From 1a07eec5990800a4888a6bacfcd09b955568d153 Mon Sep 17 00:00:00 2001
From: li jin <lijinwithyou@gmail.com>
Date: Wed, 29 Mar 2017 13:40:19 +0800
Subject: [PATCH] fix url bugs

---
 .../01.01-python-overview.ipynb               |     0
 .../01.02-ipython-interpreter.ipynb           |     0
 .../01.03-ipython-notebook.ipynb              |   100 +-
 .../01.04-use-anaconda.ipynb                  |   644 +-
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 .../02.03-numbers.ipynb                       |     0
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 .../02.06-lists.ipynb                         |     0
 ....07-mutable-and-immutable-data-types.ipynb |     0
 .../02.08-tuples.ipynb                        |   626 +-
 ...peed-comparison-between-list-&-tuple.ipynb |     0
 .../02.10-dictionaries.ipynb                  |     0
 .../02.11-sets.ipynb                          |  2270 +--
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 .../02.17-functions.ipynb                     |  1536 +-
 .../02.18-modules-and-packages.ipynb          |  1318 +-
 .../02.19-exceptions.ipynb                    |  1572 +-
 .../02.20-warnings.ipynb                      |   218 +-
 .../02.21-file-IO.ipynb                       | 13060 ++++++++--------
 .../03.01-numpy-overview.ipynb                |     0
 .../03.02-matplotlib-basics.ipynb             |  2450 +--
 .../03.03-numpy-arrays.ipynb                  |  4432 +++---
 .../03.04-array-types.ipynb                   |     0
 .../03.05-array-calculation-method.ipynb      |  2044 +--
 .../03.06-sorting-numpy-arrays.ipynb          |  1182 +-
 .../03.07-array-shapes.ipynb                  |  2848 ++--
 .../03.08-diagonals.ipynb                     |     0
 .../03.09-data-to-&-from-string.ipynb         |   428 +-
 ...0-array-attribute-&-method-overview-.ipynb |     0
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 .../03.14-vectorizing-functions.ipynb         |   416 +-
 .../03.15-binary-operators.ipynb              |   868 +-
 .../03.16-universal-functions.ipynb           |  1172 +-
 .../03.17-choose.ipynb                        |   490 +-
 .../03.18-array-broadcasting.ipynb            |   908 +-
 .../03.19-reading-and-writing-arrays.ipynb    |  2618 ++--
 .../03.20-structured-arrays.ipynb             |  1790 +--
 .../03.21-record-arrays.ipynb                 |     0
 .../03.22-memory-maps.ipynb                   |   154 +-
 .../03.23-from-matlab-to-numpy.ipynb          |   756 +-
 .../04.01-scienticfic-python-overview.ipynb   |     0
 .../04.02-interpolation-with-scipy.ipynb      |  2034 +--
 .../04.03-statistics-with-scipy.ipynb         |  2814 ++--
 .../04.04-curve-fitting.ipynb                 |  1964 +--
 .../04.05-minimization-in-python.ipynb        |  1956 +--
 .../04.06-integration-in-python.ipynb         |  2736 ++--
 .../04.07-ODEs.ipynb                          |   516 +-
 .../04.08-sparse-matrix.ipynb                 |  1244 +-
 .../04.09-linear-algbra.ipynb                 |     0
 .../04.10-sparse-linear-algebra.ipynb         |     0
 {04. scipy => 04-scipy}/JANAF_CH4.txt         |   134 +-
 .../05.01-overview-of-the-sys-module.ipynb    |   764 +-
 .../05.02-interacting-with-the-OS---os.ipynb  |     0
 .../05.03-comma-separated-values.ipynb        |   924 +-
 .../05.04-regular-expression.ipynb            |  1038 +-
 .../05.05-datetime.ipynb                      |   742 +-
 .../05.06-sql-databases.ipynb                 |   506 +-
 .../05.07-object-relational-mappers.ipynb     |   544 +-
 .../05.08-functions.ipynb                     |  1776 +--
 .../05.09-iterators.ipynb                     |  1254 +-
 .../05.10-generators.ipynb                    |   750 +-
 ...text-managers-and-the-with-statement.ipynb |  1694 +-
 .../05.12-decorators.ipynb                    |  1414 +-
 .../05.13-decorator-usage.ipynb               |  1094 +-
 ...ools-itertools-toolz-fn-funcy-module.ipynb |   702 +-
 .../05.15-scope.ipynb                         |  1084 +-
 .../05.16-dynamic-code-execution.ipynb        |   866 +-
 .../my_database.sqlite                        |   Bin
 .../06.01-pyplot-tutorial.ipynb               |  1560 +-
 ...-customizing-plots-with-style-sheets.ipynb |   572 +-
 .../06.03-working-with-text---basic.ipynb     |   914 +-
 ...-working-with-text---math-expression.ipynb |   588 +-
 .../06.05-image-tutorial.ipynb                |   906 +-
 .../06.06-annotating-axes.ipynb               |  1206 +-
 .../06.07-legend.ipynb                        |  1028 +-
 ...08-figures,-subplots,-axes-and-ticks.ipynb |   596 +-
 .../06.09-do-not-trust-the-defaults.ipynb     |  1632 +-
 .../06.10-different-plots.ipynb               |  3174 ++--
 .../artists_figure.png                        |   Bin
 .../artists_tree.png                          |   Bin
 .../stinkbug.png                              |   Bin
 .../07-02-example.zip                         |   Bin
 .../07-03-fib.zip                             |   Bin
 .../07-04-extern.zip                          |   Bin
 .../07-05-particle.zip                        |   Bin
 .../07-06-cython-sum.zip                      |   Bin
 .../07.01-introduction.ipynb                  |   186 +-
 .../07.02-python-extension-modules.ipynb      |  1192 +-
 .../07.03-cython-part-1.ipynb                 |   848 +-
 .../07.04-cython-part-2.ipynb                 |  1144 +-
 .../07.05-cython-part-3.ipynb                 |  1154 +-
 .../07.06-cython-part-4.ipynb                 |   622 +-
 .../07.07-profiling-with-annotations.ipynb    |   200 +-
 .../07.08-ctypes.ipynb                        |   736 +-
 .../fib_orig.c                                |     0
 .../fib_orig.html                             |     0
 .../fib_orig.png                              |   Bin
 .../fib_orig.pyx                              |     8 +-
 .../08.01-oop-introduction.ipynb              |   466 +-
 .../08.02-using-oop-model-a-forest-fire.ipynb |   674 +-
 .../08.03-what-is-a-object.ipynb              |   640 +-
 .../08.04-writing-classes.ipynb               |   546 +-
 .../08.05-special-method.ipynb                |  1108 +-
 .../08.06-properties.ipynb                    |   928 +-
 .../08.07-forest-fire-simulation.ipynb        |   704 +-
 .../08.08-inheritance.ipynb                   |   534 +-
 .../08.09-super.ipynb                         |   872 +-
 ...factoring-the-forest-fire-simutation.ipynb |   466 +-
 .../08.11-interfaces.ipynb                    |   692 +-
 ....12-public-private-special-in-python.ipynb |   294 +-
 .../08.13-multiple-inheritance.ipynb          |   604 +-
 .../09.01-introduction-and-installation.ipynb |     0
 .../09.02-theano-basics.ipynb                 |     0
 .../09.03-gpu-on-windows.ipynb                |     0
 .../09.04-graph-structures.ipynb              |     0
 ...uration-settings-and-compiling-modes.ipynb |     0
 .../09.06-conditions-in-theano.ipynb          |     0
 .../09.07-loop-with-scan.ipynb                |     0
 .../09.08-linear-regression.ipynb             |     0
 .../09.09-logistic-regression-.ipynb          |     0
 .../09.10-softmax-on-mnist.ipynb              |     0
 .../09.11-net-on-mnist.ipynb                  |     0
 .../09.12-random-streams.ipynb                |     0
 .../09.13-modern-net-on-mnist.ipynb           |     0
 .../09.14-convolutional-net-on-mnist.ipynb    |     0
 .../09.15-tensor-basics.ipynb                 |     0
 .../09.16-tensor-indexing.ipynb               |     0
 ...-operator-and-elementwise-operations.ipynb |     0
 .../09.18-tensor-nnet-.ipynb                  |     0
 .../09.19-tensor-conv.ipynb                   |     0
 {09. theano => 09-theano}/apply1.png          |   Bin
 {09. theano => 09-theano}/apply2.png          |   Bin
 {09. theano => 09-theano}/apply_no_opti.png   |   Bin
 {09. theano => 09-theano}/apply_opti.png      |   Bin
 {09. theano => 09-theano}/download_mnist.py   |     0
 {09. theano => 09-theano}/load.py             |     0
 .../10.01-maps-using-basemap.ipynb            |   204 +-
 .../10.02-maps-using-cartopy.ipynb            |   200 +-
 .../10.03-nba-data.ipynb                      |     0
 .../10.04-louis-cha's-kungfu-world.ipynb      |     0
 .../_Player.py                                |     0
 .../bangs.txt                                 |     0
 .../kungfu.txt                                |     0
 .../names.txt                                 |     0
 .../11.01-pprint.ipynb                        |     0
 .../11.02-pickle-and-cPickle.ipynb            |     0
 .../11.03-json.ipynb                          |     0
 .../11.04-glob.ipynb                          |     0
 .../11.05-shutil.ipynb                        |     0
 .../11.06-gzip,-zipfile,-tarfile.ipynb        |     0
 .../11.07-logging.ipynb                       |     0
 .../11.08-string.ipynb                        |     0
 .../11.09-collections.ipynb                   |     0
 .../11.10-requests.ipynb                      |     0
 .../12.01-ten-minutes-to-pandas.ipynb         |     0
 .../12.02-series-in-pandas.ipynb              |     0
 .../12.03-dataframe-in-pandas.ipynb           |     0
 README.md                                     |   307 +-
 generate index.ipynb                          |    26 +-
 index.md                                      |   306 +-
 168 files changed, 54537 insertions(+), 54538 deletions(-)
 rename 01. python tools/01.01 python overview.ipynb => 01-python-tools/01.01-python-overview.ipynb (100%)
 rename 01. python tools/01.02 ipython interpreter.ipynb => 01-python-tools/01.02-ipython-interpreter.ipynb (100%)
 rename 01. python tools/01.03 ipython notebook.ipynb => 01-python-tools/01.03-ipython-notebook.ipynb (95%)
 rename 01. python tools/01.04 use anaconda.ipynb => 01-python-tools/01.04-use-anaconda.ipynb (97%)
 rename 02. python essentials/02.01 a tour of python.ipynb => 02-python-essentials/02.01-a-tour-of-python.ipynb (100%)
 rename 02. python essentials/02.02 python data types.ipynb => 02-python-essentials/02.02-python-data-types.ipynb (100%)
 rename 02. python essentials/02.03 numbers.ipynb => 02-python-essentials/02.03-numbers.ipynb (100%)
 rename 02. python essentials/02.04 strings.ipynb => 02-python-essentials/02.04-strings.ipynb (100%)
 rename 02. python essentials/02.05 indexing and slicing.ipynb => 02-python-essentials/02.05-indexing-and-slicing.ipynb (95%)
 rename 02. python essentials/02.06 lists.ipynb => 02-python-essentials/02.06-lists.ipynb (100%)
 rename 02. python essentials/02.07 mutable and immutable data types.ipynb => 02-python-essentials/02.07-mutable-and-immutable-data-types.ipynb (100%)
 rename 02. python essentials/02.08 tuples.ipynb => 02-python-essentials/02.08-tuples.ipynb (94%)
 rename 02. python essentials/02.09 speed comparison between list & tuple.ipynb => 02-python-essentials/02.09-speed-comparison-between-list-&-tuple.ipynb (100%)
 rename 02. python essentials/02.10 dictionaries.ipynb => 02-python-essentials/02.10-dictionaries.ipynb (100%)
 rename 02. python essentials/02.11 sets.ipynb => 02-python-essentials/02.11-sets.ipynb (94%)
 rename 02. python essentials/02.12 frozen sets.ipynb => 02-python-essentials/02.12-frozen-sets.ipynb (95%)
 rename 02. python essentials/02.13 how python assignment works.ipynb => 02-python-essentials/02.13-how-python-assignment-works.ipynb (95%)
 rename 02. python essentials/02.14 if statement.ipynb => 02-python-essentials/02.14-if-statement.ipynb (95%)
 rename 02. python essentials/02.15 loops.ipynb => 02-python-essentials/02.15-loops.ipynb (95%)
 rename 02. python essentials/02.16 list comprehension.ipynb => 02-python-essentials/02.16-list-comprehension.ipynb (94%)
 rename 02. python essentials/02.17 functions.ipynb => 02-python-essentials/02.17-functions.ipynb (95%)
 rename 02. python essentials/02.18 modules and packages.ipynb => 02-python-essentials/02.18-modules-and-packages.ipynb (94%)
 rename 02. python essentials/02.19 exceptions.ipynb => 02-python-essentials/02.19-exceptions.ipynb (96%)
 rename 02. python essentials/02.20 warnings.ipynb => 02-python-essentials/02.20-warnings.ipynb (95%)
 rename 02. python essentials/02.21 file IO.ipynb => 02-python-essentials/02.21-file-IO.ipynb (96%)
 rename 03. numpy/03.01 numpy overview.ipynb => 03-numpy/03.01-numpy-overview.ipynb (100%)
 rename 03. numpy/03.02 matplotlib basics.ipynb => 03-numpy/03.02-matplotlib-basics.ipynb (99%)
 rename 03. numpy/03.03 numpy arrays.ipynb => 03-numpy/03.03-numpy-arrays.ipynb (94%)
 rename 03. numpy/03.04 array types.ipynb => 03-numpy/03.04-array-types.ipynb (100%)
 rename 03. numpy/03.05 array calculation method.ipynb => 03-numpy/03.05-array-calculation-method.ipynb (94%)
 rename 03. numpy/03.06 sorting numpy arrays.ipynb => 03-numpy/03.06-sorting-numpy-arrays.ipynb (94%)
 rename 03. numpy/03.07 array shapes.ipynb => 03-numpy/03.07-array-shapes.ipynb (94%)
 rename 03. numpy/03.08 diagonals.ipynb => 03-numpy/03.08-diagonals.ipynb (100%)
 rename 03. numpy/03.09 data to & from string.ipynb => 03-numpy/03.09-data-to-&-from-string.ipynb (94%)
 rename 03. numpy/03.10 array attribute & method overview .ipynb => 03-numpy/03.10-array-attribute-&-method-overview-.ipynb (100%)
 rename 03. numpy/03.11 array creation functions.ipynb => 03-numpy/03.11-array-creation-functions.ipynb (99%)
 rename 03. numpy/03.12 matrix object.ipynb => 03-numpy/03.12-matrix-object.ipynb (94%)
 rename 03. numpy/03.13 general functions.ipynb => 03-numpy/03.13-general-functions.ipynb (94%)
 rename 03. numpy/03.14 vectorizing functions.ipynb => 03-numpy/03.14-vectorizing-functions.ipynb (98%)
 rename 03. numpy/03.15 binary operators.ipynb => 03-numpy/03.15-binary-operators.ipynb (94%)
 rename 03. numpy/03.16 universal functions.ipynb => 03-numpy/03.16-universal-functions.ipynb (94%)
 rename 03. numpy/03.17 choose.ipynb => 03-numpy/03.17-choose.ipynb (95%)
 rename 03. numpy/03.18 array broadcasting.ipynb => 03-numpy/03.18-array-broadcasting.ipynb (99%)
 rename 03. numpy/03.19 reading and writing arrays.ipynb => 03-numpy/03.19-reading-and-writing-arrays.ipynb (95%)
 rename 03. numpy/03.20 structured arrays.ipynb => 03-numpy/03.20-structured-arrays.ipynb (95%)
 rename 03. numpy/03.21 record arrays.ipynb => 03-numpy/03.21-record-arrays.ipynb (100%)
 rename 03. numpy/03.22 memory maps.ipynb => 03-numpy/03.22-memory-maps.ipynb (96%)
 rename 03. numpy/03.23 from matlab to numpy.ipynb => 03-numpy/03.23-from-matlab-to-numpy.ipynb (97%)
 rename 04. scipy/04.01 scienticfic python overview.ipynb => 04-scipy/04.01-scienticfic-python-overview.ipynb (100%)
 rename 04. scipy/04.02 interpolation with scipy.ipynb => 04-scipy/04.02-interpolation-with-scipy.ipynb (99%)
 rename 04. scipy/04.03 statistics with scipy.ipynb => 04-scipy/04.03-statistics-with-scipy.ipynb (99%)
 rename 04. scipy/04.04 curve fitting.ipynb => 04-scipy/04.04-curve-fitting.ipynb (99%)
 rename 04. scipy/04.05 minimization in python.ipynb => 04-scipy/04.05-minimization-in-python.ipynb (99%)
 rename 04. scipy/04.06 integration in python.ipynb => 04-scipy/04.06-integration-in-python.ipynb (98%)
 rename 04. scipy/04.07 ODEs.ipynb => 04-scipy/04.07-ODEs.ipynb (99%)
 rename 04. scipy/04.08 sparse matrix.ipynb => 04-scipy/04.08-sparse-matrix.ipynb (94%)
 rename 04. scipy/04.09 linear algbra.ipynb => 04-scipy/04.09-linear-algbra.ipynb (100%)
 rename 04. scipy/04.10 sparse linear algebra.ipynb => 04-scipy/04.10-sparse-linear-algebra.ipynb (100%)
 rename {04. scipy => 04-scipy}/JANAF_CH4.txt (98%)
 rename 05. advanced python/05.01 overview of the sys module.ipynb => 05-advanced-python/05.01-overview-of-the-sys-module.ipynb (95%)
 rename 05. advanced python/05.02 interacting with the OS - os.ipynb => 05-advanced-python/05.02-interacting-with-the-OS---os.ipynb (100%)
 rename 05. advanced python/05.03 comma separated values.ipynb => 05-advanced-python/05.03-comma-separated-values.ipynb (94%)
 rename 05. advanced python/05.04 regular expression.ipynb => 05-advanced-python/05.04-regular-expression.ipynb (95%)
 rename 05. advanced python/05.05 datetime.ipynb => 05-advanced-python/05.05-datetime.ipynb (94%)
 rename 05. advanced python/05.06 sql databases.ipynb => 05-advanced-python/05.06-sql-databases.ipynb (95%)
 rename 05. advanced python/05.07 object-relational mappers.ipynb => 05-advanced-python/05.07-object-relational-mappers.ipynb (95%)
 rename 05. advanced python/05.08 functions.ipynb => 05-advanced-python/05.08-functions.ipynb (95%)
 rename 05. advanced python/05.09 iterators.ipynb => 05-advanced-python/05.09-iterators.ipynb (95%)
 rename 05. advanced python/05.10 generators.ipynb => 05-advanced-python/05.10-generators.ipynb (95%)
 rename 05. advanced python/05.11 context managers and the with statement.ipynb => 05-advanced-python/05.11-context-managers-and-the-with-statement.ipynb (96%)
 rename 05. advanced python/05.12 decorators.ipynb => 05-advanced-python/05.12-decorators.ipynb (94%)
 rename 05. advanced python/05.13 decorator usage.ipynb => 05-advanced-python/05.13-decorator-usage.ipynb (95%)
 rename 05. advanced python/05.14 the operator functools itertools toolz fn funcy module.ipynb => 05-advanced-python/05.14-the-operator-functools-itertools-toolz-fn-funcy-module.ipynb (95%)
 rename 05. advanced python/05.15 scope.ipynb => 05-advanced-python/05.15-scope.ipynb (94%)
 rename 05. advanced python/05.16 dynamic code execution.ipynb => 05-advanced-python/05.16-dynamic-code-execution.ipynb (94%)
 rename {05. advanced python => 05-advanced-python}/my_database.sqlite (100%)
 rename 06. matplotlib/06.01 pyplot tutorial.ipynb => 06-matplotlib/06.01-pyplot-tutorial.ipynb (99%)
 rename 06. matplotlib/06.02 customizing plots with style sheets.ipynb => 06-matplotlib/06.02-customizing-plots-with-style-sheets.ipynb (99%)
 rename 06. matplotlib/06.03  working with text - basic.ipynb => 06-matplotlib/06.03-working-with-text---basic.ipynb (99%)
 rename 06. matplotlib/06.04 working with text - math expression.ipynb => 06-matplotlib/06.04-working-with-text---math-expression.ipynb (99%)
 rename 06. matplotlib/06.05 image tutorial.ipynb => 06-matplotlib/06.05-image-tutorial.ipynb (99%)
 rename 06. matplotlib/06.06 annotating axes.ipynb => 06-matplotlib/06.06-annotating-axes.ipynb (99%)
 rename 06. matplotlib/06.07 legend.ipynb => 06-matplotlib/06.07-legend.ipynb (99%)
 rename 06. matplotlib/06.08 figures, subplots, axes and ticks.ipynb => 06-matplotlib/06.08-figures,-subplots,-axes-and-ticks.ipynb (99%)
 rename 06. matplotlib/06.09 do not trust the defaults.ipynb => 06-matplotlib/06.09-do-not-trust-the-defaults.ipynb (99%)
 rename 06. matplotlib/06.10 different plots.ipynb => 06-matplotlib/06.10-different-plots.ipynb (99%)
 rename {06. matplotlib => 06-matplotlib}/artists_figure.png (100%)
 rename {06. matplotlib => 06-matplotlib}/artists_tree.png (100%)
 rename {06. matplotlib => 06-matplotlib}/stinkbug.png (100%)
 rename {07. interfacing with other languages => 07-interfacing-with-other-languages}/07-02-example.zip (100%)
 rename {07. interfacing with other languages => 07-interfacing-with-other-languages}/07-03-fib.zip (100%)
 rename {07. interfacing with other languages => 07-interfacing-with-other-languages}/07-04-extern.zip (100%)
 rename {07. interfacing with other languages => 07-interfacing-with-other-languages}/07-05-particle.zip (100%)
 rename {07. interfacing with other languages => 07-interfacing-with-other-languages}/07-06-cython-sum.zip (100%)
 rename 07. interfacing with other languages/07.01 introduction.ipynb => 07-interfacing-with-other-languages/07.01-introduction.ipynb (95%)
 rename 07. interfacing with other languages/07.02 python extension modules.ipynb => 07-interfacing-with-other-languages/07.02-python-extension-modules.ipynb (95%)
 rename 07. interfacing with other languages/07.03 cython part 1.ipynb => 07-interfacing-with-other-languages/07.03-cython-part-1.ipynb (95%)
 rename 07. interfacing with other languages/07.04 cython part 2.ipynb => 07-interfacing-with-other-languages/07.04-cython-part-2.ipynb (95%)
 rename 07. interfacing with other languages/07.05 cython part 3.ipynb => 07-interfacing-with-other-languages/07.05-cython-part-3.ipynb (96%)
 rename 07. interfacing with other languages/07.06 cython part 4.ipynb => 07-interfacing-with-other-languages/07.06-cython-part-4.ipynb (96%)
 rename 07. interfacing with other languages/07.07 profiling with annotations.ipynb => 07-interfacing-with-other-languages/07.07-profiling-with-annotations.ipynb (94%)
 rename 07. interfacing with other languages/07.08 ctypes.ipynb => 07-interfacing-with-other-languages/07.08-ctypes.ipynb (95%)
 rename {07. interfacing with other languages => 07-interfacing-with-other-languages}/fib_orig.c (100%)
 rename {07. interfacing with other languages => 07-interfacing-with-other-languages}/fib_orig.html (100%)
 rename {07. interfacing with other languages => 07-interfacing-with-other-languages}/fib_orig.png (100%)
 rename {07. interfacing with other languages => 07-interfacing-with-other-languages}/fib_orig.pyx (95%)
 rename 08. object-oriented programming/08.01 oop introduction.ipynb => 08-object-oriented-programming/08.01-oop-introduction.ipynb (95%)
 rename 08. object-oriented programming/08.02 using oop model a forest fire.ipynb => 08-object-oriented-programming/08.02-using-oop-model-a-forest-fire.ipynb (99%)
 rename 08. object-oriented programming/08.03 what is a object.ipynb => 08-object-oriented-programming/08.03-what-is-a-object.ipynb (94%)
 rename 08. object-oriented programming/08.04 writing classes.ipynb => 08-object-oriented-programming/08.04-writing-classes.ipynb (95%)
 rename 08. object-oriented programming/08.05 special method.ipynb => 08-object-oriented-programming/08.05-special-method.ipynb (95%)
 rename 08. object-oriented programming/08.06 properties.ipynb => 08-object-oriented-programming/08.06-properties.ipynb (95%)
 rename 08. object-oriented programming/08.07 forest fire simulation.ipynb => 08-object-oriented-programming/08.07-forest-fire-simulation.ipynb (99%)
 rename 08. object-oriented programming/08.08 inheritance.ipynb => 08-object-oriented-programming/08.08-inheritance.ipynb (95%)
 rename 08. object-oriented programming/08.09 super.ipynb => 08-object-oriented-programming/08.09-super.ipynb (98%)
 rename 08. object-oriented programming/08.10 refactoring the forest fire simutation.ipynb => 08-object-oriented-programming/08.10-refactoring-the-forest-fire-simutation.ipynb (99%)
 rename 08. object-oriented programming/08.11 interfaces.ipynb => 08-object-oriented-programming/08.11-interfaces.ipynb (99%)
 rename 08. object-oriented programming/08.12 public private special in python.ipynb => 08-object-oriented-programming/08.12-public-private-special-in-python.ipynb (95%)
 rename 08. object-oriented programming/08.13 multiple inheritance.ipynb => 08-object-oriented-programming/08.13-multiple-inheritance.ipynb (95%)
 rename 09. theano/09.01 introduction and installation.ipynb => 09-theano/09.01-introduction-and-installation.ipynb (100%)
 rename 09. theano/09.02 theano basics.ipynb => 09-theano/09.02-theano-basics.ipynb (100%)
 rename 09. theano/09.03 gpu on windows.ipynb => 09-theano/09.03-gpu-on-windows.ipynb (100%)
 rename 09. theano/09.04 graph structures.ipynb => 09-theano/09.04-graph-structures.ipynb (100%)
 rename 09. theano/09.05 configuration settings and compiling modes.ipynb => 09-theano/09.05-configuration-settings-and-compiling-modes.ipynb (100%)
 rename 09. theano/09.06 conditions in theano.ipynb => 09-theano/09.06-conditions-in-theano.ipynb (100%)
 rename 09. theano/09.07 loop with scan.ipynb => 09-theano/09.07-loop-with-scan.ipynb (100%)
 rename 09. theano/09.08 linear regression.ipynb => 09-theano/09.08-linear-regression.ipynb (100%)
 rename 09. theano/09.09 logistic regression .ipynb => 09-theano/09.09-logistic-regression-.ipynb (100%)
 rename 09. theano/09.10 softmax on mnist.ipynb => 09-theano/09.10-softmax-on-mnist.ipynb (100%)
 rename 09. theano/09.11 net on mnist.ipynb => 09-theano/09.11-net-on-mnist.ipynb (100%)
 rename 09. theano/09.12 random streams.ipynb => 09-theano/09.12-random-streams.ipynb (100%)
 rename 09. theano/09.13 modern net on mnist.ipynb => 09-theano/09.13-modern-net-on-mnist.ipynb (100%)
 rename 09. theano/09.14 convolutional net on mnist.ipynb => 09-theano/09.14-convolutional-net-on-mnist.ipynb (100%)
 rename 09. theano/09.15 tensor basics.ipynb => 09-theano/09.15-tensor-basics.ipynb (100%)
 rename 09. theano/09.16 tensor indexing.ipynb => 09-theano/09.16-tensor-indexing.ipynb (100%)
 rename 09. theano/09.17 tensor operator and elementwise operations.ipynb => 09-theano/09.17-tensor-operator-and-elementwise-operations.ipynb (100%)
 rename 09. theano/09.18 tensor nnet .ipynb => 09-theano/09.18-tensor-nnet-.ipynb (100%)
 rename 09. theano/09.19 tensor conv.ipynb => 09-theano/09.19-tensor-conv.ipynb (100%)
 rename {09. theano => 09-theano}/apply1.png (100%)
 rename {09. theano => 09-theano}/apply2.png (100%)
 rename {09. theano => 09-theano}/apply_no_opti.png (100%)
 rename {09. theano => 09-theano}/apply_opti.png (100%)
 rename {09. theano => 09-theano}/download_mnist.py (100%)
 rename {09. theano => 09-theano}/load.py (100%)
 rename 10. something interesting/10.01 maps using basemap.ipynb => 10-something-interesting/10.01-maps-using-basemap.ipynb (99%)
 rename 10. something interesting/10.02 maps using cartopy.ipynb => 10-something-interesting/10.02-maps-using-cartopy.ipynb (99%)
 rename 10. something interesting/10.03 nba data.ipynb => 10-something-interesting/10.03-nba-data.ipynb (100%)
 rename 10. something interesting/10.04 louis cha's kungfu world.ipynb => 10-something-interesting/10.04-louis-cha's-kungfu-world.ipynb (100%)
 rename {10. something interesting => 10-something-interesting}/_Player.py (100%)
 rename {10. something interesting => 10-something-interesting}/bangs.txt (100%)
 rename {10. something interesting => 10-something-interesting}/kungfu.txt (100%)
 rename {10. something interesting => 10-something-interesting}/names.txt (100%)
 rename 11. useful tools/11.01 pprint.ipynb => 11-useful-tools/11.01-pprint.ipynb (100%)
 rename 11. useful tools/11.02 pickle and cPickle.ipynb => 11-useful-tools/11.02-pickle-and-cPickle.ipynb (100%)
 rename 11. useful tools/11.03 json.ipynb => 11-useful-tools/11.03-json.ipynb (100%)
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 rename 12. pandas/12.01 ten minutes to pandas.ipynb => 12-pandas/12.01-ten-minutes-to-pandas.ipynb (100%)
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 rename 12. pandas/12.03 dataframe in pandas.ipynb => 12-pandas/12.03-dataframe-in-pandas.ipynb (100%)

diff --git a/01. python tools/01.01 python overview.ipynb b/01-python-tools/01.01-python-overview.ipynb
similarity index 100%
rename from 01. python tools/01.01 python overview.ipynb
rename to 01-python-tools/01.01-python-overview.ipynb
diff --git a/01. python tools/01.02 ipython interpreter.ipynb b/01-python-tools/01.02-ipython-interpreter.ipynb
similarity index 100%
rename from 01. python tools/01.02 ipython interpreter.ipynb
rename to 01-python-tools/01.02-ipython-interpreter.ipynb
diff --git a/01. python tools/01.03 ipython notebook.ipynb b/01-python-tools/01.03-ipython-notebook.ipynb
similarity index 95%
rename from 01. python tools/01.03 ipython notebook.ipynb
rename to 01-python-tools/01.03-ipython-notebook.ipynb
index f7a2b431..397de26c 100644
--- a/01. python tools/01.03 ipython notebook.ipynb	
+++ b/01-python-tools/01.03-ipython-notebook.ipynb
@@ -1,50 +1,50 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Ipython notebook"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在命令行下输入命令：\n",
-    "\n",
-    "    ipython notebook\n",
-    "\n",
-    "会打开一个notebook本地服务器，一般地址是 http://localhost:8888\n",
-    "\n",
-    "**`ipython notebook`** 支持两种模式的cell：\n",
-    "\n",
-    "* Markdown\n",
-    "* Code\n",
-    "\n",
-    "这里不做过多介绍。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ipython notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在命令行下输入命令：\n",
+    "\n",
+    "    ipython notebook\n",
+    "\n",
+    "会打开一个notebook本地服务器，一般地址是 http://localhost:8888\n",
+    "\n",
+    "**`ipython notebook`** 支持两种模式的cell：\n",
+    "\n",
+    "* Markdown\n",
+    "* Code\n",
+    "\n",
+    "这里不做过多介绍。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/01. python tools/01.04 use anaconda.ipynb b/01-python-tools/01.04-use-anaconda.ipynb
similarity index 97%
rename from 01. python tools/01.04 use anaconda.ipynb
rename to 01-python-tools/01.04-use-anaconda.ipynb
index 666caaf4..cd0587d3 100644
--- a/01. python tools/01.04 use anaconda.ipynb	
+++ b/01-python-tools/01.04-use-anaconda.ipynb
@@ -1,322 +1,322 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 使用 Anaconda"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Anaconda](http://www.continuum.io/downloads)是一个很好用的Python IDE，它集成了很多科学计算需要使用的**python**第三方工具包。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## conda 的使用 "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "根据自己的操作系统安装好[Anaconda](http://www.continuum.io/downloads)后，在命令行下输入：\n",
-    "\n",
-    "    conda list\n",
-    "\n",
-    "可以看已经安装好的**python**第三方工具包，这里我们使用 `magic` 命令 `%%cmd` 在 `ipython cell` 中来执行这个命令："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# packages in environment at C:\\Anaconda:\n",
-      "#\n",
-      "_license                  1.1                      py27_0  \n",
-      "alabaster                 0.7.3                    py27_0  \n",
-      "anaconda                  2.3.0                np19py27_0  \n",
-      "argcomplete               0.8.9                    py27_0  \n",
-      "astropy                   1.0.3                np19py27_0  \n",
-      "babel                     1.3                      py27_0  \n",
-      "backports.ssl-match-hostname 3.4.0.2                   <pip>\n",
-      "basemap                   1.0.7                np19py27_0  \n",
-      "bcolz                     0.9.0                np19py27_0  \n",
-      "beautiful-soup            4.3.2                    py27_1  \n",
-      "beautifulsoup4            4.3.2                     <pip>\n",
-      "binstar                   0.11.0                   py27_0  \n",
-      "bitarray                  0.8.1                    py27_1  \n",
-      "blaze                     0.8.0                     <pip>\n",
-      "blaze-core                0.8.0                np19py27_0  \n",
-      "blz                       0.6.2                np19py27_1  \n",
-      "bokeh                     0.9.0                np19py27_0  \n",
-      "boto                      2.38.0                   py27_0  \n",
-      "bottleneck                1.0.0                np19py27_0  \n",
-      "cartopy                   0.13.0               np19py27_0  \n",
-      "cdecimal                  2.3                      py27_1  \n",
-      "certifi                   14.05.14                 py27_0  \n",
-      "cffi                      1.1.0                    py27_0  \n",
-      "clyent                    0.3.4                    py27_0  \n",
-      "colorama                  0.3.3                    py27_0  \n",
-      "conda                     3.17.0                   py27_0  \n",
-      "conda-build               1.14.1                   py27_0  \n",
-      "conda-env                 2.4.2                    py27_0  \n",
-      "configobj                 5.0.6                    py27_0  \n",
-      "cryptography              0.9.1                    py27_0  \n",
-      "cython                    0.22.1                   py27_0  \n",
-      "cytoolz                   0.7.3                    py27_0  \n",
-      "datashape                 0.4.5                np19py27_0  \n",
-      "decorator                 3.4.2                    py27_0  \n",
-      "docutils                  0.12                     py27_1  \n",
-      "dynd-python               0.6.5                np19py27_0  \n",
-      "enum34                    1.0.4                    py27_0  \n",
-      "fastcache                 1.0.2                    py27_0  \n",
-      "flask                     0.10.1                   py27_1  \n",
-      "funcsigs                  0.4                      py27_0  \n",
-      "geopy                     1.11.0                    <pip>\n",
-      "geos                      3.4.2                         3  \n",
-      "gevent                    1.0.1                    py27_0  \n",
-      "gevent-websocket          0.9.3                    py27_0  \n",
-      "greenlet                  0.4.7                    py27_0  \n",
-      "grin                      1.2.1                    py27_2  \n",
-      "h5py                      2.5.0                np19py27_1  \n",
-      "hdf5                      1.8.15.1                      2  \n",
-      "idna                      2.0                      py27_0  \n",
-      "ipaddress                 1.0.7                    py27_0  \n",
-      "ipython                   3.2.0                    py27_0  \n",
-      "ipython-notebook          3.2.0                    py27_0  \n",
-      "ipython-qtconsole         3.2.0                    py27_0  \n",
-      "itsdangerous              0.24                     py27_0  \n",
-      "jdcal                     1.0                      py27_0  \n",
-      "jedi                      0.8.1                    py27_0  \n",
-      "jinja2                    2.7.3                    py27_2  \n",
-      "jsonschema                2.4.0                    py27_0  \n",
-      "launcher                  1.0.0                         1  \n",
-      "libpython                 1.0                      py27_1  \n",
-      "llvmlite                  0.5.0                    py27_0  \n",
-      "lxml                      3.4.4                    py27_0  \n",
-      "markupsafe                0.23                     py27_0  \n",
-      "matplotlib                1.4.3                np19py27_1  \n",
-      "menuinst                  1.0.4                    py27_0  \n",
-      "mingw                     4.7                           1  \n",
-      "mistune                   0.5.1                    py27_1  \n",
-      "mock                      1.3.0                    py27_0  \n",
-      "multipledispatch          0.4.7                    py27_0  \n",
-      "networkx                  1.9.1                    py27_0  \n",
-      "nltk                      3.0.3                np19py27_0  \n",
-      "node-webkit               0.10.1                        0  \n",
-      "nose                      1.3.7                    py27_0  \n",
-      "numba                     0.19.1               np19py27_0  \n",
-      "numexpr                   2.4.3                np19py27_0  \n",
-      "numpy                     1.9.2                    py27_0  \n",
-      "odo                       0.3.2                np19py27_0  \n",
-      "openpyxl                  1.8.5                    py27_0  \n",
-      "owslib                    0.9.0                    py27_0  \n",
-      "pandas                    0.16.2               np19py27_0  \n",
-      "patsy                     0.3.0                np19py27_0  \n",
-      "pbr                       1.3.0                    py27_0  \n",
-      "pep8                      1.6.2                    py27_0  \n",
-      "pillow                    2.9.0                    py27_0  \n",
-      "pip                       7.1.2                    py27_0  \n",
-      "ply                       3.6                      py27_0  \n",
-      "proj4                     4.9.1                    py27_1  \n",
-      "psutil                    2.2.1                    py27_0  \n",
-      "py                        1.4.27                   py27_0  \n",
-      "pyasn1                    0.1.7                    py27_0  \n",
-      "pycosat                   0.6.1                    py27_0  \n",
-      "pycparser                 2.14                     py27_0  \n",
-      "pycrypto                  2.6.1                    py27_3  \n",
-      "pyepsg                    0.2.0                    py27_0  \n",
-      "pyflakes                  0.9.2                    py27_0  \n",
-      "pygments                  2.0.2                    py27_0  \n",
-      "pyopenssl                 0.15.1                   py27_1  \n",
-      "pyparsing                 2.0.3                    py27_0  \n",
-      "pyqt                      4.10.4                   py27_1  \n",
-      "pyreadline                2.0                      py27_0  \n",
-      "pyshp                     1.2.1                    py27_0  \n",
-      "pytables                  3.2.0                np19py27_0  \n",
-      "pytest                    2.7.1                    py27_0  \n",
-      "python                    2.7.10                        0  \n",
-      "python-dateutil           2.4.2                    py27_0  \n",
-      "pytz                      2015.4                   py27_0  \n",
-      "pywin32                   219                      py27_0  \n",
-      "pyyaml                    3.11                     py27_2  \n",
-      "pyzmq                     14.7.0                   py27_0  \n",
-      "requests                  2.7.0                    py27_0  \n",
-      "rope                      0.9.4                    py27_1  \n",
-      "runipy                    0.1.3                    py27_0  \n",
-      "scikit-image              0.11.3               np19py27_0  \n",
-      "scikit-learn              0.16.1               np19py27_0  \n",
-      "scipy                     0.16.0               np19py27_0  \n",
-      "setuptools                18.1                     py27_0  \n",
-      "shapely                   1.5.11                 nppy27_0  \n",
-      "six                       1.9.0                    py27_0  \n",
-      "snowballstemmer           1.2.0                    py27_0  \n",
-      "sockjs-tornado            1.0.1                    py27_0  \n",
-      "sphinx                    1.3.1                    py27_0  \n",
-      "sphinx-rtd-theme          0.1.7                     <pip>\n",
-      "sphinx_rtd_theme          0.1.7                    py27_0  \n",
-      "spyder                    2.3.5.2                  py27_0  \n",
-      "spyder-app                2.3.5.2                  py27_0  \n",
-      "sqlalchemy                1.0.5                    py27_0  \n",
-      "ssl_match_hostname        3.4.0.2                  py27_0  \n",
-      "statsmodels               0.6.1                np19py27_0  \n",
-      "sympy                     0.7.6                    py27_0  \n",
-      "tables                    3.2.0                     <pip>\n",
-      "theano                    0.7.0                     <pip>\n",
-      "toolz                     0.7.2                    py27_0  \n",
-      "tornado                   4.2                      py27_0  \n",
-      "ujson                     1.33                     py27_0  \n",
-      "unicodecsv                0.9.4                    py27_0  \n",
-      "werkzeug                  0.10.4                   py27_0  \n",
-      "wheel                     0.24.0                   py27_0  \n",
-      "xlrd                      0.9.3                    py27_0  \n",
-      "xlsxwriter                0.7.3                    py27_0  \n",
-      "xlwings                   0.3.5                    py27_0  \n",
-      "xlwt                      1.0.0                    py27_0  \n",
-      "zlib                      1.2.8                         0  \n"
-     ]
-    }
-   ],
-   "source": [
-    "!conda list"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "第一次安装好 [Anaconda](http://www.continuum.io/downloads) 以后，可以在命令行输入以下命令使 [Anaconda](http://www.continuum.io/downloads) 保持最新：\n",
-    "\n",
-    "    conda update conda\n",
-    "    conda update anaconda\n",
-    "\n",
-    "conda 是一种很强大的工具，具体用法可以参照它的[文档](http://conda.pydata.org/docs/)。\n",
-    "\n",
-    "也可以参考它的 [cheat sheet](http://conda.pydata.org/docs/_downloads/conda-cheatsheet.pdf) 来快速查看它的用法。\n",
-    "\n",
-    "可以使用它来安装，更新，卸载第三方的 **python** 工具包：\n",
-    "\n",
-    "    conda install <some package>\n",
-    "    conda update <some package>\n",
-    "    conda remove <some package>\n",
-    "\n",
-    "在安装或更新时可以指定安装的版本号，例如需要使用 `numpy 1.8.1`：\n",
-    "\n",
-    "    conda install numpy=1.8.1\n",
-    "    conda update numpy=1.8.1\n",
-    "\n",
-    "查看 `conda` 的信息：\n",
-    "\n",
-    "    conda info"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Current conda install:\n",
-      "\n",
-      "             platform : win-64\n",
-      "        conda version : 3.17.0\n",
-      "  conda-build version : 1.14.1\n",
-      "       python version : 2.7.10.final.0\n",
-      "     requests version : 2.7.0\n",
-      "     root environment : C:\\Anaconda  (writable)\n",
-      "  default environment : C:\\Anaconda\n",
-      "     envs directories : C:\\Anaconda\\envs\n",
-      "        package cache : C:\\Anaconda\\pkgs\n",
-      "         channel URLs : https://repo.continuum.io/pkgs/free/win-64/\n",
-      "                        https://repo.continuum.io/pkgs/free/noarch/\n",
-      "                        https://repo.continuum.io/pkgs/pro/win-64/\n",
-      "                        https://repo.continuum.io/pkgs/pro/noarch/\n",
-      "          config file : None\n",
-      "    is foreign system : False\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "!conda info"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "一个很棒的功能是 `conda` 可以产生一个自定义的环境，假设在安装的是 **Python 2.7** 的情况下，想使用 **Python 3.4**，只需要在命令行下使用 `conda` 产生一个新的环境：\n",
-    "\n",
-    "    conda create -n py34 python=3.4\n",
-    "\n",
-    "这里这个环境被命名为 `py34` ，可以根据喜好将 `py34` 改成其他的名字。\n",
-    "\n",
-    "使用这个环境时，只需要命令行下输入：\n",
-    "\n",
-    "``` python\n",
-    "activate py34 #(windows)\n",
-    "source activate py34 #(linux, mac)\n",
-    "```\n",
-    "\n",
-    "此时，我们的 **Python** 版本便是 **`python 3.4`**了。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## spyder 编辑器"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Anaconda` 默认使用的编辑器是 `spyder`，可以在命令行下输入：\n",
-    "\n",
-    "    spyder\n",
-    "\n",
-    "来进入这个编辑器，具体使用方法不做介绍。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 使用 Anaconda"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Anaconda](http://www.continuum.io/downloads)是一个很好用的Python IDE，它集成了很多科学计算需要使用的**python**第三方工具包。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## conda 的使用 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "根据自己的操作系统安装好[Anaconda](http://www.continuum.io/downloads)后，在命令行下输入：\n",
+    "\n",
+    "    conda list\n",
+    "\n",
+    "可以看已经安装好的**python**第三方工具包，这里我们使用 `magic` 命令 `%%cmd` 在 `ipython cell` 中来执行这个命令："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# packages in environment at C:\\Anaconda:\n",
+      "#\n",
+      "_license                  1.1                      py27_0  \n",
+      "alabaster                 0.7.3                    py27_0  \n",
+      "anaconda                  2.3.0                np19py27_0  \n",
+      "argcomplete               0.8.9                    py27_0  \n",
+      "astropy                   1.0.3                np19py27_0  \n",
+      "babel                     1.3                      py27_0  \n",
+      "backports.ssl-match-hostname 3.4.0.2                   <pip>\n",
+      "basemap                   1.0.7                np19py27_0  \n",
+      "bcolz                     0.9.0                np19py27_0  \n",
+      "beautiful-soup            4.3.2                    py27_1  \n",
+      "beautifulsoup4            4.3.2                     <pip>\n",
+      "binstar                   0.11.0                   py27_0  \n",
+      "bitarray                  0.8.1                    py27_1  \n",
+      "blaze                     0.8.0                     <pip>\n",
+      "blaze-core                0.8.0                np19py27_0  \n",
+      "blz                       0.6.2                np19py27_1  \n",
+      "bokeh                     0.9.0                np19py27_0  \n",
+      "boto                      2.38.0                   py27_0  \n",
+      "bottleneck                1.0.0                np19py27_0  \n",
+      "cartopy                   0.13.0               np19py27_0  \n",
+      "cdecimal                  2.3                      py27_1  \n",
+      "certifi                   14.05.14                 py27_0  \n",
+      "cffi                      1.1.0                    py27_0  \n",
+      "clyent                    0.3.4                    py27_0  \n",
+      "colorama                  0.3.3                    py27_0  \n",
+      "conda                     3.17.0                   py27_0  \n",
+      "conda-build               1.14.1                   py27_0  \n",
+      "conda-env                 2.4.2                    py27_0  \n",
+      "configobj                 5.0.6                    py27_0  \n",
+      "cryptography              0.9.1                    py27_0  \n",
+      "cython                    0.22.1                   py27_0  \n",
+      "cytoolz                   0.7.3                    py27_0  \n",
+      "datashape                 0.4.5                np19py27_0  \n",
+      "decorator                 3.4.2                    py27_0  \n",
+      "docutils                  0.12                     py27_1  \n",
+      "dynd-python               0.6.5                np19py27_0  \n",
+      "enum34                    1.0.4                    py27_0  \n",
+      "fastcache                 1.0.2                    py27_0  \n",
+      "flask                     0.10.1                   py27_1  \n",
+      "funcsigs                  0.4                      py27_0  \n",
+      "geopy                     1.11.0                    <pip>\n",
+      "geos                      3.4.2                         3  \n",
+      "gevent                    1.0.1                    py27_0  \n",
+      "gevent-websocket          0.9.3                    py27_0  \n",
+      "greenlet                  0.4.7                    py27_0  \n",
+      "grin                      1.2.1                    py27_2  \n",
+      "h5py                      2.5.0                np19py27_1  \n",
+      "hdf5                      1.8.15.1                      2  \n",
+      "idna                      2.0                      py27_0  \n",
+      "ipaddress                 1.0.7                    py27_0  \n",
+      "ipython                   3.2.0                    py27_0  \n",
+      "ipython-notebook          3.2.0                    py27_0  \n",
+      "ipython-qtconsole         3.2.0                    py27_0  \n",
+      "itsdangerous              0.24                     py27_0  \n",
+      "jdcal                     1.0                      py27_0  \n",
+      "jedi                      0.8.1                    py27_0  \n",
+      "jinja2                    2.7.3                    py27_2  \n",
+      "jsonschema                2.4.0                    py27_0  \n",
+      "launcher                  1.0.0                         1  \n",
+      "libpython                 1.0                      py27_1  \n",
+      "llvmlite                  0.5.0                    py27_0  \n",
+      "lxml                      3.4.4                    py27_0  \n",
+      "markupsafe                0.23                     py27_0  \n",
+      "matplotlib                1.4.3                np19py27_1  \n",
+      "menuinst                  1.0.4                    py27_0  \n",
+      "mingw                     4.7                           1  \n",
+      "mistune                   0.5.1                    py27_1  \n",
+      "mock                      1.3.0                    py27_0  \n",
+      "multipledispatch          0.4.7                    py27_0  \n",
+      "networkx                  1.9.1                    py27_0  \n",
+      "nltk                      3.0.3                np19py27_0  \n",
+      "node-webkit               0.10.1                        0  \n",
+      "nose                      1.3.7                    py27_0  \n",
+      "numba                     0.19.1               np19py27_0  \n",
+      "numexpr                   2.4.3                np19py27_0  \n",
+      "numpy                     1.9.2                    py27_0  \n",
+      "odo                       0.3.2                np19py27_0  \n",
+      "openpyxl                  1.8.5                    py27_0  \n",
+      "owslib                    0.9.0                    py27_0  \n",
+      "pandas                    0.16.2               np19py27_0  \n",
+      "patsy                     0.3.0                np19py27_0  \n",
+      "pbr                       1.3.0                    py27_0  \n",
+      "pep8                      1.6.2                    py27_0  \n",
+      "pillow                    2.9.0                    py27_0  \n",
+      "pip                       7.1.2                    py27_0  \n",
+      "ply                       3.6                      py27_0  \n",
+      "proj4                     4.9.1                    py27_1  \n",
+      "psutil                    2.2.1                    py27_0  \n",
+      "py                        1.4.27                   py27_0  \n",
+      "pyasn1                    0.1.7                    py27_0  \n",
+      "pycosat                   0.6.1                    py27_0  \n",
+      "pycparser                 2.14                     py27_0  \n",
+      "pycrypto                  2.6.1                    py27_3  \n",
+      "pyepsg                    0.2.0                    py27_0  \n",
+      "pyflakes                  0.9.2                    py27_0  \n",
+      "pygments                  2.0.2                    py27_0  \n",
+      "pyopenssl                 0.15.1                   py27_1  \n",
+      "pyparsing                 2.0.3                    py27_0  \n",
+      "pyqt                      4.10.4                   py27_1  \n",
+      "pyreadline                2.0                      py27_0  \n",
+      "pyshp                     1.2.1                    py27_0  \n",
+      "pytables                  3.2.0                np19py27_0  \n",
+      "pytest                    2.7.1                    py27_0  \n",
+      "python                    2.7.10                        0  \n",
+      "python-dateutil           2.4.2                    py27_0  \n",
+      "pytz                      2015.4                   py27_0  \n",
+      "pywin32                   219                      py27_0  \n",
+      "pyyaml                    3.11                     py27_2  \n",
+      "pyzmq                     14.7.0                   py27_0  \n",
+      "requests                  2.7.0                    py27_0  \n",
+      "rope                      0.9.4                    py27_1  \n",
+      "runipy                    0.1.3                    py27_0  \n",
+      "scikit-image              0.11.3               np19py27_0  \n",
+      "scikit-learn              0.16.1               np19py27_0  \n",
+      "scipy                     0.16.0               np19py27_0  \n",
+      "setuptools                18.1                     py27_0  \n",
+      "shapely                   1.5.11                 nppy27_0  \n",
+      "six                       1.9.0                    py27_0  \n",
+      "snowballstemmer           1.2.0                    py27_0  \n",
+      "sockjs-tornado            1.0.1                    py27_0  \n",
+      "sphinx                    1.3.1                    py27_0  \n",
+      "sphinx-rtd-theme          0.1.7                     <pip>\n",
+      "sphinx_rtd_theme          0.1.7                    py27_0  \n",
+      "spyder                    2.3.5.2                  py27_0  \n",
+      "spyder-app                2.3.5.2                  py27_0  \n",
+      "sqlalchemy                1.0.5                    py27_0  \n",
+      "ssl_match_hostname        3.4.0.2                  py27_0  \n",
+      "statsmodels               0.6.1                np19py27_0  \n",
+      "sympy                     0.7.6                    py27_0  \n",
+      "tables                    3.2.0                     <pip>\n",
+      "theano                    0.7.0                     <pip>\n",
+      "toolz                     0.7.2                    py27_0  \n",
+      "tornado                   4.2                      py27_0  \n",
+      "ujson                     1.33                     py27_0  \n",
+      "unicodecsv                0.9.4                    py27_0  \n",
+      "werkzeug                  0.10.4                   py27_0  \n",
+      "wheel                     0.24.0                   py27_0  \n",
+      "xlrd                      0.9.3                    py27_0  \n",
+      "xlsxwriter                0.7.3                    py27_0  \n",
+      "xlwings                   0.3.5                    py27_0  \n",
+      "xlwt                      1.0.0                    py27_0  \n",
+      "zlib                      1.2.8                         0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "!conda list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "第一次安装好 [Anaconda](http://www.continuum.io/downloads) 以后，可以在命令行输入以下命令使 [Anaconda](http://www.continuum.io/downloads) 保持最新：\n",
+    "\n",
+    "    conda update conda\n",
+    "    conda update anaconda\n",
+    "\n",
+    "conda 是一种很强大的工具，具体用法可以参照它的[文档](http://conda.pydata.org/docs/)。\n",
+    "\n",
+    "也可以参考它的 [cheat sheet](http://conda.pydata.org/docs/_downloads/conda-cheatsheet.pdf) 来快速查看它的用法。\n",
+    "\n",
+    "可以使用它来安装，更新，卸载第三方的 **python** 工具包：\n",
+    "\n",
+    "    conda install <some package>\n",
+    "    conda update <some package>\n",
+    "    conda remove <some package>\n",
+    "\n",
+    "在安装或更新时可以指定安装的版本号，例如需要使用 `numpy 1.8.1`：\n",
+    "\n",
+    "    conda install numpy=1.8.1\n",
+    "    conda update numpy=1.8.1\n",
+    "\n",
+    "查看 `conda` 的信息：\n",
+    "\n",
+    "    conda info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current conda install:\n",
+      "\n",
+      "             platform : win-64\n",
+      "        conda version : 3.17.0\n",
+      "  conda-build version : 1.14.1\n",
+      "       python version : 2.7.10.final.0\n",
+      "     requests version : 2.7.0\n",
+      "     root environment : C:\\Anaconda  (writable)\n",
+      "  default environment : C:\\Anaconda\n",
+      "     envs directories : C:\\Anaconda\\envs\n",
+      "        package cache : C:\\Anaconda\\pkgs\n",
+      "         channel URLs : https://repo.continuum.io/pkgs/free/win-64/\n",
+      "                        https://repo.continuum.io/pkgs/free/noarch/\n",
+      "                        https://repo.continuum.io/pkgs/pro/win-64/\n",
+      "                        https://repo.continuum.io/pkgs/pro/noarch/\n",
+      "          config file : None\n",
+      "    is foreign system : False\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!conda info"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "一个很棒的功能是 `conda` 可以产生一个自定义的环境，假设在安装的是 **Python 2.7** 的情况下，想使用 **Python 3.4**，只需要在命令行下使用 `conda` 产生一个新的环境：\n",
+    "\n",
+    "    conda create -n py34 python=3.4\n",
+    "\n",
+    "这里这个环境被命名为 `py34` ，可以根据喜好将 `py34` 改成其他的名字。\n",
+    "\n",
+    "使用这个环境时，只需要命令行下输入：\n",
+    "\n",
+    "``` python\n",
+    "activate py34 #(windows)\n",
+    "source activate py34 #(linux, mac)\n",
+    "```\n",
+    "\n",
+    "此时，我们的 **Python** 版本便是 **`python 3.4`**了。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## spyder 编辑器"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Anaconda` 默认使用的编辑器是 `spyder`，可以在命令行下输入：\n",
+    "\n",
+    "    spyder\n",
+    "\n",
+    "来进入这个编辑器，具体使用方法不做介绍。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/02. python essentials/02.01 a tour of python.ipynb b/02-python-essentials/02.01-a-tour-of-python.ipynb
similarity index 100%
rename from 02. python essentials/02.01 a tour of python.ipynb
rename to 02-python-essentials/02.01-a-tour-of-python.ipynb
diff --git a/02. python essentials/02.02 python data types.ipynb b/02-python-essentials/02.02-python-data-types.ipynb
similarity index 100%
rename from 02. python essentials/02.02 python data types.ipynb
rename to 02-python-essentials/02.02-python-data-types.ipynb
diff --git a/02. python essentials/02.03 numbers.ipynb b/02-python-essentials/02.03-numbers.ipynb
similarity index 100%
rename from 02. python essentials/02.03 numbers.ipynb
rename to 02-python-essentials/02.03-numbers.ipynb
diff --git a/02. python essentials/02.04 strings.ipynb b/02-python-essentials/02.04-strings.ipynb
similarity index 100%
rename from 02. python essentials/02.04 strings.ipynb
rename to 02-python-essentials/02.04-strings.ipynb
diff --git a/02. python essentials/02.05 indexing and slicing.ipynb b/02-python-essentials/02.05-indexing-and-slicing.ipynb
similarity index 95%
rename from 02. python essentials/02.05 indexing and slicing.ipynb
rename to 02-python-essentials/02.05-indexing-and-slicing.ipynb
index e277d480..51fa3f0c 100644
--- a/02. python essentials/02.05 indexing and slicing.ipynb	
+++ b/02-python-essentials/02.05-indexing-and-slicing.ipynb
@@ -1,488 +1,488 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 索引和分片"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 索引"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于一个有序序列，可以通过索引的方法来访问对应位置的值。字符串便是一个有序序列的例子，**Python**使用 `[]` 来对有序序列进行索引。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'h'"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s = \"hello world\"\n",
-    "s[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Python**中索引是从 `0` 开始的，所以索引 `0` 对应与序列的第 `1` 个元素。为了得到第 `5` 个元素，需要使用索引值 `4` 。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'o'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[4]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "除了正向索引，**Python**还引入了负索引值的用法，即从后向前开始计数，例如，索引 `-2` 表示倒数第 `2` 个元素："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'l'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[-2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "单个索引大于等于字符串的长度时，会报错："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "string index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-4-79ffc22473a3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mIndexError\u001b[0m: string index out of range"
-     ]
-    }
-   ],
-   "source": [
-    "s[11]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 分片"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "分片用来从序列中提取出想要的子序列，其用法为：\n",
-    "\n",
-    "    var[lower:upper:step]\n",
-    "\n",
-    "其范围包括 `lower` ，但不包括 `upper` ，即 `[lower, upper)`， `step` 表示取值间隔大小，如果没有默认为`1`。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'hello world'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'el'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[1:3]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "分片中包含的元素的个数为 `3-1=2` 。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "也可以使用负索引来指定分片的范围："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'ello wor'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[1:-2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "包括索引 `1` 但是不包括索引 `-2` 。\n",
-    "\n",
-    "lower和upper可以省略，省略lower意味着从开头开始分片，省略upper意味着一直分片到结尾。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'hel'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'rld'"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[-3:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'hello world'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "每隔两个取一个值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'hlowrd'"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[::2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "当step的值为负时，省略lower意味着从结尾开始分片，省略upper意味着一直分片到开头。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'dlrow olleh'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[::-1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "当给定的upper超出字符串的长度（注意：因为不包含upper，所以可以等于）时，Python并不会报错，不过只会计算到结尾。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'hello world'"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[:100]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 使用“0”作为索引开头的原因"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 使用`[low, up)`形式的原因"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "假设需要表示字符串 `hello` 中的内部子串 `el` ：\n",
-    "\n",
-    "|方式|`[low, up)`|`(low, up]`|`(lower, upper)`|`[lower, upper]`\n",
-    "|--|--|--|--|--|\n",
-    "|表示|`[1,3)`|`(0,2]`|`(0,3)`|`[1,2]`\n",
-    "|序列长度|`up - low`|`up - low`|`up - low - 1`|`up - low + 1`\n",
-    "\n",
-    "对长度来说，前两种方式比较好，因为不需要烦人的加一减一。\n",
-    "\n",
-    "现在只考虑前两种方法，假设要表示字符串`hello`中的从头开始的子串`hel`：\n",
-    "\n",
-    "|方式|`[low, up)`|`(low, up]`\n",
-    "|--|--|\n",
-    "|表示|`[0,3)`|`(-1,2]`|\n",
-    "|序列长度|`up - low`|`up - low`|\n",
-    "\n",
-    "第二种表示方法从`-1`开始，不是很好，所以选择使用第一种`[low, up)`的形式。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 使用0-base的形式"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> Just to beautiful to ignore.  \n",
-    "----Guido van Rossum"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "两种简单的情况：\n",
-    "\n",
-    "- 从头开始的n个元素；\n",
-    "    - 使用0-base：`[0, n)`\n",
-    "    - 使用1-base：`[1, n+1)`\n",
-    "\n",
-    "- 第`i+1`个元素到第`i+n`个元素。\n",
-    "    - 使用0-base：`[i, n+i)`\n",
-    "    - 使用1-base：`[i+1, n+i+1)`\n",
-    "\n",
-    "1-base有个`+1`部分，所以不推荐。\n",
-    "\n",
-    "综合这两种原因，**Python**使用0-base的方法来进行索引。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 索引和分片"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 索引"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于一个有序序列，可以通过索引的方法来访问对应位置的值。字符串便是一个有序序列的例子，**Python**使用 `[]` 来对有序序列进行索引。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'h'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = \"hello world\"\n",
+    "s[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Python**中索引是从 `0` 开始的，所以索引 `0` 对应与序列的第 `1` 个元素。为了得到第 `5` 个元素，需要使用索引值 `4` 。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'o'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "除了正向索引，**Python**还引入了负索引值的用法，即从后向前开始计数，例如，索引 `-2` 表示倒数第 `2` 个元素："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'l'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[-2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "单个索引大于等于字符串的长度时，会报错："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "string index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-4-79ffc22473a3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mIndexError\u001b[0m: string index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "s[11]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 分片"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "分片用来从序列中提取出想要的子序列，其用法为：\n",
+    "\n",
+    "    var[lower:upper:step]\n",
+    "\n",
+    "其范围包括 `lower` ，但不包括 `upper` ，即 `[lower, upper)`， `step` 表示取值间隔大小，如果没有默认为`1`。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'hello world'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'el'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[1:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "分片中包含的元素的个数为 `3-1=2` 。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "也可以使用负索引来指定分片的范围："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'ello wor'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[1:-2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "包括索引 `1` 但是不包括索引 `-2` 。\n",
+    "\n",
+    "lower和upper可以省略，省略lower意味着从开头开始分片，省略upper意味着一直分片到结尾。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'hel'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'rld'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[-3:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'hello world'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "每隔两个取一个值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'hlowrd'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[::2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "当step的值为负时，省略lower意味着从结尾开始分片，省略upper意味着一直分片到开头。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'dlrow olleh'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[::-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "当给定的upper超出字符串的长度（注意：因为不包含upper，所以可以等于）时，Python并不会报错，不过只会计算到结尾。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'hello world'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[:100]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 使用“0”作为索引开头的原因"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 使用`[low, up)`形式的原因"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "假设需要表示字符串 `hello` 中的内部子串 `el` ：\n",
+    "\n",
+    "|方式|`[low, up)`|`(low, up]`|`(lower, upper)`|`[lower, upper]`\n",
+    "|--|--|--|--|--|\n",
+    "|表示|`[1,3)`|`(0,2]`|`(0,3)`|`[1,2]`\n",
+    "|序列长度|`up - low`|`up - low`|`up - low - 1`|`up - low + 1`\n",
+    "\n",
+    "对长度来说，前两种方式比较好，因为不需要烦人的加一减一。\n",
+    "\n",
+    "现在只考虑前两种方法，假设要表示字符串`hello`中的从头开始的子串`hel`：\n",
+    "\n",
+    "|方式|`[low, up)`|`(low, up]`\n",
+    "|--|--|\n",
+    "|表示|`[0,3)`|`(-1,2]`|\n",
+    "|序列长度|`up - low`|`up - low`|\n",
+    "\n",
+    "第二种表示方法从`-1`开始，不是很好，所以选择使用第一种`[low, up)`的形式。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 使用0-base的形式"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> Just to beautiful to ignore.  \n",
+    "----Guido van Rossum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "两种简单的情况：\n",
+    "\n",
+    "- 从头开始的n个元素；\n",
+    "    - 使用0-base：`[0, n)`\n",
+    "    - 使用1-base：`[1, n+1)`\n",
+    "\n",
+    "- 第`i+1`个元素到第`i+n`个元素。\n",
+    "    - 使用0-base：`[i, n+i)`\n",
+    "    - 使用1-base：`[i+1, n+i+1)`\n",
+    "\n",
+    "1-base有个`+1`部分，所以不推荐。\n",
+    "\n",
+    "综合这两种原因，**Python**使用0-base的方法来进行索引。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/02. python essentials/02.06 lists.ipynb b/02-python-essentials/02.06-lists.ipynb
similarity index 100%
rename from 02. python essentials/02.06 lists.ipynb
rename to 02-python-essentials/02.06-lists.ipynb
diff --git a/02. python essentials/02.07 mutable and immutable data types.ipynb b/02-python-essentials/02.07-mutable-and-immutable-data-types.ipynb
similarity index 100%
rename from 02. python essentials/02.07 mutable and immutable data types.ipynb
rename to 02-python-essentials/02.07-mutable-and-immutable-data-types.ipynb
diff --git a/02. python essentials/02.08 tuples.ipynb b/02-python-essentials/02.08-tuples.ipynb
similarity index 94%
rename from 02. python essentials/02.08 tuples.ipynb
rename to 02-python-essentials/02.08-tuples.ipynb
index d5680087..3ea45ca9 100644
--- a/02. python essentials/02.08 tuples.ipynb	
+++ b/02-python-essentials/02.08-tuples.ipynb
@@ -1,313 +1,313 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 元组"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 基本操作"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "与列表相似，元组`Tuple`也是个有序序列，但是元组是不可变的，用`()`生成。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10, 11, 12, 13, 14)"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t = (10, 11, 12, 13, 14)\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以索引，切片："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(11, 12)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t[1:3]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "但是元组是不可变的："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "'tuple' object does not support item assignment",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-4-da6c1cabf0b0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 会报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
-     ]
-    }
-   ],
-   "source": [
-    "# 会报错\n",
-    "t[0] = 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 单个元素的元组生成"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "由于`()`在表达式中被应用，只含有单个元素的元组容易和表达式混淆，所以采用下列方式定义只有一个元素的元组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(10,)\n",
-      "<type 'tuple'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = (10,)\n",
-    "print a\n",
-    "print type(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<type 'int'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = (10)\n",
-    "print type(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "将列表转换为元组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10, 11, 12, 13, 14)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [10, 11, 12, 13, 14]\n",
-    "tuple(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 元组方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "由于元组是不可变的，所以只能有一些不可变的方法，例如计算元素个数 `count` 和元素位置 `index` ，用法与列表一样。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.count(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.index(12)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 为什么需要元组"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "旧式字符串格式化中参数要用元组；\n",
-    "\n",
-    "在字典中当作键值；\n",
-    "\n",
-    "数据库的返回值……"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 元组"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 基本操作"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "与列表相似，元组`Tuple`也是个有序序列，但是元组是不可变的，用`()`生成。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 11, 12, 13, 14)"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t = (10, 11, 12, 13, 14)\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以索引，切片："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(11, 12)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t[1:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "但是元组是不可变的："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'tuple' object does not support item assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-4-da6c1cabf0b0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 会报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
+     ]
+    }
+   ],
+   "source": [
+    "# 会报错\n",
+    "t[0] = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 单个元素的元组生成"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "由于`()`在表达式中被应用，只含有单个元素的元组容易和表达式混淆，所以采用下列方式定义只有一个元素的元组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10,)\n",
+      "<type 'tuple'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = (10,)\n",
+    "print a\n",
+    "print type(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<type 'int'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = (10)\n",
+    "print type(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将列表转换为元组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 11, 12, 13, 14)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = [10, 11, 12, 13, 14]\n",
+    "tuple(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 元组方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "由于元组是不可变的，所以只能有一些不可变的方法，例如计算元素个数 `count` 和元素位置 `index` ，用法与列表一样。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.count(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.index(12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 为什么需要元组"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "旧式字符串格式化中参数要用元组；\n",
+    "\n",
+    "在字典中当作键值；\n",
+    "\n",
+    "数据库的返回值……"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/02. python essentials/02.09 speed comparison between list & tuple.ipynb b/02-python-essentials/02.09-speed-comparison-between-list-&-tuple.ipynb
similarity index 100%
rename from 02. python essentials/02.09 speed comparison between list & tuple.ipynb
rename to 02-python-essentials/02.09-speed-comparison-between-list-&-tuple.ipynb
diff --git a/02. python essentials/02.10 dictionaries.ipynb b/02-python-essentials/02.10-dictionaries.ipynb
similarity index 100%
rename from 02. python essentials/02.10 dictionaries.ipynb
rename to 02-python-essentials/02.10-dictionaries.ipynb
diff --git a/02. python essentials/02.11 sets.ipynb b/02-python-essentials/02.11-sets.ipynb
similarity index 94%
rename from 02. python essentials/02.11 sets.ipynb
rename to 02-python-essentials/02.11-sets.ipynb
index 5ef90d4a..158086d0 100644
--- a/02. python essentials/02.11 sets.ipynb	
+++ b/02-python-essentials/02.11-sets.ipynb
@@ -1,1135 +1,1135 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 集合"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "之前看到的列表和字符串都是一种有序序列，而集合 `set` 是一种无序的序列。\n",
-    "\n",
-    "因为集合是无序的，所以当集合中存在两个同样的元素的时候，Python只会保存其中的一个（唯一性）；同时为了确保其中不包含同样的元素，集合中放入的元素只能是不可变的对象（确定性）。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 集合生成"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以用`set()`函数来显示的生成空集合："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "set"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = set()\n",
-    "type(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "也可以使用一个列表来初始化一个集合："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3}"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = set([1, 2, 3, 1])\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "集合会自动去除重复元素 `1`。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以看到，集合中的元素是用大括号`{}`包含起来的，这意味着可以用`{}`的形式来创建集合："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = {1, 2, 3, 1}\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "但是创建空集合的时候只能用`set`来创建，因为在Python中`{}`创建的是一个空的字典："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s = {}\n",
-    "type(s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 集合操作"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "假设有这样两个集合："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = {1, 2, 3, 4}\n",
-    "b = {3, 4, 5, 6}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 并"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "两个集合的并，返回包含两个集合所有元素的集合（去除重复）。\n",
-    "\n",
-    "可以用方法 `a.union(b)` 或者操作 `a | b` 实现。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3, 4, 5, 6}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.union(b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3, 4, 5, 6}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b.union(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3, 4, 5, 6}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a | b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 交"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "两个集合的交，返回包含两个集合共有元素的集合。\n",
-    "\n",
-    "可以用方法 `a.intersection(b)` 或者操作 `a & b` 实现。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{3, 4}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.intersection(b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{3, 4}"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b.intersection(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{3, 4}"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a & b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "set([3, 4])\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(a & b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "注意：一般使用print打印set的结果与表示方法并不一致。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 差"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`a` 和 `b` 的差集，返回只在 `a` 不在 `b` 的元素组成的集合。\n",
-    "\n",
-    "可以用方法 `a.difference(b)` 或者操作 `a - b` 实现。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2}"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.difference(b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2}"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a - b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "注意，`a - b` 与 `b - a`并不一样，`b - a` 返回的是返回 b 不在 a 的元素组成的集合："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{5, 6}"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b.difference(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{5, 6}"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b - a "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 对称差"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`a` 和`b` 的对称差集，返回在 `a` 或在 `b` 中，但是不同时在 `a` 和 `b` 中的元素组成的集合。\n",
-    "\n",
-    "可以用方法 `a.symmetric_difference(b)` 或者操作 `a ^ b` 实现（异或操作符）。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 5, 6}"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.symmetric_difference(b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 5, 6}"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b.symmetric_difference(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 5, 6}"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a ^ b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 包含关系"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "假设现在有这样两个集合："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = {1, 2, 3}\n",
-    "b = {1, 2}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "要判断 `b` 是不是 `a` 的子集，可以用 `b.issubset(a)` 方法，或者更简单的用操作 `b <= a` ："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b.issubset(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b <= a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "与之对应，也可以用 `a.issuperset(b)` 或者 `a >= b` 来判断："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.issuperset(b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a >= b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "方法只能用来测试子集，但是操作符可以用来判断真子集："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a <= a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "自己不是自己的真子集："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a < a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 集合方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `add` 方法向集合添加单个元素"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "跟列表的 `append` 方法类似，用来向集合添加单个元素。\n",
-    "\n",
-    "    s.add(a)\n",
-    "\n",
-    "将元素 `a` 加入集合 `s` 中。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3, 5}"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t = {1, 2, 3}\n",
-    "t.add(5)\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "如果添加的是已有元素，集合不改变："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3, 5}"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t.add(3)\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `update` 方法向集合添加多个元素"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "跟列表的`extend`方法类似，用来向集合添加多个元素。\n",
-    "\n",
-    "    s.update(seq)\n",
-    "\n",
-    "将`seq`中的元素添加到`s`中。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3, 5, 6, 7}"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t.update([5, 6, 7])\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `remove` 方法移除单个元素"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    s.remove(ob)\n",
-    "\n",
-    "从集合`s`中移除元素`ob`，如果不存在会报错。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{2, 3, 5, 6, 7}"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t.remove(1)\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "10",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-31-3bc25c5e1ff4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m: 10"
-     ]
-    }
-   ],
-   "source": [
-    "t.remove(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### pop方法弹出元素"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "由于集合没有顺序，不能像列表一样按照位置弹出元素，所以`pop` 方法删除并返回集合中任意一个元素，如果集合中没有元素会报错。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{3, 5, 6, 7}"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t.pop()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "set([3, 5, 6, 7])\n"
-     ]
-    }
-   ],
-   "source": [
-    "print t"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'pop from an empty set'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-34-9f9e06c962e6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# 报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m: 'pop from an empty set'"
-     ]
-    }
-   ],
-   "source": [
-    "s = set()\n",
-    "# 报错\n",
-    "s.pop()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### discard 方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "作用与 `remove` 一样，但是当元素在集合中不存在的时候不会报错。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "t.discard(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{5, 6, 7}"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不存在的元素不会报错："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "t.discard(20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{5, 6, 7}"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### difference_update方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    a.difference_update(b)\n",
-    "\n",
-    "从a中去除所有属于b的元素："
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 集合"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "之前看到的列表和字符串都是一种有序序列，而集合 `set` 是一种无序的序列。\n",
+    "\n",
+    "因为集合是无序的，所以当集合中存在两个同样的元素的时候，Python只会保存其中的一个（唯一性）；同时为了确保其中不包含同样的元素，集合中放入的元素只能是不可变的对象（确定性）。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 集合生成"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以用`set()`函数来显示的生成空集合："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = set()\n",
+    "type(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "也可以使用一个列表来初始化一个集合："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = set([1, 2, 3, 1])\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "集合会自动去除重复元素 `1`。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以看到，集合中的元素是用大括号`{}`包含起来的，这意味着可以用`{}`的形式来创建集合："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = {1, 2, 3, 1}\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "但是创建空集合的时候只能用`set`来创建，因为在Python中`{}`创建的是一个空的字典："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = {}\n",
+    "type(s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 集合操作"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "假设有这样两个集合："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "a = {1, 2, 3, 4}\n",
+    "b = {3, 4, 5, 6}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 并"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "两个集合的并，返回包含两个集合所有元素的集合（去除重复）。\n",
+    "\n",
+    "可以用方法 `a.union(b)` 或者操作 `a | b` 实现。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5, 6}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.union(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5, 6}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.union(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5, 6}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a | b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 交"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "两个集合的交，返回包含两个集合共有元素的集合。\n",
+    "\n",
+    "可以用方法 `a.intersection(b)` 或者操作 `a & b` 实现。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{3, 4}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.intersection(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{3, 4}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.intersection(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{3, 4}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a & b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "set([3, 4])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a & b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "注意：一般使用print打印set的结果与表示方法并不一致。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 差"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`a` 和 `b` 的差集，返回只在 `a` 不在 `b` 的元素组成的集合。\n",
+    "\n",
+    "可以用方法 `a.difference(b)` 或者操作 `a - b` 实现。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.difference(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a - b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "注意，`a - b` 与 `b - a`并不一样，`b - a` 返回的是返回 b 不在 a 的元素组成的集合："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{5, 6}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.difference(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{5, 6}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b - a "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 对称差"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`a` 和`b` 的对称差集，返回在 `a` 或在 `b` 中，但是不同时在 `a` 和 `b` 中的元素组成的集合。\n",
+    "\n",
+    "可以用方法 `a.symmetric_difference(b)` 或者操作 `a ^ b` 实现（异或操作符）。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 5, 6}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.symmetric_difference(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 5, 6}"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.symmetric_difference(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 5, 6}"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a ^ b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 包含关系"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "假设现在有这样两个集合："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "a = {1, 2, 3}\n",
+    "b = {1, 2}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "要判断 `b` 是不是 `a` 的子集，可以用 `b.issubset(a)` 方法，或者更简单的用操作 `b <= a` ："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.issubset(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b <= a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "与之对应，也可以用 `a.issuperset(b)` 或者 `a >= b` 来判断："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.issuperset(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a >= b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "方法只能用来测试子集，但是操作符可以用来判断真子集："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a <= a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "自己不是自己的真子集："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a < a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 集合方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `add` 方法向集合添加单个元素"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "跟列表的 `append` 方法类似，用来向集合添加单个元素。\n",
+    "\n",
+    "    s.add(a)\n",
+    "\n",
+    "将元素 `a` 加入集合 `s` 中。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 5}"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t = {1, 2, 3}\n",
+    "t.add(5)\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "如果添加的是已有元素，集合不改变："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 5}"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.add(3)\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `update` 方法向集合添加多个元素"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "跟列表的`extend`方法类似，用来向集合添加多个元素。\n",
+    "\n",
+    "    s.update(seq)\n",
+    "\n",
+    "将`seq`中的元素添加到`s`中。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 5, 6, 7}"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.update([5, 6, 7])\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `remove` 方法移除单个元素"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    s.remove(ob)\n",
+    "\n",
+    "从集合`s`中移除元素`ob`，如果不存在会报错。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{2, 3, 5, 6, 7}"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.remove(1)\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "10",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-31-3bc25c5e1ff4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m: 10"
+     ]
+    }
+   ],
+   "source": [
+    "t.remove(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### pop方法弹出元素"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "由于集合没有顺序，不能像列表一样按照位置弹出元素，所以`pop` 方法删除并返回集合中任意一个元素，如果集合中没有元素会报错。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{3, 5, 6, 7}"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.pop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "set([3, 5, 6, 7])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print t"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'pop from an empty set'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-34-9f9e06c962e6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# 报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m: 'pop from an empty set'"
+     ]
+    }
+   ],
+   "source": [
+    "s = set()\n",
+    "# 报错\n",
+    "s.pop()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### discard 方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "作用与 `remove` 一样，但是当元素在集合中不存在的时候不会报错。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "t.discard(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{5, 6, 7}"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不存在的元素不会报错："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "t.discard(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{5, 6, 7}"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### difference_update方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    a.difference_update(b)\n",
+    "\n",
+    "从a中去除所有属于b的元素："
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/02. python essentials/02.12 frozen sets.ipynb b/02-python-essentials/02.12-frozen-sets.ipynb
similarity index 95%
rename from 02. python essentials/02.12 frozen sets.ipynb
rename to 02-python-essentials/02.12-frozen-sets.ipynb
index 48cccf4a..054e74a7 100644
--- a/02. python essentials/02.12 frozen sets.ipynb	
+++ b/02-python-essentials/02.12-frozen-sets.ipynb
@@ -1,153 +1,153 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 不可变集合"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对应于元组（`tuple`）与列表（`list`）的关系，对于集合（`set`），**Python**提供了一种叫做不可变集合（`frozen set`）的数据结构。\n",
-    "\n",
-    "使用 `frozenset` 来进行创建："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "frozenset({1, 2, 3, 'a'})"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s = frozenset([1, 2, 3, 'a', 1])\n",
-    "s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "与集合不同的是，不可变集合一旦创建就不可以改变。\n",
-    "\n",
-    "不可变集合的一个主要应用是用来作为字典的键，例如用一个字典来记录两个城市之间的距离："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{frozenset({'Austin', 'New York'}): 1515,\n",
-       " frozenset({'Austin', 'Los Angeles'}): 1233,\n",
-       " frozenset({'Los Angeles', 'New York'}): 2498}"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flight_distance = {}\n",
-    "city_pair = frozenset(['Los Angeles', 'New York'])\n",
-    "flight_distance[city_pair] = 2498\n",
-    "flight_distance[frozenset(['Austin', 'Los Angeles'])] = 1233\n",
-    "flight_distance[frozenset(['Austin', 'New York'])] = 1515\n",
-    "flight_distance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "由于集合不分顺序，所以不同顺序不会影响查阅结果："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1515"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flight_distance[frozenset(['New York','Austin'])]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1515"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flight_distance[frozenset(['Austin','New York'])]"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 不可变集合"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对应于元组（`tuple`）与列表（`list`）的关系，对于集合（`set`），**Python**提供了一种叫做不可变集合（`frozen set`）的数据结构。\n",
+    "\n",
+    "使用 `frozenset` 来进行创建："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "frozenset({1, 2, 3, 'a'})"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = frozenset([1, 2, 3, 'a', 1])\n",
+    "s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "与集合不同的是，不可变集合一旦创建就不可以改变。\n",
+    "\n",
+    "不可变集合的一个主要应用是用来作为字典的键，例如用一个字典来记录两个城市之间的距离："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{frozenset({'Austin', 'New York'}): 1515,\n",
+       " frozenset({'Austin', 'Los Angeles'}): 1233,\n",
+       " frozenset({'Los Angeles', 'New York'}): 2498}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flight_distance = {}\n",
+    "city_pair = frozenset(['Los Angeles', 'New York'])\n",
+    "flight_distance[city_pair] = 2498\n",
+    "flight_distance[frozenset(['Austin', 'Los Angeles'])] = 1233\n",
+    "flight_distance[frozenset(['Austin', 'New York'])] = 1515\n",
+    "flight_distance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "由于集合不分顺序，所以不同顺序不会影响查阅结果："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1515"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flight_distance[frozenset(['New York','Austin'])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1515"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flight_distance[frozenset(['Austin','New York'])]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/02. python essentials/02.13 how python assignment works.ipynb b/02-python-essentials/02.13-how-python-assignment-works.ipynb
similarity index 95%
rename from 02. python essentials/02.13 how python assignment works.ipynb
rename to 02-python-essentials/02.13-how-python-assignment-works.ipynb
index b6082346..330adc2e 100644
--- a/02. python essentials/02.13 how python assignment works.ipynb	
+++ b/02-python-essentials/02.13-how-python-assignment-works.ipynb
@@ -1,623 +1,623 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Python 赋值机制"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "先看一个例子："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 100, 3]\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = [1, 2, 3]\n",
-    "y = x\n",
-    "x[1] = 100\n",
-    "print y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "改变变量`x`的值，变量`y`的值也随着改变，这与**Python**内部的赋值机制有关。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 简单类型"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "先来看这一段代码在**Python**中的执行过程。\n",
-    "\n",
-    "```python\n",
-    "x = 500\n",
-    "y = x\n",
-    "y = 'foo'\n",
-    "```\n",
-    "\n",
-    "- `x = 500`\n",
-    "\n",
-    "**Python**分配了一个 `PyInt` 大小的内存 `pos1` 用来储存对象 `500` ，然后，Python在命名空间中让变量 `x` 指向了这一块内存，注意，整数是不可变类型，所以这块内存的内容是不可变的。\n",
-    "\n",
-    "|内存|命名空间|\n",
-    "|---|---|\n",
-    "|`pos1 : PyInt(500)` (不可变)|          `x : pos1`         |\n",
-    "\n",
-    "- `y = x `\n",
-    "\n",
-    "**Python**并没有使用新的内存来储存变量 `y` 的值，而是在命名空间中，让变量 `y` 与变量 `x` 指向了同一块内存空间。\n",
-    "\n",
-    "|内存|命名空间|\n",
-    "|---|---|\n",
-    "|`pos1 : PyInt(500)` (不可变)|`x : pos1`<br> `y : pos1`|\n",
-    "\n",
-    "- `y = 'foo'`\n",
-    "\n",
-    "**Python**此时分配一个 `PyStr` 大小的内存 `pos2` 来储存对象 `foo` ，然后改变变量 `y` 所指的对象。\n",
-    "\n",
-    "|内存|命名空间|\n",
-    "|---|---|\n",
-    "|`pos1 : PyInt(500)` (不可变)<br> `pos2 : PyStr('foo')` (不可变)|`x : pos1`<br>`y : pos2`|\n",
-    "\n",
-    "对这一过程进行验证，可以使用 `id` 函数。\n",
-    "\n",
-    "    id(x)\n",
-    "\n",
-    "返回变量 `x` 的内存地址。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "48220272L"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = 500\n",
-    "id(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "48220272L"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y = x\n",
-    "id(y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "也可以使用 `is` 来判断是不是指向同一个事物："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x is y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "现在 `y` 指向另一块内存："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "39148320L"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y = 'foo'\n",
-    "id(y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x is y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "**Python**会为每个出现的对象进行赋值，哪怕它们的值是一样的，例如："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "48220296L"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = 500\n",
-    "id(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "48220224L"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y = 500\n",
-    "id(y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x is y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "不过，为了提高内存利用效率，对于一些简单的对象，如一些数值较小的int对象，**Python**采用了重用对象内存的办法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6579504L"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = 2\n",
-    "id(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6579504L"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y = 2\n",
-    "id(y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x is y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "## 容器类型"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "现在来看另一段代码：\n",
-    "\n",
-    "``` python\n",
-    "x = [500, 501, 502]\n",
-    "y = x\n",
-    "y[1] = 600\n",
-    "y = [700, 800]\n",
-    "```\n",
-    "\n",
-    "- `x = [500, 501, 502]`\n",
-    "\n",
-    "Python为3个PyInt分配内存 `pos1` ， `pos2` ， `pos3` （不可变），然后为列表分配一段内存 `pos4` ，它包含3个位置，分别指向这3个内存，最后再让变量 `x` 指向这个列表。\n",
-    "\n",
-    "|内存|命名空间|\n",
-    "|---|---|\n",
-    "|`pos1 : PyInt(500)` (不可变) <br> `pos2 : PyInt(501)` (不可变) <br>`pos3 : PyInt(502)` (不可变) <br> `pos4 : PyList(pos1, pos2, pos3)` (可变)|`x : pos4`|\n",
-    "\n",
-    "- `y = x`\n",
-    "\n",
-    "并没有创建新的对象，只需要将 `y` 指向 `pos4` 即可。\n",
-    "\n",
-    "|内存|命名空间|\n",
-    "|---|---|\n",
-    "|`pos1 : PyInt(500)` (不可变) <br> `pos2 : PyInt(501)` (不可变) <br> `pos3 : PyInt(502)` (不可变) <br> `pos4 : PyList(pos1, pos2, pos3)` (可变)|`x : pos4`<br>`y : pos4`|\n",
-    "\n",
-    "- `y[1] = 600`\n",
-    "\n",
-    "原来 `y[1]` 这个位置指向的是 `pos2` ，由于不能修改 `pos2` 的值，所以首先为 `600` 分配新内存 `pos5` 。\n",
-    "\n",
-    "再把 `y[1]` 指向的位置修改为 `pos5` 。此时，由于 `pos2` 位置的对象已经没有用了，**Python**会自动调用垃圾处理机制将它回收。\n",
-    "\n",
-    "|内存|命名空间|\n",
-    "|---|---|\n",
-    "|`pos1 : PyInt(500)` (不可变) <br> `pos2 :` 垃圾回收 <br> `pos3 : PyInt(502)` (不可变) <br> `pos4 : PyList(pos1, pos5, pos3)` (可变)<br>`pos5 : PyInt(600)` (不可变) |`x : pos4`<br> `y : pos4`|\n",
-    "\n",
-    "- `y = [700, 800]`\n",
-    "\n",
-    "首先创建这个列表，然后将变量 `y` 指向它。\n",
-    "\n",
-    "|内存|命名空间|\n",
-    "|---|---|\n",
-    "|`pos1 : PyInt(500)` (不可变) <br> `pos3 : PyInt(502)` (不可变) <br>`pos4 : PyList(pos1, pos5, pos3)` (可变)<br>`pos5 : PyInt(600)` (不可变) <br>`pos6 : PyInt(700)` (不可变)<br>`pos7 : PyInt(800)` (不可变)<br>`pos8 : PyList(pos6, pos7)` (可变)|`x : pos4` <br> `y : pos8`|\n",
-    "\n",
-    "对这一过程进行验证："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "48220224\n",
-      "48220248\n",
-      "48220200\n",
-      "54993032\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = [500, 501, 502]\n",
-    "print id(x[0])\n",
-    "print id(x[1])\n",
-    "print id(x[2])\n",
-    "print id(x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "赋值，`id(y)` 与 `id(x)` 相同。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "54993032\n"
-     ]
-    }
-   ],
-   "source": [
-    "y = x\n",
-    "print id(y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x is y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "修改 `y[1]` ，`id(y)` 并不改变。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "54993032\n"
-     ]
-    }
-   ],
-   "source": [
-    "y[1] = 600\n",
-    "print id(y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`id(x[1])` 和 `id(y[1])` 的值改变了。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "48220272\n",
-      "48220272\n"
-     ]
-    }
-   ],
-   "source": [
-    "print id(x[1])\n",
-    "print id(y[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "更改 `y` 的值，`id(y)` 的值改变"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "54995272\n",
-      "54993032\n"
-     ]
-    }
-   ],
-   "source": [
-    "y = [700, 800]\n",
-    "print id(y)\n",
-    "print id(x)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Python 赋值机制"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "先看一个例子："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 100, 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = [1, 2, 3]\n",
+    "y = x\n",
+    "x[1] = 100\n",
+    "print y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "改变变量`x`的值，变量`y`的值也随着改变，这与**Python**内部的赋值机制有关。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 简单类型"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "先来看这一段代码在**Python**中的执行过程。\n",
+    "\n",
+    "```python\n",
+    "x = 500\n",
+    "y = x\n",
+    "y = 'foo'\n",
+    "```\n",
+    "\n",
+    "- `x = 500`\n",
+    "\n",
+    "**Python**分配了一个 `PyInt` 大小的内存 `pos1` 用来储存对象 `500` ，然后，Python在命名空间中让变量 `x` 指向了这一块内存，注意，整数是不可变类型，所以这块内存的内容是不可变的。\n",
+    "\n",
+    "|内存|命名空间|\n",
+    "|---|---|\n",
+    "|`pos1 : PyInt(500)` (不可变)|          `x : pos1`         |\n",
+    "\n",
+    "- `y = x `\n",
+    "\n",
+    "**Python**并没有使用新的内存来储存变量 `y` 的值，而是在命名空间中，让变量 `y` 与变量 `x` 指向了同一块内存空间。\n",
+    "\n",
+    "|内存|命名空间|\n",
+    "|---|---|\n",
+    "|`pos1 : PyInt(500)` (不可变)|`x : pos1`<br> `y : pos1`|\n",
+    "\n",
+    "- `y = 'foo'`\n",
+    "\n",
+    "**Python**此时分配一个 `PyStr` 大小的内存 `pos2` 来储存对象 `foo` ，然后改变变量 `y` 所指的对象。\n",
+    "\n",
+    "|内存|命名空间|\n",
+    "|---|---|\n",
+    "|`pos1 : PyInt(500)` (不可变)<br> `pos2 : PyStr('foo')` (不可变)|`x : pos1`<br>`y : pos2`|\n",
+    "\n",
+    "对这一过程进行验证，可以使用 `id` 函数。\n",
+    "\n",
+    "    id(x)\n",
+    "\n",
+    "返回变量 `x` 的内存地址。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "48220272L"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = 500\n",
+    "id(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "48220272L"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = x\n",
+    "id(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "也可以使用 `is` 来判断是不是指向同一个事物："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x is y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "现在 `y` 指向另一块内存："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "39148320L"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = 'foo'\n",
+    "id(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x is y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "**Python**会为每个出现的对象进行赋值，哪怕它们的值是一样的，例如："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "48220296L"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = 500\n",
+    "id(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "48220224L"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = 500\n",
+    "id(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x is y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "不过，为了提高内存利用效率，对于一些简单的对象，如一些数值较小的int对象，**Python**采用了重用对象内存的办法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6579504L"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = 2\n",
+    "id(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6579504L"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = 2\n",
+    "id(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x is y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## 容器类型"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "现在来看另一段代码：\n",
+    "\n",
+    "``` python\n",
+    "x = [500, 501, 502]\n",
+    "y = x\n",
+    "y[1] = 600\n",
+    "y = [700, 800]\n",
+    "```\n",
+    "\n",
+    "- `x = [500, 501, 502]`\n",
+    "\n",
+    "Python为3个PyInt分配内存 `pos1` ， `pos2` ， `pos3` （不可变），然后为列表分配一段内存 `pos4` ，它包含3个位置，分别指向这3个内存，最后再让变量 `x` 指向这个列表。\n",
+    "\n",
+    "|内存|命名空间|\n",
+    "|---|---|\n",
+    "|`pos1 : PyInt(500)` (不可变) <br> `pos2 : PyInt(501)` (不可变) <br>`pos3 : PyInt(502)` (不可变) <br> `pos4 : PyList(pos1, pos2, pos3)` (可变)|`x : pos4`|\n",
+    "\n",
+    "- `y = x`\n",
+    "\n",
+    "并没有创建新的对象，只需要将 `y` 指向 `pos4` 即可。\n",
+    "\n",
+    "|内存|命名空间|\n",
+    "|---|---|\n",
+    "|`pos1 : PyInt(500)` (不可变) <br> `pos2 : PyInt(501)` (不可变) <br> `pos3 : PyInt(502)` (不可变) <br> `pos4 : PyList(pos1, pos2, pos3)` (可变)|`x : pos4`<br>`y : pos4`|\n",
+    "\n",
+    "- `y[1] = 600`\n",
+    "\n",
+    "原来 `y[1]` 这个位置指向的是 `pos2` ，由于不能修改 `pos2` 的值，所以首先为 `600` 分配新内存 `pos5` 。\n",
+    "\n",
+    "再把 `y[1]` 指向的位置修改为 `pos5` 。此时，由于 `pos2` 位置的对象已经没有用了，**Python**会自动调用垃圾处理机制将它回收。\n",
+    "\n",
+    "|内存|命名空间|\n",
+    "|---|---|\n",
+    "|`pos1 : PyInt(500)` (不可变) <br> `pos2 :` 垃圾回收 <br> `pos3 : PyInt(502)` (不可变) <br> `pos4 : PyList(pos1, pos5, pos3)` (可变)<br>`pos5 : PyInt(600)` (不可变) |`x : pos4`<br> `y : pos4`|\n",
+    "\n",
+    "- `y = [700, 800]`\n",
+    "\n",
+    "首先创建这个列表，然后将变量 `y` 指向它。\n",
+    "\n",
+    "|内存|命名空间|\n",
+    "|---|---|\n",
+    "|`pos1 : PyInt(500)` (不可变) <br> `pos3 : PyInt(502)` (不可变) <br>`pos4 : PyList(pos1, pos5, pos3)` (可变)<br>`pos5 : PyInt(600)` (不可变) <br>`pos6 : PyInt(700)` (不可变)<br>`pos7 : PyInt(800)` (不可变)<br>`pos8 : PyList(pos6, pos7)` (可变)|`x : pos4` <br> `y : pos8`|\n",
+    "\n",
+    "对这一过程进行验证："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "48220224\n",
+      "48220248\n",
+      "48220200\n",
+      "54993032\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = [500, 501, 502]\n",
+    "print id(x[0])\n",
+    "print id(x[1])\n",
+    "print id(x[2])\n",
+    "print id(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "赋值，`id(y)` 与 `id(x)` 相同。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "54993032\n"
+     ]
+    }
+   ],
+   "source": [
+    "y = x\n",
+    "print id(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x is y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "修改 `y[1]` ，`id(y)` 并不改变。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "54993032\n"
+     ]
+    }
+   ],
+   "source": [
+    "y[1] = 600\n",
+    "print id(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`id(x[1])` 和 `id(y[1])` 的值改变了。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "48220272\n",
+      "48220272\n"
+     ]
+    }
+   ],
+   "source": [
+    "print id(x[1])\n",
+    "print id(y[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "更改 `y` 的值，`id(y)` 的值改变"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "54995272\n",
+      "54993032\n"
+     ]
+    }
+   ],
+   "source": [
+    "y = [700, 800]\n",
+    "print id(y)\n",
+    "print id(x)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/02. python essentials/02.14 if statement.ipynb b/02-python-essentials/02.14-if-statement.ipynb
similarity index 95%
rename from 02. python essentials/02.14 if statement.ipynb
rename to 02-python-essentials/02.14-if-statement.ipynb
index fd4bcd95..dcbc780e 100644
--- a/02. python essentials/02.14 if statement.ipynb	
+++ b/02-python-essentials/02.14-if-statement.ipynb
@@ -1,387 +1,387 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 判断语句"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 基本用法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "判断，基于一定的条件，决定是否要执行特定的一段代码，例如判断一个数是不是正数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hey!\n",
-      "x is positive\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = 0.5\n",
-    "if x > 0:\n",
-    "    print \"Hey!\"\n",
-    "    print \"x is positive\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在这里，如果 `x > 0` 为 `False` ，那么程序将不会执行两条 `print` 语句。\n",
-    "\n",
-    "虽然都是用 `if` 关键词定义判断，但与**C，Java**等语言不同，**Python**不使用 `{}` 将 `if` 语句控制的区域包含起来。**Python**使用的是缩进方法。同时，也不需要用 `()` 将判断条件括起来。\n",
-    "\n",
-    "上面例子中的这两条语句：\n",
-    "```python\n",
-    "    print \"Hey!\"\n",
-    "    print \"x is positive\"\n",
-    "```\n",
-    "就叫做一个代码块，同一个代码块使用同样的缩进值，它们组成了这条 `if` 语句的主体。\n",
-    "\n",
-    "不同的缩进值表示不同的代码块，例如：\n",
-    "\n",
-    "`x > 0` 时："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hey!\n",
-      "x is positive\n",
-      "This is still part of the block\n",
-      "This isn't part of the block, and will always print.\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = 0.5\n",
-    "if x > 0:\n",
-    "    print \"Hey!\"\n",
-    "    print \"x is positive\"\n",
-    "    print \"This is still part of the block\"\n",
-    "print \"This isn't part of the block, and will always print.\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`x < 0` 时："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This isn't part of the block, and will always print.\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = -0.5\n",
-    "if x > 0:\n",
-    "    print \"Hey!\"\n",
-    "    print \"x is positive\"\n",
-    "    print \"This is still part of the block\"\n",
-    "print \"This isn't part of the block, and will always print.\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在这两个例子中，最后一句并不是`if`语句中的内容，所以不管条件满不满足，它都会被执行。\n",
-    "\n",
-    "一个完整的 `if` 结构通常如下所示（注意：条件后的 `:` 是必须要的，缩进值需要一样）：\n",
-    "    \n",
-    "    if <condition 1>:\n",
-    "        <statement 1>\n",
-    "        <statement 2>\n",
-    "    elif <condition 2>: \n",
-    "        <statements>\n",
-    "    else:\n",
-    "        <statements>\n",
-    "\n",
-    "当条件1被满足时，执行 `if` 下面的语句，当条件1不满足的时候，转到 `elif` ，看它的条件2满不满足，满足执行 `elif` 下面的语句，不满足则执行 `else` 下面的语句。\n",
-    "\n",
-    "对于上面的例子进行扩展："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x is zero\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = 0\n",
-    "if x > 0:\n",
-    "    print \"x is positive\"\n",
-    "elif x == 0:\n",
-    "    print \"x is zero\"\n",
-    "else:\n",
-    "    print \"x is negative\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`elif` 的个数没有限制，可以是1个或者多个，也可以没有。\n",
-    "\n",
-    "`else` 最多只有1个，也可以没有。\n",
-    "\n",
-    "可以使用 `and` ， `or` , `not` 等关键词结合多个判断条件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = 10\n",
-    "y = -5\n",
-    "x > 0 and y < 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "not x > 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x < 0 or y < 0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里使用这个简单的例子，假如想判断一个年份是不是闰年，按照闰年的定义，这里只需要判断这个年份是不是能被4整除，但是不能被100整除，或者正好被400整除："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This is not a leap year.\n"
-     ]
-    }
-   ],
-   "source": [
-    "year = 1900\n",
-    "if year % 400 == 0:\n",
-    "    print \"This is a leap year!\"\n",
-    "# 两个条件都满足才执行\n",
-    "elif year % 4 == 0 and year % 100 != 0:\n",
-    "    print \"This is a leap year!\"\n",
-    "else:\n",
-    "    print \"This is not a leap year.\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 值的测试"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Python**不仅仅可以使用布尔型变量作为条件，它可以直接在`if`中使用任何表达式作为条件：\n",
-    "\n",
-    "大部分表达式的值都会被当作`True`，但以下表达式值会被当作`False`：\n",
-    "\n",
-    "- False\n",
-    "- None\n",
-    "- 0\n",
-    "- 空字符串，空列表，空字典，空集合"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The first element is: 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "mylist = [3, 1, 4, 1, 5, 9]\n",
-    "if mylist:\n",
-    "    print \"The first element is:\", mylist[0]\n",
-    "else:\n",
-    "    print \"There is no first element.\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "修改为空列表："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There is no first element.\n"
-     ]
-    }
-   ],
-   "source": [
-    "mylist = []\n",
-    "if mylist:\n",
-    "    print \"The first element is:\", mylist[0]\n",
-    "else:\n",
-    "    print \"There is no first element.\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "当然这种用法并不推荐，推荐使用 `if len(mylist) > 0:` 来判断一个列表是否为空。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 判断语句"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 基本用法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "判断，基于一定的条件，决定是否要执行特定的一段代码，例如判断一个数是不是正数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hey!\n",
+      "x is positive\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 0.5\n",
+    "if x > 0:\n",
+    "    print \"Hey!\"\n",
+    "    print \"x is positive\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在这里，如果 `x > 0` 为 `False` ，那么程序将不会执行两条 `print` 语句。\n",
+    "\n",
+    "虽然都是用 `if` 关键词定义判断，但与**C，Java**等语言不同，**Python**不使用 `{}` 将 `if` 语句控制的区域包含起来。**Python**使用的是缩进方法。同时，也不需要用 `()` 将判断条件括起来。\n",
+    "\n",
+    "上面例子中的这两条语句：\n",
+    "```python\n",
+    "    print \"Hey!\"\n",
+    "    print \"x is positive\"\n",
+    "```\n",
+    "就叫做一个代码块，同一个代码块使用同样的缩进值，它们组成了这条 `if` 语句的主体。\n",
+    "\n",
+    "不同的缩进值表示不同的代码块，例如：\n",
+    "\n",
+    "`x > 0` 时："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hey!\n",
+      "x is positive\n",
+      "This is still part of the block\n",
+      "This isn't part of the block, and will always print.\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 0.5\n",
+    "if x > 0:\n",
+    "    print \"Hey!\"\n",
+    "    print \"x is positive\"\n",
+    "    print \"This is still part of the block\"\n",
+    "print \"This isn't part of the block, and will always print.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`x < 0` 时："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This isn't part of the block, and will always print.\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = -0.5\n",
+    "if x > 0:\n",
+    "    print \"Hey!\"\n",
+    "    print \"x is positive\"\n",
+    "    print \"This is still part of the block\"\n",
+    "print \"This isn't part of the block, and will always print.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在这两个例子中，最后一句并不是`if`语句中的内容，所以不管条件满不满足，它都会被执行。\n",
+    "\n",
+    "一个完整的 `if` 结构通常如下所示（注意：条件后的 `:` 是必须要的，缩进值需要一样）：\n",
+    "    \n",
+    "    if <condition 1>:\n",
+    "        <statement 1>\n",
+    "        <statement 2>\n",
+    "    elif <condition 2>: \n",
+    "        <statements>\n",
+    "    else:\n",
+    "        <statements>\n",
+    "\n",
+    "当条件1被满足时，执行 `if` 下面的语句，当条件1不满足的时候，转到 `elif` ，看它的条件2满不满足，满足执行 `elif` 下面的语句，不满足则执行 `else` 下面的语句。\n",
+    "\n",
+    "对于上面的例子进行扩展："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x is zero\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 0\n",
+    "if x > 0:\n",
+    "    print \"x is positive\"\n",
+    "elif x == 0:\n",
+    "    print \"x is zero\"\n",
+    "else:\n",
+    "    print \"x is negative\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`elif` 的个数没有限制，可以是1个或者多个，也可以没有。\n",
+    "\n",
+    "`else` 最多只有1个，也可以没有。\n",
+    "\n",
+    "可以使用 `and` ， `or` , `not` 等关键词结合多个判断条件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = 10\n",
+    "y = -5\n",
+    "x > 0 and y < 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "not x > 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x < 0 or y < 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里使用这个简单的例子，假如想判断一个年份是不是闰年，按照闰年的定义，这里只需要判断这个年份是不是能被4整除，但是不能被100整除，或者正好被400整除："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This is not a leap year.\n"
+     ]
+    }
+   ],
+   "source": [
+    "year = 1900\n",
+    "if year % 400 == 0:\n",
+    "    print \"This is a leap year!\"\n",
+    "# 两个条件都满足才执行\n",
+    "elif year % 4 == 0 and year % 100 != 0:\n",
+    "    print \"This is a leap year!\"\n",
+    "else:\n",
+    "    print \"This is not a leap year.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 值的测试"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Python**不仅仅可以使用布尔型变量作为条件，它可以直接在`if`中使用任何表达式作为条件：\n",
+    "\n",
+    "大部分表达式的值都会被当作`True`，但以下表达式值会被当作`False`：\n",
+    "\n",
+    "- False\n",
+    "- None\n",
+    "- 0\n",
+    "- 空字符串，空列表，空字典，空集合"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The first element is: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "mylist = [3, 1, 4, 1, 5, 9]\n",
+    "if mylist:\n",
+    "    print \"The first element is:\", mylist[0]\n",
+    "else:\n",
+    "    print \"There is no first element.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "修改为空列表："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There is no first element.\n"
+     ]
+    }
+   ],
+   "source": [
+    "mylist = []\n",
+    "if mylist:\n",
+    "    print \"The first element is:\", mylist[0]\n",
+    "else:\n",
+    "    print \"There is no first element.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "当然这种用法并不推荐，推荐使用 `if len(mylist) > 0:` 来判断一个列表是否为空。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/02. python essentials/02.15 loops.ipynb b/02-python-essentials/02.15-loops.ipynb
similarity index 95%
rename from 02. python essentials/02.15 loops.ipynb
rename to 02-python-essentials/02.15-loops.ipynb
index 504405bf..399ca7de 100644
--- a/02. python essentials/02.15 loops.ipynb	
+++ b/02-python-essentials/02.15-loops.ipynb
@@ -1,451 +1,451 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 循环"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "循环的作用在于将一段代码重复执行多次。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## while 循环"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    while <condition>:\n",
-    "        <statesments>\n",
-    "\n",
-    "**Python**会循环执行`<statesments>`，直到`<condition>`不满足为止。\n",
-    "\n",
-    "例如，计算数字`0`到`1000000`的和："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "499999500000\n"
-     ]
-    }
-   ],
-   "source": [
-    "i = 0\n",
-    "total = 0\n",
-    "while i < 1000000:\n",
-    "    total += i\n",
-    "    i += 1\n",
-    "print total"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "之前提到，空容器会被当成 `False` ，因此可以用 `while` 循环来读取容器中的所有元素："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Perform King Lear\n",
-      "Perform Macbeth\n",
-      "Perform Hamlet\n"
-     ]
-    }
-   ],
-   "source": [
-    "plays = set(['Hamlet', 'Macbeth', 'King Lear'])\n",
-    "while plays:\n",
-    "    play = plays.pop()\n",
-    "    print 'Perform', play"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "循环每次从 `plays` 中弹出一个元素，一直到 `plays` 为空为止。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## for 循环"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    for <variable> in <sequence>:\n",
-    "        <indented block of code>\n",
-    "\n",
-    "`for` 循环会遍历完`<sequence>`中所有元素为止\n",
-    "\n",
-    "上一个例子可以改写成如下形式："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Perform King Lear\n",
-      "Perform Macbeth\n",
-      "Perform Hamlet\n"
-     ]
-    }
-   ],
-   "source": [
-    "plays = set(['Hamlet', 'Macbeth', 'King Lear'])\n",
-    "for play in plays:\n",
-    "    print 'Perform', play"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `for` 循环时，注意尽量不要改变 `plays` 的值，否则可能会产生意想不到的结果。\n",
-    "\n",
-    "之前的求和也可以通过 `for` 循环来实现："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4999950000\n"
-     ]
-    }
-   ],
-   "source": [
-    "total = 0\n",
-    "for i in range(100000):\n",
-    "    total += i\n",
-    "print total"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "然而这种写法有一个缺点：在循环前，它会生成一个长度为 `100000` 的临时列表。\n",
-    "\n",
-    "生成列表的问题在于，会有一定的时间和内存消耗，当数字从 `100000` 变得更大时，时间和内存的消耗会更加明显。\n",
-    "\n",
-    "为了解决这个问题，我们可以使用 `xrange` 来代替 `range` 函数，其效果与`range`函数相同，但是 `xrange` 并不会一次性的产生所有的数据："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4999950000\n"
-     ]
-    }
-   ],
-   "source": [
-    "total = 0\n",
-    "for i in xrange(100000):\n",
-    "    total += i\n",
-    "print total"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "比较一下两者的运行时间："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10 loops, best of 3: 40.7 ms per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit for i in xrange(1000000): i = i"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10 loops, best of 3: 96.6 ms per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit for i in range(1000000): i = i"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以看出，`xrange` 用时要比 `range` 少。 "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## continue 语句"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "遇到 `continue` 的时候，程序会返回到循环的最开始重新执行。\n",
-    "\n",
-    "例如在循环中忽略一些特定的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3\n",
-      "2\n",
-      "1\n"
-     ]
-    }
-   ],
-   "source": [
-    "values = [7, 6, 4, 7, 19, 2, 1]\n",
-    "for i in values:\n",
-    "    if i % 2 != 0:\n",
-    "        # 忽略奇数\n",
-    "        continue\n",
-    "    print i/2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## break 语句"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "遇到 `break` 的时候，程序会跳出循环，不管循环条件是不是满足："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "start\n",
-      "process\n",
-      "process\n",
-      "process\n"
-     ]
-    }
-   ],
-   "source": [
-    "command_list = ['start', \n",
-    "                'process', \n",
-    "                'process',\n",
-    "                'process', \n",
-    "                'stop', \n",
-    "                'start', \n",
-    "                'process', \n",
-    "                'stop']\n",
-    "while command_list:\n",
-    "    command = command_list.pop(0)\n",
-    "    if command == 'stop':\n",
-    "        break\n",
-    "    print(command)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在遇到第一个 `'stop'` 之后，程序跳出循环。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## else语句"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "与 `if` 一样， `while` 和 `for` 循环后面也可以跟着 `else` 语句，不过要和`break`一起连用。\n",
-    "\n",
-    "- 当循环正常结束时，循环条件不满足， `else` 被执行；\n",
-    "- 当循环被 `break` 结束时，循环条件仍然满足， `else` 不执行。\n",
-    "\n",
-    "不执行："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found: 7\n"
-     ]
-    }
-   ],
-   "source": [
-    "values = [7, 6, 4, 7, 19, 2, 1]\n",
-    "for x in values:\n",
-    "    if x <= 10:\n",
-    "        print 'Found:', x\n",
-    "        break\n",
-    "else:\n",
-    "    print 'All values greater than 10'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "执行："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All values greater than 10\n"
-     ]
-    }
-   ],
-   "source": [
-    "values = [11, 12, 13, 100]\n",
-    "for x in values:\n",
-    "    if x <= 10:\n",
-    "        print 'Found:', x\n",
-    "        break\n",
-    "else:\n",
-    "    print 'All values greater than 10'"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 循环"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "循环的作用在于将一段代码重复执行多次。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## while 循环"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    while <condition>:\n",
+    "        <statesments>\n",
+    "\n",
+    "**Python**会循环执行`<statesments>`，直到`<condition>`不满足为止。\n",
+    "\n",
+    "例如，计算数字`0`到`1000000`的和："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "499999500000\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "total = 0\n",
+    "while i < 1000000:\n",
+    "    total += i\n",
+    "    i += 1\n",
+    "print total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "之前提到，空容器会被当成 `False` ，因此可以用 `while` 循环来读取容器中的所有元素："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Perform King Lear\n",
+      "Perform Macbeth\n",
+      "Perform Hamlet\n"
+     ]
+    }
+   ],
+   "source": [
+    "plays = set(['Hamlet', 'Macbeth', 'King Lear'])\n",
+    "while plays:\n",
+    "    play = plays.pop()\n",
+    "    print 'Perform', play"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "循环每次从 `plays` 中弹出一个元素，一直到 `plays` 为空为止。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## for 循环"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    for <variable> in <sequence>:\n",
+    "        <indented block of code>\n",
+    "\n",
+    "`for` 循环会遍历完`<sequence>`中所有元素为止\n",
+    "\n",
+    "上一个例子可以改写成如下形式："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Perform King Lear\n",
+      "Perform Macbeth\n",
+      "Perform Hamlet\n"
+     ]
+    }
+   ],
+   "source": [
+    "plays = set(['Hamlet', 'Macbeth', 'King Lear'])\n",
+    "for play in plays:\n",
+    "    print 'Perform', play"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `for` 循环时，注意尽量不要改变 `plays` 的值，否则可能会产生意想不到的结果。\n",
+    "\n",
+    "之前的求和也可以通过 `for` 循环来实现："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4999950000\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "for i in range(100000):\n",
+    "    total += i\n",
+    "print total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "然而这种写法有一个缺点：在循环前，它会生成一个长度为 `100000` 的临时列表。\n",
+    "\n",
+    "生成列表的问题在于，会有一定的时间和内存消耗，当数字从 `100000` 变得更大时，时间和内存的消耗会更加明显。\n",
+    "\n",
+    "为了解决这个问题，我们可以使用 `xrange` 来代替 `range` 函数，其效果与`range`函数相同，但是 `xrange` 并不会一次性的产生所有的数据："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4999950000\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "for i in xrange(100000):\n",
+    "    total += i\n",
+    "print total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "比较一下两者的运行时间："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 loops, best of 3: 40.7 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit for i in xrange(1000000): i = i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 loops, best of 3: 96.6 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit for i in range(1000000): i = i"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以看出，`xrange` 用时要比 `range` 少。 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## continue 语句"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "遇到 `continue` 的时候，程序会返回到循环的最开始重新执行。\n",
+    "\n",
+    "例如在循环中忽略一些特定的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "2\n",
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "values = [7, 6, 4, 7, 19, 2, 1]\n",
+    "for i in values:\n",
+    "    if i % 2 != 0:\n",
+    "        # 忽略奇数\n",
+    "        continue\n",
+    "    print i/2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## break 语句"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "遇到 `break` 的时候，程序会跳出循环，不管循环条件是不是满足："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start\n",
+      "process\n",
+      "process\n",
+      "process\n"
+     ]
+    }
+   ],
+   "source": [
+    "command_list = ['start', \n",
+    "                'process', \n",
+    "                'process',\n",
+    "                'process', \n",
+    "                'stop', \n",
+    "                'start', \n",
+    "                'process', \n",
+    "                'stop']\n",
+    "while command_list:\n",
+    "    command = command_list.pop(0)\n",
+    "    if command == 'stop':\n",
+    "        break\n",
+    "    print(command)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在遇到第一个 `'stop'` 之后，程序跳出循环。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## else语句"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "与 `if` 一样， `while` 和 `for` 循环后面也可以跟着 `else` 语句，不过要和`break`一起连用。\n",
+    "\n",
+    "- 当循环正常结束时，循环条件不满足， `else` 被执行；\n",
+    "- 当循环被 `break` 结束时，循环条件仍然满足， `else` 不执行。\n",
+    "\n",
+    "不执行："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "values = [7, 6, 4, 7, 19, 2, 1]\n",
+    "for x in values:\n",
+    "    if x <= 10:\n",
+    "        print 'Found:', x\n",
+    "        break\n",
+    "else:\n",
+    "    print 'All values greater than 10'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "执行："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All values greater than 10\n"
+     ]
+    }
+   ],
+   "source": [
+    "values = [11, 12, 13, 100]\n",
+    "for x in values:\n",
+    "    if x <= 10:\n",
+    "        print 'Found:', x\n",
+    "        break\n",
+    "else:\n",
+    "    print 'All values greater than 10'"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/02. python essentials/02.16 list comprehension.ipynb b/02-python-essentials/02.16-list-comprehension.ipynb
similarity index 94%
rename from 02. python essentials/02.16 list comprehension.ipynb
rename to 02-python-essentials/02.16-list-comprehension.ipynb
index c588a649..df732d31 100644
--- a/02. python essentials/02.16 list comprehension.ipynb	
+++ b/02-python-essentials/02.16-list-comprehension.ipynb
@@ -1,274 +1,274 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 列表推导式"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "循环可以用来生成列表："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[100, 441, 16, 49, 144]\n"
-     ]
-    }
-   ],
-   "source": [
-    "values = [10, 21, 4, 7, 12]\n",
-    "squares = []\n",
-    "for x in values:\n",
-    "    squares.append(x**2)\n",
-    "print squares"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "列表推导式可以使用更简单的方法来创建这个列表："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[100, 441, 16, 49, 144]\n"
-     ]
-    }
-   ],
-   "source": [
-    "values = [10, 21, 4, 7, 12]\n",
-    "squares = [x**2 for x in values]\n",
-    "print squares"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "还可以在列表推导式中加入条件进行筛选。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "例如在上面的例子中，假如只想保留列表中不大于`10`的数的平方："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[100, 16, 49]\n"
-     ]
-    }
-   ],
-   "source": [
-    "values = [10, 21, 4, 7, 12]\n",
-    "squares = [x**2 for x in values if x <= 10]\n",
-    "print squares"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "也可以使用推导式生成集合和字典："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "set([16, 49, 100])\n",
-      "{10: 100, 4: 16, 7: 49}\n"
-     ]
-    }
-   ],
-   "source": [
-    "square_set = {x**2 for x in values if x <= 10}\n",
-    "print(square_set)\n",
-    "square_dict = {x: x**2 for x in values if x <= 10}\n",
-    "print(square_dict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "再如，计算上面例子中生成的列表中所有元素的和："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "165\n"
-     ]
-    }
-   ],
-   "source": [
-    "total = sum([x**2 for x in values if x <= 10])\n",
-    "print(total)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "但是，**Python**会生成这个列表，然后在将它放到垃圾回收机制中（因为没有变量指向它），这毫无疑问是种浪费。\n",
-    "\n",
-    "为了解决这种问题，与xrange()类似，**Python**使用产生式表达式来解决这个问题："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "165\n"
-     ]
-    }
-   ],
-   "source": [
-    "total = sum(x**2 for x in values if x <= 10)\n",
-    "print(total)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "与上面相比，只是去掉了括号，但这里并不会一次性的生成这个列表。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "比较一下两者的用时："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "x = range(1000000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 loops, best of 3: 3.86 s per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit total = sum([i**2 for i in x])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 loops, best of 3: 2.58 s per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit total = sum(i**2 for i in x)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 列表推导式"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "循环可以用来生成列表："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[100, 441, 16, 49, 144]\n"
+     ]
+    }
+   ],
+   "source": [
+    "values = [10, 21, 4, 7, 12]\n",
+    "squares = []\n",
+    "for x in values:\n",
+    "    squares.append(x**2)\n",
+    "print squares"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "列表推导式可以使用更简单的方法来创建这个列表："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[100, 441, 16, 49, 144]\n"
+     ]
+    }
+   ],
+   "source": [
+    "values = [10, 21, 4, 7, 12]\n",
+    "squares = [x**2 for x in values]\n",
+    "print squares"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "还可以在列表推导式中加入条件进行筛选。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "例如在上面的例子中，假如只想保留列表中不大于`10`的数的平方："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[100, 16, 49]\n"
+     ]
+    }
+   ],
+   "source": [
+    "values = [10, 21, 4, 7, 12]\n",
+    "squares = [x**2 for x in values if x <= 10]\n",
+    "print squares"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "也可以使用推导式生成集合和字典："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "set([16, 49, 100])\n",
+      "{10: 100, 4: 16, 7: 49}\n"
+     ]
+    }
+   ],
+   "source": [
+    "square_set = {x**2 for x in values if x <= 10}\n",
+    "print(square_set)\n",
+    "square_dict = {x: x**2 for x in values if x <= 10}\n",
+    "print(square_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "再如，计算上面例子中生成的列表中所有元素的和："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "165\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = sum([x**2 for x in values if x <= 10])\n",
+    "print(total)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "但是，**Python**会生成这个列表，然后在将它放到垃圾回收机制中（因为没有变量指向它），这毫无疑问是种浪费。\n",
+    "\n",
+    "为了解决这种问题，与xrange()类似，**Python**使用产生式表达式来解决这个问题："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "165\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = sum(x**2 for x in values if x <= 10)\n",
+    "print(total)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "与上面相比，只是去掉了括号，但这里并不会一次性的生成这个列表。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "比较一下两者的用时："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x = range(1000000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loops, best of 3: 3.86 s per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit total = sum([i**2 for i in x])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loops, best of 3: 2.58 s per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit total = sum(i**2 for i in x)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/02. python essentials/02.17 functions.ipynb b/02-python-essentials/02.17-functions.ipynb
similarity index 95%
rename from 02. python essentials/02.17 functions.ipynb
rename to 02-python-essentials/02.17-functions.ipynb
index 6bd2cc39..092c5764 100644
--- a/02. python essentials/02.17 functions.ipynb	
+++ b/02-python-essentials/02.17-functions.ipynb
@@ -1,768 +1,768 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 定义函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "函数`function`，通常接受输入参数，并有返回值。\n",
-    "\n",
-    "它负责完成某项特定任务，而且相较于其他代码，具备相对的独立性。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def add(x, y):\n",
-    "    \"\"\"Add two numbers\"\"\"\n",
-    "    a = x + y\n",
-    "    return a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "函数通常有一下几个特征：\n",
-    "- 使用 `def` 关键词来定义一个函数。\n",
-    "-  `def` 后面是函数的名称，括号中是函数的参数，不同的参数用 `,` 隔开， `def foo():` 的形式是必须要有的，参数可以为空；\n",
-    "- 使用缩进来划分函数的内容；\n",
-    "-  `docstring` 用 `\"\"\"` 包含的字符串，用来解释函数的用途，可省略；\n",
-    "-  `return` 返回特定的值，如果省略，返回 `None` 。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 使用函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用函数时，只需要将参数换成特定的值传给函数。\n",
-    "\n",
-    "**Python**并没有限定参数的类型，因此可以使用不同的参数类型："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n",
-      "foobar\n"
-     ]
-    }
-   ],
-   "source": [
-    "print add(2, 3)\n",
-    "print add('foo', 'bar')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在这个例子中，如果传入的两个参数不可以相加，那么**Python**会将报错："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "unsupported operand type(s) for +: 'int' and 'str'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-3-6f8dcf7eb280>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"foo\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m<ipython-input-1-e831943cfaf2>\u001b[0m in \u001b[0;36madd\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;34m\"\"\"Add two numbers\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
-     ]
-    }
-   ],
-   "source": [
-    "print add(2, \"foo\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "如果传入的参数数目与实际不符合，也会报错："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "add() takes exactly 2 arguments (3 given)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-4-ed7bae31fc7d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m: add() takes exactly 2 arguments (3 given)"
-     ]
-    }
-   ],
-   "source": [
-    "print add(1, 2, 3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "add() takes exactly 2 arguments (1 given)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-5-a954233d3b0d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m: add() takes exactly 2 arguments (1 given)"
-     ]
-    }
-   ],
-   "source": [
-    "print add(1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "传入参数时，Python提供了两种选项，第一种是上面使用的按照位置传入参数，另一种则是使用关键词模式，显式地指定参数的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n",
-      "barfoo\n"
-     ]
-    }
-   ],
-   "source": [
-    "print add(x=2, y=3)\n",
-    "print add(y=\"foo\", x=\"bar\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以混合这两种模式："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "print add(2, y=3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 设定参数默认值"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以在函数定义的时候给参数设定默认值，例如："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def quad(x, a=1, b=0, c=0):\n",
-    "    return a*x**2 + b*x + c"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以省略有默认值的参数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print quad(2.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以修改参数的默认值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print quad(2.0, b=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "12.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print quad(2.0, 2, c=4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里混合了位置和指定两种参数传入方式，第二个2是传给 `a` 的。\n",
-    "\n",
-    "注意，在使用混合语法时，要注意不能给同一个值赋值多次，否则会报错，例如："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "quad() got multiple values for keyword argument 'a'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-12-101d0c090bbb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0mquad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m: quad() got multiple values for keyword argument 'a'"
-     ]
-    }
-   ],
-   "source": [
-    "print quad(2.0, 2, a=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 接收不定参数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用如下方法，可以使函数接受不定数目的参数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def add(x, *args):\n",
-    "    total = x\n",
-    "    for arg in args:\n",
-    "        total += arg\n",
-    "    return total"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里，`*args` 表示参数数目不定，可以看成一个元组，把第一个参数后面的参数当作元组中的元素。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n",
-      "3\n"
-     ]
-    }
-   ],
-   "source": [
-    "print add(1, 2, 3, 4)\n",
-    "print add(1, 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这样定义的函数不能使用关键词传入参数，要使用关键词，可以这样："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def add(x, **kwargs):\n",
-    "    total = x\n",
-    "    for arg, value in kwargs.items():\n",
-    "        print \"adding \", arg\n",
-    "        total += value\n",
-    "    return total"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里， `**kwargs` 表示参数数目不定，相当于一个字典，关键词和值对应于键值对。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "adding  y\n",
-      "adding  z\n",
-      "adding  w\n",
-      "46\n"
-     ]
-    }
-   ],
-   "source": [
-    "print add(10, y=11, z=12, w=13)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "再看这个例子，可以接收任意数目的位置参数和键值对参数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 3) {'x': 'bar', 'z': 10}\n"
-     ]
-    }
-   ],
-   "source": [
-    "def foo(*args, **kwargs):\n",
-    "    print args, kwargs\n",
-    "\n",
-    "foo(2, 3, x='bar', z=10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不过要按顺序传入参数，先传入位置参数 `args` ，在传入关键词参数 `kwargs` 。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 返回多个值"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "函数可以返回多个值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5.0 0.927295218002\n"
-     ]
-    }
-   ],
-   "source": [
-    "from math import atan2\n",
-    "\n",
-    "def to_polar(x, y):\n",
-    "    r = (x**2 + y**2) ** 0.5\n",
-    "    theta = atan2(y, x)\n",
-    "    return r, theta\n",
-    "\n",
-    "r, theta = to_polar(3, 4)\n",
-    "print r, theta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上，**Python**将返回的两个值变成了元组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(5.0, 0.9272952180016122)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print to_polar(3, 4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "因为这个元组中有两个值，所以可以使用\n",
-    "\n",
-    "    r, theta = to_polar(3, 4)\n",
-    "\n",
-    "给两个值赋值。\n",
-    "\n",
-    "列表也有相似的功能："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 2 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "a, b, c = [1, 2, 3]\n",
-    "print a, b, c"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上，不仅仅返回值可以用元组表示，也可以将参数用元组以这种方式传入："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "def add(x, y):\n",
-    "    \"\"\"Add two numbers\"\"\"\n",
-    "    a = x + y\n",
-    "    return a\n",
-    "    \n",
-    "z = (2, 3)\n",
-    "print add(*z)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里的`*`必不可少。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上，还可以通过字典传入参数来执行函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "def add(x, y):\n",
-    "    \"\"\"Add two numbers\"\"\"\n",
-    "    a = x + y\n",
-    "    return a\n",
-    "\n",
-    "w = {'x': 2, 'y': 3}\n",
-    "print add(**w)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## map 方法生成序列"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以通过 `map` 的方式利用函数来生成序列："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[4, 9, 16]\n"
-     ]
-    }
-   ],
-   "source": [
-    "def sqr(x): \n",
-    "    return x ** 2\n",
-    "\n",
-    "a = [2,3,4]\n",
-    "print map(sqr, a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "其用法为：\n",
-    "    \n",
-    "    map(aFun, aSeq)\n",
-    "\n",
-    "将函数 `aFun` 应用到序列 `aSeq` 上的每一个元素上，返回一个列表，不管这个序列原来是什么类型。\n",
-    "\n",
-    "事实上，根据函数参数的多少，`map` 可以接受多组序列，将其对应的元素作为参数传入函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[12, 8, 7]\n"
-     ]
-    }
-   ],
-   "source": [
-    "def add(x, y): \n",
-    "    return x + y\n",
-    "\n",
-    "a = (2,3,4)\n",
-    "b = [10,5,3]\n",
-    "print map(add,a,b)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 定义函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "函数`function`，通常接受输入参数，并有返回值。\n",
+    "\n",
+    "它负责完成某项特定任务，而且相较于其他代码，具备相对的独立性。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def add(x, y):\n",
+    "    \"\"\"Add two numbers\"\"\"\n",
+    "    a = x + y\n",
+    "    return a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "函数通常有一下几个特征：\n",
+    "- 使用 `def` 关键词来定义一个函数。\n",
+    "-  `def` 后面是函数的名称，括号中是函数的参数，不同的参数用 `,` 隔开， `def foo():` 的形式是必须要有的，参数可以为空；\n",
+    "- 使用缩进来划分函数的内容；\n",
+    "-  `docstring` 用 `\"\"\"` 包含的字符串，用来解释函数的用途，可省略；\n",
+    "-  `return` 返回特定的值，如果省略，返回 `None` 。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 使用函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用函数时，只需要将参数换成特定的值传给函数。\n",
+    "\n",
+    "**Python**并没有限定参数的类型，因此可以使用不同的参数类型："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n",
+      "foobar\n"
+     ]
+    }
+   ],
+   "source": [
+    "print add(2, 3)\n",
+    "print add('foo', 'bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在这个例子中，如果传入的两个参数不可以相加，那么**Python**会将报错："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unsupported operand type(s) for +: 'int' and 'str'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-3-6f8dcf7eb280>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"foo\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-1-e831943cfaf2>\u001b[0m in \u001b[0;36madd\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;34m\"\"\"Add two numbers\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
+     ]
+    }
+   ],
+   "source": [
+    "print add(2, \"foo\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "如果传入的参数数目与实际不符合，也会报错："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "add() takes exactly 2 arguments (3 given)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-4-ed7bae31fc7d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m: add() takes exactly 2 arguments (3 given)"
+     ]
+    }
+   ],
+   "source": [
+    "print add(1, 2, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "add() takes exactly 2 arguments (1 given)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-5-a954233d3b0d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m: add() takes exactly 2 arguments (1 given)"
+     ]
+    }
+   ],
+   "source": [
+    "print add(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "传入参数时，Python提供了两种选项，第一种是上面使用的按照位置传入参数，另一种则是使用关键词模式，显式地指定参数的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n",
+      "barfoo\n"
+     ]
+    }
+   ],
+   "source": [
+    "print add(x=2, y=3)\n",
+    "print add(y=\"foo\", x=\"bar\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以混合这两种模式："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print add(2, y=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 设定参数默认值"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以在函数定义的时候给参数设定默认值，例如："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def quad(x, a=1, b=0, c=0):\n",
+    "    return a*x**2 + b*x + c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以省略有默认值的参数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print quad(2.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以修改参数的默认值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print quad(2.0, b=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print quad(2.0, 2, c=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里混合了位置和指定两种参数传入方式，第二个2是传给 `a` 的。\n",
+    "\n",
+    "注意，在使用混合语法时，要注意不能给同一个值赋值多次，否则会报错，例如："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "quad() got multiple values for keyword argument 'a'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-12-101d0c090bbb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0mquad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m: quad() got multiple values for keyword argument 'a'"
+     ]
+    }
+   ],
+   "source": [
+    "print quad(2.0, 2, a=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 接收不定参数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用如下方法，可以使函数接受不定数目的参数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def add(x, *args):\n",
+    "    total = x\n",
+    "    for arg in args:\n",
+    "        total += arg\n",
+    "    return total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里，`*args` 表示参数数目不定，可以看成一个元组，把第一个参数后面的参数当作元组中的元素。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n",
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print add(1, 2, 3, 4)\n",
+    "print add(1, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这样定义的函数不能使用关键词传入参数，要使用关键词，可以这样："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def add(x, **kwargs):\n",
+    "    total = x\n",
+    "    for arg, value in kwargs.items():\n",
+    "        print \"adding \", arg\n",
+    "        total += value\n",
+    "    return total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里， `**kwargs` 表示参数数目不定，相当于一个字典，关键词和值对应于键值对。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding  y\n",
+      "adding  z\n",
+      "adding  w\n",
+      "46\n"
+     ]
+    }
+   ],
+   "source": [
+    "print add(10, y=11, z=12, w=13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "再看这个例子，可以接收任意数目的位置参数和键值对参数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 3) {'x': 'bar', 'z': 10}\n"
+     ]
+    }
+   ],
+   "source": [
+    "def foo(*args, **kwargs):\n",
+    "    print args, kwargs\n",
+    "\n",
+    "foo(2, 3, x='bar', z=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不过要按顺序传入参数，先传入位置参数 `args` ，在传入关键词参数 `kwargs` 。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 返回多个值"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "函数可以返回多个值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.0 0.927295218002\n"
+     ]
+    }
+   ],
+   "source": [
+    "from math import atan2\n",
+    "\n",
+    "def to_polar(x, y):\n",
+    "    r = (x**2 + y**2) ** 0.5\n",
+    "    theta = atan2(y, x)\n",
+    "    return r, theta\n",
+    "\n",
+    "r, theta = to_polar(3, 4)\n",
+    "print r, theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上，**Python**将返回的两个值变成了元组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5.0, 0.9272952180016122)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print to_polar(3, 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "因为这个元组中有两个值，所以可以使用\n",
+    "\n",
+    "    r, theta = to_polar(3, 4)\n",
+    "\n",
+    "给两个值赋值。\n",
+    "\n",
+    "列表也有相似的功能："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 2 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "a, b, c = [1, 2, 3]\n",
+    "print a, b, c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上，不仅仅返回值可以用元组表示，也可以将参数用元组以这种方式传入："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "def add(x, y):\n",
+    "    \"\"\"Add two numbers\"\"\"\n",
+    "    a = x + y\n",
+    "    return a\n",
+    "    \n",
+    "z = (2, 3)\n",
+    "print add(*z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里的`*`必不可少。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上，还可以通过字典传入参数来执行函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "def add(x, y):\n",
+    "    \"\"\"Add two numbers\"\"\"\n",
+    "    a = x + y\n",
+    "    return a\n",
+    "\n",
+    "w = {'x': 2, 'y': 3}\n",
+    "print add(**w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## map 方法生成序列"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以通过 `map` 的方式利用函数来生成序列："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[4, 9, 16]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def sqr(x): \n",
+    "    return x ** 2\n",
+    "\n",
+    "a = [2,3,4]\n",
+    "print map(sqr, a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "其用法为：\n",
+    "    \n",
+    "    map(aFun, aSeq)\n",
+    "\n",
+    "将函数 `aFun` 应用到序列 `aSeq` 上的每一个元素上，返回一个列表，不管这个序列原来是什么类型。\n",
+    "\n",
+    "事实上，根据函数参数的多少，`map` 可以接受多组序列，将其对应的元素作为参数传入函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[12, 8, 7]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def add(x, y): \n",
+    "    return x + y\n",
+    "\n",
+    "a = (2,3,4)\n",
+    "b = [10,5,3]\n",
+    "print map(add,a,b)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/02. python essentials/02.18 modules and packages.ipynb b/02-python-essentials/02.18-modules-and-packages.ipynb
similarity index 94%
rename from 02. python essentials/02.18 modules and packages.ipynb
rename to 02-python-essentials/02.18-modules-and-packages.ipynb
index 323c11b4..612e1f22 100644
--- a/02. python essentials/02.18 modules and packages.ipynb	
+++ b/02-python-essentials/02.18-modules-and-packages.ipynb
@@ -1,659 +1,659 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 模块和包"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 模块"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Python会将所有 `.py` 结尾的文件认定为Python代码文件，考虑下面的脚本 `ex1.py` ："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwriting ex1.py\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%writefile ex1.py\n",
-    "\n",
-    "PI = 3.1416\n",
-    "\n",
-    "def sum(lst):\n",
-    "    tot = lst[0]\n",
-    "    for value in lst[1:]:\n",
-    "        tot = tot + value\n",
-    "    return tot\n",
-    "    \n",
-    "w = [0, 1, 2, 3]\n",
-    "print sum(w), PI"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以执行它："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6 3.1416\n"
-     ]
-    }
-   ],
-   "source": [
-    "%run ex1.py"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这个脚本可以当作一个模块，可以使用`import`关键词加载并执行它（这里要求`ex1.py`在当前工作目录）："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6 3.1416\n"
-     ]
-    }
-   ],
-   "source": [
-    "import ex1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<module 'ex1' from 'ex1.py'>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ex1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在导入时，**Python**会执行一遍模块中的所有内容。\n",
-    "\n",
-    "`ex1.py` 中所有的变量都被载入了当前环境中，不过要使用\n",
-    "\n",
-    "    ex1.变量名\n",
-    "\n",
-    "的方法来查看或者修改这些变量："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.1416\n"
-     ]
-    }
-   ],
-   "source": [
-    "print ex1.PI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.141592653\n"
-     ]
-    }
-   ],
-   "source": [
-    "ex1.PI = 3.141592653\n",
-    "print ex1.PI"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "还可以用\n",
-    "\n",
-    "    ex1.函数名\n",
-    "\n",
-    "调用模块里面的函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "9\n"
-     ]
-    }
-   ],
-   "source": [
-    "print ex1.sum([2, 3, 4])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "为了提高效率，**Python**只会载入模块一次，已经载入的模块再次载入时，Python并不会真正执行载入操作，哪怕模块的内容已经改变。\n",
-    "\n",
-    "例如，这里重新导入 `ex1` 时，并不会执行 `ex1.py` 中的 `print` 语句："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import ex1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "需要重新导入模块时，可以使用`reload`强制重新载入它，例如："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6 3.1416\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<module 'ex1' from 'ex1.pyc'>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reload(ex1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "删除之前生成的文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('ex1.py')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## `__name__` 属性"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "有时候我们想将一个 `.py` 文件既当作脚本，又能当作模块用，这个时候可以使用 `__name__` 这个属性。\n",
-    "\n",
-    "只有当文件被当作脚本执行的时候， `__name__`的值才会是 `'__main__'`，所以我们可以："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing ex2.py\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%writefile ex2.py\n",
-    "\n",
-    "PI = 3.1416\n",
-    "\n",
-    "def sum(lst):\n",
-    "    \"\"\" Sum the values in a list\n",
-    "    \"\"\"\n",
-    "    tot = 0\n",
-    "    for value in lst:\n",
-    "        tot = tot + value\n",
-    "    return tot\n",
-    "\n",
-    "def add(x, y):\n",
-    "    \" Add two values.\"\n",
-    "    a = x + y\n",
-    "    return a\n",
-    "\n",
-    "def test():\n",
-    "    w = [0,1,2,3]\n",
-    "    assert(sum(w) == 6)\n",
-    "    print 'test passed.'\n",
-    "    \n",
-    "if __name__ == '__main__':\n",
-    "    test()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "运行文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "test passed.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%run ex2.py"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "当作模块导入， `test()` 不会执行："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import ex2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "但是可以使用其中的变量："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.1416"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ex2.PI"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用别名："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.1416"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import ex2 as e2\n",
-    "e2.PI"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 其他导入方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以从模块中导入变量："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from ex2 import add, PI"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `from` 后，可以直接使用 `add` ， `PI`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "add(2, 3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "或者使用 `*` 导入所有变量："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7.5"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from ex2 import *\n",
-    "add(3, 4.5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这种导入方法不是很提倡，因为如果你不确定导入的都有哪些，可能覆盖一些已有的函数。\n",
-    "\n",
-    "删除文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('ex2.py')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 包"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "假设我们有这样的一个文件夹：\n",
-    "\n",
-    "foo/\n",
-    "- `__init__.py` \n",
-    "- `bar.py` (defines func)\n",
-    "- `baz.py` (defines zap)\n",
-    "\n",
-    "这意味着 foo 是一个包，我们可以这样导入其中的内容：\n",
-    "\n",
-    "```python    \n",
-    "from foo.bar import func\n",
-    "from foo.baz import zap\n",
-    "```\n",
-    "\n",
-    "`bar` 和 `baz` 都是 `foo` 文件夹下的 `.py` 文件。\n",
-    "\n",
-    "导入包要求：\n",
-    "- 文件夹 `foo` 在**Python**的搜索路径中\n",
-    "- `__init__.py` 表示 `foo` 是一个包，它可以是个空文件。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "## 常用的标准库"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- re 正则表达式\n",
-    "- copy 复制\n",
-    "- math, cmath 数学\n",
-    "- decimal, fraction\n",
-    "- sqlite3 数据库\n",
-    "- os, os.path 文件系统\n",
-    "- gzip, bz2, zipfile, tarfile 压缩文件\n",
-    "- csv, netrc 各种文件格式\n",
-    "- xml\n",
-    "- htmllib\n",
-    "- ftplib, socket\n",
-    "- cmd 命令行\n",
-    "- pdb \n",
-    "- profile, cProfile, timeit\n",
-    "- collections, heapq, bisect 数据结构\n",
-    "- mmap\n",
-    "- threading, Queue 并行\n",
-    "- multiprocessing\n",
-    "- subprocess\n",
-    "- pickle, cPickle\n",
-    "- struct"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "## PYTHONPATH设置"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Python的搜索路径可以通过环境变量PYTHONPATH设置，环境变量的设置方法依操作系统的不同而不同，具体方法可以网上搜索。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 模块和包"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 模块"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python会将所有 `.py` 结尾的文件认定为Python代码文件，考虑下面的脚本 `ex1.py` ："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting ex1.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile ex1.py\n",
+    "\n",
+    "PI = 3.1416\n",
+    "\n",
+    "def sum(lst):\n",
+    "    tot = lst[0]\n",
+    "    for value in lst[1:]:\n",
+    "        tot = tot + value\n",
+    "    return tot\n",
+    "    \n",
+    "w = [0, 1, 2, 3]\n",
+    "print sum(w), PI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以执行它："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 3.1416\n"
+     ]
+    }
+   ],
+   "source": [
+    "%run ex1.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这个脚本可以当作一个模块，可以使用`import`关键词加载并执行它（这里要求`ex1.py`在当前工作目录）："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 3.1416\n"
+     ]
+    }
+   ],
+   "source": [
+    "import ex1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'ex1' from 'ex1.py'>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ex1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在导入时，**Python**会执行一遍模块中的所有内容。\n",
+    "\n",
+    "`ex1.py` 中所有的变量都被载入了当前环境中，不过要使用\n",
+    "\n",
+    "    ex1.变量名\n",
+    "\n",
+    "的方法来查看或者修改这些变量："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.1416\n"
+     ]
+    }
+   ],
+   "source": [
+    "print ex1.PI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.141592653\n"
+     ]
+    }
+   ],
+   "source": [
+    "ex1.PI = 3.141592653\n",
+    "print ex1.PI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "还可以用\n",
+    "\n",
+    "    ex1.函数名\n",
+    "\n",
+    "调用模块里面的函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9\n"
+     ]
+    }
+   ],
+   "source": [
+    "print ex1.sum([2, 3, 4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "为了提高效率，**Python**只会载入模块一次，已经载入的模块再次载入时，Python并不会真正执行载入操作，哪怕模块的内容已经改变。\n",
+    "\n",
+    "例如，这里重新导入 `ex1` 时，并不会执行 `ex1.py` 中的 `print` 语句："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import ex1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "需要重新导入模块时，可以使用`reload`强制重新载入它，例如："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 3.1416\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<module 'ex1' from 'ex1.pyc'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reload(ex1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "删除之前生成的文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('ex1.py')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `__name__` 属性"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "有时候我们想将一个 `.py` 文件既当作脚本，又能当作模块用，这个时候可以使用 `__name__` 这个属性。\n",
+    "\n",
+    "只有当文件被当作脚本执行的时候， `__name__`的值才会是 `'__main__'`，所以我们可以："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing ex2.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile ex2.py\n",
+    "\n",
+    "PI = 3.1416\n",
+    "\n",
+    "def sum(lst):\n",
+    "    \"\"\" Sum the values in a list\n",
+    "    \"\"\"\n",
+    "    tot = 0\n",
+    "    for value in lst:\n",
+    "        tot = tot + value\n",
+    "    return tot\n",
+    "\n",
+    "def add(x, y):\n",
+    "    \" Add two values.\"\n",
+    "    a = x + y\n",
+    "    return a\n",
+    "\n",
+    "def test():\n",
+    "    w = [0,1,2,3]\n",
+    "    assert(sum(w) == 6)\n",
+    "    print 'test passed.'\n",
+    "    \n",
+    "if __name__ == '__main__':\n",
+    "    test()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "运行文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test passed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%run ex2.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "当作模块导入， `test()` 不会执行："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import ex2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "但是可以使用其中的变量："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.1416"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ex2.PI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用别名："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.1416"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import ex2 as e2\n",
+    "e2.PI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 其他导入方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以从模块中导入变量："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from ex2 import add, PI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `from` 后，可以直接使用 `add` ， `PI`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "add(2, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "或者使用 `*` 导入所有变量："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.5"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from ex2 import *\n",
+    "add(3, 4.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这种导入方法不是很提倡，因为如果你不确定导入的都有哪些，可能覆盖一些已有的函数。\n",
+    "\n",
+    "删除文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('ex2.py')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 包"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "假设我们有这样的一个文件夹：\n",
+    "\n",
+    "foo/\n",
+    "- `__init__.py` \n",
+    "- `bar.py` (defines func)\n",
+    "- `baz.py` (defines zap)\n",
+    "\n",
+    "这意味着 foo 是一个包，我们可以这样导入其中的内容：\n",
+    "\n",
+    "```python    \n",
+    "from foo.bar import func\n",
+    "from foo.baz import zap\n",
+    "```\n",
+    "\n",
+    "`bar` 和 `baz` 都是 `foo` 文件夹下的 `.py` 文件。\n",
+    "\n",
+    "导入包要求：\n",
+    "- 文件夹 `foo` 在**Python**的搜索路径中\n",
+    "- `__init__.py` 表示 `foo` 是一个包，它可以是个空文件。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## 常用的标准库"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- re 正则表达式\n",
+    "- copy 复制\n",
+    "- math, cmath 数学\n",
+    "- decimal, fraction\n",
+    "- sqlite3 数据库\n",
+    "- os, os.path 文件系统\n",
+    "- gzip, bz2, zipfile, tarfile 压缩文件\n",
+    "- csv, netrc 各种文件格式\n",
+    "- xml\n",
+    "- htmllib\n",
+    "- ftplib, socket\n",
+    "- cmd 命令行\n",
+    "- pdb \n",
+    "- profile, cProfile, timeit\n",
+    "- collections, heapq, bisect 数据结构\n",
+    "- mmap\n",
+    "- threading, Queue 并行\n",
+    "- multiprocessing\n",
+    "- subprocess\n",
+    "- pickle, cPickle\n",
+    "- struct"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## PYTHONPATH设置"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python的搜索路径可以通过环境变量PYTHONPATH设置，环境变量的设置方法依操作系统的不同而不同，具体方法可以网上搜索。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/02. python essentials/02.19 exceptions.ipynb b/02-python-essentials/02.19-exceptions.ipynb
similarity index 96%
rename from 02. python essentials/02.19 exceptions.ipynb
rename to 02-python-essentials/02.19-exceptions.ipynb
index e77a789b..5b3feac8 100644
--- a/02. python essentials/02.19 exceptions.ipynb	
+++ b/02-python-essentials/02.19-exceptions.ipynb
@@ -1,786 +1,786 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 异常"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## try & except 块"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "写代码的时候，出现错误必不可免，即使代码没有问题，也可能遇到别的问题。\n",
-    "\n",
-    "看下面这段代码：\n",
-    "\n",
-    "```python \n",
-    "import math\n",
-    "\n",
-    "while True:\n",
-    "    text = raw_input('> ')\n",
-    "    if text[0] == 'q':\n",
-    "        break\n",
-    "    x = float(text)\n",
-    "    y = math.log10(x)\n",
-    "    print \"log10({0}) = {1}\".format(x, y)\n",
-    "```\n",
-    "\n",
-    "这段代码接收命令行的输入，当输入为数字时，计算它的对数并输出，直到输入值为 `q` 为止。\n",
-    "\n",
-    "乍看没什么问题，然而当我们输入0或者负数时："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "> -1\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "math domain error",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-1-ceb8cf66641b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m         \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog10\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[1;32mprint\u001b[0m \u001b[1;34m\"log10({0}) = {1}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: math domain error"
-     ]
-    }
-   ],
-   "source": [
-    "import math\n",
-    "\n",
-    "while True:\n",
-    "    text = raw_input('> ')\n",
-    "    if text[0] == 'q':\n",
-    "        break\n",
-    "    x = float(text)\n",
-    "    y = math.log10(x)\n",
-    "    print \"log10({0}) = {1}\".format(x, y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`log10` 函数会报错，因为不能接受非正值。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "一旦报错，程序就会停止执行，如果不希望程序停止执行，那么我们可以添加一对 `try & except`： "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "import math\n",
-    "\n",
-    "while True:\n",
-    "    try:\n",
-    "        text = raw_input('> ')\n",
-    "        if text[0] == 'q':\n",
-    "            break\n",
-    "        x = float(text)\n",
-    "        y = math.log10(x)\n",
-    "        print \"log10({0}) = {1}\".format(x, y)\n",
-    "    except ValueError:\n",
-    "        print \"the value must be greater than 0\"\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "一旦 `try` 块中的内容出现了异常，那么 `try` 块后面的内容会被忽略，**Python**会寻找 `except` 里面有没有对应的内容，如果找到，就执行对应的块，没有则抛出这个异常。\n",
-    "\n",
-    "在上面的例子中，`try` 抛出的是 `ValueError`，`except` 中有对应的内容，所以这个异常被 `except` 捕捉到，程序可以继续执行："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "> -1\n",
-      "the value must be greater than 0\n",
-      "> 0\n",
-      "the value must be greater than 0\n",
-      "> 1\n",
-      "log10(1.0) = 0.0\n",
-      "> q\n"
-     ]
-    }
-   ],
-   "source": [
-    "import math\n",
-    "\n",
-    "while True:\n",
-    "    try:\n",
-    "        text = raw_input('> ')\n",
-    "        if text[0] == 'q':\n",
-    "            break\n",
-    "        x = float(text)\n",
-    "        y = math.log10(x)\n",
-    "        print \"log10({0}) = {1}\".format(x, y)\n",
-    "    except ValueError:\n",
-    "        print \"the value must be greater than 0\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 捕捉不同的错误类型"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "``` python\n",
-    "import math\n",
-    "\n",
-    "while True:\n",
-    "    try:\n",
-    "        text = raw_input('> ')\n",
-    "        if text[0] == 'q':\n",
-    "            break\n",
-    "        x = float(text)\n",
-    "        y = 1 / math.log10(x)\n",
-    "        print \"log10({0}) = {1}\".format(x, y)\n",
-    "    except ValueError:\n",
-    "        print \"the value must be greater than 0\"\n",
-    "```\n",
-    "\n",
-    "假设我们将这里的 `y` 更改为 `1 / math.log10(x)`，此时输入 `1`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "> 1\n"
-     ]
-    },
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "float division by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-3-7607f1ae6af9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m             \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog10\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m         \u001b[1;32mprint\u001b[0m \u001b[1;34m\"log10({0}) = {1}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
-     ]
-    }
-   ],
-   "source": [
-    "import math\n",
-    "\n",
-    "while True:\n",
-    "    try:\n",
-    "        text = raw_input('> ')\n",
-    "        if text[0] == 'q':\n",
-    "            break\n",
-    "        x = float(text)\n",
-    "        y = 1 / math.log10(x)\n",
-    "        print \"log10({0}) = {1}\".format(x, y)\n",
-    "    except ValueError:\n",
-    "        print \"the value must be greater than 0\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "因为我们的 `except` 里面并没有 `ZeroDivisionError`，所以会抛出这个异常，我们可以通过两种方式解决这个问题："
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 捕捉所有异常"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "将`except` 的值改成 `Exception` 类，来捕获所有的异常。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "> 1\n",
-      "invalid value\n",
-      "> 0\n",
-      "invalid value\n",
-      "> -1\n",
-      "invalid value\n",
-      "> 2\n",
-      "1 / log10(2.0) = 3.32192809489\n",
-      "> q\n"
-     ]
-    }
-   ],
-   "source": [
-    "import math\n",
-    "\n",
-    "while True:\n",
-    "    try:\n",
-    "        text = raw_input('> ')\n",
-    "        if text[0] == 'q':\n",
-    "            break\n",
-    "        x = float(text)\n",
-    "        y = 1 / math.log10(x)\n",
-    "        print \"1 / log10({0}) = {1}\".format(x, y)\n",
-    "    except Exception:\n",
-    "        print \"invalid value\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 指定特定值"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里，我们把 `ZeroDivisionError` 加入 `except` 。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "> 1\n",
-      "invalid value\n",
-      "> -1\n",
-      "invalid value\n",
-      "> 0\n",
-      "invalid value\n",
-      "> q\n"
-     ]
-    }
-   ],
-   "source": [
-    "import math\n",
-    "\n",
-    "while True:\n",
-    "    try:\n",
-    "        text = raw_input('> ')\n",
-    "        if text[0] == 'q':\n",
-    "            break\n",
-    "        x = float(text)\n",
-    "        y = 1 / math.log10(x)\n",
-    "        print \"1 / log10({0}) = {1}\".format(x, y)\n",
-    "    except (ValueError, ZeroDivisionError):\n",
-    "        print \"invalid value\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "或者另加处理："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "> 1\n",
-      "the value must not be 1\n",
-      "> -1\n",
-      "the value must be greater than 0\n",
-      "> 0\n",
-      "the value must be greater than 0\n",
-      "> 2\n",
-      "1 / log10(2.0) = 3.32192809489\n",
-      "> q\n"
-     ]
-    }
-   ],
-   "source": [
-    "import math\n",
-    "\n",
-    "while True:\n",
-    "    try:\n",
-    "        text = raw_input('> ')\n",
-    "        if text[0] == 'q':\n",
-    "            break\n",
-    "        x = float(text)\n",
-    "        y = 1 / math.log10(x)\n",
-    "        print \"1 / log10({0}) = {1}\".format(x, y)\n",
-    "    except ValueError:\n",
-    "        print \"the value must be greater than 0\"\n",
-    "    except ZeroDivisionError:\n",
-    "        print \"the value must not be 1\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上,我们还可以将这两种方式结合起来,用 `Exception` 来捕捉其他的错误："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "> 1\n",
-      "the value must not be 1\n",
-      "> -1\n",
-      "the value must be greater than 0\n",
-      "> 0\n",
-      "the value must be greater than 0\n",
-      "> q\n"
-     ]
-    }
-   ],
-   "source": [
-    "import math\n",
-    "\n",
-    "while True:\n",
-    "    try:\n",
-    "        text = raw_input('> ')\n",
-    "        if text[0] == 'q':\n",
-    "            break\n",
-    "        x = float(text)\n",
-    "        y = 1 / math.log10(x)\n",
-    "        print \"1 / log10({0}) = {1}\".format(x, y)\n",
-    "    except ValueError:\n",
-    "        print \"the value must be greater than 0\"\n",
-    "    except ZeroDivisionError:\n",
-    "        print \"the value must not be 1\"\n",
-    "    except Exception:\n",
-    "        print \"unexpected error\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 得到异常的具体信息"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在上面的例子中，当我们输入不能转换为浮点数的字符串时，它输出的是 `the value must be greater than 0`，这并没有反映出实际情况。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "could not convert string to float: a",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-8-99859da4e72c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: a"
-     ]
-    }
-   ],
-   "source": [
-    "float('a')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "为了得到异常的具体信息，我们将这个 `ValueError` 具现化："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "> 1\n",
-      "the value must not be 1\n",
-      "> -1\n",
-      "the value must be greater than 0\n",
-      "> aa\n",
-      "could not convert 'aa' to float\n",
-      "> q\n"
-     ]
-    }
-   ],
-   "source": [
-    "import math\n",
-    "\n",
-    "while True:\n",
-    "    try:\n",
-    "        text = raw_input('> ')\n",
-    "        if text[0] == 'q':\n",
-    "            break\n",
-    "        x = float(text)\n",
-    "        y = 1 / math.log10(x)\n",
-    "        print \"1 / log10({0}) = {1}\".format(x, y)\n",
-    "    except ValueError as exc:\n",
-    "        if exc.message == \"math domain error\":\n",
-    "            print \"the value must be greater than 0\"\n",
-    "        else:\n",
-    "            print \"could not convert '%s' to float\" % text\n",
-    "    except ZeroDivisionError:\n",
-    "        print \"the value must not be 1\"\n",
-    "    except Exception as exc:\n",
-    "        print \"unexpected error:\", exc.message"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "同时，我们也将捕获的其他异常的信息显示出来。\n",
-    "\n",
-    "这里，`exc.message` 显示的内容是异常对应的说明，例如\n",
-    "\n",
-    "    ValueError: could not convert string to float: a\n",
-    "\n",
-    "对应的 `message` 是 \n",
-    "\n",
-    "    could not convert string to float: a\n",
-    "\n",
-    "当我们使用 `except Exception` 时，会捕获所有的 `Exception` 和它派生出来的子类，但不是所有的异常都是从 `Exception` 类派生出来的，可能会出现一些不能捕获的情况，因此，更加一般的做法是使用这样的形式：\n",
-    "\n",
-    "```python\n",
-    "try:\n",
-    "    pass\n",
-    "except:\n",
-    "    pass\n",
-    "```\n",
-    "\n",
-    "这样不指定异常的类型会捕获所有的异常，但是这样的形式并不推荐。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 自定义异常"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "异常是标准库中的类，这意味着我们可以自定义异常类："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class CommandError(ValueError):\n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里我们定义了一个继承自 `ValueError` 的异常类，异常类一般接收一个字符串作为输入，并把这个字符串当作异常信息，例如："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "> bad command\n"
-     ]
-    },
-    {
-     "ename": "CommandError",
-     "evalue": "Invalid commmand: bad command",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mCommandError\u001b[0m                              Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-11-0e1f81a1136d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mcommand\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mraw_input\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'> '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcommand\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mvalid_commands\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mCommandError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Invalid commmand: %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mcommand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mCommandError\u001b[0m: Invalid commmand: bad command"
-     ]
-    }
-   ],
-   "source": [
-    "valid_commands = {'start', 'stop', 'pause'}\n",
-    "\n",
-    "while True:\n",
-    "    command = raw_input('> ')\n",
-    "    if command.lower() not in valid_commands:\n",
-    "        raise CommandError('Invalid commmand: %s' % command)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们使用 `raise` 关键词来抛出异常。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们可以使用 `try/except` 块来捕捉这个异常："
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "``` python\n",
-    "valid_commands = {'start', 'stop', 'pause'}\n",
-    "\n",
-    "while True:\n",
-    "    command = raw_input('> ')\n",
-    "    try:\n",
-    "        if command.lower() not in valid_commands:\n",
-    "            raise CommandError('Invalid commmand: %s' % command)\n",
-    "    except CommandError:\n",
-    "        print 'Bad command string: \"%s\"' % command\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "由于 `CommandError` 继承自 `ValueError`，我们也可以使用 `except ValueError` 来捕获这个异常。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## finally"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "try/catch 块还有一个可选的关键词 finally。\n",
-    "\n",
-    "不管 try 块有没有异常， finally 块的内容总是会被执行，而且会在抛出异常前执行，因此可以用来作为安全保证，比如确保打开的文件被关闭。。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "finally was called.\n"
-     ]
-    }
-   ],
-   "source": [
-    "try:\n",
-    "    print 1\n",
-    "finally:\n",
-    "    print 'finally was called.'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在抛出异常前执行："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "finally was called.\n"
-     ]
-    },
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "integer division or modulo by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-13-87ecdf8b9265>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[1;32mprint\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32mprint\u001b[0m \u001b[1;34m'finally was called.'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero"
-     ]
-    }
-   ],
-   "source": [
-    "try:\n",
-    "    print 1 / 0\n",
-    "finally:\n",
-    "    print 'finally was called.'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "如果异常被捕获了，在最后执行："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "divide by 0.\n",
-      "finally was called.\n"
-     ]
-    }
-   ],
-   "source": [
-    "try:\n",
-    "    print 1 / 0\n",
-    "except ZeroDivisionError:\n",
-    "    print 'divide by 0.'\n",
-    "finally:\n",
-    "    print 'finally was called.'"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 异常"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## try & except 块"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "写代码的时候，出现错误必不可免，即使代码没有问题，也可能遇到别的问题。\n",
+    "\n",
+    "看下面这段代码：\n",
+    "\n",
+    "```python \n",
+    "import math\n",
+    "\n",
+    "while True:\n",
+    "    text = raw_input('> ')\n",
+    "    if text[0] == 'q':\n",
+    "        break\n",
+    "    x = float(text)\n",
+    "    y = math.log10(x)\n",
+    "    print \"log10({0}) = {1}\".format(x, y)\n",
+    "```\n",
+    "\n",
+    "这段代码接收命令行的输入，当输入为数字时，计算它的对数并输出，直到输入值为 `q` 为止。\n",
+    "\n",
+    "乍看没什么问题，然而当我们输入0或者负数时："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> -1\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "math domain error",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-1-ceb8cf66641b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m         \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog10\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[1;32mprint\u001b[0m \u001b[1;34m\"log10({0}) = {1}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: math domain error"
+     ]
+    }
+   ],
+   "source": [
+    "import math\n",
+    "\n",
+    "while True:\n",
+    "    text = raw_input('> ')\n",
+    "    if text[0] == 'q':\n",
+    "        break\n",
+    "    x = float(text)\n",
+    "    y = math.log10(x)\n",
+    "    print \"log10({0}) = {1}\".format(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`log10` 函数会报错，因为不能接受非正值。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "一旦报错，程序就会停止执行，如果不希望程序停止执行，那么我们可以添加一对 `try & except`： "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "import math\n",
+    "\n",
+    "while True:\n",
+    "    try:\n",
+    "        text = raw_input('> ')\n",
+    "        if text[0] == 'q':\n",
+    "            break\n",
+    "        x = float(text)\n",
+    "        y = math.log10(x)\n",
+    "        print \"log10({0}) = {1}\".format(x, y)\n",
+    "    except ValueError:\n",
+    "        print \"the value must be greater than 0\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "一旦 `try` 块中的内容出现了异常，那么 `try` 块后面的内容会被忽略，**Python**会寻找 `except` 里面有没有对应的内容，如果找到，就执行对应的块，没有则抛出这个异常。\n",
+    "\n",
+    "在上面的例子中，`try` 抛出的是 `ValueError`，`except` 中有对应的内容，所以这个异常被 `except` 捕捉到，程序可以继续执行："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> -1\n",
+      "the value must be greater than 0\n",
+      "> 0\n",
+      "the value must be greater than 0\n",
+      "> 1\n",
+      "log10(1.0) = 0.0\n",
+      "> q\n"
+     ]
+    }
+   ],
+   "source": [
+    "import math\n",
+    "\n",
+    "while True:\n",
+    "    try:\n",
+    "        text = raw_input('> ')\n",
+    "        if text[0] == 'q':\n",
+    "            break\n",
+    "        x = float(text)\n",
+    "        y = math.log10(x)\n",
+    "        print \"log10({0}) = {1}\".format(x, y)\n",
+    "    except ValueError:\n",
+    "        print \"the value must be greater than 0\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 捕捉不同的错误类型"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "``` python\n",
+    "import math\n",
+    "\n",
+    "while True:\n",
+    "    try:\n",
+    "        text = raw_input('> ')\n",
+    "        if text[0] == 'q':\n",
+    "            break\n",
+    "        x = float(text)\n",
+    "        y = 1 / math.log10(x)\n",
+    "        print \"log10({0}) = {1}\".format(x, y)\n",
+    "    except ValueError:\n",
+    "        print \"the value must be greater than 0\"\n",
+    "```\n",
+    "\n",
+    "假设我们将这里的 `y` 更改为 `1 / math.log10(x)`，此时输入 `1`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> 1\n"
+     ]
+    },
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "float division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-3-7607f1ae6af9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m             \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog10\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m         \u001b[1;32mprint\u001b[0m \u001b[1;34m\"log10({0}) = {1}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "import math\n",
+    "\n",
+    "while True:\n",
+    "    try:\n",
+    "        text = raw_input('> ')\n",
+    "        if text[0] == 'q':\n",
+    "            break\n",
+    "        x = float(text)\n",
+    "        y = 1 / math.log10(x)\n",
+    "        print \"log10({0}) = {1}\".format(x, y)\n",
+    "    except ValueError:\n",
+    "        print \"the value must be greater than 0\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "因为我们的 `except` 里面并没有 `ZeroDivisionError`，所以会抛出这个异常，我们可以通过两种方式解决这个问题："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 捕捉所有异常"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将`except` 的值改成 `Exception` 类，来捕获所有的异常。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> 1\n",
+      "invalid value\n",
+      "> 0\n",
+      "invalid value\n",
+      "> -1\n",
+      "invalid value\n",
+      "> 2\n",
+      "1 / log10(2.0) = 3.32192809489\n",
+      "> q\n"
+     ]
+    }
+   ],
+   "source": [
+    "import math\n",
+    "\n",
+    "while True:\n",
+    "    try:\n",
+    "        text = raw_input('> ')\n",
+    "        if text[0] == 'q':\n",
+    "            break\n",
+    "        x = float(text)\n",
+    "        y = 1 / math.log10(x)\n",
+    "        print \"1 / log10({0}) = {1}\".format(x, y)\n",
+    "    except Exception:\n",
+    "        print \"invalid value\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 指定特定值"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里，我们把 `ZeroDivisionError` 加入 `except` 。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> 1\n",
+      "invalid value\n",
+      "> -1\n",
+      "invalid value\n",
+      "> 0\n",
+      "invalid value\n",
+      "> q\n"
+     ]
+    }
+   ],
+   "source": [
+    "import math\n",
+    "\n",
+    "while True:\n",
+    "    try:\n",
+    "        text = raw_input('> ')\n",
+    "        if text[0] == 'q':\n",
+    "            break\n",
+    "        x = float(text)\n",
+    "        y = 1 / math.log10(x)\n",
+    "        print \"1 / log10({0}) = {1}\".format(x, y)\n",
+    "    except (ValueError, ZeroDivisionError):\n",
+    "        print \"invalid value\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "或者另加处理："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> 1\n",
+      "the value must not be 1\n",
+      "> -1\n",
+      "the value must be greater than 0\n",
+      "> 0\n",
+      "the value must be greater than 0\n",
+      "> 2\n",
+      "1 / log10(2.0) = 3.32192809489\n",
+      "> q\n"
+     ]
+    }
+   ],
+   "source": [
+    "import math\n",
+    "\n",
+    "while True:\n",
+    "    try:\n",
+    "        text = raw_input('> ')\n",
+    "        if text[0] == 'q':\n",
+    "            break\n",
+    "        x = float(text)\n",
+    "        y = 1 / math.log10(x)\n",
+    "        print \"1 / log10({0}) = {1}\".format(x, y)\n",
+    "    except ValueError:\n",
+    "        print \"the value must be greater than 0\"\n",
+    "    except ZeroDivisionError:\n",
+    "        print \"the value must not be 1\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上,我们还可以将这两种方式结合起来,用 `Exception` 来捕捉其他的错误："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> 1\n",
+      "the value must not be 1\n",
+      "> -1\n",
+      "the value must be greater than 0\n",
+      "> 0\n",
+      "the value must be greater than 0\n",
+      "> q\n"
+     ]
+    }
+   ],
+   "source": [
+    "import math\n",
+    "\n",
+    "while True:\n",
+    "    try:\n",
+    "        text = raw_input('> ')\n",
+    "        if text[0] == 'q':\n",
+    "            break\n",
+    "        x = float(text)\n",
+    "        y = 1 / math.log10(x)\n",
+    "        print \"1 / log10({0}) = {1}\".format(x, y)\n",
+    "    except ValueError:\n",
+    "        print \"the value must be greater than 0\"\n",
+    "    except ZeroDivisionError:\n",
+    "        print \"the value must not be 1\"\n",
+    "    except Exception:\n",
+    "        print \"unexpected error\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 得到异常的具体信息"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在上面的例子中，当我们输入不能转换为浮点数的字符串时，它输出的是 `the value must be greater than 0`，这并没有反映出实际情况。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "could not convert string to float: a",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-8-99859da4e72c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: a"
+     ]
+    }
+   ],
+   "source": [
+    "float('a')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "为了得到异常的具体信息，我们将这个 `ValueError` 具现化："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> 1\n",
+      "the value must not be 1\n",
+      "> -1\n",
+      "the value must be greater than 0\n",
+      "> aa\n",
+      "could not convert 'aa' to float\n",
+      "> q\n"
+     ]
+    }
+   ],
+   "source": [
+    "import math\n",
+    "\n",
+    "while True:\n",
+    "    try:\n",
+    "        text = raw_input('> ')\n",
+    "        if text[0] == 'q':\n",
+    "            break\n",
+    "        x = float(text)\n",
+    "        y = 1 / math.log10(x)\n",
+    "        print \"1 / log10({0}) = {1}\".format(x, y)\n",
+    "    except ValueError as exc:\n",
+    "        if exc.message == \"math domain error\":\n",
+    "            print \"the value must be greater than 0\"\n",
+    "        else:\n",
+    "            print \"could not convert '%s' to float\" % text\n",
+    "    except ZeroDivisionError:\n",
+    "        print \"the value must not be 1\"\n",
+    "    except Exception as exc:\n",
+    "        print \"unexpected error:\", exc.message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "同时，我们也将捕获的其他异常的信息显示出来。\n",
+    "\n",
+    "这里，`exc.message` 显示的内容是异常对应的说明，例如\n",
+    "\n",
+    "    ValueError: could not convert string to float: a\n",
+    "\n",
+    "对应的 `message` 是 \n",
+    "\n",
+    "    could not convert string to float: a\n",
+    "\n",
+    "当我们使用 `except Exception` 时，会捕获所有的 `Exception` 和它派生出来的子类，但不是所有的异常都是从 `Exception` 类派生出来的，可能会出现一些不能捕获的情况，因此，更加一般的做法是使用这样的形式：\n",
+    "\n",
+    "```python\n",
+    "try:\n",
+    "    pass\n",
+    "except:\n",
+    "    pass\n",
+    "```\n",
+    "\n",
+    "这样不指定异常的类型会捕获所有的异常，但是这样的形式并不推荐。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 自定义异常"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "异常是标准库中的类，这意味着我们可以自定义异常类："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class CommandError(ValueError):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里我们定义了一个继承自 `ValueError` 的异常类，异常类一般接收一个字符串作为输入，并把这个字符串当作异常信息，例如："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> bad command\n"
+     ]
+    },
+    {
+     "ename": "CommandError",
+     "evalue": "Invalid commmand: bad command",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mCommandError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-11-0e1f81a1136d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mcommand\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mraw_input\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'> '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcommand\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mvalid_commands\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mCommandError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Invalid commmand: %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mcommand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mCommandError\u001b[0m: Invalid commmand: bad command"
+     ]
+    }
+   ],
+   "source": [
+    "valid_commands = {'start', 'stop', 'pause'}\n",
+    "\n",
+    "while True:\n",
+    "    command = raw_input('> ')\n",
+    "    if command.lower() not in valid_commands:\n",
+    "        raise CommandError('Invalid commmand: %s' % command)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们使用 `raise` 关键词来抛出异常。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们可以使用 `try/except` 块来捕捉这个异常："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "``` python\n",
+    "valid_commands = {'start', 'stop', 'pause'}\n",
+    "\n",
+    "while True:\n",
+    "    command = raw_input('> ')\n",
+    "    try:\n",
+    "        if command.lower() not in valid_commands:\n",
+    "            raise CommandError('Invalid commmand: %s' % command)\n",
+    "    except CommandError:\n",
+    "        print 'Bad command string: \"%s\"' % command\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "由于 `CommandError` 继承自 `ValueError`，我们也可以使用 `except ValueError` 来捕获这个异常。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## finally"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "try/catch 块还有一个可选的关键词 finally。\n",
+    "\n",
+    "不管 try 块有没有异常， finally 块的内容总是会被执行，而且会在抛出异常前执行，因此可以用来作为安全保证，比如确保打开的文件被关闭。。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "finally was called.\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    print 1\n",
+    "finally:\n",
+    "    print 'finally was called.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在抛出异常前执行："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finally was called.\n"
+     ]
+    },
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "integer division or modulo by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-13-87ecdf8b9265>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[1;32mprint\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32mprint\u001b[0m \u001b[1;34m'finally was called.'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    print 1 / 0\n",
+    "finally:\n",
+    "    print 'finally was called.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "如果异常被捕获了，在最后执行："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "divide by 0.\n",
+      "finally was called.\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    print 1 / 0\n",
+    "except ZeroDivisionError:\n",
+    "    print 'divide by 0.'\n",
+    "finally:\n",
+    "    print 'finally was called.'"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/02. python essentials/02.20 warnings.ipynb b/02-python-essentials/02.20-warnings.ipynb
similarity index 95%
rename from 02. python essentials/02.20 warnings.ipynb
rename to 02-python-essentials/02.20-warnings.ipynb
index e56a4bc5..a71faef7 100644
--- a/02. python essentials/02.20 warnings.ipynb	
+++ b/02-python-essentials/02.20-warnings.ipynb
@@ -1,109 +1,109 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 警告"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "出现了一些需要让用户知道的问题，但又不想停止程序，这时候我们可以使用警告：\n",
-    "\n",
-    "首先导入警告模块："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import warnings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在需要的地方，我们使用 `warnings` 中的 `warn` 函数：\n",
-    "\n",
-    "    warn(msg, WarningType = UserWarning)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:4: RuntimeWarning: month (13) is not between 1 and 12\n"
-     ]
-    }
-   ],
-   "source": [
-    "def month_warning(m):\n",
-    "    if not 1<= m <= 12:\n",
-    "        msg = \"month (%d) is not between 1 and 12\" % m\n",
-    "        warnings.warn(msg, RuntimeWarning)\n",
-    "\n",
-    "month_warning(13)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "有时候我们想要忽略特定类型的警告，可以使用 `warnings` 的 `filterwarnings` 函数：\n",
-    "\n",
-    "    filterwarnings(action, category)\n",
-    "\n",
-    "将 `action` 设置为 `'ignore'` 便可以忽略特定类型的警告："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "warnings.filterwarnings(action = 'ignore', category = RuntimeWarning)\n",
-    "\n",
-    "month_warning(13)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 警告"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "出现了一些需要让用户知道的问题，但又不想停止程序，这时候我们可以使用警告：\n",
+    "\n",
+    "首先导入警告模块："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import warnings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在需要的地方，我们使用 `warnings` 中的 `warn` 函数：\n",
+    "\n",
+    "    warn(msg, WarningType = UserWarning)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:4: RuntimeWarning: month (13) is not between 1 and 12\n"
+     ]
+    }
+   ],
+   "source": [
+    "def month_warning(m):\n",
+    "    if not 1<= m <= 12:\n",
+    "        msg = \"month (%d) is not between 1 and 12\" % m\n",
+    "        warnings.warn(msg, RuntimeWarning)\n",
+    "\n",
+    "month_warning(13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "有时候我们想要忽略特定类型的警告，可以使用 `warnings` 的 `filterwarnings` 函数：\n",
+    "\n",
+    "    filterwarnings(action, category)\n",
+    "\n",
+    "将 `action` 设置为 `'ignore'` 便可以忽略特定类型的警告："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "warnings.filterwarnings(action = 'ignore', category = RuntimeWarning)\n",
+    "\n",
+    "month_warning(13)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/02. python essentials/02.21 file IO.ipynb b/02-python-essentials/02.21-file-IO.ipynb
similarity index 96%
rename from 02. python essentials/02.21 file IO.ipynb
rename to 02-python-essentials/02.21-file-IO.ipynb
index 57695539..ac126e98 100644
--- a/02. python essentials/02.21 file IO.ipynb	
+++ b/02-python-essentials/02.21-file-IO.ipynb
@@ -1,6530 +1,6530 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 文件读写"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "写入测试文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing test.txt\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%writefile test.txt\n",
-    "this is a test file.\n",
-    "hello world!\n",
-    "python is good!\n",
-    "today is a good day."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 读文件"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `open` 函数或者 `file` 函数来读文件，使用文件名的字符串作为输入参数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "f = open('test.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "f = file('test.txt')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这两种方式没有太大区别。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "默认以读的方式打开文件，如果文件不存在会报错。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以使用 `read` 方法来读入文件中的所有内容："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "this is a test file.\n",
-      "hello world!\n",
-      "python is good!\n",
-      "today is a good day.\n"
-     ]
-    }
-   ],
-   "source": [
-    "text = f.read()\n",
-    "print text"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "也可以按照行读入内容，`readlines` 方法返回一个列表，每个元素代表文件中每一行的内容："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['this is a test file.\\n', 'hello world!\\n', 'python is good!\\n', 'today is a good day.']\n"
-     ]
-    }
-   ],
-   "source": [
-    "f = open('test.txt')\n",
-    "lines = f.readlines()\n",
-    "print lines"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用完文件之后，需要将文件关闭。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "f.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上，我们可以将 `f` 放在一个循环中，得到它每一行的内容："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "this is a test file.\n",
-      "\n",
-      "hello world!\n",
-      "\n",
-      "python is good!\n",
-      "\n",
-      "today is a good day.\n"
-     ]
-    }
-   ],
-   "source": [
-    "f = open('test.txt')\n",
-    "for line in f:\n",
-    "    print line\n",
-    "f.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "删除刚才创建的文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('test.txt')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 写文件"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们使用 `open` 函数的写入模式来写文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "f = open('myfile.txt', 'w')\n",
-    "f.write('hello world!')\n",
-    "f.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `w` 模式时，如果文件不存在会被创建，我们可以查看是否真的写入成功："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hello world!\n"
-     ]
-    }
-   ],
-   "source": [
-    "print open('myfile.txt').read()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "如果文件已经存在， `w` 模式会覆盖之前写的所有内容："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "another hello world!\n"
-     ]
-    }
-   ],
-   "source": [
-    "f = open('myfile.txt', 'w')\n",
-    "f.write('another hello world!')\n",
-    "f.close()\n",
-    "print open('myfile.txt').read()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "除了写入模式，还有追加模式 `a` ，追加模式不会覆盖之前已经写入的内容，而是在之后继续写入："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "another hello world!... and more\n"
-     ]
-    }
-   ],
-   "source": [
-    "f = open('myfile.txt', 'a')\n",
-    "f.write('... and more')\n",
-    "f.close()\n",
-    "print open('myfile.txt').read()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "写入结束之后一定要将文件关闭，否则可能出现内容没有完全写入文件中的情况。\n",
-    "\n",
-    "还可以使用读写模式 `w+`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "world!\n"
-     ]
-    }
-   ],
-   "source": [
-    "f = open('myfile.txt', 'w+')\n",
-    "f.write('hello world!')\n",
-    "f.seek(6)\n",
-    "print f.read()\n",
-    "f.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里 `f.seek(6)` 移动到文件的第6个字符处，然后 `f.read()` 读出剩下的内容。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('myfile.txt')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 二进制文件"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "二进制读写模式 b："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "'\\x86H\\x93\\xe1\\xd8\\xef\\xc0\\xaa(\\x17\\xa9\\xc9\\xa51\\xf1\\x98'\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "f = open('binary.bin', 'wb')\n",
-    "f.write(os.urandom(16))\n",
-    "f.close()\n",
-    "\n",
-    "f = open('binary.bin', 'rb')\n",
-    "print repr(f.read())\n",
-    "f.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('binary.bin')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 换行符"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不同操作系统的换行符可能不同：\n",
-    "\n",
-    "- `\\r`\n",
-    "- `\\n`\n",
-    "- `\\r\\n`\n",
-    "\n",
-    "使用 `U` 选项，可以将这三个统一看成 `\\n` 换行符。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 关闭文件"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在**Python**中，如果一个打开的文件不再被其他变量引用时，它会自动关闭这个文件。\n",
-    "\n",
-    "所以正常情况下，如果一个文件正常被关闭了，忘记调用文件的 `close` 方法不会有什么问题。\n",
-    "\n",
-    "关闭文件可以保证内容已经被写入文件，而不关闭可能会出现意想不到的结果："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "''\n"
-     ]
-    }
-   ],
-   "source": [
-    "f = open('newfile.txt','w')\n",
-    "f.write('hello world')\n",
-    "g = open('newfile.txt', 'r')\n",
-    "print repr(g.read())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "虽然这里写了内容，但是在关闭之前，这个内容并没有被写入磁盘。\n",
-    "\n",
-    "使用循环写入的内容也并不完整："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hello world: 0\n",
-      "hello world: 1\n",
-      "hello world: 2\n",
-      "hello world: 3\n",
-      "hello world: 4\n",
-      "hello world: 5\n",
-      "hello world: 6\n",
-      "hello world: 7\n",
-      "hello world: 8\n",
-      "hello world: 9\n",
-      "hello world: 10\n",
-      "hello world: 11\n",
-      "hello world: 12\n",
-      "hello world: 13\n",
-      "hello world: 14\n",
-      "hello world: 15\n",
-      "hello world: 16\n",
-      "hello world: 17\n",
-      "hello world: 18\n",
-      "hello world: 19\n",
-      "hello world: 20\n",
-      "hello world: 21\n",
-      "hello world: 22\n",
-      "hello world: 23\n",
-      "hello world: 24\n",
-      "hello world: 25\n",
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-      "hello world: 2791\n",
-      "hello \n"
-     ]
-    }
-   ],
-   "source": [
-    "f = open('newfile.txt','w')\n",
-    "for i in range(3000):\n",
-    "    f.write('hello world: ' + str(i) + '\\n')\n",
-    "\n",
-    "g = open('newfile.txt', 'r')\n",
-    "print g.read()\n",
-    "f.close()\n",
-    "g.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('newfile.txt')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "出现异常时候的读写："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "float division by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-20-8dc1cc9b29ec>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'newfile.txt'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'w'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1.0\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'hello world: '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'\\n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
-     ]
-    }
-   ],
-   "source": [
-    "f = open('newfile.txt','w')\n",
-    "for i in range(3000):\n",
-    "    x = 1.0 / (i - 1000)\n",
-    "    f.write('hello world: ' + str(i) + '\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看已有内容："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hello world: 0\n",
-      "hello world: 1\n",
-      "hello world: 2\n",
-      "hello world: 3\n",
-      "hello world: 4\n",
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-      "hell\n"
-     ]
-    }
-   ],
-   "source": [
-    "g = open('newfile.txt', 'r')\n",
-    "print g.read()\n",
-    "f.close()\n",
-    "g.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以看到，出现异常的时候，磁盘的写入并没有完成，为此我们可以使用 `try/except/finally` 块来关闭文件，这里 `finally` 确保关闭文件，所有的写入已经完成。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "something bad happened\n"
-     ]
-    }
-   ],
-   "source": [
-    "f = open('newfile.txt','w')\n",
-    "try:\n",
-    "    for i in range(3000):\n",
-    "        x = 1.0 / (i - 1000)\n",
-    "        f.write('hello world: ' + str(i) + '\\n')\n",
-    "except Exception:\n",
-    "    print \"something bad happened\"\n",
-    "finally:\n",
-    "    f.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hello world: 0\n",
-      "hello world: 1\n",
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-      "hello world: 997\n",
-      "hello world: 998\n",
-      "hello world: 999\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "g = open('newfile.txt', 'r')\n",
-    "print g.read()\n",
-    "g.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## with 方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上，**Python**提供了更安全的方法，当 `with` 块的内容结束后，**Python**会自动调用它的`close` 方法，确保读写的安全："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "float division by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-24-9d2a70065b27>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'newfile.txt'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'w'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1.0\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m         \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'hello world: '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'\\n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
-     ]
-    }
-   ],
-   "source": [
-    "with open('newfile.txt','w') as f:\n",
-    "    for i in range(3000):\n",
-    "        x = 1.0 / (i - 1000)\n",
-    "        f.write('hello world: ' + str(i) + '\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "与 `try/exception/finally` 效果相同，但更简单。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hello world: 0\n",
-      "hello world: 1\n",
-      "hello world: 2\n",
-      "hello world: 3\n",
-      "hello world: 4\n",
-      "hello world: 5\n",
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-      "hello world: 823\n",
-      "hello world: 824\n",
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-      "hello world: 850\n",
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-      "hello world: 864\n",
-      "hello world: 865\n",
-      "hello world: 866\n",
-      "hello world: 867\n",
-      "hello world: 868\n",
-      "hello world: 869\n",
-      "hello world: 870\n",
-      "hello world: 871\n",
-      "hello world: 872\n",
-      "hello world: 873\n",
-      "hello world: 874\n",
-      "hello world: 875\n",
-      "hello world: 876\n",
-      "hello world: 877\n",
-      "hello world: 878\n",
-      "hello world: 879\n",
-      "hello world: 880\n",
-      "hello world: 881\n",
-      "hello world: 882\n",
-      "hello world: 883\n",
-      "hello world: 884\n",
-      "hello world: 885\n",
-      "hello world: 886\n",
-      "hello world: 887\n",
-      "hello world: 888\n",
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-      "hello world: 890\n",
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-      "hello world: 892\n",
-      "hello world: 893\n",
-      "hello world: 894\n",
-      "hello world: 895\n",
-      "hello world: 896\n",
-      "hello world: 897\n",
-      "hello world: 898\n",
-      "hello world: 899\n",
-      "hello world: 900\n",
-      "hello world: 901\n",
-      "hello world: 902\n",
-      "hello world: 903\n",
-      "hello world: 904\n",
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-      "hello world: 907\n",
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-      "hello world: 910\n",
-      "hello world: 911\n",
-      "hello world: 912\n",
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-      "hello world: 914\n",
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-      "hello world: 918\n",
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-      "hello world: 920\n",
-      "hello world: 921\n",
-      "hello world: 922\n",
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-      "hello world: 924\n",
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-      "hello world: 926\n",
-      "hello world: 927\n",
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-      "hello world: 938\n",
-      "hello world: 939\n",
-      "hello world: 940\n",
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-      "hello world: 942\n",
-      "hello world: 943\n",
-      "hello world: 944\n",
-      "hello world: 945\n",
-      "hello world: 946\n",
-      "hello world: 947\n",
-      "hello world: 948\n",
-      "hello world: 949\n",
-      "hello world: 950\n",
-      "hello world: 951\n",
-      "hello world: 952\n",
-      "hello world: 953\n",
-      "hello world: 954\n",
-      "hello world: 955\n",
-      "hello world: 956\n",
-      "hello world: 957\n",
-      "hello world: 958\n",
-      "hello world: 959\n",
-      "hello world: 960\n",
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-      "hello world: 962\n",
-      "hello world: 963\n",
-      "hello world: 964\n",
-      "hello world: 965\n",
-      "hello world: 966\n",
-      "hello world: 967\n",
-      "hello world: 968\n",
-      "hello world: 969\n",
-      "hello world: 970\n",
-      "hello world: 971\n",
-      "hello world: 972\n",
-      "hello world: 973\n",
-      "hello world: 974\n",
-      "hello world: 975\n",
-      "hello world: 976\n",
-      "hello world: 977\n",
-      "hello world: 978\n",
-      "hello world: 979\n",
-      "hello world: 980\n",
-      "hello world: 981\n",
-      "hello world: 982\n",
-      "hello world: 983\n",
-      "hello world: 984\n",
-      "hello world: 985\n",
-      "hello world: 986\n",
-      "hello world: 987\n",
-      "hello world: 988\n",
-      "hello world: 989\n",
-      "hello world: 990\n",
-      "hello world: 991\n",
-      "hello world: 992\n",
-      "hello world: 993\n",
-      "hello world: 994\n",
-      "hello world: 995\n",
-      "hello world: 996\n",
-      "hello world: 997\n",
-      "hello world: 998\n",
-      "hello world: 999\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "g = open('newfile.txt', 'r')\n",
-    "print g.read()\n",
-    "g.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "所以，写文件时候要确保文件被正确关闭。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('newfile.txt')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 文件读写"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "写入测试文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing test.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile test.txt\n",
+    "this is a test file.\n",
+    "hello world!\n",
+    "python is good!\n",
+    "today is a good day."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 读文件"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `open` 函数或者 `file` 函数来读文件，使用文件名的字符串作为输入参数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "f = open('test.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "f = file('test.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这两种方式没有太大区别。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "默认以读的方式打开文件，如果文件不存在会报错。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以使用 `read` 方法来读入文件中的所有内容："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "this is a test file.\n",
+      "hello world!\n",
+      "python is good!\n",
+      "today is a good day.\n"
+     ]
+    }
+   ],
+   "source": [
+    "text = f.read()\n",
+    "print text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "也可以按照行读入内容，`readlines` 方法返回一个列表，每个元素代表文件中每一行的内容："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['this is a test file.\\n', 'hello world!\\n', 'python is good!\\n', 'today is a good day.']\n"
+     ]
+    }
+   ],
+   "source": [
+    "f = open('test.txt')\n",
+    "lines = f.readlines()\n",
+    "print lines"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用完文件之后，需要将文件关闭。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上，我们可以将 `f` 放在一个循环中，得到它每一行的内容："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "this is a test file.\n",
+      "\n",
+      "hello world!\n",
+      "\n",
+      "python is good!\n",
+      "\n",
+      "today is a good day.\n"
+     ]
+    }
+   ],
+   "source": [
+    "f = open('test.txt')\n",
+    "for line in f:\n",
+    "    print line\n",
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "删除刚才创建的文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('test.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 写文件"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们使用 `open` 函数的写入模式来写文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "f = open('myfile.txt', 'w')\n",
+    "f.write('hello world!')\n",
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `w` 模式时，如果文件不存在会被创建，我们可以查看是否真的写入成功："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello world!\n"
+     ]
+    }
+   ],
+   "source": [
+    "print open('myfile.txt').read()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "如果文件已经存在， `w` 模式会覆盖之前写的所有内容："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "another hello world!\n"
+     ]
+    }
+   ],
+   "source": [
+    "f = open('myfile.txt', 'w')\n",
+    "f.write('another hello world!')\n",
+    "f.close()\n",
+    "print open('myfile.txt').read()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "除了写入模式，还有追加模式 `a` ，追加模式不会覆盖之前已经写入的内容，而是在之后继续写入："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "another hello world!... and more\n"
+     ]
+    }
+   ],
+   "source": [
+    "f = open('myfile.txt', 'a')\n",
+    "f.write('... and more')\n",
+    "f.close()\n",
+    "print open('myfile.txt').read()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "写入结束之后一定要将文件关闭，否则可能出现内容没有完全写入文件中的情况。\n",
+    "\n",
+    "还可以使用读写模式 `w+`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "world!\n"
+     ]
+    }
+   ],
+   "source": [
+    "f = open('myfile.txt', 'w+')\n",
+    "f.write('hello world!')\n",
+    "f.seek(6)\n",
+    "print f.read()\n",
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里 `f.seek(6)` 移动到文件的第6个字符处，然后 `f.read()` 读出剩下的内容。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('myfile.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 二进制文件"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "二进制读写模式 b："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'\\x86H\\x93\\xe1\\xd8\\xef\\xc0\\xaa(\\x17\\xa9\\xc9\\xa51\\xf1\\x98'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "f = open('binary.bin', 'wb')\n",
+    "f.write(os.urandom(16))\n",
+    "f.close()\n",
+    "\n",
+    "f = open('binary.bin', 'rb')\n",
+    "print repr(f.read())\n",
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('binary.bin')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 换行符"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不同操作系统的换行符可能不同：\n",
+    "\n",
+    "- `\\r`\n",
+    "- `\\n`\n",
+    "- `\\r\\n`\n",
+    "\n",
+    "使用 `U` 选项，可以将这三个统一看成 `\\n` 换行符。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 关闭文件"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在**Python**中，如果一个打开的文件不再被其他变量引用时，它会自动关闭这个文件。\n",
+    "\n",
+    "所以正常情况下，如果一个文件正常被关闭了，忘记调用文件的 `close` 方法不会有什么问题。\n",
+    "\n",
+    "关闭文件可以保证内容已经被写入文件，而不关闭可能会出现意想不到的结果："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "''\n"
+     ]
+    }
+   ],
+   "source": [
+    "f = open('newfile.txt','w')\n",
+    "f.write('hello world')\n",
+    "g = open('newfile.txt', 'r')\n",
+    "print repr(g.read())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "虽然这里写了内容，但是在关闭之前，这个内容并没有被写入磁盘。\n",
+    "\n",
+    "使用循环写入的内容也并不完整："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello world: 0\n",
+      "hello world: 1\n",
+      "hello world: 2\n",
+      "hello world: 3\n",
+      "hello world: 4\n",
+      "hello world: 5\n",
+      "hello world: 6\n",
+      "hello world: 7\n",
+      "hello world: 8\n",
+      "hello world: 9\n",
+      "hello world: 10\n",
+      "hello world: 11\n",
+      "hello world: 12\n",
+      "hello world: 13\n",
+      "hello world: 14\n",
+      "hello world: 15\n",
+      "hello world: 16\n",
+      "hello world: 17\n",
+      "hello world: 18\n",
+      "hello world: 19\n",
+      "hello world: 20\n",
+      "hello world: 21\n",
+      "hello world: 22\n",
+      "hello world: 23\n",
+      "hello world: 24\n",
+      "hello world: 25\n",
+      "hello world: 26\n",
+      "hello world: 27\n",
+      "hello world: 28\n",
+      "hello world: 29\n",
+      "hello world: 30\n",
+      "hello world: 31\n",
+      "hello world: 32\n",
+      "hello world: 33\n",
+      "hello world: 34\n",
+      "hello world: 35\n",
+      "hello world: 36\n",
+      "hello world: 37\n",
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+      "hello world: 2608\n",
+      "hello world: 2609\n",
+      "hello world: 2610\n",
+      "hello world: 2611\n",
+      "hello world: 2612\n",
+      "hello world: 2613\n",
+      "hello world: 2614\n",
+      "hello world: 2615\n",
+      "hello world: 2616\n",
+      "hello world: 2617\n",
+      "hello world: 2618\n",
+      "hello world: 2619\n",
+      "hello world: 2620\n",
+      "hello world: 2621\n",
+      "hello world: 2622\n",
+      "hello world: 2623\n",
+      "hello world: 2624\n",
+      "hello world: 2625\n",
+      "hello world: 2626\n",
+      "hello world: 2627\n",
+      "hello world: 2628\n",
+      "hello world: 2629\n",
+      "hello world: 2630\n",
+      "hello world: 2631\n",
+      "hello world: 2632\n",
+      "hello world: 2633\n",
+      "hello world: 2634\n",
+      "hello world: 2635\n",
+      "hello world: 2636\n",
+      "hello world: 2637\n",
+      "hello world: 2638\n",
+      "hello world: 2639\n",
+      "hello world: 2640\n",
+      "hello world: 2641\n",
+      "hello world: 2642\n",
+      "hello world: 2643\n",
+      "hello world: 2644\n",
+      "hello world: 2645\n",
+      "hello world: 2646\n",
+      "hello world: 2647\n",
+      "hello world: 2648\n",
+      "hello world: 2649\n",
+      "hello world: 2650\n",
+      "hello world: 2651\n",
+      "hello world: 2652\n",
+      "hello world: 2653\n",
+      "hello world: 2654\n",
+      "hello world: 2655\n",
+      "hello world: 2656\n",
+      "hello world: 2657\n",
+      "hello world: 2658\n",
+      "hello world: 2659\n",
+      "hello world: 2660\n",
+      "hello world: 2661\n",
+      "hello world: 2662\n",
+      "hello world: 2663\n",
+      "hello world: 2664\n",
+      "hello world: 2665\n",
+      "hello world: 2666\n",
+      "hello world: 2667\n",
+      "hello world: 2668\n",
+      "hello world: 2669\n",
+      "hello world: 2670\n",
+      "hello world: 2671\n",
+      "hello world: 2672\n",
+      "hello world: 2673\n",
+      "hello world: 2674\n",
+      "hello world: 2675\n",
+      "hello world: 2676\n",
+      "hello world: 2677\n",
+      "hello world: 2678\n",
+      "hello world: 2679\n",
+      "hello world: 2680\n",
+      "hello world: 2681\n",
+      "hello world: 2682\n",
+      "hello world: 2683\n",
+      "hello world: 2684\n",
+      "hello world: 2685\n",
+      "hello world: 2686\n",
+      "hello world: 2687\n",
+      "hello world: 2688\n",
+      "hello world: 2689\n",
+      "hello world: 2690\n",
+      "hello world: 2691\n",
+      "hello world: 2692\n",
+      "hello world: 2693\n",
+      "hello world: 2694\n",
+      "hello world: 2695\n",
+      "hello world: 2696\n",
+      "hello world: 2697\n",
+      "hello world: 2698\n",
+      "hello world: 2699\n",
+      "hello world: 2700\n",
+      "hello world: 2701\n",
+      "hello world: 2702\n",
+      "hello world: 2703\n",
+      "hello world: 2704\n",
+      "hello world: 2705\n",
+      "hello world: 2706\n",
+      "hello world: 2707\n",
+      "hello world: 2708\n",
+      "hello world: 2709\n",
+      "hello world: 2710\n",
+      "hello world: 2711\n",
+      "hello world: 2712\n",
+      "hello world: 2713\n",
+      "hello world: 2714\n",
+      "hello world: 2715\n",
+      "hello world: 2716\n",
+      "hello world: 2717\n",
+      "hello world: 2718\n",
+      "hello world: 2719\n",
+      "hello world: 2720\n",
+      "hello world: 2721\n",
+      "hello world: 2722\n",
+      "hello world: 2723\n",
+      "hello world: 2724\n",
+      "hello world: 2725\n",
+      "hello world: 2726\n",
+      "hello world: 2727\n",
+      "hello world: 2728\n",
+      "hello world: 2729\n",
+      "hello world: 2730\n",
+      "hello world: 2731\n",
+      "hello world: 2732\n",
+      "hello world: 2733\n",
+      "hello world: 2734\n",
+      "hello world: 2735\n",
+      "hello world: 2736\n",
+      "hello world: 2737\n",
+      "hello world: 2738\n",
+      "hello world: 2739\n",
+      "hello world: 2740\n",
+      "hello world: 2741\n",
+      "hello world: 2742\n",
+      "hello world: 2743\n",
+      "hello world: 2744\n",
+      "hello world: 2745\n",
+      "hello world: 2746\n",
+      "hello world: 2747\n",
+      "hello world: 2748\n",
+      "hello world: 2749\n",
+      "hello world: 2750\n",
+      "hello world: 2751\n",
+      "hello world: 2752\n",
+      "hello world: 2753\n",
+      "hello world: 2754\n",
+      "hello world: 2755\n",
+      "hello world: 2756\n",
+      "hello world: 2757\n",
+      "hello world: 2758\n",
+      "hello world: 2759\n",
+      "hello world: 2760\n",
+      "hello world: 2761\n",
+      "hello world: 2762\n",
+      "hello world: 2763\n",
+      "hello world: 2764\n",
+      "hello world: 2765\n",
+      "hello world: 2766\n",
+      "hello world: 2767\n",
+      "hello world: 2768\n",
+      "hello world: 2769\n",
+      "hello world: 2770\n",
+      "hello world: 2771\n",
+      "hello world: 2772\n",
+      "hello world: 2773\n",
+      "hello world: 2774\n",
+      "hello world: 2775\n",
+      "hello world: 2776\n",
+      "hello world: 2777\n",
+      "hello world: 2778\n",
+      "hello world: 2779\n",
+      "hello world: 2780\n",
+      "hello world: 2781\n",
+      "hello world: 2782\n",
+      "hello world: 2783\n",
+      "hello world: 2784\n",
+      "hello world: 2785\n",
+      "hello world: 2786\n",
+      "hello world: 2787\n",
+      "hello world: 2788\n",
+      "hello world: 2789\n",
+      "hello world: 2790\n",
+      "hello world: 2791\n",
+      "hello \n"
+     ]
+    }
+   ],
+   "source": [
+    "f = open('newfile.txt','w')\n",
+    "for i in range(3000):\n",
+    "    f.write('hello world: ' + str(i) + '\\n')\n",
+    "\n",
+    "g = open('newfile.txt', 'r')\n",
+    "print g.read()\n",
+    "f.close()\n",
+    "g.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('newfile.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "出现异常时候的读写："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "float division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-20-8dc1cc9b29ec>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'newfile.txt'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'w'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1.0\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'hello world: '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'\\n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "f = open('newfile.txt','w')\n",
+    "for i in range(3000):\n",
+    "    x = 1.0 / (i - 1000)\n",
+    "    f.write('hello world: ' + str(i) + '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看已有内容："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello world: 0\n",
+      "hello world: 1\n",
+      "hello world: 2\n",
+      "hello world: 3\n",
+      "hello world: 4\n",
+      "hello world: 5\n",
+      "hello world: 6\n",
+      "hello world: 7\n",
+      "hello world: 8\n",
+      "hello world: 9\n",
+      "hello world: 10\n",
+      "hello world: 11\n",
+      "hello world: 12\n",
+      "hello world: 13\n",
+      "hello world: 14\n",
+      "hello world: 15\n",
+      "hello world: 16\n",
+      "hello world: 17\n",
+      "hello world: 18\n",
+      "hello world: 19\n",
+      "hello world: 20\n",
+      "hello world: 21\n",
+      "hello world: 22\n",
+      "hello world: 23\n",
+      "hello world: 24\n",
+      "hello world: 25\n",
+      "hello world: 26\n",
+      "hello world: 27\n",
+      "hello world: 28\n",
+      "hello world: 29\n",
+      "hello world: 30\n",
+      "hello world: 31\n",
+      "hello world: 32\n",
+      "hello world: 33\n",
+      "hello world: 34\n",
+      "hello world: 35\n",
+      "hello world: 36\n",
+      "hello world: 37\n",
+      "hello world: 38\n",
+      "hello world: 39\n",
+      "hello world: 40\n",
+      "hello world: 41\n",
+      "hello world: 42\n",
+      "hello world: 43\n",
+      "hello world: 44\n",
+      "hello world: 45\n",
+      "hello world: 46\n",
+      "hello world: 47\n",
+      "hello world: 48\n",
+      "hello world: 49\n",
+      "hello world: 50\n",
+      "hello world: 51\n",
+      "hello world: 52\n",
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+      "hello world: 57\n",
+      "hello world: 58\n",
+      "hello world: 59\n",
+      "hello world: 60\n",
+      "hello world: 61\n",
+      "hello world: 62\n",
+      "hello world: 63\n",
+      "hello world: 64\n",
+      "hello world: 65\n",
+      "hello world: 66\n",
+      "hello world: 67\n",
+      "hello world: 68\n",
+      "hello world: 69\n",
+      "hello world: 70\n",
+      "hello world: 71\n",
+      "hello world: 72\n",
+      "hello world: 73\n",
+      "hello world: 74\n",
+      "hello world: 75\n",
+      "hello world: 76\n",
+      "hello world: 77\n",
+      "hello world: 78\n",
+      "hello world: 79\n",
+      "hello world: 80\n",
+      "hello world: 81\n",
+      "hello world: 82\n",
+      "hello world: 83\n",
+      "hello world: 84\n",
+      "hello world: 85\n",
+      "hello world: 86\n",
+      "hello world: 87\n",
+      "hello world: 88\n",
+      "hello world: 89\n",
+      "hello world: 90\n",
+      "hello world: 91\n",
+      "hello world: 92\n",
+      "hello world: 93\n",
+      "hello world: 94\n",
+      "hello world: 95\n",
+      "hello world: 96\n",
+      "hello world: 97\n",
+      "hello world: 98\n",
+      "hello world: 99\n",
+      "hello world: 100\n",
+      "hello world: 101\n",
+      "hello world: 102\n",
+      "hello world: 103\n",
+      "hello world: 104\n",
+      "hello world: 105\n",
+      "hello world: 106\n",
+      "hello world: 107\n",
+      "hello world: 108\n",
+      "hello world: 109\n",
+      "hello world: 110\n",
+      "hello world: 111\n",
+      "hello world: 112\n",
+      "hello world: 113\n",
+      "hello world: 114\n",
+      "hello world: 115\n",
+      "hello world: 116\n",
+      "hello world: 117\n",
+      "hello world: 118\n",
+      "hello world: 119\n",
+      "hello world: 120\n",
+      "hello world: 121\n",
+      "hello world: 122\n",
+      "hello world: 123\n",
+      "hello world: 124\n",
+      "hello world: 125\n",
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+      "hello world: 127\n",
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+      "hello world: 130\n",
+      "hello world: 131\n",
+      "hello world: 132\n",
+      "hello world: 133\n",
+      "hello world: 134\n",
+      "hello world: 135\n",
+      "hello world: 136\n",
+      "hello world: 137\n",
+      "hello world: 138\n",
+      "hello world: 139\n",
+      "hello world: 140\n",
+      "hello world: 141\n",
+      "hello world: 142\n",
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+      "hello world: 150\n",
+      "hello world: 151\n",
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+      "hello world: 153\n",
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+      "hello world: 750\n",
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+      "hello world: 760\n",
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+      "hello world: 763\n",
+      "hello world: 764\n",
+      "hello world: 765\n",
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+      "hello world: 768\n",
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+      "hello world: 773\n",
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+      "hello world: 780\n",
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+      "hello world: 783\n",
+      "hello world: 784\n",
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+      "hello world: 800\n",
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+      "hello world: 802\n",
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+      "hello world: 805\n",
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+      "hello world: 809\n",
+      "hello world: 810\n",
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+      "hello world: 812\n",
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+      "hello world: 815\n",
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+      "hello world: 819\n",
+      "hello world: 820\n",
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+      "hello world: 822\n",
+      "hello world: 823\n",
+      "hello world: 824\n",
+      "hello world: 825\n",
+      "hello world: 826\n",
+      "hello world: 827\n",
+      "hello world: 828\n",
+      "hello world: 829\n",
+      "hello world: 830\n",
+      "hello world: 831\n",
+      "hello world: 832\n",
+      "hello world: 833\n",
+      "hello world: 834\n",
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+      "hello world: 836\n",
+      "hello world: 837\n",
+      "hello world: 838\n",
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+      "hello world: 840\n",
+      "hello world: 841\n",
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+      "hello world: 843\n",
+      "hello world: 844\n",
+      "hello world: 845\n",
+      "hello world: 846\n",
+      "hello world: 847\n",
+      "hello world: 848\n",
+      "hello world: 849\n",
+      "hello world: 850\n",
+      "hello world: 851\n",
+      "hello world: 852\n",
+      "hello world: 853\n",
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+      "hello world: 855\n",
+      "hello world: 856\n",
+      "hello world: 857\n",
+      "hello world: 858\n",
+      "hello world: 859\n",
+      "hello world: 860\n",
+      "hello world: 861\n",
+      "hello world: 862\n",
+      "hello world: 863\n",
+      "hello world: 864\n",
+      "hello world: 865\n",
+      "hello world: 866\n",
+      "hello world: 867\n",
+      "hello world: 868\n",
+      "hello world: 869\n",
+      "hello world: 870\n",
+      "hello world: 871\n",
+      "hello world: 872\n",
+      "hello world: 873\n",
+      "hello world: 874\n",
+      "hello world: 875\n",
+      "hello world: 876\n",
+      "hello world: 877\n",
+      "hello world: 878\n",
+      "hello world: 879\n",
+      "hello world: 880\n",
+      "hello world: 881\n",
+      "hello world: 882\n",
+      "hello world: 883\n",
+      "hello world: 884\n",
+      "hello world: 885\n",
+      "hello world: 886\n",
+      "hello world: 887\n",
+      "hello world: 888\n",
+      "hello world: 889\n",
+      "hello world: 890\n",
+      "hello world: 891\n",
+      "hello world: 892\n",
+      "hello world: 893\n",
+      "hello world: 894\n",
+      "hello world: 895\n",
+      "hello world: 896\n",
+      "hello world: 897\n",
+      "hello world: 898\n",
+      "hello world: 899\n",
+      "hello world: 900\n",
+      "hello world: 901\n",
+      "hello world: 902\n",
+      "hello world: 903\n",
+      "hello world: 904\n",
+      "hello world: 905\n",
+      "hello world: 906\n",
+      "hello world: 907\n",
+      "hello world: 908\n",
+      "hello world: 909\n",
+      "hello world: 910\n",
+      "hello world: 911\n",
+      "hello world: 912\n",
+      "hello world: 913\n",
+      "hello world: 914\n",
+      "hello world: 915\n",
+      "hello world: 916\n",
+      "hello world: 917\n",
+      "hello world: 918\n",
+      "hello world: 919\n",
+      "hello world: 920\n",
+      "hello world: 921\n",
+      "hello world: 922\n",
+      "hello world: 923\n",
+      "hello world: 924\n",
+      "hello world: 925\n",
+      "hello world: 926\n",
+      "hello world: 927\n",
+      "hello world: 928\n",
+      "hello world: 929\n",
+      "hello world: 930\n",
+      "hello world: 931\n",
+      "hello world: 932\n",
+      "hello world: 933\n",
+      "hello world: 934\n",
+      "hello world: 935\n",
+      "hello world: 936\n",
+      "hello world: 937\n",
+      "hello world: 938\n",
+      "hello world: 939\n",
+      "hello world: 940\n",
+      "hello world: 941\n",
+      "hello world: 942\n",
+      "hello world: 943\n",
+      "hello world: 944\n",
+      "hello world: 945\n",
+      "hello world: 946\n",
+      "hello world: 947\n",
+      "hello world: 948\n",
+      "hello world: 949\n",
+      "hello world: 950\n",
+      "hello world: 951\n",
+      "hello world: 952\n",
+      "hello world: 953\n",
+      "hello world: 954\n",
+      "hello world: 955\n",
+      "hello world: 956\n",
+      "hello world: 957\n",
+      "hello world: 958\n",
+      "hello world: 959\n",
+      "hello world: 960\n",
+      "hello world: 961\n",
+      "hello world: 962\n",
+      "hello world: 963\n",
+      "hello world: 964\n",
+      "hello world: 965\n",
+      "hello world: 966\n",
+      "hello world: 967\n",
+      "hello world: 968\n",
+      "hello world: 969\n",
+      "hell\n"
+     ]
+    }
+   ],
+   "source": [
+    "g = open('newfile.txt', 'r')\n",
+    "print g.read()\n",
+    "f.close()\n",
+    "g.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以看到，出现异常的时候，磁盘的写入并没有完成，为此我们可以使用 `try/except/finally` 块来关闭文件，这里 `finally` 确保关闭文件，所有的写入已经完成。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "something bad happened\n"
+     ]
+    }
+   ],
+   "source": [
+    "f = open('newfile.txt','w')\n",
+    "try:\n",
+    "    for i in range(3000):\n",
+    "        x = 1.0 / (i - 1000)\n",
+    "        f.write('hello world: ' + str(i) + '\\n')\n",
+    "except Exception:\n",
+    "    print \"something bad happened\"\n",
+    "finally:\n",
+    "    f.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello world: 0\n",
+      "hello world: 1\n",
+      "hello world: 2\n",
+      "hello world: 3\n",
+      "hello world: 4\n",
+      "hello world: 5\n",
+      "hello world: 6\n",
+      "hello world: 7\n",
+      "hello world: 8\n",
+      "hello world: 9\n",
+      "hello world: 10\n",
+      "hello world: 11\n",
+      "hello world: 12\n",
+      "hello world: 13\n",
+      "hello world: 14\n",
+      "hello world: 15\n",
+      "hello world: 16\n",
+      "hello world: 17\n",
+      "hello world: 18\n",
+      "hello world: 19\n",
+      "hello world: 20\n",
+      "hello world: 21\n",
+      "hello world: 22\n",
+      "hello world: 23\n",
+      "hello world: 24\n",
+      "hello world: 25\n",
+      "hello world: 26\n",
+      "hello world: 27\n",
+      "hello world: 28\n",
+      "hello world: 29\n",
+      "hello world: 30\n",
+      "hello world: 31\n",
+      "hello world: 32\n",
+      "hello world: 33\n",
+      "hello world: 34\n",
+      "hello world: 35\n",
+      "hello world: 36\n",
+      "hello world: 37\n",
+      "hello world: 38\n",
+      "hello world: 39\n",
+      "hello world: 40\n",
+      "hello world: 41\n",
+      "hello world: 42\n",
+      "hello world: 43\n",
+      "hello world: 44\n",
+      "hello world: 45\n",
+      "hello world: 46\n",
+      "hello world: 47\n",
+      "hello world: 48\n",
+      "hello world: 49\n",
+      "hello world: 50\n",
+      "hello world: 51\n",
+      "hello world: 52\n",
+      "hello world: 53\n",
+      "hello world: 54\n",
+      "hello world: 55\n",
+      "hello world: 56\n",
+      "hello world: 57\n",
+      "hello world: 58\n",
+      "hello world: 59\n",
+      "hello world: 60\n",
+      "hello world: 61\n",
+      "hello world: 62\n",
+      "hello world: 63\n",
+      "hello world: 64\n",
+      "hello world: 65\n",
+      "hello world: 66\n",
+      "hello world: 67\n",
+      "hello world: 68\n",
+      "hello world: 69\n",
+      "hello world: 70\n",
+      "hello world: 71\n",
+      "hello world: 72\n",
+      "hello world: 73\n",
+      "hello world: 74\n",
+      "hello world: 75\n",
+      "hello world: 76\n",
+      "hello world: 77\n",
+      "hello world: 78\n",
+      "hello world: 79\n",
+      "hello world: 80\n",
+      "hello world: 81\n",
+      "hello world: 82\n",
+      "hello world: 83\n",
+      "hello world: 84\n",
+      "hello world: 85\n",
+      "hello world: 86\n",
+      "hello world: 87\n",
+      "hello world: 88\n",
+      "hello world: 89\n",
+      "hello world: 90\n",
+      "hello world: 91\n",
+      "hello world: 92\n",
+      "hello world: 93\n",
+      "hello world: 94\n",
+      "hello world: 95\n",
+      "hello world: 96\n",
+      "hello world: 97\n",
+      "hello world: 98\n",
+      "hello world: 99\n",
+      "hello world: 100\n",
+      "hello world: 101\n",
+      "hello world: 102\n",
+      "hello world: 103\n",
+      "hello world: 104\n",
+      "hello world: 105\n",
+      "hello world: 106\n",
+      "hello world: 107\n",
+      "hello world: 108\n",
+      "hello world: 109\n",
+      "hello world: 110\n",
+      "hello world: 111\n",
+      "hello world: 112\n",
+      "hello world: 113\n",
+      "hello world: 114\n",
+      "hello world: 115\n",
+      "hello world: 116\n",
+      "hello world: 117\n",
+      "hello world: 118\n",
+      "hello world: 119\n",
+      "hello world: 120\n",
+      "hello world: 121\n",
+      "hello world: 122\n",
+      "hello world: 123\n",
+      "hello world: 124\n",
+      "hello world: 125\n",
+      "hello world: 126\n",
+      "hello world: 127\n",
+      "hello world: 128\n",
+      "hello world: 129\n",
+      "hello world: 130\n",
+      "hello world: 131\n",
+      "hello world: 132\n",
+      "hello world: 133\n",
+      "hello world: 134\n",
+      "hello world: 135\n",
+      "hello world: 136\n",
+      "hello world: 137\n",
+      "hello world: 138\n",
+      "hello world: 139\n",
+      "hello world: 140\n",
+      "hello world: 141\n",
+      "hello world: 142\n",
+      "hello world: 143\n",
+      "hello world: 144\n",
+      "hello world: 145\n",
+      "hello world: 146\n",
+      "hello world: 147\n",
+      "hello world: 148\n",
+      "hello world: 149\n",
+      "hello world: 150\n",
+      "hello world: 151\n",
+      "hello world: 152\n",
+      "hello world: 153\n",
+      "hello world: 154\n",
+      "hello world: 155\n",
+      "hello world: 156\n",
+      "hello world: 157\n",
+      "hello world: 158\n",
+      "hello world: 159\n",
+      "hello world: 160\n",
+      "hello world: 161\n",
+      "hello world: 162\n",
+      "hello world: 163\n",
+      "hello world: 164\n",
+      "hello world: 165\n",
+      "hello world: 166\n",
+      "hello world: 167\n",
+      "hello world: 168\n",
+      "hello world: 169\n",
+      "hello world: 170\n",
+      "hello world: 171\n",
+      "hello world: 172\n",
+      "hello world: 173\n",
+      "hello world: 174\n",
+      "hello world: 175\n",
+      "hello world: 176\n",
+      "hello world: 177\n",
+      "hello world: 178\n",
+      "hello world: 179\n",
+      "hello world: 180\n",
+      "hello world: 181\n",
+      "hello world: 182\n",
+      "hello world: 183\n",
+      "hello world: 184\n",
+      "hello world: 185\n",
+      "hello world: 186\n",
+      "hello world: 187\n",
+      "hello world: 188\n",
+      "hello world: 189\n",
+      "hello world: 190\n",
+      "hello world: 191\n",
+      "hello world: 192\n",
+      "hello world: 193\n",
+      "hello world: 194\n",
+      "hello world: 195\n",
+      "hello world: 196\n",
+      "hello world: 197\n",
+      "hello world: 198\n",
+      "hello world: 199\n",
+      "hello world: 200\n",
+      "hello world: 201\n",
+      "hello world: 202\n",
+      "hello world: 203\n",
+      "hello world: 204\n",
+      "hello world: 205\n",
+      "hello world: 206\n",
+      "hello world: 207\n",
+      "hello world: 208\n",
+      "hello world: 209\n",
+      "hello world: 210\n",
+      "hello world: 211\n",
+      "hello world: 212\n",
+      "hello world: 213\n",
+      "hello world: 214\n",
+      "hello world: 215\n",
+      "hello world: 216\n",
+      "hello world: 217\n",
+      "hello world: 218\n",
+      "hello world: 219\n",
+      "hello world: 220\n",
+      "hello world: 221\n",
+      "hello world: 222\n",
+      "hello world: 223\n",
+      "hello world: 224\n",
+      "hello world: 225\n",
+      "hello world: 226\n",
+      "hello world: 227\n",
+      "hello world: 228\n",
+      "hello world: 229\n",
+      "hello world: 230\n",
+      "hello world: 231\n",
+      "hello world: 232\n",
+      "hello world: 233\n",
+      "hello world: 234\n",
+      "hello world: 235\n",
+      "hello world: 236\n",
+      "hello world: 237\n",
+      "hello world: 238\n",
+      "hello world: 239\n",
+      "hello world: 240\n",
+      "hello world: 241\n",
+      "hello world: 242\n",
+      "hello world: 243\n",
+      "hello world: 244\n",
+      "hello world: 245\n",
+      "hello world: 246\n",
+      "hello world: 247\n",
+      "hello world: 248\n",
+      "hello world: 249\n",
+      "hello world: 250\n",
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+      "hello world: 252\n",
+      "hello world: 253\n",
+      "hello world: 254\n",
+      "hello world: 255\n",
+      "hello world: 256\n",
+      "hello world: 257\n",
+      "hello world: 258\n",
+      "hello world: 259\n",
+      "hello world: 260\n",
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+      "hello world: 263\n",
+      "hello world: 264\n",
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+      "hello world: 268\n",
+      "hello world: 269\n",
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+      "hello world: 275\n",
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+      "hello world: 280\n",
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+      "hello world: 285\n",
+      "hello world: 286\n",
+      "hello world: 287\n",
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+      "hello world: 293\n",
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+      "hello world: 295\n",
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+      "hello world: 299\n",
+      "hello world: 300\n",
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+      "hello world: 360\n",
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+      "hello world: 652\n",
+      "hello world: 653\n",
+      "hello world: 654\n",
+      "hello world: 655\n",
+      "hello world: 656\n",
+      "hello world: 657\n",
+      "hello world: 658\n",
+      "hello world: 659\n",
+      "hello world: 660\n",
+      "hello world: 661\n",
+      "hello world: 662\n",
+      "hello world: 663\n",
+      "hello world: 664\n",
+      "hello world: 665\n",
+      "hello world: 666\n",
+      "hello world: 667\n",
+      "hello world: 668\n",
+      "hello world: 669\n",
+      "hello world: 670\n",
+      "hello world: 671\n",
+      "hello world: 672\n",
+      "hello world: 673\n",
+      "hello world: 674\n",
+      "hello world: 675\n",
+      "hello world: 676\n",
+      "hello world: 677\n",
+      "hello world: 678\n",
+      "hello world: 679\n",
+      "hello world: 680\n",
+      "hello world: 681\n",
+      "hello world: 682\n",
+      "hello world: 683\n",
+      "hello world: 684\n",
+      "hello world: 685\n",
+      "hello world: 686\n",
+      "hello world: 687\n",
+      "hello world: 688\n",
+      "hello world: 689\n",
+      "hello world: 690\n",
+      "hello world: 691\n",
+      "hello world: 692\n",
+      "hello world: 693\n",
+      "hello world: 694\n",
+      "hello world: 695\n",
+      "hello world: 696\n",
+      "hello world: 697\n",
+      "hello world: 698\n",
+      "hello world: 699\n",
+      "hello world: 700\n",
+      "hello world: 701\n",
+      "hello world: 702\n",
+      "hello world: 703\n",
+      "hello world: 704\n",
+      "hello world: 705\n",
+      "hello world: 706\n",
+      "hello world: 707\n",
+      "hello world: 708\n",
+      "hello world: 709\n",
+      "hello world: 710\n",
+      "hello world: 711\n",
+      "hello world: 712\n",
+      "hello world: 713\n",
+      "hello world: 714\n",
+      "hello world: 715\n",
+      "hello world: 716\n",
+      "hello world: 717\n",
+      "hello world: 718\n",
+      "hello world: 719\n",
+      "hello world: 720\n",
+      "hello world: 721\n",
+      "hello world: 722\n",
+      "hello world: 723\n",
+      "hello world: 724\n",
+      "hello world: 725\n",
+      "hello world: 726\n",
+      "hello world: 727\n",
+      "hello world: 728\n",
+      "hello world: 729\n",
+      "hello world: 730\n",
+      "hello world: 731\n",
+      "hello world: 732\n",
+      "hello world: 733\n",
+      "hello world: 734\n",
+      "hello world: 735\n",
+      "hello world: 736\n",
+      "hello world: 737\n",
+      "hello world: 738\n",
+      "hello world: 739\n",
+      "hello world: 740\n",
+      "hello world: 741\n",
+      "hello world: 742\n",
+      "hello world: 743\n",
+      "hello world: 744\n",
+      "hello world: 745\n",
+      "hello world: 746\n",
+      "hello world: 747\n",
+      "hello world: 748\n",
+      "hello world: 749\n",
+      "hello world: 750\n",
+      "hello world: 751\n",
+      "hello world: 752\n",
+      "hello world: 753\n",
+      "hello world: 754\n",
+      "hello world: 755\n",
+      "hello world: 756\n",
+      "hello world: 757\n",
+      "hello world: 758\n",
+      "hello world: 759\n",
+      "hello world: 760\n",
+      "hello world: 761\n",
+      "hello world: 762\n",
+      "hello world: 763\n",
+      "hello world: 764\n",
+      "hello world: 765\n",
+      "hello world: 766\n",
+      "hello world: 767\n",
+      "hello world: 768\n",
+      "hello world: 769\n",
+      "hello world: 770\n",
+      "hello world: 771\n",
+      "hello world: 772\n",
+      "hello world: 773\n",
+      "hello world: 774\n",
+      "hello world: 775\n",
+      "hello world: 776\n",
+      "hello world: 777\n",
+      "hello world: 778\n",
+      "hello world: 779\n",
+      "hello world: 780\n",
+      "hello world: 781\n",
+      "hello world: 782\n",
+      "hello world: 783\n",
+      "hello world: 784\n",
+      "hello world: 785\n",
+      "hello world: 786\n",
+      "hello world: 787\n",
+      "hello world: 788\n",
+      "hello world: 789\n",
+      "hello world: 790\n",
+      "hello world: 791\n",
+      "hello world: 792\n",
+      "hello world: 793\n",
+      "hello world: 794\n",
+      "hello world: 795\n",
+      "hello world: 796\n",
+      "hello world: 797\n",
+      "hello world: 798\n",
+      "hello world: 799\n",
+      "hello world: 800\n",
+      "hello world: 801\n",
+      "hello world: 802\n",
+      "hello world: 803\n",
+      "hello world: 804\n",
+      "hello world: 805\n",
+      "hello world: 806\n",
+      "hello world: 807\n",
+      "hello world: 808\n",
+      "hello world: 809\n",
+      "hello world: 810\n",
+      "hello world: 811\n",
+      "hello world: 812\n",
+      "hello world: 813\n",
+      "hello world: 814\n",
+      "hello world: 815\n",
+      "hello world: 816\n",
+      "hello world: 817\n",
+      "hello world: 818\n",
+      "hello world: 819\n",
+      "hello world: 820\n",
+      "hello world: 821\n",
+      "hello world: 822\n",
+      "hello world: 823\n",
+      "hello world: 824\n",
+      "hello world: 825\n",
+      "hello world: 826\n",
+      "hello world: 827\n",
+      "hello world: 828\n",
+      "hello world: 829\n",
+      "hello world: 830\n",
+      "hello world: 831\n",
+      "hello world: 832\n",
+      "hello world: 833\n",
+      "hello world: 834\n",
+      "hello world: 835\n",
+      "hello world: 836\n",
+      "hello world: 837\n",
+      "hello world: 838\n",
+      "hello world: 839\n",
+      "hello world: 840\n",
+      "hello world: 841\n",
+      "hello world: 842\n",
+      "hello world: 843\n",
+      "hello world: 844\n",
+      "hello world: 845\n",
+      "hello world: 846\n",
+      "hello world: 847\n",
+      "hello world: 848\n",
+      "hello world: 849\n",
+      "hello world: 850\n",
+      "hello world: 851\n",
+      "hello world: 852\n",
+      "hello world: 853\n",
+      "hello world: 854\n",
+      "hello world: 855\n",
+      "hello world: 856\n",
+      "hello world: 857\n",
+      "hello world: 858\n",
+      "hello world: 859\n",
+      "hello world: 860\n",
+      "hello world: 861\n",
+      "hello world: 862\n",
+      "hello world: 863\n",
+      "hello world: 864\n",
+      "hello world: 865\n",
+      "hello world: 866\n",
+      "hello world: 867\n",
+      "hello world: 868\n",
+      "hello world: 869\n",
+      "hello world: 870\n",
+      "hello world: 871\n",
+      "hello world: 872\n",
+      "hello world: 873\n",
+      "hello world: 874\n",
+      "hello world: 875\n",
+      "hello world: 876\n",
+      "hello world: 877\n",
+      "hello world: 878\n",
+      "hello world: 879\n",
+      "hello world: 880\n",
+      "hello world: 881\n",
+      "hello world: 882\n",
+      "hello world: 883\n",
+      "hello world: 884\n",
+      "hello world: 885\n",
+      "hello world: 886\n",
+      "hello world: 887\n",
+      "hello world: 888\n",
+      "hello world: 889\n",
+      "hello world: 890\n",
+      "hello world: 891\n",
+      "hello world: 892\n",
+      "hello world: 893\n",
+      "hello world: 894\n",
+      "hello world: 895\n",
+      "hello world: 896\n",
+      "hello world: 897\n",
+      "hello world: 898\n",
+      "hello world: 899\n",
+      "hello world: 900\n",
+      "hello world: 901\n",
+      "hello world: 902\n",
+      "hello world: 903\n",
+      "hello world: 904\n",
+      "hello world: 905\n",
+      "hello world: 906\n",
+      "hello world: 907\n",
+      "hello world: 908\n",
+      "hello world: 909\n",
+      "hello world: 910\n",
+      "hello world: 911\n",
+      "hello world: 912\n",
+      "hello world: 913\n",
+      "hello world: 914\n",
+      "hello world: 915\n",
+      "hello world: 916\n",
+      "hello world: 917\n",
+      "hello world: 918\n",
+      "hello world: 919\n",
+      "hello world: 920\n",
+      "hello world: 921\n",
+      "hello world: 922\n",
+      "hello world: 923\n",
+      "hello world: 924\n",
+      "hello world: 925\n",
+      "hello world: 926\n",
+      "hello world: 927\n",
+      "hello world: 928\n",
+      "hello world: 929\n",
+      "hello world: 930\n",
+      "hello world: 931\n",
+      "hello world: 932\n",
+      "hello world: 933\n",
+      "hello world: 934\n",
+      "hello world: 935\n",
+      "hello world: 936\n",
+      "hello world: 937\n",
+      "hello world: 938\n",
+      "hello world: 939\n",
+      "hello world: 940\n",
+      "hello world: 941\n",
+      "hello world: 942\n",
+      "hello world: 943\n",
+      "hello world: 944\n",
+      "hello world: 945\n",
+      "hello world: 946\n",
+      "hello world: 947\n",
+      "hello world: 948\n",
+      "hello world: 949\n",
+      "hello world: 950\n",
+      "hello world: 951\n",
+      "hello world: 952\n",
+      "hello world: 953\n",
+      "hello world: 954\n",
+      "hello world: 955\n",
+      "hello world: 956\n",
+      "hello world: 957\n",
+      "hello world: 958\n",
+      "hello world: 959\n",
+      "hello world: 960\n",
+      "hello world: 961\n",
+      "hello world: 962\n",
+      "hello world: 963\n",
+      "hello world: 964\n",
+      "hello world: 965\n",
+      "hello world: 966\n",
+      "hello world: 967\n",
+      "hello world: 968\n",
+      "hello world: 969\n",
+      "hello world: 970\n",
+      "hello world: 971\n",
+      "hello world: 972\n",
+      "hello world: 973\n",
+      "hello world: 974\n",
+      "hello world: 975\n",
+      "hello world: 976\n",
+      "hello world: 977\n",
+      "hello world: 978\n",
+      "hello world: 979\n",
+      "hello world: 980\n",
+      "hello world: 981\n",
+      "hello world: 982\n",
+      "hello world: 983\n",
+      "hello world: 984\n",
+      "hello world: 985\n",
+      "hello world: 986\n",
+      "hello world: 987\n",
+      "hello world: 988\n",
+      "hello world: 989\n",
+      "hello world: 990\n",
+      "hello world: 991\n",
+      "hello world: 992\n",
+      "hello world: 993\n",
+      "hello world: 994\n",
+      "hello world: 995\n",
+      "hello world: 996\n",
+      "hello world: 997\n",
+      "hello world: 998\n",
+      "hello world: 999\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "g = open('newfile.txt', 'r')\n",
+    "print g.read()\n",
+    "g.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## with 方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上，**Python**提供了更安全的方法，当 `with` 块的内容结束后，**Python**会自动调用它的`close` 方法，确保读写的安全："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "float division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-24-9d2a70065b27>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'newfile.txt'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'w'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1.0\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m         \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'hello world: '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'\\n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "with open('newfile.txt','w') as f:\n",
+    "    for i in range(3000):\n",
+    "        x = 1.0 / (i - 1000)\n",
+    "        f.write('hello world: ' + str(i) + '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "与 `try/exception/finally` 效果相同，但更简单。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello world: 0\n",
+      "hello world: 1\n",
+      "hello world: 2\n",
+      "hello world: 3\n",
+      "hello world: 4\n",
+      "hello world: 5\n",
+      "hello world: 6\n",
+      "hello world: 7\n",
+      "hello world: 8\n",
+      "hello world: 9\n",
+      "hello world: 10\n",
+      "hello world: 11\n",
+      "hello world: 12\n",
+      "hello world: 13\n",
+      "hello world: 14\n",
+      "hello world: 15\n",
+      "hello world: 16\n",
+      "hello world: 17\n",
+      "hello world: 18\n",
+      "hello world: 19\n",
+      "hello world: 20\n",
+      "hello world: 21\n",
+      "hello world: 22\n",
+      "hello world: 23\n",
+      "hello world: 24\n",
+      "hello world: 25\n",
+      "hello world: 26\n",
+      "hello world: 27\n",
+      "hello world: 28\n",
+      "hello world: 29\n",
+      "hello world: 30\n",
+      "hello world: 31\n",
+      "hello world: 32\n",
+      "hello world: 33\n",
+      "hello world: 34\n",
+      "hello world: 35\n",
+      "hello world: 36\n",
+      "hello world: 37\n",
+      "hello world: 38\n",
+      "hello world: 39\n",
+      "hello world: 40\n",
+      "hello world: 41\n",
+      "hello world: 42\n",
+      "hello world: 43\n",
+      "hello world: 44\n",
+      "hello world: 45\n",
+      "hello world: 46\n",
+      "hello world: 47\n",
+      "hello world: 48\n",
+      "hello world: 49\n",
+      "hello world: 50\n",
+      "hello world: 51\n",
+      "hello world: 52\n",
+      "hello world: 53\n",
+      "hello world: 54\n",
+      "hello world: 55\n",
+      "hello world: 56\n",
+      "hello world: 57\n",
+      "hello world: 58\n",
+      "hello world: 59\n",
+      "hello world: 60\n",
+      "hello world: 61\n",
+      "hello world: 62\n",
+      "hello world: 63\n",
+      "hello world: 64\n",
+      "hello world: 65\n",
+      "hello world: 66\n",
+      "hello world: 67\n",
+      "hello world: 68\n",
+      "hello world: 69\n",
+      "hello world: 70\n",
+      "hello world: 71\n",
+      "hello world: 72\n",
+      "hello world: 73\n",
+      "hello world: 74\n",
+      "hello world: 75\n",
+      "hello world: 76\n",
+      "hello world: 77\n",
+      "hello world: 78\n",
+      "hello world: 79\n",
+      "hello world: 80\n",
+      "hello world: 81\n",
+      "hello world: 82\n",
+      "hello world: 83\n",
+      "hello world: 84\n",
+      "hello world: 85\n",
+      "hello world: 86\n",
+      "hello world: 87\n",
+      "hello world: 88\n",
+      "hello world: 89\n",
+      "hello world: 90\n",
+      "hello world: 91\n",
+      "hello world: 92\n",
+      "hello world: 93\n",
+      "hello world: 94\n",
+      "hello world: 95\n",
+      "hello world: 96\n",
+      "hello world: 97\n",
+      "hello world: 98\n",
+      "hello world: 99\n",
+      "hello world: 100\n",
+      "hello world: 101\n",
+      "hello world: 102\n",
+      "hello world: 103\n",
+      "hello world: 104\n",
+      "hello world: 105\n",
+      "hello world: 106\n",
+      "hello world: 107\n",
+      "hello world: 108\n",
+      "hello world: 109\n",
+      "hello world: 110\n",
+      "hello world: 111\n",
+      "hello world: 112\n",
+      "hello world: 113\n",
+      "hello world: 114\n",
+      "hello world: 115\n",
+      "hello world: 116\n",
+      "hello world: 117\n",
+      "hello world: 118\n",
+      "hello world: 119\n",
+      "hello world: 120\n",
+      "hello world: 121\n",
+      "hello world: 122\n",
+      "hello world: 123\n",
+      "hello world: 124\n",
+      "hello world: 125\n",
+      "hello world: 126\n",
+      "hello world: 127\n",
+      "hello world: 128\n",
+      "hello world: 129\n",
+      "hello world: 130\n",
+      "hello world: 131\n",
+      "hello world: 132\n",
+      "hello world: 133\n",
+      "hello world: 134\n",
+      "hello world: 135\n",
+      "hello world: 136\n",
+      "hello world: 137\n",
+      "hello world: 138\n",
+      "hello world: 139\n",
+      "hello world: 140\n",
+      "hello world: 141\n",
+      "hello world: 142\n",
+      "hello world: 143\n",
+      "hello world: 144\n",
+      "hello world: 145\n",
+      "hello world: 146\n",
+      "hello world: 147\n",
+      "hello world: 148\n",
+      "hello world: 149\n",
+      "hello world: 150\n",
+      "hello world: 151\n",
+      "hello world: 152\n",
+      "hello world: 153\n",
+      "hello world: 154\n",
+      "hello world: 155\n",
+      "hello world: 156\n",
+      "hello world: 157\n",
+      "hello world: 158\n",
+      "hello world: 159\n",
+      "hello world: 160\n",
+      "hello world: 161\n",
+      "hello world: 162\n",
+      "hello world: 163\n",
+      "hello world: 164\n",
+      "hello world: 165\n",
+      "hello world: 166\n",
+      "hello world: 167\n",
+      "hello world: 168\n",
+      "hello world: 169\n",
+      "hello world: 170\n",
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+      "hello world: 834\n",
+      "hello world: 835\n",
+      "hello world: 836\n",
+      "hello world: 837\n",
+      "hello world: 838\n",
+      "hello world: 839\n",
+      "hello world: 840\n",
+      "hello world: 841\n",
+      "hello world: 842\n",
+      "hello world: 843\n",
+      "hello world: 844\n",
+      "hello world: 845\n",
+      "hello world: 846\n",
+      "hello world: 847\n",
+      "hello world: 848\n",
+      "hello world: 849\n",
+      "hello world: 850\n",
+      "hello world: 851\n",
+      "hello world: 852\n",
+      "hello world: 853\n",
+      "hello world: 854\n",
+      "hello world: 855\n",
+      "hello world: 856\n",
+      "hello world: 857\n",
+      "hello world: 858\n",
+      "hello world: 859\n",
+      "hello world: 860\n",
+      "hello world: 861\n",
+      "hello world: 862\n",
+      "hello world: 863\n",
+      "hello world: 864\n",
+      "hello world: 865\n",
+      "hello world: 866\n",
+      "hello world: 867\n",
+      "hello world: 868\n",
+      "hello world: 869\n",
+      "hello world: 870\n",
+      "hello world: 871\n",
+      "hello world: 872\n",
+      "hello world: 873\n",
+      "hello world: 874\n",
+      "hello world: 875\n",
+      "hello world: 876\n",
+      "hello world: 877\n",
+      "hello world: 878\n",
+      "hello world: 879\n",
+      "hello world: 880\n",
+      "hello world: 881\n",
+      "hello world: 882\n",
+      "hello world: 883\n",
+      "hello world: 884\n",
+      "hello world: 885\n",
+      "hello world: 886\n",
+      "hello world: 887\n",
+      "hello world: 888\n",
+      "hello world: 889\n",
+      "hello world: 890\n",
+      "hello world: 891\n",
+      "hello world: 892\n",
+      "hello world: 893\n",
+      "hello world: 894\n",
+      "hello world: 895\n",
+      "hello world: 896\n",
+      "hello world: 897\n",
+      "hello world: 898\n",
+      "hello world: 899\n",
+      "hello world: 900\n",
+      "hello world: 901\n",
+      "hello world: 902\n",
+      "hello world: 903\n",
+      "hello world: 904\n",
+      "hello world: 905\n",
+      "hello world: 906\n",
+      "hello world: 907\n",
+      "hello world: 908\n",
+      "hello world: 909\n",
+      "hello world: 910\n",
+      "hello world: 911\n",
+      "hello world: 912\n",
+      "hello world: 913\n",
+      "hello world: 914\n",
+      "hello world: 915\n",
+      "hello world: 916\n",
+      "hello world: 917\n",
+      "hello world: 918\n",
+      "hello world: 919\n",
+      "hello world: 920\n",
+      "hello world: 921\n",
+      "hello world: 922\n",
+      "hello world: 923\n",
+      "hello world: 924\n",
+      "hello world: 925\n",
+      "hello world: 926\n",
+      "hello world: 927\n",
+      "hello world: 928\n",
+      "hello world: 929\n",
+      "hello world: 930\n",
+      "hello world: 931\n",
+      "hello world: 932\n",
+      "hello world: 933\n",
+      "hello world: 934\n",
+      "hello world: 935\n",
+      "hello world: 936\n",
+      "hello world: 937\n",
+      "hello world: 938\n",
+      "hello world: 939\n",
+      "hello world: 940\n",
+      "hello world: 941\n",
+      "hello world: 942\n",
+      "hello world: 943\n",
+      "hello world: 944\n",
+      "hello world: 945\n",
+      "hello world: 946\n",
+      "hello world: 947\n",
+      "hello world: 948\n",
+      "hello world: 949\n",
+      "hello world: 950\n",
+      "hello world: 951\n",
+      "hello world: 952\n",
+      "hello world: 953\n",
+      "hello world: 954\n",
+      "hello world: 955\n",
+      "hello world: 956\n",
+      "hello world: 957\n",
+      "hello world: 958\n",
+      "hello world: 959\n",
+      "hello world: 960\n",
+      "hello world: 961\n",
+      "hello world: 962\n",
+      "hello world: 963\n",
+      "hello world: 964\n",
+      "hello world: 965\n",
+      "hello world: 966\n",
+      "hello world: 967\n",
+      "hello world: 968\n",
+      "hello world: 969\n",
+      "hello world: 970\n",
+      "hello world: 971\n",
+      "hello world: 972\n",
+      "hello world: 973\n",
+      "hello world: 974\n",
+      "hello world: 975\n",
+      "hello world: 976\n",
+      "hello world: 977\n",
+      "hello world: 978\n",
+      "hello world: 979\n",
+      "hello world: 980\n",
+      "hello world: 981\n",
+      "hello world: 982\n",
+      "hello world: 983\n",
+      "hello world: 984\n",
+      "hello world: 985\n",
+      "hello world: 986\n",
+      "hello world: 987\n",
+      "hello world: 988\n",
+      "hello world: 989\n",
+      "hello world: 990\n",
+      "hello world: 991\n",
+      "hello world: 992\n",
+      "hello world: 993\n",
+      "hello world: 994\n",
+      "hello world: 995\n",
+      "hello world: 996\n",
+      "hello world: 997\n",
+      "hello world: 998\n",
+      "hello world: 999\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "g = open('newfile.txt', 'r')\n",
+    "print g.read()\n",
+    "g.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "所以，写文件时候要确保文件被正确关闭。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('newfile.txt')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.01 numpy overview.ipynb b/03-numpy/03.01-numpy-overview.ipynb
similarity index 100%
rename from 03. numpy/03.01 numpy overview.ipynb
rename to 03-numpy/03.01-numpy-overview.ipynb
diff --git a/03. numpy/03.02 matplotlib basics.ipynb b/03-numpy/03.02-matplotlib-basics.ipynb
similarity index 99%
rename from 03. numpy/03.02 matplotlib basics.ipynb
rename to 03-numpy/03.02-matplotlib-basics.ipynb
index 31d7685a..b676d75b 100644
--- a/03. numpy/03.02 matplotlib basics.ipynb	
+++ b/03-numpy/03.02-matplotlib-basics.ipynb
@@ -1,1225 +1,1225 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Matplotlib 基础"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在使用**Numpy**之前，需要了解一些画图的基础。\n",
-    "\n",
-    "**Matplotlib**是一个类似**Matlab**的工具包，主页地址为\n",
-    "\n",
-    "http://matplotlib.org \n",
-    "\n",
-    "导入 `matplotlib` 和 `numpy`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using matplotlib backend: Qt4Agg\n",
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pylab"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## plot 二维图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "plot(y)\n",
-    "plot(x, y)\n",
-    "plot(x, y, format_string)\n",
-    "```\n",
-    "\n",
-    "只给定 `y` 值，默认以下标为 `x` 轴："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xa086fd0>]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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xcOqpcOKJsHRp6Iiq7/HH4a67fKvkI44IHU1m0Zi+SASZwd13wyGHQOvW8Nxz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa05afd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "x = linspace(0, 2 * pi, 50)\n",
-    "plot(sin(x))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "给定 `x` 和 `y` 值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xa241898>]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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YvUjMNGoEH3zgu+X166Fdu9CJfmnaNGjVCu6+22+9LOGp0IvE0AknQGGh75rX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\ngg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa071a20>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(x, sin(x))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "多条数据线："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xa508b00>,\n",
-       " <matplotlib.lines.Line2D at 0xa508d30>]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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cpXugWtGbCZcz+t0ZuwmsHUi96vUA+OQTePJJ8PLSWZhOqA3ZsunaFQID4c8/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\nKBQKN+f/AQEk9vM2SbiHAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa22c860>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(x, sin(x),\n",
-    "    x, sin(2 * x))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用字符串，给定线条参数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xba6ea20>]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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l1W/TJjjhBLj6an8CChkzwxs6NH4zl5wcCgAtYzeOSjci6bLffhCLxevM27b5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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa5121d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(x, sin(x), 'r-^')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "多线条："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xbcf1710>,\n",
-       " <matplotlib.lines.Line2D at 0xbcf1940>]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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5JCSUBmGLimDmTBg3zmS7oo129CcRUoltSaPUueeG2Sp74VmyhJTiYtfrKbkJ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\nDuf/AbP7N0Qt3HMbAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xba59c88>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(x, sin(x), 'b-o',\n",
-    "    x, sin(2 * x), 'r-^')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "更多参数设置，请查阅帮助。事实上，字符串使用的格式与**Matlab**相同。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## scatter 散点图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "scatter(x, y)\n",
-    "scatter(x, y, size)\n",
-    "scatter(x, y, size, color)\n",
-    "```\n",
-    "\n",
-    "假设我们想画二维散点图："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xbd6c0b8>]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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TQIkwmMSo0LoBCqLqg0lOjMoPhR4oiCqfSFXH+UOR0KMHCqSqJ1IxfxgdTpgCSibtRKqy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\nbKpvNy2mHwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbcfe0b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(x, sin(x), 'bo')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以使用 `scatter` 达到同样的效果："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0xbd996d8>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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/Z+qy7OyZ1wPX6CPisNT6taGHTZIeioiHs7G3S3qdpAcGPS6A4cir25+8DVNaWPhlszav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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbd78908>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scatter(x, sin(x))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上，scatter函数与**Matlab**的用法相同，还可以指定它的大小，颜色等参数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar instance at 0x000000000C31F448>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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jt98WSkdHN2lt7SA/+KC/TEpKyrJ/n/6DpYWzr7SwsJV/gAwGWcrBQWfHVmJi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\nDQ0H4P8DJu8dXcDMQQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbd78c50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x = rand(200)\n",
-    "y = rand(200)\n",
-    "size = rand(200) * 30\n",
-    "color = rand(200)\n",
-    "scatter(x, y, size, color)\n",
-    "# 显示颜色条\n",
-    "colorbar()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 多图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用figure()命令产生新的图像："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xc680cf8>]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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xcOqpcOKJsHRp6Iiq7/HH4a67fKvkI44IHU1m0Zi+SASZwd13wyGHQOvW8Nxz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbfac080>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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i/PnP8PrrKvgShoq+ZBUz34/m97/3jdk+/DB0ouLZssW3mOjcGd54A048MXQi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\nERV9EZEY+X9EO9oF1xEZ7QAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbd09668>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "t = linspace(0, 2*pi, 50)\n",
-    "x = sin(t)\n",
-    "y = cos(t)\n",
-    "figure()\n",
-    "plot(x)\n",
-    "figure()\n",
-    "plot(y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "或者使用 `subplot` 在一幅图中画多幅子图：\n",
-    "\n",
-    "    subplot(row, column, index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xcd47518>]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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zvl1LWZkmzrSyev4BRZd3wpaFy+Ckuvxy+MIX4NprfUfSeC+8ALffrok/iMZe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\nMI+06AAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xc99c518>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "subplot(1, 2, 1)\n",
-    "plot(x)\n",
-    "subplot(1, 2, 2)\n",
-    "plot(y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 向图中添加数据"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "默认多次 `plot` 会叠加："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xcbcfd30>]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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vvaT2b5g8WS0dNlLmXtw9Rm8bzcIGC3VHsbTYWGjYEGbNgsyZ1edCQkIICQlJ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\nir5hGIYH+R9/BjfcPdcAvwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xcd57630>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(x)\n",
-    "plot(y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以跟**Matlab**类似用 hold(False)关掉，这样新图会将原图覆盖："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xcf4b9b0>]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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i/PnP8PrrKvgShoq+ZBUz34/m97/3jdk+/DB0ouLZssW3mOjcGd54A048MXQi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\nERV9EZEY+X9EO9oF1xEZ7QAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbf89630>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(x)\n",
-    "hold(False)\n",
-    "plot(y)\n",
-    "# 恢复原来设定\n",
-    "hold(True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 标签"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以在 `plot` 中加入 `label` ，使用 `legend` 加上图例："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0xd2089b0>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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mX91RjLvYWvQLAjF3fXwq+XMPe02hlA6WP7+NaTxY35p9mbxzMtduXdMdxdI+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\nYXgQU/QNwzA8iCn6hmEYHuT/Hiuv2gfQi84AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xc428320>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(x, label='sin')\n",
-    "plot(y, label='cos')\n",
-    "legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "或者直接在 `legend`中加入："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0xd51fb00>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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mX91RjLvYWvQLAjF3fXwq+XMPe02hlA6WP7+NaTxY35p9mbxzMtduXdMdxdI+\n/BCmTlVz/IZtpu+ZTp2idcyuWDbauVPtHmgvti6MTO0NgXvnm1L8uqCgoL/f9/f3x9/fP12hPFHp\n3KWpU7QO0/dMp1cN8yNTej3+uNq/YepUtXTYSJ9bibf4ZNsn/NjyR91RLC0hAVq3hrlzIXdu9bnQ\n0FBCQ0PTfUybbuQKIWoAQVLKBskfDwCS7r6ZK4SYAoRKKRcmfxwO1JVSnr/nWA/cGN14uJ2nd/L6\nd68T0TOCDN4ZdMexrAMHVAuQyEjIlEl3Gmuas28O8w7MY83bpj+ULRYuhMmTYdOm+7/G2dsl7gJK\nCiGKCSEyAK2AJfe8ZgnwdnK4GsDlewu+YR/VClaj1KOl+ObgN7qjWFqFClC5MsybpzuJNSXJJEK2\nhJhNUmzkqMaTNhV9KWUC0B1YCRwGvpVSHhFCdBZCdE5+zXIgUggRAUwFutqY2XiAwNqBhGwJIUkm\n6Y5iaYGBapOVRLNXTZotPbqULL5ZePHxF3VHsbQVKyApCRo2tO9xXerhLFfJYmVSSqp9VY0hdYbQ\npMy9C6mM1JISatVST+qaPlCpJ6Xk2RnP8mHND2lRroXuOJZWty507gxvvvng1zl7esdwMUIIAmsH\nErw52GyyYoM7bZdDQkzb5bTYGL2R2PhYmpVppjuKpW3dCidPQsuW9j+2KfpuqFmZZsTGx7IxeqPu\nKJbWqBHEx8PatbqTWMeYLWPoV6sf3l7euqNYmiMbT5qi74a8vbzpV6sfIVtMBzFbeHlB//6m7XJq\n7T+3nwPnD9C2QlvdUSzt0CHYsQM6dHDM8U3Rd1NtK7Rl//n97D9n+gXbIiAAIiJUIzbjwUK2hNC7\nRm8y+mTUHcXSxo5VLUEyZ3bM8c2NXDc2bus49pzdw9evf607iqVNnAjr1sGiRbqTuK7IS5FU/6o6\nkb0iyZExh+44lhUdDVWqwIkTkDNn6r7G6V027cUUffuLuxlH8c+Ls+PdHTzh94TuOJZ1/bp6Ujc0\nFMqWfejLPVLXZV3xy+zHyBdG6o5iab16qQcC07K3gyn6xr8MXjeYi9cvMqXRFN1RLG3UKDh+HGbP\n1p3E9Zy7eo5yk8sR3j2cvFnz6o5jWRcvQqlSak4/LX3ITNE3/uWPa39QelJpDnU9RP7spqNdel2+\nDE88AXv3QpEiutO4lv6r+xOfEM+EhqY9qS2GDYNz51Tfp7QwRd/4j16/9iKjT0bGvmS2VbRFv35w\n44ZpvXwRZ9ROAAAchUlEQVS3S/GXKDGxBHs776XII+Z/w/T66y+1Y9u2bVCiRNq+1hR94z9OXjlJ\n5amViegRQa7MuXTHsayzZ6F8eTh6FPLk0Z3GNYzcOJKI2AhmN52tO4qlffqpWqb57bdp/1pT9I0U\nvfPzOzye83GG1B2iO4qldemiWtyONPcruXbrGsUnFCe0XShl85g73Ol186Ya5S9bBpUqpf3rTRsG\nI0X9a/VnYthEs8mKjfr2hSlTzCYroDZJqV2ktin4NpozRxX79BT89DBF30OUzl2ausXq8tWer3RH\nsbQnnoCXX1aF35PdSrzFuG3jGFDbzn1/PUxCglqeOXCg885pir4HGVB7AOO2juNmwk3dUSytf38Y\nP17d1PVU8w/Mp2zuslQtUFV3FEv79lsoVEh1dHUWU/Q9SJX8VSiftzzzD8zXHcXSKlZUm6zMmaM7\niR6JSYmEbAkxo3wbJSWpvk7OHOWDKfoeZ2DtgYRsCSExyewOYosBA1SPlIQE3Umc76cjP+GX2Q//\nYv66o1ja0qWQMaOaLnQmU/Q9TJ2idcidJTc/HjEbVtuidm0oUAC++053EueSUhK8OZgBtQcgRKoX\njBj3kBJGj1ajfGdfRlP0PYwQggG1B5hNVuxg0CD1jZvkQTtTrjqxiluJt2hUqpHuKJa2bp1aAdZM\nw14zpuh7oFdLvUpiUiIrIlbojmJp9eur5lg//6w7ifOM3jyaAbUH4CVM6bDFqFFqitBLw2U0f3Me\nyEt4EVg7kNGbR+uOYmlCwODB6kEtT/ihaWvMVmKuxNDqyVa6o1jatm0QGan2atDBFH0P1bJ8S87+\nddZsqWij116DW7dghQf80DRy40j61eqHj5cD9vDzIMHBqo+Tr6+e85ui76F8vHwYUHsAIzaO0B3F\n0ry81Nz+iBHuPdrfdWYXB84foEMlB+3h5yEOHICdOx23FWJqmKLvwdpWbMvxP4+z/dR23VEs7Y03\nVC/00FDdSRznzijfbIVomzFjoHdvx22FmBqm6HuwDN4Z6F+rvxnt28jbWy29c9cmbAfOH2DH6R10\nqtJJdxRLO34cVq1STft0MkXfw3Wo3IH95/az+8xu3VEs7a231L6mW7fqTmJ/IzeOpM+zfcjsq3F4\n6gZGjYIePSCH5i2ETWtlgwk7JrA+aj2LWpmdv20xZYp6ynLZMt1J7OfIH0fwn+PPiZ4nyJYhm+44\nlnXiBFSvDhERkMvOW1qY1spGmnWq0ontp7Zz4PwB3VEsrX172LcP9uzRncR+Rm0axf+e+Z8p+DYK\nDoZu3exf8NPDjPQNAMZtHUfY6TC+e8PD+grY2fjxsGkT/OgGXS6O/3mcmjNrcqLnCXJk1DwnYWFR\nUfD002pO38/P/sc3I30jXbpU7cKG6A0c+eOI7iiW1qkTbN4Mhw7pTmK70ZtH071ad1PwbRQcrG7e\nOqLgp4cZ6Rt/G71pNIf/OMz85qb1si3GjIGDB2HBAt1J0u/3S79T9auqZl9lG508qXbEOnZMbbPp\nCGaPXCPd4m7G8cSEJ9j6zlZKPlpSdxzLiotTO2xt3gylS+tOkz6dl3Ymd5bcjHpxlO4oltatG2TL\npnbHchRT9A2bBIUGcfLKSWY2mak7iqWNHAnh4TDfgj80xVyJoeKUihzrcYzcWRw0PPUAp0/DU0+p\nfwd58zruPKboGza5FH+JEhNLsPu93RTLWUx3HMuKi4MSJWDDBihrsX3DeyzvQSafTHz88se6o1ha\nz56QIQOMG+fY85iib9hs4NqB/Hn9T6Y2nqo7iqUFB6teK998oztJ6p396yzlvyjP4W6HeSzbY7rj\nWNbZs1C+PBw+DI85+DKaom/Y7OL1i5SeVJpdnXbxeK7HdcexrL/+UqP9detUAbCCnr/2xMfLh0/r\nf6o7iqV98IFqwPfZZ44/lyn6hl0MWTeE03+dNnP7Nho7Fnbvhm+/1Z3k4U5eOUmlKZUI7x5O3qwO\nnIR2c+fPqym9335TW2o6min6hl1cir9EyYkl2dZxm1nJY4Nr19RKntWr1U09V9Z5aWdyZc7FmHpj\ndEextL594eZNmDDBOeczRd+wmxEbRnD0z6Nm3b6NPvlE7Zb0ww+6k9xf5KVIqn1VjWPdj/Folkd1\nx7Gss2fhySdh/34oVMg55zRF37CbuJtxlJhQgtD2oZTLU053HMu6fl2N9lesgIoVdadJWYefO1Ak\nRxGGPz9cdxRL69FD7Yj1qRNviZiib9jV2C1j2XVml+nJY6PPPoONG2GRCzYyPfbnMWrNrMXxHsfJ\nmSmn7jiWFR0NVarAkSOOXZd/L1P0Dbu6dusaJSaWYMVbK6j4mIsOUy0gPl6t5PnlF6hcWXeaf3vr\np7col7scg+oM0h3F0jp2hPz5nb+Zjin6ht2N3z6e0KhQFrderDuKpU2YAGvXws8/607yj0MXDvHC\n3BeI6BFB9ozZdcexrGPHoFYt1Ukzp5N/WDJdNg2761K1C7vO7GLXmV26o1jae++p5Zu7XOgyBm0I\n4sNnPzQF30bDhqm9b51d8NPDjPSNVPli5xf8cuwXlr+1XHcUS5s8We2stdwFLuO+c/t4ZcErRPSM\nIItvFt1xLOvAAXj5ZbUrVjYNe82Ykb7hEB0rd+TQH4fYGuOGm8A60bvvqht9GzboTgLDQofRv1Z/\nU/BtNGQIBAbqKfjpYYq+kSoZfTIypM4QhqwfojuKpWXMqG709e+vHtPXJex0GHvO7qFz1c76QriB\nHTvU9phduuhOknqm6Bup1q5iO6IvR7P+9/W6o1haQADcuAGLNd4XH7p+KANrDySTTyZ9IdzAoEFq\npJ/JQpcx3UVfCOEnhFgthDgmhFglhEjxFoYQIkoIcUAIsVcIEZb+qIZuvt6+jHh+BP3W9CNJJumO\nY1leXmp3rQEDICHB+edfE7mGiNgIOlbp6PyTu5H169X+tx066E6SNraM9AOB1VLKUsDa5I9TIgF/\nKWVlKWV1G85nuIBWT7ZCSsn3h77XHcXS6tdXzbhmzXLueZNkEv1W9yP4xWAyeGdw7sndiJRqlB8U\npJ7AtRJbiv5rwJzk9+cATR/w2lTfWTZcm5fw4uOXPmbguoHcTLipO45lCaFG+8OHqzYNzvLNwW/w\n9falRbkWzjupG1q+HK5cUVN1VmNL0c8npTyf/P55IN99XieBNUKIXUKITjacz3ARzz/+PGVyl2HK\nrim6o1ha9erw7LPO68Z4I+EGg9YNYtxL4xDCjMPSKyEB+vWD0aPB21t3mrTzedBvCiFWAynt+/Kv\n57WllFIIcb+1CLWklGeFEHmA1UKIcCnlppReGBQU9Pf7/v7++Pv7PyieoVFIvRBenPsi7Sq1M/1a\nbDBqlHqS8733wM/PseeaHDaZSo9V4rmizzn2RG5uxgzIkwdee03P+UNDQwkNDU3316f74SwhRDhq\nrv6cECI/sF5KWeYhXzMMuCql/CSF3zMPZ1nMu0veJU+WPATXC9YdxdK6dIHs2eFjB25JGxsfS5lJ\nZdjYYSNlcj/w29R4gL/+glKlVA+lp5/WnUZx5sNZS4B2ye+3A/6zAE0IkUUIkT35/azAy8BBG85p\nuJDh/sOZtmcaMVdidEextKFDYeZMiHHgZRy9aTTNyzY3Bd9GISHw0kuuU/DTw5aRvh/wHVAEiAJa\nSikvCyEKAF9JKV8VQhQHfkr+Eh9ggZQyxWGhGelb0+B1gzkVd4rZTWfrjmJpgwapDThmOmB3yqjL\nUTw97WkOdT1kNju3QUwMVKoE+/ZB4cK60/zDdNk0nCruZhylJpZiZZuVpvWyDa5cgZIl1SbqTz5p\n32O3+akNJfxKEOQfZN8De5i334YiRZzfOvlhTNE3nG5S2CR+OfYLK9qs0B3F0j7/XC0FXLFCLem0\nhz1n99Do60Yc63GMbBks0hzGBe3eDY0aqRbK2V2sIalpuGY4XeenOxN5KZLVJ1brjmJpXbuqKYQl\nS+xzPCklfVf3ZVjdYabg20BK6NNHPVPhagU/PUzRN2zm6+1L8IvB9FvTj8SkRN1xLMvXV432P/hA\n9eax1YqIFZyOO23aLdhoyRK4eBHeeUd3EvswRd+wi+Zlm5MtQzZm7J2hO4qlvfQSVKhg+8batxJv\n0Xtlbz5+6WN8vB74OI7xALdvqwexxo0DHze5jGZO37Cb/ef28/L8lznc9TCPZnlUdxzLioxUT+vu\n3w8FC6bvGCGbQ9gcs5mlAUvtG87DTJyo1uSvXKk7yf2ZG7mGVj1/7cnNhJtMbTxVdxRLGzxYdXCc\nPz/tXxtzJYbKUysT1imM4rmK2z2bp7h0CcqUgTVr4KmndKe5P1P0Da0u37hM2cllWRqwlKoFquqO\nY1nXrqmCs3ChatOQFi2/b0nZ3GUZ/vxwx4TzEF27ql+/+EJvjocxq3cMrXJmysmYF8fQdVlX03Pf\nBlmzwtix0LMnJKbh3viayDXsOrOLwNr363RupEZYmNrkZvRo3UnszxR9w+7aVmyLr7cvM/aYm7q2\naN0asmRJ/VO6txJv0X15d8Y3GE9m38yODefGEhJUP6SPP4acbthL0BR9w+68hBeTX5nM4PWDiY2P\n1R3HsoRQNxKHDIHLlx/++vHbx/OE3xM0LtXY8eHc2OTJkCsXvPmm7iSOYeb0DYfpvrw7iUmJfNno\nS91RLK1LF7UH6/jx93/NqbhTVJpSie3vbqeEXwnnhXMzp09DxYqwebO6p2IF5kau4TIuxV+i7OSy\n/PLmL+amrg0uXoRy5WD1alWQUtLqh1aUfrQ0Hz3/kXPDuZmWLaF0aRgxQneS1DM3cg2XkStzLoJf\nDKb78u7mpq4NcudWLX3feSfljdTXRq4l7HSYuXlroxUrVI+dgQN1J3EsU/QNh2pXqR1CCGbudUDP\nYA/Svr0q/uPG/fvztxJv0ePXHnxW/zOy+GbRks0dxMdDt25qPj+zm98DN9M7hsPtPbuXBgsasLfz\nXgpkL6A7jmVFRUG1amq+uXRp9bnhocPZeWYnSwOWmn1vbTB0KBw5At9/rztJ2pk5fcMlDV0/lL3n\n9rKk9RJTnGwwaRJ8+y1s2AD7z++l/vz67O28l4I50tmvweDoUfUAnC1tL3Qyc/qGSxpcZzAxV2KY\nu3+u7iiW1rWravU7YfIt2i1uxycvf2IKvg2SkuD991XbCysW/PQwRd9wigzeGZjTdA59V/flVNwp\n3XEsy8sLZsyAgSs/Il/Gx2lToY3uSJY2aZKaz+/eXXcS5zFF33Caio9VpEf1Hry75F3MVF76xWXb\niXe1r7j141TATJWl1+HD8NFHMG+e+7RNTg1T9A2nCqwdyMXrF03f/XS6kXCD9j+3Z0rTz7l67jFm\nz9adyJpu3YK2bVVvnRIe9iybuZFrON1vF37j+TnPs6vTLormLKo7jqX0X92fE5dO8P0b33PggOCl\nl9QNyPz5dSezlkGD4MABtSuW1dcVmNU7hiWM2TyG1ZGrWd12NV7C/MCZGttittHs22YceP8AebPm\nBVRfnv374eefrV+8nGXLFmjRAvbtg3z5dKexnVm9Y1jChzU/5Oqtq0zdZTZbSY342/G0/7k9k16Z\n9HfBB7Xq5MwZmDBBYzgL+esvePtt+PJL9yj46WFG+oY24RfDqT2zNjve3cETfk/ojuPSPlj5AWev\nnuWb17/5z+9FRkKNGmpbv+rVNYSzkHffVUteZ7jRLSUz0jcso0zuMgypM4RWP7TiRsIN3XFc1tKj\nS/n+8PdMbDgxxd8vXlyNXFu1Ulv8GSn7+WdYt+7B3Uo9gRnpG1pJKWn9Y2uyZ8jO9Nem647jciJi\nI6g5oyZLApZQo1CNB762Z0+IiYGffjLz+/c6fx4qVYIffkj79pOuzoz0DUsRQjDjtRlsjdnK9D2m\n6N/t2q1rNP+2OcP9hz+04IPa6enUKTO/f6+EBGjTBjp0cL+Cnx5mpG+4hKMXj/LcrOdY9uYyqhWs\npjuOdlJK2ixqg4+XD7ObzE51vyIzv/9fvXurB7GWLXPPh7DMSN+wpNK5SzO10VRafN+Ci9cv6o6j\n3aSwSRy6cIgvX/0yTQ3qiheHKVPM/P4ds2apYr9woXsW/PQwI33DpQSuCWT32d2seGsF3l7euuNo\nsfnkZl7/7nW2ddxG8VzF03WMXr0gOhoWLfLc+f1t26BJE9WRtGxZ3Wkcx4z0DUsb+cJIkmQSQ9YP\n0R1Fi7N/naXVD62Y1WRWugs+wNixar/XsWPtGM5CTp1SD2DNnu3eBT89TNE3XIqPlw8LX1/IgoML\nWBy+WHccp7qdeJtWP7TivSrv8UrJV2w6VsaMapT/xReqoZgniY+Hpk3VTzuv2HYZ3ZKZ3jFcUtjp\nMF79+lXWvr2WCvkq6I7jcFJKui7rysm4kywNWGq31hSHD8MLL6gRb4MGdjmkS5MS3npLtaCeN88z\nprbM9I7hFqoXrM7kVybTYH4Dwi+G647jUFJKBqwdwM4zO/m6+dd27UVUrpxat9+2LYSF2e2wLisk\nBCIi4KuvPKPgp4e5n224rJblW3L99nVemvcSG9tv5PFcj+uO5BCjNo1i2fFlhLYL5ZFMj9j9+DVr\nwsyZ/9zULFXK7qdwCQsXqk1Rduxw/83NbWFG+oZLa1+pPYG1Aqk3rx6n407rjmN3n237jLn757K6\n7WoezfKow87TuDGMHAn166sGbe5mwQL44ANYscJztj1MLzPSN1xet+rduHb7GvXm1WND+w3/6jJp\nZdN2T+PzHZ+zscNGHsv2mMPP17EjnDsHDRvCxo3wiP1/qNBi3jwIDIQ1a9R0lvFg5kauYRlD1w9l\nydElrGu3Dr/Mfrrj2GT+gfkErgkktH0oJfyct3WTlNCjB/z2G/z6q/WnQWbPVu2l16yBMmV0p9HD\nbKJiuC0pJX1W9WFLzBbWtF1D9ozZdUdKl5+O/ETXZV1Z+/Zayuct7/TzJyaqPjTHj6vOk3kt+oPT\njBkQFKQKfunSutPoY1bvGG5LCMEnL39CpXyVaLigIX9c+0N3pDT79rdv6fJLF5a/tVxLwQfw9oY5\nc6BePXj2WQi34OKoadNg+HDVKtmTC356mKJvWIoQgi8bfUmdonWo9lU19pzdoztSqiQmJRK4JpDA\ntYGsaruKKvmraM0jBIwYofaKrVtXreqxii+/hFGjVMEvWVJ3Gusx0zuGZf1w+AfeX/Y+4+uP560K\nb+mOc1+x8bG8+eOb3E66zbctviV3lty6I/3LmjXw5pvw6aeqBbGrun4d/vc/WL8eVq5UzeUMM71j\neJAW5Vqwvt16hoUO44OVH5CQlKA70n8cPH+Q6l9Vp3ye8qxss9LlCj6oaZ5169QN0REj1M1eV3P4\nsGoVffUq7N5tCr4tzEjfsLzY+FgCfgwgISnBpUbSVvlJ5I6zZ9V6/rJl1UYsuXLpTqT+A5o5Uy3J\nHDMG3nnHPGl7LzPSNzyOX2Y/lr+5nGoFqlHtq2psP7Vda54bCTcIXBPIh6s+ZGWblZYo+AD586u5\n/WzZ1Hr3uXP1jvrj4lQfnfHjVa6OHU3BtwdT9A234O3lzZh6Y/j4pY95/bvXafVDKyJiI5yaITEp\nkTn75lB6UmmO/nmUnZ12ar9hm1ZZs6obpUuWqNG+v79a0+9s27fD009D9uyqZ5B56Mp+0l30hRBv\nCCEOCSEShRD3/ZcthGgghAgXQhwXQvRP7/kMIzValGvBse7HqJC3AjWm16DH8h5cuHbBoeeUUrL8\n+HIqT63MtD3T+Lr51yxqtYg8WfM49LyOVK2a6mHTqhU8/zz07avm0x1JSjWir19f9cIfNQqmTrX+\nA2QuR0qZrjegDFAKWA9Uuc9rvIEIoBjgC+wDyt7ntdJQ1q9frzuCy7DlWly4ekH2+rWXfDTkUTk8\ndLj86+Zf9guWLOxUmPSf7S/LTCojFx9ZLJOSkux+jjt0/bs4d07Kt9+WsnBhKadNkzI21r7HT0qS\nculSKZ99VsoSJaScPl3KGzce/DXme+QfybUz1bU73SN9KWW4lPLYQ15WHYiQUkZJKW8DC4Em6T2n\npwgNDdUdwWXYci3yZM3D+AbjCesUxtE/j1JqYil6/dqLZceWcfVW+oatUkqO/XmMSWGTaLigIc2+\nbcZbT73FwfcP0qRMkzTtZ5tWuv5d5MunHuZasEA1NCtWDBo1Uj1v4uLSf9wbN+Cbb6BiRbVy6H//\nUw+KdeyoNoF5EPM9kn6ObrhWEIi56+NTwDMOPqdh/EvxXMVZ0HwBhy4cYumxpXyy7RNa/9iaqgWq\n8nLxl3npiZeokr/KffvYx8bHsu73daw6sYpVJ1aRkJRA/Sfq065iOxqXakzWDFmd/CfS47nn1Ftc\nnJrz//Zb6NZNLfls1QrKlwc/P7Xq594pmdu34dAh2LkTdu1Sv4aHq2WYISFqgxdzk9Y5Hlj0hRCr\ngZTa/w2UUi5NxfHNGkzDZZTPW57yecsTWDuQq7eusjF6I6tOrOLtRW8TfSWajN4pDy8TkhJ4ruhz\nvFz8ZXrX6E2Z3GUcOqJ3dTlyqIe42rSBS5dg8WK10icqCmJj1ZuXlyr+fn6QIQMcPQpFi6p7BVWr\nqt4/FSua+XodbF6nL4RYD/SRUv7neXghRA0gSErZIPnjAUCSlDIkhdea/yAMwzDSQaZhnb69pnfu\nd8JdQEkhRDHgDNAKCEjphWkJbRiGYaSPLUs2mwkhYoAawDIhxK/Jny8ghFgGIKVMALoDK4HDwLdS\nyiO2xzYMwzDSw2XaMBiGYRiOp/2JXE9+eEsIMVMIcV4IcfCuz/kJIVYLIY4JIVYJIXLqzOgsQojC\nQoj1yQ/8/SaE6Jn8eY+7HkKITEKIHUKIfUKIw0KI4OTPe9y1uEMI4S2E2CuEWJr8sUdeCyFElBDi\nQPK1CEv+XJquhdaiL4TwBiYBDYByQIAQoqzOTE42C/Vnv1sgsFpKWQpYm/yxJ7gN9JZSlkdNGXZL\n/rfgcddDSnkDeF5KWQmoADwvhKiNB16Lu/RCTRHfmZrw1GshAX8pZWUpZfXkz6XpWuge6Xv0w1tS\nyk3ApXs+/RowJ/n9OUBTp4bSREp5Tkq5L/n9q8AR1HMenno9rie/mwH1ZPslPPRaCCEKAa8A0/ln\n0YhHXotk9y56SdO10F30U3p4q6CmLK4in5TyfPL754F8OsPokLzaqzKwAw+9HkIILyHEPtSfeb2U\n8hAeei2Az4C+QNJdn/PUayGBNUKIXUKITsmfS9O1cPQTuQ9j7iI/gJRSetrzC0KIbMCPQC8p5V93\nPwTlSddDSpkEVBJCPAKsFEI8f8/ve8S1EEI0Ai5IKfcKIfxTeo2nXItktaSUZ4UQeYDVQoh/7XCc\nmmuhe6R/Gih818eFUaN9T3ZeCPEYgBAiP+DYFpEuRAjhiyr486SUi5M/7bHXA0BKeQVYBjyNZ16L\nmsBrQojfgW+AF4QQ8/DMa4GU8mzyr38Ai1BT5Gm6FrqL/t8PbwkhMqAe3lqiOZNuS4B2ye+3AxY/\n4LVuQ6gh/QzgsJRy/F2/5XHXQwiR+84KDCFEZuAlYC8eeC2klAOllIWllI8DrYF1Usq2eOC1EEJk\nEUJkT34/K/AycJA0Xgvt6/SFEA2B8aibVTOklMFaAzmREOIboC6QGzUXNxT4GfgOKAJEAS2llJd1\nZXSW5NUpG4ED/DPtNwAIw8OuhxDiKdQNOa/kt3lSyo+FEH542LW4mxCiLqrly2ueeC2EEI+jRveg\npuYXSCmD03ottBd9wzAMw3l0T+8YhmEYTmSKvmEYhgcxRd8wDMODmKJvGIbhQUzRNwzD8CCm6BuG\nYXgQU/QNwzA8iCn6hmEYHuT/Hiuv2gfQi84AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xd4f5828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(x)\n",
-    "plot(y)\n",
-    "legend(['sin', 'cos'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 坐标轴，标题，网格"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以设置坐标轴的标签和标题："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.text.Text at 0xd727dd8>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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glJlNAvap46Gb3f2FFJ5CC3yIiCRMYhaUMrPXgRvcfXodj3UBBrh7t2j7JqDS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\nQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xd70b2e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(x, sin(x))\n",
-    "xlabel('radians')\n",
-    "# 可以设置字体大小\n",
-    "ylabel('amplitude', fontsize='large')\n",
-    "title('Sin(x)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "用 'grid()' 来显示网格："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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YeWdYZ52QPP70pzB8NWyYkoeIlAf9Uxazd96BtdcOS4688QZcfnlIHJdcEvYj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\niIiI5EUJRERE8qIEIiIieVECERGRvPwfMOmeP1xnpUIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xd3220b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(x, sin(x))\n",
-    "xlabel('radians')\n",
-    "ylabel('amplitude', fontsize='large')\n",
-    "title('Sin(x)')\n",
-    "grid()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 清除、关闭图像"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "清除已有的图像使用：\n",
-    "\n",
-    "    clf()\n",
-    "\n",
-    "关闭当前图像：\n",
-    "\n",
-    "    close()\n",
-    "\n",
-    "关闭所有图像：\n",
-    "\n",
-    "    close('all')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## imshow 显示图片"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "灰度图片可以看成二维数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[162, 162, 162, ..., 170, 155, 128],\n",
-       "       [162, 162, 162, ..., 170, 155, 128],\n",
-       "       [162, 162, 162, ..., 170, 155, 128],\n",
-       "       ..., \n",
-       "       [ 43,  43,  50, ..., 104, 100,  98],\n",
-       "       [ 44,  44,  55, ..., 104, 105, 108],\n",
-       "       [ 44,  44,  55, ..., 104, 105, 108]])"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 导入lena图片\n",
-    "from scipy.misc import lena\n",
-    "img = lena()\n",
-    "img"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们可以用 `imshow()` 来显示图片数据："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar instance at 0x000000000DECFD88>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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7WT7lWTxO1kW0tATqumGsSRciaYyBvdUo+8tNBqklKwuoHENM0LUq2VRKCX5w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\ncF0fnhfCD8iATdRhDxr0//l8gvl8SAE3kkJX4ma8FK7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TyQFJeMIEgEAYJyiLHEpVMBIc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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xdc7df60>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "imshow(img,\n",
-    "       # 设置坐标范围\n",
-    "      extent = [-25, 25, -25, 25],\n",
-    "       # 设置colormap\n",
-    "      cmap = cm.bone)\n",
-    "colorbar()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "更多参数和用法可以参阅帮助。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里 `cm` 表示 `colormap`，可以看它的种类："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[u'Accent',\n",
-       " u'Accent_r',\n",
-       " u'Blues',\n",
-       " u'Blues_r',\n",
-       " u'BrBG',\n",
-       " u'BrBG_r',\n",
-       " u'BuGn',\n",
-       " u'BuGn_r',\n",
-       " u'BuPu',\n",
-       " u'BuPu_r',\n",
-       " u'CMRmap',\n",
-       " u'CMRmap_r',\n",
-       " u'Dark2',\n",
-       " u'Dark2_r',\n",
-       " u'GnBu',\n",
-       " u'GnBu_r',\n",
-       " u'Greens',\n",
-       " u'Greens_r',\n",
-       " u'Greys',\n",
-       " u'Greys_r',\n",
-       " 'LUTSIZE',\n",
-       " u'OrRd',\n",
-       " u'OrRd_r',\n",
-       " u'Oranges',\n",
-       " u'Oranges_r',\n",
-       " u'PRGn',\n",
-       " u'PRGn_r',\n",
-       " u'Paired',\n",
-       " u'Paired_r',\n",
-       " u'Pastel1',\n",
-       " u'Pastel1_r',\n",
-       " u'Pastel2',\n",
-       " u'Pastel2_r',\n",
-       " u'PiYG',\n",
-       " u'PiYG_r',\n",
-       " u'PuBu',\n",
-       " u'PuBuGn',\n",
-       " u'PuBuGn_r',\n",
-       " u'PuBu_r',\n",
-       " u'PuOr',\n",
-       " u'PuOr_r',\n",
-       " u'PuRd',\n",
-       " u'PuRd_r',\n",
-       " u'Purples',\n",
-       " u'Purples_r',\n",
-       " u'RdBu',\n",
-       " u'RdBu_r',\n",
-       " u'RdGy',\n",
-       " u'RdGy_r',\n",
-       " u'RdPu',\n",
-       " u'RdPu_r',\n",
-       " u'RdYlBu',\n",
-       " u'RdYlBu_r',\n",
-       " u'RdYlGn',\n",
-       " u'RdYlGn_r',\n",
-       " u'Reds',\n",
-       " u'Reds_r',\n",
-       " 'ScalarMappable',\n",
-       " u'Set1',\n",
-       " u'Set1_r',\n",
-       " u'Set2',\n",
-       " u'Set2_r',\n",
-       " u'Set3',\n",
-       " u'Set3_r',\n",
-       " u'Spectral',\n",
-       " u'Spectral_r',\n",
-       " u'Wistia',\n",
-       " u'Wistia_r',\n",
-       " u'YlGn',\n",
-       " u'YlGnBu',\n",
-       " u'YlGnBu_r',\n",
-       " u'YlGn_r',\n",
-       " u'YlOrBr',\n",
-       " u'YlOrBr_r',\n",
-       " u'YlOrRd',\n",
-       " u'YlOrRd_r',\n",
-       " '__builtins__',\n",
-       " '__doc__',\n",
-       " '__file__',\n",
-       " '__name__',\n",
-       " '__package__',\n",
-       " '_generate_cmap',\n",
-       " '_reverse_cmap_spec',\n",
-       " '_reverser',\n",
-       " 'absolute_import',\n",
-       " u'afmhot',\n",
-       " u'afmhot_r',\n",
-       " u'autumn',\n",
-       " u'autumn_r',\n",
-       " u'binary',\n",
-       " u'binary_r',\n",
-       " u'bone',\n",
-       " u'bone_r',\n",
-       " u'brg',\n",
-       " u'brg_r',\n",
-       " u'bwr',\n",
-       " u'bwr_r',\n",
-       " 'cbook',\n",
-       " 'cmap_d',\n",
-       " 'cmapname',\n",
-       " 'colors',\n",
-       " u'cool',\n",
-       " u'cool_r',\n",
-       " u'coolwarm',\n",
-       " u'coolwarm_r',\n",
-       " u'copper',\n",
-       " u'copper_r',\n",
-       " 'cubehelix',\n",
-       " u'cubehelix_r',\n",
-       " 'datad',\n",
-       " 'division',\n",
-       " u'flag',\n",
-       " u'flag_r',\n",
-       " 'get_cmap',\n",
-       " u'gist_earth',\n",
-       " u'gist_earth_r',\n",
-       " u'gist_gray',\n",
-       " u'gist_gray_r',\n",
-       " u'gist_heat',\n",
-       " u'gist_heat_r',\n",
-       " u'gist_ncar',\n",
-       " u'gist_ncar_r',\n",
-       " u'gist_rainbow',\n",
-       " u'gist_rainbow_r',\n",
-       " u'gist_stern',\n",
-       " u'gist_stern_r',\n",
-       " u'gist_yarg',\n",
-       " u'gist_yarg_r',\n",
-       " u'gnuplot',\n",
-       " u'gnuplot2',\n",
-       " u'gnuplot2_r',\n",
-       " u'gnuplot_r',\n",
-       " u'gray',\n",
-       " u'gray_r',\n",
-       " u'hot',\n",
-       " u'hot_r',\n",
-       " u'hsv',\n",
-       " u'hsv_r',\n",
-       " u'jet',\n",
-       " u'jet_r',\n",
-       " 'ma',\n",
-       " 'mpl',\n",
-       " u'nipy_spectral',\n",
-       " u'nipy_spectral_r',\n",
-       " 'np',\n",
-       " u'ocean',\n",
-       " u'ocean_r',\n",
-       " 'os',\n",
-       " u'pink',\n",
-       " u'pink_r',\n",
-       " 'print_function',\n",
-       " u'prism',\n",
-       " u'prism_r',\n",
-       " u'rainbow',\n",
-       " u'rainbow_r',\n",
-       " 'register_cmap',\n",
-       " 'revcmap',\n",
-       " u'seismic',\n",
-       " u'seismic_r',\n",
-       " 'six',\n",
-       " 'spec',\n",
-       " 'spec_reversed',\n",
-       " u'spectral',\n",
-       " u'spectral_r',\n",
-       " u'spring',\n",
-       " u'spring_r',\n",
-       " u'summer',\n",
-       " u'summer_r',\n",
-       " u'terrain',\n",
-       " u'terrain_r',\n",
-       " 'unicode_literals',\n",
-       " u'winter',\n",
-       " u'winter_r']"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(cm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用不同的 `colormap` 会有不同的显示效果。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0xe0883c8>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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AFlKZ2djG455l7FUQW+tvjJ20fA6d9KJ4o9ls5hajLMjZbOZSj0VRUBQFa2trDmF000tZ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\nq3vUfZsozrjxC7/Gtz71BjY3N4tbHykVHjPpF7GJMo7uUZ2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NwqNSYxn6+3meR6/XY2Fh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KTCMpfDFmrOFESWSxQlWrWpilJsWlhJTK0ugcn+ovRsiN08huURJdIoZufOK8i4DgKT+lqVqakp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\nSUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xdefe9e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "imshow(img, cmap=cm.RdGy_r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 从脚本中运行"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在脚本中使用 `plot` 时，通常图像是不会直接显示的，需要增加 `show()` 选项，只有在遇到 `show()` 命令之后，图像才会显示。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 直方图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "从高斯分布随机生成1000个点得到的直方图："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([   2.,    7.,   37.,  119.,  216.,  270.,  223.,   82.,   31.,   13.]),\n",
-       " array([-3.65594649, -2.98847032, -2.32099415, -1.65351798, -0.98604181,\n",
-       "        -0.31856564,  0.34891053,  1.0163867 ,  1.68386287,  2.35133904,\n",
-       "         3.01881521]),\n",
-       " <a list of 10 Patch objects>)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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7J13LfJJsSnJ39//7viTvnXRNMyU5Jcm+bv8+lOTDk65pPklOTHIgyW3zrTexUO8C8s2s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\noS5JDTHUJakhhrokNcRQl6SG/D/cw+79AFjUjgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xdf2a5f8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "hist(randn(1000))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "更多例子请参考下列网站：\n",
-    "\n",
-    "http://matplotlib.org/gallery.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Matplotlib 基础"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在使用**Numpy**之前，需要了解一些画图的基础。\n",
+    "\n",
+    "**Matplotlib**是一个类似**Matlab**的工具包，主页地址为\n",
+    "\n",
+    "http://matplotlib.org \n",
+    "\n",
+    "导入 `matplotlib` 和 `numpy`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using matplotlib backend: Qt4Agg\n",
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plot 二维图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "plot(y)\n",
+    "plot(x, y)\n",
+    "plot(x, y, format_string)\n",
+    "```\n",
+    "\n",
+    "只给定 `y` 值，默认以下标为 `x` 轴："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0xa086fd0>]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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xcOqpcOKJsHRp6Iiq7/HH4a67fKvkI44IHU1m0Zi+SASZwd13wyGHQOvW8Nxz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa05afd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "x = linspace(0, 2 * pi, 50)\n",
+    "plot(sin(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "给定 `x` 和 `y` 值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0xa241898>]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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YvUjMNGoEH3zgu+X166Fdu9CJfmnaNGjVCu6+22+9LOGp0IvE0AknQGGh75rX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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa071a20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(x, sin(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "多条数据线："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0xa508b00>,\n",
+       " <matplotlib.lines.Line2D at 0xa508d30>]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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cpXugWtGbCZcz+t0ZuwmsHUi96vUA+OQTePJJ8PLSWZhOqA3ZsunaFQID4c8/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\nKBQKN+f/AQEk9vM2SbiHAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa22c860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(x, sin(x),\n",
+    "    x, sin(2 * x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用字符串，给定线条参数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0xba6ea20>]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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l1W/TJjjhBLj6an8CChkzwxs6NH4zl5wcCgAtYzeOSjci6bLffhCLxevM27b5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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa5121d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(x, sin(x), 'r-^')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "多线条："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0xbcf1710>,\n",
+       " <matplotlib.lines.Line2D at 0xbcf1940>]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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5JCSUBmGLimDmTBg3zmS7oo129CcRUoltSaPUueeG2Sp74VmyhJTiYtfrKbkJ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\nDuf/AbP7N0Qt3HMbAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xba59c88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(x, sin(x), 'b-o',\n",
+    "    x, sin(2 * x), 'r-^')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "更多参数设置，请查阅帮助。事实上，字符串使用的格式与**Matlab**相同。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## scatter 散点图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "scatter(x, y)\n",
+    "scatter(x, y, size)\n",
+    "scatter(x, y, size, color)\n",
+    "```\n",
+    "\n",
+    "假设我们想画二维散点图："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0xbd6c0b8>]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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TQIkwmMSo0LoBCqLqg0lOjMoPhR4oiCqfSFXH+UOR0KMHCqSqJ1IxfxgdTpgCSibtRKqy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\nbKpvNy2mHwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbcfe0b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(x, sin(x), 'bo')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以使用 `scatter` 达到同样的效果："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0xbd996d8>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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/Z+qy7OyZ1wPX6CPisNT6taGHTZIeioiHs7G3S3qdpAcGPS6A4cir25+8DVNaWPhlszav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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbd78908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter(x, sin(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上，scatter函数与**Matlab**的用法相同，还可以指定它的大小，颜色等参数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar instance at 0x000000000C31F448>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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jt98WSkdHN2lt7SA/+KC/TEpKyrJ/n/6DpYWzr7SwsJV/gAwGWcrBQWfHVmJi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\nDQ0H4P8DJu8dXcDMQQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbd78c50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = rand(200)\n",
+    "y = rand(200)\n",
+    "size = rand(200) * 30\n",
+    "color = rand(200)\n",
+    "scatter(x, y, size, color)\n",
+    "# 显示颜色条\n",
+    "colorbar()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 多图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用figure()命令产生新的图像："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0xc680cf8>]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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xcOqpcOKJsHRp6Iiq7/HH4a67fKvkI44IHU1m0Zi+SASZwd13wyGHQOvW8Nxz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbfac080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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i/PnP8PrrKvgShoq+ZBUz34/m97/3jdk+/DB0ouLZssW3mOjcGd54A048MXQi\niStN70jWeukl+Mtf4KGH4OqrQ6fZtdWr4aqr/I7bF1+Egw4KnUhyiaZ3JDaaNYMpU6BHD3+c4ObN\noRPt7OOP/UazOnVgwgQVfAlPI33JeqtXQ8uWvuhHaSSdLa9EJLtppC+xU7GivzF6yin+7b33wubZ\nsMHP3Xfs6Ef3KvgSJSr6khNKl4b+/eH++30v+hYt/PmymbRlC4wY4Q+EWbTI99LRDVuJmoSLvpn9\n0czmmdkWM9vlt7aZNTazhWa2xMw6Jfp8IsXRrBksXuw7czZoAO3awTffpPc5nfOvNOrV87tsX3gB\nXn0VDj44vc8rkohkRvofA02Aqbt6gJmVBgYDjYHaQEszq5XEc8ZCfn5+6AiRkci12HdfuOsuWLDA\nvwKoXdvvfv3pp9TnmzULzj7bbxrr08efetWwYeqfB/R9UZiuReISLvrOuYXOucV7eFh9YKlzbplz\nbhMwCrgs0eeMC31Db5fMtTj4YH8TdeZMP91Sowa0b+87Wib6C8A5/0pi8GC/QaxJEz9n//HHcNll\n6T3PVt8X2+laJC7dvXcqAysKfbwSODXNzynyK9Wr+5O45s3zB5QMGgRXXgknn+x7+Zx3np9731Uf\n+9Wrfb+fN97wb5s3wwUXQJs2cMkl/pWFSLbYbdE3s4lAUf3/ujrnxhXj79caTImMOnX8W+fOfqQ/\ndaov4tdcA198AXvtVfTXbd4MjRr5XxAdOsCxx6Z3RC+STkmv0zezKcDtzrmdNsSbWQOgp3OuccHH\nXYCtzrkBRTxWvyBERBJQknX6qZre2dUTvg8cY2ZHAf8LtABaFvXAkoQWEZHEJLNks4mZrQAaAK+Z\n2X8KPn+Emb0G4JzbDNwCTADmAy865xYkH1tERBIRmTYMIiKSfsF35MZ585aZDTOzVWb2caHPVTSz\niWa22MzeMLMDQmbMFDOramZTCjb8fWJmtxZ8PnbXw8z2NrMZZvaRmc03s34Fn4/dtdjGzEqb2Wwz\nG1fwcSyvhZktM7O5BddiZsHnSnQtghZ9bd5iOP7/vbDOwETnXA1gcsHHcbAJ6OCcq4OfMvxrwfdC\n7K6Hc24DcJZz7gSgLnCWmZ1BDK9FIe3xU8Tbpibiei0ckOecq+ecq1/wuRJdi9Aj/Vhv3nLOTQPW\n7PDpS4ERBe+PAC7PaKhAnHNfO+c+Knj/J2ABfp9HXK/HzwXvlgNK479PYnktzKwKcCEwlO2LRmJ5\nLQrsuOi266lLAAAB/ElEQVSlRNcidNEvavNW5UBZouJQ59yqgvdXAYeGDBNCwWqvesAMYno9zKyU\nmX2E/3+e4pybR0yvBfAgcAewtdDn4notHDDJzN43sxsLPleia5HuHbl7orvIu+Gcc3Hbv2BmvwFe\nBto75/7PCu2CitP1cM5tBU4ws/2BCWZ21g5/HotrYWYXA98452abWV5Rj4nLtSjQ0Dn3lZkdDEw0\ns4WF/7A41yL0SP9LoGqhj6viR/txtsrMDgMws8OBNPeIjA4zK4sv+M8658YUfDq21wPAOfcj8Bpw\nEvG8FqcDl5rZ58BI4Gwze5Z4Xgucc18V/Pdb4FX8FHmJrkXoov/fzVtmVg6/eWts4EyhjQXaFLzf\nBhizm8fmDPND+qeA+c65hwr9Ueyuh5kdtG0FhpmVB84DZhPDa+Gc6+qcq+qcqwZcCbzpnGtNDK+F\nme1jZhUK3t8XOB/f7bhE1yL4On0z+wPwEP5m1VPOuX5BA2WQmY0EzgQOws/F3Q38CxgNHAksA5o7\n534IlTFTClanTAXmsn3arwswk5hdDzM7Dn9DrlTB27POufvNrCIxuxaFmdmZ+JYvl8bxWphZNfzo\nHvzU/PPOuX4lvRbBi76IiGRO6OkdERHJIBV9EZEYUdEXEYkRFX0RkRhR0RcRiREVfRGRGFHRFxGJ\nERV9EZEY+X9EO9oF1xEZ7QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbd09668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "t = linspace(0, 2*pi, 50)\n",
+    "x = sin(t)\n",
+    "y = cos(t)\n",
+    "figure()\n",
+    "plot(x)\n",
+    "figure()\n",
+    "plot(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "或者使用 `subplot` 在一幅图中画多幅子图：\n",
+    "\n",
+    "    subplot(row, column, index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0xcd47518>]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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zvl1LWZkmzrSyev4BRZd3wpaFy+Ckuvxy+MIX4NprfUfSeC+8ALffrok/iMZe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\nMI+06AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc99c518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "subplot(1, 2, 1)\n",
+    "plot(x)\n",
+    "subplot(1, 2, 2)\n",
+    "plot(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 向图中添加数据"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "默认多次 `plot` 会叠加："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0xcbcfd30>]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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vvaT2b5g8WS0dNlLmXtw9Rm8bzcIGC3VHsbTYWGjYEGbNgsyZ1edCQkIICQlJ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\nir5hGIYH+R9/BjfcPdcAvwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xcd57630>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(x)\n",
+    "plot(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以跟**Matlab**类似用 hold(False)关掉，这样新图会将原图覆盖："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0xcf4b9b0>]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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i/PnP8PrrKvgShoq+ZBUz34/m97/3jdk+/DB0ouLZssW3mOjcGd54A048MXQi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\nERV9EZEY+X9EO9oF1xEZ7QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbf89630>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(x)\n",
+    "hold(False)\n",
+    "plot(y)\n",
+    "# 恢复原来设定\n",
+    "hold(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 标签"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以在 `plot` 中加入 `label` ，使用 `legend` 加上图例："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0xd2089b0>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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mX91RjLvYWvQLAjF3fXwq+XMPe02hlA6WP7+NaTxY35p9mbxzMtduXdMdxdI+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\nYXgQU/QNwzA8iCn6hmEYHuT/Hiuv2gfQi84AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc428320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(x, label='sin')\n",
+    "plot(y, label='cos')\n",
+    "legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "或者直接在 `legend`中加入："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0xd51fb00>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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mX91RjLvYWvQLAjF3fXwq+XMPe02hlA6WP7+NaTxY35p9mbxzMtduXdMdxdI+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\nYXgQU/QNwzA8iCn6hmEYHuT/Hiuv2gfQi84AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd4f5828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(x)\n",
+    "plot(y)\n",
+    "legend(['sin', 'cos'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 坐标轴，标题，网格"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以设置坐标轴的标签和标题："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0xd727dd8>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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glJlNAvap46Gb3f2FFJ5CC3yIiCRMYhaUMrPXgRvcfXodj3UBBrh7t2j7JqDS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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd70b2e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(x, sin(x))\n",
+    "xlabel('radians')\n",
+    "# 可以设置字体大小\n",
+    "ylabel('amplitude', fontsize='large')\n",
+    "title('Sin(x)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "用 'grid()' 来显示网格："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YeWdYZ52QPP70pzB8NWyYkoeIlAf9Uxazd96BtdcOS4688QZcfnlIHJdcEvYj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\niIiI5EUJRERE8qIEIiIieVECERGRvPwfMOmeP1xnpUIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd3220b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(x, sin(x))\n",
+    "xlabel('radians')\n",
+    "ylabel('amplitude', fontsize='large')\n",
+    "title('Sin(x)')\n",
+    "grid()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 清除、关闭图像"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "清除已有的图像使用：\n",
+    "\n",
+    "    clf()\n",
+    "\n",
+    "关闭当前图像：\n",
+    "\n",
+    "    close()\n",
+    "\n",
+    "关闭所有图像：\n",
+    "\n",
+    "    close('all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## imshow 显示图片"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "灰度图片可以看成二维数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[162, 162, 162, ..., 170, 155, 128],\n",
+       "       [162, 162, 162, ..., 170, 155, 128],\n",
+       "       [162, 162, 162, ..., 170, 155, 128],\n",
+       "       ..., \n",
+       "       [ 43,  43,  50, ..., 104, 100,  98],\n",
+       "       [ 44,  44,  55, ..., 104, 105, 108],\n",
+       "       [ 44,  44,  55, ..., 104, 105, 108]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 导入lena图片\n",
+    "from scipy.misc import lena\n",
+    "img = lena()\n",
+    "img"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们可以用 `imshow()` 来显示图片数据："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar instance at 0x000000000DECFD88>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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7WT7lWTxO1kW0tATqumGsSRciaYyBvdUo+8tNBqklKwuoHENM0LUq2VRKCX5w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\ncF0fnhfCD8iATdRhDxr0//l8gvl8SAE3kkJX4ma8FK7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TyQFJeMIEgEAYJyiLHEpVMBIc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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xdc7df60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imshow(img,\n",
+    "       # 设置坐标范围\n",
+    "      extent = [-25, 25, -25, 25],\n",
+    "       # 设置colormap\n",
+    "      cmap = cm.bone)\n",
+    "colorbar()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "更多参数和用法可以参阅帮助。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里 `cm` 表示 `colormap`，可以看它的种类："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[u'Accent',\n",
+       " u'Accent_r',\n",
+       " u'Blues',\n",
+       " u'Blues_r',\n",
+       " u'BrBG',\n",
+       " u'BrBG_r',\n",
+       " u'BuGn',\n",
+       " u'BuGn_r',\n",
+       " u'BuPu',\n",
+       " u'BuPu_r',\n",
+       " u'CMRmap',\n",
+       " u'CMRmap_r',\n",
+       " u'Dark2',\n",
+       " u'Dark2_r',\n",
+       " u'GnBu',\n",
+       " u'GnBu_r',\n",
+       " u'Greens',\n",
+       " u'Greens_r',\n",
+       " u'Greys',\n",
+       " u'Greys_r',\n",
+       " 'LUTSIZE',\n",
+       " u'OrRd',\n",
+       " u'OrRd_r',\n",
+       " u'Oranges',\n",
+       " u'Oranges_r',\n",
+       " u'PRGn',\n",
+       " u'PRGn_r',\n",
+       " u'Paired',\n",
+       " u'Paired_r',\n",
+       " u'Pastel1',\n",
+       " u'Pastel1_r',\n",
+       " u'Pastel2',\n",
+       " u'Pastel2_r',\n",
+       " u'PiYG',\n",
+       " u'PiYG_r',\n",
+       " u'PuBu',\n",
+       " u'PuBuGn',\n",
+       " u'PuBuGn_r',\n",
+       " u'PuBu_r',\n",
+       " u'PuOr',\n",
+       " u'PuOr_r',\n",
+       " u'PuRd',\n",
+       " u'PuRd_r',\n",
+       " u'Purples',\n",
+       " u'Purples_r',\n",
+       " u'RdBu',\n",
+       " u'RdBu_r',\n",
+       " u'RdGy',\n",
+       " u'RdGy_r',\n",
+       " u'RdPu',\n",
+       " u'RdPu_r',\n",
+       " u'RdYlBu',\n",
+       " u'RdYlBu_r',\n",
+       " u'RdYlGn',\n",
+       " u'RdYlGn_r',\n",
+       " u'Reds',\n",
+       " u'Reds_r',\n",
+       " 'ScalarMappable',\n",
+       " u'Set1',\n",
+       " u'Set1_r',\n",
+       " u'Set2',\n",
+       " u'Set2_r',\n",
+       " u'Set3',\n",
+       " u'Set3_r',\n",
+       " u'Spectral',\n",
+       " u'Spectral_r',\n",
+       " u'Wistia',\n",
+       " u'Wistia_r',\n",
+       " u'YlGn',\n",
+       " u'YlGnBu',\n",
+       " u'YlGnBu_r',\n",
+       " u'YlGn_r',\n",
+       " u'YlOrBr',\n",
+       " u'YlOrBr_r',\n",
+       " u'YlOrRd',\n",
+       " u'YlOrRd_r',\n",
+       " '__builtins__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '_generate_cmap',\n",
+       " '_reverse_cmap_spec',\n",
+       " '_reverser',\n",
+       " 'absolute_import',\n",
+       " u'afmhot',\n",
+       " u'afmhot_r',\n",
+       " u'autumn',\n",
+       " u'autumn_r',\n",
+       " u'binary',\n",
+       " u'binary_r',\n",
+       " u'bone',\n",
+       " u'bone_r',\n",
+       " u'brg',\n",
+       " u'brg_r',\n",
+       " u'bwr',\n",
+       " u'bwr_r',\n",
+       " 'cbook',\n",
+       " 'cmap_d',\n",
+       " 'cmapname',\n",
+       " 'colors',\n",
+       " u'cool',\n",
+       " u'cool_r',\n",
+       " u'coolwarm',\n",
+       " u'coolwarm_r',\n",
+       " u'copper',\n",
+       " u'copper_r',\n",
+       " 'cubehelix',\n",
+       " u'cubehelix_r',\n",
+       " 'datad',\n",
+       " 'division',\n",
+       " u'flag',\n",
+       " u'flag_r',\n",
+       " 'get_cmap',\n",
+       " u'gist_earth',\n",
+       " u'gist_earth_r',\n",
+       " u'gist_gray',\n",
+       " u'gist_gray_r',\n",
+       " u'gist_heat',\n",
+       " u'gist_heat_r',\n",
+       " u'gist_ncar',\n",
+       " u'gist_ncar_r',\n",
+       " u'gist_rainbow',\n",
+       " u'gist_rainbow_r',\n",
+       " u'gist_stern',\n",
+       " u'gist_stern_r',\n",
+       " u'gist_yarg',\n",
+       " u'gist_yarg_r',\n",
+       " u'gnuplot',\n",
+       " u'gnuplot2',\n",
+       " u'gnuplot2_r',\n",
+       " u'gnuplot_r',\n",
+       " u'gray',\n",
+       " u'gray_r',\n",
+       " u'hot',\n",
+       " u'hot_r',\n",
+       " u'hsv',\n",
+       " u'hsv_r',\n",
+       " u'jet',\n",
+       " u'jet_r',\n",
+       " 'ma',\n",
+       " 'mpl',\n",
+       " u'nipy_spectral',\n",
+       " u'nipy_spectral_r',\n",
+       " 'np',\n",
+       " u'ocean',\n",
+       " u'ocean_r',\n",
+       " 'os',\n",
+       " u'pink',\n",
+       " u'pink_r',\n",
+       " 'print_function',\n",
+       " u'prism',\n",
+       " u'prism_r',\n",
+       " u'rainbow',\n",
+       " u'rainbow_r',\n",
+       " 'register_cmap',\n",
+       " 'revcmap',\n",
+       " u'seismic',\n",
+       " u'seismic_r',\n",
+       " 'six',\n",
+       " 'spec',\n",
+       " 'spec_reversed',\n",
+       " u'spectral',\n",
+       " u'spectral_r',\n",
+       " u'spring',\n",
+       " u'spring_r',\n",
+       " u'summer',\n",
+       " u'summer_r',\n",
+       " u'terrain',\n",
+       " u'terrain_r',\n",
+       " 'unicode_literals',\n",
+       " u'winter',\n",
+       " u'winter_r']"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(cm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用不同的 `colormap` 会有不同的显示效果。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0xe0883c8>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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U85cp5iVB5FPkFcbA9NqM3a9eZPOvP014x0NsbGxweHjoEJak0R9//HE++9nP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\nq3vUfZsozrjxC7/Gtz71BjY3N4tbHykVHjPpF7GJMo7uUZ2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NwqNSYxn6+3meR6/XY2Fh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KTCMpfDFmrOFESWSxQlWrWpilJsWlhJTK0ugcn+ovRsiN08huURJdIoZufOK8i4DgKT+lqVqakp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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xdefe9e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imshow(img, cmap=cm.RdGy_r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 从脚本中运行"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在脚本中使用 `plot` 时，通常图像是不会直接显示的，需要增加 `show()` 选项，只有在遇到 `show()` 命令之后，图像才会显示。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 直方图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "从高斯分布随机生成1000个点得到的直方图："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([   2.,    7.,   37.,  119.,  216.,  270.,  223.,   82.,   31.,   13.]),\n",
+       " array([-3.65594649, -2.98847032, -2.32099415, -1.65351798, -0.98604181,\n",
+       "        -0.31856564,  0.34891053,  1.0163867 ,  1.68386287,  2.35133904,\n",
+       "         3.01881521]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noS5JDTHUJakhhrokNcRQl6SG/D/cw+79AFjUjgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xdf2a5f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hist(randn(1000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "更多例子请参考下列网站：\n",
+    "\n",
+    "http://matplotlib.org/gallery.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.03 numpy arrays.ipynb b/03-numpy/03.03-numpy-arrays.ipynb
similarity index 94%
rename from 03. numpy/03.03 numpy arrays.ipynb
rename to 03-numpy/03.03-numpy-arrays.ipynb
index a05a9ed7..86fd3bc1 100644
--- a/03. numpy/03.03 numpy arrays.ipynb	
+++ b/03-numpy/03.03-numpy-arrays.ipynb
@@ -1,2216 +1,2216 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Numpy 数组及其索引"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "先导入numpy："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from numpy import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 产生数组"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "从列表产生数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1, 2, 3])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lst = [0, 1, 2, 3]\n",
-    "a = array(lst)\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "或者直接将列表传入："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 2, 3, 4])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = array([1, 2, 3, 4])\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 数组属性"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看类型："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看数组中的数据类型："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dtype('int32')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 32比特的整数\n",
-    "a.dtype"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看每个元素所占的字节："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.itemsize"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看形状，会返回一个元组，每个元素代表这一维的元素数目："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4L,)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 1维数组，返回一个元组\n",
-    "a.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "或者使用："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4L,)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "shape(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`shape` 的使用历史要比 `a.shape` 久，而且还可以作用于别的类型："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4L,)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lst = [1,2,3,4]\n",
-    "shape(lst)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看元素数目："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.size"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "size(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看所有元素所占的空间："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.nbytes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "但事实上，数组所占的存储空间要比这个数字大，因为要用一个header来保存shape，dtype这样的信息。\n",
-    "\n",
-    "查看数组维数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.ndim"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 使用fill方法设定初始值"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以使用 `fill` 方法将数组设为指定值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-4, -4, -4, -4])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.fill(-4.8)\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "但是与列表不同，数组中要求所有元素的 `dtype` 是一样的，如果传入参数的类型与数组类型不一样，需要按照已有的类型进行转换。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 索引与切片"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "和列表相似，数组也支持索引和切片操作。\n",
-    "\n",
-    "索引第一个元素："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = array([0, 1, 2, 3])\n",
-    "a[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "修改第一个元素的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([10,  1,  2,  3])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[0] = 10\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "切片，支持负索引："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([12, 13])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = array([11,12,13,14,15])\n",
-    "a[1:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([12, 13])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[1:-2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([12, 13])"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[-4:3]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "省略参数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([11, 13, 15])"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[::2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([14, 15])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[-2:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "假设我们记录一辆汽车表盘上每天显示的里程数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "od = array([21000, 21180, 21240, 22100, 22400])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以这样计算每天的旅程："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([180,  60, 860, 300])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dist = od[1:] - od[:-1]\n",
-    "dist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在本质上，**Python**会将array的各种计算转换为类似这样的**C**代码：\n",
-    "\n",
-    "```c\n",
-    "int compute_sum(int *arr, int N) {\n",
-    "    int sum = 0;\n",
-    "    int i;\n",
-    "    for (i = 0; i < N; i++) {\n",
-    "        sum += arr[i];\n",
-    "    }\n",
-    "    return sum;\n",
-    "}\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 多维数组及其属性"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`array` 还可以用来生成多维数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0,  1,  2,  3],\n",
-       "       [10, 11, 12, 13]])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = array([[ 0, 1, 2, 3],\n",
-    "           [10,11,12,13]])\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上我们传入的是一个以列表为元素的列表，最终得到一个二维数组。\n",
-    "\n",
-    "甚至可以扩展到3D或者4D的情景。\n",
-    "\n",
-    "查看形状："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2L, 4L)"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里2代表行数，4代表列数。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看总的元素个数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 2 * 4 = 8\n",
-    "a.size"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看维数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.ndim"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 多维数组索引"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于二维数组，可以传入两个数字来索引："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "13"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[1, 3]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "其中，1是行索引，3是列索引，中间用逗号隔开，事实上，**Python**会将它们看成一个元组(1,3)，然后按照顺序进行对应。\n",
-    "\n",
-    "可以利用索引给它赋值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0,  1,  2,  3],\n",
-       "       [10, 11, 12, -1]])"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[1, 3] = -1\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上，我们还可以使用单个索引来索引一整行内容："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([10, 11, 12, -1])"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 返回第二行元组组成的array\n",
-    "a[1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Python**会将这单个元组当成对第一维的索引，然后返回对应的内容。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 多维数组切片"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "多维数组，也支持切片操作："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0,  1,  2,  3,  4,  5],\n",
-       "       [10, 11, 12, 13, 14, 15],\n",
-       "       [20, 21, 22, 23, 24, 25],\n",
-       "       [30, 31, 32, 33, 34, 35],\n",
-       "       [40, 41, 42, 43, 44, 45],\n",
-       "       [50, 51, 52, 53, 54, 55]])"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = array([[ 0, 1, 2, 3, 4, 5],\n",
-    "           [10,11,12,13,14,15],\n",
-    "           [20,21,22,23,24,25],\n",
-    "           [30,31,32,33,34,35],\n",
-    "           [40,41,42,43,44,45],\n",
-    "           [50,51,52,53,54,55]])\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "想得到第一行的第 4 和第 5 两个元素："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([3, 4])"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[0, 3:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "得到最后两行的最后两列："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[44, 45],\n",
-       "       [54, 55]])"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[4:, 4:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "得到第三列："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 2, 12, 22, 32, 42, 52])"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[:, 2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "每一维都支持切片的规则，包括负索引，省略："
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    [lower:upper:step]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "例如，取出3，5行的奇数列："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[20, 22, 24],\n",
-       "       [40, 42, 44]])"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[2::2, ::2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 切片是引用"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "切片在内存中使用的是引用机制。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2 3]\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = array([0,1,2,3,4])\n",
-    "b = a[2:4]\n",
-    "print b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "引用机制意味着，**Python**并没有为 `b` 分配新的空间来存储它的值，而是让 `b` 指向了 `a` 所分配的内存空间，因此，改变 `b` 会改变 `a` 的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0,  1, 10,  3,  4])"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b[0] = 10\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "而这种现象在列表中并不会出现："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 2, 3, 4, 5]\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = [1,2,3,4,5]\n",
-    "b = a[2:3]\n",
-    "b[0] = 13234\n",
-    "print a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这样做的好处在于，对于很大的数组，不用大量复制多余的值，节约了空间。\n",
-    "\n",
-    "缺点在于，可能出现改变一个值改变另一个值的情况。\n",
-    "\n",
-    "一个解决方法是使用copy()方法产生一个复制，这个复制会申请新的内存："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1, 2, 3, 4])"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = array([0,1,2,3,4])\n",
-    "b = a[2:4].copy()\n",
-    "b[0] = 10\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 花式索引"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "切片只能支持连续或者等间隔的切片操作，要想实现任意位置的操作，需要使用花式索引 `fancy slicing` 。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 一维花式索引"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "与 range 函数类似，我们可以使用 arange 函数来产生等差数组。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0, 10, 20, 30, 40, 50, 60, 70])"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = arange(0, 80, 10)\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "花式索引需要指定索引位置："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[10 20 50]\n"
-     ]
-    }
-   ],
-   "source": [
-    "indices = [1, 2, -3]\n",
-    "y = a[indices]\n",
-    "print y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "还可以使用布尔数组来花式索引："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "mask = array([0,1,1,0,0,1,0,0],\n",
-    "            dtype=bool)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([10, 20, 50])"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[mask]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "或者用布尔表达式生成 `mask`，选出了所有大于0.5的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.37214708,  0.48594733,  0.73365131,  0.15769295,  0.30786017,\n",
-       "        0.62068734,  0.36940654,  0.09424167,  0.53085308,  0.12248951])"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from numpy.random import rand\n",
-    "a = rand(10)\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.73365131,  0.62068734,  0.53085308])"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mask = a > 0.5\n",
-    "a[mask]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "mask 必须是布尔数组。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 二维花式索引"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0,  1,  2,  3,  4,  5],\n",
-       "       [10, 11, 12, 13, 14, 15],\n",
-       "       [20, 21, 22, 23, 24, 25],\n",
-       "       [30, 31, 32, 33, 34, 35],\n",
-       "       [40, 41, 42, 43, 44, 45],\n",
-       "       [50, 51, 52, 53, 54, 55]])"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = array([[ 0, 1, 2, 3, 4, 5],\n",
-    "           [10,11,12,13,14,15],\n",
-    "           [20,21,22,23,24,25],\n",
-    "           [30,31,32,33,34,35],\n",
-    "           [40,41,42,43,44,45],\n",
-    "           [50,51,52,53,54,55]])\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于二维花式索引，我们需要给定 `row` 和 `col` 的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1, 12, 23, 34, 45])"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[(0,1,2,3,4), (1,2,3,4,5)]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "返回的是一条次对角线上的5个值。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[30, 32, 35],\n",
-       "       [40, 42, 45],\n",
-       "       [50, 52, 55]])"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[3:, [0,2,5]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "返回的是最后三行的第1，3，5列。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "也可以使用mask进行索引："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 2, 22, 52])"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mask = array([1,0,1,0,0,1],\n",
-    "            dtype=bool)\n",
-    "a[mask, 2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "与切片不同，花式索引返回的是原对象的一个复制而不是引用。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### “不完全”索引"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "只给定行索引的时候，返回整行："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0,  1,  2,  3,  4,  5],\n",
-       "       [10, 11, 12, 13, 14, 15],\n",
-       "       [20, 21, 22, 23, 24, 25]])"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y = a[:3]\n",
-    "y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这时候也可以使用花式索引取出第2，3，5行："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[10, 11, 12, 13, 14, 15],\n",
-       "       [20, 21, 22, 23, 24, 25],\n",
-       "       [40, 41, 42, 43, 44, 45]])"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "condition = array([0,1,1,0,1],\n",
-    "                 dtype=bool)\n",
-    "a[condition]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 三维花式索引"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[ 0,  1,  2,  3],\n",
-       "        [ 4,  5,  6,  7],\n",
-       "        [ 8,  9, 10, 11],\n",
-       "        [12, 13, 14, 15]],\n",
-       "\n",
-       "       [[16, 17, 18, 19],\n",
-       "        [20, 21, 22, 23],\n",
-       "        [24, 25, 26, 27],\n",
-       "        [28, 29, 30, 31]],\n",
-       "\n",
-       "       [[32, 33, 34, 35],\n",
-       "        [36, 37, 38, 39],\n",
-       "        [40, 41, 42, 43],\n",
-       "        [44, 45, 46, 47]],\n",
-       "\n",
-       "       [[48, 49, 50, 51],\n",
-       "        [52, 53, 54, 55],\n",
-       "        [56, 57, 58, 59],\n",
-       "        [60, 61, 62, 63]]])"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = arange(64)\n",
-    "a.shape = 4,4,4\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[ 2,  3],\n",
-       "        [ 6,  7],\n",
-       "        [10, 11],\n",
-       "        [14, 15]],\n",
-       "\n",
-       "       [[18, 19],\n",
-       "        [22, 23],\n",
-       "        [26, 27],\n",
-       "        [30, 31]],\n",
-       "\n",
-       "       [[34, 35],\n",
-       "        [38, 39],\n",
-       "        [42, 43],\n",
-       "        [46, 47]],\n",
-       "\n",
-       "       [[50, 51],\n",
-       "        [54, 55],\n",
-       "        [58, 59],\n",
-       "        [62, 63]]])"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y = a[:,:,[2, -1]]\n",
-    "y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "## where语句"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    where(array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`where` 函数会返回所有非零元素的索引。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 一维数组"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "先看一维的例子："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = array([0, 12, 5, 20])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "判断数组中的元素是不是大于10："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([False,  True, False,  True], dtype=bool)"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a > 10"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "数组中所有大于10的元素的索引位置："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([1, 3], dtype=int64),)"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "where(a > 10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "注意到 `where` 的返回值是一个元组。\n",
-    "\n",
-    "使用元组是由于 where 可以对多维数组使用，此时返回值就是多维的。\n",
-    "\n",
-    "在使用的时候，我们可以这样："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 3], dtype=int64)"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "indices = where(a > 10)\n",
-    "indices = indices[0]\n",
-    "indices"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "或者："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 3], dtype=int64)"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "indices = where(a>10)[0]\n",
-    "indices"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以直接用 `where` 的返回值进行索引："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([12, 20])"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "loc = where(a > 10)\n",
-    "a[loc]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 多维数组"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "考虑二维数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = array([[0, 12, 5, 20],\n",
-    "           [1, 2, 11, 15]])\n",
-    "loc = where(a > 10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "返回结果是一个二维的元组，每一维代表这一维的索引值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([0, 0, 1, 1], dtype=int64), array([1, 3, 2, 3], dtype=int64))"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "loc"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "也可以直接用来索引a："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([12, 20, 11, 15])"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[loc]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "或者可以这样："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "rows, cols = where(a>10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 1, 1], dtype=int64)"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 3, 2, 3], dtype=int64)"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cols"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([12, 20, 11, 15])"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[rows, cols]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "再看另一个例子："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0,  1,  2,  3,  4],\n",
-       "       [ 5,  6,  7,  8,  9],\n",
-       "       [10, 11, 12, 13, 14],\n",
-       "       [15, 16, 17, 18, 19],\n",
-       "       [20, 21, 22, 23, 24]])"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = arange(25)\n",
-    "a.shape = 5,5\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[False, False, False, False, False],\n",
-       "       [False, False, False, False, False],\n",
-       "       [False, False, False,  True,  True],\n",
-       "       [ True,  True,  True,  True,  True],\n",
-       "       [ True,  True,  True,  True,  True]], dtype=bool)"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a > 12"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4], dtype=int64),\n",
-       " array([3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4], dtype=int64))"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "where(a > 12)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Numpy 数组及其索引"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "先导入numpy："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from numpy import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 产生数组"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "从列表产生数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 3])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lst = [0, 1, 2, 3]\n",
+    "a = array(lst)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "或者直接将列表传入："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = array([1, 2, 3, 4])\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 数组属性"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看类型："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看数组中的数据类型："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('int32')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 32比特的整数\n",
+    "a.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看每个元素所占的字节："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.itemsize"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看形状，会返回一个元组，每个元素代表这一维的元素数目："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4L,)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 1维数组，返回一个元组\n",
+    "a.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "或者使用："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4L,)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`shape` 的使用历史要比 `a.shape` 久，而且还可以作用于别的类型："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4L,)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lst = [1,2,3,4]\n",
+    "shape(lst)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看元素数目："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "size(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看所有元素所占的空间："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.nbytes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "但事实上，数组所占的存储空间要比这个数字大，因为要用一个header来保存shape，dtype这样的信息。\n",
+    "\n",
+    "查看数组维数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.ndim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 使用fill方法设定初始值"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以使用 `fill` 方法将数组设为指定值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-4, -4, -4, -4])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.fill(-4.8)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "但是与列表不同，数组中要求所有元素的 `dtype` 是一样的，如果传入参数的类型与数组类型不一样，需要按照已有的类型进行转换。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 索引与切片"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "和列表相似，数组也支持索引和切片操作。\n",
+    "\n",
+    "索引第一个元素："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = array([0, 1, 2, 3])\n",
+    "a[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "修改第一个元素的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([10,  1,  2,  3])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[0] = 10\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "切片，支持负索引："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([12, 13])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = array([11,12,13,14,15])\n",
+    "a[1:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([12, 13])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[1:-2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([12, 13])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[-4:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "省略参数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([11, 13, 15])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[::2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([14, 15])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[-2:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "假设我们记录一辆汽车表盘上每天显示的里程数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "od = array([21000, 21180, 21240, 22100, 22400])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以这样计算每天的旅程："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([180,  60, 860, 300])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dist = od[1:] - od[:-1]\n",
+    "dist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在本质上，**Python**会将array的各种计算转换为类似这样的**C**代码：\n",
+    "\n",
+    "```c\n",
+    "int compute_sum(int *arr, int N) {\n",
+    "    int sum = 0;\n",
+    "    int i;\n",
+    "    for (i = 0; i < N; i++) {\n",
+    "        sum += arr[i];\n",
+    "    }\n",
+    "    return sum;\n",
+    "}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 多维数组及其属性"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`array` 还可以用来生成多维数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3],\n",
+       "       [10, 11, 12, 13]])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = array([[ 0, 1, 2, 3],\n",
+    "           [10,11,12,13]])\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上我们传入的是一个以列表为元素的列表，最终得到一个二维数组。\n",
+    "\n",
+    "甚至可以扩展到3D或者4D的情景。\n",
+    "\n",
+    "查看形状："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2L, 4L)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里2代表行数，4代表列数。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看总的元素个数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 2 * 4 = 8\n",
+    "a.size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看维数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.ndim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 多维数组索引"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于二维数组，可以传入两个数字来索引："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[1, 3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "其中，1是行索引，3是列索引，中间用逗号隔开，事实上，**Python**会将它们看成一个元组(1,3)，然后按照顺序进行对应。\n",
+    "\n",
+    "可以利用索引给它赋值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3],\n",
+       "       [10, 11, 12, -1]])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[1, 3] = -1\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上，我们还可以使用单个索引来索引一整行内容："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([10, 11, 12, -1])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 返回第二行元组组成的array\n",
+    "a[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Python**会将这单个元组当成对第一维的索引，然后返回对应的内容。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 多维数组切片"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "多维数组，也支持切片操作："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4,  5],\n",
+       "       [10, 11, 12, 13, 14, 15],\n",
+       "       [20, 21, 22, 23, 24, 25],\n",
+       "       [30, 31, 32, 33, 34, 35],\n",
+       "       [40, 41, 42, 43, 44, 45],\n",
+       "       [50, 51, 52, 53, 54, 55]])"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = array([[ 0, 1, 2, 3, 4, 5],\n",
+    "           [10,11,12,13,14,15],\n",
+    "           [20,21,22,23,24,25],\n",
+    "           [30,31,32,33,34,35],\n",
+    "           [40,41,42,43,44,45],\n",
+    "           [50,51,52,53,54,55]])\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "想得到第一行的第 4 和第 5 两个元素："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 4])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[0, 3:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "得到最后两行的最后两列："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[44, 45],\n",
+       "       [54, 55]])"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[4:, 4:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "得到第三列："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2, 12, 22, 32, 42, 52])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[:, 2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "每一维都支持切片的规则，包括负索引，省略："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    [lower:upper:step]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "例如，取出3，5行的奇数列："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[20, 22, 24],\n",
+       "       [40, 42, 44]])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[2::2, ::2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 切片是引用"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "切片在内存中使用的是引用机制。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = array([0,1,2,3,4])\n",
+    "b = a[2:4]\n",
+    "print b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "引用机制意味着，**Python**并没有为 `b` 分配新的空间来存储它的值，而是让 `b` 指向了 `a` 所分配的内存空间，因此，改变 `b` 会改变 `a` 的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1, 10,  3,  4])"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b[0] = 10\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "而这种现象在列表中并不会出现："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = [1,2,3,4,5]\n",
+    "b = a[2:3]\n",
+    "b[0] = 13234\n",
+    "print a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这样做的好处在于，对于很大的数组，不用大量复制多余的值，节约了空间。\n",
+    "\n",
+    "缺点在于，可能出现改变一个值改变另一个值的情况。\n",
+    "\n",
+    "一个解决方法是使用copy()方法产生一个复制，这个复制会申请新的内存："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 3, 4])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = array([0,1,2,3,4])\n",
+    "b = a[2:4].copy()\n",
+    "b[0] = 10\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 花式索引"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "切片只能支持连续或者等间隔的切片操作，要想实现任意位置的操作，需要使用花式索引 `fancy slicing` 。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 一维花式索引"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "与 range 函数类似，我们可以使用 arange 函数来产生等差数组。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0, 10, 20, 30, 40, 50, 60, 70])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = arange(0, 80, 10)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "花式索引需要指定索引位置："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[10 20 50]\n"
+     ]
+    }
+   ],
+   "source": [
+    "indices = [1, 2, -3]\n",
+    "y = a[indices]\n",
+    "print y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "还可以使用布尔数组来花式索引："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "mask = array([0,1,1,0,0,1,0,0],\n",
+    "            dtype=bool)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([10, 20, 50])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[mask]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "或者用布尔表达式生成 `mask`，选出了所有大于0.5的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.37214708,  0.48594733,  0.73365131,  0.15769295,  0.30786017,\n",
+       "        0.62068734,  0.36940654,  0.09424167,  0.53085308,  0.12248951])"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from numpy.random import rand\n",
+    "a = rand(10)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.73365131,  0.62068734,  0.53085308])"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask = a > 0.5\n",
+    "a[mask]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "mask 必须是布尔数组。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 二维花式索引"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4,  5],\n",
+       "       [10, 11, 12, 13, 14, 15],\n",
+       "       [20, 21, 22, 23, 24, 25],\n",
+       "       [30, 31, 32, 33, 34, 35],\n",
+       "       [40, 41, 42, 43, 44, 45],\n",
+       "       [50, 51, 52, 53, 54, 55]])"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = array([[ 0, 1, 2, 3, 4, 5],\n",
+    "           [10,11,12,13,14,15],\n",
+    "           [20,21,22,23,24,25],\n",
+    "           [30,31,32,33,34,35],\n",
+    "           [40,41,42,43,44,45],\n",
+    "           [50,51,52,53,54,55]])\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于二维花式索引，我们需要给定 `row` 和 `col` 的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1, 12, 23, 34, 45])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[(0,1,2,3,4), (1,2,3,4,5)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "返回的是一条次对角线上的5个值。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[30, 32, 35],\n",
+       "       [40, 42, 45],\n",
+       "       [50, 52, 55]])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[3:, [0,2,5]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "返回的是最后三行的第1，3，5列。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "也可以使用mask进行索引："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2, 22, 52])"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask = array([1,0,1,0,0,1],\n",
+    "            dtype=bool)\n",
+    "a[mask, 2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "与切片不同，花式索引返回的是原对象的一个复制而不是引用。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### “不完全”索引"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "只给定行索引的时候，返回整行："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4,  5],\n",
+       "       [10, 11, 12, 13, 14, 15],\n",
+       "       [20, 21, 22, 23, 24, 25]])"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = a[:3]\n",
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这时候也可以使用花式索引取出第2，3，5行："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[10, 11, 12, 13, 14, 15],\n",
+       "       [20, 21, 22, 23, 24, 25],\n",
+       "       [40, 41, 42, 43, 44, 45]])"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "condition = array([0,1,1,0,1],\n",
+    "                 dtype=bool)\n",
+    "a[condition]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 三维花式索引"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 0,  1,  2,  3],\n",
+       "        [ 4,  5,  6,  7],\n",
+       "        [ 8,  9, 10, 11],\n",
+       "        [12, 13, 14, 15]],\n",
+       "\n",
+       "       [[16, 17, 18, 19],\n",
+       "        [20, 21, 22, 23],\n",
+       "        [24, 25, 26, 27],\n",
+       "        [28, 29, 30, 31]],\n",
+       "\n",
+       "       [[32, 33, 34, 35],\n",
+       "        [36, 37, 38, 39],\n",
+       "        [40, 41, 42, 43],\n",
+       "        [44, 45, 46, 47]],\n",
+       "\n",
+       "       [[48, 49, 50, 51],\n",
+       "        [52, 53, 54, 55],\n",
+       "        [56, 57, 58, 59],\n",
+       "        [60, 61, 62, 63]]])"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = arange(64)\n",
+    "a.shape = 4,4,4\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 2,  3],\n",
+       "        [ 6,  7],\n",
+       "        [10, 11],\n",
+       "        [14, 15]],\n",
+       "\n",
+       "       [[18, 19],\n",
+       "        [22, 23],\n",
+       "        [26, 27],\n",
+       "        [30, 31]],\n",
+       "\n",
+       "       [[34, 35],\n",
+       "        [38, 39],\n",
+       "        [42, 43],\n",
+       "        [46, 47]],\n",
+       "\n",
+       "       [[50, 51],\n",
+       "        [54, 55],\n",
+       "        [58, 59],\n",
+       "        [62, 63]]])"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = a[:,:,[2, -1]]\n",
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## where语句"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    where(array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`where` 函数会返回所有非零元素的索引。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 一维数组"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "先看一维的例子："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "a = array([0, 12, 5, 20])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "判断数组中的元素是不是大于10："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False,  True, False,  True], dtype=bool)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a > 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "数组中所有大于10的元素的索引位置："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1, 3], dtype=int64),)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "where(a > 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "注意到 `where` 的返回值是一个元组。\n",
+    "\n",
+    "使用元组是由于 where 可以对多维数组使用，此时返回值就是多维的。\n",
+    "\n",
+    "在使用的时候，我们可以这样："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 3], dtype=int64)"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "indices = where(a > 10)\n",
+    "indices = indices[0]\n",
+    "indices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "或者："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 3], dtype=int64)"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "indices = where(a>10)[0]\n",
+    "indices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以直接用 `where` 的返回值进行索引："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([12, 20])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loc = where(a > 10)\n",
+    "a[loc]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 多维数组"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "考虑二维数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "a = array([[0, 12, 5, 20],\n",
+    "           [1, 2, 11, 15]])\n",
+    "loc = where(a > 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "返回结果是一个二维的元组，每一维代表这一维的索引值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0, 0, 1, 1], dtype=int64), array([1, 3, 2, 3], dtype=int64))"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "也可以直接用来索引a："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([12, 20, 11, 15])"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[loc]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "或者可以这样："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "rows, cols = where(a>10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 1, 1], dtype=int64)"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 3, 2, 3], dtype=int64)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([12, 20, 11, 15])"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[rows, cols]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "再看另一个例子："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4],\n",
+       "       [ 5,  6,  7,  8,  9],\n",
+       "       [10, 11, 12, 13, 14],\n",
+       "       [15, 16, 17, 18, 19],\n",
+       "       [20, 21, 22, 23, 24]])"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = arange(25)\n",
+    "a.shape = 5,5\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False, False, False, False, False],\n",
+       "       [False, False, False, False, False],\n",
+       "       [False, False, False,  True,  True],\n",
+       "       [ True,  True,  True,  True,  True],\n",
+       "       [ True,  True,  True,  True,  True]], dtype=bool)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a > 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4], dtype=int64),\n",
+       " array([3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4], dtype=int64))"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "where(a > 12)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.04 array types.ipynb b/03-numpy/03.04-array-types.ipynb
similarity index 100%
rename from 03. numpy/03.04 array types.ipynb
rename to 03-numpy/03.04-array-types.ipynb
diff --git a/03. numpy/03.05 array calculation method.ipynb b/03-numpy/03.05-array-calculation-method.ipynb
similarity index 94%
rename from 03. numpy/03.05 array calculation method.ipynb
rename to 03-numpy/03.05-array-calculation-method.ipynb
index 662b222b..4ad49974 100644
--- a/03. numpy/03.05 array calculation method.ipynb	
+++ b/03-numpy/03.05-array-calculation-method.ipynb
@@ -1,1022 +1,1022 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 数组方法"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using matplotlib backend: Qt4Agg\n",
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pylab"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 求和"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "a = array([[1,2,3], \n",
-    "           [4,5,6]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "求所有元素的和："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "21"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "指定求和的维度："
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "沿着第一维求和："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([5, 7, 9])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(a, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "沿着第二维求和："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 6, 15])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(a, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "沿着最后一维求和："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 6, 15])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(a, axis=-1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "或者使用 `sum` 方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "21"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([5, 7, 9])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.sum(axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 6, 15])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.sum(axis=-1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 求积"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "求所有元素的乘积："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "720"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.prod()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "或者使用函数形式："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 4, 10, 18])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "prod(a, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 求最大最小值"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.444,  0.06 ,  0.668,  0.02 ],\n",
-       "       [ 0.793,  0.302,  0.81 ,  0.381],\n",
-       "       [ 0.296,  0.182,  0.345,  0.686]])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from numpy.random import rand\n",
-    "a = rand(3, 4)\n",
-    "%precision 3\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "全局最小："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.020"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.min()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "沿着某个轴的最小："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.296,  0.06 ,  0.345,  0.02 ])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.min(axis=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "全局最大："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.810"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.max()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "沿着某个轴的最大："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.668,  0.81 ,  0.686])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.max(axis=-1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 最大最小值的位置"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `argmin, argmax` 方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.argmin()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2, 0, 2, 0], dtype=int64)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.argmin(axis=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 均值"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以使用 `mean` 方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = array([[1,2,3],[4,5,6]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.500"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 2.,  5.])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.mean(axis=-1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "也可以使用 `mean` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.500"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mean(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "还可以使用 `average` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 2.5,  3.5,  4.5])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "average(a, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`average` 函数还支持加权平均："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 3.,  4.,  5.])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "average(a, axis = 0, weights=[1,2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 标准差"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "用 `std` 方法计算标准差："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.816,  0.816])"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.std(axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "用 `var` 方法计算方差："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.667,  0.667])"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.var(axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "或者使用函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.667,  0.667])"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "var(a, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.816,  0.816])"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "std(a, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## clip 方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "将数值限制在某个范围："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3],\n",
-       "       [4, 5, 6]])"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[3, 3, 3],\n",
-       "       [4, 5, 5]])"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.clip(3,5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "小于3的变成3，大于5的变成5。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## ptp 方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "计算最大值和最小值之差："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2, 2])"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.ptp(axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.ptp()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## round 方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "近似，默认到整数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = array([1.35, 2.5, 1.5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里，.5的近似规则为近似到偶数值，可以参考：\n",
-    "\n",
-    "https://en.wikipedia.org/wiki/Rounding#Round_half_to_odd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.,  2.,  2.])"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.round()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "近似到一位小数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.4,  2.5,  1.5])"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.round(decimals=1)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 数组方法"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using matplotlib backend: Qt4Agg\n",
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 求和"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a = array([[1,2,3], \n",
+    "           [4,5,6]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "求所有元素的和："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "指定求和的维度："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "沿着第一维求和："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5, 7, 9])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(a, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "沿着第二维求和："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 6, 15])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(a, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "沿着最后一维求和："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 6, 15])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(a, axis=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "或者使用 `sum` 方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5, 7, 9])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 6, 15])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.sum(axis=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 求积"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "求所有元素的乘积："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "720"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.prod()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "或者使用函数形式："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 4, 10, 18])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prod(a, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 求最大最小值"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.444,  0.06 ,  0.668,  0.02 ],\n",
+       "       [ 0.793,  0.302,  0.81 ,  0.381],\n",
+       "       [ 0.296,  0.182,  0.345,  0.686]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from numpy.random import rand\n",
+    "a = rand(3, 4)\n",
+    "%precision 3\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "全局最小："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.020"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.min()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "沿着某个轴的最小："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.296,  0.06 ,  0.345,  0.02 ])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.min(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "全局最大："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.810"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "沿着某个轴的最大："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.668,  0.81 ,  0.686])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.max(axis=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 最大最小值的位置"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `argmin, argmax` 方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.argmin()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 0, 2, 0], dtype=int64)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.argmin(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 均值"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以使用 `mean` 方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "a = array([[1,2,3],[4,5,6]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.500"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.,  5.])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.mean(axis=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "也可以使用 `mean` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.500"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "还可以使用 `average` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.5,  3.5,  4.5])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average(a, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`average` 函数还支持加权平均："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 3.,  4.,  5.])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average(a, axis = 0, weights=[1,2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 标准差"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "用 `std` 方法计算标准差："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.816,  0.816])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.std(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "用 `var` 方法计算方差："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.667,  0.667])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.var(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "或者使用函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.667,  0.667])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "var(a, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.816,  0.816])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "std(a, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## clip 方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将数值限制在某个范围："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6]])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3, 3, 3],\n",
+       "       [4, 5, 5]])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.clip(3,5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "小于3的变成3，大于5的变成5。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ptp 方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "计算最大值和最小值之差："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 2])"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.ptp(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.ptp()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## round 方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "近似，默认到整数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "a = array([1.35, 2.5, 1.5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里，.5的近似规则为近似到偶数值，可以参考：\n",
+    "\n",
+    "https://en.wikipedia.org/wiki/Rounding#Round_half_to_odd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.,  2.,  2.])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.round()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "近似到一位小数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.4,  2.5,  1.5])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.round(decimals=1)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.06 sorting numpy arrays.ipynb b/03-numpy/03.06-sorting-numpy-arrays.ipynb
similarity index 94%
rename from 03. numpy/03.06 sorting numpy arrays.ipynb
rename to 03-numpy/03.06-sorting-numpy-arrays.ipynb
index d6fb21f2..6cfe7bcb 100644
--- a/03. numpy/03.06 sorting numpy arrays.ipynb	
+++ b/03-numpy/03.06-sorting-numpy-arrays.ipynb
@@ -1,591 +1,591 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 数组排序"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using matplotlib backend: Qt4Agg\n",
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pylab"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## sort 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "先看这个例子："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 20.8,  53.4,  61.8,  93.2])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "names = array(['bob', 'sue', 'jan', 'ad'])\n",
-    "weights = array([20.8, 93.2, 53.4, 61.8])\n",
-    "\n",
-    "sort(weights)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`sort` 返回的结果是从小到大排列的。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## argsort 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`argsort` 返回从小到大的排列在数组中的索引位置："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 2, 3, 1], dtype=int64)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ordered_indices = argsort(weights)\n",
-    "ordered_indices"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以用它来进行索引："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 20.8,  53.4,  61.8,  93.2])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "weights[ordered_indices]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['bob', 'jan', 'ad', 'sue'], \n",
-       "      dtype='|S3')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "names[ordered_indices]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用函数并不会改变原来数组的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 20.8,  93.2,  53.4,  61.8])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "weights"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## sort 和 argsort 方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "数组也支持方法操作："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 2, 3, 1], dtype=int64)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = array([20.8,  93.2,  53.4,  61.8])\n",
-    "data.argsort()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`argsort` 方法与 `argsort` 函数的使用没什么区别，也不会改变数组的值。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 20.8,  93.2,  53.4,  61.8])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "但是 `sort`方法会改变数组的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "data.sort()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 20.8,  53.4,  61.8,  93.2])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 二维数组排序"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于多维数组，sort方法默认沿着最后一维开始排序："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.2,  0.1,  0.5],\n",
-       "       [ 0.4,  0.8,  0.3],\n",
-       "       [ 0.9,  0.6,  0.7]])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = array([\n",
-    "        [.2, .1, .5], \n",
-    "        [.4, .8, .3],\n",
-    "        [.9, .6, .7]\n",
-    "    ])\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于二维数组，默认相当于对每一行进行排序："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.1,  0.2,  0.5],\n",
-       "       [ 0.3,  0.4,  0.8],\n",
-       "       [ 0.6,  0.7,  0.9]])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sort(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "改变轴，对每一列进行排序："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.2,  0.1,  0.3],\n",
-       "       [ 0.4,  0.6,  0.5],\n",
-       "       [ 0.9,  0.8,  0.7]])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sort(a, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## searchsorted 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    searchsorted(sorted_array, values)\n",
-    "\n",
-    "`searchsorted` 接受两个参数，其中，第一个必需是已排序的数组。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "sorted_array = linspace(0,1,5)\n",
-    "values = array([.1,.8,.3,.12,.5,.25])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 4, 2, 1, 2, 1], dtype=int64)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "searchsorted(sorted_array, values)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "排序数组：\n",
-    "\n",
-    "|0|1|2|3|4|\n",
-    "|-|-|-|-|-|\n",
-    "|0.0|0.25|0.5|0.75|1.0\n",
-    "\n",
-    "数值：\n",
-    "\n",
-    "|值|0.1|0.8|0.3|0.12|0.5|0.25|\n",
-    "|-|-|-|-|-|-|-|\n",
-    "|插入位置|1|4|2|1|2|1|\n",
-    "\n",
-    "`searchsorted` 返回的值相当于保持第一个数组的排序性质不变，将第二个数组中的值插入第一个数组中的位置：\n",
-    "\n",
-    "例如 `0.1` 在 [0.0, 0.25) 之间，所以插入时应当放在第一个数组的索引 `1` 处，故第一个返回值为 `1`。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from numpy.random import rand\n",
-    "data = rand(100)\n",
-    "data.sort()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不加括号，默认是元组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.4, 0.6)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bounds = .4, .6\n",
-    "bounds"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "返回这两个值对应的插入位置："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "low_idx, high_idx = searchsorted(data, bounds)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "利用插入位置，将数组中所有在这两个值之间的值提取出来："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.41122674,  0.4395727 ,  0.45609773,  0.45707137,  0.45772076,\n",
-       "        0.46029997,  0.46757401,  0.47525517,  0.4969198 ,  0.53068779,\n",
-       "        0.55764166,  0.56288568,  0.56506548,  0.57003042,  0.58035233,\n",
-       "        0.59279233,  0.59548555])"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data[low_idx:high_idx]"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 数组排序"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using matplotlib backend: Qt4Agg\n",
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## sort 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "先看这个例子："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 20.8,  53.4,  61.8,  93.2])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names = array(['bob', 'sue', 'jan', 'ad'])\n",
+    "weights = array([20.8, 93.2, 53.4, 61.8])\n",
+    "\n",
+    "sort(weights)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`sort` 返回的结果是从小到大排列的。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## argsort 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`argsort` 返回从小到大的排列在数组中的索引位置："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 2, 3, 1], dtype=int64)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordered_indices = argsort(weights)\n",
+    "ordered_indices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以用它来进行索引："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 20.8,  53.4,  61.8,  93.2])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weights[ordered_indices]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['bob', 'jan', 'ad', 'sue'], \n",
+       "      dtype='|S3')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names[ordered_indices]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用函数并不会改变原来数组的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 20.8,  93.2,  53.4,  61.8])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## sort 和 argsort 方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "数组也支持方法操作："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 2, 3, 1], dtype=int64)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = array([20.8,  93.2,  53.4,  61.8])\n",
+    "data.argsort()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`argsort` 方法与 `argsort` 函数的使用没什么区别，也不会改变数组的值。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 20.8,  93.2,  53.4,  61.8])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "但是 `sort`方法会改变数组的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data.sort()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 20.8,  53.4,  61.8,  93.2])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 二维数组排序"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于多维数组，sort方法默认沿着最后一维开始排序："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.2,  0.1,  0.5],\n",
+       "       [ 0.4,  0.8,  0.3],\n",
+       "       [ 0.9,  0.6,  0.7]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = array([\n",
+    "        [.2, .1, .5], \n",
+    "        [.4, .8, .3],\n",
+    "        [.9, .6, .7]\n",
+    "    ])\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于二维数组，默认相当于对每一行进行排序："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.1,  0.2,  0.5],\n",
+       "       [ 0.3,  0.4,  0.8],\n",
+       "       [ 0.6,  0.7,  0.9]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sort(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "改变轴，对每一列进行排序："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.2,  0.1,  0.3],\n",
+       "       [ 0.4,  0.6,  0.5],\n",
+       "       [ 0.9,  0.8,  0.7]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sort(a, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## searchsorted 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    searchsorted(sorted_array, values)\n",
+    "\n",
+    "`searchsorted` 接受两个参数，其中，第一个必需是已排序的数组。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "sorted_array = linspace(0,1,5)\n",
+    "values = array([.1,.8,.3,.12,.5,.25])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 4, 2, 1, 2, 1], dtype=int64)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "searchsorted(sorted_array, values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "排序数组：\n",
+    "\n",
+    "|0|1|2|3|4|\n",
+    "|-|-|-|-|-|\n",
+    "|0.0|0.25|0.5|0.75|1.0\n",
+    "\n",
+    "数值：\n",
+    "\n",
+    "|值|0.1|0.8|0.3|0.12|0.5|0.25|\n",
+    "|-|-|-|-|-|-|-|\n",
+    "|插入位置|1|4|2|1|2|1|\n",
+    "\n",
+    "`searchsorted` 返回的值相当于保持第一个数组的排序性质不变，将第二个数组中的值插入第一个数组中的位置：\n",
+    "\n",
+    "例如 `0.1` 在 [0.0, 0.25) 之间，所以插入时应当放在第一个数组的索引 `1` 处，故第一个返回值为 `1`。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from numpy.random import rand\n",
+    "data = rand(100)\n",
+    "data.sort()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不加括号，默认是元组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.4, 0.6)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bounds = .4, .6\n",
+    "bounds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "返回这两个值对应的插入位置："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "low_idx, high_idx = searchsorted(data, bounds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "利用插入位置，将数组中所有在这两个值之间的值提取出来："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.41122674,  0.4395727 ,  0.45609773,  0.45707137,  0.45772076,\n",
+       "        0.46029997,  0.46757401,  0.47525517,  0.4969198 ,  0.53068779,\n",
+       "        0.55764166,  0.56288568,  0.56506548,  0.57003042,  0.58035233,\n",
+       "        0.59279233,  0.59548555])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[low_idx:high_idx]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.07 array shapes.ipynb b/03-numpy/03.07-array-shapes.ipynb
similarity index 94%
rename from 03. numpy/03.07 array shapes.ipynb
rename to 03-numpy/03.07-array-shapes.ipynb
index bc25dfc6..4882d5d9 100644
--- a/03. numpy/03.07 array shapes.ipynb	
+++ b/03-numpy/03.07-array-shapes.ipynb
@@ -1,1424 +1,1424 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 数组形状"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using matplotlib backend: Qt4Agg\n",
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pylab"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 修改数组的形状"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1, 2, 3, 4, 5])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = arange(6)\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "将形状修改为2乘3："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 1, 2],\n",
-       "       [3, 4, 5]])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.shape = 2,3\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "与之对应的方法是 `reshape` ，但它不会修改原来数组的值，而是返回一个新的数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 1],\n",
-       "       [2, 3],\n",
-       "       [4, 5]])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.reshape(3,2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 1, 2],\n",
-       "       [3, 4, 5]])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`shape` 和 `reshape` 方法不能改变数组中元素的总数，否则会报错："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "total size of new array must be unchanged",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-6-1a35a76a1693>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m: total size of new array must be unchanged"
-     ]
-    }
-   ],
-   "source": [
-    "a.reshape(4,2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 使用 newaxis 增加数组维数"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3L,)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = arange(3)\n",
-    "shape(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1L, 3L)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y = a[newaxis, :]\n",
-    "shape(y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "根据插入位置的不同，可以返回不同形状的数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3L, 1L)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y = a[:, newaxis]\n",
-    "shape(y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "插入多个新维度："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1L, 1L, 3L)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y = a[newaxis, newaxis, :]\n",
-    "shape(y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## squeeze 方法去除多余的轴"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = arange(6)\n",
-    "a.shape = (2,1,3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2L, 3L)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b = a.squeeze()\n",
-    "b.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "squeeze 返回一个将所有长度为1的维度去除的新数组。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 数组转置"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `transpose` 返回数组的转置，本质上是将所有维度反过来："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[0, 1, 2]],\n",
-       "\n",
-       "       [[3, 4, 5]]])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于二维数组，这相当于交换行和列："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[0, 3]],\n",
-       "\n",
-       "       [[1, 4]],\n",
-       "\n",
-       "       [[2, 5]]])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.transpose()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "或者使用缩写属性："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[0, 3]],\n",
-       "\n",
-       "       [[1, 4]],\n",
-       "\n",
-       "       [[2, 5]]])"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "注意：\n",
-    "- 对于复数数组，转置并不返回复共轭，只是单纯的交换轴的位置\n",
-    "- 转置可以作用于多维数组"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
-       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
-       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
-       "       51, 52, 53, 54, 55, 56, 57, 58, 59])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = arange(60)\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[ 0,  1,  2,  3,  4],\n",
-       "        [ 5,  6,  7,  8,  9],\n",
-       "        [10, 11, 12, 13, 14],\n",
-       "        [15, 16, 17, 18, 19]],\n",
-       "\n",
-       "       [[20, 21, 22, 23, 24],\n",
-       "        [25, 26, 27, 28, 29],\n",
-       "        [30, 31, 32, 33, 34],\n",
-       "        [35, 36, 37, 38, 39]],\n",
-       "\n",
-       "       [[40, 41, 42, 43, 44],\n",
-       "        [45, 46, 47, 48, 49],\n",
-       "        [50, 51, 52, 53, 54],\n",
-       "        [55, 56, 57, 58, 59]]])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.shape = 3,4,5\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5L, 4L, 3L)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b = a.T\n",
-    "b.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "转置只是交换了轴的位置。\n",
-    "\n",
-    "另一方面，转置返回的是对原数组的另一种view，所以改变转置会改变原来数组的值。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 1, 2],\n",
-       "       [3, 4, 5]])"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = arange(6)\n",
-    "a.shape = (2,3)\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "修改转置："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "b = a.T\n",
-    "b[0,1] = 30"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "原数组的值也改变："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0,  1,  2],\n",
-       "       [30,  4,  5]])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 数组连接"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "有时我们需要将不同的数组按照一定的顺序连接起来："
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    concatenate((a0,a1,...,aN), axis=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "注意，这些数组要用 `()` 包括到一个元组中去。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "除了给定的轴外，这些数组其他轴的长度必须是一样的。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2L, 3L)\n",
-      "(2L, 3L)\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = array([\n",
-    "        [0,1,2],\n",
-    "        [10,11,12]\n",
-    "    ])\n",
-    "y = array([\n",
-    "        [50,51,52],\n",
-    "        [60,61,62]\n",
-    "    ])\n",
-    "print x.shape\n",
-    "print y.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "默认沿着第一维进行连接："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0,  1,  2],\n",
-       "       [10, 11, 12],\n",
-       "       [50, 51, 52],\n",
-       "       [60, 61, 62]])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "z = concatenate((x,y))\n",
-    "z"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4L, 3L)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "z.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "沿着第二维进行连接："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0,  1,  2, 50, 51, 52],\n",
-       "       [10, 11, 12, 60, 61, 62]])"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "z = concatenate((x,y), axis=1)\n",
-    "z"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2L, 6L)"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "z.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "注意到这里 `x` 和 `y` 的形状是一样的，还可以将它们连接成三维的数组，但是 `concatenate` 不能提供这样的功能，不过可以这样："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "z = array((x,y))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2L, 2L, 3L)"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "z.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上，**Numpy**提供了分别对应这三种情况的函数：\n",
-    "\n",
-    "- vstack\n",
-    "- hstack\n",
-    "- dstack"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4L, 3L)"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vstack((x, y)).shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2L, 6L)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hstack((x, y)).shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2L, 3L, 2L)"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dstack((x, y)).shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Flatten 数组"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`flatten` 方法的作用是将多维数组转化为1维数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1, 2, 3])"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = array([[0,1],\n",
-    "           [2,3]])\n",
-    "b = a.flatten()\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "返回的是数组的复制，因此，改变 `b` 并不会影响 `a` 的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[10  1  2  3]\n",
-      "[[0 1]\n",
-      " [2 3]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "b[0] = 10\n",
-    "print b\n",
-    "print a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## flat 属性"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "还可以使用数组自带的 `flat` 属性："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<numpy.flatiter at 0x3d546a0>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.flat"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`a.flat` 相当于返回了所有元组组成的一个迭代器："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "b = a.flat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "但此时修改 `b` 的值会影响 `a` ："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[10  1]\n",
-      " [ 2  3]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "b[0] = 10\n",
-    "print a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([10,  1,  2,  3])"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.flat[:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## ravel 方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "除此之外，还可以使用 `ravel` 方法，`ravel` 使用高效的表示方式："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1, 2, 3])"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = array([[0,1],\n",
-    "           [2,3]])\n",
-    "b = a.ravel()\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "修改 `b` 会改变 `a` ："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[10,  1],\n",
-       "       [ 2,  3]])"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b[0] = 10\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "但另一种情况下："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 2, 1, 3])"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = array([[0,1],\n",
-    "           [2,3]])\n",
-    "aa = a.transpose()\n",
-    "b = aa.ravel()\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "b[0] = 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 2],\n",
-       "       [1, 3]])"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "aa"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 1],\n",
-       "       [2, 3]])"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以看到，在这种情况下，修改 `b` 并不会改变 `aa` 的值，原因是我们用来 `ravel` 的对象 `aa` 本身是 `a` 的一个view。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## atleast_xd 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "保证数组至少有 `x` 维："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1])"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = 1\n",
-    "atleast_1d(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1L, 3L)"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = array([1,2,3])\n",
-    "b = atleast_2d(a)\n",
-    "b.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3]])"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "c = atleast_3d(b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1L, 3L, 1L)"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`x` 可以取值 1，2，3。\n",
-    "\n",
-    "在**Scipy**库中，这些函数被用来保证输入满足一定的条件：“ \n",
-    "\n",
-    "|用法|**Scipy**中出现次数|\n",
-    "|-|-|\n",
-    "|value.flaten() <br> value.flat <br>  value.ravel() |     ~2000次\n",
-    "| atleast_1d(value) <br> atleast_2d(value)  |~700次\n",
-    "| asarray(value)    |~4000次"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 数组形状"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using matplotlib backend: Qt4Agg\n",
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 修改数组的形状"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 3, 4, 5])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = arange(6)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将形状修改为2乘3："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2],\n",
+       "       [3, 4, 5]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.shape = 2,3\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "与之对应的方法是 `reshape` ，但它不会修改原来数组的值，而是返回一个新的数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1],\n",
+       "       [2, 3],\n",
+       "       [4, 5]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.reshape(3,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2],\n",
+       "       [3, 4, 5]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`shape` 和 `reshape` 方法不能改变数组中元素的总数，否则会报错："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "total size of new array must be unchanged",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-6-1a35a76a1693>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m: total size of new array must be unchanged"
+     ]
+    }
+   ],
+   "source": [
+    "a.reshape(4,2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 使用 newaxis 增加数组维数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3L,)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = arange(3)\n",
+    "shape(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1L, 3L)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = a[newaxis, :]\n",
+    "shape(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "根据插入位置的不同，可以返回不同形状的数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3L, 1L)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = a[:, newaxis]\n",
+    "shape(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "插入多个新维度："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1L, 1L, 3L)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = a[newaxis, newaxis, :]\n",
+    "shape(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## squeeze 方法去除多余的轴"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "a = arange(6)\n",
+    "a.shape = (2,1,3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2L, 3L)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = a.squeeze()\n",
+    "b.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "squeeze 返回一个将所有长度为1的维度去除的新数组。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 数组转置"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `transpose` 返回数组的转置，本质上是将所有维度反过来："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[0, 1, 2]],\n",
+       "\n",
+       "       [[3, 4, 5]]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于二维数组，这相当于交换行和列："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[0, 3]],\n",
+       "\n",
+       "       [[1, 4]],\n",
+       "\n",
+       "       [[2, 5]]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "或者使用缩写属性："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[0, 3]],\n",
+       "\n",
+       "       [[1, 4]],\n",
+       "\n",
+       "       [[2, 5]]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "注意：\n",
+    "- 对于复数数组，转置并不返回复共轭，只是单纯的交换轴的位置\n",
+    "- 转置可以作用于多维数组"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
+       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
+       "       51, 52, 53, 54, 55, 56, 57, 58, 59])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = arange(60)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 0,  1,  2,  3,  4],\n",
+       "        [ 5,  6,  7,  8,  9],\n",
+       "        [10, 11, 12, 13, 14],\n",
+       "        [15, 16, 17, 18, 19]],\n",
+       "\n",
+       "       [[20, 21, 22, 23, 24],\n",
+       "        [25, 26, 27, 28, 29],\n",
+       "        [30, 31, 32, 33, 34],\n",
+       "        [35, 36, 37, 38, 39]],\n",
+       "\n",
+       "       [[40, 41, 42, 43, 44],\n",
+       "        [45, 46, 47, 48, 49],\n",
+       "        [50, 51, 52, 53, 54],\n",
+       "        [55, 56, 57, 58, 59]]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.shape = 3,4,5\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5L, 4L, 3L)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = a.T\n",
+    "b.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "转置只是交换了轴的位置。\n",
+    "\n",
+    "另一方面，转置返回的是对原数组的另一种view，所以改变转置会改变原来数组的值。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2],\n",
+       "       [3, 4, 5]])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = arange(6)\n",
+    "a.shape = (2,3)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "修改转置："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "b = a.T\n",
+    "b[0,1] = 30"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "原数组的值也改变："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2],\n",
+       "       [30,  4,  5]])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 数组连接"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "有时我们需要将不同的数组按照一定的顺序连接起来："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    concatenate((a0,a1,...,aN), axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "注意，这些数组要用 `()` 包括到一个元组中去。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "除了给定的轴外，这些数组其他轴的长度必须是一样的。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2L, 3L)\n",
+      "(2L, 3L)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = array([\n",
+    "        [0,1,2],\n",
+    "        [10,11,12]\n",
+    "    ])\n",
+    "y = array([\n",
+    "        [50,51,52],\n",
+    "        [60,61,62]\n",
+    "    ])\n",
+    "print x.shape\n",
+    "print y.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "默认沿着第一维进行连接："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2],\n",
+       "       [10, 11, 12],\n",
+       "       [50, 51, 52],\n",
+       "       [60, 61, 62]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z = concatenate((x,y))\n",
+    "z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4L, 3L)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "沿着第二维进行连接："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2, 50, 51, 52],\n",
+       "       [10, 11, 12, 60, 61, 62]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z = concatenate((x,y), axis=1)\n",
+    "z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2L, 6L)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "注意到这里 `x` 和 `y` 的形状是一样的，还可以将它们连接成三维的数组，但是 `concatenate` 不能提供这样的功能，不过可以这样："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "z = array((x,y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2L, 2L, 3L)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上，**Numpy**提供了分别对应这三种情况的函数：\n",
+    "\n",
+    "- vstack\n",
+    "- hstack\n",
+    "- dstack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4L, 3L)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vstack((x, y)).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2L, 6L)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hstack((x, y)).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2L, 3L, 2L)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dstack((x, y)).shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Flatten 数组"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`flatten` 方法的作用是将多维数组转化为1维数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 3])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = array([[0,1],\n",
+    "           [2,3]])\n",
+    "b = a.flatten()\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "返回的是数组的复制，因此，改变 `b` 并不会影响 `a` 的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[10  1  2  3]\n",
+      "[[0 1]\n",
+      " [2 3]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "b[0] = 10\n",
+    "print b\n",
+    "print a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## flat 属性"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "还可以使用数组自带的 `flat` 属性："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<numpy.flatiter at 0x3d546a0>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.flat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`a.flat` 相当于返回了所有元组组成的一个迭代器："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "b = a.flat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "但此时修改 `b` 的值会影响 `a` ："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[10  1]\n",
+      " [ 2  3]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "b[0] = 10\n",
+    "print a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([10,  1,  2,  3])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.flat[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ravel 方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "除此之外，还可以使用 `ravel` 方法，`ravel` 使用高效的表示方式："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 3])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = array([[0,1],\n",
+    "           [2,3]])\n",
+    "b = a.ravel()\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "修改 `b` 会改变 `a` ："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[10,  1],\n",
+       "       [ 2,  3]])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b[0] = 10\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "但另一种情况下："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 2, 1, 3])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = array([[0,1],\n",
+    "           [2,3]])\n",
+    "aa = a.transpose()\n",
+    "b = aa.ravel()\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "b[0] = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 2],\n",
+       "       [1, 3]])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "aa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1],\n",
+       "       [2, 3]])"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以看到，在这种情况下，修改 `b` 并不会改变 `aa` 的值，原因是我们用来 `ravel` 的对象 `aa` 本身是 `a` 的一个view。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## atleast_xd 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "保证数组至少有 `x` 维："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1])"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = 1\n",
+    "atleast_1d(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1L, 3L)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = array([1,2,3])\n",
+    "b = atleast_2d(a)\n",
+    "b.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3]])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "c = atleast_3d(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1L, 3L, 1L)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`x` 可以取值 1，2，3。\n",
+    "\n",
+    "在**Scipy**库中，这些函数被用来保证输入满足一定的条件：“ \n",
+    "\n",
+    "|用法|**Scipy**中出现次数|\n",
+    "|-|-|\n",
+    "|value.flaten() <br> value.flat <br>  value.ravel() |     ~2000次\n",
+    "| atleast_1d(value) <br> atleast_2d(value)  |~700次\n",
+    "| asarray(value)    |~4000次"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.08 diagonals.ipynb b/03-numpy/03.08-diagonals.ipynb
similarity index 100%
rename from 03. numpy/03.08 diagonals.ipynb
rename to 03-numpy/03.08-diagonals.ipynb
diff --git a/03. numpy/03.09 data to & from string.ipynb b/03-numpy/03.09-data-to-&-from-string.ipynb
similarity index 94%
rename from 03. numpy/03.09 data to & from string.ipynb
rename to 03-numpy/03.09-data-to-&-from-string.ipynb
index 056a4ba6..ba94c781 100644
--- a/03. numpy/03.09 data to & from string.ipynb	
+++ b/03-numpy/03.09-data-to-&-from-string.ipynb
@@ -1,214 +1,214 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 数组与字符串的转换"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## tostring 方法"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "a = np.array([[1,2],\n",
-    "           [3,4]], \n",
-    "          dtype = np.uint8)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "转化为字符串："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\x01\\x02\\x03\\x04'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.tostring()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们可以使用不同的顺序来转换字符串："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\x01\\x03\\x02\\x04'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.tostring(order='F')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里使用了**Fortran**的格式，按照列来读数据。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## fromstring 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以使用 `fromstring` 函数从字符串中读出数据，不过要指定类型："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 2, 3, 4], dtype=uint8)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s = a.tostring()\n",
-    "a = np.fromstring(s, \n",
-    "                  dtype=np.uint8)\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "此时，返回的数组是一维的，需要重新设定维度："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2],\n",
-       "       [3, 4]], dtype=uint8)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.shape = 2,2\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于文本文件，推荐使用\n",
-    "- `loadtxt`\n",
-    "- `genfromtxt`\n",
-    "- `savetxt`\n",
-    "\n",
-    "对于二进制文本文件，推荐使用 \n",
-    "- `save` \n",
-    "- `load`\n",
-    "- `savez`"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 数组与字符串的转换"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## tostring 方法"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a = np.array([[1,2],\n",
+    "           [3,4]], \n",
+    "          dtype = np.uint8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "转化为字符串："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\x01\\x02\\x03\\x04'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.tostring()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们可以使用不同的顺序来转换字符串："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\x01\\x03\\x02\\x04'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.tostring(order='F')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里使用了**Fortran**的格式，按照列来读数据。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## fromstring 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以使用 `fromstring` 函数从字符串中读出数据，不过要指定类型："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4], dtype=uint8)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = a.tostring()\n",
+    "a = np.fromstring(s, \n",
+    "                  dtype=np.uint8)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "此时，返回的数组是一维的，需要重新设定维度："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2],\n",
+       "       [3, 4]], dtype=uint8)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.shape = 2,2\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于文本文件，推荐使用\n",
+    "- `loadtxt`\n",
+    "- `genfromtxt`\n",
+    "- `savetxt`\n",
+    "\n",
+    "对于二进制文本文件，推荐使用 \n",
+    "- `save` \n",
+    "- `load`\n",
+    "- `savez`"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.10 array attribute & method overview .ipynb b/03-numpy/03.10-array-attribute-&-method-overview-.ipynb
similarity index 100%
rename from 03. numpy/03.10 array attribute & method overview .ipynb
rename to 03-numpy/03.10-array-attribute-&-method-overview-.ipynb
diff --git a/03. numpy/03.11 array creation functions.ipynb b/03-numpy/03.11-array-creation-functions.ipynb
similarity index 99%
rename from 03. numpy/03.11 array creation functions.ipynb
rename to 03-numpy/03.11-array-creation-functions.ipynb
index 9606b52c..46ab8228 100644
--- a/03. numpy/03.11 array creation functions.ipynb	
+++ b/03-numpy/03.11-array-creation-functions.ipynb
@@ -1,1217 +1,1217 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 生成数组的函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## arange"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`arange` 类似于**Python**中的 `range` 函数，只不过返回的不是列表，而是数组：\n",
-    "\n",
-    "    arange(start, stop=None, step=1, dtype=None)\n",
-    "\n",
-    "产生一个在区间 `[start, stop)` 之间，以 `step` 为间隔的数组，如果只输入一个参数，则默认从 `0` 开始，并以这个值为结束："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1, 2, 3])"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "np.arange(4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "与 `range` 不同， `arange` 允许非整数值输入，产生一个非整型的数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.        ,  0.78539816,  1.57079633,  2.35619449,  3.14159265,\n",
-       "        3.92699082,  4.71238898,  5.49778714])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.arange(0, 2 * np.pi, np.pi / 4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "数组的类型默认由参数 `start, stop, step` 来确定，也可以指定："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.        ,  0.78539819,  1.57079637,  2.3561945 ,  3.14159274,\n",
-       "        3.92699099,  4.71238899,  5.49778748], dtype=float32)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.arange(0, 2 * np.pi, np.pi / 4, dtype=np.float32)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "由于存在精度问题，使用浮点数可能出现问题："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.5,  1.8,  2.1])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.arange(1.5, 2.1, 0.3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`stop` 的值 `2.1` 出现在了数组中，所以使用浮点数的时候需要注意。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## linspace"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    linspace(start, stop, N)\n",
-    "\n",
-    "产生 `N` 个等距分布在 `[start, stop]`间的元素组成的数组，包括 `start, stop`。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.linspace(0, 1, 5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## logspace"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    logspace(start, stop, N)\n",
-    "\n",
-    "产生 N 个对数等距分布的数组，默认以10为底："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  1.        ,   1.77827941,   3.16227766,   5.62341325,  10.        ])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.logspace(0, 1, 5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "产生的值为$\\left[10^0, 10^{0.25},10^{0.5},10^{0.75},10^1\\right]$。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## meshgrid"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "有时候需要在二维平面中生成一个网格，这时候可以使用 `meshgrid` 来完成这样的工作："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "x_ticks = np.linspace(-1, 1, 5)\n",
-    "y_ticks = np.linspace(-1, 1, 5)\n",
-    "\n",
-    "x, y = np.meshgrid(x_ticks, y_ticks)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里产生的 `x, y`如下："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-1. , -0.5,  0. ,  0.5,  1. ],\n",
-       "       [-1. , -0.5,  0. ,  0.5,  1. ],\n",
-       "       [-1. , -0.5,  0. ,  0.5,  1. ],\n",
-       "       [-1. , -0.5,  0. ,  0.5,  1. ],\n",
-       "       [-1. , -0.5,  0. ,  0.5,  1. ]])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-1. , -1. , -1. , -1. , -1. ],\n",
-       "       [-0.5, -0.5, -0.5, -0.5, -0.5],\n",
-       "       [ 0. ,  0. ,  0. ,  0. ,  0. ],\n",
-       "       [ 0.5,  0.5,  0.5,  0.5,  0.5],\n",
-       "       [ 1. ,  1. ,  1. ,  1. ,  1. ]])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`x` 对应网格的第一维，`y` 对应网格的第二维。\n",
-    "\n",
-    "图例："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Miniconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:9: RuntimeWarning: invalid value encountered in divide\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.text.Text at 0x9ac1630>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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jRzNnzpyE3V+fJF0FXSVDhWybmprCZNuRn7Or2gUK2Xq9XiwWS5tleLqjj+D3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\nYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x3aa46d8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "from matplotlib import cm\n",
-    "\n",
-    "def f(x, y):\n",
-    "    # sinc 函数\n",
-    "    r = np.sqrt(x ** 2 + y ** 2)\n",
-    "    result = np.sin(r) / r\n",
-    "    result[r == 0] = 1.0\n",
-    "    return result\n",
-    "\n",
-    "x_ticks = np.linspace(-10, 10, 51)\n",
-    "y_ticks = np.linspace(-10, 10, 51)\n",
-    "\n",
-    "x, y = np.meshgrid(x_ticks, y_ticks)\n",
-    "\n",
-    "z = f(x, y)\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
-    "ax.plot_surface(x, y, z,\n",
-    "                rstride=1, cstride=1,\n",
-    "                cmap=cm.YlGnBu_r)\n",
-    "ax.set_xlabel('x')\n",
-    "ax.set_ylabel('y')\n",
-    "ax.set_zlabel('z')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上，`x, y` 中有很多冗余的元素，这里提供了一个 `sparse` 的选项： "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "x_ticks = np.linspace(-1, 1, 5)\n",
-    "y_ticks = np.linspace(-1, 1, 5)\n",
-    "\n",
-    "x, y = np.meshgrid(x_ticks, y_ticks, sparse=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-1. , -0.5,  0. ,  0.5,  1. ]])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-1. ],\n",
-       "       [-0.5],\n",
-       "       [ 0. ],\n",
-       "       [ 0.5],\n",
-       "       [ 1. ]])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在这个选项下，`x, y` 变成了单一的行向量和列向量。\n",
-    "\n",
-    "但这并不影响结果："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Miniconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:9: RuntimeWarning: invalid value encountered in divide\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.text.Text at 0xba147f0>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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jRzNnzpyE3V+fJF0FXSVDhWybmprCZNuRn7Or2gUK2Xq9XiwWS5tleLqjj+D3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\nYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x3a1ab38>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x_ticks = np.linspace(-10, 10, 51)\n",
-    "y_ticks = np.linspace(-10, 10, 51)\n",
-    "\n",
-    "x, y = np.meshgrid(x_ticks, y_ticks, sparse=True)\n",
-    "\n",
-    "z = f(x, y)\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
-    "ax.plot_surface(x, y, z,\n",
-    "                rstride=1, cstride=1,\n",
-    "                cmap=cm.YlGnBu_r)\n",
-    "ax.set_xlabel('x')\n",
-    "ax.set_ylabel('y')\n",
-    "ax.set_zlabel('z')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`meshgrid` 可以设置轴排列的先后顺序：\n",
-    "- 默认为 `indexing='xy'` 即笛卡尔坐标，对于2维数组，返回行向量 `x` 和列向量 `y`\n",
-    "- 或者使用 `indexing='ij'` 即矩阵坐标，对于2维数组，返回列向量 `x` 和行向量 `y`。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## ogrid , mgrid"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Matlab**中有 `meshgrid` 的用法："
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "    meshgrid(-1:.5:1, -1:.5:1)\n",
-    "\n",
-    "**Numpy**的 `meshgrid` 并不支持这样的用法，但我们可以使用 `ogrid / mgrid` 来实现类似这样的用法。\n",
-    "\n",
-    "`ogrid` 与 `mgrid` 的区别在于：\n",
-    "- `ogrid` 相当于 `meshgrid(indexing='ij', sparse=True)`\n",
-    "- `mgrid` 相当于 `meshgrid(indexing='ij', sparse=False)`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "x, y = np.ogrid[-1:1:.5, -1:1:.5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-1. ],\n",
-       "       [-0.5],\n",
-       "       [ 0. ],\n",
-       "       [ 0.5]])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-1. , -0.5,  0. ,  0.5]])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "注意：\n",
-    "- 这里使用的是中括号\n",
-    "- **Matlab** 使用的是 `start:step:end` 的表示，**Numpy** 使用的是 `start:end:step` 的表示\n",
-    "- 这里的结果不包括 `end` 的值\n",
-    "\n",
-    "为了包含 `end` 的值，我们可以使用这样的技巧："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "x, y = np.ogrid[-1:1:5j, -1:1:5j]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([[-1. ],\n",
-       "        [-0.5],\n",
-       "        [ 0. ],\n",
-       "        [ 0.5],\n",
-       "        [ 1. ]]), array([[-1. , -0.5,  0. ,  0.5,  1. ]]))"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x, y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们在 `step` 的位置传入一个复数 `5j` ，表示我们需要一个 `5` 个值的数组，此时返回值就会包含 `end` 的值。\n",
-    "\n",
-    "重复之前的画图："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Miniconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:9: RuntimeWarning: invalid value encountered in divide\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.text.Text at 0x9e34278>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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jRzNnzpyE3V+fJF0FXSVDhWybmprCZNuRn7Or2gUK2Xq9XiwWS5tleLqjj+D3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\nYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x99de780>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# exchange here\n",
-    "y, x = np.ogrid[-10:10:51j, -10:10:51j]\n",
-    "\n",
-    "z = f(x, y)\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
-    "ax.plot_surface(x, y, z,\n",
-    "                rstride=1, cstride=1,\n",
-    "                cmap=cm.YlGnBu_r)\n",
-    "ax.set_xlabel('x')\n",
-    "ax.set_ylabel('y')\n",
-    "ax.set_zlabel('z')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里，我们交换了 `x, y` 输出值的顺序。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## r`_` , c`_`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们可以使用 `r_ / c_` 来产生行向量或者列向量。\n",
-    "\n",
-    "使用切片产生："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.r_[0:1:.1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "复数步长制定数组长度："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ])"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.r_[0:1:5j]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "连接多个序列，产生数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  3.,  22.,  11.,   4.,  15.,   6.])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.r_[(3,22,11), 4.0, [15, 6]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "列向量："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1. ],\n",
-       "       [ 1.5],\n",
-       "       [ 2. ],\n",
-       "       [ 2.5],\n",
-       "       [ 3. ]])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.c_[1:3:5j]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## ones , zeros"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "ones(shape, dtype=float64)\n",
-    "zeros(shape, dtype=float64)\n",
-    "```\n",
-    "\n",
-    "产生一个制定形状的全 `0` 或全 `1` 的数组，还可以制定数组类型："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.,  0.,  0.])"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.zeros(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.,  1.,  1.],\n",
-       "       [ 1.,  1.,  1.]], dtype=float32)"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.ones([2,3], dtype=np.float32)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "产生一个全是 `5` 的数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 5.,  5.,  5.],\n",
-       "       [ 5.,  5.,  5.]])"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.ones([2,3]) * 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## empty"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    empty(shape, dtype=float64, order='C')\n",
-    "\n",
-    "也可以使用 `empty` 方法产生一个制定大小的数组（数组所指向的内存未被初始化，所以值随机），再用 `fill` 方法填充："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-0.03412165,  0.05516321])"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.empty(2)\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 5.,  5.])"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.fill(5)\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "另一种替代方法使用索引，不过速度会稍微慢一些："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 5.,  5.])"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[:] = 5\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## empty`_`like, ones`_`like, zeros`_`like"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    empty_like(a)\n",
-    "    ones_like(a)\n",
-    "    zeros_like(a)\n",
-    "\n",
-    "产生一个跟 `a` 大小一样，类型一样的对应数组。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0. ,  2.5,  5. ,  7.5])"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.arange(0, 10, 2.5)\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.,  0.,  0.,  0.])"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.empty_like(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.,  0.,  0.,  0.])"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.zeros_like(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.,  1.,  1.,  1.])"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.ones_like(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## identity"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    indentity(n, dtype=float64)\n",
-    "产生一个 `n` 乘 `n` 的单位矩阵："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.,  0.,  0.],\n",
-       "       [ 0.,  1.,  0.],\n",
-       "       [ 0.,  0.,  1.]])"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.identity(3)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 生成数组的函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## arange"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`arange` 类似于**Python**中的 `range` 函数，只不过返回的不是列表，而是数组：\n",
+    "\n",
+    "    arange(start, stop=None, step=1, dtype=None)\n",
+    "\n",
+    "产生一个在区间 `[start, stop)` 之间，以 `step` 为间隔的数组，如果只输入一个参数，则默认从 `0` 开始，并以这个值为结束："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 3])"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.arange(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "与 `range` 不同， `arange` 允许非整数值输入，产生一个非整型的数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.        ,  0.78539816,  1.57079633,  2.35619449,  3.14159265,\n",
+       "        3.92699082,  4.71238898,  5.49778714])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.arange(0, 2 * np.pi, np.pi / 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "数组的类型默认由参数 `start, stop, step` 来确定，也可以指定："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.        ,  0.78539819,  1.57079637,  2.3561945 ,  3.14159274,\n",
+       "        3.92699099,  4.71238899,  5.49778748], dtype=float32)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.arange(0, 2 * np.pi, np.pi / 4, dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "由于存在精度问题，使用浮点数可能出现问题："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.5,  1.8,  2.1])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.arange(1.5, 2.1, 0.3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`stop` 的值 `2.1` 出现在了数组中，所以使用浮点数的时候需要注意。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## linspace"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    linspace(start, stop, N)\n",
+    "\n",
+    "产生 `N` 个等距分布在 `[start, stop]`间的元素组成的数组，包括 `start, stop`。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linspace(0, 1, 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## logspace"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    logspace(start, stop, N)\n",
+    "\n",
+    "产生 N 个对数等距分布的数组，默认以10为底："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  1.        ,   1.77827941,   3.16227766,   5.62341325,  10.        ])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.logspace(0, 1, 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "产生的值为$\\left[10^0, 10^{0.25},10^{0.5},10^{0.75},10^1\\right]$。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## meshgrid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "有时候需要在二维平面中生成一个网格，这时候可以使用 `meshgrid` 来完成这样的工作："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x_ticks = np.linspace(-1, 1, 5)\n",
+    "y_ticks = np.linspace(-1, 1, 5)\n",
+    "\n",
+    "x, y = np.meshgrid(x_ticks, y_ticks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里产生的 `x, y`如下："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1. , -0.5,  0. ,  0.5,  1. ],\n",
+       "       [-1. , -0.5,  0. ,  0.5,  1. ],\n",
+       "       [-1. , -0.5,  0. ,  0.5,  1. ],\n",
+       "       [-1. , -0.5,  0. ,  0.5,  1. ],\n",
+       "       [-1. , -0.5,  0. ,  0.5,  1. ]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1. , -1. , -1. , -1. , -1. ],\n",
+       "       [-0.5, -0.5, -0.5, -0.5, -0.5],\n",
+       "       [ 0. ,  0. ,  0. ,  0. ,  0. ],\n",
+       "       [ 0.5,  0.5,  0.5,  0.5,  0.5],\n",
+       "       [ 1. ,  1. ,  1. ,  1. ,  1. ]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`x` 对应网格的第一维，`y` 对应网格的第二维。\n",
+    "\n",
+    "图例："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Miniconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:9: RuntimeWarning: invalid value encountered in divide\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x9ac1630>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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jRzNnzpyE3V+fJF0FXSVDhWybmprCZNuRn7Or2gUK2Xq9XiwWS5tleLqjj+D3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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x3aa46d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "from matplotlib import cm\n",
+    "\n",
+    "def f(x, y):\n",
+    "    # sinc 函数\n",
+    "    r = np.sqrt(x ** 2 + y ** 2)\n",
+    "    result = np.sin(r) / r\n",
+    "    result[r == 0] = 1.0\n",
+    "    return result\n",
+    "\n",
+    "x_ticks = np.linspace(-10, 10, 51)\n",
+    "y_ticks = np.linspace(-10, 10, 51)\n",
+    "\n",
+    "x, y = np.meshgrid(x_ticks, y_ticks)\n",
+    "\n",
+    "z = f(x, y)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ax.plot_surface(x, y, z,\n",
+    "                rstride=1, cstride=1,\n",
+    "                cmap=cm.YlGnBu_r)\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "ax.set_zlabel('z')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上，`x, y` 中有很多冗余的元素，这里提供了一个 `sparse` 的选项： "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x_ticks = np.linspace(-1, 1, 5)\n",
+    "y_ticks = np.linspace(-1, 1, 5)\n",
+    "\n",
+    "x, y = np.meshgrid(x_ticks, y_ticks, sparse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1. , -0.5,  0. ,  0.5,  1. ]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1. ],\n",
+       "       [-0.5],\n",
+       "       [ 0. ],\n",
+       "       [ 0.5],\n",
+       "       [ 1. ]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在这个选项下，`x, y` 变成了单一的行向量和列向量。\n",
+    "\n",
+    "但这并不影响结果："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Miniconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:9: RuntimeWarning: invalid value encountered in divide\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0xba147f0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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jRzNnzpyE3V+fJF0FXSVDhWybmprCZNuRn7Or2gUK2Xq9XiwWS5tleLqjj+D3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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x3a1ab38>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_ticks = np.linspace(-10, 10, 51)\n",
+    "y_ticks = np.linspace(-10, 10, 51)\n",
+    "\n",
+    "x, y = np.meshgrid(x_ticks, y_ticks, sparse=True)\n",
+    "\n",
+    "z = f(x, y)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ax.plot_surface(x, y, z,\n",
+    "                rstride=1, cstride=1,\n",
+    "                cmap=cm.YlGnBu_r)\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "ax.set_zlabel('z')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`meshgrid` 可以设置轴排列的先后顺序：\n",
+    "- 默认为 `indexing='xy'` 即笛卡尔坐标，对于2维数组，返回行向量 `x` 和列向量 `y`\n",
+    "- 或者使用 `indexing='ij'` 即矩阵坐标，对于2维数组，返回列向量 `x` 和行向量 `y`。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ogrid , mgrid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Matlab**中有 `meshgrid` 的用法："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "    meshgrid(-1:.5:1, -1:.5:1)\n",
+    "\n",
+    "**Numpy**的 `meshgrid` 并不支持这样的用法，但我们可以使用 `ogrid / mgrid` 来实现类似这样的用法。\n",
+    "\n",
+    "`ogrid` 与 `mgrid` 的区别在于：\n",
+    "- `ogrid` 相当于 `meshgrid(indexing='ij', sparse=True)`\n",
+    "- `mgrid` 相当于 `meshgrid(indexing='ij', sparse=False)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x, y = np.ogrid[-1:1:.5, -1:1:.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1. ],\n",
+       "       [-0.5],\n",
+       "       [ 0. ],\n",
+       "       [ 0.5]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1. , -0.5,  0. ,  0.5]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "注意：\n",
+    "- 这里使用的是中括号\n",
+    "- **Matlab** 使用的是 `start:step:end` 的表示，**Numpy** 使用的是 `start:end:step` 的表示\n",
+    "- 这里的结果不包括 `end` 的值\n",
+    "\n",
+    "为了包含 `end` 的值，我们可以使用这样的技巧："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x, y = np.ogrid[-1:1:5j, -1:1:5j]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[-1. ],\n",
+       "        [-0.5],\n",
+       "        [ 0. ],\n",
+       "        [ 0.5],\n",
+       "        [ 1. ]]), array([[-1. , -0.5,  0. ,  0.5,  1. ]]))"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x, y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们在 `step` 的位置传入一个复数 `5j` ，表示我们需要一个 `5` 个值的数组，此时返回值就会包含 `end` 的值。\n",
+    "\n",
+    "重复之前的画图："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Miniconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:9: RuntimeWarning: invalid value encountered in divide\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x9e34278>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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jRzNnzpyE3V+fJF0FXSVDhWybmprCZNuRn7Or2gUK2Xq9XiwWS5tleLqjj+D3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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x99de780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# exchange here\n",
+    "y, x = np.ogrid[-10:10:51j, -10:10:51j]\n",
+    "\n",
+    "z = f(x, y)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ax.plot_surface(x, y, z,\n",
+    "                rstride=1, cstride=1,\n",
+    "                cmap=cm.YlGnBu_r)\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "ax.set_zlabel('z')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里，我们交换了 `x, y` 输出值的顺序。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## r`_` , c`_`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们可以使用 `r_ / c_` 来产生行向量或者列向量。\n",
+    "\n",
+    "使用切片产生："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.r_[0:1:.1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "复数步长制定数组长度："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.r_[0:1:5j]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "连接多个序列，产生数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  3.,  22.,  11.,   4.,  15.,   6.])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.r_[(3,22,11), 4.0, [15, 6]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "列向量："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1. ],\n",
+       "       [ 1.5],\n",
+       "       [ 2. ],\n",
+       "       [ 2.5],\n",
+       "       [ 3. ]])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.c_[1:3:5j]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ones , zeros"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "ones(shape, dtype=float64)\n",
+    "zeros(shape, dtype=float64)\n",
+    "```\n",
+    "\n",
+    "产生一个制定形状的全 `0` 或全 `1` 的数组，还可以制定数组类型："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.,  0.,  0.])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.zeros(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.,  1.,  1.],\n",
+       "       [ 1.,  1.,  1.]], dtype=float32)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.ones([2,3], dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "产生一个全是 `5` 的数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 5.,  5.,  5.],\n",
+       "       [ 5.,  5.,  5.]])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.ones([2,3]) * 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## empty"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    empty(shape, dtype=float64, order='C')\n",
+    "\n",
+    "也可以使用 `empty` 方法产生一个制定大小的数组（数组所指向的内存未被初始化，所以值随机），再用 `fill` 方法填充："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.03412165,  0.05516321])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.empty(2)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 5.,  5.])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.fill(5)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "另一种替代方法使用索引，不过速度会稍微慢一些："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 5.,  5.])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[:] = 5\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## empty`_`like, ones`_`like, zeros`_`like"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    empty_like(a)\n",
+    "    ones_like(a)\n",
+    "    zeros_like(a)\n",
+    "\n",
+    "产生一个跟 `a` 大小一样，类型一样的对应数组。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0. ,  2.5,  5. ,  7.5])"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.arange(0, 10, 2.5)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.,  0.,  0.,  0.])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.empty_like(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.,  0.,  0.,  0.])"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.zeros_like(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.,  1.,  1.,  1.])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.ones_like(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## identity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    indentity(n, dtype=float64)\n",
+    "产生一个 `n` 乘 `n` 的单位矩阵："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.,  0.,  0.],\n",
+       "       [ 0.,  1.,  0.],\n",
+       "       [ 0.,  0.,  1.]])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.identity(3)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.12 matrix object.ipynb b/03-numpy/03.12-matrix-object.ipynb
similarity index 94%
rename from 03. numpy/03.12 matrix object.ipynb
rename to 03-numpy/03.12-matrix-object.ipynb
index fa3ae4ae..555c6751 100644
--- a/03. numpy/03.12 matrix object.ipynb	
+++ b/03-numpy/03.12-matrix-object.ipynb
@@ -1,250 +1,250 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 矩阵"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `mat` 方法将 `2` 维数组转化为矩阵："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "matrix([[1, 2, 4],\n",
-       "        [2, 5, 3],\n",
-       "        [7, 8, 9]])"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "a = np.array([[1,2,4],\n",
-    "              [2,5,3], \n",
-    "              [7,8,9]])\n",
-    "A = np.mat(a)\n",
-    "A"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "也可以使用 **Matlab** 的语法传入一个字符串来生成矩阵："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "matrix([[1, 2, 4],\n",
-       "        [2, 5, 3],\n",
-       "        [7, 8, 9]])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A = np.mat('1,2,4;2,5,3;7,8,9')\n",
-    "A"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "利用分块创造新的矩阵："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "matrix([[ 1,  2, 10, 20],\n",
-       "        [ 3,  4, 30, 40],\n",
-       "        [10, 20,  1,  2],\n",
-       "        [30, 40,  3,  4]])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([[ 1, 2],\n",
-    "              [ 3, 4]])\n",
-    "b = np.array([[10,20], \n",
-    "              [30,40]])\n",
-    "\n",
-    "np.bmat('a,b;b,a')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "矩阵与向量的乘法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1],\n",
-       "       [2],\n",
-       "       [3]])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = np.array([[1], [2], [3]])\n",
-    "x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "matrix([[17],\n",
-       "        [21],\n",
-       "        [50]])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A * x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`A.I` 表示 `A` 矩阵的逆矩阵："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[  1.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
-      " [  0.00000000e+00   1.00000000e+00   2.08166817e-17]\n",
-      " [  2.22044605e-16  -8.32667268e-17   1.00000000e+00]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print A * A.I"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "矩阵指数表示矩阵连乘："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 6497  9580  9836]\n",
-      " [ 7138 10561 10818]\n",
-      " [18434 27220 27945]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print A ** 4"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 矩阵"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `mat` 方法将 `2` 维数组转化为矩阵："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "matrix([[1, 2, 4],\n",
+       "        [2, 5, 3],\n",
+       "        [7, 8, 9]])"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "a = np.array([[1,2,4],\n",
+    "              [2,5,3], \n",
+    "              [7,8,9]])\n",
+    "A = np.mat(a)\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "也可以使用 **Matlab** 的语法传入一个字符串来生成矩阵："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "matrix([[1, 2, 4],\n",
+       "        [2, 5, 3],\n",
+       "        [7, 8, 9]])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A = np.mat('1,2,4;2,5,3;7,8,9')\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "利用分块创造新的矩阵："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "matrix([[ 1,  2, 10, 20],\n",
+       "        [ 3,  4, 30, 40],\n",
+       "        [10, 20,  1,  2],\n",
+       "        [30, 40,  3,  4]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([[ 1, 2],\n",
+    "              [ 3, 4]])\n",
+    "b = np.array([[10,20], \n",
+    "              [30,40]])\n",
+    "\n",
+    "np.bmat('a,b;b,a')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "矩阵与向量的乘法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1],\n",
+       "       [2],\n",
+       "       [3]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array([[1], [2], [3]])\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "matrix([[17],\n",
+       "        [21],\n",
+       "        [50]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A * x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`A.I` 表示 `A` 矩阵的逆矩阵："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[  1.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
+      " [  0.00000000e+00   1.00000000e+00   2.08166817e-17]\n",
+      " [  2.22044605e-16  -8.32667268e-17   1.00000000e+00]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print A * A.I"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "矩阵指数表示矩阵连乘："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 6497  9580  9836]\n",
+      " [ 7138 10561 10818]\n",
+      " [18434 27220 27945]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print A ** 4"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.13 general functions.ipynb b/03-numpy/03.13-general-functions.ipynb
similarity index 94%
rename from 03. numpy/03.13 general functions.ipynb
rename to 03-numpy/03.13-general-functions.ipynb
index 29114833..d4b92bc4 100644
--- a/03. numpy/03.13 general functions.ipynb	
+++ b/03-numpy/03.13-general-functions.ipynb
@@ -1,551 +1,551 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 一般函数"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 三角函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    sin(x)\n",
-    "    cos(x)\n",
-    "    tan(x)\n",
-    "    sinh(x)\n",
-    "    conh(x)\n",
-    "    tanh(x)\n",
-    "    arccos(x)\n",
-    "    arctan(x)\n",
-    "    arcsin(x)\n",
-    "    arccosh(x)\n",
-    "    arctanh(x)\n",
-    "    arcsinh(x)\n",
-    "    arctan2(x,y)\n",
-    "\n",
-    "`arctan2(x,y)` 返回 `arctan(x/y)` 。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 向量操作"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    dot(x,y)\n",
-    "    inner(x,y)\n",
-    "    cross(x,y)\n",
-    "    vdot(x,y)\n",
-    "    outer(x,y)\n",
-    "    kron(x,y)\n",
-    "    tensordot(x,y[,axis])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 其他操作"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    exp(x)\n",
-    "    log(x)\n",
-    "    log10(x)\n",
-    "    sqrt(x)\n",
-    "    absolute(x)\n",
-    "    conjugate(x)\n",
-    "    negative(x)\n",
-    "    ceil(x)\n",
-    "    floor(x)\n",
-    "    fabs(x)\n",
-    "    hypot(x)\n",
-    "    fmod(x)\n",
-    "    maximum(x,y)\n",
-    "    minimum(x,y)\n",
-    "\n",
-    "`hypot` 返回对应点 `(x,y)` 到原点的距离。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 4.12310563,  5.38516481,  6.70820393])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = np.array([1,2,3])\n",
-    "y = np.array([4,5,6])\n",
-    "np.hypot(x,y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 类型处理"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    iscomplexobj\n",
-    "    iscomplex\n",
-    "    isrealobj\n",
-    "    isreal\n",
-    "    imag\n",
-    "    real\n",
-    "    real_if_close\n",
-    "    isscalar\n",
-    "    isneginf\n",
-    "    isposinf\n",
-    "    isinf\n",
-    "    isfinite\n",
-    "    isnan\n",
-    "    nan_to_num\n",
-    "    common_type\n",
-    "    typename\n",
-    "\n",
-    "正无穷："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inf"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.inf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "负无穷："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-inf"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "-np.inf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "非法值（Not a number）："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "nan"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.nan"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "检查是否为无穷："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.isinf(1.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.isinf(np.inf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.isinf(-np.inf)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "非法值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Miniconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:1: RuntimeWarning: invalid value encountered in divide\n",
-      "  if __name__ == '__main__':\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([ nan])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.array([0]) / 0.0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这并不会报错，而是返回一个非法值。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "只有 `0/0` 会得到 `nan`，非0值除以0会得到无穷："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Miniconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:2: RuntimeWarning: divide by zero encountered in divide\n",
-      "  from IPython.kernel.zmq import kernelapp as app\n",
-      "c:\\Miniconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:2: RuntimeWarning: invalid value encountered in divide\n",
-      "  from IPython.kernel.zmq import kernelapp as app\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([ nan,  inf,  inf,  inf,  inf])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.arange(5.0)\n",
-    "b = a / 0.0\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`nan` 与任何数进行比较都是 `False`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([False, False, False, False, False], dtype=bool)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b == np.nan"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "想要找出 `nan` 值需要使用 `isnan`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ True, False, False, False, False], dtype=bool)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.isnan(b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 修改形状"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    atleast_1d\n",
-    "    atleast_2d\n",
-    "    atleast_3d\n",
-    "    expand_dims\n",
-    "    apply_over_axes\n",
-    "    apply_along_axis\n",
-    "    hstack\n",
-    "    vstack\n",
-    "    dstack\n",
-    "    column_stack\n",
-    "    hsplit\n",
-    "    vsplit\n",
-    "    dsplit\n",
-    "    split\n",
-    "    squeeze"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 其他有用函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    fix\n",
-    "    mod\n",
-    "    amax\n",
-    "    amin\n",
-    "    ptp\n",
-    "    sum\n",
-    "    cumsum\n",
-    "    prod\n",
-    "    cumprod\n",
-    "    diff\n",
-    "    angle\n",
-    "\n",
-    "    unwrap\n",
-    "    sort_complex\n",
-    "    trim_zeros\n",
-    "    fliplr\n",
-    "    flipud\n",
-    "    rot90\n",
-    "    diag\n",
-    "    eye\n",
-    "    select\n",
-    "    extract\n",
-    "    insert\n",
-    "\n",
-    "    roots\n",
-    "    poly\n",
-    "    any\n",
-    "    all\n",
-    "    disp\n",
-    "    unique\n",
-    "    nansum\n",
-    "    nanmax\n",
-    "    nanargmax\n",
-    "    nanargmin\n",
-    "    nanmin\n",
-    "\n",
-    "`nan` 开头的函数会进行相应的操作，但是忽略 `nan` 值。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 一般函数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 三角函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    sin(x)\n",
+    "    cos(x)\n",
+    "    tan(x)\n",
+    "    sinh(x)\n",
+    "    conh(x)\n",
+    "    tanh(x)\n",
+    "    arccos(x)\n",
+    "    arctan(x)\n",
+    "    arcsin(x)\n",
+    "    arccosh(x)\n",
+    "    arctanh(x)\n",
+    "    arcsinh(x)\n",
+    "    arctan2(x,y)\n",
+    "\n",
+    "`arctan2(x,y)` 返回 `arctan(x/y)` 。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 向量操作"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    dot(x,y)\n",
+    "    inner(x,y)\n",
+    "    cross(x,y)\n",
+    "    vdot(x,y)\n",
+    "    outer(x,y)\n",
+    "    kron(x,y)\n",
+    "    tensordot(x,y[,axis])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 其他操作"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    exp(x)\n",
+    "    log(x)\n",
+    "    log10(x)\n",
+    "    sqrt(x)\n",
+    "    absolute(x)\n",
+    "    conjugate(x)\n",
+    "    negative(x)\n",
+    "    ceil(x)\n",
+    "    floor(x)\n",
+    "    fabs(x)\n",
+    "    hypot(x)\n",
+    "    fmod(x)\n",
+    "    maximum(x,y)\n",
+    "    minimum(x,y)\n",
+    "\n",
+    "`hypot` 返回对应点 `(x,y)` 到原点的距离。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 4.12310563,  5.38516481,  6.70820393])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array([1,2,3])\n",
+    "y = np.array([4,5,6])\n",
+    "np.hypot(x,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 类型处理"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    iscomplexobj\n",
+    "    iscomplex\n",
+    "    isrealobj\n",
+    "    isreal\n",
+    "    imag\n",
+    "    real\n",
+    "    real_if_close\n",
+    "    isscalar\n",
+    "    isneginf\n",
+    "    isposinf\n",
+    "    isinf\n",
+    "    isfinite\n",
+    "    isnan\n",
+    "    nan_to_num\n",
+    "    common_type\n",
+    "    typename\n",
+    "\n",
+    "正无穷："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.inf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "负无穷："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-inf"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-np.inf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "非法值（Not a number）："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.nan"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "检查是否为无穷："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.isinf(1.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.isinf(np.inf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.isinf(-np.inf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "非法值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Miniconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:1: RuntimeWarning: invalid value encountered in divide\n",
+      "  if __name__ == '__main__':\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([ nan])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array([0]) / 0.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这并不会报错，而是返回一个非法值。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "只有 `0/0` 会得到 `nan`，非0值除以0会得到无穷："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Miniconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:2: RuntimeWarning: divide by zero encountered in divide\n",
+      "  from IPython.kernel.zmq import kernelapp as app\n",
+      "c:\\Miniconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:2: RuntimeWarning: invalid value encountered in divide\n",
+      "  from IPython.kernel.zmq import kernelapp as app\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([ nan,  inf,  inf,  inf,  inf])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.arange(5.0)\n",
+    "b = a / 0.0\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`nan` 与任何数进行比较都是 `False`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False, False, False], dtype=bool)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b == np.nan"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "想要找出 `nan` 值需要使用 `isnan`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True, False, False, False, False], dtype=bool)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.isnan(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 修改形状"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    atleast_1d\n",
+    "    atleast_2d\n",
+    "    atleast_3d\n",
+    "    expand_dims\n",
+    "    apply_over_axes\n",
+    "    apply_along_axis\n",
+    "    hstack\n",
+    "    vstack\n",
+    "    dstack\n",
+    "    column_stack\n",
+    "    hsplit\n",
+    "    vsplit\n",
+    "    dsplit\n",
+    "    split\n",
+    "    squeeze"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 其他有用函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    fix\n",
+    "    mod\n",
+    "    amax\n",
+    "    amin\n",
+    "    ptp\n",
+    "    sum\n",
+    "    cumsum\n",
+    "    prod\n",
+    "    cumprod\n",
+    "    diff\n",
+    "    angle\n",
+    "\n",
+    "    unwrap\n",
+    "    sort_complex\n",
+    "    trim_zeros\n",
+    "    fliplr\n",
+    "    flipud\n",
+    "    rot90\n",
+    "    diag\n",
+    "    eye\n",
+    "    select\n",
+    "    extract\n",
+    "    insert\n",
+    "\n",
+    "    roots\n",
+    "    poly\n",
+    "    any\n",
+    "    all\n",
+    "    disp\n",
+    "    unique\n",
+    "    nansum\n",
+    "    nanmax\n",
+    "    nanargmax\n",
+    "    nanargmin\n",
+    "    nanmin\n",
+    "\n",
+    "`nan` 开头的函数会进行相应的操作，但是忽略 `nan` 值。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.14 vectorizing functions.ipynb b/03-numpy/03.14-vectorizing-functions.ipynb
similarity index 98%
rename from 03. numpy/03.14 vectorizing functions.ipynb
rename to 03-numpy/03.14-vectorizing-functions.ipynb
index c3c44bde..68ade026 100644
--- a/03. numpy/03.14 vectorizing functions.ipynb	
+++ b/03-numpy/03.14-vectorizing-functions.ipynb
@@ -1,208 +1,208 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 向量化函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "自定义的 `sinc` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "def sinc(x):\n",
-    "    if x == 0.0:\n",
-    "        return 1.0\n",
-    "    else:\n",
-    "        w = np.pi * x\n",
-    "        return np.sin(w) / w"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "作用于单个数值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.0"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sinc(0.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.8981718325193755e-17"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sinc(3.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "但这个函数不能作用于数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-4-9d4f36f2aa7a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0msinc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m<ipython-input-1-dffe464e3332>\u001b[0m in \u001b[0;36msinc\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0msinc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0.0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
-     ]
-    }
-   ],
-   "source": [
-    "x = np.array([1,2,3])\n",
-    "sinc(x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以使用 `numpy` 的 `vectorize` 将函数 `sinc` 向量化，产生一个新的函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  3.89817183e-17,  -3.89817183e-17,   3.89817183e-17])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vsinc = np.vectorize(sinc)\n",
-    "vsinc(x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "其作用是为 `x` 中的每一个值调用 `sinc` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xa24e4e0>]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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x8H7o3z/8rf/9758//sEH4VvsfvuF98PEidn6RptlSvQZYRaWXr70UlgaNmxY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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa0b3ac8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "x = np.linspace(-5,5,101)\n",
-    "plt.plot(x, vsinc(x))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "因为这样的用法涉及大量的函数调用，因此，向量化函数的效率并不高。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 向量化函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "自定义的 `sinc` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def sinc(x):\n",
+    "    if x == 0.0:\n",
+    "        return 1.0\n",
+    "    else:\n",
+    "        w = np.pi * x\n",
+    "        return np.sin(w) / w"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "作用于单个数值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sinc(0.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.8981718325193755e-17"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sinc(3.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "但这个函数不能作用于数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-4-9d4f36f2aa7a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0msinc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-1-dffe464e3332>\u001b[0m in \u001b[0;36msinc\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0msinc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0.0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.array([1,2,3])\n",
+    "sinc(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以使用 `numpy` 的 `vectorize` 将函数 `sinc` 向量化，产生一个新的函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  3.89817183e-17,  -3.89817183e-17,   3.89817183e-17])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vsinc = np.vectorize(sinc)\n",
+    "vsinc(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "其作用是为 `x` 中的每一个值调用 `sinc` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0xa24e4e0>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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x8H7o3z/8rf/9758//sEH4VvsfvuF98PEidn6RptlSvQZYRaWXr70UlgaNmxY\nuJBJ9+5hZ+2pp4YJqwkTwuRVlp16ahgHjssnn4QTOYcMia/OcnDAAeHbXlweeiicqrrzzvHVGbfN\nNgur0saPD99ounUL74dddglxjxsXDhB78knYfvu0oy0fkc66iVOez7ppD/ewg7ZzZ/jKV0p/7kmc\n1q0L57U8+2zomUX18MPh+Ig2nuNU9h56CH71q5Dc4nDEEWH/xrBh8dSXBPcwDLhkSTixdNNN044o\ne1pz1o0SvZTEqFEh4V9/ffS6fvjDsNrmvPOi11VOPvkkfKN7++2wSiuKBQvCGUhJHmAmyUjiUDOR\nRg0fHjZPffJJtHpqa8OZ6UcfHU9c5aRz57CL+vHHo9d1222hJ68kX5mU6KUk+vQJk2Z//GO0eiZO\nDGO0/frFE1e5OfroMHQVxaefhjmTpM6dl+xRopeSGTky7FaMMiL38MOV2Zuvc9RRoUcf5Qjo++4L\nV66qlM1m8mVK9FIyhx0GH38cbYdnpSf6HXYIO59feKH9ddxyS/jQlcqlRC8ls8EGYcnozTe37/mL\nFoWJyEo/v+SYY9o/fPPqq2HFSlb3XUgylOilpE4+OQw9tOf44kceCUc9lNPS0lKIMk5/yy1hbD5r\nO2ElWUr0UlJduoRzSK69tu3P/fOfK3vYps5ee4XVS9Omte158+aFD4gsHWAm6dA6eim5xYvDRcsn\nTQqXVGyNN94IY/zz5sEmm5Q2vnJw2WVhLXxbdhwPGxZ2lLbndFQpH9owJZlx8cVhrLi1ieqkk2D3\n3cPGK4Hly8N1eSdPDruOWzJ1Khx+eDgkT1dbyreSJ3oz6wrcC+wIvAUc5+4rG5TpCfwO2BZw4A53\n/3UjdSnR59jKlWEt/HPPtXwswrx54VybefPCQVcSnH9+2G08enTLZY8+Gg45JBx/LfmWRKK/BvjA\n3a8xs1HAVu5+QYMy3YHu7j7ZzDYDJgDfdvcZDcop0efctdeGQ9seeKD5ciNGQNeucOWVycRVLt59\nNwyBzZoVzj9qyvPPh3mRWbPCgXiSb0kcgXAMUHeV0LuBL52Q7u5L3H1y8fYqYAagc+cq0MiRYadr\nc7tlFy+Ge+8NVxuSL9p++3B91V9/6fvw51auDGcDXXmlkrx8LmqPfoW7b1W8bcDyuvtNlO8NPAcM\nLCb9+o+pR18Bpk4NVwW6//5wjkt9a9eGnuh228GNN6YTX9a9+WYY1nr66bAap741a2Do0DC3of9/\nlaM1PfrCT96EAAAF3ElEQVQWVyib2VNA90Yeurj+HXd3M2syUxeHbe4HzmmY5OtUV1d/drtQKFAo\nFFoKT8rMoEHh4hff+144a71uW/7HH4drfpqFS8NJ43beGe64I1wM+957w0VeIBwzcfrpsOWWcMMN\n6cYopVVTU0NNG8/sjtqjnwkU3H2JmW0HjHP3XRsptyHwCPCYuzc6laQefWUZMwZ+8pPQq99nn7A5\nqqoK7rwzXGBcmjduHBx/fNh5vHhxmPvYaKPwc51QWVmSmoxd5u5Xm9kFQJdGJmONMH6/zN3PbaYu\nJfoKM39+uGTihAlhcvH880OPXlpn8mS49dZw1ah99oHBg0Oyl8qS1PLKPwG9qLe80sy2B+509yPN\n7OvAX4GphOWVABe6++MN6lKiFxFpI22YEhHJOV1hSkRElOhFRPJOiV5EJOeU6EVEck6JXkQk55To\nRURyToleRCTnlOhFRHJOiV5EJOeU6EVEck6JXkQk55ToRURyToleRCTnlOhFRHJOiV5EJOeU6EVE\nck6JXkQk55ToRURyrt2J3sy6mtlTZjbbzJ40sy7NlO1gZpPM7OH2ticiIu0TpUd/AfCUu/cDnine\nb8o5wHQ+vzh4xampqUk7hJLJ82sDvb5yl/fX1xpREv0xwN3F23cD326skJn1AI4A/gto9gK2eZbn\nP7Y8vzbQ6yt3eX99rREl0Xdz9/eKt98DujVR7lfAT4DaCG2JiEg7dWzuQTN7CujeyEMX17/j7m5m\nXxqWMbOjgPfdfZKZFaIEKiIi7WPu7Rs2N7OZQMHdl5jZdsA4d9+1QZmrgGHAOmBjYAvgAXf/fiP1\nVez4vYhIFO7e7LB4lER/DbDM3a82swuALu7e5ISsmX0TON/dj25XgyIi0i5Rxuh/CRxmZrOBg4v3\nMbPtzez/mniOeu0iIglrd49eRETKQ6Z2xprZWWY2w8zeMLOr046nFMzsPDOrNbOuaccSJzO7tvi7\nm2JmD5rZlmnHFAczG2pmM81sjpmNSjueOJlZTzMbZ2bTiu+5s9OOKW553qxpZl3M7P7i+266me3b\nVNnMJHozO4iwNn+Qu+8GXJdySLEzs57AYcDbacdSAk8CA919D2A2cGHK8URmZh2Am4GhwADgBDPr\nn25UsVoLnOvuA4F9gTNz9vog35s1bwQedff+wCBgRlMFM5PogRHAL9x9LYC7L005nlK4Afhp2kGU\ngrs/5e51eyVeBnqkGU9MhgBz3f2t4t/lPcCxKccUG3df4u6Ti7dXERLF9ulGFZ88b9YsfmP+hruP\nAXD3de7+YVPls5To+wIHmtlLZlZjZvukHVCczOxYYJG7T007lgScCjyadhAx2AFYWO/+ouLPcsfM\negN7ET6k8yLPmzV3Apaa2Vgzm2hmd5pZ56YKN7thKm4tbMDqCGzl7vua2VeBPwF9kowvqhZe34XA\n4fWLJxJUjJp5fRe5+8PFMhcDa9z9fxINrjTy+HX/S8xsM+B+4Jxiz77sVcBmzY7A3sBId3/VzEYT\nzhu7pKnCiXH3w5p6zMxGAA8Wy71anLDc2t2XJRZgRE29PjPbjfAJPMXMIAxrTDCzIe7+foIhRtLc\n7w/AzH5A+Kp8SCIBld47QM9693sSevW5YWYbAg8Af3D3v6QdT4z2B44xsyMobtY0s981tlmzTC0i\njBC8Wrx/P80cLJmloZu/ENbjY2b9gE7llOSb4+5vuHs3d9/J3Xci/JL2Lqck3xIzG0r4mnysu3+a\ndjwxeQ3oa2a9zawTcDzwUMoxxcZCr+MuYLq7j047nji5+0Xu3rP4fvtX4NkcJXncfQmwsJgrAQ4F\npjVVPtEefQvGAGPM7HVgDZCbX0oj8jgkcBPQCXiq+K1lvLufkW5I0bj7OjMbCTwBdADucvcmVzaU\noQOAk4CpZjap+LML3f3xFGMqlTy+584C/rvYCXkTOKWpgtowJSKSc1kauhERkRJQohcRyTklehGR\nnFOiFxHJOSV6EZGcU6IXEck5JXoRkZxTohcRybn/B44x+J9qVCmGAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa0b3ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "x = np.linspace(-5,5,101)\n",
+    "plt.plot(x, vsinc(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "因为这样的用法涉及大量的函数调用，因此，向量化函数的效率并不高。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.15 binary operators.ipynb b/03-numpy/03.15-binary-operators.ipynb
similarity index 94%
rename from 03. numpy/03.15 binary operators.ipynb
rename to 03-numpy/03.15-binary-operators.ipynb
index 2f92d54a..a827794a 100644
--- a/03. numpy/03.15 binary operators.ipynb	
+++ b/03-numpy/03.15-binary-operators.ipynb
@@ -1,434 +1,434 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 二元运算"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 四则运算 "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "运算|函数\n",
-    "--- | --- \n",
-    "`a + b` | `add(a,b)`\n",
-    "`a - b` | `subtract(a,b)`\n",
-    "`a * b` | `multiply(a,b)`\n",
-    "`a / b` | `divide(a,b)`\n",
-    "`a ** b` | `power(a,b)`\n",
-    "`a % b` | `remainder(a,b)`\n",
-    "\n",
-    "以乘法为例，数组与标量相乘，相当于数组的每个元素乘以这个标量："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([3, 6])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([1,2])\n",
-    "a * 3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "数组逐元素相乘："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([3, 8])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([1,2])\n",
-    "b = np.array([3,4])\n",
-    "a * b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([3, 8])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.multiply(a, b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上，函数还可以接受第三个参数，表示将结果存入第三个参数中："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([3, 8])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.multiply(a, b, a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([3, 8])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 比较和逻辑运算"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "运算|函数<\n",
-    "--- | --- \n",
-    "`==` | `equal`\n",
-    "`!=` | `not_equal`\n",
-    "`>` | `greater`\n",
-    "`>=` | `greater_equal`\n",
-    "`<` | `less`\n",
-    "`<=` | `less_equal`\n",
-    "| `logical_and`\n",
-    "| `logical_or`\n",
-    "| `logical_xor`\n",
-    "| `logical_not`\n",
-    "`&` | `bitwise_and`\n",
-    " | `bitwise_or`\n",
-    "`^` | `bitwise_xor`\n",
-    "`~` | `invert`\n",
-    "`>>` | `right_shift`\n",
-    "`<<` | `left_shift`\n",
-    "\n",
-    "等于操作也是逐元素比较的："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ True,  True, False,  True],\n",
-       "       [False,  True,  True,  True]], dtype=bool)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([[1,2,3,4],\n",
-    "              [2,3,4,5]])\n",
-    "b = np.array([[1,2,5,4],\n",
-    "              [1,3,4,5]])\n",
-    "a == b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这意味着，如果我们在条件中要判断两个数组是否一样时，不能直接使用\n",
-    "\n",
-    "    if a == b:\n",
-    "\n",
-    "而要使用：\n",
-    "\n",
-    "    if all(a==b):\n",
-    "\n",
-    "对于浮点数，由于存在精度问题，使用函数 `allclose` 会更好：\n",
-    "\n",
-    "    if allclose(a,b):\n",
-    "\n",
-    "`logical_and` 也是逐元素的 `and` 操作："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([False,  True, False], dtype=bool)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([0,1,2])\n",
-    "b = np.array([0,10,0])\n",
-    "\n",
-    "np.logical_and(a, b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`0` 被认为是 `False`，非零则是 `True`。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "比特操作："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 17,  34,  68, 136])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([1,2,4,8])\n",
-    "b = np.array([16,32,64,128])\n",
-    "\n",
-    "a | b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "取反："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([254, 253, 252, 251], dtype=uint8)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([1,2,3,4], np.uint8)\n",
-    "~a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "左移："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 8, 16, 24, 32], dtype=uint8)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a << 3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "要注意的是 `&` 的运算优先于比较运算如 `>` 等，所以必要时候需要加上括号："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([False, False,  True, False], dtype=bool)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([1,2,4,8])\n",
-    "b = np.array([16,32,64,128])\n",
-    "\n",
-    "(a > 3) & (b < 100)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 二元运算"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 四则运算 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "运算|函数\n",
+    "--- | --- \n",
+    "`a + b` | `add(a,b)`\n",
+    "`a - b` | `subtract(a,b)`\n",
+    "`a * b` | `multiply(a,b)`\n",
+    "`a / b` | `divide(a,b)`\n",
+    "`a ** b` | `power(a,b)`\n",
+    "`a % b` | `remainder(a,b)`\n",
+    "\n",
+    "以乘法为例，数组与标量相乘，相当于数组的每个元素乘以这个标量："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 6])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([1,2])\n",
+    "a * 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "数组逐元素相乘："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 8])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([1,2])\n",
+    "b = np.array([3,4])\n",
+    "a * b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 8])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.multiply(a, b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上，函数还可以接受第三个参数，表示将结果存入第三个参数中："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 8])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.multiply(a, b, a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 8])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 比较和逻辑运算"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "运算|函数<\n",
+    "--- | --- \n",
+    "`==` | `equal`\n",
+    "`!=` | `not_equal`\n",
+    "`>` | `greater`\n",
+    "`>=` | `greater_equal`\n",
+    "`<` | `less`\n",
+    "`<=` | `less_equal`\n",
+    "| `logical_and`\n",
+    "| `logical_or`\n",
+    "| `logical_xor`\n",
+    "| `logical_not`\n",
+    "`&` | `bitwise_and`\n",
+    " | `bitwise_or`\n",
+    "`^` | `bitwise_xor`\n",
+    "`~` | `invert`\n",
+    "`>>` | `right_shift`\n",
+    "`<<` | `left_shift`\n",
+    "\n",
+    "等于操作也是逐元素比较的："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ True,  True, False,  True],\n",
+       "       [False,  True,  True,  True]], dtype=bool)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([[1,2,3,4],\n",
+    "              [2,3,4,5]])\n",
+    "b = np.array([[1,2,5,4],\n",
+    "              [1,3,4,5]])\n",
+    "a == b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这意味着，如果我们在条件中要判断两个数组是否一样时，不能直接使用\n",
+    "\n",
+    "    if a == b:\n",
+    "\n",
+    "而要使用：\n",
+    "\n",
+    "    if all(a==b):\n",
+    "\n",
+    "对于浮点数，由于存在精度问题，使用函数 `allclose` 会更好：\n",
+    "\n",
+    "    if allclose(a,b):\n",
+    "\n",
+    "`logical_and` 也是逐元素的 `and` 操作："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False,  True, False], dtype=bool)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([0,1,2])\n",
+    "b = np.array([0,10,0])\n",
+    "\n",
+    "np.logical_and(a, b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`0` 被认为是 `False`，非零则是 `True`。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "比特操作："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 17,  34,  68, 136])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([1,2,4,8])\n",
+    "b = np.array([16,32,64,128])\n",
+    "\n",
+    "a | b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "取反："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([254, 253, 252, 251], dtype=uint8)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([1,2,3,4], np.uint8)\n",
+    "~a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "左移："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 8, 16, 24, 32], dtype=uint8)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a << 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "要注意的是 `&` 的运算优先于比较运算如 `>` 等，所以必要时候需要加上括号："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False,  True, False], dtype=bool)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([1,2,4,8])\n",
+    "b = np.array([16,32,64,128])\n",
+    "\n",
+    "(a > 3) & (b < 100)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.16 universal functions.ipynb b/03-numpy/03.16-universal-functions.ipynb
similarity index 94%
rename from 03. numpy/03.16 universal functions.ipynb
rename to 03-numpy/03.16-universal-functions.ipynb
index b7b4ee12..467688c0 100644
--- a/03. numpy/03.16 universal functions.ipynb	
+++ b/03-numpy/03.16-universal-functions.ipynb
@@ -1,586 +1,586 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ufunc 对象"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Numpy** 有两种基本对象：`ndarray (N-dimensional array object)` 和 `ufunc (universal function object)`。`ndarray` 是存储单一数据类型的多维数组，而 `ufunc` 则是能够对数组进行处理的函数。\n",
-    "\n",
-    "例如，我们之前所接触到的二元操作符对应的 **Numpy** 函数，如 `add`，就是一种 `ufunc` 对象，它可以作用于数组的每个元素。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2, 4, 6])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([0,1,2])\n",
-    "b = np.array([2,3,4])\n",
-    "\n",
-    "np.add(a, b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看支持的方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__call__',\n",
-       " '__class__',\n",
-       " '__delattr__',\n",
-       " '__doc__',\n",
-       " '__format__',\n",
-       " '__getattribute__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__name__',\n",
-       " '__new__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__subclasshook__',\n",
-       " 'accumulate',\n",
-       " 'at',\n",
-       " 'identity',\n",
-       " 'nargs',\n",
-       " 'nin',\n",
-       " 'nout',\n",
-       " 'ntypes',\n",
-       " 'outer',\n",
-       " 'reduce',\n",
-       " 'reduceat',\n",
-       " 'signature',\n",
-       " 'types']"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(np.add)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "除此之外，大部分能够作用于数组的数学函数如三角函数等，都是 `ufunc` 对象。\n",
-    "\n",
-    "特别地，对于二元操作符所对应的 `ufunc` 对象，支持以下方法："
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## reduce 方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    op.reduce(a)\n",
-    "\n",
-    "将`op`沿着某个轴应用，使得数组 `a` 的维数降低一维。\n",
-    "\n",
-    "add 作用到一维数组上相当于求和：\n",
-    "\n",
-    "$$\n",
-    "\\begin{align}\n",
-    "y & = add.recuce(a) \\\\\n",
-    "& = a[0] + a[1] + ... + a[N-1] \\\\\n",
-    "& = \\sum_{n=0}^{N-1} a[n]\n",
-    "\\end{align}\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([1,2,3,4])\n",
-    "\n",
-    "np.add.reduce(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "多维数组默认只按照第一维进行运算："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([5, 7, 9])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([[1,2,3],[4,5,6]])\n",
-    "\n",
-    "np.add.reduce(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "指定维度："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 6, 15])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.add.reduce(a, 1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "作用于字符串："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'abcdef'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array(['ab', 'cd', 'ef'], np.object)\n",
-    "\n",
-    "np.add.reduce(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "逻辑运算："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([1,1,0,1])\n",
-    "\n",
-    "np.logical_and.reduce(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.logical_or.reduce(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## accumulate 方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    op.accumulate(a)\n",
-    "\n",
-    "`accumulate` 可以看成保存 `reduce` 每一步的结果所形成的数组。\n",
-    "\n",
-    "$$\n",
-    "\\begin{align}\n",
-    "y & = add.accumulate(a) \\\\\n",
-    "& = \\left[\\sum_{n=0}^{0} a[n], \\sum_{n=0}^{1} a[n], ..., \\sum_{n=0}^{N-1} a[n]\\right]\n",
-    "\\end{align}\n",
-    "$$\n",
-    "\n",
-    "与之前类似："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1,  3,  6, 10])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([1,2,3,4])\n",
-    "\n",
-    "np.add.accumulate(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['ab', 'abcd', 'abcdef'], dtype=object)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array(['ab', 'cd', 'ef'], np.object)\n",
-    "\n",
-    "np.add.accumulate(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ True,  True, False, False], dtype=bool)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([1,1,0,1])\n",
-    "\n",
-    "np.logical_and.accumulate(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ True,  True,  True,  True], dtype=bool)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.logical_or.accumulate(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## reduceat 方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "    op.reduceat(a, indices)\n",
-    "\n",
-    "`reduceat` 方法将操作符运用到指定的下标上，返回一个与 `indices` 大小相同的数组：\n",
-    "\n",
-    "$$\n",
-    "\\begin{align}\n",
-    "y & = add.reduceat(a, indices) \\\\\n",
-    "& = \\left[\\sum_{n=indice[0]}^{indice[1]-1} a[n], \\sum_{n=indice[1]}^{indice[2]-1} a[n], ..., \\sum_{n=indice[-1]}^{N-1} a[n]\\right]\n",
-    "\\end{align}\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([60, 90])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([0, 10, 20, 30, 40, 50])\n",
-    "indices = np.array([1,4])\n",
-    "\n",
-    "np.add.reduceat(a, indices)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里，`indices` 为 `[1, 4]`，所以 `60` 表示从下标1（包括）加到下标4（不包括）的结果，`90` 表示从下标4（包括）加到结尾的结果。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## outer 方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    op.outer(a, b)\n",
-    "\n",
-    "对于 `a` 中每个元素，将 `op` 运用到它和 `b` 的每一个元素上所得到的结果："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3],\n",
-       "       [2, 3, 4]])"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([0,1])\n",
-    "b = np.array([1,2,3])\n",
-    "\n",
-    "np.add.outer(a, b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "注意有顺序的区别："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2],\n",
-       "       [2, 3],\n",
-       "       [3, 4]])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.add.outer(b, a)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ufunc 对象"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Numpy** 有两种基本对象：`ndarray (N-dimensional array object)` 和 `ufunc (universal function object)`。`ndarray` 是存储单一数据类型的多维数组，而 `ufunc` 则是能够对数组进行处理的函数。\n",
+    "\n",
+    "例如，我们之前所接触到的二元操作符对应的 **Numpy** 函数，如 `add`，就是一种 `ufunc` 对象，它可以作用于数组的每个元素。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 4, 6])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([0,1,2])\n",
+    "b = np.array([2,3,4])\n",
+    "\n",
+    "np.add(a, b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看支持的方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__call__',\n",
+       " '__class__',\n",
+       " '__delattr__',\n",
+       " '__doc__',\n",
+       " '__format__',\n",
+       " '__getattribute__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__name__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " 'accumulate',\n",
+       " 'at',\n",
+       " 'identity',\n",
+       " 'nargs',\n",
+       " 'nin',\n",
+       " 'nout',\n",
+       " 'ntypes',\n",
+       " 'outer',\n",
+       " 'reduce',\n",
+       " 'reduceat',\n",
+       " 'signature',\n",
+       " 'types']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(np.add)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "除此之外，大部分能够作用于数组的数学函数如三角函数等，都是 `ufunc` 对象。\n",
+    "\n",
+    "特别地，对于二元操作符所对应的 `ufunc` 对象，支持以下方法："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## reduce 方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    op.reduce(a)\n",
+    "\n",
+    "将`op`沿着某个轴应用，使得数组 `a` 的维数降低一维。\n",
+    "\n",
+    "add 作用到一维数组上相当于求和：\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "y & = add.recuce(a) \\\\\n",
+    "& = a[0] + a[1] + ... + a[N-1] \\\\\n",
+    "& = \\sum_{n=0}^{N-1} a[n]\n",
+    "\\end{align}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([1,2,3,4])\n",
+    "\n",
+    "np.add.reduce(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "多维数组默认只按照第一维进行运算："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5, 7, 9])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([[1,2,3],[4,5,6]])\n",
+    "\n",
+    "np.add.reduce(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "指定维度："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 6, 15])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.add.reduce(a, 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "作用于字符串："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'abcdef'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array(['ab', 'cd', 'ef'], np.object)\n",
+    "\n",
+    "np.add.reduce(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "逻辑运算："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([1,1,0,1])\n",
+    "\n",
+    "np.logical_and.reduce(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.logical_or.reduce(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## accumulate 方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    op.accumulate(a)\n",
+    "\n",
+    "`accumulate` 可以看成保存 `reduce` 每一步的结果所形成的数组。\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "y & = add.accumulate(a) \\\\\n",
+    "& = \\left[\\sum_{n=0}^{0} a[n], \\sum_{n=0}^{1} a[n], ..., \\sum_{n=0}^{N-1} a[n]\\right]\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "与之前类似："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1,  3,  6, 10])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([1,2,3,4])\n",
+    "\n",
+    "np.add.accumulate(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['ab', 'abcd', 'abcdef'], dtype=object)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array(['ab', 'cd', 'ef'], np.object)\n",
+    "\n",
+    "np.add.accumulate(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True,  True, False, False], dtype=bool)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([1,1,0,1])\n",
+    "\n",
+    "np.logical_and.accumulate(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True,  True,  True,  True], dtype=bool)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.logical_or.accumulate(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## reduceat 方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "    op.reduceat(a, indices)\n",
+    "\n",
+    "`reduceat` 方法将操作符运用到指定的下标上，返回一个与 `indices` 大小相同的数组：\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "y & = add.reduceat(a, indices) \\\\\n",
+    "& = \\left[\\sum_{n=indice[0]}^{indice[1]-1} a[n], \\sum_{n=indice[1]}^{indice[2]-1} a[n], ..., \\sum_{n=indice[-1]}^{N-1} a[n]\\right]\n",
+    "\\end{align}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([60, 90])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([0, 10, 20, 30, 40, 50])\n",
+    "indices = np.array([1,4])\n",
+    "\n",
+    "np.add.reduceat(a, indices)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里，`indices` 为 `[1, 4]`，所以 `60` 表示从下标1（包括）加到下标4（不包括）的结果，`90` 表示从下标4（包括）加到结尾的结果。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## outer 方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    op.outer(a, b)\n",
+    "\n",
+    "对于 `a` 中每个元素，将 `op` 运用到它和 `b` 的每一个元素上所得到的结果："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [2, 3, 4]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([0,1])\n",
+    "b = np.array([1,2,3])\n",
+    "\n",
+    "np.add.outer(a, b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "注意有顺序的区别："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2],\n",
+       "       [2, 3],\n",
+       "       [3, 4]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.add.outer(b, a)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.17 choose.ipynb b/03-numpy/03.17-choose.ipynb
similarity index 95%
rename from 03. numpy/03.17 choose.ipynb
rename to 03-numpy/03.17-choose.ipynb
index 09e0693a..be5351f0 100644
--- a/03. numpy/03.17 choose.ipynb	
+++ b/03-numpy/03.17-choose.ipynb
@@ -1,245 +1,245 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# choose 函数实现条件筛选"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于数组，我们有时候需要进行类似 `switch` 和 `case` 进行条件选择，此时使用 choose 函数十分方便："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[11, 10, 11],\n",
-       "       [12, 11, 10],\n",
-       "       [11, 12, 12]])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "control = np.array([[1,0,1],\n",
-    "                    [2,1,0],\n",
-    "                    [1,2,2]])\n",
-    "\n",
-    "np.choose(control, [10, 11, 12])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在上面的例子中，`choose` 将 `0,1,2` 对应的值映射为了 `10, 11, 12`，这里的 `0,1,2` 表示对应的下标。\n",
-    "\n",
-    "事实上， `choose` 不仅仅能接受下标参数，还可以接受下标所在的位置："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[10,  1, 10],\n",
-       "       [23, 10,  5],\n",
-       "       [10, 27, 28]])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "i0 = np.array([[0,1,2],\n",
-    "               [3,4,5],\n",
-    "               [6,7,8]])\n",
-    "i2 = np.array([[20,21,22],\n",
-    "               [23,24,25],\n",
-    "               [26,27,28]])\n",
-    "control = np.array([[1,0,1],\n",
-    "                    [2,1,0],\n",
-    "                    [1,2,2]])\n",
-    "\n",
-    "np.choose(control, [i0, 10, i2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里，`control` 传入第一个 `1` 对应的是 10，传入的第一个 `0` 对应于 `i0` 相应位置的值即 `1`，剩下的以此类推。 \n",
-    "\n",
-    "下面的例子将数组中所有小于 `10` 的值变成了 `10`。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ True,  True,  True],\n",
-       "       [False, False, False],\n",
-       "       [False, False, False]], dtype=bool)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([[ 0, 1, 2], \n",
-    "              [10,11,12], \n",
-    "              [20,21,22]])\n",
-    "\n",
-    "a < 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[10, 10, 10],\n",
-       "       [10, 11, 12],\n",
-       "       [20, 21, 22]])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.choose(a < 10, (a, 10))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "下面的例子将数组中所有小于 10 的值变成了 10，大于 15 的值变成了 15。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 1, 1],\n",
-       "       [0, 0, 0],\n",
-       "       [2, 2, 2]])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([[ 0, 1, 2], \n",
-    "              [10,11,12], \n",
-    "              [20,21,22]])\n",
-    "\n",
-    "lt = a < 10\n",
-    "gt = a > 15\n",
-    "\n",
-    "choice = lt + 2 * gt\n",
-    "choice"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[10, 10, 10],\n",
-       "       [10, 11, 12],\n",
-       "       [15, 15, 15]])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.choose(choice, (a, 10, 15))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# choose 函数实现条件筛选"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于数组，我们有时候需要进行类似 `switch` 和 `case` 进行条件选择，此时使用 choose 函数十分方便："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[11, 10, 11],\n",
+       "       [12, 11, 10],\n",
+       "       [11, 12, 12]])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "control = np.array([[1,0,1],\n",
+    "                    [2,1,0],\n",
+    "                    [1,2,2]])\n",
+    "\n",
+    "np.choose(control, [10, 11, 12])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在上面的例子中，`choose` 将 `0,1,2` 对应的值映射为了 `10, 11, 12`，这里的 `0,1,2` 表示对应的下标。\n",
+    "\n",
+    "事实上， `choose` 不仅仅能接受下标参数，还可以接受下标所在的位置："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[10,  1, 10],\n",
+       "       [23, 10,  5],\n",
+       "       [10, 27, 28]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "i0 = np.array([[0,1,2],\n",
+    "               [3,4,5],\n",
+    "               [6,7,8]])\n",
+    "i2 = np.array([[20,21,22],\n",
+    "               [23,24,25],\n",
+    "               [26,27,28]])\n",
+    "control = np.array([[1,0,1],\n",
+    "                    [2,1,0],\n",
+    "                    [1,2,2]])\n",
+    "\n",
+    "np.choose(control, [i0, 10, i2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里，`control` 传入第一个 `1` 对应的是 10，传入的第一个 `0` 对应于 `i0` 相应位置的值即 `1`，剩下的以此类推。 \n",
+    "\n",
+    "下面的例子将数组中所有小于 `10` 的值变成了 `10`。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ True,  True,  True],\n",
+       "       [False, False, False],\n",
+       "       [False, False, False]], dtype=bool)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([[ 0, 1, 2], \n",
+    "              [10,11,12], \n",
+    "              [20,21,22]])\n",
+    "\n",
+    "a < 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[10, 10, 10],\n",
+       "       [10, 11, 12],\n",
+       "       [20, 21, 22]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.choose(a < 10, (a, 10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "下面的例子将数组中所有小于 10 的值变成了 10，大于 15 的值变成了 15。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 1, 1],\n",
+       "       [0, 0, 0],\n",
+       "       [2, 2, 2]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([[ 0, 1, 2], \n",
+    "              [10,11,12], \n",
+    "              [20,21,22]])\n",
+    "\n",
+    "lt = a < 10\n",
+    "gt = a > 15\n",
+    "\n",
+    "choice = lt + 2 * gt\n",
+    "choice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[10, 10, 10],\n",
+       "       [10, 11, 12],\n",
+       "       [15, 15, 15]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.choose(choice, (a, 10, 15))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.18 array broadcasting.ipynb b/03-numpy/03.18-array-broadcasting.ipynb
similarity index 99%
rename from 03. numpy/03.18 array broadcasting.ipynb
rename to 03-numpy/03.18-array-broadcasting.ipynb
index 94c26146..07196f89 100644
--- a/03. numpy/03.18 array broadcasting.ipynb	
+++ b/03-numpy/03.18-array-broadcasting.ipynb
@@ -1,454 +1,454 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 数组广播机制"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "正常的加法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0,  1,  2],\n",
-       "       [10, 11, 12],\n",
-       "       [20, 21, 22],\n",
-       "       [30, 31, 32]])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([[ 0, 0, 0],\n",
-    "              [10,10,10],\n",
-    "              [20,20,20],\n",
-    "              [30,30,30]])\n",
-    "b = np.array([[ 0, 1, 2],\n",
-    "              [ 0, 1, 2],\n",
-    "              [ 0, 1, 2],\n",
-    "              [ 0, 1, 2]])\n",
-    "a + b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "将 `b` 的值变成一维的 `[0,1,2]` 之后的加法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0,  1,  2],\n",
-       "       [10, 11, 12],\n",
-       "       [20, 21, 22],\n",
-       "       [30, 31, 32]])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b = np.array([0,1,2])\n",
-    "\n",
-    "a + b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "结果一样，虽然两个数组的维数不一样，但是 **Numpy** 检测到 `b` 的维度与 `a` 的维度匹配，所以将 `b` 扩展为之前的形式，得到相同的形状。\n",
-    "\n",
-    "对于更高维度，这样的扩展依然有效。 \n",
-    "\n",
-    "如果我们再将 `a` 变成一个列向量呢？"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0],\n",
-       "       [10],\n",
-       "       [20],\n",
-       "       [30]])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([0,10,20,30])\n",
-    "a.shape = 4,1\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1, 2])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0,  1,  2],\n",
-       "       [10, 11, 12],\n",
-       "       [20, 21, 22],\n",
-       "       [30, 31, 32]])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a + b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以看到，虽然两者的维度并不相同，但是**Numpy**还是根据两者的维度，自动将它们进行扩展然后进行计算。\n",
-    "\n",
-    "对于 **Numpy** 来说，维度匹配当且仅当：\n",
-    "\n",
-    "- 维度相同\n",
-    "- 有一个的维度是1\n",
-    "\n",
-    "匹配会从最后一维开始进行，直到某一个的维度全部匹配为止，因此对于以下情况，**Numpy** 都会进行相应的匹配：\n",
-    "\n",
-    "A|B|Result\n",
-    "---|---|---\n",
-    "3d array: 256 x 256 x 3 | 1d array: 3 | 3d array: 256 x 256 x 3\n",
-    "4d array: 8 x 1 x 6 x 1 | 3d array: 7 x 1 x 5 | 3d array: 8 x 7 x 6 x 5\n",
-    "3d array: 5 x 4 x 3 | 1d array: 1 | 3d array: 5 x 4 x 3\n",
-    "3d array: 15 x 4 x 13 | 1d array: 15 x 1 x 13 | 3d array: 15 x 4 x 13\n",
-    "2d array: 4 x 1 | 1d array: 3 | 2d array: 4 x 3\n",
-    "\n",
-    "匹配成功后，**Numpy** 会进行运算得到相应的结果。\n",
-    "\n",
-    "当然，如果相应的维度不匹配，那么**Numpy**会报错："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4L,)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([0,10,20,30])\n",
-    "a.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3L,)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "operands could not be broadcast together with shapes (4,) (3,) ",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-9-f96fb8f649b6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (4,) (3,) "
-     ]
-    }
-   ],
-   "source": [
-    "a + b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "将 `a` 转换为列向量，还是可以计算出结果："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0,  1,  2],\n",
-       "       [10, 11, 12],\n",
-       "       [20, 21, 22],\n",
-       "       [30, 31, 32]])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[:, np.newaxis] + b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 例子"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "x = np.linspace(-.5,.5, 21)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "y = x[:, np.newaxis]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(21L,)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(21L, 1L)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "先形成一个 21 乘 21 的网格，再计算网格到原点的距离："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "radius = np.sqrt(x ** 2 + y ** 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0xa2cb358>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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YLpkp8H64zVM4DwjG/S9iEA5dszNG03+7Uvoj1HfQv0MijwF8y8z/Z7o3g/4J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\nhx32wOyA/rDDHpgd0B922AOz/wcPcIpSAXPsiQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa1177b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "plt.imshow(radius)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 数组广播机制"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "正常的加法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2],\n",
+       "       [10, 11, 12],\n",
+       "       [20, 21, 22],\n",
+       "       [30, 31, 32]])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([[ 0, 0, 0],\n",
+    "              [10,10,10],\n",
+    "              [20,20,20],\n",
+    "              [30,30,30]])\n",
+    "b = np.array([[ 0, 1, 2],\n",
+    "              [ 0, 1, 2],\n",
+    "              [ 0, 1, 2],\n",
+    "              [ 0, 1, 2]])\n",
+    "a + b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将 `b` 的值变成一维的 `[0,1,2]` 之后的加法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2],\n",
+       "       [10, 11, 12],\n",
+       "       [20, 21, 22],\n",
+       "       [30, 31, 32]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = np.array([0,1,2])\n",
+    "\n",
+    "a + b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "结果一样，虽然两个数组的维数不一样，但是 **Numpy** 检测到 `b` 的维度与 `a` 的维度匹配，所以将 `b` 扩展为之前的形式，得到相同的形状。\n",
+    "\n",
+    "对于更高维度，这样的扩展依然有效。 \n",
+    "\n",
+    "如果我们再将 `a` 变成一个列向量呢？"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0],\n",
+       "       [10],\n",
+       "       [20],\n",
+       "       [30]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([0,10,20,30])\n",
+    "a.shape = 4,1\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2],\n",
+       "       [10, 11, 12],\n",
+       "       [20, 21, 22],\n",
+       "       [30, 31, 32]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a + b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以看到，虽然两者的维度并不相同，但是**Numpy**还是根据两者的维度，自动将它们进行扩展然后进行计算。\n",
+    "\n",
+    "对于 **Numpy** 来说，维度匹配当且仅当：\n",
+    "\n",
+    "- 维度相同\n",
+    "- 有一个的维度是1\n",
+    "\n",
+    "匹配会从最后一维开始进行，直到某一个的维度全部匹配为止，因此对于以下情况，**Numpy** 都会进行相应的匹配：\n",
+    "\n",
+    "A|B|Result\n",
+    "---|---|---\n",
+    "3d array: 256 x 256 x 3 | 1d array: 3 | 3d array: 256 x 256 x 3\n",
+    "4d array: 8 x 1 x 6 x 1 | 3d array: 7 x 1 x 5 | 3d array: 8 x 7 x 6 x 5\n",
+    "3d array: 5 x 4 x 3 | 1d array: 1 | 3d array: 5 x 4 x 3\n",
+    "3d array: 15 x 4 x 13 | 1d array: 15 x 1 x 13 | 3d array: 15 x 4 x 13\n",
+    "2d array: 4 x 1 | 1d array: 3 | 2d array: 4 x 3\n",
+    "\n",
+    "匹配成功后，**Numpy** 会进行运算得到相应的结果。\n",
+    "\n",
+    "当然，如果相应的维度不匹配，那么**Numpy**会报错："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4L,)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([0,10,20,30])\n",
+    "a.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3L,)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "operands could not be broadcast together with shapes (4,) (3,) ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-9-f96fb8f649b6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (4,) (3,) "
+     ]
+    }
+   ],
+   "source": [
+    "a + b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将 `a` 转换为列向量，还是可以计算出结果："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2],\n",
+       "       [10, 11, 12],\n",
+       "       [20, 21, 22],\n",
+       "       [30, 31, 32]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[:, np.newaxis] + b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 例子"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x = np.linspace(-.5,.5, 21)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y = x[:, np.newaxis]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(21L,)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(21L, 1L)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "先形成一个 21 乘 21 的网格，再计算网格到原点的距离："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "radius = np.sqrt(x ** 2 + y ** 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0xa2cb358>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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YLpkp8H64zVM4DwjG/S9iEA5dszNG03+7Uvoj1HfQv0MijwF8y8z/Z7o3g/4J\n7FdKrzH9AZe9r1bp+4M0h4N+DL6xYA/FX8M+A3/l/p/wxmEFeZbks4q+hF76iyPcrq1qIo9kyqgB\neHTx1SB0RbeuPYy6KQAMlDLDncX319CPabDiY7IKf6bws2s/broZ+LXaxz2kyq63e0heDvce0JfJ\nK/DtfTo8vo+6CzFZdzXUdjUw5x2z9+szuFP6p6CvlF7X/ZVTxb1frj9QY6CPk1+MR2U9oLm6z8Cv\nQ4DZE7BQv2LE8dmMO3eg13VM5GXAc1dzC74Abz0A84A4UXDtrdob8EutrW/aAk+5yu+hHzPcRjU/\nJgMQld72xdse9XlZK79VcQ1lpI2NulMwO7KDCKileUaF2eVJ+rIaShsVfuXKv3P2ngH850R0Avh3\nmfnfc3tX0K/UPYN55+4bAzDeDy+w91dH27H1LxPwc9LuSAHN1P0J9Guln8swBjngK+g78Bn4su5Q\ny83Y61HpNZEXlL4bgQ7+uPELe/e+byv8XLva27heQT9gobcu/b541x6TAcihvxPbW/PBE+gI0FPR\ncTgCPNrLM/tDPfonI/A7A3B3/Rmh/yPM/DtE9DcB+FUi+h+Z+dd15w//0jjw258Evv1b5EuuJsgs\n+ZpNht61y1oB77PeSAKv9hdOzGqaPwtv4/k95DncD6HnhcpziOk5JO9YESk4WZSvCtiVwF3dCVxL\nX/eYPgEcSAyA3MwscDPQXXsWAFgMAZUKyCue21tkuL0rXt4ZT7JmVDBVaCLuIHmrLOQ1VDSeoqto\n13CeDaeATbtXeMCq6o6FLEiYU4L7tu5jkfEApM+euHTvh8CoaIk91Bb3e3h50X5v/Wv/K/Brv735\nsWb5IOiZ+Xdk/VeJ6M8C+DkAA/q/P3zgoXvPB+RVJANwyPPwKOhr78qPqapjHL8v2bPwcX8cvbcG\nfgu9hZ0t7PIdWJReYe9rYwDYwG+hNrBzpd6mhmAPPIYc2YneNX1N0umkQa7GqETtehRxhQuhUkUp\njCqGgIhat5ZOQEkk8FeZoaZ2g1BQUNzc9TLACuazPOr9fnQ32ocvOejxGT7z/CJxeyhHwC+lQH4V\nNEVJYOAUuM8W++MEcBDolHu6sof/DOuk/dufbkWXX/pv1r/rzdAT0V8H4GDm/5eI/noA/zCAX3IH\nZe79Qs1z9afhupu6gt8MAUmWntJ3y510SP/8lcp7xZ8VPevOm7v3dsA7pWcL/YvfVqXvCTqFfLT5\nfR56C3shD4nmAAAgAElEQVS14FeN4UPs3oHXjL6694N9fa0TtDdPnlYkGX4KKu1xUwe+KJ5Az4VR\nqKA9lXKO98mRTkJZ5Q2yFUU8NWZRdTOXHVv4U8CfQe+759YufEnrNqMgsNOBSoxTDEApY7gRcWlZ\nfQGfjQHAwe1hnZOa8ivUUd2z9gfLhyj9TwD4s9Qs7QuA/4CZ/4I7ooRPRKXPQE9ce+7qTt0A6LRX\nk9K710Wb7DitFFwhz+qz2//c5Q/tTuVfOuQD+HFMrQbsnoxTV17Al7pO5pCBb7cxKTxGmxKtRkAh\nJxJXniSOp+7qaxsEdp0yuoHuoW+uvwxiIRKXv6l7lampGvQNFO8VDNRggM9fSrGHPoM8ywh4wK3C\n+9TpiOsPGW7MIGqufUHBWUe9vVCjQU1B8blSm4Irc+Gj2sf154jpmfk3APzs9qBVIi/CnsCv3XBd\n7bu6k69r4o5kQsv+yinj4i9gX89Y6915Oyw39uU/Ad66968WeAP7qyo9HwNwhb+SqY84vrvyAj0s\n6MYQQN37LfDGIGAk88arnEhifHRXv7vuReGHDEVtbQN+9NifSwEp8KUBX5mbypeWomuwmycFiRK1\n1xvL3JubW3Kk+PTIOdfvFd6r/+pJ/dKfLzhatoJkCFGRUQdccRKBUNpkHA5aBiqBTm73e+UZ6i8o\nkbdfdu79zUReV3QDeX85hYzAa3G8ToZRzIsmtatuD/rqGfgIez5SL/bhX3X5mTV7A9Dhr6291gG6\ngl1NnV0dA2yF24JvoQeM4suF6cCb7e7Ko8MODNdeUvjtevUx59zjewWdHfQDeFuvRbr8ZNpp4pYI\nrJTATgPFcWON5fr+z1V9Bfo8S4FtM5N/CujEBYXGI0RnKa2NC06WJ/EOdKWnChQL/Cqht4npnyyf\nFvro3lvgt7H8KP21Ux126m+l0efmh9LbeF6TeFrudZWtu/Oi8nvwr93/kSvooCOofNV1a+PTqPhp\nQD/DuraM8IAe85rHusO9A7679wTN4muKmqFdebItMbwHfsAO85w5SgEdFSTrKqDTwSCurQhMHXCZ\n3qzrrJ3MEoruk8Wr+xr2rLgOUg99h7/lJBrkbV0l90Kg5s4L+EVi9J7AM9sU4d7F9F+00l+pvEvi\ntaKw9+fl9ZFZ8yjtKd07dlac8a74I4BvIbRuffZobFT+Nfh3gO9Kzy9e5atZi9J32E8CFHIDPE4F\n30IPA3zS1kXOuviYDICN6bVZ66Sw2Sx+QYe+ufXtxu4GQOE/GHQ24FGNAWAGjgF8MzWlz2fIta3b\ndxZjEyazvLsMwAEL/N6l95DP04AJ7GjjCXWG/oIWdhYmnCzZ+4NBlVFOlmQ0t5l3ziZwtIP7PWP6\nW8tVTJ+pvXXte1xvFF/c+v46Ks3aQ56k08ErkrHvmfttIi+6/bun5u6CngPfFV7rxq1X4HUNgbyt\ni6v7fTPsfftEDj0wGQCv+HLJjMIDI76HxtJaL809VdgVfJhthb7d9AWota0PltFu7QsSWlKwJ/Eg\n4Gs3nY43eJPSa8w+6vcUnmfIrUGg5qkU0vGCAj4J+FxQ0LoecQKlAFX65otcJ1X3NvUW57Cv3P4H\ny5cZ09ttE9dbF1/d/HoU6cO1sId55G/11fsuvFdkSh8H7VyN0su7/1ThncoL7F3p6yHZXAP4q0Au\na7yqAUAHGxUG9FBOOMA5NQBmOyi8zhjb1B4jlJbHSu310utHuq1ewMFt3rjKAIvCm3gaoqAd+kJj\nyDBjzE9vuxlpDz25us0EXAE/T+m9WxeqOLmiUHsIuIgInRLja7edAtthV4Gr3Ofau5XIs/seLJ83\nprfu/S6mN202cWfd+vFQDQ1XHuLmqwFAdOtz+F/dOg6VTRJwifLvEndpu4XewG+hxzlDroXtdld6\nGAOw2GbkgGfbFmzy22y3aQBvjTUOdLXvRYF/YZkzzhQwiORVMsTmGQDvjKN4db+r9CNhd63sZVrX\nC/jl6QDSpwbMPxJPFC17rxNsoFDrq68C/EnGOEpSb+faf5HufZbIizfHsW7TG8a+PrqP0AqnW4ep\nVozk3Wiz49QP2GfVY9swFm8oHI1JA9sBfx44a1vXWlDPY3S/nUVccvLAW7hfQzmbu+jg3tXvwJ7E\n9Cn8XemTaxjrul0BvIhK6/cyj5dyBUi3S8tf1FJRjoJaDtDBOPkAAThNHOKgjQ/DJG2rpwHjw0En\nDrnLdAoPG7PH9YB93Ht2hoBD0pOAPrBUqcogKDIeE8+QF+zbvhjoV+59LEvwm8JDTkob1KHj6lfP\nVvv55IYBWL0L3hqBUeJDLbeB500bG+DPA7UeqGdBPcvIzp9luPQT7BjlDPUr6HV9pfS2fQd6bNsY\n7+mGjbAzWmgi2/0NMZrAO0rrwy8M4gN0SBgb3zKh30sNlmtr/+kOeoXcrnVf9pqN8TpNC7qFfZ7r\nt6JIiqSigkBU2hBkub9RzGSaGdwZP1FcL5b3ce+vVMEphMJO/d3xOjqr0oB7Bt9PMuHjfAv5qO9A\nn9sSYyAj6141ZrdKr+68AH9KqWcA3ybnnKJjuPYWfoU+A3xlCIAc9JV7vwLdllVyNt6wLyTuvCas\nJDR5YfSRgKL4OEpze7mVegxo24Ev3fPrD7qE6axi2+yu69QcHvLMAKzBrwF0q+76T/ZT6RFRU/gx\n484A37j58fyt6g+Wzw/9TdjHnPXDve/ggzrwFupc9W2J7nxx9Qh3rvIb2HFIn/uLV3eTuKv1wClu\nvcJeXw8BvoBfB/gDesz1ldLHelxfgb5z71cGYKXw2drmF4wBQJ8f3ih+e1mBDDlmecGE7JMv0Z2S\n3n2ov8fA342ADQqj2z7D7vePt+l5F1/voRDHT+pvlF6+OVF7CWaRe9sp986Vz5T/wfI+0F+B37fF\n6ql739V+XJJZzeeSxfyZ0q+V/YaLb8fNs6q9T9g1915hb+sGuaxfSxK3w7j4EJcf3t1fgZ7VVwr/\nBPqsbLpee9sJE783yLlSy1Z3V98qfgO+KX3pwPd7CeOeAGGofX/OH9BeAR/bh6z7Vu1tvUxKP6b7\nWImOV//m3rcHj9qjEO1JxGJyVxzBvwL+i07krWL6VdKn980Ppa9UDPhzHJ+VObaPSu/VPkK+nrzS\nZ//7djcAY4RdV3oBns+C+lo6+PW1oL62OJ5fS6LyFnhgiu13oMeYfwc6sIceSVsGeITddjuFeJ6Z\nJJNP7rv0h4YUeP16MgyYqT1FWBT4Mn7AgHx+FLYk0K/Ufq4P4+An91r9G/eg/iMwKpraFzvMuKs9\nzSK4i+cLvmDob4Ku22xieqibr+Cj9PfS5a58Xlaw27jeQh4V3+7LJr94jYpfX/p4+ld+cZn6Bj51\nlefvSgBeYUdI5CVJPVvqZvtTKv1uvIXdNoOHdKw5S73H+BXAS3G2SNMR+shve1KvyBqAKnkxqs6Z\nix/7fmZ3foZ9qHxNtneiE0sBo5ICX/qThyjNAPbzpdctc/vj9hcDfZa9vwA9GoOevTcxPYM22fur\nksOeK/yD7juW0XfWvTdj6bU+EnYyhPa1NHX/TsFXtSfgu+DCfxdgV/h3wGeu/8eEvuAaeqv2LxBi\nJTDv6s7os/ZY0gVF/XMscTCJ8adSUSoNqAs341Es8LGPPUKfq30EfwX7OMan7pbxvTyYU8nCL+LG\nGMqdcXIuth8s75/IW8F/aBbTJ/Fm197H7au43oM+w75X+Gdx/fQQjQy6UaV34+ZPAhT811bHdwp8\n4s7v1H4FvC1XoFujoEDfAX8H/KTwcCrfY/w01CjiDBdAZthpz+YTUJpnwJUG7OD2MAubrL3UC62g\nXyt8tj3DPt6+5137eXLPVtrrri3sfe4DffEqo43Bz9z7zN1/sHwZibwLF9932ZUU/jlpF13+Fegz\n7CvgK2YD8LpYN3feuPii9m7wjcAOq+zfNdXHd1hD/x327v0d6O8o/h2Fz9z7XXHvcDOgx/nfeXAv\nFMsDPu1lEtqtRaWgliqHDcCJK4qsNXm3du+92uegj/sitp/OZMT7L4O/dsXvffQazzOa4EHOQ8ZH\nFt8/WL6cRF4o2mUHTdrYEXm0GpH33M2P7RF2azwi8K4bkEtz37kNujl1rSPvalv38fNm1J0bXvtK\nDerJlUcO/FPwV7Bn7TvIV0p/Ve4mE3vuXd368feYigAvk3QUQuXa5wxUF/3Uh2B47nNXdfeaPFz3\nK9DneykepSW/R5nGo8Ns7vEe3qzYWDH0YPm00FOyHW6WaWy2+WHN9fHZWu2nH+DHmcuszb42Cqv3\nva89iDji78CYoLKMKa50dJ0oe3sSjjANrc2AvwL9u1C3UO8MwCuuld6272Av2ENvp3POxv4DCej5\n/cKE5s7He4cwHvw5pHvP6LnOvNPmFrR97Qqqgp69PnNc41V/+9pP2N1/5t6l2rfdPS7ufcpGMb/Z\nlgfLu0HP2RfXdrmgfXCOAb5ZRzl5U0LPp2Z4eeLvGoV8SO/sIRx9/roxfZWC3+L39cMz2MfqEfBV\nWwZ4tp3BvmqLwGcGQNviOwoqciOQQQ+zPd0rArtOw0Vorv2r3C+a6GVqD2YZIejToFPpE4meZPU4\nc8Wfe425x2lFyYpTpvTm/jbAKyMRcv/IMr7wLruNuse2AXxM4pE/eaGMlyBYC7y+KNclHwMwx/zj\nxqraLde75mgeVhtG2k1Da68gj+sId9YWoY+wx+0d8FHpdeCNBTwqvo3ngTXw9n4xNzo5A0D+e0hl\n3CulTbfF9jrmrvlqnLzvg39iCAizx0lm3xCsIWRG4JSbwEaHfBVm3Vze170Pis/hIsMpPTz4dkiu\nOamZws+ulwc+H70Xb4x5PL8rblpqcrPQjmffDfBmLP2oY1b7FeTfYZ3Qi+DbdYR7B/4V7Lqt7n18\n3nsFf4Q+Aq/3jTMshD7fvtat4osHiLMM4Gtpc8+ryhv33kfgN7rZbhYrSOMNPLk4TfDHYsbfk1V+\nYwzcvAY3l88P/ULdObZ1qyfdGCC5FzVmm0/mTuHnByuflTjXngf+6HPS86Typc90Mz8x19bp0NqV\n4l9BH+txvQI+q1+pu63vXqtsX9t0R917XUEn6IsyyP5t+x2syssEogo8idpTgP0trvyVwnsDEAVH\nnxvRQTkRdGFAL4Vx7dnCbl3+L869j9Aba5WpvQXfJ/AwW0N7Epeg7yzyFdx7DyDN8rOJ7U1MzzrF\nVXDr55F2mEFfxfa2rIDP6jGhtqtncGX1gjFGYPeG1V3SLt4v2d8QEaDXYQggcTD6k2qq9BW1NuBJ\nlJ4+IG6/p/Je8SPsPraXezhm76vWvSjqDEQUr8EXp/RZTG8BD6B3AzAl8siDD7M2J7Gak5qf/Hvg\nv6nYN8/U0t4sUwtQjcq7+H3hzkfQM3XPoI+Ar9oyyFfgL8Cb2hT6OLY+mSTjliJl3kV36dH7tOlV\n9r/KPVIkQVbU6FZ5808z0s1PnDvW5pHzq0E1O+BXY/1sfO9j+0okc/3J98e4/9XoKuiq7n3buPxP\nXfz3c++t9VbQbRzf13N3nb7XLLpUd2F/e8lH4tlYf2TubRKv9H55to/FnhnsNwzACvqsrKBfrW39\nDvC6tiqfwa7bu3slKwb2Pi0Xobv76HFtU/cGvFV5BklKXN38zLsbT71bM3AH/nifre5Bgd++oosT\nESuQkXkB9BjDf5/c+8mtN6Cr8rtn6Q3w2UsP7ij7hxuAbBSfv2mmd8yp2pvMfXuYBotCHvDVcTvw\nM6WPxbr3V+sM7p3Sr8od1z67d/rfUdjZ/F3qKt8EQ4yqvECDjypvAxLoReWv3Hs1AB7+DPThsufe\n46z2UfnHq7lE2ORbNvCHCDpFL8MIfK/ce5fAC0rP5kLrc9LmFWxSF4u5ADs+Mf0hZQe77q/yltkB\nPDnFdyPv7oC5g3wF/u7zT5TerlfQx/U0rn5RVuCvVH76WzQbm14Xj6rU1l8vwDeFP9An3kzd+1nR\ndWjtPMR2zhnlQrO+L7unykHlQf0UWa8XlCfxJvhvLl/G4Jyg6g70hcoPSzuXDPz5GPv52AvgjUn/\nTOIesly86t4fR2b+edN1Zx+IqWG9G1CzMxRL4Dn//zrYHFx6nt18VXt19fWFdrpd6cK1XNyJK7At\nzBoynKau68PsO9Cm/O7b8Tq060MyGUfl7F4o/TmOeI/s7pX8tRf5vZjeh7T/LBP1NwXp+Vn21T9Y\n3iem7+CTgRvOIPTXGa1c+yXom5PYL2A2UmphSDjp9mNzs6CMkXi2TPDDZ893j7/uBtds3Xjet50w\nsAvku+0ihkoHwoA8/PooqG73ottxiB1m0NVFt/UV9AH2fr4O9Pf1rUqVV36VErrVaL6mu/vMK3Kr\nY3GfXf8fHvDxf47TCsIYlOQ4Yd9t98XG9OmNAfOD6FLh/ai87ARHlY+G4Ur1F5bags7Ft08vk6T+\n9tgJeAv+HUNwR+0n4Nn8H6GuYDMPdY/zz3f4yVwnuTAlbBO1Y7th2N0A8NBnkMf1Dna7bc+1eljR\nEEvPivcYzbWkhSLfBhkX+5P/Q9We5bMEt2/wwWIALD/DA3iyvFMiz7jwJcDutg3wJnvvL5AFPTvR\naxf/6kJ2V5Dz7e4FTOpO0NdItRF5mEHP1H33oMyuW+472y6QO/eeDfQPCtFceNVmLrq+zbbLj4kJ\noqJnBiBCH8F3sM9F39pLTu1bjF9YwrTgzteQVdfZmTKReFpieODu4RjXh4y+67kgjAlAext/4dA7\n9x3OACBVenVzvGW8a033sVa2fx1/WeB7bG8esKmqLBF4s07LFfArA7AyBg54s1bwmcUXFidS61m7\nzRxRMaALrVpnwEHtrr80WP4j7Prds/oV6Mag6jkfsKMrfmVCqYRaYtxeHOg+Bn+Dm37XELguO3jY\nBQgWPsgoPRvQXffdg+WzZ+877D1DPwD34HuLlwFfp7rtEhnrVYImHw89x+8ReKf0rEpfxkSOJnE3\nAZ/F8R9D7RX2Dj971e/QK9h1UxfoFXQu6MNhoXXtT9K+JeTuvRoEpnFMhH4F/51yAOO1XtR/iiZQ\nIUYZleUa1QTu2H22E4MVyLNrP0/JuTEE099sDDRHSr0uNi8U5bnf/ubyDko/YkIdhafGYGTuKcTy\nC7UnhXQ+sfsYfXbbVp+zsXs1Cbuo9JoltvG8BT5182M2P7ZFqHexfd/HHviu8jzc+0GGyuPcBsbo\nE+IGd69byKVTGcHFB+DkPSp9BDtL4K3CoE23oIJO3eAq/AW1MsgofXfp7XWmnUjcLRex/WqAjlV+\nvb91tiBZs03g2dj+wfIFdNlRd2NGPJ+ru03g3b0QcyJvf0wK/A5+qfsuu8FPhP+Wsmf7r/rzs8Rf\nh99C7whpB0botY0OuOFgMPGjvbYcXbpwA2g6Wpuiwu9gt+cjJvGsa382uxThRx2uPbiAeQy26eDv\nrncvdhuhfp3Ay+/HcX/XBP7BhsyWSwzq4Ae1f7C8D/QmocfTmrpRyN0ebwzmBMlY24t13VefAB/A\nVoWP8LtEXlyvsvQfEttnmXsLdty2bar0Fm5Llt1mbheLwtsUYldcfKcch3rvf8Ie+pisWxnANKan\nruyuGPCpNvh7UrjM98b9LrsnkM9dwy6J1+9niay64KHH9ByAnxT/wfIu/fQWbB/XhzqCRZ1Av29V\n9/uiy79XePvcvA65bQ92EGIGf5d4ury5V5Cv2lzJjEBU9OgzS72/4F5ceWaM+cytZJPE9NldRwZ4\n6N08Q29Bf0UO/pWbn7r85hpo9ynP3a2Z4qvSxqTeh4Cfft4al5jFx0jasQN/5FadiD7w8D8/9Lp2\nbn40AuHHf1TAs0Set8qrATqpay8qAgt8j+nh4V/dqKsbemcEljE/AzXE85WBWtuaM4uzIYxV5bUc\n/gIq+P3u08UaB9P2Sh72HehXgCew9/ERzCDTm6ID2l0Mb0HjofJ7N38UpO33XX13fOZdGNgb6CRr\n9vH8F6X0m4XNN223B5m6LtSPG8fbz837OKztZ+zFQvj7bOqrv93XcrOApY1JerrUkhmPV+v2D9wp\n9aI93c+mmIy8zcynlmO1tvNfxYnu7BfQOcsl+WefGLEvqKTFx1f1q4KwdoXALFdM6hBjrUlH5rFm\nyq91ev078KNuj41t87787+Sf0VYyPzbue7a8G/T5Mk7mWGt7W8Y1zoxA/L9WJ3ccP4xDVHf5DnKT\nMA/g9cMdfL3JMOpw6+nLxy9wD/4IjFW5CXj9Yll8cZUVtK6DnQVD1/aLm7NpE3odeG4fIx7n4wr8\nO6An7f2deBDg+3UR1VfwO/DDlQcjgG8V3bd54NHrFvbMIIxlFiP/Gf3/PPAe8tkI3F3eGfo1sJzU\nZ9Ctose29SnxF0QvYPz7s9dgwR8KTx34CPmbFP4K/isPwILf43frB6+e1slm4fwG3r+O811hnHfb\n3H8zocfy1v5cgX/1Wy+MJzOcwpMD3wIfQTf7+m/zoM/A72FfGYSxnsVrbsvO+duAB94degDJybD7\n2B1n28gdHwG1n5ldtSy0sNYdvs4RfOPSwxoAc6ze6DCG4GOCPu1jXxDBj6796jld23aigR+/gL0m\n9mZlcxi1v6vga9j/FO6nBePvW4VX8O216grfQc/gRtLmjUH/kxdKn9+b/v+fjxufXav+s+VdofeQ\nZ9CNfRnocR/MOopBq/sLsL5IV+DDuP8YN44BXmP68ULGoIh3YL8CfQu+Am+746Jrr3D/CB78H8n+\nuvgyFEoxv02NogHeuvmXnsobijuf7W8yIyh8Wzv3vsfy5roR+vZsBHaq79tngNeQ65K1aTstVf/5\n8u5Kn/8E7+rMJyNP2HnQd2rvP7OCf7rYQd37jSOqrjeOvwFh9oWStV2VFHgFS4+roywz9VHlf2TW\nWr9SeAW+jHY2BfF7bX7HW+DHXG+uPZm/G117uS7kjbgH3sK9EqMYAlig1/vsOVzfw8OYMOLdDdP2\ntmU1lGp8NaJfJqLfJaK/Ytr+RiL6VSL6n4joLxDR3/AB3yEFf2cJc6iztr17fwW/U3ezf8AOA7ze\n7OiGYXdzbkG4A0fcVyFdcrFYA5B1/FvQ/z9T/pqp/wjeGMTZOBcjZ7JHdrO4/u652JxDju3GC4Pp\nbRkGG4hGfOVp5sDPx+feady3D1njPfwplkvoAfwpAD8f2v5FAL/KzH87gP9Ctj9o8Vpy5Q6tlZ3D\nPv83VvCvlN6o+wT5+F79htL9oHAjWvW/We5k7Sc49I86S4A5no/AX4GfAW+f55XQwRoY24ugr6S+\n6qZ7i4vfLxr17ZFAteALvOzhH+s13Bno8/HxuFhv26N9fHW/D+Ezfsnu66fLJfTM/OsA/q/Q/I8C\n+BWp/wqAf/ztX8HHh+xOyGz5PNS5EciPz0/q3F1iVT0YAAf5UHytu+68DPjxh0bdtn2QMeCkrNz7\nzLW3wP81eKVfgW+V3iq8BZ/n738F/FP4+zm0xlf3mWvTPTETkrm29n/kqj/fTzvg433s7+l2LPpn\n4j57f49w42MAD7w9pv8JZv5dqf8ugJ94/l9YoOdlBab/fLSKa9B3fyNT/Rl8sy/cNL5rzhsDbwDs\ncTfKnaRXmswz8fwyts+SeVHp7Xkl+LGz9s2U5ziGCX3orutJYAk/KIf/aYnnUH46dcOrybxxrHpq\nEbIphmcPZw68V/3suNyVz+7PTJBGLmDstffz203AByfymJmJ8mf7fvgro/7t3w18+/d86F/78Djn\nvuv0tr9161K85Xp9sJlfWZ4PWd/4Yv6uvf81b/736/+Dxoen/+PjxssfS4E/ZPm1/w74tf/+3rFv\nhf53iegPMPP/QUR/EMDvZQf98Bd3/4WeqvwCZCkRSm62XVvmEO3+X6/xq3wtg4j7+GcyD0EA6O0a\n2venpMYf8GXVrsJ6t14gfeGy0R+H1bqOoVdltn3wVnJ1KQB+nyk/kPKNlBdTVPX1+XtCn2WnxHry\nG1a/eVfiedOfTtwLheNIr1dyXV2+nK7uE3985vCjt6HXV/9fbkRjL7/ui9epLd/+bCu6/NKfng7p\ny51EXrb8JwAU6V8E8B+/8f9B9Nv8NZ1VKab0/L6sbTYA6wtqv8MCeK138Ee9Ozxyg7U65zeqrT8p\nK+D7VFbmC5BCmLnkLxgAf4MB9e9DDnsGvP0/Cxz4bnqt8J3jzDl3DdylARjn2gI/rg37balb441g\nFJwxCPeDhT2CnwcIOej5cXrMx47obyg9Ef0ZAH8UwO8not8C8C8D+NcA/EdE9McA/CaAf/JDv4he\nP54gh1nvDMDOKMDty+vD4LgLbOAme5Mw3Gf6cxwkv8ApPM83/5vh3rR1yEhUvmLAyJjjcKvycSnw\nwO/Al2fuya4JS/DvwnwJua1z37YGl1y7McwLoCnsi5F8fvwdIwDY+3dnBFYBg/3JH7JcQs/Mv7DY\n9Q99hL8PQGGf22bIW323z7fdUfn4+QC8gzsaAwyDAPR5AhrsBnT9A7H+sYyAPk8dwSedz44bjADG\ngzPfmN8bz74aCusBfBNKdO+N0lsJV7deX0N19TveaAicKw/069O2LfzsVV3bUvDney7CnPU3ZfVV\n2s4fy5jv74+/vPuIvAz4aPWyk5AZgPmk2n1r2HOlnzvtnOqzAV4nOgC7+QGswjej0P/AfQMQQVgp\nfJENzZ4r7NPZtQ/O2P32CymdCv03oZ7F89a1p1A2v+lDYnvM9RHXa7sFfNTd9dX9zp2Gqc/3w+wN\nrMD1xiMD3d6v/n7O3XqaWp4t7wq9B95C2PbMVjMzACvln9382IGyVvrkolvVJwFen9eW7QY+xpRG\nwEjy6Vzwd2HfgbCEJsAGgV9/GwFutEov8Y9YpVfIo9J/Aw+8uPX6+Qz+VQy/NGY3izufPOB3wOs5\nEAiNh9avN61h33fijntnf19h+vxYcgGD+/8+jvq/u9JbpfFGoO2j6bi9hdwZgFHn6XP2gvkLjwl4\nBccqRJvlxIMvB+dql920K3f3Sg0L0OdQcsDLb+snSs9wvIEy6K2afxPWi8z9Tu0/tluPsJ7aExee\n5FOWD7UAACAASURBVPpTrF8D7cFfqXnuQdqzfN9jzY6Lx7xteWfoVWXmdkrqOwMwn9z1ickvUPx/\nk5tA1Z0G/EMxuM9ppgGm7crLVelBuTIIPa7PwLf/UabwNg5XiHXmnDvFqLztMejwhz+zAv6OEdid\nP9k3fjJPig/CgFzBhwGacqA92N4A3IE9gzrec2OxbZm6f5jifwFK35Z2O+pJaVtryMfJX1vS6Obn\nsdEcl0mxUPc1+noohMKtYQDBvpwgTeZ9CPxL4A3sJLCTZu/9Gcr/eIQ+myortsV43vw/Lp63ddxX\n9iceADCSdVMSb7SNaxbAzwyAU/Wo6HaNCX5MbSuo5/9nfBbhOLu8Hfx3g57A/WcN4CkcxdMJzhV9\n5frPJze7CJbD3d/ua+I2LRNB1hLDy03mu+vUKIQ/9hTwVT2L6QF0lbeTKLobJcJ+mrVOMB/B3hX5\nv5zhkbrN3K9+w8d08RVymLpATc5YD3VfXeu5zRqD0WaP3SXgMoMxlvwz1/ueLZ8W+pUxMtw1IW3A\nN09ZFJXZXE9z0T5qySfCno4TyIlkrlxbL4xC3NS9sNy8bZvkBQVceL7xo0cd6zFUjl3stm499e5Z\nSCMDY766iv7SivTiEPooPtvHT6svY1S9FOCgRdGPkP8dV3YknoesVzAUIu6DAlEYVAbotl6ojnaY\ntlv3zfpexGZffrzZ5rBfnl3oHTHg0SHDwPQw083l80NfG9BtEkP0GU2oKuwkbQa47ISQh3b3btHs\nXSXzawrHi60KkldomBvC1VHBVIBS5UYrAn8Ce7x5VyxFyO3zMlkbkLgrlLQJ/OryczieTwy/2A7s\nKQJ+SNbZTP1LAnoG9iIlcAv87Pyl8AvgCnlhUKkgAd0C3+v2/qD5+t8t2X047rfwf3E8HuZeF+Hr\nQsgtWusTfibl5vL5oQ+Wys1hpgovBqErrANdTlRX2/WJv3rJcP7OG3OxydTBKFyHKljjUGq78YuY\nZFV8KWxvyOxGfYsBsEUB1yWPV+T8W+DZ1DUGkSfm9AWWEfKpTVx4C/zLop6lBu4YgZUH5M5pBrpv\nK2UBvKmvgH82+/1e7fv9GwxA/6zWLfCdF8zg18W1XizvA31Vtx4BfHSX35+UYAAoO5k1WFO/Xlns\nvYcQPkOhjZohYCrgwqgGeC5abzcdr1T+LvA76G06JKp9vBZMAXyF3a4rehLQwb5YlwRyC2+s31H4\nHfiJ2lvHQ7etS18KN6UX8K/U/JnCryG/fEcOr+tQjzcFnjsrKWeb5R2gZ+jkharu/ctXdft5Vnhu\nsfNkLR+ofZlc+XHMpVdAQ9kL5KbR71gqQARS0OVmay4+5CZkr/ir2P4K9qx9gj7E9e78F4x3z8OD\nr/tIJsDQhJzG7at6KfJ9LPBW5fFc3e8aggn2ce5XwHd11+to1X55D3zoi63CfQtGSdXdbBv3funa\nS1j8ZPm00Ee3oxiFlzndqM+Yig48TCKvW1GTTOtrp+SZcs8voPbxVV3s87D3ixGUXl18poJaKkoh\nVEncKeQO9rfc2Ap2pvRs1gAG4AF2p/Ka/GEDPJt9Cr5AbyF3/f802osm6CzkFna67Oa/Hd8vYnh1\n8bvKFx5rMcLFwN2vK3mDPnlzNwRhpfa7995GtS8O+sGACiI54IPKf1HQxy9jVd249zqbCvUbkswI\nOKP6Afh1TL4zBCvjMIqL12HiewO+vUlakoi6wlO/+RR8rFX+KfBR4S30nXPCyN4bA9BvkhJAN7D3\nOs+wr0pJYNd6hDobur8KBZ6EPs7FH6BrEq8BX1GKuW7WqIf8zVapP7hUD3kAPqo8DNyj3Vz7LzqR\n5ywVTZZrzHFmTkAAv83DYAGcQb57Ya7cemf9uU7un1V6FEY1iSOWriPqWXzksJ9Jm5YIeYTdPjsD\nDNe+A2/PvVH0adZceNi1rSVTQoGHXYfYHgnsTuUTtY9u/91cxyZEGqADOoCqg6/uPhnDbkPGxACs\nY/t9su5K9R3kGIB76AfgXukjNwlrm+XzQj/N3EquzVq1vIuOveonlji2uTj8aTKPfMaeMOB36t/j\nx6b4eqPdgv0IbRn0FvjMAKibTpPkj+ugRrWKglvY3cSaaMfof1MC7AXzdiEPcuyPv+Par8DPMvYp\n/PFc2+uipS676GaX/l4Mryd3ZwiiV+m2ncBZwA30FTPwlYMBwO3lXbvsouvCYgTm7H0AcXNSB+Qr\ni7tO5qWfszGeyyWMRFCtAnshpzQ93xXBtjfvCvQIewQ+uncAurqn3pOUDrwxuDDXoaIBZJW+SN0C\nX8z+GJL0sonlYxx/x8XfwE9O5fNStG6vH7xR3wE/31MI6+uyDDVTdVfYuYfFoy0pN5d3GZyjP4DN\nF7Y/jvXHIZxMwvLk7bpdssTMnMyr3SBkXoECnv2fRa4EW+A1k38w6GSvRFHZLfgR+gj5C9YX2wl8\nUHt7/isNqO26St32CJQba/ubrpT9Knv/JJO/UH5nAKhdn2KUfnl9H3uD67fXZ1110//N12WCXe6J\n710/vfYIwfyQ3jVs1GZYUSkcTjgNUK2K24sX3fsn5UDFmba3Vl2zxLh9XWpT/YNAZ2ngHwy88ABq\nysBT2F6U7BzrCXqFBzEr8W+s1hH6HfhXsMdE3q5sjQOnf4sOMa5HbaWY/vgi9wmdi2t84kC+z94H\n+b7ZYMR7ryB4FAJ74ZBTSlQe1bj2Cewumfdg+exdds46mWK78kiLJo/BIYsfu9Es8PdAp82+UU4c\nDvN1AZ3t8hGhUAUXApXSbsJagBcGVQa7mJwMaDzDf/diWiiviv2/M9Cd4UUOeab0K8izcgtweNgn\nQ8Ju/wCfBfoqWfsKEuAPA/kV7IcpT4Qihg3OIwzHLRN2dYBPO9i/F9n78EUVdGKj9Or+yyhQquIu\nmwRekYE6FmAPfH4R3l4Ufr0dKqwPwEIAC+xN6Uu78Y4CqgZ8+a0ji44E/hCHX11YhU+VPjMAMWSI\nkGdGIIN8pfg76OP2ldo7gxCBj7BrWx3AHywKb1z6jdpnsD+9Rzzsubg00OOaB+wdeO7iZyHvfETY\nvw/uvf4AtutoAIqJ6+XEFMggBtdfr9hlap8r/D2VXxuA0v+NNiaBXtelgo+CWitwFAG8ApVALy2Z\ntocfe9DtkoF+LrbvAr9z72M9xvO7+mr2rcwbsOsIu21ThbdKT0PpbRxvFf5IyocLRLv/KNTtdm9P\n++UxlN2Arkrv1jYkfrB8dvc+JvC64gdDgCpuvcIfTxCNLL23qjP83sWy5WNd5ApGBVNFpeLcSzrK\nSMqAHVwDflqDd2exSl+S+mG2V7BnxuYK9pjI23XDrYDO6pu8AHX4Nb7n1nYwYOL5lq2vLqa37nyr\n3ylvU/sIvk8cm2Sf65prcLhuOstDCIlveYHJ8j7uvfkRHOrWklm3pyk8Arwx+54Dv7PImXt3759x\n76mIyleJ54sM1JHEUn+hY4HLxDMAeayYu+LTfBE5rIEGnK1nsJ9hre79HeBtTL+C3dZ3sEdPIKr4\nN8k+p+7sgdekaN/HPa4nTeiJ2qvSfzwVvwJ/Vnx/f1rQa7uvY5+8KS6OT+rfH/c+qnxwWbraw9xn\nFAyA1o1yR/hzC3wN/j3XvoR2SeKV5tJblcchFhw8njt4MQNkKoVYXur2HNJmvVL5CL2+3yLeMKvt\nK9hXXXa7+g763fybE+RStwk8o/alGPBRu4s/kngf26XPFN7fX8th3qvuukTdKat/6e69Az35IWwt\nnQHdla722Zj5eJI/TiIvgt48gqH2hSoqmViSKypLckm+b/vhcCqPKirGJDG+KbvFQq/w6ctotR5n\nwHrFGva77v1K6Vf97Nn2pcLPhTLgbTmqAM/DvbdJPFT4bP1w4T80tp/vsSyOT4Z99/vZerbrOP6y\n3Fzezb1X1yWL72F+fJ9f0ih7W1cQisnkzy7/fRVnA/IK+OqOqab9QAO+32BFgOd28S1NVNG67Zzi\nm20dLRcvZHTnM+itW58ZgOje7wyAhX4Hv4U+Ap7Bv0rYZdvfwIPeP8/dxSctJSTytK/e9tOTKn12\njX3bU/jn5PAsSva+tPeyK3WGP5ZlFv/m8r7ufZacsD+O5CRQ6NZABXFBjJlWin+n5MBH0PWYCLx0\n6fWx3aI4clW0q1GV3nk6E3wBeAu7bkfoLdgr4LVcgW73rSDfKf1VyZT8kcqPQl3lxcXveZQBexyF\np5DPUD9L2s3ue1R3q/DBC+AB/py9l3vkTuLOeMtPls/r3hPmH7GxZn3qYm6ZzT6ZBo8YvoDeeLGG\ny6dbq3YPepE++7H/AKFSwUEFtZw4uKDSiUIFpejn2porAbV4xRcjoM/LMEfSsYfPxvOZ4mssn8X0\nOyNwR+XvQh8z/LeBD5Aftl6Bg1GO5l2V45SM/VgfmrmnOChHXXvr3s8Ddq5H7N0xFnOXsvbTFwt8\n5bXCb1j5spT+CnrrcmaFxL2Xp6R8wqOiMG2gV9iOqT225KCXaVtBj6MAxmgBySIQtUE6SB7HOAq4\nVnAtQCUwE5hLi+m5CPzyuG42yKYA4yk3rJU+wm6378aIK8h3ffU74CfoeQ/8NxX0DQOyJjMIB92N\nb9n6F3rFQSde6BUvdOLQNV4RR+FlI/OGIajIRuztRvLF5OB8H9r/xxgCDQHNOPt27/PMQryGcd+D\n5f2gz4C3P0wnmZRCRVXewj6gty74OZ30iP/aAOzBz4f+9E5EIum+s9veAHBh8EFg6aZT0Ie6l2a0\nxxNHpijsLAaAhtIfuAbeuvcfA/pVBn8FvAOfvdIfnCi/AP/CoJcqRbrkzFj7UmoDvrziKKfAL+pu\n6wnoKwMwBvDMRsF7ACulj/v8cRSAp2oLQCvAV+XB8j7QR+BjoknbVOkLgDrAV7eoncBZ6Xd97TPs\nM/x78M8ceiqobKEnmXaOANZtQuUCrgV8MJiLDMaTej9PxUHPFvYIvz76ap/Y26n8x1T66N5ngJdQ\nnxTe18nVpXxTURT6o6IY6NWNfyEF/rXBLyr/YmB/WcCt8EYXf/YAZnf/cIDfG9TTYvra7mEFXtb9\nmu0UPjMID5ZPC310O+zNuVJ809VEFU3l7fh7UfsKEsXfJ+HWrv28beGOCn+Y+kjNnHBP4pO46wI9\nimxXEviBWg7Ug8HMqE3iO/xN60lei1W7kus0UNzn0Oeh8tG1t+DHG+RTKb117yPkq5i+j5mHid1t\nm7jyLwN4uy5d5U9R+tfm2hdV+Fej9pmLv1b8GMvnsEdjsC/TvdgT0gZ4ieuXah5Bj4b85vJ5lZ5x\nDXxw+YngXJ9SGiyFKxgk971341f/jgXsdnsFPge1P5wBME9Qk3HlMeCHuPAtBj068Hqa2uxT1Pr6\nC0ZMb96Jp2/FnWJ9695noNu2FeBI2jK4kbRF9z4qf/QA0mQdYIfWOqU/BPSX2pJ1Fvij4iinA/zF\nAr9V+QGvV/x9vB+V/3nmX7JAkpty4GtMv4rhV9tfLPSrmD669/1hEYbO4uISeKDmEqPKR/KTO7v5\n5wb+K/AztTfrALsS0qamo17qceDkBj6Fc1M1nun5DASwSKbhIuCVh5tfkAMe26wreKXyer10fUfp\nV7DbtXXrD5jsPOaBNxq39zjegF8qjuPsSv9iYceI573iX6l9rvgR8Bz2/dh9p/hcJYlngYeL6Sew\nr+L77wX0F2qvyYz+eG3I3Gsui4Gu8lksH70AHUlnu9524Gtm3qt9UHh16yP80t6SE+3LnszABDzJ\nS2fblFsoNKDXhN2rjecX7v0R1ll8eFfld+49kra70DulN/H8EYB3z8g3d76pvKj7cTboj7Mn7zSO\nH/G8j+mvQPeKX93awm9H8sURfdcJvXE3aUxf+kSl2IOeXdPvTfY+3qCrZJ66+GoB5SQxsyS/KtS9\nn+P6eZy8B74ayAfs1o1X8GcDkLj1otItpoe0wRuE0P/Ock64l+KmbmZ13RV28X6oEDg+O2/PYwZ7\nhB64hv8KdG237n2sxzYz+QUZVz4baQedCUfH0wv8h4LeiwAuibuXkMC7p/AW/ryLzir9yBF5lb+O\n5cd6lcHfqvwK/AfL54d+1VUX4dcbXJ48i0rfvP7m166Bj26+Aq8wW+CTabBAiLBb48AWepPE690O\nSrXCwaPazgcBJ6ESgfojuQTU0n67GIMO9iu8S2+VPjuPq4TPlcpr2wr2rG0FfayvhuP2J+fGIBzS\nUXaq9PqorLj1x/E6wI9gL7Z3Gfw16BF4m9SLKr5x6w3449Ha6oG3ybzVNczaHiyfP3u/i0uCQRjP\nEZuYBxVF/qN2f8Yuu8zVt+69B9uDP6Cej1NDMAyCNQppEs9sd1rIcCVufalt4o1aCShtph2Z4H+8\nKCPOf2e3T8rDpFXCJ4M925bvu4TdbluwI+iTe89O1V0Mb7d1WK2ATzrKToso/ksRlV+UO6p+vfbu\n/Oz+Z279ansemDOG3wbg76j8Fx3Ta/beqrldx32HAk+tB0uymlwYdAClVqCWPnXWQfMY+YNGvO7V\nu7VNCTkL8aa0nzPqd5Z+XQgyFh8+dp+uXCOqG40++WZp9ULAKwEH9RuBM0NaAVRy0PMd8C+AJ7ut\nidcIu3Y5dugV7jpc+SNAL6PtGtStG0773t3IOwF6htyDHg2APc62Zet8RF+e3T9w4mDr4hvFZ26G\noVaUyq7QyS1/dbJx5/leYlb3PVg+r9Jb9z5bZ20FZlppgA7q8LebqPZJEqo+Qkl2XJ6oOFGAfjXz\nPSX7+sOxBnhfv7uQ+PgKfRtijD4fO/q3qS0HcJQOvQLPpU3LhULgUuTik59nsFIwAP7R3f64proe\nBnw20NMEPIdtdI/ETvNNwQiMbTbPvjcFh90uY/vQQTeakCsSs1MwAImy75Vf63sDcMgxIyx4DetQ\nFHi2sIt7L7H84UCXojmrE2MkXix10f7FddmtBufcUXnN3It7Sz2jzygmcUWi/BXcYKf2WKutM52o\n8hCMB9xOYrw2AB74rN6WO+ed9Ek7C7/mLjSJJ2YH4XvCDvOlAj4IOCv4bGrPAXCqvm3cHGQUX0kH\n+jsFddsBPxISTuHBHmpTz9sU8Opidj+TrSi9DqlNVF5hz5V+D32E3MM9t+3Uvh9jgRfQDzPy7pAn\n64qAPlQeQ+VrK13xM9AzldcE7c3l87v3EfLM3T8AnEY15BgqQClAPYAix/PR4iOFvNBw8w+cqNTG\ntw/gkz72Wyq/A/6+i0/6CRprglF7HWZM7A0QtSf0mAq4iNrXBj6f1EBnTMDrmiVEApMBfbxPkAG4\nZ/nHF27GKDEAZPcH0Ndr7o+/unWP28f6MCPs+tBa0w33QjPgR1DwvJyIir8yApfqHpWeVd0FfilF\nnxuptQ0yE9BLUHnKgN5tf3Fddpl7/zimh4vjURvwfACoDD7JTJRQcRCjlpGxH8+7W6Dvwj4DbpNy\nA/iH0JNE6swdeijw/RmD2r6fKnstqNXAXtrTelXAV+gb3KrwZBRej2EBnQDI9F39LcHGte8x/Qz8\n2FZvBQZo471IaGbrHXqj6MXUbXtz6Q3oNEDPlP4K+OMG+NcKn8OvbT2zXxX2UQ4ern3pD9YYF9+A\nTTuVz7YfLO8b029ce7cuDNJklcaJJ6MUAh/AAUYlxlG4gV5PcAkvriYP/Qy7bYvuvYXfGgD9UfeX\nrvRgUfvgT5TaxyNUDFe+UpH596hDX7m0STX1EV2n6qMN1gB017495dc8dJmYU8IOrffwA6beXXz2\nRqCPLzBwd/hNO40Zbfqro80baboRoKb04yk5W59d8iu3/rgAfxgMD/xa5WPbUPVYnAHogIu6V3X5\nkcf1K3f+U8b0RPTLAP4RAL/HzH+XtP0QwD8D4K/KYf8SM//56cOrfnqj5LlrP1z6BjkBZ7tx+ABY\nlJ4rwGdzg7tLLy+cqLXiKDItNc4U8H22PoLu4/fxg9bQZ3ua0id/UefyN2MR2pN7BnguKLWgysM6\nVOt4aq9DT90ITCpfqT23D/SxDzpgiBz46K+n14YBPJuYnsXFZ6Pi7AYY9bClt9c+pVh/i6yZz66/\nlabUBpUFvm+PuD5m3K8VfkBuQbdKH4G3oG9d++7inx3yQ4GXGP6oVZSceyzfVF3jeF7DvcvgP1ju\nKP2fAvBvAfjTpo0B/Alm/hPbT+7c+10G36g8FUh8T62LR45tCbxWZzTlONBeMlErNeD5bOPdiVo9\n9KPXG3UPOk3lSeYe/RNiTigAjzre4ANuwHNBVeBlm6RUeZ98fzbfAM+VWr+/ws+S5de4XRTdg2/c\ne73M6r4bwK0HoNOAWdjLBP5YT9NYmTLazHx2qu4KPMTdh2+7C3zevedB90bAD+jZxvWSzGuu/Imj\nShcdc1P72pQe1cOucTyZ+lblM9V/sFxCz8y/TkQ/nd6/V8vKvc9c+HTdVJ5OGOCpZTcPgCsDpw7O\nMSPwCoG5dXlVuQBjaOw9d94r/GwEQi/Xcoknyb+dJ1F7sEyl1FS8oqDy0esKPOHoXkFlAz0L7LWg\nKPz9/XjkQKcJfJYZfNraue7AAniM97+X6oGnZJu4Q11goEed2luyzqhsT+BlMfVz4O8YgdydXyX1\nqlP5nshT4EXt0YFHhx3OrV+o/Qr+z9hl988T0T8N4C8D+BeY+f+ejojQxxh+lcl3Ln4bg04nNeWX\nrD5LNh8H4yBC67Sj9tIJJlQ+UZhw4OzaqkNnZxd/ncj7sGX+H1wszxLjova1nd33hCh974swwION\nq1PbDDyVJARo4NZamhvf36Aj3kkKfjJgx7j0S4U30Gss7ur6TjmN2fXFEzRGqR194kpV+DHU1Sm8\nVVraQ3+t8BeZ+ETxL0vP3p8d9tJLU/ty1g4rOSXnMQr1KobPymeA/t8B8K9I/V8F8G8A+GPxoB/+\n16P+7U8C3/4UvJpfJfDUxdcuPGMUyBgH7kovsSGoGQI++1RU7OBfq/ha4Umc8ifLnOqLyj5lFEjN\nT/tNZ0eAQR2NVofA31z98ZacpvZtenA1AtVAT3JObHw/uuq0bmP5EY64ON4Yggb0gL2vTcyu226k\nGvKhr1q/O1b+LvQ7z2Ct+j7B57+TbHMAv0aVb659H1h25cJfJfJC+6/9dit3ljdBz8y/129ioj8J\n4D/Njvvh3xsaHrn2Zv16sYbpAoOddphQiHDQ2bqwKINeImyK8G9+/wZ+ckdxj/7Hfv/Xzw74XCrG\nk/+6tnXbxvK/9Cm57AhEknUp0icvv1fg17pO6wU1Cr0/3qi6qUONVKb0MNDDbDvAjdovDMFOhSP0\ncaTdDP65NAYrDyAm+V44GpoBe4vftS++gs7WJ4+enZd7Vsu5Wcdy0f7tH2hFl1/6b9f375ugJ6I/\nyMy/I5v/BIC/kh4Y3fs2yGwN7wr6KwMgXVDjbTIKfG2KT60DAMweOTLZeQMDUlzDOUhdd/3ESNZB\ntkf2IMJ9IL4BZTwZoGjn0B84uyfQoZffNRkCs6/F9hh1oMf7ANwjwD0U6bG9Bd62eTXv4GOstZ6r\n+mwAcuDz9tUAndhFl0O/9xgOnrvzXtgbCE3elXp2l952zfU43cK9A/6pEfjIXXZ/BsAfBfD7iei3\nAPxxAN8S0c/Kn/oNAP9s+uGnibwIv32SbFUnn0svqPKoaoXMSYmjYPRNq7qHiS8c/DTO4ZO++Ai8\nbtvAwGcQcoUfxqB2JDLoe53mp/465KLwLc9ROuQsj/528Ale6UGi6rabER58HVhEmIEno/rGCNiy\nhj7E9BeqP5R+PdJu1a13YG0Isu68F/bq3r9DbeNDnCt/VjPGHjnkV3C/R0zPzL+QNP/yrf/9KfQR\nfi2rVzAr/O2bQt3NctYepqJAPAwe0NNQNRC66g1UZ5XP4J8y89OR1sWPSt8U/jQGYH5Lz3D07as4\nIvRL2DFm5rXtTFbRxbWHV3qF3iXunMqbDEiAe6fyz6D3sO8MQQR7NcQ2Ar9WfavyYgTYw65tL6L0\n6tp39948WIPX5uZP7n0G/F11twbjwfL5R+Rlan7l2q9g72rfbtHe901tlBNTHcDL8FYXVduJL5g6\nxXPf/G6xR1qVn4FHr69cfBvP34P+gJ28k1wMvzICQIjl1eCJwg/3PsTxpi2CPwFvjQC8EbiCXrdn\n2K/i/Az8FfRR4c/QZlXegM7yf/AwCt29t1l6UftJ6SPsb1H47DMPlk8LfbRANj6P71G/gj6DXdsA\njKRSczVBGCPSKvWx7T695sF301dvlwi6rw+V17WBo6Oaqd+Af8TzxwT9gWrwMIk8hZ0X4JuQxgMP\ncfHNN9VEnjFcHvyg9DauBzu1j8q/gryYtnneuvUUVjnkcR3H6Z8B+PyRWwXcgx6y9Xjt893Z0Xeq\n9pCY3oGfqfbKCNwxDJ+pn/56WSXy7sTzuraALwwA9SLKVACqFaW2LiyS7qn2EIsoubr1wEbM8x2r\nw72a2+LbLK4njoXy98eFBHnvBxTM0DuXPoJPxUznZZRevzH7bw/GiOf1NxgjkIGvCt+V3/ze+NvX\n0A/Xfu3ir5N5dr2aJMPX15n/wybrwjDb7tqzZu7NxBgne5V/ZVCM32O5q+grF/+LhV5j+l3W3u5T\nQbb1VVFXlNCetyeZKloePmldzAFH7aZzM9miJ/P2S6bwFvC4Hjd9A93H9xrbez2P78i9Ab0BPXPv\n5UzNwJP9BSaR51oS8K3SbxTeuvkR+Bn6FfBrtc+B19g8Qu/d+NWTdkvFN/AfAj4xo8iUbvq4rD4+\n20bc8Rrkt5RPmb3/oOVuIu+Oa78tDJL54nWsfikEFpWvMtKpiLqN2SbVVYhffE7kjWUAPo720DuX\nOKh+VPmo8PFdubPKqw8QZl0zBmzaJuPem282XreV/wq43wDzWzAy94D7bdYAZMAT+Cb0e+AzA7B/\nDv4a/tjd96KuvP0bfDYPwJUT+uZZBV/ndBzgoyXzMpW/UvwrY/HFJ/KeuPaxbNWe+yQbXNAeQpE5\n43QEmvbFd0XHcPHZ3uwp796UkqvfGcvXSlT52b0/Ou476G37FLcHhU+VXmB3RiBV+g34GOBbbE+q\nDAAAGsdJREFUF94pfDAA8ZfcgX5Vz5Q+V/kr6GfgXXKQtZxpOfg0L6vAeGdDLwnsO4DvegP2uAfL\nl5W9j25/BvqifQg493e/aWlTbrUx/DqzDhdq4/SpgnGa/2gecUdTfdz0rS268bMBsBNzzS5+c++j\nB5C79prmO7raZ678yt2PPsjaT0FvBa7Bz+L3J9Bn4N8FPlf6Gf4d9BF4p/ShFDv6Tt9WY0DH6evZ\n9qXSPx2w82B53+w9YQ287YNfqfsWfujgOrCp9ye+6njM8ygEphNHoZZslGQg+p8PWJDHY6fs8bGe\nqOw2Oo/1XOW92o936q3Bj+3+m7tfZrR7r/TxU9lUo9nv/xjQx1dLtXrsY58flkkz+zzD3qGvAno9\n+2QYRbPz/W2zouzfoYNLr3n9llt/dcz3LpHHmJU8GoAFzLvSXvgY2qgnoPufJjT3v8oz31xZnrun\nBjzO8fdZP2XQ0Gw1Dzd2DXq82RU9H7vbuN7W1yrvoa+ptq701qp9Dr6tx5Y1+NfG7i3Q3wc+g323\n/Toy8NrGM/QK/IC/humr0aFXwOkp7LuMfgQ9U/0vPpGnXXa7obYxc3+3YKh8tu6KX9CAL9zmm2PC\nYVP10p8PNje2gX0ksHL31sJekpt/uPE5+Kr2ra04sGcXf2yv0MvgB66B1+Up+CvYPwR6D/oK/vgQ\nzsYAmKG0wwAk8NvHZM86j6vXEXcVwHei9rq+awBWwMf4Pmv7XkBvE3kW9g8FPgkDJvD1kIP7fG3l\naO/DG3vbDXxSgN7Cz9xHqRFjUvvVVJtW6QfkI2l3Oti9iz+XuWd/p+rWxQfuAa9KP/dE6N77ycvc\n23nu4kfgV0qfwT4n9MTF7yPtAvSi8n3GGxlX3+BnM9cd2nPwAXA1AG9S+BXkK9f+i4feuvaZ0l/B\nvXL95f+PMb269TL4rMVfRwMeaK+7bktz10+d2YblBidVeIwZb1jVf3+Deze/GYCh7sWtY9b+CfR5\nQHHftc+MwLhkHwZ+3oewK3YAz6zya9XPlT5r7yqvg284KL5k5UucCOMchWqV7rhW8F2u7JSBv4vf\nY/tK3b837n1046+G2D6J6yPsNP5kd/GB9joscd813mgz16CPHa9UQVTczU2Q/WoAbPt0g9uHZ7wB\nWL8lbzYEb4E+DzSeAz+U/h7493wN3vwKD/xa5Xdq7x933br77GN77W/v7j/rQzR+LH2DnvNx9d/h\nOqF3lay76xF8r/rpM8gj7B/BxddN69oD6IMo2g0IaLq+AU9teicuMk8d0Mf086j3/6erfeyTztBr\nJQJfN+sPg37th9wBflyyXVcdNn9l/osRev9r1k/hRZXP16tZdnLFd33xOuDG1BV6skNrdc56hf61\nAR9j9lTdP0ZCb9dX/2B5P+gvxtNnIF8VVXX7uX77qqvP+jXaI6kgAlGbTJIIbWqpUmUuOmAMNZV6\nkrnP3Hir8H4KTvswTa7ue5XPMbkCPQf+4w3OuaPyet7vQp+9KXYF/moeu/zFFMOt90k8bWvQk+ma\nK30G2wF8UQU/sY3hb6n8WzwAawweLO8L/V0Fv3sshhsPmPHzZFdN4Vn3E4OJJCQgcIU8iVsCRphg\n38ev+TPymqirC7W/du0LsmEuGfAeRYR9V331b+myy+G3fzmDew39XuVH4m8P+W4Aj3Pt3Xj6V3nj\nEPpIuw7+if4QjXuY5i1qfxXf3y0PlveBPnPp3xDHW9iztXXr/TKy8UQ6bJf6nO9c0dSe5hv6jjub\nTX9llT4C71X+yrXPcuAr3PJvDFwDj+mo3K3H8i+vv9E9MzZDbyGP7v+d4h6Uia59gP7gVxTtg+9D\na4c7r3F875tXyJ+49lewP3H1HyyfFHqOCYaNek/vOr+I2ZfQ31jGLW232TUWlqG7xO1lktReD30U\n+bLmu/YbevICwvTW0EE3w4lvNVvfx/IZGndh3wEfOie70t+J5+3/cgf+t7r4M/Sj7iaodIpfhytv\nobfFvXdOYni2ao6enScDd1f4CPxq/YGFd4biwfJJoa8b6O1LEbOhs7ehz5Y7hoDHIZKQbyovda7o\nL2MsavLlUV0U4b7II7tqA2RKa523vqC0OnN/iYOf705V/hr6Wd19+w70HPoMfl2PE3df5XfBxDjm\nCvJoFPZK71Xfvi66bRsvwL5KWobVHjUBvfJw6xfuOmUw3wE9tq268zLYZR8v2ruC3Vg+O/Q7qOMI\nOv3MEnrz/z5aEuA1yQeWekV/LVMhBmQUH0qbgqsDL+9ra7Afo5+fGISWFyhUUfhAod00WLp19tt5\nH8OTq1+XmJ1AWM9Yr+P5uL7n1mdKH+H32z6BV9yZCTE9G/DZtAVD0F8m2Sex5D6evj8Lr/Ar5Ctl\nj7H7HdDfou6vAJ9hOxqCLx16C/dqyOwtpYdZY7Edlwg8D+B1u3159Ed1Ia9bRqnjyT1GH8RTiHHS\nITmCYyg+zCg7ajH8ehqs2dXPgc+3P9zFHydvVnrbugoG7rn316Db7QfQq4qb98Mr9OM98aebvHIM\nvBnj6TVxBzOe3sIdt1O473gCd43C6YF30J+m7f9v71xCdVmuOv5f1UcHPkCCmEi4eiMoqJPcSRCi\nGEXlZhJ1oghCcCAORMWRcaLOFEEJIgjiVVTEB4jRkeQqHh8TQ8SbxBdRkgteyT1xoGBGObtrOai1\nqlatXtXd37f3+fbeZ/eC3lVd3d+3v3781n/Vq/vOQO8aGDzsHcBpADxW8nD5NbOw63rkABT8R6Li\nk4T3Cv1UtpcW/vIeuazva1PgKRXFT6kHfwi5X1e1X+Lgu+laukftbegeodunMfB9qp8Yq/pY6ft8\nO1JbkdkHvWnU09dD11dM9eAnO8IuM2ju19PMbU68VfoI9DWItyKAPYsJ9fnKQT+79Z0YABduyKtK\nvtaIdwr05zgAD7uUdcAzAGZQIqRcoE5TBiYZtDNBXvJYXqWVodAzUtLZcYzEbaZcIlt/t632Iycw\nrwC/hD9a4sk26+rOi5M4Bt2na6D3Sr8ffA/5EHyj8BVw8wLJDnp580wBPNdx9C3Fsk5v4A0n0Iwg\n36P2K6G9h90DX7edYLcW3qc10Ef1euB0yNUiV2hghwFe4U8TkDNkYg7KgzWTvPxxKmAzqLwrXqbr\nzllSMBITEhJmKq+ZTtSA7wGPoE8dAnvUfg/85bDHat9vH4X37YTq9j2uxz5IJAr1rwW9hV/T3NKm\n8mwG2ZiXUZi30dSZcwHgZ8N/DZWPnEC+T9Ar7NIIvq34CPKjssg87A50D7tdR2akCdCh+swATwyS\nGKHd1hn6ckkiSVN5o0wS4DMt6+tb0C9Bj+HfUnfFLWMEe19WTtN6eN/veb3wPgJ/H/StPl8iq17h\nC/BsGu9yGzNf3zHnYNehta6vfHN+/J46/tr+e1Rf6vce+AVnG3ZR6Elht+Ab4MOWe02jMmsB3Gvm\nwe9gV7XPkEdol3Be3/fGYFhICvIygy4lzFzeDZ9Z8kQulI+fcBtD3yPh4R8p/Vodv5yepQNop82e\n3Bj0/pP7YN+j8go7gUPgl9D3ZZPAX8A3+foSCi7XdAZwxbAPrdR3ztWBN4M+8VUHsNVQd4b6+0Y7\nr/b5LnXZRXX65GDf3XI/gv7UEN9CHpR3ai9P02WZpCNvh+jPLxEyWbXPEtKL0gv8qQtatx922QO9\nDfwyiF6r18MdRw9+OSXkjnQd/jHwy/6FCPxrQ6/97ZLat81MEsprQ119vZROlhmt27I9YfgNAO6/\ncwS8Kn++a112c9B6D3lQJQR8+8459vDjhNTamurz+mLVHhmlQY/t97gvpzI5d+YCSQaVqblZQKaE\nzBmJCTO327SDns6Bfk94H7fs2x9vwS9HtzyhNsy3e9rtPdgjFzQ+Egv7Enqz8Di0V+gnmSjTWuRV\n6VmeQY/4QRV71rfK94Tzp4zYi4AX2LNR+zvTev/UrRNMd7f2gOVyEL5hLy2+rf+eziyoUXm0354l\nu/yjYFtG6cabpE44EWgujX9IALKmAJKMkid7qLwYvpupPNyj3M4rb6WlHpsFcrTETc/gEnab1/2s\nC+jzfu+Fa+FxfZ5QoKTIAXBf70/sWve5ucQk35OQ69TXZBvl6sw4FEg19dCO8iO498yIG4HsQOeV\nLr5sl7mIaM5mYVlwl6BfiiISA5MDflK1p7Zf/xCrVg60AxxG9pETGMF8CvTeAdh0BjCh1AmnUh9U\nR5Ams530ZkcdtVcewltgzyjj/AvUuQXHZGEWTNjkA9AzR/BHSj+qz7eTt4R/qfQh7GyBl2O3+7CJ\nVdi4MIWZ1Uk0RSfjDPTzOsddR9a1FnlzfaRFfhX8NQewF/gdqs/OAbDbl5+2ersFfhbgZwG+zre5\nK+F9pPQTStU4McACvl4UVUB9fiajj+KHsEfAbm3fA3pUHgE/lUVBh6RJ3pibs4zsywAJyKXrkkoQ\nIAN6isIzMpEBn0TtDcBMbd2G8OYFlR3sZn89g1v1eb81Cu1tPjnAfd5C3pWxOQp2jkDC9eoMuEUI\niWU/Tbt+9gY+jANYvFpqT7o31N9bf3eAswfflKnKz1cCvcA/C/z1bVmnyDxuAXoNSSaVcvXC6Nvs\nRseh22w6tFPD+Qj6vJIa8Glm8CQt/gk1pEyi/jyVFswydz+Vej9IZvElAV9gJ4Gd5N3zZADWN9iQ\ngdyqO6i+ucZv65V+O8S3wI9Ce/2mEPgOdk3b/snsV+F3ZQp93Z9znDdPtakvnjBpOB31JqHfCusD\n9edRapxAntsyz8BVAPzV8s5ftctCL8CzBcoAP0taxrUvv28I+jlwn+IERupe8wxM1G6uScLKqWxP\nqUQ1NAOcIM/gEyBJwE9tPcs03m5R4AlN5TuosYA7W6dg31jbxU9jDYfbexTaaxop+DJFD3hd4GC3\n22TJ/Xqy5a4u3/rj0dfnt4Df28B33XA/AD5UegU+94vCr197il22Tk898Ax09Xh1CkkaKdZC+wX8\nzxr6SOWDOj1NDMxURvFJmwVPBfryrP3yeC42kHMilCdxm3JNAeMMYBwBeuADwL0T4IpoOZNbIb7d\nwwMfd9lhAHoAvYCeLPTeCTjYoVNevQOQuns3qk7LTN2+e63UCPS9dftzgHchvgfeKj0/RRuMo/X4\nuQF/xf1yil1c6XVkm0JFWRr40EJ/TrKP/SzQ3YIh8Fu2N6SPAI9UX5+qInV6ZAbPVMDPVGIvCflZ\nQnxO5URwKmCDYEAHUMFFD7emyaq6T01EYN5Ia51ADHs7o9yfWVcyigkc5Ar3AngLs4UXg21tH4zy\n3MMOAzjZlq6R0m9Bvwb2KdDrvoHSh8Cryqu6G7W/cuB7zrbs4nV6BspF0zJGnbWWMpBTWUbh/dCe\nhcKPVN2H99oQOaGE+gI8qcLLNpaGPU5U0jIRv8CeSJxhAVXT4hDQ8tk4geSg97AH9fp2uvaE+PEn\nrMLrnov6vFd1qFo7oNXxV9hRHQFkqqsqfF23qVwj7X+3Ib11AmGdfssBXEfl17YP+umrA5B8nkUI\nTYv9laYG+LsFvVdroZ5gFJ50mmphpdzUAsiKrSr9TcMfqb2F38Cuqk5SnycJ7yE3qQJuYV/kp/K/\nWBwBy2CmCnOSn5fLPuWQjcoDBv5lnd+exZF29zYO7fVbFuG9hR19SK+wFth76JEN+PU8i7qb9S5v\nlFyH0uq5V5A71V8D/qYa9EbbA4XnCPirIhYa/drGuyuB/ynL0l+OTbt4eA+oMsjFJrnXc0mlkbuo\nv/+8ybMti2C1O45U/BTgRwqvIX5GDee1Aa/W8+2+8lAOnjQvywTxdvKzpc+yAi4Hr7tomA/Wn+vg\nHjTw6Vnrke3PsHUMbevIPTTVX8APxqJ+Lue2he5osHdOAC0s13Nrz+NszusihOe6vqnyW47gJoB3\n2/yAHA9+VvCzgZ7bYVmlv+KluG7ZxcN7wMHPBXYSxb+iBrsOydXtAGpfft1H/+jNdF1Vf+TyEfid\nwrtFbzRTz+8G7wjsVB1A+xzqKL5SpqpehyhrPoszkH05YdnAJ3OY7XprvbdXo4d8WacfuwgbAXiw\na8PbolFOjl3hdnnrACzgXRqVWWj9+qjsFMVfg3vFEXBQHk2Yqenc0jnLVyvgaMqusN+58P7zbl1B\nrbcVo2+hM31yj7g4AWKJAjKQ5hoBg/X7HOgcgB86guzyI+AjsCNnMGMB/7BMQU8u78sI5TXcyeTF\n4+n8BR48cajsy+IluZRRO+X+Eth1bzHobW8N37vWdavqZtHzS+a823x3bfbAHir9YP0ctd8Dvlln\nV+ah16mxtjvOttDrwvKRzxuwK+Rc2HqKlp4i9rei9EMzTiBzG67bAT+XKkAyn7E3l4e7PhHHh/Qe\neq/2FvgI8oHKL2APVB46LDfK2zINaQTsDn6FPbWICAJ5mbXYgC9dhGxe8VVOcjnd1uuObp5RC76s\nVyVX+HvIbSOdqnqru/dlIfSngH+O0kcqHwG/kffPr4vKuqG1Bnw7nl6H2GpDnYKvgEfLnYa+C/G1\nYc8ovFX6rPV8Ted2cLa/X28or/JR2WYY7wfhrEHuWu9Xld6uj4D38BvoNa/w27Rur4vCz3XwD0mq\nJ9srPMwlWJoP+h34VdU9+IEDyH0aKvxe6KPw/rpq7x3ACHZXFsFtQ3kt4yt0Q2q7VMBX6G34XsH3\nas/3AXpzZ9Uf6oAX8SgDdQR+adDGpPubGymN4DZlndpHKm/TUWi/pfgR/NH6FvCad2F9lCKEHhV8\nGOCJSHA/Bfh2pbzaq5XG1B78hQNQ2K2zdg4gdAjngH+K2o9UfhTir4T+iyfUGuj1ibb5Cm2gjYE9\nGk+vdfgaynOs8r4avWW3G97bO40h/dNlNYvaTxmtxdp+zihHzkvgKaMfBOSV3gM+mESzqfh+lt2W\nykdgR/X71Nfh+7r6sk7fnjKso/7QKXzdZ3FFYtXvL1L/ibDcqHrtVw8iLw81uXRxfSzcW+BfV+23\nGvOiNFD8xZNrBfpuLL3ct1cOeu2HnwdK7+v0N96QR0QvAPgdAF8hl+HXmflXiOgtAP4QwFcDeB3A\n9zHz//rP76rTO/B1Rx25l1lOmIVWgKCMOpDHDvIJQbfARzeXB94PxNmj9h72ker7MH6k8j6sN3l4\nta/K7vJJwUd9zXa7GhHwviQOHMlvU1XvHDDHeQf5IgLz12UP+Htg9+tbdflofQS9hT9Q+dpiL8CH\n4+gd+BHgXu112ym2pfRPAfwkM79GRF8C4B+I6FUAPwTgVWb+RSL6KQAfkGXxYWu+1b5TbrsDy32S\n5fq7fWtfbioRQTI3ilYJohA/hN7C7vN71P4R1kP6yBGMAPd9974Bz8Be4R+24Ku6S4ivzgGQGnoc\n5vcWA2+N6sWy55rjcx8A7hWe/HWKoN8K789R+jWVX4Pf5EePtOqeazcXoCv4LKAz+jH16JU+Uvdn\n0mXHzG8CeFPynyOifwXwdgDvA/CtsttvA3iMAHpf1+husCim5Hab1fskt/2qgGXU/vss/XeUG+w+\nrO/q+xH0e2BfU/utkN46gjXgfZnvqnOwdy33yZ4gtOqAdNehOgC4LrjItmH3u5MBfQF8DhyDV3Xr\nEPy12Qv+Xvj31OPPcQhrCm/K7GCbCnlufe920M2w1d45gVOu2O46PRG9COAlAH8P4K3M/EQ2PQHw\n1ugza633APro0DkAnXDD+kfvZ41aqah8JgC5NeZZ6MPW+xH0W7Dvac3fCvEz+nB+C3hXl4cJ8RcO\nwCp83TdwAHohtuxE7pfAcwf0oopl8jQo3wX+HuC3lH4v6Cvb/DvmIuC1Ec+PsLOQV4XnbdCfaZed\nhPZ/DOAnmPn/iNpdw8xMfWWx2odN/kUAX6OfiXbmeFudbWfvXxImJCU5keAl/LX13tYjI3W/jgOw\nN99WiB+F86Mw38LsQnxfHi2UeFn+rGwE9RrwEehr0G+p/l7499blT3QEUcNdB/5cwvsMtEddcftq\nO4HGQ7/WR/8UwKdQGtf22Cb0RPQFKMD/LjN/SIqfENHbmPlNIvpKAJ+NPvset751/RflbCIARlfn\n777Xqfmo++6sRdXZl5Er17LRd0Tb1soBMwLJ/I61vDf7O5+1bcF+KvCjz0fbtv7v1uf3OKq9/8Mt\n7PK1FsRuVx7kd57WF2VR++uVS7V6O1CR9FcA/Aszf9Bs+jMA75f8+wF8yH92ZJ/au+M9ssf/edu/\n4Obt8Wu3/Qtu3p7H63QOT1sa8G4APwjg24joH2V5GcAvAPhOIvokgG+X9V326TN+5F23x2/c9i+4\neXv8sdv+BTdvz+N1Ooenrdb7v8PYMXzHGf/vsMMOu2W7RG3vsMMOu0NG7B9Gd1NfPGjRP+ywwy5j\nzL4jvNgzg/6www67m3aE94cd9sDsgP6wwx6YXQx6InqZiP6NiP5dJuk8F0ZErxPRx6U78yO3/XvO\nMSL6TSJ6QkSfMGVvIaJXieiTRPRhIvqy2/yNp9rgmH6OiN5w3c/3xojoBSL6KyL6ZyL6JyL6cSk/\n6VpdBHoimgD8KoCXAXwDgB8goq+/xP++gDGA9zDzS8z8rtv+MWfab6FcG2sfQJlJ+XUA/hLBhKo7\nbtExMYBflmv1EjP/+S38ruuYznr9RgDfBOBHhaOTrtWllP5dAP6DmV9n5qcA/gDAd1/of1/CnuWo\n9mduzPy3AP7HFb8PZQYlJP2ei/6oa9rgmIB7fK2Y+U1mfk3ynwNgZ73uvlaXgv7tAOwgyDek7Hkw\nBvAXRPRRIvrh2/4xN2i7ZlLeQ/sxIvoYEb1y36os1s6Z9ap2Keif537BdzPzSwDeixJufctt/6Cb\nNuY6beS+268BeAeAdwL4DIBfut2fc575Wa92255rdSno/wvAC2b9BRS1v/fGzJ+R9L8B/AlKVeZ5\nsCdE9DYAWJtJeZ+MmT/LYgB+A/fwWq3NepXtm9fqUtB/FMDXEtGLRPSFAL4fZabevTYi+iIi+lLJ\nfzGA7wLwifVP3Rs7eyblXTUBQu17cc+u1U3Ner3YiDwiei+AD6I8IuIVZv75i/zjZ2hE9A4UdQfK\n5KXfu4/HRUS/j/L4sy9HqRP+DIA/BfBHAL4KKw8/vasWHNPPojzi4Z0o4e+nAfyIqQvfeSOibwbw\nNwA+jhbC/zSAj+CEa3UMwz3ssAdmx4i8ww57YHZAf9hhD8wO6A877IHZAf1hhz0wO6A/7LAHZgf0\nhx32wOyA/rDDHpgd0B922AOz/wcPcIpSAXPsiQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa1177b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "plt.imshow(radius)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.19 reading and writing arrays.ipynb b/03-numpy/03.19-reading-and-writing-arrays.ipynb
similarity index 95%
rename from 03. numpy/03.19 reading and writing arrays.ipynb
rename to 03-numpy/03.19-reading-and-writing-arrays.ipynb
index 9f5ac0e3..509c473d 100644
--- a/03. numpy/03.19 reading and writing arrays.ipynb	
+++ b/03-numpy/03.19-reading-and-writing-arrays.ipynb
@@ -1,1309 +1,1309 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 数组读写"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 从文本中读取数组"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 空格（制表符）分割的文本"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "假设我们有这样的一个空白分割的文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing myfile.txt\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%writefile myfile.txt\n",
-    "2.1 2.3 3.2 1.3 3.1\n",
-    "6.1 3.1 4.2 2.3 1.8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "为了生成数组，我们首先将数据转化成一个列表组成的列表，再将这个列表转换为数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "data = []\n",
-    "\n",
-    "with open('myfile.txt') as f:\n",
-    "    # 每次读一行\n",
-    "    for line in f:\n",
-    "        fileds = line.split()\n",
-    "        row_data = [float(x) for x in fileds]\n",
-    "        data.append(row_data)\n",
-    "\n",
-    "data = np.array(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 2.1,  2.3,  3.2,  1.3,  3.1],\n",
-       "       [ 6.1,  3.1,  4.2,  2.3,  1.8]])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不过，更简便的是使用 `loadtxt` 方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 2.1,  2.3,  3.2,  1.3,  3.1],\n",
-       "       [ 6.1,  3.1,  4.2,  2.3,  1.8]])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = np.loadtxt('myfile.txt')\n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 逗号分隔文件"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwriting myfile.txt\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%writefile myfile.txt\n",
-    "2.1, 2.3, 3.2, 1.3, 3.1\n",
-    "6.1, 3.1, 4.2, 2.3, 1.8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于逗号分隔的文件（通常为`.csv`格式）,我们可以稍微修改之前繁琐的过程，将 `split` 的参数变成 `','`即可。\n",
-    "\n",
-    "不过，`loadtxt` 函数也可以读这样的文件，只需要制定分割符的参数即可："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 2.1,  2.3,  3.2,  1.3,  3.1],\n",
-       "       [ 6.1,  3.1,  4.2,  2.3,  1.8]])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = np.loadtxt('myfile.txt', delimiter=',')\n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### loadtxt 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    loadtxt(fname, dtype=<type 'float'>, \n",
-    "            comments='#', delimiter=None, \n",
-    "            converters=None, skiprows=0, \n",
-    "            usecols=None, unpack=False, ndmin=0)\n",
-    "\n",
-    "`loadtxt` 有很多可选参数，其中 `delimiter` 就是刚才用到的分隔符参数。\n",
-    "\n",
-    "`skiprows` 参数表示忽略开头的行数，可以用来读写含有标题的文本"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwriting myfile.txt\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%writefile myfile.txt\n",
-    "X Y Z MAG ANG\n",
-    "2.1 2.3 3.2 1.3 3.1\n",
-    "6.1 3.1 4.2 2.3 1.8"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 2.1,  2.3,  3.2,  1.3,  3.1],\n",
-       "       [ 6.1,  3.1,  4.2,  2.3,  1.8]])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.loadtxt('myfile.txt', skiprows=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "此外，有一个功能更为全面的 `genfromtxt` 函数，能处理更多的情况，但相应的速度和效率会慢一些。\n",
-    "\n",
-    "    genfromtxt(fname, dtype=<type 'float'>, comments='#', delimiter=None, \n",
-    "               skiprows=0, skip_header=0, skip_footer=0, converters=None, \n",
-    "               missing='', missing_values=None, filling_values=None, usecols=None, \n",
-    "               names=None, excludelist=None, deletechars=None, replace_space='_', \n",
-    "               autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, \n",
-    "               usemask=False, loose=True, invalid_raise=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### loadtxt 的更多特性"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于这样一个文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwriting myfile.txt\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%writefile myfile.txt\n",
-    " -- BEGINNING OF THE FILE\n",
-    "% Day, Month, Year, Skip, Power\n",
-    "01, 01, 2000, x876, 13 % wow!\n",
-    "% we don't want have Jan 03rd\n",
-    "04, 01, 2000, xfed, 55"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[   1,    1, 2000,   13],\n",
-       "       [   4,    1, 2000,   55]])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = np.loadtxt('myfile.txt', \n",
-    "                  skiprows=1,         #忽略第一行\n",
-    "                  dtype=np.int,      #数组类型\n",
-    "                  delimiter=',',     #逗号分割\n",
-    "                  usecols=(0,1,2,4), #指定使用哪几列数据\n",
-    "                  comments='%'       #百分号为注释符\n",
-    "                 )\n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### loadtxt 自定义转换方法"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwriting myfile.txt\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%writefile myfile.txt\n",
-    "2010-01-01 2.3 3.2\n",
-    "2011-01-01 6.1 3.1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "假设我们的文本包含日期，我们可以使用 `datetime` 在 `loadtxt` 中处理："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[datetime.datetime(2010, 1, 1, 0, 0), 2.3, 3.2],\n",
-       "       [datetime.datetime(2011, 1, 1, 0, 0), 6.1, 3.1]], dtype=object)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import datetime\n",
-    "\n",
-    "def date_converter(s):\n",
-    "    return datetime.datetime.strptime(s, \"%Y-%m-%d\")\n",
-    "\n",
-    "data = np.loadtxt('myfile.txt',\n",
-    "                  dtype=np.object, #数据类型为对象\n",
-    "                  converters={0:date_converter,  #第一列使用自定义转换方法\n",
-    "                              1:float,           #第二第三使用浮点数转换\n",
-    "                              2:float})\n",
-    "\n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "移除 `myfile.txt`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('myfile.txt')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 读写各种格式的文件"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "如下表所示：\n",
-    "\n",
-    "文件格式|使用的包|函数\n",
-    "----|----|----\n",
-    "txt | numpy | loadtxt, genfromtxt, fromfile, savetxt, tofile\n",
-    "csv | csv | reader, writer\n",
-    "Matlab | scipy.io | loadmat, savemat\n",
-    "hdf | pytables, h5py| \n",
-    "NetCDF | netCDF4, scipy.io.netcdf | netCDF4.Dataset, scipy.io.netcdf.netcdf_file\n",
-    "**文件格式**|**使用的包**|**备注**\n",
-    "wav | scipy.io.wavfile | 音频文件\n",
-    "jpeg,png,...| PIL, scipy.misc.pilutil | 图像文件\n",
-    "fits | pyfits | 天文图像\n",
-    "\n",
-    "此外， `pandas` ——一个用来处理时间序列的包中包含处理各种文件的方法，具体可参见它的文档：\n",
-    "\n",
-    "http://pandas.pydata.org/pandas-docs/stable/io.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 将数组写入文件"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`savetxt` 可以将数组写入文件，默认使用科学计数法的形式保存："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "data = np.array([[1,2], \n",
-    "                 [3,4]])\n",
-    "\n",
-    "np.savetxt('out.txt', data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.000000000000000000e+00 2.000000000000000000e+00\n",
-      "3.000000000000000000e+00 4.000000000000000000e+00\n"
-     ]
-    }
-   ],
-   "source": [
-    "with open('out.txt') as f:\n",
-    "    for line in f:\n",
-    "        print line,"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "也可以使用类似**C**语言中 `printf` 的方式指定输出的格式："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "data = np.array([[1,2], \n",
-    "                 [3,4]])\n",
-    "\n",
-    "np.savetxt('out.txt', data, fmt=\"%d\") #保存为整数"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 2\n",
-      "3 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "with open('out.txt') as f:\n",
-    "    for line in f:\n",
-    "        print line,"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "逗号分隔的输出："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "data = np.array([[1,2], \n",
-    "                 [3,4]])\n",
-    "\n",
-    "np.savetxt('out.txt', data, fmt=\"%.2f\", delimiter=',') #保存为2位小数的浮点数，用逗号分隔"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.00,2.00\n",
-      "3.00,4.00\n"
-     ]
-    }
-   ],
-   "source": [
-    "with open('out.txt') as f:\n",
-    "    for line in f:\n",
-    "        print line,"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "复数值默认会加上括号："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "data = np.array([[1+1j,2], \n",
-    "                 [3,4]])\n",
-    "\n",
-    "np.savetxt('out.txt', data, fmt=\"%.2f\", delimiter=',') #保存为2位小数的浮点数，用逗号分隔"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " (1.00+1.00j), (2.00+0.00j)\n",
-      " (3.00+0.00j), (4.00+0.00j)\n"
-     ]
-    }
-   ],
-   "source": [
-    "with open('out.txt') as f:\n",
-    "    for line in f:\n",
-    "        print line,"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "更多参数："
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    savetxt(fname, \n",
-    "            X, \n",
-    "            fmt='%.18e', \n",
-    "            delimiter=' ', \n",
-    "            newline='\\n', \n",
-    "            header='', \n",
-    "            footer='', \n",
-    "            comments='# ')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "移除 `out.txt`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('out.txt')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Numpy 二进制格式"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "数组可以储存成二进制格式，单个的数组保存为 `.npy` 格式，多个数组保存为多个`.npy`文件组成的 `.npz` 格式，每个 `.npy` 文件包含一个数组。\n",
-    "\n",
-    "与文本格式不同，二进制格式保存了数组的 `shape, dtype` 信息，以便完全重构出保存的数组。\n",
-    "\n",
-    "保存的方法：\n",
-    "\n",
-    "- `save(file, arr)` 保存单个数组，`.npy` 格式\n",
-    "- `savez(file, *args, **kwds)` 保存多个数组，无压缩的 `.npz` 格式\n",
-    "- `savez_compressed(file, *args, **kwds)` 保存多个数组，有压缩的 `.npz` 格式\n",
-    "\n",
-    "读取的方法：\n",
-    "\n",
-    "- `load(file, mmap_mode=None)` 对于 `.npy`，返回保存的数组，对于 `.npz`，返回一个名称-数组对组成的字典。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 单个数组的读写"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "a = np.array([[1.0,2.0], [3.0,4.0]])\n",
-    "\n",
-    "fname = 'afile.npy'\n",
-    "np.save(fname, a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.,  2.],\n",
-       "       [ 3.,  4.]])"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "aa = np.load(fname)\n",
-    "aa"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "删除生成的文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('afile.npy')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 二进制与文本大小比较"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = np.arange(10000.)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "保存为文本："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "np.savetxt('a.txt', a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看大小："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "260000L"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import os\n",
-    "os.stat('a.txt').st_size"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "保存为二进制："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "np.save('a.npy', a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看大小："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "80080L"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.stat('a.npy').st_size"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "删除生成的文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "os.remove('a.npy')\n",
-    "os.remove('a.txt')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以看到，二进制文件大约是文本文件的三分之一。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 保存多个数组"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = np.array([[1.0,2.0], \n",
-    "              [3.0,4.0]])\n",
-    "b = np.arange(1000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "保存多个数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "np.savez('data.npz', a=a, b=b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看里面包含的文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Archive:  data.npz\n",
-      "  Length      Date    Time    Name\n",
-      "---------  ---------- -----   ----\n",
-      "      112  2015/08/10 00:46   a.npy\n",
-      "     4080  2015/08/10 00:46   b.npy\n",
-      "---------                     -------\n",
-      "     4192                     2 files\n"
-     ]
-    }
-   ],
-   "source": [
-    "!unzip -l data.npz"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "载入数据："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "data = np.load('data.npz')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "载入后可以像字典一样进行操作："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a', 'b']"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.,  2.],\n",
-       "       [ 3.,  4.]])"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data['a']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1000L,)"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data['b'].shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "删除文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# 要先删除 data，否则删除时会报错\n",
-    "del data\n",
-    "\n",
-    "os.remove('data.npz')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 压缩文件"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "当数据比较整齐时："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = np.arange(20000.)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "无压缩大小："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "160188L"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.savez('a.npz', a=a)\n",
-    "os.stat('a.npz').st_size"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "有压缩大小："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "26885L"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.savez_compressed('a2.npz', a=a)\n",
-    "os.stat('a2.npz').st_size"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "大约有 6x 的压缩效果。\n",
-    "\n",
-    "当数据比较混乱时："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = np.random.rand(20000.)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "无压缩大小："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "160188L"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.savez('a.npz', a=a)\n",
-    "os.stat('a.npz').st_size"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "有压缩大小："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "151105L"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.savez_compressed('a2.npz', a=a)\n",
-    "os.stat('a2.npz').st_size"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "只有大约 1.06x 的压缩效果。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "os.remove('a.npz')\n",
-    "os.remove('a2.npz')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 数组读写"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 从文本中读取数组"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 空格（制表符）分割的文本"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "假设我们有这样的一个空白分割的文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing myfile.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile myfile.txt\n",
+    "2.1 2.3 3.2 1.3 3.1\n",
+    "6.1 3.1 4.2 2.3 1.8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "为了生成数组，我们首先将数据转化成一个列表组成的列表，再将这个列表转换为数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data = []\n",
+    "\n",
+    "with open('myfile.txt') as f:\n",
+    "    # 每次读一行\n",
+    "    for line in f:\n",
+    "        fileds = line.split()\n",
+    "        row_data = [float(x) for x in fileds]\n",
+    "        data.append(row_data)\n",
+    "\n",
+    "data = np.array(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2.1,  2.3,  3.2,  1.3,  3.1],\n",
+       "       [ 6.1,  3.1,  4.2,  2.3,  1.8]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不过，更简便的是使用 `loadtxt` 方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2.1,  2.3,  3.2,  1.3,  3.1],\n",
+       "       [ 6.1,  3.1,  4.2,  2.3,  1.8]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = np.loadtxt('myfile.txt')\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 逗号分隔文件"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting myfile.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile myfile.txt\n",
+    "2.1, 2.3, 3.2, 1.3, 3.1\n",
+    "6.1, 3.1, 4.2, 2.3, 1.8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于逗号分隔的文件（通常为`.csv`格式）,我们可以稍微修改之前繁琐的过程，将 `split` 的参数变成 `','`即可。\n",
+    "\n",
+    "不过，`loadtxt` 函数也可以读这样的文件，只需要制定分割符的参数即可："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2.1,  2.3,  3.2,  1.3,  3.1],\n",
+       "       [ 6.1,  3.1,  4.2,  2.3,  1.8]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = np.loadtxt('myfile.txt', delimiter=',')\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### loadtxt 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    loadtxt(fname, dtype=<type 'float'>, \n",
+    "            comments='#', delimiter=None, \n",
+    "            converters=None, skiprows=0, \n",
+    "            usecols=None, unpack=False, ndmin=0)\n",
+    "\n",
+    "`loadtxt` 有很多可选参数，其中 `delimiter` 就是刚才用到的分隔符参数。\n",
+    "\n",
+    "`skiprows` 参数表示忽略开头的行数，可以用来读写含有标题的文本"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting myfile.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile myfile.txt\n",
+    "X Y Z MAG ANG\n",
+    "2.1 2.3 3.2 1.3 3.1\n",
+    "6.1 3.1 4.2 2.3 1.8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2.1,  2.3,  3.2,  1.3,  3.1],\n",
+       "       [ 6.1,  3.1,  4.2,  2.3,  1.8]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.loadtxt('myfile.txt', skiprows=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "此外，有一个功能更为全面的 `genfromtxt` 函数，能处理更多的情况，但相应的速度和效率会慢一些。\n",
+    "\n",
+    "    genfromtxt(fname, dtype=<type 'float'>, comments='#', delimiter=None, \n",
+    "               skiprows=0, skip_header=0, skip_footer=0, converters=None, \n",
+    "               missing='', missing_values=None, filling_values=None, usecols=None, \n",
+    "               names=None, excludelist=None, deletechars=None, replace_space='_', \n",
+    "               autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, \n",
+    "               usemask=False, loose=True, invalid_raise=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### loadtxt 的更多特性"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于这样一个文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting myfile.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile myfile.txt\n",
+    " -- BEGINNING OF THE FILE\n",
+    "% Day, Month, Year, Skip, Power\n",
+    "01, 01, 2000, x876, 13 % wow!\n",
+    "% we don't want have Jan 03rd\n",
+    "04, 01, 2000, xfed, 55"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[   1,    1, 2000,   13],\n",
+       "       [   4,    1, 2000,   55]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = np.loadtxt('myfile.txt', \n",
+    "                  skiprows=1,         #忽略第一行\n",
+    "                  dtype=np.int,      #数组类型\n",
+    "                  delimiter=',',     #逗号分割\n",
+    "                  usecols=(0,1,2,4), #指定使用哪几列数据\n",
+    "                  comments='%'       #百分号为注释符\n",
+    "                 )\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### loadtxt 自定义转换方法"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting myfile.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile myfile.txt\n",
+    "2010-01-01 2.3 3.2\n",
+    "2011-01-01 6.1 3.1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "假设我们的文本包含日期，我们可以使用 `datetime` 在 `loadtxt` 中处理："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[datetime.datetime(2010, 1, 1, 0, 0), 2.3, 3.2],\n",
+       "       [datetime.datetime(2011, 1, 1, 0, 0), 6.1, 3.1]], dtype=object)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import datetime\n",
+    "\n",
+    "def date_converter(s):\n",
+    "    return datetime.datetime.strptime(s, \"%Y-%m-%d\")\n",
+    "\n",
+    "data = np.loadtxt('myfile.txt',\n",
+    "                  dtype=np.object, #数据类型为对象\n",
+    "                  converters={0:date_converter,  #第一列使用自定义转换方法\n",
+    "                              1:float,           #第二第三使用浮点数转换\n",
+    "                              2:float})\n",
+    "\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "移除 `myfile.txt`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('myfile.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 读写各种格式的文件"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "如下表所示：\n",
+    "\n",
+    "文件格式|使用的包|函数\n",
+    "----|----|----\n",
+    "txt | numpy | loadtxt, genfromtxt, fromfile, savetxt, tofile\n",
+    "csv | csv | reader, writer\n",
+    "Matlab | scipy.io | loadmat, savemat\n",
+    "hdf | pytables, h5py| \n",
+    "NetCDF | netCDF4, scipy.io.netcdf | netCDF4.Dataset, scipy.io.netcdf.netcdf_file\n",
+    "**文件格式**|**使用的包**|**备注**\n",
+    "wav | scipy.io.wavfile | 音频文件\n",
+    "jpeg,png,...| PIL, scipy.misc.pilutil | 图像文件\n",
+    "fits | pyfits | 天文图像\n",
+    "\n",
+    "此外， `pandas` ——一个用来处理时间序列的包中包含处理各种文件的方法，具体可参见它的文档：\n",
+    "\n",
+    "http://pandas.pydata.org/pandas-docs/stable/io.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 将数组写入文件"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`savetxt` 可以将数组写入文件，默认使用科学计数法的形式保存："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "data = np.array([[1,2], \n",
+    "                 [3,4]])\n",
+    "\n",
+    "np.savetxt('out.txt', data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.000000000000000000e+00 2.000000000000000000e+00\n",
+      "3.000000000000000000e+00 4.000000000000000000e+00\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('out.txt') as f:\n",
+    "    for line in f:\n",
+    "        print line,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "也可以使用类似**C**语言中 `printf` 的方式指定输出的格式："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data = np.array([[1,2], \n",
+    "                 [3,4]])\n",
+    "\n",
+    "np.savetxt('out.txt', data, fmt=\"%d\") #保存为整数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 2\n",
+      "3 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('out.txt') as f:\n",
+    "    for line in f:\n",
+    "        print line,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "逗号分隔的输出："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data = np.array([[1,2], \n",
+    "                 [3,4]])\n",
+    "\n",
+    "np.savetxt('out.txt', data, fmt=\"%.2f\", delimiter=',') #保存为2位小数的浮点数，用逗号分隔"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.00,2.00\n",
+      "3.00,4.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('out.txt') as f:\n",
+    "    for line in f:\n",
+    "        print line,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "复数值默认会加上括号："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data = np.array([[1+1j,2], \n",
+    "                 [3,4]])\n",
+    "\n",
+    "np.savetxt('out.txt', data, fmt=\"%.2f\", delimiter=',') #保存为2位小数的浮点数，用逗号分隔"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " (1.00+1.00j), (2.00+0.00j)\n",
+      " (3.00+0.00j), (4.00+0.00j)\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('out.txt') as f:\n",
+    "    for line in f:\n",
+    "        print line,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "更多参数："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    savetxt(fname, \n",
+    "            X, \n",
+    "            fmt='%.18e', \n",
+    "            delimiter=' ', \n",
+    "            newline='\\n', \n",
+    "            header='', \n",
+    "            footer='', \n",
+    "            comments='# ')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "移除 `out.txt`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('out.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Numpy 二进制格式"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "数组可以储存成二进制格式，单个的数组保存为 `.npy` 格式，多个数组保存为多个`.npy`文件组成的 `.npz` 格式，每个 `.npy` 文件包含一个数组。\n",
+    "\n",
+    "与文本格式不同，二进制格式保存了数组的 `shape, dtype` 信息，以便完全重构出保存的数组。\n",
+    "\n",
+    "保存的方法：\n",
+    "\n",
+    "- `save(file, arr)` 保存单个数组，`.npy` 格式\n",
+    "- `savez(file, *args, **kwds)` 保存多个数组，无压缩的 `.npz` 格式\n",
+    "- `savez_compressed(file, *args, **kwds)` 保存多个数组，有压缩的 `.npz` 格式\n",
+    "\n",
+    "读取的方法：\n",
+    "\n",
+    "- `load(file, mmap_mode=None)` 对于 `.npy`，返回保存的数组，对于 `.npz`，返回一个名称-数组对组成的字典。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 单个数组的读写"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a = np.array([[1.0,2.0], [3.0,4.0]])\n",
+    "\n",
+    "fname = 'afile.npy'\n",
+    "np.save(fname, a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.,  2.],\n",
+       "       [ 3.,  4.]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "aa = np.load(fname)\n",
+    "aa"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "删除生成的文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('afile.npy')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 二进制与文本大小比较"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "a = np.arange(10000.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "保存为文本："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "np.savetxt('a.txt', a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看大小："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "260000L"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.stat('a.txt').st_size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "保存为二进制："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "np.save('a.npy', a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看大小："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "80080L"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.stat('a.npy').st_size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "删除生成的文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "os.remove('a.npy')\n",
+    "os.remove('a.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以看到，二进制文件大约是文本文件的三分之一。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 保存多个数组"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "a = np.array([[1.0,2.0], \n",
+    "              [3.0,4.0]])\n",
+    "b = np.arange(1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "保存多个数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "np.savez('data.npz', a=a, b=b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看里面包含的文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Archive:  data.npz\n",
+      "  Length      Date    Time    Name\n",
+      "---------  ---------- -----   ----\n",
+      "      112  2015/08/10 00:46   a.npy\n",
+      "     4080  2015/08/10 00:46   b.npy\n",
+      "---------                     -------\n",
+      "     4192                     2 files\n"
+     ]
+    }
+   ],
+   "source": [
+    "!unzip -l data.npz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "载入数据："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "data = np.load('data.npz')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "载入后可以像字典一样进行操作："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b']"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.,  2.],\n",
+       "       [ 3.,  4.]])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['a']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000L,)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['b'].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "删除文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# 要先删除 data，否则删除时会报错\n",
+    "del data\n",
+    "\n",
+    "os.remove('data.npz')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 压缩文件"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "当数据比较整齐时："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "a = np.arange(20000.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "无压缩大小："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "160188L"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.savez('a.npz', a=a)\n",
+    "os.stat('a.npz').st_size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "有压缩大小："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26885L"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.savez_compressed('a2.npz', a=a)\n",
+    "os.stat('a2.npz').st_size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "大约有 6x 的压缩效果。\n",
+    "\n",
+    "当数据比较混乱时："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "a = np.random.rand(20000.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "无压缩大小："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "160188L"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.savez('a.npz', a=a)\n",
+    "os.stat('a.npz').st_size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "有压缩大小："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "151105L"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.savez_compressed('a2.npz', a=a)\n",
+    "os.stat('a2.npz').st_size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "只有大约 1.06x 的压缩效果。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "os.remove('a.npz')\n",
+    "os.remove('a2.npz')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.20 structured arrays.ipynb b/03-numpy/03.20-structured-arrays.ipynb
similarity index 95%
rename from 03. numpy/03.20 structured arrays.ipynb
rename to 03-numpy/03.20-structured-arrays.ipynb
index b3d8d6f2..cc2607a4 100644
--- a/03. numpy/03.20 structured arrays.ipynb	
+++ b/03-numpy/03.20-structured-arrays.ipynb
@@ -1,895 +1,895 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 结构化数组"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "假设我们要保存这样的数据：\n",
-    "\n",
-    "|name|age|wgt\n",
-    "--|--|--|--\n",
-    "0|dan|1|23.1\n",
-    "1|ann|0|25.1\n",
-    "2|sam|2|8.3\n",
-    "\n",
-    "希望定义一个一维数组，每个元素有三个属性 `name, age, wgt`，此时我们需要使用结构化数组。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "定义数组 `a`：\n",
-    "\n",
-    "0|1|2|3\n",
-    "-|-|-|-\n",
-    "1.0|2.0|3.0|4.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = np.array([1.0,2.0,3.0,4.0], np.float32)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `view` 方法，将 `a` 对应的内存按照复数来解释："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.+2.j,  3.+4.j], dtype=complex64)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.view(np.complex64)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "0|1|2|3\n",
-    "-|-|-|-\n",
-    "1.0|2.0|3.0|4.0\n",
-    "real|imag|real|imag\n",
-    "\n",
-    "事实上，我们可以把复数看成一个结构体，第一部分是实部，第二部分是虚部，这样这个数组便可以看成是一个结构化数组。\n",
-    "\n",
-    "换句话说，我们只需要换种方式解释这段内存，便可以得到结构化数组的效果！\n",
-    "\n",
-    "0|1|2|3\n",
-    "-|-|-|-\n",
-    "1.0|2.0|3.0|4.0\n",
-    "mass|vol|mass|vol\n",
-    "\n",
-    "例如，我们可以将第一个浮点数解释为质量，第二个浮点数解释为速度，则这段内存还可以看成是包含两个域（质量和速度）的结构体。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "my_dtype = np.dtype([('mass', 'float32'), ('vol', 'float32')])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([(1.0, 2.0), (3.0, 4.0)], \n",
-       "      dtype=[('mass', '<f4'), ('vol', '<f4')])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.view(my_dtype)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里，我们使用 `dtype` 创造了自定义的结构类型，然后用自定义的结构来解释数组 `a` 所占的内存。\n",
-    "\n",
-    "这里 `f4` 表示四字节浮点数，`<` 表示小字节序。\n",
-    "\n",
-    "利用这个自定义的结构类型，我们可以这样初始化结构化数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(1.0, 1.0) (1.0, 2.0) (2.0, 1.0) (1.0, 3.0)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_data = np.array([(1,1), (1,2), (2,1), (1,3)], my_dtype)\n",
-    "\n",
-    "print my_data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "第一个元素："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1.0, 1.0)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_data[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "得到第一个元素的速度信息，可以使用域的名称来索引："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.0"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_data[0]['vol']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "得到所有的质量信息："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.,  1.,  2.,  1.], dtype=float32)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_data['mass']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "自定义排序规则，先按速度，再按质量："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(1.0, 1.0) (2.0, 1.0) (1.0, 2.0) (1.0, 3.0)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_data.sort(order=('vol', 'mass'))\n",
-    "\n",
-    "print my_data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "回到最初的例子，定义一个人的结构类型："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "person_dtype = np.dtype([('name', 'S10'), ('age', 'int'), ('weight', 'float')])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看类型所占字节数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "22"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "person_dtype.itemsize"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "产生一个 3 x 4 共12人的空结构体数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "people = np.empty((3,4), person_dtype)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "分别赋值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "people['name'] = [['Brad', 'Jane', 'John', 'Fred'],\n",
-    "                  ['Henry', 'George', 'Brain', 'Amy'],\n",
-    "                  ['Ron', 'Susan', 'Jennife', 'Jill']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "people['age'] = [[33, 25, 47, 54],\n",
-    "                 [29, 61, 32, 27],\n",
-    "                 [19, 33, 18, 54]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "people['weight'] = [[135., 105., 255., 140.],\n",
-    "                    [154., 202., 137., 187.],\n",
-    "                    [188., 135., 88., 145.]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[('Brad', 33, 135.0) ('Jane', 25, 105.0) ('John', 47, 255.0)\n",
-      "  ('Fred', 54, 140.0)]\n",
-      " [('Henry', 29, 154.0) ('George', 61, 202.0) ('Brain', 32, 137.0)\n",
-      "  ('Amy', 27, 187.0)]\n",
-      " [('Ron', 19, 188.0) ('Susan', 33, 135.0) ('Jennife', 18, 88.0)\n",
-      "  ('Jill', 54, 145.0)]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print people"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('Jill', 54, 145.0)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "people[-1,-1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 从文本中读取结构化数组"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们有这样一个文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing people.txt\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%writefile people.txt\n",
-    "name age weight\n",
-    "amy 11 38.2\n",
-    "john 10 40.3\n",
-    "bill 12 21.2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "利用 `loadtxt` 指定数据类型，从这个文件中读取结构化数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([('amy', 11, 38.2), ('john', 10, 40.3), ('bill', 12, 21.2)], \n",
-       "      dtype=[('name', 'S10'), ('age', '<i4'), ('weight', '<f8')])"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "person_dtype = np.dtype([('name', 'S10'), ('age', 'int'), ('weight', 'float')])\n",
-    "\n",
-    "people = np.loadtxt('people.txt', \n",
-    "                    skiprows=1,\n",
-    "                    dtype=person_dtype)\n",
-    "\n",
-    "people"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看 `name` 域："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['amy', 'john', 'bill'], \n",
-       "      dtype='|S10')"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "people['name']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "删除文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('people.txt')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于下面的文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing wood.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%writefile wood.csv\n",
-    "item,material,number\n",
-    "100,oak,33\n",
-    "110,maple,14\n",
-    "120,oak,7\n",
-    "145,birch,3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "定义转换函数处理材料属性，使之对应一个整数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "tree_to_int = dict(oak = 1,\n",
-    "                   maple=2,\n",
-    "                   birch=3)\n",
-    "\n",
-    "def convert(s):\n",
-    "    return tree_to_int.get(s, 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `genfromtxt` 载入数据，可以自动从第一行读入属性名称："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "data = np.genfromtxt('wood.csv',\n",
-    "                     delimiter=',', # 逗号分隔\n",
-    "                     dtype=np.int, # 数据类型\n",
-    "                     names=True,   # 从第一行读入域名\n",
-    "                     converters={1:convert}\n",
-    "                    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([(100, 1, 33), (110, 2, 14), (120, 1, 7), (145, 3, 3)], \n",
-       "      dtype=[('item', '<i4'), ('material', '<i4'), ('number', '<i4')])"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看域："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 2, 1, 3])"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data['material']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "删除文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "os.remove('wood.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 嵌套类型"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "有时候，结构数组中的域可能包含嵌套的结构，例如，在我们希望在二维平面上纪录一个质点的位置和质量：\n",
-    "\n",
-    "<table> \n",
-    "  <th colspan=\"2\">position\n",
-    "  <th rowspan=\"2\">mass\n",
-    "  <tr><th>x<th>y\n",
-    "</table>\n",
-    "\n",
-    "那么它的类型可以这样嵌套定义："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "particle_dtype = np.dtype([('position', [('x', 'float'), \n",
-    "                                         ('y', 'float')]),\n",
-    "                           ('mass', 'float')\n",
-    "                          ])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "假设数据文件如下："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwriting data.txt\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%writefile data.txt\n",
-    "2.0 3.0 42.0\n",
-    "2.1 4.3 32.5\n",
-    "1.2 4.6 32.3\n",
-    "4.5 -6.4 23.3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "读取数据："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "data = np.loadtxt('data.txt', dtype=particle_dtype)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([((2.0, 3.0), 42.0), ((2.1, 4.3), 32.5), ((1.2, 4.6), 32.3),\n",
-       "       ((4.5, -6.4), 23.3)], \n",
-       "      dtype=[('position', [('x', '<f8'), ('y', '<f8')]), ('mass', '<f8')])"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看位置的 `x` 轴："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 2. ,  2.1,  1.2,  4.5])"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data['position']['x']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "删除生成的文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "os.remove('data.txt')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 结构化数组"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "假设我们要保存这样的数据：\n",
+    "\n",
+    "|name|age|wgt\n",
+    "--|--|--|--\n",
+    "0|dan|1|23.1\n",
+    "1|ann|0|25.1\n",
+    "2|sam|2|8.3\n",
+    "\n",
+    "希望定义一个一维数组，每个元素有三个属性 `name, age, wgt`，此时我们需要使用结构化数组。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "定义数组 `a`：\n",
+    "\n",
+    "0|1|2|3\n",
+    "-|-|-|-\n",
+    "1.0|2.0|3.0|4.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "a = np.array([1.0,2.0,3.0,4.0], np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `view` 方法，将 `a` 对应的内存按照复数来解释："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.+2.j,  3.+4.j], dtype=complex64)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.view(np.complex64)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "0|1|2|3\n",
+    "-|-|-|-\n",
+    "1.0|2.0|3.0|4.0\n",
+    "real|imag|real|imag\n",
+    "\n",
+    "事实上，我们可以把复数看成一个结构体，第一部分是实部，第二部分是虚部，这样这个数组便可以看成是一个结构化数组。\n",
+    "\n",
+    "换句话说，我们只需要换种方式解释这段内存，便可以得到结构化数组的效果！\n",
+    "\n",
+    "0|1|2|3\n",
+    "-|-|-|-\n",
+    "1.0|2.0|3.0|4.0\n",
+    "mass|vol|mass|vol\n",
+    "\n",
+    "例如，我们可以将第一个浮点数解释为质量，第二个浮点数解释为速度，则这段内存还可以看成是包含两个域（质量和速度）的结构体。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "my_dtype = np.dtype([('mass', 'float32'), ('vol', 'float32')])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([(1.0, 2.0), (3.0, 4.0)], \n",
+       "      dtype=[('mass', '<f4'), ('vol', '<f4')])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.view(my_dtype)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里，我们使用 `dtype` 创造了自定义的结构类型，然后用自定义的结构来解释数组 `a` 所占的内存。\n",
+    "\n",
+    "这里 `f4` 表示四字节浮点数，`<` 表示小字节序。\n",
+    "\n",
+    "利用这个自定义的结构类型，我们可以这样初始化结构化数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(1.0, 1.0) (1.0, 2.0) (2.0, 1.0) (1.0, 3.0)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_data = np.array([(1,1), (1,2), (2,1), (1,3)], my_dtype)\n",
+    "\n",
+    "print my_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "第一个元素："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1.0, 1.0)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_data[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "得到第一个元素的速度信息，可以使用域的名称来索引："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_data[0]['vol']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "得到所有的质量信息："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.,  1.,  2.,  1.], dtype=float32)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_data['mass']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "自定义排序规则，先按速度，再按质量："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(1.0, 1.0) (2.0, 1.0) (1.0, 2.0) (1.0, 3.0)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_data.sort(order=('vol', 'mass'))\n",
+    "\n",
+    "print my_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "回到最初的例子，定义一个人的结构类型："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "person_dtype = np.dtype([('name', 'S10'), ('age', 'int'), ('weight', 'float')])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看类型所占字节数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "22"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "person_dtype.itemsize"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "产生一个 3 x 4 共12人的空结构体数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "people = np.empty((3,4), person_dtype)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "分别赋值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "people['name'] = [['Brad', 'Jane', 'John', 'Fred'],\n",
+    "                  ['Henry', 'George', 'Brain', 'Amy'],\n",
+    "                  ['Ron', 'Susan', 'Jennife', 'Jill']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "people['age'] = [[33, 25, 47, 54],\n",
+    "                 [29, 61, 32, 27],\n",
+    "                 [19, 33, 18, 54]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "people['weight'] = [[135., 105., 255., 140.],\n",
+    "                    [154., 202., 137., 187.],\n",
+    "                    [188., 135., 88., 145.]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[('Brad', 33, 135.0) ('Jane', 25, 105.0) ('John', 47, 255.0)\n",
+      "  ('Fred', 54, 140.0)]\n",
+      " [('Henry', 29, 154.0) ('George', 61, 202.0) ('Brain', 32, 137.0)\n",
+      "  ('Amy', 27, 187.0)]\n",
+      " [('Ron', 19, 188.0) ('Susan', 33, 135.0) ('Jennife', 18, 88.0)\n",
+      "  ('Jill', 54, 145.0)]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print people"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('Jill', 54, 145.0)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[-1,-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 从文本中读取结构化数组"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们有这样一个文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing people.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile people.txt\n",
+    "name age weight\n",
+    "amy 11 38.2\n",
+    "john 10 40.3\n",
+    "bill 12 21.2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "利用 `loadtxt` 指定数据类型，从这个文件中读取结构化数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([('amy', 11, 38.2), ('john', 10, 40.3), ('bill', 12, 21.2)], \n",
+       "      dtype=[('name', 'S10'), ('age', '<i4'), ('weight', '<f8')])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "person_dtype = np.dtype([('name', 'S10'), ('age', 'int'), ('weight', 'float')])\n",
+    "\n",
+    "people = np.loadtxt('people.txt', \n",
+    "                    skiprows=1,\n",
+    "                    dtype=person_dtype)\n",
+    "\n",
+    "people"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看 `name` 域："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['amy', 'john', 'bill'], \n",
+       "      dtype='|S10')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people['name']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "删除文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('people.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于下面的文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing wood.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile wood.csv\n",
+    "item,material,number\n",
+    "100,oak,33\n",
+    "110,maple,14\n",
+    "120,oak,7\n",
+    "145,birch,3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "定义转换函数处理材料属性，使之对应一个整数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "tree_to_int = dict(oak = 1,\n",
+    "                   maple=2,\n",
+    "                   birch=3)\n",
+    "\n",
+    "def convert(s):\n",
+    "    return tree_to_int.get(s, 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `genfromtxt` 载入数据，可以自动从第一行读入属性名称："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "data = np.genfromtxt('wood.csv',\n",
+    "                     delimiter=',', # 逗号分隔\n",
+    "                     dtype=np.int, # 数据类型\n",
+    "                     names=True,   # 从第一行读入域名\n",
+    "                     converters={1:convert}\n",
+    "                    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([(100, 1, 33), (110, 2, 14), (120, 1, 7), (145, 3, 3)], \n",
+       "      dtype=[('item', '<i4'), ('material', '<i4'), ('number', '<i4')])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看域："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 1, 3])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['material']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "删除文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "os.remove('wood.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 嵌套类型"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "有时候，结构数组中的域可能包含嵌套的结构，例如，在我们希望在二维平面上纪录一个质点的位置和质量：\n",
+    "\n",
+    "<table> \n",
+    "  <th colspan=\"2\">position\n",
+    "  <th rowspan=\"2\">mass\n",
+    "  <tr><th>x<th>y\n",
+    "</table>\n",
+    "\n",
+    "那么它的类型可以这样嵌套定义："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "particle_dtype = np.dtype([('position', [('x', 'float'), \n",
+    "                                         ('y', 'float')]),\n",
+    "                           ('mass', 'float')\n",
+    "                          ])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "假设数据文件如下："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting data.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile data.txt\n",
+    "2.0 3.0 42.0\n",
+    "2.1 4.3 32.5\n",
+    "1.2 4.6 32.3\n",
+    "4.5 -6.4 23.3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "读取数据："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data = np.loadtxt('data.txt', dtype=particle_dtype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([((2.0, 3.0), 42.0), ((2.1, 4.3), 32.5), ((1.2, 4.6), 32.3),\n",
+       "       ((4.5, -6.4), 23.3)], \n",
+       "      dtype=[('position', [('x', '<f8'), ('y', '<f8')]), ('mass', '<f8')])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看位置的 `x` 轴："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2. ,  2.1,  1.2,  4.5])"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['position']['x']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "删除生成的文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "os.remove('data.txt')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.21 record arrays.ipynb b/03-numpy/03.21-record-arrays.ipynb
similarity index 100%
rename from 03. numpy/03.21 record arrays.ipynb
rename to 03-numpy/03.21-record-arrays.ipynb
diff --git a/03. numpy/03.22 memory maps.ipynb b/03-numpy/03.22-memory-maps.ipynb
similarity index 96%
rename from 03. numpy/03.22 memory maps.ipynb
rename to 03-numpy/03.22-memory-maps.ipynb
index d0bcad43..29b4ffb0 100644
--- a/03. numpy/03.22 memory maps.ipynb	
+++ b/03-numpy/03.22-memory-maps.ipynb
@@ -1,77 +1,77 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 内存映射"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Numpy** 有对内存映射的支持。\n",
-    "\n",
-    "内存映射也是一种处理文件的方法，主要的函数有：\n",
-    "\n",
-    "- `memmap`\n",
-    "- `frombuffer`\n",
-    "- `ndarray constructor`\n",
-    "\n",
-    "内存映射文件与虚拟内存有些类似，通过内存映射文件可以保留一个地址空间的区域，同时将物理存储器提交给此区域，内存文件映射的物理存储器来自一个已经存在于磁盘上的文件，而且在对该文件进行操作之前必须首先对文件进行映射。\n",
-    "\n",
-    "使用内存映射文件处理存储于磁盘上的文件时，将不必再对文件执行I/O操作，使得内存映射文件在处理大数据量的文件时能起到相当重要的作用。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## memmap"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    memmap(filename,\n",
-    "           dtype=uint8,\n",
-    "           mode='r+'\n",
-    "           offset=0\n",
-    "           shape=None\n",
-    "           order=0)\n",
-    "\n",
-    "`mode` 表示文件被打开的类型： \n",
-    "\n",
-    "- `r` 只读\n",
-    "- `c` 复制+写，但是不改变源文件\n",
-    "- `r+` 读写，使用 `flush` 方法会将更改的内容写入文件\n",
-    "- `w+` 写，如果存在则将数据覆盖\n",
-    "\n",
-    "`offset` 表示从第几个位置开始。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 内存映射"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Numpy** 有对内存映射的支持。\n",
+    "\n",
+    "内存映射也是一种处理文件的方法，主要的函数有：\n",
+    "\n",
+    "- `memmap`\n",
+    "- `frombuffer`\n",
+    "- `ndarray constructor`\n",
+    "\n",
+    "内存映射文件与虚拟内存有些类似，通过内存映射文件可以保留一个地址空间的区域，同时将物理存储器提交给此区域，内存文件映射的物理存储器来自一个已经存在于磁盘上的文件，而且在对该文件进行操作之前必须首先对文件进行映射。\n",
+    "\n",
+    "使用内存映射文件处理存储于磁盘上的文件时，将不必再对文件执行I/O操作，使得内存映射文件在处理大数据量的文件时能起到相当重要的作用。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## memmap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    memmap(filename,\n",
+    "           dtype=uint8,\n",
+    "           mode='r+'\n",
+    "           offset=0\n",
+    "           shape=None\n",
+    "           order=0)\n",
+    "\n",
+    "`mode` 表示文件被打开的类型： \n",
+    "\n",
+    "- `r` 只读\n",
+    "- `c` 复制+写，但是不改变源文件\n",
+    "- `r+` 读写，使用 `flush` 方法会将更改的内容写入文件\n",
+    "- `w+` 写，如果存在则将数据覆盖\n",
+    "\n",
+    "`offset` 表示从第几个位置开始。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/03. numpy/03.23 from matlab to numpy.ipynb b/03-numpy/03.23-from-matlab-to-numpy.ipynb
similarity index 97%
rename from 03. numpy/03.23 from matlab to numpy.ipynb
rename to 03-numpy/03.23-from-matlab-to-numpy.ipynb
index d97dff19..30df3678 100644
--- a/03. numpy/03.23 from matlab to numpy.ipynb	
+++ b/03-numpy/03.23-from-matlab-to-numpy.ipynb
@@ -1,378 +1,378 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 从 Matlab 到 Numpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##Numpy  和 Matlab 比较"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**`Numpy`** 和 **`Matlab`** 有很多相似的地方，但 **`Numpy`** 并非 **`Matlab`** 的克隆，它们之间存在很多差异，例如：\n",
-    "\n",
-    "`MATLAB®`|`Numpy`\n",
-    "---|---\n",
-    "基本类型为双精度浮点数组，以二维矩阵为主 | 基本类型为 `ndarray`，有特殊的 `matrix` 类\n",
-    "1-based 索引 | 0-based 索引\n",
-    "脚本主要用于线性代数计算 | 可以使用其他的 **Python** 特性 \n",
-    "采用值传递的方式进行计算<br>切片返回复制 | 采用引用传递的方式进行计算<br>切片返回引用\n",
-    "文件名必须和函数名相同 | 函数可以在任何地方任何文件中定义\n",
-    "收费 | 免费\n",
-    "2D，3D图像支持 | 依赖第三方库如 `matplotlib` 等\n",
-    "完全的编译环境 | 依赖于 **Python** 提供的编译环境"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## array 还是 matrix？"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Numpy` 中不仅提供了 `array` 这个基本类型，还提供了支持矩阵操作的类 `matrix`，但是一般推荐使用 `array`：\n",
-    "\n",
-    "- 很多 `numpy` 函数返回的是 `array`，不是 `matrix`\n",
-    "- 在 `array` 中，逐元素操作和矩阵操作有着明显的不同\n",
-    "- 向量可以不被视为矩阵\n",
-    "\n",
-    "具体说来：\n",
-    "\n",
-    "- `*， dot(), multiply()`\n",
-    "    - `array`：`*` -逐元素乘法，`dot()` -矩阵乘法\n",
-    "    - `matrix`：`*` -矩阵乘法，`multiply()` -逐元素乘法\n",
-    "- 处理向量 \n",
-    "    - `array`：形状为 `1xN, Nx1, N` 的向量的意义是不同的，类似于 `A[:,1]` 的操作返回的是一维数组，形状为 `N`，一维数组的转置仍是自己本身\n",
-    "    - `matrix`：形状为 `1xN, Nx1`，`A[:,1]` 返回的是二维 `Nx1` 矩阵\n",
-    "- 高维数组\n",
-    "    - `array`：支持大于2的维度\n",
-    "    - `matrix`：维度只能为2\n",
-    "- 属性\n",
-    "    - `array`：`.T` 表示转置\n",
-    "    - `matrix`：`.H` 表示复共轭转置，`.I` 表示逆，`.A` 表示转化为 `array` 类型\n",
-    "- 构造函数\n",
-    "    - `array`：`array` 函数接受一个（嵌套）序列作为参数——`array([[1,2,3],[4,5,6]])`\n",
-    "    - `matrix`：`matrix` 函数额外支持字符串参数——`matrix(\"[1 2 3; 4 5 6]\")`\n",
-    "\n",
-    "其优缺点各自如下：\n",
-    "\n",
-    "- **`array`**\n",
-    "    - `[GOOD]` 一维数组既可以看成列向量，也可以看成行向量。`v` 在 `dot(A,v)` 被看成列向量，在 `dot(v,A)` 中被看成行向量，这样省去了转置的麻烦\n",
-    "    - `[BAD!]` 矩阵乘法需要使用 `dot()` 函数，如： `dot(dot(A,B),C)` vs `A*B*C`\n",
-    "    - `[GOOD]` 逐元素乘法很简单： `A*B`\n",
-    "    - `[GOOD]` 作为基本类型，是很多基于 `numpy` 的第三方库函数的返回类型\n",
-    "    - `[GOOD]` 所有的操作 `*,/,+,**,...` 都是逐元素的\n",
-    "    - `[GOOD]` 可以处理任意维度的数据\n",
-    "    - `[GOOD]` 张量运算\n",
-    "\n",
-    "- **`matrix`**\n",
-    "    - `[GOOD]` 类似与 **`MATLAB`** 的操作\n",
-    "    - `[BAD!]` 最高维度为2\n",
-    "    - `[BAD!]` 最低维度也为2\n",
-    "    - `[BAD!]` 很多函数返回的是 `array`，即使传入的参数是 `matrix`\n",
-    "    - `[GOOD]` `A*B` 是矩阵乘法\n",
-    "    - `[BAD!]` 逐元素乘法需要调用 `multiply` 函数\n",
-    "    - `[BAD!]` `/` 是逐元素操作\n",
-    "\n",
-    "当然在实际使用中，二者的使用取决于具体情况。\n",
-    "\n",
-    "二者可以互相转化：\n",
-    "\n",
-    "- `asarray` ：返回数组\n",
-    "- `asmatrix`（或者`mat`） ：返回矩阵\n",
-    "- `asanyarray` ：返回数组或者数组的子类，注意到矩阵是数组的一个子类，所以输入是矩阵的时候返回的也是矩阵"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 类 Matlab 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "有很多类似的函数：\n",
-    "\n",
-    "- `ones, zeros, empty, eye, rand, repmat`\n",
-    "\n",
-    "通常这些函数的返回值是 `array`，不过 `numpy` 提供了一个 `matlib` 的子模块，子模块中的这些函数返回值为 `matrix`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy\n",
-    "import numpy.matlib"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(7L,)\n",
-      "<type 'numpy.ndarray'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = numpy.ones(7)\n",
-    "\n",
-    "print a.shape\n",
-    "print type(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(1L, 7L)\n",
-      "<class 'numpy.matrixlib.defmatrix.matrix'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = numpy.matlib.ones(7)\n",
-    "\n",
-    "print a.shape\n",
-    "print type(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`mat` 函数将一个数组转化为矩阵："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'numpy.matrixlib.defmatrix.matrix'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = numpy.array([1,2,3])\n",
-    "\n",
-    "b = numpy.mat(a)\n",
-    "\n",
-    "print type(b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "有些函数被放到子模块中了，例如调用 `rand()` 函数需要使用 `numpy.random.rand()` （或者从 `matlib` 模块中生成矩阵）："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0.66007267  0.34794294  0.5040946   0.65044648  0.74763248  0.42486999\n",
-      "  0.90922612  0.69071747  0.33541076  0.08570178]\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = numpy.random.rand(10)\n",
-    "print a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 等效操作"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "假定我们已经这样导入了 `Numpy`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from numpy import *\n",
-    "import scipy.linalg"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "以下 `linalg` 表示的是 `numpy.linalg`，与 `scipy.linalg` 不同。\n",
-    "\n",
-    "注意：**`MATLAB`** 与 **`Numpy`** 下标之间有这样几处不同：\n",
-    "- `1-base` vs `0-base`\n",
-    "- `()` vs `[]`\n",
-    "- `MATLAB`：`beg(:step):end`，包含结束值 `end`\n",
-    "- `Numpy`：`beg:end(:step)`，不包含结束值 `end`\n",
-    "\n",
-    "MATLAB|Numpy|注释\n",
-    "---|---|---\n",
-    "`help func` | `info(func)`， `help(func)`， `func?`(IPython)| 查看函数帮助\n",
-    "`which func` |  | 查看函数在什么地方定义\n",
-    "`type func` | `source(func)`， `func?？`(IPython)| 查看函数源代码\n",
-    "`a && b` | `a and b` | 逻辑 `AND`\n",
-    "`1*i, 1*j, 1i, 1j` | `1j` | 复数\n",
-    "`eps` | `spacing(1)` | `1` 与最近浮点数的距离\n",
-    "`ndims(a)` | `ndim(a), a.ndim` | `a` 的维数\n",
-    "`numel(a)` | `size(a), a.size` | `a` 的元素个数\n",
-    "`size(a)` | `shape(a), a.shape` | `a` 的形状\n",
-    "`size(a,n)` | `a.shape[n-1]` | 第 n 维的大小\n",
-    "`a(2,5)` | `a[1,4]` | 第 2 行第 5 列元素\n",
-    "`a(2,:)` | `a[1], a[1,:]` | 第 2 行 \n",
-    "`a(1:5,:)` | `a[0:5]` | 第 1 至 5 行\n",
-    "`a(end-4:end,:)` | `a[-5:]` | 后 5 行\n",
-    "`a(1:3,5:9)` | `a[0:3][:,4:9]` | 特定行列（1~3 行，5~9 列）\n",
-    "`a([2,4,5],[1,3])` | `a[ix_([1,3,4],[0,2])]` | 特定行列（2,4,5 行的 1,3 列）\n",
-    "`a(3:2:21,:)` | `a[2:21:2,:]` | 特定行列（3,5,...,21 行）\n",
-    "`a(1:2:end,:)` | `a[ ::2,:]` | 奇数行\n",
-    "`a([1:end 1],:)` | `a[r_[:len(a),0]]` | 将第一行添加到末尾\n",
-    "`a.'` | `a.T` | 转置\n",
-    "`a ./ b` | `a/b` | 逐元素除法\n",
-    "`(a>0.5)` | `(a>0.5)` | 各个元素是否大于 0.5\n",
-    "`find(a>0.5)` | `nonzero(a>0.5)` | 大于 0.5 的位置\n",
-    "`a(a<0.5)=0` | `a[a<0.5]=0` | 小于 0.5 的设为 0\n",
-    "`a(:) = 3` | `a[:] = 3` | 所有元素设为 3\n",
-    "`y=x` | `y=x.copy()` | 将 y 设为 x\n",
-    "`y=x(2,:)` | `y=x[1,:].copy()` | 注意值传递和引用传递的区别\n",
-    "`y=x(:)` | `y=x.flatten(1)` | 将矩阵变为一个向量，这里 `1` 表示沿着列进行转化\n",
-    "`max(max(a))` | `a.max()` | 最大值\n",
-    "`max(a)` | `a.max(0)` | 每一列的最大值\n",
-    "`max(a,[],2)` | `a.max(1)` | 每一行的最大值\n",
-    "`max(a,b)` | `maximum(a,b)` | 逐元素比较，取较大的值\n",
-    "`a & b` | `logical_and(a, b)` | 逻辑 AND\n",
-    "`bitand(a, b)` | `a & b` | 逐比特 AND\n",
-    "`inv(a)` | `linalg.inv(a)` | a 的逆\n",
-    "`pinv(a)` | `linalg.inv(a)` | 伪逆\n",
-    "`rank(a)` | `linalg.matrix_rank(a)` | 秩\n",
-    "`a\\b` | `linalg.solve(a,b)(如果a是方阵),linalg.lstsq(a,b)` | 解 `a x = b`\n",
-    "`b/a` | 求解 `a.T x.T = b.T` | 解 `x a = b`\n",
-    "`[U,S,V]=svd(a)` | `U, S, Vh = linalg.svd(a), V = Vh.T` | 奇异值分解\n",
-    "`chol(a)` | `linalg.cholesky(a).T` | Cholesky 分解\n",
-    "`[V,D]=eig(a)` | `D,V = linalg.eig(a)` | 特征值分解\n",
-    "`[V,D]=eig(a,b)` | `V,D = scipy.linalg.eig(a,b)` | \n",
-    "`[V,D]=eigs(a,k)` |  | 前 k 大特征值对应的特征向量\n",
-    "`` | `` |\n",
-    "`` | `` |\n",
-    "`` | `` |\n",
-    "`` | `` |\n",
-    "\n",
-    "MATLAB|numpy.array|numpy.matrix|注释\n",
-    "---|---|---|---\n",
-    "`[1,2,3;4,5,6]` | `array([[1.,2.,3.],[4.,5.,6.]])` | `mat([[1.,2.,3.],[4.,5.,6.]]), mat('1,2,3;4,5,6')` | `2x3` 矩阵\n",
-    "`[a b;c d]` | `vstack([hstack([a,b]), hsatck([c,d])]])` | `bmat('a b;c d')` | 分块矩阵构造\n",
-    "`a(end)` | `a[-1]` | `a[:,-1][0,0]` | 最后一个元素\n",
-    "`a'` | `a.conj().T` | `a.H` | 复共轭转置\n",
-    "`a * b` | `dot(a,b)` | `a * b` | 矩阵乘法\n",
-    "`a .* b` | `a * b` | `multiply(a,b)` | 逐元素乘法\n",
-    "`a.^3` | `a**3` | `power(a,3)` | 逐元素立方\n",
-    "`a(:,find(v>0.5))` | `a[:,nonzero(v>0.5)[0]]` | `a[:,nonzero(v.A>0.5)[0]]` | 找出行向量 `v>0.5` 对应的 `a` 中的列\n",
-    "`a(:,find(v>0.5))` | `a[:,v.T>0.5]` | `a[:,v.T>0.5)]` | 找出列向量 `v>0.5` 对应的 `a` 中的列\n",
-    "`a .* (a>0.5)` | `a * (a>0.5)` | `mat(a.A * (a>0.5).A)` | 将所有小于 0.5 的元素设为 0\n",
-    "`1:10` | `arange(1.,11.), r_[1.:11.], r_[1:10:10j]` | `mat(arange(1.,11.)), r_[1.:11., 'r']` | 这里 `1.` 是为了将其转化为浮点数组\n",
-    "`0:9` | `arange(10.), r_[:10.], r_[:9:10j]` | `mat(arange(10.)), r_[:10., 'r']` | \n",
-    "`[1:10]'` | `arange(1.,11.)[:,newaxis]` | `r_[1.:11.,'c']` | 列向量\n",
-    "`zeros, ones, eye, diag, linspace` | `zeros, ones, eye, diag, linspace` | `mat(...)` |\n",
-    "`rand(3,4)` | `random.rand(3,4)` | `mat(...)` | 0~1 随机数\n",
-    "`[x,y]=meshgrid(0:8,0:5)` | `mgrid[0:9., 0:6.], meshgrid(r_[0:9.],r_[0:6.])` | `mat(...)` | 网格\n",
-    "| `ogrid[0:9.,0:6.], ix_(r_[0:9.],r_[0:6.])` | `mat()` | 建议在 `Numpy` 中使用\n",
-    "`[x,y]=meshgrid([1,2,4],[2,4,5])`|`meshgrid([1,2,4],[2,4,5])`|`mat(...)`|\n",
-    "|`ix_([1,2,4],[2,4,5])`|`mat(...)`|\n",
-    "`repmat(a, m, n)`|`tile(a, (m,n))`|`mat(...)`| 产生 `m x n` 个 `a`\n",
-    "`[a b]` | `c_[a,b]`|`concatenate((a,b),1)`| 列对齐连接\n",
-    "`[a; b]` | `r_[a,b]`|`concatenate((a,b))`| 行对齐连接\n",
-    "`norm(v)` | `sqrt(dot(v,v)), linalg.norm(v)` | `sqrt(dot(v.A,v.A)), linalg.norm(v)` | 模\n",
-    "`[Q,R,P]=qr(a,0)` | `Q,R = scipy.linalg.qr(a)` | `mat(...)` | QR 分解\n",
-    "`[L,U,P]=lu(a)` | `L,U = Sci.linalg.lu(a)` | `mat(...)` | LU 分解\n",
-    "`fft(a)` | `fft(a)` | `mat(...)` | FFT\n",
-    "`ifft(a)` | `ifft(a)` | `mat(...)` | IFFT\n",
-    "`sort(a)` | `sort(a),a.sort` | `mat(...)` | 排序\n",
-    "\n",
-    "参考：http://wiki.scipy.org/NumPy_for_Matlab_Users#whichNotes"
-   ]
-  }
- ],
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-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 从 Matlab 到 Numpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Numpy  和 Matlab 比较"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**`Numpy`** 和 **`Matlab`** 有很多相似的地方，但 **`Numpy`** 并非 **`Matlab`** 的克隆，它们之间存在很多差异，例如：\n",
+    "\n",
+    "`MATLAB®`|`Numpy`\n",
+    "---|---\n",
+    "基本类型为双精度浮点数组，以二维矩阵为主 | 基本类型为 `ndarray`，有特殊的 `matrix` 类\n",
+    "1-based 索引 | 0-based 索引\n",
+    "脚本主要用于线性代数计算 | 可以使用其他的 **Python** 特性 \n",
+    "采用值传递的方式进行计算<br>切片返回复制 | 采用引用传递的方式进行计算<br>切片返回引用\n",
+    "文件名必须和函数名相同 | 函数可以在任何地方任何文件中定义\n",
+    "收费 | 免费\n",
+    "2D，3D图像支持 | 依赖第三方库如 `matplotlib` 等\n",
+    "完全的编译环境 | 依赖于 **Python** 提供的编译环境"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## array 还是 matrix？"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Numpy` 中不仅提供了 `array` 这个基本类型，还提供了支持矩阵操作的类 `matrix`，但是一般推荐使用 `array`：\n",
+    "\n",
+    "- 很多 `numpy` 函数返回的是 `array`，不是 `matrix`\n",
+    "- 在 `array` 中，逐元素操作和矩阵操作有着明显的不同\n",
+    "- 向量可以不被视为矩阵\n",
+    "\n",
+    "具体说来：\n",
+    "\n",
+    "- `*， dot(), multiply()`\n",
+    "    - `array`：`*` -逐元素乘法，`dot()` -矩阵乘法\n",
+    "    - `matrix`：`*` -矩阵乘法，`multiply()` -逐元素乘法\n",
+    "- 处理向量 \n",
+    "    - `array`：形状为 `1xN, Nx1, N` 的向量的意义是不同的，类似于 `A[:,1]` 的操作返回的是一维数组，形状为 `N`，一维数组的转置仍是自己本身\n",
+    "    - `matrix`：形状为 `1xN, Nx1`，`A[:,1]` 返回的是二维 `Nx1` 矩阵\n",
+    "- 高维数组\n",
+    "    - `array`：支持大于2的维度\n",
+    "    - `matrix`：维度只能为2\n",
+    "- 属性\n",
+    "    - `array`：`.T` 表示转置\n",
+    "    - `matrix`：`.H` 表示复共轭转置，`.I` 表示逆，`.A` 表示转化为 `array` 类型\n",
+    "- 构造函数\n",
+    "    - `array`：`array` 函数接受一个（嵌套）序列作为参数——`array([[1,2,3],[4,5,6]])`\n",
+    "    - `matrix`：`matrix` 函数额外支持字符串参数——`matrix(\"[1 2 3; 4 5 6]\")`\n",
+    "\n",
+    "其优缺点各自如下：\n",
+    "\n",
+    "- **`array`**\n",
+    "    - `[GOOD]` 一维数组既可以看成列向量，也可以看成行向量。`v` 在 `dot(A,v)` 被看成列向量，在 `dot(v,A)` 中被看成行向量，这样省去了转置的麻烦\n",
+    "    - `[BAD!]` 矩阵乘法需要使用 `dot()` 函数，如： `dot(dot(A,B),C)` vs `A*B*C`\n",
+    "    - `[GOOD]` 逐元素乘法很简单： `A*B`\n",
+    "    - `[GOOD]` 作为基本类型，是很多基于 `numpy` 的第三方库函数的返回类型\n",
+    "    - `[GOOD]` 所有的操作 `*,/,+,**,...` 都是逐元素的\n",
+    "    - `[GOOD]` 可以处理任意维度的数据\n",
+    "    - `[GOOD]` 张量运算\n",
+    "\n",
+    "- **`matrix`**\n",
+    "    - `[GOOD]` 类似与 **`MATLAB`** 的操作\n",
+    "    - `[BAD!]` 最高维度为2\n",
+    "    - `[BAD!]` 最低维度也为2\n",
+    "    - `[BAD!]` 很多函数返回的是 `array`，即使传入的参数是 `matrix`\n",
+    "    - `[GOOD]` `A*B` 是矩阵乘法\n",
+    "    - `[BAD!]` 逐元素乘法需要调用 `multiply` 函数\n",
+    "    - `[BAD!]` `/` 是逐元素操作\n",
+    "\n",
+    "当然在实际使用中，二者的使用取决于具体情况。\n",
+    "\n",
+    "二者可以互相转化：\n",
+    "\n",
+    "- `asarray` ：返回数组\n",
+    "- `asmatrix`（或者`mat`） ：返回矩阵\n",
+    "- `asanyarray` ：返回数组或者数组的子类，注意到矩阵是数组的一个子类，所以输入是矩阵的时候返回的也是矩阵"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 类 Matlab 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "有很多类似的函数：\n",
+    "\n",
+    "- `ones, zeros, empty, eye, rand, repmat`\n",
+    "\n",
+    "通常这些函数的返回值是 `array`，不过 `numpy` 提供了一个 `matlib` 的子模块，子模块中的这些函数返回值为 `matrix`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy\n",
+    "import numpy.matlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(7L,)\n",
+      "<type 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = numpy.ones(7)\n",
+    "\n",
+    "print a.shape\n",
+    "print type(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1L, 7L)\n",
+      "<class 'numpy.matrixlib.defmatrix.matrix'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = numpy.matlib.ones(7)\n",
+    "\n",
+    "print a.shape\n",
+    "print type(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`mat` 函数将一个数组转化为矩阵："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'numpy.matrixlib.defmatrix.matrix'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = numpy.array([1,2,3])\n",
+    "\n",
+    "b = numpy.mat(a)\n",
+    "\n",
+    "print type(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "有些函数被放到子模块中了，例如调用 `rand()` 函数需要使用 `numpy.random.rand()` （或者从 `matlib` 模块中生成矩阵）："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.66007267  0.34794294  0.5040946   0.65044648  0.74763248  0.42486999\n",
+      "  0.90922612  0.69071747  0.33541076  0.08570178]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = numpy.random.rand(10)\n",
+    "print a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 等效操作"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "假定我们已经这样导入了 `Numpy`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from numpy import *\n",
+    "import scipy.linalg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "以下 `linalg` 表示的是 `numpy.linalg`，与 `scipy.linalg` 不同。\n",
+    "\n",
+    "注意：**`MATLAB`** 与 **`Numpy`** 下标之间有这样几处不同：\n",
+    "- `1-base` vs `0-base`\n",
+    "- `()` vs `[]`\n",
+    "- `MATLAB`：`beg(:step):end`，包含结束值 `end`\n",
+    "- `Numpy`：`beg:end(:step)`，不包含结束值 `end`\n",
+    "\n",
+    "MATLAB|Numpy|注释\n",
+    "---|---|---\n",
+    "`help func` | `info(func)`， `help(func)`， `func?`(IPython)| 查看函数帮助\n",
+    "`which func` |  | 查看函数在什么地方定义\n",
+    "`type func` | `source(func)`， `func?？`(IPython)| 查看函数源代码\n",
+    "`a && b` | `a and b` | 逻辑 `AND`\n",
+    "`1*i, 1*j, 1i, 1j` | `1j` | 复数\n",
+    "`eps` | `spacing(1)` | `1` 与最近浮点数的距离\n",
+    "`ndims(a)` | `ndim(a), a.ndim` | `a` 的维数\n",
+    "`numel(a)` | `size(a), a.size` | `a` 的元素个数\n",
+    "`size(a)` | `shape(a), a.shape` | `a` 的形状\n",
+    "`size(a,n)` | `a.shape[n-1]` | 第 n 维的大小\n",
+    "`a(2,5)` | `a[1,4]` | 第 2 行第 5 列元素\n",
+    "`a(2,:)` | `a[1], a[1,:]` | 第 2 行 \n",
+    "`a(1:5,:)` | `a[0:5]` | 第 1 至 5 行\n",
+    "`a(end-4:end,:)` | `a[-5:]` | 后 5 行\n",
+    "`a(1:3,5:9)` | `a[0:3][:,4:9]` | 特定行列（1~3 行，5~9 列）\n",
+    "`a([2,4,5],[1,3])` | `a[ix_([1,3,4],[0,2])]` | 特定行列（2,4,5 行的 1,3 列）\n",
+    "`a(3:2:21,:)` | `a[2:21:2,:]` | 特定行列（3,5,...,21 行）\n",
+    "`a(1:2:end,:)` | `a[ ::2,:]` | 奇数行\n",
+    "`a([1:end 1],:)` | `a[r_[:len(a),0]]` | 将第一行添加到末尾\n",
+    "`a.'` | `a.T` | 转置\n",
+    "`a ./ b` | `a/b` | 逐元素除法\n",
+    "`(a>0.5)` | `(a>0.5)` | 各个元素是否大于 0.5\n",
+    "`find(a>0.5)` | `nonzero(a>0.5)` | 大于 0.5 的位置\n",
+    "`a(a<0.5)=0` | `a[a<0.5]=0` | 小于 0.5 的设为 0\n",
+    "`a(:) = 3` | `a[:] = 3` | 所有元素设为 3\n",
+    "`y=x` | `y=x.copy()` | 将 y 设为 x\n",
+    "`y=x(2,:)` | `y=x[1,:].copy()` | 注意值传递和引用传递的区别\n",
+    "`y=x(:)` | `y=x.flatten(1)` | 将矩阵变为一个向量，这里 `1` 表示沿着列进行转化\n",
+    "`max(max(a))` | `a.max()` | 最大值\n",
+    "`max(a)` | `a.max(0)` | 每一列的最大值\n",
+    "`max(a,[],2)` | `a.max(1)` | 每一行的最大值\n",
+    "`max(a,b)` | `maximum(a,b)` | 逐元素比较，取较大的值\n",
+    "`a & b` | `logical_and(a, b)` | 逻辑 AND\n",
+    "`bitand(a, b)` | `a & b` | 逐比特 AND\n",
+    "`inv(a)` | `linalg.inv(a)` | a 的逆\n",
+    "`pinv(a)` | `linalg.inv(a)` | 伪逆\n",
+    "`rank(a)` | `linalg.matrix_rank(a)` | 秩\n",
+    "`a\\b` | `linalg.solve(a,b)(如果a是方阵),linalg.lstsq(a,b)` | 解 `a x = b`\n",
+    "`b/a` | 求解 `a.T x.T = b.T` | 解 `x a = b`\n",
+    "`[U,S,V]=svd(a)` | `U, S, Vh = linalg.svd(a), V = Vh.T` | 奇异值分解\n",
+    "`chol(a)` | `linalg.cholesky(a).T` | Cholesky 分解\n",
+    "`[V,D]=eig(a)` | `D,V = linalg.eig(a)` | 特征值分解\n",
+    "`[V,D]=eig(a,b)` | `V,D = scipy.linalg.eig(a,b)` | \n",
+    "`[V,D]=eigs(a,k)` |  | 前 k 大特征值对应的特征向量\n",
+    "`` | `` |\n",
+    "`` | `` |\n",
+    "`` | `` |\n",
+    "`` | `` |\n",
+    "\n",
+    "MATLAB|numpy.array|numpy.matrix|注释\n",
+    "---|---|---|---\n",
+    "`[1,2,3;4,5,6]` | `array([[1.,2.,3.],[4.,5.,6.]])` | `mat([[1.,2.,3.],[4.,5.,6.]]), mat('1,2,3;4,5,6')` | `2x3` 矩阵\n",
+    "`[a b;c d]` | `vstack([hstack([a,b]), hsatck([c,d])]])` | `bmat('a b;c d')` | 分块矩阵构造\n",
+    "`a(end)` | `a[-1]` | `a[:,-1][0,0]` | 最后一个元素\n",
+    "`a'` | `a.conj().T` | `a.H` | 复共轭转置\n",
+    "`a * b` | `dot(a,b)` | `a * b` | 矩阵乘法\n",
+    "`a .* b` | `a * b` | `multiply(a,b)` | 逐元素乘法\n",
+    "`a.^3` | `a**3` | `power(a,3)` | 逐元素立方\n",
+    "`a(:,find(v>0.5))` | `a[:,nonzero(v>0.5)[0]]` | `a[:,nonzero(v.A>0.5)[0]]` | 找出行向量 `v>0.5` 对应的 `a` 中的列\n",
+    "`a(:,find(v>0.5))` | `a[:,v.T>0.5]` | `a[:,v.T>0.5)]` | 找出列向量 `v>0.5` 对应的 `a` 中的列\n",
+    "`a .* (a>0.5)` | `a * (a>0.5)` | `mat(a.A * (a>0.5).A)` | 将所有小于 0.5 的元素设为 0\n",
+    "`1:10` | `arange(1.,11.), r_[1.:11.], r_[1:10:10j]` | `mat(arange(1.,11.)), r_[1.:11., 'r']` | 这里 `1.` 是为了将其转化为浮点数组\n",
+    "`0:9` | `arange(10.), r_[:10.], r_[:9:10j]` | `mat(arange(10.)), r_[:10., 'r']` | \n",
+    "`[1:10]'` | `arange(1.,11.)[:,newaxis]` | `r_[1.:11.,'c']` | 列向量\n",
+    "`zeros, ones, eye, diag, linspace` | `zeros, ones, eye, diag, linspace` | `mat(...)` |\n",
+    "`rand(3,4)` | `random.rand(3,4)` | `mat(...)` | 0~1 随机数\n",
+    "`[x,y]=meshgrid(0:8,0:5)` | `mgrid[0:9., 0:6.], meshgrid(r_[0:9.],r_[0:6.])` | `mat(...)` | 网格\n",
+    "| `ogrid[0:9.,0:6.], ix_(r_[0:9.],r_[0:6.])` | `mat()` | 建议在 `Numpy` 中使用\n",
+    "`[x,y]=meshgrid([1,2,4],[2,4,5])`|`meshgrid([1,2,4],[2,4,5])`|`mat(...)`|\n",
+    "|`ix_([1,2,4],[2,4,5])`|`mat(...)`|\n",
+    "`repmat(a, m, n)`|`tile(a, (m,n))`|`mat(...)`| 产生 `m x n` 个 `a`\n",
+    "`[a b]` | `c_[a,b]`|`concatenate((a,b),1)`| 列对齐连接\n",
+    "`[a; b]` | `r_[a,b]`|`concatenate((a,b))`| 行对齐连接\n",
+    "`norm(v)` | `sqrt(dot(v,v)), linalg.norm(v)` | `sqrt(dot(v.A,v.A)), linalg.norm(v)` | 模\n",
+    "`[Q,R,P]=qr(a,0)` | `Q,R = scipy.linalg.qr(a)` | `mat(...)` | QR 分解\n",
+    "`[L,U,P]=lu(a)` | `L,U = Sci.linalg.lu(a)` | `mat(...)` | LU 分解\n",
+    "`fft(a)` | `fft(a)` | `mat(...)` | FFT\n",
+    "`ifft(a)` | `ifft(a)` | `mat(...)` | IFFT\n",
+    "`sort(a)` | `sort(a),a.sort` | `mat(...)` | 排序\n",
+    "\n",
+    "参考：http://wiki.scipy.org/NumPy_for_Matlab_Users#whichNotes"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/04. scipy/04.01 scienticfic python overview.ipynb b/04-scipy/04.01-scienticfic-python-overview.ipynb
similarity index 100%
rename from 04. scipy/04.01 scienticfic python overview.ipynb
rename to 04-scipy/04.01-scienticfic-python-overview.ipynb
diff --git a/04. scipy/04.02 interpolation with scipy.ipynb b/04-scipy/04.02-interpolation-with-scipy.ipynb
similarity index 99%
rename from 04. scipy/04.02 interpolation with scipy.ipynb
rename to 04-scipy/04.02-interpolation-with-scipy.ipynb
index 759e04a0..4aff2faf 100644
--- a/04. scipy/04.02 interpolation with scipy.ipynb	
+++ b/04-scipy/04.02-interpolation-with-scipy.ipynb
@@ -1,1017 +1,1017 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 插值"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "设置 **`Numpy`** 浮点数显示格式："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "np.set_printoptions(precision=2, suppress=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "从文本中读入数据，数据来自 http://kinetics.nist.gov/janaf/html/C-067.txt ，保存为结构体数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "data = np.genfromtxt(\"JANAF_CH4.txt\", \n",
-    "                  delimiter=\"\\t\", # TAB 分隔\n",
-    "                  skiprows=1,     # 忽略首行\n",
-    "                  names=True,     # 读入属性\n",
-    "                  missing_values=\"INFINITE\",  # 缺失值\n",
-    "                  filling_values=np.inf)      # 填充缺失值"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "显示部分数据："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0\t0.0\n",
-      "100.0\t33.258\n",
-      "200.0\t33.473\n",
-      "250.0\t34.216\n",
-      "298.15\t35.639\n",
-      "300.0\t35.708\n",
-      "350.0\t37.874\n",
-      "...\t...\n"
-     ]
-    }
-   ],
-   "source": [
-    "for row in data[:7]:\n",
-    "    print \"{}\\t{}\".format(row['TK'], row['Cp'])\n",
-    "print \"...\\t...\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "绘图："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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NdFXMukteEapdXrjFUhjjHTdx68SsfrjFYt0gu3p9LOMmbp2YtYaSQFUckqJodSq6alus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\nZmaWKwcWMzPLlQOLmZnl6n8BKYzjzfpoiFsAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa0dc208>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "p = plt.plot(data['TK'], data['Cp'], 'kx')\n",
-    "t = plt.title(\"JANAF data for Methane $CH_4$\")\n",
-    "a = plt.axis([0, 6000, 30, 120])\n",
-    "x = plt.xlabel(\"Temperature (K)\")\n",
-    "y = plt.ylabel(r\"$C_p$ ($\\frac{kJ}{kg K}$)\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 插值"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "假设我们要对这组数据进行插值。\n",
-    "\n",
-    "先导入一维插值函数 `interp1d`：\n",
-    "\n",
-    "    interp1d(x, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.interpolate import interp1d"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "ch4_cp = interp1d(data['TK'], data['Cp'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`interp1d` 的返回值可以像函数一样接受输入，并返回插值的结果。\n",
-    "\n",
-    "单个输入值，注意返回的是数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(39.565144000000004)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ch4_cp(382.2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "输入数组，返回的是对应的数组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 10.71,  36.71])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ch4_cp([32.2,323.2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "默认情况下，输入值要在插值允许的范围内，否则插值会报错："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "A value in x_new is above the interpolation range.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-10-5d727af9aa33>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mch4_cp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m8752\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32md:\\Miniconda\\lib\\site-packages\\scipy\\interpolate\\polyint.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     77\u001b[0m         \"\"\"\n\u001b[0;32m     78\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_shape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prepare_x\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 79\u001b[1;33m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_evaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     80\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_finish_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_shape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     81\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32md:\\Miniconda\\lib\\site-packages\\scipy\\interpolate\\interpolate.pyc\u001b[0m in \u001b[0;36m_evaluate\u001b[1;34m(self, x_new)\u001b[0m\n\u001b[0;32m    496\u001b[0m         \u001b[1;31m#    The behavior is set by the bounds_error variable.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    497\u001b[0m         \u001b[0mx_new\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_new\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 498\u001b[1;33m         \u001b[0mout_of_bounds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_bounds\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_new\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    499\u001b[0m         \u001b[0my_new\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_new\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    500\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_new\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32md:\\Miniconda\\lib\\site-packages\\scipy\\interpolate\\interpolate.pyc\u001b[0m in \u001b[0;36m_check_bounds\u001b[1;34m(self, x_new)\u001b[0m\n\u001b[0;32m    526\u001b[0m                 \"range.\")\n\u001b[0;32m    527\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbounds_error\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mabove_bounds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 528\u001b[1;33m             raise ValueError(\"A value in x_new is above the interpolation \"\n\u001b[0m\u001b[0;32m    529\u001b[0m                 \"range.\")\n\u001b[0;32m    530\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: A value in x_new is above the interpolation range."
-     ]
-    }
-   ],
-   "source": [
-    "ch4_cp(8752)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "但我们可以通过参数设置允许超出范围的值存在："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ch4_cp = interp1d(data['TK'], data['Cp'], \n",
-    "                  bounds_error=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不过由于超出范围，所以插值的输出是非法值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(nan)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ch4_cp(8752)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以使用指定值替代这些非法值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ch4_cp = interp1d(data['TK'], data['Cp'], \n",
-    "                  bounds_error=False, fill_value=-999.25)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(-999.25)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ch4_cp(8752)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 线性插值"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`interp1d` 默认的插值方法是线性，关于线性插值的定义，请参见：\n",
-    "\n",
-    "- 维基百科-线性插值： https://zh.wikipedia.org/wiki/%E7%BA%BF%E6%80%A7%E6%8F%92%E5%80%BC\n",
-    "- 百度百科-线性插值： http://baike.baidu.com/view/4685624.htm\n",
-    "\n",
-    "其基本思想是，已知相邻两点 $x_1,x_2$ 对应的值 $y_1,y_2$ ，那么对于 $(x_1,x_2)$ 之间的某一点 $x$ ，线性插值对应的值 $y$ 满足：点 $(x,y)$ 在 $(x_1,y_1),(x_2,y_2)$ 所形成的线段上。\n",
-    "\n",
-    "应用线性插值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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QX6N+hvBGBnTjDculNy/X2p4/PjnJ0UOHVizBPAKMT0y0/AxJ7al0qA9iCaOZ1QJ669at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\nSSViqEtSiRjqklQihroklcj/A8xgms4BB0nlAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb861358>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "T = np.arange(100,355,5)\n",
-    "plt.plot(T, ch4_cp(T), \"+k\")\n",
-    "p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "其中红色的圆点为原来的数据点，黑色的十字点为对应的插值点，可以明显看到，相邻的数据点的插值在一条直线上。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 多项式插值"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们可以通过 `kind` 参数来调节使用的插值方法，来得到不同的结果：\n",
-    "\n",
-    "- `nearest` 最近邻插值\n",
-    "- `zero` 0阶插值\n",
-    "- `linear` 线性插值\n",
-    "- `quadratic` 二次插值\n",
-    "- `cubic` 三次插值\n",
-    "- `4,5,6,7` 更高阶插值\n",
-    "\n",
-    "最近邻插值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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1aY6IIDNj2dd3O9TfuWePm0wkaZFWGupdX35Zy5tMetVeuzpJ6pSerKmv1U0mhrqk0vTs\nQambTCSp+3q6+WgtbDLpVXtudpG0KlbyIfd2L9b4JpNetdeuTpJmsV42H7nJRJK6ryehvlY3mfSq\nvXZ1ktQpXf+c+h179rjJRJIWac1vPupm+5JUmjW/+UiS1DstQz0iLomIRyNiMiKORsSdVfmhiPhS\nVf5QRLysN92VJLXSMtQz8zlgV2YOAa8EdkXEdcBdmfmrVfkngOHud3V9c0fpOY7FDMfhHMeic9ou\nv2RmszrcDGwEvpeZP5hzyqXAqS70rShO2nMcixmOwzmORee03VEaERuALwLXAPdk5tGq/N3A7wNN\n4LXd7KQkaXEWc6d+plpmuRK4PiJqVfkdmbkd+BvgA93spCRpcZb0kcaIOABMZ+bdc8q2A/+Smb98\nkfP9PKMkLdFKPtLYcvklIgaB5zNzKiK2AjcCByPi5Zn5jeq0NwETne6YJGnp2q2pXwHcX62rbwAe\nyMyHIuLjEfELwAvAk8CfdrmfkqRF6OqOUklSby17R2lEfDgiTkbEY3PKXhIR/xoRX4+IsYgYmFN3\ne0T8d0R8LSL2rLTja8kCY1GPiBMRMVG93jCnruSxeFlEPBwRj0fEVyLi7VV5382NFmPRd3OjxUbG\nfpwXC41FZ+bFcr+zF3gdsAN4bE7ZXcBt1fF+4D3V8S8Bk8CLgKuAbwAbVvKdwWvptcBYDAN/cZFz\nSx+Ly4Gh6vhS4AngF/txbrQYi36dGy+u/rkJ+BxwXT/OixZj0ZF5sew79cz8D+CZecVvBO6vju8H\nfqc6fhPw0cz8cWZ+s+rUa5b73mvNAmMBcLEHxaWPxdOZOVkdPwt8FfgZ+nButBgL6M+5MX8j4zP0\n4byABccCOjAvOv2FXtsy82R1fBLYVh2/FDgx57wTnJvcJbu5+o6c++b8Wtk3YxERVzHzG8yj9Pnc\nmDMWn6uK+m5uRMSGiJhk5v//w5n5OH06LxYYC+jAvOjatzTmzO8NrZ7Clv6E9h7gamAI+A7wvhbn\nFjcWEXEp8A/An+X5XyvRd3OjGouPMzMWz9KncyMv3Mi4a15938yLi4xFjQ7Ni06H+smIuBwgIq4A\nvluVfwuY+02OV1ZlxcrM72YF+BDnfl0qfiwi4kXMBPoDmfmJqrgv58acsfjb2bHo57kBkJnfBz4N\n/Bp9Oi9mzRmLazs1Lzod6v8MvK06fhsz3+A4W/7WiNgcEVcDPw98vsPvvaZUE3TWXmD2kzFFj0VE\nBHAfcDQzPzinqu/mxkJj0Y9zIyIGZ5cT4txGxgn6c15cdCxmf7hVlj8vVvD09qPAt4EfAU8Bfwi8\nBPgs8HVgDBiYc/5fMbPA/zXgt1b76XMnXxcZiz8CPgJ8GfgSMxN1W5+MxXXAGWae1k9Ur9f349xY\nYCze0I9zA/gVZr4YcLL6b7+1Ku/HebHQWHRkXrj5SJIK4p+zk6SCGOqSVBBDXZIKYqhLUkEMdUkq\niKEuSQUx1CWpIIa6JBXk/wGLsxItFlRd2gAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb8612e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cp_ch4 = interp1d(data['TK'], data['Cp'], kind=\"nearest\")\n",
-    "p = plt.plot(T, cp_ch4(T), \"k+\")\n",
-    "p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "0阶插值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb989f98>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cp_ch4 = interp1d(data['TK'], data['Cp'], kind=\"zero\")\n",
-    "p = plt.plot(T, cp_ch4(T), \"k+\")\n",
-    "p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "二次插值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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k+Je+tGIJ5hFgbHx88fWS1sdQF9B+eLc7zLDRnslJrr3vvuWjX4Cbd+7kkN8yJHWNoT5E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\nqEtShRjqklQh/w+L/6C1jqXsbQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb995860>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cp_ch4 = interp1d(data['TK'], data['Cp'], kind=\"quadratic\")\n",
-    "p = plt.plot(T, cp_ch4(T), \"k+\")\n",
-    "p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "三次插值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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o6rz3Xpb1l/3fJPWqdKHuD9vg9Hve+zADfxR+uUhlYKjrDP24uLp8X7PZ5Jb9+5mbmWHz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\nAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb995a58>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cp_ch4 = interp1d(data['TK'], data['Cp'], kind=\"cubic\")\n",
-    "p = plt.plot(T, cp_ch4(T), \"k+\")\n",
-    "p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上，我们可以使用更高阶的多项式插值，只要将 `kind` 设为对应的数字即可："
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "四次多项式插值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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9sCmhPswjXHptkEMkNyvwG40G1+/dy4GjR9nZtP74kSNcd//9HJ6eXg72Zo5kkdavdKGu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\nAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbe85278>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cp_ch4 = interp1d(data['TK'], data['Cp'], kind=4)\n",
-    "p = plt.plot(T, cp_ch4(T), \"k+\")\n",
-    "p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以参见：\n",
-    "\n",
-    "- 维基百科-多项式插值：https://zh.wikipedia.org/wiki/%E5%A4%9A%E9%A1%B9%E5%BC%8F%E6%8F%92%E5%80%BC\n",
-    "- 百度百科-插值法：http://baike.baidu.com/view/754506.htm\n",
-    "\n",
-    "对于二维乃至更高维度的多项式插值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.interpolate import interp2d, interpnd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "其使用方法与一维类似。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "### 径向基函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "关于径向基函数，可以参阅：\n",
-    "- 维基百科-Radial basis fucntion：https://en.wikipedia.org/wiki/Radial_basis_function\n",
-    "\n",
-    "径向基函数，简单来说就是点 $x$ 处的函数值只依赖于 $x$ 与某点 $c$ 的距离：\n",
-    "\n",
-    "$$\\Phi(x,c) = \\Phi(\\|x-c\\|)$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "x = np.linspace(-3,3,100)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "常用的径向基（`RBF`）函数有：\n",
-    "\n",
-    "高斯函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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w5JNh4+ZWreCyy2InSi6Vhjy1Y0doxWzYEHZkadYsdiKR+jv8cHj88XBep1kz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\nwKmQi4gUOBVyEZEC9/8BloffFHVXiTcAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa0dcb38>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(x, np.exp(-1 * x **2))\n",
-    "t = plt.title(\"Gaussian\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Multiquadric` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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LTjbbLHZEpWXBAjj00LAfzuDBSuKlTIlcGqRnT1i0KAx8jhsHm24aO6LSsHhx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\nJJwSuYhIwv0/gG56R7Cwt98AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb9a6908>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(x, np.sqrt(1 + x **2))\n",
-    "t = plt.title(\"Multiquadric\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Inverse Multiquadric` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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PhNtuC4sNieQ6dzjrrPDJc9AgfQItTyomBLUEZrv7XHdfAwwGTirrtaqYMe+t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\n9/9saTRo/aYs3AAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbd3d438>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(x, 1. / np.sqrt(1 + x **2))\n",
-    "t = plt.title(\"Inverse Multiquadric\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 径向基函数插值"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于径向基函数，其插值的公式为：\n",
-    "\n",
-    "$$\n",
-    "f(x) = \\sum_j n_j \\Phi(\\|x-x_j\\|)\n",
-    "$$\n",
-    "\n",
-    "我们通过数据点 $x_j$ 来计算出 $n_j$ 的值，来计算 $x$ 处的插值结果。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.interpolate.rbf import Rbf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `multiquadric` 核的："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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QEYk6f5JXZR4REd/4F/xa0SMi4gt/gl8rekREfKNSj4hIklGpR0QkyajUIyKS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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbebf828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cp_rbf = Rbf(data['TK'], data['Cp'], function = \"multiquadric\")\n",
-    "plt.plot(data['TK'], data['Cp'], 'k+')\n",
-    "p = plt.plot(data['TK'], cp_rbf(data['TK']), 'r-')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `gaussian` 核："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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mnruBF8zsUkLDQKP69Z4EADO/ZeTKldC1KxucK1ksru+++2oEkIhIFeIWAJxz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\nwgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xc576550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cp_rbf = Rbf(data['TK'], data['Cp'], function = \"gaussian\")\n",
-    "plt.plot(data['TK'], data['Cp'], 'k+')\n",
-    "p = plt.plot(data['TK'], cp_rbf(data['TK']), 'r-')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `nverse_multiquadric` 核："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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ERI6Bt8EvIiJR503wb9yoi7dERDyiGb+ISJJR8IuIJBkFv4hIklHwi4gkGQW/\niEiSUfCLiCQZBb+ISJLxJvj37YMaNTw5tIhIsvMm+OvW1e0aREQ84k3w66pdERHPeBP8qu+LiHhG\nwS8ikmQU/CIiSUbBLyKSZCIe/GbW2cxWmdn/zKxPkRsp+EVEPBPR4DezFGAk0BloAVxjZs0P2zCB\ngz8QCHjdhXKl8cW3RB5fIo8t0iI94/8t8K1zLtM5lwf8B+h62FYK/ril8cW3RB5fIo8t0iId/KcA\n68Ke/xhqKyiBg19EJNZFOvhdibY64YQIH1ZERErKnCtZVpdoZ2bnAH7nXOfQ837AQefc0LBtIndA\nEZEk4pyLyL1uIh38FYFvgAuBDcBc4Brn3MqIHURERMqkYiR35pzbb2Z3Ap8AKcBrCn0RkdgS0Rm/\niIjEvqheuVuii7tijJmNNrMsM1sa1lbLzKaa2Wozm2JmNcNe6xca3yoz+0NY+9lmtjT02rPRHseR\nmFkDM5thZsvNbJmZ9Qy1J8QYzayKmc0xs8VmtsLMHg+1J8T4IHj9jJktMrPJoeeJNLZMM1sSGt/c\nUFsija+mmY03s5Whn892URmfcy4qXwRLP98CjYFKwGKgebSOX4Z+nw+0AZaGtT0B3B963AcYEnrc\nIjSuSqFxfssvf1XNBX4bevwh0NnrsYX6Ug84K/S4KsFzNM0TbIypoe8VgdlAhwQbX2/gLWBSAv58\nfg/UKtSWSOMbA9wU9vNZIxrji+YA2wMfhz3vC/T1+h++hH1vTMHgXwWkhx7XA1aFHvcD+oRt9zFw\nDnASsDKsvRvwktfjOsJYM4BOiThGIBWYB7RMlPEB9YFpwO+ByYn280kw+E8s1JYQ4yMY8muKaC/3\n8UWz1FOyi7viQ7pzLiv0OAvIvyLtZILjypc/xsLt64nBsZtZY4J/3cwhgcZoZhXMbDHBccxwzi0n\nccY3HLgPOBjWlihjg+C1QdPMbL6Z3RJqS5TxNQE2m9nrZrbQzF4xszSiML5oBn9CnkV2wV+xcT82\nM6sKTADuds7tDH8t3sfonDvonDuL4Oz4d2b2+0Kvx+X4zOwSYJNzbhFQ5PrueB1bmPOcc22Ai4E7\nzOz88BfjfHwVgbbAC865tkAOwUrIIeU1vmgG/3qgQdjzBhT8LRVPssysHoCZnQRsCrUXHmN9gmNc\nH3oc3r4+Cv0sETOrRDD033DOZYSaE2qMAM65HcAHwNkkxvjOBS41s++Bd4ALzOwNEmNsADjnfgp9\n3wy8R/BvPb0qAAABWUlEQVR+YIkyvh+BH51z80LPxxP8RbCxvMcXzeCfD5xuZo3NrDJwNTApiseP\npEnA9aHH1xOsi+e3dzOzymbWBDgdmOuc2whkh87YG3Bd2Hs8FerPa8AK59wzYS8lxBjNrHb+qggz\nOx64CFhEAozPOfeAc66Bc64Jwbrup86560iAsQGYWaqZVQs9TgP+ACwlQcYX6tc6Mzsj1NQJWA5M\nprzHF+WTGRcTXDXyLdDP65MrJezzOwSvQt5H8BzFjUAtgifUVgNTgJph2z8QGt8q4I9h7WcT/KH9\nFnjO63GF9asDwfrwYoKBuIjgbbUTYozAr4GFofEtAe4LtSfE+ML61pFfVvUkxNgI1sAXh76W5WdG\noowv1K/WBBccfA1MJHjCt9zHpwu4RESSjDcfvSgiIp5R8IuIJBkFv4hIklHwi4gkGQW/iEiSUfCL\niCQZBb+ISJJR8IuIJJn/D8ptq7/3lHsDAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa1c3240>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cp_rbf = Rbf(data['TK'], data['Cp'], function = \"inverse_multiquadric\")\n",
-    "plt.plot(data['TK'], data['Cp'], 'k+')\n",
-    "p = plt.plot(data['TK'], cp_rbf(data['TK']), 'r-')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不同的 `RBF` 核的结果也不同。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 高维 `RBF` 插值"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from mpl_toolkits.mplot3d import Axes3D"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "三维数据点："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "x, y = np.mgrid[-np.pi/2:np.pi/2:5j, -np.pi/2:np.pi/2:5j]\n",
-    "z = np.cos(np.sqrt(x**2 + y**2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x176b4da0>"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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hbN261f76xRdfxNDQUOT9CAqL1ZBwiqxG5b+Lqi1qJ4zsS0rwqdVq9q5S5XIZ\nU1NTSn10UQxsqm0S8rWWz2U3lgmOrDIifpO+RGtBrVbTKumL773O0VkMJuG6ep2/0dHRWLZaXbly\nJW688UZceOGFePTRRzF37lztLQAAi1Vl9HJktVPEBJ9ms+m4e1dUlgaVg7Dqz0AiQa4v29/f31Xh\n/rhFZdztM8GQ7QV0X1LtSJ2SvnQWXDr3LQnoev4A79Ja4+PjSsTqBz7wATz00EMYHR3F0qVLce21\n19q7XK5atQpnnXUW1q5di5GREZTLZXz3u98NvQ8qYLEaEnFGVnUWq04JPsVi0XVzhCjFahKPT+dz\n586doRbuF48fBTpPMExnyMJLh6Qvpnt0Pdc6C33Cq4/j4+NYsGBB6G3eeuutbX/mxhtvDL1d1bBY\nVURU2fOAHjVQZZz2lPeT4BNF+aIoBHGYx6coarVaRbPZhGEYSgr3JzXSzCSPIElfYk3ZTpK+dL73\ndBZcOvcN0L9/gPe912w2u1oJm23wmQoJGiTp5hS/Vv1ARSHwqB2vh89paTronvK9ElntFpqgxcL9\nxWLRPseqdpiKamJPwkTDxEOYSV9i4pc4NjPJJyljiFMfdX6B0hUWqwrReXm+03ZkUSwv89NuSG7L\n/H7a6AWx2unx2xXup+iSClQP/KKQcGuLB3GmHUGSvqgUl1gST4ekL7G/ugounfsG6N8/wL2PlKis\ne/91gsVqiMgihZbnVe+zHmU1AKoda1mWLaqo3JSfZf52zEax6uTrjaNwv6pj+x2QeeBmusXJXtBo\nNGBZFgqFglZJX7qTBDGoM142wJ07d2JgYCCGXiUXFqsK6bXIKrB3r+Pdu3dPW+b3W8fTD7NJrHZa\nuL8X4Imw9/DKfNYFv0lf9LfqpK8knDNdScoY4tTH0dFRJclVvQyL1RCJqyKA6uxzsY6nYRgolUqh\nZqCL9IJYBdyXs8lf103h/qRFVsVj02dMwiRDsDVhdhBl0lcS0F0MJrl/cdVYTTIsVhUSVcRThSgW\no34AkM/nUS6XUavV7DqKKuiFagBOA1RYhfs5sz5aVJzvzZs34447fgzLsnD22e/C4YcfHurxmemE\nIWrCTvqif0dhE+uUJIhBnaPSXudvbGyMI6sBYbEaIk6RVfJ4RkG3g4sc9ctmsyiXy7Z30jTNSKKe\n1BeVA2UUNgC5OoLTJgg6EbcQjrv9KNi4cSPOPvsj2LPnQrRaeXz3ux/Hrbd+DcuWLYu7a0wXBE36\nIosBBQURRVFEAAAgAElEQVQajYY2SV9JQfexwmsO27FjB0dWA8JiVSFRela7KZNFyT1UEskt6heF\nrSEKsar6uoiiP51Od1UdQSapgi6p/Q6bf/zH72Ny8jL09V0OAKhUFuOrX/0O/vVfWaz2Kl72gkql\nYluqxOoFOiR96R5ZBfS2E3mdv/HxcRx88MER9yjZsFgNkbg8q2JbfpdF5BJJuVyubdQvSluD7nVQ\nZVqtlr3Mr7Jwv9he2J+DBaV6JidrMIzXlv9SqYWoVKox9ig8dBU3uvaLCBqV5Z2+9qL7dW1nAxgc\nHIy4R8mGxapCopz8/bTltMzvViKp3XFUDhK6ZOu3Q0w+Ewv3N5tNmKapRKjqPDh7IZ5zOm+zcZnz\nve89E/ff/1XUavvDMPIwjC/jfe97f2z9qVQquOOOH2Pr1h14wxsOxVlnvUNrH2Cv0W4sVZH05Tcq\nm2RPqA6wZzVcWKyGSJyRVS8BJib3pFIpO1kq6EDUrd0gSDs6i9V2hfvr9Xok/U9aZFXegleeUOl7\npmn2rJBdvnw5vvKVSXz969fCNE18+MPn4qKLLgx0jLCufaPRwKc/fQ2effZAZDJvxN13/wQbN/4B\na9Z8outjM+pRlfQV1TjfLUnon9scOzo6ypHVgLBYVYg8IatEFsaioGo2m6El9/RCaalOjk8iKu7C\n/UmDkswsy0KlUrHvQ9M0pz0flHQCYMaE2mtF2s899xyce+45cXcDzzzzDJ5/PovBwc/AMAxY1ltx\nxx0X4eMfvxilUinu7oWGzhFC1d78TpO+xJ8TV0F0eYlMwvjqdW2r1SrK5XLEPUo2LFZDRHwjpa+B\naN4ADcOwBxtx69NisRhacg8Qza5cOonVTgr369T/uI5rWRaq1aptj0ilUnZ9XrENMbpDwrZYLAJo\n79dziwzRtYl7QtWdvS8MRfs8GUYOrVa0FUyYePCyFwB7n71KpYJMZq9E0CXpy+lz6IrbvC/rA8Yf\nLFYVozpZCNg7kJimCdM00Wg0lO6ENBsiq7K3V8VOXd2ga+TWq1TX7t27p507P+cxiF/PKTI0WxNP\n/HLkkUdi4cJ/xiuv3I5C4fWYnFyLU045HP39/R0dT/dlWd3Q8Rkm6DrSi6ZIJ0lfYUdlk3CvefUx\nCf3XDRarISMLCYp4hh2JlIVBOp1GJpPBnDlzQm1HppfFKhfu30vQgVQ+b06luoKcEz/te/n1qB15\nD3gx8cQrKjRbJpFyuYybbvobfPOb38cf//jfOPbYEXzsY5+Ju1uho7sw0LlvTnSS9EX/7jbpS2xH\n9/Pm1sdKpWKvHjH+YbGqmDAjq7JvMpPJ2MKAIoGq6QWxSlAbYRfujzsy3M1x/eIVRfX7+4Zh4Omn\nn8a2bdswPDyMI444otOuT0MUsX4ST7wmU4oUibaDXmJwcBDXXvuXcXdjVqK74Oqkf6qTvqK01nWD\n1/g8OjrKlQA6gMVqyDgl23RbEcBp61N5mT8qgReVWFVZRYGu0eTk5AzRH8YAmFSxKh7b7Tw4RZ+p\nqHlQbrzxn3DLLb+EYRyNVus2fPrT78I557yr24/QlnaJJ/Jk2mq1MDU1pZVXj2FUonJ86SbpS3zO\nqESgzisiTn0aGxvj3as6gMWqYjqNrMqRq3a+yajKZKkWktSGisGy1WpNE/2GkbzC/XFA0Y9qtdpx\n9Fm+plu3bsUtt/wUc+Z8H+l0PxqNMXz96x/Caae9NbYs2WaziUceeQSbN/8JS5YM4pRTToZhGLAs\nC4VCYYZH1m9UiIVsvOj6HOraL5Gok6XaJX2Jzx2gb+UQr2vLkdXOYLEaMt1EVsnfI2996idyFVVk\nNZVSny0c5mdxsk4Ui0VMTk4in88nsnB/FJFVINwoqtzGzp07kU7vh3R6bzJPNjsfhrEPdu/ejaGh\noa4/R1BarRa+/e1/wX/+505kMsfCNH+LJ59cjyuu+PC0frfz6sn2Aq+kk15L+EqC+GL8oeO1FKOy\ntFtjPp8HoEfSl0g7scqR1eCwWFWMH3FHy/xUTL6byJXqQSYpntV21ompqamu++lFu+X0MI6tAopa\nVCoVuxJCWNFnsc/Dw8PI51/ExMQj6Ot7M3bt+i/ss08V++67bywT5fj4ONau/R2Ghq5DOp1Ds3ka\nfv7zv8Z73/sn7Lvvvm1/P0jSCQlZp+XN2ZzwNdvQURASuieIyudOh6Qvr/6JjI+PY2RkJPAxZzss\nVkPGb2SVREG3W5+K7agUSGI7uopVOYqazWZRLpdjKdwfVaQ7LJrNJqrVqm2VKBQKHVVCcEM+zpw5\nc/DNb/41Pve5r+Cll3bggAP2w/XXf8GOlERNvV5HKlVAKpUFAKRSGaRSZXu5sRv8Jp24TaSykJUn\nWcYbPk+dofM5C3JNo0r68tu/0dFRnHjiiQE+LQOwWFWO7FklMSUu84clCnQWkirbmE2F+8M8tvzC\nlMvlkEqlbIGvmiOPPBJr195iv1gAe5Pe4mBwcBAjIzls2PBj7LPPm7Br11MYGprC4sWLlbdNy5t+\nKxeQvWByctLTI6uz2GD0FtE6940I80W6XdKX+ELplvQlPnte1r+xsTH2rHYAi9WQcYrg0W4+tJd8\nmEurcltJz9SnNtp9Dieh1dfX51tkJS3yKdNN32VxL0ZRd+/ereS8eJ1vEqpxkk6ncfXVl+O7370D\n69f/A444YhEuvXQ1crlcrDs6OU2kjUbDTnRrl/ClS8IJw4RJVGI6aFSWXiRpzJicnEQqlcIDDzyA\n559/HsPDw9i5c6eSFaR7770Xa9asgWVZuOyyy3DVVVdN+/8HH3wQZ599Ng466CAAwPve9z5cffXV\nofdDFSxWFSGKKWDvBNPNMr8foqgIEMWOXF7+26QU7tctstqtuJ8NDAwMYM2aj077Xhg2AFUETfjy\nu1Vtp+OTrtE47ldwdO4boE//3KKy1WoVqVQKmUwGzWYT5XIZ4+Pj+PWvf42nnnoKy5YtQzabxcEH\nH4yDDjoIBx98MFauXIk3v/nNHfXDsixcccUVeOCBBzA0NITjjz8eK1euxOGHHz7t50499VTcdddd\nHbURNzxTKWBqasq+WfP5PBqNRqj+PzeijBaqHCzk48plvGZz4X46tt+XEieLhNe9GNU95NZO0iPe\nuhA04Yu3qmVEdBGDbiShf/TcpFIpnHHGGTjjjDPQarVw1lln4ec//znGxsbwwgsv4IUXXsCmTZuw\nZ8+ejttbt24dRkZGMDw8DAC48MILceedd84Qq0keW1mshgwN7qKYqlarSrZcdWo7iqhnVIlcjUYD\njUYD9XqdC/cHwCnRzKtGbxSwCNUHr6VNukaiiPVbuUBHdL7ndBdcOqPzdQW8ry0J2YULF2LhwoUd\nR1NFtm3bhqVLl9pfL1myBI899ti0nzEMA4888gje8IY3YGhoCNdff31ouwZGAYtVBRSLxWmRr6gm\n6ig3BlD1eZrNpl3Ca3JyEoVCQZm/V/W5itoG0Emimd9jM7MDUcQGrVwA7N33XMeELxaFwdBZSNO9\npmv/APfzZ1lWbLW9ly1bhq1bt6JUKuGee+7BOeecg/Xr14feF1WwWI2AXtpdSmwnrIdOjgRmMhmk\nUimUSiXkcrlQ2pCJIrKq8tjU9yDluuIkqntzNhDny4RX5YI9e/bYO32JUVlO+HKGImw6onPfCJ3v\nGTexOj4+jnnz5oXe3tDQELZu3Wp/vXXrVixZsmTaz/T399v/XrFiBS6//HJl/VEBi1UFOFUEiNMH\nGDZhJVl5Fe6fmJjo+vheJN0G0Gq1UK1WUa1WAQCFQiFwFNUJHSKrcbcvosP5cEK3iZrOkZ9933mr\nWqYbdI76At7j19jYGObPnx96m8cddxw2bNiALVu2YPHixbj99ttx6623TvuZl19+GYODgzAMA+vW\nrUOr1UqMUAVYrEZCVJHVJNgA5Kx0t0hgFCIhaWKVoqjVahWmaU6riarz4B2EXvkczHSCVi7ws1Ut\nHcvrntFZ2HDfOkPnvgGY9vIls2PHDiVbrWYyGdx44414xzveAcuy8NGPfhSHH344br75ZgDAqlWr\ncMcdd+Cmm25CJpNBqVTCbbfdFno/VMJiVQG9HlntpB3LsuyMfvJTlstl10hgkpfp6fhhvTiQj5fq\n9GazWTSbTfT19YVyfBFV510+blQvVoz+BK1c4JTwpZtHNunouJqQFLzE9NjYmBKxCuxd2l+xYsW0\n761atcr+9+rVq7F69WolbUcBi9UISKVSkdRr1E2sdlPbM+nL9N1CySvVatX2olKdXsuyYJqm0rZV\no/O5Z/TBq3IB4JzwRfVkSTS0Wnu3EHbaKjNOdH8G4j4/biQlsuqESrHa67BYVUBckVVdErnCKNyf\ndLHa6fFpYqUoqlNGv+oarlFEVgH9Jx1Gf7wSvsg2U6/XYRjGNGuBmPAVZ1RW1/tf52dT574B7cXq\nYYcdFnGPegMWqwqQb9SolzxVP8ypVGrGFpRhF+5XnT2um1iljH6qKVssFl1rykaRvBUXuke8GWf8\njDljY2NYvfrzeOyxddhnn/n46lf/GqeffrqyPolRVLmqiJ+EL65coCe6i1UvVCVYzQZYrEZAlMvz\n1FZUpZNkkRVW4X7dxKSK45PAr1ardhR1YGAg1pIxSZ0EGP35+Mc/i8cfPxLZ7M0YG/stLrvs47jv\nvn/ByMiIsjbdnsHt27dj3bp1yOVyeOtb34o5c+ZM+x054UvFVrU6iy7uW+d49W90dBSLFi2KuEe9\nAYtVBbhFVqN4yKgt1YLHsizs2rXLFllhF+6PKsFK5TVx63+QKKoTSYyscsR0dmNZFtatewz5/L/B\nMLLI5U6EZS3H448/rlSsAjPH4/Xr12PVqmtRqZwCYAKLF/8I//zPX8E+++xj/3zYlQvomDqLLIKf\n0+7wmlPGx8fZs9ohLFYVIU7OUQ5QqkQB+b9IZBmGobRsUhTiRmUUWj5m2DYJOmbYfZ/tXj1GDalU\nCsViHxqNjchkDker1QTwAubOfUvkffn613+AWm0VFi58BwBg27Yb8aMf3YXLLruk7e+GUbmAfl8U\nuDoKWd36Q7Raem9Y4NW/Wq2GYrEYcY96AxarEUERTxVbrYmELfLkwv2FQgH5fB6Tk5PIZrOhtSMT\npVhVeWzK6A/TJhGVxSNs/B6XIzu9h2EYuO66v8Jf/uUHUa+vRDr9LI45JoMzzzxTabtOL3Tj4xMo\nFPa3v06nD8DY2O+6bitI5QISqbS1tC4JX9RPXYUqkNz+0XVmOoPFqiLkST9JFQHkklNy4X4xIqCK\nJItVMVlj9+7dSm0SOg/aIkEsDkzy8HMvvve95+Kggw7Er371KyxYcC7e9a532S+8r7zyCv74xz+i\nWCzisMMOU/pSf9ppx+Lb3/4estnPw7L2oNm8A299618oa48QKxfQGFEqlQC8JmTlqGwcCV+6jytJ\n7V8cK629BIvViEjC7lJUcoqW+duVnFI5aCRRrMoluwBg7ty5iRucVJ932oXLsqwZE6+Ke+qJJ57A\nk08+iX333RcrVqzQegmx1znmmGNwzDHHTPvehg0b8LWvrYVlHQnL2oBjjnkSq1Z9QJlg/chHPoiJ\niW/hrrs+jFwui8997n046aSTlLTlFxKyTvipXCCK2F6vXKB7dNJtDNuzZ4+SjVxmCyxWFSHfrFFW\nBAgiiuUoqp/C/VFUHaDjq36L7vaauHlRU6kUXn311ZB6ORNV95PKc02R5maziWw2a0/OYgSJrrco\nZLtJUPn+9/8Vf/VX16PVejdSqR/hlFPuxA9+8I+BBKvuk2PS+cEPHkCpdD4GBg5Aq9XCk0/+C557\n7jkcddRRXR/bafzIZrP47GdX47OfjW83nyDjWtCEL7+VC+jY3fQtLnTun9v547JV3cFiNSKiiqw6\n1UB1otvC/VFl66ukmzaczl8ul4s0QSkJWfvieWq1WrZnlyZVeQKuVqsAYC+VyjsSOe0P7yZkTdPE\n5z9/DSzrYaRSI7CsOn7+85Pw8MMP45RTTvHV/7gmxbGxMfzyl0+jWjVx9NEH4tBDD4mlH1Gwc+ck\n5s/fW85nb4RxEJVKJeZeJYOgCV/03HlVLiDhqys6i2mvpf7R0VGuBNAFLFYVEWdk1a2dMDPSk56t\nLx7fLxSFpiXsducvab7SsHCK1pfLZVQqFbs4u9s5ISuAU/KevBTaTshOTEyg2QQM4+A/t5lDKnUY\nxsbG1H34EHj11Vdx8833wDSPRyZTwhNPPIoPfrCB17/+iLi7poRjjz0Ajz76MwwNnYmpqVEYxjNY\nuvS8uLullCjGBa+ELxr3nJ4nErKVSsXVIxvXmKaziAba11hlsdo5LFYVId+wqVQKjUYjknbFCC4N\nQmEX7g9qN+i0DdXRWz/H7zSKGkW1AZ2OK1aOMAwDhULBjtaHEa0RE1RknIRsoVDA8PAwNm36exjG\np9FsPoZM5uc4+ujPwrKsjq0Fqvn97zdgaupILF36egBALlfCL37xoNZitRvxdcEF70Kz+Z948sm/\nR39/AatXvw2LFy+OvV+9jChi5eepXq/bVh2nWrJxVi4QPbo60q7G6oIFCyLuUe/AYjUioqwGQIMK\nRVFbrfB3R6J2VBKnWHWKDvb393t6ed2Oo4Ko7ic/UMJUo9FANptFX18f0ul0pBOKm5C9447v4eKL\nr8Bvf3sd5s1bhBtv/L8YGhpCrVbzVcQ9DvZGir3/v5colUr46EfPn1XCUvfP2m3C12zdqrZdZPXw\nww+PuEe9A4tVRThFVlVHIimi1Gw2sWvXLmSzWZRKJSWF+5OYre/n+F7RwU6OnzT8nnOylIhbxZZK\nJdeXobjE9ZIlS/DTn/7Ys5yM11Io9btWq0W2G9HrXncIfvaztdi+vYRMpoQ9ex7FO995pJK2dCKJ\nz0sv0k5I+034EoWsn4QvP9dfd5Hv1b+xsTG2AXQBi9WIUDlZy4X7AYRe11OmV8SqOKAGqYjg9/i9\nFlkVLRGdbBXrhyirHLSzFpimiXq9DgCOuxE5JXx5teeHefPm4eMffyd++cunUatZOProY2YkWOk8\nYesEeZl1Q2fR1c05C1q5IOhWtTqfN4DFqkpYrCpCdWTVTWCl02ns3LkzEvN+FGJVZTSaxMiuXbtg\nGO3rygZFN7H6wgsv4P/9v7UwDANnn/1uDA8P+zruyy+/jOeeex5AE4ceeij22WefUF6GnM6zThOR\nuHSZz+en/Z+cYR22kF2wYAHe8563hf+hGCYm/AhZ8blyeqboZxuNhhYJXzLtxOrg4GDEPeodWKwq\nxGni7/bN0E/h/qiEpJ8SWd22EfbnIIFKHkvDMDryogZpTweeffZZvP/9qzE5eT4AC9/+9kfw4x9/\nGyMjI66/02w2sXHjRnzjG2vRaBwDwzCxePHdWLMmeMF2uj91j4z4xS0iK2dZBxGyST4vvXJdo0Ln\n8xVH30TB6ZVAKa5yyLWZdfCee0Wld+3ahblz50bWl16DxWpE0APUbDYDT/Ryyal2y9QUkVRpA4gi\nwSrMNmQvaj6fRz6fR7VaVSZUVQ6SQYX8DTf8M6amPoW5cy8EAOzePYCbbroFX/3q3077OTrmnj17\n0Gg0cM89j6JYPAvDw3sTA/7whwfxq189idNP91en1AudJ+xO8cqynq1CNk568R5TjY7nTHw5JI88\nIVsL3LaqjVPIiuX1mM5gsaoQWVAEFRidlkzqhUx9aqMbGwBFUWu1mmOmummaWpTG6pQgx96zZwrp\n9GtLUKnUIuza9ZtpxxJ9z+l0GqVSCaZpoFh8LRqQyfRjamq0677rtHQXFWEJWUpUoV2+Ztt57AV0\nFIRJwOm86VS5wE9yGtMZLFYjxI/4kqOonRTu74Xkp27acIqiumWq6yZWJyYm8E//dBt++9utWLx4\nH3ziE+djaGio62Ofc84ZWLfua6jVBtFqWTCMf8A556yCZVmoVqt2Dd5isYg9e/Ygn88jlUrh+OMP\nxu23/wyZzDvRaEzBNH+Fww/vzEvpJ0EirsSxuGknZJ0mXafyW51kWDOMiM5COmh0MmjCl6qtahuN\nhrIVvNkCnz2FOCVZOU3EVDInrML9qhOTqA2dxKpTFLVcLnuW7dItCa3VauHv//5b+M1vDsbChe/H\n73+/Hldf/Q/4xjf+F/r6+mYcO8g1Pv/892Fqagr/9E+fRypl4GMfuwCnnHISdu/ePeOFSDwvJ598\nIizLwi9+8UOUyxlccMFbcOCBB/putxeIWzzLfj6aSHO5nKOQddtSczYKWV2Fl85LwnHf716EeT2D\nJnz53arWifHxccyfPz+Ufs9WWKxGiCwwVBXuj8oGoIMgpnNIe8oXCgXPep9Bj98twZbq9+CZZ3Zg\nyZLPwTAMFAoL8NJLv8aWLVvw+te/vqt+GIaBiy++CBdccJ5dM9TNViKeF8MwcPrpJ+P000/uqn35\nuElBR6Ej4pWY4pZh7VQqqFshq6soZDpD12sZ1fjR7rkCnLeqBYCpqSl7/vnSl76E/fffH+VyGeVy\nGZZlhZ5Lcu+992LNmjWwLAuXXXYZrrrqqhk/c+WVV+Kee+5BqVTC9773PRx77LGh9iEKWKwqxCmy\nSm9nYgQw7ML9UQlJQO0k5SZuOomieh1f1WcIesy9wrEB05xENtuHVqsJy9o5o2wSHdvPwC2fq7Bq\nyDL6042Q9VoC1VXIJAldxX0SXibjPm9ulp1Wq4XJyUmUy2U7iDIwMIAnn3wSGzZswO9//3uUy2Uc\ncMABGBkZwcjICA499FCsXr26475YloUrrrgCDzzwAIaGhnD88cdj5cqV03bKWrt2LTZu3IgNGzbg\nsccewyc/+Uk8+uijnZ+AmOAZSyHiQ9VsNmGaJhqNBkzTbLvjT7ftRiFW/XgQu21DHDydItHdnEPd\nbAD5fB5/8Ren4XvfuwGp1BvRbG7EW986gIMPPjjwscWEqVarpV3E2YskTJhJJ2whq3q86RRdRaHu\n6HrOdL6e1DdK+CoWi/if//N/AgB+9KMf4dVXX8UnPvEJbN68GRs3bsQLL7yAbdu2ddXmunXrMDIy\nguE/18y+8MILceedd04Tq3fddRcuueQSAMAJJ5yAnTt34uWXX8aiRYu6ajtqWKwqREyWMk3TfhOb\nM2eO0gcuChsAEN0OUxQZrNfroUeiVQruTs7Pe9/7bhx00BJs3vxHLFhwJN7ylrcEEuNywpSq7XY7\nwc/50KGfsx0/QlbehYi+npycnCZkZ9O+8EHQVXTp2i9C5/559W10dBT77bcfisUijjjiCBxxxBGh\ntLlt2zYsXbrU/nrJkiV47LHH2v7Miy++yGKVeY1ms4mpqSl7f3nTNFGpVLSL6OnYDgl9AHZ2ehh+\nXhnV5yrosQ3DwLHHHtvWUyT2m8qxVKtVu4JEN+cq7shqO7hmYXy4JaXU63U0m03kcjnf2dUsZPVD\ndzGoM17nbmxsDEcddVTobfq9VvK50/Uae8FiVSGZTAYDAwP211FGPKNYllMhasQoKvkq58yZo0yY\nqBRmKgcEusaVSiVwHd64CONcr1+/Eb/85SZYVguHHbYQb3rT0ey/1QB6eRBL+8j/H6RMUFhCVlfx\n9fzzz+OZZzahVMrj9NNPxIIFC+LuUmLQ8XoC7cXqwoULQ29zaGgIW7dutb/eunUrlixZ4vkzL774\nomM5RN3h0IRCnLKsoxCRUYlir1IdQSB/5a5du7Bnzx6kUikMDAygv78/0VFoFcemKGqlUrEn/v7+\nfsyZMwf5fD5Ua4RuvPTSS3jwwe2YO/d0LFr0Tjz7bBa/+c3zcXeL8QEJz0wmg2w2i3w+j2KxiFKp\nhHK5jGKxiFwuZ+9QREmok5OTmJycRKVSse0tpmnaHtok8utfP4Hrr/8v/OIXh+Cee/bB3/zNdzA+\nPh53twDoK+4BvfsGtBerg4ODjv/XDccddxw2bNiALVu2oF6v4/bbb8fKlSun/czKlSvx/e9/HwDw\n6KOPYu7cuYmzAAAcWVWOXAYIUP/QRdlONxOGHEUtFoszastG4YtNglgVS3QZhoFsNgvLslAul0M5\nvi54nbMdO15FLrc/crkCAGD+/IOxdesTWLYsyh4yYeNmLQDcI7J+diDSlTvvfBQDA+dj/vxDYBgG\n/vCHKn796yewfPmZcXdNa0Goc98A7/6Nj48riZ5nMhnceOONeMc73gHLsvDRj34Uhx9+OG6++WYA\nwKpVq3DWWWdh7dq1GBkZQblcxne/+93Q+xEFLFYjJIoMerGtZrMZek03uY2gYkxMOvOzQ9dsF6ty\nchmV6KJotApUnZNuj1sq5VGv77S/npzchaGhbBhdYzSlWyELwK4rrItH1jQtpFI5++tUKgvTVPMs\n9xJJEKtuL0n1et2xBGEYrFixAitWrJj2vVWrVk37+sYbb1TSdpSwWFWMPEHT0rnqN/+oNgbw24ac\npe53h64olqR1W06UBX2hUJiRMKVbnzsh6MQzPHwADjhgHbZufQyGkUOpNIrjjjtOUe/0RfdJOyra\nCVlKcKVdv1TuCR+E5cvfgJtv/hEMYyXq9Qnkco/imGMuVtqmX3S+t3TuG+Dev14Yq3WAxWrE9EKm\nvtiGl2c1aBTVrQ3VkVWVxw6y6QBtuUsJU16CXveIsAoymQzOPPME7NixA5ZlYf78Q1EoFOLuVizo\nNmnrVp1BFLHZ7PTou1xDljyyXkKWvg7jvJ966kkwzQaeeOIBlMtZvOc9F2K//fbr+rhhoONznxTa\niVXdntmkwWJVMfINGlZSkp92o9gYwKmNIKLLTxtJtgG0o9Xau8NUtVqFaZrI5XKBBL3u0QYR+Vx3\nEolIp9PYd999lfTPDZ7Ak4nb/eXXWqBSyL7lLW/G8uVv6+rzqULX8UT3sc6tf7t378acOXNi6FFv\nwWJVMU4VAaLK1FfdjthGGFFUJ5IsVsXjy/eBnDCVz+fR19fnezDWOXGu27Z1Qrf+BGXz5s145JFH\nMDAwgHe84x0zoozMdHQQsnGiW4RcJKlidWxsjEuThQCL1YiJMrIalQ1AZa1P1RHiKCLQ4nVwS5jq\nJqWPmIoAACAASURBVOqs8wAu4nRPJqn/SePhhx/GBRd8FK3WmTCMzTjssG9h7dofKkv00I2wx78w\nhKz4u2J9WaY9ugtpN0ZHR5XUWJ1tsFhVjFNkNek2ABqIq9WqPRiHEUV1IgqxGkVktVaroVqtotVq\nKduNKyyietHR1RvbK6xe/XlUKt9CKvUutFpNPPfce3D77bfj4ov1SOaJgqiEYBAhSzVip6amZghZ\n+d9RC1mdXx517hvh1L/R0VGOrIYAi9WISaVSaDQakbRjWVaox5S9qLlcDqZpKq31mWQbAE1KExMT\nSKfTjnVku0Fl31Wec7KMkAUiiculSWBs7BUYxhsBAIaRQq22DNu3b1fSVhKERFzIQtY0TQBAsVgM\nFJGN4lnh69gZXueNI6vhwGJVMXF5VsNqhwbPWq1mJwD19/fbtT4rlYrSAU619zbs6yGfLwAolUpK\nll5V3UsqJ6t6vW6XE6J7iM4ZTdoAUKlUpvn+WMwG54QTTsTPf/5lWNZXAfwB+fxtOPHEr8fdrchI\nQtQ+LGvBbHjp01lIe/VtfHwcRx11VMQ96j1YrEZMUqoBNJtNO4pqGAYKhcKMBCBxKVfVIJKUyKrb\n+ZqYmNB2gI2CVqs1rcZuOp22X3bq9fqMe6fVamFychLZbHaakHWaoOXamLP5PDvxrW/9H1x00So8\n/vhcZDJ5XHvt/4eTTz457m5Fio73hN/xMoiQJXtBt0I2qYIwbtpFVlVstTrbYLGqmCRVA3CKovb1\n9SGTcb9NkiImVR2fyk41Gg1ks1n09fUhnU7b1z2JWfthHFdc6iefLtVEpSLtlmXBsix7AhUnUqek\nMyfvH03OlHzhlomt6ySnkvnz5+Pee++wk/lm4znoVVQJ2aQKwrhpF1llG0D3sFiNAHHyp3+rfvCC\nCA45KhikjJLuYlLF8Z2EWKlUcpw4VPZfR89qs9lEtVq1fc2iT7dSqaDZbNr2CHGCpAgs+awbjYaj\niJUzqsX+isullmXZEVnx95wm6F4nl8u1/6EeRFdxE8XY36mQBYBqtaqdtUB3Swd7VtXDYjViolg6\np3a8RHGrNbMYfbsoqlc7qtDp+GKCWSaT8ZUwFVUkPUyC3pd0L9VqNTQajRkbG5B4NAwDpmmiXq/b\nEyH9DAnVTCZjC38xakqiU0walIVsOp2eUZGCzr04OYtbb4oTu077xycNXYUhMx0vIUtlCLPZbNuI\nbFwWHF3vMa/7f2JigjcFCAEWqxEgCxaaiFWWLnITxd1EUd3a0UVMdoOXqBetEUE3O+jlyKocYS4U\nCrbQlCOdwN5tL3O5nP1/jUZjmm+VxCw9G/QnnU7bxyQvNolYJyErWgLaCVn6fbFP4uRMv1+v17WJ\nMjHJRndh7xSwkJ8TqnQSlZDV/ZzRmOP0fQDalilMEixWYyAqASZGV8XIl5O3slOiShhTNVi5HdNP\ngplfkiZW231GcalfjjBTFJXuOzoeHZMEYa1WQyqVQrFYnOZPdVqiFAWkKF7Ff9PvOolY8d6RhayX\nrYA+S61W40SvBKK7wNERt/NlGIbrC7qTkBVfKMN4VnS/lu36p3PfkwKL1QiQb9SoBB4AO/mHoqhu\n3spOiSKyqto2IX4GeTm7E2uE27GThNxnJ9uI01I/3dfyUiOJPnpZKpfLjpMfTYpO/ye2Qd5Xmhzl\nCTGdTiObzdoRWXFCBeAYjRX7LE6otVpt2q5s7SZnJ28s2woYwi0KFzedjrHdCFknb6yTkE2qWK3X\n67PWMx42LFYjQL6JVS/fkuCiwaGbLT3bEYUYU9lGs9nEli1bAACLFy+2s9bDEvW0bK2CKCKrTkv9\nFGF2WuoXJxn5XqQavZ2eV8MwXJcoxb5YloV6vW5/LfpjaVLMZrP2McUJVYwIi+LbNM1pgrPd5OwU\nGQZmd6IXM/sIS8iKz7iOqxduYnV0dBTz58+PoUe9B4vVGFARWW02m6jX6/aSZaFQQKvVQi6Xsydm\nFagUY2IbKkRZrVbDtdd+FQ89tA3p9BwsXrwHN9zwvzF37tzQ2lAt5lWJVUq2CGOpn+5BlZFxt6QR\ncTlfjMhS/+WNB8gaY5qmLTLFCG23/lhZVIsTsFfpLd3RMfKlY58A7hfRiZClLWr9RmSjwu3cjY2N\ncSWAkGCxGgFOkdUwBB5NwmKdz1KpZEdRxciQKpIWWSXvYbVaxX33/QQ/+1kTixbdgGy2gO3b78LX\nvvY9fPnLnw+lLbFNFYR97ikSShNCq9UKvNRfr9ftup50L8ZJOyErR0HpZY9+V/TEuvlj6fcJNyEr\nHkfuhzw5e+1URAlouooeJpnodD85CdlWa2+ZwKDWAtX1lr3G4NHRUSxYsCD0NmcjLFZjIJVKodFo\ndPz7rVbLTv6hB9hp2ZomNJUkJbIqJkylUink83mMj08gm30j0uksgBbmzFmGTZvuD6fTf0bl4B/m\nS498P5mmiXK5HPlSf5TQhEiRVNM0kU6nkc/n7dUPv/5Y+nc3/li/dTFpFYUqFEQ5MTPdk0QPe9yI\nQtpPRFaut6xSyFLf3GwAHFkNBxarERCWZ5VEQb1et+tRenlRoxCSUQniTtoQhZRTwtQhhxyAZvNB\nWNbpAIrYufMhHHfcAVr0PQrkurF0P7VaLVSrVc+lfvKyRrXUHzY0kdVqNViWZVfIEAWjH/EYpj+W\nkCdPWchOTU0hm83aO4GJESYnf+xsTfTSNZEJ0DM7XKfIqozfa9kuQVOFkPU6b2NjY1i6dKn/D8q4\nwmI1BoJ4Vp2iXgMDA74e3G4juH7Q0QYgnzO3hKnTTjsN5523AXfccTlSqSIOO6wPV175v2Ltu+pj\nk4CnrH65bqwopiYmJmZED8lGIdtOkgL1n+q75nI5lEol35N02P5YcTlfFrJyRJbap/+jY7h9TrdE\nL/EzOHn+mN5k06ZN2LBhA/bZZx8cd9xxM+5hncVqGHQiZMUXv0785GNjY1i2bJmSzzPbSM4sk2A6\niazKUVQ/uyU5taubkFTZRtDIcyqVwpo1q3Deee9Co9HA/vvv77vYf9h9V31sUcADmLYZhNNSf39/\n/7SIXb1enyaaaKKj5XESXrpOdvLSeT6fD71CRlB/bND6sbT7lzihevlj/SZ6Ofljg07MvS50wiSO\nc/Wznz2EL3zhX9BqnQDgQbztbT/HX//1Z7SNPMuoPmfdCFnqF60y7dq1C6ZpYnBwEGNjY0o9q+Pj\n47jgggvwhz/8AcPDw/j3f/93xwTh4eFhOyiRzWaxbt06ZX1SBYvViBCFBf1bfgBpaVXcc95vFLVd\nm6pw+ywq2nBCXI62LKujczZv3jwA7uWIkozbUj+d03ZL/ZQVbxiG/cIkCy/K/HdbBo9z+dlvfVfV\ntJsMverHis9YNptFsVhEOp2eEYF1i8ZS+0ESvWhi9rsRgo6wgN5Ls9nEl770HZTL16NYPADNpokH\nHvgfWLnyaRx77LH2z+lsm4jzWrZ7dulF3jD2JjXfc889uPrqq9FoNLBw4UK88sorOProo3HIIYfg\nkEMOwcjICBYuXBjK57nuuuuwfPlyfO5zn8Pf/d3f4brrrsN1113n+BkefPBBe65LIixWY0CcgCi5\no9soqhMqSmTJiMJGpViVP0ez+douSul0GoVCoeNzptLbG0dk1c9Sv5+s/kajYd+P4lJ/GMvgopgN\nOxlIjEJalqV90pdhzKwfK0bCDcOwfa9UvqcTf6ybrcDLHyv2R/bqiscG9m5AIr+gUBvMa0QtvBqN\nBqamGujv3x8AkEplkE4vxc6dO2PtVxB07Rs9O+l02i7+/6EPfQgf+tCHMD4+jg9/+MM477zzsHnz\nZtx///246aabsGHDBlxzzTW48soru27/rrvuwkMPPQQAuOSSS3Daaac5ilUg+Yl9LFYjQhYWhmHY\nYoIigkH2nA/SpuoHXXWSlfg55F2U+vv7u/ZMql5eikqsksCpVqswDKPtUr9TFFUUeHLCkZ/++FkG\ntyzLjh5Sf+RIbCe2Aopy1Ot1ALCrZOg4yblBVSsajQbS6bS9oYdMVP5Yv0J2cnLSTs7jjRCciUss\n5PN5HHnkAfjd727DggXnY3JyPVKpp/C6170vlv50gs5Cy21+nTdvHur1Oj70oQ/N+H955aNTXn75\nZSxatAgAsGjRIrz88suOP2cYBs4880yk02msWrUKH/vYx0JpP0pYrEYE3axUF5WWT8OKonq1qZoo\n7AamaWLXrl0zRFgYRNF/lS8MdE9RZF7csczPUj8JPMMwAicc+UVcSpM3qZDLMgW1FcgCj5bJkySG\nyK5gmqZjZQIZVf5YADOqFTiV3nKKiMseYLrf5L7QMcXPEDRxxQ+6RONeeuklXHHF/8bTTz+DhQsX\n4otf/EucccYZkfbhi1/8S1xzzf/F00/finnz5uBv/3YVFi9ePO1ndDlfMuK4pSNu9gmvOSVIUGr5\n8uXYvn37jO9/8YtfnPa113Pz3//939hvv/2wY8cOLF++HIcddhhOPvlk333QARarEVGv1zE5OWlH\nUTOZDPL5vPJ9g8kKoNKnp0rskT2ClkKp7JQKIaUy+gmEPxFQJNSyLOzevTuUpf64BJ6fpWf6LGL0\nUDy3ZAWh+0PXiU2EriEJ83w+j2Kx2HXf/SSLiOfTq36sGJF18seK3yNvs+yP9Ur0Eo/htRGCKsuI\nalqtFi699DPYsOGdKJd/gFdf/RWuuGIN7r//dRgaGoqsHwsWLMA3v/lFu4pEEtH1uruN7fT9bvt9\n//3utb8XLVqE7du3Y99998VLL72EwcFBx5/bb7/9AAALFy7Eueeei3Xr1rFYZZwxDGOar3JycjKS\npY0oooZhtiEmTNEE3tfXh0qlomzbWNXnKGyRKi71A8DcuXMjW+qPElHw0FI4Jf1QWbJMJmO/kInJ\niWHYClThZFeIqkatm5B18qR61Y8ln7cYzRbtOmH6Y2WPrFc0VofrK7J7925s3LgV5fIn/zwHnIha\n7Xj89re/jVSsEl7Pus6RVR37Rbj1b+fOnaFu3e3EypUrccstt+Cqq67CLbfcgnPOOWfGz1QqFViW\nhf7+fkxOTuInP/kJrrnmGqX9UgGL1YjI5XLTBgoa7FUTlVjt9rOIWetywpQovlQQhVjt9vjiUj9l\ntadSKezevdv+fz9L/UByvZwkUlOp1LQoqoyTl1OHagVi+Szd7ArtbAV0TkURK/5/o9GIzB8rRmOd\nCruLYpoiiXGd473VN1qwrK3IZPZHq9VAs7kJ++xzXiz98UJXUahrvwi3/qkuWwUAn//853H++efj\nO9/5Dob/XLoKAP70pz/hYx/7GO6++25s374d733vewHsXa286KKL8Pa3v11pv1TAYjUi5Js5lUqF\nZrL2IoqKAJ0mWIlRMkqYckoyS4KYVHF8+fzIZblo0q5UKo7RQ52W+juFPoMo0ttZWgxjZnY94G4r\nEEWNimoFYvmsXC4XW/msThGjqOSppS1p4/DH+t0IAYD9/ABwfDlRLWSz2Sy+8IX/gWuuuQiNxhkA\nfoPTTjsAxx9/vLI2O0HnBCbdcROrO3bsUC5W582bhwceeGDG9xcvXoy7774bAHDQQQfhqaeeUtqP\nKGCxGhO9FlkN0gYlxJAX1W/ClKo3bN3EqrikTfYRt6z+fD4/TXTJXk5RpOq63C/jZFcIo/SUk61A\nbFMUOnK1AqdorJfwV/UZokT+DE6eWpX+WCcRK9/fopCV/bGmaaJQKMywJ5DwFhO9ZG9smP7YCy44\nD0cc8To888wzWLjwWJx44onavjCq6teOHTuwbds2DAwMYHh4OFA7OkdWvcb1sbExLFy4MMLe9DYs\nViPCKbLaS55VP8Kbyk6JBdr9JEzRpJFUsQr4i1w4LfW3y+ovFAr28cnrC2Cal5M2TBAnY1l46TAZ\niJFkIFq7QjvR5VQiShQ6onillw16EUua5SKs6+DXH0vny80fm0rtrR8rJ3rRc+AUjaW/xYg5EO5G\nCEHOx1FHHYWjjjrK3mRDN1QKwqeeehrf+MZ9aLVGYFnbcNZZ++P881f6bk93sep2L4yOjiqPrM4m\nWKzGRFSR1SjsBl5ij0SUuCtXqVQKHGGKSlCqEsNebTYaDVSrVccduEShRMdyy+pPp9MolUqOEb8g\nS+CypWA2ezkBf7YCEiGyD5O+F/U57QTxhaedL7gb/PpjnaLcTvcp1XcVXxRM07Rf1uh4cvtBE73E\nsltiVDcJiV5x0Ww2cfPN/4mBgctRLu8Ly6pj7dqv4YQT/oDh4WFfx0iCWHVibGzM92dk2sNiNSJm\nW2RV3uaz23qyKj9HHJFb2QpRKBSQy+Ucl/rFPgKdZfUHWQKXfYeqMuvpM5APMsleToqGi1uhBvFy\ntrMVqITuRfI2x3kd2glZr3MqPsPZbNaOxhLt/LFi+36ErPiMum2EIItZXYWXqn5Vq1VMTRlYsGBf\nAEA6nUM6vR8mJiZi71sYePVtfHycbQAhwmI1QkTRIvqoVD6IUYhVcXlOTggKa1cuXZbqO0Hsu7i1\nLhV/F8syxZHV77UELooDMSPfbbmWBFc7L2fYtUWjhK4T2Suc/Khh2QpUWjWCbkQQN07nlIQ2ReXp\n/5rNpr1aEcQfKyZnAe6JXrI/VsRLVBPi1rSitSAuVI19xWIRBxzQhz/96ZdYtOjNmJz8E9LpTVi8\nONpNEVThNX+Pjo6yWA0RFqsRIotVldE8IopqADQ40w5TYkJQWKgWqyqvAQm03bt3w7IsFAqFGUv9\n4iQpR5bEyFfUy+TtlkhF0SVHDuUoLFkWUqmUvTFG0kRqWF7ObqsVdGorcBLauotUJ2Sh7Za81ok/\nVoyC+vHHis+rl5Cll81mc+8mLfIzI4pq+d9R+bZVHPNTn/ogvvGNW7Fly93o709jzZpzMH/+fN/H\noPFER9qJVbci/UxwWKzGCC0jqnwQVYk8mvQoYarVaqG/v99xEg6DKMRq2McXRSawt+ZikKV+UVTo\nFvnys1xLdTkpiipCk7RuSV5OxOHlDGrVaGcrIKFdr9enecd1PeduUFS+E/uLTLf+WFHIiqshXrYC\n6ou8wUlUiV5uqAyaLFiwANde+ynUajV7/NOlb93iNWdUKhWUy+UIe9PbsFiNkDh8q2GLMKeEqWKx\niF27diVyS1cVx5eX+mmAzufzsS31Rw1NtiQqZJEeZZ3TTtHNy9mprYB+RvRy6pTA1g4n60hY9pdO\n/bHyfSqXhpOFrChAU6nUjK1pgWAbIYjHdvPH6nZ98/l8R7+ns1gFnCPSNLbrElzoBVisxkgUPsyw\nvLFiWaUwEqaCortYpYmIfHLiUj99zyurP86l/rAgof3cc89hz549eP3rX29vBUv4iRxaljWjzmmU\n26cGjd7FjZOtQPRyipG8ZrOJqamp2CtA+MHJdhH3trTUL6cXLid/LJ1PeoHIZDLTroVTopebP9YJ\nv4leTtYC+Tg6XHMnVM+R3dDOoqDrOU0iLFYjxCmyqtpP2o03VhZgXglT9FlURZ50FatiVj95MeUo\nYqvVsoWoHDUkgafjUr9fyAdYrVaxZs1f4d57f4FMZhDl8k7cffftGBkZaXsMURzIS6Ttkrzclr+D\n3O9i9K7VaiGXyyUyoi3vltXX1xc4CciPrUAlUdkuOsVNyMpL+VTtQlxBoe97+WPpWE7VCug47RK9\nRI+teI3puOLvp1Ipu7yarqJVxz4B7iK/Wq3aNbCZcGCxGiNRRFY7acdLgIXVRlBooFd5/CD9lzc4\ncMrqbzab2LFjB8rlMvr7+6dNGrKPkyYbiqzqFOFyQ05yue+++3DffZtgmr+BZZVQqdyEj3/8s/jp\nT+/sqp12y6OykHVbqnUSXLIwSmriV7vqBDLd2AqczmsYUW566aHM/lKppMwDrwLZykOWhVwuZ38/\nLH+sk5B1shW0E9XyigbZE+JM9JLRVUAD7n3jDQHCJzkjQQ/gFFlVXbCf2mkX9RQjS2JUxu9koWvk\nM8zji5Fm0TtHokAc+F955RVccsmV2LhxO4Aq1qz5KFav/hgajca0pX6KZviNcKlc/vaDk3+QSk9t\n2rQZU1PLkc2WAACGcTY2bvyKsr4EXaqVBRf9TDqdRqFQiNTWEgaqosFOtgJqz6/nOIitQCdvcKfI\n18LJshClPxYIVj+WrDYkkOX+iH1x8saqErI6WwAAFqtRwmI1RnSIrNISNQ2yhUJByx2m4jy+HGkW\nhY3sGaNjfeYzX8D69WegXP40LGsMN9zwARxyyAE49dRTHSdjPxEuFcvffpEjkLlcbsZk/LrXHYpi\n8R/QaHwahtGPVuuHeN3rXhd6X/zgJmQpI940Tdty0Gq1/ly8fEq7JC8nxCQ8wzAiiwaLS8dhVCsA\nMG1TiCRaYERfbafXIkx/LG2zLNoASMR6+WPpZUMUx259EftElWBUJXqRGNTl2RPxygXhGqvhw2I1\nQuLwrFK7shCTE6Zoya3TQaEXxKp8Lfws9YsDljioPv30sygUvvLnQXweTPOd2LRpE975zncG6pNb\nhKvd8ndY0VhxO1fxPnHi3HPPxU9/+kv88IdHIZOZj7lzTXzrW7cGbjNsnKLBThHIIElespCNAnmZ\nXKckvCCCizzaBO34RZ7uqM9rJ8gvb6quhV9/rNPLrNs4IP+uOHZQIqjYftBEL/llBZie6CUKWp2v\nsV+cPsPY2BhHVkOGxWqMRBVZJVEcJGEqKLp5Sjs9vjgJtVvqp98ToxA0GS9aNIjNm3+Jcvk9AExk\ns09g8eL3hNZXv37DTnaccvJA+ol6GYaBG2/8Cj73uU9h9+7dGBkZiTXJQI5AOkWDRfwmeZGAp69V\nR7nlF4akLZPTfSdGBml1AsC08xr2JghhI44PcfpqRQHp1Md2/lhxVYi2CRY9t2H7Y2VxLCZ6eVkL\nkuhXBfaK1YMOOijiHvU2LFYjJK7IKgBbpKbTaV8JU0ERl55UoFqsUvRt586dMzyMbkv94uAuF/C/\n4YZrcNFFV6LR+DGaze045ZQlePe7362s/0Q7v2G7HaeA1yalTj2Q+++/f/cfpAtURCC7SfLqNKs+\naSW0nBCXyQHn8lOdnle3Fy8ViPeU7i8MXkKWVossy7JfyprNJiYnJz39sd0megEzrU6yiKV7hf4t\nClYx8UsXW0A7sfqmN70p4h71NixWI0YUXWI0L+yHT1z6pFqLqneYUh1ZVXF8Grwp+iaeo3ZL/fJE\nLIq7o48+Gg8++CP85je/QX9/P5YtWxar0PCawMRyR/Qz5GWme6ddNFYH5OoEUQiKIFFuP1n1hmFM\nqxaR5E0huik/FZaPs1u7hpj8FdU9pQLxxSefz6NcLrvaYFT7Y2Uh6xUdphUMsexWHIleTnjN2+xZ\nDR8WqxEji9WwlzqcEqay2awdEVBFFMv0QDhlTGgQFHfh6uvrQ7VanZENS207LfVT5M5tIp4/fz5O\nP/30rvqqknY1Of1EY+OqxSn2kepZ6haBbBflFoWBeF6BvZM4RSCpBqaOLwgy8rOhYpncr4+zG7uG\n/OKjyz0VFFmker34qPTHOolYcSyXhazsaxV3v5KjsZ1uhNAtXnPR+Pg4BgcHQ21vtsNiNWbCEnli\nFFVOmCKPkEqSIFYpSkJ2CHEXLhrsaJtTpygqTX5RRu7CRk428qrJ2c4XJ0dh5Kihymis0/JyUiKQ\n4nlNp9P2tUin09M2lNAxycsNMQIZ1zK5n/u1na2AVnB0e/EJgviM08t4N89Gt/5Y+eWA5iVRvIrR\nWQB2JNU0TfuFjfri5o+l/rhdZ/FzhJHo5TUXvfrqq5g3b17gYzLusFiNGDffaicDu7jUJm/xKbcZ\nhVhV7b/t1BcrZvWTOJOX+oG9n2HPnj0zBjQSRgCm1RVNEkGTjdrRLgrTzhvbqddQhR81DvyIOzHJ\nK2jUMKpkpKREINvZCuieEgUWJbWFaStQiZNIVV07OMgLQrv6sWQtEO9vWp2g+x8IbyME2R/rFo11\nO39eYpW21mXCg89mzHQiJJvNJqrVqp2R2q6gOQlilXQqJIMQ5Fw5LfW3y+rv6+uzBzXTNKfVTxSP\nSTU641r6DoIsilSLuzCjsU4+zqR7BzsVd91GDcO2a8jJX/39/do+A27Iqwxy4mmYtoIoPke1WgUA\nbbanDeo7pn8TmUzGMdELaO+PpfaD+GO9Er2corHNZtPV5sOED4vViHGLrLZDHJBM00Qul/Nddkpc\nclEpUnQQq7KQF5f65QGJjum11C/6OOWJyykBQYdEJPocYnUCHSJeQaKxNHmJ15tezOQdfHTHSRSF\nGZ1vJwraJXm5vSB4fY5WK7wds6JGFnduEcgwbAVO5zbMzyG+UOsiUv0g3rP0OcibXSgU7JW6bvyx\nYjACcE/0EgWojByNFXfzEr9nGAYefvhhlMtlHHzwwUoDAj/84Q/xhS98Ac8//zwef/xxLFu2zPHn\n7r33XqxZswaWZeGyyy7DVVddpaQ/UcFiNWbaCTBa9qxWq7bRvK+vL9CDEMXgFacgFidRWuoXhTxN\n2GL/ZJFKOxvRJOwkJsQB1m2JVo4UOEW2uinS74Xs40yKmJBFgRjZAPZ+DnqpE6NbYsRDFgU6fGYn\nX23UW7rSMqpT39rds+IfEg0AErU1bb1en/ayGpa4Cxo1lF9qu7EVJFmkisifo1gsei6du92zXv5Y\nOq/t/LF0fHEcEoWxE5OTk9PurQceeAAPP/wwNm3aBNM0ceKJJ+LQQw+1/xxyyCE4+uiju35hOeqo\no/Af//EfWLVqlevPWJaFK664Ag888ACGhoZw/PHHY+XKlTj88MO7ajtOWKxGjFNkVVy6IMSEKVr2\n7GZA6sYb64coBTEhL/WLW8U6vV3Lb8+y/7HTbStFseW2DaU4uLpFCTr1GcqfI6mTV5DP4VdsxWHX\nEL3kul4Pv/csvcSJ0MqDzh7OrVu34oMfXIXnnvsNyuV+3HDDl3DmmW9DKqVutymi3QpCp7YC8b6K\n4nOoIqhIJfzcs0Ei3eSFFVfdxMgs4G0rkK/zl7/8ZQDA7373O9xwww24/PLLsX79eqxfvx63sllI\nLQAAIABJREFU3XYbtmzZgscff7zr83fYYYe1/Zl169ZhZGQEw8PDAIALL7wQd955J4tVpnNEASYO\nRrRc6JQw1W07qqClG5WCmAYVWuonH6bfpX4AdqkjisKq9D8GWfoOumVqL/k4g+7QFPQFwSmyFXY0\n1k/SVBIQVxroc4hJLroleTnxgQ98HM8//x6k0/+FSuUprF59Hn7608Ninay7sRXQ2GYYBrLZLLLZ\nrHYvCO2g+6pardpiO6wkpLAi3TSeiP5YWciKY7ZoqaE/o6OjWLp0KU466SScdNJJoXy+oGzbtg1L\nly61v16yZAkee+yxWPoSFixWI8YtslqpVHwnTHXarmqxKj7gYUMDRa1Ww9TUVFdL/Sp8g0FpN3F5\nbZkqLmuJIjVpE1cnW7r6wU9ky2801o/PMCkZ8e0gsV2v1x1f4oKILafM7yisMMDe5dnnn38WqdRP\n/2yDWIZU6m146qmntI0sOd2z4sqR+FJGL+sqNkFQgShSVdXe9cJvpLudP5bOJ22eQv55UcyOjY3h\n5ptvRn9/v21N6ITly5dj+/btM77/pS99Ce95T/ttu3W59mHCYjUmxAeYylz4TZjqBL+JXN2gQhCL\nAzZFbefOnTvDh+RnqT+VSnW81B8lTj5DEurkqyVx2mw2UalU7O/pPGkBzv7gqHy1fpcR3ZLnnHxw\n4mYEScyIB6aLba+6u160i2zJSV5eCTOd3rcktk3TRKFQRK32LFKpo9Bq1QE8i8HBswMdLy7EZz2T\nyczYrEP8uW5sBVF8jjhFajvavXyJL7amacI0zWm/+8wzz+Df/u3fMDIygiVLluAXv/gFfv3rX2PN\nmjU499xzu3phvf/++zv+XQAYGhrC1q1b7a+3bt2KJUuWdHXMuNHnzplFTE1N2X6dXC4H0zRRLpeV\nthmVDaBdG+vXr8cTTzyBcrmMt73tbSiVSo4/RxOouNQvljbRaalfJeLSMg34TlFUOaolTlpukZco\no3+y3063lwZRbLVLnqNzK/4eTcxxnNtOkCPbKlcanF6+qA9OQjZopFuObPf39+Mb3/g7rF59Lgzj\nTPz/7Z17WFTl2v+/M5wPCmgIiBriATANNA9tfd1qhW2P2dZrq729+pYVaoq+tTNrV+r+7RTPmVi5\nMyXNSMXtlleBnVmQSYiV7UogwCJBE0VERc4z8/vD91mtWaw1s4Y5rLXG+3NdXcnMgnnWmplnfZ/7\nue/vDXyPceP6qrqbHGC+sJaTRmJPWoGlVBh7PwPCnG21iVQ5CHfkTCaT2XkYjUaEhIQgPDwcubm5\nKCsrw+XLl9Hc3IzVq1cjIyMD/fv3x9SpU3H//fc7bZxS99uhQ4eirKwMFRUV6N69O/bt24f09HSn\njcMVaOsT5AawLwF/tdzY2MhtXTvzdZUWq/n5+XjuuW1obR0Pna4CH36Yjffe28AJVpPJsj0Xi0Kz\nqmTh5Cq21e/r66t64SBEKCTkbC1LCSRbI4aOjsYK8zi1duPip5Owz55er+cWDcLoiyuvbUdg3zFH\ndTeyB7liS+rasp0Fo9HYLmf70UenITY2Bt988w3CwibhgQceUO084IxcZ0flcNqyQ+MOIhVo/x0R\nK5BsampCZmYmjh49ivnz52POnDnw8vLCzZs3UV5ezhVWXb9+3eHjO3ToEJKTk1FTU4NJkyZh8ODB\nyM7OxsWLF/H000/j6NGj8PT0RGpqKh5++GEYDAbMmzdPtSkwctFZETDkbusE+LY8wO3WbI4qpJKC\nCT1nRnBv3brFJc6LMW1aEmprl6JTp3sBANXV/w8rVtyDyZMnc1FUNjn4+PiYiV9hPhE/D47dtFiF\nshqrr+UgtkXONyl39GsJb1rs346opmeRbRbt8vHxUa1YsIQw2uXt7W31Bix1ba1Fup25PSuswFZb\nZFsufCFhMBjMvv/Ca6t0kZc1+CJVDd8RsbQC/udWKq0AAPfZ4ruqaBEWLGELOWHtSGNjI3bu3In9\n+/fjySefxLx58+Dt7a3giN0S0S+qNj9RboYaop6OwFqB1Y0b9fDx6c79bDJ1R23tNdTV1ZlF3aSq\n+j09PbktWr6w40cEjEYjGhsb29lBqfWGBSiTV2trNb0lI3m+iHWm+b0rEW4ty4l2HTlyBEuWvIyb\nN+swbtxD2LFjCzp16mR2jNxIt6PyjoXRLi3bHQk/WwEBAWbnoaYiL0sIRapaCvLk5HAKCz/51k4s\nkssPHKh1zhViTaQ2Nzfj/fffx969e/Ff//VfOHHiBHx9fRUc8Z0HiVUFEH55WWTAmTmV7DWcibXX\nePDBYfjHP95Bly5JaGy8AL0+GwkJy+2u6pdqo2ppi0sNW7Mst5TvpauGvFpr1bNSdlv832WVslrC\nHoeCM2fO4L//ezEaG/cDiMGxYy/gyScX48CBNLPj5OTG2pJ3LCUGbM1/VCv81CDAcmMFW4q8rHVF\ncsa8ICxkU4tIlQNfyLIFEEtbEDbt4M+5AFRd+MmP0ou58LS0tGDv3r1IS0vDzJkzkZeXJ1lnQTgX\nEqsqwFpEUiuvYSl6azKZ8Oyz/42mpreRlzcfISEBWLZsIeLj4x1a1S/HpkSObZGzoi7s5iusItfC\nTUsYeeG/J0x8sQWLUAyoxX9TDGGOWkccCj777DO0ts4GMAYA0Ny8GZ9+2lf273ckf5MtEoQCgEUU\ntVxYKExbsDe1x9lFXpYQc1tQw+feVqzlpFpbgPGLE63t0jj7+ghFqvD73tbWho8++gg7duzAo48+\nik8//bTdLgnhWkisKoDwi8hWpc5+TSXEqrCq/7XX/mxxq184UTnK+N7WbW9HCy020bNuQEoWttiL\nLb6i1qKxSkZd+O+JvXmcISEh8PIqQFubCbdTrsoQGBjkkHHKica2tbVx15WfksGislpIhwHau0a4\notuUtUVCRwuRDAYDVyvgTiJV7hxsa1qBVLtfR352+e+J2BxsMBiQkZGBd955BxMnTsSxY8cQFOSY\n7zFhH1RgpQAsOsJoaGiATqeTLExyBEajEXV1dejSpYvTXqO1tRWNjY3o1KmTWVW/j48PfHx8zLb6\nmVjhuyDwt/r5gpEVGrk6+iicUPkTq1yhJYw+suugtZuWWM6gvcVfwpsV/9/OLEISe0/sLQi5desW\nRo0aj6qqnmhpiYW39x688846zJgx3a6/awkx+yn2nojlb9r62XUlJtNv3qJqL9KRU4jEjmPb5K70\nN3UUQpHqqtQeqWsrllYgdxdMKFKFc5fRaMThw4eRmpqKBx54AM8//7xT75WERUTfSBKrCsCiS4zG\nxkYYjUanVuqbTCZcu3YNISEhTpswW1tbUV9fz03KTKTyI67CrX4pYeeqQqOOIiUEmNDiCwZPT09N\n5nAC5tuxgOWcQUe+ptj1tTfvmN/W1cvLixMRjuLWrVtIT0/HtWvXMHbsWAwbNsxhf5uPmP2ULe+J\ntc+ulBBwxnsuzK3lL2q1BlugM7s5tmPGXySopcjLEkqJVDnjsrQIE0srAMClXEmJ1KysLGzZsgWj\nRo3CsmXLcNdddyl1isRtSKyqBaFYZdvcgYGBTn1dZ1lksVUrs1zq1KmT2VY/P5IKWN/q1+oNi7+t\nzEQqAMnJVG25m3zUGBG2FtESE7EeHh7tiqaUiNI7AmfbT7kyGms0qsu2qaOw6LalSnLhsXKFlquj\nsWoVqXIQ2wVra2vj7jnss7VmzRr06dMHffr0wZUrV/Duu+9i6NChWL58OcLDw5U8BeI3SKyqBTYp\nMFg70c6dOzv1devq6hAYGOiQ7TX+jZNt9Xt7e+PGjRsICQkBAE1s9TsCfkGRVERYLEeL/VtN27L8\nhQN7T7RwwxITsfyOZ6zAhr9QUOMiQQyhiFBi4cAXsWJCS25etzDfWcsi1Z7otvBvWZobHFHkZe31\ntSpShRiNRrOmMj4+PtzjjY2N2Lp1K7777jucO3cO586dg7e3N2JiYhATE4P+/fsjISEBU6ZMUfgs\n7njIZ1WtuKJS31Gvw3LLmpqauCrdwMBAs61+YQ9lYVU/X9h5e3s7fVvZWdhiPWWt2IAvsKxZFjla\nqGjZoYDBF0xsIcWvIudfYyVcIDqCcItcycp+KXEkFo0VK6BjLgW2WoKpDaGVliMakDiiyKsjVnx8\nkar058tehCJVWMym1+vx7bffIj8/H9HR0XjzzTdx99134+rVqygtLcWPP/6I0tJSFBQUkFhVKRRZ\nVQi2ZQ7cFj38iKSzuHnzJhcBtRX+Vj/LwRTb6m9oaEBbW1u7iRS4LWL5ERUtToxiws5ZEWFLhQaO\n2DZUIh/VWXQkbUEoBPj/VnJbVphbq9XoI1uYssUXOwdbo7FqwNkpGB0Zj5yUGKncWL5I1epcDJin\nk4jNxSaTCYWFhUhJSUF4eDhee+019OnTR8ERA5WVlZgzZw4uX74MnU6HZ555BsnJye2OS05ORnZ2\nNvz9/ZGWlobBgwcrMFpFoMiqWmHRIP52uTPQ6WxrDCC21W/NwJ8ZJrPn2O/zF0VsK1DpLW9bEBN2\nzraekmNZxG5StnTqEQo7rbanBezrq86/vnys2W05q0hGy6bxfKxtkVuLxqqpCEkoUtXyXZETjZW6\nvuz3vby8zOZypc/JFoQ5z8LvislkwpkzZ7BmzRoEBQXhzTffRP/+/VVxjl5eXti8eTMSEhJQX1+P\n++67D4mJiYiLi+OOycrKQnl5OcrKynDq1CksWLAABQUFCo5aeUisKgR/25w/iTvzyyQ3DYCJGamt\nfv5kyMYvnCj4W/38ziBCEeDqLW9bEQo7NbSr5N+oxDxjLXXqYcfwRaoWBRGLbjtD2MlN2RC7vrZG\nC8Xsp7Taola4RS4VqZdaJLC/4cjra8+5uNLv1ZEIry/fFozfslrs+ipd5GUNOSL1hx9+wJo1a+Dl\n5YW1a9finnvuUc34ASA8PJwr5goMDERcXBwuXrxoJlYzMzMxd+5cAMCIESNQV1eH6upqhIWFKTJm\nNUBiVSW4Im+VL5DF4G/1sxxMWw38+Tmcwg4n7HekOsnwIwFiuVmurKLniyE1tUK1hvD68quVDQYD\nJ07ZzbixsVE0901tNylA3OvV1cJOzufXUu4m/9qyY1j0UatNIhwZfZR7fZ0V7RYWG4nNYVpBKFIt\nzWFydhOkhKwr4AcNpERqcXEx1qxZA6PRiJUrVyI+Pl7136eKigqcOXMGI0aMMHv8woUL6NmzJ/dz\njx49UFVVRWKVcD1i0Qaj0ehUQcRukHzYjYZvmMy3t+JPYOxv8EWMWFV/R4pzrG15W+uA1JECAyFq\nEEOOgr2vLDeatd4UnotYSoGwAEnpLVkt5NZaihYKRWxTU5PZ94nZawEw+wyrHaGwc2b00VHRWKmF\n2J0qUhlKFXnZci5SIrW8vBwpKSmor6/Hq6++imHDhqlqbpCivr4eM2bMwJYtW0RtK4WBJS2ckzPR\n5rfRDXFFZJX/GmJb/fzuN2Jb/VKTuzOr+i1NokIR0NEqb+G5KF08YQ+2nou1lAJhXqwrt2RdKYac\nCbsurCUqOxf2fbS0m+AsEWAPfAGhBmFnKRorJ1rIjvH09FT8XOyhIyJVDtZyu8WErFRal9w5Qngu\nYmk+P//8M9auXYsrV67glVdewciRIxX/bsiltbUV06dPx+OPP45p06a1ez4yMhKVlZXcz1VVVYiM\njHTlEFWHNr+VboBYZNUVaQBGoxG3bt0yW6kyAeCorX5XIRWBEptAxUQWizSzrX4t36jsKTSSwtJN\nSmrLG4DdIssZ56IUcs5FTgGdpYWYq7Zk+VuxWnhfLC10+X7C7PqxuVELuZt8nCVSrSFnocv+44tY\nYVoM/1oDsHoulZWVWLduHc6fP4+//OUvGDNmjCrfFylMJhPmzZuHAQMGYOnSpaLHTJ06FampqZg1\naxYKCgoQHBx8R6cAAGRdpRhMJDEaGhqg0+ng5+fn8Ndi26iNjY0wGAzw9fWFr6+v2Va/MIrK/78r\n7ZqcCTvP1tZWTlwxka6VvE0hTIir4X2RstORK7LcxTAecN65KGG3JSxq0fL7Ilw8CG2bxKKxQoN+\nqbQCVyMUqVqxoBLmHvOvNfBbpNxgMCA/Px8xMTHo2bMnLl++jA0bNqCkpAQvv/wyHnroIVXPzVJ8\n8cUX+P3vf497772XG//q1atx/vx5AEBSUhIAYNGiRcjJyUFAQAB27dqFIUOGKDZmF0MdrNQEE02M\nxsZGGI1GBAQEOPQ1mpubzbwBGxoa0KVLF7N0AGtb/S0tLdDpdJo28LeU9ygWyRKKLLHcWKWug1hu\nrbDntdqwJrLYMZ6enlzXLLUvFMRQavFgq8iSk3vsTosHRwhuJRYKUuPQokgVQ5jqwzzAjUYjLl26\nhGeeeQbl5eWoq6uDh4cHEhISMGbMGK7rVExMDIKDgxU+C8LBkFhVE0KxyiZSsURrW2E3TLZVz+/i\nc+3aNQQFBXE3N0B6q59FH/jiQWvwty7ZZGhLPqqUAODnZLlqO9bdcmtZYR8AUWszsSp6pRcKYqh9\n8WBNZAmvMfucqSFaby+uiArLWSg4Yp4QpmG4k0gVa/FaU1ODLVu2oKCgAMnJyejTpw/Ky8vx448/\ncv9169YN2dnZCp0F4SRIrKoJ9mVlsC9up06dOvz3+FX9vr6+ZhMzu2HduHHD7AYlzN90l5uUs3vc\nS213i0VZ7C0+Em5dMsGtRWwR3FJbhUosFKTORe0uBZYQiizWYY6hth0FWxA2WFBqLnNENFYoUvkp\nXFpDjki9du0atm7dis8//xxLly7FjBkzFD/fJ598EkePHkW3bt3w/ffft3s+NzcXjzzyCKKjowEA\n06dPxyuvvOLqYboLJFbVhFCstrW14datWwgKCrLp7/C3+tnNn1/Vz45hW/38n1mvbr6dlYeHB+fF\nqYRNkT2IRbi8vLxcOtFZi7LYYrXlbtuw/BuuPYLbloWCM7Zj3S3CLdZtCoDVHYWOVHk7G7WIVGuI\nzRNi+d3sGL4bhhbhB1M8PDy47wyfGzduYNu2bfj444+RnJyMWbNmqeZ8T5w4gcDAQMyZM0dSrG7a\ntAmZmZkKjM7toHaraoJN7PyteFvcAIRb/YGBgdyX31pVv16v50Sq0WiEl5cXFxFiv8e3KVJDFMsS\nrrLRkoM9Vlv8a8qqZ319fTXr9Qq0F9yO6DTFdynoiCdvRyOFarNssgcmUi11m5Jjzs+v8gbsd4Lo\nKPzGF8zrWc3fGblOBSx4YDQaUV9fL7oYU3PEm7/7oNPpRL8z9fX12L59OzIzM7Fw4UKcPHlSdd+r\n0aNHo6KiwuIxznbzudNR1yfiDkav13M3ValJR2yrX2jgL1YwZamq35p4EEaxmMCSyndj/3YFwtxa\ntYsHqWvDBBaLPAK/OTEwoSd2g1Ir7Hz4hUau6HNvSQAIRay1Nr/8ayxMw1C7ZZMlhOLB1m5Tcjw3\n+XOFs9M2+AVtWu4CBrTv0iRsrqJWSzMxhJ8zPz+/dnNzQ0MDduzYgYMHD+Kpp57CyZMnuQIrraHT\n6ZCfn4/4+HhERkZiw4YNGDBggNLDcivUe2e/AxBGVqUQbvX7+vqKVrLLqeoHYNOkLjeKxbw2xdqj\nOnIrVikh5CyEBWBMCAl9b/lRLGdf447CblCsa5aaxAOzwxEiJbDYNWbHsPw6LW/3O7PBgrWFgqVr\nLBRZcj7H7iZSLfW7Z8jxNVWiy5RwHEKRKvycNTU1IS0tDenp6Zg7dy6++OIL+Pj4OHQcrmbIkCGo\nrKyEv78/srOzMW3aNJSWlio9LLeCxKqKYNFVFrVg23T8SUzuVj/QPvLoyBuU1M1JbPXviK1YYTGL\nt7e3pm9QwgIwsWidnCiW2DV2dWGMlnM4xRZj7HtnMBi4nGf+rkZHraCUQA2pC9YWvLZECtn5qG0x\n1BHkilQ52DJXOCMay08rkYrYt7S0YPfu3dizZw9mz56Nzz//3Cm+4krAL4yeMGECFi5ciNraWnTp\n0kXBUbkXJFYVRDghsJxRYZGQv7+/U7f6HX1Ollb//EgsG6Olmz8/KuwO0S3heyPc6pOD3Gsstd3t\nqG1CYVRY7WkYlhArzgsICBC9NmJ5sWId0pQsPhIWtKkxdUFupJA/VzA8PDxE8721MC84UqRaQ841\nlhvxFlv0CnOfxebn1tZWpKen47333sOMGTPw2WefOcSiUU1UV1ejW7du0Ol0KCwshMlkIqHqYLR5\nZ3FDWFSsvr6eE2Wu2up3Fda2YvlRQhbB4v+elkWq0OLIWe+NLdvd1nKPpW7+/Ii9l5eXKoWQXDpi\nPyXnGiu1FetKIeRM2PVi1xGAWd6jVBGdmiPeantv7I14A+AWEPz7FaOtrQ0HDhzA9u3bMWXKFBw/\nfhydO3d27Uk6iNmzZyMvLw81NTXo2bMnVq1axfmkJyUlISMjA2+//TZXO/HRRx8pPGL3g6yrFIRV\nsLKtfp3udpcptjXCnzTkbPUzSxA1VerbgjC65eXl1e7mpJbiLjkII49qfG/Eco/56SX8KCF7f1ie\noFptgeTgytQFsZu/mE2RmC+vXNzJ5kyY9yj3vZGyjOvIgsyRCEWqlt8b/s4f++yyz/bGjRtx4sQJ\n9OvXDz4+Pvjiiy/w0EMPYeXKlQgNDVV66IR2IJ9VtdHQ0ICbN2/Cx8cHPj4+Zvk+wigq//9iRUbu\nIBzktnWVElhqKTziV/azm5MWI4/8KGxra6uZNYuaI1iWEFtAKJm6ICVi5QosoWWTj4+P6t8DKToq\nUuX8XX7EW2pB5ugcb3cSqcBvudx8P17+Pam6uhoHDhzAiRMn0NDQAA8PD/z000+4cOECoqKiEBsb\ni2XLlmHkyJEKnwmhcshnVW2wyYtt9et0Oi7CKpUfpPatflsQWgLJLQBzdXGXHMQWEB3JR1UL/Mp+\nVtXLhIPYNRZ68opVdyuJWu2nrBXGCN02+Nvd7BgvLy/4+/ur4jp3BGGU2xlOBWLXGGjvfWyLpZkU\natvutxdLIhW4fb4ff/wxNm/ejOHDh2Pnzp3o1q0b93xTUxPXJjU8PNwlY7bWcQoAkpOTkZ2dDX9/\nf6SlpWHw4MEuGRvRMSiyqiD8yla2vcKfLPmilU2m/BZ1Wr0x8UWdqyIOUpFYe/PcOpLzqGbsSV0Q\n5sXy/61UxNvdtsf5woEtHviCy9FFdM5EaKeldJSbj5zPslj6ET+XW8ufNcDcHkwsJ9VoNOKzzz7D\nhg0bcO+99+Lll19GRESEgiP+DWsdp7KyspCamoqsrCycOnUKS5YsQUFBgQIjJUSgyKra+Pzzz/Hp\np58iNjYWsbGx6N27NxcpNRgMyM/PR2RkJLp27cpNiAaDAQ0NDZI5bmq8KQHtPThdbT1lS3GXnCgh\n3xJIr9dr2qUAcEzk0ZaCDWdHvPk3Wi10NLKE3AWRVFEMf+GrhjlDKFLV6CJh62eZFRqx39PpdGbN\nPLT02eOnlojt3plMJpw4cQLr1q1Dv379sGfPHvTq1UvBEbfHWsepzMxMzJ07FwAwYsQI1NXVobq6\nGmFhYS4aIWEr6poh7jAGDBiAGzduoKioCDk5Ofj55585wXD9+nXo9XqsWLECEydO5HLR5N74bTHY\ndiZSOYJqmbwtbcNKVc8z9Ho91+NeC/maYrBoPuul7owtS1vtzDoaJeQX6CmxIHI0whxOawsiuSkF\nrkyPEY6DLfDUlIphC/zPsl6v5xa2rG4AgKzFghrmZiFyRGpBQQHWrl2LHj164L333kPv3r0VHHHH\nuXDhAnr27Mn93KNHD1RVVZFYVTEkVhUkNDQUU6ZMwZQpU/DLL79g27Zt2LlzJ4YMGYLHHnsMOp0O\nubm52LFjB1paWtCtWzfExMQgJiYGcXFx6N+/P3x9fbkJRbhtxbcbcdUNicE3vdeivRH/xu/p6cmd\nD7sxsep4/gQvtj2othsSIO4p6ufnp8gY5Ua8LVltsTQZfmGOllMxhJFHe3M4pXK8AdtzNjsyDv6C\nVasilY+1nFQ5xvxqstuSI1K//vprpKSkoGvXrti2bRv69evnkrE5E2EKpFbnizsFEqsq4YMPPoDB\nYEBhYSGio6PbPW80GlFdXY2ioiKcPXsW77//PsrKytDU1ITg4GDExsZyIjYmJsbM0FyuGb+9ItZR\npvdqwZbUBSWLu2w9Hy3k18qJEvJb/DL0ej3a2to0aRbPjzy6antcqmBIjmG8tSihWovaOkpHC6es\n7SwIF2ViDSackbrBz+eWEqnfffcd1qxZAz8/P2zYsAFxcXGa+C5ZIzIyEpWVldzPVVVViIyMVHBE\nhDWowErjmEwmXL16FWfPnkVRURGKiopQUlKChoYGBAYGIiYmhsuJjY2NRVBQkJmIFRYcSW3BWlrt\ni7kUqFUEycHRHpzOKu6y5fX5IkiNfq+2IFUEZs3LVK1WW8LIo5qtzsQWZez/fM9Y9pn39PTkCkK1\nCl+kusomUCx1g3+d7Vn88kWqmN2ZyWRCcXExVq9eDb1ej9deew2DBg1SxXfFFioqKjBlyhSrBVYF\nBQVYunQpFVipB/JZvZMwmUy4fv06zp49i+LiYhQVFaG4uBg3btyAr68vF4ll0diuXbvaLGJZEUFb\nW5vZTVZrkxpDGAli+ajOQs51tmcLVng+ahZBcujo+Tj7OjvifLRePW4ymZCRkYG8vC/Ro0c3PPHE\nEwgMDLTbBkpJjEYjmpqaOFGnFi9rS80PpK4zq3fgn4+YSC0tLUVKSgqamprw6quv4r777tPkfM7v\nOBUWFtau4xQALFq0CDk5OQgICMCuXbswZMgQJYdM/AaJVeL2hFRfX88JWCZia2tr4e3tjX79+nEC\nNjY2Ft26deMmaLbN//PPPyMiIoLbqmIesVLFXWqHX2SkBtEgZptjq1G8u9g1Ac47H6WstoSRLbWI\noI5iMBjw6qv/D+++exQNDU/Ax+ck+ve/hM8/z4a3t7fNNlBKp26oVaRaQ6r5gTBNxsvLCzU1Naiv\nr0d0dDS8vb1x7tw5pKSk4Nq1a3j11Vdx//33a2LuJtwSEquENCaTCY2NjSgtLeVSCoqV1SZ5AAAg\nAElEQVSLi1FdXQ1PT09ERUUBAL799ltcv34dn376KcLCwsxM4sUmSSlxpfTkL1Zk5O3treoJ2lrn\nLr5bBF/UqfmcpBBrsuCq7kzO2oJ1p25TwG+iu6GhAdHR/WEw/AIgDIAJgYG/Q1raC5gwYYLk74ul\nFCiZuqFVkSoFi9y3tLRwRaHA7fftn//8J/72t7/h119/RWhoKJqbm5GYmIiHHnqISxkLCQlR+AyI\nOxQSq4Tt1NTU4O2338a2bdvQtWtXjBs3DtXV1bh48SL0ej2i/q+NHsuNvfvuu822nSyJK6mcWGfe\nwPn5tTqd9dauaoefXwuAS1tgN361FHfJRWg/pbb8Z7HPs6U8b51Ox4kGVm2t9kWRNYSiu7W1FZGR\nUTAYboLV7AYGTsO2bX/EjBkzOvQallI3HF145G6RbjnpJRcuXMD69etx7tw5PP744wgMDERpaSlK\nSkq4/xYsWIB169YpdBbEHQyJVcI2TCYThg0bhkGDBmHJkiVISEgwe85gMODcuXNmxV2VlZUwGo3o\n2bOnWWFX7969zdp1WirScIZXrFRRjlZFg9wiMGvFXXKL6FxxPs7oC+8qxD7P7DoD4CrBXbkwczT8\nRgtC0f3gg4/gm2/uRkvLnwGcROfOf8GZM/kOb68ptYtjNN72P5bKixW7ztYKjbSGHJF66dIlbNy4\nET/88ANeeukljB8/XtINorm5Gb6+vi4Ze05ODpYuXQqDwYCnnnoKL774otnzubm5eOSRRzinnOnT\np+OVV15xydgIl0NilbAdNvHJhd1MfvnlF85mq6ioCD///DPa2trQvXt3LgobFxeHPn36mN30pHLb\nxESsnAihMN+Rvx2mRRxVNKWWoiOhp6g7LCL49mCsSE8qRUZt+ZpiWBKpjLq6Ojz77DLk5xcgMjIS\nb721Fvfee6/LxshfAFtbmAHg7Lh8fHzcQqSyhbiUSL1y5QreeOMNnD59Gi+++CImTZqkmuixwWBA\nTEwMPvnkE0RGRmLYsGFIT09HXFwcd0xubi42bdqEzMxMBUdKuAhqt0rYji1CFfjNHzM6OhrR0dGY\nPHky95zRaERVVRWKi4tx9uxZ5OXloby83KzhAYvEChseCCOE/IYHUluvzOBcSdN7RyEU3fZ2mrLk\nY8q/4TurZafQrknrHpzWuk3JMYq39Jl2deoGv+EFS8ew1A0sODgYe/f+3SVjE8NS4wN2nVtbWznv\nY3YezBPaUSkFrkTYEUxsTqitrcWWLVtw8uRJPPfcc9i4caNqRCqjsLAQffv25eoiZs2ahcOHD5uJ\nVaC9iT9xZ0FilXAZer0evXr1Qq9evfDwww9zjxuN9jU8YDf8lpYW7mYE/CbImJBQk7emHMSKjJzd\n454vYsV6ovMXDPxrLTdCKNyqdAeR2pFuU9aM4vmRbmd0lbJ0PmrOGe4I7DMn3O639JlWc663HJF6\n/fp1pKam4vjx41iyZAlSUlJU+z0Ta3166tQps2N0Oh3y8/MRHx+PyMhIbNiwAQMGDHD1UAkFIbFK\nKI5er0dERAQiIiLw4IMPco8LGx7s379ftOFBeHg4vvjiC+zZswdHjhxBXFwc9Hq92Y2IRb2kCmHU\ncBNiiHWaUrrHvVTkSm7nLp1Ox+Vxent72x0ZVhphowVHim6dznILWn7U21EFi0ykNjU1AdB+Yw+g\nfU6qcKFnKRorzIsVWzCI2Zo5E6FIFfvM3bx5E++88w6OHj2KRYsWYdWqVU7vgmYvcq7bkCFDUFlZ\nCX9/f2RnZ2PatGkoLS11wegItUA5q4TmYA0Pjhw5gu3bt+P06dMYMWIEfH190dbWJqvhgTUPUyW8\nYh3dOUtpmHBlN3q2gFBbcZctCNMX1NBoQa7Vlliut9YL28RwZuGUtflDSsTa8/r8eUHqM3fr1i28\n++67OHToEJKSkjB37lybU7iUoqCgACtXrkROTg4AYM2aNdDr9e2KrPj07t0bX3/9Nbp06eKqYRKu\ng3JWCfdAp9Phtddew/79+7Fw4UL84x//QGhoaLuGB8ePH0dqaqrshgf8aIowauVMr1ihU4EresI7\nE2tbyWKRWGHUW6kFgxTC9AU1RYZtiRDy82L5DT28vLzg5eWlimvdUaxFUh2B3DQZqR0GW1IKhCkm\nYpHUxsZG7Nq1C/v27cMTTzyBkydPwtvb26Hn7GyGDh2KsrIyVFRUoHv37ti3bx/S09PNjqmurka3\nbt2g0+lQWFgIk8lEQvUOgyKrTuSFF17AkSNH4O3tjT59+mDXrl0ICgpqd5w12w6iPaWlpejVq5cs\naxVrDQ+io6PNbLYiIyPNRKyzvGLdzanA3iidLR2lXFUI424enOw9amxshF5/u5uR8DMutsPgyMWZ\no1G7BVVH2qOy75GHhwd8fX3bzQvNzc3YvXs3PvjgAzz++ONISkpymc2UM8jOzubugfPmzcNLL72E\n7du3A7jdHnXbtm14++234enpCX9/f2zatAn333+/wqMmnARZV7maY8eO4cEHH4Rer8fy5csBACkp\nKWbHyLHtIJwDi1yUlZVxPrFFRUW4cOEC9PrfGh6wtAKxhge2esUCv91cWf6mOwggZ9pPSS0YbC3u\nsgW+XZMaBZCtiL1H1vJi1W61xRepWmy2ILY4a2tra+fNm5+fj5aWFsTGxiIiIgIfffQRdu3ahT/9\n6U9YuHAhAgICFD4TgnAolAbgahITE7l/jxgxAgcPHmx3jFzbDsLxsOjfwIEDMXDgQO5xsYYHBw8e\nlGx4wPprS3nF8rdeGZ6enlzEREs3WD6usp/qaHGXrfZPQvcFNRS22YtQpFpLMbFkaaYWqy1+By0t\n29LxI9jsffLw8OB2WNi1LikpwZEjR1BaWora2lp07doVI0eORENDA44ePWpm9UcQ7gqJVRexc+dO\nzJ49u93jcmw7CNfCqrFZkdYf//hHAOIND7Kzs/Hzzz/DYDAgIiKiXcMDg8GAPXv24MqVK/if//kf\nLneTCSvmY6m0r6Yt8G3ClMzflGv/JKeamwlvOZ6iWkBO5bgt2Gu15YjKeaFIdYf3iJ82I1xIsO9/\naGgompqakJSUhCeffBKXL19GcXExSkpKsG/fPhQXF+OVV17BY489puDZEIRzIbFqJ4mJibh06VK7\nx1evXo0pU6YAAF5//XV4e3uLTiZanmzvNGxpeJCTk4OTJ0+ipqYGgwYNwvDhw5GVlSXZ8IAfsbJ0\ns1eyal6YG6imIiMh1uyfmI0WK+xiv+Ph4QGDwQAAqinusgUlmi3YYrXVkQYT7i5S/fz82l0/o9GI\nzMxMbN26FePGjUN2djZXUNSrVy8MHTpUiaFzyKmzSE5ORnZ2Nvz9/ZGWlobBgwcrMFLCXSCxaifH\njh2z+HxaWhqysrJw/Phx0ecjIyNRWVnJ/VxZWYkePXo4dIyE89Hrbzc8CAgIwOHDh5GVlYUZM2Zg\n6dKl6NKli1nDg9LSUjQ3N9vU8EApr1ixrXGtbrsC4EQSE08sosWaRzjDw9QV8N0K1NIRzN7KeZ1O\nx70P7iJSmZetTte+yxlw+33Mzs7GG2+8gZEjRyIzMxOhoaEKjro9BoMBixYtMquzmDp1qlnqWlZW\nFsrLy1FWVoZTp05hwYIFKCgoUHDUhNYhsepEcnJysH79euTl5UnmE8mx7XA0Bw4cwMqVK1FSUoLT\np09jyJAhosdFRUWhc+fO3M2msLDQqeNyB/z8/BAWFobi4mKEh4dzj3e04QH7LygoSNIrlhUDOdIr\nVsx+yh3EgrVuU87q3OUs1GypJYU1qy3+55mJVnaOatllsAW5IvX48ePYuHEjhgwZgoMHD5rNH2pC\nTp1FZmYm5s6dC+B2vUZdXR2qq6sRFhamxJAJN4DEqhNZvHgxWlpauEKr3/3ud3jrrbdw8eJFPP30\n0zh69Cg8PT2RmpqKhx9+mLPtcHZx1aBBgzjzaEvodDrk5uaSn50N+Pv7Y8WKFVaP0+l0uOuuuzBm\nzBiMGTOGe5w1PDh79iyKi4tx5MgRrF+/Hjdu3ICvry8XiWUiVqrhQUe9Yt3RJN6eblO2FHe5suBI\niyLVGsLtfn7RoqVdBrVabQkXfGIi1WQyIS8vD+vXr0dcXBw+/PBD1e+syamzEDumqqqKxCrRYUis\nOpGysjLRx7t3746jR49yP0+YMAETJkxw1bAQGxsr+1gr1maEg9HpdAgODsaoUaMwatQo7nFhw4NP\nPvkEW7dutdjwwMfHh/tdseig0I6I3VyZt6PWRaozt8YdVdxla3RQS3nDcpGTk2rJpcDSAs0ZHaWs\nIVek5ufnY+3atYiKisKuXbu4SKXascU3uSO/RxBikFglJNHpdHjooYfg4eGBpKQkPP3000oP6Y5F\np9OhU6dOGD58OIYPH849Lmx4cPLkSezYscNiwwO+iK2ursaVK1fQq1cvs8r4hoYGyXQCtd90lI46\nyinuEm53W0vfcEeRyvey7WiaiS1WW/bYmtlyTszhQ9i5jY3r9OnTSElJQVhYGLZv344+ffrY9Zqu\nRk6dhfCYqqoqREZGumyMhPtBYtVNkeNSYI2TJ08iIiICV65cQWJiImJjYzF69GhHD5WwA1YglJCQ\ngISEBO5xYcODM2fOYO/evVzDg65du+LWrVs4ffo05s+fj5deesks+iP0ihUrgBHrNa8kahd0cqKD\nYsVd7BhPT0/RPFut4QiRag1H2prJud5yROq3336LNWvWoHPnznjjjTcQExOjyfdRTp3F1KlTkZqa\nilmzZqGgoADBwcGUAkDYBYlVN8WaS4EcIiIiAAChoaF49NFHUVhYSGJVI0g1PPjqq6+wdu1aHD9+\nHOPHj8fSpUtRWlqKyZMny2p4ILzRK2UMz0fYbcoZPeGdiVjVPBM/BoMBXl5eXMSbRWItdUlT67m7\nQqTKoaNWW2I530zsGgwG+Pr6iorUs2fPYs2aNfD09ERKSgruuece1b5HcpCqs+C3R504cSKysrLQ\nt29fBAQEYNeuXQqPmtA61G71DmbcuHHYsGED7rvvvnbPNTQ0wGAwoFOnTrh16xbGjx+PFStWYPz4\n8U4bj1yXAjkef0R7zp8/j9GjR2Pp0qV4+umnERgYyD0n1vCgqKjIYsMDKREr1f/ckVXcYpZaWmu3\nKYZQ0Emdk9h1VnrRIIXcc1IrYjnf7N/Ab+L38uXL+P777xEbG4uoqCiUl5cjJSUFbW1tWLFiBeLj\n4zV13gShEKJfEhKrdyCHDh1CcnIyampqEBQUhMGDByM7O9vMpeCnn37iOje1tbXhP//zP/HSSy85\ndVwlJSXQ6/VISkriLFyEGAwGxMTEmHn8paenU3tamRgMBpuLjFjDg6KiIu6/8vJytLS0oFu3bmbu\nBNYaHtgrYsUstYTRLK3BhJClbWRb/5bwWivRYELrIlUMYTGYp6cn9xn/6quvsHr1apSVlaGmpgZe\nXl4YMWIERo4ciQEDBiAuLo7aohKEdUisEtpg3LhxkmL1yy+/xKpVq5CTkwMASElJAQAsX77cpWMk\nbovY6upqs0isrQ0PxISslBUREz8A3MKtwJXCW7hosHa97cmL5YtUsa1xLWLJVotRUVGBtWvXorq6\nGs8//zy6dOmCkpISlJSUoLi4GMXFxejatSs+//xzhc6CIDSB6GRBOauEppDj8Ue4Br1ej4iICKc2\nPGAWW2xRzQpm2PNq8NO0Fb5JPACXRIc7WtxlS+cuoUjVehMJwLxoTyrPtqqqCuvWrUNFRQX+8pe/\nYOzYsdwxwhQrJa0Aa2trMXPmTPzyyy+IiorC/v37ERwc3O44agZDqBESq4RLsdelQOs3vzsBRzQ8\n6NmzJw4ePIg9e/bgX//6F0JCQuDh4WHRK1bN7VCB9g0X1BAdtrclKmtTy57z8/NzO5EqVbR36dIl\nrF+/HsXFxXj55ZeRmJho9byVvC4pKSlITEzEsmXLsHbtWqSkpHA7U3yoGQyhRkisEi7FXpcCOR5/\nhDqR0/CgsLAQf/3rX/HVV19h8ODBiImJwerVq602PJBrs6VExbwaRao1rLVE5XeRMplM3LkwgaeW\n4i5bMRqNaGpqsihSL1++jM2bN+Obb77B8uXLsW3bNk1E9zMzM5GXlwcAmDt3LsaOHSsqVgFqBkOo\nDxKrhCqRmizlePwR2oI1PPj000+xfv16TJs2De+99x769+9vc8MDvmhQ2iuWidSmpibo9Xq38EgF\nfhN0rDsTS2EQRmId2bnL2cgRqVevXsWWLVvw5Zdf4vnnn8fmzZs1IVIZ1dXVnNdpWFgYqqurRY+j\nZjCEGqECK0I1yHEpAIDs7GzOumrevHlOdykAKN/LFXzyySfo378/evXqZfE4fsMDllJQVFTENTyI\niooyE7GsO5eUV6yjbZ/Y+Jqbm+Hh4cFVjWsZexwLXFncZSv8bmfe3t7w9vZuJ0Dr6uqwdetW5OXl\nYenSpZg+fbrD2vY6Gqk0q9dffx1z587FtWvXuMe6dOmC2tradsf++uuvZs1gtm7dSv7ahCshNwCC\n6CjLli3DXXfdxeV7Xbt2TXQLrXfv3vj6668p30sBWOHSTz/9xBV3FRUV4fz58zCZTKIND/jCyF6v\nWBKptv9tKb9YZ+chC1vy+vj4tBOpN27cwFtvvYV//etfWLx4MWbPnq1akSqH2NhY5ObmIjw8HL/+\n+ivGjRuHkpISi7+zatUqBAYG4vnnn3fRKAmCxCpBdJjY2Fjk5eUhLCwMly5dwtixY0Un+t69e+Or\nr75C165dFRglIYYjGh5Y84plos7T05NEqgNeW2rhYG8eshyRWl9fj7///e/IzMzEggUL8Pjjj5sV\nn2mVZcuWoWvXrnjxxReRkpKCurq6dgtuJZrBEIQAEqsE0VFCQkK4LTSTyYQuXbqYbakxoqOjERQU\nRPleGoHf8IClFIg1PIiLi0O/fv3MGh7U1NTgu+++w3333ceJViaulN7e7ihqb7rQ0c5dLIe2paVF\nUqQ2NjZix44dyMjIwFNPPYUnnngC3t7eCp2p46mtrcWf/vQnnD9/3iyVSelmMAQhgMQqQViC8r0I\nhqWGB35+fjAYDPj2228xefJkbNiwAYGBgRYbHvC3t8V6zCtdqKN2kWoNSykcrPhLr9fD29sbzc3N\n0Ov1XLvhpqYmvP/++/jwww8xZ84cPPPMM5zbBEEQLofEKkF0FMr3Ii5cuIB169Zh9+7dGDt2LP7j\nP/4DFRUVNjU8kCruUsorVusiVQqTyYTm5mY0NzfD09PTrC3q4cOHsWjRIoSGhqJHjx746aef8Pvf\n/x4LFixAQkICQkJClB4+QdzJkFgliI6itnyvnJwczhHhqaeewosvvtjumOTkZGRnZ8Pf3x9paWkY\nPHiww8dxp2AymfC73/0OI0eOxJ///Gd079693fOs4UFRURHXXlOs4UFsbCy6du3aTsSK5cU6yyvW\n3UVqS0sLlz8sLIpqbW3F3r17kZGRgXvuuQehoaE4d+4c954FBARg8uTJ2LFjh0JnQRB3NCRWCaKj\nqCnfy2AwICYmBp988gkiIyMxbNgwpKenIy4ujjsmKysLqampyMrKwqlTp7BkyRIUFBQ4fCx3EgaD\nweZqcH7DA+ZOUFxcjKtXr8LHxwf9+vVr1/DAklesJRErx2bLnUUqc2KQEqltbW3IyMjA9u3bMWnS\nJCxZsgRBQUHt/s6FCxdw9epVxMfHu/IUOA4cOICVK1eipKQEp0+fxpAhQ0SPk7NgJQgNQmKVINyB\nL7/8EqtWrUJOTg4AcBHe5cuXc8fMnz8f48aNw8yZMwGYuxkQymMymcwaHjARyxoe9OnTxywSK2x4\nYKtXrE6n41qIMjN/tXfRkoMckWowGPDPf/4T27ZtQ2JiIp577jlVb/WXlJRAr9cjKSkJGzduFBWr\nchasBKFRRCclbfurEMQdyIULF9CzZ0/u5x49euDUqVNWj6mqqiKxqhJ0Oh38/f2RkJCAhIQE7nFh\nw4MzZ85g7969Fhse8COjYl2kmIgFAA8PD7P8TTV1kbIFoadtQEBAO5FqNBpx9OhRbNmyBaNHj8aR\nI0dw1113KTRi+cTGxlo9prCwEH379kVUVBQAYNasWTh8+DCJVcJtIbFKEBpDrrgQ7ppoUZTcaeh0\nOvj4+GDgwIEYOHAg97hYw4ODBw9KNjyIiopCTk4Otm7dip07dyIiIsLMWqu1tRXNzc2aaIXKR65I\nPXbsGDZt2oRhw4bh0KFDbrdIk7NgJQh3gsQqQWiMyMhIVFZWcj9XVlaiR48eFo+pqqpCZGSky8ZI\nOBadTgcvLy/ExMQgJiaGy40WNjz44YcfsH37dnz99dcIDQ3FiBEjsGfPHsTFxXEND3x8fCRttlg+\nq9q8Yk0mE1pbW9HU1AQPDw/4+/u3a7xgNBqRm5uLDRs2YODAgdi3b1+7Qji1IGWTt3r1akyZMsXq\n76txIUEQzoTEKkFojKFDh6KsrAwVFRXo3r079u3bh/T0dLNjpk6ditTUVMyaNQsFBQUIDg52u+gS\nAU5QRkdH46effsK+ffsAALt378bkyZNx8eJFzis2Ly9PdsMDMRGrhFcsE6nNzc1c6oRQpJpMJnzx\nxRdYt24d+vTpg/fffx933323w8fiSI4dO2bX78tZsBKEO0FilSA0hqenJ1JTU/Hwww/DYDBg3rx5\niIuLw/bt2wEASUlJmDhxIrKystC3b18EBARg165dLh2jtUrl3NxcPPLII4iOjgYATJ8+Ha+88opL\nx+hutLa2YuXKlZg6dSonOnv16oVevXrhD3/4A3ecsOFBWloa1/AgODiYs9mKi4tDTEwMAgICLHrF\ntra2OtwrVihS/fz8REXqqVOnkJKSgsjISLz77rvc58ldkCqAlrNgJQh3gtwACIJwKHIqlXNzc7Fp\n0yZkZmYqOFKCj8lkwtWrV7mc2KKiIpsbHojZbAEQzYsVE7FCkerr69su9cBkMuGbb75BSkoKQkJC\n8Nprr6F///6uu1BO5tChQ0hOTkZNTQ2CgoIwePBgZGdnm9nkAUB2dja3IJw3bx61RSXcBbKuIgjC\n+cix1srNzcXGjRvxv//7v4qMkZCPsOEBE7FyGh4A8rxi9Xo9J1SZSBVaa5lMJnz//fdYs2YNfH19\nsWLFCsTFxVH+JkG4F2RdRRCE85FTqazT6ZCfn4/4+HhERkZiw4YNGDBggKuHSshAp9MhODgYo0aN\nwqhRo7jHhQ0PPvnkE2zduhW1tbXw9va22vCAORxcuXIFAQEB3GsZjUY0NTUhJSUFfn5+iIuLg7+/\nPz744APo9Xr89a9/xb333ksilSDuIEisEgThUOSIiCFDhqCyshL+/v7Izs7GtGnTUFpa6oLREY5C\np9OhU6dOGD58OIYPH849Lmx4cPLkSezYscOs4UH//v1hMBhw4MABhIaGIiMjg4ukspzYgQMH4tSp\nU3jrrbfw448/oqGhAX369MHf/vY3DBgwAAMGDMCYMWMQHh6u4FUgCMIVkFglCMKhyKlU7tSpE/fv\nCRMmYOHChaitrUWXLl1cNk7COVhqeNDc3IwPPvgA69evR11dHR544AGcP38ekydPNmt4EBgYiM8+\n+wy1tbXYtGkT7r//fjQ1NaG0tJRLRdi/fz9CQ0MVE6ty26JGRUWhc+fO8PDwgJeXFwoLC108UoLQ\nPiRWCYJwKHIqlaurq9GtWzfodDoUFhbCZDK5TKg++eSTOHr0KLp164bvv/9e9Jjk5GRkZ2fD398f\naWlpGDx4sEvG5s7odDrMnTsX3377LVasWIGZM2fCw8NDtOFBRkYG3nzzTYwePZqL1Pv5+SE+Ph7x\n8fEKn8ltBg0ahEOHDiEpKcnicTqdDrm5ubQQIwg7ILFKEIRDkWOtlZGRgbfffhuenp7w9/fHRx99\n5LLxPfHEE1i8eDHmzJkj+nxWVhbKy8tRVlaGU6dOYcGCBSgoKHDZ+NyZlStXol+/fmY2VGIND7Rg\nYyanLSrDSiEzQRBWIDcAgiDuOCoqKjBlyhTRyOr8+fMxbtw4zJw5E8BtUZKXl0dNFQhRxo0bh40b\nN0qmAURHRyMoKAgeHh5ISkrC008/7eIREoSmIDcAgiAIa4i5GVRVVZFYvQOxty0qAJw8eRIRERG4\ncuUKEhMTERsbi9GjRzt6qATh1pBYJQiCECDccSKbpDsTe9uiAkBERAQAIDQ0FI8++igKCwtJrBKE\njTi+mTNBEISGEboZVFVVITIyUsEREWpHKp2uoaEBN2/eBADcunULH3/8MQYNGuTKoRGEW0BilSAI\ngsfUqVOxe/duAEBBQQGCg4MpBYBox6FDh9CzZ08UFBRg0qRJmDBhAgDg4sWLmDRpEgDg0qVLGD16\nNBISEjBixAhMnjwZ48ePV3LYBKFJqMCKIIg7itmzZyMvLw81NTUICwvDqlWr0NraCgCcDdGiRYuQ\nk5ODgIAA7Nq1S7J4xhlYs9bKzc3FI488gujoaADA9OnTNVE9TxAEIQPRnCsSqwRBECrixIkTCAwM\nxJw5cyTF6qZNm5CZmanA6AiCIJyKqFilNACCIAgVMXr0aISEhFg8hnw7CYK4kyCxShAEoSF0Oh3y\n8/MRHx+PiRMnoqioSOkhEQRBOBUSqwRBEBpiyJAhqKysxL///W8sXrwY06ZNU3pIquaFF15AXFwc\n4uPj8cc//hHXr18XPS4nJwexsbHo168f1q5d6+JREgRhCRKrBEEQGqJTp07w9/cHAEyYMAGtra2o\nra1VeFTqZfz48Th79iz+/e9/o3///lizZk27YwwGA1dUV1RUhPT0dBQXFyswWoIgxCCxShAEoSGq\nq6u5nNXCwkKYTCZ06dJF4VGpl8TEROj1t291I0aMQFVVVbtjCgsL0bdvX0RFRcHLywuzZs3C4cOH\nXT1UgiAkILFKEAShImbPno2RI0fixx9/RM+ePbFz505s374d27dvBwBkZGRg0KBBSEhIwNKlS/HR\nRx+5fIyVlZUYN24c7rnnHgwcOBBvvvmm6HHJycno168f4uPjcebMGRePsj07d4Ydjv4AAAJKSURB\nVO7ExIkT2z0u1mL3woULrhwaQRAWoHarBEEQKiI9Pd3i888++yyeffZZF41GHC8vL2zevBkJCQmo\nr6/Hfffdh8TERMTFxXHHZGVloby8HGVlZTh16hQWLFiAgoICp4wnMTERly5davf46tWrMWXKFADA\n66+/Dm9vbzz22GPtjqN2ugShbkisEgRBEDYRHh6O8PBwAEBgYCDi4uJw8eJFM7GamZmJuXPnAri9\n/V5XV4fq6mqndAM7duyYxefT0tKQlZWF48ePiz4vbLFbWVmJHj16OHSMBEF0HEoDIAiCIDpMRUUF\nzpw5gxEjRpg9Lra1LpYv6mxycnKwfv16HD58GL6+vqLHDB06FGVlZaioqEBLSwv27duHqVOnunik\nBEFIQWKVIAiC6BD19fWYMWMGtmzZgsDAwHbPC5sXKLHdvnjxYtTX1yMxMRGDBw/GwoULAQAXL17E\npEmTAACenp5ITU3Fww8/jAEDBmDmzJlmUWKCIJSF2q0SBEEQNtPa2orJkydjwoQJWLp0abvn58+f\nj7Fjx2LWrFkAgNjYWOTl5TklDYAgCLeB2q0SBEEQ9mMymTBv3jwMGDBAVKgCwNSpU7F7924AQEFB\nAYKDg0moEgTRIaxFVgmCIAjCDJ1O9x8APgfwHX7bgXsZQC8AMJlM2//vuFQAfwBwC8ATJpPpG9eP\nliAIrUNilSAIgiAIglAtlAZAEARBEARBqBYSqwRBEARBEIRqIbFKEARBEARBqBYSqwRBEARBEIRq\nIbFKEARBEARBqJb/D6nRM2cdX17QAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x17697cc0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(12,6))\n",
-    "ax = fig.gca(projection=\"3d\")\n",
-    "ax.scatter(x,y,z)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "3维 `RBF` 插值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "zz = Rbf(x, y, z)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x176e5c50>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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WHVHIsatzq66uLnn+FKKebiYKtX+33XYbt9xyCz5k6j6ICLk+SKQyggjTnsAa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gINL4JHUcN4OMqZjsgXB7UGBAW+gRIjI8c9T8GxBlB+GLjlRpwcgKx5F0zpIPAOnKoK+VtC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\nBAKBafl/CyJH4aoVOLIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x174b17b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xx, yy = np.mgrid[-np.pi/2:np.pi/2:50j, -np.pi/2:np.pi/2:50j]\n",
-    "fig = plt.figure(figsize=(12,6))\n",
-    "ax = fig.gca(projection=\"3d\")\n",
-    "ax.plot_surface(xx,yy,zz(xx,yy),rstride=1, cstride=1, cmap=plt.cm.jet)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 插值"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "设置 **`Numpy`** 浮点数显示格式："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "np.set_printoptions(precision=2, suppress=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "从文本中读入数据，数据来自 http://kinetics.nist.gov/janaf/html/C-067.txt ，保存为结构体数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data = np.genfromtxt(\"JANAF_CH4.txt\", \n",
+    "                  delimiter=\"\\t\", # TAB 分隔\n",
+    "                  skiprows=1,     # 忽略首行\n",
+    "                  names=True,     # 读入属性\n",
+    "                  missing_values=\"INFINITE\",  # 缺失值\n",
+    "                  filling_values=np.inf)      # 填充缺失值"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "显示部分数据："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0\t0.0\n",
+      "100.0\t33.258\n",
+      "200.0\t33.473\n",
+      "250.0\t34.216\n",
+      "298.15\t35.639\n",
+      "300.0\t35.708\n",
+      "350.0\t37.874\n",
+      "...\t...\n"
+     ]
+    }
+   ],
+   "source": [
+    "for row in data[:7]:\n",
+    "    print \"{}\\t{}\".format(row['TK'], row['Cp'])\n",
+    "print \"...\\t...\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "绘图："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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NdFXMukteEapdXrjFUhjjHTdx68SsfrjFYt0gu3p9LOMmbp2YtYaSQFUckqJodSq6alus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\nZmaWKwcWMzPLlQOLmZnl6n8BKYzjzfpoiFsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa0dc208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = plt.plot(data['TK'], data['Cp'], 'kx')\n",
+    "t = plt.title(\"JANAF data for Methane $CH_4$\")\n",
+    "a = plt.axis([0, 6000, 30, 120])\n",
+    "x = plt.xlabel(\"Temperature (K)\")\n",
+    "y = plt.ylabel(r\"$C_p$ ($\\frac{kJ}{kg K}$)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 插值"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "假设我们要对这组数据进行插值。\n",
+    "\n",
+    "先导入一维插值函数 `interp1d`：\n",
+    "\n",
+    "    interp1d(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.interpolate import interp1d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ch4_cp = interp1d(data['TK'], data['Cp'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`interp1d` 的返回值可以像函数一样接受输入，并返回插值的结果。\n",
+    "\n",
+    "单个输入值，注意返回的是数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(39.565144000000004)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ch4_cp(382.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "输入数组，返回的是对应的数组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 10.71,  36.71])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ch4_cp([32.2,323.2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "默认情况下，输入值要在插值允许的范围内，否则插值会报错："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "A value in x_new is above the interpolation range.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-10-5d727af9aa33>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mch4_cp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m8752\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32md:\\Miniconda\\lib\\site-packages\\scipy\\interpolate\\polyint.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     77\u001b[0m         \"\"\"\n\u001b[0;32m     78\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_shape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prepare_x\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 79\u001b[1;33m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_evaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     80\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_finish_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_shape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     81\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32md:\\Miniconda\\lib\\site-packages\\scipy\\interpolate\\interpolate.pyc\u001b[0m in \u001b[0;36m_evaluate\u001b[1;34m(self, x_new)\u001b[0m\n\u001b[0;32m    496\u001b[0m         \u001b[1;31m#    The behavior is set by the bounds_error variable.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    497\u001b[0m         \u001b[0mx_new\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_new\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 498\u001b[1;33m         \u001b[0mout_of_bounds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_bounds\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_new\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    499\u001b[0m         \u001b[0my_new\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_new\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    500\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_new\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32md:\\Miniconda\\lib\\site-packages\\scipy\\interpolate\\interpolate.pyc\u001b[0m in \u001b[0;36m_check_bounds\u001b[1;34m(self, x_new)\u001b[0m\n\u001b[0;32m    526\u001b[0m                 \"range.\")\n\u001b[0;32m    527\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbounds_error\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mabove_bounds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 528\u001b[1;33m             raise ValueError(\"A value in x_new is above the interpolation \"\n\u001b[0m\u001b[0;32m    529\u001b[0m                 \"range.\")\n\u001b[0;32m    530\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: A value in x_new is above the interpolation range."
+     ]
+    }
+   ],
+   "source": [
+    "ch4_cp(8752)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "但我们可以通过参数设置允许超出范围的值存在："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "ch4_cp = interp1d(data['TK'], data['Cp'], \n",
+    "                  bounds_error=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不过由于超出范围，所以插值的输出是非法值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(nan)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ch4_cp(8752)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以使用指定值替代这些非法值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "ch4_cp = interp1d(data['TK'], data['Cp'], \n",
+    "                  bounds_error=False, fill_value=-999.25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(-999.25)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ch4_cp(8752)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 线性插值"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`interp1d` 默认的插值方法是线性，关于线性插值的定义，请参见：\n",
+    "\n",
+    "- 维基百科-线性插值： https://zh.wikipedia.org/wiki/%E7%BA%BF%E6%80%A7%E6%8F%92%E5%80%BC\n",
+    "- 百度百科-线性插值： http://baike.baidu.com/view/4685624.htm\n",
+    "\n",
+    "其基本思想是，已知相邻两点 $x_1,x_2$ 对应的值 $y_1,y_2$ ，那么对于 $(x_1,x_2)$ 之间的某一点 $x$ ，线性插值对应的值 $y$ 满足：点 $(x,y)$ 在 $(x_1,y_1),(x_2,y_2)$ 所形成的线段上。\n",
+    "\n",
+    "应用线性插值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QX6N+hvBGBnTjDculNy/X2p4/PjnJ0UOHVizBPAKMT0y0/AxJ7al0qA9iCaOZ1QJ669at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\nSSViqEtSiRjqklQihroklcj/A8xgms4BB0nlAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb861358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "T = np.arange(100,355,5)\n",
+    "plt.plot(T, ch4_cp(T), \"+k\")\n",
+    "p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "其中红色的圆点为原来的数据点，黑色的十字点为对应的插值点，可以明显看到，相邻的数据点的插值在一条直线上。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 多项式插值"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们可以通过 `kind` 参数来调节使用的插值方法，来得到不同的结果：\n",
+    "\n",
+    "- `nearest` 最近邻插值\n",
+    "- `zero` 0阶插值\n",
+    "- `linear` 线性插值\n",
+    "- `quadratic` 二次插值\n",
+    "- `cubic` 三次插值\n",
+    "- `4,5,6,7` 更高阶插值\n",
+    "\n",
+    "最近邻插值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1aY6IIDNj2dd3O9TfuWePm0wkaZFWGupdX35Zy5tMetVeuzpJ6pSerKmv1U0mhrqk0vTs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\niKEuSQUx1CWpIIa6JBXk/wGLsxItFlRd2gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb8612e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cp_ch4 = interp1d(data['TK'], data['Cp'], kind=\"nearest\")\n",
+    "p = plt.plot(T, cp_ch4(T), \"k+\")\n",
+    "p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "0阶插值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb989f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cp_ch4 = interp1d(data['TK'], data['Cp'], kind=\"zero\")\n",
+    "p = plt.plot(T, cp_ch4(T), \"k+\")\n",
+    "p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "二次插值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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k+Je+tGIJ5hFgbHx88fWS1sdQF9B+eLc7zLDRnslJrr3vvuWjX4Cbd+7kkN8yJHWNoT5E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\nqEtShRjqklQh/w+L/6C1jqXsbQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb995860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cp_ch4 = interp1d(data['TK'], data['Cp'], kind=\"quadratic\")\n",
+    "p = plt.plot(T, cp_ch4(T), \"k+\")\n",
+    "p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "三次插值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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o6rz3Xpb1l/3fJPWqdKHuD9vg9Hve+zADfxR+uUhlYKjrDP24uLp8X7PZ5Jb9+5mbmWHz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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb995a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cp_ch4 = interp1d(data['TK'], data['Cp'], kind=\"cubic\")\n",
+    "p = plt.plot(T, cp_ch4(T), \"k+\")\n",
+    "p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上，我们可以使用更高阶的多项式插值，只要将 `kind` 设为对应的数字即可："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "四次多项式插值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbe85278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cp_ch4 = interp1d(data['TK'], data['Cp'], kind=4)\n",
+    "p = plt.plot(T, cp_ch4(T), \"k+\")\n",
+    "p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以参见：\n",
+    "\n",
+    "- 维基百科-多项式插值：https://zh.wikipedia.org/wiki/%E5%A4%9A%E9%A1%B9%E5%BC%8F%E6%8F%92%E5%80%BC\n",
+    "- 百度百科-插值法：http://baike.baidu.com/view/754506.htm\n",
+    "\n",
+    "对于二维乃至更高维度的多项式插值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.interpolate import interp2d, interpnd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "其使用方法与一维类似。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "### 径向基函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "关于径向基函数，可以参阅：\n",
+    "- 维基百科-Radial basis fucntion：https://en.wikipedia.org/wiki/Radial_basis_function\n",
+    "\n",
+    "径向基函数，简单来说就是点 $x$ 处的函数值只依赖于 $x$ 与某点 $c$ 的距离：\n",
+    "\n",
+    "$$\\Phi(x,c) = \\Phi(\\|x-c\\|)$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x = np.linspace(-3,3,100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "常用的径向基（`RBF`）函数有：\n",
+    "\n",
+    "高斯函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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w5JNh4+ZWreCyy2InSi6Vhjy1Y0doxWzYEHZkadYsdiKR+jv8cHj88XBep1kz\n+NrXYidKJhXyPLRtW1iQaMsWmDYtTPwRKVTHHguPPQYDBoStCL/3vdiJkkejVvLM++/DOedA8+Yw\nc6Z2+pFk6NULnn0W7rgD/v3fQeMeMkuFPI+sWRNODp1wQhgr3rx57EQimXPooWEi2+OPw7e+FUZj\nSWaokOeJadPg5JPDcK1f/1pL0koytW8PTz0V5kV86UvwxhuxEyWDykVklZVw7bVw1VUwdSr84Aca\nYijJ1rp1GIU1YgT06RN+76VxNCEoopdeCpvYtmkTts1q1y52IpHcWrgwrB10zjnwX/8FBx4YO1H+\n0RT9PFVeHk74nHlmKOSzZqmIS3Hq0yesy1JSElZNnDw5dqLCpBZ5DrmHX9TrroMePeB3v9POPiK7\nzJ8fzhF16wa//GX4GxG1yPPKvHmh9XHLLWEI1sSJKuIiVZ12GixbBmecEf5bHTkS/v732KkKgwp5\nFu3YAZMmhV/QK66AH/4w7K/Zv3/sZCL5qUUL+PGPYdWqMMW/d+9wUnTZstjJ8pu6VrJgw4YwDvzu\nu6FjR/jRj8ImyVorRaR+Nm2Ce++Fu+4KG6lceWVYCbSYZjtrY4kc2rQJZswIo09eeCEU7n/919Cd\nIiKNU1EBEybA/feH3YcuuACGDQsLciV9HaKMFHIz6w/8FigB/ujuY2o45k7gXOBjYKS7L63hmEQV\n8srK8O/eE0+EqfQvvRT69oYPh8GDi6vFIJJLGzbA+PFhLPrKlWFBroEDobQ0zB5N2jyMRhdyMysB\nXgXOBtYDLwDD3H1FlWMGAFe5+wAzOxm4w90/0w4t5ELuDmvXwtKl4fLcc7BoERx8cDgpM3AgmJXx\nla+Uxo6aNWVlZZSWlsaOkRVJfm2Q7Nf3zjvwm9+UsWZNKc88E4r4aaeFWdK9e4c1Xtq0iZ2ycTKx\nscRJwGp3fzP1gOOBIcCKKsecBzwA4O6LzOxAM+vg7hsbnDyCnTvDL8Vbb4XL6tXw2mvhsmJF+Pet\nd2847rgw+/JLX/r05rKjR6uQF6okvzZI9uv7l3+B5s3LGD++FPcw5X/+/NC9OWFCGKPeti0cdRQc\neWT4eOihoRF28MFhw5YkqKuQdwbWVrm9Djg5jWO6ADkr5O6hD628PFy2bYOPPtpz+fDDsCTsli2h\nL3vX5d13YeNGePvtUMQPOAA+//lwtvzww8NWVSNHhh9+hw65ejUi0hBm4e/28MPD3y2EBtobb+xp\nlL3ySphxRyVIAAAEPUlEQVSA9/e/h4tZ+Ns+6KDwsW3b0EBr2zbMMt1//3DZb7+wOUbLluHjvvuG\nETYtWoTJTLHVVcjT7Qup3uyv8X5nnx2K7q7Lzp17PtZ02bFjz6Wy8tOXigrYvn3PpUmTPd/YffcN\n3+xd3/gDDgg/iP32Cz+kjh3DLLK2bcMPcNcPsUWLNF+tiBSEffYJo12OOCKsh16Ve2jc7WrMbdy4\np5G3cWMo/LsagB9+GBqFH38cPu5qMJaXhzeDZs3CpWnTcGnSJHwsKQnXS0rCZZ999nysejELl6rX\nd13SUVcfeR9gtLv3T92+DthZ9YSnmf0eKHP38anbK4G+1btWzKwwO8hFRCJrbB/5EqCbmR0CbAAu\nBoZVO2YacBUwPlX4N9fUP15XEBERaZhaC7m7V5rZVcAcwvDD+9x9hZmNSn39HnefZWYDzGw18BHw\nzaynFhGR3XI2IUhERLIjp2utmNkvzOxvZrbMzOaaWddcPn82mdltZrYi9fommVlCBjYFZjbUzF4x\nsx1mdnzsPJliZv3NbKWZrTKza2PnySQz+5OZbTSzl2JnyQYz62pm81K/ly+b2Q9iZ8oUM2thZotS\ntXK5mf2y1uNz2SI3s/3c/cPU9e8Dvdz9ipwFyCIz6wfMdfedZvYrAHf/WeRYGWNmRwM7gXuAq939\n/yJHarR0JrwVMjM7DdgK/I+7fyF2nkwzs4OAg9x9mZm1Bv4XOD9BP7+W7v6xmTUBngV+4u7P1nRs\nTlvku4p4SmvgvVw+fza5+xPuvjN1cxFhLH1iuPtKd38tdo4M2z3hzd0rgF0T3hLB3ecD78fOkS3u\n/ra7L0td30qYqJiYxaHd/ePU1WaEc5Sb9nZszpexNbP/NLO3gBHAr3L9/DnyLWBW7BBSp5oms3WO\nlEUaITWyrjehEZUIZraPmS0jTK6c5+7L93ZsxhdWNbMngINq+NL17j7d3W8AbjCznwG/oYBGudT1\n2lLH3ABsd/eHcxouA9J5fQmjM/0JkOpWmQD8MNUyT4TUf/jHpc63zTGzUncvq+nYjBdyd++X5qEP\nU2Ct1rpem5mNBAYAZ+UkUIbV42eXFOuBqifcuxJa5VIgzKwpMBF40N2nxM6TDe7+gZnNBE4Eymo6\nJtejVrpVuTkE+Mxyt4UqtdzvT4Eh7l4eO0+WJWVy1+4Jb2bWjDDhbVrkTJImMzPgPmC5u/82dp5M\nMrN2ZnZg6vq+QD9qqZe5HrUyATgK2AG8DnzH3d/JWYAsMrNVhJMSu05ILHD370aMlFFmdgFwJ9AO\n+ABY6u7nxk3VeGZ2LnvW27/P3Wsd5lVIzOwRoC/QFngHuNHdx8VNlTlm9mXgGeBF9nSTXefuj8VL\nlRlm9gXCqrL7pC5/dvfb9nq8JgSJiBQ2bb4sIlLgVMhFRAqcCrmISIFTIRcRKXAq5CIiBU6FXESk\nwKmQi4gUOBVyEZEC9/8BloffFHVXiTcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa0dcb38>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, np.exp(-1 * x **2))\n",
+    "t = plt.title(\"Gaussian\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Multiquadric` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LTjbbLHZEpWXBAjj00LAfzuDBSuKlTIlcGqRnT1i0KAx8jhsHm24aO6LSsHhx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\nJJwSuYhIwv0/gG56R7Cwt98AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb9a6908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, np.sqrt(1 + x **2))\n",
+    "t = plt.title(\"Multiquadric\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Inverse Multiquadric` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PhNtuC4sNieQ6dzjrrPDJc9AgfQItTyomBLUEZrv7XHdfAwwGTirrtaqYMe+t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\n9/9saTRo/aYs3AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbd3d438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, 1. / np.sqrt(1 + x **2))\n",
+    "t = plt.title(\"Inverse Multiquadric\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 径向基函数插值"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于径向基函数，其插值的公式为：\n",
+    "\n",
+    "$$\n",
+    "f(x) = \\sum_j n_j \\Phi(\\|x-x_j\\|)\n",
+    "$$\n",
+    "\n",
+    "我们通过数据点 $x_j$ 来计算出 $n_j$ 的值，来计算 $x$ 处的插值结果。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.interpolate.rbf import Rbf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `multiquadric` 核的："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QEYk6f5JXZR4REd/4F/xa0SMi4gt/gl8rekREfKNSj4hIklGpR0QkyajUIyKS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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbebf828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cp_rbf = Rbf(data['TK'], data['Cp'], function = \"multiquadric\")\n",
+    "plt.plot(data['TK'], data['Cp'], 'k+')\n",
+    "p = plt.plot(data['TK'], cp_rbf(data['TK']), 'r-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `gaussian` 核："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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mnruBF8zsUkLDQKP69Z4EADO/ZeTKldC1KxucK1ksru+++2oEkIhIFeIWAJxz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\nwgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc576550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cp_rbf = Rbf(data['TK'], data['Cp'], function = \"gaussian\")\n",
+    "plt.plot(data['TK'], data['Cp'], 'k+')\n",
+    "p = plt.plot(data['TK'], cp_rbf(data['TK']), 'r-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `nverse_multiquadric` 核："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ERI6Bt8EvIiJR503wb9yoi7dERDyiGb+ISJJR8IuIJBkFv4hIklHwi4gkGQW/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\niCQZBb+ISJJR8IuIJJn/D8ptq7/3lHsDAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa1c3240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cp_rbf = Rbf(data['TK'], data['Cp'], function = \"inverse_multiquadric\")\n",
+    "plt.plot(data['TK'], data['Cp'], 'k+')\n",
+    "p = plt.plot(data['TK'], cp_rbf(data['TK']), 'r-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不同的 `RBF` 核的结果也不同。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 高维 `RBF` 插值"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "三维数据点："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x, y = np.mgrid[-np.pi/2:np.pi/2:5j, -np.pi/2:np.pi/2:5j]\n",
+    "z = np.cos(np.sqrt(x**2 + y**2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x176b4da0>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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hbN261f76xRdfxNDQUOT9CAqL1ZBwiqxG5b+Lqi1qJ4zsS0rwqdVq9q5S5XIZ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\nIbFKEARBEARBqJb/D6nRM2cdX17QAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x17697cc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(12,6))\n",
+    "ax = fig.gca(projection=\"3d\")\n",
+    "ax.scatter(x,y,z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3维 `RBF` 插值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "zz = Rbf(x, y, z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x176e5c50>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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WHVHIsatzq66uLnn+FKKebiYKtX+33XYbt9xyCz5k6j6ICLk+SKQyggjTnsAa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gINL4JHUcN4OMqZjsgXB7UGBAW+gRIjI8c9T8GxBlB+GLjlRpwcgKx5F0zpIPAOnKoK+VtC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\nBAKBafl/CyJH4aoVOLIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x174b17b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xx, yy = np.mgrid[-np.pi/2:np.pi/2:50j, -np.pi/2:np.pi/2:50j]\n",
+    "fig = plt.figure(figsize=(12,6))\n",
+    "ax = fig.gca(projection=\"3d\")\n",
+    "ax.plot_surface(xx,yy,zz(xx,yy),rstride=1, cstride=1, cmap=plt.cm.jet)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/04. scipy/04.03 statistics with scipy.ipynb b/04-scipy/04.03-statistics-with-scipy.ipynb
similarity index 99%
rename from 04. scipy/04.03 statistics with scipy.ipynb
rename to 04-scipy/04.03-statistics-with-scipy.ipynb
index 6ca66a27..65de31da 100644
--- a/04. scipy/04.03 statistics with scipy.ipynb	
+++ b/04-scipy/04.03-statistics-with-scipy.ipynb
@@ -1,1407 +1,1407 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 概率统计方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 简介"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**`Python`** 中常用的统计工具有 **`Numpy, Pandas, PyMC, StatsModels`** 等。\n",
-    "\n",
-    "**`Scipy`** 中的子库 `scipy.stats` 中包含很多统计上的方法。\n",
-    "\n",
-    "导入 `numpy` 和 `matplotlib`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pylab inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "heights = array([1.46, 1.79, 2.01, 1.75, 1.56, 1.69, 1.88, 1.76, 1.88, 1.78])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Numpy` 自带简单的统计方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean,  1.756\n",
-      "min,  1.46\n",
-      "max,  2.01\n",
-      "standard deviation,  0.150811140172\n"
-     ]
-    }
-   ],
-   "source": [
-    "print 'mean, ', heights.mean()\n",
-    "print 'min, ', heights.min()\n",
-    "print 'max, ', heights.max()\n",
-    "print 'standard deviation, ', heights.std()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "导入 **`Scipy`** 的统计模块："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import scipy.stats.stats as st"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "其他统计量："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "median,  1.77\n",
-      "mode,  (array([ 1.88]), array([ 2.]))\n",
-      "skewness,  -0.393524456473\n",
-      "kurtosis,  -0.330672097724\n",
-      "and so many more...\n"
-     ]
-    }
-   ],
-   "source": [
-    "print 'median, ', st.nanmedian(heights)    # 忽略nan值之后的中位数\n",
-    "print 'mode, ', st.mode(heights)           # 众数及其出现次数\n",
-    "print 'skewness, ', st.skew(heights)       # 偏度\n",
-    "print 'kurtosis, ', st.kurtosis(heights)   # 峰度\n",
-    "print 'and so many more...'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 概率分布"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "常见的[连续概率分布](https://zh.wikipedia.org/wiki/Category:%E8%BF%9E%E7%BB%AD%E5%88%86%E5%B8%83)有：\n",
-    "\n",
-    "- 均匀分布\n",
-    "- 正态分布\n",
-    "- 学生`t`分布\n",
-    "- `F`分布\n",
-    "- `Gamma`分布\n",
-    "- ...\n",
-    "\n",
-    "[离散概率分布](https://zh.wikipedia.org/wiki/Category:%E7%A6%BB%E6%95%A3%E5%88%86%E5%B8%83)：\n",
-    "\n",
-    "- 伯努利分布\n",
-    "- 几何分布\n",
-    "- ...\n",
-    "\n",
-    "这些都可以在 `scipy.stats` 中找到。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 连续分布"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 正态分布"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "以[正态分布](https://zh.wikipedia.org/wiki/%E6%AD%A3%E6%80%81%E5%88%86%E5%B8%83)为例，先导入正态分布："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.stats import norm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "它包含四类常用的函数：\n",
-    "\n",
-    "- `norm.cdf` 返回对应的[累计分布函数](https://zh.wikipedia.org/wiki/%E7%B4%AF%E7%A7%AF%E5%88%86%E5%B8%83%E5%87%BD%E6%95%B0)值\n",
-    "- `norm.pdf` 返回对应的[概率密度函数](https://zh.wikipedia.org/wiki/%E6%A9%9F%E7%8E%87%E5%AF%86%E5%BA%A6%E5%87%BD%E6%95%B8)值\n",
-    "- `norm.rvs` 产生指定参数的随机变量\n",
-    "- `norm.fit` 返回给定数据下，各参数的[最大似然估计](https://zh.wikipedia.org/wiki/%E6%9C%80%E5%A4%A7%E4%BC%BC%E7%84%B6%E4%BC%B0%E8%AE%A1)（MLE）值\n",
-    "\n",
-    "从正态分布产生500个随机点："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x_norm = norm.rvs(size=500)\n",
-    "type(x_norm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "直方图："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "counts,  [   7.   21.   42.   97.  120.   91.   64.   38.   17.    3.]\n",
-      "bin centers [-2.68067801 -2.13266147 -1.58464494 -1.0366284  -0.48861186  0.05940467\n",
-      "  0.60742121  1.15543774  1.70345428  2.25147082  2.79948735]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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3As/ut6rJqqqfdIunsXh+8nsrtevt0xaT/HWSbwG7gev6qmMG3gh8qu8itCrfENeIJOcC\nL2BxINWMJE9KcjewANxZVQdXaje1T1tMMg/sWOGmd1XVbVV1DXBNkj3AB4A3TKuWaTjZ8XVtrgEe\nqaobZ1rcBKzl+BrilQEN6KZbPg68rRupN6N7xf8b3fm4TycZVNVwebupBXpVXbrGpjeyCUewJzu+\nJK8HXs3iNfmbzjr+fi14EFh6Yn4ni6N0bRJJngx8AviXqrql73qmpap+kOTfgd9khQ+v6esql/OX\nrF4O7O+jjmlJsgt4B3B5VT3cdz1T1t8HSU/OfwPnJzk3yWnAVcCtPdekNcrih5l/BDhYVR/su55J\nS3Jmkm3d8unApZwgM/u6yuXjwK8CPwW+CfxJVT0080KmJMl9LJ68ePzExeer6uoeS5qoJL8P/ANw\nJvADYH9VvarfqsaT5FXAB/nZG+JOeGnYZpPkJuAS4JeAh4B3V9UN/VY1OUleAnwG+Co/mz7bW1X/\n0V9Vk5PkYmAfiwPwJwEfq6r3r9jWNxZJUhv8TlFJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANd\nkhphoEtSI/4P1qxllG6H6EYAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa955048>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "h = hist(x_norm)\n",
-    "print 'counts, ', h[0]\n",
-    "print 'bin centers', h[1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "归一化直方图（用出现频率代替次数），将划分区间变为 `20`（默认 `10`）："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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luRK4sqruTfIE4FPAyzbR3+/yqno0yTbg48AfV9XHV2s71R75YyHe8wTgoWmuf5Kq6sNV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\nAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa922eb8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "h = hist(x_norm, normed=True, bins=20)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在这组数据下，正态分布参数的最大似然估计值为："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean,  -0.0426135499965\n",
-      "x_std,  0.950754110144\n"
-     ]
-    }
-   ],
-   "source": [
-    "x_mean, x_std = norm.fit(x_norm)\n",
-    "\n",
-    "print 'mean, ', x_mean\n",
-    "print 'x_std, ', x_std"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "将真实的概率密度函数与直方图进行比较："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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xDmN4l9acylMYG8IOLas0tLLR6tVw991w663Bhcxrr4V99930cWZj3BDtP12j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\nKZGLiETc/wfoqVYxTpVDEwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1586bf28>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "h = hist(x_norm, normed=True, bins=20)\n",
-    "\n",
-    "x = linspace(-3,3,50)\n",
-    "p = plot(x, norm.pdf(x), 'r-')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "导入积分函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.integrate import trapz "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "通过积分，计算落在某个区间的概率大小："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "95.45% of the values lie between -2 and 2\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x15cbb8d0>]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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ffML4RYtY4TqIMSVoiCoT9+9nwpAh3PbSS67jhJ2kpCSSkpKKNKbQ0yRFJB5I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\nFXhjjIlSVuCNMSZK/T8+DStxCxquAQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x15cbb748>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x1 = linspace(-2,2,108)\n",
-    "p = trapz(norm.pdf(x1), x1) \n",
-    "print '{:.2%} of the values lie between -2 and 2'.format(p)\n",
-    "\n",
-    "fill_between(x1, norm.pdf(x1), color = 'red')\n",
-    "plot(x, norm.pdf(x), 'k-')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "默认情况，正态分布的参数为均值0，标准差1，即标准正态分布。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以通过 `loc` 和 `scale` 来调整这些参数，一种方法是调用相关函数时进行输入："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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LSKqIPFbGcf1EJFdEvunfEE1QpaVpiaFtW68j8cZVV+luQdnZXkdijM/KTOQiUh14GUgA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\nAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x15e78400>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "p = plot(x, norm.pdf(x, loc=0, scale=1))\n",
-    "p = plot(x, norm.pdf(x, loc=0.5, scale=2))\n",
-    "p = plot(x, norm.pdf(x, loc=-0.5, scale=.5))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "另一种则是将 `loc, scale` 作为参数直接输给 `norm` 生成相应的分布："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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apZeju9h3Yh/N6jWj5SUtaVW/VdGl5SUtaVm/JU3rNqVxncY0qdOExnUa06hO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\nAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x160ef2b0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "p = plot(x, norm(loc=0, scale=1).pdf(x))\n",
-    "p = plot(x, norm(loc=0.5, scale=2).pdf(x))\n",
-    "p = plot(x, norm(loc=-0.5, scale=.5).pdf(x))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 其他连续分布"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.stats import lognorm, t, dweibull"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "支持与 `norm` 类似的操作，如概率密度函数等。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不同参数的[对数正态分布](https://zh.wikipedia.org/wiki/%E5%AF%B9%E6%95%B0%E6%AD%A3%E6%80%81%E5%88%86%E5%B8%83)："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x15781c88>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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gq8Bu4FNgmjFmfZs6xcBPjDFTTnKcoAT3116D//gPeOYZuPrqjutsqtjED979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\n9P8BfIyZcbomsyAAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x158a85f8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x = linspace(0.01, 3, 100)\n",
-    "\n",
-    "plot(x, lognorm.pdf(x, 1), label='s=1')\n",
-    "plot(x, lognorm.pdf(x, 2), label='s=2')\n",
-    "plot(x, lognorm.pdf(x, .1), label='s=0.1')\n",
-    "\n",
-    "legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不同的[韦氏分布](https://zh.wikipedia.org/wiki/%E9%9F%A6%E4%BC%AF%E5%88%86%E5%B8%83)："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0xaa9bc50>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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dOvDoo4/SvHlzJk6cmG/KvOK23OvWrUuNGjVy9X3nnC8o5V5ISAi1atUqVvq+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\nAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa980d68>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x = linspace(0.01, 3, 100)\n",
-    "\n",
-    "plot(x, dweibull.pdf(x, 1), label='s=1, constant failure rate')\n",
-    "plot(x, dweibull.pdf(x, 2), label='s>1, increasing failure rate')\n",
-    "plot(x, dweibull.pdf(x, .1), label='0<s<1, decreasing failure rate')\n",
-    "\n",
-    "legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不同自由度的[学生 `t` 分布](https://zh.wikipedia.org/wiki/%E5%AD%A6%E7%94%9Ft-%E5%88%86%E5%B8%83)："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x164582e8>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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6ytflq11f2SS7e7dI6dIiDx8mQsEUpnVrkblzbZP1D/KXyjMrS2RUpH2D7N4t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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x15bb9f98>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x = linspace(-3, 3, 100)\n",
-    "\n",
-    "plot(x, t.pdf(x, 1), label='df=1')\n",
-    "plot(x, t.pdf(x, 2), label='df=2')\n",
-    "plot(x, t.pdf(x, 100), label='df=100')\n",
-    "plot(x[::5], norm.pdf(x[::5]), 'kx', label='normal')\n",
-    "\n",
-    "legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 离散分布"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "导入离散分布："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.stats import binom, poisson, randint"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "离散分布没有概率密度函数，但是有[概率质量函数](https://zh.wikipedia.org/wiki/%E6%A6%82%E7%8E%87%E8%B4%A8%E9%87%8F%E5%87%BD%E6%95%B0)。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[离散均匀分布](https://zh.wikipedia.org/wiki/%E9%9B%A2%E6%95%A3%E5%9E%8B%E5%9D%87%E5%8B%BB%E5%88%86%E4%BD%88)的概率质量函数（PMF）："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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DXpIaYNhLUgMMe0lqgGEvSQ2YGPZJlpKcSPJikntH9DnYtR9LsnMtYyVJl97YsE+yBXgY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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa980fd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "high = 10\n",
-    "low = -10\n",
-    "\n",
-    "x = arange(low, high+1, 0.5)\n",
-    "p = stem(x, randint(low, high).pmf(x))  # 杆状图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[二项分布](https://zh.wikipedia.org/wiki/%E4%BA%8C%E9%A0%85%E5%88%86%E4%BD%88)："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x1738a198>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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WVpafpw2fGlPjs726vjrMkfgvlpN9Skqdz/bU1Mh9ajgaY1aRo6ysjLKysoD3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\naLJXSqk48P8B4O96y1bc/nYAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x16550a58>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "num_trials = 60\n",
-    "x = arange(num_trials)\n",
-    "\n",
-    "plot(x, binom(num_trials, 0.5).pmf(x), 'o-', label='p=0.5')\n",
-    "plot(x, binom(num_trials, 0.2).pmf(x), 'o-', label='p=0.2')\n",
-    "\n",
-    "legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[泊松分布](https://zh.wikipedia.org/wiki/%E6%B3%8A%E6%9D%BE%E5%88%86%E4%BD%88)："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x1763e320>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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RSZNEDhxwy2t5xKlTIm++KdKpk6S3aCHjGzYs+rvYbJKemOjrKJU6w547nc+1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\ngg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x158c6828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x = arange(0,21)\n",
-    "\n",
-    "plot(x, poisson(1).pmf(x), 'o-', label=r'$\\lambda$=1')\n",
-    "plot(x, poisson(4).pmf(x), 'o-', label=r'$\\lambda$=4')\n",
-    "plot(x, poisson(9).pmf(x), 'o-', label=r'$\\lambda$=9')\n",
-    "\n",
-    "legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 自定义离散分布"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "导入要用的函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.stats import rv_discrete"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "一个不均匀的骰子对应的离散值及其概率："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "xk = [1, 2, 3, 4, 5, 6]\n",
-    "pk = [.3, .35, .25, .05, .025, .025]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "定义离散分布："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "loaded = rv_discrete(values=(xk, pk))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "此时， `loaded` 可以当作一个离散分布的模块来使用。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "产生两个服从该分布的随机变量："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([3, 1])"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "loaded.rvs(size=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "产生100个随机变量，将直方图与概率质量函数进行比较："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Container object of 3 artists>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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L2gRcGhFHp+tb5DdjZmYvyJuiISJ2AbumtG2tW/4Fk6diGvY1M7OF4TtZzcwS5YA3M0uU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\niXLAm5klygFvZpYoB7yZWaIc8GZmifp/J4pqDI9mKMcAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1764bef0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "samples = loaded.rvs(size=100)\n",
-    "bins = linspace(.5,6.5,7)\n",
-    "\n",
-    "hist(samples, bins=bins, normed=True)\n",
-    "stem(xk, loaded.pmf(xk), markerfmt='ro', linefmt='r-')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 假设检验"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "导入相关的函数：\n",
-    "\n",
-    "- 正态分布\n",
-    "- 独立双样本 `t` 检验，配对样本 `t` 检验，单样本 `t` 检验\n",
-    "- 学生 `t` 分布\n",
-    "\n",
-    "`t` 检验的相关内容请参考：\n",
-    "- 百度百科-`t` 检验：http://baike.baidu.com/view/557340.htm\n",
-    "- 维基百科-学生 `t` 检验：https://en.wikipedia.org/wiki/Student%27s_t-test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.stats import norm\n",
-    "from scipy.stats import ttest_ind, ttest_rel, ttest_1samp\n",
-    "from scipy.stats import t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 独立样本 t 检验"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "两组参数不同的正态分布："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "n1 = norm(loc=0.3, scale=1.0)\n",
-    "n2 = norm(loc=0, scale=1.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "从分布中产生两组随机样本："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "n1_samples = n1.rvs(size=100)\n",
-    "n2_samples = n2.rvs(size=100)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "将两组样本混合在一起："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "samples = hstack((n1_samples, n2_samples)) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "最大似然参数估计："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "loc, scale = norm.fit(samples)\n",
-    "n = norm(loc=loc, scale=scale)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "比较："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x17ca7278>]"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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GJqySRC5eGTcOMmeGJk1sNtu+HY4ehS+/DL1dA11qQ79dkMPC5BLhPH74ASZN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\nIYRwcpLIhRDCyUkiF0IIJ/d/XdaGnd6OVMIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x17c1c208>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x = linspace(-3,3,100)\n",
-    "\n",
-    "hist([samples, n1_samples, n2_samples], normed=True)\n",
-    "plot(x, n.pdf(x), 'b-')\n",
-    "plot(x, n1.pdf(x), 'g-')\n",
-    "plot(x, n2.pdf(x), 'r-')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "独立双样本 `t` 检验的目的在于判断两组样本之间是否有显著差异："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t = 0.868384594123\n",
-      "p-value = 0.386235148899\n"
-     ]
-    }
-   ],
-   "source": [
-    "t_val, p = ttest_ind(n1_samples, n2_samples)\n",
-    "\n",
-    "print 't = {}'.format(t_val)\n",
-    "print 'p-value = {}'.format(p)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "`p` 值小，说明这两个样本有显著性差异。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 配对样本 t 检验"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "配对样本指的是两组样本之间的元素一一对应，例如，假设我们有一组病人的数据："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "pop_size = 35\n",
-    "\n",
-    "pre_treat = norm(loc=0, scale=1)\n",
-    "n0 = pre_treat.rvs(size=pop_size)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "经过某种治疗后，对这组病人得到一组新的数据："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "effect = norm(loc=0.05, scale=0.2)\n",
-    "eff = effect.rvs(size=pop_size)\n",
-    "\n",
-    "n1 = n0 + eff"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "新数据的最大似然估计："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "loc, scale = norm.fit(n1)\n",
-    "post_treat = norm(loc=loc, scale=scale)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "画图："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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ngHbuvrK4jmILKynZG2/AL7+UfzBQgHGLxrFi/Qp+27icYzTIdrm5kacDu3eH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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x17ed5fd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = figure(figsize=(10,4))\n",
-    "\n",
-    "ax1 = fig.add_subplot(1,2,1)\n",
-    "h = ax1.hist([n0, n1], normed=True)\n",
-    "p = ax1.plot(x, pre_treat.pdf(x), 'b-')\n",
-    "p = ax1.plot(x, post_treat.pdf(x), 'g-')\n",
-    "\n",
-    "ax2 = fig.add_subplot(1,2,2)\n",
-    "h = ax2.hist(eff, normed=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "独立 `t` 检验："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t = -0.347904839913\n",
-      "p-value = 0.728986322039\n"
-     ]
-    }
-   ],
-   "source": [
-    "t_val, p = ttest_ind(n0, n1)\n",
-    "\n",
-    "print 't = {}'.format(t_val)\n",
-    "print 'p-value = {}'.format(p)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "高 `p` 值说明两组样本之间没有显著性差异。\n",
-    "\n",
-    "配对 `t` 检验："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t = -1.89564459709\n",
-      "p-value = 0.0665336223673\n"
-     ]
-    }
-   ],
-   "source": [
-    "t_val, p = ttest_rel(n0, n1)\n",
-    "\n",
-    "print 't = {}'.format(t_val)\n",
-    "print 'p-value = {}'.format(p)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "配对 `t` 检验的结果说明，配对样本之间存在显著性差异，说明治疗时有效的，符合我们的预期。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `p` 值计算原理 "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`p` 值对应的部分是下图中的红色区域，边界范围由 `t` 值决定。 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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e9e0Lo0bB9Ol2EQ9jorF/G4Phw9N773jroklw06fDlVfCrFnW725MtHbtcgsA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\nMSZFWYE3xpgU9f8B8tTCMYTHdwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x179aeac8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "my_t = t(pop_size) # 传入参数为自由度，这里自由度为50\n",
-    "\n",
-    "p = plot(x, my_t.pdf(x), 'b-')\n",
-    "lower_x = x[x<= -abs(t_val)]\n",
-    "upper_x = x[x>= abs(t_val)]\n",
-    "\n",
-    "p = fill_between(lower_x, my_t.pdf(lower_x), color='red')\n",
-    "p = fill_between(upper_x, my_t.pdf(upper_x), color='red')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 概率统计方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 简介"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**`Python`** 中常用的统计工具有 **`Numpy, Pandas, PyMC, StatsModels`** 等。\n",
+    "\n",
+    "**`Scipy`** 中的子库 `scipy.stats` 中包含很多统计上的方法。\n",
+    "\n",
+    "导入 `numpy` 和 `matplotlib`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "heights = array([1.46, 1.79, 2.01, 1.75, 1.56, 1.69, 1.88, 1.76, 1.88, 1.78])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Numpy` 自带简单的统计方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean,  1.756\n",
+      "min,  1.46\n",
+      "max,  2.01\n",
+      "standard deviation,  0.150811140172\n"
+     ]
+    }
+   ],
+   "source": [
+    "print 'mean, ', heights.mean()\n",
+    "print 'min, ', heights.min()\n",
+    "print 'max, ', heights.max()\n",
+    "print 'standard deviation, ', heights.std()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "导入 **`Scipy`** 的统计模块："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import scipy.stats.stats as st"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "其他统计量："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "median,  1.77\n",
+      "mode,  (array([ 1.88]), array([ 2.]))\n",
+      "skewness,  -0.393524456473\n",
+      "kurtosis,  -0.330672097724\n",
+      "and so many more...\n"
+     ]
+    }
+   ],
+   "source": [
+    "print 'median, ', st.nanmedian(heights)    # 忽略nan值之后的中位数\n",
+    "print 'mode, ', st.mode(heights)           # 众数及其出现次数\n",
+    "print 'skewness, ', st.skew(heights)       # 偏度\n",
+    "print 'kurtosis, ', st.kurtosis(heights)   # 峰度\n",
+    "print 'and so many more...'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 概率分布"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "常见的[连续概率分布](https://zh.wikipedia.org/wiki/Category:%E8%BF%9E%E7%BB%AD%E5%88%86%E5%B8%83)有：\n",
+    "\n",
+    "- 均匀分布\n",
+    "- 正态分布\n",
+    "- 学生`t`分布\n",
+    "- `F`分布\n",
+    "- `Gamma`分布\n",
+    "- ...\n",
+    "\n",
+    "[离散概率分布](https://zh.wikipedia.org/wiki/Category:%E7%A6%BB%E6%95%A3%E5%88%86%E5%B8%83)：\n",
+    "\n",
+    "- 伯努利分布\n",
+    "- 几何分布\n",
+    "- ...\n",
+    "\n",
+    "这些都可以在 `scipy.stats` 中找到。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 连续分布"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 正态分布"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "以[正态分布](https://zh.wikipedia.org/wiki/%E6%AD%A3%E6%80%81%E5%88%86%E5%B8%83)为例，先导入正态分布："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.stats import norm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "它包含四类常用的函数：\n",
+    "\n",
+    "- `norm.cdf` 返回对应的[累计分布函数](https://zh.wikipedia.org/wiki/%E7%B4%AF%E7%A7%AF%E5%88%86%E5%B8%83%E5%87%BD%E6%95%B0)值\n",
+    "- `norm.pdf` 返回对应的[概率密度函数](https://zh.wikipedia.org/wiki/%E6%A9%9F%E7%8E%87%E5%AF%86%E5%BA%A6%E5%87%BD%E6%95%B8)值\n",
+    "- `norm.rvs` 产生指定参数的随机变量\n",
+    "- `norm.fit` 返回给定数据下，各参数的[最大似然估计](https://zh.wikipedia.org/wiki/%E6%9C%80%E5%A4%A7%E4%BC%BC%E7%84%B6%E4%BC%B0%E8%AE%A1)（MLE）值\n",
+    "\n",
+    "从正态分布产生500个随机点："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_norm = norm.rvs(size=500)\n",
+    "type(x_norm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "直方图："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "counts,  [   7.   21.   42.   97.  120.   91.   64.   38.   17.    3.]\n",
+      "bin centers [-2.68067801 -2.13266147 -1.58464494 -1.0366284  -0.48861186  0.05940467\n",
+      "  0.60742121  1.15543774  1.70345428  2.25147082  2.79948735]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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3As/ut6rJqqqfdIunsXh+8nsrtevt0xaT/HWSbwG7gev6qmMG3gh8qu8itCrfENeIJOcC\nL2BxINWMJE9KcjewANxZVQdXaje1T1tMMg/sWOGmd1XVbVV1DXBNkj3AB4A3TKuWaTjZ8XVtrgEe\nqaobZ1rcBKzl+BrilQEN6KZbPg68rRupN6N7xf8b3fm4TycZVNVwebupBXpVXbrGpjeyCUewJzu+\nJK8HXs3iNfmbzjr+fi14EFh6Yn4ni6N0bRJJngx8AviXqrql73qmpap+kOTfgd9khQ+v6esql/OX\nrF4O7O+jjmlJsgt4B3B5VT3cdz1T1t8HSU/OfwPnJzk3yWnAVcCtPdekNcrih5l/BDhYVR/su55J\nS3Jmkm3d8unApZwgM/u6yuXjwK8CPwW+CfxJVT0080KmJMl9LJ68ePzExeer6uoeS5qoJL8P/ANw\nJvADYH9VvarfqsaT5FXAB/nZG+JOeGnYZpPkJuAS4JeAh4B3V9UN/VY1OUleAnwG+Co/mz7bW1X/\n0V9Vk5PkYmAfiwPwJwEfq6r3r9jWNxZJUhv8TlFJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANd\nkhphoEtSI/4P1qxllG6H6EYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa955048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "h = hist(x_norm)\n",
+    "print 'counts, ', h[0]\n",
+    "print 'bin centers', h[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "归一化直方图（用出现频率代替次数），将划分区间变为 `20`（默认 `10`）："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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luRK4sqruTfIE4FPAyzbR3+/yqno0yTbg48AfV9XHV2s71R75YyHe8wTgoWmuf5Kq6sNV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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa922eb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "h = hist(x_norm, normed=True, bins=20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在这组数据下，正态分布参数的最大似然估计值为："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean,  -0.0426135499965\n",
+      "x_std,  0.950754110144\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_mean, x_std = norm.fit(x_norm)\n",
+    "\n",
+    "print 'mean, ', x_mean\n",
+    "print 'x_std, ', x_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将真实的概率密度函数与直方图进行比较："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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xDmN4l9acylMYG8IOLas0tLLR6tVw991w663Bhcxrr4V99930cWZj3BDtP12j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\nKZGLiETc/wfoqVYxTpVDEwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1586bf28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "h = hist(x_norm, normed=True, bins=20)\n",
+    "\n",
+    "x = linspace(-3,3,50)\n",
+    "p = plot(x, norm.pdf(x), 'r-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "导入积分函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.integrate import trapz "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "通过积分，计算落在某个区间的概率大小："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "95.45% of the values lie between -2 and 2\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x15cbb8d0>]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ffML4RYtY4TqIMSVoiCoT9+9nwpAh3PbSS67jhJ2kpCSSkpKKNKbQ0yRFJB5I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\nFXhjjIlSVuCNMSZK/T8+DStxCxquAQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15cbb748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x1 = linspace(-2,2,108)\n",
+    "p = trapz(norm.pdf(x1), x1) \n",
+    "print '{:.2%} of the values lie between -2 and 2'.format(p)\n",
+    "\n",
+    "fill_between(x1, norm.pdf(x1), color = 'red')\n",
+    "plot(x, norm.pdf(x), 'k-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "默认情况，正态分布的参数为均值0，标准差1，即标准正态分布。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以通过 `loc` 和 `scale` 来调整这些参数，一种方法是调用相关函数时进行输入："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LSKqIPFbGcf1EJFdEvunfEE1QpaVpiaFtW68j8cZVV+luQdnZXkdijM/KTOQiUh14GUgA\nugG3icg3LnLcM8BcwLr7w1lhfTxaR23Urw9xcbb1mwkr5bXI+wNbnXM7nXM5wDTg+lKO+wnwAXDA\nz/GZYIvm+nghK6+YMFNeIo8Bdhe7nV7wsyIiEoMm99cKfmSDxcNZNNfHC1kiN2GmRjn3+5KUXwAm\nO+eciAhllFamTJlSdD0+Pp74+Hgfnt4EzdmzWlK46iqvI/G7s2d134jFi/Wyf3/px4nA8NaD+NXS\nJ9mbolWWaK0yGW8kJiaSmJhYoceUObNTRAYCU5xzCQW3HwfynXPPFDtmO+eSd3PgFHCfc+6TEs9l\nMztD3Zo1cPfd8NVXXkdSZWfPwtKl5xL3qlWalK+5BoYOhdjY0h+XlwdfrsvntgeaM6z5RjLyWjF0\nqD4uPh569LDEboLLl5md5SXyGsAWYCSwB1gF3Oac23SR498BZjjnPizlPkvkoe6VV2DdOvjrX72O\npNJOnNDwn39eVxkYPlyT8ODBury6z8aOhR/+kF29ry/6MPj0U13Zd/JkmDDBEroJjipP0XfO5QL3\nA/OAjcD7zrlNIjJJRCb5L1QTEsK4Pn74MDz5JHTsCMuWwccf68v5wx9g3LgKJnEoqpPHxsJ3v6sf\nDtu3wwMPwK9+BVdcAe+9p/tvGOM1WzTLnNO1K3zwAfTs6XUkPsvI0Nb3O+/AjTfCo4/qy6iyefP0\nU2Dhwgvucg7mzoWnn9bzP/II3HUX1Knjh/MaU4ItmmV8d/gw7N0L3bp5HYlPcnK0Bd6zJ+Tnax/t\nW2/5KYmDrrmyZk2pTW4RrbwsXgx//zvMmqX199mz/XRuYyqovFErJlqsWqWjVapX9zqScm3cCN/7\nHjRvrv2yMTHlP6bCmjTRgviGDdCr10UPu/pqmDEDEhO1n3jUKP2G0KBBAGIy5iKsRW5UGNTH8/M1\nSV5zDdx3H8yZE6AkXmjQIFi+3KdD4+P1W4FzWj9ftCiAcRlTgiVyo0I8ke/YoSNQPvxQQ500KQij\nRio4MahhQ5g6FV56CW6/HR56CE6fDmB8xhSwRG5CesVD5zQ59uunQ/4WLYJOnYJ08krO8JwwQVvn\n6elw5ZVaajcmkGzUioHUVC3u7trldSTnyc2F++/XiT3//rdOxgmqvDytle/cCU2bVvjhzsG0afDg\ng/Dyy3Dzzf4P0UQ+X0atWGenCcnW+PHjmvic00TesKEHQVSvrh3Aq1ZBQkKFHy4Ct92mA4EmTNDy\n0KOP2kQi439WWjEhl8jT03Uafbt2OiLEkyReyA8LaPXqpX2m//631vZzcvwUmzEFLJGbkErk69bp\nYJHbb4fXXw+B/Z8HDvR55EpZ2rSBJUtg926YOBGOHfNDbMYUsBp5tDtzRuu/Bw9CvXqehjJnDtx5\nZ4jVkzMzoUsXnTBVrertntxc+MlPdBmBWbM0wRtTFpvZacq3di184xueJ/E334R77tE1UkImiQO0\naAHNmsGWLX55uho14NVX4Y479JvHunV+eVoT5SyRR7sQKKv85S+6rMmSJbpKYcjx80YTIro+y/PP\nw5gx+llqTFVYIo92HifyV16BP/9Z16bq3NmzMMoWoB2Dvv1t7QcYO9a2CDVVY4k82hVutuyB116D\nP/1Jk/jFNnoICQHc+u3GG/XDbMwY+PLLgJzCRAEbRx7N9u2DrCztzAuyN988t0ps+/ZBP33F9OoF\nW7fq4PYArIZ1000692jMGJg/34OJTybsWYs8mi1apAO2/TAaoyKmToXf/Q4+/1w3ggh5tWpB796w\nenXATnHzzVpiuvZaXXDRmIqwRB7NFi2CYcOCesq339Z1xD//PIhrpvjDoEEBK68UuvVWePZZGD1a\nl+o1xleWyKNZkBP53/8Ov/41fPZZCHdsXkwA6+TF3X47/PGPmsz9NOLRRAGbEBStCie6HDoUlM0k\nZs7UNcQXLoTLLw/46fwvPR369oX9+4OyWMo77+g3l2XLdBNpE738MiFIRBJEZLOIpIrIY6Xcf72I\nrBeRtSLyhYiMqErQJkgWL4YhQ4KSxFes0Mk+06eHaRIHnYJZu7Z2egbB3Xfruixjx2p/tDFlKTOR\ni0h14GUgAegG3CYi3yhx2ALnXC/nXB/gLuDNQARq/CwxUbe1CbAtW+CGG+Ddd3UbzLAWH1/qZsyB\nMnmy9kXfeCNkZwfttCYMldci7w9sdc7tdM7lANOA64sf4Jw7WexmfeCgf0M0ARGE+vjevdqifPpp\nGDcuoKcKjhEjgprIReDFF3UpnDvv1K3ujClNeYk8Bthd7HZ6wc/OIyI3iMgmYA7wgP/CMwFx8CCk\npUGfPgE7xbFjmsTvuUfLBBFh+HBN5EHs66leHf75T/1Q/PnPg3pqE0bKmxDk05+Nc+5j4GMRGQr8\nA+ha2nFTpkwpuh4fH098EL7am1IsXqzbv9cIzHyws2fhm9/UEXu//GVATuGN9u11cbFNm3S3iCCp\nU0cXExs6FJ57Dh5+OGinNh5ITEwkMTGxQo8pc9SKiAwEpjjnEgpuPw7kO+eeKeMx24D+zrlDJX5u\no1ZCxYMP6lCIxy7ou66y/Hxd2e/0afjgg6D0pQbXvffqN5n77w/6qdPTdVGxp5+G73wn6Kc3HvHH\nqJU1QJyItBeRWsAtwCclTtJJRMdjiUhfgJJJ3ISYxMSA1ccnT9aqzXvvRWASh6DXyYtr00bXbH/o\nIR2Lb0yhMhO5cy4XuB+YB2wE3nfObRKRSSIyqeCwm4CvRGQt8CJwayADNlV0+LBuHnnllX5/6qlT\n4aOPdJhh3bp+f/rQMHy4fhB61PPYvTu8/75OHLIJQ6aQTQiKNh9/rMsOzpvn16f9/HPdaHjJEk/W\n4Aquyy+HadN0/RWPvP22llhWrNB9L0zksh2CzIUWLfL7+PGUFE3i06ZFQRIHbZV//rmnIdxzj3Yo\nf/Ob2rlsopsl8mjj5/Hjhw7B+PHw+99rfosKhcMQPfb00zrG/Ic/tGGJ0c5KK9HkyBFo106zb61a\nVX66s2d12dV+/XSDiKhRuE7NwYMBG8Lpq5MndVjiLbcEZBCSCQFWWjHnS0rSVfz8kMSdgx/9CBo3\n1g0iokqLFtC2LSQnex0Jl1wCM2bASy/Bhx96HY3xiiXyaOLHYYfPPqt57J//jNBhhuXxcBhiSTEx\nOlJo0iT44guvozFesEQeTfzU0Tl9uq4BMmMG1K9f9bDCUgh0eBZ35ZW6fd4NN0BGhtfRmGCzGnm0\nyMrSGSUHD+pyrJX05ZcwahTMmqW18ah15IjuGH3woF9KVf7y9NNaYlm8OILH8kcZq5Gbc5KSNPNW\nIYlnZsJ118Ff/hLlSRygSROIi4NVq7yO5DyTJ2s/7D332EiWaGKJPFpUsaySna1jlr/7Xd1b0hBS\ndfJCIjrDdvt2HRJqooMl8mhRhY7OwhEqLVro9mOmQIiMJy+pbl2dwPvGGzaSJVpYjTwaHD8Ol12m\n9dw6dSr88Oef142Tk5KiuHOzNMeP6yqSBw5U6v810L74AhISYP58T1cTMFVkNXKjli6Fq66qVLKZ\nPVuHGk6fbkn8Ag0aQI8esHy515GU6sor4ZVX4Prrdc9oE7kskUeDSpZVNm6Eu+7SdcVjY/0eVWQY\nMSKkhiGWdPPN+ju0fT8jmyXyaFCJjs5Dh3SEyh//qJsZmIsI0Tp5cU88oRWgH/zARrJEKquRR7qD\nB6FTJ9i3z+eBxVG7hkplnDqlvcD79oV07alwTZZbb4VHH/U6GlMRViM3MHOmzuDxMYk7p7uYNWwY\nhWuoVEa9elqMXrrU60jKdMkl8MknOgdg+nSvozH+Zok80k2frr1dPnrxRd2s4F//itI1VCojBMeT\nl6ZNGx2O+P3vw/r1Xkdj/MkSeSQ7dUo3dxw/3qfD58zRmviMGTogw/goxNZdKUv//vDyyzaSJdJY\nIo9kCxbo134f9gLbsAHuvNNGqFTKwIGQmqp18jBwyy3nRrKcOeN1NMYffErkIpIgIptFJFVELli+\nXkS+IyLrReRLEVkqIlf4P1RTYR9/rMvhlePgQR2h8txzNkKlUmrVgnHj9P87TPz611pque8+G8kS\nCcpN5CJSHXgZSAC6AbeJyDdKHLYduMY5dwXwW+BNfwdqKigvTzs6y6mPnz2ra6jcfLOuo2Iq6aab\n4H//8zoKn1WrBu++C5s2Wad2JPClRd4f2Oqc2+mcywGmAedlB+fccudcVsHNlUAb/4ZpKmzZMh08\n3L79RQ8pXEOlWTNbYKnKEhJ0JcRDh7yOxGf16mlf+Kuv2pos4c6XDQdjgN3FbqcDA8o4/l5gdlWC\nMn7gQ1nlmWd0l58lS7SFZirOOceZ3DNk5R+jQfzVZP3zNfbdPI4zuWc4nXOaM7lnyM7LxjmHwxU9\npvB6NalG7eq1qV2jNrWr16ZOjTpF1y+pdQmNajeiQe0GVJPA/IJiYvRPJSFBd6+L+uWJw5Qvidzn\nCpqIDAfuAa4u7f4pU6YUXY+PjyfeD7vVmFI4p02t//73oodMm6YtseXLQ3oeS9Dl5ueSeTKTfSf2\nse/EPvaf2H/u+sn9HDlzhKNnjp53qSbVaFi7ITc3zudbf13Mz+p8SJ0adahTow51a9alVvVaCIKI\nzukovC4IeS6P7NxssvOyyc7NLkr82bnZnDh7gqzsLE7lnKJ+rfo0qt2IRnUa0ah2I5rXa07LS1rS\n4pIWtKxf8G/B7TYN29Cgtu/Djq68Et56S6twS5dChw6B+t81vkhMTCQxMbFCjyl3ZqeIDASmOOcS\nCm4/DuQ7554pcdwVwIdAgnNuaynPYzM7g+Xrr3XI4c6dukB1CUlJWhdfsACuiLJu6Zy8HHYc3cH2\nI9vZdXQXaVlp7MrapZeju9h3Yh/N6jWj5SUtaVW/VdGl5SUtaVm/JU3rNqVxncY0qdOExnUa06hO\nI+rUKFiM7Phx7UFMS4NGjfwWc15+Hseyj5GVnUXWmSyysrM4eOogmSczyTyZyf4T+8k8lVn0AZR+\nLJ1a1WvRrlE72jVqR9uGbYuud27ambimcTSp2+SC87z0Erz2mibzJhfebTziy8xOX1rka4A4EWkP\n7AFuAW4rcaJ2aBK/o7QkboKscBJQKUk8JQW+9S3dNDlSk7hzjn0n9rHxwEZSD6eSciil6JKWlUbr\nBq3p2KQjsY1iiW0cy+iOo4ltHEtso1jaNGxDzeo1K3fiBg10TZsZM+COO/z2eqpXq06Tuk1KTb6l\ncc5x+PRh0rLS2H1sN2lZaaRlpZG8N5mth7eSejiV2tVrE9csjrimcXRu2pmuzboy/JbupG7vwje/\nWYt580JqBztTDp/WWhGRscALQHXgLefc0yIyCcA594aITAVuBNIKHpLjnOtf4jmsRR4s/fppAXzE\niPN+fOAADBqk24F9//sexeZnR04fYcOBDXyd+TVf7f+Krw98zdeZX1NNqtHt0m50adqFLs3OXTo2\n6UjtGpXf7q5cf/87fPSRXkKUc47Mk5mkHk4l9VAqqYdT2XJoCxsyN7Araxc1T3SgSW537hrXnZ4t\ne9CzRU/imsUFrE5vyuZLi9wWzYo06enQq5dOTql5rmV5+jSMHKmTEMN1hErmyUyS9ybzxZ4vSN6n\n/x46fYjul3anR4se9GihSadHix60uKRFUU06qI4c0ZFCGRlh2fmQnZvNuvQt3P7Tr2ndewPNLt/A\n+v3rOXjqIL1a9qJPqz70vawvfS7rQ7dLu1GrujXbA80SeTQq7MH8xz+KfpSfr7P5atbUkko4jFA5\nnn2c1XtWsyJ9BSszVvLFni84mXOSvpf1pW+rvlzZ+kr6XtaXzk07h15LcexYuPtuHZwfpvbv129v\nTzyhM36PnD7Cun3rSN6bzNp9a0nem8zOozvp3qI7A2IG6KXNAOKaxnnzARrBLJFHozFjdLret75V\n9KNHHoGVK3XLr9oBrCpUVr7LZ9OBTaxIX6GXjBVsP7KdPq36FCWIq1pfRYfGHcIjSUydqv/Z77/v\ndSRVsmmTlvzfe0+/zZV08uxJ1u5by8r0lazM0Mvx7OP0j+nPgJgBDG47mEFtB9GwdsOgxx5JLJFH\nm6wsHQyckVG06tWzz+rQsqQkn5ZcCYqzeWdJ3pvMkl1LWJK2hKW7l9KodiMGtx3MwDYDGdhmIFe0\nvCJ8v7bRuWHkAAAYaUlEQVQfOABxcbB3r8/LB4eqRYu0TTBnju4WWJ59J/axMn0lK9JXsCx9GV/s\n+YIuzbowpN2QokvrBq0DH3gEsUQebaZN05LKrFkAvPOO7nq/ZInmd6+cyT3DivQVLNyxkMVpi1md\nsZrOTTsztN1QhsYOjcw394gR8OCDFVpCOFR9/LHOAE5MhK5dK/bY7Nxskvcmk5SWRNLuJJLSkmhU\nuxHD2g9jePvhxLePp12jdgGJO1JYIo82t96q34Hvu69Kb76qOpt3ltUZq1m4cyELdy5kVcYqul3a\njeHth3NN7DUMbjuYxnUaBzeoYCulryKc+atRkO/y2XxwM4k7E4suDWo3ID42nuEdNLG3aWgrfBRn\niTyaZGdDq1awaROJm1tx8836dfjKKwN/auccX2V+xfxt81mwYwFL05YS1yyO4e2HM7z9cIbGDo2+\nOunevdC9u44eipAB2c89p+X/JUugeXP/PKdzjo0HNmpS35XIwh0LaV6vOaM6jmJ0x9HEt4+nUR3/\nTa4KR5bIo8m8efDkkyS/vIyEBO1nGz48cKfLOJbB/O3zmb99Pgu2L6Bh7YaM6jCK0Z30zde0btPA\nnTxcDBkCv/yljmKJEI8/rnuVfPZZYDYfyXf5rN+3ngXbF7BgxwKW7V5GjxY9GNVhFKM6jmJw28GV\nn7AVpiyRR5Mf/YgD9TvQ61+P8uqrPi1DXiFncs+wZNcS5m6dy9xtc9l3Yh8jO4xkdMfRjO40mvaN\n2/v3hJHgz3/W5RLeesvrSPzGOZg0CXbs0FWSAz0K6kzuGZbtXsaC7Qv4dNunbD28lfj28YzpNIaE\nzgl0aBL5C8NYIo8Wp06R16Ydw+qt4d7ftufuu6v+lM45Ug+nMnfrXOZtm8eSXUvo2bInCZ0SGNN5\nDFdediXVq9mmnmVKS9Pa1t69UMOX1TDCQ16ezksQ0f71YO7teuDkAeZvn8/crXP5dNunNKzdkDGd\nxjA2bizx7eOpV7Ne8IIJEkvkUeLos3/liydnsvaJ6Tz8cOWf53TOaRbtWsTs1NnMTp3NmdwzJHRO\nYEynMYzqOMrntT5MMf37w9NPlz4QO4xlZ8OECboM7ltvebNRd77L58v9X+q3xK1zSd6bzJB2QxgX\nN46xncfSqWmn4AcVAJbIo8DePY6sjr1Jvv1Zbn97dIUfv/PozqLEvXjXYnq36s24uHGMixtHzxY9\nw2MCTih75hldhfK117yOxO9OndJk3rYtvP22N8m8uKNnjrJg+wJmp85mztY5NKzdkHGdxzG+y3iu\nib0mbOclWCKPcHv2wMMDlvDSmftotn+jT3Pvc/NzWZG+gpkpM5mZMpPMk5mMjRvLuM7juLbTtdbq\n9rddu7S8snNnWK69Up5Tp2DiRG2Zv/OO98m8UGGn6azUWcxOnc3GAxsZ2XEk4+PGMy5uHK3qt/I6\nRJ9ZIo9gGRk6KmV6nVv4xn1D4Cc/ueixR04fYd62ecxMmcncrXNp26gtE+ImML7LePq17me17kD7\n9rdh6FB44AGvIwmIU6d08+7LLtN9QEMlmRd34OQB5mydw6zUWXy67VPimsYxPm48E7pMoO9lfUP6\nm6cl8ghVmMQf/FYGP369p7b2Gp4/TjvlUAoztsxgRsoMkvcmM6z9sKLkbRMugmzFCrj9dkhNDc0s\n5wenTukk1pYt4W9/C+2XmZOXQ1Jakn4rTZ3JibMnmBA3gYldJzKyw0jq1gytZRUskUeg9HRN4vfd\nB4+e+LVu9vvKK+Tk5bB099Ki5H0y52TRH+eIDiMisjc/rFx9NfzsZ+ctZhZpTp/WlnmLFprMw2Wg\nTslGT3z7eCZ0mcCELhNCYukIS+QRpjCJ/+AH8MgD2eTHtmPum4/yr9xk5qTOoUOTDkzsMpGJXSaG\n/NfFqPPhh7qC2bJlXkcSUKdPa8u8eXPdYyNcknmhw6cPM3frXGakzGDe1nl0atqJiV0mcl3X6+jV\nspcn7ylL5BFk0yadIHjbj7fRcugMTv7trwyev4VnfzOG67pcx4QuE4hpGON1mOZi8vKgSxddEH7Q\nIK+jCajTp+HGG3VlgmnToF6YfhksLMHMSJnBJ1s+4WzeWSZ0mcDELhMZ3mH4ub1aA8wSeQTIy8/j\nr3NX8PM3ZtB4wAzyah1ifNx4/jRlKfV+MYU6377V6xCNr156SdeF/eADryMJuLNn4d57YetW3cLU\nX2uzeMU5x+aDm4uS+leZXzGyw0gmdpnI+C7jaXFJi4Cd22+JXEQSOLdn51Tn3DMl7r8ceAfoA/zS\nOfdcKc9hidxHx7OP8+m2T5mRMoOPN8zmxL5WfPuK6/jp2In0i+lHteS18M1vwrZt4ffdNZqdOKHb\nwK1aBR07eh1NwOXn69os06fD3Ln60iPFwVMHmZ06mxkpM5i/bT7dLu2mZc2uE+l+aXe/lmD8kshF\npDqwBRgFZACrgducc5uKHXMpEAvcAByxRF5xu47uYkbKDGamzGTZ7mUMbDOQpgeu4/PXJzDrX+3p\n16/YwXffrWvTTp7sWbymkiZP1trDiy96HUnQ/OUvOi9q1izo3dvraPwvOzebRbsWFXWYiggTu0xk\nQpcJDIsdVuXNvv2VyAcBTzjnEgpuTwZwzv2hlGOfAE5YIi9fXn4eKzNWMjNlJjNSZrD/xH7GxY1j\nYpeJjOo4mueeash772lLpnPnYg88eFB3n0lNDf/vq9EoIwN69tRvU02iZ/LVf/8LP/4x/PvfEbda\nwXmcc3yd+TUzUjSpbzqwiVEdRzGxy0TGxY3j0ksurfBz+iuRfwsY45y7r+D2HcAA59wFM1AskZct\n60wW87bNY1bqLOakzqFV/VZFn9z9Y/pTvVp1cnPhhz+E9eu1BdOiZOntmWdg82adRmfC0/e+p2uV\nP/aY15EEVWKi7kf9wgs6rD4aZJ7MLCrBLNi+gG6Xdiuaz+HrKBh/JfKbgARL5BXnnGPLoS3MSpnF\nzNSZfLHnC4bGDmV83HjGx40ntnHseccfOKCb/NSqpS2YC2Z05+VpbfXDD4OzY4QJjHXrdJGS7dsj\nZtMJX331FYwfD3feCVOmhPbEIX/Lzs1mSdqSouUxsvOyGdd5HBO6TGBkx5EXnevhr0Q+EJhSrLTy\nOJBfssOz4L4yE/kTTzxRdDs+Pp74+Pgyzx2OTuecJnFnInO2zmF26mzO5p3VxN1lfJkTc1av1rki\n3/kO/Pa3F/kDnz4d/vAH3ULMhLeRI7Wv4447vI4k6Pbv12Vw69SB996DplG4B4lzjpRDKcxKncXM\nlJms2bOGwW0HMy5uHM32NyM1ObXo2CeffNIvibwG2tk5EtgDrKJEZ2exY6cAx6OtRb79yPaiFQST\n0pLoc1kfxnUex9i4sT6tIDh1qvbuv/mmjr8tVU4O9O2rzZibbvL7azBBNnu27h6UnKwLe0eZnBzt\n9/3oI/2CGYmdoBVxLPtY0cqNs1NnU79W/aJVSMd0HuO34YdjOTf88C3n3NMiMgnAOfeGiLRCR7M0\nBPKB40A359yJYs8RMYn8VM4pFu1cVLRbzrHsY4ztPJaxnccyutNonzcWzs7Wta6WLNE/6MsvL+Pg\nP/1J99eaMycq3/gRJz8fevSAl1+GESO8jsYz778P99+v+4F+73teRxManHOs37+e2amzmZU6i2X3\nLrMJQf5QuEFs4W45y9OX0/eyvkW75fRu1ZtqUv4SssXt3q0N63bttN+yzP0PC5dCXbkSOkXGYvkG\nXSrw9ddh6dLoKhaX8PXXOi3i2mvh+eejrtugXDazswoyT2ayYPsC3WB423xqVKtBQucEEjonMKLD\niCrtCj9zpi569dBD8PDD5TSwndOViAYO1K/iJnLk52utfPx4qrS1UwTIytIW+YED8I9/WHulOEvk\nFXA65zRJaUlFO8PvOLKDYe2HcW3HaxndaTRxTeOqPFsrK0sXwFu4UBtjw4b58KCPPtIEvm6dNVUi\n0fbtuh1cUlI5tbXIl5+vQxOffhp+8xsdhmtVREvkZcrJy2HNnjV8tuMzPt/xOasyVnFFyyu4ttO1\njO44mv4x/alZvabfzrdgga49MXaslrvLLKUUOn4cunWDf/0LrrnGb7GYEPPKK/o7XrIkqksshTZt\n0uGJjRvrfqBt23odkbcskReTl5/Hl/u/ZOHOhXy+43OWpC2hQ+MOjOwwkhEdRjA0dmiVyiUXc+KE\nzvv45BMdnTJmTAUe/LOfwdGjNvkn0hWWWCZMgJ//3OtoQkJuLvzxj/DnP2vD5847o7d1HtWJPC8/\nj3X71rFo1yISdyaSlJZEy/otGRY7jJEdRjK8w3Ca1wvsFPekJLjrLt1T4MUXtYXhs7VrISEBNmyw\nqfjRoLDEsnSprqNjAJ3h/L3v6aCAN9/U7eSiTVQl8uzcbL7Y+wVLdi1hSdoSktKSiGkYw7DYYcS3\nj+ea2GuCtuHqvn3wi1/oOimvvgo33FDBJ8jL0zWrf/QjnTRiosPLL+sMGSuxnOfsWa2Zv/GGvq9+\n/OPo6i6K6ESedSaLZbuXkZSWxJK0JSTvTaZr864MaTuEobFDuSb2moCuEVyaM2f0q+Bzz8E992gf\nZaNGlXiiV17RAbaLFkXv98lolJ+vY8qvu06HNJnzbNqk/y3bt+t7bPz46Hh7REwiz3f5pBxKYfnu\n5SxP18vOozvp17ofQ9oNYWi7oQxsM5AGtX3pQfQ/53R22iOPwBVX6I5e561YWBF79+qTLFqkHZ0m\numzbBgMGWImlDHPmaPdRbKw2nCL9bRK2ifzQqUOs3rOa1RmrWZ6+nBXpK2hcpzGD2g5iUBu9XNHy\nCr+OKqmsdevgpz/VPZBfeKGKS3QeOaJPcNNNNmY8mr30ku6RtnixlVguIidHy5a/+50uNDdlCjRr\n5nVUgREWifzk2ZOs3beWVRmrWL1nNasyVnHg5AGuan0V/Vr3Y2CbgQxqOyho9W1frVql412XL4cn\nntAJPlXarCcrC0aP1mGGf/pTdHxnNKUrLLH066dDN+xv4aIOHtT337RpWs586KHI6xANuUR+9PRR\n1u1bR/LeZJL3JZO8N5kdR3bQo0UP+sf0p1/rfvSP6U/X5l0rPOU9GJzTyTxPPaX7OjzyiP7xVHlz\n2ePHdX5y//7arLc3rjl4UEctDRigLfRqofd+CCVpaVo3/8c/dGXFRx+FDh28jso/Qi6RX/L7S7ii\n5RX0vaxv0aXbpd2oVT20u6Dz83Va/VNP6bDuyZN1YXy/9JyfPKlv2J49tZPTkrgplJUFEyfqZpdv\nv237s/ogM1OH+r7+Oowbp+/V7t29jqpqQi6R5+TlUKNa+PwxZmbqJ/zUqVC3rg59uvFGP5YtT53S\nrvdOnXSQrLW6TEmnTmmfSZ06Wj+oXbX9H6NFVpbW0F98UZfIvfdeHQwUjv99IZfIQ2mK/sXk5cGn\nn+rU4AULdAz4vffCkCF+biyfOaOtrdatdeamJXFzMWfP6gYUR47Axx/DJZd4HVHYOH1aR5RNnapz\n6+64Q9/P4dRKt0ReAamp2vp+5x3tLLn3Xu0Nr9Q48PKcOqXbATVurCe1kQmmPHl58IMf6GDq2bMr\nOE3YAGzdqu/vd9/V9Vu+/339shPqe2BbIi9Dfj6sWaMNnOnT4fBh3Rj23nt1GHdAOKcn++lPdZjh\nG29Y3dP4Lj9f12JZuFCbmR07eh1RWMrN1VnXb7+t37r79YPrr9dLbGz5jw82S+QlnDmjO3l//LEu\nYtW4sf7ybrhBf5kBrW5s2wYPPKDT0l55Jap3hTFV4JzOgnnqKfjud+H//b/IHUAdBCdPwvz52r6a\nORPatDmX1Hv1Co2KZ9Qn8hMndJz3okU6tyI5WX85N9ygv6guXYIQxOnT8Mwzuo7GI4/olLRoWijC\nBMb+/fDkk/Df/+pYu5/8RDtETaXl5sKyZZrUP/lEuySGDtWpHcOGae7wogoaVYk8P18bvevW6Y70\nixbpFlJ9++ov4ZprYPBgqF8/YCGczzmYNQsefBD69NE9rNq1C9LJTdTYskXH2K1dC7//Pdx2W2g0\nIyNARoY2ABcv1nyyZ4/mkKFDdefF3r2hRRCWc/JLIheRBM5tvDzVOfdMKcf8BRgLnALucs6tLeUY\nvyXyY8cgJUWXuFy7VpP3l19C06b6n9u3rybuAQN02GDQOKdTPv/zH/jgA9094tlndZy4MYG0ZIl+\n4zt7VhfvnjjRauh+lpmp/81JSZpz1q3T/NKnj+ad3r21f61DB/9+6a5yIheR6sAWYBSQAawGbnPO\nbSp2zDjgfufcOBEZALzonBtYynP5nMjz83XvvvR0bWWnpmqPc2qqXk6cgLg4/apT+B/Yq5cm8qAr\nSN6Jzz9P/IoV+pu9+Wb49rd1l/QImeCTmJhIfHy812EERMS8Nud0RMuHH+q3wWbNYOJEEmNiiP+/\n/4vY0VFe/f6c0xmlhUl93Tr46ivNW61ba46Ki9MF9OLiNMHHxEDDhhVLC74k8vKGTPQHtjrndhY8\n4TTgemBTsWOuA/6mL8ytFJHGItLSObe/5JMdPaozj0te9uzRrzHp6frv3r36YmNidK5MXJyO477r\nLr1+2WUe5cczZ2DzZq3ZFF7WroUGDUhs0YL4mTMjKnkXFzHJrhQR89pEdILZ+PHnhmXNmEHi739P\n/G9+o2v59Oypqyp27aoZJhxnyJTg1e9PREe5xMZqn1uhnBzYufP8Bujcufqz9HR9XEyMdqzGxOil\nVSvdP6bkxdflP8pL5DHA7mK304EBPhzTBrggkcfGXhhos2b66dWv37kX1rp1kPpt8vK0M/LUKb0c\nParfnw4c0Evh9cxMreXs3KmfLD166OW++/Tfjh2146lnzyAEbYwPqlXTtXv699eW+D33wGef6Tj0\nd9/V2vquXfqm69pV/66bNbvw0rSpTqaoW1fflBHYSPG3mjXPtcZLck5Lw8UbrunpmuyXLz+/gXvg\ngO9fospL5L4WtUv+dkt9XNbQCef/4BBw0GnxptSzO71c7Lpz2vLIzz933TlN0Lm5pV+ys88l75wc\n/cirW1cvjRtr78Wll57796qr9N+4OP2DtxEnJhy1a3fhblNnz+pw2C1bYMcOXYt540b999AhnVxx\n6JDOdz9zRt87tWufS+p162rWqlFDM07Jf6tV08Rf+G/J63D+vyV/VprS7ktJ0W8fYfAhI0Cjgkup\ny6jXQZvBbTSJ5uVCzbk+PG85NfKBwBTnXELB7ceB/OIdniLyOpDonJtWcHszMKxkaUVEQmc2kDHG\nhJGq1sjXAHEi0h7YA9wC3FbimE+A+4FpBYn/aGn18fICMcYYUzllJnLnXK6I3A/MQ4cfvuWc2yQi\nkwruf8M5N1tExonIVuAkYLsFG2NMEAVtQpAxxpjACOoUMBH5rYisF5F1IvKZiLQN5vkDSUT+JCKb\nCl7fhyISiHUTPSMi3xaRDSKSJyJ9vY7HX0QkQUQ2i0iqiDzmdTz+JCJvi8h+EfnK61gCQUTaisjC\ngr/Lr0XkAa9j8hcRqSMiKwty5UYRebrM44PZIheRBs654wXXfwL0cs59P2gBBJCIjAY+c87li8gf\nAJxzkz0Oy29E5HIgH3gD+LlzLtnjkKrMlwlv4UxEhgIngL875yJubKyItAJaOefWiUh94Avghgj6\n/dVzzp0SkRpAEvCwcy6ptGOD2iIvTOIF6gMHg3n+QHLOzXfO5RfcXIkOIooYzrnNzrkUr+Pws6IJ\nb865HKBwwltEcM4tAY54HUegOOf2OefWFVw/gU5UbO1tVP7jnDtVcLUW2kd5+GLHBn11HRH5vYik\nAXcCfwj2+YPkHmC210GYcpU2mS3Go1hMFRSMrOuDNqIigohUE5F16OTKhc65jRc71u+7GojIfKBV\nKXf9wjk3wzn3S+CXIjIZ+DNhNMqlvNdWcMwvgbPOufeCGpwf+PL6Ioz19EeAgrLKB8CDBS3ziFDw\nDb93QX/bPBGJd84llnas3xO5c260j4e+R5i1Wst7bSJyFzAOGBmUgPysAr+7SJEBFO9wb4u2yk2Y\nEJGawP+AfzrnPvY6nkBwzmWJyCzgKiCxtGOCPWql+OoD1wMXLHcbrgqW+30EuN45d8breAIsUiZ3\nFU14E5Fa6IS3TzyOyfhIRAR4C9jonHvB63j8SUSai0jjgut1gdGUkS+DPWrlA6ArkAdsA37knMsM\nWgABJCKpaKdEYYfEcufc/3kYkl+JyI3AX4DmQBaw1jk31tuoqk5ExnJuvf23nHNlDvMKJyLyb2AY\n0AzIBH7tnHvH26j8R0SGAIuBLzlXJnvcOefD6iShTUR6oqvKViu4/MM596eLHm8TgowxJrzZnlDG\nGBPmLJEbY0yYs0RujDFhzhK5McaEOUvkxhgT5iyRG2NMmLNEbowxYc4SuTHGhLn/D/ClPVPdyI77\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15e78400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = plot(x, norm.pdf(x, loc=0, scale=1))\n",
+    "p = plot(x, norm.pdf(x, loc=0.5, scale=2))\n",
+    "p = plot(x, norm.pdf(x, loc=-0.5, scale=.5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "另一种则是将 `loc, scale` 作为参数直接输给 `norm` 生成相应的分布："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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apZeju9h3Yh/N6jWj5SUtaVW/VdGl5SUtaVm/JU3rNqVxncY0qdOExnUa06hO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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x160ef2b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = plot(x, norm(loc=0, scale=1).pdf(x))\n",
+    "p = plot(x, norm(loc=0.5, scale=2).pdf(x))\n",
+    "p = plot(x, norm(loc=-0.5, scale=.5).pdf(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 其他连续分布"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.stats import lognorm, t, dweibull"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "支持与 `norm` 类似的操作，如概率密度函数等。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不同参数的[对数正态分布](https://zh.wikipedia.org/wiki/%E5%AF%B9%E6%95%B0%E6%AD%A3%E6%80%81%E5%88%86%E5%B8%83)："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x15781c88>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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gq8Bu4FNgmjFmfZs6xcBPjDFTTnKcoAT3116D//gPeOYZuPrqjutsqtjED979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\n9P8BfIyZcbomsyAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x158a85f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = linspace(0.01, 3, 100)\n",
+    "\n",
+    "plot(x, lognorm.pdf(x, 1), label='s=1')\n",
+    "plot(x, lognorm.pdf(x, 2), label='s=2')\n",
+    "plot(x, lognorm.pdf(x, .1), label='s=0.1')\n",
+    "\n",
+    "legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不同的[韦氏分布](https://zh.wikipedia.org/wiki/%E9%9F%A6%E4%BC%AF%E5%88%86%E5%B8%83)："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0xaa9bc50>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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dOvDoo4/SvHlzJk6cmG/KvOK23OvWrUuNGjVy9X3nnC8o5V5ISAi1atUqVvq+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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa980d68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = linspace(0.01, 3, 100)\n",
+    "\n",
+    "plot(x, dweibull.pdf(x, 1), label='s=1, constant failure rate')\n",
+    "plot(x, dweibull.pdf(x, 2), label='s>1, increasing failure rate')\n",
+    "plot(x, dweibull.pdf(x, .1), label='0<s<1, decreasing failure rate')\n",
+    "\n",
+    "legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不同自由度的[学生 `t` 分布](https://zh.wikipedia.org/wiki/%E5%AD%A6%E7%94%9Ft-%E5%88%86%E5%B8%83)："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x164582e8>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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6ytflq11f2SS7e7dI6dIiDx8mQsEUpnVrkblzbZP1D/KXyjMrS2RUpH2D7N4t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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15bb9f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = linspace(-3, 3, 100)\n",
+    "\n",
+    "plot(x, t.pdf(x, 1), label='df=1')\n",
+    "plot(x, t.pdf(x, 2), label='df=2')\n",
+    "plot(x, t.pdf(x, 100), label='df=100')\n",
+    "plot(x[::5], norm.pdf(x[::5]), 'kx', label='normal')\n",
+    "\n",
+    "legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 离散分布"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "导入离散分布："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.stats import binom, poisson, randint"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "离散分布没有概率密度函数，但是有[概率质量函数](https://zh.wikipedia.org/wiki/%E6%A6%82%E7%8E%87%E8%B4%A8%E9%87%8F%E5%87%BD%E6%95%B0)。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[离散均匀分布](https://zh.wikipedia.org/wiki/%E9%9B%A2%E6%95%A3%E5%9E%8B%E5%9D%87%E5%8B%BB%E5%88%86%E4%BD%88)的概率质量函数（PMF）："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DXpIaYNhLUgMMe0lqgGEvSQ2YGPZJlpKcSPJikntH9DnYtR9LsnMtYyVJl97YsE+yBXgY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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa980fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "high = 10\n",
+    "low = -10\n",
+    "\n",
+    "x = arange(low, high+1, 0.5)\n",
+    "p = stem(x, randint(low, high).pmf(x))  # 杆状图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[二项分布](https://zh.wikipedia.org/wiki/%E4%BA%8C%E9%A0%85%E5%88%86%E4%BD%88)："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1738a198>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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WVpafpw2fGlPjs726vjrMkfgvlpN9Skqdz/bU1Mh9ajgaY1aRo6ysjLKysoD3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\naLJXSqk48P8B4O96y1bc/nYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x16550a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_trials = 60\n",
+    "x = arange(num_trials)\n",
+    "\n",
+    "plot(x, binom(num_trials, 0.5).pmf(x), 'o-', label='p=0.5')\n",
+    "plot(x, binom(num_trials, 0.2).pmf(x), 'o-', label='p=0.2')\n",
+    "\n",
+    "legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[泊松分布](https://zh.wikipedia.org/wiki/%E6%B3%8A%E6%9D%BE%E5%88%86%E4%BD%88)："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1763e320>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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RSZNEDhxwy2t5xKlTIm++KdKpk6S3aCHjGzYs+rvYbJKemOjrKJU6w547nc+1\nruzsiZu3kv2aPWuk5fSWkpuX63Lb3FyROnVEfv/dA4H50LCPhsn4ZePd0teE6OgiybHgNrFtW5E/\n/3TLa3hFfr5M6NrV8e8SE+Pr6JQ6w9VkHzLDOLNXzWbU5aMIqxLmctuwMLjmGlheYo2twPbcdc8x\nZ80cfj78c6X7Cs92XDc/rFkzqFOn0v17jTGE13R8VbXO1VeBLCSS/ZGsIyzasoiRl4+scB+BXt/e\nkebnNOfvPf7OI6mPVLqv3GrVHG7Piwy8Rc9zIxyvc5B3+rSXI1HKfUIi2S/YuIBoWzRNajWpcB/B\ndpK2wENXPsTqPatJ25lW8U6OHyf68GEmVC9ahsHVOfL+wuFc/caN6ffDD/DCC9Z8XKUCTNBPvRQR\nLp59MbNumEVU66gK95OTY03H3rkT6td3W3h+4f0f3ufZ5c+yZtQa14e59uyBgQOhSxcyBg4kdfbs\nSs2R9xcO5+pffDEMG2YtdPD229C8ua/DVCFM59kXk7Erg9GJo9n8wGZMJS+Tj4mBBx6AQYPcFJyf\nEBF6/bsXwzoNY1SXUc43/P57K9Hfdx88/nholCHIy4Nnn4VZs2Du3OB7M6iAocm+mCEfDqFH8x6M\n6zGu0n1Nm2aVcXnlFTcE5mfW7V1H76d60+VkF/JMHhEmgrihcQzoV8qReWoq3HmndWHSkCHeDdYf\n/O9/1u/fvz+8/DLUqOHriFSIcftFVcaY/saYrcaY7caYxxw8394Ys8IYk2WMeciVtp627/g+lu5Y\nyvDL3FPFrKACZjDas2kP+Tvy+fL8L0lvk05K6xTGzRpHUqqDi4nefNMazvjww9BM9GAVVFu/Ho4c\nga5dYcMGX0ekVJnKTPbGmDDgVaA/0BEYYowpfk35QSAWeKkCbT1q/vr53NL+FupG1nVLf926WRd/\nHjnilu78Svw78Ry75liRbZmdM0l4N+HsBhGYONH6ipORAdde6+Uo/UydOvDf/8ITT0Dfvta3nABY\nDUyFpvKO7LsDO0Rkp4jkAAuBIoOUIrJfRFYDxcsIltvWk/Ly85i7Zi73d3OulLEzIiKshP+//7mt\nS7+RLY7nyWfl2+eWZ2dbR/NffAHffgvt2nkxOj9mjLVY8bffWtU0BwyAP/7wdVRKlVBesm8O7C70\n+Ff7NmdUpm2lJWcmc26Nc+narKtb+w3G+fYAEcbx3PLIKpFw6BBER1sJ/8svoWFDL0cXAGw2+Ppr\n6NwZLrvLh8qqAAAcmUlEQVSMjKlTtbaO8ivlLV5Sme+kTredMmXKmftRUVFERUVV4mUts1fP5r4u\n91W6n+J69oQJE9zerc/FDY0jc1YmmZ3Prt3aYlULHr39Nmt8+i9/sSpWVgmJSzMqpmpVmDaNjFq1\nSJ40iWl5eWee0nVwVWWlpaWRlpZW4fZlzsYxxvQApohIf/vjJ4B8EXnewb6TgeMi8rIrbT0xG2fX\nn7u4/PXL+eXBX8pcULwiTp60Dmz/+ANKuao+YCWlJpHwbgJZ+VkcOHGAduGHWZSWj5k0yZpzqpxS\n6lq6ug6uciN3z8ZZDbQ1xrQ2xlQD7gA+Le21K9HWrV5f8zrDLhnm9kQP1gy7yy6zFiEPNrVPQ9ff\nhKidMGh7OCM/2cfCuD6a6F1Uap0gra2jfKjMYRwRyTXGjAWSgTBgnohsMcaMtj8/1xjTBFgFnAPk\nG2PGAR1F5Lijtp78ZQBO551m3rp5fDX8K4+9RsG4fd++HnsJr8tISiJ53DimZZ4dxnmsWVP++2Mi\nHX5fz2VNLvNhdIGl1No6+/ZZs3VC4eIz5XfKHYAVkSUi0k5ELhCRf9q3zRWRufb7v4tICxGpIyL1\nRKSliBwvra2nLd66mA4NO9ChoedmeQbjfPuU+PgiiR7g+T17if6xJcMXD+d0nhYBc5bD2jotW9Lv\n1CkYORL0CF/5QHknaAPO7NWzub+r+6ZbOnLVVbBmjfV/NgCLOjoUXkoCahFenwN16vBMxjM81fsp\nL0cVmApOwk4qVFunf2wsPXv1ghEjICoKPvrItyt1qZATVMl+y/4tbNm/hZva3+TR16ldGzp2hO++\ns2bnBDwRcn92XNM+v3p15g6cy2VzL2NQu0F0adbFy8EFpp4DBjieefP++1ZtnW7dYNEiuPJK7wen\nQlJQzaObs3oOIzuPpFqY49rq7hQ08+1F4MEHiY6MZEKbNkWeKihR3LR2U6bHTGf44uFk5zo++aic\nZIw1d7egiNq8eb6OSIWIoCmEduL0CVrOaMnaUWtpVbeVGyIr22efWVfHL1vm8ZfyHBF46CFrCa7U\nVDK++aZkWV/70amIMPj9wbQ/tz3PXvesjwMPElu3Wgm/Xz+YPt2ap6+Uk0K26uWb697k460f89mQ\nz9wQVfkOH4aWLa0qmKUs0uTfRODRR60rYpctg3r1ym2y7/g+Lp1zKZ8O+ZTuzbt7IcgQ8OefVvXM\nEyfggw/06mTlNLdXvQwU3jgxW1i9etYV8mvWeO0l3UfEKt61bJlVqtiJRA/QuFZj4q+PZ/ji4WTl\n6owSt6hbFz79FK6+2hrHX7fO1xGpIBUUyX71ntUcOHmAGFuMV183IMftCypXLlliJXsXl926/aLb\nuaTRJTz51ZMeCjAEhYVZlURfeMGqQfTuu76OSAWhgB7GSUpNIv6deDbs38A5Vc9h+gPTS19swwM+\n+gj+9S/4/HOvvWTlPfkkfPxxpQqa7T+xn05zOvHh7R9yVYur3BxgiNuwAW66CW6/nYyrryZl1izC\ns7PJjYggOi5Oa+uoM0JmzD4pNYlxs8YVKdxlW2dj5piZXkv4+/fDBRdY4/bhgTCJdepUa+rfV19B\no0aV6urDzR8y/svxrBu9jhpVdZUmtzpwgIw+fUjOzGTayZNnNk+w2YiZOVMTvgJCaMw+/p34Ioke\nHCy24WENG8J551kLFvm9Z56BhQutI/pKJnqAwR0H06VpFyZ+OdENwakizj2XlCZNiiR6gGmZmaQm\neO/9rYJLwCb7chfb8JKAKJ3wz3/Cf/5jJfrGjd3WbcL1CSzctJDlu5a7rU9lCT/tuDyFFlNTFRWw\nyb7MxTa8qGdPPz9J+8ILMH++leibNnVr1w1qNGD2gNmM+GQEJ06fcGvfoa7UYmo6F19VUMAm+2E3\nD6PKl0XDt621ETsk1qtx9OxpXZOUn+/Vl3XOK6/A669bY/QeqsMyqP0grmxxJeO/GO+R/kOVw2Jq\ntWrRb+tW2LTJR1GpQBawJ2gfSHqAPZv2kLUti6z8LCKrRBI7JNars3EKtG0LH34InTp5/aVLN2MG\nJCRAWhq0aOHRlzp06hCdZnfiv7f8l16te3n0tUJJRlJSySua9++HRx6B556Dv/1NyyWHsJCYjbPt\nwDaumX8NW8dspUGNBh6KzDlJSRmMHp1CZGQ4NlsucXHRDBjg/epoGUlJpMTHW9P09u0j+tAheq5a\nZV3m6wWJPyYStySOjfdvpFa1Wl55zZC1eTPcdpu13u2cOVBL/96hyNVkHwgTBkt44osneOSqR/wi\n0Y8bl8xvv00DIDMTMjOtBWq9mfAdLTwyoVUr+P57enop2Q+8cCAz3ptBx1s6cv655xNhIogbGueT\nb1pBr2NHWLUKYmOhSxerzIJffa1UfklEfHqzQnDe17u+lhavtJCTp0+61M4ToqMniHVJatFbTMxE\nr8YxITq6ZBAgE2NivBZDYkqitPlLG2EKZ262QTZJTEn0Wgwh6e23Rc49V2TuXJH8fF9Ho7zInjud\nzrUBdYJWRHh02aM83ftpqlet7utwyM52/MUoKyvMq3GUtvCIN6fpxb8Tz89ditbE9/Z1DyHprrus\nGQKvvgpDh8LRo76OSPmpcpO9Maa/MWarMWa7MeaxUvaJtz+/wRjTudD2ncaYjcaYdcaY7yob7OKt\nizl++jjDOg2rbFduERGR63B7ZGSe94LIzSW32HKCBfK8uIyWv1z3EJLat4eVK61Vdbp00WJqyqEy\nk70xJgx4FegPdASGGGM6FNvnBuACEWkLjAJmF3pagCgR6SwilaqJm5OXw+NfPM4LfV8grIp3j5xL\nExcXjc02oci28PDx3HNPP+8EcPIkDB5MdMOGpS484i2lXfeQn+ePc1KDUPXq1jTbp56yiqm99po1\nmKeUXXknaLsDO0RkJ4AxZiEwCNhSaJ8bgbcARGSlMaauMaaxiOyzP++WuWH/WvsvWtZpSbQt2h3d\nuUXBSdiEhElkZYURGZnH6dP9ycjoya23evjFDx2Cv/wF2rSh58qVkJpacs1TL9ZQiRsaR+aszCIl\nLBquaMgW2xa2H9xO2wZtvRZLSBsyxDq6v+MO+OorMm67jZR587SYmir7BC1wK/BGocfDgIRi+3wG\nXFXo8TLgcvv9n4B1wGrg3lJeo9wTEUezjkqTl5rI2j1rK3M+wysOHhRp2lTk6689+CK7dol06CDy\n0EMieXkefCHXJKYkSsyIGOk1vJfEjIiRxJREmbd2npz3ynmy7cA2X4cXWk6dkvQbbpDx4eFFTtqP\nt9kkPVFPmgcDXDxBW96RvbPfA0s7er9GRPYYYxoCqcaYrSLiciGVl1e8zHVtrqNz087l7+xj9etb\n1zKNHGkVSHP7sPmmTXD99fDgg9aSgn5kQL8BDqdaGgx93urDF3/9gnbntvNBZCEoMpKU3Fym5RY9\nrzQtM5NJCQl6dB+Cykv2vwGFL79sAfxazj7n2bchInvsP/cbYz7GGhYqkeynTJly5n5UVBRRUVFn\nHu89tpeE7xJYMypwloQaPNhaf2LqVKsGmdtkZFgX00yfbs28CBAjOo+giqnCdW9fx7K/LqP9ue19\nHVJICM92fNJci6kFprS0NNLS0ireQVmH/VgfBplAa6AasB7oUGyfG4DP7fd7AN/a79cAatvv1wS+\nAaIdvEaZX1VGfzZaHkp+yD3fe7xo716RRo1E1qxxU4cffSTSsKFISoqbOvS+t9e/Lc1ebiab/9js\n61BCQqnXXzRsKPLzz74OT1USLg7jOHPR0/XANmAH8IR922hgdKF9XrU/v4Gz4/Xn2z8c1gObCto6\n6L/UX2bL/i1y7gvnysGTB937V/KSt94SufRSkdOnK9nR7NnWiYDVq90Sly8t2LBAmr3cTH744wdf\nhxL00hMTZbzNViTRP3H++ZJ+110iDRqIPPWUyKlTvg5TVZCryd6va+Pc/N7NXHXeVTxy9SNejso9\nROCGG6y1pCdWZI0PEZg8Gd55B5KTrRXOg8A737/DwykPk3JXChc3utjX4QQ1h8XUBgyAXbvg73+H\njRshPt56o6qAEjSF0L7+5Wvu/OhOto3dRmS4d2vUu9Mvv1gz4dLTrZImTsvNhfvvty6Q+fxzt6wu\n5U8WblrIP5L/QfKwZC5pfImvwwldS5daNXYuusiqlNq6ta8jUk4KimUJRYRHUh/hmd7PBHSiB6vo\n5FNPWdVo85y9sNZ+sRS//OKW9WL90f9d/H9Mj5lO9H+i2bhvo6/DCV39+1szvLp1g65d4emnQU/g\nBiW/TPYfbfmIUzmnuLPTnb4OxS1Gj4aICOvbsiMZSUlMjIlhSlQUE/v0IaNLF+vS988+s34GqTsu\nvoOZ/WcS858YNvy+wdfhhK6ICJgwAdassb5JXnyx9W1SBRW/G8bJycvhotcuYtYNs+hn81LZAS/Y\nvh2uvNIqYVJ46N1heeI6dYhZsICef/mLDyL1vkWbFzH287EsHbaUy5pc5utw1NKlEBdnjTvOmEHG\nDz+cXStBr8L1GwFfz/6NtW/Qum7roEr0YK1m9fjjcO+98MUXZxcYSomPL5LoAaYdOcKkWbNCJtnf\n2vFWDIb+/+nPEy2e4POln5Mt2VoT31f694fvv4eXXybjkktIrlqVaYcPn3l6gv39qgk/sPjVMM6x\n7GM8lf4Uz/d93teheMSDD8Lx4/DGG2e3hZ9wvFB3qF34MrjjYEbWH8k/XvsHKa1TSG+TTkrrFMbN\nGkdSapKvwws9EREwfjwpnTsXSfRgXYWbmqClqwONXyX7F//3ItG26IAoi1AR4eHw5pvW8OivuwU+\n+IDcVasc7uvN8sT+YvXy1eT3KVolU2vi+1Z4FccpIqzYB4Dyf36T7Pce28usVbN4uvfTvg7Foy6+\nGCYO28neLgORqVOJfvppJhSbP+/t8sT+Qmvi+5/cCMelq/PWroWbb7Zmi2kp5YDgN2P2U9KmMLLz\nSFrVbeXrUDwnJwdmzCBuwfPEhz/Ejkc+ZsjwanDRRT4tT+wvSquJv/vwbo5mH+WciHO8HJGKjotj\nQmZmkfNK4202+j/3HBw4AGPGWF9Z4+LgzjutuvrKL/nFbJzNf2ym1797sW3sNupVr+fTeDzm22+t\nOZhNmsBrr7H6sI0BA6zzYEE4jb5CklKTGDdrXJGa+K1Wt+KC7hewqfomJvWcxKguo6gaVtWHUYae\nUq/CBeuoftkymDnTmmp2zz3wwAPQokXZnapKC8graG9890Z6tuzJQ1f5V8letzhyBMaPh48/hlde\nsRaVsE/Feewx2LkT3nvPtyH6k6TUJBLeTSArP4vIKpHEDollQL8BbPh9A4+kPsKuI7t47rrnuKn9\nTRjjlnVxlLts326thbtgAfTtC+PGwVVXkfH55zp10wNcTfZOF9Hx1A2QyN6R8tGSj9xSHMhv5OeL\nvP++SLNmIqNGiRw6VGKXkydFLrxQ5OOPfRBfgFq6falc8tolcs2b18i3u7/1dTjKkSNHRGbOFLng\nAkm32WR8o0a6gIoHEIiF0JgCtnU2Zo6ZGXBzqjOSkkoetVx0kTWWuWsXzJ1rVUIrxfLlcNNNGVx6\naQr5+eFEROQSFxd9ZslDVVJefh5vb3ibSV9N4uqWV/Nsn2ex1Q+OInFBJT+fiV278oyDBdAnxcTw\n9NKlPggqeATsRVUFU+wCKdk7vPp1zRrIzqbnhAnW0E21amX2cfRoBrm5yXz11bQz2zIzrUXMNeE7\nFlYljBGdR3DHxXcwfcV0rvjXFdzV6S4m9pxIgxoNSEpNIv6deL0wy9eqVCH8HMcn1cPWrIF//xti\nYqBpU+/GFaL8ZuolBN4UO4dXvx48SGrnztblsuUkeoD4+BSOHp1WZFtm5jQSElLdGmswqlG1BhN6\nTuCHB34gOy+b9rPaM2LmCOJejdMLs/xEqVM3GzeGpCSrJEPnzvDEE9ZKbDk5Xo4wdPhVso+sElgX\nEoX/+afD7WGlXIjiSHa24y9XJ0+GVSimUNS4VmNeG/Aay0csJ2lpEj9d/lOR5/XCLN+JjotzfB3J\n88/DBx/A/v3Wos1hYVZ9/YYNrYqvb7wBv55dAbVIscCYGDKS9MPbVX4zjGNbayN2bABcSPTzz9ab\n9IMPyN3guFKjK1e/RkTkOty+cmUeEyZYtXS0xLhz2p/bno6NO5JOeonnDmcftk5S6QweryqYdVPq\ndSTh4XDNNdbtmWfg998hJQWWLLG+HTdrRkbbtiSvXMm0PXvO9Kv1eSrAlbO5nrgBEjMiRhJT/Pjs\n/E8/iTz/vEjXrtY6sKNGiaSmSvonn5Rc9s3FmQaJielis40vskyozfaEzJ6dLg8+aK0eN2CASGKi\nSG6uB3/HIBF9d7QwhRK3iN4R0mp6Kxn5yUh59/t3Zd/xfb4OVZUnN1dkxQqZcP75Rf6PnVlLt0cP\nkaNHfR2lzxCIs3F8GYPD2TQDBhQ5gmfXLuvS8Ntug6go62ikUPtSLzhxUlJSBgkJqWRlhREZmUds\nbL8zJ2dPnrTm4c+ZA/v2wahR1kIoTZq4868QPBxdmGVba2PGmBnYOttY9tMylv28jPSd6bSq24q+\nbfrS9/y+XNvqWmpVq1WkHz3J6x+mREUxJb3kt7UpNWsyJT/fuirx4ouL3tq3h2LfsEv9vx6g3H5R\nlTGmPzADCAP+JSIlSlIaY+KxFiY/CdwtIutcaOuzZO9wNk2DBsTUq0fPI0dKTfC+smaNlfQXLYLo\naGvVwl69rGu0kpIyiI9PITtbp2+WdmFWYbn5uazes9pK/j8tY/We1Vze9HL6nt+X6r9VZ+57c8m8\nvNAHRoBODQ4GE2NieCYlpcT2STExPJ2UBD/9ZK229cMP1s9NmyAzE1q1OpP8M7KySH7nHabt3n2m\n/QSbjZiZM11K+P70geHWi6qwkvQOoDVQFVgPdCi2zw3A5/b7VwDfOtvWvl+Fv8akJybKhOhomdyr\nl0yIjnZ++CQ/X2TfPplwxRWOvx527SqSk1PhuDztzz9FEhJEOnYUad9e5N5706VNm4KhoK/sQ0Hj\nJTEx3dehBozj2cdl6fal8nDyw1K7b+2zQ0DDzw4FXTH0Ctl9ZLfk5pU/npaYkijRd0dLr+G9JPru\n6AoNU7qjD3/y1VdfVahdemKi68Ol2dki338v8u67IhMmyISGDR3/X2/RQuSZZ0TmzRP5/HORtWtF\n9u51OGbqKA5XLxCrcM5y0AcuDuOUl+yvBJYWevw48HixfeYAdxR6vBVo4kxb+/YK/dLl/uGPHhXZ\nsEFk8WKR6dNFYmNFBg4UuegikZo1RerXl8m1ajl8A0zu1cvlfwBfyM8XycgQadJkQqHwJ5+536vX\nRMnKcr6/xMR0iY6eIL16TZbo6Akuf1hUtr2/9NFreK+zyb7X2WRfq18tafpSU6n6VFVp8UoLufJf\nV8rtH9wuDyU/JNNXTJdFPyySlb+ulLc/eVua9G1W5JxBk77NXErWiSmJle5DRGTytOekwSVtpM6l\nraTBJW1k8rTnXGrvjj4K2kc0rlPhGEbdNVyurFVdrqsZIVfWqi6j7hruWgy9ejn+v26ziTz+uMjw\n4SIxMSKdOlnn5cLDRZo0EencWeSGG0RGjpQJbdo4/sDo2dO6Qr6cA8T0xES5r379Im3vq1/f5Q+L\ngj5cTfbljU00B3YXevyr/ei9vH2aA82caAvAMykpjs+u5+XBqVPWAsjFfqZMnVpyjntmJpOGDqVn\n1arWYHebNkVvvXufvV+nDrkxMdaZ/2ICpZa8MXDttdCuXTi//17y+f/9L4zataFGDWtYs1Eja2Zb\nwf3C27ZsyeCll5LZubNiF3clJWUwblwymZkVvzjMX/o4euAktCm5/cKIjqx5aCWn806z59gedh/Z\nze6ju/n16K9kHsokbWcau4/uZuN/N5Lbp+gsq9+v2cNdzw/n1qxbqFm1JjWr1Szz58PTH+P3a/aU\n6GNS/FRu6HuDU7OKpjz7PNPef47cwWenCE97/znrufGPOfW3qGwfRdp/Bdm9j1Qohjc3fkLuw6fO\nbFv18Sc0ffZ5p/vY+tseh9u3VQmDf/6z5BM5OfDHH7B3rzVDaO9ejn34ocM+5JtvrJxy7Jh1bU3t\n2g5vi5YuZfbRo0Xazj50iFF/+xs9X37ZalutmrVwTCk/Xx8by38OHXLqdy6uvGTv7GB6peezTcvM\nZNKtt9Kzdu2zST0vzyqZGhlZ4mf4jz867Cfs/POtaVuNG59d+68UpZZvDbBa8qVN3+zTJ48lS+DP\nP6337f791s+C27Zt8PXX1v1Vq1I4caLkxV033TSJunV7Eh5unbYIC+PM/cKPd+xwfHHYX/86icsu\n64kxZ/85Svu5enUKBw+W7OPuuyfRvXvJRO3on3flyhQOHHDcR48eziX7HSvbwM5DcFuhg4n3bWz/\now3WSpHVsEYnW5do2wzYuKcV8EuJ547sF779+HJyq5wgr8oJ8qr8SV6VPeSFnSi07QS5YSc4cmSb\nw9jWHVtFlaeqYPLDMYRjJJwqEg4SRhX744LbyfRdcHPR90buzX8y9bMnmfHnhxgxWP91DQYDUsX6\nWeh2KGMl3JxVso/FU5l14Auwt8Z+7yzr/v6v0+HmUw7bz96f4fB3LO6Pr78qo4/lTvVxkl3cUQ/e\nK7Tmyu31YInspPHfnVv+s2X+UYfbk2sKb4y4FkSonptPrdO51MzJpdZp+y3nALWO/c7RbMftzaE/\nWDTtcarl5VMtL5+qeflE5OVTNT//zDbrJrQ4etKpWB2+jpRxctQY0wOYIiL97Y+fAPKl0IlWY8wc\nIE1EFtofbwV6YR0bldnWvl1XPlBKqQoQN9bGWQ20Nca0BvYAdwBDiu3zKTAWWGj/cPhTRPYZYw46\n0da1s8lKKaUqpMxkLyK5xpixQDLW7Jp5IrLFGDPa/vxcEfncGHODMWYHcAIYUVZbT/4ySimlHPP5\nRVVKKaU8z6eF0Iwx/Y0xW40x240xzp1WV6Uyxuw0xmw0xqwzxnzn63gCiTHmTWPMPmPM94W21TfG\npBpjfjTGpBhj6voyxkBSyt9zijHmV/v7c539oktVDmNMC2PMV8aYH4wxm4wxcfbtLr0/fZbsjTFh\nwKtAf6AjMMQY08FX8QQJAaJEpLOIdPd1MAFmPtZ7sbDHgVQRuRD4wv5YOcfR31OAV+zvz84ioquX\nOCcH+LuIXAT0AMbYc6VL709fHtl3B3aIyE4RyQEWAoN8GE+w0BPeFSAiy4HDxTbfCLxlv/8WcJNX\ngwpgpfw9Qd+fLhOR30Vkvf3+cWAL1rVMLr0/fZnsS7sYS1WcAMuMMauNMff6Opgg0FhE9tnv7wMa\n+zKYIBFrjNlgjJmnw2Kus89u7AysxMX3py+TvZ4Zdr+rRaQzVlG6McaYa30dULAQayaDvmcrZzbW\n9TeXAXuBl30bTmAxxtQCPgTGicixws858/70ZbL/DWhR6HELrKN7VUEistf+cz/wMdZQmaq4fcaY\nJgDGmKbAHz6OJ6CJyB8FNV6Af6HvT6cZY6piJfoFIrLYvtml96cvk/2ZC7aMMdWwLrr61IfxBDRj\nTA1jTG37/ZpANPB92a1UOT4FhtvvDwcWl7GvKoc9IRW4GX1/OsVYhZDmAZtFZEahp1x6f/p0nr0x\n5nrO1rufJyIOKhIpZxhj2mAdzYN1sdx/9e/pPGPMu1hlPs7FGv98EvgEeB9oCewEbhcRxwsPqyIc\n/D0nA1FYQzgC/AyMLjTmrEphjLkGyAA2cnao5gngO1x4f+pFVUopFQJ8elGVUkop79Bkr5RSIUCT\nvVJKhQBN9kopFQI02SulVAjQZK+UUiFAk71SSoUATfZKKRUC/h8A3P8rbZVVhAAAAABJRU5ErkJg\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x158c6828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = arange(0,21)\n",
+    "\n",
+    "plot(x, poisson(1).pmf(x), 'o-', label=r'$\\lambda$=1')\n",
+    "plot(x, poisson(4).pmf(x), 'o-', label=r'$\\lambda$=4')\n",
+    "plot(x, poisson(9).pmf(x), 'o-', label=r'$\\lambda$=9')\n",
+    "\n",
+    "legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 自定义离散分布"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "导入要用的函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.stats import rv_discrete"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "一个不均匀的骰子对应的离散值及其概率："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "xk = [1, 2, 3, 4, 5, 6]\n",
+    "pk = [.3, .35, .25, .05, .025, .025]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "定义离散分布："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "loaded = rv_discrete(values=(xk, pk))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "此时， `loaded` 可以当作一个离散分布的模块来使用。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "产生两个服从该分布的随机变量："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 1])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loaded.rvs(size=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "产生100个随机变量，将直方图与概率质量函数进行比较："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Container object of 3 artists>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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L2gRcGhFHp+tb5DdjZmYvyJuiISJ2AbumtG2tW/4Fk6diGvY1M7OF4TtZzcwS5YA3M0uU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\niXLAm5klygFvZpYoB7yZWaIc8GZmifp/J4pqDI9mKMcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1764bef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "samples = loaded.rvs(size=100)\n",
+    "bins = linspace(.5,6.5,7)\n",
+    "\n",
+    "hist(samples, bins=bins, normed=True)\n",
+    "stem(xk, loaded.pmf(xk), markerfmt='ro', linefmt='r-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 假设检验"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "导入相关的函数：\n",
+    "\n",
+    "- 正态分布\n",
+    "- 独立双样本 `t` 检验，配对样本 `t` 检验，单样本 `t` 检验\n",
+    "- 学生 `t` 分布\n",
+    "\n",
+    "`t` 检验的相关内容请参考：\n",
+    "- 百度百科-`t` 检验：http://baike.baidu.com/view/557340.htm\n",
+    "- 维基百科-学生 `t` 检验：https://en.wikipedia.org/wiki/Student%27s_t-test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.stats import norm\n",
+    "from scipy.stats import ttest_ind, ttest_rel, ttest_1samp\n",
+    "from scipy.stats import t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 独立样本 t 检验"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "两组参数不同的正态分布："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "n1 = norm(loc=0.3, scale=1.0)\n",
+    "n2 = norm(loc=0, scale=1.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "从分布中产生两组随机样本："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "n1_samples = n1.rvs(size=100)\n",
+    "n2_samples = n2.rvs(size=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将两组样本混合在一起："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "samples = hstack((n1_samples, n2_samples)) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "最大似然参数估计："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "loc, scale = norm.fit(samples)\n",
+    "n = norm(loc=loc, scale=scale)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "比较："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x17ca7278>]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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GJqySRC5eGTcOMmeGJk1sNtu+HY4ehS+/DL1dA11qQ79dkMPC5BLhPH74ASZN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\nIYRwcpLIhRDCyUkiF0IIJ/d/XdaGnd6OVMIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x17c1c208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = linspace(-3,3,100)\n",
+    "\n",
+    "hist([samples, n1_samples, n2_samples], normed=True)\n",
+    "plot(x, n.pdf(x), 'b-')\n",
+    "plot(x, n1.pdf(x), 'g-')\n",
+    "plot(x, n2.pdf(x), 'r-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "独立双样本 `t` 检验的目的在于判断两组样本之间是否有显著差异："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t = 0.868384594123\n",
+      "p-value = 0.386235148899\n"
+     ]
+    }
+   ],
+   "source": [
+    "t_val, p = ttest_ind(n1_samples, n2_samples)\n",
+    "\n",
+    "print 't = {}'.format(t_val)\n",
+    "print 'p-value = {}'.format(p)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "`p` 值小，说明这两个样本有显著性差异。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 配对样本 t 检验"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "配对样本指的是两组样本之间的元素一一对应，例如，假设我们有一组病人的数据："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "pop_size = 35\n",
+    "\n",
+    "pre_treat = norm(loc=0, scale=1)\n",
+    "n0 = pre_treat.rvs(size=pop_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "经过某种治疗后，对这组病人得到一组新的数据："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "effect = norm(loc=0.05, scale=0.2)\n",
+    "eff = effect.rvs(size=pop_size)\n",
+    "\n",
+    "n1 = n0 + eff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "新数据的最大似然估计："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "loc, scale = norm.fit(n1)\n",
+    "post_treat = norm(loc=loc, scale=scale)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "画图："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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8adhjJVx5Fuwd81zDcLafsWrSBE4+OfJ6wwZ45x149FGAiTD/Djjkg6SnvenYm2jcrzHs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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x17ed5fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = figure(figsize=(10,4))\n",
+    "\n",
+    "ax1 = fig.add_subplot(1,2,1)\n",
+    "h = ax1.hist([n0, n1], normed=True)\n",
+    "p = ax1.plot(x, pre_treat.pdf(x), 'b-')\n",
+    "p = ax1.plot(x, post_treat.pdf(x), 'g-')\n",
+    "\n",
+    "ax2 = fig.add_subplot(1,2,2)\n",
+    "h = ax2.hist(eff, normed=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "独立 `t` 检验："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t = -0.347904839913\n",
+      "p-value = 0.728986322039\n"
+     ]
+    }
+   ],
+   "source": [
+    "t_val, p = ttest_ind(n0, n1)\n",
+    "\n",
+    "print 't = {}'.format(t_val)\n",
+    "print 'p-value = {}'.format(p)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "高 `p` 值说明两组样本之间没有显著性差异。\n",
+    "\n",
+    "配对 `t` 检验："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t = -1.89564459709\n",
+      "p-value = 0.0665336223673\n"
+     ]
+    }
+   ],
+   "source": [
+    "t_val, p = ttest_rel(n0, n1)\n",
+    "\n",
+    "print 't = {}'.format(t_val)\n",
+    "print 'p-value = {}'.format(p)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "配对 `t` 检验的结果说明，配对样本之间存在显著性差异，说明治疗时有效的，符合我们的预期。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `p` 值计算原理 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`p` 值对应的部分是下图中的红色区域，边界范围由 `t` 值决定。 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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e9e0Lo0bB9Ol2EQ9jorF/G4Phw9N773jroklw06fDlVfCrFnW725MtHbtcgsA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\nMSZFWYE3xpgU9f8B8tTCMYTHdwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x179aeac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "my_t = t(pop_size) # 传入参数为自由度，这里自由度为50\n",
+    "\n",
+    "p = plot(x, my_t.pdf(x), 'b-')\n",
+    "lower_x = x[x<= -abs(t_val)]\n",
+    "upper_x = x[x>= abs(t_val)]\n",
+    "\n",
+    "p = fill_between(lower_x, my_t.pdf(lower_x), color='red')\n",
+    "p = fill_between(upper_x, my_t.pdf(upper_x), color='red')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/04. scipy/04.04 curve fitting.ipynb b/04-scipy/04.04-curve-fitting.ipynb
similarity index 99%
rename from 04. scipy/04.04 curve fitting.ipynb
rename to 04-scipy/04.04-curve-fitting.ipynb
index 1767b106..1a519687 100644
--- a/04. scipy/04.04 curve fitting.ipynb	
+++ b/04-scipy/04.04-curve-fitting.ipynb
@@ -1,982 +1,982 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 曲线拟合"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "导入基础包："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib as mpl\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 多项式拟合"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "导入线多项式拟合工具："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from numpy import polyfit, poly1d"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "产生数据："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "x = np.linspace(-5, 5, 100)\n",
-    "y = 4 * x + 1.5\n",
-    "noise_y = y + np.random.randn(y.shape[-1]) * 2.5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "画出数据："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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TBPYzz4TRoyNeqEQd9KhRL6eeGixzp/rqItLPSk7FmNmtwHjgPeB14Fx335R+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\n/wEcY+GYZJlNlgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa1224a8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "p = plt.plot(x, noise_y, 'rx')\n",
-    "p = plt.plot(x, y, 'b:')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "进行线性拟合，`polyfit` 是多项式拟合函数，线性拟合即一阶多项式："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 3.93921315  1.59379469]\n"
-     ]
-    }
-   ],
-   "source": [
-    "coeff = polyfit(x, noise_y, 1)\n",
-    "print coeff"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "一阶多项式 $y = a_1 x + a_0$ 拟合，返回两个系数 $[a_1, a_0]$。\n",
-    "\n",
-    "画出拟合曲线："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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E3FwtGDFC3x45UhtWqaf1a63RNDQtnmrJzVXt3Vv19devvKY/gdiR/snKYt15CWFgJypt\nglwIovD0aV3Vt6/e0KqVduvWTT9bvtxYHzQjo+h8oQrA7tpqgYUsoh0DO1Fp5NwLVjWVoiksVF25\n0qYJLVtq+zZtND093Rgt6lh9yHGOUKVMvH05uLafQoqBnai08dYL9tDD/utf92pc3NdapUq6Llu2\nTC9fvmzsF2jO3gxP5162jD32MGNgJypNvAVwNwF/9epDWrv251q+fI4OH27TCxcuRbT5zLGXDDOB\nnfOxE0UL52H/judA0dQAu3cD7drhW5sNAx/9H/bv74oBA/Zh8eLOiIuLLdn2OThPXcC5XUoEV1Ai\nKk2ca8YdUwA4th8+jB/uvx9jb70Vnfv0wa2dDuPIkUpIS+uJOFwwgmq4+Zq6wFPNO4N6iWNgJwpG\nevqVg3oc85UHc5zT8P1Tmzdj4s03o83x46jSvj3+c++9WFD+a9SvcrlkBwb5sXweRZivXE24HmCO\nnawg0LyyieNOnvyfDuy9TONRQcfcd5/m5OQU7TtypOrChZHJYbPqJaLAm6dEJSDQ2m0Px/3003kd\nMuQjLVcuW+tc/ZluW7n1yvNGKriyTj3iGNiJSkqggdbpuPz8SzphwjqtUGGfVqu6XxcmppiukCkR\nrHqJCgzsRCUhmB57v356edcuXda7t9avO0SvvjpLX0z5SgufTfFeJx6J4BrOungyjYGdKBhmAlkQ\nOfbCsWM1fdEibV+jht7ctq2uv+tuzc/M8n48g2uZZyaws46dyBPXRSZcXwNFtdtbtxbVcDtqtx3b\n3ZT7bfnjHzE1LQ2nzpzBC1OmYND27ZAxY4CpU69c7ILIiZk6dvbYibwxm2Yx2XP/5JMMbdx4lVar\nNlMXL16sBQUFxhvRVGnCXwVRDUzFEJngK5CZDbpevgS+/PI/euONy1TklHbvnqHZ2RdMHRcRvEka\n1RjYiczwc44Wr1y+BLKzD2vXrktU5Ji2bbtXd+/+yfy1IynavmzoFwzsRGY5AtnChcbgH+eg7jrt\nra90TFaWHh8+XJ8YO1bj4+P11ls/1s8+O+P+mGDSHuFOmURTeoh+wcBOZUOoApyJxaA9ntce1POy\ns/WZZ57R+GrVdHybNvrfBQvCF3zD2dtnjz1qMbBT2RCKAOccyEaONB5+BLWf3n9fpz75itasWVMf\neughzXIsFRfuuvNwBOBoTQ+RqjKwU1kSTIBzF8jsi0H7SkPk5+frM88s09jYf2qVKkd11659oW2b\nGaFOmbAqJqqZCeysYyfryM4GmjQBsrKAxo3NH+c6j3heHvD008AttwBffnnlDIbp6bjcpQvmL9mC\nadMu4+LFXhg38gRm3fENYgfdGdq2+eKorU9KAubM4WyLZQDr2KnsCFWv2EcaorCwUNOWLtX6lUdp\nTPk8ffDBLM3LdjnGtcfrmI1xwoSiG7PO7wXaE2bKpEwCUzFkKb7W2QxFgPOShvj000+1S5cu2rZt\nW138Rpr+8NAk918kruWSjpx9dnbRc+eqm0ADMVMmZRIDO1mLpx7qsmXeA5ynAJiSYiowfvHFF3r7\n7bdrs2bN9J133ilaLNpbbttd+aRjeyTnUqdSj4GdrMdTwHS856UU8YovBHeljE6vd+/eqzff/Ipe\nd11PXfDoo5r/44/Fz+krQHsK/KwPpyAwsJM1udabqxp/9utnBGvVol66I9h7CsRucvPffvud3nbb\nXI2J2asNGx7V7dvPe06veEqpeMr5sz6cgsTATtbjrd7cuQfuLvh6KmG0f1H8sGOH/va3czUmZpvW\nqPGjLlnykxYWurm2r18LAf5CIDKDgZ2sxUy9uafA72nQUW6unnr4YZ00ZozGVbxO46v9oPPm/U8v\nXfLQBjNplCBz+kTeRDSwA+gL4CCATACT3Lwf5o9PluOpjNA1veIcfD2lbcaN07P79ukLCQlaPT5e\nR40apd/v3auFY83NBcOeNkVKxAI7gPIAvgHQGEAFAF8D+JXLPmH/CyAL85XucO6lu6ROLly4oK++\nOEtrV6mig3/7Wz106FDx8/pzA5bBnUqYmcAelpGnItIVQIqq9rW/nmyP5LOc9tFwXJvKCNfRogBw\n+DDw6KPGCkSAMXoUAObOBQAUTJmCvzRvj6nTzyE2tivWrbsaHTq0D/x6jpWS3KyQRBQuZkaehiuw\n3wugj6qOsr8eCqCzqj7mtA8DO4WWc/B1PAdQuGULlp/Lxx8e34fc02PQs3sB/vy3umjWzH4cAzSV\nImYCe0yYrm0qYqempv7yPDExEYmJiWFqDpUJzoG5f3+oKjZs2ICxj3+Go0efQLt2PbDu/UrosHwK\nUH0GAJd1TImikM1mg81m8+uYcPXYuwBIdUrFTAFQqKp/dNqHPXYKm23btmHKlCk4fvw47rxzEQYM\n6Ixf/7qc8SYnzqJSLJKpmBgA/wHQC8APAL4AMFhVDzjtw8BOvvmZ287IyEBycjL27NmD1NRUPPjg\ng4iJcfPDNFyzLRKFmZnAXi4cF1bVAgDjAawHsB/AcuegTmRat25G7zovz3jt6G3b8+cOmZmZ6N//\nSdxxR1/ccccdOHToEEaMGOE+qOflGT31rCzjT8e5iSyC87FT9POSOsnJyUFS0nysXt0WMTED8dln\nQMeOlX2fy3EO19dEUS5iPXaygPT0K3uyeXnG9kDO4XjufA6z54uLM4J6kybGn3FxOHHiBMaNexbN\nm3+AtLRnMH78IBw9Wtl7UAeMFI5zEI+LM15v3Wr+cxFFO1+F7uF6gAOUolsoBuT4O3GWr/NkZemZ\n//s/TZk0Sa+9trvGxp7Vhx46p//9b2Afkag0gokBSuyxk3uOnmxysnGj0V26wlev3vkczvv5k/6w\n73t+2jS8tHIlWqxZg6y0NOz85DXs21cFixdXRu3aIfnERJYRrjp2sgLnFEhW1pVB2HFj012+2tM5\nAM/nc+PSZ59hUYsWeL5TJyQkJODTTZtwY716RurkZpOjRonKGl9d+nA9wFRM9DMz6ZWvfbxNs+u8\nr8sEX5cvX9alf/mr1qk2RNu0eV537NgRhg9IVPqA0/ZSwPxZhi4jQ91OZWsmx+44n/114enT+uG7\n72qzWvfoNRW2acP6/9OPPiqRT0xUKjCwU+DMLhydna3aurUR3L31wl1XNHJzPtvatdo+vrteE7NW\na1Q6rgtfP+d5XnSiMspMYGcdO/nPkUsfPRoYMgRYuxZo1CjgmvAvbTZMHTYM34gg7twSPHA6DY8d\nnIDY6xuF8UMQlU6sYyf/mK1dd9wQbdcOWLrUCOqO7X7UhB84cAD33nsvBgwZgnseeQQHjhzBl/3+\ngqSsxxD72myOCCUKEAM7FTE5fL/YkPw33ywegOPifE5/e/jwYQwf/jB69uyJhIQEZO7cibHZ2ag4\ndChQqRKwYwcwceKVbfFncBRRWeYrVxOuB5hjj05mq1zM3FR1WY3o2LFjOm7cE3r11RO1evUfNScn\nz/1NVMdN1uzsotw8VysiUlXePKVAeVuw2exNVadgnJubq1OmJGvlyo/otdee1Ntuu6C7dpk8H9cX\nJSqGgZ08B05363o63gs0oLocey4nR2fOnKlVq96h1aod0Q4dLuimTX60ydsXDFEZxcBO/s35Eor5\nYbKy9CKgr6emap06dfT+++/XNWu+01WrVAsL/bhOMF8wRBbGwE4Gs0HS3969i4KTJ3Xxbbdp4/r1\n9c6GDfVLmy2wNoXiC4bIoswEdtaxW5G7VYd27zbKE8OwYpCqYvWSJZg0fg6qNWuAl+ZNRvc2bXzX\ntHtaxcjPVZOIyhLWsZdVrmWLhw8bA4kyMkK6YpCqYuPGjejQoTfGjL+E44U7MfnO54yg7lzT7q5U\n0dsqRv37X/llYKKMkojsfHXpw/UAUzHh5UhfZGQYQ/6zs4tvDzKtsX37dr311r4aH/+qVqlyQceO\nLTTmRfcnf85UC5HfwBx7GeeoKsnIKL7dj7y5q927d+uAAQO0Xr3mGh9/Vh944LJ+843LTr5y+kHm\n8onKMjOBnTl2q/KyTmggvv32W6SkpODjjz/G5MmTMWbMGJw+HYu6dT0c4Cl/TkRBYY69rHKejKtx\n46JVjJYv93sd06NHj2LMmDHo3Lkzrr/+emRmZuKJJ55AbKyXoO4tf05E4eerSx+uB5iKCR8zo0PX\nrjXy7q65bns65OTJk/r000/rNdf00a5dN+jJkyfNXZv5c6KwAnPsERDt+WMfN1X/d+SIPvfcc1q1\n6i3auPHXWrfuJX3rLafBRb5E++cnKuXMBHbm2EPNdU7yAOcoDytH/jsjw5idMSkJF158EX9u1Agz\nXl6JqlVfQV7eLUhOjsG4cUBsbKQbTEQOzLFHgqN+OznZCKDRFtRdptwtGDkSf2vSBC3XrsWmzz/H\nkCFrMXhwD3z3XQyefNIe1M3O005EUSEm0g2wJMdCFI6qkGgK6vYvmsJrr8V7v/oVnu3aFfU6dcKK\nBg3QZeFC9211DHhy9yuEiKIOe+zhEK1VIVu3Ql94AR9t24YObW/G3Oen44233sKn/fqhy8sve17Y\nItp/hRBRcb6S8OF6wKo3T31VhYTz5qKPc2/evFm7deuu9eolaa24U7pt/ZnibfS1sAWn0SWKOISz\nKgbAfQD2AbgM4CaX96YAyARwEMAdHo4vgb+CCPAVuMNZDujh3F999pn27XunXnfdcG3Q4JR27lxo\nzIvu7lhfKydxGl2iiAp3YG8FoCWATc6BHcANAL4GUAFAYwDfACjn5vgS+UuISuEMkk7nPjh4sN4/\naJBed11Hbd48R1u1KtTVq72ULnrqkbM2nShqmAnsAefYVfWgqh5y89ZAAO+q6iVVzbYH9oRAr2NJ\nzjdXk5L8z1V7q1KJi8OR3/8e/9ekCW5dvx4dEhKwf/9nSEmph717BffcA4i7Qilv9wW2bi2eU3ee\nuZGIok44bp7WBZDj9DoHQL0wXKf0Cvbmquu0vPYqlR9btsQfxo1Dh169UGvcOGQOGoTJY8agevXK\nGDoUKF/eS3vcTUHgOD+n0SUqVbyWO4rIRgC13bw1VVU/9OM6bkcipaam/vI8MTERiYmJfpyylHId\nsOQIov5UmTgfl5SEvBdewEvVqmF+pzvRp8bN2L97N2q1bGl+cJS3HjmDN1FE2Ww22Gw2v44JeuSp\niGwC8JSqfmV/PRkAVHWW/fU/AaSo6g6X4zTYa5dK/qwO5GPfnw8cwPwbbsCc+IZo0OgVZGf2w1OP\nK5JfuMr3uYmoVCrJkafOF/kAwAMiUlFEmgBoAeCLEF2n9PMnreEh5ZLfqRP+NHcumt98C9751RvA\nz1/j+iZ34YtdscWDurdzE5FlBTzyVEQGAXgNQA0A6SKyS1XvVNX9IrICwH4ABQDGlc2ueQg4p1w6\ndsTlf/0L73bujGcTEtCysBBNbziIa1SweEMM2i/7A1BjBgAOGiIq6zgJWCmgWVn4oGlTJFetimtb\ntsTMQYOQ+PvfI2/664jr3Qno08fY0ZFyYfqFyLI4CZgFfLJmDbp06YJnW7XCrIQEbG3TBomDBwOz\nZyNu7jQjqCcnGzs7gnpyspHGIaIyiYE92thr1Hfs2IHbExMxYuiLqNTIhi3PvYS7VqyA5OcXr3/n\nPC5E5IKzO0aZffHxmHbTTdj+U3U0jX8dFyrejHsqrEHFHr2MHSpVAhYuLL6OabTOJklEEcEeeyQ5\njSD97rvvMGzYMPS8azhOV3oDl85swm29WiFz0CQ8md7LmBc9ORmYOxd4+OHig4iidTZJIooI3jyN\npLw8/PeJJ/BCTAyWrV6Nx0aNQsLe6viozgRMe+RH1E5oaATrxo0917SvXw9s3hzdKzYRUcjw5mkU\nO336NCbPmoUb16xB7L//jYMbNiD17Fn0WzIK8+ecR+3Fs4r3wD3Vv1epwnlciKgY9thL2Llz5zBv\n3jy8/PKrGDjwfjz//BTULygonh+P9jVTiShi2GOPIhcvXsRrr72G5s1bYMOGGNSsmYPWrd9A/SpV\niufH169nD5yIgsLAHmYFBQVYtGgRWrZsiRUrjqBevUycODEJs2ZVwh9GuJlVcfPmK0/CaQGIyA9M\nxbjjz0RdHhQWFmLVqlV45plncN11dQGsQFZWdaSmAsOGATExobkOEZUtZlIxDOzuuOa1/chzqyo2\nbNiAZPvciektAAAKWklEQVRo0JkzZ6J3795ITxfcfjuMskV3GOSJyAQzgZ2LWXsSwPJ1W7du1R49\nemirVq30vffe00KPa9B5uR6XnyMiL2BiaTz22L3Jzi6qVmnc2ONuGRkZSE5Oxu7d3+Gee17Hyy/3\nRExMAIN6Hb8MkpKKjywlIrJjVUwwTIzmzMzMxODBg9Gnz9246qokXLq0D7m5vVCuXIAzNQS7FioR\nERjY3fOxBmhOTg4eeeQRdO3aDZcvP4CrrsrGuXM9sW6d4B//AMq5/q16W3zadRunBiCiIDGwu+Nh\nDdATH32Ep556Cu3atUP16tUxZsxhHDkyEIsWlcO6dUD79h7O52ElpGJT6/paUJqIyCTm2H1JT8f/\n2rTBSwsXYv4rr2Dw736H5KeeQp1vv8X52/oj9kIeZJuJyhVf+XNWxRCRCcyxB+n8+fOY+9VXaN6q\nFbIPHcK/t2zBfFXUmTsX6NYNV13Mg0wzuaiFr/y5P2uhEhF5UTYDu4+c96VLl/Dmm2+iRYsW+Hhr\nDrr9OhujL3ZEk6pVi/ZftQp4+uniPW93eXPn8zN/TkQlwVc9ZLgeiGQdu4ea8cunTunSpUu1WbNm\n2qPHb3Tw4P9qfLzqtGmqeRnZqoBR156VZTwfOtRc3Tlr1IkoRMA6di+cct46ezbWdu+O5BdfRKVK\n1dC69SJ8+GFTPPAAMG0aUDvWKT/+wgvG8dOmFT2/5RZg2zZjEQzn3rsjP878ORGFCHPs3thz3rYm\nTdBtxw5MnTkT06dPx8aNNsTENMWOHcD8+U5B3fVmZ1ycEcgvXgRGjjT+dHCtemH+nIhKUJntsf97\n0yZMHTYM35Yrh+dbtsQDy5ahfPXqV+7o3Nt2PAeM3na3bkae3dFjB4yePEeNElGYcK4YN/bv36+/\nufturXN1ZZ3z/Bt68eLFwHLe7vLmQ4cW5eGJiMIAJnLsZSYVk52djeHDh6Nnz56odbkzmrY5hS/2\njEPFihUDW8zCdRATAFSqBCxcyKoXIoqoMpGKOXLkCG666Sbce28Kvv9+DPbsqYDnnjPmRS9fPgQX\nCGKaXyIif/DmqV3Dhg0xbFgOVq16DL16VcChQ8CIEfagbnYeF2/cTUHQo4exzF0w5yUiCkCZCOwA\nMGRILDIzgSefdFnswsw8Lr64q3rp08dY5i6Y8xIRBSDgVIyIzAFwF4B8AN8CGKGqZ+zvTQHwMIDL\nACao6gY3x5dYKsancM2DzvnViSjEwro0noj0BvCJqhaKyCwAUNXJInIDgHcAdAJQD8DHAFqqaqHL\n8dET2AHTi2pEzXmJqEwKa45dVTc6BesdAOrbnw8E8K6qXlLVbADfAEgI9DolIlzzuHB+GCKKgFDl\n2B8G8JH9eV0AOU7v5cDouUencM2DzvnViShCvAZ2EdkoInvcPO522icZQL6qvuPlVFGUc3HhYVEN\nv2raS/K8REQ+eF2cU1V7e3tfRIYD6Aegl9PmowAaOL2ub992hdTU1F+eJyYmIjEx0dvlwsN5vhbn\n6QMc2wOdrMvd/pwfhoj8ZLPZYLPZ/DommJunfQG8BKCnqp502u64eZqAopunzV3vlEbdzVOAA42I\nKOqFuyomE0BFAKftm7ar6jj7e1Nh5N0LADyuquvdHB99gR1giSIRRbWwBvZglUhgD3QedJYoElGU\n4pQCgYwqZYkiEZVy1u6xA0XBvGNH76scOe/LHDsRRSn22IFfVkryucoRwBJFIrIE6wd259RKpUrG\nikfZ2e574lzCjogswNqpGHeplcceA5Ys4Y1RIiqVmIrhKkdEVAZZu8fujDdGicgCWMfuLNCadiKi\nKMLATkRkMcyxExGVQQzsREQWw8BORGQxDOxERBbDwE5EZDEM7EREFsPATkRkMQzsREQWw8BORGQx\nDOxERBbDwE5EZDEM7EREFsPATkRkMQzsREQWw8BORGQxDOxERBbDwE5EZDEM7EREFsPATkRkMQEH\ndhGZLiIZIvK1iHwiIg2c3psiIpkiclBE7ghNU4mIyIxgeuyzVbWdqrYHkAYgBQBE5AYAvwNwA4C+\nAP4kImXul4HNZot0E8KKn690s/Lns/JnMyvggKuqZ51eVgFw0v58IIB3VfWSqmYD+AZAQsAtLKWs\n/j8XP1/pZuXPZ+XPZlZMMAeLyAwADwI4j6LgXRfA50675QCoF8x1iIjIPK89dhHZKCJ73DzuBgBV\nTVbVhgAWAXjVy6k0hG0mIiIvRDX4mCsiDQF8pKqtRWQyAKjqLPt7/wSQoqo7XI5hsCciCoCqirf3\nA07FiEgLVc20vxwIYJf9+QcA3hGRl2GkYFoA+MLfhhERUWCCybG/KCLXA7gM4FsAYwFAVfeLyAoA\n+wEUABinofhZQEREpoQkFUNERNEj4vXlIvKYiBwQkb0i8sdItyccROQpESkUkfhItyWURGSO/b9d\nhoisEpGqkW5TsESkr31gXaaITIp0e0JJRBqIyCYR2Wf/9zYh0m0KBxEpLyK7ROTDSLcl1EQkTkTe\nt/+72y8iXdztF9HALiK/BjAAQFtVbQ1gbiTbEw72Ebm9ARyOdFvCYAOAG1W1HYBDAKZEuD1BEZHy\nAObDGFh3A4DBIvKryLYqpC4B+IOq3gigC4BHLfb5HB6HkQq2YjpiHoxClV8BaAvggLudIt1jHwvg\nRVW9BACqeiLC7QmHlwFMjHQjwkFVN6pqof3lDgD1I9meEEgA8I2qZtv/n1wGozDAElT1mKp+bX9+\nDkZQqBvZVoWWiNQH0A/A3wBYqkDD/ou4u6q+BQCqWqCqZ9ztG+nA3gJADxH5XERsInJzhNsTUiIy\nEECOqu6OdFtKwMMAPop0I4JUD8D3Tq8tO7hORBoD6ADjC9lKXgGQBKDQ146lUBMAJ0RkkYh8JSJ/\nFZGr3e0Y1MhTM0RkI4Dabt5Ktl+/mqp2EZFOAFYAaBruNoWSj883BYDzJGilrgfh5fNNVdUP7fsk\nA8hX1XdKtHGhZ8Wf7lcQkSoA3gfwuL3nbgkicheAH1V1l4gkRro9YRAD4CYA41V1p4i8CmAygGfd\n7RhWqtrb03siMhbAKvt+O+03GKur6qlwtytUPH0+EWkN4xs2Q0QAI03xpYgkqOqPJdjEoHj77wcA\nIjIcxk/fXiXSoPA6CqCB0+sGMHrtliEiFQCsBLBEVdMi3Z4QuwXAABHpByAWwLUi8ndVHRbhdoVK\nDowMwE776/dhBPYrRDoVkwbgNgAQkZYAKpamoO6Nqu5V1Vqq2kRVm8D4j3JTaQrqvohIXxg/eweq\n6oVItycE/g2ghYg0FpGKMGYp/SDCbQoZMXoYCwHsV1VvU4CUSqo6VVUb2P+9PQDgUwsFdajqMQDf\n22MlANwOYJ+7fcPeY/fhLQBvicgeAPkALPMfwQ0r/sx/HUBFABvtv0q2q+q4yDYpcKpaICLjAawH\nUB7AQlV1W3VQSnUDMBTAbhFxjBSfoqr/jGCbwsmK/+YeA7DU3vH4FsAIdztxgBIRkcVEOhVDREQh\nxsBORGQxDOxERBbDwE5EZDEM7EREFsPATkRkMQzsREQWw8BORGQx/w/bVCtp0vAwowAAAABJRU5E\nrkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa1ec278>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "p = plt.plot(x, noise_y, 'rx')\n",
-    "p = plt.plot(x, coeff[0] * x + coeff[1], 'k-')\n",
-    "p = plt.plot(x, y, 'b--')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "还可以用 `poly1d` 生成一个以传入的 `coeff` 为参数的多项式函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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yYABMOOBPTO0/l0V9rqR4SGcvqE+e7AX1sWO9jaNB3T/Huoi0OgrshSZSo754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\nkFFgFxEJGQV2EZGQ+X/DyfQNy7jRVwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa25e1d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f = poly1d(coeff)\n",
-    "p = plt.plot(x, noise_y, 'rx')\n",
-    "p = plt.plot(x, f(x))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "poly1d([ 3.93921315,  1.59379469])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "显示 `f`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " \n",
-      "3.939 x + 1.594\n"
-     ]
-    }
-   ],
-   "source": [
-    "print f"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "还可以对它进行数学操作生成新的多项式："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       2\n",
-      "31.03 x + 29.05 x + 6.674\n"
-     ]
-    }
-   ],
-   "source": [
-    "print f + 2 * f ** 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 多项式拟合正弦函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "正弦函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "x = np.linspace(-np.pi,np.pi,100)\n",
-    "y = np.sin(x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "用一阶到九阶多项式拟合，类似泰勒展开："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "y1 = poly1d(polyfit(x,y,1))\n",
-    "y3 = poly1d(polyfit(x,y,3))\n",
-    "y5 = poly1d(polyfit(x,y,5))\n",
-    "y7 = poly1d(polyfit(x,y,7))\n",
-    "y9 = poly1d(polyfit(x,y,9))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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HHx51rLHEiN8UP1U70QwSvzKMmfsFFX19TDovi2BQmlHKS/Vktj+/C11iIJ/1\n7uO3U37r9Hz19XIQ9cMPwd/BBzTvqEhqEjtYcGIJhfWFrEweg/ztxkZYuFCx6Sw9FiQvCb2fstky\nOTk53HTTTYrO6SzLly/n3nvvRQhxQXA+KyuLrKysUedw1rjnAamSJE0GaoCbgG8OHSBJUgzQIIQQ\nkiQtAaThDDuca9wnEuXl5ZhMJqZNmzbqWFcEUweZ98159L56kv3t7Rca9zPSzJxo5VL+PJWOvH68\nYzopqC1w2ogKIevst94Ko5xlG5XeyAZiSydzuH6/cxM5isKyjBp6uxCCnJwcnhlaXtMNSE5ORqfT\ncfr0aVJSUs5573zH96GHHhp2DqdkGSGECfghsAU4BvxbCHFckqQ7JUm688ywbwCHJUk6CDwF3OzM\nmuORwcdCW9LnjCXqp0EOkrwkGZ3JQk7u6Qveiw2K1TJmzhBaHQFT6smIzyDA27lA9xtvQFERWLlf\n7SI8tZ/o2hgKG8YoqKpwQFUNSebEiRMYDAYSEs4PFY4tkiQ5rbs7necuhNgshJghhJgmhPj9mdde\nEEK8cObffxNCzBVCpAshlgshvnR2zfHGnj17WLFihU1jXZEpM4gkSdQlttGy+8KAXFyQljED0NHe\nTUKVP41LKpzW22tq4N57YeNG8FUgpLLqWwtIrAzgaO3JsQl+K+y5q5EGac+952qWL1/ulO6unVB1\nA3Jzc0etJzOIWqV+rREwRyLihP4C46CdUpXZ8lo2TVEDfOqV65RxF0I+hXrXXaBUxdnky5bSEjHA\ngvqLKW8vV2ZSWxHCI2QZe+49V+NsUFUz7mNMT08PJ0+eJD093abxrjbus66azOwjglKj8ZzXtYNM\nMpU76miLauZUWxlLEpY4PM9rr0F1tdw6TzF0OtqiG8koW+r6fPeODvnxQ8Hj/GrIMrm5uSxZ4vjv\nTU0WLlzIiRMn6O7uduh6zbiPMQcOHGD27Nk21bQwdZkwtZnwTVA/DXKQ9BvTSayU2HXqXEOuHWSS\n8aoIpDe2hpXJK/HWOyYZVFbKDTg2bpTrbCm6v7gWEqpTOFzv4nRIDzid2tXVRUlJCfPnq9MK0Vn8\n/PxIS0tj/37HAuKacR9jcnNzueiii2waaywx4jfVNWmQg/gafKmO7uTgltPnvK557jJRtaG0TClx\nWJIRQq7R/pOfQFqawpsDZqwKJ7Eq3PW57ioY94FWZWWZ/Px80tLS8FH6L6qCOBNU1Yz7GGOXcXdh\nMHUo3VNHUIBiAAAgAElEQVR7sRT0n/NanCFuwhcPKzleRUi7F58lbnfYuL/4IrS1wa8uKLmnDKtu\nX4dvr0RlkYtPqapREbJFWVnGnntvrHAmqKoZ9zHGng9YX2Ufvsmuk2QGSVoeTGKxD+YhQdUQ3xD6\nzf30DDjfVMBTyfp7DrUJ3VToWpgXM8/u68vK4De/kfV2L+ULHQKgj4ykPqGDpCNTXZsxo5YsE6ac\nLOMJxn0wqOrI704z7mNIXV0dnZ2dpKam2jS+v7YfnzjXP0Je9K35zD4mcbyz6+xrkiTJue4TWHdv\nP9hHe1Q9qyatQifZdytZLHDbbbLWPlvlakt9kfXMqE6jqWf45iuqoHDpATgjy4RPLM89ISGBgIAA\niovtb7qiGfcxZDBSb2vt7/66fnzjXO+5J8xLwOg9wPbtJ855faLr7oG1obTHnuLi5IvtvvaZZ2Bg\nQG6dpzYRqQMk1iRQ0lKi/mKDNDa6tSxTXV1NX1/fBac/3ZGlS5eSm5tr93WacR9D7PUc+mr7xsRz\nB6hJ6KI8+9ym2BM5Y8ZsNhNfZaBwUg6XTL7ErmuLi+Hhh2U5Rq9OO9BzWPb1eSRUB3Cs9qT6iw3i\n5rLM4L2nZFMVtVi8eLFDGTOacR9D7DXu/bX9+MSOUWR/ugWfcx33Ce25Z3+Sz4C3YFf0IebH2J5K\nZzbLdWPuvx9sVOOcZvK6FXSEmDi4vcw1C4I6tdwVlGU8QZIZRDPuHobZbCYvL8+uAxT9dWOjuQPM\nvDiapNN+WIYEdiZyCYLD7x+nIb6NZZNWotfZ7n7/+c/g7Q0//KGKmzsfLy9aopvxznOhpKew5y4s\nAlObCa/QiWfcMzIyKCwspL+/f/TBQ9CM+xhx4sQJoqKiiIyMtGm8pc+CudOMd4TyLcasMsSQr7hp\nAVNOweGmtrOvxRniqOuemOmQpmJv2qIquXiS7Xr7sWNyu7xXXgGdi++8/qh6omsSXbegwsbd3GlG\n769H5+38D85sNpOfn++2J1PPx2AwMHnyZI4cOWLXdZpxHyPslmTq+vGO9nbNAaa2Nrj5ZrjkEjCZ\nAAiODaYxtI8vNh8/O2wiZ8uE1oVRGXOISybZprebTLIc88gjMGWKunsbjpjZemJrlZVJrKJGXRkF\nJZmjR4+SkJDgVp2XRsMRaUYz7mOEI8bdJZkyOTmQni7rpd7e8Mc/nn2rPqmHmj1fdV+aqJp7e2sX\ncbV+7Ez6nIVxtjWjePJJCAmBO+8cfawarL3tUmLrfCmtckEBMTevK+NJkswgmnH3IPbt22fXY6FL\nMmX+9jf4xjfkPL2nn4ZXX4X/+z84LB9d954l4V/81ZPDRM2W2fpyFk1R/SSmzbGpnkxhoay1v/wy\njFVyRkT6LJqi+/jkte3qL6ZGpkyLcpky9t577oBm3D0Eo9FoVyVIcEGmjBDw2GOwbRtcfbX8WnKy\n7HJ+5zvQ38+8yxJIHhJUjQqIorW3lQHzgHr7ckOqshtoimmyKb+9vx9uuQWeeEL+cY4ZkkRzVAPt\n+4yjj3UWtTJlFPLc9+/f73HGff78+RQXF9tVIVIz7mPAoUOHmDVrlk2VIAdRPVPm6FH5UXrOeW3z\nbr0VEhPhkUdYcf18EqolDlbK+e56nZ6ogCjqu+vV25cboq8OoimyxKb89kcfhfh4+TTqWNMTVY2h\n2rYAvlOolOOuhOZuNBopLi4mTY0qbSri6+vLnDlzKCgosPkazbiPAXl5eSxatMiua1QvPbB1K6xd\ne6FuIElydatnnsGvs4XqGCM7Pj529u2JWEAsvD6U41Ffsjh+8YjjDhyA556Tf3zucFbGf3YvsXUu\naGiuQukBpQ4wDTpWvkq0unIx9kozmnEfAxw27mrKMlu2wLp1w78XFydLNe+9R+MkI437O796a4Ll\nujfUtBDZ6I1xUS++XtYNRF+frGb93/+Bu7TnTL95MWEtXpSfUvmPsQqlB5TqwpSXl0eGUq2uXIxm\n3D0Ah4y7mtkyRiPs2QOXXmp9zA03wLvv4j/Hi6Dirw7tTLSMme1/z6I+tpfFaSPr7Q89BNOmwX/9\nl4s2ZgOzZi+mLr6HHS9nq7uQG8syjtx77oJm3N2crq4uysrKmHO+tj0KqmbLZGfD/Plyrp41Lr8c\nCgu56KIQJpd/FVSdaLnudfvaaI5uYNWkVVbH5ObKB5VeeME95JhBogOjaYqqoTG/a/TBzqBGLXeF\nZBlPNu6zZs2irq6O1tbW0QejGXeXc/DgQebOnWtX9xdhEQw0DOATo5JxH9TbR8LPD666iiWdRwlr\nhf3Hq4GJ1yhbX2OgNuIESxOHb6psNMox6KefhpgY1+5tNCRJojmyBL/aEf6IK4EaXZgUkGUcdazc\nBb1ez8KFC8nLy7NpvGbcXYwjnsNA8wB6gx6dr0q/LluMO8A3voH+/Xc5nWhk10dyFbGJJstE1ofQ\nkFRCkE/QsO//9rcwbx7ceKOLN2Yj3dNria3xPOOuxCEmRxwrd2Px4sXs27fPprGacXcxjgR0VM2U\nqamBqipYPHLmByAHXAsKaE/qob1AzredSAeZqktrCG/2JmLN8C55Tg688QY8+6yLN2YHwZfGEGDU\ncThXxfK/askyTjbH9mRJZpDFixdrnru74mimjGrB1M8/h8sus62wuL8/rF9PQmg9hlPyjZYYnEhV\nR5U6e3Mzsl7JoS7eyMp5Fwaeu7vlXPZnnwUba8GNCVMTZlEb30Xuv2zz/uxGCGhqckvPfTwY94yM\nDPLz820aqxl3F9LR0UFlZSWz7eyr1l+nYhrkSCmQw/GNb7CsPpvJFX4IIYgLiqPZ2Ey/2b5ypJ5I\nQ34njVF1rEhaccF7//u/sHQpXHfdGGzMDqaFT6MpqobOIyZ1FhisK6NgHrkwC0wdJrxCNOM+depU\nOjs7aWhoGHWsZtxdyIEDB5g/fz5ednZDVi1TxmKRPXdb9PZBrrySaYe3ENgN+49UotfpiQ2Kpbqj\nWvn9uRledcE0RpYQE3SuLLNjB7z/vhxEdXdSw1OpjDqOX4NKFRHVkGTaTXgFeyHpHU896ujooKqq\nilmzZim4M9cjSRILFy60yXvXjLsLcdRzUE1zP3UKAgLsK3ri74/uinVUJ3ST84ms2yaHJFPRXqH8\n/tyMqPpQ+me2nPNaZyd897ty2mNY2BhtzA5ig2LJS9xLVF3wOfX6FcNNg6mOOlbuiK3SjGbcXYjD\nxl0tWaakxLFeb+vWYQlpoLWgB5CNe2VHpcKbcy9OHTlNSJsXc689t9jbz38un/266qox2pidSJJE\n2/Q2/Ht1HN1qf+u2UamthdhYRadUIg1yPEgyg2jG3Q1xS8996lT7r7vkEhJ7DxFUJgdVk4KTxr3n\nnvPqbmoTelg99/Kzr23dCp99Bn/60xhuzAEmhU2iOqGL/e8cVH7yU6cU70aiVKaMp5YdOJ9FixZp\nxt2daG1tpb6+nhkzZth9rWrZMqWljhn3KVOY3XPwbFB1IsgyDYU9NEbVMiVMNlxtbXD77XKN9pEO\n9rojySHJtEQ30VGswu3v6GdqBLRMmXOZMmWKTUFVzbi7iPz8fNLT09HbknJ4HqrJMqWljnlZkkTK\n0mQCu+HA4YoJIcv4NYTREV2JdKaewL33yrXU1qwZ4405QFJwEq3Jdfg0qVAh0tHP1Ag4W8vdGcfK\nHbE1qKoZdxeRn5/vkOdg6jIhzAJ9sP1/FEbFCS9Ll3kJdfHd7PqkaELIMlF1oQSkywHITz+FnTvl\nPiaeSHJIMtVpVUTWh8jlK5VEDc+9xTnP/cCBAw47Vu6KLbq7ZtxdRH5+vkOa36DeLildgUoIxzV3\ngMxMpMAqWgq6x70sU1JQgqFTz6X/vY7mZrkP6quvQtDwFQjcnqSQJKqij+HXq+P4BzuUm3hgAKqr\nYdIk5ebEec3d0XvPnbFFd9eMu4tw2LirJcnU18tpkMHBjl0/bRoR4iShpV6E+oViERbae9uV3aOb\nsGPjLmoTelg85SLuuUeufnzJ6E2Y3JbkkGSqOiupTugm/5Mi5SYuL5fbTilcu8VZWWY8GnfNc3cT\nWltbaWhoYPr06XZfq1qmjLOPz5LElORekqoCAFnHHa+6e8vRPpoj6/j4Qy/275dbzXoyScFJVHVU\n0RHXQXu5gp8tFSQZcF6WGY/G3ZagqtPGXZKkKyRJOiFJUrEkSb+yMubpM+8fkiRpgbNrehrOaH6q\nZso4Gfiasz6dgB6JQ2eCquNVmgloiKQ7upG774bXXpMfeDwZf29/DL4GxEwL3i0KHjhSIQ0SnJNl\n2traxlUwdRBbgqpOGXdJkvTAM8AVwGzgm5IkzTpvzHpgmhAiFbgDeM6ZNT0RZzwH1RpjK+Bl6Vdn\n0hDfya5PTsoZM+3j03OPrg+jxBTHf/0XLF8+1rtRhuSQZEIvNRBRHypLdEqgkufujCwzeDJ1PAVT\nBxlNd3fWc18ClAghTgshBoC3gGvPG3MNsBFACJELhEqS5GZtDNTFGePeV9unXhqkszfijBmIwGra\n97WP24yZ4/lHMHToKSi/ht/9bqx3oxzJIcmEzh7At09H0bvblJlULVnGiTz38SjJDDKa7u6scU8A\nhrprVWdeG21MopPrehTj1XNHkvANbyb4tK8sy3SMP+P+6fNfUBvfw8ZXw/DzG+vdKEdScBLVnZVU\nJfZQsE2hJy4VNXdHZZmJbNydraJja+Wh8/P4hr3uwQcfPPvvzMxMMjMzHdqUO+Gs5jfQOIB3lPO9\nIy/AmTTIIaRmBOH3aiARIYZxJ8sIAR3H/bBENNvUy8STGDx4FpmQgL5agZzOwdRahTV3S78FS68F\nvcExWSU/P5/f/va3iu5prMnKyiIrKwshBIGBgVbHOWvcq4GkId8nIXvmI41JPPPaBQw17uMFZzU/\nZ7wWq3R2yl9xcU5Ptei/L6XxuWaMNX7jTpb5xz8gqjOcrqnj6/8LZOP+ZdWXxKavRrcpVq7D7mha\nLMi6vZ+f4rUY+uv68Y7xduicR3t7O7W1tcycOVPRPY01Qx3fhx56yOrPxllZJg9IlSRpsiRJPsBN\nwMfnjfkY+A6AJElLgTYhhEIRHPfH2cdCZ3N8h+XUKUhJAQUORunnzqUhrpPSTQ1Ud1ZjERYFNjj2\nVFXBz35uIbo+hOmXJI1+gYcxGCNZsC6F0IYw2L3buQkVehI8H2eyxcZzMNUWnDLuQggT8ENgC3AM\n+LcQ4rgkSXdKknTnmTGbgFOSJJUALwD/4+SePQpnjLswC8xdZqc70FyAktqoTkdvWCM9B7oI8wuj\nvsvz/24LIRcFu/mbOwlp92LD9zeM9ZYUZ1CWWX3ZLHz6dJR9uMu5CVXS252JOY1nvd0WnM5zF0Js\nFkLMEEJME0L8/sxrLwghXhgy5odn3p8vhDhgbS5bWkd5Gs58wExtZzrQ6BQuPaDwjegf20FgdSBJ\nIeMjY+bll8+0ATXmUBtvxDdgHEVSzxAbFEtzTzNmYaI82cjRQqNzE6pk3J3JFtOMuxtha+NXT8FZ\nzU8VSQYUf4SesTKK2BrDuDjIVF4u90N97TXgVCCtUY1jvSVV0Ov0xBviqe6spnFSHy09idDV5fiE\nannuTpzQ1oy7G5GXlzfWW1CUgoIC54KpCtSxHhaFb8SLbrkcv14d4Z2TPboEgcUit8z72c9g7lwI\nbU6AxO6x3pZqDP4x9p/nS+9ACuzZ4/hkKpT6Bcdlmfb2dmpqasZdMNUe3Mq4jzfP3VnPwdRiwjtM\nhTRIhW9EfUwM9fFdhOdFebTn/txz0N0tt86r6qgiui6cWasnj/W2VGNQRltweTLh9aGQleX4ZGp6\n7g7IMgUFBaSlpY2LnqmOohl3FcnPz2fhwoUOXz/QOoBXuMIfzoEBORVk8mRFp+0JbyK4JMJjjXtp\nKTzwgCzHeHnBlu0fEdLmxaW3XjrWW1ON5GC5ZMSll87Cp19H7RcFjk2kYGrt+TiaLTPRJRlwM+Pe\n1dU1roKqeXl5LHbi9IsqskxFhdzA2FfZYmS+yUZC6yI9UpaxWOC22+C++2DwKb7o49PUJvTgFzj+\ngqmDDMoy3no9Zcl9HGuJkh9d7KWsTE6t1SlvThyVZZy998YDbmXcbe3q7Qm0tbU5fYDC1KqCLKNS\nPvLMNcnE1gZ7pOf+l7/I6Y8//vFXr/lXRNEW1TJ2m3IBSSFJZ0tGNE/q43TwIti71/6JVJJkhEXQ\nX9+PT4xjxn289Ex1FLcz7uMlqKrEAYqBFhVkGZVuxBXfWYNvr46AegO9pl7F51eLkyfh0UdlOWbw\nV9XW20ZE8yS8Jyncgs7NGFrJ03eeN/09SXL/QHtRqxpk8wB6gx6dr31mqrW1lbq6unFX5tde3M64\njxfPXQnPQRVZpqoKkpQ/can386MuvovV1Zd6TI0ZsxluvRUeeuhc27Sncg8xtRHMuFzZdnHuRnJI\nMuXt5QghWLgmmag6A3zxhf0TuVmmzHjsmeoIbmXcbekL6CkoZdwVl2UaGiA6Wtk5z9AV1UpK7RzK\n2spUmV9p/vhH8PeHu+469/Wc7VsJ6tST+W0P7qVnAyG+ch2Y9r52Ll09E58BPU1FTVBba99EBQUw\nZ47i+3M0U0aTZGTcyrinpKTQ3d09LoKqSnzABlpUOMTU2AhRCnbfGYLPFBMRjYmUtJSoMr+SHDki\nG/dXXrkwDtiZ3UdNQjc+fiqUWnYjJEk6K834enlxanIfe+d9HT74wPZJWlrg6FFVupg4mimjGXcZ\ntzLutrSO8gSam5tpampyqGfqUEytJuU198ZG1Tz32VenElMXSklzsSrzK8XAgCzHPPbYhRmhvaZe\nwmtT6IxqHYutuZyhp4qbJ/VRIs2Ed96xfYLt22HVKtQodu9o6QHNuMu4lXGH8RFUHcxv1zmZGqaa\nLKOS577qG8vx7dXR9uVpVeZXiscfh8hIuTjY+eTV5BHdNAWfKQOu39gYMLSDlu8cb2gIlWUWW1vv\nbd0Ka9eqsjdHNPempiZaWlpITU1VZU+ehNsZ90WLFnm8cVfKc/A0WUav11OT0IXhsPt2UTx4EJ5+\nGv7+9+ErHueUZxNdG8as9dNcv7kxIC4ojtouWWOff3kyyVWBsH69bdKMELBlC6xbp8reHKkro5Rj\nNR5wu5/A4sWL2b9//1hvwymUMO6WAQuiTzjcgWZY+vvl4lBhYcrNeR6dMW1EN8zAbDGrtoaj9PfD\nLbfAH/4AiVYaPR7JyiXAqOPiG8ZJJ+xRiDPEUdspG/fLM2fiZdFRtnAtvPvu6BefPCkbeJXqtzgS\nUNUkma9wO+M+adIkTCYT1dXDNmvyCBRLgwz1cqgDjVWamiAiQpWThIP4z4TI5slUdZzfkGvs+d3v\nIDlZNvDDYREW/A+FURPfhZf3xKhJMtRz9/Pyojilj22NEbB/v/yUNxJbt8peu5Kf0SE4Istoxv0r\n3M64S5Lk0d57fX09nZ2dTHXyUIcqOe4qSjKDLLw5ndjqEIqrj6m6jr3s3w8vvih/WbNFRxqOkNyU\nRld0m2s3N4bEGb4y7gAtU/ppKOyHK66ADz8c+eItW1TT28GxbBnNuH+F2xl38GxpZrBgkbMetyp6\nu4o57oMsvmQ2Fh0c/ciJ8rEK09srZ8c89dTIta2yy7OJbErGN9X9JCW1iAuKo66r7uz3AfP9CD7l\nA9/4xshZM319kJ0Na9aosi9TlwlhEuiDbZcl6+rq6OrqYooKB6o8Ebc17vv27RvrbTiEUp6DqVWF\nxtgu8Nz1ej01ie10uNGv74EHYNYsuPnmkcdln95JTG0o866d5ZqNuQExQTE0djeejZEsunIKk6sC\nsFxxBeTmWs+a2b1b/qGGh6uyr0FJxh4nKT8/n0WLFikrZXowbmvc8/LyEEKM9VbsRknjrooso7Ln\nDtAe04R/g5WIpYvZuxdefx2efXZkaVgIQd3uErwHJJZvmDiP9T56H0L8QmjqaQJg9aIp9ATpOPxl\nlZwr+pOfDH/hoN6uEo5kymiSzLm4pXGPjo4mODiYkhL3P+l4Pm6dBqlijvtQfGZDWGO86uuMRk+P\nHDx95pnR/6YVNRcxr2oxtfFdE64mydCgqq9OR2lKH3s/LYFHHpFz3t9++8KLXKC3a5kyzuGWxh08\nU3evrq6mv7+fSZOcLzjlqbIMQOYtK4mrDqJvtGwLlfn1r2HRIvj610cfu7N8J4kNaXTFTJxg6iCx\nQbFn0yEBWqcO0HaoVy688/rrcM89UHdGl7dY5LoNdXVw0UWq7cneTBkhBPv375/wDTqG4rbGfcmS\nJR5n3HNzc1myZIkimp8nyzKLMmbRGWzms+dHybZQkV27ZIfzmWdsHF++i7DGRAJmTzy99vyMmZCF\ngYSV+8vfLFkiyzN33ikXFFu3Tj7gtHcveKvQAvIM9mbKVFRUIIRQxLEaL7itcffEoGpubi4XKeTN\neLIsA1AT30JZ7th4wV1dcmel55+3Ld4nhGBPSRZx1cEs+++J170nLijuHM99+dWpJNcG0G/sl1+4\n/344fVo+rLR8uVzzXeE2jedjb12ZwXtPC6Z+hdsa94yMDA4dOoTJZBrrrdjMvn37FDPunuy5AzTF\n1qCrd80fkvP55S/lWlYbNtg2vqytjPnFqfT5WZi/XJ3Tlu7M+emQy6fFUx8jceCTI/ILPj5yzvvn\nn8vF713QdNpeWUbJe2+84LbGPTg4mKSkJI4ePTrWW7EJs9lMfn4+S5YsUWQ+VTR3F3ruxtldhDRF\ny8fTXci2bfDpp3JOu63sPL2TtNqLqYubeHo7XCjL+On1lKX0cWBr+VeDUlJkicZF2Jsto+RT83jB\nbY07eFZQ9dixY8TGxhKuUN7vQKvCskx/v9z8ODRUuTlHYOaGScTU+tN5otQl6wF0dMD3vgcvvWTf\n/+bO8p2ENUyhP7FTvc25MUOzZQbpnGam+8jYPTXbky0zMDBAQUHBhG+IfT6acVcIpT0HU4vCskxT\nk1zn1kXV8hZPn0tT5ABZr2a5ZD2An/5UPjVvb/r1zvKdhDdEE7MsRJ2NuTlDi4cNErHYQGRFwJjs\nxzJgwdRqwifaNuN+5MgRkpOTCQmZmL8/a7i1cV+yZInHBFUVN+5KyzIulGQApoZPpTqhgaqDrmmW\nvWmT3Dfij3+077qK9goCG81E1/mx9vur1dmcmxMbFEttV+05hwZXrp9BXLM/3S3dLt/PQMMA3pHe\nSHrbgqOaJDM8bm3c58+fT1FRET09PWO9lVFR8gNmNpoRFoHOX8FfjwuDqQDBvsGcji1GNKpzPH0o\nra1wxx1yyzyDwb5rd5XvYl3VFTTE9BEV6xrJyt0I8gnCS+dFe1/72dcuio/k9GTY//ZBl+/H0UwZ\njXNxa+Pu5+fH3Llz3b55R1dXF6WlpcyfP1+R+QYzZRRN63LRAaahVMwqI6w+Uu5rpyI//jF87Wuw\n2gHHe+fpncRUzaIldmIGUwc5Px3ST6+nNLWfI5+7vvS2FkxVBrc27gDLly9n7969Y72NEcnLyyMt\nLQ0fH2UaKntaez1rhGVIhLR5U/qfbNXW+Ogj2LNHbp3nCDvLdxLUmIiU4hr5yF2JM5ybDglgnA3i\nmOtNRH9NPz7xtt1L7e3tVFRUMG/ePJV35Xm4vXFftmwZe/a4T/nY4VDacxhoHfCoxtjWmBmTSmVy\nF3veUSedtakJ7roLXn0VAgPtv76yvZKOnlYia8OYsWFitNWzxnAZM8mXxpBYGezyAn7GEiP+U/1t\nGrt//34WLFiAlwty7z0NjzDue/fudesKkW6fKQNjIsvMipxFTXw17WW23aj2cvfdchnfVascu357\n2Xa+bswkoEfPZTcuVXZzHsb5sgxA5rIpmHx0lOWUuXQvPcU9+Kfa9pnRJBnruL1xT0pKwtfXl1On\nTo31VqyiSqbMOJBl5sXM43jyQfyblG+Y/fbbcrPrRx91fI7tZdtJPjGXmoQuvCdIWz1rnH+QCWC+\nwcCxeToK/n3EpXsxFhsJSLUtDVMz7tZxe+MO7i3NVFVV0d/fT0pKimJzjhdZZmrYVHYmbSGuKoje\ncuV6qtbXy4UKN26UCxc6ghCC7ae241eZQOcEPZk6lOFkGV+djsrpA9Tu63LZPoRF0HuqF7+pfqOP\nFUIz7iPgsHGXJClckqTPJUkqkiRpqyRJw+aRSZJ0WpKkQkmSCiRJcihpfVCacUcGa1oomdmiSl2Z\nMfDc9To9k5KjaAszsePFzxWZUwhZZ7/1VljqhJJyoukE3npvApqjCZw3seq3D8dwB5kAdAt9MZx2\nIKDhIH1VfXiFeeEVNPrnv6KiAp1OR1JSkgt25nk447n/P+BzIcR0YPuZ74dDAJlCiAVCCIeKUyxf\nvtxtPfc9e/aw1BkrMwymFhVkmTHw3AHmRc+jNr6Jsjxlziq88QYUFcn1q5xhe9l21kesIr7KwMpb\nJ7beDl8dZDqfBWsmE90WSGe9a0ozGIuNNuvtg/eeVglyeJwx7tcAG8/8eyNw3QhjnfrpL1iwgOLi\nYjo73a/2R3Z2NqscjehZQZW6Mj09LqsrM5S0mDTqkyuQ6iKdnqu2Vi4xsHEj+Npe6ntYtpdtZ1b+\nVDqCTczN0Boqn18ZcpAVcZGUTrFQ8GaBS/ZhTzBVjXtvPOGMcY8RQgx2z60HrEXNBLBNkqQ8SZK+\n78hCPj4+pKenu12dme7ubo4cOaJYJchBTK0mZTX3xkaIiBi5iahKpMWkcXRGIeF1EeBE+WYh4Pvf\nl3tGONtsx2wxk3U6C45FU5/Q6txk44Rw/3B6BnowDhjPeX12QABH53tRtlm5mMlIGEuM+E+zzbjn\n5ORoxn0ERjTuZzT1w8N8XTN0nJDzFK3lKq4QQiwArgTuliTJod+GO0ozubm5zJ8/H39Ho3pWUFyW\nGSNJBuSMmd1Bmwns1nPsgyyH53ntNaiuht/8xvk9Hag9QLwhHu+6GJg+sQ8vDSJJErFBsRd47146\nHU2zzPSdcE3uha2ZMq2trZSVlZGenu6CXXkmI7qHQojLrb0nSVK9JEmxQog6SZLigAYrc9Se+W+j\nJBwmGdAAAB1sSURBVEkfAEuAYY8sPvjgg2f/nZmZSWZm5tnvly1bxssvvzzSdl2OWo+FissyY5Dj\nPkhkQCQBvn5UJXXS+EEls29YY/cclZVyA47t2+W+Ec7yRdkXXB5/MTFV4UQ8plyWk6czmDGTEnbu\nzyRyZRjxvxdYzBZ0enWNvK2a++7du7nooovwVrHVn7uSlZVFVlbWqOOcsSAfA7cAT5z57wUNMyVJ\nCgD0QohOSZICgbWA1VDYUON+PsuWLeP222/HYrGgc1HZ2tHIycnhxz/+seLzKi7LNDSMmecOsjTT\nEt+A4bT9JWSFkGu0/+QnkJamzH62l23n5tOZgGDllZrnN4i1jJmlcxJoNzRStLWImVeq16lKmAW9\nZb02nU6dyJLM+Y7vQ1ayC5yxko8Dl0uSVARceuZ7JEmKlyTpP2fGxALZkiQdBHKBT4UQWx1ZLC4u\njpCQEIqKipzYsnKYTCZyc3NZvny5ovMKIdSRZcbIcwc5Y6ZtZh2BDfb/gXnxRWhrg1/9Spm99Jn6\n2Fu1F+O+IKqT2tHrtTTIQYbLdQdYEhzMiTQ9x989rur6fVV9eIV7oQ8c/XeSnZ3NypUrVd2Pp+Ow\ncRdCtAgh1gghpgsh1goh2s68XiOEuOrMv08JIdLPfM0VQvzemc0uX76c3bt3OzOFYhQUFDBp0iTF\nOi8NYu42I3lL6HwVfDoZgxz3oaTFpHF64UniqgNoO2H7SeOyMlljf+015dp27q7czeyo2VAdRf9k\n98u+GkuGK0EAMD0ggCPzvWjZo+5hJlszZYxGIwcPHlQ8BXm84R76ho1ccskl7NixY6y3Aaj3WOjp\njbGHIy0mjWN9BTTE9LHlb9tsusZige9+V9baZ89Wbi+bizezPmUd4XWRTLlKO/wyFGu57npJonuJ\nF2Gnw1St8WRrMHX//v3MnTuXQEeqxU0gPMq4r169mqysLLcoIqbWY6Gp3YRXiOcXDRvKzMiZlLaW\n0pTURP0h2z5yzzwjp+f/9KfK7mVzyWYyyhIxdHix/paJqdlaY7iyv4PMyYjFpJeo2F2h2vrGEtuC\nqZokYxseZdynTZPLspaUlIzpPoQQqnnu5k4zeoPCOvAYyzK+Xr5MDZuKmN9NYG3sqOOLiuDhh2U5\nRklJvLytnPrueuo/76cqqRNf34mXaTES1jR3gItCQjieZubgK+p1ZjIW25bjPpGDqfbgUcZdkiRW\nr1495tJMUVER/v7+qtS0MHepYNybmsbUuIMszfhfqyO+KpDmk9Z1d7NZrhtz//2QmqrsHjaXbOaK\naVfQc9pAV7JWLOx8ogKjaOxuHPa9xcHBFKwMoD2rfdj3lcCWNEiz2czevXtZsWKFavsYL3iUcQfc\nwrireezZ3GVGH6SwcW9rG5PSA0OZFz2P06KIurhePnv6C6vj/vxnOZf9hz9Ufg+bijexfuqVhNTH\nEL1yYvZLHYmogCgaexqHlT2n+PlRuMSL8IoIhEV5WVSYBcay0Zt0FBYWEhcXR9QYOyuegMca97HU\n3dV8LDR3mvEyKKy5d3RASIiyc9pJWkwahQ2FtCY20VQ4/Mfu2DG5Xd4rr4DSRxn6TH3sLN9JelM8\n0XW+XP0/DjRcHef4e/vjo/ehs//CLCJJkkhNDaPNr5dTnyvfW6G3shefKB/0ASM7NpokYzseZ9xT\nUlLw8/PjxIkTY7K+EIKsrCzP8dz7+uTUE2crbTlJWkwahfWFhGboMdReWIbIZJLlmN/9DqaoUMdr\nV/ku5kTNIf/1I1QnGgmPMCi/yDggKiCKhu5hD5tzUXAwpWlmDm88rPi6turtO3fu1Iy7jXiccYex\nlWaKi4sxmUzMmjVLlfkVD6gOeu1jXBY1MTgRIQTzvjeLuOoAGk6c27rtySflbf7gB+qsv6l4E+tT\n19N63EBLSrM6i4wDRtTdDQZOXmKgO7tb8XVt0dtNJhPbt29nzRr7S1hMRDTjbidbt25l7dq1qtWQ\nVtxzb2+H4GDl5nMQSZJYmbySIssxauONfPbXr35/hYWy1v7yy+r9DdpUson1KWsJr44jcb2m11pj\nUHcfjsUGA7uW+RNeHY5lwKLourakQe7fv5/k5GTi4uIUXXu84pHGPTMzk6ysLCwWZT9gtrBlyxbW\nrVun2vymTpOyxt0N9PZBViavJKcih/bEJlrP5Lv398Mtt8ATT0BysjrrlraU0tHXgfi8luB2b67/\nn8vUWWgcMJLnHuvriy7Kl1rfZoo/KVZ0XVs8d7XvvfGGRxr35ORkgoODOXr0qEvX7e/vZ9euXao+\nFiqeCukmnjvAquRVZFdkE77Yi+Ba+cTsY49BfDzcdpt66w6mQO5/v5LTU1q1/PYRiA6Itqq5Aywx\nGKiab+LYP48ptqYQgs4DnQTNCxpx3OBTs4ZteKRxh7GRZvbs2cOMGTOIiIhQbQ3FZRk38tznx86n\nor2CpT9YRGyNP9ver+DZZ+XiYGqGBD46+RFXp16NqIhhYJbrmj17IlGB1mUZkPPdqy6LoHePcnXw\ne8t7wQJ+U6w3xW5tbeXIkSPayVQ78Gjj/sUX1vOl1WDr1q2qPxYqHlB1I8/dS+fFRYkXUWwpoiah\nh/ceOcSf/gQJCeqt2dTTxL7qfVwWupj48giWfX+heouNA0bS3EH23A9nhhHaEMpA24Aia7bntBOy\nMmTEONYXX3zBihUr8POz/gdA41w81rivXbuWHTt20Nvruk46rngsVCWg6iaeO5yRZsqzqYvqYCp9\nfPvb6q73wfEPWDd1Hdl/+ZzuQDPLLpur7oIezkiaO0CGwUChr4WT/kUcev6QImu257QTvGJkB8QV\njtV4w2ONe1RUFPPmzbOpI4kSNDY2UlJSonqZUcUPMXV0uI3nDnJQdfOxHHY1pjK5MgyL2fG+qrbw\n7vF3uWH2DZz+0kxtSpOqa40HRvPcg728mOTnR/tlPpz+52lF1uzY3UHISusOiBCCLVu2aHq7nXis\ncQfYsGEDn3zyiUvW2rZtG5mZmaq39Rrvnnta+EUcaTrItx+agUUn2PTXTaqt1dzTzJdVX7J+2pUE\n1sQTtFRrzDEa0YEjB1RBTonsvnU6QSeCMPeYnVpvoHWA3tO9BM23HkxV+2zJeMWjjfs111zDxx9/\n7JJSBK5KwzJ3jt+AKsDjvwskbGAOk1cWUJtSS/En6h0o+vDEh1w+5XJa950ktjqQa3+mHX4ZjUFZ\nZqR7aklwMM0z4zgpneTUm86VIujY04FhiQGdt3VTNHjvqXW2ZLzi0cZ95syZ+Pn5cfCgemVIQX4s\ndFUa1nhOhczJgTfegJuWriKnIofIpRbCy9Q7kPLOsXe4YfYNfP78XqqSuomNV7Zr1ngkwDsAvU5P\nV7/1rKLFBgN53d10pHVw4iXnyoC0724fUZIBLQXSUTzauEuSdNZ7V5PCwkICAgKYOnWqqusIi8Dc\nY7aph6TNuInn3t0t57I/+yxcPkM+zHTtb68josmH/M8LFF+vxdjC3qq9XDX9KrpPhtI+tUXxNcYr\no+nu84OCKDYaSf7edLwPeGPpc/wwYXtOOyErrH8+e3t7VT9bMl7xaOMOuMS4v/POO1x//fWqrgFg\n7jGj89Mh6RV8/HQTz/3//T9YuhSuuw5WJK9gT+Ue/MOCqJjSTPazBxRf76MTH7Fmyhr09R0klsaz\n8A5Nr7WV0XR3X52OOYGBRG9YTpm5jLr/DN+9aTQsfRY6D3QSvNT653Pz5s1kZGSoerZkvOLxxn3F\nihWcPn2aqqoqVeYXQvDWW29x8803qzL/UFSp5e4GnvuOHfDBB/D00/L30YHRxAbFcqThCF4zm/Er\nilR8zUFJ5oMHP6A9pJ/Lrlus+BrjldHSIUHOdz8BVKZUcuS5Iw6t03mgk4DUALyCrWeHuereG494\nvHH38vJi/fr1qmXN5Ofno9PpWLBggSrzD0WVFntj7Ll3dsqNrl98EcLCvnp99eTVbCndwrqfriS5\nLJjaipGNiT00dDewp3IPV6VeRWtBGI1zRs7+0DiX0WQZkIOq+zo7SfxWIpYcCxaT/dLM4OEla3R1\ndfH/2zv3uKiqtY9/FxcREAwNMgXUk2beUtM0I46XMjE6mmleOvqal8QUT1Kab1CRx1K84DmmZt7S\n8tP7KvqqZZ7IehPFS+YtNU2PoBgXL4jYIBcdmHX+GLwlyMywhxk26/v58NE9s/baz8aZn89+1rOe\nJzExsUqemvVItRd3sG9o5obnUBUr9Xr03CdPhp494bnn7nx9YKuBrDu+jiZd2pAVlMemDxM1u+bK\nQysZ0HIAMiOX4JQGhETZ/z9mPeHvVbHn/riPDz8ZDPQe0Zus4ixy/z/X6utUtJi6efNmQkJCuP9+\n7Z/sagK6EPfevXuza9cu8vLu7iBTGUwmE2vXrq2yx0LN0yClNLvOPo5pTLF1KyQmwrx5d7/XrUk3\nzl45y+nc0xQFZ3H9gDbNREzSxJIDSxjXaRwbY7/iYkARXZ9pq8ncNYWK6ssAtPDyItto5L7gYPb6\n7+XYNOuK+EkpK9yZqkIylUMX4u7r60vPnj1Zu3atpvPu3r0bPz8/WrVqpem85VFyVePdqfn5ULs2\nuGncts8CrlyBMWNg+fKyHxzcXNx4seWLrDu2jtBRf+JPJ/3JTK/8DtLvUr/Dz9OPTg07kf+LP1fa\nahfuqSlYspHJVQg6+fiwz2AgaEwQ+YfzuXrE8qJsVw9exc3XjdqBZdeKyc3NJSkpiX79+lllu+IW\nuhB3gLFjx7J06VJN56xqz0Fzz92B8faoKAgPh169yh8zqPUgEo4n0G7os2QF57J2SuV3q35y4BPG\ndRxHzrEUglMCePot+5aL0COWxNyhNDSTl8fIiJGsl+s5PcPyDU3p89JpOK5hue9v2rSJp59+mrpO\nkMZbXdGNuPfu3Zvz589z6JA2OdPFxcWsW7eOwYMHazKfJWi+gclB8favv4bt22HOnHuP+3PjP5Nh\nyCD1cipeHTLx+ymgUtfNMGSwPW07Q9sOZdP0rWQ1yqd91xaVmrMmYkm2DJgXVffl5dGoUSMM3Q1k\nf51NYVphhecVphVyOfEyDSPKF3cVkqk8uhF3V1dXxowZw7JlyzSZLykpicaNG9t949Lt6KHFXk4O\nRETAypVQ5969F3BzcWNAywGsO76OQXMH42NwZ8My22v0rzi4giFthlCnVh2MxxuQ304VCrMFqzx3\ngwEpJa9MeIVk32Qy5lWckpzxjwweHP0gbnXLDhdevHiRvXv3Eh4ebrXtilvoRtwBRo0axZo1a8jP\nr3wD31WrVlW551CcV1ztPfe//Q0GDoRu3SwbP6j1IBKOJeDRIIDzLVI486ltaYvFpmKWH1pORMcI\nTn6dTNCZevR5t4dNc9V0/L39uZh/scKaTUEeHggg/do1wsLCWM96sj7L4vql6+WeY8wxcmH1BQJf\nDyx3zBdffEF4eDje3t623oICnYl7YGAgTz31VKUXVk+fPk1iYiKjRo3SyDLLqO6e+4YNsG8fzJxp\n+TmhwaFk5WWRcjmFp0YE8PAvAZzPsr5UwJcnviTQN5B2Ddqxdea/OdX6Io+0bWz1PArwdvdGIMg3\n3ttJEkKY890NBlxdXXkp4iVSA1PJ+Ef53nvmokzu738/Ho3Kzo66du0a8+bN44033qjUPSh0Ju6g\nzcLq7NmziYiI4L777tPIKsuwy4JqFXnu2dkwYQKsWgVeXpaf5+riag7NHFtH+1df4FzQFb6Y+o1V\n1zaWGIn5IYZ3//wuZ/ccofHRpjz2dlPrbkBxEyGExXH3G4uqAKNHj2ZWxizOrTpH1rKsu8aWFJSQ\nuSiToMlB5c63evVqWrduTceOHW2/AQWgQ3Hv06cPmZmZHD5sW5eYzMxMEhISmDRpksaWVUx1XVCV\nEsaPh2HD4MknrT9/2KPDWHZwGddKruPZJg2/3dZVb1x+cDmBvoH0adaHr97eS9pDl+n2QmfrDVHc\nxNK4+41FVTA/Obfo1oLTE09zdvpZMj/OvDnOmGMkdUoqvk/44t2y7HBLcXExcXFxxMTEaHMTNRzd\niburqysRERHExcXZdP68efMYMWIE/v7+GltWMdU1FXLtWjh2DKZPt+38rkFdaeXfio/3fczAmf3x\nyXNnxaxvLTrXcM3A33f8nTm95pB96jeCDj/EQxOq9olLj1jjuR/Iy6OkND7/2muvEbc6jjbftyF9\nTjpp09I4GXGSvc32Yiow0XxB83LnSkhIoGHDhoSGhmp2HzUZ3Yk7QFRUFHv27LG6gfalS5dYuXIl\nb775pp0suzeab2KqAs/9/Hl4/XX47DPzfilbmfXMLGbunElRw7pc7/QTHgtcMBgKKjxv9q7ZPPvQ\ns3R4sANrJyaSGfg74WNUedjK4u/lX+FGJoB67u4EuLtzosD8bxUWFkZQUBBLvlpC+6T2/L77d2o9\nWIvOJzrzyMpHqB1c9ofEZDIxc+ZMoqOjNb2Pmowuxd3b25v58+czfvx4rl8vf+X+j8yfP5+BAwcS\nGFj+Sr49qW4LqlKa0x5ffRUer2TRxdYBrenXoh8zd85kyMrRFNW5ysL/une9oAxDBov3L+aDHh9g\nuJBDw/3NeGC4Lj/SVU6Ad4BFYRkoDc0YDIA5Xr9w4ULi4uLIdsmm3bftaPp+U2o9UOuec2zevBkP\nDw/VBFtDdPtN6Nu3L82bNyc+Pt6i8adOnWLx4sVMnTrVzpaVj+ZVIe3sua9eDWlp8N572sw3rcc0\nVhxaQYZHEW1ezKDNDwHs3lF2px8pJZO3TiaiYwRBdYNYOWQTlwLyGfiW2q6uBZYUD7tBRx8fDly9\nVXqgWbNmREZGEhUVZdH5eXl5REdHExMTo1rpaYjN4i6EeEkIcUwIUSKEeOwe48KEECeEEKeEEFWm\nnEIIPvroI+Lj40lLS7vn2NzcXJ5//nlmzJhRpZuW/kh18twzM80VHz/7DGrd2ymzmIY+DZnw+ATe\n2fYOT3wwgXMtf2XfxJN3jbsh7GeunCE6NJqlIz8l8EgTHou3X8u+moYlxcNu0LFOHQ78oWjf1KlT\n+fnnn0lMvHe1z5KSEl5++WVCQkJ44YUXbLZXcTeV8dyPAv2BHeUNEEK4AguBMKAVMFQIUWUtcZo2\nbUpUVBTjxo2jsLDsbdFGo5GBAwcSHh7O2LFjq8q0MinOK64WzbGlNBcFi4yE9u21nXvKk1PYdmYb\ns/fM5aUZj/LARS/mRm68Y8y07dP4/sz3fPPXb/huwTYabGiCS1QOjz+nGnJohaUxd4DHfHw4fPUq\nxaZbNd09PT1ZsGABkZGRXLhwodxzp0yZQkFBAYsWLVJeu8bYLO5SyhNSyn9XMKwzkCKlTJNSGoE1\nQJU+N0+ePJm6devSuXNnjh49esd7UkoiIyPx9PRkTkWFUKqA6tIce8UKc177229rPjU+Hj7sHr2b\nDb9uYGT2XHxDf6DxWl9mdPs/LmUbmLt7Lmt+WcPWYVs5/WMmppleXAw/Sf93BmlvTA3GGs/d182N\nQA8Pfi24cwG8T58+DB8+nA4dOvCvf91dFG7JkiVs2bKF9evX4+7urondilvYuxZsIyD9tuMMoIud\nr3kHHh4erFmzhs8//5yePXsSExNDkyZNSE5OZvv27RiNRnbu3Imrq8ZNMqzEZDQhiyUutTVcBrGD\n5372rFnUt20De30fg+sGs2PkDqZsncLErmt4r+5x6v/4V7Z03MPWvqm8/Pu7/O+nO2ly2o+s9ilM\n/J/X7GNIDSbAO8DimDuUxt3z8mj7h4JCsbGx9OjRg+HDh9OvXz/69u1LcnIyycnJHD9+nOTkZPxu\nb9Gl0Axxr/oRQojvgAZlvBUtpdxcOmYb8KaU8q4ux0KIAUCYlPLV0uNhQBcp5cQyxsrY2Nibx927\nd6d79+7W3U0FpKSkMH78eFxcXAgNDSU0NJQuXbrg4aFNo4jKYMw18mPTHwm9omGOr7c3XLhQcQUv\nCzGZzCV8e/UyN7yuCr488SVHLx4lpCCAtA/O4/bbExT4XcLl/nM8HOJDt3de0S7or7hJ3rU8GsQ3\nID/asjpN8enpnCksZOHDD5f5fm5uLpMmTSI1NfXmdy8kJESV9LWBpKQkkpKSbh5PmzYNKeVdMa17\nirslVCDuTwDvSynDSo/fBkxSyllljJWVtaU6U5RexMGuB3kyw4YtnmVRXGxOPDcaQaNY5qJF5gyZ\nnTsd0v/DfE+HD0PbtkrQ7YyUEs8PPcl5KwfvWhUX8Np+5Qr/ffo0ex4rN7dCYSeEEGWKu1Zf0fLU\nYz/QXAjRBMgCBgNDNbqmrijJs8MGJh8fzYQ9NRViY2HXLgcJO5gvrGqOVAk368sUZFsk7h3q1OFI\n6aKqm4tuM6yrFZVJhewvhEgHngC2CCG+KX29oRBiC4CUshiIBL4FjgNrpZS/Vt5s/aF5GqSG8XaT\nCUaOhOhoaKF6X9QY/L38uVRgWU38G4uqxwsq3lWsqBps9sGklBuBjWW8ngWE33b8DWBdmb8aiDPX\nlZk/35z++PrrmkynqCbU86zH5ULLyy93Kl1UfVSjNR5F5VDPT06Cs1aEPHkSPvzQ3FnJwQlFiiqm\nvld9q8T9RsaMwjlQ4u4kaL6BSQPPvbgYRoyA99+HZs20MUtRfahXux45BTkWj+/o48N+Je5OgxJ3\nJ8EuG5gq6bnHx5sbb4wfr5FNimqFtWGZDnXqcDQ//46dqgrHocTdSXC2BdVffoE5c+DTT0ElP9RM\n6nvVJ6fQcs/d182NILWo6jSor62ToHlFyEqEZYxGczhmxgxo0kQ7kxTVC2s9d1ChGWdCibuT4Eye\ne1wc+Pub67Qrai71Pa1bUIVbGTMKx6PE3UlwllTIn3+Gjz6C5cs12/+kqKbU86xnVVgGzBUiD95W\n213hOJS4OwnO0GLv+nVzOGbOHHBQMyqFE2FLWKattzfH8vMx1eBSIs6CEncnwRk89+nTITjYLPAK\nhbV57mDuqVrXzY2zRUV2skphKY6qEqL4A47exLR/Pyxdag7LqHCMAsCvth+5hbmYpAkXYbkf+Ki3\nN0fy82nq6WlH6xQVoTx3J8GRLfaKisze+j//CQ+qTnWKUtxd3fFy98JwzWDVeW29vTmi4u4OR4m7\nk1CcV+wwzz02Flq2hCFDtLu8Qh/YEpp5tE4djuRbVgdeYT+UuDsJjvLc9+yBzz+Hjz9W4RjF3dTz\ntK4EAZSGZZTn7nCUuDsJmi6oSmn23CsQ94ICeOUVWLgQAgK0ubRCX9iS697Cy4vfrl2joKTETlYp\nLEGJuxMgpbTZc7+93dZNiorMNQMqaB8YE2PufTFggNWXrVGU+TuuIdiSDunu4kILT0+OWxGaqcm/\nY3uhxN0JMBWaEO4CF3fr/znK/FJYEG/fsQMSEmDBAqsvWeOoycJjy0YmsD7uXpN/x/ZCibsToPkG\npgri7VevmjsrffIJ1K+v3WUV+sOWsAyouLszoMTdCajqujJTp0JoKPzlL9pdUqFPbFlQBZUx4wwI\n6STbhIUQzmGIQqFQVDOklHflujmNuCsUCoVCO1RYRqFQKHSIEneFQqHQIUrcdYAQ4n0hRIYQ4lDp\nT5ijbdILQogwIcQJIcQpIcRUR9ujR4QQaUKII6Wf3Z8cbY9eUDF3HSCEiAXypJTzHG2LnhBCuAIn\ngWeATGAfMFRK+atDDdMZQogzQEcppfU5l4pyUZ67flCVYbSnM5AipUyTUhqBNUA/B9ukV9TnV2OU\nuOuHiUKIw0KIFUKI+xxtjE5oBKTfdpxR+ppCWyTwvRBivxBCde7VCCXu1QQhxHdCiKNl/PQFFgNN\ngfbAOSDeocbqBxWzrBpCpJQdgD7ABCFEqKMN0gOqE1M1QUrZy5JxQojlwGY7m1NTyASCbjsOwuy9\nKzRESnmu9M9sIcRGzOGwZMdaVf1RnrsOEELc3j+pP3DUUbbojP1AcyFEEyFELWAw8JWDbdIVQggv\nIYRP6d+9gWdRn19NUJ67PpglhGiPOYxwBohwsD26QEpZLISIBL4FXIEVKlNGcx4ANgpzpxg34Asp\n5VbHmqQPVCqkQqFQ6BAVllEoFAodosRdoVAodIgSd4VCodAhStwVCoVChyhxVygUCh2ixF2hUCh0\niBJ3hUKh0CFK3BUKhUKH/AdKQZTK2jwwogAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa3f1e80>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x = np.linspace(-3 * np.pi,3 * np.pi,100)\n",
-    "\n",
-    "p = plt.plot(x, np.sin(x), 'k')\n",
-    "p = plt.plot(x, y1(x))\n",
-    "p = plt.plot(x, y3(x))\n",
-    "p = plt.plot(x, y5(x))\n",
-    "p = plt.plot(x, y7(x))\n",
-    "p = plt.plot(x, y9(x))\n",
-    "\n",
-    "a = plt.axis([-3 * np.pi, 3 * np.pi, -1.25, 1.25])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "黑色为原始的图形，可以看到，随着多项式拟合的阶数的增加，曲线与拟合数据的吻合程度在逐渐增大。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 最小二乘拟合"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "导入相关的模块："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.linalg import lstsq\n",
-    "from scipy.stats import linregress"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xbc98518>]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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PnJM3s9uANwEvIOpKuR84pxe1bzGza4HfBH4InAbe7+7fTNiPZ62LSBOkyV2PupGpm5yz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\nAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbc0e320>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x = np.linspace(0,5,100)\n",
-    "y = 0.5 * x + np.random.randn(x.shape[-1]) * 0.35\n",
-    "\n",
-    "plt.plot(x,y,'x')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "一般来书，当我们使用一个 N-1 阶的多项式拟合这 M 个点时，有这样的关系存在：\n",
-    "\n",
-    "$$XC = Y$$\n",
-    "\n",
-    "即\n",
-    "\n",
-    "$$\\left[ \\begin{matrix}\n",
-    "x_0^{N-1} & \\dots & x_0 & 1 \\\\\\\n",
-    "x_1^{N-1} & \\dots & x_1 & 1 \\\\\\\n",
-    "\\dots & \\dots & \\dots & \\dots \\\\\\\n",
-    "x_M^{N-1} & \\dots & x_M & 1\n",
-    "\\end{matrix}\\right] \n",
-    "\\left[ \\begin{matrix} C_{N-1} \\\\\\ \\dots \\\\\\ C_1 \\\\\\ C_0 \\end{matrix} \\right] =\n",
-    "\\left[ \\begin{matrix} y_0 \\\\\\ y_1 \\\\\\ \\dots \\\\\\ y_M \\end{matrix} \\right]$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scipy.linalg.lstsq 最小二乘解"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "要得到 `C` ，可以使用 `scipy.linalg.lstsq` 求最小二乘解。\n",
-    "\n",
-    "这里，我们使用 1 阶多项式即 `N = 2`，先将 `x` 扩展成 `X`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.05050505,  1.        ],\n",
-       "       [ 0.1010101 ,  1.        ],\n",
-       "       [ 0.15151515,  1.        ],\n",
-       "       [ 0.2020202 ,  1.        ]])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X = np.hstack((x[:,np.newaxis], np.ones((x.shape[-1],1))))\n",
-    "X[1:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "求解："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([ 0.50432002,  0.0415695 ]),\n",
-       " 12.182942535066523,\n",
-       " 2,\n",
-       " array([ 30.23732043,   4.82146667]))"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "C, resid, rank, s = lstsq(X, y)\n",
-    "C, resid, rank, s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "画图："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sum squared residual = 12.183\n",
-      "rank of the X matrix = 2\n",
-      "singular values of X = [ 30.23732043   4.82146667]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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acgl+q1bpiUOH9I033tBwONyyDyOfy8mgy5Y8gzyRLxlItRgNfsePH9ejR49aUy8ngy5z\n8gzyRI4y2qq3u/VvMPh98MEHevfdd2u3bt30iSeecGy/lmHvGgZ5IkcZDXJ2B8M0wS8cDmtVVZVe\nffXVGggE9MYbb2yZesBs0CzgoOs0Bnkis3INWEbTFS7lkt9++20955xz9De/+Y0ePnw4eZ0KMP2R\nbxjkyX353qozE/CMXnh0qVfIiRMnUr9YoBcy842RIG/Z8n9ESbmx3JmV4peJC4UiP++9t+3C34ka\nGoAHHgBqayM/E5fyy7ZcDsLhMNasWYNQKJT09Xbt0vz7BwLA9OlA796Rn5k+L3lXprOAUzewJe9f\nfmgVZtPaTmztL1nSeo6XWJklS2xJixw+fFh//etf65e+9CUdOHCgbtiwoW0hC3vlWCbfv/W5AEzX\nkGeY7Kvt6j9/tgEvsb719ZEgv2RJ6+0tWWLp59q3b59OnDhRA4GAXn311bphw4aWvu1G61Rf715O\nntcCssYgT95gwahL10eTmt23Ay3jUCik99xzj3744Yep9x//ORLXNY295uZJ1Q/f+hzEIE/uy6Mg\nmZTdU/k6KdkxdLtOyXixTh7FIE/u81OQNMOCk1Q4HNZXX31Vr7zyyuR5diPij6EXW81erJOHORLk\nATwOoA7A2yleDwJoRMtC37NSlLP5cFBeSZVDrqjIv39+k99mPvnkE3300Uf1wgsv1L59++r8+fO1\nsbEx93rU1iZd8MP1NWSZk8+aU0H+mwAGZAjyLxjYjp3HgvJN4oXA+KCUb//8OX6bCYfD+sorr2iX\nLl308ssv17Vr17ZcSM2W0R4/XlxDlr1rUjIS5CVSzhwRKQGwUlUvTPJaEMDtqvp/M2xDragL+Uis\nT/2gQcAbbwDz5rX0125oAKqqgPJyd+tos8bGRjQ0NKBXr17mNrR6dWRsQnx/dyePYex3OX16ZDyA\nkbEGlJGIQFUlbRkHgvwwAM8DOADgHwDuUNUdScoxyFNboVBkQE5tLVBS4nZtbHPo0CEEAgEUFRWZ\n25DbwTydAvldOslIkG/vQD3eAtBDVZtE5DIAywGcl6zgnDlzPr8fDAYRDAYdqB55VuJoUB+2/rZu\n3YqHHnoIy5cvx8tz5mDgtdeaC9CxEcaxYxVrQd97rz0fwKgC+F06obKyEpWVldm9KVM+x8gNQAlS\n5OSTlK0F0CXJ89Ymqyi/+fgi3LFjx3Tx4sX69a9/XXv27Kn333+/Hjx40Lrctdd6qPj4d+k2ONWF\nMl2QB1CMlrTQEAChFOVsPRiUZ3x6ES4cDuvTTz+tl1xyiS5btkybm5tbFzASoI0ETS91N/Xp79IL\njAR50zl5EfkTgGEAzkSkK+VsAB2iUXuBiEwBcDOAZgBNAG5T1Y1JtqNm60KUD1QVImnSqEZy1+ku\nZPIiZ8Fw7MKrFRjkyU8++eQTPP300xg/fjxOPfVU42/MJkAnOxnE5+ATc/IM9L5jJMhzqmEiC+3a\ntQvTpk1Dr169sG7dOtTX1xt/c3xALilpmeI42fTDqaYorqpqHdBjUyVXVZn+bJSf2JInykVCV8UN\nGzbg5z/7GbbX1GDilCmYPHkyunfvbmqbAJL3rmFrnaKYrqHCZmef8YTAunHtWvz9V7/C9596CicV\nF5vbdiZe7gtPjmK6hvLH6tVt0xINDZHnc2XnqlQJK0aVLl+Oq//8Z3sCfOKxiQXy+GMTCDDAU3KZ\nut84dQO7UBY2u/pSp+qSmGW3vqamJn3iiSd06NChWldX1/KCE10V/dTPnN0pLQVONUx5xa5BPMkC\nscHAWVtbqzNmzNAvfOELOmLECF21alVL33YnBx15bYBTrvx0wvIABnnKP1a3jNMFxwyBc/78+XrG\nGWforbfeqrt3727dCo29NxRqed7uYOWlAU5m+OWE5QEM8pRfrP7nNzkytK6uTo8cOZJ8e6tWRQJ8\n4vYzpR1yTVf4LTD65YTlMgZ5yh92fI03OMdL7WuvGd+X2WCby+f0W4rDbycsFzHIU/5w+ILcZx99\npH/+7nd1WFmZduvWTRv37TMecMy2QrMNcn66WOm3E5bLjAR59pOnglJXV4fHHnsMCx58EH3OOw9T\nfvxjjBkzBh06dDDW19yqeWEKdW519vG3FAdDESX45S9/if3792Pq1Kn4yle+kt2brRppygnEyCIM\n8pQ9trRSs+LYcEoCshBHvFL27Bwl6pC9e/di3rx5sLzRUF7eNhBnO9KUE4iRwxjkqbWE4fr50soM\nh8NYs2YNysvLUVpairq6Onz22WduV6stK04URFlguqYQGUk7xC4M1tQA8blrD6ZuFi9ejF/84hfo\n3LkzpkyZgh/+8Ic45ZRT3K4Wke2YrqHkMqVkYnOV19QA11wD7NuXvJxHnHnmmXjqqaewZcsW3HDD\nDQzwRHHYki9UqXp4JF4I3LcP+N73gKefBhYsyIvUTVK8oEw+xN41lF6yvtrJguHf/gb07+9an+5/\n/vOfWLBgAV566SVUVVWhXbscvoCyVwv5kO3pGhF5XETqROTtNGXmi8h7IlIjIgPM7M8UO+Yrz2ep\nlo9LvDDY0BBpwSeWs5mq4q9//SuuvPJK9OvXDx9//DEWLlyYW4AH8vaCMpFpmYbEprsB+CaAAQDe\nTvH6SAAvRu9/HcDGNNuyYpRvahxO3cLosXDxmI0bN0779u2r8+fP14aGBus2zImxyEfgxNw1AErS\nBPlHAfxn3ONdAIpTlLX1YKgqJ0aKMToXiotzptTV1Wk4HLZ2o/z9k88YCfKmc/IiUgJgpapemOS1\nlQDuU9U3oo/XAZihqluTlFWzdTGkUOcM8aATJ05g9+7dOP/88+3fGXPy5ENGcvLtnahHwuOUkXzO\nnDmf3w8GgwgGg9bWJDEPzX/w3JnorXLo0CFUVFTgkUcewZe//GWsWbMGImn/Ts3XCUg90pS9ayhP\nVFZWorKyMrs3ZWrqZ7ohc7rmqrjH9qZr0qUXmJO3Vg7Hc8uWLXrddddpIBDQa6+9Vt98803X60SU\nz+CBnHz8hddS2H3hNd0/uZ/m5HZL4jGsr1edMEG1osJQMJ08ebLef//9evDgweTbi23TzO+EeXcq\nILYHeQB/AvBPAJ8B2A/gBgCTAEyKK/MQgD0AagAMTLMtaz41/8ntk+wkOnZs5M+ooiL7gG1Xy5s9\naKhAGAny/hwMxYur9okfKXvPPZHnZs0C7rkHqorK0aOxPRTCtHHjjF3YNDu3emIevqEBuOMOYOhQ\nYOtWXnchXyvMuWtSDfIhawQCkYDcuzfw738D8+bhkzPOwMN9+6Lf889j2vjxOLWpyXjPlfjtTZ9u\nLCDHD2yLzcOzbx/wzDORAA8AY8a0DH7i3wAVskxNfadusDsnT9aIHdOKCtUJE3T6tGl6+umn6w9+\n8ANdv3KlhhcuzC5Vkkt6LfH3Ggqp9uun+rvfRa4RJF434HUX8ikU3ELevLhqryQn0WUjRuj+7dtb\nv240YJs5KSfuq6aGeXgqOIUX5Mk24XDY+i6qZk/KsQusNTW82E4FiUHea5z+pmFyf+FwWDdu3Khj\nx47V73//+7buK2uxk0hNTSRVEwq1fp6BngoAg7zXOH3NIMf9NTU16RNPPKGDBg3SPn366AMPPKAf\nf/yxPXXMReL4h1Co7edkio4KgJEg788ulF5mtsugzftTVfTr1w+9evXClClTMGLECBQVFdlXv1xw\nARAiAFw0xLuc7sef5f4aGxvRuXNn26tFROYUZj95r3O6H3+K/TU2NuKdd95J+hYGeCL/YJB3Uvz0\ntiUl9g/WSbK/7ZMn4+YbbkBJSQmeeeYZe/bLVbiIPINB3klVVamnu7Vxfyc6dcLSpUsRHD0al776\nKoqPHcOOHTtw991327Pf2CjUWKCPnWzKyuzZHxGlxJx8AQiHwxg3bhwuv/xyXHHFFejQoYP9O011\nwZcXTYkswwuv5K5kF3y5QhORZXjhtYA0NTVh4cKFWLRokdtViUh1gTmWopo5M3ISYIAnshWDfJ7b\nu3cvbr/9dvTs2RMrVqxAr1693K5S5gvMucw8SUQ5YZC3gwO9S5qamlBeXo7S0lIUFRVh8+bNWLly\nJYYNG2bZPnKW6QIzp4Mmck6mIbFO3RA/rUG+zybp0LD7ZcuWadNzz+XXseJ00ESWQd7OXeOHQGDh\nBFrHjx/PvJ98OVb5fgIn8pD8DfKq/lir1cRUuP/+97/1j3/8o5aVlemsWbPSF/bDsSKirDkS5AGM\nALALwHsAZiR5PQigEcC26G1Wiu20/QRmFmR2u8WY46IWBw4c0Lvuuku7du2qF198sS5dujR9Sz6G\ni1dbw+2/G6Is2B7kARQB2AOgBEAHANUAzk8oEwTwgoFtta692dapm2mMVMvTxVr0Kerw8ccf6xln\nnKE333yzbo+ttpTN/qJL8nH5OxPyLf1FBc2JIP8NAP8b9/inAH6aUCYIYKWBbbXU3Kp/NDvTGOla\nfPGvxeoQCiVfQSnBp59+ml094rdXXx8J8rFAzwCVG6a/KE84EeR/AOAPcY/HAvhdQplhAA4BqAHw\nIoALUmwrUuv6etXZs637ymxXGsPoiSjJyWDXm2/qe489Zk1qIHEbsUBfUcEAZQbTX5QHnAjy3zcQ\n5DsBODV6/zIAu1NsS2fPmKGzBw/W2TNm6Pr1680fAbtbZFlsv7m5WVesWKGXXnqpnnXWWfrss8/a\nlxpggDKHLXnyqPXr1+vs2bM/vzkR5EsT0jU/S3bxNeE9tQC6JHne2n8op3KrGQJqY2Oj3n///dqr\nVy8dMmSILlq0qHVKxuqAwgBlDnPylEecCPLtAeyNXnjtmOLCazFaJkIbAiCUYlvWtjyd6CVhIKAe\nOnRIJ0yYoJs3b069Hata3gxQ5rF3DeURp7pQXgbg3Wgvm59Fn5sEYFL0/hQA26MngDcAlKbYTn4F\nJC9eHGaAIiooRoK8t6Yarq/Pn1kJ4+ZF379/Px599FEMGzQIl550kvF50TntLhGZkH9TDdu9UpKF\ndORIvPLWWxgzZgz69++PI0eO4JyvfjW7hS+cXCkq3aRpXK6PyLe81ZL3SF0y2b17N0aPHo127dph\n6tSpGDt2LE477TS3q5Veum8NAL9REOUhrgxlk2PHjmHjxo0YNmwYRNIeX29JtSRfpteIyJMY5E1q\nbm5GOBxGx44d3a6KdZItyWfkNSLynPzLyXvERx99hLlz56JPnz5Y7ae8dLrFOriQB5EvMchHqSo2\nbdqEcePGoW/fvvj73/+OFStW4IorrnC7atZItyRfpuX6iChvMV0T9dprr+G6667DLbfcguuvvx5d\nunRxrS62iOvy+bmGhpaePKley6a3EBE5ijn5LITDYagqioqKXKsDEVE2/JOTt6gfdzgcxksvvYSD\nBw+2ea1du3YM8ETkO/kR5MvKWueIYznksjJDb29sbMSDDz6I888/H3feeScOHDjQ8iIHAhGRj+VH\nkI+NBJ05M9LNz+BAndraWkyePBklJSXYuHEjHn/8cWzbtg0DBgxoKWTyBGIZnmyIyAb5lZPPsh93\nTU0NVqxYgRtvvBFnn3126oJeGAiUOMr0mWeAtWuBefNaD1jixVAiivJPTh7IqR93//79cdddd6UP\n8EAkiE6fHjmBDBqUfN92t6gTv62sXdu2Dm58wyCi/JZpmkqnbkhcyDteiml9w4cP6+uvv65XXXWV\nvvvuuxkm5Uwjfrrf+DVSk+3bbvFzy3MBECJKAwamGs6PlnzCbI1NHTtiYd++GDh4MK6//nqUlpai\na9euuW07cSDQvHmR5++4I6v8vyUSv60ALd8wpk/nXDJElLX8yskDWL16Na699loMHToUU6ZMwfDh\nw9GunYlzVapBQs8/D0yY4Nw8Lslmibzjjshrs2Zx0jAiasNfOfmowYMHY/PmzXjhhRfw3e9+11yA\nByIXMZMFzq1bnZ3HJXFu+ZjhwznVABHlzLMt+YaGBnTu3Nn5qXy9slpTumkI2LuGiOBQS15ERojI\nLhF5T0RmpCgzP/p6jYgMSFYmZtu2bZg4cSJ69+6NvXv3mq1e9pxcrSmdZN8w4rtSxmN/eiJKwVSQ\nF5EiAA8BGAHgAgA/FJHzE8qMBHCOqp4L4CYAv0+1vbKyMowaNQq9e/fGu+++i3POOcdM9XITH1xj\nA5QCgZbOGqasAAAGIUlEQVTWs9sB1SuDt4goL5htyQ8BsEdVQ6p6HMASAJcnlBkF4EkAUNVNAAIi\nUpxsY7fffjtqa2sxc+ZMnHXWWSarZgEvBtQcR/8SUWEyG+S7Adgf9/hA9LlMZbon29iYb38b7du3\nb3kiU6vZ7qkAvBpQ4wdvsWslEaVhNsgbvWqbeGEg+fuybTXn2tLO5uTgxYDKVZyIyKD2mYuk9Q8A\nPeIe90CkpZ6uTPfoc23M6dQp0mWwrAzBUAjB//mf9EE1vqWdzbwzsZNDsh40iRIDqtst+cTePrHP\n73a9iMh2lZWVqKyszO5NmYbEprshcpLYC6AEQEcA1QDOTygzEsCL0fulADam2FZknG78sH6jcnmP\nkSkDUkyn4Or0AqtWtd1/fX3keSIqKDAwrYEVc85cBuBdAHsA/Cz63CQAk+LKPBR9vQbAwBTbyW2u\nFjPzu2Q6OTCgEpGHORLkrboByL7VbKalncvJgUGfiDwk/4J8tgE016Cb68nBi+kbIipYRoK8Z6c1\nsJWZKQOSLTBSVcUpCIjIcUamNci/IO+FOV0SV6jyynw3RFRQfDkLpeujUJP1UffqoCkiKnj515IH\n3FuTNVOLPcs1aImIzPBnuibGjYCaLlUU+4bh5mLgRFRQ/JmuAdwb1p9q+t/4EbRc4IOIPCT/WvJe\nvMjphYvBRFRw/JmuKcSAWoifmYgy8meQL0Re/PZCRK5jkPcTt3oUEZFnMcj7DbtoElEc//auKURc\nKISIcsAgnw/ic/DsoklEWWC6Jh+wdw0RJcGcPBGRjzEnT0RU4BjkiYh8jEGeiMjH2uf6RhHpAuAZ\nAL0AhABcqaptunuISAjAvwCcAHBcVYfkuk8iIsqOmZb8TwGsVdXzALwcfZyMAgiq6gAGeGMqKyvd\nroJn8FhE8Di04LHIjpkgPwrAk9H7TwIYnaZs2qu/1Br/iFvwWETwOLTgsciOmSBfrKp10ft1AIpT\nlFMA60Rki4jcaGJ/RESUpbQ5eRFZC6Brkpdmxj9QVRWRVJ3cy1T1AxH5AoC1IrJLVV/LrbpERJSN\nnAdDicguRHLtH4rI2QDWq+qXM7xnNoAjqvrrJK9xJBQRUZYyDYbKuXcNgBcAXAvg/0V/Lk8sICKn\nAihS1U9E5P8AuBTAL3KpKBERZc9MS74LgGcB9ERcF0oR+SKAP6hquYj0AfB89C3tATytqveZrzYR\nERnhmblriIjIeq6PeBWRESKyS0TeE5EZbtfHLSLyuIjUicjbbtfFbSLSQ0TWi8g7IrJdRH7kdp3c\nIiIni8gmEakWkR0iUvDfhEWkSES2ichKt+viJhEJicjfosfizZTl3GzJi0gRgHcBXALgHwA2A/ih\nqu50rVIuEZFvAjgCYJGqXuh2fdwkIl0BdFXVahE5DcBWAKML8e8CiFzbUtUmEWkP4HUAd6jq627X\nyy0ichuAQQA6qeoot+vjFhGpBTBIVQ+nK+d2S34IgD2qGlLV4wCWALjc5Tq5ItqttN7teniBqn6o\nqtXR+0cA7ATwRXdr5R5VbYre7QigCEDaf2o/E5HuAEYCWAgOsgQMHAO3g3w3APvjHh+IPkcEABCR\nEgADAGxytybuEZF2IlKNyKDD9aq6w+06uei3AKYDCLtdEQ8wNNDU7SDPq76UUjRVsxTAj6Mt+oKk\nqmFV/SqA7gC+JSJBl6vkChH5HoCPVHUb2IoHIgNNBwC4DMCUaMq3DbeD/D8A9Ih73AOR1jwVOBHp\nAOA5AE+papsxGIVIVRsBrAYw2O26uGQogFHRXPSfAHxbRBa5XCfXqOoH0Z8HASxDJP3dhttBfguA\nc0WkREQ6AvhPRAZZUQETEQFQAWCHqv632/Vxk4icKSKB6P1TAAwHsM3dWrlDVf9LVXuoam8AVwF4\nRVXHu10vN4jIqSLSKXo/NtA0ac88V4O8qjYDmArgLwB2AHimgHtQ/AnAGwDOE5H9InK923VyURmA\nsQAujnYP2yYiI9yulEvOBvBKNCe/CcBKVX3Z5Tp5RSGne4sBvBb3d7FKVV9KVpCDoYiIfMztdA0R\nEdmIQZ6IyMcY5ImIfIxBnojIxxjkiYh8jEGeiMjHGOSJiHyMQZ6IyMf+P3gCky4HG7kGAAAAAElF\nTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa25ed30>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "p = plt.plot(x, y, 'rx')\n",
-    "p = plt.plot(x, C[0] * x + C[1], 'k--')\n",
-    "print \"sum squared residual = {:.3f}\".format(resid)\n",
-    "print \"rank of the X matrix = {}\".format(rank)\n",
-    "print \"singular values of X = {}\".format(s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scipy.stats.linregress 线性回归"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于上面的问题，还可以使用线性回归进行求解："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.50432001884393252, 0.041569499438028901)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope, intercept, r_value, p_value, stderr = linregress(x, y)\n",
-    "slope, intercept"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "R-value = 0.903\n",
-      "p-value (probability there is no correlation) = 8.225e-38\n",
-      "Root mean squared error of the fit = 0.156\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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2rqE84lQXyksBvBvtZfOT6HPTAUyP3p8BYEf0BPAagJIU28mvgOTFi8MMUEQF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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbff7e10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "p = plt.plot(x, y, 'rx')\n",
-    "p = plt.plot(x, slope * x + intercept, 'k--')\n",
-    "print \"R-value = {:.3f}\".format(r_value)\n",
-    "print \"p-value (probability there is no correlation) = {:.3e}\".format(p_value)\n",
-    "print \"Root mean squared error of the fit = {:.3f}\".format(np.sqrt(stderr))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以看到，两者求解的结果是一致的，但是出发的角度是不同的。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 更高级的拟合"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.optimize import leastsq"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "先定义这个非线性函数：$y = a e^{-b sin( f x + \\phi)}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def function(x, a , b, f, phi):\n",
-    "    \"\"\"a function of x with four parameters\"\"\"\n",
-    "    result = a * np.exp(-b * np.sin(f * x + phi))\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "画出原始曲线："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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vfqM57lI4ZoZzrsGpANlMB3wAmAOcaGZrzewy4NfAaDNbBZyV/lxKzIcfwief+L05pH5q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\nLSKSMApuEZGEUXCLiCSMgltEJGH+Px9Ir5f410BLAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa6f17f0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x = np.linspace(0, 2 * np.pi, 50)\n",
-    "actual_parameters = [3, 2, 1.25, np.pi / 4]\n",
-    "y = function(x, *actual_parameters)\n",
-    "p = plt.plot(x,y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "加入噪声："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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XLdkvxMiE5ak/aiH/h8vDhun8GTO0efPmWqdOHf3LX/6i586du/m1hR3IZmw1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\nAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa3f1c18>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from scipy.stats import norm\n",
-    "y_noisy = y + 0.8 * norm.rvs(size=len(x))\n",
-    "p = plt.plot(x, y, 'k-')\n",
-    "p = plt.plot(x, y_noisy, 'rx')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scipy.optimize.leastsq"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "定义误差函数，将要优化的参数放在前面："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def f_err(p, y, x):\n",
-    "    return y - function(x, *p)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "将这个函数作为参数传入 `leastsq` 函数，第二个参数为初始值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([ 3.03199715,  1.97689384,  1.30083191,  0.6393337 ]), 1)"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c, ret_val = leastsq(f_err, [1, 1, 1, 1], args=(y_noisy, x))\n",
-    "c, ret_val"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`ret_val` 是 1~4 时，表示成功找到最小二乘解："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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eeXsvgCRVPSIiUQC+UNUeLr6PpxFSaElPBxITMe1//ge5ubl46623uFo9+Z1fV6V3EeDT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\nA5MAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa3d7b00>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "p = plt.plot(x, y_noisy, 'rx')\n",
-    "p = plt.plot(x, function(x, *c), 'k--')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scipy.optimize.curve_fit"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "更高级的做法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.optimize import curve_fit"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不需要定义误差函数，直接传入 `function` 作为参数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "p_est, err_est = curve_fit(function, x, y_noisy)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 3.03199711  1.97689385  1.3008319   0.63933373]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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eeXsvgCRVPSIiUQC+UNUeLr6PpxFSaElPBxITMe1//ge5ubl46623uFo9+Z1fV6V3EeDT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\nA5MAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa22b828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "print p_est\n",
-    "p = plt.plot(x, y_noisy, \"rx\")\n",
-    "p = plt.plot(x, function(x, *p_est), \"k--\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里第一个返回的是函数的参数，第二个返回值为各个参数的协方差矩阵："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 0.08483704 -0.02782318  0.00967093 -0.03029038]\n",
-      " [-0.02782318  0.00933216 -0.00305158  0.00955794]\n",
-      " [ 0.00967093 -0.00305158  0.0014972  -0.00468919]\n",
-      " [-0.03029038  0.00955794 -0.00468919  0.01484297]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print err_est"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "协方差矩阵的对角线为各个参数的方差："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "normalized relative errors for each parameter\n",
-      "   a\t  b\t f\tphi\n",
-      "[ 0.09606473  0.0488661   0.02974528  0.19056043]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print \"normalized relative errors for each parameter\"\n",
-    "print \"   a\\t  b\\t f\\tphi\"\n",
-    "print np.sqrt(err_est.diagonal()) / p_est"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 曲线拟合"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "导入基础包："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 多项式拟合"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "导入线多项式拟合工具："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from numpy import polyfit, poly1d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "产生数据："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x = np.linspace(-5, 5, 100)\n",
+    "y = 4 * x + 1.5\n",
+    "noise_y = y + np.random.randn(y.shape[-1]) * 2.5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "画出数据："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TBPYzz4TRoyNeqEQd9KhRL6eeGixzp/rqItLPSk7FmNmtwHjgPeB14Fx335R+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\n/wEcY+GYZJlNlgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa1224a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "p = plt.plot(x, noise_y, 'rx')\n",
+    "p = plt.plot(x, y, 'b:')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "进行线性拟合，`polyfit` 是多项式拟合函数，线性拟合即一阶多项式："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 3.93921315  1.59379469]\n"
+     ]
+    }
+   ],
+   "source": [
+    "coeff = polyfit(x, noise_y, 1)\n",
+    "print coeff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "一阶多项式 $y = a_1 x + a_0$ 拟合，返回两个系数 $[a_1, a_0]$。\n",
+    "\n",
+    "画出拟合曲线："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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E3FwtGDFC3x45UhtWqaf1a63RNDQtnmrJzVXt3Vv19devvKY/gdiR/snKYt15CWFgJypt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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa1ec278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = plt.plot(x, noise_y, 'rx')\n",
+    "p = plt.plot(x, coeff[0] * x + coeff[1], 'k-')\n",
+    "p = plt.plot(x, y, 'b--')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "还可以用 `poly1d` 生成一个以传入的 `coeff` 为参数的多项式函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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yYABMOOBPTO0/l0V9rqR4SGcvqE+e7AX1sWO9jaNB3T/Huoi0OgrshSZSo754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\nkFFgFxEJGQV2EZGQ+X/DyfQNy7jRVwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa25e1d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = poly1d(coeff)\n",
+    "p = plt.plot(x, noise_y, 'rx')\n",
+    "p = plt.plot(x, f(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "poly1d([ 3.93921315,  1.59379469])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "显示 `f`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " \n",
+      "3.939 x + 1.594\n"
+     ]
+    }
+   ],
+   "source": [
+    "print f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "还可以对它进行数学操作生成新的多项式："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       2\n",
+      "31.03 x + 29.05 x + 6.674\n"
+     ]
+    }
+   ],
+   "source": [
+    "print f + 2 * f ** 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 多项式拟合正弦函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "正弦函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x = np.linspace(-np.pi,np.pi,100)\n",
+    "y = np.sin(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "用一阶到九阶多项式拟合，类似泰勒展开："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y1 = poly1d(polyfit(x,y,1))\n",
+    "y3 = poly1d(polyfit(x,y,3))\n",
+    "y5 = poly1d(polyfit(x,y,5))\n",
+    "y7 = poly1d(polyfit(x,y,7))\n",
+    "y9 = poly1d(polyfit(x,y,9))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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HHx51rLHEiN8UP1U70QwSvzKMmfsFFX19TDovi2BQmlHKS/Vktj+/C11iIJ/1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\niBJ3hUKh0CFK3BUKhUKH/AdKQZTK2jwwogAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa3f1e80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-3 * np.pi,3 * np.pi,100)\n",
+    "\n",
+    "p = plt.plot(x, np.sin(x), 'k')\n",
+    "p = plt.plot(x, y1(x))\n",
+    "p = plt.plot(x, y3(x))\n",
+    "p = plt.plot(x, y5(x))\n",
+    "p = plt.plot(x, y7(x))\n",
+    "p = plt.plot(x, y9(x))\n",
+    "\n",
+    "a = plt.axis([-3 * np.pi, 3 * np.pi, -1.25, 1.25])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "黑色为原始的图形，可以看到，随着多项式拟合的阶数的增加，曲线与拟合数据的吻合程度在逐渐增大。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 最小二乘拟合"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "导入相关的模块："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.linalg import lstsq\n",
+    "from scipy.stats import linregress"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0xbc98518>]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Q9QrBzJ5rZuf1HvcHmib2zAsa5N39h8B7ga8CJ4EvzHAPituAu4ELzOwxM/u1\n0HUKaCdwNfDmXvew42Z2RehKBfIi4Ou9nPwx4HZ3vytwnapiltO9m4F/iP27OOzuf5dUUIOhREQa\nLHS6RkRECqQgLyLSYAryIiINpiAvItJgCvIiIg2mIC8i0mAK8iIiDaYgLyLSYP8Pt2y1T8p57b8A\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbc0e320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(0,5,100)\n",
+    "y = 0.5 * x + np.random.randn(x.shape[-1]) * 0.35\n",
+    "\n",
+    "plt.plot(x,y,'x')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "一般来书，当我们使用一个 N-1 阶的多项式拟合这 M 个点时，有这样的关系存在：\n",
+    "\n",
+    "$$XC = Y$$\n",
+    "\n",
+    "即\n",
+    "\n",
+    "$$\\left[ \\begin{matrix}\n",
+    "x_0^{N-1} & \\dots & x_0 & 1 \\\\\\\n",
+    "x_1^{N-1} & \\dots & x_1 & 1 \\\\\\\n",
+    "\\dots & \\dots & \\dots & \\dots \\\\\\\n",
+    "x_M^{N-1} & \\dots & x_M & 1\n",
+    "\\end{matrix}\\right] \n",
+    "\\left[ \\begin{matrix} C_{N-1} \\\\\\ \\dots \\\\\\ C_1 \\\\\\ C_0 \\end{matrix} \\right] =\n",
+    "\\left[ \\begin{matrix} y_0 \\\\\\ y_1 \\\\\\ \\dots \\\\\\ y_M \\end{matrix} \\right]$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scipy.linalg.lstsq 最小二乘解"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "要得到 `C` ，可以使用 `scipy.linalg.lstsq` 求最小二乘解。\n",
+    "\n",
+    "这里，我们使用 1 阶多项式即 `N = 2`，先将 `x` 扩展成 `X`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.05050505,  1.        ],\n",
+       "       [ 0.1010101 ,  1.        ],\n",
+       "       [ 0.15151515,  1.        ],\n",
+       "       [ 0.2020202 ,  1.        ]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = np.hstack((x[:,np.newaxis], np.ones((x.shape[-1],1))))\n",
+    "X[1:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "求解："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 0.50432002,  0.0415695 ]),\n",
+       " 12.182942535066523,\n",
+       " 2,\n",
+       " array([ 30.23732043,   4.82146667]))"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C, resid, rank, s = lstsq(X, y)\n",
+    "C, resid, rank, s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "画图："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sum squared residual = 12.183\n",
+      "rank of the X matrix = 2\n",
+      "singular values of X = [ 30.23732043   4.82146667]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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9Hata3gxQ5rF3DeURp7pQXgbg3Wgvm59Fn5sEYFL0/hQA26MngDcAlKbYTn4F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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa25ed30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = plt.plot(x, y, 'rx')\n",
+    "p = plt.plot(x, C[0] * x + C[1], 'k--')\n",
+    "print \"sum squared residual = {:.3f}\".format(resid)\n",
+    "print \"rank of the X matrix = {}\".format(rank)\n",
+    "print \"singular values of X = {}\".format(s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scipy.stats.linregress 线性回归"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于上面的问题，还可以使用线性回归进行求解："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.50432001884393252, 0.041569499438028901)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "slope, intercept, r_value, p_value, stderr = linregress(x, y)\n",
+    "slope, intercept"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R-value = 0.903\n",
+      "p-value (probability there is no correlation) = 8.225e-38\n",
+      "Root mean squared error of the fit = 0.156\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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2rqE84lQXyksBvBvtZfOT6HPTAUyP3p8BYEf0BPAagJIU28mvgOTFi8MMUEQF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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbff7e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = plt.plot(x, y, 'rx')\n",
+    "p = plt.plot(x, slope * x + intercept, 'k--')\n",
+    "print \"R-value = {:.3f}\".format(r_value)\n",
+    "print \"p-value (probability there is no correlation) = {:.3e}\".format(p_value)\n",
+    "print \"Root mean squared error of the fit = {:.3f}\".format(np.sqrt(stderr))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以看到，两者求解的结果是一致的，但是出发的角度是不同的。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 更高级的拟合"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.optimize import leastsq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "先定义这个非线性函数：$y = a e^{-b sin( f x + \\phi)}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def function(x, a , b, f, phi):\n",
+    "    \"\"\"a function of x with four parameters\"\"\"\n",
+    "    result = a * np.exp(-b * np.sin(f * x + phi))\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "画出原始曲线："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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vfqM57lI4ZoZzrsGpANlMB3wAmAOcaGZrzewy4NfAaDNbBZyV/lxKzIcfwief+L05pH5q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\nLSKSMApuEZGEUXCLiCSMgltEJGH+Px9Ir5f410BLAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa6f17f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(0, 2 * np.pi, 50)\n",
+    "actual_parameters = [3, 2, 1.25, np.pi / 4]\n",
+    "y = function(x, *actual_parameters)\n",
+    "p = plt.plot(x,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "加入噪声："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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XLdkvxMiE5ak/aiH/h8vDhun8GTO0efPmWqdOHf3LX/6i586du/m1hR3IZmw1\nkTkUcWzY7XZdvny5Pvnkk1qjRg0dNWqUHnr3XUf/8YKv9+RntCSfX/aYyWWuBF5SRiUsT355pKdr\nVkyMbv7XvzQ+MlLrVq2q/fv3192TJ2tWwcTtygUmM9UtyRxcPF5/+eUXnTx5skZERGjjxo119uzZ\nevpWfchd3Xdhx6o7n1/2mLmBfyRwVeMSVim+PLKzs3XPnj06c+ZM7dKlizaqXFkV0I8mTtS0tLQb\n39+dA5YtcCqMm63U7Oxs3bhxoz733HMaFBSkjRo10ldeeUW//vprvXDhgvv7L+4z6urn18X6fomP\ne4u15E2TwP/zn/+U/H/hqVMqL9fjUlNTde3atTpz5kzt37+/hoSEaGRkpI4cOVKXL1yol4cOdb2F\nUlSsixeXyZYIeVdWVpbu2LFD//rXv+rjjz+ugYGB2rp1a3399dd10aJFun//fr127VrRb+BuC9zb\ntfESfH6uXLmi+/bt02XLlhX/3j5kmgRerVo1DQ4O1vbt2+uoUaP0//7v/zQ+Pl6TkpL0t99+K/p/\n4MlTKg/U4347flz379+vcXFxOmfOHB0zZox27NhR77jjDu0fGKjRbdroyy+/rB999JH+8ssvridd\nT7RQiDwkMzNT169fr5MnT9Z+/fppgwYNtGLFitqkSRMdNGiQ/u1vf9NPP/1Ut2/frqcSEwvvMVVc\nDbw0n99SNHSunz2rJw8c0DNPPaXL/+d/9LvmzXVgt27aoEEDve2227RBgwbap0+fwnvsGMCVBF7q\nJdVEpDOA2QACAPxDVacVeFztdjvOnDmDpKQkJCYmIikpCYcPH8apU6dw4sQJVKhQAXfffTfq1KmD\n0NBQ1KhRAzVq1EDTU6dwpXlzVKlTB0FBQahUqRIqX7+OagcOIKBdOwROnYpyr78OmTkzb1rOuLjc\n5ahy2WzAtm3QRx6BvvkmMl98ETJrFv7z8su4GBCAjIwMZGRkwGaz4dy5c0hLS8Olkyfx+MaN+KBu\nXRw+exa2lBSMz8jAgogI1KhXD2FhYahXrx4aN26Mxo0b465KlSB//vPN6xu2awd06lRoPIiOLnxJ\nLa4YTyaTmZmJpKQk7Nu3D0lJSTh+/Dh++eUXRB46hLUZGahSpw7q1KmDkJAQ3B0YiGaZmahatSqu\nNG+OqnXrIjAwEJUrV0aV7GxUT0pC+fbtUXXaNMi4cSg3a1bxx32+tUKzq1TB5TNnEPDWWzg3dCgC\n58zBiZH7YoWwAAAIX0lEQVQjka6KSydPot6CBdjQoQPS09PRLj4ei+vUweM//IC3AgJw5Nw5hISE\n4OGQECzbuxfvjB6N2m3a4Pe//z3uvfde3Hbbbb79pd6C19fEFJEAAD8D6AjgFIDvAQxQ1YP5nqPF\n7UNVYbPZcPLkSZw8eRKnT59Geno6zp8/n/vv+fPnYbPZkJmZicuXL+f+G5KRgSPZ2YgsXx4nAgIQ\nEBCAGuXK4a1r1/DXSpWQrorK16/jL1euYAKA83Y7IkRwTBUP1ayJ81WrorPdjkPBwdBq1RAUFITg\n4GDUrlwZHQ8cQEqvXqgWFoZatWohLCwMNQMCINu3F70uobvJ+BaL2hJZwZUrV3DixAmcOHEitwFU\n8N9Lly7lNpRyfoIvXcLhrCzUE8HJ8uVRPt+PqsJutyM7OxtPXr+Orao4b7fDbrejYsWKuPO229Au\nIAAHqlVDrM2GLyIi8MK5c1jZujVur1ULNWrUQJgqBr31FnZ/8QVCHn4YoaGhqJCRYZkGkysJvLTl\nkdYA1uS7/waANwo8xzvnFzmnRYcPa1ZMjGaePq0XL17UCxcuaHpysl4eOlQv7N2rV4cP18tnzuj1\n69cd3aa83bfanQuuLIlQWeX8nNmPHdPsUaP0SmqqXrp0SS9cuKDnzp3TtLQ0tdls+ttvv2lGRoZe\nvnxZr127VvjqU652t/X0Z93L4O0aOIC+AObnuz8QwN/V2wnclT9EwT9qca/xVM8O9hAhf+XJxoaH\nu+i6nKiLGsls0gaTLxJ4H0MS+K0OppJc+S5tV0WLfbsTucWTx7cXB8lZMVEXxZUEXtoaeCsAk1S1\ns/P+eAB2zXchU0R04sSJua+JiopCVFRUifd5SyWpK3viQmIxF0+LrJkTWYnZLrj72WcuISEBCQkJ\nuffffvttr1/ELA/HRczHAZwGsBNuXsT0OHf/qLyQSOS6lBQgIsKx2HJ4uNHR+DVXLmKWalV6Vc0C\n8BKAeABJAD7Ln7wNER19c+INCir6G7m4lcGJypriVrG32Rwt7+Rkx78Fn0c+V+p+4LfcQf4WuJ+d\n8hD5naLOSMeNA6ZP55mqD3m9Be62Nm0cf/Scb+6cg+DSpaK/9YnId3LOQCdMcJRLcpL0gQM8UzUh\n37bAgcIvhACsQxOZCWvdhjNfCxxwJOTYWMfBERvruF/Utz6TN5HvsdZtGb5P4EUdHIUldiLyrfxn\nv+HheQ0rJnFT8m0CL+7g4Lc+kfHYK8tSzNELJT4e2LyZNXAiIievz0boYhC3HsjD7oVERDewTgIn\nIqIbmLMXChEReQQTOBGRRTGBExFZFBM4EZFFMYETEVkUEzgRkUUxgRMRWRQTOBGRRTGBExFZFBM4\nEZFFMYETEVkUEzgRkUUxgRMRWRQTOBGRRTGBExFZFBM4EZFFMYETEVkUEzgRkUUxgRMRWRQTOBGR\nRZU4gYvIJBE5KSI/On86ezIwIiIqXmla4ArgHVVt5vxZ46mgzCQhIcHoEErFyvFbOXaA8RvN6vG7\norQllGKXvPcHVj8IrBy/lWMHGL/RrB6/K0qbwEeLyF4RWSAiQR6JiIiIXFJsAheRdSKyv5CfHgDm\nAogA0BTAGQCzfBAvERE5iaqW/k1EwgGsUNUHCnms9DsgIiqDVLXYMnX5kr6xiISq6hnn3d4A9pck\nACIiKpkSJ3AA00SkKRy9UZIBxHgmJCIicoVHSihEROR7XhuJKSKdReQnETksIq97az/eIiIfisiv\nIlJoacjMRKSOiGwSkUQROSAiLxsdkztE5HYR2SEie0QkSUSmGh1TSYhIgHOQ2wqjY3GXiKSIyD5n\n/DuNjscdIhIkIl+IyEHn8dPK6JhcJSIN8g2O/FFELhT3+fVKC1xEAgD8DKAjgFMAvgcwQFUPenxn\nXiIijwK4BOBfhV2cNTMRqQWglqruEZFAALsB9LLY77+SqmaKSHkAWwH8SVW3Gh2XO0RkLIDmAKqo\nag+j43GHiCQDaK6q542OxV0ishDAN6r6ofP4qayqF4yOy10iUg6O/NlCVU8U9hxvtcBbADiiqimq\neh3AYgA9vbQvr1DVLQDSjY6jJFQ1VVX3OG9fAnAQwF3GRuUeVc103vwdgAAAlkokInI3gK4A/gHr\nDnizXNwiUg3Ao6r6IQCoapYVk7dTRwBHi0regPcSeG0A+Xd60rmNfMzZxbMZgB3GRuIeESknInsA\n/Apgk6omGR2Tm94FEAvAbnQgJaQA1ovILhEZbnQwbogAcFZEPhKRH0RkvohUMjqoEnoGwCfFPcFb\nCZxXRk3AWT75AsArzpa4ZaiqXVWbArgbQDsRiTI4JJeJSDcA/1HVH2HBVqxTG1VtBqALgBedJUUr\nKA/gQQBzVPVBABkA3jA2JPeJyO8AdAewpLjneSuBnwJQJ9/9OnC0wslHRKQCgC8B/FtVlxkdT0k5\nT3/jADxkdCxueARAD2cd+VMAHUTkXwbH5JacMR6qehbAUjjKolZwEsBJVf3eef8LOBK61XQBsNv5\n+y+StxL4LgCRIhLu/CbpD+BrL+2LChARAbAAQJKqzjY6HneJSHDO3DoiUhHAEwB+NDYq16nqm6pa\nR1Uj4DgN3qiqg42Oy1UiUklEqjhvVwbwJIoYqGc2qpoK4ISI3Ovc1BFAooEhldQAOL78i1WagTxF\nUtUsEXkJQDwcF6AWWKkHBACIyKcA2gOoKSInALylqh8ZHJar2gAYCGCfiOQkvvEWmvI3FMBC51X4\ncgA+VtUNBsdUGlYrKd4JYKmjHYDyABap6lpjQ3LLaACLnI3HowBeMDgetzi/NDsCuOW1Bw7kISKy\nKC6pRkRkUUzgREQWxQRORGRRTOBERBbFBE5EZFFM4EREFsUETkRkUUzgREQW9f/dYlve7WDutgAA\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa3f1c18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import norm\n",
+    "y_noisy = y + 0.8 * norm.rvs(size=len(x))\n",
+    "p = plt.plot(x, y, 'k-')\n",
+    "p = plt.plot(x, y_noisy, 'rx')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scipy.optimize.leastsq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "定义误差函数，将要优化的参数放在前面："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def f_err(p, y, x):\n",
+    "    return y - function(x, *p)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将这个函数作为参数传入 `leastsq` 函数，第二个参数为初始值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 3.03199715,  1.97689384,  1.30083191,  0.6393337 ]), 1)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c, ret_val = leastsq(f_err, [1, 1, 1, 1], args=(y_noisy, x))\n",
+    "c, ret_val"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`ret_val` 是 1~4 时，表示成功找到最小二乘解："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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eeXsvgCRVPSIiUQC+UNUeLr6PpxFSaElPBxITMe1//ge5ubl46623uFo9+Z1fV6V3EeDT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\nA5MAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa3d7b00>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = plt.plot(x, y_noisy, 'rx')\n",
+    "p = plt.plot(x, function(x, *c), 'k--')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scipy.optimize.curve_fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "更高级的做法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.optimize import curve_fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不需要定义误差函数，直接传入 `function` 作为参数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "p_est, err_est = curve_fit(function, x, y_noisy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 3.03199711  1.97689385  1.3008319   0.63933373]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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eeXsvgCRVPSIiUQC+UNUeLr6PpxFSaElPBxITMe1//ge5ubl46623uFo9+Z1fV6V3EeDT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\nA5MAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa22b828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print p_est\n",
+    "p = plt.plot(x, y_noisy, \"rx\")\n",
+    "p = plt.plot(x, function(x, *p_est), \"k--\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里第一个返回的是函数的参数，第二个返回值为各个参数的协方差矩阵："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.08483704 -0.02782318  0.00967093 -0.03029038]\n",
+      " [-0.02782318  0.00933216 -0.00305158  0.00955794]\n",
+      " [ 0.00967093 -0.00305158  0.0014972  -0.00468919]\n",
+      " [-0.03029038  0.00955794 -0.00468919  0.01484297]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print err_est"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "协方差矩阵的对角线为各个参数的方差："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "normalized relative errors for each parameter\n",
+      "   a\t  b\t f\tphi\n",
+      "[ 0.09606473  0.0488661   0.02974528  0.19056043]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print \"normalized relative errors for each parameter\"\n",
+    "print \"   a\\t  b\\t f\\tphi\"\n",
+    "print np.sqrt(err_est.diagonal()) / p_est"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/04. scipy/04.05 minimization in python.ipynb b/04-scipy/04.05-minimization-in-python.ipynb
similarity index 99%
rename from 04. scipy/04.05 minimization in python.ipynb
rename to 04-scipy/04.05-minimization-in-python.ipynb
index 7d97aee4..32716756 100644
--- a/04. scipy/04.05 minimization in python.ipynb	
+++ b/04-scipy/04.05-minimization-in-python.ipynb
@@ -1,978 +1,978 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 最小化函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## minimize 函数"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pylab inline\n",
-    "set_printoptions(precision=3, suppress=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "已知斜抛运动的水平飞行距离公式：\n",
-    "\n",
-    "$d = 2 \\frac{v_0^2}{g} \\sin(\\theta) \\cos (\\theta)$\n",
-    "\n",
-    "- $d$ 水平飞行距离\n",
-    "- $v_0$ 初速度大小\n",
-    "- $g$ 重力加速度\n",
-    "- $\\theta$ 抛出角度\n",
-    "\n",
-    "希望找到使 $d$ 最大的角度 $\\theta$。\n",
-    "\n",
-    "定义距离函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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/wLtU38HdHhiY2sEd7dMRuLQQR0O5w/77wwknhDZNESleCxfCDjvA009D+/bp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\nAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9f52780>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def dist(theta, v0):\n",
-    "    \"\"\"calculate the distance travelled by a projectile launched\n",
-    "    at theta degrees with v0 (m/s) initial velocity.\n",
-    "    \"\"\"\n",
-    "    g = 9.8\n",
-    "    theta_rad = pi * theta / 180\n",
-    "    return 2 * v0 ** 2 / g * sin(theta_rad) * cos(theta_rad)\n",
-    "theta = linspace(0,90,90)\n",
-    "p = plot(theta, dist(theta, 1.))\n",
-    "xl = xlabel(r'launch angle $\\theta (^{\\circ})$')\n",
-    "yl = ylabel('horizontal distance traveled')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "因为 `Scipy` 提供的是最小化方法，所以最大化距离就相当于最小化距离的负数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def neg_dist(theta, v0):\n",
-    "    return -1 * dist(theta, v0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "导入 `scipy.optimize.minimize`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "optimal angle = 45.0 degrees\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.optimize import minimize\n",
-    "result = minimize(neg_dist, 40, args=(1,))\n",
-    "print \"optimal angle = {:.1f} degrees\".format(result.x[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`minimize` 接受三个参数：第一个是要优化的函数，第二个是初始猜测值，第三个则是优化函数的附加参数，默认 `minimize` 将优化函数的第一个参数作为优化变量，所以第三个参数输入的附加参数从优化函数的第二个参数开始。\n",
-    "\n",
-    "查看返回结果："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   status: 0\n",
-      "  success: True\n",
-      "     njev: 18\n",
-      "     nfev: 54\n",
-      " hess_inv: array([[ 8110.515]])\n",
-      "      fun: -0.10204079220645729\n",
-      "        x: array([ 45.02])\n",
-      "  message: 'Optimization terminated successfully.'\n",
-      "      jac: array([ 0.])\n"
-     ]
-    }
-   ],
-   "source": [
-    "print result"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Rosenbrock 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Rosenbrock 函数是一个用来测试优化函数效果的一个非凸函数：\n",
-    "\n",
-    "$f(x)=\\sum\\limits_{i=1}^{N-1}{100\\left(x_{i+1}^2 - x_i\\right) ^2 + \\left(1-x_{i}\\right)^2 }$\n",
-    "\n",
-    "导入该函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.optimize import rosen\n",
-    "from mpl_toolkits.mplot3d import Axes3D"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `N = 2` 的 Rosenbrock 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "x, y = meshgrid(np.linspace(-2,2,25), np.linspace(-0.5,3.5,25))\n",
-    "z = rosen([x,y])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "图像和最低点 `(1,1)`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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o0KEDqqqi1+t94nogEGghjFWBW1564f949dXnaNNKZe264m16k46dG2xUr2HA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\nI/UCtvtf5dDY9WV+G5k7T5C+NZW4b1/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XSLg1RlN25Te7qPfMTZpyqqqyc+hSwv/1sEc5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\nbxj8dmObAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x15fc5518>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = figure(figsize=(12,5.5))\n",
-    "ax = fig.gca(projection=\"3d\")\n",
-    "ax.azim = 70; ax.elev = 48\n",
-    "ax.set_xlabel(\"X\"); ax.set_ylabel(\"Y\")\n",
-    "ax.set_zlim((0,1000))\n",
-    "p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)\n",
-    "rosen_min = ax.plot([1],[1],[0],\"ro\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "传入初始值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 1.  1.  1.  1.  1.]\n"
-     ]
-    }
-   ],
-   "source": [
-    "x0 = [1.3, 1.6, -0.5, -1.8, 0.8]\n",
-    "result = minimize(rosen, x0)\n",
-    "print result.x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "随机给定初始值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0.815 -2.086  0.297  1.079 -0.528  0.461 -0.13  -0.715  0.734  0.621]\n",
-      "[-0.993  0.997  0.998  0.999  0.999  0.999  0.998  0.997  0.994  0.988]\n"
-     ]
-    }
-   ],
-   "source": [
-    "x0 = np.random.randn(10)\n",
-    "result = minimize(rosen, x0)\n",
-    "print x0\n",
-    "print result.x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于 `N > 3`，函数的最小值为 $(x_1,x_2, ..., x_N) = (1,1,...,1)$，不过有一个局部极小值点 $(x_1,x_2, ..., x_N) = (-1,1,...,1)$，所以随机初始值如果选的不好的话，有可能返回的结果是局部极小值点："
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 优化方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### BFGS 算法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`minimize` 函数默认根据问题是否有界或者有约束，使用 `'BFGS', 'L-BFGS-B', 'SLSQP'` 中的一种。\n",
-    "\n",
-    "可以查看帮助来得到更多的信息："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " minimize(fun, x0, args=(), method=None, jac=None, hess=None, hessp=None,\n",
-      "          bounds=None, constraints=(), tol=None, callback=None, options=None)\n",
-      "\n",
-      "Minimization of scalar function of one or more variables.\n",
-      "\n",
-      "Parameters\n",
-      "----------\n",
-      "fun : callable\n",
-      "    Objective function.\n",
-      "x0 : ndarray\n",
-      "    Initial guess.\n",
-      "args : tuple, optional\n",
-      "    Extra arguments passed to the objective function and its\n",
-      "    derivatives (Jacobian, Hessian).\n",
-      "method : str or callable, optional\n",
-      "    Type of solver.  Should be one of\n",
-      "\n",
-      "        - 'Nelder-Mead'\n",
-      "        - 'Powell'\n",
-      "        - 'CG'\n",
-      "        - 'BFGS'\n",
-      "        - 'Newton-CG'\n",
-      "        - 'Anneal (deprecated as of scipy version 0.14.0)'\n",
-      "        - 'L-BFGS-B'\n",
-      "        - 'TNC'\n",
-      "        - 'COBYLA'\n",
-      "        - 'SLSQP'\n",
-      "        - 'dogleg'\n",
-      "        - 'trust-ncg'\n",
-      "        - custom - a callable object (added in version 0.14.0)\n",
-      "\n",
-      "    If not given, chosen to be one of ``BFGS``, ``L-BFGS-B``, ``SLSQP``,\n",
-      "    depending if the problem has constraints or bounds.\n",
-      "jac : bool or callable, optional\n",
-      "    Jacobian (gradient) of objective function. Only for CG, BFGS,\n",
-      "    Newton-CG, L-BFGS-B, TNC, SLSQP, dogleg, trust-ncg.\n",
-      "    If `jac` is a Boolean and is True, `fun` is assumed to return the\n",
-      "    gradient along with the objective function. If False, the\n",
-      "    gradient will be estimated numerically.\n",
-      "    `jac` can also be a callable returning the gradient of the\n",
-      "    objective. In this case, it must accept the same arguments as `fun`.\n",
-      "hess, hessp : callable, optional\n",
-      "    Hessian (matrix of second-order derivatives) of objective function or\n",
-      "    Hessian of objective function times an arbitrary vector p.  Only for\n",
-      "    Newton-CG, dogleg, trust-ncg.\n",
-      "    Only one of `hessp` or `hess` needs to be given.  If `hess` is\n",
-      "    provided, then `hessp` will be ignored.  If neither `hess` nor\n",
-      "    `hessp` is provided, then the Hessian product will be approximated\n",
-      "    using finite differences on `jac`. `hessp` must compute the Hessian\n",
-      "    times an arbitrary vector.\n",
-      "bounds : sequence, optional\n",
-      "    Bounds for variables (only for L-BFGS-B, TNC and SLSQP).\n",
-      "    ``(min, max)`` pairs for each element in ``x``, defining\n",
-      "    the bounds on that parameter. Use None for one of ``min`` or\n",
-      "    ``max`` when there is no bound in that direction.\n",
-      "constraints : dict or sequence of dict, optional\n",
-      "    Constraints definition (only for COBYLA and SLSQP).\n",
-      "    Each constraint is defined in a dictionary with fields:\n",
-      "        type : str\n",
-      "            Constraint type: 'eq' for equality, 'ineq' for inequality.\n",
-      "        fun : callable\n",
-      "            The function defining the constraint.\n",
-      "        jac : callable, optional\n",
-      "            The Jacobian of `fun` (only for SLSQP).\n",
-      "        args : sequence, optional\n",
-      "            Extra arguments to be passed to the function and Jacobian.\n",
-      "    Equality constraint means that the constraint function result is to\n",
-      "    be zero whereas inequality means that it is to be non-negative.\n",
-      "    Note that COBYLA only supports inequality constraints.\n",
-      "tol : float, optional\n",
-      "    Tolerance for termination. For detailed control, use solver-specific\n",
-      "    options.\n",
-      "options : dict, optional\n",
-      "    A dictionary of solver options. All methods accept the following\n",
-      "    generic options:\n",
-      "        maxiter : int\n",
-      "            Maximum number of iterations to perform.\n",
-      "        disp : bool\n",
-      "            Set to True to print convergence messages.\n",
-      "    For method-specific options, see :func:`show_options()`.\n",
-      "callback : callable, optional\n",
-      "    Called after each iteration, as ``callback(xk)``, where ``xk`` is the\n",
-      "    current parameter vector.\n",
-      "\n",
-      "Returns\n",
-      "-------\n",
-      "res : OptimizeResult\n",
-      "    The optimization result represented as a ``OptimizeResult`` object.\n",
-      "    Important attributes are: ``x`` the solution array, ``success`` a\n",
-      "    Boolean flag indicating if the optimizer exited successfully and\n",
-      "    ``message`` which describes the cause of the termination. See\n",
-      "    `OptimizeResult` for a description of other attributes.\n",
-      "\n",
-      "\n",
-      "See also\n",
-      "--------\n",
-      "minimize_scalar : Interface to minimization algorithms for scalar\n",
-      "    univariate functions\n",
-      "show_options : Additional options accepted by the solvers\n",
-      "\n",
-      "Notes\n",
-      "-----\n",
-      "This section describes the available solvers that can be selected by the\n",
-      "'method' parameter. The default method is *BFGS*.\n",
-      "\n",
-      "**Unconstrained minimization**\n",
-      "\n",
-      "Method *Nelder-Mead* uses the Simplex algorithm [1]_, [2]_. This\n",
-      "algorithm has been successful in many applications but other algorithms\n",
-      "using the first and/or second derivatives information might be preferred\n",
-      "for their better performances and robustness in general.\n",
-      "\n",
-      "Method *Powell* is a modification of Powell's method [3]_, [4]_ which\n",
-      "is a conjugate direction method. It performs sequential one-dimensional\n",
-      "minimizations along each vector of the directions set (`direc` field in\n",
-      "`options` and `info`), which is updated at each iteration of the main\n",
-      "minimization loop. The function need not be differentiable, and no\n",
-      "derivatives are taken.\n",
-      "\n",
-      "Method *CG* uses a nonlinear conjugate gradient algorithm by Polak and\n",
-      "Ribiere, a variant of the Fletcher-Reeves method described in [5]_ pp.\n",
-      "120-122. Only the first derivatives are used.\n",
-      "\n",
-      "Method *BFGS* uses the quasi-Newton method of Broyden, Fletcher,\n",
-      "Goldfarb, and Shanno (BFGS) [5]_ pp. 136. It uses the first derivatives\n",
-      "only. BFGS has proven good performance even for non-smooth\n",
-      "optimizations. This method also returns an approximation of the Hessian\n",
-      "inverse, stored as `hess_inv` in the OptimizeResult object.\n",
-      "\n",
-      "Method *Newton-CG* uses a Newton-CG algorithm [5]_ pp. 168 (also known\n",
-      "as the truncated Newton method). It uses a CG method to the compute the\n",
-      "search direction. See also *TNC* method for a box-constrained\n",
-      "minimization with a similar algorithm.\n",
-      "\n",
-      "Method *Anneal* uses simulated annealing, which is a probabilistic\n",
-      "metaheuristic algorithm for global optimization. It uses no derivative\n",
-      "information from the function being optimized.\n",
-      "\n",
-      "Method *dogleg* uses the dog-leg trust-region algorithm [5]_\n",
-      "for unconstrained minimization. This algorithm requires the gradient\n",
-      "and Hessian; furthermore the Hessian is required to be positive definite.\n",
-      "\n",
-      "Method *trust-ncg* uses the Newton conjugate gradient trust-region\n",
-      "algorithm [5]_ for unconstrained minimization. This algorithm requires\n",
-      "the gradient and either the Hessian or a function that computes the\n",
-      "product of the Hessian with a given vector.\n",
-      "\n",
-      "**Constrained minimization**\n",
-      "\n",
-      "Method *L-BFGS-B* uses the L-BFGS-B algorithm [6]_, [7]_ for bound\n",
-      "constrained minimization.\n",
-      "\n",
-      "Method *TNC* uses a truncated Newton algorithm [5]_, [8]_ to minimize a\n",
-      "function with variables subject to bounds. This algorithm uses\n",
-      "gradient information; it is also called Newton Conjugate-Gradient. It\n",
-      "differs from the *Newton-CG* method described above as it wraps a C\n",
-      "implementation and allows each variable to be given upper and lower\n",
-      "bounds.\n",
-      "\n",
-      "Method *COBYLA* uses the Constrained Optimization BY Linear\n",
-      "Approximation (COBYLA) method [9]_, [10]_, [11]_. The algorithm is\n",
-      "based on linear approximations to the objective function and each\n",
-      "constraint. The method wraps a FORTRAN implementation of the algorithm.\n",
-      "\n",
-      "Method *SLSQP* uses Sequential Least SQuares Programming to minimize a\n",
-      "function of several variables with any combination of bounds, equality\n",
-      "and inequality constraints. The method wraps the SLSQP Optimization\n",
-      "subroutine originally implemented by Dieter Kraft [12]_. Note that the\n",
-      "wrapper handles infinite values in bounds by converting them into large\n",
-      "floating values.\n",
-      "\n",
-      "**Custom minimizers**\n",
-      "\n",
-      "It may be useful to pass a custom minimization method, for example\n",
-      "when using a frontend to this method such as `scipy.optimize.basinhopping`\n",
-      "or a different library.  You can simply pass a callable as the ``method``\n",
-      "parameter.\n",
-      "\n",
-      "The callable is called as ``method(fun, x0, args, **kwargs, **options)``\n",
-      "where ``kwargs`` corresponds to any other parameters passed to `minimize`\n",
-      "(such as `callback`, `hess`, etc.), except the `options` dict, which has\n",
-      "its contents also passed as `method` parameters pair by pair.  Also, if\n",
-      "`jac` has been passed as a bool type, `jac` and `fun` are mangled so that\n",
-      "`fun` returns just the function values and `jac` is converted to a function\n",
-      "returning the Jacobian.  The method shall return an ``OptimizeResult``\n",
-      "object.\n",
-      "\n",
-      "The provided `method` callable must be able to accept (and possibly ignore)\n",
-      "arbitrary parameters; the set of parameters accepted by `minimize` may\n",
-      "expand in future versions and then these parameters will be passed to\n",
-      "the method.  You can find an example in the scipy.optimize tutorial.\n",
-      "\n",
-      ".. versionadded:: 0.11.0\n",
-      "\n",
-      "References\n",
-      "----------\n",
-      ".. [1] Nelder, J A, and R Mead. 1965. A Simplex Method for Function\n",
-      "    Minimization. The Computer Journal 7: 308-13.\n",
-      ".. [2] Wright M H. 1996. Direct search methods: Once scorned, now\n",
-      "    respectable, in Numerical Analysis 1995: Proceedings of the 1995\n",
-      "    Dundee Biennial Conference in Numerical Analysis (Eds. D F\n",
-      "    Griffiths and G A Watson). Addison Wesley Longman, Harlow, UK.\n",
-      "    191-208.\n",
-      ".. [3] Powell, M J D. 1964. An efficient method for finding the minimum of\n",
-      "   a function of several variables without calculating derivatives. The\n",
-      "   Computer Journal 7: 155-162.\n",
-      ".. [4] Press W, S A Teukolsky, W T Vetterling and B P Flannery.\n",
-      "   Numerical Recipes (any edition), Cambridge University Press.\n",
-      ".. [5] Nocedal, J, and S J Wright. 2006. Numerical Optimization.\n",
-      "   Springer New York.\n",
-      ".. [6] Byrd, R H and P Lu and J. Nocedal. 1995. A Limited Memory\n",
-      "   Algorithm for Bound Constrained Optimization. SIAM Journal on\n",
-      "   Scientific and Statistical Computing 16 (5): 1190-1208.\n",
-      ".. [7] Zhu, C and R H Byrd and J Nocedal. 1997. L-BFGS-B: Algorithm\n",
-      "   778: L-BFGS-B, FORTRAN routines for large scale bound constrained\n",
-      "   optimization. ACM Transactions on Mathematical Software 23 (4):\n",
-      "   550-560.\n",
-      ".. [8] Nash, S G. Newton-Type Minimization Via the Lanczos Method.\n",
-      "   1984. SIAM Journal of Numerical Analysis 21: 770-778.\n",
-      ".. [9] Powell, M J D. A direct search optimization method that models\n",
-      "   the objective and constraint functions by linear interpolation.\n",
-      "   1994. Advances in Optimization and Numerical Analysis, eds. S. Gomez\n",
-      "   and J-P Hennart, Kluwer Academic (Dordrecht), 51-67.\n",
-      ".. [10] Powell M J D. Direct search algorithms for optimization\n",
-      "   calculations. 1998. Acta Numerica 7: 287-336.\n",
-      ".. [11] Powell M J D. A view of algorithms for optimization without\n",
-      "   derivatives. 2007.Cambridge University Technical Report DAMTP\n",
-      "   2007/NA03\n",
-      ".. [12] Kraft, D. A software package for sequential quadratic\n",
-      "   programming. 1988. Tech. Rep. DFVLR-FB 88-28, DLR German Aerospace\n",
-      "   Center -- Institute for Flight Mechanics, Koln, Germany.\n",
-      "\n",
-      "Examples\n",
-      "--------\n",
-      "Let us consider the problem of minimizing the Rosenbrock function. This\n",
-      "function (and its respective derivatives) is implemented in `rosen`\n",
-      "(resp. `rosen_der`, `rosen_hess`) in the `scipy.optimize`.\n",
-      "\n",
-      ">>> from scipy.optimize import minimize, rosen, rosen_der\n",
-      "\n",
-      "A simple application of the *Nelder-Mead* method is:\n",
-      "\n",
-      ">>> x0 = [1.3, 0.7, 0.8, 1.9, 1.2]\n",
-      ">>> res = minimize(rosen, x0, method='Nelder-Mead')\n",
-      ">>> res.x\n",
-      "[ 1.  1.  1.  1.  1.]\n",
-      "\n",
-      "Now using the *BFGS* algorithm, using the first derivative and a few\n",
-      "options:\n",
-      "\n",
-      ">>> res = minimize(rosen, x0, method='BFGS', jac=rosen_der,\n",
-      "...                options={'gtol': 1e-6, 'disp': True})\n",
-      "Optimization terminated successfully.\n",
-      "         Current function value: 0.000000\n",
-      "         Iterations: 52\n",
-      "         Function evaluations: 64\n",
-      "         Gradient evaluations: 64\n",
-      ">>> res.x\n",
-      "[ 1.  1.  1.  1.  1.]\n",
-      ">>> print res.message\n",
-      "Optimization terminated successfully.\n",
-      ">>> res.hess\n",
-      "[[ 0.00749589  0.01255155  0.02396251  0.04750988  0.09495377]\n",
-      " [ 0.01255155  0.02510441  0.04794055  0.09502834  0.18996269]\n",
-      " [ 0.02396251  0.04794055  0.09631614  0.19092151  0.38165151]\n",
-      " [ 0.04750988  0.09502834  0.19092151  0.38341252  0.7664427 ]\n",
-      " [ 0.09495377  0.18996269  0.38165151  0.7664427   1.53713523]]\n",
-      "\n",
-      "\n",
-      "Next, consider a minimization problem with several constraints (namely\n",
-      "Example 16.4 from [5]_). The objective function is:\n",
-      "\n",
-      ">>> fun = lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2\n",
-      "\n",
-      "There are three constraints defined as:\n",
-      "\n",
-      ">>> cons = ({'type': 'ineq', 'fun': lambda x:  x[0] - 2 * x[1] + 2},\n",
-      "...         {'type': 'ineq', 'fun': lambda x: -x[0] - 2 * x[1] + 6},\n",
-      "...         {'type': 'ineq', 'fun': lambda x: -x[0] + 2 * x[1] + 2})\n",
-      "\n",
-      "And variables must be positive, hence the following bounds:\n",
-      "\n",
-      ">>> bnds = ((0, None), (0, None))\n",
-      "\n",
-      "The optimization problem is solved using the SLSQP method as:\n",
-      "\n",
-      ">>> res = minimize(fun, (2, 0), method='SLSQP', bounds=bnds,\n",
-      "...                constraints=cons)\n",
-      "\n",
-      "It should converge to the theoretical solution (1.4 ,1.7).\n"
-     ]
-    }
-   ],
-   "source": [
-    "info(minimize)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "默认没有约束时，使用的是 [BFGS 方法](https://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm)。\n",
-    "\n",
-    "利用 `callback` 参数查看迭代的历史："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(37L, 2L)\n",
-      "[ 1.  1.]\n",
-      "in 200 function evaluations.\n"
-     ]
-    }
-   ],
-   "source": [
-    "x0 = [-1.5, 4.5]\n",
-    "xi = [x0]\n",
-    "result = minimize(rosen, x0, callback=xi.append)\n",
-    "xi = np.asarray(xi)\n",
-    "print xi.shape\n",
-    "print result.x\n",
-    "print \"in {} function evaluations.\".format(result.nfev)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "绘图显示轨迹："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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2L6n9Z0ei1BGnT59m4pTJhB1aWMBerv8Ax9efolEusWqzyszsepR6PR5RVapKtsmsbr+S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\n4JgH9qlwvBPooWixoEgmAVrdiT6+CKKaFy1QTV8Iu9pC2L3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twB1m8FStYMcSeL87dJoC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uB6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\nYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x15fc54e0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x, y = meshgrid(np.linspace(-2.3,1.75,25), np.linspace(-0.5,4.5,25))\n",
-    "z = rosen([x,y])\n",
-    "fig = figure(figsize=(12,5.5))\n",
-    "ax = fig.gca(projection=\"3d\"); ax.azim = 70; ax.elev = 75\n",
-    "ax.set_xlabel(\"X\"); ax.set_ylabel(\"Y\"); ax.set_zlim((0,1000))\n",
-    "p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)\n",
-    "intermed = ax.plot(xi[:,0], xi[:,1], rosen(xi.T), \"g-o\")\n",
-    "rosen_min = ax.plot([1],[1],[0],\"ro\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`BFGS` 需要计算函数的 Jacobian 矩阵：\n",
-    "\n",
-    "给定 $\\left[y_1,y_2,y_3\\right] = f(x_0, x_1, x_2)$\n",
-    "\n",
-    "$$J=\\left[ \\begin{matrix} \\frac{\\partial y_1}{\\partial x_0} & \\frac{\\partial y_1}{\\partial x_1} & \\frac{\\partial y_1}{\\partial x_2} \\\\\\ \\frac{\\partial y_2}{\\partial x_0} & \\frac{\\partial y_2}{\\partial x_1} & \\frac{\\partial y_2}{\\partial x_2} \\\\\\ \\frac{\\partial y_3}{\\partial x_0} & \\frac{\\partial y_3}{\\partial x_1} & \\frac{\\partial y_3}{\\partial x_2} \\end{matrix} \\right]$$\n",
-    "\n",
-    "在我们的例子中\n",
-    "\n",
-    "$$J= \\left[ \\begin{matrix}\\frac{\\partial rosen}{\\partial x_0} & \\frac{\\partial rosen}{\\partial x_1} \\end{matrix} \\right] $$\n",
-    "\n",
-    "导入 `rosen` 函数的 `Jacobian` 函数 `rosen_der`： "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.optimize import rosen_der"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "此时，我们将 `Jacobian` 矩阵作为参数传入："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(38L, 2L)\n",
-      "in 49 function evaluations and 49 jacobian evaluations.\n"
-     ]
-    }
-   ],
-   "source": [
-    "xi = [x0]\n",
-    "result = minimize(rosen, x0, jac=rosen_der, callback=xi.append)\n",
-    "xi = np.asarray(xi)\n",
-    "print xi.shape\n",
-    "print \"in {} function evaluations and {} jacobian evaluations.\".format(result.nfev, result.njev)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以看到，函数计算的开销大约减少了一半，迭代路径与上面的基本吻合："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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2L6n9Z0ei1BGnT59m4pTJhB1aWMBerv8Ax9efolEusWqzyszsepR6PR5RVapKtsmsbr+S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\n4JgH9qlwvBPooWixoEgmAVrdiT6+CKKaFy1QTV8Iu9pC2L3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twB1m8FStYMcSeL87dJoC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y0X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\nCOGxJFgVQgghhBAe6/8D6bfN7+5DVO4AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1632da90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x, y = meshgrid(np.linspace(-2.3,1.75,25), np.linspace(-0.5,4.5,25))\n",
-    "z = rosen([x,y])\n",
-    "fig = figure(figsize=(12,5.5))\n",
-    "ax = fig.gca(projection=\"3d\"); ax.azim = 70; ax.elev = 75\n",
-    "ax.set_xlabel(\"X\"); ax.set_ylabel(\"Y\"); ax.set_zlim((0,1000))\n",
-    "p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)\n",
-    "intermed = ax.plot(xi[:,0], xi[:,1], rosen(xi.T), \"g-o\")\n",
-    "rosen_min = ax.plot([1],[1],[0],\"ro\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Nelder-Mead Simplex 算法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "改变 `minimize` 使用的算法，使用 [Nelder–Mead 单纯形算法](https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method)："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(120L, 2L)\n",
-      "Solved the Nelder-Mead Simplex method with 226 function evaluations.\n"
-     ]
-    }
-   ],
-   "source": [
-    "xi = [x0]\n",
-    "result = minimize(rosen, x0, method=\"nelder-mead\", callback = xi.append)\n",
-    "xi = np.asarray(xi)\n",
-    "print xi.shape\n",
-    "print \"Solved the Nelder-Mead Simplex method with {} function evaluations.\".format(result.nfev)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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S0oJ2EMoL9FYd0P5pncwKc7y11WrFbDZjMBhITk6m9/0D2WaPJm3492COzTpg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\nEEIIIYTP+v/YLseLtZ7EnQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x15ea7668>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x, y = meshgrid(np.linspace(-1.9,1.75,25), np.linspace(-0.5,4.5,25))\n",
-    "z = rosen([x,y])\n",
-    "fig = figure(figsize=(12,5.5))\n",
-    "ax = fig.gca(projection=\"3d\"); ax.azim = 70; ax.elev = 75\n",
-    "ax.set_xlabel(\"X\"); ax.set_ylabel(\"Y\"); ax.set_zlim((0,1000))\n",
-    "p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)\n",
-    "intermed = ax.plot(xi[:,0], xi[:,1], rosen(xi.T), \"g-o\")\n",
-    "rosen_min = ax.plot([1],[1],[0],\"ro\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Powell 算法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 [Powell 算法](https://en.wikipedia.org/wiki/Powell%27s_method)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(31L, 2L)\n",
-      "Solved Powell's method with 855 function evaluations.\n"
-     ]
-    }
-   ],
-   "source": [
-    "xi = [x0]\n",
-    "result = minimize(rosen, x0, method=\"powell\", callback=xi.append)\n",
-    "xi = np.asarray(xi)\n",
-    "print xi.shape\n",
-    "print \"Solved Powell's method with {} function evaluations.\".format(result.nfev)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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2L6n9Z0ei1BGnT59m4pTJhB1aWMBerv8Ax9efolEusWqzyszsepR6PR5RVapKtsmsbr+S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\n4JgH9qlwvBPooWixoEgmAVrdiT6+CKKaFy1QTV8Iu9pC2L3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twB1m8FStYMcSeL87dJoC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\nQgghhBBeS4JVIYQQQgjhtf4/QWwBBPzDxt8AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x167624a8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x, y = meshgrid(np.linspace(-2.3,1.75,25), np.linspace(-0.5,4.5,25))\n",
-    "z = rosen([x,y])\n",
-    "fig = figure(figsize=(12,5.5))\n",
-    "ax = fig.gca(projection=\"3d\"); ax.azim = 70; ax.elev = 75\n",
-    "ax.set_xlabel(\"X\"); ax.set_ylabel(\"Y\"); ax.set_zlim((0,1000))\n",
-    "p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)\n",
-    "intermed = ax.plot(xi[:,0], xi[:,1], rosen(xi.T), \"g-o\")\n",
-    "rosen_min = ax.plot([1],[1],[0],\"ro\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 最小化函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## minimize 函数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab inline\n",
+    "set_printoptions(precision=3, suppress=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "已知斜抛运动的水平飞行距离公式：\n",
+    "\n",
+    "$d = 2 \\frac{v_0^2}{g} \\sin(\\theta) \\cos (\\theta)$\n",
+    "\n",
+    "- $d$ 水平飞行距离\n",
+    "- $v_0$ 初速度大小\n",
+    "- $g$ 重力加速度\n",
+    "- $\\theta$ 抛出角度\n",
+    "\n",
+    "希望找到使 $d$ 最大的角度 $\\theta$。\n",
+    "\n",
+    "定义距离函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/wLtU38HdHhiY2sEd7dMRuLQQR0O5w/77wwknhDZNESleCxfCDjvA009D+/bp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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9f52780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def dist(theta, v0):\n",
+    "    \"\"\"calculate the distance travelled by a projectile launched\n",
+    "    at theta degrees with v0 (m/s) initial velocity.\n",
+    "    \"\"\"\n",
+    "    g = 9.8\n",
+    "    theta_rad = pi * theta / 180\n",
+    "    return 2 * v0 ** 2 / g * sin(theta_rad) * cos(theta_rad)\n",
+    "theta = linspace(0,90,90)\n",
+    "p = plot(theta, dist(theta, 1.))\n",
+    "xl = xlabel(r'launch angle $\\theta (^{\\circ})$')\n",
+    "yl = ylabel('horizontal distance traveled')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "因为 `Scipy` 提供的是最小化方法，所以最大化距离就相当于最小化距离的负数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def neg_dist(theta, v0):\n",
+    "    return -1 * dist(theta, v0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "导入 `scipy.optimize.minimize`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "optimal angle = 45.0 degrees\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.optimize import minimize\n",
+    "result = minimize(neg_dist, 40, args=(1,))\n",
+    "print \"optimal angle = {:.1f} degrees\".format(result.x[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`minimize` 接受三个参数：第一个是要优化的函数，第二个是初始猜测值，第三个则是优化函数的附加参数，默认 `minimize` 将优化函数的第一个参数作为优化变量，所以第三个参数输入的附加参数从优化函数的第二个参数开始。\n",
+    "\n",
+    "查看返回结果："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   status: 0\n",
+      "  success: True\n",
+      "     njev: 18\n",
+      "     nfev: 54\n",
+      " hess_inv: array([[ 8110.515]])\n",
+      "      fun: -0.10204079220645729\n",
+      "        x: array([ 45.02])\n",
+      "  message: 'Optimization terminated successfully.'\n",
+      "      jac: array([ 0.])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Rosenbrock 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rosenbrock 函数是一个用来测试优化函数效果的一个非凸函数：\n",
+    "\n",
+    "$f(x)=\\sum\\limits_{i=1}^{N-1}{100\\left(x_{i+1}^2 - x_i\\right) ^2 + \\left(1-x_{i}\\right)^2 }$\n",
+    "\n",
+    "导入该函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.optimize import rosen\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `N = 2` 的 Rosenbrock 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x, y = meshgrid(np.linspace(-2,2,25), np.linspace(-0.5,3.5,25))\n",
+    "z = rosen([x,y])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "图像和最低点 `(1,1)`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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o0KEDqqqi1+t94nogEGghjFWBW1564f949dXnaNNKZe264m16k46dG2xUr2HA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\nI/UCtvtf5dDY9WV+G5k7T5C+NZW4b1/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XSLg1RlN25Te7qPfMTZpyqqqyc+hSwv/1sEc5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\nbxj8dmObAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15fc5518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = figure(figsize=(12,5.5))\n",
+    "ax = fig.gca(projection=\"3d\")\n",
+    "ax.azim = 70; ax.elev = 48\n",
+    "ax.set_xlabel(\"X\"); ax.set_ylabel(\"Y\")\n",
+    "ax.set_zlim((0,1000))\n",
+    "p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)\n",
+    "rosen_min = ax.plot([1],[1],[0],\"ro\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "传入初始值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1.  1.  1.  1.  1.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x0 = [1.3, 1.6, -0.5, -1.8, 0.8]\n",
+    "result = minimize(rosen, x0)\n",
+    "print result.x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "随机给定初始值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.815 -2.086  0.297  1.079 -0.528  0.461 -0.13  -0.715  0.734  0.621]\n",
+      "[-0.993  0.997  0.998  0.999  0.999  0.999  0.998  0.997  0.994  0.988]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x0 = np.random.randn(10)\n",
+    "result = minimize(rosen, x0)\n",
+    "print x0\n",
+    "print result.x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于 `N > 3`，函数的最小值为 $(x_1,x_2, ..., x_N) = (1,1,...,1)$，不过有一个局部极小值点 $(x_1,x_2, ..., x_N) = (-1,1,...,1)$，所以随机初始值如果选的不好的话，有可能返回的结果是局部极小值点："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 优化方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### BFGS 算法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`minimize` 函数默认根据问题是否有界或者有约束，使用 `'BFGS', 'L-BFGS-B', 'SLSQP'` 中的一种。\n",
+    "\n",
+    "可以查看帮助来得到更多的信息："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " minimize(fun, x0, args=(), method=None, jac=None, hess=None, hessp=None,\n",
+      "          bounds=None, constraints=(), tol=None, callback=None, options=None)\n",
+      "\n",
+      "Minimization of scalar function of one or more variables.\n",
+      "\n",
+      "Parameters\n",
+      "----------\n",
+      "fun : callable\n",
+      "    Objective function.\n",
+      "x0 : ndarray\n",
+      "    Initial guess.\n",
+      "args : tuple, optional\n",
+      "    Extra arguments passed to the objective function and its\n",
+      "    derivatives (Jacobian, Hessian).\n",
+      "method : str or callable, optional\n",
+      "    Type of solver.  Should be one of\n",
+      "\n",
+      "        - 'Nelder-Mead'\n",
+      "        - 'Powell'\n",
+      "        - 'CG'\n",
+      "        - 'BFGS'\n",
+      "        - 'Newton-CG'\n",
+      "        - 'Anneal (deprecated as of scipy version 0.14.0)'\n",
+      "        - 'L-BFGS-B'\n",
+      "        - 'TNC'\n",
+      "        - 'COBYLA'\n",
+      "        - 'SLSQP'\n",
+      "        - 'dogleg'\n",
+      "        - 'trust-ncg'\n",
+      "        - custom - a callable object (added in version 0.14.0)\n",
+      "\n",
+      "    If not given, chosen to be one of ``BFGS``, ``L-BFGS-B``, ``SLSQP``,\n",
+      "    depending if the problem has constraints or bounds.\n",
+      "jac : bool or callable, optional\n",
+      "    Jacobian (gradient) of objective function. Only for CG, BFGS,\n",
+      "    Newton-CG, L-BFGS-B, TNC, SLSQP, dogleg, trust-ncg.\n",
+      "    If `jac` is a Boolean and is True, `fun` is assumed to return the\n",
+      "    gradient along with the objective function. If False, the\n",
+      "    gradient will be estimated numerically.\n",
+      "    `jac` can also be a callable returning the gradient of the\n",
+      "    objective. In this case, it must accept the same arguments as `fun`.\n",
+      "hess, hessp : callable, optional\n",
+      "    Hessian (matrix of second-order derivatives) of objective function or\n",
+      "    Hessian of objective function times an arbitrary vector p.  Only for\n",
+      "    Newton-CG, dogleg, trust-ncg.\n",
+      "    Only one of `hessp` or `hess` needs to be given.  If `hess` is\n",
+      "    provided, then `hessp` will be ignored.  If neither `hess` nor\n",
+      "    `hessp` is provided, then the Hessian product will be approximated\n",
+      "    using finite differences on `jac`. `hessp` must compute the Hessian\n",
+      "    times an arbitrary vector.\n",
+      "bounds : sequence, optional\n",
+      "    Bounds for variables (only for L-BFGS-B, TNC and SLSQP).\n",
+      "    ``(min, max)`` pairs for each element in ``x``, defining\n",
+      "    the bounds on that parameter. Use None for one of ``min`` or\n",
+      "    ``max`` when there is no bound in that direction.\n",
+      "constraints : dict or sequence of dict, optional\n",
+      "    Constraints definition (only for COBYLA and SLSQP).\n",
+      "    Each constraint is defined in a dictionary with fields:\n",
+      "        type : str\n",
+      "            Constraint type: 'eq' for equality, 'ineq' for inequality.\n",
+      "        fun : callable\n",
+      "            The function defining the constraint.\n",
+      "        jac : callable, optional\n",
+      "            The Jacobian of `fun` (only for SLSQP).\n",
+      "        args : sequence, optional\n",
+      "            Extra arguments to be passed to the function and Jacobian.\n",
+      "    Equality constraint means that the constraint function result is to\n",
+      "    be zero whereas inequality means that it is to be non-negative.\n",
+      "    Note that COBYLA only supports inequality constraints.\n",
+      "tol : float, optional\n",
+      "    Tolerance for termination. For detailed control, use solver-specific\n",
+      "    options.\n",
+      "options : dict, optional\n",
+      "    A dictionary of solver options. All methods accept the following\n",
+      "    generic options:\n",
+      "        maxiter : int\n",
+      "            Maximum number of iterations to perform.\n",
+      "        disp : bool\n",
+      "            Set to True to print convergence messages.\n",
+      "    For method-specific options, see :func:`show_options()`.\n",
+      "callback : callable, optional\n",
+      "    Called after each iteration, as ``callback(xk)``, where ``xk`` is the\n",
+      "    current parameter vector.\n",
+      "\n",
+      "Returns\n",
+      "-------\n",
+      "res : OptimizeResult\n",
+      "    The optimization result represented as a ``OptimizeResult`` object.\n",
+      "    Important attributes are: ``x`` the solution array, ``success`` a\n",
+      "    Boolean flag indicating if the optimizer exited successfully and\n",
+      "    ``message`` which describes the cause of the termination. See\n",
+      "    `OptimizeResult` for a description of other attributes.\n",
+      "\n",
+      "\n",
+      "See also\n",
+      "--------\n",
+      "minimize_scalar : Interface to minimization algorithms for scalar\n",
+      "    univariate functions\n",
+      "show_options : Additional options accepted by the solvers\n",
+      "\n",
+      "Notes\n",
+      "-----\n",
+      "This section describes the available solvers that can be selected by the\n",
+      "'method' parameter. The default method is *BFGS*.\n",
+      "\n",
+      "**Unconstrained minimization**\n",
+      "\n",
+      "Method *Nelder-Mead* uses the Simplex algorithm [1]_, [2]_. This\n",
+      "algorithm has been successful in many applications but other algorithms\n",
+      "using the first and/or second derivatives information might be preferred\n",
+      "for their better performances and robustness in general.\n",
+      "\n",
+      "Method *Powell* is a modification of Powell's method [3]_, [4]_ which\n",
+      "is a conjugate direction method. It performs sequential one-dimensional\n",
+      "minimizations along each vector of the directions set (`direc` field in\n",
+      "`options` and `info`), which is updated at each iteration of the main\n",
+      "minimization loop. The function need not be differentiable, and no\n",
+      "derivatives are taken.\n",
+      "\n",
+      "Method *CG* uses a nonlinear conjugate gradient algorithm by Polak and\n",
+      "Ribiere, a variant of the Fletcher-Reeves method described in [5]_ pp.\n",
+      "120-122. Only the first derivatives are used.\n",
+      "\n",
+      "Method *BFGS* uses the quasi-Newton method of Broyden, Fletcher,\n",
+      "Goldfarb, and Shanno (BFGS) [5]_ pp. 136. It uses the first derivatives\n",
+      "only. BFGS has proven good performance even for non-smooth\n",
+      "optimizations. This method also returns an approximation of the Hessian\n",
+      "inverse, stored as `hess_inv` in the OptimizeResult object.\n",
+      "\n",
+      "Method *Newton-CG* uses a Newton-CG algorithm [5]_ pp. 168 (also known\n",
+      "as the truncated Newton method). It uses a CG method to the compute the\n",
+      "search direction. See also *TNC* method for a box-constrained\n",
+      "minimization with a similar algorithm.\n",
+      "\n",
+      "Method *Anneal* uses simulated annealing, which is a probabilistic\n",
+      "metaheuristic algorithm for global optimization. It uses no derivative\n",
+      "information from the function being optimized.\n",
+      "\n",
+      "Method *dogleg* uses the dog-leg trust-region algorithm [5]_\n",
+      "for unconstrained minimization. This algorithm requires the gradient\n",
+      "and Hessian; furthermore the Hessian is required to be positive definite.\n",
+      "\n",
+      "Method *trust-ncg* uses the Newton conjugate gradient trust-region\n",
+      "algorithm [5]_ for unconstrained minimization. This algorithm requires\n",
+      "the gradient and either the Hessian or a function that computes the\n",
+      "product of the Hessian with a given vector.\n",
+      "\n",
+      "**Constrained minimization**\n",
+      "\n",
+      "Method *L-BFGS-B* uses the L-BFGS-B algorithm [6]_, [7]_ for bound\n",
+      "constrained minimization.\n",
+      "\n",
+      "Method *TNC* uses a truncated Newton algorithm [5]_, [8]_ to minimize a\n",
+      "function with variables subject to bounds. This algorithm uses\n",
+      "gradient information; it is also called Newton Conjugate-Gradient. It\n",
+      "differs from the *Newton-CG* method described above as it wraps a C\n",
+      "implementation and allows each variable to be given upper and lower\n",
+      "bounds.\n",
+      "\n",
+      "Method *COBYLA* uses the Constrained Optimization BY Linear\n",
+      "Approximation (COBYLA) method [9]_, [10]_, [11]_. The algorithm is\n",
+      "based on linear approximations to the objective function and each\n",
+      "constraint. The method wraps a FORTRAN implementation of the algorithm.\n",
+      "\n",
+      "Method *SLSQP* uses Sequential Least SQuares Programming to minimize a\n",
+      "function of several variables with any combination of bounds, equality\n",
+      "and inequality constraints. The method wraps the SLSQP Optimization\n",
+      "subroutine originally implemented by Dieter Kraft [12]_. Note that the\n",
+      "wrapper handles infinite values in bounds by converting them into large\n",
+      "floating values.\n",
+      "\n",
+      "**Custom minimizers**\n",
+      "\n",
+      "It may be useful to pass a custom minimization method, for example\n",
+      "when using a frontend to this method such as `scipy.optimize.basinhopping`\n",
+      "or a different library.  You can simply pass a callable as the ``method``\n",
+      "parameter.\n",
+      "\n",
+      "The callable is called as ``method(fun, x0, args, **kwargs, **options)``\n",
+      "where ``kwargs`` corresponds to any other parameters passed to `minimize`\n",
+      "(such as `callback`, `hess`, etc.), except the `options` dict, which has\n",
+      "its contents also passed as `method` parameters pair by pair.  Also, if\n",
+      "`jac` has been passed as a bool type, `jac` and `fun` are mangled so that\n",
+      "`fun` returns just the function values and `jac` is converted to a function\n",
+      "returning the Jacobian.  The method shall return an ``OptimizeResult``\n",
+      "object.\n",
+      "\n",
+      "The provided `method` callable must be able to accept (and possibly ignore)\n",
+      "arbitrary parameters; the set of parameters accepted by `minimize` may\n",
+      "expand in future versions and then these parameters will be passed to\n",
+      "the method.  You can find an example in the scipy.optimize tutorial.\n",
+      "\n",
+      ".. versionadded:: 0.11.0\n",
+      "\n",
+      "References\n",
+      "----------\n",
+      ".. [1] Nelder, J A, and R Mead. 1965. A Simplex Method for Function\n",
+      "    Minimization. The Computer Journal 7: 308-13.\n",
+      ".. [2] Wright M H. 1996. Direct search methods: Once scorned, now\n",
+      "    respectable, in Numerical Analysis 1995: Proceedings of the 1995\n",
+      "    Dundee Biennial Conference in Numerical Analysis (Eds. D F\n",
+      "    Griffiths and G A Watson). Addison Wesley Longman, Harlow, UK.\n",
+      "    191-208.\n",
+      ".. [3] Powell, M J D. 1964. An efficient method for finding the minimum of\n",
+      "   a function of several variables without calculating derivatives. The\n",
+      "   Computer Journal 7: 155-162.\n",
+      ".. [4] Press W, S A Teukolsky, W T Vetterling and B P Flannery.\n",
+      "   Numerical Recipes (any edition), Cambridge University Press.\n",
+      ".. [5] Nocedal, J, and S J Wright. 2006. Numerical Optimization.\n",
+      "   Springer New York.\n",
+      ".. [6] Byrd, R H and P Lu and J. Nocedal. 1995. A Limited Memory\n",
+      "   Algorithm for Bound Constrained Optimization. SIAM Journal on\n",
+      "   Scientific and Statistical Computing 16 (5): 1190-1208.\n",
+      ".. [7] Zhu, C and R H Byrd and J Nocedal. 1997. L-BFGS-B: Algorithm\n",
+      "   778: L-BFGS-B, FORTRAN routines for large scale bound constrained\n",
+      "   optimization. ACM Transactions on Mathematical Software 23 (4):\n",
+      "   550-560.\n",
+      ".. [8] Nash, S G. Newton-Type Minimization Via the Lanczos Method.\n",
+      "   1984. SIAM Journal of Numerical Analysis 21: 770-778.\n",
+      ".. [9] Powell, M J D. A direct search optimization method that models\n",
+      "   the objective and constraint functions by linear interpolation.\n",
+      "   1994. Advances in Optimization and Numerical Analysis, eds. S. Gomez\n",
+      "   and J-P Hennart, Kluwer Academic (Dordrecht), 51-67.\n",
+      ".. [10] Powell M J D. Direct search algorithms for optimization\n",
+      "   calculations. 1998. Acta Numerica 7: 287-336.\n",
+      ".. [11] Powell M J D. A view of algorithms for optimization without\n",
+      "   derivatives. 2007.Cambridge University Technical Report DAMTP\n",
+      "   2007/NA03\n",
+      ".. [12] Kraft, D. A software package for sequential quadratic\n",
+      "   programming. 1988. Tech. Rep. DFVLR-FB 88-28, DLR German Aerospace\n",
+      "   Center -- Institute for Flight Mechanics, Koln, Germany.\n",
+      "\n",
+      "Examples\n",
+      "--------\n",
+      "Let us consider the problem of minimizing the Rosenbrock function. This\n",
+      "function (and its respective derivatives) is implemented in `rosen`\n",
+      "(resp. `rosen_der`, `rosen_hess`) in the `scipy.optimize`.\n",
+      "\n",
+      ">>> from scipy.optimize import minimize, rosen, rosen_der\n",
+      "\n",
+      "A simple application of the *Nelder-Mead* method is:\n",
+      "\n",
+      ">>> x0 = [1.3, 0.7, 0.8, 1.9, 1.2]\n",
+      ">>> res = minimize(rosen, x0, method='Nelder-Mead')\n",
+      ">>> res.x\n",
+      "[ 1.  1.  1.  1.  1.]\n",
+      "\n",
+      "Now using the *BFGS* algorithm, using the first derivative and a few\n",
+      "options:\n",
+      "\n",
+      ">>> res = minimize(rosen, x0, method='BFGS', jac=rosen_der,\n",
+      "...                options={'gtol': 1e-6, 'disp': True})\n",
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.000000\n",
+      "         Iterations: 52\n",
+      "         Function evaluations: 64\n",
+      "         Gradient evaluations: 64\n",
+      ">>> res.x\n",
+      "[ 1.  1.  1.  1.  1.]\n",
+      ">>> print res.message\n",
+      "Optimization terminated successfully.\n",
+      ">>> res.hess\n",
+      "[[ 0.00749589  0.01255155  0.02396251  0.04750988  0.09495377]\n",
+      " [ 0.01255155  0.02510441  0.04794055  0.09502834  0.18996269]\n",
+      " [ 0.02396251  0.04794055  0.09631614  0.19092151  0.38165151]\n",
+      " [ 0.04750988  0.09502834  0.19092151  0.38341252  0.7664427 ]\n",
+      " [ 0.09495377  0.18996269  0.38165151  0.7664427   1.53713523]]\n",
+      "\n",
+      "\n",
+      "Next, consider a minimization problem with several constraints (namely\n",
+      "Example 16.4 from [5]_). The objective function is:\n",
+      "\n",
+      ">>> fun = lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2\n",
+      "\n",
+      "There are three constraints defined as:\n",
+      "\n",
+      ">>> cons = ({'type': 'ineq', 'fun': lambda x:  x[0] - 2 * x[1] + 2},\n",
+      "...         {'type': 'ineq', 'fun': lambda x: -x[0] - 2 * x[1] + 6},\n",
+      "...         {'type': 'ineq', 'fun': lambda x: -x[0] + 2 * x[1] + 2})\n",
+      "\n",
+      "And variables must be positive, hence the following bounds:\n",
+      "\n",
+      ">>> bnds = ((0, None), (0, None))\n",
+      "\n",
+      "The optimization problem is solved using the SLSQP method as:\n",
+      "\n",
+      ">>> res = minimize(fun, (2, 0), method='SLSQP', bounds=bnds,\n",
+      "...                constraints=cons)\n",
+      "\n",
+      "It should converge to the theoretical solution (1.4 ,1.7).\n"
+     ]
+    }
+   ],
+   "source": [
+    "info(minimize)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "默认没有约束时，使用的是 [BFGS 方法](https://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm)。\n",
+    "\n",
+    "利用 `callback` 参数查看迭代的历史："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(37L, 2L)\n",
+      "[ 1.  1.]\n",
+      "in 200 function evaluations.\n"
+     ]
+    }
+   ],
+   "source": [
+    "x0 = [-1.5, 4.5]\n",
+    "xi = [x0]\n",
+    "result = minimize(rosen, x0, callback=xi.append)\n",
+    "xi = np.asarray(xi)\n",
+    "print xi.shape\n",
+    "print result.x\n",
+    "print \"in {} function evaluations.\".format(result.nfev)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "绘图显示轨迹："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2L6n9Z0ei1BGnT59m4pTJhB1aWMBerv8Ax9efolEusWqzyszsepR6PR5RVapKtsmsbr+S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\n4JgH9qlwvBPooWixoEgmAVrdiT6+CKKaFy1QTV8Iu9pC2L3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twB1m8FStYMcSeL87dJoC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uB6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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15fc54e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x, y = meshgrid(np.linspace(-2.3,1.75,25), np.linspace(-0.5,4.5,25))\n",
+    "z = rosen([x,y])\n",
+    "fig = figure(figsize=(12,5.5))\n",
+    "ax = fig.gca(projection=\"3d\"); ax.azim = 70; ax.elev = 75\n",
+    "ax.set_xlabel(\"X\"); ax.set_ylabel(\"Y\"); ax.set_zlim((0,1000))\n",
+    "p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)\n",
+    "intermed = ax.plot(xi[:,0], xi[:,1], rosen(xi.T), \"g-o\")\n",
+    "rosen_min = ax.plot([1],[1],[0],\"ro\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`BFGS` 需要计算函数的 Jacobian 矩阵：\n",
+    "\n",
+    "给定 $\\left[y_1,y_2,y_3\\right] = f(x_0, x_1, x_2)$\n",
+    "\n",
+    "$$J=\\left[ \\begin{matrix} \\frac{\\partial y_1}{\\partial x_0} & \\frac{\\partial y_1}{\\partial x_1} & \\frac{\\partial y_1}{\\partial x_2} \\\\\\ \\frac{\\partial y_2}{\\partial x_0} & \\frac{\\partial y_2}{\\partial x_1} & \\frac{\\partial y_2}{\\partial x_2} \\\\\\ \\frac{\\partial y_3}{\\partial x_0} & \\frac{\\partial y_3}{\\partial x_1} & \\frac{\\partial y_3}{\\partial x_2} \\end{matrix} \\right]$$\n",
+    "\n",
+    "在我们的例子中\n",
+    "\n",
+    "$$J= \\left[ \\begin{matrix}\\frac{\\partial rosen}{\\partial x_0} & \\frac{\\partial rosen}{\\partial x_1} \\end{matrix} \\right] $$\n",
+    "\n",
+    "导入 `rosen` 函数的 `Jacobian` 函数 `rosen_der`： "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.optimize import rosen_der"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "此时，我们将 `Jacobian` 矩阵作为参数传入："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(38L, 2L)\n",
+      "in 49 function evaluations and 49 jacobian evaluations.\n"
+     ]
+    }
+   ],
+   "source": [
+    "xi = [x0]\n",
+    "result = minimize(rosen, x0, jac=rosen_der, callback=xi.append)\n",
+    "xi = np.asarray(xi)\n",
+    "print xi.shape\n",
+    "print \"in {} function evaluations and {} jacobian evaluations.\".format(result.nfev, result.njev)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以看到，函数计算的开销大约减少了一半，迭代路径与上面的基本吻合："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2L6n9Z0ei1BGnT59m4pTJhB1aWMBerv8Ax9efolEusWqzyszsepR6PR5RVapKtsmsbr+S\n8JYNCaheMIRuCA/iyD5zHrE67ONM1Q0AzCYbU7pG0XBQA7z9Xe8l9Q8yEBlv5PvVEs2aypQv73Z6\nYuNg4hSZyVvVhf+NGTJfvn+ZZ4c8gG+QFyEV/Nkde17VWIHA0xFiVSAQCP7l5BaZVqs1j7fUXeg+\nvyjN7Sm9U2RZ5v2eHxLw+QfoI0oVOB/Q5nmOd9ue59imqbGYs6DxqCaqrnF46iHSrxl5ZMYHDs/r\nalbi4F+RtOyU/fPOTWaOHTAz46K6BgDLRsehNXjTaFADt7ZepQL5cY2RvadkYleomp7eAyXqPO5L\nnQbua84CzBiWiKGYD899UgeAkAp+XIgVjQEE/w6EWBUIBIJ/CWpC93axqtVqc2xBfei+MJg5exZx\nShahH7ZyeN7v5cYktbZw/UImIeV8SU82s+jTSJ6f/aqqUlLpCelsHbSZB+b2RHJScb9EiwfYP+kI\n4IfNpjC0RzrN3i+Lb4D7x2JCrJHfx5yn/Ya33doCFK8TwrqfE+jWBQID3dv/uR227VD4PU5dqaqY\nEyaW/HidT3bfqvEaUMKAMdNEeno6/v7qWssKBJ6KEKsCgUBwn3E3oXsgx1uq0+nuqSh1xKVLl/ji\nq1GE/DkDjVbr0EaSJHxKhxK57TpPtfVlyWfRBFUIpuZbtVRdY+NHGwioW4FSrzv3epZ6+3G29JpF\nVpbCigUmUlI0dByjrgHA9B7RlHuiLBEN1YXo9T56fA3w3Wj3tlYrdOul4ZUPgikW7P4RLcsKIzpe\npt4r5Sj3QPGc4xqNhpLlixMXF0ft2upKfAkEnooQqwKBQOCB5M+6zy1M7d7QOwndZ2VlAeDlpa5m\nZ2HfU/c+vfH9sBXetau4tJUfqsuJjWeo9L8g/vw5lg4H1JWqit1ynph1Z3g6ZrJLO+/QQPyK6Tm0\n28Ko/hm0GV1Dldf20PokTu5Mpk98V1XryUjM5Oj8KBQbqPk+MOMnDakZEh+PcVxdID+rZt/kcpyV\nr3c3LHAutLw/sbGxQqwK7nuEWBUIBIJ/kNxe0vye0txCNLcgvdvQfVF5UfPz22+/cfjCOUquGOXW\nNuCd5zjW8y+SLxop36wSJWq6L1VlzbKyuuNKIj5+EUPJYm7t9eGhDOpyDb/i3jzbxX3I3WKWmdwl\nkga9/4dPkPskL4A/PtxKsZplMEbFEnNeoZoLjZ6cDEOGK3w6u7Qq4Xwj0cqEfldoPe0JdPqCXuri\nFQzExcWpWqdA4MkIsSoQCARFQGFk3RdG6N5e77SouX79Or0HDSTw97FovPRu7f1ebUJS2yxiDlj5\n8FInVdfYM3oXsqSn5ijHBfrzo69RnvjfrzByszrP48rx8cjoaDaioBfTEWc3xBK97hzPnR3LngZD\nOHEyzaVYHTpSokwlb5q95V5oA3zX+xph1Yvz6DsVHZ4vVsFL1FoV/CsQYlUgEAgKiXsVuv830H/I\np3i3bI7P4/XUDTCZ0ei01GjzAF5uSkMB3IhJZve3u3hkw2eqplcUBdPF6/gW01Hn6WC39kkXTfz6\nZQxtVqrokQqYMyws67COqp+8iKFkIJQuybETabzxqmP7E5Hwy0KZOYfV7YM9tC2DbStvMjz6dac2\nIeX9OHdY1FoV3P8IsSoQCAS3ibvQvcViAbLD9Pk9pkWZde9s7UUtgDdt2sT6v7YTfmKp6jHXe32D\n2abFkmpxa6soCmve/4PgxnUIaVhD1fxXluwmLeoSGGXSki0EBLv29s78+DSlHy5FpaYRLu3sbB76\nF9oAP2oPzVangQ9X5MDhWMDmYP3Qo7fE4y/4U766++0FFrPM8I6XeKp7DYJKO68JG1LBnwOi1qrg\nX4AQqwKBQOCEOw3dAzkC9J/Kund1T/dqDfn31cqyTFpaGl17f0yxqZ8i+bsvtg+Q/sd2UhZvhFGT\nOPdVb7drPrU0iqtHEmh6aYSq+bMSUzn6wXSCxw3ANOw7ovfc5H8vON8Te/zPZA5vTKL3eXVJXpcP\nJbB/+jGa7vsi51hYs1ocW7rZof2K1RAZrbDqT3WlquZ9m4zFpuON0Q+5tAsp78eF2Iuq5hQIPJmi\n37gkEAgEHkTuYvlZWVkYjUbS09NJSUnh5s2bpKamkpaWRnp6OpmZmZhMppz/zGZzzr5SnU6Ht7c3\nfn5+6PV6dDodBoMBLy+vPGL134CiKNhsNiwWC1lZWZhMJjIzM8nIyMBoNGKxWFAUBa1Wy9fjxsKT\n9fB7/klVc1sTk7nabgjyJyPg7bZYjFZuxNxwap+VmsW6bmuoOqoNOl91SU/Hu0zDu2Zlgt57A1uF\nipz6K8X5eiwyP7wXycPd6uEX6l5s26wyS9uupWzrxylW+1ZIP6x5LRITFTIz89qbTPBRP2g3uCQG\ng/tH8qVzZn7++hod5zd0u/c4MNyHtJTs34lAcD8jPKsCgeA/gbvQPeDUY+oobO9KfGo0mpw5PY3b\n8azmf83s/9lfE/vroNVq0ev1BV6TAwcOsHDZEtXhf0VRuNb+MzSVasB7HwKgLV2W2M3nCa7ieF/p\ntsFb8Q4PpmKPZ1Vd48pve0ncepKI82sB8G7agGMbna/vjx8uYjJpaPFNI1Xz7/nuEJk3rTw9vWOe\n4zpfAwHFdESesvK/+reOj5+kQW/Q0f4T99UOFEVh1PtXqPp0ONUahbu1lyQNoWWLERsbS82aNVWt\nXyDwRIRYFQgE/yruJus+d5KTJ4XuC5P8YtVR6N5ZeazcgtTda2I2m3m/54cEjuuHNrS4S1s7aT+t\nIHPPceTdt5KCTP9rxLnVu6nf9eEC9lcPX+Hoz0doeOhbVfObr6dx9L2pBH/bF11wEACB777AuTEz\nsdkUtNq893TjahYLPj/Dm4teVldK6nwKm7/YxeMrezu09woN5ERkco5YvXQZvhmvMG6NuqSqrb+l\ncuqwka8vvqjKPi3RRPK1FPbu3SvEquC+RohVgUBw35FfYDmrTepIoP7bs+6dkXs/rdlszvP65X9N\nCkOoj53wHSnlQglu87wqe0vsJa59PBp5/Ky8PUnbdCa29QIUWUEj3VqLbJNZ3WEV4S0bElBdndg7\n0XUGXlXKE9T1VptUr+oV0Rt0XIhMp0LdgDz2s/ueoWSdktR4sbLbuRVF4beO6ynxdA3CmzkuhaWp\nWJojx24C2b+H/p9KVKtv4OHG7tuhZqTZ+LrrZV4aUR9vP/elvwB+6boPm00hIyNDlb1A4KkIsSoQ\nCDwWV6F7s9mM9u92nYURuv+34Cp0n/venYXuC4PTp08z6ccphB1aqGpuxWbj6tsDUJ5oDC/lKw1V\n/1E0Oi3XjiUQ9uCt0PeRaYdJT8jkkRkfqFrT1RX7Sdh4jIiYNQXO6UqVJHp3Sh6xGrXrJvtWJtDz\n9Puq5j86P4qrx5N44eIQpzYhj1XhwJooAHbvg7UbZJacLadq/imDkwgI86dpT3XVDg4vjydy8xUe\n7NuI6JjTqsYIBJ6KEKsCgeAf505D9zabrVAL5t8vOAvdOxLquQWpoigYjUa8vb3v2dpkWeb9nh8S\n8PkH6CNKqRpzc8wczPHXUJbsdnheKVOR2E3nc8RqekI6WwZt4oE5PZF07h9j5uR0jnT6keJf9kTn\nYEuCXK8OJ/48zrMfZGfj22wKP3SO5IEOdQgsrcLrmZTJ6p5beGBiW5dJXuHP1WH/+OXIMnTrqeHZ\ntkGEhrv3kp46ZGTVT9cZfOglt7YAGclZzO28mwZfPY9vqUCi50SrGicQeCpCrAoEgiKhsEP3ZrMZ\nnU6HXq8uJFqUaDSanJD73ZD7NcgvTO+ks1VRJH3NmDWTOCWL0A9bqbLPOhpN0shpKAvWgJfj4v/m\nJ1twdtVyGvR/HIBNPTcQUKcCpV5voOoaJz+chb5iWYr3bOPwvP9rTYgcsC3n5/VTL5J600aX75uq\nmn/NR38SULUUFTs85dKu+COVMJlg7ERISJKYNdm9mLfZFIZ3uMzDrSpSqrq6zlYLPzpAQMUSPPBh\nQxKPXmZ/zB5V4wQCT0WIVYFAUKg4C93nTtpRk3WfW4w5El/2gvv/BtyF7nML9bsJ3d/rhgDHjx9n\n6IjhhP/1MxptwV71+ZFNWVx5qx/Ka23gkSecG7Z7n4vNvsdmtnFhZzxn157h6ZjJqtaU8Mchrv5x\niIizfzi18X+1MbHts0hLtqDICnMHn+aV2S+oSqo6uzGWU3/E8OwZ90lekiQREOzF5yPNDJkVhlbr\nfv7fpt4gOVGm3/TH3NoCHF97iaOrL/Du6YEAFKsUzMVz8f9IMwiBoLAQYlUgENwRd1MwvzBC955c\nHsoR+T3L+f99p1n3t7uGeyVYjEYj7d7rijkrC30ldcXtkwdPwmbWwDc/uDasWAV9gC/xO+L5o9NK\nIj5+EUNJ915Gy80MjrT/gaAveqArGeLUTjIY8C3hz+m9N/lrcSIhVUOo81Y1t/ObMy381mEdVQe8\ngE94kFt7ALOio1xlhZc6qGjxetXCD4Ou0ml+I3Q698LWmGrmp/a7+N9nzfELz05S8wowYAjw4cqV\nK5QuXVrVGgUCT0OIVYFA4BRHeyOtVqtHZN0XVqi9sMn9etmbBuQO3ed+Xe6VKC1qFEWhW8/eXC5e\nB21yIpmb9+L/0tMux2T+uZ8bM5ahrN0LKjyY1ohqrO64EkWrp+ao1qrWdbLnT+jKlSa4b3u3trYK\nFfnjhwuc2HaDD092UjX/ls92Ifn7Ufvz11TZX9t2irTrJho8HejeGBjzYQJlHwjhwVfVJWEt7n0I\n3/Bi1B/QOM/x4MoliYmJEWJVcN8ixKpAICiQtGSz2bBarVgslpxQqLvQvUajKdJSUP+0Z9VV6D73\n+u5l1v2drLmw16AoCpN/nMr6fScxfbIbZrQhc/EGl2LVlpLGlVafoHTrB5XdezABrA83JH32IR7f\nNlyV/bV1R7i8fB/lo1epsvdq/AgHRx2lfqfaFC/v3mt7+XAC+6Yepcnez1XNb0kzsuedH/Fv/DBR\nB465td+7MZ29G9MYee4Nt7YAUZuvcGBJLK1P9i9wLqBycWJiYnjqKdd7agUCT0WIVYHgP8Tthu5t\nNlvOWE/Lui8KsZr/NXHUxSm3l9TuNbV7n+9l1v2dcDdi1Vn1gb/++osR34zDNHAPePtC8z6kTn2J\nEjab032rSd2+hNDS0HeouosbjbBuJYokEVg3wq25JSWTw+1+IGhoV3SlS6q8QdBI8NKPz7g1tVll\nlrVdS5lWjxFUR53X82jvhWiDi1Ph15FEhT2HOUvGy9uxRznLJDOi0yWa9q1NQKj7FrKmdAuz2/5F\nvf5PExBRsNqBT+VAzsSccTBSILg/EGJVIPiX4Sx0n7+8kZrQvSzLeHt7o1NRHqioKUyxWthdnP5p\nr+/d4K7Fqv3edTodly9fpnO3DzF1mg8lKmZPUP1pNFodpj3H8Gn4UIH505ZtIu2Pncg7I1WvSRrc\nE9ChCwnl+taThL/6iEv7yN4/ow0vQcjAzqrmNx05xc0J8/EKMJB48jql64e5tN8z4RAZyRYazVS3\nXeDq+uPEL95HtZOL0AUXwzfQi3Mns6hR38eh/U+jriN5efPKF/VUzf/bJ0fQB/nTYFgLh+cDKwdz\naq0oXyW4f/G8J5BAIFCFo9C9q6z73KJUbejearV6rOiyC8Lb8RY68w466uLkKaH7wsT+WhWGODeZ\nTLzdtiPpTfpA7eZ5riOH1SVj2eYCYtV6JZGEzsOQh42FUJUez6ULUNauRJkehXniBySuOuRSrCZu\nOsalpXsod3K5qunlTCNXXu+D8mY7OLiduJ2XXIrVG7EpbB62i8dXOG6pmh/zjQz2vDuNEp91xisi\ne15dyRBOHzE5FKtxp7NYMD6RPn86Fp75ObPzGrvmnqXV0b5ObYpVDiE65i9V8wkEnogQqwKBh6Mm\ndO/IUwp5RUduQapWgEmS5JFJTIDLWqKuiubn9w7+m9ut5q84YLVagex2q3fTYlVRFLp/3Ic4n0rY\nWgwoeP6prqT+2peQcf1y5lIUhYR3P4U6D0Hrjupu4Gw0DO6J0udnCCkNzTtxZcb71HVibk0zcvjd\n7wka+B5eKhsSXO89BlkywNeTyBrUk5gNf/L4x/Ud2iqKwvJOGwhtVJ3wZxy3VM3P4W5z8SpXirBP\n2uUcs1X1gOvXAAAgAElEQVSpROT+U7ySz/GrKAojO12mZosyVHykhNu5zUYrM1vvpM5HTxJUOdSp\nXWClEOJj4lStVyDwRIRYFQg8gMIM3ecWHHcrwCRJyrNv1ZOwe3zt3t+7Dd3fz6it02r3pHt5ed3V\n6zB9xkzW7j6KceAecDTPo62RF36A+dR5vGtWAiD1x8WYjp5B3huj7iLGTDTtX0d5/A1o9Fb2sYav\nY/2uLRlnr+JXJbzAkKg+c5FCgwkZqq4Fa/qqP0lZuA558+Hs+3jjHeI6zXPqrT+24BSXjybywsVP\nVc1/8feDXF53jGpnluY57tf4IY7PO1zAft0vKZw/ZebrzU+qmn/FkKNovL1pOPoFl3a+Yf5kmUyk\npKRQrJi6xgICgSchxKpAUITkD91bLJYcMagmdG8XHkWVdS9JEhaL5Z7NrwZHiU25RanFYkGr1eaE\n7gtLqHsarrzFauu0Go1GtFrtXb02u3fv5vNR32D8ZBd4+zk2kiQ0JaqR8dtmvIdUwnw6lmsDxqNM\nXQS+vqquIw3qCbIWpf/PeeaVSkZwbf0RKlZ5Lo990tYTXFj0F+VO/qZqfuvVJK62G4o8+Eso+3fS\n1iNPINsUkmNuElIlb6JS5nUjqz/cTJ3x77psqWrHdC2V/Z1mEfZtT/Ql89ZULfba05wbOhlZVpCk\n7N9F6g0bY3pe4Y2xj+BlcP9oPr8viW3TTvP2gV5ubTUaDSGVwoiJiaF+fcdeY4HAkxFiVSC4B6gN\n3du9grlrht5t6L4wsW8DKIruN+4Se3ILdbsYM5lMHtty9U5xtZ/0Tlqs5p/7bn6Ply9fpmW7jhg7\nzIGSlV3a2h5uTfrCGRQf2Imrb/WHpi9As+dcjslhyXzkdatgRnSBGqyWui249vt2Kn54ay5ruolD\nbSZRrH8HvMqXcTu9IsskvDMQatWDjt1vnZAktGHhxO24VECsrum5Fb8qpajUuZH7+RWFg51nY6hV\nidCurxc4b6hcFp1e4mKMmYiq2RUjJg24Rkj5QJ58r6rb+S1ZNma8s5OanR8luKbrZDA7XiV92L59\nuxCrgvsSIVYFgjvkTkL3+ZN57CLVYDB4pEfQvp7CEquFnXXvqS1X1VQDUBu695Rkr6ysLN5o3Y6M\npz6CuipEZ9OPyFr9GYk9vsKSlIayeq66C505BZ/2gr5zILhgqJ9XPiLpo2nIZiuSV/Yj7NSA+UjF\nihH6RQ9Vl0iZtADTiXPIe84WOGes9SjnN0dRv1OdnGMxm+OIWhVDi9PuW6oCxP+ym2t/naH6eede\nXu/QYkQfNhFR1ZvjezLZsOgGn598VdX8f3xxHKus5cmJr6iyv7o3ngu7Yrj88GVV9gKBpyHEqkDg\nBldZ964EqZrQvaIoZGRk5Nh4Inbv6u2sz1ltUkdZ93dTs9XTS0TdaZ3WeyVK7/RLh6IofNSnP+f1\n5bA+P1jdIG9fpOLhpMxbhfLbVlBT/iwzI3ufasO34Kk3HduUq47O35/k3acJfboW17dHEj9vG+WO\nLlO1rKzjZ0gc8j3KT7873pLw0hvEDO+a86PFaGFZ+3VU6fc8vqXct1TNvHSDgz3mUvrHQeiCnHeq\nspYvT/TBizR5I5Dh7S/xWIcqhJb3dzt//OFkNk2K4o2/PlT1N2nJMLP2rXkYakYQe/WSW3uBwBMR\nYlUg+Jt/IuvenvByu2KwKHFVEeCf9g56SsvV/B5j+/vG/kXEU1qs3qlYnTX7J1ZtP+A8ocoR1+Ox\nJSVBxcpQ72FVQ6SBHwF6lL6zXdpZS9Uiac1hgh6pzKE2kwj8uC1eld0X55eNpuwyVa++A082cWz0\n3CtkftyO9IQM/MP82PLZbiQfH+p8UTCcnx9FUdjfdjq+j9Qh+N1nXdr6PF6X4zvP8uvEZNIzNLzz\nvevasQBWi8zM1jup2ro+JR50v90BYMfHK9H4+VNt4gdE9V+saoxA4GkIsSr4T+EoDK3WU5pffOX2\ngt2N6NBqtdhsNo8svA/ZgtDefvVOE3vu5dqK0rOqtk6rXeD7+vp6zLaOO32d9u7dy5DhozB+8hcY\n3Hv+AMhMQTO2KUqZJyB2ByRfh+AQ12MWz0XZuAZl+qkC+1TzIz/1DleWf40t04zGx48SX32salnJ\n/cYh27Tw7RTnRjod3iVDidt5ieDKQez98TBN9gxTNf+56du4cfwiNeJXurUt9vKTRE2cz6lDGXT9\nrYmqL6vrvjqJMV2h8XR1LVjPr47k9OKjPHpyKpKPF6eizxTJ/nOBoLDxzKejQHCX5BaZdpGlNnTv\nqBTUvQzNarVasrKy7snct4O7/aRwy4PsCe1W4d6JVXfJXu7qtNq/AHmiKFBTQ9V+/5cuXeLtdztg\nbP8ThLlP/AHAakGa9BJIAShvrEeaXxV542po1cH5mNNRMKQPSv95jvep5ufZzmTO6kPc7C2UO/Sr\nqmVlrN3JzXmrkTccdCuGjZXrcm7zBbYM202Ztx8jqK57r236+USO9ltI2V9GIKmoFuDToDZWq0KV\nhiWp1aK0W/vLJ2+y7tsTvLqlmyphm3ktnY3tfqXCVx0wRJTI/p2ikJSURIkS7mu4CgSehBCrgvsa\nR6FXu1CwnzObzTmeUGfJPP9k1n1RZtzD7YfuIbvkkT0JzJO4G7Fa2Mlejub3JPK/v9xVHbBarbRq\n35n0J7tBvRfVXgTp506QGIfc8QxIEnKZF5GW/ILsTKza96k++TY0dB9qByArE/R6fN94Bq/qFd2a\nW69d50qbwcj9h0N59/bKs69w4LPe+IT602i2+5atiiyzt9WP+DV5hKBX3VcLALgxYyVaLy1NPq7h\n1tZmlZnxzk4qvV6X8AYR7tejKGxq9yu+tcoT0TM7CUuj0RBUPYLo6GghVgX3HUKsCu4Lcguq20ly\nyp3NfrfJPPcKu0AszK0A+YVI/n/fTmKPXXR5YvjwdrPu84uzwkz2yr8uTyF3cqCiKBiNRlX7irt/\n3Ifz2tJYnx+i+lrSqhEox9ahtD8JuuySTDQYhDy7EqTchGIFE5SkAT1A8kbpM0vdRawWpGGvINu8\n0KSkq7r/hDaDoVpt6NJT3TXq1gcJ6v3YQZUX88yEjaTHJVN950+qpjdFnudS34loSpbiwqEbPPSa\nawG6afwp0q5beG1uK1Xzn5y+j6sHLvJYXN71GKqXJjo6miefVNd0QCDwFIRYFXgMd5N17yp0b7Va\nsVgseHl5/dO36BR7ktXt4s47VhiJPfY5PM1TmBv72u6nUlCFiZovJ/bfu5qqA58OGcqyjTswDd7v\nNmSew665yOvGQssd4Jer9qd/abRBpbBt/APeejfvmEVzUDavQ5l5WvV1pB8/hoR46LaetGmNCbNa\n0bj4kpfywyJMh08j7z6j7j4y0tF0fxetrw9aL/ePyNRTlzn+2TIqrByLpOIzRjaaOP/qJ0ivvQah\nJTiz03UVg4TTqawafpQX/3hPlXC+eTaJHX1XUXPBAHT+easdSFXDiYw+5XYOgcDTEGJVUOS4C93n\n/+9uQ/darRaTyeSRnkE7Wq3WZaeofzrr3r5VQavVFtqcd4IjcQ6QkZEB/PtbrN7N1gX7lzZ33vs5\nc+bxww+T0TZ4B3ycl17KQ/Q2mNcdnl8AYQ8WOG0LfxbtsoXYcovV6Ej4rA/KgF8gqKSqy2jWzkDe\nsgD6H4OQ8kgGX4y7juLbyHG1gayTZ0kcNBFlxmLwV5EcpihIfbqA1hdrxUe4timSUi/Uc2ouW23s\nafkjgS83IqCZ+2x+gCu9JiDbtGgn/4By8BBxP091+tkkywozW+8k4rkalG3sugmDfT1r35xPyLMP\nU/LVxwuc96lRhqNzj6pap0DgSQixKrhnqCkF5S7rvjCSeezloaxWq8d2OrIL6tyC3VliT+7XpKi8\ng67KV90L3HmM839Z8fb2vus2op6E2qoDhf3lZOasnxgybCzUXoLt4LvQPsN5S1U7l6Ng0svw2BdQ\n1UlR+waDsc2tDulp4B+Q7b1s/zrKU+/AE6+pW9zJXSg/9oaOSyGkPABySG2MK/90KFblLHN2maoX\n34DGzVVdQjNnGsqOrSizYmDLL1xZ8zX1xrd2ah/99R+YrmdSbcFwVfPfXL6N6ws3ot+9O/tv6uH6\nyFaFGxczCS5X8HXe+n001y8a6bC3jar5D4zcQmaikQYHBzo871e9LGfOLMVqtXps9RGBwBHi3Sq4\nK/KH7nNn39vFjSdk3et0Omw2m0eIVWdCDLITmTzROyhJkkvP751SGB5jm80GeNY+UbXcbdWB272W\nq7HTp8/ksxETMNbcAr5VkOJLIB/6HR5v63zSlAQY2xSqtoRHBzi3K1YeKTAMefNaeOVtpP7dQOuD\n0lflPtXEizDsJWjyCdR6/tY9PdyB1GWfEzK2X4Ehyf3HY8sCxs9Qd40jB1C+/BQ+Xw6BwdC8A+mz\n+mK+mYFXUEEhefNoPJGjV1Npy2RV4XnzhQTiO4xE+9UopIjs6gKSJKEtUZzY/dcLiNWk82n8/ulh\nnvutA5IKYXl1XzwHx/7Jg9u+dWrvU6UUl8/Hk5GRQUBAgMfWdhYI8iPEqkAVrkL39v729n2N+T2m\nnpB1by8PVZRbAW7XO2avWuCJe2vvxrNamMlejvDELla513Svqw6oxdV7f8qP0xj+1Q8Ya20Fn0oA\nyIFvIP05BdmZWM3KRDO+ORSrgfLsTLfXl8OaIS1bgJyRgbJ1Y/Y+VTVkGdEMeRYiHkN5Pl+900fa\nY13xEZa4y+jL3yr/lLFhFzd/WoG8bq+6vbDJ16HDG/Dih1D/by+sXyD6kGCSdpym9MsP5TG3ZVnY\n/fZkglo/i1+DOg4mzItisxH3xmCkRxug65C3KoIpoiqxe69T/41bSVaKojDr3V2UaVKF8s9Wdzu/\nJcPM2jfnUarL8xT7n/MSY1ofb/zDQjh16hQPPPAA3t7eQrAK7guEWBXk4U5D95D3oetpWff2dRV2\n8X1He2vv1Dtm9/56Inbx5UrwFEWyl6u1/dPk/tuw/x4zMzPvadWBwmDS95MZ9e10jDX/BJ8Kt05U\nGIa8txRcj4eQfNnqsg1p6ltgMiO336zuQg2GIM+rDbu2owxcqG6fqqIgjW2ffZ3eqwue1+nRhkSQ\n8ccOgnpkZ8rbkm5w9Z2ByH2GQiUVtWFlGalbGyhZAbnLt3lOmUvV5drGyAJiNfLz5VjNUHH6IPfz\nA9eGzybrYhLadX8WPNnwSc5sm5Pn0PZpZ7h6Oo32F9U1O9jRayUaXz+qTfjArW1A9bKcP3+eypWz\n98B6Ykk6gSA/Qqz+B3EUus8tSuH2QvcajQaTyYSXl5dH74PS6XR3vFerKLxjWq0Ws9nskYlg9t+7\nLMs5/y/q/ZSu1laUYlXt1gUgx3PlCb9PR++r7yZ8z+hxs7I9qobyeQfog5D8qqHsmoPy8md5TkmL\neqGcP4zS6Yz6agF6X9B5Qf0W8PgrqoZolnyLcmgLyuBop9exVn6BjMUbCOrRKrtMVdshUKk6dO+r\n6hrSxNEoUVEoP50vcE5p+CZX1o0gd8rY9b0xnP5hE5X3zFTllUzffpiEcQvQr12L5GAbkvbVV7gw\naVzO7yf5QgZLBxyk2bw26Azuoyzn/4ji9K/ZXarUoKtWiri4uJwyZpIk4eXl5RHvUYHAGf85/3/n\nzp0JCwujbt26OceSk5Np3rw51apVo0WLFty8eTPn3Ndff03VqlWpUaMGGzZsyDl+8OBB6tatS9Wq\nVenVq1eR3oNa7B4ei8VCVlYWmZmZpKWlkZKSQkpKCmlpaaSnp5ORkYHRaMRkMpGVlYXJZMoT2tfr\n9RgMBvz8/PDz88PHxwdvb2/0en2OYLULQU/G7rl0JWzyv2Ymk4nMzMyc18hisWCv2ert7Y2vry9+\nfn74+vpiMBhyBPudCBS7vad4CfO/FvaHW0ZGBllZWTneQ51Oh7e3t9P3x71+CN4LsWoXpFarFbPZ\nTFZWVs69575/eykoHx+fPPdv38rhSUlf+cXqmLETGD3+J4y1/iwoVP9GDvsYtk2DXK+vZuME5F3z\nUVrtAi+17VeT0Cx6EmwGJHOmujH716H8MhKlyxrwD3Vu93RvMvccQ840kjp1CcZ9J7HN/0PdNXZs\nQZ4yDmXkWjD4FjzftB0ZcUlkJWfXc7VmZrH77ckEd3sd37pV3E5vTU4h9s1PkT7uhfSg46oCUq1a\naLRaEmPSUBSFnzvsJvyxilR+zf32AmNiOhvbLaLCl+0xRKgr9G+8doOlK5bj7++PxWLBaDTm/H0L\nBJ7Kf06sdurUiXXr1uU5Nnr0aJo3b87p06dp1qwZo0ePBiAyMpJff/2VyMhI1q1bR48ePXL+oLt3\n786sWbM4c+YMZ86cKTBnUZJbVJhMJjIyMkhNTSUlJYXU1FTS0tJIS0vLEVx2EWZ/4NpFqZeXV85D\nN7/4cvfQ1ev1OQLXU7ELyNyhWrsQyy9E7MLbLkpdCfXCEiP2qgVFuRUgvyjLLc7zizL7l5Lc74+i\nFKXOuNsuVs7u32g0YjabczzGuUVpYXw5+adQFIWvvh7D2Elz/xaqLlqJhncCUwbE7M7++fAKlN+H\nwqurIch9JygAslLRLH4ajS4cnt6PfHgLZKS4HnMhGka1hJe+hQoNXNuGlEcbWJybUxZzbcB4bBN+\ngkAVJbeuXIIPWkObYVClvmMbX3/0IaEkbY8G4MQnS8DgS5lx7h0UiqJwoe1wNBEV0A92nJ1vR1ci\nhNgD19k95xzxR2/w3AoXrWlzzb+p3WJ8a0QQ0ctJFYZ8XP1lK4lr9pP5d4QkICAAk8lESkqKxzsb\nBP9tPDdme4946qmniI2NzXNs5cqVbNu2DYAOHTrQuHFjRo8ezYoVK2jdujV6vZ4KFSpQpUoV9u7d\nS/ny5UlLS+PRRx8FoH379ixfvpznnnvunq1bTeg+/37Sosy6t+/J9KTi+85KQBmNRsAza3La99UW\ndtUCZwlOzkL39tch92thsViwWq0eJ8put4uVJ2xdKAryv//tnxcjR41m2uwVGGttB+9SrieRJBS/\nBkg7ZiBr9TDjXWgyGcqq7IBkyUSztBka2Qv5qR0gSWj9S2HbtQKat3c8JiMFzactUOq8Bk/1UHUZ\na4mHSBowHl5vBc88736AxYKm01tQ9VGUt11UMQCySj/AtQ2R6IN8ifl5B1WPzle1putTfiN9byS6\no+7rmpoq1ODI7+c5sfYST097Cy9f95+hkTP3c2XfBR6LVdc1K/P0JU51nUzAmEHEDpmQEyGyl/XL\nysrK+RwXCDwN8a4EEhISCAvL7rgSFhZGQkICAJcvX+axxx7LsStbtiyXLl1Cr9dTtmzZnONlypTh\n0qVLhbKW/A9Ve7a9PVMccChIHWXd325WtZ0nn36az4cMoUWLFrc1TqfTYTabc8ROUZH7dXBXk1OS\nJMxmM35+fh4pRuwPjjulMEpBOcNe7cHTyJ95X1SloNytyb6ee/0+c/SFzNH7X1EUvh37HdN+Wo2x\n1jbwDld3gYiRyPsaw+EV8GAvqOPe6weANQvp9xcgMw258YmcPae24i8jrZ+F7Eis2mxII98E72CU\ntnPVvgBgyQIvL5g4W9UQaeQguJaIPHufe+On3ubS0oFc+O0Aof3fxVC5rNshxuNnufzJD2jnz0NS\n4+Vt9BQHvthK2acqUq31Q27NU2Kus6PPSmr8MgBdoIPtC/mwmcwce3kE3i83w69bG7I+m5jz3LPZ\nbPj6+pKRkVFgz7VA4CkIsZqPf8qzNnXqVJ555hlCQ0Mxm80kJycTGhpaYO9g/qzqe5FV/OjjT/DW\n228z9PNhDOjbR/W8Wq0250F5LzodFYYQUxQlZ9+pJ4pVe/LSnWTdO/rSUpgeYzVrKwqceczT09Nz\n1ulpHvPCwJ0Qt/8NOHr/K4rCFyO+YvYvG7JD/17qOkYB4BUMGi2EPgRPjVI3RrYirXoTbsQhN44C\nKdejpvpQ5E0RkJIExfLuRZVmD0I5dwJl6DnVy5NWD0S+dAy8feDEEajnuJtVDmuWIy+aA98fATVe\nxCZtME7uhm/VcpT6ootbcznTROyrA9G8+Ra6Zs1U3YNGAY1Ww/PL3X8RkK021r41n+LN6zvsUuWI\nsx9NxWrVEPLLeDQaDb61qxEZGUlwcDBarRaDwYCiKDmC1WAwCMEq8CiEWCXbm3r16lXCw8O5cuUK\nJUtmf5CXKVOGCxcu5NhdvHiRsmXLUqZMGS5evJjneJkyZW77umlpaURHRxMVFcWmTZv46aefuHHj\nBvHx8TRu3Ji5c+fmPHzs3iMfH5+7v2E3fD5oIAsX/cpXY8dy4NgxfpoyGT8/N11sIGdfn8ViuWOx\n6kyIOKvJebtCJHc3K0/ZrpCb3PtWc4t/dx7joiiFZJ+7qMSqWo+5/ffp4+Nz34fv78UXEUVRGDJ0\nOLN/2Yix5lbwUpeIA0DaQTjyDBCKZMlAVaVdRUZa2xbl6mGUxlGgM+Q9byiJFBCBvGMpvNTt1vEt\nC5D/mAp99oGXe28hgGbHZJSd0+C1vWi2d4K1K1BcidVzZ6H3+9BjMpRx374UQLPpZ9B7ETzARXOE\nXFz+aBw2rTe6SRNU2csHDmAdMxYvfwNpcTcwBLu+9wNfbiEjIYMG+9WVzUr4dTtXFu+kxIm1OQJU\nqVWFqKgoGjRokLPlyGAwYLPZyMzMzBGs9/PfkuDfhfjqBLzyyivMmZNd527OnDm89tprOccXLVqE\n2Wzm/PnznDlzhkcffZTw8HACAwPZu3cviqIwb968nDHuuHjxIs2bN6dcuXKEhYXx/vvvs2bNGurU\nqYO3tzcTJ04kPj6eJUuW5Enm8fb2zrM/9V4SFBTEiKFD8K5aiz/x54lmz3D+fMGyLo6wVwVQu4fQ\nWWKLxWLJKZNU2Iktnla5IH+ylyzLOYlyjioQ5E/2Ksokn7tpDuAMV8luJpPJ7f0722Pryah5/6tJ\nfHT3O5dlmfc/+JDZC7fcvlBN3giHG4PfB1DmCPK1Y3DjrLsbQ9rUDSV2K0qjo+DlOAQuh76NtC5X\nI4Ezh2BiF2g1C8Jqqlvf8ZUoKweitFgOxWugVOkIyxc7tzdmomn/GjzyEjzjZL9sfo5sRZk+ACW4\nFsath9ya31y6heSlW9GuXKnKM6kkJWFu2Rpe74UmtBxXdse5tE/Yf4GDY/6k1orPVXW1yjx7maj3\nJxE4ZQS6iFtNE+TalTkcdRKz2ZzzpV2j0eDn55ezr99eRk8g8AT+c57V1q1bs23bNpKSkihXrhwj\nRoxg0KBBtGzZklmzZlGhQgUWL87+wKtVqxYtW7akVq1a6HQ6pkyZkvNgmDJlCh07dsRoNPLCCy+o\nTq4KCQmhb9++1KxZk4iIiDwfaDVq1GDr1q08+WTB5AW719JsNmMwGAqcL2w6d+rI97NmEdv4ZeJq\nP8yTz7Rg/szpNGnSxOW43J4uvV5/T/dQ3ilarTZnHUUV6srvMcv/7/xeUqvViq+vr8eJrzsVq0Xh\nMffEB+s/+f5PTk6mfftubN+5HeqtBy8X5Z/ykzAPTnWD4mMgMDvJSeNVF83xaciNxjgdJu34BCX6\nN5Snj4DBxfWqDkTeMD67japOD0Oeg8e6wkMt1a0vdg/MbQMNJ0OZvz+TqndC2d8XzsdAxXxeU0VB\nGtADrBqUgb+ou8blGBj+GrQYAaFVubm6I2VcRBXMcVeI7zwK7ehvkFRE2hSrFWubtmjK1kD54Cuy\nEi9y5c9jPNDjCYf2OV2q3n+OYo9Uczu/nGXh2Csj8X6uMX5t81YL0NWuypGFG3Lee3Y0Gg3+/v6k\npqZiNBpznjue9jkk+O+hcfMB73mf/v9ibDYbDRs2ZMmSJQQFBRU4b99T5OvrWyQia/v27bTs9hGZ\na6LgyB58+rVmUK+e9O75UZ4PL2dZx3YBUViJX4WJyWTKEQiFibMwrj107SjhK78os/+ePTEJzGKx\nYLPZnH5hKoz7vxOMRiN6vf4fyWS237Oj5EjA4Xv/Xr//Dxw4QMuWHUlNewOL5SiasHLINea4HwhI\nF8Ygnx8BIfPBL5fIyVgNKe2gxzXQFvy7kfaMRNk/HuWpfRDgvnOUtL028itt0fy1DBQ/lI+2qbu5\na2dg/CNQqxc8OjzvnL/XRX7/XejeJ89xzYLZMGIwyvRTEKwisSwjFU33eiilH4O2C7O7XH3hT/XD\nc/CuWrDUl2K1cubR9zGHRaD/9VdVt2H77HOsi5Yi/xKXnRy2/Xf8pnSk8+WhDu23dllG3PYLPBo9\nXdX8p7tP4dr6o4Sc3VLgeWG7dp2UGs9x4WyMwy1eNpuN1NRUfH198fHxKfTPSYHABQ4/GMU2AA9C\nq9XStWtXpk93/GGUe09oUdCoUSMef7Au2p/GQYPGGH/dwzcLl9Cm83vcvHnTaU1K+zd1e8j2dmu2\nFgWFkXWfP3TtqnmAPXStJoxrFzRFseXjdsmdZOUsdH+3938nFIVn1VXoPjMzs0CjAAAfH58893yv\na9IqisLkyVN58cVWJCV9h9n8HYr8A3LCYjAnuRksI8V8jBI7CkpuyitUAfxeQpIMcK5gwX3NoYnI\n+8agPLFVlVAFkMPbwc9D0SQnonRT2bI17Rr80BgiXi4gVAHksm8hrci3FeDEUZTP+6MMmK9OqNps\nSCNfR6Mvli1UASQJqXgEaRsdVw9I+Gwm5qs30f6izmtrW7Uay6yfkcdtzRaqAE+8jCk5E2NiegH7\n2DVRRC86Qt0NI1XNf+23XVz+ZSvFt/7i0LEhlQhGljR5GuDkRqvV4u/vn/O+9qRtU4L/JkKsehht\n27Zl/fr1OZnN+bGL1Xv5YM4tREYP+xz9z+Mh4TKUKU/mL3+x2eJFkxde4vLly06FiF6vz3lweyJq\nu1m5ax5wr7o4FXVzAEfkrutrv3/7F5L8DQN0Op3LLmf3Uxer3L9zV/tJ3XUxK+o9tKmpqbRq1ZGR\nIxdiNO4GzRvZJzS1kLSV0Fxx0Y5TNiNFvY1y5VeU8MNgcFyIX9a+jHR4Up5jmuOzUHYMhQZrIOhB\nh+IHaS8AACAASURBVOMKoChoTPEg6ZDbzFeXlZ+VgWZKMzT+VaDpPMc2dXshR0dC0rXsn1NuQvvX\noEVnaPCSqqVJM/vD+SjkD3flOW6NaEbaip0F7NO2HuTapF/RLl2iah+pfOYM5q7dUXr+ABE1bp3Q\n6fAKCebqnvg89sbEdDa0XUSFke3wKR/mdn7j+atEdvyOgAmfoSvveDuCRqPBt1ZVoqOjnc6j1+vx\n9fUlPT09Zy+9QPBPIcSqA77++mtq165N3bp1adOmDVlZWXfUkvVO0Ov1vPfee8ye7bheoH1P4916\nV3N7iVwJsYoVK9K5fTsM3/2deerji+nb+cS+1J5GLZ5j165dDh/Int7RKnfWvSuPmavWmveyi1NR\nitX7wWPojjsRq+4Su+zv36LsYnannDhxgkcfbcLWP0PJzNwFmrx7NmXLMJQL34Hs4HPDmop0tBnc\nOIgSHgV6F52pin+FfHk3pP5dJSV6McqWXvC/xRCqslGAIiMd7QIXlqAxVEUTtdb9GJsVafZraLKs\nKC9tdW5nCEIKKgub1mTvU+3RHikwHHp8r25t62ejrJuN3H1HwYoET3QndcdhlFxeRmvSTeLeHoLU\ntx9SHfftUZX0dCxvtoSGr8PzBctUmUpW48rOW0lWiqKwqcNifKtHENHbfRKvbLZw/JUv8W7yBP6d\n33ZtXDu7IoArvL29MRgMOX8TQrAK/imEWM1HbGwsM2bM4NChQxw/fhybzcaiRYtuqyXr3f5Bd+jQ\ngeXLl5OZ6biHtpeXl2rvqjshYjabXQoxLy8vhnzyCd67NsKxv0NgGg22Tv1I/WYeb3boxOSpUwus\nxf4A/6e9g7nJ7zFTFMVl1r0zj1lRZd278/zeLoXlMfTE5gDOxKqj97+jL2X53///hHf4Tpk7dz7N\nmr3ClSufkZU1HTQO9hNrWqLBGxKX5T2edRXNoQZgSkEudRp0wa4vpgtF8q6B5sRMOLcG1nWCB2dD\nuIquUQCKDelQO5RLq1CqHkYJ/Qxl9yxw9ZmpKEiLP4BLkcivHchpLuAMuUQLtMsXI00eh3LkIPK3\nKvfCnvwLJvdEaf0LhDooa1WqDlqDL5n7Iv9elsKFd79AU6kK+gH93E6vKAq2rt1B6weDHe8fVh5q\nzqWtMTk/R806wJU9F3hgvbrwf0y/2WSlmgj6fYpbW7l2FQ5HnXRrZzAY0Gq1OX8vnva3L/hvIMRq\nPgIDA9Hr9WRmZmK1WsnMzKR06dKsXLmSDh2yvwnbxSTgsCXrvn0quqK4wGAw0LZtW+bOddzBxZ6g\nkdu7mt9L5E6I+Pn54e/vr0qIBQQE8PWwYfh93Su7W4ydJ57BtGg3I2fNpXP3HphMpjzjinJ/bW7c\nhe7tHjN7Mo5dlHmSx8x+7Tt5MNxrj6Gn7qd1VQrKvn3hXpRCK2rsAjw1NZX27bsyYMAkjMZtKLRz\nPc7SAU38V7cOZJ6Bg/XRUAY57AhI6uoOy74DUQ5MgFUtoc5EKKsyg1+2IB1oBQlbUaodB6+yEPQ2\nGpsVzu1wOkzaMBLl6HLk1/aBl/t6zzw4ANuencgTvkIZtgp8A9yPSYiDz1+CxgOhlvPtAnJQFdLW\n7wXg+qTFpB+MRlr+u/v5AXnyFKw7dyNP3OFccD/bjsRjl5GtNlLOXWd77xVUm9VLVZeqxJV7ufTz\nJoK3zFeVgKurXZVd+/e7tbOXtAJyHBxCsAqKGiFW8xEcHEy/fv2IiIigdOnSBAUF0bx5c5ctWXO3\nXrW3ZL1bunTpwuLFi8nKyspz3C5E7G1DHQkxwK0Qud0Hcps2rSkjm2H1wrwnylUic9Ee1tzIotGz\nz+e5d/u+0HshbO4mdJ+7PqmnCi9wvRXgTu+/MN4L/6Rn1Vlim31PXVEndqlZ792MdZbEd/z4cRo2\nbMG6dRqMxv2gqaVixuEoxjhI2Qup++HgI6Bvjlxyk1tvZb6VgWyDch2hwvv/Z++846Oo1jf+PWfT\nGyV0EFR6EaSDUkUREASRIigWLjYExYbYsQECYrl2EURFsaGUCwIKAtJrEEjovUMIySa72XLO74/J\nbDbJbnYiRX735vl88mGZOWdmzuzszDvved7nsdZFuZBre8HpNahaWyE8x0FLSnREK2xrPg/cb+1U\n1O8T0Lf8DnEWjVc8WRAWAR0HQT0LDk8OO+K5zoir2kPnlwofRt3bSJ/1J1mbd3L0uY+xfT4VGRcX\nchfelatwvT4G/eosSCgke13xKmzRkZxOOsavfb6m1I2NKXdbYCkrfzgPnmTbXW8RP/FZwqpXC9le\na41zyo8c3Le/QJIhEExJK4/Hkyf5UYxiXCr8z+mshsKePXt455132L9/PyVKlKBv3758/fXXedqE\nKpw434egUooTJ07QoEEDRo4cidvtZteuXdSsWZPx48cXkPyJjo6+6MUcUkreHz+OnvcMxtGpJ8T4\nZThiYnG8/T27P3uTVh068v2X02jdurWv8OZ83KJCSSGdr4uTGVD/E5JHoWAqFvjrwgYav/nvpXCx\nAgpk9S8GQlmLmuM0xwyGHNmlcHizCqvfQSgtVnM75pjnzJnLY4+NwuF8Ha0eAKvftYgA1QGx60F0\n1m6IewxKW7RPBWMKP20UKv1jUNcg7cnWHK28TuSa7nBuN6rmNgjLZxRQ4TW8m9pC308gwu/7S1kE\nPwyDTjOgbGNrx5i2C2a1A10aW9pxQpKQlEKO6Q8qDHX3zNDbb/0gjtdeZn+PpxEDBmDr2D5kF338\nOO4Bd8LA56BRaF6vSKzMonu+w5maTcu1z4Zsr9we/ur5BpFtmxP34IDQYwCyPpxO1pwlhIdHsnPn\nTurWrRtSnkpKSXx8POnp6b5rsVjSqhiXCpffE/ofxvr167nuuutITEwEoHfv3qxatYoKFSpYtmQt\nqvVqZmYmEydOJCUlheTkZHbu3EliYiI1a9YkPT2dfv360bdvX+rVq5dHe9PMql2qquNWrVrRoGZ1\nNrx4P2rMFIj048YJgeeBUZyr3YieA+/ijeef4/4h/yI8PByn0xlSWPqfEk+32Ww4nU4iIiL+0enf\nwsbvcDj+EfOEYLhQ2ejCXkTM/Vg1CvDXOr0cp/FDjdW8xs3P/moC5rjT0tIYMOA+Nm0+gNOxAEST\nIIqEwQ7CBaIK2r4QSr4IJZ+z3ledQ57qg87+C1gLsizqTFXI3AexhRRkeTKRq7pA5nFU7e0gA0xn\nxzRBRpZGbZ0NTfobyw5vhim9ocV4uLKHtWNM3w+/XA+lb4WqT+Fd3wyyHRAZ/AVGTnsevWM9euQu\na9nl6FKIqCi8kbFETHorZHPtduPuPwBRsyn67sD6qfmRXeZq3JsW0eTPCZbUBfaNmobzlJ0y6wpR\ne/Df/ooNpI0cj35/NpHTJrJ3716uuOIKEhISQtpk22w24uPjycjI8F2fl+OLfjH++1BMA8iHOnXq\nsHr1ahwOh1GJ+dtv1KtXjx49ehTJkrUoiIiIIDs7m27dujF58mROnDjBoUOHWLx4MT169KBEiRJ0\n7NiR8uXL53kQmzeWS1nE9MHECejFsxG9GsOBANaL7bvi/GYFL370GQ8MfxS32+0rtMo/dR1MgcB8\nY78UUkhmgHApqABWFRj8paDAqLy/nIp9TC6t1WlAczo7lLWozWYjIiLibxW2XS4Bqv93rLXG5XIV\n4M76U3UiIiKIiIjw0VLM6mtTZcIsbpk69QsaNGjG6tXbcGV3MgLVIh3YNoRohBSzEaIaUp+w3te9\nC3GkEWSfQuu9IOuCLIOUDZD7PyikXwZiRQfIOo2qtS1woJoDFdkduTIn2Eo9AB92gjr3wzWPWDtG\n+xH45Too0QHqTYW4+sjoRFhXiNLA4m9Qs95H378YogLbwuaHnPcMyp6NbNnKUns16jn08TOocb9a\nas/JQ7BlBba4GEsuVafnrePQJ/Mp9ds0S4Gt9+gJzvR4ED14JFx/I5m1GrFt+3aio6PJyMiwdB8M\nCwsjNjbWd8+6XGlUxfjvQrGDVQCMHz+eadOmIaWkSZMmTJ48mYyMDPr168fBgwd9lqymy9SYMWOY\nMmUKYWFhvPvuu9x8880X7FjS0tLo3LkzCxcuDPjW63a7cbvdPirApcBrY8Yy4a23IDwCxnwOXQMU\nWNgziB41iKvPHuWbzydTtqzhSX4xXYz+LrKzsxFC/G2qQn4UJWMYavz/pDNTYcjKyiIyMjLPNWk1\nO55/3BcKmZmZREdHXxJ3NytT96ZBhv/36//nT+EIhqVLl/LII09z4kQcWVnPYkyG9QV2gbAwg6O9\nCPkWWr0C9AQ+BNaD6AlXHAFbQae8PMhaACf7grgVZF46FGoRyNuh20mw5VMgcKUhVnRAeDSq+gaQ\nIa5f90lIqQYjNyE+7golr0XfbK1wiawTiJktIOoadKO5ucv/GoithhPvSwGm93esg5Edod8X0KiP\npd2I5e/Bry+im49DbHuByL27Cr1+vT/8iHvEk+jJW6DilRbGYUc80BRdsiZy9yJa7/yUqCrBLWud\nh0+zpv5QYl9/kvjhd4fcvHa5ONWyL57YiqgpvxkL537Ljct+YO533/qKiuPj4y39Ls2Xr7i4OKKi\noi6ZdXUx/usR8OIrDlb/H+C5556jdu3a9O7du8A6rTVZWVm+DMylgMPhoH6T5pxu1AuWTkV2H4B6\n4T2IiMzbUCnCPnqduO8/4avPPqFt27aX5Q3NzPjFxISuuPVHMK/7QHzavxucmZW3kZGRoRtfApiB\nuNPp9I0nEJ/0n3gRCRRAnw+C2ajmD0qDBaJutxullC+ALuza9zegUEqxb98+Ro58mZUrN+FwPAPc\njHkPl7IviOtQKoQ8kd6LkP1BH0TrqUAuX1LamqFL3IcuEYQTqTUiYyI6dTSIN0EOC9hMyitQ14yB\nqn5KBNlnEH+2RahYVPU1lou35O66KNcRZKk6qNssKqo4zyBmtkKEV0Vdm88FK2MrbGoBP57JSwU4\nfQQeagTNH4BbxmAJST/AjPug6zyo0AYxvSQRC+cj6wcubFPbt5PdqTOMnAodLaglKIV8phscOYR6\n4S8iXr+KmhP6U2FAYE6s8njZ0Pop3KXKk7hwqqUhnBvyHFkLVuNdsDfXhGH3dsoP78mB7VvRWmO3\n233V/6F+s+azx+v1+gLWy2WGoxj/r1Fst/r/FY8//jiffPJJwOkWMyPocrkuyr4DuTgBjB39IjEp\nS2D8Zli6EHFbEzi0N29nKfE88hJpL33EbXfexfBHR/wjUlah4F/AFAhWpLDM7VxIBQZzm/+EVm1h\nagMmRcYMyi8XKajzkfoKZYrg8XjyjNecvvefxvefujfpDDabzccrN/dlXkvmfjIyMkhPT8dut3P2\n7FlGj36d66+/kaVLa+BwzAe64H//Vuo1lPcL0MeCDQjBJ0BD0GXRejv+gSqA8j6HTpsAOrtgf+VE\nnhkAZ8eCWBg0UAVQ7kGI3RNzFzhPIJa1BF26SIEqnjRUthOURvVcGbo9QHYaYlZbhK0cquGiguvj\nGyCjS8GGBX59HIjnbkZUaWY9UN2zzAhU234GldqBlIj46qgFgQ1g9Ll0XLf3gxsHWQtUAfnx0+id\nm1EjjXPmKnMt535PCtp+//Nf4TyaRql5n1nafuan35H5w694p6/M6xZ2ZS3OHDvqC1Lj4uLwer2W\nFQLMF3zzd1KsEFCMi4XiYNUi0tLS6NOnD3Xr1qVevXqsWbPmkrlalS1bltatWzNv3ryA68PCwny2\nmH8XRZVC6tOnD7XKlUAk/Yr69x502bpwayNYGGDKrdOteEZ/zFc//ECz1u3ZsGHD3z7OiwHTzcrt\ndp+XFNbF4JOaxUwX6yFQmDxSMH3e2NhYIiMjfS9Kl4s+aajCr1DcWdMgA/LySQMFpP6f/c+BmT01\n92UqOtjtdl9QampVgvHbjY6OJj4+nvnz59OkyfV89tlunM5ZeDxDgQAC/9RByhpIW4BgSx9DyhuB\n54DP0HoGEKhiuzdSRIP9m7yLPUcQx5uDYx2aFJDXF37SxcvozL2QthEcRxBLWyBsV6KvXmY9UHUd\nROxsgtCJgIQzwYM0H9x2xJyOCBWNuja4bqmKbIvt9xxrVq2Rb96JcLrQgwPfSwvg2F/weXdo8hLU\nyq20V5V7oWfNLtBcK4Vn8L8Q8eXgSWsFT2LeFNScz9BPLIeoHBmsxn04EyRYPbNwIwc/mEvJBV9Y\n4qlmr9lM2uNvoCbMgApV8q4MCyOmRl22bt1qHIsQxMfHk52dXUA2MeCxF0taFeMSoZgGYBH33HMP\n7du3Z/DgwXg8HjIzM3njjTcoU6YMI0eO5M033+Ts2bOMGzeO7du3M3DgQNatW8eRI0e48cYb2blz\n53lNgR87dox+/foxd+7cgNsxRc/Nopxg8J/SzD+9WRiXNFAgsnXrVm64pRfOt5MhvjQs+QKmDEf2\nGoR69u28tACtEbc3Rx+1E63SGHjH7bz28gvEWdAovJAIxSf15xheDnxaMLIWZkD0dxFKCirYdx8M\nZvbFFAu/HGBSJiIiIookBfV3+aT+15I5fW/+a55X8+XF5XL5Mq75z+vGjRsZOvRJ9u51kJn5HNDU\nwmi3AQOBfSBy/OL198D9CNEQrb8HQn037yDCp6Ir7wEhwbkaTtyCEM3Qer7lYFPom6GsRKdtQUQ1\nRl81N3QnE1mbYU8nhGyHjv0ZkXkjosaVqPaTg/dxZyHndoIsO6rZZpCF0D4ytsCmVvBTKvKH8eif\n/40euQNiQjh1AZw9BG83hqvugLbv513nOA3fVCZq1w5EyRK+xd4Jb+H+8BP09H0QY+HelrQMRnaD\nId/DNd1yl7uciCcTaHt0GuGJucVf2cdSWV3vYWJefJSEJwaH3Lz3xGlONOiG6v0QPBk4kxzz/GDe\nbN+E+++/37fM4/GQkZFBXFycJXkqr9dLenq67+XdVLMoRjH+BoppAH8X586dY/ny5QwebNwcwsLC\nKFGixCV1tapYsSINGzZk8eLFAdeHh4f7pirBuovT+fieN2jQgD69ehDxQ46Qdsd7YeIW+H0e4vZm\ncGhfbmMh0K9/Bo5DOLrMY/rKDBo2acWCBQsCbvt8Udj4g1mLAgGzZf/0TdcqFeBCGCVYzQ6bxgD/\npDlAfmUJs9jQdOoyOaOmukKwDGn+LKn/tW/uy/9aysrKwm63+6buTc90KSWRkZHExcWRkJBAfHy8\n79zGxcUVqJzesWMHXbr0pHPn2/nrr15kZv6AtUAVoD5SXo2U40CnIuXtCHE/MBat/0PoQBXgUVDp\n4JgH9qlwvBPooWixoEgmAVrdiT6+CKKaFy1QTV8Iu9pC2L3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twB1m8FStYMcSeL87dJoC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y0X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\nCOGxJFgVQgghhBAe6/8D6bfN7+5DVO4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1632da90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x, y = meshgrid(np.linspace(-2.3,1.75,25), np.linspace(-0.5,4.5,25))\n",
+    "z = rosen([x,y])\n",
+    "fig = figure(figsize=(12,5.5))\n",
+    "ax = fig.gca(projection=\"3d\"); ax.azim = 70; ax.elev = 75\n",
+    "ax.set_xlabel(\"X\"); ax.set_ylabel(\"Y\"); ax.set_zlim((0,1000))\n",
+    "p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)\n",
+    "intermed = ax.plot(xi[:,0], xi[:,1], rosen(xi.T), \"g-o\")\n",
+    "rosen_min = ax.plot([1],[1],[0],\"ro\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Nelder-Mead Simplex 算法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "改变 `minimize` 使用的算法，使用 [Nelder–Mead 单纯形算法](https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method)："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(120L, 2L)\n",
+      "Solved the Nelder-Mead Simplex method with 226 function evaluations.\n"
+     ]
+    }
+   ],
+   "source": [
+    "xi = [x0]\n",
+    "result = minimize(rosen, x0, method=\"nelder-mead\", callback = xi.append)\n",
+    "xi = np.asarray(xi)\n",
+    "print xi.shape\n",
+    "print \"Solved the Nelder-Mead Simplex method with {} function evaluations.\".format(result.nfev)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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S0oJ2EMoL9FYd0P5pncwKc7y11WrFbDZjMBhITk6m9/0D2WaPJm3492COzTpg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\nEEIIIYTP+v/YLseLtZ7EnQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15ea7668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x, y = meshgrid(np.linspace(-1.9,1.75,25), np.linspace(-0.5,4.5,25))\n",
+    "z = rosen([x,y])\n",
+    "fig = figure(figsize=(12,5.5))\n",
+    "ax = fig.gca(projection=\"3d\"); ax.azim = 70; ax.elev = 75\n",
+    "ax.set_xlabel(\"X\"); ax.set_ylabel(\"Y\"); ax.set_zlim((0,1000))\n",
+    "p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)\n",
+    "intermed = ax.plot(xi[:,0], xi[:,1], rosen(xi.T), \"g-o\")\n",
+    "rosen_min = ax.plot([1],[1],[0],\"ro\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Powell 算法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 [Powell 算法](https://en.wikipedia.org/wiki/Powell%27s_method)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(31L, 2L)\n",
+      "Solved Powell's method with 855 function evaluations.\n"
+     ]
+    }
+   ],
+   "source": [
+    "xi = [x0]\n",
+    "result = minimize(rosen, x0, method=\"powell\", callback=xi.append)\n",
+    "xi = np.asarray(xi)\n",
+    "print xi.shape\n",
+    "print \"Solved Powell's method with {} function evaluations.\".format(result.nfev)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2L6n9Z0ei1BGnT59m4pTJhB1aWMBerv8Ax9efolEusWqzyszsepR6PR5RVapKtsmsbr+S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\n4JgH9qlwvBPooWixoEgmAVrdiT6+CKKaFy1QTV8Iu9pC2L3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twB1m8FStYMcSeL87dJoC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\nQgghhBBeS4JVIYQQQgjhtf4/QWwBBPzDxt8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x167624a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x, y = meshgrid(np.linspace(-2.3,1.75,25), np.linspace(-0.5,4.5,25))\n",
+    "z = rosen([x,y])\n",
+    "fig = figure(figsize=(12,5.5))\n",
+    "ax = fig.gca(projection=\"3d\"); ax.azim = 70; ax.elev = 75\n",
+    "ax.set_xlabel(\"X\"); ax.set_ylabel(\"Y\"); ax.set_zlim((0,1000))\n",
+    "p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)\n",
+    "intermed = ax.plot(xi[:,0], xi[:,1], rosen(xi.T), \"g-o\")\n",
+    "rosen_min = ax.plot([1],[1],[0],\"ro\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/04. scipy/04.06 integration in python.ipynb b/04-scipy/04.06-integration-in-python.ipynb
similarity index 98%
rename from 04. scipy/04.06 integration in python.ipynb
rename to 04-scipy/04.06-integration-in-python.ipynb
index 84b22380..a169518b 100644
--- a/04. scipy/04.06 integration in python.ipynb	
+++ b/04-scipy/04.06-integration-in-python.ipynb
@@ -1,1368 +1,1368 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 积分"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 符号积分"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "积分与求导的关系：\n",
-    "\n",
-    "$$\\frac{d}{dx} F(x) = f(x)\n",
-    "\\Rightarrow F(x) = \\int f(x) dx$$\n",
-    "\n",
-    "符号运算可以用 `sympy` 模块完成。\n",
-    "\n",
-    "先导入 `init_printing` 模块方便其显示："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from sympy import init_printing\n",
-    "init_printing()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from sympy import symbols, integrate\n",
-    "import sympy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "产生 x 和 y 两个符号变量，并进行运算："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFYAAAAlCAYAAADY4B6YAAAABHNCSVQICAgIfAhkiAAAA4FJREFU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\ngg==\n",
-      "text/latex": [
-       "$$\\sqrt{x^{2} + y^{2}}$$"
-      ],
-      "text/plain": [
-       "   _________\n",
-       "  ╱  2    2 \n",
-       "╲╱  x  + y  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x, y = symbols('x y')\n",
-    "sympy.sqrt(x ** 2 + y ** 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于生成的符号变量 `z`，我们将其中的 `x` 利用 `subs` 方法替换为 `3`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEoAAAAlCAYAAADlcn/+AAAABHNCSVQICAgIfAhkiAAAA49JREFU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\n3TKcAAAAAElFTkSuQmCC\n",
-      "text/latex": [
-       "$$\\sqrt{y^{2} + 9}$$"
-      ],
-      "text/plain": [
-       "   ________\n",
-       "  ╱  2     \n",
-       "╲╱  y  + 9 "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "z = sympy.sqrt(x ** 2 + y ** 2)\n",
-    "z.subs(x, 3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "再替换 `y`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAsAAAASCAYAAACNdSR1AAAABHNCSVQICAgIfAhkiAAAAMxJREFU\nKJHN0TFLQmEUBuDHFIKGFhVcmvwFDW35T/wHQXOLo3/AsSVozTlwCwcbGhoaxHLJTVAIAo0cbLjf\nhevHTW5bL5zh/d5zXt7vHP6AcsQn+MAyaOe4xhMW8fA2qm9cpGIlap5hgDrecIPxb7Ee9mU+2CfG\nKEX8EfeoYoMmrvCaNzzFSYa3MUejSKwyVugVjfoeamd1Q8kfWjnutdjhE885zl+Sy+7gTrKFLE4l\nl+zEzWfo4yjwEm4xwmH6kEULl1jjGC/ohij/BT8UwSaNbxctpgAAAABJRU5ErkJggg==\n",
-      "text/latex": [
-       "$$5$$"
-      ],
-      "text/plain": [
-       "5"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "z.subs(x, 3).subs(y, 4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "还可以从 `sympy.abc` 中导入现成的符号变量："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAADgAAAAXCAYAAABefIz9AAAABHNCSVQICAgIfAhkiAAAAwFJREFU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\nsxfhbTwvakRb4T8H94xyrglMYAKnCf8B3eKPw+iETXAAAAAASUVORK5CYII=\n",
-      "text/latex": [
-       "$$\\sin^{2}{\\left (\\theta \\right )}$$"
-      ],
-      "text/plain": [
-       "   2   \n",
-       "sin (θ)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sympy.abc import theta\n",
-    "y = sympy.sin(theta) ** 2\n",
-    "y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对 y 进行积分："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJ0AAAAZCAYAAAArBywYAAAABHNCSVQICAgIfAhkiAAABZBJREFU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\n9yxvAAAAAElFTkSuQmCC\n",
-      "text/latex": [
-       "$$\\frac{\\theta}{2} - \\frac{1}{2} \\sin{\\left (\\theta \\right )} \\cos{\\left (\\theta \\right )}$$"
-      ],
-      "text/plain": [
-       "θ   sin(θ)⋅cos(θ)\n",
-       "─ - ─────────────\n",
-       "2         2      "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Y = integrate(y)\n",
-    "Y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "计算 $Y(\\pi) - Y(0)$："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJMAAAASCAYAAABfCexoAAAABHNCSVQICAgIfAhkiAAABchJREFU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\nlqIAAAAASUVORK5CYII=\n",
-      "text/latex": [
-       "$$1.5707963267949$$"
-      ],
-      "text/plain": [
-       "1.57079632679490"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "np.set_printoptions(precision=3)\n",
-    "\n",
-    "Y.subs(theta, np.pi) - Y.subs(theta, 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "计算 $\\int_0^\\pi y d\\theta$ ："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAACEAAAAZCAYAAAC/zUevAAAABHNCSVQICAgIfAhkiAAAAc5JREFU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\nh284OVxxrb4HkAAAAABJRU5ErkJggg==\n",
-      "text/latex": [
-       "$$\\frac{\\pi}{2}$$"
-      ],
-      "text/plain": [
-       "π\n",
-       "─\n",
-       "2"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "integrate(y, (theta, 0, sympy.pi))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "显示的是字符表达式，查看具体数值可以使用 `evalf()` 方法，或者传入 `numpy.pi`，而不是 `sympy.pi` ："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJMAAAASCAYAAABfCexoAAAABHNCSVQICAgIfAhkiAAABchJREFU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\nlqIAAAAASUVORK5CYII=\n",
-      "text/latex": [
-       "$$1.5707963267949$$"
-      ],
-      "text/plain": [
-       "1.57079632679490"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "integrate(y, (theta, 0, sympy.pi)).evalf()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJMAAAASCAYAAABfCexoAAAABHNCSVQICAgIfAhkiAAABchJREFU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\nlqIAAAAASUVORK5CYII=\n",
-      "text/latex": [
-       "$$1.5707963267949$$"
-      ],
-      "text/plain": [
-       "1.57079632679490"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "integrate(y, (theta, 0, np.pi))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "根据牛顿莱布尼兹公式，这两个数值应该相等。\n",
-    "\n",
-    "产生不定积分对象："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAF8AAAAxCAYAAAC8snXfAAAABHNCSVQICAgIfAhkiAAABXZJREFU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\n",
-      "text/latex": [
-       "$$\\int \\sin^{2}{\\left (\\theta \\right )}\\, d\\theta$$"
-      ],
-      "text/plain": [
-       "⌠           \n",
-       "⎮    2      \n",
-       "⎮ sin (θ) dθ\n",
-       "⌡           "
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Y_indef = sympy.Integral(y)\n",
-    "Y_indef"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'sympy.integrals.integrals.Integral'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "print type(Y_indef)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "定积分："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAHIAAAA4CAYAAAAhHqx3AAAABHNCSVQICAgIfAhkiAAABoBJREFU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\nHA6HIwn+B7jjVvTlLJflAAAAAElFTkSuQmCC\n",
-      "text/latex": [
-       "$$\\int_{0}^{\\pi} \\sin^{2}{\\left (\\theta \\right )}\\, d\\theta$$"
-      ],
-      "text/plain": [
-       "π           \n",
-       "⌠           \n",
-       "⎮    2      \n",
-       "⎮ sin (θ) dθ\n",
-       "⌡           \n",
-       "0           "
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Y_def = sympy.Integral(y, (theta, 0, sympy.pi))\n",
-    "Y_def"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "产生函数 $Y(x) = \\int_0^x sin^2(\\theta) d\\theta$，并将其向量化："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "Y_raw = lambda x: integrate(y, (theta, 0, x))\n",
-    "Y = np.vectorize(Y_raw)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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jB/zlL2EKvohIKVEiz2L06LA5xKhRcOqpUUcjIrI5XeyswR13wLXXhmVpO3aM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\ngg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb8bd320>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "x = np.linspace(0, 2 * np.pi)\n",
-    "p = plt.plot(x, Y(x))\n",
-    "t = plt.title(r'$Y(x) = \\int_0^x sin^2(\\theta) d\\theta$')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 数值积分"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "数值积分：\n",
-    "\n",
-    "$$F(x) = \\lim_{n \\rightarrow \\infty} \\sum_{i=0}^{n-1} f(x_i)(x_{i+1}-x_i) \n",
-    "\\Rightarrow F(x) = \\int_{x_0}^{x_n} f(x) dx$$\n",
-    "\n",
-    "导入贝塞尔函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.special import jv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def f(x):\n",
-    "    return jv(2.5, x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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s6VEF3xna01fKQyUnJ9O6dWv27NnjVU98+qJTp05Rs2ZNli5dSuPGjW3H+R/a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\nUn7k/wDY8//eOSbGSgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbfe3a58>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x = np.linspace(0, 10)\n",
-    "p = plt.plot(x, f(x), 'k-')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `quad` 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Quadrature 积分的原理参见：\n",
-    "\n",
-    "http://en.wikipedia.org/wiki/Numerical_integration#Quadrature_rules_based_on_interpolating_functions\n",
-    "\n",
-    "quad 返回一个 (积分值，误差) 组成的元组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.integrate import quad\n",
-    "interval = [0, 6.5]\n",
-    "value, max_err = quad(f, *interval)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "积分值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.28474297234\n"
-     ]
-    }
-   ],
-   "source": [
-    "print value"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "最大误差："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.34181853668e-09\n"
-     ]
-    }
-   ],
-   "source": [
-    "print max_err"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "积分区间图示，蓝色为正，红色为负："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "integral = 1.284742972\n",
-      "upper bound on error: 2.34e-09\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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fj/r3n8batV/iec+6jmKO29n4fA3o2vU910FMnHrhhReYP38+Y8aMOe5tQ745S0SaAq8A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\nR6zoG2NMHLGib4wxceT/AZhppPNX9o1QAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xc126860>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "print \"integral = {:.9f}\".format(value)\n",
-    "print \"upper bound on error: {:.2e}\".format(max_err)\n",
-    "x = np.linspace(0, 10, 100)\n",
-    "p = plt.plot(x, f(x), 'k-')\n",
-    "x = np.linspace(0, 6.5, 45)\n",
-    "p = plt.fill_between(x, f(x), where=f(x)>0, color=\"blue\")\n",
-    "p = plt.fill_between(x, f(x), where=f(x)<0, color=\"red\", interpolate=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 积分到无穷"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from numpy import inf\n",
-    "interval = [0., inf]\n",
-    "\n",
-    "def g(x):\n",
-    "    return np.exp(-x ** 1/2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "upper bound on error: 7.2e-11\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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gixQRkThUYg+VmYWAFcAAYAMwH7jUObe8SJtTgWXOuR1mNhgY65zrddB51EMl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\nTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xc260dd8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "value, max_err = quad(g, *interval)\n",
-    "x = np.linspace(0, 10, 50)\n",
-    "fig = plt.figure(figsize=(10,3))\n",
-    "p = plt.plot(x, g(x), 'k-')\n",
-    "p = plt.fill_between(x, g(x))\n",
-    "plt.annotate(r\"$\\int_0^{\\infty}e^{-x^1/2}dx = $\" + \"{}\".format(value), (4, 0.6),\n",
-    "         fontsize=16)\n",
-    "print \"upper bound on error: {:.1e}\".format(max_err)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 双重积分"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "假设我们要进行如下的积分：\n",
-    "\n",
-    "$$ I_n = \\int \\limits_0^{\\infty} \\int \\limits_1^{\\infty} \\frac{e^{-xt}}{t^n}dt dx = \\frac{1}{n}$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def h(x, t, n):\n",
-    "    \"\"\"core function, takes x, t, n\"\"\"\n",
-    "    return np.exp(-x * t) / (t ** n)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "一种方式是调用两次 `quad` 函数，不过这里 `quad` 的返回值不能向量化，所以使用了修饰符 `vectorize` 将其向量化："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from numpy import vectorize\n",
-    "@vectorize\n",
-    "def int_h_dx(t, n):\n",
-    "    \"\"\"Time integrand of h(x).\"\"\"\n",
-    "    return quad(h, 0, np.inf, args=(t, n))[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "@vectorize\n",
-    "def I_n(n):\n",
-    "    return quad(int_h_dx, 1, np.inf, args=(n))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([ 1.97,  1.  ,  0.5 ,  0.2 ]),\n",
-       " array([  9.804e-13,   1.110e-14,   5.551e-15,   2.220e-15]))"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "I_n([0.5, 1.0, 2.0, 5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "或者直接调用 `dblquad` 函数，并将积分参数传入，传入方式有多种，后传入的先进行积分："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.integrate import dblquad\n",
-    "@vectorize\n",
-    "def I(n):\n",
-    "    \"\"\"Same as I_n, but using the built-in dblquad\"\"\"\n",
-    "    x_lower = 0\n",
-    "    x_upper = np.inf\n",
-    "    return dblquad(h,\n",
-    "                   lambda t_lower: 1, lambda t_upper: np.inf,\n",
-    "                   x_lower, x_upper, args=(n,))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([ 1.97,  1.  ,  0.5 ,  0.2 ]),\n",
-       " array([  9.804e-13,   1.110e-14,   5.551e-15,   2.220e-15]))"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "I_n([0.5, 1.0, 2.0, 5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 采样点积分"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### trapz 方法 和 simps 方法"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.integrate import trapz, simps"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`sin` 函数， `100` 个采样点和 `5` 个采样点："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "x_s = np.linspace(0, np.pi, 5)\n",
-    "y_s = np.sin(x_s)\n",
-    "x = np.linspace(0, np.pi, 100)\n",
-    "y = np.sin(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Rw+lEZbm6uvLwww9zzz330KdPnzIHq3h4ePD//t//q9La/MI5yB2qDurKlSsMHDiQ7Oxs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\nQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xc1954e0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "p = plt.plot(x, y, 'k:')\n",
-    "p = plt.plot(x_s, y_s, 'k+-')\n",
-    "p = plt.fill_between(x_s, y_s, color=\"gray\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "采用 [trapezoidal 方法](https://en.wikipedia.org/wiki/Trapezoidal_rule) 和 [simpson 方法](https://en.wikipedia.org/wiki/Simpson%27s_rule) 对这些采样点进行积分（函数积分为 2）："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Trapezoidal Integration over 5 points : 1.896\n",
-      "Simpson Integration over 5 points : 2.005\n",
-      "Trapezoidal Integration over 100 points : 2.000\n"
-     ]
-    }
-   ],
-   "source": [
-    "result_s = trapz(y_s, x_s)\n",
-    "result_s_s = simps(y_s, x_s)\n",
-    "result = trapz(y, x)\n",
-    "print \"Trapezoidal Integration over 5 points : {:.3f}\".format(result_s)\n",
-    "print \"Simpson Integration over 5 points : {:.3f}\".format(result_s_s)\n",
-    "print \"Trapezoidal Integration over 100 points : {:.3f}\".format(result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 使用 ufunc 进行积分"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Numpy` 中有很多 `ufunc` 对象："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ufunc"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(np.add)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "accumulate(array, axis=0, dtype=None, out=None)\n",
-      "\n",
-      "Accumulate the result of applying the operator to all elements.\n",
-      "\n",
-      "For a one-dimensional array, accumulate produces results equivalent to::\n",
-      "\n",
-      "  r = np.empty(len(A))\n",
-      "  t = op.identity        # op = the ufunc being applied to A's  elements\n",
-      "  for i in range(len(A)):\n",
-      "      t = op(t, A[i])\n",
-      "      r[i] = t\n",
-      "  return r\n",
-      "\n",
-      "For example, add.accumulate() is equivalent to np.cumsum().\n",
-      "\n",
-      "For a multi-dimensional array, accumulate is applied along only one\n",
-      "axis (axis zero by default; see Examples below) so repeated use is\n",
-      "necessary if one wants to accumulate over multiple axes.\n",
-      "\n",
-      "Parameters\n",
-      "----------\n",
-      "array : array_like\n",
-      "    The array to act on.\n",
-      "axis : int, optional\n",
-      "    The axis along which to apply the accumulation; default is zero.\n",
-      "dtype : data-type code, optional\n",
-      "    The data-type used to represent the intermediate results. Defaults\n",
-      "    to the data-type of the output array if such is provided, or the\n",
-      "    the data-type of the input array if no output array is provided.\n",
-      "out : ndarray, optional\n",
-      "    A location into which the result is stored. If not provided a\n",
-      "    freshly-allocated array is returned.\n",
-      "\n",
-      "Returns\n",
-      "-------\n",
-      "r : ndarray\n",
-      "    The accumulated values. If `out` was supplied, `r` is a reference to\n",
-      "    `out`.\n",
-      "\n",
-      "Examples\n",
-      "--------\n",
-      "1-D array examples:\n",
-      "\n",
-      ">>> np.add.accumulate([2, 3, 5])\n",
-      "array([ 2,  5, 10])\n",
-      ">>> np.multiply.accumulate([2, 3, 5])\n",
-      "array([ 2,  6, 30])\n",
-      "\n",
-      "2-D array examples:\n",
-      "\n",
-      ">>> I = np.eye(2)\n",
-      ">>> I\n",
-      "array([[ 1.,  0.],\n",
-      "       [ 0.,  1.]])\n",
-      "\n",
-      "Accumulate along axis 0 (rows), down columns:\n",
-      "\n",
-      ">>> np.add.accumulate(I, 0)\n",
-      "array([[ 1.,  0.],\n",
-      "       [ 1.,  1.]])\n",
-      ">>> np.add.accumulate(I) # no axis specified = axis zero\n",
-      "array([[ 1.,  0.],\n",
-      "       [ 1.,  1.]])\n",
-      "\n",
-      "Accumulate along axis 1 (columns), through rows:\n",
-      "\n",
-      ">>> np.add.accumulate(I, 1)\n",
-      "array([[ 1.,  1.],\n",
-      "       [ 0.,  1.]])\n"
-     ]
-    }
-   ],
-   "source": [
-    "np.info(np.add.accumulate)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`np.add.accumulate` 相当于 `cumsum` ："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "result_np = np.add.accumulate(y) * (x[1] - x[0]) - (x[1] - x[0]) / 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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I++hLVFCpRyQngso727dDiwa7OOObqYwZsY8St7f6e5BXeUcimEo9IjkR9N2419fZR/XU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\nRoFfRCTGKPCLiMSY/weoVZxsAST89wAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xc612a20>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "p = plt.plot(x, - np.cos(x) + np.cos(0), 'rx')\n",
-    "p = plt.plot(x, result_np)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 速度比较"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "计算积分：$$\\int_0^x sin \\theta d\\theta$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import sympy\n",
-    "from sympy.abc import x, theta\n",
-    "sympy_x = x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "x = np.linspace(0, 20 * np.pi, 1e+4)\n",
-    "y = np.sin(x)\n",
-    "sympy_y = vectorize(lambda x: sympy.integrate(sympy.sin(theta), (theta, 0, x)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`numpy` 方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The slowest run took 4.32 times longer than the fastest. This could mean that an intermediate result is being cached \n",
-      "10000 loops, best of 3: 56.2 µs per loop\n",
-      "-2.34138044756e-17\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit np.add.accumulate(y) * (x[1] - x[0])\n",
-    "y0 = np.add.accumulate(y) * (x[1] - x[0])\n",
-    "print y0[-1] "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`quad` 方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10000 loops, best of 3: 40.5 µs per loop\n",
-      "result =  3.43781337153e-15\n",
-      "number of evaluations 21\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit quad(np.sin, 0, 20 * np.pi)\n",
-    "y2 = quad(np.sin, 0, 20 * np.pi, full_output=True)\n",
-    "print \"result = \", y2[0]\n",
-    "print \"number of evaluations\", y2[-1]['neval']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`trapz` 方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10000 loops, best of 3: 105 µs per loop\n",
-      "-4.4408920985e-16\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit trapz(y, x)\n",
-    "y1 = trapz(y, x)\n",
-    "print y1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`simps` 方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1000 loops, best of 3: 801 µs per loop\n",
-      "3.28428554968e-16\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit simps(y, x)\n",
-    "y3 = simps(y, x)\n",
-    "print y3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`sympy` 积分方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "100 loops, best of 3: 6.86 ms per loop\n",
-      "0\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit sympy_y(20 * np.pi)\n",
-    "y4 = sympy_y(20 * np.pi)\n",
-    "print y4"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 积分"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 符号积分"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "积分与求导的关系：\n",
+    "\n",
+    "$$\\frac{d}{dx} F(x) = f(x)\n",
+    "\\Rightarrow F(x) = \\int f(x) dx$$\n",
+    "\n",
+    "符号运算可以用 `sympy` 模块完成。\n",
+    "\n",
+    "先导入 `init_printing` 模块方便其显示："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sympy import init_printing\n",
+    "init_printing()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sympy import symbols, integrate\n",
+    "import sympy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "产生 x 和 y 两个符号变量，并进行运算："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFYAAAAlCAYAAADY4B6YAAAABHNCSVQICAgIfAhkiAAAA4FJREFU\naIHt2UuIHFUUxvFfEskYoxFfmBiNY4JEGYzGiEqiaNABSVw4IrNQcZOVulBIFAV3IkRRwfiAgIsy\nii9ERfCBJMYEUUQFdaGoEB8Iig9E1BAFExenm+kuu0xN1e2Zsak/NN236ta5H7fOveec2zQ0/J+Y\nlcDGgQQ2Gnrw7HQLmInMrvn8cnyeQsigUXdi1+PlFEIaunlG/ZczkNSZlCPwB/Yn0jJQ1JnYUWxP\nJaRhgq04ZrpFzFTqeOzR+DmVkEGj6sSuxIcphQwah1R8LlWadR5W40iswZ3YncBuSqZU43MJbByO\nzR3tcezF4gS2UzGlGo8VgasuK0SqtqzVXiDOHcYT2E7FlGq8FlcmsDNLLLP2QdCIEL0yge1UTKnG\nTBQHqXkc9/XBbkpKa+yVFSzBnIL+c3AYfqumq5AN+A6batpZoXpAPhi1NB6K94X792INNlbTVcjl\nQnR7/OEatrKazxcxaY15j90oIt4lBf1Tn2ZdhONbNhfiMixKaD8FtTWehLOwDW8U9HmhpK1VeAD3\n43lRpd2Ou/GEiLJLxZZyIPdZMBnROTLlPXYYD4oJuzp370ZxDpJU43XYh3m56ydiS4nnl+JhE6sh\nE4fhq8VWsl/67aRNpvzEPoK5uAkf5+69i6frCOkVvHZgSExCJ+vwagmbm3CbiePE+fgFb+Mb4cVZ\nBa0puQBv4S+xtDv/BZmPs7GrHwN/qrvigCfFxn0wTsm1v8VdKUSVIFPOYxcK51mMvzHWcW9ULPeR\nOkKKUpPtugPYXOHd+0rY/LLj93IhfmcldcU8hjN7XF+Cc4Un5tmAD1q/v299j+N3vNLR70L8hE+S\nKM1xhXiTR7Xao7ihgp3r8afIfdssK+ibgszk0q3X8GLu2psi4Nai6Nhwp1gOa1vt9brfahHzcA/O\naLVHRWDY2zHeLZWU9oeT8VlHe0jk8LVPr4om9ldRKFzaag/jqxL21omJG8FpOFV4bJs7xDKeKXwt\nUsE2m0UcqR24/qv824GrxOR8UdLeLrEcV4nIer5Ia7aKfe8lvFNRaz+4GY/iIbGqzhFO9VE/B10r\ntoN7cXE/B0pIpnpJO1sEtW2pxBQxJN7iD/p3sJGaLSKVKsNTuguDMbGqTk8tqhevS/NvwUzkR1HS\nwgnYg2tSGS86HmyzCO/5d8k3COzBcaLyGsOtylWWDQ0NDQ3TzD+oDaLToDx6/gAAAABJRU5ErkJg\ngg==\n",
+      "text/latex": [
+       "$$\\sqrt{x^{2} + y^{2}}$$"
+      ],
+      "text/plain": [
+       "   _________\n",
+       "  ╱  2    2 \n",
+       "╲╱  x  + y  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x, y = symbols('x y')\n",
+    "sympy.sqrt(x ** 2 + y ** 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于生成的符号变量 `z`，我们将其中的 `x` 利用 `subs` 方法替换为 `3`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEoAAAAlCAYAAADlcn/+AAAABHNCSVQICAgIfAhkiAAAA49JREFU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\n3TKcAAAAAElFTkSuQmCC\n",
+      "text/latex": [
+       "$$\\sqrt{y^{2} + 9}$$"
+      ],
+      "text/plain": [
+       "   ________\n",
+       "  ╱  2     \n",
+       "╲╱  y  + 9 "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z = sympy.sqrt(x ** 2 + y ** 2)\n",
+    "z.subs(x, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "再替换 `y`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAsAAAASCAYAAACNdSR1AAAABHNCSVQICAgIfAhkiAAAAMxJREFU\nKJHN0TFLQmEUBuDHFIKGFhVcmvwFDW35T/wHQXOLo3/AsSVozTlwCwcbGhoaxHLJTVAIAo0cbLjf\nhevHTW5bL5zh/d5zXt7vHP6AcsQn+MAyaOe4xhMW8fA2qm9cpGIlap5hgDrecIPxb7Ee9mU+2CfG\nKEX8EfeoYoMmrvCaNzzFSYa3MUejSKwyVugVjfoeamd1Q8kfWjnutdjhE885zl+Sy+7gTrKFLE4l\nl+zEzWfo4yjwEm4xwmH6kEULl1jjGC/ohij/BT8UwSaNbxctpgAAAABJRU5ErkJggg==\n",
+      "text/latex": [
+       "$$5$$"
+      ],
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z.subs(x, 3).subs(y, 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "还可以从 `sympy.abc` 中导入现成的符号变量："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAADgAAAAXCAYAAABefIz9AAAABHNCSVQICAgIfAhkiAAAAwFJREFU\nWIXt1luIV1UUBvCf5jRTpqNEmdeGSaickHoIqYx8CZoIwiAhUCh96KVMnHqIsIjCSzREQw9JEIE+\n2IUukJQPGjVRQSlRURFCklFZ2kOF0lDaw9qH/57tmWb+MzqCzAebc9Z31t57rX1Z3+Esx6RxnGsJ\nrkc7bsAT+GAc5z+tuACbM3sFjmLumQnn1GMxjuOyZE/HCZHoWYFJ4ohWV6JLJHjNGYuowDL8jvWn\naLxt6B3GpwUdQ3zrwFZswWuYhlmYOdqA7hIrvnW0A2RYg6f8f4Gbgsdwfs23DuzD7GT3oA+TsWmI\nPiPCwjTxWHCbSBDaDL1DPeLeljgXe7E64+7ED+l9Np7OO0xuIrj9+KcJ/xI3iWO0E5fgFo1dyDFN\nyMkXNd8eSH22Z1w75osj/bOo2AuaCawVl2Mprm6mY4ZO/CmOed6m1/jegQdr+DYcFvqZozeN1Zrs\nFXioLojl4m48jMfxXGpXYVca5KXC/8s06QbcjI14Fh/hutpUh0ef2O0Sd6cYugr+E/yR2Z14q+x8\nJfYU3EqDE/qssOFCsTM7Ne5WFeSBmiBHgtednAQR9DG8m7Xd+Bf9mV+7WHg07uBiXCzOf4U38Vdm\n5+8VjqTWiRcz/itciouGy6YGU/F3wZ0jdvUNcXer1ptyeC/zPSqKERoJ9osEfxQadT/Ow30jDOpz\ncXwqDGTBNovfnKxnc8XOfFzw3en5asbNxK+VUSX4k/jT2CFEvQ8Hhf6NBOWKjwXfYl7BzUrPrzNu\niigo/bIjKSrqN5VRJXitEN57k8MCvCyEvdX44h3cWHCVPP2Scd3i1G0ofJeKmoBGgl0G79ZBIabH\nMWNs8TaNvULrWjKuEvJch3vwAt4v+i8Ri4TBQr8OczJ7Hr7DoWS3qP+TqeNbimezeAarMvuIkJ4r\nkr1anKy1Rb9bRSEaKHgr8UgaeCOeFBo4X1SvT0URGRByMhW3i9Wu+A/Fyr8ifsxP4Hs8Osoku7Eo\nsxfhbTwvakRb4T8H94xyrglMYAKnCf8B3eKPw+iETXAAAAAASUVORK5CYII=\n",
+      "text/latex": [
+       "$$\\sin^{2}{\\left (\\theta \\right )}$$"
+      ],
+      "text/plain": [
+       "   2   \n",
+       "sin (θ)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sympy.abc import theta\n",
+    "y = sympy.sin(theta) ** 2\n",
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对 y 进行积分："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJ0AAAAZCAYAAAArBywYAAAABHNCSVQICAgIfAhkiAAABZBJREFU\naIHt2nusXFUVx/FPoaWVlhQCCC0CV3ILVAIWGorWIqUJJkAaAgQrj5iC4h+mhEdjMEExQVM1iqaF\n8FCI4gMaFAIpFYRAqqVAoCUkGh5/+QAJCqEEDEjB1j/Wmdx9991z55w7U4fqfJPJzFmzH7+91tmP\ns2YYMGAX5qx+CxjQF/oW9yNxT786H9A3JhT33drYh3AzvotfY68O7ZyHOzLbAtyN+/EH3IqDmgrs\ngsV4HVf0oK0pwic5Q8p+OgD79KDfbmmnm7L2prp7FvchPI1Z1fVKrOlQ52l8KLk+Dg9i7+p6Bn6P\nf2jvhF5zLnYIx3bDZHwDe2b2Ie39tBu+Xajz36Sdbtprb6q7J3HfA1twUWI7B38dp+MF+HlmW4/h\nzHasuAnWjtNWrxkWzu+GlTgms9Xx0yx8v8u+u6Gkm87a6+ruWdy/gpcrYS2+WFWa0qbOapya2f4p\nBvHhzL4Vr7Xr/APIXrirYK/rp5twyE5T1552uqmnvY7uCcc9PdNNw5ViD96W2OcWyrbYHSfjocz+\nJ3E+mJ7Z3zV6Od5ZTMURWIR5XbRzCh7PbE389AiWddH/RCnppr72Trq7inu69XwO+xq7DH4Kb1UV\nc5ZgI97P7J8Qs+2VxDa7ErShOIzmnIlPilk0TWiHFZiDa/EZ3IblSZ1rxBayGk8I503H8WJLSoO1\n2NgVo4mfNuN8fC+xzcPVeAP/EoG+pSrb4gRcgr+LGO2NVXih5vhLuptoL+lO6Vnc78U7eCB5PYx/\nVx2U+EkluA7fqdpaWLP8eMwVszHlAvw0s20u2PYVDl6PLyT2NfhzVvZuHJXZmvhppniCa7EQb4qb\nosXP8Jfkeqm4ufZPbHMrbccm1+ONv6S7ifZcd05P4r67mHm3Z/bTxF5/TaGxaXgGk2p0PCz2+2/V\nFNqJZfij0amcGbg+K7fB2JuOCOBzRmv/khhrGuzfGn0wbuqnKUZWp0nV5/VZmR/izmQMr+GygubV\nwt90Hn+uu6n2VHdO13Fv7eEHibs7Pwe0Doq/KjR4usjF7OjQ8VQx0B/hazWE1mGjOKy+JJ6gLhFn\nhhUN2njGaO2tM056HnnV6LxVUz/tI9IFxJZ5uHhyTLkcn60+ny5W4ucLel/AxzFf5/HnuptqT3Xn\ndB331k13QPX+bPLdZOGMjcpL7bn4ZYeOJ4ml+AHlJO3RwglP1HzdVNV7WQRxrdiq1uDFSlNdSmfU\nnOfxkeS6qZ8OFisqI3mql8bp77DqPT8rwXvV+7DO4891N9We6s7pRdwxkktJzwFLK9tJhfIz8WSH\njoll9euZ7fM16nXieCMBIpx0mzgvTU3sG7TfXnP7cjHeocQ2Hz9Irpv66VKcUX3+dFXuykK5XMN5\nhe++Wn23WOfx57qbak91p/Qk7q2VrpUYTGfYSvwYvys0erY4rI7HhdiOb2b2RR3q1eEoo1e1F0Wy\nc7uRbHgv2CKedFv5q6Z+OkFsRfCYWOWWFMqdVfWzDm+LdE/O/Kr+ozqPP9fdVHuqO6Xncd9k5O6+\nSGx709o0/CAOHafjJeJA/IvstdbY3+omwnJxbpmd2A41dhZuqvrN+VvBfrGY9XMy+wKjs/d1/XSa\nyPSnnCLSJOkqsj9uTK7PFzfXrMT2UeHPk6vr5TqPP9ddV3tJd4uex/1juE+cm9YUxLQ4UHlWp2wV\nASy98hkwES7AVeLJb5VYzq8X2wyxXTxV9bdNpBemC4dvSeyPiuDeKf4csEMkOK/O+jtV+Id6fpot\nZnyJBfiNOGRfW+mfmZU5sfr+BlwnkrlHNxh/SXcd7ePp7mvcL8OXu2lgwC5JX+O+Cfv1q/MBfaNn\ncW/3f7p2DIsE4670o/2A7ulp3JvedKU/7Q3436evcV8nfm4Z8P/FIO4DBgwYMGDAB5v/ACgJsG1Y\n9yxvAAAAAElFTkSuQmCC\n",
+      "text/latex": [
+       "$$\\frac{\\theta}{2} - \\frac{1}{2} \\sin{\\left (\\theta \\right )} \\cos{\\left (\\theta \\right )}$$"
+      ],
+      "text/plain": [
+       "θ   sin(θ)⋅cos(θ)\n",
+       "─ - ─────────────\n",
+       "2         2      "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Y = integrate(y)\n",
+    "Y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "计算 $Y(\\pi) - Y(0)$："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJMAAAASCAYAAABfCexoAAAABHNCSVQICAgIfAhkiAAABchJREFU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\nlqIAAAAASUVORK5CYII=\n",
+      "text/latex": [
+       "$$1.5707963267949$$"
+      ],
+      "text/plain": [
+       "1.57079632679490"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.set_printoptions(precision=3)\n",
+    "\n",
+    "Y.subs(theta, np.pi) - Y.subs(theta, 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "计算 $\\int_0^\\pi y d\\theta$ ："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAACEAAAAZCAYAAAC/zUevAAAABHNCSVQICAgIfAhkiAAAAc5JREFU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\nh284OVxxrb4HkAAAAABJRU5ErkJggg==\n",
+      "text/latex": [
+       "$$\\frac{\\pi}{2}$$"
+      ],
+      "text/plain": [
+       "π\n",
+       "─\n",
+       "2"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "integrate(y, (theta, 0, sympy.pi))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "显示的是字符表达式，查看具体数值可以使用 `evalf()` 方法，或者传入 `numpy.pi`，而不是 `sympy.pi` ："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJMAAAASCAYAAABfCexoAAAABHNCSVQICAgIfAhkiAAABchJREFU\naIHt2WuMXVUVB/DfQEsZWoHwHAkNLS3iI1FKKkVAYkRISEggTeQRG0C+CBgCRh5KBIsFITXRiGCx\nEBiF8LAEmkAT1AhaPwA18hAioZKYajHIEFCgUrTt+GHt4+x7Zp/be+7cfiHzT07u3Wuvs9ZZ+6y9\nHvswjWkMCEMN9L3wFD7ZQtbLWIEnsAWL8Q1cgpcSz6m4Dc/jPbyPHZmM3+PW9P9I3IhNGMcBuAL/\nyPjbyDsc38ZWbMdsXIXXCrZ8BMszme+l8TsZz7H4SpobFmv23fQsOY5J6zCMQ7EB1+LVPvTuKntz\nXI6ZYu1z9CXv0+mhxneitI7x2vUffLXG8/UCX36dmvj2wWYsy+69Gi9ijz7kzccbWJrdu0y8lBm1\nZ/w4/orj0ngEfxEvtcIiPIo9M9oqvI2jMtrR+CX2TeM5WI/XMa8PvbvC3hyHiUCwvEZvLe9jWIdR\nEZXaOtMmrMbDWJnk1bEKc4Xn75bRj8ct2fgGseD5g+6H/+KiPuStxZs1nmGxy87PaDNEhL0so83F\nGC7NaD8Q63NWRjst0W7OaOuwUCcWJb77+9A7aHvrWJ2ebXmN3q88hEO1dabf9MBzS4E2B4+JNFFh\nIx4p8L6Ax1vK20M44YYC78siwlS4QETUfQu8Oc7Fv3ByRjtbrNlNGe1dEW0Oqt3/ltjpbfUO2t4c\nS03YsDyj9yvv/xi1a5yphFVYko0/lHTfWuD9hXiJbeSNJHnrC7wbRASs8Cv8aSfym7AS2/CpjPaC\nqGvm13hfE+lkEHqnYm+FObgj/a87U8/yuuXPtpiFa7C/8OQFovDc2OWe47E7ns5oh6Xftwv8W7B3\n0vV+j/LG8G+d9U2FQ3CgWIftOEEs0Ik4RSzyPFyHZ7vYMR/n4WKdBfixYnPkReohONjE5huagt6p\n2Lsto3/T5IJ7KvI6MKp9ZHpF5PMKy0TnNdLlnj+YvGuPS7qvK/DfneYObiEP7hRpJe9eR0QnNC7S\n0AHp/0u4MOP7nOimPlGQe5roYP8oGoTdCjx13CQctyq0+9FbYSr2VjhKOFOFUs3URt4kjGrvTPWF\n3F149M0FXjhJObQvUTaIKFrHxY7oVR6xezaKVp7YSdfjmSRvf+Gg46KoHK7dv1n32mCGSFVPCedo\nwkJRR12f0frVO1V7iXf2U50dcmnte5LXy07qFTtq4+0iRJ7ewH+xWPw6xrromJ1+3ynMNcmrZC4R\nu+n7YrFuF8+8VXQqbybeV8QZTo7Notie1SB/m4hQS8Q5UAmzcK/omL6V0fvVO1V7CecYFcV/N/Qq\nr4hR7SLTevyuQN+clNUxU+zQGwpzs9ND/rAw97johNrI64a/4dfZ+HVlO34r1uPDafxRkw909048\nO0TNk2NIONJ3Gp6jV70VBmHvCH5U4GnKCjuTN7DItMjkBSRC/qYC/RjhNG8U5raIonNuYW4hnmsp\nrwkHihPpNRmtKU1VBf+YcJpnRYhfkPFsT79DIsXnWCFqomsz2rkt9eYYhL1fEJtibXatS3Nnp/FS\nzSitXxGjukemI3Tm9zUm8nCF6nDumsL956W5iwpzxA5+VWfBtyDdUz9V70XeZWL3H5rRLheRM+9S\nzhF1Xm7bEP6Jn6fxnqJb/bM4SK2wOD1D3lnBl5Uj0uqWenMMyt465ilHpp7kNUWmimGvwtyJ4rBq\nbUZbiZ9k/EP4Gp5Mc3VU1X9Trl4ldl7+OeUSUXDe3oe8OeJlVe3rIvGd70ydafgB0drnDvtF4TxX\npvFWfE80FnnKvVSknvwFfz7xHo57sut+cWTQRm+OQdlbx8zab9/yDhKHgi+a+NYzJj7cfinjO1K0\n/D+u3f9ZEaF+JhxtheZdcIbYdYsbzYqWdZ34dHEHHlJOfb3IGxYv9S48KDqvzzTw7ici8xpR59yn\nM51VOD/N3Z3kPSA+1OZ4S/N3tBV96mWw9hKp+wn83URn+WTS04+8aUxjGtOYxjQ+aPgf1hMJbOhz\nlqIAAAAASUVORK5CYII=\n",
+      "text/latex": [
+       "$$1.5707963267949$$"
+      ],
+      "text/plain": [
+       "1.57079632679490"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "integrate(y, (theta, 0, sympy.pi)).evalf()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJMAAAASCAYAAABfCexoAAAABHNCSVQICAgIfAhkiAAABchJREFU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\nlqIAAAAASUVORK5CYII=\n",
+      "text/latex": [
+       "$$1.5707963267949$$"
+      ],
+      "text/plain": [
+       "1.57079632679490"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "integrate(y, (theta, 0, np.pi))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "根据牛顿莱布尼兹公式，这两个数值应该相等。\n",
+    "\n",
+    "产生不定积分对象："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAF8AAAAxCAYAAAC8snXfAAAABHNCSVQICAgIfAhkiAAABXZJREFU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\n",
+      "text/latex": [
+       "$$\\int \\sin^{2}{\\left (\\theta \\right )}\\, d\\theta$$"
+      ],
+      "text/plain": [
+       "⌠           \n",
+       "⎮    2      \n",
+       "⎮ sin (θ) dθ\n",
+       "⌡           "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Y_indef = sympy.Integral(y)\n",
+    "Y_indef"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'sympy.integrals.integrals.Integral'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print type(Y_indef)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "定积分："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAHIAAAA4CAYAAAAhHqx3AAAABHNCSVQICAgIfAhkiAAABoBJREFU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\nHA6HIwn+B7jjVvTlLJflAAAAAElFTkSuQmCC\n",
+      "text/latex": [
+       "$$\\int_{0}^{\\pi} \\sin^{2}{\\left (\\theta \\right )}\\, d\\theta$$"
+      ],
+      "text/plain": [
+       "π           \n",
+       "⌠           \n",
+       "⎮    2      \n",
+       "⎮ sin (θ) dθ\n",
+       "⌡           \n",
+       "0           "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Y_def = sympy.Integral(y, (theta, 0, sympy.pi))\n",
+    "Y_def"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "产生函数 $Y(x) = \\int_0^x sin^2(\\theta) d\\theta$，并将其向量化："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Y_raw = lambda x: integrate(y, (theta, 0, x))\n",
+    "Y = np.vectorize(Y_raw)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jB/zlL2EKvohIKVEiz2L06LA5xKhRcOqpUUcjIrI5XeyswR13wLXXhmVpO3aM\nOhoRkeopkVfDPUy7//vfw673e+8ddUQiIlumRJ5h3Tq46KKwrvjkybDLLlFHJCJSMyXyNMuWwdln\nQ6NGYdr9dttFHZGISHa62Jny/PPQoUOohY8ZoyQuIvFR8T3ydevCRhAjRoRb9+5RRyQikp+KTuTv\nvgv9+8OOO8LMmbDrrlFHJCKSv4osrbjDI49A585hyv1TTymJi0h8VVyPfMYMuPLKcGFz3Dj4znei\njkhEpG4qpkf+4Ydw3nlhc+TTTw9rpyiJi0g5KPtEvnp1uJjZvj3svju8/TZcfHEYYigiUg7KNp29\n9x4MGwb33guJRCip7LVX1FGJiBRe1h65mfU0s/lmtsDMfrWFNrelXp9tZh0KH2Zu1q2Dxx8P5ZOO\nHWHlSnjuOXjwQSVxESlfNSZyM2sI/AXoCRwE9DezAzPa9Ab2dff9gAuBO4sUa7XWrYNXXoGrrw7J\n+uabw5DCDz6A224r3FZsyWSyMAeKQJxjB8UfNcVf+rL1yDsD77j7u+6+DngYODGjTR9gBIC7TwGa\nm1nLgkeasn49TJkC118PPXvCzjvDT34SEvqECWHt8LPPhm22KeznxvmXIc6xg+KPmuIvfdlq5K2A\nxWmPPwAOz6FNa2BpbYP68sswPHDhQli0KNw23p8/H9q2DXXviy8OZZOddqrtJ4mIxF+2RO45Hsdy\neV+vXlBV9c3b2rWwalW4rV4dfgK0aAH77BOWkN1nHzj++HB///1DL1xERAJz33KuNrMuwGB375l6\nPBCocvcb0trcBSTd/eHU4/nA0e6+NONYuX4piIhIGnfP7Cx/Q7Ye+XRgPzNrC3wEnA70z2jzBHAJ\n8HAq8a/MTOK5BCIiIrVTYyJ39/VmdgnwLNAQGObub5rZRanX73b3cWbW28zeAT4Hzi961CIiskmN\npRURESl9RZ+in8uEolJlZsPNbKmZzYk6ltowszZmNtHM3jCzuWZ2WdQx5cPMmpjZFDObZWbzzOyP\nUceULzNraGYzzezJqGOpDTN718xeT/0bpkYdTz7MrLmZ/cPM3kz9/nSJOqZcmdn+qXO+8fZZTf9/\ni9ojT00oegvoDnwITAP6u/ubRfvQAjKz7wKrgZHufnDU8eTLzHYDdnP3WWbWFHgN+GFczj+AmW3r\n7mvMrBEwCbjK3SdFHVeuzOznQCdge3fvE3U8+TKzfwGd3H1F1LHky8xGAC+4+/DU78927v5Z1HHl\ny8waEPJWE/2+AAACU0lEQVRnZ3dfXF2bYvfIc5lQVLLc/SXg06jjqC13X+Lus1L3VwNvAntEG1V+\n3H1N6u5WhOs0sUkoZtYa6A3cy+ZDdOMkdrGbWTPgu+4+HML1vjgm8ZTuwMItJXEofiKvbrJQqyJ/\nplQjNfKoAzAl2kjyY2YNzGwWYYLZRHefF3VMeRgC/AKoijqQOnBggplNN7P/jjqYPHwL+MTM7jOz\nGWY21My2jTqoWuoHPFhTg2Incl1JLQGpsso/gJ+leuax4e5V7n4oYbbw98wsEXFIOTGzE4Bl7j6T\nGPZo03R19w5AL+CnqXJjHDQCOgJ3uHtHwoi6X0cbUv7MbCvgB8Dfa2pX7ET+IdAm7XEbQq9c6omZ\nNQYeA0a5+5io46mt1J/FTwNx2Q7kSKBPqsb8EHCMmY2MOKa8ufvHqZ+fAI8TyqVx8AHwgbtPSz3+\nByGxx00v4LXU+d+iYifyTROKUt8spxMmEEk9MDMDhgHz3P2WqOPJl5m1MLPmqfvbAMcBM6ONKjfu\nfrW7t3H3bxH+NP4/dz8n6rjyYWbbmtn2qfvbAT2AWIzgcvclwGIza5d6qjvwRoQh1VZ/QkegRkXd\nWGJLE4qK+ZmFZGYPAUcDO5vZYuC37n5fxGHloytwFvC6mW1MgAPdfXyEMeVjd2BE6qp9A+B+d38+\n4phqK45lxpbA46E/QCPgAXd/LtqQ8nIp8ECqE7mQmE1WTH15dgeyXpvQhCARkZgr+z07RUTKnRK5\niEjMKZGLiMScErmISMwpkYuIxJwSuYhIzCmRi4jEnBK5iEjM/T8bTzxBOTN6TgAAAABJRU5ErkJg\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb8bd320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = np.linspace(0, 2 * np.pi)\n",
+    "p = plt.plot(x, Y(x))\n",
+    "t = plt.title(r'$Y(x) = \\int_0^x sin^2(\\theta) d\\theta$')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 数值积分"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "数值积分：\n",
+    "\n",
+    "$$F(x) = \\lim_{n \\rightarrow \\infty} \\sum_{i=0}^{n-1} f(x_i)(x_{i+1}-x_i) \n",
+    "\\Rightarrow F(x) = \\int_{x_0}^{x_n} f(x) dx$$\n",
+    "\n",
+    "导入贝塞尔函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.special import jv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def f(x):\n",
+    "    return jv(2.5, x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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s6VEF3xna01fKQyUnJ9O6dWv27NnjVU98+qJTp05Rs2ZNli5dSuPGjW3H+R/a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\nUn7k/wDY8//eOSbGSgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbfe3a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(0, 10)\n",
+    "p = plt.plot(x, f(x), 'k-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `quad` 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Quadrature 积分的原理参见：\n",
+    "\n",
+    "http://en.wikipedia.org/wiki/Numerical_integration#Quadrature_rules_based_on_interpolating_functions\n",
+    "\n",
+    "quad 返回一个 (积分值，误差) 组成的元组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.integrate import quad\n",
+    "interval = [0, 6.5]\n",
+    "value, max_err = quad(f, *interval)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "积分值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.28474297234\n"
+     ]
+    }
+   ],
+   "source": [
+    "print value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "最大误差："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.34181853668e-09\n"
+     ]
+    }
+   ],
+   "source": [
+    "print max_err"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "积分区间图示，蓝色为正，红色为负："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "integral = 1.284742972\n",
+      "upper bound on error: 2.34e-09\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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fj/r3n8batV/iec+6jmKO29n4fA3o2vU910FMnHrhhReYP38+Y8aMOe5tQ745S0SaAq8A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\nR6zoG2NMHLGib4wxceT/AZhppPNX9o1QAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc126860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print \"integral = {:.9f}\".format(value)\n",
+    "print \"upper bound on error: {:.2e}\".format(max_err)\n",
+    "x = np.linspace(0, 10, 100)\n",
+    "p = plt.plot(x, f(x), 'k-')\n",
+    "x = np.linspace(0, 6.5, 45)\n",
+    "p = plt.fill_between(x, f(x), where=f(x)>0, color=\"blue\")\n",
+    "p = plt.fill_between(x, f(x), where=f(x)<0, color=\"red\", interpolate=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 积分到无穷"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from numpy import inf\n",
+    "interval = [0., inf]\n",
+    "\n",
+    "def g(x):\n",
+    "    return np.exp(-x ** 1/2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "upper bound on error: 7.2e-11\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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gixQRkThUYg+VmYWAFcAAYAMwH7jUObe8SJtTgWXOuR1mNhgY65zrddB51EMl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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc260dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "value, max_err = quad(g, *interval)\n",
+    "x = np.linspace(0, 10, 50)\n",
+    "fig = plt.figure(figsize=(10,3))\n",
+    "p = plt.plot(x, g(x), 'k-')\n",
+    "p = plt.fill_between(x, g(x))\n",
+    "plt.annotate(r\"$\\int_0^{\\infty}e^{-x^1/2}dx = $\" + \"{}\".format(value), (4, 0.6),\n",
+    "         fontsize=16)\n",
+    "print \"upper bound on error: {:.1e}\".format(max_err)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 双重积分"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "假设我们要进行如下的积分：\n",
+    "\n",
+    "$$ I_n = \\int \\limits_0^{\\infty} \\int \\limits_1^{\\infty} \\frac{e^{-xt}}{t^n}dt dx = \\frac{1}{n}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def h(x, t, n):\n",
+    "    \"\"\"core function, takes x, t, n\"\"\"\n",
+    "    return np.exp(-x * t) / (t ** n)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "一种方式是调用两次 `quad` 函数，不过这里 `quad` 的返回值不能向量化，所以使用了修饰符 `vectorize` 将其向量化："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from numpy import vectorize\n",
+    "@vectorize\n",
+    "def int_h_dx(t, n):\n",
+    "    \"\"\"Time integrand of h(x).\"\"\"\n",
+    "    return quad(h, 0, np.inf, args=(t, n))[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "@vectorize\n",
+    "def I_n(n):\n",
+    "    return quad(int_h_dx, 1, np.inf, args=(n))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 1.97,  1.  ,  0.5 ,  0.2 ]),\n",
+       " array([  9.804e-13,   1.110e-14,   5.551e-15,   2.220e-15]))"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "I_n([0.5, 1.0, 2.0, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "或者直接调用 `dblquad` 函数，并将积分参数传入，传入方式有多种，后传入的先进行积分："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.integrate import dblquad\n",
+    "@vectorize\n",
+    "def I(n):\n",
+    "    \"\"\"Same as I_n, but using the built-in dblquad\"\"\"\n",
+    "    x_lower = 0\n",
+    "    x_upper = np.inf\n",
+    "    return dblquad(h,\n",
+    "                   lambda t_lower: 1, lambda t_upper: np.inf,\n",
+    "                   x_lower, x_upper, args=(n,))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 1.97,  1.  ,  0.5 ,  0.2 ]),\n",
+       " array([  9.804e-13,   1.110e-14,   5.551e-15,   2.220e-15]))"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "I_n([0.5, 1.0, 2.0, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 采样点积分"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### trapz 方法 和 simps 方法"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.integrate import trapz, simps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`sin` 函数， `100` 个采样点和 `5` 个采样点："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x_s = np.linspace(0, np.pi, 5)\n",
+    "y_s = np.sin(x_s)\n",
+    "x = np.linspace(0, np.pi, 100)\n",
+    "y = np.sin(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Rw+lEZbm6uvLwww9zzz330KdPnzIHq3h4ePD//t//q9La/MI5yB2qDurKlSsMHDiQ7Oxs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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc1954e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = plt.plot(x, y, 'k:')\n",
+    "p = plt.plot(x_s, y_s, 'k+-')\n",
+    "p = plt.fill_between(x_s, y_s, color=\"gray\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "采用 [trapezoidal 方法](https://en.wikipedia.org/wiki/Trapezoidal_rule) 和 [simpson 方法](https://en.wikipedia.org/wiki/Simpson%27s_rule) 对这些采样点进行积分（函数积分为 2）："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trapezoidal Integration over 5 points : 1.896\n",
+      "Simpson Integration over 5 points : 2.005\n",
+      "Trapezoidal Integration over 100 points : 2.000\n"
+     ]
+    }
+   ],
+   "source": [
+    "result_s = trapz(y_s, x_s)\n",
+    "result_s_s = simps(y_s, x_s)\n",
+    "result = trapz(y, x)\n",
+    "print \"Trapezoidal Integration over 5 points : {:.3f}\".format(result_s)\n",
+    "print \"Simpson Integration over 5 points : {:.3f}\".format(result_s_s)\n",
+    "print \"Trapezoidal Integration over 100 points : {:.3f}\".format(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 使用 ufunc 进行积分"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Numpy` 中有很多 `ufunc` 对象："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ufunc"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(np.add)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accumulate(array, axis=0, dtype=None, out=None)\n",
+      "\n",
+      "Accumulate the result of applying the operator to all elements.\n",
+      "\n",
+      "For a one-dimensional array, accumulate produces results equivalent to::\n",
+      "\n",
+      "  r = np.empty(len(A))\n",
+      "  t = op.identity        # op = the ufunc being applied to A's  elements\n",
+      "  for i in range(len(A)):\n",
+      "      t = op(t, A[i])\n",
+      "      r[i] = t\n",
+      "  return r\n",
+      "\n",
+      "For example, add.accumulate() is equivalent to np.cumsum().\n",
+      "\n",
+      "For a multi-dimensional array, accumulate is applied along only one\n",
+      "axis (axis zero by default; see Examples below) so repeated use is\n",
+      "necessary if one wants to accumulate over multiple axes.\n",
+      "\n",
+      "Parameters\n",
+      "----------\n",
+      "array : array_like\n",
+      "    The array to act on.\n",
+      "axis : int, optional\n",
+      "    The axis along which to apply the accumulation; default is zero.\n",
+      "dtype : data-type code, optional\n",
+      "    The data-type used to represent the intermediate results. Defaults\n",
+      "    to the data-type of the output array if such is provided, or the\n",
+      "    the data-type of the input array if no output array is provided.\n",
+      "out : ndarray, optional\n",
+      "    A location into which the result is stored. If not provided a\n",
+      "    freshly-allocated array is returned.\n",
+      "\n",
+      "Returns\n",
+      "-------\n",
+      "r : ndarray\n",
+      "    The accumulated values. If `out` was supplied, `r` is a reference to\n",
+      "    `out`.\n",
+      "\n",
+      "Examples\n",
+      "--------\n",
+      "1-D array examples:\n",
+      "\n",
+      ">>> np.add.accumulate([2, 3, 5])\n",
+      "array([ 2,  5, 10])\n",
+      ">>> np.multiply.accumulate([2, 3, 5])\n",
+      "array([ 2,  6, 30])\n",
+      "\n",
+      "2-D array examples:\n",
+      "\n",
+      ">>> I = np.eye(2)\n",
+      ">>> I\n",
+      "array([[ 1.,  0.],\n",
+      "       [ 0.,  1.]])\n",
+      "\n",
+      "Accumulate along axis 0 (rows), down columns:\n",
+      "\n",
+      ">>> np.add.accumulate(I, 0)\n",
+      "array([[ 1.,  0.],\n",
+      "       [ 1.,  1.]])\n",
+      ">>> np.add.accumulate(I) # no axis specified = axis zero\n",
+      "array([[ 1.,  0.],\n",
+      "       [ 1.,  1.]])\n",
+      "\n",
+      "Accumulate along axis 1 (columns), through rows:\n",
+      "\n",
+      ">>> np.add.accumulate(I, 1)\n",
+      "array([[ 1.,  1.],\n",
+      "       [ 0.,  1.]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "np.info(np.add.accumulate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`np.add.accumulate` 相当于 `cumsum` ："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "result_np = np.add.accumulate(y) * (x[1] - x[0]) - (x[1] - x[0]) / 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/uQfabHfnV93vKvGra9fyDzep0Ui3ff4Kb2xamnOJid7Pzp2913HO+5mY6ERE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\nRoFfRCTGKPCLiMSY/weoVZxsAST89wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc612a20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = plt.plot(x, - np.cos(x) + np.cos(0), 'rx')\n",
+    "p = plt.plot(x, result_np)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 速度比较"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "计算积分：$$\\int_0^x sin \\theta d\\theta$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import sympy\n",
+    "from sympy.abc import x, theta\n",
+    "sympy_x = x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x = np.linspace(0, 20 * np.pi, 1e+4)\n",
+    "y = np.sin(x)\n",
+    "sympy_y = vectorize(lambda x: sympy.integrate(sympy.sin(theta), (theta, 0, x)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`numpy` 方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The slowest run took 4.32 times longer than the fastest. This could mean that an intermediate result is being cached \n",
+      "10000 loops, best of 3: 56.2 µs per loop\n",
+      "-2.34138044756e-17\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit np.add.accumulate(y) * (x[1] - x[0])\n",
+    "y0 = np.add.accumulate(y) * (x[1] - x[0])\n",
+    "print y0[-1] "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`quad` 方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10000 loops, best of 3: 40.5 µs per loop\n",
+      "result =  3.43781337153e-15\n",
+      "number of evaluations 21\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit quad(np.sin, 0, 20 * np.pi)\n",
+    "y2 = quad(np.sin, 0, 20 * np.pi, full_output=True)\n",
+    "print \"result = \", y2[0]\n",
+    "print \"number of evaluations\", y2[-1]['neval']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`trapz` 方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10000 loops, best of 3: 105 µs per loop\n",
+      "-4.4408920985e-16\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit trapz(y, x)\n",
+    "y1 = trapz(y, x)\n",
+    "print y1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`simps` 方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1000 loops, best of 3: 801 µs per loop\n",
+      "3.28428554968e-16\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit simps(y, x)\n",
+    "y3 = simps(y, x)\n",
+    "print y3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`sympy` 积分方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100 loops, best of 3: 6.86 ms per loop\n",
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit sympy_y(20 * np.pi)\n",
+    "y4 = sympy_y(20 * np.pi)\n",
+    "print y4"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/04. scipy/04.07 ODEs.ipynb b/04-scipy/04.07-ODEs.ipynb
similarity index 99%
rename from 04. scipy/04.07 ODEs.ipynb
rename to 04-scipy/04.07-ODEs.ipynb
index 5b665452..6dcb1069 100644
--- a/04. scipy/04.07 ODEs.ipynb	
+++ b/04-scipy/04.07-ODEs.ipynb
@@ -1,258 +1,258 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 解微分方程"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pylab inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 积分求解"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 简单的例子"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$$\\frac{dy}{dt} = sin(t)$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def dy_dt(y, t):\n",
-    "    return np.sin(t)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "积分求解："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.integrate import odeint\n",
-    "\n",
-    "t = np.linspace(0, 2*pi, 100)\n",
-    "\n",
-    "result = odeint(dy_dt, 0, t)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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U69atlVJKGY1G9eGHH0Yfo1GjRq8dt2PHjurMmTOx9u3bt09NmTJFKaVUYGCgsrW1VX4v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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa183a58>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = figure(figsize=(12,4))\n",
-    "p = plot(t, result, \"rx\", label=r\"$\\int_{0}^{x}sin(t) dt $\")\n",
-    "p = plot(t, -cos(t) + cos(0), label=r\"$cos(0) - cos(t)$\")\n",
-    "p = plot(t, dy_dt(0, t), \"g-\", label=r\"$\\frac{dy}{dt}(t)$\")\n",
-    "l = legend(loc=\"upper right\")\n",
-    "xl = xlabel(\"t\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 高阶微分方程"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "抛物运动（竖直方向）：\n",
-    "\n",
-    "$$\n",
-    "\\frac{d^2x}{dt^2} = g - \\frac{D}{m}\\frac{dx}{dt}\n",
-    "$$\n",
-    "\n",
-    "改写成如下形式：\n",
-    "\n",
-    "$$y = \\left[x, \\frac{dx}{dt}\\right] $$\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "\\frac{dy_0}{dt} &= y_1 \\\\\\\n",
-    "\\frac{dy_1}{dt} &= -g - \\frac{D}{m} y_1 \\\\\\\n",
-    "\\end{aligned}\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def dy_dt(y, t):\n",
-    "    \"\"\"Governing equations for projectile motion with drag.\n",
-    "    y[0] = position\n",
-    "    y[1] = velocity\n",
-    "    g = gravity (m/s2)\n",
-    "    D = drag (1/s) = force/velocity\n",
-    "    m = mass (kg)\n",
-    "    \"\"\"\n",
-    "    g = -9.8\n",
-    "    D = 0.1\n",
-    "    m = 0.15\n",
-    "    dy1 = g - (D/m) * y[1]\n",
-    "    dy0 = y[1] if y[0] >= 0 else 0.\n",
-    "    return [dy0, dy1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "position_0 = 0.\n",
-    "velocity_0 = 100\n",
-    "t = linspace(0, 12, 100)\n",
-    "y = odeint(dy_dt, [position_0, velocity_0], t)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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B6zFj4NRT82PwOu8TxJw50LFj7ChERDavefNQUHTVqrCw7tNPY0e0aXmdINau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\nSUoJQkREklKCEBGRpP4fPzntqp99LaMAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa183438>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "p = plot(t, y[:,0])\n",
-    "yl = ylabel(\"Height (m)\")\n",
-    "xl = xlabel(\"Time (s)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Integration successful.\n",
-      "['hu', 'imxer', 'leniw', 'lenrw', 'message', 'mused', 'nfe', 'nje', 'nqu', 'nst', 'tcur', 'tolsf', 'tsw']\n",
-      "cumulative number of function evaluations at each calculated point: [ 45  49  51  53  55  59  61  61  63  65  67  67  69  71  73  73  75  77\n",
-      "  77  79  79  81  81  83  85  85  87  87  89  89  91  91  93  95  95  97\n",
-      "  97  99  99 101 101 103 103 105 107 107 109 109 111 111 113 113 115 115\n",
-      " 117 117 119 119 121 121 123 123 123 125 125 127 127 129 129 131 131 131\n",
-      " 133 133 135 135 135 137 137 139 139 139 141 141 143 143 143 145 145 147\n",
-      " 147 149 149 149 154 158 274 280 280]\n",
-      "cumulative number of time steps [ 20  22  23  24  25  27  28  28  29  30  31  31  32  33  34  34  35  36\n",
-      "  36  37  37  38  38  39  40  40  41  41  42  42  43  43  44  45  45  46\n",
-      "  46  47  47  48  48  49  49  50  51  51  52  52  53  53  54  54  55  55\n",
-      "  56  56  57  57  58  58  59  59  59  60  60  61  61  62  62  63  63  63\n",
-      "  64  64  65  65  65  66  66  67  67  67  68  68  69  69  69  70  70  71\n",
-      "  71  72  72  72  73  75 130 133 133]\n"
-     ]
-    }
-   ],
-   "source": [
-    "y, infodict = odeint(dy_dt, [position_0, velocity_0], t, full_output=True, printmessg=True, )\n",
-    "print sorted(infodict.keys())\n",
-    "print \"cumulative number of function evaluations at each calculated point:\", infodict['nfe']\n",
-    "print \"cumulative number of time steps\", infodict['nst']"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 解微分方程"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 积分求解"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 简单的例子"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$\\frac{dy}{dt} = sin(t)$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def dy_dt(y, t):\n",
+    "    return np.sin(t)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "积分求解："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.integrate import odeint\n",
+    "\n",
+    "t = np.linspace(0, 2*pi, 100)\n",
+    "\n",
+    "result = odeint(dy_dt, 0, t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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V6mczdVjCQqxbt47q1auTO3duALJkycLSpUtj7bt69SqZM2emRYsWHD58mIoV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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa183a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = figure(figsize=(12,4))\n",
+    "p = plot(t, result, \"rx\", label=r\"$\\int_{0}^{x}sin(t) dt $\")\n",
+    "p = plot(t, -cos(t) + cos(0), label=r\"$cos(0) - cos(t)$\")\n",
+    "p = plot(t, dy_dt(0, t), \"g-\", label=r\"$\\frac{dy}{dt}(t)$\")\n",
+    "l = legend(loc=\"upper right\")\n",
+    "xl = xlabel(\"t\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 高阶微分方程"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "抛物运动（竖直方向）：\n",
+    "\n",
+    "$$\n",
+    "\\frac{d^2x}{dt^2} = g - \\frac{D}{m}\\frac{dx}{dt}\n",
+    "$$\n",
+    "\n",
+    "改写成如下形式：\n",
+    "\n",
+    "$$y = \\left[x, \\frac{dx}{dt}\\right] $$\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\frac{dy_0}{dt} &= y_1 \\\\\\\n",
+    "\\frac{dy_1}{dt} &= -g - \\frac{D}{m} y_1 \\\\\\\n",
+    "\\end{aligned}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def dy_dt(y, t):\n",
+    "    \"\"\"Governing equations for projectile motion with drag.\n",
+    "    y[0] = position\n",
+    "    y[1] = velocity\n",
+    "    g = gravity (m/s2)\n",
+    "    D = drag (1/s) = force/velocity\n",
+    "    m = mass (kg)\n",
+    "    \"\"\"\n",
+    "    g = -9.8\n",
+    "    D = 0.1\n",
+    "    m = 0.15\n",
+    "    dy1 = g - (D/m) * y[1]\n",
+    "    dy0 = y[1] if y[0] >= 0 else 0.\n",
+    "    return [dy0, dy1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "position_0 = 0.\n",
+    "velocity_0 = 100\n",
+    "t = linspace(0, 12, 100)\n",
+    "y = odeint(dy_dt, [position_0, velocity_0], t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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B6zFj4NRT82PwOu8TxJw50LFj7ChERDavefNQUHTVqrCw7tNPY0e0aXmdINau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\nSUoJQkREklKCEBGRpP4fPzntqp99LaMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa183438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = plot(t, y[:,0])\n",
+    "yl = ylabel(\"Height (m)\")\n",
+    "xl = xlabel(\"Time (s)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Integration successful.\n",
+      "['hu', 'imxer', 'leniw', 'lenrw', 'message', 'mused', 'nfe', 'nje', 'nqu', 'nst', 'tcur', 'tolsf', 'tsw']\n",
+      "cumulative number of function evaluations at each calculated point: [ 45  49  51  53  55  59  61  61  63  65  67  67  69  71  73  73  75  77\n",
+      "  77  79  79  81  81  83  85  85  87  87  89  89  91  91  93  95  95  97\n",
+      "  97  99  99 101 101 103 103 105 107 107 109 109 111 111 113 113 115 115\n",
+      " 117 117 119 119 121 121 123 123 123 125 125 127 127 129 129 131 131 131\n",
+      " 133 133 135 135 135 137 137 139 139 139 141 141 143 143 143 145 145 147\n",
+      " 147 149 149 149 154 158 274 280 280]\n",
+      "cumulative number of time steps [ 20  22  23  24  25  27  28  28  29  30  31  31  32  33  34  34  35  36\n",
+      "  36  37  37  38  38  39  40  40  41  41  42  42  43  43  44  45  45  46\n",
+      "  46  47  47  48  48  49  49  50  51  51  52  52  53  53  54  54  55  55\n",
+      "  56  56  57  57  58  58  59  59  59  60  60  61  61  62  62  63  63  63\n",
+      "  64  64  65  65  65  66  66  67  67  67  68  68  69  69  69  70  70  71\n",
+      "  71  72  72  72  73  75 130 133 133]\n"
+     ]
+    }
+   ],
+   "source": [
+    "y, infodict = odeint(dy_dt, [position_0, velocity_0], t, full_output=True, printmessg=True, )\n",
+    "print sorted(infodict.keys())\n",
+    "print \"cumulative number of function evaluations at each calculated point:\", infodict['nfe']\n",
+    "print \"cumulative number of time steps\", infodict['nst']"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/04. scipy/04.08 sparse matrix.ipynb b/04-scipy/04.08-sparse-matrix.ipynb
similarity index 94%
rename from 04. scipy/04.08 sparse matrix.ipynb
rename to 04-scipy/04.08-sparse-matrix.ipynb
index 82e4a923..f24de6bb 100644
--- a/04. scipy/04.08 sparse matrix.ipynb	
+++ b/04-scipy/04.08-sparse-matrix.ipynb
@@ -1,622 +1,622 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 稀疏矩阵"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Scipy` 提供了稀疏矩阵的支持（`scipy.sparse`）。\n",
-    "\n",
-    "稀疏矩阵主要使用 位置 + 值 的方法来存储矩阵的非零元素，根据存储和使用方式的不同，有如下几种类型的稀疏矩阵：\n",
-    "\n",
-    "类型|描述\n",
-    "---|----\n",
-    "`bsr_matrix(arg1[, shape, dtype, copy, blocksize])`\t| Block Sparse Row matrix\n",
-    "`coo_matrix(arg1[, shape, dtype, copy])`\t| A sparse matrix in COOrdinate format.\n",
-    "`csc_matrix(arg1[, shape, dtype, copy])`\t| Compressed Sparse Column matrix\n",
-    "`csr_matrix(arg1[, shape, dtype, copy])`\t| Compressed Sparse Row matrix\n",
-    "`dia_matrix(arg1[, shape, dtype, copy])`\t| Sparse matrix with DIAgonal storage\n",
-    "`dok_matrix(arg1[, shape, dtype, copy])`\t| Dictionary Of Keys based sparse matrix.\n",
-    "`lil_matrix(arg1[, shape, dtype, copy])`\t| Row-based linked list sparse matrix\n",
-    "\n",
-    "在这些存储格式中：\n",
-    "\n",
-    "- COO 格式在构建矩阵时比较高效\n",
-    "- CSC 和 CSR 格式在乘法计算时比较高效"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 构建稀疏矩阵"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.sparse import *\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "创建一个空的稀疏矩阵："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<2x3 sparse matrix of type '<type 'numpy.float64'>'\n",
-       "\twith 0 stored elements in COOrdinate format>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coo_matrix((2,3))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "也可以使用一个已有的矩阵或数组或列表中创建新矩阵："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  (0, 0)\t1\n",
-      "  (0, 1)\t2\n",
-      "  (1, 2)\t3\n",
-      "  (2, 0)\t4\n",
-      "  (2, 2)\t5\n"
-     ]
-    }
-   ],
-   "source": [
-    "A = coo_matrix([[1,2,0],[0,0,3],[4,0,5]])\n",
-    "print A"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不同格式的稀疏矩阵可以相互转化："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "scipy.sparse.coo.coo_matrix"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(A)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "scipy.sparse.csr.csr_matrix"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "B = A.tocsr()\n",
-    "type(B)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以转化为普通矩阵："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "matrix([[1, 2, 0],\n",
-       "        [0, 0, 3],\n",
-       "        [4, 0, 5]])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "C = A.todense()\n",
-    "C"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "与向量的乘法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1, -3, -1])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v = np.array([1,0,-1])\n",
-    "A.dot(v)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "还可以传入一个 `(data, (row, col))` 的元组来构建稀疏矩阵："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "I = np.array([0,3,1,0])\n",
-    "J = np.array([0,3,1,2])\n",
-    "V = np.array([4,5,7,9])\n",
-    "A = coo_matrix((V,(I,J)),shape=(4,4))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  (0, 0)\t4\n",
-      "  (3, 3)\t5\n",
-      "  (1, 1)\t7\n",
-      "  (0, 2)\t9\n"
-     ]
-    }
-   ],
-   "source": [
-    "print A"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "COO 格式的稀疏矩阵在构建的时候只是简单的将坐标和值加到后面，对于重复的坐标不进行处理："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  (0, 0)\t1\n",
-      "  (0, 2)\t1\n",
-      "  (1, 1)\t1\n",
-      "  (3, 3)\t1\n",
-      "  (1, 1)\t1\n",
-      "  (0, 0)\t1\n",
-      "  (0, 0)\t1\n"
-     ]
-    }
-   ],
-   "source": [
-    "I = np.array([0,0,1,3,1,0,0])\n",
-    "J = np.array([0,2,1,3,1,0,0])\n",
-    "V = np.array([1,1,1,1,1,1,1])\n",
-    "B = coo_matrix((V,(I,J)),shape=(4,4))\n",
-    "print B"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "转换成 CSR 格式会自动将相同坐标的值合并："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  (0, 0)\t3\n",
-      "  (0, 2)\t1\n",
-      "  (1, 1)\t2\n",
-      "  (3, 3)\t1\n"
-     ]
-    }
-   ],
-   "source": [
-    "C = B.tocsr()\n",
-    "print C"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 求解微分方程"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.sparse import lil_matrix\n",
-    "from scipy.sparse.linalg import spsolve\n",
-    "from numpy.linalg import solve, norm\n",
-    "from numpy.random import rand"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "构建 `1000 x 1000` 的稀疏矩阵："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "A = lil_matrix((1000, 1000))\n",
-    "A[0, :100] = rand(100)\n",
-    "A[1, 100:200] = A[0, :100]\n",
-    "A.setdiag(rand(1000))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "转化为 CSR 之后，用 `spsolve` 求解 $Ax=b$："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "A = A.tocsr()\n",
-    "b = rand(1000)\n",
-    "x = spsolve(A, b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "转化成正常数组之后求解："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "x_ = solve(A.toarray(), b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看误差："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6.4310987107687431e-13"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "err = norm(x-x_)\n",
-    "err"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## sparse.find 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "返回一个三元组，表示稀疏矩阵中非零元素的 `(row, col, value)`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0 0 1 3] [0 2 1 3] [3 1 2 1]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy import sparse\n",
-    "\n",
-    "row, col, val = sparse.find(C)\n",
-    "print row, col, val"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## sparse.issparse 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看一个对象是否为稀疏矩阵："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sparse.issparse(B)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "或者"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sparse.isspmatrix(B.todense())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "还可以查询是否为指定格式的稀疏矩阵："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sparse.isspmatrix_coo(B)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sparse.isspmatrix_csr(B)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 稀疏矩阵"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Scipy` 提供了稀疏矩阵的支持（`scipy.sparse`）。\n",
+    "\n",
+    "稀疏矩阵主要使用 位置 + 值 的方法来存储矩阵的非零元素，根据存储和使用方式的不同，有如下几种类型的稀疏矩阵：\n",
+    "\n",
+    "类型|描述\n",
+    "---|----\n",
+    "`bsr_matrix(arg1[, shape, dtype, copy, blocksize])`\t| Block Sparse Row matrix\n",
+    "`coo_matrix(arg1[, shape, dtype, copy])`\t| A sparse matrix in COOrdinate format.\n",
+    "`csc_matrix(arg1[, shape, dtype, copy])`\t| Compressed Sparse Column matrix\n",
+    "`csr_matrix(arg1[, shape, dtype, copy])`\t| Compressed Sparse Row matrix\n",
+    "`dia_matrix(arg1[, shape, dtype, copy])`\t| Sparse matrix with DIAgonal storage\n",
+    "`dok_matrix(arg1[, shape, dtype, copy])`\t| Dictionary Of Keys based sparse matrix.\n",
+    "`lil_matrix(arg1[, shape, dtype, copy])`\t| Row-based linked list sparse matrix\n",
+    "\n",
+    "在这些存储格式中：\n",
+    "\n",
+    "- COO 格式在构建矩阵时比较高效\n",
+    "- CSC 和 CSR 格式在乘法计算时比较高效"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 构建稀疏矩阵"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.sparse import *\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "创建一个空的稀疏矩阵："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<2x3 sparse matrix of type '<type 'numpy.float64'>'\n",
+       "\twith 0 stored elements in COOrdinate format>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coo_matrix((2,3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "也可以使用一个已有的矩阵或数组或列表中创建新矩阵："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  (0, 0)\t1\n",
+      "  (0, 1)\t2\n",
+      "  (1, 2)\t3\n",
+      "  (2, 0)\t4\n",
+      "  (2, 2)\t5\n"
+     ]
+    }
+   ],
+   "source": [
+    "A = coo_matrix([[1,2,0],[0,0,3],[4,0,5]])\n",
+    "print A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不同格式的稀疏矩阵可以相互转化："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "scipy.sparse.coo.coo_matrix"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "scipy.sparse.csr.csr_matrix"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "B = A.tocsr()\n",
+    "type(B)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以转化为普通矩阵："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "matrix([[1, 2, 0],\n",
+       "        [0, 0, 3],\n",
+       "        [4, 0, 5]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C = A.todense()\n",
+    "C"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "与向量的乘法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1, -3, -1])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v = np.array([1,0,-1])\n",
+    "A.dot(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "还可以传入一个 `(data, (row, col))` 的元组来构建稀疏矩阵："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "I = np.array([0,3,1,0])\n",
+    "J = np.array([0,3,1,2])\n",
+    "V = np.array([4,5,7,9])\n",
+    "A = coo_matrix((V,(I,J)),shape=(4,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  (0, 0)\t4\n",
+      "  (3, 3)\t5\n",
+      "  (1, 1)\t7\n",
+      "  (0, 2)\t9\n"
+     ]
+    }
+   ],
+   "source": [
+    "print A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "COO 格式的稀疏矩阵在构建的时候只是简单的将坐标和值加到后面，对于重复的坐标不进行处理："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  (0, 0)\t1\n",
+      "  (0, 2)\t1\n",
+      "  (1, 1)\t1\n",
+      "  (3, 3)\t1\n",
+      "  (1, 1)\t1\n",
+      "  (0, 0)\t1\n",
+      "  (0, 0)\t1\n"
+     ]
+    }
+   ],
+   "source": [
+    "I = np.array([0,0,1,3,1,0,0])\n",
+    "J = np.array([0,2,1,3,1,0,0])\n",
+    "V = np.array([1,1,1,1,1,1,1])\n",
+    "B = coo_matrix((V,(I,J)),shape=(4,4))\n",
+    "print B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "转换成 CSR 格式会自动将相同坐标的值合并："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  (0, 0)\t3\n",
+      "  (0, 2)\t1\n",
+      "  (1, 1)\t2\n",
+      "  (3, 3)\t1\n"
+     ]
+    }
+   ],
+   "source": [
+    "C = B.tocsr()\n",
+    "print C"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 求解微分方程"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.sparse import lil_matrix\n",
+    "from scipy.sparse.linalg import spsolve\n",
+    "from numpy.linalg import solve, norm\n",
+    "from numpy.random import rand"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "构建 `1000 x 1000` 的稀疏矩阵："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "A = lil_matrix((1000, 1000))\n",
+    "A[0, :100] = rand(100)\n",
+    "A[1, 100:200] = A[0, :100]\n",
+    "A.setdiag(rand(1000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "转化为 CSR 之后，用 `spsolve` 求解 $Ax=b$："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "A = A.tocsr()\n",
+    "b = rand(1000)\n",
+    "x = spsolve(A, b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "转化成正常数组之后求解："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x_ = solve(A.toarray(), b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看误差："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.4310987107687431e-13"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "err = norm(x-x_)\n",
+    "err"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## sparse.find 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "返回一个三元组，表示稀疏矩阵中非零元素的 `(row, col, value)`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 0 1 3] [0 2 1 3] [3 1 2 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy import sparse\n",
+    "\n",
+    "row, col, val = sparse.find(C)\n",
+    "print row, col, val"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## sparse.issparse 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看一个对象是否为稀疏矩阵："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sparse.issparse(B)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "或者"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sparse.isspmatrix(B.todense())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "还可以查询是否为指定格式的稀疏矩阵："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sparse.isspmatrix_coo(B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sparse.isspmatrix_csr(B)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/04. scipy/04.09 linear algbra.ipynb b/04-scipy/04.09-linear-algbra.ipynb
similarity index 100%
rename from 04. scipy/04.09 linear algbra.ipynb
rename to 04-scipy/04.09-linear-algbra.ipynb
diff --git a/04. scipy/04.10 sparse linear algebra.ipynb b/04-scipy/04.10-sparse-linear-algebra.ipynb
similarity index 100%
rename from 04. scipy/04.10 sparse linear algebra.ipynb
rename to 04-scipy/04.10-sparse-linear-algebra.ipynb
diff --git a/04. scipy/JANAF_CH4.txt b/04-scipy/JANAF_CH4.txt
similarity index 98%
rename from 04. scipy/JANAF_CH4.txt
rename to 04-scipy/JANAF_CH4.txt
index 6fc1d022..c39c4e75 100644
--- a/04. scipy/JANAF_CH4.txt	
+++ b/04-scipy/JANAF_CH4.txt
@@ -1,67 +1,67 @@
-Methane (CH4)	C1H4(g)
-T(K)	Cp	S	-[G-H(Tr)]/T	H-H(Tr)	delta-f H	delta-f G	log Kf
-0	0.	0.	INFINITE	-10.024	-66.911	-66.911	INFINITE
-100	33.258	149.500	216.485	-6.698	-69.644	-64.353	33.615
-200	33.473	172.577	189.418	-3.368	-72.027	-58.161	15.190
-250	34.216	180.113	186.829	-1.679	-73.426	-54.536	11.395
-298.15	35.639	186.251	186.251	0.	-74.873	-50.768	8.894
-300	35.708	186.472	186.252	0.066	-74.929	-50.618	8.813
-350	37.874	192.131	186.694	1.903	-76.461	-46.445	6.932
-400	40.500	197.356	187.704	3.861	-77.969	-42.054	5.492
-450	43.374	202.291	189.053	5.957	-79.422	-37.476	4.350
-500	46.342	207.014	190.614	8.200	-80.802	-32.741	3.420
-600	52.227	215.987	194.103	13.130	-83.308	-22.887	1.993
-700	57.794	224.461	197.840	18.635	-85.452	-12.643	0.943
-800	62.932	232.518	201.675	24.675	-87.238	-2.115	0.138
-900	67.601	240.205	205.532	31.205	-88.692	8.616	-0.500
-1000	71.795	247.549	209.370	38.179	-89.849	19.492	-1.018
-1100	75.529	254.570	213.162	45.549	-90.750	30.472	-1.447
-1200	78.833	261.287	216.895	53.270	-91.437	41.524	-1.807
-1300	81.744	267.714	220.558	61.302	-91.945	52.626	-2.115
-1400	84.305	273.868	224.148	69.608	-92.308	63.761	-2.379
-1500	86.556	279.763	227.660	78.153	-92.553	74.918	-2.609
-1600	88.537	285.413	231.095	86.910	-92.703	86.088	-2.810
-1700	90.283	290.834	234.450	95.853	-92.780	97.265	-2.989
-1800	91.824	296.039	237.728	104.960	-92.797	108.445	-3.147
-1900	93.188	301.041	240.930	114.212	-92.770	119.624	-3.289
-2000	94.399	305.853	244.057	123.592	-92.709	130.802	-3.416
-2100	95.477	310.485	247.110	133.087	-92.624	141.975	-3.531
-2200	96.439	314.949	250.093	142.684	-92.521	153.144	-3.636
-2300	97.301	319.255	253.007	152.371	-92.409	164.308	-3.732
-2400	98.075	323.413	255.854	162.141	-92.291	175.467	-3.819
-2500	98.772	327.431	258.638	171.984	-92.174	186.622	-3.899
-2600	99.401	331.317	261.359	181.893	-92.060	197.771	-3.973
-2700	99.971	335.080	264.020	191.862	-91.954	208.916	-4.042
-2800	100.489	338.725	266.623	201.885	-91.857	220.058	-4.105
-2900	100.960	342.260	269.171	211.958	-91.773	231.196	-4.164
-3000	101.389	345.690	271.664	222.076	-91.705	242.332	-4.219
-3100	101.782	349.021	274.106	232.235	-91.653	253.465	-4.271
-3200	102.143	352.258	276.498	242.431	-91.621	264.598	-4.319
-3300	102.474	355.406	278.842	252.662	-91.609	275.730	-4.364
-3400	102.778	358.470	281.139	262.925	-91.619	286.861	-4.407
-3500	103.060	361.453	283.391	273.217	-91.654	297.993	-4.447
-3600	103.319	364.360	285.600	283.536	-91.713	309.127	-4.485
-3700	103.560	367.194	287.767	293.881	-91.798	320.262	-4.521
-3800	103.783	369.959	289.894	304.248	-91.911	331.401	-4.555
-3900	103.990	372.658	291.982	314.637	-92.051	342.542	-4.588
-4000	104.183	375.293	294.032	325.045	-92.222	353.687	-4.619
-4100	104.363	377.868	296.045	335.473	-92.422	364.838	-4.648
-4200	104.531	380.385	298.023	345.918	-92.652	375.993	-4.676
-4300	104.688	382.846	299.967	356.379	-92.914	387.155	-4.703
-4400	104.834	385.255	301.879	366.855	-93.208	398.322	-4.729
-4500	104.972	387.612	303.758	377.345	-93.533	409.497	-4.753
-4600	105.101	389.921	305.606	387.849	-93.891	420.679	-4.777
-4700	105.223	392.182	307.424	398.365	-94.281	431.869	-4.800
-4800	105.337	394.399	309.213	408.893	-94.702	443.069	-4.822
-4900	105.445	396.572	310.973	419.432	-95.156	454.277	-4.843
-5000	105.546	398.703	312.707	429.982	-95.641	465.495	-4.863
-5100	105.642	400.794	314.414	440.541	-96.157	476.722	-4.883
-5200	105.733	402.847	316.095	451.110	-96.703	487.961	-4.902
-5300	105.818	404.861	317.750	461.688	-97.278	499.210	-4.920
-5400	105.899	406.840	319.382	472.274	-97.882	510.470	-4.938
-5500	105.976	408.784	320.990	482.867	-98.513	521.741	-4.955
-5600	106.049	410.694	322.575	493.469	-99.170	533.025	-4.972
-5700	106.118	412.572	324.137	504.077	-99.852	544.320	-4.988
-5800	106.184	414.418	325.678	514.692	-100.557	555.628	-5.004
-5900	106.247	416.234	327.197	525.314	-101.284	566.946	-5.019
-6000	106.306	418.020	328.696	535.942	-102.032	578.279	-5.034
+Methane (CH4)	C1H4(g)
+T(K)	Cp	S	-[G-H(Tr)]/T	H-H(Tr)	delta-f H	delta-f G	log Kf
+0	0.	0.	INFINITE	-10.024	-66.911	-66.911	INFINITE
+100	33.258	149.500	216.485	-6.698	-69.644	-64.353	33.615
+200	33.473	172.577	189.418	-3.368	-72.027	-58.161	15.190
+250	34.216	180.113	186.829	-1.679	-73.426	-54.536	11.395
+298.15	35.639	186.251	186.251	0.	-74.873	-50.768	8.894
+300	35.708	186.472	186.252	0.066	-74.929	-50.618	8.813
+350	37.874	192.131	186.694	1.903	-76.461	-46.445	6.932
+400	40.500	197.356	187.704	3.861	-77.969	-42.054	5.492
+450	43.374	202.291	189.053	5.957	-79.422	-37.476	4.350
+500	46.342	207.014	190.614	8.200	-80.802	-32.741	3.420
+600	52.227	215.987	194.103	13.130	-83.308	-22.887	1.993
+700	57.794	224.461	197.840	18.635	-85.452	-12.643	0.943
+800	62.932	232.518	201.675	24.675	-87.238	-2.115	0.138
+900	67.601	240.205	205.532	31.205	-88.692	8.616	-0.500
+1000	71.795	247.549	209.370	38.179	-89.849	19.492	-1.018
+1100	75.529	254.570	213.162	45.549	-90.750	30.472	-1.447
+1200	78.833	261.287	216.895	53.270	-91.437	41.524	-1.807
+1300	81.744	267.714	220.558	61.302	-91.945	52.626	-2.115
+1400	84.305	273.868	224.148	69.608	-92.308	63.761	-2.379
+1500	86.556	279.763	227.660	78.153	-92.553	74.918	-2.609
+1600	88.537	285.413	231.095	86.910	-92.703	86.088	-2.810
+1700	90.283	290.834	234.450	95.853	-92.780	97.265	-2.989
+1800	91.824	296.039	237.728	104.960	-92.797	108.445	-3.147
+1900	93.188	301.041	240.930	114.212	-92.770	119.624	-3.289
+2000	94.399	305.853	244.057	123.592	-92.709	130.802	-3.416
+2100	95.477	310.485	247.110	133.087	-92.624	141.975	-3.531
+2200	96.439	314.949	250.093	142.684	-92.521	153.144	-3.636
+2300	97.301	319.255	253.007	152.371	-92.409	164.308	-3.732
+2400	98.075	323.413	255.854	162.141	-92.291	175.467	-3.819
+2500	98.772	327.431	258.638	171.984	-92.174	186.622	-3.899
+2600	99.401	331.317	261.359	181.893	-92.060	197.771	-3.973
+2700	99.971	335.080	264.020	191.862	-91.954	208.916	-4.042
+2800	100.489	338.725	266.623	201.885	-91.857	220.058	-4.105
+2900	100.960	342.260	269.171	211.958	-91.773	231.196	-4.164
+3000	101.389	345.690	271.664	222.076	-91.705	242.332	-4.219
+3100	101.782	349.021	274.106	232.235	-91.653	253.465	-4.271
+3200	102.143	352.258	276.498	242.431	-91.621	264.598	-4.319
+3300	102.474	355.406	278.842	252.662	-91.609	275.730	-4.364
+3400	102.778	358.470	281.139	262.925	-91.619	286.861	-4.407
+3500	103.060	361.453	283.391	273.217	-91.654	297.993	-4.447
+3600	103.319	364.360	285.600	283.536	-91.713	309.127	-4.485
+3700	103.560	367.194	287.767	293.881	-91.798	320.262	-4.521
+3800	103.783	369.959	289.894	304.248	-91.911	331.401	-4.555
+3900	103.990	372.658	291.982	314.637	-92.051	342.542	-4.588
+4000	104.183	375.293	294.032	325.045	-92.222	353.687	-4.619
+4100	104.363	377.868	296.045	335.473	-92.422	364.838	-4.648
+4200	104.531	380.385	298.023	345.918	-92.652	375.993	-4.676
+4300	104.688	382.846	299.967	356.379	-92.914	387.155	-4.703
+4400	104.834	385.255	301.879	366.855	-93.208	398.322	-4.729
+4500	104.972	387.612	303.758	377.345	-93.533	409.497	-4.753
+4600	105.101	389.921	305.606	387.849	-93.891	420.679	-4.777
+4700	105.223	392.182	307.424	398.365	-94.281	431.869	-4.800
+4800	105.337	394.399	309.213	408.893	-94.702	443.069	-4.822
+4900	105.445	396.572	310.973	419.432	-95.156	454.277	-4.843
+5000	105.546	398.703	312.707	429.982	-95.641	465.495	-4.863
+5100	105.642	400.794	314.414	440.541	-96.157	476.722	-4.883
+5200	105.733	402.847	316.095	451.110	-96.703	487.961	-4.902
+5300	105.818	404.861	317.750	461.688	-97.278	499.210	-4.920
+5400	105.899	406.840	319.382	472.274	-97.882	510.470	-4.938
+5500	105.976	408.784	320.990	482.867	-98.513	521.741	-4.955
+5600	106.049	410.694	322.575	493.469	-99.170	533.025	-4.972
+5700	106.118	412.572	324.137	504.077	-99.852	544.320	-4.988
+5800	106.184	414.418	325.678	514.692	-100.557	555.628	-5.004
+5900	106.247	416.234	327.197	525.314	-101.284	566.946	-5.019
+6000	106.306	418.020	328.696	535.942	-102.032	578.279	-5.034
diff --git a/05. advanced python/05.01 overview of the sys module.ipynb b/05-advanced-python/05.01-overview-of-the-sys-module.ipynb
similarity index 95%
rename from 05. advanced python/05.01 overview of the sys module.ipynb
rename to 05-advanced-python/05.01-overview-of-the-sys-module.ipynb
index 17167e92..488b492b 100644
--- a/05. advanced python/05.01 overview of the sys module.ipynb	
+++ b/05-advanced-python/05.01-overview-of-the-sys-module.ipynb
@@ -1,382 +1,382 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# sys 模块简介"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import sys"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 命令行参数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`sys.argv` 显示传入的参数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing print_args.py\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%writefile print_args.py\n",
-    "import sys\n",
-    "print sys.argv"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "运行这个程序："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['print_args.py', '1', 'foo']\n"
-     ]
-    }
-   ],
-   "source": [
-    "%run print_args.py 1 foo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "第一个参数 （`sys.args[0]`） 表示的始终是执行的文件名，然后依次显示传入的参数。\n",
-    "\n",
-    "删除刚才生成的文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('print_args.py')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 异常消息"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`sys.exc_info()` 可以显示 `Exception` 的信息，返回一个 `(type, value, traceback)` 组成的三元组，可以与 `try/catch` 块一起使用： "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(<type 'exceptions.ZeroDivisionError'>, ZeroDivisionError('integer division or modulo by zero',), <traceback object at 0x0000000003C6FA08>)\n"
-     ]
-    }
-   ],
-   "source": [
-    "try:\n",
-    "    x = 1/0\n",
-    "except Exception:\n",
-    "    print sys.exc_info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`sys.exc_clear()` 用于清除所有的异常消息。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 标准输入输出流"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- sys.stdin\n",
-    "- sys.stdout\n",
-    "- sys.stderr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 退出Python"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`sys.exit(arg=0)` 用于退出 Python。`0` 或者 `None` 表示正常退出，其他值表示异常。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Python Path"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`sys.path` 表示 Python 搜索模块的路径和查找顺序："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['',\n",
-       " 'C:\\\\Anaconda\\\\python27.zip',\n",
-       " 'C:\\\\Anaconda\\\\DLLs',\n",
-       " 'C:\\\\Anaconda\\\\lib',\n",
-       " 'C:\\\\Anaconda\\\\lib\\\\plat-win',\n",
-       " 'C:\\\\Anaconda\\\\lib\\\\lib-tk',\n",
-       " 'C:\\\\Anaconda',\n",
-       " 'C:\\\\Anaconda\\\\lib\\\\site-packages',\n",
-       " 'C:\\\\Anaconda\\\\lib\\\\site-packages\\\\Sphinx-1.3.1-py2.7.egg',\n",
-       " 'C:\\\\Anaconda\\\\lib\\\\site-packages\\\\cryptography-0.9.1-py2.7-win-amd64.egg',\n",
-       " 'C:\\\\Anaconda\\\\lib\\\\site-packages\\\\win32',\n",
-       " 'C:\\\\Anaconda\\\\lib\\\\site-packages\\\\win32\\\\lib',\n",
-       " 'C:\\\\Anaconda\\\\lib\\\\site-packages\\\\Pythonwin',\n",
-       " 'C:\\\\Anaconda\\\\lib\\\\site-packages\\\\setuptools-17.1.1-py2.7.egg',\n",
-       " 'C:\\\\Anaconda\\\\lib\\\\site-packages\\\\IPython\\\\extensions']"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sys.path"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在程序中可以修改，添加新的路径。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 操作系统信息"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`sys.platform` 显示当前操作系统信息：\n",
-    "\n",
-    "- `Windows: win32`\n",
-    "- `Mac OSX: darwin`\n",
-    "- `Linux:   linux2`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'win32'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sys.platform"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "返回 `Windows` 操作系统的版本："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "sys.getwindowsversion(major=6, minor=2, build=9200, platform=2, service_pack='')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sys.getwindowsversion()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "标准库中有 `planform` 模块提供更详细的信息。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Python 版本信息"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'2.7.10 |Anaconda 2.3.0 (64-bit)| (default, May 28 2015, 16:44:52) [MSC v.1500 64 bit (AMD64)]'"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sys.version"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "sys.version_info(major=2, minor=7, micro=10, releaselevel='final', serial=0)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sys.version_info"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# sys 模块简介"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 命令行参数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`sys.argv` 显示传入的参数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing print_args.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile print_args.py\n",
+    "import sys\n",
+    "print sys.argv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "运行这个程序："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['print_args.py', '1', 'foo']\n"
+     ]
+    }
+   ],
+   "source": [
+    "%run print_args.py 1 foo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "第一个参数 （`sys.args[0]`） 表示的始终是执行的文件名，然后依次显示传入的参数。\n",
+    "\n",
+    "删除刚才生成的文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('print_args.py')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 异常消息"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`sys.exc_info()` 可以显示 `Exception` 的信息，返回一个 `(type, value, traceback)` 组成的三元组，可以与 `try/catch` 块一起使用： "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(<type 'exceptions.ZeroDivisionError'>, ZeroDivisionError('integer division or modulo by zero',), <traceback object at 0x0000000003C6FA08>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    x = 1/0\n",
+    "except Exception:\n",
+    "    print sys.exc_info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`sys.exc_clear()` 用于清除所有的异常消息。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 标准输入输出流"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- sys.stdin\n",
+    "- sys.stdout\n",
+    "- sys.stderr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 退出Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`sys.exit(arg=0)` 用于退出 Python。`0` 或者 `None` 表示正常退出，其他值表示异常。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Python Path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`sys.path` 表示 Python 搜索模块的路径和查找顺序："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['',\n",
+       " 'C:\\\\Anaconda\\\\python27.zip',\n",
+       " 'C:\\\\Anaconda\\\\DLLs',\n",
+       " 'C:\\\\Anaconda\\\\lib',\n",
+       " 'C:\\\\Anaconda\\\\lib\\\\plat-win',\n",
+       " 'C:\\\\Anaconda\\\\lib\\\\lib-tk',\n",
+       " 'C:\\\\Anaconda',\n",
+       " 'C:\\\\Anaconda\\\\lib\\\\site-packages',\n",
+       " 'C:\\\\Anaconda\\\\lib\\\\site-packages\\\\Sphinx-1.3.1-py2.7.egg',\n",
+       " 'C:\\\\Anaconda\\\\lib\\\\site-packages\\\\cryptography-0.9.1-py2.7-win-amd64.egg',\n",
+       " 'C:\\\\Anaconda\\\\lib\\\\site-packages\\\\win32',\n",
+       " 'C:\\\\Anaconda\\\\lib\\\\site-packages\\\\win32\\\\lib',\n",
+       " 'C:\\\\Anaconda\\\\lib\\\\site-packages\\\\Pythonwin',\n",
+       " 'C:\\\\Anaconda\\\\lib\\\\site-packages\\\\setuptools-17.1.1-py2.7.egg',\n",
+       " 'C:\\\\Anaconda\\\\lib\\\\site-packages\\\\IPython\\\\extensions']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sys.path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在程序中可以修改，添加新的路径。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 操作系统信息"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`sys.platform` 显示当前操作系统信息：\n",
+    "\n",
+    "- `Windows: win32`\n",
+    "- `Mac OSX: darwin`\n",
+    "- `Linux:   linux2`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'win32'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sys.platform"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "返回 `Windows` 操作系统的版本："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sys.getwindowsversion(major=6, minor=2, build=9200, platform=2, service_pack='')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sys.getwindowsversion()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "标准库中有 `planform` 模块提供更详细的信息。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Python 版本信息"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2.7.10 |Anaconda 2.3.0 (64-bit)| (default, May 28 2015, 16:44:52) [MSC v.1500 64 bit (AMD64)]'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sys.version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sys.version_info(major=2, minor=7, micro=10, releaselevel='final', serial=0)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sys.version_info"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/05. advanced python/05.02 interacting with the OS - os.ipynb b/05-advanced-python/05.02-interacting-with-the-OS---os.ipynb
similarity index 100%
rename from 05. advanced python/05.02 interacting with the OS - os.ipynb
rename to 05-advanced-python/05.02-interacting-with-the-OS---os.ipynb
diff --git a/05. advanced python/05.03 comma separated values.ipynb b/05-advanced-python/05.03-comma-separated-values.ipynb
similarity index 94%
rename from 05. advanced python/05.03 comma separated values.ipynb
rename to 05-advanced-python/05.03-comma-separated-values.ipynb
index f4d047be..a632dc2c 100644
--- a/05. advanced python/05.03 comma separated values.ipynb	
+++ b/05-advanced-python/05.03-comma-separated-values.ipynb
@@ -1,462 +1,462 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# CSV 文件和 csv 模块"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "标准库中有自带的 `csv` (逗号分隔值) 模块处理 `csv` 格式的文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import csv"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 读 csv 文件"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "假设我们有这样的一个文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing data.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file data.csv\n",
-    "\"alpha 1\",  100, -1.443\n",
-    "\"beat  3\",   12, -0.0934\n",
-    "\"gamma 3a\", 192, -0.6621\n",
-    "\"delta 2a\",  15, -4.515"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "打开这个文件，并产生一个文件 reader："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "fp = open(\"data.csv\")\n",
-    "r = csv.reader(fp)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以按行迭代数据："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['alpha 1', '  100', ' -1.443']\n",
-      "['beat  3', '   12', ' -0.0934']\n",
-      "['gamma 3a', ' 192', ' -0.6621']\n",
-      "['delta 2a', '  15', ' -4.515']\n"
-     ]
-    }
-   ],
-   "source": [
-    "for row in r:\n",
-    "    print row\n",
-    "    \n",
-    "fp.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "默认数据内容都被当作字符串处理，不过可以自己进行处理："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[['alpha 1', 100, -1.443],\n",
-       " ['beat  3', 12, -0.0934],\n",
-       " ['gamma 3a', 192, -0.6621],\n",
-       " ['delta 2a', 15, -4.515]]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = []\n",
-    "\n",
-    "with open('data.csv') as fp:\n",
-    "    r = csv.reader(fp)\n",
-    "    for row in r:\n",
-    "        data.append([row[0], int(row[1]), float(row[2])])\n",
-    "    \n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('data.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 写 csv 文件"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以使用 `csv.writer` 写入文件，不过相应地，传入的应该是以写方式打开的文件，不过一般要用 `'wb'` 即二进制写入方式，防止出现换行不正确的问题："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "data = [('one', 1, 1.5), ('two', 2, 8.0)]\n",
-    "with open('out.csv', 'wb') as fp:\n",
-    "    w = csv.writer(fp)\n",
-    "    w.writerows(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "显示结果："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "one,1,1.5\n",
-      "two,2,8.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "!cat 'out.csv'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 更换分隔符"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "默认情况下，`csv` 模块默认 `csv` 文件都是由 `excel` 产生的，实际中可能会遇到这样的问题："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "data = [('one, \\\"real\\\" string', 1, 1.5), ('two', 2, 8.0)]\n",
-    "with open('out.csv', 'wb') as fp:\n",
-    "    w = csv.writer(fp)\n",
-    "    w.writerows(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\"one, \"\"real\"\" string\",1,1.5\n",
-      "two,2,8.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "!cat 'out.csv'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以修改分隔符来处理这组数据："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "data = [('one, \\\"real\\\" string', 1, 1.5), ('two', 2, 8.0)]\n",
-    "with open('out.psv', 'wb') as fp:\n",
-    "    w = csv.writer(fp, delimiter=\"|\")\n",
-    "    w.writerows(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\"one, \"\"real\"\" string\"|1|1.5\n",
-      "two|2|8.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "!cat 'out.psv'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('out.psv')\n",
-    "os.remove('out.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 其他选项"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`numpy.loadtxt()` 和 `pandas.read_csv()` 可以用来读写包含很多数值数据的 `csv` 文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing trades.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file trades.csv\n",
-    "Order,Date,Stock,Quantity,Price\n",
-    "A0001,2013-12-01,AAPL,1000,203.4\n",
-    "A0002,2013-12-01,MSFT,1500,167.5\n",
-    "A0003,2013-12-02,GOOG,1500,167.5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `pandas` 进行处理，生成一个 `DataFrame` 对象："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "             Date Stock  Quantity  Price\n",
-      "Order                                   \n",
-      "A0001  2013-12-01  AAPL      1000  203.4\n",
-      "A0002  2013-12-01  MSFT      1500  167.5\n",
-      "A0003  2013-12-02  GOOG      1500  167.5\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas\n",
-    "df = pandas.read_csv('trades.csv', index_col=0)\n",
-    "print df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "通过名字进行索引："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Order\n",
-       "A0001    203400\n",
-       "A0002    251250\n",
-       "A0003    251250\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Quantity'] * df['Price']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('trades.csv')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CSV 文件和 csv 模块"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "标准库中有自带的 `csv` (逗号分隔值) 模块处理 `csv` 格式的文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 读 csv 文件"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "假设我们有这样的一个文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing data.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file data.csv\n",
+    "\"alpha 1\",  100, -1.443\n",
+    "\"beat  3\",   12, -0.0934\n",
+    "\"gamma 3a\", 192, -0.6621\n",
+    "\"delta 2a\",  15, -4.515"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "打开这个文件，并产生一个文件 reader："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "fp = open(\"data.csv\")\n",
+    "r = csv.reader(fp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以按行迭代数据："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['alpha 1', '  100', ' -1.443']\n",
+      "['beat  3', '   12', ' -0.0934']\n",
+      "['gamma 3a', ' 192', ' -0.6621']\n",
+      "['delta 2a', '  15', ' -4.515']\n"
+     ]
+    }
+   ],
+   "source": [
+    "for row in r:\n",
+    "    print row\n",
+    "    \n",
+    "fp.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "默认数据内容都被当作字符串处理，不过可以自己进行处理："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['alpha 1', 100, -1.443],\n",
+       " ['beat  3', 12, -0.0934],\n",
+       " ['gamma 3a', 192, -0.6621],\n",
+       " ['delta 2a', 15, -4.515]]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = []\n",
+    "\n",
+    "with open('data.csv') as fp:\n",
+    "    r = csv.reader(fp)\n",
+    "    for row in r:\n",
+    "        data.append([row[0], int(row[1]), float(row[2])])\n",
+    "    \n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('data.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 写 csv 文件"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以使用 `csv.writer` 写入文件，不过相应地，传入的应该是以写方式打开的文件，不过一般要用 `'wb'` 即二进制写入方式，防止出现换行不正确的问题："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "data = [('one', 1, 1.5), ('two', 2, 8.0)]\n",
+    "with open('out.csv', 'wb') as fp:\n",
+    "    w = csv.writer(fp)\n",
+    "    w.writerows(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "显示结果："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "one,1,1.5\n",
+      "two,2,8.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat 'out.csv'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 更换分隔符"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "默认情况下，`csv` 模块默认 `csv` 文件都是由 `excel` 产生的，实际中可能会遇到这样的问题："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data = [('one, \\\"real\\\" string', 1, 1.5), ('two', 2, 8.0)]\n",
+    "with open('out.csv', 'wb') as fp:\n",
+    "    w = csv.writer(fp)\n",
+    "    w.writerows(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\"one, \"\"real\"\" string\",1,1.5\n",
+      "two,2,8.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat 'out.csv'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以修改分隔符来处理这组数据："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data = [('one, \\\"real\\\" string', 1, 1.5), ('two', 2, 8.0)]\n",
+    "with open('out.psv', 'wb') as fp:\n",
+    "    w = csv.writer(fp, delimiter=\"|\")\n",
+    "    w.writerows(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\"one, \"\"real\"\" string\"|1|1.5\n",
+      "two|2|8.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat 'out.psv'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('out.psv')\n",
+    "os.remove('out.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 其他选项"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`numpy.loadtxt()` 和 `pandas.read_csv()` 可以用来读写包含很多数值数据的 `csv` 文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing trades.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file trades.csv\n",
+    "Order,Date,Stock,Quantity,Price\n",
+    "A0001,2013-12-01,AAPL,1000,203.4\n",
+    "A0002,2013-12-01,MSFT,1500,167.5\n",
+    "A0003,2013-12-02,GOOG,1500,167.5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `pandas` 进行处理，生成一个 `DataFrame` 对象："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             Date Stock  Quantity  Price\n",
+      "Order                                   \n",
+      "A0001  2013-12-01  AAPL      1000  203.4\n",
+      "A0002  2013-12-01  MSFT      1500  167.5\n",
+      "A0003  2013-12-02  GOOG      1500  167.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas\n",
+    "df = pandas.read_csv('trades.csv', index_col=0)\n",
+    "print df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "通过名字进行索引："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Order\n",
+       "A0001    203400\n",
+       "A0002    251250\n",
+       "A0003    251250\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Quantity'] * df['Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('trades.csv')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/05. advanced python/05.04 regular expression.ipynb b/05-advanced-python/05.04-regular-expression.ipynb
similarity index 95%
rename from 05. advanced python/05.04 regular expression.ipynb
rename to 05-advanced-python/05.04-regular-expression.ipynb
index fa2a2d5e..e37cdca4 100644
--- a/05. advanced python/05.04 regular expression.ipynb	
+++ b/05-advanced-python/05.04-regular-expression.ipynb
@@ -1,519 +1,519 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 正则表达式和 re 模块"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 正则表达式"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[正则表达式](http://baike.baidu.com/view/94238.htm)是用来匹配字符串或者子串的一种模式，匹配的字符串可以很具体，也可以很一般化。\n",
-    "\n",
-    "`Python` 标准库提供了 `re` 模块。 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import re"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## re.match & re.search"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在 `re` 模块中， `re.match` 和 `re.search` 是常用的两个方法：\n",
-    "\n",
-    "    re.match(pattern, string[, flags])\n",
-    "    re.search(pattern, string[, flags])\n",
-    "\n",
-    "两者都寻找第一个匹配成功的部分，成功则返回一个 `match` 对象，不成功则返回 `None`，不同之处在于 `re.match` 只匹配字符串的开头部分，而 `re.search` 匹配的则是整个字符串中的子串。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## re.findall & re.finditer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`re.findall(pattern, string)` 返回所有匹配的对象， `re.finditer` 则返回一个迭代器。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## re.split"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`re.split(pattern, string[, maxsplit])` 按照 `pattern` 指定的内容对字符串进行分割。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## re.sub"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`re.sub(pattern, repl, string[, count])` 将 `pattern` 匹配的内容进行替换。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## re.compile"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`re.compile(pattern)` 生成一个 `pattern` 对象，这个对象有匹配，替换，分割字符串的方法。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 正则表达式规则"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "正则表达式由一些普通字符和一些元字符（metacharacters）组成。普通字符包括大小写的字母和数字，而元字符则具有特殊的含义：\n",
-    "\n",
-    "子表达式|匹配内容\n",
-    "---|---\n",
-    "`.`| 匹配除了换行符之外的内容\n",
-    "`\\w` | 匹配所有字母和数字字符\n",
-    "`\\d` | 匹配所有数字，相当于 `[0-9]`\n",
-    "`\\s` | 匹配空白，相当于 `[\\t\\n\\t\\f\\v]`\n",
-    "`\\W,\\D,\\S`| 匹配对应小写字母形式的补\n",
-    "`[...]` | 表示可以匹配的集合，支持范围表示如 `a-z`, `0-9` 等\n",
-    "`(...)` | 表示作为一个整体进行匹配\n",
-    "&#166; | 表示逻辑或\n",
-    "`^` | 表示匹配后面的子表达式的补\n",
-    "`*` | 表示匹配前面的子表达式 0 次或更多次\n",
-    "`+` | 表示匹配前面的子表达式 1 次或更多次\n",
-    "`?` | 表示匹配前面的子表达式 0 次或 1 次\n",
-    "`{m}` | 表示匹配前面的子表达式 m 次\n",
-    "`{m,}` | 表示匹配前面的子表达式至少 m 次\n",
-    "`{m,n}` | 表示匹配前面的子表达式至少 m 次，至多 n 次\n",
-    "\n",
-    "例如：\n",
-    "\n",
-    "- `ca*t       匹配： ct, cat, caaaat, ...`\n",
-    "- `ab\\d|ac\\d  匹配： ab1, ac9, ...`\n",
-    "- `([^a-q]bd) 匹配： rbd, 5bd, ...`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 例子"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "假设我们要匹配这样的字符串："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<_sre.SRE_Match object at 0x0000000003A5DA80>\n"
-     ]
-    }
-   ],
-   "source": [
-    "string = 'hello world'\n",
-    "pattern = 'hello (\\w+)'\n",
-    "\n",
-    "match = re.match(pattern, string)\n",
-    "print match"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "一旦找到了符合条件的部分，我们便可以使用 `group` 方法查看匹配的部分："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hello world\n"
-     ]
-    }
-   ],
-   "source": [
-    "if match is not None:\n",
-    "    print match.group(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "world\n"
-     ]
-    }
-   ],
-   "source": [
-    "if match is not None:\n",
-    "    print match.group(1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们可以改变 string 的内容："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hello there\n",
-      "there\n"
-     ]
-    }
-   ],
-   "source": [
-    "string = 'hello there'\n",
-    "pattern = 'hello (\\w+)'\n",
-    "\n",
-    "match = re.match(pattern, string)\n",
-    "if match is not None:\n",
-    "    print match.group(0)\n",
-    "    print match.group(1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "通常，`match.group(0)` 匹配整个返回的内容，之后的 `1,2,3,...` 返回规则中每个括号（按照括号的位置排序）匹配的部分。\n",
-    "\n",
-    "如果某个 `pattern` 需要反复使用，那么我们可以将它预先编译："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "there\n"
-     ]
-    }
-   ],
-   "source": [
-    "pattern1 = re.compile('hello (\\w+)')\n",
-    "\n",
-    "match = pattern1.match(string)\n",
-    "if match is not None:\n",
-    "    print match.group(1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "由于元字符的存在，所以对于一些特殊字符，我们需要使用 `'\\'` 进行逃逸字符的处理，使用表达式 `'\\\\'` 来匹配 `'\\'` 。\n",
-    "\n",
-    "但事实上，`Python` 本身对逃逸字符也是这样处理的："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\\n"
-     ]
-    }
-   ],
-   "source": [
-    "pattern = '\\\\'\n",
-    "print pattern"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "因为逃逸字符的问题，我们需要使用四个 `'\\\\\\\\'` 来匹配一个单独的 `'\\'`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['C:', 'foo', 'bar', 'baz.txt']\n"
-     ]
-    }
-   ],
-   "source": [
-    "pattern = '\\\\\\\\'\n",
-    "path = \"C:\\\\foo\\\\bar\\\\baz.txt\"\n",
-    "print re.split(pattern, path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这样看起来十分麻烦，好在 `Python` 提供了 `raw string` 来忽略对逃逸字符串的处理，从而可以这样进行匹配："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['C:', 'foo', 'bar', 'baz.txt']\n"
-     ]
-    }
-   ],
-   "source": [
-    "pattern = r'\\\\'\n",
-    "path = r\"C:\\foo\\bar\\baz.txt\"\n",
-    "print re.split(pattern, path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "如果规则太多复杂，正则表达式不一定是个好选择。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Numpy 的 fromregex()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing test.dat\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file test.dat \n",
-    "1312 foo\n",
-    "1534    bar\n",
-    "444  qux"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    fromregex(file, pattern, dtype)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`dtype` 中的内容与 `pattern` 的括号一一对应："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(1312L, 'foo') (1534L, 'bar') (444L, 'qux')]\n"
-     ]
-    }
-   ],
-   "source": [
-    "pattern = \"(\\d+)\\s+(...)\"\n",
-    "dt = [('num', 'int64'), ('key', 'S3')]\n",
-    "\n",
-    "from numpy import fromregex\n",
-    "output = fromregex('test.dat', pattern, dt)\n",
-    "print output"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "显示 `num` 项："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1312 1534  444]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print output['num']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('test.dat')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 正则表达式和 re 模块"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 正则表达式"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[正则表达式](http://baike.baidu.com/view/94238.htm)是用来匹配字符串或者子串的一种模式，匹配的字符串可以很具体，也可以很一般化。\n",
+    "\n",
+    "`Python` 标准库提供了 `re` 模块。 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import re"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## re.match & re.search"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在 `re` 模块中， `re.match` 和 `re.search` 是常用的两个方法：\n",
+    "\n",
+    "    re.match(pattern, string[, flags])\n",
+    "    re.search(pattern, string[, flags])\n",
+    "\n",
+    "两者都寻找第一个匹配成功的部分，成功则返回一个 `match` 对象，不成功则返回 `None`，不同之处在于 `re.match` 只匹配字符串的开头部分，而 `re.search` 匹配的则是整个字符串中的子串。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## re.findall & re.finditer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`re.findall(pattern, string)` 返回所有匹配的对象， `re.finditer` 则返回一个迭代器。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## re.split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`re.split(pattern, string[, maxsplit])` 按照 `pattern` 指定的内容对字符串进行分割。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## re.sub"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`re.sub(pattern, repl, string[, count])` 将 `pattern` 匹配的内容进行替换。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## re.compile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`re.compile(pattern)` 生成一个 `pattern` 对象，这个对象有匹配，替换，分割字符串的方法。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 正则表达式规则"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "正则表达式由一些普通字符和一些元字符（metacharacters）组成。普通字符包括大小写的字母和数字，而元字符则具有特殊的含义：\n",
+    "\n",
+    "子表达式|匹配内容\n",
+    "---|---\n",
+    "`.`| 匹配除了换行符之外的内容\n",
+    "`\\w` | 匹配所有字母和数字字符\n",
+    "`\\d` | 匹配所有数字，相当于 `[0-9]`\n",
+    "`\\s` | 匹配空白，相当于 `[\\t\\n\\t\\f\\v]`\n",
+    "`\\W,\\D,\\S`| 匹配对应小写字母形式的补\n",
+    "`[...]` | 表示可以匹配的集合，支持范围表示如 `a-z`, `0-9` 等\n",
+    "`(...)` | 表示作为一个整体进行匹配\n",
+    "&#166; | 表示逻辑或\n",
+    "`^` | 表示匹配后面的子表达式的补\n",
+    "`*` | 表示匹配前面的子表达式 0 次或更多次\n",
+    "`+` | 表示匹配前面的子表达式 1 次或更多次\n",
+    "`?` | 表示匹配前面的子表达式 0 次或 1 次\n",
+    "`{m}` | 表示匹配前面的子表达式 m 次\n",
+    "`{m,}` | 表示匹配前面的子表达式至少 m 次\n",
+    "`{m,n}` | 表示匹配前面的子表达式至少 m 次，至多 n 次\n",
+    "\n",
+    "例如：\n",
+    "\n",
+    "- `ca*t       匹配： ct, cat, caaaat, ...`\n",
+    "- `ab\\d|ac\\d  匹配： ab1, ac9, ...`\n",
+    "- `([^a-q]bd) 匹配： rbd, 5bd, ...`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 例子"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "假设我们要匹配这样的字符串："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<_sre.SRE_Match object at 0x0000000003A5DA80>\n"
+     ]
+    }
+   ],
+   "source": [
+    "string = 'hello world'\n",
+    "pattern = 'hello (\\w+)'\n",
+    "\n",
+    "match = re.match(pattern, string)\n",
+    "print match"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "一旦找到了符合条件的部分，我们便可以使用 `group` 方法查看匹配的部分："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello world\n"
+     ]
+    }
+   ],
+   "source": [
+    "if match is not None:\n",
+    "    print match.group(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "world\n"
+     ]
+    }
+   ],
+   "source": [
+    "if match is not None:\n",
+    "    print match.group(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们可以改变 string 的内容："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello there\n",
+      "there\n"
+     ]
+    }
+   ],
+   "source": [
+    "string = 'hello there'\n",
+    "pattern = 'hello (\\w+)'\n",
+    "\n",
+    "match = re.match(pattern, string)\n",
+    "if match is not None:\n",
+    "    print match.group(0)\n",
+    "    print match.group(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "通常，`match.group(0)` 匹配整个返回的内容，之后的 `1,2,3,...` 返回规则中每个括号（按照括号的位置排序）匹配的部分。\n",
+    "\n",
+    "如果某个 `pattern` 需要反复使用，那么我们可以将它预先编译："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "there\n"
+     ]
+    }
+   ],
+   "source": [
+    "pattern1 = re.compile('hello (\\w+)')\n",
+    "\n",
+    "match = pattern1.match(string)\n",
+    "if match is not None:\n",
+    "    print match.group(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "由于元字符的存在，所以对于一些特殊字符，我们需要使用 `'\\'` 进行逃逸字符的处理，使用表达式 `'\\\\'` 来匹配 `'\\'` 。\n",
+    "\n",
+    "但事实上，`Python` 本身对逃逸字符也是这样处理的："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\\n"
+     ]
+    }
+   ],
+   "source": [
+    "pattern = '\\\\'\n",
+    "print pattern"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "因为逃逸字符的问题，我们需要使用四个 `'\\\\\\\\'` 来匹配一个单独的 `'\\'`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['C:', 'foo', 'bar', 'baz.txt']\n"
+     ]
+    }
+   ],
+   "source": [
+    "pattern = '\\\\\\\\'\n",
+    "path = \"C:\\\\foo\\\\bar\\\\baz.txt\"\n",
+    "print re.split(pattern, path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这样看起来十分麻烦，好在 `Python` 提供了 `raw string` 来忽略对逃逸字符串的处理，从而可以这样进行匹配："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['C:', 'foo', 'bar', 'baz.txt']\n"
+     ]
+    }
+   ],
+   "source": [
+    "pattern = r'\\\\'\n",
+    "path = r\"C:\\foo\\bar\\baz.txt\"\n",
+    "print re.split(pattern, path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "如果规则太多复杂，正则表达式不一定是个好选择。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Numpy 的 fromregex()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing test.dat\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file test.dat \n",
+    "1312 foo\n",
+    "1534    bar\n",
+    "444  qux"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    fromregex(file, pattern, dtype)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`dtype` 中的内容与 `pattern` 的括号一一对应："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(1312L, 'foo') (1534L, 'bar') (444L, 'qux')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "pattern = \"(\\d+)\\s+(...)\"\n",
+    "dt = [('num', 'int64'), ('key', 'S3')]\n",
+    "\n",
+    "from numpy import fromregex\n",
+    "output = fromregex('test.dat', pattern, dt)\n",
+    "print output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "显示 `num` 项："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1312 1534  444]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print output['num']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('test.dat')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/05. advanced python/05.05 datetime.ipynb b/05-advanced-python/05.05-datetime.ipynb
similarity index 94%
rename from 05. advanced python/05.05 datetime.ipynb
rename to 05-advanced-python/05.05-datetime.ipynb
index 73cfdca0..0d38b01d 100644
--- a/05. advanced python/05.05 datetime.ipynb	
+++ b/05-advanced-python/05.05-datetime.ipynb
@@ -1,371 +1,371 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# datetime 模块"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import datetime as dt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`datetime` 提供了基础时间和日期的处理。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## date 对象"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以使用 `date(year, month, day)` 产生一个 `date` 对象："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "d1 = dt.date(2007, 9, 25)\n",
-    "d2 = dt.date(2008, 9, 25)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以格式化 `date` 对象的输出："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2007-09-25\n",
-      "Tuesday, 09/25/07\n",
-      "Tue, 09-25-2007\n"
-     ]
-    }
-   ],
-   "source": [
-    "print d1\n",
-    "print d1.strftime('%A, %m/%d/%y')\n",
-    "print d1.strftime('%a, %m-%d-%Y')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以看两个日期相差多久："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "366 days, 0:00:00\n"
-     ]
-    }
-   ],
-   "source": [
-    "print d2 - d1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "返回的是一个 `timedelta` 对象："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "366\n",
-      "0\n"
-     ]
-    }
-   ],
-   "source": [
-    "d = d2 - d1\n",
-    "print d.days\n",
-    "print d.seconds"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看今天的日期："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2015-09-10\n"
-     ]
-    }
-   ],
-   "source": [
-    "print dt.date.today()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## time 对象"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以使用 `time(hour, min, sec, us)` 产生一个 `time` 对象："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "t1 = dt.time(15, 38)\n",
-    "t2 = dt.time(18)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "改变显示格式："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "15:38:00\n",
-      "03:38, PM\n",
-      "15:38:00, PM\n"
-     ]
-    }
-   ],
-   "source": [
-    "print t1\n",
-    "print t1.strftime('%I:%M, %p')\n",
-    "print t1.strftime('%H:%M:%S, %p')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "因为没有具体的日期信息，所以 `time` 对象不支持减法操作。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## datetime 对象"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以使用 `datetime(year, month, day, hr, min, sec, us)` 来创建一个 `datetime` 对象。 \n",
-    "\n",
-    "获得当前时间："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2015-09-10 20:58:50.148000\n"
-     ]
-    }
-   ],
-   "source": [
-    "d1 = dt.datetime.now()\n",
-    "print d1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "给当前的时间加上 `30` 天，`timedelta` 的参数是 `timedelta(day, hr, min, sec, us)`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2015-10-10 20:58:50.148000\n"
-     ]
-    }
-   ],
-   "source": [
-    "d2 = d1 + dt.timedelta(30)\n",
-    "print d2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "除此之外，我们还可以通过一些指定格式的字符串来创建 `datetime` 对象："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2001-02-10 00:00:00\n"
-     ]
-    }
-   ],
-   "source": [
-    "print dt.datetime.strptime('2/10/01', '%m/%d/%y')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## datetime 格式字符表"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "字符|含义\n",
-    "--|--\n",
-    "`%a` | 星期英文缩写\n",
-    "`%A` | 星期英文\n",
-    "`%w` | 一星期的第几天，`[0(sun),6]`\n",
-    "`%b` | 月份英文缩写\n",
-    "`%B` | 月份英文\n",
-    "`%d` | 日期，`[01,31]`\n",
-    "`%H` | 小时，`[00,23]`\n",
-    "`%I` | 小时，`[01,12]`\n",
-    "`%j` | 一年的第几天，`[001,366]`\n",
-    "`%m` | 月份，`[01,12]`\n",
-    "`%M` | 分钟，`[00,59]`\n",
-    "`%p` | AM 和 PM\n",
-    "`%S` | 秒钟，`[00,61]` （大概是有闰秒的存在）\n",
-    "`%U` | 一年中的第几个星期，星期日为第一天，`[00,53]`\n",
-    "`%W` | 一年中的第几个星期，星期一为第一天，`[00,53]`\n",
-    "`%y` | 没有世纪的年份\n",
-    "`%Y` | 完整的年份"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# datetime 模块"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import datetime as dt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`datetime` 提供了基础时间和日期的处理。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## date 对象"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以使用 `date(year, month, day)` 产生一个 `date` 对象："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "d1 = dt.date(2007, 9, 25)\n",
+    "d2 = dt.date(2008, 9, 25)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以格式化 `date` 对象的输出："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2007-09-25\n",
+      "Tuesday, 09/25/07\n",
+      "Tue, 09-25-2007\n"
+     ]
+    }
+   ],
+   "source": [
+    "print d1\n",
+    "print d1.strftime('%A, %m/%d/%y')\n",
+    "print d1.strftime('%a, %m-%d-%Y')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以看两个日期相差多久："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "366 days, 0:00:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "print d2 - d1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "返回的是一个 `timedelta` 对象："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "366\n",
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "d = d2 - d1\n",
+    "print d.days\n",
+    "print d.seconds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看今天的日期："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2015-09-10\n"
+     ]
+    }
+   ],
+   "source": [
+    "print dt.date.today()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## time 对象"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以使用 `time(hour, min, sec, us)` 产生一个 `time` 对象："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "t1 = dt.time(15, 38)\n",
+    "t2 = dt.time(18)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "改变显示格式："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15:38:00\n",
+      "03:38, PM\n",
+      "15:38:00, PM\n"
+     ]
+    }
+   ],
+   "source": [
+    "print t1\n",
+    "print t1.strftime('%I:%M, %p')\n",
+    "print t1.strftime('%H:%M:%S, %p')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "因为没有具体的日期信息，所以 `time` 对象不支持减法操作。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## datetime 对象"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以使用 `datetime(year, month, day, hr, min, sec, us)` 来创建一个 `datetime` 对象。 \n",
+    "\n",
+    "获得当前时间："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2015-09-10 20:58:50.148000\n"
+     ]
+    }
+   ],
+   "source": [
+    "d1 = dt.datetime.now()\n",
+    "print d1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "给当前的时间加上 `30` 天，`timedelta` 的参数是 `timedelta(day, hr, min, sec, us)`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2015-10-10 20:58:50.148000\n"
+     ]
+    }
+   ],
+   "source": [
+    "d2 = d1 + dt.timedelta(30)\n",
+    "print d2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "除此之外，我们还可以通过一些指定格式的字符串来创建 `datetime` 对象："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2001-02-10 00:00:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "print dt.datetime.strptime('2/10/01', '%m/%d/%y')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## datetime 格式字符表"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "字符|含义\n",
+    "--|--\n",
+    "`%a` | 星期英文缩写\n",
+    "`%A` | 星期英文\n",
+    "`%w` | 一星期的第几天，`[0(sun),6]`\n",
+    "`%b` | 月份英文缩写\n",
+    "`%B` | 月份英文\n",
+    "`%d` | 日期，`[01,31]`\n",
+    "`%H` | 小时，`[00,23]`\n",
+    "`%I` | 小时，`[01,12]`\n",
+    "`%j` | 一年的第几天，`[001,366]`\n",
+    "`%m` | 月份，`[01,12]`\n",
+    "`%M` | 分钟，`[00,59]`\n",
+    "`%p` | AM 和 PM\n",
+    "`%S` | 秒钟，`[00,61]` （大概是有闰秒的存在）\n",
+    "`%U` | 一年中的第几个星期，星期日为第一天，`[00,53]`\n",
+    "`%W` | 一年中的第几个星期，星期一为第一天，`[00,53]`\n",
+    "`%y` | 没有世纪的年份\n",
+    "`%Y` | 完整的年份"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/05. advanced python/05.06 sql databases.ipynb b/05-advanced-python/05.06-sql-databases.ipynb
similarity index 95%
rename from 05. advanced python/05.06 sql databases.ipynb
rename to 05-advanced-python/05.06-sql-databases.ipynb
index 18c2e14c..08340e19 100644
--- a/05. advanced python/05.06 sql databases.ipynb	
+++ b/05-advanced-python/05.06-sql-databases.ipynb
@@ -1,253 +1,253 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# SQL 数据库"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Python` 提供了一系列标准的数据库的 API，这里我们介绍 sqlite 数据库的用法，其他的数据库的用法大同小异："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import sqlite3 as db"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "首先我们要建立或者连接到一个数据库上："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "connection = db.connect(\"my_database.sqlite\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不同的数据库有着不同的连接方法，例如 cx-oracle 数据库的链接方式为：\n",
-    "\n",
-    "    connection = db.connect(username, password, host, port,  'XE')\n",
-    "\n",
-    "一旦建立连接，我们可以利用它的 `cursor()` 来执行 SQL 语句："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "cursor = connection.cursor()\n",
-    "cursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS orders(\n",
-    "        order_id TEXT PRIMARY KEY,\n",
-    "        date TEXT,\n",
-    "        symbol TEXT,\n",
-    "        quantity INTEGER,\n",
-    "        price NUMBER)\"\"\")\n",
-    "cursor.execute(\"\"\"INSERT INTO orders VALUES\n",
-    "        ('A0001', '2013-12-01', 'AAPL', 1000, 203.4)\"\"\")\n",
-    "connection.commit()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不过为了安全起见，一般不将数据内容写入字符串再传入，而是使用这样的方式："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "orders = [\n",
-    "          (\"A0002\",\"2013-12-01\",\"MSFT\",1500,167.5),\n",
-    "          (\"A0003\",\"2013-12-02\",\"GOOG\",1500,167.5)\n",
-    "]\n",
-    "cursor.executemany(\"\"\"INSERT INTO orders VALUES\n",
-    "    (?, ?, ?, ?, ?)\"\"\", orders)\n",
-    "connection.commit()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "cx-oracle 数据库使用不同的方式：\n",
-    "\n",
-    "    cursor.executemany(\"\"\"INSERT INTO orders VALUES\n",
-    "    (:order_id, :date, :symbol, :quantity, :price)\"\"\",\n",
-    "    orders)\n",
-    "\n",
-    "查看支持的数据库格式："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'qmark'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "db.paramstyle"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在 `query` 语句执行之后，我们需要进行 `commit`，否则数据库将不会接受这些变化，如果想撤销某个 `commit`，可以使用 `rollback()` 方法撤销到上一次 `commit()` 的结果：\n",
-    "\n",
-    "    try:\n",
-    "        ... # perform some operations\n",
-    "    except:\n",
-    "        connection.rollback()\n",
-    "        raise\n",
-    "    else:\n",
-    "        connection.commit()\n",
-    "\n",
-    "使用 `SELECT` 语句对数据库进行查询："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(u'A0002', u'2013-12-01', u'MSFT', 1500, 167.5)\n"
-     ]
-    }
-   ],
-   "source": [
-    "stock = 'MSFT'\n",
-    "cursor.execute(\"\"\"SELECT *\n",
-    "    FROM orders\n",
-    "    WHERE symbol=?\n",
-    "    ORDER BY quantity\"\"\", (stock,))\n",
-    "for row in cursor:\n",
-    "    print row"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`cursor.fetchone()` 返回下一条内容， `cursor.fetchall()` 返回所有查询到的内容组成的列表（可能非常大）："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(u'A0001', u'2013-12-01', u'AAPL', 1000, 203.4)]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stock = 'AAPL'\n",
-    "cursor.execute(\"\"\"SELECT *\n",
-    "    FROM orders\n",
-    "    WHERE symbol=?\n",
-    "    ORDER BY quantity\"\"\", (stock,))\n",
-    "cursor.fetchall()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "关闭数据库："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "cursor.close()\n",
-    "connection.close()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SQL 数据库"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Python` 提供了一系列标准的数据库的 API，这里我们介绍 sqlite 数据库的用法，其他的数据库的用法大同小异："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import sqlite3 as db"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "首先我们要建立或者连接到一个数据库上："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "connection = db.connect(\"my_database.sqlite\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不同的数据库有着不同的连接方法，例如 cx-oracle 数据库的链接方式为：\n",
+    "\n",
+    "    connection = db.connect(username, password, host, port,  'XE')\n",
+    "\n",
+    "一旦建立连接，我们可以利用它的 `cursor()` 来执行 SQL 语句："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "cursor = connection.cursor()\n",
+    "cursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS orders(\n",
+    "        order_id TEXT PRIMARY KEY,\n",
+    "        date TEXT,\n",
+    "        symbol TEXT,\n",
+    "        quantity INTEGER,\n",
+    "        price NUMBER)\"\"\")\n",
+    "cursor.execute(\"\"\"INSERT INTO orders VALUES\n",
+    "        ('A0001', '2013-12-01', 'AAPL', 1000, 203.4)\"\"\")\n",
+    "connection.commit()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不过为了安全起见，一般不将数据内容写入字符串再传入，而是使用这样的方式："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "orders = [\n",
+    "          (\"A0002\",\"2013-12-01\",\"MSFT\",1500,167.5),\n",
+    "          (\"A0003\",\"2013-12-02\",\"GOOG\",1500,167.5)\n",
+    "]\n",
+    "cursor.executemany(\"\"\"INSERT INTO orders VALUES\n",
+    "    (?, ?, ?, ?, ?)\"\"\", orders)\n",
+    "connection.commit()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "cx-oracle 数据库使用不同的方式：\n",
+    "\n",
+    "    cursor.executemany(\"\"\"INSERT INTO orders VALUES\n",
+    "    (:order_id, :date, :symbol, :quantity, :price)\"\"\",\n",
+    "    orders)\n",
+    "\n",
+    "查看支持的数据库格式："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'qmark'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "db.paramstyle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在 `query` 语句执行之后，我们需要进行 `commit`，否则数据库将不会接受这些变化，如果想撤销某个 `commit`，可以使用 `rollback()` 方法撤销到上一次 `commit()` 的结果：\n",
+    "\n",
+    "    try:\n",
+    "        ... # perform some operations\n",
+    "    except:\n",
+    "        connection.rollback()\n",
+    "        raise\n",
+    "    else:\n",
+    "        connection.commit()\n",
+    "\n",
+    "使用 `SELECT` 语句对数据库进行查询："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(u'A0002', u'2013-12-01', u'MSFT', 1500, 167.5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "stock = 'MSFT'\n",
+    "cursor.execute(\"\"\"SELECT *\n",
+    "    FROM orders\n",
+    "    WHERE symbol=?\n",
+    "    ORDER BY quantity\"\"\", (stock,))\n",
+    "for row in cursor:\n",
+    "    print row"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`cursor.fetchone()` 返回下一条内容， `cursor.fetchall()` 返回所有查询到的内容组成的列表（可能非常大）："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(u'A0001', u'2013-12-01', u'AAPL', 1000, 203.4)]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stock = 'AAPL'\n",
+    "cursor.execute(\"\"\"SELECT *\n",
+    "    FROM orders\n",
+    "    WHERE symbol=?\n",
+    "    ORDER BY quantity\"\"\", (stock,))\n",
+    "cursor.fetchall()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "关闭数据库："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "cursor.close()\n",
+    "connection.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/05. advanced python/05.07 object-relational mappers.ipynb b/05-advanced-python/05.07-object-relational-mappers.ipynb
similarity index 95%
rename from 05. advanced python/05.07 object-relational mappers.ipynb
rename to 05-advanced-python/05.07-object-relational-mappers.ipynb
index 14dc6248..3a9e4876 100644
--- a/05. advanced python/05.07 object-relational mappers.ipynb	
+++ b/05-advanced-python/05.07-object-relational-mappers.ipynb
@@ -1,272 +1,272 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 对象关系映射"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "数据库中的记录可以与一个 `Python` 对象对应。\n",
-    "\n",
-    "例如对于上一节中的数据库：\n",
-    "\n",
-    "Order|Date|Stock|Quantity|Price\n",
-    "--|--|--|--|--\n",
-    "A0001|2013-12-01|AAPL|1000|203.4\n",
-    "A0002|2013-12-01|MSFT|1500|167.5\n",
-    "A0003|2013-12-02|GOOG|1500|167.5\n",
-    "\n",
-    "可以用一个类来描述：\n",
-    "\n",
-    "Attr.|Method\n",
-    "--|--\n",
-    "Order id| Cost\n",
-    "Date|\n",
-    "Stock|\n",
-    "Quant.|\n",
-    "Price|\n",
-    "\n",
-    "可以使用 `sqlalchemy` 来实现这种对应："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from sqlalchemy.ext.declarative import declarative_base\n",
-    "from sqlalchemy import Column, Date, Float, Integer, String\n",
-    "\n",
-    "Base = declarative_base()\n",
-    "\n",
-    "class Order(Base):\n",
-    "    __tablename__ = 'orders'\n",
-    "    \n",
-    "    order_id = Column(String, primary_key=True)\n",
-    "    date = Column(Date)\n",
-    "    symbol = Column(String)\n",
-    "    quantity = Column(Integer)\n",
-    "    price = Column(Float)\n",
-    "    \n",
-    "    def get_cost(self):\n",
-    "        return self.quantity*self.price"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "生成一个 `Order` 对象："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import datetime\n",
-    "order = Order(order_id='A0004', date=datetime.date.today(), symbol='MSFT', quantity=-1000, price=187.54)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "调用方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-187540.0"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "order.get_cost()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用上一节生成的数据库产生一个 `session`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from sqlalchemy import create_engine\n",
-    "from sqlalchemy.orm import sessionmaker\n",
-    "\n",
-    "engine = create_engine(\"sqlite:///my_database.sqlite\")   # 相当于 connection\n",
-    "Session = sessionmaker(bind=engine) # 相当于 cursor\n",
-    "session = Session()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用这个 `session` 向数据库中添加刚才生成的对象："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "session.add(order)\n",
-    "session.commit()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "显示是否添加成功："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(u'A0001', u'2013-12-01', u'AAPL', 1000, 203.4)\n",
-      "(u'A0002', u'2013-12-01', u'MSFT', 1500, 167.5)\n",
-      "(u'A0003', u'2013-12-02', u'GOOG', 1500, 167.5)\n",
-      "(u'A0004', u'2015-09-10', u'MSFT', -1000, 187.54)\n"
-     ]
-    }
-   ],
-   "source": [
-    "for row in engine.execute(\"SELECT * FROM orders\"):\n",
-    "    print row"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `filter` 进行查询，返回的是 `Order` 对象的列表："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "A0001 2013-12-01 203400.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "for order in session.query(Order).filter(Order.symbol==\"AAPL\"):\n",
-    "    print order.order_id, order.date, order.get_cost()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "返回列表的第一个："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "order_2 = session.query(Order).filter(Order.order_id=='A0002').first()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "u'MSFT'"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "order_2.symbol"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 对象关系映射"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "数据库中的记录可以与一个 `Python` 对象对应。\n",
+    "\n",
+    "例如对于上一节中的数据库：\n",
+    "\n",
+    "Order|Date|Stock|Quantity|Price\n",
+    "--|--|--|--|--\n",
+    "A0001|2013-12-01|AAPL|1000|203.4\n",
+    "A0002|2013-12-01|MSFT|1500|167.5\n",
+    "A0003|2013-12-02|GOOG|1500|167.5\n",
+    "\n",
+    "可以用一个类来描述：\n",
+    "\n",
+    "Attr.|Method\n",
+    "--|--\n",
+    "Order id| Cost\n",
+    "Date|\n",
+    "Stock|\n",
+    "Quant.|\n",
+    "Price|\n",
+    "\n",
+    "可以使用 `sqlalchemy` 来实现这种对应："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sqlalchemy.ext.declarative import declarative_base\n",
+    "from sqlalchemy import Column, Date, Float, Integer, String\n",
+    "\n",
+    "Base = declarative_base()\n",
+    "\n",
+    "class Order(Base):\n",
+    "    __tablename__ = 'orders'\n",
+    "    \n",
+    "    order_id = Column(String, primary_key=True)\n",
+    "    date = Column(Date)\n",
+    "    symbol = Column(String)\n",
+    "    quantity = Column(Integer)\n",
+    "    price = Column(Float)\n",
+    "    \n",
+    "    def get_cost(self):\n",
+    "        return self.quantity*self.price"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "生成一个 `Order` 对象："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "order = Order(order_id='A0004', date=datetime.date.today(), symbol='MSFT', quantity=-1000, price=187.54)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "调用方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-187540.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "order.get_cost()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用上一节生成的数据库产生一个 `session`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sqlalchemy import create_engine\n",
+    "from sqlalchemy.orm import sessionmaker\n",
+    "\n",
+    "engine = create_engine(\"sqlite:///my_database.sqlite\")   # 相当于 connection\n",
+    "Session = sessionmaker(bind=engine) # 相当于 cursor\n",
+    "session = Session()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用这个 `session` 向数据库中添加刚才生成的对象："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "session.add(order)\n",
+    "session.commit()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "显示是否添加成功："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(u'A0001', u'2013-12-01', u'AAPL', 1000, 203.4)\n",
+      "(u'A0002', u'2013-12-01', u'MSFT', 1500, 167.5)\n",
+      "(u'A0003', u'2013-12-02', u'GOOG', 1500, 167.5)\n",
+      "(u'A0004', u'2015-09-10', u'MSFT', -1000, 187.54)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for row in engine.execute(\"SELECT * FROM orders\"):\n",
+    "    print row"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `filter` 进行查询，返回的是 `Order` 对象的列表："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A0001 2013-12-01 203400.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "for order in session.query(Order).filter(Order.symbol==\"AAPL\"):\n",
+    "    print order.order_id, order.date, order.get_cost()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "返回列表的第一个："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "order_2 = session.query(Order).filter(Order.order_id=='A0002').first()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "u'MSFT'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "order_2.symbol"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/05. advanced python/05.08 functions.ipynb b/05-advanced-python/05.08-functions.ipynb
similarity index 95%
rename from 05. advanced python/05.08 functions.ipynb
rename to 05-advanced-python/05.08-functions.ipynb
index 44fb8677..8b47d46a 100644
--- a/05. advanced python/05.08 functions.ipynb	
+++ b/05-advanced-python/05.08-functions.ipynb
@@ -1,888 +1,888 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 函数进阶：参数传递，高阶函数，lambda 匿名函数，global 变量，递归"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 函数是基本类型"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在 `Python` 中，函数是一种基本类型的对象，这意味着\n",
-    "\n",
-    "- 可以将函数作为参数传给另一个函数\n",
-    "- 将函数作为字典的值储存\n",
-    "- 将函数作为另一个函数的返回值"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def square(x):\n",
-    "    \"\"\"Square of x.\"\"\"\n",
-    "    return x*x\n",
-    "\n",
-    "def cube(x):\n",
-    "    \"\"\"Cube of x.\"\"\"\n",
-    "    return x*x*x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "作为字典的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "funcs = {\n",
-    "    'square': square,\n",
-    "    'cube': cube,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "例子："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4\n",
-      "8\n",
-      "cube 8\n",
-      "square 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = 2\n",
-    "\n",
-    "print square(x)\n",
-    "print cube(x)\n",
-    "\n",
-    "for func in sorted(funcs):\n",
-    "    print func, funcs[func](x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 函数参数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 引用传递"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Python` 中的函数传递方式是 `call by reference` 即引用传递，例如，对于这样的用法：\n",
-    "\n",
-    "    x = [10, 11, 12]\n",
-    "    f(x)\n",
-    "\n",
-    "传递给函数 `f` 的是一个指向 `x` 所包含内容的引用，如果我们修改了这个引用所指向内容的值（例如 `x[0]=999`），那么外面的 `x` 的值也会被改变。不过如果我们在函数中赋给 `x` 一个新的值（例如另一个列表），那么在函数外面的 `x` 的值不会改变："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 2, 3]\n",
-      "[999, 2, 3]\n",
-      "[999, 2, 3]\n"
-     ]
-    }
-   ],
-   "source": [
-    "def mod_f(x):\n",
-    "    x[0] = 999\n",
-    "    return x\n",
-    "\n",
-    "x = [1, 2, 3]\n",
-    "\n",
-    "print x\n",
-    "print mod_f(x)\n",
-    "print x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 2, 3]\n",
-      "[4, 5, 6]\n",
-      "[1, 2, 3]\n"
-     ]
-    }
-   ],
-   "source": [
-    "def no_mod_f(x):\n",
-    "    x = [4, 5, 6]\n",
-    "    return x\n",
-    "\n",
-    "x = [1,2,3]\n",
-    "\n",
-    "print x\n",
-    "print no_mod_f(x)\n",
-    "print x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 默认参数是可变的！"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "函数可以传递默认参数，默认参数的绑定发生在函数定义的时候，以后每次调用默认参数时都会使用同一个引用。\n",
-    "\n",
-    "这样的机制会导致这种情况的发生："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def f(x = []):\n",
-    "    x.append(1)\n",
-    "    return x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "理论上说，我们希望调用 `f()` 时返回的是 `[1]`， 但事实上："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1]\n",
-      "[1, 1]\n",
-      "[1, 1, 1]\n",
-      "[9, 9, 9, 1]\n",
-      "[1, 1, 1, 1]\n",
-      "[1, 1, 1, 1, 1]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print f()\n",
-    "print f()\n",
-    "print f()\n",
-    "print f(x = [9,9,9])\n",
-    "print f()\n",
-    "print f()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "而我们希望看到的应该是这样："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1]\n",
-      "[1]\n",
-      "[1]\n",
-      "[9, 9, 9, 1]\n",
-      "[1]\n",
-      "[1]\n"
-     ]
-    }
-   ],
-   "source": [
-    "def f(x = None):\n",
-    "    if x is None:\n",
-    "        x = []\n",
-    "    x.append(1)\n",
-    "    return x\n",
-    "\n",
-    "print f()\n",
-    "print f()\n",
-    "print f()\n",
-    "print f(x = [9,9,9])\n",
-    "print f()\n",
-    "print f()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 高阶函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "以函数作为参数，或者返回一个函数的函数是高阶函数，常用的例子有 `map` 和 `filter` 函数：\n",
-    "\n",
-    "`map(f, sq)` 函数将 `f` 作用到 `sq` 的每个元素上去，并返回结果组成的列表，相当于：\n",
-    "```python\n",
-    "[f(s) for s in sq]\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 4, 9, 16]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "map(square, range(5))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`filter(f, sq)` 函数的作用相当于，对于 `sq` 的每个元素 `s`，返回所有 `f(s)` 为 `True` 的 `s` 组成的列表，相当于：\n",
-    "```python\n",
-    "[s for s in sq if f(s)]\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 2, 4]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def is_even(x):\n",
-    "    return x % 2 == 0\n",
-    "\n",
-    "filter(is_even, range(5))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "一起使用："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 4, 16]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "map(square, filter(is_even, range(5)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`reduce(f, sq)` 函数接受一个二元操作函数 `f(x,y)`，并对于序列 `sq` 每次合并两个元素："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "15"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def my_add(x, y):\n",
-    "    return x + y\n",
-    "\n",
-    "reduce(my_add, [1,2,3,4,5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "传入加法函数，相当于对序列求和。\n",
-    "\n",
-    "返回一个函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def make_logger(target):\n",
-    "    def logger(data):\n",
-    "        with open(target, 'a') as f:\n",
-    "            f.write(data + '\\n')\n",
-    "    return logger\n",
-    "\n",
-    "foo_logger = make_logger('foo.txt')\n",
-    "foo_logger('Hello')\n",
-    "foo_logger('World')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hello\n",
-      "World\n"
-     ]
-    }
-   ],
-   "source": [
-    "!cat foo.txt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('foo.txt')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 匿名函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在使用 `map`， `filter`，`reduce` 等函数的时候，为了方便，对一些简单的函数，我们通常使用匿名函数的方式进行处理，其基本形式是：\n",
-    "\n",
-    "    lambda <variables>: <expression>\n",
-    "\n",
-    "例如，我们可以将这个："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0, 1, 4, 9, 16]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print map(square, range(5))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "用匿名函数替换为："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0, 1, 4, 9, 16]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print map(lambda x: x * x, range(5))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "匿名函数虽然写起来比较方便（省去了定义函数的烦恼），但是有时候会比较难于阅读："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "285\n"
-     ]
-    }
-   ],
-   "source": [
-    "s1 = reduce(lambda x, y: x+y, map(lambda x: x**2, range(1,10)))\n",
-    "print(s1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "当然，更简单地，我们可以写成这样："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "285\n"
-     ]
-    }
-   ],
-   "source": [
-    "s2 = sum(x**2 for x in range(1, 10))\n",
-    "print s2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# global 变量"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "一般来说，函数中是可以直接使用全局变量的值的："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "15\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = 15\n",
-    "\n",
-    "def print_x():\n",
-    "    print x\n",
-    "    \n",
-    "print_x()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "但是要在函数中修改全局变量的值，需要加上 `global` 关键字："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "18\n",
-      "18\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = 15\n",
-    "\n",
-    "def print_newx():\n",
-    "    global x\n",
-    "    x = 18\n",
-    "    print x\n",
-    "    \n",
-    "print_newx()\n",
-    "\n",
-    "print x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "如果不加上这句 `global` 那么全局变量的值不会改变："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "18\n",
-      "15\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = 15\n",
-    "\n",
-    "def print_newx():\n",
-    "    x = 18\n",
-    "    print x\n",
-    "    \n",
-    "print_newx()\n",
-    "\n",
-    "print x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 递归"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "递归是指函数在执行的过程中调用了本身，一般用于分治法，不过在 `Python` 中这样的用法十分地小，所以一般不怎么使用：\n",
-    "\n",
-    "Fibocacci 数列："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 1, 2, 3, 5, 8, 13, 21, 34, 55]\n"
-     ]
-    }
-   ],
-   "source": [
-    "def fib1(n):\n",
-    "    \"\"\"Fib with recursion.\"\"\"\n",
-    "\n",
-    "    # base case\n",
-    "    if n==0 or n==1:\n",
-    "        return 1\n",
-    "    # recurssive caae\n",
-    "    else:\n",
-    "        return fib1(n-1) + fib1(n-2)\n",
-    "\n",
-    "print [fib1(i) for i in range(10)]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "一个更高效的非递归版本："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 1, 2, 3, 5, 8, 13, 21, 34, 55]\n"
-     ]
-    }
-   ],
-   "source": [
-    "def fib2(n):\n",
-    "    \"\"\"Fib without recursion.\"\"\"\n",
-    "    a, b = 0, 1\n",
-    "    for i in range(1, n+1):\n",
-    "        a, b = b, a+b\n",
-    "    return b\n",
-    "\n",
-    "print [fib2(i) for i in range(10)]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "速度比较："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "100 loops, best of 3: 5.35 ms per loop\n",
-      "100000 loops, best of 3: 2.2 µs per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit fib1(20)\n",
-    "%timeit fib2(20)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于第一个递归函数来说，调用 `fib(n+2)` 的时候计算 `fib(n+1), fib(n)`，调用 `fib(n+1)` 的时候也计算了一次 `fib(n)`，这样造成了重复计算。\n",
-    "\n",
-    "使用缓存机制的递归版本，这里利用了默认参数可变的性质，构造了一个缓存："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 1, 2, 3, 5, 8, 13, 21, 34, 55]\n",
-      "100 loops, best of 3: 5.37 ms per loop\n",
-      "100000 loops, best of 3: 2.19 µs per loop\n",
-      "The slowest run took 150.16 times longer than the fastest. This could mean that an intermediate result is being cached \n",
-      "1000000 loops, best of 3: 230 ns per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "def fib3(n, cache={0: 1, 1: 1}):\n",
-    "    \"\"\"Fib with recursion and caching.\"\"\"\n",
-    "\n",
-    "    try:\n",
-    "        return cache[n]\n",
-    "    except KeyError:\n",
-    "        cache[n] = fib3(n-1) + fib3(n-2)\n",
-    "        return cache[n]\n",
-    "\n",
-    "print [fib3(i) for i in range(10)]\n",
-    "\n",
-    "%timeit fib1(20)\n",
-    "%timeit fib2(20)\n",
-    "%timeit fib3(20)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 函数进阶：参数传递，高阶函数，lambda 匿名函数，global 变量，递归"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 函数是基本类型"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在 `Python` 中，函数是一种基本类型的对象，这意味着\n",
+    "\n",
+    "- 可以将函数作为参数传给另一个函数\n",
+    "- 将函数作为字典的值储存\n",
+    "- 将函数作为另一个函数的返回值"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def square(x):\n",
+    "    \"\"\"Square of x.\"\"\"\n",
+    "    return x*x\n",
+    "\n",
+    "def cube(x):\n",
+    "    \"\"\"Cube of x.\"\"\"\n",
+    "    return x*x*x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "作为字典的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "funcs = {\n",
+    "    'square': square,\n",
+    "    'cube': cube,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "例子："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n",
+      "8\n",
+      "cube 8\n",
+      "square 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 2\n",
+    "\n",
+    "print square(x)\n",
+    "print cube(x)\n",
+    "\n",
+    "for func in sorted(funcs):\n",
+    "    print func, funcs[func](x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 函数参数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 引用传递"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Python` 中的函数传递方式是 `call by reference` 即引用传递，例如，对于这样的用法：\n",
+    "\n",
+    "    x = [10, 11, 12]\n",
+    "    f(x)\n",
+    "\n",
+    "传递给函数 `f` 的是一个指向 `x` 所包含内容的引用，如果我们修改了这个引用所指向内容的值（例如 `x[0]=999`），那么外面的 `x` 的值也会被改变。不过如果我们在函数中赋给 `x` 一个新的值（例如另一个列表），那么在函数外面的 `x` 的值不会改变："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3]\n",
+      "[999, 2, 3]\n",
+      "[999, 2, 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def mod_f(x):\n",
+    "    x[0] = 999\n",
+    "    return x\n",
+    "\n",
+    "x = [1, 2, 3]\n",
+    "\n",
+    "print x\n",
+    "print mod_f(x)\n",
+    "print x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3]\n",
+      "[4, 5, 6]\n",
+      "[1, 2, 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def no_mod_f(x):\n",
+    "    x = [4, 5, 6]\n",
+    "    return x\n",
+    "\n",
+    "x = [1,2,3]\n",
+    "\n",
+    "print x\n",
+    "print no_mod_f(x)\n",
+    "print x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 默认参数是可变的！"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "函数可以传递默认参数，默认参数的绑定发生在函数定义的时候，以后每次调用默认参数时都会使用同一个引用。\n",
+    "\n",
+    "这样的机制会导致这种情况的发生："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def f(x = []):\n",
+    "    x.append(1)\n",
+    "    return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "理论上说，我们希望调用 `f()` 时返回的是 `[1]`， 但事实上："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1]\n",
+      "[1, 1]\n",
+      "[1, 1, 1]\n",
+      "[9, 9, 9, 1]\n",
+      "[1, 1, 1, 1]\n",
+      "[1, 1, 1, 1, 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print f()\n",
+    "print f()\n",
+    "print f()\n",
+    "print f(x = [9,9,9])\n",
+    "print f()\n",
+    "print f()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "而我们希望看到的应该是这样："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1]\n",
+      "[1]\n",
+      "[1]\n",
+      "[9, 9, 9, 1]\n",
+      "[1]\n",
+      "[1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def f(x = None):\n",
+    "    if x is None:\n",
+    "        x = []\n",
+    "    x.append(1)\n",
+    "    return x\n",
+    "\n",
+    "print f()\n",
+    "print f()\n",
+    "print f()\n",
+    "print f(x = [9,9,9])\n",
+    "print f()\n",
+    "print f()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 高阶函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "以函数作为参数，或者返回一个函数的函数是高阶函数，常用的例子有 `map` 和 `filter` 函数：\n",
+    "\n",
+    "`map(f, sq)` 函数将 `f` 作用到 `sq` 的每个元素上去，并返回结果组成的列表，相当于：\n",
+    "```python\n",
+    "[f(s) for s in sq]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 4, 9, 16]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "map(square, range(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`filter(f, sq)` 函数的作用相当于，对于 `sq` 的每个元素 `s`，返回所有 `f(s)` 为 `True` 的 `s` 组成的列表，相当于：\n",
+    "```python\n",
+    "[s for s in sq if f(s)]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 2, 4]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def is_even(x):\n",
+    "    return x % 2 == 0\n",
+    "\n",
+    "filter(is_even, range(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "一起使用："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 4, 16]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "map(square, filter(is_even, range(5)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`reduce(f, sq)` 函数接受一个二元操作函数 `f(x,y)`，并对于序列 `sq` 每次合并两个元素："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def my_add(x, y):\n",
+    "    return x + y\n",
+    "\n",
+    "reduce(my_add, [1,2,3,4,5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "传入加法函数，相当于对序列求和。\n",
+    "\n",
+    "返回一个函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_logger(target):\n",
+    "    def logger(data):\n",
+    "        with open(target, 'a') as f:\n",
+    "            f.write(data + '\\n')\n",
+    "    return logger\n",
+    "\n",
+    "foo_logger = make_logger('foo.txt')\n",
+    "foo_logger('Hello')\n",
+    "foo_logger('World')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello\n",
+      "World\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat foo.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('foo.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 匿名函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在使用 `map`， `filter`，`reduce` 等函数的时候，为了方便，对一些简单的函数，我们通常使用匿名函数的方式进行处理，其基本形式是：\n",
+    "\n",
+    "    lambda <variables>: <expression>\n",
+    "\n",
+    "例如，我们可以将这个："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 1, 4, 9, 16]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print map(square, range(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "用匿名函数替换为："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 1, 4, 9, 16]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print map(lambda x: x * x, range(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "匿名函数虽然写起来比较方便（省去了定义函数的烦恼），但是有时候会比较难于阅读："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "285\n"
+     ]
+    }
+   ],
+   "source": [
+    "s1 = reduce(lambda x, y: x+y, map(lambda x: x**2, range(1,10)))\n",
+    "print(s1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "当然，更简单地，我们可以写成这样："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "285\n"
+     ]
+    }
+   ],
+   "source": [
+    "s2 = sum(x**2 for x in range(1, 10))\n",
+    "print s2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# global 变量"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "一般来说，函数中是可以直接使用全局变量的值的："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 15\n",
+    "\n",
+    "def print_x():\n",
+    "    print x\n",
+    "    \n",
+    "print_x()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "但是要在函数中修改全局变量的值，需要加上 `global` 关键字："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "18\n",
+      "18\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 15\n",
+    "\n",
+    "def print_newx():\n",
+    "    global x\n",
+    "    x = 18\n",
+    "    print x\n",
+    "    \n",
+    "print_newx()\n",
+    "\n",
+    "print x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "如果不加上这句 `global` 那么全局变量的值不会改变："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "18\n",
+      "15\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 15\n",
+    "\n",
+    "def print_newx():\n",
+    "    x = 18\n",
+    "    print x\n",
+    "    \n",
+    "print_newx()\n",
+    "\n",
+    "print x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 递归"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "递归是指函数在执行的过程中调用了本身，一般用于分治法，不过在 `Python` 中这样的用法十分地小，所以一般不怎么使用：\n",
+    "\n",
+    "Fibocacci 数列："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 1, 2, 3, 5, 8, 13, 21, 34, 55]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def fib1(n):\n",
+    "    \"\"\"Fib with recursion.\"\"\"\n",
+    "\n",
+    "    # base case\n",
+    "    if n==0 or n==1:\n",
+    "        return 1\n",
+    "    # recurssive caae\n",
+    "    else:\n",
+    "        return fib1(n-1) + fib1(n-2)\n",
+    "\n",
+    "print [fib1(i) for i in range(10)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "一个更高效的非递归版本："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 1, 2, 3, 5, 8, 13, 21, 34, 55]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def fib2(n):\n",
+    "    \"\"\"Fib without recursion.\"\"\"\n",
+    "    a, b = 0, 1\n",
+    "    for i in range(1, n+1):\n",
+    "        a, b = b, a+b\n",
+    "    return b\n",
+    "\n",
+    "print [fib2(i) for i in range(10)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "速度比较："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100 loops, best of 3: 5.35 ms per loop\n",
+      "100000 loops, best of 3: 2.2 µs per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit fib1(20)\n",
+    "%timeit fib2(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于第一个递归函数来说，调用 `fib(n+2)` 的时候计算 `fib(n+1), fib(n)`，调用 `fib(n+1)` 的时候也计算了一次 `fib(n)`，这样造成了重复计算。\n",
+    "\n",
+    "使用缓存机制的递归版本，这里利用了默认参数可变的性质，构造了一个缓存："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 1, 2, 3, 5, 8, 13, 21, 34, 55]\n",
+      "100 loops, best of 3: 5.37 ms per loop\n",
+      "100000 loops, best of 3: 2.19 µs per loop\n",
+      "The slowest run took 150.16 times longer than the fastest. This could mean that an intermediate result is being cached \n",
+      "1000000 loops, best of 3: 230 ns per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "def fib3(n, cache={0: 1, 1: 1}):\n",
+    "    \"\"\"Fib with recursion and caching.\"\"\"\n",
+    "\n",
+    "    try:\n",
+    "        return cache[n]\n",
+    "    except KeyError:\n",
+    "        cache[n] = fib3(n-1) + fib3(n-2)\n",
+    "        return cache[n]\n",
+    "\n",
+    "print [fib3(i) for i in range(10)]\n",
+    "\n",
+    "%timeit fib1(20)\n",
+    "%timeit fib2(20)\n",
+    "%timeit fib3(20)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/05. advanced python/05.09 iterators.ipynb b/05-advanced-python/05.09-iterators.ipynb
similarity index 95%
rename from 05. advanced python/05.09 iterators.ipynb
rename to 05-advanced-python/05.09-iterators.ipynb
index be5b630a..a9f5a503 100644
--- a/05. advanced python/05.09 iterators.ipynb	
+++ b/05-advanced-python/05.09-iterators.ipynb
@@ -1,627 +1,627 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 迭代器"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 简介"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "迭代器对象可以在 `for` 循环中使用："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\n",
-      "4\n",
-      "6\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = [2, 4, 6]\n",
-    "\n",
-    "for n in x:\n",
-    "    print n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "其好处是不需要对下标进行迭代，但是有些情况下，我们既希望获得下标，也希望获得对应的值，那么可以将迭代器传给 `enumerate` 函数，这样每次迭代都会返回一组 `(index, value)` 组成的元组："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pos 0 is 2\n",
-      "pos 1 is 4\n",
-      "pos 2 is 6\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = [2, 4, 6]\n",
-    "\n",
-    "for i, n in enumerate(x):\n",
-    "    print 'pos', i, 'is', n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "迭代器对象必须实现 `__iter__` 方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<listiterator object at 0x0000000003CAE630>\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = [2, 4, 6]\n",
-    "i = x.__iter__()\n",
-    "print i"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`__iter__()` 返回的对象支持 `next` 方法，返回迭代器中的下一个元素："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\n"
-     ]
-    }
-   ],
-   "source": [
-    "print i.next()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "当下一个元素不存在时，会 `raise` 一个 `StopIteration` 错误："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4\n",
-      "6\n"
-     ]
-    }
-   ],
-   "source": [
-    "print i.next()\n",
-    "print i.next()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "StopIteration",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-6-e590fe0d22f8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mStopIteration\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "i.next()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "很多标准库函数返回的是迭代器："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<listreverseiterator object at 0x0000000003D615F8>\n"
-     ]
-    }
-   ],
-   "source": [
-    "r = reversed(x)\n",
-    "print r"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "调用它的 `next()` 方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6\n",
-      "4\n",
-      "2\n"
-     ]
-    }
-   ],
-   "source": [
-    "print r.next()\n",
-    "print r.next()\n",
-    "print r.next()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "字典对象的 `iterkeys, itervalues, iteritems` 方法返回的都是迭代器："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<dictionary-itemiterator object at 0x0000000003D51B88>\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = {'a':1, 'b':2, 'c':3}\n",
-    "i = x.iteritems()\n",
-    "print i"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "迭代器的 `__iter__` 方法返回它本身："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<dictionary-itemiterator object at 0x0000000003D51B88>\n"
-     ]
-    }
-   ],
-   "source": [
-    "print i.__iter__()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('a', 1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print i.next()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 自定义迭代器"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "自定义一个 list 的取反迭代器："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class ReverseListIterator(object):\n",
-    "    \n",
-    "    def __init__(self, list):\n",
-    "        self.list = list\n",
-    "        self.index = len(list)\n",
-    "        \n",
-    "    def __iter__(self):\n",
-    "        return self\n",
-    "    \n",
-    "    def next(self):\n",
-    "        self.index -= 1\n",
-    "        if self.index >= 0:\n",
-    "            return self.list[self.index]\n",
-    "        else:\n",
-    "            raise StopIteration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "9 8 7 6 5 4 3 2 1 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = range(10)\n",
-    "for i in ReverseListIterator(x):\n",
-    "    print i,"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "只要我们定义了这三个方法，我们可以返回任意迭代值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Collatz(object):\n",
-    "    \n",
-    "    def __init__(self, start):\n",
-    "        self.value = start\n",
-    "        \n",
-    "    def __iter__(self):\n",
-    "        return self\n",
-    "    \n",
-    "    def next(self):\n",
-    "        if self.value == 1:\n",
-    "            raise StopIteration\n",
-    "        elif self.value % 2 == 0:\n",
-    "            self.value = self.value / 2\n",
-    "        else:\n",
-    "            self.value = 3 * self.value + 1\n",
-    "        return self.value"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里我们实现 [Collatz 猜想](http://baike.baidu.com/view/736196.htm)：\n",
-    "\n",
-    "- 奇数 n：返回 3n + 1\n",
-    "- 偶数 n：返回 n / 2\n",
-    "\n",
-    "直到 n 为 1 为止："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "for x in Collatz(7):\n",
-    "    print x,"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不过迭代器对象存在状态，会出现这样的问题："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "22 11\n",
-      "34 17\n",
-      "52 26\n",
-      "13 40\n",
-      "20 10\n",
-      "5 16\n",
-      "8 4\n",
-      "2 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "i = Collatz(7)\n",
-    "for x, y in zip(i, i):\n",
-    "    print x, y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "一个比较好的解决方法是将迭代器和可迭代对象分开处理，这里提供了一个二分树的中序遍历实现："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class BinaryTree(object):\n",
-    "    def __init__(self, value, left=None, right=None):\n",
-    "        self.value = value\n",
-    "        self.left = left\n",
-    "        self.right = right\n",
-    "\n",
-    "    def __iter__(self):\n",
-    "        return InorderIterator(self)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class InorderIterator(object):\n",
-    "    \n",
-    "    def __init__(self, node):\n",
-    "        self.node = node\n",
-    "        self.stack = []\n",
-    "    \n",
-    "    def next(self):\n",
-    "        if len(self.stack) > 0 or self.node is not None:\n",
-    "            while self.node is not None:\n",
-    "                self.stack.append(self.node)\n",
-    "                self.node = self.node.left\n",
-    "            node = self.stack.pop()\n",
-    "            self.node = node.right\n",
-    "            return node.value\n",
-    "        else:\n",
-    "            raise StopIteration()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "tree = BinaryTree(\n",
-    "    left=BinaryTree(\n",
-    "        left=BinaryTree(1),\n",
-    "        value=2,\n",
-    "        right=BinaryTree(\n",
-    "            left=BinaryTree(3),\n",
-    "            value=4,\n",
-    "            right=BinaryTree(5)\n",
-    "        ),\n",
-    "    ),\n",
-    "    value=6,\n",
-    "    right=BinaryTree(\n",
-    "        value=7,\n",
-    "        right=BinaryTree(8)\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 2 3 4 5 6 7 8\n"
-     ]
-    }
-   ],
-   "source": [
-    "for value in tree:\n",
-    "    print value,"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不会出现之前的问题："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 1\n",
-      "2 2\n",
-      "3 3\n",
-      "4 4\n",
-      "5 5\n",
-      "6 6\n",
-      "7 7\n",
-      "8 8\n"
-     ]
-    }
-   ],
-   "source": [
-    "for x,y in zip(tree, tree):\n",
-    "    print x, y"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 迭代器"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 简介"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "迭代器对象可以在 `for` 循环中使用："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "4\n",
+      "6\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = [2, 4, 6]\n",
+    "\n",
+    "for n in x:\n",
+    "    print n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "其好处是不需要对下标进行迭代，但是有些情况下，我们既希望获得下标，也希望获得对应的值，那么可以将迭代器传给 `enumerate` 函数，这样每次迭代都会返回一组 `(index, value)` 组成的元组："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pos 0 is 2\n",
+      "pos 1 is 4\n",
+      "pos 2 is 6\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = [2, 4, 6]\n",
+    "\n",
+    "for i, n in enumerate(x):\n",
+    "    print 'pos', i, 'is', n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "迭代器对象必须实现 `__iter__` 方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<listiterator object at 0x0000000003CAE630>\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = [2, 4, 6]\n",
+    "i = x.__iter__()\n",
+    "print i"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`__iter__()` 返回的对象支持 `next` 方法，返回迭代器中的下一个元素："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "print i.next()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "当下一个元素不存在时，会 `raise` 一个 `StopIteration` 错误："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n",
+      "6\n"
+     ]
+    }
+   ],
+   "source": [
+    "print i.next()\n",
+    "print i.next()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "StopIteration",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-6-e590fe0d22f8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mStopIteration\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "i.next()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "很多标准库函数返回的是迭代器："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<listreverseiterator object at 0x0000000003D615F8>\n"
+     ]
+    }
+   ],
+   "source": [
+    "r = reversed(x)\n",
+    "print r"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "调用它的 `next()` 方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6\n",
+      "4\n",
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "print r.next()\n",
+    "print r.next()\n",
+    "print r.next()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "字典对象的 `iterkeys, itervalues, iteritems` 方法返回的都是迭代器："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<dictionary-itemiterator object at 0x0000000003D51B88>\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = {'a':1, 'b':2, 'c':3}\n",
+    "i = x.iteritems()\n",
+    "print i"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "迭代器的 `__iter__` 方法返回它本身："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<dictionary-itemiterator object at 0x0000000003D51B88>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print i.__iter__()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('a', 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print i.next()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 自定义迭代器"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "自定义一个 list 的取反迭代器："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class ReverseListIterator(object):\n",
+    "    \n",
+    "    def __init__(self, list):\n",
+    "        self.list = list\n",
+    "        self.index = len(list)\n",
+    "        \n",
+    "    def __iter__(self):\n",
+    "        return self\n",
+    "    \n",
+    "    def next(self):\n",
+    "        self.index -= 1\n",
+    "        if self.index >= 0:\n",
+    "            return self.list[self.index]\n",
+    "        else:\n",
+    "            raise StopIteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9 8 7 6 5 4 3 2 1 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = range(10)\n",
+    "for i in ReverseListIterator(x):\n",
+    "    print i,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "只要我们定义了这三个方法，我们可以返回任意迭代值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Collatz(object):\n",
+    "    \n",
+    "    def __init__(self, start):\n",
+    "        self.value = start\n",
+    "        \n",
+    "    def __iter__(self):\n",
+    "        return self\n",
+    "    \n",
+    "    def next(self):\n",
+    "        if self.value == 1:\n",
+    "            raise StopIteration\n",
+    "        elif self.value % 2 == 0:\n",
+    "            self.value = self.value / 2\n",
+    "        else:\n",
+    "            self.value = 3 * self.value + 1\n",
+    "        return self.value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里我们实现 [Collatz 猜想](http://baike.baidu.com/view/736196.htm)：\n",
+    "\n",
+    "- 奇数 n：返回 3n + 1\n",
+    "- 偶数 n：返回 n / 2\n",
+    "\n",
+    "直到 n 为 1 为止："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "for x in Collatz(7):\n",
+    "    print x,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不过迭代器对象存在状态，会出现这样的问题："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22 11\n",
+      "34 17\n",
+      "52 26\n",
+      "13 40\n",
+      "20 10\n",
+      "5 16\n",
+      "8 4\n",
+      "2 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = Collatz(7)\n",
+    "for x, y in zip(i, i):\n",
+    "    print x, y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "一个比较好的解决方法是将迭代器和可迭代对象分开处理，这里提供了一个二分树的中序遍历实现："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class BinaryTree(object):\n",
+    "    def __init__(self, value, left=None, right=None):\n",
+    "        self.value = value\n",
+    "        self.left = left\n",
+    "        self.right = right\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return InorderIterator(self)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class InorderIterator(object):\n",
+    "    \n",
+    "    def __init__(self, node):\n",
+    "        self.node = node\n",
+    "        self.stack = []\n",
+    "    \n",
+    "    def next(self):\n",
+    "        if len(self.stack) > 0 or self.node is not None:\n",
+    "            while self.node is not None:\n",
+    "                self.stack.append(self.node)\n",
+    "                self.node = self.node.left\n",
+    "            node = self.stack.pop()\n",
+    "            self.node = node.right\n",
+    "            return node.value\n",
+    "        else:\n",
+    "            raise StopIteration()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "tree = BinaryTree(\n",
+    "    left=BinaryTree(\n",
+    "        left=BinaryTree(1),\n",
+    "        value=2,\n",
+    "        right=BinaryTree(\n",
+    "            left=BinaryTree(3),\n",
+    "            value=4,\n",
+    "            right=BinaryTree(5)\n",
+    "        ),\n",
+    "    ),\n",
+    "    value=6,\n",
+    "    right=BinaryTree(\n",
+    "        value=7,\n",
+    "        right=BinaryTree(8)\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 2 3 4 5 6 7 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "for value in tree:\n",
+    "    print value,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不会出现之前的问题："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 1\n",
+      "2 2\n",
+      "3 3\n",
+      "4 4\n",
+      "5 5\n",
+      "6 6\n",
+      "7 7\n",
+      "8 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "for x,y in zip(tree, tree):\n",
+    "    print x, y"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/05. advanced python/05.10 generators.ipynb b/05-advanced-python/05.10-generators.ipynb
similarity index 95%
rename from 05. advanced python/05.10 generators.ipynb
rename to 05-advanced-python/05.10-generators.ipynb
index 6a70eb99..ca4a6823 100644
--- a/05. advanced python/05.10 generators.ipynb	
+++ b/05-advanced-python/05.10-generators.ipynb
@@ -1,375 +1,375 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 生成器"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`while` 循环通常有这样的形式：\n",
-    "\n",
-    "```python\n",
-    "<do setup>\n",
-    "result = []\n",
-    "while True:\n",
-    "    <generate value>\n",
-    "    result.append(value)\n",
-    "    if <done>:\n",
-    "        break\n",
-    "```\n",
-    "\n",
-    "使用迭代器实现这样的循环：\n",
-    "\n",
-    "```python\n",
-    "class GenericIterator(object):\n",
-    "    def __init__(self, ...):\n",
-    "        <do setup>\n",
-    "        # 需要额外储存状态\n",
-    "        <store state>\n",
-    "    def next(self): \n",
-    "        <load state>\n",
-    "        <generate value>\n",
-    "        if <done>:\n",
-    "            raise StopIteration()\n",
-    "        <store state>\n",
-    "        return value\n",
-    "```\n",
-    "\n",
-    "更简单的，可以使用生成器：\n",
-    "\n",
-    "```python\n",
-    "def generator(...):\n",
-    "    <do setup>\n",
-    "    while True:\n",
-    "        <generate value>\n",
-    "        # yield 说明这个函数可以返回多个值！\n",
-    "        yield value\n",
-    "        if <done>:\n",
-    "            break\n",
-    "```\n",
-    "\n",
-    "生成器使用 `yield` 关键字将值输出，而迭代器则通过 `next` 的 `return` 将值返回；与迭代器不同的是，生成器会自动记录当前的状态，而迭代器则需要进行额外的操作来记录当前的状态。\n",
-    "\n",
-    "对于之前的 `collatz` 猜想，简单循环的实现如下："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "def collatz(n):\n",
-    "    sequence = []\n",
-    "    while n != 1:\n",
-    "        if n % 2 == 0:\n",
-    "            n /= 2\n",
-    "        else:\n",
-    "            n = 3*n + 1\n",
-    "        sequence.append(n)\n",
-    "    return sequence\n",
-    "\n",
-    "for x in collatz(7):\n",
-    "    print x,"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "迭代器的版本如下："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "class Collatz(object):\n",
-    "    def __init__(self, start):\n",
-    "        self.value = start\n",
-    "\n",
-    "    def __iter__(self):\n",
-    "        return self\n",
-    "    \n",
-    "    def next(self):\n",
-    "        if self.value == 1:\n",
-    "            raise StopIteration()\n",
-    "        elif self.value % 2 == 0:\n",
-    "            self.value = self.value/2\n",
-    "        else:\n",
-    "            self.value = 3*self.value + 1\n",
-    "        return self.value\n",
-    "\n",
-    "for x in Collatz(7):\n",
-    "    print x,"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "生成器的版本如下："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "def collatz(n):\n",
-    "    while n != 1:\n",
-    "        if n % 2 == 0:\n",
-    "            n /= 2\n",
-    "        else:\n",
-    "            n = 3*n + 1\n",
-    "        yield n\n",
-    "\n",
-    "for x in collatz(7):\n",
-    "    print x,"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上，生成器也是一种迭代器："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<generator object collatz at 0x0000000003B63750>\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = collatz(7)\n",
-    "print x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "它支持 `next` 方法，返回下一个 `yield` 的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "22\n",
-      "11\n"
-     ]
-    }
-   ],
-   "source": [
-    "print x.next()\n",
-    "print x.next()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`__iter__` 方法返回的是它本身："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<generator object collatz at 0x0000000003B63750>\n"
-     ]
-    }
-   ],
-   "source": [
-    "print x.__iter__()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "之前的二叉树迭代器可以改写为更简单的生成器模式来进行中序遍历："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class BinaryTree(object):\n",
-    "    def __init__(self, value, left=None, right=None):\n",
-    "        self.value = value\n",
-    "        self.left = left\n",
-    "        self.right = right\n",
-    "\n",
-    "    def __iter__(self):\n",
-    "        # 将迭代器设为生成器方法\n",
-    "        return self.inorder()\n",
-    "    \n",
-    "    def inorder(self):\n",
-    "        # traverse the left branch\n",
-    "        if self.left is not None:\n",
-    "            for value in self.left:\n",
-    "                yield value\n",
-    "                \n",
-    "        # yield node's value\n",
-    "        yield self.value\n",
-    "        \n",
-    "        # traverse the right branch\n",
-    "        if self.right is not None:\n",
-    "            for value in self.right:\n",
-    "                yield value"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "非递归的实现："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def inorder(self):\n",
-    "    node = self\n",
-    "    stack = []\n",
-    "    while len(stack) > 0 or node is not None:\n",
-    "        while node is not None:\n",
-    "            stack.append(node)\n",
-    "            node = node.left\n",
-    "        node = stack.pop()\n",
-    "        yield node.value\n",
-    "        node = node.right"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 2 3 4 5 6 7 8\n"
-     ]
-    }
-   ],
-   "source": [
-    "tree = BinaryTree(\n",
-    "    left=BinaryTree(\n",
-    "        left=BinaryTree(1),\n",
-    "        value=2,\n",
-    "        right=BinaryTree(\n",
-    "            left=BinaryTree(3),\n",
-    "            value=4,\n",
-    "            right=BinaryTree(5)\n",
-    "        ),\n",
-    "    ),\n",
-    "    value=6,\n",
-    "    right=BinaryTree(\n",
-    "        value=7,\n",
-    "        right=BinaryTree(8)\n",
-    "    )\n",
-    ")\n",
-    "for value in tree:\n",
-    "    print value,"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 生成器"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`while` 循环通常有这样的形式：\n",
+    "\n",
+    "```python\n",
+    "<do setup>\n",
+    "result = []\n",
+    "while True:\n",
+    "    <generate value>\n",
+    "    result.append(value)\n",
+    "    if <done>:\n",
+    "        break\n",
+    "```\n",
+    "\n",
+    "使用迭代器实现这样的循环：\n",
+    "\n",
+    "```python\n",
+    "class GenericIterator(object):\n",
+    "    def __init__(self, ...):\n",
+    "        <do setup>\n",
+    "        # 需要额外储存状态\n",
+    "        <store state>\n",
+    "    def next(self): \n",
+    "        <load state>\n",
+    "        <generate value>\n",
+    "        if <done>:\n",
+    "            raise StopIteration()\n",
+    "        <store state>\n",
+    "        return value\n",
+    "```\n",
+    "\n",
+    "更简单的，可以使用生成器：\n",
+    "\n",
+    "```python\n",
+    "def generator(...):\n",
+    "    <do setup>\n",
+    "    while True:\n",
+    "        <generate value>\n",
+    "        # yield 说明这个函数可以返回多个值！\n",
+    "        yield value\n",
+    "        if <done>:\n",
+    "            break\n",
+    "```\n",
+    "\n",
+    "生成器使用 `yield` 关键字将值输出，而迭代器则通过 `next` 的 `return` 将值返回；与迭代器不同的是，生成器会自动记录当前的状态，而迭代器则需要进行额外的操作来记录当前的状态。\n",
+    "\n",
+    "对于之前的 `collatz` 猜想，简单循环的实现如下："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "def collatz(n):\n",
+    "    sequence = []\n",
+    "    while n != 1:\n",
+    "        if n % 2 == 0:\n",
+    "            n /= 2\n",
+    "        else:\n",
+    "            n = 3*n + 1\n",
+    "        sequence.append(n)\n",
+    "    return sequence\n",
+    "\n",
+    "for x in collatz(7):\n",
+    "    print x,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "迭代器的版本如下："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Collatz(object):\n",
+    "    def __init__(self, start):\n",
+    "        self.value = start\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return self\n",
+    "    \n",
+    "    def next(self):\n",
+    "        if self.value == 1:\n",
+    "            raise StopIteration()\n",
+    "        elif self.value % 2 == 0:\n",
+    "            self.value = self.value/2\n",
+    "        else:\n",
+    "            self.value = 3*self.value + 1\n",
+    "        return self.value\n",
+    "\n",
+    "for x in Collatz(7):\n",
+    "    print x,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "生成器的版本如下："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "def collatz(n):\n",
+    "    while n != 1:\n",
+    "        if n % 2 == 0:\n",
+    "            n /= 2\n",
+    "        else:\n",
+    "            n = 3*n + 1\n",
+    "        yield n\n",
+    "\n",
+    "for x in collatz(7):\n",
+    "    print x,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上，生成器也是一种迭代器："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<generator object collatz at 0x0000000003B63750>\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = collatz(7)\n",
+    "print x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "它支持 `next` 方法，返回下一个 `yield` 的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22\n",
+      "11\n"
+     ]
+    }
+   ],
+   "source": [
+    "print x.next()\n",
+    "print x.next()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`__iter__` 方法返回的是它本身："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<generator object collatz at 0x0000000003B63750>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print x.__iter__()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "之前的二叉树迭代器可以改写为更简单的生成器模式来进行中序遍历："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class BinaryTree(object):\n",
+    "    def __init__(self, value, left=None, right=None):\n",
+    "        self.value = value\n",
+    "        self.left = left\n",
+    "        self.right = right\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        # 将迭代器设为生成器方法\n",
+    "        return self.inorder()\n",
+    "    \n",
+    "    def inorder(self):\n",
+    "        # traverse the left branch\n",
+    "        if self.left is not None:\n",
+    "            for value in self.left:\n",
+    "                yield value\n",
+    "                \n",
+    "        # yield node's value\n",
+    "        yield self.value\n",
+    "        \n",
+    "        # traverse the right branch\n",
+    "        if self.right is not None:\n",
+    "            for value in self.right:\n",
+    "                yield value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "非递归的实现："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def inorder(self):\n",
+    "    node = self\n",
+    "    stack = []\n",
+    "    while len(stack) > 0 or node is not None:\n",
+    "        while node is not None:\n",
+    "            stack.append(node)\n",
+    "            node = node.left\n",
+    "        node = stack.pop()\n",
+    "        yield node.value\n",
+    "        node = node.right"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 2 3 4 5 6 7 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "tree = BinaryTree(\n",
+    "    left=BinaryTree(\n",
+    "        left=BinaryTree(1),\n",
+    "        value=2,\n",
+    "        right=BinaryTree(\n",
+    "            left=BinaryTree(3),\n",
+    "            value=4,\n",
+    "            right=BinaryTree(5)\n",
+    "        ),\n",
+    "    ),\n",
+    "    value=6,\n",
+    "    right=BinaryTree(\n",
+    "        value=7,\n",
+    "        right=BinaryTree(8)\n",
+    "    )\n",
+    ")\n",
+    "for value in tree:\n",
+    "    print value,"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/05. advanced python/05.11 context managers and the with statement.ipynb b/05-advanced-python/05.11-context-managers-and-the-with-statement.ipynb
similarity index 96%
rename from 05. advanced python/05.11 context managers and the with statement.ipynb
rename to 05-advanced-python/05.11-context-managers-and-the-with-statement.ipynb
index cf3f9aff..adf2ce36 100644
--- a/05. advanced python/05.11 context managers and the with statement.ipynb	
+++ b/05-advanced-python/05.11-context-managers-and-the-with-statement.ipynb
@@ -1,847 +1,847 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# with 语句和上下文管理器"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "# create/aquire some resource\n",
-    "...\n",
-    "try:\n",
-    "    # do something with the resource\n",
-    "    ...\n",
-    "finally:\n",
-    "    # destroy/release the resource\n",
-    "    ...\n",
-    "```\n",
-    "\n",
-    "处理文件，线程，数据库，网络编程等等资源的时候，我们经常需要使用上面这样的代码形式，以确保资源的正常使用和释放。\n",
-    "\n",
-    "好在`Python` 提供了 `with` 语句帮我们自动进行这样的处理，例如之前在打开文件时我们使用： "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "with open('my_file', 'w') as fp:\n",
-    "    # do stuff with fp\n",
-    "    data = fp.write(\"Hello world\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这等效于下面的代码，但是要更简便："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "fp = open('my_file', 'w')\n",
-    "try:\n",
-    "    # do stuff with f\n",
-    "    data = fp.write(\"Hello world\")\n",
-    "finally:\n",
-    "    fp.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 上下文管理器"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "其基本用法如下：\n",
-    "```\n",
-    "with <expression>:\n",
-    "    <block>\n",
-    "```\n",
-    "\n",
-    "`<expression>` 执行的结果应当返回一个实现了上下文管理器的对象，即实现这样两个方法，`__enter__` 和 `__exit__`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<built-in method __enter__ of file object at 0x0000000003C1D540>\n",
-      "<built-in method __exit__ of file object at 0x0000000003C1D540>\n"
-     ]
-    }
-   ],
-   "source": [
-    "print fp.__enter__\n",
-    "print fp.__exit__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`__enter__` 方法在 `<block>` 执行前执行，而 `__exit__` 在 `<block>` 执行结束后执行：\n",
-    "\n",
-    "比如可以这样定义一个简单的上下文管理器："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class ContextManager(object):\n",
-    "    \n",
-    "    def __enter__(self):\n",
-    "        print \"Entering\"\n",
-    "    \n",
-    "    def __exit__(self, exc_type, exc_value, traceback):\n",
-    "        print \"Exiting\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `with` 语句执行："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Entering\n",
-      "  Inside the with statement\n",
-      "Exiting\n"
-     ]
-    }
-   ],
-   "source": [
-    "with ContextManager():\n",
-    "    print \"  Inside the with statement\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "即使 `<block>` 中执行的内容出错，`__exit__` 也会被执行："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Entering\n",
-      "Exiting\n"
-     ]
-    },
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "integer division or modulo by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-6-b509c97cb388>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mContextManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[1;32mprint\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero"
-     ]
-    }
-   ],
-   "source": [
-    "with ContextManager():\n",
-    "    print 1/0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## `__`enter`__` 的返回值"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "如果在 `__enter__` 方法下添加了返回值，那么我们可以使用 `as` 把这个返回值传给某个参数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class ContextManager(object):\n",
-    "    \n",
-    "    def __enter__(self):\n",
-    "        print \"Entering\"\n",
-    "        return \"my value\"\n",
-    "    \n",
-    "    def __exit__(self, exc_type, exc_value, traceback):\n",
-    "        print \"Exiting\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "将 `__enter__` 返回的值传给 `value` 变量："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Entering\n",
-      "my value\n",
-      "Exiting\n"
-     ]
-    }
-   ],
-   "source": [
-    "with ContextManager() as value:\n",
-    "    print value"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "一个通常的做法是将 `__enter__` 的返回值设为这个上下文管理器对象本身，文件对象就是这样做的："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<open file 'my_file', mode 'r' at 0x0000000003B63030>\n"
-     ]
-    }
-   ],
-   "source": [
-    "fp = open('my_file', 'r')\n",
-    "print fp.__enter__()\n",
-    "fp.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('my_file')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "实现方法非常简单："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class ContextManager(object):\n",
-    "    \n",
-    "    def __enter__(self):\n",
-    "        print \"Entering\"\n",
-    "        return self\n",
-    "    \n",
-    "    def __exit__(self, exc_type, exc_value, traceback):\n",
-    "        print \"Exiting\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Entering\n",
-      "<__main__.ContextManager object at 0x0000000003D48828>\n",
-      "Exiting\n"
-     ]
-    }
-   ],
-   "source": [
-    "with ContextManager() as value:\n",
-    "    print value"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 错误处理"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "上下文管理器对象将错误处理交给 `__exit__` 进行，可以将错误类型，错误值和 `traceback` 等内容作为参数传递给 `__exit__` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class ContextManager(object):\n",
-    "    \n",
-    "    def __enter__(self):\n",
-    "        print \"Entering\"\n",
-    "    \n",
-    "    def __exit__(self, exc_type, exc_value, traceback):\n",
-    "        print \"Exiting\"\n",
-    "        if exc_type is not None:\n",
-    "            print \"  Exception:\", exc_value"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "如果没有错误，这些值都将是 `None`, 当有错误发生的时候："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Entering\n",
-      "Exiting\n",
-      "  Exception: integer division or modulo by zero\n"
-     ]
-    },
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "integer division or modulo by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-14-b509c97cb388>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mContextManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[1;32mprint\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero"
-     ]
-    }
-   ],
-   "source": [
-    "with ContextManager():\n",
-    "    print 1/0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在这个例子中，我们只是简单的显示了错误的值，并没有对错误进行处理，所以错误被向上抛出了，如果不想让错误抛出，只需要将 `__exit__` 的返回值设为 `True`： "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class ContextManager(object):\n",
-    "    \n",
-    "    def __enter__(self):\n",
-    "        print \"Entering\"\n",
-    "    \n",
-    "    def __exit__(self, exc_type, exc_value, traceback):\n",
-    "        print \"Exiting\"\n",
-    "        if exc_type is not None:\n",
-    "            print \" Exception suppresed:\", exc_value\n",
-    "            return True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Entering\n",
-      "Exiting\n",
-      " Exception suppresed: integer division or modulo by zero\n"
-     ]
-    }
-   ],
-   "source": [
-    "with ContextManager():\n",
-    "    print 1/0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在这种情况下，错误就不会被向上抛出。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 数据库的例子"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于数据库的 transaction 来说，如果没有错误，我们就将其 `commit` 进行保存，如果有错误，那么我们将其回滚到上一次成功的状态。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Transaction(object):\n",
-    "    \n",
-    "    def __init__(self, connection):\n",
-    "        self.connection = connection\n",
-    "    \n",
-    "    def __enter__(self):\n",
-    "        return self.connection.cursor()\n",
-    "    \n",
-    "    def __exit__(self, exc_type, exc_value, traceback):\n",
-    "        if exc_value is None:\n",
-    "            # transaction was OK, so commit\n",
-    "            self.connection.commit()\n",
-    "        else:\n",
-    "            # transaction had a problem, so rollback\n",
-    "            self.connection.rollback()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "建立一个数据库，保存一个地址表："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import sqlite3 as db\n",
-    "connection = db.connect(\":memory:\")\n",
-    "\n",
-    "with Transaction(connection) as cursor:\n",
-    "    cursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS addresses (\n",
-    "        address_id INTEGER PRIMARY KEY,\n",
-    "        street_address TEXT,\n",
-    "        city TEXT,\n",
-    "        state TEXT,\n",
-    "        country TEXT,\n",
-    "        postal_code TEXT\n",
-    "    )\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "插入数据："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "with Transaction(connection) as cursor:\n",
-    "    cursor.executemany(\"\"\"INSERT OR REPLACE INTO addresses VALUES (?, ?, ?, ?, ?, ?)\"\"\", [\n",
-    "        (0, '515 Congress Ave', 'Austin', 'Texas', 'USA', '78701'),\n",
-    "        (1, '245 Park Avenue', 'New York', 'New York', 'USA', '10167'),\n",
-    "        (2, '21 J.J. Thompson Ave.', 'Cambridge', None, 'UK', 'CB3 0FA'),\n",
-    "        (3, 'Supreme Business Park', 'Hiranandani Gardens, Powai, Mumbai', 'Maharashtra', 'India', '400076'),\n",
-    "    ])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "假设插入数据之后出现了问题："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "Exception",
-     "evalue": "out of addresses",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-20-ed8abdd56558>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m         \u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'2100 Pennsylvania Ave'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Washington'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'DC'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'USA'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'78701'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     )\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"out of addresses\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mException\u001b[0m: out of addresses"
-     ]
-    }
-   ],
-   "source": [
-    "with Transaction(connection) as cursor:\n",
-    "    cursor.execute(\"\"\"INSERT OR REPLACE INTO addresses VALUES (?, ?, ?, ?, ?, ?)\"\"\",\n",
-    "        (4, '2100 Pennsylvania Ave', 'Washington', 'DC', 'USA', '78701'),\n",
-    "    )\n",
-    "    raise Exception(\"out of addresses\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "那么最新的一次插入将不会被保存，而是返回上一次 `commit` 成功的状态："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(0, u'515 Congress Ave', u'Austin', u'Texas', u'USA', u'78701')\n",
-      "(1, u'245 Park Avenue', u'New York', u'New York', u'USA', u'10167')\n",
-      "(2, u'21 J.J. Thompson Ave.', u'Cambridge', None, u'UK', u'CB3 0FA')\n",
-      "(3, u'Supreme Business Park', u'Hiranandani Gardens, Powai, Mumbai', u'Maharashtra', u'India', u'400076')\n"
-     ]
-    }
-   ],
-   "source": [
-    "cursor.execute(\"SELECT * FROM addresses\")\n",
-    "for row in cursor:\n",
-    "    print row"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## contextlib 模块"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "很多的上下文管理器有很多相似的地方，为了防止写入很多重复的模式，可以使用 `contextlib` 模块来进行处理。\n",
-    "\n",
-    "最简单的处理方式是使用 `closing` 函数确保对象的 `close()` 方法始终被调用："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<!DOCTYPE html><!--STATUS OK--><html><head><meta http-equiv=\"content-type\" content=\"text/html;charse\n"
-     ]
-    }
-   ],
-   "source": [
-    "from contextlib import closing\n",
-    "import urllib\n",
-    "\n",
-    "with closing(urllib.urlopen('http://www.baidu.com')) as url:\n",
-    "    html = url.read()\n",
-    "\n",
-    "print html[:100]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "另一个有用的方法是使用修饰符 `@contextlib`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Enter\n",
-      "  Inside the with statement\n",
-      "Exit\n"
-     ]
-    }
-   ],
-   "source": [
-    "from contextlib import contextmanager\n",
-    "\n",
-    "@contextmanager\n",
-    "def my_contextmanager():\n",
-    "    print \"Enter\"\n",
-    "    yield\n",
-    "    print \"Exit\"\n",
-    "\n",
-    "with my_contextmanager():\n",
-    "    print \"  Inside the with statement\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`yield` 之前的部分可以看成是 `__enter__` 的部分，`yield` 的值可以看成是 `__enter__` 返回的值，`yield` 之后的部分可以看成是 `__exit__` 的部分。\n",
-    "\n",
-    "使用 `yield` 的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Enter\n",
-      "my value\n",
-      "Exit\n"
-     ]
-    }
-   ],
-   "source": [
-    "@contextmanager\n",
-    "def my_contextmanager():\n",
-    "    print \"Enter\"\n",
-    "    yield \"my value\"\n",
-    "    print \"Exit\"\n",
-    "    \n",
-    "with my_contextmanager() as value:\n",
-    "    print value"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "错误处理可以用 `try` 块来完成："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "@contextmanager\n",
-    "def my_contextmanager():\n",
-    "    print \"Enter\"\n",
-    "    try:\n",
-    "        yield\n",
-    "    except Exception as exc:\n",
-    "        print \"   Error:\", exc\n",
-    "    finally:\n",
-    "        print \"Exit\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Enter\n",
-      "   Error: integer division or modulo by zero\n",
-      "Exit\n"
-     ]
-    }
-   ],
-   "source": [
-    "with my_contextmanager():\n",
-    "    print 1/0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于之前的数据库 `transaction` 我们可以这样定义："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "@contextmanager\n",
-    "def transaction(connection):\n",
-    "    cursor = connection.cursor()\n",
-    "    try:\n",
-    "        yield cursor\n",
-    "    except:\n",
-    "        connection.rollback()\n",
-    "        raise\n",
-    "    else:\n",
-    "        connection.commit()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# with 语句和上下文管理器"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "# create/aquire some resource\n",
+    "...\n",
+    "try:\n",
+    "    # do something with the resource\n",
+    "    ...\n",
+    "finally:\n",
+    "    # destroy/release the resource\n",
+    "    ...\n",
+    "```\n",
+    "\n",
+    "处理文件，线程，数据库，网络编程等等资源的时候，我们经常需要使用上面这样的代码形式，以确保资源的正常使用和释放。\n",
+    "\n",
+    "好在`Python` 提供了 `with` 语句帮我们自动进行这样的处理，例如之前在打开文件时我们使用： "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "with open('my_file', 'w') as fp:\n",
+    "    # do stuff with fp\n",
+    "    data = fp.write(\"Hello world\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这等效于下面的代码，但是要更简便："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "fp = open('my_file', 'w')\n",
+    "try:\n",
+    "    # do stuff with f\n",
+    "    data = fp.write(\"Hello world\")\n",
+    "finally:\n",
+    "    fp.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 上下文管理器"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "其基本用法如下：\n",
+    "```\n",
+    "with <expression>:\n",
+    "    <block>\n",
+    "```\n",
+    "\n",
+    "`<expression>` 执行的结果应当返回一个实现了上下文管理器的对象，即实现这样两个方法，`__enter__` 和 `__exit__`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<built-in method __enter__ of file object at 0x0000000003C1D540>\n",
+      "<built-in method __exit__ of file object at 0x0000000003C1D540>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print fp.__enter__\n",
+    "print fp.__exit__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`__enter__` 方法在 `<block>` 执行前执行，而 `__exit__` 在 `<block>` 执行结束后执行：\n",
+    "\n",
+    "比如可以这样定义一个简单的上下文管理器："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class ContextManager(object):\n",
+    "    \n",
+    "    def __enter__(self):\n",
+    "        print \"Entering\"\n",
+    "    \n",
+    "    def __exit__(self, exc_type, exc_value, traceback):\n",
+    "        print \"Exiting\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `with` 语句执行："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Entering\n",
+      "  Inside the with statement\n",
+      "Exiting\n"
+     ]
+    }
+   ],
+   "source": [
+    "with ContextManager():\n",
+    "    print \"  Inside the with statement\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "即使 `<block>` 中执行的内容出错，`__exit__` 也会被执行："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Entering\n",
+      "Exiting\n"
+     ]
+    },
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "integer division or modulo by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-6-b509c97cb388>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mContextManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[1;32mprint\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero"
+     ]
+    }
+   ],
+   "source": [
+    "with ContextManager():\n",
+    "    print 1/0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `__`enter`__` 的返回值"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "如果在 `__enter__` 方法下添加了返回值，那么我们可以使用 `as` 把这个返回值传给某个参数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class ContextManager(object):\n",
+    "    \n",
+    "    def __enter__(self):\n",
+    "        print \"Entering\"\n",
+    "        return \"my value\"\n",
+    "    \n",
+    "    def __exit__(self, exc_type, exc_value, traceback):\n",
+    "        print \"Exiting\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将 `__enter__` 返回的值传给 `value` 变量："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Entering\n",
+      "my value\n",
+      "Exiting\n"
+     ]
+    }
+   ],
+   "source": [
+    "with ContextManager() as value:\n",
+    "    print value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "一个通常的做法是将 `__enter__` 的返回值设为这个上下文管理器对象本身，文件对象就是这样做的："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<open file 'my_file', mode 'r' at 0x0000000003B63030>\n"
+     ]
+    }
+   ],
+   "source": [
+    "fp = open('my_file', 'r')\n",
+    "print fp.__enter__()\n",
+    "fp.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('my_file')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "实现方法非常简单："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class ContextManager(object):\n",
+    "    \n",
+    "    def __enter__(self):\n",
+    "        print \"Entering\"\n",
+    "        return self\n",
+    "    \n",
+    "    def __exit__(self, exc_type, exc_value, traceback):\n",
+    "        print \"Exiting\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Entering\n",
+      "<__main__.ContextManager object at 0x0000000003D48828>\n",
+      "Exiting\n"
+     ]
+    }
+   ],
+   "source": [
+    "with ContextManager() as value:\n",
+    "    print value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 错误处理"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "上下文管理器对象将错误处理交给 `__exit__` 进行，可以将错误类型，错误值和 `traceback` 等内容作为参数传递给 `__exit__` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class ContextManager(object):\n",
+    "    \n",
+    "    def __enter__(self):\n",
+    "        print \"Entering\"\n",
+    "    \n",
+    "    def __exit__(self, exc_type, exc_value, traceback):\n",
+    "        print \"Exiting\"\n",
+    "        if exc_type is not None:\n",
+    "            print \"  Exception:\", exc_value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "如果没有错误，这些值都将是 `None`, 当有错误发生的时候："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Entering\n",
+      "Exiting\n",
+      "  Exception: integer division or modulo by zero\n"
+     ]
+    },
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "integer division or modulo by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-14-b509c97cb388>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mContextManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[1;32mprint\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero"
+     ]
+    }
+   ],
+   "source": [
+    "with ContextManager():\n",
+    "    print 1/0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在这个例子中，我们只是简单的显示了错误的值，并没有对错误进行处理，所以错误被向上抛出了，如果不想让错误抛出，只需要将 `__exit__` 的返回值设为 `True`： "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class ContextManager(object):\n",
+    "    \n",
+    "    def __enter__(self):\n",
+    "        print \"Entering\"\n",
+    "    \n",
+    "    def __exit__(self, exc_type, exc_value, traceback):\n",
+    "        print \"Exiting\"\n",
+    "        if exc_type is not None:\n",
+    "            print \" Exception suppresed:\", exc_value\n",
+    "            return True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Entering\n",
+      "Exiting\n",
+      " Exception suppresed: integer division or modulo by zero\n"
+     ]
+    }
+   ],
+   "source": [
+    "with ContextManager():\n",
+    "    print 1/0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在这种情况下，错误就不会被向上抛出。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 数据库的例子"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于数据库的 transaction 来说，如果没有错误，我们就将其 `commit` 进行保存，如果有错误，那么我们将其回滚到上一次成功的状态。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Transaction(object):\n",
+    "    \n",
+    "    def __init__(self, connection):\n",
+    "        self.connection = connection\n",
+    "    \n",
+    "    def __enter__(self):\n",
+    "        return self.connection.cursor()\n",
+    "    \n",
+    "    def __exit__(self, exc_type, exc_value, traceback):\n",
+    "        if exc_value is None:\n",
+    "            # transaction was OK, so commit\n",
+    "            self.connection.commit()\n",
+    "        else:\n",
+    "            # transaction had a problem, so rollback\n",
+    "            self.connection.rollback()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "建立一个数据库，保存一个地址表："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import sqlite3 as db\n",
+    "connection = db.connect(\":memory:\")\n",
+    "\n",
+    "with Transaction(connection) as cursor:\n",
+    "    cursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS addresses (\n",
+    "        address_id INTEGER PRIMARY KEY,\n",
+    "        street_address TEXT,\n",
+    "        city TEXT,\n",
+    "        state TEXT,\n",
+    "        country TEXT,\n",
+    "        postal_code TEXT\n",
+    "    )\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "插入数据："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "with Transaction(connection) as cursor:\n",
+    "    cursor.executemany(\"\"\"INSERT OR REPLACE INTO addresses VALUES (?, ?, ?, ?, ?, ?)\"\"\", [\n",
+    "        (0, '515 Congress Ave', 'Austin', 'Texas', 'USA', '78701'),\n",
+    "        (1, '245 Park Avenue', 'New York', 'New York', 'USA', '10167'),\n",
+    "        (2, '21 J.J. Thompson Ave.', 'Cambridge', None, 'UK', 'CB3 0FA'),\n",
+    "        (3, 'Supreme Business Park', 'Hiranandani Gardens, Powai, Mumbai', 'Maharashtra', 'India', '400076'),\n",
+    "    ])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "假设插入数据之后出现了问题："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "Exception",
+     "evalue": "out of addresses",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-20-ed8abdd56558>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m         \u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'2100 Pennsylvania Ave'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Washington'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'DC'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'USA'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'78701'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     )\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"out of addresses\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mException\u001b[0m: out of addresses"
+     ]
+    }
+   ],
+   "source": [
+    "with Transaction(connection) as cursor:\n",
+    "    cursor.execute(\"\"\"INSERT OR REPLACE INTO addresses VALUES (?, ?, ?, ?, ?, ?)\"\"\",\n",
+    "        (4, '2100 Pennsylvania Ave', 'Washington', 'DC', 'USA', '78701'),\n",
+    "    )\n",
+    "    raise Exception(\"out of addresses\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "那么最新的一次插入将不会被保存，而是返回上一次 `commit` 成功的状态："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0, u'515 Congress Ave', u'Austin', u'Texas', u'USA', u'78701')\n",
+      "(1, u'245 Park Avenue', u'New York', u'New York', u'USA', u'10167')\n",
+      "(2, u'21 J.J. Thompson Ave.', u'Cambridge', None, u'UK', u'CB3 0FA')\n",
+      "(3, u'Supreme Business Park', u'Hiranandani Gardens, Powai, Mumbai', u'Maharashtra', u'India', u'400076')\n"
+     ]
+    }
+   ],
+   "source": [
+    "cursor.execute(\"SELECT * FROM addresses\")\n",
+    "for row in cursor:\n",
+    "    print row"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## contextlib 模块"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "很多的上下文管理器有很多相似的地方，为了防止写入很多重复的模式，可以使用 `contextlib` 模块来进行处理。\n",
+    "\n",
+    "最简单的处理方式是使用 `closing` 函数确保对象的 `close()` 方法始终被调用："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<!DOCTYPE html><!--STATUS OK--><html><head><meta http-equiv=\"content-type\" content=\"text/html;charse\n"
+     ]
+    }
+   ],
+   "source": [
+    "from contextlib import closing\n",
+    "import urllib\n",
+    "\n",
+    "with closing(urllib.urlopen('http://www.baidu.com')) as url:\n",
+    "    html = url.read()\n",
+    "\n",
+    "print html[:100]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "另一个有用的方法是使用修饰符 `@contextlib`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter\n",
+      "  Inside the with statement\n",
+      "Exit\n"
+     ]
+    }
+   ],
+   "source": [
+    "from contextlib import contextmanager\n",
+    "\n",
+    "@contextmanager\n",
+    "def my_contextmanager():\n",
+    "    print \"Enter\"\n",
+    "    yield\n",
+    "    print \"Exit\"\n",
+    "\n",
+    "with my_contextmanager():\n",
+    "    print \"  Inside the with statement\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`yield` 之前的部分可以看成是 `__enter__` 的部分，`yield` 的值可以看成是 `__enter__` 返回的值，`yield` 之后的部分可以看成是 `__exit__` 的部分。\n",
+    "\n",
+    "使用 `yield` 的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter\n",
+      "my value\n",
+      "Exit\n"
+     ]
+    }
+   ],
+   "source": [
+    "@contextmanager\n",
+    "def my_contextmanager():\n",
+    "    print \"Enter\"\n",
+    "    yield \"my value\"\n",
+    "    print \"Exit\"\n",
+    "    \n",
+    "with my_contextmanager() as value:\n",
+    "    print value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "错误处理可以用 `try` 块来完成："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "@contextmanager\n",
+    "def my_contextmanager():\n",
+    "    print \"Enter\"\n",
+    "    try:\n",
+    "        yield\n",
+    "    except Exception as exc:\n",
+    "        print \"   Error:\", exc\n",
+    "    finally:\n",
+    "        print \"Exit\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter\n",
+      "   Error: integer division or modulo by zero\n",
+      "Exit\n"
+     ]
+    }
+   ],
+   "source": [
+    "with my_contextmanager():\n",
+    "    print 1/0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于之前的数据库 `transaction` 我们可以这样定义："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "@contextmanager\n",
+    "def transaction(connection):\n",
+    "    cursor = connection.cursor()\n",
+    "    try:\n",
+    "        yield cursor\n",
+    "    except:\n",
+    "        connection.rollback()\n",
+    "        raise\n",
+    "    else:\n",
+    "        connection.commit()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/05. advanced python/05.12 decorators.ipynb b/05-advanced-python/05.12-decorators.ipynb
similarity index 94%
rename from 05. advanced python/05.12 decorators.ipynb
rename to 05-advanced-python/05.12-decorators.ipynb
index ae172720..6d1ea18d 100644
--- a/05. advanced python/05.12 decorators.ipynb	
+++ b/05-advanced-python/05.12-decorators.ipynb
@@ -1,707 +1,707 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 修饰符"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 函数是一种对象"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在 `Python` 中，函数是也是一种对象。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<type 'function'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "def foo(x):\n",
-    "    print x\n",
-    "    \n",
-    "print(type(foo))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看函数拥有的方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__call__',\n",
-       " '__class__',\n",
-       " '__closure__',\n",
-       " '__code__',\n",
-       " '__defaults__',\n",
-       " '__delattr__',\n",
-       " '__dict__',\n",
-       " '__doc__',\n",
-       " '__format__',\n",
-       " '__get__',\n",
-       " '__getattribute__',\n",
-       " '__globals__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__module__',\n",
-       " '__name__',\n",
-       " '__new__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__subclasshook__',\n",
-       " 'func_closure',\n",
-       " 'func_code',\n",
-       " 'func_defaults',\n",
-       " 'func_dict',\n",
-       " 'func_doc',\n",
-       " 'func_globals',\n",
-       " 'func_name']"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(foo)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在这些方法中，`__call__` 是最重要的一种方法： "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "42\n"
-     ]
-    }
-   ],
-   "source": [
-    "foo.__call__(42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "相当于："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "42\n"
-     ]
-    }
-   ],
-   "source": [
-    "foo(42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "因为函数是对象，所以函数可以作为参数传入另一个函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def bar(f, x):\n",
-    "    x += 1\n",
-    "    f(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "bar(foo, 4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 修饰符"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "修饰符是这样的一种函数，它接受一个函数作为输入，通常输出也是一个函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def dec(f):\n",
-    "    print 'I am decorating function', id(f)\n",
-    "    return f"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "将 `len` 函数作为参数传入这个修饰符函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "I am decorating function 33716168\n"
-     ]
-    }
-   ],
-   "source": [
-    "declen = dec(len)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用这个新生成的函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "declen([10,20,30])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "上面的例子中，我们仅仅返回了函数的本身，也可以利用这个函数生成一个新的函数，看一个新的例子："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def loud(f):\n",
-    "    def new_func(*args, **kw):\n",
-    "        print 'calling with', args, kw\n",
-    "        rtn = f(*args, **kw)\n",
-    "        print 'return value is', rtn\n",
-    "        return rtn\n",
-    "    return new_func"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "loudlen = loud(len)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "calling with ([10, 20, 30],) {}\n",
-      "return value is 3\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "loudlen([10, 20, 30])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 用 @ 来使用修饰符"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Python` 使用 `@` 符号来将某个函数替换为修饰符之后的函数： "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "例如这个函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "I am decorating function 64021672\n"
-     ]
-    }
-   ],
-   "source": [
-    "def foo(x):\n",
-    "    print x\n",
-    "    \n",
-    "foo = dec(foo)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以替换为："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "I am decorating function 64021112\n"
-     ]
-    }
-   ],
-   "source": [
-    "@dec\n",
-    "def foo(x):\n",
-    "    print x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上，如果修饰符返回的是一个函数，那么可以链式的使用修饰符：\n",
-    "\n",
-    "```python\n",
-    "@dec1\n",
-    "@dec2\n",
-    "def foo(x):\n",
-    "    print x\n",
-    "```\n",
-    "\n",
-    "使用修饰符 `loud` 来定义这个函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "@loud\n",
-    "def foo(x):\n",
-    "    print x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "calling with (42,) {}\n",
-      "42\n",
-      "return value is None\n"
-     ]
-    }
-   ],
-   "source": [
-    "foo(42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 例子"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "定义两个修饰器函数，一个将原来的函数值加一，另一个乘二："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def plus_one(f):\n",
-    "    def new_func(x):\n",
-    "        return f(x) + 1\n",
-    "    return new_func\n",
-    "\n",
-    "def times_two(f):\n",
-    "    def new_func(x):\n",
-    "        return f(x) * 2\n",
-    "    return new_func"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "定义函数，先乘二再加一："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "@plus_one\n",
-    "@times_two\n",
-    "def foo(x):\n",
-    "    return int(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "27"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "foo(13)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 修饰器工厂"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`decorators factories` 是返回修饰器的函数，例如："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def super_dec(x, y, z):\n",
-    "    def dec(f):\n",
-    "        def new_func(*args, **kw):\n",
-    "            print x + y + z\n",
-    "            return f(*args, **kw)\n",
-    "        return new_func\n",
-    "    return dec"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "它的作用在于产生一个可以接受参数的修饰器，例如我们想将 `loud` 输出的内容写入一个文件去，可以这样做："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def super_loud(filename):\n",
-    "    fp = open(filename, 'w')\n",
-    "    def loud(f):\n",
-    "        def new_func(*args, **kw):\n",
-    "            fp.write('calling with' + str(args) + str(kw))\n",
-    "            # 确保内容被写入\n",
-    "            fp.flush()\n",
-    "            fp.close()\n",
-    "            rtn = f(*args, **kw)\n",
-    "            return rtn\n",
-    "        return new_func\n",
-    "    return loud"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以这样使用这个修饰器工厂："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "@super_loud('test.txt')\n",
-    "def foo(x):\n",
-    "    print x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "调用 `foo` 就会在文件中写入内容："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "12\n"
-     ]
-    }
-   ],
-   "source": [
-    "foo(12)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看文件内容："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "calling with(12,){}\n"
-     ]
-    }
-   ],
-   "source": [
-    "with open('test.txt') as fp:\n",
-    "    print fp.read()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.remove('test.txt')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 修饰符"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 函数是一种对象"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在 `Python` 中，函数是也是一种对象。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<type 'function'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "def foo(x):\n",
+    "    print x\n",
+    "    \n",
+    "print(type(foo))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看函数拥有的方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__call__',\n",
+       " '__class__',\n",
+       " '__closure__',\n",
+       " '__code__',\n",
+       " '__defaults__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__doc__',\n",
+       " '__format__',\n",
+       " '__get__',\n",
+       " '__getattribute__',\n",
+       " '__globals__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__module__',\n",
+       " '__name__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " 'func_closure',\n",
+       " 'func_code',\n",
+       " 'func_defaults',\n",
+       " 'func_dict',\n",
+       " 'func_doc',\n",
+       " 'func_globals',\n",
+       " 'func_name']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(foo)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在这些方法中，`__call__` 是最重要的一种方法： "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "42\n"
+     ]
+    }
+   ],
+   "source": [
+    "foo.__call__(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "相当于："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "42\n"
+     ]
+    }
+   ],
+   "source": [
+    "foo(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "因为函数是对象，所以函数可以作为参数传入另一个函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def bar(f, x):\n",
+    "    x += 1\n",
+    "    f(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "bar(foo, 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 修饰符"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "修饰符是这样的一种函数，它接受一个函数作为输入，通常输出也是一个函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def dec(f):\n",
+    "    print 'I am decorating function', id(f)\n",
+    "    return f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将 `len` 函数作为参数传入这个修饰符函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I am decorating function 33716168\n"
+     ]
+    }
+   ],
+   "source": [
+    "declen = dec(len)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用这个新生成的函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "declen([10,20,30])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "上面的例子中，我们仅仅返回了函数的本身，也可以利用这个函数生成一个新的函数，看一个新的例子："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def loud(f):\n",
+    "    def new_func(*args, **kw):\n",
+    "        print 'calling with', args, kw\n",
+    "        rtn = f(*args, **kw)\n",
+    "        print 'return value is', rtn\n",
+    "        return rtn\n",
+    "    return new_func"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "loudlen = loud(len)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calling with ([10, 20, 30],) {}\n",
+      "return value is 3\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loudlen([10, 20, 30])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 用 @ 来使用修饰符"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Python` 使用 `@` 符号来将某个函数替换为修饰符之后的函数： "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "例如这个函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I am decorating function 64021672\n"
+     ]
+    }
+   ],
+   "source": [
+    "def foo(x):\n",
+    "    print x\n",
+    "    \n",
+    "foo = dec(foo)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以替换为："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I am decorating function 64021112\n"
+     ]
+    }
+   ],
+   "source": [
+    "@dec\n",
+    "def foo(x):\n",
+    "    print x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上，如果修饰符返回的是一个函数，那么可以链式的使用修饰符：\n",
+    "\n",
+    "```python\n",
+    "@dec1\n",
+    "@dec2\n",
+    "def foo(x):\n",
+    "    print x\n",
+    "```\n",
+    "\n",
+    "使用修饰符 `loud` 来定义这个函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "@loud\n",
+    "def foo(x):\n",
+    "    print x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calling with (42,) {}\n",
+      "42\n",
+      "return value is None\n"
+     ]
+    }
+   ],
+   "source": [
+    "foo(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 例子"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "定义两个修饰器函数，一个将原来的函数值加一，另一个乘二："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plus_one(f):\n",
+    "    def new_func(x):\n",
+    "        return f(x) + 1\n",
+    "    return new_func\n",
+    "\n",
+    "def times_two(f):\n",
+    "    def new_func(x):\n",
+    "        return f(x) * 2\n",
+    "    return new_func"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "定义函数，先乘二再加一："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "@plus_one\n",
+    "@times_two\n",
+    "def foo(x):\n",
+    "    return int(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "foo(13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 修饰器工厂"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`decorators factories` 是返回修饰器的函数，例如："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def super_dec(x, y, z):\n",
+    "    def dec(f):\n",
+    "        def new_func(*args, **kw):\n",
+    "            print x + y + z\n",
+    "            return f(*args, **kw)\n",
+    "        return new_func\n",
+    "    return dec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "它的作用在于产生一个可以接受参数的修饰器，例如我们想将 `loud` 输出的内容写入一个文件去，可以这样做："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def super_loud(filename):\n",
+    "    fp = open(filename, 'w')\n",
+    "    def loud(f):\n",
+    "        def new_func(*args, **kw):\n",
+    "            fp.write('calling with' + str(args) + str(kw))\n",
+    "            # 确保内容被写入\n",
+    "            fp.flush()\n",
+    "            fp.close()\n",
+    "            rtn = f(*args, **kw)\n",
+    "            return rtn\n",
+    "        return new_func\n",
+    "    return loud"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以这样使用这个修饰器工厂："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "@super_loud('test.txt')\n",
+    "def foo(x):\n",
+    "    print x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "调用 `foo` 就会在文件中写入内容："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12\n"
+     ]
+    }
+   ],
+   "source": [
+    "foo(12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看文件内容："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calling with(12,){}\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('test.txt') as fp:\n",
+    "    print fp.read()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove('test.txt')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/05. advanced python/05.13 decorator usage.ipynb b/05-advanced-python/05.13-decorator-usage.ipynb
similarity index 95%
rename from 05. advanced python/05.13 decorator usage.ipynb
rename to 05-advanced-python/05.13-decorator-usage.ipynb
index c288c258..6a8fbf8d 100644
--- a/05. advanced python/05.13 decorator usage.ipynb	
+++ b/05-advanced-python/05.13-decorator-usage.ipynb
@@ -1,547 +1,547 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 修饰符的使用"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## @classmethod 修饰符"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "在 `Python` 标准库中，有很多自带的修饰符，例如 `classmethod` 将一个对象方法转换了类方法： "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Foo(object):\n",
-    "    @classmethod\n",
-    "    def bar(cls, x):\n",
-    "        print 'the input is', x\n",
-    "        \n",
-    "    def __init__(self):\n",
-    "        pass\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "类方法可以通过 `类名.方法` 来调用："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "the input is 12\n"
-     ]
-    }
-   ],
-   "source": [
-    "Foo.bar(12)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## @property 修饰符"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "有时候，我们希望像 __Java__ 一样支持 `getters` 和 `setters` 的方法，这时候就可以使用 `property` 修饰符："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Foo(object):\n",
-    "    def __init__(self, data):\n",
-    "        self.data = data\n",
-    "    \n",
-    "    @property\n",
-    "    def x(self):\n",
-    "        return self.data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "此时可以使用 `.x` 这个属性查看数据（不需要加上括号）："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "23"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "foo = Foo(23)\n",
-    "foo.x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这样做的好处在于，这个属性是只读的："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "can't set attribute",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-5-e5e7e6c675ef>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfoo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m: can't set attribute"
-     ]
-    }
-   ],
-   "source": [
-    "foo.x = 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "如果想让它变成可读写，可以加上一个修饰符 `@x.setter`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Foo(object):\n",
-    "    def __init__(self, data):\n",
-    "        self.data = data\n",
-    "    \n",
-    "    @property\n",
-    "    def x(self):\n",
-    "        return self.data\n",
-    "    \n",
-    "    @x.setter\n",
-    "    def x(self, value):\n",
-    "        self.data = value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "23\n"
-     ]
-    }
-   ],
-   "source": [
-    "foo = Foo(23)\n",
-    "print foo.x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以通过属性改变它的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n"
-     ]
-    }
-   ],
-   "source": [
-    "foo.x = 1\n",
-    "print foo.x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Numpy 的 @vectorize 修饰符"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`numpy` 的 `vectorize` 函数讲一个函数转换为 `ufunc`，事实上它也是一个修饰符："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,\n",
-       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from numpy import vectorize, arange\n",
-    "\n",
-    "@vectorize\n",
-    "def f(x):\n",
-    "    if x <= 0:\n",
-    "        return x\n",
-    "    else:\n",
-    "        return 0\n",
-    "\n",
-    "f(arange(-10.0,10.0))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 注册一个函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "来看这样的一个例子，定义一个类："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Registry(object):\n",
-    "    def __init__(self):\n",
-    "        self._data = {}\n",
-    "    def register(self, f, name=None):\n",
-    "        if name == None:\n",
-    "            name = f.__name__\n",
-    "        self._data[name] = f\n",
-    "        setattr(self, name, f)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`register` 方法接受一个函数，将这个函数名作为属性注册到对象中。\n",
-    "\n",
-    "产生该类的一个对象："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "registry = Registry()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用该对象的 `register` 方法作为修饰符："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "@registry.register\n",
-    "def greeting():\n",
-    "    print \"hello world\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这样这个函数就被注册到 `registry` 这个对象中去了："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'greeting': <function __main__.greeting>}"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "registry._data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function __main__.greeting>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "registry.greeting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[flask](flask.pocoo.org) ，一个常用的网络应用，处理 url 的机制跟这个类似。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 使用 @wraps"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "一个通常的问题在于："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "None wrapper\n"
-     ]
-    }
-   ],
-   "source": [
-    "def logging_call(f):\n",
-    "    def wrapper(*a, **kw):\n",
-    "        print 'calling {}'.format(f.__name__)\n",
-    "        return f(*a, **kw)\n",
-    "    return wrapper\n",
-    "\n",
-    "@logging_call\n",
-    "def square(x):\n",
-    "    '''\n",
-    "    square function.\n",
-    "    '''\n",
-    "    return x ** 2\n",
-    "\n",
-    "print square.__doc__, square.__name__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们使用修饰符之后，`square` 的 `metadata` 完全丢失了，返回的函数名与函数的 `docstring` 都不对。\n",
-    "\n",
-    "一个解决的方法是从 `functools` 模块导入 `wraps` 修饰符来修饰我们的修饰符："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "    square function.\n",
-      "     square\n"
-     ]
-    }
-   ],
-   "source": [
-    "import functools\n",
-    "\n",
-    "def logging_call(f):\n",
-    "    @functools.wraps(f)\n",
-    "    def wrapper(*a, **kw):\n",
-    "        print 'calling {}'.format(f.__name__)\n",
-    "        return f(*a, **kw)\n",
-    "    return wrapper\n",
-    "\n",
-    "@logging_call\n",
-    "def square(x):\n",
-    "    '''\n",
-    "    square function.\n",
-    "    '''\n",
-    "    return x ** 2\n",
-    "\n",
-    "print square.__doc__, square.__name__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "现在这个问题解决了，所以在自定义修饰符方法的时候为了避免出现不必要的麻烦，尽量使用 `wraps` 来修饰修饰符！"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Class 修饰符"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "与函数修饰符类似，类修饰符是这样一类函数，接受一个类作为参数，通常返回一个新的类。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 修饰符的使用"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## @classmethod 修饰符"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "在 `Python` 标准库中，有很多自带的修饰符，例如 `classmethod` 将一个对象方法转换了类方法： "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Foo(object):\n",
+    "    @classmethod\n",
+    "    def bar(cls, x):\n",
+    "        print 'the input is', x\n",
+    "        \n",
+    "    def __init__(self):\n",
+    "        pass\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "类方法可以通过 `类名.方法` 来调用："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the input is 12\n"
+     ]
+    }
+   ],
+   "source": [
+    "Foo.bar(12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## @property 修饰符"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "有时候，我们希望像 __Java__ 一样支持 `getters` 和 `setters` 的方法，这时候就可以使用 `property` 修饰符："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Foo(object):\n",
+    "    def __init__(self, data):\n",
+    "        self.data = data\n",
+    "    \n",
+    "    @property\n",
+    "    def x(self):\n",
+    "        return self.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "此时可以使用 `.x` 这个属性查看数据（不需要加上括号）："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "foo = Foo(23)\n",
+    "foo.x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这样做的好处在于，这个属性是只读的："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "can't set attribute",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-5-e5e7e6c675ef>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfoo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m: can't set attribute"
+     ]
+    }
+   ],
+   "source": [
+    "foo.x = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "如果想让它变成可读写，可以加上一个修饰符 `@x.setter`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Foo(object):\n",
+    "    def __init__(self, data):\n",
+    "        self.data = data\n",
+    "    \n",
+    "    @property\n",
+    "    def x(self):\n",
+    "        return self.data\n",
+    "    \n",
+    "    @x.setter\n",
+    "    def x(self, value):\n",
+    "        self.data = value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23\n"
+     ]
+    }
+   ],
+   "source": [
+    "foo = Foo(23)\n",
+    "print foo.x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以通过属性改变它的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "foo.x = 1\n",
+    "print foo.x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Numpy 的 @vectorize 修饰符"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`numpy` 的 `vectorize` 函数讲一个函数转换为 `ufunc`，事实上它也是一个修饰符："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,\n",
+       "         0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from numpy import vectorize, arange\n",
+    "\n",
+    "@vectorize\n",
+    "def f(x):\n",
+    "    if x <= 0:\n",
+    "        return x\n",
+    "    else:\n",
+    "        return 0\n",
+    "\n",
+    "f(arange(-10.0,10.0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 注册一个函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "来看这样的一个例子，定义一个类："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Registry(object):\n",
+    "    def __init__(self):\n",
+    "        self._data = {}\n",
+    "    def register(self, f, name=None):\n",
+    "        if name == None:\n",
+    "            name = f.__name__\n",
+    "        self._data[name] = f\n",
+    "        setattr(self, name, f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`register` 方法接受一个函数，将这个函数名作为属性注册到对象中。\n",
+    "\n",
+    "产生该类的一个对象："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "registry = Registry()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用该对象的 `register` 方法作为修饰符："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "@registry.register\n",
+    "def greeting():\n",
+    "    print \"hello world\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这样这个函数就被注册到 `registry` 这个对象中去了："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'greeting': <function __main__.greeting>}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "registry._data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.greeting>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "registry.greeting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[flask](flask.pocoo.org) ，一个常用的网络应用，处理 url 的机制跟这个类似。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 使用 @wraps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "一个通常的问题在于："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None wrapper\n"
+     ]
+    }
+   ],
+   "source": [
+    "def logging_call(f):\n",
+    "    def wrapper(*a, **kw):\n",
+    "        print 'calling {}'.format(f.__name__)\n",
+    "        return f(*a, **kw)\n",
+    "    return wrapper\n",
+    "\n",
+    "@logging_call\n",
+    "def square(x):\n",
+    "    '''\n",
+    "    square function.\n",
+    "    '''\n",
+    "    return x ** 2\n",
+    "\n",
+    "print square.__doc__, square.__name__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们使用修饰符之后，`square` 的 `metadata` 完全丢失了，返回的函数名与函数的 `docstring` 都不对。\n",
+    "\n",
+    "一个解决的方法是从 `functools` 模块导入 `wraps` 修饰符来修饰我们的修饰符："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "    square function.\n",
+      "     square\n"
+     ]
+    }
+   ],
+   "source": [
+    "import functools\n",
+    "\n",
+    "def logging_call(f):\n",
+    "    @functools.wraps(f)\n",
+    "    def wrapper(*a, **kw):\n",
+    "        print 'calling {}'.format(f.__name__)\n",
+    "        return f(*a, **kw)\n",
+    "    return wrapper\n",
+    "\n",
+    "@logging_call\n",
+    "def square(x):\n",
+    "    '''\n",
+    "    square function.\n",
+    "    '''\n",
+    "    return x ** 2\n",
+    "\n",
+    "print square.__doc__, square.__name__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "现在这个问题解决了，所以在自定义修饰符方法的时候为了避免出现不必要的麻烦，尽量使用 `wraps` 来修饰修饰符！"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Class 修饰符"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "与函数修饰符类似，类修饰符是这样一类函数，接受一个类作为参数，通常返回一个新的类。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/05. advanced python/05.14 the operator functools itertools toolz fn funcy module.ipynb b/05-advanced-python/05.14-the-operator-functools-itertools-toolz-fn-funcy-module.ipynb
similarity index 95%
rename from 05. advanced python/05.14 the operator functools itertools toolz fn funcy module.ipynb
rename to 05-advanced-python/05.14-the-operator-functools-itertools-toolz-fn-funcy-module.ipynb
index 8bb250ee..a79f0da2 100644
--- a/05. advanced python/05.14 the operator functools itertools toolz fn funcy module.ipynb	
+++ b/05-advanced-python/05.14-the-operator-functools-itertools-toolz-fn-funcy-module.ipynb
@@ -1,351 +1,351 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# operator, functools, itertools, toolz, fn, funcy 模块"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## operator 模块"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import operator as op"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`operator` 模块提供了各种操作符（`+,*,[]`）的函数版本方便使用："
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "加法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "45\n"
-     ]
-    }
-   ],
-   "source": [
-    "print reduce(op.add, range(10))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "乘法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "362880\n"
-     ]
-    }
-   ],
-   "source": [
-    "print reduce(op.mul, range(1,10))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`[]`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[('a', 1), ('bb', 4), ('ccc', 2), ('dddd', 3)]\n",
-      "[('a', 1), ('ccc', 2), ('dddd', 3), ('bb', 4)]\n",
-      "[('a', 1), ('bb', 4), ('ccc', 2), ('dddd', 3)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_list = [('a', 1), ('bb', 4), ('ccc', 2), ('dddd', 3)]\n",
-    "\n",
-    "# 标准排序\n",
-    "print sorted(my_list)\n",
-    "\n",
-    "# 使用元素的第二个元素排序\n",
-    "print sorted(my_list, key=op.itemgetter(1))\n",
-    "\n",
-    "# 使用第一个元素的长度进行排序：\n",
-    "print sorted(my_list, key=lambda x: len(x[0]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## functools 模块"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`functools` 包含很多跟函数相关的工具，比如之前看到的 `wraps` 函数，不过最常用的是 `partial` 函数，这个函数允许我们使用一个函数中生成一个新函数，这个函数使用原来的函数，不过某些参数被指定了："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n",
-      "24\n"
-     ]
-    }
-   ],
-   "source": [
-    "from functools import partial\n",
-    "\n",
-    "# 将 reduce 的第一个参数指定为加法，得到的是类似求和的函数\n",
-    "sum_ = partial(reduce, op.add)\n",
-    "\n",
-    "# 将 reduce 的第一个参数指定为乘法，得到的是类似求连乘的函数\n",
-    "prod_ = partial(reduce, op.mul)\n",
-    "\n",
-    "print sum_([1,2,3,4])\n",
-    "print prod_([1,2,3,4])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`partial` 函数还可以按照键值对传入固定参数。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## itertools 模块"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`itertools` 包含很多与迭代器对象相关的工具，其中比较常用的是排列组合生成器 `permutations` 和 `combinations`，还有在数据分析中常用的 `groupby` 生成器："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from itertools import cycle, groupby, islice, permutations, combinations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`cycle` 返回一个无限的迭代器，按照顺序重复输出输入迭代器中的内容，`islice` 则返回一个迭代器中的一段内容："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['a', 'b', 'c', 'd', 'a', 'b', 'c', 'd', 'a', 'b']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print list(islice(cycle('abcd'), 0, 10))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`groupby` 返回一个字典，按照指定的 `key` 对一组数据进行分组，字典的键是 `key`，值是一个迭代器： "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3 ['pig', 'cow', 'dog', 'cat']\n",
-      "4 ['lion']\n",
-      "5 ['hippo', 'tiger']\n",
-      "7 ['giraffe']\n",
-      "8 ['elephant']\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "animals = sorted(['pig', 'cow', 'giraffe', 'elephant',\n",
-    "                  'dog', 'cat', 'hippo', 'lion', 'tiger'], key=len)\n",
-    "\n",
-    "# 按照长度进行分组\n",
-    "for k, g in groupby(animals, key=len):\n",
-    "    print k, list(g)\n",
-    "print"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "排列："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['abc', 'acb', 'bac', 'bca', 'cab', 'cba']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print [''.join(p) for p in permutations('abc')]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "组合："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1, 2], [1, 3], [1, 4], [2, 3], [2, 4], [3, 4]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print [list(c) for c in combinations([1,2,3,4], r=2)]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## toolz, fn 和 funcy 模块"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这三个模块的作用是方便我们在编程的时候使用函数式编程的风格。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# operator, functools, itertools, toolz, fn, funcy 模块"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## operator 模块"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import operator as op"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`operator` 模块提供了各种操作符（`+,*,[]`）的函数版本方便使用："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "加法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "45\n"
+     ]
+    }
+   ],
+   "source": [
+    "print reduce(op.add, range(10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "乘法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "362880\n"
+     ]
+    }
+   ],
+   "source": [
+    "print reduce(op.mul, range(1,10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`[]`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('a', 1), ('bb', 4), ('ccc', 2), ('dddd', 3)]\n",
+      "[('a', 1), ('ccc', 2), ('dddd', 3), ('bb', 4)]\n",
+      "[('a', 1), ('bb', 4), ('ccc', 2), ('dddd', 3)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_list = [('a', 1), ('bb', 4), ('ccc', 2), ('dddd', 3)]\n",
+    "\n",
+    "# 标准排序\n",
+    "print sorted(my_list)\n",
+    "\n",
+    "# 使用元素的第二个元素排序\n",
+    "print sorted(my_list, key=op.itemgetter(1))\n",
+    "\n",
+    "# 使用第一个元素的长度进行排序：\n",
+    "print sorted(my_list, key=lambda x: len(x[0]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## functools 模块"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`functools` 包含很多跟函数相关的工具，比如之前看到的 `wraps` 函数，不过最常用的是 `partial` 函数，这个函数允许我们使用一个函数中生成一个新函数，这个函数使用原来的函数，不过某些参数被指定了："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n",
+      "24\n"
+     ]
+    }
+   ],
+   "source": [
+    "from functools import partial\n",
+    "\n",
+    "# 将 reduce 的第一个参数指定为加法，得到的是类似求和的函数\n",
+    "sum_ = partial(reduce, op.add)\n",
+    "\n",
+    "# 将 reduce 的第一个参数指定为乘法，得到的是类似求连乘的函数\n",
+    "prod_ = partial(reduce, op.mul)\n",
+    "\n",
+    "print sum_([1,2,3,4])\n",
+    "print prod_([1,2,3,4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`partial` 函数还可以按照键值对传入固定参数。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## itertools 模块"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`itertools` 包含很多与迭代器对象相关的工具，其中比较常用的是排列组合生成器 `permutations` 和 `combinations`，还有在数据分析中常用的 `groupby` 生成器："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from itertools import cycle, groupby, islice, permutations, combinations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`cycle` 返回一个无限的迭代器，按照顺序重复输出输入迭代器中的内容，`islice` 则返回一个迭代器中的一段内容："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['a', 'b', 'c', 'd', 'a', 'b', 'c', 'd', 'a', 'b']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print list(islice(cycle('abcd'), 0, 10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`groupby` 返回一个字典，按照指定的 `key` 对一组数据进行分组，字典的键是 `key`，值是一个迭代器： "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3 ['pig', 'cow', 'dog', 'cat']\n",
+      "4 ['lion']\n",
+      "5 ['hippo', 'tiger']\n",
+      "7 ['giraffe']\n",
+      "8 ['elephant']\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "animals = sorted(['pig', 'cow', 'giraffe', 'elephant',\n",
+    "                  'dog', 'cat', 'hippo', 'lion', 'tiger'], key=len)\n",
+    "\n",
+    "# 按照长度进行分组\n",
+    "for k, g in groupby(animals, key=len):\n",
+    "    print k, list(g)\n",
+    "print"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "排列："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['abc', 'acb', 'bac', 'bca', 'cab', 'cba']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print [''.join(p) for p in permutations('abc')]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "组合："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1, 2], [1, 3], [1, 4], [2, 3], [2, 4], [3, 4]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print [list(c) for c in combinations([1,2,3,4], r=2)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## toolz, fn 和 funcy 模块"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这三个模块的作用是方便我们在编程的时候使用函数式编程的风格。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/05. advanced python/05.15 scope.ipynb b/05-advanced-python/05.15-scope.ipynb
similarity index 94%
rename from 05. advanced python/05.15 scope.ipynb
rename to 05-advanced-python/05.15-scope.ipynb
index 1799b0ee..ff7dd8df 100644
--- a/05. advanced python/05.15 scope.ipynb	
+++ b/05-advanced-python/05.15-scope.ipynb
@@ -1,542 +1,542 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 作用域"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在函数中，`Python` 从命名空间中寻找变量的顺序如下：\n",
-    "\n",
-    "- `local function scope`\n",
-    "- `enclosing scope`\n",
-    "- `global scope`\n",
-    "- `builtin scope`\n",
-    "\n",
-    "例子："
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# local 作用域"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def foo(a,b):\n",
-    "    c = 1\n",
-    "    d = a + b + c"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里所有的变量都在 `local` 作用域。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## global 作用域"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "c = 1\n",
-    "def foo(a,b):\n",
-    "    d = a + b + c"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里的 `c` 就在 `global` 作用域。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## global 关键词"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `global` 关键词可以在 `local` 作用域中修改 `global` 作用域的值。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "2\n"
-     ]
-    }
-   ],
-   "source": [
-    "c = 1\n",
-    "def foo():\n",
-    "    global c\n",
-    "    c = 2\n",
-    "    \n",
-    "print c\n",
-    "foo()\n",
-    "print c"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "其作用是将 `c` 指向 `global` 中的 `c`。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "如果不加关键词，那么 `local` 作用域的 `c` 不会影响 `global` 作用域中的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "1\n"
-     ]
-    }
-   ],
-   "source": [
-    "c = 1\n",
-    "def foo():\n",
-    "    c = 2\n",
-    "    \n",
-    "print c\n",
-    "foo()\n",
-    "print c"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## built-in 作用域"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3\n"
-     ]
-    }
-   ],
-   "source": [
-    "def list_length(a):\n",
-    "    return len(a)\n",
-    "\n",
-    "a = [1,2,3]\n",
-    "print list_length(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里函数 `len` 就是在 `built-in` 作用域中："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function len>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import __builtin__\n",
-    "\n",
-    "__builtin__.len"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## class 中的作用域"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Global | MyClass\n",
-    "---|---\n",
-    "`var = 0` <br> `MyClass` <br> `access_class` | `var = 1`<br>`access_class` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# global\n",
-    "var = 0\n",
-    "\n",
-    "class MyClass(object):\n",
-    "    # class variable\n",
-    "    var = 1\n",
-    "    \n",
-    "    def access_class_c(self):\n",
-    "        print 'class var:', self.var\n",
-    "    \n",
-    "    def write_class_c(self):\n",
-    "        MyClass.var = 2\n",
-    "        print 'class var:', self.var\n",
-    "        \n",
-    "    def access_global_c(self):\n",
-    "        print 'global var:', var\n",
-    "    \n",
-    "    def write_instance_c(self):\n",
-    "        self.var = 3\n",
-    "        print 'instance var:', self.var"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Global | MyClass | obj\n",
-    "---|---|----\n",
-    "`var = 0` <br> `MyClass` <br> [`access_class`] <br> `obj` | `var = 1`<br>`access_class`  |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "obj = MyClass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查询 `self.var` 时，由于 `obj` 不存在 `var`，所以跳到 MyClass 中：\n",
-    "\n",
-    "Global | MyClass | obj\n",
-    "---|---|----\n",
-    "`var = 0` <br> `MyClass` <br> [`access_class` <br> `self`] <br> `obj` | `var = 1`<br>`access_class`  |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "class var: 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "obj.access_class_c()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查询 `var` 直接跳到 `global` 作用域：\n",
-    "\n",
-    "Global | MyClass | obj\n",
-    "---|---|----\n",
-    "`var = 0` <br> `MyClass` <br> [`access_class` <br> `self`] <br> `obj` | `var = 1`<br>`access_class`  |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "global var: 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "obj.access_global_c()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "修改类中的 `MyClass.var`：\n",
-    "\n",
-    "Global | MyClass | obj\n",
-    "---|---|----\n",
-    "`var = 0` <br> `MyClass` <br> [`access_class` <br> `self`] <br> `obj` | `var = 2`<br>`access_class`  |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "class var: 2\n"
-     ]
-    }
-   ],
-   "source": [
-    "obj.write_class_c()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "修改实例中的 `var` 时，会直接在 `obj` 域中创建一个：\n",
-    "\n",
-    "Global | MyClass | obj\n",
-    "---|---|----\n",
-    "`var = 0` <br> `MyClass` <br> [`access_class` <br> `self`] <br> `obj` | `var = 2`<br>`access_class`  | `var = 3`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "instance var: 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "obj.write_instance_c()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "MyClass.var"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`MyClass` 中的 `var` 并没有改变。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 词法作用域"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于嵌套函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "a = 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "def outer():\n",
-    "    a = 1\n",
-    "    def inner():\n",
-    "        print \"a =\", a\n",
-    "    inner()\n",
-    "    \n",
-    "outer()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "如果里面的函数没有找到变量，那么会向外一层寻找变量，如果再找不到，则到 `global` 作用域。\n",
-    "\n",
-    "返回的是函数的情况："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "a (1): 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "def outer():\n",
-    "    a = 1\n",
-    "    def inner():\n",
-    "        return a\n",
-    "    return inner\n",
-    "    \n",
-    "func = outer()\n",
-    "\n",
-    "print 'a (1):', func()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "func() 函数中调用的 `a` 要从它定义的地方开始寻找，而不是在 `func` 所在的作用域寻找。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 作用域"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在函数中，`Python` 从命名空间中寻找变量的顺序如下：\n",
+    "\n",
+    "- `local function scope`\n",
+    "- `enclosing scope`\n",
+    "- `global scope`\n",
+    "- `builtin scope`\n",
+    "\n",
+    "例子："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# local 作用域"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def foo(a,b):\n",
+    "    c = 1\n",
+    "    d = a + b + c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里所有的变量都在 `local` 作用域。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## global 作用域"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "c = 1\n",
+    "def foo(a,b):\n",
+    "    d = a + b + c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里的 `c` 就在 `global` 作用域。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## global 关键词"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `global` 关键词可以在 `local` 作用域中修改 `global` 作用域的值。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "c = 1\n",
+    "def foo():\n",
+    "    global c\n",
+    "    c = 2\n",
+    "    \n",
+    "print c\n",
+    "foo()\n",
+    "print c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "其作用是将 `c` 指向 `global` 中的 `c`。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "如果不加关键词，那么 `local` 作用域的 `c` 不会影响 `global` 作用域中的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "c = 1\n",
+    "def foo():\n",
+    "    c = 2\n",
+    "    \n",
+    "print c\n",
+    "foo()\n",
+    "print c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## built-in 作用域"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "def list_length(a):\n",
+    "    return len(a)\n",
+    "\n",
+    "a = [1,2,3]\n",
+    "print list_length(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里函数 `len` 就是在 `built-in` 作用域中："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function len>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import __builtin__\n",
+    "\n",
+    "__builtin__.len"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## class 中的作用域"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Global | MyClass\n",
+    "---|---\n",
+    "`var = 0` <br> `MyClass` <br> `access_class` | `var = 1`<br>`access_class` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# global\n",
+    "var = 0\n",
+    "\n",
+    "class MyClass(object):\n",
+    "    # class variable\n",
+    "    var = 1\n",
+    "    \n",
+    "    def access_class_c(self):\n",
+    "        print 'class var:', self.var\n",
+    "    \n",
+    "    def write_class_c(self):\n",
+    "        MyClass.var = 2\n",
+    "        print 'class var:', self.var\n",
+    "        \n",
+    "    def access_global_c(self):\n",
+    "        print 'global var:', var\n",
+    "    \n",
+    "    def write_instance_c(self):\n",
+    "        self.var = 3\n",
+    "        print 'instance var:', self.var"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Global | MyClass | obj\n",
+    "---|---|----\n",
+    "`var = 0` <br> `MyClass` <br> [`access_class`] <br> `obj` | `var = 1`<br>`access_class`  |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "obj = MyClass()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查询 `self.var` 时，由于 `obj` 不存在 `var`，所以跳到 MyClass 中：\n",
+    "\n",
+    "Global | MyClass | obj\n",
+    "---|---|----\n",
+    "`var = 0` <br> `MyClass` <br> [`access_class` <br> `self`] <br> `obj` | `var = 1`<br>`access_class`  |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "class var: 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "obj.access_class_c()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查询 `var` 直接跳到 `global` 作用域：\n",
+    "\n",
+    "Global | MyClass | obj\n",
+    "---|---|----\n",
+    "`var = 0` <br> `MyClass` <br> [`access_class` <br> `self`] <br> `obj` | `var = 1`<br>`access_class`  |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "global var: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "obj.access_global_c()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "修改类中的 `MyClass.var`：\n",
+    "\n",
+    "Global | MyClass | obj\n",
+    "---|---|----\n",
+    "`var = 0` <br> `MyClass` <br> [`access_class` <br> `self`] <br> `obj` | `var = 2`<br>`access_class`  |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "class var: 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "obj.write_class_c()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "修改实例中的 `var` 时，会直接在 `obj` 域中创建一个：\n",
+    "\n",
+    "Global | MyClass | obj\n",
+    "---|---|----\n",
+    "`var = 0` <br> `MyClass` <br> [`access_class` <br> `self`] <br> `obj` | `var = 2`<br>`access_class`  | `var = 3`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "instance var: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "obj.write_instance_c()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MyClass.var"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`MyClass` 中的 `var` 并没有改变。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 词法作用域"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于嵌套函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a = 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "def outer():\n",
+    "    a = 1\n",
+    "    def inner():\n",
+    "        print \"a =\", a\n",
+    "    inner()\n",
+    "    \n",
+    "outer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "如果里面的函数没有找到变量，那么会向外一层寻找变量，如果再找不到，则到 `global` 作用域。\n",
+    "\n",
+    "返回的是函数的情况："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a (1): 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "def outer():\n",
+    "    a = 1\n",
+    "    def inner():\n",
+    "        return a\n",
+    "    return inner\n",
+    "    \n",
+    "func = outer()\n",
+    "\n",
+    "print 'a (1):', func()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "func() 函数中调用的 `a` 要从它定义的地方开始寻找，而不是在 `func` 所在的作用域寻找。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/05. advanced python/05.16 dynamic code execution.ipynb b/05-advanced-python/05.16-dynamic-code-execution.ipynb
similarity index 94%
rename from 05. advanced python/05.16 dynamic code execution.ipynb
rename to 05-advanced-python/05.16-dynamic-code-execution.ipynb
index 93f3800f..1d63c2c3 100644
--- a/05. advanced python/05.16 dynamic code execution.ipynb	
+++ b/05-advanced-python/05.16-dynamic-code-execution.ipynb
@@ -1,433 +1,433 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 动态编译"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 标准编程语言"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于 **C** 语言，代码一般要先编译，再执行。\n",
-    "\n",
-    "    .c -> .exe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 解释器语言"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "shell 脚本\n",
-    "\n",
-    "    .sh -> interpreter"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Byte Code 编译"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Python, Java** 等语言先将代码编译为 byte code（不是机器码），然后再处理：\n",
-    "\n",
-    "    .py -> .pyc -> interpreter"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## eval 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    eval(statement, glob, local)\n",
-    "\n",
-    "使用 `eval` 函数动态执行代码，返回执行的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = 1\n",
-    "\n",
-    "eval(\"a+1\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以接收明明空间参数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "local = dict(a=2)\n",
-    "glob = {}\n",
-    "eval(\"a+1\", glob, local)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里 `local` 中的 `a` 先被找到。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## exec 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    exec(statement, glob, local)\n",
-    "\n",
-    "使用 `exec` 可以添加修改原有的变量。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = 1\n",
-    "\n",
-    "exec(\"b = a+1\")\n",
-    "\n",
-    "print b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'a': 2, 'b': 3}\n"
-     ]
-    }
-   ],
-   "source": [
-    "local = dict(a=2)\n",
-    "glob = {}\n",
-    "exec(\"b = a+1\", glob, local)\n",
-    "\n",
-    "print local"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "执行之后，`b` 在 `local` 命名空间中。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 警告"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "动态执行的时候要注意，不要执行不信任的用户输入，因为它们拥有 `Python` 的全部权限。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## compile 函数生成 byte code"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    compile(str, filename, mode)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = 1\n",
-    "c = compile(\"a+2\", \"\", 'eval')\n",
-    "\n",
-    "eval(c)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = 1\n",
-    "c = compile(\"b=a+2\", \"\", 'exec')\n",
-    "\n",
-    "exec(c)\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## abstract syntax trees"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import ast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Expression(body=BinOp(left=Name(id='a', ctx=Load()), op=Add(), right=Num(n=2)))\""
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tree = ast.parse(\"a+2\", \"\", \"eval\")\n",
-    "\n",
-    "ast.dump(tree)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "改变常数的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Expression(body=BinOp(left=Name(id='a', ctx=Load()), op=Add(), right=Num(n=3)))\""
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tree.body.right.n = 3\n",
-    "\n",
-    "ast.dump(tree)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = 1\n",
-    "c = compile(tree, '', 'eval')\n",
-    "\n",
-    "eval(c)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "安全的使用方法 `literal_eval` ，只支持基本值的操作："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[10.0, 2, True, 'foo']"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ast.literal_eval(\"[10.0, 2, True, 'foo']\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 动态编译"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 标准编程语言"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于 **C** 语言，代码一般要先编译，再执行。\n",
+    "\n",
+    "    .c -> .exe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 解释器语言"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "shell 脚本\n",
+    "\n",
+    "    .sh -> interpreter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Byte Code 编译"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Python, Java** 等语言先将代码编译为 byte code（不是机器码），然后再处理：\n",
+    "\n",
+    "    .py -> .pyc -> interpreter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## eval 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    eval(statement, glob, local)\n",
+    "\n",
+    "使用 `eval` 函数动态执行代码，返回执行的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = 1\n",
+    "\n",
+    "eval(\"a+1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以接收明明空间参数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "local = dict(a=2)\n",
+    "glob = {}\n",
+    "eval(\"a+1\", glob, local)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里 `local` 中的 `a` 先被找到。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## exec 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    exec(statement, glob, local)\n",
+    "\n",
+    "使用 `exec` 可以添加修改原有的变量。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = 1\n",
+    "\n",
+    "exec(\"b = a+1\")\n",
+    "\n",
+    "print b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'a': 2, 'b': 3}\n"
+     ]
+    }
+   ],
+   "source": [
+    "local = dict(a=2)\n",
+    "glob = {}\n",
+    "exec(\"b = a+1\", glob, local)\n",
+    "\n",
+    "print local"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "执行之后，`b` 在 `local` 命名空间中。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 警告"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "动态执行的时候要注意，不要执行不信任的用户输入，因为它们拥有 `Python` 的全部权限。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## compile 函数生成 byte code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    compile(str, filename, mode)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = 1\n",
+    "c = compile(\"a+2\", \"\", 'eval')\n",
+    "\n",
+    "eval(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = 1\n",
+    "c = compile(\"b=a+2\", \"\", 'exec')\n",
+    "\n",
+    "exec(c)\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## abstract syntax trees"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import ast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Expression(body=BinOp(left=Name(id='a', ctx=Load()), op=Add(), right=Num(n=2)))\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tree = ast.parse(\"a+2\", \"\", \"eval\")\n",
+    "\n",
+    "ast.dump(tree)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "改变常数的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Expression(body=BinOp(left=Name(id='a', ctx=Load()), op=Add(), right=Num(n=3)))\""
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tree.body.right.n = 3\n",
+    "\n",
+    "ast.dump(tree)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = 1\n",
+    "c = compile(tree, '', 'eval')\n",
+    "\n",
+    "eval(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "安全的使用方法 `literal_eval` ，只支持基本值的操作："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[10.0, 2, True, 'foo']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ast.literal_eval(\"[10.0, 2, True, 'foo']\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/05. advanced python/my_database.sqlite b/05-advanced-python/my_database.sqlite
similarity index 100%
rename from 05. advanced python/my_database.sqlite
rename to 05-advanced-python/my_database.sqlite
diff --git a/06. matplotlib/06.01 pyplot tutorial.ipynb b/06-matplotlib/06.01-pyplot-tutorial.ipynb
similarity index 99%
rename from 06. matplotlib/06.01 pyplot tutorial.ipynb
rename to 06-matplotlib/06.01-pyplot-tutorial.ipynb
index da2e6240..02bde092 100644
--- a/06. matplotlib/06.01 pyplot tutorial.ipynb	
+++ b/06-matplotlib/06.01-pyplot-tutorial.ipynb
@@ -1,780 +1,780 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Pyplot 教程"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Matplotlib 简介"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**`matplotlib`** 是一个 **`Python`** 的 `2D` 图形包。\n",
-    "\n",
-    "在线文档：http://matplotlib.org ，提供了 [Examples](http://matplotlib.org/examples/index.html), [FAQ](http://matplotlib.org/faq/index.html), [API](http://matplotlib.org/contents.html), [Gallery](http://matplotlib.org/gallery.html)，其中 [Gallery](http://matplotlib.org/gallery.html) 是很有用的一个部分，因为它提供了各种画图方式的可视化，方便用户根据需求进行选择。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 使用 Pyplot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "导入相关的包："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`matplotlib.pyplot` 包含一系列类似 **`MATLAB`** 中绘图函数的相关函数。每个 `matplotlib.pyplot` 中的函数对当前的图像进行一些修改，例如：产生新的图像，在图像中产生新的绘图区域，在绘图区域中画线，给绘图加上标记，等等…… `matplotlib.pyplot` 会自动记住当前的图像和绘图区域，因此这些函数会直接作用在当前的图像上。\n",
-    "\n",
-    "下文中，以 `plt` 作为 `matplotlib.pyplot` 的省略。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## plt.show() 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "默认情况下，`matplotlib.pyplot` 不会直接显示图像，只有调用 `plt.show()` 函数时，图像才会显示出来。\n",
-    "\n",
-    "`plt.show()` 默认是在新窗口打开一幅图像，并且提供了对图像进行操作的按钮。\n",
-    "\n",
-    "不过在 `ipython` 命令行中，我们可以使用 `magic` 命令将它插入 `notebook` 中，并且不需要调用 `plt.show()` 也可以显示：\n",
-    "\n",
-    "- `%matplotlib notebook`\n",
-    "- `%matplotlib inline`\n",
-    "\n",
-    "不过在实际写程序中，我们还是需要调用 `plt.show()` 函数将图像显示出来。\n",
-    "\n",
-    "这里我们使图像输出在 `notebook` 中："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## plt.plot() 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 例子"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`plt.plot()` 函数可以用来绘图："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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yc8B+4JUjzS4Fbhm02cPSV4ie0rB+dYBjCVK3NBlj+LvBv+8A/riqPp/k2vW+UZJtwNnA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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa0a4a58>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot([1,2,3,4])\n",
-    "plt.ylabel('some numbers')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 基本用法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`plot` 函数基本的用法有以下四种：\n",
-    "\n",
-    "默认参数\n",
-    "- `plt.plot(x,y)` \n",
-    "\n",
-    "指定参数\n",
-    "- `plt.plot(x,y, format_str)`\n",
-    "\n",
-    "默认参数，`x` 为 `0~N-1`\n",
-    "- `plt.plot(y)`\n",
-    "\n",
-    "指定参数，`x` 为 `0~N-1`\n",
-    "- `plt.plot(y, format_str)`\n",
-    "\n",
-    "因此，在上面的例子中，我们没有给定 `x` 的值，所以其默认值为 `[0,1,2,3]`。\n",
-    "\n",
-    "传入 `x` 和 `y`： "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xa48a550>]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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vzM7cNIqkspj4zjwzDwGH2sc/iYgDwLOAA0WvOWnd0ijbtplGkVQ9I+lmiYg1wAuBPaO4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\nSaoBg7kk1YDBXJJqwGAuSTVgMJekGvhf3kAwE/Ra4D0AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa20de10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot([1,2,3,4], [1,4,9,16])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 字符参数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "和 **`MATLAB`** 中类似，我们还可以用字符来指定绘图的格式：\n",
-    "\n",
-    "表示颜色的字符参数有：\n",
-    "\n",
-    "字符 | 颜色\n",
-    "-- | -- \n",
-    "`‘b’`|\t蓝色，blue\n",
-    "`‘g’`|\t绿色，green\n",
-    "`‘r’`|\t红色，red\n",
-    "`‘c’`|\t青色，cyan\n",
-    "`‘m’`|\t品红，magenta\n",
-    "`‘y’`|\t黄色，yellow\n",
-    "`‘k’`|\t黑色，black\n",
-    "`‘w’`|\t白色，white\n",
-    "\n",
-    "表示类型的字符参数有：\n",
-    "\n",
-    "字符|类型 | 字符|类型\n",
-    "---|--- | --- | ---\n",
-    "`  '-'\t`| 实线 | `'--'`|\t虚线\n",
-    "`'-.'`|\t虚点线 | `':'`|\t点线\n",
-    "`'.'`|\t点 | `','`| 像素点\n",
-    "`'o'`\t|圆点 | `'v'`|\t下三角点\n",
-    "`'^'`|\t上三角点 | `'<'`|\t左三角点\n",
-    "`'>'`|\t右三角点 | `'1'`|\t下三叉点\n",
-    "`'2'`|\t上三叉点 | `'3'`|\t左三叉点\n",
-    "`'4'`|\t右三叉点 | `'s'`|\t正方点\n",
-    "`'p'`\t| 五角点 | `'*'`|\t星形点\n",
-    "`'h'`|\t六边形点1 | `'H'`|\t六边形点2 \n",
-    "`'+'`|\t加号点 | `'x'`|\t乘号点\n",
-    "`'D'`|\t实心菱形点 | `'d'`|\t瘦菱形点 \n",
-    "`'_'`|\t横线点 | |\n",
-    "\n",
-    "例如我们要画出红色圆点："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa4a6a58>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot([1,2,3,4], [1,4,9,16], 'ro')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以看出，有两个点在图像的边缘，因此，我们需要改变轴的显示范围。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 显示范围"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "与 **`MATLAB`** 类似，这里可以使用 `axis` 函数指定坐标轴显示的范围：\n",
-    "\n",
-    "    plt.axis([xmin, xmax, ymin, ymax])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa4e8908>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot([1,2,3,4], [1,4,9,16], 'ro')\n",
-    "# 指定 x 轴显示区域为 0-6，y 轴为 0-20\n",
-    "plt.axis([0,6,0,20])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 传入 `Numpy` 数组"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "之前我们传给 `plot` 的参数都是列表，事实上，向 `plot` 中传入 `numpy` 数组是更常用的做法。事实上，如果传入的是列表，`matplotlib` 会在内部将它转化成数组再进行处理："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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ZK9Oh0u6XfmhsBrr6QjPL8Ys4Y8VKW+1goKvnml2O3+8zVvq92u6HPqgzDHT1XLO7AfX7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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb688198>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# evenly sampled time at 200ms intervals\n",
-    "t = np.arange(0., 5., 0.2)\n",
-    "\n",
-    "# red dashes, blue squares and green triangles\n",
-    "plt.plot(t, t, 'r--', \n",
-    "         t, t**2, 'bs', \n",
-    "         t, t**3, 'g^')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 传入多组数据"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上，在上面的例子中，我们不仅仅向 `plot` 函数传入了数组，还传入了多组 `(x,y,format_str)` 参数，它们在同一张图上显示。\n",
-    "\n",
-    "这意味着我们不需要使用多个 `plot` 函数来画多组数组，只需要可以将这些组合放到一个 `plot` 函数中去即可。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 线条属性"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "之前提到，我们可以用字符串来控制线条的属性，事实上还可以通过关键词来改变线条的性质，例如 `linwidth` 可以改变线条的宽度，`color` 可以改变线条的颜色："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb67c588>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x = np.linspace(-np.pi,np.pi)\n",
-    "y = np.sin(x)\n",
-    "\n",
-    "plt.plot(x, y, linewidth=2.0, color='r')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 使用 plt.plot() 的返回值来设置线条属性"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`plot` 函数返回一个 `Line2D` 对象组成的列表，每个对象代表输入的一对组合，例如：\n",
-    "\n",
-    "- line1, line2 为两个 Line2D 对象\n",
-    "\n",
-    "    `line1, line2 = plt.plot(x1, y1, x2, y2)`\n",
-    "\n",
-    "- 返回 3 个 Line2D 对象组成的列表\n",
-    "\n",
-    "    `lines = plt.plot(x1, y1, x2, y2, x3, y3)`\n",
-    "\n",
-    "我们可以使用这个返回值来对线条属性进行设置："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb7d6080>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 加逗号 line 中得到的是 line2D 对象，不加逗号得到的是只有一个 line2D 对象的列表\n",
-    "line, = plt.plot(x, y, 'r-')\n",
-    "\n",
-    "# 将抗锯齿关闭\n",
-    "line.set_antialiased(False)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### plt.setp() 修改线条性质"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "更方便的做法是使用 `plt` 的 `setp` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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iR5R7o0Z5++Zdd/XavM7TFamWRvRJ166dr61ft65ylJtm//2vLy0FP0VKSV4k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\nfxeZmr/ZF8WeAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbcff048>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lines = plt.plot(x, y)\n",
-    "\n",
-    "# 使用键值对\n",
-    "plt.setp(lines, color='r', linewidth=2.0)\n",
-    "\n",
-    "# 或者使用 MATLAB 风格的字符串对\n",
-    "plt.setp(lines, 'color', 'r', 'linewidth', 2.0)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以设置的属性有很多，可以使用 `plt.setp(lines)` 查看 `lines` 可以设置的属性，各属性的含义可参考 `matplotlib` 的文档。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  agg_filter: unknown\n",
-      "  alpha: float (0.0 transparent through 1.0 opaque)         \n",
-      "  animated: [True | False]         \n",
-      "  antialiased or aa: [True | False]         \n",
-      "  axes: an :class:`~matplotlib.axes.Axes` instance         \n",
-      "  clip_box: a :class:`matplotlib.transforms.Bbox` instance         \n",
-      "  clip_on: [True | False]         \n",
-      "  clip_path: [ (:class:`~matplotlib.path.Path`,         :class:`~matplotlib.transforms.Transform`) |         :class:`~matplotlib.patches.Patch` | None ]         \n",
-      "  color or c: any matplotlib color         \n",
-      "  contains: a callable function         \n",
-      "  dash_capstyle: ['butt' | 'round' | 'projecting']         \n",
-      "  dash_joinstyle: ['miter' | 'round' | 'bevel']         \n",
-      "  dashes: sequence of on/off ink in points         \n",
-      "  drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' |                   'steps-post']         \n",
-      "  figure: a :class:`matplotlib.figure.Figure` instance         \n",
-      "  fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']         \n",
-      "  gid: an id string         \n",
-      "  label: string or anything printable with '%s' conversion.         \n",
-      "  linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` |                   ``' '`` | ``''``]\n",
-      "  linewidth or lw: float value in points         \n",
-      "  lod: [True | False]         \n",
-      "  marker: :mod:`A valid marker style <matplotlib.markers>`\n",
-      "  markeredgecolor or mec: any matplotlib color         \n",
-      "  markeredgewidth or mew: float value in points         \n",
-      "  markerfacecolor or mfc: any matplotlib color         \n",
-      "  markerfacecoloralt or mfcalt: any matplotlib color         \n",
-      "  markersize or ms: float         \n",
-      "  markevery: [None | int | length-2 tuple of int | slice |         list/array of int | float | length-2 tuple of float]\n",
-      "  path_effects: unknown\n",
-      "  picker: float distance in points or callable pick function         ``fn(artist, event)``         \n",
-      "  pickradius: float distance in points         \n",
-      "  rasterized: [True | False | None]         \n",
-      "  sketch_params: unknown\n",
-      "  snap: unknown\n",
-      "  solid_capstyle: ['butt' | 'round' |  'projecting']         \n",
-      "  solid_joinstyle: ['miter' | 'round' | 'bevel']         \n",
-      "  transform: a :class:`matplotlib.transforms.Transform` instance         \n",
-      "  url: a url string         \n",
-      "  visible: [True | False]         \n",
-      "  xdata: 1D array         \n",
-      "  ydata: 1D array         \n",
-      "  zorder: any number         \n"
-     ]
-    }
-   ],
-   "source": [
-    "plt.setp(lines)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "## 子图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`figure()` 函数会产生一个指定编号为 `num` 的图：\n",
-    "\n",
-    "    plt.figure(num)\n",
-    "\n",
-    "这里，`figure(1)` 其实是可以省略的，因为默认情况下 `plt` 会自动产生一幅图像。\n",
-    "\n",
-    "使用 `subplot` 可以在一副图中生成多个子图，其参数为：\n",
-    "\n",
-    "    plt.subplot(numrows, numcols, fignum)\n",
-    "\n",
-    "当 `numrows * numcols < 10` 时，中间的逗号可以省略，因此 `plt.subplot(211)` 就相当于 `plt.subplot(2,1,1)`。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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mfPnyDBs2jLFjx4Z13c03d6NMmRuBB8ndSza5XwrdurBYLBbf4TR7Zdxwyy23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\nSXD+P5ePy54P9ccKAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbb32ef0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def f(t):\n",
-    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
-    "\n",
-    "t1 = np.arange(0.0, 5.0, 0.1)\n",
-    "t2 = np.arange(0.0, 5.0, 0.02)\n",
-    "\n",
-    "plt.figure(1)\n",
-    "plt.subplot(211)\n",
-    "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
-    "\n",
-    "plt.subplot(212)\n",
-    "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "## 图形上加上文字"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "`plt.hist()` 可以用来画直方图。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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X2dgliYioPchvWZFdXncAMyRNlzQROBRYWlNmKXA4DDZAq6u6sxpZChyRvz8C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\n60kAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbd0b358>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mu, sigma = 100, 15\n",
-    "x = mu + sigma * np.random.randn(10000)\n",
-    "\n",
-    "# the histogram of the data\n",
-    "n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)\n",
-    "\n",
-    "\n",
-    "plt.xlabel('Smarts')\n",
-    "plt.ylabel('Probability')\n",
-    "plt.title('Histogram of IQ')\n",
-    "plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
-    "plt.axis([40, 160, 0, 0.03])\n",
-    "plt.grid(True)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于这幅图形，我们使用 `xlabel` ，`ylabel`，`title`，`text` 方法设置了文字，其中：\n",
-    "\n",
-    "- `xlabel` ：x 轴标注\n",
-    "\n",
-    "- `ylabel` ：y 轴标注\n",
-    "\n",
-    "- `title` ：图形标题\n",
-    "\n",
-    "- `text` ：在指定位置放入文字\n",
-    "\n",
-    "输入特殊符号支持使用 `Tex` 语法，用 `$<some Tex code>$` 隔开。\n",
-    "\n",
-    "除了使用 `text` 在指定位置标上文字之外，还可以使用 `annotate` 函数进行注释，`annotate` 主要有两个参数：\n",
-    "\n",
-    "- `xy` ：注释位置 \n",
-    "- `xytext` ：注释文字位置"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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DmAHgSwDVkFj8LQBuA3BbVJm/ANgCYHWssE2kDJeXM59zjlgVoMpZXI4cYW7dWs539uwz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\nRVGUgPP/Qde6gvF4TtQAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xba841d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = plt.subplot(111)\n",
-    "\n",
-    "t = np.arange(0.0, 5.0, 0.01)\n",
-    "s = np.cos(2*np.pi*t)\n",
-    "line, = plt.plot(t, s, lw=2)\n",
-    "\n",
-    "plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),\n",
-    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
-    "            )\n",
-    "\n",
-    "plt.ylim(-2,2)\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pyplot 教程"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matplotlib 简介"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**`matplotlib`** 是一个 **`Python`** 的 `2D` 图形包。\n",
+    "\n",
+    "在线文档：http://matplotlib.org ，提供了 [Examples](http://matplotlib.org/examples/index.html), [FAQ](http://matplotlib.org/faq/index.html), [API](http://matplotlib.org/contents.html), [Gallery](http://matplotlib.org/gallery.html)，其中 [Gallery](http://matplotlib.org/gallery.html) 是很有用的一个部分，因为它提供了各种画图方式的可视化，方便用户根据需求进行选择。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 使用 Pyplot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "导入相关的包："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`matplotlib.pyplot` 包含一系列类似 **`MATLAB`** 中绘图函数的相关函数。每个 `matplotlib.pyplot` 中的函数对当前的图像进行一些修改，例如：产生新的图像，在图像中产生新的绘图区域，在绘图区域中画线，给绘图加上标记，等等…… `matplotlib.pyplot` 会自动记住当前的图像和绘图区域，因此这些函数会直接作用在当前的图像上。\n",
+    "\n",
+    "下文中，以 `plt` 作为 `matplotlib.pyplot` 的省略。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plt.show() 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "默认情况下，`matplotlib.pyplot` 不会直接显示图像，只有调用 `plt.show()` 函数时，图像才会显示出来。\n",
+    "\n",
+    "`plt.show()` 默认是在新窗口打开一幅图像，并且提供了对图像进行操作的按钮。\n",
+    "\n",
+    "不过在 `ipython` 命令行中，我们可以使用 `magic` 命令将它插入 `notebook` 中，并且不需要调用 `plt.show()` 也可以显示：\n",
+    "\n",
+    "- `%matplotlib notebook`\n",
+    "- `%matplotlib inline`\n",
+    "\n",
+    "不过在实际写程序中，我们还是需要调用 `plt.show()` 函数将图像显示出来。\n",
+    "\n",
+    "这里我们使图像输出在 `notebook` 中："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plt.plot() 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 例子"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`plt.plot()` 函数可以用来绘图："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa0a4a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([1,2,3,4])\n",
+    "plt.ylabel('some numbers')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 基本用法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`plot` 函数基本的用法有以下四种：\n",
+    "\n",
+    "默认参数\n",
+    "- `plt.plot(x,y)` \n",
+    "\n",
+    "指定参数\n",
+    "- `plt.plot(x,y, format_str)`\n",
+    "\n",
+    "默认参数，`x` 为 `0~N-1`\n",
+    "- `plt.plot(y)`\n",
+    "\n",
+    "指定参数，`x` 为 `0~N-1`\n",
+    "- `plt.plot(y, format_str)`\n",
+    "\n",
+    "因此，在上面的例子中，我们没有给定 `x` 的值，所以其默认值为 `[0,1,2,3]`。\n",
+    "\n",
+    "传入 `x` 和 `y`： "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0xa48a550>]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSaoBg7kk1YDBXJJqwGAuSTVgMJekGvhf3kAwE/Ra4D0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa20de10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([1,2,3,4], [1,4,9,16])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 字符参数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "和 **`MATLAB`** 中类似，我们还可以用字符来指定绘图的格式：\n",
+    "\n",
+    "表示颜色的字符参数有：\n",
+    "\n",
+    "字符 | 颜色\n",
+    "-- | -- \n",
+    "`‘b’`|\t蓝色，blue\n",
+    "`‘g’`|\t绿色，green\n",
+    "`‘r’`|\t红色，red\n",
+    "`‘c’`|\t青色，cyan\n",
+    "`‘m’`|\t品红，magenta\n",
+    "`‘y’`|\t黄色，yellow\n",
+    "`‘k’`|\t黑色，black\n",
+    "`‘w’`|\t白色，white\n",
+    "\n",
+    "表示类型的字符参数有：\n",
+    "\n",
+    "字符|类型 | 字符|类型\n",
+    "---|--- | --- | ---\n",
+    "`  '-'\t`| 实线 | `'--'`|\t虚线\n",
+    "`'-.'`|\t虚点线 | `':'`|\t点线\n",
+    "`'.'`|\t点 | `','`| 像素点\n",
+    "`'o'`\t|圆点 | `'v'`|\t下三角点\n",
+    "`'^'`|\t上三角点 | `'<'`|\t左三角点\n",
+    "`'>'`|\t右三角点 | `'1'`|\t下三叉点\n",
+    "`'2'`|\t上三叉点 | `'3'`|\t左三叉点\n",
+    "`'4'`|\t右三叉点 | `'s'`|\t正方点\n",
+    "`'p'`\t| 五角点 | `'*'`|\t星形点\n",
+    "`'h'`|\t六边形点1 | `'H'`|\t六边形点2 \n",
+    "`'+'`|\t加号点 | `'x'`|\t乘号点\n",
+    "`'D'`|\t实心菱形点 | `'d'`|\t瘦菱形点 \n",
+    "`'_'`|\t横线点 | |\n",
+    "\n",
+    "例如我们要画出红色圆点："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa4a6a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([1,2,3,4], [1,4,9,16], 'ro')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以看出，有两个点在图像的边缘，因此，我们需要改变轴的显示范围。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 显示范围"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "与 **`MATLAB`** 类似，这里可以使用 `axis` 函数指定坐标轴显示的范围：\n",
+    "\n",
+    "    plt.axis([xmin, xmax, ymin, ymax])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAEACAYAAACTXJylAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa4e8908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([1,2,3,4], [1,4,9,16], 'ro')\n",
+    "# 指定 x 轴显示区域为 0-6，y 轴为 0-20\n",
+    "plt.axis([0,6,0,20])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 传入 `Numpy` 数组"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "之前我们传给 `plot` 的参数都是列表，事实上，向 `plot` 中传入 `numpy` 数组是更常用的做法。事实上，如果传入的是列表，`matplotlib` 会在内部将它转化成数组再进行处理："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb688198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# evenly sampled time at 200ms intervals\n",
+    "t = np.arange(0., 5., 0.2)\n",
+    "\n",
+    "# red dashes, blue squares and green triangles\n",
+    "plt.plot(t, t, 'r--', \n",
+    "         t, t**2, 'bs', \n",
+    "         t, t**3, 'g^')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 传入多组数据"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上，在上面的例子中，我们不仅仅向 `plot` 函数传入了数组，还传入了多组 `(x,y,format_str)` 参数，它们在同一张图上显示。\n",
+    "\n",
+    "这意味着我们不需要使用多个 `plot` 函数来画多组数组，只需要可以将这些组合放到一个 `plot` 函数中去即可。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 线条属性"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "之前提到，我们可以用字符串来控制线条的属性，事实上还可以通过关键词来改变线条的性质，例如 `linwidth` 可以改变线条的宽度，`color` 可以改变线条的颜色："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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iR5R7o0Z5++Zdd/XavM7TFamWRvRJ166dr61ft65ylJtm//2vLy0FP0VKSV4k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\nfxeZmr/ZF8WeAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb67c588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-np.pi,np.pi)\n",
+    "y = np.sin(x)\n",
+    "\n",
+    "plt.plot(x, y, linewidth=2.0, color='r')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 使用 plt.plot() 的返回值来设置线条属性"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`plot` 函数返回一个 `Line2D` 对象组成的列表，每个对象代表输入的一对组合，例如：\n",
+    "\n",
+    "- line1, line2 为两个 Line2D 对象\n",
+    "\n",
+    "    `line1, line2 = plt.plot(x1, y1, x2, y2)`\n",
+    "\n",
+    "- 返回 3 个 Line2D 对象组成的列表\n",
+    "\n",
+    "    `lines = plt.plot(x1, y1, x2, y2, x3, y3)`\n",
+    "\n",
+    "我们可以使用这个返回值来对线条属性进行设置："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb7d6080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 加逗号 line 中得到的是 line2D 对象，不加逗号得到的是只有一个 line2D 对象的列表\n",
+    "line, = plt.plot(x, y, 'r-')\n",
+    "\n",
+    "# 将抗锯齿关闭\n",
+    "line.set_antialiased(False)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### plt.setp() 修改线条性质"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "更方便的做法是使用 `plt` 的 `setp` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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iR5R7o0Z5++Zdd/XavM7TFamWRvRJ166dr61ft65ylJtm//2vLy0FP0VKSV4k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\nfxeZmr/ZF8WeAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbcff048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lines = plt.plot(x, y)\n",
+    "\n",
+    "# 使用键值对\n",
+    "plt.setp(lines, color='r', linewidth=2.0)\n",
+    "\n",
+    "# 或者使用 MATLAB 风格的字符串对\n",
+    "plt.setp(lines, 'color', 'r', 'linewidth', 2.0)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以设置的属性有很多，可以使用 `plt.setp(lines)` 查看 `lines` 可以设置的属性，各属性的含义可参考 `matplotlib` 的文档。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  agg_filter: unknown\n",
+      "  alpha: float (0.0 transparent through 1.0 opaque)         \n",
+      "  animated: [True | False]         \n",
+      "  antialiased or aa: [True | False]         \n",
+      "  axes: an :class:`~matplotlib.axes.Axes` instance         \n",
+      "  clip_box: a :class:`matplotlib.transforms.Bbox` instance         \n",
+      "  clip_on: [True | False]         \n",
+      "  clip_path: [ (:class:`~matplotlib.path.Path`,         :class:`~matplotlib.transforms.Transform`) |         :class:`~matplotlib.patches.Patch` | None ]         \n",
+      "  color or c: any matplotlib color         \n",
+      "  contains: a callable function         \n",
+      "  dash_capstyle: ['butt' | 'round' | 'projecting']         \n",
+      "  dash_joinstyle: ['miter' | 'round' | 'bevel']         \n",
+      "  dashes: sequence of on/off ink in points         \n",
+      "  drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' |                   'steps-post']         \n",
+      "  figure: a :class:`matplotlib.figure.Figure` instance         \n",
+      "  fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']         \n",
+      "  gid: an id string         \n",
+      "  label: string or anything printable with '%s' conversion.         \n",
+      "  linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` |                   ``' '`` | ``''``]\n",
+      "  linewidth or lw: float value in points         \n",
+      "  lod: [True | False]         \n",
+      "  marker: :mod:`A valid marker style <matplotlib.markers>`\n",
+      "  markeredgecolor or mec: any matplotlib color         \n",
+      "  markeredgewidth or mew: float value in points         \n",
+      "  markerfacecolor or mfc: any matplotlib color         \n",
+      "  markerfacecoloralt or mfcalt: any matplotlib color         \n",
+      "  markersize or ms: float         \n",
+      "  markevery: [None | int | length-2 tuple of int | slice |         list/array of int | float | length-2 tuple of float]\n",
+      "  path_effects: unknown\n",
+      "  picker: float distance in points or callable pick function         ``fn(artist, event)``         \n",
+      "  pickradius: float distance in points         \n",
+      "  rasterized: [True | False | None]         \n",
+      "  sketch_params: unknown\n",
+      "  snap: unknown\n",
+      "  solid_capstyle: ['butt' | 'round' |  'projecting']         \n",
+      "  solid_joinstyle: ['miter' | 'round' | 'bevel']         \n",
+      "  transform: a :class:`matplotlib.transforms.Transform` instance         \n",
+      "  url: a url string         \n",
+      "  visible: [True | False]         \n",
+      "  xdata: 1D array         \n",
+      "  ydata: 1D array         \n",
+      "  zorder: any number         \n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.setp(lines)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## 子图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`figure()` 函数会产生一个指定编号为 `num` 的图：\n",
+    "\n",
+    "    plt.figure(num)\n",
+    "\n",
+    "这里，`figure(1)` 其实是可以省略的，因为默认情况下 `plt` 会自动产生一幅图像。\n",
+    "\n",
+    "使用 `subplot` 可以在一副图中生成多个子图，其参数为：\n",
+    "\n",
+    "    plt.subplot(numrows, numcols, fignum)\n",
+    "\n",
+    "当 `numrows * numcols < 10` 时，中间的逗号可以省略，因此 `plt.subplot(211)` 就相当于 `plt.subplot(2,1,1)`。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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mfPnyDBs2jLFjx4Z13c03d6NMmRuBB8ndSza5XwrdurBYLBbf4TR7Zdxwyy23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\nSXD+P5ePy54P9ccKAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbb32ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def f(t):\n",
+    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
+    "\n",
+    "t1 = np.arange(0.0, 5.0, 0.1)\n",
+    "t2 = np.arange(0.0, 5.0, 0.02)\n",
+    "\n",
+    "plt.figure(1)\n",
+    "plt.subplot(211)\n",
+    "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
+    "\n",
+    "plt.subplot(212)\n",
+    "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## 图形上加上文字"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "`plt.hist()` 可以用来画直方图。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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X2dgliYioPchvWZFdXncAMyRNlzQROBRYWlNmKXA4DDZAq6u6sxpZChyRvz8C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\n60kAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbd0b358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mu, sigma = 100, 15\n",
+    "x = mu + sigma * np.random.randn(10000)\n",
+    "\n",
+    "# the histogram of the data\n",
+    "n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)\n",
+    "\n",
+    "\n",
+    "plt.xlabel('Smarts')\n",
+    "plt.ylabel('Probability')\n",
+    "plt.title('Histogram of IQ')\n",
+    "plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
+    "plt.axis([40, 160, 0, 0.03])\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于这幅图形，我们使用 `xlabel` ，`ylabel`，`title`，`text` 方法设置了文字，其中：\n",
+    "\n",
+    "- `xlabel` ：x 轴标注\n",
+    "\n",
+    "- `ylabel` ：y 轴标注\n",
+    "\n",
+    "- `title` ：图形标题\n",
+    "\n",
+    "- `text` ：在指定位置放入文字\n",
+    "\n",
+    "输入特殊符号支持使用 `Tex` 语法，用 `$<some Tex code>$` 隔开。\n",
+    "\n",
+    "除了使用 `text` 在指定位置标上文字之外，还可以使用 `annotate` 函数进行注释，`annotate` 主要有两个参数：\n",
+    "\n",
+    "- `xy` ：注释位置 \n",
+    "- `xytext` ：注释文字位置"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DmAHgSwDVkFj8LQBuA3BbVJm/ANgCYHWssE2kDJeXM59zjlgVoMpZXI4cYW7dWs539uwz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\nRVGUgPP/Qde6gvF4TtQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xba841d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = plt.subplot(111)\n",
+    "\n",
+    "t = np.arange(0.0, 5.0, 0.01)\n",
+    "s = np.cos(2*np.pi*t)\n",
+    "line, = plt.plot(t, s, lw=2)\n",
+    "\n",
+    "plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),\n",
+    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
+    "            )\n",
+    "\n",
+    "plt.ylim(-2,2)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/06. matplotlib/06.02 customizing plots with style sheets.ipynb b/06-matplotlib/06.02-customizing-plots-with-style-sheets.ipynb
similarity index 99%
rename from 06. matplotlib/06.02 customizing plots with style sheets.ipynb
rename to 06-matplotlib/06.02-customizing-plots-with-style-sheets.ipynb
index d8ae787f..287ef509 100644
--- a/06. matplotlib/06.02 customizing plots with style sheets.ipynb	
+++ b/06-matplotlib/06.02-customizing-plots-with-style-sheets.ipynb
@@ -1,286 +1,286 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 使用 style 来配置 pyplot 风格"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`style` 是 `pyplot` 的一个子模块，方便进行风格转换， `pyplot` 有很多的预设风格，可以使用 `plt.style.available` 来查看："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[u'dark_background', u'bmh', u'grayscale', u'ggplot', u'fivethirtyeight']"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plt.style.available"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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YvUjMNGoEH3zgu+X166Fdu9CJfmnaNGjVCu6+22+9LOGp0IvE0AknQGGh75rX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\ngg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa1069e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x = np.linspace(0, 2 * np.pi)\n",
-    "y = np.sin(x)\n",
-    "\n",
-    "plt.plot(x, y)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "例如，我们可以模仿 `R` 语言中常用的 `ggplot` 风格："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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PVEUkaRh0zw7oga9NpwQMzcuFLp4Na8SDplNchYOfqIpIWE3IoBGw57xnOiVg\n6IIPIR16QK69znSKq3DwE1Uh6TkAOHYEujvNdIrr6XeHoFvWQ4aOMp3iOhz8RFVIqlWHlTwa9od/\nhRZyKwdf2LP/Bhl4JyQyynSK63DwE1W1Np2Aeg2gKxeYLnEt3Z0GfHcQ0meI6RRX4uAnqmIiAuvu\nh6FLPoaezDKd4zpqF8H+6B1Ydz4Aqc6tGbzBwU9kgDSIgfS8FTr7b6ZTXEc3rgRqhgPtu5hOcS0O\nfiJDZNAIaGYGdM9O0ymuoWfyofM+KL5YS8R0jmtx8BMZIqFhsJIfgv3BdGhhoekcV9A570FaJ0Ca\nNDed4moc/EQmte8CRHugqz81XeJ4mrkbmvY55M4HTKe4Hgc/kUEiAuueR4r37M/JNp3jWFpQAPu9\nKbDufhgSUct0jutx8BMZJg1jId2ToB/PMp3iWLr4I6D+tUD7rqZTAgIHP5EDyOBk6J5d0D27TKc4\njh45AF29CNaoX/AD3UrCwU/kABJWE9a9v4A9azJv03gRtW3Y770FuW0UxFPPdE7A4OAncghp0xFy\nUyuc/ftU0ymOoeuWAACk162GSwILBz+Rg8jIn6Nw5zboji2mU4zT7BPFa/Z/9jjE4qiqTPxpEjmI\n1AxH+JhnYf99CjQv13SOMaoK+8PpkD6DIDGNTecEHA5+Ioep1rItpGNP2O9PhaqazjFCN68Bjh2B\nDBxhOiUgcfATOZAMvw84chD6+VrTKVVOvzsITZkJ65FnuAmbn3DwEzmQVK8B66FfQ1NmQrNPmM6p\nMnr+POzpL0Hu+BkktonpnIDFwU/kUHJ9M0jikOIlnrZtOqdK6D//CrkuDtI9yXRKQOPgJ3IwGXgX\ncO5sUNy0xf5sNTRzN+TeMbxQy884+IkcTEJCYD38NHTpJ9D0VNM5flNyXv/R30LCaprOCXgc/EQO\nJ9c0hPXIM7Bnvgo9esh0TqW79Lx+nOmcoMDBT+QCcuNPILf/FPZbE6H5p03nVCr9518hsU14Xr8K\ncfATuYTVoz/kJ+1h//UlaFGR6ZxKYa9ZXHxe/76xPK9fhTj4iVxERowGINCP3jGd4jN7ywbown/B\neuIFntevYhz8RC4iISGwHn0Gums77HVLTed4TXdth344HdYv/wCpH2M6J+hw8BO5jITXgvX476Fz\n/+HKG7Xrvq9gz3wV1tjnIdfxw1wTOPiJXEgaNoL18NOwp78EzfjSdE656aFvYE+ZCGv0U5AbWprO\nCVoc/EQsiRx2AAAIN0lEQVQuJS3awHr0Wdj/9xfol1+YzimTfn8U9uTxkJE/h7S6xXROUOPgJ3Ix\nueknsJ74H9jvvQXbwRu6aU427Nf+BzI4GVanXqZzgh4HP5HLSVxzWL+eAJ09C/YPd6xyEv02E/ak\nZyA9BsDqPch0DoGDnyggSKPrYT0zEbpoNuylc0znlLA3r4H9+nhYyQ/BGnin6Rz6AQc/UYCQ+jGw\nfvtn6IZlsGf/DVpYYKxFi4pgp8yEzv8A1tMTIbd0NdZCl+PgJwog4qkH65lJ0CMHYU98Gnrg6ypv\nsPNOwZ48Hnr4W1i/ewXS6Poqb6Cr4+AnCjASFV18NWzSbbBf/wPseR9U2bt//TYTp383BtK4Kawn\n/wCJiKyS16WKqebtN3722Wf46KOPcPjwYUyaNAlNmza94nFpaWmYNWsWbNtGYmIihg8f7nUsEZWP\niEC69oW2aAv7H1OhE38D64FfQq5v5pfX06OHoPM/hO7ZifD7H8f51h398jpUObx+x9+4cWM8/fTT\naNmy9IswbNvGzJkzMW7cOLz66qvYuHEjDh0KvG1liZxK6tSF9fjvIQNuhz15POzZs6BZxyvt+TXr\nOOxZk2G/+BzQ6HpYE6ejRre+lfb85B9ev+Nv1KhRmcdkZmaiYcOGqF+/PgCgW7du2Lp1K2JjY719\nWSKqIBGBdO4DvbkNdFEK7D8+BTRqAumaCGnf1asN0jT7e+iSj6FfrIf0Gghr4jRIeC0/1JM/eD34\nyyM7Oxt169YteezxeJCZmenPlySiUki0BzLqF9ARDwE7voC9aRX0XzMgbTpBOvYE6jUAakUC4bUg\n1n9PBmhREXD4G+i+PcC+DOjXe4D8PEjXfrD+dwokKtrg3xV546qDf8KECcjJybnsr99zzz1ISEjw\nWxQR+Y9Urw7c0g0ht3SD5p6Ebl4L+9MU4FQ2kJ8HnDsLhEcAEVFAWE3g6GHAUw/S9CbgplawBo0A\nGsZe8h8HcperDv4XXnjBpyf3eDzIysoqeZyVlQWPx3PFY9PT05Genl7yODk5GTEx7t6uNTLSvSsa\n3NwOsL/cYmKAm+OBB8ZW6tPy529WSkpKyZ/j4+MRHx9/ydf9+p/sZs2a4ejRozh+/DgKCwuxadOm\nUn9TiI+PR3Jycsn/Lg53Izf3u7kdYL9p7DcrJSXlkln646EP+DD4v/jiC4wZMwZ79+7FpEmT8Kc/\n/QlA8Xn9SZMmAQBCQkIwevRoTJw4EU899RS6du3KD3aJiAzz+sPdjh07omPHy9fqejwePP/88yWP\n27Vrh3bt2nn7MkREVMkc++nMlX49cRM397u5HWC/aew3qzz9oqpaBS1EROQQjn3HT0RE/sHBT0QU\nZPx65a433Lyp29SpU5GamoqoqCi88sorpnMq7MSJE5gyZQpOnToFEUHfvn0xaJB77ph04cIFjB8/\nHgUFBSgsLESHDh0watQo01kVYts2nnvuOXg8Hjz33HOmcyrsscceQ82aNWFZFkJCQkpW+LlBfn4+\npk2bVrKf2JgxY3DjjTcariqfI0eO4PXXXy95fOzYMYwcObL0f3/VQYqKivTxxx/XY8eOaUFBgT79\n9NN68OBB01nltnv3bv3666/117/+tekUr5w8eVL379+vqqpnz57VJ5980lU/f1XVc+fOqapqYWGh\njhs3TjMyMgwXVcyCBQt08uTJ+uc//9l0ilfGjh2reXl5pjO88uabb+rKlStVtfifn/z8fMNF3ikq\nKtKHH35Yv//++1KPcdSpnos3datWrVrJpm5u0aJFC0RERJjO8Fp0dDSaNGkCAAgLC0OjRo1w8uRJ\ns1EVFBoaCgAoLCyEbduoVcs9G4dlZWUhNTUViYmJUBevuXBj+5kzZ/DVV18hMTERQPE1SOHh4Yar\nvLNz5040aNAA9erVK/UYR53q4aZuznH8+HF88803aN68uemUCrFtG88++yyOHTuG/v37u+qCwXff\nfRf33Xcfzp49azrFayKCCRMmwLIs9OvXD/369TOdVC7Hjx9HVFQUpk6dim+//RZxcXF48MEHS95I\nuMnGjRvRvXv3qx7jqHf85Aznzp3Dq6++igceeABhYWGmcyrEsiy8/PLLmDZtGjIyMi7Z/8nJtm3b\nhqioKMTFxbnyHfN/TJgwAS+99BLGjRuHpUuXIiMjw3RSuRQVFWH//v3o378/XnzxRYSFhWHu3Lmm\nsyqssLAQ27ZtQ5cuXa56nKMGf0U2dSP/KCwsxCuvvIIePXpc8cpstwgPD0e7du2wb98+0ynlsmfP\nHmzbtg2PPfYYJk+ejPT0dLz11lumsyqsTp06AICoqCh07NjRNb+x161bFx6PBzfccAMAoHPnzti/\nf7/hqopLTU1F06ZNERUVddXjHDX4K7KpG1U+VcW0adPQqFEjDB482HROheXm5iI/Px9A8QqfnTt3\nIi4uznBV+YwaNQpvv/02pkyZgl/96leIj4/H448/bjqrQs6fP19ymurcuXPYsWMHGjdubLiqfKKj\no1GvXj0cOXIEALBjxw5XnSb8j40bN6Jbt25lHueoc/wXb+r2n+Wcbvrhv/7668jIyEBeXh7GjBmD\n5ORk9OnTx3RWue3Zswfr169H48aN8dvf/hZA8UBq27at4bLyycnJwZQpU2DbNlQVPXv2RKtWrUxn\neUVETCdU2KlTp/Dyyy8DKP6spXv37mjTpo3hqvJ78MEH8eabb6KwsBANGjTA2LGVu1W1v507dw47\nd+7Eo48+Wuax3LKBiCjIOOpUDxER+R8HPxFRkOHgJyIKMhz8RERBhoOfiCjIcPATEQUZDn4ioiDD\nwU9EFGT+Pxvu78Bmq8eQAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa250320>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.style.use('ggplot')\n",
-    "\n",
-    "plt.plot(x, y)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "有时候，我们不希望改变全局的风格，只是想暂时改变一下分隔，则可以使用 `context` 将风格改变限制在某一个代码块内："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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TPkAhsD/G+bUc/7Rx2g03sA4lfTdLAxLKzqtXr+buu++mvLy8wc+ys7OZNWsW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\nSUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa39ceb8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with plt.style.context(('dark_background')):\n",
-    "    plt.plot(x, y, 'r-o')\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在代码块外绘图则仍然是全局的风格。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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cfftaHE9PScG8bdva/P2i83tLDvkNU6fCetddME+f3uHfK4f83oiPj2/zHN65\nS+Rj7CYRK+TIEYQUFcHM+yncYuEn8jHO8RFL/8YbqJozB+Dr7RYLP5GPuZrjE2wf7SdK6MGD0H77\nLWoef1x0FFnjB7EQ+VjTOT4h5eVQFxfjzqwsbux2osaZPBFHjsB6/fW448sv+Xq3goWfqBM0neMT\nO2cOGk6dQtVvfiM4VWBqMZOnqoozedrApR6iTlaVng7dpk1QX74sOkpA4kyejmPhJ+pkDTfcgJqJ\nExG1erXoKAGJXVQdx8JP5AfVL72EiJ07oWl2ZUres4WEuD7Orh63WPiJ/MAeFwfz9OnQ5+SIjhJw\nRsbHI6vZ3bnsomodN3eJ/MT87LP4ZvBgfPLAA9CGhHCGjw+oy8tx92efwfbKK0jftYszedqJhZ/I\nTw58/TXyNRrkHD7sOMYZPt6JWrkSlgcfROKUKUicMkV0HMXgUg+Rn+QZjVhWXu50jN0nntOeOYOI\nv/8dVb/7negoisPCT+Qn7D7xrejsbFTPng27wSA6iuKw8BP5CWf4+E7ol19Ce/o0zNzA9QjX+In8\nJCktDRnnzzvdbDS/Vy92n7RT41gGrcUC1YkTSJ42DYPcvJlS61j4ifyk6QwfrcUClJZiTHw8buHG\nbptajGUAkPHxx6gbPJgb4x5g4Sfyo6YzfFQ1NbguJQWmvDzUJSUJTiZveUajU9EHrm2Mp2/axMLv\nAa7xEwkiRUai8r//GzF/+ANQXy86jqxxY9y3WPiJBLLcey8auneHji2dreLGuG9xqYdIJJUKFdnZ\nOHnfffjks8+glSRIkZEYOnUqlzCaSHrqKSzYvx9LbDbHMY5l8BwLP5FgeefP46BajZyvv3Ycyzh7\nFgDv6G005uRJRA0YgPToaGitVo5l8BILP5FgeUYjciornY5x4/L/05aUIHLzZgzYtQsJ8fGi4wQE\nrvETCcaNy1bY7YhNT0fV734HO4u+z/CKn0gwblw6c9yoZbXCfuUKxgHoxwFsPsXCTySYqzt6M7p1\nC8qNS5c3asXHo/Lzz7ns5UMs/ESCNb+jV1Vfj3vOnkW/22+HXXA2f3N5o1ZpKfc7fIyFn0gGmt7R\nq9froUpPhyYzE1fffRdQqQSn8x/ud/gHN3eJZKhy/nxoT59GxIcfio7iV9zv8A9e8RPJUXg4TG+9\nhROPPIJPtm2DFgiKj2pMmjwZC/LysKTJCAveqOV7LPxEMvWvixeRr9Ui58ABx7FA/6jGcZ9/Dl1i\nItJDQ3kyS9fjAAAI6UlEQVSjVidi4SeSqTyjETkmk9OxQL6xK+Ivf0HowYMY8M9/4hadTnScgMbC\nTyRTwbTRqT19GtGLFqHsgw8gseh3OhZ+IpkK9I1Ox41atbVQnTiBYY89htv69xcdKyiw8BPJlKsb\nu7K0WiQ9+qjAVL7h8katzz+HNTc3IJex5IaFn0immt/YZQsPx+iQEKR88AHK778f0Cr3ny8/UUss\n5f7NIQoCTW/sAgDYbMCUKTg2Ywb21NVd63xRYJtnMO1fyBELP5GSaLX452OP4dCcOchp8qEkSmvz\nbDCbXR4PlP0LueOdu0QKk/fBB06fRAVcWybJU8jHN4YUFGD8uXPI7NHD6Thv1PIfXvETKYzSlkma\njlm21dfj3pIS3LpuHUx2u9P+BW/U8h8WfiKFUVKbp6vuncyuXWGy21vuX5DfcKmHSGGS0tKQ0aeP\n07EstRop//VfghK5l2c0OrWjAsCyK1cUsywVqHjFT6Qwrto8h48di3vWrMEuiwV7z52TTbeP0pal\nggULP5ECuVom+YdWi/wFC5DT0OA4Jrrbx95s1lAjOS5LBRMWfqIA8a9PPnEq+oB/b4pq3MQNs9lg\nVasxOjIS95SVISM+HstLSx3nsXtHPBZ+ogAhclnF1SZuVng4hqxYgTuioti9IzMs/EQBwl23D378\nEfmffoq8rVs7be3f1QiGpRYL0nfswLxt21joZcbjwv/1119jx44d+OGHH7B06VLceOONLs87evQo\nNm/eDLvdjlGjRmHChAkehyUi91wNdZvfqxd6qlQ49PzzyGnyqVberP079eX//CYS4mYtn5u48uRx\n4e/duzd+//vf47333nN7jt1ux8aNG7Fw4UIYDAZkZWUhMTERvXr18vRpicgNV90+SdOmIW/jRuT8\nXOgbNV37d1XI3b0huFzSKSiAqbbW5fncxJUnjwt/z5492zznzJkz6NGjB7p16wYASE5ORkFBAQs/\nUSdx1e1zcN06l+eGfvcd8v/xDxxYssR5PHKTN4nmbwgul3TMZkzt1w8ZNTXOP21wE1e2OnWNv7y8\nHHFxcY7HBoMBZ86c6cynJKJm3K39SyYTCmbNwnIXnUBTc3LQo6rK+Y7b48dhbbJc1FR8TAyGZGYi\nfdMmhNXXwxoSwk1cGWu18GdnZ8PkYu3uiSeeQGJiYqeFIiLfcbn236cP7nztNRx66y3g8OEWv8d6\n4gSWNzu2rLwcqZGRLp/DFh7u+GlDr9ejqqrKl38E8jGVJEmSN99g0aJFeOqpp1xu7paUlGDHjh14\n+eWXAQAffvghVCqVyw3eoqIiFBUVOR6npqZ6E4uIKGht377d8euEhAQkJCQ4fb1TZ/XcdNNN+Omn\nn3Dp0iXYbDbk5eW5/UkhISEBqampjv+aBlciJedXcnaA+UVjfrG2b9/uVEubF33Ai8J/8OBBzJw5\nEyUlJVi6dCmWLFkC4Nq6/tKlSwEAGo0GaWlpWLx4MebNm4ekpCRu7BIRCebx5u6QIUMwZMiQFscb\n2zYbDRo0CIMGDfL0aYiIyMdkO5bZ1Y8nSqLk/ErODjC/aMwvVnvye725S0REyiLbK34iIuocLPxE\nREFGdtM5lTzU7Z133kFhYSGio6OxYsUK0XE67MqVK1i7di0qKiqgUqkwevRo3HvvvaJjtVtdXR1e\nffVV1NfXw2azYfDgwZg0aZLoWB1it9uRmZkJg8GAzMxM0XE6bPbs2YiIiIBarYZGo3F0+CmB2WzG\n+vXr8f333wMAZs6ciV/96leCU7VPaWkpVq1a5Xh88eJFPPbYY+7//Uoy0tDQIL3wwgvSxYsXpfr6\neun3v/+99N1334mO1W4nT56Uvv32W+m3v/2t6CgeuXr1qnTu3DlJkiSptrZWmjNnjqJef0mSJIvF\nIkmSJNlsNmnBggVScXGx4EQd8/HHH0urV6+Wli1bJjqKR2bNmiVVVVWJjuGRNWvWSHv27JEk6drf\nH7PZLDiRZxoaGqRnnnlGunz5sttzZLXU03Som1ardQx1U4p+/fpBp9OJjuGx2NhY/OIXvwAAhIeH\no2fPnrh69arYUB0U9vNcGpvNBrvdjqioKMGJ2q+srAyFhYUYNWoUJAX3XCgxe01NDb755huMGjUK\nwLV7kCLdjKeQu+PHj6N79+7o2rWr23NktdTDoW7ycenSJZw/fx4333yz6CgdYrfbkZGRgYsXL2Lc\nuHGKumFwy5YtmDx5MmrdjDhWApVKhezsbKjVaowZMwZjFDKk7dKlS4iOjsY777yDCxcuoG/fvpg2\nbZrjQkJJ9u/fj2HDhrV6jqyu+EkeLBYLVq5ciaeffhrhCpunrlarkZOTg/Xr16O4uNhp/pOcHT58\nGNHR0ejbt68ir5gbZWdn4/XXX8eCBQuwa9cuFBcXi47ULg0NDTh37hzGjRuH5cuXIzw8HDt37hQd\nq8NsNhsOHz6MO++8s9XzZFX4DQYDysrKHI/LyspgMBgEJgo+NpsNK1aswPDhw13ema0UkZGRGDRo\nEM6ePSs6SrucOnUKhw8fxuzZs7F69WoUFRXh7bffFh2rw7p06QIAiI6OxpAhQxTzE3tcXBwMBgN+\n+ctfAgDuuOMOnDt3TnCqjissLMSNN96I6OjoVs+TVeHvyFA38j1JkrB+/Xr07NkT9913n+g4HVZZ\nWQmz2QzgWofP8ePH0bdvX8Gp2mfSpElYt24d1q5di7lz5yIhIQEvvPCC6FgdYrVaHctUFosFx44d\nQ+/evQWnap/Y2Fh07doVpaWlAIBjx44papmw0f79+5GcnNzmebJa42861K2xnVNJL/6qVatQXFyM\nqqoqzJw5E6mpqRg5cqToWO126tQpfPXVV+jduzfmz58P4FpBuu222wQnax+TyYS1a9fCbrdDkiTc\nddddGDBggOhYHlGpVKIjdFhFRQVycnIAXNtrGTZsGAYOHCg4VftNmzYNa9asgc1mQ/fu3TFr1izR\nkTrEYrHg+PHjeO6559o8lyMbiIiCjKyWeoiIqPOx8BMRBRkWfiKiIMPCT0QUZFj4iYiCDAs/EVGQ\nYeEnIgoyLPxEREHm/wEVP/FL2qENYwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa7d8278>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with plt.style.context(('dark_background')):\n",
-    "    pass\n",
-    "plt.plot(x, y, 'r-o')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "还可以混搭使用多种风格，不过最右边的一种风格会将最左边的覆盖："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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cfftaHE9PScG8bdva/P2i83tLDvkNU6fCetddME+f3uHfK4f83oiPj2/zHN65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\nYeEnIgoyLPxEREHm/wEVP/FL2qENYwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb3b00b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.style.use(['dark_background', 'ggplot'])\n",
-    "\n",
-    "plt.plot(x, y, 'r-o')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上，我们还可以自定义风格文件。\n",
-    "\n",
-    "自定义文件需要放在 `matplotlib` 的配置文件夹 `mpl_configdir` 的子文件夹 `mpl_configdir/stylelib/` 下，以 `.mplstyle` 结尾。\n",
-    "\n",
-    "`mpl_configdir` 的位置可以这样查看："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "u'c:/Users/Jin\\\\.matplotlib'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import matplotlib\n",
-    "matplotlib.get_configdir()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "里面的内容以 `属性：值` 的形式保存：\n",
-    "\n",
-    "```\n",
-    "axes.titlesize : 24\n",
-    "axes.labelsize : 20\n",
-    "lines.linewidth : 3\n",
-    "lines.markersize : 10\n",
-    "xtick.labelsize : 16\n",
-    "ytick.labelsize : 16\n",
-    "```\n",
-    "\n",
-    "假设我们将其保存为 `mpl_configdir/stylelib/presentation.mplstyle`，那么使用这个风格的时候只需要调用：\n",
-    "\n",
-    "    plt.style.use('presentation')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 使用 style 来配置 pyplot 风格"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`style` 是 `pyplot` 的一个子模块，方便进行风格转换， `pyplot` 有很多的预设风格，可以使用 `plt.style.available` 来查看："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[u'dark_background', u'bmh', u'grayscale', u'ggplot', u'fivethirtyeight']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plt.style.available"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YvUjMNGoEH3zgu+X166Fdu9CJfmnaNGjVCu6+22+9LOGp0IvE0AknQGGh75rX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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa1069e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(0, 2 * np.pi)\n",
+    "y = np.sin(x)\n",
+    "\n",
+    "plt.plot(x, y)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "例如，我们可以模仿 `R` 语言中常用的 `ggplot` 风格："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PVEUkaRh0zw7oga9NpwQMzcuFLp4Na8SDplNchYOfqIpIWE3IoBGw57xnOiVg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\nwU9EFGT+Pxvu78Bmq8eQAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa250320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.style.use('ggplot')\n",
+    "\n",
+    "plt.plot(x, y)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "有时候，我们不希望改变全局的风格，只是想暂时改变一下分隔，则可以使用 `context` 将风格改变限制在某一个代码块内："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TPkAhsD/G+bUc/7Rx2g03sA4lfTdLAxLKzqtXr+buu++mvLy8wc+ys7OZNWsW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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa39ceb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with plt.style.context(('dark_background')):\n",
+    "    plt.plot(x, y, 'r-o')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在代码块外绘图则仍然是全局的风格。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cfftaHE9PScG8bdva/P2i83tLDvkNU6fCetddME+f3uHfK4f83oiPj2/zHN65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\nYeEnIgoyLPxEREHm/wEVP/FL2qENYwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa7d8278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with plt.style.context(('dark_background')):\n",
+    "    pass\n",
+    "plt.plot(x, y, 'r-o')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "还可以混搭使用多种风格，不过最右边的一种风格会将最左边的覆盖："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cfftaHE9PScG8bdva/P2i83tLDvkNU6fCetddME+f3uHfK4f83oiPj2/zHN65\nS+Rj7CYRK+TIEYQUFcHM+yncYuEn8jHO8RFL/8YbqJozB+Dr7RYLP5GPuZrjE2wf7SdK6MGD0H77\nLWoef1x0FFnjB7EQ+VjTOT4h5eVQFxfjzqwsbux2osaZPBFHjsB6/fW448sv+Xq3goWfqBM0neMT\nO2cOGk6dQtVvfiM4VWBqMZOnqoozedrApR6iTlaVng7dpk1QX74sOkpA4kyejmPhJ+pkDTfcgJqJ\nExG1erXoKAGJXVQdx8JP5AfVL72EiJ07oWl2ZUres4WEuD7Orh63WPiJ/MAeFwfz9OnQ5+SIjhJw\nRsbHI6vZ3bnsomodN3eJ/MT87LP4ZvBgfPLAA9CGhHCGjw+oy8tx92efwfbKK0jftYszedqJhZ/I\nTw58/TXyNRrkHD7sOMYZPt6JWrkSlgcfROKUKUicMkV0HMXgUg+Rn+QZjVhWXu50jN0nntOeOYOI\nv/8dVb/7negoisPCT+Qn7D7xrejsbFTPng27wSA6iuKw8BP5CWf4+E7ol19Ce/o0zNzA9QjX+In8\nJCktDRnnzzvdbDS/Vy92n7RT41gGrcUC1YkTSJ42DYPcvJlS61j4ifyk6QwfrcUClJZiTHw8buHG\nbptajGUAkPHxx6gbPJgb4x5g4Sfyo6YzfFQ1NbguJQWmvDzUJSUJTiZveUajU9EHrm2Mp2/axMLv\nAa7xEwkiRUai8r//GzF/+ANQXy86jqxxY9y3WPiJBLLcey8auneHji2dreLGuG9xqYdIJJUKFdnZ\nOHnfffjks8+glSRIkZEYOnUqlzCaSHrqKSzYvx9LbDbHMY5l8BwLP5FgeefP46BajZyvv3Ycyzh7\nFgDv6G005uRJRA0YgPToaGitVo5l8BILP5FgeUYjciornY5x4/L/05aUIHLzZgzYtQsJ8fGi4wQE\nrvETCcaNy1bY7YhNT0fV734HO4u+z/CKn0gwblw6c9yoZbXCfuUKxgHoxwFsPsXCTySYqzt6M7p1\nC8qNS5c3asXHo/Lzz7ns5UMs/ESCNb+jV1Vfj3vOnkW/22+HXXA2f3N5o1ZpKfc7fIyFn0gGmt7R\nq9froUpPhyYzE1fffRdQqQSn8x/ud/gHN3eJZKhy/nxoT59GxIcfio7iV9zv8A9e8RPJUXg4TG+9\nhROPPIJPtm2DFgiKj2pMmjwZC/LysKTJCAveqOV7LPxEMvWvixeRr9Ui58ABx7FA/6jGcZ9/Dl1i\nItJDQ3kyS9fjAAAI6UlEQVSjVidi4SeSqTyjETkmk9OxQL6xK+Ivf0HowYMY8M9/4hadTnScgMbC\nTyRTwbTRqT19GtGLFqHsgw8gseh3OhZ+IpkK9I1Ox41atbVQnTiBYY89htv69xcdKyiw8BPJlKsb\nu7K0WiQ9+qjAVL7h8katzz+HNTc3IJex5IaFn0immt/YZQsPx+iQEKR88AHK778f0Cr3ny8/UUss\n5f7NIQoCTW/sAgDYbMCUKTg2Ywb21NVd63xRYJtnMO1fyBELP5GSaLX452OP4dCcOchp8qEkSmvz\nbDCbXR4PlP0LueOdu0QKk/fBB06fRAVcWybJU8jHN4YUFGD8uXPI7NHD6Thv1PIfXvETKYzSlkma\njlm21dfj3pIS3LpuHUx2u9P+BW/U8h8WfiKFUVKbp6vuncyuXWGy21vuX5DfcKmHSGGS0tKQ0aeP\n07EstRop//VfghK5l2c0OrWjAsCyK1cUsywVqHjFT6Qwrto8h48di3vWrMEuiwV7z52TTbeP0pal\nggULP5ECuVom+YdWi/wFC5DT0OA4Jrrbx95s1lAjOS5LBRMWfqIA8a9PPnEq+oB/b4pq3MQNs9lg\nVasxOjIS95SVISM+HstLSx3nsXtHPBZ+ogAhclnF1SZuVng4hqxYgTuioti9IzMs/EQBwl23D378\nEfmffoq8rVs7be3f1QiGpRYL0nfswLxt21joZcbjwv/1119jx44d+OGHH7B06VLceOONLs87evQo\nNm/eDLvdjlGjRmHChAkehyUi91wNdZvfqxd6qlQ49PzzyGnyqVberP079eX//CYS4mYtn5u48uRx\n4e/duzd+//vf47333nN7jt1ux8aNG7Fw4UIYDAZkZWUhMTERvXr18vRpicgNV90+SdOmIW/jRuT8\nXOgbNV37d1XI3b0huFzSKSiAqbbW5fncxJUnjwt/z5492zznzJkz6NGjB7p16wYASE5ORkFBAQs/\nUSdx1e1zcN06l+eGfvcd8v/xDxxYssR5PHKTN4nmbwgul3TMZkzt1w8ZNTXOP21wE1e2OnWNv7y8\nHHFxcY7HBoMBZ86c6cynJKJm3K39SyYTCmbNwnIXnUBTc3LQo6rK+Y7b48dhbbJc1FR8TAyGZGYi\nfdMmhNXXwxoSwk1cGWu18GdnZ8PkYu3uiSeeQGJiYqeFIiLfcbn236cP7nztNRx66y3g8OEWv8d6\n4gSWNzu2rLwcqZGRLp/DFh7u+GlDr9ejqqrKl38E8jGVJEmSN99g0aJFeOqpp1xu7paUlGDHjh14\n+eWXAQAffvghVCqVyw3eoqIiFBUVOR6npqZ6E4uIKGht377d8euEhAQkJCQ4fb1TZ/XcdNNN+Omn\nn3Dp0iXYbDbk5eW5/UkhISEBqampjv+aBlciJedXcnaA+UVjfrG2b9/uVEubF33Ai8J/8OBBzJw5\nEyUlJVi6dCmWLFkC4Nq6/tKlSwEAGo0GaWlpWLx4MebNm4ekpCRu7BIRCebx5u6QIUMwZMiQFscb\n2zYbDRo0CIMGDfL0aYiIyMdkO5bZ1Y8nSqLk/ErODjC/aMwvVnvye725S0REyiLbK34iIuocLPxE\nREFGdtM5lTzU7Z133kFhYSGio6OxYsUK0XE67MqVK1i7di0qKiqgUqkwevRo3HvvvaJjtVtdXR1e\nffVV1NfXw2azYfDgwZg0aZLoWB1it9uRmZkJg8GAzMxM0XE6bPbs2YiIiIBarYZGo3F0+CmB2WzG\n+vXr8f333wMAZs6ciV/96leCU7VPaWkpVq1a5Xh88eJFPPbYY+7//Uoy0tDQIL3wwgvSxYsXpfr6\neun3v/+99N1334mO1W4nT56Uvv32W+m3v/2t6CgeuXr1qnTu3DlJkiSptrZWmjNnjqJef0mSJIvF\nIkmSJNlsNmnBggVScXGx4EQd8/HHH0urV6+Wli1bJjqKR2bNmiVVVVWJjuGRNWvWSHv27JEk6drf\nH7PZLDiRZxoaGqRnnnlGunz5sttzZLXU03Som1ardQx1U4p+/fpBp9OJjuGx2NhY/OIXvwAAhIeH\no2fPnrh69arYUB0U9vNcGpvNBrvdjqioKMGJ2q+srAyFhYUYNWoUJAX3XCgxe01NDb755huMGjUK\nwLV7kCLdjKeQu+PHj6N79+7o2rWr23NktdTDoW7ycenSJZw/fx4333yz6CgdYrfbkZGRgYsXL2Lc\nuHGKumFwy5YtmDx5MmrdjDhWApVKhezsbKjVaowZMwZjFDKk7dKlS4iOjsY777yDCxcuoG/fvpg2\nbZrjQkJJ9u/fj2HDhrV6jqyu+EkeLBYLVq5ciaeffhrhCpunrlarkZOTg/Xr16O4uNhp/pOcHT58\nGNHR0ejbt68ir5gbZWdn4/XXX8eCBQuwa9cuFBcXi47ULg0NDTh37hzGjRuH5cuXIzw8HDt37hQd\nq8NsNhsOHz6MO++8s9XzZFX4DQYDysrKHI/LyspgMBgEJgo+NpsNK1aswPDhw13ema0UkZGRGDRo\nEM6ePSs6SrucOnUKhw8fxuzZs7F69WoUFRXh7bffFh2rw7p06QIAiI6OxpAhQxTzE3tcXBwMBgN+\n+ctfAgDuuOMOnDt3TnCqjissLMSNN96I6OjoVs+TVeHvyFA38j1JkrB+/Xr07NkT9913n+g4HVZZ\nWQmz2QzgWofP8ePH0bdvX8Gp2mfSpElYt24d1q5di7lz5yIhIQEvvPCC6FgdYrVaHctUFosFx44d\nQ+/evQWnap/Y2Fh07doVpaWlAIBjx44papmw0f79+5GcnNzmebJa42861K2xnVNJL/6qVatQXFyM\nqqoqzJw5E6mpqRg5cqToWO126tQpfPXVV+jduzfmz58P4FpBuu222wQnax+TyYS1a9fCbrdDkiTc\nddddGDBggOhYHlGpVKIjdFhFRQVycnIAXNtrGTZsGAYOHCg4VftNmzYNa9asgc1mQ/fu3TFr1izR\nkTrEYrHg+PHjeO6559o8lyMbiIiCjKyWeoiIqPOx8BMRBRkWfiKiIMPCT0QUZFj4iYiCDAs/EVGQ\nYeEnIgoyLPxEREHm/wEVP/FL2qENYwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb3b00b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.style.use(['dark_background', 'ggplot'])\n",
+    "\n",
+    "plt.plot(x, y, 'r-o')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上，我们还可以自定义风格文件。\n",
+    "\n",
+    "自定义文件需要放在 `matplotlib` 的配置文件夹 `mpl_configdir` 的子文件夹 `mpl_configdir/stylelib/` 下，以 `.mplstyle` 结尾。\n",
+    "\n",
+    "`mpl_configdir` 的位置可以这样查看："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "u'c:/Users/Jin\\\\.matplotlib'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.get_configdir()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "里面的内容以 `属性：值` 的形式保存：\n",
+    "\n",
+    "```\n",
+    "axes.titlesize : 24\n",
+    "axes.labelsize : 20\n",
+    "lines.linewidth : 3\n",
+    "lines.markersize : 10\n",
+    "xtick.labelsize : 16\n",
+    "ytick.labelsize : 16\n",
+    "```\n",
+    "\n",
+    "假设我们将其保存为 `mpl_configdir/stylelib/presentation.mplstyle`，那么使用这个风格的时候只需要调用：\n",
+    "\n",
+    "    plt.style.use('presentation')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/06. matplotlib/06.03  working with text - basic.ipynb b/06-matplotlib/06.03-working-with-text---basic.ipynb
similarity index 99%
rename from 06. matplotlib/06.03  working with text - basic.ipynb
rename to 06-matplotlib/06.03-working-with-text---basic.ipynb
index 15d28a18..a42c18f2 100644
--- a/06. matplotlib/06.03  working with text - basic.ipynb	
+++ b/06-matplotlib/06.03-working-with-text---basic.ipynb
@@ -1,457 +1,457 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 处理文本（基础）"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`matplotlib` 对文本的支持十分完善，包括数学公式，`Unicode` 文字，栅格和向量化输出，文字换行，文字旋转等一系列操作。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 基础文本函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在 `matplotlib.pyplot` 中，基础的文本函数如下：\n",
-    "\n",
-    "- `text()` 在 `Axes` 对象的任意位置添加文本\n",
-    "- `xlabel()` 添加 x 轴标题\n",
-    "- `ylabel()` 添加 y 轴标题\n",
-    "- `title()` 给 `Axes` 对象添加标题\n",
-    "- `figtext()` 在 `Figure` 对象的任意位置添加文本\n",
-    "- `suptitle()` 给 `Figure` 对象添加标题\n",
-    "- `anotate()` 给 `Axes` 对象添加注释（可选择是否添加箭头标记）"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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CMwAAAOAR4RkAAADwiPAMAAAAeER4BgAAADwiPAMAAAAeEZ4BAAAAjwjPAAAA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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa15d6a0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# -*- coding: utf-8 -*-\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# plt.figure() 返回一个 Figure() 对象\n",
-    "fig = plt.figure(figsize=(12, 9))\n",
-    "\n",
-    "# 设置这个 Figure 对象的标题\n",
-    "# 事实上，如果我们直接调用 plt.suptitle() 函数，它会自动找到当前的 Figure 对象\n",
-    "fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')\n",
-    "\n",
-    "# Axes 对象表示 Figure 对象中的子图\n",
-    "# 这里只有一幅图像，所以使用 add_subplot(111)\n",
-    "ax = fig.add_subplot(111)\n",
-    "fig.subplots_adjust(top=0.85)\n",
-    "\n",
-    "# 可以直接使用 set_xxx 的方法来设置标题\n",
-    "ax.set_title('axes title')\n",
-    "# 也可以直接调用 title()，因为会自动定位到当前的 Axes 对象\n",
-    "# plt.title('axes title')\n",
-    "\n",
-    "ax.set_xlabel('xlabel')\n",
-    "ax.set_ylabel('ylabel')\n",
-    "\n",
-    "# 添加文本，斜体加文本框\n",
-    "ax.text(3, 8, 'boxed italics text in data coords', style='italic',\n",
-    "        bbox={'facecolor':'red', 'alpha':0.5, 'pad':10})\n",
-    "\n",
-    "# 数学公式，用 $$ 输入 Tex 公式\n",
-    "ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)\n",
-    "\n",
-    "# Unicode 支持\n",
-    "ax.text(3, 2, unicode('unicode: Institut f\\374r Festk\\366rperphysik', 'latin-1'))\n",
-    "\n",
-    "# 颜色，对齐方式\n",
-    "ax.text(0.95, 0.01, 'colored text in axes coords',\n",
-    "        verticalalignment='bottom', horizontalalignment='right',\n",
-    "        transform=ax.transAxes,\n",
-    "        color='green', fontsize=15)\n",
-    "\n",
-    "# 注释文本和箭头\n",
-    "ax.plot([2], [1], 'o')\n",
-    "ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),\n",
-    "            arrowprops=dict(facecolor='black', shrink=0.05))\n",
-    "\n",
-    "# 设置显示范围\n",
-    "ax.axis([0, 10, 0, 10])\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 文本属性和布局"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们可以通过下列关键词，在文本函数中设置文本的属性：\n",
-    "\n",
-    "关键词|值\n",
-    "---|---\n",
-    "alpha\t\t|\t    float\n",
-    "backgroundcolor\t\t|    any matplotlib color\n",
-    "bbox\t\t|\t    rectangle prop dict plus key ``'pad'`` which is a pad in points\n",
-    "clip_box\t|\t    a matplotlib.transform.Bbox instance\n",
-    "clip_on\t\t|\t    [True ， False]\n",
-    "clip_path\t|\t    a Path instance and a Transform instance, a Patch\n",
-    "color\t|\t\t    any matplotlib color\n",
-    "family\t|\t\t    [ ``'serif'`` , ``'sans-serif'`` , ``'cursive'`` , ``'fantasy'`` , ``'monospace'`` ]\n",
-    "fontproperties\t\t|    a matplotlib.font_manager.FontProperties instance\n",
-    "horizontalalignment or ha  | [ ``'center'`` , ``'right'`` , ``'left'`` ]\n",
-    "label\t\t\t  |  any string\n",
-    "linespacing\t\t  |  float\n",
-    "multialignment\t\t |   [``'left'`` , ``'right'`` , ``'center'`` ]\n",
-    "name or fontname\t |   string e.g., [``'Sans'`` , ``'Courier'`` , ``'Helvetica'`` ...]\n",
-    "picker\t\t|\t    [None,float,boolean,callable]\n",
-    "position\t|\t    (x,y)\n",
-    "rotation\t|\t    [ angle in degrees ``'vertical'`` , ``'horizontal'``\n",
-    "size or fontsize\t |   [ size in points , relative size, e.g., ``'smaller'``, ``'x-large'`` ]\n",
-    "style or fontstyle\t|    [ ``'normal'`` , ``'italic'`` , ``'oblique'``]\n",
-    "text\t\t|\t    string or anything printable with '%s' conversion\n",
-    "transform\t|\t    a matplotlib.transform transformation instance\n",
-    "variant\t\t|\t    [ ``'normal'`` , ``'small-caps'`` ]\n",
-    "verticalalignment or va\t  |  [ ``'center'`` , ``'top'`` , ``'bottom'`` , ``'baseline'`` ]\n",
-    "visible\t\t|\t    [True , False]\n",
-    "weight or fontweight\t|    [ ``'normal'`` , ``'bold'`` , ``'heavy'`` , ``'light'`` , ``'ultrabold'`` , ``'ultralight'``]\n",
-    "x\t\t|\t    float\n",
-    "y\t\t|\t    float\n",
-    "zorder\t|\t\t    any number\n",
-    "\n",
-    "其中 `va`, `ha`, `multialignment` 可以用来控制布局。\n",
-    "- `horizontalalignment` or `ha` ：x 位置参数表示的位置 \n",
-    "- `verticalalignment` or `va`：y 位置参数表示的位置\n",
-    "- `multialignment`：多行位置控制"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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9lO4Zbff5g4D3AHcBTqQt9PLg4Um1Se4FHAjcGXhiVX1/Odf3EbAkLUC+P0v2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\n/g8NfX/IA8ubvAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa62d4e0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.patches as patches\n",
-    "\n",
-    "# build a rectangle in axes coords\n",
-    "left, width = .25, .5\n",
-    "bottom, height = .25, .5\n",
-    "right = left + width\n",
-    "top = bottom + height\n",
-    "\n",
-    "fig = plt.figure(figsize=(10,7))\n",
-    "ax = fig.add_axes([0,0,1,1])\n",
-    "\n",
-    "# axes coordinates are 0,0 is bottom left and 1,1 is upper right\n",
-    "p = patches.Rectangle(\n",
-    "    (left, bottom), width, height,\n",
-    "    fill=False, transform=ax.transAxes, clip_on=False\n",
-    "    )\n",
-    "\n",
-    "ax.add_patch(p)\n",
-    "\n",
-    "ax.text(left, bottom, 'left top',\n",
-    "        horizontalalignment='left',\n",
-    "        verticalalignment='top',\n",
-    "        transform=ax.transAxes,\n",
-    "        size='xx-large')\n",
-    "\n",
-    "ax.text(left, bottom, 'left bottom',\n",
-    "        horizontalalignment='left',\n",
-    "        verticalalignment='bottom',\n",
-    "        transform=ax.transAxes,\n",
-    "        size='xx-large')\n",
-    "\n",
-    "ax.text(right, top, 'right bottom',\n",
-    "        horizontalalignment='right',\n",
-    "        verticalalignment='bottom',\n",
-    "        transform=ax.transAxes,\n",
-    "        size='xx-large')\n",
-    "\n",
-    "ax.text(right, top, 'right top',\n",
-    "        horizontalalignment='right',\n",
-    "        verticalalignment='top',\n",
-    "        transform=ax.transAxes,\n",
-    "        size='xx-large')\n",
-    "\n",
-    "ax.text(right, bottom, 'center top',\n",
-    "        horizontalalignment='center',\n",
-    "        verticalalignment='top',\n",
-    "        transform=ax.transAxes,\n",
-    "        size='xx-large')\n",
-    "\n",
-    "ax.text(left, 0.5*(bottom+top), 'right center',\n",
-    "        horizontalalignment='right',\n",
-    "        verticalalignment='center',\n",
-    "        rotation='vertical',\n",
-    "        transform=ax.transAxes,\n",
-    "        size='xx-large')\n",
-    "\n",
-    "ax.text(left, 0.5*(bottom+top), 'left center',\n",
-    "        horizontalalignment='left',\n",
-    "        verticalalignment='center',\n",
-    "        rotation='vertical',\n",
-    "        transform=ax.transAxes,\n",
-    "        size='xx-large')\n",
-    "\n",
-    "ax.text(0.5*(left+right), 0.5*(bottom+top), 'middle',\n",
-    "        horizontalalignment='center',\n",
-    "        verticalalignment='center',\n",
-    "        fontsize=20, color='red',\n",
-    "        transform=ax.transAxes)\n",
-    "\n",
-    "ax.text(right, 0.5*(bottom+top), 'centered',\n",
-    "        horizontalalignment='center',\n",
-    "        verticalalignment='center',\n",
-    "        rotation='vertical',\n",
-    "        transform=ax.transAxes,\n",
-    "        size='xx-large')\n",
-    "\n",
-    "ax.text(left, top, 'rotated\\nwith newlines',\n",
-    "        horizontalalignment='center',\n",
-    "        verticalalignment='center',\n",
-    "        rotation=45,\n",
-    "        transform=ax.transAxes,\n",
-    "        size='xx-large')\n",
-    "\n",
-    "ax.set_axis_off()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 注释文本"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`text()` 函数在 Axes 对象的指定位置添加文本，而 `annotate()` 则是对某一点添加注释文本，需要考虑两个位置：一是注释点的坐标 `xy` ，二是注释文本的位置坐标 `xytext`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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DmAHgSwDVkFj8LQBuA3BbVJm/ANgCYHWssE2kDJeXM59zjlgVoMpZXI4cYW7dWs539uwz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\nRVGUgPP/Qde6gvF4TtQAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb7872e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure()\n",
-    "ax = fig.add_subplot(111)\n",
-    "\n",
-    "t = np.arange(0.0, 5.0, 0.01)\n",
-    "s = np.cos(2*np.pi*t)\n",
-    "line, = ax.plot(t, s, lw=2)\n",
-    "\n",
-    "ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),\n",
-    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
-    "            )\n",
-    "\n",
-    "ax.set_ylim(-2,2)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在上面的例子中，两个左边使用的都是原始数据的坐标系，不过我们还可以通过 `xycoords` 和 `textcoords` 来设置坐标系（默认是 `'data'`）：\n",
-    "\n",
-    "参数|坐标系\n",
-    "--|--\n",
-    "‘figure points’|\tpoints from the lower left corner of the figure\n",
-    "‘figure pixels’|\tpixels from the lower left corner of the figure\n",
-    "‘figure fraction’|\t0,0 is lower left of figure and 1,1 is upper right\n",
-    "‘axes points’|\tpoints from lower left corner of axes\n",
-    "‘axes pixels’|\tpixels from lower left corner of axes\n",
-    "‘axes fraction’|\t0,0 is lower left of axes and 1,1 is upper right\n",
-    "‘data’|\tuse the axes data coordinate system\n",
-    "\n",
-    "使用一个不同的坐标系："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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pXtLCydHBCUtNRLOJqIyIdhDRA82UeSK8fSMRWVrX9VLDm1uPpAaqhYmXtHCq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\n/wdcAmhbrixx1QAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa455470>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure()\n",
-    "ax = fig.add_subplot(111)\n",
-    "\n",
-    "t = np.arange(0.0, 5.0, 0.01)\n",
-    "s = np.cos(2*np.pi*t)\n",
-    "line, = ax.plot(t, s, lw=2)\n",
-    "\n",
-    "ax.annotate('local max', xy=(3, 1),  xycoords='data',\n",
-    "            xytext=(0.8, 0.95), textcoords='axes fraction',\n",
-    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
-    "            horizontalalignment='right', verticalalignment='top',\n",
-    "            )\n",
-    "\n",
-    "ax.set_ylim(-2,2)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 极坐标系注释文本"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "产生极坐标系需要在 `subplot` 的参数中设置 `polar=True`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Qcg1btmwJuC5TUQD9z/cB1moAzin/XSshjk8P6P6qqiqXuZTVag3a7CdWiLQ3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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb97a390>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure()\n",
-    "ax = fig.add_subplot(111, polar=True)\n",
-    "r = np.arange(0,1,0.001)\n",
-    "theta = 2*2*np.pi*r\n",
-    "line, = ax.plot(theta, r, color='#ee8d18', lw=3)\n",
-    "\n",
-    "ind = 800\n",
-    "thisr, thistheta = r[ind], theta[ind]\n",
-    "ax.plot([thistheta], [thisr], 'o')\n",
-    "ax.annotate('a polar annotation',\n",
-    "            xy=(thistheta, thisr),  # theta, radius\n",
-    "            xytext=(0.05, 0.05),    # fraction, fraction\n",
-    "            textcoords='figure fraction',\n",
-    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
-    "            horizontalalignment='left',\n",
-    "            verticalalignment='bottom',\n",
-    "            )\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 处理文本（基础）"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`matplotlib` 对文本的支持十分完善，包括数学公式，`Unicode` 文字，栅格和向量化输出，文字换行，文字旋转等一系列操作。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 基础文本函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在 `matplotlib.pyplot` 中，基础的文本函数如下：\n",
+    "\n",
+    "- `text()` 在 `Axes` 对象的任意位置添加文本\n",
+    "- `xlabel()` 添加 x 轴标题\n",
+    "- `ylabel()` 添加 y 轴标题\n",
+    "- `title()` 给 `Axes` 对象添加标题\n",
+    "- `figtext()` 在 `Figure` 对象的任意位置添加文本\n",
+    "- `suptitle()` 给 `Figure` 对象添加标题\n",
+    "- `anotate()` 给 `Axes` 对象添加注释（可选择是否添加箭头标记）"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CMwAAAOAR4RkAAADwiPAMAAAAeER4BgAAADwiPAMAAAAeEZ4BAAAAjwjPAAAA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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa15d6a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# -*- coding: utf-8 -*-\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# plt.figure() 返回一个 Figure() 对象\n",
+    "fig = plt.figure(figsize=(12, 9))\n",
+    "\n",
+    "# 设置这个 Figure 对象的标题\n",
+    "# 事实上，如果我们直接调用 plt.suptitle() 函数，它会自动找到当前的 Figure 对象\n",
+    "fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')\n",
+    "\n",
+    "# Axes 对象表示 Figure 对象中的子图\n",
+    "# 这里只有一幅图像，所以使用 add_subplot(111)\n",
+    "ax = fig.add_subplot(111)\n",
+    "fig.subplots_adjust(top=0.85)\n",
+    "\n",
+    "# 可以直接使用 set_xxx 的方法来设置标题\n",
+    "ax.set_title('axes title')\n",
+    "# 也可以直接调用 title()，因为会自动定位到当前的 Axes 对象\n",
+    "# plt.title('axes title')\n",
+    "\n",
+    "ax.set_xlabel('xlabel')\n",
+    "ax.set_ylabel('ylabel')\n",
+    "\n",
+    "# 添加文本，斜体加文本框\n",
+    "ax.text(3, 8, 'boxed italics text in data coords', style='italic',\n",
+    "        bbox={'facecolor':'red', 'alpha':0.5, 'pad':10})\n",
+    "\n",
+    "# 数学公式，用 $$ 输入 Tex 公式\n",
+    "ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)\n",
+    "\n",
+    "# Unicode 支持\n",
+    "ax.text(3, 2, unicode('unicode: Institut f\\374r Festk\\366rperphysik', 'latin-1'))\n",
+    "\n",
+    "# 颜色，对齐方式\n",
+    "ax.text(0.95, 0.01, 'colored text in axes coords',\n",
+    "        verticalalignment='bottom', horizontalalignment='right',\n",
+    "        transform=ax.transAxes,\n",
+    "        color='green', fontsize=15)\n",
+    "\n",
+    "# 注释文本和箭头\n",
+    "ax.plot([2], [1], 'o')\n",
+    "ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),\n",
+    "            arrowprops=dict(facecolor='black', shrink=0.05))\n",
+    "\n",
+    "# 设置显示范围\n",
+    "ax.axis([0, 10, 0, 10])\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 文本属性和布局"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们可以通过下列关键词，在文本函数中设置文本的属性：\n",
+    "\n",
+    "关键词|值\n",
+    "---|---\n",
+    "alpha\t\t|\t    float\n",
+    "backgroundcolor\t\t|    any matplotlib color\n",
+    "bbox\t\t|\t    rectangle prop dict plus key ``'pad'`` which is a pad in points\n",
+    "clip_box\t|\t    a matplotlib.transform.Bbox instance\n",
+    "clip_on\t\t|\t    [True ， False]\n",
+    "clip_path\t|\t    a Path instance and a Transform instance, a Patch\n",
+    "color\t|\t\t    any matplotlib color\n",
+    "family\t|\t\t    [ ``'serif'`` , ``'sans-serif'`` , ``'cursive'`` , ``'fantasy'`` , ``'monospace'`` ]\n",
+    "fontproperties\t\t|    a matplotlib.font_manager.FontProperties instance\n",
+    "horizontalalignment or ha  | [ ``'center'`` , ``'right'`` , ``'left'`` ]\n",
+    "label\t\t\t  |  any string\n",
+    "linespacing\t\t  |  float\n",
+    "multialignment\t\t |   [``'left'`` , ``'right'`` , ``'center'`` ]\n",
+    "name or fontname\t |   string e.g., [``'Sans'`` , ``'Courier'`` , ``'Helvetica'`` ...]\n",
+    "picker\t\t|\t    [None,float,boolean,callable]\n",
+    "position\t|\t    (x,y)\n",
+    "rotation\t|\t    [ angle in degrees ``'vertical'`` , ``'horizontal'``\n",
+    "size or fontsize\t |   [ size in points , relative size, e.g., ``'smaller'``, ``'x-large'`` ]\n",
+    "style or fontstyle\t|    [ ``'normal'`` , ``'italic'`` , ``'oblique'``]\n",
+    "text\t\t|\t    string or anything printable with '%s' conversion\n",
+    "transform\t|\t    a matplotlib.transform transformation instance\n",
+    "variant\t\t|\t    [ ``'normal'`` , ``'small-caps'`` ]\n",
+    "verticalalignment or va\t  |  [ ``'center'`` , ``'top'`` , ``'bottom'`` , ``'baseline'`` ]\n",
+    "visible\t\t|\t    [True , False]\n",
+    "weight or fontweight\t|    [ ``'normal'`` , ``'bold'`` , ``'heavy'`` , ``'light'`` , ``'ultrabold'`` , ``'ultralight'``]\n",
+    "x\t\t|\t    float\n",
+    "y\t\t|\t    float\n",
+    "zorder\t|\t\t    any number\n",
+    "\n",
+    "其中 `va`, `ha`, `multialignment` 可以用来控制布局。\n",
+    "- `horizontalalignment` or `ha` ：x 位置参数表示的位置 \n",
+    "- `verticalalignment` or `va`：y 位置参数表示的位置\n",
+    "- `multialignment`：多行位置控制"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9lO4Zbff5g4D3AHcBTqQt9PLg4Um1Se4FHAjcGXhiVX1/Odf3EbAkLUC+P0v2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\n/g8NfX/IA8ubvAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa62d4e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as patches\n",
+    "\n",
+    "# build a rectangle in axes coords\n",
+    "left, width = .25, .5\n",
+    "bottom, height = .25, .5\n",
+    "right = left + width\n",
+    "top = bottom + height\n",
+    "\n",
+    "fig = plt.figure(figsize=(10,7))\n",
+    "ax = fig.add_axes([0,0,1,1])\n",
+    "\n",
+    "# axes coordinates are 0,0 is bottom left and 1,1 is upper right\n",
+    "p = patches.Rectangle(\n",
+    "    (left, bottom), width, height,\n",
+    "    fill=False, transform=ax.transAxes, clip_on=False\n",
+    "    )\n",
+    "\n",
+    "ax.add_patch(p)\n",
+    "\n",
+    "ax.text(left, bottom, 'left top',\n",
+    "        horizontalalignment='left',\n",
+    "        verticalalignment='top',\n",
+    "        transform=ax.transAxes,\n",
+    "        size='xx-large')\n",
+    "\n",
+    "ax.text(left, bottom, 'left bottom',\n",
+    "        horizontalalignment='left',\n",
+    "        verticalalignment='bottom',\n",
+    "        transform=ax.transAxes,\n",
+    "        size='xx-large')\n",
+    "\n",
+    "ax.text(right, top, 'right bottom',\n",
+    "        horizontalalignment='right',\n",
+    "        verticalalignment='bottom',\n",
+    "        transform=ax.transAxes,\n",
+    "        size='xx-large')\n",
+    "\n",
+    "ax.text(right, top, 'right top',\n",
+    "        horizontalalignment='right',\n",
+    "        verticalalignment='top',\n",
+    "        transform=ax.transAxes,\n",
+    "        size='xx-large')\n",
+    "\n",
+    "ax.text(right, bottom, 'center top',\n",
+    "        horizontalalignment='center',\n",
+    "        verticalalignment='top',\n",
+    "        transform=ax.transAxes,\n",
+    "        size='xx-large')\n",
+    "\n",
+    "ax.text(left, 0.5*(bottom+top), 'right center',\n",
+    "        horizontalalignment='right',\n",
+    "        verticalalignment='center',\n",
+    "        rotation='vertical',\n",
+    "        transform=ax.transAxes,\n",
+    "        size='xx-large')\n",
+    "\n",
+    "ax.text(left, 0.5*(bottom+top), 'left center',\n",
+    "        horizontalalignment='left',\n",
+    "        verticalalignment='center',\n",
+    "        rotation='vertical',\n",
+    "        transform=ax.transAxes,\n",
+    "        size='xx-large')\n",
+    "\n",
+    "ax.text(0.5*(left+right), 0.5*(bottom+top), 'middle',\n",
+    "        horizontalalignment='center',\n",
+    "        verticalalignment='center',\n",
+    "        fontsize=20, color='red',\n",
+    "        transform=ax.transAxes)\n",
+    "\n",
+    "ax.text(right, 0.5*(bottom+top), 'centered',\n",
+    "        horizontalalignment='center',\n",
+    "        verticalalignment='center',\n",
+    "        rotation='vertical',\n",
+    "        transform=ax.transAxes,\n",
+    "        size='xx-large')\n",
+    "\n",
+    "ax.text(left, top, 'rotated\\nwith newlines',\n",
+    "        horizontalalignment='center',\n",
+    "        verticalalignment='center',\n",
+    "        rotation=45,\n",
+    "        transform=ax.transAxes,\n",
+    "        size='xx-large')\n",
+    "\n",
+    "ax.set_axis_off()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 注释文本"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`text()` 函数在 Axes 对象的指定位置添加文本，而 `annotate()` 则是对某一点添加注释文本，需要考虑两个位置：一是注释点的坐标 `xy` ，二是注释文本的位置坐标 `xytext`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DmAHgSwDVkFj8LQBuA3BbVJm/ANgCYHWssE2kDJeXM59zjlgVoMpZXI4cYW7dWs539uwz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\nRVGUgPP/Qde6gvF4TtQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb7872e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111)\n",
+    "\n",
+    "t = np.arange(0.0, 5.0, 0.01)\n",
+    "s = np.cos(2*np.pi*t)\n",
+    "line, = ax.plot(t, s, lw=2)\n",
+    "\n",
+    "ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),\n",
+    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
+    "            )\n",
+    "\n",
+    "ax.set_ylim(-2,2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在上面的例子中，两个左边使用的都是原始数据的坐标系，不过我们还可以通过 `xycoords` 和 `textcoords` 来设置坐标系（默认是 `'data'`）：\n",
+    "\n",
+    "参数|坐标系\n",
+    "--|--\n",
+    "‘figure points’|\tpoints from the lower left corner of the figure\n",
+    "‘figure pixels’|\tpixels from the lower left corner of the figure\n",
+    "‘figure fraction’|\t0,0 is lower left of figure and 1,1 is upper right\n",
+    "‘axes points’|\tpoints from lower left corner of axes\n",
+    "‘axes pixels’|\tpixels from lower left corner of axes\n",
+    "‘axes fraction’|\t0,0 is lower left of axes and 1,1 is upper right\n",
+    "‘data’|\tuse the axes data coordinate system\n",
+    "\n",
+    "使用一个不同的坐标系："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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pXtLCydHBCUtNRLOJqIyIdhDRA82UeSK8fSMRWVrX9VLDm1uPpAaqhYmXtHCq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\n/wdcAmhbrixx1QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa455470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111)\n",
+    "\n",
+    "t = np.arange(0.0, 5.0, 0.01)\n",
+    "s = np.cos(2*np.pi*t)\n",
+    "line, = ax.plot(t, s, lw=2)\n",
+    "\n",
+    "ax.annotate('local max', xy=(3, 1),  xycoords='data',\n",
+    "            xytext=(0.8, 0.95), textcoords='axes fraction',\n",
+    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
+    "            horizontalalignment='right', verticalalignment='top',\n",
+    "            )\n",
+    "\n",
+    "ax.set_ylim(-2,2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 极坐标系注释文本"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "产生极坐标系需要在 `subplot` 的参数中设置 `polar=True`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Qcg1btmwJuC5TUQD9z/cB1moAzin/XSshjk8P6P6qqiqXuZTVag3a7CdWiLQ3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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb97a390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111, polar=True)\n",
+    "r = np.arange(0,1,0.001)\n",
+    "theta = 2*2*np.pi*r\n",
+    "line, = ax.plot(theta, r, color='#ee8d18', lw=3)\n",
+    "\n",
+    "ind = 800\n",
+    "thisr, thistheta = r[ind], theta[ind]\n",
+    "ax.plot([thistheta], [thisr], 'o')\n",
+    "ax.annotate('a polar annotation',\n",
+    "            xy=(thistheta, thisr),  # theta, radius\n",
+    "            xytext=(0.05, 0.05),    # fraction, fraction\n",
+    "            textcoords='figure fraction',\n",
+    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
+    "            horizontalalignment='left',\n",
+    "            verticalalignment='bottom',\n",
+    "            )\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/06. matplotlib/06.04 working with text - math expression.ipynb b/06-matplotlib/06.04-working-with-text---math-expression.ipynb
similarity index 99%
rename from 06. matplotlib/06.04 working with text - math expression.ipynb
rename to 06-matplotlib/06.04-working-with-text---math-expression.ipynb
index e6d55067..a36032f6 100644
--- a/06. matplotlib/06.04 working with text - math expression.ipynb	
+++ b/06-matplotlib/06.04-working-with-text---math-expression.ipynb
@@ -1,294 +1,294 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 处理文本（数学表达式）"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在字符串中使用一对 `$$` 符号可以利用 `Tex` 语法打出数学表达式，而且并不需要预先安装 `Tex`。在使用时我们通常加上 `r` 标记表示它是一个原始字符串（raw string）"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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LNOublhHfH70ktyb5VpK5KZc4NSO8P9YkuTbJbV1fnL0CZU5Fkk8luS/JNwe0GS83q2pi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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa0d7518>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plain text\n",
-    "plt.title('alpha > beta')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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oqn9L8u/AKxndf/N8M3M3531Z5uhNT0lOZXTT0+ofztuA98PRO2CPe9NTAxPXIslLgVuB\n91bVgS2Y46JMXIuqenlVvayqXsbouvtHmoUdpvv5+Efgj5JsS3IaozfPfrDgeS7CNGuxD7gEYHx9\n+ZXADxc6y5PHzN2c65l7edPTUdOsBfBJ4MXADeMz1sNVdeFWzXmzTLkW7U3587EvyR3Ag8DTwE1V\n1S7uU35PfBq4JckDjE5EP1ZVP92ySW+iJF8C3grsSHIQuI7RJbp1d9ObmCSpIf83e5LUkHGXpIaM\nuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGvp/zy4/4DuVo0MAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa162860>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# math text\n",
-    "plt.title(r'$\\alpha > \\beta$')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 上下标"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `_` 和 `^` 表示上下标：\n",
-    "\n",
-    "$\\alpha_i > \\beta_i$：\n",
-    "\n",
-    "    r'$\\alpha_i > \\beta_i$'\n",
-    "\n",
-    "$\\sum\\limits_{i=0}^\\infty x_i$：\n",
-    "\n",
-    "    r'$\\sum_{i=0}^\\infty x_i$'\n",
-    "\n",
-    "注：\n",
-    "\n",
-    "- 希腊字母和特殊符号可以用 '\\ + 对应的名字' 来显示\n",
-    "- `{}` 中的内容属于一个部分；要打出花括号是需要使用 `\\{\\}`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 分数，二项式系数，stacked numbers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$\\frac{3}{4}, \\binom{3}{4}, \\stackrel{3}{4}$：\n",
-    "\n",
-    "    r'$\\frac{3}{4}, \\binom{3}{4}, \\stackrel{3}{4}$'\n",
-    "\n",
-    "$\\frac{5 - \\frac{1}{x}}{4}$：\n",
-    "\n",
-    "    r'$\\frac{5 - \\frac{1}{x}}{4}$'\n",
-    "\n",
-    "在 Tex 语言中，括号始终是默认的大小，如果要使括号大小与括号内部的大小对应，可以使用 `\\left` 和 `\\right` 选项：\n",
-    "\n",
-    "$(\\frac{5 - \\frac{1}{x}}{4})$\n",
-    "\n",
-    "    r'$(\\frac{5 - \\frac{1}{x}}{4})$'\n",
-    "\n",
-    "$\\left(\\frac{5 - \\frac{1}{x}}{4}\\right)$：\n",
-    "\n",
-    "    r'$\\left(\\frac{5 - \\frac{1}{x}}{4}\\right)$'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 根号"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$\\sqrt{2}$：\n",
-    "\n",
-    "    r'$\\sqrt{2}$'\n",
-    "\n",
-    "$\\sqrt[3]{x}$：\n",
-    "\n",
-    "    r'$\\sqrt[3]{x}$'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 特殊字体"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "默认显示的字体是斜体，不过可以使用以下方法显示不同的字体：\n",
-    "\n",
-    "命令|显示\n",
-    "--|--\n",
-    "\\mathrm{Roman}|$\\mathrm{Roman}$\n",
-    "\\mathit{Italic}|$\\mathit{Italic}$\n",
-    "\\mathtt{Typewriter}|$\\mathtt{Typewriter}$\n",
-    "\\mathcal{CALLIGRAPHY}|$\\mathcal{CALLIGRAPHY}$\n",
-    "\\mathbb{blackboard}|$\\mathbb{blackboard}$\n",
-    "\\mathfrak{Fraktur}|$\\mathfrak{Fraktur}$\n",
-    "\\mathsf{sansserif}|$\\mathsf{sansserif}$\n",
-    "\n",
-    "$s(t) = \\mathcal{A}\\ \\sin(2 \\omega t)$：\n",
-    "\n",
-    "    s(t) = \\mathcal{A}\\ \\sin(2 \\omega t)\n",
-    "\n",
-    "注：\n",
-    "\n",
-    "- Tex 语法默认忽略空格，要打出空格使用 `'\\ '`\n",
-    "- \\sin 默认显示为 Roman 字体"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 音调"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "命令|结果\n",
-    "--|--\n",
-    "`\\acute a`| $\\acute a$\n",
-    "`\\bar a`| $\\bar a$\n",
-    "`\\breve a` | $\\breve a$\n",
-    "`\\ddot a`| $\\ddot a$\n",
-    "`\\dot a` | $\\dot a$\n",
-    "`\\grave a`| $\\grave a$\n",
-    "`\\hat a`| $\\hat a$\n",
-    "`\\tilde a` | $\\tilde a$\n",
-    "`\\4vec a` | $\\vec a$\n",
-    "`\\overline{abc}`|$\\overline{abc}$\n",
-    "`\\widehat{xyz}`|$\\widehat{xyz}$\n",
-    "`\\widetilde{xyz}`|$\\widetilde{xyz}$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 特殊字符表"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "参见：http://matplotlib.org/users/mathtext.html#symbols"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 例子"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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uherV/Y7GZJdX89RzuSUMAFVdpqr/CkNMIbOkkb+UFNevkZbmdySJ5ccf4cIL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\noFnSMMYYE7T/Bxi7FV50SWh9AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa567160>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "t = np.arange(0.0, 2.0, 0.01)\n",
-    "s = np.sin(2*np.pi*t)\n",
-    "\n",
-    "plt.plot(t,s)\n",
-    "plt.title(r'$\\alpha_i > \\beta_i$', fontsize=20)\n",
-    "plt.text(1, -0.6, r'$\\sum_{i=0}^\\infty x_i$', fontsize=20)\n",
-    "plt.text(0.6, 0.6, r'$\\mathcal{A}\\ \\mathrm{sin}(2 \\omega t)$',\n",
-    "         fontsize=20)\n",
-    "plt.xlabel('time (s)')\n",
-    "plt.ylabel('volts (mV)')\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 处理文本（数学表达式）"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在字符串中使用一对 `$$` 符号可以利用 `Tex` 语法打出数学表达式，而且并不需要预先安装 `Tex`。在使用时我们通常加上 `r` 标记表示它是一个原始字符串（raw string）"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LNOublhHfH70ktyb5VpK5KZc4NSO8P9YkuTbJbV1fnL0CZU5Fkk8luS/JNwe0GS83q2pi\nD/pTN9uAY4BDgNuAly5ocwZwTbd8EnDTJGs4WB4j9sWrgZ/tlmeeyn0xr91XgC8Bv7fSda/Qa+II\n4NvAUd36mpWuewX7Yhb4q8f6AXgQWL3StS9Tf/wm8Ergm4tsHzs3Jz1y96anfYb2RVXdWFU/7lZv\npt37A0Z5XQCcD3weuH+axU3RKP3wVuCKqtoBUFUPTLnGaRmlL74HHN4tHw48WFW7p1jj1FTV14Af\nDmgydm5OOty96WmfUfpivncD1yxrRStnaF8kWUv/zf3Yr69o8WLQKK+JdcBzk3w1yaYk75haddM1\nSl9cBvxqknuB24E/nVJtB6Oxc3PYVyHH5U1P+4z8MyV5HfAu4DXLV86KGqUvPga8v6oqSXjia6QF\no/TDIcCrgFOBw4Abk9xUVVuXtbLpG6UvPgDcVlW9JC8EvpzkuKp6eJlrO1iNlZuTDvedwNHz1o+m\n/wkzqM1R3XOtGaUv6C6iXgbMVNWg/5Y9mY3SF8fTv1cC+vOrpyfZVVVXT6fEqRilH7YDD1TVI8Aj\nSa4HjgNaC/dR+uI3gA8DVNV3knwXeDH9+2+easbOzUlPy+y96SnJofRvelr45rwaeCfsvQN2vzc9\nNWBoXyR5PnAl8Paq2rYCNU7L0L6oql+uqhdU1Qvoz7v/cWPBDqO9P64CXptkVZLD6F88u3PKdU7D\nKH2xBTgNoJtffjFw91SrPHiMnZsTHbmXNz3tNUpfAB8EngNc0o1Yd1XViStV83IZsS+aN+L7Y0uS\na4E7gD3AZVXVXLiP+Jr4CHB5ktvpD0TfV1UPrVjRyyjJZ4FTgDVJtgMX0Z+iW3JuehOTJDXIP7Mn\nSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJatD/A8TB+T0A8shJAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa0d7518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plain text\n",
+    "plt.title('alpha > beta')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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oqn9L8u/AKxndf/N8M3M3531Z5uhNT0lOZXTT0+ofztuA98PRO2CPe9NTAxPXIslLgVuB\n91bVgS2Y46JMXIuqenlVvayqXsbouvtHmoUdpvv5+Efgj5JsS3IaozfPfrDgeS7CNGuxD7gEYHx9\n+ZXADxc6y5PHzN2c65l7edPTUdOsBfBJ4MXADeMz1sNVdeFWzXmzTLkW7U3587EvyR3Ag8DTwE1V\n1S7uU35PfBq4JckDjE5EP1ZVP92ySW+iJF8C3grsSHIQuI7RJbp1d9ObmCSpIf83e5LUkHGXpIaM\nuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGvp/zy4/4DuVo0MAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa162860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# math text\n",
+    "plt.title(r'$\\alpha > \\beta$')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 上下标"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `_` 和 `^` 表示上下标：\n",
+    "\n",
+    "$\\alpha_i > \\beta_i$：\n",
+    "\n",
+    "    r'$\\alpha_i > \\beta_i$'\n",
+    "\n",
+    "$\\sum\\limits_{i=0}^\\infty x_i$：\n",
+    "\n",
+    "    r'$\\sum_{i=0}^\\infty x_i$'\n",
+    "\n",
+    "注：\n",
+    "\n",
+    "- 希腊字母和特殊符号可以用 '\\ + 对应的名字' 来显示\n",
+    "- `{}` 中的内容属于一个部分；要打出花括号是需要使用 `\\{\\}`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 分数，二项式系数，stacked numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$\\frac{3}{4}, \\binom{3}{4}, \\stackrel{3}{4}$：\n",
+    "\n",
+    "    r'$\\frac{3}{4}, \\binom{3}{4}, \\stackrel{3}{4}$'\n",
+    "\n",
+    "$\\frac{5 - \\frac{1}{x}}{4}$：\n",
+    "\n",
+    "    r'$\\frac{5 - \\frac{1}{x}}{4}$'\n",
+    "\n",
+    "在 Tex 语言中，括号始终是默认的大小，如果要使括号大小与括号内部的大小对应，可以使用 `\\left` 和 `\\right` 选项：\n",
+    "\n",
+    "$(\\frac{5 - \\frac{1}{x}}{4})$\n",
+    "\n",
+    "    r'$(\\frac{5 - \\frac{1}{x}}{4})$'\n",
+    "\n",
+    "$\\left(\\frac{5 - \\frac{1}{x}}{4}\\right)$：\n",
+    "\n",
+    "    r'$\\left(\\frac{5 - \\frac{1}{x}}{4}\\right)$'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 根号"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$\\sqrt{2}$：\n",
+    "\n",
+    "    r'$\\sqrt{2}$'\n",
+    "\n",
+    "$\\sqrt[3]{x}$：\n",
+    "\n",
+    "    r'$\\sqrt[3]{x}$'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 特殊字体"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "默认显示的字体是斜体，不过可以使用以下方法显示不同的字体：\n",
+    "\n",
+    "命令|显示\n",
+    "--|--\n",
+    "\\mathrm{Roman}|$\\mathrm{Roman}$\n",
+    "\\mathit{Italic}|$\\mathit{Italic}$\n",
+    "\\mathtt{Typewriter}|$\\mathtt{Typewriter}$\n",
+    "\\mathcal{CALLIGRAPHY}|$\\mathcal{CALLIGRAPHY}$\n",
+    "\\mathbb{blackboard}|$\\mathbb{blackboard}$\n",
+    "\\mathfrak{Fraktur}|$\\mathfrak{Fraktur}$\n",
+    "\\mathsf{sansserif}|$\\mathsf{sansserif}$\n",
+    "\n",
+    "$s(t) = \\mathcal{A}\\ \\sin(2 \\omega t)$：\n",
+    "\n",
+    "    s(t) = \\mathcal{A}\\ \\sin(2 \\omega t)\n",
+    "\n",
+    "注：\n",
+    "\n",
+    "- Tex 语法默认忽略空格，要打出空格使用 `'\\ '`\n",
+    "- \\sin 默认显示为 Roman 字体"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 音调"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "命令|结果\n",
+    "--|--\n",
+    "`\\acute a`| $\\acute a$\n",
+    "`\\bar a`| $\\bar a$\n",
+    "`\\breve a` | $\\breve a$\n",
+    "`\\ddot a`| $\\ddot a$\n",
+    "`\\dot a` | $\\dot a$\n",
+    "`\\grave a`| $\\grave a$\n",
+    "`\\hat a`| $\\hat a$\n",
+    "`\\tilde a` | $\\tilde a$\n",
+    "`\\4vec a` | $\\vec a$\n",
+    "`\\overline{abc}`|$\\overline{abc}$\n",
+    "`\\widehat{xyz}`|$\\widehat{xyz}$\n",
+    "`\\widetilde{xyz}`|$\\widetilde{xyz}$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 特殊字符表"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "参见：http://matplotlib.org/users/mathtext.html#symbols"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 例子"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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uherV/Y7GZJdX89RzuSUMAFVdpqr/CkNMIbOkkb+UFNevkZbmdySJ5ccf4cIL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\noFnSMMYYE7T/Bxi7FV50SWh9AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa567160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "t = np.arange(0.0, 2.0, 0.01)\n",
+    "s = np.sin(2*np.pi*t)\n",
+    "\n",
+    "plt.plot(t,s)\n",
+    "plt.title(r'$\\alpha_i > \\beta_i$', fontsize=20)\n",
+    "plt.text(1, -0.6, r'$\\sum_{i=0}^\\infty x_i$', fontsize=20)\n",
+    "plt.text(0.6, 0.6, r'$\\mathcal{A}\\ \\mathrm{sin}(2 \\omega t)$',\n",
+    "         fontsize=20)\n",
+    "plt.xlabel('time (s)')\n",
+    "plt.ylabel('volts (mV)')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/06. matplotlib/06.05 image tutorial.ipynb b/06-matplotlib/06.05-image-tutorial.ipynb
similarity index 99%
rename from 06. matplotlib/06.05 image tutorial.ipynb
rename to 06-matplotlib/06.05-image-tutorial.ipynb
index b73c975c..2ccc0c37 100644
--- a/06. matplotlib/06.05 image tutorial.ipynb	
+++ b/06-matplotlib/06.05-image-tutorial.ipynb
@@ -1,453 +1,453 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 图像基础"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "导入相应的包："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.image as mpimg\n",
-    "import numpy as np\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![臭虫](stinkbug.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 导入图像"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们首先导入上面的图像，注意 `matplotlib` 默认只支持 `PNG` 格式的图像，我们可以使用 `mpimg.imread` 方法读入这幅图像："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "img = mpimg.imread('stinkbug.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(375L, 500L, 3L)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "img.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这是一个 `375 x 500 x 3` 的 `RGB` 图像，并且每个像素使用 uint8 分别表示 `RGB` 三个通道的值。不过在处理的时候，`matplotlib` 将它们的值归一化到 `0.0~1.0` 之间："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dtype('float32')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "img.dtype"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 显示图像"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `plt.imshow()` 可以显示图像："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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6SKwzSc7Jr1lTkKyMO9YWzjC16CvZJwWAUD2lKM/PWYMFWGZgi4uL+Pmf/3k0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VIpCCH7Qv3NVBJXrlxBLpcLr2IBDgCx3W4/FGBNhqesfHFxMZidzrkANqPRCIVCAYPBIAik+rgJ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\ndJpO02l6yukUWE/TaTpNp+kpp/8PTYoQ8rA9sPAAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa0cc5f8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "imgplot = plt.imshow(img)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 伪彩色图像"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "从单通道模拟彩色图像："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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0khakZoqa53ayKDgp6NhAdmA4MPnd8q9qlttrHJY3AFFgKpmxduJbjcua13pc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JV67Ond5x5IjNEPuMlIs0sxES1ClsV4ls9REUj8piEkXps6cUGDYPaJgJrKjEqbeWMSZllkam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\n2+yGbQPWzTbbbLMbtv8GmbHHpC2s7ygAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa491438>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lum_img = img[:,:,0]\n",
-    "imgplot = plt.imshow(lum_img)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 改变 colormap"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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8D4L3bTthlkn8rjbHs+4ThDN3VrHKmjh6zFhk5ZK5u58DnNZ5aADXmb28oUKq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\ncwI053Lg4uv5oZs2+INnXsj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q4UAf1XmYR+PsXod41OL10IIbTa8/x28NWBlz4EmbPOdV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4D74Za3Q+u749Bc6mJqOPRXMPoA7P0w8IOp8gvRZ6OQH0lzU9695CUtBt3gL/m/kBMUcmytFj9p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\naJom0+TibCB9rWfGguCbFoP4zxOLuYUAN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pFOBzdOl8AXq09uJJPGPatyhgS0k+TpWuvI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\n5bbcltslbsvAutyW23Jbbpe4/X9nGwcxiuGaUwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa6a04e0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "imgplot = plt.imshow(lum_img)\n",
-    "imgplot.set_cmap('hot')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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lbQNW3m7Jo48XIAXaDXAD0iS6T2oL93Ukl8r+1iOKUwExLtJCL9TZ4m/LNK1d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ZOcUt3O0DiMWcCMgYUlKxwieMlcWDJNUbZPhFkuzg2B6OqZimc0wV5aSiWVyTJC74XDL1coVkuv\niJF0FIDYV/WihahLNNNQ7GdvS7WLB39ffptYmBmCLbXUPsqodLahFaPmIouKIDxzyu3SwlVIxnor\nCcvTx3UQdDc2st7qcnoMzBam+JvQfs8nxpjDgCcDvwKWWWu3+13bCSutHARsUadtwQniHprCAZTd\nlMOsBmd5qDg0IiYRQiKINF/q3/1IZkS9lC+477LOuyDR+IVqG5aoQ92kV/WQmVfsrbHXs3SsqJGm\nV+DL81Vzx0gyqw90w4ATlVG3QcKmxBTQSoLqr+1KMRqIqZ0GFBILiiwSMjB7CbYscR7rH+fwiUn4\n8hxoJhm0VjM6AmNDQBuefXgTmm+G4UFXsWftNBzyK9gB8++GP94M+SCw7L+gYWA6g5NvheE3g6nT\nzYapMMUz7r0H5i2E7hCM/Tvz1sKeGs7+NAQ04ZODcM8k/Pe0ywaTZVO0Ci7osFDvU3oD7UXd959Z\nqt7pLMhSn6N1U202kNJ/Jg+mBTEvyD2kfJ7wZGFfn+WdynsX2z/0D6fqJ8xK+5OQwg3h/HjM7IuK\nVSey3ibvizfFRyL2XC3cpGaHiY7Xpj/ov7LtY6H9EqzGmGHgcuBt1toJvc9aq+fXftR331zc6gEV\nXJhV02+fMa6wSilGM7qCxaWs6YeQB8lNWf0WklhVbToQe6gurKuN4XEMqz6nlA7o7yX50zKT633a\nDCCfuiaBtFdsYjq2legaqXUCVUKpEmCqGtTzrvE2swhhSD+IdzgjMKAhDK5CPcoDgs7VdYpr2WAW\nKKEW/08SCnRBEOnTrnHpkJUUnrDQVyv71VLXkiqMDAAL4PzrgaMvgQ0fhrkVWItjltZ5XGbh8Afh\n67cuhYEZOONqsDfB17q+gnqbaebTZJCPfeB2aD0DHtoG637MRTfCh7q4rJS5VdJdQ9Sr8JNdkLX9\ncuney184cmx4DqAoQiKea23bLPpC8ZB4yYu00KQsaLVAzJQdt8QkBAQdo0Mphyfe7mICTUIOv1wm\nLkKkx8psoXU61lQ0mHj1Y2mTfpbZSHhLxoiQtrXqSyQEM5icI/bUUqKM0uS6Ud9LbQN9vlxbogX+\nn8SxGmOqOKF6sbX2m37zdmPMcmvtNmPMCmCH3/4IzlwqdLDf1kNXv88F8wMcuR6euj5kUTWgVNYv\nN72CTKpapaqTLRSBzvLbEJCbFmSGshDWs7WQLMMsgkbCqoo/9RJRxxTtQHk/KddkBeWR9ReMU1SL\nY224nmSPNDKHBiXMTGIPU4Lg1PyhhZs8W5KGvqopVVTuM5M6W3Y36e9ok8w07QyTAZHkZYEhqrD8\nzgzMb8NQFW6bhNcsgEVHjTPy0BgY+KdB+EID0jr8yyEbedNDf0p++4u54ohv8NHj4fqf3cY9u6Cz\nEtLdO8gyYNEb4cUVsJfAd2+DY66h267RrRioPw+2fh9W/wlMj/GU1XC1BVYD9yesP3uKLRXI5sDT\nFwCTFAH+Peo75d8Wikwneb64v6XPW6kXAnk4togHTsrvXne4TSinxxrfPuP7OqGIHy142JTNY7qA\njg2H9KjrCWBt+Rjh95h3+5UerOaQeORcOIiTcJ70j7Z8JHEj/M/YH1qxYbKQeGtxChfrefljC+ez\nR6rtpByL3k2CU/qma+GGn/C4kbE27ha10xiDs6GOWGvfrrb/vd/2UWPMu4H51tp3e+fVV3F21ZXA\nD4E1NrqJMcb+nXWV2zQN45GsZdZ6qTGJYM0JMw74TBN5uQptGAIS3NeMaggFe4FQqEQJkjioX1Os\nxguqi1dB1SQFRORZ5JBSrKp/NklSkBhTnbQA4RnTvIw2IASMQ4iQMID1jiWZzAQZt41aKoegRqW2\nN+d6f0jsl4sA7oLPngrzEvg3C4cAP+zAjY+6AT61BC4YhFPbVW5tdPjvO+B3D4f2FvjkSjhhAA4f\ngbt++lfAHHfSQy+G5RuYc9ES3njdXXz8I9th8bVQfwIc8k+sXAePJMAjcO4qeEkF/iKHxQa+1IHD\npqHRdH3RswzJXqjtEeNAtz9/tRLH28JD/cIBwXvTkzC5S3k/vVKB9qDrFU1jyo1rTzFRxny5l3Gg\nEV+cqCKxpf2oNP5yhUA9z7Q9X4npTABQv8Lzs7VJJmhN2hEnCLaThGSYuopSmK3vAY4fBGsfe0Tr\nvhDracArgduNMbf4bX8JXAhcZox5HfAg8LsA1tq7jTGXAXfjAOibY6G6LyqKUeNVyf04W2Y1UTG1\nF7Ck4kAhTfYmVAuTQaSe9DulNNNH147tqMWKBJGQEwGYWHqmf5kY5LsI0cw4VGBsQLHxUjKy5LYs\nSSNtk3Caeu5m8fEqTPh1ioa7QX0f6rhstnbq0vW1/JT+zgkrCEj8a1V94u8ltRJkMNTxA6QD7IbL\nDbw/g8+0YLjjYvjrOVT3wA9WwxMNnGk7vHgChrefz5PmX87iRbBkBk5vwg9+dgHY+fC9F8Mz/g8c\nfg3YBmuvG2SsUnOrDjaeDrYLu4b4eG2Kl90OX17rEk1mLByRODQwlEG14+yWsogeqv/0uxYSE4dM\nvN3EFaXRanqhFaEGnn932klZJKV4dGU8rCxqEJheLU7WlpJ3LzwsVajkfev3AmH9Jy3PhM8sClRk\nveh0NqFq1PmaRHMRx6fcR3buC+iIDVX/1mNCzFxCwm8SW1tVQrWIFnjMonPvtFfBaq29jtntsGfN\ncs6HgQ/v68YxWk2ApTjP/5Rxqr7UXq1QTlctzomQZJFFIW1Rx1gTcqabfZCHBDEbykHve+v3wq5L\nsFNCEMLxmuXQG6eX+5toW1W/l23V8bm6rth0E8UkmWcia8JgE4Eq/WT9fcYq8M91uGgS2m04dQjW\nD8IFTVi6HWq7oLkUOsMOuY0PBiecmD1kIM2o8BhwiQLSB/qZEgsD03DHAmB7HfacxbVbruAXh8KS\nChzbhJe2YNtyOGwKFrfg1w34DvDeBBqHX86NF18Kz3sT5x85xnU/mwutN4D9HJw9BNlzfSZEnU3p\nICckOQytgO4IVFbD2Am87KGfUt89RKM+xWYDVwPXj8BZwy5LqRCoEd/NqukogWvw2URp4D+N6vtN\ntuDRqZ8sdd3TuLxgcbtIeBkokjaExP4oaq+Uz5OJDryZKg+/ZQl3SRKRa4saX7xfO7twLSpNWXcD\nEYIiqOW0dhK26YScxJbHio3+8NeSe6S61i9hPCd4oEDQ+OLImsyE1Vr7aZGPhfbLefU/QXv83wxu\nafiFOPCy0fQK0S5O2MYapy4oomfYHMekurK4VGGazUutq9poXtFZWTnlgSDNlE7UAckShK3TV3Uc\noLzAfqtRaobX62uVgrNN8NSKXS0zYcE0qYUpjiy5jQRjZwb2VB1K/OJt0P7ukXDFXH5+eZUP3wjn\ndmHHQki2wtAlMO9amHsVLL+vPBByEzyumYGhbug3QdO53941bgDMmYJ7F8NzLNDqQvVEOjvhi8Cb\nt8P7E4eQR+tQGYcV2+CWDC6ahskKvHcNcPhWeOuFXHM/dLdbqEw5O+roOphZxvP/cA/cdjDd1g6+\nkhwCfAW6twITUP8D2HAUF66fYjCFp3bg2TPQvRsebjkGkFAmjVaBothKPEEWxxvHe3UfB5vETIub\n4CWCRP8V15Ei4OLFVqq09GfS59oSwSIOHL1NkwAHQ9inEbNExmhEKZOnmDpi+6VORBCNSCNfq++l\n2iP8Lf0mWVj6efUn+BhuqxJyKMeXC9ARh5Rs0+NVO+5EbiTWjZ+pxyEf9YAufz2JQ6gJrt7qKlyJ\nwJTyTDxsZ4l1VehBzzTGX1Nm3ljNgbLA1Ntlm1a/Y4ErTgJBfbKt1AYbmEpepKEsmIW5KzGjqcZq\nlSq+H/RRz9Uz6CpVhZ3OOtWym8C7G/DdO4C7XgC1p4F5BCprYcP1bNn5PU48eZT3/g78ft1lfCZH\nQT7krjlZcYMqte67PH8rVaErhPJsWeqW7gD48hK4sAtTdwE7LOSbYHQNp/EAl+6BX9XgdcAv74GR\nLuxuwp7rYM0p8OymM+AvWvcORn7/M7Dl+bCwAe84FN54JKdceA+LRic5hPtYtWUV87L53L9yOWe/\n4j38fP0ixg+aB2dvgEUP8I8t2NN2UVyPtoERuHscblkBz5wJtuki5dQGJ5HueOERA+VUVPX+0tgL\nI+/MM1LPKqoRFE19YkVqKKUFV7o+QkBO6zMBV6yr2iRCWZCoaEui3Rhbtr1XLKUKdIl1k0KBWm0w\nJYgDtmTWiviyCIdKyu3VqDgmXatDulZ8FEbtk+cx6pjMOFOiCHgRnLISSMWG6IZc3U+Px8dKB0yw\ndvHLruA6qYGLaV3VB4rnuGiBnu2m/F2ESKHWK5QYkxaocUprJwnpstp5JYUbBI0V6pEJs2RxHSD3\n+7pQiq8TZhJ1R9oZUwKlbKi4T6T9GrUXNqScHjVNkEErgY01uGEM2DzkkJ4dhMog0IDac2AsYde1\nV/CWVaN85nTLH50Fqy1s84j/1HYYKLlxKqYUFNbOQSnVJn31n8PwtnFcyNQIsCuHxkXQHGLcwsBC\n2FGBHRuWwD2v40unXMixixxyfngM2ruqfOeuNZA9HSqHwwMfZuEJX2H0IzdCt8Iv33AILDqW135k\nmLYdZoattM9PsOMJ46u3waF12LkR5h7FpvENMNxiZAUwjmPIHfCNlbDeUFSnAiUw++h4IpRmK4Bd\nEVuprv2pNSe5jzpG1sES4adnV11bAAtJtyz4dZigds4IciylhlpndpPD6lkoYh0np2gTgPCvXFNU\nag0+IIyTGkoAq8cR85T0V676DYIpSdqrQym1ecmofpTuKkXdoBxx/t2mXjOQyUHa0E/A/6Z0wASr\nFDQSM1YTF5slpQCn1NNNG4da95dEwAiiaqYhJnNvAcZyLoSObqWB2TNCwQbd+yJouxEzJ4C1ZQ+6\nzKQ6PrVnET7FgJoZY9qbB1XH44lhX6iZwqcT2P4wMHYKmMMgaUPaxPVSFWrPgu5quOti7nn017xt\nKVSWu8mCBBqHwqeAo6yzgS5oOhPOyIBjqgqhcIvYhe+ZA28fAcZwaskQ0BkE24GBKS6X1JIUmDsG\nm17A3xyykfWrLiNfDO1fAxvWwU0XQVqBKwy83bJn91WQfAKSz8PKxbC7xpd+5wRYDgM8AJ2Uq/5m\nCcy9DOwfQfpHcN/X4ZArYPOX4Z4pOPQBF1Bdh5uByTrMb1KsICAUe+blHfUjicjQs58WqPEaYO4L\nPUigWDureLnlzyR3fCZoN+aZroHuLCYwsT8KtdKyYNHp1NVcCUy5vQ1qf2p7ebJU4MiDCuF/HUMq\nwEOvgdUvUSEWkrpL9HuQNlkckJD7SaF2KbgC7nsjC5EV/6sXExzwN58GNvjfBmcSmGvcb79WJgtx\nCQGC/PQKA3VbThaoE4RVV3R2fNB0Tsmjqmd2LcwqSqcQRJZYVwlHhLMObM4ph4fF5gFBwaKSV/KA\nfo1158o9NML+TV6wRsJIGwnMK6E7kxX4WAo/eBC4PwVzJGSD8MgaWFmBygTYBuRzXUOH/wyaD8PG\nu+hubELlETD3Mb05442r4Mhl8Nmae7Z5LVg8Bd2qs8W1E1fQopXAw3W4YAd0bsDZeyywcTFU1zjj\n5sxiFs/b6opWJ3DsUIdrjtlI6yvn8v3By+CeJ4F5CGb+AL4Pc+7fzAn1HVw7/lS6g58Bk5LcsJzn\nfuF6rnzryW4WvMOSUcP8qMuZN+zk1he9nPEX/R6MDsGF0/DR9dA4BrgYJh9wRSsmYXQEJgdcmcq4\nilSxNLVCh0nMS16Yppk/X/hVV62aDSio7aV7KnW9VI/UtxGZSDN3Hx2hktJrominwdapJ97iMxIw\nCSHAXqJQUoLZTCZP4eEitDBRoMMLTTF3FZrgLF0hArYfbwtKFuQqmZMxeJKShPXMHTNdCSi5Smjn\ntJKE+1MDY190wATrdhxAmIcTrstxqHUQJ0AncOaBubjsg0U4Ido0ZXvrhAhDXMdOGHeswQlmPVO3\n0qD2SK1KYx2yyo1DyXPy3o5NbAjVEGeNhHWJDU2YUycsaIdTNQ82Lq3SGHUPCEI/4LcAACAASURB\nVN57KB9ThJGZMMAqamDoSAEhbRowvi++WYOvjQC3ptB6MdRPhk7VHbh7lQveNAmkHTdCK6OQLIXq\ncZC0XE/nG+HXl8HWnWxYNMZZB3d53mq4oAFnTcG8Mffu9sxzTN1O4GMGxu6uwejRsPA2N1vW1znB\n3vwl5A2WD8MbDbxlJ1wzWXejf/J4uON4GHqZ45b5u+A9DzDx/SNYfPko5scWe8FO+OA68oc3cuXr\n18Hxj8AxKekH2sxZVMceV2Hs1nms/eY4N5y+BhZugX9+CFgKk6th+BCYGYZNk5BC+zAXflasYOon\n2kKgqe8y8ULQEHSQej80W1pZwIbracGpHVOlYi5yL3Fw2XDN3DsnTAa2EpxceRIm/q5/H1WP2ipZ\n4NW4YIrwfGYcohPhCYH3q7lHxEnZNKTjWA0ByKguLISgJZT5q1hXDjRR4EVAiSFUwSp8DCbYW8Vs\nYAnLyde81qptx9b3Y7xN+yJ+WzqgzitwJgGDM3HFJC/B4sZh04RIAilOMI4L0xrDpUWC907jUr4W\nAEvVzKhn4cyEc6q44kki2GS2FBPCTBoM3lXPIJLJoYP38fcoIdiEnmVoCoEnqlJS9pbuLfEgjYR/\nEXrV5wTdpl1VuKSFK40z/SqonwZ2LpDDTA53DsLZj0D2cbBbIPkAjD8FquPQHYLBzc77nhwNQ+8D\nJmDkEXjwQr63bYarjoAfHAzH5DDYgjmTDm19axF85zZg5wugsQpGboNHgcoLId8Bi6uwfAfNBC4F\nuB7YfQbsOA7qAzB4AZi5OK55ENrnwzl/weUnvgzGDAxcDu/fBa89AQ6rw8xSyGdYtWuUwQkLD8Jt\nL17tmGblxdD5FSRPhdZyaNVh+FSY/HgYDVKcJsEtV+3tVcZvEwYRz3VcF0JoX+mRJcfVLMfFKbL9\n7qHTZt2FXbtNUj6/7cFEXAJTBIkuw9k1FNJLeL2ROUckBAdu5lGgwQmx6bQ39lzQvEapmleNfw4R\n+AO+fRMVd089bsWkVmhiJght+S1rybUTpd4nZWFfLIdDOXqgX2jbY6EDWui6jhNAM2rbHoLQjXlt\nG+Hlb/afg8AoAcUO4oRqhhOqc60Tak3jbICiwuwwDi2L7XbSuFi2QRvsvplnLkGiRY69n9VklrX4\nF+uPjdNJZyNjQxyqRc2UJsycEnSeEJgrRtTGlpMihLRaJJPDxB5g5xyonQF2njd6DcGcBJ71Y1j6\neldqZydwyyeh9kIwD0PlAthzKAzt8hcehIlhGNwNcwdh6zbybfdw1tzv8ZR18JIl8HszsGwrLJ8D\nNOtOmHW+5154s+pc1Qz4lwPPBL62E2ieDFMfhusH4bkbwU4Ch8D4ETBxFHznPF7w6A6uOO1usudu\nhtG3wgMNnsQD3Da+yOXJ3pdyysQoG0h46Y9/wr1U2VVZxCPzzoUVz4SFv4Q8c6EpA0ugcT6M/Rjq\nW2lmMOHVdp15BUEl14JsXyhHC1BshExVBS2jB7f1DizoTakVoFDYGNRvmUjToLGIHVXaKchUUKrE\ncGrnlP4tNK3qE4iTyeI1uMQnkqikCG3rFSQ52/JEYlKwJpj2GlkIkdSp4yhbr6BPQanaBivfM+PX\nzlLZXhotF2jaj6HHAbAeWMEqpBthcbWMhwgoT0KzhnDIVCjDmRFk6eylOLNBCydol+JMAyl+SRfj\njm3j0mc7hLXQq5RnbGmL9ox2E+e8aeQU4TWyW9Ct2JQing+MQwiBEYbQvFaynxEGm9jxhAnjgRwn\n38lusWd1E7jBwMgUMHMY2AVgvSsuacG8BtS/y9Ah8JFFcO0CuH7hz3mk/XPyCeCe22DoaTCwC6bW\nwOi5rgahmQ/DTwDWQv4M2Nnmpqvv4aaDm1z5xBGefxisSeH8U1t8/eZ3YR+quwZVO9C8BBrnwdQ6\nuH8rX1iyGTsCNJe7WWwh0GpAbSHYARj6KjTOhmes5dvTd8HJH4TN34d/BB4Z5bYPnQCLZsBAZV6N\nBbS4mZXcxCo4pMGiRzZiProHMzyf/A9Ohyf9LdReDDvWw+FrYMlXIYGBirMNZxWodIINU5OuhC8I\nSTLnjDqm9DIVFbbbqNhKEWEAvSm0pvypz4udX9YojcvzjZSJ7Ki2FJXKIn7SPFwE1JuyQMKjUJ2m\n2/HoW2fawd6TCWTekvAobZpoJcG8YHBCXC8hr0kEvowR/zp6x4YSvjoaQNqwLwf3/tABFax+OSGO\noVyppQksxtlZobyygKZJHOoc8L83EdDqYeq43MJm465pcah2BIdoh62rqNWhN/KgYAYDNnfMM5Ap\n1cm/7JSgOgmajGc9McJLbK1QRSEgsb9Kup8Uq9Zxtbo2glC/6AAd/yrX2GQh2wM0DwWWQGXSRQJU\n90BnPiSWOQZObcGpBqYWwS8r8DDw6bXXwcR1bkbaBfzoezD6FYc8x46FpoW8C4s+APV7YNvD/KTz\nfn6yDEwd7CrgaGBRC+7CzXB5DtNfBzMI0yeTb9jsCk9WRyHdCScOQb4Q7rgAPgn86zzIarDmUlj+\nt/Dgn8C3Uthm4eVLYD5QnYS0TZeD+BzHYJcuhh07Yc4AIyuOJNnS4qW7f8V/fPJQ7Hs+DWs+Bub/\ngH2677iw+Fy1FYRbsSyLt1lqoSaB77nMtlAqPtJHLgcyZQGsi770mAdi4afQLlCEYUllK+ElLZg6\nppxqDO5Zh7PejESdSy/eem2HjTUom5TTRCWpRvhYQIRV17P+mpKU0DXO3FDLg81WL24o6dMyVnpM\nAcoRp6lCSDGWFWCLWHUTxvnj4biS+x0QkjKYKa56tqj/kuoq5oEGTlDO4BrbwQnS5ThAM6SuOe0/\nJ3GmgoNxTD1lXPKBmA9m1H3ExhqXKtQk6r3YW4XhDIFh+pUq1OqSIQhVmfUTKMVF6nJmEBivqAcg\nhi3CNYUPGllYGFAQsTB4x7jvv7I4r2F6FkwudrAsnXGhVlkC7cMZtc4K8IS2sw68sOWA6WsMjAw7\nRLFhGfzJE39GN3ktLJ6ARgIPnQePvgxGl0F7LgweDCOXwM47sWyFjV921VUWAsfh7Dd3N6H6SjDL\noH2ZY4opIF8Od6+CbwB0GR7bxGRnPly1Et76KqjeCnd8CMwz4XnAGQYWd2Foh+ug7hCMWywpzKlC\nezls7MCf1shX1bl042Ek/7IA+2gFFvwZzDsV5rzBFQrYCQsH3eW6VYqgf+s7PK+UbYYixETFllUb\ndGaWLK1SaB95EILaeaX3x5Roga61mszFseaVMBl3UiVcTHmytybYSYUkUL6W9yLEruJfbcfEs6I0\nR3i2GTmXZF8J6RLaYwgOMFHnc1z0ijzmVKXM7zKG5NpS3NrYkNSREQSmgAydmCD0m6xw8JvQAROs\nVZyNVeB7TN76xk5cI2v4lZBxKbAQhPOQF3LDOPvMXJyQlbDI3F+nSlhiWzq4YZ39pWV8FpgtyTpA\nCTTvRYUw40I5S0tI9hXhVLhUPBGo8j2mOHQLgiqnvc7C9FKVqh+DGAJymTJwT4bz9mVHwu4aNBp+\nZHagvgcqy2k3YWsVjmu7ZzU4+Wtw/T7UgZUN+LPj4AdrfkI3hSfW4RubbmL6vrugdRCMr4Cx02Bo\nATTuAHMo7PwdGNvmUPKKB5wjae3d8NCD0F4L6WLYdSbUfgLto5n7wUdZz6OcTJtxYMuJVb76vk87\nx9vOr8PWY9wL+/MZeM0ALDAOzeZ1aNfhFlxPzmRQA1OfwO5Y7F7WoR0WrZ2iuT1jYsl8+NmZcPov\nYPqv4MRf8NKBSaqtMEHJiqzdxE0ytSx4wRPrhJ04VNrGCSgdwlQ4t5RwtVry4GytxfItCknFamwp\nOcCfn6siOyLoi+QYE+y5vgl9Sdo+4CdoOS6uZwpBVTd6v2qnmMOEBHy0o2tIm+tZEPhxNS2dMSiq\nfSvidYuKDpBtJghyHeGWmRC2WQAXtd/CrEk5vwkdMMHaIdhQJwmIVdN8wuoCB+GcV5rEBDBlghrf\nxQnaKlAzDqUmlIWlXluraVxFLRFm+3JEyMwOZftpPyoKQRuKmgFa4MoAlNg/CQETEkaAMiqQcBZB\nOol1zNbPaSbMuS3BGajbR0Jzjq9wMwi1QRe7WpkEVsNuBxor3vQhCEau001gfgdeb+GVqRO+w3vg\nVYfCTw7/KhdmQAvsfWDvfwckT4Z8CJqvhO5uGLgI7v8MbM2dpF7yeXjo81B7EwycC5O7YfBqZl79\nElZdtJWHgW8/YStbv/Uhp9rs+VuYOQ4WTMBd810ntIE9KdQWu4fejWOYJy+CW7twcoWBu3aRXTJD\ny66CE45g507glhE3sZyewP0LYfRzkFzBx29+D8l547yzSVFPt5k6rWBOu7yksrx/rUK20lCmTlPs\n+CqFVPVR//sdJ8wgJgqTeZNAJWhHcc3Xol1JQNU9bbPBFCAprwXKVM/WUWrStHdWtdJgv5UqXnr5\ndbGVatL9NVOZfcyJxqZJT1raDKbjaEVj0GOziFP3vwUvyeWbpjd657HSXuux/k+RMcb+uXXCdBBK\nQf9Cc3DCV9bEkljXqjpmKwG9guuomg12z0nVqbsoRx+s8lOVwQllUedjGalrUKaWov5oHIcqgs/0\nOa+I3/P7rQnfY4pDdGRWTSjneEs9BWEkqZuqZ+EiP9rAT+vwJ1uAX50LWz8Mk4POHjL/QWfTNDnY\ncZj3Ri54FnxyytcUTYKdbqbitsm9uh7B595uNe29snsM3FCBP20CVx4P+ftg4gkOSS64Eyoj0L4G\nzDTkX3fcXAc4Aiqvg8mPwqZrmfvBzVTe8jNGT7wY6u+A1pNgch40EmofaXLEhhHuecexsMI6j2K9\n63pmR8WZGsSzeT9ww6M8rbuFu+ctoL3AMLPsIDghgTOvgc7nYeYc2PL7sHQChsdh/uUcufrTPPco\neHvbJT9kabDPSahRghvk7Uhg1VWsqkEJR9nmf5ciASKSvhe+gCBQC3uu2l8E6ytwIGteCWlNC0Js\nqqwoIbZLaapGfkKFIFJeeHGUSfzoYDeg3FqmkCfByy8TUuwvKIrZ2P79olPCRbBKKJWg21y1eza7\nqYxZQbGyLQWenfA/Wo/1f5S6OL4XtCpqehP3gE3CKgNtnClgBPfgTWYnvT45/h7GX3/Snz/or9mw\nQfgIatXMrL3yQn3z+kWgq4EiA09eXpWyzVS8t6V7UmZ8YW6rmCwxbhAkBKZJ6BXYoj79X+rePMqy\nrKj3/+xz7ph5c6jKrHkeeix6npupm1FkejxEBAEVR3ChqKCgD0VBHk8XigNLHiBPBJn0pwhoQzfQ\nzWQ39ABd1d3V3VVd1VWVVZU1ZeV8h3Pu2b8/IuLufbMKUFpfrXfWynUzb957zh5ix/5GxDdiJzoG\nwkkbg6yuKA8YWAZJUxVrBaZgX6HZKkl/u6yClWWxeGRBLjoN4nehksPyMqzvQKsBb0kfk+fVjkF5\nANrrIB+FypBK/S9B6X7wt0MxDd17hCu75TvM/vOd0PkCtD8MJ7fAyMPQHGbs7TnXHj/MLS/cCpce\nkwBcPiS1DQuLMiK78yDwkIfccZKEV80cZHCm4BuPH+PkPWPsPfVsih8pwcj/hsH3wEMfhOEbIflx\n9jx8AXse/T986LK9/OXqOW52/ZtntaBXhtIsEpvTLkgdVYKyixWenV1sAShn4Wwd7D53gH42pleZ\npRL7PT0hyBPp3N7/40pPSfS+ox/ZxdZTTlB8cbp3jBTNh2rWF2g2FCEN1rrXSfrdVwVnKr6ltRes\nfdYvc2/ZXFgtYlsrtueU1SLUIe4LGscJNYW216rq/Weg1nOqWEvIYBh3NdHXIwiYmiMkDmzT165+\nfg1istpgN3THbbrgFrDXkgtuA3MDJAhn1ZCHpZXGmVNEE2tyZNzVvn74/omyL5kwW3mz+Jygnv8O\nQvR/iW/KrjiDyugglkrba+BZfo/R6wgI/Pd3Q7oAviEf6gyIwkvaQBn8au4rJns+26UL2BB3LKiN\nLCyqhYr4Ydupum6SNTC9FroNQYIuB18CX4fpi+FUAX49nLccOq+TqFG7gDWvhYMpZJ+HhWEoL0I+\ngvstOOXnueWXroId3UAtmU0FoZakG6z1sgPNoqZLyl4arKXNM2hyHjnP6R7gsU9P8nffvoriFz8J\nm+6BJ90NZHDoJljXhuwDtO5a5OfKb2P9jV/j82MyjNVCEHopOZPiUzgYylW2Eklt9U58l4088gkW\nYTxj0/6M+U/65yAuSgLBhP9BJwPk0RzGMmi1V2MFWtDPd4VQJNsi8WUvKP5siDBPoqNTIpRcUVSb\nqYLNI45q7C4oQUDjLHGRueCWKhWygRm9yyiPMVK1I5HikwxsDMsFpG5JUsMPjVOXtP9cXANn+b1J\n/26xGjH37f8DXncWpOEj9B8Rm3D2gtggftSmE/9rlzMd1KYAY7MnFma7jO5hecoQAlNLrzga2vc+\nIekAQkpgr/zZEsSdFsHXZXnZS10R36PbvaDXBg+Xr4Hvnv8g5O+F6m/AQj18MF2EZB6yISp+sock\njKyeeFng1g5rfzeRFERbqF0n77VS2OEAfwAerQusvXgYlp2EZAZa/wIb3gkbZuHkY8IndW+F6keh\nsQ8m3gnZZeAH4fRyWLkIzZX411VgZkyqXjcS2J4J0m4DjwDnIbtzbRZmG3BHCnsnqXKCJzPDNAkf\nocE+VpO+apihtQdo3Aezv3MYt2k5ft2r4RoPawtBzuQSEOv+LhP3foHLh+/kmqu/yV/UZc5HOxqJ\nTvQcsiRk/cQ1KTqJbN6Zjs1QJkqlRP86js8diystxScRlJFsp6oWDompdXaZ0oyDRfZ+/Gq/WyT/\nDApVJGs+stktP3/pFVO0LNMrvl83EljLtLL2n42XuvQRvUM6ff/nLQki9dLOmAHQe6QLLgMbk3gz\ntM+3/l9WrCXoHR5YJlCpTOHNIWb7Bv27AE64UGrwqH5/AwJUMidZVks75PS+KQHVVnTGIlAqfDoX\nTHf43r4Z81N9vyBXLBS97Ck1b2I6VUI4ysRuFz/XzDKbfDNrem3Vh5mfzGQ/gR7J23lYncEbqvCb\n58OpUx+B9BpoPkukMB+AilKVioL2LBwdhZFcFbkq1F5KoOs3c22BgxS0tkDPSiC9LKebvA9YDXO3\nQ/c+KF0IpSE4dRXwHDhQgjU5XPxXMJ/D/vdBZSO0N4iNvOkB2H2pnL5aFLA1kQODzpuBgUlY3ADZ\nYDiCYhFYHIF7ga+cZkV2kG3k7KHKoWQDXLcKXjlDd+UXmfaLcMUD8LpDeL8FPprDXTfCA0MkT99A\n8YxBgeFbH4DWUyG/iLtvO4/rz/s0bF1k6DQszg2xrDbHi6owXIPlFXAlGE1hs4eNOj+jevJj4jXA\nFAVg4voR0K/4lpqw5tu02qIxkrWNLS36EWpkSPVAQOzbdIR7WVtiUBC7D1z0k7sQzDX/c3wZWjUf\nrrnbzEp00Vq0yxCqI6TfOoLrydxoPc+Ij3zQ0WZUVmUbB4UTdA1FD3TRGHvOnh32H73OKY91AAEX\nBeLyW9qfLoF7ugFRtlP0Ky37/zICIrXv2iS2CH5Jez8mB4PcsCA4zr9X7nZcciwOpJ1tYSw9wjtX\nPkgaOfNjkz5WqPZ7/Bq74UyoCsKbS8sW9nx9+t4VbfjlAfiDLQUcfCsM3Qh5HdK2jIofgNJ2yB9j\nMoHtTsbNMmnMXLRAmpmQJvRdB3OJ/HRSeHEGHK7C1Oeg/vNQej2Uj8DcNTAxBosePu7hzffApa+A\nx8fB/TYs3gzzHWnP6D649XLx++w9CCvXwcV13Y0zehylHBGoo8ABRLLvAtotPHAXo+CG4OrV8AtH\nYXAXLPwjVK6A5BbcJRNs3A7+pXDkOHS/DEX+eXjbJvipVHaX6l4gg6m3wCM/Ars+w9zl/wSjc5z8\nxr/w4W4K7kHh5Ca7oFHAMg/rOtLe5Z7rB+FdBexYlDGKs6vi40Vi+lxGiLIbwrJkk/jqBZIijXm2\nGhR98QKVG/OJxjLYXSJX5grw0X3aSfjO9+KBg3LBoVcPuIcerT8IWCj5kDoO8r84eaBESF21Y2ji\n/rRSBRNF0BMGguyeqd5v6Wkiuev3KT+R65z6WFPEgluJBKUMwYEo0XWIny5HaFfWaU9Qmiv0taav\nB5xkWNluaHGMwSWTfjaKlE3CUtPEdjFDiku/agqs1y/fT62yXdPM+Ljqejd6Vqw84/ztmAtrbVvq\nejBKl6EIU3bOOqbfubqA5StgajyDU0dh7nwojcsKTzKoXgbzX+TQSg1MRYEZqzCfFnJUlXdSJOvr\nHiamYXIf7Jm7mlZ3NbS3wcwW6FwFB5dDUhMEuf9JMOqp3vUoncuH8B/4VSjdC48/C3g1dBfgcBdW\nVOUMl7eugMmTcNU4bN8kNK0MmeTFZdBow0JZjq98ECn9twX5+5Qc23eSQZGUy8bgRV6dnrNQuwkW\nPwWXTLBxK/xxARsK2DsGx34Cbmu/gLufV+f05Hb8oTIcXwcDz4PhndBeCZX/Bg9eDPwujL8ABq+A\n2/8QvvERuDET98XCFBzch1QJ63BXY5Jn1T/IlZfD24fh8mbYpHLXzy6Ia6WebbN3BKsk/pfJ1VKk\nawVMTF4SBO31MrRccF3Efn27qkVQwiUvvFMXyai1wxGsMGPSGD/c+xDwM7eatRmV8TyBrhd59YTE\nF0dAx7XumfTC2OqLXWUDWUD4tkZKHtBgrLnizPUXZ4H+sNc5VawOQa5VZJ00ov+10GgfgZY1SVC8\ndp1A+K45Ql+EsOMccYJkK17iF1ZgBQTdVnxAryakZrr0TgXwS0wFF875sSveyY0KBf0mjV8ivPH/\n84TeOe6mEE3YbZHEKX6Of9+uakJm7e86WNGFHVX4+kgb5nZDcr74KLs1ia77DDw0HCykQZk+UIGd\nTehOw/u7cOr0KpjaANOrYW6r1FVt7UCKs6wQztUD8vDk83Okw/Nk2+rwwCxsKzN27e0ced9t8PgM\nTP8pcCE0V0CnJStgPoM/rMKpfZRpk91dqBR4+ExVXAFrSlCMwN4yfLkLcwWsKAtf9zRQaJqJ2wiN\nQdiQiGmUtMDVoZiEDYepb4E3prBJK/lcmkkTbkhhblmTQ8t28fUd8HBxH4/d9TlO7f4H2LMJzlsN\nA98A/3o4dS1M/xZc+0K48vXwsR+DTy6DbWOw/XxBEOPA6ALFshdyz/H/xU8+/U7eu7zghkLr1qZB\nBiEyXwt6aa52Eq7JVa4Cs3Sz76VAF/3urVguCoKbpwcCHD06k8mMyaMVbY8RYEG/JWXtW6pUDTQY\n8izi+1t/iYKA+jwrfmSbTOyXbVrAzdak73dRWUaXBZyz6H929Yodad/nXXA/PpHr+ypW59wG4G8R\nUOmBD3jv/9w593bg5wj8/d/23t+i33kr8FpEL/6K9/7Ws917GAk+NRFkeoT+0oENBIVWEOVrxVeW\nKlboL8wCopRXePHHTugkjaK7IeKcNtPDJtf8lUUkOPGgx075s6EH+wza8Rjtpj5QQmLSdkxZiXdT\nCOgWBO0WkelS64bCvPHpmvGVR0LkCYig3oU1NWC8C8f2Q7UDlMQdkI1ApQSpuFych2/V4JWnSrCz\nBsdukmpXfhZa18LJtaKoBpBE7F1OVtRdngoZnYcmSErTjNUKTrRWwfwQfHQeVzrB0Sf/Eex8PnRe\nL6cYlEswfQSKTbD2EPzGKuAIDeZZoETCBFvosI/t+KMN+EcHNwKPDcFBD7O5jPaekjjjTwLdDlCG\nZXWhg3R1AErTQAb5p+D8ghvG4epclFuu1of5AFdn4iu9sQOLCSxeD5+98cf4q7+/mcUT74PGleDH\nwS1A5yNw6h7o/BG85/3wxy+B978KPrsZVtYl/Dw0ADfvgG0fYObLf8nPbPtrnnxDhw83hYprlLlY\nrmI2ZbkIm/jiktVrymtp6mZ8YGB8e/u8pej27kNgy4AgS0OhpuA8gmBbafiuVYcy5kAtAiCmoHv+\nS9fveoOg/E2eez5op4E+fb+p5n7qtViOIs5KV9Z3nJxgll2cUWm1B3qHcUbjlSEB8Sd6/SDEmgG/\n5r3/rnOuAdzrnLsNacefeO//JP6wc+5i4OVIXZV1wJecc+d7H8cTz7ycfji+LO9/GdLR1chi/14c\nswVkIdSQoNe8EzfBOMFXu1IXzRRSRyBHkCsIa2AhkZoBZo4MFlEUEvHbOIKT3rJrLEJuftaC4ETv\nFWjRy+oB2GWKtxR9zv5vqNX4o3Yt9Q3FCtkhG0ZLs2Li59j9rgA+PQoU9wBN6I4IB7Q6BcV+GIG7\nPXz8QcfuE6+A2evg8FOhPSzlwhyiTOeQXawN3JOy5bvfYf/wVlzzOEPrhzn19scpNr+fExd5eOw1\nMHUzTO3hz172Bn79rkHy/CUwfzm0B2SS882wIYP/sR7Zs4eZx1NjlhaDZGRcwT7uG10OW5woy2Hg\nntM4JvFuC4w62WkXjOnbhekOrK1IjYJ1k5AsQvsbcEHGpWvhpwoYyQIaayfBKuk6SYqwKPRoBj+R\nwDNfcjt/Nf18Pve5/wkfvwBe2ZRJr10G81+DW98Fzz4Mr38bfOQ1cMvNcF8LTiyDmTJsqMFT3gSz\nT+Xbg6/kwEWwrROsnXiubfM0n6ohyqXE/5jcb1cSyaMR8g0s9OQnkvOeXzUJz7NbZonEOuMAV7WL\n1H2NlKsh1Vj2ekWrde8195YFRbHZitpozzalaPn/pmCrS+S7nYZnG/o3F4ttKIULBa5tEyickFaO\noRnfPPHr+ypW7/0kSkf03s8753YTdOBS6wPgxcAnvPcZ8Lhzbi9yqOZdSz84Qr/pD6Hk30pCMgCI\nspwhFMVeerBgi36FW0YoWAte0l3L+qxYuzcJgR0rUZggA5wgboNYmZV8CNRUu/3HVic++GmMEO2W\nCK9dccHemIJiQSEzq5YuEAtmxL5UCArdaxsttXWg27/4YmHf6tGsjDuhdhqmVLEO3gmbdsEDa/jn\noxvAvxkOXwoTJbgd0cg7PYw7+Fobxspw7DiOEwzfDBPP/Qr84i58BZ504d9quAAAIABJREFU0e9z\n9Sa4IIGPNuHrcw/Dvju4/Kb7+ZXvvBaaF0JRg8pRWBiBzY/Dwga4rwyZzeZpIKFFDcg5xBAHGYDT\nXTiUyMQeAvIuPh2DS+syqVmhaUBVmdnBEtwEbJ+C6iFgERpfpbwNfqwM52WBueEJhZbtstjYaCZo\nqVIIA+Vdjf1c+aqf4O9G3sbeIz8tQvtvF4tTf/F34Mf3wn2/x8BLf53FS28EdxWMfwgqTSlTeeAN\ncO8byY7Cwg6ZeO+DDBgybOkz46ypmNhvcmbB0KXVzs4g4C/539ncBKZwYuXtOZO90k37A1AQ0r57\nmWmm3AmurCxRyyDpb7vVFjZL0tphdQ8Kwji00nCUt7XTnlsggVd8iAtUCinoYpmElmiQFjDklKHk\nxNh5ote/28fqnNuMLK27gCcDb3DOvQa4B/gN7/00kqEdK9EJzgSjgAzyvH5hwItCG0SUKsigr/Ww\n38nONocIt2VrNRDzvooMRC2692lkYMecPKOD8DgPOCmuZC6BaeidEGuHF6b64xE0W49MepOx3GmQ\nWP1JtQjdWCEXq/W49CTJOL/cTPTYB2bCY1d8fAQEH5XdyyqkW1Fg49caadwqchlaMDfGSA1mRoDO\nDGQe1t4Gl74R9q6DxddD6zI4skMk5O9zONYkfXAKWIZPytRHHiRpF9R2NJh9x8sYe0HGb5bhOl0U\nAzkMK3l//wB8fWMHjv8eDxx9lpw59fDNUP4aDNwA278FfjW4VCIH5QQR8xKOGWpkNBnGMwJuVFO7\ntFMP5OCbMDIuQas6wtrvJLAng4EqPC+BLR4ax4EStL8MF0wxPg5XFAEBln3wX1qh5MEIvpivO56L\nl+ew7b+9gzftfRdTt36UfMd1cD9CiSiGoPhVFid+G658iOds/jeeioDtdg4/e+iLsP6NsvijCJTx\nXhe14AuEEyxSHwKbZkrH3M7YpI9Tba2+hEM23d59S+F0XfPn22WyW+0GUn/P76tCZW4vU6wx9Q5t\no7ncegFXbUcrCcrNUG28J6Q+KhNIACyWVOCBdpleogVo0oYPwbvMFH+kwLMkjJOh5cKLp2adD1bs\nE7n+XYpV3QD/APyqIte/Av5A//0O4D3IUfBnu84G3HpBntPIzpLRXwIQRNkuQ9gzHf18hwDV55Hl\nV0fAQoFUpmsTygIu01dTnAWCZguCEj+J0jgQRZ8Bh00Jm8J14hpICGT+DsG0sKudQifqcRIJlIez\n4nwTGquWbv7bShH8QeZXsyIaFp09g92gu7H5mXI1vTJta6WAVR6uS+DWEeD4A+DWwYo3w/1/CY9d\nB59sw9EZVnUf5Liv4WerlJml9Oo1FFOTdFdNs/DOt7Gy1aFIy/zpxoyLM1GkPeRBiLjemAHrM5aN\n/CutXXvJT74fvtmEyYvgl3fBZAHrT4HfJJ0cTeS4VzLZ4Mh11jsyWqMIUp3x4DtADbJSGMgETc9T\nBbwVGJmFogzJYRi5hfJG+EVgpAhzY0EVXGBC9PLvCciplQbWhwOuacO/bu5y6GWv5CWf/Q6sd7A4\nJLVhSyXY9yGYKPPQs36Uvx6TkngPlwAehVqTZFQ25G4SAixmvsdV8WM6k5nvcX2AOLgpwhc2aocE\naxsdqGrKYrciBIm8BElJlBIEczyWaeiv42p+3F5Q1YfgUqEAw1C3fSe+SoWsKUfw6Z7tMkWeRWvM\nEVwbuZP/lZHNYimK7/FgfSg/2E2iQFoi6ezfceLd2sh/TkT/B97DOVcG/j/gY977zwB4749H//8Q\nkgcDgjfioNp6vgd74V/fLq8V4JKb4LKb5O8jiHIbIZwEsBqF7ohLYA5BqjH3tYqkhlfRlEMfjnho\nePG5jkedbhISCqqRIFhqW9nJ751YIFRSLN01FsA4mktkukC/orUrJfhmzZTx9PumOkk/AombYnza\npYErW/y5E4Ve8pB0hTNZUV9tvQsXl1Wxcj+svA4m/xxufTqsOgUv/wJcvsDxh1O4qUn9WMrl/7Ke\n9p+/jPrJFsMD8OaGjDEehpuhOHGqY2Y83lohwvqqGuz89pXs/NZ7YHQDrD8Nr0+h3gY/D74OaQeK\nChy0ShDLdbYrOmOL4Bcgr8lkT+QIcbUhKHWdCsUKZNdtIbt1TQc5OQSVt8N1TV45As/oSoopLkSk\ny/ZRH+bV/Hr2Xj1CkZaPXvKwcRm8+1VX8JYv/TQ8cBXkz4Q/XgdPnYObjzFx1yd52gW/yHu2zCg0\nq0JllqTUj5KXzqdtpFmkMNIkKC77nrmJzAIyShUqa/Vcind3y/TquhrbwKhUrTS4AKzGr7XJlJaN\nS2IyqvJsplF8UoHFFOKAko3dUn+qKdcYtZoitCs+pcMyEVP6Nx2HvG+ULFO01SIchR0/Y87B1B3w\nlTtk+/6+AaF/5/WDWAEO+GvgIe/9e6P313jvj+qfLwF26e+fBT7unPsTRMzPQ46GO+O66e3hCGwI\n5rm5BmzHrhOUXt1D3YmiPY6gsHUI6o2rXDn9TtkL6jUWgOoBDqH8VhXa+NyrjirUQd2J7RXoocEe\n9y4R5JAtifZD/44fVx7S28jkJZosgJaaK868T3ydLRvHxqmVBme+Kel4odl9HSLwlztwQ+BrM+Ae\nhc/eCKe68Pw7YPXvs/WaLu99IRz38OlFOPgs+CsHfoXw3uudgFhmyhL8OVmVzKul6b6ph7cuwou/\n8SaY2ggbH4HnboDaY0jduw5wAqqnYXEVZLZ9jiBbZYEo1jLUhoWr+jiwYHwQL+6DRUTPnkYUqpk0\nwx7qh6D+XrhslpePw095esVAkiIsNLdk0SVI0DI+WtkQeV3dL+VCNpksgec72PGcj3DbdR/j69Wc\nw92vMvs52PKBU+x+/cXs3/1pfqH2Xv73llvAXQA+Jymr++kscrJU0ZobaKlSMlPbIv3Wh7gAUOKh\nSM/crE2GbXOJ2QAWcbexsaBsXDvCEhAsODpf6ld05SJYqI4gHzaWsYVj/TYf65Lu9zNrou95Ty/D\nyhIP4jVhY2GvRiNcdCIy198EG28KKfS3/z5P6PpBiPXJCGNwp3PuO/rebwOvcM5dLl1jP2JV4b1/\nyDn3aYSenQOv99+jLqFDAEUZkf3jyMBvRASk7eTvAS8oddaJEjWlu9JJMGuGoFRPE3irtpsPeEG+\nyoCUc62cYKEmwsxZpb/XkEQCE2wzc5ZGOKFfcZqfyybczCTzadl3HEFI4zPdLeBki9r8TuZ7sgUW\n+67iQV2KWs33GtfxjE8UKByMF7BsEKaqR2Duu7DnmbCtED9nqcLTqk3GMtjYhQurAgSrXvxzdltD\nauMd6UMj7w+E2BhmiZhhq65+hL23XgfuU5C/GfIGVKYRCXBQasnxLfeOwGITYQaYN7EEaB3Zg2jx\nlTIiJYMSmr4N0cWPdQR6rCnDNgfDk5C+Ey7cxSWb4KVOcvVNsfaSLtRmbuucVyKFEM95Sv+5UQmh\n8n21gI2J52dGcn7Og3vd05n8ZWF4fWfhan797z/F6W3b5PvJJsg7JOVgDqde0FxMVYp96kvn2d6K\nEWuWhMM1q5H/sUiQExCWyJGlxloUvVKIW6CThnvafDvE8ollOmYjtAmBtljuGoX6N1XhxSyEpZzx\nHgpfEoSzdPDUnxmLgLCmLOZg6b/mJvEuWgMI4NqNxGv2oQFKQkW9J3L9IFbANzg7dfSW7/OddwHv\n+kEPLgiKdQV6fDXiN/CJLKkaun6cMGuWFkdYo9+fQAVE77HBhaJHQzoJ84gv1SFW5CJCu6oj2CjR\ne3X0eUuZBzbppkQLF0jJ5gzvpGcqFhMUQ7rx0dVd5PdBrV1p0d8sDUESqwIUZ6bgtMCF/l4q1GWh\ni8OQQEyKNvPQEwUzPJA+BK3z4fET8KJ1UAzAomeyCwNq2g9nAbnYvSHwFc03Z+jYxssEGySiXqQK\nc/Nd0PgunL5Gag2WlUiX5pJYv64KewapMkWdLjMWuKIkyvLpKjC3DECnBlfWhOWwG5joyvlb9VRg\n+bZ5KP0h7Libi7bC2xys7QafdYzCOols6sagMHdGL+ChfWsmAfXEjIwen1J3Q5urjQW4YVheuYdG\ndTfzX38Sf3YJUL4A0haluixE81UayoqrV5U8UtYhCWNv7bExtjUQB+MKVTI299beXOXF5sprvK+V\nyLHTmcqyzbGZ3TFvuqyBv3ZkmpuL2xMYAWkhHGCT5xgF2zog6n8va7EIKN7Wn8UrbO3YeFnFKxsT\nc5OVvMyXpWMvOrFYPaIjSrqmKjp2df4vJAj8V14ziHKrIcEph+wWU/qzRl/nnLgH4l0kQdxmAwiS\ndchAVwguBYcgi6Yq2ZgfOIJA/g6iZOcRBLsHesJWQixO52Sh9OoCuP5d25zq36sgS8+f4/vfQ9tk\nEVcIdU7jzJtaNwi2Nc9MvS5KA3PR3/S7K6zd9vjUSwS01kUOaR0HJp4F0yuh5KBYBcUAmQ8Vb50+\np9Q9E8HHl40LhBRBCD4u5xMxDyo5ND4DJ66Bk1thtAH1CbG5y16KrBwdoz3foc08MAbDy2FVIs4l\ngEMeOiU4L5HVkADHVKPVy3BtAld3Yflfwo4vMH4BvDmB89r9dB7rT8ULUu0VroFe0ZvuUnKp9slk\nAYKf01CkjZnRnxIPQxXYcvFX2fW5n+POOxPwFSidoD4AI90wrrYZ44P/0BRcN5JPU1J2GT/UdKbz\nws4oeyljWMrEijEfqwVADSjEZfYSo+upgkpUyWWJ/M8RNh6gd4w7iFluMpg7Gb+qrRcCEj2bGFkb\nKgW9gj9mWfTuT9j8jEFh/7dNzhEsPAMpp1N5/lokKXA0umcBvRrHSw8V/WGuc6ZY5wmoNSGUBhxE\nTPxJRKgaBCXYd+mIzBC46ikyOHsQALMW4cJaanlbn2dVtIaQiZhF0Ow8ouhtHc06sUyNElUuwo5q\nu2MlEvi42AWcqWxLhQh0H2pIIqI1wdyJj/g1BWu55Ko+elfXaUpkIsrPFqjt8naZSWnZZWmqgzB/\nJVyQijR0lkPHcSqDrCJF+WMkYChtqW9saUAgvipdUwItuL/DwIU7GRzbyYnlL4OvXQ3XrQCnLHx0\n0uY7yBaYyszNzsGqEdmNbwOOngAKSakd1Em3ayyBqz1s/Ras/zvqO+DdKVzZDqako9//5hD93E2C\ngjQL42yXISQbVxtvm49SNE4Q0iu57o/hc78E0z8DyQDkJyDVwj76WcvDN6VZ9sE0N3+wZV3ZvJjy\n7iZBliyVM0cRXKpKrxAShRUTMiRr/bHN3iP9j+sJ4MJ6yJ2sJftcLGtLubGLS3y75guOr7gmRSeN\n3C1J8OUa2u25TqLXkrqd2qphbQzMTVL2sNsJALP0+Av0d4/omqXc+h/2OmeKdUpfUwLdyXL9y9Dj\nl+bR5+3wwfj0AFOCC8j6mkMWyElkkE7o92YRxZkh/lQNhbCckHhQ1u8a6rWMrTLiXnBOkYJOuFf4\naGahXb1FRTCfev9ToW1HCrbrJaiQQK+2p+Vau0I6bYJvsmZFXczBvzQl1tpni9sQVNeJL3S2rO13\nwB0r4Nm2quVY1qkMutVgVhoK66Fp+mkt9uA4PTIOxKUe0s5+qJUYmoMVT4YTk/8LtnwUDtRg+yiU\nFmSwcxBbf05noS5vTim94egCeHXkzCMO+uOdMHM31uHyO2HDG1l95SLvSuH6pqJ01+83jNkcvRNX\nXT/VyhAshCI8iW2o6pJZVH9zaYlCiX2J3QQ+m8KW2T1w+tkwPAPFLlwpKPKuk+BPvHEu6txakMmG\n2zZ5T5CxJJIJy0RyiLzlZclYLuehjisEilQr6TfRHUH27DK/s+Xl981/jLjtfz5Yj10ilKwylNl9\n6WfZ2H17vG+C5WD+UTvfLkWUfysVC7dMCF7H/tUyobDTmD6jTCiaD/3n4T2R63uBjP/yaxY9zw5Z\nNk2kc00EgY4TfKYVfe80olTT6GceQa2WPDAf3ecA4awrQ7Y2YVV97gpCPVjzs3j9ewwJhm314m9M\noJehUy1kZ48DCEv9Rp4QnbT/G1IqFYG8XzgharcSCVwYKs2cFEJZKNHLQql25dlGJ0m8CHu10NOs\ntTG2+w/mEr220mtGMHcJjFUAdzN8aQGWGQyoQROmC1mMHRfKvPVQKyHoUajCMIpOloSNx8bDlJkr\n7oUhSKs3s3kEuOA+2PoZmPQw3YB8UCbjfDCPd5U2AlNPw6l5OJTLKmIUGIJmC47YdtmCq2rwozth\n2U8xeN1p3jEINzRhWUcWWjkacwuC9HyBOpb1brBOzMdnexAEs9+sDuMq14qgXI16ZqjTAkHVHCob\n/gX+eRw4CNU2pSq9fPVyNGe2YduJvjbupkjNNPdO5qKpG3bfhqd9bafyM5fCfEXm1e5lh/1Z5X0I\nfYv7akkGdk9rT3yuVgwkCv1crn7bVtS2jhNueUd/5l1EcdM1YvINkm4+78KPAa4FJ35Tr88e8GEu\nbVzsmQecgDXLzDT+u0d0wwCqQ2J08kNe57S6VQsJ8BpyNFpVA+mwQzpv/tWLkAYnCBLtItzv48jS\nm0dI/xOI0p3Rz1bgjBoDQ4gCfky/F6NXMwfG9X2ja5lCtUyQIhI4Q2l5Ij7MpUValqYVVnzYrb2j\nd7hcbHYvXRxm0tjVTMPi7SSB7B6bVHaZGQf0UgxfXIKHDmyGgROSnjpZE7oFjnZXfcBFcCF4bbfz\nIcvLng260dDve7PFK50+AIMnce2H+c0c9myFRw6+F67+UZgYhnpDPrwBxJkzTcIUvaTksYZoqLkK\nSlCFkRLMKKN5yxD8/MOw5rVUngJ/VoFrWiHJIlF0s7wTgi6WSx5XL7MAoPlIbdMwAB4HUExJxBlZ\naRE2RwgLu+ylZvYrXzLN39y+GhY+CcNPp1wLrA/vgzKpFmH8umnYBOJz2Ky0nil48zuWtV8mIzGH\nc1HlINNN0doa9yG+DE3bZgoaCFLvkYlWntAr7ZnqOCw9RdUhcY9FH2oo2zjNJzCkLoVO2u/LH/By\n73Yk/wWy9gcQK29AQUPZR5WvomsYQat2iwb9FfPM/3ro7MPwH7rOGWLdg/C0ZhGX2lGCiT9HOFF1\nhGCKtfTHDEQQRdjWz3T0XnZO3mm9T4dw7MsoMsAL+hljD+TIwLYRRWvvaxIkC052TVsAcSAhRgj2\nf0f/Z1ppOH64F8VVhazWfh+1qudLciEab9+PjyfupR0W/e3Kkv4foJe0YG1b8MC/XARjK2G+ptt3\nCfIy7UIWhqHjHs1Fn9lWlGMLzXK2LaBWKoJSqmoH3ThQWwm1d7O+DT9fBa44AcveCMtaYv3niKnw\nkjKkw2Q9foaHhQIW7OB0dejMzMkMr67BG6dhzesYvnKWNw3AlR1xexQEJGmsDOcFFda7qpxcGCNH\nMLFtDkx52PsFwYdXK/rdPU7/33X0H2TnxfpYPfAJWFEHvwb87Qylcg+TH6u8ZO0peznNoVyIv9ra\n4KIN3pJNDJXXu4GU710ofrJQEvR4uiy/Z9rGVOfMvlfvLnFruDA2ls6aRvPeduKyyPRZi06UY9PJ\nTwaccgJSZvT3o06m/BjhmPpmEhRwHr0u6H0KnflJBBRNIpbpbuBeF04TqSh6rdkYEapWLUfMf3P3\ntZA2ZYhOmOWJX+cMsS4iHajR86Axr//LUE4g0uEUQZ0JglOWIbvNEYIStGOyZwk+U/Q+04ggjkTP\nsKNg0Pe6+vccPSOT4/qsg8hOZjt24qOaAEm/iQghVc5M/V45QUcvjc/5YFZZ2p0pKqN0mZKMg1lo\nO2xLtAVryiOOlvZ4lz7cp6QmZN3DV6aAY8+AVzdEWitAawiqqyE/xMkUVvvgmzRFY5lIi8rd7PFm\nu4KAetWKfEBEXaeDOb4I2Th5Ak/N4Npx+Pa6r0Lnu3DH9bBdJ+9i4P4V5Psz8DNAC1oLwAIlTpEz\noDNWg9oIvMDD2r+ByyZ4zRi8alHanRIQqLXTqHIlH+bTTG1DhJUC0iRQyXpjTUC5njAuheunQLno\ntYcwETP/hu05XDMHs78F215IqxsqmFlGndf5tEzA3EndgmYaaIcVRWj2PXSuLC06S4P8ORexS3xo\noFM0GyvS2CpKCBu/KVVzEXgPs0ngnIPW/PCwV+8xjJxOvs8FemWBxExmCMWRViHrcD/Sp0HUH2oy\nrPe39TurbVrQNppV20TW71pH71Tko4TDREH0wRCSFno/ss4H9TMGyJ7odc4Uq0XiUkSpKu4AZLCX\nIVXeHKHQtaFTq0w1jwzqvN5rTn8a+r0FQuWr1XrvEqKEnb7G6DdBBtyCZyujNtW8fDf1gVIEQNHP\n83MEhdrLPooE1S5TgJaSZ4gQtHCGcqpsHcT+ukUHI4q+zNe6lCwevwK9Ck1OFyMFzHwbuHsIrkd2\nOo9onvooNGVDeZI2wAILHRd8V8WS56G0mi4RjUkj1FkKy9YBA0263WEKRWm/XoafPR8WTnwUNl4P\njyIrqgRcBUyvgqkScJJRpijwLCdnAsgpAwPwnBSe/mG44H08dz28OhNFUThxy2BK1QX+bjsNUfi4\nWlnilU7kJYpe6Yr/2yyEXpUxfa0oWm1Fpj8oPSoNChtCQGlrAaz4Bbj3E/DUYDKbYhvJglWylN5W\nLoIP244ySSP6UycNqLfnjnD0TihNfDCzC+3HUuaDbQZWUMU2dZNz52XumopSjxEUSRdY7mRdWfLb\nnMreFGE9WWIQ+hlL4/SE4kgnCG65QWRtGg3T4iUGpGyIFoiokQRUOh+9F2dpVpF17vRzy/i/kCDw\nX3kVqpxmoh20m8iAFUgHLWNKyxJTQTpdInI06+et+pWdgDxCGMBMfyxIpSWRe3UFOoRJTgg73eN6\nn2FChLSii9AyWmLz3JCJMQKMU2hBCDvfyA56M1RqwSUItCWLvJoZXlUF3k4F9RYEzqojKHVDq+j7\nll9ua8eSGPIuHE1/HyotSBsykCnirErPg+ZtHHYauNLvmrncTPuDF9bnrt7CAjeVQhGYKtFrHXwu\nW6A4UBFBTiQD7Jo1cMfaL8D8e+H+N8CuBDZrw8XgpEKHDXSo4imAUxTM0YAfqcOLPgib383a8+FN\nhSimkg9FcswfbDVAIZjpcWZSWoiv2xU6VzoXAyowmfY7Pn+qp7QiC8EGxebOEf6XJWICDz1/irk/\narL+V3ewJX2QRls+P1cKG6k2IQRAFXFbERHzzZuShBCEwsvZY20viLKUBNSWAFNOAYwqSavfW9Yd\nxEBBR+fZ3FujHU1ocTDioFGSBbPfhQDwPmsLwTI1EDRCWIfjiFlubsCqvm4gBJEzfX9Kv2PFkmJQ\nVDK50/uv1LYfdRK8XqGfWdDnVXV854EdiFvCFPAYwQ35RK5zF7xqQY/voOkPdoaO/XsBGUALZpWR\nwZxBdq+4CEuXUJwlI0yCmQlG2XQE82NB/7dam7Ko73f0ezP6/DEC6di4pz2Hd6R4LDUV/VzvuGpd\ngLFJCP0cWENChghK+n9HUFSecJ59Fj3LgjAx3craZJ+xhZiqojvZdeS7dsDGunCHWokMUstJRtC0\nFHl4eRKUid3D7t/nc4NeFpb53cw/7BEF/yQPzEzRqq7mWEk2x6ECfr6Ag1fCPv8BeOl6+IcXwT1l\n+eJyB7NDdPJRhlhkNTk7qTG/YhU8I4WX/QVsfC+bL4Vfr4TAVFMd82a2JhZY0/GyzKISEjQBUaCJ\nmrVxQLJIQkTbEKpD7llErpGmmt7tJIyRIeQugvzaCexxMFc/AekpJg4+jf+540GmqmKlxG4XH8mL\nZUo56B23kkfPMcVuG1zhtPaMswKMwmxZSASoHFHZPu1ge0n61E4ExbeTEADCyfjMOTl0wvy0iQ+U\nwRVqDVhAuaNrZhFRZNOIcrNSn56Qfj5HQJmeEA+ZQhSf1RxN9LMZsEM3i7VIquy+yCSoIwrb1nEV\nKRnaduL+sjWSFFBVV9YoAUl3EdfAE73OKSug5+VXszcroK7KdREZcCv/Z6gUZAc8hijKIX1vSD8z\nD70qWAqEqSMT5AgTlRL8KSuQCVvhhZJhwTCnzzyBCIFDFlJXlVvvWGpEsPbrgqijO6OT/PqYokK0\nAMs+CGgHQQxm0hniMxRq37eIf4cQze4hLh+i/4bUDP3YAq8W8vobSQP+Zg28NIHGMXh0tUD8wRIU\no9CGAz6Ym/OlYC6b66GXUUXYdCxxII4wD2aSWtxKALef+dpq5nJBO8MZXN6FD5bhHde2uaPxW1D5\nIjz9XfDIuAhAqQSf2MQ9B8q468Zpv7QJK78KtXfABadZth3+R0XK91n2WlvRpR1E11REpmAOUJeG\nDp5tdp1UKpv1qGTRRuKAWi4KNSmE8ouHvCqZtQ5VnmmIxudOaHDxdSgBHpqB13fgzjqXPEOQauph\nvhxcPAnBN47+XusKwrRAZKabmSMEJuOCMXbV9POzSLBnEKnLAeInXeZFUWeo+a+fP5xIYOk8FBkW\n0rfchSpficrjpMq+xUO2AGNerFKPKHNTWk29n/2dI+tuDmUcEM65Q3+vI+t6vZP2j+SCni+yZAek\nHTNO9EMDWYePu9CuhUQ2c6vPmnqp0dNxoUbEYjRuP+x17hSrOUJSqCbQVuXapkek6flbFhBz3Dho\nhlyXIYIyhAya0arqyCAb2h1DBtiUsD1+nQ+nvNp31yKD0kIUqtfvWcDDFgvQI2zPJz1qes/XO0KI\nDCf6eVM+hkpj4e+6UIbOglAWUY/J4w75TNmFaHWPErQkwNXSRZ4QAldt3biO7K6K9K49ArUDMLFa\nBss7GYF2mXY3Y6YkO31VOYVVVd6m5O3qulB5vxeB9yEQl3RhwyxQnqC4BYb+O4yNSik7gOEO/HkZ\n/nYHfGLjVzh89Hq4cQy605IC9oJr6czXIZ+G+rcY2Ob50TG4sA5PKWBtWwqreMKGZ8VFvA6+uWsq\najqXC8h1w6gUMFeW11YpBLJsDkHnRPsCkFXlQ1kpRPLjxJBWEuhq9v3Ew9oCWf1rboMHXs2723/C\nL6sLwDK0bOO1gJSh315tXxcQ91Ae5Ms28SzRKlNpsBjmgczLseYIWlnIAAAgAElEQVSHtW8NrwEl\n3WSMX91KpSbxCWSNGNpvlkK03pguuxPYq3KwTMdtObDcS3uHUZYAQVGOI8pvHRLdnyYwcUAU6SjS\n5usRHWCUyRmEWljrSqILLgRRG14aYGv+tPZhM1JwKUPksa6yvLIdNq4skYy2q6OA5Q97nXvEqggR\n3XVzRFmayQD0aiQmBMU5qF+3MSihp43oZxaQyakSHN/2yEEfFKEh05MORrswmIhSHQVGPazTSTAU\nsJAqlxX1oRZaLCYJft81BLMDQnGP2JSuLFGsxjDQtdQX/EKfZfQsQ54WbW+l6ipRJGOVlkDMuRyh\nntgJl48BfHMN1FdB4w/Ab4XajcJVOwX4CmSraC1OCEnAh3oGSaHBNoJJbOmUphjKBQyqbZV0oUhF\nIVXaAKfhyVCZgmEtwYqaaSMOfmMAXjkABy6ET7hTrHHwiG8y4b7KTA7VNlxchzd3BbEMLcJIW/ih\nnkBlM+TW2wQI429R9TwJZzYl0KsqlkCv6r0n8HV76NXmyUGzQq/YjdM5qyjBNbYU7Cp7eMADySpo\nfRXGfpGd31pP91kTFB7KXSjMeR5tsrah5qoAW2nYdM2KsQCVcYs9wR86AKzUDWGhJGUXrA8mh7Vu\neIZDFBMI6lxwcDSBdUVwi3UcnE5Enk4gLrUUUZqroj6XvKzZERc45mVkXz+GKLsRZJ0a6rUEonFE\nSZ4kZFeaX7VZCpuoWUpdlaVGoUXnkbXfRDaTjODquQKgIoAg0034pIOv/j+NWHOCzUCozWrRuwUk\nQLwVGUyjVxiSaCADZBSpAWTAB5BBHEEoHAkBHFd96LCZr3aywHGgkorJWiJERIcKennQeSIBATMn\nzBQaLHoU9r4MmdgUsx3VzMudJTjfh8UYZ2GZC8DuY6dUxjSYTiJmayS/EnRQNGzZUAmhznNJF83O\nFlD+XXgeMPIJOH4hbPhxeKQKFwLdQWA1eXeit6FVjeqTCjdUKmP1I5yqBvUqhTTedUOEPXHg2jop\ne2TAy8tlYypKusATyUzaNA9rU7gi1eM7PMyWYDaFgRLU2qJU8TDSlGc1WhJwqnelPmyeCJoxZLqU\nmbGQRgdHephPBfkZako9vRRM+9v6OaDj2yyJa6BwglrNV17Szcd7zTAqCTJP4gYcb8DgT8DxkzzS\nejfHeBWnUlivczpUhE3fUm0tCyr291tGnlH5QObJeelP1wV2AigbIglj8mAip9aUVL7NoikVcDjV\nxBtTiE42yQ0WrEMUYY2QTm5raz0hMHh3Kv8/hKxNtYmYQgJdFyMK9LR+L0EQ7EbgUi+mvNULmkdc\ndk6fP9JRF00hczhfkrGfSyV4ZUr6PC8K+itOlHSCmP7jOuYHkboBj/CfkyBw7hSrchpqiiqqak6b\nyZ8jimo1mkdMSEM1/2mX/qyJmJsKMtFDhE62FR03vHyv5UISAchCqxRQcgHBxOese/0M6GJKQkDK\nEdCD+R7bTs15L89yTvoLkiZrStVQZsupOZ0o2tXXThLcEPMlehV9YsUNoliWsgKseHZFkVQCPHYM\n+OaVcMGfwUUZNI/AkztwV1WgR2c51Ou0C/G/NQgK3JCpoVNLV41TOO2guDSFShK5ParAigIeKFG8\nokZroEVZFaRTZYwLimQkg0aqyLKQhRXXHE2RvIZaN8xDMw1o1bih9tPbcPRRuxIYTmBTETEuvPRr\nQJkaxmlNvJLOFQF2VQm1K/RKR8ZZc6kq1/io5R7PNAPyCrSvhssn6O5vcKwFKwcCnXDUB6K+cVut\noLa5fEyxZio3hdM2qWJtIq+dRCyXGUXzHScsAYcoHNvAjcZ13GkdcWAbooisONI+BKU2EI7zFi+0\nq5Z+bgahIpsbqJUEv+5xBKE6xP9qaBR9/yiiOMeRtT6PyMJFhYAZQ7EDiKKvONigQcg5XeQlLzJr\n/ttxZC+fRI5faWpfjyLK9HxEB8wD9yFgzo5zeiLXOVOsF6aiQNcg/peD+v4s0rGVSNm+IQIP1dgB\nBSGVzjpg/hejWmWIkh4juAsGVYjmXRBgO3iwSj/ZupEH5WU+L/Np9YJWLgRDEi+LwHK1MwdHnAhj\n4aSfFW3LcSeuhoeB1bobT6DIOxFhHiokQmsL1XLCrZhH35ExeiWOvkwfU4LmYzUU/JnsmfCZDD53\nB5V10Jk6Bflfwo/9GnyzJh1uVVmcgoXxUO/SE7K7LCBj5nXqBfmlXsbe6aZgwaNKgZwRWCTwlAGy\nxgry9BA+oZfO69NQtb4AKAdk1qvYRQjWWZDKeJZGCVq0eyia80nkh9b7zToxdecRK8SZXDnZDNqp\nKFFT4tWubEyFjmWhaNfmpVf31ot1UfZBKVYLDY6kgrwfWgCSZdAZhqFPw4Ov5YRfzjammETrt3p6\nRXRSL4jMTPxuEopRd9XVY+4YAwTzqbJifAiGzkYAYdyHDSh3gc6XO1mDVqs4l2mghsQfcl1j6730\n8VQSknRO62ePoEfNp+JKNrCjLmkOIet6A3CZjuM9TtZ9jijnUWQd2HwvIqb8KeBBJz7WBJhO1bJA\n1rudCDLjBIE6/V4duCaHizqwswK3luBOZBMogH/V7z9P2/9Er3OmWLfo6wKiVC9HJmUKGaiVyC65\nQj9jPNYBIhOJsMjssvKD5mOcIhy85fTDXv+fIRMGoXJW5sS0jylOcyUlXhMUTBx5tYU/XQ6uBY8s\nXKs7ewSZ/MzJexdFfTAmwxj95RHjqPRiOVJg7uwnSaZqIlklJEtzhZBhdLoMPLAGLpjleRc8xIU1\n+NPzgJkPw1ULsOadsDgGNXFa7gUuSvqLfiRe+YnlEJ1OCln8RszHyyZiYzhTVn7hONCpsmdlgwv1\nu+1yUKjma7Y2GxOhrcrWCpKkPtDM4hNpzb9sh/DNOxnn0Qi9OqRt5qs3WTFuaxdxCcXZboZSZ8rB\njC4VEuw4WRZLJFHLwLK6FlKRnfgoksJBJwNqr4FWB6oPQWuYd07Uue18OVnYqo6ZbM2Xgpy31D3S\n86US8uct0LvRw6pMxnExVcYKsnFPI4BljEDjWlCFbS6zdQiQqauiLvuAwM33bFlrazPYrlbHvjr8\no5NnPKTrxGhTVmh+P8Id3UFIADILYpW+Gnr12pfZBHbqfA0jLJI1Ha2J7ESmp8oy1w1EqT4KXKNK\nO3VwUQ5XHIDaNGxfC8tWCYLfB3wdeKwLl6SCbE+dubT+w9c5U6yPImtsHaIMjyK72Byy880iMN2g\nv3FWl+urOb89gSlgtQYahMpVxxDnupHXzew3f26HkHCwiCy0TqILogjBJghCAErlcSHCbAEd+/ig\nF+qW8fIsjW6BcBQN2v4K/am2jwLbEhHs2ZIu2iXPl1yk8DeIQK1SJNHL6uqG2poAf+SA226En51n\nZCDnxR0YHIE/ua5g8Z5PQroGFn8K0vUwL5Z7RRGX5dgvOkEKFrnNnaDNHqI2hI8s+tFcfXYDsK0+\ny2OtlN/rVLhhOJxAaxQ0c3PY4iUNSsoKjnQSeMwJajoCbHTqwkkEZcZ+7jRS2OafnHQy19OEDbZM\ncBvVELfMWCryWEK+O6uTWzH/pw/HUmcO0kQUUDsNAat5XWFGk1rRhjwHfANcBrWvQwb+zudx3wUf\n4kKVSaN8daF3LLfd2+qROsJYV73I+yABGJQKUcJ7nJjiNZ2TErDTSX/NpzzmZN3Ze0eAcRcyn0qp\nsm8UzTiV27qX+a3nYhm8sCaybTU6Onq/awuhIy5z8ApELpchJv6jDrZ7CRxVEAW7XNt70IXfx4CL\nuzDeplcEe0VLZSaFmhMEfQxRyMcRBV9BeLjTy6FYDrM1OJIKIt6PgJx9qfT/OPAi4PM8seucKVYr\nCXgaUaoFMiBXErIz7MhrC5J6AqcUAvJoIqjU3k8RBbZf/7bSgimyg7YJfkOjZW0iZH0ZUmgpR30+\nEQd6hkw6BErXXicC7RTt1AksgyqyeGvIdzuIkLS1rcParklEQa1HdsvliNLMnbzXdhp40wVsqbWb\nPb2D2u5JYXMB43kQuoWSRIVN+bYTeHwfMPVMWPF57s/hzwbFhbClAQ9u83D04zD0FCgGoBDEeqMi\nKEO/BTIegwjaMTO9nmgAycum0yikrZNlaf9JB09zTR47CYcX4LjWqG6XJBC0U9HOxUgkulvIfSyN\ndrAQX+JgLnW596Yy9pWu+GNt8zCifEddJgmhdkPhxPT/ro690XKu8KGG76jeyHnp0wNOAyPAJYiP\ncZvKbo4Ec6aRqPdcAstzDTomkR9dX09V4eFFoLMC2g2Jwl00CTO/zKm5D1E0QsQ9QWIPCwjqmkuF\nc1ntqnyqH6qmsjjvlD7lA0pe2YaNNbnfGJIKvd/JmlmFBIISVapbtZ0TLoCYispmqnO6P5HnLapM\nX5XCVEW4vIva1sOEOMgKBCANJxKMOgX8E3BRKuvzWm27WW4WfD6g7x3XtbkBWNaFnSlUBiTZoZFD\nWocTifTrHhfqNk84WV9H9P4PleAVDbi4I5v0NPBVYLSQNVzMwqkBsZ6+whO/zplinUWU50FE4WxC\nuGYD+poSFsoEMskJgnIfR3wjhgYH9F6D+gUzF80sXyD4WQ3xWmrrCLITZwQfrJ3nPpcExThDKJA9\niCi8IS9oaR8hZW8roYzhowSkvdraqO1IkQVccdK/I9pfo5nFnL6jwICDhvZnuBDT1ojojRye1aJH\nUC8p5xQXiOpzKXwthYcevxkeKUF3jt2PwO4xcGPg49JgA21BVG2Y98HPuODg3mg8TiALYNKFjJcE\nQc4jyB9XeyGbr3CiaGpMyUCchH0XCipanou1sQpR5PchZwBuUwU56cQ1ZAFDy29f7wXtXErw5XYS\nHQO1JBZVSZxwoiQt4rtJuzqMZgV1YVW3v6Zpx4lS3YeYohcgC/di3SR2If0+ov0seU2dLImb5LAT\nrjQEpfeA03GeG4KpQbhkLcz9BSz+Ifceg5VDIpfrtV1TiZiqt6kcnO/gGYmUUVjXFl/3qYr6dNOw\nicyUpGhLJ5F+no88/9su8K33A8ec+P+HETQ54JU25QUBHkZ8mmXd0K9StD6fBE6zR8xxhwS01hDc\nBztL8G/6vGXIWtyp/XmTytBaxOe70ss6fG8iz32qytoHkfknlfHOEOvgKRXZLP4BSdG9RMfsC8DR\nJryiLsejvQ+4owsbKlAqi0x/Sdt0F5Bp/mzTwd2ExIkncp0zxTqTyQ5XSeQY6jaCGFJkd1pOcHqX\nEFRnxeJLwIPQOwxsi34uIXA5hxMpIFLW743oTp77cLCZuQ4yJ2Rmo09ZdLuBtM0BQ07MwlPIppAA\nm11gLJjiNq7cQUS52v8PI0jMuHstRFFs8HANIa1vRBdi6iSH+RDBF1xDNpVSIveqqfK0qLP5Hoe1\nQnyzFPiVJa8+qSM/AYNTkO+VAT8E3nJ9Z8ag/jpor4HSXliAfyrgNYmgxekkRHZXIItxBHHnHESO\n5j2AKC2QBVOoQnlUvzvUfEQm7PE1nHf9/cwkgkIOOWnOQ8jiaSGm5VYvi+q0jvFYFlwCWQKjDg4k\nchoLyBwdV0VgForTOcgI0emDyIa6CVEEh1PxsZnFMq1j3tS5niGkO15ewEgiimcaUXgnnWTaVRC5\nWYFUdTI6UD2RsVibIpkFiwNys2IbXPUg/DM8cPIFTKz4PEkTDrVgbsI6sA38KkjGOeEPcOeG+7n2\nUvh9db/MpWJBOO1LE1ESWxQVpkgCRrcEjzip6DSqfbpOZeoKZI4nEzkPyjnZSJ5UyNx8x8FzM1jX\nEUW+J5FU0k0luMaJJTGdwHedAKMNRUDGP17IAY63l0VxdZCN42+8tH8T4vo6qutrUf+/E9EFK5H1\nv0Vlb7ILO9R0P4go4PMQJfltL5v19rqsp39DFPZICn+fwSfKEjC+AbFarktgX1UDVouyKfYCNU/g\n+r6K1TlXQxBzFZGZf/bev9U5txz4FOGE9x/33k/rd94KvBaRw1/x3t961geXxdeU5eDLglROIE5t\n48VZilsZEeBdiMBuQBaJpayZSWOEdVCzRf9X1caMdPWIZkLkuKx+q6qG0w0ll70ISysRBLPFCbWk\ni5i4Vh/1OMHPC6Fm7CIhQ2yYoGi3R4PeRHbacS9mjupJUYSJLNSW/v0oQv9M0fhPIhtBVSPIZmq2\nHBwp6+mziWSe1BHFensnga+Mw2AT0o+FEupWIqh8M1TXQPUItL8Fy6DZgfcNSvbLJCHYN6D9GiRU\nA2sj81hTYdmMLNI8ESWbAB03rylNr2ai/AVWFKIcJ3QcN6hA7dSxK5ym7yIIMi3B6pxeuuVuBCHb\nuWcQkkJMGdopFFbndxWyEC0foolwHs3E3aV9W6597iCWyDLt08lE3DMWUL0SVc6EbKY1Xdjc1EqM\n6nPNEvjHFGhvhrwstJD918DwZ6GSM7n7Iib3fx3yzRJSdzshuRlKF0B6DPwguOdS7L6fu05+gF97\nGvxOWfyI1/pwXpv5FL/tZFo3I6myDzhRXlZlaiXwoypXmxF/5t0qq4WOzYQTebvJi4V2oCaMjwsL\nIXhMAr+Uyvw+H3hmBreU4T0JTDt4q4exQhTax7xkfpHBjIdOFy4ehMMeko7ULJ9OZYOdLOAKJ26K\nR3VRPViFS2uCOHd34VQqiPVB4IMdQcgDVdiXy3x8t6SZWw5+EnFJfdjDG3UuG4rIV3rZVP+pAVNe\n5nMXT+z6vorVe99yzt3svV90zpWAbzjnnoL4d2/z3v+Rc+63gLcAb3HOXQy8HAFU64AvOefO994X\nS+/dReB83hYEuYiYMgeQnychwn2MUGHKhLiDLIzLvOyaqxAl6BBld8gF03rcy9HLRpaGwMHM0mAq\nV4qQGmgHnaUeTlRD4KSmQQE7uG+qLDVC7Lwcq0cwgSiCk8C3gGchPrkyIthzCA/fduIZB9sN8tLP\ndEgQ82yVKsjHEBJzAlymPrfUS+S06QWJ7HGyeA7pBI8D9zv48sEa3DcGN7ZDWHiQUCYsfwRcGfwi\npP+KVbLZjZijBmytnOI2ZEGNIYtwCHgBcA9iTj1G4D8OAJd7+FKjKyt5bBXfnIWfrIs1MOXgi8hm\naWcG3NWFv3FAAeuU+L0tlUDDZh2ffTrOeGhaGTNLPPFQLomindNoT8lJm2eRTfpSRFbuAu7Q8doG\nfAMxiTcj/d+m7dqLKIwbkMX7VR2bzdrfq4CnFnDhnASuuk6YBN8pw616X9Y9Cut+El72cej8rPhb\nH52DtU+GbdsguxYGvwCLTWh/GYZv01QjwNWAnTAu/s6vOGHULO+K6T9dlg1nDlk7X0TWxyonU7wP\ncc3sLoQjfDuCBO8EHnYybD9XwKYOzFSk/aeBTzr4oofVKfwS4p7ZgGxebeBpyOb/sTI81BW0XCBZ\nTHeXRO6f6uBuB0NVeBVwXSFj/twclhVQa8KtA/B/Erhcg1AFsKEmfOEtiWzwuxMomhoArsFPp4Ki\nv40oysESPBvZIO6Yh29U4WfVMqo6+EMEjX8O+B2d26sy+HYdjrTgb6d5wtcPdAV47w2kGA3zNKJY\nn67vfwQZn7cALwY+4b3PgMedc3sR//RdZ9y3LZtyL081kaCAd4JaM8Sfd4igVFv6ej2Cbh9yUukG\nxBSudUUJjZRkUD1KsyoJurNIeSuVnywJFde7TswAT6A0Jcj7U05Q1OpEzP/eiQEedunvRmXZAGzz\nATFfT0gym9PXJ6E7phc/XIY40Dci5uO0E92zHRHWsrVFn2EBrweAq5xsBFOI0OzTPttJC6sRxfN4\nF9hdwrU6+MseFY2XEhzMy4GZXdDaFRxiFWAW9o0Jb7OdQkVpPFOpKKTdyMZXR5TLKkJqr0cW9mVI\nlPaUk+9zZwbnbeb2GfjuMJwoYGoRTna04ZbpYT8dOeqKOjyumSD7U0LIudC2ekJtSS1z1vbQrtGr\nSZchxWWoiCDv84LAigwO60QdSKUDjw9IQCQvwR6llg2lct/PeFnsy4HDBdylPLmHq+BSuH5UnvWI\nExm+x2u94EXExBg8BYO3QHcOmp+Cpz0F9l4CG98gEG/14VD5ZB5BHlWgeH8vqDCUikx8GVF4A6n4\nQ7+JKI5diElt1aWen8EdZfhiBk9JZV5uQKyDr+jQvVrnM0vELTCqgGWVg5GKZNL9XSp924QAhP+f\nuveOkqu8tn1/e+/K1TmouxVaOSIJASKKJETOOZhgg8FGRBtsgw2YAwbb4IBNsDEOZA4GTDYiByGE\nAElIIKlRaKUO6hwrV+393T/mLhrf8944912/9+6gxtCQutVdtcO317fWXHPOdTLwNxTg9/Bgjj0y\nVn5vdP4T0RpuykBnCD70YcCncvBmCE4NKMA9bmlzKkeb3hadKi0eHOU3x3bzIBbUOPFXcvCIIyhj\nor8ODzHwlgWtvn3kEUE9Kw0oux1IwJIojHLgwRx0B+DwKHzqQs8g/68Ysv63gdWyLBu/nwD80Riz\n3rKsOmNMcdJBJyPN8tH8axBtZcRQ6l9fniCAsYERm68kylZXGzjCzwoO8g8yih7iIqF/rqdu4Go/\nvSuzhHttY2Rzz6LyJmGBHRop2Qdt0UNCrsqZIocShEkOWwokEaOfzxt9b6UFMyx13YvPbgJliUWJ\nXh9qAuyBgn8HWtxFfG4XyvhiKLbNMHqPdksZz3ZLATXiX7xd/t/1/vsFGDGqCaAsaMi/AUW6SBro\nK4DtqLkTM5BNAd3fxmyPwoyPtOKLtbFBD24tI7ZDQb4Ejr0MWCFIFyCdUiZDULhVK3zJA3OAoSL3\nzIK0p/ceY8M/Leg10FYKnB6ENtiwswasnhFW+Fdnk+Of6HAQslEwSZUKmSg4/nCdQgSNzS7WDPkR\n7pyNdmQPvX/cPzeHEXeeABSKwnOPEWmPzZcWZQVXH5UK60YnfMwjWKEqaVvRdd1Xrwz3wl8qIBDQ\n/UgBb3sw8FVT4SAweSekrhlJ58s2wvY6qLqQQ+fdyjJPQgNKoC4KAwbOCflQOPCxB+E8VATh267W\n0Kf+0zzVP6SDUTZdlKxuDmpT7w3BwZ6C3w6jZOE2D1b4z89WGz6wYV0ergjqe08bzW2cGIIbXdGv\nhvGrBYTVLnQlcJnkwae2TvN1H34IosptXETZ6lsG5hRgjxB84cFztrDfBSj4zUEJSqcRPk1BUMRx\nASVK/8zA73IQDME3jBKTsQXYIw/bwzq2YwLwKfBOCqqiypa3p8GExTjpBPYLwIFZeCECM43Gvv2X\nAXn/G6//lYzVA+ZZllUOvGZZ1sL/6f+NZf3Po/P+9S3+L7/7c/1PMgBTD4WSQ/3OL/CypXJyH7Qw\nB4ED0ELpwm+S2NrVQujGNTMCfBcxPlDZDLDODxKT/J8jqN22OJYlgIJEKb4zlaWbE0E77lL0XNjo\n5hcnHhSTpGIjzULxIWLUPCv3328X2nWyKL3vQpvBNiA1CE4MDgrq/z9HO3VRX51AgX6GNeL01Qfc\nCOyWhq0h+LMz4i07hDCqGNq4+ixw24D0+VARAtM2ctLl+C4t/sUvgsVfncCYQ96gYf8Eu/T3YC0K\nQMX5GAVGXMODfMk/e6M4g6MYPANouzVHwrYnRki5Xx0NQQkEp0Bwhg4+twEK06RY8qr0g8HpYIUh\n3wSp/9SFcYG2uL7PgC8pYgQIDn7lRlb7/44wIucrZodV/t+uf1zFr30+XT4H+Sr/Yhft8v2bnQ/A\nPQlGbM4sRviCrWgnjqPSotxfGG/cAIFlsHM27+5tccgoGXp/MAidMR3XQ1/NpPyK4xUL7rbgdEew\nUzliADQb2JiGe2IKrF0+HnpIRuvyW2FleJP9kn6NPSIhXwEsCMB8tJk/56kMv8OBBX1KRh6ogPsc\neNBv+noBOGMIAgUYduGGegXbccDFPmT3JPDHDLwWgWvzYi6cihqbbxl4w4Wr/Q3pFp9a1x3QJbs9\nomMJeWKYzImo+XQocFVOdK8bAgrQJ+RgSUS38qwoJNPweLvWn1UOpkqeE9kW+NCDD13gY1j9AQTK\nBH//u6//ZVaAMWbQsqx/oky907KsemNMh2VZDYzw3dv8a1l8FUVP//V1q/DBibaCXxyVMI3+QbX7\nZfae/vcq0LM421NW2eSX0GPRzpNEpWkvWljVqFRJoBtTVHW9D6zyRMPZzdJC2omem+noOcn4v9vO\niLw27n+91T8WP5lgGAX14q4Mih9LrJHYZFAQj6NM1kJlyTr/ONeX6Vhmo7hUHDHT4r/nbAQtzEG7\negtyirooDFdEBTccZ+BDS5hfvz85MRWUMkhGtVOwlpRgH5zEdd4bmSNePMAizmIYGasQ8U/IZYRJ\n7/kXpZiZ5lCw6UQBqIwR3aOf3YWAXNFFfDAKpRmwC7Bh7MiA96EQWDPA/QICOWAihBeIvRDcE0L7\nqsbObwNvGIJT9ZQQhuB+UDoGMivAroJQu1Jst8v/4Dy4GeGTmQrwWqF0SCsz8pVzSX7lvGsYyWRj\njJBFix1Gz/9ZFyob/DlJAb2HA1xeAskSXZqnCrok4RDsyvrXp0hmLod4DJI1u7SgghOhtYwvqgY5\nPwCLyuHBtCq5L8swHyoJl8ExLkyx4YfGn5phwV7GVz7F4EVgaV7Q+U0WdDtwgaUkvzcOyxI6n2Mj\nKo3vzCvbHpOBkE8Zm5mD80KiUn2vWsYt9yZ17kdFYK8gtPRCcyVck4bKBLywC16vgaUWPGHU7Pvm\nENSm4fwYjPoCmubAQAAeD8FpwFV5dfyfTsI75ZrI81C3guTLDtw3DK/V6Vla7MFdBXjPaBNY3gVL\nqnQtLo9IdbXnEGws89dtDfymAU5Kwb6D0FukfMQRzncgsC+Mnqyhv4O38W+9/jtWQA1QMMYMWJYV\nRZjwLeh+fRO4w//7ef9XXgSesCzrtygOTkWY8n99GTUSig5Uzaizv7cPxn/i44ZN/ofWGyU1nbbW\nVod/8FOMGkjF0jqNgtEsxHP8zFIA2+Z/TqrgP2thWB2EvW343FUy1uMoY50GLPJUkr9nw0OICdAO\ndBnAGZlgkDZKZHpArAIDe1ojloUB1LDYhQDouS7U5KEipaAvCxoAACAASURBVPHTr5bCXX7WUI+e\ntVYU4FPA51no9f3SdgZ9d3pXXNVCHp4KakOqs6DFCAccSgEZsCvAK6Ao3TYVXmnDXBuRf5yPn5Jl\nBFMqDgzzYGZQ2ULCQMbTwURLlQAOFOeKf5VnVsxui/pcGy3oDOQKjGSELlA7DKENUOgb+XxjgdcG\ndgCYDnYMnB5lp3YcTAbcPnCqwRmnf3sDYNeDGYLcOrB3gd0NXjUE54H7KMzs03FtAtwIeClw8rp5\nJWgjyDJCOC6gINrmn5vjH3tR+2ozgv0E9bv9A1/5WQNWHN7Pqbk0kIdRQZgXhbe/OtgNBL24kCz6\n440fhEAXrLyVQ3e7mmoLPixAohltQNVwSQge82D/HHzUDPfXgV0ilGTAwIokNMdU4vamoL8E+nsh\nUAq3B+G6AFwH3JaHpkGwI4LkVhiJJL4JvJOBLgeSSZhpQXUIptoKdONsNX16orC7Cz8IwstJiJSK\nB74gDPeEYY+sMuVHgzC54O+fFfBMJSzsgsDrEJkOz5bA5iFIl8DiECxMwZKYGtp1g/AzD1qH4Xtr\nofIArfWzs3BnCA7wRK3b2AejY7BqUOqqwCA8nRL0VBEWbBEHnnPh2iGY2gC9TfDzyfCTokIoCgfa\nsHwASm3+7dd/l7E2AA/7OKsNPGqMecuyrE+BpyzL+jY+3QrAGLPBsqynULVeAC4zpjhg4l9fE2x9\n+GuoJDkDke3b0FqfjLI72z/ICa74mWlHwWQYX/3kY5Kb/d8rRRndMtQd3wv9KcJ2zeC7hGhBDSDZ\nY9ITiD8FZchL7a/o+D1YP6AEKWvDKgNjLOjxxL8rSmvrEF6Ff/LFEb11wEcFGBcQzavagUAEnkV4\n0g5XF2ySo6z5myizLgGqwnBqGB7NQbZYRrtQ8IA0bM+qyVLUkB5gww4/KfOK2GkeaD0Ey+rDq+hn\nSj3Mjsh4Itfr38EJfImfHFguPNkxkCn6vIWFr6aLDt4ptPPBiHLCx1YxqHSIMDJfp8inKlkA6Qhk\nCxCuUKmQBMJjIbsTIt+C4GTILIfyp6F7NJjRUNgueYzbCtFjIPVn8FwIzYfQLJ28tVEyruDxkFsF\n0Quh6UHJc4MzwRmCzNtK94tTI8No18uFIJbTjlx0Ri4OUjOMyOp2+uc4E2XpRZLyWLC3yVbv1kO1\nnp7NwMAW6CqHt+uALNhjYFYM1rUx4qE3oGTd/HklPLs/9mA9f590Ev2HvUxJ0GXyaOj1tbWv5mGP\nAPwkCNFGWNANZ5XABsfmB3hc6EmcMNGGJX0KzOc1wLEGrrP1nKw3oiqRBa8bDp0A37TgFgu2BGFH\nsYk4DKeP85V5KXgmBL9s01DIPhueseHKPPw6qoB7IHCUD0F4KWjYF77xMXwrCovjSjqezsE5Qej4\nIfwtAvdk1bU/MAFbymBxGqpKtWSPi8MvclCdgFWHwZIUfNQN75RCoV9Mi+oYuLVabuNL9OxcFQHT\nBW/ZYpFE4jC4BpZ1KEBsDkBgEtzciUqBWcAmFQseajy+xr/3sv5v4t7/py/LssyDRkHtdbRWD0Xl\n7BpLWeoMFJgWeeoggrh6E3Nqqr4Y4csByFuADwz0GO10B6Bnog8FaAs9u5uN1kvWVdDZM6jP7USB\neJtRJ3KXpfK/Hf1cTxbCYempowi/3N1WrOj1fyZn5EZV7TfR5qH/jzDS4Qcd21MoQx0PfGqEgban\nwAkqUwwXhDW9AHziiltnOTCcUEJjA7kM/tgSgfkmCURhYrXoYUNGaqrTQvDPT2Dw7ythXQRO+w84\n6hkCdVAoqq2Ktu/FMtNTGZjLMxIYi5jhaEawig5GiLUF/rVUzjIyoLAIMRSAzmuJ3XUqsVMtehru\ngjlPq46OopIhsT+ED4LMUrCW62ZnS0R0dCaDPQEaNkFvHaTTEBgn/JUCOGN0EPlV4MxUu99tF6m0\nf5JvBfWZeEkVeQHh/cBwyJ9ml1OnpMojujeU2NDdB8fH4eV1/jmPRws3ysjskDKworDbFFjXByU5\n2Rl+qUIo8CWBdkJajbD8eOjPAs1QNk0d/klrp/F+NsaUf17Elkd3hysAaxNU+dBJ0IJgEiIPQdcP\ntSHUISqA3QAT2oFh5l5wLeEyONqDcxOwvhRuHJZJyuFhWNIB11XDM33QHAfjQTQPY8ugfxj62uCs\nqfBkO1zQCE0BmOHCa30wvlId/Kqc71URFF3w4QKUBeA/hmBFXE5m17VDS62eje0JqClVlXhmJ3yv\nFJqicKQnJ6xwDl7Kwo/i8JkHDwXggiVw2hj4pBHWlytTrvHgHBeaHMEe9yOfgMEOqAzD7DJVlVMd\n2DsD/4jAJwZYCxXjYFpI0yYWVkAiD3cB/TkVQLPrYd1mfJkaGPNVp5D/Z6//Y4G1Oi+98AQLHvNH\nNE709L1BhCMMoqRigg9+H4LW6lF+R/NDWxnfS6gbHbQ1N6vB1prfD2GOjfgwXk7Q2xQUSPdGiccT\nrjLPWaBuNvAdF2Z3AR50lsNtJXqfKiNN+Jw8PBBUw2CMB6sL0BiCzoxcjkwEZtnKQOd50GRE6n/N\ngt4cDBZgQkyl+9YsXBRRtTqI/s7ju1XlIJ0A8+ZB0HkUtBwC9S7EE3rYSjywfi1UP7EveJskSXVc\nBQ7HhR0nwO9OUdfi3FOxD1nHlErYmhOPGBhpOvnZZqzBn/2TRAFlC9oNSlEQ7mFkAPsYRrrvxQmM\nHYyoI0ABuxb46CHCfx5H9tdXwWALDDdCrAMKPTA4DuxaCIwH5zndfBMT3mKnwdsDnCZoyKguTR4A\n1noI9UJ+OmTbIBQXhttvw7h+RbiyjI+tzILCJpkNTEO40tDukBiCmnZI5qVaKQEOhYNL4POP4NhJ\n8Hg30BaE9tFQs0MbTBxYacOkIM8fkOXyHLS1QqxEWvZ9YtDeBmtKgL5qiPQKEg5Bfh0KzlP9azcM\nJQ0yFXs0AN/58BhaNiwCr0H3OezbFSUt2FUOJZ9AbQvBv84n/+118HyQ4Map1HYY2u/5gJqJT3D+\nHtt5IapT6miGH8+CcBJ+sl0P1sGTxe+c4sKyAvSEtIGvKsBCW3F8wzBcloeuUkjlpZg8OAf3VsBb\nDjyegOYgbAzBQ2k1o4+NiBlzegaqC/BeHtpDEAjBT7aXcFUgT+foLKvyUJqE31XADxMq2/eNwvwU\nPJKBt7Pwiwr4IAYrC9DUqerymnpYOaxnY3QNPNYHpCFeDfcE4U5bj8cVH8KKw7Q8N3XBxEr4WYeW\n49HjYfcU3LE+ztwpSTbkodCLL9MEZn1NA2upUQCrQUGwHPg0DwT19TmMCAOKjdU69Pz3oYZXDj3v\nK4BCEoJhOCgAlxjFmcscuMn//+MQtHino7L7RiNV0gSg2pUrzmuWQOIzgaOAhqzgh4QjPtwDUWG1\n7+fh2CBcYGCZpThzewFIwRFlqm4/MHCtBfvkYZWvhNoFPG5g4wAEgxAogdMRCN1ltBi3ejDDVoxb\nl5Hiq/nlICz7BD6OwMVt4FwD3AWxdZC9BQoXQ8d3lCW6iF9lIbzAANWDUL0KCm/BrKdhDMytlCTz\n410oYy12xF2gCpxtysKMB3YWxo+DLW+gbqHDCMUhC2MmQH8fpHbCl7NzehHeWAGmEyhzYNs90HwU\nVLXCIYsku9vhL4Auyzd0PQJK3hjp4leg0iEFOCeCNQSN745IwKaiEqctrBktcX9RbaiAPQeg2Qb3\nl1g192NKt+rmGLR79SG8aRM4eym5pRVCh0CuB2WmRULo5jAszI64BeF/Tpm/GBvRpjIOrDREk5Aa\n8o+vxT8HB+g+FKfyXdyiQGMM2ry6gJZGqN7Jl3PBWysgfa46T9YekBlNoLsBu74DM/Vw8p0lkLoa\ndv85rApD1QxY+hjWlAswM1d/qQ0PFKA0BvTDuWFYZMNZBSik4IJ6eNnVsfT0gpWB08bDS8PwHyXw\nlCV++Io2OCkCe1bCpX1wdo0SgqqoTHqMB0d4MFyAxwOqREMB+I6B0wbgV5VwnCv11xRLXgGtrtZg\n2tZ7nFiAvYfghkr4W7+yymYPdnbBnQ3wmxysTsP34vCk32T5Ra2mrf5gALw1cPlEuGIZzB6DNs9a\niUQOtuEw4K4B6On06ZWO+pn72XBmneLCmS58P6jj/FoGVlxoSMOkuALhnRZs6IdgGRxiwekuXAbM\nCUK/UcfsVT9rrXE1OG5ZWNBBD1IHJgycY8GPt8OENsjHoL8RPqsQBcQOqltarOp6UbA7Az1vc4BW\nIwpLhdHF7zXQMux7UZaBm4QxMS2OjzzdsL8W5/LGYWZYEjw3CfUlGsn8oIHNBSnLTrTgHx7Mc9So\nK6Bjewe4wIG7czApKBzsGhveysGnz0+GzGKw71HHO+yDrEWqRFM15HaDwTpwBmH8kFLhUFJOGZ0b\nITgW8r1QGPa7z/iSNSACC0phu6cuMGF4vgA3RaAjAeVBOc0vWc+Ik0wW4pMg6TO4w/WQbdd7kQZG\ng5WFSAzSr8+D/lsk1ax5Dva4Gwb6tHtsQyD4O+NgWgtsLoeJg3ra+jz9zEwU8JOodBk24NRBaaeC\nRz9qzgU8GLVVzicu1DVC52uTYJRFtKyZ9DAs2g3eG4LCqvGw9w7YMgUqtnDKXvD8ajD9MLMcmkK6\nNnZGQcN0+8e5DQXXsUACyspgaK1/bLOAnRCYBSfVwj+K8r8gxGKw0IK1GdgrDi8kUUCeBHcPwFUZ\nKKuBoSTQXAFzByAcgDYXKg17xmHtxiC1B+T5dh6eCEBbhxqDVlr4YG4A7qiDJTa826l7RB1cUgpV\nA3B6FJaktf7X2NCZh+9HYGZa5P91JbDRhncHYHQvLK+CYBb+EIALgT/lYbwLh3TDixPhzCEIbIey\n3WBREvoaoHc5nDUGJsRhTg7+Ohru2lDB2Mmw/8AAT5fDHnF4aCUcWg5/r4JZIXglCosd+DgNdQlY\nXA/D22DNeFWg2RS0DcHcDFwxBt5OwMPNcO1ceDUJB5XCe8NwSRCmBSSX/e56dfgnTZfmIhGG60rh\n+xnobAEyECyHfDXa4LPgdEPQKiVzzPDXM7COM5AxMNVSp7zRFjl+I+KJNuRhe0BO4VlHKqUFHrxj\nSX42Gng+LILzi5bMTPaxVPEtTsPYFCTjSljaQkoaHs6JtjQqqOexDAXWdF6+p8N5uCok6eoUYGZW\nvqwFS+5IncPw4SA8bUGoGqJRfeY1LhzqQsRWd77UhgEPegdgYhwuCsADngJrLgBNQ1BVpub0jQEY\nl5Vx/7PAd4Hrk+qp3BcRq+HztPo073fJXX58FeyqhC0DesZnl8KGLNht4EVgWgO0ZCE5BNOzsDEB\n1EK4Cmp3QGs/lE+HwW6U+dUBBagr85MxF96w4Mcl8J0gfOc9FIBnwX7DsMI3SKicAIPbwRuNsrui\nKH/LidBQUA08ahiabwInAjW3QM0LypCL7seZCIzJEZnmkekGtsHE3WHbJhRcy+MQ83e2or9i6yyc\nug24eSAbhqEytZxjm6mfBx0pYNcsGLUBXHW+A1HIbbP4Q2AMe8b62a9KF3liFYzrhqVFr7w0kuNt\nRBnPKH/RliHQfBAmTZEenS4oTsaLl8NF5XDKVnh4Ljy8Hr3fEFqsPSgYbwzB7NyXJgXTxsGtZbAh\nDC9/Aas9oAq+Vw5/GIL8gAjt88dBQwC6U5DoAasWLivA4g6Y3wjrmqG+TuSKyyvESrgvC39Jw/ha\nWNAMe9iwxxSYlIfHtsE3xsLLYah14PcDcHoAfr4NfjoengzAuRaEMzB7SCKHUFBDD35qYLBEGedz\nObENKrNSZFWG4XsGlufg5AxcGYObhuHxXdA4Ddo92KcAdxtJTI919KzVpsCuhk9yCrRVFszIQV8H\nPFIJ421Jbldk4FsxeHQnPDoa7rVhaSeUhaC0DCYWlFCtH4BLSmBzHFpysHYIRlfA9x24ZzNUlUNv\nBPp2gFsOmSyCuYq0u0lf04z1bAPPZP2xECEprNJGzjTfMvA9S8TiQ114MwALDPzdggdd0RynRGUg\n0WqUBY4uSGY5JufLVI2Stiej4ne2G2hx4TgHLvIx226UYO3hwkcO/NUSFWWG39w6LyD8KYeC5e6W\nAs9TCVFc8oNgpSBWoWzZHQLKJLUnBnPC8Bvghw6szYuq9I4NL1hqxH/bwDFpLb7DLHjB0eipC86F\nx/8CFWUK6DMLsCmgTH3fHs2hGwjBWwFJJk9Kw8sRODotj9I5AXjfgbuG4VdxuHgH3FEP9xkYXo2C\npAFqpNCqqlFwbstIIRR0IJdVl5tSGTC91AMnpvXgNlRqsF+yyG1tgdkLoOmPz+GmZsM9XTiH1OA+\n34rjGdwjxmOf/V28wEooS4jHtXdOOE4GZYJNNuG9PPJN4DWC3RbC7PIwczzJZHaCnQJvX7BX3oy3\n2y3aSCYhoD0vmpOZjHaj4lybonVYL8psQ6j87/R/Lwr2weCtF/5uimq0KphUC1u3IlVJGg4fB292\nqVw2LpSOg5ODcMA2uCYL6Xp1oDO9wGaomw+hWmj5HI4thyXtcM58eOIjsQMCcZi9C9YGwXwBXoU+\nlygcUAdDMdiSgTvC8J+eZLPhISiMVTV1lgcLcwoW6Wa4aILW8ZZ1cOxEuL0bZo2C1S7sE1ScP9+B\nR9aA2V3X5aJSaAtIAXi3J6XZbUHJwg/Lwu69It9vN3DGKIj0wWfDuvd71WtibglwdBLODkK+Be6e\nJFXjvAz8OguPuUAAvl8Fj2bgEQM7DbwRg+c6YfEoQQ59n8HiuXBvn9bd1CCM6VajrTYBS0vhpxvg\nP6dB4wBMfgyCBwmGbw3DNBesEnirDLDVmNpkQ/sOGBwLtwbhXgc2roHeath3gs5nVgze2IY2ymqU\nbOz7NQ2sCw2sS4o71haVnGyZBzcX4I0AfODAU1np0j8JwgMFaA7IY2ByGK53JZ37nlEfwxmEsyvg\nMA8sF94MwseuOLB7o6B2b1ANmdnInGW8C5NzcJsrr9LDovDdNhgXhqZK+EcAnvFgOCeNe02pYMD2\nFMQzMnT+Ro3KusoInGXBEmCzB6f4/MeEA5ekYKcLe3TAWBvyPcryWqvh6Jjw5AUpyW7XONK3v24r\nWx9MwYKYYs8STw9NcBLsVwIf5eE+Cy5PwU97IdQNHY3QUyE44osyZdAXunBqWg4+Gwzs3g/vGyAC\nM2OwMwrHONq1PwrC9xxo2gmL6uExC976EE7dV3EoYsG6TXD/LLikHW4OWNzy0t8oj33Bfj/en9da\nY8x+52F2vH0Mw6dcwEmXXsTy71cxtO+dZLuBoai6+Wmgag8wc4GXIJpkdDpA3+QkmWaIxSG/K0A+\nGIR8GsogOg3Sg/JEODUmvX46Bvsn4MMa3zGtCRiYCBXblC2GEHQxGsEYG4E6+GkMbnWAbmnsU+1i\nZbk9QE8ATIFRY0J0JXMjHFxTDslh7FkeN1TCPwfU8Z5bC0dH4YJBOMvneTb0arM9fQIsT8Cn20Rq\nsJqhkIHQFKjOwq9D8EAlfLEe9qmDl/wNIJgHa5zEMEeOh9e6IFYvV6mTB2BbNZybhYe74GbgzSpY\nWoBlfvPmwywEyuG+HritEmo+gtf2EX3uSuC6OKzvh4PK4N0MHLgdVjbAHz24PgAXxeHiJMxOQnq0\nqsand8H5ZXBTGLJJsMrg8hZoLlPFWHDgmw7cGBHl6oUoJDzYacMFaTglBE+7cFfG9zZIwNQaTePF\nhl8lYEJU/YxLjBRsTR68YEPvp3DhbHihH/o2AePlq1HVAPs5er+mPJztwpkt4FTBbXHoi8LHGd93\ndQBmpGHXgCDCB8ohNgDr86J1BeLqoVIJ1H9NA+ujfn8lV4ArLfA8ue0MJaAxqsVeb8N5Bj7Iw8qQ\nAuZMR6qK+pxYAJOzcG0JvJWCvWJQkYPVtlQkxxYkmXvJhgpH1MlllpQoUwNweUBZ7GEDcFcFrHPh\nclsu/Muba/lzzwBVs/Mc6sE7YfA64PcT4ZIuEaKHO+E7jTA+BSvK4McuPB6ElQlYWwLjXLjcgXE5\n2OzAvgZucGCfJhieKVnuVQb274KfZCDZAHMDcGgWjuuBP5dAa0yc+WU5WaY1DSvwlzhw41Y4bypM\n9uDUhHiCpxXgpAKUV4IbFKxxxzAcOgw31cCF3TC7RpntyQX4mwObl8Mh+8P4AfjANw8p64Gr6+Bm\nB7KD6uh2bofDZsKaz+GN6bAoBAOfToTBbRAeDS2Xi7kw5yjoqQSnWTjhnDzsCkNvOXvv08XZgyVM\nD15G+8d3cv2esoF1uiCXhtFRSPRVMhTrHwloA4t4bs5bXJSE/hYgCxNnRdj2SRSCA5w+1bApAp9t\nCnPZmCwHVcAjSWhxoKMK0kmoTEHrDsA5krI9X2doPcwOwbgJMLhJHfysBZ3jZJW4pAv2HKOmaGkB\nrvPgg7GCe27ZCeRhXhrWxMXeSho59e/04ZCLxsCj3VCRhtcbYUGTje147NYo8+cKB9ztMFADuS9Q\nPZyFcw0kkrBiKhyWg7SrMvxIS3OZgsCPyuCMFjipBuJl0JyAqaVwy2Z4oxb2NBLU3Fcp0sjOFBwV\nVPa2oB3sUYLW/ur7DP5lK/xkHBySgPdjkpJODML1thg576UFY90dhod95dRmV1DAeRlojat6anDh\ngSScHZfpTFMebBv+7sA52+HgBlWVmx0Yl4e3+qFsKWyJwf6L4MyY3jMG/KYfWkPwszKIZ8W7fjID\nuzzIDML1DTL2edxTorkxBTPL4ISs+jLVBTWrHwvCH1JKqA7OwDu7INwIJ8RhfxvWp+HIMFziqALI\nBuCxTjD/ZmDFGPP/+x/AsB1TPYzZK48hgaEXY7VjJhvMBA9zloupdzHxBGamh1lkMKMLmFG9mNqd\nmJjBrElhLnH1u+UG87KLeTWNudHDPJjHfJTFvJvFNHdjTjCYNWnMX3MYqw9TkcH8wmAWeZiudsyv\ncpgPk5i5BnNYGsNaTKwdMyqFqd6BcQqYaXkM3RhWYE7I6t9OAnPvAGZNP6Z3M+a9HOZNF7M8hwll\nMaEhzD89zKcDmE1DmM3dmM39mHMN5qMkZm4Kc66L+WI7JpTDPG8w8/KYKwzm6QTm+DxmVgHDF5jV\nScyMLOY3HmZCOyaexNCECaQxJ3gYNmL4CHN5ATM/i7k6hTnbwyxswvT/CcNLUcOjz5jZnZjaAcxn\nR2EuzWOcPsxL3ZgGF3NxAbOkG3PuRgyrMGzARFdh2IwpdzE/8TAvpTFvuJiLXcyobZjmHsxZCcwR\nBd2Hg3IYe8k+hpUY1vt/1mDsZsy0Xgy7MKGdmG/0YSavxMzvxESGMDRj+AzDUkwgj7E+w7ycx4S2\nYy7KY67zMDVDmP1dzAVJzM1ZTHUew7uY6wsYejDlPZjoOsybKcyxecyebZjyYUywFXNAEvPaEMbe\noK/LW/T+P12KmbUG07sV804XZoGLqUxjpvdjnvQwdUbHdaHBOBsxR3Rgtg5hXvAwJ3VjFhoMg5ij\nhzEPpzGlw5hYFjMjiYntwCwoYB5wMb93MRVrMDcbTFUH5sce5sMs5uUMZoHB7O9h9nAxz7iYt/KY\nzR2Ytg2YN7KYF1zMLBfjpDGNXZg/GsyaBOaSAmZFFrOjF1PrYjb3aN0/42LecjG3GEz/BszHXZhL\nezHHFzCv5jCzk5hAJ2ZPg4m3Yg7PYtYlMa0tmJXDmKkeZqzBzEph2DqyFmZ6GKsFs4+HCecx9iCm\n2sVYaUw0hQm2YWLvYcYNYvbzMMd6GFKYijzm1CxmnsHEU5j1Cczw53pevkhg2vswTf2YZTnMbz2M\nncM8mcP8xGD2N7r3BxoMHmaei7nRYGoNpsHDjMphyGld0Y/5tYdZlcG8nMMcZTChAYzTi3mwgKlw\ndb+u9zCvePr/gw1mpsHUG8xfPMzJBqPQ+L8f4/7PsQLSCGgcA2Tg1CqYbcMz3bChBIIFOL0UPstB\nhwORvPTA7f5s6ws8WFSlDO+aYZgZkXhm77Qs0ea1Q2QUXBeFpTnNRJoUhSoPXu2AZTYkamBLSJLN\n5Sl4Ngp1WX1etgBXxuG3a+DCuWqCLSzAEx2w7xiNx/huChrDUlKtG4LXHXgkCt/yYPIwDPwDUrvB\nr/aHnWnhyZUR+OGg35z/Dlz/AMTD0F0COzNwbiXMGlK2enga1oXhzIJA+t1sONPAdZ0wdxT8ow9O\nTsL0Gjh8OyRGwfVx+HwARg1CVwPMrYLPumH/SqnFxvc5HGu53FYLHw9pmNvoUvhZJ7xVo+b7CmDH\nZnhgrJprzibYVQ8zBuDpbjhmJlxfAa0ZWLZdbvNeDWwfhJ5SiA1CslZGH6tDyh5ueBheOQ0uXgP7\nHSRS+PROOKoO7jRwaB/8LS1O8/Y6eLka1ichOwQ/rYeTtsOna2DhYXBODE504IAv4JWpkO6FeA0k\nhiFeItL477LwsxwU4lo7LQbmxyGYgi1d0F0LkwKQyoK7CVpLYWw5zAvBW2GYFYQzczDDH+2wNgQ7\nSuHTFMyNweocNIdhTTtsqYY7XHhsGSTGQLoOWvJQWQ6fR0QkODcFD4Vk6h0OwC+N1slCFzYGYc8c\nNHbDeTU67/FlMN6BF7vgsgaY7cImD/5kwatpuDID51UJmunzZL24woKSoK8eNGK0NHqQCcD0Ajy7\nFt6bAn9wYLaB+0rkEnd5tySv4x0JU2ak4eMYLMpJcPOMA7+xIJxS1vqeA6dYOieThxsqxHQZ58gp\n6ugMZGJiGFyXkgvaZRH4xBZ1shYNvfxtAW53BIfnLXCy0vrHXfhRTCKhHQZuS8MyAz+ISCQzLwK1\nYTW5Iwau92XCow0cGZTvwKw0tG2DSCPcH4KdEQl1hnNwRhD2L8AnITWJ+5BtYlsLmCh4YdQr+TpC\nAWzXv79VDZtD0gv3DYE1Fo4Lws5haAlBtwsnWFJlVARhVAQ27YCzJ0B4EE4BTjewICNd/5U1sPgz\n2fCdOwuOCcrublQvZOtguVGH/vg8nJaFCzLweJkc+5eG4Pd5uM+D5zIqweeF4N4heLEGNmfgrV5w\nKuHHADGB7udk1PC5PAWPxWBNWBvDgAN5F6Zl4O4OMwpDowAAIABJREFUmDkeaodUYn3YCVvL4N0K\n+CwN8SAsfR6WHgL7RuBNA/dGYdtGmLYbdEbk9rU2I/pJUx4mB+CCgoL7P8Pw7i54Ni4dtRUQb3Bp\nK1wRg+ficGEKbitXY+rJHlgwWcYXfy3AO90wrUwq09qUSuNb2oAcnBtS0Lm3DYL1sHepzJT/moH7\nU8Lxzq+BjVVaqJ09wtz27YfqcSJib/9PBa/oIVCIwS9K4IAwrO+AoX6oaoRTDOyMwa9cuN0SZrop\nJQz10riYE60h2DsL9/ZDWRieicAVnWI81GTgkzhMicCogqCy7wLHh2BzDk4Kq+m7GvGXj8uJzrY8\nDucXYOEAOAVYmoJxJTDnPuiZCY+eIHrcvIJcnF4Ow6YhcY6vd+R0NVQJPa6CxNGubCZfj8HpSeGT\nOwpQHoa7AuJI/76gAHtIARqDcnNbloRxUbgtKwhrQVhr5/gQ1Bbg3oiUgf059Q0+sXUuswPwmRFh\nohnoLsCvA3BeFq4KiEWzOgS39Ug5dXQAGpNaEw22pgaEsnBsAG5/HjLT4U8zoCIAB+ZgeRCez8Cu\noLr5q4OSfO5v4F0LPuwXY+BvJfCfiNJ7cAEeC0BHDqYFRWWssaCpAGe48EpIDnITfYOavV01oCYl\nIZ6D7aVQ6sGaIJwwoGbzHyrktPUjW3OujgSmGzWmnzbwC1d92dYAHNUOpQOwazQ8W6UpARE0LmZ6\nTiybWBDuD8C3PfimLcvHURHoSgJ1X9fAuhlCo6EiApfZcOAQvJSEv0dhZhyuQUYj1+SFM17oKjjV\nVIKXg9KchoX92YDdB00vwJjF4ve9EIDxFixKwytBzbjZZcEHFrQmoaUPyoLwZhxeLBMO2efC0w5c\nYsOeebjAhaX98OdqGbVEUVayYReYMjg2DU1lcGyJ/F6Trswe9i7AX4GLbfhRK9SPg80bYFGZsnIr\nDpf1ASnpyn9ZCte1wrXT4bc5uGAX1E6Qq020C54Jw50xNSO2dEOuWtTUH4Xh0RRcEIS/heC0MFy3\nFTpGwazlcOqeeiAXpqCzSkYz32qHk0fBuaWw9wCcvhHu2geOWw/vBMCaAM87sL8Hi8NajHsiGtwH\nPkPgxmr40zBcVQoze4VHng+MqpRf6DNJuNaGM3aBWQ+dR2qMxim75KOyoQJ+E4d3KmS4/ISjIDPP\ngeNCcP8OOCgK96Iu/co4HLEK2svglenwhdE0geVGGc2jPWDthG+NhocyMK1RFcqUKLT3wq8rxEt+\neRBGh9RkWoA/ZcGSi/2LFtzTL2rO5rC4/CcnNSMqWpAl7NhuKE/CQJ0mA4zfDEt2VzY+/wsgBBsm\n+MEiIb3Czog+Y14OPgiJXpsFJhX0vvfHRzQIs4BzPBEQ5luwxtNGelcrfFEPfwqLDfSkgVkpuCEK\nVw6qcVTtSGkbNXLsryzAxQH5TZwI/IdRBVYCfDsoksUuS6ZA38nLCPoEJE19DsHacQP3+ucRMTA6\nDedV6DO6LG0ORxfUXH7JhsaCWDO1WVgbFcH+HQMPZKBxEB6v1Wyuc4YFne+MiBDyYVD3sTEPHwcg\nbolV9wQSzxxkZDBTsOGftvwtrkSimjM8neuSABxm9Nk/isKHKdHFUo6w090Qu2gKcHFuxB1zdExW\npBvRBIW6HMwPwQu9QM3XNbB+BHvNgZogrO6CvkpRoS51YDALuSBUJqWaODsDV5aKvhGIwhWWLvbU\nvGhRU0rh7T7NPK+Pw3ud8Oxo0Ui+l4U/eCLfz2mCsrmwMg9Ph+DJvEr6lzPQVgJtn8HCmTBzCG7a\nAVNnQ1uneIPVu0MmB5PTsKlEO97S1WDmw80BZai9YXU/n7CgJgD7uHD6INxSDR8MwKxKeHcr5MfK\ncOXnefhdCDa0w4/HiWaStOR8d42BiW1wUwZuHQffT8FvIvBiSLOsft8KrXVSwVxeL+ZSiw/8rxqE\nu6OiaMU9uCsML/fLJf+DmB7qM41mBN0ah+uyGjZ3eC38YhCeDWmcNg48PQCnV6msPNyWsOL8lMZY\nPOLBoAtXu7DgBkhWnsKclufYcA2snyxXsN8PQyImQdErGahPw1+qIT9FTcjJHtydhzUpeLIMZgxB\nxQAsnqSM+w0b7hlWYDkcDXfcEoLX0vDUALxZC/GNEB/WKK/sbHi+VoY6+xgZoX/s6lw+H4RIAK4P\nw9u2xCbHF+C+oEyVW21pESoMTMzAjBRgKXjtcuCIFmkQciXySH0mJg/SVFhTKJaF9XAesxk610Jt\nDXQvUEAhrwZQyNP6mZSEv5do49pkwd2IDfK3ghpLbwfUbFqcU1MynhXN7nLgFFvZV2PehypCsD4C\nlWklAPEQPBjQiKIw8EZBpPkfe5qJtsTni48xkmcf54pTuh25Lu2DjrPewEYHzmoHbFhbDY0Z+DwK\nm/xgHgZMQR4ZFTnBXSkHXgrBGRl9beXhrbiubZmBLQbm58F14aU4nJyATFj+A8vjSpreCYklNxs4\nKCd5d39A0JVjRPvb4MHHtoLxha7gvRetkdH2a4BZnu7hLAueHYabg7BfBj4tka9rABkr7bAkqV9h\nwf0FSIa+roH1VQemuhwe1m63slwLttuDH7bD9ZYIv9cmoKMO7s8DBg7rgKaJcHifeOGxAiyKSDix\n3oPaTumd96+FTwbhegOvVsLFGZXXu78ALadoBEQ4BX9x4IluPbQnl8B7JbAgApcauA1Y0QplMVFX\nztkFDfVwYxvcNAXuSMGFPfBpDE5NwY0h0bGOHwVfBCDcA3fF4achOC4F7QG5DJ2dUtZ2aR4Ygkds\nuDwHV9cKM64zcE8GFoY19ohykaqfCMpbYb0Fx3swUIDVARhlazjbHq4sCWe68Jgj7PP73boWE11l\nlPOTMmPaowf+MFmE7Xk23J6G+WEF1CM9WGRgegc8sDPK87PSnOLAgzH4gSUe7uc5uDUA2zJw009h\n7VngbIXjw/DBoXB/BexnhMElAjBUADcE76dlLgxAwr9XNfBZRlSgiWWwzZMpyEd5KEnDTfUKBAkD\ni1Fm9fOsWB3HAz9x9CBtcWGvDOwVEKm+zMi+rtKC1wOiA9V40iUcndXGsj3oU+hCMuGZ76tidxuE\nSBAGIrAmpjEf322HaK+yVi8k/9HSvIyeGzrA8mCoAiL9MBSH1ioYU1Aw2xCCGhvWBXUMjQVVShuC\n8EtknHOmBSfmlMU+FRJckQeOzimReCYqU6GU0dTUI3NSNxkj6bVlw7YoXBHSzw85mj81F1GaJiHZ\neFdCm01DBRwaUCbci+6LY+vWjPGgztb4ljMTsC4quKy6ILZFuQWrLEEPpxWEaSYCMhDaFZNl4m5Z\nrcf1JQrmDnBGHmpzur+pEIzu05Sa/nrYXiFa5FYHogGxezIIshkIqFIrGNEuP0cQwpl5mJSGphj8\nKiDvjjJgIQqwq/yvPzW6V6/kpPjO2xIFFSxhunVJcW/bS2FpFBbbX9fA+ixEx8Oq0XByFyxrgKXl\nMDYPp0bgZguOSMK9MVhbEBWmIwAHuqIKdbXCOQ2wtwPvWzA3D0tCcHoa9t0Ah8xUMD6/D3rrIdMJ\nUwpqLhSG4OlqWOzCxq1Q1wCnlMP8gsa9/NmB5n74WbkaatdnYO+IiNGXRuCODMxy4YgK+IGnBVPZ\npZLpolHCtCosuCaohlRNDHrSIicvTsGMEihph1c3wz0HqDH1bgJGfQrfOQRu3QWXW/DdCMzrgs+m\nw0suHBbSeIpmD47IaNBhRV4WAv2uMpBJnqwVy7IwJgXRbnAysGUqbC9RNlTrZ2I/icFrLsTS8McI\nHL9VGCJG2fDEz8ErhaVToXEIxiZhW4NmVyUD8oL5VVgUoW8OwE9L4Jw+6IlpnM39ZWpU7OdBdQbu\nj4ritTilkjjvy5fPN9BsCS9sNxouuL+RE/zqIDxmw0keTMspy4gYlYHBEJwYgJcR398x4HaDKYF9\nY/rsa9MwvgecnBpWyYACQEMGSrKQCPnKzz4oeRS8+WDGwHAVfDxKJfzRBT2AdVmo61JQLcT0cIby\nEB+Q8U4uAq+OEaUoaaDBgwl+lvipDT+zRaw/1lYp/TrKmJ7JawDepZZEK39Pw0lRXdd/WBpR1Gk0\nC60Ula0TEaTxHtoIvpFWwO4KazrpowY2DEEmA7NHSRvRkhJFMeBCQ1wUrhAShZUgn4waYHNWQX9h\nFE4z/vwqS5OEmxxt3K2O/I4HLYlzXEsN4kQA6rOqirK2egPdjiwGzzIq3TOOAr9rweSEPMdzIdhY\nrllUCRTo61AFthK40Iha2RnWuKW1KKM+KifLhbQF14VgkwuX2nBiHpaHFFDPNHrGFgDHZ0Sf2xj1\nPX6AsRk5ZDlGk4DLCzC59OsaWAegrAXMDBEDJuQ0IK6QV7m/CFg5CI1l2iXPzMNxg/DDOs0OP8kS\nBri4G7pDMC2sDvbOMJy1Cz6ql1plrAt/KgAxZYXHhWFMEpbE4YwdsDkq8D8TkAPVDyrhxpwWwwOO\nysLf5lQe/yMJF4XgEFszdmYMwdIymJqE6ig0+N3J4204JaoM5rwM3BDTnPV0AXYPwfycMpRLbM2T\nOswSvPFHC65sk0zc+xQyc4E4FMog2uebVhttDq0VUJ2Ti0+uAPUvQP5AaJ8IA2EYlfDtUDNQNSjZ\nXnO1zsuyYH6vgujfS+CeJNxTBtMS0GGES17jquz9yNE023O64Ldl8EpAI4QnD2n21YoymJWB5WHJ\nYItOgR3IXWwNKhd3oGt5uwtbA8q0EgVYGhOmmUEY2CKjB6UhBVV5+Eu5JMSf5cEE1MhciHCyL4AT\nDYwfht4A3BGVmi1udB5jPJiaA1xldW0lypZcS1lmRU4E8WACnDRkKyGQhkSd+IypsBRuHirz53j6\nvVYHJhSg3H+oAz6skgxoLeYcZV6lBRmYvBGVN+g6HQopZGp+BsLjWwtwZEAahiTKEiehDL3G07jv\nkNFU4M8dTbHYhT77RA9W2brm44x6CxEjf4OsgZwn+OgjA/taClTVRqXxNoSZznFhXUCVTBIF615k\nfXkQ6povMAr671oKdDd7up9DDpQbNZqSljjg9WkoT2mIY3cYtkdkJDY+L8OjhJ8pNjmwMClGQH9Y\nmXrSlm/IPCRDX+2vn8f8QOxZgjgqjT5/0JJtxouONoB9gfGexHdRAwt835yesOZ4HeBqTM0Ga2TS\ncKceMyyj+5I2cJHzdQ2sj0QIHpqhulYLbegzmF8NmybCUI8UUc0FCI2Hb6c0lvZbbXD1RGiMw7Ie\noFQZ6gsD0siXjlaWclEKTnOV4n9cDvsMQmcUprVKbrfZgquH4P0AdObULNtnCK6ugm8WgAC8PgQX\nIXOKQje8UitwfYyj7ubfLfj1IGRK4X5bHpK/y0k+u9aGMS6sCGnw2reBcRbcHoBPPG0kvbbI3w8U\nfAlhQR3LeRbMTyvraLDgN2Fl2r0p6bBtB44pwEZ/EEASmFtQ1tlcKmehy7MKwqGAHoqxrhzhl4bh\n7GGVPJvD8lDY6ij47VNQGfi2rY5zEAWlkC244aD/0d6ZB1t23PX98zvn3OXdt8ybN/uMRtJI1u5F\n8o5tYRkKYxPbQIrFlRRLEidVIYVJoIDYVJGEVAxFEZykkpAihJQhYCqJwdjBcbwhHMCWsS3JtmRZ\nGmkkzaLZ581b7nrO6fzx/fXtMyNjAxpr0OR21at737ln6e7T/e3vb+1aeqhfGyoh+bmOwuj3okGa\no1wOrwya6HO1XGeWKhkVTrXg3W24McAba+kdlypNsLMGv+tD+PnAdRXcNIE961rwNtvaQvoDpvu+\nsIa1Cs71VOdv7cPCREBfmtIzfhy9q81Mevhr3DsjM0klp3IZG7fVcONZWfOto61sSoOzbVnB948F\n8sfmpW88k8HNJeydwNjFyseWpHt+8QB6E0kGgxwezDVR2YRvXYWHl+E989r1YneA3TVQwmMtifFX\nu0g/Me0rVQMLpUB6nMGHOgLG30ML2CKKgP22UikqJ5Ukvpaf362TyPuhtmcpDErj0K6VPXGzUOTi\nE5lAa3cpfeuG+bbZtd5FZdq99u5cDPdNQeD1CEqh8J1B9X5lJf309oGkgXMd7cq6joBrATn09zOB\n96scMFcm6rO7C+0ScBUpp/gu4PtquGqsZ6y2ZVB9HI3vRxDzXsYXJD9+W9Cc2kCBfm11NwfRgjGH\ncpJ8tqUEafuQ+iWgfcSem8B6FHgK/sGN8KsTePEy3PuwYrDpwZv3wwePAWP4h9fDEYOf6sN3TjQR\n37oAX6jgNS25d6xV8B+DVvm/2YLtI+2P/uNB4PDtXeUP+LmxmMiPIEbzM8A7TastiOW2gizbV9fw\n6nPwtm3wM8dgcQ4Ob4X/acqx+v5Mq+zLgkJO73XF+SND+KOuonYmwG/0tJPkyzOF2P7SZ+H2G5Sp\n39MLkNXwviAVwH8y7d3+0onUHu9EK+jBACd8Nf+OSszgKx2Jd7snyhx/PtMe81/KpKtdNLFFyzSg\nXhKUK3TPBJYm8LlF+Lu1rK67gLuC3FjOGHzzJly9AT+wU9ts31lLhGvXAoAaJanZLBSCu4EiXGJy\n/tuAm0ewcyzd31dysfX1Aj6XpyRbqwgsVzMB1Uk8Sf8Edg3EANdaYoCtGlb6YphPbIN3Lar/rkOL\nzGE0oc6aJtqLJ/DJlqzL+/sCm81cLjnva0vU3TFSO7ccgdESnN4GT82pjUUQsx3lYqXn2rB1LPF1\n4V60IeYt8MXr4LoNpb8929X1oPredlQLT9WGE0sCBkOAErfhrE05Z9rBjyN23K30GwjsCeqHMlMm\n/FvGivxba+n3TiUmO8wEyFmAI3PaP86CJKeX9NWuIui6WFZbWlTuLxS+/L2VxPm5oONlgLsz9euT\nKPPikwiwXh/Enrs17BrK9lH7AnWiDedzSRIY3O/tefVY7c2Q18GJLnywK7XFJ1BKh4PISPZaV61s\nGnw4E3uukUpgiMbSGxEL/zAaR/0gldyttVj2OFNq26VCAPpNJgB+PwLyJ0rYnkva4jmrYz2FLBI5\n/Ps7tPr954E2OfuJXfBfzylvIkvK5LMwgV4HdrTgS33p0J5v8IpKjvk/VsI/C/CJ47BtrxjegUWt\n4psjOf1+Sy5G9K8qTcy39+BfB3hXkJj+i30YjYF5+BsL8KOldIWPt5RtZ0+tvdbvBW6vxEjawCcC\nfJfJifu/5QpBLDfhbYti428ZiW18fqxkLP+hC8/bVBrDicGxrvpl61hqjy0TKCbKoPdIobDCpYkM\nII+YEre0Mg3ahRIOtbT1LwYfysVqvnUdnpqX5Xi1JR3fAWChkqi4XAkk+yZm9vlczPOtQcy0F+At\nZ2DbEe3GemSbQKXj141MRoR9CHg2igRaf5ArC9l1KIz3MJoQLwwC+10VrGUSa7+CVAB3TgQW9+ea\nKLtQutYDmbYB6QTtcb97pDZXwME5bdNzC9L7bSvhvjb8rMmH9YdR/bYEWC51XR58B96WQCsgYN26\nDt1VCJmMqIN5scZJJkCqTSA1P4DWGDqrSpkXtsFoh7I+EaAYKmnJYA7OdgSSB45DZ0O7ddeF0q2O\nu0oePcjhCU9nuWFyPdoeYKlW/RYc3Pu5xNm5Urre+P/z1iSZVZlY/Uah8wPpujVPwLJlont2aljZ\nlEqoLNQXUTw/29EGgD3k1dLxd/qFTCB2HyIyPQRKn0WuWm9z6WOugidzOesvTdRvG4XG+WYmZnge\neR/kAV410jWbubxqPmDaxXiIVBODWqqA78gEtH8M/GmtpNtZ7VsUIde86XbtpX8foVW7RMi7RRtR\nYMrh+qKWksGcHPh9xkyzlTH/HAXWfxTgi5tKUHHHAuws4MND+OEtMrB8cAyPHUOKwgJYhu/N4XQG\nJyr48mENaoawuF0612Xg7iAF/MTg4ED7oH9upIQOjOGGDryuBZ+aSFS+0wSMb0a+c4eG8NY5TdxX\nBr3glVIuTqMM7jGx0FvQuzoA9Ct42UR6poc7ilTpAfdU8LOVHOXXluB3W4r3/u423NTXHua/k8v3\n9ZtHcLjQ7pin5x1kh3B8TtE7J0z3v2MicDxTwL9rpz3xjnldMrRtcRtFoU0yZU/ayGRsWs2UEev1\nlVyD5ku5roD2zjpjyhy/s4QD67DnMRjPw+ndcPcW7aDw/DWoB/CpLXLtuXUowD3e1nP7Ofx8WxPv\nJiSuHsQBtJaz+9j11B/LxcBfFATYx3NZmzvAa4LA7N5Mk/GbgrbV2DLR39C0uWsraJK1xwKufq6t\nPravKZnJYEnPW1iH1imgBf3tcH4JqOVqVht0RtpqZLOnPmlXAp1hJoDruRGrXcLcKmQbulfVhdGi\np4ztegrWoLrNVfJ/7a7p3pM5GLZh0JJ/9udzuecdQYvAS5HO73tcVTBXCRRPZnCmhleNE3i2aul5\nawSYp+ckTawVev4gl4vSot9jsdR7vuoMFCMtIsN5Rf0d6sB7Ci10e5Eb2A21Ah3uM1nXn0TMbivy\nu70GRVJdHWQ8ehLpfleQ1NAOnhfcmfhR0/07SAy/Mwhobw16l2dMhsgPozkGmkcLSKXiTjQcnfg/\nzoCnG0Fm/leTdrSomW7yyIS0WWQgbag3Fo4wD515SSe0nqPAmpWKQPrgOnzxOHpbZ8BWYP+yQOCW\nHN5zGL1Nc5+/FmxUsFDIQn6HucK9jzqooxf32q7Y2APIMjoYKRP+zhyqbUo5eIKU9KiDXDVWkF/m\n7+fwqwNFy5w9B7+wDI/n8L4S1kdwTU8W6j3I0Xp3rYH+cXdfeS8adKs1vB2JbPd2pPPZ7s/tBPgl\nkwjzQ+uySH5sXuz4hgHsmig71qd7GhtXBU3wPopWawUxwU3gthr+KQpmeDPS9ZGJjY0zDdCVscbV\nhxbFSv7JQCqBooRPLOv8RWS5vek8zI3kg7rREkB9fov0utf0NY7PtTR5t4/hTFuTfc9ADPn+Oehl\ncNVIRpfPZQLaa4EbfIJ/pZAY9iTwj4HbSiXo+H1/1/t9Ybs+CByWXCe4d6B9pfIS2msSsTtrsvyX\nHb3nrBRDjDvG5mNt5WHOSkJLSX3qFowXBY5nt6gNFrQgTTIB6bIbJTMkSWQVWAnnlqUe6BeydBe1\nAHlpAkubYsAgsI8gVra1xdbEpC54Xyax13eh5mq0D9siikZanqjdK33VfXVJE/9MW0w1eq61azHT\nuUp1WSgFwL1Sx8a5xtKeIWxdTSqIKoPVLXBoXsEhDyB96C40rs6S0sqeRXr17/Bjqwj0Xobvx4hY\n7UPIF3aXt2uCgPEphGktf8bDSF1UIXXOnyH/47hBAwEWTOevIBXB2Qo1euSfhhSobhuZpsTM/cYT\n/xyRtnuP123xlzoG2rCwqPqyqRfwnARWHgBKbdI3OoaW6zmSlvm0nxzQ25xHs6zQZwbUO6AzANsO\nw7N+/nHdZ24X3NWT6HAHSuV3/DTcsl1hfA8Ap8fQM+VdnZg2M3yXiWXtDPDuU1CPVJ8swO27BQyb\nKKplD2LJrwDeEGS9fjlSvn8Qib1vMXiBA+ATiJEcmMgQcH+m5jyG9hUa5wrLrdFAe3kp8PxMC96F\notHGwHuAEzWMRvCqOYX1XhfklTBBjPYDPQHWCq4amMDNZxUG/NROuGeLJsWXvdteGmQQeNjg9aVc\nUHoT6Ldk+Pt4Lr3WHQHu7Gt/+DNdie8nugKV3UMH/lzHhrnGcQsxZQsa2xsm/8cH0P/XoXq/Ei02\n874ojDPpyuPuvLnrF/MAK4chW9f8sIneUWiDnfIxs+o3XYFyv4CToOTi+VgeG2UbRstq/6QHVe4M\nNU9jdZTJx3W5lK44+O37hX6rzDezy3T+Sgk7R0oJmE9g0Bbo9Qsx2NqvH+YC8a+0ZK1e8+c9HyV+\nn2RSbfRcHbA8lE/t8kQuVWfa8FCuKbM3iEQsV+5yZvrs1PpcKBNx61VwvtCx4GL6WgEPtWQ7+KiL\n1yuFDME3IUw6isb+ik/RCSIHb67ggRw+iUAxptftkvaTvJ2Ea9v8vPsRwXjCnxHB+FM+v0ApB+fQ\njs6Puh2gxh8+8JNq0hbMHaYstNdSHggGJICtG585xN2HWfDPWgEkQ9/36hsGrGbWRd4XHQR3vx9C\neIeZ/XPgbWiuAbwzhPC//Zp3IIN6Bbw9hPCRr3LfwIfRm1pD1oo9/v95v3JI2h54yS/s+vlO/7s3\nKLHIowMghy1dOB+3SclV4zuX5Zh9ptKLOWyw1pcbyxOVfFpZgb81L6fllQJ+HfnmPVr6y6n0zPk5\nWJjTKjtC4tstwI9Uysu5C+1hVwS4N5f4m+n2bEOD5BBaIzro/KcQOB9GQBj3ADuPtksaAr+FRz4Z\nvLlUrtrHXCWx5Ne/BgH5thIGhQbqR0xj6IVBngTXrcNCXyxttacJWpn+7iu0o8M+pK/dOZY+b7NQ\nfP7BliYBKKHwrkrrXaeSbs+Q8WX3SGL1E204mIll3zLUhB7kmsjDDL7kRoKR98/zkAvNBEWLWRAo\nzLuxLAsSdyeZ9gbsTCTO5iOBpAXIJkqGbUOghHoJxisSvyeFUthVft8MsU7cGj5ui72ttQXoIKb9\nRKH6namlBllC9elWYogbedq0Naby3OGeEC0X1zOAAN1SzLWfq1/PtdWew6YFE+QKtb1Oi1DXvVvm\nS7Hjs22934MtLYIjBGRngdsmemZlei9zldpSBIFnhhj4QbcNdNG+b18xhYh+2nWXTV1jx50995sW\nvlc7STiCwHZNp03zFGSkde2cj899iIV+HxI+P48s+ud9ip9EaqwbkA43qjuPAedr/d8DljL5a08q\nRT7GreCp5NFhpvSjuRu0D5WIGo98IkV1QO2VLkgst6fcFL2x8OGZhrQWX+vHEMLQzF4XQuibWQH8\nsZm9xqv4yyGEX26eb2a3At+PVDD7gI+Z2Y0hhPppN8+8U3roLZs3fg51hPlfjd5ShhAp1nqnVv1j\nbT+nreij6Z7YQfe5Z6TkuQ9nfh/TRp4bLfT23M/xt0v19WRT9zvX0j1tTgCyHUWw/GCl7PyPoZX2\nWqSU3+IDLkf6sJ5X9RCacC9HrHJiGmRr6B6g0d4xAAAgAElEQVRv9PPmUR7VXSNZzddb2pfoUcRi\nuqbP+Vyi4pI/+88QgK8B15v2nN9fa8ubDeBjriPrtGCyDHs9/HKjENCt5nJ72Y7UGUVQhFJA7kvB\nVLe4i8uTSA+613WjWZAK46pSr+VER4D6JdTOFxjsLTTJt7nhaa2AF7ThukzGuApt5PZiEzOMLG2+\nTNbzylSXYQ7WhbKlzFWdWqqMrNL+TNmCDEgECLmL+l2Nn7HpvUfV3NaRqwPyBDqFj9RJpuctIwD5\nSqZx8JIgfXtWSG848nZe6+9km+tFQaAWEEi2EahuFEnFkAUZZe/wBWPsusDKp3PhC0plztiz6e7k\ndE1jbOD1A7HihVL6+Xat60oE5P1CksLnTCC85tNkgkDuDDIUXtOBj7eVW4CBpCI6MGqpjY+ZxvQG\nytewXsOuPO0E3kcEMPd7x+l5M5pfpxGgbyNt7JAjEN2FwHgV9WsP2OtzaR8iH8eNJPZHBurvKpi8\nX5b9vlQkpG/5dR3/DEwJEwEYazfbtYk//BmWrwmsACGEvn+Ndre46fFXQ/PvBN4bQpgAj5vZQYQp\nn37amdsRqNVo5i6iHj+BOsF1Jb19cFsBf7ZG2pq2z1RPMoCkc/EOYgnuaimyakstQ8b/aitN2nCs\nmPm1TBOKbcDEDTaFchMcq6UfHAQR6VtRtqEDzjj2IjA7hG/BZLBmmnhHvAqPekcd8Y57KbK2r3tn\nXIN0VefQIChQIpTlXIacQ66T7KKx8AXvlqMmBpUhb4Hc0oaphhymd5vu90LgpbWOHTa1+YhHJEU3\nl1ZQRM+OoIFZGuwYCgTX2wL52tS1jwEfq+QLekOmPlgwZS46U4gl1KjecUurzyAGcWcmXWXbBaQs\niN1uNb2HUZYG1NiBJ5hYc1ZpI7jVlgBoM3dbRZDao2q7mqALC26Vt6D7lm0BbzBl/Oo02DBButi2\nj6duDp0FBUSsF7DVGeB8JsYWy9D0/JH3yw1BVvwsuEdHcF5Qqy2dWgEKg1ztqkz33eaGp6jiiHrR\nVXeBak4wc91v5sC9tYIXZNoHajcCtUAC4XPOvPOgvn0gk07zDFLBbDC111CTNpy9F2U/o0Uy+rhq\n5JP+bqP6cs2NRKec4Ve+KI0daEe1xt3AxFQzH8+RO3URS4WpKpyut9uzNbIL7U93zM+xAFt6CucG\n9KBSodHzfu0E7V9HFMYjPY9UODLXNf/uWylNy9dFxa9fvu4tzCxD7P164FdCCA+Y2fcAP2pmP4g8\nLn4ihLCK5ngTRKOx8+klKo5HXouAVpJFYA3y7VDNyxftaIF6LTLbHtMOBb9PDrt72p1xe1Du0j5S\nFaxnArTrCiV3WUGD/gGDmw1e0dFtWkBhioU+gEBjiz9mV/AdmtHAfNzP/xM0sL8NGdMWLDkuL6Ac\nBDtQVvelWrrGGGP9JHpOgcTs25HzdqeGWzLIC/iy6fhNwE21HKtPIbA9ZBp4O4MCE0Z+HzLVbU/Q\nJF8JWsnvM2UdKtBCEv1N5xBI9Exbf2TIlWfkQDA2Tdw2sJJrErr3Cn3EQjJ0TheN2RGq2wHk5xvQ\nhC9cbxwBqGWysvdzqRQi8Excf1lmss73xhJvT3f0rjfzZGACGYwy5D6Um7s0FS72B4EztYB6Ylos\n8hJazm6rFlQdAVdhul/bwayTiSnX6NjWWvWcZAK5+UrPWJqIKU5LDWudNL8jU66QOJ8HsdIVzwVQ\nob7Z6s7yQ19s8ihJuN45ODBTw02Z3OcWzc8x9WXsl1EGD2YSsZ/09/QmBKz3oQW7RvNjI8Bmn8T0\nOkylum5XY/8Erv+Les2ANnU0vwbNwXG0ymcKTjmJIitf7OPuiM+LwsdN1DEfC8qYBlrwi3bydb4d\neIWr2E4Wqsc6evaCj09i/SKw5t6OaLhqIdQOiBVlJMV3xJOol3kG5S/CWGvgdjPbAvwfM7sL+BXg\n5/yUf4n2zPt7f94tvurRd5No+iuQkjBDL2gZqugWUcOxSOHRpNk6B6sTiYJT9toR+/uWXPq6m4AV\n77RWEOLf6C91Nxqg6wHur2FrpkxHtyJm8YagCRJMg7tCUULtXHqtNYTzT5B0SefQ3ug3opV/v1dr\nC1oDMsR0xiZjzRwa1E/5uWMcsFtpTKwyVRWz4m1/GA2cCj3rOgfPvSY28pjXa8nbuyOX7nIO1Tsy\n5BFimgN0z29GfbMvkzgfncs3WmLkeJvv8vqM0RhdQIvILf7c7YgJ7fDvWxBTv94B83zL2aODa+71\nX55I9B/lAobIvAJaBEE6zUHj9+jk3q30OTHY7HhU0UQJQbJKxqqsStb5vNC7ySb6v879t1wMVgNN\nIFwhkOrWqku71t/IgbYVdE7U22ZVqu+onfxmI5tcbbQ/IJa6OJbhbJyncyEx1oDabD6mJpZwbegL\nZd9k+Js4wAXgWC6p6qSP+Zf4uNhVi8EO/f/H/H1uoIGZ11pMxt5Xkbye9/e9gnI7lFGULrVw57mC\ncbaiyLYJSsITAW2Sy93KSEabU7j3QYBhtNhHUb0Lp2vp7q/3OdP2MVsG1avyDjo/Zkqw9EJIxQnZ\nlpYAv2zLP30qMZfIGv1JEpt9huUvTHpDCOfN7A+Al4YQ7o7HzezXkBEcpO7Z37jsKpIK6MLydlzx\nRJIDcjS7e5rYoxFCgL6Of39XA2k3sqo/hOKQNUs08W8EXheEyTnwxUI+eAV6mS9F4PJHaBDuMN1n\nD+rPW53Vkcm/tEb/X1skK+c1SGTqIlZ2ld/7tH+O0AAcIeDcg6JNzpoGQx/4KAKfHjKGbUUD7mG0\nkEaysIQG0hmvb+2/n0CDLUcX3hqkrzyJxsg68D/QevUq032WvP2f9m6+0bt/NxLjl9EY28wUKdPz\nGR7QZNqF9FwTJMKV3varghjvyOsf2/LKoLp/xuRsf4svcgTpefu5PlsIvGpnW9F1KQ8JkPLgQFYr\ny1KGgHjedYrg+mD3kd1oy6shegFkpf7qQv/j/TZFrwh+Lj0NCwHM0kTssqydPQbVp+sWeEOMeuR1\nDpmDa6Xk5+1SWZzOt/y6Wm0wXJfsbmOYIpPKLC0c8XlR/xtZcumMfM0ZYt+vHWQaMz3vq9PevD0+\nVgrU1w9mmpR7EQY9hQBuziThVQ5kpY/ha33MthHTfMJ/oyVxdL4lIrPfz4+E8KMthctmKLVf7s/a\nhebqYR//e3D9ooN0nM+Mga6+Rqeho37dlJW68ar5HqeYEsmZaZGfQwtA4XXsz0lHzAAxi1f6DyPg\n3/KMytcEVjPbDpQhhFUzm0MS778ws90hhON+2nejLF6gBDm/bWa/jPriBqRme3px4xAVyYclaruH\nyh4kRQ5TpfOHu/C3ESh8GbkcxXu93B/WQlEsNVqAoi5pBQHLb/n1p1HURWVu9UX9uY5ewBA4USj1\nXAsZiTa8mjEbUMfvuROB07J36KNoTJz1+97r91sHTtaKmppHA+Z5aCzc702ZI72URQS+16OB9yAa\n2NFKegqBWLQ+78yVVnBI0nEWXgd3zSNDjBnSeHwxcE2QYaCL2Hke3ICDDAi7cUMRWtwmJn3vaaRf\nBuUB2GrTHbH5tCUfxZ0IfAYIUKLlvV9oO/NuKVCZq9xZv9bfQqksVHXuEUReN0yqgQX3glgYJlY6\nV0pXHkwstOw6sPpYC7lE/8gcQcawcSFVQJW5+F2rTrlLMIPcGSECidon8SBPwlcwdboVqmPl7cyD\n1BfdOmHAxMRSM5eORrn6ZeheAxZ0fpkl33fQOQPTRoFj06LWwpOumCdeR4vsfjSlCqb8hAp/z/5e\nbvaxaz7ejvr4fjHS468EOJYptLVvGoMbmRb3fQgY3bA+1a8fQzr+pyzF/K8H35U805R+kV9rKC/G\npwqpyqoosvlEGCFW/SRizWcgOfCWJAyJHRR/byycMZDmRgQpB+NPEXtKNHHKdM0zKV+Pse4B3uN6\n1gz4zRDCx83sN8zsdq/SIbQDBiGEB83svyMMKIEfCX+eP1cLveUavYnog1YDc0qCe4FVr5TV/0uu\nT7kvaBdl3Gr52Z50ji8j6agfQhPgen/c/V6pz8BU3s5ypR58PorsOWoC7aO49dQ02KKlMzbGHQzY\ng8BlAw3O3D/PosFdIfHnnD8zb0nJf03mUVt+3jpiEGfRgL0KgW5ASSbcWYEn/PMq7+SRi9kLrqLo\nens7fu3NXuE1FxVbzl6jBiWuW3NBSactpJDITqWJvV5ILRBcnM+97dfVcmHaQRLvlmoB74ppQTnl\n/TGPDEAdZ3m1A8m8i9tlpr8iJH1pDDkduzEn1qvtYGN+r/mJi99BDDUrIXQFWASJftZ2tywfU5UD\nbwS1TWeUZMmdKor0G23V1RojuQgp4XJk1ufbic226uTvOsmkUho7AGe1WG7mDHXccrCMOm1XP2WW\nmGr0MMCnRDckVjsf3HAWtNBOMr3bmxxozzlQLCAwfoEl08bVJnXNK0gW+0gSXojsBp1Kx9s+Pq7P\ntTXPYTRvOi6xxJwCXQSymz4vbkIgUQVNiH6WSEjUrW76GLEMTkdXAp/74yC7RWG6r0UwdOPZBdb9\nWKI6wPW/Gy0t+tF7soWM09Pggsh+4/2eYbl8AQKPoje6yIU6jYLkhxqtIH2miLV7XsDxJ+fjzfRn\nC7CYJXcPSMCR4f6FCBCPxx99Yt6Yi+0uk7LvPIL696EKQg373OjVQoCIV71ArkW7UL2imtjQ6noa\nRXQdRu0xv89ub9p1JFfdAgHrk2igXeN/1yFmcHWQjneAQMwQwEag+xRakY+R9Ljx7W4gcM+9jXuC\nMkURZFmPEUdF0CRu18mQFEvuE2fgOsraJ3zfJxWIPUdXoi9kAtd9aMFbdLG27T7FLdfjQooiyoOr\nC7xNhVvBW67XbNUJgAyx2nnXoVoQcFatNDaCs5jaHAQdLKdMsvYEKA2K0a3c+8/1qKUlNUUEM/z3\nCHhF7XaSKtU/C2LZ/TzZURYnWqzyoGe3ncGOMzHaysEkGg5B7ySWWJfa2xL8e/ytW6X2jjK5ANZI\n8ipCAkfQteNMoJih6TY0jZU9QdKAkXIsRI4zyFLylrjYGtBylUiJDLQVmksPI3JxHB3bjebj1alZ\nDJCkV6N5Axq/8bd1aCiWSQanJrBOxQYSxS+ANuwokscBwJEAdQwLi+A6JoHsi/jG+bF+Q0tGomFt\nLkSkaMGLq5ZHRTBSEpTjsRMjIOcQRsoNCfLNBJLvRSuJtQtBSTQmVXpuT6dwgvROSjQourkMC0cq\npYw74ExsHgHsSfReCv8e05fdqupyCok8qyg9XNfPj/7Ju0kDKbpWdRAIXuvXXhumBlb2BYmQOx0Q\nKsRIHvU69YJi7KMf49BkXd2NAPUpk59r25lIu04TN3ZrFpJVeehgNszFJCtnBhlJBG4FgV9pmnSd\noLo9P8iolvt98zih0fkBd3x39hXnwsjZKJYs4jFAIKoQ4oivzZ3/DTpD+a3WufKpRjyKbDBGIk0s\nxdrn1hCUnEVutpKBeK52gCrE3CeZWGDZmHJZkF9p14GFeJ8inRdPj//HcRY9IIaZWFVcyE66uA+w\nv0rvMy4mmTPmvhu8ClfTRFXK2NUqmYN67MOatKhEFVLbmXMHsd5dXq/4W+HvMzgzj4Ebo0zHMm9D\n5WqTDUuavU0EkMs+7qOvflS9gVQO1+N5Jrxf2qQdAKIbe535xUYCVkjMInZ0/J4xnWjn0SaL0zIm\ngeqkcX4E62dYLh+wenzuNCIiyq6gjorWvbpxbEKqcQTdEdMZaW2dttdP76JsPcPgjtAVjGInVn7t\nnF7cBlpFo5I+xjOPffIxVnjiai4Gdgr4U9Iidy3JSrkDRdCUJkbpeT9iNO40bnqb17HtzX/Ur62Q\nGmCL/77NwaWyxFQje6yjrjLW38QcomFnmMNioWvnTCI6CLhGWdIh9spkIDI0YUufIIOWunjdWUqc\n/F232B4xpWLbGtQf5zO1aSnAjjoxqqJOInTp4LZRKBdthgBv4CJ37pO+MvdbdYAovb1ZSJb1zVzO\n85jE7cgiR37NOFObBr5IQDJ+5VXSnU6cqRNQ5idTMMNmIePTwJQa8hTJ8LiBB3Ag9lb4AlPmquPA\nF/nCmdSoAWrRGDfMfZuQTK6BFkQeTiHpZsXvERemuNiMswSAtY/5cZYCDYwkFdTevpYbEOdKX5gd\nrIsqeSTEz0EhtUphyUWt1bhvjZhr5u9h1QSqj/vciSqupjflmi+Y0UZxo8+JTTSvov2iIulBPeEc\nNS6+x4ip6D3QBMOcpCKIv5tcwKZRV012GgG68v9j8MEzLJcPWGvUo11SJtocLW+RwjeL61KnMz9m\nrfWoGly02pXJraSFdKwVOr8fHYNb/lxnqyGIaWYIzI6h1fNGf8zRTID16BzUQSvtbUi3GvNAHkeT\nrO/V6SLH6BX/fnWQ2HPcxET3edVXSNZ/EAjHMbPgXdHCxT3EiHpVYpWjTBO+mytTUI3CQXulBv7E\ndZa9UuAwdrbVIgFUFI3BGQjSC8ZJB4ml9n2CR2APiNFci0TOBy3Fee9B6o1pFFGDFdfmukV/RqdO\nFm8QIGYkNcB8lSKimqoJfMLH+k0cQGOkVmVigvG+kBhvt1KfDBttaldJPwliYBFwgyUjXQ+ds4kW\nv8Ilh+gihvdPDCeFFJZbmZ4ZKl1HllzH5sdidgHYWrirnzlDzrQIRG+EWO+e9816S9JJXMQwvZO5\n4IzUj0fmOcqTaqAIWpyit0EgLXz9PLm9FUFuTvEdxL6sTdnXHvc+ieRkDc2NGPp6rqG73Migbfot\nQzrYvf4e4mkxCmsRAet5QxMmiv7ugkkMw43vOWvcJJAMCs1zI9JfDKqRLj/DcvmANYZebJCYafRV\nihQudlCz4fhv0XIYwTXIJeZkJnejqK8cR6th7PQK5noabGM0GLchP8wDft0uv+VONCCOIYZZmF5w\nNFxFjYX574tepWPonnFBXDCJx9EN6YxX5RwC0wMo8ikLYkUjPB4iJBG6VXsEjwngo7jTrhUl1naW\nZz5pFnxgWVAG/pFPxLiYZyQdX9zyIlqkV/PETGoHy22hIU76s6MIPbbkfRGFDlDKuXOk/AchSwAU\nXYQMLQ4TcwHEJ3TXxdOYa6NrUl1EHaN53wwz/x8ZPCPjiiGwTdG76bwf+7RXJqNk1QDICKrD3Jk9\nmp8xqfYQvc+F0NCBhgRMeQNE8oueO1+6eO3HOpV0r0aKOlsoJWH0c3mRTBej4OI+elZUFwydZT7q\n76vr9TtlEtW3IzVRZPo1atcwSwt17Lcz6L7mwN/C89kEpa6EtIBtmFjpuvePr3Gc9//76H4bdeNH\nZ5hnfYxgMixtoKl/PWKu60wJJ2soN6s6sfHpIa3TyKqIF4E0QCMQR9ba9ESCpIOKAUiXoFw+YI1Z\nZ0AosolGa2STMVMJJCSI4jukEB/8mAPtJNOk7UNS9ER5ogMHCg2yq5wFnkMgeDXqjH3+mBi3G5Nm\n3eW3GyIQjfpUd7Vju/++RNIJ7fTrC5J4dbbR7NiM7SH5NnZC0p81S2RsNRrwRa3PaKUHF2XdBSka\ndzAdm0MiYm5Jz4hP3CLIEnvGF58tQVFmrUZ9F0yp+yITnvqWZuriZkKVDZLjN35s01Kqh2FkT2gC\nd/GIIb/P7sbgrqNBZ+Kv08Eu9k+ZCbjGDhJ5kIqimYV/5MCUhcS2o36xQKqT2uSGFedu6exz7Nfm\nJH/dVuw/0mLjXU23TGt4XACqzD0U0Lnx91ZIYL80cQ8Cnm4wrBtjYbrYWCMXAfJmOef9Hl2rzvk7\naOO+x8428yazRW0654vQUyRAW/drt/k9xyaQz0geGvNB4/AkaTpGXekIzYccqWpqvC1xPhtMXBQf\nWVLBHSOBckDRlxdY6yNoNgjG07wCssa5zQnXVDE2gT5Oovqi8/+K5fLqWKOeNbpG1CQgbIbJNb0G\nIkuNPq/NNGAe5dFvX3RdQxQcIsaxg+R03EeGqwwNhHVS2q6DaIDegQZdFAFrBCAZ8hXc5udv8+bM\nk4D1FClWeh8Sf2LCrlMoHLHONOnmXHcV9YOx5FkyNsU9jaJ4FmrtDLrgC8tGofMWy8RGywxO5/J2\nmHeQJ5foeZ4LF/DHTF28xf/fSfJtnXN1RDTMGHruTZZydkbSYN7eFlowIrNbCIlB5g7Sj/l5Mf55\nbGki9hDIRTE7qjGiLnZcJGs4fm6vTCGekYlVWQo8KBx8B66SiEMkgjCona1ablStWsOtbUmjlPvz\no3sVCCyzIENXDKeNIB/rWDSOjzIPhok6ZH92DAQY5QrYOI0Ml6Ula/9i7VuwhIQ1C6gvh+jdbqCF\nrSa5bl2snokSxDlS/tTIQPHxERNNb/NrzZJhKor6sZxGwDLvn4vxvSCpbxOm9pIFV99sxTc6RFP5\nNG5fiuSoiQERHJtAayR9aUbyb724NF04mwYwuGSgCpcTWKNLVZzRkaZHPWrsSHe0vsCNgsY1ccXa\n4AJ96/QTkgrBM+4/jsT9NgLF6HQ7RmzzSVJ0SYylf8hvkaEBcw0CxTUk6szhOzwiIKr9vjuAwd1w\n/C79FvNxB5K/4EkkCuUNBhpIzK4iuTHVrgeMEzKyx1YQ+4t7pnerNEljzP1hb1vcSbUkRaQseLv2\n4+5ofk70mR674SMasJoW/iK4K1UGh+6GHXdp8sRoxoWQrP+lKaqpVaX5UATptMdIj7Y1TF/XdA4V\nJECYhn2awlbN9ZeR/UUgjSw1b/xWZsnpfmIJCKPOGC7UR0bWO7ZU18KB2YDP/xG88s40DCemuva8\n/9t10tsO8+Sb69XX+p9daDTKwoUqjE5Q1F5pAtcCMcV+pvd51pIL3onGO1tDoBbzXTzpC0jHlPui\nbymhzALJoH7K390qSUM3B3zxbpjcpXGy1dtwxD+jb3RkudHTpUbS4GmcZMTGt5PP9wFSaPSZRhuq\nCKoxwVI0WBkptWEN032so4gfSVlTfRh/u7hEESUC8yXIEwCXE1gjS23oPqf0PG/8XpK8BKzxF/Un\nzZWpeS5cqKB2Y9dqphjq+9HkP+Sn9xEjPUTSFcZkJ8MglhdF/wyxghg40IyWWiP5oPbRYLnnbnjJ\nXUlfeN7PX0WDdAmtCVtCalbfNBjPxSabdodd8InQN4FZhSbWYa/XTZl0YbRS8p61TLo29zyb5gzA\n69BBIN9FAztGqkX34RrFjm+xCxf5jGQYqf1ed98Nb7pLz+l5m2L6v+hLWtRJr2e1jndw16eQHETi\nc6L+F3R9HPtxsTEuNBxFEXyhTOy2KTaPsyQV9Er1bQTYeH58fgS7pm9qBFwDPvtJeMlrG04qvuDF\nMsgTcMb2NCXbuCgCUz/iuHdUmclYFB3qj6J3sBJkSDPSohPHa8SUyFiv8Xd5lT87iuiRHR8lZUtr\nEr1ASkDX82ccvRvuuEvjPap1oqqsSR5B7z8GCjQTS01MetkuMlbd0HjuGTSGzsQOiheFix4SwbK+\n6HjzWDRSNXWuEUPivSNhg+QlcInK5QPWmB8xMte4Z0N07o55BBqDdDrLm0atJvNt+KZeEAM4naFa\nBc+TUqhF8XzD/++j39fQvfu+Uga7UF1bInDN0Iq7xIXiUCCpBzZIxo/zyK0q5sO8GQH0YWDJ9L1L\nEsWiSvk8nsDCkhqhqWhfUzVlLDJthzwwDfyAQDrqrY6Rdg4AbQUcuyqKf496t+5udPdDdmEiiKWa\nadglKIHLvgAvahg4iosG8HzpIGNJNwnusZB5ukyvzFrmgIesyCCGFGJ7TSn9bm6wSPDQ21o6y677\nlMYcs1P3LS5UHwQS+DbF8Zh6L5a68VvcQTUanGKJ3hMXF+PC3QlAw7kXPQT8nH6R1ARrXqeYcGNM\n8k5oaL+mJUY0bUcG2T2oT3veNwuN9kVwP9S4fp200MYSVUIxJLTR1GmJW8vkpMxnoPEbk7gs+7M2\ns+RqFTcGga+Ba3HeNwH3YhG+SdDi58V62Wj1j0DbBNlLCKpwOYF1iWTdiCtJfAOu2J42PE7M6Mfa\n7NR4vKkaiEv5xeoA7+i1trIgnTcpzx8xqGq5oYAr1JtiREurbNZWJqwDSNyPu8dEPe023H+TNMii\nPe4plFAhst6BV+duNIhjir2z/tgY5x9fUB+B706/bwTXCRrI0VhUIvDrXzT6z3gdYj5NSAEJT3hd\nYuai6EbWFA9jO5tlbHLniRb4ZokAVEU/ziiuO5uEZAAJDTE7uheNnU31TZM9+nJHnd06yVA4MK+b\nG7JK03OLkMCxJvmORhVB6eJBDFCI7DcarCJbjXrJmHQlzsmoZx7k6fhmlqLQSmQE3B0uUPODXx/9\nP8ssuWMNc/0f0Lg8T0oVuoXkmbjLj20lDfEuadgvkdyXxni/465aLg2t+V/cNiXmC+iSJKkFpIrI\ngXuCVE25v5fIeWJ4LGgOXJwnOmLXHCIxx0mADxrXMUtcHxLwRWod53YTKGOEZtH4PZZ4LlwIyvG8\nixO9xBcKTzeC/RXL5QtpnZVZmZVZ+WtcnnObCc7KrMzKrFzJ5WIJZVZmZVZmZVaeYZkB66zMyqzM\nyiUuzzqwmtkbzOwhM3vEzH762X7+pS5m9utmdsLMvtg4tmJmHzWzh83sI2a23PjtHd72h8zs9Zen\n1n+1Ymb7zewPzewBM/uSmb3dj1+p7e2a2T1mdp+ZPWhmP+/Hr8j2AphZbmb3mtkH/f8rua2Pm9kX\nvL2f8WOXpr0hhGftD9noDpKSQd0H3PJs1uEb0KY7UWDWFxvHfhH4Kf/+08Av+Pdbvc0t74ODQHa5\n2/CXaOtu4Hb/voA8wG65Utvrbej5Z4F2tXnNFd7eH0cbbXzA/7+S23oIWLno2CVp77PNWF8OHAwh\nPB60RfbvoC2zn7MlhPB/Sf72sbwFeI9/fw/wXf59uj14COFx9HJe/mzU81KUEMLxEMJ9/n0DucPu\n4wptL0D46tu/X5HtNbOr0K7sv0ZyQLoi29ooF1v+L0l7n21g3Ycn0/dyhD9ve+zndtkVQjjh30+Q\n3A73kqIA4TncfjO7FjH1e7iC22tmmazz9p0AAAIDSURBVJndh9r1hyGEB7hy2/tu4Ce5MCznSm0r\nyGP1Y2b2WTP7+37skrT32Q4Q+P/OtyuEEL6O3+5zrk/MbAF4H/BjIYR1s7ToX2ntDU/f/v11F/1+\nRbTXzN4EnAwh3Otb3D+tXCltbZRXhxCeMrMdwEfN7KHmj8+kvc82Y714e+z9XLgKXCnlhJntBjCz\nPSjPCvxltgf/a1rMrIVA9TdDCO/3w1dse2MJIZwH/gDlUb8S2/sq4C1mdgh4L/AtZvabXJltBSCE\n8JR/ngJ+D4n2l6S9zzawfha4wcyuNbM28P1oy+wrrXwA+CH//kPA+xvH32pmbTM7wNfaHvyvYTFR\n0/8CPBhC+DeNn67U9m6PVuHG9u/3cgW2N4TwzhDC/hDCAeCtwCdCCD/AFdhWADPrmdmif58HXo+i\nzi9Ney+DJe6NyJp8EHjH5bYMXoL2vBflNRkj/fHfQSH3H0O5Xj4CLDfOf6e3/SHg2y93/f+SbX0N\n0r/dhwDmXuANV3B7XwB83tv7BeAn/fgV2d5GG15L8gq4ItuKUnPc539filh0qdo7C2mdlVmZlVm5\nxGUWeTUrszIrs3KJywxYZ2VWZmVWLnGZAeuszMqszMolLjNgnZVZmZVZucRlBqyzMiuzMiuXuMyA\ndVZmZVZm5RKXGbDOyqzMyqxc4jID1lmZlVmZlUtc/h8Kz3lJYE3qlwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa6a0dd8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "imgplot = plt.imshow(lum_img)\n",
-    "imgplot.set_cmap('spectral')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "显示色度条："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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69BAPPSwU7CF7EtD3NCC+tJGA8HIP7FpHcn/iNFhZFhspAa2dCNhO8h8BpJ2i\nMHa0E/cqmGTAZmnbSckMa1VC0dkGQBcW89KNYydrpxVAhMzaKM00lyUNEF2cejfekRs3rrQeZzMt\n0x4C9kF96hxJ+m/Ji/UZCGf+/N5oPQ5w7TTXZ53uoCFO4lemDMR6HQIiBWUOEoX7ViMqAQyDUlLz\nVKw9qXWq38WnzA60jDqwpNkScttqJwN2APVosZLJ1/GzbN1I3PQaAF5+/qWyVL8T4+9ye1LdLmXc\nR/exejw35LBpuXGbvWZ6i88xy9xT5VTkU7r13L8jLrsP429AzOx8M7vUzC5zQlnf/w0z+4T/fdrM\npma2dyguANtbQGFmX0Le++JVIYRXm9n1IYTj/L4BuI6/5bnwJk/2uOA7ZrmagfsZjBxgqaOl8acJ\nUb0waeJiCo7MVB0AuYGqEWwIYIYY0Wwhc+fs27JTkhml684Eax/PIb1pU4F1cZ/6ZurdOKXn7zaX\nj+XofbrajzEz1S3KXOsQPexW2JTqIxU8aoNVMhSFcqo7HGl+bl6eU1oBCO6fNMh4JQ9qBC3uCeOa\neS5knX/yGrBZIKaeNr0XQ1oCyDS7cRxspituaLPM1vX9Ma/9KKYzXZ7NY1o8UgNxKD+LZyTsUJU3\nVf3V8WvYWjXBxUYTi4Cqq9642Kf339xwiGGmTf7OxRXXIoLvAZSge22sUrzCsO0FFOGOhxH+y2V6\nZtYC+HfEwyyvAvBxAI8dPD09hn84gP8RQnjIvDS2q2r43hDC1WZ2O8Rz5IuN3kMIwWy4230Dfoqr\ndyhuYJO8DeLl6Igdcl9M1vQ+G8gKhVPwqdtSbPibyaGm4TS2AZ6O6u+sBE/VVya9bw3CiHkL0kGZ\nZ5OwwUFUQVL1r3wmlYN1QcDrZ9NlvHW5C30p5DmGsXwvDQRisRtUkQjwsxyFm1gdXFloyOFUt3mo\nTW3UW2UI7FnfMwZKGRiHDKcIWc0DUUkURkUZEPjO2Tb4vov2oPVvKL1iOmlLVdmKawLOtF3UCzfm\nCY14ybcXANrct5q6fkS4HWahHw/ZCEdhM1rpIvgmjwgfoKYW7ToTAKdanN1zd7MNRPbLg1qPiMxh\nsluU+wG4PIRwBQCY2ZsBPBLAvBOFHgfgws0i3BbwhhCu9s9vmtnbPYPXmtkpIYRrzOxURIydkfdf\nENkuANz3+4H7PCi2R/VL1aW1AfGFz2yMEma/k2104zx9Ux3vYAeso2UH9mfZkMhMaF1Pja8rG3Jo\nhTVVoiDa45OvAAAgAElEQVREdyerGHCtoiiAUUFxGgcZ6rdTmCo9NTI1nLpb2clr67+yNEpyytf8\nDnT0mUFCADVd2+TZVIYaGOcMKilOia/2HKnTVQa/qYTyXQ5xryJfMjjXxkkOWgHC2snOSSR0ENd6\nqtmoXKu9SwaLIWF1Nzsgkx7mLZXLSZEa17gPNaXxhywgetd4ODaR5Qp80cdwk8bPQQyZ/QYAl18E\nfOkit9hvXqStyymHDpJk9kji01AeoHElgPsPPWpmOwH8EIBf2iyJmw28nkAbQrjRzHYB+EEAzwXw\nLgBPAPD7/vmOoecfdYEvDw6lSoFCH8C0TSLi50aDtOFzsPgCi8btKofOT4ykGqB1oOzbCJq8PiTJ\n91Km5H3FFtN0U8GuRbEqqeg0IacXLDIkrphKagoG198BRQecATRzVkVWJp4cCdR7gKe6qi48ua5N\nq7QwACQitNLX+dCw6ppEVUHyfQ1V3AGl6sJBpDAUdu5BoIA/AJgzYBtmwxYgp2mErGoYKlOhXqhF\nmbq32aRaqPTMBTAjP1O3SX2+thMMLbc+1GS8JrIW8r69Q9tEpoDIoElfccPsxjrpEE/vy9R6dZY3\n6Flzb5Ee8R8HgZMDsMeiuqE5D7j7edGVbALg4uduXq4tySaM96JvAhdtvpnDoYZmlUcAuHjIk0tl\nO4z3ZABvj2pcjAD8RQjhvWZ2CYC/NLMnwd3Jhh6+EWlz+bxfrfsKcgObzgBjAwvZ+JbaKzutAGRo\nkRc8CNDxmUY6uBpZhiSYg7DGHTK7LbYHdLVE02XQJwgk41yI8YUmG8h6ui1Ny86ZFkoIEKayUM/L\n8rUoFitoPO1azNvGTmBpLf6erni5e09H2BxZmKoTFMzqmYICAg1aQ6wZcODsgSGzQhC1Tr26kFPj\nQq/NgbBKX6/xPWn98Trrsp721xvxFHpZ97YB69gBdLqcjbOhEfBG+S6Y/34k71fuNfDZGveM6GbL\nm8rQ+wyragsAsDpy33cydRIYQx5PQjmjbPrMahV3ufPc6iiDJxBBd9zHjXGAcnk490BJWoyQT7ww\n5L1IgueD7HclRLC+zvKOZkdMNgHe83YD590x/37u7MmYV6E8ZvAM5IOWa/kpHELNAGwDeEMIXwZw\nr4Hr1yEqoTeVZQBnVR1wzaf0u6bVqItS7xSAYrenYiVWk9UCapTQXZpoQZ5xD6vL0qBgRexM3bxa\no+qgcgHiTzLvbpwd81ux0gPD4JPy4mxed+oCgGYjpmV99isNLTA+CBz7kt8CHvENXHb/1+DkVWB9\nT6zA0WoE4zSIVa284fHaUo5kka/KnIC2q65ZCRzFQCHqjzRYVEA4VA9DeU3XK2CeZzc9FDMs3sFQ\nfio1iYIuGX496GhZ2kmZBzXoqWtZDbYJuDXOVj69zneoVw2ywQuIngbLbiwkyRk7iNMvnqAKxH5j\nqEDX09d9QkiUqIIYeXl0X+BRyEa95Q6YSj/iCR0w3w8X8xWoN0u2p+O9BMCdzOwsxHM/HwPgsXUg\nMzsWwPcj6ng3lS3Ysm8ZWQNwhTBR9Q0cOp21h2zoYrPb3SWPBo66dcmsZMbdGDPTzGT80CnvFkVd\nnpL7jpUgQRcyCgE8qTYO5WExL11Reyhz7VvgCz/3fPzGDe/HuXd6I+76nlfj8j3AyvXAZGfW9epC\nk1qSz25ldGp6/xsAwVpFYZ0zclU9VICWjE+su2pGkSP3j7Ys90w+59RlvahjM2+O2qVvxouCTLz2\nsLBN8o+qjUFc5JrIniHtFEDyM65BV4XGraSLdbJRExgCKMFz3EfApfdB7ac7k3dkDyTz77p5PY3e\nOrBwu1Q9zWK9nd3ZDshM90rk8xWPiGzDnSyEMAXwVADvAfA5AG8JIXzezJ5iZk+RoD8K4D0hhEN6\nAh+1/Xj/JMSRDYh63jbkRtHCt2kMGWzVlxeIo7rux5AklJ2rWPUzwKjo/lRbkoE5HcfKOAsj0KHK\nrXlxIfupyzAPOGoDj+pSNd+JcRnQ9cD1B3bgrZ95ED7+h4/Dtc/6Kl58l2fjbjty/gmMWwJ/qjuo\nzzyUbrHOH701NO9kyEN1IeoLLfOM25jN5kVZMN/1zXFMmtHRNlUehHHW9diNsrsjXf+sy+20NgID\nZZ45iCfjl8SfGK/UG1UIul9vQAZbsmDA1XpdVCXw7LyA6I2QjldqMgMmo+W5bpSNKi0eS6+vUjdR\n517ZNLoRlKeIq9qAuIn6dQCebdi+O9lh6IntOdtLbyty1BjvdYgKYGbCkHVD9QkG1EsF5JGRLzb5\nMDIgpGPxHhvxwDQ5tG5wogHE8vVkANE/SJwVM57nMsVdp5KuUp4ZdOavQFTTmVmSKmWdYXtkp2Pg\nhL2reOp9/x7f87onYONd38b/fNJz8PQr7432oAezzDSHLOh1WqGuCw0mg1Lhe+p1POO1oAAuoF6o\nKyoGmN4tmW8rz4ccr/5OsxBFA8n/DEudVw8pI/JOBSDrwaudevv1crS1fjeUaXNxRPK1rcmFxTZb\ne6Qk3apl0NsQ1qszvlGfd6KbOBBOHDwJhEyyCVm1x0VMurc0m306fbnJ7o3qotZAwJ/A74Nhvc8D\nEEF4zybVf1iyzQUUR1qOGvACcZ8GIL84AEn5zjXe82Ta+LSIAOkdPHVARiZA2o8rQK2mrcBs5+Pq\nnUIYZzWlruOl0Og05I5E9cNcQ5+mTXUI9aUC5miqsMigx47cj4CfPyXgoie/BOc9/av48E//GL77\nPc/GP14NNAeQdMhpE/G2jLJwaStulNcTG6/yn1ZbeacMbcnw0sIMCHhKHRbpDYGi5kPqoAbTUNVp\nUs8wfzLADA2kaaDVwUDTGHqPIddvDf6pTir9fgpi5TNky42AbWoLMiACeYFDb754wYF40kRwXWvi\n/UmTTwIxSZfseYP2BFcl8IBNEiCed0iPDj2pukFWYSRVn7QRsuw2xMVUuzzMEVMzAHmT76383Qpy\n1IB3GflYn3VpJAbfBpK6KBm91c+QZ09Rl0WxLk+z6k6fJAyAhwj1aOqyMwjAQAnu2mH1U8PqVJds\nYGARQj8aABv/rI041JkO+e8Whr42+jdvnAk8/QF/ig/+/W/jlM/3ePKT3oC7fONeaDccoJvM3AaN\nWzL1r12dks5VBsD0W2YXQLzfdN5RuX/GAINO5Z8HuFLeGanUNhyoNtPtbsXAV896OP0fWiXJ96D6\nd7oSAvH7zFlvWs4BsGabTH7uNlwthgy0QAY8MmMWrbeoAqB7F70iig1uENNca/Nm+ar+I9hy/+x0\nEknIDHjoTL/iqCdeQz4m/ojI6DD+bgU5asC7It+XQ24gQNxEmQY27lTGZYgTiy/9YJtVDUAE37Qi\np2pcwEDnCXOuuwwZMTbT+gz5Pg4x0KRnlOkzAUenjzqNrNkUAbFedaasSSV5IwR3L3Od4vgY4D1P\neT5e+YZXYe9jj8dJ//B3+MxX27wzGzJgAnEwKAxHMpvQWcOMqkU6FuMAhMGFcsn3PFFDWpHeUD1p\n2hW7rI1mm6Y5oPtP8QiQJp23+iOnSHL++P5rFUffIG2Mw2sp7YrFMn0yXlUt1FIDnZ7ebHOu87xB\n1dOutfFvvVIVANl4phv0Jy+KpjzrT4pTbCvJ62TcKyESsyO1Sc4CeEWuxKwzXI/Z3Y+mli20fLkc\nZdeGWKGLbhE544IzwKw0nFrXh4RhazCu18wzbBGmTqONBhhNiy5KBZBWeZ6nsx4Ci7TAogGWDmRf\n3slJwENPuBj/8Lp/xOrTbo8H/df34O3NdyLQ91hdwHRhxzxmuoW8dGO33iO71rE8yQ/2cJw4ayDT\nQWuUWflm+zsctnAqrW59ssx7SNKmRare6FDsy8FyjNb8npep2Jg/FS5eb/uSNa63GSjTHgnVsyQ0\nQLkadKXLfawNfjx9m+Pgse7q3sm9eenLq6eYLPcZZPhZnw5DLwmN0zz8EVsyvADeKDchruI7Hcgn\n7FYVT1ANHoYjaUDWTQ2R0MTQKmDsRxkwh0Cz3tkMEHYKYSYK4laqJtSAoVIs5yXTc9bUuh9uSkN8\nM4t9DqoOWzBMTbNi1GRaLEe3VO241gDj7w7Y99l74/i/eDj+8T6/iHP+7GFYusb14v48lybXkty/\nKqAtXOm8QXN3Mi4g4W81jOlxS/1AR0juaKpbr+uGt9xVLtXlYQB6PcjV/r0F460W7egubbXxrng3\nDtR9U8bLwUjfE/XvRf4qNmyuptNNyDvLYKfHt1M/rP64ZLpA7HNrba5qqhVq0FB1hKougNx3qRMG\nsuFO3cm4eU5n0ah2wOM8nJW+m8oCeKNwW8jd/rICyk7dhOzSQgY8tYHjyyENRYB00M1LG22bQa4b\n5e30ZlyfZDo9xOIUdGAlqKf9HgbAOOllLW+AzQUVdQcu5oXSYYtlyyZAoNNZ5lvLofkIsQ4aA/od\nAZ+5xxqe8AdvwNr7z8ID3/Za/PMqMKFvaZV+Uhl0ZdoFKPo7oE5zyKWukEoFQB/XNCMI5bsApP4r\n2QrDLYxclfpJdeSF10H9Lln2dGH4vs6ginfcy/7RKMtUtDXDoApM87RG33DLCyWoaw3IPrd8ZqYs\nyK+nUG34JxlvfQIxQZOqAx0TTZ7v5Tll4WrUIy4AR/DIngXwRpkg78PZII+WHDnTOU42O1UZWtVG\nBX7X+GqbPrOJYnWPiwIfW0bvnburgHNIt1swF59yztNTagOv96SgfpODADdy13RmOvI8y7+qMASY\nCHiDbM98GotY/vEScJ8HX4LXP/uVOPuyD+EXfvLleN0ngH4prmaj0S8Z2Kp0asBIoBxkoBgAQ7Lc\neUDZVANQrVYY0tnO+N7Kc+lyPchi4B0y3aHplcZRDTYqyXOhGhR1QEnxDbzzoTY41NZ2ettZcT93\nvnKSE50l1kWaWuxvKgqQ6WBTAU9mgQybxKievQLlyTL0diDrpmH9FgOkBfBGOQmR9U4AIGRFe92W\nuia/pKGG1pkb40bRHxGI+q4abNOUHfl3Yk4Qzwm/T7BMKgl23LrVCtOkJ0UNtDOFmsOYZlyfBphu\nAbSbGX7YyStfvWKa7iyIG9CQXTYAfuC+wGtf8Ge494lX4MWPfynecvmPIMjZcilvVb4UaFM+pY7S\nFL1SjXCHtkKPrGXyMEOr/HQQmAeOg4Y/AdrinQk4prQ2Y+lSjjS3rsKnOrCsPqGBLPktA4Pqg7nJ\nST0pGVl3N8BJk0kNT5+ml0E9Trn6P+4XUQEqVQK6YZVe16LzrDYSIbqeUXitYZoh+wYDZR9f2rz4\nhycrh/F3K8hRA97dnvhKyPonvgS+uI3Gd623YtOt1CCn0kACsutZYqwSdoiNKWjQDYYjNePg6jiy\n0TrOmeWvAtDqF8qwgXlryg6t+r3kQ6u9YwhwAwaZHnW8tQeAsvs0QHinpytZMAfAAIRjgD9/+Ytw\nxrP24ZLz74U3fP4H0X0rF1NnFbWLnta3rjQbWmTSj2O6/QhpA5khoKPum+BbrPKSeh/yYFHWn9RD\nnOnQzc1yvGmRhqis0owh5PdKFUwjuuSsO0NePNNKXTEa6vN1YJJ64cDfS14Lg2yTXSm13dYqBq46\n0yPYDW43Yd0KYSATpTEtZ8izz7T8Hs9ABESV4X1HPRbgVTPygWD3NAMwpO9RhlzPbrYsGG8WQ17p\nAuQpR7BoaV2SBqMZ5fJhxqHx6Q5JnNIUHUik1tlpQ2YcgIA4oi6usE4PMBuNvzasAAKuHpbuW60f\nqTO0fHku4wol8y3UKX0ZrvbLVTBUHW0aBDpguht474Oeg3e+/Ep86tHfg8d8+QFYvQapEzaTElgR\nSkan6dT5ovGpXS8ZZ99Gr4uZopocxSSDGYDovWBlWXKCMuC1mZWngaZ6RrfpnDuwEbn8kyqpZgqE\nKfC1EXDRcSdix65TcNLJTWEkZLppAGm2tmHTPAas7VabyYYAfUAE4BVfir/UZ4ab4pE+01ZAOHVg\n5gkxBlEvmHs0QPochpssWW5ANNwxP8keAvFYOgTjPyxZAG+WHdV0hTKuGrsCar2cWKWz7EYTANw0\njo2va/2zAlay182mdBayu07bO/sQ1jHPpWreKrTC0CIARUv2VBwXQ5v1zjNqCKBk7x4/r6cC8uvQ\n9L2ryqJeFC7tBGhPBD7/0NfhHS95AU7/yXNxyud+DO1VyHshowT1AmTneBHU2zmGJoI4Dy0dD2wz\nomfZcYOhIr82XB71OdYd0tRAqN4C6ms8pLpIQN3EAWJjT8z3dBn4oeOB4z/yM7jLj38WP3zh2zFa\nuQGfWw/50FJ3n0teHtPMEOeqSZDDsA3Xm0QB7qEg7YH7nay1eSMcegrRR1fVCuyLvZWqCHXvnHja\nOtukkOHq0mE+z7DJV99d3TYa37msj6eFU0YhHoJ7xGQBvFFYPn2pDVBsmAGUTtkqaQf9Kt7l3jdW\nR2x4jKoN2deRDZceB3XcRToNMG3LZ1TYqdVolNjVQLwJnKrTZ2u3KQILO2zSOxaRIYOJzQJfwX7D\nLKjWxrfCY6J6tm2Ay88N+PTzv4jTfvUXcea//AzwVQE1ka0uTqA0E4B73OoMYTNpJz7T1wGpUr1w\nV7TCFWzA8KdGroKdy8BSlC8A3bLPzG4CvrQbuMdXzsGxT3wr/unpF+O8O30U/3zh9+L6Hz0XNxxY\nw/EHQ6oX67P3Sjtxb5qBQXzIbRGYnY6rUDUHRIBca2d3+qOxbCia2jNBXcK4e1kndTJ02jCrrh1o\nA1qty11JsNTARrXhgSZ7N2xb/n8GvLdSMrPCJYX0Mxz5XH7cl87d6v4C5B2TuGco7437+OLWmziN\naoOfctrLiNtUeidOcYBkjNDGwN86DabuT1UZaXltK9P3TRoMATBNuT2OpgMCmeMcgKd6oxuVDHDI\nOh9/5PjVqFawRDJxqhj8OY3LOqA/DrjoQX+Nx151Ai57/k/iRRe2+HV7bTymfjQMuMnVTVzOhnSd\nBiBU6ob6ZA5+b/zEjb5BuS2ls6dG6pYjb6jzFnI+VOWgIF6nS+nbyMjfvQv4vS8/BR97/APRfs9N\neMmzX4wHf8dHcc6616vryuHGy6SaCphRPQCzg68uWlHVVKEfFzXYqB/eUlVFWanGIdVSJMHfS9w9\nzW/WnCIg9zMl77wmavjYn7zdcdUbv/PeyD8PcWzilmWeG97RkqMGvBOL4Eu9DgFvo4l6VL44Noql\nvtQf7ejyPTqB85kNAixyw0zW3Aqg2FCADLIUbok36nMgC9nTgsYPPWE4gcsh9FNDBiDV7RYLHIAE\nXqEF0LsLmBhxyHjTwgqyYQJtnSftDZKXZNgLEo7pT4FwCnDhY16NJy8DL//vwBPfAJxwWg5fD1DF\n5uj8KvWl99QDJLmPkal3UjbPV+GD63VTHEuk5W0w6zEhZUyYRrtC7++DDLgHuh3A0vXAR08E/ve/\nPxYf/YOfwO1P+hc873lPwS/cZQ3LEwAbMqBbzFPTATbJRkQF4DRjEuBXHSk3nQkNCsMukEmI7sVQ\ni7ZvPqNEXk+h4Pchom/y7JBIkyuqdty7WgNx/4Xey0D97lqbnUDYt4AIwjvCkQPeI7Zi8QjJUduP\n931iIAMiuHFlzEjusSGQ+dLqqlOu2vjG+KZNaRldngIHx9m1hg2XosdTc+s7rkGfNOUu/OnU1BoM\nBlhfsQEKAdFbW+GH6s83vaga6Bss4F/UJZnQUI+QDl4wWyC5kFmHZFoOIxQqhxgQM72wHwH9V4Hf\nfN934oMfeRm+9iuvxLV3++v0EmaMegSYeU1Ne+tAOdL0XwaXBKJNWa76ueQhIeUoZhSav+pZAOmI\nnn4MjFaBn2nvg/e/8LHApSfgcS96J15wwjvjir4+hu2WUcxm9Bw1XZWoqqGUZk0KyHg5za/Ccy9b\n9gueKKGiO4IBwyo7G+g/KR9eVWSm2hz4OQp5S0l9jttD8p6y84BSfcFBRdUXPYDVBni4Ydv78a4f\nxnEWy3fdXnpbkUMScDN7nZlda2aflmvHm9n7zOwLZvZeM9sr955lZpeZ2aVm9oNz45Xvurabq9PY\nNxog78zEkdmfY0MY2iBkyJF76i+6t/zSp6IL48F/S33OU93IgMx2J03ZqA+1yYuGqRcSFN4PkKW0\njesf3eWpLuiQWqIMkD9rg5bqdtXdrTYsFQayEAFmdHvg0Y+5FPf82o046VE/iff/5V70PkjM+Ngy\nHzJAJPWNtsDNQBeRcXOw6pdym9DBRKVWp6Q05tRX4dngumEey3PJdcBpb/sevPtxL8SJJ/01Pnfh\nz+J5p74Tk52zzwGZ8dJo2o1RqqQMhe5Z6yq1BUgbCUhbQfJecrW03HanFbgR0FS9UJ+HVvuwc0Oq\nHlknzAMJUjmljEM2knrGCgz79GpYsnuW4UATN9A6EjJodJ3zd2vIVjQffwrg/OraMwG8L4RwZwAf\n8N8ws7shnkd0N3/mFWY2mMYolAp4jnor3nkn0jjSqC7PTy3qc9mf1RCgvoeTJsbVWTbapbXiwu6a\nEBsy9ylNltmBRqXub7UOMjHYkDtJvW1iLYUxx7LlG4gduRWXLZgvca7dj6p8JnepKk8pH62AgHo0\n6FTde2NtaOv92QcsAb/x0mfihza+iT/7rT/CRX80yjpq5GcHDWXCgocWKOheB/xNFYiFMp8FeOlz\nmr4N5KUGOs23v4d+FXjH0gl4xJs+i4Mvewre9OqH4RNP+AhWPMxoPb6jZgJ07gKnPsn1jmP9KMbP\nk6GHVtcRZAnUbGeGrKvcaPNJDpx9BSCdb8a/Wn0G5P5EaeWZgDyDbJDJTdqsynJauiNZLaxKJVVA\nuXJuh6sU04IKv8f87AxHzqVsu8BrZuc7mbzMzJ4xJ8x5ZvYJM/uMmV20WX4OCbwhhA8BuL66/CMA\nXu/fX4941hAAPBLAhSGESQjhCgCXA7jfULwTiwBG97B6RCWATq3UQXGVi+4DytG6Bkq+73leC9wY\nmumrK86kyf6OG1UtcWnyzloPiwwCXFff9BkkB5d96tJlpSnMu4BsOqDTynt6EnIqOwG3mmJrw0p7\n/laAvJn0I9d/jgC0wN3P/gJ+9d9/G2dhHx75mgtx4JrY0bslJB1yW5/pJkxajUdF/it3r+SREZCm\nPMWqxMproS57SlpAmVMqsn0eNNl0wHQnsLwfuN0134tfuvNfYP0eH8E1730KfmD3BJ2fKswd5AAg\njNw7w+u5OKZdB645hjStFwBpr93QYNAgttxFkrLalrrZeheydWnjFPYrBVNKTTRqgOZnnU46rdj/\n2Gd0hzQg7xlhAG4a5X7bexw8y419e+UIMV7dHOtQf7WYWQvg5Yhk8m4AHmtmd63C7AXwJwAeEUK4\nO4BHbZafm2vrOzmEcK1/vxbxqHcgHqN2pYS7EsBpQxGofrfuk6MALAnAUnQ3/aH3UV/jC6XKoG5U\napCgHkrj4BRuuc964c3S7BphVWTFTKMfeECEBjoFwn6UwTpUjYLTVWAWtGoZXGpdHfaZwqq6wVlf\nLUHK1C8BZzQ34AOv+T08cO3f8cBXvgujq+OiCOYtVAw15b+RuGy2HDOrAuu89vJpSH7R6Zq+0IBS\nFWH5OvWt06W4gm+yA9i3Btzhr/8YDzr/V3DCRT+Lff/55zA6JpRLsVGWQQfJeVb0eQPbZv7kupCH\nXgvrbQRdJR/cK1eZqKvv82GSNvvaGV4PrlS3M1UJ1NuyUnRG2kvYUSjzk9zOLPYp7VeG0vf3SMo2\nGe/9AFweQrgihDAB8GZEkqnyOABvCyFcCQAhhG9tlp+bC7y5QNE6t9m4NHhPpxxcKgxkktLLd6A0\nhNVTJXov1PFTiCODu99LwwXy6h7dl3Se7pYeGWwsIx5LP9SJpPMUcfCFK4hWYTlFLZiTbJRD4xDZ\nb73yTZdKd378UevHt6sfMsMmi7tl41JKt0deUkx3rAb4yL2uxr6nfRLHv+lq/PSf/QTafwO4oivl\nvZE4aJR0wNWlvoXbmc5gKje8fAMzwKqDR1H/bY5H67Jfynr1lxlwxxe9Hfe48Bz8wnv/AJ/a+/W0\nrJhuc6ozZj6badbxoppdaFmTkXDIIHgIwFEDb01A0okPEg+JTN3O50nt56v50fRUncG/2oasYF8D\nKcGcBsEG5UILfq4eoj62KtsE3tMAfE1+DxHKOwE43sz+0cwuMbP/vll+bq472bVmdkoI4RozOxXA\nN/z6VQDOkHCn+7UZecMFWb9z7wcB3/t9wIrro0ZA2lxZ+w2XGtaLLnoMeDX49Gypn51qUerGoQ0U\niCC8FMrGxykUjx4y8xVtBqAtjXnqqJ9ckzokg0cyMHHu5V91rX+KS0B4ZnlvU+YxNHEwaKeY9XV1\n6dsM2Ar2Ka16GuKfaZ+JUAJmuB1wyQ//FX780/fEV173CPzs2V/Hq7/rI+h2RRap+Vaf5FRoLfM8\ndAg5bwp+ablwZQSsqV1hzJR73RLQbMRVaNc1wPOe92bc5+NX4X+85g34/tMuiacET/MgVMxK2jK+\neQs/ZurU3zv3cK7zBOS20fhsjEbhUe/FC9njhsxUFzKwb5DBBmQdMPy+Ve2bKi/eg+W+QQBtkPti\nr3FVeW/DbN9rgm/iI4NED0Bf3b9+EPjkRfH7nInmYctmS7I/9GHg4g9v+vhWFB5jAPcB8F8A7ATw\nETP7aAjhsqHAW3InM7OzAPxNCOEe/vuFAL4dQvh9M3smgL0hhGe6ce1NiNT8NADvB3BOqBIxs3DR\nNIPpDq9d1SE18ptsVzexYaMC8oILbQRACYJcbFGviutsvhpBdcvqqqOLLAxI/sHc3AcQYO2lcQs7\nHdobIHWAAdA1zz+NUd046lp1v9iZqXpwsBCwLHyFuYzVe01a+DHUJAaArGDMXt52Ffh/H/dMPO9f\nH4BXvO/lOP+u74961ZANSkQDMmrmP4Gu1FNKaqh8c1zp6j0pinvMt9dP0rX2wN/uAn71j1+Be/z9\n5/H0F74a537HWmmQbJA2teGmOHT70zwnwyUHWgK1zISSD7gMRtZX6qTqO8H1pnFst8t9CbTpWHcr\n3VrEYSoAACAASURBVLi4SInV1Pl19gmt1nQasM3+VtDmaROAGOeaTELoI0+pT8IIEM8ieX/cEP2A\nAXv6eB7jDzfYtjvZ9d/eevjjTijTM7MHALgghHC+/34WgD6E8PsS5hkAdoQQLvDfrwHw9yGEvxpK\n45CqBjO7EMD/AXAXM/uamT0RwAsAPNTMvgDgwf4bIYTPAfhLAJ8D8G4Av1SDLoVAaogNhOx2aO8G\nALNHg2iHQAZdAmG9EGLorRFA19u86GK9zY1KF1ys+BLHeh8JTXvi4MljijrXy078FAeqDLjhOgGR\nrkYpTgGzmdVKhuSUr9drUGon2RiXQLdeGWWyVBZI0+l0tprlcJrfWi1hXbTuWwf0y8De//UCnH3a\nl/HKx/84PnVJjLOZerzCVJknejekJbsCYFq+GQMcQbdmtpu0ajYLs7i3gnUxT//f5ct49PvejGtu\neBD+9rUvw/fdeS3tUqZlThsBtdW7sVxv3JGM0sv7ZLkBlKdJADN64XrpMttWE2I7VMBiG6SqoF76\nq2M6ML9PcAUZRUGYYzJ1tmlHtApk65Vw9Vg+lK4aug1xqXBv+SDc7YrucXGovwG5BMCdzOwsM1tC\n9Nx6VxXmnQC+z8xaM9sJ4P6IODgoR20BxT+5PnS5i3+GcgrDmSeBeLPNcYA8MqsUhjlEVQZXuAVE\nMJ02MW6yBI7EXFVDnRpH5yUfAPRIan1u5xQz+mZgeO16USeVFbydzLKf5Ihvwh4Hnp+NXIDjEHO3\nxmciZHJk6y19aMn6REbrEcQa3zWsHwEf+MBD8dInPQCf2Xk2vnLhk9DdvcdIFiOMDyIv4EDJEvtR\nNM4pUxzK96aLR+ZIO83ueONVYP0Y4N1XLOGxr/o0fubPP4wPX/yb+MRp34rLgg8Ck52lqmZoypry\nbtXU3fPN2YkeDaXvtulnQbeOH0DhM7veRvDlDC4gg+7ceOCsVIgJ2z9QGtY4waEHA1V/QzJ0Kgzj\nKcJZJjXr0oaYnsYzNeCgxbh+zLbPeK+5YevhT9k7m56ZPQzASxEX4L02hPB7ZvYUAAghvMrD/AaA\nJyJm+9UhhD+em6ejBbwfmuQXSyk2a0YJvOnZMGskYxg9+2moocxj00w3ILq4cL9QYHjtOwF7qCFy\niWQbZtkxRTfracnktvAaVI0xcxw4ShDTa5u5iNXuXPVKOgXtmeuWn1u+EThwO2DpoLvQGfC1j98Z\nz33Us/CPr/wrfPb7/ha7dyL5sZIpp8UEVMkI8FLUXWy7Ml2JaY0PAut7gP9zLfCw3/w4nvO+S/CO\n638dH9x3ECPEfFA3XZ9Rp0fUax7LC0gDgupxgZINbyZ1nHpqdlGmJhvE9Cge2jl6CZf0w36N/Yyi\nfUl/6zXmpbe8QpTCTa7q/lez4tVWVB/CztUQdySB98p9Ww9/+rHbS28rckhVwy0lhqyz1b13aeDS\naYwKp1s6LWCj47M8lYLPEohrH19NY6PJm+KobpkMVzvOmqgjNL5xn58ZCRtpeuDgKKe/OspnYwXk\nabX6pQ5VGKfhQ6ALxkMGZEhbWG4qJoAu0+mUJ06PO5l2E3gMyVth9bjIIOn21i8BJ977C/joW5+H\ntV/8HXzo2junlXfqfoWQ9aVUhajeVz0Q+nFZxrrMdV3ofeaXngsbu6Kf7s/9+Vtx9vsuxTvf+Fx8\naP9BjF39YT2ie5pl0O3byOznpl/Np8lide+Gmeonoxc0rMutYVvXy/JzoynbNVlr2uIRQkb6rJlR\nNy/+VpAdh2ys44IjIKvRmjC7AAqeH7Jk7UdcldYZcLAtgSdIvCpLYXYBwc0VqjK28ndryFEDXiC+\ncO58r4YsHfWGiGAAZo60pjKfCy5UBVC7qagvIadEHMGXnBFsNPGPfsP1MSeqS2NZ2Ak6y/udrrXA\n+ghpifGkyWmuteUUsyeQIXe+FLknzNMI6gM1C/cm/86NXtIJxwMdPwGT6FPr0xgYblO3OuqHgKSD\n3jkCPn3W5bDf/CB+4iPPx2sPNFEXTHAjc2Z6WscNCiCiQSu5Y7U5TykeBdm6DpGvd0vAjuuBe77q\nBTjzJas4+bd/CR/9zmvQLwHoPZ0Q86MuerqRTUq/H1Z5cM8NfRdNP1yHyfgpOt0Zv2eb1dMGxPbK\nxRQkLzWDZT+jP3tyk/QwCpApPWQg5CZUdCczDANV2kgduQ8BmQXTfrIUch7ZF9mXydAPWFy1Jhvw\nbUvqRSOb/d0aclSBF8grxob6tPr31sKXro0wWNXQII3VwYGjOxuHNrBRiKoGNUio4WyGaArYq6xX\no/e6gC3B2ZBVEckKbDLy83kqu/27ej/UwNtXQD4ExgraQ+BdXwOyMRAYZpeDYoAZ0JwM3PCjT8ee\nN5yKV//kRfjAWoyjqXuUVaDEAU302jWQJrA1AWoH5eQ9ov7OvvfC+Abgu97xaKy+ZIw7PPNS/MP5\nN2LjVAdKNy6y/EP7ThQbDw34fvJdqBC4k1dEn8Nudf9i1fGqEYgzsDZEDyGy19SEKmarzVX3u66n\n+1Npx3VfUHVFyh9yf2B8JCeGmDdmu94lUPeL0HjXtlY1hxTux7KVv1tDjhrwchXamgNaAgZknJm3\nQg3IagqTsDpq9QA25A12yCyVDSON6J6mhXx2VJEP/rFxSF75ObXcgMgsdATtJU6Nu6vyAmQADhY3\n9gltBsQ0vecnUAIAwahB4WNKvWQwARe9ZvleinMAxAMwc4R9CkP1g0VD29SX3x44DnjEuW/ByZ//\nLP767U9CWMveFCqqJknXPI/dUkw8qSmUlXPp9KhkjtyLgnFOVyL43ufAD2L/C5+IH/5Zw5/+zPOx\ncQdgtAaMbyr1uf0oqjeUYavaQvNJUC4GLi1PxeYJwGmw0GvyTtkO2EaCZWaqHjcEUF2iW7tlArNE\nJulX/TfbefrtcXH2B2T3NC6amFr2tyXTZX3obHNqsU+yz2ufYT9QGTiE5GbLgvG69NI4tKzKQIEM\nlhOpGE71OSqnk4hDftHFbmfVi51anMLo6DxposJ/YrnxAf5bnu38WbX1JIOGx7fWzrLepCtF7jyr\nbZ7CcaRl5+q9syYG0lR/cvQNwZN+oAqMU3dfgwGdu4ulkzQCip3PqFcsDnsUQKfOWNkaWXJSZzjw\nTXdk1tnuAF7/hD/GnrMa/MsF5+OKG07CdElYLMGljcBH1YO6X9F3mQOIehpwcYMBhV8zLKsm0ERw\n/ZdrgS898gKcd8pHcf4zno7J7lie6Y7owdBuoDiah8bHbpTjq/fjUFWOsmIdpAFx7evyszOqEWeT\nypjZBtg3eLoE204iMa3bF6Tts69A2ntNAPR+5zMyqu6YfQVssmvmhxuZ9xDPCn+eLpbcfIq6Y41L\nCYz2q/3V7+3IAnhdkmXf8hQcyNsz1qKMS3WpurwRqNaay7RcV6XxcL3CSoscXwL7Zng6hTlpp8bq\nF2vjGz852rP8G00EeHamiV/jgLI2yudl8fysxJibrJ4gyNYbfqgKg9e44xU7mwJn8jvVaRcBpctg\nTaH+MgF+mwGvW4qAc809gD2/ezWOx378/Et+GXu+FBlot4S0io7pEMDSXhUEcNmpDUBaAt35FpE0\n0qn0TWTe0yVg/WsNHvzoT+Mxq5/AE178XJx3XK4fUrzkV2w5DeuQNp6ft6y0b1DurTHQgZMR0wcy\nDaKDu0lY9oVJk/3NGWalE1uEX1djm1dd0daYVmKsltUWDE+pPRHM47JQ6oMTEPu1JfcxpsuY5nki\n5VThs2sG3GQRcDdQLoPdjiyMay5awFqdoNP0Yrpuw+FpeSX7pYzcmEF3G41LWTGlXimnzFeFgKrs\nnFZcDT60CxSncPUG7FRx9HBDRBP1zTRI0CqtMsfZO0md97oo9f3itForGfbU93gg8wMyAHXjDNz1\nElQgPrOjA/74nAsw/bE1HHzrnfCQczJLb6fDz/DgSyDvHEYmSvY4JGn/2waR6W5Ed7fTL3shfu0r\n/4BffvvLcO69IgMmiKu7WL2d5tAAlAPLV1nUUhj15oB1fTx74SXg18fuVcMFPJqNefpIevbUWa3V\nDHX1aZ+sDcqarrYbnYXOUwsy3JDwMlWH3I2sQdxv4EgB1ILxDghf8E2j0uikQj2rLn3kC28gOjDk\nBqSboWt96rJHMluenKphVG9cswXKEEPg9KsWnbqpkElQ51XvEkUVCsvDTke2o3nTJdGc3vGPv1Xn\ntpkoEJh8mdHr8lYo1Q+UpgeWVoGD5wAPefjv4A6TKc581AU45p+R9xYWMCfjLAA0lLrXemlzMl7p\nyR2uWO9b4N6Xnom1jz0Sj7jkdbjrnS9FN/ZFKqMc56EMh2rATHUw9J75nsTflwDfVfWSrPlNjrdr\nnGEjv18a0JYG+oaKvlMap5vqPlUg7E+c9WxF2uCrPP03+25StzWlnpnMep6RXIUGcQDY5fn+xmYP\nHIYsgNeFLx3Iela6fxEc6hGdVlbqYwH314VbXZErburT9DQNr9QT1GMxL+xAjTyjeuFaPzYktf5Z\n853KLaxXWUWDzLLJxtUdboh5c7CpdX5USRD8J1V59PSN9aqO1a2vF1WL6v9q4w5VATQOmf5GBsSd\nE+Dp516Lq572Xnz84nviydNdWafsYJtO/tUZkT/fjX3wEjctFeqCqY7g8ud3X3lffPVxr8HT/uYt\nuP/1n8J0BWn7x2KDGo2zesnJy0HDA4U73jx/XiAPDE0f63Hd1RKc6uug3YR4Xw11nN5rn+ChrioK\ncA3fG0pQMa8rVW+wnfE+bSrqD98h+7rzcIEaqOq2oVtCEvA1bNvnmSlXn+70z+tx5A6FXKgaXAgY\n6bQHCDA2cZEBw+goSuvquI+ZXwqzrl7KHlW3Sl/I5LrlafEakF3bJpZZJxuoellMm/IsKV1VRH1X\n8PxSRcA2p94UtSqF6SnA6YISTW/IGqwb/mz4vVon1yMPXDQCsUNTZWMhh1Odu26CooWq96gl46zd\n1SZ7gf/5mH/G2bvW8PePfwXsyiVMRlmNkfZrUM+EJgMzBNABUQc0/jwioPKZ8Y3Ao1/13/ADq1fj\nzLe9E2t3D9GAtoQZN660ko5pYRYsCvBlOL4PV8lA3klff69UMow7vV/Li23WpC32Fv11GW7kzFOL\noGovEgWqsbSj62CudgiqKKYWmecoxGd9O4uZjas422T+O88jV35yxqjumbrPgw4a/mqLnQb3AjiI\nIyNXHcbfrSFHXdXQAMm/FsiMUlULQ/Sfq9PohaDGLiUBU2n4tX6KMnApLbesgS5YHhVbYce1f2Xw\nfw3ySKpMV6dfjTdu5l310OwktZ64nnGyzMqIyHjY4EN1fdxnBjwK3tE9nY023qOOkaKDQ8H6mK6z\nzn5UsjxN+OEnX4Zdf/hhjPaP8aL+oWjWkXdGcyAr9kdlOhafn9GXWv7rluMKus73gXjaPz0G//nP\nz8DoJ76Ip+78+OC7Zhw0pKVz44IY/bSuOdgQhJpZ10YNy0GeUQ0te6esD/VIb1/FCSlaJ0xrTtF0\nljV0T5/nK9AZqSHnX9sTwzGQzih5EGetYtA2yPTVX7iulyNlXNt/GH+3hhw14O0QrZdTm6X6E/1t\ns8wQiGGokK/dz2qPg16uE9hqL4ZaNF41IJDlkn0DJdjrlJHb7wVkpkhPicScrdRbW3U9Ae1APqmW\nKHR1OsjIc+qtMTUUx7No3LqMmivtiiluxZJ0MEtuZwQw6i/bEsCme4D/fa8/wbl33MBb7v84XP61\nXVhbmgNyZLtA2nw97T9BIOaUvInLgKc7YvhvHgQ+/fqTgWMCTn/x72D1ZCTkMG0cEDWB5bgS+xYA\nVs+GvikHWk6XDVllxUGH7RrIKgbWvboTDulwl7pZQNrM6HtzpOgHIbdRtqEGszMsfV8dkNzG+Jzm\nK9VflXc97of3KX661BGRfYfxd2vIUQPeVWUA1T1a8DlFpy60Hj0nTWQBBG8Kw9bTa6DUQdUNVTcb\nIUOlG2gr8dUAqKAeUBoqksrAO3SPrHvmQMK17fOU+2QsQ/1KGQS9H9Rvud4eU5/Tae4QY+Y9GjsD\nSp0fqrICefpdG5jopta4O9qZpwIXX/zL+Jadjod/6Rzs/BYKv9h6t67aS4BpFctsA7C2F8nX9tc+\n95v43MUPxnc+4R34w/1OikV1oT7J6v+rqwNhZX7IhJlXgw+qTWkbSAeiNpn96UrFDblOrwXWN5Ne\n7ubr9+ujdKZzwm3GdlVq3XDdbgLygow04Fv5vD6jWTHmYyB/KY1KHQcMz+xuriyA16UF0jrsGyvA\nKfS1wnyBWXDqEV2VNppsVLpplBlpfYS0ijJAneLprk69ZUMCkDfcqXW+FE1H88nvVD0Q3JkPpjs0\nwGi+gMy42fi1gWqa/Dq04RC9JYYYPzsZmTQNcPykMU/9nOkapV4NuqtYWuDRuvtYA/zb+kH86nEf\nwY4nPhN/9/VS76xSLIqoRFUcwSLjRQ+MVoG//d1H4u64Eb/9nLdi/VikY49Upsso3L2o8kg6WKDY\nrHwq+xtzwDo4zix3KjMxrU9DBUYhAivb7ME2D3oBftq2h+VuX23IKgYK3xPT57XNdudT//b6vp7V\nBmTSQekx3D757kZ9ZO2161ur00LMkiWKIfeBHSF6NxwJWagaXL4NYG+Im2GwL/BdUY/KU4jJfFX3\nqQapg3rIIfJL5+YcVPKrwYqgpcYBxkmjBe9pg0wg7Z2LW0Cq9PJcvW5epXbXqkGQXxlHwCwDGtqH\nmBKw+Qtmh6i35jTkehsSdvBxxVIaAa1kLGrypxqu+hbAroATvvByrK3fHi+6+Fwcsy/HMVpHsSBj\nCCgofYPkUbG+J4LjAz7wR3jAJ6/AA//Xu3DC2gb6UYyzlhrk0+5qUnH0YYY5u0VWI7V99NYIcOAM\npfeNChfB0C2s8FBwRkyXShIILgdeb/LJ1/XgWoM6DdB1u0wzLPEiqMNoPOpVox4RQ7MynmbRNdl1\nEYjlTO2Df55m7ZeuBmQOVgvGe4TlIIAr2RD8mnohAJkVEoTplUAhQOpgSusqvSB0Qw+GW2/ywgSy\nOqoelOGqakFXyxHs9HmGYz5q/98h4S1lzhxUdAOgaXWfRou6ww2Jus0N3aunouvtcMdSISAnnSXK\nKbKmN/e0XcR0n3zNlTj/XhfjY298Bv5mB9KJHd0S0oo4YFj/W6gAQmSrEwP2Xgas/c7pmJ6/hP/2\nX9+atqPkyrKkngiih56j4mDcSY9tZTnrUwtM6kd141PL6oYhUB65axg9F/SEFrLOcZ+PydJ6rOtn\nSQbEIb0v46u3O63jrVUJ9E8fah7meeB2lWTFdA0leUj64YE4moEyHymAWgCvyzKAm6pM6OhaqxTS\nPWnotLATsAmMnXaSkAFX/VvVBzI1Psugpj61PWKHZjy1lXozIZAe7oypPuqoHghS2TEbt3pxBGRX\nvXn5Kww9fl39eWuZt5KqVr8EDC//BhxUG+D6U4AHvvS38f3fmuLDb/s5oANG9MMVIBwCRN1qMVjU\nHa+sAbs/+1vYe/Ua7vvU5+GcOzgAdVmNoIa0/hBsenDjHm9XSU0wiqoCuj1SFUPhCSdKALRtsx2u\nO6vuPP4GecAncyTrBcoZIgeEIVvAkGEuyHP1PbpDEkC1f05tGDR1gKfBrN5KlYMPQZVnJQLC3OWZ\n3g69YGSr8rXD+Ls15JDAa2avM7NrzezTcu0CM7vSzD7hfw+Te88ys8vM7FIz+8F58R5A1vFuAOAs\nsEOe/rY+Qq5XnVl3vVcQHAIhQ57e8cy0zrJvokkl6GqbINdHDtQEv4nNpsPfCnCNfIYqnEq9so4d\novHvbdVYeXRL8maQ/OhgYfJX51O/J5BFuRKJcbDT0E+TeaTxjoDCQZB7PxgcnPvZDt6N47NLAB53\ndofb//xf4a/eeD6m+5FWiCU3wwC03SxQJF2y52OjBa67YgcO/soP4dP3HeOC7/4kzHJ6fYvCP5gZ\nL/Jm+TN5TshAHsz3TBCD2VKf80DWpgMz31mwbEsgoOgG5XyWp/NqW6L6YKOJaQbmEVnHr4Mf42Ye\n1Bc81R2EgVYkJHkMoWwnupevti32kQblcV0rXWnoZn4pShLYD7igYjTQbm6ubJfxmtn5jmmX+cGW\n9f3zzGyfYOKzN8vPVhjvnwI4v7oWAPxhCOHe/vduT/xuiAfB3c2feYWZDabxTQA3Io4wE4sGtoDo\n7aDMtUd0K9EXVxh1LC+mALKvI13GyLjoHqXLcRkfvQGGdk3SVW76rAJYCoNyehfg0yuUU6x50zVN\nOzEkSU/dwmr3MrKdwvcYpWGQ+aMEiWvQKCfl5DX6/CbXqZAHCKpreLxM5++JCxsYUTBgvBGvWwfc\nsAd4ztPfiFM/1uJ2n38hvjQB9vvuZaN1JKMW1QnJ+4FuagaEHti5DzjrAw/AaevfxNtf9msYc9Ma\nB0Vlu7oTGJBBtt53WN8z67ZWGaw1mSRw8Fd3RK4qhL97Hh1FHeqkiYBMn2m1UdSGOi7ZTV458t4K\nLx5/p3zHahvgu6JfNAdMPlevdNQ9e9knA3J/SIa/JttVqGahyo+DtqEEHarMCM4Ecw5UhzqrcKuy\nHeOambUAXo6IaXcD8Fgzu+tA0A8KJv4/m+XnkMAbQvgQhk/gGMKORwK4MIQwCSFcAeByxKPeZ2Q3\ngGP9+9c9spu84teqmJWRsRElXVJfAoq6peg0eTNXlhpE9TeBXNSCg7uWMU3tcARujRvIurB5eaBw\nFzL1eqjDE9iB2TL28lza70HC1EybopZt6rN3TeNBnstdNR1sMHMUEctHI05LtUmPtGvZZAkY+zRn\nZw98agn4xKPuCTz9R/Dki16F42+M96ZLMXzaON1Qst7G1RLLwL98a4Tmgt/H1/fsxvcef9Wm+h1d\niqteC0yDz2rZWB4C44of1DoOpU6yQTx6nQPsSgfsmgArchAqVTVLfY5HhUbOIcOssul5RVR/4no/\nXi7TZT0UZZRBlXpZdXvUWZzGyb6ogxJd5Jb62H7q5emaJuNUY++hDrg9HNkm470fgMtDCFeEECYA\n3oyIdbVsxqcK2Y6O92lm9m9m9loz2+vXbg/gSglzJYDThh6+EVHdAAC3Q9T5LofoPrIsYEImAZTA\nwuWUAGYsrWttNEJwdN6OLPvSR3WlWu6y4U5lM19c1ctttLOLRqg6SUsr+zwFDcgudaOqI6mb3TzR\n5Zq1zHO6H0nn05OW+TcTjwMt2fagOMvsmqjH5em7TQc84lrgDuFROP/LH8O//vqdce2euJ/CaB1J\n51FvvtNOYpiuBZavAR7e3wf32PNVfOWDP4pwDDbtBgRc7utrXf5T4f65HCiVhbLueFI2z+ujgUwB\ndeK6ZILRhgze9ao0lfpMNZV57WwzT5oibJ+9ERTkhrYzZZxkuPN0rw3ygKJlAPx0DLnOI4vGPvho\nuodyrTxs6Q7jb1ZOQ6n+HcK1AOCBjol/57P/uXJz96B4JYDf8e+/C+DFAJ40J+xg1X38gljGJQB3\nOg948HlRlzsO8XRR+u9RDbBuObOq/+VJverkHfp86J42vs0aIvVnS31uKEt9NJzsnGb3n1FfGk6m\nbelzCeR19J3FxqauPQ02Hwx6i2kkSzkB0PXanV9TEKVOsLZQk333XngCKQ0kynjnqT8IpDxVYLl3\nxh/KWcXqKHdieBmT/3CTQW5jHFdi9a5m4KKK/ScCP/X4U3H529Zw9jeuwQPe8uf44iMen7aHDE0G\nas1c3wLLB4F3rwC7Hvpc2P9+E/aeuJYGm1rqU5frVXWp0Ia0CXrtP8wFMEBmzazDGrQ4OHMqTibI\no9kJUGM3FNZGRNYpvQO4mEhdK1VWurJ99ijZVQ3WQu5Tu9MZhaoBlI02IXojqX8742kkYytd6f0C\n+BFAoXxW5V8/GP+OqGyPgW0F/v8VwBkhhINu83oHgDvPC3yzgDeEkHZrM7PXAPgb/3kVyuXVp2PO\nvhP/5YJoXNsj15a9eLXTdGfA2L+rvglAss6yajrLDU0tyTx2PbR5Aw9ai5nccp+X0wZEp/AaaNUY\nYcjGls4iQJN9KyNS4wLd0XSXqwB3T/OMF47llo1Y6oXBjkDwG3S3UlULcocii6AejQDd+b2Jxbqg\nzq1eWdX4c7oF5ZIz3t5ZadvLoEi9rIMzXbp0o5iVCfD0+/0dfuihPw28b4SznnEQ4ewlNHfdQD8G\nzE+ZoC/waD3qiEfrwJs3gJ9795tx8h1PxP0f9YZ4XI83jmCZ1QbD7D4PLgR0HsXOUyK0LhPoWy6T\n3lc9rnqFUHfbhtimAspNwgmuNeguuy6YiyZ01qIDqCG3xxtHeW/n1sGRMyXDrDdOZ6XXharACJhD\nsyUObLqoRz8ZN5DJh4YHYhtbbbNqYkfntPH7gPufmxeOvHZTbekWZbOjLP7J/+ZLjWtnoJzZI4Rw\no3x/t5m9wsyODyFcNxThzVI1mNmp8vPHANDj4V0AfsrMlszsjgDuBOBjQ3EcRAm6U0RWy/rpEb0d\ngGhwA0p9p0o9mAVklpm8APx6j2zsUEbCrSa5SQyNdklP2udwBHt1nbIwX4eV8mlZT819EICct3nD\napA/ZVd6bytTsjoNNZQwDjWmsAMnI0kTP1UV+n/ZO+9oS6oq/39O1Q0vv9evc6Ib6G6gyQiIZAQd\nFEQR408wDTOOjAF1/KkTHLM4MmJWdNQxYsIAIgMoyJCzhG5SN3TO4XW/eEPV+f1xzq6zq9593YCt\nuH6LvdZd7766VadO1dlnn32+OxnCwiTaUoazq8VF5zuQG+YqFxvorMPik2/BkPBAY39+vbia5X0Q\nrTOuuyQ4jXZ3719ug/O/+FP4+gI2PtLgzeThAy10jerTRBQl+f5GPueo2BQyTwKjjGtxngdlkde8\nJecL+0pgkMBYEnmpF13xqZ6IN7KtfxrGv1w4URQP+S67pSxDWJpf/MXgJ141RS0+965s3hPDSBv+\neg0VZIZYG3ZamRLS4j6xdYJ4d+P1lKmxi88LgA+oz3i6G1hojJlvjKngHAiu0CcYY6Yb43xoBaCQ\n5wAAIABJREFUjDFHA2YioQtPQeM1xlwGnARMMcasBv4dONkYcxhujJ4E3gZgrV1qjPkpsBQnSy+w\n1rZ8dfLMZaDdd6RdVmj/f8V/b7d5Fx01V4HW2KUmEcRiiJLv4i/ZiMKKLFuxYjL2kg2ubpbA7KOx\nu64jyeOwrajIwBKN1jTjJ0wx+Y9MeGFWyDNl8ZattBSZhKZwvtbc2r0RpBmF3YDAHCW/I5CLMwNN\ncfmW/tGCrDewyaKjtN5GG7z4pN+yhRNYPmx509LDGVz0v6Tew8F4IdisgK1DfQjO+/7P4NIFzGs8\nysyP/JL9OiBNIfGaNrJ19oIvapJF7JgEjAm/S390FY3s3aThOUWA6/ERgSnQQVtBwxKBJhUWMper\nwnkigCG/u9vVoiz+rpJPJDFuHOtxqHAtw+ZjSbJ2hco2zC2Bw3IRiBQ8N0zYLRWVAd237PnV/8V5\na3BafSsBX5wXz5h2pfHuhqy1TWPMO4BrcK/wW9bah40xIvcuBV4FvN0Y08Tpla/bVZtmArn4ZyVj\njP2EdRrvXJwAjnEGNnHYjnAuZp1+RWzgoIhsQhAstC0nOGEV1lvmomuVZu7iwAsmqrfUsqXTbWd9\nUUwqJFvCiYSx7rtmXC0cZaJrzBRau9roZxI4Qkg/c3HnIMflnBQXxjoWubGQGHxwgllb4Y1qW24n\nC4fAD/rdSqIaEaRYJxBT7zJ20GUfYM2FL6J99hRW/O9htHvPBhs7jbdZgVELJ//qIB79xM/Zb919\nHPK66/n2Z7/ptusRRH67lJYJBS/l3oZ8gh2UUE3JqlJAflGS83IJdEyArXQyIXkXkjh8Ih4VGovH\nC2shyRCnNXYZi7E4eJ6INjkau99T493UbNhpyWJfFObSZw1F6HGV5Euo32hxvWV8DgbtainnyvEM\nLiOv+Oh7Ht4O1j5zj15jjGXL07hgyp92v6dCf4pXw59EsvKuxuVtEMGq4Dk6bcCmqoAYiEAJMv96\nWmU/Eg1UtjHaQFDMUVBKx7v0iAEkd8z3R/ww2xLH5KJxFCNysr6Y1lZo0TLEPzOHDfvtqBawsi0s\n9rX4zFkuC4KWbwhwgo5I0sLaEPyis8Q+Ni+AIr+VxgbssxYFKERi9UcU9JJECtYQY4vK3ZBUndCL\n6lCdejlnsJa/XXsPqx+KXQrGshfOEdgS9KyFR6/8HmbdCvaqDLH/l77pYAn/UqVasSRoNwnENQ8/\neNe0KHGGu6xMkBfuGczgi2vmDHpKExYhKDitPkW20BJxJtt6PZb6e1uLbXWUhh1VezMILL0ACv/p\nSEfx6BFBG1mHpxZdwzKt2+a1UOmvXCMk0IFAC/Kxfnzl/trQpq8Vrw9tYxCFpaz64Idwz1L6ND5/\nAXrWBK+mGUAbDuMdM/lnzzmFq/+LQqzoaA55LRHyq7ZeXSHvMiZU/F/chMppcAYX0q5kOkVfMTQ0\nZWJ3K9FSigEN8nzyTNmWvwXlJnZBuLZi7KJ7GgToRX4Xdymhsn8HtQjadvqQ2ZK7bqAC7Q3XZkcS\nJmSpAXFT3ds6oSY4rBSztBF85cgS+7GdrzOFP86a5wTgmPNAaLZB1IB5K4Bb2jmVbRz12bt5X8M9\nYOTrskWpF5x1d1yqEWfarH9mXaIo51JmyaoYa8r626QlteJDCB44WXHLlHEpNSE/fobggiXBP9pN\nS49JUajrkHjJNQJhvIXHtPDVAlEvvNWCMIoYL5B1cIT4OmeKgw3vQF+j2xAsu9xC4O8R+tPcyfY4\nPWuCdxgYxeG4Y7htbcl6rVdpX8Iosp0S7Uxb+CciA7nIHcgPaMULUHFiF6oq7bdUmAiaLEF73BVp\nv8qJXrhE2slHMMHEjJ+ghtaTtkjSLcHing4vF2EPIQldHY3hVxGc9CS8fCk8WnfCsefF0P4l6Fju\notNGY/dJY2iWXKfiphNejUroaOphBBtBZ/IIl8w9ioSY9z5piYf9MzSdYa22HLb981Ww8wl2vBbe\netalrtaar6Nm8ZrzJqjeC+13Qfc10LkByiOwbLLzhhCcMm6Ej35oqYDckkRzTF12Mh0wIp84Jcsn\nURSMEngSKRhCxlV7WOhE6poXizux3Nj5e1WSUIpHk/B78XgRfipqxfJ/8ZicK1BIsV3ZtYlbZCvK\n5ofN92N39punTH9lgndP1ZJ72jQJByvUcdqu5E+wFkoKExKoQLaQGVbn2xH3MckZoK3youHKgMr5\n1v8mLlHapSqy+dy8UpNNu5WJkBatQWshRVwVWmivE7wTgRHE8KdLf4tFXJ7zqYZS6v4UQ0onArEi\nGzSsovZWSR2E8NM2eP+PLwYOgmaVl61cD4P9HPqJX3Hwsq9z8WegfiXMPQYaXRC9CNL9wPZBrQPS\n6RAn0LaDsAJvB7sVnp9A+wvLJN9tUH/8s9h7X0X5RLAJ2Plw/qRJ2PVTOZt7OOmc/2TODogHIa3A\n6IeADZA8DCstdE2axLZLxvj0kaP8dMWBMHA03DvA907/JedsJbcVkFpx2LDrEiw4TsmgjNQQjISe\nN3U+X3ehhy0K77YlLu+1Xwm/NgRvA+leEf+tFIW274s2eMq1pXT8RBejmOaDoozTthFRYire60N4\nupIyLpBInkHvqpoKmtG5VoQs5IyaQruKOH1a1Nj9KX9JetaMa//hmawCzMQxRofvSoSDHao2YL/a\nKiqCUsgSmEOEZ1FYSbua70X4isGsldAUIV1JQwhvNSXnKZET6p5RUt/PWE0GjblpfpJnSwlGEiv9\nNX6ySj9bMKfcX2L35RnlNMHcmgUsUnYRmmShaxr3nDLx9HtJE9jaAe8YmMf1P/kdlCNoH4ahdjAR\nTH0Y4lV8cejDXPA/W2AAoh2QjEDpANxKuw+wLy470g3AXLDTwS6HsV/A1J91Eh//fQZfsogtDx5E\nXwPis8AOQLTPNfBZ2PcFcO8hf0PPMNAPA1fDlkc9zwBt50/hlecPccfyV0B8Atg5MDaLE048jR9P\n2kHPkNNyM68GEZZWwQ/K2CbY9LhMaX4Ai9WPtbtcsWqykLH+vhR4WjBz8tcVDZWRuNrZ0JbkpMgS\nCMn1uECXjmbwPdf3EHfHjF+MN3qnYUcmPCG7slR58oiXkPBalmvFhjwOohzJvfSrlOuFh0W47xHj\n2rKnccGCP79x7VnTeBs4odtBePkj/lG7lGCqm7CC6kHS3zXjiNCVSC5Z1T1P5utEmTxjNiOwBWhB\nPABGlfYiWrREmQn2LFqi1iybhlzEnSVoEsWACGFonV5PtKDEH5cENVqjENLaQaTalYCOuGBg2RWV\nbZiYHQUtzZZgSg0+MWklxy58Fdz/C2h0OoEaNdwJo4fwrq4P0/7Rd/Gaj0NlFpTvBU6FdDZEk4FV\nYBfA6IlOe0urzgAWXQhf6q+ysrSVj1+9isf/HY6aDs3D4PYrejEXjbI3MUd+8B/pvgHog/QQaLsT\nuvth/Wg7vRd0M/u0Bqw4HcpnQX0K1LopL3oBX57cpG97EJSymGVCVgtRvyLGTW+wM3kesjJoFAQg\n7njWdsELBKPOTwHxV1YaeDZMltyBIrwmbWcLRprvZzY/Iqc5ZwLXhsUdAm6fBVP468SzRfOuLOB1\npWik+F2sCYqEIUQ66gROQOYtIxpz6jureXuitKJPm/5CRrOnSs8axtuJm6fFkHr5XikwV6v3JsJU\nsjCB3/6La5A6t4gdSZUAjRcXDRsiKLXHhKSHFOE7GpM50uuSJ9L9yIbqvZpaYaja00CT9t0t+jJP\nRKJ5SB+8l1XOqi0TSxstBZ+TrW2M046K6QbHYphfg0tPux/2uh+2W0jGcCFmI9DxGJh2/u6hT9P7\niv/gh+ftx5MRMB2i7ZA0oXkArD4AtkyDrTNgRx/cvw+MtsFB1tIsN+hnhCeP7+LJ82D5PHj7iw/B\nVg/gLJbx4R3bqL8Fmn/jnq3tXdD3FvjJilFmHxPD9vPAHOOk6WgX+y54AQ8c1mT+qH9u8apQAxF5\n7wJdAogoGP7k3WaeD3oHkajr/W86QCRHlqyiMSjBrKRO7pincVBFYREQ74ysDzZ/mo64i8kLVI3h\nys4ssr48vJ8nZcXjej6JTUQiP3VIswhdnXVPPo0o2A3EFqMz8P3/ivE+a4LXKxZsJh97V8KVAxoq\nCqrC9TI4Lfg5o9S45CRiUc6ujcLvInSaUX4LGStmkf8TU6jN5iEHMXRo53dDYNpivyDPtLLdk22e\nNmrktFir8O4WZAjXCeNqOEF+F0NGJpgJ7VbTMBkjXEatahIisaLU4fKddfdsZ43C1W94JV0nLoDE\n53Ya2QfHwU1I9ofmY5z/qx+x8Esvp/Z52PpGGDsDbA1mrYbpm5yz/819cFUFLpwDMMAQ2zieIR6Y\ndwJbKrBjEjx09aV011bx1YuPY/bJQ/xmATxwNAzPidjwRti+FS5Z3gnp+8EOQTpMqfQr9v/bY7ni\nSJi3CUpj4aVnW3XUX9EsvYCJJYQSBQ3ocfF4q4YszC4EZO6wbOf8ALWqK5f9NsHxVm3qAqOJ4m0p\nzSP2CQkPl3BmnWtaG6VlIZbbidAUGiznvXqSKI/jai8fmbtjUVAkdISeJUSX7sqI+LTor0zwPmtQ\ng6YSLqJnzEyMgUvKyFbF72omGOk0/pV4Ad7thWbZjgf1MxyYoE3oTGJSSLNpXGo7Cf8EL4y9FiCu\nZ5KkRARoTQllbSCJC/20uE40pS9KYMdeexHfYSFLEOwaPhA4QTunW+M08lbn6fvVovAM7U13k9i6\ntIbNsutcexOSkhNaVQNHDcAdL4DlJxzLWTefCGsuAtsNZhvE68DMhhPvhtXLmPEdWHbLMWw873Y4\nAfa5DErbYerJMNYGlOGqETi/w/LV8kLmjDZpLok5bW9YeRvwnwn7spHLT34ZPafAKw+E5A8wchjs\nf+V+7Nj5GljTCYxAYwkcdxnXzxnloM0uSiwL3BDh6TXPzDjWIpqt6X2MtQZa3O6nkYMjsuu0JpwS\nckBYj716gZslZPd/sqASuZXvW9GukbUtht6IXB4KuU4wU+HtpMCHAqNB2AVp/DXyPCKajwTPCL/r\n4gElxW96hynVlAXzTdVxkwRlpVUQ0x6j56AGRwO4QZqNSxFZN+6v/AZOaK4lGNq606ANi0bc9L9V\nCO9WVtXYQq8/GJFfwYWEySTXQlNtfSLyRR+HS2SJdYRif50EUUgEUsMEoSa4mmQsE21Db/uFYUVT\nBjdZBGsVPBnCQqG11mLUXM4pnhD+q+QJKc7XtrMZtA7xx2z3AkAYREqhy9Y79j7NJnVuXjPH4AVD\ncNML/peuBa8Gs8PfqQFRP4x8D+odDNy9Pwt2/B3Tutx92eKYoOsxeNWDcPYGqEZwezvE8/fjKOos\nngTH3w5pdTGk2zh50gq6z6hjzoHkZlg5ACv2Sdmx4+0w9Buwe0PzCU580R08MX2Uw7a5DGal0fAs\nRF7bjclXKlZBHeAghpLSeHMaqqIoVcK0hdYkWK8uEy8LQDao/q9O+pSDN1oIj6w8UqFP1kcoZfmk\nBVprhlOF3wWWyrb54XVk80VrwqNawJOH5YTv5VxdSVtryTpfr9BE3jd7hP7KNN5nTfBanBfRapxX\nQ4qzRNdxSdI7rTOy7aNWQAHyDe63VqQHSk7RYcatLhNrsTZ2aZjBEnwWLYHxdGSWdpxveGOEhGdC\nSJg9UdIROV9DCTL/ivXXBG+NCucWYQhLEPICy0jVWhHMAo8I3KDfU7GbRjVs1Emx19rKTTh4Jyya\ntM2p7o17IN0Mzceh4xwoPQk8wsC0j/O5x6CtVGXDl6vUa1CbB0OzYJ8xeOIeWHgllF4DNQzrK7Dm\nOthZP4DZbOD+0j5MOS2lfiOsnDSLrnPhmvOB2k3QdhIkd0LHz/lAL0wZdFCBxnMlZNnGwXtBP6DW\nQjOvB8JvOsgiex9J+Fh1L1DQhVW4cZF0W6Kdlrwm64+LkW6i3MHjmozIyuhYnBAerjr+lcQ+DS/o\ndZJ8edxWCXqKWKwlaLHyvw47lmMioBtqISnOA/lXtGzxpNgjtKskOcXPX4CeVahBeDPBldxoIyTG\n2WicABaqFQSqaLx+9wv+ug41mGIwsOQFUlHTzPpjAyZqbRBcEmYruRsSwjZdPA3Ewb09CYa27H5+\n8giMMJHjenEV1NBCRNCIS2n+/IkWFEOY5KIUSfUMSUpStiF3cTHs1eIw8nIyvt1x9/ICpVmBlY0K\nDB0K7ddAYykkm6HUB01f3nTuCi56EOrXvYjBK67jomuhPYK+u6B2BpQ+AwdfC/GHHYMe2wV/f/R+\nvH5JSh/w8L+XMNfAlttgyjsG6DuyD9oHoHoNjBwEyf0wc4x9vLEL6wMhLJkngby4KMk/g87DAEEI\nRv5Yasi5j+X8HAnXFF3GtJdBBkfobQ6hnUxzbvXShRFk8P312rgssITO8ZBTLFCLsc3zTy7dKvno\ntokoZ6/w99fasvbUqRTep75Wu4BqGGyP0F9Ik32q9KxpvE1CgctRQvmfMdxkk5SRIwTDWkTQdMXl\nrM2GihWtVkcJsBChWczOr/9KyK6MkcSXQ16bja0TXLHnTgmNlHZ0P0ryu1XO7ep3zVj6OgkNzvK6\nErRaaUOn5GuVrV/mdqvcEeJBkRAirsrNvAwwlnHZs0ziOyMC3X+P/PGdMWwdmAU91wInQnIrpFsg\nqcOkUWj2w9p/ZXTjt/j0ilfw5VPejPk1JHcAJ0HXbbD9Udj7G8B1Neawmp5frOb6Jz/HNruNqQyT\npl/FHgDb45iRBSMweX945F9gqA06boe+UZhrmTQWsPOiQSwjEUbyXHJMC0HI0lNmeR08RUUNyYRr\nW20dMsGktEJJ2jPOy0K1aXMDQ1A15VpvRLMmVDrWMFmKM2bVopBXQ4Rj0f9cDW+OHzQLZSHs8j/q\nxMJ1orSIIiReOinjbS6aIibYHTwTeg5qcKRV7SYuY3oZp/VCmAMdOAE9hhIU/q8emJINeG52D9s6\nYEF+E204si4ip2m8u4wNAk58ECXMsrvhzmlPfDx6gTHGZKISfGcl+U0j8q455KGILDEJjEvWI88g\nxjph+InS5YmgLbrhaHxbfpKAi6rHp9M4lKTJrMlacyswZRba6o0/NoYtMbBtiPbytzlgnwtdIo5o\nOzSX+ov2B46BzSeRtqWk6Q+49TDY9AUw/wBsgikXQ+MmaK4psZp5nDu1G65bwAce+SeOZZR08h1s\n+eYk9v1ewqyevWHn3pCeAJXZ0AfMg6mzAv6cabry/LKSG3IGKUmqo88V39qoqbRWqwSCkkhxI3zP\nMqL5T+Y2pq7NBLiSUk2Pzcp4ZXl8hR997oimwBBRKCskEAIEI3DTOEGby8pX4DsIv4s3gvYlF1kv\nAlQ0WB2AIcI6Ufwv7QovRuq+AtPp3Sfkd4rS9h6h9Gl8/gL0rGq8mrYTarAJCd8O4GCHJi4puvV/\nu72GWodcJWII2yQhHSMuseOWoFWKVbaYob/TTzgRRK3i2bN2vYDuSIJAFk1aot9EgFYKQlsL1cSM\nTxQijJ/LAVvoS47JcYtDbAOum7mMJSGrVVFAixGu4t9LLlhADFEtZoMkDb8rBpLVzD1iBV+dC6xt\nd+B9d4MsCqS8HKZsgPRJMCOc+sZ30nwlJANgb4Kd6+CTl3QzslcnDxBjvn0J0+9fz7Qvt3ELk4gG\nr6T03e184kURdK2AxgyIBiG933VmCnyjzYWe56yJLch4DdZKAENE3tMghrQUPqKRSoYznUIyl1Cn\ncE8RsrocvbxPcXc0xrso+neslWdJsGOsz3mBW/DEGKxxV/kU1+asLXWu3FvCeDNN1+bPFap7+0Vd\ntZ8Zxfy7kXa1x0Nx3ohmLhBXqQUvdjZ3D3M8ZXpO43XUClzuLP5v3WcKAfPu9Ctupw0DW0ggFe6x\nm0HT2bv0MUPIQAb5ooXaxxYCBqwThxQFYjEDVDGzk2G8Aa1IoskW7yWZwzTjyrFie3K8HgcBXkxa\nouP/ZbLrrFrgNdw4WNTFXSopw+9LwDR47DF4SQfQ9wUodzg8aWQxtJ0Co1dC213Q8y0Y/A5M+gYv\n/gKUz4NDfg39fQv4xC2G/3v9zazuPhxb7WPTqp1s/bf5rGUqmy6bRv9YmU9eNcUl/aAC8cbAVLfB\nyiZExvVVG9CK2cZkq54oYSaCLym7j06ek1QgLUOzLQjOlp8CY+Xy+BZIFrha7LTXkVK+sGgOB03z\nZYP091bsk5L3nfWPHJ5ngj7pBV6fEhf4W/vt6ueBieeflHyXxUGwaEmNqoMvnvNq+DPTVMYrJmJA\ni4EeP4g14/xyDc63V7u+CJXs+K14Fs1WOK7ljg7IEEVJl+fRNFGUmT63Fe6qz5EsUhLKqyGF4r1k\nMYC8G45MzmYUjtfEal3otBy3BC2pmPrSELw2DK0xtqycjsI/I79Nq4/iAPqx2YzdBdgxSP7BDVRj\nEGp3QdQDpUe45fhNmH3fDAOdPHLN89n3jE4euvEDJBsvpXzP7/kc+1G/9AqI3Z46GRxg2b99Ed4Q\nwfqrYetbKC2wtD/vEhj5rrPMrj8IGjHTGy78GMj8YEUA5xKdR85PFwNJ1eOlpSCgbQT1jiCERWMV\n1zBZcIr5G8YlUBehNwE/SOh6rBZTWTx1SlDIG0FljHW61FbpSTVJwJBouBb3XZQOvftqRiFNaC7P\ncxGaIC/gtVdQPXKLiZSQgvyOMLK+nQLsILlR9gg9BzUEEjzX4nx4x/z/4pcrhrR2G+AEcIEWQK6M\niRjQDOM1TiPbbBus+/KJ1bFIMbZmchGG2t9WVuiyJQuDjBTjSmLnUuGv9AOb70eWvwFlsLAhXFPq\nv+Umoj9HYINiOr3ieyj6+kr9OD05Uzw0kQY8Uqc0tBSEitcYLU5gnZ3Az3qBjo3QD1Q+CGOvhc4u\n6F4N5d9B/U5ofoPj7gCbTILuAd435XGe37sYzn4l5l8N9re92AtnwNqzYUeTI9gJgyksOB+GG/Du\nybz01F9S2Q6jjTpMucsxVPkgeF7CHP8yrYcHkgohostrs2LMEh/ZLNqr5DVlBS3kosFK5HMq+IVI\nBPG4skHksV09CMVQbD02Dd+WLP5JBGOl4AIo4yfXCp9pXs1cxaJ8xCYEw6we+wxfVlqoPkfbGkzh\nOeRf/R38fW0wrGUZ/9S1CQEW62yGe06URvJpU/1pfFqQMeZ0Y8wjxpjHjTGtK7O5844yxjSNMa/c\nVXd2KXiNMXONMTcYY5YYYx4yxrzLH+83xlxnjHnMGHOtMaZPXfMh37lHjDEv3lX73p+dXpygFUFc\nM+M1g5pxGG8TBztEOI8Gndc6E4CF+4iwEw1OrKpSWSFSv2fnEwxeWkCCu6eEWSKaKsGzyBA0iZTw\nLOJTqZlZjGw6w1gEtKVhMZEcEIINWwLMEBEEsmbSRE1oMWQUt5q5rGbyHtUk1+9ESCKhsoaMwzuT\nEmyvwNn3wal3AT1NSv3Qd/Iobz3sffSVK94CY2HSGqjNgK1v4G2Hb+dfTk5ZOW0bqzrvovS2MU4d\nXE1jpiGZ+ST8tAEvSbjnc0fDC2+CZAalze0c9LIrOPfAxyhVgSdwkMM06D7sxxywH+w/4p9HBsMo\nfFoEgxamkovBa7MioDX0gCGraCFa9K6yj+U8GMz4BUufI+Mg7zfzNiCv1erQ20xbVdplqq7Nkt3Y\nvOdNluaRvEap+yILbIYHe0EssIBowjppvtzf4rx+hO909r+i3UC/Dr071FDfHqE/QeM1xsTAl4HT\ngcXA640xB0xw3meA/2GXloXda7wN4D3W2gOBY4B/9Df8IHCdtXYR8Hv/P8aYxbgKnIt9J79qjGl5\nj5hQc63Xf4RKjMdtu2z+s6vB07/p1bqVoMl+U991kp3iuaKByu9SulvjWiKwwQt21RcJ1tATQ9qy\nBOZsmoAty/kaB5Y8EaU0xLRLv4tQxe4gEennWAF2aPGawjU2GKGMdZFsy9vgtsNh+ESgEdFcB2f0\nWv5r8Gb+5cRtYHugJ/WwQzc0hvjuCJwewZoeuO2hl9BszuHGT86DSgRmJrzre3Tc9ATvfe/vYdNm\nKN1Kc98BHnrrp5k/Am2bgHQylOCAObB4f7jUb9HTOBi1dDQYBGGaS+NYFJBeAOdqsxUCMSaizD2r\nVLiHEvaaEuO2480onw9ESHBR2M1uWPF4qzSikhwKyNzPJmqmOP7ioqYx2qz/5A3Tkf+IO6chH56u\n245tSDcJCpbhr8a4djSwzFq7wlrbAH4MvLzFee8Efo5LQbNL2qXgtdZusNb+0X8fAh7GRfmeBXzX\nn/Zd4BX++8uBy6y1DWvtCmCZ7/Q42o6DF1YTPBwmWiJaZShKCoOjNT69lWoloCUBjjiVaw0wpzna\n8fexhXPFr1dIM2Mrr4HE5K+PCkwo39u8wB31Kn1FtrMmz5hybllBDfJMEhsv7RYNKRqDk8muM25p\n3FK+j8iKaIIAGWp3v98QwUtH4LrJcMJeKayazRXrIB2G96yC9lkvBNMLk4Hpj8H8XzP2IExeAQsn\nw7kLHoRqSuOW2bD/DjjscIi20DUS0UnKOb+cBvM+Dzu6ecOZlhkGNpWBzq1Qh3dOg0cbTjCUvGuX\nGNcEGhAXrNQb1DLNVsEIoh1LmG2zNF4zlHcg52cCPiLnrqZd8HKasBprqVHXlkw8B2TxbagFu8ja\nWjvW9xLoQBZqOXei+ZH1sUVnxB1Sk2jcuilpdzQOEWhFJUhsEqK8SPCEVmL+SrKTzcaJKqE1/lhG\nxpjZOPn3NX9olz1/yhivMWY+cDhwBzDdWrvR/7QRmO6/zyKfbGxcB4V6VM9kedC72Ny91ffi0+gt\nuxgoZIsijKN9ecVlTLbw4hEg/2s/wojgxZBzG0vDRJBcCnI8wi0kIpRbke6LKTyfrPCSnanitbdh\nX4E3baGZSiXfdp9JTIov6qKVomm0olyYtSkIXBHm3rJfaaFutTedcLo5gng63FCGm27dG8wG0g5I\nJ0G8DM7q+RXseDdMNXAkUCvDli66fwXJLfCDaD0cvwQOHYRvNmBWAs0VjLU1uZFeLv+IGCGFAAAg\nAElEQVTQk3DHS2HWrfzwkRqXdQDrgT6D2TGJ2MLA3U4INrzBzBqyHBPyXDqhjPY20HiuzuglVPTu\nkDSMzRLjXe1MXqvNPEG8QG/GahHzlw2V3PZcL/aZ2xf5+aGNV1n/TN5oBkGIWYIhTO+oJ9J4W+26\ni4qHPq4NyZKXRP8mQlWEdBbBVuBJ3U5sxz/jM6Y/zbj2VMT/54EPWldZojitx9FTChk2xnQBlwPv\nttYOGhPatNZaY3YZX9Lytz98JMAJC0+GmSe7OmyTGJ+Lt2XH1XZfBBKQJUHXZd9zWyKTZ5BEO9AT\n2oSAsWb3TMd/1z6QEuIrpcSKgq5YN0tbcUUIS4imuH3VvWW3ZJ3225aMTy6SeK07iULfM8ZP/WT3\nz1NTE0KwP5moxQXOEDSmmCB0dHgqOKEcGTg2hdtHYX43MHUQtjsvh7X7wfz18M9T4SerL4F7/pVb\nX/Vxjj2wCb9/IesevYbmG4CbT4NDzoWOUei9EX5/B0y/mp3JdJhdhvXHQe+7YPHXYD38xxzgQOCh\nNs48dTuPpFDaC/qSoGlqgVukLGBC8UAqQL2isRiI8yWfYusFtAljoDXILAuav08WWGEDLp+1Tett\ndXEa6PHRhlKZ5bFlt1Z5gaikvzJf9D2E37L+2vyCI+8ga9MLyJE4GOwSE7Lc6fZECbKEKsma9yxw\n+01w1//u+jmeNu0KL17qPxPTWhwqKjSXvIIJ8Dzgx142TgFeYoxpWGuvaNXgbgWvMaaME7rft9b+\nyh/eaIyZYa3dYIyZCWyaoINz/LFxdOpH8rkYNuCM4K1krhacxWUkUQ+ReQt4rU2YLCJoqbsiybEg\nK7tY9OWyzFKsromTgL9m/Y3Ctq+l8PX91Ct65tVgg7Zclknib6oXJPGdlTywufywfiES74xOiV3w\nbY75CdIw7uUI4ydem5bgidx7bqEFZ7+V3Hu4cQya98HsY+Hik7dwfROWWvhZCd43A7p74L0vHuTO\nkc9wYQXGtlimnXYNgydBVz/MfdE1rL57EYy9Gvb6OTz0Zug9mEmDgyTVEsQ3Qfuh8Njv4CSwvcBW\neOGZo7y0HT6YwMtmQ/+wn9xKiNrCLkFIGxPrkU95WRAy5TS/e2kaKFouMshItNXCfSWDmPBFMc+s\nZ4ls/MXyr+0JE1UOyQIVIsYlSi+G5OoaglozlnvEqVvsNWxXVKtkx1bsjixMWqMV4SskqUmN/15N\n80ZEAxx9Ihx1YphrX/tU6+d+WrQrwbuf/whdPu6Mu4GFfte/DmfHer0+wVq7j3w3xnwHuHIioQu7\n92owwLeApdbaz6ufrgDe5L+/CfiVOv46Y0zFGLM3sBC4s2Xbhf8buPBgcIt2fQIm05T6dgSvtSa/\nkovQ1RpxKxJ3Lb2NFnewnEuQ+iueDK00FbEgF+ubiXaOmkwQhK8wX9ME67F+LrEqNyOoedxRtOKG\nN3oMl0LY71AJVnXCik4YqPo4/Ri6asGgobWNLIuVb0tb0bXrktxL/m5u84IBYCv8yMDxTfjqGFxf\ngyMsmAEYLMHaNjivt84XR6F79evpi6Fehg/WYfXDB8PWM2DpeTDaA4fdDO2DnLJsB/V5sXuhba+G\nkaO4tMeVdvtoP1wQQ5+FqTG8EOhqEIImjBJ+ZjwfWHUstu5dxkroyHPK+AgVI8NEM5TKFdhw30yQ\n23CekEBCEl2oF+tSmoe/dE6OzD2R8H+luBOaiOft+N8EN87SRBYW8yLpn3TosLzP1ITgD3lG8UsW\nhUPbJco2KBryelopYc+Y/oTsZNbaJvAO4BqcbvwTa+3Dxpi3GWPe9ky6szuN9zjgXOABY8x9/tiH\ngIuAnxpj/hZYAbzGd3CpMeanvnNN4AI7QTVNHaXWBbTj3MOG/La2jhO+ZZxfryYNBRhAfGJF05Ot\nTcM4ZpQ8uZq0tll079Ikt9ZYmazEettYjFjLWZKNe9HFsGAxKkaEHaJg1RLGLBqHGGSaxjGpJcAk\nwviRJcucNhjDl9vg21thdAQW98EJ3fB/R2DSUieYhveFUg129jj/UPG6EA1HkqxkWa2igI/r5Cuz\ndsKWDtgyDAy/gluW/YrvLIJDyvDCERiIoD4L+nfArDa4xMJvDZgDfsOqC7/LS//j7UzZPgKPnwDV\nfeGAH4E9CEanQZRwd7XBlJ4B19nGEqhewNsePB9sidm9NTYDv0lg2WbnpVZOCkLDD1q2Zbb530Tr\nFZ6oxWFLLFqcaIpQ2IanASc2qi19e13+R76Ki6O8W1lgJXpLcjhr461B9cdAyeSDJITvU8K4FY1e\nEOAjI/xGgKLkProNTUXcVeab8K+xwe6h3cGk7RTnKin3KKn2REkSHHiPeTTArjXep0DW2quBqwvH\nLp3g3Lfsrr1dCl5r7c1MrBWfNsE1nwJ2uzkYxgmeXlzgRA8uFWRf4TyLE8Y6/25h3mQMbfHwgt/K\nCbxQFLqQZ55WLmeyIovrS3HLpZldtlZSzsgaN86i4bYJzGHD78WwyiwXqpqIYlCRWSyGEZ2TQX+X\n7etYBJ+twrfviGDl86G2jaXlNpbOuJ9fHA23LoJ9r4Cey8D0QftJsOHofGIc0UaKRsUkDpNqLIae\nUafxHh7D0GYgmkGytZs7zCCXPgEf3gcqdXheF8xdDpf1waCBa6rwpb0GueCsdSQ/OJcth30Dqg14\nbD+YtA+LlsYc8q27+c27X0o0/AgPbJoCD26Dg54HzQ5YciJvfe0NDEQwqQbtO4HH4aoZcF4MbTVc\n9JlS6aW6hK50oBfsOA1BOaLxySJb1clFlCBF8UWWGAdyodTCV5pXddY76Y+4DooJRcZAsONxYbxK\n0RAhKsY48QePFD9JG9orQhYonchGexckvmGBHrIgDILQBu9fboJAtQTB0TROiWrz78fgDdDq3IZR\nihQhAKgYWfmMaTfY91+anrV8vL44C+C02lFc4cuidttm87l4IY936WAHvdq3inippONj2g1eSItG\na/M4rj5X+93mjHuEnLZJgZGz+9g8U+nuFY1/u6Jiv8TnVyzHAI+2w7dXAqtfBaVTINoI0SzYuoXN\n13yEhcfV+dK58JpHoWcHjO7r3H5kAYmsgyXKNkxieSfl1An2ZuTKhF/bB++KYOhBYDnAjTAwmVMY\n5I8WPlYDbuxl+PBhPrG6yebHoX8avD3Cbev6L4Ha12H9k3D9m2FjOzPvX8WOqmFBEtG9bojDSSlt\nTTj2Y2fwvUXPg5MjeMOtfHsLEEF/Fbb5LEt3b4WNnTCv5jqcCUPrtv5F7FqEnh4vGc9KSt5XVy+O\nXrvNspn5QTG4/yN/bSzGOxvc9BLfD3FX02MvKUTTyPn1Sh8lAZOQQD96tyT8UC1ouhKUIfOiGY3X\npmSRzYSyCRqpUHHeQFBMBE6J/b200MW3MxY5O4XMUWODD7pk+tNzDPZkEEXb7k/JaGz3p/yJ9KwJ\n3h04bXc2bhDXApNF+1PnpYwXxvrfLAqNkOS7mrQ2ROgEHJpKqOoWNhjXRCAbyLKZaaEsJJNWyg0J\nRKAZfVx1WEWt2syeLwruYjqxTVFAi8FicwneYIDHAE6CZAbEMVABMw/sJ+DqL/POBat450I48lD3\nvlfjHLL3GXX3LKduy65hkprCTaMUVrTD62q4LPYRzjG7+ggMWX7WwGUls8DQO/j0Db/j7DPvoNSA\nzUPAY5Ng7RdgZH94dDK8Cg579XH88WfvYP1bZsHOo7noxy+HfniAlSx/w14sG5kFB38cSi+AVT+D\nJy6CyXewrd9LtSoM7YSNfTA3VjuUXWxZWwXTJFF+wmc+uvL8lryngv9dV+XIkc1j+7HPiC/CSQst\nbaco8oWGeET5aJVUP+f+KJp2QWOV9mTupOSNbUV2LRrpWikIOkxdNOZWJIelDVlQZN40olDXbY/l\n4zXFFFy7ov+PBa/cWFwephKKVuoxHjEOZhg2rQtdQtiWi9CLjWIUxXBxmrdYZ4ysPRJKTtMrpT4P\nA+NdbkppXoBr7VcWAmlfSPOpTCrZFk5Ecs9WlmkR6NJeZJ3x6nNlGHwcWD8dKvNgZL7DA0wd0g4w\n86H7/bBuOSz/PneXt7qtRjecOR9658PHIzh7J3Q0oCN1OWBl0erwfsIPdMArN+ME/DxgJWAOhNHN\nMG0Ta7YCnXBuG/xk3qdovP9Rjjv0+TRWboeNJRh9M1x4MNidsPcozPkSf1yyBijR+eU+hk23c/Ce\nnDLAVPjCGMy0cNCrgc3wmcXw6i9Bx82wcinMvhT6wY7BtTEcbqDkE7tndc60e5eirPSTGLEKWm5W\nCNPvi40lhxlmKSU1FOCFaJR69iqOsyVXZFNIw2LV1O880qCNqstzLlrFYzmNl/xuTHgnM5yqRcAU\n2pHzdXKbVjyrIY6K9bXX1C5QC/SxkrKTqOcR/NoSEuRM5Av/tCnq2P05GW3dQzedmJ41wTuMe+kV\n3KBLyR8I/r01HBxRxWFvQyaszjGO96tqxRZsrB6FSVCPgxtWQ62gqXE5fauM92OUQo6ipIhmIZNJ\nNGHZouoSJWIca0Z5ASv4ryG/ndLGFWlDGFhXhtVYsxzT3hD1CL5bgctXAbcDbX8PaQm2t0O0CEoj\nkLZB1HCJeqNJ0HUw2M1uJgw/BKt/yI5HG7xvIVw2Gz7QCUcMObndOQrbJ7sJVY/gCzUYub8bto7C\nnKYb0OopMPo12LoPr5z8BHMi+FITTNPCmq007jjFm+oXQ9wJH06pfq6PVzx8Jz+pzYO7ZsNNg0wa\n3cTwR0tusfiPGnbvMvG0iPPvuJlLv3IIvHUhnLsWptRh8CRoe9htoYaALnjoADKtVKr2Zkltihqi\nGvs0wmVeayUk0wAVZIf9ebEvFqp9eDNfZwVDyPXaY8Ckjm+la9pHux454VuPHE9qQZQt7uTtDLUo\n7Ni0ViqeMuKPLUpIxlM2PLbmQ4NTZGIvTMdU+/pcwYMFZzbqWGxDmLGOnBOBbAnnwHgD7h6hp6Xx\n/vnpWRO8AvE1cApX0dlXXtMwAePdhvP1BadgTQI2GNdGGec03MQZb2aorVrOsmuC54TgTWKRjgjb\n+hFv5Zdk4mORMw7E3qoQFZhbC8VxAQY2HM8mpzqe8ZdsRclrAyK8NenrEwMrKvCNQeChNmj7KJgZ\n0GgHm8Adk+D5w1D6KsR9MPx6iGuQtENlK8TDUJ4B8dEwsIrGnfdwW9/vefUBKW+cBf8cuSjfrlEn\nSH7ZC1ffB2x/DUT3waZ7wbwC4oXQU4IZT7CpDH8cBXtVhE0/BO1TID0D4lUQLQSWwawLqH32/fzk\ntydA551w8hWw4+9ZM2syVAagNMbsLVuo7Oxh2+wKlx5/Ehw2Am3vg7lzHX69eS50ngSDP4LunVBz\nC3JSglKdLEdDpmF5zFWSAOn8w/Ky9TjlB4iAG/tFXAIuMs3NBCHcyh1LyrzrhOuZASwKYyvaYmZg\n9W2XbDCCCnyt+bzDGz3luLEhRWg1dUEO5UJ/M+XA36io0YpBt+yl5Gicz4YnvGjU+cKX0l7i32kz\nCq54WgkRxUK0c8hDF38yxVOfxsmP7KGbTkzPmuAt47RNTaM4tzJwAlfT6sLxXnxQgP9/Ek7o7lDt\nDhmnETdxwmyjgenWQRejXuNtt17Qee5tilHFqkglgpDGa5qRGY/D6TpaRdK5IYSKW0HRMIptFDFe\nIZ0w+skIxrYAW/aFtkXubaRlKMVw1D0w9zUuyusJAyumu9y45cNh+8HQs8mpho39YPgkmHQs7DyU\nxq0r+FbPTVx1xEYumApvGoOZa2BLN7DpUIgXQO2HzqWcA9ybb3ZAT41DgZs3AI1/coEQr4ygfRNE\nc2F4FgzvTXzTyZzwcJMbF1yJrc6C+Gxm3jXE+lPbYHsfbIQTdy7nfgY5+eZVbCZmybaD4fD3Q+VJ\nGCvDUBna50HpSNi6FCZvcDY+2cYrLRPIcjBk1OKFiwCQopXFZDiRHw9JKSkXmASIyDKXgRKwhlwQ\nBf56CK5qAiEJli6Le0rQWA151y/tbSPRY9rVL42C9liLnCKhk0BZgjLRSsgZG6pmN/zv5TQYxXSt\nQXmN2muiFgdBHJtgWJOFQis7EASw2Gz2GD2n8QZStpqMhnGuZbLjmoWLamsjQN4WJ2BFUHcSdprt\nOKG+2biAjNS448afN6RuJtt/odR4J3QvUMdiX/oH5SNsgw9mFl2mBGDROiwYV66EjnwpCG/ZNmpt\nF3WseL14cdQj+AO4LUHlDGj0QXmH2zd316HjcqYsgM/MhC9Ps6yY9yG2x8BDM6DtXKhugdHZsPN1\nMNIGlfnQk4A5DQaPYsPvvsqHp1p+feQqXrkvLCrBYS+8nz/evgS2N93KNvwZ6Poo1I+AZcv5+qwV\nsD6C0iy3Sg4ASSck3VD9BZRPJ1mwkD+cci30XAeXfhmWDrP+kBocl0C5AUNV+hljKXNZyiyYU2Le\n0iVs/tR8RhacAK/7V+hYDpvOhLl7wZTroQr741AWU1NberXrEMEpOKcY0rKKzBOsnuIiJsl1pD1E\niy26Psm4m3AeBKxZBl5gAgkgkAW/oe6hcycLP0aQeeHkfMjxuzwTYChw7dfiYLPIqh+b1lt7aUcL\ne6msXVOLRWxdP8bifNBQ1kYU3q0IXtlponYGidyIsNjsMbjhOcHrKCW4k/VDVrwyweG9o/7vevLb\nrRJOAHcRciIM4IRtN84PuIrTdFcYJ4gTnPFuIy6Iut26ZOplm38BsWLIktJ4heGyVd1PoiyqrLDl\nku+isUjgRKIYzljl+mbzwlU7lYOb8JlWTX6iSZ/X1oHtbRAdDvWqeyHlARibCiZicgTHjcKBJWjM\nhFtjuH7WBh4euphmG6SDsOG318LWH0CzDkMLYWs7TJkBXXvBUJ177voI9/Svgw4L+9bhyKZzIVsN\njO0PY1dB1A8jR5KsWOGL4m2G/iFo74INfwOfiOEj+0L/Jjj+k9D8GWy8AjakcEgnnN4BbQNQGoJo\nDl/hQJjcD9tHoLeTld3Po3/bQ6TXPMFY56fgrF9A/2uh2p+9pC1AqREES+aBYAOmK7uMnKeAF8rW\nv3OvoI7XitVCKhpvVoVDk1pBjaiucn/vhmajkExfgjGyfCNqyy3dqDaDRiyCWPueW4JGK/9nvBQF\npQAC70qSfc1bogRIMAc4+E18iw1B802MC+YwhMUiNcGXPTF5XNh3hdhr31IMNsOrVRt7jJ6Wce3P\nT8+a4B0ilHCXxOZV/5EE6TXyRiuDcz+r44RuUStci7NHTgPWGFfgVrDjIVwSCUsomNkqxR2ESDbR\nVkuFCYD6X+NYQnorKNcLM8lfbZEWmEGMCvVoPCQhQqQY0dM07vxHR4Adc2B4P3d3WwLTdJ1p7M/y\nhsPKp9fcNa8wcEbkvDgqdVjVDWe+4C6a5f1gAZitYCs/hwcPhflHQcd2aH4VRjZC41ZY+U2YY91L\nngrctRSaFwH7Qv2bsDzy0mMR0bsNpZExeljGIH3ULovgm2fCmj7YcCuknXBO5Nrq2QxRHZrdbhCJ\noa8CIw3oMfAmyzazP3wtgv4UNp8DHSfD5NNgPphhOAMnRJMSIXG5MniJxTzzmTUKs1cMkWG9Nvjl\nTlR+vSh0JXhCn29SxxwCd8hWvKo1YEJ/WgX+6BwPmaHVBOVEMFR9jiXwTC0KeKxAXxKmrkkep5w6\ngSssN6L61NQJpqLQJn43KMZhbVATlzUJzNC3TQtt7lF6TuN11I0TvDsn+H0a8Lg/T7RfMawJE3RY\nZSQA9gF2mCBsH/b3mDXBPerGvYBWWaGESYquY0I6wkdi6DWJ1tDKD9EWzst8kYtteObNqmYU2qkm\nTlBsNDAwgkuMu6PXrV7txnswbIbyLJrDsKEKM8eUP27i+l1OnFHmyr3g3r2h28BqC5/d9HrYt9PN\n1o0fhbFjofwglPeHsc/D8u3w6OUw40E4GHj8Y7DzQqgcB/U2aLsShrq5cOQuehkjIeX2M7dx7dff\nCb87Aga+DzsrcH8T7krhjRFUJ4HxQnsbQD+srEE5wty7Ctu7FxwbQ81wzPfv4Pb3PB/ungz73g3m\nB7DoY0RAeQwaHQ6LBydEJbexvOZmFCAt2eKL1pdlGpMtchTef9H4VkxWpH/T+Xghj//KGMq9tNBv\nFbEli7LUE8yeQ+28ikI3VecINaLAawJHTFTfTCqe6IrBImB1yLPOcSE4tvYWKiaE0n0aNm5+l8kb\n15rG/bZH6DnB62iUgjVZURXH5J3+bzcOIhBelPEcNuGYeCn0WjK3smm4tGkbcQ861zqtzxJCkCdQ\nYIAQPNCSTF4rlaxOsn2teMGsAyiKO1YhOSaCPIncNkwoUkyrhbO4lC0zuMzy8UkB0G52QTwIVf/0\nI84T5MjUxzWIQPLf2xNYPArzY/cd4Oy+BuuOHmCFgSXNd/LfT87APvRGSI6HrftB9xqojMC66bD+\n9zB5BPgU1GeCOR3qr4XSdXxpxj/wxg33cvnZf2DgLbfA3X8HbUdAZwr/U4c76nBcl9vi1MrO/L7T\nrzTHxk4Qdxr6r93C1pumQr0djoXbz9ofpq6AAzfDk0fBinOwzOVfn/w2l594G1f5bXI9DotbbEMx\nz8gGH1vRMIupHzWTZuG/LbRbyMMPmYeC36plkIBvU4Yxu6cXnDLODbWzEXhKLh+Ttn0bGlqA4I5Y\ni5RLmA2Z6SxhjMXf1hAiO4UsTsPVfsJCOmufkLipZZVQvMtaSQnkVoEX4p8vykzDOCgwwcGCe4Se\nllfDn5+eVYzXEOAGTRHOoLY3zmDewNtv/O/DhNyT4str/XkxwathnTonJgjpnjQw8rhEzGpbZE1+\nBRYS4Wr8M8iEk1h7MTBoh3UhrS3oYxn5tusxOa1aY3kQMLOmgfUGB5hH+zsAvAzUer2BrQFpFbbD\n/cDZkY9ME/9W1c8uX7Why+OIc2owP4FjjZuwO/ffwOUPfhM4Djq3QX0alE+G8iHAabB1MyRLIboN\n7A0QHQSjV9M46t1859z/JR1bBzu+BEkv1GPKv7SccM3d3HLKQmrnjHiJ1QZpBdJ2WAAs8h38foMG\nnbxh+E4Gr4+4k142TJ8JF5Rg2vdg34/AljPghxfA2Qdz3zWXMWv2Tzn7iPV8FhcIkhifuc0WhInA\nPYVVWCLI0pISmihMeAK/XvEZzqpeKAjBKiEpwr2hjmlZXwwlF3cyYYMkCkJfV/+VZ6ukZImUVPcz\n3FXSoEqYuBiLZRESvpBkSULaBU+Ot3uekfkg9o/UhGcteZ9l+b+odct8qpk9KHAz+nNhGM+MnlWv\nhhTnjdCDM5b144KVenEasbiQzcYJ25L/qzcNFbx2gNuC1f22pYGDDCMc7tvtv/fYPBMJ/pVLpk4w\nHuhtVhZeafLaqxyL0yA0iwX+WmV60n2QVI6SDjLL5yCTnLyFWRauSupSIjqfuAHPuUCtGyqToDQI\ntgtGOrjWjPARQvpAsYjHuP7WY6dsNv3CIMEpUeL+vszC5XEJts/1yQN2Qm0ylKqQzoXtc8COwcx7\nwF4GpcehfQzefDBp/SgwH4cts2Dq4/BfezPj1uX84fgjSV835t0zOmHUv4A23AISeWaol9hJlSvp\n5kK2sJBR2jdu4rpPzuaOV38aTlkKc78Pr/shrHw9zPkbWHcMv1y5ihsXfY+3LVrCm6tu8kdeq7K4\n522YME41jwVL7gCdB0Ncs2Q3ksZkhjaTBBw+LeWhAx0xmZqQL8QSBNA4OMu4V1JNwzkReY3Ren7R\nGK3epUkQj9xn1AcTiWtamx/XUY8zi9eD+NkKVBGhXMJs3u4ik0CM0vIerQkBE5GXqnoBilDzpqCE\naKE7QdHfZ0Ct9rbPHj2rfrya14YJLmNlnNDcgPNKKOGghg3ATH/+GlzAREVpnqCi1PyqWTMw2wYf\nRHHvMeQjfMTIZQlCs2nIoooy7dOOF6BZNJFoPBRwQgIGJ22PwwIJDJkQtKcsSILQtpwvbm17Az37\nwM41X4d5p8HA1PCwcQ2iIbC9dHg/ktGSm2i56CW1JRRXK3mODq8N1Qxgt8K9PTDbwMxhB6aODkH3\nEpi3Anb8EnqnwOB0J7kXNWHpFx3GtmEvMKNQmwrHdLB6r4OcV8SIcQNdi91qG+OA+ckDMNoJt5Rh\nYDv97GAhY3ybXtZTJd2nh72nbWP+LetY8fO9MC//OHZhHWatdvBEYzo0FrBt2dF8eusH+eLilXxw\n7gZeU3Nb7Woddra5wBiLW8RGSm4M6pFbcGqxm/yyPRcBI/yWq7WmGFEKSaaQ5QYWPL+43ZbUjzoC\nMtNq1cIrO7RItaX5TfhMLjaWLBsbuOsk8kzuK4JZIAgZ/4zHUK5fvmlZsLXCL65fMr+0m6TxF2b5\nS/yNpAJK5mmidiE6VeSeoec0XsDBASlOyE7FuQBJhPQoThMWOKHpz53sf9vif2vgotS61EQANSlw\nv4lmkypmFXzPkIcXiiSuLjpRT1E5KYb9Cpaow3zFSCfagN5qidCFMPmkb5mgtcHdRu4vONsBDfjb\nHrjk8DWw5Rzo/R2MVaDZ6SLTSCGtsnnY5XOQxUmwvcSQJcaRRN0Cm0jSkrYEFqVgFqfYwRuhw8DY\nV6CyEmwfjKRg3w+1F8KambDoIthRg4d+CCOLoTwMMx+HLTPgJ1Oc9W6GgVOBvm0Qj8HIXu6GEh0z\n0Af3AlduZHJjJYfQ4CE62Ny5P7y7CofcyBOsAKZAaRi78iD4wmYYXEx01HTS01Kod8C87TD0Xobv\n3sG/3fdJ/u2YtXSXmgyOQF/cyRuiYRaUYHbsBNUkoN9Cp99u99dcVHUtdu5cxgbru1HW/GwHJF+s\nC1PX7lnigSDGpqIBV4eZFyueyPeGHy8dESm8JnkdRMBro2zD5HlXfpewZCFRMkpp2L1p2AszPul6\nwz+HNKOFtfRdcOamemei/Ej/5VwRzoN7TPI+J3gB583Qh4MYtpAXKECWMQuckF1dOEd+m0Y+gc6o\ncRoaOPtMp2+rk3yCD2FIawOTF0nCM4s/aXexYr+1cUGMYjoUU1uuiyVmWpU4yjUotpAAACAASURB\nVPnzksecxTG9LYXXNODRafDbfdbCxrtgw3FgGk4ls20QzWasuYJaBco+KUYzCpNPY43yTClusg7E\nbnv9Kgt2SwXM58H+I7S9B9IE1h7vMKLbgGOXwOtOghV7QXIRjC2G+zvgBavh4Xlwbztc9wRMmgmn\nt3tLatP1s2nctmcYuBkngG8GGiPUMNzAdOjsg0+XYcbtMPoTiKdD/B2io9ay9zmw4z2w4z5oPPFP\n8LU3wcwEZlehuhbSMgxcAzffzWDXv8DzVzNw28l8Zet7oPQY1H4A8X3Qm8LkFGY1oGro6Lf8uALP\nG3SCKC2F8a2kDvMcK+f5RCcshwBtZGPvj2v8X3vJaE8Z4TcpqZ7jIbVgQ949TYxlWVa7VpoFHlby\n10h4sZQ7ipWgrCQhW58hYMOZv3Dk5pMkmRIai/Phv6IwZDi7h3W0+1wGnbTu8jOg56CG7MYWl0mw\nj/wL3oqDEUS4rmM8iYuYjNV647BincGs1+aNbUUczRLChROUQFPCuS3Jhy7KbzIZWrmAZdbZKOB3\nOsJHtFrx1x31EXKGAIkUJ4nczxafw3/vasLx7fDbOcCaG2H4GChPcftlk0D1AEZGbqFZdZcMlUI5\nccE0n/Tg+VUluNbCEw/C0JNTGWseB9ERMHwQjC2EJW0wKYJvW+gwmCceJ0pGSK7+CkS/hyfOgej1\nwAa4oh3OeQI+swgeWgtTZ8KCvWFV3e3hJwODM6FjwOFHv8OB9McDS4CtdaDKEH3QMRPO7XQJ06lB\n2/Ew9G04fC3PnwsXNdx4Dh4Gdxx2MZeddTFPbu+hvrTkFoLOC6HvdkjaYegi+O3FEF8FR94It/wn\nfPW7cKaFmU0Y2ABPbIfKZkZK2zlr6EP0ngCf2AteOeyzhnkmGikHbVNXKtEkgqeV26Eugio/6yxj\nEmyTKQhp8NfVLmPgciTrQAfRZPWCLf1JjBO4+rfOpuNHMUJmGfKMY8CEkMZxqBTakjaK+XO1W5w8\nU9VnctMeE2KQFhuH9Hkze4qe03gzyjQ4HIZbUcel9NEsnDtnMWnUOkIQRQ03CSbhjPubjMOCdS4G\nyGvGkl1MDFZa+OYSfpjxGaPak2DgkpSzsXVfxABjaI3hWoIAlZ9TQu5RmZxFHFAmX0TeJ1JvTQ9I\ncIJs7KfQ9T6wVah3Q9sWsKOuUnEcBO49nfDJBowtg0cGYXTbYZA8HwYPh7gJQ8+DkS7Y1uHa/Qkw\nAJXHV2J7yjQaCWybAqf2k178TnisBEOfh/R50CzDxsVw6jr49Fx4dAUVO4jdOEpj4xQ3wj+c7ipZ\n9UawtQ+uSeGRGkyuwmMRrAKSJtAH1Skwr0IIY0yguQbmrWLSQngvzjstAnqacGYEpxsYmbSTB06C\nJSdv43/W/T2rr/s+/OBIeMsNwF7AmXDv7dD7PvjvefCbS+DD3XDEXjBnntO+p1vY+yXsuOMa3jvw\nb6w8qM6bEuhtOLxc8uiKJqiNaVnhVPJGXU0idCOl5co1VnjT5DVLIRH0Iogl9FjbF8RwK+cL5FZJ\nA59qI2KmeChviyzhuRfAou1LMUu5l2itKaGOmgh/EcpZsVf9jvyzpfJMBlYZmN76lT0DKu/+lF2Q\nMeZ0XAn3GPgva+1nCr+/HPgYQTS831p7/UTt7VLwGmPmAt/D7egt8A1r7ReNMR8BzicsSP/saxJh\njPkQ8FacHHuXtfbaVm334OwpEc7jQNeZiwn54tfhtOFWHdUWT4kKLRmHGe/wHYiNN4rjYI2a8Ylz\njDOGaVxJrLciHHVUj3bR0deIFVdjaTolpP6/6EaTy6dqVdSRgiuqaZjYkpthuJSPuhLqTWB6B2yc\nMgjbtsLQTO9T1A4VQ+yF7ter8J17YdvWs2DTidA4EmwDRmfASIcTNjNT+EMdHjUwNeWwB5exZIuh\nObeL+Lwmo3TC/CoseIDFp/0fltz1Mqi9HrYcCqUK3DYKx4/Cpf3wyE5KNGhSJqXEPjzGGqZQb06H\nXwMnAHdF8PigG+1ym1tthwHbBCrQXXZzZzLQvRIYA/NrONDy+i7YuxYyZkkV5Urq+OGUGhwXw9um\n1Ln/vNfygcNfzJbb/wlGPghTRsCcBHY5PPgQHPdOmD0FLv4Y3DDDSYfeMszohsmvovHSA7h49Ye4\n59QlfN17gWh5mhhnTJMxbvMGOQ01WPJuVkKRhdSSpRYVQaSTL2kNVBLfyP+G4CLWMJ5n0vA7BAhD\naqPBeM8KSS9pU7IyVRqu0G0OlYKiYMkXrdTabkcSNHNpUx69moaqJqIA1fx8WsWeomeu8RpjYuDL\nuHJna4G7jDFXWGsfVqf9zlr7a3/+wcAvcQ6RLWl3Gm8DeI+19o/GmC7gHmPMdbj3+jlr7ecKHVyM\nK328GOcF9jtjzCJrbVpsWHs1dOA0VT/FSHAYsNhYqrROTVwnJMpJcG+kH+i2LhmOFEdI/IN0+b8N\n4+4vWme2TcIzoXETQOLYiaCk8zZEwbuhYcgqvepJpK3fukClQAWiAeUMLZ55c/6kNrRTNFgUNahe\nCy8qwQ/6gaHl0JwFzXaX98CuopbAIQ8aePxsGPp7eHwebKk4LqgAj6TQC+bBLcRJg+61O+npbWNl\nbw9/POkq+v9lIwf0/4w7LXD3u2DJ23nzgW/lv++bBzv+jxuJRhnGRuHoTuex8JiDBZr0U2IDlhE2\nEbMXwyzDDwrAMJhkA7YyFXoNPJTCqCy5CTRTODKGBduhvB4aS2G/TZw6FV6WhPdv8BofypMF6G44\noXRYE67Z/1p+NONa/vNb34QlJ8HchvNDTF7oYI/pD8GlL4Jl58GP3gk9d0L1SacNNF4A936fG+Yf\nweh+TkhpzF8MkpUkL3hkt5OqczRpA5aco2Es0ahzZANPyG/in5tVgjCB7yyhuoPwn/gni9Ih3g3i\nhyvvT8qyiwYvBmQR9tKfbBdnwqfdu9qNeXgr8ddIOSBdLDS27n8xtu85d7I/CWo4GlhmrV0BYIz5\nMfByXHAsANZanVBRTFcT0u6KXW7AeXFhrR0yxjyME6jQgg98Zy6z1jaAFcaYZb7TtxdP7PKfMd9Q\nBy7CTF5PisN/24FlOMaQVJI6paQUzesApnjGXWfcHBGYoowT7Jv9/f4fe2ceZddRnftfnXPu2LPU\nrdbQmi1ZkuVBxvOIARsbbGwgCRAcTJinQCAPQsjLCwQIAZK8hMmBGBKHIYSAsSEMnjAeAGPLtizZ\nsjVas1pSz33ne8+p98eufeu08ABYL6yVxVmrV3ffe4Y6Vbt27f3tb+/S7YZCKxkykTtHU4gTPAad\nGJdsgLNqkxRUkHLBUnNE+g5vqVjExTfM5FXqNUdTjNSKNu4eDecWqrBmYz9BYabSXxggoPnBPQ6z\nqcLx/xsWPEDtx1dDcBUcPAUeNrA+gekqTEUQR0SVQ4RzcrTCKq1r7iN/2ffpWfhjLhiEqwtwel3c\nxo8lcGPffbBygH+9ez38JIIXfxTG3gddZejZB4eXwx05qLpyb0xhCbBElOhmOwWIm5DLSErdjgqW\nPKzoFQQgE0A1CzsjwXTPCuHkOnTtAOoQ/gscBy8Poa/hFUF614N8kkp2cd/3uOI5r+2GC9/7Rq65\n688Y33QxfGexZP91XgFvOh9qe+m44HpaJ17P3CFY0AfFCG77zkmw7dtQn7kLc9O5TG04IPRRe93h\noT3mxrNb0oeWXwTPCW+nzwYp5W28waDcdcWQ05xe49qhz1IYTa1iXTBU1rUwVDVMybSTeYvvS/UI\nW8a/S8s4BWpTcQ6VZ2jXplArX1kTaU5826pORATmGFeu45gczyq4tgAfcgKJRpx59EnGmKuAjyFI\n5yVPd8NfGuM1xiwB1iFK9Fzgj4wxrwHWA39irZ1AdF1aye7DK+oZxyS+2E0DUXyK5yh2WkO0vird\nGh6O6MRDEgZRwFNGdE6/O0fhC+OeMQ9ZRQ+7zwMjjWsB48ajQHknqBnry/HNyBhygqMUNdw91AWL\nU8KUPnQC1J1gKqZnmBmUS0eANdMpYz0dJ04Jey30W8RkE0n0ohtIHodWE058J4wPwI5vw4FO+Mte\nwiuqxD+yUJpioOMgdDdp5jqZ+Px9JFPzKC79PG87dSOXWQm2gORB6MS9KAc3DtzL4OJ7ObTteih+\nFt7yGnh1E4p74exJiLNCJZmdg1GxWuM2Kl+EsAsWZsQu2Gah3qCdGjML2OU6bWEAvTkR854JsBlI\nNsLaBi/ogkXJzIXraMYJrm8zLkFgOoIO9/fKBtxx7se4ce3H+GBlm3RybwVy28FeQ/meN8CCh/nm\nCX9LnECuBusyG6GrhXGFo431KbxagFyfGyXe6m5bkKn/ZyQPuO+tG+NCDJ0V2oV5WhnJpp7KiCI7\nms2gMqeHpu9qg9LwlsITakA00+1Anq0BujREoYfBv5cqU/0nwQfIIuvZEdrGtidovYJXK1uht1Ig\nafCL8XP52R9PY/E++ig8uvnpLj56Kj/5SdbeCNxojDkf+DJw/FOd+0spXgczfBN4l7N8r0WAZIAP\nA38HvP5XafQdH5Tf3cD858KFzxWDow+/DdAgnr+71zU2QqzgaXdtgAiCshyKVvj4Hc6a1cI6ne68\n46wE2SzOcnatKxt/reLAqojT1qgGKlpGuJxqxcYGoidZVNOBuTRbQv+up87NJb+4t5U+Wq2HRmoh\nyFghLGjADIQ9RTdQ/x7Mfh8cOQ32vB3uTmBVFT6UkHRZzB/cz4v/qZ+7/u/XqM3+Nt0tuGPITQ4L\ns5qidMvOPUzvFvv8Orz2BPjXbz8Hhr8uq+MHW7DoEWg2IemSmdowMKojOY1PRRiHOIbaoKycR6rI\nkjgoAzYXWeLXI6vtCsSlIQC7ARZ9lmgVvBOY25yJubcj5ylwK40ndrZcsMeIjOUSuKIX/uWdK9h3\n82eI77kIVpwCPynC84bhSJkLH8pxx4l1mkqByR8hkxHFmU6t1UPHLk7Ndc2Q0xiC7uKglqt7O8Fe\nE9ntQ3fL0Fq+mURYC+WIdlW6bOK31Ukr/XbBGiuQh8YE6kGqzKNrXyEloyD3T1ugT3aky6GCD5S7\nps7ocz0vH7uEJ+uNF4MsNjpOsYFb7oRtP5bPmhyr42kU7wknyY8e3/zW0Wfsx6cV4P7e91S3s9be\nbYyJjDGzrbVPuoHbMypeY0wG+BbwFafRsdYeTn1/HfDdp2jgEL+4qw8A535Q9EN6bHvwFB11zwYR\nJdppZNpWEIv1eKRW73x8l0Z4a6diZjoXB9x5c43n+TaMKNwIqd8QG6FwztjIUq1YZ0GoEOuRXvHV\n1VPL1lgRYrUam261Tyvj9MRLH7kYWqlyfOnIsj5Ls+3ysW9DTjuyYxKSe2HjJXAX8ObrYcVHWHUq\nvC8Uptbfnd/Fx/qnOdPBJ7PqnsMJ3hJJKzSQSfayFvzrrV+FgSbMzsLcbfI22UlgGnJTMN7lej5B\nQp4dtDdGy/TCEmAL0Bj3vdDhLhlG3BelrHSXIfsQDPwNhZPhqyH0xs795RcDluD7SxVcO0PR9Vfg\nxrTDwk0RbLn8HdxyfpavT/VSfuAelr4n4Ikrnk/lkps5c/g2bnvhR8AsgSQmmwoqpY8nK0gOUM34\nfjWIstMFWBk2euj27mkB1gSa9KEZhkpJe6rthtLBsMjJZDp7rB5I8Eufnfa4dBFJ/5+49qQFN3N0\nkDHwHOAw8d4j1rcnH0tbdJwaAYwZWP1cMcTuRwym2z/0i+/0Kx/JL+3cP9mxHljhvP4DSBzrVekT\njDHLgZ3WWmuMORXgqZQuPDOrwQBfBDZba/8h9fk8a+1B9+9LgU3u7+8AXzPG/D3ixa8A7nuqB1eQ\neVZBLNo+oC8RhZhBss72OBesy8o1xoiRNIzX8BpgyzsB7sRbsAkuboIo6UPAoFPKFSRDSd39XDIz\n4QG8W5+2qlSo2vVxUxNKzzuaDD+dEWEtJB6bU5dRMcmjgzEw875J6tz0RE3Ty0ILlxv4r36Ez/v4\nmTDQgCDE5OFvA6nJewpwaf80ResnnT4nNtK+Qgrv0+ZkE4FoVsfACXthLC9VyWr9UNxJGyXPVWF1\nL2ztRZbIvam7dMlM3o/bViQLhJDLSbiiC9jRkMGbm4ElRrDdrg+QP63Bn3TAYFMw3HSbNQCkf1t8\nPyWp/mkEHueshiILHS1YHcKKngbv7jrM4X9cSe3/QmMyz1XffBTqr2TKfARYAnGNTEaelcMrrzZs\nZLwnpAuxZgKCd6/bW+jgFZ5x7bYOIyZ1TV1xY8fWMIjVqinHafgiSlnTmilpcbCe9TWwQcZfZaia\nsuBjF3FLJxgdncChfar1PhTyMG7OqOVt8Rly9cg/Q42RhoERI5LShbAZ5sCTGiW/1pHkn/mcpzis\ntS1jzDuAm5FX/aK19jFjzJvd958HXg68xhjTRKyLVz7dPZ9pGTgXuBrYaIx5yH32AeBVxphTkP58\nAtAGbDbGfAPYjHhyb7NWHYmZR82dkMGXb0yAzkAgB5B04F5EQKqpERh0n+3Hk+Z6oe3rdDqYYNpI\ncC1ElPFhZLeKfnwh9oaRn9xRrVQMNnZUoKxGyt1LJ8FMoTi6Pq9ScSyCKyr3txZ6uk/eBVAUvrCp\nH01VVout5ZgVqkyUkwteuTcCeddeEF2WPA7bm/CybrBFbFnYYQvc9XMavsjK0RajWji5xFtbqjAs\nUGgCnQ1B9Ne+DXb9AIIW5IfBNCBqCeDc2wUTJbo4QI08TeZI44ayki68B7ilG4YycHwgK+rOWBgN\nxRycbWD5ASj+EeGZ47y9By6JZy4WCb4fMymF0w5uBX5BVCqenq+ByVroqFDOEp0XgAkg31vj4ksv\n4tZP38FfXNkN4VIIKxSyMxdZtQTBK9XQjWNa6ap1CtCM/DUkjv7mrNZ0UR0L7RoM2tbpyMNSivur\nLGjasP5oeUhrZsIA6n3VA/lRZk2aVZOmr7UL3iR+UVC8Nh00VrhEx0DHRxcnfU5ixEDa696xgiBK\nGptpAGs5Rsezs3hxdNkfHPXZ51N/fwL4xC97v2diNdzDk4cDf/Akn+k1fw389TM9uIJQvaYRZTgL\n8SofQTxlZTUM4RSJO7TOwIgTtDpideh28d3IgI3hmUq4c2L3MiXESh7H7+OWMQJDdLlnRHhFqpk6\nqiCfbBVuu7uufSrYgbMo604pKok8NP46DY6hlztBTPC4nEbFo9QzcilrwuIVRyFCXIneM2Fbn7gI\n8UJoSlAz3eZ09Sv9TJWBLiJaU1j/1xKFBAVJqz1zF0zshL0rYH4HdDwBQSyzaG4Ak/1MW9f7mR5Y\nlBfC4SHgwaZMiq5ABmjYQtlCmIezAzi9Al3vhjP38coBeEVDxkML1iRIv6r1mG6/LmBHH+nNHtt4\nu/WBObWWlVHyyfwYp2ywPPqjudCdA3OAgZzgre0x0z6zM+UFPDzVXgCMX9i06E0m8UG1jgaErlsa\nGR+sSgfVMqk2KtsF6+ldocPlteqelrjU9qryTdcgjkPaRZOOtpYSQxtn1gJKygdOMxiixLdJ5UWV\ndGIkEKnvOh2JPM5CHJ056XFDbIfi0Q35dQ/7G80V+4XjN9YaJbkVEMWrVcia7rscMm/3IQpaLZs2\nZoYo2TKiPBNkECvIDjHj7vopfABLqwyOumcqS1QZE2Wk0lcXkDee7xi45+ZiP3nS+2CpItbPAivu\neDMQQdai75nYC3xgpQKiEto1UaIWOoqcM9vUitDAg7p1kfWMDVz7lMrTAoFUN6yD42sQFiHphaq4\nIr+Dx+70aCeJWNrcz3T2E/i/NVAEW+A/Z/E3H4t5f8ffw5FPQ9gD84Yk6hcjxXBsBbTOVbMM5Zyk\nFz7SgvIeoA+mZ8GUhSNuyZkbwXktGPoCnLqeV8+HP25Ad9NP/EzsLXHwlqVxCkddfz3UEk5ngCle\nqdZZWomoUgx6S/DcOlT+D0QPQ7yXOPTei1K02um5TtnoQgDepdbnK47afoZ7fsNA1s1K4/pZy1aq\nQtNAVoiMeRjPxJPVzY+ND/Cl4xIqt3qUUztzqLwdre/SVDSFUmJ3ozQmXDeOo29T3OAAKoGjiyfe\nqi4mcNjJ6ziuIB2yHvcg9sKTWX2/1vFbxSvHNCI4dUQJVvCMhQ73uSpGtX7TwpIgA6+7EtcRS9Yi\nA9eNQBFNZrouPe76jPvJIoNbd78ngayRVON22mfiBTLt1umKrkKqFoAqZg1+6LYo05HHkttcy7YS\nk0mnAqsVwdSa0UysNK0ngLaVo/+bxJXOHAC+1gmrCy46lkBJLIuGWy00sSM9J3X32HQN1UQXC7zy\nzdaBoBuO7+TyCrz/5FugcQ/c8Vzo64HctLgcHRbKhwlokGg+4mhRtgYql9zIJDBpJUmCCgRVeGk/\nrP4UnPFZ/mAxvLMBc2rSHoVq0pNSFWcu8XCJKlodl7pTWm3r2M603mBmgCpx35scmOf8I/aWP4GL\ntkG8k2zGL5bqOuuh1MDAOms35U3o520l71zxlhEvqBm42tOBW+BSkFYtmKng9UgrXXC7bgQytm26\nWUC7OI1hZnv1OWpZN1xfKg6cdx2Zxm/dMtrm8oIo3RjHFsH3r2ahaQylnXWHpHZtxXNOs8yMzh+1\nXvz6x7OEGo71ccwWlF/1KCOW5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MvwLKEupGDLIlwxKCfLGhSuRH4cR42ncQ4iYrkeUcgn\nZGCNiz00Qpmv6zlGx28tXnfoTAjFrc84AcwiQvQ4UpI1QoRgjrvksPtbAfwOZDKrNdwtt+QwQl11\nKIYU4HGWT9nIuXMQhZzgAxYgE1DpVeMOI9OatFrYucN6jDFd9V8zjSJnSVTc9Unqe52AauEqzqfu\nve7ymt7dOHCQh7rZ9VA2d+yxnn3RDOCRx3qgUYQV/wYHjsAJ43D3gJvBC6Ap6dAJvg11p9SsUxz6\nWKWotXP8AyRFtQXlLMQDQNCAbRCeLIFjGwDW4aYGBmtS2zdoyf0bAZwUeVjFWJmYPU6pFxtCwNB6\ntWNOUWbjlPXqFob9BmYFEryZyHpOdSH22W0K7Sger7V4FSOvOQhJ6ylkYw8plSO5lwa1GO6C4GWw\n6CDf3b+OC9f9FwsRZVVD3jOnY+tkInCyVQ7lHs0AkhS1UAOEWrSnHvg946YDb3CU3XOaAYzlRLaM\nhU3OejyCKMpxoNspui4HTagXWEJ4sUNubLMJHAqlROgRPP0rpl0vjkH3+wiigEcRj/Q4oG5hhZHv\njrNilMDM+r66eB4wAusprrzGhR/2u3bVESV7OPQ003uQth2T49gV9j0mx29M8fblxDLtR4Sl6n5m\nIwKwGF/EXPO2y/jUYD0Uf4rwu1F0IlhSHQkSgCvBbUQha4Cu4c7PuIlpY0/zAeh0yrZdiCTxRHut\n0t8yMglGoF0ZLLJ+x2SMYMhZI7VGR4CuEAYCF2F31rJirY3A5ecHM2lObQ6xa5vuJKvucWQl64ef\nvgTeDaw5JCta42Y48WrYCJzaCePwoya8x7nYWMdxDpwVm4gFqDilQiNqzenW3MZCs4gkUZSvYarr\neootzypIwpn4JxmPk+cSv2hpxt501qe4JqnFRxkd9dCna6sllwWeMGAybgEMfUCss+WTWAJEoWZi\niIyDjCKXqGM8S8S6tvQ2ZawrkSvbGMDdIdCsQn0JzL8DHj2Ok18si/guYK31QVOttQue0YChXeku\nvWADjEcS0Tc4FzvlXuui1+l+t4usG3jQyfMpiPLLITGHKaDfCJMgQbBoxWFzeKJLJZQgWMl9d9D9\nXgucCKyxcMQITFFCOLYD7nkg9x83Mm/3GuH6BsBK51Wmy0kOunPLbt5tcrj0Mrd4qIJVZtOtCORx\nMfAljsHxW4tXjrPc7ykEChhAlFIFcYXmIEpYoYce2lR7qct71P063GfF1GcxMOEmQ8kpwDq+tjZI\nQKM7lomqk37aKaW2UZ5yd8FlBIV+LFuIQE0gi0jOiGKounc5aDxOFuBqfBs/6bMtUTRaf6AVSPAh\nfeSsvKDWbSjGHjvWClPDdWDqjbDuA/zOLMsPIiiP/A28Mgv3/h40uyARl7mBQB1qYYeB0KzKDvZR\nVzuXeOWnVneUuHTiHiBrSIIXEofXS32NrFs0jLcc9TBuTKzx+KwFpt0z1fvV1Fp1xXVD0UwiiRuJ\nk4MajnjvlNEw0GfEG6mEzsV1bn+Ce0/r2zcZ+YWlnRGWyPiDp6qBoyAXfh+qFrrugj0f5S8r8MGi\nWH+6COqiEbj3mNK0a6d060bapQbDrETkTxfbKSNychCR83V4z0BpbyCL1AIjiYE9CGSWVw8iEAgM\n5HnHxWKRbsrAg0gQbTTw9UnGkPmmtXAHkYqes53MjblnjDj53uieNxrI39Zdf0YTulsypw5GMBxJ\nxb98Iu91BFhpZX70Wvj9w9A9CqOD8OVZ8E0jqcIPADsTuDB4hh0jf5Xjt4pXjv2ItTuE3wOtF1G0\nO5CVOXI/e/EBtBBRdBV8PYcmopxB4IURJGut4c7VIjQ5fElI/Xs2rjBJONMKSScUGCsCHCG/i846\nrTurK0k9a9i1U4uz73Ht6nVtriFKfxdiGVgEFyviqUgWUbQTKaVVNWL1dOEz6ZQ4r22+dno23FCA\nf/gG72nAsh743BlVSvf9OVzQA+VzIHM8VLZguj22HFrpo6lIrKPOxBWPd8pYFW42kcYVXLCt0glk\nx6lPrWQkhJyTJqWBqbWn7IB0hpZF2j4aCEe1bhyH1GGjOZeBoAosbe0HDsaxTnZ2uoWtx1lo+w0s\nC914BUK/mop8oEzd+inXXp0ELevTa6cin11WiF0yQLASbA2im2H0k4w/vJixc3YzDSwzouzrDrtv\nusVF6V6JERlROcpbKW06GyF71EKBFrY42UmQ2iF7jSiqMURGCkZ0SKeVuZJ3chUiY9bpFo/YdVdn\nDMtq0NOApR2wIC8KULe12+Tev2TF0n6/FWgsAww7KGDIClsmQLDdeUjbE+Q5ZwKDDV9HY3EFZmWg\npyDvdSQQo2R5Iu9TRhZHQpjqh6m8yN18PL2zPxDo4Q3A9RyD47fBNTm02piDBNmACNoQokir+Jq6\nARK57cJv+V5EFOdJ7m+DKLc97vsEoc9k3LVamUyronUg1mh7UgTeoj0SirLvds/XjKE9yEp/2OG4\nCn/E7r429ewORLkvdG14zP1e5tozG1EYBhG4MtCVCg7FBuY5BTQdSmT6othH6EuRBNti14ETEWzf\nsRYOT0MePuc2RTx9FtyxNoG7PwfdQ9AYgGQLJSPPa3NlgY1GAiQN9741A1EkQlIxUqQ+YwVzL1iY\nb4BwmPjIYkZC6Ailb8sRPGTknquQ4KWxUA58tlV3LAp8loFtofRrR9O76ZrVpSnWukBkYrHofoLf\no28AOMmKklhqPcYaG1mEHwplfNYiynGJFYhiofUZkhMh9LekL5S+ppCABW6wQHMAqkVYVoG+KbZO\nvptljT9m3C3cReQ9S6HjeiM4fIDU68At1DGigGcjC6wW2R8EurLyeejkfxKxapc7V70DUa4lI5CZ\ncm97kH6dDMVw6UIU8wIDtgDjWXn3guu3x518X+j6DwM/BL5r4DQj8nqCm4+H3aKYdXJ+GBizsMjA\nuVYSWzZlBdbrbMFoF+zPyEL4I+PTlO908+sQUmi+owsWNYX6thFZEJqJ3LtSgmaP31PsWR+/tXjl\n0OI2TWQwL0AmUQ4RDqCd+nscInATeOhBSz+G+PReg9DSyu6eCifMQV60hQjkfByhwoowWWRlHneC\nMYII1zQi0EPuGYuNTNgjrq3zkGjubjzdZh4ipLOQhUGx3qy7J8jEUmUHMvm2Grc1u3vW7KbgmuUI\nZjXgKodX1gIfMCo7V3o8EkoWd7wA1gmD4Sub8ATmIzjqQgPCOVCTIJm6vz91VtgUMgGqzsIacOMx\nid/h4yxEURwOxPrCbIXKYu6I4KwIumK5fjbwUze+qwLoT2DYuAQVXH0IREnOC2Sh7QoEM20an0CS\nIErGAKOhtFGVlGZXzXGKfFCDWA662G1k36nNCDVqCcId3W4EtzxiRDZWJCJzlVBghhED/VbarlQz\njgDVbtjTAWfOhwPfIN6ygm9dBqen5HQ6lKDQd614P+cEcIaB8xNhcExlPLxj3LCMZyU5pOoU4/JE\nMs0mnZw8jsQH5ju5m2vEYlxsBA54wgjtLOsWzHNdwk4dV+c6kQpniZEA2AsQGaoa+E4Adzq5jd2Y\nfR/4CKLYh4B1LsB6fUa273sucq8vAV8xcJKRPu4wsCwDZyNb7I4YOMON0e0Ile/9GbgR+FoMc3Nw\nblbe81H3vN0J4hZGsNFKuvMxOZ6l4jXGXAr8A6JyrrPWfvyo718NvA8Z1mngrdbajU91v6dVvMaY\nPDIumv13k7X2z4wxs4D/QGJgu4Dfc9u7Y4z5M+B1yKu+01p7y5Pd+7DjkHZnRBlO4oH9Ij4FGGQF\nD5hJ2Lb4vZO1BKM1QgfTDLiic4ut4np42MEikXSDD1hoqnC3EUEfQ+ZbBVjlrlOIo44o3Am80g3d\nZyciQnTItXedFctcUy9zDjPtCsTqHTeetzyKtP9EF+lPAsEEa1YmbE9T7jHmsFRjZRLmR4A9l8Ml\nden5rUiVkTwwNRcKb4L6LDAtmIR7huClsSiyMWQBWYAsNMsQS30TooiH3HssR5gK0w6DfiQGWv8B\n817I80sRSVeLxwPBEPcjCuMg8owLnJIbx7mVdRmz2IiS2GtkIdHkl+3uWSEei4yRRXEYUQAF4FQE\n1+2P4AIHTVSMPPcQrj4QHgZaG0uN9T2I8n0EeDSQhb0LWWBWWhioe+s3Nq5RcZcIabwYzjwA9d/n\n2i3wqQ4I6lDe5R7KGghXQTTED+u3c/MZj/K25fCqnHgRD4SijObj18Q1IdwfwGktgQcez8EtiHEx\nhuysuA9Y7cb7B87AGDTCENgeiPJ7U1Xmw9483BuIZX1RRqCNaSNjuhLoDqQPTwDeVIM783Atvq7C\nBywMGJHbbZHIxhSyuNyDBL263XhsAg4mErdYZmR7moudHP2z67pFwIqMXGuBlSFcW4HPFUUuXoEo\n/RUR/KwDymUolaE3vY3Mszkqz3zKUx3GmBD4DLJm7QfuN8Z8x1r7WOq0ncAF1tpJp6S/gA9l/cLx\ntIrXWlszxlxkra0YYyLgHmPMecBLgFuttZ8wxvwp8H7g/caYNUgfrkHG6jZjzEpr7S8gLMUMVFow\nEYtrNoUM8hiiAEPks05kQBTzKiLWVC8iBP3WBzWUnB0jwYQAt5WKU7BTqbfVOqzpUo0BEnCJQ3iO\ns6LDWHTXZCS59AV/OjUcxxVxzVYikMZ9iND1ItfuNoJnz3arrmK5RQtDxu8LtxuZ/P04CCTwKdDq\nem/IibUXAzsMzDFSQ/y28VVwcye88Hs+PajufgfnSQZG/nGYvBMy8HWHoU6699AdQLoQS/eA+yyH\n6HDdnuluhI62CLFkSeqwL8u+XCfzmKDTyIQcR6CcB9zY7TdyX4WLmjmx7Cxwr7NAO9x1qmgHcXoO\nmeCzEcXwHOAtiEexzbWthEAk+10/noP3Mua6tq8CNkQe3lro+noYUQ5rLBxXhTkl4fuO56Tvd4Rg\nDyErwVZg61uh76vwlSJTC14EdpU8rfFtsE0IeiH7ADR2QOaPsT/7Kp9t/pjx1WJ1r7QCcxik3OJu\nAzc5RWoiWWg3OFkqu/c930qQtmHg+8ZTJu8H1hiYH0vK7Z6cq4aGzKMtwHuNWMeXAmck8DcB3G/h\nlQFc1oLv5WVH29gKv7rm5HFhCBc14WU1ONIJ/8dA3BIKXxDIfqRMQakPTjTwmJWkiFOQIN6nW/J+\nuRC2VeHxEKKsvPd7gFVFeH0MnwxE3k5wi/Pb6jBWgHcEYtnt4xgc1Wc+5WmOM4Dt1tpdAMaYrwNX\nItMeAGvtz1Ln/xyxV57yeEaowVqra4XGtsYRxXuh+/x64MeI8r0S+HdrbRPYZYzZ7hp979H3bQC6\n9UMrEvxvSyCCZZGJsANPKZuDrM4hMkEiZKKmK349HIjQnOos3YGGWIShle8VUsgkYnUZK9bnjM0C\nQ3H7l1joiz3VqxHAQcdkmECs7T4L4xYOBSLkmmMeIIpg0r3HUkQQ54UiSGUrC8qoEStXcT/lYx5G\nJs6cAM5wlu4GIxbnfmQh2oBfoHYbmNy/BuwohPt9ZXclZJZvh9LDYKZh4STkpF3/ZOU5A8bjzCVE\nEc1C+usyRCnW3e2ecO+jk5RZDdhueO3ukFtXyM496w38qCVsjUoWGnXYb4GKbGuTzYk1P2mgL4Rq\nCSaaMiYNm5I2F7mMLNisK26TSPH8RiDc76GsjPnuGhQDyZCyCWwMpLbBFKJ4tV87XJ/tRVzZrHX1\nAQJZGFYA9/WKVV8O4TbjJv584NG3wzu+AHYpBMe7snmvFzckez30lyAaBrNT3qG+H+y1MG8jzJdZ\nqrhuR0tYBsNGZDlGvt+EyMgkMFGFvQEsy4nLrVZ6HnHnVzUl/TtE+u5eA5+vw7ocXGjkXfNOVkcR\nvHS9Fbk/LZA+WR/JovYC4OvOnbs6gAELj1uBLxYUhMzxPAPHR37DgnVZ2FiEz4XwOaA+BXuz0qbz\nMnBVJBBDAZhTkHG4t+l41xmR4YEQPuXGYwni6dgsbAggmoKfPDuF6Y9nYfEipsze1P/7kJjiUx2v\nRxCbpzyeUfEaYwJEbywHrrXWPmqMGbTWanW3Q/iiYPOZqWT34Xf1mHG0KmBy0OlA0KzDpA450H4K\nv+/aYkSAFgHzrERYZ1s5JzFSA6HsBG0YeMDAUAj5SJQm+LKCxZYIqrq47aIzxivgPIJ7zs2IW6ck\n9seQZzQQBXTIWZzdiGur29WrtbgOWST2IKmPY4g1ttTIOcOIa/AfiIWcQc4tIi7wHOBhQ7tc3j36\nXDdwE8hCvq8E3LsU0z+CHfykJ0PHyN/lcWi6vZu7AAujNblpzcBEDbYFkM8L/3Oxe0e1BqeRsdkB\n7HFWet3AXSGCgTxhYHPAi5eIkqUE1KDiqszvt/I/U9BoSX2EkjOrSoF7CUu7YJK4Km7QNZMt6166\n6q7pEuW7RfPELVTKUMm49+6GLVmgBQdzcu97Q9oFmzsC8XoOuPD/sIUb8hI7a7kx/GYTSi3xPKgD\nC3ZB9SNQrUCwHpa8AYbnwbnnyKq1Rp7b14DpEqzuaNHgQcazMGElzbrPCC6/PSuvsxnBiPe7Z54E\nXBHDVQF8sgBXWRmyfe61uqx4HGUEq95gZL7UgEcsFENRllvxmwtcAFwHnA9cEsLSGO4JBdfd4drw\nh8CmDgmQ7QPOSeBvE2hmJJD5czcEa5xc34Fsp5fPy7w5K4BMHi6L4D+RexeAdwELW/Cg4yo/0gBb\nlHlyOxJMu7ciNMSxDJzWkHTz46yr+XysolBPp8AP3wtHfv50V9un+zJ9GGMuQqDWc5/uvF/G4k2A\nU4wxPcDN7sbp760x5uka9uTfRcIWOC8QrT2CKK9tiII6ERfJRmz2zQje2GlE+W4w0pchonSP4GGA\nYcTFmsrIwweR78eB063DaRPBFJtGzq/Tnuc8hCi+AuL+ZPB0r2484TyHTIoL3fcDyIR41P2MI9H9\nLuDBlrzvotARxxE3fCPyHl+1ovRarr1Zd04mhs2hCL7qorEykIGSiwxGI9DadhE23yn+80J85Z+S\na1gLH+nLuP+NTJZmCDSg1oLRDmfhKT0jcQOhJOosfKYp0frOANECc4Ha+2hsfa+cp9sQaFVs8lDv\ngWQxtLaKyx4MQrQC4v0QLYX4ILR2ykhlYghcaDTf9DUINVoauo7QIrA51zbdxiDCmwK6rUEfbcU+\nLy9UvUklkWchKcPPM9J1d8dCh5qKnTBqkdwhYOQuGdCFwIM/huJlmHLA2rMSjkzCSBPGmxB2yD1m\nuSaUDZxrhEnyeCQu4hEEjtpkZUeVzyL9enso3tQ7Y/jHEF6IKKIu1637gZ8hQdXHJ+B/9QiWOmzh\nrATeWBcs+Ja8dNViC79r4NUlYb9UIpG5OxGraA3wt8DbY9gfwoMx7ArhbaFYpS0LS5yyfwB4fixM\nhrtCWNKAT+fhHcCBrMybi4FFiUBhL6pCFEiM5IoAPp+Bu6bh70O4IIbHGtDqhNkRjE7DHYl0zIYB\nmRhh5zEiJDyd4u06S370eOxTR5+xn5lbwS3kSRAQY8xJyFBcaq0df7rm/NLriQONv4dAToeMMXOt\ntcPGmHn4gP3RDRziqYrIf0RkeX0Ai54Lk88VRXPQSOfvCyRwEiHz+veci/RIINk2pyBY6lZksDVf\nvuB+VxEscQKZoxusZNZc5CzaWaGLl7gGr0SU5ekJ7A58HV/dgy2LWNiPIG5bJ3L9RnzKqOa115Ag\nYT8y328DNkdA4lM3t7vvNrg2DBrvGjyGCO6/B/CGEN5hZeG53bhNeJ0CqSC/eWI+2b2zaJy03Rcf\nNniiM+5lwtR3DjtoOm6u1pqsJvgSWVoVXqkmDpwu1MQNLwdAfYV0zN6XQf975ZpaAE3NxA+g412Q\nqUJyBDJL5QWScUhGIbsOMmtoVwxo7YTGg1B7DDLHwcRWMAXhzxrHVbE56JqQ1TrB1wC1iB/c6QZB\ni8tG+G2ns3Bwwr1TVfDHukU0YBnu64BzcmLlb6lAJgt1zdjQPh2E/gEY6fgWHHkRdtub2bLoWs5Y\nAG8DrmvCnjrcWXZC0wGds0QGXx7B94ByC/4ggpfE8IVQMrUKwA0G3mLhdyrw0aJwY38UwsfHoL8P\n3hKIWz9WhdNycFkPfCKBsTG4qh/eGsB/Ohz7J1W4vQS/3w+PNeGEPJxWhb4RWJOHTd1QbYHJCzPh\n1AY8L4YPPg7DS+Cbs+F4K937jVja994mvLUA28fgygLcXBMr/DsFoTve2IJv12FWVmC4sAgXtkSJ\n94zDB5Ct68jC8gx8pgQXT8EupTgFiGu3Qep/zDe+atqzOp4d1LAeWGGMWYKYFa9A4p3twxizCLgB\nuNpa+4xNNtY+tbFqjOkHWtbaCWNMAbgZ+BCyCI9aaz9ujHk/0Gut1eDa1xBcdwGic46zRz3EGGNp\nwaoQznOtnUjgkkCwpo3IyloCXoYvyDGAYFwRgn2uNzLv9uL5judZwQ7/GrEsM0gAr+4m4mJH4zmC\nWNUlxHV6fSLj/q8u2psBdjagLyuhyU5kMg7jN+jrd8/UMpU/RuA2AojEAAAgAElEQVSBN1gpj7ig\nAj1leGIWfDQv15+HuGF7kBXpVmC3248o1y0uvHCopL0XZH30eAnw9Ya7uB+ZEVPAXW8g85JX0fy3\nXXDV630hU1VIU7TdgTAvwfmoAq0xeYZpgS0jK0c6RXAMH4nSDJUK3hrOAv+1Guo3Qt9uiC8RK3Sy\nAEkRwmkwJ0J0HGQWQTwJ0SK5YWs3ZFa7ng6lh5t3Q3OjaIJwCKIlUP0MnD4lq93jyLnhXOm9DtcP\nqhR1z5kud8sGXvlqlo26L2o9d+O9gZZkQBe6BWJ4fgQ7Eti1x72zgziwrj/vOhe2/BPUClzwjnX8\neXGaf23BfzwCyUqYVYQ/CuHDVTD7IS5C93yX2BFD+RCsmAulhqxXpxm4exqOnw1XGriqCeeNQZKD\nXJfIximBKK/bLXyxLskhYR16c/CtnEBm7w/gNOCVrszfezLwQAyXRoIhXw1cU4H/ysPmAG6vwpcz\nkuwx7wCsfid878vwdRxs0AFfb0rdjZ93wtI62Ek46QhkbofZF8E/nwA3hTL//rQFtWFYOSRiPG1g\nehucOARX5uH6FhzIwDXAdUdg/mw4sAneuxo+eYj2poj9eRg9DHYhWKsFP3/1wxhjOWvHL3/Bvct/\n4XnGmMvwdLIvWms/Zox5M4C19vPGmOuAl+JzpprW2jOesk3PoHhPRIJngfv5srX2k45O9g0Edt3F\nTDrZBxCMowW8y1p785Pc1y6yokxHgZe7GzUQl+sQYg3WEbxxyOXOP5yFnxhx385H5sIWRD9MIEaO\nksozCGRQAnY1oDENFKC7KLplbwuIJGB3kvsJ3L0+VRGLOSgKK2JhCFsTWB54D34R3nttIQGfXqRY\n9ktDWXmyCGa93cBXnGW5IJS6qS8G/h7RX8sSCehtr0qbsFLzwGr2VkFwwY5A6HL37seTmCvA176I\nef8S7E1f5/IX/jMPAAc1fU9LtxXgrD5ZrvfHEFel4UEdMj1QL+FfLnAdafH52WrtabWWAzjKxhfh\n++fBxQeg82K5QWkxNI5A8Q8hHIDpz0HvONRfC8mkwAvJBOSfD5VrwSyC7MkQDkLrNshsh+alYOui\nnGtfgqATMpdCfT3YrULaTRBFuBeprNPTkpVwO7Kq7nP9pPSYPcgqvgxZwVWZOuL3u18oY3bdFIwe\nADMHMt3QOAydAxAfgqrCNSNAxyD8/j2wLICzN3D2NS+nuwM6SnDDKHQthlYLPpSB8+vwl5PwRBec\nnBeGwRsn5bXeHsFnD0DcB18P4d0hnO6CZj8oQ60u7/CSJXBRC74QwZYaBBVRvG+aLayFK6vw0Rx8\nawze2i+yf1YCb0ygPxTebTmBVVMwfxNM3Q6v+d8Cu3wshNe3YEMJJnPw0RBqCZybgWsjaB6G2iyB\npS5K4H3jYFuQ9MKVT0DnCjhcgUoMF3fDYwGMxLIt36eycPUY1PZC71KYrkI8BeEOMQIya52x+4Tc\nD2Qidq2FFQ14sPsYKN4Vv4Li3faLivdYH0+reP+/PdQY+0Ur8+VGZK5chsyjO5F5MQZcnsDtgRgo\n58Uwtw73FwQyKCDQwJ2JzIEhhxWrPuh3z3rEyvwqxwJHviQUOOAu4GErCnev8YGksUSi530ZUazj\nFlYbaesIEjGvW2c4BQJd7Mft0IosFAGygHwDcQ1CxPQfb4jbujIvAvnGEL7l2ng4ke2Q4gOQaToL\n3VWtDvNiLHbMgb6CBJUOV+B3C3DLz2Hysxshn4UXPQ9z4QFsBnGtFRR2G1gFiVQNm1FlaATpkBJi\nku9DVpAishIqgbbpzgnwJa+qnXDPA7zmPzfxb++5Dk7cAFPDLtqYgfrlEMyByldFG1WyEIRgZkF0\nisz+gydAbYOcZx0jOnsyVL8vGDBAvAfmZ2D0bGj+AAqRYL/9ibznRFZqMWZjiQ4tg5NWwsYxOCkL\nG3cgLs4SfOEB6z8bWgz7JqFjGspFJ0QpMnmmCIvqMLEGRh1HevZqWBXDT356JkQRmSv/huY1ESzY\nC7OmpWpbLgbThP7NcP/5vqr4+oVw4V5oNTn77a+nlIE3J3B+Db6WhX8O4YQI1g/DW2fBt0dhX7fc\nMgwlAJjfCy/qhZ9U4HmDsDWCwSbcf1is6jUxfLAJ78uL1/iBpnhvnxqHG3rhkgROm4RSEXY3IJuF\nHVm45Ai8rQib8/CSAE6zcH0gPOzzrHB+f+en8Lo5sH4x3NAD+ytQLcA/1OC6SHjRYwYmDkImB78z\nC24ow5/kJKni01VgPQycC0vLAqe8PQ83ZeCrzpo+fQA2PAHNWcC8Y6B4h34Fxbvvf7Di7arBGseT\n/HkiluWqSOIuK6C9P5TyZEPE0j0nkcpGtyJR0XIC+UACAAvdtfsQBRgB9RYcjsTw24TwPB4ERixc\nbmEsEOPuzS1Ytw+wcPdC+EoEL7DCXLioAT/MCs5yXCKBjZyRFNdNFpbkJJvnghhGEikQ8jVg7xRE\neTg+A4/U4Lwc5ENR0IcRC72aCNexctfJ8Mg18OBZ0JuFoaqYvXMmIP8tqC2CegFyRyCqu60rGrDl\nYvjkafCm3XDFJfScKv11UHOjq/itnMswuBwOHcLzl+YgirILWXmUc7UYUcQjqftoRFHpJqzF3PEt\nbOUxOPMqqF8GdiME+6G0CoIBSA5C/3bH5StArQq2H0wVFpfhQBGaF0DhNgjyokSjXpi/C/Z1wMpp\nGJ4NPaMw2QnTfRDuhecBD4aQzBEsadUY7E2glMBz4I0nwJcfkxqvDzSReoN7eqB3TNycbcBoBy++\nssxwER7YAX3zIBmD/lkQTsLW0UEoHBLXqxeCYTHYORUYhUwErV44uQ9eshP+ausr4KCU3qR4LtAp\nivamQXjfx+DW08lQpJn5GdEjv0v3d/oY+8IG5iz9F849/UHuC2FyHM6eC2+uwjUjUJ6GdSvgo0Ze\n7w4jRsEBhMN+noVPTcKfh1AqwM9iWBdAswmfz8KHqrCxQ4b79gY8Xoc/75QA22V1SWT6jxG4eiFc\nMQ3n7J5F9YQxNpfgdRkJft+Yh5st3DcJb63D9hhe1Q0/L8KdDahXpTbG7/XAl7bB6YvheyWRse5+\n4ZhOAplD0HcEmnPh1ga8ez68d6fI2SUnwoGDsHfnAK8/9Qh//7ibwBlg7TFQvLN/BcU7+j9Y8Rac\ntdmFzO3NTTFawhAuD6Vox1rHjx1BuGzHJVJP4IeOPjMN7G9IYe5XBXBhAnEddhTEY64BpyTw3UC8\nz6sRpZy1QrDfa4Rmcy7wskQyhiYRi3ZrBD8JhC3VYUQJbzfwcAtuK8sGglcW4PtuAflYAlsDuCeB\nkRDuaUCjAmf0SGrtASSCe0oW7k/g8gDus5JEceBxaNx/Pdy9Cs75T1h4C5SeCx37wd4Ee74CI6c7\n/NIpXTMOtKDjNih2QfBvsHgz4QCcXoT7DcRKz3IBtUw3DE1ALgtBGRb1wQ8fQ6zA2UCfuL7JCPQe\nBxO7EIWdoW0pdnXA9GNAfDrs+JKAlSvOhnOrsqIVgH2dEJWgcQ4s+KkvjqzVX5LFMFqA2Y/72pzL\ngY0RNPogGoXVCWwrwLIqbF8GxRfTuebTlLYjkdAteIB+Jyw8G/ZukAW8sQziMaBfjODGfVk4peGr\neStDYTuy6FjE7SrBH03Dp6eQCEUJgSeSLKctbLB+1F1/PDKgYx0wWZZx6YkELB89TwpZBMtgapDz\n5mV5KDhEuOaLTP34RCHw5vbIanv4nXQOfIZSxxR0wcBiOGESLozgthC+1pTYw2MZOKkFP++GS0vw\ntQasGxCsFQMHcnCkAjsOwseXw8oG/EsCB8swnoH3ZOHfI/hfAQzW4aYEbojghXnYPipBtueEMh/e\nFMN3Yzi+5BJouiQAOViHuTH8RSQ83781YBrw4Q74JwNfnoZTa5LMs7ZPMvBuasH4RnjXcogm4ZY+\n2FSFCxbDy5pQz8D6MtxzAA46mlytE97dC3dEcPoReGwe3BMcA8Vb+BUUb/V/sOLNNaHRgNcW4UXA\nn1ZgXwZemYF31AXnWhyIMntT4uvhntGA3Vm4LZCUyl2OhvWROvzBJohrUFkEm+eLAn0oEmL5CGI9\nTyL4bD+i6FcZuMXId7MSuV8yDaYDHrJABU7rEOrMKwLYNyVB10Xd0IhhuAGdVXhRt3im9ybCKz4j\nhFtieHkoeuLSBnwiI3S4rS3YbCV778YWXFGBqZ+dJwGn8n4x4XtafrvXHYhyGp4Hxx0Ui1TZCCNA\nEQZzzpLtRpRoAORlV3VCySpaHsDNDTipWyhocwKJTi8uSo68qUAwG+LHoXM5lKoIeK70jGF5Fnd+\nGMZeCT23wrq3+XZMInSOrQOw5AhsgoXrYO8m19bV0hY518geRNESGNolkMjWhQ703iuKsQJzl8Lw\n3UvILdtFfRcsWwPndsO/338GrbX3wdYhuvr2sagfHt3l2tuFj9nFiNW5Wvpx/lI4kACHoZCD6lbo\nXwXnrIfbnge5HIxPuXddBIs74bXAh5pS0GfsUWAefK4bPj4BuR7YOgynDcC2ARm+Vgec3An79sP4\nMrjkIPxsLnSPwPg05BfBxBH4eDf8P/beO8yyqkr//+xzbq4curs6R7pJQiMgUSQIIqKIAVF0UMFR\nx6zjOKOjfkfHrzpmR0VmAJVRwQAoKogKEiQ2OXRDBzp3dXVXV7x14zln/f5496lbhnFwYL7+Hp85\nz1NP3bp14j57v3vtd71rrfu64Lu74LQpqC4T/7+oDP+UgTNK8IM9avL3z4L5VTilDN9vh0/lYMU6\n2Lkf1Ebg6g44uQLXNmB3O5y0A962CtZvFCP0khqsWQHjd8AXDxbN8MteeKQBn9t6Bvfudx3nocyh\nl6/T6uwri+DMKjwvCxcU4Ny9cM0suHoXLOyGm0xJ2dkAxy6B82vwwVF4wXy4bhTO7oKHi/CZJoxs\nga90wPb1sOMwqG8D5sNlXfDGfTBnTMlxmgGiglI9574inFl9+sDLnwC8/AUD72EGOww+AuxXh9sK\nwpMpRCFsMhlB+zs41eCbJh3sXzfhRz7s8FcGr0D88OMohLg3guuzrYQvt5XhoBD6ixrbdwPZKchm\nIcjB+4HVkc+e1RB/Vo9U1fX02+Bjx8BX6nBsOwwkcJqDt44q41MmgIUF2DIFC3MK99yZwHscPNHU\ncmp2SQEfXwikeggdnGLwpQ3Q6IbzZksq9p6GlpOZEM41uD+Ab0YKNDm3UxKkbAVyRdibwL4MvC6G\nezbC4DyYmAI2QvbZ0ByEZTthaLFWydYO8RjMmaOgld1VOVhGH4ShFfD8ufDr++V1X3I4DE1BcwgI\n24ieGICNn4buLvjJIli8HbYthRd9HYqfEcAFeEVBO8yN6Tukyr61tNQQUxkR2EO9MHefvKf7A2vf\nSduZX2bqCeDJLNQXwtwn6T5I/CBPvAYWfVcPMY442vt7aJv/GporvkpjCI5ZJM3rdU/koNr0uT2h\nVIDKAL6wGLRXFPvPHETK98HSLhgqSi0wqySVwIsGxbHHe6DQB5UNED4rIA69FrAOF86BsytwcQ2u\n3apzkYPMFHSUxKW/eSHcFsAHJuHMNpg3Dqd0wBV16Miqzz5WgA9MQUcBDt4EewcUTHBvn7z+d9dh\nchC+uBB+5KAzhFfW4HVl6OiGLznYXIOP7wY2w/LVsLkK82bDwgTurMOcEpyRg9snYX0F6IF/zcHA\nPbBxCXy3By7bB11VWJmHnU14bhmGF8NbsvDVSTi5D24ehbZeeG4Ef5XAP0/CJ7vE0T7UDlENfrwF\nzuxS9ODfVRR8sSkCSvDZPtjdhFwJvj4lDviIEhyWwOgOOHE2fGYKhuswNUZLYbMWOPEZsHhVJuAp\nbqv+x4EXM/t//gPYaxuYW4Nl9mF3l7HlU1hbAwsjbJ5ht0XYHQ3s1xH2LsMWGHaqYc837MYYu7+C\nfdKwy2Ps5zH2+Ai2rozdU8P27sFub2Brp3T8GYZ9IdH5Nk9gI3uwd8dYUMcOqWufK2Ks37BewxjC\n+gw72DBGsO4ydmSCnW3Ya5uYG8V6E4wK5spYpo69LME+YFhxAntPgm2awH6cYOWtWHsd+5xh+xl2\numGfjbH7qtiZMXZ9jD1Sxv4hwZ4fYa+oYw9PYT9JsIemsPtq2JmGZfdi549hbhv2qxgLx7FgEvtC\nDeuJsQ/WMPZi4cMYP8Uua2KMYR+JsGO3Y7Ob2KtqWHabnpV9mHsSO2kCczsxytirEoybMHdV1ngw\nsEMvfrOd5a4w5t9i+fBK6/j3Lnv2geeZe9s11rUT436Mq04ybsH4Acb9obEO49K59oEGxgMYt2OX\n1TFuzxhXzzbWYEeNYYxi85pY+H3svDLGHZi7G8veibEL45vzjUGMX2I8hPEYxq0YGzHWY9n7MZ7E\n+Bm2cgpjDVaqYPwK4149+yHrMa7G2I2xB+MKrCPCwl9jx49jbiN2wj0YVezKis7t1mNfGMWWPoi5\nxzCGsTOqGBswtmF/nWCZXdjZVYwfY+FmjKsw7sMKu/UO3ljV71kRdoFhH0mwZYZdV8FOa2KZBpar\nYG9IsI/HWPAY9iLDlkXY5SOY24yxFTs1UZ9Zug47soa9eQf2+PVYR6L3d2GCPTaBnbtdfXG+YYtj\nzO3DHpnCjm1itzaxjybYxA7szgb2N4ZtHcFeda/v3zvVn2+Ote/sBvaRKnbuGNbfwB6sYgck2DsT\nbKCBZYaxtgh7dRN7Th2bcyOW34N1DGOsw56TYK9rYusnsF9FWHYn1juhvvLFQew8w65sYO1NjBr2\n9cS35ZPYkQ0scyfGjWpPtum5BFNPD2/gsT/h5+ld7ynd058LeC8y7PIEu6mJFStYkGDPSrD+CnZo\ngr0vUWf4coLlDVsZY/+SYHdVsRub2FUN7JWGMYktN2xgQuBcamI/bwqcXutBty1Rp3yfYf+eYEM7\nsWsqWNsUdkkNu7eKfauBdd2FMY5dkmigv2wHdsCkOjnrsR9G2G/GsX8w7JYIO6iCFWKd+/sx9okE\n+1iM3VIVoK+sYJ+OsJ/E2O1T2Bs3Y0snsU9G2G1VrH0btnErtqyMXZFgp5WxN49j82Lsx3XMPYz9\na4Jd1cQ2jmEX78SesxF7cBj7lmF/k2CHjmPBI9hDm7EjRrAzI+zEKezwOuZuwwoT2L81sOwo1jWK\nXRZhxxp2WFNAUdyMLUmwnruwtWWsM8aKazB+0WX5n15ofO52C37RbVy60rg6b+0X9VtuV8G4/gTj\nHuxHI9hPbviObXhJjx1+a6+VpgKB7yPOuKHNWOOBczvG1a+wt96PHTCOcQMW3IK9PMG46kRjCHvT\nZk10q9dj1w/nrf1ejAcFoq/b7cHvygXGJLbwUSz4MfbicSz3JFbYirFFEwijGNsC4xFs4TbsjHXY\n8ZXAnl/FuAfrrWLdT2CFO7G+XRibsfBXGJuwC6sYd2F/PYy9czPGVRljCJs7JRA4aj22YBTr+Kmu\n11/XZMU+rDiJBeux9mFNHrmdWGkddlgD6x/ClpSxcxrYFxvYfds08d7YwB4sY6si7Cc17OXbsUc2\nYVdXsbHt2A/q2Otj7KQYy2zEbpwUmC2JsSvr2HkNLFfFsiPYcwy7voaFQ9jiUezbhv1yHHusjP1j\njN1Ux4Id2GWGvd703p9n2NxJbOlm7K3m26+MPbQL2/tp7Mox7CDDggnsBMNGdmI/irCzExkLp1UF\n3DyJHbwXe2cDOyTSe51b0fh7oqz7vLmGXdrQxBWM+z5xL8Yg9oIY+1ETu7uOXWhYEGH/WMPy254p\n4L3vT/j5nwfePxvVcNAQzJ4t/8aOYfGdzUTOqEUB3JZA2ADLKu/CeiC7F/Kz4OKmPLzvqIPrgH+L\nFQN/WQgfqGplmy3DzR3w4ibckoN3jMGqXjjDwaomzG7CQxl4NAftNfjWmk6CuRMwT+Gjh/SJ43xy\nFFb0yQlBD3yhory5Rxrc0qdl//tHYW4RvpiD5VU54/Kjyu970XzlYv2PGC4fhrP74W9zcnCMNeCy\nMXhJO7gxWN0tOqOjCBeNwAafaf2gObB6L9xRg/IkfOxgORbXxvDwNrjuR3D8katZXz+aE07+OhdV\n4e/HYUMWXgP8cxbO7oC3TMLJD8NJK+DmnVCaDeUueEcX7LcPDuxVFNQvhgpclsS80fJcmF3OFc0J\npn5zOCz6oa/VjZxLS+GwxfDQbRAuhhUBLIjg1z0QDUF/GwzfC5wK76nBrZHUZNYF907CQuc4vgi3\nDxvbJlWh6GcDAVE1IX4SbjhcbVodh1/kgW3wpqVwiV/ez6rAu/ugchsUe+Dd7fCL/cSh742VGzeX\ng8c2wLP3gxu2Qc88eGMZkhy8JYFTc3D/EHywF95Zh2WdSqFYH4Hnz4GFTZWhyRfg20PSqJ6WwM4I\nrixCvQlvyMHcDBxdgdm74eeLYbmD96EafYsa8NYI7s7B4Q2YV4EP98EVg7CnDdaWIMxAeQLmj8Nb\n5ymE9h9juLwOv6zA8/qlDf5GF5w0BR0V+MhsuDKAw5rwq2FJDg8vwMvmwA+ykr5VQ+jNiEbbB4Sj\n0NgFy/eHOCeaZrIKH+1QyHAxgasacOgmeKwb4m6fG6IAazLwzQQ+WYPvlmBHDEdl4TvAtgQ+HCh/\nw/WJrxzTVP8NsvCrnIpvrg3hnxw8OQS3zobT67B/EU4ysUAXxdCXk9LvTSZn+vfdM0E1/NFcDL+z\nHfW0rvdUtj8b8E4nVl0EhPCOImytwLWhPO4v6IHhSPrZVwIf2CFA/Wo/bMvAi7Lw9kk4sgBdWeVP\nPaIJLynD2tlwTgjzp2B1QQlZkl3wyvmwKA9Dkc71t3U4IqNUh/UYvpSDt98NPz8MLg9gTQ1OyivU\n8r01WJpTKsTbq/CiEM7NKKH06mvgqg7YdBrc1tDEsS8H7x6HnRvhzLycNO+Yp6xdZ5YULfudXvhp\nDU4ow4Vt8GgeXjgOd9XAzYHrxuClE9DfDvtNwP3dcGkCBze172md8IsheNksccrv2J7lhv2b5Kbg\ne4HkuK9pQncJ7orh3gTKe+DsbqhWdf7lc+CHj8LJ+8NXeuCKGObtU8a3xwLxdN0J/CyGF7TDxhL0\nT8DrboTh50NzA3xyKbxwDE5cpgTYh+6Gy5vwsaZ49D1L4MtjcEEeeupw5vdh5TnwoSIck4EDboF3\nr4DTZsO+Mejph+8Gyi1wWRUO7dDA7ssqYc0rgA9WJR0sOshthg0dcFxJuvDDeuWbPGtCBZefiGBx\nF1w7Bi/rlgP0BcAxk3BJCU5+Ek6sw+PL4J46HNcO/wd4qw/zviVQJY7vOMmpDkWT+avKUqr8lcGr\nM1DvhqtH4fQeeHYCaxvwU+DbEXy1AGc7CSCGyooyW1hQno+VEXwtgjfmNSQO2wNXZJXR690N+FAn\nHGTw8R2Q6YdzS/Dssjj5BXXoDpVp7nsGXTV4V5t8BQ8kcM0InD9Ln5eEsKoCr2/CcJsA8ksTcHRJ\n+uUTkd/zSIOrIzm4xxJ43MHKBJ4Vw+dzPom9wbsqCq54Ig9rxuB5HdAXSvL5tzEQwdwYVhXhwwlU\nh5Tg6KYe2JSFdaEy2J0WSghSCltpPq4ZBgJo+JwcTx94b/8TjjjuLxh4t0BXCC+eLzXATzZBc7YG\n9+5J2JmHkQieX4JfTEB7AeZUlD3spCa828HhAZw1CVe1y/pYcC+M98Gly6G3CAxCdrZyOrzF4EOT\ncGo7fKgOr43h0DZ1krtj+FZVeXjP6oCv7IOf9MBbhkT69xsckIXrTbrIH5jAqK1NtaH2OhhswIoI\nLhmE5y6GlRU4rQy31uDXA3B/AhN1uORRCBbB4qJkbjd1wJ1bYc5KWR8LpqSqGm/Ayhy8vApbu1XS\npbANjp4HX6rDh/Nwzw6B2fcy8jvd2wM9W+Cbi+DaCL6Sgev3SrUwu1sW/D8V4LQ9cFIJDiqrcsGG\nDCzrg+EAvlOBz1SVG+CIxbAiB+17YVMTzixD3wKYNwr1x2HtPli6CP52OZzVD7ftVuKZ7gF4RSJp\n0JsT+PdJ6O6Hx4bgDQGMtcPuIhxdh1fthHO74Ked8LZB2NOh8udJHyxrQJ9T7t3nhCohdEBW4eTz\nEIBc1YQHivDWmixOxiTEf8WjULofvvZmyHTBy+sQZeGiArjdkHTAu2twTQDZNphoSrUyHsDDeXhO\nXZPE5R3QHcHKDLw/UlrLE2KYnVF6zl+Mwhu7FbmYxLCqAa/OwdwIvlJQHt19BqdW4L0FODwDv4nl\n8H0M2NuEj2XhryP4biRp8ofLcnQdUYRVU/CjktJWbkmkpLm7DG2/hu+cAbUCnFyH3+Tg2zVoL8KX\nq3BBUflMYtPkFTbgW93K5n2CwbMS+IzBQABnJdKXr4nhtQlcnZXabUkkQFyCkkktH4PNXVoRPJCF\nl+6FW3vgs1ntY8iqPjvRBHOJwTciJQVaGMPBm5WP9weLIR/oPBsdHFCX0nBdRuW3QgefmPS13nxs\n/tMH3lv+hCOe9xcMvNugvw/ailr+H7oHvgTMblceT2JpY6sOOquwpwFzupXMo38c1rVBdgx+/D04\n+QK4sAhrQoHxFxM4sw7NgqSYt1VgVx3ONnhFE26aBWcZvCGrWh0HRXCBwXdG4d48jHeoCOO3d8NA\nJ7y8CV8J4B/apY/dHsEbMzAyAcMdcNF2ePkCuGInnFeA5xbgjk64fBf0dym3S3YHHHQA/NrgzH2w\ncpHKphywCd5TgItnwa8HoTxbHf7vGnBVAG9tg8sCeGdZaotiHcIudeTrYhjogFlV5R7+dAFOLyqS\nqJCBK4bgtAKs7IXNTUVxfTgLG2K1+Y1jEguc1QPDU3BeCU4chgvKsH2u2vLIKbghA++ZUh714lGK\n+HvzL5VTdUsGPnCKovByDXhnRRFSJ7TB7dvg/fPgqLqv8jEBlV1wx2qIYmjkYW0iAJqzDRbdCcsG\nYPchqkwwUBQF87F2UTU31ODoInwoC7sTuCOAl0bSvL51Cu4sSk6717T8LiY+93IDBkah7JMMh5Nw\nzUJ44TB074GRhbCnAEvL8GBBAXZdMewKBPw3xvCyAOZW4aCUgCgAACAASURBVKJ2acSXoVpkn4rh\n86aUVLNDKQR2t8NleTnlPx2rFMGeDHyuAl0lrxKM4I6MKvOenNXKrgz81OedeFeov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lVn3TT0DOd9cQZfwK/bMFaL+RQHkVnkDW4Tyku93hFB58r+8P48BIyp86tVMSqN92oMm9A9Uv\nq6cKg3R/L+8KUBpUImVUG4lhtKDn7kWT79z0Wr4fjMdgdSCRs3FTIMu6Cyk7NgHPcjDoZCgEOQVR\njKNrJimAhkwPnJwH/+nvHNOTyDQmRDwj2x9zri1nJctZOf33A1z7u7vcC+znnFuCoOBVKOr9D21/\niAn4ve0pqxrMbNw59zPgCDO7efoqzl0C/MT/+bszwwL/3e9vX6QFyUeheMgCsESDK3bApITy5JS7\n9ECnTrAWdXKmgFDgdRoilheiTp/m9WxHS/31qPV6nKyCBcB8Uz7XglND7Bd7YbefqftN0TZT/tzb\n0aB+FGlNV/hbno9aezfyIDfDVphpysv8hpZw/JaMAG4jsl47/X73BJIBjaEBeB6SVgW+iX7ulIpw\nDwLKZweabLr9fTzb31sXLYOiDx8b71RdOPVZgvi6PjTQR2hFEu3nP2/z/xtGE+DheiXc7xR0koJY\nNtEgSpAVF5gUC2Un/elYVkAV5f3YClrLd0P/623qPCkYx4HGYL4pbWh+QrRDxokDDWLxwgRKuhNE\n+j/o2JrXxmabAtbY6X7zsarmZkyWdtN5azvx1IbT50wCkzlJ9ur+ftsj3V9vTY7BOKuMbBN+P2gp\nFKqhADm1lpve4o5dKxKt6ifI2E+2E2jyPti/B4cGTz8KgEjpnvYQcln1gSYKy+5Fk2gfAuisk5wu\n46Dg+fR+3zeMVkBO0oRMQYl/xv2QaqfFAdeAsZrvuKE/ONG1KmjYxr7/3WFQS5h2rtVSVUPKa6RG\nUyJaaA5KRzmRhVFTjt+kCfwSeQJT6uEZ2J4O1WBmkXPu7cjPHgKXmtk659yb/f8vds4NIHanE0ic\nc+8CDjSz8h865x8FXudcPxCZ2ZhzrogozH9yzg2Y2W6/29lodQPKxfJd59znER7th9Lh/v72Ln/1\n6fWd/ztSftS4QovdnyUq4DhkxW3BsxSdsiSXoQ7Xhd7XE37WXeD/dzsCkn0IYALEZ+1zusG5/vod\nGVhimrXXIIBLjQZPL3MIAvc+fw8j6E0MIvBcgzrvWvU4dwAAFrpJREFULtTnYjR4EmSNVP19HujP\nkdHj0YEkcHv9Pvvr8dRXTVb1SU4dfB36vXXGcy9A1nWHP1+7P8f+nuboRct9Q8AUOXnAsyYLbMoD\nRCo72u3P0eVfz1Zk1aQ+zyjQoHSIlwyaHoS99Zc6ngK/7A5NARMBohgcOgYEjIG3AjMND66Bl3Nl\n/DK8KNXG9OA1Sa3SrZnTyiaw1tI950G8GIk6AF+t2o/Bulcu4K8X+33Mqx5gRmixt7wy5i3MSEEG\naVRcwwlsS95SyyUteqIWqh/kfZ/pMq2o9qIosbLvI0v9O0/xarvTcQO+P5X8OU4K1N+6UP6IhWhi\n3I7n+P35aoGvnu37xWw0qZcRsBZDgW+IQH8qhjmhjjnI799AyYLWJd6XEvp3kCi0fMj3lU3IGT3N\n18702aRb+u5y6s8gam4MrejqTr95LhLOl/05vsLT3p6ujtertq7/ne8unvF5N79tdP7R7b+yeOcC\n3/I8bwD8h5nd6Jy73Dm3GjXrZqSqwszWOue+j4zSCPgb+8/0akXUG6Zo9bQYaPOzZnp3fj38RFEd\nYQ2wp860lufWkqK5ViNAeohWNe8EpcbIo8TgoC/n5mQhH2eyZO5GlsQkgJMF+buNNI68hHvRTN9F\nK7Bmvd/vAbzA3GsZQ1NmsPkIrGI0AZT9c6z09zkHgWaEBswupIb4NXBgoM7ZmXgnmL+PHcCF3hou\nO0m7ZjtZNaCB1EA86GqT9RV6C6zTBwlk/bI9DjSoYwTW65wA25w40P4ABpxm0Am9IllPgWRYnQ2V\n+U41tKlVXfL8aca8vMsDZtZ0sdD5xNempX4QKwTaApVuqrd7TtVkLcc5WaMukbVpoT6bk3WaeLBs\njwSU5rQyGs225nfQM2dM4BygCaOeFU9biFv0QORaFEObl3o58xNNTmBed3quSkbP3XQC5lrYul4+\nFm1meJ+yQZ/JeZnud6SJ926kz2DqH/OcjpmDADildid9f3kW8iEUI5X4KcRq08lAjpleBOgRinJc\n7zShprLHAAH2lO+35UClq/B9PsXO+QHsKkKc6nQTAea9/hlLzDBOU+/c727On6wBE6EKIaRbFk+N\npMfW/cWbv3+a/86WeabI4mdo+7NmJ6MfnxWG31qGTC9Nqv6nCEGvUuDdUZe+Nd3fdciRFTPtd2OM\nljd2NrJK98A0b9QVakLtQR1zGJWHr6Mk4UGg5NMhsiTyCDgLiO9dhQZBOvlPIkC6Bw/CTXBZWaE9\n/jFTGqvT38s2/4gHITBegKzpcZOzrM0/w0JaHu2y/ztV4qV9ewRNBnn/vD3eu55PvKfdA1/d82pN\n1/JjZLxTJ7XKEs+5zvRqg86xNhDHvASBfzEFUQRi2cSLUrxjKevBPv1/0TvX6p6eqHhwS51RBT9y\ng8hbnx4AzA/QOPChqN7qNPSciV9Wp4Df1pR6wpDVZ07XSoEZBKxNf75c4mkJf6/OZJVXvDWYi8Vd\np7KxfNI6RzVsgWfs2zV9ZvPfRd7n4Mw7G/33ubj1XBVv7YUo5WYuadEWqea47vnZCSeLd4lBj19N\ntEf6nT7iqA8vdubvy1+vFuo6qYJhK1K27ETgO8/310W0+sgWWn08tdyd37+aWrkNWrJPTzX81phO\nPwc6QWdOxkvav7bHcqhTR7NLCsAN4JCnr+P9BF97yvt/iL/5yw0Znl6/z1Q0gJ/6aGlofNRLUlG2\nr+kX6iVG1pAndVoH6ICGlqmN7AwAqGlpSL4VrTU84zJ11PnaQpgyeDRR8cbU17k/4l1Tw3w36jjz\nkQNrB7JQxoGhrM4/jgz71FKZQMCb1oucQiB2jGn/LKIJCih6L/YTynYncO0G5ifS0DpkHY0Hsmiq\nwJCDWbEGeVuk505zrwYzwKDhrcxa2LJSQcAy4Qd/BoHhWCiAjZ3Clee4lpMoXbK3NQWmxViAml4T\nFKxQ8FZrCgyFWPeQvq7EA1YlD6WaX+pnBJ4JAo2ad2y1eVUE6FoZD2oJLcCfyGnyaIt1/nJGzr5G\nIKfiTFOjZK17wIPYVFagnD7DzCEYoHM3Qz1b2euGJ0I5RkHvJ1VvOGTNpxNcgqdUfPs2AlntHSg8\n3KHzxc4rMVxrYsij74vAPGv17ULcetdhSq8kLcddgFckeJpl1Ld3Go80G03cXlTEXj8eSojKW4Co\nCfPfFdEEPAYtpE9ROgXZmcgS0mpM7xCvABMzreKqv2iV1qwPz5iqIfn/mcX75wNeL/eZ1unOnClD\nWuGHCa1Yb88twYzfDrLe0TALWb0jhdYMvTmG5kyuyZ8/1blVaAXLpOHkgeeptuXFg1aRNZsu8xYy\nPXHT4695tOlc42ip5vBhpMDxpuQlsxBd0Otv4wD0AjJOiU6cX953esuqHmrpB+rsaf8txbJwmgF0\neEDKBbL8zS/n0uitUuQtLA90lbDlcY+DFoiZb8tiomiuvQ46M5JdDflj55uE//gletYDTAqg5Yw8\n/qk0quItynKo5W16bXNeDoieM0SWozkFiaQ8beJa91oLBSrVsGWZliKBVt1z1rFrZfsCKSemQkm2\npvzSe69/7zXEj69C18kkrXtLUJsGtCzqiayeN6VPKqF+moG6cIUW3z/pJ4PQWhZ14rx6I/Fd1z9T\nw7dRGq2GpzJCv4IoRK33k7aL8/eXOuvSVUAjEH2TsZblXQ1bE9tOJ7Dd7ofCCC1xUNWPF0z9t41W\nKHEbGl8xMg42+uMTZEhMkHbkGWNs5thNKYaIaWCOoBW1llIM6Tg1/12NZwyh7JmX4j6t7c8HvDN1\nelVaa/FUzDqzndLolRR5jJbZ5V9UyckirCFLtmbQ8HKZ6eP8zyRytM1DfOoyZNEW/fGDoRKPgIBz\nBS0HxW4EuOntF1Fn7nSynlKLoEprHvFRj9MKixqylmf72+/yAyVvygubCu9riSiLuaF4vJy1tLDg\nuUonoKwFrXNYoEGbGiMNv1Qd93Kn1Juf9QCZbqlVnDOpP0Zo5VTtxlMQ1hrcqTIhDcUF6aITD4Lp\n96ksrMN/TvWxCaIM4qAl6Wx4qyyidZ4UPCK/KmqiNkivnT6CMz1rainG/rljJ3qn4Ns8pXB6Td3I\naCktsv7eMtYCP/z/89Y6J2iS6fEOw2oI8/wkWA88vZCI38U/V+CpmbqncyYDH6jppEJIJ5R0UCbo\nvQa0wDSdVNLnS4dJ1bdNKpOLXGuCi5BUcisyKsfQEKvgFTqoFDyoMaYCWe9Nb0DkkdHQ49tqLxpD\nnQiIZ47D6dWrj66b7oTpC8bfRLp/nRYgxzN+p2N8hgP16Wz/C7zplpLmM0MGG+hNFplWOOD1htOz\nXzpS0uVIQ4NiW1ag2ETVJtLgCkJJVvqyAr6q//ogvCbVXy7vP+/w5wj9rF9BTrt+f+wuf6vtCJge\nRpZTgvSzHSaQdYiHM0QBHIy4u4wHoXSZmPa/FJBSwJrJNzo/YMOkRZVN92c/+HLW4vLSY7BWGG3K\nEaagu9EpW1nXjHsyf7040HOn95/1zTmBLOGFaIJx6HozreZJH4ob+7brthagTma8ZtYDZOQniJTD\nbDodk+prU+40sBYfnQJh1gRqqcQL1wKfmreAp3yUHGiicwg8RlGtsoK17julBdJ2Tf9h/rsOH9Xm\n/P3lvLVfC7QySTnsdOUQ+3fTCGTJRk77VVAe4wairlJ7YA6S/PWi6h3w22Ab+Qmm4W9yMNBkEgMu\nVJ+rITooF7QWhzEtTnjCP5ajpciJkRHyu+rTskE5kbRztn8v+Hc66T83UNj5tOY2PUcKwCkvl65u\n0xkyHZvJjN8pRRHOOFcK6M/A9r9UQ7pNotms4P+O/Xdpw6d6k3TmM7Sm89mqph1wfv+6qWNPz6Ku\n9bM0kHyrSEuxkDoI0uCAzf77bQhIl824xQ3+ZwhJ2voR8M7xx3b6Y7ejiLF2f2yagAkEFJFrWaPh\nH+hQwYwBF9i03p1SammF0rWm4abgB3UsZ9CEE5hUU9oBaUVTNUQJOe5SiVjBWrKrVD+bzmk1WvlN\nGr7p99K6pxhNTtAabwEC7oq3skZQFqsg8GMzAJdpccyVUOoEaDnmHNLlhiZgSzOHJciBNjNyzPBa\n4BnWX2p5x67FRqUqgm6/T7s/PjPDoVaIPQ/rrXi85ZsGR6TdKfDgm0laOS2i9N3SonpSx13dOxBT\nqz4F70GnNh6lFS2Wduf0OdKJJG3fulNmtCKeX6UFpmXUtzc6SSRnOtpK9tuOxVQe24FkS2PIVxDg\nVQv+XaWTQg4ZIQVkeJT98TPDg3+LIkw3T9lNP0QKtCnYQuulp/9Pv09n+5nnexrb/1q86ZZ6LRNa\ncoTA/071mikIpw41RyvULG3HyozvZ+qCjWkLOs0hugAFIuxCS6w0Ln03LQ3sNDfsj8kiHvBR1EmH\nEDDn/eWWIDqiE2j8GhafpH3Trdc/0gZvKbYj51FgLZAAScI6Qy1fSVqZsMBbsE4gGvqVwLSFHGpw\nP+zkHMzMWIp2o0Hahz5vR5b3YNpc1vJ6g6zRKa8GWOxlZPfdAl0n6hnHEWgXTdF3+diDZ9CSpRkC\nsylvobX7z20z+n0KvKmeOAXX0ANhCq5pgEZaULHkB3HWWlb6b7kBvBWdnrPonU7TyXn89UODB26B\nY49vOQhnhvumIDwT/NJEP2nXqnuao+mphXronW6e2njQwSqn508pkoZTNrw0sCem1a7/X3tnF2LX\nVcXx3//OR5LJJOaTpNoo0TYivhiEWOyDCYKGIMUnaUEpIuqDYvVBbH2QvumbwQdB8INSRPGlRcGH\nRmyKTw2FfLRN0zRiqLHpmCbVpJlJ5uMuH9Zas89MMumkud6ZM+x/GHLOPueeu9c9e//32muvvVZ6\nVwoPcLMdJ7vBGMjekrexndEGB/D2ew4nwVFg6jB8aG9o7/M02OwWlykm1KH42x6f/1fj5lW4Lfyj\njWe8QoMHuxSXrwz72CTU/MKc2SbhZgTCKeaSata3WZbT0x5gplcM3iMsHfEm4eYoeT3OV1GmJ9mj\nZnsMRftNcwKUXTX58qcaz5/2xZ0T8oZ8hMLpHbwRn4/HvYo3/i6uHeYCzBs4YeX6XiJ3pU3ihP3S\nc3B5XxFnHG/oqyn24yHFLi95h3qDosiPd9yfdJX58brQls6pdM6P4BrZdEyNrw14LrWsT8bpHMUH\nnM3x/0ZKaIwr8X1jHbdzT+Od9Wr8zoPmi3od4LnD8Pm9RUveZe4tkIs54IQ5EJ0uV92H8Nxd+XqI\n78hNC1MUwm/61HZVfF6n5bvarjfkNUChXY0PhPdAaMLNQaSJJHKIRdEZOHHYiTfvvx7tcLb/Rz2a\nj8tpP5o7OEyH2WMYN+tcHnDSehXf7Qc+sL5N4Z4zFE66Eu9nM8XFcQpPAbQLJ/RIVM0wrkCcjM+t\ni7JR4PhhJ17w2dgw3v7+26h/k8e2UKKl5Y61K/L2uh0nefD3d5oIsjPjC65co5Bs9rd8+Px3MNO4\nb5Ki2SY5NzXh5INxeka6UDXeghz1MkpZLtvnohkUu09OY/LlJLN1GudDlLlRc+SdgYkuXBzwfGOb\nKdtoX8eJ6ioRSnQ6bFYxn+7inWUSt4MN4tO41XijT+X8w8QCRtyb235fx9vPB/EOdYFYpFDpFBmE\nJCNHdeSLUDO4fQ2KO+METsibgI0DZTPHBZxUO3iHzgHhXnwcex/Fde4UTrrDUffTHa9TYq25ljga\nU/kt5nZDQu5V3WISyXxjacdMZGyDVTiRbjZfSFpjxUvjrZBrHHfh22ZuH+9Y8QgYmSlpcQZxUutS\nIpQ1NdCuisadM4mMkZA8MGDhuTBQzBTQMFPM6w3ZVSc7c/34u/hW88GwTw9ZRDQb8IWpKZVdZO+o\nxGd4O95rTuzW4ueb8A1Am/DcfGlG2dggsAmYE8blKmWzTHoX/J0IfcmNa1KXKb/FSHx3IjXtN3GT\nwxp8sTn3N+XEdJZb84fpzLvQVCpTs82+ODvazrt/oFGWI+tk47xHqMSbWEcZ1Qzvpandzh8JGwFM\nZlOKZHmugCZZQyHgvNb1nVVXh127ney67XGiGwsW6dKSKm34fY4MueZyD0WTTC+XdCPbWj7CBN54\nT8b5f/CG+2rX4+PuwEl6PcXbIW3NU7j2bXE9NcyMeDmBr0pfomyqyLHoIi7XpUb91uPk9g/czLA5\nnpFxHWbwDpi2wkR2ztQEhwkSTu01VuwHu2UzwRQlwSP52W5oavFezuI26ElKIKo07awnZgIUwkiN\nOv1fr4cG3vRxze/KsjQXNAkV/N5Ow87ZDZPAtVilFD4grAkBJvHZyIY8b7StXOibtBLKciLMLWPy\nqf/qeA9bcAK+K97zSFybjrLUOzbF+QxuTsqMIJM4WV+i7EQcjd9oBG8jG+J41OB4DJJpHr2WvxGF\nw7YyN/xBh6Jxr8GVhffHM6dw80KX2MWWJJvxJGHuDrXsy01CZt59Od1MLXmm8Tf/eXl/D7DcFteW\nbudaRUVFxSJxpzvXvsvji77/II+vzJ1r/2+hKioqKpqY8GgUywZLZ2qoqKio6BPGZz2Rlwcq8VZU\nVKx4XPIwWcsGnXe/pbeQtF/SKUmvSfpBv7//vULSryWNSXqxUbZJ0iFJpyU9I2lD49pjIeMpSZ9b\nmlrfGpJ2SHpW0suSXpL0nShvu1yrJT0v6Zikk5J+HOWtlgtA0oCko5L+FOcrQaazkk6EXEeirKdy\njd/Gv36gr8TbyE+/H99M9pCkj/WzDneA3+D1buJR4JCZ7cITwD4KzM+2vB/4ecQ0Xm6YAr5nZh/H\nI2V+K95Hq+Uys2vAPjP7BB5tc19kx261XIFHcMeZXKBeCTIZsNfMdpvZnijrqVwTXF30Xz/Q7xex\nBzhjZmcjE/Hv8czEyx5m9jeKi2ziAeCJOH4C+GIcz2ZbNrOzuHvtHpYZzOxNMzsWx+/g3kMfoOVy\nAQtlx261XJLuBg4Av6Q4WrVapgbmL7j3VK47Jd7FzNQl/SyuH5e0+1b16Tfx3iw//c2zELcD28xs\nLI7H8A1D4K6Q5xr3LXs5I5HfbjwhR+vlktSRdAyv/7Nm9jLtl+unwPeZ6yHbdpnANd6/SHpB0tej\nrKdy3YmpYTEzdUkHgHvM7F7gG3hC4AXR78W1Feu/a2b2Lv7Jy1Z2SaN4lqRHzOyKGhv92yrXTbJj\n75t3vVVySfoC8G8zOyrP9H0D2iZTA/eb2XlJW4FDkubkWu+FXHdoQpidqQNIypn6K417ZjV0M3te\n0gZJzcFjDvpNvD3PT7/EGMvEn5Lugtml08VnW15iSBrCSfdJM3s6ilsvV6KRHfuTtFuuTwMPhGa1\nGlgv6UnaLRMAZnY+/r8g6Smc6Hoq1x0S781m6p9axD1349r6Dei3qWE2P72kYdxIfkMS+xbhj8DD\ncfww8HSj/EFJw5J2cqtsy0sIuWr7K+CkmR1sXGq7XFtyFVwlO/ZRWiyXmf3QzHaY2U7gQeCvZvYV\nWiwTgKQRSevieC2eh/ZFeizXRS4s+u8mWOxMYb6desHP9VXjXSg/fT/r8F4h6XfAZ4Atkv4J/Aj4\nCfAHSV/DwxF8Cbi9bMtLi/uBLwMnJB2Nssdov1wLZcc+SrvlaiLr1/Z3tQ14Ksxbg8BvzewZSS+w\nfORazEz9tjTxJYnVUFFRUdEWSBrEo3x+Fg8keAR4qKk0hgno22Z2QNJ9wEEzu2+hZ9adaxUVFRW3\nwEIzdUnfjOu/MLM/Szog6QwelPCrt3pm1XgrKioq+ozlupOloqKiYsWiEm9FRUVFn1GJt6KioqLP\nqMRbUVFR0WdU4q2oqKjoMyrxVlRUVPQZlXgrKioq+oxKvBUVFRV9xv8AMGeY9urwVxwAAAAASUVO\nRK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xc12e320>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "imgplot = plt.imshow(lum_img)\n",
-    "imgplot.set_cmap('spectral')\n",
-    "plt.colorbar()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 限制显示范围"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "先查看直方图："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xc8b1160>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist(lum_img.flatten(), 256, range=(0.0,1.0), fc='k', ec='k')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "将显示范围设为 `0.0-0.7`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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P636/hUFUCp47LnS2S6rH3++nMDfv2U1Vdd1GhIOZ86rdXfczClnzuANwHCPzvSEreoIO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\nbtWA5gcAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xcb72f98>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "imgplot = plt.imshow(lum_img)\n",
-    "imgplot.set_clim(0.0,0.7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## resize 操作"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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QbJ0pF21Q9xDOHG/cuDEBxePjY7z2ta/FzZs3sbu7i83NzcnizqIMhloZsz1rd154dej1\n5yEmc2+pLXMv91gpX2tBOJrXzL/Bo0kE8KKmU8l536vDXEeA5LKysoKNjQ1sb2/j5s2byDljZWUF\nJycnePTRR3Hz5k3s7OxMwPKqAqWUMdp16FY1bxxFF2VmXa4hgFmKZ61ZyHw1cPTClWSmPstZ+S1q\nGaUVttWEv0qidSL6JWbJfZRnZ2fY3d2dHBsbG5e2DUXyichQkPFk1tZBS1k8kPSAQErJ9B4iHsB5\nOzZqd3OUdLAAU16zQHJhVsNrVklLCziaCR4BSO+apof3/zqAZ6nsHCw5y+T7LDc3N6fM8Jb8h7hF\n5ABZdIkAmwaK2nkUKC3h8XqBZk17tfgwPTY41mIXl7l9Cre3WCb3GGb4dQRPKSsrK1hfX0dKaQKU\ntEWIP72ztrY2ZYbX+s68QRNZkIiks0hSMl21/x5gltKdlUnu6WDJ0DbzALPFHC/Jwm4dqvVXWuFK\nDCbacUv51saZp0Q6BzFLAkztvnV+VfzCiwywEihLZKNmcXKRpLYNooyyFL+lny4sWEYXh6REFoui\n6Yyd9yIPVpKaSSrCYlo6duRei0k3a+HP25+dnU3OAVxi6vTrMWt50HUeRsbR0vHu9xIv7ZpxME+w\nn6nPMvo7VKLgVGsyAmUd5wGYvUB3yMRQGnS9nPfyXm1a8xTa6H94eIijoyMcHR3h8PAQAKZ8wJub\nm1hZWZm8Qk+rAwmSdE071/6PIUMWkiILRDVgH62XGpmZz7I13hgMc4hEwHNegGnpI2XMSWkMptLD\ntB/qG+shtE/14OAA9+/fnxwAJrsL+M4Db+FMsskWoOzVViXWOEa6Q2XhFnjGktrV9TGlFzD20jXq\njxyStrYqy+9rg3Bo2YYs1PWYtGvz1eJysNzb28Pdu3fx6quvAgBOTk4mQLm2tobt7e1L7g1+rtV1\njX5jAdwY6UTSH5tVAjMEyxZTvJVVchkKQhG/2KIB5lAprWxHTHBrEPcqXxQ8rRXm0sAZwzUin7e/\nc+cOXn755ck9YpRbW1tTz9tb6cvzqPvDA9h5AWWt/3kevssrySxrpRWEtE51nQFTA5YoYNI17Zen\nPVb5Iqa6NvmOberxPDmzJLD867/+a+ScJ1u1tra2Jua4rL8akBviPxwqQ4CS/4+u/A9hmDW6zhUs\nvUEzb4ZZAxKeXj2Boaep7pmYGpDUmr3chKSBT5+loP/00mBazOCLGi1S69vsDRzW5MCvESDS8/a0\nd/XRRx/F7u4utre3sb6+fqkeImx+qM496mMoUPYMb7kwrPOSzHU13Lo/lkTBptcgGgPYxwZMutci\nViekLTN8u8zZ2dmlz1Osra0NAktNBy6af9WSmnoumbqUDrFHejkJmd4AcPPmTTzyyCPY3t7GxsaG\nWg/zMD1rpDfweRPEPKQIlimlnwXwdwB8Nef8TRfXXgvglwA8DuBFAN+Rc75jxLfSNe/XsMoWxuiF\nnzdQSj3GXgwac/LgzPL09BTHx8eT4/T0FJubm9jY2MDGxgYAuEBZwwBKYWsXQWQfrDFxJXhznyQt\n5gAPV8M9sBxSFi1sT1Y5FkPsCZBDd1VEmOXPAfhnAH6eXftBAM/knH88pfQDF/9/UFNOno9hrnoV\nMGRFMyoWU+4hJZO5Rkrp9MhH65AcLOlbPgSY9C5MYl2e3rWAqUlLvck68twUlp8xpTR5xp4+PUyP\nkQLT+yzJDO8F+rVS8hlGdZg3E/SkBTiLYJlz/u2U0pvF5fcBeMfF+UcBPAsFLC1pWQGf12q4lFJa\nY7gSZsUkx2LVHCwPDg6wv7+Po6OjiRm6srKCtbW1qZcIW2kNZQe9RQKm5xcjZil9lwCmntwpuSN6\n+xa989b+skhtxGVI/2n1Wb4+5/zSxflLAF4fidQD/FqkJ2D2Bt9Z5zsP/WnLDD2xcv/+/cmTKwQQ\nm5ubl8ByXrsCaoVAkoMlCQdKWsBaX183TXZ5Lgf3ooIQl0XSsefkOniBJ+ecU0qqNp/61Kcm57dv\n38aTTz45NLu5S8mHWLsw0LJC38Mf2gt4I/oQc1xfX8fm5iZOT0+nzO6TkxPs7+/j7OwMR0dHWF1d\nnTqIdfE8F1Wig7PFJzmW/847j+gVuT6WRBaJPHn++efx3HPPhfJqBcuXUkrfkHP+SkrpDQC+qgV6\n73vfayZgAUzkfF4yZHXUSqPUmC1+xogM9f1F0yJGRUBJYbmpeXJygrOzMxwcHGB1dXWy8EO/QBxc\n5iVaP23d5TGrxZYWkPTSnxVQRvP3dinQ761bt3Dr1q3J/aefftrMtxUsPw7gAwB+7OL3Y6UI8wa6\noVLTgSIzci3wRhYsxvKXcmnJg5jl5ubm5P/6+rq6nYjexL6zs4PT01PknCcrxosqQ1fqI3F7rB5r\neVuAWZv+2EBZq9cY+kS2Dv0iHizmvC6l9CUA/wjAPwHwyymlD+Ji69BQRYawS211slZ6r2CX0m8F\nTS9OLzN9qB5SCCzpnN6TeXh4iIODg8kjgAcHBzg6OpqslOecJ0zT08OTsSfplhX6Wj9aTzDVxkoE\nMHst5vD26FWuKFAOLUNkNfz9xq13leJ6wHaR9uDOLJ3iLYA5C9ZrAVlE31qgjdZriwugJj4JvUl9\ndXV18h0f2kJDYHn//n28+uqr2NvbmwLKjY2Nyf8WmZf7Zpam+JA+X/Jb9sy7dmdDC8uNAKU0xaNl\nmLt9Y3XmWfkuF51RenG8tLx6qh0IQ+tIe4zx7OwMh4eHSClNFnju3LmDO3cePNtAQEmfsxgii+Dv\njkot8A01NyMLPWPlHc0nmu/YLoK5gyWX0gCPmOm1aQ/1+UU7lMcoh/ovh7DWmny1PGpEWgFra2vY\n2NjA1tYWdnZ2Jo8A3rhxAzs7O9ja2pp8PXKsslmilVn7rTVZvfuzBspeadTkFTXDewFiz/ItFFhK\n0Sq3BiS9+Fb4saTEAi3xwpcAZCiz1NJprSNttZX8kXwxZ2NjY/Jd8t3d3aknWrjZ1OL3a3ErSJ1T\nSs31buXhXa9NZ0jcUj+cFTiPCZStJjiw4GAJXO6sEZNc+id6+0mHSMRU18JHQHaRmaVm3hFY7u7u\nTv7TSyZu3LiB3d1dbG1tYW1trUvZWny5Vv1z8I5aNi33eoSPxJklw/RkERklycKDJYkcbEMZZkRq\nWWA0jUiaLb7OMTpILwDW2ojM8Jzz5FlpepnE9vb25PvknFlazKBlV4F3X/slVskP/uagWfjfatMZ\nEleWp/S/d/6l+z0BNFKOKwOWJNwEqwHJFkY5dkdoYZS1zHSo1JiY0XSkGc5XvWmlfH19ffIrmaXW\n9j0tBp6mlm5KaQLeBJoac47mE71em85Y8YZKrfuk9/VWU3zhwVIbrBpgWnFqzXaeT1SnUvgh6cyL\nUVo6DclLAhw9xsif7Mk5T14ILF8MTC8N7sVopMh+ww9eBu63lEzzKgDl0DacFTgvmkk+c7AsMYDS\nYJBAWTt4IoyU36tdvRvKbiKMctY+155l01ia5g+U1wkoNVO8l57n5+eTp4j4E0V8Iz0dvA9dFdN7\nlqb9WHmPDYiejAqW1gCXg0Z28lrgi/g8WtnRrDqYBo5DVrNrV35bfau1+UZEgiH5BS225+lZU66z\ns7PJy4npSaLj42Osrq5O/KdbW1vIOU+9bzKik6Zb6fpQAJgnOC6iHkNlZsxSA8QagLDCtcRZRBlS\nH1Z6JNEJhYdvlRYQtoRAkpu4HEhbwMmrCwJLeucm/W5sbGBnZ2fyvRximqurqyZQ1iwOWgy5RRYN\nmK4C+41apjNnliWgKyldswrOw1v5WfnW+Ah7LyzUsuBaFhnVI5J2JK0hC2ulPtLKIjWR3/W+d+8e\n9vb2ph655G9QkgCu6VEzSUXC15apR/gWiTL/sfKIiFyYK8lMmWVptm0dVBEfqJZfJO3eYS2ZB5uL\n1l9UejJ8CxCjcaL68LicWe7t7eHu3bu4e/cutra2AGAKKOkrlTzfoaDVk2EOSWeoFTYWUM7bPJ8p\ns+Q+xtZV6RL7stJdZBMciLkWIvXDw0VZdM3s6omVzhB26ZU5yqijk4EGli+//DK2t7envutNn7Ad\n8o7NWsY5Rh5e+F59Yog+s0orKjN7o6rs/EMZnuWzqr2+qBJlGYvYAVsGqTZQrcHLD75yrq2sW/pY\n5jO9mZ2eWafN8fTYJW1nqi1T5Lqnb61cpb4+a+F9pGYymAmzLDGcGp9bdB9i6Xok36suLT7hWUiU\nOUufknVwcOXWSyRPni//4uLJycnER7m5uTn1rLr1MuJW89MCyllbRLLeWvOu2crXKjV59NJnJj5L\naX6X/IgRs2koaNbmOwtp9a9G0qxl8vOuC0BvH41VWsxUS0/WBT8nU3t7e3vyuVraV0lvb+fPqkfq\ntAZAS+ya6mBM8dxkLemUrkXFcse0AqaciCPpzO0JnshMPyvQlGHGllkDUUu5agZMC8BGdNLS40DJ\nwZJ/GbKVxXG/ZEoPP1dLn8Tg5viQlfchZngNOPSM2yK9GJ2XRi+GOffVcInctbOWNZtYeQy5PkuZ\ntXnVKhE9SyyuJq2SaEDJ3/5DgBkZQBo40TPq3BznX6Kkb3prb0HS0o3eWxQzfBElOqm2AGYtmM/V\nDG/xj/QCx3mDpue3W6QBErEAImEj+ZB4jFKa4fTMuPRf1uZLzJJe3sHv8dV3ayFpKEjK/9IM79En\nZuFL7JlnTbxZlG1hXqTRCyRqQWhe4GTluUhASWLVUa8FgZp8yT/JTW/tuXFPzs7Opp4D58+Ay2+W\n87e0t7ozrpO0lKeVzY0FgK36LJTPcl7A1ZLvmCBRK/Nkyb1Av9RpORDytw9JoOThrHbVngE/Ojqa\nvBaOvlm+ubk5MfdbpeQna/Xdzlp6glbN5FMbNurf5OlG6zfyKdyfBfB3AHw15/xNF9d+CMB3Afja\nRbAP5Zw/GcmwpFwPwGxdcGhd/JH3WvQfk1X3kJo6bdWhBjBLCxjSjOV5nJ2d4ejoaPLsNx30DPjO\nzs7Ugo+WTq9yWSxn3gDZm/1p4WpcGVHQLNWhZg31ZJY/B+CfAfh5di0D+HDO+cOhXAwZ2zSuTafX\n7Fnb4VtZ7ZAVz9b8S3XaYxHHyk+a2R679/ysxCz39/dx7949vPrqq7h37x62tramngGnt7j36BdD\nTPlZSu/+UwNMkTqKgFupD8p8ujHLnPNvp5TerOUZyqGc/sx8ib3TbGXJQ3QYY7D1qJcxmZBkidLk\nthZeLJ2IWe7v7+PVV1/FK6+8gjt37kwxyo2NjcnG9J5iMawWH1pvKeXb0k8sv3YENC3dWuuoRRcu\nQx53/N6U0v+dUvpISuk1A9Ixle3RacYCFzlwW2UI8+3FerS0a8LOymSkRR3+gl466Br5MCm8Vq7T\n09MpZvnKK6/ga1/7Gl5++WXcvXsXe3t7ODw8nLDM3jJvE7tWhvZ3izDU1MNQv7Hc0aDdL+XRusDz\n0wB+5OL8RwH8BIAPykBPP/305PzWrVu4ffs2gH5+yavQ6UoLWVelHFxa9e1ZVrmIw9PmeRDA8kFO\noAo8eGqHngWnvZT0/HftYK4BEc93NpRdltwktQtOFiOr1clKG6hfwBm6OEbX//RP/xTPP/98MR2g\nESxzzl9lmf4MgF/Twr3nPe9pSV7mNVPTuUVK6UUXj+a1MNQznTGFgyLpyzenS3OcXqNG4EjnJycn\nAID19fXJS31XV1exs7ODRx99FDdu3Jh8VZLSqh2YrYses5ASYALjWA+RfC0ZaxHsrW99K9761rdO\n/n/yk/Y6dRNYppTekHP+y4u/3w7g8y3pRGVRVgg1ieokAdNinDVpzwMoh/qXh7YlB0nODjXABB5+\nV4eb7Kenp0gpTT69Sx9N498s55/g1fStXWRoWQ0fc7EuutgC9N82NNTfWOtr7CWRrUO/COAdAF6X\nUvoSgP8BwDtTSm/Dg1XxFwB8dyCdgar2XSVvlVpfi8y3BJgyvMy7p7Q46y3pufMgOknwz9FqzJJW\nvslHeXJyMnkXJYElvVXoxo0bk0cc6fAea2xhkdGwWh5DZKgJXDsRRvRpSU/qWzOB9QD/yGr4+5XL\nP1ub0TxXt3v7yoakpQEmMHvW3BMoSXpOZh5ISf+kdZC/8vT0FCcnJzg6OsLR0dHkPr31nICWf72R\nfJiR8tQv8Nx1AAAgAElEQVQO2lmuhvN6qjGBe6w2DxUL4Ia6Clr76Mye4FlEE7pFxgKDWYLmGEBJ\nMiZgakDJfZjyAHAJLA8PDydvEdKe1pEHT6uWOdUupPQWq/5a4nLprXsErLU6HctVYMncv8Ezq3Tm\nnX80HS/9q7hyDjx8PFH+8rLwc/lWIb4yLbexyGv8+uHhIQ4PD3FwcDB1bG1tTd4wRMC5u7uLlKYf\nn+RbhyyfqCceaErg6umT83TrAdQeSNWmX7Ighl7vKQvzIo1ZSMRHuCjmphcHWFymrjFm8hvyhRby\nG1IcftBr0Phq9Nra2hSQ8Y+F0Uq3XP0moKTP2tJBq98bGxuTl/yura1N0qIVc74YRFuKtBdsROuk\nBCwln9y8xXIhAH02jUfy65k+STStKw+WLQsLXmVH/SBjAmYk7UUcUJYuBD785RUnJydTTI0zSTKR\n+XUCJwJEakMOxnQQ0GlgeXh4CACTb+xQWmtra5PvgtOCEPk4+avb6Fnx1dXV5vopAUuND7RFIszV\nY3xaXI8p16SvhYsCZhRER1vguQpS2yg1leWBUs9VxFbQWwTTvMTWCSy5SUwgpB3EGldXVyfsksCM\n0uS/GhjTVxrlcXh4iJWVlcmLfan++Hd1+Fce9/f3sbq6OrWRfXV1FTln98NlNYs8Jd9byeytybsU\nNyoR4O/VL3sxzKGTzpVc4KkBiHmbzNcdMCP5EvgcHh5if38f9+/fx8HBweT9kQSS3LwloCRQIiZH\npjIfrASWtIBDoChBks7X19dx48YNnJycTDFLMvM5WN6/f38CjgAmwNlSL5HBWoo31Fc6RCJm+Fi+\nw6GA2UOnK7fAM0bepdk8ms7YEnURzEqi/l3OLOl5bAIheZBvkLb1cDADMPExcpbJwZIATvopOVhu\nbW1Nnv3mPsvT01MAl78fTvstuV4tg08u4kQsIt7nrPAW8+zpO9T0kmHmBZgl8eLUpLcwZnir73Fo\n+LFX0OYNsFJqdSn5rmrSkAs5wMOVbP7BMfI70kHmNX9ZhkyP+y4JOIk5Ag+fAacPjvEN53LhiefB\n8+khJVCR+QwJP5aMnX5Pia5NRORKvym9dpEjmn4PgBgSB7D3Gbbm0UP31jRo5Zk+MQtgslCihSfg\no9eopZSmTF8Ovuvr6xNWSj5EAl96jHFtbW2S9/n5OW7evIlHHnkEm5ubAIDj42Pcu3dvAsw550nc\nGzduTOIT0NashNfW1VAZA8hKafL7MqzFQFvKqulh5d2LTXKZKVjKStLArtU3KMVKo5R+T6CslZay\na+F76NoClJYufJsO+SDpP3/JBX+9Gq1Ip/RgBfz4+HiypYjMdX5oG8iJTXK/6MrKyuRt6Jubm0gp\n4fj4GHt7exPzmwMtuQD4Z3CHfmpC1k9Nu9cyxzGZZg2Iymu1LqUhgF2TjiczZ5YRRtljy0F0FXsM\nVqaVryW9WmDvOYC1/63Mkl6BBmDyqYbNzc1LL7cgM5gzS/JHkq9RfhuHA6hklpQPbUWiX2KH5Acl\ngKR4nFlubGxMfJU8bk8Z0r+ii0a1K+QtgBMFrJo0a+MM1duTuZjhQxhlDYusSdeSXj4+Lj1Wycdi\nuj2BkuLx73Gvr69PPt8gt/rwPAg4aWFoZWVlavWathQRePEnfcgPSoC3u7s7dRAokn+SFnPkqjzp\nLBefxjLDx5QIYMqFnJJJbV2fhU+zBMxRHa7MAk+vxY+S77I1nzGAsofUgGfNolYEKCPMQ8bhK9x8\nQYdWqDnISbOcm+e0cr2+vj61Sk7p8zwpLL2G7dFHH8VrXvMaPProoxNwpPzpPy0AEUBKFkvHmF98\n7BG+JZ1WM9gDyijrrZWSLmMAJbBAq+E9pOfKc0+glJ20J9OdpY+1xDysvAlguBD4kfktn//mq9L0\nS0yUVsk1E54f3AeqAR3lT+z2/Px88tgjMeIxzO55rSZHTFRtMowwx1Z22bqoK//XAmWLXCuwrAWO\nWQJrKUyJLfYESW1ARFwZ/LdWtMHIN4Hz7TvEPgng+Gcezs/PJ1uDaKV8b29vsq/y8PAQx8fHOD09\nnYQh3yR/5pseZTw6Opp6YUaPwdZrwM4jnVqmOMQErpV5mPtc5gqWHhgMSWvI9aH5Rs3eIQs3vXyY\ntSuxwPAOKgeffAEGLfCQ8Bf8kr7kxwQesEMOluT/JEAlVsifIuJ50eGV0Vuga62PMUG5ZZW6Nq9Z\ngqRMu3Te6mooydzAssZsHZJObRztfrSShwLzWCDp+Rmji2MeqMrrkXy471J+sZH7B8l05sySmCGd\n379/f/IIJTHLk5OTKXObAPb+/fuTdLleln49QGFWDKg3SA5d0PHys/pyqb5bgVLe430gKnMBSw8E\neOetWUSI3htrEaYnMx4CkmMuMg3xCWsDTK5KkxnOzW6+Qs3NcG6O88cbiVmSaU3h6JlwevWb3KfJ\nX6QxVObhk4yCRM29UthWZutJlCjVAqUWf+HBskbBVv9YTyBp8Uf2WsDpDZIt9Sk7VY9FNMkqpVlM\nunKfJYFZznkCqiT7+/uXfJbkmySGydnp5uYmtra2pg4PLKW/tRQuej0qvSao0vXSPSvcrM1vDRyt\n857kYe4LPK2FGctHWavPVQFKHraH37HVz8k7umYWSTbJtwZJgKX/HBy535Pnwc0uerJHPmdeehO6\nBZpjgaSXb8vCS8v1UtghPtteQObp0zOfuYMll6gfY1aLObULUGMCZU/pDZhRs4kfBGjEHPnbfOQG\ncDpobyUd9LZ1/iijfJO5fAUcvVyYs0r+ZA53AVjt0MLMon24VIfetVaf5RBTvFW8yUj7HzW1Zd/u\niQkuWKaUHgPw8wD+BoAM4J/nnP9pSum1AH4JwOMAXgTwHTnnO7WZ17C9kinUUinReyU9e7Lj1kHU\nwohn4VvjJjf/5aveHCwJ/DSQ45+ioLQBmOE1vyQ9eskff6Rv8cjnyKN1OsTcHTIxzhooxxLN1VMj\nVl/ubU2WmOUJgO/POX8upXQDwL9OKT0D4L8G8EzO+cdTSj8A4Acvjinp0SFIei7m1IBfC6C1Lk6V\nrvX2xbSAgaZPiYHJxRxuRnNmCeASs5OmMd/mw8FSA0wOjPRsN/88hDysxxl7MK9aE54kwm5nsbij\nhW8F2girHJLP0PFtiQuWOeevAPjKxfleSumPAbwRwPsAvOMi2EcBPAsFLFk6o5mWswLKWjO8xkS1\n0vfyHLNOPdHyLZnk1mIOmdW0F5LYIKUlf7X9kJS3ZoYTWJLZTb/8zUXa75jMspZAaOZ7yUfXkm7v\n8Ja0+rpr7o0lYZ9lSunNAL4ZwO8DeH3O+aWLWy8BeH1txj0WRnoBZQmoepnLVtze6Y8tFmBaYSWz\n5Isw3K9Ijxry9CzAyTlPNqxr3/Hhbwra2trC9vb25OC+Sc3fOabPkk8qQ/2WrQstiwiUUb+3l3Yr\nA+1lhlNiNwD8CoDvyznfEyZKTimpWj7zzDOT81u3buHWrVshpYI6dbleCjsUyEqNGE3fAo3o4JMm\nsZVmCcg1BlmqDwmU3qdwuY7y4OBIC0EALpnqPF1ukhN4yu+Sc2Yq07DqMXq9dG/RZWzd5103zz33\nHJ5//vlQ2CJYppTW8QAofyHn/LGLyy+llL4h5/yVlNIbAHxVi/vud7+bp+PmMy+zsjbvVgbcMvNH\n4pVMYB5OvpiCszvvsMBI09ECem3zuZWfBZa8XujFHJyZUt6UF/88xfHx8RSD1VglpctfwBEVr61m\n0a9r+lgLC1s00cpQ6/rKOePJJ5/Ek08+Obn2iU98wgxfWg1PAD4C4As5559ktz4O4AMAfuzi92NK\ndJ6Oqqjn+2qRIXGjLK1VR2rcoT4bDZgifjACEL5Zm3+wSzIsCST8vyyTpgu/z32W/Nve1kIOpSNB\nkn45UHKwpDASLDlQ0qq63JZE6RJQ8nyjEgWhUh+IujiiE1UkbosMIQGebl7cqF6lvPn1Xmb42wH8\nfQB/mFL67MW1DwH4JwB+OaX0QVxsHYoqXZJaB7gnXqeLAngpzSH61EjJDLfC8Hv8+9r0WCAHSwIU\nzsBoMzgBigb61kTAQY4zS2J7HIDJvOYDUAKlxiw5K+R50WOTBJgUJqU0tfLN/aSUFl+tr53ghvZb\nyy3TCnoeMPUCTCt9S3r5TGvyjrioSlJaDf8dANaX5N8VysFPvzuDIynNzq3p93QXRNKJmOGRtHLO\nE+A4PDycHBrLIhA5OzubvLCX8rDMcI1lkvBVcMkstf2RFlhyXyTXQ74lXTJL6Uag+/yjacRQOUjO\nWrw2jJj5JdAcCzB7piOlBQjHaru5P8EzJmAOyXveEllIiLJK4KEZTqzy4OAA+/v7OD09VT8ERm8j\nJ9HMcK6H5cbgZrj0IxJrJaDkZZGmN2ee0nSXzJK/vUgu3HCXANefTHDJKntJTVvx8C3mZy0LXVTA\nbE2rpq5r8pg7WAJtgFkDdCXzVFauZEuzBtQIUPL/ERbNWR0B5uHh4QQsyTSVYKytEPP/8pD3pc+S\nL/LQPQJNy0dJ5wSY9AvgEsjJg7+FXdYT93mOxSqjZrVk61rYGp9orzKMxRhLMnaeLekvxNcdvet0\nDyj7ICOdSkur5Mus0bWH1DIAWW7t4J9hkJ9eIOF7DfnbxQGowCMZnrY4RGlon70l3clvKh9nLNUR\nyd7e3uT1bPT6Nkpbslqus/RPlqQ3iI4JCLXpD/GLAv3qpjWdWbTfQjBLkhIIRRoz2kk8gIywyh6A\n6aXRYjJJFsefx+bfrZGPHJIeBCJkkgOYYmiUvwWW8kkYMo85MEu95MsxLIbFy0dx6Zy/y5JW+LlJ\nSmDJfZxra2tVQDmGzMtysaQFMIfU3SwArmfbjgqWFiMcM44XP+rv83xwVnpRqSnXUMDkICU/8KV9\n0Es+LigBju/LlAyTb/ymg0xs60uNmu6kCzfp6Vd+xIx/ypa+pyOZJdedrtEquawDafbPQhYFKEk8\nv6fsd4vIAksutyEyE2bZMnt6Pp1SQ9aaDRazLF2bhbQAplx95gsr3AyXrIpMZ2KGlA6xUv7dbgmY\n9J1tLV2NWcr9jPywfKHya458AuD7RzkQ5/zwDUcEnvQoJNe1diC1TOpjiedSagWIsdwEY5jzYwIk\nl4Uywz2JVnKNE9xKW94rMcwaaUmjxrUgWSVf1JE+S87A5GLH6urqpc830IKQ9FNSPOtlutLsl2a4\n9iYiDZD5p3D5ubawI81w7htdW1tTmSWFt+q2V3t6sgjgS1Lr0irp3gJqPcFz6AQ3M7DsDTqUDok0\nH6INV/KRLgrDLAk3JSVgaos7EiS4Gc4fB6Q0OTvVwJJvbpcLPNb3vTWwtHyYBJa0mZ6fa/Ug+wP9\n0v5KqYP0h/L686wQSnusPjG2T7A1vue6qHFZ9QLJGiBuBc0rwyw18RZHhsSfpZQGWktnkmApV781\nkJRP8NCmdApL161nuuUTPycnJ8g5T/yIBHD8ExA8bWKnAKbAi5vQJycnl3SS251kPfCDrnH3hNwo\nT2yKuzG0NxTJV7hZA7B2YHr9oaYvtPoWS2nx/xEGF9V5DB9lb7nSYKlJK1DOEjhbBoR3Xw4MyS4J\nDOTqLwdKDpj8jeIUjvx8EizpvgQO8nHSOd+MTvpwdsofR+RgJn2v3NSXm9mlWKvvfPLgPl3awyk3\n0Gub9uUClGwL6zoXrw+09McWAIoyMg98a8o8FCRnCY5SZgaWPcCoF6B5QBkxs3qbXUM7tRZWAwbO\n0kg4M5SPO9J9Akq+eCLBMuc8ZWZrprf85cDD8z4+Pp4wQL7AxP2P3AVgCV8BB6ACJT/InUC60VNG\n/C3rvE7koqLWXhGrocZsjYTrCUgRkJT/o+VpZb8l0fp3KVxE5uKzrJUxHeiLYIp7UuOLoWsSLDVm\nSe0h2SUHS/ovwZaE1935+TmOjo4moEnPoB8fH7uvhuOmP30Lh4CS0qUFKpkvf/uQV0dyNVxOIPzT\numdnZ1hZWZl60cfGxsbUJEGuBq0tWkFzKJPsYdaWQLIWwCNmeNTnGc1Tu95rjF8pZumlG2nIElBa\nphWlP2tTveUe3beAwfNZcjOcng0n8KL0LCFA4qb34eEhDg4O1FVwABPQ4eyVTH0CQgLLo6Mj1W8o\nn1WXFgBfGfeAknyomnuBb8hfWVmZ7CHV+kTEJG3pXx4YRnyINSvVHpBZ/4F6M7x03uLLH1MW3mfp\nVZhmPluVWOoss2aYVgceApIyrHaQaAs0FhBZg5H/p4UQAJMV+MPDQ+zv718KS2W3NsVbq/DyY2IU\nh86lyGfCJZBKneRkwHXjLxiRTwlp/VBLS4KwjN/CrCJtU0rbA8oaVlkL+FaevD6GuCh4Wj1kLmBZ\n2xkijvDePg+twazznvnWXNeEAES+OYhMy5OTk0mavCz8kE/tWJvJedzT01Ps7e1hb28P+/v7ExNc\nWz3nq8nczCZGur+/P9kWRMDE8+JAZdUBgEk4Yq0ExltbW9jZ2Zk6tre31TcqAZjyrdITQ9xvKctG\nIK/t/eRuDvnNH9I9Ysb2Eo89cumhS6/yWGN+bMKz8MwSKAPm2HlY4Yb4WLR0I9ciQqBA5iJfDOHm\nOc9DAqa2ACI3cvPj9PQU+/v7U89p08q3/AiYZI9kupO5TRvgCSy5z7HkDtDqj0CNJpDt7e0JQNKx\ns7NjgiVfrT8/P5/4YbWdBPQWJO0pI/J/8gO4/Hgtv+aVq4b1LYqUALNmLA0dd7XgPSpY9kT6nsDk\n5RFhkaX/UR1bTe5SZyNmSf+56UoshxgfpaexSmJ8co+k3IpDQMdfKszBjtcJBxZuivMXXfD8iFla\nQOlNMpzxcRbIwXJnZwe7u7vY2dkJ+fVoEqGy8FV8/iYjuYGePm1BX5qkyUrbs6mBZ0Skm6UUxrru\nubKs+tbys8Lz8snfHjKWr/NKMEsukQ5Rk1ZrGmOY4TztyDVNuO9PnhOo8QUUnj4HJPI7Ess7PDyc\n+hSF9pINDgwUlhgYPZNNwCXf+iNBmh+aGc7/W/UjwYwOziY5aHI/IheLZct9qWRan52dXZo4Dg8P\nsbq6it3d3akFro2NDRNQWtp/0WSI6R0dX7VstFWfmYPlGGyzZ9o1Psqx/ZZDzHDqFPxt5ASA/FML\nlA8HIvolZkkLNQcHBzg8PLy0Dcjat0hAxzePc78d5Wm9EUn6SXmdkA+QA6ast/X19SmfJR3SX0mA\naW1wp8Uq+ZYjyoO/bYmeO6c30fODnooCHgIllZUvVlE9RayLHqZ4TZ+TekXyHcP3WvJZjzG5XDlm\n6cmYbI/SBy6vqraYESX2OGQ2tkwiYooWUHLATClNfG78UxQcNLRtSRJIef7Sv0dgKs196Uflwp8d\np3StuuMb3tfX17G5uYnNzc0pXyU/rJX1lZWVqUUo8q2SPtKHy7dN3b9/H/fv38fe3t5kkYl0kY9v\n8v41tC/Xmsa9JJqvZYpHxKoXyxXWq7zXCixbpMQYrfvA5S0OtY2upTmWaH5JGqjyhRIkHGTIdOTm\ns9xsbq2ay5cC04qx1IsfUiQ4Wve58F0Bm5ubE3/h1tYWNjc3J0xQrkbLdPni0Obm5pTu/Llx4OHr\n6CiMfO2dXP2m9iBXBf8ttSf/9cJoZeJhtD4bcQXJCWrsRdhSXkPdaiUpfTf8MQA/D+BvAMgA/nnO\n+Z+mlH4IwHcB+NpF0A/lnD9ZymxM1jck/dZK1kC1R8cYCzglUHrbgjgj49uP6GkWCxzlYgyvI/5r\nMVoLLGV8/muFI4CmlWcOlhsbG1hfX59ambdAhT9hxAFI28IkJx3uo+WThQRLyoeAstQHSlaItwCp\n3bfSttKP9NF5MNiasVzrwigxyxMA359z/lxK6QaAf51SegYPgPPDOecPh3IxpNdM1NtXKf9HZs0e\nZsTYwkGJg6XcDkQ6ElgCD1kaPemigaN1aHlROpYLQEqpzjQw4FuoiFnu7OxMgFJjllp68u1G/DFQ\nPgFJxi31sICSl52AcigYeX3WKmsLaNbmTflp1pgFfprOXj5yTEYmhsHMMuf8FQBfuTjfSyn9MYA3\nXtweNOJLlXmR56B0aiVqSngNFNFJlqumvFbcSDw5sAmwtPc6Apj41wgsiFV6prP8PT8/v/SKNvL9\neenwslm+KE14HP7oJjfDCSQ507PSoHqQC1T0JA/fS6ktSNUyS60eSu1q1YEGGlqdlphjC0NstdY8\nHWqZ5JA61CTss0wpvRnANwP4PQBvB/C9KaXvBPAHAP5BzvlONK2KPN2CjM3QSjOXpUstw+zdqF58\nySw5gGrMkrMqDRC1mZnXxfn5OQ4ODnBwcDABA9qn6IGlZAUe8+Nh+Dn3WXIznIOWfL7cAmj+YhGq\nM1owo3ICmGLpUg8LLEuTRq14bEzWEw/P9bHueWNOY3Feu9VKC2DWpF2SEFhemOD/B4Dvu2CYPw3g\nRy5u/yiAnwDwQRnv6aefnpzfunULt2/fjmQXkrGAsrXiazp3xLyvybcmDc9s1AY5DSx+8LS0cvFf\net0Z+fhoJVkblBazjIimJ/dZcmYpny+X5dLSBR6++IOEv6yYTwCaCZ5zLjJLijMULGX/svqINIOl\naKAZsbBKgFmjQyn9aD5a+JwznnvuOXzxi18MxSmCZUppHcCvAPjfcs4fu8joq+z+zwD4NS3uu9/9\nbgozUdBiU7KgpYorzZ5Dwks9x2Sw0fSHskoSGsCcUfH/HnBoaXl6eWAqWaB1eGlzM1muuK+treHG\njRvY3d2dLOjwp3gsPbRfrzzac/j865H8l57g2dzcnLyOTk5IJYlYWrV9qlff4ulqk6rsL56pH+1X\n1uTtMWcut2/fxu3btyfXn3nmGTPf0mp4AvARAF/IOf8ku/6GnPNfXvz9dgCf99KxKqvFdLXSFnpX\nhdfijgmSXmeqFc0clenSPXlwsOTXaoBLA0yv7qx0rTy8vClf7lMlFrmxsTHZcE5gqW0TKgGkJxws\nOZukzefyoBV1uRI/pL/KsLWA6wHJkPy0eyX22qJHC560MvcSs3w7gL8P4A9TSp+9uPYPAbw/pfQ2\nABnACwC+21IK0B3O/D+/1kOGgI/VyLWDKMKMrc7Uqy5kOhIoPWapgaQHgJ5QWSMM0gJnr075Rm/a\nGiR/aVGnhkmWJKWHLwKmMtKTUtK9QY998le9yc9TRMDFu051xH+1ePL+ELPf67MtaQ6ZHKJxoy4F\nKaXV8N8BoO2O/URIK6GcB5JWpXsdI5pvTRwZv5Zp8s7fApiRvLT70bJxoJRg6bG+WrH8UDUMNpov\nmd0Elru7u9jd3Z0wTDqkr1L7jebNWS35LkvPvFO5+Us3vHduWnl696PjgtqmlwneaokNBdQI4A1l\nrySjPsHTApK1swOXaAf3xJqVKb6XRw0bkjr1MsWsPDVQkma4BppDpOSz1ACT3+dxrLLlnCfMkvZR\nPvLII3jkkUcmpi5/0YXcgB4FZ6tOqQx8lZxASB5aPlZZ5URd0kXWjzXJW5NYDymZ5b3y6H0vKjN5\n3NEDSbrWwwQd08/YKtGOWetv8tilN/FIoPJMcJluq2jgUGKYNWlLM3x3dxc3b96cevOSx+BaLA5e\nFi0NaepJ4NS2atXkbd3zwEoDUAvQW6Xkq2whOa3p9MqXZFSw5E5vElmZHpOLSMkv2pLeUJ2kRACz\nxFgtf1OtaINVe6uPjBPVVRO+ACOfbsn54RM+9N1uqSuJ9u7I1dVV3Lhxw1zI6cGQeZl79QuLNIzF\n6umalm8JVHrlXRO+Nn1NtHFD5y0yEzO8ZBqQ1ACBZZrUduoaIIiY+VaYWrZUI5bLQP732E0Ns/DC\naWxX215DbU17FPlr4zSdcs4TcKRN5vTIIoGltyXHAqASMyyVL1Innlj50zVpNkcmXs/UtshFqQw1\nFlIPGcNNMHQCmimz9EDSu++JB5I9/TO1pkJrw3hg28tV4R1DRWtD/ow2/aeneYhRHh8fmy8k5syX\ntt9sbW1NVrvp5b2cWXITv0VqJ1rJiK1wVvolYK9tfwmC0cmgRay0rHK3MMkh7dirrDNjlpHCeqDJ\nC2398nDazFmj91CfXW0De8yiJo6li/zVmKWXd40esuzELOmcGCK9SFe+0EKyX64nvflIrnp7m72t\nuvLuRcQDyFI8CXyRSbJGIoAt8/XGXFSXIfdr7tWOrR6AOROwpPNSASP3rU4w1JdksYSe0gKetSaY\nJSWgHMIuPcbDzXB6BRmBI/9shbZBm4MlfzqGVr53d3fxyCOPTG30pi1C3puEStaNJ5bLowVY5MRe\nYpUtYBU12XleEZ219EuTa8RdVNJ51hMbl5mY4YDOGi3TIOK7lP4szfSuZV5jypg+SymeWSRZmzRz\nvfRKs7vWDgCmttfQvfX19cknKwg8+eOIUk/6oBmZ4cQsH3300akX61pvErLEAlSvrHLgSTDzJnWe\nhgaUJT2iYrWZVa6hbh4tP5meHJMlcPXqbKhpbuXpycy+7jjU5LEamSpNpt8KTtbsXqtrCyOUjVjy\nM5UGqEybf9KBgCXnhwsnWkf2XBxSb/m/ZFrSRvKdnZ2p7/XI91/S+c2bN3Hz5k3cuHED29vb2Nzc\nvPQGIQ62Vp1Yk6ulK6+HknUj64ynY21WB6afovIeg6y1LjTGq+lYqgcPdCMmfiSdSPyxiEREZrLP\nshXIak13a5amtLy4Q/TU8hjSKCXQ0hh4yRSmg3/mgIOlBJrIAIiApAWwcm8kPeFCK+baW9x3d3cn\nL8fY3t6e8k9ysJT1JM/5tdIEEDGttXyseFQ2esEG/4AZtQnF0epR1q/XTrVAFJl0vTqNxquVoQBZ\nyj+q29yYZQ/ArGE01gxrDf4eQBlhJZae0YHpMVitfixm6QFNaYYvWRBafAJs+r5PSmnqP2dfdM6f\n9+bMksqlWQWeOVoz2Wh1UgMWdJ9vlaIDwKVPVmgTVg2A14hk2pHw2nk0L0+GjMFIngvLLFuZm+bb\nsGbZSNolMNGYqKePpwd18poZWqYl2Y5l5nlxNF09M5w/K87LoJXfA8SayZDAkQPlzs6OufgkF3KI\nWW5p2c4AAB+YSURBVFJ+ljXBfz0TXKtDq695JrdnxRCzpMWt4+PjS7rx3QOa1E7CESY6BPRKzFzW\nv9WftPhDx2UvoARm+HVHq/A1gBlNR7vnmSv8sNhCpIFlx/B0sAaUx3BkPG2QWwOfyhcxw2XaUkft\nt3RPKyd9soJ/Q5ve3q4BnVzEoUOrHzlA+bk2EXlAaYGwlFpmSQtcPD8+ibVIDXBqOnvjRN63ANMb\nG5Ixe5O8R3y0fD3pAZpzYZbeORero0bSiZoVEix5PnQ/Mgis/54eHgPxwkog04BSS5PAkoPO+fm5\n+akDSycLFCNgKcGbfzUx2ok1QJOMXg5M7b9s76j1MEQ4WNK32CkfmsT4p3S1MnN9S8AY1d2b5OX1\n0oTtxbXysMBWTuDRMnnEp1UW/rvhNYPWCmOZCa0zkkbz5QDVOkoNq9b+Rxo75zz1HRjy+52cnODw\n8BBHR0eTlWcCK2t/o5xIIqxTgqEsq1Y2r3wWS7Hie+DI/1uTiwTTUh+J9CGpK7URX+DhbJMmM771\nTuYXmZgiE7RW99Z44fe8duPxpUvF2qYmxxQftxL4rPw0MK0lOp7M3AwfItrsU9OhZfwSWGmdwWoM\nyYysgeixLU0HeW6FJyHmwr86KBcUTk5OkHOeevMQ75TevssSk7GYugWcGqDxNrLqrAS63rlM1xps\nkfay6sPSmU9g5L/MOU+1E01gGlhS2tRukZ0A8lqJJcqyW1YXL5MmkkB4E5b8X2KRVrwoztQAKsnM\nwNLqeEMkyg55eHke7WCl/9pMqzWiBviWRBtRloE+CsYP+alWi1nyAa2Bn2WWyfsyjlYn2gDSBqkE\nNq2OvF/tmgXEpfbWBnDNANX2WcqXiVB7EOuUQu0m/bbyg2paOTwiIAFIy1cLa/3nDNR7tNaS2nAa\n+Fpl4fEWDixbgTLSMVvTLpkw0cqsYS8l3UvMzfsPPGSW5A/b39/HwcEBTk9Pp8BLshL+xAsHUx6+\nVDcWuFoM02IdXvyaQe+FIV1ku5Ta3GLIWtmsCZmAg2+4l0AJwAVLeosT/acXk3iiTVLafVk+baLk\nQGgx0hKz1OpQ0yXCGi2WWiJFNeC90MzSYnpWI1pxSFrAKgKYJfZC+ViNZ7E1GT7CSolZHh8f4+Dg\nAHt7e9jb25t8kpZ//8X7jjX/DAKAydM1GhPkZaQ4GtjJ+rC2CGkALeu2xIw88LTMthKY8PCW1VBq\nG8kqCTA1U5o+rytFmujENCNigRbXX5aTfilfCUwWq9TyK9VrqY29iVem55ELrR+XZOEXeEg8Zqax\nt0gFe3m1/NeA0mOWmplKYbVOVQJKYJpZ7u/vY29vD6+++irOzs4mb+cBHj5epz29w5kl5ScHCmdI\nHCQIULVN4hJoZBp0Lr/AKPd/enUUaR+vDkssyJoES+0imRgHSnqiR/Zl/iw9F6pjCs8/muaVrwRa\nFgBqpESmqV2Xv16beTpqJEGOGSm835TGfEknkrmApceuZDiLAfCB7VFsq2NredbMPBEWozWwxWSt\nTqIBrtVpgYcmNC+zZHzeR7Msps7r2dKTD2JPV562BpYWQ61hHVrdyDqU7eKlr7WhVl/aIOZ1CDx8\nTR29l5NYPzF+/so6TbTnxyOTh9enNZ3li5ol2Ev/t1Y/Vp1qdcjHtKa7BZDaOLPEG2clKX03fAvA\nvwKwCWADwK/mnD+UUnotgF8C8DiAFwF8R875jqaYLJBWQAtMJFBYaXvnXr6y0aKzZEkfTTSGpaWv\nNaDWuSwd+B49DozEQOSGbu0bPFqZZFt4wC7rVqtDCZAaWHpgJvXzwmvl8QaYNSClXt4kAzwERw0s\n6TMbBDbULtrH1aRO/JV3nHVrnwepmexlGElIZDtpX67kdcXdNjKfErjyvqBNXNYk5mEJ/bYAJVD+\nFO5hSumpnPN+SmkNwO+klP42gPcBeCbn/OMppR8A8IMXR5XIGdkCMg30+OCU1/l5BKTlPQuwtPS9\nsmmHJdrMLTuHVi/ylzNLypODpQaY2hM8Ui+vfiRQppSmFoi0clnXJEvh9WnVfaSurbay+gA/t9LP\nOV8CLK+OOFiur69PwtODAVqbWDrzN0VxULNAwSqv1b95G3LdeVm435VcARwoeb3IX2uykguLWrtp\noM7PrbHmAWUEMItmeM55/+J0A8AqgFfwACzfcXH9owCehQKW2iDTOqfGumQca6CUZkyt0lqAzPvV\nJNKAUlcNPHg4bQaW4eTTH5xZaiDpsUutbkszs9aWEsTlr1ZmS7T7NDjlrxXeA84SQGp5UD1rdSXL\nTWH5m+NzzpfaQb4yTyuvBFRtMpITpyyn1Y9kWA0kJbOUfZ5vZdLGrDYp8jD8utdmcqLWGLk2ifAw\nXcAypbQC4N8AuAXgp3POf5RSen3O+aWLIC8BeL2XBq8cD6Tkfw6UUcCsAUtuNpRmZA28vBlbXtfK\nJtO2mIjU0dKJd2aKIwegxSo9ZmnlZYGlPNdet8ZBRA5sq44t4eClbYXS6lxrAw6E3rnsjxxMtLbg\nB+lLZjQxTN4GVlvwepV6yXqU9euxY62NJav08qCdElw/2QYeWGrtVCIxEtg1sPSAUitzSSLM8hzA\n21JKjwL4VErpKXE/p5TU3EqVQqKxSw0oZYNRHq1gCTyc2S09vfzkYLHS4I1ogbAGlLyjaw2qxeMv\nouAMBMAlkKQBawGmpZsFnFY7yxf5yscxtTJrdW+JXKziZebtJP/LfCRg8YMAgfdF3rZW36Fy84lA\n6mcxWn6/JJS2NjFR2Szm7U18FrPk57T1iY8Ba8KiPL0yUdpWGfl/qS/P13NLjAKWTLG7KaVfB/C3\nALyUUvqGnPNXUkpvAPBVLc6zzz47OX/iiSdw69atqYJpLEayPEu0mV2m5aUh8/ZmMZkmcNkBzQEm\npTS1L9Fa3YuUz2J6Wtl4ZyEwXF9fn2xu5p9v0LZVWKzfAnF5zwIhbYDJwWANYK2uouccgKRo+mvm\npBWHfnkcvomcA6ScUKx6jgCkNj6syUVjXLyPcOao5cPLyM9lWaQLwWP3Xn3K/CXJ0ES6kLR6sNLP\nOeOFF17Aiy++6OpJUloNfx2A05zznZTSNoB3A/hhAB8H8AEAP3bx+zEt/lNPPTVRTM6cXGkqkMeg\nHB0vgZaXFqWnAU1ptpOAoS1G8DS4OSzLUQI9YJr1WpOBFoeA8uzsbOqNPuvr6+qWE6m/5Z7QTGd5\naIzRAz6NSZVAwAIgb3Wal0/6ECNtodW9BbRyUrDKXDK3NbHqQAMH3pYas+RjReopgUe2K0+fAybv\nXx5YyjGq1ZEGktp4kxO710elvOUtb8ETTzwx+c8JnpQSs3wDgI+mB37LFQC/kHP+zZTSZwH8ckrp\ng7jYOqRF1liLdo+EswEPNC3WxdOIgK4GFF54usYHAx8gEvjJfOP3PdZSYjTWfx6Pg6UcAPKDXha7\n5mXi+ZU6IjcB+bkFWhbz8cBSG7jShaAxNvmfgJIDplYfsn5lO3DQlROlBpZyUtIWhvhvKV/ZJlb9\nev5XrZ41ZqmNK9JfWyz0VvMt/zQfJxJwtQlRqzMtbUsPb9xJKW0d+jyAv6lcfxnAu0qJe4NRu3+R\nduiaBCYJpPLc+pW68TwtcLeYjQREuQ1CmxWterDqxhp4JBwsZT3QPW2llXdO6pAc6OSg1BZn+NMo\n3DcpGQ1nrvyat92E6pO+8sjBLgKS/OAulFL9exOpBEuur1ZXEiglGHgDnF/jk7U8l2BigWQJLGW9\n030PjOWhkQM5oXjkhtePrDP6lZMGd/HIOuPpyjEUkZl9sEwCTxQoI2mX0igBpdSpxPI0RmGBrTd4\no2XyyiXTBi6/fYaDpZyxZTjZQWW5LTObgIy/Fo5Ak7MOWtiQg8DaW8j140BJ55SWNyFp4CQZlNcO\nWh+jOiXftBygGvO2AIaDv1Z2q+2zYPMUTtatxbo9sLQmRguMra1osv6sSUeWUxIA6+CTNB+/JSzh\nYaMy0w+WaaDpAVkJ9Uv3tZnFy8djGjJdPoN5HbHEciQ4yPrRZl+NWfL0OFhSR5bmsCaafrzMFlhS\nXWjvzqRH+YjtUj4SuLmLQLaZ1I8vpFDdeIAgV7m53rKMWp1oIpmXbG8eRmNrEmwoHAc9K19Z7wQY\n0rLhdWu1rQaW3lYhQAdja5FH1qFGKLS618aknHSlm8vyq2p5e9csGRUsucJRBJeNqAGEHBwyvGda\naIyCKpze8mLNdlzH0iHLI9PiensD1tLfS4ODAon8r3UmPvA4UMt6l8yK7km2SfnKJ4d422nbmax6\n0Oqe8pD9Qpr9FsPSwlnPzcv6ktdLwGqlyUWbpLR21tpD1o+ni1YWEs6+rX4t05NAy5meVk/WWKUw\nsk9LHzt3w2iTUKmMLTITMzxCibWCSPYC4NKMpS0KRA45m1N+ET058FhgqZVZAyjtvjXba9t2KD0P\nMDmbonhy8FMenLVJsCSGIhkHr1MOOPTCYY1xSJZQYpal+tYmUckouekmdaXB7fneNNCsGYwyfSu8\nTNOasDX/rtRZMlopGnhSPOlPlXUgAVJL22onbWeFVwdafrzPesRgKEiSzIRZesIHupxRgelVRR6H\nfrnZGTmks11rcI8t8IHI/2sDygIor648nfk5T5P/WnpqICvByPK7AdOmF9eNrnMA4luXOPOUenGg\ntJilLBMH8whoasDHQZNcFFxPyU4tMNbaj+smz71+IuNowtuYtysHNQ2Eo+OQ5yGBUjOp6Z4kGbxv\naPVIda8BpjcZ8bRl/twlJsEyUu6IzB0sAd+noc0ekllKsORxNfYj7/Hw1iCh/CJAKfPwAFMrrweS\nWhk5YFKavCy8LayZXKsTrl9KD18wa+lBbcEHgrUySelpYOmBumQnJcDUQFPWKZ9ALWDTBj4vj6an\n9+uJ1o4aGFMd8j6nsdbSOJTjj4M470MyvDXpWmDJrUDZp3l82Qb8nhyv8tyr5+ikZMncwdJqKIrP\nV7uoAXihaZBZQMk7Gw0ubUaja9wHpHU8OpdvWZEHH4jR+tCA0gJMGd4DYckuLSCX+mmdXerKzwko\nrTLIOuHskr+5ndeVnGjIXKY6Lk1cGrO09Jf1xgeexk6tOrX08fLV2s0TOQny+DI/Tzggy8legiQX\nDyipXbRJSiMSVj145EMSIBlP07UVJEnmvsCjNRS/RxVzeno6GZA8Xc4seRw651s7JAOjsPyZZbnF\nhesuZy0PLGkwA5ed0KU6s0BGmwC065auvD5lnhoY00TE65pPVtqvdk0+G04Tk+W3tNKjeuULUNb2\nLQny/JBl19qAC03KMh1r8HJ9JBPVGLcFFLydNZHEQWt/q4xafnKSkXWiATCdy/El/cKy/r1JP0qg\npOltYYhXh7KuPBkVLHlh6Feey87izZgkspPKwS87DDfXrFmNp8cbm/syo4AndZEdUGtUKRpTkenK\nBRMLCHiH5p1ZA1oeRuvoGlhqdcPDrKysTC2gkJXgvflIdnKePgdKy2Ug60j2PRKNPclyWfXA65TO\nuS5aX7f+a6JN7lrY0oKG1MP6L+NqfVcjQCVWqDE9K66U0kSspV0aVxbzLMlMwBLQG1AyQ27aarM0\nr3g+w1jMjucrwVUzxWXDSLBvFSqnHBwRwKRwctbldSdNRAvM6B6fCCx2KMHSG1heB+V6E1BSXVgf\nTZN1JNORizGyrDwNr3418JT/PbDkdSoBUNaB9kt5lUBTAyiue4Q1lQCTp2VNzKSLxYYtHyU/90Qj\nJRIPvEmBx5HnGujWjuuZgaUmKaVLPiq6Lv/L1Ti5J9BaeZMzs3dYYbxyaMzEGgDeuWxga7bm+Vpg\nyetTzsKSMXszvNbZNVCIDAIOvgTSklla4MXriOdPaUn9vUmW14UMowkHS2syLoEk/6/1cwus5T2t\nT3j1Jf/XlFlLR3M98Hua20PmZ9WVNla5Ph5YynKUwFILF5GZgaXVmakC+P463igUToIlZ5YAphZ6\nKJw2SDi79IDSA0ieJl9k0MrKdamd0axBzzukfIejBEAJlhIwPVC32EekDDI93qZygUdjrpo+nPXS\nuewrvA2tQaSBhjWQ5UQsJ6USIFuDXqsnrQ4tYJHn0QnL0lX7lenL8UF1L5m+Vk+Wjta4kPVUAkyu\nt+wvVtlqZaZgqQ1eWXAqqHTca2BJQClBhc458+A6WQsnXE861/yhslHkqqzVmKWBwcui1Ys2aLWn\nTbQOwsGSgFLmX2IcnnjllIxEToIWUHoTniybtrshUt9W/5L6S115mWTZS4yypJMl1liJtJkGlhIA\ntTx4meiXL5oCl5/d5hO4VwaZjwVsmn5ef/PiLTxYyoFizfzWzKINJD5AJIhQOOnY95ilVrFywPFO\nQ+Gkz8zS2WtQS3hecuaWq8h0rqWvgaXG/CRwWJOI1l4ltiNZghZOpqHpq6XDXQp8C1ikTmVemvB6\n53VViltqW1lmnp8Wn1+3wM8qr6YvnzS1iZXnK3WSgCkncb4WIdO16kVO9BqT1OpDlleeW5OBFc+S\nue2z1MCjxAa0dOmcFg7kIoL8lIG1CizZW4nt8HNLZ21CKInVqT2dvPw8AKI60hzzlJ7srBrYSL01\n3fhAkJOOFl7TWUuHh+HmYMmN4k1kmkhGxX2vNQNa1oEnVhivj8i8LNCT4Swyw9vKOpf5lJhdaRKR\nh7dQ7IlFglrZ5UyeDSeRM5TGGnjleAOOpyEXe0hWVlamXhWmvc6KdzxvQYPrXwOYUlc6t+qHp6k5\nyGWapc5UAgYL5Gp09X65rvLcA08tnlYemQ7f3yfjStYvfWqajjw815PvoZV7LK364OBSYlkyneh1\nT7T6kDtD6FwD4lK7cXCSz2tb55qOEiityTpSVmsSaJGZgaXFRuQAlnsiLUbBKyOl6beccBYgX0hr\ndWy+FaTm0MrE9eLnkQ5Tk5cES01/i21adeyVx7oXqQtZ7ghQWiLz5QDGwcirb2uCsxiQ1R5yQGtx\npK5aXqXye21YAlSv/uVWOrnPlsfT8tHAX+60iArXSQKcFo6XUUvLOhYSLOVMzs8thiAHvDaYtQGX\nUpo85SPBUj49ojVALUhq23SsOpCsolRPsqNK3yuPw01CLb5V716baPnIyUqLy/OT53LAynrhdeXV\nj1YnMj3evtavlY+Mq7UNj18KK/uyLJMGmLIetAlFA09LPODywBJ4WM8eyFjjV973dPSYYGl8yXx4\nOpbbTYtTkpma4doiAoksjNY5LaYgK5oAhM908pANWAIVDSgt8NZmwhJQkvB0pRluxeXl4SBesxFY\n+5V6yzJYAKsBCo8rgYWH0con07aASEvfu6blZQ1U2T800dKVenIwqmFeFmDWDHapIy+jBEu5g0HW\nsZe21kc8fS2dImCppcPHvQaYWh+PyEzNcAIavmqtVQwHOzmzeYxBxpWVVhoA1gGUt49oHUWyhdq6\nsmZNnrbsADk/fBKKDmtzsOwsEcCUOnpgyf+XJhSrfNp/OXHJ9DTgs/5bv7zfSB2tsnqgLt08Wj/R\nBq6crLw6K4lWDxqRsLbLeflbk5I3png8/r8GMC3wlW3IyyX7URQwZ8osuWgDITKLyAGsdWCt4ay0\ntIEnG1X7jXbUEihoOpWut8zO/LqWbrQ8HjvggO0NCJ5OpLyeLvI80vE18JagVMM4eP5av9HCWn1M\ni9cy4XLR+ob1PyJSH02/FkbI40WZZeSeNtlok3NJSt8N3wLwrwBsAtgA8Ks55w+llH4IwHcB+NpF\n0A/lnD+pxJ/8ajOKxVhkHKtjyVnf6rARVhTtuDLfSOOWgEOKNlg9xqHpJ2dXeV2LI+u1tZNq6Wmg\nxMui9ZFS57bK4eko87dAVj4ZpEnNZKptZeKMs6SnJlqbeWFLkxYnDdZkJicVnrc2VrzJ1fs/VCyL\noMSQPSl9CvcwpfRUznk/pbQG4HdSSn8bQAbw4Zzzh734JSVKrMsCQBne+rV00syD0rPJmr5RaYnn\nNahsdMuUkQO0NFt7JhHP14uj3YtMiF5ZtXRLYBKxUHha2v2opeP9WvVlTeZykNcAppaPF0+WQxtv\nJdDUiIA3/kqTmUU2PGLgSQkoa6Rohuec9y9ONwCsAniF9IhmolW210l5nBrmV6oICyhrWQ0vA/1G\nzQZLr9b/Ejjkr/Z8vjbrRnTUBn5kQMs8KT1ZxzX135InT09OANZk5E0q0UnaYnSaPloe3mTllVO7\n77FYznStMcHrxQIgq39ZddqThHh17ekZkSJYppRWAPwbALcA/HTO+Y9SSv8FgO9NKX0ngD8A8A9y\nzneiSmrKypnYm+W8tLWZjq7L/6XZ09K9xMC8sJ6eNWARHdCA/fanUtlKg9wKY0kEZLxzi61FRetj\n1qCPMierLNbkxePKfYwRwIxIDQuzAK/mLVA1EgX2GnYpw2ttyX9bwTrCLM8BvC2l9CiAT6WU3gng\npwH8yEWQHwXwEwA+KOO2UN4SQFrXLnQNpe+xy5JoAMU7uscsvc6vlSeikwduvJwyH3nulS0iVO4a\npqr9RnWs0S8y2XA9LHPSGpAl5iL7hNbnKIx8umwIg7buWX3P0k/2Sat+WvNvEa2Pau3j1XXtJBRe\nDc85300p/TqAfz/n/CxdTyn9DIBf0+L87u/+LoXBY489hscff/xSoTSpGXAaG9DCyYFRA5AksoJL\n+Vk6R695M7uVtmUWaQNCO+dha8w775qXn5Uu10Ge13Tw2vCtcYDLusoj0o6liaAHgMo0vP7nTTQR\naZl8vT4UmUiidZ1zxgsvvIAXX3wxpFdpNfx1AE5zzndSStsA3g3gh1NK35Bz/spFsG8H8Hkt/tvf\n/nZK55KSNVIaJF6DtDIPGV8CiAeUtXlZzNlKy2ND3qxfYkIkPWZ+LV9PNJZXAsrIoPBAwTPHSozd\n0pmu1zIqDVy5RIGilA9Pj4evZestojHAyHgpjXv5K/Oz8s854/HHH8fjjz8+ufZbv/Vbpi4lZvkG\nAB9ND/yWKwB+Ief8mymln08pvQ1ABvACgO8upKMqTxJhHTUsp4X6a/lqaUZYl5WWBlIagEVYpQaY\nll6eydjKomT6sjyl8BFAjABETbvWgKv1a5VF6s5/W+u3BTBrGJ+MJ/tlreWlSakeLasnSoysMJoO\n/H9L+5S2Dn0ewN9Urn9nJHEPNGSjaw3VwkC186honZ2fe+aLZ+Zq5ZPnEiy1NDV95bnWOT2g1Mo1\npKN6OnIpAaZ23qKHZxl4cSyGZ6UZZTdaOjzPkpQmxYhYfbU0oVv5tIzTmklMG3cafnh1ok1mpYlQ\nylye4LFmzJZZLEK7h8yOJXbpgaQlvcBSS6s0Y2r5eJ1Ni+91tCHtqAGa1oalbztJfUt68TDWEdG5\nNGC9yZaHrQG9UlhZf1rfsiaoiMUn84mGqR2bVt157FvmVWs1SLG/ot5R/vzP/xxAfWckKcXTzlup\ntsxXi2/pzk2XP/uzP5v6L80a65523xIexvqejQXM1kCo7UAkzz//vKqfJ1a9Sl34vdqDvxfAekeA\nNri8ODlnPPfcc6H0S5O5NQasia5mIrXaMNIHtXy0NvbKZ4XxJhRPSpOdlqYcw/LlGpEXOJPMBCy/\n9KUvXbrWCl7RzqgNhJY8tfxLklLCF7/4xan/8n7kl4f3GGyp01vAaIFxLWDmnKfK20NK7UrnpYOH\n4/FLA7uU7vPPPx+erEvl866R1LA8K60I2Hj59GzjUjtEgTFSL1bba23myUzA0pKh4LWUpSxlKbOS\nuYLlVZQh/s+rIl8PZVzKUmoljcXuUkpL2riUpSzlyknOWWULo4HlUpaylKVcJ1ma4UtZylKWEpAl\nWC5lKUtZSkBGB8uU0ntTSn+SUnoupfQDY+c3a0kp/WxK6aWU0ufZtdemlJ5JKf1pSunplNJr5qlj\nb0kpPZZS+nRK6Y9SSv9PSum/u7h+LcudUtpKKf1+SulzKaUvpJT+8cX1a1lekpTSakrpsymlX7v4\nf63LW5JRwTKltArgfwbwXgD/DoD3p5T+7THznIP8HB6Uj8sPAngm5/xWAL958f86yQmA7885/7sA\n/gMA/81Fu17LcuecDwE8lXN+G4B/D8BT6cEXA65leZl8H4AvAKCFjeteXlfGZpbfCuD5nPOLOecT\nAP8CwN8dOc+ZSs75t/Hw7fEk7wPw0YvzjwL4z2eq1MiSc/5KzvlzF+d7AP4YwBtxjcud9S8GXNvy\nppS+EcB/CuBngMlXEa5teSMyNli+EQB/fOcvLq5dd3l9zvmli/OXALx+nsqMKSmlNwP4ZgC/j2tc\n7pTSSkrpc3hQrk/nnP8I17i8AP4nAP89AP4g/nUub1HGBsuv+31J+cHerGtZDymlGwB+BcD35Zzv\n8XvXrdw55/MLM/wbAfxHKaWnxP1rU96U0n8G4Ks5588C+re2rlN5ozI2WH4ZwGPs/2N4wC6vu7yU\nUvoGAEgpvQHAV+esT3dJKa3jAVD+Qs75YxeXr325c853Afw6gL+F61ve/xDA+1JKLwD4RQD/cUrp\nF3B9yxuSscHyDwA8mVJ6c0ppA8DfA/DxkfNcBPk4gA9cnH8AwMecsFdO0oPnIT8C4As5559kt65l\nuVNKr6OV3/TwiwGfxTUtb875H+acH8s5PwHgvwTwf+ac/ytc0/JGZfQneFJK/wmAn8QDp/hHcs7/\neNQMZywppV8E8A4Ar8MDP84/AvCrAH4ZwJsAvAjgO7Ly9curKhcrwb8F4A/x0BT7EID/C9ew3Cml\nb8KDBQ3+xYD/MaX0WlzD8nJJKb0DD77e+r6vh/J6snzccSlLWcpSArJ8gmcpS1nKUgKyBMulLGUp\nSwnIEiyXspSlLCUgS7BcylKWspSALMFyKUtZylICsgTLpSxlKUsJyBIsl7KUpSwlIEuwXMpSlrKU\ngPz/QCjzUOP0/xAAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa5a94a8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from PIL import Image\n",
-    "img = Image.open('stinkbug.png')\n",
-    "rsize = img.resize((img.size[0]/10,img.size[1]/10))\n",
-    "rsizeArr = np.asarray(rsize) \n",
-    "imgplot = plt.imshow(rsizeArr)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "上面我们将这个图像使用 PIL 的 `Image` 对象导入，并将其 `resize` 为原来的 1/100，可以看到很多细节都丢失了。\n",
-    "\n",
-    "在画图时，由于画面的大小与实际像素的大小可能不一致，所以不一致的地方会进行插值处理，尝试一下不同的插值方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa5b2908>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "imgplot = plt.imshow(rsizeArr)\n",
-    "imgplot.set_interpolation('nearest')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Ja62jl/ApXW/dFlrL6m2391vbr7m/0g6vP6Si5PdVcrLkZEjESf9Xbj0D3uuV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\nHVnusMMOOySwI8sddthhhwT+Pzl/C8KJDU7hAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xcec14a8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "imgplot = plt.imshow(rsizeArr)\n",
-    "imgplot.set_interpolation('bicubic')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 图像基础"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "导入相应的包："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.image as mpimg\n",
+    "import numpy as np\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![臭虫](stinkbug.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 导入图像"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们首先导入上面的图像，注意 `matplotlib` 默认只支持 `PNG` 格式的图像，我们可以使用 `mpimg.imread` 方法读入这幅图像："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "img = mpimg.imread('stinkbug.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(375L, 500L, 3L)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这是一个 `375 x 500 x 3` 的 `RGB` 图像，并且每个像素使用 uint8 分别表示 `RGB` 三个通道的值。不过在处理的时候，`matplotlib` 将它们的值归一化到 `0.0~1.0` 之间："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('float32')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 显示图像"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `plt.imshow()` 可以显示图像："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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R2mCIcX4rrcWaQ/p6SZtFH03P9+FqOkPAzfPGvhCE6/a6+Dm+hKOVKwVaqR6erUrlS5K1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VIpCCH7Qv3NVBJXrlxBLpcLr2IBDgCx3W4/FGBNhqesfHFxMZidzrkANqPRCIVCAYPBIAik+rgJ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\ndJpO02l6yukUWE/TaTpNp+kpp/8PTYoQ8rA9sPAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa0cc5f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imgplot = plt.imshow(img)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 伪彩色图像"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "从单通道模拟彩色图像："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0khakZoqa53ayKDgp6NhAdmA4MPnd8q9qlttrHJY3AFFgKpmxduJbjcua13pc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JV67Ond5x5IjNEPuMlIs0sxES1ClsV4ls9REUj8piEkXps6cUGDYPaJgJrKjEqbeWMSZllkam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\n2+yGbQPWzTbbbLMbtv8GmbHHpC2s7ygAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa491438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lum_img = img[:,:,0]\n",
+    "imgplot = plt.imshow(lum_img)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 改变 colormap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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8D4L3bTthlkn8rjbHs+4ThDN3VrHKmjh6zFhk5ZK5u58DnNZ5aADXmb28oUKq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\ncwI053Lg4uv5oZs2+INnXsj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q4UAf1XmYR+PsXod41OL10IIbTa8/x28NWBlz4EmbPOdV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4D74Za3Q+u749Bc6mJqOPRXMPoA7P0w8IOp8gvRZ6OQH0lzU9695CUtBt3gL/m/kBMUcmytFj9p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\naJom0+TibCB9rWfGguCbFoP4zxOLuYUAN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pFOBzdOl8AXq09uJJPGPatyhgS0k+TpWuvI\nSjOFiRFgN5PZD62M4wwpSATLM6Qvcz353ViLZII3/fNVLPUfmtp0sr9XQDvZn9/B0m+dcKNCasAr\nyQodz6SwMu3acq9+dM8yWEtmqQhqulwsOjFtbWsf/yEybW2SPLbwAOlXNeA0SYKKGm4vuLiYzz1J\nnnWhZimgaIEIqGtJwSJozJAamvQVXA3cCPqrCL5QY3T8uh9O9s8Ezlnya2LUhDeQftuzZMqkgG81\nlkUVi+TX1hwnDyxyj5hGpY/f8crfs/0++W2RPIfkApmu2cgT8VaTvusT5BkRU3FP+94rFVj/AaH2\nv4fQenaQNd4e+PE0QYS39+s3ESk/cyzVfHRQmwi8hSDqDjJZezOxqC+W/9WYzG+EPBV/B8F4NxF5\nfm8jFvAgeTrVcXIBV5NSUn+dPqI3E5rh6v7cpwizWT+eeXzHyRJDzbCzpFaopqIJvKE/Y9qKAQjL\nBRUaaiJ+5mEa08Rmf4HcpGv6zwIB5Nd0mntYjsEdfXpurpNkorXakiXDawnBeI780rsz5T7zez1s\nZ66Pe12/3xLYlWTamUn5T5Gn15vSJDh57wnyOMEJEkw81EUQ3zEB5+ZzjdXw9JPqk13dn9uyOyZw\nah88sZBFJKfJr4geJwTuWH/vUTKoKM8xQjs1KIM93megRoHq2cRGyQ0o0cdrXvcJUqudJvhdkNNX\nCSmYLAO1klFXQCNA0jUSVBWqat1WPymgNN+tUvOdBqUaWW59gDyMZSWpiKwlBcVJln5Xm3vIsmhd\nGYsk2Cr8dd/oStFFMkkGu+ah/ciVetC1E/ksEbzR/2mZ3F5SdbfG/wSxGEfJdJGed3bxBKHnet8v\nkeaBQLeOMD2smDH4YsrL48RCCS6rSY3xGdJvZE6cuZKnCUYyYuri7ial8dY+TquCthLMZuoJxMac\nJrXKlwkG2k0WEJgnuUhoISvIDQd5YLGJ7gK+2rCRUE2qjeThI2qumogbga0T8N75YHi1wHGyKk5T\nVTfHBZZqdybJayZKKzVbI+ZGmDW9J0mz2vGcJzWhGgH2EJ+VnUZmNHj+QY2WV21Q36NCZpjPg8mN\n4uvbHMhUqilgzUS/8AqMLcQaCXoGp86Qm34Nmb6mFul5FtJeIa2mOJDmqVkfK/p81Pj1gSrozpNW\nlS4YfdarS79r+lq6DwUwNfkV/XMtnxnSetD3Camteo7G0OkvcKm3abYLhBNkGWq1oAxaLZKpbwoh\nrRKB2r1LoRGkgJ4tfejnt0rL9DOFrEUW8vHrbJcPWG8jiHqQMKUEqTvJlCNz6AxOVXPjSWID6KxW\nat5EJl6bRKz6bw26IHeG/AoU02tOEwAPGcnV33OWBBdz4XSgryclqoEOK6m2kd/7JGMrzY/2Z68h\nNQ4DMNv6/8dIDXQbuRmOlnEYPHMjHuzzFEDdbBfIAMN4H9c06SdV01lJZ9AGaxqsHTIFZnN5xwWW\n5l6uJE34ebLEVTN7DWnemWoEubFNWBfwIE1Hs0DMINlGAtYYqSFbEroZmGpwcuhm5xhMr43BnDkH\nJ3pSv9qypbaC+0T/3xLlSRIYViz0hfLQAFLYQZ6u5qlrmqjny4/anAfTqJ0a4BLMTT9UAxc8N5Iu\nJ5/Vr63JbQpZIwHjGlLDdwyn+9g1iQVv81FnSDCXNvqwBd85UqNthY4Kxgsk0Avuhwr95VXdEtKx\nHoBjSqHCzrxXgdLgnPfJy66NwkuFpu4JhbNC43W0yweshwkAmyWrcI6SeamC7GOEz3AgN9w8EXwZ\nI7W3A+QmHyNNaZlQqawGsY5czNXktwlc03+OEoGpVQTg3MlS7cISWgMo+n00YU6QBzmfJ0/dkvEP\nkZU6+iNXE5q0gKeTXg1OUNZvpe9Lab6/96GpfpoQJG5Aa7VfJJhMTXOKTFYfJ78naiWwdy5NJN0F\nSnfNTghwUCAaxaXQRj+iQSzIDXKcBH77NrtBv5+bXdAQtLYBs2OwtkWp84VFmJgIgTDb1dPNi13o\nTEDr16bHYMsEDHNwtgf1jpCg4FjlD8hg0jhwfoD9M6kVKrwEt1UEbxp117y9wFJfsEANKXxMWteX\nvbKvh0J2Ban5a+2YpiTIqRmbYD9PBsmkPSwtLtBnrEbtCWmU31XgzZIuBYFOAFQAqsUqXKv7xyCV\nCtQ5MrgKCZwqIq4JpV/HoU94ul8bL/MUrB2P2Rhq+/Kt++ksr7tdPmB9msgtXUGAIuSZq+cIMNME\nMGAh46vyb+nXNpNSbi/pOzEiuJFgTiurqulbI4fXEIDne9Qop4jI+Q1kjqfEN7o5QTC95ooaxnoy\nqnyBzDOVcdeQOa9WcFlxtI40oQ73cZ7t9xq19sxaCCvASOf+/r6TnY6byROTDJqdJHM09Wdp3p8l\nN/oJ0q8lc5vZYO6mzKk2KjDqT1RrgvzqEpld7Wiy/38tqXFYDqlGYnrNtnGYWAVtgDVdpRlmYYXm\nywZYMRmLMr8fZgZoF2BqLkpQGYe5OTi9mGOytFTQoc/5xT4fS0chz/PVVaAm1gvMLq7p+fK/kWgB\naCX5/ViCq9q/5ixkNFxLZrb8P0ZaStVlYDpS1fwMbL1Mam0GBl0TMzv0pU+VPixLFdwMKpkp4PxW\nFroo9DXpx0mNfZz8hgqFKaTGKFhrofl8BVWVARUChb/ZGwulj4XyfyO/JcEcX62mSxB2+mOBtbU2\nTaTq6wX79WEYPtZa+wngR0jD5+PDMHyiP/Mx4If7FH5syAMClzYrLjwh5wz5bZ8GaCSmIKImSL9n\nC8EsAuEasm7fQyB2kX6T63v/r5A+O9NgdpJRYI+H0+wymGBOoeazWorRZpPvTflwA5ikrJlrCpIp\nKqv6HNRQ1XIMCJ0gAVwaGYAyAKEvWXCoidbV/2Yg4No+dz9zQ1l1IwCo4U+UPo6QpycZjW6dbkr9\n2fI+XQeaXJr2vkNw8L1uwhqIkV6zfd7HF2DF6QTsRuTrjp0kvo/mFVg8DWfPxzsFg2GAsQHaYmow\nakMVDCCj2rqj1NAmylici6b3UPp0wzu+9aRgaqVv1wDSreHc7U9ftPzquwUJTVz9/4KLwloeMare\nynMzZPBLc726ZszJNZ1JoacCAEv9utLRACLkVyDJ87pGpKPrrzXjvHQnOSfjElW7NsNBRWyqvMfx\nqAm7vgrxqs2afVG8O99o+2OBdRiG8621DwzDcK61NgF8obX23j7EnxqG4afq/a21O4hvVb+DgLTP\ntNZuHYZh8VWdu8jWGGsWmyKhdqMT2wCA0sc8Uc3m6mOS4UzfWE9qXqZYeJjFBRJsfcY0nCnyRCw3\nwGbSee57NXs0l0z/gNx4BlBcXDeYGovuC8Fcjdb3rCQjraaKGO1US1Fz0V9tqpRa1hx5sLUaiub4\nOAmqagr6kzX1jLobYFjF0qg+ZEK4Plb9qOaNVoFZ/WmC1oryM136V0vx3QpPAWScOPBlYgamZ3L+\ngopg5waTD7QI6NcFAsG2RsC1OMyvNJtDoBBcBMXqB5Q/qqZrsEu3lJFud4ugLN01WwVh10UNW17R\nxWMkX7CUFq6lgk0emCYLF/TPCujuB/vSZNb/XAWLGmf92z2uO8Cse1fyAAAWKklEQVTxSQeVilGt\nUheU+0y6KhQFT3+qa0KBJ2a4Tvbvs+57hcV/amAFGIahph+Pk4kkr5WK8D3ALw7DMAc831rbQxSR\n/cdX3Wn6kEDhSTUHSX8aBDxbJ20QyzQmiVK1H8FqA2H6uvk9sFgwcQE2lf+NuFZ/zBT57a/6Yk3Z\nkdkFTyVgBWi1rZXlmnl6a0nprkbsQb9quwogtVy15KrRqU1CMiYkILv55jsd1Goggcs+zFFcy9Iv\nXoRMqzIKfaH8fZoUHAKhJukxMitAjmss/drsUfNbk2yK1MLVwGt5o/XdjkO3gvOq5p39QmrZuiKk\n8+nyDn2dFSylcU2XUsA6Bvu2T2lbsxJq5N/Am0JIN1PdYa38licVkPovpX0FUkFG99RC+e271QC1\nfhSEfqaw0efqvpsjwU3N2jFWgaiG6Gf2WzVz5zdWnhOAq/Zp0Ku6I1QMKqDXAFjNxfW6FiGkoPfZ\nS+Ag/bpdtNbGiELMm4GfGYbhq621Pw/89dbaf00ccPY3h2E4QRjUFURfIivwlzYrYmRcJ7uOmPBW\nlqZrGMDQHBEwIBfAjVpNFqV9DSrUIIkJ05DEdQPar8S+UJ5z82p2G52WsWVqtSI1Vn1tSkhzcPW5\nOU8ZURdIBUn7VlM2NcnN61w0d9zobpoq3f1tUEhgUGupJnL1IVatw3WozC94mHYDCcRVO65R36pZ\n1kTvapVUbUqtBNI/p2CrQs13Vn+k41MrVit1vc2ZFXzt1/FKF0FO2tUdpbnPyDVdKo7LANNQ/q85\noioBNfXLZ10P51etCtdEU7/mnCr01OBdR1OdVDQEad0V8l/V8t0L1Tdqf1UxkBdURqrby/50Q0ir\nVu6pgnOyPFd5GXLtpacqocLdORrUre4gx/A6259EY10E7m2trQc+1Vp7P/AzwP/Wb/n7wD8G/vIf\n1cVrXfyJ+7lIiPfvhvffTBJnA3k4iD4g8+oEK90GOswFNZlUZveZSdIHJZCbaWBE3oiipj+kZNcM\nEmwEg4Xyo1nkYvpu5zVf+lIrqtqZvsCqfVamURK7aYzoOj+DKFUDr9K8+hIXyQDGLFk2aU6hqVvV\nP2lmgWOpWpmb0LnoA5NOvn+mPKs1IdA6Xl0b0kINR4Hk5xUsBTuFzYpyvW6sqmHpPqq+1eoTXMFS\nTbfOyz4ULq5R1Zx0b1R/9ag2qmDXqvC+Vu6BpWlKrh/lXsdeAc+107UlqOlC8pkLpV8BVKEiH/gu\n+cq821Hz3jWXR7WUpJ+gKd9UwV01YMi8cfeMNK7Bq6H0K/2rbxdSeallwgqE/r77n4mfi5r462x/\nYqV3GIaTrbXfBN4+DMP9Xm+t/SxxoidELPq68ti1/dqr2k+8j6UObAFoLbm4OulnCMC0/LU6nUef\nN6oqYxr9hzTJlcyQWqQa4grSua/vS7+UTG/E0XdWp/0oGJhLZ4qNjGHte9U61CorENa8PAFOxtVv\nNZDBsvnyrpPkV3Tbp1kLk6SwUuutwSPBGXKjSXdpAOl7UwBZYdPI9C6dSc7Xv2dLv4LFaEAIckNX\njVE3gMUBjlPTd4bUck1bEhBrYGh0g6p1O4/qwlGQVJ6j9OtaaSUIaOfJVMEKEGpwCnpTpGZ5baGq\nhiatBCBpJY/6Yx9aDu4JtdfRtDdBVyCTLgogQVQ+1LJy3wmegrunl8HS9Cf3izxmxN49pODVZ+y7\nfEaNXD6pERxp5ZpokVQryz1YUsvefxu8/0YuFqX8vd/jdbWvlxWwBZgfhuFEa20lcezy32utbR+G\nwXjgnyWyTSG+Tej/aa39FOECuIUoCH11G92QNQgwS0rx2XL9FFkGpzluX5raalkDqfLXDWnOZGWQ\nGrCRSReJKHcFvMWRe2T80eh41QA0gyo4jrN0c0EmyldNpQKv/VTtUX+vDKNpqgCQ4arPqvq1bJa5\naioK2FVbV+urgSw3pIJLi0CBovByfAYH1K7h1dUuVdhqqnsQiKc6VUAz2GnARppUi+N8uea8akQd\ncsNWgSZNXWMBFnJzutnd0MPI/xUonHs1WX1nDSKpNKgxCjrVVaVW5joL5KO+z8nyrPOq1obavfRS\neCpUFEqVNgKa/U+W/hbKPabc1dzTCoIKPt9r9N+1EZ1qcKkKlkqLGijzXZVmNRimAB9dMy3I14oe\n/Snb19NYdwA/1/2sY8AvDMPwO621n2+t3duHtBf4bwGGYXiitfbLRIbqPPDXhj/qMAKjxGpvmu5K\n7tGoXgWpMTKYYWWJGq7+PE0hiSk4QkpsF89otU7xmhJUTc5K8BogqvdBbkiBWA3EjaXLwEWGV0nQ\nJZrWbLlPM8U0laH0IzM5X8fneGU8N6TXpUPdIF7TbPRaZdiq8dqvfY+T5qKgOqqBmVanOS5A6CJQ\nA6oC0PdVsJku/QrIsFTQVf+uoFOBRsFp/xXAzemtm1SeGEofaqV1DKbrzZU+qu+7+kQFboU85LrW\na3WNpEs1213TleVZ71PALJZ7qw+3lXtqeW21zARLNUrX0zFVIF8gram6zkN5P+X5yfJs5blq6lfX\nSs0sqEhj0LCm6o2zlIa6pubLT6XX62iX7xCW/4WMPlefRpWYaqxn+/X1ZAndAZY6t404Vy0EcrNU\nk7Omm0D692Swqs26WWpgogYa1JCV/LUpDEw6F1jV0BZZ6jt2vIK6+Xj1twzqvGV2x6pfqgKD/crE\naq0+W10RNbAwqmG4+eZYytTVpyVYKMjUMkeDQTK+Y9T/NZqxUMcqX1wo75gY6VsQh9x8zt81o9BC\nkKkBi2p2LpYf32urflGFStWWVQC0YPQrV9fVROmruhzkAUp/jtt5jfPqOU6WvxdG+qjgKH3my/MV\nrHQVKbgqmBtorM9Ie0FJa8U9rOWpa6FaKfaj/7XyqXOp/FszVZxjHZ8g7ZqMxlzoczCdUUXKn3lo\nP80VeghL47X9TnWBJFjVSD3OTQCk/10joTJiNb/GRu5dYCmDQ5qR1Tx0cSp4qsEZhKqmXiv3ybhq\ntX4+CoyVkaomMUmAxopyXS3O32rztbxwLUvTpyqgXyCBVM2pbtw6jkoDTbuh3FO1UAWFvjotj0rP\n6rPVHSHD1wDQXOljRbleXQj2oyBS+FVz2rH6f/V9Vu1abUd+qBvXNdbXr8Y16terPkjnWt1H1fyf\nLNeq6TxT+tHKodCxChPpLhhVEBGEqnVQ/14YeWayXJMvFXxVM/dH91TNUHAeo1qo6yqth/Lb9XF+\nlGvOb4alPKcVWgG98hgjz8vjar02NW5BtWrci7zudvmA1U2pb7GWzlX/lsRzI6uNCLoCRTWBJXT1\nqegaqCfg1IDB6EaDpQxazVH9kaY4wVLwVnNxs2oCyhBqXzUVyrn4nD7NVvpeLNeqqQXJ8LoUDOzo\nO1osn8FSrcg5VVPOZ9RQqiulmsxV03Lsru2oVuIGq0B6nqWFBtWHXDdE3UyO33WyPFJBNlf+rkCi\nf9vnDbhUn+GoRmcBhgFBf9d5VN9y9UO6pnW8ZoN4v/7LmfKcY61uFGni7+rrlk5eE9xdA2k3X65V\nX/yotidPm45YfcXVHSN9pJfKkJp95U9b1S4NejkOAb5qxozQVZrVzIw65lHXYRVg1fesAK4WSd0P\nr7NdPmDVX+qBEoJRTYmqzQoQCa8mofbl4nhkm1K/MolAZ06dizCa3qHpUMEOcsHGSO3NzTKqdbmJ\nZWLBuGosbmLBQhO0BsLcFEPpp2rYbnQj4zUYUSO91celhkHpy/5k6KqVjWoidVxuDn3DdS7Vp1XN\n+6pJuybVbK3AXJPhHYv9VfCsPkzH7lzq5natreQRtKr14Jgd0yhAVxO1RpmrS0Ea10BbBV2vVV+v\nBSqwtGTVvtXAxkofavRV87KNCgBYCsAKLzM06hp4n2Y8LFV2IGkp/XV5VH6pQFvdLdXKs9VyVsdf\nfcBaMNWShVS4Ko/V92hpOGbHWelbM1FGg7vfQLt8wKqmaTWHLgHrhquJ6CacIw9MqIngFVhNpapS\nqjJ4BaGqIY9GKGU6TZ5q9latyVbB1dQb+68+H+/1d/WhyvBqBmMjz6olVK26pvpUU1qmU8BUBpap\nnaPA7TxrSXHVJmTwCm5V4o+660f9r7A0Y8LruoQoc6vFFpB+1sVyj3MdyrPOpZqdtjqOqjFVE7D+\nVL6rfs2J8nf1Cdcx1Gujf1dlQEGqMqDWZ3BTwHM8ZsgMLAU5gaP64BWu/l15pK535etRvyvlfjVF\nrzkmf4+C+lA+qwEmr+u+c70d9xRLQZ7SDywNEtec7ToeBUN9dwXQyq/VH/sNe1WXtssHrFWKWA2h\ndJwiARSSSNUsNM0Glkr3qk1Us1Qgr1+pAUtNYZnc6/W35xTYZ/UDVrN0vDxTo9eOZ5QB1UJkFjXS\nuhkpn1dhUBnIsar1+17HoTCqPkU1OjWT0fSz6jOURlXLGt2M1R1RN2elCeV69cFJG81xRu7xOTdk\nnbMbrOZF1k1aU6sEEQGyChd5BZZu/KH0VU1nWGpZtNIn5bkqCEd5xc1c+bmCWrWEnG8VTvJDdWf4\nnoXyrL7GhdKnrVoPArvjGHUn2LdrVpWFUcFax11dDdWyqwKwuktGheSo26vuPcdU318tnFHhujDy\nzDBy/yXICri8Gqtg5+G4TkpNpPpWbS5+LWtbLM/UoJTPVU1F7aumkVRiSnj/d3PVEtqqJbjhqhSF\n3LD2Vc38OZYyZGUSN++oWVc3h8AnQwxkabAuFgG2ArqCq4L2KOg551F/mpqd7oiaxK4ZW/1atprC\nVTeZ/bp+NW8TXu03k19g6cZQKFTzrQboquakllmDKfJM9RO6vh6d5ylUlM+q1l/XX/eOAU61pjq3\n6j5QE3Q9K28JeIJdDRD6f/VJVlo5V3/XYGd1VzinqllWejkWhU/NqhAgawaDn0kb+aO6UaR7VXBq\nPrbgWs30igH2V4G2guSor7m2KlheSxBcAlCFV+tFb1yrqSxufMFSYtZ6aXi1iVUX05N2LHms5ab1\n0Imqmcjso+aJR97pMxxjqQ+qug7GSt81gAEX/br3P1HGZhJyBfJ5lo7V/71PxtKPViPUVXt0HNXX\nCK82a60d9wzNCiZqz6+VlrNQnqV8Vnx193+V3CR1s3ti1WR5ZvRz194NWbWaunFGgdKxTpafukkE\nv7HSl/zmutSgV6UbLD0oRM19Zfzc/9VCM9dyscyn33dRc5Zm9lddVqNRdptAUcudx8s1eb9aCu4p\nwUpAHQ0Az43cVy2jhfKzCPc/TO5JaTOqvAiqdfzV1bVQrvlOM1/8vGZfuD6um+tejwGs2rzBwbqf\n5HlT317LfeTfoyD+DbbLB6zVTPT/Uf+ajFI372hQxw0Br/YNVbO9sRSkPIDCk+0F1VrdJLBdKPfU\nex1T1ftlVvL++79c3l03kQBezU3HvVDuqePy62E8K9TP/K4r+/as2ZP9d63RHwUVmU+6OF/HW0HZ\n+72vMvsC3P8QS01IQXNUo/CnairVPK8ms+tYNvnFzVDNVrVax1r93K6ldHUuC+VaBdW62SAtBbXO\nPrb7H3wN+vj36JhGebOat/ZZXUI18DRe+nANK0A7R69JZwWNbpxRf3BVcOq865y74Lr/8X69rh+v\n8Vydi8+Pl5/q3zZY7fjnRn5XYVI1aP+vLo+6fj5bU6rqPaPuhhqkuwTt8rkCNOProRmCVNUkK/NV\nf1PVwtxA1Q0gQMPShfG5mic4yVLQrNJyeI1+6mJovtd8VPusQqPO5UK5pslqFkHNFBgFkBqdHW1V\nU1CjrrSrwCHw2jxYpc5R0Kxz9zg5W10rSM1wdbmnBmkg02so1xyfGqZzmS/3OI/RdLsxsvJKukj7\n2fJ33Yyjboc6hvouAXcUjPxssjxX175Gv0fbqEY0sLQCz3hAFQCwVFhXP2p1mcDSAEzNI647fRTg\nL4x8Nnp/dW+NFsGM9ll92PZffcpV4NbMnD+uVf9sBdFRV0Adh/dVgaFmXt0Y1cq7hO3yAWv9ehOB\n0E1QpWElXk3voPxdJXn9qYtmYEPGrMBTXQW+s24WwbBWQI36jXQFVK2j+tE05WsEeiC0T32n9eyE\nyqiUcdc0KudfmWUghVJtVWOzSfeaJ1ndGaP2zOj/VciNvq9qij6rhmrzXW6Mms4zqk1JkxrBrvOu\nYxzdONUfXccmLapgHvXvDSP9CWaNdNvUfEsFm//PsfR8gLqBq9VVq73kgXomr+Osflmv0a/rBx2l\ndaWdPFlzOIeRvqpGOl6eq1kF9bnq46xWwmhzjxuglCY18CYNBfzKRxUo3QeVJrbKE6NBs3mWrmet\ninOMXw/o/wTtspW0vuEvXW7Lbbkttz9Fez0lrZcFWJfbcltuy+1qbpcveLXclttyW25XaVsG1uW2\n3JbbcrvE7Q0H1tbah1prT7bWnmmt/Z03+v2XurXW/nlr7VBr7bFybVNr7bdba0+31j7dWttQPvtY\nn/uTrbXvvDyj/sZaa+261tpnW2tfba093lr7sX79ap3vdGvtgdbaI621J1prP9mvX5XzBWitjbfW\nHm6t/fv+/9U81+dba4/2+f5hv3Zp5jsMwxv2Q8Tw9gA3EPHBR4Db38gx/CeY0/uA+4DHyrV/BPzt\n/vffAf6P/vcdfc6TnQZ7gLHLPYc/xVy3A/f2v9cATwG3X63z7XNY1X9PEF+U+d6rfL4/Dvxr4Df6\n/1fzXPcCm0auXZL5vtEa6zuBPcMwPD/EV2T/EvGV2VdsG4bh8+RXgts+Cvxc//vngO/tf1/8evBh\nGJ4nFuedb8Q4L0UbhuHgMAyP9L/PAF8jvoLnqpwvwPDaX/9+Vc63tXYt8GeAnyUTkK7KuZY2Gvm/\nJPN9o4F1F7Cv/P8Sf9TXY1/ZbdswDIf634eAbf3vncScbVfs/FtrNxCa+gNcxfNtrY211h4h5vXZ\nYRi+ytU7338C/C2WZgZfrXOFyFj9TGvtwdbaj/Zrl2S+b3SBwP/vcruGYRi+Tt7uFUeT1toa4FeA\n/2EYhtOtpdC/2uY7vPrr3z8w8vlVMd/W2keAw8MwPNy/4v5V7WqZa2nvGYbh5dbaVuC3W2tP1g9f\nz3zfaI11P0u/Hvs6lkqBq6Udaq1tB2it7QAO9+uj87+WP+Lrwf9zba21SQJUf2EYhl/rl6/a+dqG\nYTgJ/CbwNq7O+X4z8NHW2l7gF4EPttZ+gatzrgAMw/By/30E+FXCtL8k832jgfVB4JbW2g2ttSng\nB4ivzL7a2m8Af6n//ZeAXyvXf7C1NtVau5E/7uvB/zNsLVTTfwY8MQzDT5ePrtb5bjEqXL7+/WGu\nwvkOw/DxYRiuG4bhRuAHgd8dhuEvchXOFaC1tqq1trb/vRr4TuAxLtV8L0Mk7sNENHkP8LHLHRm8\nBPP5ReI7Yy8Q/uP/BtgEfAZ4Gvg0sKHc//E+9yeB77rc4/9TzvW9hP/tEQJgHgY+dBXP9y3AQ32+\njwJ/q1+/Kudb5vCtZFbAVTlX4Ma+ro8Aj4tFl2q+yyWty225LbfldonbcuXVcltuy225XeK2DKzL\nbbktt+V2idsysC635bbcltslbsvAutyW23Jbbpe4LQPrcltuy225XeK2DKzLbbktt+V2idsysC63\n5bbcltslbsvAutyW23Jbbpe4/X9nGwcxiuGaUwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa6a04e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imgplot = plt.imshow(lum_img)\n",
+    "imgplot.set_cmap('hot')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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lbQNW3m7Jo48XIAXaDXAD0iS6T2oL93Ukl8r+1iOKUwExLtJCL9TZ4m/LNK1d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ZOcUt3O0DiMWcCMgYUlKxwieMlcWDJNUbZPhFkuzg2B6OqZimc0wV5aSiWVyTJC74XDL1coVkuv\niJF0FIDYV/WihahLNNNQ7GdvS7WLB39ffptYmBmCLbXUPsqodLahFaPmIouKIDxzyu3SwlVIxnor\nCcvTx3UQdDc2st7qcnoMzBam+JvQfs8nxpjDgCcDvwKWWWu3+13bCSutHARsUadtwQniHprCAZTd\nlMOsBmd5qDg0IiYRQiKINF/q3/1IZkS9lC+477LOuyDR+IVqG5aoQ92kV/WQmVfsrbHXs3SsqJGm\nV+DL81Vzx0gyqw90w4ATlVG3QcKmxBTQSoLqr+1KMRqIqZ0GFBILiiwSMjB7CbYscR7rH+fwiUn4\n8hxoJhm0VjM6AmNDQBuefXgTmm+G4UFXsWftNBzyK9gB8++GP94M+SCw7L+gYWA6g5NvheE3g6nT\nzYapMMUz7r0H5i2E7hCM/Tvz1sKeGs7+NAQ04ZODcM8k/Pe0ywaTZVO0Ci7osFDvU3oD7UXd959Z\nqt7pLMhSn6N1U202kNJ/Jg+mBTEvyD2kfJ7wZGFfn+WdynsX2z/0D6fqJ8xK+5OQwg3h/HjM7IuK\nVSey3ibvizfFRyL2XC3cpGaHiY7Xpj/ov7LtY6H9EqzGmGHgcuBt1toJvc9aq+fXftR331zc6gEV\nXJhV02+fMa6wSilGM7qCxaWs6YeQB8lNWf0WklhVbToQe6gurKuN4XEMqz6nlA7o7yX50zKT633a\nDCCfuiaBtFdsYjq2legaqXUCVUKpEmCqGtTzrvE2swhhSD+IdzgjMKAhDK5CPcoDgs7VdYpr2WAW\nKKEW/08SCnRBEOnTrnHpkJUUnrDQVyv71VLXkiqMDAAL4PzrgaMvgQ0fhrkVWItjltZ5XGbh8Afh\n67cuhYEZOONqsDfB17q+gnqbaebTZJCPfeB2aD0DHtoG637MRTfCh7q4rJS5VdJdQ9Sr8JNdkLX9\ncuney184cmx4DqAoQiKea23bLPpC8ZB4yYu00KQsaLVAzJQdt8QkBAQdo0Mphyfe7mICTUIOv1wm\nLkKkx8psoXU61lQ0mHj1Y2mTfpbZSHhLxoiQtrXqSyQEM5icI/bUUqKM0uS6Ud9LbQN9vlxbogX+\nn8SxGmOqOKF6sbX2m37zdmPMcmvtNmPMCmCH3/4IzlwqdLDf1kNXv88F8wMcuR6euj5kUTWgVNYv\nN72CTKpapaqTLRSBzvLbEJCbFmSGshDWs7WQLMMsgkbCqoo/9RJRxxTtQHk/KddkBeWR9ReMU1SL\nY224nmSPNDKHBiXMTGIPU4Lg1PyhhZs8W5KGvqopVVTuM5M6W3Y36e9ok8w07QyTAZHkZYEhqrD8\nzgzMb8NQFW6bhNcsgEVHjTPy0BgY+KdB+EID0jr8yyEbedNDf0p++4u54ohv8NHj4fqf3cY9u6Cz\nEtLdO8gyYNEb4cUVsJfAd2+DY66h267RrRioPw+2fh9W/wlMj/GU1XC1BVYD9yesP3uKLRXI5sDT\nFwCTFAH+Peo75d8Wikwneb64v6XPW6kXAnk4togHTsrvXne4TSinxxrfPuP7OqGIHy142JTNY7qA\njg2H9KjrCWBt+Rjh95h3+5UerOaQeORcOIiTcJ70j7Z8JHEj/M/YH1qxYbKQeGtxChfrefljC+ez\nR6rtpByL3k2CU/qma+GGn/C4kbE27ha10xiDs6GOWGvfrrb/vd/2UWPMu4H51tp3e+fVV3F21ZXA\nD4E1NrqJMcb+nXWV2zQN45GsZdZ6qTGJYM0JMw74TBN5uQptGAIS3NeMaggFe4FQqEQJkjioX1Os\nxguqi1dB1SQFRORZ5JBSrKp/NklSkBhTnbQA4RnTvIw2IASMQ4iQMID1jiWZzAQZt41aKoegRqW2\nN+d6f0jsl4sA7oLPngrzEvg3C4cAP+zAjY+6AT61BC4YhFPbVW5tdPjvO+B3D4f2FvjkSjhhAA4f\ngbt++lfAHHfSQy+G5RuYc9ES3njdXXz8I9th8bVQfwIc8k+sXAePJMAjcO4qeEkF/iKHxQa+1IHD\npqHRdH3RswzJXqjtEeNAtz9/tRLH28JD/cIBwXvTkzC5S3k/vVKB9qDrFU1jyo1rTzFRxny5l3Gg\nEV+cqCKxpf2oNP5yhUA9z7Q9X4npTABQv8Lzs7VJJmhN2hEnCLaThGSYuopSmK3vAY4fBGsfe0Tr\nvhDracArgduNMbf4bX8JXAhcZox5HfAg8LsA1tq7jTGXAXfjAOibY6G6LyqKUeNVyf04W2Y1UTG1\nF7Ck4kAhTfYmVAuTQaSe9DulNNNH147tqMWKBJGQEwGYWHqmf5kY5LsI0cw4VGBsQLHxUjKy5LYs\nSSNtk3Caeu5m8fEqTPh1ioa7QX0f6rhstnbq0vW1/JT+zgkrCEj8a1V94u8ltRJkMNTxA6QD7IbL\nDbw/g8+0YLjjYvjrOVT3wA9WwxMNnGk7vHgChrefz5PmX87iRbBkBk5vwg9+dgHY+fC9F8Mz/g8c\nfg3YBmuvG2SsUnOrDjaeDrYLu4b4eG2Kl90OX17rEk1mLByRODQwlEG14+yWsogeqv/0uxYSE4dM\nvN3EFaXRanqhFaEGnn932klZJKV4dGU8rCxqEJheLU7WlpJ3LzwsVajkfev3AmH9Jy3PhM8sClRk\nveh0NqFq1PmaRHMRx6fcR3buC+iIDVX/1mNCzFxCwm8SW1tVQrWIFnjMonPvtFfBaq29jtntsGfN\ncs6HgQ/v68YxWk2ApTjP/5Rxqr7UXq1QTlctzomQZJFFIW1Rx1gTcqabfZCHBDEbykHve+v3wq5L\nsFNCEMLxmuXQG6eX+5toW1W/l23V8bm6rth0E8UkmWcia8JgE4Eq/WT9fcYq8M91uGgS2m04dQjW\nD8IFTVi6HWq7oLkUOsMOuY0PBiecmD1kIM2o8BhwiQLSB/qZEgsD03DHAmB7HfacxbVbruAXh8KS\nChzbhJe2YNtyOGwKFrfg1w34DvDeBBqHX86NF18Kz3sT5x85xnU/mwutN4D9HJw9BNlzfSZEnU3p\nICckOQytgO4IVFbD2Am87KGfUt89RKM+xWYDVwPXj8BZwy5LqRCoEd/NqukogWvw2URp4D+N6vtN\ntuDRqZ8sdd3TuLxgcbtIeBkokjaExP4oaq+Uz5OJDryZKg+/ZQl3SRKRa4saX7xfO7twLSpNWXcD\nEYIiqOW0dhK26YScxJbHio3+8NeSe6S61i9hPCd4oEDQ+OLImsyE1Vr7aZGPhfbLefU/QXv83wxu\nafiFOPCy0fQK0S5O2MYapy4oomfYHMekurK4VGGazUutq9poXtFZWTnlgSDNlE7UAckShK3TV3Uc\noLzAfqtRaobX62uVgrNN8NSKXS0zYcE0qYUpjiy5jQRjZwb2VB1K/OJt0P7ukXDFXH5+eZUP3wjn\ndmHHQki2wtAlMO9amHsVLL+vPBByEzyumYGhbug3QdO53941bgDMmYJ7F8NzLNDqQvVEOjvhi8Cb\nt8P7E4eQR+tQGYcV2+CWDC6ahskKvHcNcPhWeOuFXHM/dLdbqEw5O+roOphZxvP/cA/cdjDd1g6+\nkhwCfAW6twITUP8D2HAUF66fYjCFp3bg2TPQvRsebjkGkFAmjVaBothKPEEWxxvHe3UfB5vETIub\n4CWCRP8V15Ei4OLFVqq09GfS59oSwSIOHL1NkwAHQ9inEbNExmhEKZOnmDpi+6VORBCNSCNfq++l\n2iP8Lf0mWVj6efUn+BhuqxJyKMeXC9ARh5Rs0+NVO+5EbiTWjZ+pxyEf9YAufz2JQ6gJrt7qKlyJ\nwJTyTDxsZ4l1VehBzzTGX1Nm3ljNgbLA1Ntlm1a/Y4ErTgJBfbKt1AYbmEpepKEsmIW5KzGjqcZq\nlSq+H/RRz9Uz6CpVhZ3OOtWym8C7G/DdO4C7XgC1p4F5BCprYcP1bNn5PU48eZT3/g78ft1lfCZH\nQT7krjlZcYMqte67PH8rVaErhPJsWeqW7gD48hK4sAtTdwE7LOSbYHQNp/EAl+6BX9XgdcAv74GR\nLuxuwp7rYM0p8OymM+AvWvcORn7/M7Dl+bCwAe84FN54JKdceA+LRic5hPtYtWUV87L53L9yOWe/\n4j38fP0ixg+aB2dvgEUP8I8t2NN2UVyPtoERuHscblkBz5wJtuki5dQGJ5HueOERA+VUVPX+0tgL\nI+/MM1LPKqoRFE19YkVqKKUFV7o+QkBO6zMBV6yr2iRCWZCoaEui3Rhbtr1XLKUKdIl1k0KBWm0w\nJYgDtmTWiviyCIdKyu3VqDgmXatDulZ8FEbtk+cx6pjMOFOiCHgRnLISSMWG6IZc3U+Px8dKB0yw\ndvHLruA6qYGLaV3VB4rnuGiBnu2m/F2ESKHWK5QYkxaocUprJwnpstp5JYUbBI0V6pEJs2RxHSD3\n+7pQiq8TZhJ1R9oZUwKlbKi4T6T9GrUXNqScHjVNkEErgY01uGEM2DzkkJ4dhMog0IDac2AsYde1\nV/CWVaN85nTLH50Fqy1s84j/1HYYKLlxKqYUFNbOQSnVJn31n8PwtnFcyNQIsCuHxkXQHGLcwsBC\n2FGBHRuWwD2v40unXMixixxyfngM2ruqfOeuNZA9HSqHwwMfZuEJX2H0IzdCt8Iv33AILDqW135k\nmLYdZoattM9PsOMJ46u3waF12LkR5h7FpvENMNxiZAUwjmPIHfCNlbDeUFSnAiUw++h4IpRmK4Bd\nEVuprv2pNSe5jzpG1sES4adnV11bAAtJtyz4dZigds4IciylhlpndpPD6lkoYh0np2gTgPCvXFNU\nag0+IIyTGkoAq8cR85T0V676DYIpSdqrQym1ecmofpTuKkXdoBxx/t2mXjOQyUHa0E/A/6Z0wASr\nFDQSM1YTF5slpQCn1NNNG4da95dEwAiiaqYhJnNvAcZyLoSObqWB2TNCwQbd+yJouxEzJ4C1ZQ+6\nzKQ6PrVnET7FgJoZY9qbB1XH44lhX6iZwqcT2P4wMHYKmMMgaUPaxPVSFWrPgu5quOti7nn017xt\nKVSWu8mCBBqHwqeAo6yzgS5oOhPOyIBjqgqhcIvYhe+ZA28fAcZwaskQ0BkE24GBKS6X1JIUmDsG\nm17A3xyykfWrLiNfDO1fAxvWwU0XQVqBKwy83bJn91WQfAKSz8PKxbC7xpd+5wRYDgM8AJ2Uq/5m\nCcy9DOwfQfpHcN/X4ZArYPOX4Z4pOPQBF1Bdh5uByTrMb1KsICAUe+blHfUjicjQs58WqPEaYO4L\nPUigWDureLnlzyR3fCZoN+aZroHuLCYwsT8KtdKyYNHp1NVcCUy5vQ1qf2p7ebJU4MiDCuF/HUMq\nwEOvgdUvUSEWkrpL9HuQNlkckJD7SaF2KbgC7nsjC5EV/6sXExzwN58GNvjfBmcSmGvcb79WJgtx\nCQGC/PQKA3VbThaoE4RVV3R2fNB0Tsmjqmd2LcwqSqcQRJZYVwlHhLMObM4ph4fF5gFBwaKSV/KA\nfo1158o9NML+TV6wRsJIGwnMK6E7kxX4WAo/eBC4PwVzJGSD8MgaWFmBygTYBuRzXUOH/wyaD8PG\nu+hubELlETD3Mb05442r4Mhl8Nmae7Z5LVg8Bd2qs8W1E1fQopXAw3W4YAd0bsDZeyywcTFU1zjj\n5sxiFs/b6opWJ3DsUIdrjtlI6yvn8v3By+CeJ4F5CGb+AL4Pc+7fzAn1HVw7/lS6g58Bk5LcsJzn\nfuF6rnzryW4WvMOSUcP8qMuZN+zk1he9nPEX/R6MDsGF0/DR9dA4BrgYJh9wRSsmYXQEJgdcmcq4\nilSxNLVCh0nMS16Yppk/X/hVV62aDSio7aV7KnW9VI/UtxGZSDN3Hx2hktJrominwdapJ97iMxIw\nCSHAXqJQUoLZTCZP4eEitDBRoMMLTTF3FZrgLF0hArYfbwtKFuQqmZMxeJKShPXMHTNdCSi5Smjn\ntJKE+1MDY190wATrdhxAmIcTrstxqHUQJ0AncOaBubjsg0U4Ido0ZXvrhAhDXMdOGHeswQlmPVO3\n0qD2SK1KYx2yyo1DyXPy3o5NbAjVEGeNhHWJDU2YUycsaIdTNQ82Lq3SGHUPCEI/4LcAACAASURB\nVN57KB9ThJGZMMAqamDoSAEhbRowvi++WYOvjQC3ptB6MdRPhk7VHbh7lQveNAmkHTdCK6OQLIXq\ncZC0XE/nG+HXl8HWnWxYNMZZB3d53mq4oAFnTcG8Mffu9sxzTN1O4GMGxu6uwejRsPA2N1vW1znB\n3vwl5A2WD8MbDbxlJ1wzWXejf/J4uON4GHqZ45b5u+A9DzDx/SNYfPko5scWe8FO+OA68oc3cuXr\n18Hxj8AxKekH2sxZVMceV2Hs1nms/eY4N5y+BhZugX9+CFgKk6th+BCYGYZNk5BC+zAXflasYOon\n2kKgqe8y8ULQEHSQej80W1pZwIbracGpHVOlYi5yL3Fw2XDN3DsnTAa2EpxceRIm/q5/H1WP2ipZ\n4NW4YIrwfGYcohPhCYH3q7lHxEnZNKTjWA0ByKguLISgJZT5q1hXDjRR4EVAiSFUwSp8DCbYW8Vs\nYAnLyde81qptx9b3Y7xN+yJ+WzqgzitwJgGDM3HFJC/B4sZh04RIAilOMI4L0xrDpUWC907jUr4W\nAEvVzKhn4cyEc6q44kki2GS2FBPCTBoM3lXPIJLJoYP38fcoIdiEnmVoCoEnqlJS9pbuLfEgjYR/\nEXrV5wTdpl1VuKSFK40z/SqonwZ2LpDDTA53DsLZj0D2cbBbIPkAjD8FquPQHYLBzc77nhwNQ+8D\nJmDkEXjwQr63bYarjoAfHAzH5DDYgjmTDm19axF85zZg5wugsQpGboNHgcoLId8Bi6uwfAfNBC4F\nuB7YfQbsOA7qAzB4AZi5OK55ENrnwzl/weUnvgzGDAxcDu/fBa89AQ6rw8xSyGdYtWuUwQkLD8Jt\nL17tmGblxdD5FSRPhdZyaNVh+FSY/HgYDVKcJsEtV+3tVcZvEwYRz3VcF0JoX+mRJcfVLMfFKbL9\n7qHTZt2FXbtNUj6/7cFEXAJTBIkuw9k1FNJLeL2ROUckBAdu5lGgwQmx6bQ39lzQvEapmleNfw4R\n+AO+fRMVd089bsWkVmhiJght+S1rybUTpd4nZWFfLIdDOXqgX2jbY6EDWui6jhNAM2rbHoLQjXlt\nG+Hlb/afg8AoAcUO4oRqhhOqc60Tak3jbICiwuwwDi2L7XbSuFi2QRvsvplnLkGiRY69n9VklrX4\nF+uPjdNJZyNjQxyqRc2UJsycEnSeEJgrRtTGlpMihLRaJJPDxB5g5xyonQF2njd6DcGcBJ71Y1j6\neldqZydwyyeh9kIwD0PlAthzKAzt8hcehIlhGNwNcwdh6zbybfdw1tzv8ZR18JIl8HszsGwrLJ8D\nNOtOmHW+5154s+pc1Qz4lwPPBL62E2ieDFMfhusH4bkbwU4Ch8D4ETBxFHznPF7w6A6uOO1usudu\nhtG3wgMNnsQD3Da+yOXJ3pdyysQoG0h46Y9/wr1U2VVZxCPzzoUVz4SFv4Q8c6EpA0ugcT6M/Rjq\nW2lmMOHVdp15BUEl14JsXyhHC1BshExVBS2jB7f1DizoTakVoFDYGNRvmUjToLGIHVXaKchUUKrE\ncGrnlP4tNK3qE4iTyeI1uMQnkqikCG3rFSQ52/JEYlKwJpj2GlkIkdSp4yhbr6BPQanaBivfM+PX\nzlLZXhotF2jaj6HHAbAeWMEqpBthcbWMhwgoT0KzhnDIVCjDmRFk6eylOLNBCydol+JMAyl+SRfj\njm3j0mc7hLXQq5RnbGmL9ox2E+e8aeQU4TWyW9Ct2JQing+MQwiBEYbQvFaynxEGm9jxhAnjgRwn\n38lusWd1E7jBwMgUMHMY2AVgvSsuacG8BtS/y9Ah8JFFcO0CuH7hz3mk/XPyCeCe22DoaTCwC6bW\nwOi5rgahmQ/DTwDWQv4M2Nnmpqvv4aaDm1z5xBGefxisSeH8U1t8/eZ3YR+quwZVO9C8BBrnwdQ6\nuH8rX1iyGTsCNJe7WWwh0GpAbSHYARj6KjTOhmes5dvTd8HJH4TN34d/BB4Z5bYPnQCLZsBAZV6N\nBbS4mZXcxCo4pMGiRzZiProHMzyf/A9Ohyf9LdReDDvWw+FrYMlXIYGBirMNZxWodIINU5OuhC8I\nSTLnjDqm9DIVFbbbqNhKEWEAvSm0pvypz4udX9YojcvzjZSJ7Ki2FJXKIn7SPFwE1JuyQMKjUJ2m\n2/HoW2fawd6TCWTekvAobZpoJcG8YHBCXC8hr0kEvowR/zp6x4YSvjoaQNqwLwf3/tABFax+OSGO\noVyppQksxtlZobyygKZJHOoc8L83EdDqYeq43MJm465pcah2BIdoh62rqNWhN/KgYAYDNnfMM5Ap\n1cm/7JSgOgmajGc9McJLbK1QRSEgsb9Kup8Uq9Zxtbo2glC/6AAd/yrX2GQh2wM0DwWWQGXSRQJU\n90BnPiSWOQZObcGpBqYWwS8r8DDw6bXXwcR1bkbaBfzoezD6FYc8x46FpoW8C4s+APV7YNvD/KTz\nfn6yDEwd7CrgaGBRC+7CzXB5DtNfBzMI0yeTb9jsCk9WRyHdCScOQb4Q7rgAPgn86zzIarDmUlj+\nt/Dgn8C3Uthm4eVLYD5QnYS0TZeD+BzHYJcuhh07Yc4AIyuOJNnS4qW7f8V/fPJQ7Hs+DWs+Bub/\ngH2677iw+Fy1FYRbsSyLt1lqoSaB77nMtlAqPtJHLgcyZQGsi770mAdi4afQLlCEYUllK+ElLZg6\nppxqDO5Zh7PejESdSy/eem2HjTUom5TTRCWpRvhYQIRV17P+mpKU0DXO3FDLg81WL24o6dMyVnpM\nAcoRp6lCSDGWFWCLWHUTxvnj4biS+x0QkjKYKa56tqj/kuoq5oEGTlDO4BrbwQnS5ThAM6SuOe0/\nJ3GmgoNxTD1lXPKBmA9m1H3ExhqXKtQk6r3YW4XhDIFh+pUq1OqSIQhVmfUTKMVF6nJmEBivqAcg\nhi3CNYUPGllYGFAQsTB4x7jvv7I4r2F6FkwudrAsnXGhVlkC7cMZtc4K8IS2sw68sOWA6WsMjAw7\nRLFhGfzJE39GN3ktLJ6ARgIPnQePvgxGl0F7LgweDCOXwM47sWyFjV921VUWAsfh7Dd3N6H6SjDL\noH2ZY4opIF8Od6+CbwB0GR7bxGRnPly1Et76KqjeCnd8CMwz4XnAGQYWd2Foh+ug7hCMWywpzKlC\nezls7MCf1shX1bl042Ek/7IA+2gFFvwZzDsV5rzBFQrYCQsH3eW6VYqgf+s7PK+UbYYixETFllUb\ndGaWLK1SaB95EILaeaX3x5Roga61mszFseaVMBl3UiVcTHmytybYSYUkUL6W9yLEruJfbcfEs6I0\nR3i2GTmXZF8J6RLaYwgOMFHnc1z0ijzmVKXM7zKG5NpS3NrYkNSREQSmgAydmCD0m6xw8JvQAROs\nVZyNVeB7TN76xk5cI2v4lZBxKbAQhPOQF3LDOPvMXJyQlbDI3F+nSlhiWzq4YZ39pWV8FpgtyTpA\nCTTvRYUw40I5S0tI9hXhVLhUPBGo8j2mOHQLgiqnvc7C9FKVqh+DGAJymTJwT4bz9mVHwu4aNBp+\nZHagvgcqy2k3YWsVjmu7ZzU4+Wtw/T7UgZUN+LPj4AdrfkI3hSfW4RubbmL6vrugdRCMr4Cx02Bo\nATTuAHMo7PwdGNvmUPKKB5wjae3d8NCD0F4L6WLYdSbUfgLto5n7wUdZz6OcTJtxYMuJVb76vk87\nx9vOr8PWY9wL+/MZeM0ALDAOzeZ1aNfhFlxPzmRQA1OfwO5Y7F7WoR0WrZ2iuT1jYsl8+NmZcPov\nYPqv4MRf8NKBSaqtMEHJiqzdxE0ytSx4wRPrhJ04VNrGCSgdwlQ4t5RwtVry4GytxfItCknFamwp\nOcCfn6siOyLoi+QYE+y5vgl9Sdo+4CdoOS6uZwpBVTd6v2qnmMOEBHy0o2tIm+tZEPhxNS2dMSiq\nfSvidYuKDpBtJghyHeGWmRC2WQAXtd/CrEk5vwkdMMHaIdhQJwmIVdN8wuoCB+GcV5rEBDBlghrf\nxQnaKlAzDqUmlIWlXluraVxFLRFm+3JEyMwOZftpPyoKQRuKmgFa4MoAlNg/CQETEkaAMiqQcBZB\nOol1zNbPaSbMuS3BGajbR0Jzjq9wMwi1QRe7WpkEVsNuBxor3vQhCEau001gfgdeb+GVqRO+w3vg\nVYfCTw7/KhdmQAvsfWDvfwckT4Z8CJqvhO5uGLgI7v8MbM2dpF7yeXjo81B7EwycC5O7YfBqZl79\nElZdtJWHgW8/YStbv/Uhp9rs+VuYOQ4WTMBd810ntIE9KdQWu4fejWOYJy+CW7twcoWBu3aRXTJD\ny66CE45g507glhE3sZyewP0LYfRzkFzBx29+D8l547yzSVFPt5k6rWBOu7yksrx/rUK20lCmTlPs\n+CqFVPVR//sdJ8wgJgqTeZNAJWhHcc3Xol1JQNU9bbPBFCAprwXKVM/WUWrStHdWtdJgv5UqXnr5\ndbGVatL9NVOZfcyJxqZJT1raDKbjaEVj0GOziFP3vwUvyeWbpjd657HSXuux/k+RMcb+uXXCdBBK\nQf9Cc3DCV9bEkljXqjpmKwG9guuomg12z0nVqbsoRx+s8lOVwQllUedjGalrUKaWov5oHIcqgs/0\nOa+I3/P7rQnfY4pDdGRWTSjneEs9BWEkqZuqZ+EiP9rAT+vwJ1uAX50LWz8Mk4POHjL/QWfTNDnY\ncZj3Ri54FnxyytcUTYKdbqbitsm9uh7B595uNe29snsM3FCBP20CVx4P+ftg4gkOSS64Eyoj0L4G\nzDTkX3fcXAc4Aiqvg8mPwqZrmfvBzVTe8jNGT7wY6u+A1pNgch40EmofaXLEhhHuecexsMI6j2K9\n63pmR8WZGsSzeT9ww6M8rbuFu+ctoL3AMLPsIDghgTOvgc7nYeYc2PL7sHQChsdh/uUcufrTPPco\neHvbJT9kabDPSahRghvk7Uhg1VWsqkEJR9nmf5ciASKSvhe+gCBQC3uu2l8E6ytwIGteCWlNC0Js\nqqwoIbZLaapGfkKFIFJeeHGUSfzoYDeg3FqmkCfByy8TUuwvKIrZ2P79olPCRbBKKJWg21y1eza7\nqYxZQbGyLQWenfA/Wo/1f5S6OL4XtCpqehP3gE3CKgNtnClgBPfgTWYnvT45/h7GX3/Snz/or9mw\nQfgIatXMrL3yQn3z+kWgq4EiA09eXpWyzVS8t6V7UmZ8YW6rmCwxbhAkBKZJ6BXYoj79X+rePMqy\nrKj3/+xz7ph5c6jKrHkeeix6npupm1FkejxEBAEVR3ChqKCgD0VBHk8XigNLHiBPBJn0pwhoQzfQ\nzWQ39ABd1d3V3VVd1VWVVZU1ZeV8h3Pu2b8/IuLufbMKUFpfrXfWynUzb957zh5ix/5GxDdiJzoG\nwkkbg6yuKA8YWAZJUxVrBaZgX6HZKkl/u6yClWWxeGRBLjoN4nehksPyMqzvQKsBb0kfk+fVjkF5\nANrrIB+FypBK/S9B6X7wt0MxDd17hCu75TvM/vOd0PkCtD8MJ7fAyMPQHGbs7TnXHj/MLS/cCpce\nkwBcPiS1DQuLMiK78yDwkIfccZKEV80cZHCm4BuPH+PkPWPsPfVsih8pwcj/hsH3wEMfhOEbIflx\n9jx8AXse/T986LK9/OXqOW52/ZtntaBXhtIsEpvTLkgdVYKyixWenV1sAShn4Wwd7D53gH42pleZ\npRL7PT0hyBPp3N7/40pPSfS+ox/ZxdZTTlB8cbp3jBTNh2rWF2g2FCEN1rrXSfrdVwVnKr6ltRes\nfdYvc2/ZXFgtYlsrtueU1SLUIe4LGscJNYW216rq/Weg1nOqWEvIYBh3NdHXIwiYmiMkDmzT165+\nfg1istpgN3THbbrgFrDXkgtuA3MDJAhn1ZCHpZXGmVNEE2tyZNzVvn74/omyL5kwW3mz+Jygnv8O\nQvR/iW/KrjiDyugglkrba+BZfo/R6wgI/Pd3Q7oAviEf6gyIwkvaQBn8au4rJns+26UL2BB3LKiN\nLCyqhYr4Ydupum6SNTC9FroNQYIuB18CX4fpi+FUAX49nLccOq+TqFG7gDWvhYMpZJ+HhWEoL0I+\ngvstOOXnueWXroId3UAtmU0FoZakG6z1sgPNoqZLyl4arKXNM2hyHjnP6R7gsU9P8nffvoriFz8J\nm+6BJ90NZHDoJljXhuwDtO5a5OfKb2P9jV/j82MyjNVCEHopOZPiUzgYylW2Eklt9U58l4088gkW\nYTxj0/6M+U/65yAuSgLBhP9BJwPk0RzGMmi1V2MFWtDPd4VQJNsi8WUvKP5siDBPoqNTIpRcUVSb\nqYLNI45q7C4oQUDjLHGRueCWKhWygRm9yyiPMVK1I5HikwxsDMsFpG5JUsMPjVOXtP9cXANn+b1J\n/26xGjH37f8DXncWpOEj9B8Rm3D2gtggftSmE/9rlzMd1KYAY7MnFma7jO5hecoQAlNLrzga2vc+\nIekAQkpgr/zZEsSdFsHXZXnZS10R36PbvaDXBg+Xr4Hvnv8g5O+F6m/AQj18MF2EZB6yISp+sock\njKyeeFng1g5rfzeRFERbqF0n77VS2OEAfwAerQusvXgYlp2EZAZa/wIb3gkbZuHkY8IndW+F6keh\nsQ8m3gnZZeAH4fRyWLkIzZX411VgZkyqXjcS2J4J0m4DjwDnIbtzbRZmG3BHCnsnqXKCJzPDNAkf\nocE+VpO+apihtQdo3Aezv3MYt2k5ft2r4RoPawtBzuQSEOv+LhP3foHLh+/kmqu/yV/UZc5HOxqJ\nTvQcsiRk/cQ1KTqJbN6Zjs1QJkqlRP86js8diystxScRlJFsp6oWDompdXaZ0oyDRfZ+/Gq/WyT/\nDApVJGs+stktP3/pFVO0LNMrvl83EljLtLL2n42XuvQRvUM6ff/nLQki9dLOmAHQe6QLLgMbk3gz\ntM+3/l9WrCXoHR5YJlCpTOHNIWb7Bv27AE64UGrwqH5/AwJUMidZVks75PS+KQHVVnTGIlAqfDoX\nTHf43r4Z81N9vyBXLBS97Ck1b2I6VUI4ysRuFz/XzDKbfDNrem3Vh5mfzGQ/gR7J23lYncEbqvCb\n58OpUx+B9BpoPkukMB+AilKVioL2LBwdhZFcFbkq1F5KoOs3c22BgxS0tkDPSiC9LKebvA9YDXO3\nQ/c+KF0IpSE4dRXwHDhQgjU5XPxXMJ/D/vdBZSO0N4iNvOkB2H2pnL5aFLA1kQODzpuBgUlY3ADZ\nYDiCYhFYHIF7ga+cZkV2kG3k7KHKoWQDXLcKXjlDd+UXmfaLcMUD8LpDeL8FPprDXTfCA0MkT99A\n8YxBgeFbH4DWUyG/iLtvO4/rz/s0bF1k6DQszg2xrDbHi6owXIPlFXAlGE1hs4eNOj+jevJj4jXA\nFAVg4voR0K/4lpqw5tu02qIxkrWNLS36EWpkSPVAQOzbdIR7WVtiUBC7D1z0k7sQzDX/c3wZWjUf\nrrnbzEp00Vq0yxCqI6TfOoLrydxoPc+Ij3zQ0WZUVmUbB4UTdA1FD3TRGHvOnh32H73OKY91AAEX\nBeLyW9qfLoF7ugFRtlP0Ky37/zICIrXv2iS2CH5Jez8mB4PcsCA4zr9X7nZcciwOpJ1tYSw9wjtX\nPkgaOfNjkz5WqPZ7/Bq74UyoCsKbS8sW9nx9+t4VbfjlAfiDLQUcfCsM3Qh5HdK2jIofgNJ2yB9j\nMoHtTsbNMmnMXLRAmpmQJvRdB3OJ/HRSeHEGHK7C1Oeg/vNQej2Uj8DcNTAxBosePu7hzffApa+A\nx8fB/TYs3gzzHWnP6D649XLx++w9CCvXwcV13Y0zehylHBGoo8ABRLLvAtotPHAXo+CG4OrV8AtH\nYXAXLPwjVK6A5BbcJRNs3A7+pXDkOHS/DEX+eXjbJvipVHaX6l4gg6m3wCM/Ars+w9zl/wSjc5z8\nxr/w4W4K7kHh5Ca7oFHAMg/rOtLe5Z7rB+FdBexYlDGKs6vi40Vi+lxGiLIbwrJkk/jqBZIijXm2\nGhR98QKVG/OJxjLYXSJX5grw0X3aSfjO9+KBg3LBoVcPuIcerT8IWCj5kDoO8r84eaBESF21Y2ji\n/rRSBRNF0BMGguyeqd5v6Wkiuev3KT+R65z6WFPEgluJBKUMwYEo0XWIny5HaFfWaU9Qmiv0taav\nB5xkWNluaHGMwSWTfjaKlE3CUtPEdjFDiku/agqs1y/fT62yXdPM+Ljqejd6Vqw84/ztmAtrbVvq\nejBKl6EIU3bOOqbfubqA5StgajyDU0dh7nwojcsKTzKoXgbzX+TQSg1MRYEZqzCfFnJUlXdSJOvr\nHiamYXIf7Jm7mlZ3NbS3wcwW6FwFB5dDUhMEuf9JMOqp3vUoncuH8B/4VSjdC48/C3g1dBfgcBdW\nVOUMl7eugMmTcNU4bN8kNK0MmeTFZdBow0JZjq98ECn9twX5+5Qc23eSQZGUy8bgRV6dnrNQuwkW\nPwWXTLBxK/xxARsK2DsGx34Cbmu/gLufV+f05Hb8oTIcXwcDz4PhndBeCZX/Bg9eDPwujL8ABq+A\n2/8QvvERuDET98XCFBzch1QJ63BXY5Jn1T/IlZfD24fh8mbYpHLXzy6Ia6WebbN3BKsk/pfJ1VKk\nawVMTF4SBO31MrRccF3Efn27qkVQwiUvvFMXyai1wxGsMGPSGD/c+xDwM7eatRmV8TyBrhd59YTE\nF0dAx7XumfTC2OqLXWUDWUD4tkZKHtBgrLnizPUXZ4H+sNc5VawOQa5VZJ00ov+10GgfgZY1SVC8\ndp1A+K45Ql+EsOMccYJkK17iF1ZgBQTdVnxAryakZrr0TgXwS0wFF875sSveyY0KBf0mjV8ivPH/\n84TeOe6mEE3YbZHEKX6Of9+uakJm7e86WNGFHVX4+kgb5nZDcr74KLs1ia77DDw0HCykQZk+UIGd\nTehOw/u7cOr0KpjaANOrYW6r1FVt7UCKs6wQztUD8vDk83Okw/Nk2+rwwCxsKzN27e0ced9t8PgM\nTP8pcCE0V0CnJStgPoM/rMKpfZRpk91dqBR4+ExVXAFrSlCMwN4yfLkLcwWsKAtf9zRQaJqJ2wiN\nQdiQiGmUtMDVoZiEDYepb4E3prBJK/lcmkkTbkhhblmTQ8t28fUd8HBxH4/d9TlO7f4H2LMJzlsN\nA98A/3o4dS1M/xZc+0K48vXwsR+DTy6DbWOw/XxBEOPA6ALFshdyz/H/xU8+/U7eu7zghkLr1qZB\nBiEyXwt6aa52Eq7JVa4Cs3Sz76VAF/3urVguCoKbpwcCHD06k8mMyaMVbY8RYEG/JWXtW6pUDTQY\n8izi+1t/iYKA+jwrfmSbTOyXbVrAzdak73dRWUaXBZyz6H929Yodad/nXXA/PpHr+ypW59wG4G8R\nUOmBD3jv/9w593bg5wj8/d/23t+i33kr8FpEL/6K9/7Ws917GAk+NRFkeoT+0oENBIVWEOVrxVeW\nKlboL8wCopRXePHHTugkjaK7IeKcNtPDJtf8lUUkOPGgx075s6EH+wza8Rjtpj5QQmLSdkxZiXdT\nCOgWBO0WkelS64bCvPHpmvGVR0LkCYig3oU1NWC8C8f2Q7UDlMQdkI1ApQSpuFych2/V4JWnSrCz\nBsdukmpXfhZa18LJtaKoBpBE7F1OVtRdngoZnYcmSErTjNUKTrRWwfwQfHQeVzrB0Sf/Eex8PnRe\nL6cYlEswfQSKTbD2EPzGKuAIDeZZoETCBFvosI/t+KMN+EcHNwKPDcFBD7O5jPaekjjjTwLdDlCG\nZXWhg3R1AErTQAb5p+D8ghvG4epclFuu1of5AFdn4iu9sQOLCSxeD5+98cf4q7+/mcUT74PGleDH\nwS1A5yNw6h7o/BG85/3wxy+B978KPrsZVtYl/Dw0ADfvgG0fYObLf8nPbPtrnnxDhw83hYprlLlY\nrmI2ZbkIm/jiktVrymtp6mZ8YGB8e/u8pej27kNgy4AgS0OhpuA8gmBbafiuVYcy5kAtAiCmoHv+\nS9fveoOg/E2eez5op4E+fb+p5n7qtViOIs5KV9Z3nJxgll2cUWm1B3qHcUbjlSEB8Sd6/SDEmgG/\n5r3/rnOuAdzrnLsNacefeO//JP6wc+5i4OVIXZV1wJecc+d7H8cTz7ycfji+LO9/GdLR1chi/14c\nswVkIdSQoNe8EzfBOMFXu1IXzRRSRyBHkCsIa2AhkZoBZo4MFlEUEvHbOIKT3rJrLEJuftaC4ETv\nFWjRy+oB2GWKtxR9zv5vqNX4o3Yt9Q3FCtkhG0ZLs2Li59j9rgA+PQoU9wBN6I4IB7Q6BcV+GIG7\nPXz8QcfuE6+A2evg8FOhPSzlwhyiTOeQXawN3JOy5bvfYf/wVlzzOEPrhzn19scpNr+fExd5eOw1\nMHUzTO3hz172Bn79rkHy/CUwfzm0B2SS882wIYP/sR7Zs4eZx1NjlhaDZGRcwT7uG10OW5woy2Hg\nntM4JvFuC4w62WkXjOnbhekOrK1IjYJ1k5AsQvsbcEHGpWvhpwoYyQIaayfBKuk6SYqwKPRoBj+R\nwDNfcjt/Nf18Pve5/wkfvwBe2ZRJr10G81+DW98Fzz4Mr38bfOQ1cMvNcF8LTiyDmTJsqMFT3gSz\nT+Xbg6/kwEWwrROsnXiubfM0n6ohyqXE/5jcb1cSyaMR8g0s9OQnkvOeXzUJz7NbZonEOuMAV7WL\n1H2NlKsh1Vj2ekWrde8195YFRbHZitpozzalaPn/pmCrS+S7nYZnG/o3F4ttKIULBa5tEyickFaO\noRnfPPHr+ypW7/0kSkf03s8753YTdOBS6wPgxcAnvPcZ8Lhzbi9yqOZdSz84Qr/pD6Hk30pCMgCI\nspwhFMVeerBgi36FW0YoWAte0l3L+qxYuzcJgR0rUZggA5wgboNYmZV8CNRUu/3HVic++GmMEO2W\nCK9dccHemIJiQSEzq5YuEAtmxL5UCArdaxsttXWg27/4YmHf6tGsjDuhdhqmVLEO3gmbdsEDa/jn\noxvAvxkOXwoTJbgd0cg7PYw7+Fobxspw7DiOEwzfDBPP/Qr84i58BZ504d9quAAAIABJREFU0e9z\n9Sa4IIGPNuHrcw/Dvju4/Kb7+ZXvvBaaF0JRg8pRWBiBzY/Dwga4rwyZzeZpIKFFDcg5xBAHGYDT\nXTiUyMQeAvIuPh2DS+syqVmhaUBVmdnBEtwEbJ+C6iFgERpfpbwNfqwM52WBueEJhZbtstjYaCZo\nqVIIA+Vdjf1c+aqf4O9G3sbeIz8tQvtvF4tTf/F34Mf3wn2/x8BLf53FS28EdxWMfwgqTSlTeeAN\ncO8byY7Cwg6ZeO+DDBgybOkz46ypmNhvcmbB0KXVzs4g4C/539ncBKZwYuXtOZO90k37A1AQ0r57\nmWmm3AmurCxRyyDpb7vVFjZL0tphdQ8Kwji00nCUt7XTnlsggVd8iAtUCinoYpmElmiQFjDklKHk\nxNh5ote/28fqnNuMLK27gCcDb3DOvQa4B/gN7/00kqEdK9EJzgSjgAzyvH5hwItCG0SUKsigr/Ww\n38nONocIt2VrNRDzvooMRC2692lkYMecPKOD8DgPOCmuZC6BaeidEGuHF6b64xE0W49MepOx3GmQ\nWP1JtQjdWCEXq/W49CTJOL/cTPTYB2bCY1d8fAQEH5XdyyqkW1Fg49caadwqchlaMDfGSA1mRoDO\nDGQe1t4Gl74R9q6DxddD6zI4skMk5O9zONYkfXAKWIZPytRHHiRpF9R2NJh9x8sYe0HGb5bhOl0U\nAzkMK3l//wB8fWMHjv8eDxx9lpw59fDNUP4aDNwA278FfjW4VCIH5QQR8xKOGWpkNBnGMwJuVFO7\ntFMP5OCbMDIuQas6wtrvJLAng4EqPC+BLR4ax4EStL8MF0wxPg5XFAEBln3wX1qh5MEIvpivO56L\nl+ew7b+9gzftfRdTt36UfMd1cD9CiSiGoPhVFid+G658iOds/jeeioDtdg4/e+iLsP6NsvijCJTx\nXhe14AuEEyxSHwKbZkrH3M7YpI9Tba2+hEM23d59S+F0XfPn22WyW+0GUn/P76tCZW4vU6wx9Q5t\no7ncegFXbUcrCcrNUG28J6Q+KhNIACyWVOCBdpleogVo0oYPwbvMFH+kwLMkjJOh5cKLp2adD1bs\nE7n+XYpV3QD/APyqIte/Av5A//0O4D3IUfBnu84G3HpBntPIzpLRXwIQRNkuQ9gzHf18hwDV55Hl\nV0fAQoFUpmsTygIu01dTnAWCZguCEj+J0jgQRZ8Bh00Jm8J14hpICGT+DsG0sKudQifqcRIJlIez\n4nwTGquWbv7bShH8QeZXsyIaFp09g92gu7H5mXI1vTJta6WAVR6uS+DWEeD4A+DWwYo3w/1/CY9d\nB59sw9EZVnUf5Liv4WerlJml9Oo1FFOTdFdNs/DOt7Gy1aFIy/zpxoyLM1GkPeRBiLjemAHrM5aN\n/CutXXvJT74fvtmEyYvgl3fBZAHrT4HfJJ0cTeS4VzLZ4Mh11jsyWqMIUp3x4DtADbJSGMgETc9T\nBbwVGJmFogzJYRi5hfJG+EVgpAhzY0EVXGBC9PLvCciplQbWhwOuacO/bu5y6GWv5CWf/Q6sd7A4\nJLVhSyXY9yGYKPPQs36Uvx6TkngPlwAehVqTZFQ25G4SAixmvsdV8WM6k5nvcX2AOLgpwhc2aocE\naxsdqGrKYrciBIm8BElJlBIEczyWaeiv42p+3F5Q1YfgUqEAw1C3fSe+SoWsKUfw6Z7tMkWeRWvM\nEVwbuZP/lZHNYimK7/FgfSg/2E2iQFoi6ezfceLd2sh/TkT/B97DOVcG/j/gY977zwB4749H//8Q\nkgcDgjfioNp6vgd74V/fLq8V4JKb4LKb5O8jiHIbIZwEsBqF7ohLYA5BqjH3tYqkhlfRlEMfjnho\nePG5jkedbhISCqqRIFhqW9nJ751YIFRSLN01FsA4mktkukC/orUrJfhmzZTx9PumOkk/AombYnza\npYErW/y5E4Ve8pB0hTNZUV9tvQsXl1Wxcj+svA4m/xxufTqsOgUv/wJcvsDxh1O4qUn9WMrl/7Ke\n9p+/jPrJFsMD8OaGjDEehpuhOHGqY2Y83lohwvqqGuz89pXs/NZ7YHQDrD8Nr0+h3gY/D74OaQeK\nChy0ShDLdbYrOmOL4Bcgr8lkT+QIcbUhKHWdCsUKZNdtIbt1TQc5OQSVt8N1TV45As/oSoopLkSk\ny/ZRH+bV/Hr2Xj1CkZaPXvKwcRm8+1VX8JYv/TQ8cBXkz4Q/XgdPnYObjzFx1yd52gW/yHu2zCg0\nq0JllqTUj5KXzqdtpFmkMNIkKC77nrmJzAIyShUqa/Vcind3y/TquhrbwKhUrTS4AKzGr7XJlJaN\nS2IyqvJsplF8UoHFFOKAko3dUn+qKdcYtZoitCs+pcMyEVP6Nx2HvG+ULFO01SIchR0/Y87B1B3w\nlTtk+/6+AaF/5/WDWAEO+GvgIe/9e6P313jvj+qfLwF26e+fBT7unPsTRMzPQ46GO+O66e3hCGwI\n5rm5BmzHrhOUXt1D3YmiPY6gsHUI6o2rXDn9TtkL6jUWgOoBDqH8VhXa+NyrjirUQd2J7RXoocEe\n9y4R5JAtifZD/44fVx7S28jkJZosgJaaK868T3ydLRvHxqmVBme+Kel4odl9HSLwlztwQ+BrM+Ae\nhc/eCKe68Pw7YPXvs/WaLu99IRz38OlFOPgs+CsHfoXw3uudgFhmyhL8OVmVzKul6b6ph7cuwou/\n8SaY2ggbH4HnboDaY0jduw5wAqqnYXEVZLZ9jiBbZYEo1jLUhoWr+jiwYHwQL+6DRUTPnkYUqpk0\nwx7qh6D+XrhslpePw095esVAkiIsNLdk0SVI0DI+WtkQeV3dL+VCNpksgec72PGcj3DbdR/j69Wc\nw92vMvs52PKBU+x+/cXs3/1pfqH2Xv73llvAXQA+Jymr++kscrJU0ZobaKlSMlPbIv3Wh7gAUOKh\nSM/crE2GbXOJ2QAWcbexsaBsXDvCEhAsODpf6ld05SJYqI4gHzaWsYVj/TYf65Lu9zNrou95Ty/D\nyhIP4jVhY2GvRiNcdCIy198EG28KKfS3/z5P6PpBiPXJCGNwp3PuO/rebwOvcM5dLl1jP2JV4b1/\nyDn3aYSenQOv99+jLqFDAEUZkf3jyMBvRASk7eTvAS8oddaJEjWlu9JJMGuGoFRPE3irtpsPeEG+\nyoCUc62cYKEmwsxZpb/XkEQCE2wzc5ZGOKFfcZqfyybczCTzadl3HEFI4zPdLeBki9r8TuZ7sgUW\n+67iQV2KWs33GtfxjE8UKByMF7BsEKaqR2Duu7DnmbCtED9nqcLTqk3GMtjYhQurAgSrXvxzdltD\nauMd6UMj7w+E2BhmiZhhq65+hL23XgfuU5C/GfIGVKYRCXBQasnxLfeOwGITYQaYN7EEaB3Zg2jx\nlTIiJYMSmr4N0cWPdQR6rCnDNgfDk5C+Ey7cxSWb4KVOcvVNsfaSLtRmbuucVyKFEM95Sv+5UQmh\n8n21gI2J52dGcn7Og3vd05n8ZWF4fWfhan797z/F6W3b5PvJJsg7JOVgDqde0FxMVYp96kvn2d6K\nEWuWhMM1q5H/sUiQExCWyJGlxloUvVKIW6CThnvafDvE8ollOmYjtAmBtljuGoX6N1XhxSyEpZzx\nHgpfEoSzdPDUnxmLgLCmLOZg6b/mJvEuWgMI4NqNxGv2oQFKQkW9J3L9IFbANzg7dfSW7/OddwHv\n+kEPLgiKdQV6fDXiN/CJLKkaun6cMGuWFkdYo9+fQAVE77HBhaJHQzoJ84gv1SFW5CJCu6oj2CjR\ne3X0eUuZBzbppkQLF0jJ5gzvpGcqFhMUQ7rx0dVd5PdBrV1p0d8sDUESqwIUZ6bgtMCF/l4q1GWh\ni8OQQEyKNvPQEwUzPJA+BK3z4fET8KJ1UAzAomeyCwNq2g9nAbnYvSHwFc03Z+jYxssEGySiXqQK\nc/Nd0PgunL5Gag2WlUiX5pJYv64KewapMkWdLjMWuKIkyvLpKjC3DECnBlfWhOWwG5joyvlb9VRg\n+bZ5KP0h7Libi7bC2xys7QafdYzCOols6sagMHdGL+ChfWsmAfXEjIwen1J3Q5urjQW4YVheuYdG\ndTfzX38Sf3YJUL4A0haluixE81UayoqrV5U8UtYhCWNv7bExtjUQB+MKVTI299beXOXF5sprvK+V\nyLHTmcqyzbGZ3TFvuqyBv3ZkmpuL2xMYAWkhHGCT5xgF2zog6n8va7EIKN7Wn8UrbO3YeFnFKxsT\nc5OVvMyXpWMvOrFYPaIjSrqmKjp2df4vJAj8V14ziHKrIcEph+wWU/qzRl/nnLgH4l0kQdxmAwiS\ndchAVwguBYcgi6Yq2ZgfOIJA/g6iZOcRBLsHesJWQixO52Sh9OoCuP5d25zq36sgS8+f4/vfQ9tk\nEVcIdU7jzJtaNwi2Nc9MvS5KA3PR3/S7K6zd9vjUSwS01kUOaR0HJp4F0yuh5KBYBcUAmQ8Vb50+\np9Q9E8HHl40LhBRBCD4u5xMxDyo5ND4DJ66Bk1thtAH1CbG5y16KrBwdoz3foc08MAbDy2FVIs4l\ngEMeOiU4L5HVkADHVKPVy3BtAld3Yflfwo4vMH4BvDmB89r9dB7rT8ULUu0VroFe0ZvuUnKp9slk\nAYKf01CkjZnRnxIPQxXYcvFX2fW5n+POOxPwFSidoD4AI90wrrYZ44P/0BRcN5JPU1J2GT/UdKbz\nws4oeyljWMrEijEfqwVADSjEZfYSo+upgkpUyWWJ/M8RNh6gd4w7iFluMpg7Gb+qrRcCEj2bGFkb\nKgW9gj9mWfTuT9j8jEFh/7dNzhEsPAMpp1N5/lokKXA0umcBvRrHSw8V/WGuc6ZY5wmoNSGUBhxE\nTPxJRKgaBCXYd+mIzBC46ikyOHsQALMW4cJaanlbn2dVtIaQiZhF0Ow8ouhtHc06sUyNElUuwo5q\nu2MlEvi42AWcqWxLhQh0H2pIIqI1wdyJj/g1BWu55Ko+elfXaUpkIsrPFqjt8naZSWnZZWmqgzB/\nJVyQijR0lkPHcSqDrCJF+WMkYChtqW9saUAgvipdUwItuL/DwIU7GRzbyYnlL4OvXQ3XrQCnLHx0\n0uY7yBaYyszNzsGqEdmNbwOOngAKSakd1Em3ayyBqz1s/Ras/zvqO+DdKVzZDqako9//5hD93E2C\ngjQL42yXISQbVxtvm49SNE4Q0iu57o/hc78E0z8DyQDkJyDVwj76WcvDN6VZ9sE0N3+wZV3ZvJjy\n7iZBliyVM0cRXKpKrxAShRUTMiRr/bHN3iP9j+sJ4MJ6yJ2sJftcLGtLubGLS3y75guOr7gmRSeN\n3C1J8OUa2u25TqLXkrqd2qphbQzMTVL2sNsJALP0+Av0d4/omqXc+h/2OmeKdUpfUwLdyXL9y9Dj\nl+bR5+3wwfj0AFOCC8j6mkMWyElkkE7o92YRxZkh/lQNhbCckHhQ1u8a6rWMrTLiXnBOkYJOuFf4\naGahXb1FRTCfev9ToW1HCrbrJaiQQK+2p+Vau0I6bYJvsmZFXczBvzQl1tpni9sQVNeJL3S2rO13\nwB0r4Nm2quVY1qkMutVgVhoK66Fp+mkt9uA4PTIOxKUe0s5+qJUYmoMVT4YTk/8LtnwUDtRg+yiU\nFmSwcxBbf05noS5vTim94egCeHXkzCMO+uOdMHM31uHyO2HDG1l95SLvSuH6pqJ01+83jNkcvRNX\nXT/VyhAshCI8iW2o6pJZVH9zaYlCiX2J3QQ+m8KW2T1w+tkwPAPFLlwpKPKuk+BPvHEu6txakMmG\n2zZ5T5CxJJIJy0RyiLzlZclYLuehjisEilQr6TfRHUH27DK/s+Xl981/jLjtfz5Yj10ilKwylNl9\n6WfZ2H17vG+C5WD+UTvfLkWUfysVC7dMCF7H/tUyobDTmD6jTCiaD/3n4T2R63uBjP/yaxY9zw5Z\nNk2kc00EgY4TfKYVfe80olTT6GceQa2WPDAf3ecA4awrQ7Y2YVV97gpCPVjzs3j9ewwJhm314m9M\noJehUy1kZ48DCEv9Rp4QnbT/G1IqFYG8XzgharcSCVwYKs2cFEJZKNHLQql25dlGJ0m8CHu10NOs\ntTG2+w/mEr220mtGMHcJjFUAdzN8aQGWGQyoQROmC1mMHRfKvPVQKyHoUajCMIpOloSNx8bDlJkr\n7oUhSKs3s3kEuOA+2PoZmPQw3YB8UCbjfDCPd5U2AlNPw6l5OJTLKmIUGIJmC47YdtmCq2rwozth\n2U8xeN1p3jEINzRhWUcWWjkacwuC9HyBOpb1brBOzMdnexAEs9+sDuMq14qgXI16ZqjTAkHVHCob\n/gX+eRw4CNU2pSq9fPVyNGe2YduJvjbupkjNNPdO5qKpG3bfhqd9bafyM5fCfEXm1e5lh/1Z5X0I\nfYv7akkGdk9rT3yuVgwkCv1crn7bVtS2jhNueUd/5l1EcdM1YvINkm4+78KPAa4FJ35Tr88e8GEu\nbVzsmQecgDXLzDT+u0d0wwCqQ2J08kNe57S6VQsJ8BpyNFpVA+mwQzpv/tWLkAYnCBLtItzv48jS\nm0dI/xOI0p3Rz1bgjBoDQ4gCfky/F6NXMwfG9X2ja5lCtUyQIhI4Q2l5Ij7MpUValqYVVnzYrb2j\nd7hcbHYvXRxm0tjVTMPi7SSB7B6bVHaZGQf0UgxfXIKHDmyGgROSnjpZE7oFjnZXfcBFcCF4bbfz\nIcvLng260dDve7PFK50+AIMnce2H+c0c9myFRw6+F67+UZgYhnpDPrwBxJkzTcIUvaTksYZoqLkK\nSlCFkRLMKKN5yxD8/MOw5rVUngJ/VoFrWiHJIlF0s7wTgi6WSx5XL7MAoPlIbdMwAB4HUExJxBlZ\naRE2RwgLu+ylZvYrXzLN39y+GhY+CcNPp1wLrA/vgzKpFmH8umnYBOJz2Ky0nil48zuWtV8mIzGH\nc1HlINNN0doa9yG+DE3bZgoaCFLvkYlWntAr7ZnqOCw9RdUhcY9FH2oo2zjNJzCkLoVO2u/LH/By\n73Yk/wWy9gcQK29AQUPZR5WvomsYQat2iwb9FfPM/3ro7MPwH7rOGWLdg/C0ZhGX2lGCiT9HOFF1\nhGCKtfTHDEQQRdjWz3T0XnZO3mm9T4dw7MsoMsAL+hljD+TIwLYRRWvvaxIkC052TVsAcSAhRgj2\nf0f/Z1ppOH64F8VVhazWfh+1qudLciEab9+PjyfupR0W/e3Kkv4foJe0YG1b8MC/XARjK2G+ptt3\nCfIy7UIWhqHjHs1Fn9lWlGMLzXK2LaBWKoJSqmoH3ThQWwm1d7O+DT9fBa44AcveCMtaYv3niKnw\nkjKkw2Q9foaHhQIW7OB0dejMzMkMr67BG6dhzesYvnKWNw3AlR1xexQEJGmsDOcFFda7qpxcGCNH\nMLFtDkx52PsFwYdXK/rdPU7/33X0H2TnxfpYPfAJWFEHvwb87Qylcg+TH6u8ZO0peznNoVyIv9ra\n4KIN3pJNDJXXu4GU710ofrJQEvR4uiy/Z9rGVOfMvlfvLnFruDA2ls6aRvPeduKyyPRZi06UY9PJ\nTwaccgJSZvT3o06m/BjhmPpmEhRwHr0u6H0KnflJBBRNIpbpbuBeF04TqSh6rdkYEapWLUfMf3P3\ntZA2ZYhOmOWJX+cMsS4iHajR86Axr//LUE4g0uEUQZ0JglOWIbvNEYIStGOyZwk+U/Q+04ggjkTP\nsKNg0Pe6+vccPSOT4/qsg8hOZjt24qOaAEm/iQghVc5M/V45QUcvjc/5YFZZ2p0pKqN0mZKMg1lo\nO2xLtAVryiOOlvZ4lz7cp6QmZN3DV6aAY8+AVzdEWitAawiqqyE/xMkUVvvgmzRFY5lIi8rd7PFm\nu4KAetWKfEBEXaeDOb4I2Th5Ak/N4Npx+Pa6r0Lnu3DH9bBdJ+9i4P4V5Psz8DNAC1oLwAIlTpEz\noDNWg9oIvMDD2r+ByyZ4zRi8alHanRIQqLXTqHIlH+bTTG1DhJUC0iRQyXpjTUC5njAuheunQLno\ntYcwETP/hu05XDMHs78F215IqxsqmFlGndf5tEzA3EndgmYaaIcVRWj2PXSuLC06S4P8ORexS3xo\noFM0GyvS2CpKCBu/KVVzEXgPs0ngnIPW/PCwV+8xjJxOvs8FemWBxExmCMWRViHrcD/Sp0HUH2oy\nrPe39TurbVrQNppV20TW71pH71Tko4TDREH0wRCSFno/ss4H9TMGyJ7odc4Uq0XiUkSpKu4AZLCX\nIVXeHKHQtaFTq0w1jwzqvN5rTn8a+r0FQuWr1XrvEqKEnb7G6DdBBtyCZyujNtW8fDf1gVIEQNHP\n83MEhdrLPooE1S5TgJaSZ4gQtHCGcqpsHcT+ukUHI4q+zNe6lCwevwK9Ck1OFyMFzHwbuHsIrkd2\nOo9onvooNGVDeZI2wAILHRd8V8WS56G0mi4RjUkj1FkKy9YBA0263WEKRWm/XoafPR8WTnwUNl4P\njyIrqgRcBUyvgqkScJJRpijwLCdnAsgpAwPwnBSe/mG44H08dz28OhNFUThxy2BK1QX+bjsNUfi4\nWlnilU7kJYpe6Yr/2yyEXpUxfa0oWm1Fpj8oPSoNChtCQGlrAaz4Bbj3E/DUYDKbYhvJglWylN5W\nLoIP244ySSP6UycNqLfnjnD0TihNfDCzC+3HUuaDbQZWUMU2dZNz52XumopSjxEUSRdY7mRdWfLb\nnMreFGE9WWIQ+hlL4/SE4kgnCG65QWRtGg3T4iUGpGyIFoiokQRUOh+9F2dpVpF17vRzy/i/kCDw\nX3kVqpxmoh20m8iAFUgHLWNKyxJTQTpdInI06+et+pWdgDxCGMBMfyxIpSWRe3UFOoRJTgg73eN6\nn2FChLSii9AyWmLz3JCJMQKMU2hBCDvfyA56M1RqwSUItCWLvJoZXlUF3k4F9RYEzqojKHVDq+j7\nll9ua8eSGPIuHE1/HyotSBsykCnirErPg+ZtHHYauNLvmrncTPuDF9bnrt7CAjeVQhGYKtFrHXwu\nW6A4UBFBTiQD7Jo1cMfaL8D8e+H+N8CuBDZrw8XgpEKHDXSo4imAUxTM0YAfqcOLPgib383a8+FN\nhSimkg9FcswfbDVAIZjpcWZSWoiv2xU6VzoXAyowmfY7Pn+qp7QiC8EGxebOEf6XJWICDz1/irk/\narL+V3ewJX2QRls+P1cKG6k2IQRAFXFbERHzzZuShBCEwsvZY20viLKUBNSWAFNOAYwqSavfW9Yd\nxEBBR+fZ3FujHU1ocTDioFGSBbPfhQDwPmsLwTI1EDRCWIfjiFlubsCqvm4gBJEzfX9Kv2PFkmJQ\nVDK50/uv1LYfdRK8XqGfWdDnVXV854EdiFvCFPAYwQ35RK5zF7xqQY/voOkPdoaO/XsBGUALZpWR\nwZxBdq+4CEuXUJwlI0yCmQlG2XQE82NB/7dam7Ko73f0ezP6/DEC6di4pz2Hd6R4LDUV/VzvuGpd\ngLFJCP0cWENChghK+n9HUFSecJ59Fj3LgjAx3craZJ+xhZiqojvZdeS7dsDGunCHWokMUstJRtC0\nFHl4eRKUid3D7t/nc4NeFpb53cw/7BEF/yQPzEzRqq7mWEk2x6ECfr6Ag1fCPv8BeOl6+IcXwT1l\n+eJyB7NDdPJRhlhkNTk7qTG/YhU8I4WX/QVsfC+bL4Vfr4TAVFMd82a2JhZY0/GyzKISEjQBUaCJ\nmrVxQLJIQkTbEKpD7llErpGmmt7tJIyRIeQugvzaCexxMFc/AekpJg4+jf+540GmqmKlxG4XH8mL\nZUo56B23kkfPMcVuG1zhtPaMswKMwmxZSASoHFHZPu1ge0n61E4ExbeTEADCyfjMOTl0wvy0iQ+U\nwRVqDVhAuaNrZhFRZNOIcrNSn56Qfj5HQJmeEA+ZQhSf1RxN9LMZsEM3i7VIquy+yCSoIwrb1nEV\nKRnaduL+sjWSFFBVV9YoAUl3EdfAE73OKSug5+VXszcroK7KdREZcCv/Z6gUZAc8hijKIX1vSD8z\nD70qWAqEqSMT5AgTlRL8KSuQCVvhhZJhwTCnzzyBCIFDFlJXlVvvWGpEsPbrgqijO6OT/PqYokK0\nAMs+CGgHQQxm0hniMxRq37eIf4cQze4hLh+i/4bUDP3YAq8W8vobSQP+Zg28NIHGMXh0tUD8wRIU\no9CGAz6Ym/OlYC6b66GXUUXYdCxxII4wD2aSWtxKALef+dpq5nJBO8MZXN6FD5bhHde2uaPxW1D5\nIjz9XfDIuAhAqQSf2MQ9B8q468Zpv7QJK78KtXfABadZth3+R0XK91n2WlvRpR1E11REpmAOUJeG\nDp5tdp1UKpv1qGTRRuKAWi4KNSmE8ouHvCqZtQ5VnmmIxudOaHDxdSgBHpqB13fgzjqXPEOQauph\nvhxcPAnBN47+XusKwrRAZKabmSMEJuOCMXbV9POzSLBnEKnLAeInXeZFUWeo+a+fP5xIYOk8FBkW\n0rfchSpficrjpMq+xUO2AGNerFKPKHNTWk29n/2dI+tuDmUcEM65Q3+vI+t6vZP2j+SCni+yZAek\nHTNO9EMDWYePu9CuhUQ2c6vPmnqp0dNxoUbEYjRuP+x17hSrOUJSqCbQVuXapkek6flbFhBz3Dho\nhlyXIYIyhAya0arqyCAb2h1DBtiUsD1+nQ+nvNp31yKD0kIUqtfvWcDDFgvQI2zPJz1qes/XO0KI\nDCf6eVM+hkpj4e+6UIbOglAWUY/J4w75TNmFaHWPErQkwNXSRZ4QAldt3biO7K6K9K49ArUDMLFa\nBss7GYF2mXY3Y6YkO31VOYVVVd6m5O3qulB5vxeB9yEQl3RhwyxQnqC4BYb+O4yNSik7gOEO/HkZ\n/nYHfGLjVzh89Hq4cQy605IC9oJr6czXIZ+G+rcY2Ob50TG4sA5PKWBtWwqreMKGZ8VFvA6+uWsq\najqXC8h1w6gUMFeW11YpBLJsDkHnRPsCkFXlQ1kpRPLjxJBWEuhq9v3Ew9oCWf1rboMHXs2723/C\nL6sLwDK0bOO1gJSh315tXxcQ91Ae5Ms28SzRKlNpsBjmgczLseYIWlnIAAAgAElEQVSHtW8NrwEl\n3WSMX91KpSbxCWSNGNpvlkK03pguuxPYq3KwTMdtObDcS3uHUZYAQVGOI8pvHRLdnyYwcUAU6SjS\n5usRHWCUyRmEWljrSqILLgRRG14aYGv+tPZhM1JwKUPksa6yvLIdNq4skYy2q6OA5Q97nXvEqggR\n3XVzRFmayQD0aiQmBMU5qF+3MSihp43oZxaQyakSHN/2yEEfFKEh05MORrswmIhSHQVGPazTSTAU\nsJAqlxX1oRZaLCYJft81BLMDQnGP2JSuLFGsxjDQtdQX/EKfZfQsQ54WbW+l6ipRJGOVlkDMuRyh\nntgJl48BfHMN1FdB4w/Ab4XajcJVOwX4CmSraC1OCEnAh3oGSaHBNoJJbOmUphjKBQyqbZV0oUhF\nIVXaAKfhyVCZgmEtwYqaaSMOfmMAXjkABy6ET7hTrHHwiG8y4b7KTA7VNlxchzd3BbEMLcJIW/ih\nnkBlM+TW2wQI429R9TwJZzYl0KsqlkCv6r0n8HV76NXmyUGzQq/YjdM5qyjBNbYU7Cp7eMADySpo\nfRXGfpGd31pP91kTFB7KXSjMeR5tsrah5qoAW2nYdM2KsQCVcYs9wR86AKzUDWGhJGUXrA8mh7Vu\neIZDFBMI6lxwcDSBdUVwi3UcnE5Enk4gLrUUUZqroj6XvKzZERc45mVkXz+GKLsRZJ0a6rUEonFE\nSZ4kZFeaX7VZCpuoWUpdlaVGoUXnkbXfRDaTjODquQKgIoAg0034pIOv/j+NWHOCzUCozWrRuwUk\nQLwVGUyjVxiSaCADZBSpAWTAB5BBHEEoHAkBHFd96LCZr3aywHGgkorJWiJERIcKennQeSIBATMn\nzBQaLHoU9r4MmdgUsx3VzMudJTjfh8UYZ2GZC8DuY6dUxjSYTiJmayS/EnRQNGzZUAmhznNJF83O\nFlD+XXgeMPIJOH4hbPhxeKQKFwLdQWA1eXeit6FVjeqTCjdUKmP1I5yqBvUqhTTedUOEPXHg2jop\ne2TAy8tlYypKusATyUzaNA9rU7gi1eM7PMyWYDaFgRLU2qJU8TDSlGc1WhJwqnelPmyeCJoxZLqU\nmbGQRgdHephPBfkZako9vRRM+9v6OaDj2yyJa6BwglrNV17Szcd7zTAqCTJP4gYcb8DgT8DxkzzS\nejfHeBWnUlivczpUhE3fUm0tCyr291tGnlH5QObJeelP1wV2AigbIglj8mAip9aUVL7NoikVcDjV\nxBtTiE42yQ0WrEMUYY2QTm5raz0hMHh3Kv8/hKxNtYmYQgJdFyMK9LR+L0EQ7EbgUi+mvNULmkdc\ndk6fP9JRF00hczhfkrGfSyV4ZUr6PC8K+itOlHSCmP7jOuYHkboBj/CfkyBw7hSrchpqiiqqak6b\nyZ8jimo1mkdMSEM1/2mX/qyJmJsKMtFDhE62FR03vHyv5UISAchCqxRQcgHBxOese/0M6GJKQkDK\nEdCD+R7bTs15L89yTvoLkiZrStVQZsupOZ0o2tXXThLcEPMlehV9YsUNoliWsgKseHZFkVQCPHYM\n+OaVcMGfwUUZNI/AkztwV1WgR2c51Ou0C/G/NQgK3JCpoVNLV41TOO2guDSFShK5ParAigIeKFG8\nokZroEVZFaRTZYwLimQkg0aqyLKQhRXXHE2RvIZaN8xDMw1o1bih9tPbcPRRuxIYTmBTETEuvPRr\nQJkaxmlNvJLOFQF2VQm1K/RKR8ZZc6kq1/io5R7PNAPyCrSvhssn6O5vcKwFKwcCnXDUB6K+cVut\noLa5fEyxZio3hdM2qWJtIq+dRCyXGUXzHScsAYcoHNvAjcZ13GkdcWAbooisONI+BKU2EI7zFi+0\nq5Z+bgahIpsbqJUEv+5xBKE6xP9qaBR9/yiiOMeRtT6PyMJFhYAZQ7EDiKKvONigQcg5XeQlLzJr\n/ttxZC+fRI5faWpfjyLK9HxEB8wD9yFgzo5zeiLXOVOsF6aiQNcg/peD+v4s0rGVSNm+IQIP1dgB\nBSGVzjpg/hejWmWIkh4juAsGVYjmXRBgO3iwSj/ZupEH5WU+L/Np9YJWLgRDEi+LwHK1MwdHnAhj\n4aSfFW3LcSeuhoeB1bobT6DIOxFhHiokQmsL1XLCrZhH35ExeiWOvkwfU4LmYzUU/JnsmfCZDD53\nB5V10Jk6Bflfwo/9GnyzJh1uVVmcgoXxUO/SE7K7LCBj5nXqBfmlXsbe6aZgwaNKgZwRWCTwlAGy\nxgry9BA+oZfO69NQtb4AKAdk1qvYRQjWWZDKeJZGCVq0eyia80nkh9b7zToxdecRK8SZXDnZDNqp\nKFFT4tWubEyFjmWhaNfmpVf31ot1UfZBKVYLDY6kgrwfWgCSZdAZhqFPw4Ov5YRfzjammETrt3p6\nRXRSL4jMTPxuEopRd9XVY+4YAwTzqbJifAiGzkYAYdyHDSh3gc6XO1mDVqs4l2mghsQfcl1j6730\n8VQSknRO62ePoEfNp+JKNrCjLmkOIet6A3CZjuM9TtZ9jijnUWQd2HwvIqb8KeBBJz7WBJhO1bJA\n1rudCDLjBIE6/V4duCaHizqwswK3luBOZBMogH/V7z9P2/9Er3OmWLfo6wKiVC9HJmUKGaiVyC65\nQj9jPNYBIhOJsMjssvKD5mOcIhy85fTDXv+fIRMGoXJW5sS0jylOcyUlXhMUTBx5tYU/XQ6uBY8s\nXKs7ewSZ/MzJexdFfTAmwxj95RHjqPRiOVJg7uwnSaZqIlklJEtzhZBhdLoMPLAGLpjleRc8xIU1\n+NPzgJkPw1ULsOadsDgGNXFa7gUuSvqLfiRe+YnlEJ1OCln8RszHyyZiYzhTVn7hONCpsmdlgwv1\nu+1yUKjma7Y2GxOhrcrWCpKkPtDM4hNpzb9sh/DNOxnn0Qi9OqRt5qs3WTFuaxdxCcXZboZSZ8rB\njC4VEuw4WRZLJFHLwLK6FlKRnfgoksJBJwNqr4FWB6oPQWuYd07Uue18OVnYqo6ZbM2Xgpy31D3S\n86US8uct0LvRw6pMxnExVcYKsnFPI4BljEDjWlCFbS6zdQiQqauiLvuAwM33bFlrazPYrlbHvjr8\no5NnPKTrxGhTVmh+P8Id3UFIADILYpW+Gnr12pfZBHbqfA0jLJI1Ha2J7ESmp8oy1w1EqT4KXKNK\nO3VwUQ5XHIDaNGxfC8tWCYLfB3wdeKwLl6SCbE+dubT+w9c5U6yPImtsHaIMjyK72Byy880iMN2g\nv3FWl+urOb89gSlgtQYahMpVxxDnupHXzew3f26HkHCwiCy0TqILogjBJghCAErlcSHCbAEd+/ig\nF+qW8fIsjW6BcBQN2v4K/am2jwLbEhHs2ZIu2iXPl1yk8DeIQK1SJNHL6uqG2poAf+SA226En51n\nZCDnxR0YHIE/ua5g8Z5PQroGFn8K0vUwL5Z7RRGX5dgvOkEKFrnNnaDNHqI2hI8s+tFcfXYDsK0+\ny2OtlN/rVLhhOJxAaxQ0c3PY4iUNSsoKjnQSeMwJajoCbHTqwkkEZcZ+7jRS2OafnHQy19OEDbZM\ncBvVELfMWCryWEK+O6uTWzH/pw/HUmcO0kQUUDsNAat5XWFGk1rRhjwHfANcBrWvQwb+zudx3wUf\n4kKVSaN8daF3LLfd2+qROsJYV73I+yABGJQKUcJ7nJjiNZ2TErDTSX/NpzzmZN3Ze0eAcRcyn0qp\nsm8UzTiV27qX+a3nYhm8sCaybTU6Onq/awuhIy5z8ApELpchJv6jDrZ7CRxVEAW7XNt70IXfx4CL\nuzDeplcEe0VLZSaFmhMEfQxRyMcRBV9BeLjTy6FYDrM1OJIKIt6PgJx9qfT/OPAi4PM8seucKVYr\nCXgaUaoFMiBXErIz7MhrC5J6AqcUAvJoIqjU3k8RBbZf/7bSgimyg7YJfkOjZW0iZH0ZUmgpR30+\nEQd6hkw6BErXXicC7RTt1AksgyqyeGvIdzuIkLS1rcParklEQa1HdsvliNLMnbzXdhp40wVsqbWb\nPb2D2u5JYXMB43kQuoWSRIVN+bYTeHwfMPVMWPF57s/hzwbFhbClAQ9u83D04zD0FCgGoBDEeqMi\nKEO/BTIegwjaMTO9nmgAycum0yikrZNlaf9JB09zTR47CYcX4LjWqG6XJBC0U9HOxUgkulvIfSyN\ndrAQX+JgLnW596Yy9pWu+GNt8zCifEddJgmhdkPhxPT/ro690XKu8KGG76jeyHnp0wNOAyPAJYiP\ncZvKbo4Ec6aRqPdcAstzDTomkR9dX09V4eFFoLMC2g2Jwl00CTO/zKm5D1E0QsQ9QWIPCwjqmkuF\nc1ntqnyqH6qmsjjvlD7lA0pe2YaNNbnfGJIKvd/JmlmFBIISVapbtZ0TLoCYispmqnO6P5HnLapM\nX5XCVEW4vIva1sOEOMgKBCANJxKMOgX8E3BRKuvzWm27WW4WfD6g7x3XtbkBWNaFnSlUBiTZoZFD\nWocTifTrHhfqNk84WV9H9P4PleAVDbi4I5v0NPBVYLSQNVzMwqkBsZ6+whO/zplinUWU50FE4WxC\nuGYD+poSFsoEMskJgnIfR3wjhgYH9F6D+gUzF80sXyD4WQ3xWmrrCLITZwQfrJ3nPpcExThDKJA9\niCi8IS9oaR8hZW8roYzhowSkvdraqO1IkQVccdK/I9pfo5nFnL6jwICDhvZnuBDT1ojojRye1aJH\nUC8p5xQXiOpzKXwthYcevxkeKUF3jt2PwO4xcGPg49JgA21BVG2Y98HPuODg3mg8TiALYNKFjJcE\nQc4jyB9XeyGbr3CiaGpMyUCchH0XCipanou1sQpR5PchZwBuUwU56cQ1ZAFDy29f7wXtXErw5XYS\nHQO1JBZVSZxwoiQt4rtJuzqMZgV1YVW3v6Zpx4lS3YeYohcgC/di3SR2If0+ov0seU2dLImb5LAT\nrjQEpfeA03GeG4KpQbhkLcz9BSz+Ifceg5VDIpfrtV1TiZiqt6kcnO/gGYmUUVjXFl/3qYr6dNOw\nicyUpGhLJ5F+no88/9su8K33A8ec+P+HETQ54JU25QUBHkZ8mmXd0K9StD6fBE6zR8xxhwS01hDc\nBztL8G/6vGXIWtyp/XmTytBaxOe70ss6fG8iz32qytoHkfknlfHOEOvgKRXZLP4BSdG9RMfsC8DR\nJryiLsejvQ+4owsbKlAqi0x/Sdt0F5Bp/mzTwd2ExIkncp0zxTqTyQ5XSeQY6jaCGFJkd1pOcHqX\nEFRnxeJLwIPQOwxsi34uIXA5hxMpIFLW743oTp77cLCZuQ4yJ2Rmo09ZdLuBtM0BQ07MwlPIppAA\nm11gLJjiNq7cQUS52v8PI0jMuHstRFFs8HANIa1vRBdi6iSH+RDBF1xDNpVSIveqqfK0qLP5Hoe1\nQnyzFPiVJa8+qSM/AYNTkO+VAT8E3nJ9Z8ag/jpor4HSXliAfyrgNYmgxekkRHZXIItxBHHnHESO\n5j2AKC2QBVOoQnlUvzvUfEQm7PE1nHf9/cwkgkIOOWnOQ8jiaSGm5VYvi+q0jvFYFlwCWQKjDg4k\nchoLyBwdV0VgForTOcgI0emDyIa6CVEEh1PxsZnFMq1j3tS5niGkO15ewEgiimcaUXgnnWTaVRC5\nWYFUdTI6UD2RsVibIpkFiwNys2IbXPUg/DM8cPIFTKz4PEkTDrVgbsI6sA38KkjGOeEPcOeG+7n2\nUvh9db/MpWJBOO1LE1ESWxQVpkgCRrcEjzip6DSqfbpOZeoKZI4nEzkPyjnZSJ5UyNx8x8FzM1jX\nEUW+J5FU0k0luMaJJTGdwHedAKMNRUDGP17IAY63l0VxdZCN42+8tH8T4vo6qutrUf+/E9EFK5H1\nv0Vlb7ILO9R0P4go4PMQJfltL5v19rqsp39DFPZICn+fwSfKEjC+AbFarktgX1UDVouyKfYCNU/g\n+r6K1TlXQxBzFZGZf/bev9U5txz4FOGE9x/33k/rd94KvBaRw1/x3t961geXxdeU5eDLglROIE5t\n48VZilsZEeBdiMBuQBaJpayZSWOEdVCzRf9X1caMdPWIZkLkuKx+q6qG0w0ll70ISysRBLPFCbWk\ni5i4Vh/1OMHPC6Fm7CIhQ2yYoGi3R4PeRHbacS9mjupJUYSJLNSW/v0oQv9M0fhPIhtBVSPIZmq2\nHBwp6+mziWSe1BHFensnga+Mw2AT0o+FEupWIqh8M1TXQPUItL8Fy6DZgfcNSvbLJCHYN6D9GiRU\nA2sj81hTYdmMLNI8ESWbAB03rylNr2ai/AVWFKIcJ3QcN6hA7dSxK5ym7yIIMi3B6pxeuuVuBCHb\nuWcQkkJMGdopFFbndxWyEC0foolwHs3E3aV9W6597iCWyDLt08lE3DMWUL0SVc6EbKY1Xdjc1EqM\n6nPNEvjHFGhvhrwstJD918DwZ6GSM7n7Iib3fx3yzRJSdzshuRlKF0B6DPwguOdS7L6fu05+gF97\nGvxOWfyI1/pwXpv5FL/tZFo3I6myDzhRXlZlaiXwoypXmxF/5t0qq4WOzYQTebvJi4V2oCaMjwsL\nIXhMAr+Uyvw+H3hmBreU4T0JTDt4q4exQhTax7xkfpHBjIdOFy4ehMMeko7ULJ9OZYOdLOAKJ26K\nR3VRPViFS2uCOHd34VQqiPVB4IMdQcgDVdiXy3x8t6SZWw5+EnFJfdjDG3UuG4rIV3rZVP+pAVNe\n5nMXT+z6vorVe99yzt3svV90zpWAbzjnnoL4d2/z3v+Rc+63gLcAb3HOXQy8HAFU64AvOefO994X\nS+/dReB83hYEuYiYMgeQnychwn2MUGHKhLiDLIzLvOyaqxAl6BBld8gF03rcy9HLRpaGwMHM0mAq\nV4qQGmgHnaUeTlRD4KSmQQE7uG+qLDVC7Lwcq0cwgSiCk8C3gGchPrkyIthzCA/fduIZB9sN8tLP\ndEgQ82yVKsjHEBJzAlymPrfUS+S06QWJ7HGyeA7pBI8D9zv48sEa3DcGN7ZDWHiQUCYsfwRcGfwi\npP+KVbLZjZijBmytnOI2ZEGNIYtwCHgBcA9iTj1G4D8OAJd7+FKjKyt5bBXfnIWfrIs1MOXgi8hm\naWcG3NWFv3FAAeuU+L0tlUDDZh2ffTrOeGhaGTNLPPFQLomindNoT8lJm2eRTfpSRFbuAu7Q8doG\nfAMxiTcj/d+m7dqLKIwbkMX7VR2bzdrfq4CnFnDhnASuuk6YBN8pw616X9Y9Cut+El72cej8rPhb\nH52DtU+GbdsguxYGvwCLTWh/GYZv01QjwNWAnTAu/s6vOGHULO+K6T9dlg1nDlk7X0TWxyonU7wP\ncc3sLoQjfDuCBO8EHnYybD9XwKYOzFSk/aeBTzr4oofVKfwS4p7ZgGxebeBpyOb/sTI81BW0XCBZ\nTHeXRO6f6uBuB0NVeBVwXSFj/twclhVQa8KtA/B/Erhcg1AFsKEmfOEtiWzwuxMomhoArsFPp4Ki\nv40oysESPBvZIO6Yh29U4WfVMqo6+EMEjX8O+B2d26sy+HYdjrTgb6d5wtcPdAV47w2kGA3zNKJY\nn67vfwQZn7cALwY+4b3PgMedc3sR//RdZ9y3LZtyL081kaCAd4JaM8Sfd4igVFv6ej2Cbh9yUukG\nxBSudUUJjZRkUD1KsyoJurNIeSuVnywJFde7TswAT6A0Jcj7U05Q1OpEzP/eiQEedunvRmXZAGzz\nATFfT0gym9PXJ6E7phc/XIY40Dci5uO0E92zHRHWsrVFn2EBrweAq5xsBFOI0OzTPttJC6sRxfN4\nF9hdwrU6+MseFY2XEhzMy4GZXdDaFRxiFWAW9o0Jb7OdQkVpPFOpKKTdyMZXR5TLKkJqr0cW9mVI\nlPaUk+9zZwbnbeb2GfjuMJwoYGoRTna04ZbpYT8dOeqKOjyumSD7U0LIudC2ekJtSS1z1vbQrtGr\nSZchxWWoiCDv84LAigwO60QdSKUDjw9IQCQvwR6llg2lct/PeFnsy4HDBdylPLmHq+BSuH5UnvWI\nExm+x2u94EXExBg8BYO3QHcOmp+Cpz0F9l4CG98gEG/14VD5ZB5BHlWgeH8vqDCUikx8GVF4A6n4\nQ7+JKI5diElt1aWen8EdZfhiBk9JZV5uQKyDr+jQvVrnM0vELTCqgGWVg5GKZNL9XSp924QAhP+f\nuveOkqu8tn1/e+/K1TmouxVaOSIJASKKJETOOZhgg8FGRBtsgw2YAwbb4IBNsDEOZA4GTDYiByGE\nAElIIKlRaKUO6hwrV+393T/mLhrf8944912/9+6gxtCQutVdtcO317fWXHPOdTLwNxTg9/Bgjj0y\nVn5vdP4T0RpuykBnCD70YcCncvBmCE4NKMA9bmlzKkeb3hadKi0eHOU3x3bzIBbUOPFXcvCIIyhj\nor8ODzHwlgWtvn3kEUE9Kw0oux1IwJIojHLgwRx0B+DwKHzqQs8g/68Ysv63gdWyLBu/nwD80Riz\n3rKsOmNMcdJBJyPN8tH8axBtZcRQ6l9fniCAsYERm68kylZXGzjCzwoO8g8yih7iIqF/rqdu4Go/\nvSuzhHttY2Rzz6LyJmGBHRop2Qdt0UNCrsqZIocShEkOWwokEaOfzxt9b6UFMyx13YvPbgJliUWJ\nXh9qAuyBgn8HWtxFfG4XyvhiKLbNMHqPdksZz3ZLATXiX7xd/t/1/vsFGDGqCaAsaMi/AUW6SBro\nK4DtqLkTM5BNAd3fxmyPwoyPtOKLtbFBD24tI7ZDQb4Ejr0MWCFIFyCdUiZDULhVK3zJA3OAoSL3\nzIK0p/ceY8M/Leg10FYKnB6ENtiwswasnhFW+Fdnk+Of6HAQslEwSZUKmSg4/nCdQgSNzS7WDPkR\n7pyNdmQPvX/cPzeHEXeeABSKwnOPEWmPzZcWZQVXH5UK60YnfMwjWKEqaVvRdd1Xrwz3wl8qIBDQ\n/UgBb3sw8FVT4SAweSekrhlJ58s2wvY6qLqQQ+fdyjJPQgNKoC4KAwbOCflQOPCxB+E8VATh267W\n0Kf+0zzVP6SDUTZdlKxuDmpT7w3BwZ6C3w6jZOE2D1b4z89WGz6wYV0ergjqe08bzW2cGIIbXdGv\nhvGrBYTVLnQlcJnkwae2TvN1H34IosptXETZ6lsG5hRgjxB84cFztrDfBSj4zUEJSqcRPk1BUMRx\nASVK/8zA73IQDME3jBKTsQXYIw/bwzq2YwLwKfBOCqqiypa3p8GExTjpBPYLwIFZeCECM43Gvv2X\nAXn/G6//lYzVA+ZZllUOvGZZ1sL/6f+NZf3Po/P+9S3+L7/7c/1PMgBTD4WSQ/3OL/CypXJyH7Qw\nB4ED0ELpwm+S2NrVQujGNTMCfBcxPlDZDLDODxKT/J8jqN22OJYlgIJEKb4zlaWbE0E77lL0XNjo\n5hcnHhSTpGIjzULxIWLUPCv3328X2nWyKL3vQpvBNiA1CE4MDgrq/z9HO3VRX51AgX6GNeL01Qfc\nCOyWhq0h+LMz4i07hDCqGNq4+ixw24D0+VARAtM2ctLl+C4t/sUvgsVfncCYQ96gYf8Eu/T3YC0K\nQMX5GAVGXMODfMk/e6M4g6MYPANouzVHwrYnRki5Xx0NQQkEp0Bwhg4+twEK06RY8qr0g8HpYIUh\n3wSp/9SFcYG2uL7PgC8pYgQIDn7lRlb7/44wIucrZodV/t+uf1zFr30+XT4H+Sr/Yhft8v2bnQ/A\nPQlGbM4sRviCrWgnjqPSotxfGG/cAIFlsHM27+5tccgoGXp/MAidMR3XQ1/NpPyK4xUL7rbgdEew\nUzliADQb2JiGe2IKrF0+HnpIRuvyW2FleJP9kn6NPSIhXwEsCMB8tJk/56kMv8OBBX1KRh6ogPsc\neNBv+noBOGMIAgUYduGGegXbccDFPmT3JPDHDLwWgWvzYi6cihqbbxl4w4Wr/Q3pFp9a1x3QJbs9\nomMJeWKYzImo+XQocFVOdK8bAgrQJ+RgSUS38qwoJNPweLvWn1UOpkqeE9kW+NCDD13gY1j9AQTK\nBH//u6//ZVaAMWbQsqx/oky907KsemNMh2VZDYzw3dv8a1l8FUVP//V1q/DBibaCXxyVMI3+QbX7\nZfae/vcq0LM421NW2eSX0GPRzpNEpWkvWljVqFRJoBtTVHW9D6zyRMPZzdJC2omem+noOcn4v9vO\niLw27n+91T8WP5lgGAX14q4Mih9LrJHYZFAQj6NM1kJlyTr/ONeX6Vhmo7hUHDHT4r/nbAQtzEG7\negtyirooDFdEBTccZ+BDS5hfvz85MRWUMkhGtVOwlpRgH5zEdd4bmSNePMAizmIYGasQ8U/IZYRJ\n7/kXpZiZ5lCw6UQBqIwR3aOf3YWAXNFFfDAKpRmwC7Bh7MiA96EQWDPA/QICOWAihBeIvRDcE0L7\nqsbObwNvGIJT9ZQQhuB+UDoGMivAroJQu1Jst8v/4Dy4GeGTmQrwWqF0SCsz8pVzSX7lvGsYyWRj\njJBFix1Gz/9ZFyob/DlJAb2HA1xeAskSXZqnCrok4RDsyvrXp0hmLod4DJI1u7SgghOhtYwvqgY5\nPwCLyuHBtCq5L8swHyoJl8ExLkyx4YfGn5phwV7GVz7F4EVgaV7Q+U0WdDtwgaUkvzcOyxI6n2Mj\nKo3vzCvbHpOBkE8Zm5mD80KiUn2vWsYt9yZ17kdFYK8gtPRCcyVck4bKBLywC16vgaUWPGHU7Pvm\nENSm4fwYjPoCmubAQAAeD8FpwFV5dfyfTsI75ZrI81C3guTLDtw3DK/V6Vla7MFdBXjPaBNY3gVL\nqnQtLo9IdbXnEGws89dtDfymAU5Kwb6D0FukfMQRzncgsC+Mnqyhv4O38W+9/jtWQA1QMMYMWJYV\nRZjwLeh+fRO4w//7ef9XXgSesCzrtygOTkWY8n99GTUSig5Uzaizv7cPxn/i44ZN/ofWGyU1nbbW\nVod/8FOMGkjF0jqNgtEsxHP8zFIA2+Z/TqrgP2thWB2EvW343FUy1uMoY50GLPJUkr9nw0OICdAO\ndBnAGZlgkDZKZHpArAIDe1ojloUB1LDYhQDouS7U5KEipaAvCxoAACAASURBVPHTr5bCXX7WUI+e\ntVYU4FPA51no9f3SdgZ9d3pXXNVCHp4KakOqs6DFCAccSgEZsCvAK6Ao3TYVXmnDXBuRf5yPn5Jl\nBFMqDgzzYGZQ2ULCQMbTwURLlQAOFOeKf5VnVsxui/pcGy3oDOQKjGSELlA7DKENUOgb+XxjgdcG\ndgCYDnYMnB5lp3YcTAbcPnCqwRmnf3sDYNeDGYLcOrB3gd0NXjUE54H7KMzs03FtAtwIeClw8rp5\nJWgjyDJCOC6gINrmn5vjH3tR+2ozgv0E9bv9A1/5WQNWHN7Pqbk0kIdRQZgXhbe/OtgNBL24kCz6\n440fhEAXrLyVQ3e7mmoLPixAohltQNVwSQge82D/HHzUDPfXgV0ilGTAwIokNMdU4vamoL8E+nsh\nUAq3B+G6AFwH3JaHpkGwI4LkVhiJJL4JvJOBLgeSSZhpQXUIptoKdONsNX16orC7Cz8IwstJiJSK\nB74gDPeEYY+sMuVHgzC54O+fFfBMJSzsgsDrEJkOz5bA5iFIl8DiECxMwZKYGtp1g/AzD1qH4Xtr\nofIArfWzs3BnCA7wRK3b2AejY7BqUOqqwCA8nRL0VBEWbBEHnnPh2iGY2gC9TfDzyfCTokIoCgfa\nsHwASm3+7dd/l7E2AA/7OKsNPGqMecuyrE+BpyzL+jY+3QrAGLPBsqynULVeAC4zpjhg4l9fE2x9\n+GuoJDkDke3b0FqfjLI72z/ICa74mWlHwWQYX/3kY5Kb/d8rRRndMtQd3wv9KcJ2zeC7hGhBDSDZ\nY9ITiD8FZchL7a/o+D1YP6AEKWvDKgNjLOjxxL8rSmvrEF6Ff/LFEb11wEcFGBcQzavagUAEnkV4\n0g5XF2ySo6z5myizLgGqwnBqGB7NQbZYRrtQ8IA0bM+qyVLUkB5gww4/KfOK2GkeaD0Ey+rDq+hn\nSj3Mjsh4Itfr38EJfImfHFguPNkxkCn6vIWFr6aLDt4ptPPBiHLCx1YxqHSIMDJfp8inKlkA6Qhk\nCxCuUKmQBMJjIbsTIt+C4GTILIfyp6F7NJjRUNgueYzbCtFjIPVn8FwIzYfQLJ28tVEyruDxkFsF\n0Quh6UHJc4MzwRmCzNtK94tTI8No18uFIJbTjlx0Ri4OUjOMyOp2+uc4E2XpRZLyWLC3yVbv1kO1\nnp7NwMAW6CqHt+uALNhjYFYM1rUx4qE3oGTd/HklPLs/9mA9f590Ev2HvUxJ0GXyaOj1tbWv5mGP\nAPwkCNFGWNANZ5XABsfmB3hc6EmcMNGGJX0KzOc1wLEGrrP1nKw3oiqRBa8bDp0A37TgFgu2BGFH\nsYk4DKeP85V5KXgmBL9s01DIPhueseHKPPw6qoB7IHCUD0F4KWjYF77xMXwrCovjSjqezsE5Qej4\nIfwtAvdk1bU/MAFbymBxGqpKtWSPi8MvclCdgFWHwZIUfNQN75RCoV9Mi+oYuLVabuNL9OxcFQHT\nBW/ZYpFE4jC4BpZ1KEBsDkBgEtzciUqBWcAmFQseajy+xr/3sv5v4t7/py/LssyDRkHtdbRWD0Xl\n7BpLWeoMFJgWeeoggrh6E3Nqqr4Y4csByFuADwz0GO10B6Bnog8FaAs9u5uN1kvWVdDZM6jP7USB\neJtRJ3KXpfK/Hf1cTxbCYempowi/3N1WrOj1fyZn5EZV7TfR5qH/jzDS4Qcd21MoQx0PfGqEgban\nwAkqUwwXhDW9AHziiltnOTCcUEJjA7kM/tgSgfkmCURhYrXoYUNGaqrTQvDPT2Dw7ythXQRO+w84\n6hkCdVAoqq2Ktu/FMtNTGZjLMxIYi5jhaEawig5GiLUF/rVUzjIyoLAIMRSAzmuJ3XUqsVMtehru\ngjlPq46OopIhsT+ED4LMUrCW62ZnS0R0dCaDPQEaNkFvHaTTEBgn/JUCOGN0EPlV4MxUu99tF6m0\nf5JvBfWZeEkVeQHh/cBwyJ9ml1OnpMojujeU2NDdB8fH4eV1/jmPRws3ysjskDKworDbFFjXByU5\n2Rl+qUIo8CWBdkJajbD8eOjPAs1QNk0d/klrp/F+NsaUf17Elkd3hysAaxNU+dBJ0IJgEiIPQdcP\ntSHUISqA3QAT2oFh5l5wLeEyONqDcxOwvhRuHJZJyuFhWNIB11XDM33QHAfjQTQPY8ugfxj62uCs\nqfBkO1zQCE0BmOHCa30wvlId/Kqc71URFF3w4QKUBeA/hmBFXE5m17VDS62eje0JqClVlXhmJ3yv\nFJqicKQnJ6xwDl7Kwo/i8JkHDwXggiVw2hj4pBHWlytTrvHgHBeaHMEe9yOfgMEOqAzD7DJVlVMd\n2DsD/4jAJwZYCxXjYFpI0yYWVkAiD3cB/TkVQLPrYd1mfJkaGPNVp5D/Z6//Y4G1Oi+98AQLHvNH\nNE709L1BhCMMoqRigg9+H4LW6lF+R/NDWxnfS6gbHbQ1N6vB1prfD2GOjfgwXk7Q2xQUSPdGiccT\nrjLPWaBuNvAdF2Z3AR50lsNtJXqfKiNN+Jw8PBBUw2CMB6sL0BiCzoxcjkwEZtnKQOd50GRE6n/N\ngt4cDBZgQkyl+9YsXBRRtTqI/s7ju1XlIJ0A8+ZB0HkUtBwC9S7EE3rYSjywfi1UP7EveJskSXVc\nBQ7HhR0nwO9OUdfi3FOxD1nHlErYmhOPGBhpOvnZZqzBn/2TRAFlC9oNSlEQ7mFkAPsYRrrvxQmM\nHYyoI0ABuxb46CHCfx5H9tdXwWALDDdCrAMKPTA4DuxaCIwH5zndfBMT3mKnwdsDnCZoyKguTR4A\n1noI9UJ+OmTbIBQXhttvw7h+RbiyjI+tzILCJpkNTEO40tDukBiCmnZI5qVaKQEOhYNL4POP4NhJ\n8Hg30BaE9tFQs0MbTBxYacOkIM8fkOXyHLS1QqxEWvZ9YtDeBmtKgL5qiPQKEg5Bfh0KzlP9azcM\nJQ0yFXs0AN/58BhaNiwCr0H3OezbFSUt2FUOJZ9AbQvBv84n/+118HyQ4Map1HYY2u/5gJqJT3D+\nHtt5IapT6miGH8+CcBJ+sl0P1sGTxe+c4sKyAvSEtIGvKsBCW3F8wzBcloeuUkjlpZg8OAf3VsBb\nDjyegOYgbAzBQ2k1o4+NiBlzegaqC/BeHtpDEAjBT7aXcFUgT+foLKvyUJqE31XADxMq2/eNwvwU\nPJKBt7Pwiwr4IAYrC9DUqerymnpYOaxnY3QNPNYHpCFeDfcE4U5bj8cVH8KKw7Q8N3XBxEr4WYeW\n49HjYfcU3LE+ztwpSTbkodCLL9MEZn1NA2upUQCrQUGwHPg0DwT19TmMCAOKjdU69Pz3oYZXDj3v\nK4BCEoJhOCgAlxjFmcscuMn//+MQtHino7L7RiNV0gSg2pUrzmuWQOIzgaOAhqzgh4QjPtwDUWG1\n7+fh2CBcYGCZpThzewFIwRFlqm4/MHCtBfvkYZWvhNoFPG5g4wAEgxAogdMRCN1ltBi3ejDDVoxb\nl5Hiq/nlICz7BD6OwMVt4FwD3AWxdZC9BQoXQ8d3lCW6iF9lIbzAANWDUL0KCm/BrKdhDMytlCTz\n410oYy12xF2gCpxtysKMB3YWxo+DLW+gbqHDCMUhC2MmQH8fpHbCl7NzehHeWAGmEyhzYNs90HwU\nVLXCIYsku9vhL4Auyzd0PQJK3hjp4leg0iEFOCeCNQSN745IwKaiEqctrBktcX9RbaiAPQeg2Qb3\nl1g192NKt+rmGLR79SG8aRM4eym5pRVCh0CuB2WmRULo5jAszI64BeF/Tpm/GBvRpjIOrDREk5Aa\n8o+vxT8HB+g+FKfyXdyiQGMM2ry6gJZGqN7Jl3PBWysgfa46T9YekBlNoLsBu74DM/Vw8p0lkLoa\ndv85rApD1QxY+hjWlAswM1d/qQ0PFKA0BvTDuWFYZMNZBSik4IJ6eNnVsfT0gpWB08bDS8PwHyXw\nlCV++Io2OCkCe1bCpX1wdo0SgqqoTHqMB0d4MFyAxwOqREMB+I6B0wbgV5VwnCv11xRLXgGtrtZg\n2tZ7nFiAvYfghkr4W7+yymYPdnbBnQ3wmxysTsP34vCk32T5Ra2mrf5gALw1cPlEuGIZzB6DNs9a\niUQOtuEw4K4B6On06ZWO+pn72XBmneLCmS58P6jj/FoGVlxoSMOkuALhnRZs6IdgGRxiwekuXAbM\nCUK/UcfsVT9rrXE1OG5ZWNBBD1IHJgycY8GPt8OENsjHoL8RPqsQBcQOqltarOp6UbA7Az1vc4BW\nIwpLhdHF7zXQMux7UZaBm4QxMS2OjzzdsL8W5/LGYWZYEjw3CfUlGsn8oIHNBSnLTrTgHx7Mc9So\nK6Bjewe4wIG7czApKBzsGhveysGnz0+GzGKw71HHO+yDrEWqRFM15HaDwTpwBmH8kFLhUFJOGZ0b\nITgW8r1QGPa7z/iSNSACC0phu6cuMGF4vgA3RaAjAeVBOc0vWc+Ik0wW4pMg6TO4w/WQbdd7kQZG\ng5WFSAzSr8+D/lsk1ax5Dva4Gwb6tHtsQyD4O+NgWgtsLoeJg3ra+jz9zEwU8JOodBk24NRBaaeC\nRz9qzgU8GLVVzicu1DVC52uTYJRFtKyZ9DAs2g3eG4LCqvGw9w7YMgUqtnDKXvD8ajD9MLMcmkK6\nNnZGQcN0+8e5DQXXsUACyspgaK1/bLOAnRCYBSfVwj+K8r8gxGKw0IK1GdgrDi8kUUCeBHcPwFUZ\nKKuBoSTQXAFzByAcgDYXKg17xmHtxiC1B+T5dh6eCEBbhxqDVlr4YG4A7qiDJTa826l7RB1cUgpV\nA3B6FJaktf7X2NCZh+9HYGZa5P91JbDRhncHYHQvLK+CYBb+EIALgT/lYbwLh3TDixPhzCEIbIey\n3WBREvoaoHc5nDUGJsRhTg7+Ohru2lDB2Mmw/8AAT5fDHnF4aCUcWg5/r4JZIXglCosd+DgNdQlY\nXA/D22DNeFWg2RS0DcHcDFwxBt5OwMPNcO1ceDUJB5XCe8NwSRCmBSSX/e56dfgnTZfmIhGG60rh\n+xnobAEyECyHfDXa4LPgdEPQKiVzzPDXM7COM5AxMNVSp7zRFjl+I+KJNuRhe0BO4VlHKqUFHrxj\nSX42Gng+LILzi5bMTPaxVPEtTsPYFCTjSljaQkoaHs6JtjQqqOexDAXWdF6+p8N5uCok6eoUYGZW\nvqwFS+5IncPw4SA8bUGoGqJRfeY1LhzqQsRWd77UhgEPegdgYhwuCsADngJrLgBNQ1BVpub0jQEY\nl5Vx/7PAd4Hrk+qp3BcRq+HztPo073fJXX58FeyqhC0DesZnl8KGLNht4EVgWgO0ZCE5BNOzsDEB\n1EK4Cmp3QGs/lE+HwW6U+dUBBagr85MxF96w4Mcl8J0gfOc9FIBnwX7DsMI3SKicAIPbwRuNsrui\nKH/LidBQUA08ahiabwInAjW3QM0LypCL7seZCIzJEZnmkekGtsHE3WHbJhRcy+MQ83e2or9i6yyc\nug24eSAbhqEytZxjm6mfBx0pYNcsGLUBXHW+A1HIbbP4Q2AMe8b62a9KF3liFYzrhqVFr7w0kuNt\nRBnPKH/RliHQfBAmTZEenS4oTsaLl8NF5XDKVnh4Ljy8Hr3fEFqsPSgYbwzB7NyXJgXTxsGtZbAh\nDC9/Aas9oAq+Vw5/GIL8gAjt88dBQwC6U5DoAasWLivA4g6Y3wjrmqG+TuSKyyvESrgvC39Jw/ha\nWNAMe9iwxxSYlIfHtsE3xsLLYah14PcDcHoAfr4NfjoengzAuRaEMzB7SCKHUFBDD35qYLBEGedz\nObENKrNSZFWG4XsGlufg5AxcGYObhuHxXdA4Ddo92KcAdxtJTI919KzVpsCuhk9yCrRVFszIQV8H\nPFIJ421Jbldk4FsxeHQnPDoa7rVhaSeUhaC0DCYWlFCtH4BLSmBzHFpysHYIRlfA9x24ZzNUlUNv\nBPp2gFsOmSyCuYq0u0lf04z1bAPPZP2xECEprNJGzjTfMvA9S8TiQ114MwALDPzdggdd0RynRGUg\n0WqUBY4uSGY5JufLVI2Stiej4ne2G2hx4TgHLvIx226UYO3hwkcO/NUSFWWG39w6LyD8KYeC5e6W\nAs9TCVFc8oNgpSBWoWzZHQLKJLUnBnPC8Bvghw6szYuq9I4NL1hqxH/bwDFpLb7DLHjB0eipC86F\nx/8CFWUK6DMLsCmgTH3fHs2hGwjBWwFJJk9Kw8sRODotj9I5AXjfgbuG4VdxuHgH3FEP9xkYXo2C\npAFqpNCqqlFwbstIIRR0IJdVl5tSGTC91AMnpvXgNlRqsF+yyG1tgdkLoOmPz+GmZsM9XTiH1OA+\n34rjGdwjxmOf/V28wEooS4jHtXdOOE4GZYJNNuG9PPJN4DWC3RbC7PIwczzJZHaCnQJvX7BX3oy3\n2y3aSCYhoD0vmpOZjHaj4lybonVYL8psQ6j87/R/Lwr2weCtF/5uimq0KphUC1u3IlVJGg4fB292\nqVw2LpSOg5ODcMA2uCYL6Xp1oDO9wGaomw+hWmj5HI4thyXtcM58eOIjsQMCcZi9C9YGwXwBXoU+\nlygcUAdDMdiSgTvC8J+eZLPhISiMVTV1lgcLcwoW6Wa4aILW8ZZ1cOxEuL0bZo2C1S7sE1ScP9+B\nR9aA2V3X5aJSaAtIAXi3J6XZbUHJwg/Lwu69It9vN3DGKIj0wWfDuvd71WtibglwdBLODkK+Be6e\nJFXjvAz8OguPuUAAvl8Fj2bgEQM7DbwRg+c6YfEoQQ59n8HiuXBvn9bd1CCM6VajrTYBS0vhpxvg\nP6dB4wBMfgyCBwmGbw3DNBesEnirDLDVmNpkQ/sOGBwLtwbhXgc2roHeath3gs5nVgze2IY2ymqU\nbOz7NQ2sCw2sS4o71haVnGyZBzcX4I0AfODAU1np0j8JwgMFaA7IY2ByGK53JZ37nlEfwxmEsyvg\nMA8sF94MwseuOLB7o6B2b1ANmdnInGW8C5NzcJsrr9LDovDdNhgXhqZK+EcAnvFgOCeNe02pYMD2\nFMQzMnT+Ro3KusoInGXBEmCzB6f4/MeEA5ekYKcLe3TAWBvyPcryWqvh6Jjw5AUpyW7XONK3v24r\nWx9MwYKYYs8STw9NcBLsVwIf5eE+Cy5PwU97IdQNHY3QUyE44osyZdAXunBqWg4+Gwzs3g/vGyAC\nM2OwMwrHONq1PwrC9xxo2gmL6uExC976EE7dV3EoYsG6TXD/LLikHW4OWNzy0t8oj33Bfj/en9da\nY8x+52F2vH0Mw6dcwEmXXsTy71cxtO+dZLuBoai6+Wmgag8wc4GXIJpkdDpA3+QkmWaIxSG/K0A+\nGIR8GsogOg3Sg/JEODUmvX46Bvsn4MMa3zGtCRiYCBXblC2GEHQxGsEYG4E6+GkMbnWAbmnsU+1i\nZbk9QE8ATIFRY0J0JXMjHFxTDslh7FkeN1TCPwfU8Z5bC0dH4YJBOMvneTb0arM9fQIsT8Cn20Rq\nsJqhkIHQFKjOwq9D8EAlfLEe9qmDl/wNIJgHa5zEMEeOh9e6IFYvV6mTB2BbNZybhYe74GbgzSpY\nWoBlfvPmwywEyuG+HritEmo+gtf2EX3uSuC6OKzvh4PK4N0MHLgdVjbAHz24PgAXxeHiJMxOQnq0\nqsand8H5ZXBTGLJJsMrg8hZoLlPFWHDgmw7cGBHl6oUoJDzYacMFaTglBE+7cFfG9zZIwNQaTePF\nhl8lYEJU/YxLjBRsTR68YEPvp3DhbHihH/o2AePlq1HVAPs5er+mPJztwpkt4FTBbXHoi8LHGd93\ndQBmpGHXgCDCB8ohNgDr86J1BeLqoVIJ1H9NA+ujfn8lV4ArLfA8ue0MJaAxqsVeb8N5Bj7Iw8qQ\nAuZMR6qK+pxYAJOzcG0JvJWCvWJQkYPVtlQkxxYkmXvJhgpH1MlllpQoUwNweUBZ7GEDcFcFrHPh\nclsu/Muba/lzzwBVs/Mc6sE7YfA64PcT4ZIuEaKHO+E7jTA+BSvK4McuPB6ElQlYWwLjXLjcgXE5\n2OzAvgZucGCfJhieKVnuVQb274KfZCDZAHMDcGgWjuuBP5dAa0yc+WU5WaY1DSvwlzhw41Y4bypM\n9uDUhHiCpxXgpAKUV4IbFKxxxzAcOgw31cCF3TC7RpntyQX4mwObl8Mh+8P4AfjANw8p64Gr6+Bm\nB7KD6uh2bofDZsKaz+GN6bAoBAOfToTBbRAeDS2Xi7kw5yjoqQSnWTjhnDzsCkNvOXvv08XZgyVM\nD15G+8d3cv2esoF1uiCXhtFRSPRVMhTrHwloA4t4bs5bXJSE/hYgCxNnRdj2SRSCA5w+1bApAp9t\nCnPZmCwHVcAjSWhxoKMK0kmoTEHrDsA5krI9X2doPcwOwbgJMLhJHfysBZ3jZJW4pAv2HKOmaGkB\nrvPgg7GCe27ZCeRhXhrWxMXeSho59e/04ZCLxsCj3VCRhtcbYUGTje147NYo8+cKB9ztMFADuS9Q\nPZyFcw0kkrBiKhyWg7SrMvxIS3OZgsCPyuCMFjipBuJl0JyAqaVwy2Z4oxb2NBLU3Fcp0sjOFBwV\nVPa2oB3sUYLW/ur7DP5lK/xkHBySgPdjkpJODML1thg576UFY90dhod95dRmV1DAeRlojat6anDh\ngSScHZfpTFMebBv+7sA52+HgBlWVmx0Yl4e3+qFsKWyJwf6L4MyY3jMG/KYfWkPwszKIZ8W7fjID\nuzzIDML1DTL2edxTorkxBTPL4ISs+jLVBTWrHwvCH1JKqA7OwDu7INwIJ8RhfxvWp+HIMFziqALI\nBuCxTjD/ZmDFGPP/+x/AsB1TPYzZK48hgaEXY7VjJhvMBA9zloupdzHxBGamh1lkMKMLmFG9mNqd\nmJjBrElhLnH1u+UG87KLeTWNudHDPJjHfJTFvJvFNHdjTjCYNWnMX3MYqw9TkcH8wmAWeZiudsyv\ncpgPk5i5BnNYGsNaTKwdMyqFqd6BcQqYaXkM3RhWYE7I6t9OAnPvAGZNP6Z3M+a9HOZNF7M8hwll\nMaEhzD89zKcDmE1DmM3dmM39mHMN5qMkZm4Kc66L+WI7JpTDPG8w8/KYKwzm6QTm+DxmVgHDF5jV\nScyMLOY3HmZCOyaexNCECaQxJ3gYNmL4CHN5ATM/i7k6hTnbwyxswvT/CcNLUcOjz5jZnZjaAcxn\nR2EuzWOcPsxL3ZgGF3NxAbOkG3PuRgyrMGzARFdh2IwpdzE/8TAvpTFvuJiLXcyobZjmHsxZCcwR\nBd2Hg3IYe8k+hpUY1vt/1mDsZsy0Xgy7MKGdmG/0YSavxMzvxESGMDRj+AzDUkwgj7E+w7ycx4S2\nYy7KY67zMDVDmP1dzAVJzM1ZTHUew7uY6wsYejDlPZjoOsybKcyxecyebZjyYUywFXNAEvPaEMbe\noK/LW/T+P12KmbUG07sV804XZoGLqUxjpvdjnvQwdUbHdaHBOBsxR3Rgtg5hXvAwJ3VjFhoMg5ij\nhzEPpzGlw5hYFjMjiYntwCwoYB5wMb93MRVrMDcbTFUH5sce5sMs5uUMZoHB7O9h9nAxz7iYt/KY\nzR2Ytg2YN7KYF1zMLBfjpDGNXZg/GsyaBOaSAmZFFrOjF1PrYjb3aN0/42LecjG3GEz/BszHXZhL\nezHHFzCv5jCzk5hAJ2ZPg4m3Yg7PYtYlMa0tmJXDmKkeZqzBzEph2DqyFmZ6GKsFs4+HCecx9iCm\n2sVYaUw0hQm2YWLvYcYNYvbzMMd6GFKYijzm1CxmnsHEU5j1Cczw53pevkhg2vswTf2YZTnMbz2M\nncM8mcP8xGD2N7r3BxoMHmaei7nRYGoNpsHDjMphyGld0Y/5tYdZlcG8nMMcZTChAYzTi3mwgKlw\ndb+u9zCvePr/gw1mpsHUG8xfPMzJBqPQ+L8f4/7PsQLSCGgcA2Tg1CqYbcMz3bChBIIFOL0UPstB\nhwORvPTA7f5s6ws8WFSlDO+aYZgZkXhm77Qs0ea1Q2QUXBeFpTnNRJoUhSoPXu2AZTYkamBLSJLN\n5Sl4Ngp1WX1etgBXxuG3a+DCuWqCLSzAEx2w7xiNx/huChrDUlKtG4LXHXgkCt/yYPIwDPwDUrvB\nr/aHnWnhyZUR+OGg35z/Dlz/AMTD0F0COzNwbiXMGlK2enga1oXhzIJA+t1sONPAdZ0wdxT8ow9O\nTsL0Gjh8OyRGwfVx+HwARg1CVwPMrYLPumH/SqnFxvc5HGu53FYLHw9pmNvoUvhZJ7xVo+b7CmDH\nZnhgrJprzibYVQ8zBuDpbjhmJlxfAa0ZWLZdbvNeDWwfhJ5SiA1CslZGH6tDyh5ueBheOQ0uXgP7\nHSRS+PROOKoO7jRwaB/8LS1O8/Y6eLka1ichOwQ/rYeTtsOna2DhYXBODE504IAv4JWpkO6FeA0k\nhiFeItL477LwsxwU4lo7LQbmxyGYgi1d0F0LkwKQyoK7CVpLYWw5zAvBW2GYFYQzczDDH+2wNgQ7\nSuHTFMyNweocNIdhTTtsqYY7XHhsGSTGQLoOWvJQWQ6fR0QkODcFD4Vk6h0OwC+N1slCFzYGYc8c\nNHbDeTU67/FlMN6BF7vgsgaY7cImD/5kwatpuDID51UJmunzZL24woKSoK8eNGK0NHqQCcD0Ajy7\nFt6bAn9wYLaB+0rkEnd5tySv4x0JU2ak4eMYLMpJcPOMA7+xIJxS1vqeA6dYOieThxsqxHQZ58gp\n6ugMZGJiGFyXkgvaZRH4xBZ1shYNvfxtAW53BIfnLXCy0vrHXfhRTCKhHQZuS8MyAz+ISCQzLwK1\nYTW5Iwau92XCow0cGZTvwKw0tG2DSCPcH4KdEQl1hnNwRhD2L8AnITWJ+5BtYlsLmCh4YdQr+TpC\nAWzXv79VDZtD0gv3DYE1Fo4Lws5haAlBtwsnWFJlVARhVAQ27YCzJ0B4EE4BTjewICNd/5U1sPgz\n2fCdOwuOCcrublQvZOtguVGH/vg8nJaFCzLweJkc+5eG4Pd5uM+D5zIqweeF4N4heLEGNmfgrV5w\nKuHHADGB7udk1PC5PAWPxWBNWBvDgAN5F6Zl4O4OMwpDowAAIABJREFUmDkeaodUYn3YCVvL4N0K\n+CwN8SAsfR6WHgL7RuBNA/dGYdtGmLYbdEbk9rU2I/pJUx4mB+CCgoL7P8Pw7i54Ni4dtRUQb3Bp\nK1wRg+ficGEKbitXY+rJHlgwWcYXfy3AO90wrUwq09qUSuNb2oAcnBtS0Lm3DYL1sHepzJT/moH7\nU8Lxzq+BjVVaqJ09wtz27YfqcSJib/9PBa/oIVCIwS9K4IAwrO+AoX6oaoRTDOyMwa9cuN0SZrop\nJQz10riYE60h2DsL9/ZDWRieicAVnWI81GTgkzhMicCogqCy7wLHh2BzDk4Kq+m7GvGXj8uJzrY8\nDucXYOEAOAVYmoJxJTDnPuiZCY+eIHrcvIJcnF4Ow6YhcY6vd+R0NVQJPa6CxNGubCZfj8HpSeGT\nOwpQHoa7AuJI/76gAHtIARqDcnNbloRxUbgtKwhrQVhr5/gQ1Bbg3oiUgf059Q0+sXUuswPwmRFh\nohnoLsCvA3BeFq4KiEWzOgS39Ug5dXQAGpNaEw22pgaEsnBsAG5/HjLT4U8zoCIAB+ZgeRCez8Cu\noLr5q4OSfO5v4F0LPuwXY+BvJfCfiNJ7cAEeC0BHDqYFRWWssaCpAGe48EpIDnITfYOavV01oCYl\nIZ6D7aVQ6sGaIJwwoGbzHyrktPUjW3OujgSmGzWmnzbwC1d92dYAHNUOpQOwazQ8W6UpARE0LmZ6\nTiybWBDuD8C3PfimLcvHURHoSgJ1X9fAuhlCo6EiApfZcOAQvJSEv0dhZhyuQUYj1+SFM17oKjjV\nVIKXg9KchoX92YDdB00vwJjF4ve9EIDxFixKwytBzbjZZcEHFrQmoaUPyoLwZhxeLBMO2efC0w5c\nYsOeebjAhaX98OdqGbVEUVayYReYMjg2DU1lcGyJ/F6Trswe9i7AX4GLbfhRK9SPg80bYFGZsnIr\nDpf1ASnpyn9ZCte1wrXT4bc5uGAX1E6Qq020C54Jw50xNSO2dEOuWtTUH4Xh0RRcEIS/heC0MFy3\nFTpGwazlcOqeeiAXpqCzSkYz32qHk0fBuaWw9wCcvhHu2geOWw/vBMCaAM87sL8Hi8NajHsiGtwH\nPkPgxmr40zBcVQoze4VHng+MqpRf6DNJuNaGM3aBWQ+dR2qMxim75KOyoQJ+E4d3KmS4/ISjIDPP\ngeNCcP8OOCgK96Iu/co4HLEK2svglenwhdE0geVGGc2jPWDthG+NhocyMK1RFcqUKLT3wq8rxEt+\neRBGh9RkWoA/ZcGSi/2LFtzTL2rO5rC4/CcnNSMqWpAl7NhuKE/CQJ0mA4zfDEt2VzY+/wsgBBsm\n+MEiIb3Czog+Y14OPgiJXpsFJhX0vvfHRzQIs4BzPBEQ5luwxtNGelcrfFEPfwqLDfSkgVkpuCEK\nVw6qcVTtSGkbNXLsryzAxQH5TZwI/IdRBVYCfDsoksUuS6ZA38nLCPoEJE19DsHacQP3+ucRMTA6\nDedV6DO6LG0ORxfUXH7JhsaCWDO1WVgbFcH+HQMPZKBxEB6v1Wyuc4YFne+MiBDyYVD3sTEPHwcg\nbolV9wQSzxxkZDBTsOGftvwtrkSimjM8neuSABxm9Nk/isKHKdHFUo6w090Qu2gKcHFuxB1zdExW\npBvRBIW6HMwPwQu9QM3XNbB+BHvNgZogrO6CvkpRoS51YDALuSBUJqWaODsDV5aKvhGIwhWWLvbU\nvGhRU0rh7T7NPK+Pw3ud8Oxo0Ui+l4U/eCLfz2mCsrmwMg9Ph+DJvEr6lzPQVgJtn8HCmTBzCG7a\nAVNnQ1uneIPVu0MmB5PTsKlEO97S1WDmw80BZai9YXU/n7CgJgD7uHD6INxSDR8MwKxKeHcr5MfK\ncOXnefhdCDa0w4/HiWaStOR8d42BiW1wUwZuHQffT8FvIvBiSLOsft8KrXVSwVxeL+ZSiw/8rxqE\nu6OiaMU9uCsML/fLJf+DmB7qM41mBN0ah+uyGjZ3eC38YhCeDWmcNg48PQCnV6msPNyWsOL8lMZY\nPOLBoAtXu7DgBkhWnsKclufYcA2snyxXsN8PQyImQdErGahPw1+qIT9FTcjJHtydhzUpeLIMZgxB\nxQAsnqSM+w0b7hlWYDkcDXfcEoLX0vDUALxZC/GNEB/WKK/sbHi+VoY6+xgZoX/s6lw+H4RIAK4P\nw9u2xCbHF+C+oEyVW21pESoMTMzAjBRgKXjtcuCIFmkQciXySH0mJg/SVFhTKJaF9XAesxk610Jt\nDXQvUEAhrwZQyNP6mZSEv5do49pkwd2IDfK3ghpLbwfUbFqcU1MynhXN7nLgFFvZV2PehypCsD4C\nlWklAPEQPBjQiKIw8EZBpPkfe5qJtsTni48xkmcf54pTuh25Lu2DjrPewEYHzmoHbFhbDY0Z+DwK\nm/xgHgZMQR4ZFTnBXSkHXgrBGRl9beXhrbiubZmBLQbm58F14aU4nJyATFj+A8vjSpreCYklNxs4\nKCd5d39A0JVjRPvb4MHHtoLxha7gvRetkdH2a4BZnu7hLAueHYabg7BfBj4tka9rABkr7bAkqV9h\nwf0FSIa+roH1VQemuhwe1m63slwLttuDH7bD9ZYIv9cmoKMO7s8DBg7rgKaJcHifeOGxAiyKSDix\n3oPaTumd96+FTwbhegOvVsLFGZXXu78ALadoBEQ4BX9x4IluPbQnl8B7JbAgApcauA1Y0QplMVFX\nztkFDfVwYxvcNAXuSMGFPfBpDE5NwY0h0bGOHwVfBCDcA3fF4achOC4F7QG5DJ2dUtZ2aR4Ygkds\nuDwHV9cKM64zcE8GFoY19ohykaqfCMpbYb0Fx3swUIDVARhlazjbHq4sCWe68Jgj7PP73boWE11l\nlPOTMmPaowf+MFmE7Xk23J6G+WEF1CM9WGRgegc8sDPK87PSnOLAgzH4gSUe7uc5uDUA2zJw009h\n7VngbIXjw/DBoXB/BexnhMElAjBUADcE76dlLgxAwr9XNfBZRlSgiWWwzZMpyEd5KEnDTfUKBAkD\ni1Fm9fOsWB3HAz9x9CBtcWGvDOwVEKm+zMi+rtKC1wOiA9V40iUcndXGsj3oU+hCMuGZ76tidxuE\nSBAGIrAmpjEf322HaK+yVi8k/9HSvIyeGzrA8mCoAiL9MBSH1ioYU1Aw2xCCGhvWBXUMjQVVShuC\n8EtknHOmBSfmlMU+FRJckQeOzimReCYqU6GU0dTUI3NSNxkj6bVlw7YoXBHSzw85mj81F1GaJiHZ\neFdCm01DBRwaUCbci+6LY+vWjPGgztb4ljMTsC4quKy6ILZFuQWrLEEPpxWEaSYCMhDaFZNl4m5Z\nrcf1JQrmDnBGHmpzur+pEIzu05Sa/nrYXiFa5FYHogGxezIIshkIqFIrGNEuP0cQwpl5mJSGphj8\nKiDvjjJgIQqwq/yvPzW6V6/kpPjO2xIFFSxhunVJcW/bS2FpFBbbX9fA+ixEx8Oq0XByFyxrgKXl\nMDYPp0bgZguOSMK9MVhbEBWmIwAHuqIKdbXCOQ2wtwPvWzA3D0tCcHoa9t0Ah8xUMD6/D3rrIdMJ\nUwpqLhSG4OlqWOzCxq1Q1wCnlMP8gsa9/NmB5n74WbkaatdnYO+IiNGXRuCODMxy4YgK+IGnBVPZ\npZLpolHCtCosuCaohlRNDHrSIicvTsGMEihph1c3wz0HqDH1bgJGfQrfOQRu3QWXW/DdCMzrgs+m\nw0suHBbSeIpmD47IaNBhRV4WAv2uMpBJnqwVy7IwJgXRbnAysGUqbC9RNlTrZ2I/icFrLsTS8McI\nHL9VGCJG2fDEz8ErhaVToXEIxiZhW4NmVyUD8oL5VVgUoW8OwE9L4Jw+6IlpnM39ZWpU7OdBdQbu\nj4ritTilkjjvy5fPN9BsCS9sNxouuL+RE/zqIDxmw0keTMspy4gYlYHBEJwYgJcR398x4HaDKYF9\nY/rsa9MwvgecnBpWyYACQEMGSrKQCPnKzz4oeRS8+WDGwHAVfDxKJfzRBT2AdVmo61JQLcT0cIby\nEB+Q8U4uAq+OEaUoaaDBgwl+lvipDT+zRaw/1lYp/TrKmJ7JawDepZZEK39Pw0lRXdd/WBpR1Gk0\nC60Ula0TEaTxHtoIvpFWwO4KazrpowY2DEEmA7NHSRvRkhJFMeBCQ1wUrhAShZUgn4waYHNWQX9h\nFE4z/vwqS5OEmxxt3K2O/I4HLYlzXEsN4kQA6rOqirK2egPdjiwGzzIq3TOOAr9rweSEPMdzIdhY\nrllUCRTo61AFthK40Iha2RnWuKW1KKM+KifLhbQF14VgkwuX2nBiHpaHFFDPNHrGFgDHZ0Sf2xj1\nPX6AsRk5ZDlGk4DLCzC59OsaWAegrAXMDBEDJuQ0IK6QV7m/CFg5CI1l2iXPzMNxg/DDOs0OP8kS\nBri4G7pDMC2sDvbOMJy1Cz6ql1plrAt/KgAxZYXHhWFMEpbE4YwdsDkq8D8TkAPVDyrhxpwWwwOO\nysLf5lQe/yMJF4XgEFszdmYMwdIymJqE6ig0+N3J4204JaoM5rwM3BDTnPV0AXYPwfycMpRLbM2T\nOswSvPFHC65sk0zc+xQyc4E4FMog2uebVhttDq0VUJ2Ti0+uAPUvQP5AaJ8IA2EYlfDtUDNQNSjZ\nXnO1zsuyYH6vgujfS+CeJNxTBtMS0GGES17jquz9yNE023O64Ldl8EpAI4QnD2n21YoymJWB5WHJ\nYItOgR3IXWwNKhd3oGt5uwtbA8q0EgVYGhOmmUEY2CKjB6UhBVV5+Eu5JMSf5cEE1MhciHCyL4AT\nDYwfht4A3BGVmi1udB5jPJiaA1xldW0lypZcS1lmRU4E8WACnDRkKyGQhkSd+IypsBRuHirz53j6\nvVYHJhSg3H+oAz6skgxoLeYcZV6lBRmYvBGVN+g6HQopZGp+BsLjWwtwZEAahiTKEiehDL3G07jv\nkNFU4M8dTbHYhT77RA9W2brm44x6CxEjf4OsgZwn+OgjA/taClTVRqXxNoSZznFhXUCVTBIF615k\nfXkQ6povMAr671oKdDd7up9DDpQbNZqSljjg9WkoT2mIY3cYtkdkJDY+L8OjhJ8pNjmwMClGQH9Y\nmXrSlm/IPCRDX+2vn8f8QOxZgjgqjT5/0JJtxouONoB9gfGexHdRAwt835yesOZ4HeBqTM0Ga2TS\ncKceMyyj+5I2cJHzdQ2sj0QIHpqhulYLbegzmF8NmybCUI8UUc0FCI2Hb6c0lvZbbXD1RGiMw7Ie\noFQZ6gsD0siXjlaWclEKTnOV4n9cDvsMQmcUprVKbrfZgquH4P0AdObULNtnCK6ugm8WgAC8PgQX\nIXOKQje8UitwfYyj7ubfLfj1IGRK4X5bHpK/y0k+u9aGMS6sCGnw2reBcRbcHoBPPG0kvbbI3w8U\nfAlhQR3LeRbMTyvraLDgN2Fl2r0p6bBtB44pwEZ/EEASmFtQ1tlcKmehy7MKwqGAHoqxrhzhl4bh\n7GGVPJvD8lDY6ij47VNQGfi2rY5zEAWlkC244aD/0d6ZB1t23PX98zvn3OXdt8ybN/uMRtJI1u5F\n8o5tYRkKYxPbQIrFlRRLEidVIYVJoIDYVJGEVAxFEZykkpAihJQhYCqJwdjBcbwhHMCWsS3JtmRZ\nGmkkzaLZ581b7nrO6fzx/fXtMyNjAxpr0OR21at737ln6e7T/e3vb+1aeqhfGyoh+bmOwuj3okGa\no1wOrwya6HO1XGeWKhkVTrXg3W24McAba+kdlypNsLMGv+tD+PnAdRXcNIE961rwNtvaQvoDpvu+\nsIa1Cs71VOdv7cPCREBfmtIzfhy9q81Mevhr3DsjM0klp3IZG7fVcONZWfOto61sSoOzbVnB948F\n8sfmpW88k8HNJeydwNjFyseWpHt+8QB6E0kGgxwezDVR2YRvXYWHl+E989r1YneA3TVQwmMtifFX\nu0g/Me0rVQMLpUB6nMGHOgLG30ML2CKKgP22UikqJ5Ukvpaf362TyPuhtmcpDErj0K6VPXGzUOTi\nE5lAa3cpfeuG+bbZtd5FZdq99u5cDPdNQeD1CEqh8J1B9X5lJf309oGkgXMd7cq6joBrATn09zOB\n96scMFcm6rO7C+0ScBUpp/gu4PtquGqsZ6y2ZVB9HI3vRxDzXsYXJD9+W9Cc2kCBfm11NwfRgjGH\ncpJ8tqUEafuQ+iWgfcSem8B6FHgK/sGN8KsTePEy3PuwYrDpwZv3wwePAWP4h9fDEYOf6sN3TjQR\n37oAX6jgNS25d6xV8B+DVvm/2YLtI+2P/uNB4PDtXeUP+LmxmMiPIEbzM8A7TastiOW2gizbV9fw\n6nPwtm3wM8dgcQ4Ob4X/acqx+v5Mq+zLgkJO73XF+SND+KOuonYmwG/0tJPkyzOF2P7SZ+H2G5Sp\n39MLkNXwviAVwH8y7d3+0onUHu9EK+jBACd8Nf+OSszgKx2Jd7snyhx/PtMe81/KpKtdNLFFyzSg\nXhKUK3TPBJYm8LlF+Lu1rK67gLuC3FjOGHzzJly9AT+wU9ts31lLhGvXAoAaJanZLBSCu4EiXGJy\n/tuAm0ewcyzd31dysfX1Aj6XpyRbqwgsVzMB1Uk8Sf8Edg3EANdaYoCtGlb6YphPbIN3Lar/rkOL\nzGE0oc6aJtqLJ/DJlqzL+/sCm81cLjnva0vU3TFSO7ccgdESnN4GT82pjUUQsx3lYqXn2rB1LPF1\n4V60IeYt8MXr4LoNpb8929X1oPredlQLT9WGE0sCBkOAErfhrE05Z9rBjyN23K30GwjsCeqHMlMm\n/FvGivxba+n3TiUmO8wEyFmAI3PaP86CJKeX9NWuIui6WFZbWlTuLxS+/L2VxPm5oONlgLsz9euT\nKPPikwiwXh/Enrs17BrK9lH7AnWiDedzSRIY3O/tefVY7c2Q18GJLnywK7XFJ1BKh4PISPZaV61s\nGnw4E3uukUpgiMbSGxEL/zAaR/0gldyttVj2OFNq26VCAPpNJgB+PwLyJ0rYnkva4jmrYz2FLBI5\n/Ps7tPr954E2OfuJXfBfzylvIkvK5LMwgV4HdrTgS33p0J5v8IpKjvk/VsI/C/CJ47BtrxjegUWt\n4psjOf1+Sy5G9K8qTcy39+BfB3hXkJj+i30YjYF5+BsL8KOldIWPt5RtZ0+tvdbvBW6vxEjawCcC\nfJfJifu/5QpBLDfhbYti428ZiW18fqxkLP+hC8/bVBrDicGxrvpl61hqjy0TKCbKoPdIobDCpYkM\nII+YEre0Mg3ahRIOtbT1LwYfysVqvnUdnpqX5Xi1JR3fAWChkqi4XAkk+yZm9vlczPOtQcy0F+At\nZ2DbEe3GemSbQKXj141MRoR9CHg2igRaf5ArC9l1KIz3MJoQLwwC+10VrGUSa7+CVAB3TgQW9+ea\nKLtQutYDmbYB6QTtcb97pDZXwME5bdNzC9L7bSvhvjb8rMmH9YdR/bYEWC51XR58B96WQCsgYN26\nDt1VCJmMqIN5scZJJkCqTSA1P4DWGDqrSpkXtsFoh7I+EaAYKmnJYA7OdgSSB45DZ0O7ddeF0q2O\nu0oePcjhCU9nuWFyPdoeYKlW/RYc3Pu5xNm5Urre+P/z1iSZVZlY/Uah8wPpujVPwLJlont2aljZ\nlEqoLNQXUTw/29EGgD3k1dLxd/qFTCB2HyIyPQRKn0WuWm9z6WOugidzOesvTdRvG4XG+WYmZnge\neR/kAV410jWbubxqPmDaxXiIVBODWqqA78gEtH8M/GmtpNtZ7VsUIde86XbtpX8foVW7RMi7RRtR\nYMrh+qKWksGcHPh9xkyzlTH/HAXWfxTgi5tKUHHHAuws4MND+OEtMrB8cAyPHUOKwgJYhu/N4XQG\nJyr48mENaoawuF0612Xg7iAF/MTg4ED7oH9upIQOjOGGDryuBZ+aSFS+0wSMb0a+c4eG8NY5TdxX\nBr3glVIuTqMM7jGx0FvQuzoA9Ct42UR6poc7ilTpAfdU8LOVHOXXluB3W4r3/u423NTXHua/k8v3\n9ZtHcLjQ7pin5x1kh3B8TtE7J0z3v2MicDxTwL9rpz3xjnldMrRtcRtFoU0yZU/ayGRsWs2UEev1\nlVyD5ku5roD2zjpjyhy/s4QD67DnMRjPw+ndcPcW7aDw/DWoB/CpLXLtuXUowD3e1nP7Ofx8WxPv\nJiSuHsQBtJaz+9j11B/LxcBfFATYx3NZmzvAa4LA7N5Mk/GbgrbV2DLR39C0uWsraJK1xwKufq6t\nPravKZnJYEnPW1iH1imgBf3tcH4JqOVqVht0RtpqZLOnPmlXAp1hJoDruRGrXcLcKmQbulfVhdGi\np4ztegrWoLrNVfJ/7a7p3pM5GLZh0JJ/9udzuecdQYvAS5HO73tcVTBXCRRPZnCmhleNE3i2aul5\nawSYp+ckTawVev4gl4vSot9jsdR7vuoMFCMtIsN5Rf0d6sB7Ci10e5Eb2A21Ah3uM1nXn0TMbivy\nu70GRVJdHWQ8ehLpfleQ1NAOnhfcmfhR0/07SAy/Mwhobw16l2dMhsgPozkGmkcLSKXiTjQcnfg/\nzoCnG0Fm/leTdrSomW7yyIS0WWQgbag3Fo4wD515SSe0nqPAmpWKQPrgOnzxOHpbZ8BWYP+yQOCW\nHN5zGL1Nc5+/FmxUsFDIQn6HucK9jzqooxf32q7Y2APIMjoYKRP+zhyqbUo5eIKU9KiDXDVWkF/m\n7+fwqwNFy5w9B7+wDI/n8L4S1kdwTU8W6j3I0Xp3rYH+cXdfeS8adKs1vB2JbPd2pPPZ7s/tBPgl\nkwjzQ+uySH5sXuz4hgHsmig71qd7GhtXBU3wPopWawUxwU3gthr+KQpmeDPS9ZGJjY0zDdCVscbV\nhxbFSv7JQCqBooRPLOv8RWS5vek8zI3kg7rREkB9fov0utf0NY7PtTR5t4/hTFuTfc9ADPn+Oehl\ncNVIRpfPZQLaa4EbfIJ/pZAY9iTwj4HbSiXo+H1/1/t9Ybs+CByWXCe4d6B9pfIS2msSsTtrsvyX\nHb3nrBRDjDvG5mNt5WHOSkJLSX3qFowXBY5nt6gNFrQgTTIB6bIbJTMkSWQVWAnnlqUe6BeydBe1\nAHlpAkubYsAgsI8gVra1xdbEpC54Xyax13eh5mq0D9siikZanqjdK33VfXVJE/9MW0w1eq61azHT\nuUp1WSgFwL1Sx8a5xtKeIWxdTSqIKoPVLXBoXsEhDyB96C40rs6S0sqeRXr17/Bjqwj0Xobvx4hY\n7UPIF3aXt2uCgPEphGktf8bDSF1UIXXOnyH/47hBAwEWTOevIBXB2Qo1euSfhhSobhuZpsTM/cYT\n/xyRtnuP123xlzoG2rCwqPqyqRfwnARWHgBKbdI3OoaW6zmSlvm0nxzQ25xHs6zQZwbUO6AzANsO\nw7N+/nHdZ24X3NWT6HAHSuV3/DTcsl1hfA8Ap8fQM+VdnZg2M3yXiWXtDPDuU1CPVJ8swO27BQyb\nKKplD2LJrwDeEGS9fjlSvn8Qib1vMXiBA+ATiJEcmMgQcH+m5jyG9hUa5wrLrdFAe3kp8PxMC96F\notHGwHuAEzWMRvCqOYX1XhfklTBBjPYDPQHWCq4amMDNZxUG/NROuGeLJsWXvdteGmQQeNjg9aVc\nUHoT6Ldk+Pt4Lr3WHQHu7Gt/+DNdie8nugKV3UMH/lzHhrnGcQsxZQsa2xsm/8cH0P/XoXq/Ei02\n874ojDPpyuPuvLnrF/MAK4chW9f8sIneUWiDnfIxs+o3XYFyv4CToOTi+VgeG2UbRstq/6QHVe4M\nNU9jdZTJx3W5lK44+O37hX6rzDezy3T+Sgk7R0oJmE9g0Bbo9Qsx2NqvH+YC8a+0ZK1e8+c9HyV+\nn2RSbfRcHbA8lE/t8kQuVWfa8FCuKbM3iEQsV+5yZvrs1PpcKBNx61VwvtCx4GL6WgEPtWQ7+KiL\n1yuFDME3IUw6isb+ik/RCSIHb67ggRw+iUAxptftkvaTvJ2Ea9v8vPsRwXjCnxHB+FM+v0ApB+fQ\njs6Puh2gxh8+8JNq0hbMHaYstNdSHggGJICtG585xN2HWfDPWgEkQ9/36hsGrGbWRd4XHQR3vx9C\neIeZ/XPgbWiuAbwzhPC//Zp3IIN6Bbw9hPCRr3LfwIfRm1pD1oo9/v95v3JI2h54yS/s+vlO/7s3\nKLHIowMghy1dOB+3SclV4zuX5Zh9ptKLOWyw1pcbyxOVfFpZgb81L6fllQJ+HfnmPVr6y6n0zPk5\nWJjTKjtC4tstwI9Uysu5C+1hVwS4N5f4m+n2bEOD5BBaIzro/KcQOB9GQBj3ADuPtksaAr+FRz4Z\nvLlUrtrHXCWx5Ne/BgH5thIGhQbqR0xj6IVBngTXrcNCXyxttacJWpn+7iu0o8M+pK/dOZY+b7NQ\nfP7BliYBKKHwrkrrXaeSbs+Q8WX3SGL1E204mIll3zLUhB7kmsjDDL7kRoKR98/zkAvNBEWLWRAo\nzLuxLAsSdyeZ9gbsTCTO5iOBpAXIJkqGbUOghHoJxisSvyeFUthVft8MsU7cGj5ui72ttQXoIKb9\nRKH6namlBllC9elWYogbedq0Naby3OGeEC0X1zOAAN1SzLWfq1/PtdWew6YFE+QKtb1Oi1DXvVvm\nS7Hjs22934MtLYIjBGRngdsmemZlei9zldpSBIFnhhj4QbcNdNG+b18xhYh+2nWXTV1jx50995sW\nvlc7STiCwHZNp03zFGSkde2cj899iIV+HxI+P48s+ud9ip9EaqwbkA43qjuPAedr/d8DljL5a08q\nRT7GreCp5NFhpvSjuRu0D5WIGo98IkV1QO2VLkgst6fcFL2x8OGZhrQWX+vHEMLQzF4XQuibWQH8\nsZm9xqv4yyGEX26eb2a3At+PVDD7gI+Z2Y0hhPppN8+8U3roLZs3fg51hPlfjd5ShhAp1nqnVv1j\nbT+nreij6Z7YQfe5Z6TkuQ9nfh/TRp4bLfT23M/xt0v19WRT9zvX0j1tTgCyHUWw/GCl7PyPoZX2\nWqSU3+IDLkf6sJ5X9RCacC9HrHJiGmRr6B6g0d4xAAAgAElEQVRv9PPmUR7VXSNZzddb2pfoUcRi\nuqbP+Vyi4pI/+88QgK8B15v2nN9fa8ubDeBjriPrtGCyDHs9/HKjENCt5nJ72Y7UGUVQhFJA7kvB\nVLe4i8uTSA+613WjWZAK46pSr+VER4D6JdTOFxjsLTTJt7nhaa2AF7ThukzGuApt5PZiEzOMLG2+\nTNbzylSXYQ7WhbKlzFWdWqqMrNL+TNmCDEgECLmL+l2Nn7HpvUfV3NaRqwPyBDqFj9RJpuctIwD5\nSqZx8JIgfXtWSG848nZe6+9km+tFQaAWEEi2EahuFEnFkAUZZe/wBWPsusDKp3PhC0plztiz6e7k\ndE1jbOD1A7HihVL6+Xat60oE5P1CksLnTCC85tNkgkDuDDIUXtOBj7eVW4CBpCI6MGqpjY+ZxvQG\nytewXsOuPO0E3kcEMPd7x+l5M5pfpxGgbyNt7JAjEN2FwHgV9WsP2OtzaR8iH8eNJPZHBurvKpi8\nX5b9vlQkpG/5dR3/DEwJEwEYazfbtYk//BmWrwmsACGEvn+Ndre46fFXQ/PvBN4bQpgAj5vZQYQp\nn37amdsRqNVo5i6iHj+BOsF1Jb19cFsBf7ZG2pq2z1RPMoCkc/EOYgnuaimyakstQ8b/aitN2nCs\nmPm1TBOKbcDEDTaFchMcq6UfHAQR6VtRtqEDzjj2IjA7hG/BZLBmmnhHvAqPekcd8Y57KbK2r3tn\nXIN0VefQIChQIpTlXIacQ66T7KKx8AXvlqMmBpUhb4Hc0oaphhymd5vu90LgpbWOHTa1+YhHJEU3\nl1ZQRM+OoIFZGuwYCgTX2wL52tS1jwEfq+QLekOmPlgwZS46U4gl1KjecUurzyAGcWcmXWXbBaQs\niN1uNb2HUZYG1NiBJ5hYc1ZpI7jVlgBoM3dbRZDao2q7mqALC26Vt6D7lm0BbzBl/Oo02DBButi2\nj6duDp0FBUSsF7DVGeB8JsYWy9D0/JH3yw1BVvwsuEdHcF5Qqy2dWgEKg1ztqkz33eaGp6jiiHrR\nVXeBak4wc91v5sC9tYIXZNoHajcCtUAC4XPOvPOgvn0gk07zDFLBbDC111CTNpy9F2U/o0Uy+rhq\n5JP+bqP6cs2NRKec4Ve+KI0daEe1xt3AxFQzH8+RO3URS4WpKpyut9uzNbIL7U93zM+xAFt6CucG\n9KBSodHzfu0E7V9HFMYjPY9UODLXNf/uWylNy9dFxa9fvu4tzCxD7P164FdCCA+Y2fcAP2pmP4g8\nLn4ihLCK5ngTRKOx8+klKo5HXouAVpJFYA3y7VDNyxftaIF6LTLbHtMOBb9PDrt72p1xe1Du0j5S\nFaxnArTrCiV3WUGD/gGDmw1e0dFtWkBhioU+gEBjiz9mV/AdmtHAfNzP/xM0sL8NGdMWLDkuL6Ac\nBDtQVvelWrrGGGP9JHpOgcTs25HzdqeGWzLIC/iy6fhNwE21HKtPIbA9ZBp4O4MCE0Z+HzLVbU/Q\nJF8JWsnvM2UdKtBCEv1N5xBI9Exbf2TIlWfkQDA2Tdw2sJJrErr3Cn3EQjJ0TheN2RGq2wHk5xvQ\nhC9cbxwBqGWysvdzqRQi8Excf1lmss73xhJvT3f0rjfzZGACGYwy5D6Um7s0FS72B4EztYB6Ylos\n8hJazm6rFlQdAVdhul/bwayTiSnX6NjWWvWcZAK5+UrPWJqIKU5LDWudNL8jU66QOJ8HsdIVzwVQ\nob7Z6s7yQ19s8ihJuN45ODBTw02Z3OcWzc8x9WXsl1EGD2YSsZ/09/QmBKz3oQW7RvNjI8Bmn8T0\nOkylum5XY/8Erv+Les2ANnU0vwbNwXG0ymcKTjmJIitf7OPuiM+LwsdN1DEfC8qYBlrwi3bydb4d\neIWr2E4Wqsc6evaCj09i/SKw5t6OaLhqIdQOiBVlJMV3xJOol3kG5S/CWGvgdjPbAvwfM7sL+BXg\n5/yUf4n2zPt7f94tvurRd5No+iuQkjBDL2gZqugWUcOxSOHRpNk6B6sTiYJT9toR+/uWXPq6m4AV\n77RWEOLf6C91Nxqg6wHur2FrpkxHtyJm8YagCRJMg7tCUULtXHqtNYTzT5B0SefQ3ug3opV/v1dr\nC1oDMsR0xiZjzRwa1E/5uWMcsFtpTKwyVRWz4m1/GA2cCj3rOgfPvSY28pjXa8nbuyOX7nIO1Tsy\n5BFimgN0z29GfbMvkzgfncs3WmLkeJvv8vqM0RhdQIvILf7c7YgJ7fDvWxBTv94B83zL2aODa+71\nX55I9B/lAobIvAJaBEE6zUHj9+jk3q30OTHY7HhU0UQJQbJKxqqsStb5vNC7ySb6v879t1wMVgNN\nIFwhkOrWqku71t/IgbYVdE7U22ZVqu+onfxmI5tcbbQ/IJa6OJbhbJyncyEx1oDabD6mJpZwbegL\nZd9k+Js4wAXgWC6p6qSP+Zf4uNhVi8EO/f/H/H1uoIGZ11pMxt5Xkbye9/e9gnI7lFGULrVw57mC\ncbaiyLYJSsITAW2Sy93KSEabU7j3QYBhtNhHUb0Lp2vp7q/3OdP2MVsG1avyDjo/Zkqw9EJIxQnZ\nlpYAv2zLP30qMZfIGv1JEpt9huUvTHpDCOfN7A+Al4YQ7o7HzezXkBEcpO7Z37jsKpIK6MLydlzx\nRJIDcjS7e5rYoxFCgL6Of39XA2k3sqo/hOKQNUs08W8EXheEyTnwxUI+eAV6mS9F4PJHaBDuMN1n\nD+rPW53Vkcm/tEb/X1skK+c1SGTqIlZ2ld/7tH+O0AAcIeDcg6JNzpoGQx/4KAKfHjKGbUUD7mG0\nkEaysIQG0hmvb+2/n0CDLUcX3hqkrzyJxsg68D/QevUq032WvP2f9m6+0bt/NxLjl9EY28wUKdPz\nGR7QZNqF9FwTJMKV3varghjvyOsf2/LKoLp/xuRsf4svcgTpefu5PlsIvGpnW9F1KQ8JkPLgQFYr\ny1KGgHjedYrg+mD3kd1oy6shegFkpf7qQv/j/TZFrwh+Lj0NCwHM0kTssqydPQbVp+sWeEOMeuR1\nDpmDa6Xk5+1SWZzOt/y6Wm0wXJfsbmOYIpPKLC0c8XlR/xtZcumMfM0ZYt+vHWQaMz3vq9PevD0+\nVgrU1w9mmpR7EQY9hQBuziThVQ5kpY/ha33MthHTfMJ/oyVxdL4lIrPfz4+E8KMthctmKLVf7s/a\nhebqYR//e3D9ooN0nM+Mga6+Rqeho37dlJW68ar5HqeYEsmZaZGfQwtA4XXsz0lHzAAxi1f6DyPg\n3/KMytcEVjPbDpQhhFUzm0MS778ws90hhON+2nejLF6gBDm/bWa/jPriBqRme3px4xAVyYclaruH\nyh4kRQ5TpfOHu/C3ESh8GbkcxXu93B/WQlEsNVqAoi5pBQHLb/n1p1HURWVu9UX9uY5ewBA4USj1\nXAsZiTa8mjEbUMfvuROB07J36KNoTJz1+97r91sHTtaKmppHA+Z5aCzc702ZI72URQS+16OB9yAa\n2NFKegqBWLQ+78yVVnBI0nEWXgd3zSNDjBnSeHwxcE2QYaCL2Hke3ICDDAi7cUMRWtwmJn3vaaRf\nBuUB2GrTHbH5tCUfxZ0IfAYIUKLlvV9oO/NuKVCZq9xZv9bfQqksVHXuEUReN0yqgQX3glgYJlY6\nV0pXHkwstOw6sPpYC7lE/8gcQcawcSFVQJW5+F2rTrlLMIPcGSECidon8SBPwlcwdboVqmPl7cyD\n1BfdOmHAxMRSM5eORrn6ZeheAxZ0fpkl33fQOQPTRoFj06LWwpOumCdeR4vsfjSlCqb8hAp/z/5e\nbvaxaz7ejvr4fjHS468EOJYptLVvGoMbmRb3fQgY3bA+1a8fQzr+pyzF/K8H35U805R+kV9rKC/G\npwqpyqoosvlEGCFW/SRizWcgOfCWJAyJHRR/byycMZDmRgQpB+NPEXtKNHHKdM0zKV+Pse4B3uN6\n1gz4zRDCx83sN8zsdq/SIbQDBiGEB83svyMMKIEfCX+eP1cLveUavYnog1YDc0qCe4FVr5TV/0uu\nT7kvaBdl3Gr52Z50ji8j6agfQhPgen/c/V6pz8BU3s5ypR58PorsOWoC7aO49dQ02KKlMzbGHQzY\ng8BlAw3O3D/PosFdIfHnnD8zb0nJf03mUVt+3jpiEGfRgL0KgW5ASSbcWYEn/PMq7+SRi9kLrqLo\nens7fu3NXuE1FxVbzl6jBiWuW3NBSactpJDITqWJvV5ILRBcnM+97dfVcmHaQRLvlmoB74ppQTnl\n/TGPDEAdZ3m1A8m8i9tlpr8iJH1pDDkduzEn1qvtYGN+r/mJi99BDDUrIXQFWASJftZ2tywfU5UD\nbwS1TWeUZMmdKor0G23V1RojuQgp4XJk1ufbic226uTvOsmkUho7AGe1WG7mDHXccrCMOm1XP2WW\nmGr0MMCnRDckVjsf3HAWtNBOMr3bmxxozzlQLCAwfoEl08bVJnXNK0gW+0gSXojsBp1Kx9s+Pq7P\ntTXPYTRvOi6xxJwCXQSymz4vbkIgUQVNiH6WSEjUrW76GLEMTkdXAp/74yC7RWG6r0UwdOPZBdb9\nWKI6wPW/Gy0t+tF7soWM09Pggsh+4/2eYbl8AQKPoje6yIU6jYLkhxqtIH2miLV7XsDxJ+fjzfRn\nC7CYJXcPSMCR4f6FCBCPxx99Yt6Yi+0uk7LvPIL696EKQg373OjVQoCIV71ArkW7UL2imtjQ6noa\nRXQdRu0xv89ub9p1JFfdAgHrk2igXeN/1yFmcHWQjneAQMwQwEag+xRakY+R9Ljx7W4gcM+9jXuC\nMkURZFmPEUdF0CRu18mQFEvuE2fgOsraJ3zfJxWIPUdXoi9kAtd9aMFbdLG27T7FLdfjQooiyoOr\nC7xNhVvBW67XbNUJgAyx2nnXoVoQcFatNDaCs5jaHAQdLKdMsvYEKA2K0a3c+8/1qKUlNUUEM/z3\nCHhF7XaSKtU/C2LZ/TzZURYnWqzyoGe3ncGOMzHaysEkGg5B7ySWWJfa2xL8e/ytW6X2jjK5ANZI\n8ipCAkfQteNMoJih6TY0jZU9QdKAkXIsRI4zyFLylrjYGtBylUiJDLQVmksPI3JxHB3bjebj1alZ\nDJCkV6N5Axq/8bd1aCiWSQanJrBOxQYSxS+ANuwokscBwJEAdQwLi+A6JoHsi/jG+bF+Q0tGomFt\nLkSkaMGLq5ZHRTBSEpTjsRMjIOcQRsoNCfLNBJLvRSuJtQtBSTQmVXpuT6dwgvROSjQourkMC0cq\npYw74ExsHgHsSfReCv8e05fdqupyCok8qyg9XNfPj/7Ju0kDKbpWdRAIXuvXXhumBlb2BYmQOx0Q\nKsRIHvU69YJi7KMf49BkXd2NAPUpk59r25lIu04TN3ZrFpJVeehgNszFJCtnBhlJBG4FgV9pmnSd\noLo9P8iolvt98zih0fkBd3x39hXnwsjZKJYs4jFAIKoQ4oivzZ3/DTpD+a3WufKpRjyKbDBGIk0s\nxdrn1hCUnEVutpKBeK52gCrE3CeZWGDZmHJZkF9p14GFeJ8inRdPj//HcRY9IIaZWFVcyE66uA+w\nv0rvMy4mmTPmvhu8ClfTRFXK2NUqmYN67MOatKhEFVLbmXMHsd5dXq/4W+HvMzgzj4Ebo0zHMm9D\n5WqTDUuavU0EkMs+7qOvflS9gVQO1+N5Jrxf2qQdAKIbe535xUYCVkjMInZ0/J4xnWjn0SaL0zIm\ngeqkcX4E62dYLh+wenzuNCIiyq6gjorWvbpxbEKqcQTdEdMZaW2dttdP76JsPcPgjtAVjGInVn7t\nnF7cBlpFo5I+xjOPffIxVnjiai4Gdgr4U9Iidy3JSrkDRdCUJkbpeT9iNO40bnqb17HtzX/Ur62Q\nGmCL/77NwaWyxFQje6yjrjLW38QcomFnmMNioWvnTCI6CLhGWdIh9spkIDI0YUufIIOWunjdWUqc\n/F232B4xpWLbGtQf5zO1aSnAjjoxqqJOInTp4LZRKBdthgBv4CJ37pO+MvdbdYAovb1ZSJb1zVzO\n85jE7cgiR37NOFObBr5IQDJ+5VXSnU6cqRNQ5idTMMNmIePTwJQa8hTJ8LiBB3Ag9lb4AlPmquPA\nF/nCmdSoAWrRGDfMfZuQTK6BFkQeTiHpZsXvERemuNiMswSAtY/5cZYCDYwkFdTevpYbEOdKX5gd\nrIsqeSTEz0EhtUphyUWt1bhvjZhr5u9h1QSqj/vciSqupjflmi+Y0UZxo8+JTTSvov2iIulBPeEc\nNS6+x4ip6D3QBMOcpCKIv5tcwKZRV012GgG68v9j8MEzLJcPWGvUo11SJtocLW+RwjeL61KnMz9m\nrfWoGly02pXJraSFdKwVOr8fHYNb/lxnqyGIaWYIzI6h1fNGf8zRTID16BzUQSvtbUi3GvNAHkeT\nrO/V6SLH6BX/fnWQ2HPcxET3edVXSNZ/EAjHMbPgXdHCxT3EiHpVYpWjTBO+mytTUI3CQXulBv7E\ndZa9UuAwdrbVIgFUFI3BGQjSC8ZJB4ml9n2CR2APiNFci0TOBy3Fee9B6o1pFFGDFdfmukV/RqdO\nFm8QIGYkNcB8lSKimqoJfMLH+k0cQGOkVmVigvG+kBhvt1KfDBttaldJPwliYBFwgyUjXQ+ds4kW\nv8Ilh+gihvdPDCeFFJZbmZ4ZKl1HllzH5sdidgHYWrirnzlDzrQIRG+EWO+e9816S9JJXMQwvZO5\n4IzUj0fmOcqTaqAIWpyit0EgLXz9PLm9FUFuTvEdxL6sTdnXHvc+ieRkDc2NGPp6rqG73Migbfot\nQzrYvf4e4mkxCmsRAet5QxMmiv7ugkkMw43vOWvcJJAMCs1zI9JfDKqRLj/DcvmANYZebJCYafRV\nihQudlCz4fhv0XIYwTXIJeZkJnejqK8cR6th7PQK5noabGM0GLchP8wDft0uv+VONCCOIYZZmF5w\nNFxFjYX574tepWPonnFBXDCJx9EN6YxX5RwC0wMo8ikLYkUjPB4iJBG6VXsEjwngo7jTrhUl1naW\nZz5pFnxgWVAG/pFPxLiYZyQdX9zyIlqkV/PETGoHy22hIU76s6MIPbbkfRGFDlDKuXOk/AchSwAU\nXYQMLQ4TcwHEJ3TXxdOYa6NrUl1EHaN53wwz/x8ZPCPjiiGwTdG76bwf+7RXJqNk1QDICKrD3Jk9\nmp8xqfYQvc+F0NCBhgRMeQNE8oueO1+6eO3HOpV0r0aKOlsoJWH0c3mRTBej4OI+elZUFwydZT7q\n76vr9TtlEtW3IzVRZPo1atcwSwt17Lcz6L7mwN/C89kEpa6EtIBtmFjpuvePr3Gc9//76H4bdeNH\nZ5hnfYxgMixtoKl/PWKu60wJJ2soN6s6sfHpIa3TyKqIF4E0QCMQR9ba9ESCpIOKAUiXoFw+YI1Z\nZ0AosolGa2STMVMJJCSI4jukEB/8mAPtJNOk7UNS9ER5ogMHCg2yq5wFnkMgeDXqjH3+mBi3G5Nm\n3eW3GyIQjfpUd7Vju/++RNIJ7fTrC5J4dbbR7NiM7SH5NnZC0p81S2RsNRrwRa3PaKUHF2XdBSka\ndzAdm0MiYm5Jz4hP3CLIEnvGF58tQVFmrUZ9F0yp+yITnvqWZuriZkKVDZLjN35s01Kqh2FkT2gC\nd/GIIb/P7sbgrqNBZ+Kv08Eu9k+ZCbjGDhJ5kIqimYV/5MCUhcS2o36xQKqT2uSGFedu6exz7Nfm\nJH/dVuw/0mLjXU23TGt4XACqzD0U0Lnx91ZIYL80cQ8Cnm4wrBtjYbrYWCMXAfJmOef9Hl2rzvk7\naOO+x8428yazRW0654vQUyRAW/drt/k9xyaQz0geGvNB4/AkaTpGXekIzYccqWpqvC1xPhtMXBQf\nWVLBHSOBckDRlxdY6yNoNgjG07wCssa5zQnXVDE2gT5Oovqi8/+K5fLqWKOeNbpG1CQgbIbJNb0G\nIkuNPq/NNGAe5dFvX3RdQxQcIsaxg+R03EeGqwwNhHVS2q6DaIDegQZdFAFrBCAZ8hXc5udv8+bM\nk4D1FClWeh8Sf2LCrlMoHLHONOnmXHcV9YOx5FkyNsU9jaJ4FmrtDLrgC8tGofMWy8RGywxO5/J2\nmHeQJ5foeZ4LF/DHTF28xf/fSfJtnXN1RDTMGHruTZZydkbSYN7eFlowIrNbCIlB5g7Sj/l5Mf55\nbGki9hDIRTE7qjGiLnZcJGs4fm6vTCGekYlVWQo8KBx8B66SiEMkgjCona1ablStWsOtbUmjlPvz\no3sVCCyzIENXDKeNIB/rWDSOjzIPhok6ZH92DAQY5QrYOI0Ml6Ula/9i7VuwhIQ1C6gvh+jdbqCF\nrSa5bl2snokSxDlS/tTIQPHxERNNb/NrzZJhKor6sZxGwDLvn4vxvSCpbxOm9pIFV99sxTc6RFP5\nNG5fiuSoiQERHJtAayR9aUbyb724NF04mwYwuGSgCpcTWKNLVZzRkaZHPWrsSHe0vsCNgsY1ccXa\n4AJ96/QTkgrBM+4/jsT9NgLF6HQ7RmzzSVJ0SYylf8hvkaEBcw0CxTUk6szhOzwiIKr9vjuAwd1w\n/C79FvNxB5K/4EkkCuUNBhpIzK4iuTHVrgeMEzKyx1YQ+4t7pnerNEljzP1hb1vcSbUkRaQseLv2\n4+5ofk70mR674SMasJoW/iK4K1UGh+6GHXdp8sRoxoWQrP+lKaqpVaX5UATptMdIj7Y1TF/XdA4V\nJECYhn2awlbN9ZeR/UUgjSw1b/xWZsnpfmIJCKPOGC7UR0bWO7ZU18KB2YDP/xG88s40DCemuva8\n/9t10tsO8+Sb69XX+p9daDTKwoUqjE5Q1F5pAtcCMcV+pvd51pIL3onGO1tDoBbzXTzpC0jHlPui\nbymhzALJoH7K390qSUM3B3zxbpjcpXGy1dtwxD+jb3RkudHTpUbS4GmcZMTGt5PP9wFSaPSZRhuq\nCKoxwVI0WBkptWEN032so4gfSVlTfRh/u7hEESUC8yXIEwCXE1gjS23oPqf0PG/8XpK8BKzxF/Un\nzZWpeS5cqKB2Y9dqphjq+9HkP+Sn9xEjPUTSFcZkJ8MglhdF/wyxghg40IyWWiP5oPbRYLnnbnjJ\nXUlfeN7PX0WDdAmtCVtCalbfNBjPxSabdodd8InQN4FZhSbWYa/XTZl0YbRS8p61TLo29zyb5gzA\n69BBIN9FAztGqkX34RrFjm+xCxf5jGQYqf1ed98Nb7pLz+l5m2L6v+hLWtRJr2e1jndw16eQHETi\nc6L+F3R9HPtxsTEuNBxFEXyhTOy2KTaPsyQV9Er1bQTYeH58fgS7pm9qBFwDPvtJeMlrG04qvuDF\nMsgTcMb2NCXbuCgCUz/iuHdUmclYFB3qj6J3sBJkSDPSohPHa8SUyFiv8Xd5lT87iuiRHR8lZUtr\nEr1ASkDX82ccvRvuuEvjPap1oqqsSR5B7z8GCjQTS01MetkuMlbd0HjuGTSGzsQOiheFix4SwbK+\n6HjzWDRSNXWuEUPivSNhg+QlcInK5QPWmB8xMte4Z0N07o55BBqDdDrLm0atJvNt+KZeEAM4naFa\nBc+TUqhF8XzD/++j39fQvfu+Uga7UF1bInDN0Iq7xIXiUCCpBzZIxo/zyK0q5sO8GQH0YWDJ9L1L\nEsWiSvk8nsDCkhqhqWhfUzVlLDJthzwwDfyAQDrqrY6Rdg4AbQUcuyqKf496t+5udPdDdmEiiKWa\nadglKIHLvgAvahg4iosG8HzpIGNJNwnusZB5ukyvzFrmgIesyCCGFGJ7TSn9bm6wSPDQ21o6y677\nlMYcs1P3LS5UHwQS+DbF8Zh6L5a68VvcQTUanGKJ3hMXF+PC3QlAw7kXPQT8nH6R1ARrXqeYcGNM\n8k5oaL+mJUY0bUcG2T2oT3veNwuN9kVwP9S4fp200MYSVUIxJLTR1GmJW8vkpMxnoPEbk7gs+7M2\ns+RqFTcGga+Ba3HeNwH3YhG+SdDi58V62Wj1j0DbBNlLCKpwOYF1iWTdiCtJfAOu2J42PE7M6Mfa\n7NR4vKkaiEv5xeoA7+i1trIgnTcpzx8xqGq5oYAr1JtiREurbNZWJqwDSNyPu8dEPe023H+TNMii\nPe4plFAhst6BV+duNIhjir2z/tgY5x9fUB+B706/bwTXCRrI0VhUIvDrXzT6z3gdYj5NSAEJT3hd\nYuai6EbWFA9jO5tlbHLniRb4ZokAVEU/ziiuO5uEZAAJDTE7uheNnU31TZM9+nJHnd06yVA4MK+b\nG7JK03OLkMCxJvmORhVB6eJBDFCI7DcarCJbjXrJmHQlzsmoZx7k6fhmlqLQSmQE3B0uUPODXx/9\nP8ssuWMNc/0f0Lg8T0oVuoXkmbjLj20lDfEuadgvkdyXxni/465aLg2t+V/cNiXmC+iSJKkFpIrI\ngXuCVE25v5fIeWJ4LGgOXJwnOmLXHCIxx0mADxrXMUtcHxLwRWod53YTKGOEZtH4PZZ4LlwIyvG8\nixO9xBcKTzeC/RXL5QtpnZVZmZVZ+WtcnnObCc7KrMzKrFzJ5WIJZVZmZVZmZVaeYZkB66zMyqzM\nyiUuzzqwmtkbzOwhM3vEzH762X7+pS5m9utmdsLMvtg4tmJmHzWzh83sI2a23PjtHd72h8zs9Zen\n1n+1Ymb7zewPzewBM/uSmb3dj1+p7e2a2T1mdp+ZPWhmP+/Hr8j2AphZbmb3mtkH/f8rua2Pm9kX\nvL2f8WOXpr0hhGftD9noDpKSQd0H3PJs1uEb0KY7UWDWFxvHfhH4Kf/+08Av+Pdbvc0t74ODQHa5\n2/CXaOtu4Hb/voA8wG65Utvrbej5Z4F2tXnNFd7eH0cbbXzA/7+S23oIWLno2CVp77PNWF8OHAwh\nPB60RfbvoC2zn7MlhPB/Sf72sbwFeI9/fw/wXf59uj14COFx9HJe/mzU81KUEMLxEMJ9/n0DucPu\n4wptL0D46tu/X5HtNbOr0K7sv0ZyQLoi29ooF1v+L0l7n21g3Ycn0/dyhD9ve+zndtkVQjjh30+Q\n3A73kqIA4TncfjO7FjH1e7iC22tmmazz9p0AAAIDSURBVJndh9r1hyGEB7hy2/tu4Ce5MCznSm0r\nyGP1Y2b2WTP7+37skrT32Q4Q+P/OtyuEEL6O3+5zrk/MbAF4H/BjIYR1s7ToX2ntDU/f/v11F/1+\nRbTXzN4EnAwh3Otb3D+tXCltbZRXhxCeMrMdwEfN7KHmj8+kvc82Y714e+z9XLgKXCnlhJntBjCz\nPSjPCvxltgf/a1rMrIVA9TdDCO/3w1dse2MJIZwH/gDlUb8S2/sq4C1mdgh4L/AtZvabXJltBSCE\n8JR/ngJ+D4n2l6S9zzawfha4wcyuNbM28P1oy+wrrXwA+CH//kPA+xvH32pmbTM7wNfaHvyvYTFR\n0/8CPBhC+DeNn67U9m6PVuHG9u/3cgW2N4TwzhDC/hDCAeCtwCdCCD/AFdhWADPrmdmif58HXo+i\nzi9Ney+DJe6NyJp8EHjH5bYMXoL2vBflNRkj/fHfQSH3H0O5Xj4CLDfOf6e3/SHg2y93/f+SbX0N\n0r/dhwDmXuANV3B7XwB83tv7BeAn/fgV2d5GG15L8gq4ItuKUnPc539filh0qdo7C2mdlVmZlVm5\nxGUWeTUrszIrs3KJywxYZ2VWZmVWLnGZAeuszMqszMolLjNgnZVZmZVZucRlBqyzMiuzMiuXuMyA\ndVZmZVZm5RKXGbDOyqzMyqxc4jID1lmZlVmZlUtc/h8Kz3lJYE3qlwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa6a0dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imgplot = plt.imshow(lum_img)\n",
+    "imgplot.set_cmap('spectral')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "显示色度条："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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69BAPPSwU7CF7EtD3NCC+tJGA8HIP7FpHcn/iNFhZFhspAa2dCNhO8h8BpJ2i\nMHa0E/cqmGTAZmnbSckMa1VC0dkGQBcW89KNYydrpxVAhMzaKM00lyUNEF2cejfekRs3rrQeZzMt\n0x4C9kF96hxJ+m/Ji/UZCGf+/N5oPQ5w7TTXZ53uoCFO4lemDMR6HQIiBWUOEoX7ViMqAQyDUlLz\nVKw9qXWq38WnzA60jDqwpNkScttqJwN2APVosZLJ1/GzbN1I3PQaAF5+/qWyVL8T4+9ye1LdLmXc\nR/exejw35LBpuXGbvWZ6i88xy9xT5VTkU7r13L8jLrsP429AzOx8M7vUzC5zQlnf/w0z+4T/fdrM\npma2dyguANtbQGFmX0Le++JVIYRXm9n1IYTj/L4BuI6/5bnwJk/2uOA7ZrmagfsZjBxgqaOl8acJ\nUb0waeJiCo7MVB0AuYGqEWwIYIYY0Wwhc+fs27JTkhml684Eax/PIb1pU4F1cZ/6ZurdOKXn7zaX\nj+XofbrajzEz1S3KXOsQPexW2JTqIxU8aoNVMhSFcqo7HGl+bl6eU1oBCO6fNMh4JQ9qBC3uCeOa\neS5knX/yGrBZIKaeNr0XQ1oCyDS7cRxspituaLPM1vX9Ma/9KKYzXZ7NY1o8UgNxKD+LZyTsUJU3\nVf3V8WvYWjXBxUYTi4Cqq9642Kf339xwiGGmTf7OxRXXIoLvAZSge22sUrzCsO0FFOGOhxH+y2V6\nZtYC+HfEwyyvAvBxAI8dPD09hn84gP8RQnjIvDS2q2r43hDC1WZ2O8Rz5IuN3kMIwWy4230Dfoqr\ndyhuYJO8DeLl6Igdcl9M1vQ+G8gKhVPwqdtSbPibyaGm4TS2AZ6O6u+sBE/VVya9bw3CiHkL0kGZ\nZ5OwwUFUQVL1r3wmlYN1QcDrZ9NlvHW5C30p5DmGsXwvDQRisRtUkQjwsxyFm1gdXFloyOFUt3mo\nTW3UW2UI7FnfMwZKGRiHDKcIWc0DUUkURkUZEPjO2Tb4vov2oPVvKL1iOmlLVdmKawLOtF3UCzfm\nCY14ybcXANrct5q6fkS4HWahHw/ZCEdhM1rpIvgmjwgfoKYW7ToTAKdanN1zd7MNRPbLg1qPiMxh\nsluU+wG4PIRwBQCY2ZsBPBLAvBOFHgfgws0i3BbwhhCu9s9vmtnbPYPXmtkpIYRrzOxURIydkfdf\nENkuANz3+4H7PCi2R/VL1aW1AfGFz2yMEma/k2104zx9Ux3vYAeso2UH9mfZkMhMaF1Pja8rG3Jo\nhTVVoiDa45OvAAAgAElEQVREdyerGHCtoiiAUUFxGgcZ6rdTmCo9NTI1nLpb2clr67+yNEpyytf8\nDnT0mUFCADVd2+TZVIYaGOcMKilOia/2HKnTVQa/qYTyXQ5xryJfMjjXxkkOWgHC2snOSSR0ENd6\nqtmoXKu9SwaLIWF1Nzsgkx7mLZXLSZEa17gPNaXxhywgetd4ODaR5Qp80cdwk8bPQQyZ/QYAl18E\nfOkit9hvXqStyymHDpJk9kji01AeoHElgPsPPWpmOwH8EIBf2iyJmw28nkAbQrjRzHYB+EEAzwXw\nLgBPAPD7/vmOoecfdYEvDw6lSoFCH8C0TSLi50aDtOFzsPgCi8btKofOT4ykGqB1oOzbCJq8PiTJ\n91Km5H3FFtN0U8GuRbEqqeg0IacXLDIkrphKagoG198BRQecATRzVkVWJp4cCdR7gKe6qi48ua5N\nq7QwACQitNLX+dCw6ppEVUHyfQ1V3AGl6sJBpDAUdu5BoIA/AJgzYBtmwxYgp2mErGoYKlOhXqhF\nmbq32aRaqPTMBTAjP1O3SX2+thMMLbc+1GS8JrIW8r69Q9tEpoDIoElfccPsxjrpEE/vy9R6dZY3\n6Flzb5Ee8R8HgZMDsMeiuqE5D7j7edGVbALg4uduXq4tySaM96JvAhdtvpnDoYZmlUcAuHjIk0tl\nO4z3ZABvj2pcjAD8RQjhvWZ2CYC/NLMnwd3Jhh6+EWlz+bxfrfsKcgObzgBjAwvZ+JbaKzutAGRo\nkRc8CNDxmUY6uBpZhiSYg7DGHTK7LbYHdLVE02XQJwgk41yI8YUmG8h6ui1Ny86ZFkoIEKayUM/L\n8rUoFitoPO1azNvGTmBpLf6erni5e09H2BxZmKoTFMzqmYICAg1aQ6wZcODsgSGzQhC1Tr26kFPj\nQq/NgbBKX6/xPWn98Trrsp721xvxFHpZ97YB69gBdLqcjbOhEfBG+S6Y/34k71fuNfDZGveM6GbL\nm8rQ+wyragsAsDpy33cydRIYQx5PQjmjbPrMahV3ufPc6iiDJxBBd9zHjXGAcnk490BJWoyQT7ww\n5L1IgueD7HclRLC+zvKOZkdMNgHe83YD590x/37u7MmYV6E8ZvAM5IOWa/kpHELNAGwDeEMIXwZw\nr4Hr1yEqoTeVZQBnVR1wzaf0u6bVqItS7xSAYrenYiVWk9UCapTQXZpoQZ5xD6vL0qBgRexM3bxa\no+qgcgHiTzLvbpwd81ux0gPD4JPy4mxed+oCgGYjpmV99isNLTA+CBz7kt8CHvENXHb/1+DkVWB9\nT6zA0WoE4zSIVa284fHaUo5kka/KnIC2q65ZCRzFQCHqjzRYVEA4VA9DeU3XK2CeZzc9FDMs3sFQ\nfio1iYIuGX496GhZ2kmZBzXoqWtZDbYJuDXOVj69zneoVw2ywQuIngbLbiwkyRk7iNMvnqAKxH5j\nqEDX09d9QkiUqIIYeXl0X+BRyEa95Q6YSj/iCR0w3w8X8xWoN0u2p+O9BMCdzOwsxHM/HwPgsXUg\nMzsWwPcj6ng3lS3Ysm8ZWQNwhTBR9Q0cOp21h2zoYrPb3SWPBo66dcmsZMbdGDPTzGT80CnvFkVd\nnpL7jpUgQRcyCgE8qTYO5WExL11Reyhz7VvgCz/3fPzGDe/HuXd6I+76nlfj8j3AyvXAZGfW9epC\nk1qSz25ldGp6/xsAwVpFYZ0zclU9VICWjE+su2pGkSP3j7Ys90w+59RlvahjM2+O2qVvxouCTLz2\nsLBN8o+qjUFc5JrIniHtFEDyM65BV4XGraSLdbJRExgCKMFz3EfApfdB7ac7k3dkDyTz77p5PY3e\nOrBwu1Q9zWK9nd3ZDshM90rk8xWPiGzDnSyEMAXwVADvAfA5AG8JIXzezJ5iZk+RoD8K4D0hhEN6\nAh+1/Xj/JMSRDYh63jbkRtHCt2kMGWzVlxeIo7rux5AklJ2rWPUzwKjo/lRbkoE5HcfKOAsj0KHK\nrXlxIfupyzAPOGoDj+pSNd+JcRnQ9cD1B3bgrZ95ED7+h4/Dtc/6Kl58l2fjbjty/gmMWwJ/qjuo\nzzyUbrHOH701NO9kyEN1IeoLLfOM25jN5kVZMN/1zXFMmtHRNlUehHHW9diNsrsjXf+sy+20NgID\nZZ45iCfjl8SfGK/UG1UIul9vQAZbsmDA1XpdVCXw7LyA6I2QjldqMgMmo+W5bpSNKi0eS6+vUjdR\n517ZNLoRlKeIq9qAuIn6dQCebdi+O9lh6IntOdtLbyty1BjvdYgKYGbCkHVD9QkG1EsF5JGRLzb5\nMDIgpGPxHhvxwDQ5tG5wogHE8vVkANE/SJwVM57nMsVdp5KuUp4ZdOavQFTTmVmSKmWdYXtkp2Pg\nhL2reOp9/x7f87onYONd38b/fNJz8PQr7432oAezzDSHLOh1WqGuCw0mg1Lhe+p1POO1oAAuoF6o\nKyoGmN4tmW8rz4ccr/5OsxBFA8n/DEudVw8pI/JOBSDrwaudevv1crS1fjeUaXNxRPK1rcmFxTZb\ne6Qk3apl0NsQ1qszvlGfd6KbOBBOHDwJhEyyCVm1x0VMurc0m306fbnJ7o3qotZAwJ/A74Nhvc8D\nEEF4zybVf1iyzQUUR1qOGvACcZ8GIL84AEn5zjXe82Ta+LSIAOkdPHVARiZA2o8rQK2mrcBs5+Pq\nnUIYZzWlruOl0Og05I5E9cNcQ5+mTXUI9aUC5miqsMigx47cj4CfPyXgoie/BOc9/av48E//GL77\nPc/GP14NNAeQdMhpE/G2jLJwaStulNcTG6/yn1ZbeacMbcnw0sIMCHhKHRbpDYGi5kPqoAbTUNVp\nUs8wfzLADA2kaaDVwUDTGHqPIddvDf6pTir9fgpi5TNky42AbWoLMiACeYFDb754wYF40kRwXWvi\n/UmTTwIxSZfseYP2BFcl8IBNEiCed0iPDj2pukFWYSRVn7QRsuw2xMVUuzzMEVMzAHmT76383Qpy\n1IB3GflYn3VpJAbfBpK6KBm91c+QZ09Rl0WxLk+z6k6fJAyAhwj1aOqyMwjAQAnu2mH1U8PqVJds\nYGARQj8aABv/rI041JkO+e8Whr42+jdvnAk8/QF/ig/+/W/jlM/3ePKT3oC7fONeaDccoJvM3AaN\nWzL1r12dks5VBsD0W2YXQLzfdN5RuX/GAINO5Z8HuFLeGanUNhyoNtPtbsXAV896OP0fWiXJ96D6\nd7oSAvH7zFlvWs4BsGabTH7uNlwthgy0QAY8MmMWrbeoAqB7F70iig1uENNca/Nm+ar+I9hy/+x0\nEknIDHjoTL/iqCdeQz4m/ojI6DD+bgU5asC7It+XQ24gQNxEmQY27lTGZYgTiy/9YJtVDUAE37Qi\np2pcwEDnCXOuuwwZMTbT+gz5Pg4x0KRnlOkzAUenjzqNrNkUAbFedaasSSV5IwR3L3Od4vgY4D1P\neT5e+YZXYe9jj8dJ//B3+MxX27wzGzJgAnEwKAxHMpvQWcOMqkU6FuMAhMGFcsn3PFFDWpHeUD1p\n2hW7rI1mm6Y5oPtP8QiQJp23+iOnSHL++P5rFUffIG2Mw2sp7YrFMn0yXlUt1FIDnZ7ebHOu87xB\n1dOutfFvvVIVANl4phv0Jy+KpjzrT4pTbCvJ62TcKyESsyO1Sc4CeEWuxKwzXI/Z3Y+mli20fLkc\nZdeGWKGLbhE544IzwKw0nFrXh4RhazCu18wzbBGmTqONBhhNiy5KBZBWeZ6nsx4Ci7TAogGWDmRf\n3slJwENPuBj/8Lp/xOrTbo8H/df34O3NdyLQ91hdwHRhxzxmuoW8dGO33iO71rE8yQ/2cJw4ayDT\nQWuUWflm+zsctnAqrW59ssx7SNKmRare6FDsy8FyjNb8npep2Jg/FS5eb/uSNa63GSjTHgnVsyQ0\nQLkadKXLfawNfjx9m+Pgse7q3sm9eenLq6eYLPcZZPhZnw5DLwmN0zz8EVsyvADeKDchruI7Hcgn\n7FYVT1ANHoYjaUDWTQ2R0MTQKmDsRxkwh0Cz3tkMEHYKYSYK4laqJtSAoVIs5yXTc9bUuh9uSkN8\nM4t9DqoOWzBMTbNi1GRaLEe3VO241gDj7w7Y99l74/i/eDj+8T6/iHP+7GFYusb14v48lybXkty/\nKqAtXOm8QXN3Mi4g4W81jOlxS/1AR0juaKpbr+uGt9xVLtXlYQB6PcjV/r0F460W7egubbXxrng3\nDtR9U8bLwUjfE/XvRf4qNmyuptNNyDvLYKfHt1M/rP64ZLpA7HNrba5qqhVq0FB1hKougNx3qRMG\nsuFO3cm4eU5n0ah2wOM8nJW+m8oCeKNwW8jd/rICyk7dhOzSQgY8tYHjyyENRYB00M1LG22bQa4b\n5e30ZlyfZDo9xOIUdGAlqKf9HgbAOOllLW+AzQUVdQcu5oXSYYtlyyZAoNNZ5lvLofkIsQ4aA/od\nAZ+5xxqe8AdvwNr7z8ID3/Za/PMqMKFvaZV+Uhl0ZdoFKPo7oE5zyKWukEoFQB/XNCMI5bsApP4r\n2QrDLYxclfpJdeSF10H9Lln2dGH4vs6ginfcy/7RKMtUtDXDoApM87RG33DLCyWoaw3IPrd8ZqYs\nyK+nUG34JxlvfQIxQZOqAx0TTZ7v5Tll4WrUIy4AR/DIngXwRpkg78PZII+WHDnTOU42O1UZWtVG\nBX7X+GqbPrOJYnWPiwIfW0bvnburgHNIt1swF59yztNTagOv96SgfpODADdy13RmOvI8y7+qMASY\nCHiDbM98GotY/vEScJ8HX4LXP/uVOPuyD+EXfvLleN0ngH4prmaj0S8Z2Kp0asBIoBxkoBgAQ7Lc\neUDZVANQrVYY0tnO+N7Kc+lyPchi4B0y3aHplcZRDTYqyXOhGhR1QEnxDbzzoTY41NZ2ettZcT93\nvnKSE50l1kWaWuxvKgqQ6WBTAU9mgQybxKievQLlyTL0diDrpmH9FgOkBfBGOQmR9U4AIGRFe92W\nuia/pKGG1pkb40bRHxGI+q4abNOUHfl3Yk4Qzwm/T7BMKgl23LrVCtOkJ0UNtDOFmsOYZlyfBphu\nAbSbGX7YyStfvWKa7iyIG9CQXTYAfuC+wGtf8Ge494lX4MWPfynecvmPIMjZcilvVb4UaFM+pY7S\nFL1SjXCHtkKPrGXyMEOr/HQQmAeOg4Y/AdrinQk4prQ2Y+lSjjS3rsKnOrCsPqGBLPktA4Pqg7nJ\nST0pGVl3N8BJk0kNT5+ml0E9Trn6P+4XUQEqVQK6YZVe16LzrDYSIbqeUXitYZoh+wYDZR9f2rz4\nhycrh/F3K8hRA97dnvhKyPonvgS+uI3Gd623YtOt1CCn0kACsutZYqwSdoiNKWjQDYYjNePg6jiy\n0TrOmeWvAtDqF8qwgXlryg6t+r3kQ6u9YwhwAwaZHnW8tQeAsvs0QHinpytZMAfAAIRjgD9/+Ytw\nxrP24ZLz74U3fP4H0X0rF1NnFbWLnta3rjQbWmTSj2O6/QhpA5khoKPum+BbrPKSeh/yYFHWn9RD\nnOnQzc1yvGmRhqis0owh5PdKFUwjuuSsO0NePNNKXTEa6vN1YJJ64cDfS14Lg2yTXSm13dYqBq46\n0yPYDW43Yd0KYSATpTEtZ8izz7T8Hs9ABESV4X1HPRbgVTPygWD3NAMwpO9RhlzPbrYsGG8WQ17p\nAuQpR7BoaV2SBqMZ5fJhxqHx6Q5JnNIUHUik1tlpQ2YcgIA4oi6usE4PMBuNvzasAAKuHpbuW60f\nqTO0fHku4wol8y3UKX0ZrvbLVTBUHW0aBDpguht474Oeg3e+/Ep86tHfg8d8+QFYvQapEzaTElgR\nSkan6dT5ovGpXS8ZZ99Gr4uZopocxSSDGYDovWBlWXKCMuC1mZWngaZ6RrfpnDuwEbn8kyqpZgqE\nKfC1EXDRcSdix65TcNLJTWEkZLppAGm2tmHTPAas7VabyYYAfUAE4BVfir/UZ4ab4pE+01ZAOHVg\n5gkxBlEvmHs0QPochpssWW5ANNwxP8keAvFYOgTjPyxZAG+WHdV0hTKuGrsCar2cWKWz7EYTANw0\njo2va/2zAlay182mdBayu07bO/sQ1jHPpWreKrTC0CIARUv2VBwXQ5v1zjNqCKBk7x4/r6cC8uvQ\n9L2ryqJeFC7tBGhPBD7/0NfhHS95AU7/yXNxyud+DO1VyHshowT1AmTneBHU2zmGJoI4Dy0dD2wz\nomfZcYOhIr82XB71OdYd0tRAqN4C6ms8pLpIQN3EAWJjT8z3dBn4oeOB4z/yM7jLj38WP3zh2zFa\nuQGfWw/50FJ3n0teHtPMEOeqSZDDsA3Xm0QB7qEg7YH7nay1eSMcegrRR1fVCuyLvZWqCHXvnHja\nOtukkOHq0mE+z7DJV99d3TYa37msj6eFU0YhHoJ7xGQBvFFYPn2pDVBsmAGUTtkqaQf9Kt7l3jdW\nR2x4jKoN2deRDZceB3XcRToNMG3LZ1TYqdVolNjVQLwJnKrTZ2u3KQILO2zSOxaRIYOJzQJfwX7D\nLKjWxrfCY6J6tm2Ay88N+PTzv4jTfvUXcea//AzwVQE1ka0uTqA0E4B73OoMYTNpJz7T1wGpUr1w\nV7TCFWzA8KdGroKdy8BSlC8A3bLPzG4CvrQbuMdXzsGxT3wr/unpF+O8O30U/3zh9+L6Hz0XNxxY\nw/EHQ6oX67P3Sjtxb5qBQXzIbRGYnY6rUDUHRIBca2d3+qOxbCia2jNBXcK4e1kndTJ02jCrrh1o\nA1qty11JsNTARrXhgSZ7N2xb/n8GvLdSMrPCJYX0Mxz5XH7cl87d6v4C5B2TuGco7437+OLWmziN\naoOfctrLiNtUeidOcYBkjNDGwN86DabuT1UZaXltK9P3TRoMATBNuT2OpgMCmeMcgKd6oxuVDHDI\nOh9/5PjVqFawRDJxqhj8OY3LOqA/DrjoQX+Nx151Ai57/k/iRRe2+HV7bTymfjQMuMnVTVzOhnSd\nBiBU6ob6ZA5+b/zEjb5BuS2ls6dG6pYjb6jzFnI+VOWgIF6nS+nbyMjfvQv4vS8/BR97/APRfs9N\neMmzX4wHf8dHcc6616vryuHGy6SaCphRPQCzg68uWlHVVKEfFzXYqB/eUlVFWanGIdVSJMHfS9w9\nzW/WnCIg9zMl77wmavjYn7zdcdUbv/PeyD8PcWzilmWeG97RkqMGvBOL4Eu9DgFvo4l6VL44Noql\nvtQf7ejyPTqB85kNAixyw0zW3Aqg2FCADLIUbok36nMgC9nTgsYPPWE4gcsh9FNDBiDV7RYLHIAE\nXqEF0LsLmBhxyHjTwgqyYQJtnSftDZKXZNgLEo7pT4FwCnDhY16NJy8DL//vwBPfAJxwWg5fD1DF\n5uj8KvWl99QDJLmPkal3UjbPV+GD63VTHEuk5W0w6zEhZUyYRrtC7++DDLgHuh3A0vXAR08E/ve/\nPxYf/YOfwO1P+hc873lPwS/cZQ3LEwAbMqBbzFPTATbJRkQF4DRjEuBXHSk3nQkNCsMukEmI7sVQ\ni7ZvPqNEXk+h4Pchom/y7JBIkyuqdty7WgNx/4Xey0D97lqbnUDYt4AIwjvCkQPeI7Zi8QjJUduP\n931iIAMiuHFlzEjusSGQ+dLqqlOu2vjG+KZNaRldngIHx9m1hg2XosdTc+s7rkGfNOUu/OnU1BoM\nBlhfsQEKAdFbW+GH6s83vaga6Bss4F/UJZnQUI+QDl4wWyC5kFmHZFoOIxQqhxgQM72wHwH9V4Hf\nfN934oMfeRm+9iuvxLV3++v0EmaMegSYeU1Ne+tAOdL0XwaXBKJNWa76ueQhIeUoZhSav+pZAOmI\nnn4MjFaBn2nvg/e/8LHApSfgcS96J15wwjvjir4+hu2WUcxm9Bw1XZWoqqGUZk0KyHg5za/Ccy9b\n9gueKKGiO4IBwyo7G+g/KR9eVWSm2hz4OQp5S0l9jttD8p6y84BSfcFBRdUXPYDVBni4Ydv78a4f\nxnEWy3fdXnpbkUMScDN7nZlda2aflmvHm9n7zOwLZvZeM9sr955lZpeZ2aVm9oNz45Xvurabq9PY\nNxog78zEkdmfY0MY2iBkyJF76i+6t/zSp6IL48F/S33OU93IgMx2J03ZqA+1yYuGqRcSFN4PkKW0\njesf3eWpLuiQWqIMkD9rg5bqdtXdrTYsFQayEAFmdHvg0Y+5FPf82o046VE/iff/5V70PkjM+Ngy\nHzJAJPWNtsDNQBeRcXOw6pdym9DBRKVWp6Q05tRX4dngumEey3PJdcBpb/sevPtxL8SJJ/01Pnfh\nz+J5p74Tk52zzwGZ8dJo2o1RqqQMhe5Z6yq1BUgbCUhbQfJecrW03HanFbgR0FS9UJ+HVvuwc0Oq\nHlknzAMJUjmljEM2knrGCgz79GpYsnuW4UATN9A6EjJodJ3zd2vIVjQffwrg/OraMwG8L4RwZwAf\n8N8ws7shnkd0N3/mFWY2mMYolAp4jnor3nkn0jjSqC7PTy3qc9mf1RCgvoeTJsbVWTbapbXiwu6a\nEBsy9ylNltmBRqXub7UOMjHYkDtJvW1iLYUxx7LlG4gduRWXLZgvca7dj6p8JnepKk8pH62AgHo0\n6FTde2NtaOv92QcsAb/x0mfihza+iT/7rT/CRX80yjpq5GcHDWXCgocWKOheB/xNFYiFMp8FeOlz\nmr4N5KUGOs23v4d+FXjH0gl4xJs+i4Mvewre9OqH4RNP+AhWPMxoPb6jZgJ07gKnPsn1jmP9KMbP\nk6GHVtcRZAnUbGeGrKvcaPNJDpx9BSCdb8a/Wn0G5P5EaeWZgDyDbJDJTdqsynJauiNZLaxKJVVA\nuXJuh6sU04IKv8f87AxHzqVsu8BrZuc7mbzMzJ4xJ8x5ZvYJM/uMmV20WX4OCbwhhA8BuL66/CMA\nXu/fX4941hAAPBLAhSGESQjhCgCXA7jfULwTiwBG97B6RCWATq3UQXGVi+4DytG6Bkq+73leC9wY\nmumrK86kyf6OG1UtcWnyzloPiwwCXFff9BkkB5d96tJlpSnMu4BsOqDTynt6EnIqOwG3mmJrw0p7\n/laAvJn0I9d/jgC0wN3P/gJ+9d9/G2dhHx75mgtx4JrY0bslJB1yW5/pJkxajUdF/it3r+SREZCm\nPMWqxMproS57SlpAmVMqsn0eNNl0wHQnsLwfuN0134tfuvNfYP0eH8E1730KfmD3BJ2fKswd5AAg\njNw7w+u5OKZdB645hjStFwBpr93QYNAgttxFkrLalrrZeheydWnjFPYrBVNKTTRqgOZnnU46rdj/\n2Gd0hzQg7xlhAG4a5X7bexw8y419e+UIMV7dHOtQf7WYWQvg5Yhk8m4AHmtmd63C7AXwJwAeEUK4\nO4BHbZafm2vrOzmEcK1/vxbxqHcgHqN2pYS7EsBpQxGofrfuk6MALAnAUnQ3/aH3UV/jC6XKoG5U\napCgHkrj4BRuuc964c3S7BphVWTFTKMfeECEBjoFwn6UwTpUjYLTVWAWtGoZXGpdHfaZwqq6wVlf\nLUHK1C8BZzQ34AOv+T08cO3f8cBXvgujq+OiCOYtVAw15b+RuGy2HDOrAuu89vJpSH7R6Zq+0IBS\nFWH5OvWt06W4gm+yA9i3Btzhr/8YDzr/V3DCRT+Lff/55zA6JpRLsVGWQQfJeVb0eQPbZv7kupCH\nXgvrbQRdJR/cK1eZqKvv82GSNvvaGV4PrlS3M1UJ1NuyUnRG2kvYUSjzk9zOLPYp7VeG0vf3SMo2\nGe/9AFweQrgihDAB8GZEkqnyOABvCyFcCQAhhG9tlp+bC7y5QNE6t9m4NHhPpxxcKgxkktLLd6A0\nhNVTJXov1PFTiCODu99LwwXy6h7dl3Se7pYeGWwsIx5LP9SJpPMUcfCFK4hWYTlFLZiTbJRD4xDZ\nb73yTZdKd378UevHt6sfMsMmi7tl41JKt0deUkx3rAb4yL2uxr6nfRLHv+lq/PSf/QTafwO4oivl\nvZE4aJR0wNWlvoXbmc5gKje8fAMzwKqDR1H/bY5H67Jfynr1lxlwxxe9Hfe48Bz8wnv/AJ/a+/W0\nrJhuc6ozZj6badbxoppdaFmTkXDIIHgIwFEDb01A0okPEg+JTN3O50nt56v50fRUncG/2oasYF8D\nKcGcBsEG5UILfq4eoj62KtsE3tMAfE1+DxHKOwE43sz+0cwuMbP/vll+bq472bVmdkoI4RozOxXA\nN/z6VQDOkHCn+7UZecMFWb9z7wcB3/t9wIrro0ZA2lxZ+w2XGtaLLnoMeDX49Gypn51qUerGoQ0U\niCC8FMrGxykUjx4y8xVtBqAtjXnqqJ9ckzokg0cyMHHu5V91rX+KS0B4ZnlvU+YxNHEwaKeY9XV1\n6dsM2Ar2Ka16GuKfaZ+JUAJmuB1wyQ//FX780/fEV173CPzs2V/Hq7/rI+h2RRap+Vaf5FRoLfM8\ndAg5bwp+ablwZQSsqV1hzJR73RLQbMRVaNc1wPOe92bc5+NX4X+85g34/tMuiacET/MgVMxK2jK+\neQs/ZurU3zv3cK7zBOS20fhsjEbhUe/FC9njhsxUFzKwb5DBBmQdMPy+Ve2bKi/eg+W+QQBtkPti\nr3FVeW/DbN9rgm/iI4NED0Bf3b9+EPjkRfH7nInmYctmS7I/9GHg4g9v+vhWFB5jAPcB8F8A7ATw\nETP7aAjhsqHAW3InM7OzAPxNCOEe/vuFAL4dQvh9M3smgL0hhGe6ce1NiNT8NADvB3BOqBIxs3DR\nNIPpDq9d1SE18ptsVzexYaMC8oILbQRACYJcbFGviutsvhpBdcvqqqOLLAxI/sHc3AcQYO2lcQs7\nHdobIHWAAdA1zz+NUd046lp1v9iZqXpwsBCwLHyFuYzVe01a+DHUJAaArGDMXt52Ffh/H/dMPO9f\nH4BXvO/lOP+u74961ZANSkQDMmrmP4Gu1FNKaqh8c1zp6j0pinvMt9dP0rX2wN/uAn71j1+Be/z9\n5/H0F74a537HWmmQbJA2teGmOHT70zwnwyUHWgK1zISSD7gMRtZX6qTqO8H1pnFst8t9CbTpWHcr\n3VrEYSoAACAASURBVLi4SInV1Pl19gmt1nQasM3+VtDmaROAGOeaTELoI0+pT8IIEM8ieX/cEP2A\nAXv6eB7jDzfYtjvZ9d/eevjjTijTM7MHALgghHC+/34WgD6E8PsS5hkAdoQQLvDfrwHw9yGEvxpK\n45CqBjO7EMD/AXAXM/uamT0RwAsAPNTMvgDgwf4bIYTPAfhLAJ8D8G4Av1SDLoVAaogNhOx2aO8G\nALNHg2iHQAZdAmG9EGLorRFA19u86GK9zY1KF1ys+BLHeh8JTXvi4MljijrXy078FAeqDLjhOgGR\nrkYpTgGzmdVKhuSUr9drUGon2RiXQLdeGWWyVBZI0+l0tprlcJrfWi1hXbTuWwf0y8De//UCnH3a\nl/HKx/84PnVJjLOZerzCVJknejekJbsCYFq+GQMcQbdmtpu0ajYLs7i3gnUxT//f5ct49PvejGtu\neBD+9rUvw/fdeS3tUqZlThsBtdW7sVxv3JGM0sv7ZLkBlKdJADN64XrpMttWE2I7VMBiG6SqoF76\nq2M6ML9PcAUZRUGYYzJ1tmlHtApk65Vw9Vg+lK4aug1xqXBv+SDc7YrucXGovwG5BMCdzOwsM1tC\n9Nx6VxXmnQC+z8xaM9sJ4P6IODgoR20BxT+5PnS5i3+GcgrDmSeBeLPNcYA8MqsUhjlEVQZXuAVE\nMJ02MW6yBI7EXFVDnRpH5yUfAPRIan1u5xQz+mZgeO16USeVFbydzLKf5Ihvwh4Hnp+NXIDjEHO3\nxmciZHJk6y19aMn6REbrEcQa3zWsHwEf+MBD8dInPQCf2Xk2vnLhk9DdvcdIFiOMDyIv4EDJEvtR\nNM4pUxzK96aLR+ZIO83ueONVYP0Y4N1XLOGxr/o0fubPP4wPX/yb+MRp34rLgg8Ck52lqmZoypry\nbtXU3fPN2YkeDaXvtulnQbeOH0DhM7veRvDlDC4gg+7ceOCsVIgJ2z9QGtY4waEHA1V/QzJ0Kgzj\nKcJZJjXr0oaYnsYzNeCgxbh+zLbPeK+5YevhT9k7m56ZPQzASxEX4L02hPB7ZvYUAAghvMrD/AaA\nJyJm+9UhhD+em6ejBbwfmuQXSyk2a0YJvOnZMGskYxg9+2moocxj00w3ILq4cL9QYHjtOwF7qCFy\niWQbZtkxRTfracnktvAaVI0xcxw4ShDTa5u5iNXuXPVKOgXtmeuWn1u+EThwO2DpoLvQGfC1j98Z\nz33Us/CPr/wrfPb7/ha7dyL5sZIpp8UEVMkI8FLUXWy7Ml2JaY0PAut7gP9zLfCw3/w4nvO+S/CO\n638dH9x3ECPEfFA3XZ9Rp0fUax7LC0gDgupxgZINbyZ1nHpqdlGmJhvE9Cge2jl6CZf0w36N/Yyi\nfUl/6zXmpbe8QpTCTa7q/lez4tVWVB/CztUQdySB98p9Ww9/+rHbS28rckhVwy0lhqyz1b13aeDS\naYwKp1s6LWCj47M8lYLPEohrH19NY6PJm+KobpkMVzvOmqgjNL5xn58ZCRtpeuDgKKe/OspnYwXk\nabX6pQ5VGKfhQ6ALxkMGZEhbWG4qJoAu0+mUJ06PO5l2E3gMyVth9bjIIOn21i8BJ977C/joW5+H\ntV/8HXzo2junlXfqfoWQ9aVUhajeVz0Q+nFZxrrMdV3ofeaXngsbu6Kf7s/9+Vtx9vsuxTvf+Fx8\naP9BjF39YT2ie5pl0O3byOznpl/Np8lide+Gmeonoxc0rMutYVvXy/JzoynbNVlr2uIRQkb6rJlR\nNy/+VpAdh2ys44IjIKvRmjC7AAqeH7Jk7UdcldYZcLAtgSdIvCpLYXYBwc0VqjK28ndryFEDXiC+\ncO58r4YsHfWGiGAAZo60pjKfCy5UBVC7qagvIadEHMGXnBFsNPGPfsP1MSeqS2NZ2Ak6y/udrrXA\n+ghpifGkyWmuteUUsyeQIXe+FLknzNMI6gM1C/cm/86NXtIJxwMdPwGT6FPr0xgYblO3OuqHgKSD\n3jkCPn3W5bDf/CB+4iPPx2sPNFEXTHAjc2Z6WscNCiCiQSu5Y7U5TykeBdm6DpGvd0vAjuuBe77q\nBTjzJas4+bd/CR/9zmvQLwHoPZ0Q86MuerqRTUq/H1Z5cM8NfRdNP1yHyfgpOt0Zv2eb1dMGxPbK\nxRQkLzWDZT+jP3tyk/QwCpApPWQg5CZUdCczDANV2kgduQ8BmQXTfrIUch7ZF9mXydAPWFy1Jhvw\nbUvqRSOb/d0aclSBF8grxob6tPr31sKXro0wWNXQII3VwYGjOxuHNrBRiKoGNUio4WyGaArYq6xX\no/e6gC3B2ZBVEckKbDLy83kqu/27ej/UwNtXQD4ExgraQ+BdXwOyMRAYZpeDYoAZ0JwM3PCjT8ee\nN5yKV//kRfjAWoyjqXuUVaDEAU302jWQJrA1AWoH5eQ9ov7OvvfC+Abgu97xaKy+ZIw7PPNS/MP5\nN2LjVAdKNy6y/EP7ThQbDw34fvJdqBC4k1dEn8Nudf9i1fGqEYgzsDZEDyGy19SEKmarzVX3u66n\n+1Npx3VfUHVFyh9yf2B8JCeGmDdmu94lUPeL0HjXtlY1hxTux7KVv1tDjhrwchXamgNaAgZknJm3\nQg3IagqTsDpq9QA25A12yCyVDSON6J6mhXx2VJEP/rFxSF75ObXcgMgsdATtJU6Nu6vyAmQADhY3\n9gltBsQ0vecnUAIAwahB4WNKvWQwARe9ZvleinMAxAMwc4R9CkP1g0VD29SX3x44DnjEuW/ByZ//\nLP767U9CWMveFCqqJknXPI/dUkw8qSmUlXPp9KhkjtyLgnFOVyL43ufAD2L/C5+IH/5Zw5/+zPOx\ncQdgtAaMbyr1uf0oqjeUYavaQvNJUC4GLi1PxeYJwGmw0GvyTtkO2EaCZWaqHjcEUF2iW7tlArNE\nJulX/TfbefrtcXH2B2T3NC6amFr2tyXTZX3obHNqsU+yz2ufYT9QGTiE5GbLgvG69NI4tKzKQIEM\nlhOpGE71OSqnk4hDftHFbmfVi51anMLo6DxposJ/YrnxAf5bnu38WbX1JIOGx7fWzrLepCtF7jyr\nbZ7CcaRl5+q9syYG0lR/cvQNwZN+oAqMU3dfgwGdu4ulkzQCip3PqFcsDnsUQKfOWNkaWXJSZzjw\nTXdk1tnuAF7/hD/GnrMa/MsF5+OKG07CdElYLMGljcBH1YO6X9F3mQOIehpwcYMBhV8zLKsm0ERw\n/ZdrgS898gKcd8pHcf4zno7J7lie6Y7owdBuoDiah8bHbpTjq/fjUFWOsmIdpAFx7evyszOqEWeT\nypjZBtg3eLoE204iMa3bF6Tts69A2ntNAPR+5zMyqu6YfQVssmvmhxuZ9xDPCn+eLpbcfIq6Y41L\nCYz2q/3V7+3IAnhdkmXf8hQcyNsz1qKMS3WpurwRqNaay7RcV6XxcL3CSoscXwL7Zng6hTlpp8bq\nF2vjGz852rP8G00EeHamiV/jgLI2yudl8fysxJibrJ4gyNYbfqgKg9e44xU7mwJn8jvVaRcBpctg\nTaH+MgF+mwGvW4qAc809gD2/ezWOx378/Et+GXu+FBlot4S0io7pEMDSXhUEcNmpDUBaAt35FpE0\n0qn0TWTe0yVg/WsNHvzoT+Mxq5/AE178XJx3XK4fUrzkV2w5DeuQNp6ft6y0b1DurTHQgZMR0wcy\nDaKDu0lY9oVJk/3NGWalE1uEX1djm1dd0daYVmKsltUWDE+pPRHM47JQ6oMTEPu1JfcxpsuY5nki\n5VThs2sG3GQRcDdQLoPdjiyMay5awFqdoNP0Yrpuw+FpeSX7pYzcmEF3G41LWTGlXimnzFeFgKrs\nnFZcDT60CxSncPUG7FRx9HBDRBP1zTRI0CqtMsfZO0md97oo9f3itForGfbU93gg8wMyAHXjDNz1\nElQgPrOjA/74nAsw/bE1HHzrnfCQczJLb6fDz/DgSyDvHEYmSvY4JGn/2waR6W5Ed7fTL3shfu0r\n/4BffvvLcO69IgMmiKu7WL2d5tAAlAPLV1nUUhj15oB1fTx74SXg18fuVcMFPJqNefpIevbUWa3V\nDHX1aZ+sDcqarrYbnYXOUwsy3JDwMlWH3I2sQdxv4EgB1ILxDghf8E2j0uikQj2rLn3kC28gOjDk\nBqSboWt96rJHMluenKphVG9cswXKEEPg9KsWnbqpkElQ51XvEkUVCsvDTke2o3nTJdGc3vGPv1Xn\ntpkoEJh8mdHr8lYo1Q+UpgeWVoGD5wAPefjv4A6TKc581AU45p+R9xYWMCfjLAA0lLrXemlzMl7p\nyR2uWO9b4N6Xnom1jz0Sj7jkdbjrnS9FN/ZFKqMc56EMh2rATHUw9J75nsTflwDfVfWSrPlNjrdr\nnGEjv18a0JYG+oaKvlMap5vqPlUg7E+c9WxF2uCrPP03+25StzWlnpnMep6RXIUGcQDY5fn+xmYP\nHIYsgNeFLx3Iela6fxEc6hGdVlbqYwH314VbXZErburT9DQNr9QT1GMxL+xAjTyjeuFaPzYktf5Z\n853KLaxXWUWDzLLJxtUdboh5c7CpdX5USRD8J1V59PSN9aqO1a2vF1WL6v9q4w5VATQOmf5GBsSd\nE+Dp516Lq572Xnz84nviydNdWafsYJtO/tUZkT/fjX3wEjctFeqCqY7g8ud3X3lffPVxr8HT/uYt\nuP/1n8J0BWn7x2KDGo2zesnJy0HDA4U73jx/XiAPDE0f63Hd1RKc6uug3YR4Xw11nN5rn+ChrioK\ncA3fG0pQMa8rVW+wnfE+bSrqD98h+7rzcIEaqOq2oVtCEvA1bNvnmSlXn+70z+tx5A6FXKgaXAgY\n6bQHCDA2cZEBw+goSuvquI+ZXwqzrl7KHlW3Sl/I5LrlafEakF3bJpZZJxuoellMm/IsKV1VRH1X\n8PxSRcA2p94UtSqF6SnA6YISTW/IGqwb/mz4vVon1yMPXDQCsUNTZWMhh1Odu26CooWq96gl46zd\n1SZ7gf/5mH/G2bvW8PePfwXsyiVMRlmNkfZrUM+EJgMzBNABUQc0/jwioPKZ8Y3Ao1/13/ADq1fj\nzLe9E2t3D9GAtoQZN660ko5pYRYsCvBlOL4PV8lA3klff69UMow7vV/Li23WpC32Fv11GW7kzFOL\noGovEgWqsbSj62CudgiqKKYWmecoxGd9O4uZjas422T+O88jV35yxqjumbrPgw4a/mqLnQb3AjiI\nIyNXHcbfrSFHXdXQAMm/FsiMUlULQ/Sfq9PohaDGLiUBU2n4tX6KMnApLbesgS5YHhVbYce1f2Xw\nfw3ySKpMV6dfjTdu5l310OwktZ64nnGyzMqIyHjY4EN1fdxnBjwK3tE9nY023qOOkaKDQ8H6mK6z\nzn5UsjxN+OEnX4Zdf/hhjPaP8aL+oWjWkXdGcyAr9kdlOhafn9GXWv7rluMKus73gXjaPz0G//nP\nz8DoJ76Ip+78+OC7Zhw0pKVz44IY/bSuOdgQhJpZ10YNy0GeUQ0te6esD/VIb1/FCSlaJ0xrTtF0\nljV0T5/nK9AZqSHnX9sTwzGQzih5EGetYtA2yPTVX7iulyNlXNt/GH+3hhw14O0QrZdTm6X6E/1t\ns8wQiGGokK/dz2qPg16uE9hqL4ZaNF41IJDlkn0DJdjrlJHb7wVkpkhPicScrdRbW3U9Ae1APqmW\nKHR1OsjIc+qtMTUUx7No3LqMmivtiiluxZJ0MEtuZwQw6i/bEsCme4D/fa8/wbl33MBb7v84XP61\nXVhbmgNyZLtA2nw97T9BIOaUvInLgKc7YvhvHgQ+/fqTgWMCTn/x72D1ZCTkMG0cEDWB5bgS+xYA\nVs+GvikHWk6XDVllxUGH7RrIKgbWvboTDulwl7pZQNrM6HtzpOgHIbdRtqEGszMsfV8dkNzG+Jzm\nK9VflXc97of3KX661BGRfYfxd2vIUQPeVWUA1T1a8DlFpy60Hj0nTWQBBG8Kw9bTa6DUQdUNVTcb\nIUOlG2gr8dUAqKAeUBoqksrAO3SPrHvmQMK17fOU+2QsQ/1KGQS9H9Rvud4eU5/Tae4QY+Y9GjsD\nSp0fqrICefpdG5jopta4O9qZpwIXX/zL+Jadjod/6Rzs/BYKv9h6t67aS4BpFctsA7C2F8nX9tc+\n95v43MUPxnc+4R34w/1OikV1oT7J6v+rqwNhZX7IhJlXgw+qTWkbSAeiNpn96UrFDblOrwXWN5Ne\n7ubr9+ujdKZzwm3GdlVq3XDdbgLygow04Fv5vD6jWTHmYyB/KY1KHQcMz+xuriyA16UF0jrsGyvA\nKfS1wnyBWXDqEV2VNppsVLpplBlpfYS0ijJAneLprk69ZUMCkDfcqXW+FE1H88nvVD0Q3JkPpjs0\nwGi+gMy42fi1gWqa/Dq04RC9JYYYPzsZmTQNcPykMU/9nOkapV4NuqtYWuDRuvtYA/zb+kH86nEf\nwY4nPhN/9/VS76xSLIqoRFUcwSLjRQ+MVoG//d1H4u64Eb/9nLdi/VikY49Upsso3L2o8kg6WKDY\nrHwq+xtzwDo4zix3KjMxrU9DBUYhAivb7ME2D3oBftq2h+VuX23IKgYK3xPT57XNdudT//b6vp7V\nBmTSQekx3D757kZ9ZO2161ur00LMkiWKIfeBHSF6NxwJWagaXL4NYG+Im2GwL/BdUY/KU4jJfFX3\nqQapg3rIIfJL5+YcVPKrwYqgpcYBxkmjBe9pg0wg7Z2LW0Cq9PJcvW5epXbXqkGQXxlHwCwDGtqH\nmBKw+Qtmh6i35jTkehsSdvBxxVIaAa1kLGrypxqu+hbAroATvvByrK3fHi+6+Fwcsy/HMVpHsSBj\nCCgofYPkUbG+J4LjAz7wR3jAJ6/AA//Xu3DC2gb6UYyzlhrk0+5qUnH0YYY5u0VWI7V99NYIcOAM\npfeNChfB0C2s8FBwRkyXShIILgdeb/LJ1/XgWoM6DdB1u0wzLPEiqMNoPOpVox4RQ7MynmbRNdl1\nEYjlTO2Df55m7ZeuBmQOVgvGe4TlIIAr2RD8mnohAJkVEoTplUAhQOpgSusqvSB0Qw+GW2/ywgSy\nOqoelOGqakFXyxHs9HmGYz5q/98h4S1lzhxUdAOgaXWfRou6ww2Jus0N3aunouvtcMdSISAnnSXK\nKbKmN/e0XcR0n3zNlTj/XhfjY298Bv5mB9KJHd0S0oo4YFj/W6gAQmSrEwP2Xgas/c7pmJ6/hP/2\nX9+atqPkyrKkngiih56j4mDcSY9tZTnrUwtM6kd141PL6oYhUB65axg9F/SEFrLOcZ+PydJ6rOtn\nSQbEIb0v46u3O63jrVUJ9E8fah7meeB2lWTFdA0leUj64YE4moEyHymAWgCvyzKAm6pM6OhaqxTS\nPWnotLATsAmMnXaSkAFX/VvVBzI1Psugpj61PWKHZjy1lXozIZAe7oypPuqoHghS2TEbt3pxBGRX\nvXn5Kww9fl39eWuZt5KqVr8EDC//BhxUG+D6U4AHvvS38f3fmuLDb/s5oANG9MMVIBwCRN1qMVjU\nHa+sAbs/+1vYe/Ua7vvU5+GcOzgAdVmNoIa0/hBsenDjHm9XSU0wiqoCuj1SFUPhCSdKALRtsx2u\nO6vuPP4GecAncyTrBcoZIgeEIVvAkGEuyHP1PbpDEkC1f05tGDR1gKfBrN5KlYMPQZVnJQLC3OWZ\n3g69YGSr8rXD+Ls15JDAa2avM7NrzezTcu0CM7vSzD7hfw+Te88ys8vM7FIz+8F58R5A1vFuAOAs\nsEOe/rY+Qq5XnVl3vVcQHAIhQ57e8cy0zrJvokkl6GqbINdHDtQEv4nNpsPfCnCNfIYqnEq9so4d\novHvbdVYeXRL8maQ/OhgYfJX51O/J5BFuRKJcbDT0E+TeaTxjoDCQZB7PxgcnPvZDt6N47NLAB53\ndofb//xf4a/eeD6m+5FWiCU3wwC03SxQJF2y52OjBa67YgcO/soP4dP3HeOC7/4kzHJ6fYvCP5gZ\nL/Jm+TN5TshAHsz3TBCD2VKf80DWpgMz31mwbEsgoOgG5XyWp/NqW6L6YKOJaQbmEVnHr4Mf42Ye\n1Bc81R2EgVYkJHkMoWwnupevti32kQblcV0rXWnoZn4pShLYD7igYjTQbm6ubJfxmtn5jmmX+cGW\n9f3zzGyfYOKzN8vPVhjvnwI4v7oWAPxhCOHe/vduT/xuiAfB3c2feYWZDabxTQA3Io4wE4sGtoDo\n7aDMtUd0K9EXVxh1LC+mALKvI13GyLjoHqXLcRkfvQGGdk3SVW76rAJYCoNyehfg0yuUU6x50zVN\nOzEkSU/dwmr3MrKdwvcYpWGQ+aMEiWvQKCfl5DX6/CbXqZAHCKpreLxM5++JCxsYUTBgvBGvWwfc\nsAd4ztPfiFM/1uJ2n38hvjQB9vvuZaN1JKMW1QnJ+4FuagaEHti5DzjrAw/AaevfxNtf9msYc9Ma\nB0Vlu7oTGJBBtt53WN8z67ZWGaw1mSRw8Fd3RK4qhL97Hh1FHeqkiYBMn2m1UdSGOi7ZTV458t4K\nLx5/p3zHahvgu6JfNAdMPlevdNQ9e9knA3J/SIa/JttVqGahyo+DtqEEHarMCM4Ecw5UhzqrcKuy\nHeOambUAXo6IaXcD8Fgzu+tA0A8KJv4/m+XnkMAbQvgQhk/gGMKORwK4MIQwCSFcAeByxKPeZ2Q3\ngGP9+9c9spu84teqmJWRsRElXVJfAoq6peg0eTNXlhpE9TeBXNSCg7uWMU3tcARujRvIurB5eaBw\nFzL1eqjDE9iB2TL28lza70HC1EybopZt6rN3TeNBnstdNR1sMHMUEctHI05LtUmPtGvZZAkY+zRn\nZw98agn4xKPuCTz9R/Dki16F42+M96ZLMXzaON1Qst7G1RLLwL98a4Tmgt/H1/fsxvcef9Wm+h1d\niqteC0yDz2rZWB4C44of1DoOpU6yQTx6nQPsSgfsmgArchAqVTVLfY5HhUbOIcOssul5RVR/4no/\nXi7TZT0UZZRBlXpZdXvUWZzGyb6ogxJd5Jb62H7q5emaJuNUY++hDrg9HNkm470fgMtDCFeEECYA\n3oyIdbVsxqcK2Y6O92lm9m9m9loz2+vXbg/gSglzJYDThh6+EVHdAAC3Q9T5LofoPrIsYEImAZTA\nwuWUAGYsrWttNEJwdN6OLPvSR3WlWu6y4U5lM19c1ctttLOLRqg6SUsr+zwFDcgudaOqI6mb3TzR\n5Zq1zHO6H0nn05OW+TcTjwMt2fagOMvsmqjH5em7TQc84lrgDuFROP/LH8O//vqdce2euJ/CaB1J\n51FvvtNOYpiuBZavAR7e3wf32PNVfOWDP4pwDDbtBgRc7utrXf5T4f65HCiVhbLueFI2z+ujgUwB\ndeK6ZILRhgze9ao0lfpMNZV57WwzT5oibJ+9ERTkhrYzZZxkuPN0rw3ygKJlAPx0DLnOI4vGPvho\nuodyrTxs6Q7jb1ZOQ6n+HcK1AOCBjol/57P/uXJz96B4JYDf8e+/C+DFAJ40J+xg1X38gljGJQB3\nOg948HlRlzsO8XRR+u9RDbBuObOq/+VJverkHfp86J42vs0aIvVnS31uKEt9NJzsnGb3n1FfGk6m\nbelzCeR19J3FxqauPQ02Hwx6i2kkSzkB0PXanV9TEKVOsLZQk333XngCKQ0kynjnqT8IpDxVYLl3\nxh/KWcXqKHdieBmT/3CTQW5jHFdi9a5m4KKK/ScCP/X4U3H529Zw9jeuwQPe8uf44iMen7aHDE0G\nas1c3wLLB4F3rwC7Hvpc2P9+E/aeuJYGm1rqU5frVXWp0Ia0CXrtP8wFMEBmzazDGrQ4OHMqTibI\no9kJUGM3FNZGRNYpvQO4mEhdK1VWurJ99ijZVQ3WQu5Tu9MZhaoBlI02IXojqX8742kkYytd6f0C\n+BFAoXxW5V8/GP+OqGyPgW0F/v8VwBkhhINu83oHgDvPC3yzgDeEkHZrM7PXAPgb/3kVyuXVp2PO\nvhP/5YJoXNsj15a9eLXTdGfA2L+rvglAss6yajrLDU0tyTx2PbR5Aw9ai5nccp+X0wZEp/AaaNUY\nYcjGls4iQJN9KyNS4wLd0XSXqwB3T/OMF47llo1Y6oXBjkDwG3S3UlULcocii6AejQDd+b2Jxbqg\nzq1eWdX4c7oF5ZIz3t5ZadvLoEi9rIMzXbp0o5iVCfD0+/0dfuihPw28b4SznnEQ4ewlNHfdQD8G\nzE+ZoC/waD3qiEfrwJs3gJ9795tx8h1PxP0f9YZ4XI83jmCZ1QbD7D4PLgR0HsXOUyK0LhPoWy6T\n3lc9rnqFUHfbhtimAspNwgmuNeguuy6YiyZ01qIDqCG3xxtHeW/n1sGRMyXDrDdOZ6XXharACJhD\nsyUObLqoRz8ZN5DJh4YHYhtbbbNqYkfntPH7gPufmxeOvHZTbekWZbOjLP7J/+ZLjWtnoJzZI4Rw\no3x/t5m9wsyODyFcNxThzVI1mNmp8vPHANDj4V0AfsrMlszsjgDuBOBjQ3EcRAm6U0RWy/rpEb0d\ngGhwA0p9p0o9mAVklpm8APx6j2zsUEbCrSa5SQyNdklP2udwBHt1nbIwX4eV8mlZT819EICct3nD\napA/ZVd6bytTsjoNNZQwDjWmsAMnI0kTP1UV+n/ZO+9oS6oq/39O1Q0vv9evc6Ib6G6gyQiIZAQd\nFEQR408wDTOOjAF1/KkTHLM4MmJWdNQxYsIAIgMoyJCzhG5SN3TO4XW/eEPV+f1xzq6zq9593YCt\nuH6LvdZd7766VadO1dlnn32+OxnCwiTaUoazq8VF5zuQG+YqFxvorMPik2/BkPBAY39+vbia5X0Q\nrTOuuyQ4jXZ3719ug/O/+FP4+gI2PtLgzeThAy10jerTRBQl+f5GPueo2BQyTwKjjGtxngdlkde8\nJecL+0pgkMBYEnmpF13xqZ6IN7KtfxrGv1w4URQP+S67pSxDWJpf/MXgJ141RS0+965s3hPDSBv+\neg0VZIZYG3ZamRLS4j6xdYJ4d+P1lKmxi88LgA+oz3i6G1hojJlvjKngHAiu0CcYY6Yb43xoBaCQ\n5wAAIABJREFUjDFHA2YioQtPQeM1xlwGnARMMcasBv4dONkYcxhujJ4E3gZgrV1qjPkpsBQnSy+w\n1rZ8dfLMZaDdd6RdVmj/f8V/b7d5Fx01V4HW2KUmEcRiiJLv4i/ZiMKKLFuxYjL2kg2ubpbA7KOx\nu64jyeOwrajIwBKN1jTjJ0wx+Y9MeGFWyDNl8ZattBSZhKZwvtbc2r0RpBmF3YDAHCW/I5CLMwNN\ncfmW/tGCrDewyaKjtN5GG7z4pN+yhRNYPmx509LDGVz0v6Tew8F4IdisgK1DfQjO+/7P4NIFzGs8\nysyP/JL9OiBNIfGaNrJ19oIvapJF7JgEjAm/S390FY3s3aThOUWA6/ERgSnQQVtBwxKBJhUWMper\nwnkigCG/u9vVoiz+rpJPJDFuHOtxqHAtw+ZjSbJ2hco2zC2Bw3IRiBQ8N0zYLRWVAd237PnV/8V5\na3BafSsBX5wXz5h2pfHuhqy1TWPMO4BrcK/wW9bah40xIvcuBV4FvN0Y08Tpla/bVZtmArn4ZyVj\njP2EdRrvXJwAjnEGNnHYjnAuZp1+RWzgoIhsQhAstC0nOGEV1lvmomuVZu7iwAsmqrfUsqXTbWd9\nUUwqJFvCiYSx7rtmXC0cZaJrzBRau9roZxI4Qkg/c3HnIMflnBQXxjoWubGQGHxwgllb4Y1qW24n\nC4fAD/rdSqIaEaRYJxBT7zJ20GUfYM2FL6J99hRW/O9htHvPBhs7jbdZgVELJ//qIB79xM/Zb919\nHPK66/n2Z7/ptusRRH67lJYJBS/l3oZ8gh2UUE3JqlJAflGS83IJdEyArXQyIXkXkjh8Ih4VGovH\nC2shyRCnNXYZi7E4eJ6INjkau99T493UbNhpyWJfFObSZw1F6HGV5Euo32hxvWV8DgbtainnyvEM\nLiOv+Oh7Ht4O1j5zj15jjGXL07hgyp92v6dCf4pXw59EsvKuxuVtEMGq4Dk6bcCmqoAYiEAJMv96\nWmU/Eg1UtjHaQFDMUVBKx7v0iAEkd8z3R/ww2xLH5KJxFCNysr6Y1lZo0TLEPzOHDfvtqBawsi0s\n9rX4zFkuC4KWbwhwgo5I0sLaEPyis8Q+Ni+AIr+VxgbssxYFKERi9UcU9JJECtYQY4vK3ZBUndCL\n6lCdejlnsJa/XXsPqx+KXQrGshfOEdgS9KyFR6/8HmbdCvaqDLH/l77pYAn/UqVasSRoNwnENQ8/\neNe0KHGGu6xMkBfuGczgi2vmDHpKExYhKDitPkW20BJxJtt6PZb6e1uLbXWUhh1VezMILL0ACv/p\nSEfx6BFBG1mHpxZdwzKt2+a1UOmvXCMk0IFAC/Kxfnzl/trQpq8Vrw9tYxCFpaz64Idwz1L6ND5/\nAXrWBK+mGUAbDuMdM/lnzzmFq/+LQqzoaA55LRHyq7ZeXSHvMiZU/F/chMppcAYX0q5kOkVfMTQ0\nZWJ3K9FSigEN8nzyTNmWvwXlJnZBuLZi7KJ7GgToRX4Xdymhsn8HtQjadvqQ2ZK7bqAC7Q3XZkcS\nJmSpAXFT3ds6oSY4rBSztBF85cgS+7GdrzOFP86a5wTgmPNAaLZB1IB5K4Bb2jmVbRz12bt5X8M9\nYOTrskWpF5x1d1yqEWfarH9mXaIo51JmyaoYa8r626QlteJDCB44WXHLlHEpNSE/fobggiXBP9pN\nS49JUajrkHjJNQJhvIXHtPDVAlEvvNWCMIoYL5B1cIT4OmeKgw3vQF+j2xAsu9xC4O8R+tPcyfY4\nPWuCdxgYxeG4Y7htbcl6rVdpX8Iosp0S7Uxb+CciA7nIHcgPaMULUHFiF6oq7bdUmAiaLEF73BVp\nv8qJXrhE2slHMMHEjJ+ghtaTtkjSLcHing4vF2EPIQldHY3hVxGc9CS8fCk8WnfCsefF0P4l6Fju\notNGY/dJY2iWXKfiphNejUroaOphBBtBZ/IIl8w9ioSY9z5piYf9MzSdYa22HLb981Ww8wl2vBbe\netalrtaar6Nm8ZrzJqjeC+13Qfc10LkByiOwbLLzhhCcMm6Ej35oqYDckkRzTF12Mh0wIp84Jcsn\nURSMEngSKRhCxlV7WOhE6poXizux3Nj5e1WSUIpHk/B78XgRfipqxfJ/8ZicK1BIsV3ZtYlbZCvK\n5ofN92N39punTH9lgndP1ZJ72jQJByvUcdqu5E+wFkoKExKoQLaQGVbn2xH3MckZoK3youHKgMr5\n1v8mLlHapSqy+dy8UpNNu5WJkBatQWshRVwVWmivE7wTgRHE8KdLf4tFXJ7zqYZS6v4UQ0onArEi\nGzSsovZWSR2E8NM2eP+PLwYOgmaVl61cD4P9HPqJX3Hwsq9z8WegfiXMPQYaXRC9CNL9wPZBrQPS\n6RAn0LaDsAJvB7sVnp9A+wvLJN9tUH/8s9h7X0X5RLAJ2Plw/qRJ2PVTOZt7OOmc/2TODogHIa3A\n6IeADZA8DCstdE2axLZLxvj0kaP8dMWBMHA03DvA907/JedsJbcVkFpx2LDrEiw4TsmgjNQQjISe\nN3U+X3ehhy0K77YlLu+1Xwm/NgRvA+leEf+tFIW274s2eMq1pXT8RBejmOaDoozTthFRYire60N4\nupIyLpBInkHvqpoKmtG5VoQs5IyaQruKOH1a1Nj9KX9JetaMa//hmawCzMQxRofvSoSDHao2YL/a\nKiqCUsgSmEOEZ1FYSbua70X4isGsldAUIV1JQwhvNSXnKZET6p5RUt/PWE0GjblpfpJnSwlGEiv9\nNX6ySj9bMKfcX2L35RnlNMHcmgUsUnYRmmShaxr3nDLx9HtJE9jaAe8YmMf1P/kdlCNoH4ahdjAR\nTH0Y4lV8cejDXPA/W2AAoh2QjEDpANxKuw+wLy470g3AXLDTwS6HsV/A1J91Eh//fQZfsogtDx5E\nXwPis8AOQLTPNfBZ2PcFcO8hf0PPMNAPA1fDlkc9zwBt50/hlecPccfyV0B8Atg5MDaLE048jR9P\n2kHPkNNyM68GEZZWwQ/K2CbY9LhMaX4Ai9WPtbtcsWqykLH+vhR4WjBz8tcVDZWRuNrZ0JbkpMgS\nCMn1uECXjmbwPdf3EHfHjF+MN3qnYUcmPCG7slR58oiXkPBalmvFhjwOohzJvfSrlOuFh0W47xHj\n2rKnccGCP79x7VnTeBs4odtBePkj/lG7lGCqm7CC6kHS3zXjiNCVSC5Z1T1P5utEmTxjNiOwBWhB\nPABGlfYiWrREmQn2LFqi1iybhlzEnSVoEsWACGFonV5PtKDEH5cENVqjENLaQaTalYCOuGBg2RWV\nbZiYHQUtzZZgSg0+MWklxy58Fdz/C2h0OoEaNdwJo4fwrq4P0/7Rd/Gaj0NlFpTvBU6FdDZEk4FV\nYBfA6IlOe0urzgAWXQhf6q+ysrSVj1+9isf/HY6aDs3D4PYrejEXjbI3MUd+8B/pvgHog/QQaLsT\nuvth/Wg7vRd0M/u0Bqw4HcpnQX0K1LopL3oBX57cpG97EJSymGVCVgtRvyLGTW+wM3kesjJoFAQg\n7njWdsELBKPOTwHxV1YaeDZMltyBIrwmbWcLRprvZzY/Iqc5ZwLXhsUdAm6fBVP468SzRfOuLOB1\npWik+F2sCYqEIUQ66gROQOYtIxpz6jureXuitKJPm/5CRrOnSs8axtuJm6fFkHr5XikwV6v3JsJU\nsjCB3/6La5A6t4gdSZUAjRcXDRsiKLXHhKSHFOE7GpM50uuSJ9L9yIbqvZpaYaja00CT9t0t+jJP\nRKJ5SB+8l1XOqi0TSxstBZ+TrW2M046K6QbHYphfg0tPux/2uh+2W0jGcCFmI9DxGJh2/u6hT9P7\niv/gh+ftx5MRMB2i7ZA0oXkArD4AtkyDrTNgRx/cvw+MtsFB1tIsN+hnhCeP7+LJ82D5PHj7iw/B\nVg/gLJbx4R3bqL8Fmn/jnq3tXdD3FvjJilFmHxPD9vPAHOOk6WgX+y54AQ8c1mT+qH9u8apQAxF5\n7wJdAogoGP7k3WaeD3oHkajr/W86QCRHlqyiMSjBrKRO7pincVBFYREQ74ysDzZ/mo64i8kLVI3h\nys4ssr48vJ8nZcXjej6JTUQiP3VIswhdnXVPPo0o2A3EFqMz8P3/ivE+a4LXKxZsJh97V8KVAxoq\nCqrC9TI4Lfg5o9S45CRiUc6ujcLvInSaUX4LGStmkf8TU6jN5iEHMXRo53dDYNpivyDPtLLdk22e\nNmrktFir8O4WZAjXCeNqOEF+F0NGJpgJ7VbTMBkjXEatahIisaLU4fKddfdsZ43C1W94JV0nLoDE\n53Ya2QfHwU1I9ofmY5z/qx+x8Esvp/Z52PpGGDsDbA1mrYbpm5yz/819cFUFLpwDMMAQ2zieIR6Y\ndwJbKrBjEjx09aV011bx1YuPY/bJQ/xmATxwNAzPidjwRti+FS5Z3gnp+8EOQTpMqfQr9v/bY7ni\nSJi3CUpj4aVnW3XUX9EsvYCJJYQSBQ3ocfF4q4YszC4EZO6wbOf8ALWqK5f9NsHxVm3qAqOJ4m0p\nzSP2CQkPl3BmnWtaG6VlIZbbidAUGiznvXqSKI/jai8fmbtjUVAkdISeJUSX7sqI+LTor0zwPmtQ\ng6YSLqJnzEyMgUvKyFbF72omGOk0/pV4Ad7thWbZjgf1MxyYoE3oTGJSSLNpXGo7Cf8EL4y9FiCu\nZ5KkRARoTQllbSCJC/20uE40pS9KYMdeexHfYSFLEOwaPhA4QTunW+M08lbn6fvVovAM7U13k9i6\ntIbNsutcexOSkhNaVQNHDcAdL4DlJxzLWTefCGsuAtsNZhvE68DMhhPvhtXLmPEdWHbLMWw873Y4\nAfa5DErbYerJMNYGlOGqETi/w/LV8kLmjDZpLok5bW9YeRvwnwn7spHLT34ZPafAKw+E5A8wchjs\nf+V+7Nj5GljTCYxAYwkcdxnXzxnloM0uSiwL3BDh6TXPzDjWIpqt6X2MtQZa3O6nkYMjsuu0JpwS\nckBYj716gZslZPd/sqASuZXvW9GukbUtht6IXB4KuU4wU+HtpMCHAqNB2AVp/DXyPCKajwTPCL/r\n4gElxW96hynVlAXzTdVxkwRlpVUQ0x6j56AGRwO4QZqNSxFZN+6v/AZOaK4lGNq606ANi0bc9L9V\nCO9WVtXYQq8/GJFfwYWEySTXQlNtfSLyRR+HS2SJdYRif50EUUgEUsMEoSa4mmQsE21Db/uFYUVT\nBjdZBGsVPBnCQqG11mLUXM4pnhD+q+QJKc7XtrMZtA7xx2z3AkAYREqhy9Y79j7NJnVuXjPH4AVD\ncNML/peuBa8Gs8PfqQFRP4x8D+odDNy9Pwt2/B3Tutx92eKYoOsxeNWDcPYGqEZwezvE8/fjKOos\nngTH3w5pdTGk2zh50gq6z6hjzoHkZlg5ACv2Sdmx4+0w9Buwe0PzCU580R08MX2Uw7a5DGal0fAs\nRF7bjclXKlZBHeAghpLSeHMaqqIoVcK0hdYkWK8uEy8LQDao/q9O+pSDN1oIj6w8UqFP1kcoZfmk\nBVprhlOF3wWWyrb54XVk80VrwqNawJOH5YTv5VxdSVtryTpfr9BE3jd7hP7KNN5nTfBanBfRapxX\nQ4qzRNdxSdI7rTOy7aNWQAHyDe63VqQHSk7RYcatLhNrsTZ2aZjBEnwWLYHxdGSWdpxveGOEhGdC\nSJg9UdIROV9DCTL/ivXXBG+NCucWYQhLEPICy0jVWhHMAo8I3KDfU7GbRjVs1Emx19rKTTh4Jyya\ntM2p7o17IN0Mzceh4xwoPQk8wsC0j/O5x6CtVGXDl6vUa1CbB0OzYJ8xeOIeWHgllF4DNQzrK7Dm\nOthZP4DZbOD+0j5MOS2lfiOsnDSLrnPhmvOB2k3QdhIkd0LHz/lAL0wZdFCBxnMlZNnGwXtBP6DW\nQjOvB8JvOsgiex9J+Fh1L1DQhVW4cZF0W6Kdlrwm64+LkW6i3MHjmozIyuhYnBAerjr+lcQ+DS/o\ndZJ8edxWCXqKWKwlaLHyvw47lmMioBtqISnOA/lXtGzxpNgjtKskOcXPX4CeVahBeDPBldxoIyTG\n2WicABaqFQSqaLx+9wv+ug41mGIwsOQFUlHTzPpjAyZqbRBcEmYruRsSwjZdPA3Ewb09CYa27H5+\n8giMMJHjenEV1NBCRNCIS2n+/IkWFEOY5KIUSfUMSUpStiF3cTHs1eIw8nIyvt1x9/ICpVmBlY0K\nDB0K7ddAYykkm6HUB01f3nTuCi56EOrXvYjBK67jomuhPYK+u6B2BpQ+AwdfC/GHHYMe2wV/f/R+\nvH5JSh/w8L+XMNfAlttgyjsG6DuyD9oHoHoNjBwEyf0wc4x9vLEL6wMhLJkngby4KMk/g87DAEEI\nRv5Yasi5j+X8HAnXFF3GtJdBBkfobQ6hnUxzbvXShRFk8P312rgssITO8ZBTLFCLsc3zTy7dKvno\ntokoZ6/w99fasvbUqRTep75Wu4BqGGyP0F9Ik32q9KxpvE1CgctRQvmfMdxkk5SRIwTDWkTQdMXl\nrM2GihWtVkcJsBChWczOr/9KyK6MkcSXQ16bja0TXLHnTgmNlHZ0P0ryu1XO7ep3zVj6OgkNzvK6\nErRaaUOn5GuVrV/mdqvcEeJBkRAirsrNvAwwlnHZs0ziOyMC3X+P/PGdMWwdmAU91wInQnIrpFsg\nqcOkUWj2w9p/ZXTjt/j0ilfw5VPejPk1JHcAJ0HXbbD9Udj7G8B1Neawmp5frOb6Jz/HNruNqQyT\npl/FHgDb45iRBSMweX945F9gqA06boe+UZhrmTQWsPOiQSwjEUbyXHJMC0HI0lNmeR08RUUNyYRr\nW20dMsGktEJJ2jPOy0K1aXMDQ1A15VpvRLMmVDrWMFmKM2bVopBXQ4Rj0f9cDW+OHzQLZSHs8j/q\nxMJ1orSIIiReOinjbS6aIibYHTwTeg5qcKRV7SYuY3oZp/VCmAMdOAE9hhIU/q8emJINeG52D9s6\nYEF+E204si4ip2m8u4wNAk58ECXMsrvhzmlPfDx6gTHGZKISfGcl+U0j8q455KGILDEJjEvWI88g\nxjph+InS5YmgLbrhaHxbfpKAi6rHp9M4lKTJrMlacyswZRba6o0/NoYtMbBtiPbytzlgnwtdIo5o\nOzSX+ov2B46BzSeRtqWk6Q+49TDY9AUw/wBsgikXQ+MmaK4psZp5nDu1G65bwAce+SeOZZR08h1s\n+eYk9v1ewqyevWHn3pCeAJXZ0AfMg6mzAv6cabry/LKSG3IGKUmqo88V39qoqbRWqwSCkkhxI3zP\nMqL5T+Y2pq7NBLiSUk2Pzcp4ZXl8hR997oimwBBRKCskEAIEI3DTOEGby8pX4DsIv4s3gvYlF1kv\nAlQ0WB2AIcI6Ufwv7QovRuq+AtPp3Sfkd4rS9h6h9Gl8/gL0rGq8mrYTarAJCd8O4GCHJi4puvV/\nu72GWodcJWII2yQhHSMuseOWoFWKVbaYob/TTzgRRK3i2bN2vYDuSIJAFk1aot9EgFYKQlsL1cSM\nTxQijJ/LAVvoS47JcYtDbAOum7mMJSGrVVFAixGu4t9LLlhADFEtZoMkDb8rBpLVzD1iBV+dC6xt\nd+B9d4MsCqS8HKZsgPRJMCOc+sZ30nwlJANgb4Kd6+CTl3QzslcnDxBjvn0J0+9fz7Qvt3ELk4gG\nr6T03e184kURdK2AxgyIBiG933VmCnyjzYWe56yJLch4DdZKAENE3tMghrQUPqKRSoYznUIyl1Cn\ncE8RsrocvbxPcXc0xrso+neslWdJsGOsz3mBW/DEGKxxV/kU1+asLXWu3FvCeDNN1+bPFap7+0Vd\ntZ8Zxfy7kXa1x0Nx3ohmLhBXqQUvdjZ3D3M8ZXpO43XUClzuLP5v3WcKAfPu9Ctupw0DW0ggFe6x\nm0HT2bv0MUPIQAb5ooXaxxYCBqwThxQFYjEDVDGzk2G8Aa1IoskW7yWZwzTjyrFie3K8HgcBXkxa\nouP/ZbLrrFrgNdw4WNTFXSopw+9LwDR47DF4SQfQ9wUodzg8aWQxtJ0Co1dC213Q8y0Y/A5M+gYv\n/gKUz4NDfg39fQv4xC2G/3v9zazuPhxb7WPTqp1s/bf5rGUqmy6bRv9YmU9eNcUl/aAC8cbAVLfB\nyiZExvVVG9CK2cZkq54oYSaCLym7j06ek1QgLUOzLQjOlp8CY+Xy+BZIFrha7LTXkVK+sGgOB03z\nZYP091bsk5L3nfWPHJ5ngj7pBV6fEhf4W/vt6ueBieeflHyXxUGwaEmNqoMvnvNq+DPTVMYrJmJA\ni4EeP4g14/xyDc63V7u+CJXs+K14Fs1WOK7ljg7IEEVJl+fRNFGUmT63Fe6qz5EsUhLKqyGF4r1k\nMYC8G45MzmYUjtfEal3otBy3BC2pmPrSELw2DK0xtqycjsI/I79Nq4/iAPqx2YzdBdgxSP7BDVRj\nEGp3QdQDpUe45fhNmH3fDAOdPHLN89n3jE4euvEDJBsvpXzP7/kc+1G/9AqI3Z46GRxg2b99Ed4Q\nwfqrYetbKC2wtD/vEhj5rrPMrj8IGjHTGy78GMj8YEUA5xKdR85PFwNJ1eOlpSCgbQT1jiCERWMV\n1zBZcIr5G8YlUBehNwE/SOh6rBZTWTx1SlDIG0FljHW61FbpSTVJwJBouBb3XZQOvftqRiFNaC7P\ncxGaIC/gtVdQPXKLiZSQgvyOMLK+nQLsILlR9gg9BzUEEjzX4nx4x/z/4pcrhrR2G+AEcIEWQK6M\niRjQDOM1TiPbbBus+/KJ1bFIMbZmchGG2t9WVuiyJQuDjBTjSmLnUuGv9AOb70eWvwFlsLAhXFPq\nv+Umoj9HYINiOr3ieyj6+kr9OD05Uzw0kQY8Uqc0tBSEitcYLU5gnZ3Az3qBjo3QD1Q+CGOvhc4u\n6F4N5d9B/U5ofoPj7gCbTILuAd435XGe37sYzn4l5l8N9re92AtnwNqzYUeTI9gJgyksOB+GG/Du\nybz01F9S2Q6jjTpMucsxVPkgeF7CHP8yrYcHkgohostrs2LMEh/ZLNqr5DVlBS3kosFK5HMq+IVI\nBPG4skHksV09CMVQbD02Dd+WLP5JBGOl4AIo4yfXCp9pXs1cxaJ8xCYEw6we+wxfVlqoPkfbGkzh\nOeRf/R38fW0wrGUZ/9S1CQEW62yGe06URvJpU/1pfFqQMeZ0Y8wjxpjHjTGtK7O5844yxjSNMa/c\nVXd2KXiNMXONMTcYY5YYYx4yxrzLH+83xlxnjHnMGHOtMaZPXfMh37lHjDEv3lX73p+dXpygFUFc\nM+M1g5pxGG8TBztEOI8Gndc6E4CF+4iwEw1OrKpSWSFSv2fnEwxeWkCCu6eEWSKaKsGzyBA0iZTw\nLOJTqZlZjGw6w1gEtKVhMZEcEIINWwLMEBEEsmbSRE1oMWQUt5q5rGbyHtUk1+9ESCKhsoaMwzuT\nEmyvwNn3wal3AT1NSv3Qd/Iobz3sffSVK94CY2HSGqjNgK1v4G2Hb+dfTk5ZOW0bqzrvovS2MU4d\nXE1jpiGZ+ST8tAEvSbjnc0fDC2+CZAalze0c9LIrOPfAxyhVgSdwkMM06D7sxxywH+w/4p9HBsMo\nfFoEgxamkovBa7MioDX0gCGraCFa9K6yj+U8GMz4BUufI+Mg7zfzNiCv1erQ20xbVdplqq7Nkt3Y\nvOdNluaRvEap+yILbIYHe0EssIBowjppvtzf4rx+hO909r+i3UC/Dr071FDfHqE/QeM1xsTAl4HT\ngcXA640xB0xw3meA/2GXloXda7wN4D3W2gOBY4B/9Df8IHCdtXYR8Hv/P8aYxbgKnIt9J79qjGl5\nj5hQc63Xf4RKjMdtu2z+s6vB07/p1bqVoMl+U991kp3iuaKByu9SulvjWiKwwQt21RcJ1tATQ9qy\nBOZsmoAty/kaB5Y8EaU0xLRLv4tQxe4gEennWAF2aPGawjU2GKGMdZFsy9vgtsNh+ESgEdFcB2f0\nWv5r8Gb+5cRtYHugJ/WwQzc0hvjuCJwewZoeuO2hl9BszuHGT86DSgRmJrzre3Tc9ATvfe/vYdNm\nKN1Kc98BHnrrp5k/Am2bgHQylOCAObB4f7jUb9HTOBi1dDQYBGGaS+NYFJBeAOdqsxUCMSaizD2r\nVLiHEvaaEuO2480onw9ESHBR2M1uWPF4qzSikhwKyNzPJmqmOP7ioqYx2qz/5A3Tkf+IO6chH56u\n245tSDcJCpbhr8a4djSwzFq7wlrbAH4MvLzFee8Efo5LQbNL2qXgtdZusNb+0X8fAh7GRfmeBXzX\nn/Zd4BX++8uBy6y1DWvtCmCZ7/Q42o6DF1YTPBwmWiJaZShKCoOjNT69lWoloCUBjjiVaw0wpzna\n8fexhXPFr1dIM2Mrr4HE5K+PCkwo39u8wB31Kn1FtrMmz5hybllBDfJMEhsv7RYNKRqDk8muM25p\n3FK+j8iKaIIAGWp3v98QwUtH4LrJcMJeKayazRXrIB2G96yC9lkvBNMLk4Hpj8H8XzP2IExeAQsn\nw7kLHoRqSuOW2bD/DjjscIi20DUS0UnKOb+cBvM+Dzu6ecOZlhkGNpWBzq1Qh3dOg0cbTjCUvGuX\nGNcEGhAXrNQb1DLNVsEIoh1LmG2zNF4zlHcg52cCPiLnrqZd8HKasBprqVHXlkw8B2TxbagFu8ja\nWjvW9xLoQBZqOXei+ZH1sUVnxB1Sk2jcuilpdzQOEWhFJUhsEqK8SPCEVmL+SrKTzcaJKqE1/lhG\nxpjZOPn3NX9olz1/yhivMWY+cDhwBzDdWrvR/7QRmO6/zyKfbGxcB4V6VM9kedC72Ny91ffi0+gt\nuxgoZIsijKN9ecVlTLbw4hEg/2s/wojgxZBzG0vDRJBcCnI8wi0kIpRbke6LKTyfrPCSnanitbdh\nX4E3baGZSiXfdp9JTIov6qKVomm0olyYtSkIXBHm3rJfaaFutTedcLo5gng63FCGm27dG8wG0g5I\nJ0G8DM7q+RXseDdMNXAkUCvDli66fwXJLfCDaD0cvwQOHYRvNmBWAs0VjLU1uZFeLv+IGCGFAAAg\nAElEQVTQk3DHS2HWrfzwkRqXdQDrgT6D2TGJ2MLA3U4INrzBzBqyHBPyXDqhjPY20HiuzuglVPTu\nkDSMzRLjXe1MXqvNPEG8QG/GahHzlw2V3PZcL/aZ2xf5+aGNV1n/TN5oBkGIWYIhTO+oJ9J4W+26\ni4qHPq4NyZKXRP8mQlWEdBbBVuBJ3U5sxz/jM6Y/zbj2VMT/54EPWldZojitx9FTChk2xnQBlwPv\nttYOGhPatNZaY3YZX9Lytz98JMAJC0+GmSe7OmyTGJ+Lt2XH1XZfBBKQJUHXZd9zWyKTZ5BEO9AT\n2oSAsWb3TMd/1z6QEuIrpcSKgq5YN0tbcUUIS4imuH3VvWW3ZJ3225aMTy6SeK07iULfM8ZP/WT3\nz1NTE0KwP5moxQXOEDSmmCB0dHgqOKEcGTg2hdtHYX43MHUQtjsvh7X7wfz18M9T4SerL4F7/pVb\nX/Vxjj2wCb9/IesevYbmG4CbT4NDzoWOUei9EX5/B0y/mp3JdJhdhvXHQe+7YPHXYD38xxzgQOCh\nNs48dTuPpFDaC/qSoGlqgVukLGBC8UAqQL2isRiI8yWfYusFtAljoDXILAuav08WWGEDLp+1Tett\ndXEa6PHRhlKZ5bFlt1Z5gaikvzJf9D2E37L+2vyCI+8ga9MLyJE4GOwSE7Lc6fZECbKEKsma9yxw\n+01w1//u+jmeNu0KL17qPxPTWhwqKjSXvIIJ8Dzgx142TgFeYoxpWGuvaNXgbgWvMaaME7rft9b+\nyh/eaIyZYa3dYIyZCWyaoINz/LFxdOpH8rkYNuCM4K1krhacxWUkUQ+ReQt4rU2YLCJoqbsiybEg\nK7tY9OWyzFKsromTgL9m/Y3Ctq+l8PX91Ct65tVgg7Zclknib6oXJPGdlTywufywfiES74xOiV3w\nbY75CdIw7uUI4ydem5bgidx7bqEFZ7+V3Hu4cQya98HsY+Hik7dwfROWWvhZCd43A7p74L0vHuTO\nkc9wYQXGtlimnXYNgydBVz/MfdE1rL57EYy9Gvb6OTz0Zug9mEmDgyTVEsQ3Qfuh8Njv4CSwvcBW\neOGZo7y0HT6YwMtmQ/+wn9xKiNrCLkFIGxPrkU95WRAy5TS/e2kaKFouMshItNXCfSWDmPBFMc+s\nZ4ls/MXyr+0JE1UOyQIVIsYlSi+G5OoaglozlnvEqVvsNWxXVKtkx1bsjixMWqMV4SskqUmN/15N\n80ZEAxx9Ihx1YphrX/tU6+d+WrQrwbuf/whdPu6Mu4GFfte/DmfHer0+wVq7j3w3xnwHuHIioQu7\n92owwLeApdbaz6ufrgDe5L+/CfiVOv46Y0zFGLM3sBC4s2Xbhf8buPBgcIt2fQIm05T6dgSvtSa/\nkovQ1RpxKxJ3Lb2NFnewnEuQ+iueDK00FbEgF+ubiXaOmkwQhK8wX9ME67F+LrEqNyOoedxRtOKG\nN3oMl0LY71AJVnXCik4YqPo4/Ri6asGgobWNLIuVb0tb0bXrktxL/m5u84IBYCv8yMDxTfjqGFxf\ngyMsmAEYLMHaNjivt84XR6F79evpi6Fehg/WYfXDB8PWM2DpeTDaA4fdDO2DnLJsB/V5sXuhba+G\nkaO4tMeVdvtoP1wQQ5+FqTG8EOhqEIImjBJ+ZjwfWHUstu5dxkroyHPK+AgVI8NEM5TKFdhw30yQ\n23CekEBCEl2oF+tSmoe/dE6OzD2R8H+luBOaiOft+N8EN87SRBYW8yLpn3TosLzP1ITgD3lG8UsW\nhUPbJco2KBryelopYc+Y/oTsZNbaJvAO4BqcbvwTa+3Dxpi3GWPe9ky6szuN9zjgXOABY8x9/tiH\ngIuAnxpj/hZYAbzGd3CpMeanvnNN4AI7QTVNHaXWBbTj3MOG/La2jhO+ZZxfryYNBRhAfGJF05Ot\nTcM4ZpQ8uZq0tll079Ikt9ZYmazEettYjFjLWZKNe9HFsGAxKkaEHaJg1RLGLBqHGGSaxjGpJcAk\nwviRJcucNhjDl9vg21thdAQW98EJ3fB/R2DSUieYhveFUg129jj/UPG6EA1HkqxkWa2igI/r5Cuz\ndsKWDtgyDAy/gluW/YrvLIJDyvDCERiIoD4L+nfArDa4xMJvDZgDfsOqC7/LS//j7UzZPgKPnwDV\nfeGAH4E9CEanQZRwd7XBlJ4B19nGEqhewNsePB9sidm9NTYDv0lg2WbnpVZOCkLDD1q2Zbb530Tr\nFZ6oxWFLLFqcaIpQ2IanASc2qi19e13+R76Ki6O8W1lgJXpLcjhr461B9cdAyeSDJITvU8K4FY1e\nEOAjI/xGgKLkProNTUXcVeab8K+xwe6h3cGk7RTnKin3KKn2REkSHHiPeTTArjXep0DW2quBqwvH\nLp3g3Lfsrr1dCl5r7c1MrBWfNsE1nwJ2uzkYxgmeXlzgRA8uFWRf4TyLE8Y6/25h3mQMbfHwgt/K\nCbxQFLqQZ55WLmeyIovrS3HLpZldtlZSzsgaN86i4bYJzGHD78WwyiwXqpqIYlCRWSyGEZ2TQX+X\n7etYBJ+twrfviGDl86G2jaXlNpbOuJ9fHA23LoJ9r4Cey8D0QftJsOHofGIc0UaKRsUkDpNqLIae\nUafxHh7D0GYgmkGytZs7zCCXPgEf3gcqdXheF8xdDpf1waCBa6rwpb0GueCsdSQ/OJcth30Dqg14\nbD+YtA+LlsYc8q27+c27X0o0/AgPbJoCD26Dg54HzQ5YciJvfe0NDEQwqQbtO4HH4aoZcF4MbTVc\n9JlS6aW6hK50oBfsOA1BOaLxySJb1clFlCBF8UWWGAdyodTCV5pXddY76Y+4DooJRcZAsONxYbxK\n0RAhKsY48QePFD9JG9orQhYonchGexckvmGBHrIgDILQBu9fboJAtQTB0TROiWrz78fgDdDq3IZR\nihQhAKgYWfmMaTfY91+anrV8vL44C+C02lFc4cuidttm87l4IY936WAHvdq3inippONj2g1eSItG\na/M4rj5X+93mjHuEnLZJgZGz+9g8U+nuFY1/u6Jiv8TnVyzHAI+2w7dXAqtfBaVTINoI0SzYuoXN\n13yEhcfV+dK58JpHoWcHjO7r3H5kAYmsgyXKNkxieSfl1An2ZuTKhF/bB++KYOhBYDnAjTAwmVMY\n5I8WPlYDbuxl+PBhPrG6yebHoX8avD3Cbev6L4Ha12H9k3D9m2FjOzPvX8WOqmFBEtG9bojDSSlt\nTTj2Y2fwvUXPg5MjeMOtfHsLEEF/Fbb5LEt3b4WNnTCv5jqcCUPrtv5F7FqEnh4vGc9KSt5XVy+O\nXrvNspn5QTG4/yN/bSzGOxvc9BLfD3FX02MvKUTTyPn1Sh8lAZOQQD96tyT8UC1ouhKUIfOiGY3X\npmSRzYSyCRqpUHHeQFBMBE6J/b200MW3MxY5O4XMUWODD7pk+tNzDPZkEEXb7k/JaGz3p/yJ9KwJ\n3h04bXc2bhDXApNF+1PnpYwXxvrfLAqNkOS7mrQ2ROgEHJpKqOoWNhjXRCAbyLKZaaEsJJNWyg0J\nRKAZfVx1WEWt2syeLwruYjqxTVFAi8FicwneYIDHAE6CZAbEMVABMw/sJ+DqL/POBat450I48lD3\nvlfjHLL3GXX3LKduy65hkprCTaMUVrTD62q4LPYRzjG7+ggMWX7WwGUls8DQO/j0Db/j7DPvoNSA\nzUPAY5Ng7RdgZH94dDK8Cg579XH88WfvYP1bZsHOo7noxy+HfniAlSx/w14sG5kFB38cSi+AVT+D\nJy6CyXewrd9LtSoM7YSNfTA3VjuUXWxZWwXTJFF+wmc+uvL8lryngv9dV+XIkc1j+7HPiC/CSQst\nbaco8oWGeET5aJVUP+f+KJp2QWOV9mTupOSNbUV2LRrpWikIOkxdNOZWJIelDVlQZN40olDXbY/l\n4zXFFFy7ov+PBa/cWFwephKKVuoxHjEOZhg2rQtdQtiWi9CLjWIUxXBxmrdYZ4ysPRJKTtMrpT4P\nA+NdbkppXoBr7VcWAmlfSPOpTCrZFk5Ecs9WlmkR6NJeZJ3x6nNlGHwcWD8dKvNgZL7DA0wd0g4w\n86H7/bBuOSz/PneXt7qtRjecOR9658PHIzh7J3Q0oCN1OWBl0erwfsIPdMArN+ME/DxgJWAOhNHN\nMG0Ta7YCnXBuG/xk3qdovP9Rjjv0+TRWboeNJRh9M1x4MNidsPcozPkSf1yyBijR+eU+hk23c/Ce\nnDLAVPjCGMy0cNCrgc3wmcXw6i9Bx82wcinMvhT6wY7BtTEcbqDkE7tndc60e5eirPSTGLEKWm5W\nCNPvi40lhxlmKSU1FOCFaJR69iqOsyVXZFNIw2LV1O880qCNqstzLlrFYzmNl/xuTHgnM5yqRcAU\n2pHzdXKbVjyrIY6K9bXX1C5QC/SxkrKTqOcR/NoSEuRM5Av/tCnq2P05GW3dQzedmJ41wTuMe+kV\n3KBLyR8I/r01HBxRxWFvQyaszjGO96tqxRZsrB6FSVCPgxtWQ62gqXE5fauM92OUQo6ipIhmIZNJ\nNGHZouoSJWIca0Z5ASv4ryG/ndLGFWlDGFhXhtVYsxzT3hD1CL5bgctXAbcDbX8PaQm2t0O0CEoj\nkLZB1HCJeqNJ0HUw2M1uJgw/BKt/yI5HG7xvIVw2Gz7QCUcMObndOQrbJ7sJVY/gCzUYub8bto7C\nnKYb0OopMPo12LoPr5z8BHMi+FITTNPCmq007jjFm+oXQ9wJH06pfq6PVzx8Jz+pzYO7ZsNNg0wa\n3cTwR0tusfiPGnbvMvG0iPPvuJlLv3IIvHUhnLsWptRh8CRoe9htoYaALnjoADKtVKr2Zkltihqi\nGvs0wmVeayUk0wAVZIf9ebEvFqp9eDNfZwVDyPXaY8Ckjm+la9pHux454VuPHE9qQZQt7uTtDLUo\n7Ni0ViqeMuKPLUpIxlM2PLbmQ4NTZGIvTMdU+/pcwYMFZzbqWGxDmLGOnBOBbAnnwHgD7h6hp6Xx\n/vnpWRO8AvE1cApX0dlXXtMwAePdhvP1BadgTQI2GNdGGec03MQZb2aorVrOsmuC54TgTWKRjgjb\n+hFv5Zdk4mORMw7E3qoQFZhbC8VxAQY2HM8mpzqe8ZdsRclrAyK8NenrEwMrKvCNQeChNmj7KJgZ\n0GgHm8Adk+D5w1D6KsR9MPx6iGuQtENlK8TDUJ4B8dEwsIrGnfdwW9/vefUBKW+cBf8cuSjfrlEn\nSH7ZC1ffB2x/DUT3waZ7wbwC4oXQU4IZT7CpDH8cBXtVhE0/BO1TID0D4lUQLQSWwawLqH32/fzk\ntydA551w8hWw4+9ZM2syVAagNMbsLVuo7Oxh2+wKlx5/Ehw2Am3vg7lzHX69eS50ngSDP4LunVBz\nC3JSglKdLEdDpmF5zFWSAOn8w/Ky9TjlB4iAG/tFXAIuMs3NBCHcyh1LyrzrhOuZASwKYyvaYmZg\n9W2XbDCCCnyt+bzDGz3luLEhRWg1dUEO5UJ/M+XA36io0YpBt+yl5Gicz4YnvGjU+cKX0l7i32kz\nCq54WgkRxUK0c8hDF38yxVOfxsmP7KGbTkzPmuAt47RNTaM4tzJwAlfT6sLxXnxQgP9/Ek7o7lDt\nDhmnETdxwmyjgenWQRejXuNtt17Qee5tilHFqkglgpDGa5qRGY/D6TpaRdK5IYSKW0HRMIptFDFe\nIZ0w+skIxrYAW/aFtkXubaRlKMVw1D0w9zUuyusJAyumu9y45cNh+8HQs8mpho39YPgkmHQs7DyU\nxq0r+FbPTVx1xEYumApvGoOZa2BLN7DpUIgXQO2HzqWcA9ybb3ZAT41DgZs3AI1/coEQr4ygfRNE\nc2F4FgzvTXzTyZzwcJMbF1yJrc6C+Gxm3jXE+lPbYHsfbIQTdy7nfgY5+eZVbCZmybaD4fD3Q+VJ\nGCvDUBna50HpSNi6FCZvcDY+2cYrLRPIcjBk1OKFiwCQopXFZDiRHw9JKSkXmASIyDKXgRKwhlwQ\nBf56CK5qAiEJli6Le0rQWA151y/tbSPRY9rVL42C9liLnCKhk0BZgjLRSsgZG6pmN/zv5TQYxXSt\nQXmN2muiFgdBHJtgWJOFQis7EASw2Gz2GD2n8QZStpqMhnGuZbLjmoWLamsjQN4WJ2BFUHcSdprt\nOKG+2biAjNS448afN6RuJtt/odR4J3QvUMdiX/oH5SNsgw9mFl2mBGDROiwYV66EjnwpCG/ZNmpt\nF3WseL14cdQj+AO4LUHlDGj0QXmH2zd316HjcqYsgM/MhC9Ps6yY9yG2x8BDM6DtXKhugdHZsPN1\nMNIGlfnQk4A5DQaPYsPvvsqHp1p+feQqXrkvLCrBYS+8nz/evgS2N93KNvwZ6Poo1I+AZcv5+qwV\nsD6C0iy3Sg4ASSck3VD9BZRPJ1mwkD+cci30XAeXfhmWDrP+kBocl0C5AUNV+hljKXNZyiyYU2Le\n0iVs/tR8RhacAK/7V+hYDpvOhLl7wZTroQr741AWU1NberXrEMEpOKcY0rKKzBOsnuIiJsl1pD1E\niy26Psm4m3AeBKxZBl5gAgkgkAW/oe6hcycLP0aQeeHkfMjxuzwTYChw7dfiYLPIqh+b1lt7aUcL\ne6msXVOLRWxdP8bifNBQ1kYU3q0IXtlponYGidyIsNjsMbjhOcHrKCW4k/VDVrwyweG9o/7vevLb\nrRJOAHcRciIM4IRtN84PuIrTdFcYJ4gTnPFuIy6Iut26ZOplm38BsWLIktJ4heGyVd1PoiyqrLDl\nku+isUjgRKIYzljl+mbzwlU7lYOb8JlWTX6iSZ/X1oHtbRAdDvWqeyHlARibCiZicgTHjcKBJWjM\nhFtjuH7WBh4euphmG6SDsOG318LWH0CzDkMLYWs7TJkBXXvBUJ177voI9/Svgw4L+9bhyKZzIVsN\njO0PY1dB1A8jR5KsWOGL4m2G/iFo74INfwOfiOEj+0L/Jjj+k9D8GWy8AjakcEgnnN4BbQNQGoJo\nDl/hQJjcD9tHoLeTld3Po3/bQ6TXPMFY56fgrF9A/2uh2p+9pC1AqREES+aBYAOmK7uMnKeAF8rW\nv3OvoI7XitVCKhpvVoVDk1pBjaiucn/vhmajkExfgjGyfCNqyy3dqDaDRiyCWPueW4JGK/9nvBQF\npQAC70qSfc1bogRIMAc4+E18iw1B802MC+YwhMUiNcGXPTF5XNh3hdhr31IMNsOrVRt7jJ6Wce3P\nT8+a4B0ilHCXxOZV/5EE6TXyRiuDcz+r44RuUStci7NHTgPWGFfgVrDjIVwSCUsomNkqxR2ESDbR\nVkuFCYD6X+NYQnorKNcLM8lfbZEWmEGMCvVoPCQhQqQY0dM07vxHR4Adc2B4P3d3WwLTdJ1p7M/y\nhsPKp9fcNa8wcEbkvDgqdVjVDWe+4C6a5f1gAZitYCs/hwcPhflHQcd2aH4VRjZC41ZY+U2YY91L\nngrctRSaFwH7Qv2bsDzy0mMR0bsNpZExeljGIH3ULovgm2fCmj7YcCuknXBO5Nrq2QxRHZrdbhCJ\noa8CIw3oMfAmyzazP3wtgv4UNp8DHSfD5NNgPphhOAMnRJMSIXG5MniJxTzzmTUKs1cMkWG9Nvjl\nTlR+vSh0JXhCn29SxxwCd8hWvKo1YEJ/WgX+6BwPmaHVBOVEMFR9jiXwTC0KeKxAXxKmrkkep5w6\ngSssN6L61NQJpqLQJn43KMZhbVATlzUJzNC3TQtt7lF6TuN11I0TvDsn+H0a8Lg/T7RfMawJE3RY\nZSQA9gF2mCBsH/b3mDXBPerGvYBWWaGESYquY0I6wkdi6DWJ1tDKD9EWzst8kYtteObNqmYU2qkm\nTlBsNDAwgkuMu6PXrV7txnswbIbyLJrDsKEKM8eUP27i+l1OnFHmyr3g3r2h28BqC5/d9HrYt9PN\n1o0fhbFjofwglPeHsc/D8u3w6OUw40E4GHj8Y7DzQqgcB/U2aLsShrq5cOQuehkjIeX2M7dx7dff\nCb87Aga+DzsrcH8T7krhjRFUJ4HxQnsbQD+srEE5wty7Ctu7FxwbQ81wzPfv4Pb3PB/ungz73g3m\nB7DoY0RAeQwaHQ6LBydEJbexvOZmFCAt2eKL1pdlGpMtchTef9H4VkxWpH/T+Xghj//KGMq9tNBv\nFbEli7LUE8yeQ+28ikI3VecINaLAawJHTFTfTCqe6IrBImB1yLPOcSE4tvYWKiaE0n0aNm5+l8kb\n15rG/bZH6DnB62iUgjVZURXH5J3+bzcOIhBelPEcNuGYeCn0WjK3smm4tGkbcQ861zqtzxJCkCdQ\nYIAQPNCSTF4rlaxOsn2teMGsAyiKO1YhOSaCPIncNkwoUkyrhbO4lC0zuMzy8UkB0G52QTwIVf/0\nI84T5MjUxzWIQPLf2xNYPArzY/cd4Oy+BuuOHmCFgSXNd/LfT87APvRGSI6HrftB9xqojMC66bD+\n9zB5BPgU1GeCOR3qr4XSdXxpxj/wxg33cvnZf2DgLbfA3X8HbUdAZwr/U4c76nBcl9vi1MrO/L7T\nrzTHxk4Qdxr6r93C1pumQr0djoXbz9ofpq6AAzfDk0fBinOwzOVfn/w2l594G1f5bXI9DotbbEMx\nz8gGH1vRMIupHzWTZuG/LbRbyMMPmYeC36plkIBvU4Yxu6cXnDLODbWzEXhKLh+Ttn0bGlqA4I5Y\ni5RLmA2Z6SxhjMXf1hAiO4UsTsPVfsJCOmufkLipZZVQvMtaSQnkVoEX4p8vykzDOCgwwcGCe4Se\nllfDn5+eVYzXEOAGTRHOoLY3zmDewNtv/O/DhNyT4str/XkxwathnTonJgjpnjQw8rhEzGpbZE1+\nBRYS4Wr8M8iEk1h7MTBoh3UhrS3oYxn5tusxOa1aY3kQMLOmgfUGB5hH+zsAvAzUer2BrQFpFbbD\n/cDZkY9ME/9W1c8uX7Why+OIc2owP4FjjZuwO/ffwOUPfhM4Djq3QX0alE+G8iHAabB1MyRLIboN\n7A0QHQSjV9M46t1859z/JR1bBzu+BEkv1GPKv7SccM3d3HLKQmrnjHiJ1QZpBdJ2WAAs8h38foMG\nnbxh+E4Gr4+4k142TJ8JF5Rg2vdg34/AljPghxfA2Qdz3zWXMWv2Tzn7iPV8FhcIkhifuc0WhInA\nPYVVWCLI0pISmihMeAK/XvEZzqpeKAjBKiEpwr2hjmlZXwwlF3cyYYMkCkJfV/+VZ6ukZImUVPcz\n3FXSoEqYuBiLZRESvpBkSULaBU+Ot3uekfkg9o/UhGcteZ9l+b+odct8qpk9KHAz+nNhGM+MnlWv\nhhTnjdCDM5b144KVenEasbiQzcYJ25L/qzcNFbx2gNuC1f22pYGDDCMc7tvtv/fYPBMJ/pVLpk4w\nHuhtVhZeafLaqxyL0yA0iwX+WmV60n2QVI6SDjLL5yCTnLyFWRauSupSIjqfuAHPuUCtGyqToDQI\ntgtGOrjWjPARQvpAsYjHuP7WY6dsNv3CIMEpUeL+vszC5XEJts/1yQN2Qm0ylKqQzoXtc8COwcx7\nwF4GpcehfQzefDBp/SgwH4cts2Dq4/BfezPj1uX84fgjSV835t0zOmHUv4A23AISeWaol9hJlSvp\n5kK2sJBR2jdu4rpPzuaOV38aTlkKc78Pr/shrHw9zPkbWHcMv1y5ihsXfY+3LVrCm6tu8kdeq7K4\n522YME41jwVL7gCdB0Ncs2Q3ksZkhjaTBBw+LeWhAx0xmZqQL8QSBNA4OMu4V1JNwzkReY3Ren7R\nGK3epUkQj9xn1AcTiWtamx/XUY8zi9eD+NkKVBGhXMJs3u4ik0CM0vIerQkBE5GXqnoBilDzpqCE\naKE7QdHfZ0Ct9rbPHj2rfrya14YJLmNlnNDcgPNKKOGghg3ATH/+GlzAREVpnqCi1PyqWTMw2wYf\nRHHvMeQjfMTIZQlCs2nIoooy7dOOF6BZNJFoPBRwQgIGJ22PwwIJDJkQtKcsSILQtpwvbm17Az37\nwM41X4d5p8HA1PCwcQ2iIbC9dHg/ktGSm2i56CW1JRRXK3mODq8N1Qxgt8K9PTDbwMxhB6aODkH3\nEpi3Anb8EnqnwOB0J7kXNWHpFx3GtmEvMKNQmwrHdLB6r4OcV8SIcQNdi91qG+OA+ckDMNoJt5Rh\nYDv97GAhY3ybXtZTJd2nh72nbWP+LetY8fO9MC//OHZhHWatdvBEYzo0FrBt2dF8eusH+eLilXxw\n7gZeU3Nb7Woddra5wBiLW8RGSm4M6pFbcGqxm/yyPRcBI/yWq7WmGFEKSaaQ5QYWPL+43ZbUjzoC\nMtNq1cIrO7RItaX5TfhMLjaWLBsbuOsk8kzuK4JZIAgZ/4zHUK5fvmlZsLXCL65fMr+0m6TxF2b5\nS/yNpAJK5mmidiE6VeSeoec0XsDBASlOyE7FuQBJhPQoThMWOKHpz53sf9vif2vgotS61EQANSlw\nv4lmkypmFXzPkIcXiiSuLjpRT1E5KYb9Cpaow3zFSCfagN5qidCFMPmkb5mgtcHdRu4vONsBDfjb\nHrjk8DWw5Rzo/R2MVaDZ6SLTSCGtsnnY5XOQxUmwvcSQJcaRRN0Cm0jSkrYEFqVgFqfYwRuhw8DY\nV6CyEmwfjKRg3w+1F8KambDoIthRg4d+CCOLoTwMMx+HLTPgJ1Oc9W6GgVOBvm0Qj8HIXu6GEh0z\n0Af3AlduZHJjJYfQ4CE62Ny5P7y7CofcyBOsAKZAaRi78iD4wmYYXEx01HTS01Kod8C87TD0Xobv\n3sG/3fdJ/u2YtXSXmgyOQF/cyRuiYRaUYHbsBNUkoN9Cp99u99dcVHUtdu5cxgbru1HW/GwHJF+s\nC1PX7lnigSDGpqIBV4eZFyueyPeGHy8dESm8JnkdRMBro2zD5HlXfpewZCFRMkpp2L1p2AszPul6\nwz+HNKOFtfRdcOamemei/Ej/5VwRzoN7TPI+J3gB583Qh4MYtpAXKECWMQuckF1dOEd+m0Y+gc6o\ncRoaOPtMp2+rk3yCD2FIawOTF0nCM4s/aXexYr+1cUGMYjoUU1uuiyVmWpU4yjUotpAAACAASURB\nVPnzksecxTG9LYXXNODRafDbfdbCxrtgw3FgGk4ls20QzWasuYJaBco+KUYzCpNPY43yTClusg7E\nbnv9Kgt2SwXM58H+I7S9B9IE1h7vMKLbgGOXwOtOghV7QXIRjC2G+zvgBavh4Xlwbztc9wRMmgmn\nt3tLatP1s2nctmcYuBkngG8GGiPUMNzAdOjsg0+XYcbtMPoTiKdD/B2io9ay9zmw4z2w4z5oPPFP\n8LU3wcwEZlehuhbSMgxcAzffzWDXv8DzVzNw28l8Zet7oPQY1H4A8X3Qm8LkFGY1oGro6Lf8uALP\nG3SCKC2F8a2kDvMcK+f5RCcshwBtZGPvj2v8X3vJaE8Z4TcpqZ7jIbVgQ949TYxlWVa7VpoFHlby\n10h4sZQ7ipWgrCQhW58hYMOZv3Dk5pMkmRIai/Phv6IwZDi7h3W0+1wGnbTu8jOg56CG7MYWl0mw\nj/wL3oqDEUS4rmM8iYuYjNV647BincGs1+aNbUUczRLChROUQFPCuS3Jhy7KbzIZWrmAZdbZKOB3\nOsJHtFrx1x31EXKGAIkUJ4nczxafw3/vasLx7fDbOcCaG2H4GChPcftlk0D1AEZGbqFZdZcMlUI5\nccE0n/Tg+VUluNbCEw/C0JNTGWseB9ERMHwQjC2EJW0wKYJvW+gwmCceJ0pGSK7+CkS/hyfOgej1\nwAa4oh3OeQI+swgeWgtTZ8KCvWFV3e3hJwODM6FjwOFHv8OB9McDS4CtdaDKEH3QMRPO7XQJ06lB\n2/Ew9G04fC3PnwsXNdx4Dh4Gdxx2MZeddTFPbu+hvrTkFoLOC6HvdkjaYegi+O3FEF8FR94It/wn\nfPW7cKaFmU0Y2ABPbIfKZkZK2zlr6EP0ngCf2AteOeyzhnkmGikHbVNXKtEkgqeV26Eugio/6yxj\nEmyTKQhp8NfVLmPgciTrQAfRZPWCLf1JjBO4+rfOpuNHMUJmGfKMY8CEkMZxqBTakjaK+XO1W5w8\nU9VnctMeE2KQFhuH9Hkze4qe03gzyjQ4HIZbUcel9NEsnDtnMWnUOkIQRQ03CSbhjPubjMOCdS4G\nyGvGkl1MDFZa+OYSfpjxGaPak2DgkpSzsXVfxABjaI3hWoIAlZ9TQu5RmZxFHFAmX0TeJ1JvTQ9I\ncIJs7KfQ9T6wVah3Q9sWsKOuUnEcBO49nfDJBowtg0cGYXTbYZA8HwYPh7gJQ8+DkS7Y1uHa/Qkw\nAJXHV2J7yjQaCWybAqf2k178TnisBEOfh/R50CzDxsVw6jr49Fx4dAUVO4jdOEpj4xQ3wj+c7ipZ\n9UawtQ+uSeGRGkyuwmMRrAKSJtAH1Skwr0IIY0yguQbmrWLSQngvzjstAnqacGYEpxsYmbSTB06C\nJSdv43/W/T2rr/s+/OBIeMsNwF7AmXDv7dD7PvjvefCbS+DD3XDEXjBnntO+p1vY+yXsuOMa3jvw\nb6w8qM6bEuhtOLxc8uiKJqiNaVnhVPJGXU0idCOl5co1VnjT5DVLIRH0Iogl9FjbF8RwK+cL5FZJ\nA59qI2KmeChviyzhuRfAou1LMUu5l2itKaGOmgh/EcpZsVf9jvyzpfJMBlYZmN76lT0DKu/+lF2Q\nMeZ0XAn3GPgva+1nCr+/HPgYQTS831p7/UTt7VLwGmPmAt/D7egt8A1r7ReNMR8BzicsSP/saxJh\njPkQ8FacHHuXtfbaVm334OwpEc7jQNeZiwn54tfhtOFWHdUWT4kKLRmHGe/wHYiNN4rjYI2a8Ylz\njDOGaVxJrLciHHVUj3bR0deIFVdjaTolpP6/6EaTy6dqVdSRgiuqaZjYkpthuJSPuhLqTWB6B2yc\nMgjbtsLQTO9T1A4VQ+yF7ter8J17YdvWs2DTidA4EmwDRmfASIcTNjNT+EMdHjUwNeWwB5exZIuh\nObeL+Lwmo3TC/CoseIDFp/0fltz1Mqi9HrYcCqUK3DYKx4/Cpf3wyE5KNGhSJqXEPjzGGqZQb06H\nXwMnAHdF8PigG+1ym1tthwHbBCrQXXZzZzLQvRIYA/NrONDy+i7YuxYyZkkV5Urq+OGUGhwXw9um\n1Ln/vNfygcNfzJbb/wlGPghTRsCcBHY5PPgQHPdOmD0FLv4Y3DDDSYfeMszohsmvovHSA7h49Ye4\n59QlfN17gWh5mhhnTJMxbvMGOQ01WPJuVkKRhdSSpRYVQaSTL2kNVBLfyP+G4CLWMJ5n0vA7BAhD\naqPBeM8KSS9pU7IyVRqu0G0OlYKiYMkXrdTabkcSNHNpUx69moaqJqIA1fx8WsWeomeu8RpjYuDL\nuHJna4G7jDFXWGsfVqf9zlr7a3/+wcAvcQ6RLWl3Gm8DeI+19o/GmC7gHmPMdbj3+jlr7ecKHVyM\nK328GOcF9jtjzCJrbVpsWHs1dOA0VT/FSHAYsNhYqrROTVwnJMpJcG+kH+i2LhmOFEdI/IN0+b8N\n4+4vWme2TcIzoXETQOLYiaCk8zZEwbuhYcgqvepJpK3fukClQAWiAeUMLZ55c/6kNrRTNFgUNahe\nCy8qwQ/6gaHl0JwFzXaX98CuopbAIQ8aePxsGPp7eHwebKk4LqgAj6TQC+bBLcRJg+61O+npbWNl\nbw9/POkq+v9lIwf0/4w7LXD3u2DJ23nzgW/lv++bBzv+jxuJRhnGRuHoTuex8JiDBZr0U2IDlhE2\nEbMXwyzDDwrAMJhkA7YyFXoNPJTCqCy5CTRTODKGBduhvB4aS2G/TZw6FV6WhPdv8BofypMF6G44\noXRYE67Z/1p+NONa/vNb34QlJ8HchvNDTF7oYI/pD8GlL4Jl58GP3gk9d0L1SacNNF4A936fG+Yf\nweh+TkhpzF8MkpUkL3hkt5OqczRpA5aco2Es0ahzZANPyG/in5tVgjCB7yyhuoPwn/gni9Ih3g3i\nhyvvT8qyiwYvBmQR9tKfbBdnwqfdu9qNeXgr8ddIOSBdLDS27n8xtu85d7I/CWo4GlhmrV0BYIz5\nMfByXHAsANZanVBRTFcT0u6KXW7AeXFhrR0yxjyME6jQgg98Zy6z1jaAFcaYZb7TtxdP7PKfMd9Q\nBy7CTF5PisN/24FlOMaQVJI6paQUzesApnjGXWfcHBGYoowT7Jv9/f4fe2ceZddRnftfnXPu2LPU\nrdbQmi1ZkuVBxvOIARsbbGwgCRAcTJinQCAPQsjLCwQIAZK8hMmBGBKHIYSAsSEMnjAeAGPLtizZ\nsjVas1pSz33ne8+p98eufeu08ABYL6yVxVmrV3ffe4Y6Vbt27f3tb+/S7YZCKxkykTtHU4gTPAad\nGJdsgLNqkxRUkHLBUnNE+g5vqVjExTfM5FXqNUdTjNSKNu4eDecWqrBmYz9BYabSXxggoPnBPQ6z\nqcLx/xsWPEDtx1dDcBUcPAUeNrA+gekqTEUQR0SVQ4RzcrTCKq1r7iN/2ffpWfhjLhiEqwtwel3c\nxo8lcGPffbBygH+9ez38JIIXfxTG3gddZejZB4eXwx05qLpyb0xhCbBElOhmOwWIm5DLSErdjgqW\nPKzoFQQgE0A1CzsjwXTPCuHkOnTtAOoQ/gscBy8Poa/hFUF614N8kkp2cd/3uOI5r+2GC9/7Rq65\n688Y33QxfGexZP91XgFvOh9qe+m44HpaJ17P3CFY0AfFCG77zkmw7dtQn7kLc9O5TG04IPRRe93h\noT3mxrNb0oeWXwTPCW+nzwYp5W28waDcdcWQ05xe49qhz1IYTa1iXTBU1rUwVDVMybSTeYvvS/UI\nW8a/S8s4BWpTcQ6VZ2jXplArX1kTaU5826pORATmGFeu45gczyq4tgAfcgKJRpx59EnGmKuAjyFI\n5yVPd8NfGuM1xiwB1iFK9Fzgj4wxrwHWA39irZ1AdF1aye7DK+oZxyS+2E0DUXyK5yh2WkO0vird\nGh6O6MRDEgZRwFNGdE6/O0fhC+OeMQ9ZRQ+7zwMjjWsB48ajQHknqBnry/HNyBhygqMUNdw91AWL\nU8KUPnQC1J1gKqZnmBmUS0eANdMpYz0dJ04Jey30W8RkE0n0ohtIHodWE058J4wPwI5vw4FO+Mte\nwiuqxD+yUJpioOMgdDdp5jqZ+Px9JFPzKC79PG87dSOXWQm2gORB6MS9KAc3DtzL4OJ7ObTteih+\nFt7yGnh1E4p74exJiLNCJZmdg1GxWuM2Kl+EsAsWZsQu2Gah3qCdGjML2OU6bWEAvTkR854JsBlI\nNsLaBi/ogkXJzIXraMYJrm8zLkFgOoIO9/fKBtxx7se4ce3H+GBlm3RybwVy28FeQ/meN8CCh/nm\nCX9LnECuBusyG6GrhXGFo431KbxagFyfGyXe6m5bkKn/ZyQPuO+tG+NCDJ0V2oV5WhnJpp7KiCI7\nms2gMqeHpu9qg9LwlsITakA00+1Anq0BujREoYfBv5cqU/0nwQfIIuvZEdrGtidovYJXK1uht1Ig\nafCL8XP52R9PY/E++ig8uvnpLj56Kj/5SdbeCNxojDkf+DJw/FOd+0spXgczfBN4l7N8r0WAZIAP\nA38HvP5XafQdH5Tf3cD858KFzxWDow+/DdAgnr+71zU2QqzgaXdtgAiCshyKVvj4Hc6a1cI6ne68\n46wE2SzOcnatKxt/reLAqojT1qgGKlpGuJxqxcYGoidZVNOBuTRbQv+up87NJb+4t5U+Wq2HRmoh\nyFghLGjADIQ9RTdQ/x7Mfh8cOQ32vB3uTmBVFT6UkHRZzB/cz4v/qZ+7/u/XqM3+Nt0tuGPITQ4L\ns5qidMvOPUzvFvv8Orz2BPjXbz8Hhr8uq+MHW7DoEWg2IemSmdowMKojOY1PRRiHOIbaoKycR6rI\nkjgoAzYXWeLXI6vtCsSlIQC7ARZ9lmgVvBOY25yJubcj5ylwK40ndrZcsMeIjOUSuKIX/uWdK9h3\n82eI77kIVpwCPynC84bhSJkLH8pxx4l1mkqByR8hkxHFmU6t1UPHLk7Ndc2Q0xiC7uKglqt7O8Fe\nE9ntQ3fL0Fq+mURYC+WIdlW6bOK31Ukr/XbBGiuQh8YE6kGqzKNrXyEloyD3T1ugT3aky6GCD5S7\nps7ocz0vH7uEJ+uNF4MsNjpOsYFb7oRtP5bPmhyr42kU7wknyY8e3/zW0Wfsx6cV4P7e91S3s9be\nbYyJjDGzrbVPuoHbMypeY0wG+BbwFafRsdYeTn1/HfDdp2jgEL+4qw8A535Q9EN6bHvwFB11zwYR\nJdppZNpWEIv1eKRW73x8l0Z4a6diZjoXB9x5c43n+TaMKNwIqd8QG6FwztjIUq1YZ0GoEOuRXvHV\n1VPL1lgRYrUam261Tyvj9MRLH7kYWqlyfOnIsj5Ls+3ysW9DTjuyYxKSe2HjJXAX8ObrYcVHWHUq\nvC8Uptbfnd/Fx/qnOdPBJ7PqnsMJ3hJJKzSQSfayFvzrrV+FgSbMzsLcbfI22UlgGnJTMN7lej5B\nQp4dtDdGy/TCEmAL0Bj3vdDhLhlG3BelrHSXIfsQDPwNhZPhqyH0xs795RcDluD7SxVcO0PR9Vfg\nxrTDwk0RbLn8HdxyfpavT/VSfuAelr4n4Ikrnk/lkps5c/g2bnvhR8AsgSQmmwoqpY8nK0gOUM34\nfjWIstMFWBk2euj27mkB1gSa9KEZhkpJe6rthtLBsMjJZDp7rB5I8Eufnfa4dBFJ/5+49qQFN3N0\nkDHwHOAw8d4j1rcnH0tbdJwaAYwZWP1cMcTuRwym2z/0i+/0Kx/JL+3cP9mxHljhvP4DSBzrVekT\njDHLgZ3WWmuMORXgqZQuPDOrwQBfBDZba/8h9fk8a+1B9+9LgU3u7+8AXzPG/D3ixa8A7nuqB1eQ\neVZBLNo+oC8RhZhBss72OBesy8o1xoiRNIzX8BpgyzsB7sRbsAkuboIo6UPAoFPKFSRDSd39XDIz\n4QG8W5+2qlSo2vVxUxNKzzuaDD+dEWEtJB6bU5dRMcmjgzEw875J6tz0RE3Ty0ILlxv4r36Ez/v4\nmTDQgCDE5OFvA6nJewpwaf80ResnnT4nNtK+Qgrv0+ZkE4FoVsfACXthLC9VyWr9UNxJGyXPVWF1\nL2ztRZbIvam7dMlM3o/bViQLhJDLSbiiC9jRkMGbm4ElRrDdrg+QP63Bn3TAYFMw3HSbNQCkf1t8\nPyWp/mkEHueshiILHS1YHcKKngbv7jrM4X9cSe3/QmMyz1XffBTqr2TKfARYAnGNTEaelcMrrzZs\nZLwnpAuxZgKCd6/bW+jgFZ5x7bYOIyZ1TV1xY8fWMIjVqinHafgiSlnTmilpcbCe9TWwQcZfZaia\nsuBjF3FLJxgdncChfar1PhTyMG7OqOVt8Rly9cg/Q42RhoERI5LShbAZ5sCTGiW/1pHkn/mcpzis\ntS1jzDuAm5FX/aK19jFjzJvd958HXg68xhjTRKyLVz7dPZ9pGTgXuBrYaIx5yH32AeBVxphTkP58\nAtAGbDbGfAPYjHhyb7NWHYmZR82dkMGXb0yAzkAgB5B04F5EQKqpERh0n+3Hk+Z6oe3rdDqYYNpI\ncC1ElPFhZLeKfnwh9oaRn9xRrVQMNnZUoKxGyt1LJ8FMoTi6Pq9ScSyCKyr3txZ6uk/eBVAUvrCp\nH01VVout5ZgVqkyUkwteuTcCeddeEF2WPA7bm/CybrBFbFnYYQvc9XMavsjK0RajWji5xFtbqjAs\nUGgCnQ1B9Ne+DXb9AIIW5IfBNCBqCeDc2wUTJbo4QI08TeZI44ayki68B7ilG4YycHwgK+rOWBgN\nxRycbWD5ASj+EeGZ47y9By6JZy4WCb4fMymF0w5uBX5BVCqenq+ByVroqFDOEp0XgAkg31vj4ksv\n4tZP38FfXNkN4VIIKxSyMxdZtQTBK9XQjWNa6ap1CtCM/DUkjv7mrNZ0UR0L7RoM2tbpyMNSivur\nLGjasP5oeUhrZsIA6n3VA/lRZk2aVZOmr7UL3iR+UVC8Nh00VrhEx0DHRxcnfU5ixEDa696xgiBK\nGptpAGs5Rsezs3hxdNkfHPXZ51N/fwL4xC97v2diNdzDk4cDf/Akn+k1fw389TM9uIJQvaYRZTgL\n8SofQTxlZTUM4RSJO7TOwIgTtDpideh28d3IgI3hmUq4c2L3MiXESh7H7+OWMQJDdLlnRHhFqpk6\nqiCfbBVuu7uufSrYgbMo604pKok8NP46DY6hlztBTPC4nEbFo9QzcilrwuIVRyFCXIneM2Fbn7gI\n8UJoSlAz3eZ09Sv9TJWBLiJaU1j/1xKFBAVJqz1zF0zshL0rYH4HdDwBQSyzaG4Ak/1MW9f7mR5Y\nlBfC4SHgwaZMiq5ABmjYQtlCmIezAzi9Al3vhjP38coBeEVDxkML1iRIv6r1mG6/LmBHH+nNHtt4\nu/WBObWWlVHyyfwYp2ywPPqjudCdA3OAgZzgre0x0z6zM+UFPDzVXgCMX9i06E0m8UG1jgaErlsa\nGR+sSgfVMqk2KtsF6+ldocPlteqelrjU9qryTdcgjkPaRZOOtpYSQxtn1gJKygdOMxiixLdJ5UWV\ndGIkEKnvOh2JPM5CHJ056XFDbIfi0Q35dQ/7G80V+4XjN9YaJbkVEMWrVcia7rscMm/3IQpaLZs2\nZoYo2TKiPBNkECvIDjHj7vopfABLqwyOumcqS1QZE2Wk0lcXkDee7xi45+ZiP3nS+2CpItbPAivu\neDMQQdai75nYC3xgpQKiEto1UaIWOoqcM9vUitDAg7p1kfWMDVz7lMrTAoFUN6yD42sQFiHphaq4\nIr+Dx+70aCeJWNrcz3T2E/i/NVAEW+A/Z/E3H4t5f8ffw5FPQ9gD84Yk6hcjxXBsBbTOVbMM5Zyk\nFz7SgvIeoA+mZ8GUhSNuyZkbwXktGPoCnLqeV8+HP25Ad9NP/EzsLXHwlqVxCkddfz3UEk5ngCle\nqdZZWomoUgx6S/DcOlT+D0QPQ7yXOPTei1K02um5TtnoQgDepdbnK47afoZ7fsNA1s1K4/pZy1aq\nQtNAVoiMeRjPxJPVzY+ND/Cl4xIqt3qUUztzqLwdre/SVDSFUmJ3ozQmXDeOo29T3OAAKoGjiyfe\nqi4mcNjJ6ziuIB2yHvcg9sKTWX2/1vFbxSvHNCI4dUQJVvCMhQ73uSpGtX7TwpIgA6+7EtcRS9Yi\nA9eNQBFNZrouPe76jPvJIoNbd78ngayRVON22mfiBTLt1umKrkKqFoAqZg1+6LYo05HHkttcy7YS\nk0mnAqsVwdSa0UysNK0ngLaVo/+bxJXOHAC+1gmrCy46lkBJLIuGWy00sSM9J3X32HQN1UQXC7zy\nzdaBoBuO7+TyCrz/5FugcQ/c8Vzo64HctLgcHRbKhwlokGg+4mhRtgYql9zIJDBpJUmCCgRVeGk/\nrP4UnPFZ/mAxvLMBc2rSHoVq0pNSFWcu8XCJKlodl7pTWm3r2M603mBmgCpx35scmOf8I/aWP4GL\ntkG8k2zGL5bqOuuh1MDAOms35U3o520l71zxlhEvqBm42tOBW+BSkFYtmKng9UgrXXC7bgQytm26\nWUC7OI1hZnv1OWpZN1xfKg6cdx2Zxm/dMtrm8oIo3RjHFsH3r2ahaQylnXWHpHZtxXNOs8yMzh+1\nXvz6x7OEGo71ccwWlF/1KCOW5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MvwLKEupGDLIlwxKCfLGhSuRH4cR42ncQ4iYrkeUcgn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lVn3TT0DOd9cQZfwK/bMFaL+RQHkVnkDW4Tyku93hFB58r+8P48BIyp86tVMSqN92oMm9A9Uv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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc12e320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imgplot = plt.imshow(lum_img)\n",
+    "imgplot.set_cmap('spectral')\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 限制显示范围"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "先查看直方图："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc8b1160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(lum_img.flatten(), 256, range=(0.0,1.0), fc='k', ec='k')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将显示范围设为 `0.0-0.7`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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P636/hUFUCp47LnS2S6rH3++nMDfv2U1Vdd1GhIOZ86rdXfczClnzuANwHCPzvSEreoIO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\nbtWA5gcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xcb72f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imgplot = plt.imshow(lum_img)\n",
+    "imgplot.set_clim(0.0,0.7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## resize 操作"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QbJ0pF21Q9xDOHG/cuDEBxePjY7z2ta/FzZs3sbu7i83NzcnizqIMhloZsz1rd154dej1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\ngPz/QCjzUOP0/xAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa5a94a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "img = Image.open('stinkbug.png')\n",
+    "rsize = img.resize((img.size[0]/10,img.size[1]/10))\n",
+    "rsizeArr = np.asarray(rsize) \n",
+    "imgplot = plt.imshow(rsizeArr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "上面我们将这个图像使用 PIL 的 `Image` 对象导入，并将其 `resize` 为原来的 1/100，可以看到很多细节都丢失了。\n",
+    "\n",
+    "在画图时，由于画面的大小与实际像素的大小可能不一致，所以不一致的地方会进行插值处理，尝试一下不同的插值方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa5b2908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imgplot = plt.imshow(rsizeArr)\n",
+    "imgplot.set_interpolation('nearest')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ja62jl/ApXW/dFlrL6m2391vbr7m/0g6vP6Si5PdVcrLkZEjESf9Xbj0D3uuV\nWMSUdb/nnCxHqdeRsd3sanrrIs0UZFbD36js/skZbIns6B4wo93u3vKmEuaUupeAdIP5frk4o4ET\nl0WYdHHLi9xSlPwPyOS/NmqPMRJhSmWpxTY123tc21Hxyl6MUo8j7fZIMDOhWJPpFPs2+7/hGjSi\naFFmWyCZEYQ5AnOpSk1JEoHxbZmWCJI+/P9ttJghJ0z+0YiS/nzs4ODgzPPfnCD5/5RrREm3Fck+\n4bb1xivnIMxe1ZUJbXl5RxN9jxvueSWEVjvPFVluCXMo3a0S/5R2ak/a8NtyePn8kULtZRUaYWok\nKf9Hh8iSVKVUlFeuXDnldnNC5CQvX64R9U1LvFLb16KKonM0MsYXEc5c8cSemK6FHtsuBFmuFfc7\nr4S5BNFKIrNeXEHtIAIiVVdrxd7e3im1ye3XyufxSXoyhx5fpKd0qFwAJ+VzhckvPr5qLkk2iqty\nZN1vL222rowtI0msdUKw8ktEijHrmreUHeHckaVHMkB/8NhC1iVZI4a5ldACoA9iSZTyBnDt2Wty\niXm/StdX1iGfyiFFSY8zEnFK4pN1EClLBczt4yROpM7bH/XRlPjbKGTL9q4zLW0rYfaqcqvcDGFe\niJjlqAt/LfXYW+/Uds9NmC3KiUNTlZzMePyQqzt5a49824+sg79JiLvdd+7cOfncvXv3lAtOhMdf\n+ivtlgqY7ANOE2dP7LfVlVyKOFvT9xB/K0Yp4RFlbIYsW5AZpF7sZ476ZL0tdWrlz0WCo8v0yuOu\nsqb+iOg4WUqiJLec0slyiTD57UFEks8999yJqiRi5OVRfm4vgDPxT37s0qVL5mOQWSWWJZUpxDOa\nVDXMsZjTUk+2/lE2rkaWUwlirrSjytiSizwHWvqeyEVbhNFIiNxh+VZySkflcrLk6pIUplSxlF+S\nLZVBF5984ocg87b0U4/LOEVRLkVkW8dIMl3lbyXWKHMN8ppq32ibp7hc2TK0FV2tXVzF0T5Kv7+/\nj0uXLp188zKoTzSylIs88s1B3KXnBMxjk7w8TXX2ojfG1pN2Sp45MdLbi8rXfkfpI6zqhlsEAfS5\nG3MuxqyFKYQ5NVY0tTwiOR571NxgTpyHh4dniJJwdHR0ZsVaU4hypZ3K0f6hkcrhZKmNKU7a/NPS\nLz3xyt60Xp2jMEW19Vy3LfX3HvOweszSIoMeksiegN7AfCuWIOURpDZHmZxQ+NMxXMXRca7iyA2W\nipQTIRE9AUeKAAAgAElEQVQdJzZJmlS+dk/ltWvXTgiTyETaANx3uyXRa4tNc/S5dVH3kOZoaHW3\nKjsrXQ/BZ72AKX22KFn2xP0Io1eZW1TmEsTaU9aaRGnVw/udK0rtdiD6UEyRSJQUJi9TKlFamdbU\nJYE/BSSf+5ZkqRE0V5Pyf3mW+C+eUapwtLocSZTZ8nvSTEmvYdX/Dff2afk4MoN06TjlUnVZ9cxR\nf6uCoguTu990Ezgdl+55KQUHBwcA7j8xQ6vXmmvNXWyNSDVlSW8TunLlyglxyvzcHi10IB+FXDuc\ns3RMMusOz22TV98odathFTd8BIktFX9siUeNrmeqyhthQ089nDApLsiflpGkZMUdtYUWqfwovfV2\ncx4GsJ77JlVJtnok2fsvj1tzmUeUkSGmKJQw1Q6LKFvanE27esyS0Eugcy6ALEWU2TJbiDKrvHvK\nzkAufmiLIkRqtIrNCcxacOFP8xBZ8r+JkLf8aOqLyuft4+mk+91LlL0kNVINTSXKLAG2KrqpaxLy\ndy9RtmAzZLm2SyMxRyxzShlZd7jFpiVUOS2QyMFMcUd5Gw9XlvQbwKlVckpL5cr7KuneSgBnFpNo\nn3wRBhGjVMUyvuq9BakHcyrOnrJbF0da1KSWrleZjyLKlvSbIctetC6ATD024nhL3qVd8dHQ3Gbt\nNhwCDV4+iPl9lESYRHh0Mzp/BpzIkgiZu/iHh4cnb0eXsUmpQrNE2YqlY3pT6x9NkjJPq0JvIcpo\nraPlXK5Clmtf2Gu77dn8c6jJKRgRa5buOZWrfcu6iTA5qR0dHZ16Jpy/ko2ImbvmtMCjvfhX3o40\nAnMRY3axZYotW1nQserqUZdT1jo2oSznIK9eYulVbSPVpLavxy7u2m4NkU0y1kmQz2zT8+acKLmy\nLKWceXyRbgGiVXGqR7OpVUku0detRDkqttmqJq3+bEGmTUvEK4GNkGUP5iDKnjyZ45ErEB1rJUqL\naHuUhRab6xnw3kfaKl+fJgmLv9yCPw9Ojznyt6JzsuQr7Px2Jr5oRHXy70z7RqSZghFENqLsORZ2\nsnbM3ceLk2Wra9lCNJnyWvKNinkutYizlNJsDcxz99pyc3l8kKfhCy3cfu6Wc4KUxMjbK2OU/H2a\n/Dhte23KtHspzEGUPfmnLHJp/b2GevSwSWXJO27E6u4oopwrZrnlBRtvILcE5vmbhyRpchLjK9L8\nmKzL+psKT7HKj1zk0bZb+2pptMQPRyz8jFrY0cponcx7CbT32nLJspTyEICfBvD5ACqAH6+1/mgp\n5fkAfg7ASwE8BeBra62f6bLgbJ1D841w1+dc2BmpaDPoXbHU6vYIU1OTHlHSqnUpp58V50qP5/WU\niFSI3k3mfHEn+78/fN8IV3eUW7oGUY5Ea3xyJDLn4FJwfB/At9VavwjAVwD45lLKFwL4TgA3a62P\nAnjf8e9ZDJxSzpxEaSmeLKy8LWqtBZY73BJPzOyL6tGezOFkRosu9Py2/OdF7xYeiyD539tqZctn\nvqN7Kb1wgncsOjctimoUUWbrHe16Z7GEWs9ew66yrLU+A+CZ4+2/LKX8IYAXA3gDgFcfJ3s7gCfR\nSZhTsTRRjiD43nABocU11mZl+d1qn6Y0LRKVBGk9wshvEucLLpKguA0aucm/fOBvPCKSzBJxy7ke\ndVHLfsnU0Utka8X+smh1reduTzpmWUp5GMCXAPhtAA/WWp89PvQsgAe1PJm41hyYw43tdcW12NkI\nuyLC9MgrUjKaG6rFkS333KpXI03Kry3iyG/pyls3lnPC5epS/kd4hiinKP3Wyail7JY0U7E0qc6p\nYltDbBwpsiylfB6AXwDwrbXWvxCKopZS1FbcvHnzZPv69eu4fv162rCkXal9PWVp5JC1odWeKTEr\nL3bHt+UCi/Y/MpwgvI9Mz0nTcsHl28y1F14Ap/82Qn7z/+uRytJ6KogTMU8rCTKrKK3JyDsHa2Dt\n+lsxkghbyrp16xZu3bqVShuSZSnlCu4R5Ttqre883v1sKeWFtdZnSikvAvBpLe/jjz+eNPk+5lSj\nI8tdQzFr8NSdJEoiKfmGHgInC0kwfL88R9Yg1YhSbluErUHWr9kXTR5RP3rbLeWNwBTXcgm3tBc9\ndrW65Fo+bWzcuHEDL3/5y09+v+c97zHLjVbDC4C3AfhwrfWt7NC7ALwJwA8ff79TyR5iCjGOJqst\nhgxaYA0mTpT8P2rkvYhki1Rr8sNt9vosUpb8L28jd14qV0nokiQ5AWuuf8stR73QyCp7rjMTUa8N\nLcdH1DEnMnVn+jwTTgJiZfkqAF8P4PdLKR883vddAH4IwM+XUt6M41uHQosakI2F9cT5onIzWEtV\nagMjIhhJVPxJF05YAFQ3lWJ6nEw4UfHzoBGWRpTyzeiyLV7c0kor65X/w0P1Xr58GQcHB6cI1yJN\nqTBbz3vPOOmpY25CnGrDFCxJxFFd0Wr4b8C+vei1nTbJOhYnH4s011KXEXoWETSiIpKkF00QaXEC\nlAsifDFF3gvJlaal7qSK48pSxiw9AuRleh9ej1a+VKN8gtBeHkz1yolhDkQhCOtcTyHM8+6uL2n/\nJp/gIWTcsymEF8X7WuqdC73xHU4enCjlOx/5S3I5IUqio39k1GKYVrzQcr/lIg/VrxGmFj+ksIJG\nxJabLRd/JFlq/zg50i2PMMrb6SHFLRLm1uwBNkKWEflExAWcdQFbBh9PP5WAR6JlwEhSsdxfIkpS\nl/z2HSJKrTxJlJpCk4SnKUtO3jxm6S3QyHYSWfL4q3xLOhGnjHNKm+Wz5EsSJKEllpmJ0Y2Oby5J\npr1jfo70Epv4wzJvPz9OyOTPDCorf8bt2orLbikvjSi5uqRvfjsOgJP4Hn2TC64RjebK8rIAmKpS\nroZLRamdS672qAz+lnTtZRpaXFU+/piJWU7FkmPFIre5SG9qmdn8vfWMavMmlCUhS0AWcUZue1RG\nKzm21DcHskSpESZf4OFEQgqSE41cMadveXuR9jcRHlnKuKIs32vb0dHRmbCCJEypkku59y+SZKv2\n6japLjlx9sYt1xgfPcTYk2cKEWXyzl1+CzZFlkCfC03Q4jZZ4mxRlZ4NGnpDDK3Q3F5Okpq7SspR\na4NGGrxNXFnKF1LQsYgs5b2evHyql7eNq0pSljIGy9vPCYBsohXxvb09VeVaCpOPDWnn5yLmVHpz\nq8ie8mcly6kud+tA9NSfhkycM4pnZu2S9WXgzfRajJJ/LGXp3edIdcq4IycqqTKJfA4PD0+5tXSM\nyE2LKVo3pst2chui0AInTK4IKaxw6dKlk1uIrAUh3lautHl4QZL55xpp9pBNlGeOMnvL1TC7spyi\nnDKNnOIay7RTXK0MpqjmbPoMUVpP71hv3ZFkxfPxRw95Ov7YIrnMMqZokaXmAmuhBU0tWws1ROAW\nYWuKXHv7EP+dmWyjY3PB8hTmKntK+rVItBWbc8NbYZHjCFdJI885Bn6rIrbKoG/NDbfIRFu4IVea\nyrPs02KawH1i4vZo8VIZs/RIkt8aZK18ay49/SaborAA9Zc2YfCYLEdvzHsu9BJlxovpOT66vjls\nyWARslxjZpX1A9twlTx3v6UMvq2RDLm/miLTVKW2Sszt5a4sr1O67Zw4OdlpKlBzvz2S1Agy68qT\nbRQusEiSvwCY8u7t7Z2UT7dXcQUuJ1PqF++8zeW5ZPZl01gTT0vZ2fpGE+4cOPfKEsgPvFGkvSb5\ne4PDc1tlLE6SnSRK/ode5JLyd0V6F5jlNlur4JZbS4Tvuc2aHZoK1iYRjShpcUrLxwmT18UXyLRF\nRs22HuJsUWJTSFI7poVJejCVcJckR4lzR5ZbUIdrIOsKaRe3jMdJaKvanDT4Aod8TpzX6cUVJdHx\nuqkOCYtwLSXJiZyHB2SIwlOuBwcHp/qU59EInt9mlYGcaOVqfStaiLKHQCOSzExW2bxrEmGm/nNF\nllOJUssvB64FOcinqMuWvC2zP9+vEZqWTypLeRsQpSey1MhDW0XmK9REQlqsVLqyFFeMFDJvh0Y+\nfBU7Oq+yHhmr5OBhB6nO+b5IWWoLR1kX3to31b3NEmV24s6mWYskW+tdhCxHqMHPNUXZS5IWtAtT\nW9zRVsUlkREJcfWmESV9pCK0iJLaoCk/L84p2wecVnzSTbYmSF43kSZPI/OSOy7jtvxceGQrF5Gs\nuGdEgkuRZGu5mfrWVJN8gsrgXCnLNSAv5FHq0quv5ZgW+5IkGH2svPK+Qq6e6FsSG7/vkR5DlItK\nvGxOwlwVRu63Zjf1h0Zu9C1fPWe9bZ3K0lQn73uaPLR8VqiAhzy0R0SjuGfPRJpN36omp4QQIlu2\nJpBmJ8utNDhywXvRS5ga8Xppo7IIkuyke83VmUU4slxJGJrq4+R49+7dk20iS24jJ0pSptzl9xZ1\nuE3cbkttclVJ/eD906P2nzy8Dw4PD0/9tkjWWkzj56LWeircoSntCFNUWlY1ZkMAGZu3oip7MCtZ\njiTKOVTc2naMiP0AZ+NdXLnQyjaRDV+gofSyPq6oOElot+1wstzf38edO3dOEaV8Z6ZUpATuzsun\nc3idvM3cbuucyInj8uXLuHr1qvohwtTerET18HgpV5Xy5SHaohqlI5Kmftjb2ztzDq029cYRvXJa\nj3uk2nJtRG7wVq55wipueG8HLNl5Le730u64lkYqSiJFIkq+yu3dl8hVJCk/vrKt3RAuXW/+FiCp\nKjU3lruskiy9VXQZGpDl8+N84rh69SquXbt26kNkyV1j7ZySvfwJH6lApTrmkwURpZxAKD9X3VkC\nHEGUWRKeqgZb44RTMbK+cxezHK3ssqS3JGH2xKqA0yqKXLyjo6MTwuQ3ZPOLlcqWpAXgTBxSI0zt\nGW0txmipWKqHu/bWY5HUTo1o5H7ax/8Cl1TktWvX8MADD7hkyftFrtATIUobNMKn9KQqaQLhipfH\nbOU5bR0La8b+1lKDXp1RDDiLc0eWwNigclRPC0GOGCiZ+JAH6RJqio0rJ40k+e02mnq01KX3dBAn\nck4unHgsd18qYUv1S5LkREmxyatXr56QJCdLilvyGCLZJ+/FlCEEqo+rSt5fnCzJ/Sb1T+RpPYcu\nJwhvXGTd4161NUqheTFmywvxyppabxaLk+XoOOYcZXrlzUWYvWqSg9cr1aVc8eW2aupJ+2j3P2rk\nqLmSUoEB9j9PyoUkL2TA1SSREO8DHiPk7rdUlZws+UTCV8EPDg7OqG5uC33kX3jQohCdE3LH5YSg\nvSrPUkVT3WOLODwXfA5oBJmtd2kFey6V5Ui0kqO2T/stEQXrrbqz0BYF+BuBrDicvG+Sf2T58j5M\nfkzGS7WnXni7uErUXG+PKCVBavs1ZSnjlESURJba445cZZMt/I/bNFWuLVBRPosoeRna00wWphCa\nR0xTyXgK5iRB3ubWNkb/G/4QgJ8G8PkAKoAfr7X+aCnlewH8cwB/cpz0u2qt9r+TM0O3iB61KFVt\npHJbB1vP4JSEKV1g2SZ5QzknJkorH3GkfUSw3C3lF7781giF7JD5JYFofS1Vq+wHGauUypKvgksX\nXHPxNVhkp91mxftZi3/y8Ic1EZBNGixFOgKRAo3EhIYoxJC1a1Q7M/VHynIfwLfVWn+3lPJ5AP5n\nKeUm7hHnW2qtb5lu5jJo7dgMYVrpPGRsGKEWtPgXL1+qO03ByRvHPZKUF79GgNzV5v2mpddsknm0\nvtReCEJEKT+cJK17K63zRX3CSV+2R8vDz5HW/9a5kMgcz8b8NHK26rHS9BDmCPRc15l9GqL/DX8G\nwDPH239ZSvlDAC8+PrxNmdiJTKd7hEnIlDE3tFghr19+5AsoODHybXnPJidHXrZ2fyaP49V6fyXZ\nsikiDE09Z4iSxyilouThBUlovB81tc77Tn74Tec8HCLVomx7RGQ8r+wXOuaRfVRmhii9ukYQ5pTr\nMsrDv+W2hnTMspTyMIAvAfBbAF4F4FtKKd8A4HcAfHut9TMZA+eYbbLlRukyajIixhHK0kMLMVv5\n6LdHTOTG0kUtVaOVX6pEIsq7d++eehGHjMvJwcu/I3WnETqRIV8F5yTJY5TWvZU8FMGh3V9JtvKY\nrVwI0urzzk8POLnz/uP92EssmXQjCVOOhZZrt7cuDymyPHbB/yuAbz1WmD8G4PuPD/8AgB8B8GaZ\n74knnjjZvn79Oq5fv76YPLcwgjD5MaCftKYO2h4XRBKbZpMkH00BWvklWZKapGNEnpKYLGjkJffJ\nxxglQcqFHPl4o/VCC9qW6pJc70ixA/df6AHgVAzVImg5cbXG8zIhhEzZU8jaEhhSSUflePtaPTxN\nQdZacfv2bdy+fTu0B0iQZSnlCoBfAPCfa63vPK7k0+z4TwB4t5b3da97nWn4aMJsIa5Mp/cQ4BTy\n24L7TpCEqa3Oeu4LkSXdPwicfozRW51vtZHstBZyLKKUMUrpGkvSkbf1RLZTHv4Ek3wuPXpaiNef\nRUv6XtLqtWEkMfd6eHL/jRs3cP369ZPfN2/eNOuMVsMLgLcB+HCt9a1s/4tqrZ86/vk1AD7klaM1\nrJdgIrSWa3U6P+Etbn6EkSq1BRYhSPdMfuSihwc+Y8sb27VFFFmvtDe6IMg+7fagBx54wL2XUhKV\npyw5InKjY/JJKTpmqUvZj/zcZMnEcr01b0lCC8VoyE7wmWtmFHlmbLLCHC11RcryVQC+HsDvl1I+\neLzvuwG8sZTyCgAVwMcAfKNnoHaiNEk+kih6XeRRM3yvXZ4bkwG/OCLlQ98tnwx4G+i5dPkGJO1l\nFZk6JInLhRxOlJwwM6qS94vVXwSuNKWbTopS3rRPZEkK01OWPeMtmuAzY8sba1baLEFFGOE9RWVM\nqSNaDf8NANodsr/SUklWWWZjDtrxlrqj9K2KcjR665WK0UrjKagRZMnLkSvqGmFqH81+mUauektV\nSUTJ3yoUud+yPwjaOaEQA701iMq1bqqXRE9k76nVjCrk9mrpI5U6laSmXCejCLVHxbbWPesTPBrB\necHrzKDwjvfK/uysPpI8lyJijxylCpVptW2eX4MkBo8ovZVoqy4qT7s9SCNK+ZIMz/322sb7iiDv\nQ6VVfrqJX3sCSfYL/7b6OuseS8KM3HFethaDbiGTSMxMIeTRsdVeWxYlSw5OlKOUXG/cb454YUT8\nI+rSlGEmrdwvyTRTrtdn2iSokRy/leby5csniz/aG3gkyWhEaT3OaC2oSGLKnhNJmDRurcdGOflo\nE9YUWziyhKnl498j0FPWyPrnKH8xsvRcK37hjVBcU0gzqzJb0JpfUzpzD6ReeGEOiyTlirC18CNB\nZWVJ0rpFSFtUyZwjbVKnc0MfeYO/JEurTEvltsIizEw+y8YRGF1u6zU+ov7FyDIinchdiMqnMuTx\nKbGUJdxkibnqzA6WKepGOx9yIebw8BBXrlw587IJIk35R2H8osgoSnnjuRanlO2bOplJWyVBatte\nmdp2q5sp82VIcwShjIh/9qT3zuEool7FDc8Qo0dWVrlax3n7uD2ZelswmmxHqEvLBdPiVVq6lvbw\nc6s9Kqm9nYffg8lv5o6I0rpNyFKtLSGLKJRktZn61dqOypblt57/JUkrm3ZO72gJcbNazJIwhxve\naku2zhFu/cjyW+xo2Z89HoFIrtZ6atGDE+XVq1dPnvIhxcnf4k7fmcWc6OUYU+OTVv9YcUIrvVVW\nhsR7z8mS7vWUulomkCxGhbEWj1lqwWYtFqT9bql3FEmNjCGNTDsCLfEsLW2232jVuNZ68qZwTV1K\nouSPFXq3B1lk6d1Mn/FcsgpUm+yjeKHnknt1z+VOR23NkOISCnOt8BiwAFlas+4oYtQwhSh741ge\nWoPRLeVaEw2H16bILR0x8WgvjSDCtP5jR/4dA/0ro0eU1p+ORYRpnZ/R5423m7Z5HaTEOaL4qFZH\nZp8sXwqb1jJHK0wPaxHm7G9K9whTS6P9luWNwhJEyaHZrg3MFhfO6l+rHk4gcp9mTwu0/pRxOm63\n9tcU5HLzf0DkZEn/ofO85z3vhCzlX9lmHiOkuqxtma+nb2QsWGszL5u/yi2qr8UryB7XiDMqryfE\nk72G11KQFjb/txJTlE1PPit/j5ti2WXl09y6TNmSMLVvKo8rGH6PYKQwo/ZEfSUnQyJD/iozIkqK\nY8r92sKO9Z/fVnusiUQjTc1uD5a64h/5FnVJlhTbpW+rPzOIiMxTkFG7W0lyLpWZwSibZidL7SJq\ndfsyjfIu3lb7WvIDOfuyrlDm4tQGu3axa4QJ4AyhWIsgMn/k5mtp+G9STJcvXz5jFxHFlStXTsUv\nqYxLly6defUav02I/39O9K5IrZ8y7m40br36tNADfyMRTQiyLktl9pKPZWdL+a2xyqhvLBuiMjNe\n2cjwwKxkOUVGR4oyc+FGiNRkxp7WgeOV4bmCUV2ccKWKke4ecP+t3bTPU2K951HrXx6Tk6qK/teb\n/9c21S9vHZI3nGdfd9bbrilur6YqtQlB/lMmgXsArXVnj/cQSEt/eITpiYXMBJVV/1PV7WJueK/y\ny5SVKd+aRVtidZ7yyxCZlo4fl+50jwtI39ofgPGVZgCnYmSSMKX7SGSRsdWCrJfvpxdQcMUlyZI/\nCSTfOu69vcf7PQf4udDccH4HgCRLjmhMT7mGLJsz5fYqS2uykmKhtV2ZcMUIhbmoG679tvYBuYsw\n6zp79VplZDs444J5tngklIGsn5OjvC2HEyZvv6fIuF2WCtbaqJ0/XiepSdr2Fj9kyMB6c5HVJ9r2\nktCUJZ0XwFau9BcV8nx53y02ZfZnlHpUR6Qw+XjxxpWGKddLZj9hUTe8Zyb0CNMbJF4H9ipLyz5Z\nT1Sn5T5oM61Wl2UHfYgY+ZMx+/v72N/fVxcWLEXGP1EfWOdAtkW63gT5EgqZ1vtYfeSdm8yFmDkn\nUX5LWXJXnJOEtIneZCTbG/WDZov1OxqvXjnWPnlcOxe8rl6itJC5djanLHuguaARYcptb588pqVp\ncTW8gSDTZNRbpk6ZjxOl/DdF+nA1YxGRViZdsJ7rzQd75J5LRcHL1ggjq6QsFZMhBp7Wm9iyqovn\n5/2oPe4piYLn5Qqa6qM+k9+WPRmVbak82SfyHGVUO09reSDRPg8eP3h5tG0Pq8Qse9Sc7JCpqrW1\n7mhfNHP21pXNy1ULJ8m7d++eIkp5qw59R0TJX5sWEYemgKw+1NSrNj4yExrfF13MEfHLerV9fL+n\nxCyFyT8WWcoVfk6WNMnI+zMtu7MEp018VtusMrTj8ltTkr2KMrLV6w+vDRybVJZzo+Ui9AZEtrN7\nZr6oXH7y5QVIZHn37l3cuXPnhCytW1UyrnjGJq0sz0WUCsVra3RBZrdl3sjNjia/jIqOiJIv7PC3\nLgG6G84Xu7gdGglZfRERndYXGrzzFtWn9VnLWMvYaE3sLcqYsEmy7CWW0ch0aNTZPW3JDGBJMpqy\nvHv37illSRel5tbJ+mW5EelpLnLkQXhkGU1ovA+8fVMvNs+eTNhBI0vtOICTfqbzCODMIhZ/X6Zm\nn7zTgOrQ6pPHMm23yupRgxZa7Iv6n6C1odX2xcjSakgUh7LKkOW1XBSZcqIZUdrViogAsmVKtcJj\nljJe6ZGa54p77dQu2imqUlN4LWTZksarw1KaXqjBqt/rQy1MIScqrQ7KE01sParSUsjSm7HaaF3n\nEQdodVq2RnVE/NBDmKspyynqUZ44rbzoIuH5rLRZwhytKDXl4OXRFg74h9/krT3i6BGmpyq9i82a\n/DRF3EOWLRe6TG8RoIXMBNOqKrk7zfdp5yEqM5rYPMKU23Q9aPfCRnV7/dqq+mSbrWPSPu0cRGMw\nKpsQ/W/4AwD+O4BrAK4C+KVa63eVUp4P4OcAvBTAUwC+ttb6GZnfIjKlnlQ6reyW/drJ1GzMzsZe\nB3uDx7r4M2SpzZDWDej8nkX+P94aQVpvBvLq19JEbeT5vItdQlNKUb3eBJohTFlW9NHaK9vGSZKe\n/5b/42PdQ+rV51343pi1+pNipRp4m3i8lY95TQlr6SxodnqipLVMrb8iwoz+Cve5Uspjtda/KqXs\nAfiNUspXAngDgJu11n9TSvkOAN95/GmC1pmRiuCdkiUumVd2rKYuLcLM1NmqXLSTaJ1AjdD4wNVe\n/8XzSYKM3iJuDajsbC/TZpRR5lxq9ViK1vMevHMly7MmGNom23m7LcIkoiylnHG1tfMij8tzJRVe\ny6Quf3sTkxxv1gRknYsWO6L0Vn2yzkj8ZMdb6IbXWv/qePMqgMsA/gz3yPLVx/vfDuBJJMlSG5xR\np7acfCtNNANlSFq7mLV6vFkucgki4tB+WyQpL2wAp1RN9CSMvPA0WzWbJDIkmSnPUxaW8vLUZVSW\nVa7861q6tUo+966RCqWnlWztAQHrHygtotbOkzVGo3HOoalL65xp/WdNwBqssnomTnnuvYm+ZYIO\nybKUcgnA/wJwHcCP1Vr/oJTyYK312eMkzwJ40CsjmgUy8NRBZlbiA1KWK220SNL6LW3UyvHqsWY3\nb9bjeT01SSqG3HD5PHX2lWa9pK7l0+zNTkbWJGi5rBZaiNd6gobq1e5F1fqK8lk34Wv2y/o0Qpf9\n57XRgyRdXobcx0mej2mr37L1cpUt7bGuXVmvllZrTwtRAjlleQTgFaWUvwHgvaWUx8TxWkpxa8uo\nLG07o1iibStf5o3UMo/WwRml0mJ3RETaBWFdkFzBcOKMCNO6EL0PT2dtR+3z2utNUKWUUwtRmvsa\nledd7DRerDItZQng1MTAbebvq5RtkduyPv7bmuA9RJMiJyhtdV07d9LubHiHlwng1KQTEZ81XjPn\nuwfp1fBa65+XUn4ZwJcBeLaU8sJa6zOllBcB+LSW5/3vf//J9iOPPIKXvexlZ9K0Kkxmz5ntVsK0\nbPBULOXVZvJeRHXJNBq5SHVCRMnvreT7PaK02soVRe/HaovVHxklIMmN79POpVYm5eF9Rfv4BSyV\nJHPSR3cAABPbSURBVIGnsfqO2yVJhm97Ckq2w2qbhHZuOQFKRSxJmKfJEKWctCyy1OyVeeS9pZ7K\n9vhEG1cf/ehH8bGPfczMwxGthr8AwEGt9TOllOcBeB2A7wPwLgBvAvDDx9/v1PI/9thjpxrjXZRW\no3rgnQiyhw8IK41mj3YBtNjVMvC1NHJbU1lytZW3V5KldW4kyXCi5ISZ2fZIztuWF7Fmk7zIAT3W\n5k0Csjz+LHzU77y9lnutkUukiDx45K+1WdbB90djUutfi+DkgpfcZ9lM/cfr1PrGI0vLfmmvtF2K\nuCeffFItC4iV5YsAvL3ci1teAvCOWuv7SikfBPDzpZQ34/jWIauADCFlBslUApXlZAjTymuRZWbg\nTbHZ27aIko7RdvbWFFm/JEHv21uV57Z627x9HrFJO7U+jvrdGhNeHZo6tWKWsg6trzUbM6rR62P5\n26rXqye67mSZ3iv0eJmWkpUTO+9T7dl3bQL1vq02ZPglunXoQwC+VNn/pwBeGxVuXQRZZBtoDTSv\nE6KLwqrLG1hWfVZdveSp2UHlcfWo2cUHtEeQUd2SQL17PKWN8g05HlnLujgx836wSF+WyceFfNku\nt0lu8zq0/rD6ybNRuvteeRZJa2Qp2yAVntaurFqVdWT6XdapEaVlv3yIQtbN+8Ky3bpWrDotLPoE\nj2WQJKtoRm0lm1GqVJbJTxKfEeesj7Y55OCS0O6Hi1bBPRu8C9YjS2vC0Ahcq5e/uFjrf0kMVhut\nNmh903JjOJUnoRFLS/9b40ojSz4OZZ2yPo0wLFWppZPvF9DK58e9srTrxytHjiVtYtaglZ/FomQZ\nKThKY21bFxtBdkSm86wTNAIZZawRQ6ZMmZf3B4+3AfcXH3haqWxaEM3KrYpHW3DSyiMCpvZo5beQ\npXaRSUKLyFJTTdL2iLikbVMm3ojEJOFk6tJIWrbTmji0azaCVbZ1zfOFtSxhaojyrvoiDa8zPSlt\nzTSRq8JhzXTSPi2vrE+zK1OHRZieLbwsrX7rNhZJoJai8VS9vLhkfbRfuzlbkhAnI40wtbZzxcoJ\nU9pnESaHfFa+RaF6Sse70OVHhiO0c6FdH9EkbO3TyDmqPwvP2/DK0a5vabO3LzOpZK+vKN2sZCln\nSb4f0Gdh+VuShDZY+b7Wk2apVwv8xEQzYATrRHt2ywvbIkzappnXmjAyk4ZMb60Uy3RkH7dXWwCQ\n935qdnGC5IRpEZ1Gdhw8b6YM7UPpebutftPK1topz6lGkrJubQx4dmr2ZtRipDC1cStfNC3TauV6\ndmYEiERWba5KloAfwPVI0yM5fpK0GdoiSo14+AVtEaE361kDONsv2v5W+7ldXN3xF8dGs3y0n+rh\nJMPr4x9tRZzK4IRp/VOjRrbyZmVNWfI6LLK0xohVhkWavCwLGoHJbXneeL975fPj0mvQ6o6gpZHh\nG8sOiyB5uRoZWYQZTTqWeMi0aQpmV5bWfm3AaOmiztBmUU1dajdT8zTSDu3i0uzMDsrM7JZRmNag\n0i5gTT1Fs3nU11rfccVHfS1vKZLlcLKU25zcKG9WWco6JNFp/cvr8VSgdtO712/R+dHSRiRJ0OLQ\n2sTVQpQ96pL2SZKk/XSOrPh4ZkxaJMnzW9dFtj8zWIQsNRXSkpcrGX4MOHth8PwaQfCbX2U6Ko+X\na3W2Nai0C6hlEGZVpQZNrVgfroq5DbLNlt0aYXJytCYnKsNywek/Z3j/8/ZEypLSamrQIkyrL6OP\nRJYwrfRa//P9EnIy9NSrhWiyb1GXtC1db+kV8Hwyv2WfJFSNZOUYG0mUwMxkKVeptNlPu9HUIg5K\nIztWG8CSLLg9HjFpykLC29dycryZNkOM0UUckW6mHl6XBq8vrad4PHWprYjLsSMXlTRVoSnClnMk\n01tueHTBym2tv7UJgbc7Y2vUB9r1Icvwyrdslr+5B8An0EzfaeDHLIETEW1URxazK8voJMrfGslZ\ns4mnKvgFpp0sbqN3kzNf5ZX1yDrl/lbIgWERp+wv7xYZrSztfkUJ6xzxNkYD2SJpKkOqS6ksZd9w\nm6Wy5OdNfmtkaZ0n7bx6k5J1AWvl0W/Zbxo5yjFnjUGPpK1zF/WD3MdJrwXe2NLsludQlsXLzE70\nWp29mF1ZSmjEyGNL3kxN0EiTKxaqR9aplaF1Pk8rX47QMutbJ12zxfsty5Mk6d3grLVRTkgeWfJt\na0KwSNOqX7ZBKssMWdK4sRSGp640MtH6Wea3Foq0dmplUTo+mfMyItLk21IQaGNU6we57dkooSl5\n7zqw9lvXtHec9kciwsJUogRWIkv6pgEo9wP2rBSpL06arfnlMe1pGN6OaKB4M6WXXrOVlyOJ0roP\nULuQvYUurZ6WC0wiqsdqg3b/oUe+3sTqkWbULmty4sd6FY42PizSjCbmSB1a51Me5/VnxICsT+tf\niYxY0PLQtzfBWzZniDiDxckSON2xXCXQMUI0w0j3Wt4qY8G64CRheApPDnhpu/bbG4DWhS7VhFQ6\nnGisPuQf6idv0HnKy7Jfu5FcfmsTmiR+62LTyGkqYcp9Whs1EojOoTZ2rPFkTaqSKDXizBKodz6z\nNkbw+jXTZ94+bQzxYxFnnAuytAYwNU67nUB2bEaSc9LUFGL0kfVEJMm3W2dfrR3ZvBZRWsqSly/v\nedTa7l1gmbbI/dp50NJLVakpIVlmK2Hy8jzylG3IXvQ8nzaReufZI1eLKK26rf3R+cwIAw/WpNIy\n5q3+0s6tljYaK17dGayiLLnC0e4XiwalJEqej5fD01q3tFCaUdAGVlaJWBcNfUtCsfZp7dfie5GK\nyE4EngqUbdWUuEVY2gUuy86SpkYaGnm2tj0DabN2vrVJxiJMzcbIdosso3OklWEds67dTF9aZOiR\npFVGVKaVPsIqN6Xzkx5dvFY+ScRysJFbqBGltoqaxegLyiNIuS8ze1sXPp805P15Wj6N8Lw2RNtW\nf1vkZalLi4g1ovQmA4soo0k6skGri/9u6c+MsszanxEfVjuscjKK00vnkVsPWVrljxJGi751SAMf\nCHLwaReuJFpNvcpV9VorDg8P3ZfSysHokdIoRSKVhaY2NJvkdlRH9rdHtARNCWWIJrLRU0NaHfKi\n1RQmt1crN0s02kWtTfhWfbJuTy1GaaO2eO2IYF0Xlm2Ux0sXtdFSthZ5t5JlixiLsJiy1AamvEha\nB5q8rYfK4AOLK6rMwkZElhZx8e8sNCXiqQfrt2yvZ0d0LFIgWQKLVFSmnVqZ2ndElB4Zye3sBdVT\nB6XLKkxZjqfKtboi8P6yXrailSsndFkm3/aIXuYZSZYtkydvl4dFlKVFlBwts6sccFxdSrKk4xph\nSvs0GyPClPk1ZN0VWRbls/pNkqRXVlaFehe3NvAjwpRltNgT2aiRpnXBtSJSk1q/W2RG257nEI1/\nWZ6so6U/ZR9FIsKrm5fljUXvGpB9uBZZRmUv5oZbitIiTW9QaYOLL/TIE+PNntqs2aowNfs1G1r7\nSv7WBltGjbfWqZFcC1FqhK6d1ynQiF1evFn1pxGjlz46Rrbwb26fRioRYWp1jiBK7SPLp/QZUaDt\nk+PJyzeSLFsnztWVpRwskmxkGkAPpGdkvPVYozcgpG2andZ+q73yxGRs19prEVQvCVu2WhddC7H1\nXLhZRBOWTMsJWiN6efFl2+kpR22/pnw9dRoRydS+zRBlVllGfab1sdy20raSnHa8RVl6iphjVrKM\n1FkGmRlNyyO3PZKUNka/NfunqBDLJk6a8lhrHdbgtlRslNdT1t7gG6EqNfu0+umYRYiRt6LZrE1U\nGbUtbdHyT7mQI1hk0aPaOLT+08ZK5FloXkDWM4jKyeaJeMZ+nu9e5gdKKb9dSvndUsqHSyk/eLz/\ne0spnyilfPD483qnjNDYwIYzpMX3yzoys5KnVHoVpayf//ZmbJknQ9CWuvSUQu8MzeEpH0sdZOrM\nTAZZm7yx4vVnNn/GdmsisepqracF1hhoPUcaWgRP9uPZnCmvty2ZfNFf4T5XSnms1vpXpZQ9AL9R\nSvlKABXAW2qtb8kYkRmkmQZ6A1zb1vJniImXkyFIDVNVVSuBROqoxw5v8GmulaeUetuesTF7zqUK\nkvZ727wNGkG32i5tsNokx2vvuIqIJKtgZTprDGhticq3CDIzjrL9MkW8hW54rfWvjjevArgM4M+o\n3ihvr2GWWrAIzBu81oUsy7GI0oI2oDOuTqZPWpRFy+DJooVMLZLJlpfpa8/G7ASh1auRKJUt7dH2\n9UzYUX3aeM0Qpocpk7QmciK75LFo/I8eqxFJ9tbnuuHHlVwqpfwugGcBvL/W+gfHh76llPJ7pZS3\nlVL+ppM/ZUjW9dFOoHVCrfI88o0I03I3M+4o32ed0Giwemk8Gz23JQtrYEr3yapD5vf62rqotDI9\nu7JKKRon2bHTMt6zE3WP6Mj0v2WP9tvK02prNAYjBayllWVHaDlPHCFZ1lqPaq2vAPASAH+/lPIa\nAD8G4GUAXgHgUwB+xDKKf7MyT76zqssbqNkB7eXz7FX6xP2dhecSeeSotckrv0UpZm3U0rUQc+tg\n9SadzCTQS5z8WOa8WLZ7/ZCdqCNE7dfgXRdyn/VbltPThp7JW+aPIG1p7d/0anit9c9LKb8M4O/W\nWp9kFf4EgHdreX7zN3/zxKiHHnoIL33pS62yT9Lxb9rWGpU9GaXoN0NHJ9uyk9JMIRZpS2SXl09z\nDzW0pNsaon7vLTMDOX7mqIPXY9XH92tprLoiNSn7tlVdeuW22rMGnnrqKTz11FOptC5ZllJeAOCg\n1vqZUsrzALwOwPeVUl5Ya33mONnXAPiQlv9Vr3qVqirlSTLqbjpRkkAyaTOEK4k8q4Q9m6wZTpux\nM/X0oqXcnsnBKr+X3D0ymapcPTus3y1jTpYx1znldXiQ5JwRJNq4ledkBFFmCD6qK1NurRUPP/zw\nKRH3a7/2a2b+SFm+CMDbSymXcM9lf0et9X2llJ8upbwCQAXwMQDfqGWOiFIS0RS0KAXtd0ZVynTy\nwvHUT8tg9AanZ49mr4W5L1ayQaunZb83Zrw29owtGbaIwhjRxJkdBx6sNvPt3rBQD2GOQovomFK+\nd164DZk+i24d+hCAL1X2f0NYsl2metKtE9biRmh1jUbmIvTsicg6q3qnDLCpitLb3wPZLxpBTlEU\nEhliyxJmqz1Zz6oFHnFm8/Z4Ti3w7IvqlP2r9bfVB1F9Lf22yivavDiJlc473nIRUZ6pA6FnsFuu\ndeSGa3laYBG850a2XnStSi5Sl9Z3q22t56nWcS/jkLBUMy8/6z7PZcPIcUdljkhvtTvbH9Fk6NVN\nCFfDR+DjH//4mX29FycN4J5ZpBe8zqg+IryPfvSjJ9vah6fV9svyPIXplS23eT7vdwtqrfjIRz5i\nHsuWkUnT+pH5+G/PhqjcW7duhfVa5Wv1tGCka2xN4Nq+27dvT6prpDfSMil751VLY2ERsnz66afV\n/VlpTr8zswNP2zJbW+glYIs8ZJ2e2x25+h7pTlGoPYOa2qu5R9l65PmKzl8vUWr1W+mtY7du3Tpj\nh2db1HavnaOQCel44y8a03Nj6oSujasWwlyELIF48PSUZ5WfTT8nLEUYERlPlznmkWErAW8ZWfWm\n5eG/+beWxspvkVurSszavTQ0b6YHWdtH8EB0HXkTUquqBBYkS4ms6sumWRot9mdcaf4dleXVEdmw\nNYw8t9mJclS51vE1VOMayF4DSwmVFtLsKn9Gwy/e6Nhhhx0uPGqtKuvORpY77LDDDhcJq7nhO+yw\nww7nCTuy3GGHHXZIYHayLKW8vpTyR6WUW6WU75i7vqVRSvnJUsqzpZQPsX3PL6XcLKX831LKE8V5\nhd15RCnloVLK+0spf1BK+d+llH95vP9CtrvY/xhwIdtLKKVcLvf+CeHdx78vdHsjzEqWpZTLAP4D\ngNcD+NsA3lhK+cI561wBP4V77eP4TgA3a62PAnjf8e+LhH0A31Zr/SIAXwHgm4/P64Vsd631OQCP\n1XuvKvw7AB4r9/4x4EK2l+FbAXwYAC1sXPT2uphbWb4SwO1a61O11n0APwvgq2euc1HUWn8d998e\nT3gDgLcfb78dwD9Z1KiZUWt9ptb6u8fbfwngDwG8GBe43VX/x4AL295SyksA/EMAPwGc/CvChW1v\nBnOT5YsB8Md3PnG876LjwVrrs8fbzwJ4cE1j5kQp5WEAXwLgt3GB2130fwy4sO0F8O8A/CsAR2zf\nRW5viLnJ8nP+vqR6796sC9kPpZTPA/ALAL611voX/NhFa3c9+48Bj4njF6a9pZR/DODTtdYPAvp/\nbV2k9mYxN1n+MYCH2O+HcE9dXnQ8W0p5IQCUUl4E4NMr2zMcpZQruEeU76i1vvN494Vvd631zwH8\nMoAvw8Vt798D8IZSyscA/AyAryqlvAMXt70pzE2WvwPg5aWUh0spVwF8HYB3zVznFvAuAG863n4T\ngHc6ac8dyr3nyt4G4MO11reyQxey3aWUF9DKb7n/jwEfxAVtb631u2utD9VaXwbgnwL41VrrP8MF\nbW8Wsz/BU0r5BwDeintB8bfVWn9w1goXRinlZwC8GsALcC+O8z0AfgnAzwP4AgBPAfjaWutn1rJx\nNI5Xgn8NwO/jviv2XQD+By5gu0spX4x7Cxr8HwP+bSnl+biA7eUopbwawLfXWt/wudBeD7vHHXfY\nYYcdEtg9wbPDDjvskMCOLHfYYYcdEtiR5Q477LBDAjuy3GGHHXZIYEeWO+ywww4J7Mhyhx122CGB\nHVnusMMOOySwI8sddthhhwT+Pzl/C8KJDU7hAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xcec14a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imgplot = plt.imshow(rsizeArr)\n",
+    "imgplot.set_interpolation('bicubic')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/06. matplotlib/06.06 annotating axes.ipynb b/06-matplotlib/06.06-annotating-axes.ipynb
similarity index 99%
rename from 06. matplotlib/06.06 annotating axes.ipynb
rename to 06-matplotlib/06.06-annotating-axes.ipynb
index 4ce7193c..8e06b128 100644
--- a/06. matplotlib/06.06 annotating axes.ipynb	
+++ b/06-matplotlib/06.06-annotating-axes.ipynb
@@ -1,603 +1,603 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 注释"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 使用文本框进行注释"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "先看一个简单的例子："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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6AGsNrz51MZ/PSOShp89yx3359n3lRS4hOsHqrX09jsVnNrq8nu0YwCvhcZUs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\nRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa0396d8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy.random\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "fig = plt.figure(1, figsize=(5,5))\n",
-    "fig.clf()\n",
-    "\n",
-    "ax = fig.add_subplot(111)\n",
-    "ax.set_aspect(1)\n",
-    "\n",
-    "x1 = -1 + numpy.random.randn(100)\n",
-    "y1 = -1 + numpy.random.randn(100)\n",
-    "x2 = 1. + numpy.random.randn(100)\n",
-    "y2 = 1. + numpy.random.randn(100)\n",
-    "\n",
-    "ax.scatter(x1, y1, color=\"r\")\n",
-    "ax.scatter(x2, y2, color=\"g\")\n",
-    "\n",
-    "# 加上两个文本框\n",
-    "bbox_props = dict(boxstyle=\"round\", fc=\"w\", ec=\"0.5\", alpha=0.9)\n",
-    "ax.text(-2, -2, \"Sample A\", ha=\"center\", va=\"center\", size=20,\n",
-    "        bbox=bbox_props)\n",
-    "ax.text(2, 2, \"Sample B\", ha=\"center\", va=\"center\", size=20,\n",
-    "        bbox=bbox_props)\n",
-    "\n",
-    "# 加上一个箭头文本框\n",
-    "bbox_props = dict(boxstyle=\"rarrow\", fc=(0.8,0.9,0.9), ec=\"b\", lw=2)\n",
-    "t = ax.text(0, 0, \"Direction\", ha=\"center\", va=\"center\", rotation=45,\n",
-    "            size=15,\n",
-    "            bbox=bbox_props)\n",
-    "\n",
-    "bb = t.get_bbox_patch()\n",
-    "bb.set_boxstyle(\"rarrow\", pad=0.6)\n",
-    "\n",
-    "ax.set_xlim(-4, 4)\n",
-    "ax.set_ylim(-4, 4)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`text()` 函数接受 `bbox` 参数来绘制文本框。\n",
-    "```python\n",
-    "bbox_props = dict(boxstyle=\"rarrow,pad=0.3\", fc=\"cyan\", ec=\"b\", lw=2)\n",
-    "t = ax.text(0, 0, \"Direction\", ha=\"center\", va=\"center\", rotation=45,\n",
-    "            size=15,\n",
-    "            bbox=bbox_props)\n",
-    "```\n",
-    "\n",
-    "可以这样来获取这个文本框，并对其参数进行修改：\n",
-    "```python\n",
-    "bb = t.get_bbox_patch()\n",
-    "bb.set_boxstyle(\"rarrow\", pad=0.6)\n",
-    "```\n",
-    "\n",
-    "可用的文本框风格有：\n",
-    "\n",
-    "class|name|attrs\n",
-    "---|---|---\n",
-    "LArrow\t|larrow\t|pad=0.3\n",
-    "RArrow\t|rarrow\t|pad=0.3\n",
-    "Round\t|round\t|pad=0.3,rounding_size=None\n",
-    "Round4\t|round4\t|pad=0.3,rounding_size=None\n",
-    "Roundtooth\t|roundtooth\t|pad=0.3,tooth_size=None\n",
-    "Sawtooth\t|sawtooth\t|pad=0.3,tooth_size=None\n",
-    "Square\t|square\t|pad=0.3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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BQK9evXDlyhWcP3++wvt/FpUqRgsLC+zYsQNnzpzBO++8o/EGPm/+6H/++Qcff/wx2rRp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\nRK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa19a710>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.patches as mpatch\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "styles = mpatch.BoxStyle.get_styles()\n",
-    "\n",
-    "figheight = (len(styles)+.5)\n",
-    "fig1 = plt.figure(figsize=(4/1.5, figheight/1.5))\n",
-    "fontsize = 0.3 * 72\n",
-    "ax = fig1.add_subplot(111)\n",
-    "\n",
-    "for i, (stylename, styleclass) in enumerate(styles.items()):\n",
-    "    ax.text(0.5, (float(len(styles)) - 0.5 - i)/figheight, stylename,\n",
-    "              ha=\"center\",\n",
-    "              size=fontsize,\n",
-    "              transform=fig1.transFigure,\n",
-    "              bbox=dict(boxstyle=stylename, fc=\"w\", ec=\"k\"))\n",
-    "\n",
-    "# 去掉轴的显示\n",
-    "ax.spines['right'].set_color('none')\n",
-    "ax.spines['top'].set_color('none')\n",
-    "ax.spines['left'].set_color('none')\n",
-    "ax.spines['bottom'].set_color('none')\n",
-    "plt.xticks([])\n",
-    "plt.yticks([])\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "各个风格的文本框如上图所示。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 使用箭头进行注释"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa396080>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(1, figsize=(3,3))\n",
-    "ax = plt.subplot(111)\n",
-    "\n",
-    "ax.annotate(\"\",\n",
-    "            xy=(0.2, 0.2), xycoords='data',\n",
-    "            xytext=(0.8, 0.8), textcoords='data',\n",
-    "            arrowprops=dict(arrowstyle=\"->\",\n",
-    "                            connectionstyle=\"arc3\"), \n",
-    "            )\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "之前介绍了 `annotate` 中 `xy, xycoords, xytext, textcoords` 参数的含义，通常我们把 `xy` 设在 `data` 坐标系，把 `xytext` 设在 `offset` 即以注释点为原点的参考系。\n",
-    "\n",
-    "箭头显示是可选的，用 `arrowprops` 参数来指定，接受一个字典作为参数。\n",
-    "\n",
-    "不同类型的绘制箭头方式："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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JGGO4ePEivvrqKxQWFuKxxx5DZmam2GXxinYgd4pGozhx4gROnDiB1atX4+GHH4ZKpRK7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\no4aCEEIIIQn7f35zqmEqULpsAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa3a2f28>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.patches as mpatches\n",
-    "\n",
-    "x1, y1 = 0.3, 0.3\n",
-    "x2, y2 = 0.7, 0.7\n",
-    "\n",
-    "fig = plt.figure(1, figsize=(8,3))\n",
-    "fig.clf()\n",
-    "from mpl_toolkits.axes_grid.axes_grid import AxesGrid\n",
-    "from mpl_toolkits.axes_grid.anchored_artists import AnchoredText\n",
-    "\n",
-    "#from matplotlib.font_manager import FontProperties\n",
-    "\n",
-    "def add_at(ax, t, loc=2):\n",
-    "    fp = dict(size=10)\n",
-    "    _at = AnchoredText(t, loc=loc, prop=fp)\n",
-    "    ax.add_artist(_at)\n",
-    "    return _at\n",
-    "\n",
-    "\n",
-    "grid = AxesGrid(fig, 111, (1, 4), label_mode=\"1\", share_all=True)\n",
-    "\n",
-    "grid[0].set_autoscale_on(False)\n",
-    "\n",
-    "ax = grid[0]\n",
-    "ax.plot([x1, x2], [y1, y2], \".\")\n",
-    "el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)\n",
-    "ax.add_artist(el)\n",
-    "ax.annotate(\"\",\n",
-    "            xy=(x1, y1), xycoords='data',\n",
-    "            xytext=(x2, y2), textcoords='data',\n",
-    "            arrowprops=dict(arrowstyle=\"-\", #linestyle=\"dashed\",\n",
-    "                            color=\"0.5\",\n",
-    "                            patchB=None,\n",
-    "                            shrinkB=0,\n",
-    "                            connectionstyle=\"arc3,rad=0.3\",\n",
-    "                            ),\n",
-    "            )\n",
-    "\n",
-    "add_at(ax, \"connect\", loc=2)\n",
-    "\n",
-    "ax = grid[1]\n",
-    "ax.plot([x1, x2], [y1, y2], \".\")\n",
-    "el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)\n",
-    "ax.add_artist(el)\n",
-    "ax.annotate(\"\",\n",
-    "            xy=(x1, y1), xycoords='data',\n",
-    "            xytext=(x2, y2), textcoords='data',\n",
-    "            arrowprops=dict(arrowstyle=\"-\", #linestyle=\"dashed\",\n",
-    "                            color=\"0.5\",\n",
-    "                            patchB=el,\n",
-    "                            shrinkB=0,\n",
-    "                            connectionstyle=\"arc3,rad=0.3\",\n",
-    "                            ),\n",
-    "            )\n",
-    "\n",
-    "add_at(ax, \"clip\", loc=2)\n",
-    "\n",
-    "\n",
-    "ax = grid[2]\n",
-    "ax.plot([x1, x2], [y1, y2], \".\")\n",
-    "el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)\n",
-    "ax.add_artist(el)\n",
-    "ax.annotate(\"\",\n",
-    "            xy=(x1, y1), xycoords='data',\n",
-    "            xytext=(x2, y2), textcoords='data',\n",
-    "            arrowprops=dict(arrowstyle=\"-\", #linestyle=\"dashed\",\n",
-    "                            color=\"0.5\",\n",
-    "                            patchB=el,\n",
-    "                            shrinkB=5,\n",
-    "                            connectionstyle=\"arc3,rad=0.3\",\n",
-    "                            ),\n",
-    "            )\n",
-    "\n",
-    "add_at(ax, \"shrink\", loc=2)\n",
-    "\n",
-    "\n",
-    "ax = grid[3]\n",
-    "ax.plot([x1, x2], [y1, y2], \".\")\n",
-    "el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)\n",
-    "ax.add_artist(el)\n",
-    "ax.annotate(\"\",\n",
-    "            xy=(x1, y1), xycoords='data',\n",
-    "            xytext=(x2, y2), textcoords='data',\n",
-    "            arrowprops=dict(arrowstyle=\"fancy\", #linestyle=\"dashed\",\n",
-    "                            color=\"0.5\",\n",
-    "                            patchB=el,\n",
-    "                            shrinkB=5,\n",
-    "                            connectionstyle=\"arc3,rad=0.3\",\n",
-    "                            ),\n",
-    "            )\n",
-    "\n",
-    "add_at(ax, \"mutate\", loc=2)\n",
-    "\n",
-    "grid[0].set_xlim(0, 1)\n",
-    "grid[0].set_ylim(0, 1)\n",
-    "grid[0].axis[\"bottom\"].toggle(ticklabels=False)\n",
-    "grid[0].axis[\"left\"].toggle(ticklabels=False)\n",
-    "fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95)\n",
-    "\n",
-    "plt.draw()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "字典中，`connectionstyle` 参数控制路径的风格：\n",
-    "\n",
-    "Name | Attr\n",
-    "----|----\n",
-    "angle|\tangleA=90,angleB=0,rad=0.0\n",
-    "angle3|\tangleA=90,angleB=0\n",
-    "arc|\tangleA=0,angleB=0,armA=None,armB=None,rad=0.0\n",
-    "arc3|\trad=0.0\n",
-    "bar|\tarmA=0.0,armB=0.0,fraction=0.3,angle=None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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FePvtt5GZmYknnngCAoEArq6usLKywsyZM9G3b19ER0ejoqJCI/bKdN/Jyclatx8QEICY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\no4KCEEIIIRqjgoIQQgghGvt/FlbD4KMSO5AAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x3bdae80>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.patches as mpatches\n",
-    "\n",
-    "fig = plt.figure(1, figsize=(8,5))\n",
-    "fig.clf()\n",
-    "from mpl_toolkits.axes_grid.axes_grid import AxesGrid\n",
-    "from mpl_toolkits.axes_grid.anchored_artists import AnchoredText\n",
-    "\n",
-    "#from matplotlib.font_manager import FontProperties\n",
-    "\n",
-    "def add_at(ax, t, loc=2):\n",
-    "    fp = dict(size=8)\n",
-    "    _at = AnchoredText(t, loc=loc, prop=fp)\n",
-    "    ax.add_artist(_at)\n",
-    "    return _at\n",
-    "\n",
-    "\n",
-    "grid = AxesGrid(fig, 111, (3, 5), label_mode=\"1\", share_all=True)\n",
-    "\n",
-    "grid[0].set_autoscale_on(False)\n",
-    "\n",
-    "\n",
-    "x1, y1 = 0.3, 0.3\n",
-    "x2, y2 = 0.7, 0.7\n",
-    "\n",
-    "\n",
-    "def demo_con_style(ax, connectionstyle, label=None):\n",
-    "\n",
-    "    if label is None:\n",
-    "        label = connectionstyle\n",
-    "\n",
-    "    x1, y1 = 0.3, 0.2\n",
-    "    x2, y2 = 0.8, 0.6\n",
-    "\n",
-    "    ax.plot([x1, x2], [y1, y2], \".\")\n",
-    "    ax.annotate(\"\",\n",
-    "                xy=(x1, y1), xycoords='data',\n",
-    "                xytext=(x2, y2), textcoords='data',\n",
-    "                arrowprops=dict(arrowstyle=\"->\", #linestyle=\"dashed\",\n",
-    "                                color=\"0.5\",\n",
-    "                                shrinkA=5, shrinkB=5,\n",
-    "                                patchA=None,\n",
-    "                                patchB=None,\n",
-    "                                connectionstyle=connectionstyle,\n",
-    "                                ),\n",
-    "                )\n",
-    "\n",
-    "    add_at(ax, label, loc=2)\n",
-    "\n",
-    "column = grid.axes_column[0]\n",
-    "\n",
-    "demo_con_style(column[0], \"angle3,angleA=90,angleB=0\",\n",
-    "               label=\"angle3,\\nangleA=90,\\nangleB=0\")\n",
-    "demo_con_style(column[1], \"angle3,angleA=0,angleB=90\",\n",
-    "               label=\"angle3,\\nangleA=0,\\nangleB=90\")\n",
-    "\n",
-    "\n",
-    "\n",
-    "column = grid.axes_column[1]\n",
-    "\n",
-    "demo_con_style(column[0], \"arc3,rad=0.\")\n",
-    "demo_con_style(column[1], \"arc3,rad=0.3\")\n",
-    "demo_con_style(column[2], \"arc3,rad=-0.3\")\n",
-    "\n",
-    "\n",
-    "\n",
-    "column = grid.axes_column[2]\n",
-    "\n",
-    "demo_con_style(column[0], \"angle,angleA=-90,angleB=180,rad=0\",\n",
-    "               label=\"angle,\\nangleA=-90,\\nangleB=180,\\nrad=0\")\n",
-    "demo_con_style(column[1], \"angle,angleA=-90,angleB=180,rad=5\",\n",
-    "               label=\"angle,\\nangleA=-90,\\nangleB=180,\\nrad=5\")\n",
-    "demo_con_style(column[2], \"angle,angleA=-90,angleB=10,rad=5\",\n",
-    "               label=\"angle,\\nangleA=-90,\\nangleB=10,\\nrad=0\")\n",
-    "\n",
-    "\n",
-    "column = grid.axes_column[3]\n",
-    "\n",
-    "demo_con_style(column[0], \"arc,angleA=-90,angleB=0,armA=30,armB=30,rad=0\",\n",
-    "               label=\"arc,\\nangleA=-90,\\nangleB=0,\\narmA=30,\\narmB=30,\\nrad=0\")\n",
-    "demo_con_style(column[1], \"arc,angleA=-90,angleB=0,armA=30,armB=30,rad=5\",\n",
-    "               label=\"arc,\\nangleA=-90,\\nangleB=0,\\narmA=30,\\narmB=30,\\nrad=5\")\n",
-    "demo_con_style(column[2], \"arc,angleA=-90,angleB=0,armA=0,armB=40,rad=0\",\n",
-    "               label=\"arc,\\nangleA=-90,\\nangleB=0,\\narmA=0,\\narmB=40,\\nrad=0\")\n",
-    "\n",
-    "\n",
-    "column = grid.axes_column[4]\n",
-    "\n",
-    "demo_con_style(column[0], \"bar,fraction=0.3\",\n",
-    "               label=\"bar,\\nfraction=0.3\")\n",
-    "demo_con_style(column[1], \"bar,fraction=-0.3\",\n",
-    "               label=\"bar,\\nfraction=-0.3\")\n",
-    "demo_con_style(column[2], \"bar,angle=180,fraction=-0.2\",\n",
-    "               label=\"bar,\\nangle=180,\\nfraction=-0.2\")\n",
-    "\n",
-    "\n",
-    "#demo_con_style(column[1], \"arc3,rad=0.3\")\n",
-    "#demo_con_style(column[2], \"arc3,rad=-0.3\")\n",
-    "\n",
-    "\n",
-    "grid[0].set_xlim(0, 1)\n",
-    "grid[0].set_ylim(0, 1)\n",
-    "grid.axes_llc.axis[\"bottom\"].toggle(ticklabels=False)\n",
-    "grid.axes_llc.axis[\"left\"].toggle(ticklabels=False)\n",
-    "fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95)\n",
-    "\n",
-    "plt.draw()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`arrowstyle` 参数控制小箭头的风格：\n",
-    "\n",
-    "Name | Attrs\n",
-    "--- |---\n",
-    "`-`\t|None\n",
-    "`->`\t|head_length=0.4,head_width=0.2\n",
-    "`-[`\t|widthB=1.0,lengthB=0.2,angleB=None\n",
-    "&#166;`-`&#166;\t|widthA=1.0,widthB=1.0\n",
-    "`-`&#166;`>`\t|head_length=0.4,head_width=0.2\n",
-    "`<-`\t|head_length=0.4,head_width=0.2\n",
-    "`<->`\t|head_length=0.4,head_width=0.2\n",
-    "`<`&#166;`-`\t|head_length=0.4,head_width=0.2\n",
-    "`<`&#166;-&#166;`>`\t|head_length=0.4,head_width=0.2\n",
-    "`fancy`\t|head_length=0.4,head_width=0.4,tail_width=0.4\n",
-    "`simple`\t|head_length=0.5,head_width=0.5,tail_width=0.2\n",
-    "`wedge`\t|tail_width=0.3,shrink_factor=0.5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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FKG77n18ApG4jIsG6uroZeXl5Dfv379c9/5IlS/w7d+484vV6V3VDeUTdKp77n8FB3UpE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\nAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa555f98>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.patches as mpatches\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "styles = mpatches.ArrowStyle.get_styles()\n",
-    "\n",
-    "ncol=2\n",
-    "nrow = (len(styles)+1) // ncol\n",
-    "figheight = (nrow+0.5)\n",
-    "fig1 = plt.figure(1, (4.*ncol/1.5, figheight/1.5))\n",
-    "fontsize = 0.2 * 70\n",
-    "\n",
-    "\n",
-    "ax = fig1.add_axes([0, 0, 1, 1], frameon=False, aspect=1.)\n",
-    "\n",
-    "ax.set_xlim(0, 4*ncol)\n",
-    "ax.set_ylim(0, figheight)\n",
-    "\n",
-    "def to_texstring(s):\n",
-    "    s = s.replace(\"<\", r\"$<$\")\n",
-    "    s = s.replace(\">\", r\"$>$\")\n",
-    "    s = s.replace(\"|\", r\"$|$\")\n",
-    "    return s\n",
-    "\n",
-    "for i, (stylename, styleclass) in enumerate(sorted(styles.items())):\n",
-    "    x = 3.2 + (i//nrow)*4\n",
-    "    y = (figheight - 0.7 - i%nrow) # /figheight\n",
-    "    p = mpatches.Circle((x, y), 0.2, fc=\"w\")\n",
-    "    ax.add_patch(p)\n",
-    "\n",
-    "    ax.annotate(to_texstring(stylename), (x, y),\n",
-    "                (x-1.2, y),\n",
-    "                #xycoords=\"figure fraction\", textcoords=\"figure fraction\",\n",
-    "                ha=\"right\", va=\"center\",\n",
-    "                size=fontsize,\n",
-    "                arrowprops=dict(arrowstyle=stylename,\n",
-    "                                patchB=p,\n",
-    "                                shrinkA=5,\n",
-    "                                shrinkB=5,\n",
-    "                                fc=\"w\", ec=\"k\",\n",
-    "                                connectionstyle=\"arc3,rad=-0.05\",\n",
-    "                                ),\n",
-    "                bbox=dict(boxstyle=\"square\", fc=\"w\"))\n",
-    "\n",
-    "ax.xaxis.set_visible(False)\n",
-    "ax.yaxis.set_visible(False)\n",
-    "\n",
-    "\n",
-    "\n",
-    "plt.draw()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 注释"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 使用文本框进行注释"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "先看一个简单的例子："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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6AGsNrz51MZ/PSOShp89yx3359n3lRS4hOsHqrX09jsVnNrq8nu0YwCvhcZUs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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa0396d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy.random\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "fig = plt.figure(1, figsize=(5,5))\n",
+    "fig.clf()\n",
+    "\n",
+    "ax = fig.add_subplot(111)\n",
+    "ax.set_aspect(1)\n",
+    "\n",
+    "x1 = -1 + numpy.random.randn(100)\n",
+    "y1 = -1 + numpy.random.randn(100)\n",
+    "x2 = 1. + numpy.random.randn(100)\n",
+    "y2 = 1. + numpy.random.randn(100)\n",
+    "\n",
+    "ax.scatter(x1, y1, color=\"r\")\n",
+    "ax.scatter(x2, y2, color=\"g\")\n",
+    "\n",
+    "# 加上两个文本框\n",
+    "bbox_props = dict(boxstyle=\"round\", fc=\"w\", ec=\"0.5\", alpha=0.9)\n",
+    "ax.text(-2, -2, \"Sample A\", ha=\"center\", va=\"center\", size=20,\n",
+    "        bbox=bbox_props)\n",
+    "ax.text(2, 2, \"Sample B\", ha=\"center\", va=\"center\", size=20,\n",
+    "        bbox=bbox_props)\n",
+    "\n",
+    "# 加上一个箭头文本框\n",
+    "bbox_props = dict(boxstyle=\"rarrow\", fc=(0.8,0.9,0.9), ec=\"b\", lw=2)\n",
+    "t = ax.text(0, 0, \"Direction\", ha=\"center\", va=\"center\", rotation=45,\n",
+    "            size=15,\n",
+    "            bbox=bbox_props)\n",
+    "\n",
+    "bb = t.get_bbox_patch()\n",
+    "bb.set_boxstyle(\"rarrow\", pad=0.6)\n",
+    "\n",
+    "ax.set_xlim(-4, 4)\n",
+    "ax.set_ylim(-4, 4)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`text()` 函数接受 `bbox` 参数来绘制文本框。\n",
+    "```python\n",
+    "bbox_props = dict(boxstyle=\"rarrow,pad=0.3\", fc=\"cyan\", ec=\"b\", lw=2)\n",
+    "t = ax.text(0, 0, \"Direction\", ha=\"center\", va=\"center\", rotation=45,\n",
+    "            size=15,\n",
+    "            bbox=bbox_props)\n",
+    "```\n",
+    "\n",
+    "可以这样来获取这个文本框，并对其参数进行修改：\n",
+    "```python\n",
+    "bb = t.get_bbox_patch()\n",
+    "bb.set_boxstyle(\"rarrow\", pad=0.6)\n",
+    "```\n",
+    "\n",
+    "可用的文本框风格有：\n",
+    "\n",
+    "class|name|attrs\n",
+    "---|---|---\n",
+    "LArrow\t|larrow\t|pad=0.3\n",
+    "RArrow\t|rarrow\t|pad=0.3\n",
+    "Round\t|round\t|pad=0.3,rounding_size=None\n",
+    "Round4\t|round4\t|pad=0.3,rounding_size=None\n",
+    "Roundtooth\t|roundtooth\t|pad=0.3,tooth_size=None\n",
+    "Sawtooth\t|sawtooth\t|pad=0.3,tooth_size=None\n",
+    "Square\t|square\t|pad=0.3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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BQK9evXDlyhWcP3++wvt/FpUqRgsLC+zYsQNnzpzBO++8o/EGPm/+6H/++Qcff/wx2rRp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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa19a710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.patches as mpatch\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "styles = mpatch.BoxStyle.get_styles()\n",
+    "\n",
+    "figheight = (len(styles)+.5)\n",
+    "fig1 = plt.figure(figsize=(4/1.5, figheight/1.5))\n",
+    "fontsize = 0.3 * 72\n",
+    "ax = fig1.add_subplot(111)\n",
+    "\n",
+    "for i, (stylename, styleclass) in enumerate(styles.items()):\n",
+    "    ax.text(0.5, (float(len(styles)) - 0.5 - i)/figheight, stylename,\n",
+    "              ha=\"center\",\n",
+    "              size=fontsize,\n",
+    "              transform=fig1.transFigure,\n",
+    "              bbox=dict(boxstyle=stylename, fc=\"w\", ec=\"k\"))\n",
+    "\n",
+    "# 去掉轴的显示\n",
+    "ax.spines['right'].set_color('none')\n",
+    "ax.spines['top'].set_color('none')\n",
+    "ax.spines['left'].set_color('none')\n",
+    "ax.spines['bottom'].set_color('none')\n",
+    "plt.xticks([])\n",
+    "plt.yticks([])\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "各个风格的文本框如上图所示。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 使用箭头进行注释"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAM8AAADICAYAAABPhLXnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa396080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(1, figsize=(3,3))\n",
+    "ax = plt.subplot(111)\n",
+    "\n",
+    "ax.annotate(\"\",\n",
+    "            xy=(0.2, 0.2), xycoords='data',\n",
+    "            xytext=(0.8, 0.8), textcoords='data',\n",
+    "            arrowprops=dict(arrowstyle=\"->\",\n",
+    "                            connectionstyle=\"arc3\"), \n",
+    "            )\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "之前介绍了 `annotate` 中 `xy, xycoords, xytext, textcoords` 参数的含义，通常我们把 `xy` 设在 `data` 坐标系，把 `xytext` 设在 `offset` 即以注释点为原点的参考系。\n",
+    "\n",
+    "箭头显示是可选的，用 `arrowprops` 参数来指定，接受一个字典作为参数。\n",
+    "\n",
+    "不同类型的绘制箭头方式："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JGGO4ePEivvrqKxQWFuKxxx5DZmam2GXxinYgd4pGozhx4gROnDiB1atX4+GHH4ZKpRK7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\no4aCEEIIIQn7f35zqmEqULpsAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa3a2f28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as mpatches\n",
+    "\n",
+    "x1, y1 = 0.3, 0.3\n",
+    "x2, y2 = 0.7, 0.7\n",
+    "\n",
+    "fig = plt.figure(1, figsize=(8,3))\n",
+    "fig.clf()\n",
+    "from mpl_toolkits.axes_grid.axes_grid import AxesGrid\n",
+    "from mpl_toolkits.axes_grid.anchored_artists import AnchoredText\n",
+    "\n",
+    "#from matplotlib.font_manager import FontProperties\n",
+    "\n",
+    "def add_at(ax, t, loc=2):\n",
+    "    fp = dict(size=10)\n",
+    "    _at = AnchoredText(t, loc=loc, prop=fp)\n",
+    "    ax.add_artist(_at)\n",
+    "    return _at\n",
+    "\n",
+    "\n",
+    "grid = AxesGrid(fig, 111, (1, 4), label_mode=\"1\", share_all=True)\n",
+    "\n",
+    "grid[0].set_autoscale_on(False)\n",
+    "\n",
+    "ax = grid[0]\n",
+    "ax.plot([x1, x2], [y1, y2], \".\")\n",
+    "el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)\n",
+    "ax.add_artist(el)\n",
+    "ax.annotate(\"\",\n",
+    "            xy=(x1, y1), xycoords='data',\n",
+    "            xytext=(x2, y2), textcoords='data',\n",
+    "            arrowprops=dict(arrowstyle=\"-\", #linestyle=\"dashed\",\n",
+    "                            color=\"0.5\",\n",
+    "                            patchB=None,\n",
+    "                            shrinkB=0,\n",
+    "                            connectionstyle=\"arc3,rad=0.3\",\n",
+    "                            ),\n",
+    "            )\n",
+    "\n",
+    "add_at(ax, \"connect\", loc=2)\n",
+    "\n",
+    "ax = grid[1]\n",
+    "ax.plot([x1, x2], [y1, y2], \".\")\n",
+    "el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)\n",
+    "ax.add_artist(el)\n",
+    "ax.annotate(\"\",\n",
+    "            xy=(x1, y1), xycoords='data',\n",
+    "            xytext=(x2, y2), textcoords='data',\n",
+    "            arrowprops=dict(arrowstyle=\"-\", #linestyle=\"dashed\",\n",
+    "                            color=\"0.5\",\n",
+    "                            patchB=el,\n",
+    "                            shrinkB=0,\n",
+    "                            connectionstyle=\"arc3,rad=0.3\",\n",
+    "                            ),\n",
+    "            )\n",
+    "\n",
+    "add_at(ax, \"clip\", loc=2)\n",
+    "\n",
+    "\n",
+    "ax = grid[2]\n",
+    "ax.plot([x1, x2], [y1, y2], \".\")\n",
+    "el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)\n",
+    "ax.add_artist(el)\n",
+    "ax.annotate(\"\",\n",
+    "            xy=(x1, y1), xycoords='data',\n",
+    "            xytext=(x2, y2), textcoords='data',\n",
+    "            arrowprops=dict(arrowstyle=\"-\", #linestyle=\"dashed\",\n",
+    "                            color=\"0.5\",\n",
+    "                            patchB=el,\n",
+    "                            shrinkB=5,\n",
+    "                            connectionstyle=\"arc3,rad=0.3\",\n",
+    "                            ),\n",
+    "            )\n",
+    "\n",
+    "add_at(ax, \"shrink\", loc=2)\n",
+    "\n",
+    "\n",
+    "ax = grid[3]\n",
+    "ax.plot([x1, x2], [y1, y2], \".\")\n",
+    "el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)\n",
+    "ax.add_artist(el)\n",
+    "ax.annotate(\"\",\n",
+    "            xy=(x1, y1), xycoords='data',\n",
+    "            xytext=(x2, y2), textcoords='data',\n",
+    "            arrowprops=dict(arrowstyle=\"fancy\", #linestyle=\"dashed\",\n",
+    "                            color=\"0.5\",\n",
+    "                            patchB=el,\n",
+    "                            shrinkB=5,\n",
+    "                            connectionstyle=\"arc3,rad=0.3\",\n",
+    "                            ),\n",
+    "            )\n",
+    "\n",
+    "add_at(ax, \"mutate\", loc=2)\n",
+    "\n",
+    "grid[0].set_xlim(0, 1)\n",
+    "grid[0].set_ylim(0, 1)\n",
+    "grid[0].axis[\"bottom\"].toggle(ticklabels=False)\n",
+    "grid[0].axis[\"left\"].toggle(ticklabels=False)\n",
+    "fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95)\n",
+    "\n",
+    "plt.draw()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "字典中，`connectionstyle` 参数控制路径的风格：\n",
+    "\n",
+    "Name | Attr\n",
+    "----|----\n",
+    "angle|\tangleA=90,angleB=0,rad=0.0\n",
+    "angle3|\tangleA=90,angleB=0\n",
+    "arc|\tangleA=0,angleB=0,armA=None,armB=None,rad=0.0\n",
+    "arc3|\trad=0.0\n",
+    "bar|\tarmA=0.0,armB=0.0,fraction=0.3,angle=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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FePvtt5GZmYknnngCAoEArq6usLKywsyZM9G3b19ER0ejoqJCI/bKdN/Jyclatx8QEICY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\no4KCEEIIIRqjgoIQQgghGvt/FlbD4KMSO5AAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x3bdae80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as mpatches\n",
+    "\n",
+    "fig = plt.figure(1, figsize=(8,5))\n",
+    "fig.clf()\n",
+    "from mpl_toolkits.axes_grid.axes_grid import AxesGrid\n",
+    "from mpl_toolkits.axes_grid.anchored_artists import AnchoredText\n",
+    "\n",
+    "#from matplotlib.font_manager import FontProperties\n",
+    "\n",
+    "def add_at(ax, t, loc=2):\n",
+    "    fp = dict(size=8)\n",
+    "    _at = AnchoredText(t, loc=loc, prop=fp)\n",
+    "    ax.add_artist(_at)\n",
+    "    return _at\n",
+    "\n",
+    "\n",
+    "grid = AxesGrid(fig, 111, (3, 5), label_mode=\"1\", share_all=True)\n",
+    "\n",
+    "grid[0].set_autoscale_on(False)\n",
+    "\n",
+    "\n",
+    "x1, y1 = 0.3, 0.3\n",
+    "x2, y2 = 0.7, 0.7\n",
+    "\n",
+    "\n",
+    "def demo_con_style(ax, connectionstyle, label=None):\n",
+    "\n",
+    "    if label is None:\n",
+    "        label = connectionstyle\n",
+    "\n",
+    "    x1, y1 = 0.3, 0.2\n",
+    "    x2, y2 = 0.8, 0.6\n",
+    "\n",
+    "    ax.plot([x1, x2], [y1, y2], \".\")\n",
+    "    ax.annotate(\"\",\n",
+    "                xy=(x1, y1), xycoords='data',\n",
+    "                xytext=(x2, y2), textcoords='data',\n",
+    "                arrowprops=dict(arrowstyle=\"->\", #linestyle=\"dashed\",\n",
+    "                                color=\"0.5\",\n",
+    "                                shrinkA=5, shrinkB=5,\n",
+    "                                patchA=None,\n",
+    "                                patchB=None,\n",
+    "                                connectionstyle=connectionstyle,\n",
+    "                                ),\n",
+    "                )\n",
+    "\n",
+    "    add_at(ax, label, loc=2)\n",
+    "\n",
+    "column = grid.axes_column[0]\n",
+    "\n",
+    "demo_con_style(column[0], \"angle3,angleA=90,angleB=0\",\n",
+    "               label=\"angle3,\\nangleA=90,\\nangleB=0\")\n",
+    "demo_con_style(column[1], \"angle3,angleA=0,angleB=90\",\n",
+    "               label=\"angle3,\\nangleA=0,\\nangleB=90\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "column = grid.axes_column[1]\n",
+    "\n",
+    "demo_con_style(column[0], \"arc3,rad=0.\")\n",
+    "demo_con_style(column[1], \"arc3,rad=0.3\")\n",
+    "demo_con_style(column[2], \"arc3,rad=-0.3\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "column = grid.axes_column[2]\n",
+    "\n",
+    "demo_con_style(column[0], \"angle,angleA=-90,angleB=180,rad=0\",\n",
+    "               label=\"angle,\\nangleA=-90,\\nangleB=180,\\nrad=0\")\n",
+    "demo_con_style(column[1], \"angle,angleA=-90,angleB=180,rad=5\",\n",
+    "               label=\"angle,\\nangleA=-90,\\nangleB=180,\\nrad=5\")\n",
+    "demo_con_style(column[2], \"angle,angleA=-90,angleB=10,rad=5\",\n",
+    "               label=\"angle,\\nangleA=-90,\\nangleB=10,\\nrad=0\")\n",
+    "\n",
+    "\n",
+    "column = grid.axes_column[3]\n",
+    "\n",
+    "demo_con_style(column[0], \"arc,angleA=-90,angleB=0,armA=30,armB=30,rad=0\",\n",
+    "               label=\"arc,\\nangleA=-90,\\nangleB=0,\\narmA=30,\\narmB=30,\\nrad=0\")\n",
+    "demo_con_style(column[1], \"arc,angleA=-90,angleB=0,armA=30,armB=30,rad=5\",\n",
+    "               label=\"arc,\\nangleA=-90,\\nangleB=0,\\narmA=30,\\narmB=30,\\nrad=5\")\n",
+    "demo_con_style(column[2], \"arc,angleA=-90,angleB=0,armA=0,armB=40,rad=0\",\n",
+    "               label=\"arc,\\nangleA=-90,\\nangleB=0,\\narmA=0,\\narmB=40,\\nrad=0\")\n",
+    "\n",
+    "\n",
+    "column = grid.axes_column[4]\n",
+    "\n",
+    "demo_con_style(column[0], \"bar,fraction=0.3\",\n",
+    "               label=\"bar,\\nfraction=0.3\")\n",
+    "demo_con_style(column[1], \"bar,fraction=-0.3\",\n",
+    "               label=\"bar,\\nfraction=-0.3\")\n",
+    "demo_con_style(column[2], \"bar,angle=180,fraction=-0.2\",\n",
+    "               label=\"bar,\\nangle=180,\\nfraction=-0.2\")\n",
+    "\n",
+    "\n",
+    "#demo_con_style(column[1], \"arc3,rad=0.3\")\n",
+    "#demo_con_style(column[2], \"arc3,rad=-0.3\")\n",
+    "\n",
+    "\n",
+    "grid[0].set_xlim(0, 1)\n",
+    "grid[0].set_ylim(0, 1)\n",
+    "grid.axes_llc.axis[\"bottom\"].toggle(ticklabels=False)\n",
+    "grid.axes_llc.axis[\"left\"].toggle(ticklabels=False)\n",
+    "fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95)\n",
+    "\n",
+    "plt.draw()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`arrowstyle` 参数控制小箭头的风格：\n",
+    "\n",
+    "Name | Attrs\n",
+    "--- |---\n",
+    "`-`\t|None\n",
+    "`->`\t|head_length=0.4,head_width=0.2\n",
+    "`-[`\t|widthB=1.0,lengthB=0.2,angleB=None\n",
+    "&#166;`-`&#166;\t|widthA=1.0,widthB=1.0\n",
+    "`-`&#166;`>`\t|head_length=0.4,head_width=0.2\n",
+    "`<-`\t|head_length=0.4,head_width=0.2\n",
+    "`<->`\t|head_length=0.4,head_width=0.2\n",
+    "`<`&#166;`-`\t|head_length=0.4,head_width=0.2\n",
+    "`<`&#166;-&#166;`>`\t|head_length=0.4,head_width=0.2\n",
+    "`fancy`\t|head_length=0.4,head_width=0.4,tail_width=0.4\n",
+    "`simple`\t|head_length=0.5,head_width=0.5,tail_width=0.2\n",
+    "`wedge`\t|tail_width=0.3,shrink_factor=0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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aNQrf+c538Itf/AIPPPAAAEDTNAwaNAgPPvgg7rnnnrZ1ozv/XokIPB4Pqqur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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa555f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.patches as mpatches\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "styles = mpatches.ArrowStyle.get_styles()\n",
+    "\n",
+    "ncol=2\n",
+    "nrow = (len(styles)+1) // ncol\n",
+    "figheight = (nrow+0.5)\n",
+    "fig1 = plt.figure(1, (4.*ncol/1.5, figheight/1.5))\n",
+    "fontsize = 0.2 * 70\n",
+    "\n",
+    "\n",
+    "ax = fig1.add_axes([0, 0, 1, 1], frameon=False, aspect=1.)\n",
+    "\n",
+    "ax.set_xlim(0, 4*ncol)\n",
+    "ax.set_ylim(0, figheight)\n",
+    "\n",
+    "def to_texstring(s):\n",
+    "    s = s.replace(\"<\", r\"$<$\")\n",
+    "    s = s.replace(\">\", r\"$>$\")\n",
+    "    s = s.replace(\"|\", r\"$|$\")\n",
+    "    return s\n",
+    "\n",
+    "for i, (stylename, styleclass) in enumerate(sorted(styles.items())):\n",
+    "    x = 3.2 + (i//nrow)*4\n",
+    "    y = (figheight - 0.7 - i%nrow) # /figheight\n",
+    "    p = mpatches.Circle((x, y), 0.2, fc=\"w\")\n",
+    "    ax.add_patch(p)\n",
+    "\n",
+    "    ax.annotate(to_texstring(stylename), (x, y),\n",
+    "                (x-1.2, y),\n",
+    "                #xycoords=\"figure fraction\", textcoords=\"figure fraction\",\n",
+    "                ha=\"right\", va=\"center\",\n",
+    "                size=fontsize,\n",
+    "                arrowprops=dict(arrowstyle=stylename,\n",
+    "                                patchB=p,\n",
+    "                                shrinkA=5,\n",
+    "                                shrinkB=5,\n",
+    "                                fc=\"w\", ec=\"k\",\n",
+    "                                connectionstyle=\"arc3,rad=-0.05\",\n",
+    "                                ),\n",
+    "                bbox=dict(boxstyle=\"square\", fc=\"w\"))\n",
+    "\n",
+    "ax.xaxis.set_visible(False)\n",
+    "ax.yaxis.set_visible(False)\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.draw()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/06. matplotlib/06.07 legend.ipynb b/06-matplotlib/06.07-legend.ipynb
similarity index 99%
rename from 06. matplotlib/06.07 legend.ipynb
rename to 06-matplotlib/06.07-legend.ipynb
index 2b0417e3..37fea74d 100644
--- a/06. matplotlib/06.07 legend.ipynb	
+++ b/06-matplotlib/06.07-legend.ipynb
@@ -1,514 +1,514 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 标签"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib as mpl\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`legend()` 函数被用来添加图像的标签，其主要相关的属性有：\n",
-    "\n",
-    "- legend entry - 一个 legend 包含一个或多个 entry，一个 entry 对应一个 key 和一个 label \n",
-    "- legend key - marker 的标记\n",
-    "- legend label - key 的说明\n",
-    "- legend handle - 一个 entry 在图上对应的对象"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 使用 legend"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "调用 `legend()` 会自动获取当前的 `Axes` 对象，并且得到这些 handles 和 labels，相当于：\n",
-    "\n",
-    "    handles, labels = ax.get_legend_handles_labels()\n",
-    "    ax.legend(handles, labels)\n",
-    "\n",
-    "我们可以在函数中指定 `handles` 的参数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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7AqHS84F6Lx6KHbHYupVKzwvqHRLFjjao9Hyj3ouBYkdkqPT8o97Li2LHGaj0YqLe84ti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\nAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa1a1940>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "line_up, = plt.plot([1,2,3], label='Line 2')\n",
-    "line_down, = plt.plot([3,2,1], label='Line 1')\n",
-    "plt.legend(handles=[line_up, line_down])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以将 labels 作为参数输入 `legend` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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92TGbJQ0zs5GlbARQCwPVO5WOJCl6jd3MxkraIOnb2Um++/7HJN3m7s9nb/+vpAXuvrXf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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa2f03c8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "line_up, = plt.plot([1,2,3])\n",
-    "line_down, = plt.plot([3,2,1])\n",
-    "plt.legend([line_up, line_down], ['Line Up', 'Line Down'])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 产生特殊形状的 marker key"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "有时我们可以产生一些特殊形状的 marker：\n",
-    "\n",
-    "块状："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa2f0a90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.patches as mpatches\n",
-    "\n",
-    "red_patch = mpatches.Patch(color='red', label='The red data')\n",
-    "plt.legend(handles=[red_patch])\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "点线组合："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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7RIwGngEuALYDvwMWZebGbn3+GtiQmXsiYh7QlplzeoyTtc4lSTpaRJCZMdDvq2flPhvo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\nEFOmWk0euQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa7d23c8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.lines as mlines\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "blue_line = mlines.Line2D([], [], color='blue', marker='*',\n",
-    "                          markersize=15, label='Blue stars')\n",
-    "plt.legend(handles=[blue_line])\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 指定 legend 的位置"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`bbox_to_anchor` 关键词可以指定 `legend` 放置的位置，例如放到图像的右上角："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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OX6hRXZxbQTJYUKr9IhYUdFi715VQK0iDLC0ofJYXghTFdSWcWwGaQ6EgKHFd7U6tICkU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\nVsmYJAjFAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb7f8630>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot([1,2,3], label=\"test1\")\n",
-    "plt.plot([3,2,1], label=\"test2\")\n",
-    "plt.legend(bbox_to_anchor=(1, 1),\n",
-    "           bbox_transform=plt.gcf().transFigure)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "更复杂的用法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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OnMjChQsZM2ZM1n3ee++9LF26lOeff57q6uo+zU/KRLH+a0CI/yucTiWtvZfT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\nEZtHunC15QAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb7d70b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.subplot(211)\n",
-    "plt.plot([1,2,3], label=\"test1\")\n",
-    "plt.plot([3,2,1], label=\"test2\")\n",
-    "# Place a legend above this legend, expanding itself to\n",
-    "# fully use the given bounding box.\n",
-    "plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\n",
-    "           ncol=2, mode=\"expand\", borderaxespad=0.)\n",
-    "\n",
-    "plt.subplot(223)\n",
-    "plt.plot([1,2,3], label=\"test1\")\n",
-    "plt.plot([3,2,1], label=\"test2\")\n",
-    "# Place a legend to the right of this smaller figure.\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 同一个 Axes 中的多个 legend"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以这样添加多个 `legend`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb8e9588>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "line1, = plt.plot([1,2,3], label=\"Line 1\", linestyle='--')\n",
-    "line2, = plt.plot([3,2,1], label=\"Line 2\", linewidth=4)\n",
-    "\n",
-    "# Create a legend for the first line.\n",
-    "first_legend = plt.legend(handles=[line1], loc=1)\n",
-    "\n",
-    "# Add the legend manually to the current Axes.\n",
-    "ax = plt.gca().add_artist(first_legend)\n",
-    "\n",
-    "# Create another legend for the second line.\n",
-    "plt.legend(handles=[line2], loc=4)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "其中 `loc` 参数可以取 0-10 或者 字符串，表示放置的位置：\n",
-    "\n",
-    "loc string | loc code\n",
-    "---|---\n",
-    "`'best'  `|          0\n",
-    "`'upper right' `   | 1\n",
-    "`'upper left'  `    |2\n",
-    "`'lower left'  `    |3\n",
-    "`'lower right' `    |4\n",
-    "`'right'       `   | 5\n",
-    "`'center left' `    |6\n",
-    "`'center right'`   | 7\n",
-    "`'lower center'`  |  8\n",
-    "`'upper center'` |   9\n",
-    "`'center'`          |10"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 更多用法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "多个 `handle` 可以通过括号组合在一个 entry 中："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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0DvzMzPaY2X8ZguuJiEicUf0dNLPHgfcmObTM3R9K8xrT3P3PZnYB8LiZPe/u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\nBe7/A4OqIDdoxkFmAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb8f56d8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from numpy.random import randn\n",
-    "\n",
-    "z = randn(10)\n",
-    "\n",
-    "red_dot, = plt.plot(z, \"ro\", markersize=15)\n",
-    "# Put a white cross over some of the data.\n",
-    "white_cross, = plt.plot(z[:5], \"w+\", markeredgewidth=3, markersize=15)\n",
-    "\n",
-    "plt.legend([red_dot, (red_dot, white_cross)], [\"Attr A\", \"Attr A+B\"])\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "自定义 `handle`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Z7pJUIMNdkgpkuEtSgQx3SSqQ4S5JBTLcJalAhrskFchwl6QCGe6SVCDDXZIKZLhLUoEM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\nIMNdkgr0v4W1q8xEcgEdAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb8609e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.patches as mpatches\n",
-    "\n",
-    "class AnyObject(object):\n",
-    "    pass\n",
-    "\n",
-    "class AnyObjectHandler(object):\n",
-    "    def legend_artist(self, legend, orig_handle, fontsize, handlebox):\n",
-    "        x0, y0 = handlebox.xdescent, handlebox.ydescent\n",
-    "        width, height = handlebox.width, handlebox.height\n",
-    "        patch = mpatches.Rectangle([x0, y0], width, height, facecolor='red',\n",
-    "                                   edgecolor='black', hatch='xx', lw=3,\n",
-    "                                   transform=handlebox.get_transform())\n",
-    "        handlebox.add_artist(patch)\n",
-    "        return patch\n",
-    "\n",
-    "plt.legend([AnyObject()], ['My first handler'],\n",
-    "           handler_map={AnyObject: AnyObjectHandler()})\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "椭圆："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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ZWE677saYXGPMNf4tUcpkDPzpT57ZCV9BjI7eJQD6ZUCvHQUz0dEwdqyr9UhJ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\nRMLQ/wcmM4RiyDPR6wAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb87d1d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from matplotlib.legend_handler import HandlerPatch\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.patches as mpatches\n",
-    "\n",
-    "\n",
-    "class HandlerEllipse(HandlerPatch):\n",
-    "    def create_artists(self, legend, orig_handle,\n",
-    "                       xdescent, ydescent, width, height, fontsize, trans):\n",
-    "        center = 0.5 * width - 0.5 * xdescent, 0.5 * height - 0.5 * ydescent\n",
-    "        p = mpatches.Ellipse(xy=center, width=width + xdescent,\n",
-    "                             height=height + ydescent)\n",
-    "        self.update_prop(p, orig_handle, legend)\n",
-    "        p.set_transform(trans)\n",
-    "        return [p]\n",
-    "\n",
-    "\n",
-    "c = mpatches.Circle((0.5, 0.5), 0.25, facecolor=\"green\",\n",
-    "                    edgecolor=\"red\", linewidth=3)\n",
-    "plt.gca().add_patch(c)\n",
-    "\n",
-    "plt.legend([c], [\"An ellipse, not a rectangle\"],\n",
-    "           handler_map={mpatches.Circle: HandlerEllipse()})\n",
-    "\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 标签"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`legend()` 函数被用来添加图像的标签，其主要相关的属性有：\n",
+    "\n",
+    "- legend entry - 一个 legend 包含一个或多个 entry，一个 entry 对应一个 key 和一个 label \n",
+    "- legend key - marker 的标记\n",
+    "- legend label - key 的说明\n",
+    "- legend handle - 一个 entry 在图上对应的对象"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 使用 legend"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "调用 `legend()` 会自动获取当前的 `Axes` 对象，并且得到这些 handles 和 labels，相当于：\n",
+    "\n",
+    "    handles, labels = ax.get_legend_handles_labels()\n",
+    "    ax.legend(handles, labels)\n",
+    "\n",
+    "我们可以在函数中指定 `handles` 的参数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAEACAYAAACnJV25AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa1a1940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "line_up, = plt.plot([1,2,3], label='Line 2')\n",
+    "line_down, = plt.plot([3,2,1], label='Line 1')\n",
+    "plt.legend(handles=[line_up, line_down])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以将 labels 作为参数输入 `legend` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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92TGbJQ0zs5GlbARQCwPVO5WOJCl6jd3MxkraIOnb2Um++/7HJN3m7s9nb/+vpAXuvrXf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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa2f03c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "line_up, = plt.plot([1,2,3])\n",
+    "line_down, = plt.plot([3,2,1])\n",
+    "plt.legend([line_up, line_down], ['Line Up', 'Line Down'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 产生特殊形状的 marker key"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "有时我们可以产生一些特殊形状的 marker：\n",
+    "\n",
+    "块状："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa2f0a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.patches as mpatches\n",
+    "\n",
+    "red_patch = mpatches.Patch(color='red', label='The red data')\n",
+    "plt.legend(handles=[red_patch])\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "点线组合："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7RIwGngEuALYDvwMWZebGbn3+GtiQmXsiYh7QlplzeoyTtc4lSTpaRJCZMdDvq2flPhvo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\nEFOmWk0euQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa7d23c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.lines as mlines\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "blue_line = mlines.Line2D([], [], color='blue', marker='*',\n",
+    "                          markersize=15, label='Blue stars')\n",
+    "plt.legend(handles=[blue_line])\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 指定 legend 的位置"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`bbox_to_anchor` 关键词可以指定 `legend` 放置的位置，例如放到图像的右上角："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OX6hRXZxbQTJYUKr9IhYUdFi715VQK0iDLC0ofJYXghTFdSWcWwGaQ6EgKHFd7U6tICkU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\nVsmYJAjFAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb7f8630>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([1,2,3], label=\"test1\")\n",
+    "plt.plot([3,2,1], label=\"test2\")\n",
+    "plt.legend(bbox_to_anchor=(1, 1),\n",
+    "           bbox_transform=plt.gcf().transFigure)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "更复杂的用法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OnMjChQsZM2ZM1n3ee++9LF26lOeff57q6uo+zU/KRLH+a0CI/yucTiWtvZfT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\nEZtHunC15QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb7d70b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplot(211)\n",
+    "plt.plot([1,2,3], label=\"test1\")\n",
+    "plt.plot([3,2,1], label=\"test2\")\n",
+    "# Place a legend above this legend, expanding itself to\n",
+    "# fully use the given bounding box.\n",
+    "plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\n",
+    "           ncol=2, mode=\"expand\", borderaxespad=0.)\n",
+    "\n",
+    "plt.subplot(223)\n",
+    "plt.plot([1,2,3], label=\"test1\")\n",
+    "plt.plot([3,2,1], label=\"test2\")\n",
+    "# Place a legend to the right of this smaller figure.\n",
+    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 同一个 Axes 中的多个 legend"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以这样添加多个 `legend`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb8e9588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "line1, = plt.plot([1,2,3], label=\"Line 1\", linestyle='--')\n",
+    "line2, = plt.plot([3,2,1], label=\"Line 2\", linewidth=4)\n",
+    "\n",
+    "# Create a legend for the first line.\n",
+    "first_legend = plt.legend(handles=[line1], loc=1)\n",
+    "\n",
+    "# Add the legend manually to the current Axes.\n",
+    "ax = plt.gca().add_artist(first_legend)\n",
+    "\n",
+    "# Create another legend for the second line.\n",
+    "plt.legend(handles=[line2], loc=4)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "其中 `loc` 参数可以取 0-10 或者 字符串，表示放置的位置：\n",
+    "\n",
+    "loc string | loc code\n",
+    "---|---\n",
+    "`'best'  `|          0\n",
+    "`'upper right' `   | 1\n",
+    "`'upper left'  `    |2\n",
+    "`'lower left'  `    |3\n",
+    "`'lower right' `    |4\n",
+    "`'right'       `   | 5\n",
+    "`'center left' `    |6\n",
+    "`'center right'`   | 7\n",
+    "`'lower center'`  |  8\n",
+    "`'upper center'` |   9\n",
+    "`'center'`          |10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 更多用法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "多个 `handle` 可以通过括号组合在一个 entry 中："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0DvzMzPaY2X8ZguuJiEicUf0dNLPHgfcmObTM3R9K8xrT3P3PZnYB8LiZPe/u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\nBe7/A4OqIDdoxkFmAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb8f56d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from numpy.random import randn\n",
+    "\n",
+    "z = randn(10)\n",
+    "\n",
+    "red_dot, = plt.plot(z, \"ro\", markersize=15)\n",
+    "# Put a white cross over some of the data.\n",
+    "white_cross, = plt.plot(z[:5], \"w+\", markeredgewidth=3, markersize=15)\n",
+    "\n",
+    "plt.legend([red_dot, (red_dot, white_cross)], [\"Attr A\", \"Attr A+B\"])\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "自定义 `handle`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIMNdkgr0v4W1q8xEcgEdAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb8609e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as mpatches\n",
+    "\n",
+    "class AnyObject(object):\n",
+    "    pass\n",
+    "\n",
+    "class AnyObjectHandler(object):\n",
+    "    def legend_artist(self, legend, orig_handle, fontsize, handlebox):\n",
+    "        x0, y0 = handlebox.xdescent, handlebox.ydescent\n",
+    "        width, height = handlebox.width, handlebox.height\n",
+    "        patch = mpatches.Rectangle([x0, y0], width, height, facecolor='red',\n",
+    "                                   edgecolor='black', hatch='xx', lw=3,\n",
+    "                                   transform=handlebox.get_transform())\n",
+    "        handlebox.add_artist(patch)\n",
+    "        return patch\n",
+    "\n",
+    "plt.legend([AnyObject()], ['My first handler'],\n",
+    "           handler_map={AnyObject: AnyObjectHandler()})\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "椭圆："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZWE677saYXGPMNf4tUcpkDPzpT57ZCV9BjI7eJQD6ZUCvHQUz0dEwdqyr9UhJ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\nRMLQ/wcmM4RiyDPR6wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb87d1d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.legend_handler import HandlerPatch\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as mpatches\n",
+    "\n",
+    "\n",
+    "class HandlerEllipse(HandlerPatch):\n",
+    "    def create_artists(self, legend, orig_handle,\n",
+    "                       xdescent, ydescent, width, height, fontsize, trans):\n",
+    "        center = 0.5 * width - 0.5 * xdescent, 0.5 * height - 0.5 * ydescent\n",
+    "        p = mpatches.Ellipse(xy=center, width=width + xdescent,\n",
+    "                             height=height + ydescent)\n",
+    "        self.update_prop(p, orig_handle, legend)\n",
+    "        p.set_transform(trans)\n",
+    "        return [p]\n",
+    "\n",
+    "\n",
+    "c = mpatches.Circle((0.5, 0.5), 0.25, facecolor=\"green\",\n",
+    "                    edgecolor=\"red\", linewidth=3)\n",
+    "plt.gca().add_patch(c)\n",
+    "\n",
+    "plt.legend([c], [\"An ellipse, not a rectangle\"],\n",
+    "           handler_map={mpatches.Circle: HandlerEllipse()})\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/06. matplotlib/06.08 figures, subplots, axes and ticks.ipynb b/06-matplotlib/06.08-figures,-subplots,-axes-and-ticks.ipynb
similarity index 99%
rename from 06. matplotlib/06.08 figures, subplots, axes and ticks.ipynb
rename to 06-matplotlib/06.08-figures,-subplots,-axes-and-ticks.ipynb
index a46a7c9a..b9523d21 100644
--- a/06. matplotlib/06.08 figures, subplots, axes and ticks.ipynb	
+++ b/06-matplotlib/06.08-figures,-subplots,-axes-and-ticks.ipynb
@@ -1,298 +1,298 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# figures, subplots, axes 和 ticks 对象"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## figures, axes 和 ticks 的关系"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这些对象的关系可以用下面的图来表示：\n",
-    "\n",
-    "示例图像：\n",
-    "\n",
-    "<img src=\"./artists_figure.png\" width = \"600\" height = \"400\" alt=\"图1\" align=left />"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "具体结构：\n",
-    "\n",
-    "<img src=\"./artists_tree.png\" width = \"600\" height = \"400\" alt=\"图2\" align=left />"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## figure 对象"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`figure` 对象是最外层的绘图单位，默认是以 `1` 开始编号（**MATLAB** 风格，`Figure 1, Figure 2, ...`），可以用 `plt.figure()` 产生一幅图像，除了默认参数外，可以指定的参数有：\n",
-    "\n",
-    "- `num` - 编号\n",
-    "- `figsize` - 图像大小\n",
-    "- `dpi` - 分辨率\n",
-    "- `facecolor` - 背景色\n",
-    "- `edgecolor` - 边界颜色\n",
-    "- `frameon` - 边框\n",
-    "\n",
-    "这些属性也可以通过 `Figure` 对象的 `set_xxx` 方法来改变。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## subplot 和 axes 对象"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### subplot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`subplot` 主要是使用网格排列子图："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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6REQCMXSJiARi6BIRCcTQJSISiKFLRCQQQ5eISCCGLhGRQAxdIiKBGLpERAIxdImIBAoY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\nAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x3bc9860>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%pylab inline\n",
-    "\n",
-    "subplot(2,1,1)\n",
-    "xticks([]), yticks([])\n",
-    "text(0.5,0.5, 'subplot(2,1,1)',ha='center',va='center',size=24,alpha=.5)\n",
-    "\n",
-    "subplot(2,1,2)\n",
-    "xticks([]), yticks([])\n",
-    "text(0.5,0.5, 'subplot(2,1,2)',ha='center',va='center',size=24,alpha=.5)\n",
-    "\n",
-    "show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "更高级的可以用 `gridspec` 来绘图："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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MzEw6OztfdHfmxHw/tj//+c958803X3Q35Efquc3pNjQ0EAqFWL58Odu2bQN4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\ntAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa0753c8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.gridspec as gridspec\n",
-    "\n",
-    "G = gridspec.GridSpec(3, 3)\n",
-    "\n",
-    "axes_1 = subplot(G[0, :])\n",
-    "xticks([]), yticks([])\n",
-    "text(0.5,0.5, 'Axes 1',ha='center',va='center',size=24,alpha=.5)\n",
-    "\n",
-    "axes_2 = subplot(G[1,:-1])\n",
-    "xticks([]), yticks([])\n",
-    "text(0.5,0.5, 'Axes 2',ha='center',va='center',size=24,alpha=.5)\n",
-    "\n",
-    "axes_3 = subplot(G[1:, -1])\n",
-    "xticks([]), yticks([])\n",
-    "text(0.5,0.5, 'Axes 3',ha='center',va='center',size=24,alpha=.5)\n",
-    "\n",
-    "axes_4 = subplot(G[-1,0])\n",
-    "xticks([]), yticks([])\n",
-    "text(0.5,0.5, 'Axes 4',ha='center',va='center',size=24,alpha=.5)\n",
-    "\n",
-    "axes_5 = subplot(G[-1,-2])\n",
-    "xticks([]), yticks([])\n",
-    "text(0.5,0.5, 'Axes 5',ha='center',va='center',size=24,alpha=.5)\n",
-    "\n",
-    "show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## axes 对象"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`subplot` 返回的是 `Axes` 对象，但是 `Axes` 对象相对于 `subplot` 返回的对象来说要更自由一点。`Axes` 对象可以放置在图像中的任意位置："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa00b0b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "axes([0.1,0.1,.8,.8])\n",
-    "xticks([]), yticks([])\n",
-    "text(0.6,0.6, 'axes([0.1,0.1,.8,.8])',ha='center',va='center',size=20,alpha=.5)\n",
-    "\n",
-    "axes([0.2,0.2,.3,.3])\n",
-    "xticks([]), yticks([])\n",
-    "text(0.5,0.5, 'axes([0.2,0.2,.3,.3])',ha='center',va='center',size=16,alpha=.5)\n",
-    "\n",
-    "show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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+b+3t3r0bPz8/p2b9TaQTJ05QX1/Prl27nmq9vb29fPbZZ2zcuJGEhIQJKfPQoUP09PSQ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\nwlkIIVRIwlkIIVRIwlkIIVTo/wC1F2xbdiTycgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa6ff128>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "axes([0.1,0.1,.5,.5])\n",
-    "xticks([]), yticks([])\n",
-    "text(0.1,0.1, 'axes([0.1,0.1,.8,.8])',ha='left',va='center',size=16,alpha=.5)\n",
-    "\n",
-    "axes([0.2,0.2,.5,.5])\n",
-    "xticks([]), yticks([])\n",
-    "text(0.1,0.1, 'axes([0.2,0.2,.5,.5])',ha='left',va='center',size=16,alpha=.5)\n",
-    "\n",
-    "axes([0.3,0.3,.5,.5])\n",
-    "xticks([]), yticks([])\n",
-    "text(0.1,0.1, 'axes([0.3,0.3,.5,.5])',ha='left',va='center',size=16,alpha=.5)\n",
-    "\n",
-    "axes([0.4,0.4,.5,.5])\n",
-    "xticks([]), yticks([])\n",
-    "text(0.1,0.1, 'axes([0.4,0.4,.5,.5])',ha='left',va='center',size=16,alpha=.5)\n",
-    "\n",
-    "show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "后面的 `Axes` 对象会覆盖前面的内容。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## ticks 对象"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "ticks 用来注释轴的内容，我们可以通过控制它的属性来决定在哪里显示轴、轴的内容是什么等等。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# figures, subplots, axes 和 ticks 对象"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## figures, axes 和 ticks 的关系"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这些对象的关系可以用下面的图来表示：\n",
+    "\n",
+    "示例图像：\n",
+    "\n",
+    "<img src=\"./artists_figure.png\" width = \"600\" height = \"400\" alt=\"图1\" align=left />"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "具体结构：\n",
+    "\n",
+    "<img src=\"./artists_tree.png\" width = \"600\" height = \"400\" alt=\"图2\" align=left />"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## figure 对象"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`figure` 对象是最外层的绘图单位，默认是以 `1` 开始编号（**MATLAB** 风格，`Figure 1, Figure 2, ...`），可以用 `plt.figure()` 产生一幅图像，除了默认参数外，可以指定的参数有：\n",
+    "\n",
+    "- `num` - 编号\n",
+    "- `figsize` - 图像大小\n",
+    "- `dpi` - 分辨率\n",
+    "- `facecolor` - 背景色\n",
+    "- `edgecolor` - 边界颜色\n",
+    "- `frameon` - 边框\n",
+    "\n",
+    "这些属性也可以通过 `Figure` 对象的 `set_xxx` 方法来改变。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## subplot 和 axes 对象"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### subplot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`subplot` 主要是使用网格排列子图："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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6REQCMXSJiARi6BIRCcTQJSISiKFLRCQQQ5eISCCGLhGRQAxdIiKBGLpERAIxdImIBAoY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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x3bc9860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%pylab inline\n",
+    "\n",
+    "subplot(2,1,1)\n",
+    "xticks([]), yticks([])\n",
+    "text(0.5,0.5, 'subplot(2,1,1)',ha='center',va='center',size=24,alpha=.5)\n",
+    "\n",
+    "subplot(2,1,2)\n",
+    "xticks([]), yticks([])\n",
+    "text(0.5,0.5, 'subplot(2,1,2)',ha='center',va='center',size=24,alpha=.5)\n",
+    "\n",
+    "show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "更高级的可以用 `gridspec` 来绘图："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MzEw6OztfdHfmxHw/tj//+c958803X3Q35Efquc3pNjQ0EAqFWL58Odu2bQN4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\ntAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa0753c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.gridspec as gridspec\n",
+    "\n",
+    "G = gridspec.GridSpec(3, 3)\n",
+    "\n",
+    "axes_1 = subplot(G[0, :])\n",
+    "xticks([]), yticks([])\n",
+    "text(0.5,0.5, 'Axes 1',ha='center',va='center',size=24,alpha=.5)\n",
+    "\n",
+    "axes_2 = subplot(G[1,:-1])\n",
+    "xticks([]), yticks([])\n",
+    "text(0.5,0.5, 'Axes 2',ha='center',va='center',size=24,alpha=.5)\n",
+    "\n",
+    "axes_3 = subplot(G[1:, -1])\n",
+    "xticks([]), yticks([])\n",
+    "text(0.5,0.5, 'Axes 3',ha='center',va='center',size=24,alpha=.5)\n",
+    "\n",
+    "axes_4 = subplot(G[-1,0])\n",
+    "xticks([]), yticks([])\n",
+    "text(0.5,0.5, 'Axes 4',ha='center',va='center',size=24,alpha=.5)\n",
+    "\n",
+    "axes_5 = subplot(G[-1,-2])\n",
+    "xticks([]), yticks([])\n",
+    "text(0.5,0.5, 'Axes 5',ha='center',va='center',size=24,alpha=.5)\n",
+    "\n",
+    "show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## axes 对象"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`subplot` 返回的是 `Axes` 对象，但是 `Axes` 对象相对于 `subplot` 返回的对象来说要更自由一点。`Axes` 对象可以放置在图像中的任意位置："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa00b0b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axes([0.1,0.1,.8,.8])\n",
+    "xticks([]), yticks([])\n",
+    "text(0.6,0.6, 'axes([0.1,0.1,.8,.8])',ha='center',va='center',size=20,alpha=.5)\n",
+    "\n",
+    "axes([0.2,0.2,.3,.3])\n",
+    "xticks([]), yticks([])\n",
+    "text(0.5,0.5, 'axes([0.2,0.2,.3,.3])',ha='center',va='center',size=16,alpha=.5)\n",
+    "\n",
+    "show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+b+3t3r0bPz8/p2b9TaQTJ05QX1/Prl27nmq9vb29fPbZZ2zcuJGEhIQJKfPQoUP09PSQ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\nwlkIIVRIwlkIIVRIwlkIIVTo/wC1F2xbdiTycgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa6ff128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axes([0.1,0.1,.5,.5])\n",
+    "xticks([]), yticks([])\n",
+    "text(0.1,0.1, 'axes([0.1,0.1,.8,.8])',ha='left',va='center',size=16,alpha=.5)\n",
+    "\n",
+    "axes([0.2,0.2,.5,.5])\n",
+    "xticks([]), yticks([])\n",
+    "text(0.1,0.1, 'axes([0.2,0.2,.5,.5])',ha='left',va='center',size=16,alpha=.5)\n",
+    "\n",
+    "axes([0.3,0.3,.5,.5])\n",
+    "xticks([]), yticks([])\n",
+    "text(0.1,0.1, 'axes([0.3,0.3,.5,.5])',ha='left',va='center',size=16,alpha=.5)\n",
+    "\n",
+    "axes([0.4,0.4,.5,.5])\n",
+    "xticks([]), yticks([])\n",
+    "text(0.1,0.1, 'axes([0.4,0.4,.5,.5])',ha='left',va='center',size=16,alpha=.5)\n",
+    "\n",
+    "show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "后面的 `Axes` 对象会覆盖前面的内容。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ticks 对象"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ticks 用来注释轴的内容，我们可以通过控制它的属性来决定在哪里显示轴、轴的内容是什么等等。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/06. matplotlib/06.09 do not trust the defaults.ipynb b/06-matplotlib/06.09-do-not-trust-the-defaults.ipynb
similarity index 99%
rename from 06. matplotlib/06.09 do not trust the defaults.ipynb
rename to 06-matplotlib/06.09-do-not-trust-the-defaults.ipynb
index a75e964a..2e050f92 100644
--- a/06. matplotlib/06.09 do not trust the defaults.ipynb	
+++ b/06-matplotlib/06.09-do-not-trust-the-defaults.ipynb
@@ -1,816 +1,816 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 不要迷信默认设置"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "导入相关的包："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "生成三角函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "x = np.linspace(-np.pi, np.pi)\n",
-    "c, s = np.cos(x), np.sin(x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 默认绘图"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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wRcLjw01HcYvVq6FsWahVy3QSs2ZEz6BnnZ5kz5bddBSPkyuXNfXW7qdPeRsp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\nF0IIH/f/QH0wXEQipAoAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9fe37f0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "# 画图\n",
-    "p = plt.plot(x,c)\n",
-    "p = plt.plot(x,s)\n",
-    "\n",
-    "# 在脚本中需要加上这句才会显示图像\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "默认效果如图所示，我们可以修改默认的属性来得到更漂亮的结果。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "图像以 `Figure #` 为窗口标题，并且数字从 1 开始，`figure()` 函数的主要参数如下：\n",
-    "\n",
-    "参数 | 默认值 | 描述\n",
-    "---|---|---\n",
-    "`num`|`1`| 图号\n",
-    "`figsize`|`figure.figsize`| 图大小（宽，高）（单位英寸）\n",
-    "`dpi`|`figure.dpi`| 分辨率（每英寸所打印的点数）\n",
-    "`facecolor`|`figure.facecolor`| 背景颜色\n",
-    "`edgecolor`|`figure.edgecolor`| 边界颜色\n",
-    "`frameon` |`True`| 是否显示图框架"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# 设置图像大小\n",
-    "f = plt.figure(figsize=(10,6), dpi=80)\n",
-    "\n",
-    "# 画图\n",
-    "p = plt.plot(x,c)\n",
-    "p = plt.plot(x,s)\n",
-    "\n",
-    "# 在脚本中需要加上这句才会显示图像\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 设置线条颜色，粗细，类型"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "首先，我们使用 figure() 函数来创建一幅新图像，并且指定它的大小，使得长宽比更合适。\n",
-    "\n",
-    "然后，我们使用 `color, linewidth, linestyle` 参数，指定曲线的颜色，粗细，类型："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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JrH9RRMVGs9au1cHRkXHMMXbBABg+HP78020+oiJLJIwGDYINGyyO3ewk5DZt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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa156860>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 设置图像大小\n",
-    "f = plt.figure(figsize=(10,6), dpi=80)\n",
-    "\n",
-    "# 画图，指定颜色，线宽，类型\n",
-    "p = plt.plot(x, c, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n",
-    "p = plt.plot(x, s, color=\"red\",  linewidth=2.5, linestyle=\"-\")\n",
-    "\n",
-    "# 在脚本中需要加上这句才会显示图像\n",
-    "# plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "也可以像 **Matlab** 中一样使用格式字符来修改参数：\n",
-    "\n",
-    "表示颜色的字符参数有：\n",
-    "\n",
-    "字符 | 颜色\n",
-    "-- | -- \n",
-    "`‘b’`|\t蓝色，blue\n",
-    "`‘g’`|\t绿色，green\n",
-    "`‘r’`|\t红色，red\n",
-    "`‘c’`|\t青色，cyan\n",
-    "`‘m’`|\t品红，magenta\n",
-    "`‘y’`|\t黄色，yellow\n",
-    "`‘k’`|\t黑色，black\n",
-    "`‘w’`|\t白色，white\n",
-    "\n",
-    "表示类型的字符参数有：\n",
-    "\n",
-    "字符|类型 | 字符|类型\n",
-    "---|--- | --- | ---\n",
-    "`  '-'\t`| 实线 | `'--'`|\t虚线\n",
-    "`'-.'`|\t虚点线 | `':'`|\t点线\n",
-    "`'.'`|\t点 | `','`| 像素点\n",
-    "`'o'`\t|圆点 | `'v'`|\t下三角点\n",
-    "`'^'`|\t上三角点 | `'<'`|\t左三角点\n",
-    "`'>'`|\t右三角点 | `'1'`|\t下三叉点\n",
-    "`'2'`|\t上三叉点 | `'3'`|\t左三叉点\n",
-    "`'4'`|\t右三叉点 | `'s'`|\t正方点\n",
-    "`'p'`\t| 五角点 | `'*'`|\t星形点\n",
-    "`'h'`|\t六边形点1 | `'H'`|\t六边形点2 \n",
-    "`'+'`|\t加号点 | `'x'`|\t乘号点\n",
-    "`'D'`|\t实心菱形点 | `'d'`|\t瘦菱形点 \n",
-    "`'_'`|\t横线点 | |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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JrH9RRMVGs9au1cHRkXHMMXbBABg+HP78020+oiJLJIwGDYINGyyO3ewk5DZt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\nH9q1swILbMRRBZZIyhUvDiNG2NFjW7fatOG2o18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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa1566d8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 设置图像大小\n",
-    "f = plt.figure(figsize=(10,6), dpi=80)\n",
-    "\n",
-    "# 画图，指定颜色，线宽，类型\n",
-    "p = plt.plot(x, c, 'b-', \n",
-    "         x, s, 'r-', linewidth=2.5)\n",
-    "\n",
-    "# 在脚本中需要加上这句才会显示图像\n",
-    "# plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 设置横轴纵轴的显示区域"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们希望将坐标轴的显示区域放大一些，这样可以看到所有的点，可以使用 `plt` 中的 `xlim` 和 `ylim` 来设置："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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P2DdpYjYecdHvv7MfDAC88QZwzz1m4/Gha67hTQLAPr1JZRjEAypVAl5/nePp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\niIiIyCP/A3dzHy2lT8uxAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa6fd748>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 设置图像大小\n",
-    "p = plt.figure(figsize=(10,6), dpi=80)\n",
-    "\n",
-    "# 画图，指定颜色，线宽，类型\n",
-    "p = plt.plot(x, c, 'b-', \n",
-    "         x, s, 'r-', linewidth=2.5)\n",
-    "\n",
-    "########################################################################\n",
-    "\n",
-    "# 设置显示范围\n",
-    "p = plt.xlim(x.min() * 1.1, x.max() * 1.1)\n",
-    "p = plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
-    "\n",
-    "########################################################################\n",
-    "\n",
-    "# 在脚本中需要加上这句才会显示图像\n",
-    "# plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 设置刻度"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于三教函数来说，我们希望将 `x` 轴的刻度设为与 $\\pi$ 有关的点，可以使用 `plt` 中的 `xticks` 和 `yticks` 函数，将需要的刻度传入："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Wrw+6G3N3smMHULgwS7oqVgSWLFETV8eYNw944w2uP/4YGDfObDw+pOM5ES/q\n148JE8BNByVMDhK/xcCwYY5KmADOI27cmOtly4CffzYbj9jo9deBSpW4/uYbYNcus/EYop0mkXtw\n5Ag3GiIj2S1540bHvW4616pVvE0EsHnRtGlm4zHkwgXOpbt0iSc2u3cDqVKZjkpssWcPM+fYWM6l\n++UX0xH5hHaaRLykTRtrosDw4UqYHCM21jEtBu7m/vuBL7/k+vBh9vQUh3jyyZvn0i1ebDYeA7TT\nJJJAK1ZYhbAO3mhwpgkTOFQQYNt3h18fi4kBnnmGu0z33ce6vpw5TUcltjh7FnjsMd4iLVgQ2LYN\nCA01HZVXaadJJIni4qz5cmnSsK5JHCJ+i4GHHnJEi4G7SZaMJV0AL0Z06mQ2HrHRAw9Y3U537eIb\nCgdR0iSSAFOn8g0VALRrx9dOcYjevTkvB+CxXJo0ZuPxExUqWJepvv2WN+vEIT7/HHj4Ya47d+Yb\nC4fQ8ZzIXVy/zj5Mx48DOXIABw44pjWP/PUXuyFHRQElSgDr1umOfTx79/KEJjYWePllR5a4ONe0\naUDt2lx36gT06GE2Hi/S8ZxIEgwdyoQJAHr2VMLkKG3aMGEC+BdBCdNN8uUDGjXieskSJU2OUrMm\nULQo14MGWT8kg5x2mkTuIH7NY6FCwNatQVfzKLcTv8VA7drAlClm4/FT+h5xsNWrgRde4PrDD4FJ\nk8zG4yXaaRJJpC+/tI7rBwzQi4FjxMVxdATAFgN9+piNx4898ADQvj3XO3cGzeumJESZMsBbb3E9\neTKH+QY57TSJ3Ma+fUCBAqzXeOklHj+IQ8yYweMHgIWunsZEcksRERwtdPw4Ww/s369jbMc4cID9\nm2JigHLlgOXLA/4YWztNIonQrh0TJpeLu0ziEDduWC0GsmZlXZPcUZo0rPcDgJMngcGDzcYjNnr8\nceDTT7lesYIz6oKYdppEbiF+OUvdusDEiUbDETsNHWo15Ro1CmjSxGw8ASI2lnXB27Zxl+ngQSB7\ndtNRiS0uXgTy5AHCwnjVeNcuIHly01ElmnaaRO5BXBzQujXXqVMH1U1auZuwMOsLnjevNTJC7io0\n1NqRDQ93fNN0Z8mcmcfYAM9mx4wxG48PKWkS+YdZs4A//uC6ZUs1snSUPn34rhlgI8sAfrdsQsWK\nwKuvcj1+POe7ikN8+iknOANA9+58AxKEdDwnEk9kJHvP/P03bwUdPAikS2c6KrHF0aPcXbpxAyhd\nmtepA7w/1kQzAAAgAElEQVSg1YRdu4Cnn+aO7WuvAfPnm45IbPPDD8A773DdunXAFoPqeE4kgUaO\nZMIE8M2SEiYH6dSJCRPAH/ZKmBKlYEGgfn2uFyzgZSpxiLffBp5/nuvhw9lRP8hop0nkfy5cYJO+\nsDDuNu3cycGk4gBbtwJFigBuN1CtGvD996YjCminTvF7KSICKFwY2LQJCNFbdGfYuBF47jmu330X\nmDnTbDyJoJ0mkQTo2dM6hu/fXwmTY7jdQNu2/DVZMjWy9IIcOaxODVu3At99ZzYesVHx4laPs1mz\ngN9/NxuPl2mnSQTAoUNA/vxAdDRbDfz2m05nHGPxYqBSJa6bNgVGjDAbT5C4do0tfE6fBnLlYrPY\n1KlNRyW2OHKE3U6jogLyB6p2mkTuokMHJkwAMHBgQH1/S1LExnKXCWABm+fatCRZ2rRW94Zjx4Bh\nw8zGIzbKndtqeLlyJbBokdFwvEk7TeJ469cDpUpxXasWMG2a2XjERt9+C9Srx3XPnkDHjkbDCTax\nsbxJt3s3kD49b6NmzWo6KrHF+fNseHnlSsBNctZOk8htuN1WI8uUKYFevczGIza6fp035gAOTPN0\nARevid/w8soV3kgVh8iS5eZJzkHyblRJkzja3LnAunVcN2vGXWVxiKFDgRMnuO7RgwPUxOsqVWLT\nSwAYO5a7TeIQzZvzVgDAo+/ISLPxeIGO58SxYmKAp54C/vwTyJSJxeCZMpmOSmxx7hyPDq5eZWOh\nbdsC5uggEG3Zwo4OAPDee8D06WbjERuNHw80bMj1wIFAq1Zm40kAHc+J3MKkSUyYABaCK2FykJ49\nmTAB7C+hhMmnnn2WyRIAzJgBbN5sNh6xUb16bHwHsP4hwMeraKdJHOn6dV6HPnGCs+X279d1aMc4\neJD9JWJigBdfBJYt03VJGxw6xNfOmBjgpZeAJUtMRyS2mTMHeOstrtu141xHP6adJpF/GDHCKmfp\n3l0Jk6N88QVfuQHuMilhskWePECjRlwvXcpcVRyialXrivKwYcDx42bjSQLtNInjXLrEYdxhYcCT\nTwLbt6v7t2Ns2gQUK8a1+kvY7swZJk/h4axx2rhR41UcY80aoEwZrj/6CPj6a7Px3IF2mkTi6dvX\nOlbv3VsJk6N4rkAnT866JrFVtmxWHfDmzcDs2WbjERs9/zzwxhtcT5wI7NljNp5E0k6TOMrx46xl\niozkbvGaNTqdcYylS4GXX+b6s884hV1sd+UKh/meO8df9+xhDisOsGcPG13GxTGBmjvXdES3pJ0m\nkf/p1s1qFdKvnxImx4iLs3aZ0qa1mlqK7dKnt6bVHDzo16c04m1PPml14P/5Z75rDTDaaRLH+PNP\ntuSJiwOqVOH3rDjEzJnWnfeuXZk9izFRUbxJ99dfPLI7eJC5rDhA/O3+kiWBtWv97t2rdppEwEtT\ncXEsPO3d23Q0YpvoaGtnKWvWgGiuF+xSpLBKys6cYXN2cYiHHmKncICDP/30iO52tNMkjrBuHVC6\nNNd167IOURziq6+AJk24HjaM83LEuLg43qDbtg1Il459nDTM1yHCwniF+dIlbjnu3OlXN3K00ySO\n5nZb5SwpU2poqKOEhwNffsl17txWoyAxLiTE6nF49ap2fx0lY0agY0eu9+4NqHexSpok6C1YAKxe\nzXXTpsDDD5uNR2w0bBhw+jTXX37JrFn8xssvA+XLcz16NHDkiNFwxE6ffmr9MO7aFYiIMBtPAilp\nkqAWG8u5cgCQIYO1Fge4cIFXJAFec65Vy2w88i8ul/UliooCunQxG4/YKFUqoEcPrk+d4hucAKCk\nSYLa1KnArl1ct2sH3H+/2XjERn36sCmQZ62hvH6pWDHgnXe4njoV2LHDbDxio/ff5xsagCONLl0y\nG08CqBBcglZkJPDEE8DRo0COHLzWnCaN6ajEFkePAnnzAjducHTDypV+d61ZLPv3s4VPbCxQuTKP\n1MUh5s9nDxjAb4b5qhBcHGn0aL52Aiz+VsLkIN26MWEC1MU0AOTNC3z8MdcLFzLHFYd47TXravOw\nYdYkdT+lnSYJSpcv80brxYvcbdq1y69utIovxR/VULUqMGeO6YgkAU6d4liViAjguefYwke5rkPE\nH+bbqBEwZozRcLTTJI4zYAATJkBDeR1HXUwDUo4cQIsWXP/+O/DTT2bjERs9/zx3nADO1TlwwGw8\nd6CdJgk6p08DefLwHWvx4sCGDXrH6hjr13MSM8AZVxMmmI1H7kn8HeL8+VkUrjc8DrFjB/DMM2ys\n9+67HH1kiHaaxFF69rRafvTtq4TJMf7ZxVTz5QJOhgzcKAQ4K3LKFLPxiI2eespqCzJrFrB5s9l4\nbkM7TRJUDh9mV/7oaOCll4AlS0xHJLZZuNDa4m/ZEhg0yGw8kijXr3Oe64kTQK5cvFmXKpXpqMQW\nhw+zCDUmhp1PFy82EoZ2msQxunZlwgSonMVR4uKszqXp01vbFRJwUqe2NgmPHTNeEyx2evRRa9TR\nkiXA8uVm47kF7TRJ0Ih/JF69Ond4xSGmTQNq1+a6Z09rrpUEpJgYoEAB7jJlycJhvunTm45KbOEH\nRanaaRJH6NiRCVNoqNWdXxwgKgro3JnrbNmAzz83G48kWbJkQK9eXJ8/DwwebDYesVH27NY1yo0b\n/a5liJImCQpr17KxLADUr89jcXGIr78G/vqL6y5dgPvuMxuPeEW1akCRIlwPGgScPWs2HrFRmzZA\n5sxcd+zIrUc/oaRJAl78S1OpUmnop6OEhwNffsn1I48ADRqYjUe8xuWyJmpcu6YaRUeJP13dz65R\nKmmSgLdoERvKAkDTpsBDD5mNR2w0bBhw5gzXPXoAKVKYjUe8qmJF4MUXuf7qK+DIEaPhiJ0+/RR4\n8EGuu3blMFE/kOSkyeVyVXK5XHtdLtcBl8vVzhtBiSTUPy9NeXacxAEuXuRkdIA9XmrWNBuP+ESf\nPvw1Kkqttxzln9cov/rKaDgeSUqaXC5XKICRACoBeBJATZfLld8bgYkkxIwZvDUHAG3bAvffbzYe\nsVG/fmwhDfDsJkQb58GoeHHg7be5njIF2L3bbDxio7p1Oc0Z4M2AK1eMhgMkfaepOICDbrf7iNvt\njgYwA0DVpIclcnf/vDTVvLnZeMRGJ04Aw4dzXbo0ULmy2XjEp3r2ZE4cFwd06mQ6GrFN/GuUFy74\nRcPapCZNDwI4Fu/5+P8+Zh+3mw2w1AvKcb7+mg1kASZPadOajUds9OWXVo2DZuUEvfz5uekA8Ab6\nhg1GwxE7VasGFC3KtR9co0xq0mQ2U9m/HyhZEqhQgSMUxDHCw61eTI88Anz8sdl4xEb79wPffMP1\na69xQroEva5drTr/9u31Ptkx4l+jDA+3dp4MSer86BMAcsV7zgXuNt2kW7zqvXLlyqFcuXJJ/LT/\nkyULsHcv1x06AK++qroGhxg+nI1jAW466NKUg3TpAsTG8oep7qE7xsMP80LVkCHAypWcsvHKK6aj\nEltUqMCrlMuWAePHs0A8Uyav/fErVqzAihUrEvR7kzRGxeVyJQOwD0AFACcBbARQ0+12/xnv9/h2\njEqfPtacqalTgfff993nEr9w8SJHFF2+DBQqBGzdyi7g4gBbtlgdD2vV4vgUcYxz5zhh4+pVoHBh\nYNMmvU92jE2beFu2Rw+fdy/22RgVt9sdA6ApgMUA9gCYGT9hskWzZmy7DrCwJSrK1k8v9vvnpSkl\nTA7ieYOULJnV1FIcI2tWoHVrrrduBWbPNhuP2KhoUQ4UNTzuITgG9n71FdCkCdcjRrDDoQSlEyeA\nxx5jDXDp0sDq1aoBdowVK4Dy5blu0gQYNcpoOGLG1avcbTp3jj8L9uwBkic3HZUEk+Af2NugAb+L\nAG7dXbtmNh7xGV2acii32+pimiaN7p07WLp01pf/4EFgwgSz8YizBEfSlDy5dZXq7Flg6FCz8YhP\nxL80VbmyLk05ys8/W/fMmzcHcuQwG48Y1agR8J//cN29OxARYTYecY7gOJ4D2PXs2WeB7ds5T+PQ\nId6uk6BRowaPtF0uYNs2Ts4QB4iN5Rd7zx7emDl8GMiY0XRUYtjkyUCdOlz37Qu00xAv8ZLgP54D\neIXCM6ToyhWrr4MEhc2bmTABvDSlhMlBpk1jwgSwQY8SJgEvShcowHXfvsClS2bjEWcInp0mgHUP\n5coBq1YBKVMCBw4AuXLd9X8m/u/ll4GlS3kSu3cvWw6IA9y4wdsyf/8N5MzJ7+k0aUxHJX7i55+B\nqv8b3NWund4ri3c4Y6cJ4LmNZ7fpxg0edkvA+/VXJkwAaxmUMDnI2LFMmAC2hFbCJPFUqQKUKsX1\nsGG8XSviS8G10+RRtSrfgoSEcCR2vnz2fn7xGrebU843bQLuu4+latmymY5KbKG75ZIAq1cDL7zA\ndcOGzLNFksI5O00evXpx10kjsQPejz8yYQKAli2VMDnKoEFMmAB+TythklsoU4YjCAHert2/32w8\nEtyCc6cJ4LWKyZO53rgRKFbM/hgkSWJigIIFgX37gPvv5y5ThgymoxJbnD3LXaZr1zg2ZeNGzcuQ\n29qxA3jmGe5Mv/suMHOm6YgkkDlvpwlgPZPnnamnKZ4ElG+/ZcIEcHqGEiYH6dnTalLbt68SJrmj\np57irVqAt2w3bzYbjwSv4N1pAtgEb/hwrpcu5ZRkCQjXrwOPP87Czly5uOWeKpXpqMQWhw+zDjE6\nmt+znlsAIncQ/6/NSy8BS5aYjkgClTN3mgCgY0dWDwPs72IqeZN7NnKkdROme3clTI7SpQtf+QDr\nNqzIXTz6KG/XAsyzf/3VbDwSnIJ7pwngNWXPNPTZs4F33jEXiyRIWBh/AF66BDz5JOsVQkNNRyW2\n2L4dKFyYb3CqV7c6mookwJkzLIULDweKFmUpnOZTyr1y7k4TALRqxSpigDtPMTFm45G76t/f6u7b\nq5cSJkfp0IEJU2go65pE7kG2bLxlC/DW7Y8/mo1Hgk/wJ03p0zNZAlgYM3Gi2Xjkjk6dsuYtlyhh\ndfsVB1i5Eli0iOsGDYC8ec3GIwGpdWu9TxbfCf6kCQA++cQap9Ktm0Zi+7Evv2QROMBLU9padwi3\n25q4mjo165pEEiH+++R9+3gLV8RbnJE0pUpljVQ5edK6USd+5cABYPx4ritVAsqWNRuP2GjOHOD3\n37n+/HPOmRNJpH++T/a8ERNJquAvBPeIjQWefppjVTJkYKdEzx6u+IX33rOa0m3dymZ14gAxMUCh\nQpzEnCkT745nzGg6Kglw334L1KvHdf/+QJs2RsORAOLsQnCP0FBrBPbly7rK7Ge2bLESppo1lTA5\nyqRJTJgAdjFVwiRe8MEHvH0L8Md9WJjZeCQ4OGenCWDdRNmynPCYIgULw//zH9NRCXgct3gxkCwZ\nXz/z5DEdkdgifhfThx7iGa2acomXzJ0LvPkm1x06AL17m41HAoN2mjxcLqBfP66jolRs6id+/ZUJ\nE8Ap5UqYHGTUKHUxFZ954w3ewgV4K9fzV00ksZy10+RRrRobeLhcwLZtHFwkRsTFcZbyli1s3n7w\nIJA9u+moxBbxu5jmywfs3MmtRhEvWrXKulTSoIF12UTkdrTT9E+9e7PGye3meBUxZuZMJkwA+5Aq\nYXKQ+F1Me/dWwiQ+8cILwOuvcz1hArBnj9l4JLA5c6cJ4JCiceO4Xr4cKF/ebDwOdOMGkD8/8Ndf\nwAMPcJcpXTrTUYktTp4EHnuMNU0lSgDr1qkpl/jM7t08UIiL45Hd3LmmIxJ/pp2mW+nWDUiThut2\n7TTM14CvvmLCBHBEoBImB1EXU7FRgQJW+4Gff+ZdIJHEcO5OEwB06sThZoCG+drs8mUWfF+4wMtT\nu3cDyZObjkpssXcvULAge6e9+iqwcKHpiMQBTpzgzxptbsrdaKfpdtq0sRpcfvEFEB1tNh4H6deP\nCRPAchYlTA7Svj0TJpfL6p0m4mMPPshm8wCwYYOG+UriODtpypCBu00A+8N8/bXZeBzi+HFgyBCu\nn3uOlxnFIVavtgpK6tbVzVWxVbt21vvkDh30PlnunbOTJoBDinLn5rp7d+DaNaPhOEHXrkBkJNcD\nBmiL3DHcbmuWRapUrGsSsVGGDEDnzlzHn3UpklBKmlKmBHr25PrMGWDwYLPxBLndu62p41WqAGXK\nGA1H7PT999ZQ3hYt2AFcxGaNGwOPPMJ19+7A1atm45HA4uxCcI+4OKBIETa6TJuWw3wfeMB0VEGp\nShVg/nwgJIS9DD2zoSTIRUXxi33oEJAlC/tLZMhgOipxqOnTgVq1uO7ShcmTiIcKwe8mJMQar3Lt\nmrXzJF61ciUTJgCoX18Jk6OMHcuECeCrlBImMahGDb5PBoBBg4DTp83GI4FDO00ebjfw0kschJY8\nOfDnnxqC5kVuN6/5btwIpE7NjYacOU1HJbaI318iTx62ZE6RwnRU4nDLlwMVKnDduDH7xokA2mlK\nmPjDfKOjrVt14hXff8+ECWA5ixImB4nfX6JPHyVM4hdefBGoVInr8eOBffvMxiOBQTtN/1SzJjBj\nBtcbN3KarCRJdDSP4g4e5HXfQ4d0OuMYx44BefPyuuRzzwHr1+u6pPiNHTuAZ57hTvhbb6l3k5B2\nmu5Fz55Wp8VWrTRexQvGjWPCBKicxXG6dFF/CfFbTz0FfPgh1z/9xC7hIneinaZbadnS6r744498\nCyKJcuUK57KeOwc8+ihLxXQ64xDx38ZXrQrMmWM6IpF/OXqUm6E3bgClS7P/qnJ7Z9NO073q1AnI\nlInrtm15XVoSZeBAJkwAx/wpYXKQtm2ZMIWGalyK+K2HHwaaNeN67VqrYb3IrShpupXMmXmsAPBc\nSdcqEuXUKV7nBXi99913zcYjNlq6FFi8mOuPPwby5TMbj8gddOhgvU9u3x6IiTEbj/gvJU2306QJ\nz5UAjnu4dMlsPAGoSxcgIoLr/v3ZDkscIC6Ou0wAcN99nJsj4scyZeLMdoC36DReRW5HL2O3kyKF\n1YLg4kU1vLxH27cD33zDdeXKvN4rDjFtGrvrA0yesmc3G49IAjRtao0h7dIFCAszGo74KRWC34nb\nDZQty8pANbxMMLcbqFiRzeNCQzkuJX9+01GJLSIjWVV77BiTpQMHOJpIJADMmsVu4QDQujUvfIrz\nqBA8sVwuqygnOpqH3XJX8+czYQKATz5RwuQoI0YwYQI40EsJkwSQ6tWBUqW4Hj7cmvwj4qGdpoSo\nXZtHDgCwZg3vpcotRUUBhQoB+/cDGTNaDS3FATxjUi5fZqa8YweQLJnpqETuycaN7MMKANWqcZqB\nOIt2mpKqd28gVSqu1fDyjr76igkTAHTurITJUbp1Y8IEsB5QCZMEoOLFgfff5/qHH4BVq8zGI/5F\nO00J9cUXnJsFANOnA++9ZzYeP3TxIi8cXrrEX3fvVl8mx9i9G3j6aSA2llX/y5apQ6AErPjTf4oU\n4e6Tbv86h3aavKF9e+CBB6y1ZzSE/F/8zgwDBihhcgy3m130Y2P5yjJkiBImCWi5crEQHAA2bwam\nTjUbj/gPJU0JlT49C1sB4O+/WSUo/7dvHzBqFNdly3JqhjjEwoXAkiVcf/wxB3qJBLh27YAcObju\n0AEIDzcbj/gHHc/di5gYHkHs2cMk6uBBIGtW01H5hapVgZ9/5gbD5s1A4cKmIxJbREcDBQuykE3f\nExJkJk4E6tfnuls39Wl1Ch3PeUuyZFbjjitX+F0kWL6cCRMA1K2rhMlRRo2yKv+7dFHCJEHlww85\ncxrgVIMTJ8zGI+Zpp+leud3Ayy+z0FWdGxEbCzz7LG+X33cfXz9z5jQdldji/Hng8cfZOlmV/xKk\nVqwAypfn+sMPgUmTjIYjNtBOkze5XMDAgfw1NtaaseVQ337LhAlgDYASJgfp1s2aNTFokBImCUrl\nygFvvsn15MnApk1GwxHDtNOUWB99BEyYwPWyZUCFCmbjMeDqVW40nDkDPPQQi8HTpDEdldgifouB\nihVZCK4bcxKkDhwAChRgCV+ZMsDKlfrrHsy00+QLPXpYGcLnn7NI3GH69mXC5FkrYXIItxto0cJq\nMTB4sF5BJKg9/jgH+gIcRfrjj2bjEXOUNCVWzpxseAkAu3axFbaD/P23NZavWDGgZk2z8YiNFiwA\nli7lulEjzs0RCXKdOwOZM3Pdti1w44bZeMQMHc8lRWQk92wPH+agtf37HXN7qFYtNkYHNI7PUeIP\nF8yQgecWDvk7LzJiBNCsGdcDBlgNMCW46HjOV1KlAoYO5TosDOjY0Ww8NtmwwUqY3n1XCZOjxG8x\n0LWrEiZxlMaNgXz5uO7RAzh3zmw8Yj/tNCWV2w1Urgz88gvrOjZuBIoWNR2Vz8TFAaVKAb//DqRM\nCezdC+TObToqscX582wtcPkyB3Pt3Kkbc+I4CxYAr7/OdaNGwJgxZuMR79NOky+5XMCwYUDy5Eyg\nmjVjZhGkJk5kwgSwFlgJk4N07cqECVCLAXGsypWBl17ietw4tSBwGu00eUu7dmwZC7D72Ycfmo3H\nBy5eBJ54ghsODz0E/PknkDat6ajEFrt2scVAXBxfMRYv1o05cay9ezliMToaeO45YN06XiSV4KCd\nJjt06mRNd2zblmNWgkznzkyYAG40KGFyCE+Lgbg4vjIMGaKESRwtXz5+SwDceZ840Ww8Yh8lTd6S\nLp2103TmDKsEg8jWrdbZ/YsvAtWrm41HbDR/Phu4AqyELVDAbDwifqBzZ+DBB7lu35478RL8dDzn\nTW438Pzz3KtNloyFsp6rFgEsLo7/WevX8z9rxw5Hj9tzluvXmST99Rfbahw4AGTJYjoqEb8waxZQ\nowbXTZrwcqkEPh3P2cXlYiMPl4sdwps3ZyIV4CZPZsIEcEtaCZOD9OnDhAkAevZUwiQST/Xq3HkH\nuBO/davZeMT3tNPkC40bA2PHcj1nDlC1qtl4kiAsjLfLz51jE/S9e3kSKQ6wfz8bWUZFAc8+y3Ya\noaGmoxLxK3/+yaLwmBigZEk2+1VReGDTTpPdevUCMmXiukULHnEEqM6drQZugwYpYXIMtxv49FMm\nTC4XxwQpYRL5l/z5OX4U4I785Mlm4xHfUtLkC/ffz6MMgEcbAweajSeRtm0DRo/munx56+xeHGD2\nbKv4u1EjoHhxs/GI+LEuXbgTD/DydFiY2XjEd3Q85ysxMUCRIqyaTp2a51oPP2w6qgRzu4EyZYC1\na1n8vW2bLk05xpUrvMBw6hTHpOzbZ+2cisgtTZ/OmZwA8NlnwPDhZuORxNPxnAnJkrEoHODxXIBN\ndpwyhQkTwCbnSpgcpFs3JkwAp5IqYRK5q/feA8qW5XrUKGD7drPxiG9op8nXatWyptsuX85zLj93\n+TI7f585A2TPzo2G9OlNRyW22LGDRd+xsewzsWqVGlmKJNCuXcAzz/Dbp3RpYPVqffsEIu00mdS/\nP5AmDddNm7Kw1s917cqECWA5lhImh4iLAz75hD/xQ0NZ0Kaf+CIJVrAgd+YB7tRPmWI2HvE+JU2+\n9tBDvIIGAHv28LjDj+3YAYwcyfULL1hn9OIAkyaxMSvA60CFCpmNRyQAdevGHXqAReGeGdcSHHQ8\nZ4eoKBaF79oFpEzJzCRvXtNR/YvbzURpzRpuNGzbxndO4gAXLvBM9sIFzob480/1lxBJpGnTgNq1\nuW7eHBg61Gw8cm90PGdaihTAuHE86rhxg1e4/TCRnDaNCRPA2x9KmBzkiy+YMAH8Ca+ESSTRatXi\nG1CAO/c7d5qNR7xHO012atrUGk40YQJQr57ZeOIJC2OTttOngWzZWPydIYPpqMQWv//OVsZuN/DK\nK8CiRaplEkminTuBwoWtOxUrV6pTeKDQTpO/6N3bGovdqhVw9qzZeOJp25YJE8CyKyVMDhEby+Jv\nt5tHxyNHKmES8YJChfg+GeAO/vjxZuMR71DSZKf06a0q60uXOGLFD/z2m/UNXbGidRYvDvDVV9aU\n0XbtgMceMxuPSBDp0QPIlYvrtm2B48fNxiNJp+M5E95+G/jpJ64XLQIqVTIWSkQEh00eOsTOCDt3\nAo8+aiwcsdPp0yz+vnKFX/Rdu9i9XkS8ZuFC4LXXuK5SBZg7V5u5/k7Hc/5mxAir0PaTT4DwcGOh\ndOvGhAnguDwlTA7SqhUTJoA7oEqYRLyucmXg/fe5njcPmDXLbDySNNppMmX0aE6RBzhixUD/pk2b\ngOeeY0/D4sXZokeD7B1i/ny+7QW48/nDD2bjEQli58/zos358xznuGcPkCWL6ajkdu6006SkyZS4\nOF6pWL+eVyr++IPjK2wSHQ0ULcqWUcmTA1u2qMWAY4SFcZjgyZOs+N+927qgICI+MWMGULMm17Vr\nq1u4P9PxnD8KCWHvpmTJmEA1bAjExNj26fv3Z8IEsEWPEiYHadmSCRMADBmihEnEBjVqAK+/zvXU\nqSxnlcCjnSbTOnUCevXievBgW27U/fknh0pGRQFPPsldppQpff5pxR8sWsQiC0A9mURsdvw4f+Ze\nvcpbdbt3q4+sP9LxnD+LjOT1tQMHeH1t924gd26ffbq4OKBMGdYvuVz8tUQJn3068SeXL3NL8fhx\n/qTevdu6Dy0ithgzhvd/APZxGjHCbDzybzqe82epUgFjx3IdEQE0aeLTESujR1szWZs1U8LkKG3a\nWI1iBg1SwiRiQMOG1oiVUaOAtWvNxiP3RjtN/qJ+fWDiRK5nzOABuJf9/Tc3Gq5d42bWzp1A2rRe\n/zTij5YuBV5+meuKFYElS3QsJ2LI/v08YLhxA8iXj/1lU6UyHZV4aKcpEAwYwLuoALeAPMNTvcTt\nBho3ZsIEcHNLCZNDXL0KNGjAddq0bP+uhEnEmLx5ge7dud67lz3yJDAoafIX99/P6fIAZ9J55oF5\nybRpwC+/cF23rrXpIA7Qrh1w9CjX/fv7tGZORBKmVSsO9AWAfv2A7dvNxiMJo+M5f+J2A2+9xT77\nALuYO/oAABlqSURBVO+lelrJJsHZs2ysdvEikC0bG6tlzpzkP1YCwfLlQIUKXJcrB/z6q0ati/iJ\nrVuBYsU4N7tIEWDDBnahEbN0PBcoXC72bnrgAT5/+qm1Q5AEzZoxYQI4LUMJk0Ncu2Ydy6VJA3zz\njRImET9SuDAH+QLA5s1smyb+TT9B/c0DDwBff8315ctAnTrsE5BIc+cCM2dy/eabQLVqXohRAkOH\nDsBff3Hdt68GC4r4oS5dWOPkWe/fbzYeuTMdz/mrhg1ZsAvwenjLlvf8R5w8yRsaFy5wWsaePUDO\nnF6OU/zTqlVA2bJclykDrFihXSYRP7V6tdWGoEgRtoVJkcJsTE6m47lANHgwkCcP1x06sD/APYiL\n4yaV5xLeV18pYXKMiAi2sACA1KmBCROUMIn4sTJlOLcd4DFd585m45Hb009Sf5U2LSc6hoRw3knt\n2mzqkUBDhgDLlnH9wQfWoEhxgI4dgUOHuO7VC3jsMbPxiMhd9epl3aYbMIB3NsT/6HjO33XubDXx\naNuWd1PvYutW4LnngOholrFs3QqkT+/jOMU/rFgBvPgib2KWKsVjutBQ01GJSALs3Qs8+yxw/TpP\nBnbsYDcasZdmzwWy6GigZEnu2bpcwG+/WbUqtxAezjPxffv4Wrl2LRMocYBz54CnnwZOnWJ74W3b\ngCeeMB2ViNyD8eNZ0grw8s6PP6oXrd1U0xTIkidnv6ZUqbh78OGHvFV3Gy1bMmECgG7dlDA5hqeI\n7dQpPg8froRJJAA1aMB2fQAwZw670Ij/0E5ToBg5EvjsM67r1AG+/fZfv+Wnn4C33+a6TBluSulk\nxiEGDuRAXoBzC6dP19tTkQB14QI3jU+c4F2OzZvZoFjsoeO5YBAXB7z6KgetAsD339/UdOnECbYX\nuHgRyJiRLfkffthQrGKv338Hnn8eiIlREZtIkFi+nLO13W7gmWfYLTxlStNROYOO54JBSAgwcSKQ\nKROfGzb8/1FMXBxP7Txdv8eOVcLkGGFhwHvvMWFKnpydTJUwiQS8F1+0uoVv2wZ88YXZeISUNAWS\nnDmZEQHMkOrXB9xuDBzIdyUAUK8e8O675kIUG7ndwMcfA0eO8LlfP6BoUaMhiYj3fPklL/YAbN3n\nOWgQc3Q8F4g++IDF4QCOth6OPEM/Q0wM2/Fs3coWT+IAY8YAn3zC9euvAz//rDomkSCzfz/bEISH\nA9mzsw1B1qymowpuqmkKNpcvs4Dp6FFEIxnKYiX+SFYK69ZxYrY4wI4dQPHibHj64IPcv8+SxXRU\nIuIDEyYAH33Etd4f+Z5qmoJNhgzAzJmICUmO5IjB93gHg9ucUsLkFOHhvCF34wZr3aZPV8IkEsTq\n1QPeeYfr+fM5FkvMUNIUoL4/XgKfxo0AAOTEKTRdWZ3jViT4NW3K1sEAm3GVKWM0HBHxLZeL/Zpy\n5eJzq1bArl1mY3IqHc8FoD17gBIlgKtX3ZiSogFqR03gv/jsMzY1lOA1dSpr2gBer1myRM24RBxi\n5UqgfHneAcmbl91GMmY0HVXw0fFcELl4EahaFbh6FQBcyDB1lHVjasQIDvmV4LR/P9C4MddZszKB\nUsIk4hhlywJdunC9fz+7jcTGmo3JaZQ0BZCYGJayHDzI5+7dgSrVUwE//GDVtDRsyCt0ElwiI/nF\nDw/n8+TJQI4cZmMSEdt16cKZdACweDHQrp3ZeJxGSVMAadMGWLaM63feATp1+t+/ePhhYMYMFgVH\nRnKWiqfTpQQ+t5t1TNu28bltW6BSJbMxiYgRISF8z1SwIJ8HDeKz2EM1TQFi4kT2sgQ4k2jtWuC+\n+/7xmwYMsFrIvvIKsGCBjm+CQfyva4kSwKpV7P4tIo51+DBbzFy8yPEqK1dqQLu3qE9TgFu3jsV/\nUVEsZfnjD+A//7nFb3S72Q78++/53LEj0LOnrbGKl/30E2cMut28OvP77zqWExEAHMr+0kusa8qR\nA9i0iYMjJGmUNAWwY8f4buLMGSBZMo5LueMN86tXuRuxZw+ff/rJOgCXwLJ5M7/Y16+zzfvatWxq\nKiLyP6NG8fQe4GvFypVA6tRmYwp0uj0XoCIigLfeYsIE8Jvjri150qUDfvyRvwKc5Ltvn0/jFB84\ndgyoUoUJU0gIB/EqYRKRf2jShPd/AJ5CNGzIjWnxDSVNfsrtZtv8zZv5HP8b466eeMKqDLx6lYXh\n7FEggeDqVSZMp07xeehQoHJlszGJiF9yudhtxvOGeupUFoeLbyhp8lP9+vFCHACUK8fXzXvy5pus\naQJ4VFenjhp6BILYWKBmTWD7dj43bcqmpSIit5EiBUtZH36Yz23bAosWmY0pWKmmyQ/Nm8cGlm43\nkDs3t1wTNVosNhZ47TU28wCARo04tEiTHv3X558Dw4Zx/eqrnMyZLJnZmEQkIGzbBpQuzdKODBl4\nb+SJJ0xHFXhUCB5ArBEpbCmwfj1QqFAS/sBLl9hGdudOPnfoAPTu7ZVYxctGjwY+/ZTrQoWANWuA\n9OnNxiQiAWX2bF6iBjRqJbFUCB4gzpwB3njDKj+aMiWJCRMAZMrEnaZHH+Vznz468PZHv/wCNGvG\ndbZsHGWuhElE7lH16kDnzlzv388E6sYNszEFE+00+Ynz59mLyTO5unt3a8aQVxw+zH3b06f5PGEC\nUK+eFz+BJNquXUCpUsyWU6XineHixU1HJSIBKi6O7d3mzOFz1arcgVJP3ITR8Zyfu3SJA+s9UzLq\n1wfGj+dNc6/auRN44QUgLIx/+Pffs6eBmHP6NNv4Hj3K59mzOSNHRCQJrl3jYIh16/hcvTrw3Xcq\nkUwIHc/5scuX+RfbkzDVrg2MG+eDhAngWd/ChUCaNHwr8t57wK+/+uATSYJcusTWAp6EqU8fJUwi\n4hVp0/LHfbFifJ49G6hbV5eok0o7TQZdvcq5q7a/E1i8mC/W0dH8zlq+3PrOEntcuAC8/DKwZQuf\n69UDvvlGNxtFxKtsO8kIItpp8kMREcxbPAnTm28C06bZtHX6yivsgOZycQ/31VeBP/+04RMLAODc\nOaBCBSthqlYNGDtWCZOIeF2mTMDSpUDBgnyeMIHt37SXkThKmgyIjGRh3sqVfK5cmY0sbS3Se/dd\nYMwYri9c4NTHv/+2MQCHOnOGFf+e5pU1axr44ouIk2TJAixbBuTLx+evvgJatFDilBhKmmx24wY3\nFpYt43PFisAPPwApUxoIpmFDq2fTiRNMnDyD7sT7Tp5ke/fdu/n8wQfsK6HKTBHxsWzZWML62GN8\nHjYMaN9eidO9UtJko+hooEYNFucB7Dk5dy5vmRvTvj3QujXXBw6wyOr8eYMBBaljx/gF37uXzx99\nBEycCISGmo1LRBwjZ06WsObOzef+/YGuXY2GFHCUNNkkJgZ4/30mSQDb8syfz4tsRrlc/M6pX5/P\n27YxuEOHzMYVTI4cYcJ08CCfGzfmFUklTCJis1y5mDjlysXnHj2AXr3MxhRIlDTZIDaWVz1nz+Zz\nsWLcbUqb1mhYFpeLhcjvv8/nAwc4y2XDBrNxBYPDh5kw/fUXn5s147gUXV0REUMeeYSJU44cfO7U\nCRg40GxMgUI/uX3s6lXWME2bxufChXnjP0MGs3H9S7JkwOTJwBdf8NnTovzHH83GFcgOHGAzUU8f\nplatgKFDdUtORIx77DEmTg88wOc2bYB27dTH6W7Up8mHDh/mLDlP3W/BgsBvv/Emg18bPx745BN+\n97hcwODBwOefm44qsOzdy+Yop07xuUMH7oErYRIRP7JzJ39UeUpZK1dmv0C/e2NvI/VpMsDTL9KT\nML36KofW+33CBAAff8yCq7RpebWiRQugeXO9BUmoNWt4JOdJmLp2VcIkIn6pUCFWYjz5JJ8XLmR1\nxv79ZuPyV0qavMztBkaOZLPnixf5sTZtgHnzAixzr1QJWL2a1y0AYPhwnjNGRJiNy5+53cCgQWwr\ncPYsP9ajB9CtmxImEfFbefIA69ez4TLAjfLnngOWLDEblz/S8ZwXRUUBn34KfP01n1Om5Lp2bbNx\nJcmxY9yv3bWLz8WLMwP0HIQLhYVxFIpnrHiKFEw0GzUyG5eISALFxQFduli36UJCWCD++efOet93\np+M5JU1ecvYsN2LWrOFzzpzATz8xxwh4ly9zkKynI+cjjwCLFgFPPGE2Ln+xdSv//zl8mM+5c/Oq\nZNGiRsMSEUmMmTP5HvD6dT7XrcsBEkaaMBugmiYf27aNr4+ehKl4ceCPP4IkYQJ4rrhgAVCnDp//\n+gsoWRL45RezcZnmdrPfUsmSVsJUpQpnyilhEpEAVaMGX888vZy+/ZZVB54yTSdT0pREs2ezF+Sx\nY3z+8EPOlPOUAgWNFCnYwbpbNz5fusTq9rp1reItJwkPZxLZqBFn44SGAv368XguUybT0YmIJMmz\nz/LNf+nSfN6wgZebNm0yG5dpSpoS6do1tt15911uYYaEsAb4228Nj0XxJZeLN8GmTAHSpePHJk3i\ntQsn9XPyVElOmcLnHDl4XbJtWzWtFJGg4ZlX99FHfD5xAihThpednHqZWjVN98jt5nlvq1acvwrw\n9GrmTOCVV8zGZqtjx7jLsmiR9bFq1fjdlD27ubh8bfp0tmQID+dz+fL8WLZsZuMSEfERz63wFi2s\nZOmZZ/gxz05UMFFNk5fs3MnXyJo1rYSpRAlg40aHJUwAD7sXLOBuS+bM/NgPP3DXafLk4BudvX8/\nUL06UKuWlTB17AgsXaqESUSCmssFfPYZf9w98gg/tm0b8PzzLElxUq2TkqYECAvjlcvChVmvBPDG\n/cSJwNq1QN68ZuMzxuViP4U9e5hQAKx1qlOHbQo840MC2alTHLD75JPA99/zY5kzM2Hs2VNDd0XE\nMcqXZ8Pm7t2tMpQpU3iRevBgIDrabHx20PHcHcTFcdOkXTurV2FoKNC0KeuhM2Y0Gp7/+eknoEkT\n4PRpPqdNy+Loxo0Dr9bn8mWgf3/OivM09HS5ONS4d2/rWomIiAMdOQK0bMkf+x758wMjRgAVKhgL\nyyvUpykRtmxhcrR+vfWxsmX5F6JQIXNx+b1Ll1jwNXGi9bFnnwWaNeM9Vn+vko+MBEaPZne3+LcC\nX30V6NMHePppc7GJiPiZxYv54z3+2JXq1XkxKlDfWyppSiC3m/VJ48bxNd8Tds6c/AtQo4azuqIm\nyZIlQMOGwN9/Wx+7/36gQQPuPOXObSy0W4qNBaZOZTvc+MeKxYtzt6xcOWOhiYj4s6goYMgQTo3y\nlHymScORpXXrBl4Ji5Kmuzh4EJg2ja+ZBw9aH0+enLcFOnWybtjLPbh2jdnmmDHWkR3AzLNKFc6c\nqVjR7NHdpUusT+rXzxoVA/CQvndv4K23lCmLiCTA8eOctTpjxs0fL16c5a81agTGBC6fJE0ul6s6\ngG4A8gEo5na7t9zm9/ll0nT+PDBrFovYNmy4+d+FhACvvcaSlnz5zMQXVKKiePA9cqTVNt3j8ceZ\nPNWpY1+R2P79nJ83bx7jid9wJGdOFqzVqwckS2ZPPCIiQWTFCtYCb9x488dDQzkLvnZt4I03uBvl\nj3yVNOUDEAdgLIBWgZA0Xb/O18mpU9leKCbm5n9fuDDwwQfAe++xX6H4wPbtwKhR3NrzFFgD/O6p\nXp1vSZ56ioVjGTJ453PGxADr1vGL/9/27jdGrqoO4/j3KZRAW9h2BYvYQiui2EJr0TQEBbdIDZCW\nSogKAf+RAC+I+AIQ+ZNIokajRhMh8MZI1QZIVCCFloSCXSVKasBut1oKLAFbQEpVKNsApTg/X5wz\n7ex0ZvZuO7Mz230+yc3M3HvO3TM9vef+5p5z7l25cmjne9m0aenmlNdc07lHspnZGLJ5czrfrlgx\ndKQGpHlCF12UAqhFizprInJLu+ckraVDgqYI2L49VU6tZWBgb39r2fHHpwlRl14Kc+eOSjENUrfY\n8uVp0HVln2ilWbNSADV/fnqdNw9OPHHfo+u99+DNN9OMtzfeSK87dqTLiWvXwurV6e9Vmz07dRMu\nXQpnnZUeFWNmZk1VKqXfrStWpB6e6ua4uzs17SecUHsZ7ZnqB23QNDiYJmqVg6ItW/Y+lbmRrq50\nUeOyy9It4cfabPiDSqmUBo3feWcKcAYHG6efNCmNKty9e2+AtHNnsb8lpYfrlgOlOXM8XsnMbBTt\n2pV+x65YAQ89lEZvDOeoo/YGUKeemoabttJ+B02S1gC1nolxU0Q8mNO0LWjavTvNYC+V6qeZMmVo\nxHr22bBkSefPfB+XSqUU/fb3p268/v60DAzs/x3Gp0xJt2tfujTdcPOYY5pbZjMz2y+vv57uGbxu\n3dCLH7t21c+zYEG6JVArNQqaGo50jYjFzSjArbfeuud9T08PPU2avj1xYuq5mTCh/mW9adN8MWHM\nmDAhdZnNng3Llu1dv3Nnug1tOZgaGIDJk9Mlw66udO221mtXV7pRiLvdzMw6zrRp6VGeV1yxd12p\nlG4mXW+YzSmnNL8cvb299Pb2FkrbrO656yLiqTrbO2YguJmZmVkjLXlgr6QLJW0FTgdWSXp4uDxm\nZmZmY5VvbmlmZmaWteRKk5mZmdl44qDJzMzMrAAHTWZmZmYFOGgyMzMzK8BBk5mZmVkBDprMzMzM\nCnDQZGZmZlbAQRE0Fb39uR18XPfjl+t+/HLdj1/trnsHTTamue7HL9f9+OW6H7/aXfcHRdBkZmZm\n1moOmszMzMwKGJVnz7X0D5iZmZk1Ub1nz7U8aDIzMzM7GLh7zszMzKwAB01mZmZmBXR00CTpu5I2\nSOqT9JikmXXS/VLSNkkb62y/VlJJUnf+vFjSk5L68+uiVn4PGzlJX5D0D0n/k3Rag3Qv5npcL+mv\nFevvzevWS3pB0vq8/n2S1koalHTbaHwXG56kcyVtlvScpBvqpPl53r5B0oLh8krqlrRG0rOSHpE0\ndTS+ixVXoO3ukbSj4li+Ja//aMW69TnNNXlbobbD2kvS4ZLW5fP7Jkk/qJHmZElPSHpH0rU1th+S\n6//BinU/lvR0bifuk9TVzHJ3dNAE/Cgi5kfEx4EHgO/USXcXcG6tDTnQWgz8s2L1dmBJRMwDvgr8\npnlFtibZCFwI/GmYdAH0RMSCiFi4Z2XExXndAuD3eQF4G7gFuK4FZbb9IOkQ4HbSMTwHuETSx6rS\nnA98OCJOAq4E7iyQ99vAmoj4CPBY/mydpW7bXeGP5WM5Ir4HEBHPVBzfnwDeAu7P6Yu2HdZGEfEO\nsCif3+cBiyR9uirZf4BvAD+ps5tvAptI54GyR4C5ETEfeBa4sZnl7uigKSIGKz5OAf5dJ93jwOt1\ndvNT4FtV6fsi4tX8cRNwhKSJB1hca6KI2BwRzxZMXnOWA4AkAV8E7sn7fSsi/gzsOvBSWpMsBAYi\n4sWI2A3cCyyrSnMB8CuAiFgHTJV07DB59+TJr59v7dewkRqm7S6re3xn5wDPR8TWvM+RtB3WRhHx\nVn57GHAI8N+q7dsj4klgd3VeSTOA84FfUPF/JCLWREQpf1wHzGhmmTs6aAKQ9H1JW0hXhH44wrzL\ngJcior9BsouAp3KDa2NPAI/mbtYramw/E9gWEc/XyGed4YPA1orPL+V1RdIc1yDv9IjYlt9vA6Y3\nq8A2agI4I3e1rJY0p0aai4G7R7lc1gSSJkjqIx2fayNi0wiy/wy4Hig1SHM5sPoAiriPtgdNeczB\nxhrLUoCIuDkijgeWk/6Riu53EnATQ7v0VJVmLikQu+pAv4eN3HB1X9Cn8iX684CrJZ1Ztf0S3KB2\nuqIB7HBXHMpp9tlfpHurOFAee/4GzMxdLbeRhmnsIekwYCnw2zaUzQ5QRJRy99wM4CxJPUXySVoC\nvBYR66nTLki6GXg3Ipra/h/azJ3tj4hYXDDp3YwsYjwRmAVsSD00zACekrQwIl7Ll/buA74cES+M\nYL/WJCOo+0b7+Fd+3S7pflJ3zeMAkg4ljW3wYNDO9jJQOcljJumKUaM0M3KaiTXWv5zfb5N0bES8\nKukDwGtNLbW1XOUQjYh4WNIdkrojotyNcx6pp2B7e0pozRAROyStAj4J9BbIcgZwQR7reDhwlKRf\nR8RXACR9jdR199lml7XtV5oakXRSxcdlwPqieSNiY0RMj4jZETGb1MCelgOmqcAq4IaIeKK5pbYW\nqPdLYpKkI/P7ycDnSINAy84Bno6IV4ru09riSeAkSbPylYMvASur0qwEyg3i6cAbueutUd6VpG59\n8usD2JgiaXoel4ikhaQbMleOe7mEPF6x3i5aWT7bf5KOLs9olXQEacJWvXP8kHqMiJsiYmY+t18M\n/KEiYDqX1G23LA82b66I6NgF+B3pJNhHmv30/rz+OGBVRbp7gFdIg3u3Al+vsa8XgO78/hZgJ6mC\nysvR7f6+XobU14W5Lt8GXgUerq574EP5/0Yf8Hfgxqp93AVcWWPfL5JmZQwCW4CT2/19x/tCumLw\nDDBQrkdSt/lVFWluz9s3kH4A1c2b13cDj5Jm0DwCTG339/SyT72X2+538/F+eWW9A1fnY7sP+Atw\nekXeyaTJQUdW7bNm2+GlsxbgVFL3ax/QD1yf11fW/7G5LneQJgxsAaZU7eczwMqKz8+RZsuXz+13\nNLPcfoyKmZmZWQEd3T1nZmZm1ikcNJmZmZkV4KDJzMzMrAAHTWZmZmYFOGgyMzMzK8BBk5mZmVkB\nDprMzMzMCnDQZGZmZlbA/wEMqEO0RIci1gAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb866278>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 设置图像大小\n",
-    "f = plt.figure(figsize=(10,6), dpi=80)\n",
-    "\n",
-    "# 画图，指定颜色，线宽，类型\n",
-    "p = plt.plot(x, c, 'b-', \n",
-    "         x, s, 'r-', linewidth=2.5)\n",
-    "\n",
-    "# 设置显示范围\n",
-    "plt.xlim(x.min() * 1.1, x.max() * 1.1)\n",
-    "plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
-    "\n",
-    "###########################################################################\n",
-    "\n",
-    "# 设置刻度\n",
-    "p = plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n",
-    "p = plt.yticks([-1, 0, 1])\n",
-    "\n",
-    "###########################################################################\n",
-    "\n",
-    "# 在脚本中需要加上这句才会显示图像\n",
-    "# plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 设定 x 轴 y 轴标题"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们想让刻度的位置显示的是含有 $\\pi$ 的标识而不是浮点数，可以在 `xticks` 中传入第二组参数，这组参数代表对应刻度的显示标识。这里，我们使用 `latex` 的语法来显示特殊符号（使用 `$$` 包围的部分）："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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AbvsEeaIFsF4rTx6uu3Rh3bS4xODB9r/x1q2B+Hiz8RikZEvEQZGRdrFw7tz8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\nJSIiIpKg/wNXpXrhRgRjdwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xba18630>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 设置图像大小\n",
-    "f = plt.figure(figsize=(10,6), dpi=80)\n",
-    "\n",
-    "# 画图，指定颜色，线宽，类型\n",
-    "p = plt.plot(x, c, 'b-', \n",
-    "         x, s, 'r-', linewidth=2.5)\n",
-    "\n",
-    "# 设置显示范围\n",
-    "plt.xlim(x.min() * 1.1, x.max() * 1.1)\n",
-    "plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
-    "\n",
-    "# 设置刻度及其标识\n",
-    "p = plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], \n",
-    "               ['$-\\pi$', '$-\\pi/2$', '$0$', '$\\pi/2$', '$\\pi$'], fontsize ='xx-large')\n",
-    "p = plt.yticks([-1, 0, 1], \n",
-    "               ['$-1$', '$0$', '$+1$'], fontsize ='xx-large')\n",
-    "\n",
-    "# 在脚本中需要加上这句才会显示图像\n",
-    "# plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 移动坐标轴的位置"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "现在坐标轴的位置是在边界上，而且有上下左右四条，我们现在想将下面和左边的两条移动到中间，并将右边和上面的两条去掉："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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hatu2Afnzm44qEDTDI3KxmTOZ7ABAy5ZKdhxl0CAmOwDQv78tkx2AJRwjR/LP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\nRETE9RTwiIiIiOsp4BERERHX+xc1HT2p8IKu8QAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbdb0ac8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 设置图像大小\n",
-    "f = plt.figure(figsize=(10,6), dpi=80)\n",
-    "\n",
-    "# 画图，指定颜色，线宽，类型\n",
-    "plt.plot(x, c, 'b-', \n",
-    "         x, s, 'r-', linewidth=2.5)\n",
-    "\n",
-    "# 设置显示范围\n",
-    "plt.xlim(x.min() * 1.1, x.max() * 1.1)\n",
-    "plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
-    "\n",
-    "# 得到轴的句柄\n",
-    "ax = plt.gca()\n",
-    "# ax.spines参数表示四个坐标轴线\n",
-    "# 将右边和上边的颜色设为透明\n",
-    "ax.spines['right'].set_color('none')\n",
-    "ax.spines['top'].set_color('none')\n",
-    "\n",
-    "###################################################################################\n",
-    "\n",
-    "# 将 x 轴的刻度设置在下面的坐标轴上\n",
-    "ax.xaxis.set_ticks_position('bottom')\n",
-    "# 设置位置\n",
-    "ax.spines['bottom'].set_position(('data',0))\n",
-    "\n",
-    "# 将 y 轴的刻度设置在左边的坐标轴上\n",
-    "ax.yaxis.set_ticks_position('left')\n",
-    "# 设置位置\n",
-    "ax.spines['left'].set_position(('data',0))\n",
-    "\n",
-    "###################################################################################\n",
-    "\n",
-    "# 设置刻度及其标识\n",
-    "p = plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], \n",
-    "           ['$-\\pi$', '$-\\pi/2$', '$0$', '$\\pi/2$', '$\\pi$'], fontsize ='xx-large')\n",
-    "p = plt.yticks([-1, 0, 1], \n",
-    "           ['$-1$', '$0$', '$+1$'], fontsize ='xx-large')\n",
-    "\n",
-    "# 在脚本中需要加上这句才会显示图像\n",
-    "# plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 加入图例"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 legend() 加入图例："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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9/HKGZ/5883ys55+XZoOOa95MekTfvjBggIxHj9alVNswDFneWrJErleuhNq1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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbdcad30>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 设置图像大小\n",
-    "plt.figure(figsize=(10,6), dpi=80)\n",
-    "\n",
-    "# 画图，指定颜色，线宽，类型\n",
-    "plt.plot(x, c, 'b-', \n",
-    "         x, s, 'r-', linewidth=2.5)\n",
-    "\n",
-    "# 设置显示范围\n",
-    "plt.xlim(x.min() * 1.1, x.max() * 1.1)\n",
-    "plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
-    "\n",
-    "# 得到画图的句柄\n",
-    "ax = plt.gca()\n",
-    "\n",
-    "# ax.spines参数表示四个坐标轴线\n",
-    "# 将右边和上边的颜色设为透明\n",
-    "ax.spines['right'].set_color('none')\n",
-    "ax.spines['top'].set_color('none')\n",
-    "\n",
-    "# 将 x 轴的刻度设置在下面的坐标轴上\n",
-    "ax.xaxis.set_ticks_position('bottom')\n",
-    "# 设置位置\n",
-    "ax.spines['bottom'].set_position(('data',0))\n",
-    "\n",
-    "# 将 y 轴的刻度设置在左边的坐标轴上\n",
-    "ax.yaxis.set_ticks_position('left')\n",
-    "# 设置位置\n",
-    "ax.spines['left'].set_position(('data',0))\n",
-    "\n",
-    "# 设置刻度及其标识\n",
-    "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], \n",
-    "           ['$-\\pi$', '$-\\pi/2$', '$0$', '$\\pi/2$', '$\\pi$'], fontsize ='xx-large')\n",
-    "plt.yticks([-1, 0, 1], \n",
-    "           ['$-1$', '$0$', '$+1$'], fontsize ='xx-large')\n",
-    "\n",
-    "##################################################################################################\n",
-    "\n",
-    "# 加入图例，frameon表示去掉图例周围的边框\n",
-    "l = plt.legend(['cosine', 'sine'], loc='upper left', frameon=False)\n",
-    "\n",
-    "##################################################################################################\n",
-    "\n",
-    "# 在脚本中需要加上这句才会显示图像\n",
-    "# plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 注释特殊点"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们可以使用 `anotate` 函数来注释特殊的点，假设我们要显示的点是 $2\\pi/3$："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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kJUjOnNKaQvdi+ZImPEr5o7NnpVXEsmVy+auv7Ne/YBbsCQ9IAnvrrbBjh9Sq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\nxpjF+z90lw18wTSaIwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbb05710>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 设置图像大小\n",
-    "plt.figure(figsize=(10,6), dpi=80)\n",
-    "\n",
-    "# 画图，指定颜色，线宽，类型\n",
-    "plt.plot(x, c, 'b-', \n",
-    "         x, s, 'r-', linewidth=2.5)\n",
-    "\n",
-    "# 设置显示范围\n",
-    "plt.xlim(x.min() * 1.1, x.max() * 1.1)\n",
-    "plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
-    "\n",
-    "# 得到画图的句柄\n",
-    "ax = plt.gca()\n",
-    "\n",
-    "# ax.spines参数表示四个坐标轴线\n",
-    "# 将右边和上边的颜色设为透明\n",
-    "ax.spines['right'].set_color('none')\n",
-    "ax.spines['top'].set_color('none')\n",
-    "\n",
-    "# 将 x 轴的刻度设置在下面的坐标轴上\n",
-    "ax.xaxis.set_ticks_position('bottom')\n",
-    "# 设置位置\n",
-    "ax.spines['bottom'].set_position(('data',0))\n",
-    "\n",
-    "# 将 y 轴的刻度设置在左边的坐标轴上\n",
-    "ax.yaxis.set_ticks_position('left')\n",
-    "# 设置位置\n",
-    "ax.spines['left'].set_position(('data',0))\n",
-    "\n",
-    "# 设置刻度及其标识\n",
-    "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], \n",
-    "           ['$-\\pi$', '$-\\pi/2$', '$0$', '$\\pi/2$', '$\\pi$'], fontsize ='xx-large')\n",
-    "plt.yticks([-1, 0, 1], \n",
-    "           ['$-1$', '$0$', '$+1$'], fontsize ='xx-large')\n",
-    "\n",
-    "# 加入图例，frameon表示图例周围是否需要边框\n",
-    "l = plt.legend(['cosine', 'sine'], loc='upper left', frameon=False)\n",
-    "\n",
-    "####################################################################################\n",
-    "\n",
-    "# 数据点\n",
-    "t = 2 * np.pi / 3\n",
-    "\n",
-    "# 蓝色虚线\n",
-    "plt.plot([t,t],[0,np.cos(t)], color ='blue', linewidth=2.5, linestyle=\"--\")\n",
-    "\n",
-    "# 该点处的 cos 值\n",
-    "plt.scatter([t,],[np.cos(t),], 50, color ='blue')\n",
-    "\n",
-    "# 在对应的点显示文本\n",
-    "plt.annotate(r'$\\sin(\\frac{2\\pi}{3})=\\frac{\\sqrt{3}}{2}$', # 文本\n",
-    "             xy=(t, np.sin(t)), # 数据点坐标位置\n",
-    "             xycoords='data',   # 坐标相对于数据\n",
-    "             xytext=(+10, +30), # 文本位置坐标\n",
-    "             textcoords='offset points', # 坐标相对于数据点的坐标\n",
-    "             fontsize=16,       # 文本大小\n",
-    "             arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\")) # 箭头\n",
-    "\n",
-    "# 红色虚线\n",
-    "p = plt.plot([t,t],[0,np.sin(t)], color ='red', linewidth=2.5, linestyle=\"--\")\n",
-    "\n",
-    "# 该点处的 sin 值\n",
-    "p = plt.scatter([t,],[np.sin(t),], 50, color ='red')\n",
-    "\n",
-    "# 显示文本\n",
-    "p = plt.annotate(r'$\\cos(\\frac{2\\pi}{3})=-\\frac{1}{2}$',\n",
-    "             xy=(t, np.cos(t)), xycoords='data',\n",
-    "             xytext=(-90, -50), textcoords='offset points', fontsize=16,\n",
-    "             arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
-    "\n",
-    "\n",
-    "#####################################################################################\n",
-    "\n",
-    "# 在脚本中需要加上这句才会显示图像\n",
-    "# plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 最后调整"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "调整刻度值的大小，并让其显示在曲线上方。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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775jm0qWmmZzs/bhSU+UXskkT0wwKujS2kiVNs08f09y3z/txXfT333+bJUuWNKdOnWpb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\nFo8THsYYY4xZvP8DkrV9v0GdesQAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa4ef5f8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 设置图像大小\n",
-    "plt.figure(figsize=(10,6), dpi=80)\n",
-    "\n",
-    "# 画图，指定颜色，线宽，类型\n",
-    "plt.plot(x, c, 'b-', \n",
-    "         x, s, 'r-', linewidth=2.5)\n",
-    "\n",
-    "# 设置显示范围\n",
-    "plt.xlim(x.min() * 1.1, x.max() * 1.1)\n",
-    "plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
-    "\n",
-    "# 得到画图的句柄\n",
-    "ax = plt.gca()\n",
-    "\n",
-    "# ax.spines参数表示四个坐标轴线\n",
-    "# 将右边和上边的颜色设为透明\n",
-    "ax.spines['right'].set_color('none')\n",
-    "ax.spines['top'].set_color('none')\n",
-    "\n",
-    "# 将 x 轴的刻度设置在下面的坐标轴上\n",
-    "ax.xaxis.set_ticks_position('bottom')\n",
-    "# 设置位置\n",
-    "ax.spines['bottom'].set_position(('data',0))\n",
-    "\n",
-    "# 将 y 轴的刻度设置在左边的坐标轴上\n",
-    "ax.yaxis.set_ticks_position('left')\n",
-    "# 设置位置\n",
-    "ax.spines['left'].set_position(('data',0))\n",
-    "\n",
-    "# 设置刻度及其标识\n",
-    "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], \n",
-    "           ['$-\\pi$', '$-\\pi/2$', '$0$', '$\\pi/2$', '$\\pi$'], fontsize ='xx-large')\n",
-    "plt.yticks([-1, 0, 1], \n",
-    "           ['$-1$', '$0$', '$+1$'], fontsize ='xx-large')\n",
-    "\n",
-    "# 加入图例，frameon表示图例周围是否需要边框\n",
-    "l = plt.legend(['cosine', 'sine'], loc='upper left', frameon=False)\n",
-    "\n",
-    "# 数据点\n",
-    "t = 2 * np.pi / 3\n",
-    "\n",
-    "# 蓝色虚线\n",
-    "plt.plot([t,t],[0,np.cos(t)], color ='blue', linewidth=2.5, linestyle=\"--\")\n",
-    "\n",
-    "# 该点处的 cos 值\n",
-    "plt.scatter([t,],[np.cos(t),], 50, color ='blue')\n",
-    "\n",
-    "# 在对应的点显示文本\n",
-    "plt.annotate(r'$\\sin(\\frac{2\\pi}{3})=\\frac{\\sqrt{3}}{2}$', # 文本\n",
-    "             xy=(t, np.sin(t)), # 数据点坐标位置\n",
-    "             xycoords='data',   # 坐标相对于数据\n",
-    "             xytext=(+10, +30), # 文本位置坐标\n",
-    "             textcoords='offset points', # 坐标相对于数据点的坐标\n",
-    "             fontsize=16,       # 文本大小\n",
-    "             arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\")) # 箭头\n",
-    "\n",
-    "# 红色虚线\n",
-    "p = plt.plot([t,t],[0,np.sin(t)], color ='red', linewidth=2.5, linestyle=\"--\")\n",
-    "\n",
-    "# 该点处的 sin 值\n",
-    "p = plt.scatter([t,],[np.sin(t),], 50, color ='red')\n",
-    "\n",
-    "# 显示文本\n",
-    "p = plt.annotate(r'$\\cos(\\frac{2\\pi}{3})=-\\frac{1}{2}$',\n",
-    "             xy=(t, np.cos(t)), xycoords='data',\n",
-    "             xytext=(-90, -50), textcoords='offset points', fontsize=16,\n",
-    "             arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
-    "\n",
-    "\n",
-    "#####################################################################################\n",
-    "\n",
-    "for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
-    "    label.set_fontsize(16)\n",
-    "    label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.65 ))\n",
-    "\n",
-    "####################################################################################\n",
-    "\n",
-    "# 在脚本中需要加上这句才会显示图像\n",
-    "# plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> The devil is in the details."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 不要迷信默认设置"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "导入相关的包："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "生成三角函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x = np.linspace(-np.pi, np.pi)\n",
+    "c, s = np.cos(x), np.sin(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 默认绘图"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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8Vhweftg2B5O4YzC2ERCvtT6qtb4DLADappTFwfsIH7JggbVxmZ2LPMC4reMY2miow0Ue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\nF0IIH/f/QH0wXEQipAoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9fe37f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "# 画图\n",
+    "p = plt.plot(x,c)\n",
+    "p = plt.plot(x,s)\n",
+    "\n",
+    "# 在脚本中需要加上这句才会显示图像\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "默认效果如图所示，我们可以修改默认的属性来得到更漂亮的结果。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "图像以 `Figure #` 为窗口标题，并且数字从 1 开始，`figure()` 函数的主要参数如下：\n",
+    "\n",
+    "参数 | 默认值 | 描述\n",
+    "---|---|---\n",
+    "`num`|`1`| 图号\n",
+    "`figsize`|`figure.figsize`| 图大小（宽，高）（单位英寸）\n",
+    "`dpi`|`figure.dpi`| 分辨率（每英寸所打印的点数）\n",
+    "`facecolor`|`figure.facecolor`| 背景颜色\n",
+    "`edgecolor`|`figure.edgecolor`| 边界颜色\n",
+    "`frameon` |`True`| 是否显示图框架"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# 设置图像大小\n",
+    "f = plt.figure(figsize=(10,6), dpi=80)\n",
+    "\n",
+    "# 画图\n",
+    "p = plt.plot(x,c)\n",
+    "p = plt.plot(x,s)\n",
+    "\n",
+    "# 在脚本中需要加上这句才会显示图像\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 设置线条颜色，粗细，类型"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "首先，我们使用 figure() 函数来创建一幅新图像，并且指定它的大小，使得长宽比更合适。\n",
+    "\n",
+    "然后，我们使用 `color, linewidth, linestyle` 参数，指定曲线的颜色，粗细，类型："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JrH9RRMVGs9au1cHRkXHMMXbBABg+HP78020+oiJLJIwGDYINGyyO3ewk5DZt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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa156860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 设置图像大小\n",
+    "f = plt.figure(figsize=(10,6), dpi=80)\n",
+    "\n",
+    "# 画图，指定颜色，线宽，类型\n",
+    "p = plt.plot(x, c, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n",
+    "p = plt.plot(x, s, color=\"red\",  linewidth=2.5, linestyle=\"-\")\n",
+    "\n",
+    "# 在脚本中需要加上这句才会显示图像\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "也可以像 **Matlab** 中一样使用格式字符来修改参数：\n",
+    "\n",
+    "表示颜色的字符参数有：\n",
+    "\n",
+    "字符 | 颜色\n",
+    "-- | -- \n",
+    "`‘b’`|\t蓝色，blue\n",
+    "`‘g’`|\t绿色，green\n",
+    "`‘r’`|\t红色，red\n",
+    "`‘c’`|\t青色，cyan\n",
+    "`‘m’`|\t品红，magenta\n",
+    "`‘y’`|\t黄色，yellow\n",
+    "`‘k’`|\t黑色，black\n",
+    "`‘w’`|\t白色，white\n",
+    "\n",
+    "表示类型的字符参数有：\n",
+    "\n",
+    "字符|类型 | 字符|类型\n",
+    "---|--- | --- | ---\n",
+    "`  '-'\t`| 实线 | `'--'`|\t虚线\n",
+    "`'-.'`|\t虚点线 | `':'`|\t点线\n",
+    "`'.'`|\t点 | `','`| 像素点\n",
+    "`'o'`\t|圆点 | `'v'`|\t下三角点\n",
+    "`'^'`|\t上三角点 | `'<'`|\t左三角点\n",
+    "`'>'`|\t右三角点 | `'1'`|\t下三叉点\n",
+    "`'2'`|\t上三叉点 | `'3'`|\t左三叉点\n",
+    "`'4'`|\t右三叉点 | `'s'`|\t正方点\n",
+    "`'p'`\t| 五角点 | `'*'`|\t星形点\n",
+    "`'h'`|\t六边形点1 | `'H'`|\t六边形点2 \n",
+    "`'+'`|\t加号点 | `'x'`|\t乘号点\n",
+    "`'D'`|\t实心菱形点 | `'d'`|\t瘦菱形点 \n",
+    "`'_'`|\t横线点 | |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JrH9RRMVGs9au1cHRkXHMMXbBABg+HP78020+oiJLJIwGDYINGyyO3ewk5DZt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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa1566d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 设置图像大小\n",
+    "f = plt.figure(figsize=(10,6), dpi=80)\n",
+    "\n",
+    "# 画图，指定颜色，线宽，类型\n",
+    "p = plt.plot(x, c, 'b-', \n",
+    "         x, s, 'r-', linewidth=2.5)\n",
+    "\n",
+    "# 在脚本中需要加上这句才会显示图像\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 设置横轴纵轴的显示区域"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们希望将坐标轴的显示区域放大一些，这样可以看到所有的点，可以使用 `plt` 中的 `xlim` 和 `ylim` 来设置："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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P2DdpYjYecdHvv7MfDAC88QZwzz1m4/Gha67hTQLAPr1JZRjEAypVAl5/nePp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\niIiIyCP/A3dzHy2lT8uxAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa6fd748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 设置图像大小\n",
+    "p = plt.figure(figsize=(10,6), dpi=80)\n",
+    "\n",
+    "# 画图，指定颜色，线宽，类型\n",
+    "p = plt.plot(x, c, 'b-', \n",
+    "         x, s, 'r-', linewidth=2.5)\n",
+    "\n",
+    "########################################################################\n",
+    "\n",
+    "# 设置显示范围\n",
+    "p = plt.xlim(x.min() * 1.1, x.max() * 1.1)\n",
+    "p = plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
+    "\n",
+    "########################################################################\n",
+    "\n",
+    "# 在脚本中需要加上这句才会显示图像\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 设置刻度"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于三教函数来说，我们希望将 `x` 轴的刻度设为与 $\\pi$ 有关的点，可以使用 `plt` 中的 `xticks` 和 `yticks` 函数，将需要的刻度传入："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Wrw+6G3N3smMHULgwS7oqVgSWLFETV8eYNw944w2uP/4YGDfObDw+pOM5ES/q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\nDprMzMzMCnDQZGZmZlbA/wEMqEO0RIci1gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb866278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 设置图像大小\n",
+    "f = plt.figure(figsize=(10,6), dpi=80)\n",
+    "\n",
+    "# 画图，指定颜色，线宽，类型\n",
+    "p = plt.plot(x, c, 'b-', \n",
+    "         x, s, 'r-', linewidth=2.5)\n",
+    "\n",
+    "# 设置显示范围\n",
+    "plt.xlim(x.min() * 1.1, x.max() * 1.1)\n",
+    "plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
+    "\n",
+    "###########################################################################\n",
+    "\n",
+    "# 设置刻度\n",
+    "p = plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n",
+    "p = plt.yticks([-1, 0, 1])\n",
+    "\n",
+    "###########################################################################\n",
+    "\n",
+    "# 在脚本中需要加上这句才会显示图像\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 设定 x 轴 y 轴标题"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们想让刻度的位置显示的是含有 $\\pi$ 的标识而不是浮点数，可以在 `xticks` 中传入第二组参数，这组参数代表对应刻度的显示标识。这里，我们使用 `latex` 的语法来显示特殊符号（使用 `$$` 包围的部分）："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AbvsEeaIFsF4rTx6uu3Rh3bS4xODB9r/x1q2B+Hiz8RikZEvEQZGRdrFw7tz8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\nJSIiIpKg/wNXpXrhRgRjdwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xba18630>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 设置图像大小\n",
+    "f = plt.figure(figsize=(10,6), dpi=80)\n",
+    "\n",
+    "# 画图，指定颜色，线宽，类型\n",
+    "p = plt.plot(x, c, 'b-', \n",
+    "         x, s, 'r-', linewidth=2.5)\n",
+    "\n",
+    "# 设置显示范围\n",
+    "plt.xlim(x.min() * 1.1, x.max() * 1.1)\n",
+    "plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
+    "\n",
+    "# 设置刻度及其标识\n",
+    "p = plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], \n",
+    "               ['$-\\pi$', '$-\\pi/2$', '$0$', '$\\pi/2$', '$\\pi$'], fontsize ='xx-large')\n",
+    "p = plt.yticks([-1, 0, 1], \n",
+    "               ['$-1$', '$0$', '$+1$'], fontsize ='xx-large')\n",
+    "\n",
+    "# 在脚本中需要加上这句才会显示图像\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 移动坐标轴的位置"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "现在坐标轴的位置是在边界上，而且有上下左右四条，我们现在想将下面和左边的两条移动到中间，并将右边和上面的两条去掉："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hatu2Afnzm44qEDTDI3KxmTOZ7ABAy5ZKdhxl0CAmOwDQv78tkx2AJRwjR/LP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\nRETE9RTwiIiIiOsp4BERERHX+xc1HT2p8IKu8QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbdb0ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 设置图像大小\n",
+    "f = plt.figure(figsize=(10,6), dpi=80)\n",
+    "\n",
+    "# 画图，指定颜色，线宽，类型\n",
+    "plt.plot(x, c, 'b-', \n",
+    "         x, s, 'r-', linewidth=2.5)\n",
+    "\n",
+    "# 设置显示范围\n",
+    "plt.xlim(x.min() * 1.1, x.max() * 1.1)\n",
+    "plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
+    "\n",
+    "# 得到轴的句柄\n",
+    "ax = plt.gca()\n",
+    "# ax.spines参数表示四个坐标轴线\n",
+    "# 将右边和上边的颜色设为透明\n",
+    "ax.spines['right'].set_color('none')\n",
+    "ax.spines['top'].set_color('none')\n",
+    "\n",
+    "###################################################################################\n",
+    "\n",
+    "# 将 x 轴的刻度设置在下面的坐标轴上\n",
+    "ax.xaxis.set_ticks_position('bottom')\n",
+    "# 设置位置\n",
+    "ax.spines['bottom'].set_position(('data',0))\n",
+    "\n",
+    "# 将 y 轴的刻度设置在左边的坐标轴上\n",
+    "ax.yaxis.set_ticks_position('left')\n",
+    "# 设置位置\n",
+    "ax.spines['left'].set_position(('data',0))\n",
+    "\n",
+    "###################################################################################\n",
+    "\n",
+    "# 设置刻度及其标识\n",
+    "p = plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], \n",
+    "           ['$-\\pi$', '$-\\pi/2$', '$0$', '$\\pi/2$', '$\\pi$'], fontsize ='xx-large')\n",
+    "p = plt.yticks([-1, 0, 1], \n",
+    "           ['$-1$', '$0$', '$+1$'], fontsize ='xx-large')\n",
+    "\n",
+    "# 在脚本中需要加上这句才会显示图像\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 加入图例"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 legend() 加入图例："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9/HKGZ/5883ys55+XZoOOa95MekTfvjBggIxHj9alVNswDFneWrJErleuhNq1\nrY3Jd13zSakJj1IZ8PLLshsZYOFCqF/f2nj8ld8lPBcuyFTe/v2QP7/smClZ0uthJCbC3XfD7t2y\ncSc8HEqV8noYygpRUbJrKzFRlrg2b5btoepKuqSlVFYtXGgmOy+/rMmOrQweLMkOwLBhliQ7ADlz\nSn9DkI07775rSRjKCrffbp5ZEx4Ow4dbG48f0hkepdLhwgXZdh4dLVuEw8Mte80LCH41wxMRIXfW\nycnSEO6ff6QLroWaN4fvv5fx3LnQoIGl4ShvSU6WDqfbt0tB19atssau0tIZHqWyYtAgSXZAbvY1\n2bEJw5D+J8nJZuWwxckOSP2Oa6NOp05yuLaygezZ4euv5WcxKQnat5fulCpdNOFR6gbCwswW/w88\nAG+9ZW08yotmzzYLRd96S34AfECxYrKyBnDgAPTvb208youqVzdPVF+5Er791tp4/IguaSl1HU4n\n1Kkjp1YHBcmxNvfdZ3VU/s8vlrTOnpXlgqNHpZPyrl1QsKDVUV3idEKtWuYK28aNeqi2bZw/L0X0\nBw/KVF94uNdaJPgBXdJSKjMmT5ZkB+Q0AU12bKRvX0l2QKb4fCjZAUnAJ06UZCc1FTp00NUN28ib\n1zxQNDYW3nvP2nj8hM7wKHUNJ07IDf7p03DLLXITlTev1VEFBp+f4dmyRYpDnU54+GFYvtzrPXfS\nq1s3GDVKxl9+KU0JlU28+KJ53ISeLOuifXiUyqjWrWHqVBn/9BM0aWJpOAHFpxMep1Oauq1ZI2eH\nbN4MlStbHdU1xcfL6kZMjKxu7NolNT7KBg4flp48585B2bJScJgzp9VRWU2XtJTKiGXLzGTn2Wfl\njEhlE5MnS7ID0srYh5MdkAaEn30m49hY+OADa+NRXlSypGwbBdi7Fz75xNp4fJzO8Ch1hYsXpfgz\nIgJuukma6pYpY3VUgcVnZ3hOnoQKFWQd89ZbZR0zTx6ro0qXxo3hl19kvHixnESgbCAlRYoLt2+X\nc2527bJ7+22d4VEqvUaOlGQHoF8/TXZspWdPSXYAxo71m2QH5CxTV7hvvy0nECgbCA42p/guXJCi\nLnVVOsOjVBrR0bIknpgoKxmbNkmvL+VePjnDs2YN1Kwp4/r14bfffLZQ+VrGjDGXtAYMgN69rY1H\neVHTptI3CmDpUqhb19JwLKRFy0qlR9pND3ogsef4XMKTkiJNBbdulQMZd+yQs4v8TEqKbC7btk2W\nYyMi7L4dUAKhAAAgAElEQVS6YSMHDsi20gsX5CiUTZtk9sd+dElLqRtZutRMdl59VZMdWxk/XpId\nkGUtP0x2QF7fxo2TcUICdO9ubTzKi0qVgl69ZLx9uxyDoi6jMzxK8d+6v4gIqVlVnuFTMzxHjkih\n8vnzUK6czO74+dbel1+GOXNkrDOVNpKYKKcc790rPQoiI6FoUauj8jad4VHqer7+WpIdkBt8TXZs\npGtXSXZAij/9PNkBKbx3fRmdO0snZmUDOXNKsT1Ij4KPPrI2Hh+jMzzK9k6fhjvukD/LlJFt6Dfd\nZHVUgc1nZnhWr5YDqUA6S7rWNANA375SuAzwzTfQpo218SgvMQx45hlYtEiK7tet85lDb71Ei5aV\nupZ33zV3df74I7zwgrXx2IFPJDxOJzz0kJwIGxIi/UsCqAdBfLzUsB48KGefRkZC/vxWR6W8IjJS\ntpkmJ0ONGubpx/agS1pKXc2OHTBhgowffVSPj7CVGTMk2QHo0iWgkh2QDswjRsj4+HEYONDaeJQX\nlS8vXcJB2i3MmGFtPD5CZ3iUbRmGdKNdskRufjZvhrvvtjoqe7B8hicuTgqVDx+G4sVh926/ajKY\nXoYhZ5+uWiU7uHbskC9b2cD58/LNPnJEDleLjIR8+ayOyht0hkepK82dK8kOQIcOmuzYyogRkuwA\nDB0akMkOSAnHuHHyZ0qKnrNlK3nzmlN8x47pFB86w6NsKu3uzYIF5Qa/cGGro7IPS2d49u+X4pbE\nROnSt25dwNc3tGsnhcsACxZITauyAcOQngSrV8sU3/bt8rMf2HSGR6m0xoyRZAdkJ4smOzbSo4d5\n0NTYsQGf7IAcqO1azXj/fUhKsjYe5SUOh+zIcE3xde4sSZBNBf4zXakrHD4sLwAgszwdOlgbj/Ki\nVatg1iwZv/KKbTry3XyzbFMHKeX4/HNr41FedN990L69jBcvlrV8m9IlLWU7rVrB9Oky/vNPePxx\na+OxI0uWtJxOqF4dNmyQBm27dkHp0t6NwUJJSVKnFhEhsz2RkVLLqmzg5EnZuXXmDNx2mzQbC4AG\nm9egS1pKAfzzj5nsNG6syY6tTJ8uyQ5Id2UbJTsAOXLIUi7AuXPw8cfWxqO8qEgRs2h53z6z8ZjN\n6AyPso0r+8zt3Ally1odlT15fYYnLk7ucI8cgRIlZHojQHdm3cizz8Jvv0lZx/r1UretbCAlRab4\nwsOlA2VUlCRCgUdneJSaPv3yPnOa7NjIsGGS7LjGNk12QGZ5goOldtXmNaz2Ehwsh6wBnD0L/ftb\nG48FdIZH2cL583KDf/QolCwpdQw2fs2znFdnePbvlwZsFy9CtWqyrmmDnVnX07UrfPKJjL//Hpo1\nszYe5SVpu60GbidKneFR9jZ8uCQ7rrEmOzbSvbskO2Cbbeg30ru37NwC+e9JSLA2HuUlDodkuq5t\n6h9+aHVEXqXPfBXwDh4072YffBBefdXaeJQX/f03zJ4t46ZNoWZNa+PxEfnzw6BBMj5wQLoxK5uo\nWhVat5bxvHmwbJmV0XiVLmmpgNe6NUydKuMVK+RsIWUtryxpOZ2S4W7cKFtwIyIgNNSzn9OPpKTA\nPfdAWJhsU4+KgqJFrY5KecXhw3DHHXDhgvTpWb8+kGY+dUlL2dOWLTBtmowbN9Zkx1amTZNkB6Bb\nN012rpC2hvXcOT1qyVZKlpTnBMCmTbY5TV1neFTAMgyoV0+aCwYHy51s+fJWR6XACzM8cXFyB+uq\nUo+MhNy5Pff5/NSVNaz6HLGR+Hh5jhw5ArfcIs+RXLmsjsoddIZH2c/vv0uyA3J8hP4it5FPPjGr\n1IcO1WTnGhwOGDXKrGHt0cPqiJTX5M5tFnIdOgSjR1sbjxfoDI8KSFqf4Ns8OsNz9CjcfrvcwQZe\nfYJHpK1zW7nSNkeMqdRUeY5s2yYJUFQUFC9udVRZpTM8yl6mTJFkB6BnT012bKVfP0l2QIpUNNm5\noUGDzKOVunTRZoS2kS2buYU1Ph769LE2Hg/TGR4VcNKeIlCqlGzOuekmq6NSaXlshic8HKpUkTvX\np5+WMxRUunz0EQwZIuNZs+QweWUTrvNGgoJkp0eVKlZHlBU6w6Ps45NPzFMEBg/WZMdWevSQZCco\nCEaMsDoav9K9uzkT2rOn2atR2cDIkTLb43RKG+4ApQmPCihHjphbbe+9F5o3tzYe5UUrVkgjNZCi\nlMqVLQ3H3+TLJ6uBIAdqjx9vaTjKmypVgnbtZLx4MSxaZG08HqJLWiqgtG8PX38t4yVL4LHHrI1H\nXZ3bl7QMAx56CNatkym93btlq63KkORkWc2IiIACBWDPHihUyOqolFccPy7F/ufPw113ydJWcLDV\nUWWGLmmpwBcWBpMmyfjZZzXZsZU5cyTZAfjgA012Mil7djlrDiA2VpaElU3cfDP06iXjsDD49ltr\n4/EAneFRASNt3d327TJLq3yTW2d4kpLgzjth714oUkSmJfLlc89j25BhQN26skKYPTvs2gVly1od\nlfKKxEQ5PT0mRhKgqCjIm9fqqDJKZ3hUYFuyxNyQ07atJju28sUXkuwA9O2ryU4WuZoRgixxuW76\nlQ3kzCmNOkGWuFw/CAFCZ3iU33M64f77Zck5cHpnBTa3zfDExkrdwalT0iY/LEymJVSWvfoqzJwp\n4zVrpERK2YDTCdWrw4YN/voLVWd4VOCaMUOSHYAPP/S356bKkuHDJdkBuTPVZMdthgyBHDlk3LWr\nNiO0jaAgs5ArPh4GDLA2HjfSGR7l1xISpMngwYNQooRsztFjk3yfW2Z4DhyQb35iItSoAatWyXqM\ncptu3cxVjZ9/hsaNrY1HeVH9+nIgYbZssHOnPx1GqDM8KjCNHSvJDsDAgZrs2Erv3pLsgDRf0mTH\n7Xr1Mrelf/ih1PQomxg+XJ5Tqanw8cdWR+MWmvAov3XqFAwbJuPKlaXXnLKJrVth2jQZN24MtWpZ\nG0+AKlhQ8kqQUg5XjytlA1Wrmp1b07Z98GO6pKX8VpcuMHq0jBcsgGeesTYelX5ZXtJ66inpCJst\nmxQqV6jgvuDUZS5elF3/+/ZBsWKS+OTJY3VUyiuio+W5lZQEderA0qX+MJOqS1oqsOzfD59/LuNH\nHpFzIpVNLF4sbwBvvqnJjoeFhMhyMcCxYzBmjLXxKC8qUwY6dpTx8uV+f+SEzvAov9S6NUydKmPd\nMut/Mj3Dk5oqPQi2bpVphqgomXZQHuV0wn33yX973rzS29F10KgKcCdPQrlycO6cnDuyebPMrPou\nneFRgWP79svLNzTZsZHvvpNXXZAqWk12vCIoyKyXO39ej5ywlSJFoHt3GW/fLs9BP6UzPMrvPP88\nzJ8vv4TDwqBiRasjUhmVqRmeixdl+Wr/fu1BYAHDgMcflzKO7NnlgNHbbrM6KuUVFy5Ig88jRyA0\nVL75OXNaHdW16AyPCgwrV0qyA/DGG5rs2MoXX0iyA3KEhCY7XuVwmLM8ycnQp4+18SgvypUL+veX\ncUwMjB9vbTyZpDM8ym8Yhuw+XrNGbi6iovRQbH+V4Rmec+ekjuDkST1CwmIvvQQ//igJ0JYtcPfd\nVkekvCIlRWp4du2SfgV790KBAlZHdTU6w6P839y5kuwAvPeeJju2MmqUJDsgBSSa7Fhm8GCpWTUM\n6NnT6miU1wQHmweLnjljHj/hR3SGR/mFlBS5kwwP9/WbC5UeGZrhOXZMZnfi4+GBB6QBmu/3Aglo\nHTrAxIkyXrZMWrQoG7hymn33brj1VqujupLO8Cj/NnWqJDsg7e412bGRgQMl2QEpItFkx3J9+0pZ\nB8gGHj1Y1CYcDhgxQsaJidCvn6XhZJTO8Cifl5AgZRuHDsnNxO7dvrxBQKVHumd49uyRyvSUFHjy\nSbPhoLLcRx/JieoAP/0ETZpYG4/yooYNYd482Sq7fTtUqmR1RGnpDI/yX599JskOwIABmuzYSp8+\nkuyAWT+gfMKHH5oHi/bqZX6blA0MGSLJjtPpV4VcmvAon3bmjPk6V6kStGplbTzKi7Zsge+/l/HL\nL0uHZeUz8ueXWR6QtiyTJ1sbj/Kiu+4yT2ueNw/+/tvScNJLl7SUT+ve3VwynjsXGjSwNh7lHula\n0nr6aTm7JzgYdu6UdU3lUxITpRdkTAyULCnLza7aHhXgDh6U52RiItSsKUmPb9TX6ZKW8j8HD8K4\ncTKuVUs6LCubWLbMPKiwbVtNdnxUzpyyzAxw+LD5fFU2cOut8O67Ml69GhYssDaedNAZHuWz2raF\nSZNkvHIl1K5tbTzKfa47w2MYckDaunUyXRAVJUdJKJ+Umgr33AM7dsgy1969Zm2PCnBnzkDZshAb\nC5UryzK09QeL6gyP8i87d5o1AQ0aaLJjK//7nyQ7IB0mNdnxadmymUdOnD2rteW2UrCgebDojh0w\nc6a18dyAzvAon9S4Mfzyi2wE2LZNauRU4LjmDE9KitwpRkTINMHevTJtoHyaYUjzwZUrISREanlK\nlbI6KuUVaQ8WLVNGnrs5clgZkc7wKP+xZo0kOwCvvabJjq1MmSK/MEH2Omuy4xccDvOkgYsXpTGh\nsolcucyTZKOj4auvLA3nenSGR/kUw4C6dWHFCrlTjIyE0FCro1LudtUZHu0w6ffSzszu2AF33ml1\nRMorkpPlm71nD9x8s/yZJ49V0egMj/IPv/8uyQ5Ax46a7NjK55+bHSb799dkxw8NGmT2o/v4Y6uj\nUV6TPbt88wGOH4cxY6yN5xp0hkf5DKdTzobcvBny5pXyjSJFrI5KecJ/ZnhiY2W3x5kzcqe4bZv0\n31F+p3VrOfsOYO1aePBBS8NR3uJ0SnPQLVus/gWuMzzK982ZI8kOQNeumuzYyvDhkuyAtK3XZMdv\n9etn1qz26mVpKMqbgoLMLXrnz/vkdj2d4VE+ITlZjo6IioKiRWUJOG9eq6NSnnLZDM/hw7LLIyEB\natSAVat8pWOryqT33oNPP5XxH3/AE09YG4/yEsOARx+F5cutLMLUGR7l2779VpIdkLV/TXZsZOBA\nSXZA7go12fF7vXqZNas9esjroLIBh8Oc2bl4UWrxfIjO8CjLpW3jULq07EoOCbE6KuVJl2Z4oqKk\nZiclRc7O+u03q0NTbtKvn/l6N2cOvPiipeEob2rUSA4/tGa73jXvmDThUZYbMcJs1jl1qp6I7q/G\njRtHZGQkxYsXZ8eOHQwYMIDy5ctf9WMvJTyvvmp2Z920Ce6914sRK086dw7KlYOTJ6F8eQgL09Is\n2wgLgypVZGqvSRP46SdvfnZNeJRvSnsUy113wdatvnAUi8qoSZMm8dVXX7F27VoAFi1aRIcOHdi1\naxc5r7K93OFwYGzebCY4TZv6fFt6lXFjxsAHH8j466/lfDxlE9Zt19MaHuWbRo6UZAdg8GBNdvzV\noEGDeO211y5dP/XUUyQlJfHdd99d+x+5tvAEB0sdjwo4b71lHjHRr59ZqqVsIO12PR8p5PKJhGfZ\nsmVWh6As8NNPyxg7VsYPPSSHhCr/ExkZyf79+6lcufKl9zkcDu666y7++OOPa//DhQvlz7ZtpYhL\nBZycOc06nkOHYMIE/X1vG2XKSMYLsHQp/Pmn5d97TXiUZYYNW3bpjm/YMN2c46/27NkDQL58+S57\nf548eYiOjv7vP0h7p3fTTdC7twejU1Zr2dKsWR0yBBYtWmZpPMqL0m7X69mTZUuXWhqO9QlPWBjE\nxVkdhfKyPXtg40YZ168vJy0r/3Tm34aBuXPnvuz9efLkufR3l5k/3xy/+y6ULOnJ8JTFgoNluRrg\n9GlYvdraeJQX3XwzdOki440bYedOS8OxLuFJTpaipipVQGd4bKdPH/NGf8gQa2NRWRP879abbFcU\nYCUnJ5Oamnr5B6emmrU7BQqY2/NUQGvUyKxZXbMGjh2zNh7lRV26mG3z//pLXvstct1dWg6Hw/oq\nI6WUUkqpdDIM46oFEtftinCDLetZt3Ur3HOPjHVbqm08+6z0l8uWDcLD4Y47rI5IZUVMTAxlypRh\n5cqV1KpV69L7a9WqRalSpZg1a5a8IykJKlSA6GgcgBEfD7lyWRO0skS9enLURPbssGuXtKRQNnDx\nonSefOstaTDq2YJNH92WXrWqNB4DmDXLPDlSBawVK8xmum3barITCEJDQ7nzzjsJDw+/9L7k5GTC\nwsJ4Iu0hSl99BWmLmDXZsR3X8nVyMvTta20syotCQuDXX+GZZyzdnWJ90fKAAWb7zZ49rY1FeZRh\nmN/inDmljkcFhjZt2jBlypRL1z/88AOFChXiVdcNTVwcDBok43LlvB+g8gkPPAAvvSTj776D7dut\njUfZi/UJT7ly0L69jH//Xfbrq4A0f765Q6NzZ92cE0jef/99HnvsMdq1a8eAAQP4+eefWbx4Mblc\nsziffmpWqmqTQVsbOFCWsw3DrF9Xyht842iJI0ek8diFC1C9upTxa1OWgJKaKuVaO3ZA/vywdy8U\nKmR1VMorTp2SYo1z52QZe9MmHNmyeb5GUPmsdu3gm29kvHIl1K5tbTwqoPhoDY9LiRLw3nsyXrtW\nTllVAeX77yXZAekynpFkJzo6mt7anM5/DRsmyQ4w7qGH6PTuuwA0bdqUyMhIKyNTFunbF3LkiAZ6\n+8qpA8oGfGOGB+RApbJl5TTJO++UxV09WCkgXLwom3P275fcNioqY/Wqw4YNo0aNGtSpU4eEhARG\njx7NyZMn2bVrF0FBQQwbNowqVap47gtQmXfwoFSmJyYy6Y47+KpgQdauXYvD4WDhwoXXPWBUBbY6\ndYaxYkUNoA6//gqPP67PbeUWPj7DA9KEzFXRGh4O06dbG49ym4kTJdkBKVTO6Oac5cuXU+ffVswD\nBw6kVatWjBkzhoULF3LfffdRu3Ztdu/e7eaolVsMGACJiQAMiovL+AGjKmBlz76c/Pnled2zJ/Tv\nr89t5Vm+k/AAdOpkVrL27XvpF6XyX+fPm5tzbr8d2rTJ2L8PDw+nYsWKACQmJjJu3LjLdgN1796d\nhIQExo0b56aIldtERsK338rw0UfZf/Roxg8YVQEpPDycKlUqXmq0vWNHImPH6nNbeZZPJTwbd+6k\nkmEQBATFxBCUKxdBQUGX3tq2bWt1iCqDRo+GEydkPGiQNBzLiJkzZ17a2pyamkqRIkWIj4+/9Pd5\n8uShUKFClw6wVD7ko4+kWt3hYM+LLwIZOGBUBTTX8/rdd6F4cYBUUlKKcO6cPrcDycaNG6lUqdJl\nr+NWvqb7TMJz4sQJBgwYwOQ5c9gUGkoPILpAAd5u25bo6Giio6OZMGGC1WGqDDhxAkaNknHFihvp\n3z/jP/jr1q2jWrVqgBxOGR0dzbBhwy79fXx8PMePH6ec9nbxLevXw48/yrhFC84UKABk4IBR5Tcy\n86Lmel7nzu1qQJib1NRoQkP1uR0oLr2mT57Mpk2b6NGjB9HR0bz99tuWvab7TMKzdu1aJk+eTPVa\ntQhr2JD6QOiZMzi2bCE0NJTQ0FBy5MhhdZgqAwYPln5zcIICBTL+g79hwwYeeOCB636O77//nly5\ncvH+++975otQGWcYshUPIEcOGDAgYweMKr+RmRe1K5/XbdqYvSgHDpRlcNDntr+79JpevTphYWHU\nr1+f0NBQHA6HZa/p1z1LKyvmzp3LjBkzbvhxhQsX5ssvv+S555679L75x48z5d57Ob95M7GbN8Px\n43LMvPIb0dHwxRcyrlJlLfPnT6Zw4UJ89913//nBv5bZs2dfVuR6pdjYWAYNGsSECRMoq4fy+I4/\n/pBTkUHOzilThqL79gHgdDov+9D4+HgK/Dv7o/yP60WtUKH0P7evfF5nzy7L3c2ayazwmDHw7rv6\n3PZ3l72mz5/PlClTOH/+PLGxsZbF5LGEp2HDhjRs2DDD/+748eMcP3GCkOHD2VevHgkpKXIAy9ix\nHohSZUZ6ktn16yEpqTDwJd988xyFC8v70/uDbxgG27dvv6zINS2n00mbNm3o168frVq1yuyXotzN\n6TRnd/LkkToeuLQscezYMW6//fZLH3769OnLrpV/yeiL2rWe1y+/DCNGyHGKI0c6WbdOn9uB4vjx\n4xw/fpyQkBD27dtHQkKCZbF4LOHJrMmTJ1O7dm144glO338/hzZulKmC996DMmWsDk9x42R2xw64\n+24ZN2kCDz4o44z84K9cuZKHH374mn/fq1cvWrRoQePGjQHYs2ePrvX7gh9+MA8B7toVihYFLj9g\n1HWiuuuA0ddff92qaJWbpPe5fa3ndVAQDB0K9etDXFwvHI4WvP66PrcDwaXXdOQG59ChQ5bF4jM1\nPAApKSlMmDBBTlh2OMjftSsbgfNJSdCvn9XhqXTq1UvKOIKCpI7HJSM/+LNnz6ZZs2ZX/buJEydS\no0aNS8kOwLRp09wTvMq8pCT4+GMZFy0KH3xw2V/f8IBR5bfS+9y+3vO6Xj0oX34iUIPFixtf6t2l\nz23/ddlrOpA/f342btzIeVehlpf51AxPREQE+fLlu3QHWK5RI0Jz5+ZEfDx5p02TO8ZrLHEo37Bq\nFfz6q4xffx3+baFz6QfftRSW9gc/b968lz1GSkoK0dHRV127nzdvHj/88AP16tUjPDwckDqQm266\nyXNflEqfb74B1xbi3r3hiu/r+++/T2xsLO3atQP47wGjyi+l97l9vec1wK+/ziNfvh+AeiQlhdOk\nCTzzjD63/dl/XtPLlSM0NJQTJ0785/e+VxiGcb036+3caRhBQYYBhtGggdXRqOtwOg2jdm35VoWE\nGEZMjPl3O3bsMCpXrmykpqYahmEYCQkJRrly5Yw9e/b853EWLVpkjB49+j/vP3nypJE7d24jKCjI\ncDgcl96CgoKMOXPmeOzrUulw/rxhFCsm3/zbbjOMixev++Hyq0cFgvQ+t6/1vDaMy5/b4Lj0ps9t\nlQnXzGl85yyt62nT5lLHVv7+G/7NFpVvWbAAXDWMXbvCyJGZe5w2bdowcOBASrq6bivfN2iQzOoA\nzJgBzZtf98MdDoeelm4z6X1eh4fLRL7TCQ0bwi+/eClAFSj84Cyt6+nXD0JCZNy9ux6t64OcTvMo\ntHz5zI06GXXx4kVOnDihyY4/OXlSttgAVK0q+4uVSiMjz+s774TWrWU8dy6sWePZ2JR9+EfCU6oU\nvPOOjNMWiSif8f33csA9SE7q2oaeUb/99hvPPvus+wJTnjdkiNktbuhQqVZXKo2MPq/T3uP26KH3\nuMo9/GNJC+D0aWnHGRsLlSrB1q0Q7FM117aVlAQVKkizweLFISoKrjhBIN2aN2/OuHHjKJzZjEl5\n1/79UL68/BDUqQNLl4LjmjPKl+iSlr1k5nndtSt88omMf/sNnn7aQ8GpQOPnS1oAhQqZayY7d8LU\nqdbGoy756itJdgD69Ml8smMYBsHBwZrs+JO+fSXZARg2LF3JjrKXzD6ve/aU5XHX+Iom3UplmP/M\n8AAkJMjd5MGDcMstEBkJuqXVUufOwe23S0v4cuWk4DCjJ6IrP+XqMGkY0Lgx/Pxzuv+pzvCo9Bg8\n2GztNH06tGhhbTzKLwTADA/ATTfBgAEyPnQIPvvM2ngUo0ZJsgOyUUeTHRv56KOrd5hUyk3ee0+W\nyUESn8REa+NR/s2/ZngAUlNlJ0hYGOTPL43OdAnEEkeOyOzOhQtw//2wbp3Wq9rGqlXwb2dd3ngD\nJk3K0D/XGR6VXhMnQocOMv7kk/808FbqSgEywwOQLZvsBAE4e9YcK6/r31+SHZCeO5rs2IRhmH0H\nQkL02BflUW3ayKYIkFnkM2esjUf5L/98iXruOXAdQPfZZ1w6dEV5za5dcpIAyO6JRx+1Nh7lRQsW\nSANQkHYRpUpZG48KaMHBUg8Pkuy4xkpllP8tabmsWQM1a8r4tdcgzaGEyvMaN5YOqA4HbNlino6u\nAlxKiiwp79wpS8p798oOygzSJS2VEYYhK6irV8uk4u7dmmerawqgJS2XGjXkVRdg2jTYts3aeGxk\n1Sqz3XurVprs2MqUKZLsgOwVzkSyo1RGORzmUTUXL0r7C6Uyyn9neEDWVSpXlkLmZ56RqXblUXqn\nZWPx8XDHHVKtXqoURETIzslM0BkelRlNmsD//qczy+q6AnCGB6BiRaloA2nFuWyZpeHYwS+/SLID\n0LmzJju2MmaMJDsg1aOZTHaUyqyhQ2XfStq6eaXSy79neAAOH5a90QkJ8OCD8M8/2u3VQ1JSZEIt\nIgIKFpSOAAULWh2V8opjx+R5FhcnNTwbN8orTybpDI/KrA4dZKs6wJIl8Nhj1sajfE6AzvAAlCwJ\n778v43Xr4KefrI0ngE2aJMkOSM85TXZsZMAASXZATkbPQrKjVFb07Ws22P/wQz1yQqWf/8/wgPTj\nKVcOTp2SGoOwMG3562ZxcfJfe/QolC4t5VM5c1odlfKKiAi46y6platXD37/PcsPqTM8Kiv69jWb\n7s+cCU2bWhuP8ikBPMMDsj3WdeDK7t0Z7vqqbmz0aEl2QMo3NNmxkZ49JdlxOGD4cKujUYquXeHm\nm2Xcq5fs3FLqRgJjhgfkJ75iRTm2u1gxiIqCPHmsjiogHD8uE2hxcXDPPVK+oV2VbSLtERKtWsHU\nqW55WJ3hUVk1fjx06iTjsWNlE4VSBPwMD8ge6UGDZHzsmBy6otwibfnG8OGa7NiGYUC3bjJO+/xS\nyge0by919AADB0plg1LXE1gvXc2ayRQESGHl4cPWxhMAdu82d0Q8+aSUcCib+Pln6WgOcmy19iBQ\nPiR7dvMoxVOn5Fe+UtcTOEtaLn/9BY8/LuNMnOKsLvfSS/DjjzLetAnuvdfaeJSXJCdDpUqyNFy4\nsPxZoIDbHl6XtJQ7GIY03V+7VtpC7d4Nt9xidVTKYjZY0nJ57DE5XBRg8mTYutXaePzY2rVmstO8\nuSY7tjJxoiQ5AL17uzXZUcpdHA5zZichQXZvKXUtgTfDA5cfOfHEE7B4sTYjzCDDgLp1YcUKyJFD\ndsYEjeAAACAASURBVCaXKWN1VMorzp2TKvWTJ6FsWQgPlx8CN9IZHuVOzz8P8+dLfeG2bdJFQdmW\njWZ4QHZrvfmmjP/8ExYutDYePzRvniQ7AB07arJjKyNGSLIDMGSI25Mdpdxt2DBJdpxOaUao1NUE\n5gwPwIkTUsJ/7hzceaek/cHBVkflF5KS5A4pKkq6Ke/eLWUcygYOHZIOkwkJUK2arGt6YHZUZ3iU\nu7VvD19/LePff9cNFjZmsxkegKJFpSMVyJT8N99YG48fGT/eLN/o00eTHVvp00eSHYCRI3UpWPmN\nAQPM1msffCBn/ymVVuDO8AAkJsry1v79kgBFRUG+fFZH5dNOnZKJsdhYudHfsUNXNGxj+3Zp6+B0\nSlHEvHke+1Q6w6M8YehQ8z73yy/NygZlKzac4QE5/2DYMBmfOGGO1TX17y/JDsCoUZrs2Er37pLs\nBAXpERLKL733HoSGyrh3b21GqC4X2AkPwCuvQPXqMh4zBmJirI3Hh+3aBRMmyPjRR+UmX9nEokVm\ncX/btlL3ppSfuekmM1c/cUJq7pVyCewlLZfVq6FWLRk3bw4zZlgbj49ybe10OKTJoKtptQpwyclQ\ntarUuuXNK1XqxYp59FPqkpbyFMOAmjXhn39khnrXLrjtNqujUl5k0yUtl5o14cUXZfzdd7B+vbXx\n+KA//5RkB6RBtSY7NjJxoiQ7AB9/7PFkRylPcjhkMh9kx2n37tbGo3yHPWZ4APbskWn65GR4+GFY\nvlx3oPwrNVW6KG/fDrlzyw1+iRJWR6W84vRpqU4/fVqaDO7cKQeFepjO8ChPe/VVmDlTxitXQu3a\n1sajvMbmMzwgnWPfeUfGK1fC//5nbTw+5NtvJdkB6NlTkx1b6d9fkh2QKnUvJDtKecOwYbJvBWSb\nutNpbTzKevaZ4QE4c0b2XJ8+LX+Ghdl+G9K5c3KDf/y47G7YtUsK/5QNhIdDlSoyxVe3rhy866VZ\nT53hUd7w0Udm4fL06dCihbXxKK/QGR5A2gb36SPjqChzS5KNDR0qyQ7IHZEmOzbSpYskOw4HjB2r\nS7wq4PToAcWLy7hnT7hwwdp4lLXslfAAvPWWzO6AtOZ0TefbUHS0WdxXvTo0bWppOMqbFi68fBt6\n1arWxqOUB+TNC4MGyfjgQfjkE2vjUday15KWy//+B02ayPj992H0aGvjsUjTpjB7toxXr4YaNayN\nR3lJcjLcfbesX+bNK7OdN9/s1RB0SUt5S2oq3H8/bN0KuXLJpoySJa2OSnmQLmldplEj2akF8Nln\n8ovfZlavNpOdpk012bGVL780f+Z79/Z6sqOUN2XLZt7TXrggdT3Knuw5wwOwebOk/YYhx+ouWmSb\nGganUxKcdetkU05EBJQubXVUyitOnZIq9TNnZOdiWJglO7N0hkd5W8OGcjycwwEbNsB991kdkfIQ\nneH5j3vvhfbtZbx4Mcyda208XjRrliQ7INs1Ndmxkf79JdkB3YaubGXkSAgOlnvcDz6QP5W92HeG\nB+DkSShfXl4AypSRpmsBvk3pwgU5QP7AAWmou3u3lHEoG9i5U2p3UlPhscekvbZFs5o6w6Os8N57\n8OmnMv75Z2jc2Np4lEfoDM9VFSkCAwfKODpa7ngD3KhRkuyA7F7QZMdGXNvQg4Jke55NlnCVcunT\nR7qTAHTtComJ1sajvMveCQ/Am2/KXS9IU5oAPk193z75EkF2Ib/+urXxKC9auFDq1ADatTN/5pWy\nkUKFoF8/Ge/dK8tcyj7svaTlsmIF1Kkj45degh9+sDYeD2nUyCxV0rNlbCTtNvR8+WQd0+KdWbqk\npaySkiIFy9u3y9ET4eFS0aAChi5pXdcjj5hd9+bMkRb7AWbhQjPZadlSkx1b+eILcxt6nz6WJztK\nWSk4GD7/XMaJidKKTdmDzvC4HDwIFSpIVe9dd8GWLfLMCAAXL0LlytJfLl8+2YbuareuAlzabeg+\ndH6czvAoq7VoAd99J+OFC6F+fWvjUW6jMzw3dOut8PHHMg4LC6hztkaNkmQH5DQNTXZspEePy7eh\n+0Cyo5QvGDnS3LTxzjtyY6gCm87wpHXxoszu7NkD+fNDZKTfT//v3w933gkJCXIw9qZNATNxpW7k\nn3/MFtr168Nvv/nMziyd4VG+YMwY6ckDMHgw9OplbTzKLa75S04TnivNnw/PPy/jtm3h66+tjSeL\nXnhB+k0ALF8u5UrKBlJSoFo1WZoNCYEdO8xDc32AJjzKFyQnSw/asDBpwbZrF4SGWh2VyiJd0kq3\n556DZ56R8aRJ0oPcT/3+u5nsNG+uyY6tTJggyQ5Az54+lewo5SuyZ4fx42WckGDO9qjApDM8V7N7\ntyxtJSdD9epy0maQf+WGFy/KEpark3JEBJQoYXVUyiuOHJEC/PPn5bysHTtk/60P0Rke5UtefRVm\nzpTx4sXw5JPWxqOyRGd4MuSOO8xUf+1amD7d2ngyYfRoSXZAGm1psmMjXbpIsgOy/9bHkh2lfM2o\nUZAnj4zfeQeSkqyNR3mGzvBcS1yc3CUfPiyHTkVESCGzH4iJkUJl1w77zZtl6lbZwJIl8MQTMn7x\nRekr5YN0hkf5mlGjoFs3GQ8bBt27WxuPyjQtWs6U77+X4heQu2Y/OWvrpZfgxx9lvHQp1K1raTjK\nW5KSpKNyRATkzi0VmLfeanVUV6UJj/I1ycly5E54OOTKJU+fUqWsjkplgi5pZUqzZmZL4k8/lVoI\nH/fnn2ay07SpJju28sknkuyArGP6aLKjlC/Knt3swHzhgtzjqsCiMzw3smUL3H8/OJ3S0+Tvv322\ngDntDX6ePHKHcsstVkelvCI6GipVkq0mfrCOqTM8ylc1bQqzZ8v4jz/MFWLlN3SGJ9Puucc8bGXN\nGpg40dp4rmPMGPMGv29fTXZspXNnSXZAzs7y4WRHKV82apSsCIMWMAcaneFJj/h4uWvev18Oo9q5\n0+eyiYMHoWJFCfXOO2HrVn3Ns41586BhQxm/9hpMmWJpOOmhMzzKl40YYRYtjxhhFjMrv6BFy1m2\ncKHZkLBJE/jpJ2vjucLLL5sbcpYsgcceszYe5SUXLshS1v79UKCATPH5wXEomvAoX5aUJAXMu3bJ\nbE94uBYw+xFd0sqyp5+WImaQ9sW//GJtPGnMm2cmOy+/rMmOrQwZIskOwNChfpHsKOXrcuSAzz6T\ncXw8vP02aH7u/3SGJyOOHZP1ojNnZElr505Z4rLQ2bNyg3/4sNzg79ypTQZtIyJC2mknJ8MDD8hh\nodmyWR1VuugMj/IHLVvCjBkynjlTCpqVz9MZHrcoVszsxXPoEHz0kbXxAB9+KMkOyK5kTXZswjCg\nY0dJdhwOKVT2k2RHKX8xZgwULSrjd96BkyetjUdljSY8GfX662Zzm/Hj5a7aIsuWwVdfyfjxxyU0\nZROzZkmxFsBbb8kMj1LKrYoUgXHjZHzypLlhV/knXdLKjMhIaXhz8SJUrgybNnl9S9SFC1JUFxUF\nN90kPRHLlvVqCMoqJ07IOubJk1Kzs2sXFCxodVQZoktayl8YhmyC/PVXuV6wwNy/onySLmm5Vfny\n8PHHMt6xw5IjJ/r1k2QHYNAgTXZspVMnc2593Di/S3aU8icOB0yYYJZrduhgns2r/IvO8GRWUhLc\ne69UCYeEwPbtcsq6F2zYANWrS/PnBx+E1au1fMM2fvpJDgUFaY/w44/yG9nP6AyP8jcTJ0qyA1I+\n5zqGQvkc7cPjEatXQ61aMn7sMTnIysMvPq4NOdu2QXCwrKZVqeLRT6l8xcmT0gDz+HEoVEiS7WLF\nrI4qUzThUf7G6ZRf88uXy/XKleZRi8qn6JKWR9SsKQWjAH/9BdOmefxTjhwpyQ5Ar16a7NhK586S\n7IAsZflpsqOUPwoKgq+/hpw55bptW0hMtDYmlTE6w5NVZ89Kb54jR+Sue9cucx+jm+3aJYXKSUny\nKTdvltU0ZQNz50KjRjJu0EAaX/rhUpaLzvAof5X22IlevWDwYGvjUf+hS1oe9fPP8MILMm7RAqZP\nd/uncDrhkUdg1Sp5nVu1Sg5vVzZw+rQsZR09GjDdJTXhUf4qJUVqKDdtkrKC9evljGnlM3RJy6Ma\nNzYPb5wxA37/3e2f4osvJMkBaYClyY6NvP++JDsAn37q98mOUv4sOBgmTZKNIikp0KaN/Kl8n87w\nuMvBg7LOFBcHJUvKrq1Chdzy0DExcoMfFwelS8tO+Dx53PLQytctWADPPSfjZ56B+fP9einLRWd4\nlL/r1UuOrwM9Ud3H6JKWV3z9NbRvL+OmTeXwlSwyDHj2WTmsHWTyqF69LD+s8gexsZLpHj4sTUDC\nwuDWW62Oyi004VH+LjFRaiojI6WQeds2r3UmUdenS1pe0bYtPP+8jGfNgu+/z/JDfvedmey89pom\nO7bSpYt5UNqYMQGT7CgVCHLmlKUtkOSnXTuptVS+S2d43O3YMdkrfuIE5M8vS1ulSmXqoY4elZMr\nTp2SEwTCw922SqZ83aJF8PTTMn7qKcl6A2Apy0VneFSg6NhROjGD/OnqVKIso0taXpV2C/Fjj8Ef\nf0gThwxwOmUpa9EiuZ49G15+2c1xKt907pwsZR08CHnzStFWaKjVUbmVJjwqUJw7JzemBw7IuYab\nNkHFilZHZWu6pOVVDRtK6T5IQ8JPP83wQ4wbZyY7r74KL73kxviUb+vWTZIdkHPaAizZUSqQ5MsH\nU6bIBGxCAjRrJudKK9+jMzyecv68NGfYu1e6A27YILcB6bB1q5yRlZQEZcrAli2yOqZs4M8/4ckn\nZfz44zI7GEBLWS46w6MCTY8eMHy4jLt0seRMaSV0ScsSq1ZJt0CnU8r51669YWvkCxegWjXpLRcU\nJOe11KzppXiVtc6ckSQ5JgZy55alrDJlrI7KIzThUYEmKUl+V2/cKNe6o9YyuqRliVq1JO0Hmbbp\n2/eG/6RrV0l2APr00WTHNgxDlkFjYuR65MiATXaUCkQ5csjG3Ny55fq112TvivIdOsPjaUlJ0hZ5\n0yZZmli+HB5++KofOm+e2bC5Vi1Ytky6eiobGD8eOnWSccOG8L//BeRSlovO8KhA9e23Zgnnc8/J\n7/UAfir7Il3SstTOnXD//dKsoUwZme3Jl++yDzl8GO6+W7ag58snH6I3+DaxZYsczpOUJC0MtmwJ\n+P4DmvCoQGUYsqP2xx/levx4ePtta2OyGV3SslSlSmY1W3Q0dO582V87nTL9eeqUXH/5pSY7thEX\nB6+8IslOtmzSnTvAkx2lApnDAV99ZbZf69JFmqQr62nC4y2dOpm7b6ZMkRPW/zVmjGzOAWjVSrY1\nKpt4+23pTQ8wYICsZSql/FrBgjB9uiQ/iYnyOz0x0eqolC5pedOhQ9KF+cwZKFyY/7d392FVl2cc\nwL9AwAwVpxbMF7zUaZmkaSXTtAHaixaTzOmcuGiZl9NcpWuZ5FIjW+Vm6mw2remgrUZZqKnYixpt\nTvGNLpYvu4Yv+ZIvJKQiCpzf/vju9NMEQTnnPOf8zvdzXVz8Hg5yHjlc59znee7nvlFUhG2HY5GQ\nAFRWAh06cDejSRPTExWfWLIESE/n9YABPNZxmQUqA5W2tCQYZGQAM2fy+tFHgZdfNjufIKEcHr/x\n1ltsLAqgasBduHH/SuzcHYqwMJ5iT0gwPD/xjZ07mddVXs6+IYWFQGys6Vn5jAIeCQaVlUDfvsCm\nTRyvXGl3jBGvUQ6P3xg+HBg5EgBw1Yd5GL57BgBg+nQFO0HjzBn+HZSXc807Ozuogh2RYBEezqPq\njRtznJ7OdotihgIeE155BSdbXwcAmIbpeLLrim/K9UgQmDQJ+OwzXk+ebOd2iYjjdOwI/OEPvD56\nFHjwQZ7kEt/TlpYBBw8CQ7vuwJqyXmiCU3A1jUbo5gKgUyfTUxNve+cdYOhQXvfpw7pMQVhsSVta\nEkwsiz0R33yT4zlzgF/+0uycHEw5PP6iogJITGSXifuwFEtxP2+Ijwc2bLDXPsV59uwBevQAysp4\njGP79qBtDKqAR4JNaSk7x+zbx62ujz9mfo94nHJ4/IFlAWPHMtgBgGvGDLFbTxQVAaNHa63TqSor\neTa1rIzj118P2mBHJBg1a8Z8nvBwPh3cf7/dSUZ8QwGPD82ezZPIAMutzJsHIDOTR5IBnuCaPdvY\n/MSLMjLsSHfCBCA11ex8RMTn+vRh5WWA+TypqTy7IL6hLS0fycsDBg1iVeW2bYGCAiAm5v83Hj8O\n3HIL1zrDwoAPPgCSkozOVzzo3XeBIUN43aMHty4jI83OyTBtaUkwmzDBTmQePpwF1tVvy2OUw2PS\n7t1Ar17czWjUiPV2evT41jdt3cpln4oK4JprgC1b7NrkErg2bwZuv51H0Rs35uOs5HQFPBLUKiuB\nu+4C1q7l+LnngClTzM7JQZTDY0pZGfCjH9mpG4sX1xDsAEDPnmyiBQDHjnGDV7XIA9sXXwApKQx2\nQkO5ZalgRyTohYcDOTlA+/YcZ2Swq7p4lwIeL6quZp7qrl0cZ2Swi26tHnjAbqtbUKBzi4Hs5EkG\nO19+yfGcOdzTFBEBuwstW2YfzB05Uk1GvU0BjxdNmQKsWsXrwYPZG7JOs2cDvXvzeuFCYNEir81P\nvMQd6RYWcvzII/wQETlPfDwLrQPAqVPcDSgpMTsnJ1MOj5dkZwOjRvG6a1fmqda7KejBg+yzdOQI\nEBEB5OczCUgCw2OPcUUHYOOcZcuCsrjgpSiHR8SWmQlMncrr5GRg9Wpue8kVUdKyLxUUAP36AWfP\nAs2bs3Fcx46X+UPy8/mXX1UFtGnDI82tWnllvuJB8+fbqzk33gh8+inQtKnZOfkhBTwiNsviaa2c\nHI4nTADmzjU7pwCmgMdXDh/mCfNDh3jCPC8P6N//Cn/Y3LnAo4/yOj6ebQiaN/fYXMXDVq8G7rmH\ntQdiYhjpqrhgjRTwiFzo9GlWXt6+neNFi4CHHjI7pwClU1q+UFEB3Hcfgx2A6ThXHOwADPPHjuV1\nURFfTE+fbvA8xQuKipiR7nKx9sDy5Qp2RKTeoqKA3FxWJQGAX/yCJUzEcxTweIjLxWjcXUx39GgP\n5KmGhLA61fDhHP/rXyxgd/ZsA3+weNSXXzIYPXmS46ws4NZbzc5JRAJOXBywdKndfmLIEOC//zU9\nK+dQwOMBLhcXYv76V45vu42pHB6pnBkWBvzlL8Ddd3O8Zg2zoaurPfDDpcHOnOERPHdTnOefZw0l\nEZEr0Lfvhe0nkpNZhF8aTgFPA1kW02wWLuT4hhvYSSAiwoN3EhEBvP02G7EAzGwbN06NRk1zuVg7\nadMmjh98EHjySbNzEpGA9/DDduXl/fvZaejAAbNzcgIFPA1gWcATT9g9UTp1Aj780N6D9aioKGDF\nCp78AYA//Um1yE2yLGDSJPtYRWIiK2WrIY6IeEBmJjBxIq/37OFKz+HDZucU6HRK6wpZFvD008DM\nmRx36MBDVG3aePmODx/mmmdxMccvvQT86ldevlO5gHtZb948jjt3ZqElnaCrN53SEqmbZbHgvvtN\ndZcuwLp1wLXXGp2Wv9OxdE+bMQN45hlex8UBn3wCtGvnozsvLmbQ4w73X3sN+PnPfXTnQc7lAsaP\nt/uetW/PDoA+e/CdQQGPSP24c0TdaRPdugEff8zWFFIjBTye9MILwOTJvG7dmis7l11YsKGKitiF\n+8QJNqbMyWFKv3hPdTUwZgzw+uscd+rEZx6vL+s5jwIekfpzufiedskSjnv2BD76CGjWzOy8/JQC\nHk95+WXg8cd5HRPDYOe66wxNZsMGYMAAoLycic3vv8+xeF51NZOSs7I4vv56PuOo+vUVUcAjcnmq\nq3lA929/4zghgYd2Vcj9Igp4POGPf7Sbmbdsyb3Url2NTol/8ffey6INUVEcu09ziWdUVfGZ5s03\nOe7alcFOTIzZeQUwBTwil6+qCvjJT4B33uG4b182qHZ3XBcAqrTccK+9Zgc73/0uT2MZD3YA4M47\ngTfe4Omg06dZ2nnpUtOzco7KSj7DuIOd7t2Zs6NgR0R87KqrWO8tJYXjTz9lh/XycrPzChQKeOoh\nK4t1EQAuH65Zw9c9v/HjHwN//jOLFFZUAEOHsq+F3kE3zNmz/N26307dfDNzdrxSd0BEpG4REUzZ\ndNeiXbuWLY0qKszOKxAo4LkEywJ+9zsgPZ3XjRuzGegtt5ieWQ0eeIB1eho35mQnTuTRaVVkvjIV\nFUwCz83lOCGBy3o6ei4ihkVGciE/OZnjNWu42H/smNl5+Tvl8NSiooIHctw5qldfzWbY/fqZnVed\ntm9nXyd3B9PBg7kGevXVZucVSM6cAVJT+SwCMCdq1SplB3qQcnhEGu70aWDQIJZFAVgdIzfXz3Yg\nfE85PJfj0CHghz+0g524OHat9ftgBwBuuolNRt0VmXNzWQX4yBGj0woYR45wrdgd7Nx+O5f1FOyI\niJ+JiuIb8bQ0jvft4/uzt982Oy9/pYDnWzZt4paVuz1S375AQQHjiIDRti2Qn28fUS8oAHr3Bnbt\nMjsvf/fJJ0CPHvbbpeRkYOVKHYEQEb/VqBH7S7/4Is+ulJcz9fCZZ1i/R2wKeM6TlcU39O4Cxg8/\nzNPHAVnGOzqaL9bp6Rzv2cOgJz/f6LT8kmXx2eL8ZjVpacyJiooyOzcRkTqEhLCv44oV9mL0jBk8\nv3LqlNm5+RMFPGBe7xNPAD/7GQ/mhIWxd8mrr3q467mvhYezKvD06RyfOMFVH/cRa+HvJDWVXc6r\nq5kN+OqrfMvUqJHp2YmI1NugQcDGjWzvBwDvvsstrj17zM7LXwR90nJpKTBiBPdBAR7Cycmxs98d\nY8kSYPRoVq4CgGnTgIwMFnYIVlu28C3Q3r0ct2/Pze+ePY1OKxgoaVnEe0pLWT4sL4/jFi341JaY\naHRavqKk5Zrs3g384Ad2sBMfz3QXxwU7AI+tr15tr3dOm8aj1tu3G52WEZbFstl9+tjBzuDBwNat\nCnZEJOA1a8ZOQ5MmcVxSAtxxB5/2gllQBjyWBWRnA7162Xm8qanAP/8JdOhgdm5e1b8/j5tdfz3H\nW7cCt94KPP009/KCwalTzM8ZNw44d477ly+9xLVfdeITEYcICwNmzeLifmQkF/fHjWNLwJIS07Mz\nI+i2tAoLgUceYUlut6lTueARGizhX0UFkJkJ/Pa3dmHCLl3YP6N3b7Nz86bPP+cW1o4dHLdqBbz1\nFo/iiU9pS0vEdzZuZDVm95mM5s2BmTOZ5RAWZnZuXqAtrRMngAkTuGPhDnZiYtg1YMaMIAp2AOA7\n32HAs3kzj2EDDAJuuw147DFWs3KSr78GfvMbrma5g53+/YFt2xTsiIjjJSQwXePOOzn+6itg7Fju\ncmzYYHZuvuT4l3mXiwsXnTvz5JXLxYh24kTm8AwZYnqGBt10E0P/55/nmqdlAXPmsGjhRx+Znl3D\nnT3L/0/HjsCzz7JARUgIl/Ty8gK03oCIyOVr3ZppnEuXsiIzwKyGPn24zRUMtWkdvaVVUACMH8/P\nbsnJwNy5ftLp3J/s3Mn1zX/8w/7aQw9xEzjQcltcLrbTmDrVTkoGWFFy1iyW0RajtKUlYk55OfDC\nC/xwp282bcrdjvHjA/7wbq1bWo4MeI4fB6ZMARYtshuGt2kD/P73TOEIqfXXEeRcLmD+fOCpp+xt\nrWuvZabbmDHA975ndn51sSy+hZk8GfjsM/vr3/8+N6z14PsNBTwi5hUXA48/DixbZn8tPh6YNy+g\nj7AHRw7P/v18XevcGVi4kK9/4eF8/d65k+W29Xp3CaGhTHQqKuIZRgA4epQZ3XFxLOyQn29Hkf5k\n40YgKYmVt9zBTmwsz2F+/rkefB/atm0bEhIScODAAdNTEZFL6NCB7Rbff587/wCf/pOS+JS5dq2z\n2lME/ApPaSkLKmVnA+vXX3jb3XczhcNddVIug2WxIvOsWdzoPV+3blz3HDnSbOuFc+cYgL3yCjem\n3Zo2BX79ayZgqzWEz7z33nvIzc1FSEgIFi9ejL179yIuLq7G79UKj4h/qajgLkhmJnDmjP31Nm34\nVJ+WxtWfAOCsLa1z54BVqxjkLF9+cQmZ+HjgueeAlBS9qW8wy+Lqyfz5wN//zl++W3Q0s93GjQM6\ndfLNfEpK+OAvX87tq6+/tm+LiGDNgaeeAlq29M185CLr169HUlKSAh6RALR/P59Cc3KAysoLb+ve\nHRg1it0JWrUyM796CPyAx7JYGDA7m6+7X3114e2xscBPf8oHo3t3BTpecfQoE6MWLAC++OLC2+64\ngxnh3brxAWjVynMPwq5d3GRevpxJ1d9eYw0L41uQGTPs4wdizLp165CcnKyARySAlZQw6MnK4mvv\n+UJCWNkjLY0nnZs0MTPHWgRGwHPyJLBvX80fxcV8vT1fVBR/2aNG8bXWgQWU/FNVFYOP+fNrP77e\nvLkd/Lg/33DDxQ05LYvVj0tLgbIyfrivt2zh/fznPxf//Oho7lmmpAADB/L+xC8o4BFxluJi4I03\nuOCwe/eFt0VGcoG/XbuaP2JifF7nzj8DnhdfZOToDmpOnKj734SFsXhSWhrbHylFw7AdO+wcmkOH\nLv29oaFMqIqMtIObsrL6Z8V17MgAJyUF6NePGenidxTwiDiTZbFebVYWUzyPHav730RE8MxLu3b8\n/OyzrAnkRf4Z8NxzD7ByZe23h4cDbdvav6ibbwaGDWPEKH7o+HGekHJ/FBYC//73lffpCg1l9eeU\nFODee9kDTHuVfk8Bj4jzVVYCH3zAVMq9e+2Fi7KyS/+7gwe9nv9T64uE0fJCXbrwF1XbUlhsrLap\nAkrLltxbPL/dfFUVt6QKCxkEFRXx69HRLGh4qc+xsfwsPpebm4vs7Ow6v69FixZYsGDBFd3HtGnT\nvrlOTExEYgAX/hAJNuHhrAIyaNCFXy8rqz015fBhPq2b4lc5PCIS+LTCIyIGBUfhQREREZGa1CAh\nyQAAAMNJREFUKOAREY9y/T8Jvbq62vBMRERsCnhExCPWrVuHYcOGIT09HSEhIRg4cCBGjBiBInfe\nloiIQcrhERGfUw6PiHiJcnhEREQkeCngEREREcdTwCMiIiKOp4BHREREHE8Bj4iIiDieAh4RERFx\nPAU8IiIi4ngKeERERMTxFPCIiIiI411Vx+21ViwUEWkAC3p+EREfqqu1hIiIiEjA05aWiIiIOJ4C\nHhEREXE8BTwiIiLieAp4RERExPEU8IiIiIjj/Q+2QDD112lDmAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbdcad30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 设置图像大小\n",
+    "plt.figure(figsize=(10,6), dpi=80)\n",
+    "\n",
+    "# 画图，指定颜色，线宽，类型\n",
+    "plt.plot(x, c, 'b-', \n",
+    "         x, s, 'r-', linewidth=2.5)\n",
+    "\n",
+    "# 设置显示范围\n",
+    "plt.xlim(x.min() * 1.1, x.max() * 1.1)\n",
+    "plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
+    "\n",
+    "# 得到画图的句柄\n",
+    "ax = plt.gca()\n",
+    "\n",
+    "# ax.spines参数表示四个坐标轴线\n",
+    "# 将右边和上边的颜色设为透明\n",
+    "ax.spines['right'].set_color('none')\n",
+    "ax.spines['top'].set_color('none')\n",
+    "\n",
+    "# 将 x 轴的刻度设置在下面的坐标轴上\n",
+    "ax.xaxis.set_ticks_position('bottom')\n",
+    "# 设置位置\n",
+    "ax.spines['bottom'].set_position(('data',0))\n",
+    "\n",
+    "# 将 y 轴的刻度设置在左边的坐标轴上\n",
+    "ax.yaxis.set_ticks_position('left')\n",
+    "# 设置位置\n",
+    "ax.spines['left'].set_position(('data',0))\n",
+    "\n",
+    "# 设置刻度及其标识\n",
+    "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], \n",
+    "           ['$-\\pi$', '$-\\pi/2$', '$0$', '$\\pi/2$', '$\\pi$'], fontsize ='xx-large')\n",
+    "plt.yticks([-1, 0, 1], \n",
+    "           ['$-1$', '$0$', '$+1$'], fontsize ='xx-large')\n",
+    "\n",
+    "##################################################################################################\n",
+    "\n",
+    "# 加入图例，frameon表示去掉图例周围的边框\n",
+    "l = plt.legend(['cosine', 'sine'], loc='upper left', frameon=False)\n",
+    "\n",
+    "##################################################################################################\n",
+    "\n",
+    "# 在脚本中需要加上这句才会显示图像\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 注释特殊点"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们可以使用 `anotate` 函数来注释特殊的点，假设我们要显示的点是 $2\\pi/3$："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kJUjOnNKaQvdi+ZImPEr5o7NnpVXEsmVy+auv7Ne/YBbsCQ9IAnvrrbBjh9Sq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\nxpjF+z90lw18wTSaIwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbb05710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 设置图像大小\n",
+    "plt.figure(figsize=(10,6), dpi=80)\n",
+    "\n",
+    "# 画图，指定颜色，线宽，类型\n",
+    "plt.plot(x, c, 'b-', \n",
+    "         x, s, 'r-', linewidth=2.5)\n",
+    "\n",
+    "# 设置显示范围\n",
+    "plt.xlim(x.min() * 1.1, x.max() * 1.1)\n",
+    "plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
+    "\n",
+    "# 得到画图的句柄\n",
+    "ax = plt.gca()\n",
+    "\n",
+    "# ax.spines参数表示四个坐标轴线\n",
+    "# 将右边和上边的颜色设为透明\n",
+    "ax.spines['right'].set_color('none')\n",
+    "ax.spines['top'].set_color('none')\n",
+    "\n",
+    "# 将 x 轴的刻度设置在下面的坐标轴上\n",
+    "ax.xaxis.set_ticks_position('bottom')\n",
+    "# 设置位置\n",
+    "ax.spines['bottom'].set_position(('data',0))\n",
+    "\n",
+    "# 将 y 轴的刻度设置在左边的坐标轴上\n",
+    "ax.yaxis.set_ticks_position('left')\n",
+    "# 设置位置\n",
+    "ax.spines['left'].set_position(('data',0))\n",
+    "\n",
+    "# 设置刻度及其标识\n",
+    "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], \n",
+    "           ['$-\\pi$', '$-\\pi/2$', '$0$', '$\\pi/2$', '$\\pi$'], fontsize ='xx-large')\n",
+    "plt.yticks([-1, 0, 1], \n",
+    "           ['$-1$', '$0$', '$+1$'], fontsize ='xx-large')\n",
+    "\n",
+    "# 加入图例，frameon表示图例周围是否需要边框\n",
+    "l = plt.legend(['cosine', 'sine'], loc='upper left', frameon=False)\n",
+    "\n",
+    "####################################################################################\n",
+    "\n",
+    "# 数据点\n",
+    "t = 2 * np.pi / 3\n",
+    "\n",
+    "# 蓝色虚线\n",
+    "plt.plot([t,t],[0,np.cos(t)], color ='blue', linewidth=2.5, linestyle=\"--\")\n",
+    "\n",
+    "# 该点处的 cos 值\n",
+    "plt.scatter([t,],[np.cos(t),], 50, color ='blue')\n",
+    "\n",
+    "# 在对应的点显示文本\n",
+    "plt.annotate(r'$\\sin(\\frac{2\\pi}{3})=\\frac{\\sqrt{3}}{2}$', # 文本\n",
+    "             xy=(t, np.sin(t)), # 数据点坐标位置\n",
+    "             xycoords='data',   # 坐标相对于数据\n",
+    "             xytext=(+10, +30), # 文本位置坐标\n",
+    "             textcoords='offset points', # 坐标相对于数据点的坐标\n",
+    "             fontsize=16,       # 文本大小\n",
+    "             arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\")) # 箭头\n",
+    "\n",
+    "# 红色虚线\n",
+    "p = plt.plot([t,t],[0,np.sin(t)], color ='red', linewidth=2.5, linestyle=\"--\")\n",
+    "\n",
+    "# 该点处的 sin 值\n",
+    "p = plt.scatter([t,],[np.sin(t),], 50, color ='red')\n",
+    "\n",
+    "# 显示文本\n",
+    "p = plt.annotate(r'$\\cos(\\frac{2\\pi}{3})=-\\frac{1}{2}$',\n",
+    "             xy=(t, np.cos(t)), xycoords='data',\n",
+    "             xytext=(-90, -50), textcoords='offset points', fontsize=16,\n",
+    "             arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
+    "\n",
+    "\n",
+    "#####################################################################################\n",
+    "\n",
+    "# 在脚本中需要加上这句才会显示图像\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 最后调整"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "调整刻度值的大小，并让其显示在曲线上方。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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775jm0qWmmZzs/bhSU+UXskkT0wwKujS2kiVNs08f09y3z/txXfT333+bJUuWNKdOnWpb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\nFo8THsYYY4xZvP8DkrV9v0GdesQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa4ef5f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 设置图像大小\n",
+    "plt.figure(figsize=(10,6), dpi=80)\n",
+    "\n",
+    "# 画图，指定颜色，线宽，类型\n",
+    "plt.plot(x, c, 'b-', \n",
+    "         x, s, 'r-', linewidth=2.5)\n",
+    "\n",
+    "# 设置显示范围\n",
+    "plt.xlim(x.min() * 1.1, x.max() * 1.1)\n",
+    "plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
+    "\n",
+    "# 得到画图的句柄\n",
+    "ax = plt.gca()\n",
+    "\n",
+    "# ax.spines参数表示四个坐标轴线\n",
+    "# 将右边和上边的颜色设为透明\n",
+    "ax.spines['right'].set_color('none')\n",
+    "ax.spines['top'].set_color('none')\n",
+    "\n",
+    "# 将 x 轴的刻度设置在下面的坐标轴上\n",
+    "ax.xaxis.set_ticks_position('bottom')\n",
+    "# 设置位置\n",
+    "ax.spines['bottom'].set_position(('data',0))\n",
+    "\n",
+    "# 将 y 轴的刻度设置在左边的坐标轴上\n",
+    "ax.yaxis.set_ticks_position('left')\n",
+    "# 设置位置\n",
+    "ax.spines['left'].set_position(('data',0))\n",
+    "\n",
+    "# 设置刻度及其标识\n",
+    "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], \n",
+    "           ['$-\\pi$', '$-\\pi/2$', '$0$', '$\\pi/2$', '$\\pi$'], fontsize ='xx-large')\n",
+    "plt.yticks([-1, 0, 1], \n",
+    "           ['$-1$', '$0$', '$+1$'], fontsize ='xx-large')\n",
+    "\n",
+    "# 加入图例，frameon表示图例周围是否需要边框\n",
+    "l = plt.legend(['cosine', 'sine'], loc='upper left', frameon=False)\n",
+    "\n",
+    "# 数据点\n",
+    "t = 2 * np.pi / 3\n",
+    "\n",
+    "# 蓝色虚线\n",
+    "plt.plot([t,t],[0,np.cos(t)], color ='blue', linewidth=2.5, linestyle=\"--\")\n",
+    "\n",
+    "# 该点处的 cos 值\n",
+    "plt.scatter([t,],[np.cos(t),], 50, color ='blue')\n",
+    "\n",
+    "# 在对应的点显示文本\n",
+    "plt.annotate(r'$\\sin(\\frac{2\\pi}{3})=\\frac{\\sqrt{3}}{2}$', # 文本\n",
+    "             xy=(t, np.sin(t)), # 数据点坐标位置\n",
+    "             xycoords='data',   # 坐标相对于数据\n",
+    "             xytext=(+10, +30), # 文本位置坐标\n",
+    "             textcoords='offset points', # 坐标相对于数据点的坐标\n",
+    "             fontsize=16,       # 文本大小\n",
+    "             arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\")) # 箭头\n",
+    "\n",
+    "# 红色虚线\n",
+    "p = plt.plot([t,t],[0,np.sin(t)], color ='red', linewidth=2.5, linestyle=\"--\")\n",
+    "\n",
+    "# 该点处的 sin 值\n",
+    "p = plt.scatter([t,],[np.sin(t),], 50, color ='red')\n",
+    "\n",
+    "# 显示文本\n",
+    "p = plt.annotate(r'$\\cos(\\frac{2\\pi}{3})=-\\frac{1}{2}$',\n",
+    "             xy=(t, np.cos(t)), xycoords='data',\n",
+    "             xytext=(-90, -50), textcoords='offset points', fontsize=16,\n",
+    "             arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
+    "\n",
+    "\n",
+    "#####################################################################################\n",
+    "\n",
+    "for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
+    "    label.set_fontsize(16)\n",
+    "    label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.65 ))\n",
+    "\n",
+    "####################################################################################\n",
+    "\n",
+    "# 在脚本中需要加上这句才会显示图像\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> The devil is in the details."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/06. matplotlib/06.10 different plots.ipynb b/06-matplotlib/06.10-different-plots.ipynb
similarity index 99%
rename from 06. matplotlib/06.10 different plots.ipynb
rename to 06-matplotlib/06.10-different-plots.ipynb
index 9bd0a714..ee22f988 100644
--- a/06. matplotlib/06.10 different plots.ipynb	
+++ b/06-matplotlib/06.10-different-plots.ipynb
@@ -1,1587 +1,1587 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "# 各种绘图实例"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 简单绘图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`plot` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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K8oeKvmPzGUdLcntaPCM9Uq2gsv7pnzAzjVqqel3WlUSAjeTSp2dPeO01uPJK\n30oKm8QsuGtX30ryh+OOg0WL3OmbTWK7XrP4CDPTGB2soGoe9yW3ZjTSJ+ECUHXX5jOOlkR72iw4\nfWrXdq7mKVN2/s36p3/CGI1zcXGNt4nxktutW+Hdd136ECM8rVq5jWYLFvhWUtjYgCYzbIVf/KjW\naKhqq6SltrFdcjtzJhx6KDRu7FtJfpHYfZv4YprPOFoS7WlGIzMqGg3rn/6pKvdUSXVvFpHYHKZq\nX8rMqSoVtVFzbBacOZ06wYcfwoYNvpUYCaqaaZwiIu+KyN0icoaIdBWR7iLyUxH5g4i8B/TPldDq\nMKOROcmjOfMZR0tpaSnTp8Phh8Nee/lWk3/Uq+dSAk2d6q6tf/qn0tVTqnpDcNjSQOBHuAOZAD4H\n3gLuUtWN2ZcYjilTYNgw3yrykyOOcKt7Pv/ct5LCxAY0NSMxqOnb17cSA0A0sWwmjxERPfxw5eOP\nfSvJX848E047Dc4/37eSwuPkk+HSS+H0030ryU/GjoU777SAeDYQEVRV0nlPwRw4aSO5mmFxjeyw\nY4dLH9Kjh28l+UvXrm6hy5a8SWZU2JjRMICdLgDzGUfLP/5RSsuWLhW9kRkNGrg0N+++a/0zDng1\nGiLST0Tmi8inInJzJfc8HLw+W0Q6VFaWGY2acfTR7mz1tWt9Kyks5syxvhkFtl8jPoQ5T2NPEfmt\niPw9uG4tIqfUtGIRqQU8AvQD2gKDROTICvecDBymqq2By4C/VlbewQdX9ooRhkRW0R07SnxLKSi+\n+KLEjEYEJIyG7dPwT5iZxpO47LbdgusvgLsiqLsTsEBVF6vqNuBZ3EqtZE4F/gmgqu8AjUVkvwjq\nNlLQq5eN5qKkrMyl9rZ4Rs3p3h2mTYNt23wrMcIYjUNV9V6CtOiquimiulsAyUfHLwv+Vt09LSOq\n36hAz57w6qulvmUUDPPmQd26pbSo2KuNtNl7b5fx4e9/L/UtpegJk+X2OxGpn7gQkUOB7yKoO+xa\n34rLwVK+b/DgwbRq1QqAxo0b0759+++nsongmV1Xfd29ewkrV8LIkaU0auRfT75fz5tXQrt28dGT\n79e9epUwe3Z89OTjdWlpKcOHDwf4/vcyXardpyEifYHbcHGHsUB3YLCqTsioxp3ldgHuUNV+wfUQ\noCyY1STueQwoVdVng+v5QC9VXVWhLC2E/SZxoG9fuOoqOPVU30ryn3POgf794cILfSspDF58EZ58\nEl55xbfJJeqkAAAbM0lEQVSSwiEr+zRU9Q3gp8BFwNNAx5oajIDpQGsRaSUidYCfASMr3DMSuAC+\nNzLrKhoMI1pslUo0qNpO8Kjp0cNlftixw7eS4ibM6qmOwEG4APgK4CAROVREwri2KkVVtwNXAWOA\necBzqvpRcHbH5cE9rwELRWQB8Djwy5rUaVRPo0alZjQi4LPP3Iq0xYtLfUspGPbdFxo2LGXuXN9K\nipswP/x/AToCc4LrY4APgb1E5BeqOibTylV1NDC6wt8er3B9VablG+nTpo0L4G7YAA0b+laTvySO\ndpW0Jv5GdbRr59q2fXvfSoqXMKunvgDaq2pHVe0ItAcW4pIY/jGb4ozc07dvCR07utQXRuZMmuSW\nMCeCkUY0DBpUYjNhz4QxGkeo6oeJC1WdB7RR1c8IvwLKyCMsrlFzLJ6RHXr0KH88sZE+M2fCypWZ\nvz+M0fhQRP4qIr1EpEREHgXmiUhdwLbaFBilpaW2ya+GLFkCmza5lPOJ5Y5GNHz2WSkNG8L8+b6V\n5C/XXw/vv5/5+8MYjcHAZ8A1wK9xrqkLcQajd+ZVG3Gla1fXqSyraGZMnmzxjGzSs6dlZM6UrVth\n+nTo1q36eyujYM7TKITPESe6dIF77gFzyafPZZfBMcfA1Vf7VlKYPPmkO2Pj6ad9K8k/3n7b7cOa\nOdNdZ2WfhogcLiIviMg8EVkUPBZmJtnIFyyukTkWz8guib5p48T0iaJvhk1Y+BiwHTgRl0DwqZpV\na8SVhA/ejEZmrFrlHkcf7a4tphEtpaWlHHKIMxiLFvlWk38kXKc1IYzRqK+q43CurMWqegcwoGbV\nGnHnhBPgnXecD9QIz+TJru1q1fKtpHARsUFNJuzY4XbUn3BCzcoJYzS+Dc6+WCAiV4nIGcCeNavW\niCuJfQWNG8Nhh8GMGX715BsVp/+2TyNaEu1pwfD0mTMHmjd3O+trQhij8WtgD+BXwA+B83Grp4wC\nx0Zz6TNxotvUZ2QXWxaePlGd7RLGaPxAVTeo6lJVHayqZ+ByURkFSLIP3oxGeqxd6/zsHZIOJbaY\nRrQk2vPII2H9eli2zK+efCKqBRphjMaQkH8zCoyePS2raDpMmeKWKteu7VtJ4SPiRs2TJ/tWkh9E\nmXW50oSFItIfOBloISIPs/MwpIbYTvCCJdkH36wZHHAAzJ4Nxx3nT1O+kOpLaTGNaEluz0RcY9Ag\nf3ryhU8+gfr14aAIfERVzTS+AGYA3wb/Jh4jgR/XvGojHzAXVXgsnpFbrG+GJ8q9Q5UaDVWdrarD\ncWeE/1NVhweP/1PVr6Op3ogbFX3w9sUMx4YNLqX88ceX/7vFNKIluT3btYMvvoDVq/3pyRdyYjRE\nZK6IzAVmJp4nPeZU9j6jsOjZ0/mNbfdt1UydCh07Qr16vpUUD7VqQffu8NZbvpXEn6hWTkEVuadE\npFVVb1TVxdFIqDmWeyq7HHoojBoFbdv6VhJfbrsNdtsN7rzTt5Li4t57YcUKePBB30riy+LF0Lmz\nS4deMYlmpLmngt3fiwPjsAV3Yt/RwOY4GQwj+9hGquopLbXkjj6wvlk9iVhbVFmXwyQsPBt4FzgL\nOBt4V0TOiqZ6I26k8sFbXKNqNm1yK8y6dt31NYtpREvF9uzYERYsgHXr/OjJByZOjHZAE2afxm+A\n41X1AlW9ADge+G10Eoy4Y1lFq2bqVLehb489fCspPurUca6XKVN8K4kvUc+CwxgNAb5Mul7Dzj0b\nRoGRal/BIYe4qe1CS4ifkqq+lLZPI1pStafNhCvn889h40a3gz4qwhiN14ExIjJYRC4CXgNGRyfB\niDuWVbRqSkttf4ZPrG9WTtTxDAhhNFT1RuBxoB0uGP64qt4UnQQjTlTmg7eAY2o2b4ZZs1LHM8Bi\nGlGTqj07d4a5c11syShP1PEMCBcIvx6YpqrXqup1qvpStBKMfMBGc6mZOhXat4c97bAAb9Sv72JK\nU6f6VhI/srGqL4x7qiHwhoi8FZynsV+0Eow4UZkP/sgj3a7npUtzqyfuVOeasphGtFTWnjao2ZWl\nS10m4Kj3V4VxT92hqkcBVwLNgUkiMj5aGUbcScQ1LKtoeWx/Rjwwo7Er2YhnQLiZRoLVwErc6qlm\n0cow4kJVPniLa5Rn82Z4/33o1q3yeyymES2VtWe3bjB9Onz7bW71xJlsDWjCxDR+KSKlwHigKXCJ\nqh4bvRQj7thorjzTprmkeRbP8E/Dhs6F+t57vpXEh2wZjUpzT31/g8gfgOdUdVb01UeD5Z7KDTt2\nwD77uNz8NT1nuBD43e9g+3a4+27fSgyAG26Avfd2ecCKnWXL3AKN1atdTrTKiDT3VAJVHRJng2Hk\njlq14IQTLK6RwOIZ8cJmwjtJxDOqMhiZkoUijXymOh+8fTEdW7bAzJlVxzPAYhpRU1V7nnCCW3a7\nfXvu9MSVbA5ozGgYaWHBcMe0aXDssdCggW8lRoImTaBVK2fMi51sGo1qYxr5gMU0cse2be7LuWSJ\n8x8XK7ffDlu3wh/+4FuJkcxVVznDccMNvpX4Y/lyt0CjungGZCmmYRjJ1K4NXbpYVlGLZ8STXr3M\nfTpxovMIZCOeAWY0jAqE8cEXe1xjyxaYMcMdNVodFtOIluras0cPd/xrWVlu9MSRbA9ozGgYaVPs\ncY1p0+CYYyyeEUf23x+aNXMJDIuVbBsNi2kYafPtt+6LuXw5NGrkW03u+d3vXGzH4hnx5LLL4Kij\n4Ne/9q0k96QTzwCLaRg5ol496NSpeF1U48dDnz6+VRiV0aeP+z8qRsaPhxNPzF48A8xoGBUI64M/\n6SQYNy67WuLI+vUwZ064eAZYTCNqwrRn797OfbptW/b1xI1x49x3M5uY0TAyolhHc5MmuVlW/fq+\nlRiV0ayZO6K42PJQqZrRMDwQ9vyHjh1dfpuVK7OrJ26k+6W08zSiJWx7FuOgZv58qFPHGcxsYkbD\nyIhatdwKjTff9K0kt1g8Iz8oRvdpYkAT9fkZFTGjYZQjHR98sX0xV650s6uOHcO/x2Ia0RK2PXv0\ncHtpiunc8FwNaMxoGBnTp48zGsWy2nn8eDe7qlXLtxKjOvbc0xn3YsnIvH2725/Ru3f26zKjYZQj\nHR/8EUe4nbcLFmRPT5wYPz79IKPFNKIlnfYsppnw9Olw8MGw337Zr8uMhpExIsXzxczVyhQjOoop\nGJ7LWJsZDaMc6frgi+WLuWCBm1Udfnh677OYRrSk057HHw8LF8KXX2ZPT1zI5YDGi9EQkSYiMlZE\nPhGRN0SkcSX3LRaROSLyvoi8m2udRvX06QMTJrijYAuZXK1MMaKjdm2X9bbQV/ht3uz2pPTsmZv6\nfM00bgHGqurhwPjgOhUKlKhqB1XtlDN1RUy6PvgDDnB+1Pffz46euJDp9N9iGtGSbnsmFmsUMm+9\nBR065C6Bpi+jcSrwz+D5P4HTqrjXxnYxp29feOMN3yqyx/btmQXBDf8k+mYhr/AbMwZ+9KPc1efL\naOynqquC56uAymL+CowTkekicmlupBU3mfjg+/WD0aOj1xIXpk1zp8E1b57+ey2mES3ptmebNu7f\n+fOj1xIXXn8d+vfPXX27Z6t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q2hPrm+nQHTgfmCMi7wd/uxU4CDLrn7a5zzAMwwhNvrin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\nYRiGYYTGjIZhGIYRGjMahmEYRmj+P0lg7oy1NXTrAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x3c9bf98>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "t = np.arange(0.0, 2.0, 0.01)\n",
-    "s = np.sin(2*np.pi*t)\n",
-    "plt.plot(t, s)\n",
-    "\n",
-    "plt.xlabel('time (s)')\n",
-    "plt.ylabel('voltage (mV)')\n",
-    "plt.title('About as simple as it gets, folks')\n",
-    "plt.grid(True)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 子图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`subplot` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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aMTtOrBg48Bq6dCmcpTpxYlvy8vK45ZaFhbblR4VZ1IxhJA6xip4aLiJDcFkg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\nLgxre1MOTEUAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9fc3f60>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.mlab as mlab\n",
-    "\n",
-    "x1 = np.linspace(0.0, 5.0)\n",
-    "x2 = np.linspace(0.0, 2.0)\n",
-    "\n",
-    "y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)\n",
-    "y2 = np.cos(2 * np.pi * x2)\n",
-    "\n",
-    "plt.subplot(2, 1, 1)\n",
-    "plt.plot(x1, y1, 'yo-')\n",
-    "plt.title('A tale of 2 subplots')\n",
-    "plt.ylabel('Damped oscillation')\n",
-    "\n",
-    "plt.subplot(2, 1, 2)\n",
-    "plt.plot(x2, y2, 'r.-')\n",
-    "plt.xlabel('time (s)')\n",
-    "plt.ylabel('Undamped')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 直方图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`hist` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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emjUwc2bUUbgq5DcOdtEbO5aZHdtFHYVLxuLFcMMNUUfhqpAnCRetr76Cl17i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\nRK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9fc3ba8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.mlab as mlab\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# example data\n",
-    "mu = 100 # mean of distribution\n",
-    "sigma = 15 # standard deviation of distribution\n",
-    "x = mu + sigma * np.random.randn(10000)\n",
-    "\n",
-    "num_bins = 50\n",
-    "# the histogram of the data\n",
-    "n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)\n",
-    "# add a 'best fit' line\n",
-    "y = mlab.normpdf(bins, mu, sigma)\n",
-    "plt.plot(bins, y, 'r--')\n",
-    "plt.xlabel('Smarts')\n",
-    "plt.ylabel('Probability')\n",
-    "plt.title(r'Histogram of IQ: $\\mu=100$, $\\sigma=15$')\n",
-    "\n",
-    "# Tweak spacing to prevent clipping of ylabel\n",
-    "plt.subplots_adjust(left=0.15)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 路径图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`matplotlib.path` 包："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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K4aKtlOqrlNqhlNqplHraGUEJ31c9uDozB81k5IKRHD97vNxlmjZtyonatTkk\n5207xc4TJ2jYoYPD4xzZeoTtn22n8187k5ScROriVCdEJ2yl7Hk2X9nKSvkDvwG9gQPAz8BQrfX2\nC5bRjmxD+LbHvn2MvMI8Ph78cbmv/7BiBYdnzuT2Bg3cHJnveXffPm5+8UViY2PtHiN1cSpjpo4h\no+0fzwJN2JTApEcmMaDPAGeEKc5TSqG1vuRnkaN72h2AXVrrTK11MTAbGOTgmMJEXun9Cmuy1jBn\n+5xyX2/fqRN7wsM5WskHJIiL5RUUcDosjLp16zo0zuT/Tb6oYANktM1gyqdTHBpX2M7Roh0D7L9g\nOuv8PLcxa1/Lyuj5VwuqRsqgFB7+9mGOnb70oGNwcDCdb7uNtHIeRis97Uybl92Zk0PD9u3x87P/\nK3/w5EE2Hyv//ugFlorvK+MKRv/s2yvAwfVt6nsMHz6c+POnJ4WHh5OYmFh2uo71D2/vdHp6ukPr\nG33aV/K/p9U9PJT6EI9EPYJS6qLXi4uL2RcRweFTp9hx/kpJ6+lu1sIl0xVPH/DzI7FtW7venzNF\nZ1gTuIapP08lMDcQ9gDWbtWec/8K8QuxeTyZLn86LS2NlJQUgLJ6WR5He9qdgAla677np58FLFrr\nVy9YRnra4ooKSgpo9347xncbz9BWQy95fc3q1WROn84Qk5+bbY+i0lLeOHiQx6dOJSQkxOb1Siwl\nfLDxA/6x4h/0vro3L/V4iS0/b7m0p70xgUmPSk/b2S7X03Z0T3s90EgpFQ8cBO4CLv3GCXEFIQEh\nzLplFjd9ehPd47sTHXbx/TGu6dCBH+fM4eDJk9QNC/NQlMaUkZtLdIsWNhdsrTXzf5vP00ueJqZ6\nDKl3p9Iuuh0AcX3iAJjy6RQKLAWE+IXw2KOPScF2I4f2tAGUUv2AtwF/YIbW+t9/et2le9ppaWmm\nvTIKfC//8cvGs+nwJhYMXXDJ+cTr1qxh53vv8ZfzZ5KkZWaa+qpIW/LXWvPBnj10HjeOFi1bXnHM\ndQfW8dTip8g5k8PEPhPp17Cfw+d1u4qvffb/zFVnj6C1Xqi1bqK1bvjngi1EZY2/YTxZ+VmkpKdc\n8lq7a6/laO3a7D8h93O21a7cXIoTEmjeokWFy+0+vpshXw5h8GeDubf1vaQ/mE7/Rv29tmCbmcN7\n2lfcgPS0RSVtPrKZXh/1YsOoDdSvUf+i1zasX8+2KVP4q5y3fUVaa6bv2cP1Tz992aKdcyaHl1a+\nxEebP2JdpOLFAAAT4ElEQVRsx7E8cd0TVAuq5uZIRXlctqcthLO1rt2axzs9zn3z78OiLRe9lti2\nLbl167I3L89D0RnH7zk5lDZqRLPmzS95raCkgNdWv0bTqU0pLC1k28PbGH/DeCnYBmD4om3WczWt\nfDX/cV3GkV+Yz7T10y6a7+/vzw133sny48dZvmePh6LzDhWdp11qsbAsP5/ut912UYvDoi38d/N/\nafpOU1bvX82q5FW8O+BdaofWdkPEzuWrn/0rcfTsESFcIsAvgJRBKXT9sCtJCUkk1Ewoe611mzas\nqlePw9u3VzCCua3IyqJ65840bdasbN6yPct4avFTBPgF8NHgj+gW182DEQp7SU9beLU3f3qTuTvm\nsnzYcvz9/Mvm/7p5Mz+9/jojGzSQg2V/kpWfz+zSUh546SXCwsLYenQr45aMY0f2Dv7d69/c0fwO\n+ZsZgPS0hSGN6TgGjWbS2kkXzW/ZqhV+rVrxy5EjHorMO50qKuKr7Gz6jxrFSU5y//z76TGrB32u\n7sO2h7dxZ4s7pWAbnOGLtln7Wla+nr+/nz8pg1L416p/sf3YH+0QpRQ14uNZWlBAUWmpByP0nD/3\ntAtKSvhk/34a/+UOZh/9jFbvtaJmlZr89uhvjO00luCAYM8E6iK+/tm/HMMXbeH7Emom8GKPFxk2\ndxgllpKy+VFRUVzdpw+rDhzwYHTeoai0lI/37mHLDdUYsWUUe/L2sHHURib2mUhEFfsfLya8j/S0\nhSFYtIWkT5LoEd+D57o+VzY/Pz+faePGMbJGDSKqVPFghO6Tuvd3JmespdCvlGCLP8Ni2rCiRi6p\ncTtpUrcpr934Wtll58K4LtfTlqItDGPfiX1c859rWPLXJbSp06Zs/splyzg8axZ3muCCm9S9vzNm\n1yIyev/xtJ+Axf7Uah3JjLEz5CpGH+KzByLN2teyMlP+9WvU59XerzJs7jCKSovKcr+ua1cORkeT\naYILbiZnrP2jYJ8/Tb2kTyltCtowoPEAUxVsM332L2T4oi3MJTkxmdjqsby08qWyeYGBgfS55x4W\n5eZi8fFfdYV+5R90LbQUujkS4SmGL9q+fJcvW5gtf6UU0wdOZ9r6aVRr9Mcl181btCC4bVs2HT7s\nwehcL9jyx7nqXNANsj6EwEzM9tm3MnzRFuYTHRbNpL6TGDZ3GAUl5x5zpZSi75AhLC8qoqCk5Aoj\nGNfohI4kLLn4bJCEjQk8NvQxD0Uk3M3wRdusfS0rs+Y/pOUQoo5GMX7Z+LJ50dHRNO7bl5UHD3ow\nMtcaENeYf1/dC5ZCyx9akrQ3ybRPjTHrZ9/wRVuYk1KKxzs9zie/fsLqfavL5vccMID0kBByzpzx\nYHSuFXJVAPV61WHK36awaOYiUxZsM5NT/oShzdk+h3FLxpH+QHrZbUV/XLmS3R98wF989L4k4/IW\n80t8CN+NXerpUIQL+ewpf8LcBjcbTMeYjjyz5JmyeR27dOFko0ZsPXbMg5G5zobAo1xTv6OnwxAe\nYviibda+lpWZ87fmPqXfFObsmMOyPcuAc/fcHpiczHdnz3K2uNiDEbrGjqAcbmh4g6nfezDvZ9/w\nRVuIiCoRTB84nRHzRpBfmA9AbGwsTQcOZLGP3Zckr/QsxwLy6dqkq6dDER4iPW3hM0bOHwnA9Jun\nA1BQUMC7zz/PbSUlxIWHezI0p/n61HaeCFtN5gtZng5FuJj0tIXPeyPpDRbvXsy3O78FICQkhH4j\nRrAgJ4cSi+UKaxvDDyV7aR5x6TMfhXkYvmibta9lZeb8/5x79eDqzBw0k1ELRnH87Ln7czRt1oxa\n3bqx2kfaJBsDj3Ft/U6Aud97MG/+dhdtpdQdSqmtSqlSpZTcB1J4hZ4NejK46WBGLxoNnPuJ2f/O\nO1kbFES2D5y7vSM4hxsa3eDpMIQH2d3TVko1BSzA+8CTWuuNl1lOetrCrU4XnSbx/UQm9p7I4GaD\nAVizejU7pk1j2NVXG/bc7WMlp4nxf5v8v50kJMh89xoxG6f3tLXWO7TWvzsWlhDOVy2oGimDUnj4\n24c5dvrcudodrruOohYtSDfwMyWXFeyhnn+0FGyTk562wZk5/4py71K/C/e0uoeHUh9Ca42fnx8D\nhw1jSVERp4uK3BekE60u2U/zmi3Lps383oN58w+o6EWl1GKgTjkvPae1XmDrRoYPH058fDwA4eHh\nJCYmlt1W0fqHt3c6PT3dofWNPm32/Cua/mfPf9LkySYMWTeEvJw8CnUhh347QPrWMP7b7+Zzy59/\nOG73859Pb57eFJRNwxPNSUtL84q/r0w7dzotLY2UlBSAsnpZHofP01ZKLUd62sJLvf3Z2zz57pNY\nev5xyl+txeH8q3YXRrW61oORVd5VJW/x5T0L6Na0m6dDEW7g6vO0jXlkR/i8hYsWXlSwAXL65PHm\nvk2GusT9cMkpTvif5bqG13k6FOFhjpzyN1gptR/oBKQqpRY6LyzbWX9emJWZ87cl90Jd/mO4Aq8K\nZ1GWca4qXHp2N3H+MQQGBJbNM/N7D+bN35GzR+Zoretpratoretorfs5MzAhnCFYBZc7Pzoqhv2x\nsezIznZzRPZZXbqfFpGtPB2G8AKGP3vE2tA3KzPnb0vuo+8eTcKmhIvmxa6LZcxfxnDLAw+Qevo0\nZwzQJtkUlE2HuItbI2Z+78G8+Vd49ogQRmd9qsuUT6dQYCkg70weB+IP0K5TO6LDoml1222kfvEF\ndzRocIWRPGtnUDY9GvfwdBjCCxh+T9usfS0rM+dva+4D+gxg0cxFpKWkkf55OqPvHM2g2YM4W3yW\nHklJHImPZ8vRo64N1gFZxSc45V9E+4T2F80383sP5s3f8EVbiMp6vtvzJNRMIHleMgEBAQweNYqF\nBQWc8tKLbpYU7KFBQD38/fw9HYrwAnI/bWFKZ4vP0n1WdwY0GsDfb/g7y777jiP//S9DvPC5kqPy\nFpDbNI4vH/za06EIN5L7aQtxgSqBVZh711xmbJrB51s/54bevclr3JhfvPDeJOnB2XSMk/OzxTmG\nL9pm7WtZmTl/R3OPDotm3pB5PPLtI2w8vJHB99/P4pIS8gvLP7fbU3YG5dCjyaUHIc383oN58zd8\n0RbCEYl1Epk+cDqDPxtMSdUSOt59N/OysvCWlt7uouMU+JXQLl5uWS/OkZ62EMArP7zCF9u+IO3e\nNGZPfo82u3bRoW5dT4fF9PwNvF3zN7Y+L3dBNhvpaQtRgae7PE2LqBYkz0/mlhHJpPn5cfT0aU+H\nxU/6AK2uSvR0GMKLGL5om7WvZWXm/J2Zu1KK6QOnc+jUISZvnkyfkSP58vBhiktLnbYNe/wSlE3H\n+PIPQpr5vQfz5m/4oi2EswQHBDPnrjl88usnbA3cTlTv3izx8E2ldgXl0KtpL4/GILyL9LSF+JNf\nj/xKz4968sXgL0j/YBH9T5+mca1abo/j98Js2gbN4OQLp/Hzk/0rs5GethA2alW7FR8O+pC7591N\n+78OZP6pU5z0wGmASwv3kBAULwVbXMTwnwaz9rWszJy/K3O/qfFNPHndkzy86mGa33kLcz1wGuAa\nfYDWddpe9nUzv/dg3vwNX7SFcJUnrnuC9nXbMy1vBgVtWrPm4EG3bv+XoBw6xXV26zaF95OethAV\nKCotos/HfUiMTCR2YQn3hIQQHRbm8u1atIUa+jXWPbCRZjHNXL494X2kpy2EHYL8g/jqzq/4Zvc3\nFPatw5dHj1LkhtMAtxdlg/KjSXQTl29LGIvhi7ZZ+1pWZs7fXblHVo1kwdAFTN4xhZPdr2bR/v0u\n3+aygj00DG5Q4UFIM7/3YN78DV+0hXCH5lHN+WjwR0w5/h82xIay1cUPTVjDAVrXkfuNiEtJT1uI\nSpiydgpT107lL1s68nB4bWpVreqS7bQqmM4jN47nwZ4PumR84f2kpy2EEzza4VF6XN2D79rsYvah\nAy65zN2iLewOyqFXM7kSUlzK8EXbrH0tKzPn74nclVJM7jeZ4NAQFl2Tw7cu6G9vLjxCgAqgUXSj\nCpcz83sP5s3f7qKtlHpNKbVdKfWLUuprpVQNZwYmhLcK9A/kizu/YIdfBp81yGXT4cNOHX95USaN\nqiQ4dUzhO+zuaSul+gBLtdYWpdQrAFrrZ8pZTnrawif9nvM7XWZ04das6/hHUGPqhIY6Zdy78r8i\ntHV7Zgyf6ZTxhDE5vaettV6stbacn1wLxNo7lhBG1LhWY2bfPpsv665mau4uCkpKnDLuluAcrmsg\nV0KK8jmrpz0C+NZJY1WKWftaVmbO3xty73V1L17u/TIpjdfzv6xdDt+fpFRb2B2UbdNBSG/I35PM\nmn9ARS8qpRYDdcp56Tmt9YLzy/wNKNJa/+9y4wwfPpz4+HgAwsPDSUxMpHv37sAff3h7p9PT0x1a\n3+jTZs/fG6ab0pRBrW7hjeLvObUmn9ZRV9H9/Oc9LTPz3PI2Tk/fuYGAwAAaXNXAa/KTafdMp6Wl\nkZKSAlBWL8vj0HnaSqnhwEigl9a64DLLSE9b+LwSSwk3zrqRwt+ymV3Yi3o17DsuPzF/NV/VzmHt\n0xudHKEwGqf3tJVSfYGngEGXK9hCmEWAXwBfD/2aw1edZmzJj5wuKrJrnHUcIjH6WidHJ3yJIz3t\nKUAosFgptUkp9a6TYqoU688LszJz/t6We3hION8P/57ldX7nxex1WOz4hbklOIfOV3exaVlvy9/d\nzJp/hT3timitKz7zXwgTSqiZwFd3f82gj2+mxYFw7o1uafO6pdpCZlAOvZv3dmGEwujk3iNCuMDU\nn6YyYeHzLDxxB9dG1LVpnZ/O7mdgyNdk/+O4i6MTRiD3HhHCjR657hEGtbqNIVUXkF1w2qZ1lhdl\n0rhaQxdHJozO8EXbrH0tKzPn7+25v3/b+1xVN57biuZSYrnyjaXW+R2ibUx7m8f39vxdzaz5G75o\nC+Gt/P38WThyEbtrneHRvO+vuPzWoBy6XH29GyITRiY9bSFcbMfhHXSc1p6Xj3fl0YgO5S5TpEsJ\n4xX2PZFF7Rq13Ryh8EbS0xbCQ5rWacont/6PZ8LTWHJyd7nL/FSwn3CqS8EWV2T4om3WvpaVmfM3\nUu4DWw9k/HUvcFeVOewuyL3k9RVFe2kc2rhSYxopf1cwa/6GL9pCGMXTfZ+mf8LNJDGbU6UXXzH5\ns98h2sbafhBSmJf0tIVwo1JLKZ3f7ETVQ8dZGno3furcftPVRe/yyqB3uLPjnR6OUHgL6WkL4QX8\n/fz57uHF7Ao/w6P53wFQYCkmK/A4PZv19HB0wggMX7TN2teyMnP+Rs09vGo4341Ywn+rb2PAyv9S\n+6vXKZ5XQtNeTZkwcYLN4xg1f2cxa/5233tECGG/5jHN6Zc/gM/yP4PzHZEccnj5q5cBmDBugueC\nE15NetpCeEhk+0hybsq5ZH6t1Fpkr8v2QETCm0hPWwgvU+Jf/jMlS/yc86xJ4ZsMX7TN2teyMnP+\nRs89oLT87mSAxbaupdHzd5RZ8zd80RbCqB6941ECvr24QAekBvDo7Y96KCJhBNLTFsKDJkycwDtf\nvkOJXwkBlnMFWw5CCrh8T1uKthBCeCGfPRBp1r6WlZnzN3PuIPmbNX/DF20hhDATaY8IIYQX8tn2\niBBCmIndRVsp9U+l1C9KqXSl1FKlVD1nBmYrs/a1rMycv5lzB8nfrPk7sqc9UWvdRmudCMwFXnBS\nTJWSnp7uic16DTPnb+bcQfI3a/52F22t9ckLJkMBj9wsIS8vzxOb9Rpmzt/MuYPkb9b8HbrLn1Lq\nZeCvwBmgk1MiEkIIcVkV7mkrpRYrpX4t55+BAFrrv2mt6wMpwFtuiPcSmZmZntis1zBz/mbOHSR/\ns+bvlFP+lFL1gW+11i3LeU3O9xNCCDuUd8qf3e0RpVQjrfXO85ODgE22blQIIYR97N7TVkp9CTQB\nSoEM4CGt9VEnxiaEEOJPXH5FpBBCCOfxmSsilVJPKqUsSqmano7FnZRSrymltp+/0OlrpVQNT8fk\nDkqpvkqpHUqpnUqppz0djzsppeoppZYrpbYqpbYopUZ7OiZ3U0r5K6U2KaUWeDoWd/OJon3+asw+\nwF5Px+IB3wMttNZtgN+BZz0cj8sppfyBd4C+QHNgqFKqmWejcqti4HGtdQvOnWr7iMnyBxgDbANM\n1yrwiaINvAmM83QQnqC1Xqy1tpyfXAvEejIeN+kA7NJaZ2qti4HZnDsYbgpa68Na6/Tz/30K2A7U\n9WxU7qOUigX6Ax8ApjvRwfBFWyk1CMjSWm/2dCxeYATwraeDcIMYYP8F01nn55mOUioeaMu5/2Gb\nxVvAU4DlSgv6IoeuiHQXpdRioE45L/2Nc+2AGy9c3C1BuVEF+T+ntV5wfpm/AUVa6/+5NTjPMN1P\n4vIopUKBL4Ex5/e4fZ5S6ibgqNZ6k1Kqu6fj8QRDFG2tdZ/y5iulWgINgF+UUnCuNbBBKdXBl04/\nvFz+Vkqp4Zz7udjLLQF53gHgwrtK1uPc3rZpKKUCga+AT7TWcz0djxt1Bm5WSvUHQoDqSqmPtNb3\nejgut/GpU/6UUnuAa7TWuZ6OxV2UUn2BN4AbtNYeuWmXuymlAoDfOPc/qYPAOmCo1nq7RwNzE3Vu\nD2UWkKO1ftzT8XiKUuoG4P+01gM9HYs7Gb6n/Se+838g203h3F0WF58/BepdTwfkalrrEuBR4DvO\nnUHwmVkK9nldgHuAHuff803n/+dtRqb7zvvUnrYQQvg6X9vTFkIInyZFWwghDESKthBCGIgUbSGE\nMBAp2kIIYSBStIUQwkCkaAshhIFI0RZCCAP5f8NyCaSKvRwCAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa78f2b0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.path as mpath\n",
-    "import matplotlib.patches as mpatches\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "\n",
-    "Path = mpath.Path\n",
-    "path_data = [\n",
-    "    (Path.MOVETO, (1.58, -2.57)),\n",
-    "    (Path.CURVE4, (0.35, -1.1)),\n",
-    "    (Path.CURVE4, (-1.75, 2.0)),\n",
-    "    (Path.CURVE4, (0.375, 2.0)),\n",
-    "    (Path.LINETO, (0.85, 1.15)),\n",
-    "    (Path.CURVE4, (2.2, 3.2)),\n",
-    "    (Path.CURVE4, (3, 0.05)),\n",
-    "    (Path.CURVE4, (2.0, -0.5)),\n",
-    "    (Path.CLOSEPOLY, (1.58, -2.57)),\n",
-    "    ]\n",
-    "codes, verts = zip(*path_data)\n",
-    "path = mpath.Path(verts, codes)\n",
-    "patch = mpatches.PathPatch(path, facecolor='r', alpha=0.5)\n",
-    "ax.add_patch(patch)\n",
-    "\n",
-    "# plot control points and connecting lines\n",
-    "x, y = zip(*path.vertices)\n",
-    "line, = ax.plot(x, y, 'go-')\n",
-    "\n",
-    "ax.grid()\n",
-    "ax.axis('equal')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 三维绘图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "导入 `Axex3D`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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zM+j9FKyQH88hlKwut7kaGBEjRb929ozZbFb1GK6nPaMdiGx7enpsfRHoWgWA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\nRK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbbb96a0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "from matplotlib import cm\n",
-    "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "ax = fig.gca(projection='3d')\n",
-    "X = np.arange(-5, 5, 0.25)\n",
-    "Y = np.arange(-5, 5, 0.25)\n",
-    "X, Y = np.meshgrid(X, Y)\n",
-    "R = np.sqrt(X**2 + Y**2)\n",
-    "Z = np.sin(R)\n",
-    "surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,\n",
-    "        linewidth=0, antialiased=False)\n",
-    "ax.set_zlim(-1.01, 1.01)\n",
-    "\n",
-    "ax.zaxis.set_major_locator(LinearLocator(10))\n",
-    "ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n",
-    "\n",
-    "fig.colorbar(surf, shrink=0.5, aspect=5)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 流向图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "主要函数：`plt.streamplot`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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zcnZX6HUttB8jqWR1SQ09UqijxatpmhN4DjgF2AMs1jRtplJqnfWeieHYIN5t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2AAAg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\nRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xbba7c88>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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wgrpqIztKleLAp0jHHYAVOu++qz2uSBHWV2sFr2vWZPJUQuPGHCjODlMfAQ8P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\nO3bs0FUWlogwduxYODk5CQV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SiNauZQ19lSqsMLJC8aYL+Y3AJUmi+fPnk7OzM7Vp04aq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\nIPAAAgggAD9FgMADCCCAAPwU/wetO4AhXrsEhgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xc38efd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "Y, X = np.mgrid[-3:3:100j, -3:3:100j]\n",
-    "U = -1 - X**2 + Y\n",
-    "V = 1 + X - Y**2\n",
-    "speed = np.sqrt(U*U + V*V)\n",
-    "\n",
-    "plt.streamplot(X, Y, U, V, color=U, linewidth=2, cmap=plt.cm.autumn)\n",
-    "plt.colorbar()\n",
-    "\n",
-    "f, (ax1, ax2) = plt.subplots(ncols=2)\n",
-    "ax1.streamplot(X, Y, U, V, density=[0.5, 1])\n",
-    "\n",
-    "lw = 5*speed/speed.max()\n",
-    "ax2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 椭圆"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Ellipse` 对象："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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1UChEZUsFSZkJmOPM2FNtVDUOLp/8nv1FKJKHlhvwIBTZJor2FSGT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\ni3wQrLY6AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa701208>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from pylab import figure, show, rand\n",
-    "from matplotlib.patches import Ellipse\n",
-    "\n",
-    "NUM = 250\n",
-    "\n",
-    "ells = [Ellipse(xy=rand(2)*10, width=rand(), height=rand(), angle=rand()*360)\n",
-    "        for i in range(NUM)]\n",
-    "\n",
-    "fig = figure()\n",
-    "ax = fig.add_subplot(111, aspect='equal')\n",
-    "for e in ells:\n",
-    "    ax.add_artist(e)\n",
-    "    e.set_clip_box(ax.bbox)\n",
-    "    e.set_alpha(rand())\n",
-    "    e.set_facecolor(rand(3))\n",
-    "\n",
-    "ax.set_xlim(0, 10)\n",
-    "ax.set_ylim(0, 10)\n",
-    "\n",
-    "show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 条状图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`bar` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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dNzMz20ymSSoi5gCrGk0eBUxPh6cDJ2UZg5mZVa5yXJPqFRHL0uFlQK8yxGBm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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xcdb8828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
-    "n_groups = 5\n",
-    "\n",
-    "means_men = (20, 35, 30, 35, 27)\n",
-    "std_men = (2, 3, 4, 1, 2)\n",
-    "\n",
-    "means_women = (25, 32, 34, 20, 25)\n",
-    "std_women = (3, 5, 2, 3, 3)\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "\n",
-    "index = np.arange(n_groups)\n",
-    "bar_width = 0.35\n",
-    "\n",
-    "opacity = 0.4\n",
-    "error_config = {'ecolor': '0.3'}\n",
-    "\n",
-    "rects1 = plt.bar(index, means_men, bar_width,\n",
-    "                 alpha=opacity,\n",
-    "                 color='b',\n",
-    "                 yerr=std_men,\n",
-    "                 error_kw=error_config,\n",
-    "                 label='Men')\n",
-    "\n",
-    "rects2 = plt.bar(index + bar_width, means_women, bar_width,\n",
-    "                 alpha=opacity,\n",
-    "                 color='r',\n",
-    "                 yerr=std_women,\n",
-    "                 error_kw=error_config,\n",
-    "                 label='Women')\n",
-    "\n",
-    "plt.xlabel('Group')\n",
-    "plt.ylabel('Scores')\n",
-    "plt.title('Scores by group and gender')\n",
-    "plt.xticks(index + bar_width, ('A', 'B', 'C', 'D', 'E'))\n",
-    "plt.legend()\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 饼状图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`pie` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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/Pdb6SQyPsokfhRC2k/v5M72z8UXLijmS6U01bWjQ0vzJ76MP7nvnvk0ux0Fn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\n6EqSJMWQDF1JkqQYkqErSZIUQ/8PQVN67hzDqWoAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa6e2208>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
-    "# The slices will be ordered and plotted counter-clockwise.\n",
-    "labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n",
-    "sizes = [15, 30, 45, 10]\n",
-    "colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']\n",
-    "explode = (0, 0.1, 0, 0) # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
-    "\n",
-    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
-    "        autopct='%1.1f%%', shadow=True, startangle=90)\n",
-    "# Set aspect ratio to be equal so that pie is drawn as a circle.\n",
-    "plt.axis('equal')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 图像中的表格 "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`table` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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v46OPPrJ9n96djqe6cuWK7e545syZDufJrX/UekWeBY5uQe2ndezYkcOHD9O8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\n+x9716LrwMNrSQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xd0309b0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
-    "data = [[  66386,  174296,   75131,  577908,   32015],\n",
-    "        [  58230,  381139,   78045,   99308,  160454],\n",
-    "        [  89135,   80552,  152558,  497981,  603535],\n",
-    "        [  78415,   81858,  150656,  193263,   69638],\n",
-    "        [ 139361,  331509,  343164,  781380,   52269]]\n",
-    "\n",
-    "columns = ('Freeze', 'Wind', 'Flood', 'Quake', 'Hail')\n",
-    "rows = ['%d year' % x for x in (100, 50, 20, 10, 5)]\n",
-    "\n",
-    "values = np.arange(0, 2500, 500)\n",
-    "value_increment = 1000\n",
-    "\n",
-    "# Get some pastel shades for the colors\n",
-    "colors = plt.cm.BuPu(np.linspace(0, 0.5, len(columns)))\n",
-    "n_rows = len(data)\n",
-    "\n",
-    "index = np.arange(len(columns)) + 0.3\n",
-    "bar_width = 0.4\n",
-    "\n",
-    "# Initialize the vertical-offset for the stacked bar chart.\n",
-    "y_offset = np.array([0.0] * len(columns))\n",
-    "\n",
-    "# Plot bars and create text labels for the table\n",
-    "cell_text = []\n",
-    "for row in range(n_rows):\n",
-    "    plt.bar(index, data[row], bar_width, bottom=y_offset, color=colors[row])\n",
-    "    y_offset = y_offset + data[row]\n",
-    "    cell_text.append(['%1.1f' % (x/1000.0) for x in y_offset])\n",
-    "# Reverse colors and text labels to display the last value at the top.\n",
-    "colors = colors[::-1]\n",
-    "cell_text.reverse()\n",
-    "\n",
-    "# Add a table at the bottom of the axes\n",
-    "the_table = plt.table(cellText=cell_text,\n",
-    "                      rowLabels=rows,\n",
-    "                      rowColours=colors,\n",
-    "                      colLabels=columns,\n",
-    "                      loc='bottom')\n",
-    "\n",
-    "# Adjust layout to make room for the table:\n",
-    "plt.subplots_adjust(left=0.2, bottom=0.2)\n",
-    "\n",
-    "plt.ylabel(\"Loss in ${0}'s\".format(value_increment))\n",
-    "plt.yticks(values * value_increment, ['%d' % val for val in values])\n",
-    "plt.xticks([])\n",
-    "plt.title('Loss by Disaster')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 散点图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`scatter` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Lyk628+Gmv2P01jBi0iWUbd7Aiy++SF5e3pnl/WgtlrgrCQQD7Nr6Dw5VlpM0\n/VIyJk6k6fAntNZ7aCqvInPMCA4UbQAgmGYnO91HwB2HL34kK9s72fX6TqZm2XHYrSyYUxDa/sbw\n8wlPr95cToJdUVG3iq1bJtPR2Tnkr5ee1tNDMV1UVMSSJUsA+u3i/AG/wayImACUUv5+3N4B4BKg\nBtjE6QdOzAUeU0rNFZFYwKiUahMRO/AhoR9q/PCkfQx4n5TH4+GJp//KzoNuJKYA5alidG6Qb3/r\nzj53xBcNYtv0f//uZ0y8fTwJKfGnLbdzywb2P/Mhv75mPnG2T8+0inaUsWBqAWt2l/PcnnEUjPh0\nUOXmIy7GzbmEOHscZfs+5u5LjFx00UVnlPfR3z7Nu2vj6PJaqW5rxzYmn/j0DByORGJjY3EeeIUr\nvjWKrLEjAPB6fWxa9z5JzZVMuGjC8e2UbSvBsGY7371+AgmOmNPus6yqhXe3J/BP//ofAz5acTBf\n+zMVLVl1zv4XUX1SIrIXuFIpVSEiM4F84B1Cw8TvFpF9SqmVZ7ofpZRfRB4APghv+zmlVLGI3Bde\n/rRSapmIXCUih4EOQr8aDJAJvBn+ADEBfz25ghosVquVB//5bsrKymhoaCAlZTTDhw+PqL6o7jJT\nsjhaffQzK6k2ZztmH8Rae/4JDqvZBP7Ok+Yq/D5/6GJZfwcWS+oXzqmU4uOP3qe24l2sgRw8tgsY\nMW0ksUlxdHS20+xspd0ViyVYSXLepxVhXW0NqY4g0nri8S+YPoLDXT5eWHGQb10/8bSVT35OAqwv\np7y8fMhv8aRpZ4t+O5MSkWnAXqWUV0SeAcyEznbeAp4nNKLuqX7Z2QDT9+47VXFxMS9/8lcu/tqF\np/2gfuOJ1xh7sIZvXTOrx+WtHR4e/MthjHm3UN+laHL7KW/xkJI5DEMwgKHsJf713iu5dP5Fpx1A\noZSipKSE2tpaYmNjGT9+PDabjS2bN7Jp7e+5/cYc7v7ORg7J9dhzxtPU0I4IJKXG0lyzhsmXOrji\nvpuPb2/D2o8YnxsgPu7U69SCwSA7X1jJXflW5kzOOe1x2rSzmgr/Bfy/W+46bTlNOxdE1JnUsYtl\nw3YDLwMe4HZCo/x29de+tME3ZswYEouS2LN2H5PmT+ixTHVJDYFKE1h6vg5JKcWR+haa/XXs2fMe\nCTP/H8aEVJLM8SQn2GnZ/RbDb5nL+nQ7q17+C5OT07nt6mtJSUmhubmZoo/eZ8/WVQSBQ41CIGUc\n1qzx4K4OJl1KAAAgAElEQVQn9o0VfPOWq1hT9AY3X5OOwSAkjrRgLV9O+d49WPNmo7xdVOxaw8SZ\nitjRCwgEghiNBoLBIB53O464nnMbDAYKL5/OP179iFkTs087uCU3K55tW8o+7+HtM7fbzc6du9i3\nr4LOTi8JCTZmzBjLmDFjIvYsXNPOxID0SYmIAbgOWKGUauv3HQywaDmTGuy26dbWVp5+4Sm86W5G\nzR1B2rA0RIRWZyuHtpTQvLOFr934dd56/g/cPFYxLO3TpsEVW0uo7PCxNmgl68r5NJU3sHt1ObUu\nO7FJGSTEeZmwaDojF8xDRAgGg1Rv3oZv1SZumnM+f/vrM1S3NmJPTqamrAaVPh3zqCuYNusiYmNj\naXfWU/n+75iXX8qD3xzDx2vLeafLwY49DQTHjuDwW3vAaCFrdiaWpnocsycRl5DPuMJCUlNS2b5x\nOedPTuDA2mLGXDCux+e/6y8reGBCAmOGp6KUwtXmASDBYT1+dtns6uIvK4I8+MNfn7CuUor6+noM\nBgNpaWmfu88qGAzy3nsfsXTpVjyeETQ1tZKXdz5ebwde705SU1u47baFA3ph9BcVLX0oOmf/i6gz\nqWNEpAD4gVLq/s8oqkWZ+Ph4/uXr32brti2s+vsqNrVvxWA0YMHKBdPmM/feuSQmJnLVV+7htSX/\nw1fnmkhNiMXnD7B0ZwVd82Yx+vK5GE1GEjJS8GWmk94Vw7iJU4jPzsRo+vTtaDAYyJ0zk44RhTz8\n/YfxN7dx4b0PEDTHsO3jw8RV7mOcvY2y0kOMnziFuOQMOpIn0Nq+nbr6dn79YglNyZm4GhqxGFOJ\nv/FBJCmVspcfxWH3ET9hBM2BBNbXV5NYWorVHzjNMw+xj81jX0UZ2ekO/r6mhC2NHQDMTLMzZ2wG\nbo+f9k4PweCJfWotLS388eUl1PmaUYEgBY4s7rnlq32+qW4wGORPf3qNVav8DBv2LaxWB0oVkZo6\nNlxiBm1ttTz22Ovcc08HF110ft9eUE2LAv3ZJ5UH/ARYTGgE3eBdft/PouVMaigppXC73QSDQWw2\n2ylNTTu2bWP5q08yMyNAXWsnHydmMurq+YgYaGrtpLLZgyEhhwlTpmMynf670kv//ghtHSlcc/lc\nfP4Aa8uMcGQvM7MdlHRkMv/iqxER9mxcSUbZL4lLtrPMM4PsLy2g9O1ldOz/BGbfhMcUj2fl86Rm\nBYi9aCZxC87DFh9Hx4EjGA6VcMslhTjsPd870R8IsnPjYWr++DYjLH72JqUw+64LMRgNbHxhFecb\njjJhhI16p4dNhxxc95Vvc+GCK0hJSeHpF57jaIEw5oLQkP09yzcxui2Z2268ucd9nezDD1fywgtV\nFBbehsHQ+38rj6eN2tpn+clPrqGgoOAzj6umDbSI+D0pEckRkSeAg8DXic5rr7TPSUSw2WzY7fYe\n+0KmTp/O4u/+nPL4OTx2sAvn6Alsr2jnkxIXlb5Eciacz+RpM/v0QWpx2LGkJbC9vBmL2YjydKA8\nXZjMlhN+PsPka6PDFaCm3YI1bzgWh538yy7CEgjg+/jPsPZZRl9fwKj/vh3xdUBFNY60BFKmjSGY\nksDq3TWn/ByH2+OnpdXN1p0VGH0d5DgUk1Nh5JgkuuqrKSuvYNSUeNISA6QlKEYNU/zLncNJDi7n\nT0/+BxUVFew+sp9R80IjA0WEkedNYvuh3X06zj6fj6VLN5Od/aXTVlBKKVpb3Rw8nM2ddz/M1//p\n5/zr9/+X995fQUtLS5/2pWmR6AtXKCKSBfwIuBewAkeBXwHTgVv7JZ12WpHeNp2WlkZXjI053/k2\nrY1N5M+ag8Vsxhpz+uuNTlY4bzZ7X1tKnQ9qTT4o2YOv5CDtI4aTGJ9C1Z511JWsprNsIyMTk+l0\nHqXTVo3zaC5HN23FYPZz1aM34zRaseZl0uYJkHHlDOre2E76xbMxWkzY4h24OgNs/mAHsxdNw+nq\nYn91Ow0eodMH3mY/2a0dVDQFMBZmsWblEfL80FTRyf51LSQPm4VxtxFv/RHG5zQxKtdBfIyTX//8\nB5iy8+h0teNISQSgo7mVePvph/Ifs2/fPtrasklJOfGHI8vKiigoWABAIBhg+469VNb6MMVcQMB3\nhPQx3wAV5PXlW1n6/h/5zgNfHrKfGYn09+kxOmdk+tyVlIikE6qc7gNiACfwn8D/KaU6ROTZ/o2o\nRavm5ma21VQw8pYr8a3dgM1qpa22nvZAgNiUZGwJffugHj1lOhUbNlDx8VI+bvYwIieJRpOJd199\nnniTj7Ez7Iydnsy0W2ZRX9bMu8/tpmnrcxhrihg7OoajBRaCNsAdxN3uIWixEJOWRDB8Zwj30RaS\nk+NxxyicZZU0ODv4pNyNuWAkCckJNH6yh2BZDVs278KclsyeLiudtjjWv7SbgH0isZc9QO7EQjq6\nfLia2ti18S38wXTyM3IorduOb2MJVe5XmHfLQkSEypV7uWP+9X167mVlNZhMp69cdu/eT2W9icT0\nSYgIruZCOlrrSMsaT96oK3A5x/Prx1/mpz+5i4yMjD7tV9MiRZ8rKRFJBX4A3A/EAi3ALwjdzSHq\nRvCdDSL921RZWRmmkfmoQAB3p5t//PIpfI5UDCYzQWc9uQUZTLx8PvFZvV8PFfD52PvWB7T7s7Hd\n9j0CZbWY05IYn5JFbUM9+Mvx1e0hLzWNiTPGMmaSn7r6Elpf2cAltw4jf3wa617fTcuafRzZ7cVZ\nG8A6LJGEkTasw7LwOF34SyvJmlhI3UEfDMtnQ0krsROnYLbH4DpSS/u61Uy6fgyZI/04Dx2lZFMj\nc35xFzseXUFX4mVgjsXV5qGlxYchYMU+/Dxq9i6jwx/HlPlXcKDUj7ejkhW/eocLZ03mvkW3MXbs\n2F6fc3cejx+D4dT/psfOojo6OiirdJGYMbfbiEETgYDveNmE5FzaXBewfMU67rj9y31+/fpLpL9P\nj9E5I9NnVlIikgx8D3gAsANtwM+AR5VSroGNp0WzstpqSElk9TMvUx+bTfyN3yAuIdTkFfT5qCne\nTc0zf2PhV68luSDvlPWVUmx9dSllnXGkf/k2xGikybSDaaPHIyI0+5oZM2shvs65LF/2JjVlH+By\nNdLhqyMuI5blv13LeV+fj9tjYtMTLcRO+hK2/ELaaqtpXFVE+txmMsYdYczYPJQCq9dAcs5oNlRX\nYWlox+NuxrVqA4WzswjYLQQMNhKGpdBS0U7jlkN4mgPYphbQWu7F2+UHv4EchwFsuXTs85MjPqoq\n6jGY0pj0pftpb7iaA7vfpq7+KN3rqEAggNPpxG63Exsbe8IxSEyMxedr7fUYV1bVIJYsQld9HDtw\nrZgtJ24nI2caq9Y/zpdv6DxlH5oWyfoycGIPoTMoBfwSKFBKPaIrqKF37MaOkepoWxs1+w7TEDcM\n4hIwhysoAIPZTPzk6ZguuZF1Ly0l4D/11o7NZRWUVrhIWngtYjTib2/D39FCc3U5zroaxBykoqqW\n0tom6rMn8eayfUy+IYukwngMowvwTh3DmnVH2fpuOyMXfoN4Wyb+qnayEkeSccFdBBsScIjQcbSV\n5r1VTB83hYrNewjGxIHbQ6bFT6rRiyngJUgsKhiDNWUMvqYuvIeasZmMNB+swhxjw2q0YDUKBhH8\nna3EmI2kxRppa25FEUQpRVL2cLLmf53n3tvJyqLQPY23bt3Co795iJf/+kMef+xfeOONF/B6vceP\nwaRJ41FqJ0oFTzg2ZWVFoWPU0okl5tOLkH1eJxbrUZJSCk8ob7bYCEoyTmevv/U5YCL9fXqMzhmZ\n+tLcdxvwX8AO4Be6aU/rK7/fT9n+clLuv53W3dt7LBObX4jTkUFD8UGyJo0/Ydnh9dsxjJ1JZ0Up\nzk/W07r7EJ5mL75YB+0dR+lMUIy47hISRo3H2eDHPvs81r27jWq/g2H3XURyl3DgozLc1QkcONyG\nfVg6icOTyMvLIxAIcMRZStmSj0gdk8/ocaOwW620tzgx+NPJjLOD30sgLp6qTWUUxDRgtiTQVOPB\n1ZCIt/oQ7tZOcJeSeOFkbGYTLe1+vP4AHYc3MjktkYbOIGabCb8yYjaF7mVojXUw7IKvsmTZc/i8\nXezd/RJ33JJGenouHo+f9z5YwdKlcOONdwKQlZXFuHEOysuLSU8/9U4fIpzw4zXtbRuYOGMqBuOp\n/7VFGPAb32paf/vMMymlVJFS6gJgKfCqiPw4fAdxbYhFetu0wePFG5eKKc5B8rwLey1nGjGemgOl\nJ8wLBgIc2XmItt37OPLEa7Ttt2DNuBpz4oU4Mi/DY70Yb8NIyl/eQPmrf8Pf2YFj4mT27VLY5kyi\nrVORkWzD39mF3+YgYLXjCSbQ2hWkraWF4lXrqdxfSnOsDcvCuexrdbHsyRfwdXXhem8Fjet3UFd5\nlPakQlrs49n8v+vY99J+Dq1ooT2QhcyfS8qcieS7D9G1+U1aK/ZgaT5E2arXMDYeoU4ycHp9uH0+\nMrJGnFA5WGMdJE29nl/87imcrfU8+WIljz59gN17Grh0YTYHDxTR0dFxvPwtt1yG17uMtraa4/OO\n9UmlJMfhdTcD0Na6DUf8AfJHzjvlGHvd7RiVk5SUlC/0Wp6JSH+fHqNzRqY+D5xQSn0AfCAiXwL+\nJiIfA79XSnUNWDotqmUkp8D+k3/q61RithDwn9ic5e3spGHPQXyWeGJyLkMMRlRQIf4AHo8HMTuw\np0/D48nGdeAwxqZ1WC6fQbvLR9XhdmLdDlprW/EaYvDU7ULFj0SUEb/Xx/ZV6wkGfVhz2/HZjaz/\n+es4EtPprDtMbIqbrCsn03rUg2vpGpIuvo7OQ/XYp96PISUTu9NJpmczbaVVxOBh5IRxKHsCZXtX\nYGl3kRUXR3xaIeYYC+1KUd4IhVNsALQ0VFC2fxXurmYCbX72HLXjrZ/O7AvG0djRzs53dzNh/SGs\nFjsdHR3Hf7IlPz+f733vWh599EWam2eRkTEDqzU0MnJYTjY79izH2biThKQK5l16J5aYU+9kUVe1\nlUUXjifmcw7/17Sh9rkv5lVKvauUupLQxbtviciDItLzZfragIr0tukpU6YQLC8h4PXhXL+613L+\nhmoS05JOmFe7ax/t5WDNmYevuZnOyjJaq6vAH6DF1UogKFhsDmJiMvHIWFoPCmXvbORouRNfXQv2\n0dlUtwTodGQiiV58217A6yzH1+ki0NmC6tiOJbMZX106xuF34S68HXfKVRzd2YAlN4PkSVmYsxKo\n/fNbJKfNJj4uibaScmx15YxLTyalTnFkp5PVRSvZs3EFXSlG5KaLcF0yhU1NZaxrCLDVOJ6W1LGs\n3r6L155/iqWv/YT6zKP4ZydSYT2IOWsUtd4sakrL2PDxRkqqvHxSkc/WXa0kJ396XVQwGKrAZ8zI\nwpGwkiNH/ouNGx+isvJ5Ghv/zIj8D8jI3M75V9xNbNypP3PSVH8Qe3ATlywcmtslRfr79BidMzJ9\n4Yt5lVJvi8g/gBuAt0XkHeCP/ZZMi3qFhYVMSUmgeOM6rKaevw8FPR7k8G5yv3Tn8XlKKQ4t30Js\n0hi6Kspoa2zCZ3YQbKgmYfgUPH7o7GzDhxmH3Y7FlonfMRvv/r8Rn5RCoLyOQ08sxTBpIu7l2/HV\n+cHUQOf738OclYMh6MGU5qWrsRAVOwKLNY6AXyBtLEol014bJOjpwtDaQYbbTFp7GzEuJ472Tkob\na1nd5KTDNgXb+bdhoJXyrUtIuSCBlLQcWojF8pXRtL69EXvOaAwqgMMqtK7fQe6CMYycNorKyjIY\nNoK2Letx1pby0Z5Wht/+U1qqD9O0fyUTkgsIBAKYTCb8fj/P/elVPtnajiVuGmIaj8mxlRTzFh56\naCYJCQmkp9/La68vZfma5zEnzCY1YxwGo5mOtgZaGraQbD3Cd75z65A09WnamTqjWxiFb3D3Zvhn\n2G8C/gHkcEJXrjZQoqFt+nt3f5U7/+cx4h/8t1OWBT0emt99nYnTx2BL/HSEmquqGmdZGxmjJnFo\newne5EIkNR+Dx4fVZMackILXW4UvKBx1NhNrUSQmJOMqVRjMXmxZ2ZgcSTQ8+wru4EjiF1xOQm4q\nzg/eIdjcgt9nxt3VgXLHgqsNHG4s8VbcxSXEJSbRsmIXbU4hcMhFWoeRgs4GDEYru+uO0NpShS/+\nemIcSaSPGkkAwVVRR7DhEF6Pl7icQrq8fuwxRqwmAxlxZqwmE/7UBDotoFAYUXTsr4ALbibWNo62\n139IY20HwVYozBlPclKAlpYWMjIyWLt2PWu3CsPH3YuEbz+lhk2n7EA6h0squPaaRQDcdusNXHB+\nBUWrN7Nt52p8vgDpaQl85eZpTJt2FTabbXBe8B5Ew/sUIjtnQ0MDn2zYSnnNUbrcXnYdqGLs8Gzm\nzJ5BQkLPPzFztuiX++yFK6vXRORvwC3AqZ9I2jlp1qxZ/OCay/n1H36Oe/5lWEaOx2A201VyEN+u\nDYwYn0/B3AUnrFNffBBXtRuTrRXf0ToCbkG63FjdHSiLjY7ODsRixttWSxArNqOZttpKfC2Cu6WG\nileaMdhNOGJcZF8ziZjRqTTub8IxMo+4Vi9VtbnYc1LptBnwr1tPV3MnQaMDVbsXKKU5dR4daROg\noAv/ri3sPlKBz5FJs9WPpA7HOuYSvG31uKoaiMlMx54+nI59a4hfaMUWF0v7JxsJmDKItyis4XsT\n2tPG0LxvBc2FmRiNRpS7g2B9PQwbRWxcMuk1RcQ5Epl24Q00bV5yvO9o+Uc7SMv58vEKCkIj9LLy\nL+bDFU9yzdVXHL8nYH5+Pnfdmc9dn56U0tzcTE1NDVarlezsbP2bU1GmrKyMN99dye4SJ5I+nbjk\neRjizQR8HnZsKuGV955i3uR8rrtqIenp6UMdd0D0681gVehijpfCD22ARcs9vO664072HTzIgcYD\neLpqaDhQSUeLwp4/nprqGKp/+RqZwxMZs2A6tXtL2PbmJzQ3xRGTmoFPnKjGGswYIHM4nd52bLFm\nYmwxWKwB3C0ttJWVY6gtxRH0MTz3PHb6YrEZq5lzVy7lJXugI5PA0Tps1WvJvmwWDUtbUKXNiMmP\n3d+Ocu7E5g5QYIujkQQCwy7HGDcaS1YsnvJSqlxVJCWmkORIxOcz4/O4cPuF9qZWbFnpGE1+/GXV\ntC5bizt+L50NAcxjryEuxkLrkR10Va0DMeBzmTj0zLvE2RX+A/XEWqDrwG7SsqZx3tVfIxBUHDm4\njVFx6vjPeDhb2knOTz7lmNZWbkM8vuPNgierr6/n1dc/YMeeegzmDIKBDlITPdx43QXMnj1jUIei\nR8v7NNJybt++k9+/uBxL4RXkXjQegzF0g+Gy3UUUTFpASs5oAr5L2Fi6jZ2P/oXvfvMmCgoKhjb0\nAIjKO5aLyCJCv/ZrBJ5VSv2qhzK/A64EOoHFx345uC/rav3LYDBw5cJLSN29i+fe347tvFvJvWoe\nRrMFAG9HB2X7trLjB89jjrGRNucOWupXEgiYwZxG2rBRtNRvBe8OjHmFiCQSUOCrrSBQW4nJ10Wi\nLUiQdHzShTV1CqqriY5GFxfcnMP6v7yCeW8z4y9LwFW6BeXsxJGTjPHQIczWXDyJY5kWSCMrycae\npnoOHT2KKXEiNm8TZoOBuFg7IxJjqa0/wOG4RDoPvY5iAh0+P6a2FtrLllM4eiadagKG+JEkjhyJ\nx92Jp6Ecf9Nyxt8yAxUMsvflTxjumEduYgytVa9imhSD5I/CW1bLOy/+gPS0EcQ3FZE2xcFjv/o+\nV9/4DUaNyKSk4QhpmSdeQ9bRXs/Y/ESMxlPvjF5XV8d//eoFfJaFDBt32/FrptpcNfzhubdpbW3n\nsssWDPjrrn1xBw4c4HcvriBt+mJiE04dDHOM0Wxh2Ji5NCdk8OunXuffH7yTzMzebzMWjQbkl3kH\nkogYgQPApUA1sBm4VSlV3K3MVcADSqmrRGQO8LhSam5f1g2vr39Pqp8dOnSI/35qKdYxN3CotoEW\nFcCUmUanqxXn0UYMSQk4dx1BVe8mszCfth0bSE76ErW1LVgcaRjMfoINm4hJtGK22kAFaaquxZA1\nFiM+gtUlJNvLMJvTachYQLu7i0z1OImZBprUMBzzL8K3cQW0VmKfNRdrewMjFuTg95op/6CS2H35\njIydSZy9lZcPb6UzLZd4fMTHTEE1LWPi+DGopjLWd3kxWu20NvoIdHkZkW3Fmj2czLGXsb+yisCI\nSQRi7DidTVC2idETK8icNRG7xUj1slXkNw+nrbMG6wUZBJNs1DS1EvB2YTiwh6nNTh6+dzrDsuKp\nrHHx0gcuMgovZMmrO1HxiwhKDCJglgDBlo/412/O5ZprQr+l1dTUxDvvrWT/4VpKSw5hTbyW4WMu\nPeV18Ho6qDv0B37zi3tJSkrq4ZXShlogEOB7j/wWNeJmEtJy+7xeXel2Rhm289ADXxvAdJ9PRP4y\n7yCYDRxWSpUBiMgrhH6qvntFcy3wFwCl1EYRSRSRTKCwD+tqZ8DtdtPQ0EBycjJxcXEUFxfz/ppl\nfLxqJ7Hj7qFgWCGZwwpwuVwU79/P4ZL9SHIcgTInYrYTM+0W2jb/kXkXXEbVwWbSUpOpaWkjMTYO\nry0bkzjxth6lw92KijHh9rRhMhjA3UDB2Diau9Lx16zGlHkhrqYJeEr3EJg3haA/lZbOLHKGWbHY\nYhhx/ggS8+y4Ww3YkkqpZxeWTitZgTTSEmdT6zaCoYtG50Fi3PvYebiUQIcX66ybSB53Hp1NbaQd\n3c6EnHhqPCNw+4P4lNBSehDruEmQkoGxq4C2yn3ET1V0NNbTuL+OxIThlDZVkp0yGa9SmFMTiW/e\nw+jJiRz5sI6lm2uZWdCGAkqOHmazL8D4m4azb93biCEfxIDHXcKoKxIpqtzK3icOct3FV/Lnl97D\naZyDLWEW22vfJ6lxK8MK5mGxnnjdvcVqR1mnsHHjNhYtumRo3iTaaRUXF+MMplLwOSoogIyCKexe\ntZL6+vqz6m730VhJ5QCV3aargDl9KJMDZPdh3agRaW3oW7Zs49mXP8RnSgXPUS6eXcie6k0Uzs+h\ncmkLdncXmS3NJCUmkZCQQF3zUVKmjyR5TD6dzS5qN1cQExuHOzEPqyMRo2EfsVYHNkMngYANMZhx\ntzah7ELmvAkoI7iKa/A0OQkYd5OccgHNzkPY4z1YJx/AYD6PxhVeOoo2QrUfS20pSXddSev6LWDN\nptMnrP3f5fiSUjFcMorSTjeb33uToDuWLmsaLamzMHo2kzkjhcKFo+mormP7x8up8gi21Axcezez\nrXESpvg4uhICmIwtOKpXE6x5AatKwz/8Kpoacmj79RsoEWwZ57E+kITdasOUmYIPEx5XK1Vv7+Ho\nqHzsC69g74hUXv5wM6rsIF95aAGHWixcuPBqLrzDT2NZDSoYJK3gAg5u2M2Ei2ZQd7iSnz31G+pr\nxjH/qvNDN6rNmIenrZSGmt0MK5x7yutkixtGVc3eQXtfRNr7tDeRknP56i3Yh83udfmxPqmTicGA\nMWMGaz/ZzI03XD2ACQdXNFZSfW2HO6NTzMWLFx/vhExMTGTq1KnH38DHLqYb6uljIiFPe3s7f/9o\nL8mT76WhYjd+0zBeWraOqQuClOw6gtdvIivLTktLC66KnXg8HrrwYc9JpW33BnweDzFZhXhr6vF6\n3NSUbGXuNV9lxctPYvW1oVqS8TUHMcV5EWM77qNBEvJH4Jhsp3rrSswZitV7VpN9yyIc5c101ZZg\nyGwjY2IAX7UT14FXsc2ej+fgEYJWGxse30BHjQG/PRdDBwSP1OCYlk3MjGSaisoxjJ6NMTuHYNpC\nnA0fkOKKJX7UNPJLlrN3y1KkcAqB4RdibKzm/7d33+FRHufCh3+zvWjVey8gUUQVvWNsjG3iFrfY\ncYnjxOntJCdOcr6045PEOXGSkziJ7dixTRz3XgAHbASYXoUAgSjqve5qtbvaNt8fEhiMAIGE2IW5\nr0sXevuj2WWffWfmnfGY2jA4OrBFVTD62/Oo2HUAb30zzYdeRzfyVoROT7fQ4o7JxDZ+DO2vrKd8\n2duM+sYXaf14M92mKHwBE1eNy6XG5cWZlY7UQH1lO0QnsXfNdoRGULhgCgB7i7dTsfsghQumkDIy\nE1NGDBXbDxBTto/0lDS6GjdBjwOfr7c6r/JQ7+uVPbL39ao6vJ5YTeuwvX9279497O/H81k+5mLG\nEwwG+ffqtSRMziAhs7cdsrK0d/uxxNR4dPdJyyduj00dzZvv/Q9xMREXJf7i4mKeffbZ3niGqBNH\nOLZJzQB+LqVc0rf8IyB4YgcIIcTjQLGU8qW+5QPAfHqr+854bN961SZ1jnbs2MFjb1aTNfEmfD0u\nDpcUs+3Dt5Geg0xckEG7NgeS5jE6pZDk5GR6vD28v241wVFJWNOSQAgadh+iuwpERxtXjrAwasq1\nHN6/nZ1r/47w9NB2tBl9jBlNbBTWzAi0miYi08A4MYG67Q101vVgSkrEEmghIlJH06Zyxn9rDomz\nxtKwdh+HXqnG0eVEH5dA58EIRPRYrFOmYIizEfT7CbZX4tzyJt3tjVju+R3a2GTcuzaj++ARkgsn\nIIypuBuP0nzkIOKq+4msO0BifDbx827Avm8F2dOdRI7IxOnpQQT8VL61E695Kfr4TKqaWwhadZhG\n54PHBR8/R0Kanvo9WzHcsIjIyAhGWoJ4YiNIKUimp8NBxxPPM/3mhUyeMuuMZe/scLDsp+/R6Z3F\n+Kwi9uwuxdu0kyVLvoLVdnK3ZCklVfuf4sffnU9+fv6FfEso58Hj8fDVh/5A1pU/Oq/je1xduEue\n4I//8/0hjuz8XK5tUtuBkUKIbKAeuJ1Tp6t/h975r17qS2qdUsomIUTbAI5VzkNERASypx2f18PG\n956lvS0La/QDJEa4OLB9Dw1N65n62YkkTeytKzcajOSmZHCwuh67xws6Hd76FoKtfmLoxhKRCkB6\nXmFLeTIAACAASURBVCFHq6YS7/83S+f7WLO1BofGQGpRAaZYK22dXur3NOPYX0faFVMx6+1kL11A\nT2sbvpZaDD47fnsn1owYPG1bkREZODsLIT4TY4wVghK9zUjArUWXP46uagHOFQTXPoUsXIzc9ho9\n7oloIq4HnRGveQrCdgg2LCd69DSMI6ag1ekJoqPL3o3F04NNCJxS4Hb78egkbqcHs9WGSInGEh0L\nMhbnjM/T1XQYT9OHmJKSCBp11LY2MnZaLgjQWMy0tHvJyDp7IomIiWTpg9NZ+fRatq56jbyEGPzx\ncRiMtpP2k1JSV7mekZn+izaVvHJmOp0OGfQjpTyvxwSCQT96fTh+rJ9e2P01Ukq/EOIbwAf0diN/\nWkpZJoR4sG/7E1LK5UKIa4UQh4Fu4AtnOvbi/CWDFyp16AAjRowgP2kN6z/4M4010RgjR2PTNDJ9\n6kxgJi+/0kln+X5YeGPf/BIwbvRYrBUWjtRW4fN6yU8dSdyYSEre+AXeBjs1rioCnnZsLeu5ckkP\nX/rKfCa9e4Bn3j/M/tVlRF0zA5mcjMfZis9bhd/uIHlxBt6aRhylB9EJP5X/PoB+ax0uJwQ72vC2\nxxB342IcZbWYo8x0N7QSSIzA7/ajs+jw15ehzfkMOt87aP3lBEQMwagZNBw2EvR58Ae0GEbdiXd/\nHXa/JDE+gaAM0uEz41pZSoQugDkxnuZtJXQfbcWYWUK8dgJ2fTQ9HZ2YUhLxHNpFd+VOAl0NRFuC\npJkD1LY60VqMON0+fH5Jy74askfkkpAQj6/HS1ebHaERRCbEoNVq2Vu8/Xj1H0BmYR5f/F02u979\nmGm6XHSYeOO9vyBMk7FEpOL1duN17mZkhpdvfv2uYX2oN5Tep2cSCnHqdDpiI824HW2n7Xp+ujYp\nAGdHIyMSL61em2GXpACklCuAFZ9a98Snlr8x0GOVwdNqtXz36/dS8/3/ocs6krTkADnZkzEYep+F\nys0aQUX9DrraG4iMTwPA5/UhAgEKMrLJyMpCp9VRtfsDvnP/9SyaP5M9e/bw7MsbafWY+PVf6liz\n7k0mT46nqUqPddZkNNEpdDZ0kTRzGgZbFNVvrKdjVznYTEQvmIYnykLj8j1YCrLwVOzFFtQSk1BI\nsNOBp60TND60Gmj7qBSt1YTD1QMuJ7qgloA3Cm3AR9AdhdDF4zONQUTHIh1V+LvtSG0WPY5q9CYT\nNRvexVHvQK+7go1PvIfVWE/cqCym3D0eKZw07H0Lx+Fo3IZc5NG3MY5KJuHKbKhzEmMvoOqJl6Fo\nMjGLJ+AORIHDSUTpUeZfM4vNb3zMnrXV+AMxSOnHGtHNlGsKkMFTq6O1Wi2jF0xm0xOr+c0PfsHU\nKRNYt34z9Y01REdamTF9Nvn5+WrUiRB39bxJvLRtO1kTlpzzsa7a7Sy6fdIFiOriCcskpfS60N/6\nuru72b59J6WlFXg8Pmw2E1OnjmbcuHHo9fpT9jebzYwaVYDWPIr4xJEnbZs5+3a6lm+hpmQFBfPu\nRqs3ULJ1E/H+TtwByf6ONmIMXWSKcm658Yt0d3fzj9c+ZEdrJL7sa9DmwKqSN1hbUkJKUR6Zi+Zj\niU9g/75mOusb6CxtxkkqjoZu9FFZmDMK6RKZBLMh9+5bqX7NSOsHq4nSavHuLicuJxp7nR1sESRd\nVYj0BejpcNEVDBIV78Ne6ka77V00nVnEjVuIpIzudi+eYCreji70nlakp4WD7/0ZZ3sLhsxbMCWM\nxWgoY/zCNGIKsomIiUGnEWSOTqa8eD+b33uTqK88gG5kLoHqMnQGH/EzxyBFgJr3l9PdUoEuIY1Y\n6eeKBRPYsXwvdXV5xGQ/iL5v+g1PVysfvbya8bP7rxKyRtvo9HXz+JPL2L2vHn9Qjwz6SU+xMaog\nA6/XO+zTdVzsu5OBCpU4p08r4uWVTxDwXXH8gfcTne4uyuVoI1I2MmbMpdWCoZKUcopAIMDbb69g\nxYqd+Hx5RETko9Ua8PlcbNq0g4iIFdx++0LmzJl5yofkjGkF7CjZRVxCwUnbnI56Zk9PZ9LkWNas\n+yO+yFG0V1eSnWwj0NlI5a6XWHz3Ddx9x/1YLBaef+FlNu4pxVtwI6kpicTExtNoM1CzqhO3Q0dF\n8V46KhvwBSRa6SVIBMLgxzhvEb7KXXRUu4hOseHRenCWlmJ1V9Nt9uMOBkkbFYMpPRopBG5M9DQ7\n0Oq1CEAfE0lXTSf+ri5ikzVY8wJ4vK14gjFozGC078Xf0krAv5ueqHz8vnaMMwuQ9v0495WQN9uK\nOSUBMz3oNb1/v1ajIWN0BEdLIYgG90vPoHdVEJFmxd8KllYPuYvy0Te3c+v1N5Ocm07phzuoq8kk\nYfTik8rRZIvHMOYWSjcsY+TUKtIKso9vk1Ky58OdbN5kpzU9jtHjb0NvtCClxNFew+MvbyVtxWa+\n+827iI8//SgGysUVHR3NVTMKWLnrbbKn3jKgtqmAz0vD7jd4YOmsfofJCmfqvj+MXYh5ZQKBAE89\n9QJvvdVIYuL9ZGcvIT5+JDExWSQmjiY7+2as1tt58snNrFix6pTjJ02ayKgRdioPv4vb1U7A76W5\ncS/7S37Fffcs4fOfu5k//Oyr3Ds3ivHWg7gPvkx0x1oe+urNfOWLd2G1WmlqamLl5reIzNCQOTEB\nl6sKT7cTg8mKVqdnTHYBnWu3IkaNQ1c0B09Qi3vbRqypcViq1qNrr0bfeASxfQ0jIivJdK1i/DgP\nsakaYpLsiIAk2qrDGm1C+AOIQACDzYDObKCnoQxnczfCVUNUqg2jvhZH1YsEzZ1Ikx1v1wawv4Bl\n0R2IcQsR8+4g0NCAad48NMHDRKXHoPV1ojVZTiqXYMBBbE407uXPM3phFEXfX0LRHRNY8I3ZjPn8\neCQeOhq7Sc5JQwjBrlVHiMo49UsAgEarw+kwsXfNwZPWH/i4lLWvNxI58h7iUiejN/bGIIQgKi6T\n7LG30Crn8rv/+ydOp3MI3zVnFi7zH4VSnLfevJTJKU4qt79BMOA/aduxLufH+HrcVG35F9dNTWTe\n3DP3BA1Hl1bKvYwEg0FqampYseoDAsEgaYnJjB079ngb0PlavbqYjz92k5t7E0L0/x3GbI4hM/M2\nXn75RXJyMhg9evTxbQaDge9++14+/HA9H675B+1OD2PGpDO7aDbjxhUCEBkZyaIrFrBg/lwOHjyI\n0WgkNzf3+DmqqqpIKjDT0xGkvrEMU+I4Wppb0dhr0LSW0XTQgOeohsCbDvzCh3Qkoakz4e9agybF\nTJJsJuVAFbFxOmbenYrRZGL7uzXkTEmi29NCq91O464Azu4AXrsXn8tH255m/O4g/oZO9Ppusid6\nWfi9BbQfbMa9NgJHxSp6GrWI6BFoe7oJNlWgjZ2EMEQh/B58RzYScDrx1u/FWJiAzhJ58usV8OGq\nqiJ3wSQy5k8k0N6K1ZBAc4cHaY1m7N0z2fHjd+hsascabcPVJYjPiUPKID6fH5BotbrjY/WZIuNp\nqjpw/Py+Hi/rXi8lZtQDtNfYT9vulJI5mYr99WzYuIWrF6sRJ0KVTqfjaw98nhdeeYs16/6ENqmI\nxOzJGC2f9Nh0OdpoqdiOaC/h9isnce2SK4d14ODhopJUGKquruapN1+hzQwmkxmNVovnwGa0/36X\nO65Ywoypp39a/Ux8Ph/vvbeZ1NTbT5ugjtHrzUREzGbFig0nJSnobZtaunQxS5cuPuM5tFotY8aM\nOWW9zWaj8XATyRMMtOzeSO2Wjbh7EhHt5Ri7BfV7rRgMs8CcjzcoiY9JQrojsCT6cbRsQBPRQVf2\ntfg8jWx4dx/ubkFidAR6nY+pN0bz7yfWY4u/lqzUDJpa7OzdVk8gcxIiPQXZ3Y2of5IJN6WjCbQS\nkajF5j1CS4sNbe5t+BpbEMmpuCtKMLo/wL+9A02EG//mMoy2dCq27SdhTB7Q+9S5z9tDwN6M42gV\n3a3djLlmMgQCmLUakpJTQIDT6aSxrYnUyalU7z9C4bwi/D4PrS2NuLq70GmCIMAfAJPZhi0yhpjM\nCeg85cfLrLLkED7yMFij8Xe3YE21nlKuxyRlzGDF6me5ctGCfgeoHWqh0tZzNqEWp16v5967buXq\nRY2s37iNDzf9BZ+wotUaqF5bgk3v5bb5k5gx7dIeh1ElqTBTV1fH7156lrgb5zMu/5O7D2aBs62D\nZ194HyklM6ed+2hP+/fvp6srnri4gb3h4+JGUFpaTHNz85DNZVNXV8dbrz5J+dYqqIlDagzEaG3k\n5xpp1+YQOfpG3HYX2o4KDJ2HaHZKCgvG0yqSyJ6Vh5QzObD5eZzllbS0t2FeWoQtP5Gu+iY615dQ\nd7Sd9LE+PAffobouj5Y2GxZLDEHaEC07kK7VWFLN+P16gl4XhggzuflujqwBr9mOMXcSXq8OTFb8\nR35P4sIv03FkM9EZcQRsLrxRI9myfDspcXqypyahDTjocfs4tLUbfUIiOp2BoL2dpMT442OiGA0G\nnFVNpI/JoKujg65uJxpbK56m3aTl5KPV9nZSkVLS7XLS1uzA23qAxbd8MrZbZWkDhpipBPx+gi4v\n1ojTJymLLZ7Wo73VqqmpqUPyuikXTnJyMrfe/BluWHo1DocDr9eL0WgkKirqkmt/6o9qkwozr37w\nPpFXTycpP5fy4k0nbYuIi2HkXUt58aOV9PT0AL0fbA0NDRw+fJgDBw5QWVmJy+Xq99w1NQ1otZkD\njkWj0SJEGo2NjWfddyD1/X6/nxeX/Z6A7whjlt7JvJvu4sqbPkfmwhuxxSRijpyHOSKR2LRsZFwe\nyZEm8uKjmTB2DHGRsXS1O9DpTeSMu56Ynh7mPDibws8vJH5UOpGzx9ETnUz58lraN9Rg9VbiqX4R\no/dtTL7XsTY8SYqphNTcdCw5N3FwdQcdNT2YRCzetiD42yApkx4s+KUFjcGPLmUkeaZOcqlFHywl\n/9YJZCyZhWneDCoqOjj67700lThoLg1iyJ1EwCfw7NlPosWCqW+mXL/Xh72yHkuHByEFwaCkrHQT\nV9+cgsGzgYC3+3j5CCGIsBqI0rbTefRFbBmfVP14PX60eiOdDW2kxaeg1Zz5DkmjM+H1es/6mgyF\nUGrrOZNQj9NgMBAfH095eTlxcXGXRYICdScVVpqbmznQ0cT4wtNXo1ljoyE3ma1bt6LRaflo+8e0\n48YQbUVoNQQ9PrzNDqbnT2DO1Jmkp6cfr8f2en0IYTzHqLQEAoFB/FWfOHToEBG6RvbbLeQtmkVz\nczXxsQbi2u3sLKkhJX3e8X1tiansL61m4tiR6HQ6stIy2VOzj+iEGCxRCbi7o7B32Un3OdDh53Ct\ni24i6dZNJinOwp7977Pkx5Ox++Nw2uM4unI/5uhs2h0mUqffSfN2Mx88/GeiE49gjNJgTDTRXfk8\nwfg5aJyN6OIiCMgsju74N9dPz6d+bAR6rR+/LQJD4XhsAT89W8opPyLx+g0YOsvx2+soEAvxltfR\nXNUEGgEuL5kJyYydMoN//e5FRl8xi6xEH+kp6cjPBfnw5ecJGiZijslEBoN4Osoxafex+NZ0Wtqr\nGBEsQKPRYIk04qnrxNXkYULhlDOUcu8Xl4DXgcViOeN+ihIKVJIKI01NTZiyUo7P0Jm/YGa/+3VH\nGPjts3+l8NoZpN8wgZEZySc1qHrdHo7uLmfLm08xwpbKF277PFarlZgYG35/2zlG5cBqPX3V0jED\nqe/v6emho8NOMGkaJouFiMh4mttb6HG0EwwmodH2dgqRSFyeAC59EtaIKADi4+OJrLXRcKie5JEp\nGIwjaF23FuaN4MBRLwFdCgF7OdbMKUhjDFZ/HRX73dTVdqIdNZOu3GxausyY/dW4mo+CKZ6MnGux\neJMpK9uMiE1ASxdCsx9NRh5S6KGxkU67m0rNWJoO7CcjV0u6COKpPEBH8SbsVVpi5t5GekoccQYX\nW/75Dzq3tbL461fjlwECwSDO5q7eu93ddcR22ag/eIDZMycAUDA1k9QRcZRvO0zt4b1otZA7L4K8\nCRMxWgzsOdhJc3MLyclJjCjKYu2rH1I45kEibZGnLWMAe1sVGUlaEhISzvqaDIVQa+s5HRVnaFJJ\nKowIISAYPOM+RysrOdRQxbirJjHxswv73cdgNjFi5njyZozjYPEOfv/UY3z7C19l3LhChHiCYHAu\nmrNUFwG43R3YbJ0n9cwbjJycHI7WBfCP6X1wNSY2jg4Eh6qP4vEbsDs9BIPQ3QMavZnY5MzjvdiE\nEEwaN4Hde/dQtf0ofl+QeL+XdT99je6YEfg7/KSnTEKvldgMWhoT89i/cQ+JS5didxvxGRKxTJiD\ncLVSt/avxGhdZIwbj685gQR7Pd4p92DyteBoKifodxIUelwZM7ClTCQq7wp0jQbaNzfSGunC2lBN\nnseOdekd5EzuHXuvw24hIn8eBZHxfPz4OuIKEyjddZRDrQY8Tkmqq53/uO9+/v7cL6jaHUvelCwA\nbDFWihaPpKifm+e4KA2OzjaSkhJpr2ggIdhEvO3Mr5uUkra6j7njrqmXZE8w5dKjklQYSU9Pp+f9\nevxeLzqDgfLiTSfdTdU3NHCwqRIrQbLGnD1xCCEYtXAKhwy7efxf/+Db93+VSZNS2bdvHykp4896\nfFPTdm67bcqA6saLi4spKiqipKSEmrIyGisq8Pf0YDCbScnLI2PUKCZMmMCEKVezbHcjphQHWq2g\noUVDclYRvpoaAvp4hNBgNgTxerqxtzbSGRFDRkYGQvR22y2aMJnOzg7WbzyM39eFDEaSnTed/EmZ\n6PCxa18tSfERVNVqiShYhKfBT5LFTYVHEmzeg7d6NwazltTUeFx+M7qgFkt0Fr66jUTN+gJx45ci\nZZCyTW9gaDpI0qQ7OFLTyFXz76Kj/hC1BzeTrZME4nrYVFLFwZ4EZECi19hIS0pl7qxp5OTk8Mbb\nb1Bd20b21XeTnJRCZ+lGKqtrWDIpm6bNh9lR20Hh4jEYLf0/UrD348PE5SfhbLOz5bkPyJZx/Ok3\nD/GHx99Er7+DyNj0U46RwSBVB96nKN/HlClFZ33NhkoojIk3ECrO0KSSVBiJjo6mKDOP8m17yJ19\ncrtDIBBg75GDWBIt0N5F+gCS1DEjZk1gV/1HbN62hVtvXUJZ2TN0dEQRE5N12mPq63eSllbH/Pmf\nOev5u7u7WbtqFR//85+MCATItFiYYrWi1+vp8Xho3rKFI2vXslqvJ3fWLNKNLTh8meCDtNwUYiZE\n0FHyW6yRFrqcPfQ4W4iyaND66+lubaG2JoqMzN4OH0L0llPhaCs5yYvYtPcARWPyCQL7KzoJSBMp\nURHo/RpkXBIRVkFstKDLOoLo+CQcwSZ0gSiS4gKUHzaQkpDEhJlz8Nkr2L/lCbrN8QS8Tji4maxF\nP8cSn4e9zo7T2U1i1lhssSkEDyxj3oLprHnszwgxBkNiKh17DlMU72fMmDEYjUamTp7KeruFrFFj\nAXBao6GnFV9Qz/dvn8TKjRV8+NdiosalkTI6hejkSPSG3v+uPW4v7fWdVB9owllh4Rt3fonZM2ej\n0Wj44bcMPPbkC1TWZxGZWITZGksw4Kej9RAB+3ZmTIznC/fcedk0uivhL+zmkxoOoTyfVEdHB4/8\n43HkrDFkTh2Ptu/Dpq6ujq27t6Ipq2DxZxaSesJwOQPRXtdE46vb+dm3fkh1dTWPPvoCTudIEhIm\nYLHEAb1VRQ5HHR0du8jIaOe7372P2NjYk87T09PDvn37aGttxWgyodPpWPfKKxQ4HMxMTcV4hg9H\nl8/Hx/X1vNTqwLToyxTMuPr4tj0fvEbF9iB2dwwpsRpcrY1k6hoZmWajtBpmzr3qk7+lvYq4uBJu\nu20xD3zjhxzqsqHVm7DYTJgtJgpHT2RvXTWHG1uIGZGPSS8xJU8nJjEZGQzi+OhxEhPzSU2ZycgR\nBcfPG/B5cbTXIYSGDR++iGH8g+gtMdgb9zGzMIHEhESq92/AWPMetY3tdGi6CEo/lmg9Zhnkpw98\nj6lTp/b+rS4XP/+/x2lJGY/GaEVbtp7/euAOXn/xMe5YGCAl0Yajq4ete+spqbFT0+4CvRYpQRcM\nkhlr4fDBHr7347+Qk5NzUjl6vV5KSvawZl0JrR1O9DothaPSmTd3Kmlpaef0vlCUwRiK+aRUkupH\nKCcpgPb2dl5+/232NFZjHJGB0GjYvupDopMjufJz15Kcl3H2k/Rj65Pv8uCCW8nPz8dut7N58zZW\nrNhKV5cR0COlm9RUA9deO4PJkyedNFCplJLijz5i1WuvEenxEAHU2+2UlZby2ZQUFs2aNeCBTXfW\n1fHzgzWk3/xdRk5ZhEano7u9mTVP/Y2GhiwSzAZs7hrmjotHCNhc5mbuFdcB4PE4aGh4mxtvHM97\naw5j146l5EgL3S11+JwWujuaCYi9pCwspOvgJrQzr4GINHyuVNIyknDuXYu+4QjTFjzAiLz8k9pt\npJQEg0G0Wi2Hdq9mT0UjpuRJdFRtZsrEkViNJta/8mfGTVyKsGTR6O8gPt1KZ/U2XIfWsuxPv2XU\nqFHHz2e321n78SY8Ph8ziyaSkZHB+nXF1JQ8zeeW5p507UAgiKfHjxACs0nH9tJGShsKuP/B753X\na60ow0ElqQsk1JPUMW+//TaZmZl0dHTw/Jq3uOoX9w9qGobDW0sZ2WDiths+e3yd3++nra0Nn8+H\nyWQiLi6u3wb3le+/z8aXXmJ2ejpWoxGnx8P7H33EUiHQ+Xxs9vl4YOlSDMaBdXHfVV/Pn+pbsI2e\nhjZ9IsJgxV5XzuYXXyBGl8c1syZjMZs4UtOJz5jLqDFjaGk5jMezjTvvnMWr7+8kKv8LBIN+3n3u\ncTpd1yGsuXR5PAQd+9EFXmbKnRZsySmUbaugrsmKpq0JbWctOWOvZvSEKWQVTMNsiyUYDHK44iiH\n6hrwBYPEWq2Mycli84pf02ZsxjAyDV1NA22bm5mz5BcUjp9OIBhgz75Smjub0KAhJQLyovfyy//6\nGsYzlIHf7+efz/yFGLGTpQuy0OlOfj2llOze38STb7Ty8G+fIC4uboCv7sUTam0oUkoqKyspLt5I\neXkVwaAkNTUem03PPffcE/JVoaFWnmdyuc7Mq/SJiopi0qRJ1NbWEnckZdDzBJkjI3Ac6TxpnU6n\nIykp6YzHtbW1seb111mcmYmxbwqPTXv3MsnvJyUyEsxm9LW1VBw5QsEJwyD5AgF6AgGsev0piW9S\naip3+Xy48+PJHW2j2+UhYvxozDf/jr/++THW7X4ZGUzHbEsnO9dMbe1uJk5M57rr7uLQ4Up8lvFY\nIxMo3fgWERFXk5g6mc7OTvxuJ6as0Whc1xN0bOGah28nYflqXv2PZzBFLSAqfQLdYjxb9nRycN9T\njCuagUuXSLnbS+SEKViMRpztbRRvLsaWHUnRzZ/B37EXQ7KN0q4ZJCRlA6DVaJk0biKBQACNRoMQ\ngsrSDkpK9jBt2tTTlqVOp+Oue7/KO2++yB+eX8vEkZCbHoFWq6G5rZvtZT4w5XL1Z5aERYIKNU6n\nk8cff5a9exswGDKIju5tEywv7+DIkWIOHKjl29/+oqoWDSEqSYWxY9+mAoEAQjv4wUM0Wg2+T424\nPBBbNm0iDY4nqHanE3ttLZOioo7vsyg5mYqjRxmRn09QCJZXVlLscOHTGUgK+rg1NYlRn3puZ25a\nGk9t2cJNd9yBzfbJ6ApPPfMPamtrqaioQK/XYzAYyMzMPD79xGvvrCUyvrf7fVtjI0bTNHRaHVFR\nUbS7nVjjEnD3xOG263E0t7Lzna3YRnwWnXE26UmpBKWkpqsHc8Y17Nz2LD5bJEnXfP7482mW2Djs\nFj3+RBsBXxcTx+ZTvOojYkZdTVd3F0l8ktRPHBsvMqmIj9atOWOSgt6RBW65/V7a2payY9smPj5U\nTjAYICommWtun0N2dnZYdR8PlW/9brebRx/9K9XVRrKyTh6M1WKJIiHhPlpbq/nVr/7K//t/3yQ5\nOfkiRnt6oVKewyWskpQQIhZ4GcgCKoHbpJSd/ey3BPgjvVPEPyWlfKRv/c+BB4CWvl1/JKVceeEj\nv7BMJhMB9+CHuPG6e4gznfsoBJUHDpB8QhIpr62lUAi0J3wI6HU6tIEALrebFY2NfJSYTeq1s9Eb\nTThamvjL6nf5ocFA+gmJzajTMTIQYNeOHcz71H/M9PR00tNP7WYNoBGid3RXwGyz0mVvRW9KRqfT\nYdLq6XG5kUEfgm5ajtbS7c0iKiadQEc9fl8MwUAPMqhFa4hAn7aE1oPPkfypu1RLci7te1eQOymK\n+NgJ+AM6Ag4vkWmnf5DWbI2lrXHgU2TExcWxeMnSAe+vnNmHHxZz9KgkJ2fCafeJj8+ksdHLsmWv\n8p//+c1hjE45nXAbu+8hYJWUMh/4sG/5JEIILfAYsAQYA3xOCHFsmG4J/F5KOanvJ6wT1LGxxuLi\n4tC5Ajjb7YM6X8ehekakZ5/zcRqNhhPb8Fqamsj8VLvLts7e7xIur5eP3X7SZ87DYDL3znWUmIx2\n6lzWNTWfcu5sq5XKffvOKZ4JY7NobyylouIorR0uqg+/xJEDe6mrqSHCZMbb2IS7fQNpE2I4sLaU\nYCAdfZebdFHGkR1fp8P+GBrH4zhrP8QSk4vPE6Snrf6ka3gcHVhr6zDvOkzlhl20HzxEPAbiE04/\nmaC3x0mEdWhmxQ31ceZOFAqx+nw+PvjgY5KTTx11/5jKyt0AJCXlsn9/LQ0NDcMV3jkJhfIcTmF1\nJwVcD8zv+/05oJhTE9U04LCUshJACPEScANQ1rc9fOpJBkin07Fg4gw27yhjzFUzzuscHqcLz6Fm\nJl036ZyPzR07ln2lpaTFxhKUErvdTlxExEn7+AIBpNlMQKtFRkSi+9S02JboGBp9p44BmBQRQfGR\nI+cUT0F+Hgd+/j1MMUvJicnGOqKDmpoX8bWPo7nVQMC3lbEjKhitK6KkpIqizMU42uvodJUxVQ9W\ncwAAGJxJREFUamwG8ZPHotFGUL1+Na4GA9G2OOwH94IhAunppGPfR5grVvGP3/+C2NhY6urqsF3R\nTZ1Xi+YMU5x0NO7ihqVjz+lvUYZGRUUF3d0G4uJsZ91XCA1CJFNaupeUlJRhiE45k3C7k0qSUjb1\n/d4E9NeinwbUnLBc27fumG8KIUqEEE8LIaIvUJzD4sS66elF0+jYVYXf5zuvc1XuLGP2mKIBdxM/\n0bTp02nUaHD19BAMBiEYRP+p6rFMjYb0vDzibTYMjnZ6uk+u9uqqryXfdOroCiadjp7TjNreHykl\nb77wAouTINL+b7rt5aSmjWfCxEKSk3YTb3qBgthifvLQF/nVfzzEPTctxWYKotEcYu49V7PontsY\nU5BDSqKJzKkJOGqfR+M8RI7nIO73foFv+UPcmFLDm0/+iqKiInJycpgzZw733Hkj3ta1eFz9383W\nV+6gs3o5NtvQDOoaTu0SoRBr78j/Z+5Zmp098fjver2Fzs6uCxzV+QmF8hxOIZekhBCrhBCl/fxc\nf+J+fX3E++snfqa+438DcoCJQAPw6JAFfpHFxsYyPW88e9/bwLl2n2+vb8a++SjzZsw+r2tHR0dz\n3d13s7auDrvbjRSCYF8MwWCQBrsdGR1Ndm4uBq2WG2OjaVj9Ph0Ndbi77NTt30PE7k3MST31W6sv\nGETX1yFjICorK2k5eJDZBfkUxProrHuHfdsfwX70n4yLquRb8xL47lVz2fDBBwQCAebMKiLQvg2P\nuwVrbBRCCAwGAwnxcYydMIooUxufv2Ykv/n2Fbz152+zfeWL/PaXPz9lvMKcnBwe+NxMGsuepr5y\nO35f71QpPW4HR0rfY+M7/4vGfxV/+tNGdu7cfV7lrJy/3hmrB94pyO/3Yjaf64wAyoUQctV9Usqr\nTrdNCNEkhEiWUjYKIVKAUxsxoA448WnWDHrvppBSHt9fCPEU8O7prnXfffeRnZ0N9H4IT5w48fg3\nmGN1whd7+di6Y8u3XX8zrc/9nfd+8ww50wspvKJ3ht59xdsAGLtg6inL7XVNrH54GTfMvOr4xIXn\nG8/1X/say19+mQN2O691dDAzOhqXENSZzTj0emYdSzYBP7M6Wmj/6F3aA0ESHR0UJcQT3TfH0seV\nlQDMyc6mxemkw+9n+fLlmC1GdpauY2/pPkwmK7fdfA9TiqaxY8eO4/Hs2bULR1MTf95fgcy+lvQZ\n+VQf3oCneRufGTeeKIuF3ZWVVDY1UV1dTU5ODrmJHnbs2E9jeRo+T98HWTCIxuQn3ghFk0bT3t7O\n+PHjz1oeGRkpPPbXp9m54m8kpY7AZIDoYAvJ8ZmMH/9ZGhp28vrr7+JwdA7q9d+9ezff+c53BvV6\nDdfyH//4x4v+/8ftdqPVOvD5eqir6635P3bndKwt6ti6ysrdNDbuYNSoJRct3jMth0J5nm65uLiY\nZ599FuD45+dghdXDvEKI3wJtUspHhBAPAdFSyoc+tY8OOAgsAuqBrcDnpJRlQogUKWVD337fBaZK\nKe/s5zph8TBvcT8P9Xk8Hp575V+UB5pJnzWW5JGZ/T4/5bJ3UbXjAM4dVXzp+rsoKCg4ZZ/zEQgE\n+OujjxK3YQMTk5KIiY3FZDLxcWUlc87jTbu+uprOuXOpbt1LxpggYyanEBllxtHpYt/ORmrLNNx9\n27fIyuodZ/BfzzxD1apVrO+MIanw1uPnaarewTzdHmaO6B1CaENNDTd///uMHj0aKSWvvf4mf335\nSXRJBkbNG0dXcyOOkg5+9OUfHR/K6FzLwefzYTAYcDqd/PSnT9DZOQY4xPe+t4jx48ed8zlP1N9r\nH6pCJdZly15k3bo20tML+91eWbmb7OyJ2O1NGI2H+PWvfxKSXf1DpTwH4rIbcaKvC/orQCYndEEX\nQqQCf5dSXte33zV80gX9aSnlr/vWL6O3qk8CFcCDJ7RxnXidsEhSpxMIBCgtLWXN9o+pcrYQNSET\nc3QEWp0On6cH+5FGAlWdzBs/jdnTZg75Q6FHjx7lnV/+knuysgb1nzwQDPKnw4fxFURy3T0ZZOac\n2nOu+mgLxa+3880v/4yoqCiWv/cea59+mj2BPJJGfdJ9u6V+P1P9m1gwKg8pJaurq/nKL39JRsYn\nN93V1dW8tfwN6lvryE7N4ZpF1x1PfgPV3t5OS0sLZrO5b3R2cXz9wYMHSUxMJC8v7zxLRBmM1tZW\nfv7zPyDEaOLi+n98we120Ni4gf/8z3sYO1Z1chmsyy5JDZdwT1Inqq+vZ8++Ujq7Hfj8fqwmC5kp\naYwfN76vnn7oSSl57L//m8mNjYzpq0I8Hzvq61lp0DDpeitzF+efdr+1K8tJ1i/mqkVX09jYyP/+\n4AccqetGO+IWbNGpeD1O2sre4J7REaTFxtJst3PEYuGhhx8esm/Kdrud5194h+2ljWiMqQT9DpJj\nvNx759UnjdenXFw1NTU8+ujfsdutxMWNJCKid4Dknp5umpoOodE08OCDtzJ16plnN1YGRiWpCyRc\nklQo3/bX1tay7Kc/5a6kJCKNxlOq+/zBIAeam1lXYafVFSDKqGFOto1xKckYtFraXC5ettvxpBu5\n5WtZxMRFnPZabS1dfPDPNh763q8B+Mfjj3Nw1Sqqu6AzYMYgPSzMimZiVjpur5d1NTXc+p3vMLno\n1DmVzqdMPR4PD//mCRp6JpOWPRONtrept7OtCkf1q/z4uzcyYsSIczrn2YTya/9poRar0+lk69Zt\nLF++jrY2J0IITCYtyclWvvKVLx9vmw1VoVaeZ6LG7lNCVnp6OnPvuYfXn36aWz41MoTb5+PvW8sp\na04nwjATsyGaOkcXT2/fS2Z0GXdOTGdlRwfXfOtbvPre34mKOXO37agYCw7nJ89S3XnffTztcmHY\ntYtkg4H0uFSEEJTW1lIbCHDV3Xf3m6DO1/btO6npTCOncO5J66PjsvB7l/LqW2v40feHNkkp5y8i\nIoIrrljIwoULcLvdBINBzGYz69evD/kEdTlSd1L9CJc7qVAnpWR9cTEbli1jocVCQd/YfP/cWc7m\n6kLSo2aeXN0mJfuaN+G3FfPfjz3M1GnT+OUj3+Pmr2QSGWU+7XXsHd28/VQ9//WD3x1fFwgEKCsr\n4+PVq6mvrESr0zGmqIjZ8+eTmpo6pH/nbx59mgbvQmISTp1oUgaD1JQ8yh9+9WWiThjySVEuB+pO\nSglpQgjmLVxIdl4ebzz1FNurqsiWknUVfrKjp/YmKClx+/3YPR6aAgE0KXOIStCS0pdIJoydQ9nu\nTUyff/o7kf27G5gwds5J67RaLYWFhRQW9t+Tayj1eHzojP0/BC00GtAY8Z3nQ9aKcrkLuYd5lYE7\n8XmpUJaZmcn4BQu46qGHKElLoyKYx/aubrbb7Wyx2zkgJa6MDArmz2fq3LmYzVM5ePAQADOnzaFs\nq4eWJke/525utHNwu4eZ0+b0u/1cnU+ZjilIo7P1cL/bXM42bKYeoqOHdnCTcHntIXxiVXGGJnUn\npQwLjUZDQUEBV15/PYfrW0lNXXx8lttPTzInhB6/v3fEhsTERG665su8sewJCmdaGTspDYvViKu7\nh707a9m/2cVnr/vq8Wk6LoZ5c6exsngZ3V2jsdo+mW4kGPDTcGQld18/JeQn0lOUUKXapPqh2qQu\nnOrqan7607fJyvrGabt/V1Y+yw9+MPWk51QaGxvZuGUtu/auR+JHoGdy4VxmzZh/1kkZh8OuXSX8\n9R8r8RvGY47MxNvjwG/fwYLpidx912dPmldKUS4Xqgv6BaKS1IUjpeThhx+noWEOiYmnjrrQ2VmJ\nTvc6jzzynX4/2KWU+Hw+9P3M5nux2e12tm7bSVV1C1GRZqZNHU9mZmbIxakow2UokpRqkwpj4VQ3\nfSxWIQQPPHAjWu1Kamo+xudzAxAIeKmv34bb/Spf+9pNp73zODYA7IX64B9MmUZFRXHVlQt54P7b\nuPWWz5A1yBE3ziQcX/tQp+IMTaqiXBl2KSkp/OxnX2DlynWsW/dHAgETQniYOTOXa665a8i7iIcC\nKSUNDQ20trai1+vJzc3FaFSjbCvK2ajqvn6o6r7h4/V6cblcmM3mS/ZDu7a2lhfff4NaTzuW9HgC\n7h78dXauKprN1Vdc1e8AwIpyKVBtUheISlLKUKmvr+fR5x8n8drJpI8dcbz6z+Vwsv+ttUyPzOP2\nGz97kaNUlAtDtUld5sKpbjpcYh3qON9c9T5xV40no3DkSe1TlsgIJn5uMRsq91BXV3fO5w2X8oTw\niVXFGZpUklKUC6Sjo4MDzdVkjBvZ73adXk/slBFs2bV9mCNTlPChqvv6oar7lKFQUVHBX4tfY9IX\nrj3tPo2HqjBtrudrdz8wjJEpyvBQ1X2KEsLMZjO+Lhdn+sLj7uom0nz6aUgU5XKnklQYC6e66XCJ\ndSjjTEpKIl5jpbWqvt/tUkradh5h6rhJ53zucClPCJ9YVZyhSSUp5bIlpcTv95/xTmcwhBDcsOBq\nDr+9ke4O+ynXPvDRVtICVkaO7L/NSlGUMGuTEkLEAi8DWUAlcJuUsrOf/f4BXAc0SynHncfxqk3q\nEnf06FGeX/keVa0tpMXEctfiaykoKLgg19q0dQsvf/QuplEp2DIS8Lo8dJZUkmdO4P7b7yYiQlX3\nKZemy+45KSHEb4FWKeVvhRA/BGKklA/1s99cwAks+1SSGujxKkldwrq6uvjx3/5ExI1XEj8yj/bK\natpfXc7DX/wqcXFxF+Sa3d3d7CrZTUNrEyaDkfGjC9W4fsol73LsOHE98Fzf788BN/a3k5RyPdBx\nvseHi3Cqmw6lWMvKyvDlZ5GQ3/twbVxOFowvYO++vRcsTqvVypxZs7n1+pv5zJLrBj2uXyiV59mE\nS6wqztAUbkkqSUrZ1Pd7E3CuczQM9njlEqDRaCAQPHllIKim01CUEBRyA8wKIVYByf1s+smJC1JK\nKYQ47zq5sx1/3333kZ2dDUB0dDQTJ05kwYIFwCffZNTyuS0fc7HjaW1tpW3VOmpzM0gsGMGul97E\nt6WEcb/9PVFRURc9vnArz7MtH1sXKvGE+/KxdaESz4nLxcXFPPvsswDHPz8HK9zapA4AC6SUjUKI\nFGCNlHLUafbNBt79VJvUgI5XbVKXvvr6el5btZLy+jpyk5K55cqryczMvNhhKcol5XJsk3oHuLfv\n93uBt4b5+JDy6W/UoSzUYk1NTeVb997PYz/6f3zv/i8dT1ChFufphEucED6xqjhDU7glqd8AVwkh\nyoEr+pYRQqQKId4/tpMQ4kVgI5AvhKgRQnzhTMcriqIooSmsqvuGi6ruUxRFGbyhqO4LuY4TinKu\n6uvr+bB4MzX17YzMSWLh/JnEx8df7LAURRkC4Vbdp5wgnOqmL1SsR44c4We//RfrjqTSYljMipJI\nfv7IMzQ2Np7X+cKlTMMlTgifWFWcoUklKSWsvfj6aozp15OWN4Oo+EwyR82nJ3I+7yxfc7FDUxRl\nCKg2qX6oNqnw4PP5+NJ3HiFr1k9OGr3B63Hi2P8XHvvdDy9idIqiXI5d0BXlOK1Wi9Wso8ftOGm9\n29lObLQatFVRLgUqSYWxcKqbvhCxajQarr2iiPqy9/F7PUDvXVTrkQ+49sqp53XOcCnTcIkTwidW\nFWdoUr37lLC25OorcLlX8MG6PyK10ehkJ3ddN43p088vSSmKElpUm1Q/VJtU+HG5XNjtdmJiYjCZ\nTBc7HEVRuAznkxouKkkpiqIMnuo4cZkLp7rpcIlVxTn0wiVWFWdoUklKURRFCVmquq8fqrpPURRl\n8FR1n6IoinJJU0kqjIVT3XS4xKriHHrhEquKMzSpJKUoiqKELNUm1Q/VJqUoijJ4qk1KURRFuaSF\nVZISQsQKIVYJIcqFEP8WQkSfZr9/CCGahBCln1r/cyFErRBiV9/PkuGJ/MIIp7rpcIlVxTn0wiVW\nFWdoCqskBTwErJJS5gMf9i335xmgvwQkgd9LKSf1/ay8QHEOi927d1/sEAYsXGJVcQ69cIlVxRma\nwi1JXQ881/f7c8CN/e0kpVwPdJzmHIOqHw0lnZ2dFzuEAQuXWFWcQy9cYlVxhqZ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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xd00d7f0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.cbook as cbook\n",
-    "\n",
-    "# Load a numpy record array from yahoo csv data with fields date,\n",
-    "# open, close, volume, adj_close from the mpl-data/example directory.\n",
-    "# The record array stores python datetime.date as an object array in\n",
-    "# the date column\n",
-    "datafile = cbook.get_sample_data('goog.npy')\n",
-    "price_data = np.load(datafile).view(np.recarray)\n",
-    "price_data = price_data[-250:] # get the most recent 250 trading days\n",
-    "\n",
-    "delta1 = np.diff(price_data.adj_close)/price_data.adj_close[:-1]\n",
-    "\n",
-    "# Marker size in units of points^2\n",
-    "volume = (15 * price_data.volume[:-2] / price_data.volume[0])**2\n",
-    "close = 0.003 * price_data.close[:-2] / 0.003 * price_data.open[:-2]\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "ax.scatter(delta1[:-1], delta1[1:], c=close, s=volume, alpha=0.5)\n",
-    "\n",
-    "ax.set_xlabel(r'$\\Delta_i$', fontsize=20)\n",
-    "ax.set_ylabel(r'$\\Delta_{i+1}$', fontsize=20)\n",
-    "ax.set_title('Volume and percent change')\n",
-    "\n",
-    "ax.grid(True)\n",
-    "fig.tight_layout()\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 设置按钮"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`matplotlib.widgets` 模块："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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udBuL2rSxdfzyS1tn1/zX5Fq3tgYVHsMUPxQ5jRpZPrHVq+0CukuHDgVay3Xu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\nBgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xdc14198>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib.widgets import Slider, Button, RadioButtons\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "plt.subplots_adjust(left=0.25, bottom=0.25)\n",
-    "t = np.arange(0.0, 1.0, 0.001)\n",
-    "a0 = 5\n",
-    "f0 = 3\n",
-    "s = a0*np.sin(2*np.pi*f0*t)\n",
-    "l, = plt.plot(t,s, lw=2, color='red')\n",
-    "plt.axis([0, 1, -10, 10])\n",
-    "\n",
-    "axcolor = 'lightgoldenrodyellow'\n",
-    "axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)\n",
-    "axamp  = plt.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)\n",
-    "\n",
-    "sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0)\n",
-    "samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)\n",
-    "\n",
-    "def update(val):\n",
-    "    amp = samp.val\n",
-    "    freq = sfreq.val\n",
-    "    l.set_ydata(amp*np.sin(2*np.pi*freq*t))\n",
-    "    fig.canvas.draw_idle()\n",
-    "sfreq.on_changed(update)\n",
-    "samp.on_changed(update)\n",
-    "\n",
-    "resetax = plt.axes([0.8, 0.025, 0.1, 0.04])\n",
-    "button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')\n",
-    "def reset(event):\n",
-    "    sfreq.reset()\n",
-    "    samp.reset()\n",
-    "button.on_clicked(reset)\n",
-    "\n",
-    "rax = plt.axes([0.025, 0.5, 0.15, 0.15], axisbg=axcolor)\n",
-    "radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)\n",
-    "def colorfunc(label):\n",
-    "    l.set_color(label)\n",
-    "    fig.canvas.draw_idle()\n",
-    "radio.on_clicked(colorfunc)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 填充曲线"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`fill` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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L8It+bpD335ws4A5VFpeWsry8nJNff53Hx46lR9euXHf55bzzzjtUenCp1HB9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\nFChb1lWmfwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9eefa90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
-    "x = np.linspace(0, 1)\n",
-    "y = np.sin(4 * np.pi * x) * np.exp(-5 * x)\n",
-    "\n",
-    "plt.fill(x, y, 'r')\n",
-    "plt.grid(True)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 时间刻度"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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UwMpdRJqKyPsiMiUodxSR6SKyUESmiUhRUt3bRWSRiCwQkTOz1fG40phsq6tW\nRYmEZ82yEXv//lbed1+49NJiIBq5d+hgqzbTUe7XXWcBuDZtgv33N7t9qtmGVq2ytHOzZ1ssklTZ\ns8cm7UIf83/+05JHDBgAF1+cejvJNKbnojbSkUWYMrC4OL9RLy+8MNpv08bmfsKMWLki05H794F5\nQDg+GQ1MV9X+wCtBGREZAFwMDADOBh4WEf+14ACmPFu0sP1vfMMCZYU/X7/4IspQFI7cO3RIb+S+\nZg384x+2v3y5KfeePW00/+67tV+/caO5YXbrZr8yUr3vq69G9w/JVKk7daN79/zmMQ0ZM8bmilRN\nuX/0UXoDhkxIW9GKSE/gHOARILQkDQPGB/vjgTAM0XBgoqruVNUSYDEwuC4djjuNxbb6+eeVfb4f\nf7x8OZRFWC9ds0xyjtDHH7cogfvvbzG+jz02WqlYFStX2l+7dlEuzIrp8KojTIIRTsQ2bQqnnZba\ntdXRWJ6LVEhXFgcckJt+pININEgJzY0LF+b2npmMou8HfgQkWy67qmpZsF8GBB7K9ACSc5CUAtUE\nHnUaE3372s/SMHtN27ZmtqiKimaZVL1lFiwo32afPjZyX7DAytVlx9m924JMLV1qL4OWLe14xaw+\n1ZH88vnjH6099z93QkLlnmvSUu4ici6wWlXfJxq1lyPM61dDM3nKBd4waCy21W3bzK4+YYLtV7W4\nKJRFuOhkn31s8cfGjandY+NGWxQ1ebIF0jrwwPLKvbowrPPn2/b+++2FApb0oX371DLZH3FEtP/t\nb5v/fl1pLM9FKjR0WdRXUpR0F0OfCAwTkXOAVkB7EXkcKBORbqq6SkS6A0FMOlYA+ydd3zM4VomR\nI0fSO3BsLioqYtCgQXv/ieHPMC83/PLixfDf/23l994rplWr1K7/y1+gXbtievaE6dMTNGlSc/2y\nMli0qJg+faBduwQff2zne/aEn/zE6m/eXPX1M2cm6NQJbr65/Plt24rZvh3efrvm/q5Zk+Cww6BD\nh2KeesrOJxKFIX8v57/8zjtWbtIk/esTiQTjxo0D2Ksvq6W6zNm1/QGnAlOC/buB24L90cBdwf4A\nYC7QAugDfApIFW3lLj14A2PGjBn57kJOsbGv/e3ZU3PdqmRx442qDz5Y/lhV7eyzj93j3nvLH1+4\nULV3bzs3ZUr5c7fconrrrapPPKF64omV2+zYUfX992vus6pqt26qpaW110uHuD8X6dDQZbFnjz1/\nInVvK9DIksN1AAAYGUlEQVSdVeroDMMYRe+GYHsX8LSIXA2UAN8ONPY8EXka86zZBYwKOuQ0Qlat\nivanTs3MDt22bXkXsk2bbNKz4lMVLlgJzSoh/fqZLf1b36rsinb//dF+6KKZzLp1lst0+nQ4/XQ7\ntnBheS8f1SiDlONURfKzktP7FIKuFXuF5bsbTo6ZORNOOsn2M/13/+IXtnx78WKb6PzgA0smHWY5\nCjnnHDv2j39UHYnxyittwdRVV0XHkl82118PDz9c/poDD4zycH7yifnk9+plcbpPPdXuN2+eJUre\nssUnUZ3qyZaCFxFUtconzX3OnXojnaX/1VFWZl4uoZINQxdUdI/s0AEuv7z6ELvhL4ANG+yLdvLJ\n0bnzz6+s2MHCx4YBnz7+2BT8F1/YCtT58+GOO2DgQPPKccXupMKGDTYgyAWu3AuMcPIkjiQS6S3c\nqEoWP/iBbcOcqv/zP7atGPFv69aavRLatjWTTphz9M03bTtpUs2p1xYuhDPPhEsvhUMOsRH6vfea\ny+Xdd1ud5BdFtojzc5EucZDFD39oYQiuu85WQeeCutrcHSdl5s2zWC2DBmXexoEHwvHHR7E5XnnF\ntt/5DvzHf0T1vvqq5pjZ27bZSPuaa8ofry1LT1GR/UoI79+lS5SvdMIEC29w5JGpfx6ncXLPPbZK\nOpdpEH3kXmCE7k9xZNkyC+Nb1WRlVVQnizZtotWlyYuUwpjtYAq3ppyrl19u27FjoxWlf/hDav0K\no1mC/Tr47W8tR+cll8AFF0ShirNJnJ+LdImLLHr1isyLucAnVJ16YdcuU8qbNkXxZDJFxJbzd+0K\nEydGx+fMsVHzl1/aF2fx4przrz7yCIwbZ6npvvoq9V8Uffua/b1lS7Oxh3HpHScdfvCDyEMrU/Xn\nE6oNiDjYE6tixQobSaej2KuTxYgRtro1WbEDzJ1r26eegrPOqj2x9ne/a7b2vn3TMxU98QQ895xN\n5tZXwu24PheZEBdZhGEtcoXb3J16YdkyG01ng65d4cEHzf69erX5lA8ZEsWKWbcuN6aRkOOPz13b\nTuOhrr9ga8NH7gVGXOyJFVm+PH3lXp0sDjrItmGkvS1bLGb7unV2fMeO3I+K6pu4PheZEBdZhM/o\nb36Tm/ZduTv1wmWXwdtvZ6etG280xb5+fXSsY8dIuW/fnvtRkePUlfAZzVWUSFfuBUZc7IlVcf31\n6dWvSRb7VQgcve++lsZu5EhT7nEbucf5uUiXuMgifEZduTsNlpIS22YzG1FF5d6xoyUAGT/eFhXF\nTbk78cNH7o2MuNgTk3nrLYv70rNnetfVJIuqlHsycVPucXwuMiUusqiYCD7buLeMk3Pefhvuuqt8\nYK+68qMfRROrUFm5V4wG6TiFRvjM+si9kRAXe2Iyn3+emWtiTbI4/nhbGRqSrNx/9SsYPjz9+xUy\ncXwuMiUusth3X9v6yN1psOzaFSUHzhXNm1vsmqIiy3jvOIVOrpW7hx9wcs6551r0u3PPzXdPHKdw\nWL/efnGWlGS+wM/DDzh5Zdeu6uOqO05jpagIbrsNDjggN+27ci8w4mJPTGbnzszMMnGURaa4LCLi\nIgsRczTIVWIXV+5O1lEtH1DLR+6OU/+4zd2pEdX0RhYzZlikxZ/9zIJ6de4MJ55oyQlOPDF3/XSc\nxkjWbO4i0kpEZonIXBGZJyK/CY53FJHpIrJQRKaJSFHSNbeLyCIRWSAiZ9btozj1zSmnwLXX1l5v\n/nxYtcrirP/sZ3bskkssabSP3B2n/klLuavqNmCoqg4CBgJDRWQIMBqYrqr9gVeCMiIyALgYGACc\nDTwsIm4KqoFCsye++aYlpqiNAQPKh8Jt0sRS4D37LLzzTmbKvdBkkU9cFhEui9RIW9GqapA9khZA\nU2A9MAwYHxwfD5wf7A8HJqrqTlUtARYDg+vSYSe3lJVVPhb649ZGaakF7gJLhn377fD441besSMb\nvXMcJ1XSVu4i0kRE5gJlwAxV/RjoqqqhWigDugb7PYDSpMtLgQpRQZxk8hk3Y+tW6NYNPv7Yyh9+\naNtdu2q+buVK2+7eDUcdZYk5Tj4Z+vWDyZPtXCYLNeISQyQbuCwiXBapkfaPZVXdAwwSkX2Al0Rk\naIXzKiI1zY5WeW7kyJH0DtaoFxUVMWjQoL3/xPBnmJdzW96xw8rPPptg9Wo47TQrz5iR4L774P/+\nr5g5cypfP25cgr59YfXqYq68Et59N8GSJdCvn53/5S8TQaz1wvq8XvZyQysnEgnGjRsHsFdfVouq\nZvwH/BT4f8ACoFtwrDuwINgfDYxOqv8icFwV7ahjzJgxI2/3/vnPVUG1Y0fbguqOHdE+qK5cWfm6\nH/5Q9bbbKh9fv96uKSvLrD/5lEWh4bKIcFlEBLqzSv2crrdMp9ATRkRaA2cA7wOTgSuCalcAzwX7\nk4ERItJCRPoA/QDPFV9AHHqorZIDc12EKKMR2OKjSZOi8p13wp495dt46y0444zKbRcVmUmnS5fs\n9tlxnNpJy89dRA7HJkybBH+Pq+rvRKQj8DRwAFACfFtVNwTX3AFcBewCvq+qL1XRrqbTDyc7TJoE\n559vMV8mTYLBgy2U7u7dtiS6c2c4+GD47DMr/+lP5hZ51FHw3nv2d8st8MYbsGCB1XUcp/6oyc/d\nFzE1YpIXJ02bZop9zpzKcdf37IleBOG5yZMttO6sWTB0KEydCq1a1V/fHcfxwGENinDypL4588zy\nyjuZJk3gggvsZfBcYHD7wQ9g9mzYuBFefTU3ij1fsihEXBYRLovUcOXu7HVTrMpuXpHhw2H6dFi8\nGG64Adq1y23fHMfJDDfLNFJ27IiSSi9fDocfbsdSjd4oAqNGwR/+kNt+Oo5TPTWZZTziRyNl7lzL\nQdq+PXz96zZpmk5Y3pkzoU+f3PXPcZy64WaZAqO+7IkzZ8IJJ0Tlnj3Tu/6EE2w1ay5x22qEyyLC\nZZEartwbKR98YC6NjuPEE7e5N1JE4Omn4VvfyndPHMfJFHeFdMqxZo1tk0P0Oo4TL1y5Fxj1YU+c\nPh1OPx323z/nt6oTbluNcFlEuCxSw5V7I2ThQh+1O07ccZt7I+TSS23kHibWcBynYeI2d6ccn35q\nPu6O48QXV+4FRq7tievWwdtvN4wIjm5bjXBZRLgsUsOVeyPjzTdh0CAL5+s4Tnxxm3sjYsMG+OMf\noaTEYrM7jtOw8dgyDi+8AP/2b5YsL0xa7ThOfHGzTIGRC3uiKpxzjm1/8hM477ys3yInuG01wmUR\n4bJIDR+5x5zvfteyJXXtCq+/Dv365btHjuPUB25zjzlhKr0HH4SbbspvXxzHyS5uc2+k7NljMdr/\n+lfLoOQ4TuMhLZu7iOwvIjNE5GMR+ZeI3BQc7ygi00VkoYhME5GipGtuF5FFIrJARM7M9geIG1XZ\nEzdvhgUL0m9r0yZo2bLhKna3rUa4LCJcFqmR7oTqTuAWVT0MOB64QUQOBUYD01W1P/BKUEZEBgAX\nAwOAs4GHRcQncdPkjjvg0EPTv27jRthnn+z3x3GcwictRauqq1R1brC/CZgP7AcMA8YH1cYD5wf7\nw4GJqrpTVUuAxcDgLPQ7thQXFwOwbZsF+IIoRG+6bNxoafQaKqEsHJdFMi6L1Mh4FC0ivYEjgVlA\nV1UtC06VAV2D/R5AadJlpdjLwKmCHTtsAnTnTvj2t6MQAfvua9tt29Jrb80aS4LtOE7jI6MJVRH5\nGvB34Puq+pVINFmrqioiNbm+VHlu5MiR9O7dG4CioiIGDRq09w0d2tgaYnn3bnj99QQitdf/5z+L\ngQTPPANTpgAUowqlpXZ+yJBiBgyAE05IcOihtbf3+efF7LdfYckjnXJ4rFD6k8/y3Llzufnmmwum\nP/ksP/DAA7HRD+mWE4kE48aNA9irL6sjbVdIEWkOPA+8oKoPBMcWAMWqukpEugMzVPUQERkNoKp3\nBfVeBMao6qwKbcbSFXLHDstT2r8/PPNM7fXtHZkAigFo1w6WLoVOncrXa9sWPvkE9qvmN9Brr9n5\nzz+Hr76Ce+/N/DPkk0QisfcBb+y4LCJcFhFZC/krNkT/CzAvVOwBk4Ergv0rgOeSjo8QkRYi0gfo\nB8xO554NmUcfhY8/NmVbGy+9FO4VA5YtqVcvawOi2OsXXmjeMzX5rBcXw7XXwp13wgEHZNb3QsC/\nwBEuiwiXRWqka3M/CbgUGCoi7wd/ZwN3AWeIyELgtKCMqs4DngbmAS8Ao2I5RK+GsWNt27RpzfV2\n7TJl/NJLcOWVcMQRlkzjgAPg1ltt/4HgVfqd75j3zDPP2Ej/qqvKt7VihY3s77nHyoWeSs9xnByh\nqnn/s27ED1Dt2lW1WTPV2bNVO3dW3bHDzu3ZE9U7/XSrq6r66qszdPdu2//pT+34nDlWfuUVu/7D\nD+04qPbuXf6eTzyhet55qi+8YOfLynL7GXPJjBkz8t2FgsFlEeGyiAh0Z5V61X3Oc0To2XL99dCq\nFQwebN4rGzbAz38OTZrAkiVmU3/5ZXjqKasvYucATjzRtqHHy2mn2YrTww831b5pE5SVwZw58O//\nDl//uo3sv/UtOOss87rp0qV+P7fjOIWBx5bJEZ98YiF2Fy+Gbt1sMdHChfDee3DKKWY3P+ccmDrV\n6lf18TdsgA4dYO3a6l0ajz0W3n3X9g86CL75Tfjtb6F169x8LsdxCgePLZMHli6F0FOpWbNoQdJT\nT8EJJ8CECdGo+oYbqm6jqMgiOtbkqz5mjIXwnTcvs1WsjuPEEzfL5IiyMujRw/ZXrLBtly5w993Q\np09598Zrron2k328wcw5NXHuuTbqj6NiryiLxozLIsJlkRqu3HPEtm1mawd45BHbhjb0Nm3Mtj57\ntplpBg7MTx8dx4kvbnPPEb//PSxaBA89ZGVVC8F72WVw3XVmd3ccx6kLbnPPA9u3W7jdEBHzd58w\nIX99chyn8eBmmRxRUbmnitsTI1wWES6LCJdFarhyzxGZKnfHcZxs4Db3HLBtm/m233473HZbvnvj\nOE5cyVrgMCc1Jk+GL7+0FaSO4zj5wJV7Dnj8cdvu2pX+tW5PjHBZRLgsIlwWqeHKPYt89BGMGAEz\nZtgCplNPzXePHMdprLjNPYscfbQF8WrTxmLHOI7j5BL3c68HXnrJFPtbb1k8dcdxnHziZpkscfbZ\ntj3+eAvJmyluT4xwWUS4LCJcFqnhyj1LnHUWPP98vnvhOI5juM09C8yda9EZp0yBI4/Md28cx2ks\nuJ97Drn3XlPoK1ZEIX4dx3HyjSv3OvLQQ3DnnTZ679q17u25PTHCZRHhsohwWaRG2spdRB4VkTIR\n+SjpWEcRmS4iC0VkmogUJZ27XUQWicgCETkzWx3PB7t3R7lRwWKxb9sGP/4xHHFEdu4xd+7c7DQU\nA1wWES6LCJdFamQycn8MOLvCsdHAdFXtD7wSlBGRAcDFwIDgmodFpMH+WujVy3KTrlxp5RdesGTU\nTZtm7x4bNmzIXmMNHJdFhMsiwmWRGmkrWlV9A1hf4fAwYHywPx44P9gfDkxU1Z2qWgIsBmpJHBdR\n1c+v6n6S5bquapQu7ze/gSlTEtx3H3z3u9m9X0lJSdb6XB91c3k/l0VEXWVRKJ8jV7IolL7Vtzxr\nIluj6K6qWhbslwGh9bkHUJpUrxTYL9VGC+lL/NVXtjhp9myzsw8blqC4uLw5Jhv3q+4nZyHJor7u\n57KIqKssCuVz5EoWhdK3QlLuGblCikhvYIqqHh6U16tqh6Tz61S1o4g8BLytqk8Exx8BpqrqMxXa\na7h+kI7jOHkk1+EHykSkm6quEpHuwOrg+Apg/6R6PYNjKXXOcRzHyYxsmWUmA1cE+1cAzyUdHyEi\nLUSkD9APmJ2lezqO4zjVkPbIXUQmAqcCnUTkM+BnwF3A0yJyNVACfBtAVeeJyNPAPGAXMKpBL0V1\nHMdpIBRE+AHHcRwnu9Sbz7mIdA62jT7McGCicgAROUZEuuS7H4WAiJwuIkfnux+FQPJCyMaOiLTI\n5LqcKncx2orIk8AkAFXdJSKNcgJVRI4SkZeBnzf2l5yIHCYibwH/BXSopXqsCZ6LF7G5qoPy3Z98\nIiLHicgkYKyIXC0irfLdp3whIieIyBPAf4lIfxFJa7lkTpW7GmFOon1FZFR93LcQEZGfAE8CT6nq\nZaqaQYbVWHEz8Kyqnquqn4ANBvLcp3pFRJqIyFhgLPAnYAJwaHgun33LB8Gvlj8Cfwv+htJIX3Yi\ncjjwe+B5zPvwGuDydNrI9ci9WeAaWQZ8F7heRDqo6u5030IxoCXwpqqOhb2jteZ57lNeCEx0e4CH\ngvKFIrI/0DooNwolr6p7gGnAyar6LPB3YKiItArONTaOBz5V1ccxubQGlue3S3njJGCBqk4EHgG2\nApemY9LN6oSqiPwHcAjwjqpOSTo+CbgBuA3YAoxV1cVZu3EBkiSLOar6nIi0A/6KeQ6dAqwCNmKj\n17/mr6e5J0kW76rqZBFpA7wH/Ai4BOgErAR2quo1+etp7kmSxXuqOinpeBPgG1gspttUdW2eulhv\nVJSFiHTDvh9/AK7GFPt84BNVvSt/Pc09VcjiSOAB4GpVXSwiY4AjMIV/RyptZmXkHtjWr8e+rCXA\nvSJypYh8LVjNWqKqpcB04HrgryLSMo4j1ypkcbeIXKOqX2Fv4COBH6rqucBrwFkicnDeOpxDqpDF\nPSLyn6q6BTNDPAxMU9WzgB8DXxeRc/LW4RxShSx+F3xH2sHeUfx8TMG3Cq6JpWmmGln8p6quwoIM\ntgJuVdXjgXHASSJyQr76m0uqkcVIbLDzBjAuGBwfiw0Om4pI61TazsqknqqqiBwP/FZVnxSR5cBV\nQQdnAr1EZApwMKbQdqnq9mzcu9CoRhZXi8hnqvo3EXlZVcOwdi8D5wBf5a3DOaS65yLYPgbcCnQO\n6q4QkTeB3fnrce6oRhZXAqtFZGowP1UqIm8D/w78Pq6mmRqei1JVnSoip2PRZQHmYDbnHfnqby6p\nQhafASOBVar6ExE5DOivqs+KyDHA5aq6NZW2Mx4ZiMjlInKqiHQMDs0H9hORZqr6MvAhcDKm0FcC\nS4GjVfU84ACJkctXCrL4ACgWkQOSFDvAmYACm4kJKT4Xp2Ff1u8Bl4vIoGD0cjo2eokFKcjiI2AI\nQYiO4JfsYsx0GStSfC6KA9PMWODW4JfLxcDXgdiYqWqRxXTsuRgqIvur6sfBfAzY92ZWqr/o0hq5\nBxNd3bFZ/T3Yg/i14ItZiv0TDgIWAE8B9wFPA99X1eQ37zcqKLkGR5qyeBKzn+0LLBeRoZhsPsPs\nq1/W/yfIHhk8Fw8Ah6rq38Vc3b4NHAZcFnrONFTq8lyo6k4RaQv0ykffs00Gz8X9QDdVfTgYzT6H\nTapeGYQMb7Bk+Fx0Aj4TkcHAb4Prrk31F13KI/fgraJAO2CFqp4GjAI2YF4PT2M/sQeLyD6quhSb\nMLxIVXeIuX01AYiBYk9XFiXAl8AFQROlwBhVHaaq8+v9A2SRDJ+LDZjpgSBi6E9Udbiq/isvHyJL\nZOG5APh/qvrT+u159snwufgSuCho4mpsMvEMVZ1X/58ge9ThubgwaGIpcKeqfiMdR5RaR+5iLou/\nBJqIyAtBB3fB3gVJ38PMLgOAidiD2hP4NWY/nRXUbfD2wyzIYnZQdxGwqN4/QBbJgizeCttq6M9G\ntp6LoH5jl8XbQd2dwJp6/wBZJIu6cw2QSPf+NY7cReRUzGWtCPsZ8QtgJ2YPGhzceDdwJzYh8DLm\nBXGSiMzCVh6m3alCxGUR4bKIcFlEuCwiCkIWqlrtH+aPfVlS+Y+YK+OVmD8mQFOgG7airE9wrAOw\nX01tN7Q/l4XLwmXhsmhIsqjN5v4O5pMeriZ9EzhAVR/D/C1vUnv79MQWoCwFUNX1qlopKUcDx2UR\n4bKIcFlEuCwi8i6LGpW7qm5V1W1BJwDOAL4I9q8CDhWRf2D2ojnZ6FCh4rKIcFlEuCwiXBYRhSCL\nlMIPiEUwVCyIzffUlsMehPmeHka0AjX2uCwiXBYRLosIl0VEPmWRkiukWgTD5tibZ2DwxvkpsFtV\n32ws/yhwWSTjsohwWUS4LCLyKYt0FjEdCXwH6AM8pqp/yU2XGgQuiwiXRYTLIsJlEZEXWaQcFVJE\nemLxhO/R8qtNGx0uiwiXRYTLIsJlEZEvWXgOVcdxnBgSy5CijuM4jR1X7o7jODHElbvjOE4MceXu\nOI4TQ1y5O47jxBBX7o7jODHElbvjOE4M+f9EYiNJNukaZgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xcae5320>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "Show how to make date plots in matplotlib using date tick locators and\n",
-    "formatters.  See major_minor_demo1.py for more information on\n",
-    "controlling major and minor ticks\n",
-    "\n",
-    "All matplotlib date plotting is done by converting date instances into\n",
-    "days since the 0001-01-01 UTC.  The conversion, tick locating and\n",
-    "formatting is done behind the scenes so this is most transparent to\n",
-    "you.  The dates module provides several converter functions date2num\n",
-    "and num2date\n",
-    "\n",
-    "\"\"\"\n",
-    "import datetime\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.dates as mdates\n",
-    "import matplotlib.cbook as cbook\n",
-    "\n",
-    "years    = mdates.YearLocator()   # every year\n",
-    "months   = mdates.MonthLocator()  # every month\n",
-    "yearsFmt = mdates.DateFormatter('%Y')\n",
-    "\n",
-    "# load a numpy record array from yahoo csv data with fields date,\n",
-    "# open, close, volume, adj_close from the mpl-data/example directory.\n",
-    "# The record array stores python datetime.date as an object array in\n",
-    "# the date column\n",
-    "datafile = cbook.get_sample_data('goog.npy')\n",
-    "r = np.load(datafile).view(np.recarray)\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "ax.plot(r.date, r.adj_close)\n",
-    "\n",
-    "\n",
-    "# format the ticks\n",
-    "ax.xaxis.set_major_locator(years)\n",
-    "ax.xaxis.set_major_formatter(yearsFmt)\n",
-    "ax.xaxis.set_minor_locator(months)\n",
-    "\n",
-    "datemin = datetime.date(r.date.min().year, 1, 1)\n",
-    "datemax = datetime.date(r.date.max().year+1, 1, 1)\n",
-    "ax.set_xlim(datemin, datemax)\n",
-    "\n",
-    "# format the coords message box\n",
-    "def price(x): return '$%1.2f'%x\n",
-    "ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')\n",
-    "ax.format_ydata = price\n",
-    "ax.grid(True)\n",
-    "\n",
-    "# rotates and right aligns the x labels, and moves the bottom of the\n",
-    "# axes up to make room for them\n",
-    "fig.autofmt_xdate()\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 金融数据"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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B2o+oyr8MO7eukHkZhHjonrutUwd0ivkE0KpBaTU4tc4dZfePmqyL/SyZ7wag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\nkUjqyf8DRMOAVtE/7MEAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2719e48>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import datetime\n",
-    "import numpy as np\n",
-    "import matplotlib.colors as colors\n",
-    "import matplotlib.finance as finance\n",
-    "import matplotlib.dates as mdates\n",
-    "import matplotlib.ticker as mticker\n",
-    "import matplotlib.mlab as mlab\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.font_manager as font_manager\n",
-    "\n",
-    "\n",
-    "startdate = datetime.date(2006,1,1)\n",
-    "today = enddate = datetime.date.today()\n",
-    "ticker = 'SPY'\n",
-    "\n",
-    "\n",
-    "fh = finance.fetch_historical_yahoo(ticker, startdate, enddate)\n",
-    "# a numpy record array with fields: date, open, high, low, close, volume, adj_close)\n",
-    "\n",
-    "r = mlab.csv2rec(fh); fh.close()\n",
-    "r.sort()\n",
-    "\n",
-    "\n",
-    "def moving_average(x, n, type='simple'):\n",
-    "    \"\"\"\n",
-    "    compute an n period moving average.\n",
-    "\n",
-    "    type is 'simple' | 'exponential'\n",
-    "\n",
-    "    \"\"\"\n",
-    "    x = np.asarray(x)\n",
-    "    if type=='simple':\n",
-    "        weights = np.ones(n)\n",
-    "    else:\n",
-    "        weights = np.exp(np.linspace(-1., 0., n))\n",
-    "\n",
-    "    weights /= weights.sum()\n",
-    "\n",
-    "\n",
-    "    a =  np.convolve(x, weights, mode='full')[:len(x)]\n",
-    "    a[:n] = a[n]\n",
-    "    return a\n",
-    "\n",
-    "def relative_strength(prices, n=14):\n",
-    "    \"\"\"\n",
-    "    compute the n period relative strength indicator\n",
-    "    http://stockcharts.com/school/doku.php?id=chart_school:glossary_r#relativestrengthindex\n",
-    "    http://www.investopedia.com/terms/r/rsi.asp\n",
-    "    \"\"\"\n",
-    "\n",
-    "    deltas = np.diff(prices)\n",
-    "    seed = deltas[:n+1]\n",
-    "    up = seed[seed>=0].sum()/n\n",
-    "    down = -seed[seed<0].sum()/n\n",
-    "    rs = up/down\n",
-    "    rsi = np.zeros_like(prices)\n",
-    "    rsi[:n] = 100. - 100./(1.+rs)\n",
-    "\n",
-    "    for i in range(n, len(prices)):\n",
-    "        delta = deltas[i-1] # cause the diff is 1 shorter\n",
-    "\n",
-    "        if delta>0:\n",
-    "            upval = delta\n",
-    "            downval = 0.\n",
-    "        else:\n",
-    "            upval = 0.\n",
-    "            downval = -delta\n",
-    "\n",
-    "        up = (up*(n-1) + upval)/n\n",
-    "        down = (down*(n-1) + downval)/n\n",
-    "\n",
-    "        rs = up/down\n",
-    "        rsi[i] = 100. - 100./(1.+rs)\n",
-    "\n",
-    "    return rsi\n",
-    "\n",
-    "def moving_average_convergence(x, nslow=26, nfast=12):\n",
-    "    \"\"\"\n",
-    "    compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'\n",
-    "    return value is emaslow, emafast, macd which are len(x) arrays\n",
-    "    \"\"\"\n",
-    "    emaslow = moving_average(x, nslow, type='exponential')\n",
-    "    emafast = moving_average(x, nfast, type='exponential')\n",
-    "    return emaslow, emafast, emafast - emaslow\n",
-    "\n",
-    "\n",
-    "plt.rc('axes', grid=True)\n",
-    "plt.rc('grid', color='0.75', linestyle='-', linewidth=0.5)\n",
-    "\n",
-    "textsize = 9\n",
-    "left, width = 0.1, 0.8\n",
-    "rect1 = [left, 0.7, width, 0.2]\n",
-    "rect2 = [left, 0.3, width, 0.4]\n",
-    "rect3 = [left, 0.1, width, 0.2]\n",
-    "\n",
-    "\n",
-    "fig = plt.figure(facecolor='white')\n",
-    "axescolor  = '#f6f6f6'  # the axes background color\n",
-    "\n",
-    "ax1 = fig.add_axes(rect1, axisbg=axescolor)  #left, bottom, width, height\n",
-    "ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1)\n",
-    "ax2t = ax2.twinx()\n",
-    "ax3  = fig.add_axes(rect3, axisbg=axescolor, sharex=ax1)\n",
-    "\n",
-    "\n",
-    "\n",
-    "### plot the relative strength indicator\n",
-    "prices = r.adj_close\n",
-    "rsi = relative_strength(prices)\n",
-    "fillcolor = 'darkgoldenrod'\n",
-    "\n",
-    "ax1.plot(r.date, rsi, color=fillcolor)\n",
-    "ax1.axhline(70, color=fillcolor)\n",
-    "ax1.axhline(30, color=fillcolor)\n",
-    "ax1.fill_between(r.date, rsi, 70, where=(rsi>=70), facecolor=fillcolor, edgecolor=fillcolor)\n",
-    "ax1.fill_between(r.date, rsi, 30, where=(rsi<=30), facecolor=fillcolor, edgecolor=fillcolor)\n",
-    "ax1.text(0.6, 0.9, '>70 = overbought', va='top', transform=ax1.transAxes, fontsize=textsize)\n",
-    "ax1.text(0.6, 0.1, '<30 = oversold', transform=ax1.transAxes, fontsize=textsize)\n",
-    "ax1.set_ylim(0, 100)\n",
-    "ax1.set_yticks([30,70])\n",
-    "ax1.text(0.025, 0.95, 'RSI (14)', va='top', transform=ax1.transAxes, fontsize=textsize)\n",
-    "ax1.set_title('%s daily'%ticker)\n",
-    "\n",
-    "### plot the price and volume data\n",
-    "dx = r.adj_close - r.close\n",
-    "low = r.low + dx\n",
-    "high = r.high + dx\n",
-    "\n",
-    "deltas = np.zeros_like(prices)\n",
-    "deltas[1:] = np.diff(prices)\n",
-    "up = deltas>0\n",
-    "ax2.vlines(r.date[up], low[up], high[up], color='black', label='_nolegend_')\n",
-    "ax2.vlines(r.date[~up], low[~up], high[~up], color='black', label='_nolegend_')\n",
-    "ma20 = moving_average(prices, 20, type='simple')\n",
-    "ma200 = moving_average(prices, 200, type='simple')\n",
-    "\n",
-    "linema20, = ax2.plot(r.date, ma20, color='blue', lw=2, label='MA (20)')\n",
-    "linema200, = ax2.plot(r.date, ma200, color='red', lw=2, label='MA (200)')\n",
-    "\n",
-    "\n",
-    "last = r[-1]\n",
-    "s = '%s O:%1.2f H:%1.2f L:%1.2f C:%1.2f, V:%1.1fM Chg:%+1.2f' % (\n",
-    "    today.strftime('%d-%b-%Y'),\n",
-    "    last.open, last.high,\n",
-    "    last.low, last.close,\n",
-    "    last.volume*1e-6,\n",
-    "    last.close-last.open )\n",
-    "t4 = ax2.text(0.3, 0.9, s, transform=ax2.transAxes, fontsize=textsize)\n",
-    "\n",
-    "props = font_manager.FontProperties(size=10)\n",
-    "leg = ax2.legend(loc='center left', shadow=True, fancybox=True, prop=props)\n",
-    "leg.get_frame().set_alpha(0.5)\n",
-    "\n",
-    "\n",
-    "volume = (r.close*r.volume)/1e6  # dollar volume in millions\n",
-    "vmax = volume.max()\n",
-    "poly = ax2t.fill_between(r.date, volume, 0, label='Volume', facecolor=fillcolor, edgecolor=fillcolor)\n",
-    "ax2t.set_ylim(0, 5*vmax)\n",
-    "ax2t.set_yticks([])\n",
-    "\n",
-    "\n",
-    "### compute the MACD indicator\n",
-    "fillcolor = 'darkslategrey'\n",
-    "nslow = 26\n",
-    "nfast = 12\n",
-    "nema = 9\n",
-    "emaslow, emafast, macd = moving_average_convergence(prices, nslow=nslow, nfast=nfast)\n",
-    "ema9 = moving_average(macd, nema, type='exponential')\n",
-    "ax3.plot(r.date, macd, color='black', lw=2)\n",
-    "ax3.plot(r.date, ema9, color='blue', lw=1)\n",
-    "ax3.fill_between(r.date, macd-ema9, 0, alpha=0.5, facecolor=fillcolor, edgecolor=fillcolor)\n",
-    "\n",
-    "\n",
-    "ax3.text(0.025, 0.95, 'MACD (%d, %d, %d)'%(nfast, nslow, nema), va='top',\n",
-    "         transform=ax3.transAxes, fontsize=textsize)\n",
-    "\n",
-    "#ax3.set_yticks([])\n",
-    "# turn off upper axis tick labels, rotate the lower ones, etc\n",
-    "for ax in ax1, ax2, ax2t, ax3:\n",
-    "    if ax!=ax3:\n",
-    "        for label in ax.get_xticklabels():\n",
-    "            label.set_visible(False)\n",
-    "    else:\n",
-    "        for label in ax.get_xticklabels():\n",
-    "            label.set_rotation(30)\n",
-    "            label.set_horizontalalignment('right')\n",
-    "\n",
-    "    ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n",
-    "\n",
-    "\n",
-    "\n",
-    "class MyLocator(mticker.MaxNLocator):\n",
-    "    def __init__(self, *args, **kwargs):\n",
-    "        mticker.MaxNLocator.__init__(self, *args, **kwargs)\n",
-    "\n",
-    "    def __call__(self, *args, **kwargs):\n",
-    "        return mticker.MaxNLocator.__call__(self, *args, **kwargs)\n",
-    "\n",
-    "# at most 5 ticks, pruning the upper and lower so they don't overlap\n",
-    "# with other ticks\n",
-    "#ax2.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))\n",
-    "#ax3.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))\n",
-    "\n",
-    "ax2.yaxis.set_major_locator(MyLocator(5, prune='both'))\n",
-    "ax3.yaxis.set_major_locator(MyLocator(5, prune='both'))\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## basemap 画地图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "需要安装 `basemap` 包："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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RpNlscOTQUf7mnk/SjTosr88TRR1sIBISfTFAyHUtQMPFZS5GgFIKy7ZIEoUU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\nWsaC0ocZlDvYsv6zhK0+5i3+Ir2D36WzEHC0NomONLXJxUzsfzFCxvQ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3AAAg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xN953DmSWccCsZaxZg4BJJNrxt/D0QjoXdNEugn5QlViPvMdyDTTrTi7fX5acHoKNbvfo06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\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TawBJqukASNDS7vwrkyMIDMo4SONTLE+z98ghRscm2bjhIu5/6GGklBQKkzz0yAN8/tNf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\nDYFS5HM5mu2IQi5AAL6Q4Pk0Ww3CwEPg0QmWcfyk9zKz5gqsCigdvoG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3m2uuKJFo7dRK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\nlowPpnzM519+wx7V5RTVN7EwVg564ysruvFTozWEGdqa46ALCKNpTMVRn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77A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\ntKIBHPVUkht/PphJU/7KHXc+yJLflikAlZ9naxKZQ8v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FZErwLzgcLI5npXQj23Hcoq08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pIz2mjx2IqlzLUd3jc8yqOmxFViNv1xzEBPlfHJFjNmDIJUBAK/KglBmmZoKVBJwil3Ps5E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QIgiJwCGdxYoYO76Mz6b4xgLdxRVK44naK0idVKxCQUA8Srt0mhGLrTWOtJ+g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\neNHFv8R1119NWTYIY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LmtueqMNUwf/3PvuvKrn/qzH3MBPWjGQ9JgAvT7/e+f8cxnpt//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\nQt4G/wW1jjOmOfSF6wAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xb96e550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "try:\n",
-    "    from mpl_toolkits.basemap import Basemap\n",
-    "    have_basemap = True\n",
-    "except ImportError:\n",
-    "    have_basemap = False\n",
-    "\n",
-    "\n",
-    "def plotmap():\n",
-    "    # create figure\n",
-    "    fig = plt.figure(figsize=(8,8))\n",
-    "    # set up orthographic map projection with\n",
-    "    # perspective of satellite looking down at 50N, 100W.\n",
-    "    # use low resolution coastlines.\n",
-    "    map = Basemap(projection='ortho',lat_0=50,lon_0=-100,resolution='l')\n",
-    "    # lat/lon coordinates of five cities.\n",
-    "    lats=[40.02,32.73,38.55,48.25,17.29]\n",
-    "    lons=[-105.16,-117.16,-77.00,-114.21,-88.10]\n",
-    "    cities=['Boulder, CO','San Diego, CA',\n",
-    "            'Washington, DC','Whitefish, MT','Belize City, Belize']\n",
-    "    # compute the native map projection coordinates for cities.\n",
-    "    xc,yc = map(lons,lats)\n",
-    "    # make up some data on a regular lat/lon grid.\n",
-    "    nlats = 73; nlons = 145; delta = 2.*np.pi/(nlons-1)\n",
-    "    lats = (0.5*np.pi-delta*np.indices((nlats,nlons))[0,:,:])\n",
-    "    lons = (delta*np.indices((nlats,nlons))[1,:,:])\n",
-    "    wave = 0.75*(np.sin(2.*lats)**8*np.cos(4.*lons))\n",
-    "    mean = 0.5*np.cos(2.*lats)*((np.sin(2.*lats))**2 + 2.)\n",
-    "    # compute native map projection coordinates of lat/lon grid.\n",
-    "    # (convert lons and lats to degrees first)\n",
-    "    x, y = map(lons*180./np.pi, lats*180./np.pi)\n",
-    "    # draw map boundary\n",
-    "    map.drawmapboundary(color=\"0.9\")\n",
-    "    # draw graticule (latitude and longitude grid lines)\n",
-    "    map.drawmeridians(np.arange(0,360,30),color=\"0.9\")\n",
-    "    map.drawparallels(np.arange(-90,90,30),color=\"0.9\")\n",
-    "    # plot filled circles at the locations of the cities.\n",
-    "    map.plot(xc,yc,'wo')\n",
-    "    # plot the names of five cities.\n",
-    "    for name,xpt,ypt in zip(cities,xc,yc):\n",
-    "        plt.text(xpt+100000,ypt+100000,name,fontsize=9,color='w')\n",
-    "    # contour data over the map.\n",
-    "    cs = map.contour(x,y,wave+mean,15,linewidths=1.5)\n",
-    "    # draw blue marble image in background.\n",
-    "    # (downsample the image by 50% for speed)\n",
-    "    map.bluemarble(scale=0.5)\n",
-    "\n",
-    "def plotempty():\n",
-    "    # create figure\n",
-    "    fig = plt.figure(figsize=(8,8))\n",
-    "    fig.text(0.5, 0.5, \"Sorry, could not import Basemap\",\n",
-    "                                horizontalalignment='center')\n",
-    "\n",
-    "if have_basemap:\n",
-    "    plotmap()\n",
-    "else:\n",
-    "    plotempty()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 对数图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`loglog, semilogx, semilogy, errorbar` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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w8cX2YDuql3y4fZL18E3MA+Qr/6huH7/B1y8P7dsAnPJ3OEKwwQaWOfTVV836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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xe36f748>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.subplots_adjust(hspace=0.4)\n",
-    "t = np.arange(0.01, 20.0, 0.01)\n",
-    "\n",
-    "# log y axis\n",
-    "plt.subplot(221)\n",
-    "plt.semilogy(t, np.exp(-t/5.0))\n",
-    "plt.title('semilogy')\n",
-    "plt.grid(True)\n",
-    "\n",
-    "# log x axis\n",
-    "plt.subplot(222)\n",
-    "plt.semilogx(t, np.sin(2*np.pi*t))\n",
-    "plt.title('semilogx')\n",
-    "plt.grid(True)\n",
-    "\n",
-    "# log x and y axis\n",
-    "plt.subplot(223)\n",
-    "plt.loglog(t, 20*np.exp(-t/10.0), basex=2)\n",
-    "plt.grid(True)\n",
-    "plt.title('loglog base 4 on x')\n",
-    "\n",
-    "# with errorbars: clip non-positive values\n",
-    "ax = plt.subplot(224)\n",
-    "ax.set_xscale(\"log\", nonposx='clip')\n",
-    "ax.set_yscale(\"log\", nonposy='clip')\n",
-    "\n",
-    "x = 10.0**np.linspace(0.0, 2.0, 20)\n",
-    "y = x**2.0\n",
-    "plt.errorbar(x, y, xerr=0.1*x, yerr=5.0+0.75*y)\n",
-    "ax.set_ylim(ymin=0.1)\n",
-    "ax.set_title('Errorbars go negative')\n",
-    "\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 极坐标"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "设置 `polar=True`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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LdjYXb9zIVo1G+A96/fXC8OcIwmw2c0NDA2u1WtZqtazT6bipqYm7urqGTHk4\nWMvEZrOxwWDgpqYm1ul0vdevr68PjSHTYhECC/t7zx49OqzL+jNqY7Vaubi4WNaUkMyCMXb69OnN\nACJ5BLybrkX2Cvi7ENHaq6++2ih1Gj5PXEOGPpu7f/qT0J8H2BIXx8W33cbWv/1N0jr6wuFwcGdn\nJ1dUVHB5eTlXVVVxV1dXUGkNA+3mOBwONhgMvfltKyoquKOjY3gpFf/5T+bISLegJCQwf/ZZUJcK\nZPjXZDINPXInAb/5zW+MSqVyA4+Ad9O1yF4BvyoJqGfOnNn2xRdfBPfkQ0RdXZ33WBu7XXB59/xv\nOXYsWw4elD1JtdFo5KqqKi4rK+PGxsaQ1GW4NhObzdab87ayspJ7enqCu9DHHzPHxbmfeVSUkHIh\nwLoE6kfS3t7ONTU1gdY2pHz77bc8Z86cTgAaHgHvKI8WMYmIiNi0fv16Wa1fHR0dXF1dPfADi4X5\nV7/qKyRnnCEE7bE8SaodDge3tbVxWVkZV1dXh9xPIpQGWIvFwjU1NVxaWsqtra2B/8c/dKivYTY6\nmvnTT/06dTgOabW1tazX6wM+L5TcdNNNxujo6Pu47z/esQA+AXAMwI8AbmYv7xSAJwGUAjgCYLa3\nYwJdZBeKISsIJJx22mndR44cGe6zDxqz2ey9advTI6QC8BSSCy8U9nsglaA4HA5uaGjg0tJSbm5u\nFq0pLsZojsPh4NbWVi4tLeX6+nr/QhJclJcz5+S4vwONZsi4nuF6trqc2uSckqS4uJhPP/10I4AU\ndr8vGQBmOddjARQDmMJ936kVAN5zri8A8DWH4l0NxUXEXOLi4h6+4447ZPvGHA4Hl5SUDBQCk2mg\nkFx/vc9oVzEFxeFwcHNzM5eWlnJHiIdKvSH20HBnZyeXlpZyU1OT/4JYUsKcmen+LhISmI8f93po\nqFzkXdcJSPhCzN13321OSEh4in3/M34LwNn99m0FsMZj+wSAdF/X8HeRXSwGrRyQvmjRIqOc87TU\n1NRwZ//IXbOZedWqvkLy+98PGTsihqC4UhBK6cQn1feh1+u5pKTE/1SXP/3EnJbm/k7y8wcECYY6\n1sZgMHBVVVVIrhUMtbW1vHTpUhOAXB74/uQBqAQQ22//LgCLPLb3AZjb//xAlxHtTh8bG3vXihUr\nlPn5+bKU39PTA7vd3jebudUKXHEFsGuXe9+99wJ/+IPgIj8InlnvbTbbsOpms9mg0+nQ3d2NwsJC\n8ebckZECodCRAAAgAElEQVTExEQUFhbCaDRCq9XCOlTWfZfPiSvjv04HXHihMP0HQpv82YVGowER\nwWAwhOR6gZKVlYUVK1YoExMT7/HcT0SxAHYC2MjM3irX/8c6fIez4aqRWAsA9ezZs7tPnDgxfPkO\nAlf3pk8T1uFgvvrqvi2SzZsDjmYdbgulublZ1v66HC1Fs9nM5eXl3OQ0bA/Krl19889edRXbnD4i\nYgTtef2tSEhFRQXPmzfPCGcLBEAEgL0AbmHv79ZWAFd4bIekmzOSWyaXLliwgCZNmiRL4TU1NcjM\nzOw7adMf/wi89JJ7+7bbhH0BTpkQbAvFbrdDp9OBmTF+/HhERUUFVO5oJjIyEgUFBVAoFNBqtb6D\nKgFhao3HH+/dtP/rXyjfvTukLRJPXBN81dTUhPza/jBu3DicfvrpIKIrSZi/4wUAx5n5CR+nvAPg\nKgAgotMBtDNz47ArMlw1EmuZPHnyiTfffHPYqh0MXvvBr7zSt0VyzTXDzq8RSAvFYDBwcXHxiIgP\nkXuuYVdc1JDBd2vXsk2t5uKNG9mcnj5sL9mh8Gpfk4i9e/fy1KlTKwGcCcAB4DCAQ87lfAA3ALiB\n3a2RpwGUQRgansMheGdlFw2vlQJmX3DBBSY5nL1cQV19mqxffNHX23L58pAlPvZHUFpaWlin08k6\nauCJ3GLCLHxPlZWVg3Z7bB0dXHz//Wx2JeiePHnAsH2o61RSUiKLd6zdbueLL77YCA/DqtTLiOzm\npKWl3b5kyRKVFBOH96e1tRVJSUnu7k19PXDJJYDFImxPmSLk1QhRc3mwLg+zMAOd1WpFXl6eaPPk\njkaICLm5uWBmVFdXu/4J9WK321He0IC8Sy5BpOu7O3ECeOABUes0ZswYNDc3i1aGLxQKBZYtWxaZ\nmZl5l+SFu5BLxXwtABIXLFhgrq+vH5ZSB4PdbufS0lL3DquVedkyd4tkzBhmkf4r92+huPJoyO1l\n6Y2R0DLxpKOjo0/KzAHDv1u3ur9DhYL54EFR61NWViZLMGBrayufccYZFgBpHG6ZAEqlcu2iRYs4\nIyND8rLr6uqQlZXl3vHAA8JcvoBgZN2xAxBpmNqzhWK1WqHVajFmzBifof5h3MTHxyMzM7P32Q0Y\n/v3Nb4CzzhLWHQ5g3Tp3S1MEsrKyeuc0drFu3Tqkp6djxowZXs/59NNPkZCQgNmzZ2P27Nl48MEH\nAy43OTkZixYtckRGRl4XVMWHixwK5msBQFOnTm341M/YilBiNptZp9O5d+zd2xsBzICQ1EgCTCYT\nHzx4UBJP1mAZaS0TF11dXXzw4EHvRuqysr6Jqh9/XNS6VFRU9Bm6//zzz/n777/n6dOnez3+k08+\n4VWrVg273IMHD/KMGTNaASj5FG+ZnDVr1qyEJUuWSF5wn1ZJWxuwdq3wswOAc84B7rnH98khgplR\nU1ODSZMmoaGhYdiObacSdrsddXV1mDx5Mqqrqwcmdxo/vq+95IEHgNZW0eqTnZ2Nurq63u3Fixcj\nKSlp0HOYh+83Nm/ePMybNy8GwgiOpIwoMcnIyLh52bJlURSg38ZwcXlW9jaLb70VaGgQ1tPThQxp\nImd2Y2ZUVlYiPT0d8fHxIfOUPRXw9GyNi4tDVlYWKioqBr6cN98MFBYK6+3twP33i1YnlUoFpVIJ\ns9ns1/FEhK+++gozZ87EihUrcPz48aDLXrp0qXrs2LGbgr5AsEjdFPK1AFDNnj27R444h8rKSneT\n9N13uY8/yVtvSVKH6urqAblS5Ehf4A8jqZvjK9amvb2dKysrB57w5pvu71apZPY0uIcYi8XSp+us\n0+l8dnM6Ozt7ZxJ87733eMKECUGX29zczKeddpoZgJpP0W7OGdOnT8fYsWMlLdRms8FutwvepB0d\nwA03uD/8xS+E2A6RaWlpQWRk5IBmcChjeU5GBou1SUhIQExMDJqamvqedOGFwrQjwgWARx4RrX4R\nEREgIlj8MPbGxcUhJiYGAHD++efDarWira0tqHJTU1Mxa9YsO4DlQV0gSEaMmCQmJl42ffr00Ps6\nD0F9fT0yMzOFjbvvBlxW+DFjgCefFL38np4eGAwGpKWlef08LCje8SdoLzU1FUajEd3d3e6dRH1t\nJy+/DFRViVbPzMxM1NfXD3lcY2Njb7fs4MGDYOZhBW9Onz5dnZqa+ougLxAMUjaDfC0AqKioqPn7\n778PumkXDHa7ncvLy4WNH37oGxz22muilx9IPoyR1OWRu5sTSBoBn/loFi92f9cbNohUUwGtVstr\n1qzhzMxMjoiI4JycHH7hhRd469atvHXrVmZmfvrpp3natGk8c+ZMXrhwIe/fv39YZRYXF/PMmTM7\nIeGUGLILCQtiMuWCCy4wSe2G3NTUJOTKcDiYzznH/eM65xxJ5rUtLy8PKPJ3pAiKnGISTD4Si8XC\nZWVlfXd+8IH7+46JYRbRObCrq4vlcML8r//6LyOA0/hUspmoVKoLi4qKSOpRnM7OTsTHxwO7dwP7\n9gk7FQrgz38OOBI4UNra2hAbGxtQ5O+p3uUJNh9JREQEEhMT+7q5n3MOUFQkrPf0AH//e4hr6yY2\nNrZvV0siioqKVOqoqIulKm9EiMnEiRPXLl26VFJ7idFohFqtBtlswCaPUbTrrxfmsxURq9UKvV7v\n004yGKeqoAw3sVFKSgq6urrcxlAi4MYb3Qc8+6zbr0gENBqN5AmUzjrrLNWEiRN/LVV5sosJEY1J\nSUkZv8xlYZeIxsZGpLt8SEpKhJ0JCULGNJGprq4e1qjVqSYoocqQlpubi+rqaveOX/4SiI3FOgDp\nP/2EGS4fFC/cfPPNmDBhAmbOnIlDhw4FXLYcAYCLFi1CWlpaOhHlSlGe7GIC4IIFCxbYpUz0w8xw\nOBxQAUJyIxd33imM4ohIV1cXoqOjh52kR2pBYWbY7XZYLBbY7XbY7XaXvUtUQplqUaVSITY2Fh0d\nHcKOuDjgyitxDYA9gODI5oX33nsPZWVlKC0txbZt23CjZ4vGT1zTpA427WqoUalUWLBgAQNYJUl5\nUhQyGLm5ub+ePXu2WsoyOzo6kJCQALz6KlBWJuxMTAR+9ztRy2VmNDQ0oHCQ/4CB4CkoBQUFCFXK\nBqvVivb29gHNcoVCAZVKhZ6eHtTU1Ax4MTQaDZKSkkI2F68YOVvT0tJQVlaG+Ph4EBHwi19g8bZt\nqACAzk7B96Sft/M777yDtWvXAgAWLFiA9vZ2d8s2AJKSkqDX65GSkhKSe/GHuXPnRhUWFFwNYIvY\nZckuJsnJyfMXLVokaZl6vR7jcnKAhx5y77zlFiA+XtRym5ubMWbMGITS0BwqQbFarWhqaoLZbIZK\npUJSUhJSU1O91tVisWBcv4nCmRkGgwENDQ2wWq2IjIxEWlpa0CIghpAAgtt6eno6GhsbkZGRASxe\nLIRMNDYCNhvwxRdupzYntbW1fbqlOTk5qKmpCVhMEhISoNPpJBWTxYsXIzY+voiIFMwsarNI1m4O\nEWWlpKRE9v9hiknvMNY77wjJcgBBRG6+WfRyOzs7RUkpMJwuT09PD7RaLerq6pCcnIyCggLk5uYi\nLi4uINEjIsTFxWHs2LEoKChAamoqGhoaUF5eHvBIhlhC4iI+Ph7d3d1Cy0qpBC691P3hm296Pad/\nly6Yfwiuc6Ts6owZMwZjx451ABgvdlly20zmTpw40SblkHDvcPDTT7t3/u53wBARncPF1SoRi0AF\nxWQyQavVCq20ceMwbtw4REdHh6w+arUaubm5yM/PR0dHB8rLy2F0TjkxGGILiYu0tDS3q/3q1e4P\nXC4CHmRnZ/cx3NbU1CA7OzuochMTE9HuwzYjFvn5+QxgrtjlyComkZGR83JyciRNsa7X65HU1ORO\neqRU9h0iFAFXqyQhIUHUcvwRFGZGXV0dGhoaMG7cOGRnZ/caB8VAoVAgKysLeXl5aGpqQm1trU/D\nrVRCAgixMAaDQajLmWcCLjvP8eOAR+oAAFi9ejW2b98OAPj666+RmJgYcBfHhRxikpubq06MjT1D\n7HJktZnk5eX9bPLkyeLG9vfD4XBAuXWre8eFFwI5OaKW2dbWJlk/eTAbisVi6ZPmQEqUSiXGjRuH\nrq4ulJaWYty4cX0c9qQUEheu4dqNGzfiMyK0QJj1+4F774V13jwAwA033IAVK1bgvffeQ2FhITQa\nDf72t78FXaYceXynTZtG43JyzhG9IKlcbb0tM2fO7KqoqBiGw3BgmM1mri4rY46Pd7tSf/ih6OWW\nlZVJnrG8v+t9V1eX9xiVIBiuO73NZuszL3Kop+z0F9fk48zM/OCD7t/EDTeIWm5tba2kU5Y0NTXx\n7FmzzBA5Tke2bg4RZaWmpkbm5kriTwMAaG9vR8KBA8IQIABMnAgsFzdK22QyISoqKqQjOP7g2UJp\nbW1FS0sLCgsLQzZ8PByUSiXGjx+P9vZ2tLS0SN4icUFEiI6ORk9PDzB/vvuD774TtVypuzpjxoxB\nTnY2Q2QjrJw2k7mFhYWSGl+7u7sR6xmDcd11QiyOiATjjxAqIiIikJycjIqKCmRnZ0suaIPhmgWv\nqqoKiYmJPoVkz549mDx5MiZMmIBHH310wOfDTcTca4idM8e98+hRYU5pkegVMAnJy811QGQjrGxi\nEhkZOT8vL09S4yv39IA+/NC9Y80acctzeo2GyokrUAwGAwwGA2bMmAGdTjeiXO9dNpLp06fDZDKh\n09Va7HfM7373O+zZswfHjx/Hjh078NNPPw04bunSpTh06BAOHTqEewLM1atSqeBwOMDJyYDLRcFi\nAY4dC+q+/EEOUc8vLFSnxMYuFrMM2dq8ubm550yaNEky46vVakXEsWOChyMALFwIiNzF6u7uhkaj\nEbUMX1gsFtTX16OwsBBE5NuxrbsbOHQI+P57wXGrqwswmYDUVCAzU5h0bNEiwJkFLBT0N7aOHTsW\n5eXliIqK6mOUPXjwIAoLC5GXlwcAuOKKK/D2229jypQpfa7Hw3Trj4+PF0bbioqAykphZ2kpMGvW\nsK47GFFRUTCZTFCrpXH+njJ1Ko3LyPi5mGXIJiYajWbKLBG/rP4YDAbE7tnj3nHFFaKX2draGrQ/\nwnBgFpJT5+fn9/4X7DPKk5gI1c6dQpDjgQPCXDKDERkpDJ/ecANw8fAi2r2N2rjErry8HIWFhb0j\nHt48Tw8cONDnep6JmLOzs/HYY49h6tSpAdUpKSkJ1dXVSCgocO/UaoO8Q/9wDU1LJSZz5swBoqNF\n9Q6VRUyISDl79uy4HJGHZD3pbm5Gmsu7kaiv16NI2O12WQye9fX1SE9PH1B2RGMj8p94Alq1GgXb\ntkHlr2eqxQJ8/LGw5OQAW7cCeXkB53wZbPhXqVQiKysLdXV1cP0u/OkOzJkzB9XV1YiJicH777+P\niy66CCWuKHA/USqVQlenoAC9JYosJhqNRnIjbFR0tJKIYphZFIONXC2TtJSUFFtERIRk5ntraSki\nXV/eggWA58x9ImCxWCQfnQCE0SOLxdLXj6SnR8h7+uSTiDCZkB8XB+1116Hg+eehMhqFrsy8eUBB\ngRBaEBkJNDUBNTXAV18JjlwuamqA118Htm0Dtm8X0jb4gT9+JLGxsWhra0NPTw9iYmIGeJ5WV1ej\n/z+guLi43vXzzz8fGzZsQFtbW8D5U6Ojo2EqKECvD3BFRUDnB4pSqYTd1eWWACJCRlKSHUAmgHIx\nypBLTLLS0tKke5JA3/80P/uZ6MW1t7fLMrVnbW1tr40BgGBIXLOmj0ExoqsL+YcPQ7t9OwrOOAOq\noUabamqA558HnnkGcOXkeOcdYMkS4P33hxTmQBzScnJyoNPpMH78eJx22mkoLS1FRUUFsrKy8Npr\nr2HHjh19jm9sbERaWhqIaFiJmBMSEtCelOQWExEn6JKLVMF+lwWRxESu0ZzM2NhYyaKd2OEAyj2e\n389FtUMBEGw0Uhtfu7u7oVar3e7x27cLLQ7PkYm5c4F9+xDx6afIX7UK2o6OoUd5cnKECatKS/uG\nHhw9Cpx/vmDE9UGgnq0KhQIxMTHo6uqCSqXC008/jXPPPRdTp07FmjVrMGXKFDz33HN47rnnAAA7\nd+7EjBkzMGvWLNxyyy149dVXhyzDG9HR0TB65tTR64O6TiAQkaRBfxqHgyC0TMRBTI84XwuA39x+\n++2SuQAajx/nuhUrBO/GuDhmi0X0Mnuz3ktIeXm5O9P9I4+4PToB5uho5ueeY+6XCT+YJNXaN98U\nJrByXfuqq7weF6xnq91uH5gAWgK0P/7ovqekJNHLa2xsZIPBIHo5Ln6/cqUNwC18MnnAKpXK7OTk\nZMkMCqZvvkGUK0J02TJ3UJdIOBwOyWMwLBYLVCqVUO5jjwGbN7s/nDoV+OYbIb9tv3oFlb5g5kzB\nCOti+3bg66/7HDKcWBuFQoHIyEi/p9YMFcqYGNhckdMS5GuNioqS9B6T7HZlfHR0nljXl0VMUlNT\nC1NSUiQr21JZiaiWFmFjsah+OwDQa0CUkqamJiFB9auvAnfc4f7grLOA/fuBadN8nhuUoFx3HXDZ\nZe5tj4mtQhG0l56ePnA2PpHRxMSg22VvGqbvij9ILZgpAHJSUkTLli6LmCQmJuaJmdujP+bWVkS6\nDIfOaFAx6e7uRmxsrOjleGKxWKAuKQGuuca9c8kS4N13/cogF5SgeObP/fBDoLU1ZNG/kZGRvRPK\nS4VGo0H3eGf4igRiEhUV5dfUoaEiIzISmpgY0Tw1ZRGTyMjI7CyRh2Z7MRph6+x0+1R4xmCIVqRR\nMmckwOndCwBXXil4rwLApEnA228H5LkasKAUFgKnny6s2+2wf/FFSIP2XF6iUhEVFQVzaqqwIYGY\nKBQKSQ2wWQkJgEKRKtb15bKZJPfO7ys2R44AzIIz0qRJoud5BQSjtpQ2E71ej8SdO92jNtHRQvrB\nIIamAxYUpzjb1WqUW60hjf5NSkqS1LGLPDPBhTDr3EghMzISdpVKtCazLGLicDhiJYuk9Qwnl6CL\nIwfdtbWIvfde947/+z/BES1IAhIUjUYQkhtuQF5FRUgd9aKjo/1K9RgyXFNgAEEJ8Ugn2WYDRUYq\niUiUwQ/JxYSIFAqFQiGZd6ina/X06dKUKTWffQZyGfLmzw9JGkp/BcVeVSUIycsvIzLEL6Dk0bWe\nrSCRU2zKARkMUKtUDEB6MSGiF4mokYh+8Ng3n4gOEtEhIvqGiOZ5fPb/iKiUiE4Q0c899q8ioiNE\n9FcASqVS6ZDsh+LprBai+WpGFBUVwA8/uLcfeyxkOVqGEhR7YyPKc3IEIWlvF6KLRzOeXq8nYcsE\nBgNUgkOjT893IjrP+f6WEtFdzn0Fznf+IyLy+WCG+tX9DcB5/fb9L4DfM/NsAPc6t0FEUwGsATDV\nec4zHjOR/xLAbAD1AIqUSqX41i0Xrkm2AGC86Nn+JYe3bBHmSwaErHEhHvr2JSj2ri6Uv/IK8l54\nQRCS6dMFf5YQI2kMS2UlyOEAKxSip6eQBYMBKuGV9ComRKQE8DSE93cqgF8Q0RQANwK4DMBDEN5l\nrwwam8PMXxBRXr/d9QBcbcBEALXO9QsB7GBmK4AKIioDsADA1xBEKwpADAB7RESEA4D4uUzsdkCn\nc484nGxiYrPB/q9/QeG6v1tvFaWYXkHZvx8Fhw+DNRqU63TIe/ppd/Dkww8HHEXsDy4xETODfi86\nHRRmM+yRkVB5xjedLBgMUMXGMny/9/MBlDFzBQAQ0asQ3msbgFjn4nO8PphAv80AviSixyCIxELn\n/iwIwuGiBoArmcc2AF8A+AhAlSQ/DACorRXC5wFhDmGPCFOxYGbp+voffwx7dzdUPT1CIqPzzxet\nqIiICOTv24cSiwVdEyZgtqeQ/Pd/AytXilIuM0sXgV1RAVtcHBxRUUKKBQmQ0s8EXV1IEPLr+Hrv\nswF4zOyOGggNgkcB/ANAO4ArfV0+GDF5AcDNzPwmEV0G4EUAvsJwGQCYeR+A0wCAiFJckaGiU1MD\nrF2L9lmzoMvKElopIsPM6OjoGHb2L78oL4ft8sthzM6G+YILgKoqUYtzTJ6MLpMJ5rQ0VF96KRRK\npRA0OXu2aM+2s7MTBoMBkkxsn5UFfU4OHAoFlBMmSPJ76ejokOZdAICzz8b42FjC7t2+zBtef7TM\nXANg2VCXD0ZM5jOzaw6OnQCed67XQph2xEUO3F0gT6yuLGCic/w48PLL0AHIb2gApCgTgE6nk+b+\nnnoKtspK1F9wAcb+5jei3p/dbkd5ZSVmv/suqohgv/hijDvzTKhE9tupra1Famqq+GJitQJ/+hMU\nl12Gsa+9BsXDDwNBpDIIBkl+K52dwLZt+P7MMxm+uyr93+GxEFonfhGM2b+MiJY615cDcI29vgPg\nCiKKJKJ8ABMAHPRyvs1ms0kzJO1pnZdwsmhJaG8HfvoJSqMRdo1GSKsoEr0u8osWIfKxx6BcuhT5\nP/sZtA0Noieplixb3U8/ARYLHCoVKCtLMiGRjBpBE2xCGgJfYvItgAlElOf0RVkD4b32i0G/JSLa\nAWApgFQiqoYwenM9gC1EFAXA6NwGMx8non8BOA7BYLOBvbf1bTabTRqjwsksJocPAwCIGZyZKZrH\npq9Ym8FmDgx1+ZJ4E7tyyxKBZs8WvzypcWasswmvpNf/AMxsI6LfAdgLYYDkBWYeOB2AD4YazfmF\nj48W+Dj+jwD+6O0zD2w2m00hiaHyZBaT0lL3ukhBk0MF7UklKJIYtD/6yL0uQWS55LhaJsIwu8/m\nJDO/D+D9YIqQ3AOWmR3M7JAkItTlHk108jkheeYoTUoKucHX3+jfoKKNRxrMQrJsF2efLV9dxKK6\nGgzAYrcP1s0ZFrLE5hBRjyS5KjydnUbAtJghxZWfBUBMRERIZ4gLNI2AWIJiNBqlGcU5etSd2zY6\nGigqEr9MqamuRjsAu9nsYGZRkqjIIibM3FpfXy9+QZ5iIpVvC4RmuehDwx5xJEkqFfQhylkabD4S\nMQRFr9cjKSkpJNcalH//G4BzXHT8eNGnjJWF4mLUA1DY7X7ObxI4sjw1i8XSUFdXJ35BzlwRyp4e\n2CRsmajVavGjXT3EKpIoJM5Pw01sFGpBMZlMiJYiFcDOnQAAS0oKIidNEr88Jw6HQxp7EDPw44+o\nA6Cw2VqGPD5IZBGTjo4OrZTdnKiWFpglnMNGo9Gg298JroLF80fonM94OCkAQ5UhLVSCYrVapXGh\n//FHYVgYQPeUKdBImKZCMs/e6mqgowMN0dHoNplE82yURUwaGxtLW1tbxXcRdbZMolpaYJZw8vCY\nmBjxZ7n3dBbr7ERaWlrQOVNDJSQuQiEovTltxeaFF3pXu88/HxoJ/UvMZrM0NiFnVHlLUhJq29p+\nFKsYuZIj1en1evEz6TpTFkY1N8MsYXo8SSJdPX/0LS29+UQDTQMYaiFxMRxBYWYYjUbxuzhGI/Dy\ny72btgULECHhPx2pxaQjJQXt3d2i+e7LZWmq7+zsFD+u3PnfW93YCJOEYuJCVCOsZ4i8M7YjLS0N\njY2Nfl9CLCFxEaygNDc3Q5KE46+/7p5sq6AALHGksCSCCfSKSVdKigOAaMZKOcVEfMuTM0qYHA53\n9LBEaDQacbs6nukUiosBCPPudnd3+9U6EVtIXAQqKMyMzs5OJIid6czhENJbOjH99rdQS5z31eFw\nSGMXOnoUAGCIjmYIKUREQS4xqWtpaRF/eMXTriDxhE4JCQniJkP29IU4dKjX2JyVlYWamsFjs6QS\nEheBCEptbS0yMjJErxN27RKMrwCg0aB99WpZ5oYWHb2+N9F4q+AoetK1TBpbWlpUontMeuQvUXV1\nSToPi1qtFneCpawsIYcJIMz163wxYmJioFAofI4mSS0kLvwRlJ6eHjgcDvHnHGIGHnrIvb1+PXoU\nCmm6HE4kizn68kuAGQygrquLcLK1TJjZZrfbe0T3NUl1TxGi0enEH67th0KhENcQu2SJe/2DD3pX\ns7KyUFtbO6BsuYTExWCC4nA4UFNTg5ycHPErsnOnMF0qAERFwXHbbSAiSRNYSzbr4+efAwDaAPQY\njXZmFm3eU9lc/Xp6ek4cdka+ioaHkTL2++9hkGD+WE+Sk5PR1tYmXgE//7l7fdeu3lWFQoHc3FxU\nVFT0GoHlFhIXvgSlsrISY8eOFf+/tdkM3HWXe/u3v4U+KkoaT1sPDAaDNLM+OsXkUGQk7BaLqFmY\nZBOT6urqj4qLi8UdYvEQk8hjx2CV2AgbFxeHrq4u8QpYscLt+v3ll72RoYDQzUpNTUVNTc2IERIX\n/QWlrq4OiYmJ0nQznnjCnUEtORm45x50dHSIb/Dth8lkEn/WR4Ohd96on8aORVV9/cdDnDEsZBMT\nk8l0QKfTiTv3Y0KC227S0wMYjdKkU3RCRFAoFOJF02ZkCBOTA4IdYPv2Ph8nJCQgKioKR48eHTFC\n4sIlKD/++CMUCoU0LYOyMuD++93b998Pe3y85F0cZpYmV/D+/b2G+YoJE+wdHR3/EbM4OSOavisv\nL1eJ+nIT9WmdxBgM4num9iM9PT0g34+AWbvWvb51K+A5HYXdjvb2dmRmZqKpqUlSIfWH5uZmpKen\no7OzU/z0BQ4HcN117rmYZ84E1q+XzqfFA8lijjxytOgAB4DvfB88fOQUk+rGxkab6EbYceN6VxNr\naiSduxYQprg0mUzivciXX+5OjlRdDbz6KoC+NpKMjAzExsZCq9VKNwfNIDgcDmi1WqjVamRmZkqT\nD2XLFuCzz4R1pRJ48UUgIgLd3d3S2C48aG9vl2YY+u23AQB6AJUNDYA7xaooyCYmzMxms/n4d9+J\nKpbAtGm9q1GHD4s7XOuD+Ph4dHZ2inPxqChgwwb39h/+ALvZPMBGkpiYiOzsbJSXl0veOvPEaDSi\nrKwMmZmZSHaGBIieYOm774Dbb3dv3347MGcOurq6oNFoQl/eEBiNRvHtJcXFwIkTAIDvIiNhsVrL\nmFdPIiYAACAASURBVFnU/ySyJm6oqqr66Pjx4+IaYT2du44cAREFHL8yXFJTU9HsSr4jBrfc0ptJ\nzl5djfLdu73aSNRqNQoLC9Ha2orq6mpJnwMzo6amBs3NzSgsLBzQzBdNUDo6hNaby/g+Z06v3USy\nYEIPXC1U0e0lzlYJAPy4cCGqa2o+E7dAmcWkp6fnQE1NjbhG2Jkz3etHjiAxMVHyrg4RQaPRiDey\nk5gI3Hkn7Gq1MIn4bbch0pWysh8KhQJjx45FcnIytFotGhsbRbWlMDOamppQXl6OxMRE5Obm+hz+\nDbmg2GzAmjWAVitsx8UBr70GqNXo7u6GWq2WxnHMA8lGjjzEpDovzyK28RWQWUwAfFdWViauEXbS\nJMAVCVpVhURAcjEBxDfE2m++GeV33CFMIl5ZKXR9BnmuGo2mt4Wg1WpRW1sbUg9hq9WKuro6lJeX\nIzIyEoWFhX7ZJkImKMzATTcBe/e69z3/fO/k9Q0NDdK47fdDkuxxjY3CSA4AKBTQtbeLbnwF5BcT\n8Y2wkZF9JtQmp6Oc1F0dhUIBjUYjiu3EbrejvLZWmNfGJZQ7dwL//OeQ58bHx2P8+PFITk5GXV0d\ntFot6urqgsoUZzKZUF9f33uNpKQkFBYWBmxsDImgPPywMLrl4t57he4OBIcxtVotTZCdB64hYdFb\nQ7t29f4j0S9YAF1lpQIiG18BmcWEmbmjo+P7//xH5BbY/Pnu9S++QGJiIjp8dAPEJCMjI+Tdij4O\naeedJwx/uli/3h3MNgTR0dEYN24cCgoKkJSUBL1eD51O17tUV1ejrq4OTU1N6O7uRl1dHaqrq/sc\n09raisTEROTn52PcuHHDGv4clqD8+c/C/McufvGLXjsJM6O+vh6ZrrgmCenq6kKcBPNd4+9/7139\nctYs9PT0nBDb+ArArZZyLQCu37x5s4nF5J//ZBa0mvmss9hut7NWqxW1SF/o9XpuaGgIybVsNhsX\nFxez2Wx27+zsZJ4wwX2/48czNzUNqxyHw8FWq5WNRiMbDAY+ceIEG41Gtlgs7HA4hnkXg2OxWLi4\nuJitVqt/JzzxhPveAebly5mNxt6Pm5ububW1VaTaDo5Op2ObzSZuIaWl7ntXKvnezZvNSqXyNpbg\nXZa7mwMA73799dcKUSN6ly51r+/fD4WzLDl8LhITE2EwGIZtYPTpIh8XB7z5JuAa8iwvBy64QHCt\nDhIigkqlglqthkajQWRkJNRqNSIiIkQflfC7hcIM3H23MLLlYvFi4J13AOcwrMuJL1mGqT8dDoc0\n+Uteesld5nnn4eDhw7Db7W/7PiF0yC4mzFzX3Nxc8+WXX4pXSHZ2r+ENJhNw8OCwcqYOl7Fjx6Kq\nKvi8vkPG2kybBvzjH+6k0998A6xaNSxBkZMhBcVsBtatE+wkLhYtAnbvdosqgOrqammikr3Q0tKC\nVI8odlGw2/uIycHFi1FXV9fGzOXiFiwgu5gAQFlZ2fZPP/1U3GQjy5a51z/5RPxMaIMQGRmJ2NhY\ntHpOX+onfgftXXQR8Mwz7u1PPwXOPdedpnCU4VNQamuF79bjJcLKlcCHH/bJZ9Pe3g61Wi2+s5gP\nurq6EO+ZrEsM9u0TngcAjBmDjy0Wm06ne0XcQt2MCDExm81vHjlyxM5iDhEvX+5ed4brx8bGihvV\nOwhpaWlob28PaDg24Ojf9euBRx91b3/1FbBwodD1GYUMEJR9+4C5c4Gvv3YfdM01QjfPI1eIzWZD\nS0sL0tPTZai1kLtEklicF190r//qVzh67Ji1q6vr3+IX7EQKw8xQCwAqKirSHzt2LCQ2KK/o9cwq\nlds4VVXFdrudy8vLxStzCCwWC5eUlPhlxPRqbPWXv/ylr1EyOZn53XeDqLGAXMZrF5aODi5+/nm2\najR9jI38+OPM/Z6lw+Hg0tLS4J5biNDpdP4bkIOlro45MrL3eWjff59nzZplAKBkid7jEdEyYWau\nra399969e8Vz/khM7Ns6eestKBQKKJXKkMyGFwwRERHIzMxEdXX1oMcNOx/JzTcDO3YIcTwA0NYm\ndAU2bRKmexhNfPABIk47Dfm33grtddfBptEIgY779gnG134G4draWqSlpcmWfsFqtYKZoRJ7Rsmn\nnnKHDCxciPe1Wm5sbHyPpRgSdiGVag21APj5Nddc0zN8iR6EZ59lzyFiZmaz2cwVFRWiFjsUDQ0N\n3NLS4vWzYbVI+rN/P3NWVt9WSmEh88cfB3QZWVomOh3zpZf2qbslLo6LH3+crbW1Xk9pa2vjWh+f\nSUVlZSWbTOJ6PnBnJ3NiovvZvPEGr1+/vgfAxSzlOyxlYYNWBIiaO3euubGxMSTP1yt1dcxE3Nss\nbm5mZqEZarFYxCvXDyoqKrizs7PPvpAKiYumJuYVK/oKCsB8ySWCj4IfSComNTXMN97IHBHRt77x\n8cx//StbzGavfihdXV2s1WpF94MZDKvVKs2z8vStKSzk9tZWnj9/vgVALJ+KYsLMyM7O3vPSSy+F\n4vH6ZtEi94N/6ilmZjaZTLK3ThwOB5eVlXFPj9A4E0VIXNjtzM89J7yQni+oSsV89dXMx48Perok\nL8ihQ0JdPOwAvctVVzHX1/ce2t+xzWg0cmlpqaxCwsxcVVXFRg+HOVGwWpnHjXM/m2ef5R07dnBe\nXt5XLHWDQOoCB60MsOKaa64R9+l7dnVmzuw12Gm1WvGNZENgt9u5pKSEe3p6xBMST2prma+4YuDL\nCjCfdx7zzp3MXuogmpjo9czbtjGfeab3Oi1ezPzll15PdQmK69nZ7XZx6ugnNptNGuP+jh3u55Oa\nytzdzevXrzcCuIxPcTFRzpgxo+XAgQMheMo+aG9njo52fwHffsvMQuuksrJSvHL9xGw288GDBwd0\neUTlwAHmZcu8v8ApKUIL4e23hb45h1hMKioEgV+1ijkqynsdTj+dee/eASM1/TEYDHzgwAHxWwN+\nUFVV1dvKFA27nbmoyP2c7ruPjx49yrNmzeoEEMGnspgwMyIiIjbfcccd4lqsrrrK/QWsX9+7u7Ky\nUtYfoqtrYzQae1sokvKf/wgvtcuu1H9RKpnnzWPt1q3ML73E/N13gjj7g83GrNUyv/8+85/+xLxm\nTd/mubey1qwRjMZ+4HpmRqMxsFgeETCZTKzT6cQv6B//cD+vmBjmpia+++67zdHR0Q+yHI0BOQod\ntEJA6qJFi8y+RjdCwmefub+E+Hjm7m5mFl7msrIy8codhP42ErvdzqWlpdK2UFxotcy//z3z2LFe\nX3Tt2rV998XFMU+ezDx/vjBKdu65QoDd4sXCf87MzL4+PoMtc+cK/iIeNpGhMBgMXFJS0htEF3Bw\nYIgpLy8Xv2yzmTk/3/3c/vu/ubOzk5cuXWoGkM1hMRGWrKysN5544gnxOr0OR9/I2mee6f2ooaGB\n9Xq9aEV7w5ex1eFwcEVFBTc7R50kx24XukCbNwv2JWeLZYCYDGeJjhbsM08+6fdokiet/7+9cw+P\nqrz2//fNPZALJJIwCSEhQAhgQEIDUmKhBa2KeFQUFFDq4+VRLKW2XqooVo62nt9RW3qKxaOgB2rF\nVizw88LPyOEeEcpFQUjIXDPJJCSZJJNJMve9fn/smcxMrjOZvWdPwv48z3qS7Nkze+3Z7/vNe13L\naCSNRtNjsFUqQTGZTGQwGMS/0H/9l/c7TE8nam2lrVu3cuPHjy8jieqt5MLRq1PA9XfccYdF1EE0\n3+m0/Hx+VJz4ChzoqlQhCGTWpr6+nvR6veSzE2Q0Eu3dS+r33iO66y6iggKihITAhSMzk+hHPyJ6\n+GGiLVv4btIgp+Q5jqOampp+K264BSVsZcdsJsrI8H6vb7xBHMfR8uXLLQB+QrKY+IkJmzZtmnb/\n/v0hfuv9YDYTjR7tfSAffdT1UltbW1gWOwUz/Wsymejy5cuSLgv34DcAy3FETU1E58/z4xtffUX0\n+edEZWVEhw7xA9x6PZGAC7fsdjtVVVUF1IIMp6DU1dVRa6BjSKHw8svecpuTQ2Sx0OHDh+naa6+t\nB8BIFpMegvLwunXrxB0NffFF70MpLvabLdBqtaIOgA5mHYnD4SClUknNzc2i+RUIUu7NaW1tpaqq\nqqAWGYZDUCwWS3gGXfV6oqQkb7ndvp2IiH71q19ZoqOj15OUdVbKi/frGDCypKTEIuoDunLFv5le\nVtb1ksvlosrKSlGarKEsSOM4jurq6iRdFyOFmDidTtJoNGQwGAb1TMQUFE/3RvQoakT8SmVPeZ0+\nncjpJIPBQPPmzbMBGEUS1tmI2OjXG0TUcfny5ff/8Y9/iJfmLSODD6rj4cUX+ccEPgC0QqGA0MGu\nQ920xxjD2LFjkZ2dDZ1Oh8bGRo/4Dluampqg0WigUCigUCgGFd1NzERf9fX1yMjIED+K2mefAbt9\nIgq89RYQHY2PPvrIVVlZ+TERhT/tgi9SKtlABmD8ggULrKKOX2g0/ku2d+/2e1mn01GHe+o4VMRY\nIm80GsM+hRyulonZbKaqqipBZ7OEbqF0dnaGp3vT0UGUl+ctpw8+SERETU1NtGjRIhuAySR1fZXa\ngYEsJSXlzxs2bBB31PGXv/Q+pIICvxkGT3cn1CasmHttXC4XGQwGUiqVgglff4gtJp2dnaRUKqm2\ntlaUZfFCCYpQZSMgfvMbbxlNT+/apPryyy/b09LS3qcIqKuSOzCgg0D69ddfb7l8+fLgHkIgNDb6\nb3r7y1/8XrZYLKRUKgc9fiLqpr1u16mpqSGlUinqrIJYYmIymUipVJJerxe9ggohKGq1OjyrlC9c\n8F/05x501el0NH/+fCsABUVCXZXagUAsMTHxt7/4xS/Endn53e+8Dyszk5869qG5uXlQi5HCJSS+\nuFwuunLlClVVVVFdXZ3gFVNIMXE6nV2+1tfXh3WDXiiC0l8MGkGx2YhmzfKWzRtu4BcTEtGvf/1r\na0pKyusUAXWUhoqYABhZXFzc9i/3pjxR6Oggys72PrQnn+xxil6vJ5PJFPBHSiEk3TGbzaTRaEil\nUlFzc7MglTVUMeE4jlpaWkilUpFaraa2tjbJFuQNRlDMZnP4NoU+84y3TMbHE7lDm164cIFKSko6\nIfEMjq9J7kCgFhsb+8TDDz8sbutkxw7vg4uK6tpR7CGYeKKRICS+cBxHRqOR1Go1qVQqqqurG3QE\nsMGIic1mo7q6ui4BaWpqkjxMgIdgBCWYuL0h87//67/p8o9/7Hpp7dq1loSEhGcpAuqmxyR3IGBH\ngbhrr7228cCBA8E9kGDgOKJFi7wPr7i4a5m9B4fDMWDBizQh6Q7HcdTR0UF6vZ7UajWp1Wqqrq6m\nlpYWslqtA1aU/sSE4ziyWq3U0tLi9/l6vZ7a29ul3xLQB4EIiue5hiUqn9Ho31K+6aau7k15eTnN\nnDmzFUAiRUDd9BgjGjprFBhj9/3sZz/bvn379gTRMslVVQFFRXxiJ4DPW/vkk36n2O126HQ6TJw4\nsUcS6pCDP0uEw+GA2WyGxWLpNcA2Y6xrHUVzczPS0tLgcrnQW/mJi4tDYmIikpKShtx3oNFokJ+f\n3yMANMdxUKlUGD9+POI9gbnFgohPsv7xx/zf6enA+fOAQgEiwmOPPWZ95513fs5x3DZxHQkSqdUs\nGAMQNXnyZPUnn3wSnMoHyyuveP8jjBxJ1EvELM/0pe9/2khvkYSCy+Uih8PRtaTf4XBETDdFSHpr\noXAcRyqVitrb28PjxNat3vIHEO3Z0/XS559/ToWFhQYAMRQBddLXJHcgaIeBG1esWGEzd5ttERSb\njWjaNO/DvP76Xne3trW1dcWOHc5C0h2p8+aITXdB0el04dnAR0R09Kj/NPCjj3a9ZLFY6P7777cC\nuIMioC52N8kdGIyNGTPmHy+88IK4tfabb/wf6gsv9Hpac3MzaTQaqqiouCqEhGj4iwmRV1B0Ol14\npoCJiKqr/UMLXHddV+AuIqJNmzbZx44d+wVFQB3szSJ2b05/NDY2Pvrpp59aDx06JN5F5swBNm3y\n/v3qq8Dhwz1OS0lJgdlsRkxMDGJjY8XzRyaseJ6nyWRCamqq+Be0WIA77wQaGvi/r7kG2LOnK83p\n119/jX379tnr6+vXiO/MIJFazQZrAG678847raJ2d5xOPgyh5z/FuHH8KHvXy96ujclkkjxPS7gY\n7i0TT4S7lpaW8MRD4Tii1au95Swmho8F48ZisdA999xjAbCcIqDu9WWSOxCKZWZmfvzss8+KG3y6\npobPzet50DffTOR09jpG0tbWRiqVatgLynAWE47jSK1W+42RiC4ov/89+Q24btni9/LGjRvtWVlZ\nEdu98ZjkDoTkPDB61qxZrQcPHhzgaYXInj1+D9v57LN9DrZ6drqGZfOXRAxXMfEEFO9tB7ZogvLu\nu/5C8vDDfkG6ysvLqaSkpB1ABkVAnevPJHcg5BsAlixbtkzc7g4R0fPP80KSkECV69eTbefOPk+1\nWq1UWVkpfo5ZiRiOYmJzpxntL9WJ4ILyySf8SmuPkCxY4Bfe0mKx0PLly62R3r3xmOQOCGEZGRkf\nix6mwOUi51138UIyahQfA6WfnC5Op5OqqqqC2sszVBhuYhJMa1IwQTl40D/p2KxZRN3KyqZNm+wK\nhSLiuzcek9wBQW4CGD1z5kxRuztOp5MqL1wg29y53gKQmdnrgjYPHMeRTqcjUZOxS8BwEpPGxkbS\narVBjXOFLCinT/O5hjzlaNIkovp6v1PKy8vpBz/4wZDo3nhMcgcEuxFgyfLly2313R6KEPgNtqpU\nfHAa3zQZA4QmaGhoILVaPWzGUYaDmLhcLtJoNIMW+kELyrffEo0Z4y0/WVl8tD8fjEYjrV692gYJ\n8gWHYpI7IKQlJyf/xyOPPGIVcvFYrytbDx/2b6Jee63flHFveNJWhm1JtogMdTHp6OjoSnIeCkEL\nysmT/ulVRo/mU4T44HA46PHHH7empqZuoQioU8GY5A4IejNAlEKhOPDUU09ZhZie7XeJ/L59fD5c\nT8GYO7dHQKXueLo9tbW1Q3r6eKiKiSeyv1arFWxfUcCCcuSIf9cmNbXXMbcXXnjBlpOT8zUicO/N\nQCa5A4LfEJBcUFBQ/dZbb4XUpwhor81f/+ofb+InPyEKoOXR0tLSlWR7KDIUxcRqtVJVVZUoOYcG\nFJSyMj4Nqm8M19One5z2/vvvu6ZOnVoPYDRFQF0K1iR3QJSbAvLnzJnTPtjYJ0Ft2tuyxVtIAKL5\n84kC2BTmdDpJq9WSXq8fcrtvh5KYcBxHtbW1pNFoRF3F2qeg7N3r3yUeO7ZH14aI6NixYzR//vxO\nAIUUAXVoMCa5A6LdGPDjn/70pzalUtnjwfXHoHb/dl/BOGsWUUNDQG81m810+fLlITWFPFTExPPd\nhisNiJ+gcBwfGc13HUlODlEvgdF1Oh0tWbLECuBWioC6M1iT3AExLT4+ft19991nDbQwhRRG4A9/\n8BeUwkJ+KX4AcBxHBoOBVCrVkFjoFuliYrPZSK1WU01NTdjHpux2O1VeukSOdev8y0N+fo9ZGyJ+\nMHj16tWWkSNHPkcRUGdCMckdEPXmAJaWlrZz3bp11oG6EoLEI9m2zf8/UV4e0cWLAb/d4XCQVqsl\nrVYbntCAgyRSxcThcJBOpyONRiPd99fSQvalS6ly/XpyjBxJXfFwelmywHEcPfnkk9aMjIxPIGHC\ncaFMcgdEv0EgLicn5+xrr73WZ+kSNLDRRx/5x0FJSSH6/POgPsJqtXbFZY3EtSmRJiYul4v0er30\nLTulkm+RAmRPTuYF5YEHiPoYaH/zzTedubm5lwAkUATUlVBNcgfCcpPAmEmTJtVu3ry5R80UJULa\nZ58RjRjhFZSoKKI33/TbwBUInZ2dpFKpSKfTRVTgpUgRE7vdTtXV1aRSqcKSybBfdu/mp3t9ujb2\nV1/tc5Zn69atzsLCwgYAWRQBdUQIk9yBsN0okF1YWHjFd8pY1FCLZ8/yA26+/eaHHuJDQgaJ1Wol\nrVYb3jik/SC1mHR0dJBarSaNRiP9GJPFQvTEE/7POT6e6MMPiaj3WZ5t27Y5p0+fbgSQSxFQN4Qy\nyR0I680CudOnTze+8847rrDEbK2vJ5o3z7+gzZnT736e/nA6nVRbW0tKpZIaGhokm1KWQkxcLhc1\nNjaSUqmkmpoacYMVBUplJR9a0ff55uXxIT998BWUnTt3cu40FZMoAuqEkCa5A2G/YWDidddd17Jv\n3z4uLF0Hq5XogQf8C1xKStd/rsHAcVxXZDeVSkWtra1hnbUIl5hIfZ/9OEa0cyefucD3uS5bRtTS\n0utb7HY7ffbZZzRnzpw2AFMpAuqC0Ca5A5LcNDCluLi45d133w3Pv3aOI3rjDf+BWYDowQcDWjHb\nH77/sTUaDbW0tIjeYhFTTDiOo9bWVtJqtV0tsIgahK6pIbr9dv/nGBfHL17sR+h27tzJuYWkiCKg\nDohhkjsg2Y0DE4uKioyhLrsPipMn+fUGvgVxypR+46IEg9PppKampq7/5AaDQZQxBaHFpHvq0MbG\nxsjoxvjichG9/TbfqvR9fgUF/PhYP2zbts3p7toMyxaJxyR3QNKbB3ILCwsbepvlEQ2TiWjlSv8C\nyRjRz39OJOBKzd5SgHoSr4facglFTFwuF5lMJqqpqfFLTRrJqUPp8mU+CprvMwOIHn98wGe2detW\nz2DrsBsj6W5DKj2oGDDGsidNmnTykUceyXjqqadiuqf7FAUiYOdOYO1aoKPDe3zcOGDLFuD220W5\nrN1uR1tbG9rb2z1iCsYYEhISkJiYiMTERMTGxmKg1KsajQYTJkzo83UigsPhgMVigdVqhcVi8bte\nUlISkpOTxU+zGSodHcDrrwOvvQZYrd7jkycD774L/OhHfb6ViLB582bX22+/3VxRUTGHiLTiOywt\nQ0pMGGM5AHYAyABAAP6biP7EGPtPALcBsANQAXiQiEyMsTwAlwBUuD/iayJa6/6spQBeAXASwIac\nnJyv7rrrroJXXnklPikpKTw3pNUCjz8O7N/vf/zuu/lCnJsrugscx3VVeIvFAofDMeB7WltbMWrU\nqH7PiY2N7RKohISEHjmZIxqOA3bsADZsAAwG7/HoaODpp4GNG4HExD7f3tnZiRdffNG2e/dujU6n\nWwSgGcBhAPEA4gDsJaLnGGP3APgtgEIAJUR0BgACLbdE9IiAdx06UjeNgjEAYwFc5/49CUAlgKkA\nbgQQ5T7+GoDX3L/nATjfx2ftAhAFYBOA6QDi09PT/7Zy5UqrapBTt4OC44j+9jf/6FuetQpPP93n\n7ICUSL3ORFQOHOg53evZvHnmzIBvr66uptWrV1vcS+QTyVveRrh/xgA4AaAUvIgUADgIoNjn3IDL\nbW/nSGWSOxCS88AeAIu6HbsTwF8DeCh/B5AI4HUAk93HWHx8/Pqbb77ZJnr6jO4YjfzsTvdCnJbG\nbyKUV8CKy6lTREuX9vz+x47l91wFMKN0/PhxWrJkiXXEiBHPo4+9NgBGADgFYJrPsWDEpEe5jRST\n3IFBO85/4ToASd2O/18AK33OaQdwFsAhAKU+5y0G8C8A/9HLZy+aO3dux9tvvx3+VWFHjvAL27oX\n6vx8ou3bI0JUhpWYHD5MdNNNPb/vxESijRsHjJ7n4b333nP98Ic/7EQfYQTcrYlzAMwA/k+313oT\nk6DLrdQmuQODcprv4vwL3bLBA9gAYLfP33FwR60CUAygGkBygNeYOGXKFP0zzzwjaEzZgOA4ol27\niCZM6FnIx43jWypi5wnqhyEvJhxH9MUXRKWlPb9fxojWrCHS6wP6KIfDQRs2bLC5I6QNOPULINXd\nzVnoc6y7mAy63EppkjsQtMNALID/B+CX3Y7/DMBx9LMDs/tDC+BaKQqF4uCjjz5qlSRdhdXKbxD0\nDULs2/156SWixsawuzVkxaStjegvfyGaObPn9xkVRXTffUTffRfwxxmNRnrssces48aN+xpBhFoE\n8CKApyjAchlsuZXKJHcgKGcBBn425w/djt8M4HsA13Q7fg2AaPfv+QBqAIwK8ppRycnJ/3nvvffa\njhw5QpLQ2spHc8vI6FkJ4uKI7r2XHzgM016dIScmZ84QPfooUVJSz+8vNpbfgNlLBLT+OHHiBK1a\ntcqWmpq6BQMEf3aXw1Hu3xMBHIHPWJ9bLGaTgOVWCpPcgaCc5UfAOXff86zbbgFQBX78xHPsLff5\nywBccB87DWBJCNf+t1mzZrVt3LjRJtl2985O/j9rb90fgGjiRF506upEdWNIiElDA79itbfxJ8+Y\nyLp1RNXVQX2sxWKhTZs22d0JsgJK2wmgCMAZd7n9DsDT7uN3AtADsACoB/AFCVxuw2mSOzCUDEB6\nZmbm3rvvvtsqWSuFiMjh4KeTS0qo14oSHU20eDEvPCIIS8SKSUMD0datRIsW+ach8bVp04g2byYa\nRJT6EydO0PLly60KheJLAJkUAWUykkxyB4aiAbhj9uzZbc8//7x0rRQP587x8TS6BebxG1C84QZ+\n0FarFeSSESMmHMeHxfzTn/g0I74hM7t3BVet4mfKBrFk32Kx0EsvvWQvKSlpB7ACwyDEohgmuQND\n1QCkjx07VvpWioeODqIdO3jh6K1C+U4xP/QQ0QcfDJjWtC8kFRO9nuj994nuv59Prdnfvc6fz0eI\nD2GQ+sSJE7RixQprdna23BoZwIbUcvpIhDF2x+zZs3fcdttt8c8880zciBEjpHYJqKkB/vlPYPdu\n4OhRfnl4X0yZApSWArNmAdddB8yYASQn9/vxA+3NEQyjETh7Fjh9Gjhzhv+pUvV9PmPA/PnAPfcA\ny5YB2dmDvrTVasUbb7zh2Lt3r/3UqVMPAfg7yZWlX2QxEQDGWHpGRsb2xYsX37xy5cq4W2+9dcDN\ncmGjoQHYuxf45BPg8GHAYhn4PZMm8cIybRqQl8fvEcrL4zcixsUJKyZWK79HSaMB1GreVCrgxjYV\n0gAAB6FJREFU22/54wMxahTw4x8DixcDd9wBZGWF5A4RoaysDB988IHtyy+/PFJfX38/EV0J6UOv\nEmQxERDG2G1TpkzZesMNN6SvWbMmobS0VGqX/LHbgZMngYMHeSsvB2y2wN/PGJCVBc2aNZhw8iRf\nkVNTvTZyJN/B4Dj/DofTCbS0eK25mf9pNAJ1dcHdQ3w835JatIgXkOJifgOeAHzzzTfYvn279ejR\no6ZLly6tBfBPuTUSOLKYCAxjLJoxdv+MGTPenDdvXuLatWsTioqKpHard6xW4Jtv+O7DuXO8XbwI\nuFz9vk2zZg0m/M//iO9fXBzf7SouBmbP5n8WFfGCIiAVFRXYsmWL7dixY7YLFy487XQ6txORU9CL\nXAXIYiISjLGE2NjYJ2bOnLmptLQ0ev369fF5eXlSuzUwVisvKOfO8d0NrRbQ6fifBgNAJKyYREcD\nOTlAfj5vEybwPwsLgenTgdhYYa7TCzU1Ndi8ebPtyJEj3Pnz51+1WCx/IKJO0S44zJHFRGQYY6nJ\nycm/KSoq+uWNN94Y9cQTT8SNGTNGarcGh90O6PXQGAyY0NkJtLYCJpPXOjuBqCi+O+Rr0dF8lygt\nDRg92vtz9GhAoRBVMHqjubkZf/7znx1lZWXc+fPn3zKZTP9ORC1hdWIYIotJmGCMZaalpf1u5syZ\nq5YuXRqzYsWK6KwQBwulImyzOQJz5coV7Nq1i/v000+d586d+3tTU9NviKhWar+GC7KYhBnGWH56\nevrLBQUF98ybN4+WLl2asGDBgsiZ/QmAoSQmRITjx49jz549tvLyclZVVfXPpqaml4ioUmrfhhuy\nmEgEYyw1Ojr6galTpz43bdq00QsXLoxftWoVS0lJkdq1ARkKYtLe3o5du3bRgQMHrBcuXGivqKh4\nzel0vk9EzVL7NlyRxURiGN8kWTh+/Pjn8vLyFpSWlmLFihVxM2bMkNq1PolkMbl48SJ27dplP3r0\nKNRq9fHq6urfAzhARP2s3JMRAllMIgjGWFZCQsJjhYWF6+fOnRu3cOHChFtuuQWpqalSu+ZHpImJ\n2WzG/v37cejQIWt5eblTqVRuaW9v30JEeql9u5qQxSQCYYzFAlial5f364yMjJLi4mJnUVFRwi23\n3MIioRJHgphUV1fjiy++wLfffms5e/ZsTENDw1m1Wv0m+IVmdkmdu0qRxSTCYYyNBHCjQqFYmZmZ\neWtBQUHUjBkzYhcvXhxTUlIiSQoJKcSEiHD69GmUlZW5vvvuO3tFRQUaGxv319bWfgDgSyIyh9Uh\nmR7IYjKEYIxFA5iTnJy8LC8vb2V2dnZ6SUkJ5syZE1daWjpgLhuhCJeYtLW14dixYzh58qT91KlT\nVFNT06rVane1tbXtBp9LRl6lGkHIYjKEYYxNjI6Ovn3ixIlrkpKSpubm5nITJkyg8ePHJxQVFbHi\n4mJRBEYMMWlra8OZM2dw/vx56HQ6i1arZWq1Orqjo6NSpVLtcLlc++Tp3MhGFpNhgrvVMgXA7LS0\ntPkKhWJhXFzchNzcXC43N5fy8/MTpk+fzoqLizF69OiQrhWqmJhMJpw9exbff/89lEqlRafTMa1W\nG+1wOLS1tbWHW1pajoEPV1ghtz6GDrKYDGN8BSY1NbU0Ozt7QWxsbH5SUhLLzMx0pKWlUVJSElJT\nU+PS0tJirrnmGigUCigUCmRlZSE5ObnXxXR9iYnZbIbBYEBdXR0MBgOMRiOam5udra2t9o6ODhiN\nRtbQ0BBjNpvhdDq1BoPhcEtLy3HwaUtk4RjiyGJyleFe15ICIAuAwm1Zqampeenp6ZMTExPHxcTE\njAGQmpCQEBUXF8fFxMRQdHQ0YmJiuJiYGEyaNCmqoqKCczqdzOVyMafTyex2e5TNZuOIqI3juMaO\njo4ao9GoNJlMGgAGAHVuMwAwyVv7hx+ymMj0CWNsBPiEUDHg8xXFuI0BcABwus3htk5ZJK5eZDGR\nkZERhPAvUpCRkRmWyGIiIyMjCLKYyMjICIIsJjIyMoIgi4mMjIwgyGIiAwBgjOUwxg4yxr5njF1g\njP3CffwjxthZt2kYY2d93vMcY6yKMVbBGLvJ5/hSxti3jLF3pLgXGWmIkdoBmYjBAeBJIjrHGEsC\ncJoxVkZEKzwnMMZeB9Dq/n0a+Ly70wBkA/iKMTbZvc5kFYBZAH7LGJtORN+H+2Zkwo/cMpEBABBR\nPRGdc//eDuAS+FWyALpWzi4H8KH70L8B+JCIHESkBaAEMNf9WhSAeAAjAMixRa4SZDGR6QFjLA98\ny+Ibn8M3ALhCRJ5kv1kAanxerwHfQgGA/wZwFICLiKpEdVYmYpC7OTJ+uLs4HwNY726heLgPwN8G\neDsBABF9BeAH4ngoE6nIYiLThTtc5G4AfyWiPT7HYwDcCaDY5/RaADk+f49zH5O5SpG7OTIAusZE\ntgG4SER/7PbyYgCXiMjgc2wfgHsZY3GMsQkAJgM4GR5vZSIRuWUi42E+gNUAvvOZ/n2OiPaDn7X5\n0PdkIrrIGPs7gIvgdw6vlXcMX93Iu4ZlZGQEQe7myMjICIIsJjIyMoIgi4mMjIwgyGIiIyMjCLKY\nyMjICIIsJjIyMoIgi4mMjIwg/H92bEfNtteOcAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xe178be0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
-    "r = np.arange(0, 3.0, 0.01)\n",
-    "theta = 2 * np.pi * r\n",
-    "\n",
-    "ax = plt.subplot(111, polar=True)\n",
-    "ax.plot(theta, r, color='r', linewidth=3)\n",
-    "ax.set_rmax(2.0)\n",
-    "ax.grid(True)\n",
-    "\n",
-    "ax.set_title(\"A line plot on a polar axis\", va='bottom')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 标注"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`legend` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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OpX79+im2GzBgAH/8YcCRWcnIkmfmQJp3fObNm5cdO3bg6upK2bJl6dixY6rt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\nJDXAMpekBljmktQAy1ySGvAJpbO10KE3ZZIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x106be390>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Make some fake data.\n",
-    "a = b = np.arange(0,3, .02)\n",
-    "c = np.exp(a)\n",
-    "d = c[::-1]\n",
-    "\n",
-    "# Create plots with pre-defined labels.\n",
-    "plt.plot(a, c, 'k--', label='Model length')\n",
-    "plt.plot(a, d, 'k:', label='Data length')\n",
-    "plt.plot(a, c+d, 'k', label='Total message length')\n",
-    "\n",
-    "legend = plt.legend(loc='upper center', shadow=True, fontsize='x-large')\n",
-    "\n",
-    "# Put a nicer background color on the legend.\n",
-    "legend.get_frame().set_facecolor('#00FFCC')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 数学公式"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 $W^{3\\beta}_{\\delta_1 \\rho_1 \\sigma_2} = U^{3\\beta}_{\\delta_1 \\rho_1} + \\frac{1}{8 \\pi 2} \\int^{\\alpha_2}_{\\alpha_2} d \\alpha^\\prime_2 \\left[\\frac{ U^{2\\beta}_{\\delta_1 \\rho_1} - \\alpha^\\prime_2U^{1\\beta}_{\\rho_1 \\sigma_2} }{U^{0\\beta}_{\\rho_1 \\sigma_2}}\\right]$\n",
-      "1 $\\alpha_i > \\beta_i,\\ \\alpha_{i+1}^j = {\\rm sin}(2\\pi f_j t_i) e^{-5 t_i/\\tau},\\ \\ldots$\n",
-      "2 $\\frac{3}{4},\\ \\binom{3}{4},\\ \\stackrel{3}{4},\\ \\left(\\frac{5 - \\frac{1}{x}}{4}\\right),\\ \\ldots$\n",
-      "3 $\\sqrt{2},\\ \\sqrt[3]{x},\\ \\ldots$\n",
-      "4 $\\mathrm{Roman}\\ , \\ \\mathit{Italic}\\ , \\ \\mathtt{Typewriter} \\ \\mathrm{or}\\ \\mathcal{CALLIGRAPHY}$\n",
-      "5 $\\acute a,\\ \\bar a,\\ \\breve a,\\ \\dot a,\\ \\ddot a, \\ \\grave a, \\ \\hat a,\\ \\tilde a,\\ \\vec a,\\ \\widehat{xyz},\\ \\widetilde{xyz},\\ \\ldots$\n",
-      "6 $\\alpha,\\ \\beta,\\ \\chi,\\ \\delta,\\ \\lambda,\\ \\mu,\\ \\Delta,\\ \\Gamma,\\ \\Omega,\\ \\Phi,\\ \\Pi,\\ \\Upsilon,\\ \\nabla,\\ \\aleph,\\ \\beth,\\ \\daleth,\\ \\gimel,\\ \\ldots$\n",
-      "7 $\\coprod,\\ \\int,\\ \\oint,\\ \\prod,\\ \\sum,\\ \\log,\\ \\sin,\\ \\approx,\\ \\oplus,\\ \\star,\\ \\varpropto,\\ \\infty,\\ \\partial,\\ \\Re,\\ \\leftrightsquigarrow, \\ \\ldots$\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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RLbb2aTRqJUe+Ieaew63moa+vomjfB9TkJVBbkEj0sj9wdvfrVrm7ytDLVnY5\nD49BEuxVTtBtHF9ppK4klcI9bzPyxvVW0zqkzmxwP8b929ADgrROjamX6R4ci9q5Za+yO3Fy8wHA\npYnhN2hrqUzbysCJN6FD2ZIp2LQ0Q+3i3SIPg7YWVGr8Ri2ykF/fUEPpsdVEXPA8oGxukPTJRcQ+\neKrFs2ULvX2fbCHnj6dxD4mloTybkFbWgTVteAm6l37SDGo0XCoirGyQ22jUgqfd3+YPq74kBbfA\naCKv+tQxXs49RMC4xXiEjCfn939gbGIQyk6uJ2vdA0RevarV2Xl/VZ11lcbxNK+Iye3EtD9uA6Nw\nDxlHfUkaoEysylhzJ0Z9A67+wzHUnkbjHYKr31B8RpxPZdpWi/RVWfFkrn8Qj+BYtNWFGI1GSg5/\nCyi9wPxt/6GuJBUA35HnY9DVmrcD62+Un9qIytkV/7FXUFd0grri1g8WFfQM/aanpvEOI3Tmo7j6\nDW1xzdkjALfA0YTMeLhlwia4BY4m7cdluAeNsZpPf0WtcSN6+RZyfv8Hx9+fhrPbAFCp8Bk+j+FL\nvmtzunlf1ll9aQblJ9ehctKgrSxgwKgF5q292qOrLraafNMkkUGT2onZPYxY/B2Z6+6noTwLo0FP\n6Jwn8Y6czekDn6J12cKQq5SlDMMXf0PWuvtJ+3G5qcFixD1kPEMWvUN19j4yfr6LjDV3MPDspQB4\nhI5j9G27zOs/G8qzAXAbOLLnK9kDVGXuwStCuYceIbFUpW83byPXUJ5DQ3kmRr2W2sRNMKx7T3wQ\nKPQbo9a4+7s11M4uxN53tN08GsrScQuIIumTi4i5+yBqje3rsor2fYjRaEBbno1bYHSL2WLWyNv+\nMsUHP6e+JBUw4uIbgUfY2Qy/5kuby7UXzm6+DDHtJ9cROquzzujL3hTufc/sJgNI/fZ6Iq/+vN10\nXXWx6WrL0FbmAio8e6GnBoqRibpxrWVYwHAGnn0jaWlpOLsPAJTnYtgVH1vLAq+IScTcta9leJON\nkPO2Pk/I9AcdZscUe6OrLkStUZYGqV080VYqp4UY9TrqS1LwHjaTuuJkDHWlvSnmX4p+437sKsUJ\nX1KS+L3yUjMaOH2g/fObGinY/Ta6ujKCJt1KyMyHyVzb/v5qmesfQqV2Zuw9hxj34CnUGk/G3nOk\nVwxaZ+mszjqjL1tRu/pYLBxui9LEHyxOSFbZuIFvV11stYVKA8vZ3R/3wGib0vRFiuSP0PiE9+tt\nmYxGA5iAldcfAAAgAElEQVRmkDb9XJ27H6/BU8nb9hLZGx7FPab1RrfAvvSbnlpXqC9JozD+PUbf\nth0A3+iLaSjLMF83GgxkbXiYwRe/0iKtvr6S/O0vEftgMgC6mhJ0taUYdPWo1Bqr6WryE6nJ2W8+\nIFOZDm1EV1uKxisQo9FI4Z53lMF4IHRm227T3qAtndlbXx2h6ebU7RE05U6OvnM2wVPvw8nFi+Bz\n/m6WP+Pnu6jK2IG2ughndz/0dWU4uQ0w96S74mKrLVCMmlcbSyj6OmUn1oFKTcSFL2DQ1aOtzO8T\nm0wD5O94jcL4d1G7eOI35jJCZz6KWuNGYfz75G75F95DZhA46TZ8Imeh8Qwy7d8J+rpyNJ5B5nxU\nTs6EznyYgt1vU5q/D8b07NKNvyrCqAGlx9cwYPRC83ddbSm+UYoLRV9XQZH8IZWpW6ymLU/agPfQ\nmeZxp/KkDXgPm4VRV0+h/I7VdOWnNuA7ar75e23RSZw9Asz76JWfXIffmEtx8R1E8pdXUp1zAM/w\nnt91oi1a01l36Ku7CBi/hOocmdLE7zDo6sxjYqcPfELw1HsYvPBNiuL/S9A5fyd/238InfWYOW1X\nXGyNRs17aP88jbgidSvaqgIGRM9HW5lPVeZuNN6hfcaohUy/n6K97xM2+0n8Y88sTg+afBvaylyL\n7fW8Bp9DbcFRfEfMoyb3AKGzngBAX3vG3ViVsRNN7P09V4G/OML9iLI4u3FfQ31dBTV5CfjFXA4o\n059Dpj+Ak6uP1bQVKX+aN6M16Oop2vseg+e/3mY6jWegxT6KuX8+y+CFb5m/1xWnUHzoKwBcA4bT\nUJ5ln4rakdZ01h366g709VXKNlGXfcjY+44RKC3n1OeXUF+WSaB0M+5Bo6lI2oBH2NnUFR63Oq0d\nOudia1zs3h97anUlqZz6fCHpP91CwguhJLwQRvKqK5WNkvsI9aXp1JUk49PsEFt9Qw1qV8vnwGfE\neTSUZ1Fy5DvcBkbjHjSKuuJkjAYdFalbKD70NUGT70Tt4ZgbP/dHRE8NCBi/mNw/n6Vo//+oLUhk\neBtT2JtTmbaFwLjlFCd8SXXuQYZe8XGrh2yay5twHbl/Psvp/Z9QX5pG4MSbLc5ICpp8B0Z9A6Cs\nZwqZ5nitvM7qrDP66g4qkn/Ha+hM88SW8HlPYzRoqc6KNy/cLk74ksirv6A86Veg5dq3zrrYavIP\no9Z44tkLez52N27+kUx8qqK3xegS5ac24jloknmyTCOVaVtbHMmjUqvNwwiN6KqLGDBqgUXY6bS0\n7hFW0AJh1ACV2qnDO/aDcrCmUVdPyPQHAGyewddeeWpnF3B2oTJtOz6RcxxyC6LO6Kyz+uoOXANG\nUHb8Z8tAoxHPCMWtWFtwDIOuDpWTMwZtLbUFiRZRO+tiqy/NQF9Xhk/k3D55esFfgYpTv+E78oIW\n4TU5MqFznmw3vUFX3x1iCWxEGLUuUJm6Ge/I2d2St66unMq0LYTN/Ue35N8bdKe+OopHSCy+UReR\nuf4hXHwHYdTV4zNiHq4DIgAoPrQK//GLAXAPHkvh3vfMaRtdbIaGqiY5qjj7n+Xtlludsx9o+9w6\nQe9hNBioSN1MyIyHrFzT23Rau0/kbPsLJrAZYdS6QG1BIr7R89uP2AmKD35B6KzHMei1VKVta/MI\n975Cd+qrM/iPuxr/cVdbvdb0MFT34DGMuuVP8/euuNiqs/cC4DO879/P/oiuuhB9fUWL44AqUv60\ny2nggu5HTBRpB4O2jvydr1NbdJz8na9j0NaZrw264Dn8x17R4XTtUZywipyNT5DwfCgJzwWj8Q7t\ncj16it7QV1+iOmsPTq4+vbaTiKBtnD0DcfEOQ1ddZA6rO51EVVY8PsNF77ovIHpq7aDWuBEy7T5C\nTJu7dnc6OHPqcl+kN/TVVzBoa6nKimfAqAWo1KI96Yio1E6MuG412RtX4OqvbHfl6h9J2OzHe1ky\nga0IoyYQ9BAVKX9i1NczYPQlvS2KoA08w88m8qr2d8cxGgyc3v8xbgOjqEzbRthc65NICuPfpzrt\nAIe+24Bv1EUEjF/S6aN4BO0jmosCQTeR8cu9JL45HqNeByiTT5zc/CxOmxb0XQp2vYFnuIT3sJmm\nZR9QevxntFWF5jjlSb+hcnLGc/JjBE+7n8C45aicNFSkbqFwz7u9JXq/Rhg1gaCbqEj5A4O2BqNR\nT31ZFqVHfyBk2v0d2ihb4LhUpm/HI2wCdaeTcPWPRFtVRPGBTy3O8ytP+hX/sVcBUFd0ErfA0VRn\n78Mncja1hcdayVnQFYT7USDoJryHTMPZKxhdTQnpP9yE28AoQmY92ttiCexAQ0UehoYqKpI3UZN/\niMEL38TZ3Q/3EMuNBPxirqA8aQP1xaUMmrgUJxcPgqfeg76+EmePgb0kfVs45PHcHUIYNYGgmxh0\nwfOkfLOEIztexTtyDlFLN7S664rRaKRw99sYDTrUGg9q8hIYPP811Bo36ksz+sy+iV3FaMOp5Y5A\nRcofBE25C58R57a53MZ72AwAytPS8Io4c/J16dEfCW7llOzepW/ovy2EURMIuglnjwCib/rNpriZ\nP9+Ni98QQmc+AkDWhkfJ3/kqXoOnovEM6hMnN3QVlbMLpz6bT2NvwS1gBDF/P9i7QrVCTe4BwuY+\nZRGmrSqirugkFWlbCBh3Tatp87e/Qn1pGrUFiQ5xLE/im+NNZzoCGPEa0rf3JBVGTSDoZapz9lOS\n+B3jH8sxh7kFjKTs5DrUGk9Cpt1L2Ym1Dn9yQ1extQHgCAye/1qLMI1XIMOvXdVu2pAZD3aHSJ1m\n7D2HelsEuyImiggEvUxF6ma8I+dYuCZVamdqcg/gN0aZ/t8XTm4QCBwB0VMTCHoZF+8w6jQeFmF1\nxcm4BY7G1W8oRoO+T5zcIBA4AsKoCQS9jP/4xdQWHqVo3weoNR4YdPUExt1CTe4B8ne8hv+4a3Hx\nCXX4kxsEAkdAGDWBoJdRqVQMOv/fLcKjlq63+N4fT24QCOyNGFMTCPoITU9uqEje1NviCAQOSbs9\nteOZ2T0hx1+GqvIK6oRO7cpfQafaUz9Rv30Fmb//E4x6PC75EXU31fmvoM+eRui052jXqLm5uPSE\nHH8Z6pychE7tzF9Bp24x1+Ad0/raJ3vyV9BnTyN02nMI96NAIBAI+g3CqAkEAoGg3yCMmkAgEAj6\nDcKoCQQCgaDfIIyaQCAQCPoNwqgJBAKBoN8gjJpAIBAI+g3CqAkEAoGg3+CwRm33ju3cc9tyllx2\nCbdct4RnnnjcplNxn3rsUa5eOJ/Kyspukasj+f/47TesW7O6W+Roj907dnD1wvl8t+rLXim/u/n2\nyy+4euF84nftbDfu9i2b+W7Vl1RXV/eAZAKBoDdxyA2NK8rLefPllwgfFMGtd/+dyooKDsoyRqMR\nlUrVfgYqFXTDsfB6vZ6rliyhvKwMd3f3duP/+O03+Pj6Mv+SS+0ui83Yoq8eRKvVotFo2o/YBjqd\njnOmzyA8IoIRUdFgNLQZf8fWLRyUZWbPm4enp2eXyhYIBI6NQxq1gvx89Ho9AwMDiZtyDp6eniy8\n7HLz9asXzmfQ4MG8+s5/2b1jB6/953muWvI3rlq8RIlgNLJ2zU9sWLuWgIEDufehRxgybBh7d+/i\n848/pvh0EV7ePkyfNYsblt1CaUkJn3ywkiOHEtDr9Vxw8XyW3LiUpx57lONHE7lwwUJ2bN3CHffc\ny9rVqzl+NJGPVn1NZloaz6x4nLPjJlFVVUl2RiZzzjuPG29ZzlOPPUpDfT2nCwu5euF8Zp17Lpde\neTVvvPgCOTk5aDQaBg8ZwrP/aXmc+yvPP8eRQwk0NDQQHBLC4utvYNI5UyksKODuW24matQo3D08\nOHnsGBMnTebehx8BlB7JZx99iEaj4ey4uFb1m5OdzduvvkxmRoaFHN9++QXff/0VDz7+BJOnTuOB\nO+8gOyuTb39Zx9HDhy3rmpnJnHlKXQH2743nq88+Iz8/j5DQMG5YtoxxE84yp5swUaKivBwwcs9D\nD/PWKy3L12q1fPvlF+zYuoXKigpGREXz9PMvmOWafe48jiUeQZo8BQ8PD7Os0SNHcvXC+YSEhTFi\nZBT79+0levQY7n3oYdatWc1BWQbgrmU3MzAoiNfefY+3XnnZfL9Dw8K456FHiBg8mDtvXkplZSWf\nf/eDnZ5mgUDQkzik+3HQ4MF4+/hwQN7HTYuv4bH772XTRsuj3tvrseVkZXPFNdeSm53NO68rR69/\n++WXaLUN3Pb3e1h0+eW4ubkB8MbLL7J7x3ZmzpnLDTcvw8fX1yKvjLQ0rr/pZsLCBzUWbnH9+NFE\nZsyazZBhw1i3ZjVyfDxXLVmCs0aDj68v9z3yKOdfNJ/f1q0l3ZTX325cSmBQsFXZR0RFmeMYjUbe\nfvUVtFqt+XpyUhIxseMIGxTBzm1bOXHsKGWlpfz3zTdwcnLiimuu5VhiYqu6+W3dWlKSk9uVo826\nDj1T19ycbF5+7t+4uLpy5bWLcXZ25qV//4vS0hJzusRDCUyZNo0Fl17GhrXWy1/9/Xes+eF7Bg8d\nyrLb72BoZKRF2YcPJXDplVczcdIkq6Lm5+URFBzM1BkzSdgv8/3XX3HO9BkMjRwOwM233c6y2+8g\nYf9+9u7exYzZc1h22+3ExI7DoNeb6+tYfVuBQNARHLKn5u7uzr9efJlffvqRhIMHSE1O5v233sQ/\nIICzJko25XH9zcsIDglh3549nDxxnNqaGkLDwzko7yNh/34ih4/g3AsupLa2lmNHjjB85EiWLr/V\nal7Lbr+DwUOHtlpW3OQpXDB/AWGDBvF/T67gWOIRblh2C2q1GldXN6bOmAlAyqkkAA7s28fwqCgu\nXrSoRV56vZ6sjAx2bd+GTqdTAlUqigoLcHZW3HYjoqO59MqrMBqNpCafoqigkMqKCnRaLXPOO59z\nL7gQlUrNe2+9YVXesPBwMBrblMPWuh49cpjTRYXo9XqSk06SnHTSLPOpEyfw9PQC4Oy4SVx65VUA\nVFVWWi1//954VCoV9z/6uLnB0ZRLLr+SeRdeCMBxK0Y7ICCAa6+/AZ1Ox6aNv3Es8Qg33rIcP38/\n0lNh4qTJBAYFkZGWhkqlIunEcTQaDWPHjWfIsGEAvPPhxzbrQiAQOB4OadT0ej0hYWHcevffAczu\np+yMTMWoqVToTS3rmuoqq3k0TioxYhpbU6m456GH2btrFynJp1jz4/es+3k1r7+3sm1hVCr8AgLa\njGIwjek0n8jSvDd54YKF+Hp7k5mdzb49e/jxm6957b/vnekBAocTDrJt85+Mm3AWCy+/nPU/r+Gg\nLKNt0JqNmpeXNwBOTk5K+QaDuUdlNDTK0vo404ULFhIeEcGxxEQLOdRqpePeqNtqK7ptXleVSmWu\n5yVXXMm4s86iUeXhERHk5eQA4N9Eh62VD0rS1iYE+Qf4t1qnprS4D+a+lxI+ZNgwXn77XQ7s28ux\nxCOsW7Oa2/5+D+eef4G5IdHVcT+BQNA7OKRRy8xI542XXmT6zFkMDApSXGkqlbm3FBQcTGF+Ptu3\nbObXtWut5vHF/z5iZPQoTp04wbDISNzd3fnkg5UEBQczZOgwfHx9yc/LQ6PREBM7jqNHDvPJBysZ\nFDGY2tqaM2N41l6wzcLk+Hh+W7eWndu3ARATGwuAl5cX5eVlbN20ieFRIzl25AiFeXlEDB1GSGgI\nmRnplJeVWxi1xhdwXV0tudk5nDx23CadRUWPQuPiwuY/ficgMJBff/651bgb16+jsrKSkJBQCzmC\nQkIAZeZkQX4+pSUlLdyP1uoaFj4IZ2dn4nftJCQ0lKqqKnbv2MGDjz/RofKlSZNJTU7mtf+8wJSp\n08jMSG+192yN4tOnWfXZp8rYndFITOw4ADy9lN7ilk2bGDM2FrVazb49u4kYMoShkZEclGXKShRX\n6b23LRdjagJBH8YhjZqfnz+DIiLY+OuvVFVW4DtgAFcvXsL4s88G4LqlN/HBu+/wzZdfMDZ2HJkZ\n6ea0KpUKVCrCIyL45aefGDR4MHfedz+g9GhWf/8dlRUV+AcMZNntd+Dk5MQ9Dz/MJytXsm3zn2i1\nWi5asNAir6ZYC4sdP4EdW7eSlZnBgksvY+KkyYDSc/n6i895543XuPa66/Hz92fXzh2Urf0Fdw8P\nLpy/gFFjxljkNe6ss5g6cybynj2o1WomTJzIrh3bW1eWSRbfAQO44557+eyjD/n5hx+IiY0lOzvL\napJG41dy+rRZjtExMUSOGMG2P//kcMJBnJ2dCQoJobCgwKa6PvTEk3z9xWf8b+X7eHh6MSZ2rNmY\n2Fr+yOhoGhoa2L5lM4mHDzEyKrpFPdu6D2Hhgyg5fRo5Pp4JEyWuuHYxAOdffDHHjyby3aoviZ0w\ngb/deBOJhw+x8df1qFUqJpw9kfMuuthcjhhTEwj6Lqq21n4tXbrU+NQLL/agOH2Lxpl9Fy5YyM23\n3W5TmrKiQgYEBnWzZPanM3XtKcqKCrn15puIGDKEV95+t7fF6fP01WfUkRE6tT+RocEYjcYWbVCH\nnP0oEAgEAkFncEj3Y18hZtw4vv1lXW+L0SM4el0dWTaBQNBziJ6aQCAQCPoNwqgJBAKBoN8gjJpA\nIBAI+g3CqAkEAoGg3yCMmkAgEAj6DcKoCQQCgaDfIIyaQNBHSU1O7m0RBAKHQxg1B+KXH39Ejo/v\nbTEEPcTH779HdXU1menpbFzf8XV2hw4e6AapBIK+jTBqDsTCyy9Hmjy5t8UQ9BDHjyby9+XL+PG7\nb5gxZy6gnJDwwjNPt5u2rLSUAX5+3SyhQND3EDuKCAS9xPxLLmX2ufMswk6dPEFIWFi7aeX4eM6Z\nMaO7RBMI+izCqDkAxadPk3Iqifhdu7jzvvvN56T1J5KTkggMDsa32anijoxWq+XwwQPmkwjsTVlp\nKQf3y6SnpDB52jQK8wtYu/onBg8dSuLhQ4wdN77VtFVVlXh6egJQUV7OJx+upKy0lLraOnx8fIid\nMIH5l1zaLXILBI7MX96o1dfV8efvGwEVOp0WvV5vPqG5pyjMz2fSOVNZ84PjnuH14r/+Dycnp1bP\nSGuL5KQk0lNTGREVZQ4rLytj/S9rMOgNpKelEhU9isuvubZLBl2r1fLBO2/j4+uLs7Mz115/Q6fz\nAuWg0KqqKnZt32Y+vdyeND5no0aP4f/+8STPvfIqv6z+kSsXL8HDw6PVdAX5+QSbzr4DyM7K5O8P\nPIS8N57BQ4YQHBJqd1kFgr5CnzdqlZWVeHt7dyqtTqdj7ZrVLLjkUlzd3AB4/IH7mDhpMhGDB9tT\nzDYZPXYsFeXluLu7O2wvbXRMTIuTvG2hoaGBn777lodXPGkOMxqNfPX5Z9x06224urrS0NDAo/fd\nQ2VlZZeOtVnz/Xc4Ozvj6elJZkZGm3H1ej0P3HUHT/37eYtTuZsza+65vPnyS4yMHkVgkG1HhxgM\nBn795WcM+panjweHhjDpnKnE79rF8aOJLF1+Ky6urqSnpaLT6aivq7MwaHt372L4yCgCBg40hx3Y\nt495F15o/j5mrHIo7bEjR4ibPMUmGQWC/kqbRs3Jycli0FqKi0OKi+tumTpEwsGD7N65g4lxcUhx\nk1CrbZ/78ucffzBt+nRqKyuoraxAr9dTkJeHtraGsqLCbpG3rqbaat7yvr0MHx7ZbeV2lRnTpwN0\nWL5f161jwoQJFukK8vM5duQwJxMPM3jwEAAmTZrEz6tXs2jRIpydO9bWatTpnp07mXPuXKZNn9Gu\nrGmpqVSWl6M26Nut04wZM/jqk4+54aabbZZp2tSprV4rKyrEoG1g/LhxlBUVkp+Xx4gRIzgk72VQ\n+CAKsrNIST7FmJixRI0YAUaDhYwVpcVUl5Va5GkwGCjIzbHL89PaMyroPEKnXUfetw95375247X5\n9tDr9Tz51NP2kqlbmH3+Bcycdx67tm/jow8/ZNxZZzH3vPPRaDRtpquuqsI/KIiQiDM9sj82bODs\nSZOJjB7VbfK2dlhgalo6c8873yEPEqwoLycvNwetVtvmOI819u3by0tvvm3RA9UajVRVVVFdW2eu\n74CAgej1elw8PPHp4LhbWVEhngP8yMnOYtzEOJt0mLF1K7ETzrIp7lmBQXzyv49x9/Yx9+i7yjmz\ngtj02wZKDyZQWJDPQyv+QXV1NS6HDnPy1CmmzZxFWkoKCftlrly85Izc6WlExYxtIXd2ViZDho+w\ny/MjDrS0P0KnXWfexfOZd/F88/eV7/3XarxedT/m5+WxYe0vBAYFkZebS/igCC5auBBQxlx8Bwyw\nKR+1Ws30WbOZPms2B+V9vPXKSwyLHM758xeYB9ObI8fHM3HSZEpLS3jn1VcJDg0lLSWZp557oVvr\nVVFRYfFwH0lIYFRMDBlpaQwfObLLZXeF+ro61vzwPaHh4ej1eo4lJhITG0tYeDg/fPMN2VmZvPvR\n/wA4uF/mmy++wNXVlUVXXEFxUREV5eUUFhRwx733oVKpyMnKwsvbu4VLNTAomI9XfW0RdirpJBFD\nhpgN2r74PXz033cpKy3FaDSiUqnM/z08Pfnv/z7F1dUVgG1bt5CUlISTszObftuAf0AAiy6/wmod\n9+7exbHERHZs3crwkSP45IOVnHfRxYQPGtSmbkaMjCLx8KEWk0ZysrPZtnkTAQEDqayowGfAAM67\n8CKb9H3uBRdafPfx9bVwvw4MDKS6utoizuGDB7l40SUt8hoUMZhr/nadTeUKBP2ZXjNq6ampvPqf\n53n6uRfMYxp3L1/G4KFDCQ0L43DCwRbTnW3hLCmOs6Q4kk6c4KP33sU/YCDzF12Cn7+/RbzKygrz\nWNyT//cvAD798AM2/PIzl3Rhokh79Uo8fJjBw0cAUFtby0/ffcvEjEksXb68U2NW1tDr9Xzw7jvo\n9bp2406bMYsJEycCsGXTH3h6eTFj9hwA/Pz9KS0pYWT0KBZdfjnvvvG6Od1ZEyXUajVvvfIyLi4u\nnG9qQT1w5x0kHjpE7IQJpKWmEBbetrEAKMjPI37XLv5hug/pqakcT0zkjfdWsnfPbiJHjMTHx4ft\nWzZbfaHPnDUbDy8fKsrLWbr81jbLmnTOVM6S4vjjtw3ceMtym+QDCAkNJT011cKopSSf4tMPP+CJ\np5/Fzc3N3JixF+VlZQwZNozS0hL8/PwxGo0YDAaHHXcVCByBXjFqRqORN15+kYsWLLQYpI8cPpy9\nu3cRHBLC7HnndamMqFGjGBEVxbervuTZJ5/g5bfesXgZWDMg9XV1HD92jJavTduwpV5nmQwIgLu7\nO//893OdLK11nJycuP3v93Q4ne+AAax8522qqqoYHRND9Ogx1Jh6Cipa6svZyRm9Xk/s+AnmMB9f\nX0pKigHlpdxaT7kRrVbLO6+/xu1338OoMTEAuLq5ccOyWwA4efwYM2bPYV/8HoKCQ1rNJzUlmWGm\nxkJ7nDx+DA8PD5sNGoCXtw852VkWYe++/hrRo8ewfctm6mpq8fUbwPU3L7M5z/aorKxA29CAp6cX\noNSxUUcCgcA6vbKjSNKJ4+RkZTFt5iyLcA8PT7IyMlCr1RYzwF55/jnKy8ttzr+hoYGN69fx6gvP\nMSgiglff+a+FQcvLzbW6wDUtNcVilllHy7WlXu7u7jbn19NMmTadZbffwcnjx3j1hed54M7bqa2p\naTONu7Wp50bln06rxVnTdrvpf++/x8JLL2PGnDnmsFDTvakoL6eurh6AzPR0XFxdWs0nPTWVyFaM\nWvPtxw4fPMiY2FircVu75xqNBqPRaP6em5NNVkYG1/ztOs678CIWXn45M+fMtWsvauy48Zx30cW4\nuCj1Hj5iJNGjR9stf4GgP9IrPbXThUUM8POzOiGgrq6WC+YvsAizdW1UdXU1G9evIy0lmTnzzuOh\nJ560Gu/o4cMMGTbMIiwvJ4eU5GRuuePODpfbiC31Kj9dZBH+yvPPccudd3VqUfLvv64nPCLCPKW7\nEZ1Ox4f/fbfD7setmzYxZfp0ps2chVar5avPP+Orzz/joSdWdFg2AJ8BAygsbH3G1+rvvyNuyhTO\nkpQZtTu3beXsuElmw7/5942MiRkLKAuVK1ppYBgMBjIz0hk2fLjV6wsvv9zi++GEBM694AIAjh89\nyvCRI82Go7V7XllZwYAmY7zVVUoPtvm4b/M1ZAKBoGfpFaMWOWIEOp0OvV5vbtkW5OeTn59HYFAw\nKpWK8vJym1/0pSUlrPt5DaXFxVwwfwGXXXV1m/Fra2tIOnGckdHRgPJS/PTDD7h68RKGj4xqNd2+\n+D3I8Xu47e57rC4dsKVelc0mirRlODPT0xk8dGiL8IaGBjb9toEtm/7gxluWt7ju7OzcKfdjYUE+\nO7Zu4dzzL0Cj0TBt5kw2bdjQoTyMRiNGU1ctKDik1Q2aN//xOyXFxQwdFsnB/TIAB/fvN/dydTod\nv65by79fegVQJgOdOHaU6bNmt8grLzcXg8HAIBvXFmZnZjAyKhqtVsuJo4mMjmnfpVdeVkbkiDM9\nwSHDhuHt40Nebq65Z5mSfIrEhIQujcl2B+09t43U1NTwyQcrmTVzpsUzuv7nNTg5OVk0NtNTU/l1\n7c8sXX6bQ3sfBH89esWohYaHs3T5rXz+8UeEDxqE0WjEx9eXu+67n7dffZUfvvmaqTNm0lBfR3LS\nKU4lneSGVsYqNv/xO0knTjD/kksYFGHbS83V1ZVhw0fw848/oNFoyM/LY8acOeYXalFhgdVyc7Ky\niN+1i0WXXUF4RESn6hVrwwu0kfhdO60aNRcXFy5auIjUlGSaeMS6jMbFhdNFRfxm2jE+PzeXa667\nnn17drPmxx8oLS7mfyvf56ZbbyNh/35+/O4bSouLee/NN7jmuuv57qtVpKUkU1dXi7OzM1OmTee9\nN99oUU5OVhYr334LvV5vMbFiVBPdnDpxgvBBg8zu4KjoUWzfusWq3OlpaURFj2rxwm5t+7ELFyxk\n51x3OBIAACAASURBVLateHl7c+ECZVZqa/e8kaQTxy12mnFxceGBx57gh2++Jio6Gp1Oh39AgMMZ\nNGj/uW2ksqKchP37iY4aSdOnNPHwYdRqtYVRy83J5tCBA1RVVgijJnAoVMY23opLly41PvXCiz0o\njiVHjxzBxcWFrX9usnALdoWc7GyKTxcxbsJZnSo3LSUFtZOaIUOHtZK6bRrXq7T3EgX4btWXXLXk\nb63m9c7rrzL73POIaWV8yBF4/aX/cOkVVzE0MtLueVdXV+Pp6cnbr7xExNBhXHLFlRbXjycmMnrs\nWFY89CDP/ufFNse72rrnFeXlvPz8v3m2F38LXaUjz61YU2V/hE7tT2RoMEajscUMNoc+eiYmNpbN\nf/zOnHkdn9rfGieOHSV6VNuD7W2Vm3TyBBGmXTC6QmFBIQMDA2mor+9yXo7MNUuu49e1P9s939qa\nGu646UYOyvtIS0ll1txzW8TpyPZjbd3zdT+v5polfXsNmL2eW4HA0XHovR91Oh2Z6Wn4DvAjPy+P\nkNCub9RaX1fX7q4QrZWbkZ7GwMDADm3F1RoxsbGsfOdtzj3/fHNYQX4+cvwe8/ekkydYt2Y1oCxB\nOP/i+S22kLLT0rZuIzQ8nIGBQSSdOEHUKPvt1OLi6sr4s87m+LFjXHjxxa2eLXb0yGFixo1rN7/W\n7nl+Xh4V5RU25eGo2PO5FQgcHYc2agaDgdDwcJJOHLfbLunWFu/aWu6QocM67XZsjrWXaHBIiMVx\nITXV1e0eH2LPMbXu4qrFS/hu1ZcEh4TYvEtMezQ9MaCtPfWOHDrE3PPOb/V6I9buuVarZeP6ddx0\n6212kbm3sOdzKxA4Og5t1FxcXLjrvgf6ZbldNdi/rVtLclISRiMYDJYLoB2RtsYGu4OObj9m7Z5r\nNBrzInCBQNA3cGij1p+xxXA6t7Ep8wXzF7RYzydQ6K7txwQCgeMjjJoD0956O4F1umv7MYFA4PiI\nkWOBQCAQ9BuEURMIBAJBv0EYNYFAIBD0G4RREwgEAkG/QRg1gUAgEPQbhFETCAQCQb9BGDWBQCAQ\n9BvaXafm5+XZE3L8ZagrdxE6tTNCp/ZF6NP+CJ32HO0atQHiRtiVUheN0KmdETq1L0Kf9kfotOcQ\n7keBQCAQ9BuEURMIBAJBv0EYNYFAIBD0G4RREwgEAkG/QRg1gUAgEPQbhFETCAQCQb9BGDWBQCAQ\n9BuEURMIBAJBv8EhjVpubi5xcXEWf3PmzLFb/mlpabz//vvs37/fHPb0008TFxfHiRMn7FaOrcTF\nxXHNNddYvXbrrbcSFxdHeXl5j8iycOFCZs2a1W683tLXsWPHiIuL45lnnrE5zenTp3n//ffZsmVL\nl8peuHAhM2fO7FIeM2bMYNGiRV3Ko7M0/q7uv//+XilfIOgJ2t1RpDcZNWoUN9xwAwAajabFdZ1O\nh7Nzx6uQkpLChx9+iEqlYuLEiQBceeWVTJ06lfDw8K4J3UlUKpXV8FtvvZWSkhI8PXtmN4JHHnkE\nrVbbI2X1FIWFhXz44YcsWLCA2bNndymv1u5TT+fRm2i1Wqu/R4HAEXDInlojfn5+5p6aJEmA0qu5\n/PLLefTRR5k9ezZVVVUsXbqUWbNmMX36dK6//noSEhLMeXzxxRdceumlTJs2jWuvvZa0tDQef/xx\nAD744APi4uLYv38/33//PStWrCAnJweAn376icsuu4wZM2Zw4403mvP85ZdfiIuLY8WKFSxevJi5\nc+fy9ddfA5Cens6NN97ItGnTmDt3LsuXL7e5rlqtln/+859Mnz6de++9l8rKSgBWrlzJihUrqK6u\nRpZl4uLiuO+++7j55puZNWsWb7zxhjkPW2S+6qqrOO+889i4cSOPPPII06ZN46GHHkKv1wPw4osv\n8vTTTwMQHx9v1t28efP+n707D4uq+h84/p5BFkURd1TADcUNt8QVlzRccLdcMhfqa5jaqi3fSqtv\ni1kuqWVpmaRZmqJpKu6hSSCLOwoiO4q4IKDsMNzfH8T9ObIjCk6f1/P4PM6Zc8/53DPDfOaee+5c\n3nvvPdLS0grEfvv2bWbPnk3//v0ZMGAArq6uJCUlFai3bNkynJ2d6dOnD2PGjGHHjh3qc/mv64IF\nCxgwYAAvv/wyGRkZAPj7+zN69GiGDBnCwYMHixzDouKYMWMGAHv27MHR0ZE9e/awd+9eRo4cSZ8+\nfRg6dCiLFy8mNzcXgOjoaF599VUGDhzIU089xdatWwv0tXbtWhwdHfnhhx/IyclhxYoVuLi48OST\nT/Lf//5X3f/4+HheeOEF+vfvz8qVK4tNaKNGjaJfv34sW7aMQYMGMX36dBISEtTn8o8S7z9azT9q\n/uKLLxg6dCgTJkzAx8eH5557jgEDBrBu3Tq9flJTU3n11Vfp168fH3zwgfol5ty5czz//PNMmzaN\n8ePHc+DAAeD/j/BeeOEF5syZw8iRI0lMTCzyNS9u5kGIh61KJzVfX1+cnZ1xdnbmzTffVMtjY2Ox\nsLDgjTfewNjYmF69evHGG2/g5uZGQkICH3/8MZD3IbZy5Urq1q3LO++8g6OjIw0aNODZZ58FYPDg\nwSxatIiWLVvq9RsQEMCiRYuoW7cu8+bNIz4+nvnz5+tNAZ48eZKnn34ajUbD119/TU5ODh4eHgQH\nB/Paa68xd+5cGjduXOp9jYmJoXHjxgwfPhwfHx+9D6L7PwhPnz7NkCFDsLS0ZNOmTcTHx5cq5nPn\nzjFhwgSSk5N5//33qV+/Pk888QTHjh3D29u7QH/m5uZMmDCBN998kyFDhnDo0CE1gedTFIV9+/YR\nGBjI1KlTeeONN7C3t1eT5L1atGjBnDlzeO2116hbty5ffPEF169fV5+/cuUKjRo1onPnzvj5+eHl\n5UVWVhYLFy7kzp07uLm5cfHixSLHsKg45syZA0C3bt1YtGgR3bp1o06dOkybNo358+fj6OjI9u3b\nOXjwIDqdjjfeeAN/f3+mTJnCSy+9hJmZmV4/Hh4erFu3DldXV1588UXWr1/PL7/8Qr9+/Xj22Wfx\n8fFh0aJFACxdupTz58/z7LPPkpKSQnp6epHxA2RkZJCamoqTkxPBwcHs3LmzwOtSlOjoaIYOHUpU\nVBTz589nxIgRWFpa8sMPP3Dnzh213tmzZ+nRowe9evVi3759bN++neTkZN544w1SUlIYP348jRs3\n5oMPPiA0NFTd7vz587Rv357Zs2fj6elZYKzzvxSUJlYhHpYqPf3o4ODA7NmzAahVq5ZaXqdOHd5/\n/30A0tLSCA4Oxt3dXf2j0mg0ZGZmcvz4cQAWLlxIixYt1O07derE5s2badWqFc7Oznp9KorC33//\nDcCsWbPo0aMH165dw93dnfPnz6v1Ro8ezTPPPMOxY8fw8/MjISEBW1tbFEXB29ubDh06MHny5FLv\na6NGjZg9ezY5OTns2rWLU6dOFVm3f//+TJ48mYiICH7//Xfi4+NLFfOIESOYOHEi7u7u3L59m3nz\n5rFv3z58fX2Ji4sr0E9GRgbbtm1Tj14hb+r2XhqNhmbNmgF5R1RdunTB2dmZevXqFWjvypUrbN26\nlczMTLUsMjKSRo0aAVC/fn1eeeUVDhw4oMYUFRXF7du3cXFx4ZlnnqFZs2ZqkrpfUXH07NmTb7/9\nliZNmqiv9/nz53F3d1ePhPL3rXXr1sTGxvLUU0/h5uZW6JgsWbKEkSNHMnfuXAD1C8G9R57+/v5A\n3pef+1/b4mi1Wt59911CQkLYt28f165dK7b+vZ5//nmsrKzYvHkzDg4OTJkyhdDQUPbu3Ut8fDw1\na9YEoHPnzkydOpWBAwdy9OhRTp06hbW1NXfu3OHOnTtERUUBea9tYGCgej67bdu2vPzyy3r7fO9Y\n161bF8g7wpekJipLlU5qtWvXxtHRsUB5/h8PgKenJz4+PgwbNowRI0awevVqLl26pHdeSFEUve3L\n+wd373YWFhYAGBkZoSgKiqIwceJEWrRowalTpzh27Bjr169n69at6odtadwfa2Hu7Rso9KiosJjz\nP9SqVauGmZmZ3vnIwtr45ptviIuL44MPPqB69eq8++67ZGVlFajn5OSEu7s7fn5++Pr6smHDBlav\nXk2PHj3UOlFRUfz888/Y29szZ84cDh8+zO7du/US3P37lZubq8afPy73Hg2UNo57vxDlW758OZmZ\nmXz++efcunWLZcuWkZmZWaC/+2m1WmrVqkVAQAA3b96kQYMGaswrVqxAq9UWGWdpXlszMzOMjY0L\nvLZarVb9f/7U9P1q1aqlbnf/Odh748mPo7B4Ro4cSdeuXbGysgKgSZMmar369eur9Yp7zbOzs9Fq\ntXLeTVSKKj39WBr5H0IpKSlcvnyZsLAw9bn8cxCffPIJO3fuZNmyZaSlpakfnqdPn+bgwYN6H6wa\njQYnJycg77zJ9u3b2bVrFxYWFjg4OBQZh6IoeHh4cPbsWZo2bUrTpk1RFIXExET1XNiXX35Z5PbX\nr19n9erVfPnllyiKoi5gyW+7pDEoT8wl0Wg06j4cOnSo0DqKonD48GGOHz9Oo0aN1CPiW7duFVo/\nIyODa9euqUcyJWnevDn16tXj2LFjbN26lR9//LHIukXFkf96X7p0iQMHDqjnfrKzs0lKStJbFdms\nWTNsbW05evQo33//PR4eHnpHVyYmJixbtozExERee+01UlNT6devHzqdjj179nDt2jV8fHzUo7bu\n3btz/fp1vv32W/W1LY+mTZuSmZmJh4cHGzZsKFcb+c6dO8fPP//MqlWrAHjiiSfo1KkTFhYW+Pj4\ncPXqVcLCwvjpp5+4efNmoW0U95o7OTkxderUB4pRiPJ67JPa8OHD6dGjBwEBAZw+fZpu3bqpz40Y\nMYLXXnuNxMRElixZgp+fH0ZGRnTt2hVHR0dOnz7NggULSE5ORqPRqAmye/fuvP/++yQmJrJixQqs\nrKxYvnw5FhYWevUA9bFGo8HU1JQ9e/awaNEizpw5w4QJE+jcubNat7iVms2aNePGjRscPnyYPn36\nMHPmTL32C/MgMd/fTmH/nzt3Lo0aNWLDhg3Y29sX2rdGo6F69ep4eXnx+eefc+TIEYYMGcLgwYP1\n6jdv3pwpU6Zw48YNPDw86Nu3b6mOmI2Njfnkk0+wtLRkw4YNtG7dusi6RcXRtGlThg0bRnR0NAsX\nLiQ6Opp58+Zhbm7OunXr6Nq1q9qGkZERy5cvp0ePHmzevJnvvvtO/dKTH6+DgwMLFy7k8uXLvP32\n27i6ujJt2jROnz7NkiVL8PX1Vb+UzJ8/HwcHB7Zv346pqWmB83P3j2lR3NzcaNiwIevXr6d58+YF\ntivNeyRf586dOXnyJAEBAQwfPpynn34aCwsLVqxYgbW1Nb/88gvu7u5Ur169yPPCJb3mMv0oKoum\nuG+Orq6uyk8//fToojFQHh4eLFmyhE2bNlGtWjW983viwUVGRsqYViAZz4onY1rx/plJKvDt6bE/\nUnscXLhwgdGjRxd7lCGEEOLBVemFIobiww8/rOwQhBDiX0GO1IQQQhgMSWpCCCEMhkw/Goj8pelZ\nWVlMnDixssMRQohKIUdqBsLS0hIbG5tiL8QWQghDJ0mtisjMzFR/wHfJkiWkpKQQFhaGh4dHhbT/\nqG5dI4QQlUmSWhVw9+5dVq9erT4+ffo0Y8aMwd3dHRcXlwrpIyUlhdWrV5f7Fy2EEOJxUGnn1Dw9\nPalRowanTp2iR48e6s88/dvodDq+/PJL5s+fr/7axJQpUxg5cmSZ2youYTVt2pT+/fuzdu1aXnrp\npXLHK4QQVVmlHKlduXKFH3/8kYEDB9KzZ0/Wrl1boe17enpy9OhRli9frndLlarY9ubNm+nXrx+W\nlpZq2a1bt/Dx8cHd3Z3o6OhStZOcnIy3tzdnz54t9Bf3Ie/nndLT0/XuNyeEEAYl/xfmC/s3Y8YM\n5WFJTExUFEVRfvjhB2XNmjUV1m5sbKwyfvx4RVEUxdvbW5k6dWqVajsiIkL9f3JysvL8888XWTcl\nJUVxdXUte6DFSExMVGbOnFmhbVa2e8dUPDgZz4onY1rx8tJXwbxVadOPNWvWZN++fcTGxvLee+89\ncHsbN27kmWeewdraWv0l9+DgYL1pzUuXLhEdHc2QIUPK1UdxbZfHzp07C8Ti5eXF6dOnmTdvHmZm\nZoSGhpKbm8uWLVsKvZ2JtbU1AwcOLPQWPRqNpsCv4VtaWmJlZcWpU6f0fvxZCCEMQaUltWrVqjF8\n+HC0Wi0LFixgyZIl5W5r7dq1DBgwgBo1agBFJ0x7e3sCAwM5ceIEvXr1KldfFZmM9+/fz8qVK/XK\nzM3N1UQXGxtL586d0Wq1TJkypdi29uzZQ2xsLH5+fmRkZDB37twifxF+0KBB7Nu3T5KaEMLgVPrq\nx7Zt23L06FH1HldlFRkZyZUrV2jbtq1alp8w+/Tpw4IFC/TqT5o0iR9//LHc13MV13ZZ3Lhxg4yM\nDPUmk/l69OhBWFgYu3btYt++fSxatKhU7RkZGdGlSxeAYhMaQLdu3fDx8Sl37EIIUVVVSlLbsWMH\nb7/9NgAJCQnUr19fvZFjWa1du5YxY8YU+lxhCbNatWr06tWLP/74o1z9Fdd2WQQFBdGmTZtCnxs7\ndixjxoxh9uzZegtIilO9enVCQkJo3bo1KSkpxdatXbs2UPSNPIUQ4nFVKUltwIAB9OvXj4MHD7J3\n715WrVqFVlv2UFJSUjhz5gzdu3dXy0qTMPv168fu3bvL3F9FJuO4uDgaNWpUrm0Ls27dOmJiYkhO\nTiY+Pr7E+g0aNChVPSGEeJxUyjm1evXqMWrUKIByL9oAOHXqVIF7lA0YMABjY2MOHjyIn59foQmz\nVatWREZGkpaWpp6HK43StF1ad+7coVatWuXatjCvvfZamepbWFiU+yhTCCGqqsf6B43PnTtXYAqv\nNAnTyMgIW1tbzp07V6YFIxWVjCHvurLKvBNurVq1JKkJIQxOpS8UeRAJCQmlPud0PwsLi0o9p6TV\nagtdov+o5ObmUq3aY/2dRgghCqiUT7XCrqmCwq+rKk5iYiI1a9YsVwyVfaRiaWnJ3bt3C5QXNTYP\norBxTUlJUReMCCGEoaiUpBYQEFDs88eOHeOvv/7i/fffL/acVf4V5OVhZGRU7n6LU9o2ateuzdWr\nVwuUlzQ2FSUlJUXvKLe0caekpLB8+XKmTJmCnZ2dWr5lyxaMjIyYMGGCWhYaGspvv/3G/Pnzy3Tu\nUgghyqvSpx+XLVtWYBVeZGQkXl5exMTEFLttnTp1Cj3aKa7tfOnp6dSpU6dc/RbXdmnbsLGx4dq1\nayX2U9b+SysuLo6mTZuqj0sbd1JSEr6+vgV+kzIwMLBAQo6OjubEiRNy2xshxCNTqUktNDS00B8F\ndnV15dtvvyUnJ6fY7Rs0aFBkUiuq7Xw3b96kfv365eq3uLZL20aHDh24fPlysXXK039p3Lp1C3Nz\nc73LEUobt7W1Nfv27WPw4MF65UuXLuXLL7/UK3N2dmbv3r00bty43LEKIURZVFpSy8nJIT4+noYN\nGxb6fFBQEC1btiy2jU6dOhEWFlbmthVF4dq1azg4OJS535LaLm3slpaWNGjQgNjY2GLrlaf/kgQE\nBNC/f/8C5aWJWwghqrJKS2rHjh2jb9++hT4XFhaGlZVViee1unXrRnBwcJnaBoiKisLGxqbAeZ7S\n9FtS26WNHWDkyJEcOHCgxHpl6b80Dh48WOB+bWWJWwghqqpK+QSLiIjAxsZGXaxx/2IPOzu7Uv0C\nvrm5OU888YTe/cFKahvgyJEjjB49ukB5Sf2Wpu3Sxg7g4uKCj49PiVN+Zem/JPnnzO5d5JH/+N96\no1YhhOGolKR28eJFQkND2bNnDwkJCRw5cqTcy+vd3NzYunVrqdvOzs7m1KlT6kXUlRU3gImJCZMn\nT+a33357ZP3/8MMPvP766+UJVwghqrxKWdJ/79TX7t27GTx4cLkvom7evDktW7bk7NmzdO7cucS2\nd+zYwYwZM8o1zVaRcecbMmQIy5YtIyoqiubNmz/U/g8cOED37t1p1qxZecMVQogqrVJPoHh6ehId\nHc2WLVtITEwsdzszZ84kMDCQtLS0Ytu+fPkydevWpWfPnlUi7nyvv/46np6epW6rPP1fuHCB5OTk\nIu9oIIQQhkBT3HkZV1dX5aeffnp00fwLREZGVupvPhoiGdOKJeNZ8WRMK55Go0FRFM395bLUTQgh\nhMGQpCaEEMJgSFITQghhMCSpCSGEMBiS1IQQQhgMSWpCCCEMhiQ1IYQQBqPEXxQJjrnyKOL410hJ\nvkOGjGmFkjGtWDKeFU/G9NEpMamZmZg8ijj+NTKMjGRMK5iMacWS8ax4MqaPjkw/CiGEMBiS1IQQ\nQhgMSWpCCCEMhiQ1IYQQBkOSmhBCCIMhSU0IIYTBkKQmhBDCYEhSE0IIYTCqZFK7cf06E0eNyPs3\neiQzpz7H+rVrKO4u3fe7k5zMxFEj+Ojd/wKw9ZdNTBw1Aj+fv8sVk9fhQ0wcNYLdO3aUa3shhBAP\nX4m/KFKZWrRqxcix4ziwdw/79+ymXYeO9HZyKlsj/9zsu7dTP5ra2GDXxv7Bgipw83AhhBBVRbFJ\nzcjIiMX/+0h93N3Rke6Ojg87Ju7cvgVArZo1cejQgdQ7yYSGhBATEUZywi1+37GdO8nJmJub07Xb\nEzw7dSparZYrsbGsW7uWxMTbDBw0CICcrGySbt7A6+B+9vzxBy/NmUu37t0JOn+O37dvJ/7aNWrV\nqsWLs2fTsGEjvlq6hBs3bgBga2vL1OkzaNK0KWl37wCQnppC4o3rbNq4gcCAAHKys6lXvz5Tpk6j\nbbt2LFn8OZdDQ/lq1deY16xZYN8y0lJJunnjoY/hv4mMacWS8ax4MqYPLjAggMCAgBLrFZvUdDod\nCz78qKJiKrWs3LxpRo2REVQzJioqGjQaOnbpRlZ2FmOfmUi1akZcDAri2FEvOj3xBP0GPslnn3xM\n/PV4npvuyqWQiwBUMzHGskFDzGqYA2BeuzbpWdl8+/XX1KxlwfSZL5KWkoK5RW0sGzSkb/8B1Klb\nj8TE2+za7sH27R4s/OQzatSyAKC6eU2S7qZw/Ngxejv1o1t3R65eiaWGhQWWDRpibGIKGg0W9RtQ\nq1atAvuWdPMGlg0aPqKR/HeQMa1YMp4VT8b0wT3lMoKnXEaoj79f812h9ar09OPZU6dwmz4VgBFj\nxtKhUyf+PnaM37f9RlJiolovNjqatLQ0oiMjaduhA6PGj6dHfG/8fX0Lb/f0KXJycnh68mSG3jNI\ntxMSOH0ykNBLl+Cf83ex0dEFtq9brx4mpqZERoRjUbs29u3a0cGhEwAfLvocRVHQaqvk6UohhDBo\nVfqTt7V9W9545780aNiI/Xt2Ex0ZifsP35ORkcEb77zL826zAMjKyvr/jf5JRrm5RS8q0Wg0enXz\nee7+g9CQEEaOGcuCTz6lbr16+m3/o7alJV99+x1DXUZw9+4dVi1dwrZffwEgJyeHnJycB9ltIYQQ\n5VSlj9RqWVjQ26kfxsYmfPnpx/z2y89oNHmJ486dZPx8fdS6NWrUoHnLVoSGhPDHju1cCg4ust1O\nXbtRzdiY7b9tQVEU0tPSaNexo7oGJOXuXS6eP8/thARqmJsX2P7a1avs2bWTlnZ2tG5jj89ff5GY\neBuATxcuIPhCED/+uqXQ6UchhBAPT5U+UsvXvWdPWtrZcdLfn6EuI6hRowbbt2ymXYeOevXmvv4G\nTayt+WPHdhpZWRVs6J8jtMZNmvDW+wuoU7cum9zXc2CfJ0ZGRgwfNZpWrVvj432cxMTb2DRrVuj2\nxiYmRIaHs+GH79m8cQP27doz9ukJ/1TRqPWEEEI8Wprirv1ydXVVPlz85SMMx/DJCeOKJ2NasWQ8\nK56MacVr2bgRiqIUOIJ4LI7UhBBCiNKQpCaEEMJgSFITQghhMCSpCSGEMBiS1IQQQhgMSWpCCCEM\nhiQ1IYQQBkOSmhBCCIMhSU0IIYTBqPJJLTI8nJMB/pUdhhBCiMdAlf5BYwBf7+Ps9NhW9g01Grb+\nsafiAxJCCFFlVemkFn/tGg0bWbF1994Kb/vG9etEhocRGRHB0BEjqFOnboX3IYQQ4tGq0tOPx/48\nwoDBgx9K25eCL1K3Xn3Ma5jr3XAUIDIinOjIyIfSrxBCiIenyh6pJSbextzcHGNj44fSfr+BT3L1\nyhXS0tNo0bKV3nNhly7xw7ery9ewTHsKIUSlqbJJzevQIYaNHFWgPDkpCc/du8jV5RIVGUEb+7aM\nnzQZIyOjMvfR1NoaG1tbDnjuZajLCCAvmdapW++hTHkKIYR4uKpkUktNTUVRFGrUqKFXrigKm3/e\nyPNuszA1NSUrK4t3Xn+Vu3fv8sKsl8rUxy8/ueM83AUjo2rEx8Wp5acCAnjyKecK2Q8hhBCPVqWe\nU7t54wbJyckFyo8ePsSgIUMKlMfHxXEp+CJxV68AYGJiQv8nB3FonyfZ2dll6rtH797EREURGR7G\nqHHjAcjKysLIyAittkqfahRCCFGESjtSiwgLY/tvW2jesiUTnp2ilmdnZ3P37t1CVyNWM65GclIS\n8XFx6nkwMzMzdDod6WlpGNeuXer+W9u3BaB7z55qWcAJXxx79S7vLgkhhKhklZbUmrdsybQX/sNH\n7/2X8RMnqefE/vrzCAMGFb7isUHDRqz/dYte2eXQS9g0a4ZFGRIawLkzp4kMC2PMMxPUstSUFMzN\nzQvUzczMZP/uPzA2MSHscihDhrtwOeQSl0KCmTR1Gja2tmXqWwghxMNRafNsWq0Wq8aNsbVthr+v\nDwA6nY5r167RuEmTUrVxPf4afj4+vDhnbpn7b96iJQH+fiiKAsD5s2dw6Nyl0Lr7d//BsFGjcRk9\nhoz0dA7t24fLmDGcO3Oa2wm3yty3EEKIh6PSTx4NHTmS/XvylsCf+Nub3k5OpdouOzub1Su+YDRb\nKgAAIABJREFU4qWXX6Vt+w5l7teidm06d+nKmZOBAMRduULjpk0L1FMUBfv27TE1Nf2n3lWcBgzA\nyMiIjVs96Ny1W5n7FkII8XBUelLr+kR3EhJuERMVRXhYGK3sWpdqO/e1axg1dhz9nnyy3H0/6TyE\nPw8dIu7qFZpYWxdaR6PRqEkz4dYt4uOv0a6jQ7n7FEII8fBUelLTarUMGe7CqmVL6dKtdEc9Oz22\n4dirl7qo4++/jpGenq5XJ8DvBN+tWkFubm6R7dRv0ICcnBy8jx4tcuoRUNsIOnuWlq3sMDMzAyDk\n4oUCdUvTb1paGt+uXEFMVJReuecfuziwV//C7aiICL5btaLA/gkhhCio0pMa5B0x1TCvQacuXUus\n63X4ELcTEtBotJw+GfjPv5NUr15dr97V2Fj8fHy4dvVqse0NHPwU9Rs0LPJ5X29v3KZPA8D/hC9N\n/pmiTE9P51JwcIH6pen37p1kzpw8qV6akC/o3DnOnz2rVxZ39QpnT50i5e6dYvdDCCEEaPIXShTG\n1dVV+XDxl48wnOJdjY3lzVfmotPp9MrbdujAx4XEGRkejtZIS7PmLcrdZ2hICHt27qB1m7a0bmvP\nvt27ae/gQFZGJkNHjsTExKRM/SbdvIFlMUlUlJ2MacWS8ax4MqYVr2XjRiiKorm/vEr+okhRmtrY\nsHnnH6WuH3opBOdhwx+ozzZt2zLvv++pj0uzKKUi+hVCCFF2VWL68WGIjoqkfoMGj/zXQSqrXyGE\nEI/ZkVpZNGve4oGmHR+3foUQQhjwkZoQQoh/H0lqQgghDIYkNSGEEAZDkpoQQgiDIUlNCCGEwZCk\nJoQQwmBIUhNCCGEwJKkJIYQwGJLUhBBCGAxJakIIIQxGiT+TVaem+aOI418jI9lExrSCyZhWLBnP\niidj+uiUmNQs5YWoUIkmxjKmFUzGtGLJeFY8GdNHR6YfhRBCGAxJakIIIQyGJDUhhBAGQ5KaEEII\ngyFJTQghhMGQpCaEEMJgSFITQghhMCSpCSGEMBglXnxdGeLi4hgzZoxeWc2aNfHy8ipXe9u2bSMx\nMRE3N7eKCE8IIUQVVSWTWr62bdsyffp0AIyNjcvdzrZt24iMjJSkJoQQBq5KJ7U6derg6OgIQLVq\n1QgPD2fp0qVcvHgRCwsLxowZw8yZMwFwdHTExsaG9u3bc/z4cRwcHFi6dCmLFy8mMjJSrdOtWzcW\nL17Me++9x4ULF9BoNLRo0YIVK1ZgaWmJo6MjLVu25Lfffqu0/RZCCFE+VTqp+fr64uzsDECnTp24\ndesWiYmJzJ07lxMnTrB27VoaNmzI6NGjAbhy5QpPPvkknTt3xtfXlz///JNnnnmGgIAAbty4waJF\ni6hTpw6enp4EBgby4osv0rBhQ4KDg8nNzVX71Wg0lbK/QgghHkyVTmoODg7Mnj0bACMjI2bNmsWw\nYcOYNGkSffr0wdvbG19fXzWp1a9fn1deeYUDBw7g6+vLtWvXcHFxwdzcHI1GoybIjIwMAPz9/enS\npQvOzs7UrVsXAD8/P0lqQgjxmKrSqx9r166No6Mjjo6OWFhYlFg/v46RkRGAevR1f5JycnLC3d2d\n3r17c+bMGWbPno2/vz8A2dnZ5OTkVORuCCGEeESq9JHavZo3b461tTV//fUXW7ZsUZNQ3759S9zW\nwsICRVHw8PCgffv2xMXFcfnyZWxsbGjRogVnz57l1q1bQF7Ck3NqQgjxeHpsklq1atVYtmwZS5Ys\n4bvvvsPCwoKXXnqJkSNHlrjts88+S0xMDF9++SVjxoxh4MCBeHl5cfXqVUxNTRkyZAiDBw9W68v0\noxBCPJ40iqIU+aSrq6vy008/Pbpo/gUiIyNp0aJFZYdhUGRMK5aMZ8WTMa14Go0GRVEKHIFU6XNq\nQgghRFlIUhNCCGEwJKkJIYQwGJLUhBBCGAxJakIIIQyGJDUhhBAGQ5KaEEIIgyFJTQghhMGQpCaE\nEMJgSFITQghhMCSpCSGEMBiS1IQQQhgMSWpCCCEMhiQ1If6FTp48yX//+19GjBhB37592blzZ4nb\nBAcHc+DAgWLrKIrCtm3bmDhxIv369aO4u3yUpW5JYmNj+eKLLxgzZgx9+/Zl/PjxHD16tNztlaQ0\nYyEqR6XcT83Hx4e9e/dy6NAhbGxs6NevHzk5Ody9e5dq1aoxffp0mjVrVhmhPVIbNmzA19eXkydP\n4uTkRKtWrXj55ZfL1VZ8fDwrV67k8OHD/PHHHzRu3Fh97p133qFVq1a4ublVVOjiMaXT6Vi2bBn1\n69fnrbfewszMjOnTpxMdHV3sdrGxsbi6utK1a1eGDh1aaB1FUXjvvfdwcnLi008/5cUXX2Tfvn24\nurqWuW5aWhpbt27Fx8eHqKgoMjIySE9PR6vVotFo6NixIytWrKBmzZp4eHgQHh7OpEmTmD9/Pteu\nXWP27NlcuHCBgQMHcuLECf744w/Onj1LQkICOp0OIyMjzM3NqVevHv3798fNzQ0TE5NSjWFxY3Hy\n5El27tzJqVOnSEhIIDc3F41GQ61atejWrRvTp0+nY8eOKIqCp6cnvr6+nD17Vk3w1atX12vv448/\npnv37ri4uODt7c3333/PlStXcHFx4Y033sDIyEit+9VXXxEYGKjeb/LfqlKSWp8+fejTpw/nz59n\n0KBBzJ07V31u69atTJs2jTVr1tC+ffvKCO+RmTFjBhqNhtDQUJYvX67enFSn0zFx4kTWrFlDgwYN\nStWWlZUV48eP58KFC3oJDWD48OHY29tXePzLli1jy5YthT5nbW3N77//XuF9PgorV64kPDycVatW\nVXYoFe7TTz/F0dERFxcXALKzs0lOTi4yUeX77bffaNWqVbF11q9fj06nY8SIEQC0bNmSJ554osx1\nk5OTmTNnDq+88grPPPMMNWvW5OTJk6xdu5bvv/9erx0PDw9u3LjBO++8o5bVr1+fnJwcRo4cybvv\nvkvnzp156623+PTTTzEzM+Ozzz4jLS2NK1eusGvXLjZs2ICJiUmpv/QVNha5ubksXrwYa2trXn/9\ndT799FNq1qzJxx9/zPXr1/nzzz/ZuXMns2bN4uuvv+bXX39lwoQJfPrppxw7dow333wTnU6n12ZQ\nUBBHjhzh7bffVvvYsGED+/fv56OPPsLS0pKZM2eq9Xv06MHff//NgQMHmDBhQqn2xRBVuenH4cOH\nk56ezs8//1zZoTwSp06dokuXLnp32w4ODubOnTulTmj5AgMD6datW4HygQMHFkh0FSElJYUpU6bg\n4+PDW2+9RdeuXfHx8eHrr78mJSWlwvt7VF577bUiE9qyZctYv379I46oYhw+fJiEhAQ1oQF4enrS\nu3dv2rZtW+R2UVFR1K9fn4YNG1LUTYWTkpJwd3dn6tSpatlXX31V6MxDSXWDgoKYM2cOvXr1ombN\nmkDeuE+ZMkWvndjYWNzd3fU+2AE2b97M3LlzOXToEFZWVkyePJk6depw9epVdQaoRo0atGnThrfe\neovWrVuTkZFR5P6XZiy2bNlCVlYW06dPp169esTFxWFjY4NGo8HKyorevXvz/fffY2JiwuzZsxk+\nfDg9e/YEID09nTp16qj7mu/3339n0KBBmJmZAdC/f380Gg3Dhw9n/PjxbN68mZycHLV+3759efnl\nlwskx3+bSjlSK05mZiaQ96YzdLm5uZw9e5b//Oc/euWBgYF07969zO0FBAQwduzYigqvRIMHD6ZG\njRoYGxur00LGxsa0adOG6dOnP7I4HqX8KfLHTW5uLqtXr1a/9efr378/Y8aMKXbbTZs28eabbxIc\nHFzkh/+OHTuoV68enTp1UsssLS3LVbdv37569YOCgoiKiqJXr1565evXr2fEiBEFpg1feOEFIO9L\nV36iyM3NJS4ujpYtW+rVzcnJITU1lVGjRhUa6/2KGosnnniCyZMnq4/j4+OxsbHR29bS0hJra2sG\nDRrE4MGD1fKLFy/SpUsXvbqZmZkcOXKExYsXFxrHuHHj8PDwICwsTO8LSVxcHD169CjVvhiqKvXX\nqdPp+Pnnn2nbti1z5sxRyzMyMli/fj3m5uaYmppy9epVZsyYQf369fHx8WHNmjXquYH4+Hhu377N\ntWvXeOWVVzh48CBGRkb4+/vj5uZG69at1XYPHz7MjRs3MDU1JSwsjC5duqjTMIW1m5SUxNWrV/ng\ngw/0jqzK69KlS6SkpKgJ7OjRo5w8eZKDBw/Srl07li1bxtNPP03z5s2BvCO43bt3Y2trS3Z2NkeP\nHuXzzz+nYcOGpKWlERwczCeffKK27+Hhwc2bN9FqtcyaNUstj4mJYcuWLTRt2hSdTkezZs0YMGBA\nmeN3cnIqtLxu3bpMmzYNyPsA+/zzz4G8P8TZs2fj4uJCTk4OFhYWtG7dmpMnT+Lo6Ehubi4XL17E\nysqK119/nT59+ui1u2fPHn744Qdu3bpF27Ztefvtt7G3t2fNmjX8+eefALz55puMGzcOZ2dn0tLS\nMDY2ZubMmXz33XcAzJw5k8zMTA4cOEBGRgbTpk1Tz+N89tln6oIJW1tbtm/frvb99ddfs3HjRvXx\npk2bAHBxceF///tfqeLctGkTK1euLDGO+xX3/vf09OSnn36idevWdO/eHS8vL7p06aJ+sOcLCAgg\nPT2d3r1765XXqVOn0D7znThxgg4dOmBmZoaZmRmJiYmF1tu/fz/9+/cvtq3y1AU4duwYnTp1Uo9Y\nIC8ZHT16lGXLlhW6TXx8PI0aNVIfR0REkJ6eTufOndUynU7Hd999x+zZs2nRokWJcRQ3FvdO78fF\nxZGWllZgyv/OnTvcvn2b0aNH65UHBgYW+GLh7e0NUOSX21atWmFiYsKlS5fUpJabm0t4eHiBI9p/\nHUVRivw3Y8YM5WEaNWqU8sorrygeHh7Khg0blAkTJihfffWVkp2drdbJzc1VZs2apfj6+qplkZGR\nyrhx45S0tDRFURTF19dXcXZ2Vvz9/dU6EyZMUBYuXKg+/vnnn5V3331Xr/9XX31V2bRpk6IoiqLT\n6ZRx48YpISEh6vNFtevn51fufY6IiNCLacCAAUpubq5alpWVpTg5OSlRUVF62wUFBSljx45VEhIS\nFEVRlF27dimDBg1Sn//7778VFxcX9fG5c+cUHx8fxd/fX3n22WfV8uDgYGXs2LHKtWvXFEVRlA8+\n+EB56623yr0/+bZt26a4ubkV+lxISIgyfvx49fHJkycVV1dXJScnR9HpdMq3336r9OjRQ9mxY4eS\nlJSk7NmzR+ndu7dy6dIlvfYHDRqk+Pj4KHfv3lXWrl2rODs7KykpKUp4eLgyadIkJTQ0VK2fnZ2t\nvP7668rx48cVRVGUzMxMZeHChUqPHj2U2bNnK5cvX1a8vLyUQ4cOqdvk5uYqWVlZiq+vrzJ48OAC\n+5HfxvLly5WsrCwlKytL77UrLs67d++WOo57leb9v2PHDmXYsGFKaGio4uXlpfz+++8F2lmxYoXy\n+uuvF9rH/fLfo+np6cqCBQvU8s8//1yZPn16gfrx8fFK9+7d1bEuTlnq5psxY4by/fff65VFRkYq\n3bt3V2JjYwvUP3nypOLs7KxXtn37dmX06NFKdna28scffyhLlixRPv74YyUoKKhUMZR2LBRFUY4c\nOaL0799fryw8PFz55JNP9Pb7t99+UwYPHqx0795d6d27t9KnTx+lT58+St++fZV+/fqV+Hf53HPP\nKd988436ePfu3cqxY8dKtT+GIC99FcxblX6kZm9vz9NPPw3kfeudPn06rVu3Vk8gHz9+nKCgIL2p\nh+bNm2NqasqePXuYMGEC1apVQ6fT4ejoqNapU6cOHTt2VB/Xrl2b27dv6/W9cuVKoqKi+PXXXzEz\nM0Or1XL58mX1G1ZR7d68ebNC9r2w82lnz57F3Ny8wOrPTz/9lLFjx1K3bl0g71tf165d1ecDAgL0\nTsrrdDp69+7NZ599ph6FKYrCBx98wKRJk7CysgLg+eefp1atWhWyP0Wxt7fHzMyM4OBg2rVrx/Hj\nxxk0aJC6ckur1dKrVy/GjRsHwIgRIzh79iybN2/mww8/JCcnh2+//Za3335bPdJwc3Nj7969HD9+\nHHt7e+zs7IiJicHW1pbr169ja2tLRESE+lqamJig1WqpW7cuy5cvx8zMDDs7O70486dPi5peNDEx\nwcjICK1Wi7GxcYHni4vT29ubYcOGlSqOe5X2/W9mZkbr1q31ZiLuFRsbW+bX2d3dXT3iBjA1NeXu\n3bsF6gUGBmJiYlKqKfOy1IW8VZAhISG8/vrreuXJyclA3t/B/bZu3ar3tw95R1lOTk5ERUWxadMm\nYmJi2Lp1a4EpwqKUdiwgb0bFwcFBjTM4OJht27YxZcoUvb/RiRMnYmlpyXfffVdgUdWYMWNKnEZs\n0KABaWlpANy6dQsfHx8WLVpUqv0xZJWe1O5Vv359evbsibu7u5rUwsPDMTU1LVDXzMyMiIgI9bG5\nuXmBOvefl1PuO8m9cuVKYmJiWLhwIZaWloVed1JYu/e3Ux65ubmcOXOG559/Xq/cz8+vwIqxixcv\nEhYWpjdFGBgYqPemDwwMVL8cAHTp0oX09HQOHz7ML7/8AsC5c+eIjIxk2LBhar38qc2HbcSIEXh6\netKuXTuOHTtWYCHGvVNLkJcI9+3bB+QtLLh79y5Llixh6dKlap20tDQSEhIAsLOzIzIykpCQEDZt\n2oSHhwepqal6i200Gg1OTk4F+qoopYmzrHGU9v1f0qKirKysIs+HpaWlFfhbOXfuHB4eHmzdupWs\nrCwg74tSYTH//fff9OnTp1T7U5a6AP7+/tSuXVtv2hCgXr16QN403b2rpC9cuICXlxfz5s1Ty7Kz\ns/Hz82PJkiXY2dmxdOlSJkyYwI8//shHH31UYgxlGYv8mNu3b4+bmxsajYYhQ4bg5uZW6Arkv/76\ni4EDB+qVXb9+nbi4OL0vrYWpXr06GRkZ6HQ6Fi1apLfP/2ZVKqlB3rfh2NhYsrKyMDExwdramrt3\n76rXluS7desWTZs2LXW7958DO3PmDFu3buXIkSPqmzM3NxcALy8vnnzyyQrYm6JdvnyZu3fvFlit\n6Ofnpy72OHPmDO3atePKlSvUrFlTTUA6nY4zZ84we/Zszp8/j62tLaGhoXTv3p2AgAD1yDL/3FyT\nJk04c+YMN27coF69egVO4CuK8sDnCEvaftiwYUybNo2nn34aMzMzrK2t9Z6Pi4vTe3zhwgX1aLVO\nnTrUrl2befPm6R01JyYm0qRJE27evImdnR2enp5ERUXRvHlzfv3110KPgO5fYfYgdDodCxYsYOHC\nhdSoUaPEOMsTh42NTane/yWNv7W1Nbt37yYxMVHvPJqfnx9bt25lyZIlZGdnY2pqSnp6OmvWrMHT\n01Mvoe7Zs4f//e9/JCQkqEklJSUFb29v1qxZU6BPRVHYuHEj7du3x9HRsUx18x08eJBJkybp7V96\nejrW1tbY29uzceNG2rRpg5OTE+fOneObb74B0Pv79fPzo3r16urRoY2NDUOHDmX//v3MnDlTfS9G\nR0er165lZWVhampKampqqccC8o7MQkJC+PDDD/UWpURGRhbY54yMDI4fP87atWv1yoODgzExMSnx\nPJ+pqal6KYGLi0uBv6l/q0pd0p8/B3qv5s2bk5ubS0hIiFrHzs4OHx8ftc7ly5cBil3pd3+79z9O\nSUnRm2q6desWMTEx6HQ6wsPDS93u/Y4dO8Ynn3yiJsiinDp1iurVqxdYSh0REUHHjh3Jysri9OnT\nmJqa0rJlS7Ta/3+ptm7dSkZGhrrIIn9qydramgsXLqj1vL29cXZ25tq1a1y/fp327duTk5Ojtww4\nJiaGDRs2PNC+6HQ6cnJyUBRFr+171a1bFzs7O1auXMmgQYMKPB8SEsKWLVtISkpi8+bNHDhwQD3h\nbWRkxOzZs1m1ahVBQUEYGxur1yYdP34cyDtSO336NAkJCYwbNw5PT0+9pJaZmUlOTg46nY6srKxC\n49TpdGo9yDu6yc7O1qtjZWXFuXPnSEpKYs+ePfj6+qpTkaWJszRx3GvAgAHlev/fb8yYMWRmZvLW\nW28RHh5OWloahw8fxsPDg48//hgvLy9WrlxJUlISq1atYubMmQWOEPOvzTp58qRatm7dOjIyMpg1\naxbjx4/n448/Zv/+/URFRbF3717+/vtv9YijLHUBwsLCiIuL47nnngPyvnR+/fXXbNu2DYDFixfT\noUMHPvzwQ6ZMmcKlS5cYMmQIvXv3pmHDhmo7u3btYvTo0Xp/Q66uriiKwueff05OTg7e3t6sW7eO\nunXr8ueff6pjsXDhwlKPBYCvry9NmzYtsMqyMMePH8fKyqrAZ8CVK1eoUaMGWq2WEydOsG7dukK3\n1+l0/Pnnn1hZWfHUU0+V2N+/haa4D2lXV1flQX66pig+Pj54enpy6NAh9Yr+mTNnqhdNrly5ksTE\nROzt7enatSu2tra4u7tTu3ZtdDodSUlJTJs2jYYNG+Lr64u7uzvnz59nxIgRvPTSS/zwww/s378f\nW1tb9VqYjRs3cvXqVYYPH84777yDoiisW7eOGzdu0LZtW9LT07G1tWXLli2MHz8ec3PzYtt97rnn\n9Kbx8v30009s3LiR9evXFzq1t3//fs6ePcvRo0fJyMhg2LBhjBs3jjZt2gCwatUqtFotFhYWPP30\n0+r0Z/7Fqubm5uo31G7dutG2bVu6du3KvHnz6N69O05OTuqH+c6dOwkPD6dx48Zqgti3bx8XLlyg\nRYsW5OTkULt27UL3ozT7km/69OkEBwcDeechf//9d/Wc3b0OHjzI+++/z2+//ab3R//9999z4sQJ\nqlevzrlz52jRogXz5s0rsMz5wIEDbNy4kaioKOrWrcvYsWN54YUXiIqKokWLFowePRonJyemT5/O\n5MmTWbBgAU899ZTeqsN8hV0cPmfOHAICAoC8Ix9FUdBqtWzbtg1bW1sAUlNTWbhwISdOnMDa2pr3\n33+/wNRYUXH+8ssvpYrjfnfu3Cny/b9//35+/vlnYmJiGDJkCFOmTCnyIukDBw6watUqEhMTsbOz\nY/z48YwZMwaNRsPOnTv57LPPgLzzrPeuPs6n0+mYMGEC5ubmTJo0iUaNGuHl5cWUKVOIiooiMDCQ\nwMBAYmJiMDExYdSoUbz00kuYmpoSEBBQqrrR0dG89dZbxMfH680gGBkZ0axZM2bMmFHk+1XJW9zG\ne++9pyaK5ORk5s2bx5dffql3RAV5Sdbd3Z3mzZszefJkdVn/vWMxf/58vaX6RY3FyJEjAXjjjTdo\n3749L774ol79yMjIAkde//vf/xg8eHCBVcSHDx/mk08+oVOnTnTt2pXJkycTFxenN8ORkZGBm5sb\nPXv21Pvxin+Tf/5GC0xRVEpSM3QhISFUq1at0Omvwt7cVVlx+1JWERERvPnmm+zYsUOvfO3atURE\nRPDFF1+Uq93HbUyrIp1Ox+bNm2nQoAFt2rSp8uOpKAq//PILqampuLi40LBhQzZt2oS5uXmhSags\n7h2Lkn5p5V7JycmMHj2aX3/9tcCpkQd9j27atAl/f3/mz5/P9evXWbFiBaNHj37gfX2clSup/ec/\n/1Hunfvv1atXgQsgRUEHDhzA2dlZb7ojX1JSUpEXpVZFxe1LaeXm5qLVatm+fTvZ2dkF/hC3bdtG\nTEwM8+fPL1f7j9uYVnWPw3hmZGTw66+/EhgYSFJSEk2aNOG5554rcXHFw3T9+nVSUlIKPVJ+0DG9\ndesW7777LsnJydSrV485c+aoKyz/LU6cOMGJEyfUx6tWrSo0qVXqdWqG6PLly8Veg3PvdWpVXUn7\nUhoxMTHKgAEDlNOnTysuLi7K9evX9Z7PyclRVq9erbz55ptKTk5Oufp4nMb0cSDjWfEqYkxTUlKU\nCxcuKFlZWRUQ0eOPqnqdmqGxs7OrkKm6qqAi9qVBgwa0bNmS999/n3feeUfvBD7kncc6deoUkLfs\nf//+/Q/UnxCGytzc3OB/5L0iSFITD5WZmVmxPwB8/3JmIYR4EFXuV/qFEEKI8pKkJoQQwmBIUhNC\nCGEwJKkJIYQwGJLUhBBCGAxJakIIIQyGJDUhhBAGQ5KaEEIIg1HixdfBMVceRRz/GinJd8iQMa1Q\nMqYVS8az4smYPjolJjUzE5NHEce/RoaRkYxpBZMxrVgynhVPxvTRkelHIYQQBkOSmhBCCIMhSU0I\nIYTBkKQmhBDCYEhSE0IIYTAkqQkhhDAYktSEEEIYDElqQgghDEaVTmr79+xm4qgRTBw1grirj/Zq\n/ONHvdj26y+kpqY+0n6FEEKUX5VOaj7H/0Kj0QDge/z4I+3b+9hRtm3+lbTUlEfarxBCiPIr8Wey\nKsvthAQuBQfTp19/Lgad5+/jf/H05GfJzs5m6y+b8D52lLt37mDXxp6PPl9MWloam9b/SKC/H2lp\nafTs3YdX5r9Jwq1buH+/hqBz5zAxMWXAoEE8O30GWq2WOS+4cufOHQYPGcqxP49g1bgJ//3gQw56\n7uV0YCAAc//zAvUbNuSrb9fw9bKlnD97Bp1OR+MmTXj1zbexsbVlzguu3L17l5+3ba/kURNCiH+3\nKpvUfL2PoygKvZ2cqGVhwf49u7kSG4Ovtze7tnvQtXt3evXpS3RUFAA/fb+Wo0cO0//JQXRwcODm\njRsAfL1sKRHhYYwaO47E27fZtd2DuvXqM3zUKACyMjNJT0/nCcceHD/qxZED++nt1I9Af3+iIsJ5\nYdZLNGjUiDMnT+Lv68MQlxG0srMjOiqKXJ0uL1iNBk1lDJIQQgg9VTqpVatWjSbW1mRkZADg89df\nnAoMQKPR8MY772JmZqbWD/T3o7alJS/Pm6+WpaenczHoPADbNv+aV6jRcP7saTWpabVaXpwzl8jw\ncI4f9eLmjRvYNGtGnbp1iIqAJ3r0pEHDhkRHRqLRaAgNCcbY2JiOnTrTrEULAFavW/90YfdEAAAg\nAElEQVQohkQIIUQJqmRSu3XzJqEhIQDMmzNbLffxPo6ZWXUUQFGUQrdVFEU9D5evecuWTHvhP/DP\nJjXMa6jPmZiYYGxsjJFR3unF3NxcAP7/2Ctvo2YtWrD0m285FeDPxaDz7N21k1mvvMrgIUPJyckB\nwNjY+IH2WwghxIOpkknN1ztvUci4iROxa2MPisKRgwc5FeDPxCnPERF2ma++WEyvPn2JiY7C9UU3\nuvfsxdHDh/hm+TLaOzhw6+ZNJj03lfYODgQHBREcFESdevUIuXABaxsbWrVuU2wM5jVrAnD0yBHa\nd3RAq9UScMIXm2bNaN6yJacDA0m6fRuA12a9KOfUhBCiCqiySU2j0TByzDhqWVgAkJOTw6nAALJz\nshn7zASOH/Ui6NxZWrexB+D5F92oZmREoL8ffj5/06NPHwBenf8WP/2wlv1795CTk0Oz5i0Y+NRT\neR1pij4TNsTFheALQWz79RccunThuRnPE3TuLAf3eaLVaOjS7Qmch7uo7cg5NSGEqHyaoqbxAFxd\nXZUPF3/5CMMxfEk3b2DZoGFlh2FQZEwrloxnxZMxrXgtGzdCUZQCxxNV+jo1IYQQoiwkqQkhhDAY\nktSEEEIYDElqQgghDIYkNSGEEAZDkpoQQgiDIUlNCCGEwZCkJoQQwmBIUhNCCGEwJKkJIYQwGJLU\nhLhPbEwMwUFBZGVlVXYoQogyqvQfNL6dkMDH77/Hfz/8CKvGjR95/wF+J2hj35balpYA6HQ6jh/1\nYuDgpx55LBXl6pUrLPnsEz5atBjLOnUeqK3jR73QarT0HTCggqJ7cPv37KZV69a0tm/7wG3l5uYS\nHRnJxaDzBF+4QMjFCxgbG2NuXpMb1+Np3qoV7Tt0pL2DA/Zt22F6zz38hBBVT6UmtezsbJZ/8TlP\nOjtXSkID6OjQiV82/MTzbrPQarVs2/wrzsOGl7u9PTt/Z9/u3Xz13RpMTEzKtO3lSyEs+uhDln69\nGkVReOvVl3n/f59g16b42+Tcr6m1NQOeHMRXXyzmg88WYWRkVKbt8x378wgpd++iKArHj3rRb+CT\nZdo+NTWVtV+vJPF2Im4vv0KNGjUIOnuWAYMHl2p7nU7Huu++JScnW6/8Rvx1jh45wuKvVpQpHsi7\n20NEWBjBF4IIvnCBxNsJNLKyopVda4a4uDDrlVepVasWkPf+jI6MJDI8DF9vb7b8/DPVjKvRxr4t\n7Tp2pG37Dpibm5ep/+zsbCLCLhMaHEJ0dBRxV65w+3YCaSkpZGVlYWpmRr369WnS1JpmLVrQvEUL\nGjdtSr169TE1MyM7O5uMjAySbt/mevw14q5eJSoigrTUVJ55dgpt2j54ohficVapSc3r0EEmTZmK\nQ5cu5dp++29bSE1N1SszNzfn6UmTS91G9Ro1GDx0KKtXLCc3NxfnocOpV79+ueIBsLFthkOXLuVK\nJA0bWTHUZQQWtWujKApDXUbQsFGjcsUxbuIkWrZujdehQzw1bFiZt8/JycHSsg4DBuUloDMnT6LT\n6cq0X6cC/Jn96uukpqby/epvsLS05MW5L5d6eyMjI2a9/IpeWXpaGmtXf82sua8UsVXhft/6G4mJ\niWRnZ9OkaVPs2tgzZLgL1WvUKHIbY2Nj7Nq00ftSodPpiImOIiIsjN82/YxOp8Pc3Jynhg0v8bVa\n8eUXpKWlYW1ri42NDc7D8t5rFrVrY2JiQnZ2Nnfv3OFaXBwxUVFER0Zw5uRJblyPJzUlBUVRqF6j\nBhYWtbFq0gQbW1ta29vz5FPO6kyDEP92cuuZR0xuQVHxSjOmmZmZmJqaPpT+c3Nzyc3NpVq1Sp/N\nrxDyHq14MqYVr6hbzxjGX6EQJXhYCQ1Aq9Wi1cqaKyGqgkpNauFhlzl6+DBNmjYlOzsb/xO+zHvn\nXerWq/fYtSGxVP1YMjMz2b/7D4xNTAi7HMqQ4S5cDrnEpZBgJk2dho2tLfv37CYpMRGtVsvE56YC\ncOH8eX7ftpUFH38CQFhoKEs//4ym1ta8/f5CYqKjyc3VYd+u/WMRgxCGrNK+XoaFhrLiiy94ZvKz\nDB81mlq1anE1NrZMH1JVpQ2J5fGIZf/uPxg2ajQuo8eQkZ7OoX37cBkzhnNnTnM74RbBFy7QuGlT\n2nXsSIDfCXW7gBO+NLKyUh8f+/MI7yz4gGEjR7Fl08/EXb1S6mRSFWIQwpBVWlJbs2olTw0dqp7g\nTrmbQrsOHR/LNiSWqh+LoijYt2+vTkPGXbmK04ABGBkZsXGrB527dgNFoXPXbhz38sKxZy912+AL\nQXTs1El97PqiGy1ataKVXWu69+ipLqZ5HGIQwtBVSlILvxxKTHQU3Xv1VsuCzp+lg4PDY9eGxPJ4\nxKLRaGjbvgMACbduER9/jXYd9dto17Ej6WlpnPjbW73sIDU1lejISNo7/H9CMTIy4mpsLImJt+lw\nT6J5HGIQwtBVSlKLv3aNGjXMaWptDeQtkw65eJH2Dg6EhoRQ3IrMqtaGxPJ4xAJ5qxQBgs6epWUr\nO8z+uZA65OIFtU5I8EXq1K1HI6u86yZDLgTRuGlTateurdYJvxyK1siIVnatATjp7/dYxSCEIauU\npGZj2wyt0f93vX/PbjIzM2nWvAUXzp9Do9EQ4HeC71atUD8EKqsNQGIxgFh8vb1xmz4NAP8TvjRp\n2hSA9PR0LgUHq/WyMrP0rl37+6+/6Nips/o40M8P72PHOBXgT0xUFF6HDxF++XKlxxD2TwyliQMg\nLS2Nb1euICYqSq/c849deP15RK8sKiKC71atID09vcj2hKgqjD766KMin9y5c+dHA59yrvBOa1ta\notPpCDp3jqiIcJq3bEVSYiKJCQm0bN2aRlaN8ff14cjBA/To1RuLe76hPuo2IO8k/ZEDFRNLl25d\nadDIqlxtVHQsVWlcHqSNjLRUzp87V2ws6WlpJN5O4NaNm/Tu58Sl4GDu3EnmckgIw0aNUi8qb2Rl\nRciFC9xOSODypUsc8NzLyLFjsbax5Xp8PLcTbjFy7DguXwrh62VLMDE1Zfp/ZqLVakscj4cZw4x/\nYijN6wLwf+3dd1gUxxvA8e+BIE3Aigoae8Su2GLsWLBgwUgUuybGJMbEmMTkZ40laqwxGk3sFRUx\nsXdFsKDYQVGQjoqACkg5hLv9/YFcPGl3cAiS+TwPj7J7+87sAPfezM7OPnsai+vWrdSpVw+b6tVV\n2/92cyMm+ona6jF3/Xw5ceQw7Tt1wtTULNt4Qu7kyUkYabn6jJC735YuYfbs2T+/ub1Y33wdEhSE\nnr4e79WoWaQxdFmXpPg4GrWwKxZ1KU7tUpAYmTe26qIuSYmJmJplvHE/CAjgl9kzWbt5q8ZLnhWH\nOhS0HuJGYd0Tbap7Od18XazvGA24f49q1d8r8hi6rEvmkFNxqEtxapfiUJeHkZGMGzaUhxERKBQK\nNq/7k88mTtIqmRSHOuiiHoLwriq2K4qEhYZQoWLFAq3UoIsYoi7/nbqUK1eO7r1643f7FqdPnuDj\nYSO0Wpe0ONRBV/UQhHdVsR5+LInEMITuiTbVLdGeuifaVPfeyeFHQRAEQdCGSGqCIAhCiSGSmiAI\nglBiiKQmCIIglBgiqQmCIAglhkhqgiAIQokhkpogCIJQYoikJgiCIJQYIqkJgiAIJYZIaoIgCEKJ\nkefaj2XNxOMSdEkebyjaVMdEm+qWaE/dE2369uSZ1CzFD0KnnhsaiDbVMdGmuiXaU/dEm749YvhR\nEARBKDFEUhMEQRBKDJHUBEEQhBJDJDVBEAShxBBJTRAEQSgxRFITBEEQSgyR1ARBEIQSQyQ1QRAE\nocQotkktJSWF5cuX07dvXz744AMcHByYMmUKUVFROi3n4MGDtGrViu3bt2t13NWrV2nVqhW//vqr\natuiRYto1aoV169f1/pYQRAEoeDyXFGkKEiSxNdff82NGzdo2bIlY8eOJSEhAU9PT548eULlypWz\nvF6pVKKvr//W6yqTyQr12PT0dEqVKpY/JkEQhGKnWPbUfHx8uHHjBjVr1uSPP/7AycmJ0aNHs3Hj\nRmxtbQFwdHSkQ4cOLFy4kC5duhAcHMzt27cZM2YMHTt2xMnJiePHj6ti7t+/HycnJzp06MDYsWO5\nd+9elnKjoqLo06cPjo6OGvcIU1NTiYuLIy4ujpcvX6rt8/LyYujQoXTo0AEXFxcuX76stv/JkyeM\nGTOGzp07s3z5ctX2Vq1a4eTkxNSpU+ncuTOJiYk5xpo+fTpt2rRBLpdz/PhxWrVqxcmTJ0lMTKR1\n69bMmDEDgPHjx9OqVSvi4+M1Oi9BEIR3UbFMav7+/gC0bdsWmUymljiUSqXqdXK5nKdPn/LNN99Q\ntmxZJk+eTGJiImPHjqVKlSrMnDmTgIAArl69yrx587C2tmbcuHHExcXx7bffqiWh+Ph4vvrqKwDW\nrl2bpTeYk/3799O9e3e6d+/OgQMHVNvDwsL44YcfMDIyYty4cZQqVYrvv/+e58+fq15z9epVevfu\nTb169di5cyeenp6qfREREZibmzN58mQePXqUbazY2FiaN2+OUqnkzp07+Pr6AnD79m18fX2RJIkW\nLVoAGb3CgvQqBUEQ3gXFclwr88038193d3dVT+bTTz9l/Pjxqv2zZ8/G1NSU8+fPk5CQQEJCAqtX\nr1bt9/HxITY2FgBvb2+8vb1V+4KDg1Vlbtu2DX19fXbu3Im1tbXGde3cuTPOzs5IksSePXs4d+4c\nkiRx+fJl0tPT8fPzw8/PT1VmYGAgtWvXBqBTp04MHjyYGjVq8MUXX3D9+nU6duwIQNmyZZk2bRoA\ne/bsyTaWn58fzZs3B8DX1xdfX18++OADfH19MTMzA1DtX7t2LZIkoadXLD/HCIIg6ESxTGoNGjQA\n4MqVKwDY29vz4sUL1q9fr9bbMDY2xtRUfeXrvn370rt3byRJAqBq1aq4u7sDMHnyZOrWrau6Bmdt\nbU1gYCAA5cqVIyYmhpMnT/LJJ59oXNdKlSrRqlUrAM6ePQuo94pGjRpFmzZtVPXR19dX/T+z1/l6\n7zNTuXLlsmx7M1bNmjWpUKECFhYW+Pj4EBAQwPLly/n2228xNDSkbNmy1KhRA8i4NqdUKildurTG\n5yYIgvCuKZYf21u2bImdnR0PHjxg0qRJXLp0SdXbykmTJk0wNzfn4sWLhISE8ODBAzZv3kx0dDTt\n27cH4NixYzx+/BhfX1+WLFlCmTJlVMcPHTqUTp068eeff3Lo0CEA/vzzT1q1asXp06e1Poc2bdpg\nYGDAmTNnePjwIffu3WPVqlUoFArVazw9PXFzc2PDhg0A2NnZZRurbdu2OcaSyWQ0a9aMK1euYG5u\nTtu2bTE1NeXGjRs0bdpUFePLL7+kffv24pqaIAglWrFMagDLly9n6NChBAUFsWjRIi5dukS3bt1U\nCerN60Pm5uasWLECGxsbVq1axaZNmzA2NqZq1arY2dkxa9YsUlJSWLRoEf/884/aG35mz2revHnU\nr1+f+fPnq4YpAY1nH77eQ6tevTq//vorxsbGLFmyBFdXV6pVq6bWs2zdujXHjx/nwYMHDBs2jA4d\nOmQbN6dYmUm5WbNmQEZiz/xXJpOphh7frJsgCEJJJcscysrO6NGjpc2bN7+92hQzP/30E76+vri7\nu+ts2C4kJISaNWvqJJaQQbSpbon21D3Rpronk8mQJCnLJ/Vi21MrDu7cucPEiRPFdShBEIR3RLGc\nKFJcvD5FXxAEQSj+RE9NEARBKDFEUhMEQRBKjFyHH/X19fn6669V37dt25a2bdsWeqVKsri4OEJC\nQoq6GiWKaFPdEu2pe6JNC+71xTNyI2Y/vmViFpTuiTbVLdGeuifaVPfE7EdBEAShxBNJTRAEQSgx\nRFITBEEQSgyR1ARBEIQSQyQ1QRAEocQQSU0QBEEoMURSEwRBEEoMkdQEQXirXr58ycqVK7NsX7Jk\nCXK5vAhqJJQkJW5BY7lczj///INMJiMtLY309HRGjx5dZPW5ffs2np6eeHt7Y2hoyIQJEzS+CTMx\nMZF58+ZhZmbG9OnTC7mm2nv58iVDhgxh5syZqme6aevixYscPnyYkydPUrFiRbp3745MJiM5OZnb\nt28TGxvLyZMntYqZmJjIjh07AKhYsSIpKSlERERgb2+vekq5tuRyOb///jv379/n1q1blC9fng8/\n/JAePXrQpk0brWK9fs41a9akXbt2avsvXLhAWFgY3bp1o0+fPln25+Tu3btMnTqVlJQUBg8ezGef\nfQZAYGAg8+fPJygoiJ49e2r8uxQbG4ujoyPp6ekavX7WrFn07ds3z9elpqZy6NAhRo4ciaWlpWp7\neHg4Gzdu5IsvvtCovEyHDh2iTp061K9fX6vj3pSens6CBQuYMWNGgeK8bsWKFYwYMUJn8QQNSJKU\n49eoUaOkd0laWpq0fv16KSUlRbVt5MiRUlBQUJHVac2aNZIkZdTN09NTCgwM1Or42NhYqW3bttKN\nGzcKo3oF4urqKtnb20vjx48vcCxHR0dp9uzZatuUSqU0adIktZ9ndoKDg1X/Dw0NlXr37i1t2bJF\n7TVPnz6VHB0dpZUrVxaonkqlUmrZsqXk5uZWoDiSlP05S5IkzZ49W+rXr5/W8caNGycdOHBAOnr0\nqNS/f39p165dUnJystS7d29pyZIlkqenp/TTTz9Jx44dyzXO6+25ePFiqV27dpKXl5d05cqVbL8u\nX74sfffdd5JCodC4rjNmzJAmTZokKZVK1TY/Pz+pY8eOkpeXl1bn/c0330gDBw6U/ve//0ljxoyR\nHBwcJBcXF8nV1VWrOBEREVLLli2lb775RpoyZYrk4uIide3aVXJ0dJT++ecfrWJlcnBwkNq1ayf1\n69dP2rhxY75iCNnLSF9Z81aJGn50d3dn6NChGBkZARmfvB49eqT2tOm3LSkpCQ8PD0qVKkWHDh3Q\n19fX6vjy5cvTrl07Dh06VEg1zB+5XE5YWBhffvkl169f5/LlyzovQyaT0bdvXx4/fqzxMXPmzEEm\nkzF8+HC17eXKlWPs2LFs3bqVK1eu5Ks+qamp+Pr6AhAUFER4eHi+4uQl849TG+np6Tg7O+Po6IiD\ngwPLly9n9+7dnDlzBjs7O6ZMmUKHDh2YOXMmBgYGGscdM2YMenp6hISE0KpVq2y/jIyMsLe3R09P\n87eTqVOnEhkZyapVq1TbGjZsSIsWLVizZo1W5z548GAUCgVnzpzh4cOHWFpaEhoaytKlS/Hw8NA4\njrW1NX369OHatWtcvHiR58+fY2VlBcD+/fu1qlOmzz77DDMzMx49esTatWtRKpX5iiNorsQktRcv\nXmBqaoqJiYlq28GDB/nwww9Vv5hFYeDAgcydO5ewsLB8x+jTpw+nT5/m5cuXOqxZwbi5ueHs7Ey/\nfv2wsbFh7dq1hVKOra2txsNfiYmJ3L59G1tb22zfYBs3bgxkDP9pQ6lU8tdff7Fw4UJVsilfvjw+\nPj78+OOPBAUFaRWvMJQqVYoePXqovq9ZsyaWlpbs3LmTfv36qbYbGRnRtWtXjeOWL18eZ2dntmzZ\nQnJycravOX78OA4ODlrV19TUlHr16rFv3z6OHTum2l6nTh06d+6sVax27dqxf/9+Ll26xPHjx3F1\ndWXfvn1UrFiRCxcuaBxHJpMxe/ZsPD09uXjxIkeOHGHnzp0MGDBANZSrrQEDBnD8+HHmzJmDvr6+\nVolfyJ8ibeHIyEiWLVuGq6srixYtYvfu3ap9z5490yrWuXPn6NChA7GxsUycOJGFCxeyf/9+fvzx\nR41jbN++nfnz5/Ptt9+SkpKitu/atWta90bi4uI4f/48NjY2zJkzR6tjX9ehQwf09PS0+tT5ukeP\nHuHk5KS2bdq0afmOl5iYSHR0NDVr1kRfX59PP/0UPz8/vLy88hUvNzY2NtStW1ej12Z+ClYoFNnu\nz+8khIULF3Lu3DlmzZpF06ZNgYye36BBg+jZsyfjx48nNDQ0X7EL03vvvUdAQADvvfdegeKMGjWK\n9PR0tm/fnmXflStXsLOz0zrm/fv3SU9PZ/78+fzyyy/cu3ePxMREjI2N+fTTTwtUXwArKyt69uzJ\n06dPCxQnOjoad3d3ra+bvql+/frUq1evQDEEzRRZUgsICGDSpEmMGDGCoUOHMnXqVFxdXbl27Rox\nMTFaf5qOj4/HwsKCChUqsGrVKn788UeaNm2qlihzo1Qq6du3Lz/88AP37t3D3d1dtS8wMJBHjx5p\n9YsdHh7OtGnT6NevH9999x23b9/G399fq3PKZGBgQOfOnTl8+HC+jvfy8qJKlSqq71++fMm5c+eo\nUaNGvuK5ubnx8ccfq77v1asXNWvWLLTemqbMzc1p1KhRjgkmc3uHDh00jpmQkMDff/9N69ats93f\nunVrEhIS2Ldvn7bVLXQVKlQAMhJwQZibmzNs2DB27NhBfHy82r6zZ89q1fPLdOzYMWxtbWnXrh3j\nx49nypQpXL16lerVqxeorq+ztrbGwsKiQDFOnz6ts95VpUqVdBJHyF2RJDVJkpg+fTrOzs5UrFhR\ntb1+/fp4eHhw+vRprYcgsvvFS0lJ4ebNmxofb2lpiYGBAf379+fAgQMAPH78mJs3b+Lo6KhxXSRJ\n4qeffmLChAlYWlrSqFEjTExMePjwocYxXnfz5k0SExPx9vbWugcL4OPjo/amfPv2bUxNTfOV1OLj\n40lISMDGxka1TSaTMWHCBAICAjh9+rTWMXVp8uTJREZGEhkZmWXfxYsX+eCDD7TqWRgZGWFqakpS\nUlK2+zOH5AqaOApDqVIZk5u1vY6bHRcXFwwMDHj9UVTnzp2jffv2+YoXHBysmvk4fPhwWrVqxaxZ\ns3T6xl+qVKkC91Kjo6OJjY3NsfevqZSUFBITEwsUQ9BMkSS127dvExISkmUc3szMjKCgIPT09DAz\nM9M4XkREBNWqVcuy/f79+/m6nubo6EhISAjXr1/nzJkzDB48WKvj/fz8SEtLU13DkclkGBsb56su\nly9fZt++fSxYsABra2uOHj2q1fEKhYLr16+rJbX8DhkB7N69m6FDh2bZ3rVrV+rXr8+ff/6p9SQH\nXWrSpAmDBg3i1KlTatvlcjk+Pj788MMPWsUzNDRk5syZeHp6Ehsbm2X/rl27aNiwIUOGDClQvYs7\nU1NTRo4cyZ49e4iNjUWSJC5evMiHH36Yr3jlypVTe2jmtGnTqF27dr4nZGQnJSWFhg0bFiiGqakp\n6enpBX7A5/Xr17l27Rp37twpUBwhb0WS1KKioihfvrzaPSqZkpOTtU4iV69ezRIrPDwcf39/tYvk\nmqpatSp169Zl8+bNuLi4aH38kydPqFOnjur7qKgoJEmiQYMGWsU5d+4cmzZtYvr06ejp6dGrVy+t\nhyD9/f2Ry+Vq9/D4+PhgZ2dHREQEUVFRGsd6+vQpaWlpOX6anjBhAiEhIWoX/t+m8PBwHj9+zJdf\nfsmNGzfU9nl7e+Pi4oKNjY1GT899XdeuXVm5ciVbtmxRXVfy8fFh1apVVKlShfXr16tm3OqKTJbl\n2YdFztnZmTJlyrBu3TpOnDhB9+7d8x3LyckJNzc3/Pz8gIwh9sWLF+Pt7a31B7ecREdHFziplS9f\nHqBAE70SEhI4d+4ctWvXZu7cuRpPfBLyp0iSWuaMttd/uA8fPiQyMpIqVaogk8l4/vw5kPHGPnfu\n3FynwiYlJXH79m3V90qlkuXLlzN+/HhVItEkzuvHW1lZERsbm+2bS16xbG1tiYuLU32/a9cuXFxc\nVMNAmtTl+PHjrF69mkWLFmFoaAhkXLsKDAzkwYMHGse5cuUKpqamqvPIvLbXqFEjLl26pLruokms\nzZs3U69ePa5evZrtV+nSpalcuTLr1q1TDddo2u6a9O7yimVmZsaBAwdIS0ujTp06apN97t27h4OD\nA5GRkao3Um3qV69ePaZMmaK6VaBly5ZMnDiRjz76SDXMp008yP2c39ynTVxNaRvTyMiIsWPHcuDA\nAby9vWnZsmW+4zZu3Bhra2t+//131bby5cvz888/s23bNq1ipaens3fvXubNm8fs2bNVX15eXkyZ\nMoWYmJh8nS9AmTJl1P7Vpl7h4eEsW7aM4cOH06JFC4YMGUJQUJBqvoAmMRITE5kzZ47qbz7Trl27\ncHNzU9sWEBDA3Llzc5yl+l9RJEmtevXqfPvtt/z222+4u7uzd+9e7t27x6xZs3j69CkbNmxQjT+H\nhIRw9uzZXO8JMjIyokmTJmzbto1du3axfPlyevXqxSeffKJ6jSZxMrm5uTFq1CgCAwOzvQ6WVyxr\na2sGDhzI+vXr2bBhAwYGBowcOVLj4+Pj41m7di0rVqxQu9BtY2ND586dVZNYNDmnK1euULVqVVav\nXs3u3bt5/vw5Q4cO5fjx45QpU0b1hpxXrNjYWPbt28f//vc/Pv/88xy/oqKiiIyMVH3azivuxYsX\nmT59OtHR0Zw7d46ff/45x3vy8opVrlw5Dh48SI8ePdi2bRtPnjxR7QsLC8PR0REnJye1YWBNfy/k\ncjlLly5l8uTJyGQyduzYwcKFC7Pc86ZJvNfP2dPTk/nz5xMWFkZYWBjz58/Hy8uLJ0+eMH36dNUb\noDa/v15eXsyfP1+1ss6kSZNYunRplh6CNjEzOTk5YWNjk2U2rTZx5XI569at48WLF9ja2qrta9my\npdrQuCZ1nDFjBosWLeLAgQMcPnxY9fX48WOqVaumum6fn/ONioqiffv2WSYK5RXr6dOnjB07lr17\n99KwYUMcHBzo168fnTp1UiVITeoTFxfHpUuXsvQUr169io+Pj9q2sLAwvL29s0zm+c/J7o5sqZit\nKOLv75/jShwhISGSt7d3geNkcnd3lx4/fixJkiQ5OzurVgTJT6zsZK7WkN/jtalHSkqK1K5dOykk\nJKTAsQpCl3Gzi/X6Chi6ilmc4hVm3MJoz5ziFlaswMBAKS0trVDq9fz583zFithPtIcAACAASURB\nVIuLk6KiolTfZ7bp06dPC1Qf4V+8yyuK+Pn5UatWrWz33bx5U3XvUEHiABw+fJhGjRpRuXJlIOPG\n6YMHD5Kenk5cXJzaMEFesQpaF13EuXnzJmXLltV4lqOu6lSYcQujjrqO+S60Y2HG1HXcvGLVqVNH\nbQhYl/XK7rq/JrEsLCyynRj25izZwmr//7Jin9QePHhA5cqVc7xXRC6Xa3SRPq84V65coVKlSmo3\nSA4YMIAGDRqwaNEi9u7dqzo2r1gFrYuu4oSEhGh8MV9XdSrMuIVRR13HfBfasTBj6jpuSY5VWO3/\nXyeTcrlYPXr0aOn1+1KEggsJCdF4lX5BM6JNdUu0p+6JNtU9mUyGJElZZvKJjwiCIAhCiSGSmiAI\nglBiiKQmCIIglBgiqQmCIAglhkhqgiAIQokhkpogCIJQYoikJgiCIJQYIqkJgiAIJYZIaoIgCEKJ\nkeeCaf7hWZ8gLORfYnwCctGmOiXaVLdEe+qeaNO3J8+kZvTqWV6Cbsj19UWb6phoU90S7al7ok3f\nHjH8KAiCIJQYIqkJgiAIJYZIaoIgCEKJIZKaIAiCUGKIpCYIgiCUGCKpCYIgCCWGSGqCIAhCiSGS\nmiAIglBiFMukFv3kCc6OfXB27IOL0wAmjBnFyiWLiX7yRONjF/48G4BVy5fh7NiH4AcPtKrD68el\npqayZ8d2PE6fys/p5JskSaxcupjhHznh7NgH31s3dV6Gl8dZ3HbuICkpSbXN2bEPUyZ+ofOyCsvK\nFctxduxDTHR0ln2JiS9YtvAXRg9xZvhHTnzz+Wd4eZzVWdlnT53E2bEPB/fteyvHCYKQuzxXFClK\nNWvXprdjf3xv38LzzGn8bt9i8e+rsbCwyPtgmQyAnr370NzODqvKlbUq+/XjUuVy9u5ypUGjxnS2\n76b1eSgUCvT19bU+LjI8nPMeHtSsXRvHgU5Uf6+G1jHycv6cBzeuXqVzt26YmpoC8PX3P2Bqaqbz\nsgrVq5/3m/a6uuJ94QIDBjtTpWpVwkJCSHzxohDKf8vHCYKQrWKd1MqWK08ne3s62dtjYGDA6ePH\nOHboIB8PG859f3+2rF9HRHgYZcuV42OX4XzYqVOWGMePHMbzzGkWLv8NszJlmPjJWOrZ2mJoYMiD\ngPv07tcfAwMD9rvvxapKFX6YPoOKlaxUxy1YvoKlv8wH4K6fL86OfRjsMoyBg53ZuWUzFzzPkSpP\npUnzZnzy+ZeYW1iwavkyPM+cpptDL65duUw/p0EYGhrivnsX8XFxWJYtS+9+/XEc6JTr+Wf2lkKC\ngli5dAmr12/A2bEPNtWrs2z1Gi6dP8/yRQsY7DKMwUNdmPXjVPzv+NF3oBPnTp+iTBlzvp82Hetq\n1Xj+7Bmb1/2F762bKBQKevbug4GBATeuXgXgy3FjqVjJitUbNvLb4l+xqV6dZnZ2JCa+YPO6dVz3\nuYJMJqO5XUvGjP8MUzOzXMs7efQI7rt3kRAfj4WlZbbnGx4ayrJFC4iNjqZUKQPq2doy4atJlCtf\nnj07trN3lytde/Tkzu3bJCcn8cnnX/JB+/akpaWxZuUKrnp7U79BQ+QpKSBJ2bbho8iM9fYaN2lK\n42bNVNsVCgWfjxmFhaUli1euAuC7iV/y4kUCazZtYeInY3nx4gWdutrjefYM9W0bYN+zJxvWrgUk\nvvh6Ms3s7FTxwkJDmfzFBOLj4hj4kTOOThnneurYMfa77+X582dUq16dUZ98Sv0GDdXqKEkS61av\n4uJ5L9LS0qhkZcW4CZ/TqElTVRtv2LmLMmXK5Pr7IghCMR1+zE7zV28gwQ8CSUx8wcI5s0lOTsLJ\n+WMqVqrEyqWLCQ0O1ihWUEAAdq1bY2Zuzr49u7nj50vXHj0JCwnh8P79aq+VIcNl1GgAbKpV55sf\nptK23Yf8vWc3h/75G7vWbejTvz83rl3jr9Wr1I6973+XIcNHUK++Lds2bcTMrAyjxoylZ5++lCqV\n9+eJoSNGAtCgUWO++f4HzM0zeqiyHHolmR4/fEiHzl149DCSA3+7A/Dbkl+5dN6Ljl26MnLsOMwt\nLGj7YXtq1KoNwNjPJjB2woR/z/tVGZv+/BPPM6fp2r0HXbp1x/PsGTb99WeO5R38O2M4LfN8J0z6\nOsfzNTAwoIt9N8Z8NoGefftw6/o19uzcofaae3fv0LtfP5KTktixZRMAJ44c5ryHB02at6BR06YE\nBwXl2FOzbdQIgLkzpjFm6MesWraU2JgY9PX1se/pQFhICCHBQTx+9IjwsFA6dOmKnl7Gn0WqXI5M\nJuN9W1tuXr/GlvXrGDjYmYT4eFVdMvndukmffgOwLFuWbZs2EBYSgu+tm/y1+ncsyloy+pPxxMbE\nsGjuHF680VMMDQ7m1PFjNG3egvFfTKRVm7Yolcp/fw55/LwFQfhXse6pvU569UlchowA/3skJSaS\nlJiI69YtGS+Qybjje5vWH3yQZ6y6779Pn/4DeBAQQGx0NE7OH2NVuTJHDuzP9rpdk2bNATC3tKBd\nh44ArF6xDIBTx46qXvfmNS+XkaOwa90GgKrW1jyJisLf/y71GzamQ5fOedazSfPmuG7bSiUrK1W5\nmhg57hOMjY05cmA/MdHRyOVy7vr6UrtuXUZ/Ol7ttWXLlSU0GOxat6FipUpZYt24dpVyFSowfMxY\nIOMa3M3r13IsLzr6idr5+t66Sa3adbM935dpL/Hy8CA8LFS1LSIsTO01jgMGYt/TgWOHD/Hk8WMA\n7vj6AjBi7DisKlfmkpcXQQ8Cs22LgYOdKVPGnPOeHgTev4/n2TM8ehjJL0uX062nA3/v2c3Zkycp\nV748AJ262quO1dPTY/Sn4zl78iS3rl+nk303ejk6cvBvd2KeqF+/69K9B90cHNDT02Pt779x189X\ndY3vY5fhNG7WjJiYJ/y9Zw+B9+6pHVuufHkMS5cmJDgIcwsL3re1pWHjJgDM+mUBkiSpEq0gCLl7\nZ5LazevXAahVp45qWyd7ezp26QqvRp4qWlnlNAqlxuTV9SL9V70HExNT1XGZn5Bfl1PPSE9fn59m\nzUZPlvGGo5TUjy1brrzq/7PmL8D74gXu+fmyc+tmLnp5MvfXxXlXNmtlUCgUACQnJWa736xMGaRX\n55Hd+ai9XHVRR4OGy6E+2ZWXeb4hQUE5nu++3bsJDwtl2Ogx1KxdmwWzZ5GW9lLtNWavhtz09fRR\nvvHDlVTfSzn2ZtLS0ujm4EA3BwcSE18wcdw4IsLDAShfoQJ2rdtw/pwHFSpWombt2lSrXl11rKGh\nIfr6+uiXyrgeamJiotqnVCrU6/LqvCXV70DOPaw3N1tYWrL8jzVcuXSJwID7rFyymMjwcIaMGEl6\nejqSJGEoVngXBI0U649/z54+5eypk6xesYzTJ45jWbYcPfs6Us+2PmZlynDj6jUeRkQQHhbK33v3\n8Pzp00Kph7GJCTKZjKhHj/DyOEtMdDR2rdugVCjwOHWKmOhobly7ysmjR3OMsemvP3mZmkr16u9h\nYmLC8+fPAO1nZ1aysiI6Kgovj7McPXQo6wuyyepGRkY0bNyEoMBANq/7i1PHjqmGCTOThsfp06oe\n0OtatGrNs9hYtm/exPZNG3n+7BnNW7bMtbzXz7dmrVpq5/u6zA8LLxIS8L5wQZWs89K4aVMAtm/a\nwH73vRnDjznU4/dlS1i1bCknjhzm9PHjpKbK1Sbc9Ojdm8QXLwgNDsrXJKBMZ0+d5OSxoxnD1zIZ\nDRo3osWrdtq9czsnjx7hzIkTmJUpQ9369dWOffzwIf/s3YuRsTF1670PoGqveTOmM3zQwCxDloIg\nZK9Y99RCQ4JZt3oVFpaWdOjUmSEjRqpmPv44czZbN65nx5bNGBoa8r6tLRWtrLL0TGQycv/ErMG+\nUqVK0c9pEMcOH+L3ZUv56tspDBzsTKpcznnPc/h4X6JS5cp0d+idY9zk5CR279hOSnIyVlWqMHx0\nxnAeUkYvQ9PZkcNHj2HdH6vZvWM7jRo3URu6y+36y6Tvv2fzX3/hefYM6WlpOPR1BKB7r17c9fPF\nbecOGjdrRsPGjdWOG/NquPLMiePIZDI6drVn9Kef5Vle5vnKU1KoVLnyv+f7GifnjwkNCebEkcP0\n6tcPExNT9Re8Hvu1/3Zz6MWDgACuXblCamoqtWrXJigoKNt6NGnanBNHD3PF+xJIEu/bNmDc5//e\nrtC0eQusKlchNiaa9p06Z192lrpk3de4WXNOHDlMfHwcI8aM5b0aNQH4bOJX7Hffy5YN66lWvTqj\nPx2PmVkZtbYzMDQkJCgIr7NnUCqVvG/bgAGDBr8KLa6pCYI2ZFIu43WjR4+WZi389S1Wp+SLi4nG\nsuK/167+N2UykiSxYNmKIqzVu+3NNtVUcnIy9+7c4Y/flmPbsBFTfvpfIdTu3ZPf9hRyJtpU92pV\nsUKSpCyf+Ir18GNR2vjnWkZ9PJjjRw4XKM6W9etyjJOWlkZoSAijPvm0QGUI+RPy4AEL58zG3NyC\n4WPGFHV1BEHQgWI9/FhUfLwvceXSRb74ejK/L11Mp672GBkZaR3nxlUffLwvMfHbKfz26yK1mXWQ\nMaV9575/dFVtQUsNmzRhz8GCfWgRBKF4ET21bPyzdy9du/cAwNzCAgMDg3zH6WzfDaVCWaA4giAI\ngmZET+0NUY8fE3j/HmM/+4zadevRpl27fMWJiY7G/+4dRowdR516+Y8jCIIgaE701N5w3ecKRkZG\n1KpTt2BxrvpgbGxM7boFiyMIgiBoTiS1N9zx9aXO++/nuRRVXu76+lKnXr0CxxEEQRA0J5LaG+7d\nvUPtAvbSIGPdx9p16+mgRoIgCIKmiuSaWkx0NF99Oi7HJZzsezrw2cSvAPC/c4dZP03NccWIoSNG\nMtD5Y53U69HDSF4kJFCjVq0CxYmJjubZ06fUfLVYcE6vya4NstyA/IbUVHmWlTc0bYOkxETGuAzJ\nsS21NWLMONVq9IIgCMVBkSS1ipUqsWv/Qfzv3GH5ogXEPX8OgENfRwa7DFN7xIZtw4bs+ucA1318\n+G3Jr6TK5QC4jBqNQ1/HfE21z8l9f38AatQsWFK7738XgOo1auT4mmzbQCZj8+49ucZevWIZ506f\nBrRvA1MzM3o79uPIgf2UMjBg0YqVamsdvik9PZ20tDSSEhNJiI8nLCQY/zt38L5wHrlcztlTJ0VS\nEwShWCnS4Ufbhg3p0LlLxjcyGcNGj8n2mVF6enq0bNNG9fgZcwsLBnw0WKcJDTKSmoGhIVWsrQsU\nJ+DevYw4Vavm+Vq1NtBAZicrv20wdOQorCpXIT0tjdXLl+a63mKpUqUwNjamQsWK1KpThy7de/DF\nN5P5c8s2Bgx25mFkBIH37+V4vCAIwttW5NfUSpcune3/s2NY2ujV63SbzDIF3POnqrV1gR/zEXj/\nHtY2NhrHyeu8sz8mf21QunRpPv/6a5DJCH7wgH/ccu8ZZsfYxASXkaP4ftoMzp46la96CIIgFIYi\nT2rFRUpyMpEREdhUy3k4ThNpaWmEBgerrQRf3DRo1JievfsAsHeXK2GhIfmK07JNGwwNDUlNTdVl\n9QRBEPJNJLVXgh4EgiRhU61ageKEhYSgUCiwyeVaVXEwbPQYKlayQqFQsHr5Mo0f+/KmEWPHiZVS\nBCGfLnp5cuifv/lr9SpiXj1gV6lU8jQ2loN/7+NJ1OMiruG7RyS1Vx4EBABQ1aZgSS0kKOO5aNYF\nTI6FzcjIiAmTJgEQGhyM+y7XfMXR19fXeJj15rVrrFm5gvVrVuerrMLw67y5LF3wS1FXQ3iH7dy6\nhSW/zOOv1at4Ghur8XFpaWk8jYml74CBtO/UiYVzfubgvn1IkkT5ChV4GBnJmpW/FWLNSyaR1F7J\nTGrV3itYDyvo1cM+rW1sClynwta4aTO69XQAYJ/bHkKCs38mma40s7OjXn1brl+9WqjlaMO2YUPe\nt7Ut6mroVFpaGh6nT/G32x7Wr/mDg/v2ER4aWihlJScnF0rcd8ngoS5897/pvF/flp+n/UR6erpG\nxymVSmJjYwCo+359HkZGUr1GDdWzFQcPdSHg3j3OnjpZaHUviURSeyX4QSD6+vpUqar9zMeUlBS2\nbtzA52NGcebkCZDJ3pnrTCPGfUL5ChVQKhSsXrZM4z/I/KpcuUqhxteW40An+g4YWNTV0JmHERGs\nX7Oa920bMHCwMxYWFvQZMICgwADcd+/SeXmZt5f8l2UOv3eyt8fQwBDfWzc1Oq506dLYVKvGsUMH\nMTAwwMTEhMiIcNX+8hUq0LN3H7Zt3MCLhIRCqXtJJJIa8OLFC2JjYqhS1VrjJ1BnSpXLmTPtJ8qU\nKcP8JcswMTGhQsWK7Ni0Ce8L5wupxrpjbGzMhK++BiA8LBQ31x1FXKO3JyE+nvv+d/G7fatQywkL\nDWHm1O8Z6zKE6T98x6XzWX8vUuVyTh47WqByXiQksHeXK598/qXa7SR6enp06d6DWnXqcGCfe4HK\neNO5M6dIS0vTaUxdOX38GN99NZGvJ4zHzXWnWj1T5XJ2bt3CyqWL2bJ+HRc8z+mkTOtq1bTqFXfv\n1Zv7/v6kp6djZGys1vNVKBRUqFgRI2NjHj2M1En9/gvEKv1A6Ktht2rvvaf1se67d9HPaRAftO9A\nRHg4yUlJtGjZiknffc+iuXOo36AhlmXL6rrKOtW0RQu6dO/O2ZMn2e/uTusP2ulkqTBNpcrl7HPb\njYmxCQaGhkQ/eUL/jz6ibNlyAMTGxPDPXjesbarxMDKcsmXLUdrIiHt37/Dd/6ZrFH+/+16qWFuj\nUCi46+dHw8aNqWptjfvu3URGhPPHhk0A3Lh2ld3bt1O6dGn6DRrE05gYEuLjiX7yhM+//kbrtTyV\nSiWb/vqTISNGUr9BQ2JjYtjvvpejhw7gNPhjGjZpQlJiIq7bttCxi33eAXOx330vQ0aMzHHiTnO7\nlty6fp34+HgsLCwKVBZAamoqocHBhAYHU/f99wsUKykpiY1r13D1ymVMzczoP+gj1Qzdi16euG7d\nytgJE2hu11KjeBc8z1HayIglv6/iYWQkS36Zx7Url5k1fwH6pUqx+Jf5DPxoMA2bNAHA7/Yt7t29\nQ/0GDbWqt7+fH7u2b8PUzBQjI2Pinj8nXYskr1AoqFTZiqTERJAkbly9SlJiIlGPHvH8+TP69h/I\n73+t1/rDdmLiC86ePInjQPXFEZRKZZZr4KeOHaNJ8+ZUsrLSqoziSvTUgJCgjKRWPR9JLTIinA/a\ndwAy7nMDqFWnDgBDho/Ay+OsjmpZuEZ9Mp5y5curhiHf1qdvSZJYMGc2DRo2pv9Hg+ndrz/de/Vm\n1o9TVavH/L50CfXq16eXoyP9nD7i0P5/sO/Rk6EjRmlUhsfpU5iamdGhcxc623fjw44dkSSJuu/X\np98bK6I0t2vJ0JEjeRgZgaGhIT169+GjoS48CAjA75b2PbqkxES69XSgQaPG6OnpUcnKik+/+JLP\nJ32Dp8cZJoweyfTvp1C3Xn0aNm6sdfxMkiSRlJSEVeXKub6uS/fuXLl4IV9lyF99OPjjtxXs2bGd\nv932oFQqOXn0CPv27Oav1avYtnEDz5890zr28oUL6NG7D+u376RzV3s2rPmDfXt2c8fXlx2bN9O1\nRw/eq1FT43hxz5/TvlNnIOP69vQ584h+8oRlixawbeMG+jsNUiU0gEZNmvIwUrve0O2bN/jl51kM\ndnHhh+kz6d6rN/53/DDX8AODXC7nvIcHSYlJlDE3B5mM5i1bMvrT8ZiZm2NsYkKHLl20TmhPoqLY\nuHat6pmQmTzPnmH4oIH8/ca9qZ3s7XFz3ZExA7wEED01Mmb/AVSvqfkfTSYTUzPV/wPvZayukfmp\ntYq1NSeOHtFBDQufiYkJ47/8ioVzZhMZGUFQYIDWn1rz49qVKzy4f5+mLVqotlnb2GBoYIjH6VP0\n7NOXkOAgLCwtAShbrhxJiYk8fvRI9eEhLxaWlvy1ehWJiYmvJoY0IDkpCQAZWXtepfRLoVAoaNy0\nmWqbuYUFz5491fr8ypibq95cX1elalUmTfle63g5eZGQQOU8EhrAezVqctHLM19lGBkZ0X/QR1zw\nPMfWDetVycvj9CmMjIwYNGQofQcM1PpN+PGjRxiWNlRN2HEeNhy5XM6u7duwbdCQRb/9hplZ1pWG\ncpKSkoLVG9duy1eowJffTGbR3DnotypF42bNshynzSII6enprPltBV2796BRk6YAKF/dFvOehsvs\nGRkZIU+V06hJE/T09JCUSvVetpZLtKalpXHiyGF279hOk2bN8PI4S+OmzbCuVo2U5GTcd+9i2R9r\nWbpgPu07daJiJStiY2K45nOFUqUMmP7dFPp/NJh+ToMwMTHRrvBiRCQ1Mq55IJPlugBxTtLT/+3R\nPAjMmGyS+Sy2iPAwnQzzvC0tWrWiTr161G/Q8K0kNICIsFAMs3kzMSxdmojwMCDjJu8HAQE0bd6C\noMAAKletmuu6mm9q+2F7FAoFp08c5+jBA5iYmDDt57lQvnyOxxhn90edz3Wgw0JD2LDmDyIjIqhq\nY0OffgP4oH17tdekyuV4nj1D916981cIEBgQwD973dS2+d+9k2VbQX3YsRPNWtjx2agRvHz5EoDp\nc+dTr379fMWLevyI9PQ3FukeOYob166SmirPc5HvN6WnpSFJWRdLt2vdhuo1auJ76yYRYWFZLjfI\nU+Qal3H/7l2exsbi0NdRte1BYABmZcpQp57mT+fIHGKFjF59+QoV/t2p5VOrJElCqVCCJKFUKklN\nTWXl0sU4DxtOaFAQXbt1p3KVKjg5D8Ft50569O7DH78tp0PnLiheTRBTKhRIOSw0/674zye19PR0\nHkZGYmZmRoWKFbU+vnadunhfOE+LVq2JDA+jRq3aGBgYIEkSe3ZsZ/iYcYVQ68Lh5XGW0qWNGD5m\n7Fsrs3LVqiQmJqJQKNQ+4cc9f4ZV5Yw3/nrv10ehUHD88CGSk5OZ++sSSpXS/Ff33OnTtG3fng87\ndiItLQ3XbVtx3baV7/43Tefn8yalUsmWdesYOmIU7zdoUGjX1MwtLGjavHmWpJj28iUDPhqs+l6h\nUHDewyPf5WS6deM6L9PScOjryNlTJ7nifSnfSc3MzIy7vrd5/uwZZctlXEdNTZVjbm6B/9077HXd\nifOw4RrHK2NuTmxMTJbtF86do0mzZqSlvWT5rwtZsGyFWu8sMfGFxmXEPX9OKQMD1YQchULBmZMn\n6O3YT+P7NhUKBVcvX6ZNu3YoFArkcjnVqr+WaLX8EGVoaIijkxOtPviAXdu30q2nA5262jNn2v8w\nLG3IvMVLAfigfXu8PM6yeP5cZs77hUqVK/PX6t+Z++sSrRJycVVirqn5XPZmzcoVOT7OJiePIiNR\nKhSq3pW2cfr0H4D3hQtsXLsGpVJJrTp1CH7wgI/79eXGtWsFXqFEG/ltA4CI8HDcd+1i8tQf8732\nZX7Kb9mmLdXfq8HNa//euxYWkrFsV9cePQEIDwujs303evbpq5qm/rqbN27kWm70kyjOn/MAMqZf\nf9ixI+bm5tqcGpIkIb16l9HmPJMSE+naowe2jRppfU1N6/bUYBLLzevXaPZqYfB8lfHK8cOH+fq7\n7xn72QRmzvuFKxcvqK6BvimvMmrUqo1l2bLM+mkqxw4d5MA+d36c/A0DBzszcPBg9u7exdrfV6pu\nbNakzkZGRmrT429c9eGClyfDRo/hh+kzSYhPYMkv81SJ7OjBA2pPrMirjKrVbJCUStU579q2FYA+\nr90ekleMhIR4ggIz7o+NjYnBpnp11WOvLMuWJSY6GkmSOHHkMHK5XOOfVeUqVRj32eecOXECy7Jl\nWfbHGhYu/03tg+AP02fw55ZtWFerhsfpUwz6eGiWhKZJecnJyfzx24osMz6PHNjP8cOH1LaFBgez\nZuUKUlJScq1/QZWYpPYwIoLLFy/y+OFD7Y6LjACgVu2MocfI8HCt4ujp6fH19z9Q+tVq+WdPnuDP\nVSv5eNhwTExMVPHfhvy2QUpKCiuX/MrEb6dofJE7P+XfvHaN3Tu38/zpU9b+vpKkxEQMDAyYvWAB\nd/38+NttD+67d3Hm5Anm/roEU9OMYadatesw1mUILk4DGOsyhG+/+JztmzaqJrM8fvQo13INDA2J\njYnh+JHDHD9ymPMeHnw8fAQ+3pdw3b6V50+fsumvP7PWceVvPH/2jL9WryIk6AFHDx7Ay+OsVu2c\n1zW1DTtcWbV+I90cHLRuzze1+aAdJ3O5hvvs6VMSX7xQXZ/MTxkA4aGhOH38MR927ARkXEOeOnN2\njrdG5FWGgYEB30+fgYWFJds3b+L8OQ/GjP+MZnZ2DB0xipFjx3H75g3Cw0IBzf5Gu3TvwQ2fq2z6\n609WLF7EPf+7TJ76I/r6+ljb2LBoRcZ1um8mfMaUiV9QqlQp7Fq30bjONWvVxnGgE7/MnsX8WTNI\nSIhn7qLFGBsbaxyjbNlydO7WDTfXnRz652++/Gayal//QR9hWbYsc6dPw7pa9YwkrcV7Uxlzc40f\nC9XdoReVq2S9f1ST340XCfHcvHYtyy0Hfrdv4/vGxKpHDyO5df06iS8K9547mZTLAyNHjx4tzVr4\na6FWYM+O7ezd5QoyGXsOHMr1tauWL8PzzGkqVrJi9YaNWfaHBAWhp6+n1SwpN9eduO3cweSpP6pm\nMeYnzs4tm/lnrxtzFv2quh6VXZy4mGgsK1ZSO7ao22D5ooU0adYM+55Z31i1lZ/yc+N/5w5HDx1g\nwldfY2JigkKh4GlsLHt2bMe6WjUGDnYmLiaa5wkvdFpuXnR9nroqJzIinGuXL9O5W3csLC1x27mD\nwS7DuHX9OlFRj9Wu4eRURna/o6/Lblp4btvzcx55eRvtr4syMmNYmJrm2qZvs07FuTxt1KpihSRJ\nWYYnSkxPDSDg/j31MWkNPIzI6EnVrP3vTLr8xImMiEBPT6/AcQpK2zKPRNE46gAABFdJREFUHjyA\nsYlJvhOaQqFQG57Q9TlHPXpIlapVVbOx9PX1qWRlRf0GDdVWP3nbbf22ytO2HJtq1endfwA3r13j\nb7c9xMXFcfDvfVhYWmab0PJTRk6JK7dha12319tof12U8S6ed1GWpwslZqJIWGgIFSpW1Pp6UGRE\nBKZmZqrud37jPIqMpKqNjerCc37jFIS2ZQbcu8dFLy9m/bIg32Xu27Mbx4FOGBkZFco5d+neg7Mn\nT7Br21YsLDNuYk98kYC5hQVOzh8DGT/Dt9nWb+tnm99yDAwM6GSv2aSTt3Euui7jXanzu3jeRVme\nrpSYpPZejZpad5GVSiWPHkbSqPG/N2HmJ056ejpRjx/Rseu/byT5iVNQ2pSZEB/PhrV/8MP0mVrN\nJHxdwL17RIaHq56+XVjn3OWNm0jfZFOtmk6GdjT1tn62b6Ocd7GMd6XO7+J5F2V5ulLkKView4yp\n3I/RzeyZ6CdPSE9Lo24+pyJninr8CKVSme+lgl5vg7zaQ/5q5lBB2kCpVLJ6xTJGjvtU/b4YDaWm\npnL04EHmzZhGyzZt8j5AEAThLSmynppCocD/jh+eZ7VfRupFQgJ7XXfSy7EfpmZmeR+Qg4hXs6ny\ne3/Nv3Eypg7Xe1+7OFnaQJLYtnEDzi7D1GaoQcZqAdd9rqimvhekDfa67qRR46Z5LsuUlpZGeno6\nyUlJJCTEEx4ayn1/fy56eZKclEQpAwNatmmrVdmCIAiFqUiSWmxMDN9++TmSJCED1fDVpyOG5Xpc\nWlqa6rX797lzYJ87Hw11oZ/ToHzVIyI8HGQy6mqZjLLECQultJGRVqtc5NQGnmfP4Hn2DJ3tuzFu\nwucA3Pe/y/yZM5DIuBhfkDa4df06e3fvykigmzZoc5pZNGnWXG0KsyAIQlErkqRWoWJFtu7ZWxRF\nqwkNCaZ69fcKvM5ZaEgItevW1WoFd23a4H3bBmx1K/gjQxQKBe67Xalqrf0z47LTqWvBVpUXBEHQ\ntRIzUSQ/QoOCaKbhoyxyjRMcRCf7bjqoUeHS19dnzqLFRV0NQRCEQlPkE0WKSlJiIlFRUdg2bFSg\nOPHx8cTGxNCggHEEQRCEgss1qUVFRb2terx1d+/4IQMaN2tasDi+vhgYGlK/oWar2l/18SlQeUJW\nok11S7Sn7ok2fXv+s0ntuo8Ptg0bafWcpuxc87lC0+Ytcnza8JvEL7fuiTbVLdGeuifa9O35zww/\nKpVK5s2Yjr+fHykpKXhfOE93h14FipOcnIyP9yXse/YshBoLgiAI2vrPJDW5XM4d39s8e/aUHZs3\nYVW5Ch926lSgONs3buC9mjWxa9W6EGosCIIgaCvXVfplMlk+n/VbPFlZWVGlShVSUlIIDg5WPbqk\nqOIIgiAI+ZfdKv25JjVBEARBeJf8Z4YfBUEQhJJPJDVBEAShxBBJTRAEQSgxRFITBEEQSgyR1ARB\nEIQSQyQ1QRAEocQQSU0QBEEoMf4PrAlMQrsCkYcAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xd37f828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from __future__ import print_function\n",
-    "import matplotlib.pyplot as plt\n",
-    "import os\n",
-    "import sys\n",
-    "import re\n",
-    "import gc\n",
-    "\n",
-    "# Selection of features following \"Writing mathematical expressions\" tutorial\n",
-    "mathtext_titles = {\n",
-    "    0: \"Header demo\",\n",
-    "    1: \"Subscripts and superscripts\",\n",
-    "    2: \"Fractions, binomials and stacked numbers\",\n",
-    "    3: \"Radicals\",\n",
-    "    4: \"Fonts\",\n",
-    "    5: \"Accents\",\n",
-    "    6: \"Greek, Hebrew\",\n",
-    "    7: \"Delimiters, functions and Symbols\"}\n",
-    "n_lines = len(mathtext_titles)\n",
-    "\n",
-    "# Randomly picked examples\n",
-    "mathext_demos = {\n",
-    "    0: r\"$W^{3\\beta}_{\\delta_1 \\rho_1 \\sigma_2} = \"\n",
-    "    r\"U^{3\\beta}_{\\delta_1 \\rho_1} + \\frac{1}{8 \\pi 2} \"\n",
-    "    r\"\\int^{\\alpha_2}_{\\alpha_2} d \\alpha^\\prime_2 \\left[\\frac{ \"\n",
-    "    r\"U^{2\\beta}_{\\delta_1 \\rho_1} - \\alpha^\\prime_2U^{1\\beta}_\"\n",
-    "    r\"{\\rho_1 \\sigma_2} }{U^{0\\beta}_{\\rho_1 \\sigma_2}}\\right]$\",\n",
-    "\n",
-    "    1: r\"$\\alpha_i > \\beta_i,\\ \"\n",
-    "    r\"\\alpha_{i+1}^j = {\\rm sin}(2\\pi f_j t_i) e^{-5 t_i/\\tau},\\ \"\n",
-    "    r\"\\ldots$\",\n",
-    "\n",
-    "    2: r\"$\\frac{3}{4},\\ \\binom{3}{4},\\ \\stackrel{3}{4},\\ \"\n",
-    "    r\"\\left(\\frac{5 - \\frac{1}{x}}{4}\\right),\\ \\ldots$\",\n",
-    "\n",
-    "    3: r\"$\\sqrt{2},\\ \\sqrt[3]{x},\\ \\ldots$\",\n",
-    "\n",
-    "    4: r\"$\\mathrm{Roman}\\ , \\ \\mathit{Italic}\\ , \\ \\mathtt{Typewriter} \\ \"\n",
-    "    r\"\\mathrm{or}\\ \\mathcal{CALLIGRAPHY}$\",\n",
-    "\n",
-    "    5: r\"$\\acute a,\\ \\bar a,\\ \\breve a,\\ \\dot a,\\ \\ddot a, \\ \\grave a, \\ \"\n",
-    "    r\"\\hat a,\\ \\tilde a,\\ \\vec a,\\ \\widehat{xyz},\\ \\widetilde{xyz},\\ \"\n",
-    "    r\"\\ldots$\",\n",
-    "\n",
-    "    6: r\"$\\alpha,\\ \\beta,\\ \\chi,\\ \\delta,\\ \\lambda,\\ \\mu,\\ \"\n",
-    "    r\"\\Delta,\\ \\Gamma,\\ \\Omega,\\ \\Phi,\\ \\Pi,\\ \\Upsilon,\\ \\nabla,\\ \"\n",
-    "    r\"\\aleph,\\ \\beth,\\ \\daleth,\\ \\gimel,\\ \\ldots$\",\n",
-    "\n",
-    "    7: r\"$\\coprod,\\ \\int,\\ \\oint,\\ \\prod,\\ \\sum,\\ \"\n",
-    "    r\"\\log,\\ \\sin,\\ \\approx,\\ \\oplus,\\ \\star,\\ \\varpropto,\\ \"\n",
-    "    r\"\\infty,\\ \\partial,\\ \\Re,\\ \\leftrightsquigarrow, \\ \\ldots$\"}\n",
-    "\n",
-    "\n",
-    "def doall():\n",
-    "    # Colors used in mpl online documentation.\n",
-    "    mpl_blue_rvb = (191./255., 209./256., 212./255.)\n",
-    "    mpl_orange_rvb = (202/255., 121/256., 0./255.)\n",
-    "    mpl_grey_rvb = (51./255., 51./255., 51./255.)\n",
-    "\n",
-    "    # Creating figure and axis.\n",
-    "    plt.figure(figsize=(6, 7))\n",
-    "    plt.axes([0.01, 0.01, 0.98, 0.90], axisbg=\"white\", frameon=True)\n",
-    "    plt.gca().set_xlim(0., 1.)\n",
-    "    plt.gca().set_ylim(0., 1.)\n",
-    "    plt.gca().set_title(\"Matplotlib's math rendering engine\",\n",
-    "                        color=mpl_grey_rvb, fontsize=14, weight='bold')\n",
-    "    plt.gca().set_xticklabels(\"\", visible=False)\n",
-    "    plt.gca().set_yticklabels(\"\", visible=False)\n",
-    "\n",
-    "    # Gap between lines in axes coords\n",
-    "    line_axesfrac = (1. / (n_lines))\n",
-    "\n",
-    "    # Plotting header demonstration formula\n",
-    "    full_demo = mathext_demos[0]\n",
-    "    plt.annotate(full_demo,\n",
-    "                 xy=(0.5, 1. - 0.59*line_axesfrac),\n",
-    "                 xycoords='data', color=mpl_orange_rvb, ha='center',\n",
-    "                 fontsize=20)\n",
-    "\n",
-    "    # Plotting features demonstration formulae\n",
-    "    for i_line in range(1, n_lines):\n",
-    "        baseline = 1. - (i_line)*line_axesfrac\n",
-    "        baseline_next = baseline - line_axesfrac*1.\n",
-    "        title = mathtext_titles[i_line] + \":\"\n",
-    "        fill_color = ['white', mpl_blue_rvb][i_line % 2]\n",
-    "        plt.fill_between([0., 1.], [baseline, baseline],\n",
-    "                         [baseline_next, baseline_next],\n",
-    "                         color=fill_color, alpha=0.5)\n",
-    "        plt.annotate(title,\n",
-    "                     xy=(0.07, baseline - 0.3*line_axesfrac),\n",
-    "                     xycoords='data', color=mpl_grey_rvb, weight='bold')\n",
-    "        demo = mathext_demos[i_line]\n",
-    "        plt.annotate(demo,\n",
-    "                     xy=(0.05, baseline - 0.75*line_axesfrac),\n",
-    "                     xycoords='data', color=mpl_grey_rvb,\n",
-    "                     fontsize=16)\n",
-    "\n",
-    "    for i in range(n_lines):\n",
-    "        s = mathext_demos[i]\n",
-    "        print(i, s)\n",
-    "    plt.show()\n",
-    "\n",
-    "if '--latex' in sys.argv:\n",
-    "    # Run: python mathtext_examples.py --latex\n",
-    "    # Need amsmath and amssymb packages.\n",
-    "    fd = open(\"mathtext_examples.ltx\", \"w\")\n",
-    "    fd.write(\"\\\\documentclass{article}\\n\")\n",
-    "    fd.write(\"\\\\usepackage{amsmath, amssymb}\\n\")\n",
-    "    fd.write(\"\\\\begin{document}\\n\")\n",
-    "    fd.write(\"\\\\begin{enumerate}\\n\")\n",
-    "\n",
-    "    for i in range(n_lines):\n",
-    "        s = mathext_demos[i]\n",
-    "        s = re.sub(r\"(?<!\\\\)\\$\", \"$$\", s)\n",
-    "        fd.write(\"\\\\item %s\\n\" % s)\n",
-    "\n",
-    "    fd.write(\"\\\\end{enumerate}\\n\")\n",
-    "    fd.write(\"\\\\end{document}\\n\")\n",
-    "    fd.close()\n",
-    "\n",
-    "    os.system(\"pdflatex mathtext_examples.ltx\")\n",
-    "else:\n",
-    "    doall()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# 各种绘图实例"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 简单绘图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`plot` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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K8oeKvmPzGUdLcntaPCM9Uq2gsv7pnzAzjVqqel3WlUSAjeTSp2dPeO01uPJK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\n+fOj1xIXXn8d+vfPXX27Z6t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q2hPrm+nQHTgfmCMi7wd/uxU4CDLrn7a5zzAMwwhNvrin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\nYRiGYYTGjIZhGIYRGjMahmEYRmj+P0lg7oy1NXTrAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x3c9bf98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "t = np.arange(0.0, 2.0, 0.01)\n",
+    "s = np.sin(2*np.pi*t)\n",
+    "plt.plot(t, s)\n",
+    "\n",
+    "plt.xlabel('time (s)')\n",
+    "plt.ylabel('voltage (mV)')\n",
+    "plt.title('About as simple as it gets, folks')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 子图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`subplot` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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aMTtOrBg48Bq6dCmcpTpxYlvy8vK45ZaFhbblR4VZ1IxhJA6xip4aLiJDcFkg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\nLgxre1MOTEUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9fc3f60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.mlab as mlab\n",
+    "\n",
+    "x1 = np.linspace(0.0, 5.0)\n",
+    "x2 = np.linspace(0.0, 2.0)\n",
+    "\n",
+    "y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)\n",
+    "y2 = np.cos(2 * np.pi * x2)\n",
+    "\n",
+    "plt.subplot(2, 1, 1)\n",
+    "plt.plot(x1, y1, 'yo-')\n",
+    "plt.title('A tale of 2 subplots')\n",
+    "plt.ylabel('Damped oscillation')\n",
+    "\n",
+    "plt.subplot(2, 1, 2)\n",
+    "plt.plot(x2, y2, 'r.-')\n",
+    "plt.xlabel('time (s)')\n",
+    "plt.ylabel('Undamped')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 直方图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`hist` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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emjUwc2bUUbgq5DcOdtEbO5aZHdtFHYVLxuLFcMMNUUfhqpAnCRetr76Cl17i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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9fc3ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.mlab as mlab\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# example data\n",
+    "mu = 100 # mean of distribution\n",
+    "sigma = 15 # standard deviation of distribution\n",
+    "x = mu + sigma * np.random.randn(10000)\n",
+    "\n",
+    "num_bins = 50\n",
+    "# the histogram of the data\n",
+    "n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)\n",
+    "# add a 'best fit' line\n",
+    "y = mlab.normpdf(bins, mu, sigma)\n",
+    "plt.plot(bins, y, 'r--')\n",
+    "plt.xlabel('Smarts')\n",
+    "plt.ylabel('Probability')\n",
+    "plt.title(r'Histogram of IQ: $\\mu=100$, $\\sigma=15$')\n",
+    "\n",
+    "# Tweak spacing to prevent clipping of ylabel\n",
+    "plt.subplots_adjust(left=0.15)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 路径图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`matplotlib.path` 包："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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K4aKtlOqrlNqhlNqplHraGUEJ31c9uDozB81k5IKRHD97vNxlmjZtyonatTkk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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa78f2b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.path as mpath\n",
+    "import matplotlib.patches as mpatches\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "Path = mpath.Path\n",
+    "path_data = [\n",
+    "    (Path.MOVETO, (1.58, -2.57)),\n",
+    "    (Path.CURVE4, (0.35, -1.1)),\n",
+    "    (Path.CURVE4, (-1.75, 2.0)),\n",
+    "    (Path.CURVE4, (0.375, 2.0)),\n",
+    "    (Path.LINETO, (0.85, 1.15)),\n",
+    "    (Path.CURVE4, (2.2, 3.2)),\n",
+    "    (Path.CURVE4, (3, 0.05)),\n",
+    "    (Path.CURVE4, (2.0, -0.5)),\n",
+    "    (Path.CLOSEPOLY, (1.58, -2.57)),\n",
+    "    ]\n",
+    "codes, verts = zip(*path_data)\n",
+    "path = mpath.Path(verts, codes)\n",
+    "patch = mpatches.PathPatch(path, facecolor='r', alpha=0.5)\n",
+    "ax.add_patch(patch)\n",
+    "\n",
+    "# plot control points and connecting lines\n",
+    "x, y = zip(*path.vertices)\n",
+    "line, = ax.plot(x, y, 'go-')\n",
+    "\n",
+    "ax.grid()\n",
+    "ax.axis('equal')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 三维绘图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "导入 `Axex3D`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zM+j9FKyQH88hlKwut7kaGBEjRb929ozZbFb1GK6nPaMdiGx7enpsfRHoWgWA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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbbb96a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "from matplotlib import cm\n",
+    "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca(projection='3d')\n",
+    "X = np.arange(-5, 5, 0.25)\n",
+    "Y = np.arange(-5, 5, 0.25)\n",
+    "X, Y = np.meshgrid(X, Y)\n",
+    "R = np.sqrt(X**2 + Y**2)\n",
+    "Z = np.sin(R)\n",
+    "surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,\n",
+    "        linewidth=0, antialiased=False)\n",
+    "ax.set_zlim(-1.01, 1.01)\n",
+    "\n",
+    "ax.zaxis.set_major_locator(LinearLocator(10))\n",
+    "ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n",
+    "\n",
+    "fig.colorbar(surf, shrink=0.5, aspect=5)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 流向图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "主要函数：`plt.streamplot`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zcnZX6HUttB8jqWR1SQ09UqijxatpmhN4DjgF2AMs1jRtplJqnfWeieHYIN5t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2AAAg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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbba7c88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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wgrpqIztKleLAp0jHHYAVOu++qz2uSBHWV2sFr2vWZPJUQuPGHCjODlMfAQ8P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\nO3bs0FUWlogwduxYODk5CQV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SiNauZQ19lSqsMLJC8aYL+Y3AJUmi+fPnk7OzM7Vp04aq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\nIPAAAgggAD9FgMADCCCAAPwU/wetO4AhXrsEhgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc38efd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "Y, X = np.mgrid[-3:3:100j, -3:3:100j]\n",
+    "U = -1 - X**2 + Y\n",
+    "V = 1 + X - Y**2\n",
+    "speed = np.sqrt(U*U + V*V)\n",
+    "\n",
+    "plt.streamplot(X, Y, U, V, color=U, linewidth=2, cmap=plt.cm.autumn)\n",
+    "plt.colorbar()\n",
+    "\n",
+    "f, (ax1, ax2) = plt.subplots(ncols=2)\n",
+    "ax1.streamplot(X, Y, U, V, density=[0.5, 1])\n",
+    "\n",
+    "lw = 5*speed/speed.max()\n",
+    "ax2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 椭圆"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Ellipse` 对象："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1UChEZUsFSZkJmOPM2FNtVDUOLp/8nv1FKJKHlhvwIBTZJor2FSGT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\ni3wQrLY6AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa701208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pylab import figure, show, rand\n",
+    "from matplotlib.patches import Ellipse\n",
+    "\n",
+    "NUM = 250\n",
+    "\n",
+    "ells = [Ellipse(xy=rand(2)*10, width=rand(), height=rand(), angle=rand()*360)\n",
+    "        for i in range(NUM)]\n",
+    "\n",
+    "fig = figure()\n",
+    "ax = fig.add_subplot(111, aspect='equal')\n",
+    "for e in ells:\n",
+    "    ax.add_artist(e)\n",
+    "    e.set_clip_box(ax.bbox)\n",
+    "    e.set_alpha(rand())\n",
+    "    e.set_facecolor(rand(3))\n",
+    "\n",
+    "ax.set_xlim(0, 10)\n",
+    "ax.set_ylim(0, 10)\n",
+    "\n",
+    "show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 条状图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`bar` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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dNzMz20ymSSoi5gCrGk0eBUxPh6cDJ2UZg5mZVa5yXJPqFRHL0uFlQK8yxGBm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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xcdb8828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "n_groups = 5\n",
+    "\n",
+    "means_men = (20, 35, 30, 35, 27)\n",
+    "std_men = (2, 3, 4, 1, 2)\n",
+    "\n",
+    "means_women = (25, 32, 34, 20, 25)\n",
+    "std_women = (3, 5, 2, 3, 3)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "index = np.arange(n_groups)\n",
+    "bar_width = 0.35\n",
+    "\n",
+    "opacity = 0.4\n",
+    "error_config = {'ecolor': '0.3'}\n",
+    "\n",
+    "rects1 = plt.bar(index, means_men, bar_width,\n",
+    "                 alpha=opacity,\n",
+    "                 color='b',\n",
+    "                 yerr=std_men,\n",
+    "                 error_kw=error_config,\n",
+    "                 label='Men')\n",
+    "\n",
+    "rects2 = plt.bar(index + bar_width, means_women, bar_width,\n",
+    "                 alpha=opacity,\n",
+    "                 color='r',\n",
+    "                 yerr=std_women,\n",
+    "                 error_kw=error_config,\n",
+    "                 label='Women')\n",
+    "\n",
+    "plt.xlabel('Group')\n",
+    "plt.ylabel('Scores')\n",
+    "plt.title('Scores by group and gender')\n",
+    "plt.xticks(index + bar_width, ('A', 'B', 'C', 'D', 'E'))\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 饼状图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`pie` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/Pdb6SQyPsokfhRC2k/v5M72z8UXLijmS6U01bWjQ0vzJ76MP7nvnvk0ux0Fn\nsC8hIsgtLOSBCRPo+a/3iEsJgw9OGJHcogNJTVvrgVtCrSWcUKYL9Okhorxerj6YT+vnHyKs/lc+\nrzWsm4qlTw9u8Gd6zwvULzvLkQO8CmRxJNNr1xWmtbEMzz/gXPjSLevdq+aqTG9V4vF6uXPcOOq2\nuUhedKva2TAQ3e991mayRQ9RuzUdQ5muj4ycXBqm1kN3fhguYLRZ4fu3MX/xCilWC/ONBvGv02R6\nR+E7GigKX6ZX1m9u+SM+2fDd8Be2l/7wRo7HVaEyvVXBe/PmsbmwWN71yTRlKJXQstu16E2WBsBF\nodYSLkS86fbpIYzAtQcP0/KpQYT1es07eiFWTcLcsjGvRFmZLYQ45dxyf6Z3Kb5Jtt0cyfTWNeSk\ntbYOWzHHse3VfutdudtVpvdcWLJrFx/Mn0//j6dFzDLfs0Gj1XLRbQ+ZDWbbvaHWEi5EvOkC7R2F\n1Ckpo95tV4dayl/TuAEsHY/1odvpYjGxQQjRM1C/7CzHQXyZ3klAfSDKYNKUN0y3/OjxyJn/d+cG\n17yJB1Wm9ywoKC+n/9ixXND/CRq06RxqOWFPuytv1UjpuVUIcerx0hFIRJtunx5CANft3EuTe29G\nmIN/QMNZodfDW09hyPyU2LhoJlkt4oNKMr3u7CzHJHyZXg3HMr3LkpuYvvp56F7Hp//a5gp0qq8i\nMFJKHpo4EX1iA3n14P+GWk6NICG1KdFJDQRwaai1hAMRbbpAQ7ebRofySX+0/6mHTYY7l1/s26f3\n4g48YLeyQgjRJFC/7CzHRmAIvkxvI8Boj9MfTGtt+XTn+tLVQ25e59qaXRxM6TWW71asYF7OLgZ9\nHbnLfM+GTn3ushostrtCrSMciHTTvSwnl7ptmiGbpIZaytlRJw5+/RrLq4NpaTGRrdGI/oH6HZfp\nHYEv05vgz/ROsdi1P3/4yBbn1C9Vpvd0bD50iGdnzODG/47CEh0Xajk1inY9b9F4PZ6bhRAhzcCH\nAxFrun16CDvQLb+QRvfegjHUes4FIeCJu9Au+BFrg7p8GWUTY4UQ9pP7+TO9c4CXgUL8md7EVNPG\nBi0tn8wee3DfW/dudOUfUJnek6lwu7l99GiaXX6jTL+sT6jl1Dji6jc+clxR+B1iF2Qi1nSBdhVO\njIcLaHRzwKmomkeHVr5M7w1X0Mef6Q24mWt2lmMXvkzvHxzN9GoL09pYhhccci98+db17pVZjuAJ\nrwH8Z+YE/DVZAAAgAElEQVRMCjQGedvro1RZ4Szp1Ocuq9Fqj/gSQySbbsaOPdS5uAOe+NhQS6k6\nrBYY9Samr14j2WZhnskonhFCnPLvnJ3lKAe+Az4E7BzJ9DYz/5GQYhj1zYs7Sr//r8r0AszctInR\n2dncPXy2WuZ7DrTreavG43bdFOklhoj8BPXpIeKBpkUltLz35vDO5p4tt1+LWJ2JuWVjhtitzBFC\nJJ3cx5/pXY4v05uDb5JNF5tk2JXW2jpsZZZj6yt917v2bisLtvywYV9REff//DOXP/E28Q0CzlMq\nzpDY5IbE12/iBXqEWksoiUjTBdqVlWPIL6R+n1r8z5+WAkvGYX3kDi7yZ3qvDNQvO8txCHgH+Anf\nPr3R/kzvaCnlL2/ctdE19+fIy/R6/ct867Tu7O1y+z9CLadW0OGafnaD2XpTqHWEkogzXX8297Kc\nXOIuPR+P/ZRjIWsXej288SSGqZ8TEx/DRJtFfCiEOGV078/0TgGOhE9ThBCiXmPz8uQmpi8nfLzX\n8ck/t7pKCiMn0/v+/PlschTKuz6eHnG/J9VFkwt7CI1WVwOWIVUfkfhhSgJSS8pIu6ln7SwtBKL7\nhb5Mb5fzuN9uZaUQommgftlZjk34Mr3ZHMv0HmrUxvppzsayVS/dvM61ZWXtz/Qu3b2b9+bN446h\nU4TBVENWzdQAklt0wO1y1gu0hD1SiETTTZcSmV9Ik6u6hVpKcEmIhZlfYXn9CZpbTKzUaMSAQP2y\nsxxFwKfA1/gzvVqdcKe2tEy1ROt++ujRLRWZX+yttZnegvJy+o8Zw/n9HiO1vdqnpSrR6nQ0bH9x\nOXBZqLWEikg03Qv252FKiPHVPCMNIWDwALT/G4M1tR6fR9nE+NNkerOAl4ACjmR6Gxg3pbayfPLH\n+EO5b96z0ZW/v3ZleqWUPDJpEto6Kd5rn3gz1HJqJS27XWM3WGzXhlpHqIgo0+3TQ1iAZvsOUa9P\nj5q37LcqadcC1k7BevOV9LJa2CCEOD9Qv+wsx27gNWAOvkyv1WzTFqW1tnxdeNi94OXb1rlXzKk9\nmd4fVq7kjx07ufebrIj63Qgmjc67RGg02u6h1hEqIu2D1QQQTifpvbtHtumCL9M74v8wjfg/6tks\nzDUZxL9Pk+n9HngfsAF1/ZnerIQU48gRL+0oGfX6TrezvGZnerccOsQz06dz/esj1TLfaqRus7a4\nKsrqCSEi8sjkSDPdtk4X5BeScEnAtVqRya1XIdZkYm7djBejrGQJIU45fN6f6V2JL9O7A/8+vbFJ\nht1pbazDVs8r8GV6t9bMTO/RZb6XXS/b9Lgh1HJqNVq9nrpN25QCF4ZaSyiIGNP1R8Uu2H8IY/M0\nXDVlG8dg0TAFFo/F+ugALrSYWC+ECBjr8Wd638WX6a0PRBuMmorUVpYxIGe8MXCjK2t8zcv0vjBr\nFvlCL29743u1zDcINLmgu1Wj1XUNtY5QEDGmi28WPirPQeKlnUN72m+4otPBf59AP+0LYhJi+dlm\nEUNPk+mdyrFMb31/pndFclPTlxM/3Zv/8RNbXSUFNSPT++vmzfywcqU6zTeINOzQRWe0RdWSXU/+\nHpH0CWsA4JU0vuQ8Vc89HZddABumYbmkE/farWQLIZoF6ped5diML9O7nCOZ3lhfpnfXprLsIbes\nc21eEd6Z3v1FRdz708/0GPyGWuYbROo2aY3H5Qz4uartRJLpNpMSd0ERKRd1CLWU8Cc+FqZ/ieWN\nf/oyvTqtGFjJYZhFwGfAcKAOUEerE57UlpZp1mjdT0Mf21Ix+bO9Ho87/MoNXq+Xu8aPp056J9n1\njsdCLSeiiKnXEHdFeYwQwhxqLcEmkky3dUER6LRoGyaHWkrNQAh4pD+aRWOxpNbjE7uVn4QQUSf3\n80+yzcWX6c3npExv1s+Hct+6Z6MrLze8Mr0fLljAhnyHvOuTGaqOG2Q0Wi32hLqlQMCVkbWZiDDd\nPj2EGUjen0fs+W3wnDpeU5yOts1hzRSst17FtVYzG4QQFwTql53l2IMv0/s7x2V6G7WxfFOY75r3\nSt917uWz84OovHKW79nDO3PncvtHmWqZb4iok9ZCAs1DrSPYRITpAimALCkj8YJ2kbPfQlViMcPX\n/8U08k3q2a38YTKK5yrJ9FYAPwLvAVagnhBC1m9mmVenvnHkty/vLBn5amgzvYXl5fQbPYZOfR8h\nrUOXkOmIdOo2bWMGoUy3lpKK71rrt2uOGueeAzdfCWsyMbdpxvN2K/OEEPVO7uMvN2Tjy/RuwzfJ\npj+a6V1QsOWVvutce7aEJtP7WGYm2oR63uuefDsk76/wUadRS73JHtU+1DqCTaSYbhOgtKycOq0j\nroJU9aQmw6IxWB+/i87+TG/AdfTZWY48fJnecfi+bcQYjJqKhq0sYxFMf/Puja454w4ENdP748qV\n/L5tO/d8/UekfPbDloSGzRAabetQ6wg2kfLBS/N4KC8sxtosLdRSagc6Hbw2GP2ML4lOiGW8zSI+\nFkKccsBndpbDk53lmAa8Dng4kultZF6Z3NT8xeTPcg8PGxycTO/WvDyemjaNPq98gy02odrfT3F6\nElKb4SovaxhqHcGm1ptunx5CB9TNL8SQEIvbqCq6VcqlnWHjdCwZnbnHbmWVEIFrdNlZji340g3L\n8JUbTPZYXV6jNtbPdm8pW/nizetcG5cVVZtOp9tNv9GjaZLRW7bteXO1vY/izLEn1EV6vSYhRC06\npfCvqfWmC8QBFBQR17Qh4RcWrQXExcDUz7G89S+aWkys0GnFPZVkeouBz4EvgQSOZXqn22N14z9+\nfGvFpE/2eqsj0/vir7+Sh1be/uaPqqYfJgghiKpTrwxf0iViiATTjceXXIhplqpWolUXQsDD/dAs\nHoulYTLD7FYmCCGiT+7nn2Sbj2/UexjfJKe2Tn3j5tRWlo/nTTy0542BG1x5uRVVpu33LVv4bsUK\nBn6lTvMNN8xRMRKIqN3GIuETGA9oXW7sDVPUngvVTRt/prfvNVxtNbNRCBFwJyl/pvd14FegIWAz\n27TFaW0sI0oK3HNf6bvevey3c8/0HiguZtBPP9H9sf+jTsOIXHUa1pjtsQCnLLipzUSC6dYDXBpB\nbHKdUEuJDMwm+Oo1TN+9RZLdyhyzSbxQWaY3O8sxGl+m14I/05vSzDK/TgPjtyNf3Vn87cs73BVl\nZ5fp9Xq9DBw/nrgWHWW3/oPP7aIU1YI5KlYLnPKNqDYTCaZbB6iQkuh6ynSDyo09Ye0UzG2a8azd\nynwhRMAF2P5M7wvAVo5kehMNexq1sX689n+Fm1++bZ1r9+bSv/3+QxcuZG3eYXn3ZzNVHTdMsUTH\n6VCmW+uIA5xON7Z6EXv+aOhoUM+X6f3nQM63mFgnhLguUL/sLMdhfCPesfgzvXrfPr3jNFqmvXXP\nJtfsMWee6V2xZw9vZWXR76PJaplvGGOJjjOgTLfWEQc4S8uwqpFuaNBq4ZXH0M8cTnSdOMbZLOKz\n02R6p+Pbv8EDNPBnerNTmpk/z/wiN++jx7a4ih2nz/QWVVTQb8wYzrv1YdI6RtiRzzUMkz1GozMY\n40OtI5jUatPt00NogGivF2dpOYZEdexVSOnWyZfp7X4hd9mtrBZCtAzULzvLsRVfumEJ/kyvLUZ3\nuFFb6+e528pXDLl5nWvj0sozvY9lZiLikmSvp96tlutQVB0mWzQ6gymiVqrUatPFNzkjXG50eh1e\nncouhJzYaMj8FMs7T9PEYmaZTivuPU2m9wv/LR6oo9UKT4MWlhn2ON24j/+5tXzCsD2nZHrHZGfz\n29ZtDPr6D1XHrQEYbXaERqtGurUIG+B1udEb9NTso2prEULAg33RLBmHJa0+H9mtTAp0Mqw/07sA\n36g3D1+0TFunvnFLw1aWTxZMztv9xl0bvYf2+jK92/PzeXLqNHq/MhxbnCrg1wRMtmgi7VTg2m66\nRgCXC73JqFajhRvpTWH1ZKz9ruNKf6b34kD9srMce/FlemfiW0xhM1m1xWltLN9SqN31Wt8Nnrxc\nJz+uXEnjS66V7XreGszLUJwDOr0BiKztVmu76eoBXB5luuGK2QRfvILph3dIjLLxu9kkhgghTlk5\nmJ3lcGZnOcbgSziY8Wd6U5Oi9raIr7uw8LBHxsXVod/bY1RZoQbhqigHKA+1jmASEabrdqO3mJTp\nhjPXX+7L9LZrwTNRVhYKIVIC9cvOcqzCt0/vFqCRBF2sPbauVm9y9X55OGqZb83C46oAQWg2Vg4R\ntf0TqgeE243erKKaYU/9urDwR6xP3kNHf6a3d6B+/kzv+8AYj7DaDnpFuUZvFM27XBVcwYpzxlVR\njvRKNdKtRegBJAiN+tJZI9Bq4aVH0P/6DVFJ8YyxW8UXlZQbPC66/erRxu3b48gvbdvzZrWZTQ3E\n7awA6f37yw1rMLX9U6rHZ7hel1uVF2oSXTrChGFYgBuh0uRJoxJT7E5nRXnT9lfdpgKBNRC3swKv\nV5lubUILCI0Gj8utzkaracyYi8frZcxp1v6eV1ZaZHI7ndFq5VnNxO0sx+txK9OtRXgAqdHgdVf/\naTCKKub7KZSWljMm0GPpGb00QNf83JzE1j2uR6tWvtRI3BXleN2uklDrCCa13XS9+EzX41KmW6PY\nsA0OHMYDLKqkSxpgc1eUt21/VV/luDUUt7MCj9ulRrq1CA+AVoPX7VHlhZrE+F/wCPhJSllZPbdj\nRUmRyVlWEtu482XBlKaoQlzlpW5UTrdW4QXQanBXOJXp1iS+n0JpSRk/BHrsuNJCnZaXXCv9q5oU\nNRDHvpxyYH+odQST2m66HgCLmbKiEnU+Wk1h2y7YlQvAvEq6NABiXBVlrdtfc7s+aMIUVc7h3ds9\nQE6odQSTiDBdgx6n1wulEbXupeby8yykTsckKaWnki7tnGWlxoriwsRmF/UMqjZF1eLYv1sL7Ay1\njmBS2023HJBCgNmEM88RajmKM+G7TIqKSiotLQjgkvy9OxKadenp1RvVUsOaitfjodSRZwZ2h1pL\nMKntpnt0VtRooPzQuR8uq6hmdu+DTTvQAXMq6ZIMJLgqytI7XNNPlRZqMEV5+9AZDCVSqmXAtYlS\n8E2gGfSUKtMNf36ehTQamC6ldFbSpZ2rolxfVuSo16Lr1UHVpqhaHLm70BlMuaHWEWwiwXQ1ABoN\nJQeV6YY932VSVFjMqECP+UsLl+bv3ZnQuFOGx2C2Blmdoiop2JcDQuwItY5gU6tNN3O2dAFOQCsl\nebsi7v/UmsX+Q7BmMwbg10q6JAFJzvKSlh2u7adyYjWc/NwcXGWlG0OtI9jUatP1Uwjo9Try1m7B\nFWoxisqZ9DuYjPx6mhpfG7ezQl9WcLhBq0sCnuSuqEEc3rOt3O0s3xpqHcEmEkz3MGC0WTi8Ybs6\nJy2c+W4yRQVFgUsLfi7Jz82JS21/scdkjw6aLkX1sH/rugpgR6h1BJtIMN3dgDnazuEdeyLiemsk\nefmwZA0GYEagx9MzetUBGlSUFjXveG1/VVqo4Ugpyd2YbQSWhlpLsIkEE9oDGKLtFOYXoK2obE68\nmimvgAv7QocbIb0XPPe+r/2wA3oOguZXw5X3gqMw8PN/mQctr4VmV8FbXx1r//e70P4GGPjssbbv\nM+Gj040Xw5DMOWAxM1dKWdmOU208bpe2rCC/UauMXkHVpqh68vfuQEpZLqXcG2otwSYSTDcPkFoN\nXruV0u0himGbjDDnW1g5EVZNgjmLYf4yePMr6NkFNv0Cl1/ku38yHg88+jr88hWsmwqjp8P6rVBQ\nBCvWQ/YkMOhhzSYoK4dvJ8Kj/YN+iefEd5kUOQr59jRdujlyc+KSW3ZwW2PigyVLUU3sXrMEndG0\nLNQ6QkEkmO5h8J0aYTZxeMO20AmxmH1/Ol3g8UJslG+EN/AGX/vAG3yTSSfz5ypomgppKaDXw+3X\nwuTZoNWAyw1SQmm577F3v4HBd/qOvakpFBbDwhUYgamBHk/P6BUHNCovLmx2Xq8BxuCqU1QHOav/\ndJUXOipbAFOriQTTzcd/nUKwc+ma0B3b4/X6ygtJ3aD7BdC6GezPg6QE3+NJCb77J7PnADSoe+x+\n/STYsx9sVrj2UjjvZkhOhCgr/Lka+vQIzvVUFVP/AIuJRVLKSoorpHs9bk1p4eEm6d2vD6Y0RTWx\nffm8Uim9i0OtIxREwubPJfj2YNDbreydvxwnEJLRkkbjKy8UFMFV9/lKDMcjhO92MoHajvD0vb4b\nwP0vwmuDYfh4+HUhtGsBzz9Udfqri++nUJz/V6WFfbtikxqne+zxSZHwma3VeD0e9m9dZyECJ9Eg\nAka6mbOlBLYDtoRYcrM3oK30xK0gEW2H6zJg2VpIiod9B33tuQcgMe7U/imJsGvfsfu79vlGu8ez\nYp3vz+Zp8NMsGPsBbN0FW8J8/6aSUpi9CAOQGejx9Ixe0UDzsiJHk47X9VelhVrAge3r0RmMh6SU\nEbkFVa03XT8bAGuMnQKnC+/eA8EXcCj/WDKhrNw3Eu3YCvp0h5GTfO0jJ8MNl5/63PPbwOadsGMP\nOJ0wdsapJYQhw3yjXKfLN/EGoBFQVlF911QVzJgHFhMrpJQBCisAtPJ6PaKs0NG8zeU3BlWbonrY\nvWYpGo0mIksLEDmmmwMIISDGzoFla4MvIPcg9LjbV9O9sC/07g6XXwzP3u8z4OZXw+xFvvsAew/A\ndQ/6ftbp4OMXfCWJ9N7Q9xpo1eTYa0/+HTq3gbp1ICYKOrSCdtdDhRPaNg/6pf4tfphCSX4hI07T\npVvB/j2xcfUbeaOT6gdNVzDxejwM7deZkY/7ZlR/+/xV3ri6EUP7dWZov85sXDAz4PM2LpjJ+ze1\n4d3r08n69p2j7TM+eo6P+nZi3JBBR9tWTPuBBT8Oq94LOUN2Zi8sLyty/BFqHaEiUupjR7OAQsOO\nP1eR0qdHcI/vadsclk84tT0uBn4LYDnJiTDti2P3r7nUdwvE9Zf7bkd452nfLdwpr4CZ89EBkwI9\nnp7Rywa0Ki043KjL7Y/U2gURC0YPI7FxK5wlRb4GIeg24HEuGfBEpc/xejxkvvUE930+g6g6KXxy\n58W0urQXUXWS2bsxm8fHLmPCaw+xb8sa4us3YdmU7xj0ybQgXVHlSCnZMG+6h8q37qz1RMpINw9w\nAboYOztnzCNESyQUx/PrQjAZWS+lrOyMrFbS69WWFzlatL3iplp5xl3B/t1snP8LnW8YhDwy2SCl\n73Yadq1ZQnyDJsQmp6HV62l31W2sy5qC0Grxul1IKXGWl6LV6Zn73ft06fcImjDIEe7fuhZnWUkZ\nsDrUWkJFRJhu5mzpBbYBtpQkdq7ZjK4kog59Dk9+mEKpo+i0pYUuhQf3RkUlphBXv3HQdAWTqe89\nxbVPvIHQHPerKAQLx3zKR3078fMrD1BWdOp8U+HBPcTUPVZuiU5MofDAXowWGy26Xs2wOy4gKiEZ\nozWK3WuWkJ7ROxiX85esz5rqBSZKGerp7NAREabrZzVgNxpwxUZzaP7yUMuJbJxOmPIHWikJUHSB\n9IxeFqBtcf6h1Nq6jeP6udOwxSaS3LLjCSPbi259kGembmLwmKXYE+oy/f1nTnmuOE2O8NKB/2Lw\n6CVc+883+e3zV+j5j5dZMvEbfvz3HcwZ/ka1XMuZsmrmuGJnWcn4kIoIMZFkupuO/KDXsmHmArXj\nWCiZ8ycY9WyVUla2MLullFJbUVzYuu0VN9fK0kJO9v9YP3cqb/dqzpj/3MnWJX8w7sV7sMUlIoRA\nCEHnGwexa+2SU54bVScFx75jf3UF+3cTnZRyQp+9G1YAkJDanNW/TeCOt34kb/c2DuVsqd4Lq4Ti\nwwc4uHOzAcgKiYAwIZJMdxfgBbTxsWybnqX21g0lo6dRXlh82gURFxYd2hdliYnTJDZqGSxZQeWq\nx17n2RnbeGbqJm5/43uadL6M214bQeHBY7vtr509mbpN25zy3JT0TuTt2kL+3h24XU5WzRpPq0tP\n3Ajo189eoefDL+NxO5FeX45QaDS4K0JzLPaGeTPQm8xzTnMUU0QQKekFMmdLZ58eYiOQmpzIntmL\n0OXlQ3xsqJVFHh4PTPgVPF5+CvR4ekYvE3Be8eH9DTr2ujMyDp+U8mjJYMZHz7Fv0yoQgriUNG54\n/lMACg/uZcJrD3P30MlodTr6/PtDvnnkOrweL51vuJvExq2Ovty6PzKp3/p87Am+9eP1mrfno9vO\no27zdtRt1jb41wesmjWuuLzIMTokbx5GiEiqZ/fpIXoAA4CcZWsY+PbTpPUPj/mFiOKPP+HGR9mS\nXyibBXo8PaNXOynlEztWzO/7wPDZlnrN2wVboqKKcVWU82pGotPtLE8+zUKYiCCSygsAW/DvOGYy\nkT16moqOhYLR06goKTvtCREXlhw+aDeYbbpQjcoUVcvWJXPQm8wbIt1wIYSmK4QoPun+3UKI6l4y\nswff5jeGhslsmr0IbVllp3EpqgWvF8b9gtflJuAMdnpGLwNwfuGh3JT2V/fVnW6WXlFzWJ45qrS8\npHBkqHWEA6Ec6Z5c16j2OkfmbOkBFgPxNgulUXYOzFpQ3e+qOJ5F2eD1clBKuaGSLs2klDpnaXGb\ntlfeGmnfxGolJY481s+dppUejzJdwqu8cHRII4RIE0LMFkJkCyF+E0I08Lc3EUIsEkKsEkK8LoQo\n8rfXE0LMFUKsEEKsFkJ0O837LAP0ACYDK0ZNVimGYDJ2Bs7yCr47TZfOpQV5do1Ob6qf3ilouhTV\nx/Ip33m1esN0VVrwEUrTNftNcoUQYgXwCsdGu8OAEVLK9sAPwFB/+0fAB1LKdvgiYEf63wH8IqXs\nCLQDVp7mfbcAbkCXlsL6GfPQqBJDcJASRk/D5XQxLtDj6Rm99MCFBfv31Gt35S0aVVqo+UgpWfDj\n0NKKksIPQq0lXAil6ZZJKTseuQFDODbavQj40f/z90C349qP1AJHH9f/T+AeIcRLQDsp5Qn14uPJ\nnC0rgCVAQpSN4mgbuZMDHJGjqHqWr4PyCoqofN19E8DoKi9r0+6qvqHfKEBxzuxYPp/y4oJ8YH6o\ntYQLYVleqOR+pUgp5wGX4Jso+1YIcedfPGURYACwW1k89HuVYggGY6fjcrv58TTr7s8rK8y3SOm1\npba7KKjaFNXDwtEflzrLSj6I5L0WTiacTPd4FgK3+3/uD8z1/7wIuMX/85HHEUKkAgellMOB4UDH\nv3j9TYAT0DdJZX32Bti2q6qkKwIhJfwwlYqyCsYEejw9o5cW6OLYtyupzeU3odGE60dTcaaU5B9i\nw/zpGun1qgm04wi39MKRtsfwlQuy8Znu4/72J4AnhRAr8X0VLfC3dwdWCiGWA7fhq/1Wir/E8AeQ\nqNfhiYsm+8txai+G6mTNZigoopzKz8VqDFhcFWVt2191W8SslKzNLJsyyqvVG6dKKQ+HWks4ETLT\nlVJGnXR/pJRysP/nHCnl5VLK9lLKnsdtirJHSnmRlLIDsAJfbfbIc9tKKc+TUmZIKc/kZLCF+FMM\nyUks+Wo8Xre7yi5PcRLjf8EjYexpvmZ2LC8uMLudFdFp510SVG2Kqsc3gTastKKk8MNQawk3atp3\nuE5CiJX+EfBDwL/O4bV24TvGJzopnoM6LYdnzKsSjYoAfD+F0tIyAq67T8/opQG65efmJKZf1get\nTg10azobF/xCRUnRAXyDG8Vx1CjTlVLOl1J28I+AL5NSbjvb1/KfEjwTiAGwW1n49tdqQq062Lgd\n9h/CA/yvki4NAZu7orxN+6tvV45bw5FS8svQ54srSgqfVRNop1KjTLcaWIk/s9ssjTUr1+NduT7U\nkmof43/Bq9Hws5Sysrp5h4rSYpOzrCS+yQXdg6pNUfVsXvQbjtydh4GfQ60lHIlo082cLUuABfgn\n1OJjmP/652qFWlXzfSYlxaVHc9cnkJ7RSwCX5O/dmdCi2zVenb5WHhIRMUgpmTns+ZKKkqLnTvOf\nbEQT0abr5zd8mV3RrCFLZ8yFHXtCLan2sH035Pj25J5bSZcGQIyroqxNh2tuj4y9c2sxW/+cw6Gc\nLQ5gbKi1hCsRb7qZs+VuIBuoYzZRERfDsreH4wm1rtrCz7OQeh2ZUsrKsiHtneWlxoriwsRmF/UM\nqjZF1SKlZOq7/yp1lhY/LaVUv0OVEPGm62caYAVo3ICFIycj8/JDrKiW8N1kigpL+D7QY8eXFppd\nfIVXbzIHWZ2iKlk3ZzKOfTl7OcNRrhDC4997ZY0/lfSkiIANN5Tp+tiM74j22GgbRbFRrH3jKzXa\nPVd274NNO9ABsyvpkgwkuMpLW3W4pp8qLdRgvB4PU997urSipOjxv1HLLfXvvdIG6AlcA7xUfSrD\nA2W6HI2PTQaiAZo04PfPxyD37A+trprOhF+RBgPTT3MQYTtXRbm+rMiR3Lzr1UHVpqhalk/9XpYV\n5m8EZpzN86WUB4EHgEcBhBAmIcQI/zauy4UQl/nbLUKIcUKItUKICf6tXjsJITRCiG/9W7uuEkI8\nUVXXVtUo0z3GamA/EBVtpygumqXPf4hao3YOfJdJUWHxaUsL3fL37kxodN4lHqPFFmR1iqqirDCf\nae89VVFRUvjwueRypZTbAa0QIhF4BPD4t3HtB4wUQhiBfwB5UsrWwItAJ3zbB3QEkv0rU9sBI87x\nsqoNZbp+/KdKjAHiAJqnkTV+JnLj9tDqqqkcyIPVmzAAsyrpkgQkO8tKWna89g6VE6vBTH3vKZfX\n4/lBSrm4Cl+2K75tXZFSbgR2As397WP87WuBVf7+W4HGQoihQoirgMIq1FKlKNM9kWxgOxBnMVNe\nJ5Z5T72tcrtnw6TfwWzkNyllWSVdWrtdTm1Z4eEGLS+9LqjaFFXHjhULWP3rz6XOsuJzWZIPgBCi\nMb7R7YEjTZV1PblBSukA2uPbyOohfLsNhiXKdI8jc7b04pt5jQZo2ZhFWUvwLM4Ora6ayKjJFDmK\nTjItmgEAABUzSURBVHvi76WOvTvjU9td5DbbY4KmS1F1uF1Oxr14T7mrvPQ+KWXBXz+jcoQQdYDP\n8Z0aAzAP3w6DCCGaA6nARnyLmW7zt6cDbf0/xwNaKeUEfGWH885FT3WiTPdUNuL7ypJk0ONKiueX\nQc/j9Kgswxlz2AFLVmOgkkmV9IxedYAGFaXFzTte298YXHWKqiJrxNve0oLDizj75b5HjuxaA/wK\n/AK86n/sU0AjhFiFr5ww0D8h+ylQRwixFngNWItvi9cUYI7/6K/vgGfP9rqqG7W5yElkzpayTw/x\nE74z2zQtG5O9eBUXfPIj9QbfeeanWUQymXPAYmZehbPSY5Nae9wubWnB4bRWl/UOqjZF1XAoZzNZ\n377rdJWX3n22k2dSykr9R0pZAQwK8FA5MEBKWSGEaILPrHf6F9/UiJNM1Ug3AJmz5U58ZzrVEwIa\n12fSCx/hyT3wV89UgC+14Cjk29N0ucSRmxOX3LK9xxoTHyxZiipCSsn4IfdWeN2uF89w7+qqxArM\n9x9kMAF4+DSrHcMSZbqV8xPgAUyJ8RyMsbPk0dfVpNpfUVgMC5ZjBKYGejw9o1cs0Li8uLBZx+sG\nqNJCDWTFtB/Yv3XtLo/bFfQNyqWURVLKzsdt8Toz2BrOFWW6lZA5WzrwnThcDyC9KXN+W4jrN7Ul\n82mZlgUWE4tPM7HS2utxa0oLDzdp3f36oGpTnDv5e3eQ+dbj5RUlRf1q2ggzXFCme3rm41senGDQ\n40qpy+SBz+EqLgm1rPDl+0yK809fWujm2Lc7JrFRK489oW6wZCmqALfLycgnbnK6nRVDpJSVnXWn\n+AuU6Z4G/4KJkfjqSNqmqWwCNj76ulqpFoiSUpi9CD2+JdWnkJ7RKxpoVlaU37jjdSq1UNOY+u6/\nPAX7chZ6XM53Q62lJqNM9y/InC134FtVlQLQuilTJ/5GxZQ5IZUVlvwyH8wmsqWUeZV0aeX1erRl\nhY4WbS6/MajaFOfGql9/ZsW0HwrKiwtvUkfwnBvKdM+MycD/t3fv4VHVdx7H32fuM7lfuIbEcIch\nMYACIriRIAg2RqsWlG3Xa12tS9dt3eo+7V7sVbu69WlXV9TWFkURCrVjUOQymgoUEQEBAwHJjVwJ\nuc59zpn57R8nVqsJVUhmcvm9nidP8ngOw3eeZ/Lh5/ec8/u2Amk2K6Hx49hw60OoZ3qLlmFq3Wv4\n2rvO+cz7gs7m+tT0rPHR1NHZMatLujBnaz9i88N3h8N+39VCCLnp6QWSofsFuNzCD6wBkgFTzhhq\nEx3s/8aDqPLffF0oDFt3YQJe7em4s7A4EXD6O9tyZ8q9FgYNNRhg7f3Xq5oa+p4QUdnH7QMydL8g\nl1t8hL7iHQeQN4Wd+4/i+b/1yNgFtu8Gm4XjQoimXk6ZJqJRQ9DTMS3/qhvkQyaDxB9/tjriOdu8\nM6KGfxnvWoYKGbpfzhb0DXFGmIxEpo7npQcfQ5V7M8BLW/B3eM7ZWri8q6UxJXnEWDKyJ8asLun8\nHXjtBXF05x/OBr2dK2Qft+/I0P0SXG6hAs8CVsCamUZr9mg2X/ut4d3fVVVwuTEIweaejjsLix3A\nxd72lpyZ19wiWwuDwKl9b/HqI6tDIb9niRDCE+96hhIZul+Syy0agLXodzMYJl1Ehd3Ku9fdh6oN\n0xvJ3toHZhNVQojTvZwyVQhhCHm7ZuRfdaNsLQxwDccP8sJ3blIj4fBXhBBH4l3PUCND9/y8A+xE\nHx9O/hTclXU0fPfnw3Ou2votBD2+cz4QcZnnbFOKPSXNMHLC9FiVJZ2H1tOneO6eZZoQ0bsimtrb\nbDvpAsjQPQ/dM9XWo/d3RxkMiLzJvPLbPxB80RXn4mIsEoFN2xGRKL/v6bizsNgGzPa2No+bufxm\nuavdAOZpbeaZb16liWj04ZDfe669kKULIEP3PLncIoS+t2cUSEqwE5g2nrX3Pkz4rb4cWjLA7Xof\nFIUGIURlL6dMEUIYQ37vjPwlN8nP2wAV9Hbx7N1LNDXofzbg6fhxvOsZyuQvwQVwucVZ9J3uMwDL\nqEzOTMjm5ev/CfXIiTgXFyMvv07IHzjnhIi5vvaWZLPdYR4zpSBmdUlfnBYO8fzqazVv25nXA13t\n98W7nqFOhu4FcrnFcfSd6scBxovGUj06E1fRbWinG+NcXD+LRmHDG0RUjY09HXcWFluAOV0tjWML\nrl5pUhR5DW2giUYivPTQqsjZ6or9gc42+YhvDMjQ7Rtu9P1jcwBl6niOJiXw1qJbUdsvaHLUwPbu\nBxCJ0iqEONbLKZOFEOZwwJd38dKvyc/aAKOpYdb968pI9YHdJ8MB/yIhxLC8EBxr8hehD3RfWNuE\nPjQvByBvMntCKh8svQvV549ref3mla2ooTAvnOOUS/2dbYkGo9E2bsalMatL+ttCfi+/uXe5VnNo\nT7ka9M9SQ4FgvGsaLmTo9pHubSB/Cxyj+1Hhgqm83tBMxZI7Uf29DSIfpISAl7cQDoXZ0NNxZ2Gx\nCZjX2Vw3On/JTQbZWhg4/J1trLlzkdZSc+KgPTltjgzc2JKh24dc7r9MK20ERhsMiFlONlfXc3LZ\n3ahD6aN9sBz8ATzok5N7MhGwqcFAfsHVK40xLE06h84z9Tz5jcs1b+uZt0fmTl3QUnMiFO+ahhsZ\nun3M5RZe4BdAFzCyO3g3fVTDR0NpxfvKG2iRCC+f48LLJYGudkc0GknMKZgf09qknp2tPcmTX58f\n0ULBzeOcs68+tb9MzvyLAxm6/cDlFm3AzwE/MNJoIDrbycbK05wsum3wj/sRAtaVEgyEWN/TcWdh\nsRG4vKOpbnTe4q9iMMiPWbw1VBziqX9YGFEMhqezps+6ubysNBrvmoYr+dvQT7rv4X0UPXhHGQyI\n2U5+f7qJY/NvGdwb5Hx4Ejq6CAHv9XLKeMChhvx5BVevlE+hxdnJvTt45q7FEbPV/qOsabNWl5eV\nytvC4kiGbj9yuUUL8Ajg4ZPg/UOXl3dn34j6UU2cCzxPG98kArxyjtbC7KC3y6GFQynjL/m7WJYm\nfUo0GmXHmh9FX3xgRdiWlPqtzjP1D8vAjT8Zuv3sUyveTmCMokDBNHbaLGybswJtX2+XoQawF134\nfQFe7umYs7DYACxob6zJnF54LUaTXOjGg7+zjd/cu1zbu+Hp9vSs8cs6GmufiXdNkk6Gbgy43KIV\n+BlQR/d9vM5J7B+TycbFt6OWDqIhlyeqoOksUWBPL6dcBCRqoWC+3OAmPurLD/DE12ZpradPlY+e\nnLeg8cThQfQJG/pk6MaIyy06gceAQ+g9T8OkizgxOZff3fIAocefJzoYHsDc+CZRg4FNQojeLsQU\nhPxeezjgy5g4tyimtQ13Qgj2bXpOPPPNxZrFkbB+zJT8wo/edVfEuy7pr8nQjSGXWwTQ7+PdCeQC\npnGjqC+YxtM/WUP71/4ZbaA/vfaiC6/Xz0s9HXMWFivAFe0NNZlTFyyLmsxySESsqMEAG35wm7b1\nV9/3Z+ZM/s7I3Km3l5eVdsS7LunzZOjGmMstNPQNcjaitxrs6Sl0zM1nzZ5DnJh1w8C9wFZdD9UN\nGICyXk7JBtLUUGBGwfKbzTEsbVhrqDjEL2+Zo1XuL6sbNdG5pP74wV+Vl5UO0zkmA58M3ThwuYVw\nucVrwP+ibwuZbjGjXjKDjVqEHZfciLbl7fjW2JNN2xBmE38UQvT2C31xOOi3Br1do6bMXxrT2oYj\nNRjg9V88FFlzxyJNRCNvjJmSP7/qwK4/x7su6dxk6MaRyy32AT8EQkCWokDeZPZNyGbtLd8l8N1H\niYTCcS7yU9b+EY/Hx7qejn2qtZAxaV5RxGyzx7i64aVyfxmPXe9UP3hzfePYaTMfHDl+2orju7Y2\nxbsu6W+ToRtnLreoRQ/eD9EvsJmyx3D6kjyeXFdKTcH1qB+ejG+NAPXNcKIKE3o/uidjgBFq0O+c\ndc0q2cztJ4Gudjb8xx3a2n+5IWRPSt2ZNW3Wzfak1F+Ul5UOoZ09hjYZugOAyy086BMoNqP3RZOS\nEvDNyeMFVWPrvJWojz+PiMbxwc3N2xEWC1uFEL2tvS9WQ0FzoKtj7NSFy2Na23BxdOdmHrtuula1\n/0/V2TMu/X56Vu6q8rLS3fKBh8FFkRvFDywlRcoM4B8BO9AAiNYO0iqqWDl1POnrH8ecPSb2dc1d\nQdd7R7hVCPHqZ491txZ+fKa6Ynb62NyiO556Xa50+1BbXSWuR+9Xaw/vDadl5b6RMjLrJ8AHMmwH\nJ7nSHWBcbvEh8AM+uZ/XnpFK+7wCnqlvZrezGO3x5xFaDK9Nt7TB4QoswJu9nDISGBsO+KbOlK2F\nPtN5pp5NP7xbe2LFbK2luqI8O2/Ot1NGZt1eXlZ6SAbu4CWfGBqAXG7RVVKkPAUcBG4Fko0Gmi+e\nSllLG0cffY7rn9nAqN/+FPP8Wf1fz6s7wG5lZzAketuYMk9TQ8ZAZ1vO9MLi/i9oiPO1n+WtXz8S\n2bf5OZGYPuJETv68HRa746nyslL5oMMQIEN3gOoeAbSnpEg5CdwJTAeaRqTTmpnGryuqyFt6F1+5\ncSmm/3kQU3pq/9Wy1kVXh+ecE3+v6GiozcjOmxuxJ6XKDcvPU9DbxZ/WPh7dve5XUXtKWmV23pz3\nrY7EDcAb5WWlcrPxIUL2dAeBkiLFCMwHVgEW9F5vNBDEeryKJR4fBT9cjfGelSiWPv6f+/ZOGH0F\nobBKphDC+9njzsLiTOC/644dWLjk3v+aNOf62/u2gGFADQbYs/5J8fZvHolYE5Jq08aOP2BPSnkV\nPWzb4l2f1LfkSncQ6J6/tqukSDkC3AgUAh12G+2zplPa1MJ7P13DNY88y5jHvof55mugr/YNd70F\nDju7QuHPB263GRFNNQY623Odhdf2zV86THham9m74enonvVPRi02R+PICc73E1IzSoHXystKz8S7\nPql/yJXuIFRSpEwBbke/N7YJCALUNJDb0Mw16amkPvFvmJddARc6D/KqO/Ds/DP3CSF6nPrrLCz+\nQWtd5TxrQvKSb/3uHeuF/W3DQ335Acp+95h6/E9bFEdqxqmUUeOOJ6RmbAdeLS8rrY93fVL/kqE7\nSJUUKWbg79BXvjb0YZiqEHCyhunNZ1k2ORfbj76NZemC8wtfjw9GXE44FGaUEOJzm6c4C4vTgMfr\njx2cf+WdD02bv+KeC3tTQ1jI7+Xwto3sXvfLcEdTreZIyTiSkT2h0my1HwA2lZeVVsW7Rik2ZHth\nkHK5hQrsLClS9gKLgWsBRVFonJLLsYk5VFRUkbfqARZlpOL4z/uwrFgG5i+xDc2Wt8FhY18w9PnA\n7eaMRjSDv6tt0oxF113oWxpyhBDUHzvA3g1Pa4e3bcSWmFyXkJZ5cvyshfWKwbAP2A6ckrd/DS9y\npTtElBQpacBy4Coggt52iAgBlXVMOtvOIgVGPHQ3pm/ehJLg+NuvWXwP3i1l3C+E+HVPx52FxQ+1\n1VfPN5rNV69+aZ9sLQARTaPm0G6O7NgUObJjczQSDoVsSalH07Nya62OxFZgG7CrvKz0bLxrleJD\nhu4QU1KkjAKWobceAJqBMEB9M1mNLVzp8ZG7qhjuvRnTzOk9v44/ABmXEQqGGSeE+FxAOAuLk4En\nTh99b3pEDV868yt/r+QvvsGYUzB/2I3oCQf8nNy7ncPbNqrH33ldMVttXSar/cOUkWPPOFIyPIqi\nVKI/WHJY7pEgydAdokqKlHTgSvQANgEtQACgo4vk6npmd3qZkzUS0+qvY1lVDClJn/z5zdvgrn/n\nvbZOMben13cWFs8D7hFC1Hhbm0d7Wpunq6FgXkQNJU9duFxMmb/UnFNwGZk5k1Eu9GreACOEoLPp\nNKf2v80HW18JVx14x2hPTG02Wa0fpo7OabElJofQd45zo481apAtBOljMnSHuJIiJQH9Ht/rgCT0\nycRtgIhGUarrmdDexWXtXYy/9kqit30V8+LL4JYH8G3ezveEEE/19LrOwuL7gIXoYd4BRAECXR0p\nHc11U6IRbWLI58kBzNl5cyIT5xVZcmcuULKmz8ZstcXgnfedcMBHXfn71B5+l8r9b4dPH33PENXU\niC0xpc5sc1Skjc1pM1vtYcAH/Bn9ScLK8rLSAbQxpzRQyNAdJkqKFAuQh97znQYI9MAMAnh8JFTV\nkR8MM9frIzGsIlSNXCFEY0+v5ywszkB/Sm5+9+sZul+r8+PXBAh4OpO9rc3Z4aA/N6KGJwS9nakj\ncqeqE+dcac7On2vIyJ5ExrgJ2JPT+vHdf3HhgI+2ukrqjx+k+uAurerArkhHY63ZnpTaZjCZqy02\ne01ixqhOW2KKouhL+BZgF3AYqC0vK43jXnDSYCBDdxjq7vvOBZYCiehth1b0C3DZJ6p5vqKKbCFE\nj2PWP8tZWJwATAFmowd7CnqoC/QQ9nb/jBYOmT1nm7ICXe05AnIiaig96PUkG00mUkdnaxkXTVZG\nTZhuzsierGSMm0B69gQS0kbQV/PWtHCIjqZa2uqraa+vprXulGiprlDb6qpEZ3OdUQ36DdaEJJ/Z\n5mgyGAyVjtSMxsT0UQGjyWTrfg8KUIMetOVAk2wdSF+GDN1hrKRIMaGvVheiB6YZvf1wf/dTcF9a\n9zaPqej7Ak8G8rt/Bj2wfOgh/Je9BIQQhAM+R9DTkR7ye9PVUCBdUZSREVXNDAd8yWooYAEFs9UW\nMdvswmxzRK2ORCyORKwJydgSkhWLI0FRgwER9ntFOOgjHPALNehX1GAALRRU1HBQ0cIhQ1QLGyyO\nJL/Zau80GI1tQkTPmK32DosjscOWmNxhdSSiKIZk4OM9JAJABfom87VAfXlZ6QAfHyoNZDJ0JQBK\nihQ7epvA5nKLPp2z5SwstgFZ6BOQ87q/p6L3gQV6ayKCHnAB9PbEX30wI5pmjKghi6aGrRFVtUQ1\n1RLRVEs0oloimmYV0YhJMRg1g9GoGgwm1WA0qgaTSTUYTarRZFINRrNqNJtVk9mKYjBY0R8o+Xj1\n+vEKFvRgPQqcAuqAVrmSlfqSDF0pLpyFxRYgDUjv/j4GGAeMBTLRQ/Cz/VGl+8vQfSzS/d3Y/d8M\nfBKiH3+wxWf+rBf9HuYm9FBtQ78Q2A50yim6Un+ToSsNOM7CYiOQjL4StXR/WT/1swV9skYCeksk\niL5CDgFqL18+oEPeUSDFmwxdSZKkGJLjeiRJkmJIhq4kSVIMydCVJEmKIRm6kiRJMSRDV5IkKYZk\n6EqSJMWQDF1JkqQYkqErSZIUQ/8PQVN67hzDqWoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa6e2208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "# The slices will be ordered and plotted counter-clockwise.\n",
+    "labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n",
+    "sizes = [15, 30, 45, 10]\n",
+    "colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']\n",
+    "explode = (0, 0.1, 0, 0) # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
+    "\n",
+    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
+    "        autopct='%1.1f%%', shadow=True, startangle=90)\n",
+    "# Set aspect ratio to be equal so that pie is drawn as a circle.\n",
+    "plt.axis('equal')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 图像中的表格 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`table` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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v46OPPrJ9n96djqe6cuWK7e545syZDufJrX/UekWeBY5uQe2ndezYkcOHD9O8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\n+x9716LrwMNrSQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd0309b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "data = [[  66386,  174296,   75131,  577908,   32015],\n",
+    "        [  58230,  381139,   78045,   99308,  160454],\n",
+    "        [  89135,   80552,  152558,  497981,  603535],\n",
+    "        [  78415,   81858,  150656,  193263,   69638],\n",
+    "        [ 139361,  331509,  343164,  781380,   52269]]\n",
+    "\n",
+    "columns = ('Freeze', 'Wind', 'Flood', 'Quake', 'Hail')\n",
+    "rows = ['%d year' % x for x in (100, 50, 20, 10, 5)]\n",
+    "\n",
+    "values = np.arange(0, 2500, 500)\n",
+    "value_increment = 1000\n",
+    "\n",
+    "# Get some pastel shades for the colors\n",
+    "colors = plt.cm.BuPu(np.linspace(0, 0.5, len(columns)))\n",
+    "n_rows = len(data)\n",
+    "\n",
+    "index = np.arange(len(columns)) + 0.3\n",
+    "bar_width = 0.4\n",
+    "\n",
+    "# Initialize the vertical-offset for the stacked bar chart.\n",
+    "y_offset = np.array([0.0] * len(columns))\n",
+    "\n",
+    "# Plot bars and create text labels for the table\n",
+    "cell_text = []\n",
+    "for row in range(n_rows):\n",
+    "    plt.bar(index, data[row], bar_width, bottom=y_offset, color=colors[row])\n",
+    "    y_offset = y_offset + data[row]\n",
+    "    cell_text.append(['%1.1f' % (x/1000.0) for x in y_offset])\n",
+    "# Reverse colors and text labels to display the last value at the top.\n",
+    "colors = colors[::-1]\n",
+    "cell_text.reverse()\n",
+    "\n",
+    "# Add a table at the bottom of the axes\n",
+    "the_table = plt.table(cellText=cell_text,\n",
+    "                      rowLabels=rows,\n",
+    "                      rowColours=colors,\n",
+    "                      colLabels=columns,\n",
+    "                      loc='bottom')\n",
+    "\n",
+    "# Adjust layout to make room for the table:\n",
+    "plt.subplots_adjust(left=0.2, bottom=0.2)\n",
+    "\n",
+    "plt.ylabel(\"Loss in ${0}'s\".format(value_increment))\n",
+    "plt.yticks(values * value_increment, ['%d' % val for val in values])\n",
+    "plt.xticks([])\n",
+    "plt.title('Loss by Disaster')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 散点图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`scatter` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Lyk628+Gmv2P01jBi0iWUbd7Aiy++SF5e3pnl/WgtlrgrCQQD7Nr6Dw5VlpM0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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd00d7f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.cbook as cbook\n",
+    "\n",
+    "# Load a numpy record array from yahoo csv data with fields date,\n",
+    "# open, close, volume, adj_close from the mpl-data/example directory.\n",
+    "# The record array stores python datetime.date as an object array in\n",
+    "# the date column\n",
+    "datafile = cbook.get_sample_data('goog.npy')\n",
+    "price_data = np.load(datafile).view(np.recarray)\n",
+    "price_data = price_data[-250:] # get the most recent 250 trading days\n",
+    "\n",
+    "delta1 = np.diff(price_data.adj_close)/price_data.adj_close[:-1]\n",
+    "\n",
+    "# Marker size in units of points^2\n",
+    "volume = (15 * price_data.volume[:-2] / price_data.volume[0])**2\n",
+    "close = 0.003 * price_data.close[:-2] / 0.003 * price_data.open[:-2]\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.scatter(delta1[:-1], delta1[1:], c=close, s=volume, alpha=0.5)\n",
+    "\n",
+    "ax.set_xlabel(r'$\\Delta_i$', fontsize=20)\n",
+    "ax.set_ylabel(r'$\\Delta_{i+1}$', fontsize=20)\n",
+    "ax.set_title('Volume and percent change')\n",
+    "\n",
+    "ax.grid(True)\n",
+    "fig.tight_layout()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 设置按钮"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`matplotlib.widgets` 模块："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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udBuL2rSxdfzyS1tn1/zX5Fq3tgYVHsMUPxQ5jRpZPrHVq+0CukuHDgVay3Xu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\nBgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xdc14198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.widgets import Slider, Button, RadioButtons\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "plt.subplots_adjust(left=0.25, bottom=0.25)\n",
+    "t = np.arange(0.0, 1.0, 0.001)\n",
+    "a0 = 5\n",
+    "f0 = 3\n",
+    "s = a0*np.sin(2*np.pi*f0*t)\n",
+    "l, = plt.plot(t,s, lw=2, color='red')\n",
+    "plt.axis([0, 1, -10, 10])\n",
+    "\n",
+    "axcolor = 'lightgoldenrodyellow'\n",
+    "axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)\n",
+    "axamp  = plt.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)\n",
+    "\n",
+    "sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0)\n",
+    "samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)\n",
+    "\n",
+    "def update(val):\n",
+    "    amp = samp.val\n",
+    "    freq = sfreq.val\n",
+    "    l.set_ydata(amp*np.sin(2*np.pi*freq*t))\n",
+    "    fig.canvas.draw_idle()\n",
+    "sfreq.on_changed(update)\n",
+    "samp.on_changed(update)\n",
+    "\n",
+    "resetax = plt.axes([0.8, 0.025, 0.1, 0.04])\n",
+    "button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')\n",
+    "def reset(event):\n",
+    "    sfreq.reset()\n",
+    "    samp.reset()\n",
+    "button.on_clicked(reset)\n",
+    "\n",
+    "rax = plt.axes([0.025, 0.5, 0.15, 0.15], axisbg=axcolor)\n",
+    "radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)\n",
+    "def colorfunc(label):\n",
+    "    l.set_color(label)\n",
+    "    fig.canvas.draw_idle()\n",
+    "radio.on_clicked(colorfunc)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 填充曲线"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`fill` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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L8It+bpD335ws4A5VFpeWsry8nJNff53Hx46lR9euXHf55bzzzjtUenCp1HB9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\nFChb1lWmfwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9eefa90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "x = np.linspace(0, 1)\n",
+    "y = np.sin(4 * np.pi * x) * np.exp(-5 * x)\n",
+    "\n",
+    "plt.fill(x, y, 'r')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 时间刻度"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UwMpdRJqKyPsiMiUodxSR6SKyUESmiUhRUt3bRWSRiCwQkTOz1fG40phsq6tW\nRYmEZ82yEXv//lbed1+49NJiIBq5d+hgqzbTUe7XXWcBuDZtgv33N7t9qtmGVq2ytHOzZ1ssklTZ\ns8cm7UIf83/+05JHDBgAF1+cejvJNKbnojbSkUWYMrC4OL9RLy+8MNpv08bmfsKMWLki05H794F5\nQDg+GQ1MV9X+wCtBGREZAFwMDADOBh4WEf+14ACmPFu0sP1vfMMCZYU/X7/4IspQFI7cO3RIb+S+\nZg384x+2v3y5KfeePW00/+67tV+/caO5YXbrZr8yUr3vq69G9w/JVKk7daN79/zmMQ0ZM8bmilRN\nuX/0UXoDhkxIW9GKSE/gHOARILQkDQPGB/vjgTAM0XBgoqruVNUSYDEwuC4djjuNxbb6+eeVfb4f\nf7x8OZRFWC9ds0xyjtDHH7cogfvvbzG+jz02WqlYFStX2l+7dlEuzIrp8KojTIIRTsQ2bQqnnZba\ntdXRWJ6LVEhXFgcckJt+pININEgJzY0LF+b2npmMou8HfgQkWy67qmpZsF8GBB7K9ACSc5CUAtUE\nHnUaE3372s/SMHtN27ZmtqiKimaZVL1lFiwo32afPjZyX7DAytVlx9m924JMLV1qL4OWLe14xaw+\n1ZH88vnjH6099z93QkLlnmvSUu4ici6wWlXfJxq1lyPM61dDM3nKBd4waCy21W3bzK4+YYLtV7W4\nKJRFuOhkn31s8cfGjandY+NGWxQ1ebIF0jrwwPLKvbowrPPn2/b+++2FApb0oX371DLZH3FEtP/t\nb5v/fl1pLM9FKjR0WdRXUpR0F0OfCAwTkXOAVkB7EXkcKBORbqq6SkS6A0FMOlYA+ydd3zM4VomR\nI0fSO3BsLioqYtCgQXv/ieHPMC83/PLixfDf/23l994rplWr1K7/y1+gXbtievaE6dMTNGlSc/2y\nMli0qJg+faBduwQff2zne/aEn/zE6m/eXPX1M2cm6NQJbr65/Plt24rZvh3efrvm/q5Zk+Cww6BD\nh2KeesrOJxKFIX8v57/8zjtWbtIk/esTiQTjxo0D2Ksvq6W6zNm1/QGnAlOC/buB24L90cBdwf4A\nYC7QAugDfApIFW3lLj14A2PGjBn57kJOsbGv/e3ZU3PdqmRx442qDz5Y/lhV7eyzj93j3nvLH1+4\nULV3bzs3ZUr5c7fconrrrapPPKF64omV2+zYUfX992vus6pqt26qpaW110uHuD8X6dDQZbFnjz1/\nInVvK9DIksN1AAAYGUlEQVSdVeroDMMYRe+GYHsX8LSIXA2UAN8ONPY8EXka86zZBYwKOuQ0Qlat\nivanTs3MDt22bXkXsk2bbNKz4lMVLlgJzSoh/fqZLf1b36rsinb//dF+6KKZzLp1lst0+nQ4/XQ7\ntnBheS8f1SiDlONURfKzktP7FIKuFXuF5bsbTo6ZORNOOsn2M/13/+IXtnx78WKb6PzgA0smHWY5\nCjnnHDv2j39UHYnxyittwdRVV0XHkl82118PDz9c/poDD4zycH7yifnk9+plcbpPPdXuN2+eJUre\nssUnUZ3qyZaCFxFUtconzX3OnXojnaX/1VFWZl4uoZINQxdUdI/s0AEuv7z6ELvhL4ANG+yLdvLJ\n0bnzz6+s2MHCx4YBnz7+2BT8F1/YCtT58+GOO2DgQPPKccXupMKGDTYgyAWu3AuMcPIkjiQS6S3c\nqEoWP/iBbcOcqv/zP7atGPFv69aavRLatjWTTphz9M03bTtpUs2p1xYuhDPPhEsvhUMOsRH6vfea\ny+Xdd1ud5BdFtojzc5EucZDFD39oYQiuu85WQeeCutrcHSdl5s2zWC2DBmXexoEHwvHHR7E5XnnF\ntt/5DvzHf0T1vvqq5pjZ27bZSPuaa8ofry1LT1GR/UoI79+lS5SvdMIEC29w5JGpfx6ncXLPPbZK\nOpdpEH3kXmCE7k9xZNkyC+Nb1WRlVVQnizZtotWlyYuUwpjtYAq3ppyrl19u27FjoxWlf/hDav0K\no1mC/Tr47W8tR+cll8AFF0ShirNJnJ+LdImLLHr1isyLucAnVJ16YdcuU8qbNkXxZDJFxJbzd+0K\nEydGx+fMsVHzl1/aF2fx4przrz7yCIwbZ6npvvoq9V8Uffua/b1lS7Oxh3HpHScdfvCDyEMrU/Xn\nE6oNiDjYE6tixQobSaej2KuTxYgRtro1WbEDzJ1r26eegrPOqj2x9ne/a7b2vn3TMxU98QQ895xN\n5tZXwu24PheZEBdZhGEtcoXb3J16YdkyG01ng65d4cEHzf69erX5lA8ZEsWKWbcuN6aRkOOPz13b\nTuOhrr9ga8NH7gVGXOyJFVm+PH3lXp0sDjrItmGkvS1bLGb7unV2fMeO3I+K6pu4PheZEBdZhM/o\nb36Tm/ZduTv1wmWXwdtvZ6etG280xb5+fXSsY8dIuW/fnvtRkePUlfAZzVWUSFfuBUZc7IlVcf31\n6dWvSRb7VQgcve++lsZu5EhT7nEbucf5uUiXuMgifEZduTsNlpIS22YzG1FF5d6xoyUAGT/eFhXF\nTbk78cNH7o2MuNgTk3nrLYv70rNnetfVJIuqlHsycVPucXwuMiUusqiYCD7buLeMk3Pefhvuuqt8\nYK+68qMfRROrUFm5V4wG6TiFRvjM+si9kRAXe2Iyn3+emWtiTbI4/nhbGRqSrNx/9SsYPjz9+xUy\ncXwuMiUusth3X9v6yN1psOzaFSUHzhXNm1vsmqIiy3jvOIVOrpW7hx9wcs6551r0u3PPzXdPHKdw\nWL/efnGWlGS+wM/DDzh5Zdeu6uOqO05jpagIbrsNDjggN+27ci8w4mJPTGbnzszMMnGURaa4LCLi\nIgsRczTIVWIXV+5O1lEtH1DLR+6OU/+4zd2pEdX0RhYzZlikxZ/9zIJ6de4MJ55oyQlOPDF3/XSc\nxkjWbO4i0kpEZonIXBGZJyK/CY53FJHpIrJQRKaJSFHSNbeLyCIRWSAiZ9btozj1zSmnwLXX1l5v\n/nxYtcrirP/sZ3bskkssabSP3B2n/klLuavqNmCoqg4CBgJDRWQIMBqYrqr9gVeCMiIyALgYGACc\nDTwsIm4KqoFCsye++aYlpqiNAQPKh8Jt0sRS4D37LLzzTmbKvdBkkU9cFhEui9RIW9GqapA9khZA\nU2A9MAwYHxwfD5wf7A8HJqrqTlUtARYDg+vSYSe3lJVVPhb649ZGaakF7gJLhn377fD441besSMb\nvXMcJ1XSVu4i0kRE5gJlwAxV/RjoqqqhWigDugb7PYDSpMtLgQpRQZxk8hk3Y+tW6NYNPv7Yyh9+\naNtdu2q+buVK2+7eDUcdZYk5Tj4Z+vWDyZPtXCYLNeISQyQbuCwiXBapkfaPZVXdAwwSkX2Al0Rk\naIXzKiI1zY5WeW7kyJH0DtaoFxUVMWjQoL3/xPBnmJdzW96xw8rPPptg9Wo47TQrz5iR4L774P/+\nr5g5cypfP25cgr59YfXqYq68Et59N8GSJdCvn53/5S8TQaz1wvq8XvZyQysnEgnGjRsHsFdfVouq\nZvwH/BT4f8ACoFtwrDuwINgfDYxOqv8icFwV7ahjzJgxI2/3/vnPVUG1Y0fbguqOHdE+qK5cWfm6\nH/5Q9bbbKh9fv96uKSvLrD/5lEWh4bKIcFlEBLqzSv2crrdMp9ATRkRaA2cA7wOTgSuCalcAzwX7\nk4ERItJCRPoA/QDPFV9AHHqorZIDc12EKKMR2OKjSZOi8p13wp495dt46y0444zKbRcVmUmnS5fs\n9tlxnNpJy89dRA7HJkybBH+Pq+rvRKQj8DRwAFACfFtVNwTX3AFcBewCvq+qL1XRrqbTDyc7TJoE\n559vMV8mTYLBgy2U7u7dtiS6c2c4+GD47DMr/+lP5hZ51FHw3nv2d8st8MYbsGCB1XUcp/6oyc/d\nFzE1YpIXJ02bZop9zpzKcdf37IleBOG5yZMttO6sWTB0KEydCq1a1V/fHcfxwGENinDypL4588zy\nyjuZJk3gggvsZfBcYHD7wQ9g9mzYuBFefTU3ij1fsihEXBYRLovUcOXu7HVTrMpuXpHhw2H6dFi8\nGG64Adq1y23fHMfJDDfLNFJ27IiSSi9fDocfbsdSjd4oAqNGwR/+kNt+Oo5TPTWZZTziRyNl7lzL\nQdq+PXz96zZpmk5Y3pkzoU+f3PXPcZy64WaZAqO+7IkzZ8IJJ0Tlnj3Tu/6EE2w1ay5x22qEyyLC\nZZEartwbKR98YC6NjuPEE7e5N1JE4Omn4VvfyndPHMfJFHeFdMqxZo1tk0P0Oo4TL1y5Fxj1YU+c\nPh1OPx323z/nt6oTbluNcFlEuCxSw5V7I2ThQh+1O07ccZt7I+TSS23kHibWcBynYeI2d6ccn35q\nPu6O48QXV+4FRq7tievWwdtvN4wIjm5bjXBZRLgsUsOVeyPjzTdh0CAL5+s4Tnxxm3sjYsMG+OMf\noaTEYrM7jtOw8dgyDi+8AP/2b5YsL0xa7ThOfHGzTIGRC3uiKpxzjm1/8hM477ys3yInuG01wmUR\n4bJIDR+5x5zvfteyJXXtCq+/Dv365btHjuPUB25zjzlhKr0HH4SbbspvXxzHyS5uc2+k7NljMdr/\n+lfLoOQ4TuMhLZu7iOwvIjNE5GMR+ZeI3BQc7ygi00VkoYhME5GipGtuF5FFIrJARM7M9geIG1XZ\nEzdvhgUL0m9r0yZo2bLhKna3rUa4LCJcFqmR7oTqTuAWVT0MOB64QUQOBUYD01W1P/BKUEZEBgAX\nAwOAs4GHRcQncdPkjjvg0EPTv27jRthnn+z3x3GcwictRauqq1R1brC/CZgP7AcMA8YH1cYD5wf7\nw4GJqrpTVUuAxcDgLPQ7thQXFwOwbZsF+IIoRG+6bNxoafQaKqEsHJdFMi6L1Mh4FC0ivYEjgVlA\nV1UtC06VAV2D/R5AadJlpdjLwKmCHTtsAnTnTvj2t6MQAfvua9tt29Jrb80aS4LtOE7jI6MJVRH5\nGvB34Puq+pVINFmrqioiNbm+VHlu5MiR9O7dG4CioiIGDRq09w0d2tgaYnn3bnj99QQitdf/5z+L\ngQTPPANTpgAUowqlpXZ+yJBiBgyAE05IcOihtbf3+efF7LdfYckjnXJ4rFD6k8/y3Llzufnmmwum\nP/ksP/DAA7HRD+mWE4kE48aNA9irL6sjbVdIEWkOPA+8oKoPBMcWAMWqukpEugMzVPUQERkNoKp3\nBfVeBMao6qwKbcbSFXLHDstT2r8/PPNM7fXtHZkAigFo1w6WLoVOncrXa9sWPvkE9qvmN9Brr9n5\nzz+Hr76Ce+/N/DPkk0QisfcBb+y4LCJcFhFZC/krNkT/CzAvVOwBk4Ergv0rgOeSjo8QkRYi0gfo\nB8xO554NmUcfhY8/NmVbGy+9FO4VA5YtqVcvawOi2OsXXmjeMzX5rBcXw7XXwp13wgEHZNb3QsC/\nwBEuiwiXRWqka3M/CbgUGCoi7wd/ZwN3AWeIyELgtKCMqs4DngbmAS8Ao2I5RK+GsWNt27RpzfV2\n7TJl/NJLcOWVcMQRlkzjgAPg1ltt/4HgVfqd75j3zDPP2Ej/qqvKt7VihY3s77nHyoWeSs9xnByh\nqnn/s27ED1Dt2lW1WTPV2bNVO3dW3bHDzu3ZE9U7/XSrq6r66qszdPdu2//pT+34nDlWfuUVu/7D\nD+04qPbuXf6eTzyhet55qi+8YOfLynL7GXPJjBkz8t2FgsFlEeGyiAh0Z5V61X3Oc0To2XL99dCq\nFQwebN4rGzbAz38OTZrAkiVmU3/5ZXjqKasvYucATjzRtqHHy2mn2YrTww831b5pE5SVwZw58O//\nDl//uo3sv/UtOOss87rp0qV+P7fjOIWBx5bJEZ98YiF2Fy+Gbt1sMdHChfDee3DKKWY3P+ccmDrV\n6lf18TdsgA4dYO3a6l0ajz0W3n3X9g86CL75Tfjtb6F169x8LsdxCgePLZMHli6F0FOpWbNoQdJT\nT8EJJ8CECdGo+oYbqm6jqMgiOtbkqz5mjIXwnTcvs1WsjuPEEzfL5IiyMujRw/ZXrLBtly5w993Q\np09598Zrron2k328wcw5NXHuuTbqj6NiryiLxozLIsJlkRqu3HPEtm1mawd45BHbhjb0Nm3Mtj57\ntplpBg7MTx8dx4kvbnPPEb//PSxaBA89ZGVVC8F72WVw3XVmd3ccx6kLbnPPA9u3W7jdEBHzd58w\nIX99chyn8eBmmRxRUbmnitsTI1wWES6LCJdFarhyzxGZKnfHcZxs4Db3HLBtm/m233473HZbvnvj\nOE5cyVrgMCc1Jk+GL7+0FaSO4zj5wJV7Dnj8cdvu2pX+tW5PjHBZRLgsIlwWqeHKPYt89BGMGAEz\nZtgCplNPzXePHMdprLjNPYscfbQF8WrTxmLHOI7j5BL3c68HXnrJFPtbb1k8dcdxnHziZpkscfbZ\ntj3+eAvJmyluT4xwWUS4LCJcFqnhyj1LnHUWPP98vnvhOI5juM09C8yda9EZp0yBI4/Md28cx2ks\nuJ97Drn3XlPoK1ZEIX4dx3HyjSv3OvLQQ3DnnTZ679q17u25PTHCZRHhsohwWaRG2spdRB4VkTIR\n+SjpWEcRmS4iC0VkmogUJZ27XUQWicgCETkzWx3PB7t3R7lRwWKxb9sGP/4xHHFEdu4xd+7c7DQU\nA1wWES6LCJdFamQycn8MOLvCsdHAdFXtD7wSlBGRAcDFwIDgmodFpMH+WujVy3KTrlxp5RdesGTU\nTZtm7x4bNmzIXmMNHJdFhMsiwmWRGmkrWlV9A1hf4fAwYHywPx44P9gfDkxU1Z2qWgIsBmpJHBdR\n1c+v6n6S5bquapQu7ze/gSlTEtx3H3z3u9m9X0lJSdb6XB91c3k/l0VEXWVRKJ8jV7IolL7Vtzxr\nIluj6K6qWhbslwGh9bkHUJpUrxTYL9VGC+lL/NVXtjhp9myzsw8blqC4uLw5Jhv3q+4nZyHJor7u\n57KIqKssCuVz5EoWhdK3QlLuGblCikhvYIqqHh6U16tqh6Tz61S1o4g8BLytqk8Exx8BpqrqMxXa\na7h+kI7jOHkk1+EHykSkm6quEpHuwOrg+Apg/6R6PYNjKXXOcRzHyYxsmWUmA1cE+1cAzyUdHyEi\nLUSkD9APmJ2lezqO4zjVkPbIXUQmAqcCnUTkM+BnwF3A0yJyNVACfBtAVeeJyNPAPGAXMKpBL0V1\nHMdpIBRE+AHHcRwnu9Sbz7mIdA62jT7McGCicgAROUZEuuS7H4WAiJwuIkfnux+FQPJCyMaOiLTI\n5LqcKncx2orIk8AkAFXdJSKNcgJVRI4SkZeBnzf2l5yIHCYibwH/BXSopXqsCZ6LF7G5qoPy3Z98\nIiLHicgkYKyIXC0irfLdp3whIieIyBPAf4lIfxFJa7lkTpW7GmFOon1FZFR93LcQEZGfAE8CT6nq\nZaqaQYbVWHEz8Kyqnquqn4ANBvLcp3pFRJqIyFhgLPAnYAJwaHgun33LB8Gvlj8Cfwv+htJIX3Yi\ncjjwe+B5zPvwGuDydNrI9ci9WeAaWQZ8F7heRDqo6u5030IxoCXwpqqOhb2jteZ57lNeCEx0e4CH\ngvKFIrI/0DooNwolr6p7gGnAyar6LPB3YKiItArONTaOBz5V1ccxubQGlue3S3njJGCBqk4EHgG2\nApemY9LN6oSqiPwHcAjwjqpOSTo+CbgBuA3YAoxV1cVZu3EBkiSLOar6nIi0A/6KeQ6dAqwCNmKj\n17/mr6e5J0kW76rqZBFpA7wH/Ai4BOgErAR2quo1+etp7kmSxXuqOinpeBPgG1gspttUdW2eulhv\nVJSFiHTDvh9/AK7GFPt84BNVvSt/Pc09VcjiSOAB4GpVXSwiY4AjMIV/RyptZmXkHtjWr8e+rCXA\nvSJypYh8LVjNWqKqpcB04HrgryLSMo4j1ypkcbeIXKOqX2Fv4COBH6rqucBrwFkicnDeOpxDqpDF\nPSLyn6q6BTNDPAxMU9WzgB8DXxeRc/LW4RxShSx+F3xH2sHeUfx8TMG3Cq6JpWmmGln8p6quwoIM\ntgJuVdXjgXHASSJyQr76m0uqkcVIbLDzBjAuGBwfiw0Om4pI61TazsqknqqqiBwP/FZVnxSR5cBV\nQQdnAr1EZApwMKbQdqnq9mzcu9CoRhZXi8hnqvo3EXlZVcOwdi8D5wBf5a3DOaS65yLYPgbcCnQO\n6q4QkTeB3fnrce6oRhZXAqtFZGowP1UqIm8D/w78Pq6mmRqei1JVnSoip2PRZQHmYDbnHfnqby6p\nQhafASOBVar6ExE5DOivqs+KyDHA5aq6NZW2Mx4ZiMjlInKqiHQMDs0H9hORZqr6MvAhcDKm0FcC\nS4GjVfU84ACJkctXCrL4ACgWkQOSFDvAmYACm4kJKT4Xp2Ff1u8Bl4vIoGD0cjo2eokFKcjiI2AI\nQYiO4JfsYsx0GStSfC6KA9PMWODW4JfLxcDXgdiYqWqRxXTsuRgqIvur6sfBfAzY92ZWqr/o0hq5\nBxNd3bFZ/T3Yg/i14ItZiv0TDgIWAE8B9wFPA99X1eQ37zcqKLkGR5qyeBKzn+0LLBeRoZhsPsPs\nq1/W/yfIHhk8Fw8Ah6rq38Vc3b4NHAZcFnrONFTq8lyo6k4RaQv0ykffs00Gz8X9QDdVfTgYzT6H\nTapeGYQMb7Bk+Fx0Aj4TkcHAb4Prrk31F13KI/fgraJAO2CFqp4GjAI2YF4PT2M/sQeLyD6quhSb\nMLxIVXeIuX01AYiBYk9XFiXAl8AFQROlwBhVHaaq8+v9A2SRDJ+LDZjpgSBi6E9Udbiq/isvHyJL\nZOG5APh/qvrT+u159snwufgSuCho4mpsMvEMVZ1X/58ge9ThubgwaGIpcKeqfiMdR5RaR+5iLou/\nBJqIyAtBB3fB3gVJ38PMLgOAidiD2hP4NWY/nRXUbfD2wyzIYnZQdxGwqN4/QBbJgizeCttq6M9G\ntp6LoH5jl8XbQd2dwJp6/wBZJIu6cw2QSPf+NY7cReRUzGWtCPsZ8QtgJ2YPGhzceDdwJzYh8DLm\nBXGSiMzCVh6m3alCxGUR4bKIcFlEuCwiCkIWqlrtH+aPfVlS+Y+YK+OVmD8mQFOgG7airE9wrAOw\nX01tN7Q/l4XLwmXhsmhIsqjN5v4O5pMeriZ9EzhAVR/D/C1vUnv79MQWoCwFUNX1qlopKUcDx2UR\n4bKIcFlEuCwi8i6LGpW7qm5V1W1BJwDOAL4I9q8CDhWRf2D2ojnZ6FCh4rKIcFlEuCwiXBYRhSCL\nlMIPiEUwVCyIzffUlsMehPmeHka0AjX2uCwiXBYRLosIl0VEPmWRkiukWgTD5tibZ2DwxvkpsFtV\n32ws/yhwWSTjsohwWUS4LCLyKYt0FjEdCXwH6AM8pqp/yU2XGgQuiwiXRYTLIsJlEZEXWaQcFVJE\nemLxhO/R8qtNGx0uiwiXRYTLIsJlEZEvWXgOVcdxnBgSy5CijuM4jR1X7o7jODHElbvjOE4MceXu\nOI4TQ1y5O47jxBBX7o7jODHElbvjOE4M+f9EYiNJNukaZgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xcae5320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Show how to make date plots in matplotlib using date tick locators and\n",
+    "formatters.  See major_minor_demo1.py for more information on\n",
+    "controlling major and minor ticks\n",
+    "\n",
+    "All matplotlib date plotting is done by converting date instances into\n",
+    "days since the 0001-01-01 UTC.  The conversion, tick locating and\n",
+    "formatting is done behind the scenes so this is most transparent to\n",
+    "you.  The dates module provides several converter functions date2num\n",
+    "and num2date\n",
+    "\n",
+    "\"\"\"\n",
+    "import datetime\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "import matplotlib.cbook as cbook\n",
+    "\n",
+    "years    = mdates.YearLocator()   # every year\n",
+    "months   = mdates.MonthLocator()  # every month\n",
+    "yearsFmt = mdates.DateFormatter('%Y')\n",
+    "\n",
+    "# load a numpy record array from yahoo csv data with fields date,\n",
+    "# open, close, volume, adj_close from the mpl-data/example directory.\n",
+    "# The record array stores python datetime.date as an object array in\n",
+    "# the date column\n",
+    "datafile = cbook.get_sample_data('goog.npy')\n",
+    "r = np.load(datafile).view(np.recarray)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(r.date, r.adj_close)\n",
+    "\n",
+    "\n",
+    "# format the ticks\n",
+    "ax.xaxis.set_major_locator(years)\n",
+    "ax.xaxis.set_major_formatter(yearsFmt)\n",
+    "ax.xaxis.set_minor_locator(months)\n",
+    "\n",
+    "datemin = datetime.date(r.date.min().year, 1, 1)\n",
+    "datemax = datetime.date(r.date.max().year+1, 1, 1)\n",
+    "ax.set_xlim(datemin, datemax)\n",
+    "\n",
+    "# format the coords message box\n",
+    "def price(x): return '$%1.2f'%x\n",
+    "ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')\n",
+    "ax.format_ydata = price\n",
+    "ax.grid(True)\n",
+    "\n",
+    "# rotates and right aligns the x labels, and moves the bottom of the\n",
+    "# axes up to make room for them\n",
+    "fig.autofmt_xdate()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 金融数据"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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B2o+oyr8MO7eukHkZhHjonrutUwd0ivkE0KpBaTU4tc4dZfePmqyL/SyZ7wag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\nkUjqyf8DRMOAVtE/7MEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2719e48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import datetime\n",
+    "import numpy as np\n",
+    "import matplotlib.colors as colors\n",
+    "import matplotlib.finance as finance\n",
+    "import matplotlib.dates as mdates\n",
+    "import matplotlib.ticker as mticker\n",
+    "import matplotlib.mlab as mlab\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.font_manager as font_manager\n",
+    "\n",
+    "\n",
+    "startdate = datetime.date(2006,1,1)\n",
+    "today = enddate = datetime.date.today()\n",
+    "ticker = 'SPY'\n",
+    "\n",
+    "\n",
+    "fh = finance.fetch_historical_yahoo(ticker, startdate, enddate)\n",
+    "# a numpy record array with fields: date, open, high, low, close, volume, adj_close)\n",
+    "\n",
+    "r = mlab.csv2rec(fh); fh.close()\n",
+    "r.sort()\n",
+    "\n",
+    "\n",
+    "def moving_average(x, n, type='simple'):\n",
+    "    \"\"\"\n",
+    "    compute an n period moving average.\n",
+    "\n",
+    "    type is 'simple' | 'exponential'\n",
+    "\n",
+    "    \"\"\"\n",
+    "    x = np.asarray(x)\n",
+    "    if type=='simple':\n",
+    "        weights = np.ones(n)\n",
+    "    else:\n",
+    "        weights = np.exp(np.linspace(-1., 0., n))\n",
+    "\n",
+    "    weights /= weights.sum()\n",
+    "\n",
+    "\n",
+    "    a =  np.convolve(x, weights, mode='full')[:len(x)]\n",
+    "    a[:n] = a[n]\n",
+    "    return a\n",
+    "\n",
+    "def relative_strength(prices, n=14):\n",
+    "    \"\"\"\n",
+    "    compute the n period relative strength indicator\n",
+    "    http://stockcharts.com/school/doku.php?id=chart_school:glossary_r#relativestrengthindex\n",
+    "    http://www.investopedia.com/terms/r/rsi.asp\n",
+    "    \"\"\"\n",
+    "\n",
+    "    deltas = np.diff(prices)\n",
+    "    seed = deltas[:n+1]\n",
+    "    up = seed[seed>=0].sum()/n\n",
+    "    down = -seed[seed<0].sum()/n\n",
+    "    rs = up/down\n",
+    "    rsi = np.zeros_like(prices)\n",
+    "    rsi[:n] = 100. - 100./(1.+rs)\n",
+    "\n",
+    "    for i in range(n, len(prices)):\n",
+    "        delta = deltas[i-1] # cause the diff is 1 shorter\n",
+    "\n",
+    "        if delta>0:\n",
+    "            upval = delta\n",
+    "            downval = 0.\n",
+    "        else:\n",
+    "            upval = 0.\n",
+    "            downval = -delta\n",
+    "\n",
+    "        up = (up*(n-1) + upval)/n\n",
+    "        down = (down*(n-1) + downval)/n\n",
+    "\n",
+    "        rs = up/down\n",
+    "        rsi[i] = 100. - 100./(1.+rs)\n",
+    "\n",
+    "    return rsi\n",
+    "\n",
+    "def moving_average_convergence(x, nslow=26, nfast=12):\n",
+    "    \"\"\"\n",
+    "    compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'\n",
+    "    return value is emaslow, emafast, macd which are len(x) arrays\n",
+    "    \"\"\"\n",
+    "    emaslow = moving_average(x, nslow, type='exponential')\n",
+    "    emafast = moving_average(x, nfast, type='exponential')\n",
+    "    return emaslow, emafast, emafast - emaslow\n",
+    "\n",
+    "\n",
+    "plt.rc('axes', grid=True)\n",
+    "plt.rc('grid', color='0.75', linestyle='-', linewidth=0.5)\n",
+    "\n",
+    "textsize = 9\n",
+    "left, width = 0.1, 0.8\n",
+    "rect1 = [left, 0.7, width, 0.2]\n",
+    "rect2 = [left, 0.3, width, 0.4]\n",
+    "rect3 = [left, 0.1, width, 0.2]\n",
+    "\n",
+    "\n",
+    "fig = plt.figure(facecolor='white')\n",
+    "axescolor  = '#f6f6f6'  # the axes background color\n",
+    "\n",
+    "ax1 = fig.add_axes(rect1, axisbg=axescolor)  #left, bottom, width, height\n",
+    "ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1)\n",
+    "ax2t = ax2.twinx()\n",
+    "ax3  = fig.add_axes(rect3, axisbg=axescolor, sharex=ax1)\n",
+    "\n",
+    "\n",
+    "\n",
+    "### plot the relative strength indicator\n",
+    "prices = r.adj_close\n",
+    "rsi = relative_strength(prices)\n",
+    "fillcolor = 'darkgoldenrod'\n",
+    "\n",
+    "ax1.plot(r.date, rsi, color=fillcolor)\n",
+    "ax1.axhline(70, color=fillcolor)\n",
+    "ax1.axhline(30, color=fillcolor)\n",
+    "ax1.fill_between(r.date, rsi, 70, where=(rsi>=70), facecolor=fillcolor, edgecolor=fillcolor)\n",
+    "ax1.fill_between(r.date, rsi, 30, where=(rsi<=30), facecolor=fillcolor, edgecolor=fillcolor)\n",
+    "ax1.text(0.6, 0.9, '>70 = overbought', va='top', transform=ax1.transAxes, fontsize=textsize)\n",
+    "ax1.text(0.6, 0.1, '<30 = oversold', transform=ax1.transAxes, fontsize=textsize)\n",
+    "ax1.set_ylim(0, 100)\n",
+    "ax1.set_yticks([30,70])\n",
+    "ax1.text(0.025, 0.95, 'RSI (14)', va='top', transform=ax1.transAxes, fontsize=textsize)\n",
+    "ax1.set_title('%s daily'%ticker)\n",
+    "\n",
+    "### plot the price and volume data\n",
+    "dx = r.adj_close - r.close\n",
+    "low = r.low + dx\n",
+    "high = r.high + dx\n",
+    "\n",
+    "deltas = np.zeros_like(prices)\n",
+    "deltas[1:] = np.diff(prices)\n",
+    "up = deltas>0\n",
+    "ax2.vlines(r.date[up], low[up], high[up], color='black', label='_nolegend_')\n",
+    "ax2.vlines(r.date[~up], low[~up], high[~up], color='black', label='_nolegend_')\n",
+    "ma20 = moving_average(prices, 20, type='simple')\n",
+    "ma200 = moving_average(prices, 200, type='simple')\n",
+    "\n",
+    "linema20, = ax2.plot(r.date, ma20, color='blue', lw=2, label='MA (20)')\n",
+    "linema200, = ax2.plot(r.date, ma200, color='red', lw=2, label='MA (200)')\n",
+    "\n",
+    "\n",
+    "last = r[-1]\n",
+    "s = '%s O:%1.2f H:%1.2f L:%1.2f C:%1.2f, V:%1.1fM Chg:%+1.2f' % (\n",
+    "    today.strftime('%d-%b-%Y'),\n",
+    "    last.open, last.high,\n",
+    "    last.low, last.close,\n",
+    "    last.volume*1e-6,\n",
+    "    last.close-last.open )\n",
+    "t4 = ax2.text(0.3, 0.9, s, transform=ax2.transAxes, fontsize=textsize)\n",
+    "\n",
+    "props = font_manager.FontProperties(size=10)\n",
+    "leg = ax2.legend(loc='center left', shadow=True, fancybox=True, prop=props)\n",
+    "leg.get_frame().set_alpha(0.5)\n",
+    "\n",
+    "\n",
+    "volume = (r.close*r.volume)/1e6  # dollar volume in millions\n",
+    "vmax = volume.max()\n",
+    "poly = ax2t.fill_between(r.date, volume, 0, label='Volume', facecolor=fillcolor, edgecolor=fillcolor)\n",
+    "ax2t.set_ylim(0, 5*vmax)\n",
+    "ax2t.set_yticks([])\n",
+    "\n",
+    "\n",
+    "### compute the MACD indicator\n",
+    "fillcolor = 'darkslategrey'\n",
+    "nslow = 26\n",
+    "nfast = 12\n",
+    "nema = 9\n",
+    "emaslow, emafast, macd = moving_average_convergence(prices, nslow=nslow, nfast=nfast)\n",
+    "ema9 = moving_average(macd, nema, type='exponential')\n",
+    "ax3.plot(r.date, macd, color='black', lw=2)\n",
+    "ax3.plot(r.date, ema9, color='blue', lw=1)\n",
+    "ax3.fill_between(r.date, macd-ema9, 0, alpha=0.5, facecolor=fillcolor, edgecolor=fillcolor)\n",
+    "\n",
+    "\n",
+    "ax3.text(0.025, 0.95, 'MACD (%d, %d, %d)'%(nfast, nslow, nema), va='top',\n",
+    "         transform=ax3.transAxes, fontsize=textsize)\n",
+    "\n",
+    "#ax3.set_yticks([])\n",
+    "# turn off upper axis tick labels, rotate the lower ones, etc\n",
+    "for ax in ax1, ax2, ax2t, ax3:\n",
+    "    if ax!=ax3:\n",
+    "        for label in ax.get_xticklabels():\n",
+    "            label.set_visible(False)\n",
+    "    else:\n",
+    "        for label in ax.get_xticklabels():\n",
+    "            label.set_rotation(30)\n",
+    "            label.set_horizontalalignment('right')\n",
+    "\n",
+    "    ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n",
+    "\n",
+    "\n",
+    "\n",
+    "class MyLocator(mticker.MaxNLocator):\n",
+    "    def __init__(self, *args, **kwargs):\n",
+    "        mticker.MaxNLocator.__init__(self, *args, **kwargs)\n",
+    "\n",
+    "    def __call__(self, *args, **kwargs):\n",
+    "        return mticker.MaxNLocator.__call__(self, *args, **kwargs)\n",
+    "\n",
+    "# at most 5 ticks, pruning the upper and lower so they don't overlap\n",
+    "# with other ticks\n",
+    "#ax2.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))\n",
+    "#ax3.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))\n",
+    "\n",
+    "ax2.yaxis.set_major_locator(MyLocator(5, prune='both'))\n",
+    "ax3.yaxis.set_major_locator(MyLocator(5, prune='both'))\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## basemap 画地图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "需要安装 `basemap` 包："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RpNlscOTQUf7mnk/SjTosr88TRR1sIBISfTFAyHUtQMPFZS5GgFIKy7ZIEoUU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\nWsaC0ocZlDvYsv6zhK0+5i3+Ir2D36WzEHC0NomONLXJxUzsfzFCxvQ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3AAAg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xN953DmSWccCsZaxZg4BJJNrxt/D0QjoXdNEugn5QlViPvMdyDTTrTi7fX5acHoKNbvfo06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\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TawBJqukASNDS7vwrkyMIDMo4SONTLE+z98ghRscm2bjhIu5/6GGklBQKkzz0yAN8/tNf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\nDYFS5HM5mu2IQi5AAL6Q4Pk0Ww3CwEPg0QmWcfyk9zKz5gqsCigdvoG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3m2uuKJFo7dRK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\nlowPpnzM519+wx7V5RTVN7EwVg564ysruvFTozWEGdqa46ALCKNpTMVRn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77A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\ntKIBHPVUkht/PphJU/7KHXc+yJLflikAlZ9naxKZQ8v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FZErwLzgcLI5npXQj23Hcoq08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pIz2mjx2IqlzLUd3jc8yqOmxFViNv1xzEBPlfHJFjNmDIJUBAK/KglBmmZoKVBJwil3Ps5E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QIgiJwCGdxYoYO76Mz6b4xgLdxRVK44naK0idVKxCQUA8Srt0mhGLrTWOtJ+g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\neNHFv8R1119NWTYIY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LmtueqMNUwf/3PvuvKrn/qzH3MBPWjGQ9JgAvT7/e+f8cxnpt//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\nQt4G/wW1jjOmOfSF6wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb96e550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "try:\n",
+    "    from mpl_toolkits.basemap import Basemap\n",
+    "    have_basemap = True\n",
+    "except ImportError:\n",
+    "    have_basemap = False\n",
+    "\n",
+    "\n",
+    "def plotmap():\n",
+    "    # create figure\n",
+    "    fig = plt.figure(figsize=(8,8))\n",
+    "    # set up orthographic map projection with\n",
+    "    # perspective of satellite looking down at 50N, 100W.\n",
+    "    # use low resolution coastlines.\n",
+    "    map = Basemap(projection='ortho',lat_0=50,lon_0=-100,resolution='l')\n",
+    "    # lat/lon coordinates of five cities.\n",
+    "    lats=[40.02,32.73,38.55,48.25,17.29]\n",
+    "    lons=[-105.16,-117.16,-77.00,-114.21,-88.10]\n",
+    "    cities=['Boulder, CO','San Diego, CA',\n",
+    "            'Washington, DC','Whitefish, MT','Belize City, Belize']\n",
+    "    # compute the native map projection coordinates for cities.\n",
+    "    xc,yc = map(lons,lats)\n",
+    "    # make up some data on a regular lat/lon grid.\n",
+    "    nlats = 73; nlons = 145; delta = 2.*np.pi/(nlons-1)\n",
+    "    lats = (0.5*np.pi-delta*np.indices((nlats,nlons))[0,:,:])\n",
+    "    lons = (delta*np.indices((nlats,nlons))[1,:,:])\n",
+    "    wave = 0.75*(np.sin(2.*lats)**8*np.cos(4.*lons))\n",
+    "    mean = 0.5*np.cos(2.*lats)*((np.sin(2.*lats))**2 + 2.)\n",
+    "    # compute native map projection coordinates of lat/lon grid.\n",
+    "    # (convert lons and lats to degrees first)\n",
+    "    x, y = map(lons*180./np.pi, lats*180./np.pi)\n",
+    "    # draw map boundary\n",
+    "    map.drawmapboundary(color=\"0.9\")\n",
+    "    # draw graticule (latitude and longitude grid lines)\n",
+    "    map.drawmeridians(np.arange(0,360,30),color=\"0.9\")\n",
+    "    map.drawparallels(np.arange(-90,90,30),color=\"0.9\")\n",
+    "    # plot filled circles at the locations of the cities.\n",
+    "    map.plot(xc,yc,'wo')\n",
+    "    # plot the names of five cities.\n",
+    "    for name,xpt,ypt in zip(cities,xc,yc):\n",
+    "        plt.text(xpt+100000,ypt+100000,name,fontsize=9,color='w')\n",
+    "    # contour data over the map.\n",
+    "    cs = map.contour(x,y,wave+mean,15,linewidths=1.5)\n",
+    "    # draw blue marble image in background.\n",
+    "    # (downsample the image by 50% for speed)\n",
+    "    map.bluemarble(scale=0.5)\n",
+    "\n",
+    "def plotempty():\n",
+    "    # create figure\n",
+    "    fig = plt.figure(figsize=(8,8))\n",
+    "    fig.text(0.5, 0.5, \"Sorry, could not import Basemap\",\n",
+    "                                horizontalalignment='center')\n",
+    "\n",
+    "if have_basemap:\n",
+    "    plotmap()\n",
+    "else:\n",
+    "    plotempty()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 对数图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`loglog, semilogx, semilogy, errorbar` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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w8cX2YDuql3y4fZL18E3MA+Qr/6huH7/B1y8P7dsAnPJ3OEKwwQaWOfTVV836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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xe36f748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.subplots_adjust(hspace=0.4)\n",
+    "t = np.arange(0.01, 20.0, 0.01)\n",
+    "\n",
+    "# log y axis\n",
+    "plt.subplot(221)\n",
+    "plt.semilogy(t, np.exp(-t/5.0))\n",
+    "plt.title('semilogy')\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# log x axis\n",
+    "plt.subplot(222)\n",
+    "plt.semilogx(t, np.sin(2*np.pi*t))\n",
+    "plt.title('semilogx')\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# log x and y axis\n",
+    "plt.subplot(223)\n",
+    "plt.loglog(t, 20*np.exp(-t/10.0), basex=2)\n",
+    "plt.grid(True)\n",
+    "plt.title('loglog base 4 on x')\n",
+    "\n",
+    "# with errorbars: clip non-positive values\n",
+    "ax = plt.subplot(224)\n",
+    "ax.set_xscale(\"log\", nonposx='clip')\n",
+    "ax.set_yscale(\"log\", nonposy='clip')\n",
+    "\n",
+    "x = 10.0**np.linspace(0.0, 2.0, 20)\n",
+    "y = x**2.0\n",
+    "plt.errorbar(x, y, xerr=0.1*x, yerr=5.0+0.75*y)\n",
+    "ax.set_ylim(ymin=0.1)\n",
+    "ax.set_title('Errorbars go negative')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 极坐标"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "设置 `polar=True`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LdjYXb9zIVo1G+A96/fXC8OcIwmw2c0NDA2u1WtZqtazT6bipqYm7urqGTHk4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\nyMjICIIsJjIyMoIgi4mMjIwg/H92bEfNtteOcAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xe178be0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "r = np.arange(0, 3.0, 0.01)\n",
+    "theta = 2 * np.pi * r\n",
+    "\n",
+    "ax = plt.subplot(111, polar=True)\n",
+    "ax.plot(theta, r, color='r', linewidth=3)\n",
+    "ax.set_rmax(2.0)\n",
+    "ax.grid(True)\n",
+    "\n",
+    "ax.set_title(\"A line plot on a polar axis\", va='bottom')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 标注"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`legend` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OpX79+im2GzBgAH/8YcCRWcnIkmfmQJp3fObNm5cdO3bg6upK2bJl6dixY6rt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\nJDXAMpekBljmktQAy1ySGvAJpbO10KE3ZZIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x106be390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Make some fake data.\n",
+    "a = b = np.arange(0,3, .02)\n",
+    "c = np.exp(a)\n",
+    "d = c[::-1]\n",
+    "\n",
+    "# Create plots with pre-defined labels.\n",
+    "plt.plot(a, c, 'k--', label='Model length')\n",
+    "plt.plot(a, d, 'k:', label='Data length')\n",
+    "plt.plot(a, c+d, 'k', label='Total message length')\n",
+    "\n",
+    "legend = plt.legend(loc='upper center', shadow=True, fontsize='x-large')\n",
+    "\n",
+    "# Put a nicer background color on the legend.\n",
+    "legend.get_frame().set_facecolor('#00FFCC')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 数学公式"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 $W^{3\\beta}_{\\delta_1 \\rho_1 \\sigma_2} = U^{3\\beta}_{\\delta_1 \\rho_1} + \\frac{1}{8 \\pi 2} \\int^{\\alpha_2}_{\\alpha_2} d \\alpha^\\prime_2 \\left[\\frac{ U^{2\\beta}_{\\delta_1 \\rho_1} - \\alpha^\\prime_2U^{1\\beta}_{\\rho_1 \\sigma_2} }{U^{0\\beta}_{\\rho_1 \\sigma_2}}\\right]$\n",
+      "1 $\\alpha_i > \\beta_i,\\ \\alpha_{i+1}^j = {\\rm sin}(2\\pi f_j t_i) e^{-5 t_i/\\tau},\\ \\ldots$\n",
+      "2 $\\frac{3}{4},\\ \\binom{3}{4},\\ \\stackrel{3}{4},\\ \\left(\\frac{5 - \\frac{1}{x}}{4}\\right),\\ \\ldots$\n",
+      "3 $\\sqrt{2},\\ \\sqrt[3]{x},\\ \\ldots$\n",
+      "4 $\\mathrm{Roman}\\ , \\ \\mathit{Italic}\\ , \\ \\mathtt{Typewriter} \\ \\mathrm{or}\\ \\mathcal{CALLIGRAPHY}$\n",
+      "5 $\\acute a,\\ \\bar a,\\ \\breve a,\\ \\dot a,\\ \\ddot a, \\ \\grave a, \\ \\hat a,\\ \\tilde a,\\ \\vec a,\\ \\widehat{xyz},\\ \\widetilde{xyz},\\ \\ldots$\n",
+      "6 $\\alpha,\\ \\beta,\\ \\chi,\\ \\delta,\\ \\lambda,\\ \\mu,\\ \\Delta,\\ \\Gamma,\\ \\Omega,\\ \\Phi,\\ \\Pi,\\ \\Upsilon,\\ \\nabla,\\ \\aleph,\\ \\beth,\\ \\daleth,\\ \\gimel,\\ \\ldots$\n",
+      "7 $\\coprod,\\ \\int,\\ \\oint,\\ \\prod,\\ \\sum,\\ \\log,\\ \\sin,\\ \\approx,\\ \\oplus,\\ \\star,\\ \\varpropto,\\ \\infty,\\ \\partial,\\ \\Re,\\ \\leftrightsquigarrow, \\ \\ldots$\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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RLbb2aTRqJUe+Ieaew63moa+vomjfB9TkJVBbkEj0sj9wdvfrVrm7ytDLVnY5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\nEIQSQyQ1QRAEocQQSU0QBEEoMf4PrAlMQrsCkYcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd37f828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from __future__ import print_function\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "import sys\n",
+    "import re\n",
+    "import gc\n",
+    "\n",
+    "# Selection of features following \"Writing mathematical expressions\" tutorial\n",
+    "mathtext_titles = {\n",
+    "    0: \"Header demo\",\n",
+    "    1: \"Subscripts and superscripts\",\n",
+    "    2: \"Fractions, binomials and stacked numbers\",\n",
+    "    3: \"Radicals\",\n",
+    "    4: \"Fonts\",\n",
+    "    5: \"Accents\",\n",
+    "    6: \"Greek, Hebrew\",\n",
+    "    7: \"Delimiters, functions and Symbols\"}\n",
+    "n_lines = len(mathtext_titles)\n",
+    "\n",
+    "# Randomly picked examples\n",
+    "mathext_demos = {\n",
+    "    0: r\"$W^{3\\beta}_{\\delta_1 \\rho_1 \\sigma_2} = \"\n",
+    "    r\"U^{3\\beta}_{\\delta_1 \\rho_1} + \\frac{1}{8 \\pi 2} \"\n",
+    "    r\"\\int^{\\alpha_2}_{\\alpha_2} d \\alpha^\\prime_2 \\left[\\frac{ \"\n",
+    "    r\"U^{2\\beta}_{\\delta_1 \\rho_1} - \\alpha^\\prime_2U^{1\\beta}_\"\n",
+    "    r\"{\\rho_1 \\sigma_2} }{U^{0\\beta}_{\\rho_1 \\sigma_2}}\\right]$\",\n",
+    "\n",
+    "    1: r\"$\\alpha_i > \\beta_i,\\ \"\n",
+    "    r\"\\alpha_{i+1}^j = {\\rm sin}(2\\pi f_j t_i) e^{-5 t_i/\\tau},\\ \"\n",
+    "    r\"\\ldots$\",\n",
+    "\n",
+    "    2: r\"$\\frac{3}{4},\\ \\binom{3}{4},\\ \\stackrel{3}{4},\\ \"\n",
+    "    r\"\\left(\\frac{5 - \\frac{1}{x}}{4}\\right),\\ \\ldots$\",\n",
+    "\n",
+    "    3: r\"$\\sqrt{2},\\ \\sqrt[3]{x},\\ \\ldots$\",\n",
+    "\n",
+    "    4: r\"$\\mathrm{Roman}\\ , \\ \\mathit{Italic}\\ , \\ \\mathtt{Typewriter} \\ \"\n",
+    "    r\"\\mathrm{or}\\ \\mathcal{CALLIGRAPHY}$\",\n",
+    "\n",
+    "    5: r\"$\\acute a,\\ \\bar a,\\ \\breve a,\\ \\dot a,\\ \\ddot a, \\ \\grave a, \\ \"\n",
+    "    r\"\\hat a,\\ \\tilde a,\\ \\vec a,\\ \\widehat{xyz},\\ \\widetilde{xyz},\\ \"\n",
+    "    r\"\\ldots$\",\n",
+    "\n",
+    "    6: r\"$\\alpha,\\ \\beta,\\ \\chi,\\ \\delta,\\ \\lambda,\\ \\mu,\\ \"\n",
+    "    r\"\\Delta,\\ \\Gamma,\\ \\Omega,\\ \\Phi,\\ \\Pi,\\ \\Upsilon,\\ \\nabla,\\ \"\n",
+    "    r\"\\aleph,\\ \\beth,\\ \\daleth,\\ \\gimel,\\ \\ldots$\",\n",
+    "\n",
+    "    7: r\"$\\coprod,\\ \\int,\\ \\oint,\\ \\prod,\\ \\sum,\\ \"\n",
+    "    r\"\\log,\\ \\sin,\\ \\approx,\\ \\oplus,\\ \\star,\\ \\varpropto,\\ \"\n",
+    "    r\"\\infty,\\ \\partial,\\ \\Re,\\ \\leftrightsquigarrow, \\ \\ldots$\"}\n",
+    "\n",
+    "\n",
+    "def doall():\n",
+    "    # Colors used in mpl online documentation.\n",
+    "    mpl_blue_rvb = (191./255., 209./256., 212./255.)\n",
+    "    mpl_orange_rvb = (202/255., 121/256., 0./255.)\n",
+    "    mpl_grey_rvb = (51./255., 51./255., 51./255.)\n",
+    "\n",
+    "    # Creating figure and axis.\n",
+    "    plt.figure(figsize=(6, 7))\n",
+    "    plt.axes([0.01, 0.01, 0.98, 0.90], axisbg=\"white\", frameon=True)\n",
+    "    plt.gca().set_xlim(0., 1.)\n",
+    "    plt.gca().set_ylim(0., 1.)\n",
+    "    plt.gca().set_title(\"Matplotlib's math rendering engine\",\n",
+    "                        color=mpl_grey_rvb, fontsize=14, weight='bold')\n",
+    "    plt.gca().set_xticklabels(\"\", visible=False)\n",
+    "    plt.gca().set_yticklabels(\"\", visible=False)\n",
+    "\n",
+    "    # Gap between lines in axes coords\n",
+    "    line_axesfrac = (1. / (n_lines))\n",
+    "\n",
+    "    # Plotting header demonstration formula\n",
+    "    full_demo = mathext_demos[0]\n",
+    "    plt.annotate(full_demo,\n",
+    "                 xy=(0.5, 1. - 0.59*line_axesfrac),\n",
+    "                 xycoords='data', color=mpl_orange_rvb, ha='center',\n",
+    "                 fontsize=20)\n",
+    "\n",
+    "    # Plotting features demonstration formulae\n",
+    "    for i_line in range(1, n_lines):\n",
+    "        baseline = 1. - (i_line)*line_axesfrac\n",
+    "        baseline_next = baseline - line_axesfrac*1.\n",
+    "        title = mathtext_titles[i_line] + \":\"\n",
+    "        fill_color = ['white', mpl_blue_rvb][i_line % 2]\n",
+    "        plt.fill_between([0., 1.], [baseline, baseline],\n",
+    "                         [baseline_next, baseline_next],\n",
+    "                         color=fill_color, alpha=0.5)\n",
+    "        plt.annotate(title,\n",
+    "                     xy=(0.07, baseline - 0.3*line_axesfrac),\n",
+    "                     xycoords='data', color=mpl_grey_rvb, weight='bold')\n",
+    "        demo = mathext_demos[i_line]\n",
+    "        plt.annotate(demo,\n",
+    "                     xy=(0.05, baseline - 0.75*line_axesfrac),\n",
+    "                     xycoords='data', color=mpl_grey_rvb,\n",
+    "                     fontsize=16)\n",
+    "\n",
+    "    for i in range(n_lines):\n",
+    "        s = mathext_demos[i]\n",
+    "        print(i, s)\n",
+    "    plt.show()\n",
+    "\n",
+    "if '--latex' in sys.argv:\n",
+    "    # Run: python mathtext_examples.py --latex\n",
+    "    # Need amsmath and amssymb packages.\n",
+    "    fd = open(\"mathtext_examples.ltx\", \"w\")\n",
+    "    fd.write(\"\\\\documentclass{article}\\n\")\n",
+    "    fd.write(\"\\\\usepackage{amsmath, amssymb}\\n\")\n",
+    "    fd.write(\"\\\\begin{document}\\n\")\n",
+    "    fd.write(\"\\\\begin{enumerate}\\n\")\n",
+    "\n",
+    "    for i in range(n_lines):\n",
+    "        s = mathext_demos[i]\n",
+    "        s = re.sub(r\"(?<!\\\\)\\$\", \"$$\", s)\n",
+    "        fd.write(\"\\\\item %s\\n\" % s)\n",
+    "\n",
+    "    fd.write(\"\\\\end{enumerate}\\n\")\n",
+    "    fd.write(\"\\\\end{document}\\n\")\n",
+    "    fd.close()\n",
+    "\n",
+    "    os.system(\"pdflatex mathtext_examples.ltx\")\n",
+    "else:\n",
+    "    doall()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/06. matplotlib/artists_figure.png b/06-matplotlib/artists_figure.png
similarity index 100%
rename from 06. matplotlib/artists_figure.png
rename to 06-matplotlib/artists_figure.png
diff --git a/06. matplotlib/artists_tree.png b/06-matplotlib/artists_tree.png
similarity index 100%
rename from 06. matplotlib/artists_tree.png
rename to 06-matplotlib/artists_tree.png
diff --git a/06. matplotlib/stinkbug.png b/06-matplotlib/stinkbug.png
similarity index 100%
rename from 06. matplotlib/stinkbug.png
rename to 06-matplotlib/stinkbug.png
diff --git a/07. interfacing with other languages/07-02-example.zip b/07-interfacing-with-other-languages/07-02-example.zip
similarity index 100%
rename from 07. interfacing with other languages/07-02-example.zip
rename to 07-interfacing-with-other-languages/07-02-example.zip
diff --git a/07. interfacing with other languages/07-03-fib.zip b/07-interfacing-with-other-languages/07-03-fib.zip
similarity index 100%
rename from 07. interfacing with other languages/07-03-fib.zip
rename to 07-interfacing-with-other-languages/07-03-fib.zip
diff --git a/07. interfacing with other languages/07-04-extern.zip b/07-interfacing-with-other-languages/07-04-extern.zip
similarity index 100%
rename from 07. interfacing with other languages/07-04-extern.zip
rename to 07-interfacing-with-other-languages/07-04-extern.zip
diff --git a/07. interfacing with other languages/07-05-particle.zip b/07-interfacing-with-other-languages/07-05-particle.zip
similarity index 100%
rename from 07. interfacing with other languages/07-05-particle.zip
rename to 07-interfacing-with-other-languages/07-05-particle.zip
diff --git a/07. interfacing with other languages/07-06-cython-sum.zip b/07-interfacing-with-other-languages/07-06-cython-sum.zip
similarity index 100%
rename from 07. interfacing with other languages/07-06-cython-sum.zip
rename to 07-interfacing-with-other-languages/07-06-cython-sum.zip
diff --git a/07. interfacing with other languages/07.01 introduction.ipynb b/07-interfacing-with-other-languages/07.01-introduction.ipynb
similarity index 95%
rename from 07. interfacing with other languages/07.01 introduction.ipynb
rename to 07-interfacing-with-other-languages/07.01-introduction.ipynb
index 225fb1b6..4f410331 100644
--- a/07. interfacing with other languages/07.01 introduction.ipynb	
+++ b/07-interfacing-with-other-languages/07.01-introduction.ipynb
@@ -1,93 +1,93 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 简介"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 使用 Python 和另一种语言混编的好处"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "至少有以下四个原因：\n",
-    "\n",
-    "1. `Best of both worlds` - 结合两种语言的优点：已经优化和测试过的代码库 + Python 的灵活\n",
-    "- `Python as glue` - **Python** 作为连接的桥梁，将很多其他语言的模块结合到一个大型程序中\n",
-    "- `Speed up Python` - 使用一个更快的语言帮助加速 **Python**\n",
-    "- `Division of labor` - 各司其职，让各个语言做各自更擅长的事情，例如 **Fortran** 进行数组计算，**Python** 处理测试，文件读写，文本处理，数据整理，GUI 生成，HTTP 服务等等。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 语言扩展工具"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 打包已有的代码和其他语言的库"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- 使用手写的扩展模块\n",
-    "- `Cython` - **C/C++**\n",
-    "- `SWIG` - **C/C++**\n",
-    "- `f2py` - **Fortran**\n",
-    "- `ctypes` - 其他语言库"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 加速 Python"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- 使用手写的扩展模块\n",
-    "- `Cython`\n",
-    "- `Weave`\n",
-    "- `Shedskin` 和其他模块"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 简介"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 使用 Python 和另一种语言混编的好处"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "至少有以下四个原因：\n",
+    "\n",
+    "1. `Best of both worlds` - 结合两种语言的优点：已经优化和测试过的代码库 + Python 的灵活\n",
+    "- `Python as glue` - **Python** 作为连接的桥梁，将很多其他语言的模块结合到一个大型程序中\n",
+    "- `Speed up Python` - 使用一个更快的语言帮助加速 **Python**\n",
+    "- `Division of labor` - 各司其职，让各个语言做各自更擅长的事情，例如 **Fortran** 进行数组计算，**Python** 处理测试，文件读写，文本处理，数据整理，GUI 生成，HTTP 服务等等。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 语言扩展工具"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 打包已有的代码和其他语言的库"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- 使用手写的扩展模块\n",
+    "- `Cython` - **C/C++**\n",
+    "- `SWIG` - **C/C++**\n",
+    "- `f2py` - **Fortran**\n",
+    "- `ctypes` - 其他语言库"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 加速 Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- 使用手写的扩展模块\n",
+    "- `Cython`\n",
+    "- `Weave`\n",
+    "- `Shedskin` 和其他模块"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/07. interfacing with other languages/07.02 python extension modules.ipynb b/07-interfacing-with-other-languages/07.02-python-extension-modules.ipynb
similarity index 95%
rename from 07. interfacing with other languages/07.02 python extension modules.ipynb
rename to 07-interfacing-with-other-languages/07.02-python-extension-modules.ipynb
index 805731cd..562d0be8 100644
--- a/07. interfacing with other languages/07.02 python extension modules.ipynb	
+++ b/07-interfacing-with-other-languages/07.02-python-extension-modules.ipynb
@@ -1,596 +1,596 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Python 扩展模块"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 简介"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "C Library | Interface | Python\n",
-    "---|---|---\n",
-    "`c header` <br> `c implementation` | Wrapper `C` $\\leftrightarrows$ `Python` <br> communication between `py + c` | `import fact` <br> `fact.fact(10)`\n",
-    "\n",
-    "**Python** 扩展模块将 `PyInt(10)` 转化为 `CInt(10)` 然后调用 `C` 程序中的 `fact()` 函数进行计算，再将返回的结果转换回 `PyInt`。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 产生一个扩展模块"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "假设我们有这样的一个头文件和程序："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing fact.h\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file fact.h\n",
-    "#ifndef FACT_H\n",
-    "#define FACT_h\n",
-    "int fact(int n);\n",
-    "#endif"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing fact.c\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file fact.c\n",
-    "#include \"fact.h\"\n",
-    "int fact(int n)\n",
-    "{\n",
-    "    if (n <= 1) return 1;\n",
-    "    else return n * fact(n - 1);\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "定义包装函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing fact_wrap.c\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file fact_wrap.c\n",
-    "\n",
-    "/* Must include Python.h before any standard headers*/\n",
-    "#include <Python.h>\n",
-    "#include \"fact.h\"\n",
-    "static PyObject* wrap_fact(PyObject *self, PyObject *args)\n",
-    "{\n",
-    "    /* Python->C data conversion */\n",
-    "    int n, result;\n",
-    "    // the string i here means there is only one integer\n",
-    "    if (!PyArg_ParseTuple(args, \"i\", &n))\n",
-    "        return NULL;\n",
-    "    \n",
-    "    /* C Function Call */\n",
-    "    result = fact(n);\n",
-    "    \n",
-    "    /* C->Python data conversion */\n",
-    "    return Py_BuildValue(\"i\", result);\n",
-    "}\n",
-    "\n",
-    "/* Method table declaring the names of functions exposed to Python*/\n",
-    "static PyMethodDef ExampleMethods[] = {\n",
-    "    {\"fact\",  wrap_fact, METH_VARARGS, \"Calculate the factorial of n\"},\n",
-    "    {NULL, NULL, 0, NULL}        /* Sentinel */\n",
-    "};\n",
-    "\n",
-    "/* Module initialization function called at \"import example\"*/\n",
-    "PyMODINIT_FUNC \n",
-    "initexample(void)\n",
-    "{\n",
-    "    (void) Py_InitModule(\"example\", ExampleMethods);\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 手动编译扩展模块"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "手动使用 `gcc` 编译，`Windows` 下如果没有 `gcc`，可以通过 `conda` 进行安装：\n",
-    "\n",
-    "    conda install mingw4\n",
-    "    \n",
-    "`Window 64-bit` 下编译需要加上 `-DMS_WIN64` 的选项，`include` 和 `lib` 文件夹的路径对应于本地 **Python** 安装的环境："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "!gcc -DMS_WIN64 -c fact.c fact_wrap.c -IC:\\Miniconda\\include"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "!gcc -DMS_WIN64 -shared fact.o fact_wrap.o -LC:\\Miniconda\\libs -lpython27 -o example.pyd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Windows` 下最终生成的文件后缀为 `.pyd` ， `Unix` 下生成的文件后缀名为 `.so`。\n",
-    "\n",
-    "用法为：\n",
-    "\n",
-    "- `Windows 32-bit`\n",
-    "```\n",
-    "gcc -c fact.c fact_wrap.c -I<your python path>\\include\n",
-    "gcc -shared fact.o fact_wrap.o -L<your python path>\\libs -lpython27 -o example.pyd\n",
-    "```\n",
-    "- `Unix`\n",
-    "```\n",
-    "gcc -c fact.c fact_wrap.c -I<your python path>\n",
-    "gcc -shared fact.o fact_wrap.o -L<your python path>\\config -lpython27 -o example.so\n",
-    "```\n",
-    "\n",
-    "编译完成后，我们就可以使用 `example` 这个模块了。\n",
-    "\n",
-    "导入生成的包："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['__doc__', '__file__', '__name__', '__package__', 'fact']\n"
-     ]
-    }
-   ],
-   "source": [
-    "import example\n",
-    "print dir(example)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `example` 中的函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "factorial of 10: 3628800\n"
-     ]
-    }
-   ],
-   "source": [
-    "print 'factorial of 10:', example.fact(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 使用 setup.py 进行编译"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "清理刚才生成的文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "!rm -f example.pyd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "写入 `setup.py`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing setup.py\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file setup.py\n",
-    "from distutils.core import setup, Extension\n",
-    "\n",
-    "ext = Extension(name='example', sources=['fact_wrap.c', 'fact.c'])\n",
-    "\n",
-    "setup(name='example', ext_modules=[ext])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `distutils` 中的函数，我们进行 `build` 和 `install`：\n",
-    "\n",
-    "    python setup.py build (--compiler=mingw64)\n",
-    "    python setup.py install\n",
-    "\n",
-    "括号中的内容在 `windows` 中可能需要加上。\n",
-    "\n",
-    "这里我们使用 `build_ext --inplace` 选项将其安装在本地文件夹："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "running build_ext\n",
-      "building 'example' extension\n",
-      "creating build\n",
-      "creating build\\temp.win-amd64-2.7\n",
-      "creating build\\temp.win-amd64-2.7\\Release\n",
-      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -mdll -O -Wall -IC:\\Miniconda\\include -IC:\\Miniconda\\PC -c fact_wrap.c -o build\\temp.win-amd64-2.7\\Release\\fact_wrap.o\n",
-      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -mdll -O -Wall -IC:\\Miniconda\\include -IC:\\Miniconda\\PC -c fact.c -o build\\temp.win-amd64-2.7\\Release\\fact.o\n",
-      "writing build\\temp.win-amd64-2.7\\Release\\example.def\n",
-      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -shared -s build\\temp.win-amd64-2.7\\Release\\fact_wrap.o build\\temp.win-amd64-2.7\\Release\\fact.o build\\temp.win-amd64-2.7\\Release\\example.def -LC:\\Miniconda\\libs -LC:\\Miniconda\\PCbuild\\amd64 -lpython27 -lmsvcr90 -o \"C:\\Users\\Jin\\Documents\\Git\\python-tutorial\\07. interfacing with other languages\\example.pyd\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "!python setup.py build_ext --inplace"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 使用编译的模块"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "进行测试："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "factorial of 10: 3628800\n"
-     ]
-    }
-   ],
-   "source": [
-    "import example\n",
-    "\n",
-    "print 'factorial of 10:', example.fact(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "定义 `Python` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3628800\n",
-      "3628800\n"
-     ]
-    }
-   ],
-   "source": [
-    "def pyfact(n):\n",
-    "    if n <= 1: return 1\n",
-    "    return n * pyfact(n-1)\n",
-    "\n",
-    "print pyfact(10)\n",
-    "print example.fact(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "时间测试："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The slowest run took 13.17 times longer than the fastest. This could mean that an intermediate result is being cached \n",
-      "1000000 loops, best of 3: 213 ns per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit example.fact(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1000000 loops, best of 3: 1.43 µs per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit pyfact(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "如果使用 `fact` 计算比较大的值： "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example.fact(100)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "会出现溢出的结果，因为 `int` 表示的值有限，但是 `pyfact` 不会有这样的问题："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "93326215443944152681699238856266700490715968264381621468592963895217599993229915608941463976156518286253697920827223758251185210916864000000000000000000000000L"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pyfact(100)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "将生成的文件压缩到压缩文件中："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import zipfile\n",
-    "\n",
-    "f = zipfile.ZipFile('07-02-example.zip','w',zipfile.ZIP_DEFLATED)\n",
-    "\n",
-    "names = 'fact.o fact_wrap.c fact_wrap.o example.pyd setup.py'.split()\n",
-    "for name in names:\n",
-    "    f.write(name)\n",
-    "\n",
-    "f.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "清理生成的文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "!rm -f fact*.*\n",
-    "!rm -f example.*\n",
-    "!rm -f setup*.*\n",
-    "!rm -rf build"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Python 扩展模块"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 简介"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "C Library | Interface | Python\n",
+    "---|---|---\n",
+    "`c header` <br> `c implementation` | Wrapper `C` $\\leftrightarrows$ `Python` <br> communication between `py + c` | `import fact` <br> `fact.fact(10)`\n",
+    "\n",
+    "**Python** 扩展模块将 `PyInt(10)` 转化为 `CInt(10)` 然后调用 `C` 程序中的 `fact()` 函数进行计算，再将返回的结果转换回 `PyInt`。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 产生一个扩展模块"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "假设我们有这样的一个头文件和程序："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing fact.h\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file fact.h\n",
+    "#ifndef FACT_H\n",
+    "#define FACT_h\n",
+    "int fact(int n);\n",
+    "#endif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing fact.c\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file fact.c\n",
+    "#include \"fact.h\"\n",
+    "int fact(int n)\n",
+    "{\n",
+    "    if (n <= 1) return 1;\n",
+    "    else return n * fact(n - 1);\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "定义包装函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing fact_wrap.c\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file fact_wrap.c\n",
+    "\n",
+    "/* Must include Python.h before any standard headers*/\n",
+    "#include <Python.h>\n",
+    "#include \"fact.h\"\n",
+    "static PyObject* wrap_fact(PyObject *self, PyObject *args)\n",
+    "{\n",
+    "    /* Python->C data conversion */\n",
+    "    int n, result;\n",
+    "    // the string i here means there is only one integer\n",
+    "    if (!PyArg_ParseTuple(args, \"i\", &n))\n",
+    "        return NULL;\n",
+    "    \n",
+    "    /* C Function Call */\n",
+    "    result = fact(n);\n",
+    "    \n",
+    "    /* C->Python data conversion */\n",
+    "    return Py_BuildValue(\"i\", result);\n",
+    "}\n",
+    "\n",
+    "/* Method table declaring the names of functions exposed to Python*/\n",
+    "static PyMethodDef ExampleMethods[] = {\n",
+    "    {\"fact\",  wrap_fact, METH_VARARGS, \"Calculate the factorial of n\"},\n",
+    "    {NULL, NULL, 0, NULL}        /* Sentinel */\n",
+    "};\n",
+    "\n",
+    "/* Module initialization function called at \"import example\"*/\n",
+    "PyMODINIT_FUNC \n",
+    "initexample(void)\n",
+    "{\n",
+    "    (void) Py_InitModule(\"example\", ExampleMethods);\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 手动编译扩展模块"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "手动使用 `gcc` 编译，`Windows` 下如果没有 `gcc`，可以通过 `conda` 进行安装：\n",
+    "\n",
+    "    conda install mingw4\n",
+    "    \n",
+    "`Window 64-bit` 下编译需要加上 `-DMS_WIN64` 的选项，`include` 和 `lib` 文件夹的路径对应于本地 **Python** 安装的环境："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "!gcc -DMS_WIN64 -c fact.c fact_wrap.c -IC:\\Miniconda\\include"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "!gcc -DMS_WIN64 -shared fact.o fact_wrap.o -LC:\\Miniconda\\libs -lpython27 -o example.pyd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Windows` 下最终生成的文件后缀为 `.pyd` ， `Unix` 下生成的文件后缀名为 `.so`。\n",
+    "\n",
+    "用法为：\n",
+    "\n",
+    "- `Windows 32-bit`\n",
+    "```\n",
+    "gcc -c fact.c fact_wrap.c -I<your python path>\\include\n",
+    "gcc -shared fact.o fact_wrap.o -L<your python path>\\libs -lpython27 -o example.pyd\n",
+    "```\n",
+    "- `Unix`\n",
+    "```\n",
+    "gcc -c fact.c fact_wrap.c -I<your python path>\n",
+    "gcc -shared fact.o fact_wrap.o -L<your python path>\\config -lpython27 -o example.so\n",
+    "```\n",
+    "\n",
+    "编译完成后，我们就可以使用 `example` 这个模块了。\n",
+    "\n",
+    "导入生成的包："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['__doc__', '__file__', '__name__', '__package__', 'fact']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import example\n",
+    "print dir(example)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `example` 中的函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "factorial of 10: 3628800\n"
+     ]
+    }
+   ],
+   "source": [
+    "print 'factorial of 10:', example.fact(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 使用 setup.py 进行编译"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "清理刚才生成的文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "!rm -f example.pyd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "写入 `setup.py`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing setup.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file setup.py\n",
+    "from distutils.core import setup, Extension\n",
+    "\n",
+    "ext = Extension(name='example', sources=['fact_wrap.c', 'fact.c'])\n",
+    "\n",
+    "setup(name='example', ext_modules=[ext])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `distutils` 中的函数，我们进行 `build` 和 `install`：\n",
+    "\n",
+    "    python setup.py build (--compiler=mingw64)\n",
+    "    python setup.py install\n",
+    "\n",
+    "括号中的内容在 `windows` 中可能需要加上。\n",
+    "\n",
+    "这里我们使用 `build_ext --inplace` 选项将其安装在本地文件夹："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "running build_ext\n",
+      "building 'example' extension\n",
+      "creating build\n",
+      "creating build\\temp.win-amd64-2.7\n",
+      "creating build\\temp.win-amd64-2.7\\Release\n",
+      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -mdll -O -Wall -IC:\\Miniconda\\include -IC:\\Miniconda\\PC -c fact_wrap.c -o build\\temp.win-amd64-2.7\\Release\\fact_wrap.o\n",
+      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -mdll -O -Wall -IC:\\Miniconda\\include -IC:\\Miniconda\\PC -c fact.c -o build\\temp.win-amd64-2.7\\Release\\fact.o\n",
+      "writing build\\temp.win-amd64-2.7\\Release\\example.def\n",
+      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -shared -s build\\temp.win-amd64-2.7\\Release\\fact_wrap.o build\\temp.win-amd64-2.7\\Release\\fact.o build\\temp.win-amd64-2.7\\Release\\example.def -LC:\\Miniconda\\libs -LC:\\Miniconda\\PCbuild\\amd64 -lpython27 -lmsvcr90 -o \"C:\\Users\\Jin\\Documents\\Git\\python-tutorial\\07. interfacing with other languages\\example.pyd\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python setup.py build_ext --inplace"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 使用编译的模块"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "进行测试："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "factorial of 10: 3628800\n"
+     ]
+    }
+   ],
+   "source": [
+    "import example\n",
+    "\n",
+    "print 'factorial of 10:', example.fact(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "定义 `Python` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3628800\n",
+      "3628800\n"
+     ]
+    }
+   ],
+   "source": [
+    "def pyfact(n):\n",
+    "    if n <= 1: return 1\n",
+    "    return n * pyfact(n-1)\n",
+    "\n",
+    "print pyfact(10)\n",
+    "print example.fact(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "时间测试："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The slowest run took 13.17 times longer than the fastest. This could mean that an intermediate result is being cached \n",
+      "1000000 loops, best of 3: 213 ns per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit example.fact(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1000000 loops, best of 3: 1.43 µs per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit pyfact(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "如果使用 `fact` 计算比较大的值： "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example.fact(100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "会出现溢出的结果，因为 `int` 表示的值有限，但是 `pyfact` 不会有这样的问题："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "93326215443944152681699238856266700490715968264381621468592963895217599993229915608941463976156518286253697920827223758251185210916864000000000000000000000000L"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pyfact(100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将生成的文件压缩到压缩文件中："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import zipfile\n",
+    "\n",
+    "f = zipfile.ZipFile('07-02-example.zip','w',zipfile.ZIP_DEFLATED)\n",
+    "\n",
+    "names = 'fact.o fact_wrap.c fact_wrap.o example.pyd setup.py'.split()\n",
+    "for name in names:\n",
+    "    f.write(name)\n",
+    "\n",
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "清理生成的文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "!rm -f fact*.*\n",
+    "!rm -f example.*\n",
+    "!rm -f setup*.*\n",
+    "!rm -rf build"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/07. interfacing with other languages/07.03 cython part 1.ipynb b/07-interfacing-with-other-languages/07.03-cython-part-1.ipynb
similarity index 95%
rename from 07. interfacing with other languages/07.03 cython part 1.ipynb
rename to 07-interfacing-with-other-languages/07.03-cython-part-1.ipynb
index 6d9a066c..ffa5115c 100644
--- a/07. interfacing with other languages/07.03 cython part 1.ipynb	
+++ b/07-interfacing-with-other-languages/07.03-cython-part-1.ipynb
@@ -1,424 +1,424 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cython：Cython 基础，将源代码转换成扩展模块"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cython 基础"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "之前使用了手动的方法对 `C` 程序进行编译，而 `Cython` 则简化了这个过程。\n",
-    "\n",
-    "考虑之前的斐波拉契数列，`Python` 版本：\n",
-    "\n",
-    "```python\n",
-    "def fib(n):\n",
-    "    a,b = 1,1\n",
-    "    for i in range(n):\n",
-    "        a,b = a+b, a\n",
-    "    return a\n",
-    "```\n",
-    "\n",
-    "`C` 版本：\n",
-    "\n",
-    "```cpp\n",
-    "int fib(int n) {\n",
-    "    int tmp, i, a, b;\n",
-    "    a = b = 1;\n",
-    "    for (i=0; i<n; i++) {\n",
-    "        tmp = a; a += b; b = tmp;\n",
-    "    }\n",
-    "    return a;\n",
-    "}\n",
-    "```\n",
-    "\n",
-    "`Cython` 版本：\n",
-    "\n",
-    "```cython\n",
-    "def fib(int n):\n",
-    "    cdef int i, a, b\n",
-    "    a,b = 1,1\n",
-    "    for i in range(n):\n",
-    "        a,b = a+b, a\n",
-    "    return a\n",
-    "```\n",
-    "\n",
-    "这里 `cdef` 定义了 `C` 变量的类型。 \n",
-    "\n",
-    "**Cython** 的好处在于，我们使用了 **Python** 的语法，又有 **C/C++** 的效率，同时省去了之前直接编译成扩展模块的麻烦，并且提供了原生的 **Numpy** 支持。\n",
-    "\n",
-    "官方网址：http://www.cython.org\n",
-    "\n",
-    "其主要用法有两点：\n",
-    "\n",
-    "- 将 Python 程序转化为 C 程序\n",
-    "- 包装 C/C++ 程序"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 将源代码转换成扩展模块"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ipython 中使用 Cython 命令"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "导入 `Cython` `magic` 命令："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%load_ext Cython"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `magic` 命令执行 `Cython`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%%cython\n",
-    "def cyfib(int n):\n",
-    "    cdef int i, a, b\n",
-    "    a,b = 1,1\n",
-    "    for i in range(n):\n",
-    "        a,b = a+b, a\n",
-    "    return a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "144"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cyfib(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 使用 distutils 编译 Cython"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Cython` 代码以 `.pyx` 结尾，先通过 cython 转化为 `.c` 文件，再用 `gcc` 转化为 `.so(.pyd)` 文件。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing fib.pyx\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file fib.pyx\n",
-    "def cyfib(int n):\n",
-    "    cdef int i, a, b\n",
-    "    a,b = 1,1\n",
-    "    for i in range(n):\n",
-    "        a,b = a+b, a\n",
-    "    return a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwriting setup.py\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file setup.py\n",
-    "from distutils.core import setup\n",
-    "from distutils.extension import Extension\n",
-    "from Cython.Distutils import build_ext\n",
-    "\n",
-    "ext = Extension(\"fib\", sources=[\"fib.pyx\"])\n",
-    "\n",
-    "setup(ext_modules=[ext], cmdclass={'build_ext': build_ext})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "编译成功："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "running build_ext\n",
-      "cythoning fib.pyx to fib.c\n",
-      "building 'fib' extension\n",
-      "creating build\n",
-      "creating build\\temp.win-amd64-2.7\n",
-      "creating build\\temp.win-amd64-2.7\\Release\n",
-      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -mdll -O -Wall -IC:\\Miniconda\\include -IC:\\Miniconda\\PC -c fib.c -o build\\temp.win-amd64-2.7\\Release\\fib.o\n",
-      "writing build\\temp.win-amd64-2.7\\Release\\fib.def\n",
-      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -shared -s build\\temp.win-amd64-2.7\\Release\\fib.o build\\temp.win-amd64-2.7\\Release\\fib.def -LC:\\Miniconda\\libs -LC:\\Miniconda\\PCbuild\\amd64 -lpython27 -lmsvcr90 -o \"C:\\Users\\Jin\\Documents\\Git\\python-tutorial\\07. interfacing with other languages\\fib.pyd\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "!python setup.py build_ext --inplace"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用模块："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "144"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import fib\n",
-    "\n",
-    "fib.cyfib(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import zipfile\n",
-    "\n",
-    "f = zipfile.ZipFile('07-03-fib.zip','w',zipfile.ZIP_DEFLATED)\n",
-    "\n",
-    "names = 'fib.pyd fib.pyx fib.c setup.py'.split()\n",
-    "for name in names:\n",
-    "    f.write(name)\n",
-    "\n",
-    "f.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 使用 pyximport"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "清理之前生成的文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "!rm -f fib.pyd\n",
-    "!rm -f fib.pyc\n",
-    "!rm -f fib.C"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "清理之前导入的模块："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "%reset -f"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `pyximport`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "144"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pyximport\n",
-    "pyximport.install()\n",
-    "\n",
-    "import fib\n",
-    "\n",
-    "fib.cyfib(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`install` 函数会自动检测 Cython 程序的变化，并自动导入，不过一般用于简单文件的编译。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "清理生成的文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "!rm -f setup*.*\n",
-    "!rm -f fib.*\n",
-    "!rm -rf build"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cython：Cython 基础，将源代码转换成扩展模块"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cython 基础"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "之前使用了手动的方法对 `C` 程序进行编译，而 `Cython` 则简化了这个过程。\n",
+    "\n",
+    "考虑之前的斐波拉契数列，`Python` 版本：\n",
+    "\n",
+    "```python\n",
+    "def fib(n):\n",
+    "    a,b = 1,1\n",
+    "    for i in range(n):\n",
+    "        a,b = a+b, a\n",
+    "    return a\n",
+    "```\n",
+    "\n",
+    "`C` 版本：\n",
+    "\n",
+    "```cpp\n",
+    "int fib(int n) {\n",
+    "    int tmp, i, a, b;\n",
+    "    a = b = 1;\n",
+    "    for (i=0; i<n; i++) {\n",
+    "        tmp = a; a += b; b = tmp;\n",
+    "    }\n",
+    "    return a;\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "`Cython` 版本：\n",
+    "\n",
+    "```cython\n",
+    "def fib(int n):\n",
+    "    cdef int i, a, b\n",
+    "    a,b = 1,1\n",
+    "    for i in range(n):\n",
+    "        a,b = a+b, a\n",
+    "    return a\n",
+    "```\n",
+    "\n",
+    "这里 `cdef` 定义了 `C` 变量的类型。 \n",
+    "\n",
+    "**Cython** 的好处在于，我们使用了 **Python** 的语法，又有 **C/C++** 的效率，同时省去了之前直接编译成扩展模块的麻烦，并且提供了原生的 **Numpy** 支持。\n",
+    "\n",
+    "官方网址：http://www.cython.org\n",
+    "\n",
+    "其主要用法有两点：\n",
+    "\n",
+    "- 将 Python 程序转化为 C 程序\n",
+    "- 包装 C/C++ 程序"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 将源代码转换成扩展模块"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ipython 中使用 Cython 命令"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "导入 `Cython` `magic` 命令："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext Cython"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `magic` 命令执行 `Cython`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%cython\n",
+    "def cyfib(int n):\n",
+    "    cdef int i, a, b\n",
+    "    a,b = 1,1\n",
+    "    for i in range(n):\n",
+    "        a,b = a+b, a\n",
+    "    return a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cyfib(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 使用 distutils 编译 Cython"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Cython` 代码以 `.pyx` 结尾，先通过 cython 转化为 `.c` 文件，再用 `gcc` 转化为 `.so(.pyd)` 文件。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing fib.pyx\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file fib.pyx\n",
+    "def cyfib(int n):\n",
+    "    cdef int i, a, b\n",
+    "    a,b = 1,1\n",
+    "    for i in range(n):\n",
+    "        a,b = a+b, a\n",
+    "    return a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting setup.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file setup.py\n",
+    "from distutils.core import setup\n",
+    "from distutils.extension import Extension\n",
+    "from Cython.Distutils import build_ext\n",
+    "\n",
+    "ext = Extension(\"fib\", sources=[\"fib.pyx\"])\n",
+    "\n",
+    "setup(ext_modules=[ext], cmdclass={'build_ext': build_ext})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "编译成功："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "running build_ext\n",
+      "cythoning fib.pyx to fib.c\n",
+      "building 'fib' extension\n",
+      "creating build\n",
+      "creating build\\temp.win-amd64-2.7\n",
+      "creating build\\temp.win-amd64-2.7\\Release\n",
+      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -mdll -O -Wall -IC:\\Miniconda\\include -IC:\\Miniconda\\PC -c fib.c -o build\\temp.win-amd64-2.7\\Release\\fib.o\n",
+      "writing build\\temp.win-amd64-2.7\\Release\\fib.def\n",
+      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -shared -s build\\temp.win-amd64-2.7\\Release\\fib.o build\\temp.win-amd64-2.7\\Release\\fib.def -LC:\\Miniconda\\libs -LC:\\Miniconda\\PCbuild\\amd64 -lpython27 -lmsvcr90 -o \"C:\\Users\\Jin\\Documents\\Git\\python-tutorial\\07. interfacing with other languages\\fib.pyd\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python setup.py build_ext --inplace"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用模块："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import fib\n",
+    "\n",
+    "fib.cyfib(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import zipfile\n",
+    "\n",
+    "f = zipfile.ZipFile('07-03-fib.zip','w',zipfile.ZIP_DEFLATED)\n",
+    "\n",
+    "names = 'fib.pyd fib.pyx fib.c setup.py'.split()\n",
+    "for name in names:\n",
+    "    f.write(name)\n",
+    "\n",
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 使用 pyximport"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "清理之前生成的文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "!rm -f fib.pyd\n",
+    "!rm -f fib.pyc\n",
+    "!rm -f fib.C"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "清理之前导入的模块："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%reset -f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `pyximport`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pyximport\n",
+    "pyximport.install()\n",
+    "\n",
+    "import fib\n",
+    "\n",
+    "fib.cyfib(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`install` 函数会自动检测 Cython 程序的变化，并自动导入，不过一般用于简单文件的编译。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "清理生成的文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "!rm -f setup*.*\n",
+    "!rm -f fib.*\n",
+    "!rm -rf build"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/07. interfacing with other languages/07.04 cython part 2.ipynb b/07-interfacing-with-other-languages/07.04-cython-part-2.ipynb
similarity index 95%
rename from 07. interfacing with other languages/07.04 cython part 2.ipynb
rename to 07-interfacing-with-other-languages/07.04-cython-part-2.ipynb
index ba0ff5a9..b89844ee 100644
--- a/07. interfacing with other languages/07.04 cython part 2.ipynb	
+++ b/07-interfacing-with-other-languages/07.04-cython-part-2.ipynb
@@ -1,572 +1,572 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cython：Cython 语法，调用其他C库"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cython 语法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### cdef 关键词"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`cdef` 定义 `C` 类型变量。 \n",
-    "\n",
-    "可以定义局部变量：\n",
-    "\n",
-    "```cython\n",
-    "def fib(int n):\n",
-    "    cdef int a,b,i\n",
-    "    ...\n",
-    "```\n",
-    "\n",
-    "定义函数返回值：\n",
-    "\n",
-    "```cython\n",
-    "cdef float distance(float *x, float *y, int n):\n",
-    "    cdef:\n",
-    "        int i\n",
-    "        float d = 0.0\n",
-    "    for i in range(n):\n",
-    "        d += (x[i] - y[i]) ** 2\n",
-    "    return d\n",
-    "```\n",
-    "\n",
-    "定义函数：\n",
-    "```cython\n",
-    "cdef class Particle(object):\n",
-    "    cdef float psn[3], vel[3]\n",
-    "    cdef int id\n",
-    "```\n",
-    "\n",
-    "注意函数的参数不需要使用 cdef 的定义。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### def, cdef, cpdef 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Cython` 一共有三种定义方式，`def, cdef, cpdef` 三种：\n",
-    "\n",
-    "- `def` - Python, Cython 都可以调用\n",
-    "- `cdef` - 更快，只能 Cython 调用，可以使用指针\n",
-    "- `cpdef` - Python, Cython 都可以调用，不能使用指针"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### cimport"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from math import sin as pysin\n",
-    "from numpy import sin as npsin"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%load_ext Cython"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "从标准 `C` 语言库中调用模块，`cimport` 只能在 `Cython` 中使用："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%%cython\n",
-    "from libc.math cimport sin\n",
-    "from libc.stdlib cimport malloc, free"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### cimport 和 pxd 文件"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "如果想在多个文件中复用 `Cython` 代码，可以定义一个 `.pxd` 文件（相当于头文件 `.h`）定义方法，这个文件对应于一个 `.pyx` 文件（相当于源文件 `.c`），然后在其他的文件中使用 `cimport` 导入：\n",
-    "\n",
-    "`fib.pxd, fib.pyx` 文件存在，那么可以这样调用：\n",
-    "```cython\n",
-    "from fib cimport fib\n",
-    "```\n",
-    "\n",
-    "还可以调用 `C++` 标准库和 `Numpy C Api` 中的文件：\n",
-    "```cython\n",
-    "from libcpp.vector cimport vector\n",
-    "cimport numpy as cnp\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "## 调用其他C库"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "从标准库 `string.h` 中调用 `strlen`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing len_extern.pyx\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file len_extern.pyx\n",
-    "cdef extern from \"string.h\":\n",
-    "    int strlen(char *c)\n",
-    "    \n",
-    "def get_len(char *message):\n",
-    "    return strlen(message)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不过 `Cython` 不会自动扫描导入的头文件，所以要使用的函数必须再声明一遍："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing setup_len_extern.py\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file setup_len_extern.py\n",
-    "from distutils.core import setup\n",
-    "from distutils.extension import Extension\n",
-    "from Cython.Distutils import build_ext\n",
-    "\n",
-    "setup(\n",
-    "  ext_modules=[ Extension(\"len_extern\", [\"len_extern.pyx\"]) ],\n",
-    "  cmdclass = {'build_ext': build_ext}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "编译："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "running build_ext\n",
-      "cythoning len_extern.pyx to len_extern.c\n",
-      "building 'len_extern' extension\n",
-      "creating build\n",
-      "creating build\\temp.win-amd64-2.7\n",
-      "creating build\\temp.win-amd64-2.7\\Release\n",
-      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -mdll -O -Wall -IC:\\Miniconda\\include -IC:\\Miniconda\\PC -c len_extern.c -o build\\temp.win-amd64-2.7\\Release\\len_extern.o\n",
-      "writing build\\temp.win-amd64-2.7\\Release\\len_extern.def\n",
-      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -shared -s build\\temp.win-amd64-2.7\\Release\\len_extern.o build\\temp.win-amd64-2.7\\Release\\len_extern.def -LC:\\Miniconda\\libs -LC:\\Miniconda\\PCbuild\\amd64 -lpython27 -lmsvcr90 -o \"C:\\Users\\Jin\\Documents\\Git\\python-tutorial\\07. interfacing with other languages\\len_extern.pyd\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "!python setup_len_extern.py build_ext --inplace"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "从 `Python` 中调用："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "import len_extern"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "调用这个模块后，并不能直接使用 `strlen` 函数，可以看到，这个模块中并没有 `strlen` 这个函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__builtins__',\n",
-       " '__doc__',\n",
-       " '__file__',\n",
-       " '__name__',\n",
-       " '__package__',\n",
-       " '__test__',\n",
-       " 'get_len']"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(len_extern)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不过可以调用 `get_len` 函数： "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len_extern.get_len('hello')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "因为调用的是 `C` 函数，所以函数的表现与 `C` 语言的用法一致，例如 `C` 语言以 `\\0` 为字符串的结束符，所以会出现这样的情况："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len_extern.get_len('hello\\0world!')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "除了对已有的 `C` 函数进行调用，还可以对已有的 `C` 结构体进行调用和修改："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing time_extern.pyx\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file time_extern.pyx\n",
-    "cdef extern from \"time.h\":\n",
-    "\n",
-    "    struct tm:\n",
-    "        int tm_mday\n",
-    "        int tm_mon\n",
-    "        int tm_year\n",
-    "\n",
-    "    ctypedef long time_t\n",
-    "    tm* localtime(time_t *timer)\n",
-    "    time_t time(time_t *tloc)\n",
-    "\n",
-    "def get_date():\n",
-    "    \"\"\"Return a tuple with the current day, month and year.\"\"\"\n",
-    "    cdef time_t t\n",
-    "    cdef tm* ts\n",
-    "    t = time(NULL)\n",
-    "    ts = localtime(&t)\n",
-    "    return ts.tm_mday, ts.tm_mon + 1, ts.tm_year + 1900"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里我们只使用 `tm` 结构体的年月日信息，所以只声明了要用了三个属性。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing setup_time_extern.py\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file setup_time_extern.py\n",
-    "from distutils.core import setup\n",
-    "from distutils.extension import Extension\n",
-    "from Cython.Distutils import build_ext\n",
-    "\n",
-    "setup(\n",
-    "  ext_modules=[ Extension(\"time_extern\", [\"time_extern.pyx\"]) ],\n",
-    "  cmdclass = {'build_ext': build_ext}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "编译："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "running build_ext\n",
-      "cythoning time_extern.pyx to time_extern.c\n",
-      "building 'time_extern' extension\n",
-      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -mdll -O -Wall -IC:\\Miniconda\\include -IC:\\Miniconda\\PC -c time_extern.c -o build\\temp.win-amd64-2.7\\Release\\time_extern.o\n",
-      "writing build\\temp.win-amd64-2.7\\Release\\time_extern.def\n",
-      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -shared -s build\\temp.win-amd64-2.7\\Release\\time_extern.o build\\temp.win-amd64-2.7\\Release\\time_extern.def -LC:\\Miniconda\\libs -LC:\\Miniconda\\PCbuild\\amd64 -lpython27 -lmsvcr90 -o \"C:\\Users\\Jin\\Documents\\Git\\python-tutorial\\07. interfacing with other languages\\time_extern.pyd\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "!python setup_time_extern.py build_ext --inplace"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "测试："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(19, 9, 2015)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import time_extern\n",
-    "\n",
-    "time_extern.get_date()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "清理文件："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import zipfile\n",
-    "\n",
-    "f = zipfile.ZipFile('07-04-extern.zip','w',zipfile.ZIP_DEFLATED)\n",
-    "\n",
-    "names = ['setup_len_extern.py',\n",
-    "         'len_extern.pyx',\n",
-    "         'setup_time_extern.py',\n",
-    "         'time_extern.pyx']\n",
-    "for name in names:\n",
-    "    f.write(name)\n",
-    "\n",
-    "f.close()\n",
-    "\n",
-    "!rm -f setup*.*\n",
-    "!rm -f len_extern.*\n",
-    "!rm -f time_extern.*\n",
-    "!rm -rf build"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cython：Cython 语法，调用其他C库"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cython 语法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### cdef 关键词"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`cdef` 定义 `C` 类型变量。 \n",
+    "\n",
+    "可以定义局部变量：\n",
+    "\n",
+    "```cython\n",
+    "def fib(int n):\n",
+    "    cdef int a,b,i\n",
+    "    ...\n",
+    "```\n",
+    "\n",
+    "定义函数返回值：\n",
+    "\n",
+    "```cython\n",
+    "cdef float distance(float *x, float *y, int n):\n",
+    "    cdef:\n",
+    "        int i\n",
+    "        float d = 0.0\n",
+    "    for i in range(n):\n",
+    "        d += (x[i] - y[i]) ** 2\n",
+    "    return d\n",
+    "```\n",
+    "\n",
+    "定义函数：\n",
+    "```cython\n",
+    "cdef class Particle(object):\n",
+    "    cdef float psn[3], vel[3]\n",
+    "    cdef int id\n",
+    "```\n",
+    "\n",
+    "注意函数的参数不需要使用 cdef 的定义。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### def, cdef, cpdef 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Cython` 一共有三种定义方式，`def, cdef, cpdef` 三种：\n",
+    "\n",
+    "- `def` - Python, Cython 都可以调用\n",
+    "- `cdef` - 更快，只能 Cython 调用，可以使用指针\n",
+    "- `cpdef` - Python, Cython 都可以调用，不能使用指针"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### cimport"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from math import sin as pysin\n",
+    "from numpy import sin as npsin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext Cython"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "从标准 `C` 语言库中调用模块，`cimport` 只能在 `Cython` 中使用："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%cython\n",
+    "from libc.math cimport sin\n",
+    "from libc.stdlib cimport malloc, free"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### cimport 和 pxd 文件"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "如果想在多个文件中复用 `Cython` 代码，可以定义一个 `.pxd` 文件（相当于头文件 `.h`）定义方法，这个文件对应于一个 `.pyx` 文件（相当于源文件 `.c`），然后在其他的文件中使用 `cimport` 导入：\n",
+    "\n",
+    "`fib.pxd, fib.pyx` 文件存在，那么可以这样调用：\n",
+    "```cython\n",
+    "from fib cimport fib\n",
+    "```\n",
+    "\n",
+    "还可以调用 `C++` 标准库和 `Numpy C Api` 中的文件：\n",
+    "```cython\n",
+    "from libcpp.vector cimport vector\n",
+    "cimport numpy as cnp\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## 调用其他C库"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "从标准库 `string.h` 中调用 `strlen`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing len_extern.pyx\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file len_extern.pyx\n",
+    "cdef extern from \"string.h\":\n",
+    "    int strlen(char *c)\n",
+    "    \n",
+    "def get_len(char *message):\n",
+    "    return strlen(message)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不过 `Cython` 不会自动扫描导入的头文件，所以要使用的函数必须再声明一遍："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing setup_len_extern.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file setup_len_extern.py\n",
+    "from distutils.core import setup\n",
+    "from distutils.extension import Extension\n",
+    "from Cython.Distutils import build_ext\n",
+    "\n",
+    "setup(\n",
+    "  ext_modules=[ Extension(\"len_extern\", [\"len_extern.pyx\"]) ],\n",
+    "  cmdclass = {'build_ext': build_ext}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "编译："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "running build_ext\n",
+      "cythoning len_extern.pyx to len_extern.c\n",
+      "building 'len_extern' extension\n",
+      "creating build\n",
+      "creating build\\temp.win-amd64-2.7\n",
+      "creating build\\temp.win-amd64-2.7\\Release\n",
+      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -mdll -O -Wall -IC:\\Miniconda\\include -IC:\\Miniconda\\PC -c len_extern.c -o build\\temp.win-amd64-2.7\\Release\\len_extern.o\n",
+      "writing build\\temp.win-amd64-2.7\\Release\\len_extern.def\n",
+      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -shared -s build\\temp.win-amd64-2.7\\Release\\len_extern.o build\\temp.win-amd64-2.7\\Release\\len_extern.def -LC:\\Miniconda\\libs -LC:\\Miniconda\\PCbuild\\amd64 -lpython27 -lmsvcr90 -o \"C:\\Users\\Jin\\Documents\\Git\\python-tutorial\\07. interfacing with other languages\\len_extern.pyd\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python setup_len_extern.py build_ext --inplace"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "从 `Python` 中调用："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import len_extern"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "调用这个模块后，并不能直接使用 `strlen` 函数，可以看到，这个模块中并没有 `strlen` 这个函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__builtins__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__test__',\n",
+       " 'get_len']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(len_extern)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不过可以调用 `get_len` 函数： "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len_extern.get_len('hello')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "因为调用的是 `C` 函数，所以函数的表现与 `C` 语言的用法一致，例如 `C` 语言以 `\\0` 为字符串的结束符，所以会出现这样的情况："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len_extern.get_len('hello\\0world!')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "除了对已有的 `C` 函数进行调用，还可以对已有的 `C` 结构体进行调用和修改："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing time_extern.pyx\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file time_extern.pyx\n",
+    "cdef extern from \"time.h\":\n",
+    "\n",
+    "    struct tm:\n",
+    "        int tm_mday\n",
+    "        int tm_mon\n",
+    "        int tm_year\n",
+    "\n",
+    "    ctypedef long time_t\n",
+    "    tm* localtime(time_t *timer)\n",
+    "    time_t time(time_t *tloc)\n",
+    "\n",
+    "def get_date():\n",
+    "    \"\"\"Return a tuple with the current day, month and year.\"\"\"\n",
+    "    cdef time_t t\n",
+    "    cdef tm* ts\n",
+    "    t = time(NULL)\n",
+    "    ts = localtime(&t)\n",
+    "    return ts.tm_mday, ts.tm_mon + 1, ts.tm_year + 1900"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里我们只使用 `tm` 结构体的年月日信息，所以只声明了要用了三个属性。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing setup_time_extern.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file setup_time_extern.py\n",
+    "from distutils.core import setup\n",
+    "from distutils.extension import Extension\n",
+    "from Cython.Distutils import build_ext\n",
+    "\n",
+    "setup(\n",
+    "  ext_modules=[ Extension(\"time_extern\", [\"time_extern.pyx\"]) ],\n",
+    "  cmdclass = {'build_ext': build_ext}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "编译："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "running build_ext\n",
+      "cythoning time_extern.pyx to time_extern.c\n",
+      "building 'time_extern' extension\n",
+      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -mdll -O -Wall -IC:\\Miniconda\\include -IC:\\Miniconda\\PC -c time_extern.c -o build\\temp.win-amd64-2.7\\Release\\time_extern.o\n",
+      "writing build\\temp.win-amd64-2.7\\Release\\time_extern.def\n",
+      "C:\\Miniconda\\Scripts\\gcc.bat -DMS_WIN64 -shared -s build\\temp.win-amd64-2.7\\Release\\time_extern.o build\\temp.win-amd64-2.7\\Release\\time_extern.def -LC:\\Miniconda\\libs -LC:\\Miniconda\\PCbuild\\amd64 -lpython27 -lmsvcr90 -o \"C:\\Users\\Jin\\Documents\\Git\\python-tutorial\\07. interfacing with other languages\\time_extern.pyd\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python setup_time_extern.py build_ext --inplace"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "测试："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(19, 9, 2015)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import time_extern\n",
+    "\n",
+    "time_extern.get_date()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "清理文件："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import zipfile\n",
+    "\n",
+    "f = zipfile.ZipFile('07-04-extern.zip','w',zipfile.ZIP_DEFLATED)\n",
+    "\n",
+    "names = ['setup_len_extern.py',\n",
+    "         'len_extern.pyx',\n",
+    "         'setup_time_extern.py',\n",
+    "         'time_extern.pyx']\n",
+    "for name in names:\n",
+    "    f.write(name)\n",
+    "\n",
+    "f.close()\n",
+    "\n",
+    "!rm -f setup*.*\n",
+    "!rm -f len_extern.*\n",
+    "!rm -f time_extern.*\n",
+    "!rm -rf build"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/07. interfacing with other languages/07.05 cython part 3.ipynb b/07-interfacing-with-other-languages/07.05-cython-part-3.ipynb
similarity index 96%
rename from 07. interfacing with other languages/07.05 cython part 3.ipynb
rename to 07-interfacing-with-other-languages/07.05-cython-part-3.ipynb
index e600e915..41e8e6de 100644
--- a/07. interfacing with other languages/07.05 cython part 3.ipynb	
+++ b/07-interfacing-with-other-languages/07.05-cython-part-3.ipynb
@@ -1,577 +1,577 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cython：class 和 cdef class，使用 C++"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## class 和 cdef class"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`class` 定义属性变量比较自由，`cdef class` 可以定义 `cdef`\n",
-    "\n",
-    "`class` 使用 `__init__` 初始化，`cdef class` 在使用 `__init__` 之前用 `__cinit__` 对 `C` 相关的参数进行初始化。\n",
-    "\n",
-    "`cdef class` 中的方法可以是 `def, cdef, cpdef` 三种，只有 `public` 的属性才可以被访问，不可以添加新的属性。\n",
-    "\n",
-    "`__dealloc__` 函数类似析构函数，负责释放申请的内存。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Cython` 属性可以使用关键词 `property` 来定义，然后定义 `__get__` 和  `__set__` 方法来进行获取和设置：\n",
-    "\n",
-    "```cython\n",
-    "property name:\n",
-    "    def __get__(self):\n",
-    "        return something\n",
-    "    def __set__(self):\n",
-    "        set_something\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 使用 C++ 类"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `C++` 类时要加上 `cppclass` 关键词，在编译时 `setup` 中要加上 `language=\"c++\"` 的选项。\n",
-    "\n",
-    "假设我们有这样一个 `C++` 类："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwriting particle_extern.h\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file particle_extern.h\n",
-    "#ifndef _PARTICLE_EXTERN_H_\n",
-    "#define _PARTICLE_EXTERN_H_\n",
-    "\n",
-    "class Particle {\n",
-    "\n",
-    "    public:\n",
-    "\n",
-    "        Particle() :\n",
-    "            mass(0), charge(0) {}\n",
-    "        \n",
-    "        Particle(float m, float c, float *p, float *v);\n",
-    "\n",
-    "        ~Particle() {}\n",
-    "\n",
-    "        float getMass() {return mass; }\n",
-    "\n",
-    "        void setMass(float m) { mass = m; }\n",
-    "\n",
-    "        float getCharge() { return charge; }\n",
-    "\n",
-    "        const float *getVel() { return vel; }\n",
-    "        const float *getPos() { return pos; }\n",
-    "\n",
-    "        void applyImpulse(float *f, float t);\n",
-    "\n",
-    "    private:\n",
-    "        float mass, charge;\n",
-    "        float pos[3], vel[3];\n",
-    "};\n",
-    "\n",
-    "#endif"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwriting particle_extern.cpp\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file particle_extern.cpp\n",
-    "#include \"particle_extern.h\"\n",
-    "\n",
-    "Particle::Particle(float m, float c, float *p, float *v) :\n",
-    "    mass(m), charge(c) \n",
-    "{\n",
-    "    for (int i=0; i<3; ++i) {\n",
-    "        pos[i] = p[i]; vel[i] = v[i];\n",
-    "    }\n",
-    "}\n",
-    "\n",
-    "void Particle::applyImpulse(float *f, float t)\n",
-    "{\n",
-    "    float newvi;\n",
-    "    for(int i=0; i<3; ++i) {\n",
-    "        newvi = vel[i] + t / mass * f[i];\n",
-    "        pos[i] = (newvi + vel[i]) * t / 2.;\n",
-    "        vel[i] = newvi;\n",
-    "    }\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在 `Cython` 中调用这个类："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwriting particle.pyx\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file particle.pyx\n",
-    "import numpy as np\n",
-    "\n",
-    "cdef extern from \"particle_extern.h\":\n",
-    "\n",
-    "    cppclass _Particle \"Particle\":\n",
-    "        _Particle(float m, float c, float *p, float *v)\n",
-    "        float getMass()\n",
-    "        void setMass(float m)\n",
-    "        float getCharge()\n",
-    "        const float *getVel()\n",
-    "        const float *getPos()\n",
-    "        void applyImpulse(float *f, float t)\n",
-    "\n",
-    "\n",
-    "cdef class Particle:\n",
-    "    cdef _Particle *thisptr # ptr to C++ instance\n",
-    "\n",
-    "    def __cinit__(self, m, c, float[::1] p, float[::1] v):\n",
-    "        if p.shape[0] != 3 or v.shape[0] != 3:\n",
-    "            raise ValueError(\"...\")\n",
-    "        self.thisptr = new _Particle(m, c, &p[0], &v[0])\n",
-    "\n",
-    "    def __dealloc__(self):\n",
-    "        del self.thisptr\n",
-    "\n",
-    "    def apply_impulse(self, float[::1] v, float t):\n",
-    "        self.thisptr.applyImpulse(&v[0], t)\n",
-    "\n",
-    "    def __repr__(self):\n",
-    "        args = ', '.join('%s=%s' % (n, getattr(self, n)) for n in ('mass', 'charge', 'pos', 'vel'))\n",
-    "        return 'particle.Particle(%s)' % args\n",
-    "\n",
-    "    property charge:\n",
-    "\n",
-    "        def __get__(self):\n",
-    "            return self.thisptr.getCharge()\n",
-    "\n",
-    "    property mass:  # Cython-style properties.\n",
-    "        def __get__(self):\n",
-    "            return self.thisptr.getMass()\n",
-    "\n",
-    "        def __set__(self, m):\n",
-    "            self.thisptr.setMass(m)\n",
-    "\n",
-    "    property vel:\n",
-    "\n",
-    "        def __get__(self):\n",
-    "            cdef const float *_vel = self.thisptr.getVel()\n",
-    "            cdef float[::1] arr = np.empty((3,), dtype=np.float32)\n",
-    "            for i in range(3):\n",
-    "                arr[i] = _vel[i]\n",
-    "            return np.asarray(arr)\n",
-    "\n",
-    "    property pos:\n",
-    "\n",
-    "        def __get__(self):\n",
-    "            cdef const float *_pos = self.thisptr.getPos()\n",
-    "            cdef float[::1] arr = np.empty((3,), dtype=np.float32)\n",
-    "            for i in range(3):\n",
-    "                arr[i] = _pos[i]\n",
-    "            return np.asarray(arr)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "首先从头文件声明这个类：\n",
-    "```cython\n",
-    "cdef extern from \"particle_extern.h\":\n",
-    "\n",
-    "    cppclass _Particle \"Particle\":\n",
-    "        _Particle(float m, float c, float *p, float *v)\n",
-    "        float getMass()\n",
-    "        void setMass(float m)\n",
-    "        float getCharge()\n",
-    "        const float *getVel()\n",
-    "        const float *getPos()\n",
-    "        void applyImpulse(float *f, float t)\n",
-    "```\n",
-    "\n",
-    "这里要使用 `cppclass` 关键词，并且为了方便，我们将 `Particle` 类的名字在 `Cython` 中重命名为 `_Particle`。\n",
-    "\n",
-    "```cython\n",
-    "cdef class Particle:\n",
-    "    cdef _Particle *thisptr\n",
-    "    def __cinit__(self, m, c, float[::1] p, float[::1] v):\n",
-    "        if p.shape[0] != 3 or v.shape[0] != 3:\n",
-    "            raise ValueError(\"...\")\n",
-    "        self.thisptr = new _Particle(m, c, &p[0], &v[0])\n",
-    "```\n",
-    "为了使用这个类，我们需要定义一个该类的指针，然后用指针指向一个 `_Particle` 对象。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwriting setup.py\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file setup.py\n",
-    "from distutils.core import setup\n",
-    "from distutils.extension import Extension\n",
-    "from Cython.Distutils import build_ext\n",
-    "\n",
-    "ext = Extension(\"particle\", [\"particle.pyx\", \"particle_extern.cpp\"], language=\"c++\")\n",
-    "\n",
-    "setup(\n",
-    "    cmdclass = {'build_ext': build_ext},\n",
-    "    ext_modules = [ext],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "要加上 `language=\"c++\"` 的选项，然后编译："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "running build_ext\n",
-      "cythoning particle.pyx to particle.cpp\n",
-      "building 'particle' extension\n",
-      "C:\\Anaconda\\Scripts\\gcc.bat -DMS_WIN64 -mdll -O -Wall -IC:\\Anaconda\\include -IC:\\Anaconda\\PC -c particle.cpp -o build\\temp.win-amd64-2.7\\Release\\particle.o\n",
-      "C:\\Anaconda\\Scripts\\gcc.bat -DMS_WIN64 -mdll -O -Wall -IC:\\Anaconda\\include -IC:\\Anaconda\\PC -c particle_extern.cpp -o build\\temp.win-amd64-2.7\\Release\\particle_extern.o\n",
-      "writing build\\temp.win-amd64-2.7\\Release\\particle.def\n",
-      "C:\\Anaconda\\Scripts\\g++.bat -DMS_WIN64 -shared -s build\\temp.win-amd64-2.7\\Release\\particle.o build\\temp.win-amd64-2.7\\Release\\particle_extern.o build\\temp.win-amd64-2.7\\Release\\particle.def -LC:\\Anaconda\\libs -LC:\\Anaconda\\PCbuild\\amd64 -lpython27 -lmsvcr90 -o \"C:\\Users\\lijin\\Documents\\Git\\python-tutorial\\07. interfacing with other languages\\particle.pyd\"\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "particle.cpp: In function 'void __Pyx_RaiseArgtupleInvalid(const char*, int, Py_ssize_t, Py_ssize_t, Py_ssize_t)':\n",
-      "particle.cpp:14931:59: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "particle.cpp:14931:59: warning: format '%s' expects argument of type 'char*', but argument 5 has type 'Py_ssize_t {aka long long int}' [-Wformat]\n",
-      "particle.cpp:14931:59: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "particle.cpp:14931:59: warning: too many arguments for format [-Wformat-extra-args]\n",
-      "particle.cpp: In function 'int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context*)':\n",
-      "particle.cpp:15498:78: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "particle.cpp:15498:78: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "particle.cpp:15498:78: warning: too many arguments for format [-Wformat-extra-args]\n",
-      "particle.cpp:15550:67: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "particle.cpp:15550:67: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "particle.cpp:15550:67: warning: too many arguments for format [-Wformat-extra-args]\n",
-      "particle.cpp: In function 'PyObject* __pyx_buffmt_parse_array(__Pyx_BufFmt_Context*, const char**)':\n",
-      "particle.cpp:15612:69: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "particle.cpp:15612:69: warning: format '%d' expects argument of type 'int', but argument 3 has type 'size_t {aka long long unsigned int}' [-Wformat]\n",
-      "particle.cpp:15612:69: warning: too many arguments for format [-Wformat-extra-args]\n",
-      "particle.cpp: In function 'int __Pyx_GetBufferAndValidate(Py_buffer*, PyObject*, __Pyx_TypeInfo*, int, int, int, __Pyx_BufFmt_StackElem*)':\n",
-      "particle.cpp:15797:73: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "particle.cpp:15797:73: warning: format '%s' expects argument of type 'char*', but argument 3 has type 'Py_ssize_t {aka long long int}' [-Wformat]\n",
-      "particle.cpp:15797:73: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "particle.cpp:15797:73: warning: too many arguments for format [-Wformat-extra-args]\n",
-      "particle.cpp: In function 'void __Pyx_RaiseTooManyValuesError(Py_ssize_t)':\n",
-      "particle.cpp:16216:94: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "particle.cpp:16216:94: warning: too many arguments for format [-Wformat-extra-args]\n",
-      "particle.cpp: In function 'void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t)':\n",
-      "particle.cpp:16222:48: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "particle.cpp:16222:48: warning: format '%s' expects argument of type 'char*', but argument 3 has type 'Py_ssize_t {aka long long int}' [-Wformat]\n",
-      "particle.cpp:16222:48: warning: too many arguments for format [-Wformat-extra-args]\n",
-      "particle.cpp: In function 'int __Pyx_ValidateAndInit_memviewslice(int*, int, int, int, __Pyx_TypeInfo*, __Pyx_BufFmt_StackElem*, __Pyx_memviewslice*, PyObject*)':\n",
-      "particle.cpp:16941:50: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "particle.cpp:16941:50: warning: format '%s' expects argument of type 'char*', but argument 3 has type 'Py_ssize_t {aka long long int}' [-Wformat]\n",
-      "particle.cpp:16941:50: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "particle.cpp:16941:50: warning: too many arguments for format [-Wformat-extra-args]\n"
-     ]
-    }
-   ],
-   "source": [
-    "!python setup.py build_ext -i"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import particle"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "注意这里类型要设成 `float32`，因为 `C++` 程序中接受的是 `float` 类型，默认是 `float64(double)` 类型："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "particle.Particle(mass=1.0, charge=2.0, pos=[ 0.  1.  2.], vel=[ 0.  1.  2.])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "pos = vel = np.arange(3., dtype='float32')\n",
-    "mass = 1.0\n",
-    "charge = 2.0\n",
-    "\n",
-    "p = particle.Particle(mass, charge, pos, vel)\n",
-    "p"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "调用 `apply_impulse` 方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "particle.Particle(mass=1.0, charge=2.0, pos=[ 0.   1.5  3. ], vel=[ 0.  2.  4.])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p.apply_impulse(np.arange(3., dtype='float32'), 1.0)\n",
-    "\n",
-    "p"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看质量："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.0"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p.mass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "修改质量："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "p.mass = 3.0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看 `charge`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.0"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p.charge"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "因为 `charge` 没有定义 `__set__` 方法，所以它是只读的属性，不能进行修改。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import zipfile\n",
-    "\n",
-    "f = zipfile.ZipFile('07-05-particle.zip','w',zipfile.ZIP_DEFLATED)\n",
-    "\n",
-    "names = ['particle.pyx',\n",
-    "         'particle_extern.cpp',\n",
-    "         'particle_extern.h',\n",
-    "         'setup.py']\n",
-    "for name in names:\n",
-    "    f.write(name)\n",
-    "\n",
-    "f.close()\n",
-    "\n",
-    "!rm -f setup*.*\n",
-    "!rm -f particle*.*\n",
-    "!rm -rf build"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cython：class 和 cdef class，使用 C++"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## class 和 cdef class"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`class` 定义属性变量比较自由，`cdef class` 可以定义 `cdef`\n",
+    "\n",
+    "`class` 使用 `__init__` 初始化，`cdef class` 在使用 `__init__` 之前用 `__cinit__` 对 `C` 相关的参数进行初始化。\n",
+    "\n",
+    "`cdef class` 中的方法可以是 `def, cdef, cpdef` 三种，只有 `public` 的属性才可以被访问，不可以添加新的属性。\n",
+    "\n",
+    "`__dealloc__` 函数类似析构函数，负责释放申请的内存。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Cython` 属性可以使用关键词 `property` 来定义，然后定义 `__get__` 和  `__set__` 方法来进行获取和设置：\n",
+    "\n",
+    "```cython\n",
+    "property name:\n",
+    "    def __get__(self):\n",
+    "        return something\n",
+    "    def __set__(self):\n",
+    "        set_something\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 使用 C++ 类"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `C++` 类时要加上 `cppclass` 关键词，在编译时 `setup` 中要加上 `language=\"c++\"` 的选项。\n",
+    "\n",
+    "假设我们有这样一个 `C++` 类："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting particle_extern.h\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file particle_extern.h\n",
+    "#ifndef _PARTICLE_EXTERN_H_\n",
+    "#define _PARTICLE_EXTERN_H_\n",
+    "\n",
+    "class Particle {\n",
+    "\n",
+    "    public:\n",
+    "\n",
+    "        Particle() :\n",
+    "            mass(0), charge(0) {}\n",
+    "        \n",
+    "        Particle(float m, float c, float *p, float *v);\n",
+    "\n",
+    "        ~Particle() {}\n",
+    "\n",
+    "        float getMass() {return mass; }\n",
+    "\n",
+    "        void setMass(float m) { mass = m; }\n",
+    "\n",
+    "        float getCharge() { return charge; }\n",
+    "\n",
+    "        const float *getVel() { return vel; }\n",
+    "        const float *getPos() { return pos; }\n",
+    "\n",
+    "        void applyImpulse(float *f, float t);\n",
+    "\n",
+    "    private:\n",
+    "        float mass, charge;\n",
+    "        float pos[3], vel[3];\n",
+    "};\n",
+    "\n",
+    "#endif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting particle_extern.cpp\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file particle_extern.cpp\n",
+    "#include \"particle_extern.h\"\n",
+    "\n",
+    "Particle::Particle(float m, float c, float *p, float *v) :\n",
+    "    mass(m), charge(c) \n",
+    "{\n",
+    "    for (int i=0; i<3; ++i) {\n",
+    "        pos[i] = p[i]; vel[i] = v[i];\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "void Particle::applyImpulse(float *f, float t)\n",
+    "{\n",
+    "    float newvi;\n",
+    "    for(int i=0; i<3; ++i) {\n",
+    "        newvi = vel[i] + t / mass * f[i];\n",
+    "        pos[i] = (newvi + vel[i]) * t / 2.;\n",
+    "        vel[i] = newvi;\n",
+    "    }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在 `Cython` 中调用这个类："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting particle.pyx\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file particle.pyx\n",
+    "import numpy as np\n",
+    "\n",
+    "cdef extern from \"particle_extern.h\":\n",
+    "\n",
+    "    cppclass _Particle \"Particle\":\n",
+    "        _Particle(float m, float c, float *p, float *v)\n",
+    "        float getMass()\n",
+    "        void setMass(float m)\n",
+    "        float getCharge()\n",
+    "        const float *getVel()\n",
+    "        const float *getPos()\n",
+    "        void applyImpulse(float *f, float t)\n",
+    "\n",
+    "\n",
+    "cdef class Particle:\n",
+    "    cdef _Particle *thisptr # ptr to C++ instance\n",
+    "\n",
+    "    def __cinit__(self, m, c, float[::1] p, float[::1] v):\n",
+    "        if p.shape[0] != 3 or v.shape[0] != 3:\n",
+    "            raise ValueError(\"...\")\n",
+    "        self.thisptr = new _Particle(m, c, &p[0], &v[0])\n",
+    "\n",
+    "    def __dealloc__(self):\n",
+    "        del self.thisptr\n",
+    "\n",
+    "    def apply_impulse(self, float[::1] v, float t):\n",
+    "        self.thisptr.applyImpulse(&v[0], t)\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = ', '.join('%s=%s' % (n, getattr(self, n)) for n in ('mass', 'charge', 'pos', 'vel'))\n",
+    "        return 'particle.Particle(%s)' % args\n",
+    "\n",
+    "    property charge:\n",
+    "\n",
+    "        def __get__(self):\n",
+    "            return self.thisptr.getCharge()\n",
+    "\n",
+    "    property mass:  # Cython-style properties.\n",
+    "        def __get__(self):\n",
+    "            return self.thisptr.getMass()\n",
+    "\n",
+    "        def __set__(self, m):\n",
+    "            self.thisptr.setMass(m)\n",
+    "\n",
+    "    property vel:\n",
+    "\n",
+    "        def __get__(self):\n",
+    "            cdef const float *_vel = self.thisptr.getVel()\n",
+    "            cdef float[::1] arr = np.empty((3,), dtype=np.float32)\n",
+    "            for i in range(3):\n",
+    "                arr[i] = _vel[i]\n",
+    "            return np.asarray(arr)\n",
+    "\n",
+    "    property pos:\n",
+    "\n",
+    "        def __get__(self):\n",
+    "            cdef const float *_pos = self.thisptr.getPos()\n",
+    "            cdef float[::1] arr = np.empty((3,), dtype=np.float32)\n",
+    "            for i in range(3):\n",
+    "                arr[i] = _pos[i]\n",
+    "            return np.asarray(arr)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "首先从头文件声明这个类：\n",
+    "```cython\n",
+    "cdef extern from \"particle_extern.h\":\n",
+    "\n",
+    "    cppclass _Particle \"Particle\":\n",
+    "        _Particle(float m, float c, float *p, float *v)\n",
+    "        float getMass()\n",
+    "        void setMass(float m)\n",
+    "        float getCharge()\n",
+    "        const float *getVel()\n",
+    "        const float *getPos()\n",
+    "        void applyImpulse(float *f, float t)\n",
+    "```\n",
+    "\n",
+    "这里要使用 `cppclass` 关键词，并且为了方便，我们将 `Particle` 类的名字在 `Cython` 中重命名为 `_Particle`。\n",
+    "\n",
+    "```cython\n",
+    "cdef class Particle:\n",
+    "    cdef _Particle *thisptr\n",
+    "    def __cinit__(self, m, c, float[::1] p, float[::1] v):\n",
+    "        if p.shape[0] != 3 or v.shape[0] != 3:\n",
+    "            raise ValueError(\"...\")\n",
+    "        self.thisptr = new _Particle(m, c, &p[0], &v[0])\n",
+    "```\n",
+    "为了使用这个类，我们需要定义一个该类的指针，然后用指针指向一个 `_Particle` 对象。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting setup.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file setup.py\n",
+    "from distutils.core import setup\n",
+    "from distutils.extension import Extension\n",
+    "from Cython.Distutils import build_ext\n",
+    "\n",
+    "ext = Extension(\"particle\", [\"particle.pyx\", \"particle_extern.cpp\"], language=\"c++\")\n",
+    "\n",
+    "setup(\n",
+    "    cmdclass = {'build_ext': build_ext},\n",
+    "    ext_modules = [ext],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "要加上 `language=\"c++\"` 的选项，然后编译："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "running build_ext\n",
+      "cythoning particle.pyx to particle.cpp\n",
+      "building 'particle' extension\n",
+      "C:\\Anaconda\\Scripts\\gcc.bat -DMS_WIN64 -mdll -O -Wall -IC:\\Anaconda\\include -IC:\\Anaconda\\PC -c particle.cpp -o build\\temp.win-amd64-2.7\\Release\\particle.o\n",
+      "C:\\Anaconda\\Scripts\\gcc.bat -DMS_WIN64 -mdll -O -Wall -IC:\\Anaconda\\include -IC:\\Anaconda\\PC -c particle_extern.cpp -o build\\temp.win-amd64-2.7\\Release\\particle_extern.o\n",
+      "writing build\\temp.win-amd64-2.7\\Release\\particle.def\n",
+      "C:\\Anaconda\\Scripts\\g++.bat -DMS_WIN64 -shared -s build\\temp.win-amd64-2.7\\Release\\particle.o build\\temp.win-amd64-2.7\\Release\\particle_extern.o build\\temp.win-amd64-2.7\\Release\\particle.def -LC:\\Anaconda\\libs -LC:\\Anaconda\\PCbuild\\amd64 -lpython27 -lmsvcr90 -o \"C:\\Users\\lijin\\Documents\\Git\\python-tutorial\\07. interfacing with other languages\\particle.pyd\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "particle.cpp: In function 'void __Pyx_RaiseArgtupleInvalid(const char*, int, Py_ssize_t, Py_ssize_t, Py_ssize_t)':\n",
+      "particle.cpp:14931:59: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "particle.cpp:14931:59: warning: format '%s' expects argument of type 'char*', but argument 5 has type 'Py_ssize_t {aka long long int}' [-Wformat]\n",
+      "particle.cpp:14931:59: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "particle.cpp:14931:59: warning: too many arguments for format [-Wformat-extra-args]\n",
+      "particle.cpp: In function 'int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context*)':\n",
+      "particle.cpp:15498:78: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "particle.cpp:15498:78: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "particle.cpp:15498:78: warning: too many arguments for format [-Wformat-extra-args]\n",
+      "particle.cpp:15550:67: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "particle.cpp:15550:67: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "particle.cpp:15550:67: warning: too many arguments for format [-Wformat-extra-args]\n",
+      "particle.cpp: In function 'PyObject* __pyx_buffmt_parse_array(__Pyx_BufFmt_Context*, const char**)':\n",
+      "particle.cpp:15612:69: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "particle.cpp:15612:69: warning: format '%d' expects argument of type 'int', but argument 3 has type 'size_t {aka long long unsigned int}' [-Wformat]\n",
+      "particle.cpp:15612:69: warning: too many arguments for format [-Wformat-extra-args]\n",
+      "particle.cpp: In function 'int __Pyx_GetBufferAndValidate(Py_buffer*, PyObject*, __Pyx_TypeInfo*, int, int, int, __Pyx_BufFmt_StackElem*)':\n",
+      "particle.cpp:15797:73: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "particle.cpp:15797:73: warning: format '%s' expects argument of type 'char*', but argument 3 has type 'Py_ssize_t {aka long long int}' [-Wformat]\n",
+      "particle.cpp:15797:73: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "particle.cpp:15797:73: warning: too many arguments for format [-Wformat-extra-args]\n",
+      "particle.cpp: In function 'void __Pyx_RaiseTooManyValuesError(Py_ssize_t)':\n",
+      "particle.cpp:16216:94: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "particle.cpp:16216:94: warning: too many arguments for format [-Wformat-extra-args]\n",
+      "particle.cpp: In function 'void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t)':\n",
+      "particle.cpp:16222:48: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "particle.cpp:16222:48: warning: format '%s' expects argument of type 'char*', but argument 3 has type 'Py_ssize_t {aka long long int}' [-Wformat]\n",
+      "particle.cpp:16222:48: warning: too many arguments for format [-Wformat-extra-args]\n",
+      "particle.cpp: In function 'int __Pyx_ValidateAndInit_memviewslice(int*, int, int, int, __Pyx_TypeInfo*, __Pyx_BufFmt_StackElem*, __Pyx_memviewslice*, PyObject*)':\n",
+      "particle.cpp:16941:50: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "particle.cpp:16941:50: warning: format '%s' expects argument of type 'char*', but argument 3 has type 'Py_ssize_t {aka long long int}' [-Wformat]\n",
+      "particle.cpp:16941:50: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "particle.cpp:16941:50: warning: too many arguments for format [-Wformat-extra-args]\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python setup.py build_ext -i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import particle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "注意这里类型要设成 `float32`，因为 `C++` 程序中接受的是 `float` 类型，默认是 `float64(double)` 类型："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "particle.Particle(mass=1.0, charge=2.0, pos=[ 0.  1.  2.], vel=[ 0.  1.  2.])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "pos = vel = np.arange(3., dtype='float32')\n",
+    "mass = 1.0\n",
+    "charge = 2.0\n",
+    "\n",
+    "p = particle.Particle(mass, charge, pos, vel)\n",
+    "p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "调用 `apply_impulse` 方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "particle.Particle(mass=1.0, charge=2.0, pos=[ 0.   1.5  3. ], vel=[ 0.  2.  4.])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p.apply_impulse(np.arange(3., dtype='float32'), 1.0)\n",
+    "\n",
+    "p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看质量："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p.mass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "修改质量："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "p.mass = 3.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看 `charge`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p.charge"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "因为 `charge` 没有定义 `__set__` 方法，所以它是只读的属性，不能进行修改。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import zipfile\n",
+    "\n",
+    "f = zipfile.ZipFile('07-05-particle.zip','w',zipfile.ZIP_DEFLATED)\n",
+    "\n",
+    "names = ['particle.pyx',\n",
+    "         'particle_extern.cpp',\n",
+    "         'particle_extern.h',\n",
+    "         'setup.py']\n",
+    "for name in names:\n",
+    "    f.write(name)\n",
+    "\n",
+    "f.close()\n",
+    "\n",
+    "!rm -f setup*.*\n",
+    "!rm -f particle*.*\n",
+    "!rm -rf build"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/07. interfacing with other languages/07.06 cython part 4.ipynb b/07-interfacing-with-other-languages/07.06-cython-part-4.ipynb
similarity index 96%
rename from 07. interfacing with other languages/07.06 cython part 4.ipynb
rename to 07-interfacing-with-other-languages/07.06-cython-part-4.ipynb
index bd73aace..98573127 100644
--- a/07. interfacing with other languages/07.06 cython part 4.ipynb	
+++ b/07-interfacing-with-other-languages/07.06-cython-part-4.ipynb
@@ -1,311 +1,311 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cython：Typed memoryviews"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 例子"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里 `double[::1]` 是一种 `memoryview` 方法，效率跟 `Numpy` 数组差不多，可以给 `C` 数组赋值，可以给 `Numpy` 数组赋值，可以像 `Numpy` 一样切片："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing cython_sum.pyx\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file cython_sum.pyx\n",
-    "def cython_sum(double[::1] a):\n",
-    "    cdef double s = 0.0\n",
-    "    cdef int i, n = a.shape[0]\n",
-    "    for i in range(n):\n",
-    "        s += a[i]\n",
-    "    return s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing setup.py\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file setup.py\n",
-    "from distutils.core import setup\n",
-    "from distutils.extension import Extension\n",
-    "from Cython.Distutils import build_ext\n",
-    "\n",
-    "ext = Extension(\"cython_sum\", [\"cython_sum.pyx\"])\n",
-    "\n",
-    "setup(\n",
-    "    cmdclass = {'build_ext': build_ext},\n",
-    "    ext_modules = [ext],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "running build_ext\n",
-      "cythoning cython_sum.pyx to cython_sum.c\n",
-      "building 'cython_sum' extension\n",
-      "creating build\n",
-      "creating build\\temp.win-amd64-2.7\n",
-      "creating build\\temp.win-amd64-2.7\\Release\n",
-      "C:\\Anaconda\\Scripts\\gcc.bat -DMS_WIN64 -mdll -O -Wall -IC:\\Anaconda\\include -IC:\\Anaconda\\PC -c cython_sum.c -o build\\temp.win-amd64-2.7\\Release\\cython_sum.o\n",
-      "writing build\\temp.win-amd64-2.7\\Release\\cython_sum.def\n",
-      "C:\\Anaconda\\Scripts\\gcc.bat -DMS_WIN64 -shared -s build\\temp.win-amd64-2.7\\Release\\cython_sum.o build\\temp.win-amd64-2.7\\Release\\cython_sum.def -LC:\\Anaconda\\libs -LC:\\Anaconda\\PCbuild\\amd64 -lpython27 -lmsvcr90 -o \"C:\\Users\\lijin\\Documents\\Git\\python-tutorial\\07. interfacing with other languages\\cython_sum.pyd\"\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "cython_sum.c: In function '__Pyx_BufFmt_ProcessTypeChunk':\n",
-      "cython_sum.c:13561:26: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "cython_sum.c:13561:26: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "cython_sum.c:13561:26: warning: too many arguments for format [-Wformat-extra-args]\n",
-      "cython_sum.c:13613:20: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "cython_sum.c:13613:20: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "cython_sum.c:13613:20: warning: too many arguments for format [-Wformat-extra-args]\n",
-      "cython_sum.c: In function '__pyx_buffmt_parse_array':\n",
-      "cython_sum.c:13675:25: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "cython_sum.c:13675:25: warning: format '%d' expects argument of type 'int', but argument 3 has type 'size_t' [-Wformat]\n",
-      "cython_sum.c:13675:25: warning: too many arguments for format [-Wformat-extra-args]\n",
-      "cython_sum.c: In function '__Pyx_GetBufferAndValidate':\n",
-      "cython_sum.c:13860:7: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "cython_sum.c:13860:7: warning: format '%s' expects argument of type 'char *', but argument 3 has type 'Py_ssize_t' [-Wformat]\n",
-      "cython_sum.c:13860:7: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "cython_sum.c:13860:7: warning: too many arguments for format [-Wformat-extra-args]\n",
-      "cython_sum.c: In function '__Pyx_RaiseArgtupleInvalid':\n",
-      "cython_sum.c:14032:18: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "cython_sum.c:14032:18: warning: format '%s' expects argument of type 'char *', but argument 5 has type 'Py_ssize_t' [-Wformat]\n",
-      "cython_sum.c:14032:18: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "cython_sum.c:14032:18: warning: too many arguments for format [-Wformat-extra-args]\n",
-      "cython_sum.c: In function '__Pyx_RaiseTooManyValuesError':\n",
-      "cython_sum.c:14552:18: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "cython_sum.c:14552:18: warning: too many arguments for format [-Wformat-extra-args]\n",
-      "cython_sum.c: In function '__Pyx_RaiseNeedMoreValuesError':\n",
-      "cython_sum.c:14558:18: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "cython_sum.c:14558:18: warning: format '%s' expects argument of type 'char *', but argument 3 has type 'Py_ssize_t' [-Wformat]\n",
-      "cython_sum.c:14558:18: warning: too many arguments for format [-Wformat-extra-args]\n",
-      "cython_sum.c: In function '__Pyx_ValidateAndInit_memviewslice':\n",
-      "cython_sum.c:15253:22: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "cython_sum.c:15253:22: warning: format '%s' expects argument of type 'char *', but argument 3 has type 'Py_ssize_t' [-Wformat]\n",
-      "cython_sum.c:15253:22: warning: unknown conversion type character 'z' in format [-Wformat]\n",
-      "cython_sum.c:15253:22: warning: too many arguments for format [-Wformat-extra-args]\n"
-     ]
-    }
-   ],
-   "source": [
-    "!python setup.py build_ext -i"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from cython_sum import cython_sum\n",
-    "from numpy import *"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = arange(1e6)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "检查正确性："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "499999500000.0"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cython_sum(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "499999500000.0"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "效率："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "100 loops, best of 3: 2.14 ms per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit cython_sum(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "100 loops, best of 3: 2.38 ms per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit a.sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import zipfile\n",
-    "\n",
-    "f = zipfile.ZipFile('07-06-cython-sum.zip','w',zipfile.ZIP_DEFLATED)\n",
-    "\n",
-    "names = ['cython_sum.pyx',\n",
-    "         'setup.py']\n",
-    "for name in names:\n",
-    "    f.write(name)\n",
-    "\n",
-    "f.close()\n",
-    "\n",
-    "!rm -f setup*.*\n",
-    "!rm -f cython_sum*.*\n",
-    "!rm -rf build"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cython：Typed memoryviews"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 例子"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里 `double[::1]` 是一种 `memoryview` 方法，效率跟 `Numpy` 数组差不多，可以给 `C` 数组赋值，可以给 `Numpy` 数组赋值，可以像 `Numpy` 一样切片："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing cython_sum.pyx\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file cython_sum.pyx\n",
+    "def cython_sum(double[::1] a):\n",
+    "    cdef double s = 0.0\n",
+    "    cdef int i, n = a.shape[0]\n",
+    "    for i in range(n):\n",
+    "        s += a[i]\n",
+    "    return s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing setup.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file setup.py\n",
+    "from distutils.core import setup\n",
+    "from distutils.extension import Extension\n",
+    "from Cython.Distutils import build_ext\n",
+    "\n",
+    "ext = Extension(\"cython_sum\", [\"cython_sum.pyx\"])\n",
+    "\n",
+    "setup(\n",
+    "    cmdclass = {'build_ext': build_ext},\n",
+    "    ext_modules = [ext],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "running build_ext\n",
+      "cythoning cython_sum.pyx to cython_sum.c\n",
+      "building 'cython_sum' extension\n",
+      "creating build\n",
+      "creating build\\temp.win-amd64-2.7\n",
+      "creating build\\temp.win-amd64-2.7\\Release\n",
+      "C:\\Anaconda\\Scripts\\gcc.bat -DMS_WIN64 -mdll -O -Wall -IC:\\Anaconda\\include -IC:\\Anaconda\\PC -c cython_sum.c -o build\\temp.win-amd64-2.7\\Release\\cython_sum.o\n",
+      "writing build\\temp.win-amd64-2.7\\Release\\cython_sum.def\n",
+      "C:\\Anaconda\\Scripts\\gcc.bat -DMS_WIN64 -shared -s build\\temp.win-amd64-2.7\\Release\\cython_sum.o build\\temp.win-amd64-2.7\\Release\\cython_sum.def -LC:\\Anaconda\\libs -LC:\\Anaconda\\PCbuild\\amd64 -lpython27 -lmsvcr90 -o \"C:\\Users\\lijin\\Documents\\Git\\python-tutorial\\07. interfacing with other languages\\cython_sum.pyd\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "cython_sum.c: In function '__Pyx_BufFmt_ProcessTypeChunk':\n",
+      "cython_sum.c:13561:26: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "cython_sum.c:13561:26: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "cython_sum.c:13561:26: warning: too many arguments for format [-Wformat-extra-args]\n",
+      "cython_sum.c:13613:20: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "cython_sum.c:13613:20: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "cython_sum.c:13613:20: warning: too many arguments for format [-Wformat-extra-args]\n",
+      "cython_sum.c: In function '__pyx_buffmt_parse_array':\n",
+      "cython_sum.c:13675:25: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "cython_sum.c:13675:25: warning: format '%d' expects argument of type 'int', but argument 3 has type 'size_t' [-Wformat]\n",
+      "cython_sum.c:13675:25: warning: too many arguments for format [-Wformat-extra-args]\n",
+      "cython_sum.c: In function '__Pyx_GetBufferAndValidate':\n",
+      "cython_sum.c:13860:7: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "cython_sum.c:13860:7: warning: format '%s' expects argument of type 'char *', but argument 3 has type 'Py_ssize_t' [-Wformat]\n",
+      "cython_sum.c:13860:7: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "cython_sum.c:13860:7: warning: too many arguments for format [-Wformat-extra-args]\n",
+      "cython_sum.c: In function '__Pyx_RaiseArgtupleInvalid':\n",
+      "cython_sum.c:14032:18: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "cython_sum.c:14032:18: warning: format '%s' expects argument of type 'char *', but argument 5 has type 'Py_ssize_t' [-Wformat]\n",
+      "cython_sum.c:14032:18: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "cython_sum.c:14032:18: warning: too many arguments for format [-Wformat-extra-args]\n",
+      "cython_sum.c: In function '__Pyx_RaiseTooManyValuesError':\n",
+      "cython_sum.c:14552:18: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "cython_sum.c:14552:18: warning: too many arguments for format [-Wformat-extra-args]\n",
+      "cython_sum.c: In function '__Pyx_RaiseNeedMoreValuesError':\n",
+      "cython_sum.c:14558:18: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "cython_sum.c:14558:18: warning: format '%s' expects argument of type 'char *', but argument 3 has type 'Py_ssize_t' [-Wformat]\n",
+      "cython_sum.c:14558:18: warning: too many arguments for format [-Wformat-extra-args]\n",
+      "cython_sum.c: In function '__Pyx_ValidateAndInit_memviewslice':\n",
+      "cython_sum.c:15253:22: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "cython_sum.c:15253:22: warning: format '%s' expects argument of type 'char *', but argument 3 has type 'Py_ssize_t' [-Wformat]\n",
+      "cython_sum.c:15253:22: warning: unknown conversion type character 'z' in format [-Wformat]\n",
+      "cython_sum.c:15253:22: warning: too many arguments for format [-Wformat-extra-args]\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python setup.py build_ext -i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from cython_sum import cython_sum\n",
+    "from numpy import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "a = arange(1e6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "检查正确性："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "499999500000.0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cython_sum(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "499999500000.0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "效率："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100 loops, best of 3: 2.14 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit cython_sum(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100 loops, best of 3: 2.38 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit a.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import zipfile\n",
+    "\n",
+    "f = zipfile.ZipFile('07-06-cython-sum.zip','w',zipfile.ZIP_DEFLATED)\n",
+    "\n",
+    "names = ['cython_sum.pyx',\n",
+    "         'setup.py']\n",
+    "for name in names:\n",
+    "    f.write(name)\n",
+    "\n",
+    "f.close()\n",
+    "\n",
+    "!rm -f setup*.*\n",
+    "!rm -f cython_sum*.*\n",
+    "!rm -rf build"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/07. interfacing with other languages/07.07 profiling with annotations.ipynb b/07-interfacing-with-other-languages/07.07-profiling-with-annotations.ipynb
similarity index 94%
rename from 07. interfacing with other languages/07.07 profiling with annotations.ipynb
rename to 07-interfacing-with-other-languages/07.07-profiling-with-annotations.ipynb
index bb3a708a..ffba79a2 100644
--- a/07. interfacing with other languages/07.07 profiling with annotations.ipynb	
+++ b/07-interfacing-with-other-languages/07.07-profiling-with-annotations.ipynb
@@ -1,100 +1,100 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 生成编译注释"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing fib_orig.pyx\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%file fib_orig.pyx\n",
-    "def fib(n):\n",
-    "    a,b = 1,1\n",
-    "    for i in range(n):\n",
-    "        a,b = a+b, a\n",
-    "    return a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "!cython -a fib_orig.pyx"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在浏览器中打开 `fib_orig.html` 可以查看内容，`windows` 下打开网页使用："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "!start fib_orig.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`linux` 下使用：\n",
-    "```\n",
-    "open fib_orig.html\n",
-    "```\n",
-    "\n",
-    "其界面可能如图所示：\n",
-    "![界面](fib_orig.png)\n",
-    "\n",
-    "点击某一行可以查看该 `Python` 代码对应的 `C` 代码。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 生成编译注释"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing fib_orig.pyx\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%file fib_orig.pyx\n",
+    "def fib(n):\n",
+    "    a,b = 1,1\n",
+    "    for i in range(n):\n",
+    "        a,b = a+b, a\n",
+    "    return a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "!cython -a fib_orig.pyx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在浏览器中打开 `fib_orig.html` 可以查看内容，`windows` 下打开网页使用："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "!start fib_orig.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`linux` 下使用：\n",
+    "```\n",
+    "open fib_orig.html\n",
+    "```\n",
+    "\n",
+    "其界面可能如图所示：\n",
+    "![界面](fib_orig.png)\n",
+    "\n",
+    "点击某一行可以查看该 `Python` 代码对应的 `C` 代码。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/07. interfacing with other languages/07.08 ctypes.ipynb b/07-interfacing-with-other-languages/07.08-ctypes.ipynb
similarity index 95%
rename from 07. interfacing with other languages/07.08 ctypes.ipynb
rename to 07-interfacing-with-other-languages/07.08-ctypes.ipynb
index 4bf077b9..be5b1049 100644
--- a/07. interfacing with other languages/07.08 ctypes.ipynb	
+++ b/07-interfacing-with-other-languages/07.08-ctypes.ipynb
@@ -1,368 +1,368 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ctypes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 基本用法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`ctypes` 是一个方便 `Python` 调用本地已经编译好的外部库的模块。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from ctypes import util, CDLL"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 标准 C 库"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `util` 来找到标准 `C` 库："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "msvcr90.dll\n"
-     ]
-    }
-   ],
-   "source": [
-    "libc_name = util.find_library('c')\n",
-    "\n",
-    "# on WINDOWS\n",
-    "print libc_name"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `CDLL` 来加载 `C` 库："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "libc = CDLL(libc_name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "libc 包含 `C` 标准库中的函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<_FuncPtr object at 0x0000000003CEE048>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "libc.printf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "调用这个函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "libc.printf(\"%s, %d\\n\", \"hello\", 5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里显示的 `9` 是 `printf` 的返回值表示显示的字符串的长度（包括结尾的 `'\\0'`），但是并没有显示结果，原因是 `printf` 函数默认是写在标准输出流上的，与 `IPython` 使用的输出流不一样，所以没有显示结果。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## C 数学库"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "找到数学库："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "msvcr90.dll\n"
-     ]
-    }
-   ],
-   "source": [
-    "libm_name = util.find_library('m')\n",
-    "\n",
-    "print libm_name"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "调用 `atan2` 函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "ArgumentError",
-     "evalue": "argument 1: <type 'exceptions.TypeError'>: Don't know how to convert parameter 1",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mArgumentError\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-7-e784e41ee9f3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mlibm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCDLL\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlibm_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mlibm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0matan2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mArgumentError\u001b[0m: argument 1: <type 'exceptions.TypeError'>: Don't know how to convert parameter 1"
-     ]
-    }
-   ],
-   "source": [
-    "libm = CDLL(libm_name)\n",
-    "\n",
-    "libm.atan2(1.0, 2.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "调用这个函数出错，原因是我们需要进行一些额外工作，告诉 `Python` 函数的参数和返回值是什么样的："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from ctypes import c_double\n",
-    "\n",
-    "libm.atan2.argtypes = [c_double, c_double]\n",
-    "libm.atan2.restype = c_double"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.4636476090008061"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "libm.atan2(1.0, 2.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "与 `Python` 数学库中的结果一致："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from math import atan2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.4636476090008061"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "atan2(1.0, 2.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Numpy 和 ctypes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "假设我们有这样的一个函数：\n",
-    "```c\n",
-    "float _sum(float *vec, int len) {\n",
-    "    float sum = 0.0;\n",
-    "    int i;\n",
-    "    for (i = 0; i < len; i++) {\n",
-    "        sum += vec[i];\n",
-    "    }\n",
-    "    return sum\n",
-    "}\n",
-    "```\n",
-    "\n",
-    "并且已经编译成动态链接库，那么我们可以这样调用：\n",
-    "\n",
-    "```python\n",
-    "from ctypes import c_float, CDLL, c_int\n",
-    "from numpy import array, float32\n",
-    "from numpy.ctypeslib import ndpointer\n",
-    "\n",
-    "x = array([1,2,3,4], dtype=float32)\n",
-    "\n",
-    "lib = CDLL(<path>)\n",
-    "\n",
-    "ptr = ndpointer(float32, ndim=1, flags='C')\n",
-    "lib._sum.argtypes = [ptr, c_int]\n",
-    "lib._sum.restype = c_float\n",
-    "\n",
-    "result = lib._sum(x, len(x))\n",
-    "```"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ctypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 基本用法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`ctypes` 是一个方便 `Python` 调用本地已经编译好的外部库的模块。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from ctypes import util, CDLL"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 标准 C 库"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `util` 来找到标准 `C` 库："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "msvcr90.dll\n"
+     ]
+    }
+   ],
+   "source": [
+    "libc_name = util.find_library('c')\n",
+    "\n",
+    "# on WINDOWS\n",
+    "print libc_name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `CDLL` 来加载 `C` 库："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "libc = CDLL(libc_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "libc 包含 `C` 标准库中的函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<_FuncPtr object at 0x0000000003CEE048>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libc.printf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "调用这个函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libc.printf(\"%s, %d\\n\", \"hello\", 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里显示的 `9` 是 `printf` 的返回值表示显示的字符串的长度（包括结尾的 `'\\0'`），但是并没有显示结果，原因是 `printf` 函数默认是写在标准输出流上的，与 `IPython` 使用的输出流不一样，所以没有显示结果。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## C 数学库"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "找到数学库："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "msvcr90.dll\n"
+     ]
+    }
+   ],
+   "source": [
+    "libm_name = util.find_library('m')\n",
+    "\n",
+    "print libm_name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "调用 `atan2` 函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ArgumentError",
+     "evalue": "argument 1: <type 'exceptions.TypeError'>: Don't know how to convert parameter 1",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mArgumentError\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-7-e784e41ee9f3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mlibm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCDLL\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlibm_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mlibm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0matan2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mArgumentError\u001b[0m: argument 1: <type 'exceptions.TypeError'>: Don't know how to convert parameter 1"
+     ]
+    }
+   ],
+   "source": [
+    "libm = CDLL(libm_name)\n",
+    "\n",
+    "libm.atan2(1.0, 2.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "调用这个函数出错，原因是我们需要进行一些额外工作，告诉 `Python` 函数的参数和返回值是什么样的："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from ctypes import c_double\n",
+    "\n",
+    "libm.atan2.argtypes = [c_double, c_double]\n",
+    "libm.atan2.restype = c_double"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4636476090008061"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libm.atan2(1.0, 2.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "与 `Python` 数学库中的结果一致："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from math import atan2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4636476090008061"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "atan2(1.0, 2.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Numpy 和 ctypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "假设我们有这样的一个函数：\n",
+    "```c\n",
+    "float _sum(float *vec, int len) {\n",
+    "    float sum = 0.0;\n",
+    "    int i;\n",
+    "    for (i = 0; i < len; i++) {\n",
+    "        sum += vec[i];\n",
+    "    }\n",
+    "    return sum\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "并且已经编译成动态链接库，那么我们可以这样调用：\n",
+    "\n",
+    "```python\n",
+    "from ctypes import c_float, CDLL, c_int\n",
+    "from numpy import array, float32\n",
+    "from numpy.ctypeslib import ndpointer\n",
+    "\n",
+    "x = array([1,2,3,4], dtype=float32)\n",
+    "\n",
+    "lib = CDLL(<path>)\n",
+    "\n",
+    "ptr = ndpointer(float32, ndim=1, flags='C')\n",
+    "lib._sum.argtypes = [ptr, c_int]\n",
+    "lib._sum.restype = c_float\n",
+    "\n",
+    "result = lib._sum(x, len(x))\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/07. interfacing with other languages/fib_orig.c b/07-interfacing-with-other-languages/fib_orig.c
similarity index 100%
rename from 07. interfacing with other languages/fib_orig.c
rename to 07-interfacing-with-other-languages/fib_orig.c
diff --git a/07. interfacing with other languages/fib_orig.html b/07-interfacing-with-other-languages/fib_orig.html
similarity index 100%
rename from 07. interfacing with other languages/fib_orig.html
rename to 07-interfacing-with-other-languages/fib_orig.html
diff --git a/07. interfacing with other languages/fib_orig.png b/07-interfacing-with-other-languages/fib_orig.png
similarity index 100%
rename from 07. interfacing with other languages/fib_orig.png
rename to 07-interfacing-with-other-languages/fib_orig.png
diff --git a/07. interfacing with other languages/fib_orig.pyx b/07-interfacing-with-other-languages/fib_orig.pyx
similarity index 95%
rename from 07. interfacing with other languages/fib_orig.pyx
rename to 07-interfacing-with-other-languages/fib_orig.pyx
index 70986d3b..edca9564 100644
--- a/07. interfacing with other languages/fib_orig.pyx	
+++ b/07-interfacing-with-other-languages/fib_orig.pyx
@@ -1,5 +1,5 @@
-def fib(n):
-    a,b = 1,1
-    for i in range(n):
-        a,b = a+b, a
+def fib(n):
+    a,b = 1,1
+    for i in range(n):
+        a,b = a+b, a
     return a
\ No newline at end of file
diff --git a/08. object-oriented programming/08.01 oop introduction.ipynb b/08-object-oriented-programming/08.01-oop-introduction.ipynb
similarity index 95%
rename from 08. object-oriented programming/08.01 oop introduction.ipynb
rename to 08-object-oriented-programming/08.01-oop-introduction.ipynb
index abac19fd..c0754a00 100644
--- a/08. object-oriented programming/08.01 oop introduction.ipynb	
+++ b/08-object-oriented-programming/08.01-oop-introduction.ipynb
@@ -1,233 +1,233 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 简介"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 属性 attributes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "属性是与对象绑定的一组数据，可以只读，只写，或者读写，使用时不加括号，例如："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "f = file(\"new_file\", 'w')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "显示模式属性："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'w'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f.mode"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "是否关闭："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f.closed"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`mode` 是只读属性，所以这样会报错："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "readonly attribute",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-4-d9757d46f855>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m: readonly attribute"
-     ]
-    }
-   ],
-   "source": [
-    "f.mode = 'r'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "获取属性不需要加括号："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "'str' object is not callable",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-5-6b67c2ae8f67>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m: 'str' object is not callable"
-     ]
-    }
-   ],
-   "source": [
-    "f.mode()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##  方法 method"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "方法是与属性绑定的一组函数，需要使用括号，作用于对象本身："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "f.write('Hi.\\n')\n",
-    "f.seek(0)\n",
-    "f.write('Hola!\\n')\n",
-    "f.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "!rm new_file"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 使用 OPP 的原因"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- 构建自己的类型来模拟真实世界的对象\n",
-    "- 处理抽象对象\n",
-    "- 容易复用和扩展\n",
-    "- 理解其他 OPP 代码\n",
-    "- GUI 通常使用 OPP 规则编写\n",
-    "- ..."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 简介"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 属性 attributes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "属性是与对象绑定的一组数据，可以只读，只写，或者读写，使用时不加括号，例如："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "f = file(\"new_file\", 'w')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "显示模式属性："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'w'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f.mode"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "是否关闭："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f.closed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`mode` 是只读属性，所以这样会报错："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "readonly attribute",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-4-d9757d46f855>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m: readonly attribute"
+     ]
+    }
+   ],
+   "source": [
+    "f.mode = 'r'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "获取属性不需要加括号："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'str' object is not callable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-5-6b67c2ae8f67>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m: 'str' object is not callable"
+     ]
+    }
+   ],
+   "source": [
+    "f.mode()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  方法 method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "方法是与属性绑定的一组函数，需要使用括号，作用于对象本身："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "f.write('Hi.\\n')\n",
+    "f.seek(0)\n",
+    "f.write('Hola!\\n')\n",
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "!rm new_file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 使用 OPP 的原因"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- 构建自己的类型来模拟真实世界的对象\n",
+    "- 处理抽象对象\n",
+    "- 容易复用和扩展\n",
+    "- 理解其他 OPP 代码\n",
+    "- GUI 通常使用 OPP 规则编写\n",
+    "- ..."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/08. object-oriented programming/08.02 using oop model a forest fire.ipynb b/08-object-oriented-programming/08.02-using-oop-model-a-forest-fire.ipynb
similarity index 99%
rename from 08. object-oriented programming/08.02 using oop model a forest fire.ipynb
rename to 08-object-oriented-programming/08.02-using-oop-model-a-forest-fire.ipynb
index 1d9e774b..5e3355fb 100644
--- a/08. object-oriented programming/08.02 using oop model a forest fire.ipynb	
+++ b/08-object-oriented-programming/08.02-using-oop-model-a-forest-fire.ipynb
@@ -1,337 +1,337 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 使用 OOP 对森林火灾建模"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 对森林建模"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Forest(object):\n",
-    "    def __init__(self, size=(150, 150), p_sapling=0.0025, p_lightning=5.e-6, name=None):\n",
-    "        self.size = size\n",
-    "        self.trees = np.zeros(self.size, dtype=bool)\n",
-    "        self.forest_fires = np.zeros(self.size, dtype=bool)\n",
-    "        self.p_sapling = p_sapling\n",
-    "        self.p_lightning = p_lightning\n",
-    "        if name is not None:\n",
-    "            self.name = name\n",
-    "        else:\n",
-    "            self.name = self.__class__.__name__\n",
-    "\n",
-    "    @property\n",
-    "    def num_cells(self):\n",
-    "        return self.size[0] * self.size[1]\n",
-    "\n",
-    "    @property\n",
-    "    def tree_fraction(self):\n",
-    "        return self.trees.sum() / float(self.num_cells)\n",
-    "\n",
-    "    @property\n",
-    "    def fire_fraction(self):\n",
-    "        return self.forest_fires.sum() / float(self.num_cells)\n",
-    "\n",
-    "    def advance_one_step(self):\n",
-    "        self.grow_trees()\n",
-    "        self.start_fires()\n",
-    "        self.burn_trees()\n",
-    "\n",
-    "    def grow_trees(self):\n",
-    "        growth_sites = self._rand_bool(self.p_sapling)\n",
-    "        self.trees[growth_sites] = True\n",
-    "\n",
-    "    def start_fires(self):\n",
-    "        lightning_strikes = (self._rand_bool(self.p_lightning) & \n",
-    "            self.trees)\n",
-    "        self.forest_fires[lightning_strikes] = True\n",
-    "        \n",
-    "    def burn_trees(self):\n",
-    "        fires = np.zeros((self.size[0] + 2, self.size[1] + 2), dtype=bool)\n",
-    "        fires[1:-1, 1:-1] = self.forest_fires\n",
-    "        north = fires[:-2, 1:-1]\n",
-    "        south = fires[2:, 1:-1]\n",
-    "        east = fires[1:-1, :-2]\n",
-    "        west = fires[1:-1, 2:]\n",
-    "        new_fires = (north | south | east | west) & self.trees\n",
-    "        self.trees[self.forest_fires] = False\n",
-    "        self.forest_fires = new_fires\n",
-    "\n",
-    "    def _rand_bool(self, p):\n",
-    "        return np.random.uniform(size=self.trees.shape) < p"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "定义一个森林类之后，我们创建一个新的森林类对象："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "forest = Forest()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "显示当前的状态："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[False False False ..., False False False]\n",
-      " [False False False ..., False False False]\n",
-      " [False False False ..., False False False]\n",
-      " ..., \n",
-      " [False False False ..., False False False]\n",
-      " [False False False ..., False False False]\n",
-      " [False False False ..., False False False]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print forest.trees"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[False False False ..., False False False]\n",
-      " [False False False ..., False False False]\n",
-      " [False False False ..., False False False]\n",
-      " ..., \n",
-      " [False False False ..., False False False]\n",
-      " [False False False ..., False False False]\n",
-      " [False False False ..., False False False]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print forest.forest_fires"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `matshow` 进行可视化："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAD7CAYAAACBpZo1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9f74908>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.matshow(forest.trees, cmap=plt.cm.Greens)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 模拟森林生长和火灾的过程"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "经过一段时间："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAD7CAYAAACBpZo1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH+OqGfP4E0uBAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa33b940>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "forest.advance_one_step()\n",
-    "plt.matshow(forest.trees, cmap=plt.cm.Greens)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "循环很长时间："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.253111111111\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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rt0Kuys6lhgYp9A5UCzZewn7nyvfanCvTA+2kqLGnh+0vlR3Rzik52vShRqe0\nnjZU0YZ46+seGjgcjiw27hFYk0xWkJKXFqy4ZJ9Cy4oxIkTKyR+5ereUt9hr0EPaWXpJFt7iokOD\nETGVJF4d4fZbMeeAzl3v5RFy1yWZlB63WTvAekI/i5CkVMY6fLOaoJPy/Vh0h8ORw+PCI+hxm+My\nPUy4ZNWqrcijctcWZKp1Tjtlm5UNFuGVVK8FIV26nvPIUvdr4YP1j5eawyrO1Lr9wPbG6+noOTmS\nlFiuTE96KuwEubaVyFjX7Ym3Y3tCaJ5LyrPU2lNrw2g+RVo3vlezT6rLQwOHw7GcryHH0OTYU/dS\nZWpl43paErGVqe5xd+PrWhfXOiTZZAbBQk5vmKm1a+42afoaMhG9B8BLADw4HXa6PvcweeLxdELy\nawD8PwC/zMw3JWSqf6qsNf1WqqPRZRXL9ehK1UvJ1zy7xG227Oha2+bMRNTklOS38j49/VRif7JO\n44ai9wLYH11LnnhMRM8C8HIAz5rqvJOIPPRwOHYAimQhM98+HYEe4mUAzps+XwvgVqwmgwsBfICZ\nvwfgq0R0H4DnAfiklbG5Wbk0o2vdQg1KhF9rLjr8XMo4pOwukYK5dqutSlYrlVbW6FBFWkbSf3Jk\nXkp+rt9aoZY1yKHKEUwTwY1BaPAQM58yfSYAR5n5FCK6BsAnmfn3pnvvBvBxZv5IJK969mFseGu6\nqwRJSk8io8bGt4QmPXxBj20WHITEttEo8SCp5y+FSJt+FgmkYdGu/fkNRV3pw9KJx+siqYt+GrLD\nMQ+2nYb8pb/MlmvxCO4FcH584jERXQIAzHz1VO4QgMuZ+Q8jedkfL5UQMyMgIY3WKJFzuTJW6PFi\ncs+okRHLm3OVbM2GlOpo5OfKx3V6PIhWL08lv/W7BomJ4K0AvsXMb5kG/8nMfMlEFl6PFS/wowBu\nAfB3OFIwImtgWSZX3qKjSDunZNBapcQ0NlhiafKtJlarSSGns5a5qC5ULaEBEX0AK2LwaUT0dQC/\nAeBqJE48ZuZ7iOhDAO4B8CiAX4wnAYfDsUzsiA1F4bWRJOKIVbcl5JHM7harjLW8UejdW5GTY7Vv\notYPewlgU4J8SV9DrmUNegfziJjZKqzQDD7LOLwn/pxzwhjpTmvlt9QdsSBp6xazRf41ZIfDkcNG\nv30Yz14Wq0CPmyetK3HhamGCRKbGs1mXH0Eijl4VNe60dpXuYeBb+tKcnkDN69TY8rj7PQJpGskq\nPqzZ03KAuvFZAAAKcElEQVR9pG2x3jV6QgZLuyQd2prhL02m2ri9FnJKbe6pmwtFAXho4HA48tgo\nWdjLpob3Jbl6C5e1VLaWERixuvaEBr1Ywl4GLWPf40L39MPWkKqlL8XytmHEFuNeSF+AtgMVWdNG\nOT0vciQsw4dadiPH6UgmwRa7tFkVi/JV1h2yQd7CoaQmEUldi7DRQwOHw7GsfQQ90O4LCMtYuay5\nmbwnP69ZpS1DpF4WOranBRbutPadxHUkXuFI0jRGS//ehp2woSgFbWfqZeytYDF45mbsUzZo6sT2\nlAaUxbsY1T6tIZ6UO+jJGqR0SSa+LXjWwOFw5LAYj6BnZZa4wal7cZke5rkkfzRaCaRRXpLEte7J\nOMzV/qqV1kC/JAwMr2n67fpz04+XjkBpIlijp0P3uF69sXQPtINEk8oqyZAOxDW0KStN+R43PqdT\nK18SbkhlWUDKcYjDGQ8NHA5HDovaYmzhOo7KFEhQW51K9UaERaly1h5Aqm7OnR5Nyra2fyxPmynQ\nhmZaYjWFXGgQ1k+1/44IDTTuvdVgHq1L6373pMqk9qyh5VN6OndOZi+sQ6pRsH53WmyNrcZzDRwO\nxxMAGz8ENTVTSmbJkitoQaTFMjVywutSPa0rcKm8lkizyOHnvIDwulX4FpeRZChaQoWc7JL9cV1J\n6FoLQ8J7Je8gV6ZUZzGhgRStqSPtxoy4rkU6rSUrYQXryTFXVzIZhbDMtpTux2V6Mk29yA1sSdhV\n68dVTsGzBg6HI4eNZg1CbIq8Ga27tAmkZos0h9xjm9b9bs0+hCh5YbXVshR6tHpzkjCn5K6HZTTe\nX8s+Do2uJFq+a5A5DfltAF4K4LsAvgTg1cz8F9O9IachS2DR0Nq0pcQGiW2lwSb53IPWsKXFfivb\nUmVadNTatiSzNdTSTky5hSSHapnG0CB1GvJNAP4+Mz8bwBEAlwJ+GrLDsZOhPukouvczAP4VM180\neQOPMfNbpnuHAPxHZv5kVGc2j0BbXkIsxXW1ZFUruZWzWYK5yTANJCtnS759JOkL2GzMkmZeQhkS\n4rAIqyPPons3YnUU+vWa05Ctf7w0xKhOb+FCzwkrvaNZdylbrrGnpq+lrmV7toYwLTim7ax/qoyI\nDgD4LjNfXyiWnmWmU1mvvOKgn4bscAzEsNOQp2s/C+DnAfxTZv7OdK3rNGRgfL7dOlMwerWfg5DT\n2KCp02KPNEOhDc1aPQWJrk31gdLnIgxPQ94P4O0AzmPmPwvKDTsNWQIJA7yGliUupbhS9aSscy0l\nVnouCXqYf42ckv7RYVEt3Vgqn4JV2k+aetTwIz0hwhaMTkO+HKsswZMA3ExEAHAHM/+in4bscOxc\nLOpcA6sVTzOLS1n6mq7wnpQJz+m1wCbJrVrdnixAyXvSys+9u5JsqZ2aOi26pHWP6cM79cdLARl7\nbBEn98Rgo3gNLZaWxaiFbFbxdk84EEM7uWj7p8U7al1U/GvIDocjix3hEeQgWW0krnuTi1WR2fNM\nPeFSSe5ajibMsdDZK0+bPcnpsyRla4RxrrzUM0rJlXg2NS9j8b9QJEHuRVuGCRa8Q1yvNrBHcwdz\nsvetMXKqrgVnJOGGcjq0qbuUXAv7U7K0fb42EXho4HA4dpZHEKJlNZbKBezItrmZ5BFytDpHhDat\nNmhDiRGZjlivtH5sW06OhshcVGhg/V2DuZlwjQ291zUxcFxeix5Wf+TkpU0Hjq6rDeVybnypjgal\nSeOYcMtDA4fDkcOODg3W0DK9PeTNKJna61pdc2YfJHWtYLG6tng62r0DUjviuiF6My8AdvaGop2A\n3vBkRLbAAlYxc6o+oB8wo9OrsSxtaKbVmctEaPXUJo7aROChgcPheOJ4BJYrmEZXywrWs0JqZI5m\n+HvavNSG0vpAP0vfKl+yT6Ekp8f7KIYtj4fQoKVzaBo05V6l6mlSOy3QpLVydTWbTEp1S/VzcrQY\nnWLUTr49k2bORW99xhLv0JTZ8dDA4XDksCiPYCQj3bIhZA0rJl/r0YzI24/ec9Hr0pfkArZhyxpW\nWZUS+ddDClqOhdw+go2ffRgiNFw7aHsGjHYSGcGiS1DrOJKO1ZOGlNSP28lqAFu3bSxP0p6aZ5G2\nQ2pSkOqo6c6FMyl4aOBwODYfGkg2ZmiRCzFGsPGttsV2jPBcrDBij8OmQrme8FNjm7RMCOu9Ccn7\nS/quwVzpwznj4dFo6ei5MiPts8qYjBqca8z13moYsRCW5Pt3DRwORxbqQ1CDe68D8DYAT2Pmo9O1\n2Q5B1W6G2RRLb4U5yUjLVWiuzVEt1yXI7enIQZIxsQgDpTjmXTSehnwugL8G8L7ogJPTAbwLwN8F\n8OPMfDQ41+C5+P65BnuZ+bFI5rZj0UfGtL1pyNqGDctYsbUdWupap8p6rvfoHSGzVCZEbhHSckCt\nYUDrszeFBsx8O4CHErd+E8Dro2sXYnUO4veY+asA7sPqsBOHw7FwtJx0dCGA85n514joK/i+RyA+\nBFUTGrSwx0t173M2aMMcSZlSDrnXE0jJSLnLVoRfy7OMIB1zIYFF/xyxwSkp3+IQVCL6QQCXAXhR\neLlQRZySyDWQtiFK5Wvu3AimWmpDroxWr3bjikSmpIxFhy1NjlpdksEmqTeiPXN25D63Lh6pcGbX\n8SclbdLuLDwLwBkA7pqOOzsNwGeI6PkAvgng9KDsadO1Y3DlFQe3PvtpyA7HQBx9BHjoEQDbx12M\nptOQg3thaDDkENTSzKdxj+fM+Zdg4Ym06LKWM6I9N7kvQ9KXwvI9ejWhh0UYF8pp2lAUHIL6NwE8\nCOA3mPm9wf0vA3hOkD68DKv04aMAfoWZP5GQOfSAk/XftfK5+5vagLSETUE1xBPrGi1cxqYnQW14\npelf0jIxavyI5HMJpfRhMTRg5ldW7j8j+vsqAFdVLXI4HIvC43qLcYilhAY92IlhRSjPOjNSq7eG\n1T4OjXeTCz1y5aTt0NsHHjffNbCK1awwcnNOjw29ndvKtpreku6aDaN5ilLacg1pCKAZ/C2TphT+\nXQOHw5HFjjvpqEYQzrl6lGybs65GTu9+gTmxaXI0hsYe7T4CSRZDGm6UbNgRv1CkRW0D0ujdWj2I\nX5LV5FUr38ve92QBtCFMEytescECEuZfknHIbVJK3UvJrPUZTTt4aOBwODYUGpz9NGD3iQD0Ln0P\nk/yJS95d3MXYsnVXUvfwrYe39OZCmzWsVrNQZ6g3p6Pn2Ut6rZGzM6e3xatS7Qc4+shWXy6Vb0Xp\neX/66p/b0il9R4sKDQ685BfwxssPYNf+vdUHKLlPGjx86AiuvOJgckBKZPcMksO33b6lV6qvVVdK\nZ6lOyc2t1a3pHcE1ZCfb6Hlz5VtDoZzMAy/5BRy841qxLklYUXLv1/UPnHOxqi/V3rOHBg6HYzMT\nwcHrrjlmhpKsTHMg9FIkePjQkaybb2HD+vN6lcitEFKZqfy25aq9fre9LrlVe+aet7Yyl9olrHvw\numuSMqXPktIjabewndc6Unqk/XkzHIHD4dgYFrGz0OFwLA/OETgcDp8IHA6HTwQOhwM+ETgcDvhE\n4HA4APx/JL/m/4qA3IAAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa4a7be0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(500):\n",
-    "    forest.advance_one_step()\n",
-    "plt.matshow(forest.trees, cmap=plt.cm.Greens)\n",
-    "print forest.tree_fraction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "迭代更长时间："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Nv/erBmvRtBjZaYSwbZvdq5D1lUT8ms7cjtQTq68d4peDu1Y+/4z8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\n+CsqKiomGSrxV1RUVEwy/P9JVJbE2yOX2wAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa4b1b70>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "forest = Forest()\n",
-    "tree_fractions = []\n",
-    "for i in range(5000):\n",
-    "    forest.advance_one_step()\n",
-    "    tree_fractions.append(forest.tree_fraction)\n",
-    "fig = plt.figure()\n",
-    "ax0 = fig.add_subplot(1,2,1)\n",
-    "ax0.matshow(forest.trees, cmap=plt.cm.Greens)\n",
-    "ax1 = fig.add_subplot(1,2,2)\n",
-    "ax1.plot(tree_fractions)\n",
-    "\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 使用 OOP 对森林火灾建模"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 对森林建模"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Forest(object):\n",
+    "    def __init__(self, size=(150, 150), p_sapling=0.0025, p_lightning=5.e-6, name=None):\n",
+    "        self.size = size\n",
+    "        self.trees = np.zeros(self.size, dtype=bool)\n",
+    "        self.forest_fires = np.zeros(self.size, dtype=bool)\n",
+    "        self.p_sapling = p_sapling\n",
+    "        self.p_lightning = p_lightning\n",
+    "        if name is not None:\n",
+    "            self.name = name\n",
+    "        else:\n",
+    "            self.name = self.__class__.__name__\n",
+    "\n",
+    "    @property\n",
+    "    def num_cells(self):\n",
+    "        return self.size[0] * self.size[1]\n",
+    "\n",
+    "    @property\n",
+    "    def tree_fraction(self):\n",
+    "        return self.trees.sum() / float(self.num_cells)\n",
+    "\n",
+    "    @property\n",
+    "    def fire_fraction(self):\n",
+    "        return self.forest_fires.sum() / float(self.num_cells)\n",
+    "\n",
+    "    def advance_one_step(self):\n",
+    "        self.grow_trees()\n",
+    "        self.start_fires()\n",
+    "        self.burn_trees()\n",
+    "\n",
+    "    def grow_trees(self):\n",
+    "        growth_sites = self._rand_bool(self.p_sapling)\n",
+    "        self.trees[growth_sites] = True\n",
+    "\n",
+    "    def start_fires(self):\n",
+    "        lightning_strikes = (self._rand_bool(self.p_lightning) & \n",
+    "            self.trees)\n",
+    "        self.forest_fires[lightning_strikes] = True\n",
+    "        \n",
+    "    def burn_trees(self):\n",
+    "        fires = np.zeros((self.size[0] + 2, self.size[1] + 2), dtype=bool)\n",
+    "        fires[1:-1, 1:-1] = self.forest_fires\n",
+    "        north = fires[:-2, 1:-1]\n",
+    "        south = fires[2:, 1:-1]\n",
+    "        east = fires[1:-1, :-2]\n",
+    "        west = fires[1:-1, 2:]\n",
+    "        new_fires = (north | south | east | west) & self.trees\n",
+    "        self.trees[self.forest_fires] = False\n",
+    "        self.forest_fires = new_fires\n",
+    "\n",
+    "    def _rand_bool(self, p):\n",
+    "        return np.random.uniform(size=self.trees.shape) < p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "定义一个森林类之后，我们创建一个新的森林类对象："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "forest = Forest()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "显示当前的状态："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[False False False ..., False False False]\n",
+      " [False False False ..., False False False]\n",
+      " [False False False ..., False False False]\n",
+      " ..., \n",
+      " [False False False ..., False False False]\n",
+      " [False False False ..., False False False]\n",
+      " [False False False ..., False False False]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print forest.trees"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[False False False ..., False False False]\n",
+      " [False False False ..., False False False]\n",
+      " [False False False ..., False False False]\n",
+      " ..., \n",
+      " [False False False ..., False False False]\n",
+      " [False False False ..., False False False]\n",
+      " [False False False ..., False False False]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print forest.forest_fires"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `matshow` 进行可视化："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAD7CAYAAACBpZo1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9f74908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(forest.trees, cmap=plt.cm.Greens)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 模拟森林生长和火灾的过程"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "经过一段时间："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAD7CAYAAACBpZo1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH+OqGfP4E0uBAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa33b940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "forest.advance_one_step()\n",
+    "plt.matshow(forest.trees, cmap=plt.cm.Greens)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "循环很长时间："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.253111111111\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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rt0Kuys6lhgYp9A5UCzZewn7nyvfanCvTA+2kqLGnh+0vlR3Rzik52vShRqe0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\n4HA4APx/JL/m/4qA3IAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa4a7be0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(500):\n",
+    "    forest.advance_one_step()\n",
+    "plt.matshow(forest.trees, cmap=plt.cm.Greens)\n",
+    "print forest.tree_fraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "迭代更长时间："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Nv/erBmvRtBjZaYSwbZvdq5D1lUT8ms7cjtQTq68d4peDu1Y+/4z8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\n+CsqKiomGSrxV1RUVEwy/P9JVJbE2yOX2wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa4b1b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "forest = Forest()\n",
+    "tree_fractions = []\n",
+    "for i in range(5000):\n",
+    "    forest.advance_one_step()\n",
+    "    tree_fractions.append(forest.tree_fraction)\n",
+    "fig = plt.figure()\n",
+    "ax0 = fig.add_subplot(1,2,1)\n",
+    "ax0.matshow(forest.trees, cmap=plt.cm.Greens)\n",
+    "ax1 = fig.add_subplot(1,2,2)\n",
+    "ax1.plot(tree_fractions)\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/08. object-oriented programming/08.03 what is a object.ipynb b/08-object-oriented-programming/08.03-what-is-a-object.ipynb
similarity index 94%
rename from 08. object-oriented programming/08.03 what is a object.ipynb
rename to 08-object-oriented-programming/08.03-what-is-a-object.ipynb
index 908e7a81..f12d7f57 100644
--- a/08. object-oriented programming/08.03 what is a object.ipynb	
+++ b/08-object-oriented-programming/08.03-what-is-a-object.ipynb
@@ -1,320 +1,320 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 什么是对象？"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在 `Python` 中，几乎所有的东西都是对象。\n",
-    "\n",
-    "整数是对象："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "a = 257"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "int"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "53187032L"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`b` 和 `a` 是同一个对象："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "b = a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "53187032L"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "53186960L"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c = 258\n",
-    "id(c)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "函数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def foo():\n",
-    "    print 'hi'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "function"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(foo)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "63632664L"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(foo)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`type` 函数本身也是对象："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "type"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(type)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "506070640L"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(type)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "只有一些保留的关键词不是对象："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-12-1e0d1307109a>, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-12-1e0d1307109a>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    id(if)\u001b[0m\n\u001b[1;37m        ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
-   "source": [
-    "id(if)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-13-86853fe3c6fd>, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-13-86853fe3c6fd>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    id(+)\u001b[0m\n\u001b[1;37m        ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
-   "source": [
-    "id(+)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 什么是对象？"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在 `Python` 中，几乎所有的东西都是对象。\n",
+    "\n",
+    "整数是对象："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "a = 257"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "int"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "53187032L"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`b` 和 `a` 是同一个对象："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "b = a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "53187032L"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "53186960L"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c = 258\n",
+    "id(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "函数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def foo():\n",
+    "    print 'hi'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "function"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(foo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "63632664L"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(foo)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`type` 函数本身也是对象："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(type)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "506070640L"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(type)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "只有一些保留的关键词不是对象："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-12-1e0d1307109a>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-12-1e0d1307109a>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    id(if)\u001b[0m\n\u001b[1;37m        ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "id(if)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-13-86853fe3c6fd>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-13-86853fe3c6fd>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    id(+)\u001b[0m\n\u001b[1;37m        ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "id(+)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/08. object-oriented programming/08.04 writing classes.ipynb b/08-object-oriented-programming/08.04-writing-classes.ipynb
similarity index 95%
rename from 08. object-oriented programming/08.04 writing classes.ipynb
rename to 08-object-oriented-programming/08.04-writing-classes.ipynb
index 3693ae3b..51ce3d84 100644
--- a/08. object-oriented programming/08.04 writing classes.ipynb	
+++ b/08-object-oriented-programming/08.04-writing-classes.ipynb
@@ -1,273 +1,273 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 定义 class"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 基本形式"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`class` 定义如下：\n",
-    "```python\n",
-    "class ClassName(ParentClass):\n",
-    "    \"\"\"class docstring\"\"\"\n",
-    "    def method(self):\n",
-    "        return\n",
-    "```\n",
-    "\n",
-    "- `class` 关键词在最前面\n",
-    "- `ClassName` 通常采用 `CamelCase` 记法\n",
-    "- 括号中的 `ParentClass` 用来表示继承关系\n",
-    "- 冒号不能缺少\n",
-    "- `\"\"\"\"\"\"` 中的内容表示 `docstring`，可以省略\n",
-    "- 方法定义与函数定义十分类似，不过多了一个 `self` 参数表示这个对象本身\n",
-    "- `class` 中的方法要进行缩进"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Forest(object):\n",
-    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "其中 `object` 是最基本的类型。\n",
-    "\n",
-    "查看帮助："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Forest()\n",
-      "\n",
-      "Forest can grow trees which eventually die.\n",
-      "\n",
-      "\n",
-      "Methods:\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "np.info(Forest)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "forest = Forest()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<__main__.Forest at 0x3cda358>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "forest"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 添加方法和属性"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以直接添加属性（有更好的替代方式）："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "forest.trees = np.zeros((150, 150), dtype=bool)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       ..., \n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]], dtype=bool)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "forest.trees"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "forest2 = Forest()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`forest2` 没有这个属性："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'Forest' object has no attribute 'trees'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-8-42e6a9d57a8b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mforest2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrees\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m: 'Forest' object has no attribute 'trees'"
-     ]
-    }
-   ],
-   "source": [
-    "forest2.trees"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "添加方法时，默认第一个参数是对象本身，一般为 `self`，可能用到也可能用不到，然后才是其他的参数："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Forest(object):\n",
-    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
-    "    def grow(self):\n",
-    "        print \"the tree is growing!\"\n",
-    "        \n",
-    "    def number(self, num=1):\n",
-    "        if num == 1:\n",
-    "            print 'there is 1 tree.'\n",
-    "        else:\n",
-    "            print 'there are', num, 'trees.'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "the tree is growing!\n",
-      "there are 12 trees.\n"
-     ]
-    }
-   ],
-   "source": [
-    "forest = Forest()\n",
-    "\n",
-    "forest.grow()\n",
-    "forest.number(12)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 定义 class"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 基本形式"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`class` 定义如下：\n",
+    "```python\n",
+    "class ClassName(ParentClass):\n",
+    "    \"\"\"class docstring\"\"\"\n",
+    "    def method(self):\n",
+    "        return\n",
+    "```\n",
+    "\n",
+    "- `class` 关键词在最前面\n",
+    "- `ClassName` 通常采用 `CamelCase` 记法\n",
+    "- 括号中的 `ParentClass` 用来表示继承关系\n",
+    "- 冒号不能缺少\n",
+    "- `\"\"\"\"\"\"` 中的内容表示 `docstring`，可以省略\n",
+    "- 方法定义与函数定义十分类似，不过多了一个 `self` 参数表示这个对象本身\n",
+    "- `class` 中的方法要进行缩进"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Forest(object):\n",
+    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "其中 `object` 是最基本的类型。\n",
+    "\n",
+    "查看帮助："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Forest()\n",
+      "\n",
+      "Forest can grow trees which eventually die.\n",
+      "\n",
+      "\n",
+      "Methods:\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.info(Forest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "forest = Forest()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<__main__.Forest at 0x3cda358>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forest"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 添加方法和属性"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以直接添加属性（有更好的替代方式）："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "forest.trees = np.zeros((150, 150), dtype=bool)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       ..., \n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]], dtype=bool)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forest.trees"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "forest2 = Forest()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`forest2` 没有这个属性："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Forest' object has no attribute 'trees'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-8-42e6a9d57a8b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mforest2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrees\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m: 'Forest' object has no attribute 'trees'"
+     ]
+    }
+   ],
+   "source": [
+    "forest2.trees"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "添加方法时，默认第一个参数是对象本身，一般为 `self`，可能用到也可能用不到，然后才是其他的参数："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Forest(object):\n",
+    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
+    "    def grow(self):\n",
+    "        print \"the tree is growing!\"\n",
+    "        \n",
+    "    def number(self, num=1):\n",
+    "        if num == 1:\n",
+    "            print 'there is 1 tree.'\n",
+    "        else:\n",
+    "            print 'there are', num, 'trees.'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the tree is growing!\n",
+      "there are 12 trees.\n"
+     ]
+    }
+   ],
+   "source": [
+    "forest = Forest()\n",
+    "\n",
+    "forest.grow()\n",
+    "forest.number(12)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/08. object-oriented programming/08.05 special method.ipynb b/08-object-oriented-programming/08.05-special-method.ipynb
similarity index 95%
rename from 08. object-oriented programming/08.05 special method.ipynb
rename to 08-object-oriented-programming/08.05-special-method.ipynb
index 55c9a0b6..4a873a36 100644
--- a/08. object-oriented programming/08.05 special method.ipynb	
+++ b/08-object-oriented-programming/08.05-special-method.ipynb
@@ -1,554 +1,554 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 特殊方法"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Python** 使用 `__` 开头的名字来定义特殊的方法和属性，它们有：\n",
-    "\n",
-    "- `__init__()`\n",
-    "- `__repr__()`\n",
-    "- `__str__()`\n",
-    "- `__call__()`\n",
-    "- `__iter__()`\n",
-    "- `__add__()`\n",
-    "- `__sub__()`\n",
-    "- `__mul__()`\n",
-    "- `__rmul__()`\n",
-    "- `__class__`\n",
-    "- `__name__`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 构造方法 `__init__()`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "之前说到，在产生对象之后，我们可以向对象中添加属性。事实上，还可以通过构造方法，在构造对象的时候直接添加属性："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Leaf(object):\n",
-    "    \"\"\"\n",
-    "    A leaf falling in the woods.\n",
-    "    \"\"\"\n",
-    "    def __init__(self, color='green'):\n",
-    "        self.color = color"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "默认属性值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "green\n"
-     ]
-    }
-   ],
-   "source": [
-    "leaf1 = Leaf()\n",
-    "\n",
-    "print leaf1.color"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "传入有参数的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "orange\n"
-     ]
-    }
-   ],
-   "source": [
-    "leaf2 = Leaf('orange')\n",
-    "\n",
-    "print leaf2.color"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "回到森林的例子："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "class Forest(object):\n",
-    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
-    "    def __init__(self):\n",
-    "        self.trees = np.zeros((150,150), dtype=bool)\n",
-    "        self.fires = np.zeros((150,150), dtype=bool)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们在构造方法中定义了两个属性 `trees` 和 `fires`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       ..., \n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]], dtype=bool)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "forest = Forest()\n",
-    "\n",
-    "forest.trees"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       ..., \n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]], dtype=bool)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "forest.fires"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "修改属性的值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ True, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       ..., \n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]], dtype=bool)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "forest.trees[0,0]=True\n",
-    "forest.trees"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "改变它的属性值不会影响其他对象的属性值："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       ..., \n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]], dtype=bool)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "forest2 = Forest()\n",
-    "\n",
-    "forest2.trees"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上，`__new__()` 才是真正产生新对象的方法，`__init__()` 只是对对象进行了初始化，所以：\n",
-    "\n",
-    "```python\n",
-    "leaf = Leaf()\n",
-    "```\n",
-    "\n",
-    "相当于\n",
-    "\n",
-    "```python\n",
-    "my_new_leaf = Leaf.__new__(Leaf)\n",
-    "Leaf.__init__(my_new_leaf)\n",
-    "leaf = my_new_leaf\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 表示方法 `__repr__()` 和 `__str__()`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Leaf(object):\n",
-    "    \"\"\"\n",
-    "    A leaf falling in the woods.\n",
-    "    \"\"\"\n",
-    "    def __init__(self, color='green'):\n",
-    "        self.color = color\n",
-    "    def __str__(self):\n",
-    "        \"This is the string that is printed.\"\n",
-    "        return \"A {} leaf\".format(self.color)\n",
-    "    def __repr__(self):\n",
-    "        \"This string recreates the object.\"\n",
-    "        return \"{}(color='{}')\".format(self.__class__.__name__, self.color)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`__str__()` 是使用 `print` 函数显示的结果："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "A green leaf\n"
-     ]
-    }
-   ],
-   "source": [
-    "leaf = Leaf()\n",
-    "\n",
-    "print leaf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`__repr__()` 返回的是不使用 `print` 方法的结果："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Leaf(color='green')"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "leaf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "回到森林的例子："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "class Forest(object):\n",
-    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
-    "    def __init__(self, size=(150,150)):\n",
-    "        self.size = size\n",
-    "        self.trees = np.zeros(self.size, dtype=bool)\n",
-    "        self.fires = np.zeros((self.size), dtype=bool)\n",
-    "        \n",
-    "    def __repr__(self):\n",
-    "        my_repr = \"{}(size={})\".format(self.__class__.__name__, self.size)\n",
-    "        return my_repr\n",
-    "    \n",
-    "    def __str__(self):\n",
-    "        return self.__class__.__name__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "forest = Forest()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`__str__()` 方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Forest\n"
-     ]
-    }
-   ],
-   "source": [
-    "print forest"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`__repr__()` 方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Forest(size=(150, 150))"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "forest"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`__name__` 和 `__class__` 为特殊的属性："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "__main__.Forest"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "forest.__class__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Forest'"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "forest.__class__.__name__"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 特殊方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Python** 使用 `__` 开头的名字来定义特殊的方法和属性，它们有：\n",
+    "\n",
+    "- `__init__()`\n",
+    "- `__repr__()`\n",
+    "- `__str__()`\n",
+    "- `__call__()`\n",
+    "- `__iter__()`\n",
+    "- `__add__()`\n",
+    "- `__sub__()`\n",
+    "- `__mul__()`\n",
+    "- `__rmul__()`\n",
+    "- `__class__`\n",
+    "- `__name__`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 构造方法 `__init__()`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "之前说到，在产生对象之后，我们可以向对象中添加属性。事实上，还可以通过构造方法，在构造对象的时候直接添加属性："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Leaf(object):\n",
+    "    \"\"\"\n",
+    "    A leaf falling in the woods.\n",
+    "    \"\"\"\n",
+    "    def __init__(self, color='green'):\n",
+    "        self.color = color"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "默认属性值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "green\n"
+     ]
+    }
+   ],
+   "source": [
+    "leaf1 = Leaf()\n",
+    "\n",
+    "print leaf1.color"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "传入有参数的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "orange\n"
+     ]
+    }
+   ],
+   "source": [
+    "leaf2 = Leaf('orange')\n",
+    "\n",
+    "print leaf2.color"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "回到森林的例子："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "class Forest(object):\n",
+    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
+    "    def __init__(self):\n",
+    "        self.trees = np.zeros((150,150), dtype=bool)\n",
+    "        self.fires = np.zeros((150,150), dtype=bool)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们在构造方法中定义了两个属性 `trees` 和 `fires`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       ..., \n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]], dtype=bool)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forest = Forest()\n",
+    "\n",
+    "forest.trees"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       ..., \n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]], dtype=bool)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forest.fires"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "修改属性的值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ True, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       ..., \n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]], dtype=bool)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forest.trees[0,0]=True\n",
+    "forest.trees"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "改变它的属性值不会影响其他对象的属性值："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       ..., \n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]], dtype=bool)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forest2 = Forest()\n",
+    "\n",
+    "forest2.trees"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上，`__new__()` 才是真正产生新对象的方法，`__init__()` 只是对对象进行了初始化，所以：\n",
+    "\n",
+    "```python\n",
+    "leaf = Leaf()\n",
+    "```\n",
+    "\n",
+    "相当于\n",
+    "\n",
+    "```python\n",
+    "my_new_leaf = Leaf.__new__(Leaf)\n",
+    "Leaf.__init__(my_new_leaf)\n",
+    "leaf = my_new_leaf\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 表示方法 `__repr__()` 和 `__str__()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Leaf(object):\n",
+    "    \"\"\"\n",
+    "    A leaf falling in the woods.\n",
+    "    \"\"\"\n",
+    "    def __init__(self, color='green'):\n",
+    "        self.color = color\n",
+    "    def __str__(self):\n",
+    "        \"This is the string that is printed.\"\n",
+    "        return \"A {} leaf\".format(self.color)\n",
+    "    def __repr__(self):\n",
+    "        \"This string recreates the object.\"\n",
+    "        return \"{}(color='{}')\".format(self.__class__.__name__, self.color)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`__str__()` 是使用 `print` 函数显示的结果："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A green leaf\n"
+     ]
+    }
+   ],
+   "source": [
+    "leaf = Leaf()\n",
+    "\n",
+    "print leaf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`__repr__()` 返回的是不使用 `print` 方法的结果："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Leaf(color='green')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "leaf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "回到森林的例子："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "class Forest(object):\n",
+    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
+    "    def __init__(self, size=(150,150)):\n",
+    "        self.size = size\n",
+    "        self.trees = np.zeros(self.size, dtype=bool)\n",
+    "        self.fires = np.zeros((self.size), dtype=bool)\n",
+    "        \n",
+    "    def __repr__(self):\n",
+    "        my_repr = \"{}(size={})\".format(self.__class__.__name__, self.size)\n",
+    "        return my_repr\n",
+    "    \n",
+    "    def __str__(self):\n",
+    "        return self.__class__.__name__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "forest = Forest()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`__str__()` 方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forest\n"
+     ]
+    }
+   ],
+   "source": [
+    "print forest"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`__repr__()` 方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Forest(size=(150, 150))"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forest"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`__name__` 和 `__class__` 为特殊的属性："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.Forest"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forest.__class__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Forest'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forest.__class__.__name__"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/08. object-oriented programming/08.06 properties.ipynb b/08-object-oriented-programming/08.06-properties.ipynb
similarity index 95%
rename from 08. object-oriented programming/08.06 properties.ipynb
rename to 08-object-oriented-programming/08.06-properties.ipynb
index 4a99d8ad..c2b947b3 100644
--- a/08. object-oriented programming/08.06 properties.ipynb	
+++ b/08-object-oriented-programming/08.06-properties.ipynb
@@ -1,464 +1,464 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 属性"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 只读属性"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "只读属性，顾名思义，指的是只可读不可写的属性，之前我们定义的属性都是可读可写的，对于只读属性，我们需要使用 `@property` 修饰符来得到："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Leaf(object):\n",
-    "    def __init__(self, mass_mg):\n",
-    "        self.mass_mg = mass_mg\n",
-    "    \n",
-    "    # 这样 mass_oz 就变成属性了\n",
-    "    @property\n",
-    "    def mass_oz(self):\n",
-    "        return self.mass_mg * 3.53e-5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里 `mass_oz` 就是一个只读不写的属性（注意是属性不是方法），而 `mass_mg` 是可读写的属性："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.00706\n"
-     ]
-    }
-   ],
-   "source": [
-    "leaf = Leaf(200)\n",
-    "\n",
-    "print leaf.mass_oz"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以修改 `mass_mg` 属性来改变 `mass_oz`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.005295\n"
-     ]
-    }
-   ],
-   "source": [
-    "leaf.mass_mg = 150\n",
-    "\n",
-    "print leaf.mass_oz"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "是属性不是方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "'float' object is not callable",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-4-aac6717ebc82>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mleaf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmass_oz\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m: 'float' object is not callable"
-     ]
-    }
-   ],
-   "source": [
-    "leaf.mass_oz()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "而且是只读属性，不可写："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "can't set attribute",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-5-d232052cd2dc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mleaf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmass_oz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.001\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m: can't set attribute"
-     ]
-    }
-   ],
-   "source": [
-    "leaf.mass_oz = 0.001"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "回到 `forest` 的例子，我们希望加入几个只读属性："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "class Forest(object):\n",
-    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
-    "    def __init__(self, size=(150,150)):\n",
-    "        self.size = size\n",
-    "        self.trees = np.zeros(self.size, dtype=bool)\n",
-    "        self.fires = np.zeros((self.size), dtype=bool)\n",
-    "        \n",
-    "    def __repr__(self):\n",
-    "        my_repr = \"{}(size={})\".format(self.__class__.__name__, self.size)\n",
-    "        return my_repr\n",
-    "    \n",
-    "    def __str__(self):\n",
-    "        return self.__class__.__name__\n",
-    "    \n",
-    "    @property\n",
-    "    def num_cells(self):\n",
-    "        \"\"\"Number of cells available for growing trees\"\"\"\n",
-    "        return np.prod(self.size)\n",
-    "    \n",
-    "    @property\n",
-    "    def tree_fraction(self):\n",
-    "        \"\"\"\n",
-    "        Fraction of trees\n",
-    "        \"\"\"\n",
-    "        num_trees = self.trees.sum()\n",
-    "        return float(num_trees) / self.num_cells\n",
-    "    \n",
-    "    @property\n",
-    "    def fire_fraction(self):\n",
-    "        \"\"\"\n",
-    "        Fraction of fires\n",
-    "        \"\"\"\n",
-    "        num_fires = self.fires.sum()\n",
-    "        return float(num_fires) / self.num_cells"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看属性："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "22500"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "forest = Forest()\n",
-    "\n",
-    "forest.num_cells"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "生成一个较小的森林："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "100"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "small_forest = Forest((10, 10))\n",
-    "small_forest.num_cells"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "初始状态下，树和火灾的比例都是 0："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.0"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "small_forest.tree_fraction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.0"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "small_forest.fire_fraction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 可读写的属性"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "对于 `@property` 生成的只读属性，我们可以使用相应的 `@attr.setter` 修饰符来使得这个属性变成可写的："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Leaf(object):\n",
-    "    def __init__(self, mass_mg):\n",
-    "        self.mass_mg = mass_mg\n",
-    "    \n",
-    "    # 这样 mass_oz 就变成属性了\n",
-    "    @property\n",
-    "    def mass_oz(self):\n",
-    "        return self.mass_mg * 3.53e-5\n",
-    "    \n",
-    "    # 使用 mass_oz.setter 修饰符\n",
-    "    @mass_oz.setter\n",
-    "    def mass_oz(self, m_oz):\n",
-    "        self.mass_mg = m_oz / 3.53e-5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "测试："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.00706\n",
-      "0.005295\n"
-     ]
-    }
-   ],
-   "source": [
-    "leaf = Leaf(200)\n",
-    "print leaf.mass_oz\n",
-    "\n",
-    "leaf.mass_mg = 150\n",
-    "print leaf.mass_oz"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "修改 `mass_oz` 属性："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "283.28611898\n"
-     ]
-    }
-   ],
-   "source": [
-    "leaf.mass_oz = 0.01\n",
-    "print leaf.mass_mg"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "一个等价的替代如下：\n",
-    "\n",
-    "```python\n",
-    "class Leaf(object):\n",
-    "    def __init__(self, mass_mg):\n",
-    "        self.mass_mg = mass_mg\n",
-    "    \n",
-    "    def get_mass_oz(self):\n",
-    "        return self.mass_mg * 3.53e-5\n",
-    "    \n",
-    "    def set_mass_oz(self, m_oz):\n",
-    "        self.mass_mg = m_oz / 3.53e-5\n",
-    "        \n",
-    "    mass_oz = property(get_mass_oz, set_mass_oz)\n",
-    "```"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 属性"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 只读属性"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "只读属性，顾名思义，指的是只可读不可写的属性，之前我们定义的属性都是可读可写的，对于只读属性，我们需要使用 `@property` 修饰符来得到："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Leaf(object):\n",
+    "    def __init__(self, mass_mg):\n",
+    "        self.mass_mg = mass_mg\n",
+    "    \n",
+    "    # 这样 mass_oz 就变成属性了\n",
+    "    @property\n",
+    "    def mass_oz(self):\n",
+    "        return self.mass_mg * 3.53e-5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里 `mass_oz` 就是一个只读不写的属性（注意是属性不是方法），而 `mass_mg` 是可读写的属性："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.00706\n"
+     ]
+    }
+   ],
+   "source": [
+    "leaf = Leaf(200)\n",
+    "\n",
+    "print leaf.mass_oz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以修改 `mass_mg` 属性来改变 `mass_oz`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.005295\n"
+     ]
+    }
+   ],
+   "source": [
+    "leaf.mass_mg = 150\n",
+    "\n",
+    "print leaf.mass_oz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "是属性不是方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'float' object is not callable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-4-aac6717ebc82>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mleaf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmass_oz\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m: 'float' object is not callable"
+     ]
+    }
+   ],
+   "source": [
+    "leaf.mass_oz()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "而且是只读属性，不可写："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "can't set attribute",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-5-d232052cd2dc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mleaf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmass_oz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.001\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m: can't set attribute"
+     ]
+    }
+   ],
+   "source": [
+    "leaf.mass_oz = 0.001"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "回到 `forest` 的例子，我们希望加入几个只读属性："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "class Forest(object):\n",
+    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
+    "    def __init__(self, size=(150,150)):\n",
+    "        self.size = size\n",
+    "        self.trees = np.zeros(self.size, dtype=bool)\n",
+    "        self.fires = np.zeros((self.size), dtype=bool)\n",
+    "        \n",
+    "    def __repr__(self):\n",
+    "        my_repr = \"{}(size={})\".format(self.__class__.__name__, self.size)\n",
+    "        return my_repr\n",
+    "    \n",
+    "    def __str__(self):\n",
+    "        return self.__class__.__name__\n",
+    "    \n",
+    "    @property\n",
+    "    def num_cells(self):\n",
+    "        \"\"\"Number of cells available for growing trees\"\"\"\n",
+    "        return np.prod(self.size)\n",
+    "    \n",
+    "    @property\n",
+    "    def tree_fraction(self):\n",
+    "        \"\"\"\n",
+    "        Fraction of trees\n",
+    "        \"\"\"\n",
+    "        num_trees = self.trees.sum()\n",
+    "        return float(num_trees) / self.num_cells\n",
+    "    \n",
+    "    @property\n",
+    "    def fire_fraction(self):\n",
+    "        \"\"\"\n",
+    "        Fraction of fires\n",
+    "        \"\"\"\n",
+    "        num_fires = self.fires.sum()\n",
+    "        return float(num_fires) / self.num_cells"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看属性："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "22500"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forest = Forest()\n",
+    "\n",
+    "forest.num_cells"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "生成一个较小的森林："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "small_forest = Forest((10, 10))\n",
+    "small_forest.num_cells"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "初始状态下，树和火灾的比例都是 0："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "small_forest.tree_fraction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "small_forest.fire_fraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 可读写的属性"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对于 `@property` 生成的只读属性，我们可以使用相应的 `@attr.setter` 修饰符来使得这个属性变成可写的："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Leaf(object):\n",
+    "    def __init__(self, mass_mg):\n",
+    "        self.mass_mg = mass_mg\n",
+    "    \n",
+    "    # 这样 mass_oz 就变成属性了\n",
+    "    @property\n",
+    "    def mass_oz(self):\n",
+    "        return self.mass_mg * 3.53e-5\n",
+    "    \n",
+    "    # 使用 mass_oz.setter 修饰符\n",
+    "    @mass_oz.setter\n",
+    "    def mass_oz(self, m_oz):\n",
+    "        self.mass_mg = m_oz / 3.53e-5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "测试："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.00706\n",
+      "0.005295\n"
+     ]
+    }
+   ],
+   "source": [
+    "leaf = Leaf(200)\n",
+    "print leaf.mass_oz\n",
+    "\n",
+    "leaf.mass_mg = 150\n",
+    "print leaf.mass_oz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "修改 `mass_oz` 属性："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "283.28611898\n"
+     ]
+    }
+   ],
+   "source": [
+    "leaf.mass_oz = 0.01\n",
+    "print leaf.mass_mg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "一个等价的替代如下：\n",
+    "\n",
+    "```python\n",
+    "class Leaf(object):\n",
+    "    def __init__(self, mass_mg):\n",
+    "        self.mass_mg = mass_mg\n",
+    "    \n",
+    "    def get_mass_oz(self):\n",
+    "        return self.mass_mg * 3.53e-5\n",
+    "    \n",
+    "    def set_mass_oz(self, m_oz):\n",
+    "        self.mass_mg = m_oz / 3.53e-5\n",
+    "        \n",
+    "    mass_oz = property(get_mass_oz, set_mass_oz)\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/08. object-oriented programming/08.07 forest fire simulation.ipynb b/08-object-oriented-programming/08.07-forest-fire-simulation.ipynb
similarity index 99%
rename from 08. object-oriented programming/08.07 forest fire simulation.ipynb
rename to 08-object-oriented-programming/08.07-forest-fire-simulation.ipynb
index f3315796..8701dc85 100644
--- a/08. object-oriented programming/08.07 forest fire simulation.ipynb	
+++ b/08-object-oriented-programming/08.07-forest-fire-simulation.ipynb
@@ -1,352 +1,352 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 森林火灾模拟"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "之前我们已经构建好了一些基础，但是还没有开始对火灾进行模拟。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 随机生长"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- 在原来的基础上,我们要先让树生长，即定义 `grow_trees()` 方法\n",
-    "- 定义方法之前，我们要先指定两个属性：\n",
-    "    - 每个位置随机生长出树木的概率\n",
-    "    - 每个位置随机被闪电击中的概率\n",
-    "- 为了方便，我们定义一个辅助函数来生成随机 `bool` 矩阵，大小与森林大小一致\n",
-    "- 按照给定的生长概率生成生长的位置，将 `trees` 中相应位置设为 `True`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "class Forest(object):\n",
-    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
-    "    def __init__(self, size=(150,150), p_sapling=0.0025, p_lightning=5.0e-6):\n",
-    "        self.size = size\n",
-    "        self.trees = np.zeros(self.size, dtype=bool)\n",
-    "        self.fires = np.zeros((self.size), dtype=bool)\n",
-    "        self.p_sapling = p_sapling\n",
-    "        self.p_lightning = p_lightning\n",
-    "        \n",
-    "    def __repr__(self):\n",
-    "        my_repr = \"{}(size={})\".format(self.__class__.__name__, self.size)\n",
-    "        return my_repr\n",
-    "    \n",
-    "    def __str__(self):\n",
-    "        return self.__class__.__name__\n",
-    "    \n",
-    "    @property\n",
-    "    def num_cells(self):\n",
-    "        \"\"\"Number of cells available for growing trees\"\"\"\n",
-    "        return np.prod(self.size)\n",
-    "    \n",
-    "    @property\n",
-    "    def tree_fraction(self):\n",
-    "        \"\"\"\n",
-    "        Fraction of trees\n",
-    "        \"\"\"\n",
-    "        num_trees = self.trees.sum()\n",
-    "        return float(num_trees) / self.num_cells\n",
-    "    \n",
-    "    @property\n",
-    "    def fire_fraction(self):\n",
-    "        \"\"\"\n",
-    "        Fraction of fires\n",
-    "        \"\"\"\n",
-    "        num_fires = self.fires.sum()\n",
-    "        return float(num_fires) / self.num_cells\n",
-    "    \n",
-    "    def _rand_bool(self, p):\n",
-    "        \"\"\"\n",
-    "        Random boolean distributed according to p, less than p will be True\n",
-    "        \"\"\"\n",
-    "        return np.random.uniform(size=self.trees.shape) < p\n",
-    "    \n",
-    "    def grow_trees(self):\n",
-    "        \"\"\"\n",
-    "        Growing trees.\n",
-    "        \"\"\"\n",
-    "        growth_sites = self._rand_bool(self.p_sapling)\n",
-    "        self.trees[growth_sites] = True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "测试："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0\n",
-      "0.00293333333333\n"
-     ]
-    }
-   ],
-   "source": [
-    "forest = Forest()\n",
-    "print forest.tree_fraction\n",
-    "\n",
-    "forest.grow_trees()\n",
-    "print forest.tree_fraction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 火灾模拟"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- 定义 `start_fires()`：\n",
-    "    - 按照给定的概率生成被闪电击中的位置\n",
-    "    - 如果闪电击中的位置有树，那么将其设为着火点\n",
-    "- 定义 `burn_trees()`：\n",
-    "    - 如果一棵树的上下左右有火，那么这棵树也会着火\n",
-    "- 定义 `advance_one_step()`：\n",
-    "    - 进行一次生长，起火，燃烧"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "class Forest(object):\n",
-    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
-    "    def __init__(self, size=(150,150), p_sapling=0.0025, p_lightning=5.0e-6):\n",
-    "        self.size = size\n",
-    "        self.trees = np.zeros(self.size, dtype=bool)\n",
-    "        self.fires = np.zeros((self.size), dtype=bool)\n",
-    "        self.p_sapling = p_sapling\n",
-    "        self.p_lightning = p_lightning\n",
-    "        \n",
-    "    def __repr__(self):\n",
-    "        my_repr = \"{}(size={})\".format(self.__class__.__name__, self.size)\n",
-    "        return my_repr\n",
-    "    \n",
-    "    def __str__(self):\n",
-    "        return self.__class__.__name__\n",
-    "    \n",
-    "    @property\n",
-    "    def num_cells(self):\n",
-    "        \"\"\"Number of cells available for growing trees\"\"\"\n",
-    "        return np.prod(self.size)\n",
-    "    \n",
-    "    @property\n",
-    "    def tree_fraction(self):\n",
-    "        \"\"\"\n",
-    "        Fraction of trees\n",
-    "        \"\"\"\n",
-    "        num_trees = self.trees.sum()\n",
-    "        return float(num_trees) / self.num_cells\n",
-    "    \n",
-    "    @property\n",
-    "    def fire_fraction(self):\n",
-    "        \"\"\"\n",
-    "        Fraction of fires\n",
-    "        \"\"\"\n",
-    "        num_fires = self.fires.sum()\n",
-    "        return float(num_fires) / self.num_cells\n",
-    "    \n",
-    "    def _rand_bool(self, p):\n",
-    "        \"\"\"\n",
-    "        Random boolean distributed according to p, less than p will be True\n",
-    "        \"\"\"\n",
-    "        return np.random.uniform(size=self.trees.shape) < p\n",
-    "    \n",
-    "    def grow_trees(self):\n",
-    "        \"\"\"\n",
-    "        Growing trees.\n",
-    "        \"\"\"\n",
-    "        growth_sites = self._rand_bool(self.p_sapling)\n",
-    "        self.trees[growth_sites] = True\n",
-    "        \n",
-    "    def start_fires(self):\n",
-    "        \"\"\"\n",
-    "        Start of fire.\n",
-    "        \"\"\"\n",
-    "        lightning_strikes = (self._rand_bool(self.p_lightning) & \n",
-    "            self.trees)\n",
-    "        self.fires[lightning_strikes] = True\n",
-    "    \n",
-    "    def burn_trees(self):\n",
-    "        \"\"\"\n",
-    "        Burn trees.\n",
-    "        \"\"\"\n",
-    "        fires = np.zeros((self.size[0] + 2, self.size[1] + 2), dtype=bool)\n",
-    "        fires[1:-1, 1:-1] = self.fires\n",
-    "        north = fires[:-2, 1:-1]\n",
-    "        south = fires[2:, 1:-1]\n",
-    "        east = fires[1:-1, :-2]\n",
-    "        west = fires[1:-1, 2:]\n",
-    "        new_fires = (north | south | east | west) & self.trees\n",
-    "        self.trees[self.fires] = False\n",
-    "        self.fires = new_fires\n",
-    "        \n",
-    "    def advance_one_step(self):\n",
-    "        \"\"\"\n",
-    "        Advance one step\n",
-    "        \"\"\"\n",
-    "        self.grow_trees()\n",
-    "        self.start_fires()\n",
-    "        self.burn_trees()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "forest = Forest()\n",
-    "\n",
-    "for i in range(100):\n",
-    "    forest.advance_one_step()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用 `matshow()` 显示树木图像："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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paxS0lSDueMjpTOUsrsOIDD9Hv0WF63uCGozg+aVwsdUnPa7RU9IpHWhWg9s6FyCxndqq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\nAz4ROBwO+ETgcDgA/H9fWFGYjphtAwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa180d30>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib import cm\n",
-    "\n",
-    "%matplotlib inline\n",
-    "\n",
-    "plt.matshow(forest.trees, cmap=cm.Greens)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看不同着火概率下的森林覆盖率趋势变化："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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TvdjHxwOtWikzrZejyCUOu3drYzyB1GJ/+jQPnVVryLwt5Po9jW5IrSKX2MfE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\nRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x3ce8358>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "forest = Forest()\n",
-    "forest2 = Forest(p_lightning=5e-4)\n",
-    "\n",
-    "tree_fractions = []\n",
-    "\n",
-    "for i in range(2500):\n",
-    "    forest.advance_one_step()\n",
-    "    forest2.advance_one_step()\n",
-    "    tree_fractions.append((forest.tree_fraction, forest2.tree_fraction))\n",
-    "\n",
-    "plt.plot(tree_fractions)\n",
-    "\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 森林火灾模拟"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "之前我们已经构建好了一些基础，但是还没有开始对火灾进行模拟。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 随机生长"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- 在原来的基础上,我们要先让树生长，即定义 `grow_trees()` 方法\n",
+    "- 定义方法之前，我们要先指定两个属性：\n",
+    "    - 每个位置随机生长出树木的概率\n",
+    "    - 每个位置随机被闪电击中的概率\n",
+    "- 为了方便，我们定义一个辅助函数来生成随机 `bool` 矩阵，大小与森林大小一致\n",
+    "- 按照给定的生长概率生成生长的位置，将 `trees` 中相应位置设为 `True`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "class Forest(object):\n",
+    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
+    "    def __init__(self, size=(150,150), p_sapling=0.0025, p_lightning=5.0e-6):\n",
+    "        self.size = size\n",
+    "        self.trees = np.zeros(self.size, dtype=bool)\n",
+    "        self.fires = np.zeros((self.size), dtype=bool)\n",
+    "        self.p_sapling = p_sapling\n",
+    "        self.p_lightning = p_lightning\n",
+    "        \n",
+    "    def __repr__(self):\n",
+    "        my_repr = \"{}(size={})\".format(self.__class__.__name__, self.size)\n",
+    "        return my_repr\n",
+    "    \n",
+    "    def __str__(self):\n",
+    "        return self.__class__.__name__\n",
+    "    \n",
+    "    @property\n",
+    "    def num_cells(self):\n",
+    "        \"\"\"Number of cells available for growing trees\"\"\"\n",
+    "        return np.prod(self.size)\n",
+    "    \n",
+    "    @property\n",
+    "    def tree_fraction(self):\n",
+    "        \"\"\"\n",
+    "        Fraction of trees\n",
+    "        \"\"\"\n",
+    "        num_trees = self.trees.sum()\n",
+    "        return float(num_trees) / self.num_cells\n",
+    "    \n",
+    "    @property\n",
+    "    def fire_fraction(self):\n",
+    "        \"\"\"\n",
+    "        Fraction of fires\n",
+    "        \"\"\"\n",
+    "        num_fires = self.fires.sum()\n",
+    "        return float(num_fires) / self.num_cells\n",
+    "    \n",
+    "    def _rand_bool(self, p):\n",
+    "        \"\"\"\n",
+    "        Random boolean distributed according to p, less than p will be True\n",
+    "        \"\"\"\n",
+    "        return np.random.uniform(size=self.trees.shape) < p\n",
+    "    \n",
+    "    def grow_trees(self):\n",
+    "        \"\"\"\n",
+    "        Growing trees.\n",
+    "        \"\"\"\n",
+    "        growth_sites = self._rand_bool(self.p_sapling)\n",
+    "        self.trees[growth_sites] = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "测试："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0\n",
+      "0.00293333333333\n"
+     ]
+    }
+   ],
+   "source": [
+    "forest = Forest()\n",
+    "print forest.tree_fraction\n",
+    "\n",
+    "forest.grow_trees()\n",
+    "print forest.tree_fraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 火灾模拟"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- 定义 `start_fires()`：\n",
+    "    - 按照给定的概率生成被闪电击中的位置\n",
+    "    - 如果闪电击中的位置有树，那么将其设为着火点\n",
+    "- 定义 `burn_trees()`：\n",
+    "    - 如果一棵树的上下左右有火，那么这棵树也会着火\n",
+    "- 定义 `advance_one_step()`：\n",
+    "    - 进行一次生长，起火，燃烧"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "class Forest(object):\n",
+    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
+    "    def __init__(self, size=(150,150), p_sapling=0.0025, p_lightning=5.0e-6):\n",
+    "        self.size = size\n",
+    "        self.trees = np.zeros(self.size, dtype=bool)\n",
+    "        self.fires = np.zeros((self.size), dtype=bool)\n",
+    "        self.p_sapling = p_sapling\n",
+    "        self.p_lightning = p_lightning\n",
+    "        \n",
+    "    def __repr__(self):\n",
+    "        my_repr = \"{}(size={})\".format(self.__class__.__name__, self.size)\n",
+    "        return my_repr\n",
+    "    \n",
+    "    def __str__(self):\n",
+    "        return self.__class__.__name__\n",
+    "    \n",
+    "    @property\n",
+    "    def num_cells(self):\n",
+    "        \"\"\"Number of cells available for growing trees\"\"\"\n",
+    "        return np.prod(self.size)\n",
+    "    \n",
+    "    @property\n",
+    "    def tree_fraction(self):\n",
+    "        \"\"\"\n",
+    "        Fraction of trees\n",
+    "        \"\"\"\n",
+    "        num_trees = self.trees.sum()\n",
+    "        return float(num_trees) / self.num_cells\n",
+    "    \n",
+    "    @property\n",
+    "    def fire_fraction(self):\n",
+    "        \"\"\"\n",
+    "        Fraction of fires\n",
+    "        \"\"\"\n",
+    "        num_fires = self.fires.sum()\n",
+    "        return float(num_fires) / self.num_cells\n",
+    "    \n",
+    "    def _rand_bool(self, p):\n",
+    "        \"\"\"\n",
+    "        Random boolean distributed according to p, less than p will be True\n",
+    "        \"\"\"\n",
+    "        return np.random.uniform(size=self.trees.shape) < p\n",
+    "    \n",
+    "    def grow_trees(self):\n",
+    "        \"\"\"\n",
+    "        Growing trees.\n",
+    "        \"\"\"\n",
+    "        growth_sites = self._rand_bool(self.p_sapling)\n",
+    "        self.trees[growth_sites] = True\n",
+    "        \n",
+    "    def start_fires(self):\n",
+    "        \"\"\"\n",
+    "        Start of fire.\n",
+    "        \"\"\"\n",
+    "        lightning_strikes = (self._rand_bool(self.p_lightning) & \n",
+    "            self.trees)\n",
+    "        self.fires[lightning_strikes] = True\n",
+    "    \n",
+    "    def burn_trees(self):\n",
+    "        \"\"\"\n",
+    "        Burn trees.\n",
+    "        \"\"\"\n",
+    "        fires = np.zeros((self.size[0] + 2, self.size[1] + 2), dtype=bool)\n",
+    "        fires[1:-1, 1:-1] = self.fires\n",
+    "        north = fires[:-2, 1:-1]\n",
+    "        south = fires[2:, 1:-1]\n",
+    "        east = fires[1:-1, :-2]\n",
+    "        west = fires[1:-1, 2:]\n",
+    "        new_fires = (north | south | east | west) & self.trees\n",
+    "        self.trees[self.fires] = False\n",
+    "        self.fires = new_fires\n",
+    "        \n",
+    "    def advance_one_step(self):\n",
+    "        \"\"\"\n",
+    "        Advance one step\n",
+    "        \"\"\"\n",
+    "        self.grow_trees()\n",
+    "        self.start_fires()\n",
+    "        self.burn_trees()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "forest = Forest()\n",
+    "\n",
+    "for i in range(100):\n",
+    "    forest.advance_one_step()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用 `matshow()` 显示树木图像："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cVNSAiN4L4GUAHp42O533PczueDztkPxaAP8PwM+HEG7K6BTva8ANg2rHuTJa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\nAz4ROBwO+ETgcDgA/H9fWFGYjphtAwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa180d30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "plt.matshow(forest.trees, cmap=cm.Greens)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看不同着火概率下的森林覆盖率趋势变化："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TvdjHxwOtWikzrZejyCUOu3drYzyB1GJ/+jQPnVVryLwt5Po9jW5IrSKX2MfE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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x3ce8358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "forest = Forest()\n",
+    "forest2 = Forest(p_lightning=5e-4)\n",
+    "\n",
+    "tree_fractions = []\n",
+    "\n",
+    "for i in range(2500):\n",
+    "    forest.advance_one_step()\n",
+    "    forest2.advance_one_step()\n",
+    "    tree_fractions.append((forest.tree_fraction, forest2.tree_fraction))\n",
+    "\n",
+    "plt.plot(tree_fractions)\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/08. object-oriented programming/08.08 inheritance.ipynb b/08-object-oriented-programming/08.08-inheritance.ipynb
similarity index 95%
rename from 08. object-oriented programming/08.08 inheritance.ipynb
rename to 08-object-oriented-programming/08.08-inheritance.ipynb
index 0d756d2b..228ac0ff 100644
--- a/08. object-oriented programming/08.08 inheritance.ipynb	
+++ b/08-object-oriented-programming/08.08-inheritance.ipynb
@@ -1,267 +1,267 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 继承"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "一个类定义的基本形式如下：\n",
-    "```python\n",
-    "class ClassName(ParentClass):\n",
-    "    \"\"\"class docstring\"\"\"\n",
-    "    def method(self):\n",
-    "        return\n",
-    "```\n",
-    "\n",
-    "- `class` 关键词在最前面\n",
-    "- `ClassName` 通常采用 `CamelCase` 记法\n",
-    "- 括号中的 `ParentClass` 用来表示继承关系\n",
-    "- 冒号不能缺少\n",
-    "- `\"\"\"\"\"\"` 中的内容表示 `docstring`，可以省略\n",
-    "- 方法定义与函数定义十分类似，不过多了一个 `self` 参数表示这个对象本身\n",
-    "- `class` 中的方法要进行缩进\n",
-    "\n",
-    "在里面有一个 `ParentClass` 项，用来进行继承，被继承的类是父类，定义的这个类是子类。\n",
-    "对于子类来说，继承意味着它可以使用所有父类的方法和属性，同时还可以定义自己特殊的方法和属性。\n",
-    "\n",
-    "假设我们有这样一个父类："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Leaf(object):\n",
-    "    def __init__(self, color=\"green\"):\n",
-    "        self.color = color\n",
-    "    def fall(self):\n",
-    "        print \"Splat!\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "测试："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "green\n"
-     ]
-    }
-   ],
-   "source": [
-    "leaf = Leaf()\n",
-    "\n",
-    "print leaf.color"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Splat!\n"
-     ]
-    }
-   ],
-   "source": [
-    "leaf.fall()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "现在定义一个子类，继承自 `Leaf`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class MapleLeaf(Leaf):\n",
-    "    def change_color(self):\n",
-    "        if self.color == \"green\":\n",
-    "            self.color = \"red\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "继承父类的所有方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "green\n"
-     ]
-    }
-   ],
-   "source": [
-    "mleaf = MapleLeaf()\n",
-    "\n",
-    "print mleaf.color"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Splat!\n"
-     ]
-    }
-   ],
-   "source": [
-    "mleaf.fall()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "但是有自己独有的方法，父类中没有："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "red\n"
-     ]
-    }
-   ],
-   "source": [
-    "mleaf.change_color()\n",
-    "\n",
-    "print mleaf.color"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "如果想对父类的方法进行修改，只需要在子类中重定义这个类即可："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class MapleLeaf(Leaf):\n",
-    "    def change_color(self):\n",
-    "        if self.color == \"green\":\n",
-    "            self.color = \"red\"\n",
-    "    def fall(self):\n",
-    "        self.change_color()\n",
-    "        print \"Plunk!\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "green\n",
-      "Plunk!\n",
-      "red\n"
-     ]
-    }
-   ],
-   "source": [
-    "mleaf = MapleLeaf()\n",
-    "\n",
-    "print mleaf.color\n",
-    "mleaf.fall()\n",
-    "print mleaf.color"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 继承"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "一个类定义的基本形式如下：\n",
+    "```python\n",
+    "class ClassName(ParentClass):\n",
+    "    \"\"\"class docstring\"\"\"\n",
+    "    def method(self):\n",
+    "        return\n",
+    "```\n",
+    "\n",
+    "- `class` 关键词在最前面\n",
+    "- `ClassName` 通常采用 `CamelCase` 记法\n",
+    "- 括号中的 `ParentClass` 用来表示继承关系\n",
+    "- 冒号不能缺少\n",
+    "- `\"\"\"\"\"\"` 中的内容表示 `docstring`，可以省略\n",
+    "- 方法定义与函数定义十分类似，不过多了一个 `self` 参数表示这个对象本身\n",
+    "- `class` 中的方法要进行缩进\n",
+    "\n",
+    "在里面有一个 `ParentClass` 项，用来进行继承，被继承的类是父类，定义的这个类是子类。\n",
+    "对于子类来说，继承意味着它可以使用所有父类的方法和属性，同时还可以定义自己特殊的方法和属性。\n",
+    "\n",
+    "假设我们有这样一个父类："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Leaf(object):\n",
+    "    def __init__(self, color=\"green\"):\n",
+    "        self.color = color\n",
+    "    def fall(self):\n",
+    "        print \"Splat!\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "测试："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "green\n"
+     ]
+    }
+   ],
+   "source": [
+    "leaf = Leaf()\n",
+    "\n",
+    "print leaf.color"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Splat!\n"
+     ]
+    }
+   ],
+   "source": [
+    "leaf.fall()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "现在定义一个子类，继承自 `Leaf`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class MapleLeaf(Leaf):\n",
+    "    def change_color(self):\n",
+    "        if self.color == \"green\":\n",
+    "            self.color = \"red\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "继承父类的所有方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "green\n"
+     ]
+    }
+   ],
+   "source": [
+    "mleaf = MapleLeaf()\n",
+    "\n",
+    "print mleaf.color"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Splat!\n"
+     ]
+    }
+   ],
+   "source": [
+    "mleaf.fall()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "但是有自己独有的方法，父类中没有："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "red\n"
+     ]
+    }
+   ],
+   "source": [
+    "mleaf.change_color()\n",
+    "\n",
+    "print mleaf.color"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "如果想对父类的方法进行修改，只需要在子类中重定义这个类即可："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class MapleLeaf(Leaf):\n",
+    "    def change_color(self):\n",
+    "        if self.color == \"green\":\n",
+    "            self.color = \"red\"\n",
+    "    def fall(self):\n",
+    "        self.change_color()\n",
+    "        print \"Plunk!\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "green\n",
+      "Plunk!\n",
+      "red\n"
+     ]
+    }
+   ],
+   "source": [
+    "mleaf = MapleLeaf()\n",
+    "\n",
+    "print mleaf.color\n",
+    "mleaf.fall()\n",
+    "print mleaf.color"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/08. object-oriented programming/08.09 super.ipynb b/08-object-oriented-programming/08.09-super.ipynb
similarity index 98%
rename from 08. object-oriented programming/08.09 super.ipynb
rename to 08-object-oriented-programming/08.09-super.ipynb
index 815cd22b..e6c77254 100644
--- a/08. object-oriented programming/08.09 super.ipynb	
+++ b/08-object-oriented-programming/08.09-super.ipynb
@@ -1,436 +1,436 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# super() 函数"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    super(CurrentClassName, instance)\n",
-    "    \n",
-    "返回该类实例对应的父类对象。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Leaf(object):\n",
-    "    def __init__(self, color=\"green\"):\n",
-    "        self.color = color\n",
-    "    def fall(self):\n",
-    "        print \"Splat!\"\n",
-    "\n",
-    "class MapleLeaf(Leaf):\n",
-    "    def change_color(self):\n",
-    "        if self.color == \"green\":\n",
-    "            self.color = \"red\"\n",
-    "    def fall(self):\n",
-    "        self.change_color()\n",
-    "        super(MapleLeaf, self).fall()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里，我们先改变树叶的颜色，然后再找到这个实例对应的父类，并调用父类的 `fall()` 方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "green\n",
-      "Splat!\n",
-      "red\n"
-     ]
-    }
-   ],
-   "source": [
-    "mleaf = MapleLeaf()\n",
-    "\n",
-    "print mleaf.color\n",
-    "mleaf.fall()\n",
-    "print mleaf.color"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "回到我们的森林例子，这里我们将森林 `Forest` 作为父类，并定义一个子类 `BurnableForest`："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "class Forest(object):\n",
-    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
-    "    def __init__(self, size=(150,150), p_sapling=0.0025):\n",
-    "        self.size = size\n",
-    "        self.trees = np.zeros(self.size, dtype=bool)\n",
-    "        self.p_sapling = p_sapling\n",
-    "        \n",
-    "    def __repr__(self):\n",
-    "        my_repr = \"{}(size={})\".format(self.__class__.__name__, self.size)\n",
-    "        return my_repr\n",
-    "    \n",
-    "    def __str__(self):\n",
-    "        return self.__class__.__name__\n",
-    "    \n",
-    "    @property\n",
-    "    def num_cells(self):\n",
-    "        \"\"\"Number of cells available for growing trees\"\"\"\n",
-    "        return np.prod(self.size)\n",
-    "    \n",
-    "    @property\n",
-    "    def tree_fraction(self):\n",
-    "        \"\"\"\n",
-    "        Fraction of trees\n",
-    "        \"\"\"\n",
-    "        num_trees = self.trees.sum()\n",
-    "        return float(num_trees) / self.num_cells\n",
-    "    \n",
-    "    def _rand_bool(self, p):\n",
-    "        \"\"\"\n",
-    "        Random boolean distributed according to p, less than p will be True\n",
-    "        \"\"\"\n",
-    "        return np.random.uniform(size=self.trees.shape) < p\n",
-    "    \n",
-    "    def grow_trees(self):\n",
-    "        \"\"\"\n",
-    "        Growing trees.\n",
-    "        \"\"\"\n",
-    "        growth_sites = self._rand_bool(self.p_sapling)\n",
-    "        self.trees[growth_sites] = True    \n",
-    "        \n",
-    "    def advance_one_step(self):\n",
-    "        \"\"\"\n",
-    "        Advance one step\n",
-    "        \"\"\"\n",
-    "        self.grow_trees()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- 将与燃烧相关的属性都被转移到了子类中去。\n",
-    "- 修改两类的构造方法，将闪电概率放到子类的构造方法上，同时在子类的构造方法中，用 `super` 调用父类的构造方法。\n",
-    "- 修改 `advance_one_step()`，父类中只进行生长，在子类中用 `super` 调用父类的 `advance_one_step()` 方法，并添加燃烧的部分。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "class BurnableForest(Forest):\n",
-    "    \"\"\"\n",
-    "    Burnable forest support fires\n",
-    "    \"\"\"    \n",
-    "    def __init__(self, p_lightning=5.0e-6, **kwargs):\n",
-    "        super(BurnableForest, self).__init__(**kwargs)\n",
-    "        self.p_lightning = p_lightning        \n",
-    "        self.fires = np.zeros((self.size), dtype=bool)\n",
-    "    \n",
-    "    def advance_one_step(self):\n",
-    "        \"\"\"\n",
-    "        Advance one step\n",
-    "        \"\"\"\n",
-    "        super(BurnableForest, self).advance_one_step()\n",
-    "        self.start_fires()\n",
-    "        self.burn_trees()\n",
-    "        \n",
-    "    @property\n",
-    "    def fire_fraction(self):\n",
-    "        \"\"\"\n",
-    "        Fraction of fires\n",
-    "        \"\"\"\n",
-    "        num_fires = self.fires.sum()\n",
-    "        return float(num_fires) / self.num_cells\n",
-    "    \n",
-    "    def start_fires(self):\n",
-    "        \"\"\"\n",
-    "        Start of fire.\n",
-    "        \"\"\"\n",
-    "        lightning_strikes = (self._rand_bool(self.p_lightning) & \n",
-    "            self.trees)\n",
-    "        self.fires[lightning_strikes] = True\n",
-    "        \n",
-    "    def burn_trees(self):\n",
-    "        \"\"\"\n",
-    "        Burn trees.\n",
-    "        \"\"\"\n",
-    "        fires = np.zeros((self.size[0] + 2, self.size[1] + 2), dtype=bool)\n",
-    "        fires[1:-1, 1:-1] = self.fires\n",
-    "        north = fires[:-2, 1:-1]\n",
-    "        south = fires[2:, 1:-1]\n",
-    "        east = fires[1:-1, :-2]\n",
-    "        west = fires[1:-1, 2:]\n",
-    "        new_fires = (north | south | east | west) & self.trees\n",
-    "        self.trees[self.fires] = False\n",
-    "        self.fires = new_fires"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "测试父类："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.00284444444444\n"
-     ]
-    }
-   ],
-   "source": [
-    "forest = Forest()\n",
-    "\n",
-    "forest.grow_trees()\n",
-    "\n",
-    "print forest.tree_fraction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "测试子类："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "burnable_forest = BurnableForest()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "调用自己和父类的方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.00235555555556\n"
-     ]
-    }
-   ],
-   "source": [
-    "burnable_forest.grow_trees()\n",
-    "burnable_forest.start_fires()\n",
-    "burnable_forest.burn_trees()\n",
-    "print burnable_forest.tree_fraction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "查看变化："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Log9JVHr4Jecc3xz9Bh8P+1iWz6dSbfalVaX47NBnmDtursHv08bKBusmrxMt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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x3ca1ef0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "%matplotlib inline\n",
-    "\n",
-    "forest = Forest()\n",
-    "forest2 = BurnableForest()\n",
-    "\n",
-    "tree_fractions = []\n",
-    "\n",
-    "for i in range(2500):\n",
-    "    forest.advance_one_step()\n",
-    "    forest2.advance_one_step()\n",
-    "    tree_fractions.append((forest.tree_fraction, forest2.tree_fraction))\n",
-    "\n",
-    "plt.plot(tree_fractions)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`__str__` 和 `__repr__` 中 `self.__class__` 会根据类型不同而不同："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Forest(size=(150, 150))"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "forest"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BurnableForest(size=(150, 150))"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "forest2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Forest\n"
-     ]
-    }
-   ],
-   "source": [
-    "print forest"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BurnableForest\n"
-     ]
-    }
-   ],
-   "source": [
-    "print forest2"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# super() 函数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    super(CurrentClassName, instance)\n",
+    "    \n",
+    "返回该类实例对应的父类对象。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Leaf(object):\n",
+    "    def __init__(self, color=\"green\"):\n",
+    "        self.color = color\n",
+    "    def fall(self):\n",
+    "        print \"Splat!\"\n",
+    "\n",
+    "class MapleLeaf(Leaf):\n",
+    "    def change_color(self):\n",
+    "        if self.color == \"green\":\n",
+    "            self.color = \"red\"\n",
+    "    def fall(self):\n",
+    "        self.change_color()\n",
+    "        super(MapleLeaf, self).fall()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里，我们先改变树叶的颜色，然后再找到这个实例对应的父类，并调用父类的 `fall()` 方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "green\n",
+      "Splat!\n",
+      "red\n"
+     ]
+    }
+   ],
+   "source": [
+    "mleaf = MapleLeaf()\n",
+    "\n",
+    "print mleaf.color\n",
+    "mleaf.fall()\n",
+    "print mleaf.color"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "回到我们的森林例子，这里我们将森林 `Forest` 作为父类，并定义一个子类 `BurnableForest`："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "class Forest(object):\n",
+    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
+    "    def __init__(self, size=(150,150), p_sapling=0.0025):\n",
+    "        self.size = size\n",
+    "        self.trees = np.zeros(self.size, dtype=bool)\n",
+    "        self.p_sapling = p_sapling\n",
+    "        \n",
+    "    def __repr__(self):\n",
+    "        my_repr = \"{}(size={})\".format(self.__class__.__name__, self.size)\n",
+    "        return my_repr\n",
+    "    \n",
+    "    def __str__(self):\n",
+    "        return self.__class__.__name__\n",
+    "    \n",
+    "    @property\n",
+    "    def num_cells(self):\n",
+    "        \"\"\"Number of cells available for growing trees\"\"\"\n",
+    "        return np.prod(self.size)\n",
+    "    \n",
+    "    @property\n",
+    "    def tree_fraction(self):\n",
+    "        \"\"\"\n",
+    "        Fraction of trees\n",
+    "        \"\"\"\n",
+    "        num_trees = self.trees.sum()\n",
+    "        return float(num_trees) / self.num_cells\n",
+    "    \n",
+    "    def _rand_bool(self, p):\n",
+    "        \"\"\"\n",
+    "        Random boolean distributed according to p, less than p will be True\n",
+    "        \"\"\"\n",
+    "        return np.random.uniform(size=self.trees.shape) < p\n",
+    "    \n",
+    "    def grow_trees(self):\n",
+    "        \"\"\"\n",
+    "        Growing trees.\n",
+    "        \"\"\"\n",
+    "        growth_sites = self._rand_bool(self.p_sapling)\n",
+    "        self.trees[growth_sites] = True    \n",
+    "        \n",
+    "    def advance_one_step(self):\n",
+    "        \"\"\"\n",
+    "        Advance one step\n",
+    "        \"\"\"\n",
+    "        self.grow_trees()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- 将与燃烧相关的属性都被转移到了子类中去。\n",
+    "- 修改两类的构造方法，将闪电概率放到子类的构造方法上，同时在子类的构造方法中，用 `super` 调用父类的构造方法。\n",
+    "- 修改 `advance_one_step()`，父类中只进行生长，在子类中用 `super` 调用父类的 `advance_one_step()` 方法，并添加燃烧的部分。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class BurnableForest(Forest):\n",
+    "    \"\"\"\n",
+    "    Burnable forest support fires\n",
+    "    \"\"\"    \n",
+    "    def __init__(self, p_lightning=5.0e-6, **kwargs):\n",
+    "        super(BurnableForest, self).__init__(**kwargs)\n",
+    "        self.p_lightning = p_lightning        \n",
+    "        self.fires = np.zeros((self.size), dtype=bool)\n",
+    "    \n",
+    "    def advance_one_step(self):\n",
+    "        \"\"\"\n",
+    "        Advance one step\n",
+    "        \"\"\"\n",
+    "        super(BurnableForest, self).advance_one_step()\n",
+    "        self.start_fires()\n",
+    "        self.burn_trees()\n",
+    "        \n",
+    "    @property\n",
+    "    def fire_fraction(self):\n",
+    "        \"\"\"\n",
+    "        Fraction of fires\n",
+    "        \"\"\"\n",
+    "        num_fires = self.fires.sum()\n",
+    "        return float(num_fires) / self.num_cells\n",
+    "    \n",
+    "    def start_fires(self):\n",
+    "        \"\"\"\n",
+    "        Start of fire.\n",
+    "        \"\"\"\n",
+    "        lightning_strikes = (self._rand_bool(self.p_lightning) & \n",
+    "            self.trees)\n",
+    "        self.fires[lightning_strikes] = True\n",
+    "        \n",
+    "    def burn_trees(self):\n",
+    "        \"\"\"\n",
+    "        Burn trees.\n",
+    "        \"\"\"\n",
+    "        fires = np.zeros((self.size[0] + 2, self.size[1] + 2), dtype=bool)\n",
+    "        fires[1:-1, 1:-1] = self.fires\n",
+    "        north = fires[:-2, 1:-1]\n",
+    "        south = fires[2:, 1:-1]\n",
+    "        east = fires[1:-1, :-2]\n",
+    "        west = fires[1:-1, 2:]\n",
+    "        new_fires = (north | south | east | west) & self.trees\n",
+    "        self.trees[self.fires] = False\n",
+    "        self.fires = new_fires"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "测试父类："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.00284444444444\n"
+     ]
+    }
+   ],
+   "source": [
+    "forest = Forest()\n",
+    "\n",
+    "forest.grow_trees()\n",
+    "\n",
+    "print forest.tree_fraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "测试子类："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "burnable_forest = BurnableForest()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "调用自己和父类的方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.00235555555556\n"
+     ]
+    }
+   ],
+   "source": [
+    "burnable_forest.grow_trees()\n",
+    "burnable_forest.start_fires()\n",
+    "burnable_forest.burn_trees()\n",
+    "print burnable_forest.tree_fraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看变化："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Log9JVHr4Jecc3xz9Bh8P+1iWz6dSbfalVaX47NBnmDtursHv08bKBusmrxMt\nJrNO9mVlwOIDkfBysxG1TVebAZ4DkJKfgltVt2Q7Z72TV08isygTk4Mmy3K+UPdQnLt5Tvb3erXk\nKv64+AeeDn1a9LL9nJSt2ZdVl2Hd6XV4sf+Lopftba/skgkxWTGSfBtrjlLJfk3SGgzwHKD3yquN\nPRj4oEgRmXmyX7cOsBuxHP8a/IIstYh67SzboY9HH0XGoM+LmYfXB76uV8+/MawtrRHsFiz7omjL\nTi3D1F5TYW9tL3rZSnfQbjqzCYO9B6OzQ2fRy1a6g3bxycWY1X+WZIMGGnNu74zy6nJZKyPVtdX4\n5ug3eHfIu7KdUxdmm+w5B75bmY5S10N4KuQp2c8/xHsIjmUdk/Wc2cXZ2HtuL17o94Ks5w11D5V1\nm8IaTQ2WnlqKVwa8Ikn5new6oaC8AOXV5ZKUr83Pp37Gi/3Er9UDyrbZ55fmY2f6Tszoo9u+uWJg\njMleu/818Vf4d/QXZd6HmMw22R89CuR1WYDXh7wsSe1PGyWS/Y8nfsT03tPh2M5R1vPKvQH57rO7\n4W3vjVCPUEnKb8PaoLNDZ0Vq9yn5KbhQcAH3d7tfkvK97JVrs18evxyPBD0i6SSqpsiZ7CtrKvHl\nX18avL+zlMw22c/9sRCV3dfjX2HS1P60CfMKQ9xV8XrStamsqcQv8b9INvW8JSHuIaJO/tBm8cnF\nktXq6ym1bMKCmAV4od8LBg3V04WDtQM0XIPiymJJym9OjaYGi08uxqth0k5qbIqcyX55/HL0dO0p\n+vLpYtCa7Blj4xljaYyxc4yxZtcFZoyFMcZqGGPy9Ay24OpVYG/uCtzffQI62XVSJAYfex+U15TL\nNltxZ/pO9HLtZdR2g4YKcQ/B6WunZVlw6kLBBcTlxEk+rLSLQxfZO2mLK4uxMWUj/hX2L8nOwRgT\nOmllbrffeGYjfB19jVp+2lByJfuKmgr858h/8OWoLyU/lyFaTPaMMQsAiwCMB9ATwDTG2F3bHNUd\n9w2AfQDk6wltxk9La9F26CL8e9gbisXAGBO27pOpLXt10mo8HSL+yBRdOLRzgKutqyy7Ai05uQTP\nhj4r6fpGgDI1+7VJazG261jJZ3nLPfySc445R+fgg6Hi7yGhC7ne75yjcxDmGabIHzRdaKvZDwRw\nnnN+iXNeDWADgIeaOO51AJsB5Iscn96qq4FF+/bCx7UjBnkNUjQWubbuyynJQXRmNB7r9Zjk52qO\nHO+1sqbyb3eoAAAd7klEQVQSKxJWYFb/WZKeB5B/YhXnHD/F/YSX+r0k+bnk3sQk8kIkanktJgQo\ns4ObHCOQrpVew4LYBZg/fr6k5zGGtmTvBaDhXOOsusf+wRjzgvAHoH6POkW3Ltq1C+ADF+KDkeLv\nfKOvUPdQJORJn+xXJqzEo0GPokPbDpKfqzn9O/WXvI9ic8pmhHqEGr2uuy7kXjLhSOYRVNRUYEzX\nMZKfy8tO3uGX/zvxP7w56E3Ffh/lWCLip5M/YUrQFEmGy4pFW7LXJXHPB/Bh3Y4qDAo348xdnQru\nliTLUgHahLiHIDkvWdJzaLgGy+KXyT7csrEBngNwMkfajUyWnlqKl/u/LOk56nVx7CLrAlqLTizC\na2GvyTL+XM5mnCtFVxB9ORpPBD8hy/maIvX7La8ux48nfhR9w3SxaZt5kw3Ap8HPPhBq9w31B7Ch\n7q+2C4AJjLFqzvnOxoVFRET8cz88PBzh4eH6R9yCS5eAk20W4e2BL8Ha0lrUsg3R07Unzt08h+ra\naslGV0RdioKNlY3RM/WMNcBzAOKuxkHDNZIkrPTr6Ui/ni7qjMKWuNq6oriyGJU1lZJ/lrKLsxGZ\nEYmfH/xZ0vPU87b3xsGLhm9cro9fTv2C6b2nK/qt07GdI6prq3Gr6pYkcaxMWIlB3oNE2985KioK\nUVFRopR1h5b2LITwxyADQBcAbQEkAAhq4fgVACY385wh2zbq5aU3Cnm72U48uzhb8nPpKvCHQJ6c\nlyxZ+dM2T+MLYhZIVr4+vL734hcLLkpS9oeRH/J3978rSdnN8Z3nyy/cvCD5eT778zP+r9//Jfl5\n6sVmxfL+S/pLfp6qmiru+b2n1j1X5RCwMICn5adJUnb/Jf35gfMHJCmbc5n2oOWc1wB4DcB+ACkA\nfuOcpzLGZjHGpO8l00NhIbA6aSXGBYyDp52n0uH8o7d7b8nGoN8ou4E95/YoMkO4KUGuQZKsbV9d\nW41fE3/Fc32eE73slsjRkVlZU4mlcUtlnR8h1yza38/+Dj9HP617rspBqqacM9fOIPdWLkb7jRa9\nbLFpXUCFc74XwN5Gjy1p5lj55kE3smKlBhb3LML7I39VKoQm9XbrLbTbS/B5X520Gvd3vx8d23cU\nv3AD9HDugdT8VIwPGC9qudvTtqObczfRvibrSo5Fw7akbkGwWzCCXO8a0SwZ9w7uuFF+Q9LmRQD4\nKe4nvDxAnj4WbaTqpF2VuApPhTwFizYWopctNrOYQavRAN/t2AcvZwcM8R6idDh36O3WG8nXxO+k\n5ZxLuoaKIXq49JCkZv/D8R/wrwHSTTRqjred9KM4Fh1fJPusZ8s2lnCzdUPOrRzJzpFxMwOnck7h\n0Z6PSnYOfUgxAqlWU4s1yWvwTOgzopYrFbNI9n/9BRT3WIgPR7+u+HDLxnq798bpa6dFL/dY1jFU\n11bLsgGEroJcg5B2Q9xkfzz7OK4UX8GUnlNELVcXXvZeku5ylJyXjMyiTDzQ/QHJztEcqWfRLo1b\nKsvkN11JsSZQ5IVIeNp5oqdrT1HLlYpZJPtFG9IBj3g8ETxV6VDu0tWpK66XXRd9LZKfT/2MF/rJ\nu3SzNj1chGYcMS2NE4ZbyrVkc0NSN+OsSVqDp0KeUuS9STkcsX7y20v9pZ8gpisp3u+y+GWY2Wem\nqGVKyeSTfUUF8HvOUjwb+rxqahENtWFtEOQahDPXzohWZlFFEbalbsOzoc+KVqYYOnXohMraStwo\nuyFKeSWVJdiSugXP9lHmfUo5GadWU4u1yWsVW+JCyk7aralbEeIegu7O3SUp3xBiz6LNL81HZEYk\npvWeJlqZUjP5ZL/z9xrwXuvw+jB1Jb6GxG63X5e8DmO7jpV8M3F9McbQw6UH0m+ki1LeltQtGN55\nuORrxTRHyoR4+PJhuNq6yt7pXE/KJQTU1DFbT+w/3JtSNuH+7vfLvpy4MUw+2S/Y9Qe87Doj0CVQ\n6VCaFewWLFq7Pecci04sknRlRGOI2ZSz8cxGTAtWrubkaeeJa6XXUKOpEb3sNUlr8FRv5YbMStWM\nk3EzA2nX0/BQYFNLaCnHo4MHrpddF+3/cmvqVkwJkr8fyRgmnewLCoDjlavwyj3KfBXWlZg1+1M5\np1BeXS7rnrr6CHIJQup145P9zfKbOHrlqCKdl/WsLKzgYuOCnBJxR62UVZdhW9o2RZsApOqP2JSy\nCZN7TJZ0SKchLNtYwsXGBbm3co0uK7s4G6dyTok+xFhqJp3s12wsAeu+GzPClFt3QxfBbsFIzkuu\nn0lslOXxy/Fcn+dU1THbUC/XXjiTb3z/xJaULbi3672ws7YTISrD+Tj44EqxuCNydqXvQphnmKKT\n/6SYMMY5x6rEVaptxxar6WpV4io81vMx2FjZiBCVfEw62S/6cyv6Oo2Ei42L0qG0qL7NOa80z6hy\nKmsq8duZ31TXMdtQL7deojRZKbk+f0M+9j6iJ0U1vDcvOy9cLbkqSgWkXmx2LGo0NRjeWV17r9YT\now+Gc47lCcsxs6/pjMKpZ7LJPjMTuGC3Cm+PUT4haMMYE2XZhN/P/o4Q9xD4OvqKFJn4ujh2QUF5\nAYoqigwu42LBRaReT8WEbsqsf96Qt723qGPt80vzcSTzCB4JekS0Mg1h29YW1hbWuFl+U7Qyl8cv\nx4w+M1T7rVOMpqsjmUdg1cZK8YUHDWGyyX7x2ixYeCXg4Z7KtenqI8TN+H1af038VdW1euD2UNOU\n/BSDy1iTtAZTe01FW4u2IkZmGB97cZtxfjvzGx7o/oCiq0DWE3OiUVl1GTanbFb1bFIxZtGuSFiB\nmX1nqvYPWktMNtmviFuL+7ynqHJsfVOM3ZT7Wuk1RGdGKzKTVF/GtNtzzlXRzFHPx0HcZpzVSatV\ns3CdmLNot6RswRCfIfCy99J+sEKM/eNWXl2ObWnb8GTvJ0WMSj4mmewTEzlueq/Cu/eqtxbRWKhH\nqFHJfl3yOkwKnKSKGqE2xgw1jc2OBQDVfE32tvcWrWZ/9sZZXC68jLFdx4pSnrHEnEewLH4ZZvRR\nbB1EnRg73HTX2V0Y4DkAnew6iRiVfEwy2c9dHw8bh3IM9x2qdCg66+naE+k30lFdW633aznnwiic\n0OfED0wCxtTsVycKtXq1fE32sfcRrc1+TdIaTAuepsjyCE0Ra6x92vU0pF1Pw6TASSJEJR1jRyCt\nTV6r6NwIY5lcstdogK0Zq/FEz6dUkxB0YWNlgy6OXQwagx6XE4fS6lKM7KKeRc9aEuwWbNDyEDWa\nGmxM2YinQ9XRhAMAnew64XrZdYP+SDfEOf9nLRy1EGso4s9xP2Nm35mq6GNpSX2bvSEjkG6W38Th\nS4cV71g3hskl+7+iNajw34B3xqrnl0ZXfTz6ICFX/w3I1yStwTMhz8iyP6kYvO29cavqFgrKC/R6\n3fHs4/Cy80IXxy7SBGYAyzaWcO/gjqslV40q51jWMbSzbId+nfqJFJnxfOx9kFViXDNOraYW606v\nU/3AAQCws7aDlYUVCisK9X7tpjObMC5gHOyt7SWITB6mkT0aWLg1Bs42LqpaZElXfdz1T/acc2xL\n22YSHbP1GGMIdAnUe42c3Wd3q3JWohjt9qsThY5ZNX0b9XHwQWZRplFlRGdGw93WXdXLlTRkaNPV\nutPrMD14ugQRycekkj3nwMHsrXikx8NKh2KQvp36Ij43Xq/XnMo5BWsLa/RyVWbBLEMFuQTptUYO\n5xybUjapcr0RYydWVdVWYVPKJkzvra5k0dmhMzKLMo2aWLUueR2eCFb3DPaGDGm3zyrOwulrp1VZ\nEdGHOnqKdJSQwFHW9Te8OnKf0qEYJNQ9FAm5CeCc61zD25q6FY/0eERVNUJd6LtrVfK1ZFTVVmGA\n5wAJozKMsZ20e87tQS+3XqpqngIAB2sHAEBRZZFBqzeWV5djc8pmJL0izR7LUjBkrP2mM5vwcODD\nsLa0ligqeZhUzX7+xpNwaG+LYHfTquXWc+/gjvaW7fX66rwtbRsmB02WMCpp9HDpodeuVZtTNuPR\nno+q8o+asc0dSq9w2RzGGDo7dDb4D9mO9B0Y4DkA3vbeIkcmHUOacTac2WBS316aYzLJnnNg57lt\neCjQdHvDAf2acjJuZuBm+U2EeYVJHJX49GnGqW/CUct+pY11deqKC4UXDHptYUUhIi9E4rFej4kc\nlTh87A3/Q7YqcZVJdMw2pO9EsgsFF3Cp8BJG+alzlVl9mEyyT0wESjtvxcsjTK+W25A+nbS7z+3G\nxG4TTWYUTkP+Hf2RWZSJyppKrceeyT+D0qpSDPIaJENk+vN38seFAsOS/bbUbRjtN1q1m1x0duhs\nUOdzTkkOjmUdM7mhiPrOot1wegMeDXpUNXMjjGEyWWTJljRY291CmJf62nT1oc/wy62pW/GwiXZG\nt7VoC19HX5y/eV7rsZtTNmNK0BRVNuEAgJ+THy4VXoKGa/R+7caUjZjaS317I9cztGa/NnktHunx\niMkt86vPrGHOOdYmr1Xtks36MplkvzVlB+7znaTahKArXZtxrpVeQ0JuAu7zv0+GqKQR5BKktZOW\nc46NZzaqtpkDECbEObVz0rtj70bZDfx95W9FN2DRxpCaPefcJBbla4o+Nfv6jYKG+pjOTP2WmESy\nT0sDCt134IXh6trqzBBdnbqisKIQ18uut3jcjrQdGBcwzmQWemuKLiNyEvMSUVZdhiHeQ2SKyjBd\nnbrq3ZSzPW077vO/T9XrGRnS+RybHYvSqlIM91XnuvUtcbN1Q1FFkU7Ni/V/0Ey9glnPJJL9r5vz\nANcUjPYLVzoUo7VhbTDUZygOXzrc4nFb00xvj8vGerj00Lo8xG+nf8MTwU+o/hcqoGMAzt08p9dr\nfjvzGx7v+bhEEYnDkNE4v5z6Ba8MeMUk+5LasDboZNdJ64xoDddgc8pms2nCAUwk2a+L+x1D3O4z\n+XGu9UZ1GYVDlw41+3xxZTGOZh7FhADlN+8whi7NODvSd5jE0NJerr30Wu8nvzQfsdmxmNhtooRR\nGa9+Qw9d+yOKK4uxNXUrngwxzWV+Ad2GXx7PPg6n9k4mOVO/OapP9pcuAbn2OzFzqOk34dQb7Tca\nf178s9nn957bi2Gdhym+/6qxerj0QPqN9GZnaJ6/eR4FFQWqnEjVWLBbME7n675s87a0bRgfMB62\nbW0ljMp47SzbwbGdo84bcW84vQGj/EYpun+usXSZRbs9bTseDjTNwRHNUX2y37S9DLzLITwYqO4a\nkj76ePRBzq2cZn/BNpzZoNox5/pwaOcAu7Z2zf5ibU7ZjIcDHzaJ5oAQ9xAk5ibqvLTAjvQdeKSH\naQxL9HP0w8WCizoduzppNZ4JMZ19JJqibRZt/XpUpjasVBvV/5atORaJQLsBcGrvpHQoorFoY4GR\nviNx6OLdTTlFFUX448IfJt9eX6+lTlpTacIBhNqgnbWdTuv0l1aVIvpytMmspaJr5/PFgotIu56m\nir2BjaGtGSf1eirKqsvQv1N/GaOSnqqTfXExkFK7E08PNJ8mnHrNtdvvOrsL4V3C4dDOQYGoxNdc\nss+9lYvU/FSTWaMfAMb4jWmx+a3eb2d+w3Df4aqdSNWYv5M/MgoytB63NnktHu/5uOrXrddG28bj\nO9J24OHAh1U/aEBfqk72e/fXok3g73g8xPySfXPt9qb09V8XQS5BTY7I2X12N8YFjDOpxDHabzT+\nuPhHi8fUamox5+gcvD34bZmiMl5Xp65ak/0/ewOraGMZQ3nZe7U4AmnX2V14MPBBGSOSh6qT/crI\nGLi291DdaoFi6OXWC0WVRXeMca6sqURkRiTu736/gpGJq7ma/c6zOzGpu7q3sWtsjN8YHL50GOXV\n5c0es/HMRjjbOGOM3xgZIzOOf0fty0H8dfkvMDDVLmmhj/ohwU31v+TeykVKfgpG+prON05dqTbZ\n19YCh3N3YnIv00oIumrD2mBUl1F31O5/O/MbBnoNhJutm4KRiaupZH+r6haiLkWZXNuvq60r+nbq\ni8gLkU0+X6upxZd/fYnZI2ebVBOAv5O/1mUtvj/2Pd4e/LZJva/muNm6wcbKBpeLLt/13I60HZjY\nbaLZDPNuSLXJPjYW0HTfgWcGmV8TTr3JQZOxImEFAOFr8oLYBXhz0JsKRyUub3tvlFSVoKii6J/H\ndp/djSHeQ9CxfUcFIzPM5B6TsTllc5PPbU7ZDId2Dri3670yR2UcTztP1Ghqmh0dlpibiBNXT+CZ\nUNMehdNQc2tU7UjfYbLrUWmj2mT/6+40tO1wy+x6xBuaEjQFmUWZOJBxALvO7kJJZYnJ1Xa1YYwh\n0Dnwjtr9muQ1mBZsmjMTpwZPxe5zu+8aqlhWXYYPDn6A/xf+/0yu9ssYQ79O/XAq59Rdz3HO8ea+\nN/Hp8E/R3qq9AtFJo497H8Tn3LlGVVl1GaIzo016PaqW6JTsGWPjGWNpjLFzjLEPmnj+ScZYImMs\niTF2lDEWYmxg289uxX2dza9HvCErCyssfWApHlz/IF7c9SK+vfdbkxhzrq+Gyybk3cpD9OVok9pT\ntyE3Wze8PvB1fB71+R2PL4xdiAGeA0w2UfTz6Ie4q3F3Pf5r4q8orS7FywNeViAq6QzyHoSjV47e\n8dgfF/5Av079TGYUlb60LtLMGLMAsAjAWADZAE4wxnZyzhsOsbgAYATnvIgxNh7AUgCDDQ3q4kXg\npttWvBL+raFFmIx7/e9F5luZcLV1NctED9y5bMK65HV4qMdDql4cTJu3B78Nn3k+KK4shr21PQor\nCvH9se9xZMYRpUMzWH/P/lh/ev0dj10vu44PDn6AvU/uhUUbC4Uik0Z4l3BM3zIdpVWl/8xyXpW0\nyuQ3FW+JLtllIIDznPNLnPNqABsA3NGQzjk/xjmvb5SNBWDUPmVrd2XBwvkiRnYxvVX1DOHewd1s\nEz0g1OxT8lNQq6nFkrglmNlnptIhGcWhnQOG+w7H7rO7AQBfR3+NB7o/gECXQIUjM1y/TnfX7N+L\nfA/TgqehX6d+CkUlHXtrewz0GvhPZ/v1suuIzIg0i+0Hm6NLhvEC0HBQalbdY815HsAeY4LacOp3\nDOw40Sx2hyHAcN/hiM6MxsLYhXC2ccYI3xFKh2S0qb2mYnXSasTnxGNFwgp8PeZrpUMyir+TP4or\ni3Gt9BoA4ET2CRzIOIAvR32pcGTSebTno9h4ZiMAYEX8CkwKnGQ2kxmboks21W0xEACMsVEAZgJo\ncrX/iIiIf+6Hh4cjPDz8rmPKy4E0vhO/DJ2h62mJyrnZuuGp3k/hnQPv4OjMo2bRD/NYz8fw4cEP\nMXrVaPx0/0/w6OChdEhGYYxhZJeRiMyIxPTe0/H+wffx+YjPTX4xvpZM7TUVn/z5CWKyYvD9se+x\n76l9SocEAIiKikJUVJT4BXPOW7xBaHvf1+DnjwB80MRxIQDOAwhophyuiy27SrjFp3a8qKJIp+OJ\naajV1PKsoiylwxBVfmk+T85LVjoM0ayMX8knrJnAD108xAN/COTVtdVKhyS5H4//yFkE4xGHIpQO\npVl1uVNrrtZ2Y1zLKn6MMUsA6QDGALgK4DiAabxBBy1jrDOAPwE8xTmPaaYcru1cADDx31tx3nEJ\nzn62X+uxhBDxlFeXw2eeD3wdffF0yNN4a/BbSocki/zSfLjauiodRrMYY+CcG/11WGubPee8BsBr\nAPYDSAHwG+c8lTE2izE2q+6wzwE4AVjMGItnjB03JBjOgehrO/FYb/OcNUuImrW3ao8vRn0B5/bO\neL7v80qHIxs1J3oxaa3Zi3YiHWr2Z1JrEPJrJ1z4MA6+jp1liYsQQtRMtpq9nH76/RicLLwp0RNC\niMhUlex3nd2JMT7UhEMIIWJTzUD2W7eATJudWDV2ndKhEEKI2VFNzX7VnnS0tb2F4f7mN1uPEEKU\npp5kH7sT/TpMMosJN4QQojaqSPacAwllu/DsYPPbCowQQtRAFck+JqEIVc7xeGpYuNKhEEKIWVJF\nsl+87w94a+6BbVsbpUMhhBCzpIpk/+flAxjrN07pMAghxGwpnuzLyzmu2uzHi6NNc4cfQggxBYon\n+y2HMmBpXYXBXXspHQohhJgtxZP9mpgDCLK6j4ZcEkKIhBRP9sdv7MMDPakJhxBCpKRoss+/WYkC\nh8N45T5K9oQQIiVFk/2SfdGwr+wJ747OSoZBCCFmT9FkvyVpL8KcJigZAiGEtAqKJvuUikg8PYSa\ncAghRGqKJftjSXmotrmC6eEDlAqBEEJaDcWS/ZIDf8KndiSsLFSzpD4hhJgtxZJ91JWDGNl5jFKn\nJ4SQVkWRZK/RcFyxPIjnRoxV4vSEENLqKJLs95/MALOsxqjgHkqcnhBCWh1Fkv2q6D/gqxlDSyQQ\nQohMFEn20VcOYUzX0UqcmhBCWiXZk31NDcfVtlF4cewouU9NCCGtluzJfsfRNFigHcK6dZH71IQQ\n0mrJnuzXH4uCfxuq1RNCiJxkT/Z/5xzCmK7hcp+WEEJaNVmTfU0NR267KMwcEy7naQkhpNWTNdlv\niU6BpaYD+vv7ynlaQghp9WRN9htiDiHAgtrrCSFEbrIm+5jcKIwNoGRPCCFykzXZ57WPwszR4XKe\nkhBCCGRO9pY1TujT1VvOUxJCCIHMyd6/TbicpyOEEFJHa7JnjI1njKUxxs4xxj5o5piFdc8nMsb6\nNlfW6K7UXk8IIUpoMdkzxiwALAIwHkBPANMYY0GNjpkIIIBz3g3ASwAWN1fec6PCjY3XLERFRSkd\ngmrQtbiNrsVtdC3Ep61mPxDAec75Jc55NYANAB5qdMwkAL8CAOc8FoAjY8y9qcLCAj2NDNc80Af5\nNroWt9G1uI2uhfi0JXsvAFca/JxV95i2Y6gXlhBCVERbsuc6ltN4FxJdX0cIIUQGjPPm8zJjbDCA\nCM75+LqfPwKg4Zx/0+CYnwBEcc431P2cBmAk5zyvUVn0B4AQQgzAOTd6Wz9LLc+fBNCNMdYFwFUA\nUwFMa3TMTgCvAdhQ98ehsHGiFytYQgghhmkx2XPOaxhjrwHYD8ACwDLOeSpjbFbd80s453sYYxMZ\nY+cBlAKYIXnUhBBC9NJiMw4hhBDzIPkMWl0mZZkbxtglxlgSYyyeMXa87rGOjLFIxthZxtgBxphj\ng+M/qrs+aYyx+5SL3HiMseWMsTzGWHKDx/R+74yx/oyx5LrnFsj9PsTQzLWIYIxl1X024hljExo8\nZ87XwocxdogxdoYxdpox9kbd463us9HCtZD2s8E5l+wGoennPIAuAKwAJAAIkvKcargBuAigY6PH\n5gB4v+7+BwD+W3e/Z911saq7TucBtFH6PRjx3ocD6Asg2cD3Xv9t8ziAgXX39wAYr/R7E+lazAbw\nThPHmvu18ADQp+5+BwDpAIJa42ejhWsh6WdD6pq9LpOyzFXjDul/Jp/V/ftw3f2HAKznnFdzzi9B\n+I8cKEuEEuCcRwMoaPSwPu99EGOsEwA7zvnxuuNWNXiNyWjmWgB3fzYA878WuZzzhLr7twCkQpij\n0+o+Gy1cC0DCz4bUyV6XSVnmiAM4yBg7yRh7se4xd357lFIegPpZxp4Qrks9c7xG+r73xo9nw7yu\nyet160gta9Bs0WquRd3ovr4AYtHKPxsNrkVM3UOSfTakTvattfd3KOe8L4AJAF5ljA1v+CQXvnO1\ndG3M9rrp8N7N3WIAfgD6AMgB8L2y4ciLMdYBwBYAb3LOSxo+19o+G3XXYjOEa3ELEn82pE722QB8\nGvzsgzv/EpklznlO3b/5ALZBaJbJY4x5AEDd169rdYc3vkbedY+ZE33ee1bd496NHjeLa8I5v8br\nAPgFt5vszP5aMMasICT61Zzz7XUPt8rPRoNrsab+Wkj92ZA62f8zKYsx1hbCpKydEp9TUYwxG8aY\nXd19WwD3AUiG8L6frTvsWQD1H/adAJ5gjLVljPkB6Aah08Wc6PXeOee5AIoZY4MYYwzA0w1eY9Lq\nElq9RyB8NgAzvxZ1sS8DkMI5n9/gqVb32WjuWkj+2ZCh53kChN7m8wA+UrIXXI4bhK9hCXW30/Xv\nGUBHAAcBnAVwAIBjg9d8XHd90gCMU/o9GPn+10OYbV0Fob9mhiHvHUD/ug/7eQALlX5fIl2LmRA6\n0ZIAJNb9Yrq3kmsxDICm7vcivu42vjV+Npq5FhOk/mzQpCpCCGkFZN2WkBBCiDIo2RNCSCtAyZ4Q\nQloBSvaEENIKULInhJBWgJI9IYS0ApTsCSGkFaBkTwghrcD/B0LrH2UND7W5AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x3ca1ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "forest = Forest()\n",
+    "forest2 = BurnableForest()\n",
+    "\n",
+    "tree_fractions = []\n",
+    "\n",
+    "for i in range(2500):\n",
+    "    forest.advance_one_step()\n",
+    "    forest2.advance_one_step()\n",
+    "    tree_fractions.append((forest.tree_fraction, forest2.tree_fraction))\n",
+    "\n",
+    "plt.plot(tree_fractions)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`__str__` 和 `__repr__` 中 `self.__class__` 会根据类型不同而不同："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Forest(size=(150, 150))"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BurnableForest(size=(150, 150))"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forest2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forest\n"
+     ]
+    }
+   ],
+   "source": [
+    "print forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BurnableForest\n"
+     ]
+    }
+   ],
+   "source": [
+    "print forest2"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/08. object-oriented programming/08.10 refactoring the forest fire simutation.ipynb b/08-object-oriented-programming/08.10-refactoring-the-forest-fire-simutation.ipynb
similarity index 99%
rename from 08. object-oriented programming/08.10 refactoring the forest fire simutation.ipynb
rename to 08-object-oriented-programming/08.10-refactoring-the-forest-fire-simutation.ipynb
index c0141294..4aa5018a 100644
--- a/08. object-oriented programming/08.10 refactoring the forest fire simutation.ipynb	
+++ b/08-object-oriented-programming/08.10-refactoring-the-forest-fire-simutation.ipynb
@@ -1,233 +1,233 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 重定义森林火灾模拟"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在前面的例子中，我们定义了一个 `BurnableForest`，实现了一个循序渐进的生长和燃烧过程。\n",
-    "\n",
-    "假设我们现在想要定义一个立即燃烧的过程（每次着火之后燃烧到不能燃烧为止，之后再生长，而不是每次只燃烧周围的一圈树木），由于燃烧过程不同，我们需要从 `BurnableForest` 中派生出两个新的子类 `SlowBurnForest`（原来的燃烧过程） 和 `InsantBurnForest`，为此\n",
-    "\n",
-    "- 将 `BurnableForest` 中的 `burn_trees()` 方法改写，不做任何操作，直接 `pass`（因为在 `advance_one_step()` 中调用了它，所以不能直接去掉）\n",
-    "- 在两个子类中定义新的 `burn_trees()` 方法。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from scipy.ndimage.measurements import label\n",
-    "\n",
-    "class Forest(object):\n",
-    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
-    "    def __init__(self, size=(150,150), p_sapling=0.0025):\n",
-    "        self.size = size\n",
-    "        self.trees = np.zeros(self.size, dtype=bool)\n",
-    "        self.p_sapling = p_sapling\n",
-    "        \n",
-    "    def __repr__(self):\n",
-    "        my_repr = \"{}(size={})\".format(self.__class__.__name__, self.size)\n",
-    "        return my_repr\n",
-    "    \n",
-    "    def __str__(self):\n",
-    "        return self.__class__.__name__\n",
-    "    \n",
-    "    @property\n",
-    "    def num_cells(self):\n",
-    "        \"\"\"Number of cells available for growing trees\"\"\"\n",
-    "        return np.prod(self.size)\n",
-    "    \n",
-    "    @property\n",
-    "    def tree_fraction(self):\n",
-    "        \"\"\"\n",
-    "        Fraction of trees\n",
-    "        \"\"\"\n",
-    "        num_trees = self.trees.sum()\n",
-    "        return float(num_trees) / self.num_cells\n",
-    "    \n",
-    "    def _rand_bool(self, p):\n",
-    "        \"\"\"\n",
-    "        Random boolean distributed according to p, less than p will be True\n",
-    "        \"\"\"\n",
-    "        return np.random.uniform(size=self.trees.shape) < p\n",
-    "    \n",
-    "    def grow_trees(self):\n",
-    "        \"\"\"\n",
-    "        Growing trees.\n",
-    "        \"\"\"\n",
-    "        growth_sites = self._rand_bool(self.p_sapling)\n",
-    "        self.trees[growth_sites] = True    \n",
-    "        \n",
-    "    def advance_one_step(self):\n",
-    "        \"\"\"\n",
-    "        Advance one step\n",
-    "        \"\"\"\n",
-    "        self.grow_trees()\n",
-    "\n",
-    "class BurnableForest(Forest):\n",
-    "    \"\"\"\n",
-    "    Burnable forest support fires\n",
-    "    \"\"\"    \n",
-    "    def __init__(self, p_lightning=5.0e-6, **kwargs):\n",
-    "        super(BurnableForest, self).__init__(**kwargs)\n",
-    "        self.p_lightning = p_lightning        \n",
-    "        self.fires = np.zeros((self.size), dtype=bool)\n",
-    "    \n",
-    "    def advance_one_step(self):\n",
-    "        \"\"\"\n",
-    "        Advance one step\n",
-    "        \"\"\"\n",
-    "        super(BurnableForest, self).advance_one_step()\n",
-    "        self.start_fires()\n",
-    "        self.burn_trees()\n",
-    "        \n",
-    "    @property\n",
-    "    def fire_fraction(self):\n",
-    "        \"\"\"\n",
-    "        Fraction of fires\n",
-    "        \"\"\"\n",
-    "        num_fires = self.fires.sum()\n",
-    "        return float(num_fires) / self.num_cells\n",
-    "    \n",
-    "    def start_fires(self):\n",
-    "        \"\"\"\n",
-    "        Start of fire.\n",
-    "        \"\"\"\n",
-    "        lightning_strikes = (self._rand_bool(self.p_lightning) & \n",
-    "            self.trees)\n",
-    "        self.fires[lightning_strikes] = True\n",
-    "    \n",
-    "    def burn_trees(self):    \n",
-    "        pass\n",
-    "    \n",
-    "class SlowBurnForest(BurnableForest):\n",
-    "    def burn_trees(self):\n",
-    "        \"\"\"\n",
-    "        Burn trees.\n",
-    "        \"\"\"\n",
-    "        fires = np.zeros((self.size[0] + 2, self.size[1] + 2), dtype=bool)\n",
-    "        fires[1:-1, 1:-1] = self.fires\n",
-    "        north = fires[:-2, 1:-1]\n",
-    "        south = fires[2:, 1:-1]\n",
-    "        east = fires[1:-1, :-2]\n",
-    "        west = fires[1:-1, 2:]\n",
-    "        new_fires = (north | south | east | west) & self.trees\n",
-    "        self.trees[self.fires] = False\n",
-    "        self.fires = new_fires\n",
-    "\n",
-    "class InstantBurnForest(BurnableForest):\n",
-    "    def burn_trees(self):\n",
-    "        # 起火点\n",
-    "        strikes = self.fires\n",
-    "        # 找到连通区域\n",
-    "        groves, num_groves = label(self.trees)\n",
-    "        fires = set(groves[strikes])\n",
-    "        self.fires.fill(False)\n",
-    "        # 将与着火点相连的区域都烧掉\n",
-    "        for fire in fires:\n",
-    "            self.fires[groves == fire] = True\n",
-    "        self.trees[self.fires] = False\n",
-    "        self.fires.fill(False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "测试："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "forest = Forest()\n",
-    "sb_forest = SlowBurnForest()\n",
-    "ib_forest = InstantBurnForest()\n",
-    "\n",
-    "forests = [forest, sb_forest, ib_forest]\n",
-    "\n",
-    "tree_history = []\n",
-    "\n",
-    "for i in xrange(1500):\n",
-    "    for fst in forests:\n",
-    "        fst.advance_one_step()\n",
-    "    tree_history.append(tuple(fst.tree_fraction for fst in forests))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "显示结果："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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ePZtjx44RHh7OoEGDqFSp0lu9di91796dadOmsX//fnRd59GjR/j5+fHw4UPOnz/Pli1b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\nIYRB/g+UqSYn01RDXAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x16fa2278>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "plt.figure(figsize=(10,6))\n",
-    "\n",
-    "plt.plot(tree_history)\n",
-    "plt.legend([f.__str__() for f in forests])\n",
-    "\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 重定义森林火灾模拟"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在前面的例子中，我们定义了一个 `BurnableForest`，实现了一个循序渐进的生长和燃烧过程。\n",
+    "\n",
+    "假设我们现在想要定义一个立即燃烧的过程（每次着火之后燃烧到不能燃烧为止，之后再生长，而不是每次只燃烧周围的一圈树木），由于燃烧过程不同，我们需要从 `BurnableForest` 中派生出两个新的子类 `SlowBurnForest`（原来的燃烧过程） 和 `InsantBurnForest`，为此\n",
+    "\n",
+    "- 将 `BurnableForest` 中的 `burn_trees()` 方法改写，不做任何操作，直接 `pass`（因为在 `advance_one_step()` 中调用了它，所以不能直接去掉）\n",
+    "- 在两个子类中定义新的 `burn_trees()` 方法。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.ndimage.measurements import label\n",
+    "\n",
+    "class Forest(object):\n",
+    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
+    "    def __init__(self, size=(150,150), p_sapling=0.0025):\n",
+    "        self.size = size\n",
+    "        self.trees = np.zeros(self.size, dtype=bool)\n",
+    "        self.p_sapling = p_sapling\n",
+    "        \n",
+    "    def __repr__(self):\n",
+    "        my_repr = \"{}(size={})\".format(self.__class__.__name__, self.size)\n",
+    "        return my_repr\n",
+    "    \n",
+    "    def __str__(self):\n",
+    "        return self.__class__.__name__\n",
+    "    \n",
+    "    @property\n",
+    "    def num_cells(self):\n",
+    "        \"\"\"Number of cells available for growing trees\"\"\"\n",
+    "        return np.prod(self.size)\n",
+    "    \n",
+    "    @property\n",
+    "    def tree_fraction(self):\n",
+    "        \"\"\"\n",
+    "        Fraction of trees\n",
+    "        \"\"\"\n",
+    "        num_trees = self.trees.sum()\n",
+    "        return float(num_trees) / self.num_cells\n",
+    "    \n",
+    "    def _rand_bool(self, p):\n",
+    "        \"\"\"\n",
+    "        Random boolean distributed according to p, less than p will be True\n",
+    "        \"\"\"\n",
+    "        return np.random.uniform(size=self.trees.shape) < p\n",
+    "    \n",
+    "    def grow_trees(self):\n",
+    "        \"\"\"\n",
+    "        Growing trees.\n",
+    "        \"\"\"\n",
+    "        growth_sites = self._rand_bool(self.p_sapling)\n",
+    "        self.trees[growth_sites] = True    \n",
+    "        \n",
+    "    def advance_one_step(self):\n",
+    "        \"\"\"\n",
+    "        Advance one step\n",
+    "        \"\"\"\n",
+    "        self.grow_trees()\n",
+    "\n",
+    "class BurnableForest(Forest):\n",
+    "    \"\"\"\n",
+    "    Burnable forest support fires\n",
+    "    \"\"\"    \n",
+    "    def __init__(self, p_lightning=5.0e-6, **kwargs):\n",
+    "        super(BurnableForest, self).__init__(**kwargs)\n",
+    "        self.p_lightning = p_lightning        \n",
+    "        self.fires = np.zeros((self.size), dtype=bool)\n",
+    "    \n",
+    "    def advance_one_step(self):\n",
+    "        \"\"\"\n",
+    "        Advance one step\n",
+    "        \"\"\"\n",
+    "        super(BurnableForest, self).advance_one_step()\n",
+    "        self.start_fires()\n",
+    "        self.burn_trees()\n",
+    "        \n",
+    "    @property\n",
+    "    def fire_fraction(self):\n",
+    "        \"\"\"\n",
+    "        Fraction of fires\n",
+    "        \"\"\"\n",
+    "        num_fires = self.fires.sum()\n",
+    "        return float(num_fires) / self.num_cells\n",
+    "    \n",
+    "    def start_fires(self):\n",
+    "        \"\"\"\n",
+    "        Start of fire.\n",
+    "        \"\"\"\n",
+    "        lightning_strikes = (self._rand_bool(self.p_lightning) & \n",
+    "            self.trees)\n",
+    "        self.fires[lightning_strikes] = True\n",
+    "    \n",
+    "    def burn_trees(self):    \n",
+    "        pass\n",
+    "    \n",
+    "class SlowBurnForest(BurnableForest):\n",
+    "    def burn_trees(self):\n",
+    "        \"\"\"\n",
+    "        Burn trees.\n",
+    "        \"\"\"\n",
+    "        fires = np.zeros((self.size[0] + 2, self.size[1] + 2), dtype=bool)\n",
+    "        fires[1:-1, 1:-1] = self.fires\n",
+    "        north = fires[:-2, 1:-1]\n",
+    "        south = fires[2:, 1:-1]\n",
+    "        east = fires[1:-1, :-2]\n",
+    "        west = fires[1:-1, 2:]\n",
+    "        new_fires = (north | south | east | west) & self.trees\n",
+    "        self.trees[self.fires] = False\n",
+    "        self.fires = new_fires\n",
+    "\n",
+    "class InstantBurnForest(BurnableForest):\n",
+    "    def burn_trees(self):\n",
+    "        # 起火点\n",
+    "        strikes = self.fires\n",
+    "        # 找到连通区域\n",
+    "        groves, num_groves = label(self.trees)\n",
+    "        fires = set(groves[strikes])\n",
+    "        self.fires.fill(False)\n",
+    "        # 将与着火点相连的区域都烧掉\n",
+    "        for fire in fires:\n",
+    "            self.fires[groves == fire] = True\n",
+    "        self.trees[self.fires] = False\n",
+    "        self.fires.fill(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "测试："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "forest = Forest()\n",
+    "sb_forest = SlowBurnForest()\n",
+    "ib_forest = InstantBurnForest()\n",
+    "\n",
+    "forests = [forest, sb_forest, ib_forest]\n",
+    "\n",
+    "tree_history = []\n",
+    "\n",
+    "for i in xrange(1500):\n",
+    "    for fst in forests:\n",
+    "        fst.advance_one_step()\n",
+    "    tree_history.append(tuple(fst.tree_fraction for fst in forests))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "显示结果："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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47z/o0wd271ZXE1asaO4WCSGESC6kR0uI99ixA0qVggwZ1FwsCVlCCCESQnq0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\nIYRB/g+UqSYn01RDXAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x16fa2278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "\n",
+    "plt.plot(tree_history)\n",
+    "plt.legend([f.__str__() for f in forests])\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/08. object-oriented programming/08.11 interfaces.ipynb b/08-object-oriented-programming/08.11-interfaces.ipynb
similarity index 99%
rename from 08. object-oriented programming/08.11 interfaces.ipynb
rename to 08-object-oriented-programming/08.11-interfaces.ipynb
index be345e3e..962cdadc 100644
--- a/08. object-oriented programming/08.11 interfaces.ipynb	
+++ b/08-object-oriented-programming/08.11-interfaces.ipynb
@@ -1,346 +1,346 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 接口"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在 `Python` 中，鸭子类型（`duck typing`）是一种动态类型的风格。所谓鸭子类型，来自于 `James Whitcomb Riley` 的“鸭子测试”：\n",
-    "\n",
-    "> 当看到一只鸟走起来像鸭子、游泳起来像鸭子、叫起来也像鸭子，那么这只鸟就可以被称为鸭子。\n",
-    "\n",
-    "假设我们需要定义一个函数，这个函数使用一个类型为鸭子的参数，并调用它的走和叫方法。\n",
-    "\n",
-    "在鸭子类型的语言中，这样的函数可以接受任何类型的对象，只要这个对象实现了走和叫的方法，否则就引发一个运行时错误。换句话说，任何拥有走和叫方法的参数都是合法的。\n",
-    "\n",
-    "先看一个例子，父类："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Leaf(object):\n",
-    "    def __init__(self, color=\"green\"):\n",
-    "        self.color = color\n",
-    "    def fall(self):\n",
-    "        print \"Splat!\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "子类："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class MapleLeaf(Leaf):\n",
-    "    def fall(self):\n",
-    "        self.color = 'brown'\n",
-    "        super(MapleLeaf, self).fall()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "新的类："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Acorn(object):\n",
-    "    def fall(self):\n",
-    "        print \"Plunk!\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这三个类都实现了 `fall()` 方法，因此可以这样使用："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Splat!\n",
-      "Splat!\n",
-      "Plunk!\n"
-     ]
-    }
-   ],
-   "source": [
-    "objects = [Leaf(), MapleLeaf(), Acorn()]\n",
-    "\n",
-    "for obj in objects:\n",
-    "    obj.fall()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这里 `fall()` 方法就一种鸭子类型的体现。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不仅方法可以用鸭子类型，属性也可以："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from scipy.ndimage.measurements import label\n",
-    "\n",
-    "class Forest(object):\n",
-    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
-    "    def __init__(self, size=(150,150), p_sapling=0.0025):\n",
-    "        self.size = size\n",
-    "        self.trees = np.zeros(self.size, dtype=bool)\n",
-    "        self.p_sapling = p_sapling\n",
-    "        \n",
-    "    def __repr__(self):\n",
-    "        my_repr = \"{}(size={})\".format(self.__class__.__name__, self.size)\n",
-    "        return my_repr\n",
-    "    \n",
-    "    def __str__(self):\n",
-    "        return self.__class__.__name__\n",
-    "    \n",
-    "    @property\n",
-    "    def num_cells(self):\n",
-    "        \"\"\"Number of cells available for growing trees\"\"\"\n",
-    "        return np.prod(self.size)\n",
-    "    \n",
-    "    @property\n",
-    "    def losses(self):\n",
-    "        return np.zeros(self.size)\n",
-    "    \n",
-    "    @property\n",
-    "    def tree_fraction(self):\n",
-    "        \"\"\"\n",
-    "        Fraction of trees\n",
-    "        \"\"\"\n",
-    "        num_trees = self.trees.sum()\n",
-    "        return float(num_trees) / self.num_cells\n",
-    "    \n",
-    "    def _rand_bool(self, p):\n",
-    "        \"\"\"\n",
-    "        Random boolean distributed according to p, less than p will be True\n",
-    "        \"\"\"\n",
-    "        return np.random.uniform(size=self.trees.shape) < p\n",
-    "    \n",
-    "    def grow_trees(self):\n",
-    "        \"\"\"\n",
-    "        Growing trees.\n",
-    "        \"\"\"\n",
-    "        growth_sites = self._rand_bool(self.p_sapling)\n",
-    "        self.trees[growth_sites] = True    \n",
-    "        \n",
-    "    def advance_one_step(self):\n",
-    "        \"\"\"\n",
-    "        Advance one step\n",
-    "        \"\"\"\n",
-    "        self.grow_trees()\n",
-    "\n",
-    "class BurnableForest(Forest):\n",
-    "    \"\"\"\n",
-    "    Burnable forest support fires\n",
-    "    \"\"\"    \n",
-    "    def __init__(self, p_lightning=5.0e-6, **kwargs):\n",
-    "        super(BurnableForest, self).__init__(**kwargs)\n",
-    "        self.p_lightning = p_lightning        \n",
-    "        self.fires = np.zeros((self.size), dtype=bool)\n",
-    "    \n",
-    "    def advance_one_step(self):\n",
-    "        \"\"\"\n",
-    "        Advance one step\n",
-    "        \"\"\"\n",
-    "        super(BurnableForest, self).advance_one_step()\n",
-    "        self.start_fires()\n",
-    "        self.burn_trees()\n",
-    "    \n",
-    "    @property\n",
-    "    def losses(self):\n",
-    "        return self.fires\n",
-    "    \n",
-    "    @property\n",
-    "    def fire_fraction(self):\n",
-    "        \"\"\"\n",
-    "        Fraction of fires\n",
-    "        \"\"\"\n",
-    "        num_fires = self.fires.sum()\n",
-    "        return float(num_fires) / self.num_cells\n",
-    "    \n",
-    "    def start_fires(self):\n",
-    "        \"\"\"\n",
-    "        Start of fire.\n",
-    "        \"\"\"\n",
-    "        lightning_strikes = (self._rand_bool(self.p_lightning) & \n",
-    "            self.trees)\n",
-    "        self.fires[lightning_strikes] = True\n",
-    "    \n",
-    "    def burn_trees(self):    \n",
-    "        pass\n",
-    "    \n",
-    "class SlowBurnForest(BurnableForest):\n",
-    "    def burn_trees(self):\n",
-    "        \"\"\"\n",
-    "        Burn trees.\n",
-    "        \"\"\"\n",
-    "        fires = np.zeros((self.size[0] + 2, self.size[1] + 2), dtype=bool)\n",
-    "        fires[1:-1, 1:-1] = self.fires\n",
-    "        north = fires[:-2, 1:-1]\n",
-    "        south = fires[2:, 1:-1]\n",
-    "        east = fires[1:-1, :-2]\n",
-    "        west = fires[1:-1, 2:]\n",
-    "        new_fires = (north | south | east | west) & self.trees\n",
-    "        self.trees[self.fires] = False\n",
-    "        self.fires = new_fires\n",
-    "\n",
-    "class InstantBurnForest(BurnableForest):\n",
-    "    def burn_trees(self):\n",
-    "        # 起火点\n",
-    "        strikes = self.fires\n",
-    "        # 找到连通区域\n",
-    "        groves, num_groves = label(self.trees)\n",
-    "        fires = set(groves[strikes])\n",
-    "        self.fires.fill(False)\n",
-    "        # 将与着火点相连的区域都烧掉\n",
-    "        for fire in fires:\n",
-    "            self.fires[groves == fire] = True\n",
-    "        self.trees[self.fires] = False\n",
-    "        self.fires.fill(False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "测试："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "forest = Forest()\n",
-    "b_forest = BurnableForest()\n",
-    "sb_forest = SlowBurnForest()\n",
-    "ib_forest = InstantBurnForest()\n",
-    "\n",
-    "forests = [forest, b_forest, sb_forest, ib_forest]\n",
-    "\n",
-    "losses_history = []\n",
-    "\n",
-    "for i in xrange(1500):\n",
-    "    for fst in forests:\n",
-    "        fst.advance_one_step()\n",
-    "    losses_history.append(tuple(fst.losses.sum() for fst in forests))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "显示结果："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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KSRYTQoSe6wfIXXfBjBmhaYuoN6VURAysFtbh6T1Vtd0v/Zsyj5YQQgwbBvIBLoQIMAld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\nQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x16f66748>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "plt.figure(figsize=(10,6))\n",
-    "\n",
-    "plt.plot(losses_history)\n",
-    "plt.legend([f.__str__() for f in forests])\n",
-    "\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 接口"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在 `Python` 中，鸭子类型（`duck typing`）是一种动态类型的风格。所谓鸭子类型，来自于 `James Whitcomb Riley` 的“鸭子测试”：\n",
+    "\n",
+    "> 当看到一只鸟走起来像鸭子、游泳起来像鸭子、叫起来也像鸭子，那么这只鸟就可以被称为鸭子。\n",
+    "\n",
+    "假设我们需要定义一个函数，这个函数使用一个类型为鸭子的参数，并调用它的走和叫方法。\n",
+    "\n",
+    "在鸭子类型的语言中，这样的函数可以接受任何类型的对象，只要这个对象实现了走和叫的方法，否则就引发一个运行时错误。换句话说，任何拥有走和叫方法的参数都是合法的。\n",
+    "\n",
+    "先看一个例子，父类："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Leaf(object):\n",
+    "    def __init__(self, color=\"green\"):\n",
+    "        self.color = color\n",
+    "    def fall(self):\n",
+    "        print \"Splat!\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "子类："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class MapleLeaf(Leaf):\n",
+    "    def fall(self):\n",
+    "        self.color = 'brown'\n",
+    "        super(MapleLeaf, self).fall()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "新的类："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Acorn(object):\n",
+    "    def fall(self):\n",
+    "        print \"Plunk!\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这三个类都实现了 `fall()` 方法，因此可以这样使用："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Splat!\n",
+      "Splat!\n",
+      "Plunk!\n"
+     ]
+    }
+   ],
+   "source": [
+    "objects = [Leaf(), MapleLeaf(), Acorn()]\n",
+    "\n",
+    "for obj in objects:\n",
+    "    obj.fall()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里 `fall()` 方法就一种鸭子类型的体现。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "不仅方法可以用鸭子类型，属性也可以："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.ndimage.measurements import label\n",
+    "\n",
+    "class Forest(object):\n",
+    "    \"\"\" Forest can grow trees which eventually die.\"\"\"\n",
+    "    def __init__(self, size=(150,150), p_sapling=0.0025):\n",
+    "        self.size = size\n",
+    "        self.trees = np.zeros(self.size, dtype=bool)\n",
+    "        self.p_sapling = p_sapling\n",
+    "        \n",
+    "    def __repr__(self):\n",
+    "        my_repr = \"{}(size={})\".format(self.__class__.__name__, self.size)\n",
+    "        return my_repr\n",
+    "    \n",
+    "    def __str__(self):\n",
+    "        return self.__class__.__name__\n",
+    "    \n",
+    "    @property\n",
+    "    def num_cells(self):\n",
+    "        \"\"\"Number of cells available for growing trees\"\"\"\n",
+    "        return np.prod(self.size)\n",
+    "    \n",
+    "    @property\n",
+    "    def losses(self):\n",
+    "        return np.zeros(self.size)\n",
+    "    \n",
+    "    @property\n",
+    "    def tree_fraction(self):\n",
+    "        \"\"\"\n",
+    "        Fraction of trees\n",
+    "        \"\"\"\n",
+    "        num_trees = self.trees.sum()\n",
+    "        return float(num_trees) / self.num_cells\n",
+    "    \n",
+    "    def _rand_bool(self, p):\n",
+    "        \"\"\"\n",
+    "        Random boolean distributed according to p, less than p will be True\n",
+    "        \"\"\"\n",
+    "        return np.random.uniform(size=self.trees.shape) < p\n",
+    "    \n",
+    "    def grow_trees(self):\n",
+    "        \"\"\"\n",
+    "        Growing trees.\n",
+    "        \"\"\"\n",
+    "        growth_sites = self._rand_bool(self.p_sapling)\n",
+    "        self.trees[growth_sites] = True    \n",
+    "        \n",
+    "    def advance_one_step(self):\n",
+    "        \"\"\"\n",
+    "        Advance one step\n",
+    "        \"\"\"\n",
+    "        self.grow_trees()\n",
+    "\n",
+    "class BurnableForest(Forest):\n",
+    "    \"\"\"\n",
+    "    Burnable forest support fires\n",
+    "    \"\"\"    \n",
+    "    def __init__(self, p_lightning=5.0e-6, **kwargs):\n",
+    "        super(BurnableForest, self).__init__(**kwargs)\n",
+    "        self.p_lightning = p_lightning        \n",
+    "        self.fires = np.zeros((self.size), dtype=bool)\n",
+    "    \n",
+    "    def advance_one_step(self):\n",
+    "        \"\"\"\n",
+    "        Advance one step\n",
+    "        \"\"\"\n",
+    "        super(BurnableForest, self).advance_one_step()\n",
+    "        self.start_fires()\n",
+    "        self.burn_trees()\n",
+    "    \n",
+    "    @property\n",
+    "    def losses(self):\n",
+    "        return self.fires\n",
+    "    \n",
+    "    @property\n",
+    "    def fire_fraction(self):\n",
+    "        \"\"\"\n",
+    "        Fraction of fires\n",
+    "        \"\"\"\n",
+    "        num_fires = self.fires.sum()\n",
+    "        return float(num_fires) / self.num_cells\n",
+    "    \n",
+    "    def start_fires(self):\n",
+    "        \"\"\"\n",
+    "        Start of fire.\n",
+    "        \"\"\"\n",
+    "        lightning_strikes = (self._rand_bool(self.p_lightning) & \n",
+    "            self.trees)\n",
+    "        self.fires[lightning_strikes] = True\n",
+    "    \n",
+    "    def burn_trees(self):    \n",
+    "        pass\n",
+    "    \n",
+    "class SlowBurnForest(BurnableForest):\n",
+    "    def burn_trees(self):\n",
+    "        \"\"\"\n",
+    "        Burn trees.\n",
+    "        \"\"\"\n",
+    "        fires = np.zeros((self.size[0] + 2, self.size[1] + 2), dtype=bool)\n",
+    "        fires[1:-1, 1:-1] = self.fires\n",
+    "        north = fires[:-2, 1:-1]\n",
+    "        south = fires[2:, 1:-1]\n",
+    "        east = fires[1:-1, :-2]\n",
+    "        west = fires[1:-1, 2:]\n",
+    "        new_fires = (north | south | east | west) & self.trees\n",
+    "        self.trees[self.fires] = False\n",
+    "        self.fires = new_fires\n",
+    "\n",
+    "class InstantBurnForest(BurnableForest):\n",
+    "    def burn_trees(self):\n",
+    "        # 起火点\n",
+    "        strikes = self.fires\n",
+    "        # 找到连通区域\n",
+    "        groves, num_groves = label(self.trees)\n",
+    "        fires = set(groves[strikes])\n",
+    "        self.fires.fill(False)\n",
+    "        # 将与着火点相连的区域都烧掉\n",
+    "        for fire in fires:\n",
+    "            self.fires[groves == fire] = True\n",
+    "        self.trees[self.fires] = False\n",
+    "        self.fires.fill(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "测试："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "forest = Forest()\n",
+    "b_forest = BurnableForest()\n",
+    "sb_forest = SlowBurnForest()\n",
+    "ib_forest = InstantBurnForest()\n",
+    "\n",
+    "forests = [forest, b_forest, sb_forest, ib_forest]\n",
+    "\n",
+    "losses_history = []\n",
+    "\n",
+    "for i in xrange(1500):\n",
+    "    for fst in forests:\n",
+    "        fst.advance_one_step()\n",
+    "    losses_history.append(tuple(fst.losses.sum() for fst in forests))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "显示结果："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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55+Tn55OXl8c///nPOh2rvkaOHMnixYtZsWIF5eXlTJs2jUaNGjFo0CC3zx87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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x16f66748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "\n",
+    "plt.plot(losses_history)\n",
+    "plt.legend([f.__str__() for f in forests])\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/08. object-oriented programming/08.12 public private special in python.ipynb b/08-object-oriented-programming/08.12-public-private-special-in-python.ipynb
similarity index 95%
rename from 08. object-oriented programming/08.12 public private special in python.ipynb
rename to 08-object-oriented-programming/08.12-public-private-special-in-python.ipynb
index 6ef0c9e9..bb80de79 100644
--- a/08. object-oriented programming/08.12 public private special in python.ipynb	
+++ b/08-object-oriented-programming/08.12-public-private-special-in-python.ipynb
@@ -1,147 +1,147 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 共有，私有和特殊方法和属性"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- 我们之前已经见过 `special` 方法和属性，即以 `__` 开头和结尾的方法和属性\n",
-    "- 私有方法和属性，以 `_` 开头，不过不是真正私有，而是可以调用的，但是不会被代码自动完成所记录（即 Tab 键之后不会显示）\n",
-    "- 其他都是共有的方法和属性\n",
-    "- 以 `__` 开头不以 `__` 结尾的属性是更加特殊的方法，调用方式也不同："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class MyClass(object):\n",
-    "    def __init__(self):\n",
-    "        print \"I'm special!\"\n",
-    "    def _private(self):\n",
-    "        print \"I'm private!\"\n",
-    "    def public(self):\n",
-    "        print \"I'm public!\"\n",
-    "    def __really_special(self):\n",
-    "        print \"I'm really special!\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "I'm special!\n"
-     ]
-    }
-   ],
-   "source": [
-    "m = MyClass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "I'm public!\n"
-     ]
-    }
-   ],
-   "source": [
-    "m.public()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "I'm private!\n"
-     ]
-    }
-   ],
-   "source": [
-    "m._private()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "注意调用方式："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "I'm really special!\n"
-     ]
-    }
-   ],
-   "source": [
-    "m._MyClass__really_special()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 共有，私有和特殊方法和属性"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- 我们之前已经见过 `special` 方法和属性，即以 `__` 开头和结尾的方法和属性\n",
+    "- 私有方法和属性，以 `_` 开头，不过不是真正私有，而是可以调用的，但是不会被代码自动完成所记录（即 Tab 键之后不会显示）\n",
+    "- 其他都是共有的方法和属性\n",
+    "- 以 `__` 开头不以 `__` 结尾的属性是更加特殊的方法，调用方式也不同："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class MyClass(object):\n",
+    "    def __init__(self):\n",
+    "        print \"I'm special!\"\n",
+    "    def _private(self):\n",
+    "        print \"I'm private!\"\n",
+    "    def public(self):\n",
+    "        print \"I'm public!\"\n",
+    "    def __really_special(self):\n",
+    "        print \"I'm really special!\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I'm special!\n"
+     ]
+    }
+   ],
+   "source": [
+    "m = MyClass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I'm public!\n"
+     ]
+    }
+   ],
+   "source": [
+    "m.public()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I'm private!\n"
+     ]
+    }
+   ],
+   "source": [
+    "m._private()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "注意调用方式："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I'm really special!\n"
+     ]
+    }
+   ],
+   "source": [
+    "m._MyClass__really_special()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/08. object-oriented programming/08.13 multiple inheritance.ipynb b/08-object-oriented-programming/08.13-multiple-inheritance.ipynb
similarity index 95%
rename from 08. object-oriented programming/08.13 multiple inheritance.ipynb
rename to 08-object-oriented-programming/08.13-multiple-inheritance.ipynb
index b62bdc47..bc44810b 100644
--- a/08. object-oriented programming/08.13 multiple inheritance.ipynb	
+++ b/08-object-oriented-programming/08.13-multiple-inheritance.ipynb
@@ -1,302 +1,302 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 多重继承"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "多重继承，指的是一个类别可以同时从多于一个父类继承行为与特征的功能，`Python` 是支持多重继承的："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Leaf(object):\n",
-    "    def __init__(self, color='green'):\n",
-    "        self.color = color\n",
-    "\n",
-    "class ColorChangingLeaf(Leaf):\n",
-    "    def change(self, new_color='brown'):\n",
-    "        self.color = new_color\n",
-    "\n",
-    "class DeciduousLeaf(Leaf):\n",
-    "    def fall(self):\n",
-    "        print \"Plunk!\"\n",
-    "\n",
-    "class MapleLeaf(ColorChangingLeaf, DeciduousLeaf):\n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在上面的例子中， `MapleLeaf` 就使用了多重继承，它可以使用两个父类的方法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "yellow\n",
-      "Plunk!\n"
-     ]
-    }
-   ],
-   "source": [
-    "leaf = MapleLeaf()\n",
-    "\n",
-    "leaf.change(\"yellow\")\n",
-    "print leaf.color\n",
-    "\n",
-    "leaf.fall()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "如果同时实现了不同的接口，那么，最后使用的方法以继承的顺序为准，放在前面的优先继承："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class Leaf(object):\n",
-    "    def __init__(self, color='green'):\n",
-    "        self.color = color\n",
-    "\n",
-    "class ColorChangingLeaf(Leaf):\n",
-    "    def change(self, new_color='brown'):\n",
-    "        self.color = new_color    \n",
-    "    def fall(self):\n",
-    "        print \"Spalt!\"\n",
-    "\n",
-    "class DeciduousLeaf(Leaf):\n",
-    "    def fall(self):\n",
-    "        print \"Plunk!\"\n",
-    "\n",
-    "class MapleLeaf(ColorChangingLeaf, DeciduousLeaf):\n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Spalt!\n"
-     ]
-    }
-   ],
-   "source": [
-    "leaf = MapleLeaf()\n",
-    "leaf.fall()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class MapleLeaf(DeciduousLeaf, ColorChangingLeaf):\n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plunk!\n"
-     ]
-    }
-   ],
-   "source": [
-    "leaf = MapleLeaf()\n",
-    "leaf.fall()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "事实上，这个顺序可以通过该类的 `__mro__` 属性或者 `mro()` 方法来查看："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(__main__.MapleLeaf,\n",
-       " __main__.DeciduousLeaf,\n",
-       " __main__.ColorChangingLeaf,\n",
-       " __main__.Leaf,\n",
-       " object)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "MapleLeaf.__mro__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[__main__.MapleLeaf,\n",
-       " __main__.DeciduousLeaf,\n",
-       " __main__.ColorChangingLeaf,\n",
-       " __main__.Leaf,\n",
-       " object]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "MapleLeaf.mro()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "考虑更复杂的例子："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class A(object):\n",
-    "    pass\n",
-    "\n",
-    "class B(A):\n",
-    "    pass\n",
-    "\n",
-    "class C(A):\n",
-    "    pass\n",
-    "\n",
-    "class C1(C):\n",
-    "    pass\n",
-    "\n",
-    "class B1(B):\n",
-    "    pass\n",
-    "\n",
-    "class D(B1, C):\n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "调用顺序："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[__main__.D, __main__.B1, __main__.B, __main__.C, __main__.A, object]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "D.mro()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 多重继承"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "多重继承，指的是一个类别可以同时从多于一个父类继承行为与特征的功能，`Python` 是支持多重继承的："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Leaf(object):\n",
+    "    def __init__(self, color='green'):\n",
+    "        self.color = color\n",
+    "\n",
+    "class ColorChangingLeaf(Leaf):\n",
+    "    def change(self, new_color='brown'):\n",
+    "        self.color = new_color\n",
+    "\n",
+    "class DeciduousLeaf(Leaf):\n",
+    "    def fall(self):\n",
+    "        print \"Plunk!\"\n",
+    "\n",
+    "class MapleLeaf(ColorChangingLeaf, DeciduousLeaf):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在上面的例子中， `MapleLeaf` 就使用了多重继承，它可以使用两个父类的方法："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "yellow\n",
+      "Plunk!\n"
+     ]
+    }
+   ],
+   "source": [
+    "leaf = MapleLeaf()\n",
+    "\n",
+    "leaf.change(\"yellow\")\n",
+    "print leaf.color\n",
+    "\n",
+    "leaf.fall()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "如果同时实现了不同的接口，那么，最后使用的方法以继承的顺序为准，放在前面的优先继承："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Leaf(object):\n",
+    "    def __init__(self, color='green'):\n",
+    "        self.color = color\n",
+    "\n",
+    "class ColorChangingLeaf(Leaf):\n",
+    "    def change(self, new_color='brown'):\n",
+    "        self.color = new_color    \n",
+    "    def fall(self):\n",
+    "        print \"Spalt!\"\n",
+    "\n",
+    "class DeciduousLeaf(Leaf):\n",
+    "    def fall(self):\n",
+    "        print \"Plunk!\"\n",
+    "\n",
+    "class MapleLeaf(ColorChangingLeaf, DeciduousLeaf):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Spalt!\n"
+     ]
+    }
+   ],
+   "source": [
+    "leaf = MapleLeaf()\n",
+    "leaf.fall()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class MapleLeaf(DeciduousLeaf, ColorChangingLeaf):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plunk!\n"
+     ]
+    }
+   ],
+   "source": [
+    "leaf = MapleLeaf()\n",
+    "leaf.fall()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "事实上，这个顺序可以通过该类的 `__mro__` 属性或者 `mro()` 方法来查看："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(__main__.MapleLeaf,\n",
+       " __main__.DeciduousLeaf,\n",
+       " __main__.ColorChangingLeaf,\n",
+       " __main__.Leaf,\n",
+       " object)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MapleLeaf.__mro__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[__main__.MapleLeaf,\n",
+       " __main__.DeciduousLeaf,\n",
+       " __main__.ColorChangingLeaf,\n",
+       " __main__.Leaf,\n",
+       " object]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MapleLeaf.mro()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "考虑更复杂的例子："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class A(object):\n",
+    "    pass\n",
+    "\n",
+    "class B(A):\n",
+    "    pass\n",
+    "\n",
+    "class C(A):\n",
+    "    pass\n",
+    "\n",
+    "class C1(C):\n",
+    "    pass\n",
+    "\n",
+    "class B1(B):\n",
+    "    pass\n",
+    "\n",
+    "class D(B1, C):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "调用顺序："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[__main__.D, __main__.B1, __main__.B, __main__.C, __main__.A, object]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "D.mro()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/09. theano/09.01 introduction and installation.ipynb b/09-theano/09.01-introduction-and-installation.ipynb
similarity index 100%
rename from 09. theano/09.01 introduction and installation.ipynb
rename to 09-theano/09.01-introduction-and-installation.ipynb
diff --git a/09. theano/09.02 theano basics.ipynb b/09-theano/09.02-theano-basics.ipynb
similarity index 100%
rename from 09. theano/09.02 theano basics.ipynb
rename to 09-theano/09.02-theano-basics.ipynb
diff --git a/09. theano/09.03 gpu on windows.ipynb b/09-theano/09.03-gpu-on-windows.ipynb
similarity index 100%
rename from 09. theano/09.03 gpu on windows.ipynb
rename to 09-theano/09.03-gpu-on-windows.ipynb
diff --git a/09. theano/09.04 graph structures.ipynb b/09-theano/09.04-graph-structures.ipynb
similarity index 100%
rename from 09. theano/09.04 graph structures.ipynb
rename to 09-theano/09.04-graph-structures.ipynb
diff --git a/09. theano/09.05 configuration settings and compiling modes.ipynb b/09-theano/09.05-configuration-settings-and-compiling-modes.ipynb
similarity index 100%
rename from 09. theano/09.05 configuration settings and compiling modes.ipynb
rename to 09-theano/09.05-configuration-settings-and-compiling-modes.ipynb
diff --git a/09. theano/09.06 conditions in theano.ipynb b/09-theano/09.06-conditions-in-theano.ipynb
similarity index 100%
rename from 09. theano/09.06 conditions in theano.ipynb
rename to 09-theano/09.06-conditions-in-theano.ipynb
diff --git a/09. theano/09.07 loop with scan.ipynb b/09-theano/09.07-loop-with-scan.ipynb
similarity index 100%
rename from 09. theano/09.07 loop with scan.ipynb
rename to 09-theano/09.07-loop-with-scan.ipynb
diff --git a/09. theano/09.08 linear regression.ipynb b/09-theano/09.08-linear-regression.ipynb
similarity index 100%
rename from 09. theano/09.08 linear regression.ipynb
rename to 09-theano/09.08-linear-regression.ipynb
diff --git a/09. theano/09.09 logistic regression .ipynb b/09-theano/09.09-logistic-regression-.ipynb
similarity index 100%
rename from 09. theano/09.09 logistic regression .ipynb
rename to 09-theano/09.09-logistic-regression-.ipynb
diff --git a/09. theano/09.10 softmax on mnist.ipynb b/09-theano/09.10-softmax-on-mnist.ipynb
similarity index 100%
rename from 09. theano/09.10 softmax on mnist.ipynb
rename to 09-theano/09.10-softmax-on-mnist.ipynb
diff --git a/09. theano/09.11 net on mnist.ipynb b/09-theano/09.11-net-on-mnist.ipynb
similarity index 100%
rename from 09. theano/09.11 net on mnist.ipynb
rename to 09-theano/09.11-net-on-mnist.ipynb
diff --git a/09. theano/09.12 random streams.ipynb b/09-theano/09.12-random-streams.ipynb
similarity index 100%
rename from 09. theano/09.12 random streams.ipynb
rename to 09-theano/09.12-random-streams.ipynb
diff --git a/09. theano/09.13 modern net on mnist.ipynb b/09-theano/09.13-modern-net-on-mnist.ipynb
similarity index 100%
rename from 09. theano/09.13 modern net on mnist.ipynb
rename to 09-theano/09.13-modern-net-on-mnist.ipynb
diff --git a/09. theano/09.14 convolutional net on mnist.ipynb b/09-theano/09.14-convolutional-net-on-mnist.ipynb
similarity index 100%
rename from 09. theano/09.14 convolutional net on mnist.ipynb
rename to 09-theano/09.14-convolutional-net-on-mnist.ipynb
diff --git a/09. theano/09.15 tensor basics.ipynb b/09-theano/09.15-tensor-basics.ipynb
similarity index 100%
rename from 09. theano/09.15 tensor basics.ipynb
rename to 09-theano/09.15-tensor-basics.ipynb
diff --git a/09. theano/09.16 tensor indexing.ipynb b/09-theano/09.16-tensor-indexing.ipynb
similarity index 100%
rename from 09. theano/09.16 tensor indexing.ipynb
rename to 09-theano/09.16-tensor-indexing.ipynb
diff --git a/09. theano/09.17 tensor operator and elementwise operations.ipynb b/09-theano/09.17-tensor-operator-and-elementwise-operations.ipynb
similarity index 100%
rename from 09. theano/09.17 tensor operator and elementwise operations.ipynb
rename to 09-theano/09.17-tensor-operator-and-elementwise-operations.ipynb
diff --git a/09. theano/09.18 tensor nnet .ipynb b/09-theano/09.18-tensor-nnet-.ipynb
similarity index 100%
rename from 09. theano/09.18 tensor nnet .ipynb
rename to 09-theano/09.18-tensor-nnet-.ipynb
diff --git a/09. theano/09.19 tensor conv.ipynb b/09-theano/09.19-tensor-conv.ipynb
similarity index 100%
rename from 09. theano/09.19 tensor conv.ipynb
rename to 09-theano/09.19-tensor-conv.ipynb
diff --git a/09. theano/apply1.png b/09-theano/apply1.png
similarity index 100%
rename from 09. theano/apply1.png
rename to 09-theano/apply1.png
diff --git a/09. theano/apply2.png b/09-theano/apply2.png
similarity index 100%
rename from 09. theano/apply2.png
rename to 09-theano/apply2.png
diff --git a/09. theano/apply_no_opti.png b/09-theano/apply_no_opti.png
similarity index 100%
rename from 09. theano/apply_no_opti.png
rename to 09-theano/apply_no_opti.png
diff --git a/09. theano/apply_opti.png b/09-theano/apply_opti.png
similarity index 100%
rename from 09. theano/apply_opti.png
rename to 09-theano/apply_opti.png
diff --git a/09. theano/download_mnist.py b/09-theano/download_mnist.py
similarity index 100%
rename from 09. theano/download_mnist.py
rename to 09-theano/download_mnist.py
diff --git a/09. theano/load.py b/09-theano/load.py
similarity index 100%
rename from 09. theano/load.py
rename to 09-theano/load.py
diff --git a/10. something interesting/10.01 maps using basemap.ipynb b/10-something-interesting/10.01-maps-using-basemap.ipynb
similarity index 99%
rename from 10. something interesting/10.01 maps using basemap.ipynb
rename to 10-something-interesting/10.01-maps-using-basemap.ipynb
index b1ce966a..c767b1de 100644
--- a/10. something interesting/10.01 maps using basemap.ipynb	
+++ b/10-something-interesting/10.01-maps-using-basemap.ipynb
@@ -1,102 +1,102 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 使用 basemap 画地图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 安装 basemap"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "最简单的方式是通过 [conda](http://conda.pydata.org/miniconda.html) 来进行安装：\n",
-    "\n",
-    "    conda install basemap"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "也可以下载下来自己编译。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 简单使用"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "绘制一幅世界地图："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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LB8H9L7ikuviZ1LlEJSJMJyOKaUGv36Pb7aKUoiwKyqJgNBoxLadUkyJYU3a3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CTeeesttjFw3vS8fbnlSrksYKU4Xiyh69m2jSSOMsqdrl2+kCLTcsLRYk5SsHATznc7LvrI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\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NjfTzGs/6bD4KFh9ddCTxmRJ95eqhdUo8DBKBfqEPD/0MvSz75quIfXERSNoNkIRtpeIH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K06NguqWyeCLLZXMdIXBDisBOlEiNYD28Nd5uU8opfh/VVTMStn5OkwOOpChIiod1gvsLJVswzP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5omKAiOUot+eI6mRPKZM6IAVjQucw+ubXABtDYoXzOuZ5TeoYPHa4vRiqqRshz9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\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WUaqmbaONUItnc+OVzolUtycMypdYNArVunMi5bkzpr6XRK+ySm9mgnXbcnb/zxjEIAJS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medO89jhJ1mZJ52rIsy9MZj9kXNv5POrMe1cUTReTFunI8pAi/UIRcjLijK7S9w+9c/ZDabEkLg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AJP9r6lFiLgFhre9v+C4DggbW76nCpOuMhVwJlJK+dqTz+/bocI/KVAiVYQVkSlFZS5ZlCKDb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jaWgF3Rf5Z1zDoRJBBJXayetoZyYGwPt0BFiX6vQr8q0ShKcWrCSC6ZFvsmRHPG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\nFBQUFBQUFBQUFMzg/wAV5zraNV6z8QAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x3caf278>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "from mpl_toolkits.basemap import Basemap\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "# lon_0 is central longitude of projection.\n",
-    "# resolution = 'c' means use crude resolution coastlines.\n",
-    "f = plt.figure(figsize=(16,9))\n",
-    "m = Basemap(projection='robin',lon_0=0,resolution='c')\n",
-    "m.shadedrelief(scale=0.2)\n",
-    "plt.title(\"Robinson Projection\")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 使用 basemap 画地图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 安装 basemap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "最简单的方式是通过 [conda](http://conda.pydata.org/miniconda.html) 来进行安装：\n",
+    "\n",
+    "    conda install basemap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "也可以下载下来自己编译。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 简单使用"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "绘制一幅世界地图："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LB8H9L7ikuviZ1LlEJSJMJyOKaUGv36Pb7aKUoiwKyqJgNBoxLadUkyJYU3a3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CTeeesttjFw3vS8fbnlSrksYKU4Xiyh69m2jSSOMsqdrl2+kCLTcsLRYk5SsHATznc7LvrI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\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NjfTzGs/6bD4KFh9ddCTxmRJ95eqhdUo8DBKBfqEPD/0MvSz75quIfXERSNoNkIRtpeIH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K06NguqWyeCLLZXMdIXBDisBOlEiNYD28Nd5uU8opfh/VVTMStn5OkwOOpChIiod1gvsLJVswzP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5omKAiOUot+eI6mRPKZM6IAVjQucw+ubXABtDYoXzOuZ5TeoYPHa4vRiqqRshz9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\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WUaqmbaONUItnc+OVzolUtycMypdYNArVunMi5bkzpr6XRK+ySm9mgnXbcnb/zxjEIAJS\nkZwOEbHO5HgfU/Zo68WzC9wAeO8/PLp/+8xJT9OZls/g0QM6G3rCMnn4hebNBCIveMXztrzmOJh9\nOAsW47fPCrWel6EVQ8aZRkgyKRRawEksMwBAri8XlsV+X3R73OLwk6Kxnm2fd17TavtJ5VNl2g0s\nvckhfW+9R3mhlYCxnqZ1tFrTmpgn0N94mWa2j1/MsGLAWu59cpPNC2tx8XMQgme3HHDgWrJgQKS3\nRdtF6xjlFolqqM65SMFcKXTcti1KyalyAmVZRPpJWmSato3gMd1TrqWYS2Joneo7Jlec9542WAZV\nL8Z/lGKkC9rgaK3Fi0BhaL1QhkClG+a2jQthMjgLrWOEESiVYtq02LbpcoKrVLMxK9UpUTjr8IVQ\nGE1d15iiYGPQo140LGxNORjS00JVKCbzhkNr6Y36GCXUTUOdFCytDZSmwAdLr1cgwGxeU5iCj/b2\nMYXhYDrjg/0DFi5gCfSrNfam80ib8RB8FnJ4cB44+8kLnXd5fP8/z7n0t7k9aq7xGVxkl/vKPtkg\nCIQY9UvA50fv/BG2aRCBWeW42BtF8RmJcvZGK65WfW7NTtCiKBGsaxAfEB2j7coYitKgfSw94duW\nsiyx3hK8Ah8pXzE3KVKwax+4ff8u1caI64MtDu/f4/b+IW996cu8//6P2X7pMoPBkMVsxrA3RHnP\n5PiQQoSTe59SlFVkLkwmiDEEDO10QaMLqCxNPWO4vkYxGvLa1jYf3vqQcTWgdp5ZXTOpa6qywge4\ns3+D3a3XUkmIFEnsjNhU/UBOe+1z31cSZf1NKtPj/fLBZ6C3gkIjM0FiZKVN6olLfJGtW7L7jQww\nIkD1p2iqSyeupGtJkvpZhTIkLE+W7s/CN3Ku5XEuDbs70+p1Luem/P8cXcy5shksPaInP+K789uL\nXOse54ReBZNntz21LpIA44ozIebVrVjnIY/VBOYll0uK66ZX0jkbJDk1Y9AhOjFU6j9RnVqirZv6\nVQZgy8hRcmSoMbUTlBS0XrAWvvHqP6BQws27ls3RNoPhFqYoubId11clip2NL+M3vsTu1V+grhcg\nhtYvS2vM2zaCVQGF5Xh+k/n+j/mX77zP5uU3uf7q9yiLAbV1SWAmBSgkRsO0RMCrlErgUNAIouL3\nkpzQqw55kexEOe34yFE+n5hA8ZFHMO5RXNu5zpeu3ObO/i0a31Dqklk95eLGZV7afqWzY6yLgjxt\nyhF2SviZ67/AH/zod3ht2OM6Cms0H3z8Q3Y2d9gYv5rLa+I8lMWQWdOiJaCN4nD6IWu9V2ic7YRw\nOqcKgeAyqHKEAG2h0WqI8ylC184p9RBTKOp2xqC8wv7+25T96zF1zEkHvlnpZl3fXOnfsZ/+v+y9\n17MlWXbe99smM4+5/pZvO22AGWAAjIYQQCpIUcEIuRD/AT1JoSe96n/Rm/SoJ0XwiSEpRDFCokBR\nAkCQBGZ6ZtDT077Lm+vOOWm20cPaOzPPNVW3XHdD1I6ounVP5cncmbnN+tb61rdicmqpfi0Yz9e8\nZF2kgvy0lq2liIzbhC5ovefhna/ZvnLjmed4JlgEvjh6eHd9ghKRqjUxf3AGjF3UQgwYJbxo/Fna\nxatsuYBo9uSEkRnl0+g7C3lzGxaWV9vyqn65+5WFXmiEnY/EaEApyqKgc47OD8pu31W77GbxPODt\nVUTmXjWV9PvYTm+ca3SlBMayWIxKNDetoHOK1kjdxcJpWq3Z3n+L+48/JbgTrl27zuHBAbfe/yFu\n8YS3b+7z5e17lLubvBcC/6Jp2JzPqYoiKaGKeIb3ni54tDFisCpF1CrlNmoKY9LiHFHKEGPo8xZ1\nXsyCpyyK3iM+UFiF8qq1bDwaUU6z2tBFyb0oUskJozUeyXmYTiq8kzBbl+TSQ5siOTp5/xWp8K7Q\nWUtb4Nsap0QkQiUWQmlLKZlhJCdEAHGk1UieSIBV3VAoxeOTBVf3rzBR8PhkQTSGqTX40BGQ56C1\noXOBwkpOpMWgVKTQmiPX8WTZUZUVi1XL8aphEaTe3R//+B+yajuaLuCdKLxlin9eM3hKLPFvGji7\nzHz+Lp1D+frn9UWMwwCYIcobcwQrRxOH/clHuHblHY5Xj1i2DYcnx9zTmraEH2zuoFyL7xxhtcS2\njsl0Ll55bVAEDKpXIq2UlItBQ2Et2SBpvYgmSTkYWDlH0JqoYW9nj52tLezBEY9XK956/32WB48A\nRVlVWDQTY3CrJV29oEzgbuUalBKRLA2cLJYcHh3xWdtw4+SQN7a3CHjuPjzkzQ8/4KbR/FXdUNgp\nW7MCHQLz6RyXcnRPFo9RKkqkpi9bI3mXWbKf7JhK22omCutkjE+sofUq5WiCUaoXh8vvQSkRe7Ap\nAuijCMilFMT0zFJ0MgPTOLIO1BiM5H+Px6vYS0qn/CUF2ov9A4EkEA1KyqpkZ9vZ/L38xydhmthH\nczxCse3tizQGw9ixH/u/vgfz//kd8hfNr6cdfxowkqPDKWockiWv48gs00Ijzmup7kG3MFqsSqAp\n5d6rIHNIgGFEK5Noml6c+ukdZbq06t/RACbkZ4pOKoUxkd2dD0FFFm1Edx2TQhPaO8S4ovWRK5vv\ni8J50DjncAhDR6EojMIWCu3vcPf2J9xbHDEv3+Dv/f3/Ah9E9bNJ4nQm8bS1GuoqqjTnFEI51QpM\nYivoOAaI6T76W1IJdD7r3WZFc5mXf/zh3x3Gp1Ks2hWTYiJ5o8FL6a8oIjLOC5W+04rKTPjg1u8S\n6q+5e/cu7YOWO5uPWbaH7G2/jcby+PEXtL5lPn8nmd0SqdyavUPT+bQGp3ccssM9YZoAMe2vrfNS\nLkQJBVkXJcvmnji/iw02Ztd4dPwQazWFUTRumLFq5MDPziY9XocQp36IER3ox560hGNGz/epLKFz\noEsi5ksJo/xJOsXxw3tsX7n1jPd1SbB4ePcL0XzPy2C/CabE4LywXqLFCC7m2x/dzCsEjJrxJi0L\n6XlRN792vTP+vHN+P79/CnqKXDjnHtbvLY68DBcvdv37Tp5BH6BpQWsxVCd2SQgTXOIthXMEfF4X\nCH86J/rie3revMHzzv885/m2gOnztld5vdO5LePz57U3xlyDOBUEV+B8oHUB20neojEa6xTzvXeI\n3S9pmpr96zd48uAeX9++x858TrVc0MyntHtbbC4WgEL5iA0Kr6Kc3zusFaEaY7RQXwGHx+baicnY\nIm1M3gdI3rsYwFozqrNIPxkUAhhjDJhRfUMXROraBc+yrimLAgAfAtZaus6JFzNFDDqlKJ2R0hkq\nFW6OkoOYFfPatuOkrlGlha7rvWEhbXDTqqRrHEqJ08woSwwBY6XsQOMd5XRKcA336xW6LGi9Q6Fo\nO8/GrGRWWjYnFYumoQ2SO+m9p+sCG5MZpdF0RcFBvaAsSqJSLKPnd9/799Bmzqpu6LpAm/LcfN7w\n+qUmbTIvuQa8jvlx2XOuRcyf0b5vdPP1eTjkpeTafVneJHuPYxSnSoyRjfkewQdm5QRTWK5sbvFm\nZVCdkwi8FgGOvaKU/KoQpM6XgsoU+M6hYyQEjwASoY5HkUCVemBlhYoB7wNFWRGNwbiO3dkmldd8\n/vAB29euiLPVGIpJRfQebSWXd2NjC8oJbbNKjhODKawo/SqNV5HdK1e5f3LE/WrCzsmCxjmuX7uK\niZE7D++yc32ftmm4Nt/i0eNHdC5Se0dVFFhjaJoF1swojMeYDBpJxlxWGkzBjB5E9i+A1klZAnlF\n8ozHztVMDdVa9+cYB5sygJAC6iPjLuVGZlpsPwbSITn/KecSq2RsGxRBazoVQBkRFAmSE5fppOdb\nGoNKwfogGwxHEe/JoYcRQErAtf/0OweK+X2Nfglj9H3Rd84e8EKpSemFZIeNykGFHLIJ4FPJMqmT\nKSUiQpDImAPwkBVvNRk8KrxSaOVRgd7BMIBFyQEckNX6LcvHKkX5klhOGueFVsRoabrIanFE297D\ndSdszn+IC5o2pFqwUcaZtZqme8TXd3+GDo7djR/wxq0f06Y6vj4ZBzn6qXsArBPwzeA1U1HTfBsB\nwyFKlmngGQadbaeBe//u0rjPkfg8T6tikpxoMs98AoqdF0V3l6J7wUdu7n7AF/drvi4fse88v3/1\nGkdffM0vl/+Y6+//bfb23uVo+RhUgJjKFaVyF5IrPuDUHHXLDiG02CRig8cezPugUcpTmBmT5dcc\n1F/D9SnXdt5n0ayS40mi03F9sAPyjpTSfZ5yBqyA2FLJvajzHs6gnqpj3jtGedzQOyHWcFUaWL0T\nLY7njPw8fHiXZnn0+Tmvba1dBix+s1wc6+BaClvKixsBE0kWVn2Jz0uDxnM+e1XgJj9CpcQrEvzF\n1zzVg6f07rxjxwty7Cd734+xd/kl7i2JChODQkcRImj8jDVNtIux7LfWxlGt8/79qs7/qtu3bWQ+\n7/Uu60U9e+yQw5Y56iEEfDKOOh9onafpRNnNGs1s6yaxvsPh/UfcPjzk5q03uHr1KvePa6auZT6b\nU1Zz/tbWNn+pkjgNUt7FKE1lS1z0oDQ+hkTjiRil0zUdmZrjiQL4cq0lBUordBLBkY1L9TUQNZJs\nP7ElSmtW9YoITGwJIWJtQRdcAqrSn8IYGlzKk05RA6BVkak2mBiJSoQL0AoTwAWPRxFTzmU2NjWA\nlgLr3nsqHemi5Hy5CLuzCSZGrLas2oajuqF2hvnGJsF1gAjg2KLANy27mxsU9YpaK/ZswcOmobQm\nAUPNqnNoa6iqirrtWLmGjfk+eztvc7RsWXV+KD6cvZExjtySp11yL7YOfVfRuu8i+vG869XTqG+n\njux9yKf/N6ZJmiPpPkQ2qhlaVXSu4cr2Nm9MSjg5AmsxUdFFl5QIFaZuCM7RxMDGfBPXdgQv+VWF\n8oOnPBu+Sr5XaC3gUXvJGdaaJkLVOo4OHrGzt0thpezGdDqjaluZ123LZDJjtTima1uMtWitKIwl\natDG0jUtcTKD1nOjrPhca1bb2xzcv89b+zvMixJvFBpNO51Rdo7GO0LUbM832LKGX3/zDZ/f+Tnv\n3voDKmuptaHVvjdcjRL2hMogqX+W+TkrXBgsEjFws4d+/NmQ2yg//Zm91I+/k4w1EdPRvaN49EYH\n0JiTUEk5lFq+a5S4sX0QxgJZ/GbNsBz3fHTu0b9U7k8Guunw/hvx1PHxu6u3erZlgP1soHjmm8/h\nCD7dQgIkWX9SnpsYUFncMzsJYqZWZns3hB74KSQyKdHFtHf1Y2jIn+1/5GPHgyvC+mGqH6cZpGml\n8UZSJWblFbav3eB4eZvbX/8lt968RVlsoxjqKloNxkTqdsFhZ7mx+yFXr/wgAUW5tlGSJiKRshFA\nJOfWqgEkkvudwGuCKmMq5UUg8fS7OEshzs6NNDdjpG5rKjvp51SIAtRyiaEQhtzFTjl8jNza/yHW\nzFjUh/zy8BGrScXGyTE7jz7lkQ/M51c4OH4Aap8QSDmQ6SejucMwPyIyf41KQDyJ7wRFr5ZKWVLP\nPmC2oWkdaL2iLCZ0rsUo16u4j83zTPUtrcEoRefE/ojBp5xCoT5nZ2/uSwaWg5bz8CwTDDvbRtfv\n6272/ZBeHT68jbHFg2e8vmeDxRij29jebY4e3J5cffMHxDDQxeQAnTylse/+2gQdTYbLtGcZM+uY\nKEvlrB8vD1b1RrKGs2qh57aLjslXHYPJYRGJxLzWn7mX836/rPGfyS8RyfFUyZvZuZDohHnhUT19\n5fvQvg3j8ruinL3qqODrvAeJKEZMzF5UUm23mGTcA52DOuX2WKMpTUGtdwn+GzbLTY6XK9764Id4\nF/j6Fw+5iWdeiqHXrTqKUtERKBBHhvOeyFC8PoSAU5HCWFFJTX3TWku5ixSF1Mr0kXkdYW4niUoa\nicFT2Iq6bTBagCQhYKxFAVVRSl2lENgq5hx1NYUyTJSh7loKo9Eoatf1uY8WiEZDN9SXgxQRSAbm\nzFiW+GGBTRulV4roI1OrCVFTaI3VYLVmE1DR87BeMZvM2CwKdIysgkKFmOpjQbCG3aLg9vERm7u7\nHD55QhuV5J24joPgUEbjlJDOat8wrTb5yYd/n2XTsWwdrXN0IcgzTj9jH038bsDW87RnReVevp1v\nZD/1G69wPo7PNVav7jf5FNlgNDcF+AdCtHz4gz/gV5/+Ge/t7lI0K7quwzlHWVZE54mFTRRtoUmr\n1uHqJbaciDIhgeBIuVdDjp8xJXhPcEGOSbUZo4KpKTh4fJ+u88xnM0JTo6oJXduyWrVs7URWyxNQ\nwsyxVUWmvCvA2gKN4mC1ZPPmTeq797k6nfHXh49Z7l/h/Q8/wAaPbxp2Z1NOlivqtuU4eFYhUBUl\nvutoY+Tqzg4ny8f8xUf/hJ/86D+Reax1ovqJ194oLcIkiYK9ziAaPWsArdfsk9Me9hgi3bhMhxJJ\ne6Gjxt4YG4yybCyLx16YStktJS0kNkcwaZ9O78DF0FPaxu18elm2cc7aEyFk9sXYaTwSuhqPO/Xt\n6Rr0133qHvc6+7JuKWa7cmxf9qXHktEclOrno8mWV8yjSGjkUjMz5+alf2dAFVUCogPIgn5r6cHX\n2oe5D2sIPyv45shexEUIQWoUdy4wKa/z/gf/saQnhYjGo41FRdkPW/eEnY232f3wXaFwupAi3GoA\ntSkXOLFLyVHDsShUH91knVqaRW/Oxydn7dzTDJHTc2/8f1UxSQBOGHUhSApW63wfVXQhJhYGdC7Q\nWc32/G2ubCvMDQG/ITo+/eZfox5/wbuTPaaTa5w0TsSJlEGntBzlk+CQEtsjV04QvCbvWjRBUs3G\nKNHC4KT0VesEcFutmFaKk/pnqHgVozf6sZIBaHZ0Wa2ZlSY58Bze5fESeuA3POEMGDMaiKMVJo+i\ns+9g/ITH62AGiRDRGp7c/ZKN3StH57zKtXaZyCLzzZ3jJ/e+mtx46328DpIUHMW/oJIihQ9j+sap\nRS3fjBoWvafZAhcBRgVYbQSFn79+9i1vytmTsr50nOnZJVrvxwPWF0B1UScuaJc1SPqc/OzhSptR\nBKKXsPSZQq7fs3ZRTt3ztu86H+l1tMs6Dy533+uToT9nFO+nDpFgJN9HSmlEnIooHzAuUHdeuPZO\nsXn9Qxar+xzde8D25i4uRupmiZlscPDwIfVqyd50xrtNzaPpDGKgSPkcxhoqY4UqR8CnfEPnBXSp\nKIn/ESiNxXkvIlMklVQv9Q7b4JhguVpOOFgt8SYyKSecrJZYa/qcJB8kdzEbgcu2IRDxMXDQ1kxM\ngUHmkrVWxEC0JvqAS2BLaYk4aoQW0gZPFxx1pg6KrJusKUnx1YVA0CV0kagjpZU8Lu8cy6ZlXpRo\npXhycsTWZMbCd3TOYaqK6B0b0ykP6hWqKCiU5qh1tBg8QaKfIRJMJHaBxnmqcpM//r3/TIBi01F3\nns4FXHqXOSoVyQbn2Q3lddHSX6ZdJFjxis5+6eu+siueMYaGnpzetOU/8hogv4a0lvsYub77Fr8K\nf4oNntXiBKM1k2pCt1rQdR0bZUXUEL3HWEvTtIQo+a5t8KBTLnCOAOQ/0WNVRGkRcVCpg9EFQmzw\nIbK1tYnvOiKBSYwcdZ7tjSnEQFkUtF2LLYpkNCtiCJTVBHyQiH9ZcNI2XLuyz2e//oTpbIayJUff\n3GH/1nUeLZdUCgGtBI5doPaGGCT/ObiO4+NjWm2QiE8SD0nRORWS0IZK1T+UUN5NPEW7HhtN2YDX\nQ5SkB1ZIlGGIECoGI4Pz7YwE9jsfhmtkJ3X6Zo4USyRDIpE5wpQ9/erUOZ/WTuftDZGCRO9noDbr\n0XHn1S99mXbZ+aN6w/TssaeBxKtt67bdRXT2kZ8wvW95ISGKIS22F/ig6Ot9Zlwexz+T6AmDob5m\nIyqy5u3wAb27AVJNwPybUFsliqWT6mgIQsX0PtB6qbEo71nLnugl7/be4a+5uvV20rjIUXLJ7x8i\nn6wDxfxHjkygcQCt+YmqwQRm/Z2O5k1/ixe/37PjJ2EK5fnVlz/jvZu/jdFVcm6HnjraednbhT6a\n3ocX1VKjnNQx1qKJYo3mres/BaDuAq3vCCFiNUkrIFIW8t46H/p8xdTrntYbiKgwxIOVkj2fqOmC\nAFqFx2mFiFC+y7I+TErX6f5Hz0BYEQalVhCnqS6mSpo/qc5mPx41QcU1AUvZx8880lGEd91ZcTb3\nOay9yEd3vuTmez86PHvG9XYpsLg4ePCPHt/+4r+GjEoFqCSh0VR4ct1rpWJaqtJO1C8ZCZXnMXUe\nl3l4IKcmNghdjGFSDxdZPzZTN8eo/uXagMYHainnvrRX1XLfNQod+2pdMPZ+AGHQ23vl7aJN4UWM\nrZcxzi6K0n7b7bJ00Ff1zJ51rac9lyFiJhHpnKQfQ8QrKaOhlaL1AeMcTaspjaY0gVjusb9zwqP7\nd9i68Sa7e1f4+Oc/4+/84R9w9+4X3FMa7SIbZcmqkZqG07IgRkfrWgEwaQPSiGhMCIGQogDeO7Qx\n4CPWWrx3vfHj8aigWBF56BRORUwI+OhTcrnBu8DGdIrrHAZFmRgOSkVKhEmgjcFpekEMEyAahO7m\nA9oatDICq0LaHKPGKg1alFTHLnox/CRfzCjDIkSuzysIgWmpsV68u1URqLThqK7xSnHUtujCoELk\nzd19Dh49YN+WlDZANeGkrimnE5yTmqm17yiKguN2hY+RlXf83R/9RzSdZ1W7RD8VD2tI9fkGoEhS\nRU1G4qjv39d2rmPwhYGt7ulMTzvHGTD3lHn7Iu30+TLz5uxelTdCesBPyEas4uruPjOj8doSoqer\npXSG8n6gmyrQE0tlJfKwPDmWnB6EfRJCQBndGyDeORSR0AaWqxUb802IHhMjXdcyKUu8a3EuUMwq\nVITSBIyRCH7nm8H8T/dXFAXRe+rFgqX3tFZRGHHjvPfhb3H30894slowdY6vj484No7N2LLwjgma\npvM4paldQ3BSyuaNm7e4f3hA2yLAtGl7epxOThyrpZC3ch4dB5KkUAAAIABJREFUwQ1dWgMCRuue\nBjpODRFhKOhSrko/XrJlHAeQf5q501s1UcBnICC10/KFM9Ut4ELK3w4AIiwScnTk1Ng5a82MxskF\nrc+7y70a2QjrX5Wzv6zj6GXnyekUnfHnL94Gi+l5vjLOLVZEsVFRxEAfjV2jGechkkD/aIvoe6HT\nXjY6eAABDN9fO+HItvTEPr9MBwGuUUOMgRAVNtHGjRYdixg1xhi0jlzZek/GYypbrPTgnNApb7aP\nPOlhjuQcS61F1yCXhlk1R1TljKOTx3S+ZWe2z3y62QOp4a6H96e15nh5yHyyeeGjX18jxZ5fNUs+\nvfdLtjf2uLbzJhD7aGrnwfkoasW9AyZHfxMPT4mzWSuN0gwCQwyjI4YACgqt2ZpWdD5Qdy0ntThe\nQwKhQwR6lPuvRKfFJgxUKllXXWJpdSGmFJttVp2A3DBySKl03w5P6ybYBGqdF8aSRCJTBFpl6K4l\nDSfmuOPIwTEecwzzSfX3nTaTnnkQyLRiFHjnOHp0jz/5R//9fw7/3YXvCi4LFheLjw7ufkXmYas0\n4GKIolaU0PgwHSIxdSp72caTJt+U5+yCcRoFn/UE5e+c9latL7EDtGN03OmF6LIL0wUGx4hT/Kpb\nHlTZixDys0oTo+fSx2yAvFh7HgBz0QL/OtvfhIjiyz6XF72/9e+tb7invXYBqe0TsohCDHivMCrg\nvKLrIq32NMZQmMD85o9oQsNWe5uqKnn85IDf+3f+FkerGl3uUfpD1OaM4quvWe3vUZQlnfOs6pqi\nnFBqqEPEoMn06aiU1HYLAaW1qKYWI4XTkNK2Q0RpiQKqKCqOTScFdW1hpeC0iqyahiqKJ64OIkIT\niHgtUQKdcomcghJRZQ3OY63Ba4hJHz+oKNFFZJ4V1kp+QBvwOeeLBHwl6UiAK3CwaoidZ2JmBBUo\nAlRas2xbFilP0RGxnUQwN2R3x7ctjRJQuahrGtcRg6GOUqeydh3KGJqu5tb+OxR2zuGqEaDYSc1H\nnzfNFC0O5PUymT0qXn6Je4n2UnM0+/1OrfUvZjSKOZC/+l0A5Kcb4eeBYqG35TBFforZ4C+NxtUr\noSy6SDSWrl5RlIWUq6hrqtmUEBzKlijXopSWEhkpSj+m4IE8lzxmtra2cU2N7xymKpNAlZgFITis\nnrM4PsRWk2QoKXznKKqyB7SQVf7geLlkYTTL2YQfbmyjT05YnBxT60ARIjv7V/g4nlDagrsnNW0r\nebfYEltZdPTszLeotGGzq/mmrjF2LraEUpAFaZTkVm2UNS3zPk1D+4hLBqRO89ok48loUu402GRM\nOh9pfSA6jUMM8ZicSyGn1zxFqz72gjQRrXQaz9mxqyXfKkVHukCvWOpDFPXKOBakuuga607qi47p\nx95TvdjP5zy67Nx++nHD56fnh0Hsxxz5fDkQm23CwfK79DdHgHEQvZFU0nU2QLZ1pSRLzN4EhgBJ\nTBRpYqa5kvJK81jK1OF1G1aNBI4EpMo5gpLSaDpG2cOiIgSp8WiNwkTpi1aKEHP+v0Yb3ZftyNFE\no3QfRRxA1HBNce5GUJb/82f/M8vVIVFH/t6P/kOubF7lcHnAn//1n/Cjd/6Am3tvpmeX6nyn7UYZ\nzdHyiI3p5vMFUxR8df83kqLiWnKU3CO5x/LH470AMOcjIYbErlOnThXQT/EZaB3QpeXo6Ct2Nm5C\nVIRAqjk5pKKMx0b6haCkBmOpRcwIaoya0fmUhhNFXM/HDDwHqJidOd4L5V2XCq2cgFvy1A2J0pxy\nQ2MqfJHGUR81zIEzpUZjNPVVBalDPaanp39kfRmi4vD+bbb2rvH47lfts17PpcAi8PGjO59jdAp/\nqixQIRK2KhX56cWGI4juUxjqFI2NWWKqp5Zf8igqeWqhuwgwvuii8GJNpVmwfq2n8f+z5yZHLeJT\nF9zkjWG8qce0WdG7Ecb+CRUHhaPn3QBO9/Oy//9dgLbXec3L5o6+qj48a7N/1jGXOf+YbpN/z3x8\nUVuUwtQqiFphF6ROVBc8rVPUzlF0isIqzPab1I++YHX3azavvcnq5BAVAh/94udc2Zqx/e4ttudz\nDpYnbG9cYx4Nd1zHIkZWqSK8UyEtnLnIsUT8QMBaTNHNkEprxBj6CIvrHF7L5oCGzaJibkucErqp\nsRpjSlTnKYyhLAoW9SqppEpUw6Z1KqTNFCPUVYPCRZ/EdFxac/OGADaCQcpvZIeNTiucjx6jDZUt\n8V0L1vJo2aCqgqulkWuVFtW2FGVBaSx4TwusmpbNzU3arqOazjk8OCASCBGaEHFKaKfWGtq2xgXP\nravvs2odTetpnacLAZfoL31UMRudaQkIrG92r2O8jcfdc38nLf39Kv5KgN3Zc4iKJc/0p73KdeZ8\ngzfvgamMAtlgywbaSHFQq2ToBAodiZ1EEF3bMp2XLF3H5myKaxuCd+ADxcRSL06wtqQLXkSnlOwR\n4zzJiFCyC2OFdB1lLzHWUteNxGXS0DFa47uOwpa98dz5lmjNoDLMsNc1dcPCGI7not5adkIpffj4\nEWZzixgj36yOuX5tl9g5uollVsGilYi88x6lJHd6sVhw0DrarqPUrRhvcbgP+VvTIcW3S6OJRuOU\nR4VMRZUos9SNEzn7LOQ1KTWFsSwbx0kjRbdDSHX1VKaMnn2D543SNSDOMNTGa3AIwgBofcBEnSh1\nfhQhkSuIiur54+ppcyRHFp913Mu2i/bDy1NSz/uPV9fnlwGbpx0q4+jwcP78lD06CvBKUuP03I5T\na1uvTqsSIIR+IKmYgCOqj4SLgyL2EaJeMXNN8F76pbz8W6s8zwUO5FqPCnGS6BRNUjqddzSP8j33\ntFOBwvzd3/0PyHWQ55MZALub+5ycHPCnv/rfqeyU63tv8ON3f4rRBqUMjxcP2ZnvYjOxUNF3+qL3\nkp97cI6v7n0GMfDgyW3e2H8XouzX3sekYCoO0hyxE8A4eqDDWSWoNlwk2dLp/pTCe0dhrnO4DKxa\nT9P5NE9D/srw3vPmqlRSSY1SgiQYrJ7Spb5kRfLsBBqwyrB65NP6KPRXqy0x+nTuAQfpcUQylewx\nSjj4PUxKQBI1RgNDakF+02MK67gTj25/wZU33jn3vZxulwWLH9359JcBVK9MncPyeaMb10vMVEml\nMukbehdLHOULEEHFFGKVYzPJdNwuXgAuuyi87EIUR5P02a2/tbwISMXeATKPdp2QPpUXmo7vzyLk\nFjGs1jtwOqL5bXjR/yZE+V5Fe977fJ0G99P68rSNezwepCaX/NV7wmNMCdxSJ1ApRes9OimjFlaz\nMd8nbNyku/spk1tvMdm/xpN7t7l54yZNvaLyga2dq2x+/mt29gNXqinq5BFHW1O+Dok5kMeu0jgn\nRWy8D2t9DH3feuKU0E2tiNYEnaIW7ZKu65gYy0QbWh9wwbFZFMQQOa6XoHWKSEpOZmZADLUe1TDD\nlKiO5QgkRCxSzDvGsShWKoRuC4L3vaQ2MdA2DZNqShMipiiJaRPz3vPG1hY2Bkpb8JvDAyyG248f\nsjOp2C8sTw4PebBYYScltYcW32+EdehY+Y75ZIdZucPRqqNxns6LfHg2PsN4Y8qMk6csBaejTN91\ne5ECw5dpY+NdJ8MqqpczJi/bzqRUMHbs5sV/WPczSNRKCS1JawplODr+hg/2rlIsn7CoG4yxdE3N\n5vYO+IB3XQKWmq5pMKbAuRZlLI1zWGOS0anX7lvpAVjUiyNCDJTVlC40zOZzoYl3Dq80Js1VlXP/\nNVRlSdd1IyNT5tz9wwOO9zYIBrZMSfQtMQQOl0vs7i678zlXCs2OnXKnfsJ2KbnDdQfL1QpvDdOy\nlOrb2jCbV0zKksOTZf8Ee0CE2CCtU1jdUZgC4+7QFNfB+X4+i1opFEZRWk1lCyalZlZZvrj3r7i5\n95NkjAaR0I8x1acdmDw815iJYsUkO6enycVICAjrwQW6EOg6ua70dIgMnG7njdnzmFgXw9lL9vzU\nuvCsNIfT179sBHLt96f8/4vM1THwOu9ZPC+TatwX+Wx4T74PjMjikuvX5W8Nga1s0Z3NNYspepgj\n5hksjEMrGSNqFN6DNsIsiwG8SeU40s2L0iYieMVQakap7IAamH3ZyaMUa+tQBqiTYo7zLf/sX/4v\nvPfmb7OqFzw4vEPXNWxWG9hC89ntX/Hr27+gsBXzyRylNb//zk/580//b37ygz/mxvabz3yPcs3I\nzz79M7pmiSoMi/oIow2t90nwK/Y5xSGKeqhP+13IoCzjoDPBpQysRu9DgVOemOZoFyTwlZ1S/c6f\n7Jd+3UklO4g67dWiRN660OdW5j5kB1D//s8ZdjGKwuvYyTQwCJOzYiR6czapLt/06OQJawmuEiek\nVYmun8dbave/+gTXNJ9c+HJG7bJg8bb3Pp4cPGJjZ3/UKRlUcUA6srAmD5lQM/JAGRJrc82p7FEz\nrCsNce5CeLq9fnB0XnvWQtOjaTW8PzM2F1QG0qo/PuDpy49kME5eh7Li6+k6ipd5Ri/WLoo6fF8M\nzFfVLrqfVxFNfVUG+Xmb80WU13FUkVGUOntJQxTqVkhpHSoKXSI5q3BeAF3TiQe+MJrtd36Krh/z\n8JsvuPL2B8Riwub+ddThI6Kr+PjTv6ZrWt6Kis8++YTjRYs6OaLY3SNUBR1COw2SBS5rhta4kOoq\nAiC5gbnkRQgBH0R8RuuhcppWmi4KoLIeirJiZgumheLRwQm2mvRqZoW1tM6JGmzyqhqTSgegRFFV\nZTXCTDsaInQ+7TA6qb6VtsAojYuBsjDMtKELATOZYoyhUpZD17FflVgP0Ugkw0TDlw8f4KwlKM9c\na3xRYTRsVoZPFw2zIKufNpkgogh1pHUd/+4Hf0TTBRrnJE8xSYf3IDHRXEYK/YmSGtfGxOkx8rrb\neeN/zXP/GpeSXLBdq5EowKgPr6OdptCdO/dVnnjJy6+yESjHW62xVlEUcLK4y+r4KzZcw0m9xHiP\nD57CGiaTitXipDdGdKpJGqMnKo3vPIWxYrgoiZJnY0drjUHU+1zbpfIw0LmO2caGOEPSPC2LUvan\nVF+u67pUj1ChtEb3AjqatqlpjMGryKycMTcFbrVgcXLEjVu3OPSOg+WSH994g+XxE7Zd5Enn+WK5\nhGJGNZ0m8QlN10mtVtd2whRQkRjd6GHTe8tFxMMQcRh9Da0jk0LRenrHmNEqMQ8M08IwmxTcP/oF\nRw8/4/rubzGvJqK06CNO6xTtUUlfAcSlqy/0wozHspBPTQIAgyGcbTQfRLzKudBT1caG7Pm1zEXP\n/fR4e9rvL9JeZL9/HufTAMAlQpwN5Di61sswHpQaBA2fdsyl+njO5zAEAlDDnMpMsPyGFPQlSsYU\n1vVzp5euVD4ZPfZUefyNGHhJFE4pCDGN0QQItQJjDDbnMKaSU/JvAY+6B4aq769m3ZYY/x8KHh/d\n51/8m3+Ccy0//82foZA1YD6ZsFFNZB+fzql9R9u0LPQJxhj+2c//KZPJjDd2317L9TzfwSHPQqHY\nmu7wTYxYrQh+xcPDu2xMr0pZqBSxI+9zQcaRgMUE0LKzlDxV5ZcsMnPedbOdJHvpkFk+tsP7a+bP\nk63iEdqq0ZrgA977UZRT9eeWexel8ly3sh8nPXCX/MqodV8TVylxcEsHdHI+hLSWxFOb6NoiJGNN\npwxepTHp+mEU+FIR7n/xazb3rh9wiXapTOAYY5xvbD689+Unax7btRNpJfxpLdSPfLOQ8wtkABdG\nUxaGQosX1WgNaw/wfE/VpRYuUkLra2qXomyqwVOjycnEiXuc8gxVXk1UBBWSsuPw/WzEGrIxe2qB\nucAQPN1edAMZFo2LJtn/357VXhewvvx5zzEmRqAiBnpvnfdJHTUILaLpAnXnqFuPw2D3P4T2mPb4\nkO2dLex0wsZ0gjKGzls+fO8DljVMNnZ548MfMas2+MmNq9woLHvVFIuRkhVKpwVZxnD+Od6wwsgz\nl38WtiTG2NNZW9+xip7WOQptuHu0YHtzwgRZT2ZG5PtNBEY01xhENEBqnCXZfZREEyOYILlMWuTv\npE9pObdGDONCayyauVJMfMQQKbVmrgNXJpU4hhSYCIumFnCrpEaWMoYrmzvUrsWYgliv2Iqe4zZy\nY3eH9/e2mWnFSb3knWsf8O//zn/KpNym6Txtl2g4IaYcqORpRYzimKhO4Qzn4LtrF0VDXvt1k6Nu\nHIXq9/pLOSKfvz0t4jJ4jUcJGaeMN63BWKm9ZWPg+OBjrhSaanPK7t4uRVViCwt46sWJ1NtSUE2q\n3gj12dUeJeKuSeJzuap06peLIujgoscUBWU1QSVHRVEUGG3QRhOCF2+0STVTk8EKUeaD90KhCoG6\nqTnW4KNhp5pB19H6junWNoUxBO+pipKjO99QGcvDhw+JhWa+MWdeGfamJRulAu+ZT0pKAmWKNE6q\nOUonAJbm13A/SmjZHtpeTr+T3EQl64GUBVJMrGFSWqpC473CVRW/+OqfU5Wmr3mW8xvV6CLj1eqi\nNtiSZ8e4yo7y5PcNia42GNJnx+nfpPa0eX2hQzP//pTn+jxz9Lzn/qLtadeN5LVl/fi1Pwz18HyU\nCGQWOcnAxEPKj1Wnvi8XGEemcvRrDQSlpnVyAGlFYQf6aRZy0gk8DmsNKCW1A/vSGckBlJsC6vqE\n/+PP/jFlYcR+jxGrFKXR7G9sUaAo0WxVU0yEsrBsVBPaukErw0/f/WPGTKFxEzt33eHtXMunX/wV\nhTUYFNNqQtu1abonWJXt49zJ8ZPIdk3MjtTE8Bk92/G7Cinf33uP8yHVKVYjB3YWicvO1/ROg9hQ\nmQLbdpIm48a2VVT9MeMyO0rRP+vMnKoKgzXy3owZpSKoPIpUwrx53uj+Gvl+h0eQ1xTV9z/dNC5t\nAbkgRXZK3fvyE5xr/+LcF3WqXTayiGtW/9O9Lz7+rz78yd8mv75M2cijWGsjIfIuUUqHXRKFFOae\nlQXzMlI7S905GhdQXvKKsl9dPPzDYLrMopE3TK2FVjf+7HU3lVwRiT08GuDDWxH5/eH/evg3GlAx\nZq5xRDzQ8m/xEOj08sclQ19/+75FE79PFLpX1Z6XyvPC12EwWvMck1pTCTRG8dQbLYCxMI7Cajav\nf0jz6HPuffYLdt78AOciram4df0Gxq+4/+gJNSUbmxs0iyVmtgd1wf6iZj6bcHU+5aHzLENgmWu6\ngaiSrfmX1/tKiva1XQNInpUU0ZVIR+M77i2PKG1BYUvapk4OlkD0iokx6BgISkGQPEOhiSLlM5Jj\nxruANTK/XPBYnbgASc5aay3CPAR2yjmLtqEqS5yFt0vLg65j6QOxrdm2BUQxEgJwb7kgFDZ5I8Vh\ntKElQnn/ZEEVPJsbU27OJtw/foKKAaUN79z8bXywHK9amlSqw6eNKoZB5j9vXv3mRuxTZF54nLyC\nOfZt0D0vui4gXmdGAmS9N/bUcd9GnxipDw7bwGCsKckPLLSmtIZ48ivenM04Wp4wKQt0YSlsQdfV\nKCUCSVqRwCF95E8pRds5jDFym7nCeDIwVXLMAmhj0EaJmipQVhXBBbxzeO/x3lOUZX5YoBTWWtmv\ngid4T3COZdty59Fjllrh97fZrSYYH4QOrjUxeGblBOolnXNUezssDg5p5xOa0HBlvkWFZtU0HPuW\n2bTCda2IRQRoug4SO2EtrJNazKGDFIlxQaFVCckpZFKeYlVYJoXF6hbvAw+Ov+a3rt/kV199iYod\nhdEYI0qHKinLqzi+Eika9vQxnfd9Rezfu9YKYzK9PTl0Yqanjr998blfxXz6LvbO8/qco4o6r/sj\nY/hp3xt/vnYf2YeQn+cruMVnPasYY2LVnT3mXPpqcl6NR27IsYGc+0gOxMS1tWLc+nhCCjAUShwh\npdV98EVrsUWNGpfKiGeiWoz63kcWUw8eHtyhLAtKbTHTqdi2UbGzs8fmbELb1CybjpN6gVUKayzG\nw1a1zT/4w3+IUZacjnZeG19fAYfHjyF6jDJowHUe72tx+BiZPzZRxXNuZVDClIqoAYz374D+twyq\nMlhn7eepY9Nfea8I/dsY3EH5trSKtC7QJcGdc3Mn4wB2c0BIKWE7lGm99z6CCdROnMzZFsuDOqZr\n+mQ5ZeHQ7HgcZcty3pDNIFEpGa85Qhh8y+O7X3L/y1//N+e/pfV2aY3h4+PjP7/7xa/JCF+NooXZ\nmxERDm/usGJAsiCUDq0hPP4r5qVja2qYFEYGQzqX0hqDFN59HotHaGSibjb+7GnteaJmz6IsJizf\nz+bBQ6BSVGUULUTyaBSx/7wvhKpE4EAoS3nBGhdOlW+/jDX4bUcLX+W1/r8GFE+31/mskhuGgO8X\nyUiiVMSY8nYinRMhlboLNC7Qesds9xqbu1sUvsEQmE/n3L9/j7qD/Vvvsr+zxZMHD5hVFVVZMdm+\nwvzqB9iFYr9R/E5R8vtlxbuTKfvGMEnRPwE54cJ+Z4MLwAdPWRQUWJRSlMYwNZrGdyzqlspGyjKg\ncVQlWBuYWsMk0XUCUofKpvNZBQUAohajFJTaApGVa3vhrqIocCFQmoKu7diZTAm+ha6lbjv2Y6SI\noEyB1dI3HwNRKyZlwa2NTbq2w6NZho796ZTPnjymKQqaCIdNx5cPH3DctDxZrrCmwJqSzg+CNt5L\nTkQM4qnOkeK8So6fYa+h8BLMgn7MvMR4/Lbn6nnj/fT/v84+jalcF1HFhWEia7lWsg/oZOQV1mCU\nQ9WH1MdHbJSFlJVZNiyPHiNU6pQZpQ3GGHHCGk3b1nhAW5P2WVFFlFpfcQDLjAyqtL8orURhOApp\nV0BlKneTKFFFUQjQ8aJeqpSiaVruHx3R7O0Qt3d4e2eP/WLCVVuB88QgdNjV8oSZFXr5A9ehqoLp\nfMa8nHJFlcTliierJT7AYllzvOqonXj8N6bzJLCTDbl1w22N5sdg/EmJHZGlL61mUhjKAg5Xd/nN\n3X/JDzc3ueYdU6Pp3KoHi8I4GEUxlBjJ61c6592v9YJepFwRUokD1ZfvQEnkIcoB/d0I5W3dmB3O\n+fLj9ttxnF98jbyW50yj/CbPM0LXweBZUHPqxGlfi8/9mJ4aRTwdMTwVpQoXHHPeOfrfyca7KAuL\nov5T+pdvUQ2shEwr1VqhjQCO0mihsScF1P7/szNKDeNPK1g0xz14FKdVTDZm5JMvf87PP/5TopOc\nfBsVM1OyMZmgQ8fy+Bjfdbiu68tIRB9oupad+S4xRJ4cPxwCJjxj7Cn4/IufoxBmj1WGeVFSmgqt\nk7PHpnvTRj7TmiKL+GQ7m/HcWQeK43c9/BwiiZGU+9i/15wKpohZKSfKPhuS4nYWmHMh13wc8pPX\noplKnmsGilpJ3miIQkcvC8WklDSEpHvav2sYOamiFPFQMQfUchR7DBfTuMnCCikMPrA8o/zR8Pj2\nF+xcuUmMcfWUIdi35yhIw1988+ufCexLk32gnQogWjuxPjupY4isWo/f+Snoiq6+hzViwBVG9Yny\nWqvUtdMb7bPBXzhleF5mAj9vO20Q9D1L6FinqGAON2vyJiQGgjWKwhYU1vY0GZnQep1bLu4csqJb\nfiK6v+KLLf7ftRH3vO37TIN9lbkir9qYvXgDU4NhlZPEU+TKpT+dE3WwunWiEuYjdjKjWR7QHj3G\nWM31N99mtrWLiwpVTrl6601OVg2tdxwdHNDFyP6bP0DvvMHhkwYOjrlVd9xYtVzPkvZy42efCYPD\nxWhDYQtQKW/KyozyMXJ1Kobpk64jGkulNZVVlCZidcQah3crSqWJXspgaBWI0fcL7LS0Ir2PonNS\nuFf1G6vGuxSVjEmK33ccLpbowjKdz2mOjzk4WVAaMdA1QlvtgiTn//XDh+iyIgIPXUPdtLSFYWUt\nj7RlMp1SzjdYegiTEucdnWslhyommnAU2lIgU+0ytWnYNEZb5CsbQ/82OGde9fpy+pyndzEYORLT\nGLMpAuaWd/Fdh7Iai0jFL48P0cYSUfgMhkbXapsGcSPGVGtN/tdaKw5Kpfuo89rumIBiBkY5Z1bp\nBErjMLacd/1YsNbifOAkBO6VFh8Nt/b22Meway3NasnB4oSoLc2qZr65yZumxGjNUefpmo4meqbG\n8vnhQ77uVnTK4ryiCZqgDWVRURUFy3pF17W5uwzG5zDixZCOiXZLonSRIi6GKuUqauW5e/AZV9uW\nrYNDHt57gPeeztdrDut+PRoFE541p6Qn43eejTMBoVbL/p9B0ni29gGYeNFVTn/6/OP1tKH8utqF\n5x9ufG2dyuNy/dBTUdTzEzlfSbvs+nbecRd99zLPOKei9Tlr+eGoQeKxBw3Zb9EHFcT5UCZHSGmz\nym8O3ES0ScEJFdNnEun/+tFnfPngE/6fX/5TPrv7y2RPZtGbyGp1zMef/mvm1Yz9zS0mtqRA47uW\nmTVE5ymNpm06nBOwWGiD7zrauuWNq+/w8PAudx9/xelxep59IyDPUK8Wkp+J9PtkcSIsHwOV1UxK\nw6Q0FFZAsUn3axW9Xa1GYj4Xv5v8RzGsIPK7HJCBYqYER2JWHEWN/BYZponSej//I0hBzFPXHw9n\nsm0v79b04CH2a/EQ6VX9OFGn5k9USeU0phItfTgg0qMONbYvU3AqjYU7n/2K/ZtvXQoownPQUIG/\nenTnS7p2RVlNgcSHjioZMulGogAbUVpcF2WJiBF6tGpZNgprbtB5L8foKIprqW7aULFxtHiMnYqn\n2utcBJ+Zq8jYC5B/ZmUqNeRRJveoTd6gEMUwN1rRdC4po4056tlTlsGp/BoIqCjR1+wNedX39F23\nMQ3k+9rX01SVy9B8LjrmMvf4MjSi/N3hp3ifQvY8RZl/XgW81kJH9Z5VpyhbzeaV36L79Bs2tnaw\nyyNCu2JxsmBjdw9bFrR1QzWbc/f211zZv0Jdr6iKgsePHjKZTLGbVwntgtXBXXa25rSPnzDf3KBW\nuq+RiIqJmpqeSdrKYoxYZXonVYhCaXfBo5Rhpyw5jIHxgML1AAAgAElEQVQ6KDaMYqIrVt5ztKo5\nbANlMcGFlqkpaNqu9776gGyqeKaFkXVMK5bRoY3pazVqJRtUERXb04qDgwNu7e2w6Dq2jOJIacx8\nTt22LArLXGnwDoNmpQLlZEJhLS44Ji7QmciksBKJtIbZtGSuIo9DwFiLSqpqETUk9yfK7rDZjWhs\nvXd0gIrfZwfL62qXnX/j9irpss88z8joG+8TRoNNOUe2PpE9QhlKK8qgnYbClmhtUMaAa/He9zmE\nMQYKa5JYlemfgeyZac5nCuqp55OqPEKiNefIjy4M3ge0NWnO0Yc1Yoz4GHjUtFy/dg1VWbatQbUt\ny7Zh4RzHzmGrCUUIqOAoVzVbe9vUTYOLAYxh2TQ4q4lOYXSBMdC5iLWK6BXOObTRdF2DczVE24Oq\n/pEqIMZhr9XpeRpNaQxVaZgWlllVcO/wK5bNire292hPjvDHLbPSEqJL0Rh60Jn32+feXPvCeskG\nMFnhVgy/IYtrENRKo0f29te01X0Xe+l4vxEbaaDxyrMQ+yaOosXfFX39We28Pp1Lsb1s/5NdJ/bt\n2XP0IFINn+XxbZWitIMjpDAGa3UfOTRKpdI5JOqz53h5iAstn937iK5pmdqCz7/5BZuTLa7vvdWP\ni48/+zdU2rI9neG7lo1pxclSIu/1akVVWFb1irppxXYAXGjxTcd77/wub1x7F+c6tue7kMCKPI+L\nbZ5IJPhOHDU+YMsCVZbcvv8Zj44fUJQV7934vZRjKAJRnQ9p7TRIJUYIQeG1VA8IIeeNyrXPOMzl\nBcp8DZo4FpDK9TKTc00lhxmKxDKg/z3vyfLraSTQg6JeYZmY8tO1YpLeHal0Vv7WMB4GFkqOgOa9\nfu1pRtUfo/u9JZVkyWuaGjsb5OftTz5iMtv48syLuaBdOrIYY2y296+v7n/+1/2CHBNNy6icVJvD\nwlq8kym3Ykwli0DnPXXnOKkbms7TOYd3YsC66NeR+ku2iwzzs9HBy31/nSI3in/23i8xevNLLoym\nKgvmk4JJaSkLizG657AbYfxICFrngqkp/1LpnurbGxnp86jSZMne0Eve+/O2b5uuel5Owvdx84Cn\nj43LfmfcXjc9eOxdlmTo/HnypsVB8MaFSOs8betZto7aK8yNP2DZeuxsg6lq+fI3HwPw8MEDNnf3\n8SGwf+0Gq87hnWNxcsi8qtAxUlhNuXeNBwtHaBvCyYI5kbktxdOlkn/zVJRVpYlkjMVqI7kRSqdF\nNvLF0TE2eEwInCyW1F5x2NQsnOOwCVCUHNZLgoLjdkWrIWqNMpr9+ZQ9q9AhMLGRtmvFqE4OG53q\nPlqjKbVmp6pQ3VLyqoLn3dkGReto08K/X02ZakvnRLmxMoZ9W6GjqJlGwJQVVVGyV04wrqPzjpuT\nCUXn6aKnQOFTBDTvDj3dJCL5iuRIolrzYP7bBhJfldn7ug3obADkn3q0jhvNIMKiNaar6bqO4Dzd\n4SH1wSOhMRor4NB1fdFtHyJKGbROyqfoXpgpA0kFfYFw4tnxYVPEPD+HEAIxBLQymELmWvAibuN9\noA2BXxwe8suTBZOrV3lwcsS20iJo4zq0NtREHhK47xtUYTlpWnb399isa5qmoS4FgNbRUSjLznTK\ndqmYGNisLBaPdy2rrqVtG7quoW5Wa44RlOojIkYrrBHDq7Caykp+4rS0opFQWU7qh9x5/CVaK5ZN\njVOKna1NUIrj1ROxW3o2VAI5Q2yHHulf8H7lZ7bs4+AsVjnCo/tzj1tMVuc4wnb+HB47Qi7nWPyu\n22n6ZZ4APcU3N3XqO2nNv8g+e5F7+7aex6XTmnKUSo0V9Ee1FbPzQslc1nooq1NaQ1lYqtJSWVEt\n10rsR20GgZtcX7TpVnz05Z/xxZ2PqJSkgMyKkkJbfvP5X/L44C63731O3Sx5/+0fU2rDyfER0XVE\n77AqJoEbEv3U9RHJ4AOu88wmG/z4t/4IrTSFLZlVG/0xqve65HVm5LwAvGtxXQteVqyuc7iu4/7j\nb/jszsfcfvwp3zz6NaUxWGMSE0/1opp57ud/Z0Eroa3qU7b+aEz2y2IS1Usfxp68L5TP/K3TMV8V\nVVJlpY9IrttwcbQ/DyJFCqisYaNcsDG1GGNRyHtUCS+d9iAMK8c5cz85HpTK0cRhjuWoa6Ys53Fk\njOLrT37OjXfeP7zUgOX5aKi0i8P/8ZtPPkq1W4TnLJ0dEnf7CFge9DksrgbUmylUPoD3qf5QDOsF\nLS8MIT5Pj4c+vcqWX7hKwLAXsRlfZ2SIKyLWaKaF5GhOCkORDeTe+0bPMR+eofCL87VkgjBQi1Ih\nWH3eABrd+8vc/7PAzYu2i7xyz9uH8fnWNqdXsDk8D3h7VXTUp+Z8vHKjdqAt+CSa4kNIqpuBzkca\nF1g1npO6w0+uUL39RxwdHaE0bFjPkztfEdqGLz/7hGa1YrFYEmNktrnFteu30KWUiYhdiw4eNd3k\nmzsPuHLzBkXbsU9kGg2lssnxonuAGBmcMVI2wCaxCBHwKIwlKHgQI23d0hrN/cZz1EROak/bORrf\nETTMqgkUhpmRc1zfmDENji52BOVZtQ1KKa5szLhaVSPjXhOJWKXZNoG267hSlWgFT+oFtVYUheHW\nxgZz72jblk7Jwtz4ThQlWyfqaD4Q6kaUHoFr0zk/2N7h4PCQnz18xFJJrmNlDE3XjOzToW5Y6K1l\nGP+T74Fx+K23wf4YPgrDznF6Pn0XBrSIpJznsEzORj0IVCjfsDw5pm46TPA8eXhfvNDG4JIyKUaj\ntETYrC3EFtE6OWjTnwT6MqVSpxzEOHoe2SD14VRJBiIYg1GGpmlwEcmZJWKtoTCa965c43euXkO3\nNa3WuK7juF7RKc2ybTnuWtqgOHSeAwKrCLac8OU3d9na2OAYx7SsmBYV+2bCfjSozqFweNdwvGxp\nfVqLYkApy2y6hw+Dh18zRFoKK3Vhq0LUTqeVZVZa5hP5U7tDfvbVn/Hw6B5FWXK7azEbczqr2ZxM\nePzkqyH6NXLW5siBGh7OM9rYDkipIyYZ/Wkhe7EROChmvgxt8kXbq3BiKvX03LzxcfLYn37Ny/Tn\nIufzi5zrZdo4UCK/JxDIIPKjcoChd1DImMlKp9nAL6ymLPRgR9oEoLTCakllMooEkqD1DZ/f+QUl\nGhthpg0zU1BGsD4yK+f81Ud/ws8/+uf8xb/63/jsN3+BAbaqKrFpYFaUzIsCE0CHSKkLKltgFKLe\nrDWua/jsy484rT8AkpY1tmnzfWZbN4SOrqlF3M0HiMKsKasCWxh89HzyzUeJMZDYA0Ynyr08F5uf\njVEUidJvtAAwm0V/Ro66tdEwoqD2P6Puc5VHt7LmcOvHVtqfz1nlycoyY5iqtKIqLDocojigKizT\nUlFYksNqfK4x0FbnMrKz/T8u25eVTfIeY7TBKlknrdGo4Lj3xSf8r//Df/sPzp7x/PY8NFSOj4//\n5Jtf//y/LIyWgpjZ653jo6M2zM313L5BGj9PoCw5e2qQ9fUF15u6YMEdh/9fhq73tKaGd5E2llTk\nsncEDBtxbj4EWi/HWCWDvErUns7Tg2bnxW/hkPh5zN4CBnCdQWPozeiAJ8igzgvScy58L/OsXgcI\nfd5z9jSXF4jynXee5znHeCP6vlBln/Y+zxjOaiSvnCipKuYxmTYwH9CdS44hRZxWFO/8HT795f/F\njfmUoCIhdNz56jNmP/5D7nz5GXtXr+G6Cm0LTpZLbPDM5xX4loKA3dyjbhquTiccB/idasInMRDa\nQIfv60jl/g0Rj8jMFInSEYjGsnIdCmgKw0TLxnlQ13QKVGlwzrGzsYFf1TgCh77mRjUhdjWPmwal\noShEuc1HzdFixerkBDudEg0QI4XSzKymdg1FqSm0YYphZzZDecet+SbROe4ePSFWEzas5WBZUxPA\nN2zt7HC4OkIrg59WHC0WGGuJ2hM1FFXJpCiJZcnxcsGyrVk0R2xO52usgRxsXF9zz873tXw59f2k\ndb2Kdl6txjzGn0cC7HXtF9Ih2eMGj28GaiJUYZSwSgqjiau7BNdRFRV1iEx29tFa4bxHFxZtEsVU\nRaJL583lLJQI30TEUAwhDPevRn/yPSPzXDbU4UkZbXDec9y0mKKATHGzGt84IDINnl/evctyUhGL\ngl8dPKFUCnzgsG6hKJhNJ7ig+Ph4xXah/1/y3vzHkuzK7/vcJSLekltlrd1dzSbZJIc7ZyRqZjy2\nhDGkgS3ZggH/ZMB/mQ3/YsOGARs2bMGWjIHGGojmYs2wOewh2d1ceu/aK/f3Xix38Q/n3ojIrMyq\nrKqs7h74AllZL997sdy4y/me8z3fAwc77CnFt3TJbdWio2KDkkW3IrYdtYkcNBEfNG0EFzq6rsM5\nx83r36ALEZeKc2fAKKIdkh9cJrBYmgwcLYWGX3z873iwf5sutKL8DLhqQkDx/sNdrr72Tdq9d/pY\n/RmMuceynEZb/9CPKuWijnKzs40zZnSMLdTzjNdPYy6fnA9PMzfOWm8eB9qeDArHpvZwjtPm7bPM\n5U9r7x60JmIPEiGitNyjROEGZ4VoeIiYizFZPVPGdmW1iNqYJBDZRx+HOou7h/d46/03mFjNNOXy\nN3XHzJY41xG6jp37t0WISimmFurFLqFtadPcQmtmZUXXtVgUGEuIgUIL08ETia3kM7/77ps45/jK\nl76b7GOT7lzKw421YMcBlluf/AZt0jMNER0U67MpC9eiomNzus7BsumDVFoJbV9F8EajYySoFGjS\nERtFeEaHgA9K7PSgcOg0BwPHFsf0KNRjYmd5+cyxwUfG7CNj6PRgSHYUQOTw8IjKF2xubNPFFqML\nlEppeceOM7IBTqDFk2dVihRgUj3jU2pwJsdkYrJ8/MFbXH35C3z87tuLM2/6RHsqsAj8+Ldv/r9h\nXlnddp7GD8pBfYJlD9jyrRyf5FmGPkvLS8ur54DwcyecyjU+o4058k/Xjl8nZCg2eq2REgN5Uqer\nUWkzeNRsG4ZW5lh7NDaK6mJlk0x6AsqdhiYZFV6F/r6tMRLp8X4YbFGhQiQGuZYYpfC4f9yGdsYi\n/nkBOc/azht1fFq68XmO9Xet704DESF7+eLIgAwghaiFwtA4kPHcQYzMp1u88v1/zsHHv0I/eId7\nt24xnc/56FdvsLY2hxho6hprNBtrc8rCguuIMXDz5k2893zy8YfcnBpuzKfcPzziptG8p4zUdYNh\nninx7etENSu1llzfKI6YiS1YtQ3GGqKGjRBYWENwndSXMoqiixxET+c7AoHlZEq1qNlHUwLWeXHB\nxEoEZYxNnlpRcFRR4TysVRM676iMYa1zqLqmqWtMYSAGXrp0md/t7XFEwOrIchW5srnBzemUo+Ue\nk1nJtp1Q4Thoa65M1zlsavaaBW0MHCwO0YXFt5Gd5UM25i/LnO/z649HzqN0VHpSp8/9k9H2Z18j\njx/jRTcVTweDows5ZYMe4yLxMEckD+5J+8bpp7hYR9oJV5Rcp1Y9BfXgo7fYmE+wVjGdTDB44uoI\niVIJUMw5vf1WGVL9wwT6MtVRXqZcxTxWRteW8xPTEBKnRJS6YxhDNS2Fho1Ha4vRhlpHfvrBJ9Ta\nML18mSvzGXd3dlDTGQfOMZ+VTCeesixp64a9rkPHyC6W3eWCKzdusHvwkK1qSl12HPmOhfOgDEvX\nseog6kjtHZ3rUDGyuXadV1/+Noe1owuSu5tLFujUd5PCMK2sKJ5ag9GRX37wV6ACt3c/JBCxxrK5\nPqfA0BFp09rwyo1vsvTTvgSN5Ak/avidNcMG9cK0aqm0bmlFYbMRn1QTU7Q0xDAAxXgMq586X581\nHvki2pPmxEUxek785ZHPnMch+rzr1Hmikue9hn6c5LdU6N8XNk2OAg00VKVUT6mU8i8SOZ8kwCjr\nhkpU9iygOCihLuoDShWZaMXMaoLvmJYW37VEF1irSryNNPVSBORcS7Ncsr25iU42ZUhlcqw2KTgU\nUqRQHEiNdwSrKZWhcZ733v9bOtfy6ktfYWvrCqGvTpCcvzn6nmx87zs+/vDtXhHaKnF+uc6xVk24\nPpuys1pybX1TROfS56SeYECFUQmNmANYYELEBESwzwtFkyiF6WPQeHyvdj74I87wFI3HRA95j39u\nDDNPnbPq+DecD1y6+l2q0qGips37AaBG+7wEo3L0KD6yMY6j1fm3IjmrjJRjsqmsSpGir5U13PrN\nm1y+eu3+Y2/2RHtasPh2W69oDh4w3bwKnRNDUguNtMd8ebKf2M/PmnOZy0u/dfXvPLVn/LETOypO\njeMeiz/3JIDhAfRfGRM+5dqslkkco+RiSh2442HuECPRRzRZWUkScIuk1obShKgojMcoRYgqyeMP\nA2yIHOYBJTRgFRXoKMXHUUKhPqO/Pusow4s0NM8bUXuW7z/tsT7L9iz3IPNPFvIQFEqn3EUFOCXV\n6knyBFGMKlcZpq98C3vti6zZHxP3PmIaG2aTNXbf/yV24xrN5W2Ojo4oqoq1UnITJzOL7zxXXvki\nH/3m59y8vs1EG6Yqcth23K0KOteRCgSkxS9C1BgUNnisVdRR47WQsJ0tUKmG2e/u3yPMKgxC056U\nJXcO9qCyPTXvsF6yVpV0DtrgmdkJkxhYrwxNVMzWLft1h9GajcrQhsjERNZ0SWFKCmPxXUcXA6qU\nvK57zZLSWq7PZtzzjmUnTqENo/BNw6QouTRZZ955agKH2tAeHhA01HVDA2ytrbPoWrTRzOx0mOcJ\nOHNsPSAtV/HRfekJjqHzGnunAp5Pafz3QZezTvfIdUiHBKJI0itEGfsRw/+kG/DsdlEgcvhsjjAy\nijAOBZlLk8CfsuAaunaF1aY3LEnfESEbPTIuRs81Rx5DAGV6BsH4OmJmqahEV9Wqz3XMdkhpLdE7\ngtLoqIit4+P79zksCv74699kQuD93XvM19Y46kSIRinYKBWLZkHroTKWh/Uh68bQaM27927TVRMm\nKuLChK2o2GsD2mgWnVBsO9fSeYf3jpKSr732D2g6UWYWAbj0FJOITVUYZqVlNin4+N5bPNi/zdIf\n0raNqMEazXo1ZdHWvLq1zeVVjZ7NKULLkQIX4OUrr7OzaGTvjjnCOKZBZovglIhZ/262YGRfttr0\ntLiIgMUuRDoX+hqp42Pn8XsWNHzevfuiKKzncbpmIQ+5tfNRQV9ke57zPy97KukLj+apBBgyI22g\noeb1QCVVz0St1EJRr6ylKk0qASP5ecaMKKpayk5ordg7fMB8Mme12EN1DmMksliakroLLKLj8nxG\n23bsHx0xNQW2LMAHrmxuYYmUZcnB4RFFabFJGVlqmHsy5T0rhsYo8jBtDJTKcOuTX3Pr9m95/Uvf\n5fXXvtdH1EU4yhBG1PfV4oCmPsQWFpOU/1WMvSpqZeCb25d46+4uOoaeghqiBzRaJ5skF50XK1uY\nUQFwCaR2gZCcNqHHFdmeVqfMvBT+UeoYODstpeD4M0+A8iTgyzAziu3f+iCaEO4+W/MbKL8Atsiu\np+EqxtjklPP1q4+Uw8hjSBtFYQylUZSFBKampeRxz6uCX7/5Y17/va/tPfZmTrSnAosxxnj58uV/\n87u//ek//nv/4X8ighh48dxqiD5LAisJl6rxIjUsij3yjkNnyF+GQeR5Ae2J835A93JxyfOgkmJc\nGmTj56aUhMSnpaZxhlXrcN6ncTZszjEZdjEAnRjjLiicj0xSKQCTc1cUdD7QOskfO5ko3lMwGBuN\nyYMVxdOSz/vIHY42nU8rSjBu580h+LSpJJ93EHiRbfwMehqv/AWQkhTitxHvHUqDCtROahWGSMpp\nDMyqKde/82do3/DhL/4tM73iq1/7MmuzDd766AO6ruPLX/8WTV1D8Lim5tqNG2h1xM76dT68dYtX\nb95k58FDvry9QXSeO0onR1pMIjOyShw1NbXSXLMTmrZmUlWAYorioG1pfQfzKcF5HC6VsLC0eLTP\nBnNk6Vs+WUW2pnOaoJiUFes2UCnFrz+6xeVLW7w8NUzXZjw8PGDDFjTRc785ojSWDV9QIjlgIWqs\nNayHgvfbJVd0ybrVBMQArr1nzVom3nPVTjhc7LK7EsXI23XN+mxGVc14pbI0bcNRCHTecXnjRrrv\nk+Atb2D5YZ4f/DxNu4j58Fzry1N/bdjsg0qkQg95hxnWwovvqzOvSJ1uWgzOPjHyspS5RqLZvvMo\nqyjLMinxGdCJvaJSiYs43HF/b0PYgkxLHQDko46CyHgN0LTeUZQFMSTxiiBlZuq65sN2xSdEptOK\nbnXEug+8ZqcYYyjXDC5Gmq7DF5o7wAMd2F01rE/nRCL1qubSxiYftw0vBQGg77qG2rfoIHum90HE\nM4KorH/7K38CZsJq5WhdUghOjgApHSBqkGVpmRUF73z4Bmsb66xNJ7TWYLRh92CPEANfvvYKN5zn\ncGK40TV8sDpkMtugaVtaX+J8SFG/Qd0w99FZGE1BL346/IG+kLiUM9A0XZSaqV3oweKghJq+nNbc\nwbC8uPZpOYiPzfUEFE/9XLrtU932Z1zr06wlz0OjHbencbw+YteM91SVnQhDPloGh2r0Xo4qmmQL\nGq2orCj6VilHsUr5uUZLtMgmtd1Cad698zavv/J1/uat/5voImuzNdaqknlpKZEc5EIbNsoJZVJH\nnhjDyrfMreWoXrJ9aZvl4QGLxSIprwoTsLQW7z0mqSGrEDBKqLEKy6JpKLWimpQ0XgS43n/vTS5v\n3eDK5ZdxzvGLt37I61/8HtPpmvSMVrz/3s8x2mCQqCJatrQQpBb03f0Fq5mnUgatTVKODgTdG75S\n9kr1vU2MEmkEhbIBPEQjaTZGK6GmksF8TNUFclmMmN3i6QkHglL9vFTxUaqqShHTSEzxKFkI8jMe\nt7yeeB+pO0+pZzRtRznZ5mjh+2kz3M9wn/n8iqzyyrG1R5MVcCWiWBWaeSlOhtJq1qeW1j/kd7ff\n4Vc/+2t+/Bd//mf8d//NE8d4bk8bWWRvb+9f/epnP/nHf/xn/xyj5OshkB6Y7zsOcrHtoeNy/k1f\nlDXKgxWP8Kg3h23wzIX62drZBzu5iZ5sPshnTPIckD0qIVB3nsIkKoBWKTqjKLTBpWRdSH0UA50S\nGpD3Cm9EhrwwMgg8geAZBoySSaXCowtpjjmgAjnCIMm2j++3MUg4zyJ8UaDyaTybF3XOZznO89L0\n4MR4+hRA+bNEO8ZjwKf52GcZJIyYxy4RolEQJWoR41CXsXOWaVnw2t/7j8At+ejN/wvl7lEpxWtf\n/DpRGzAWpaS49+LoiLKaEKLnm9//E959+5dU03UO/YTq3kdsv3SFA6UIJMqL1fggHs0I3F2umNmS\nNhUI72KQnL8o+ZN5+QjAUbOScgNk+0Xut6YjKiiV5kqpKFzg0AfWty5RVDC1JaFeUCnNXCtoPUsV\nmNgpddtRlROCc6AN7arGJkr5LeeYBVh2Hdtrazgf0Ra2J3N05yjLgtViyVGMvH7jZdZN4O27D2gX\nsDabsqiXGBSzckbtsppcqiPFCID02GgcoRicQM86Ri5y3F/omH9clBE4BgKTE/JxTrPnaefpwwFE\nKFABpQy9cEq6kRx10CpC6NjevkRVGZaLI5ZNiwZKPYgyjEFhbyNEWftViiI+ksOts+vzBN9FqR4F\nKSXCStoYlLZU1tCtlsQQaX3HOw8esrq0zdXLa7xsCq5UFd3BAXhHsAWaknZxBIVlEaCwhitGE4Vy\nxJFrUZXFx0i3qKluvMzD/T18CDQulc3xQs90rWNebfIPv/1Pab1i0Thq5xJrZ7j0LAxUWc3EaO7t\nfcBsMmV7ts68KLnnD/AhsD6b85WXbrLmPeVywfX1Oa5dsrNY0bFJVIbOi1NWfLxDVPHksBm/zE8x\n6pEh2UeQkkhXjDgfRo5f39NdxweTsSr/H2WajsbKk8VezhMNfHH70PFrPLm3HL/YkU9jdH1PmqPP\nFsl/+va0UchH5pt8Wea2Gua5rOE5F3EEFEe/tcqUUt2Lxwj11DApLIXRFNYMdTuTeFKhDX/zmx+x\nu3uXr736LS6v32R352O064g60NSi7FkYSZeYliVd02HQVEVJZUtUiNy4vE1sW4zSTMqStuskhSRE\nnHMpYhxB61H5NgVKs1ZNaYOTHOfg8CEymcz48U/+D6pqype/9B2+9Xv/HjqpmBMV9+9+wP0771EY\ni03OHWUVQWvQBqMVX9rcZBU7IobDepeq2MCmslbgydonWg19H0Ne6wR/hKiIRuFCnptCz5dSfflZ\nyQwWKqtCIdcZ0ngdi8cce/4Jp6gUHQaxm8ZgL8ct81dDjHgCTRcw1ZzWS9pMCIOb6Ph8kHvRKWoK\n9GxUHUFpcarnsWQS9XReWbZmZSrVp9HKsb+3y5p/ibKcAJy7bAY8A1gMIfzg53/1w6hAFQamVYEL\nEimLPZc4ARd1HIUPFJzRZidU4tFnnkqg9ZnbqYvBYwyS/OhcOJ6o7FP05WCV7iWBvMLIJPdBU7cO\nF8JAt42xV6YTWRstOYnI+UOmGfWLSPJYKBEuIC+uKiaKrHilZHOSGnQxAfA+AHHBBtOLBkAXdexP\nO2L4vBvVp+EFzecanzMmz3aIKaqQgIhR4sCQlijUQIyeEMHHVGojGVzTcsr1v/+fs/fRL5kevcfe\n7ff47e2/4bWvfRNrC6pSCoV3vuXKlavc39ll++aXiIsD9hrPxpVXWFuu+Oq1Te74wF7wdCFijFyT\nT2bv0ndYpelUpLQWoxTzcoKxlsPVkZSYiHEklJN+JweU5O4bZsZTdw26sBRty+sbG5RVQWhb1u2U\ntoK7y32OfIfCstcsmUSNcY7OtWysraMU7NVLCg0+KAKaq5tTSh/Y7QK3lkdcms7ZXxyye7RgY23G\nprZcLgzvPLjPIcCk4uHhEWgjADlLAmRnUW9YHI+MKZUcSSf+9rTz/Xnn81nOkWd1Yjz6xvGX52Uo\nHD/AxayBT6LqAj0L73gR5+FK+n/lwRK8o61b9uUaeGcAACAASURBVHYOuXJpA4LHFiUBhwpDftOx\n40tosHeAHItyZKdtlFqhx+69t2qS8weJMhgjBkdTr/DBoaPm9u4+B/N1vrK9SdPWrHzDfg02SrRB\nacPe7i6qtBTWcr2qCCHy26MdXllfY3e5YtEGZuWEg8UhyhqODg5ZNA0+eFxIqRs+8O0v/AFfuvYN\nQjQs25Zl21F3njZTUONgOxhFMprFMPr5b37Ite3LFEozt7BpLMvoWN/cYj04usM99OUruOWSuwcH\nfHS04E//4D/Gx5LWNXgfkxr7iUSYCDGne6Smc2QsRy/IgGGwC4jQeUk/6Vygc344RzY00rn60iac\nNv7Pp0T6Ih1BZ51vOGY89b2TgDF/tndvqUGo5vPSnmXtPPl9AQYDiMnOPlHwzet4ZhYoNBptEvWy\nB4qJOlhZqhRRzLRTk3J1s/jNz975S5aLHXyz4s6993l47z20UjR1YKImrM2r3llhlRWap4K6bSiU\n5nC5YG0+x7UtpbUSbfOOylqCkwDQIEwpZTS0ysEgTWktFpioAh8jk2jpouewrplPSrqu4zfv/JQv\nfOHrRCT32vuOX/3i3/YMAaMMykiQxRiDLgqqoiKqyKXJjEu25eH9j7h587sY7TBWg8t7PGTjPUqI\nTerGqiyIMwZuAa0SjFSDvZ4+QPQyn2X9lM8+mjyfMM4x/4AwP0yKJOcDjkHk+PvBQ0egViIOZI0V\nR/cxBzAMq5HqX2VHhJIlRICsApWUV02iLosadIG1Ck2H1VNuXv8G/+J/+m959UtfXdy7/clTDfSn\nBovAGzv37nD/3n0uX70KMVAajdOiPKRV7BF6brK4p9qMyMKrktCNvD8EfscLafagXiwp45RFM2bv\nLT0P+3Fe7P7t5MHOQFAF8fSYtDlPihYTloSwjmuPq8D6KLzmMPIGD7mbwj82DLUrvZdNJtMT2k6o\ngLl/JGoZk8tBaEU6b0anLH4no3fPkvN31nfOayBeBOB8HsP0rHbRAPNpIqovsp1nE8ylGbSCEDRo\n2RyywRpQFFGnsS/1AGMSbQgxKxtbLr32HepbkdX9d3n1pU2mfsluY7i2/RqoSNc2XN6+BMGxd3BE\ntXUZ3n2L9StXWZgNwn7D1dDyjetXuNM13HGepfesgkv1kSQ/1yiNi5FZYVk1DVVpaVeWrhBvm+s6\nXDgBsBDhqGWzoiot+6uaZW24UU2ZKLj78B7b5YSVLVAhUipDQOODolKWorActA2TsiS0ktuofaRT\nmqLQXJ7NmaK5s/cAM51ys5yzKApWyyVra1M25uvgOm7t77CIilhYmq5FFZq2afHO0/oGpSp0ohi1\nepDRyJGV4MNTrYyPm6sXOfaeJ7p4XkPt03YCXfS5j+e/KZTvKArN+uYM5RuK2FEVU1wndYdJjtdH\njdnBWO9Fb5LBka8zxJFh0ztPZBzlMlXKGAGgWkmusi1ZLpZ8oAKvXttm21TsxYbZbEoZwMxmGOfR\nITCrCgKKUmnaxYIOeHW2xUFbE0PHrCxY+EAIAaM19xeHuODonNBPv3Ttdf7oq3+K85GVczRtQ916\nAYre49J3JWIzgOyc37V/dJ+yLIghsHKOrm25tD7nelGyURmoa9Y2NmGxIhSWtWrKdOZwRDrn6FK6\nR96Lj0UWR4aoIqns6h7ukElh9EaifNYnqmkICMU25Mpt9OD+VAdGjDzB/Hh0LD3l/vzZNvFySJDq\ntLneD9RHwOSTHEOflYP55BqagyIS7RFKptiFqq/naaJCm1RDMdt5SpSRrdEUVqVaoSaVp0jfSzmK\nUl8UUIHV4S7NaoWN8Muf/4D5rCJ4x6QsKKxhZ38fg+Ly1gau7Vh5n1RULYeHh2xvbUmOcnB0nSeE\nwLSa4DoRtIvJziQBoj7CmGzmru3ENrUWq0XJvLQFpoJ9VqgQab3jL//if+C73/1TyrLil2/+gNA0\nlLbAKpPqQkoupCkKqsmEqAPGGMJqwb2dXXas4wsqYpTCKkW0ClxeE0M/VftpC/04i8lW7wOECSmO\ncwuJotcgwatRytnJ8aD6f47NU6uSECZqlJM8ciD0dqr8PZCrHDS03YrIWo4bHj/ZiOue39HpXuW9\nLLmZ7H5NoqEW7C/eoVIL3nuwyx+8/k9wXvHzv/4Jr3z5985dX7G/v6f9QozRbW9v//hvf/rjP/mH\nf/afohRUhRHVoRCJUeFjQrkx9/UwSRQKE3xC9llbSPWRsCzakp8TSvIXns4sOo+HaLTgj0CpRrjL\nxzbY4d6PXXMWS4gI+JMHKB6JEOFgZZiUlxCdc+mbY8A3bRpd9P1kzC0Ej9cKHTVFqiPTeZkoZSpd\n4kPsRZIge0tk08o5jTpFYnrJ8TP66GkXyPPQXp7nGOdpj3jTX3B7EZvv52lDP+4JHjZ1sdNkhuqo\n6NLEjCkCGfMC3S/MMtbmL3+HugO/+z6F7dgo5rQqsvPgATeuXeNofw+lFRtrU6Yb2yz2XmLn/h0W\nR3u8dGWLg4NDFjv3WL90ietVwcfG0JJyBYlCN1GKraKi0ppOa6wLXN3c4F7rWPmWamJZrWramLOg\nZW3xIbAKjtCIyTcrCroYwTtsWaKKgvurpXgltfAddGGpYsT4yFF0aKdYFZrCOXRh2VCajdmc0gVi\n8Kiy4uZkys7+ITfnL3HLOTa2tjg62OOXu7vMZnMxDlDEsuRgcUQIka9c/zqTcsaq9b2ynbA0fL/R\n5Pl91nP8vLXPfpx/HvokrVUnX8VAUAV1UzNfq3CtCMfVBwdMbaqR2EcIRxGZ8ZFVciXoEWMl7WHj\nXs9gJb9SSvIhxViU+e6U4v7hER8u9nntlS/wqi45Otjlpc1LkqscGspygndOimnHCNGjYiA6x3y+\nxv0HO9wrA07BsvYsQkfbdcymMw4Wh6KSqOC7r/4+337tH1B3jlXnqVtH0wlQ7HIeo5d71XrYkzND\nABRlMcEag4+OqpryrctbLA/2uFZOWR2tKJWkjxw1K7bmV9hpHtDWLaBpfZAalgnMhdH6NW5jR25u\nYuaHtOdGFDqpMkp6ipSMC/hIUlol7fkCLiPxmFGZD/852Q4e2560/58WfZcx3P+VR6KmvS0T+8+f\ndq7Pfi053o45x3qjXgIgOQhhUvmLvvyDzqUxYh8hz3mIhdEUSdm3sKlOYE5zSuU0TKKnF7akOVpR\nFgYdwZiI8g4dIiZG6FrJOawmNHXLbDoleEdwDo3i6uVtiJGuWWGMEUaDtbRtK5FPM9DbQ1IiBnoQ\n2XkvIA+I3ve252q1wsWIclGEaZQies8v/+YvpH9CFPEebSgMlFVFJGDLClOUaCLzqqKIgaWGD3cP\n+P0/+A964Z+QVU1tJPhISKX2ctw+QnJei6NpcP4kKZt4/LnldLghnzbmhzvGhgMoOfZanOvWaKZl\nQYyR1vmUwjM4tzL7IMfC8jWFWKIoiMH3I0flD2YQ1Y+vE2OvB5MkZkM6glG0bo+PH3xAuVry0AdC\nNCyaQ978dz9k9+G9f8Z//1+dd4gDzxZZZH9//399+40f/8kf/uk/pSoMhbUULgiqDnnCSO0TAX6x\nl71VEeEk5/yNQehN8qWyqEw+yonF9Lw0gbM+03t/+l6PKd1vAF4KKYeRPYHjc8YEhH0U4EZMUVGl\nBi9tUEQVWDbQdJEYBTzm94drScMhpOvpx2ca9E6jtYcoCw30wdiUw5QyUdSx8dQfQ2uBi86HUV+q\nE587f38+b7TwWYHpWe15Ihgvsj2pr8bvfx6u+2SuCYgab+9AR+op6RD6idOhB+PJ+eNOEJVpk3Dp\nte+yWr9MGW6xvPOA5eGcvd0dunrJpY0NXOsppxVFYVGFpVaGjfU5USnW19Yoq5L1jXXu7D7EzKYY\nU4ozKo3jECPL6GiWjsPlkklZ8LBtWDeWDVvhleIwjksJpSiBUlht0QGm1QRFICQK3QTDPdcQ8Kxi\noOjAolm2jn3tONQtKmipMdk24BxXp2vMtSHUHcYYdl3D2qzicLFgp4rcCI6i6bhz5w6rqWWyts6+\n80yKgrWi4HYS4iBGvv3a3xfRsOzBzV74/v/5uUEGv49jDzyufVqG2EUd8+T1XdT1XtTadOr3oz62\n7B77RBRgp6oZ9/dXGKNou45JWXJ1c5OHd29TTCenOwaylzwbDNlZA32aQ/aoyz6m+y/19EFFEtWR\nWqrOiyL3+vo6f/jyy5Rdh/KejckEnMO6Du8cQTuicyhjUCh09Bzu7lBWE7rFik8ePqR76TIhGspY\ns2hWvHz1OrsH+0lOP7JWrfOtL36fVSsgcdU56i7QdEO0z/nQO5GJUlOtt7YAkkGotGYtiWFVRwvm\n83VoHKVz+M5RbW1i0dSHB9xeLFmbX4VY0LpGzhNECdqHE5GIbH4pcQLnv2WaePYb5/pveX3MBmpI\nTmJhbOhj62TIQJOx0zbd1jnG2pPaeZzBF7knn0YrfXSve8yd9UAx9/Hpn34WO+SigOdZQDipR5CH\nQJ8Xl0CETnRLrYexYozoWmRl0/z/XOJAqKYaa6Skhkl5aWNRrA8/+Fs2ZhWTqqSrG4L3SVcDYudp\nY2BSVQmsBbq6xlpN5zxFUeDbjhBSukMah6IaLjUJfdrbe9psAo/eS6G2XHKn788gUS1tC7S1hO4Q\nYwq88nRBylUYrXGuQyuT7kXStlAGpTSXZlOWrmWrmrCzv0OtIpvra9RumfI6BTD29nISWgiDx3qY\ne8OTyg8s/TqZkxyP/S0/V00GedmJNKwJ4zmrleRqTwotZQGj9Gl4wnAPIdK0HpOfsYauVzgf4Y/H\nHiVZQ6PSgyFAoTf44rU/JMbA19eusLes+fC9D8X5GMKbZ1/Z6e2ZwGII4S9+8dc/iC4Epb0AGqGF\n6NRxgZAvXGWsLF2slWxGanj7WP5FBok2velQjyw+F9Ws1sPgSGWcJQ8hyMbEUD9xbEjH5P0LMabJ\nQp9wKveSBquSjQzoPZbjll/lpUrFfJ7cY7LpdBF8FG+UC4FlB4Q8kAQw9nXyRkcNkaSOpMArovI8\nrvset3FcRLTwefMBnrd9Wh7JJ5/j0fcv0uh97ohtWoAk50kBokLYi2X0Bb/1MCd8mjcMG2cuKjy/\n/Ar64UO216d0hWZza4utmUQDlJng246de3coqgk3Xn4V1TXs3X2forAsDg9o2pa7iyPaskKZnDMk\nnpbKWgprWDlHNZux9B1t17ITPN3iMCk2p6hMzBR5mYsTa3ujsLKWpWuY2ZJCaYxvWYRAFw1RSU6x\nCxFrSipruDmdUjc1s6KiXLOYEIltS4cSMQDveRhqpiiREz/c5/L6Bs5ErhYVe/WKNw+OKBWsViuh\n5XtFVcyQuRv6aG1Im0+AlFeVjdnxenjcgHnWXKDPg/Pice3k9X3ervfk/Mv5bY+23g0sVEXnufGd\nP2P3l39OUWi0btm5d6cHHXk/OM1QzeH/OMr3F9W/wbmjdDpZf20DNdtHcfR6H8gFObarCuqV7CdK\nEzpH07TEFCUgSEQhBgE8TesI2tI4jwuO2cYa+0rRuMjdoyN0oViuVrSp3uq0mvOf/dF/warxNM7R\ndp5mBBTbETU0gzVSGkuO0uXo4nyywcH+IVdu3OCoXtFMC3zbUCQjuLAampomeupVy3t1zfe/9A1a\n56X+cV8v+nhUMcUg+tJZg1SFiGTkZyB5ZinSP5p7Yrem+sc9BSgV3VYR3dP5j83evPI+VXuedf8i\nHTln/f04cDvNQXnsiuRzT3Hu86wLL5KmmlwKAix0TPTSiDGmzyEzOhdKjxgt1FJrRamyMJJ/aI3U\nx5MooxbBG5Up17qPKGqt+M1bP+Jo/xPmRYFJ6qSqLLBK07atKBe3HVYJbVNpla5LY5SoLhdFkfIo\nh+chdjrEtL+rrICqVFJDFbtUp+cWskEMSeBR1qu2rqkKSxEjbSfzSKX0aaUtRgtANtpI/mVRYIsC\nEzzaeW4/fEijxYhtiXRti5LMGKzWYCN4ieCTKgzFpGoqoDI5aLTY4UYpfAJ++Znl+aoUCTQPtrci\npX2ZXO4uZCzaazqkQ0i/JsVqo5DnqLXkLcd4bHwIkzH1HRAJUo4rLtG+QqmesD7a4084ORgt5f3f\nhufRdZ7DumVerVGVhv1lw7Jx/OwnP+C1r35r+fDurac2Dp4JLAJvHu7txId376or128k7vQw4Eig\nx8YBaOUBCJZ4QqQhtz7ChjxobTzRaZI4KPB8gOP4JM/hXgQgxuGhZvJmSAv6I1QRBg8ucRhgKm0K\nsi+k0Pf4nD39Jy+Gjyr4KR5d9FSMRD9ajP3wPQGW2fsQhyhEai7ma5J7OQ4oh3N8WiDuszTuXkjU\n4JztuIf1Yo/9uHaaJ/Q8m3qMURZWsjaYeN5jWoRDzmOQgwIaR0RyNDzGKYx24im1Go0l+pYiOspq\nws7BgrXKMp/PsYVBB82yiyybhsO9XSoKrr10k4Ode9y5f4/plUusbcx50Hoc4oFTStOlyIGLAZeU\n3orCcrhcgFbMigqlFEf1qu/4vNmpELgxn3HvqMZYTfQd912Hdy2N0pSmoK4DTQw0qxVblzaFNqQ9\ne+2SNVNQKkWsW9rgKYwVcQJteNiumFQF06pkQxfsdDVHy5pubcbvHt7n1sEhxeUr1N4JbciLw811\nDZ1r8VEU33yMvaeyDy5mICjuRMiRFjU8x0+jfd7oYM/aXlg0VY4+OnYGPvnv8ltoixFswdrN7zCp\nP6Kulxx2LQCTtL+cjNJo8RBLWY1okkpxvieGMdEromZJ+eMXabQiuJRTjzBhXLsanU9hTIEyERVE\n/CJ2HUoJWIwhULcdoSqYr62h6o6AY70USf1Ww8SW1G2D7zzf/+of8/Ub36FuPY3zdJ2ndlJLUYCi\nTwXsU000UnpIzEEM3QehoooYbQmupW5bGueYV1MOlkdMZ3NRTnQtXeeoqgl39vaZzjbZXL/KwdIP\nUcUYe0cMMVPWxrBt7Egd+lArlXKVUnGt7NjJc5U4ehiyZqHymItCG44KwrAvP0txl7PG73kZLi+y\nPU6y8LO2O57l/h9xCClx1qvkwFRKSjJkWmlpTV8zURvdl8YojNRMrIzCpnIYOgFCqwa9inFOo0pg\nc3m4x+HOx3LMVNJCW030AUVgXpVE76mslSUgFbw3ytC2LTEKkBXpkESL9llxM6CtEdDhHIWxOOew\nxvbxXqG8C4jMAjJaKbxzMopTQEhHqMoSqw0r6rTHdRib7jWVmNFKqLiT0mINVNZA1Cy6htpFvnb9\nJY7cEkVSYDUhATAjwSqP5Fr3IZ4wALO0PfoQcCH/7dEg1DHqJyL4NbEF1kIISkriAc6HPljTjyGV\nRfNS+aPo6HzASpLziUEXYTzs0jqhVTmEpxSCLWLOfxSGQv8V1ccTe8yhoF/HuhCpG48xLU3X4OOE\npnP88qc/4qUvfPneUw96Hj+Pz2wxxjApiz//xU//HxrnWTaSGDuEcOX3UDgD8q1ZnTo1PbDsYO29\nnmkXcBGct9Jh57ump70L2YyGap6SU6nF64fORJPBs3haC6REVTlkijKqPswYA+knb3aRGD0BTwwZ\n3Kn0M/Jujn4yhdV7UZ3NRX2PGyFhAOajv8s7g6dCqbP780V7GF/ksZ/2nBd5jRd97ue5ttNoNidf\nP8mA6L+THCIxRnwc3pccHJXycoKo/AURepLXksvQJtl45wNxepnZfE63d5dCK2ZVma5Xth5bFLzy\n6qtMZ3MuX76C04bbt+9wtKzZXF/j6nydl0zBRHusghikdplTkSZ6fIy0rmN/tcB7j02lBFZtQ70a\nA8U8n0ApQwwOGzsKC3WAunUcdgofLEaZJHMNa5ubxCBGQe0cdQx0SNmb0mgqW0Cqq+abBqciFqHd\nd9Ez0Ya1jQ0a3/LK1mX0+ibXJlMuBThqW1l7Ok/rHK1zKVcrpDVqTIvjEW9Db4rmt58wJi6qnfe4\n5x3PnyXr4MW1E32kjosdkE2bGHEEqss3efBgl+l0nSs3rrN5+ZKUtEhjiShq1yZFCKwRgYjofD93\nRRCtGwy5NH5OdVJFpAQMAnhUooIpbUBlg05y82LwBO96B2ZoG/Ae5zpMYZlMJixCwBWG9WrC7mLF\ng+USYqDpOpq25WsvfZtvvPw92iA1BwUoiox8532inw5AMaayXPknpoHe30oEHxyv3/w2ddt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medO89jhJ1mZJ52rIsy9MZj9kXNv5POrMe1cUTReTFunI8pAi/UIRcjLijK7S9w+9c/ZDabEkLg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AJP9r6lFiLgFhre9v+C4DggbW76nCpOuMhVwJlJK+dqTz+/bocI/KVAiVYQVkSlFZS5ZlCKDb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jaWgF3Rf5Z1zDoRJBBJXayetoZyYGwPt0BFiX6vQr8q0ShKcWrCSC6ZFvsmRHPG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\nFBQUFBQUFBQUFMzg/wAV5zraNV6z8QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x3caf278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "from mpl_toolkits.basemap import Basemap\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "# lon_0 is central longitude of projection.\n",
+    "# resolution = 'c' means use crude resolution coastlines.\n",
+    "f = plt.figure(figsize=(16,9))\n",
+    "m = Basemap(projection='robin',lon_0=0,resolution='c')\n",
+    "m.shadedrelief(scale=0.2)\n",
+    "plt.title(\"Robinson Projection\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/10. something interesting/10.02 maps using cartopy.ipynb b/10-something-interesting/10.02-maps-using-cartopy.ipynb
similarity index 99%
rename from 10. something interesting/10.02 maps using cartopy.ipynb
rename to 10-something-interesting/10.02-maps-using-cartopy.ipynb
index dd5011ca..86ba1920 100644
--- a/10. something interesting/10.02 maps using cartopy.ipynb	
+++ b/10-something-interesting/10.02-maps-using-cartopy.ipynb
@@ -1,100 +1,100 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 使用 cartopy 画地图"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 安装 cartopy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "最简单的方式是通过 [conda](http://conda.pydata.org/miniconda.html) 来进行安装：\n",
-    "\n",
-    "    conda install -c scitools cartopy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "也可以下载下来自己编译。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 简单使用"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "绘制一幅世界地图："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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+TDAm40oHgZRmhpTCml+H/c/th9rug7EAZz6lozozL2cAuwVaZMBilhmtYJZlZl3CnSkU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69Mjv3FFBHUwB7M+ZgjuxZLE9scDfUdIFkdr2LEsmgapvMpQQK1V0eI5GUvAd6NeXrHojR53SOf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ud0KJe2vXsp0s523gEDRj1HQ24YPCZ4M1md5GXjw5sGk8isxH75+w947eRe6sBr783iUpBXL2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\nHctzC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8UYCiWllflVAqhMTjOl6FqGoIhR2DLCEADVVNGtRaHpTOLL+yt6/RHbXva/h4cP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\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e8eoYOQVDoxVTbFClFssnjbaGxrZoHDFappzw42GuTbXGQNaQEjF7jLNkJVlvpa3M+1ya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T+4iLzbzxymzJiUf/TR5+T8MZ/fXbsd1gLo5/rqa19ked3uNLU+qXG1HvI8EdPAbrsheTP5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\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CXWmwIM2aKm9SZjqlMXV1xObFfd7jqX+tfhRVCkPHyLpv2rg3ikipNe0UmX5iihZVGEf3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inFIYGIXykTtGCrBRps+VDNv9O+xh+zsRtDaAN8zF7zMYDw5VzXCkJ/uEbaAZKNvv8eCRghdY\n+xzYwt4i6C1lye61/fEYhX4GXVm5s1F3X6K7L/U1UknhaHLD+Hyxfg8XYYjS+iMGT/qwPnbHTUqa\nRhNNqTDPFwBHmR17D42T68H7QUhRX6/zfXEmwArxrq+P4wkxAiSNq0FRvT/jHo33N2FOqkiGPkt7\nvoEZcReubTN3UxSMebErsRaNtHMo3X1tFUkZUpRWF2aseu1LPxBWU+iohOiMq9MmnKnB53lW28PA\nNCXsLu/jdHMNEOHFwvgdfogDTkAryNrj8eLiAs9vpO1GjAElLwKKUxk1V8TA2M9xoCG4LDMjfYqi\nMExRPXQQ72RtPWXRvmcpl1MkFUZiGDRuWNbVFf6qxm5Uxl8asOSG05pxWgW8aJ57e4mRCrx9gylD\n2+OIcVCjMcjD+XI6xxn9GQrboNgRwZsMd3CiD6JL3o51OFUv/8r5i+RUZOM/v/9We/2gUei/KoQ7\nOAC2tDOcfR+PMb7vcRE6HZ6vBZ//NfDx7U2G5+ivVjtmr+e8Yl1XLMuCdc3IuUJj3t97/r5/53/3\nMQQClsq4zoSCiJgmhGn2+RndSeTDFP8tiAkrH7M5yH2NjzZvJA+YgmZ8vwForviMY2ssqJLkzhDx\ngu+iZVm85CLjfXCFycbQz/P5IuGOvDKHs407qjM5pqARLTEYrdao32iUOx+wKfS939Zp3Pl9NHL7\n+WWFtm9qLEZHizajMUZpuRBI5EkMUb8DiYbVJmjpQRx3jB45tOykjRxx41AOgCir25rO8RJDr/On\nLR+S8/VBfGS7V9vPSkRPMmfks+3sPhuu/NKxvfT1gf/bOgc1FHvZUR+/ASz61+/wAxqGsdUGx+hR\nN5p5yPaSM7gtWxKaPZWIj10u+MMPb7CLrTuAtUQozhNYAbE8ak0krSRMzpqRpuckzpJ5IQ4EMUIQ\netYJauv7xpCsAAU/M5lBjaGFxBKa1whiK3X4ezwsplVyZyrgDtQWk0Q/UwDNAQgCeseW2grlP9WY\nkjiKRoRfP766dn06UqYRANfVGgsQpmRjMeZA2EUBB6ws2TnWjqhUMexhzhLqWqDtqctm0tKCILzF\nwLZ8OY0/wTJD7DwMCMsDmFUyug4CjlhL0YiloaNX1d2CO1vsnpt6x9DP59aB8xKa1I9+6Zd/Fn/o\nj3wav/mFX/0f737oB78+7MgiSil/7quf+/k/vf6J/zim/aX2hgM4CBGkeUIDcAxHtCroVMvxhN08\ngULAshYVJrIYUQu6D4cdlnWVNAMGuHUF3HKQR5WtCyyrdemLa4GSQJYC0b0ZQdMFATug8k6PNCoT\ncl7Ta138M0zaZJQcPc+8/p5uaaPjBlSNDkFAAaBRjpREKUnUI2GkXl6ryyLWe7Khdaryxk7rZwoo\n0KNdG/tsMHIH6tVfOkzwuXJI/qC+RoAZ8GpPyPtu6Ou/erg9OgcaUuX660O8thtcwfLDjan3Ogpn\nt8bg27bQH1qDWEtGo8mRVq0WMgZBMo1pckbRzwSckRj6FbOkWIsTxMat52YY+xhRtEjGZkwhShqp\nRrrM+zwyIzlyipZIVuM09C1j9rQ6SwUkQxgGQK1vtveq0lx7M5IFTWunta+y1lAlonmNSrDD5f3G\n7CyxKkkxBBzLgt3+gHma8GC34ipmnJYTKk1gDnj/8RNcX9+g5AXvvP3bmHcHBFpA0wFTI9zcrnj4\nj74JCgkomh7KQKpSV4AQkCI5jQCSVtKUNi3lKHkKqoz/kBJyrThWwqqG4UVi3N8dpF8gJA16zQWR\nG16/3GOtFTe1YU4Bb+z3uF4mvFhuMYWKtZnnXxBgW+09SM0YNMXYaq7QGlrTGksVnkGsEYky1eIF\n8Ga0WwpZTOoc0kmbUJLbDGeLrQ5DDSIAoRmdOEG5Ei+14GNNIamAG2LsBAgPk3Pr1Yn2XatV96PW\nvZ5QBaxxT42T8YdBUehxh0BDRHBwfkk9VudErFNR9jvwfPPeb42wUUm0bAHi6PeyuXdHjoxxSpPz\nLuaG25sbLEtW4zEiRBJoe+5GlkWDLP1+s7ZKd5aiHkLd8mPl4+/cMj4y3+Jhy1gWBqWEWgtSkl6p\ncU44rTcI3JAQZC42r0hSAhICcl0Q0gRGQC4rcs2a6g60nNGqpBNPU0TjKMh9Lh2NdwFoohClIQ0v\nTYTHNwmldbRYmHFE1GvpbW8tM6h/GC5dTDHTZ1LzZVQZC3cu1dqdhoymPemCtyOYYsQUo6A285ks\nICOBbYkJTHknE1Jnz3adQOJD5tSRtdA69pRkPWNUniJGXkzJ5x1C0DRxBqhpravUGk7ThMZN2htp\naQbpIOS8q1NIDo7ThdGa1df62G38JPJCWho15esyH0t3GxViA9QxWQLTddxiG7aPSNdde1xrf7sY\nAkoxGRo2qbBCO+rA6NZ3Vy1gRq/MRYYodCYtOtQoYXEOSmvW6PIQEMTQc72GYEY5u4y06TSrA3RZ\nLrpX46r3BYCKhRcxOmIEB9E9mpYPfOn9K3z88ojDxJKOqU6dGEVXYHUk+PpGyewBRBeR/EvNCKiK\nOpo0Pb418Mpo1gpJGWAMAa1UXyuJeiaZXxOUcNTmckVq1oOXnNRT9r0OUeuga3Md3kRLnCbs790H\nTRGIIqvKTUF5ccJ0eUC8iPK5WVJhW2CESuCWESbhCVIXKjRCKXgNZgB7SrgYVL1dDmLADABBSoRu\nSsOeGIWBkisSJCLYOOBmDeCaMSUrhWNklWNW5kXq+OMizvlcmxp520wsO9uG+C29Wqsa+63r2wNf\nMdqrpbo+aw6GlNIAWNUzSlh1uVarI68SBVSF6yJoWvaAqG4yLmmHhS/+lb+Ij3zyxz4UBNTx+tCN\nRWZ+/8FrDz/3d/7qz/5LP/FH/001BgCAlGFUZ7jUhJnbWY+a727M3BQOAiGXDvoQVSMwxcijKmYI\nnQlkoBssOkplCKLgmCfelHiypG6fk3619WdYWFr45ZCKpQ8zr5Qo9K0zVx0TgVzJZgghN9Jn622q\nErW2VUUIhHk3KTPuyo4pMl1z4qHJu3/E+fC5ALSD3j+uAtznpKqbanMEFkE1gmIMwtYNUl9ze4/6\nesKMOxsyD4ffdt+/AY8U+yO1obJ+akxdFhCG0csIGMKHfV2cXFZs3ay7hyuwI4w3Wy1ZCF5vIHs8\neFJVKTQb0OY+NnEm35NzzgKduwEgkNZKDQoLIDVdVucZDKQcKEX6dnprmH5T2xkZ8rDJWzrZ7j7Z\n+zDEPqtd6IY4aJvCKHPWPWLxaDZIetzhcMDlTLhMDYfEIEzgJkqep6oyg2oF+IirQ0SKFbsdAdMe\n0zSjNGCKMqZqzgHS9CNSiGqbCQGgPoZA5KmRDmUNEaz7iVEa4bBLmOKEFKQuMRIjpSTGXZCmyyhA\naZPCpieE0BBQcD98F+/zx/wsE3rNkdG20T8Pa2wr7w4rS7NpDUWh5tnPtipPbQTW6vQ3ZiXIWSV4\ng+1alAbIeZUcz6GODGL8+PkcLq5mlBmN9rPpFKhKKKhHUuAcT/kIc//L6y3Ix2WpsbWOvJc3d/Hb\nELlssSiKOQYwrPPAEj/gknWy9CJfPxrvJb+LEyXKuS4VJWcsi+yTtbyxMbTGvifGVwhWAz3Ko74m\nG3aKLrumALxzQ/jIDLyRGpbTCcu6opSC1w4XiDHgeHsN8Toq/7C7q8JN3JVpcTAW7SdaPD2VIGjE\nIZAbJr2mT3keAk6nIwhN2vGMY+WGJ8fUzyx1hXyUp30/ziOJ3P/RpWFHGhu/a2dOUu7qEDWV5zVN\n4dT6o6hp4aUNMqTLi64ubA2YUaYQjW93/hPIegRKip44/Kq+z54NxCwpn2Y0mwyoGvU1o9LkIrTU\nQQwfjc61ntobBoeOk6Pthf4r8lEj5uhnztoBeM9q1VUsE2lcjxGpsRvKFqmx1/vBZS2lkDKaLL1+\nuUmZjeWHwtYAACAASURBVD2DpHYPAELq0V+rCTS3st/b2qvpeSE2VUwM4v1uRlPHHmtPvKa1ZTYv\n2yu7exddbZDLMqkNr+4qnc6vr4ft47qsAsw2z+rEZhQmXM0VKwdcUpWWhKXJfC1aZA4RNpkFj8bZ\nmXd2rGeI9OjUWnUeQndWk8/WD1y/HEjqWUe5Iymo5iwkU430zCq/V14hRhM2F1epA427A5iy1E/W\nKm04QgClIGm1rWkdIkl9Ive5dFo1faZLRKIORmh6GQMoYtljrwGj29LEOUMdffRiinh8iliqgkbO\nMyQTs/PejtYrtbVVHXXGrEOImObZVnHzbxjpFxj0wYH+VDallHB7e4QFQZz21MlFmgFV1dEfp0lZ\nk01a9TndgF7r3s+j6QaSkRDwza9+AcwNz99757/Dh3x9qGmodj1/9vRPfv4v/4WNQureRfUCHi4O\nuHzzozg+f9I9QrVpD7e0gV0mQJpztq7k9lRM0YKFGRhD2xqM1lvPVYhBqcLmKfb2mbKEHpkxKeap\nDneUkJFx2meaRhHM07X9scbh1Tx8qmS0ZkWwGWvOyKViWRbxOrl3S88VBenHs1mbznE6wQ9caFgA\nHt5jG/t2BVyJ2s50MMjOF8PGcbY6ZzpRf/rAlahT3qBY9B3o67u9azfcx+/YQhlYiDoaSCMJRIO3\nUKMeIfkSjZEduQI66ZCP0VSGcU7nAvxsMzbrKcJDU7W0vsU+I8zQUgyHfdavNjW8Go+Cb/vk73WZ\n58ue5+lmWg86tqAQEjOxfvYUNUSYxVAEM6Z5hxSARIyZGmjag0FYc1EQmApwQyDGfmY8uNxhPwfs\ndwn7/U6yY5qkg3rKK6BCRbGkYAaL/PSTjFH2YoqSzk6kNWIA5ki42iU8uNjj3mHGYY4IJP3ipolw\nsRfghDlF7KaEi91OergyI6DgXny6XeUBnEOEc9v8tA0vGJSZ0NOJW61qSAVPZwn+WbkzM3y/m3pO\nQrAa7+DeyxikTtOAJ8Y0OK+tI/NbmlbcI/ajwDpPkwSGc0/9+y+n880ibQzFforC5ovy1iiyR3q2\nWqxxJNtnfPA1fnaIIOnahgH9h0hkiAv4JorwqgabKZibwwfgDq83T9JdoeGjJYgyTLD0cmkX894J\nuEoVu7YgrxmlFJzWDBDQakbLq9CeWoebFeNek2l9FYv2nKutK5eWPmV9Hi2R1nkbC0DE8bQgEGOK\nljYv56424Nkp3eFNfS066zMDZpQxL10XffA2MsxqvGttH3f51GpBzrWnkCdpxo3gGvHmbBrfGOl1\nm6Z4vkeDXoGRA0JLReLmTI/pkVam0FrVtMCGWovWTbeN0WZyrDuLOu35OpCt55ke4p9vZy+TyzK/\nH8YIKTAiOo3PGh02JnPdyTWuJTMIih6aizo3e6qfyTKvUzxba8KozAsfswiM9LnrqK0ANAo+Iabo\nOuNm70yp1l8C9ZTGjX7A6gg15X/4st2vao8+511GGITOI8nkI3BvloyTKLguYIbXVYqNx5tjz2Ax\n3DQbwJ9NEDArz+7ijSLlOtDAd5zOLOXT89LJx2jM02+lZ48bS5pk6emiYrSJs/xw9QC7+w9B2rS+\n5hXL06NkF1zs1BhmtFJRThmY4Aay89mATpNnwoIg58PA/qLROrGXhoGApUmmUUPHLoghYG0JayHR\nKcKI8NvlrVCu/u2pqKZ7WYq37ctLLteXuuwGuuydFJzS0lxjiJ5ZIec5AhSwrBmt9s+FISW1Wpo+\n4A5uD2wNo5Loujz/C7/wM/iJf/WP4Vu//nd/7WXD/mGuDz2yqNdfu3n83Xe/+ZXPv/XxH/8jiEEg\n45mlxud0WpDmHeqzp5h2B+Tba8xX93EqFSlG7PcTkASl0JvUm9VPvUaje217iwnNUDCaBoCe7gI4\nX90Ymxiam5MpS3YDdnRUITIN9atnTD7Brid5E03APbYCOKDjpODIXC4EoKkeLMQ9IqExjyFnQs4V\npdyqJ0K9H4ORY+kaYaNQ9nnD10y/c0ZAdo0MxxbO1s2FUTOVnNQRYmtH7g2SbwqntBTJbQRRmIPd\ne3xdsx7kp4cRfZx9jcYoJjyVddtjb1TeTDDC586mLLeGnp+p3tkghc6tNR2/rZ3sqc2tK5zDOoJA\n3Hrt4TAFZoZDWYzrP6yRvWrMhDmYTHKGxSqcYSkStg6+X+jolK4A2Lnvipx5lj3ap0qjQUsHE1hG\nezGiG7idFnWggioaE5Y1473rinfniH8snlD3b6Le3iAvRyxLxu3NNWq+xcU84xAP2M8H3DtcYZ0S\n/s43vopHT25xeUjYJfGUEoRBTkGFv0baGrrDpTXGNHB8m/LVPgksdmPpNRmB1+/vZJ5NPMCi0DC4\nVUzqxYwA0hRQSsE+BTx5dkLNt6DKiNObWI4Bc6wKiiNKh6MoNlkjU9zRqgsNUVhEUWxVzrHUcnbh\nYMLeIj62b0Y7QE/jDGZYqmJaS8U8zZh3M0CKDKvMkbl6BIzAougqjRhvZZjHf+CLtE0h3EYW2J0d\ng27vZ34TmfRTiE5rZF5eOa8xxp5m6Lzbzr5Q4VaZHu/sR9HPd+cv1J9N5PLDaNHFgBnyPmdR8Nmj\nRyZbulLsCrhmIngWitGUKnZkvMKNK/LvS2sOQRTmKql1v/SdPe597IiZgF1KaJAWPUDDvav7avxV\nLKViiqTP15QuDohJ6KG1ilLls8xW/9ZBWRrQo+GkKgKxGqgrWssI6YC5ivEIaphDxXu3jGenhDjr\n2bEDNKCMA4YKuJp5hx4p3J4jO4Bk2rezx4DdnDBp6r89o7D2l+Uup1prgBobIQZEDmgkgHqBVVb4\nuQ7Cjwfe28elyqoZdSSZULVk7HZ7afautZNgwpQmTZ2LnnraWkMpGbtJSm4aILViDKQ0gQJQ1gyi\n6BGVODTwHg0ry0JpTaMUMGRLct5gZRFRI3YGfNRqUwTxoKmIA1DgsP7M8GfaO64D2NG1f9nKMXr0\nw0ojUpowzZP6SrqMM0lM0Pp/leMhCu+yEgszaltraFkAiyJFzFPCbjeBiZCryhtN8SUEMJvjWGRk\nUGerBCck2hTMYGQx5EqpiCn6axZmEJT6QU6aDtAaai6oMQpPaHKOAOC904zXLyo+ucsIGcA+uSGG\nxgpSo7SmTigBahOezyQpq2GKqKUiUgDFzitqLhIh1CwAYvmO6DAWmW6Swq0ZB2xtgaTIT9ZczzMP\n56aVhnkSwCya5JkpzOCUtBZ1BsIeqEVlk4I8NoBLQ9pNaGVFKxkBAoZDil4vqbRBxmoZMIOqxkr0\nMQRFfWdBCK4CJMQk2UX7FFAa4SImvFhWnHLFi0Xmf8oXWE4ZtWTs93vRh/OCzECMQpuBNR2YoDWF\nUev+O4o9QK47OV9S+ivaa9oynEwoRc3OAInsrCSR3RiD2yoMSU81Q49CQJoSainu9A9E2gJJ2niJ\n2DBaHHgmSU/p6yeP8Bt/+7N4/9u/9Rngz+DDvl5JZJGZ+Xh7859/9n/7H9jCtmJQkSuwBGB/cSFG\nX5XedURyUJc1e7QBzsoHpY+FUW+EixsttB0LgFwMaVWuvvEvHbsyNd68JiNQZc08ECboxxuYfGT1\n9vNg8JgS+ZJxwng1bV+S8W5fYyaAgh+sMd1yhP++69mGMxEb4/az3+tyVaoPUiVFH1/XDgfb3H/G\n+3zwYwZv4zhxt8bH5/SLz1739LXBwNwYxOZN8rlofMpSAX0zBgGtn99EhNCF5suvrmB/8IfO3+iM\nwJ99VkS/9S7b2eaz/ehjlO+04XX5stPj5jye/U0dDOichl42N1f+TSAHwjwF3I8rKgPLKm0GRF4S\nCA33LvdI0w6FpRbx5nTCzfEa+1lqs9ZckSuBEKROSplsbex1iCkQkvIF60cU1UtoHuVTbliz0MKc\nAnZTjwTEENV7rY6nELHkhuOacVwzliIK2qlUcD0BYUIN9/G83BPjktmNiGGRfVXOeYqh9lkaCsOc\nE5IiI32bVPlSuiV0cIseiVRjfTAUg0URQ7BwI6BGXEoabUzJPfiWsrrxSuP86obN+PfLaPx7cZPu\nWHgJv+aBZ3LvObnNdBjvNYzFfs55K40K7suIUI29l7wvRmKPKlKQWrhSqtf0nguS8U9T7Mgmfj5+\ndGPUAczUSeNRY4igXirw95/vcYsdwjxjPwvsvn2GiUAxYd7tkEjbNMWhV2azFhpSGxdAmGJEpKDO\n0i67ZHBmRATkErBmSXnf72cwE45rRa4FuVYshfCdFwc0DIAixpNpTJ1m5FWQY3G2TsOX7l7dxhjO\nveUUbM9GN7y7gteVvBFIaTwTtNn/nqHT98zccAYoJkvUESml/kgQD+XR8roDWnBDigkNUjudczH9\nUrMNGJbKPbYOwRnfcMfrqGD7awMVqmLqC6eXpdien8kz9cPX4Zz3+z4obYwZCtYqxUB7JsWiYJAb\ninafqNkO8LUc2m4EowdoVG6QufZjqX3je+Fs7Xx94PVinsKpb9ioLIumr4Z95CXAW5tFI0wpqf4q\nPfEiMZZC+LHXT1gXvuuY0HVjAhAlkhjMmAga3YpB56/pzHE4DwYgQ4CnItBd6iF/1rAWzZzLrjUA\nzKhLRjllSTO1PYkBAQGJJrACWgFA3F2i5RuU2+doK4NiREwJIYnhuj6/8b67zocSISpQXTO05POf\n88EPZawqwhCjoZ/LPt+uBbdrQWkNuwT8ztMZt6cONsMsXQsaD21bmrk/2VN4PbqXojsRzW6BL73I\n+JGHxBi0U4M+T8deivBZBwgceIbV7Fu3A7Mj3BnFhhEhNbDCn8IdyWrnKYaAL/zcT+Mf/4lP3zx+\n+5v/D17B9UqMRQBorf1Pb3/ja+Xdb/z6JuUK6Ox9d7jwRbPXW6sCQd4aYB73JsRtXmO715hS4F5j\n1xW6DQ4TELwlJsZQm+C8g7c/g5LnjJMsTWJgXK7gn90DTtvwiChEEe3K1zAgXZut8OZhTpZ6pp4j\nSz+0KJ8LDIY1YTcL2xPNyF42jzdUaN0VOKZokX7RlDlbD/+YCR9LL2KcIdyRE8Q5R3MTcyPoBu6h\n86fhvW0E2PZ/a7TIU10z7cKRhvHaqrrxbyiBumKGXGoKhSleA1Ku3HZQOAfV2cRDF9rjnlI/I313\nzuY47lVPDTSFwXuOOkPZrqqtmyxXV5jsTR+/z9neCnc+C0VUtegReHTQ9MiZRZCN5lIUo+y1cEJh\n4LisWDW6x9xAXPD6/QtM84xGAadS8ex0g+fHp7i6mJUPNIQQkeJO0k4CumGk51PSLc3Qk3/NqSSA\nO4qQxlLIPk+EXYow8hf6DsM5CziuFdfHFTdLwe0iZebHNaPWjIwDjngdT9YHGolskH6ubaOwOgmi\n770okMOP0oqALUSJZluxu0WF0eciQjN4JNGQUM2rbZEUCgY4o+eLCGlKiClqmmryZ22Uav2POW0s\n9WnjODEa7XrXoLaPVLA9f3a+w2CYY/isZWdIBGBI3Xd+OqSPOcMnn6MZYOSk3/lXpy3/9jCugT+C\n3DAZU+FA0jvO0Iq30Xoe/if3G+vh72hEqmhMMQ5yrH9C5IbcOxBjrYQvPT3gcTuA04zDTppvC9gQ\nSa1timIsBkkHDYrKB8DPUVXkaAPsiQMYSD+ndnYZzBHLCpyWhlwa5t2MUoHbNWMpBc+XgPePCb/9\n7AoUJldorHaQQC4vmcVYtFILO2Ojw3M07IYT5PI36bm3xTL6cvodjAVG7xnorLMZcJcrC24Abo2R\noRRAASZAGiGAlitEAZyLoUeeXQEEq3NBEGJNFjUmaT7e+nl2BVZp1ecG5ZFGP8oLt7IAXspi54Vg\nwDFGE3A9xxuFDzzb6EJkvMzYdZxg9YIqyYymhj2w1L2mGQHTNAmfiQoiErVPn65TjMGbktteWU2w\n0Zo55m0fu742tGIjMbzlPI/yeXQQ6BpX6YNa6uDQs3nD2k11frQ5l+dK+vB9ZkbOkhou9eEVkRjv\n3SY8XiZMMyFOEhVk4m70WX1xJARtHRVi0jRU+TySrk2UWno0BirAEdLMPnY+VzTllIcUXxGCZkQD\nHRCwk5YHGtYiADeVYW4GN8r07CmhgWuWP6vyyiBgOCFGlLV4uZmtYZwCpt0Eiy4i0WB9dNlicsZI\nwMo7KQBxUsTjFLxOMRBwvWTcrlkcNYHwjcd7nBbL3gtazpVRtR+lBVSMHrg1d6wGjdI6P4ae777b\nTjNW88joAl7krvUQd9aFaUquiJk9FBUTgTT1X9oJKcAh95YcVQhHz6bJKIaBTYEIZT3ii7/4F/HR\nH/2nf+V4/fwpXsH1qtJQwczrbrf7U3/9//hffvIz/+GfhpF8Sgmn0wlEhOV0QogRuTSkdQFNsxNh\nyZLWkQIhOyOA52xzY1dwRgW7X32zrQ+dWqmeVhpIG3QzADK/vUofU/PZVHDZnKaCO0RpWGwpnjQ+\nM8AJ0jwYrQENFQbl3NhQCq12qs8D6rUw9Ly2YUzd0DDYbRfwXa46qhYUMQ2qyNnv+o8e6K2JIe91\n7ZY3X7DnnzFM9yh2b+/2htyVzmEu55ajfZxGxVOtW1tj+56hwdm9aByj37orgqwKPENR4wbPNBje\nK9OUcrunPl5OhAlM/a946Bz5yMdsTKc227Pe80rGPq4fDw9RJVlRWVstCHEa9t7m29dmPPbmZHBh\nahH61kAxDdG8UenukUmnITLPW3eEGCqswLgPCsZwWaSFmbHb7XEsWfupEZ6sARcUcZgIN1MCrQUE\nRkoRF/uEZ8cjSm5olbHEik/9yJvgBlznk9QexQnTfA+3x4KAKgh31BWhwY7WVlBB0fEYh1nSk6Q/\nImM/Rcyq1EmqHpBL0ca4DWtlLOWEtTKe3Zxwcdjj+TFjNydQK6CU8Py0w9duPoLWRDmoroSqNxU9\nq8KMOFKDhwzdj0TBGVPdAYtSNu0RJeis7jkPhAhGSpOm6RNSEkM+FzEEcpa0qVoq1nXFVHYAmtaF\nJwcSk3tFtEaCIhmVD3J3lFmtBVHvGQvq56JrV4CkQneFHKZ8Mfz8m57SFeWONCzgLAKzLshzWufF\n7HU/pszSIKR1Kf15W/rq/LhHa0TgOl3BxYDeq6cTmewJIeB4vFXABE2dM5qh/kxPEVTa8gwlbPlu\nSmrgq2ZkZ3hMQ8LwnSkCxxrxd5/M+PH7Bf/UvYL5YsZsfRApyrw0khWNTmWYaA2orSKXjIuLC0nL\nUwU/hqjAR+QbKg2kL3E8AdcvblFqRYw7PL/OyLWg8YqV9/jVt/8gXhwZc5LUWUD4jin3puC31sDW\n4oib8u/gMl0UM+NEynnUMLCaUYtGMUnauAGwMJEabMkBkrgpgiQk5avWigBGmKaOQGyGkmml6OfU\n0rNj7KnGEQSLNHXFEohJ0iyD76O2VeDmtJ615ZmBasQYnU4kKin7HEyJ5T4eW5OR3OwDDpDh6W8+\nES9rMZRUA0JrSuNoXXEWuXjO19XYNh2e+g8gIHyRBvcISS1WVEOwp4IKurvN1VKsS5XUS7CUKE0p\n4rRmlV1b53UMAZUlW8WiM40ZJZeNo8FSTH2tCDIOmCNJ+XCrAFkfSOU9pIBFpBxc5fEYUICvAwEI\nbiwCjP3hgGlKWArhjYuCbz+b8Yl7KziKczmkKKnbIMRJUldpJsR4AYL04Cv1GiEkAY2xIAcDNAVw\n6DWVFAhhn1CX6o4GTMZvWYxTZZjjPCJFEJPor63I760iTaJvt8qIGgUMTUWX0ktIAWl/hXp6AS4r\n2nIStN4UEOdLcFW0V66idjCBJou2kdRUV5G5IYqxb+sdlJi8bh+CKUARmOaAVgXYKTcFK4wMRsB7\n1y9wb0841T1+851LPF9mlHoDsJQX1VIAyBmzlN2HD67AIFRuKEUdcjFg0hTkDk6mcm5Urln72gaZ\np9kYrYp8DVqXmkvpGT5EYH0/JkFiBQMjgNCaC0BRypaalIuMpSFCX4YcLPq/ncnP/8LP4A/9c/8i\n/u8/+2f+Nbyi65UZiwCwrutPfe1v/tJ/9vjdb01vfuwPghFQa29c3LTPGkrGtNsJbC1JKgcD3i8N\nTN44eCRiY3DGSuHCpSlBdsZqXj/pq9a/AWWaJpil5KArb10X7rVYrVqk0yI6cpnnsxs50DEB1rTX\napnIGF4zH64ZvABCEHvRjA/SOrLBgGnMaKX4M+UE2RDl3m6DeP2Jfl8fZOiDpoz4e5uV6woVgNEu\n0vUamagaci7woethihhvjF8zVHlcZzXWGroQM6WwsXl3Q//py9wvq50kgDA25bUFIVd8bW6m0HZj\nrPl85Ie2H6YxISBslF+LAgHGDEyBHCKto5Xr1m2fBLP0CQyhG4r+HgBQcOOVdV7u1LBbqfJuZ4dM\nGfTxcT9Tw/6Z3QpVmEIAvK6OTYkflBi23qmqcLCtg9Q57S73eJEJT/dXeBBvsKvXePasoDXGiyfv\nI4aCdx49wac+8jqmwwHvXT/Gaw/vAQg47JJ6LGc0BOR6QuCKovUd+ymiKlpnZUYpMrOo3tvSGqYY\n4R5VAPspai2iGe7qvQ8yv1yqNhZnTFTxyTfvoZHU/4Erlkw4LgHfuPkDImQVvjxE8RTKEQ1DXVun\nbTYacWW472uM4l1MMUH6yDFCIyBEBADzlDBNEUm99NKWSIrpzQtfmMGWaqVWUJokOttaU7RLUd7n\nOWFdM1oTZSuMNZ6tO2dI6yGN3uQEOGfwORofcUh2Pbh23FkYmadZuqKuenrUKF7R8701+GwNh4wR\nUK/RsbGy0QTQey3ArUzSyDFTd7Jt/R1S27TbzQgx9hrY2tB4xeXhAGLGmosge8NklPEMfeBIGwPt\ngjT6pWtdWfm/fjVKH4SNoWNOsMbAPjR89zThjd0eH7tquMKKm+MJEs+TlOw1F4lWEGEqM6wdbCAx\nWA7TAUHBImqDylyVH0QaqWHUFvH08TVevLhGDAlLPiHMBbc3hCkSDlPC3/j2x3C9BBx2olWyGYps\nta7Kt80I0WhVq7poGAxFqIKm/5NlFAezRQ0BGd80RMKJBHzKSl4aA7WKbMzLglqlfYOglYY7+268\nO5LxRtm3KUaEAE8xG58HqiA0rVMU42+eEhAmNTqitL5ozenU+rXWUlzeNOP7bP1fB52G5C8zNs2R\nTWSKrI6/SW24rF+DH0MQpil6PZY5RQBGChIhN9khpGl9IVVXQY/QCfmTilV1ujK6Yq/rvt9Pyscq\nmAJyKZimiFnRJQH9TpA+20SaDk/S3iWr0dMzA4L3yq2tqiHa83V6vz5yJcbARBqq61ad9w7Rc5bS\nA4qMVlcEmrphuCFZ0R1Nr+n8XJ2Cg+F8Oh5FlqQ9blfC81PEKUdMgTFdJqy3VVM7NVobZb653LpB\nJ7Z1k/r8FMC5AEkB+KLoda00IKkzZhKjgSeIgaYBEJq0TpkZrbA44LU1BIHAkYGm+mNjIEp7qhBl\nnu20AoURJokOC9Ivo9w8VYdZd/K1yjjd3kjasbJ2w3dgZnBtWPJRaj+jnEGigMKMRtYnWTESmnQA\nCEFqFDWJCSEEFBaHKIWIaRfx5PktjrngweUe773Y4+uPIm5vb0BcEaedyEHjHUrHl5cHhBRxXLIA\n7HHDPO9ErjIruvEYiFB50sS4rqUihuQRcpCk58cYkVKUKCIzXjy/QUoTwAktCM5JikIL4uAR7IIU\ngyKvM1rLsP6zYieas0lp320coGm0dD9P+H//0p/HR37kU/8lXuH1ytJQAYCZn00p/fd/4//8X9VQ\nY5ScJSWBG+bDAcvxFvNu7155EGlvRREhaUqYZwnhirHQUyJabQKjbEXuJAylNktagPJhloJ+9dQY\nippdZrSMTMKVJzZBITnWIaauTbN6wwfDYUzD8ldUG7f0Eus/VHL2iJwJTfNqeLrJFnHcDaezdRYD\nW6UeMzT9xxglebptHD3lxpB9nhIlIzAsca0/BBgmCcJQt6H7tmWxnana90w1GFdnXH/bgz62rhRK\nvVUCDKV0SBeUTzSfi0GWu3PhrL6V7kzI1rGPY/MpHr45pDj1HR6+RR38Y5Ma5EQukYZIAGlvTF8D\nOh+R3N3Oj31QlIrW723p/8qY+81YPcqMoCBTfZ7w8+Iro1+TPVAgHdNUhzPnaafcDTBfCB2HkQgB\nqDThY5cVn3xQ8OZlwQLg6vIS67richfw8MEBDx9eIs4Rx3rExb2A+5d7hBhxnSW9ZDdNOFxMIGqY\nUsCMgqud9Mg0ISPn21JPbUqaetLYaTWFLuTZBxywSwKacHlxwIPLA964f4GPvfkadvsLzDHiebmP\n6/Y6juEfQdl9HDdlQgqDyqsQ+FLTUHukEejGE+Bj7VZ9Xz8zXGsRpFjzws+z8MEQoxiuLIrcNM+A\n9sJrKlDjlDClJIqQpXJRj9JFvafxQVba30ZF+3kwmPEzHWqz4UYnhF4naufAoqFWS9lpUSM3QVJi\nJdWqp/6Nh9JTSYf1ck5i533zLYaApzQFdZBvMEsz+aIolAAPpQjiPU8xYH/YI00JRA2trIJCDUKY\nJsRpkpShLGikYbMwNp5OF+wvmwEsr9dmaIOdFzFYSwsYuVYx3JoZvzLL0giPThHfuJ6x3wErGKfj\nCafTglyygjxLFIAjo6gThRswIaIuRWUEEENCLtIX1FIhcy44LSuub07Iy4J5iqCw4OHre4Q0YZeA\n25zwlUeX+M4zQqi3AryWM2pZxTDTOshAkm6YojSkjzHpPg78ZFip8dxts1A0W2GWmlv/biBMkxiT\npVaU2tPPBPE1a0qcRhk6VpLTvjsQ0MG9dtOk/dq6PBjTl2M0eSopcft52oz1dDqhNDEgTJI0zXIw\nemRA6cqU6j5PcAPX1fUBHpRLq2u0xQsxmPagBqg6r9zwG4UM3Cgxh5+lV0sEXI0wkI8jkKXe2kys\nTKY7QGNMmCaxVqR/dnXAGQMls2dbpNP4dowR024W97COMVg9o2l8ZrDqmZjSJJFK5SnTZOvfDbqo\n9dmjvjfWawrAUwWpYzgMcsP34YznWaRp01pFN8LqUJdlBbWCGAP+hU+8QOKsspYx7Qm7SYCQcmtY\nVrii5wAAIABJREFUKuO4ZgEoWbJG/xlFMwByqyhE6rSSJWdFXOupunqO1bCyKHlI5OzI0lXjrKB0\n1ZA5O1ItgF7WVBlMAetpRckFcSKEIHmnrTYgiFEa5z0WJtwuK/IpI4BQW4aA7klWU5pS1xO40xz3\n7QYzkKtm2EXpIxyicIRcgNoiblfG9SnjlAXM6nQ64bSs2E0J33qc8fVHAe+8dyPyP04aiOl6aowB\n964uME0zTkvGukiG4263EwcHqWNouNx4Z+cYCDFqazatgW3S21HkbkBeF7x48RyA8Jucq0Y3ZT5r\nzsjrgryuEk2fJkenlsCV8aIejLL/Gq9oraFywH6/w1c++3N446OfwG988a//R3iF1ys1FgHg5ubm\nJ7/0i//7+uS73wEB2B8OSFH6EZVcMO0POB6PKLcvNkKXSMK4JVex1PWy3H8Trf66MZS7Gk2/yJRl\niSR6zv9wDfaQF76Ol0RQ1MuyMTg7IMHv5RJee6Zsb+44zktfVw+HRO11Bbi592/TbBid4b1sTVyd\nGWxbz+F+icnyQVdXeu1vbBcRXRCOn7H5nF8vW7+XjYbPrejfbZxWz3DnZudOg7Pv+X9ePq47Xsjv\na1Ryeebu2Y1Edg+WnTFWmNC7+3zLyb9zsx/yMsdFCAOAhd7XcvAttWus+wgx4XQ64u33b/Hb1wcp\ngCfCFL+//TsbzZ1XRP/sr0ci5CpWdGk9ShzPlmLkOb/3U/+DXaIobaOJZvA4rULqkHvdE8Dth1mr\nH/ySmsKhhtMcT/9ARtOvczcPg5x/veo9/N2vMwMHL+MrfMcJ+Pt6EVCzpDGf1kWUD2bUXLEcT34W\nz+XJ79vwSJR7o01LY2fW3nzDARzp5NVeGrVjt2UxRXGp7nc9atYArWe12n0+c358uJc57tzQhrBB\nM2YBAsUkDiXuDmAZl9Xh/vDyAdhKGVZDZgOOowbciGEhrU2S97ojkgDBaIzRRqn4PsZjOh7gepCt\ngb0eSHp4p2m6o4theKxns5njQPUaK4W4890Pupo4jAy7wspTSutlSQAkiGlWwzgk5g54s3GmG0+R\n9M6YtmdOeh1+j3PYuEdpWT5vfSGDGZTMIJrAWp9PCk60rCsSES6uDqg1m9dDzuF+UgdqA1dBHrXe\nmszwGuSsWALjZVk4dg6ypoZKenNTo1oygb5z/QCPnxctDdA14Z6xZis1TxMqM5bl5I4aWZ/Q09eV\n952j6W+c+ewHA0SaHh/6OZB+8t0RZFfQjCNuzR0cBGDN2dGDt7oy+ePMyWYZjWmSVOVf/umfwvPH\nj/6TD97cD+d6pWmoAMDM71xdXf3Pn/vpn/r3/vX/4L8AzANOUgRNlLAcn+DB628CLJDOTAFBPWon\n7TFIZBGDMDTlNpQhBXUgArjCwF9MmZaoAsEKxSVKKERvOf6sXj/Z7CbephDBbIX5NqHmhdCSYtg2\nTLIxj62K/BKbyjz23XvWUyN64SpA6FhV8PfcTB7TL/V96P2twNxRFPWeUQuVzXtqzUERBErbDqlH\nPdRLOabQwZnl1qC072xAUbRdBPt9bKw2KB989zTbszYLZ5+ztzu6n22KpJT1dFX7mte0sgkLHTlb\nao8MhO6MxWq2+rOHfwajRFPMzni6Kwo2juE2BPFwy9/W+NpHArOst9FL/6beyP1Msu9xEMiAOzha\ntagJg1nbOkAEtmk9XqMrGqMrGH2u+j2K7qn2JRj28RwcQvO3/DtLCbicK+5NC/Juj1QJD+7fx7vv\nvMC91+5jPd5gujdhnnfItNMeQxHH4y12uxnIt5hpj+V0i4CCw2GWNBXaAVSBKjUj4vEDTtoiwlIb\nLbIYNQ2ur5/U9dRaJBJHAY0Dbo4nrSOqaFwxU8bz4wFv314hEPD0GDFFCGBCzt15wSxCtNZ+Tl3o\nQT2davDYWur6WUoxMaNUbf8xSSaDRyW4Om3HGLGWuql7EkeCOLNiYkduK7n4M3MuUisULIIsg/M6\nLmB4/SzKR5u/NvTtx0HrKoyPyDw1qoYhjdTOS+iRSQzr0r8P3afuAMNLPkPOeO1v88IKT7dzAPS+\nVCIfmtd2TdOE/WGHm5sbLMuiYyHEqK0P1hWn2xtJcwwd8GmEV7f/elDKLMbBucPcwJWUdrfeAwFI\naAP/YlfqGzMSNTxZIv7+0z1+9LVbTFPB6VQQFLAohohWtEUKiyIJIuS1eJSXQkDOGUnTy6zFR0hS\nO9dSwhwCTqcj8umIq0uJQB5iwXVOePt6hy+/e4FpbojTYeO8ExZmypJ5yC3iVJDXDFBEQOfBGFau\nl2oYiIX2m0wTdrsdllxQmRVcQhTtMb0/kNRbCl2q0gloHazJWv19+K2VhsNeIly51i7pWCKkCElq\nyB2gxepNpd0NSM5JUf0mQHvNmuFLQdYkGHy+lVZoNoyNRIgY0raknw2LALLKOQYDLQCskVzlIWaQ\nxZQ6f+em5QRCqL0Vk9Ge6URCiIYM6v0QlS4NOIxIQHrmKcHaUxhPmnd7aBUQam1S31olbfe0ZvhJ\nYcaUEliNB4pRAbeC87tq6NKs+pNGqKcpStRddSPrVWdGYQrAaclaSqTZJbXKXhXZ2zkl5UGxA+IY\nn1aHRdcRemnJqMCbbgYyROOIw36HQhM+df8Fnp4Ib7wOLEX4F4iBwogM7Bk4sbxemqB5S8aIgPYk\nBWTJ0ITPZllfwIwgbSpkYQD711m06UjWkohhtQPhECUjT6oqAG5ADKAIxClKKioR0iH4/WutUm+Z\nCGl3hXq6xVozXlw/AUrD/bc+gVqu0TgLywkJbmPWhoqGMCcEDi4oChGWUrFUaSGxo4A9BQHkYaAh\nYGliJIIzDhFYGrBLE3LJePLiFs+uT8jY49GLPW4Xq+8nB/OyVPLGjP3hAtfHk/Y2Zcy7CfNuBjMG\nQ1F1IpMrxpZY7IcYojiDWGg6hm5M277ltfiZEcdIx6uobREdJEbM+z3WZcFR9QdSfsCwLJ4zYUv9\nl6gtVr78K/8XDvdeW7/7zd/8b/CKr1duLALAzc3Nn/zyr/7Cv/sv/9v/fnj41scxzxNub66xroIe\nNe2vsNy8wP7BG2i14P9j7l1jbcuy86BvzDnX2nufc1/1tLva7XT7FWJHIUFxsBCOFGGUYBQiRULK\nD+RIIIQUfgAiIoAUGxABxzJRICgosUxMoEOIkh92EiuJu40Sv7CN3G7HuP2guuOurqquqlv3cR57\n77XWnHPwYzzmXPucW91u4epeUtU9Z5+115qPMcf4xhssDCkSQJpcXmpFHCLGMeF6f1TLhIWlNqXQ\nUWxXjCHFBnxMqAQV3tVRSgMgARDAo0n4QQsFmPfElSjTqbjItxQxOYGhMRYY4+H2vSVLWIkxGWFW\nquR2gqPlwVk4Umu2a3qdeDarFoYBKFgoSgBzULe1Ml5qeRst5r8VEmiwj134em4l2btkHOSKF2nC\nONxqRCdr4NY36tavvcl/Xq1Zp9jJ1ztQ3oXjNAsrzA6g29oUIHuvF8/p8hCt4M3aQrg+rE2nNJhh\nzzQmw6vvOdjQd6y3n7vvdWF/bWOcXonaulvIL1iLDnAHoG0dqOX4sgoLMdDAQbKJdz9Dtk7U5mHA\n34wUPaDv89uwWjMVrjo/B3uR8OQQMJeIaS6gsMW4YWyGgP3VUxAfMdELyIcFx+mIYVNw5/lzgKXH\nXK2i/M4zgcsB9+4+ADOQ6+TnUpRDyV0kIuzGwYs/hCD5YSs/BMnaXR1mnG2S5tdKqM1mCJgLIQbG\nQISL6RwTbwCSkKDLZQBqBjQ8LGqDYlM+KbTcXNt/0c9tdbmtsyoBZqywJPygRWiinkkLU7HzYWFI\nFAKIpVG8gUTAFHuFO2oUCsYX9f+2T6LM9RV+hVhb9Gyjvcr1RrU4hqW8tRBbVoAXiFrPPQPu3Rj6\nynEkSTdOs17Zkjs+YWCfLCywCXZ5d/T1lvVVfkdAGiQiREIuNacUQBoGpCShvheXlwCs35bMORJj\nGCKW+YjpOCHEAeM4gAFvbO9H18cmxsghDZhVcYGfI5bwvI7FiEVb5QlJJnTQKoMxBixL0bFIKOrj\nKeInP38Pf+SVCdhuQTuhwTpLzioVwnxcgCKekwIxpsQUPHyPCThq7phUPhww54JSF+wPR+SaQYmB\nELCJBQ+vEz755jne3p+B0k4qGNtaOxtQuZEkr9aUobwsWJZFZZ0UsOim3/bQ+KGtaRB5s9mMOMyL\nGwI0g0fXUM5PKQXLMqMsws9jUONoR3W0kjhwGTGMkpu1TLNiAOOROrJaEJWHxhgRhwHLcY9Sg4Qm\ns8x3Oh5Q5gkxWWh3ROY2Vq6Mqsn/EjHYKkeKLYPsZBk12elq8okBaB2GQMENkG60C5rHZsV4NGyO\nuZ0HUv7HbRn8bcm8XKqAVrZcQvmOVHitagxQI1wM0vuTVdErBSUvWtBI+nt6kTSdhBmx7HerqDkM\nA2Iaulwu8+yy9tOFG7ekZ5/WdAAwDlHyw7QPp7fLUI8mKdjOtaBmpTju29U0747hRHQ06VCDO4TA\n7W8lF2zOgM9enCEE4LnNgs0IRJP5urQxRmyLVOiuRR5YUDHX6saFgIqFVaZBFMoYgqcO9eeOyKLO\ngEQVMbAqIFJRk0FADFgq4SxUVDWscglYpgqqJHa+IQAhguKAYXuOfLyQc1wYIUiOYdgOmC8vkeaC\ncRwxXz0EuKKEAuJBjFWBQcaLtOgLaV70zCwh4yQF7hKAs00EB5UZQXJYr68zBlSMo/DANAbsF8bb\nT/Z48+k1drtzvHVxjrnY94KGwctZW3JGrQVDGkSnCAFcxQi82wyqwFnF8dBxBZUqbFEWBdbeyM5w\nGKRQXND88EUNFnLeYlckR3LQ46CVfvVM5ZzdSJGzGRsJBAulbzpMz71iFKNmoIIf/+hfxu/+g//6\nz7/2q5+8xG/z9b4oi8z8zvn5+ff/+N/4y//JH/8P/xxqkUatpQAUE/LhCsOD+1p2mFtiP6pUT4J4\nykqpOE4LNkPCUrRPG6BMkcXFTaQKlxxjSQkUaxtzlaIhkANlRWyqWxrRcYKmbIh10CzUCvWYPFHY\nIaAJMbvNwL8pLpDPzABu4AHUGHkDXcqelKubhVu+wa4k+qfKJUz8sobeVXV5k3qT5LvVQZ4/wxQ6\nGIRUdcgBpIU96sC5CzdkBkES3LmwMAZuTwtRPH+ng3bmuxIX7XfzDgPt3hW6WiGNPqTXoUZbaFUS\n/XNT3qxGmgFP7p6ggtFRQ/eufszgHgJ3CgLWz2se4n7Mbe1teuZNIEfIJsLXzzJLKKARK9yMCz2w\nsP112uwUPxOBBKNdVoWWIA3Su0Vh+Dr1oeCmOPeVeUHS3iJtd1j2V+Ba8fQY8aknd/Ch3SWmJ5e4\nvpak8Lv3H+D68g1QykhgbEvAnbMzUAQ2Wyk0kiIQqOCwv8ZzD8SrWEo7jykRlgWe25E0NxeQvIEU\nCLkCS60YKXjIT65Vc+zI8xUsTL2UghgrDjni8XHA2/st5kw45IBAYvUWITDqvvbnVZbBqguuqaff\nScCUROMjtm9VKxxKNcLWQJ0BCWExHmfVJdXDkpcZ7TyxFvti3/++5Q93tOXneUWPjR82I5Cc5TXQ\nbzTUJspAUM7aAZr+2X2omXMAArjmldKgsF2+H9pf+lPnVn7/2RAd+XctSsTOb9BGzGZsu77eg1kq\nlXJZwKxW4HEDALi8vEZMks8GBQ3G4/sT2gw8rbAadd7VfomcVepMjE2islcilN5wbQ/MVvP61YjX\n9lt86HzGXCvmuYAPWgyONYqACBykuJzQSQRxRM0VS15EKY0BxMA8zZjzDKBiSMBACSEQxsT4zUfA\nJ9+6h8u8A8cRgwIzPiHxaAYMIq8YKgWkyHmqKSs959SVczrp8+pIz6M9t3l6jOeJ9yMvCwjBjQIA\nIXIzzPSe6VWYrdKh9H9kzwUz6hePWks9YWZMx6PLsnleEIlQsijnXoqfrMx/76HvUkOMdvRcN35b\nXRkWm6bJK/NStrPYGxApaCUj9OvXqsZTd3b8LLJ5xlvEklQOb3LeF0OVqaQVaWsVg3EpwDiOGMaE\nUqQIoORdS1Ea9yATtBiPYpTQ1lj4h2CHeZoRlow0itIolZKjG6LAlmup51j5+W63BdgKy83Y7/cr\nBVuM/9qsHm1u4jUNsu5OkB1I73HIWojDoqpCCthoDlokxvUS8ME7E8aRtAq25XKzVyrlzjvI6r0C\ngBAHVFQsTNB62kiBMGpxHCivM4XQ+F+AVCA91oC3rrZ495gw16DVb2USxxLxytmED5wdMUZR+Ldn\nhFAYOROgRqy42cpc0wgu12LcigEhSTRNDBFMFTFJ8RYODNSIqobLsAlSpM1aOLFgQyagLMXpmwCM\ng/IGkqCkpTDycfGlPi4ZQ414cpjx6OoArgvu7TZ4dz7Db7x9jjxPCGpYaLgUKitHjWKUfZdesRvM\n8wxQQEjDCQYznNqkC9cKSsnzuoMq7OMgXulpKV5JPYSgxfBYjR0SpRi0InTStjK565NufN0r/FMv\nS5s336pBEwE/+/f/D7zwwY/gJ//2D/zLeB+u9y39ZL/ff++v/JMfnd9+7dNIw4hxHGB9dSgELPOM\n3mMmly0S3OpTStV4YPLPGtPT/VWmdnpZifo1A2BnioDDIv+OhRjJhvZgnLsqo+GEebTx3Hb1H9vz\n/JnduF2J8Yd20Mg0Uu6+Y1LTLE4nArIPyfxiL2aIk8u8krA1M6BDbcJelEfZmwNE8j3sPYpf3GVc\n2uC4n6S2Bqvxrufaj/nmv92+6xz6QiTt8y+0XjcndPsU1wpe/7mPiPt3tr5G4Hpz326Zv3zcwFEb\nTwP9Nwdr4EOFp8tW9TzduPX25zzrcyskMmfg0TwgJkats4ZCSjjRvQcfxGd+83Vs70QEAsaYEBEw\npkH3RM6ptJLoDBc+v0YSfT6KC3k9R7VbfhvvEAMmLWvfe2iFFCpyJUw5Yr9ELIVwzFFhhIbuneb9\n9McRN//rbrn16mmY7He1yNt/MucGCq1PY28JtebL/h1Vkt7z/JNRSlMoT/e8B6in/+KEBr7QUV/z\nhe45q8U8oWVqY+vvetb9/h70oLQ9y9o7MDOWvKgyRg6MgZYbZ54xD7l/xrvWs29rftv9beS3/FU/\n6I0O7PNhbGLBJknoataenT3bEy+TKozo+7JqIY1cOnpgzbcTa3ykVqkvBuBqBt66GnGsY/MOliYH\nV2tga0vNaPMs/vDsqxnTrEiVKQa9omhXqyIYHaBaL9YYgu+zN+Duabp/RtNkBZDH1m/QZDHDGn3L\n90qxXrAG9rp7uSmH/Xj1k45KTq+WsqCrcbLOBnZuHLvuebruyktsTCt21Xn6bN98sHTL3qnRygvo\nQM6J0GSb581ca/N6kufcGSY7LX4lhRBF2TaFlYh8H/VpjicESMd2lgFtrVK6s9dBFTdYiAHHWgkZ\nhAJOCvg9Y439D/o3aWkg4aS7VDDXroI0WRRFUwhyqZjVwFldxrdoLQnetddQK97GonSG/j8tTlgY\nuJgT3jkOeHO/xZv7Ld643uLN/QafuTzDPkdc5YhAjAzgbKxIgUUbCCwhfQGgJAYB0voIiEH3TtI2\nUFh6HvIa19RSgEFlqfH3QAiDVg7tclaZgTFoMUnbGwCFgTkLbjAP8pQrrqcFF/sjgIqZN3h4Nbai\nUTB67LdGUgW22w2meUbJxekn5yz5vEpNt1+8omOba9AwdKEz/YzEq2xpKca3AGjF5OL0azK5jwq7\nyUG7f1Usmsf1uL/CP/lbP4D5cP1fP2Pg/79f74tnEQCY+fHZ2dl/+/G//t9/z5/4T78fKUkZ+N2d\nu1j2F8jTAXF7Rywo1HLGrLS4HCY7SIQ7m51Ygg+TV1Xy0BSS/ilcq6ZrkPZ1MWuheNxKYQd0Yl1N\nYCuTDDgzs/AJa4ZN0JBLkN4PFwr2NOoIXw1Htg5rYQJVeEGe96IyT4kjIJcKDXjXYZEqqI25WE8v\ne2/gAqtpChjj1r9XRg0e/KjgsjrD7QJTABAKIB7eWoFifY7WjFkqylUvPsDq6dykgFyWFqpEktfh\n62DIhqsnxDevsIRiGjBiWKlxQ0J97mYTXp57EE6UWYKGZ2r56VIkpC6wNvWNraw/muADdQfZlTkF\nk4DTiwlnT+pXBmLC56biaWEN7X6pjgsXkAJKRJEZxqgeBtfmOsGvzJYlN6sUUzIbADgNkw2aW9bC\ngxuzJX0++zpwU3iY121QGGBqHngDRzEmlDIBRNgOEop1PTMuZ8b5+YjrY0AOWsgqT9iOI7ZhxPBc\nQg0SkpdiBDQPoTCwO9vi6rogxQXbjeQHmRdpHAKOU9GKh4wxMYbYM2wpzlK00uRuiJpXQUCBFhlo\nQn07EConvHyn4HNXhP0stJ4CsMwLiIJW5IsSFYEGnG2dmtGkFwjN0OWUQOSKne8nq5LQyBBFrZpF\n6aQHodbkW4SmfFhLduXGQKNVebZ9GscRy5w9H8najLAl5PYU2ylZRnLoeE8PfK2/LfcACbSaD6sh\nodRWWdHHZsWUnK/S6u+NlBudG7/zStbOM7WFjCrRpRSEmBADSSW6IoL87vk5Ntstnj55jIUlFNX6\n1z169ARp2CiPVB7HFup/cq513KUU5GKFDVpeoy2S8yuVSzYd6ZNLqLwAxRqZD7AWTGBgvwS8fFbw\n7jzipbMJ0yEjFQZvotBzrshzweG4RxoS0hgxUEQIjCVPuNhfY7vbYqpSLTWGAETp9k1RvCwxFAAF\nf/OfPoeLeUQck/TIK5pX2BnXbAVCCNJDMop8mJcMQ+Fm9Og9bdT9JPtOTscpJW+VgRAwDAnW/9Bo\nzg0rISDaPutV+7dwT8/yhug5pwtKheZBq/xmk3MRpEU9liy5oUlzWLlWhBoQOCOQ5FMiJK3EKXme\nkgcNPwMSat0whsk0gvHkoBVwT8J0VWZaWydSXkVg5KVIjqIVyFBZLthA2rD0+cdNbvENQ4+QI4vS\nrXRripU4iKTSrO9SGrEbJNdwzhnLktXooDNoGhgM7zS2KJ9X5WcOxkNEzRmHefb9HMZxFRaftcKk\n9bGzORAI83REztJSRProstOW8dgUk85AjSyd0cQiLpyrELCqFaByO6gH2vrenZ9tQZFwORE+8mBB\nioQ5A4Eqho4fSpYJIUagLBWBpKdgAQEhYMECioSSpX7G+RAxBsnFDCQtcv7ZxRZvHTeIJHJ3roR3\nDzs8mja4yq06bzBhDkIgxsUy4K3DiF9+fBfnqeDBZsHL5zN+94t7pFGKxhQQUC8RMQBxRhgjKFRw\nmFBoROWMSIThzg4lF9SlALkCnFFKRtiMXhyHqQJBeHyuFVNlJO0fGBWj9RWzc5V92m0GHJeMJRds\nktDWLjFevJuAMOKn/tkrePr0CiVPPjcAnk9ohTTHcdBw1FYMcl4WDOPY9Aanu1ZnQPOvnP6M3rgy\n4iAVyo+ThmODcZxmAIxxs8HxcEQakuCEIKHZMSbEIQFBoiRaqpudOVrxU1MczFCyGQaMm4Tj4Yif\n/bsfxSvf8C341Z/98d/2wjZ2vW/KIgAcDofv+/QnfvJPv/arnzx/5Ru/RdyweUFII+bpgHE5Iow7\nbXgZHAAIIQBAy12blgUxBA9JNcuNA1z7mcyqZ2EnFm4axFsW7DvGxFqit4GfoH17mqKg3kQADnBA\nKyDQGSftnHYKYlMgoPORPwf9XUKW9OkNZNnVKR3uCQFgIRn+CUt4KFSJ7gGNFXTon8EKbhzYdSDG\nHkkK9thCfbk4SHNFVEdB6IoPVAmrCQBY8x0QTfD3i9LCisybS8HCWCDzgVlcNZDBAaz+b22iceEH\nkvxTsBYWIGhIR6dM2Wawrb1RoC8ATMCevObGZUxohQ95/XcAFiutNGOKWZAGrN0YintsTQgL+bbi\nKDJGt2TfYgo1z/oJS7o5OMDDs03N6WSpKANYK4yuCqkFNKphJQ0bPHz4Ll76qpcx1REhJhBPuHte\ncIgb7K8WhFBwdmcEoibTk+RTVRBQCdNhj7OzHYYYkKKsqRWtqZ2istsmbErF00PGu9czdoO0xCAi\nHBfCXIAUxRLbVkwLnoQgYTwWSRCAgQiFCW9cbq3FKJgZaRhR8uLFQVyTXq1pr2b3NNDo3D9zq7YZ\nAmSOpRYHr/05NQ+I0a0BotMxRA2xcW+rGkPMkEFE0pQ5iZHKzokxMFdx/YwY7TTAZzTe8CA5nTX6\n70wVHQEaawsaus7gZtChjpcRWiQHbl6ndL02zDR+LjQTsdlIKNA8L0LLQRT/NCTkkrEsBRQTYhRP\nAYGRhgHIxeXIet58enwaHXTrz2hGupVSTgA0QKUU3UsF0CKiWPrGKUgtFbi3rfiauzOe2y2Qrmfy\nrM1mxOH6gOk4YbqesBkjNsNGeHKFNM4eAu7stohDxLxkaYDNFblmlJqlyBIz3rw6w6fe2WG/WK9D\nPe9qMOvlHEGMO1ZMDWiFI9b5QFjRUeMrRntwg5/lkvf7XHjtmWh7Qc0LoM+sUhVp9Q7Sz+0cVM0R\nkzwjDfcOzfDsPFi9X1mL2VjxFFNurLdpUsVszq3Ij2OGLuys0akTrSi3yssNJDrNm7Kpa2+Ko9Gv\nGxKM73QEafiA0J9pMfqBSHOsbY3bubZcP1BACuqJNiVQ5amNsViaTseHTG4qx0PHNFZjlb2SfbSW\nGsLiyOc1Hw5qsNdEIhaskZfsaxmTKLg5Z8/nN3kOXxNZh+JGJzH+VzU49HsSiLDZJIzjgHnO7kUS\nchAjkvTOS9LCJUTFPMBLdzIe7CakCIyhU9RVkBSuqCRGzlrkvBGkdQ+CIh3d76UySiU8nSM+f73F\n1TLg8Txgrx5CADiWhFoJFYQh9rLATpviKQCVA7gCV4ukVbx4p2LihLNxxjwRUCU/MddFfibG4zLi\nwRkBsyiKNQVM0wxesrTmCPLCOAwIY1J8aGhQyKIqxD6WghSAMUn4bggWCRQQI5BnqY56XBhTBq7m\njDubgKUCv3n5PN683GB/mLHMM0IcW8VhZgxJopPA1m91VIeSGTuExsZx6GgTJ3JPBmsG8Hb+n8qI\nAAAgAElEQVSmoK17pAqrYdtape+zFWGTfpNiQEhRQsOFtpoyGmLQ50sRQntH1TNoPFOKgUrLoWVe\n8PTRQ/zMD/91vPw7vuHP4H283rcwVABg5v0yT//Bj/6V/4atsIowxc4yS/pf58Z1AAPVvikg54JZ\nG74mtfy2Q94JZLsMsHCfq7Bm2MbQWjgX/O+nYU+wz27A7htv7hfgVgWDqJVSNrDfrZkDqluw/2rs\nrigAt7yHb/mUbygILYyN/YD3igj0QFMgd/9XDT81AWCCzQZsoX/uyTSga++rrfXCaTid7Zfo8Z3H\nxhl9p8GgAcln7YEJffFaWL8kocEGghmn63UaQqXyWpV8XZfTd6EB7NPnrBRc4GQubRbuGSVqxYh0\nmM2rZFV+uxWg26lgjepvho/1gzLl2wRWsOR6QgvLoqZAgducScdl4CtGCZKJgTAk6f+73QHDOIBp\ng+M0YRgHXE8HpE1ErhnQc1GL9B2secGQSMJQ0TCJjc/GkpI0DZ9zwWGpOGZRssfIOOQoho6eUnQ+\nVkimMnvxjRgIjw4jHh1GGEBzoGX311a7uIG5G5Sw+o31cJmysApHucF/1ltZqxSGsL5yVXtjnT5H\nxh86WiPllUHp3rK31/RghSTMYmpKnN1jY/TrhIQIHV13H9qq96vB3d/6A9TWsH14w9Ny4+rudeUM\nvmcKl0Dq9fGquAp6Q5DiA9M0aQq48SvGosbJfs+Mh9l7TpWh1bLw6V/hBCwW77Z/Rb0ctqfGE11p\nIVHShsi4t8k4G4vU9AmiMErlxyq9JFlATNCG2iY/iQjjkLQwCQEkRVeYihS9KYS3rwe8+miDh9cj\nMmvorbOPlpfLbBiLWssGgvN087BXZm3Erj37+GQ9un3v6dB2lvRlTUbj1r0wZTN3ZwKuNPJaFnU8\n1/gW0ArFWG5S0CJ51hfZjDUxSmGb9hmkDL+HB6EbfzvQ6yNjfyUXP30IvX3HI1rsodzG3qi9++8W\nvOHytmp/QcsvdbXuBpX6z9LvkX3/oV5Q1e87WkX3rC7a53TK3ftOz/uatwc/D7XC88aqYoJSWh+7\nkhdMxyNY2yb18tz4KBHrfKoBkzbLDvMNQ8LdOzucne0wjiPikBBiwJAiNpsRwxC1AFX0fnkMoFRZ\nkOd3GbtUPHTUyNC3jiSv3xTFFIHtSDjbSI9BcEEikUG5Am9cj/j00zP8xtNzvHpxjs/vt3gyjXg8\nbfB42uCYpXhNrRaSav9x968auECoTJhKwNN5wOWc8O5hwL5EhAhttUEgKjhmxpvHDQ4YEIaANGit\nD4h8p4GAREAKCGNC3I1S5dkEgdEHG36XNUiRMKSAlNQgF8wJIGOrFThmxpPDjBQJ7+4jPnd5B595\nfI43HxXsr64QUqcoKo9JIYBgvcdb8TszlAYiDa1fO0skyMicBrZfPbcwPQC6z8qjFPgILpb/0iBp\ndsMo/UCjGdG01oL0mm89fr3NDK37RrcweomGzKXgH//N/wnf9K1/cP+5X/ul3/YKqP31vnoWAaCU\n8tf2l0//4i//1D+68/Xf+ockxrdoCIVaVlr0U6+gNUugM3WW8si77Sg91Sqjdn0reuXRpJw8tnl8\nCGsLod+t73fgHGjVEsPCSvrQBX9NN18CxGLMzgbRcSe5lzvwZe+1MYFcODZGyugrk60VjgbWe8Hb\nCH0t5NvzmhW1t9o60NR1M+BoLnNmKfNveSFEEvJu+SUCfAit54yEjwwpIkDCBHNhcC3SMkVzYex1\n/Ry8TLaH1BIqtdAB6zNkISRmQHBll3lV6IdVeZGwFNZxBt8TJzLYc9SKqwzNW0mYbDb+2HZWvQbU\n6EP3xay3PfhxMLDaN8Cqop6GtLbQPrSQKFWCsXqmDaflTDiYQXuveVJNfYhmwdUhBWVk87J4heFa\nWUJMslll5amVAShTLFkaFIMi7m9n7OIi7U5qQIykzcevsNvdl1BblrAVgDWcPCMlKeM/DiMCFd/X\nRpNrmrl/NuB6LrieC/ZLwUt3RpQasVTZ8xQA7ioTWwhmYV0JhkQlMPDqo3Mcc8QYe5VJ6KKWoqGe\n5rEjV2rYeBi6S8fsa+S02WjOktx1FxsYszAoaiDZPCQml61qoXlHyEAszJoaUbgV9Cm1eF84y4vj\nXJz8XRDrPjvPQcdDDRM7H2nTbWy0qYqkwtX2T4RybXRshpNOMMs5axEPvZ5hPuLgvI3bs32v5J1R\nFRoQYZ5niKVdFGehgYr91SVC3Mg5yRUlF0zKq0opiENyRSVA+EZ/tg1s2O+w9bL1cFJgWB48KlDJ\nrPB2v32jVa82+iZiLIVQUREjsNlIyfkSGHnOKIt458/ujeBAWKii5hnbUbyJHDVEk0jmU2fskoRL\nXS0R18uIX3l3h9efBIwRnk9m7O6ESSofkRoEUoFYxmqKL9eKmrPfv9o/41Mma3Vtev5ltFxYjEcW\nTWBn0Q0s+uzboh06ily9O0bZI6OZjfb9E4OChBxLntAe8zwhDaMUrrFcUKVNezwrfx1S14t5JeOD\nKzBw3ksu35jhbQ6MuQkgDR5SKMZZ8YaW2s4hYIBcq08a/ZkcZEbNiygwIcl9NneVjxYSSib3SEZY\nKktYLqToSsMLevYqI1uRQqf9Hk+h+128wKyMkrt9ctyhfC3ECEbQthqCD1qBGiBRcK/ufDwIHaZB\nUjHYaMmqgbMoIv5ZFcNxDHIGlQemGLHdjNhuN4DSwDACIRQJvxwiDscFo3odpcqohIceZsIf+5ZL\n3BlmTWuQ9csZ4MzSpxDCUseBcHUoiBHYjgG7MWAujC0BNQg2OsyMH3/tq/Bo2mJh8ZoNVBAhyiR8\nH2RO1Qmo5zOtuq38I8VwAkmkzecvEz5wVjFErew/aD0RnvHGFPHTD5/Hv/ThPYZhAjFhOs6ITEjb\nAFajW9TqnW6OoSCyUTgsQBINfWRgt43YjVGKwhBjGMgr3ObCdixwPS3IpeDRnvALn38ZDy+Aaf9Y\njk4cYe3rADWgpQgKQJ4XLEVSLEqR/osUCIGF7sbNqFVQlduS4RwxtJGvm4XGt7Ysffi9YeuguY/C\nElTxC6KUHo+T9GTV8HIY7ev7Y2zKrtFeX9DM/pumCY/e/Cx+7f/6OL7jT/5H//gXP/7DE97H631X\nFpm5EtF3/tj//H0//nX/wrenYbPB7vwOri+eIM8T0vZMK73JZnj5+Y7ZCnAWYZGr9j2EWFWN8QUx\nj4hFxbloF9IBaIUqrBkqlEnqS82S2NsjV0pUIFgPFp0hmlLX3dfLKFcw+6spcTZvYQEtptlAaK+4\nMEyD4DY3i1+ACK4mJxvHNll6CmTt0Hg5ewfkBvDkMDTlUjw9a4+GQR12oePACCQ5Xqz5mMssAJvZ\nlU+bcGVGTFEVPNcLYOG6DZhSBxCqh9OsFKsuvMQOKgAsWcYFpxk05aPLFxMPWQ86DKwbcPZPewrw\nh/V0ZgaFXmkXT2cHMl2pta3rpG0PlHzvjYZs3xqAbffBlRFbFyUSYeQUXAEWcC8WSbPgciAUzuBa\nMBejUxl/CAEt560LH4Q0Pb53fgbUjLevR/zK8DJ+30tvoVbCdpswDgHzdIanl0dsN1uUsiAN0Uus\nJ23+W4sw45SivqOBEWbDVyxAFaIw3tkkXE0FU654emRsh4ht0vBVNqOPrImFJZmhoXLFpx7ewcWU\ncHfMOGhhm1K0ZQY6oAs4PTRPUNtjB61oe2oapZ8VrOnEgW9dAyl77trAYOefVu+WNZTxlVJw2GeE\nNHgT6pWXvgPcwgZaERA3vECAhiktMGOC7rtNrCl1zchmM/LzwcLfSJ/JGqNkKsPae2nfspe1j8jH\nbWdBFU6K7VnKvDajiLycF+QsofjjOIjVdlkwH7P2FBV6tr0MBExLBoW04nPVwGh/zlbD7EIOcfPq\nvwvA89UbzQhDKlVL2xcCakUAMNeEzz7dYJsYd8cC5oJKrK0wGGmQ9hUcKmqoOETClGdwyRgHqCUf\nWJhwlTc4loS5EJ5OEY/3EU/2kp9rAJ19z9rpNhC+2QzrtAZVHiwX3ufKLfTR7+0UZKcShhq+upND\nQaqV9vRlSg5JjqUcnZNiYMb6qKcd2achJljrK8F3onXFmBzEZW1xxaqcteiQFvpmnN+MM9Gq5TJr\nRdTVUPR8mXGp/aUXt3afjUn+LvULqAv/JFVGTGmzpwWdJ9n3IGclahuBU4zVG+O9NRJLiRWTfQwN\n9e3CKk2WFC7Obxi2P2iTcTnV0T33T1lf3O+b/u552Up4Rj9Rq04yA+fnOxS23rRFmsGr8KhQ+ac0\nYy19ZJUCQFVaNKQoOYQdXyQS8A+IwmnVv5ml4jSD8JHnjjgfMs6HGSlK3nuKBFQgRuF2hYFcM3IG\nLg/AwgMCBywlYX9kRKoum0qp+LXH57jIWwl5rQVcCwqaV9TOgBc+60O+iRqdKmZxp4F+mgtwDBGP\npxFX84y7WznTbz2t+MyjO7hYNnj5bsU3f9WE4wJcPjx4BEEBELTqMluPcKVpcl4o8olI5MhuSAga\n3g9iDENQzKY9UkvFXAJefesJzgbg7pjwibeew9OrBXmexXCwag/V8A3XisNxARDcw88FCMUqUkOa\n2cM8g7IuRu/eI7s7E24AdLqUDtk3eutSA4+5ZMQYkXPuil71OKyvFG38QC7v0KDOkxiEp6eU8LEf\n+u/w1R/5nT/zw//Dn/1OvM/X+64sAgAz/8S9+w9+4ef+3kf/wB/4N/4khs3G+5LUeUIYt3KjgSZe\n/bpeXZawHVFwyi3MRxQNyUHgEyIwQB6EKZpVaz3W9nundIly073rRo++/iFYPbRnkk2JbEqACZCi\njbOVS8pDyBTWXmS7vGoo7CbrlbATywXienJPE9en31zZxKkF8IDhyrKVeJf9UqFtLvxOU26Atvoc\nLSSUS1UrooBIBqNW6vperkdlgJUdwIiiYMJX9lKeFLgJeQE11rfOnwYJqTELc9sjs9iZYmorbF7F\nVb8wx3fsnqX1YpKPrX3UPL5uXJCBNkF1+ghDo9zG123ljau3LK93uYF98XgGFC5wn4wB8WrwQwBB\nqVCvrHy+wg42BDaZTthsNwhJvHMPtgtAQExS2UvCeXbYHzJSGHE4HDQ0TorVx5h0rgEUgHD6spN3\nqqTCboigUc7+cSmIVPF4f8TVfI4P3c8+R/F0saMTIvE+Xh4TfuPRGZ4cB8yl69lmeQwnCiFzFzrI\n7Xn9dhgcbgrR6lGyZk7X6m1TQGQ71r/PDDdNgeluYjhYN2CTc5YIgBolvLcUbQHS8zly/mhnJobm\n+bA1atbq/h+jm5v8x2fPNhv2vSI00OpCF+S3tMeoUtitV3tDxQ3aV0MRlF/GKGXNl2URwGdCvBZp\nGL9ow3gKCsCrG4RKYVCMdvjB/Zi7Ze8Ff3+m3/vq5Bw67ySZQKqoRcAZwBiHiKUC7+wH3B0z7g9S\nGGIcAC5FclARUEPAvhCul4h9DuCloNSA81GdKSSl9C/yGZ5OCccFuJoI10cxLqQkfFHm0q27yhki\nwjhGDIOAN29u3ufgMrSSeVnP91kr4RjTeKLxIFECw8qqv9psNAYg7wBbgQ8dr8pQwwIhkPS4o5Yn\nJDLOqFme1yz90eU/tLAVTAZxN2YHssanGzjvMGUbqZ3xG+LCvIM+IyeoFcu3s9nhopXWaesZWhuP\nnve43KEW/taqmsvYuDvhxr/Mm5xz1nB4GwdW2G198frHW+ZsczKp45y6HRLdbXIwLnZ9UZTMO1pz\nlogbl39kbEXpoinsss4ajaHKYQVpkcSg9UgkUmrRuhqGQSoIYyx4sJ3x8vmEWjMQGFG9wcXmSYyl\nANcT4d1DlDxDGrBo88VIjN1QsRRZ/zcvRzw8jJgykCgDXcitLpbK5lY4SqKDW/sa8+IaXz9degbh\nmIHrLKH5x0w4LAH/76OEN652ePku48U7GUsBaq6gKL7CClGaohWEIkLmJq+COWp0vU0OjWR02qJf\n7KbDVDAV4OIwYVoyPnhvi19++Dw+92TEdDyAywIi8TA76DUWSZb32uYunvSCCslTjiFgs91q0cgm\nh31NOrndnwd5CSndmNwm5SGhY/iaO66PKNXaXbkJBYAZdRjVDTPt/SKjJb1OKvrKer76iZ/Ek7df\nx/n9F74bX4bry6IsAsDlxdM/8fN//298+pu+7V/F7v4L2JzfxXJ9oc2DASMe8gNt3xQGUpgRtNfO\nos2qwdK/MZBWHwrSc4Z9s8R7UjTfYIimTjJqiJ5Mb4RtSoOHBSkD8eINnlCu4wUcJKl8uAleOqBn\n3+sVRusDZBY08Zquw7tW8hrNO1pB7gaXGzSItaqAJDjB+gEAOfAx2WKM1O7xMEVW6xCRWmkIARGM\n0jyRMIVcrDRe5lrHX4v8PWoF0hgTcilIw+hWL7nf8ij60DLtiRhOhZ15EsgVLAfXOqGiRTOClX8m\n8gqooSuFLx41rcKatOiGAaCOAjUFBg4rTDgapWmxFCZu7itf994T3Ss/5GMz8Ny8f83T1FuPDTyQ\n05Qpu2uFqnk8m3JoD+kBSjEFRb1ZUqCCXYkpBSIiSCz9En3RwgUrM0KnRJW8YBgS9vsDHjy3weVU\n8eLZHjlXVA5YsvRBOtvtwOUah+mISlL+O5AUICI2MCZ0XFnCWojW4eDtSBlgE2Fwd5twNgYwZ1zN\nwCc/f4ZNOuDeOONsqIjEIKrYDIRcA3JhDKHg1Uf38LmLHTZRGhwzSMGHVNLsj7aFT/ZV9ZrXDrde\n1B1o7jaC9Hc3Wlh4NXWCFejORdtL2BnnAlThfVYkKKaEcbtDrRXLPDvoyDmrgiw5e+Q5jfIeo0Pq\nUnz6sEGjO29jxHoKiBrvciqEK2hSREEAJzN7vkofMmhKk8zRetHyiq861mAlUtCK76QY3OMLSKPm\nZV6QNjswM6bDXivdEWIaAA+hzFoxGeJttP5wnaLTn52Vwt/tsWF3VzJ0k12haAwE7rmBntVgD5QK\npFxFrtGYkMCYM+G1JwmX+w2+4d4VXn6esOSIx4eA/UK4zhGvXW7x8DjiWCIIGZVHnA+LFsEIuFxG\nzIUwLYwySw8z4oI0bgGr9o3mXXHPN4v3ckhJPazVFTojZwlFK+rJzasFM6xns7dK48acTEGUMFyR\ngzElpW+p2mjA3hZeyta3VAH29JXWR9DmYpV4Tb/yVglKy2WpGIaEQOTtukymkytTcmVPzDfjhKyH\ntflQqSviqXKnoHR8wFeiWxOrzq4g1ioEm7dbCiFpESB9l4FZtjUEI1Hw77pc0TNDxjP0rJisZS5a\n9TQ0L56CZPh5lXNioex+i/4vOM23te2gHCzsgEAr72uP9wo3zyZszTTM0tI5Ymg85PLqWiIGhuSY\nsHDVPEdWhcpyazXHT+cn/XUrYojYbDbSu0+FW+Wqe9lVzSTxgBvN1LLgatpjDAzmhGMmnAVgyRVB\n+3xfTAFvXG7wS+/cw/UyohRGUSWWSPopmnJRGajTNZblGlMnT0wOUIcdetqTKCHJKa29omhnt6O4\nMVTsF6n8/LUvMP7v10b88ltn+LVH57i/ZfzRr32I67liKsB0WNy7HvTcEOTnpVQsGukXSYoNGV8L\nipSYgEqMUdtfpVFrCLDI/BgTrq+vcbmf8OK9u/ild87x6YcJ0/5a0lnGrXvMe+xiiqhhq15OgiVs\nORBhHDeeimO42ZRApzg934HMe9hwvZ094yciv7o90QFJTqLsS9GxJu1bPSQtpAVID9ZgURgFzIRB\ni9KlFDBdHzCMG3A54h/8wJ/Hv/bv/hn8b//Vv/8xfBkueqY37H24drvd93zod/7z3/1H//RfDI8e\nvi19ndKAcbtzsOGMXb9jm2ZVjQjmYWpK0pgCpqV4+IYB6h74WxirBcAUjacLTnAtrBUERLKY5U4I\nsnlc4EDAiMaBe29ykAmoQmqSslkxvXdRBwA9dNFykxT8mbePVHFzZqzghFwQuJ3a7/f10GF5HgTQ\nxo9eeDbFZOiULSs+VGtFRTtMUZN3DWToQ10BTproW0vBkjNgLT6I2top4IxdWwtTiiiEFiJUqjdT\n79tuCI2svb0EtKpVgIMysxDa3CV/S6vAQUKPdPV8TQ1hmNCy9WbfY2XcXbhi/zdTbkNHtxZGQ73C\nq5tpIIzVO9w85LqNfXiOKSsmUPT93RahsXknnU4J0HXQSoZN1AOmENta9VZgUxgsUXxZMmrJ2AwJ\nzz+4ixw2ePFsxnd85B2MsYCRUOs9vPrrF3j88HW8dD/i/oMdducJaUNiua+SsF41CYqiJManJP/5\nsPo9PmFpROIZQ2Ucl4Bff7jBJ9++g8eHhA/dO+Kr7sx4frvgconYhIxEBa9f7vDW9RYPDxukwKoI\nFpQ8e3Vky3vpeyCulUPT3mitSaxHd/JvG7Otafus+fgbzSjtdxbaQK2qsT2XWarEJS1x7zRZKqbp\nKBbw2Er8W0hxthLfIDcgGfjmrh0HGodZhbX18/FZkoXg6FoxoxQJL6ym6LHxdwWlehZrf/5sRTVc\nvF9Bs/iasmgqHteCw3FGCAkpDarEFAkVg65bHEEo3lDcZAd5WGrfuLt7p2qJYtBq8322scDukMGH\nWyqGAiYXBPxFLbAheyW2/YoARsTZsODDd5/iYt7g9atzUXir9TpUYM2yBqW2n+tyRCkzUFlgXYjN\nGMJrWdSMjPL5MCRpeB2oKQyVMWheZ6kFJS84Ho7CT5wmb9K754Dr/lnfPIBwttuCqCKmQUC/Kdfe\nBzNhmSbkXLwnpp0VO0fWPqVoXl0tGSlISNswDKo4SHXvlBIqSDzQJcM8htLHUpQ2y/OtzN6iS0Ib\n2c+o8yVXsluaRFMUOyOCr4x5903pjPoswxHtZ6Oj/mxa7uGQYqPh7rlWaaVVP5d3BYhRZVmkSrDk\nNYpMbDyWHJw3gxgDp3ROAOlYGvtr+9tjjX4NWgSMoZL1JbzJUlHacgjvZxwPB3CekYYR5gkuZVEl\nwbBXT8vmTW31DKQ4yYjtbgtWmolpUF5VpMomtNrlMIgiTgFTDnjl3jVePn+Ee2PGC7uA53YBjIA3\nLkf88tt38cYTRp4nwHtAGg9Xw4KFDJNUehXvaW+c7PmqynE/UqSKRsQ4jvLcKGeoGd5OZQphkxhf\ne++Arzo74ideewCiiN/7yh4PthkXx4h/+vkd/r1/8SnydAVw8hSdXFgNJSwF4oyOjGdyxwMTQFF5\nUSCkoffQyf4fc8XVYcbDyxmvPhrw6+/cx+XlFdJw1va6HSr/p8k6mWMFkEJXGImljd5mM2Dcbf29\nJutiSlisqm4IvsJm/De+ZnKmF+lWhGblVPAIBYmOG2PB2ShtdK6XEXMWbAuI0crrptQqlXePB1Gi\ng/Rz/Nj/+pfw6i/+9MVrv/rJ+zcOxPt0va/VUE+v4/H4va+/+qnrz/ziT+Hu/fsSY56XVWlyZ4L6\nnZ5gmjLZnkkkCsSYwhrkAg6CmzVyLbCAlROoXSweFxMY8n4VCKENSA5ez9zWruf+geY16zlsq4i6\nHsMpKBdrWhPa1D+nSfb1OIhum67P2RUH/0aXL6q/6zl0fcQqn53OsO+3Z0pSv0ceK05tdUjH7spY\nNzYRfBaAhdWzTeG3A9y/6PRZsizUFPzTBXHmQ7BKkXzyvP5f+IhszY2R3B5O1I9fH+QW+fe6DGD3\n7yTdkBXt230nYPqZzzy5TIFwgULrv9129Yac1b3MXkrcz518jONiAEn6QVrpemnwC3Ah1MJuuRRS\nEevje43lmTNWWkUgbFPFc7uCl84XvHy+IAXgao54Mg24nBIupgFPpwH7nJBrExroFHzzmPf84Iu7\nbucFz/7bF77Wyvp73Qe/z6oainLbaI9ZWxNolcDb9tVo5Lcy2ttIkblvEM+nf8SXuh727PW723ua\nx3TtjXYedYtMeT+vZx1bB5VmoPCxG+9jPL854HoZMJcIY/eBWk421PMHVcLBkufoKQmmVBhd8811\nuM1bboCsD+Hq97VoiOIXWtJ+242PrkC9ejlXdNPJY48ueY+rlOz3Wzhm9OI9je5ludZnA6dzZlMU\nmwFDt2R9ncqg1U+3j7cpis/kvKffaIqiItkeN7R71t+3cdvHNidmBoWk/O7mGEiVgfe6fF38lbyS\n+bft1RfiLLfzEqyVILYcVnsed/Qk/0Y1VhHW2KoRoNBc1bBPMTIUDbctrYK7Av1aGdNccZ4mlFKw\nnxP2c8IhD7heIl6/SHj7MuAw6fftHJuyGoIrTIZ7S5aWcDeihJhXla9P/wtBlFgfp575NkfYgskZ\nh4S/HnPAT7z2AIECnj+TFJ25ED719g7/zu9/gmU+wp0f+k/R51kocDDo0SlVckP7PUZTFAm5Eo4L\nq9IJTEvFxWHB4+OA6yng+rA4Hfpe6/74o8kGdFNoeRssANvtiDQOK94VghaJWoXI2woZtl5fvaLY\ny9W1orj+TgiMbarYpYIUpFe5KIrcPJ1ckVL0iB+GGLnf+dyn8XM/+r/jxa/9hh+6MZj38fqyehYB\ngIi+495LH/ixf/O7/6omIldsNlvEYQOKwfiL3AsrqNEYYc9QCcA4JE8qlbAhtQZ3zNEPqj0HFaVY\niJRWy3NGYIh1HXIqhXOCE62DqMYJnfnc8C/6nNYKAEPDB1TSG3hzBVdLXntTaH1W5zd0pbDf10BS\n3rlqbpIuhKwn2iF2XasTIN77KEY/OL0Fs9aqLvveeqnzYQB8kgSsXoMYJPytVgbF5PmUzdIIZ2SS\ncyBMoVTGMAyeAzOkoLk10mC1WMEjbY/RwmCaZR1ocfXmoexDNKrmJVjbByjItPmmoRUI6GlxvTbd\nSugzXeHoFTGGW7esyqvTKZp3157DmovR9r7RNEzIU/NA6TLCvDRQq5nd04f+Gl14WK+Typrz9QK9\nN4f4O/xvwH5/wPlugxdffB6FgTvDjK978BTf/MIVaLyLJ48HPHp4jeX6Hbzy0hk24wYUIuZ6wPY8\noS/UQwQUYiAAmyFgSNQKCzayhVdN76fl0yOMUYDr5RTwM5+9h597/S62saJWUsXWzqgBen4AACAA\nSURBVJ6ES4tcrVimQ1coSULTKot3rleyq+3vF+StNwFf24YWfn169bmEtu6mkPehdOCKKqn4UtnW\nFQFWsNOHQrecXlOGV170bnyMdiaET6w/t/EbfyYHnOz8zELXxCDQ+B28L53NS8eLbl7+PqM5uDFC\nzqycozQMiOphsbM7TTOGcYNAQduOlBXfkrEWPUua492Z46xlggG627A+hQ5idPKrO1KdEUL+F0hC\nhq3KsPCMdvbMs2hA0PbbGt9XBGQWeSlVprk7j/Y8qUhqlvNlmZEXMc6GtJHVXTOWG3PrAbV4MJLi\n6qas9ZZ5AFjmCYf9welFIbqvD6PRLyojRFLjUQJILOtSUEO+X4vkldoYQJpLnBcgJA0fs2QW2yc5\ns9J3TT3NpaDmGfcfPMC0LMjLDDMUUkwoOfues2ipynulsFGtVRWiJvtMXqUkXiI5k+oF03VjVVOC\netzWa4pO3liBoLiis0AabRMtf7LhBH+D8YJOIRbGJgVSwBCPvRWr0xoCtZQGnIP1QbZoJ3ZsYlE2\nNwF1w2Imm4z2za4uXsqViRtuxHIeskJM7dyYEtIpeV6UBOo5VM+fPLNgSBISaNgxpYgYokY1aaqS\ntQmy0RMhRax4D3R9GiiV+7Zbbe7OFRykj/CYKobIGLTQ6rvXAcfDESiz8++qaRUWFltLl4uodOJh\nyLqItg9eqCassYbxQeET0sIhDYPzcd83NRyRnlempHiDMUbCR56b8PpFwhADvubehN//gSfIteLB\nLqIsyk8g4ddcgSERIrfzWBQLEzeEEBIhjdKqh4iQC+HRnvDw6ojndxFDBA5zwadef4TfePq1eOfR\nJCknKTkBGH5LkRpWVUoi9dqbR19kclU+GaVdRoq6UC1CznFtWOsb3aK6gr+idD23tobdF7SWhyjh\nSw3YpIJv++A7uFoS3rjc4a2rc0BjBCtbhJuMZD5OOk45/x/9L/8UPvx7vg0f/1/+wi3S5v27vmw5\ni3Yx88fu3rv38f/nY3/7X/l93/lvIU8TUkxyKKqF3AGmfAgRyO8GH1Igb7eRS4EIBYbkwBaM44Ci\nydfMaMmxlhsSIhK1HkElF6QU9bDqQC2vBlKJDgamgNUhJBtZpzTq6Buooc4DaQpmB5AKd9UHV4BJ\n3ptr1ZLMpI3a9T3UhEapWsEN8l/OZs1AEw6yGL6SrPO0UXthDTJvoVrkNIwNZELcFOVe8TCmRD5v\nUPBkbC5yOAeNa5dcOAM45PcHgpZmVm9wqMqghDZyJTAkb5UACWejiE1KCEHWcrDKXaVI+FaVnIQ+\ndMFi/SsgRSyqNO+29RKhGRBC503tlEifV8e5nRE7oLV9bjmXRgeWxGzf8z2oFV48SSvhmbisHk9v\nz+z2QIWcKKJNYbR1M++90xjgP8uvlv+qFR9Xlb8aZHfA7jqFgQNGyRlEYsBZOGAXJzy/vcaHzh9j\nGM/w+FBxuF4QEDBsnsfVccZmBCgNuHr3XRBtsdltxPCjYytd/5rKQFDm3ocUebcHO+9kBa6EpvdL\nwBAYkYCsiuUYGRxO7dp6aplRlkVWXbFM5T7stHk5PH+qA4Dtab7D7RY/LXpHryit7rVTTmgllTrZ\ncaKFCDmqshaiAkPdMzZjWeNF/dfljChIq3LuQZLTaHst1ejaOTg9/15QwXMHgSElz1sTntvWwfO8\n0MCfKczVwLADfwPf8EPlYDYm5V1SiCgvlmMjfGvYiCJTqvJDHUMK2sdqyQIkQALSdY7VFWzWcSrE\n6ACSfUDc5RVbawxfX25zVgZtLJPA3gKgzX/tJakMoUUiKdnPkB6QKWEkMdwxuogICtKWqEo+bRw3\nqDnjOB2xzAukJcHoLS48F6sTSqc2j94I0JQdAWnSl7ApE8uyYJpnWCN1V4wowEICY2MeoBQ83JlV\nDgrLUuWiVozj2KJaFDhz0Rwi2wPqcpL0vqo5hNJnM4JLwUIVS8moZQFpPYOQInhZ0Dw9YiAIaRAD\nUeed7YEps4Tgxxg6FaqTh52cbTnyNkbnph5CyggARVWgzPiiCqS3SCIvgmc7RiGCaxEjbI83qoSc\nhjQKXfW8kyKWRUKyYUqmFueophR2++TnPZDLA+dwCuKJJWLEit64vCOtAonGN+08tEI0dk4cUcFe\n0Rcv0ac4jVqeaKLBvchZq6GKojiqh1D7/3rrBVmMnl5yZm3ALsYHa7MAzVWT+yqm4+T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I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\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ibcKI/F4T04JwpKW+2QOZLcxKw5lUkeERVIlMISQAHcFTJTHCgQlqtfkqAeIcTxfAdG4A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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa2d14e0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "import cartopy.crs as ccrs\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "f = plt.figure(figsize=(16,9))\n",
-    "ax = plt.axes(projection=ccrs.Robinson())\n",
-    "ax.stock_img()\n",
-    "\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 使用 cartopy 画地图"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 安装 cartopy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "最简单的方式是通过 [conda](http://conda.pydata.org/miniconda.html) 来进行安装：\n",
+    "\n",
+    "    conda install -c scitools cartopy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "也可以下载下来自己编译。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 简单使用"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "绘制一幅世界地图："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VikwgE8D+pQKrHQJpxnK/wKhUGFWMPb0MxzbH2CqBuy9bQT8XUJrxxRc2MSw1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69Mjv3FFBHUwB7M+ZgjuxZLE9scDfUdIFkdr2LEsmgapvMpQQK1V0eI5GUvAd6NeXrHojR53SOf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ud0KJe2vXsp0s523gEDRj1HQ24YPCZ4M1md5GXjw5sGk8isxH75+w947eRe6sBr783iUpBXL2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\nHctzC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8UYCiWllflVAqhMTjOl6FqGoIhR2DLCEADVVNGtRaHpTOLL+yt6/RHbXva/h4cP\neNYPOL9DlcTWbHhvv+XFaSNiNVmAs5wCYZ4I81wXoGZ7cYn13aIOnNGIyn1enn2N8ljWWn0rZ77m\n8k/Vx20j2aLwR1tM3T5rqUP7cjVbTbtB9hVNymmpLc25YI3h6mLH/f5ETmltUdOYSUqhlDB1cs6o\nnLHOkVqMUH1xheLpZ77Ab/+Xf4T/6S/8cX7oj/7Zn9xdP39nf/viPT7Fn099sKiUsm98/pv++lvf\n9G182/f/IA3J1BR0zQyFEKHk2roAjHUYZxfeunWdBCNVnKAhBq0Rujg3FS/V6hGqLw5cc+JYHLQl\npqyOTqlBQGvIWyM3Wkai0TZVk4tSVKqLbGStNYcAaOpMUrhdrAV/bd9QixFrC6jV6LSvyIIU4RXx\nn+ukb85kWaUsFoPXij/Xx6vXLI//pQ3Akh04B4DWovKW8m9nEfnyiuZlyfLI/Uu9RErSgPqzu1ue\n2vfxVhBLlSPX3rD1VtoT1I2zkLFK8+zimjkEwjyTc8KbTt5tVnil6FQhx8j1YGoNDXTO4weL7jyx\nKL7y8oZpTiK9rDSu6ygh8Mb1dkGkj4cjisSz62txfKtOvjEWp+B603O97QTpqyj3Wm5OpUUZUkqP\nAkUZ0lUhDFpAf4Y7nM2JNqJTEAnyVIpkXHvH1WCX32kFqYjCZp8K3sD1YDFKahPlNWqmmKoim8wI\nmedZMrbI9501tL5qrfagKEFAC4XTPOObulkKOK3YdZ7708wcIs7JfQ29YYw9H52e8mJ6TioGozOG\nvKyX9t9SNCEbvnr84rI+tapZsCpEVWhUoyROYB2n1moCFFQaFQVUFtU7kRXPZ2uFZa1McwAKznko\nSaZqrSnLWdDWnBLGuTN9rNqOojrTLfARKrtaQJrzdfS4mL7U+ryWGZL1nM/XfA0UclGP5kKjQi30\npTrX2pxytlJKaw1RCk24g+U8jfLahJdkw1zH2loL1W7KxEnEaarIvVvqsEKIdd4oGvXIOs8caoZ3\ncSjEQeq84+pyS1GGcY6okkWIwBhi1lyYwBevX3LRRTz3nAYFOBG0UZJhPw2eEAvGOy67zMt9ICNz\nJyTD25uRrdcY3aE1jAnmWltslGacZ3TOGKU55iv+5s1bhJcwmBmvI5/tXxBj5NVh4KPjE27yM/qL\ngY2qUvlGYXLkIe04FI32mU0JXG8vOY4T8zTxXW+JyurPfTTwwd4yuEzMip/68Ckbu+Od7Yln80ve\n3kkAHLPmYRR1Rmtk3d0+jKSUudp0GKPZHxMfTUeudx273uM7T4kFZ8RBmlPhNEdMzerMGX783Td4\nuS/kPEsApU21OWuweJ45ronaM+DhzACVM5C1ggJYRS6X/N3xm3mi32eXX5KmIw96y6ab8dbhtAZj\nMMOJzzy7QCnFlBKdlcxvVporm/i1bxz5xbstqTRwtc4nA0pnfvwrll+4TfzOL8Gb1xsm5aVZe4ok\npZhm6a/rN15seHtWpUjTTEmJvr/iePpAAkEipQQ6t1tsccmZEiKwUrFVy2A0ECVnShTannaarneA\nZZpGEjNJ2drovaBsQXcKVawwWGowoA1QZrTJJDJzVBhtGYYtOWuOs+Ph4UhOFkNiayyhGG7uBbCc\n5knu3Rs22yuGzQZlHNMUub+7lyApafYPI8457u8mcsncuQlTIsfbW6b9PQnFu3MStVElYnCdKfTe\ncXmx41AzfyFF/DDgfE/Yn+itYeg8/TCQUTzc3+OdY9hshPZZM7JtzR2nwN39A9ZojqcZqRcX9lBJ\nVUgtBCkdQTJbSglDylVBqn7ohdauJMCItS5aTJb4Ys57XNcxTyPMBVUki4U22EEySKEEUhTKaYgJ\noyVj5qycwxi7gK2lJB7twqpmfyvg0IIWpUHnglIJXRLFaHqlOIwzb193vNrPkCPeSbnDzf5ASpH3\nsiLbN7jcOGw+yPzLmcPkuA9X3Jx2pKwwVjKmpazZOmPNwrLLJWGK4nqnuPZHdr7wdAsvj4U5TDzb\nbYjzxDHcoUvhW99RFL3hg0MmxhMpRkL1qU7HI8f7O5xRXD95hh+2YETMKFVgoJztJ61PuNj5NShc\n/EylFn+XuredJzYWYLMiwO07qtmhtn80saraNqr5pVZpdNGVoSHq2zFGrLMMnWUKkRBibVkkc2Ep\nXzGmAmKFeZ7xzsumlLYAACAASURBVEH1o+T60irlnW/+dr7nh34/f/nP/jG+9/f9of8O+Ef4FH8+\n9X0Wn73zhb8+XD77rn/qX/tT4rjVBSk1YtSoqKWcpaFtQ7WttQtauiqV1QCRVhS8XmvNVq7Ol0hq\ntwL0qtJYvc52TEP0VRW+WSd0ebRSWri3SjScX0sCzZY+12eb9TmaAmd0UvU4EJNzrc6pMS0lv9Zw\nLVk9RQ0WG6J85qc/mjLnK51lwbaLrbSkuoDPn7Vdb0GZ1syWbMSqjo80dW2y2l+6fI+ee0oa2XSd\n9CzKhY0peGvpsAzW4LRe5LjnBOPhxOXFwHAxcHO3xwBD57GuymsbUUNDQYpQtCbEzDjPhBQ5pcJx\nSlzpwtYP/N2Xd1z0lneePmHjB+YQSGkSiosxhDDhdFOsXOeE0qtTv/pXYmkkyb2+t0Kl51GW+afr\n+5ARa9kpxaIaePZ+9qMgir7WuBklTmVrZq+Vqj/DFHNVtIRNJ4FbTIVTEIpmb5UoGFaaszhB0j5j\nDIlYNEq7hY6acqKzElydQmYKUWq/SmIOsnnej4n744kxzPRdT2+ld9zfuX2LX354A28i5byIU50/\n4AI3rms3J6mraxsRItqTlmyeOAl5yZicraMzfOOc0ttAowV0USKqYqxdg69qN1KUustFCKOslPMW\nsIqNaVagPH4UWXYfQ0yp1zZaL457o2Qv4i+l2b5mp6rdaO006ji1+dNEZJx3y9g1ZzuXBn5Vi1Uy\nizk9m2CNCt3GYLnxupGmMKONoesGUmktL1qwrlAlLWqBISZ5+DPbBrDb9kKJsxbU2vdMK43Shl99\n9R5vbQ5sOkWIhVy0ZDAACa1jrTnPdE6CTGdE6CDlQkiRKSqmCCEW4piIObPZbhcEPJfI1sEp9/zN\nm8/w4d5g8omUlQjzq5XxobVeakqNsTy7sFz4wLsvA0o7nE4MeuR7v3jLxgZCNsSieXVz4GLj+Pm7\n5/zSwxX3k2bjJNObi4AgqiTe2k4864489Qcu7JGX9xPZDjiVuNpZEsLISFkRsqLTgc5oTrEQYoGy\nZqWVEqptZxWDd/zMRzt+8oNneEap31syumV1xso6v9pe1cDJtn7avraY9kcTmeVbMRbIgcGcuPQP\nvK2/wjxJxr63PUO/5epiK/egCmgRBXEqE8ORZ1een3j/ip99dUUuRaizYjSXdzInySy+sdV84Qq+\n880s38+QiqLbDIu4TZvkikpprNnyMd2Q8oQuGqc6rBlQWsCRUpkJTRG1ZeCFtRAXYDfFRFEVnFYJ\n7QrGa5QWOxBTJsSMcx2bTc88R168fJAWFp0XldEYamCiGccTh+PI3e1ETnL+GCeGwTJsBy6vr+m6\nnptX93SdwViNMRboGKfE8TQT5ohRhd3O4TtHTpl5Fqrqw90d93d3dP2W/eFAeLjl7Tef8+T6ksvL\njdiHlIlTxPseY321q6mCWZmCKHu+ePXAzas7nj95wtXVFcMwSJuKIMqqkvEq7I8n6WXqPHMIxFoj\n3XwLYzS9l56BxlqK1ozjzDRNy57ZeVG99Z2j6/slcJegW8a65ITzrlJvRacihnmpcy4KsiooI8JG\nmcTCS6sgwBxm4jhijcY5h6ktnVphTStbyKKBjlIK67T0VqxrQsSZMiErbk+Jm+PE/WHEWaHSe625\n7J3sgfnEzanw8y8veDU/ZUodG/PAoA6oErifHGPeMgWpS9Z6pUquPqLsOrraqadby7PhwBN3R68e\nGKcTtr9kjic8kTeve9693/HLtzv2c0fKhXEuNHGmaZo4HQ5MxyOGwmazYbO7rG3KWPzVcrY3tO1D\n3kNZ9jPZa2Wcz8u/mh99bkia23z+Z8tjNNXryrNdQNVCrRk9s1VQlXBb4sgari63TNPE8TQSo6xp\nYVYJcNMSMJRSs9C13Yiu11wAVvEJ/s//9i/w4su/yJtf/JY/8VN/5b/+kY9t7J+Sj/6VD/n/7+cz\n3/Rr/1JK6bt+2w//uzjnFyfmcaC4op0iiyt9E5dAkU9wfvh4oHj++aRAceklxuq0tUkptAf12lk+\nHih+7WvV9gefENi/HiieB5GPj1oDxfNPy648ChQ/dhNfK1A8v8TqTLSLfdJ52h+PA9mzX9aPlqZf\n9dRyUWctvc30eqTEkcH7Rc7eKGlk67TA3LYaSasbslW4vt7SDZ4YxSm2Si3ZLHUGXLXylFKkj1HK\nIkyTc+GZLgzWc5wjWota2a7fEKPUJYIiaU1KkfaO5f3k5TGXQPH14alByuJv8xjBPw/cHweKj19D\n+xymuIAY59dIta5EK1UFTFY0DlWbu9dzj0GQXGfOA3+1iGPEWpgvKqwSqKytDeTP45w4TjMKKcTP\npdSGy3K/p3li6HqsKnijSNkwpddIDx8LFF9/bqkZOg8UXz9BKY3a/Hr93eN30LzoTzqkgUgL/fLs\nmIXqXlbFtU++13ayr7WY/j4/Z0vm/yvm97XszfL7X+mA/7dPm78pVRrXx+3seh31NccJwDuzfPfR\ncWWdswUJ2D5+Gom+nbXSdqUKQzRF1CYg1taIUSzZyCXgqfOmAQZii88udBbbrjbtNVuGsFysig0y\nIhfFnMRxbN/YbkWVz+qC03lpMM9rZ0xZVI9D1oSsH63LcTpjKTz6Xs1k6/aMMg7n42JNxurM29sT\nFSL4FeaBWsArpVjakXzsjv8+JltRiozGeYezkoktKiNo/tpWJcbEnBIigy9iMVYLLfXRTloej11B\n7F7OcDc3m1he24PqA9fMT1ErPfX8k8mkHOqe98mPVKDW5trHNrxUHkCN2UUsp/kKDfRCgmgUlxcb\nQhVTSa2NSm0JIY3EDZutlUBQK6xdsyelgmS7iy3zLN1CUmqrRfwkspxr/zBxOo7EKAJf1hi0MWLr\nyGyGnu7qCR+9vAHWchGlJGMj7ycutEcRBpSg1llH5x27iy33hwP7/Z5cCt53C9DUfKih7wiVhu6c\nWxx9rdeyiBATsZYWqVLoOl9fnczDlAqlghy51rrpCtq2aymtmed5mVtKt0TCWTarrDW950ImbXCN\nsdh+qPah1WvWnbk0qvdZqySkx5+ptOGYCiGvU9UZJSJV3hFqWcgxJO5OYQHXvFF87vLItbuR/Tlv\nOOUNDbhRZBG8SaXqJDSG2fl6qOUeOfPqELkPWx7iBpQlFc0cC0VtOJWO925mftXTkZ2fGFxYAKbm\nFHrv6YYB13WkQhVAWxMuagGlPm4X2zsTsLMs8+lja2kJND95sTU3ulUv5AWhavFAFsopa4LkfK9o\nAJ/SihATd/cH+k7qd9s5UtU0WHpuyheXmmcBW0tlw9W5WNf0d//QD2OtYz4d/vh3/8A/+yOf+BCf\ngs+nNrP4a37jP/Fjv/zTP/G7f/CP/Ic8+cwXlqzbMpuXx6rUT8XHJsj5k7dJq7WuYjPlUR3EQvFi\nVfkrFe16tJeUGmim/BghQSakUTLxmsBDhsXAtBMUhMNeb6x+ezn9cs51zZ4dW8prz7X857VNqyzj\ndH7/zfFo1Nsamzwe0jYiXyMalOERmXGQBdSM97I50DI36302x1EtGbKCuFrQdw5lHG+4r/DEvGTr\nEt57dt4xzxNPNh6jDIc5oFEMyuKVwRnDaZ7p+gGtC84ZQhCVNKMt2lh835FSZI6BOcxodKXKRWLJ\nxCKN5UNImCnxZHdJRIRavHOLUEyrvdS6sJ9HUk4MFQU9n4faKIwyIsV+/nLbuGp5KdZKT7SVviEO\nj7OtX1CpctiNfrmepdXahiTCNf3idMtYP5yCOBdGNtTDLFL8SqnaHFhLbzdVcFrXRsfyTmIuhCT9\noFLRnEIhpsJ+nChk6aelBX/LNUCcQqpBu+JwGtlUEYO7MaBzwHc9lMTTjeXdh2f84v1zYq5iF6xG\ne52b6zwuuRCmk7zTZrwpSJ9EoS41RBGlag3z4+DldRxEHIuzTYD2HqTvoTGixGqXQve8IJSScTSL\nA5Fa/8Bc1hYBBQqr49g230+6m7YxqbP/t/tDrZnFcz0tuU9xdBdwpmUlq81oWW75gngtOZ+15WAF\nkzgbh3OAqTVNzqU9WwWtzmyxUoocI9M8r0F0kZxUAxbEDkn94GN7JO/CWulPqK2T+lJjsMaQlOed\n7Z63h3ueuxtcNxBCgFQoJeJN4RueXdOcNa01RcF7DzNzyDgNO6/ZDZYxBOmhOs7MMTPPmTFkQioc\nxonrrcJ1G17mz/GTXxkYnDRt5zUHRxw7uaL3Hd/65IYdL5gOL/ipwz9ERjKal33mOz5zxzde3THO\nmsNUOBxnLi82HMuOv/3ygq8+dBJsqrwErVMQG+JN4Ym94TvfekGche7edz1975eau8OYyHni4uIJ\n772656KDy0GUWAsFaxTWSp3e7Wnib3y1473DBffzBkMS9e/FsKz7i7hLho2nBrUi3mU0jKEQs3zH\n27K0wVg0Ynh9qhdQUgf8HZ95n29+cuKDm1H61eYideEps5+kbMFg8Kbj2ZMLNoPBmMSH90f+2gef\n52a0DDbX+6ssDiX3mzL8mqvAb3grCO0Twze+sSG34LAkyUhqj1KaFCPxeACVCfGBGI9kMgaDpUcX\nKzWDpbbdKAWUJiPzXVeQMCWplZQa57QCu15je4P1hqILxjhCNJxOmRcvjkCp1P4kNXKbS8ZxrJlK\nVfcaEXS7GHp224LvLcp40ANz6ri9eWCaJp4+f5tpTnzw3nuE+cQ0HgnjHktmO/TEUkTJN0nLj5hk\nBC82nufPBrJK5Nng7QaKYhi2GCNiUc576VVZ676lN67QO41emVQ5BorSWOdrYJGElp+lzYI1ptpq\n6dmnlJb91UsLlxQjKQpzxDq32KwxSPZWI5khSqHrO4btgO8GUoqU2icvo4TdQCbEgLMO57yIrVX6\nvJSYirhNplBUpNUzNkNbVOEYZuZpxGtN7z0oodMrpYRhUuKS2VMKjNVoo0hFcQoCos5JgNjOGqYI\nX3m1x1sFWmpJS87M05GN75nnE7fjlnf3z7gbRchGgCtNzM1fE/aBsa1dS3PcGovmsbPqrGHXK678\nA1fmJV8d32I/akKxXPeRnpc4NTGWLR9O71CyjGWMM/M0SV/FEKEGUsNmg3Udse4J52Hquce95HAa\nsFH/swSGZ1tg+/EcdHy0d59tGC2Q1Vr6pzafvvleMbZMvxxvqsp5632a6r459D2+c+wf7hnHKB0C\nqh+kVPOv5N5CSkusIOeRevGWJCg5E05HfvTf/4P8uu//AUD9pz/+X/0nf4BP2edTGSxqY7+t22z/\n1m/9F/4d9YVv+64z9cwWL60OzlLTU87V9NZApSFaS/3f+aecUc+qIExDUReHCUFP2i00tcTm4Ilo\njl4c3Majbhmx2NKX1fmSlXH2LJwFh68HV/V5oEp4LHTb9gDyQ8uofs1XvTiUj/5JxnpxhNqJ272s\nNL31O6tT0eqa2opfqHA8zrq8HixqXTcsoNWcGaOlsbFRvDnc8Y79ZaY589bOs+17QcW1FHwLFQe2\nriNOE2MIizy6HKfYbAeKMRwPJ8bTjCny+0wmhIDWsNttySQOOWOzZrDSd6skLZLhap1fyiL0vfpS\nigJyZE6JvnMY2+jDlXa6AGU1ANLNcW9QAfW9tXGR3o8hSaa0aROlFiwWUcJrVMBQlRIbqBGStNQQ\nh0XQzdyCFqUWrr3Vaz2kbqIJUGvsSu0Pqbk9BvazyF9PUbKG96cRWR9y3cFZOmuw1Wgqpbg9jRxO\nI17BxXZDKpneSt/JudJCi77mMCtezk84hA0hS81Fm18FzmrnFGEayUlEE2jrOK+ZzVLKEhy2yZZb\nxoj1GZflsszHFeV/FLgpFmVIYyzaSC1taw9hrcM5U/ublYWWlfP67tfNrq19Hn8aglBtSgNRtGkt\nAlqQ1sRqmvE5Uy9VUvva7IE2RkRb9AocmVbfWN9bTqnCF2WxablJv5dG4dHiGNfgVqkqnlBYsq3e\n6nXOkZfMSBOWWOsw5TmbHV7eXzm3z60GSbPd7aS5ehXGscZQtOOLV/d8drvnje7EaUr0XhpFG63Z\n9Z6rYSAlqaHSBpxx3J8id4cjGth6x3bo8M6gDeQciVl6+hkjzu97N3s+2FtuxoEPxmtujop+GDjn\nDC82bXkFIobxRneDjbfM4z3vl1+D0SLzn3BMIfMdnzvxxasjF/bAw8Oe4yR28J3nlySz5aP7yP/x\n5UvuZk/MGq8TzkrLlika3tyM/KOfe580z0xT4DRGtCl89ukOpTXv3x0Jc+T2OJErPdobxRff2LDb\nijPdW804J96/2/PLDxe8u3/OMYmwhTFaBKNoYl6KS595vpm5cHu2dqRTieNc6Cx8dOo55YFD7Hhx\nMJySJ4WpsnkEl88to6rampR586XrO57bG1yJKAXeW3Z9z+l0iybUPTQTE9zcFgY30FmH0iPPnzt+\n8uVn+DuvhiUjtOC9RWqKrnzhN72T+In3Dfez4rd8rvDr3xbHMhSNshpjPUVZckqkeYIQSPFU650T\nkZGko1DosPQMqCWbpETQSilSghhDXfuy3gu18X3J4DLd4BabH4piv0+8eHEkhswcIofTA9tth3cW\nazS5CJBJhu12Ry6FEALD9hJjPRS71CtbbXnvvfe4ffk+u+1G+hqGmadXHduNo/eWznuGwROnCFmh\nLRin6YcLUJrjeEApRGAmGpSylFrr1ffSQsAYCdD29/ecg19KazrvJLBTNctnZN7GkJjnQC4t0ybA\nRQyBME0477Bm3TtEc2wVuRoPh9UgK7FJYZ6xxrDZDAzbLf1uR04wTTPj4UCMo+x1RsAWAUuMqJ6m\nhHKaMItKaCaTUhCV07OyEG0M8xw5hROFJP6KMiKuEwOX245c4HQ64Xu3gJdag3NrCUjB8mIfOc3S\nx1dMcWGKWeponSEVw3gaubzYEqaZ233ko+OGL4/voFXBGijohWLfwNCWCDgPxpZSifWfgMLQS6Cf\nU+bS7dnoe+7CjkO+IGdNqqrGJY5y3pyZpxPjODKNEylIzW/f9fh+oOkZ5KUfsVpUl8+wzDXYUmrp\nIND25XaUWIq6D5/t1Qt4XrPGLch8xI5TaybZGrOo0KaUROiKdQxaGLtkg1HSh7r3WGsYp4nTacYY\ngzWNLVgBqQp2Njd+DWhzZdZUn6rA3Udf5Uf/9L/Kb/y9P/z+X/1v/vy3fNpaanzqgkWl9e7NL/zq\nh2/+rt/Cr/9t//SCCuizxdGMb8uOlYpit5rB9jv12DtcAsZlfylnzaOVWiaSrpHVkj1sx9f/tKby\njykuK77irNAU8zLBVjR9nbTntL/13xpSX84UA9d3eBY004KQVQHqMaokRkPrNXtYHp3rDKWp3O/1\n648DRNVQe/X4/OcqYi0YVe07tdi4VGfRGI1WUmS8osK6vgNRqbveZD6r/xY6Zzpr6K3l5mGP94Zv\nfOsZ3jjinDBzIoaE0hY/bPE2kVWWHovWCI1lnMXRN+I8zXPg8sln2WwvyfGBeXrFXZBaEKc0V5dX\nUBTpGIgl0RqNGw3ZwIlMKPDeq1u+dH1VSSsZpasICtUktUBANYXB+p7MOvaritfj+dPmhKkGK6ZS\n6aCFFqgZLc51qD0PxWGvYhYxYU0LFipdp82VarhzzlXlFJnDlY5dkMzAnDI3hxFvNHPK3I8zx0lE\nW0IUVdXw/1D3ZrG2bel91290s1lrr7336W5XjV0u21U2sSPihoAgBKNEoZEVFyiJiAhCAkQkhBAK\nRjIWchBBvCBZQpGFoggJkQcQ3UMUCOTJJFJACAewXUXsalx1b917T51mN6uZc46Oh2+MOefa914L\neMn1Kp0696y99lyz+cb4uv/3/8fAtrFc9Y3QpWfF3emEM4bOGbat436MHLywI45RMQbNjX9Cyrrc\ng0TKJdmZz6d6HLG44XBfkihXYK0PgvbMPIN39n7pjSzlzPMkvc4kfGT9rrbKuUqqpMtrjKHtirhv\nfY5az+LCMYke4UKOxbJXPUhIFycpEK9cssy6Huv/r6Hc9cpmQKJSRT+vFgAKuc0ajoXICUjF1BKC\nL4WDvDquItXzzhn0uT5WDSDqO40ThsjJxyIIHTiNkzAgRiGvqLqfKFl/piSy4zgQk5rtPpfloo10\nFp0txbfCkrzdtCTT8cZ24LO7Az90dcPNnad3Qkffd46+bWZdywSgDcdT4nA8sm2FTj9GYbUzWksX\nz2hCShzGie/dR14fYT8ZfuPVFUO0GJWL1usDiOKq4JizsCu3jQMyG+u5bEa+ddOTY5iTUFCM0XDZ\nRS7bwJUbuWqOfPF6T8qaJ1dbrDG8u9/y4cHx4d7w/r3lbjT0Tmyrd5mvfOlDbm/37E8jpzGw2zg2\nbcft8cRlpwkJGi1apsdxYj8G3rje0bWWzsFu29I3jvsh8fKQeHmEm2nHy5OlsQZnVJEfsby1uWfn\nTjg1YpIgBhpjBcIYArve0jSapCwvh47XU8t37xyn6JiizFReNLJ3+QQJw5s7eKvdkw7fxeLZNC1P\nry7Ef6XMq/uXbC9anu4a8VWluDTEwLff3XM6Jq6ue955ZvlbH7zFu/c9t6PlohEjqt3rlGFj4Rhg\nilJwe3MLf/yLibd2hikLWiAXLUWK3SutUCmSoyd5T1BB2DIzkBUakSWqXb+YwftYtythdFWiu6i0\nJqtMJmEaBTrPtSFUz3e/e+D1iztSPGJt4M23H+GjYpwyGIvGoJPBWtG51VpxnDzOdXT9lmnKHA8n\nXr98wel4jyby9PGGZ09lztAmjdU9TdeRdSApIdlRydDZHca1YtspkfGE4AkhESbF/uAZJyFiurzY\n0LRtKdD6MpajadtGyK6UEakMnWnaBhCCOh8C4zgyjhPeB2Hq1UKy5azGGens+BCZplHWVRYkkRTE\nI1pptLEYY5km6UK6op2sNaIN2Gz49nsfcDi+oOtFsqdtHDmJ1E9MWQo+xuCnQdRKaoUqRyDRNMIM\nGuIESmbQQwzshxObXvYlpTS3J89Gl+RLq+JbDZ0TZthTkK61M5aQNT4qTlPi5d1BYLA50rct+5Ps\nlSELe7hKkSe7nu++OsHpfW7sl/nay2vx82ohXlyBZyUWK39D5ThQs2+Qwm+ckW66JD9N29LosbDS\nBm7HnmUGT/Z4Pw4c9/syj+7ZbrdQ4OCUEaAzH7eOsVd74yriZKH4W3x8LQyfR5TrY+TZFzxMQGf/\npyX+UUhBAa3m7nSNIdKDmF/2CUEObbqW68fXHI9Hbl/fFPI3Nz/zhShPGOJVPY5aEvL6hEIMGKV5\n96v/O//Tf/of8id+4Vf4z37hTzexapz8Hnj9nmND/YHf/w+9p4zlx//IPyvsgZRO1SpIqyXp2hWQ\nWYqFFKK+1tjjudrN6kGrVeClmBdmzIKVpySPtbpATT/n00hzULdmUw0lSJjr0KuKdJ2r0uugTC2B\nYdWakcoYrNLJdUl7Obf59TBRPKfQX5gh10FyuV+qvpc/8p0VeqbU+ZbwkVmkmsyr9ZUv1dicozBt\nSnawSow01jY82Qxcujs6DHFSxOgZiDx+dMmmbVHGkQZP9hEfMm23pW0aYhrLMHmeu6RGa3TXzbN5\n3id21++wu34TrTKvbr6JacWxRRTHSfHqg9fC1hYDfWvpjKV3DdoaXk9H7qdIYxxXXc/z44QNB964\n3qG1VKiV1tUEpZquFnhOZdkyuibkas77a3Vu7nQVqxVU4wJDXROp+BiFhMUo2ciCBJWNreLb8j3a\nFAavsrnJ/JIuVfL6rOQcT1MsJDkCm5lCxCeBVO+6Bh/B60hrE2RD31g2rePu5LEq82zXFTPXnNIV\nHghKJC9uh4YxNeQCOSZLAJmrLRc7qRXEFIWyO2eZTZmJC1ZJV+0kptW6qEnXuko5p1o1qqs/f7An\nnK+vWryRLq+1dp6FiGkFLV5TtJe1q/L5Olx7qlxOfHbqq/Nd04CzWoMpp6VYU4orpjK/zUlXnol4\njDHl1M6dbSyD+tXhqfLwUywzGLoWhUo4UvaLfNbdFHutxwplzsMYM5N+5HxeDKvFqnGa5FsrImGV\nGOckemKVotwaRdMIuVAqxwwRpiDBnbOGrrUoI0G7NpacIMTI6TSy3x/pnMWHgEfJ7FQuOppK4WNm\nPB34+k3Hd/ZbXp+cwPJMS2fPZx7nnfA8HioJguGdS88bW8+FHbD5xP4YeH/fAWnuDOs0cRwy45h5\nrVsuOmFL7vURYyb6ruVJe+DKKT631XywNby37/jOfc8U5byves04iIRE6yLGSBW9tTLTdeksz289\nL6ae+8kSMry43bB1nrd3E0OWIuaHe8tvv9rghT6SNy9Grtt7GiP7jk8ip6BiICslkkE5igB6zlgF\n3ic04FpNn/eYZuDyScvgDUPUhGRoncbEEz5psnZcNmCGO1Cifdg4gdnlMGC0om96otfcHDKt0zhj\nMCqzsZbPvpU5nDwhJm7uRz7f3dAQeG63HIJhjAWeV57WEOBpl3lnp7kdhdzHNZqcJkyOpDFSEQnG\naJFvSVJ4SlEQJLokHHMBRxUEBgqMReWMyZRjGJQBZTU5qHl+SilNGBPKiiyGMQ4/Tdy9fEnrEpvr\nlu3Fjs22xRhHiIqYNTEqfNBE3xSmWkWTLa7puLvbM54GhtNIChN9YzBKE6eMP0Z2zxx5hJw9KWus\ncRh1gcmhEHQYFJXtUZGTQmdHYxS2EzKsmOT9CjHFQEqK9uIKTRRYs9IMxxN+mogx0IxClhRDFDmp\nwvfgTJpjOCGKl/V/HEaCj3g/0jjpMHrv6foOP0ZikR7xSjpzzrUE70uEIt3Cm9evSXHAGLi4bGZd\n45ATx0MgBk88BnLZf6xTtE1DxhJ9wDmN94mUSndYBSIQo0epzDAFLlzL6AON0URVUTpKIKfIaMev\nfXiJT9CawBvbgc89sRynwDCONBYErSEIm66V4xmlGYr+74vbPUSPMg0qHnjWN7w4bbB23qnn2FFM\nUdBBtthGdYviQsWXtMYJI3VBb4kOo0KZlre3r2n1wNPpng9OTzjGthREgLbD+cDpeCrPJODarsQm\nC868+uIaoC4fMQAAIABJREFUW3zE2dVUdu4mntVuxSbUR/fV9QZrio+uBdVZJ3i1J/soeuTWcMYW\nv24qLa5dEkWQvfM0jORXN1xd7/Dbnvu7e6bxhDUXknvawpSaM0otRFZ1/5/HEZSQDaWc+OyP/gS/\n/x//Cv/Df/Ln+RO/+Cv/J/Ajn3CFn7rX7ymCm93jZ7/8+sPvXP7Mv/jzgDyQSsf+SUY1VxUeJopL\nRnie1DC/vTrG8qpJaQ0ja4D4cb+oyGdJZv3eWk9Zf0HKpaqh1rNn1fCW5Klex0f+1PdXQ9ysOpGf\n9Jo7mp9wA+VYlab4ozfkLKB+cIy68S/njSTZ559aFlZK4qxW5+2sxVnNrgnszL2wXCUvlUhn2PQb\ndl1LHj3JB3KIaGNxTSskCVWnLUnAW+VBtDGlcynP4umbX8QYxc3Lb6K0mmfsYlaMSfF6f+R2f4tS\naUkCFHgipxg4TQJLCgn86Ya+sWIBuRAJlASk3IryjBdbqsH8/KwePDLZ9AU2Q14SxSrVUgsLoUJT\noWgsruAg1U50JfFQ89/VFmogXJNZsUtEBLmcbyoMqDElOmtxMxQz0hhN6wxdY8kIaYgzsGlE6BzV\nEXNDoiPTcAotY+rwycmaKGyYKx/yEZtKwRP8RJ0VW0NOpZvIPCu4HGj155Mc0LlZsmws+ezth7Y7\ns61VGy4PcNljeLBeP+5YH12jNQms6+fsZ6tEMJcEU33MMc5/Z6ky12PMx5mhcvJaM66u/5xd/dzt\nZP72meyh2MmcUOWP3sP1PhVTnM/xI3e5HDMWjbQ5yZ27n7I7xZp8aY0teo4CZzdkpclJMXmRENBK\njhnKjGb5IlKWTroPnpuT4uXQ8HLsuJ0aodIvndC58nN+acu9RvbtiybxbBt4YzPxuJ3ojcBx1x1v\nciSExGmCu9FwM3XcTR3HYHh1FBh5ZyYedRNvbkfe2o487r3c7yTX/vzYCCujMXROdCJzmTlunKVt\nLJ99bMhYDnHDwAUvxy2vx5ZjsJy85jDC7WD4YN/yeug4eENvE5du5KoZuG5HHvUTTnlk9k6h0Fht\nSEHIUkRr1UgijMLi6Rl41A486kYetyOP2oHrZuTKnXjUDDxuBzo9sdk0OMqMdHm+VVPQaktOile3\nE8Mk+zJKvnvbO7YbizEwTpGL/IIf3r1m10SsLnytqzg1ZOht5otX8LjXbBrNMRqmbEqnMAtrcZ0F\npmwq5LlQBKCyQuXSuWDxt0AJYCl7vi4dfSPwSuvm+EEOvRRqU0w8e3bJcT/ip5GLi462bWgaw6Z3\nbDpH21qsM7K/KYPSDmtbgcUrgalO0zB3g2SeMHM8enLMaAfoSuyi0LpBqxatG5SSucF5P0mSfGht\nsdbQtU7u99bRtE3xR9WnLqygzjk2FwKD9tNECIFp8njvCb50K8vIhyJRa1sZKcYLiY9ABkMIc4Ep\nxYRr2tmvCkJr0WbOZW48FrSDynVeUOKFxlmcMbSdRhlD8CPBT0x+xPuJED0KuS++yAeBJvhAClHk\nl3JCIWRJd/uD6MNGmWWv26gkCHJtn704cD86vnW7ZT8Z7k6Zq172p6pNrGtWpdTMqNo3hpjhOAbI\n0s3duRGnJ6yOUpicTe+j/qFuL6Y0H+a9uhbiS/yjtJ7HoRIOMGxd5KIJXDanItUmtmydw1iL6zoJ\ng2r8rCThld7CUlVd+4jz3bHWA5dGysf55d8ldF2us/q/B59dI42kiKtm/dyPfeV67oVkKgaGYeDm\n9S273Q5tBBE4joMUUcv3a2Nom0Ziyo85du1WVwm9n/hjf4r+8prf+NW/+uXv//E/+E/+7lf46Xn9\nnoGhbi4f/XM557/ycz//yzx55wszRDHlMsPCqjMGS7JU3pG1KP+uMFKWv1ZdiQcdvdU5SCchz0md\nGFU6M/La/dGrYKK+XzdDCfbTYlh5SXyNrqynNQlI8+ajlAiBz4tttcFACVqVdOlm6ChLQLmGsmpj\na5m/dGiq/s8yh7l+1crVAlFTpR1fksDyXbEOEM9PYnWMLPNblQBonaTUblftfrimoesaMgajIk/c\ncz67+YDOdug48v1vP+bFIfP69pacEtvGcq1aSBpdHJ7pjEhuzJulnHNCIIEV+/Do2ffRtB1KRV6/\n+han08hEYMwCwctjoCGx63f0/YYxjrjGMmrRJRsmz/3+HtB8eDdwZUd+8stfYBwLhXiB48YY5ns1\n5xGaBc0m/o6yB5cOS6ngzqYsULxxWoLrnMGnxKaxjEHkAUIU7b/W6ZIkLmxhsdhBY81MbV27PJQk\nNJYAJuXM87uhSBKIraWUccZgtSZkGEPmcDrOx9l1jtYZdp3Bx1RmHhVGZ77++oL/4/lTQpRgSqvF\nmTyEr1RK+pQWhtMaGKSyTnOm0NavbTXPlcOzLvjHbXUfk8R8xHLV+QckgRO4VEqxwHTLHKO284xO\nPbY8xsXW55nKdeeRWohaVSdRM5S8QoRrmUmXTuZiFvI5GeivUFMxLGO0iFNrLayTWe5MbWSLPIDA\nrFCVMU7O1U/jXFBRsKztmhjnFaID6TQbI3YVg3S4x2mc92RTiWlKUDcbfqFMFzsRCB9aZr/qvRFi\nKAnyutZxebnlflR86ck933d54FF7ZBwyVkmXJOVECosmrSKzaS1X256UCvmR0rw6TnQl2Jxi5nj3\nPb7j3+E39p8nhxGFyCxp2873bdtkpmnkMD4o/JV/GaPRxvGoG/kH3r7hUTswTpFf/W3Ft45v4ExG\nG9GWkyRX5uZc0dEcvGLbwpvdDW+0N3z/48iroeO9fc+7tz0/+XnProP/+VtbDpPhskv81LPn2HDH\n0cPVheai6Xhxe8+3js84pg3XncxpfuP+Cc45egs+wttX8JndwJN+4OClYm/waO3QeaRTk8zYJ3n+\noZCAQRIJg2Lr281G6OVTYhwHjLGimKsENuyDl+Q2Bow2xAR944hR0A8+VD8ksO6YE8SAM5GURdev\naS+ISe7ty9sbrPFse3jj0U6IwchMU2I6ZH7z9opv71tuRsPRC09AKkHtTz8b+ZnPRBJ2JugJKTFF\nsE3pQI0Dbd+JjpJUQspaKYRZpZNjtKHmiyjJK1XjCitnIOdAzpEESxIUJ6x1NK0RTUArAu85K15+\neEf0gbc+81TId5RIEoHlcDhxHEZiNOz3R3Jy3N7cSMFhmjidTqTpxJtPr4TArQTHz647+osLmrYl\na/HDRjdoZea9Y+FKyOQyqyx+v+xJq3WulMK4lipar4wWUpasmQ57IHM6VUhjRhmRLEspFwKwSNtf\nyv4ZgkgplYRIFx+z39+RMzgrSalWkKOf455Q5M9s2yFSMRlSpOsajqeJ/WHPrm/Y7DbiK2KGmIg5\nkpTCx0lkS4zMkkr8ZFDKcRyLJnMsCJYa7xi5TcdpICbPo6ueKShGH5i859HlBo1IVN0dRl75HX/7\nu0/Ye0fIhsbCz335hqfdLT6MKKRbnFDcHj23x4lhEn83BEPyAzoH0B2v42NeTzteDX3ZL2pR9dxz\nKSUooU3fSmG3aD3XxK6SvsQYBYJvrTQ2tEirbBrPF/qvSrFHZ949PuWbN8/QRs1F7uP9PS8/eJ/N\n9gJj3fx8a+xaY+2UlyJkjY0V4pdySXZrDFL96zqe/KTXxyZli4MusXuNTUsMpCReX2uUV5WBVOxb\nq6VADvLv692GlCKHw5H7/QHX9jjrhC3YaJqmmWOfnOtcZJDrSquRjpxn2arT/o7/6i/8WX76Z/8F\nfu2v/5d///e+/Vt/53e94E/B6/cEDFUp9fjy6Vt/5R/8yr/C4zc/Jw8lLfOCdYD2wS/JX6XiVwO2\nVZ5Ys5Ri4HUId2W1czWydu7yKvBTZ99TznOuciyZ4ZLoiSEusLJ1F0lXmFfO8wKTBC9RoQIV+lbn\nL3IlSGFZPPK7lfCmBthL904uVQSXpXK6XEc8O9aKcbJkMBKkVigj+CTSDKokGXUUqd6jGlTOJwkl\ncAClzaoapGpJiipLkHPG+0AmYqxn60a2TcPpOPDGdUtIog918pGLznHV9OQgFeAQIv3uEgwo5XG2\nii+XSnWxm0hGZdjfvWK3uyD4IzkmlFHoLHMrJiTatmHbbnGt4+hHUBkfPMeYuD+MQrDSbtifRloj\n3TRJWvO8QeVSHFClmrzUMvJ87yrMby605SXnqKaYWSCrqQb0KeGMzCkqRakSClwv5wo5XBdGMoNf\nKPaNETbXFNLyXUqV+wtTEPbTkApjaoZgMo11HKbA4XhEKUXrDBedo3OGxmpCzExB4wyENPDhvuVm\n0MSsUeXekDN+nNaLbf7vvHI01bnU66mGNlfl8zrZk5+rXK13ycBVXd3zzx68Vkv/YZK4fLgURmIs\ns5LLOq72VZ+WQHvMXMip8E61Pj7McO48f8OCJKjPrK5IVfaWdXGHuYq7mknUtTtvZmbUSiVvKAUB\n+XKUMmdSAWf7ZHnV+Y51gnje4ax0+koY8spt0AXaXBOimvjWAFSh5m64qsUIJ515pWoRSZdKeklm\ntML7SG8VuyaysYEYM1on0U61Fd4qHc5aVW5LgcvqTFJi904Jm12OMpN2l6/59vExOY7yDLXFWMfb\nFyMXzhMSfOOVw6mJGBVKiczAw6DNakXMRsTLjUKrwBBqBTrOFXkZi5fz9EXaQMS/4XvHjoN/zCEe\nueoU+7DhEBt+/bnj2cbPRZzDpPnG3QU/ej3i8sj+FOls5LJ3XCfPcAp867aHJJ1/bQ1KJxqVeNxF\nNmZAxRO9Suw6YfONcSqJUUIrQ2cl6UumwSCzfSAJpJ8CKSS0k1n0xjm0bSAs3X2tDVkrxihdyRwT\nh+NYihuGprWEkNC6iHbHjLIO1/SCJMiBNA0ME1jXS6IdhNU5xAmrWgl+teJVbPjNVxumKKdQOwZG\nSbD/d+9Eu27nMj/+SDpUk59omoYUKgxfMQ2DMB/XZ5uLxqdWC6ojy/2pckgVwRJTAOOhQGCdMigd\nsE7hWAqtUxB+VS0tP0x7SSDw/ocjygjhzjCepNsRhPF0GgPTyePHIzonxtMRZzLPrhucadlttiUx\n09IRKTBPY0RAPKsEFJZIJMmq9sjMaiz3TK324LoR1nEAhUHrDEmRo5d7XBhJ+64tmbN8rzILmY9s\nYZaIZUoKjON4PGK0wN1yjhjncE6hojhLVRhzqzySVkLMEnxgClG6dEZL93Ia2bTSBVNJZginKWB0\nngteORVN7Cg7dUyRrCLGaVT1vQiDelZWih1ZcZoOaC2yPjEkLjctIWm0cjIjlzMvj4Zv7Tc8Pzi2\nLjJES2cjf+CtA1fNgFFwHxyvhpabweKT4VE/0TV7Hm2FhG4/Zm72AUfkjqcMvmWIwr6+wPg1JJES\nUav5PedsKRhnfBBNSSkmBlIpWNSOdyyjAjWO9lFxE57yqLknxoG3u9foi1d89/g2Y76QPVtrLq6v\nGfZ7etusYufivVStpUgsqoqPoMQ8a/9XlQxW0ejZ68xHPwz0q0Nf+eqcK/O8Kscv90kp0U4tx6gc\n69poVB2fKM65/ixleH1/pG9k1MRYN9tFLoiQM39VUEbGGFRSJHXeTCrhN912xz/xZ/89/rv/6N/k\nKz//y7/2CZf+qXp96pNFpZR69rkf+M3PfPkn+KGf+sNUnaKchB44FpFrSpW92k0NoNYGs/xdNsFS\nRajBlzzTepylgzYnmaizzQ7q762ZmJagvpz/CkudZ2dTj7VOCCi/V2m4l1wizQEcK5bK+fgrO5Pj\nioGmXLqoJciTBV0o/NfdC7VA5s4SvSW0nl9zYFw6I0prmeco3UlxQGutm/NVUBPpczKcep+XxBol\ns51KWyKOzomWosqJvin6TyrRNjLkP5ExMaJC5tHjJ4SMVKbnxF2Vjg5oLYyhSbQcyMORzmliEn0x\nrQ3744kuK0whETDOEXMiJKkYnWJiiInBR9qmIydNDkJk8aPf9wVCzLNt5NrlUoX5Ueezm5LPH+kq\nJ1GrfxeIZa7QxAXTr5D7bbXoJvosTJS6dFfI0kQtQiYcp4gC9qP87LJvGLzQl2tdAvGUmHzkfgjc\nDwJ58ykxxYRR0tkLKTJOHh8DrXM82/UFaipi4GMwNCZzPylOE3x46Hk9NHIWqkDxYiDFAHpJpGb7\nmoMUXQgt8owArkWZueK9XkSrdA1EoDerJfj5xNfDzPEjmeTyXqZCc1dOMoNSFelQX9KDUSjySogu\nr6/vE79suVbywh738HyUXuaXRdJDvqfOGdb3qi1qpSEFcmZmVa3rXp1/sfxdnezqTKvdLXvHcj0h\nCCS7wpx03UPnglY+K2DBsu5VWavO2bOfVfdujcY1FmctMSs+s/Vct57eRkKExmmaxmBNZWDORVhZ\nzV2UnMr6yDJ73luDj2JbNmcGfcmrqaNRoQiZS7JtdaDTE2OKDJNC2bx4/wdPssKvctmHfYSjV0zJ\nre5FYaxd7cfSbS4wtwin5Jhyg9KG77v8kOenHq0bPri3eC9i3qCYkuaDQ8ebm54L4wk+MobIo03L\no5S584FvTwql+kL6sARz/9fzln/4nXvGBBdNoNNC8jPGIOeiJMkyWtAnCoMGmqYtMMFI1lm6S4XZ\nUYqBmUDpYBdSl1Ag4inH0lGMZBLb7Xaeoa9FB2u0dNuULT4xoVLEoSAFLOBTJCdDSpoqvm60wrXw\nY8+O/Op3tlidZ7i9UmAU7L3i794anrSRH7xStEpQEk31y+VJSqcgzetvCRSqX2FVhZuXNzkFGQMI\nCmVKNwWBBCqzFF5y1kyjwp8E3mi0ZfJwPKUZYtm2mWH0xJBomnam909RE0MihIDVisudY3dhccZi\njUUrR0wB1zqsa2hcW4rWomkJi55xmruHC9x29v9z7FG3BSX7N0WMfEZRVZsurM01MUsRTENSgigY\nhhHnLCoEkaYZR9q+ZwyydzijCcFjNSir0MWGpCHlhIzLe4wqDM4F1k1B4KQYRatwmrBGCJV89AIr\nbdTc8cm5zLRTEB3FucRwwrQiLYOC+uUpy3dZYxn9ibZrBRbsNK4Uh4YAPsGjTWZz0Lz3fEPITvbj\nlOlthBy5GQzv3vd8eOy4GSwhKT44dOyanre2Rz5/OaJN4jRNmJxQueeQd4xZbM6YhcW/RMRnz2ic\nAn3nJAHOWcj9jC4JpLxn7Pl4VkaI5U7e8EF+gyfdAaIggnoz0Os7hiCkN03bMg4nUkpM00DTbXj4\nehjXkmu8uf7MSvqpnAMP3flyhNVnPmqX62Mub6t5rYpr0aiCaFF5ib9lXlbPaBc1r235zOQDqUje\nyax5QTXNIxcJa534lFJ0VVlIiObrq0VSues8+dwX+Uf+1L/Gf/8rv8Qf/fZv/zf/41/697/ykZv4\nKXqZX/qlX/p7fQ6/6+uv/m9ff//mw/fe+mP/6i/RtK1UIGIowQQrWmqKTdQqgj6b36tsqaawQ6KY\nW8RLMrgsuHXwUisTtcqttZnbyVArGZUcRBIyRXGslem0dJcW6NiCr1dldci1RQxlvkwvhsZKK7JC\nBqW9rlews1q9X8EOxUsuBDarALzCT6pxL8n1Kkkux6y6MfOrzIzk6igKJXsN7Gr1VZeVKMnN8kxY\n/2GBIlY9G6Ugp0TTbnjr4jXv9PfoHLHacLFxbDctt0PkOERaDWaK2KyFwrlxTKMnxBEfAymW7yYX\nci8ZRD8eB662W1LIkni2jkQiZMX+ds/jboMPiSF7Dvs90zjgrOZwmnh5Gkk50/YXaN0Sj0fQEde1\nbNsGk1NhwAxnhYNlCL3c5rw8g1WVA2ZblOAllRgl5dIppsjCpEqsIMQPoguXaJ1dhO9X66kGr/dj\n4PboeXTRcD94DmNg9FHmaqzi1X7ixX7i5WHkMIpm2xgSQxDWNqUbTqcTPgRQml3fcNE1KBLHKfE7\nd5e8dRl5fnS8f294dVR88+4djqGVCmaBH/lpwBRIhypC0BUCrco6BsrMWizXUJKOFGVNIdIpdS3O\nt5ZK2rNOuusCf5CeZR688bu/qo1XW14SrWX91GBy/vbSYatSGrl0IupcbXV+KuezNSL7yzpRXLrv\n5EzVkVoSRflM7eKJVltJhqwhh6l0QFXRNlsx89ZEs3QKxTGa2XiWRKg6/SpYL78Xo5CAaCWQ03l/\nLolhpS9fz5OglMw6FYpzYzRd10pAY0TnL8aEs5aua9luOlwjrII/9Zk9z/qBzngSmk3foHKeu5BW\nI0yOMeFjxMeAyonWtSglRafWypyOwHU1z8MT3r01bFrHRWdpnSbnyGnM7MfE67uRfejJ2s3JegmD\n5v3TKEkEGxN5s98TQuDDveW9/UZIYmyZf3zItl3sMie5h9YKNOyUWp5uBt5pvssUG14PjiHqsu+K\nPxg93HvHG5sTCrnuk4882nWEkLkdMkPeFghawCdFyJregSFwFzY86wtUNGuGUbrDzmgaY0BJktI4\ni61yQFGSb2M0xhpSCjTWYI10MiYfiSGy2WxQSuGnyHA6ErwvlXzZe13jqDIzAuOa2G63KG0ZR2Hg\nTElhM3RNQ2MEMnh7PHG1a9n0O6xuSSqhTebl4Hj3vuX5weG0dIdnH1fWh1ZSYHvzwvC4y2BbjGvQ\nxsosX5FimnVWc90/JUCf1z71savZplOOxDxJpyo2KAzoPMcS1hpOY0Drjsm3fPjhPc+fv+b+MHI8\nTrx8dcNpPBZ2cFmDKRuurh+htOM0eChQXts4Lrc9l7sNV9cb2i2QNW3T4fqMsx1ts8FoYW4mheJb\nqtB9Ls+8+qmyB5zFQVWrrkJTASWdFm0bgSzbBuN6snLFxxp8FLKyiOY4RW73A+++/xzvPVPI3Nze\nczieOOz3DFPgNIz4mLm5P3IYAyEV2RatgAQmgO5RJWGpXWNZT9JFzUmYmF3T4NoO27S8fP0CrRJW\nGWIpAqT5PswVSGKOMnPZtgxFjzllTUiZYZzQ2hPTRN9atn1D27k5wckp8+LU8HzvOEyab970nILj\nGAydFYXOm0HzZj/wWy87/s73HvHBoeN2dBy9Ze8t37nv0aZh0yg+dx24HxJZWzYu8GK85jAZWrsq\nfGglEi+rWFb8np4ToRADXeOIMTL5qcA/TbHzOk+raZ1dxiqy4s5f8ub2HkOic7A/DdyGpzMpWh2F\nGI4H0Ume7X/tJZdC6jr5kxxcn5HC1JhnQdTks2NVH7PY6FLwmrVg61+r4qTRdc58/e1qjq1q7B9K\nIqfVIpFXM4NKXjWjB5UqrLdxRglWnWXRHZa51/X31mR3HTM8/dwX+eDrv8Hp7tWP/K334p/8Z/7Q\nj/9FPqWvT3Vnsd1c/FFtzFtf+bf/Y7S1c2BqrFD1xlSpqWcLeWBsq6CcClfNpV1fKkykQohxTlJz\n7ggkjaz/L4FhYUZjmUmrEFNUpeuVxVI7DXWBZJaKxAz5PNuoxYRTXCCoKi94Z1bXVxeRGGLtvIhI\nulIFZpHV7Ojm5HBVBp3XY10cM1xgtb6Wk0OSO7VoWK3Og3Ie9ZoqxHc53Y8tG5XnU/HjFJZFQ04T\no1eElOi0YncpQ/4+JC4ay75xmJxBS8UvZSUMZsTCBBrBKsZpoGs6shGHkJOia2SjN9aw2XW8vLsV\nfbUx0GtHaxp0NozJkzrNmGW+KGlN3zgRpZ8i4+nAdSvUosZ7CAFfgmOtdWHO1Wdzqjmr9RM8q0rL\n4y0Ocn2rymcWyLIi5iyisCkzTLHc3rwqSqgCk6qbrKKxitELjPs4RoYp4mPCGs0YImMIMqdYAv/W\nGmGqRQiHjHaM48QUAr2zpJzpnOE4eWJSHIPBZ82u8bwymkjHy2E7P/foJUlUSmGbbmVCS7WOklhU\nYXupfC/BWK005vn3pBtfRXZrwsMc2FEjnOWe14xybegPzDJ/5EfLB9YmvMBB6/EWp1YDTEKQGaQK\nM18dZP7vnFi0Tev+VfadVVGsiv+KE1Zl7cgPK+FOJalJGWJCksYkuopVBL0mrtJBB0Vi8iO2BCJR\nifOM5X5VeJacblnjqnYey0ylVrMDrdD9syT+bO3LwWKK5VpyYYJuyGoR+NYFfj96T4hRdFNtyzAl\nJquw2mJNRlOQG8qiKwV6bwgxEaLYkUYXCG7GRy/wQKRLkrLmsd2zcZaELZX5hPeRMRpMUuS8lc6I\nynUA/sxg6j48TRMqJb72vIFkuBsVIQthV1vWTb1eUIQCA7fWEGN5pkoDmk1rsRp+55UEl51NTMnI\nvG4qPiolDt7ywfCIN7o9Vg0cp0BzPKKS481dTzMcuZvk3kpXSNbcvXfsOo3RBieCsnRGrktr2X9b\nIx3+cRSCMWsMTWPxY561Z5WxgMF7KZZppTCuYRwD0Qt5iNy7iabtyNTOXSWUyVirMKYhplgKwxqt\nGsia4/2t7BvWCpzLOqDlsB/xNqFMZkyBb7xs+e59g9W5zEHWIgdlZl6e0atB8dVXmXCp+cF3ronT\nSJgGEVtvG5IKkBVGGXTpXEocUTpmJYFKqiKBVEGjasgaVCIScQXmnVWW9awMjWu4PwTu93syIzBC\nDmhlaV1gs+0lCTMNSlkaYzkcT3gf5mKhcQ4/jUzZsB8ySScutxYVE0GP6KyLxMcKgVRmSClrMUsQ\nIwltTme2LMm8gPJSGXrX2pT1VwhxsmHy0pEZp3tiSIUEpM7VS+C92W5wRrHpmuIHM4+ud5yGiWma\n2LQyYzeFSNd1xBSJWTFFxfF+wGroWsVF7wXmO4o/zYXrIeUJlcremBIxRSYlkPj9eODNx9fYbCFF\nBPRYi7QF/aOAlLFNI4zitmEYTjgzgbKE7FFl5KNtm2LHch9TEm3iD+8t79513I6Gg9dMUeMKk7Ds\nMZlf+3CHVpmNDfioCgxdtBg7m3h9sgxBiuZvPdry/qt7wPPD1x/wtO354NByCD3OlP23FNuNXpLF\nmDO5+HRnnTA/+1CKf0VYPgacW+bCp5AwyqCyJ2bH0+1AzhI/xJRpu0su4p5DvMJoYZG+LzGOn0aa\nrueMdoCyTh74zOo/0mxr54lgtcuHvrbGxtX31POuvrO6+Hq3q+3Jv2vBJcmzT8t+sNioonY+64gM\nZe+dQ8DSAAAgAElEQVQgM/uhWrRunMUYxTB6rFJQ2dzrOSdZ687aMqKVC3N7Ors3/+if/jf4L/78\nv8TbP/jjX7h+8zNfvPnwva/zKXx9apNFpVT77PM/9Nd/9A/90zx6+/P13RmqsjBgMT/gWg1bV/Rr\npVbVz2mhUg9BmK4qa9/aij9CEVuCobnSRmWYZGXPecmx6g9K8EOus0SmVI2Xz55BV0syMYdVqnYq\nl+StJrJ1UdROY4UtaVVBNDVBXPDiqhwPyR/n854D61WCW7OUrCpktCxotYKq1Y5BPUy9DpbguWov\nqbxg189+XYFadYVyrlXOVDpniaNv6a3FWdHsM1qT0OwngayYCCCOO6aMnzyJiPcRbUS4XYqHmpzD\nvLGYCpuyjtvjsTCagh4DTy4uMYhTzECwmjEFkoJN33J/N9Colmk4YZEANKdIZzQ6VXgLoqG1SsBn\nO13bV3kea3MSfcS8/kh9/GRg8LFs5MJMOkzSKTIKLloJRM3KlpZOkOghZrJoJYZURIIpos8ik3F/\n8rgyGyGI2pooWk6Dx/sRRaKxhpASYwhSSc6OQ2jYNQEfI/ej4WsvnmD1Ah+PQaqbumnmYsjZBlqT\nvCz09TWgWeV6y78zWKsKO5l0jlUqCU6Z3VwSxf9vr/wJ//3/50A5ZVIJFNfENLKkVvYwe69l/1II\nFAtqx1FTwSyLTXG+h60KXhW6q5VAh+ut0DCTg2mlUDmSw5EUDbkOd2U170sCX0uotNp/6lfXPbd+\nv9bzc6sXpNQqOK37Tk3eC2uvdFGFLCGmVDorGnQuc2CRKYN1hk4bXh4MrbFYnendgtIgmzkh17ro\n4OVIKtqiPsWS0EWwoEuRKkTF4CNWJYFdh4jKUdZMgmPQGNPQmEqEtn4Aq0eeZQ8bfea9qRGiiaxx\nRjq92mjR8svL/LrIsWayNhgt61EpsDqxa0U79r37HUflRCybZb9USoKvlByvxy3bJqKMpO8fvPZo\nZ3i2jVw2kXfvEy/HvkgKSXI7BY3RdZRggRbb0nnIoRRtoqxVY/QcfOmiH5dVRmeZnY4pLTquWnE6\njWiVCgTYABZXOhlalwDfKJjJfhRkTUyexhmMs6Sk2Fw+YjzcCoolZzZtj9Wt7PkxzKzM1t/w6vQY\nqxdCuLrR1jJTzHAKivf3cN1ofsTf0Vy+xf2L90jJk7Il5iTEKErNqBitlOj0VT+vZIXmUqjIxfeS\nhDAqq4wPIy430NRihhEotT9x3N/j/ch2Y4CIdVo6yjZhLZKMTaLZa41if79ns+ln36+1RtmG+2Fk\n9BN+1OycxqhA2zo0dnEwZeXXtViDe0GrsN5MVhsUJcGMKGUALVqM2sksn08MUyDEyPG4L+s5M46j\naCw2TWGhzHSN5XK3IYQo/sM5tLaEmAkxYa3hqu+5ucsYElplIlq0eH0kZAhpz7bv0WjCOGHLLGZW\nWahua5E6R1ScOI4eaxX74cCTqxYzFsZXvRT0KheERuOngWZzwalIccQMxiS0PE7RJm0drsw/16J/\nyvD7nu751k3HB/sWq0Vj0qgaE8GrsZE9JihOUTRphdtA4gOrYQwKHxUpK4xOPN51nEaPiUeumiOT\n35AShNyRS8JrlBTGTGE2FRkg6ZZVPoNUxnKUNtK0KAmRKXOLWWkpuCnIBFojtl25DD57NfHBqUOl\nOiufefT0Gc/f+46wIZ9FMMz+Kj/wvXNTZWVi6yRy6Sc8PN7ZDkvdvWYbreZbipjz7H5dI0ahkiCX\nkoplDGlVNEmrmX8qIaCa9zBdPtd1Hd4H2qZh8hPgl8SzBva5kFgWG3ErdmRCKN8p8W27ueCP/Mu/\nyF/7i7/Y/cl/9y/99mrD+lS9PrXJ4rPP/sDfvHjyJr/vD/8ssDB15pzJMQispFQNagIpdlK6gXox\nmBqMNk2t5hZxaAUxLKWQOfXUy7OqTJLGLMlMdRzkMl9IQmWNVuK1K2ywJoNKW2poVQOlnNMCk63/\nrgEe4qBTgWvVbmCtoEDtIC5Q0VohU0qY5bQxM7HDus2/fP9SfcnkufJKzjNhDUrmrSRwlw3DlHmE\nNM821MXGco5KoElLMrzMZ5ZdYP68/K5c40waUBZeTpGUHdZEjG2IOfFo61BGQYJNozkcA51qSDHh\nCeisGL3HWktnLSF5tDK4fkvGY6vMgVYMpxGtLdEmToMnBsiTp7FSbQs+4LqOiZHJD6Jt2To27QW7\nSWAvz097YW9E8Xjbc9E6nHWEMKFNJfRgvs45aE7lueoloK6zY6haDKlPjZkAxSjN/uR5/27AakXv\nLE0hmhhDwAepJvatzHxpyqxRGV4fQ+LDu0HYaNEYpbjetCQyu9by+uh57/UBrRVTTMVhCZza2Yb9\n/sDoJ3IKfObxTpxGzkwpcz8aoup4NWxpTcQpO1+HUghl+jRgbINtu7piS/cwzYQRyzWvEsWZhXP9\nCTmED4na8U8plSqzBKxt02B05jSMlKEXcczzol9s8PzIiwda/6S+u7iq+t5qf1cPdvuVE2TNglzW\nR00WjJG9qnZkahGlwt5TLoG2kmqvWh2+Fs9kG0gSBGqYNbesntll6+/Vmq9AITN+GpHMw+GTZoqF\nxEJFmlYS+xgVvm5uy0XNCama13eeZ2NQag6sTqfTUuCqBQxVmPHK74uWW51nkzsbioOtv2eUQuXA\n115uiVlIlHbNgFR3FVPKdCYzTkGCp7KHTVPAGaHPD0kzjp4cI72TTv39CP/L+1dMUUE+MZYFqbUi\nxZG2EU2xGM/3v9ly1OqBI1iUlA3OabaNKUm2keSzEjhlIdF4c5tpLNyOmZO37FygMYlGRzbmxPPX\nR77nv180JlWW5H5lVyEmnl0kppB5ftrh25a3+gMvDiMmO75/N2LbxHUz8rVXkfeHS7EvlXEq0KpC\nFKKlQxtDxDglybhKTD7gp4SxYlUxpSIdIYmM0QYiHKYRpUVG4TiMTMPAcX/P1dVW7kfSONtyPE6k\nHGlbgf9aJ0nU4RCYxkDKEWMaUrSQheF5GEa897RtQyax6yR4jcoQgmLKid4qgpdOgJlh7CVwlfax\n7Ctlz7A644xGWUf0I8YgZDtxJCtFUIngR1olKAuFQZfgPucsCa5pBLGiZHZTo8keqm4bKhFzwGaL\n1Q6tLCoLvG863XN11fDs2VMOxz0omfsbJxgnWU/7uxvabss0jlhrGAaB76sUuNj2M8z85c2BO5W4\n6CxvPL5g+0jBCEoncj6fUZPEVs4/p7Rak5L41vWdc5a2l1a4tkeblqQsx0GSqeADMWWmaWIYAyoH\nYeK08Ojqmr5vhTmyxDubTcc0DNzc7bm/vych1+tjJkcgRNq2K0P5Aa1lvCQmzzAOvN6PKAtdZ9mP\nd7Qmk1WgbRySoJRxk8J4fKEVEUe7sRAtw3SPyolsKMVx2WO0MQQfsM4J4sBqSKY0GBS77QbSyPai\np2lMIRCTpCaTSUnu6x/6/C0ojY+KU9Dcj0bQHRlCUjz3TfHLwhQrO6icQ8yKy1ZQUYPPOJ257gUq\nf/KZ/eD57MVrrs1zvnr/IySzxZhEVhnnHDmL1mmMCe8Dukj9yN+WaRISolycsrVCRnc4TVIYSYqr\nNnA3Wj489jSbnjYfUBg+OFzTNYZDkOIaWhPDhGtapmFgGo64bjPHfg9dtfynEM+leeOqcWCek26W\nH62KjauMcO166sELfGiuta6KplUuSisNRoqkwddp1SVGNYXJOaU61mWou7lWkltIDGzo+w7TOHZ9\nx2mciDHJHOl6FAvhkGiLz7blOcTSrMpFLiqlzDs/9GP82M/8cf7GX/4P+IN/86/9hb/93/7lf4dP\n2etTmSwqpX5m++jZT/7sn/u3xNBVFYcWAxDGsySUtXXjr1XkUomvkL9KJW+NYhxHcXBaFw2dJfio\nm2i1YV2H+YsBphKAaa2ZxmHWVFnIAgoBR+mQLJDKsiCAavBrMezl3DNnAtdpFYYqdR5IV4prcknk\nKPclFk0ds1psaU765srGKk0E5kHpPFeCKIurBPOrgGkd6K2D/dWzm/+uydFZIP6wjFS6CxmIMYPK\nZQ7TlCHuxP3Y8H+/fsqXn5xojCwyq8EqITs4jh6dpAp32I/sLi7IClojcgFeRWIYpOtkLGPy6Cyw\nGIzldr9Hpcg4DmyUJZ5GJuNITvFqeEmjFBeXHd8+jOw/HAj+XuAEOfKlNzoa2/He6zuu+47Gtig0\nrukwtt6fALqQG8xQkdXsx6rKVttnwg4p/23N4pTuh8gY4dG2IyURnd2PgVeHYU4yaq0jZekMOKNo\nnMZqNRcQNo2Tu1/OySnF/Rj51ov9LHSulCl2o1ApMZz25BxwWrHpe14fJ5SWNeC05lGfmOKe7x23\nBO346ss3uRk7yBE/jqQYsE03FyXk3uRZR2v9SmmBitT/nXM7LWVJXWw2J4ESxRn+JAP9w+iF6lpR\noIhlvqOuglwO/KCIOXfHWYf/9e8HzmtVkK+J6/qaHiaI9V/SodEy/wGz3qErouRraLa1VhLFORNc\nXLJ1Zf6k7hpK0Vg7H3+cPMM0yr5FLbKJFmbOmXGaSFFj3K4UjuQ8rGlRKHpddMjY0jiLD5FQCIdi\nEAbKeR6Z+twgh1iKBXJPmtJNnhnqQGClhSnVGpmvHCdfOqEZSAWaKs/DWcNms8EYxaRaohp5tg10\nzohQd4j4IXDvZX7OaNEf7JuWR9srQhAIlvaxBIgjd6nh5djyzfueu1HjGNCmmeWBZH6ynwObZShh\nFeAgOo1rm1FKCSuk0hwmIXhxJpcCoZqLZErBH3j7nqfdEchMEYbjgW98T/HNF46vTddM8W36TSN0\n/mUd19xUG0ejEy/uROi7c4bWJq77gLse8Crz1Rc7TsExJkPIhq2NDFFxmDSDank1tTid+cxu5HE7\ncXcyhKyEyh/IaLSTfzsUaMV+f8fjRxuCiuQoPrJtW+6OgXd/5z1cm2gah20Nh+GEzplMQifYXnS0\nmw5SmWnX0LgGo0amKTBOmWmcGMc65yrEHm3Xom1i1+5IyuJjxp88XWd4/vIVanvB42bgab9nyhtC\nNsQkSZeKqTTZZMH3JnM7GW6nTNYK748om2Sea4jCsJ2EYfr4+oAyma6TeTVrxc+KnFUGrQh6kgJQ\nMjhlZQxCKbRyJCLTNBFiIEbF4XjixYsXfOFzj7l+/AYvXtyI3IWReSecJL3WOna7HeMoTJ3OiSRG\nCJ6u3dG3ApvcZiAFYvQcxpGTB3/a0js3F4CF7CShjCnJuCnsyAsSKWWRt0LrYqMGY4Qkx7Ytd8fE\n++8/53A6Ya2jbVshG/TSYen7DU3X49oNpmlQxjGMA42zpBgIXuYwr692NG3LaZi4ub3n5WthJb95\nfSpFdi22YzXagLWavu/oOkvMmfvDkWQbDllxfxh5pDLX/SW5BOIpRYKfJIFyBkPDNI0CJ1ZFB1GL\nn5Bk26OdJhNQKTFGxffujzzpE1dX16SsgEZ4DKaJy10z7+Uy4wo5a65N5Oe+9D1IibvR8J//+lsM\n0eJM5u9788SuifzOjePV0JRiUo0DwajM3aj5X9+75Lv3jpSgMfDObuBJe88UBqao2PQdP8Q3+Prp\nS0QMGJlNJGe8F46BthEbpcTPobDE2qZFKb3i2BBYt0meKcLdZHl64fnM7oYrN7A/KHSOXNnX/NbN\nE5HKQXgHQvDknAjThG06YS0uY15ibwuhS3WT566+JoywDM7Ucu0q+XtQgV13HWV+UK94OxbfUglp\nYkE7VKZS1wjpUPKjMMki42mNNfPvhJBmkjjx1Ratxed5L2v+GEPxy3GO91UqI29IQW2aPMZEUra0\nbVPOLROjXLjSgrj46X/qz/Bf//q/jmm6XwA+dcniRxCXf69f1jXv7J68+Tf+sT/z59hcPprfz0iF\nPXqPdU60zPLSEai47bltvKqeSWVaZl8omON1R25JfihOazVzgxBpVBHOWCrc9VWr+mlODJc07EHs\nefb+vCyyQBbPOm1zyHceZJ5/b+3c1QMvLfSl2lzP8ONe52d3FqzXzeBjruH8fPKDgPjh8R5+t3q4\nU6x/ArAk6OV4SmuMTly3E63xZ+LdIUgVMYdEa2A8TVT9IFKVkaiJyKqokEpirgwv7+7QKZJCpFEG\ng8w/dV2Hj55GW5nZiRGSaEGl5Mkq8oNPO7qmK0UtCfZNhRtQNsqcl4urSVxYFQhyPr8lqt6mahP1\nfjI7FjPbp2xG9U+VlNj1dvV1ucytgo+ZMZyTmVRmysMY+O0P70QaY7UeAGauO0UhcpJFpRRFG05E\ntFNWxKwISWik95MrAcjCBrYyHekGf0yiWA2vJonzew9fH29KZ3ZYNaVm5rhcYH+fsC4ePor/16+a\ncNYN5+PO6xN+VZV5w2Vu+fyTD58HnF9jhZuu0tGzpC3OCAV1VqBKeWWjZxXc9V3IGBUICQmW1sdj\nvfbV+UlRkna1JM7nEFTm81tDhaw1MzlNXUfyOfkOXZLfSqpTd8qNjYw+c/KKk9fEbHBFpN5ZK8FQ\nYX4kx5mIIKfI0WteD0I+Mfg6s7JKfOenV/dENV8XavUeeu7iG7109FPOGJ3ZuMRVF4uuX/mecv4p\nw6uT4dXJcD9qQgTbboWxUBkSAp9aFzbWNlNvqVKWnBU+aoGzJYH/xZTxSTElRUiyVlMuLKeqgsoU\nUzKF+GapfiwERqUgQ2FAzRnrLMsUhyAQYkxM4wnnoGsNrhEdQZSIeltjRPdzLlzULuuigYbKGAPW\ngVZTQfZE+VOfiK4XnmY+AlsC4N4YvtS8yyN3w+M+0NtY4HX1OUonOaHYe8WrIROCR+UASiQeYhII\n6npdqiL9NIVERGa8UJCV8B+AQq2G+cVE6qykiNtLIO/Y9D191+G93D+jjVhRCXSdK3IsORaiI0Qu\npELHy+cqGgctZEjaWNGhRfR4BbqpyMqAqvqrMBO71Lrvg91PUckCpVCQ0fiQ5gTYWkGOyCycrNlK\nPBhK4hiidPNDEHmHdWE8eC+zbs5gtGLbd0zjWAr+Armtsde8Z6VYzi3NBFoxBlKWOfzBy1x4RcWk\nWtTRmjAFkpECUVqW0bkfzrIWjZKxi8Zqpmw4nU6z/QjMM50VyesarElPBrJS9C7xz//YB1y2gd7K\n/X3ae55uJt7cjDzpPVdtmAu8a7t5/75jippj0HznboPPPcY4eies0NtW8cObr9GqIymp+RqMsXOD\nYd4fajxb9kBV7LuubVPkanRhPw7JMARhnkVp4v9D3pvF3LZl912/2a21dvM1p7v33LpVLruoshP3\nAZskFpJtSCAROBFICYGEOBIhIUIgHhAPIRIkRIjwghAiEkK8BBIrgYjAA0qAJAYEKLhiIogBlwt3\n1dx7zz3N1+y9VzM7Hsaca6197rmusiW7jFjSOec7+9urn3OOMf7jP/4jgyby2YuvkAnU0ME6t5Qc\nVN+MagqXALHeU1333rQtBPGPfuE1s/GRbc1sWdvKWBhJqTybWupQa4+tddKvsvji5CVpMweqagX+\nzefLTF5a5ugiVCZ2rorWLUFvnG2NzB2lwDUOY80c2Colyvu//Q//CX7yv/7z/J4/8R/ettv95cff\n8a/99usus7jZ7v6jT3zr96rPfO8PyAc1Wi/CEMa58k1BNmOIWF0a7mYx3lK4L9kZUX3LxairOeir\n1DpxmNcDWzjmWkFMYYX6iBqjBoFAkeP7MM1GBCViJhnOs21UMRhBx0tpxmz0zgLFrKgUkBlNqUFD\n2ccsLnT5XS5UDlPQr/lXi6NWHtmyo6K2L1hPsLycuSi1VoO3TKYaaVSaYP095bt1Qqqy82zc6xfK\ndaly8xnm9iKFHDQH7DklIhnvB9J0xxFDc9mhlebhRSMKXbHHcsU4jDitCWRs1zKGiZACqnDYkwE/\nTEhWxXDr78lhxBrHVjf4HNi0W9qmQSVoA1xeXXGfTrw4HshDj0oD6Mi2vaQPmXd3W37+ZY+2CZ+i\n9Dx0jhAHVMoFvSzPN0tWfHGYS5bZVDpeefZKMYZQ6IFqDpqdUlxvNWNIol4aI1MIHCdBko3SKCcL\ndzVsPiammBnLeacQMUaVmkgYvdRjvTiKoA1KRC6s1vhQHKwUcc4Qg8I1RhZPpXDGFOU0yZjErJmi\nIeF4cbJMyZLjRJwGrGtkLNSMU5Kmyq8HijlT2mQszd+X8byMznMcpFBLZyOxCpzUkrUYRmk0brQh\nUemvS8CyZPLXsdMvYaHW45qlLc+SW1rmaQ3uBalnngO6KKpR91TrtSnO3xGWhBytGpdFKbmOEYmF\nas2X1ppx6GXdmns9UgSRIKp0dntKKWmpsVKNzCkxpsSQoXEbcvCcTj22NOSW0rKljxVAjKFcp0g6\nSFZQVPYkoyZtFFKWrHLTOJqmoW1bco7YKEIisdDIqmpe1zW0Tmrd+nFiq1uMTuzMyC+8FMf+FBpC\nbrnqEt90NeBcR9O0BZyIDENP8pGpj3jvwff89KtrfvZux31wGKNpdMLYDsk4LEBNtSlaaZwVKmws\nvdpyeS9bJ9RQo1IBdxRKO7SKfMtjcQ5/8isbjsFhiKSc8SkRI/z1L2550Frevez5xH7krV3g+z7T\n8jN3O4Zxy7YJxOAXMTalSFHaXKS00GJDMrw8KnJ2XLUND3XgcFS8GhwH3xCwbJyAWrtG+hqGKAHi\n331+ycZGWhUwKpKTp/eGzrWSQQgeGMhJ1gPrGiavCN7Tdhpjtty/fMEH7/8c737qAQ8e7PHJMHrF\nNER2rYMU2W43MlZSIuvEMPVkrYleBLC0yTgn5R/WwuHQlyyRAdsweA36xKbpRN22bfDDyOP9BWjD\nOI5sGfmOBx/w5Enmf3+v4edu95y8m9ffnIEkQdWrUXN/fCniPkYRtOEwTpg+FuFvhXVS6xV94uQD\n7A16k7FZyh1yzqikUEmUT9Vcr5Yhe5pmi7RgkPfUNop33nkq7y8m9tsNd8eR7WbDaRiwpuHBVcft\nYRBV56I+mWIkW4NzDrTU2GoFrmnRxnK8v6fpNAHFGBQ2lFIFDToLzTgykZHWVFYbxjGUFUjGhTYO\nbR1KO5IyhKRI2XJ/23MaJlCG7XZDCIlT3zN6z7ZruL68FF8syedDCRRVjmw6x65rUCrj40jyCT9N\nZCWia5e7lmH0pWQgcLXfcn/o8dOE94qmtSJumD1dJ3WQN30vmdoMd4PmfoCHVxeMpwGjElYbjLZF\nNTiTghXAq4b2WcBMqcUNaF2iSOPolOLBxZ6hv+Hi8qGIZIXE8TjQmEhv4WK/IeWqcyVgmi7BdQKU\nyeiseOdiYmMVv/XdOzrj+dYHUjN9MzruR83/+KVrbgZHKD2IQRroPDttyEiw7wx8+lLz6Sd33I2K\n57cD2o08bb7E8/EhN+EROoc58FBzECeZtYx87qylbSzjOAnYnhMhKbbmCBGcu+bBNvLu/p4QE8Y4\n7sfIw01m62+x+QGBVkodUpLEjVLEaaDtNhKYR1nblNZFTbgavQxnPqossBJwVuXsNQy52lYAaJ2/\nNaljjaZt3Mz4AylPqYrp9Vy6AG5rTQiFElp5+cSUrODMpCv+6Ly/OOKl9U8Rz1EG22gmH2Z/YA32\nx5TI3kupSQE9K2ultuRQwIN3Psn3/8iP8j/9pT97+danv/U3A//tmx7FN2JTH0H1v4Hbo0988782\n9oc//Qf+9H/C9vJa5l4RNYCiJJriHPWLg6fnzJ41ZpaanpFX6hzOpU7rPHNSB6g4SlLFU3+fci1u\nXeTNz9CaXIQcqE7iOujL837lg9lRVq+hK4L2U4LiBR1ZU9kqKpOhqGuucY7XnGjWE2pBD9UqkVzd\nWL06VyoLyznyU39fnODyy3XvmMoJXyOws4jHTHV4/Zg1di1Nqcu9V9lyOW5FmxSfvJr4+9898qAV\nqm3bKVTWvDr23B0jh/sMtwfaknnYbdtC17HEBKMfMGT2FztoDDvXzpS1QYM9Ja4uLggqMA4DnW7Q\n2uJVRFtNzBOJxKAivZ84DROH08TDpuUX7u/pjOfp9UM6I/VERotIgVZKHAelyvMvY0XX5vIJ40x5\nprkYhoSPmcZJXeQYJTMQUsKHyGEI0ihbyzM6jJ4pJHat5Z3rLfvWMgZR0v3wfsAXevLTy44MTCFz\nP3h6HxmDKJ3FVJqCh4hTmV3nMKYhA/3Qo7Xi5tjTto6Nk8bOtU4m58Rl59DaMEXHT798wothy5QM\ncTxRBlrJ6Ja5mOLZPJp/nOdXyXqlZeQsiOH5iMqrv2e6yxnKt8xNmeYCgtReY5VCfjZCVaUJL4pt\nlboq/y5MghrQ1QL5GZzJde1Js6NHEQSR+SIiJiHlQlEWapAxhpBqT6+yn5J6iLr2Vcq7tWZWY67n\nrkqV4ziSc8KWfrSpvOcaOApin87mbQWJ5jUiV7l/oR35mFDKwmp9SZkSEKa5lk9Q3CUjkEuNpdGm\nUJ9L82KtaRoJAF3TcH9/L8JU5TlUQGq3adEq40PgeBrQrmXTNoAINGkjCHfM8kdpxe/7jpd85pHn\ng1eeQ++57yNfvmuYgmEYRzH0bsuXTjuUqa07hFoYs7SIaEykMVI71DnFo23iQeu53iRCGDkNkTvf\nMsSGizbxpLulsRqnIWTpf+izY6d7nuw8m0bxlbsN79/CF15ueHay+Kggi9J3TIqYJcDet5nvenLL\n//2VyIuhw1i31N7N4zZJBrM4SbXl0nA6kVLg4U6xswOf2J34meM7TEma0Tcmi0KuoVDqixOjNTZP\ndC6zNz0NR7790chGaVKIHIeJz7/Y4TZbnjZ3PGozF42S1hpZaL+f/+IzTpt3+PTTHRMdKkxs1MBb\nzYl200rbC1Md2YQ14vhj8ww0oIxkEYtQzjRJ+wxnW46nwDjKeHry8JrGakKy3B8Gcozsdh2THzn2\nR459z+7ykhQD7w+a+7xl1Hv64Ghs5uk+srUBqxN7B9b3TP1EHCOXrqVzVnQzS+15CgKuKasZ/UiK\nkW7j2F9saZ0j+ShieAZ0a5b1JIsAHUnj2g5URhWBoEKEYRp7uo1lCj3GOPyUGKdEzJYXLw+c+kyr\nPb8AACAASURBVEjSjmGYyGX9zEDTNGx3Fzz74H3efvqUGAIhBPw0sb/Y0TiHUoa+P2JUprWarjFs\nugbXNmwaTdeWmkLdgGoYpyDXh9Sz3d4diCljrRWGV4w0ztJ2LeM4zXR3EGd7HAcRrzGWw/09/elU\naMaW/W5D1zriNEABjHzwiKqmL2uRptu0KG0Yp8Cp1IVZq9AOuq4FwPvAi5tXXGwdxzFxe3vHxig6\nZ7FKQDNp22LQJIyx+OiJ2ZNz7aOqCSnIGqWkzlUCTMt9f0ArxfOj59FezwCR9yOX+5bLqy3nAKEC\nzOyXSkCTMErWRcF0Ije95hduWj7sG14MDSdvOHpRc1/7X1rV+j745gcnvvvtO5zy9FPk2I/EGOl9\n5Kt3hi+fvgVlLEaKf5fjZBGxCSEuoGWS7LSxsm5O0fItVzc83kWaPJCne8J0h8+G+9NIjgOXFxue\nj5e8d3rIKV6QwkQIgbubV0z9gHOO3f5CfIMUmQIYaxfV0+qTnwHAaQ62ltyibLWUbPFb6+9ee0bF\nljROGGGq2MwQAtPoqT1EFZRSiWrjKetLUeJOVU1+SdKI/y8Z/hn0Z7G1CgpYoGga0Ynoh3EGoGfF\n3eI/OGtpu0ZosTHg2k7a8xS7LcJQA3/x3/xjfNtv+W186f/62//EFz//3/8X/DrYft0Ei0qpze7q\nwcsf/oP/Svcbfstvmx9wDKUegIqoF8y+OKkxLmIoMZ2LtqiamUrVAa3qc4LuqYIUzDSuEoBVQywD\nYQme0lxHJotZufKCjCwOJstv5p9SXnq4VM94CeyWoKsGt8sRStaEhWYgSdUqlZ9W17JMxFpvNS9g\n6ywMFSVRM5a4ZFI4E/hZZ3PqpS91mbWusT7vQpOqweBZRKzOFoqK2tStUtBYOTzSq02EEp52r/js\n1T1PtxPWWdrG0DTS2/Hnnw08//JzrtoWi8JPgf1my36/5RRG/DgWmssW1YqxOJ5OPNxf88XnX+Ui\nOx5sL7jc7Yk6MzDSxIbRZHzw2AyPLh9wmo5ElXivP3J88YqL68c8v79F5cinr/fstztOw0DTWKyw\nN5agH8UsdlDVx4qRcVYyzyGK8los77lzmsNURL6V4sVhQtTs5a3EKmefE1fbls5pGitje/DycF8e\nR14cJy43jk0JXlOG28GjteXl4UROkcZoHuxaWmex1nGaMh/e3HM43vPW1VYktvUiQjSFVBA6RVNU\n2IagOIUtP3d7xe24oe9HasPmSnddgodlLq17fK3H7wx2lECuBlwfpe/ms7lWwQvUQruUICiWoKUG\n7GYGf9ZOeEUfYxLKCGV+So85PRt8ofIt4zzmXBTT0pz9m69Ki/OgWcCnGgQareZ6xBgzojpY69l0\nQTv12dqQkvS2m4Vwyr1aKwGWNKsOhXpWAnVW1xornWu91f8lZvrqHETnUrxfs+NpXrcEtV7eXy5B\n49n7KbUftW5Xa6lPdI3UcDSN4Xg8cTqdQEnQZo1Qyp1rUESOpyMhJIwR2fr6zLrWSSA1I8RCv3x7\n7/lNT57xf74HL/qOg9+StC3PldmZczqSlSGXQNNZzWcfZ7794T0XLtCaWBgjga7RxCmglOZ4nHCl\nfYS1wnLRxpJ1QZLL2Ares9ttwFpiiPjTxF/4wkNOsSFljdMVRMwroSJ5RqcxQ5oI4yBggbGz81Pn\nQKV0zku9UoTgid6DFgdGWhYYxmSIWWNVCcwKkFFtTwwBnzTf9lbi2fNbXvWGU2r57gfPyGHkzjve\nj0/RbkvOmXd2J5409zzSN/ikuY8bfub0NkFtSBi8n/iBT97zXU+ObJumnA9UXgIpqxuMavBhRKk8\n0zX7YZD6PQXojNEN3ifu7g8c+4EQM227YddtsbaVgHIYiX5kv9uD1vT9EWvFT7i9m5hiImtFu92C\nnnj4cIMyLTkq4slzfH6PtZar7YaYxYlsGsfkPSFFEbZAMSZPPwy0zrLpGmJUbLcbXKsRXYxMiH4B\nsnIiRXBmi2nsag3LpJCYRmFaKJ1AicjU8RR4edsTs2OaJGN9OBwIwaPJbHaXTNNITr4ArQZjGx4+\nfEvGzunEOHl2220px0jSgksr9vsdfuxJMbDpGi72G66urrg5TPSDZKfatsUY6deroIi1iQhIiLLG\nxBjxfiLlzG67nTUi6r1ZK5TIvpdz+XHg8upqHqNdI0I/xii0ccQQqK2etMpok7jcXTBEzYuXt2XM\nRpQSSvIwjIQYinpuIqeARgSLmsbimg0iKiZ8rBAjbWPwfiTjpUbawil4tAKrFEkbrA9o23I8HbBG\nC+CmYdtA1245nXq0SlxcNvOaWlMGMWqGYZrBRfGNhAFgdeZ+UHxwb/mrP/uYMVmMFvtkVBEtO1+U\n6xIKSvHO/shvfPgScs8UMp2VDOT7t4kvvnqHMW/QxgrFsRzM6KU/c4oen8SvbJpmBh0/dTXy1uae\nrXpJP4ycjgPbXYf3nsYoHu0b3rru+Oq94Sfff8wHB4fK0jP1dH/PeDqx3W5L2xGhAOdiw+YQcOX/\nVT+eAiRWX3RO1hRbYiqwmhatgddtK8U+WmulX68TMZ8YI+M4FpA0ihbGa3hznG3losLO6r3Njz9z\nti5XMHq+jlx9WEvOscQc5f7m0y3q0tXvUEr602ojpTsCjiaef/ln+bE/+Uf5zb/7R//qj/+n/97v\nfMOI+DXfft3ULH7u+37w59/9tt/Ufe77fwiQwVTl8F/fPhIoxtqjqfy+BoqvOZQ1UJwDFvX6oFsC\nxde3daC4/n7NiL0eKK6/UwPFs3tY3cubapLOA8Xlu1UA5k2B4tn9f8y1rN3DzGvO4muB4tleS4wr\nn6n1butA8U2nVb/Uf4tzsHKs50kp2RSrE04Hnm4HsnKr/Qwv7j1TP6GARmuCj3RtS9c2M6IVYxCJ\n9bY5O+9Nf0QjbQn2my1JZzwek6X+MClI3vP46iFTGMuzkM1YuY5+mvjExZZNt8GHsGS99fq9fvzb\nsEYCxVWSjZgym0YzhPwxY0o2V8SQam2UMxL2T0H2iikLtfRN51YaPze5z1x0roiBGIrGCsd++CXO\nXq+hyHUniElo0Cfv8OnrY7i/ach8PQDWm7/ycZWIb/rq8s3qoKev47zrbR0o/rL2rcFyBUbU2oJ9\nfZvWv8S9fmQt+dXZ1tTYX+5WM4e/0q1mMf6/uGn3K79vAUN+pfeuaXT8Fe056CsaLVmARk2/rH2/\neLM7q+lfl3p9zZG6YBWkUru3222/rvNWxy4lhTGK/d6hfpnDNWVpxdQ2zVI797XOW/6tNYj1E6Ug\nponkv/Y7yLlnv22pLrQxMt+326997+M0zUGa0ZpxmqQ+VOmP9UPq1thlbIYQSCmuetfKHvrj1pcK\nHK98mlgodrpQ661r3rwvsu5rY2Ywvwr5hRhEFfRqf87WWm2p6FEobWewVYIjydjnVf/a19cskxUb\n64qYlvwuaU2OgU23xceSLQbCa69uGs81LAC0zrSt+4gdE5bc8uw+dXFibZ7nKvA3DTCJnvjwJPTt\ni1bP+yrgeqv4lqv3ZgX95TDCINAVvFdSp1yD+TpHvnTb4vOmsD80rrEMvYyj0cuJRp94axswOtPZ\nRELTbTa0BSSofwTEzB810qsEQi7Pcx4zwCwZts48lgCuJg8qm+L1rbZZqixEydRpmqahilWmnD5i\np7VSZ9TV5V18xDtegdzMgd7r1yq+hJTFnR9PvJOQUtHLUGfHrD1mVZknjz7xzXz/P/YH+Lm/8z//\njn/6T/7Hf+sjN/wN2H5d1Cy22/3vtq556w/9mR+bi3NBYRsnPWJK7RRKk2KQpt4hzGiArkXbZNYp\n5xq9zynoGS2oDe5Lr5k6O88MQVUMrKpKC+qwZDJkxFcHvw6ImnJm7QTWXV8LSFW5srqAzUGZWimT\nlmtbAg9Bqs4yF+o19cW68LByRvPy3SrFv/r4LFCcA+BcJ0GdJMw1VLJvLob8XDilNiteqIa1hrGe\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e8eoYOQVDoxVTbFClFssnjbaGxrZoHDFappzw42GuTbXGQNaQEjF7jLNkJVlvpa3M+1ya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T+4iLzbzxymzJiUf/TR5+T8MZ/fXbsd1gLo5/rqa19ked3uNLU+qXG1HvI8EdPAbrsheTP5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\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CXWmwIM2aKm9SZjqlMXV1xObFfd7jqX+tfhRVCkPHyLpv2rg3ikipNe0UmX5iihZVGEf3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inFIYGIXykTtGCrBRps+VDNv9O+xh+zsRtDaAN8zF7zMYDw5VzXCkJ/uEbaAZKNvv8eCRghdY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I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\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ibcKI/F4T04JwpKW+2QOZLcxKw5lUkeERVIlMISQAHcFTJTHCgQlqtfkqAeIcTxfAdG4A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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa2d14e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "f = plt.figure(figsize=(16,9))\n",
+    "ax = plt.axes(projection=ccrs.Robinson())\n",
+    "ax.stock_img()\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/10. something interesting/10.03 nba data.ipynb b/10-something-interesting/10.03-nba-data.ipynb
similarity index 100%
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rename to 10-something-interesting/10.03-nba-data.ipynb
diff --git a/10. something interesting/10.04 louis cha's kungfu world.ipynb b/10-something-interesting/10.04-louis-cha's-kungfu-world.ipynb
similarity index 100%
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diff --git a/10. something interesting/_Player.py b/10-something-interesting/_Player.py
similarity index 100%
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rename to 10-something-interesting/_Player.py
diff --git a/10. something interesting/bangs.txt b/10-something-interesting/bangs.txt
similarity index 100%
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diff --git a/10. something interesting/kungfu.txt b/10-something-interesting/kungfu.txt
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diff --git a/10. something interesting/names.txt b/10-something-interesting/names.txt
similarity index 100%
rename from 10. something interesting/names.txt
rename to 10-something-interesting/names.txt
diff --git a/11. useful tools/11.01 pprint.ipynb b/11-useful-tools/11.01-pprint.ipynb
similarity index 100%
rename from 11. useful tools/11.01 pprint.ipynb
rename to 11-useful-tools/11.01-pprint.ipynb
diff --git a/11. useful tools/11.02 pickle and cPickle.ipynb b/11-useful-tools/11.02-pickle-and-cPickle.ipynb
similarity index 100%
rename from 11. useful tools/11.02 pickle and cPickle.ipynb
rename to 11-useful-tools/11.02-pickle-and-cPickle.ipynb
diff --git a/11. useful tools/11.03 json.ipynb b/11-useful-tools/11.03-json.ipynb
similarity index 100%
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rename to 11-useful-tools/11.03-json.ipynb
diff --git a/11. useful tools/11.04 glob.ipynb b/11-useful-tools/11.04-glob.ipynb
similarity index 100%
rename from 11. useful tools/11.04 glob.ipynb
rename to 11-useful-tools/11.04-glob.ipynb
diff --git a/11. useful tools/11.05 shutil.ipynb b/11-useful-tools/11.05-shutil.ipynb
similarity index 100%
rename from 11. useful tools/11.05 shutil.ipynb
rename to 11-useful-tools/11.05-shutil.ipynb
diff --git a/11. useful tools/11.06 gzip, zipfile, tarfile.ipynb b/11-useful-tools/11.06-gzip,-zipfile,-tarfile.ipynb
similarity index 100%
rename from 11. useful tools/11.06 gzip, zipfile, tarfile.ipynb
rename to 11-useful-tools/11.06-gzip,-zipfile,-tarfile.ipynb
diff --git a/11. useful tools/11.07 logging.ipynb b/11-useful-tools/11.07-logging.ipynb
similarity index 100%
rename from 11. useful tools/11.07 logging.ipynb
rename to 11-useful-tools/11.07-logging.ipynb
diff --git a/11. useful tools/11.08 string.ipynb b/11-useful-tools/11.08-string.ipynb
similarity index 100%
rename from 11. useful tools/11.08 string.ipynb
rename to 11-useful-tools/11.08-string.ipynb
diff --git a/11. useful tools/11.09 collections.ipynb b/11-useful-tools/11.09-collections.ipynb
similarity index 100%
rename from 11. useful tools/11.09 collections.ipynb
rename to 11-useful-tools/11.09-collections.ipynb
diff --git a/11. useful tools/11.10 requests.ipynb b/11-useful-tools/11.10-requests.ipynb
similarity index 100%
rename from 11. useful tools/11.10 requests.ipynb
rename to 11-useful-tools/11.10-requests.ipynb
diff --git a/12. pandas/12.01 ten minutes to pandas.ipynb b/12-pandas/12.01-ten-minutes-to-pandas.ipynb
similarity index 100%
rename from 12. pandas/12.01 ten minutes to pandas.ipynb
rename to 12-pandas/12.01-ten-minutes-to-pandas.ipynb
diff --git a/12. pandas/12.02 series in pandas.ipynb b/12-pandas/12.02-series-in-pandas.ipynb
similarity index 100%
rename from 12. pandas/12.02 series in pandas.ipynb
rename to 12-pandas/12.02-series-in-pandas.ipynb
diff --git a/12. pandas/12.03 dataframe in pandas.ipynb b/12-pandas/12.03-dataframe-in-pandas.ipynb
similarity index 100%
rename from 12. pandas/12.03 dataframe in pandas.ipynb
rename to 12-pandas/12.03-dataframe-in-pandas.ipynb
diff --git a/README.md b/README.md
index ceac77ec..8d17c13c 100644
--- a/README.md
+++ b/README.md
@@ -62,157 +62,156 @@ conda update anaconda
 
 ---
 
-- [01. **Python 工具**](01. python tools)
-	 - [01.01 Python 简介](01. python tools/01.01 python overview.ipynb)
-	 - [01.02 Ipython 解释器](01. python tools/01.02 ipython interpreter.ipynb)
-	 - [01.03 Ipython notebook](01. python tools/01.03 ipython notebook.ipynb)
-	 - [01.04 使用 Anaconda](01. python tools/01.04 use anaconda.ipynb)
-- [02. **Python 基础**](02. python essentials)
-	 - [02.01 Python 入门演示](02. python essentials/02.01 a tour of python.ipynb)
-	 - [02.02 Python 数据类型](02. python essentials/02.02 python data types.ipynb)
-	 - [02.03 数字](02. python essentials/02.03 numbers.ipynb)
-	 - [02.04 字符串](02. python essentials/02.04 strings.ipynb)
-	 - [02.05 索引和分片](02. python essentials/02.05 indexing and slicing.ipynb)
-	 - [02.06 列表](02. python essentials/02.06 lists.ipynb)
-	 - [02.07 可变和不可变类型](02. python essentials/02.07 mutable and immutable data types.ipynb)
-	 - [02.08 元组](02. python essentials/02.08 tuples.ipynb)
-	 - [02.09 列表与元组的速度比较](02. python essentials/02.09 speed comparison between list & tuple.ipynb)
-	 - [02.10 字典](02. python essentials/02.10 dictionaries.ipynb)
-	 - [02.11 集合](02. python essentials/02.11 sets.ipynb)
-	 - [02.12 不可变集合](02. python essentials/02.12 frozen sets.ipynb)
-	 - [02.13 Python 赋值机制](02. python essentials/02.13 how python assignment works.ipynb)
-	 - [02.14 判断语句](02. python essentials/02.14 if statement.ipynb)
-	 - [02.15 循环](02. python essentials/02.15 loops.ipynb)
-	 - [02.16 列表推导式](02. python essentials/02.16 list comprehension.ipynb)
-	 - [02.17 函数](02. python essentials/02.17 functions.ipynb)
-	 - [02.18 模块和包](02. python essentials/02.18 modules and packages.ipynb)
-	 - [02.19 异常](02. python essentials/02.19 exceptions.ipynb)
-	 - [02.20 警告](02. python essentials/02.20 warnings.ipynb)
-	 - [02.21 文件读写](02. python essentials/02.21 file IO.ipynb)
-- [03. **Numpy**](03. numpy)
-	 - [03.01 Numpy 简介](03. numpy/03.01 numpy overview.ipynb)
-	 - [03.02 Matplotlib 基础](03. numpy/03.02 matplotlib basics.ipynb)
-	 - [03.03 Numpy 数组及其索引](03. numpy/03.03 numpy arrays.ipynb)
-	 - [03.04 数组类型](03. numpy/03.04 array types.ipynb)
-	 - [03.05 数组方法](03. numpy/03.05 array calculation method.ipynb)
-	 - [03.06 数组排序](03. numpy/03.06 sorting numpy arrays.ipynb)
-	 - [03.07 数组形状](03. numpy/03.07 array shapes.ipynb)
-	 - [03.08 对角线](03. numpy/03.08 diagonals.ipynb)
-	 - [03.09 数组与字符串的转换](03. numpy/03.09 data to & from string.ipynb)
-	 - [03.10 数组属性方法总结](03. numpy/03.10 array attribute & method overview .ipynb)
-	 - [03.11 生成数组的函数](03. numpy/03.11 array creation functions.ipynb)
-	 - [03.12 矩阵](03. numpy/03.12 matrix object.ipynb)
-	 - [03.13 一般函数](03. numpy/03.13 general functions.ipynb)
-	 - [03.14 向量化函数](03. numpy/03.14 vectorizing functions.ipynb)
-	 - [03.15 二元运算](03. numpy/03.15 binary operators.ipynb)
-	 - [03.16 ufunc 对象](03. numpy/03.16 universal functions.ipynb)
-	 - [03.17 choose 函数实现条件筛选](03. numpy/03.17 choose.ipynb)
-	 - [03.18 数组广播机制](03. numpy/03.18 array broadcasting.ipynb)
-	 - [03.19 数组读写](03. numpy/03.19 reading and writing arrays.ipynb)
-	 - [03.20 结构化数组](03. numpy/03.20 structured arrays.ipynb)
-	 - [03.21 记录数组](03. numpy/03.21 record arrays.ipynb)
-	 - [03.22 内存映射](03. numpy/03.22 memory maps.ipynb)
-	 - [03.23 从 Matlab 到 Numpy](03. numpy/03.23 from matlab to numpy.ipynb)
-- [04. **Scipy**](04. scipy)
-	 - [04.01 SCIentific PYthon 简介](04. scipy/04.01 scienticfic python overview.ipynb)
-	 - [04.02 插值](04. scipy/04.02 interpolation with scipy.ipynb)
-	 - [04.03 概率统计方法](04. scipy/04.03 statistics with scipy.ipynb)
-	 - [04.04 曲线拟合](04. scipy/04.04 curve fitting.ipynb)
-	 - [04.05 最小化函数](04. scipy/04.05 minimization in python.ipynb)
-	 - [04.06 积分](04. scipy/04.06 integration in python.ipynb)
-	 - [04.07 解微分方程](04. scipy/04.07 ODEs.ipynb)
-	 - [04.08 稀疏矩阵](04. scipy/04.08 sparse matrix.ipynb)
-	 - [04.09 线性代数](04. scipy/04.09 linear algbra.ipynb)
-	 - [04.10 稀疏矩阵的线性代数](04. scipy/04.10 sparse linear algebra.ipynb)
-- [05. **Python 进阶**](05. advanced python)
-	 - [05.01 sys 模块简介](05. advanced python/05.01 overview of the sys module.ipynb)
-	 - [05.02 与操作系统进行交互：os 模块](05. advanced python/05.02 interacting with the OS - os.ipynb)
-	 - [05.03 CSV 文件和 csv 模块](05. advanced python/05.03 comma separated values.ipynb)
-	 - [05.04 正则表达式和 re 模块](05. advanced python/05.04 regular expression.ipynb)
-	 - [05.05 datetime 模块](05. advanced python/05.05 datetime.ipynb)
-	 - [05.06 SQL 数据库](05. advanced python/05.06 sql databases.ipynb)
-	 - [05.07 对象关系映射](05. advanced python/05.07 object-relational mappers.ipynb)
-	 - [05.08 函数进阶：参数传递，高阶函数，lambda 匿名函数，global 变量，递归](05. advanced python/05.08 functions.ipynb)
-	 - [05.09 迭代器](05. advanced python/05.09 iterators.ipynb)
-	 - [05.10 生成器](05. advanced python/05.10 generators.ipynb)
-	 - [05.11 with 语句和上下文管理器](05. advanced python/05.11 context managers and the with statement.ipynb)
-	 - [05.12 修饰符](05. advanced python/05.12 decorators.ipynb)
-	 - [05.13 修饰符的使用](05. advanced python/05.13 decorator usage.ipynb)
-	 - [05.14 operator, functools, itertools, toolz, fn, funcy 模块](05. advanced python/05.14 the operator functools itertools toolz fn funcy module.ipynb)
-	 - [05.15 作用域](05. advanced python/05.15 scope.ipynb)
-	 - [05.16 动态编译](05. advanced python/05.16 dynamic code execution.ipynb)
-- [06. **Matplotlib**](06. matplotlib)
-	 - [06.01 Pyplot 教程](06. matplotlib/06.01 pyplot tutorial.ipynb)
-	 - [06.02 使用 style 来配置 pyplot 风格](06. matplotlib/06.02 customizing plots with style sheets.ipynb)
-	 - [06.03 处理文本（基础）](06. matplotlib/06.03  working with text - basic.ipynb)
-	 - [06.04 处理文本（数学表达式）](06. matplotlib/06.04 working with text - math expression.ipynb)
-	 - [06.05 图像基础](06. matplotlib/06.05 image tutorial.ipynb)
-	 - [06.06 注释](06. matplotlib/06.06 annotating axes.ipynb)
-	 - [06.07 标签](06. matplotlib/06.07 legend.ipynb)
-	 - [06.08 figures, subplots, axes 和 ticks 对象](06. matplotlib/06.08 figures, subplots, axes and ticks.ipynb)
-	 - [06.09 不要迷信默认设置](06. matplotlib/06.09 do not trust the defaults.ipynb)
-	 - [06.10 各种绘图实例](06. matplotlib/06.10 different plots.ipynb)
-- [07. **使用其他语言进行扩展**](07. interfacing with other languages)
-	 - [07.01 简介](07. interfacing with other languages/07.01 introduction.ipynb)
-	 - [07.02 Python 扩展模块](07. interfacing with other languages/07.02 python extension modules.ipynb)
-	 - [07.03 Cython：Cython 基础，将源代码转换成扩展模块](07. interfacing with other languages/07.03 cython part 1.ipynb)
-	 - [07.04 Cython：Cython 语法，调用其他C库](07. interfacing with other languages/07.04 cython part 2.ipynb)
-	 - [07.05 Cython：class 和 cdef class，使用 C++](07. interfacing with other languages/07.05 cython part 3.ipynb)
-	 - [07.06 Cython：Typed memoryviews](07. interfacing with other languages/07.06 cython part 4.ipynb)
-	 - [07.07 生成编译注释](07. interfacing with other languages/07.07 profiling with annotations.ipynb)
-	 - [07.08 ctypes](07. interfacing with other languages/07.08 ctypes.ipynb)
-- [08. **面向对象编程**](08. object-oriented programming)
-	 - [08.01 简介](08. object-oriented programming/08.01 oop introduction.ipynb)
-	 - [08.02 使用 OOP 对森林火灾建模](08. object-oriented programming/08.02 using oop model a forest fire.ipynb)
-	 - [08.03 什么是对象？](08. object-oriented programming/08.03 what is a object.ipynb)
-	 - [08.04 定义 class](08. object-oriented programming/08.04 writing classes.ipynb)
-	 - [08.05 特殊方法](08. object-oriented programming/08.05 special method.ipynb)
-	 - [08.06 属性](08. object-oriented programming/08.06 properties.ipynb)
-	 - [08.07 森林火灾模拟](08. object-oriented programming/08.07 forest fire simulation.ipynb)
-	 - [08.08 继承](08. object-oriented programming/08.08 inheritance.ipynb)
-	 - [08.09 super() 函数](08. object-oriented programming/08.09 super.ipynb)
-	 - [08.10 重定义森林火灾模拟](08. object-oriented programming/08.10 refactoring the forest fire simutation.ipynb)
-	 - [08.11 接口](08. object-oriented programming/08.11 interfaces.ipynb)
-	 - [08.12 共有，私有和特殊方法和属性](08. object-oriented programming/08.12 public private special in python.ipynb)
-	 - [08.13 多重继承](08. object-oriented programming/08.13 multiple inheritance.ipynb)
-- [09. **Theano 基础**](09. theano)
-	 - [09.01 Theano 简介及其安装](09. theano/09.01 introduction and installation.ipynb)
-	 - [09.02 Theano 基础](09. theano/09.02 theano basics.ipynb)
-	 - [09.03 Theano 在 Windows 上的配置](09. theano/09.03 gpu on windows.ipynb)
-	 - [09.04 Theano 符号图结构](09. theano/09.04 graph structures.ipynb)
-	 - [09.05 Theano 配置和编译模式](09. theano/09.05 configuration settings and compiling modes.ipynb)
-	 - [09.06 Theano 条件语句](09. theano/09.06 conditions in theano.ipynb)
-	 - [09.07 Theano 循环：scan（详解）](09. theano/09.07 loop with scan.ipynb)
-	 - [09.08 Theano 实例：线性回归](09. theano/09.08 linear regression.ipynb)
-	 - [09.09 Theano 实例：Logistic 回归](09. theano/09.09 logistic regression .ipynb)
-	 - [09.10 Theano 实例：Softmax 回归](09. theano/09.10 softmax on mnist.ipynb)
-	 - [09.11 Theano 实例：人工神经网络](09. theano/09.11 net on mnist.ipynb)
-	 - [09.12 Theano 随机数流变量](09. theano/09.12 random streams.ipynb)
-	 - [09.13 Theano 实例：更复杂的网络](09. theano/09.13 modern net on mnist.ipynb)
-	 - [09.14 Theano 实例：卷积神经网络](09. theano/09.14 convolutional net on mnist.ipynb)
-	 - [09.15 Theano tensor 模块：基础](09. theano/09.15 tensor basics.ipynb)
-	 - [09.16 Theano tensor 模块：索引](09. theano/09.16 tensor indexing.ipynb)
-	 - [09.17 Theano tensor 模块：操作符和逐元素操作](09. theano/09.17 tensor operator and elementwise operations.ipynb)
-	 - [09.18 Theano tensor 模块：nnet 子模块](09. theano/09.18 tensor nnet .ipynb)
-	 - [09.19 Theano tensor 模块：conv 子模块](09. theano/09.19 tensor conv.ipynb)
-- [10. **有趣的第三方模块**](10. something interesting)
-	 - [10.01 使用 basemap 画地图](10. something interesting/10.01 maps using basemap.ipynb)
-	 - [10.02 使用 cartopy 画地图](10. something interesting/10.02 maps using cartopy.ipynb)
-	 - [10.03 探索 NBA 数据](10. something interesting/10.03 nba data.ipynb)
-	 - [10.04 金庸的武侠世界](10. something interesting/10.04 louis cha's kungfu world.ipynb)
-- [11. **有用的工具**](11. useful tools)
-	 - [11.01 pprint 模块：打印 Python 对象](11. useful tools/11.01 pprint.ipynb)
-	 - [11.02 pickle, cPickle 模块：序列化 Python 对象](11. useful tools/11.02 pickle and cPickle.ipynb)
-	 - [11.03 json 模块：处理 JSON 数据](11. useful tools/11.03 json.ipynb)
-	 - [11.04 glob 模块：文件模式匹配](11. useful tools/11.04 glob.ipynb)
-	 - [11.05 shutil 模块：高级文件操作](11. useful tools/11.05 shutil.ipynb)
-	 - [11.06 gzip, zipfile, tarfile 模块：处理压缩文件](11. useful tools/11.06 gzip, zipfile, tarfile.ipynb)
-	 - [11.07 logging 模块：记录日志](11. useful tools/11.07 logging.ipynb)
-	 - [11.08 string 模块：字符串处理](11. useful tools/11.08 string.ipynb)
-	 - [11.09 collections 模块：更多数据结构](11. useful tools/11.09 collections.ipynb)
-	 - [11.10 requests 模块：HTTP for Human](11. useful tools/11.10 requests.ipynb)
-- [12. **Pandas**](12. pandas)
-	 - [12.01 十分钟上手 Pandas](12. pandas/12.01 ten minutes to pandas.ipynb)
-	 - [12.02 一维数据结构：Series](12. pandas/12.02 series in pandas.ipynb)
-	 - [12.03 二维数据结构：DataFrame](12. pandas/12.03 dataframe in pandas.ipynb)
-
+- [01. **Python 工具**](01-python-tools)
+	 - [01.01 Python 简介](01-python-tools/01.01-python-overview.ipynb)
+	 - [01.02 Ipython 解释器](01-python-tools/01.02-ipython-interpreter.ipynb)
+	 - [01.03 Ipython notebook](01-python-tools/01.03-ipython-notebook.ipynb)
+	 - [01.04 使用 Anaconda](01-python-tools/01.04-use-anaconda.ipynb)
+- [02. **Python 基础**](02-python-essentials)
+	 - [02.01 Python 入门演示](02-python-essentials/02.01-a-tour-of-python.ipynb)
+	 - [02.02 Python 数据类型](02-python-essentials/02.02-python-data-types.ipynb)
+	 - [02.03 数字](02-python-essentials/02.03-numbers.ipynb)
+	 - [02.04 字符串](02-python-essentials/02.04-strings.ipynb)
+	 - [02.05 索引和分片](02-python-essentials/02.05-indexing-and-slicing.ipynb)
+	 - [02.06 列表](02-python-essentials/02.06-lists.ipynb)
+	 - [02.07 可变和不可变类型](02-python-essentials/02.07-mutable-and-immutable-data-types.ipynb)
+	 - [02.08 元组](02-python-essentials/02.08-tuples.ipynb)
+	 - [02.09 列表与元组的速度比较](02-python-essentials/02.09-speed-comparison-between-list-&-tuple.ipynb)
+	 - [02.10 字典](02-python-essentials/02.10-dictionaries.ipynb)
+	 - [02.11 集合](02-python-essentials/02.11-sets.ipynb)
+	 - [02.12 不可变集合](02-python-essentials/02.12-frozen-sets.ipynb)
+	 - [02.13 Python 赋值机制](02-python-essentials/02.13-how-python-assignment-works.ipynb)
+	 - [02.14 判断语句](02-python-essentials/02.14-if-statement.ipynb)
+	 - [02.15 循环](02-python-essentials/02.15-loops.ipynb)
+	 - [02.16 列表推导式](02-python-essentials/02.16-list-comprehension.ipynb)
+	 - [02.17 函数](02-python-essentials/02.17-functions.ipynb)
+	 - [02.18 模块和包](02-python-essentials/02.18-modules-and-packages.ipynb)
+	 - [02.19 异常](02-python-essentials/02.19-exceptions.ipynb)
+	 - [02.20 警告](02-python-essentials/02.20-warnings.ipynb)
+	 - [02.21 文件读写](02-python-essentials/02.21-file-IO.ipynb)
+- [03. **Numpy**](03-numpy)
+	 - [03.01 Numpy 简介](03-numpy/03.01-numpy-overview.ipynb)
+	 - [03.02 Matplotlib 基础](03-numpy/03.02-matplotlib-basics.ipynb)
+	 - [03.03 Numpy 数组及其索引](03-numpy/03.03-numpy-arrays.ipynb)
+	 - [03.04 数组类型](03-numpy/03.04-array-types.ipynb)
+	 - [03.05 数组方法](03-numpy/03.05-array-calculation-method.ipynb)
+	 - [03.06 数组排序](03-numpy/03.06-sorting-numpy-arrays.ipynb)
+	 - [03.07 数组形状](03-numpy/03.07-array-shapes.ipynb)
+	 - [03.08 对角线](03-numpy/03.08-diagonals.ipynb)
+	 - [03.09 数组与字符串的转换](03-numpy/03.09-data-to-&-from-string.ipynb)
+	 - [03.10 数组属性方法总结](03-numpy/03.10-array-attribute-&-method-overview-.ipynb)
+	 - [03.11 生成数组的函数](03-numpy/03.11-array-creation-functions.ipynb)
+	 - [03.12 矩阵](03-numpy/03.12-matrix-object.ipynb)
+	 - [03.13 一般函数](03-numpy/03.13-general-functions.ipynb)
+	 - [03.14 向量化函数](03-numpy/03.14-vectorizing-functions.ipynb)
+	 - [03.15 二元运算](03-numpy/03.15-binary-operators.ipynb)
+	 - [03.16 ufunc 对象](03-numpy/03.16-universal-functions.ipynb)
+	 - [03.17 choose 函数实现条件筛选](03-numpy/03.17-choose.ipynb)
+	 - [03.18 数组广播机制](03-numpy/03.18-array-broadcasting.ipynb)
+	 - [03.19 数组读写](03-numpy/03.19-reading-and-writing-arrays.ipynb)
+	 - [03.20 结构化数组](03-numpy/03.20-structured-arrays.ipynb)
+	 - [03.21 记录数组](03-numpy/03.21-record-arrays.ipynb)
+	 - [03.22 内存映射](03-numpy/03.22-memory-maps.ipynb)
+	 - [03.23 从 Matlab 到 Numpy](03-numpy/03.23-from-matlab-to-numpy.ipynb)
+- [04. **Scipy**](04-scipy)
+	 - [04.01 SCIentific PYthon 简介](04-scipy/04.01-scienticfic-python-overview.ipynb)
+	 - [04.02 插值](04-scipy/04.02-interpolation-with-scipy.ipynb)
+	 - [04.03 概率统计方法](04-scipy/04.03-statistics-with-scipy.ipynb)
+	 - [04.04 曲线拟合](04-scipy/04.04-curve-fitting.ipynb)
+	 - [04.05 最小化函数](04-scipy/04.05-minimization-in-python.ipynb)
+	 - [04.06 积分](04-scipy/04.06-integration-in-python.ipynb)
+	 - [04.07 解微分方程](04-scipy/04.07-ODEs.ipynb)
+	 - [04.08 稀疏矩阵](04-scipy/04.08-sparse-matrix.ipynb)
+	 - [04.09 线性代数](04-scipy/04.09-linear-algbra.ipynb)
+	 - [04.10 稀疏矩阵的线性代数](04-scipy/04.10-sparse-linear-algebra.ipynb)
+- [05. **Python 进阶**](05-advanced-python)
+	 - [05.01 sys 模块简介](05-advanced-python/05.01-overview-of-the-sys-module.ipynb)
+	 - [05.02 与操作系统进行交互：os 模块](05-advanced-python/05.02-interacting-with-the-OS---os.ipynb)
+	 - [05.03 CSV 文件和 csv 模块](05-advanced-python/05.03-comma-separated-values.ipynb)
+	 - [05.04 正则表达式和 re 模块](05-advanced-python/05.04-regular-expression.ipynb)
+	 - [05.05 datetime 模块](05-advanced-python/05.05-datetime.ipynb)
+	 - [05.06 SQL 数据库](05-advanced-python/05.06-sql-databases.ipynb)
+	 - [05.07 对象关系映射](05-advanced-python/05.07-object-relational-mappers.ipynb)
+	 - [05.08 函数进阶：参数传递，高阶函数，lambda 匿名函数，global 变量，递归](05-advanced-python/05.08-functions.ipynb)
+	 - [05.09 迭代器](05-advanced-python/05.09-iterators.ipynb)
+	 - [05.10 生成器](05-advanced-python/05.10-generators.ipynb)
+	 - [05.11 with 语句和上下文管理器](05-advanced-python/05.11-context-managers-and-the-with-statement.ipynb)
+	 - [05.12 修饰符](05-advanced-python/05.12-decorators.ipynb)
+	 - [05.13 修饰符的使用](05-advanced-python/05.13-decorator-usage.ipynb)
+	 - [05.14 operator, functools, itertools, toolz, fn, funcy 模块](05-advanced-python/05.14-the-operator-functools-itertools-toolz-fn-funcy-module.ipynb)
+	 - [05.15 作用域](05-advanced-python/05.15-scope.ipynb)
+	 - [05.16 动态编译](05-advanced-python/05.16-dynamic-code-execution.ipynb)
+- [06. **Matplotlib**](06-matplotlib)
+	 - [06.01 Pyplot 教程](06-matplotlib/06.01-pyplot-tutorial.ipynb)
+	 - [06.02 使用 style 来配置 pyplot 风格](06-matplotlib/06.02-customizing-plots-with-style-sheets.ipynb)
+	 - [06.03 处理文本（基础）](06-matplotlib/06.03-working-with-text---basic.ipynb)
+	 - [06.04 处理文本（数学表达式）](06-matplotlib/06.04-working-with-text---math-expression.ipynb)
+	 - [06.05 图像基础](06-matplotlib/06.05-image-tutorial.ipynb)
+	 - [06.06 注释](06-matplotlib/06.06-annotating-axes.ipynb)
+	 - [06.07 标签](06-matplotlib/06.07-legend.ipynb)
+	 - [06.08 figures, subplots, axes 和 ticks 对象](06-matplotlib/06.08-figures,-subplots,-axes-and-ticks.ipynb)
+	 - [06.09 不要迷信默认设置](06-matplotlib/06.09-do-not-trust-the-defaults.ipynb)
+	 - [06.10 各种绘图实例](06-matplotlib/06.10-different-plots.ipynb)
+- [07. **使用其他语言进行扩展**](07-interfacing-with-other-languages)
+	 - [07.01 简介](07-interfacing-with-other-languages/07.01-introduction.ipynb)
+	 - [07.02 Python 扩展模块](07-interfacing-with-other-languages/07.02-python-extension-modules.ipynb)
+	 - [07.03 Cython：Cython 基础，将源代码转换成扩展模块](07-interfacing-with-other-languages/07.03-cython-part-1.ipynb)
+	 - [07.04 Cython：Cython 语法，调用其他C库](07-interfacing-with-other-languages/07.04-cython-part-2.ipynb)
+	 - [07.05 Cython：class 和 cdef class，使用 C++](07-interfacing-with-other-languages/07.05-cython-part-3.ipynb)
+	 - [07.06 Cython：Typed memoryviews](07-interfacing-with-other-languages/07.06-cython-part-4.ipynb)
+	 - [07.07 生成编译注释](07-interfacing-with-other-languages/07.07-profiling-with-annotations.ipynb)
+	 - [07.08 ctypes](07-interfacing-with-other-languages/07.08-ctypes.ipynb)
+- [08. **面向对象编程**](08-object-oriented-programming)
+	 - [08.01 简介](08-object-oriented-programming/08.01-oop-introduction.ipynb)
+	 - [08.02 使用 OOP 对森林火灾建模](08-object-oriented-programming/08.02-using-oop-model-a-forest-fire.ipynb)
+	 - [08.03 什么是对象？](08-object-oriented-programming/08.03-what-is-a-object.ipynb)
+	 - [08.04 定义 class](08-object-oriented-programming/08.04-writing-classes.ipynb)
+	 - [08.05 特殊方法](08-object-oriented-programming/08.05-special-method.ipynb)
+	 - [08.06 属性](08-object-oriented-programming/08.06-properties.ipynb)
+	 - [08.07 森林火灾模拟](08-object-oriented-programming/08.07-forest-fire-simulation.ipynb)
+	 - [08.08 继承](08-object-oriented-programming/08.08-inheritance.ipynb)
+	 - [08.09 super() 函数](08-object-oriented-programming/08.09-super.ipynb)
+	 - [08.10 重定义森林火灾模拟](08-object-oriented-programming/08.10-refactoring-the-forest-fire-simutation.ipynb)
+	 - [08.11 接口](08-object-oriented-programming/08.11-interfaces.ipynb)
+	 - [08.12 共有，私有和特殊方法和属性](08-object-oriented-programming/08.12-public-private-special-in-python.ipynb)
+	 - [08.13 多重继承](08-object-oriented-programming/08.13-multiple-inheritance.ipynb)
+- [09. **Theano 基础**](09-theano)
+	 - [09.01 Theano 简介及其安装](09-theano/09.01-introduction-and-installation.ipynb)
+	 - [09.02 Theano 基础](09-theano/09.02-theano-basics.ipynb)
+	 - [09.03 Theano 在 Windows 上的配置](09-theano/09.03-gpu-on-windows.ipynb)
+	 - [09.04 Theano 符号图结构](09-theano/09.04-graph-structures.ipynb)
+	 - [09.05 Theano 配置和编译模式](09-theano/09.05-configuration-settings-and-compiling-modes.ipynb)
+	 - [09.06 Theano 条件语句](09-theano/09.06-conditions-in-theano.ipynb)
+	 - [09.07 Theano 循环：scan（详解）](09-theano/09.07-loop-with-scan.ipynb)
+	 - [09.08 Theano 实例：线性回归](09-theano/09.08-linear-regression.ipynb)
+	 - [09.09 Theano 实例：Logistic 回归](09-theano/09.09-logistic-regression-.ipynb)
+	 - [09.10 Theano 实例：Softmax 回归](09-theano/09.10-softmax-on-mnist.ipynb)
+	 - [09.11 Theano 实例：人工神经网络](09-theano/09.11-net-on-mnist.ipynb)
+	 - [09.12 Theano 随机数流变量](09-theano/09.12-random-streams.ipynb)
+	 - [09.13 Theano 实例：更复杂的网络](09-theano/09.13-modern-net-on-mnist.ipynb)
+	 - [09.14 Theano 实例：卷积神经网络](09-theano/09.14-convolutional-net-on-mnist.ipynb)
+	 - [09.15 Theano tensor 模块：基础](09-theano/09.15-tensor-basics.ipynb)
+	 - [09.16 Theano tensor 模块：索引](09-theano/09.16-tensor-indexing.ipynb)
+	 - [09.17 Theano tensor 模块：操作符和逐元素操作](09-theano/09.17-tensor-operator-and-elementwise-operations.ipynb)
+	 - [09.18 Theano tensor 模块：nnet 子模块](09-theano/09.18-tensor-nnet-.ipynb)
+	 - [09.19 Theano tensor 模块：conv 子模块](09-theano/09.19-tensor-conv.ipynb)
+- [10. **有趣的第三方模块**](10-something-interesting)
+	 - [10.01 使用 basemap 画地图](10-something-interesting/10.01-maps-using-basemap.ipynb)
+	 - [10.02 使用 cartopy 画地图](10-something-interesting/10.02-maps-using-cartopy.ipynb)
+	 - [10.03 探索 NBA 数据](10-something-interesting/10.03-nba-data.ipynb)
+	 - [10.04 金庸的武侠世界](10-something-interesting/10.04-louis-cha's-kungfu-world.ipynb)
+- [11. **有用的工具**](11-useful-tools)
+	 - [11.01 pprint 模块：打印 Python 对象](11-useful-tools/11.01-pprint.ipynb)
+	 - [11.02 pickle, cPickle 模块：序列化 Python 对象](11-useful-tools/11.02-pickle-and-cPickle.ipynb)
+	 - [11.03 json 模块：处理 JSON 数据](11-useful-tools/11.03-json.ipynb)
+	 - [11.04 glob 模块：文件模式匹配](11-useful-tools/11.04-glob.ipynb)
+	 - [11.05 shutil 模块：高级文件操作](11-useful-tools/11.05-shutil.ipynb)
+	 - [11.06 gzip, zipfile, tarfile 模块：处理压缩文件](11-useful-tools/11.06-gzip,-zipfile,-tarfile.ipynb)
+	 - [11.07 logging 模块：记录日志](11-useful-tools/11.07-logging.ipynb)
+	 - [11.08 string 模块：字符串处理](11-useful-tools/11.08-string.ipynb)
+	 - [11.09 collections 模块：更多数据结构](11-useful-tools/11.09-collections.ipynb)
+	 - [11.10 requests 模块：HTTP for Human](11-useful-tools/11.10-requests.ipynb)
+- [12. **Pandas**](12-pandas)
+	 - [12.01 十分钟上手 Pandas](12-pandas/12.01-ten-minutes-to-pandas.ipynb)
+	 - [12.02 一维数据结构：Series](12-pandas/12.02-series-in-pandas.ipynb)
+	 - [12.03 二维数据结构：DataFrame](12-pandas/12.03-dataframe-in-pandas.ipynb)
diff --git a/generate index.ipynb b/generate index.ipynb
index b5df8c95..8a3f5299 100644
--- a/generate index.ipynb	
+++ b/generate index.ipynb	
@@ -34,18 +34,18 @@
    },
    "outputs": [],
    "source": [
-    "folders = ['01. python tools', \n",
-    "           '02. python essentials',\n",
-    "           '03. numpy',\n",
-    "           '04. scipy',\n",
-    "           '05. advanced python',\n",
-    "           '06. matplotlib',\n",
-    "           '07. interfacing with other languages',\n",
-    "           '08. object-oriented programming',\n",
-    "           '09. theano',\n",
-    "           '10. something interesting',\n",
-    "           '11. useful tools',\n",
-    "           '12. pandas'\n",
+    "folders = ['01-python-tools', \n",
+    "           '02-python-essentials',\n",
+    "           '03-numpy',\n",
+    "           '04-scipy',\n",
+    "           '05-advanced-python',\n",
+    "           '06-matplotlib',\n",
+    "           '07-interfacing-with-other-languages',\n",
+    "           '08-object-oriented-programming',\n",
+    "           '09-theano',\n",
+    "           '10-something-interesting',\n",
+    "           '11-useful-tools',\n",
+    "           '12-pandas'\n",
     "          ]"
    ]
   },
@@ -347,7 +347,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "version": "2.7.11"
   }
  },
  "nbformat": 4,
diff --git a/index.md b/index.md
index 9b07d3ca..fe7faccd 100644
--- a/index.md
+++ b/index.md
@@ -1,153 +1,153 @@
-- [01. **Python 工具**](01. python tools)
-	 - [01.01 Python 简介](01. python tools/01.01 python overview.ipynb)
-	 - [01.02 Ipython 解释器](01. python tools/01.02 ipython interpreter.ipynb)
-	 - [01.03 Ipython notebook](01. python tools/01.03 ipython notebook.ipynb)
-	 - [01.04 使用 Anaconda](01. python tools/01.04 use anaconda.ipynb)
-- [02. **Python 基础**](02. python essentials)
-	 - [02.01 Python 入门演示](02. python essentials/02.01 a tour of python.ipynb)
-	 - [02.02 Python 数据类型](02. python essentials/02.02 python data types.ipynb)
-	 - [02.03 数字](02. python essentials/02.03 numbers.ipynb)
-	 - [02.04 字符串](02. python essentials/02.04 strings.ipynb)
-	 - [02.05 索引和分片](02. python essentials/02.05 indexing and slicing.ipynb)
-	 - [02.06 列表](02. python essentials/02.06 lists.ipynb)
-	 - [02.07 可变和不可变类型](02. python essentials/02.07 mutable and immutable data types.ipynb)
-	 - [02.08 元组](02. python essentials/02.08 tuples.ipynb)
-	 - [02.09 列表与元组的速度比较](02. python essentials/02.09 speed comparison between list & tuple.ipynb)
-	 - [02.10 字典](02. python essentials/02.10 dictionaries.ipynb)
-	 - [02.11 集合](02. python essentials/02.11 sets.ipynb)
-	 - [02.12 不可变集合](02. python essentials/02.12 frozen sets.ipynb)
-	 - [02.13 Python 赋值机制](02. python essentials/02.13 how python assignment works.ipynb)
-	 - [02.14 判断语句](02. python essentials/02.14 if statement.ipynb)
-	 - [02.15 循环](02. python essentials/02.15 loops.ipynb)
-	 - [02.16 列表推导式](02. python essentials/02.16 list comprehension.ipynb)
-	 - [02.17 函数](02. python essentials/02.17 functions.ipynb)
-	 - [02.18 模块和包](02. python essentials/02.18 modules and packages.ipynb)
-	 - [02.19 异常](02. python essentials/02.19 exceptions.ipynb)
-	 - [02.20 警告](02. python essentials/02.20 warnings.ipynb)
-	 - [02.21 文件读写](02. python essentials/02.21 file IO.ipynb)
-- [03. **Numpy**](03. numpy)
-	 - [03.01 Numpy 简介](03. numpy/03.01 numpy overview.ipynb)
-	 - [03.02 Matplotlib 基础](03. numpy/03.02 matplotlib basics.ipynb)
-	 - [03.03 Numpy 数组及其索引](03. numpy/03.03 numpy arrays.ipynb)
-	 - [03.04 数组类型](03. numpy/03.04 array types.ipynb)
-	 - [03.05 数组方法](03. numpy/03.05 array calculation method.ipynb)
-	 - [03.06 数组排序](03. numpy/03.06 sorting numpy arrays.ipynb)
-	 - [03.07 数组形状](03. numpy/03.07 array shapes.ipynb)
-	 - [03.08 对角线](03. numpy/03.08 diagonals.ipynb)
-	 - [03.09 数组与字符串的转换](03. numpy/03.09 data to & from string.ipynb)
-	 - [03.10 数组属性方法总结](03. numpy/03.10 array attribute & method overview .ipynb)
-	 - [03.11 生成数组的函数](03. numpy/03.11 array creation functions.ipynb)
-	 - [03.12 矩阵](03. numpy/03.12 matrix object.ipynb)
-	 - [03.13 一般函数](03. numpy/03.13 general functions.ipynb)
-	 - [03.14 向量化函数](03. numpy/03.14 vectorizing functions.ipynb)
-	 - [03.15 二元运算](03. numpy/03.15 binary operators.ipynb)
-	 - [03.16 ufunc 对象](03. numpy/03.16 universal functions.ipynb)
-	 - [03.17 choose 函数实现条件筛选](03. numpy/03.17 choose.ipynb)
-	 - [03.18 数组广播机制](03. numpy/03.18 array broadcasting.ipynb)
-	 - [03.19 数组读写](03. numpy/03.19 reading and writing arrays.ipynb)
-	 - [03.20 结构化数组](03. numpy/03.20 structured arrays.ipynb)
-	 - [03.21 记录数组](03. numpy/03.21 record arrays.ipynb)
-	 - [03.22 内存映射](03. numpy/03.22 memory maps.ipynb)
-	 - [03.23 从 Matlab 到 Numpy](03. numpy/03.23 from matlab to numpy.ipynb)
-- [04. **Scipy**](04. scipy)
-	 - [04.01 SCIentific PYthon 简介](04. scipy/04.01 scienticfic python overview.ipynb)
-	 - [04.02 插值](04. scipy/04.02 interpolation with scipy.ipynb)
-	 - [04.03 概率统计方法](04. scipy/04.03 statistics with scipy.ipynb)
-	 - [04.04 曲线拟合](04. scipy/04.04 curve fitting.ipynb)
-	 - [04.05 最小化函数](04. scipy/04.05 minimization in python.ipynb)
-	 - [04.06 积分](04. scipy/04.06 integration in python.ipynb)
-	 - [04.07 解微分方程](04. scipy/04.07 ODEs.ipynb)
-	 - [04.08 稀疏矩阵](04. scipy/04.08 sparse matrix.ipynb)
-	 - [04.09 线性代数](04. scipy/04.09 linear algbra.ipynb)
-	 - [04.10 稀疏矩阵的线性代数](04. scipy/04.10 sparse linear algebra.ipynb)
-- [05. **Python 进阶**](05. advanced python)
-	 - [05.01 sys 模块简介](05. advanced python/05.01 overview of the sys module.ipynb)
-	 - [05.02 与操作系统进行交互：os 模块](05. advanced python/05.02 interacting with the OS - os.ipynb)
-	 - [05.03 CSV 文件和 csv 模块](05. advanced python/05.03 comma separated values.ipynb)
-	 - [05.04 正则表达式和 re 模块](05. advanced python/05.04 regular expression.ipynb)
-	 - [05.05 datetime 模块](05. advanced python/05.05 datetime.ipynb)
-	 - [05.06 SQL 数据库](05. advanced python/05.06 sql databases.ipynb)
-	 - [05.07 对象关系映射](05. advanced python/05.07 object-relational mappers.ipynb)
-	 - [05.08 函数进阶：参数传递，高阶函数，lambda 匿名函数，global 变量，递归](05. advanced python/05.08 functions.ipynb)
-	 - [05.09 迭代器](05. advanced python/05.09 iterators.ipynb)
-	 - [05.10 生成器](05. advanced python/05.10 generators.ipynb)
-	 - [05.11 with 语句和上下文管理器](05. advanced python/05.11 context managers and the with statement.ipynb)
-	 - [05.12 修饰符](05. advanced python/05.12 decorators.ipynb)
-	 - [05.13 修饰符的使用](05. advanced python/05.13 decorator usage.ipynb)
-	 - [05.14 operator, functools, itertools, toolz, fn, funcy 模块](05. advanced python/05.14 the operator functools itertools toolz fn funcy module.ipynb)
-	 - [05.15 作用域](05. advanced python/05.15 scope.ipynb)
-	 - [05.16 动态编译](05. advanced python/05.16 dynamic code execution.ipynb)
-- [06. **Matplotlib**](06. matplotlib)
-	 - [06.01 Pyplot 教程](06. matplotlib/06.01 pyplot tutorial.ipynb)
-	 - [06.02 使用 style 来配置 pyplot 风格](06. matplotlib/06.02 customizing plots with style sheets.ipynb)
-	 - [06.03 处理文本（基础）](06. matplotlib/06.03  working with text - basic.ipynb)
-	 - [06.04 处理文本（数学表达式）](06. matplotlib/06.04 working with text - math expression.ipynb)
-	 - [06.05 图像基础](06. matplotlib/06.05 image tutorial.ipynb)
-	 - [06.06 注释](06. matplotlib/06.06 annotating axes.ipynb)
-	 - [06.07 标签](06. matplotlib/06.07 legend.ipynb)
-	 - [06.08 figures, subplots, axes 和 ticks 对象](06. matplotlib/06.08 figures, subplots, axes and ticks.ipynb)
-	 - [06.09 不要迷信默认设置](06. matplotlib/06.09 do not trust the defaults.ipynb)
-	 - [06.10 各种绘图实例](06. matplotlib/06.10 different plots.ipynb)
-- [07. **使用其他语言进行扩展**](07. interfacing with other languages)
-	 - [07.01 简介](07. interfacing with other languages/07.01 introduction.ipynb)
-	 - [07.02 Python 扩展模块](07. interfacing with other languages/07.02 python extension modules.ipynb)
-	 - [07.03 Cython：Cython 基础，将源代码转换成扩展模块](07. interfacing with other languages/07.03 cython part 1.ipynb)
-	 - [07.04 Cython：Cython 语法，调用其他C库](07. interfacing with other languages/07.04 cython part 2.ipynb)
-	 - [07.05 Cython：class 和 cdef class，使用 C++](07. interfacing with other languages/07.05 cython part 3.ipynb)
-	 - [07.06 Cython：Typed memoryviews](07. interfacing with other languages/07.06 cython part 4.ipynb)
-	 - [07.07 生成编译注释](07. interfacing with other languages/07.07 profiling with annotations.ipynb)
-	 - [07.08 ctypes](07. interfacing with other languages/07.08 ctypes.ipynb)
-- [08. **面向对象编程**](08. object-oriented programming)
-	 - [08.01 简介](08. object-oriented programming/08.01 oop introduction.ipynb)
-	 - [08.02 使用 OOP 对森林火灾建模](08. object-oriented programming/08.02 using oop model a forest fire.ipynb)
-	 - [08.03 什么是对象？](08. object-oriented programming/08.03 what is a object.ipynb)
-	 - [08.04 定义 class](08. object-oriented programming/08.04 writing classes.ipynb)
-	 - [08.05 特殊方法](08. object-oriented programming/08.05 special method.ipynb)
-	 - [08.06 属性](08. object-oriented programming/08.06 properties.ipynb)
-	 - [08.07 森林火灾模拟](08. object-oriented programming/08.07 forest fire simulation.ipynb)
-	 - [08.08 继承](08. object-oriented programming/08.08 inheritance.ipynb)
-	 - [08.09 super() 函数](08. object-oriented programming/08.09 super.ipynb)
-	 - [08.10 重定义森林火灾模拟](08. object-oriented programming/08.10 refactoring the forest fire simutation.ipynb)
-	 - [08.11 接口](08. object-oriented programming/08.11 interfaces.ipynb)
-	 - [08.12 共有，私有和特殊方法和属性](08. object-oriented programming/08.12 public private special in python.ipynb)
-	 - [08.13 多重继承](08. object-oriented programming/08.13 multiple inheritance.ipynb)
-- [09. **Theano 基础**](09. theano)
-	 - [09.01 Theano 简介及其安装](09. theano/09.01 introduction and installation.ipynb)
-	 - [09.02 Theano 基础](09. theano/09.02 theano basics.ipynb)
-	 - [09.03 Theano 在 Windows 上的配置](09. theano/09.03 gpu on windows.ipynb)
-	 - [09.04 Theano 符号图结构](09. theano/09.04 graph structures.ipynb)
-	 - [09.05 Theano 配置和编译模式](09. theano/09.05 configuration settings and compiling modes.ipynb)
-	 - [09.06 Theano 条件语句](09. theano/09.06 conditions in theano.ipynb)
-	 - [09.07 Theano 循环：scan（详解）](09. theano/09.07 loop with scan.ipynb)
-	 - [09.08 Theano 实例：线性回归](09. theano/09.08 linear regression.ipynb)
-	 - [09.09 Theano 实例：Logistic 回归](09. theano/09.09 logistic regression .ipynb)
-	 - [09.10 Theano 实例：Softmax 回归](09. theano/09.10 softmax on mnist.ipynb)
-	 - [09.11 Theano 实例：人工神经网络](09. theano/09.11 net on mnist.ipynb)
-	 - [09.12 Theano 随机数流变量](09. theano/09.12 random streams.ipynb)
-	 - [09.13 Theano 实例：更复杂的网络](09. theano/09.13 modern net on mnist.ipynb)
-	 - [09.14 Theano 实例：卷积神经网络](09. theano/09.14 convolutional net on mnist.ipynb)
-	 - [09.15 Theano tensor 模块：基础](09. theano/09.15 tensor basics.ipynb)
-	 - [09.16 Theano tensor 模块：索引](09. theano/09.16 tensor indexing.ipynb)
-	 - [09.17 Theano tensor 模块：操作符和逐元素操作](09. theano/09.17 tensor operator and elementwise operations.ipynb)
-	 - [09.18 Theano tensor 模块：nnet 子模块](09. theano/09.18 tensor nnet .ipynb)
-	 - [09.19 Theano tensor 模块：conv 子模块](09. theano/09.19 tensor conv.ipynb)
-- [10. **有趣的第三方模块**](10. something interesting)
-	 - [10.01 使用 basemap 画地图](10. something interesting/10.01 maps using basemap.ipynb)
-	 - [10.02 使用 cartopy 画地图](10. something interesting/10.02 maps using cartopy.ipynb)
-	 - [10.03 探索 NBA 数据](10. something interesting/10.03 nba data.ipynb)
-	 - [10.04 金庸的武侠世界](10. something interesting/10.04 louis cha's kungfu world.ipynb)
-- [11. **有用的工具**](11. useful tools)
-	 - [11.01 pprint 模块：打印 Python 对象](11. useful tools/11.01 pprint.ipynb)
-	 - [11.02 pickle, cPickle 模块：序列化 Python 对象](11. useful tools/11.02 pickle and cPickle.ipynb)
-	 - [11.03 json 模块：处理 JSON 数据](11. useful tools/11.03 json.ipynb)
-	 - [11.04 glob 模块：文件模式匹配](11. useful tools/11.04 glob.ipynb)
-	 - [11.05 shutil 模块：高级文件操作](11. useful tools/11.05 shutil.ipynb)
-	 - [11.06 gzip, zipfile, tarfile 模块：处理压缩文件](11. useful tools/11.06 gzip, zipfile, tarfile.ipynb)
-	 - [11.07 logging 模块：记录日志](11. useful tools/11.07 logging.ipynb)
-	 - [11.08 string 模块：字符串处理](11. useful tools/11.08 string.ipynb)
-	 - [11.09 collections 模块：更多数据结构](11. useful tools/11.09 collections.ipynb)
-	 - [11.10 requests 模块：HTTP for Human](11. useful tools/11.10 requests.ipynb)
-- [12. **Pandas**](12. pandas)
-	 - [12.01 十分钟上手 Pandas](12. pandas/12.01 ten minutes to pandas.ipynb)
-	 - [12.02 一维数据结构：Series](12. pandas/12.02 series in pandas.ipynb)
-	 - [12.03 二维数据结构：DataFrame](12. pandas/12.03 dataframe in pandas.ipynb)
+- [01. **Python 工具**](01-python-tools)
+	 - [01.01 Python 简介](01-python-tools/01.01-python-overview.ipynb)
+	 - [01.02 Ipython 解释器](01-python-tools/01.02-ipython-interpreter.ipynb)
+	 - [01.03 Ipython notebook](01-python-tools/01.03-ipython-notebook.ipynb)
+	 - [01.04 使用 Anaconda](01-python-tools/01.04-use-anaconda.ipynb)
+- [02. **Python 基础**](02-python-essentials)
+	 - [02.01 Python 入门演示](02-python-essentials/02.01-a-tour-of-python.ipynb)
+	 - [02.02 Python 数据类型](02-python-essentials/02.02-python-data-types.ipynb)
+	 - [02.03 数字](02-python-essentials/02.03-numbers.ipynb)
+	 - [02.04 字符串](02-python-essentials/02.04-strings.ipynb)
+	 - [02.05 索引和分片](02-python-essentials/02.05-indexing-and-slicing.ipynb)
+	 - [02.06 列表](02-python-essentials/02.06-lists.ipynb)
+	 - [02.07 可变和不可变类型](02-python-essentials/02.07-mutable-and-immutable-data-types.ipynb)
+	 - [02.08 元组](02-python-essentials/02.08-tuples.ipynb)
+	 - [02.09 列表与元组的速度比较](02-python-essentials/02.09-speed-comparison-between-list-&-tuple.ipynb)
+	 - [02.10 字典](02-python-essentials/02.10-dictionaries.ipynb)
+	 - [02.11 集合](02-python-essentials/02.11-sets.ipynb)
+	 - [02.12 不可变集合](02-python-essentials/02.12-frozen-sets.ipynb)
+	 - [02.13 Python 赋值机制](02-python-essentials/02.13-how-python-assignment-works.ipynb)
+	 - [02.14 判断语句](02-python-essentials/02.14-if-statement.ipynb)
+	 - [02.15 循环](02-python-essentials/02.15-loops.ipynb)
+	 - [02.16 列表推导式](02-python-essentials/02.16-list-comprehension.ipynb)
+	 - [02.17 函数](02-python-essentials/02.17-functions.ipynb)
+	 - [02.18 模块和包](02-python-essentials/02.18-modules-and-packages.ipynb)
+	 - [02.19 异常](02-python-essentials/02.19-exceptions.ipynb)
+	 - [02.20 警告](02-python-essentials/02.20-warnings.ipynb)
+	 - [02.21 文件读写](02-python-essentials/02.21-file-IO.ipynb)
+- [03. **Numpy**](03-numpy)
+	 - [03.01 Numpy 简介](03-numpy/03.01-numpy-overview.ipynb)
+	 - [03.02 Matplotlib 基础](03-numpy/03.02-matplotlib-basics.ipynb)
+	 - [03.03 Numpy 数组及其索引](03-numpy/03.03-numpy-arrays.ipynb)
+	 - [03.04 数组类型](03-numpy/03.04-array-types.ipynb)
+	 - [03.05 数组方法](03-numpy/03.05-array-calculation-method.ipynb)
+	 - [03.06 数组排序](03-numpy/03.06-sorting-numpy-arrays.ipynb)
+	 - [03.07 数组形状](03-numpy/03.07-array-shapes.ipynb)
+	 - [03.08 对角线](03-numpy/03.08-diagonals.ipynb)
+	 - [03.09 数组与字符串的转换](03-numpy/03.09-data-to-&-from-string.ipynb)
+	 - [03.10 数组属性方法总结](03-numpy/03.10-array-attribute-&-method-overview-.ipynb)
+	 - [03.11 生成数组的函数](03-numpy/03.11-array-creation-functions.ipynb)
+	 - [03.12 矩阵](03-numpy/03.12-matrix-object.ipynb)
+	 - [03.13 一般函数](03-numpy/03.13-general-functions.ipynb)
+	 - [03.14 向量化函数](03-numpy/03.14-vectorizing-functions.ipynb)
+	 - [03.15 二元运算](03-numpy/03.15-binary-operators.ipynb)
+	 - [03.16 ufunc 对象](03-numpy/03.16-universal-functions.ipynb)
+	 - [03.17 choose 函数实现条件筛选](03-numpy/03.17-choose.ipynb)
+	 - [03.18 数组广播机制](03-numpy/03.18-array-broadcasting.ipynb)
+	 - [03.19 数组读写](03-numpy/03.19-reading-and-writing-arrays.ipynb)
+	 - [03.20 结构化数组](03-numpy/03.20-structured-arrays.ipynb)
+	 - [03.21 记录数组](03-numpy/03.21-record-arrays.ipynb)
+	 - [03.22 内存映射](03-numpy/03.22-memory-maps.ipynb)
+	 - [03.23 从 Matlab 到 Numpy](03-numpy/03.23-from-matlab-to-numpy.ipynb)
+- [04. **Scipy**](04-scipy)
+	 - [04.01 SCIentific PYthon 简介](04-scipy/04.01-scienticfic-python-overview.ipynb)
+	 - [04.02 插值](04-scipy/04.02-interpolation-with-scipy.ipynb)
+	 - [04.03 概率统计方法](04-scipy/04.03-statistics-with-scipy.ipynb)
+	 - [04.04 曲线拟合](04-scipy/04.04-curve-fitting.ipynb)
+	 - [04.05 最小化函数](04-scipy/04.05-minimization-in-python.ipynb)
+	 - [04.06 积分](04-scipy/04.06-integration-in-python.ipynb)
+	 - [04.07 解微分方程](04-scipy/04.07-ODEs.ipynb)
+	 - [04.08 稀疏矩阵](04-scipy/04.08-sparse-matrix.ipynb)
+	 - [04.09 线性代数](04-scipy/04.09-linear-algbra.ipynb)
+	 - [04.10 稀疏矩阵的线性代数](04-scipy/04.10-sparse-linear-algebra.ipynb)
+- [05. **Python 进阶**](05-advanced-python)
+	 - [05.01 sys 模块简介](05-advanced-python/05.01-overview-of-the-sys-module.ipynb)
+	 - [05.02 与操作系统进行交互：os 模块](05-advanced-python/05.02-interacting-with-the-OS---os.ipynb)
+	 - [05.03 CSV 文件和 csv 模块](05-advanced-python/05.03-comma-separated-values.ipynb)
+	 - [05.04 正则表达式和 re 模块](05-advanced-python/05.04-regular-expression.ipynb)
+	 - [05.05 datetime 模块](05-advanced-python/05.05-datetime.ipynb)
+	 - [05.06 SQL 数据库](05-advanced-python/05.06-sql-databases.ipynb)
+	 - [05.07 对象关系映射](05-advanced-python/05.07-object-relational-mappers.ipynb)
+	 - [05.08 函数进阶：参数传递，高阶函数，lambda 匿名函数，global 变量，递归](05-advanced-python/05.08-functions.ipynb)
+	 - [05.09 迭代器](05-advanced-python/05.09-iterators.ipynb)
+	 - [05.10 生成器](05-advanced-python/05.10-generators.ipynb)
+	 - [05.11 with 语句和上下文管理器](05-advanced-python/05.11-context-managers-and-the-with-statement.ipynb)
+	 - [05.12 修饰符](05-advanced-python/05.12-decorators.ipynb)
+	 - [05.13 修饰符的使用](05-advanced-python/05.13-decorator-usage.ipynb)
+	 - [05.14 operator, functools, itertools, toolz, fn, funcy 模块](05-advanced-python/05.14-the-operator-functools-itertools-toolz-fn-funcy-module.ipynb)
+	 - [05.15 作用域](05-advanced-python/05.15-scope.ipynb)
+	 - [05.16 动态编译](05-advanced-python/05.16-dynamic-code-execution.ipynb)
+- [06. **Matplotlib**](06-matplotlib)
+	 - [06.01 Pyplot 教程](06-matplotlib/06.01-pyplot-tutorial.ipynb)
+	 - [06.02 使用 style 来配置 pyplot 风格](06-matplotlib/06.02-customizing-plots-with-style-sheets.ipynb)
+	 - [06.03 处理文本（基础）](06-matplotlib/06.03-working-with-text---basic.ipynb)
+	 - [06.04 处理文本（数学表达式）](06-matplotlib/06.04-working-with-text---math-expression.ipynb)
+	 - [06.05 图像基础](06-matplotlib/06.05-image-tutorial.ipynb)
+	 - [06.06 注释](06-matplotlib/06.06-annotating-axes.ipynb)
+	 - [06.07 标签](06-matplotlib/06.07-legend.ipynb)
+	 - [06.08 figures, subplots, axes 和 ticks 对象](06-matplotlib/06.08-figures,-subplots,-axes-and-ticks.ipynb)
+	 - [06.09 不要迷信默认设置](06-matplotlib/06.09-do-not-trust-the-defaults.ipynb)
+	 - [06.10 各种绘图实例](06-matplotlib/06.10-different-plots.ipynb)
+- [07. **使用其他语言进行扩展**](07-interfacing-with-other-languages)
+	 - [07.01 简介](07-interfacing-with-other-languages/07.01-introduction.ipynb)
+	 - [07.02 Python 扩展模块](07-interfacing-with-other-languages/07.02-python-extension-modules.ipynb)
+	 - [07.03 Cython：Cython 基础，将源代码转换成扩展模块](07-interfacing-with-other-languages/07.03-cython-part-1.ipynb)
+	 - [07.04 Cython：Cython 语法，调用其他C库](07-interfacing-with-other-languages/07.04-cython-part-2.ipynb)
+	 - [07.05 Cython：class 和 cdef class，使用 C++](07-interfacing-with-other-languages/07.05-cython-part-3.ipynb)
+	 - [07.06 Cython：Typed memoryviews](07-interfacing-with-other-languages/07.06-cython-part-4.ipynb)
+	 - [07.07 生成编译注释](07-interfacing-with-other-languages/07.07-profiling-with-annotations.ipynb)
+	 - [07.08 ctypes](07-interfacing-with-other-languages/07.08-ctypes.ipynb)
+- [08. **面向对象编程**](08-object-oriented-programming)
+	 - [08.01 简介](08-object-oriented-programming/08.01-oop-introduction.ipynb)
+	 - [08.02 使用 OOP 对森林火灾建模](08-object-oriented-programming/08.02-using-oop-model-a-forest-fire.ipynb)
+	 - [08.03 什么是对象？](08-object-oriented-programming/08.03-what-is-a-object.ipynb)
+	 - [08.04 定义 class](08-object-oriented-programming/08.04-writing-classes.ipynb)
+	 - [08.05 特殊方法](08-object-oriented-programming/08.05-special-method.ipynb)
+	 - [08.06 属性](08-object-oriented-programming/08.06-properties.ipynb)
+	 - [08.07 森林火灾模拟](08-object-oriented-programming/08.07-forest-fire-simulation.ipynb)
+	 - [08.08 继承](08-object-oriented-programming/08.08-inheritance.ipynb)
+	 - [08.09 super() 函数](08-object-oriented-programming/08.09-super.ipynb)
+	 - [08.10 重定义森林火灾模拟](08-object-oriented-programming/08.10-refactoring-the-forest-fire-simutation.ipynb)
+	 - [08.11 接口](08-object-oriented-programming/08.11-interfaces.ipynb)
+	 - [08.12 共有，私有和特殊方法和属性](08-object-oriented-programming/08.12-public-private-special-in-python.ipynb)
+	 - [08.13 多重继承](08-object-oriented-programming/08.13-multiple-inheritance.ipynb)
+- [09. **Theano 基础**](09-theano)
+	 - [09.01 Theano 简介及其安装](09-theano/09.01-introduction-and-installation.ipynb)
+	 - [09.02 Theano 基础](09-theano/09.02-theano-basics.ipynb)
+	 - [09.03 Theano 在 Windows 上的配置](09-theano/09.03-gpu-on-windows.ipynb)
+	 - [09.04 Theano 符号图结构](09-theano/09.04-graph-structures.ipynb)
+	 - [09.05 Theano 配置和编译模式](09-theano/09.05-configuration-settings-and-compiling-modes.ipynb)
+	 - [09.06 Theano 条件语句](09-theano/09.06-conditions-in-theano.ipynb)
+	 - [09.07 Theano 循环：scan（详解）](09-theano/09.07-loop-with-scan.ipynb)
+	 - [09.08 Theano 实例：线性回归](09-theano/09.08-linear-regression.ipynb)
+	 - [09.09 Theano 实例：Logistic 回归](09-theano/09.09-logistic-regression-.ipynb)
+	 - [09.10 Theano 实例：Softmax 回归](09-theano/09.10-softmax-on-mnist.ipynb)
+	 - [09.11 Theano 实例：人工神经网络](09-theano/09.11-net-on-mnist.ipynb)
+	 - [09.12 Theano 随机数流变量](09-theano/09.12-random-streams.ipynb)
+	 - [09.13 Theano 实例：更复杂的网络](09-theano/09.13-modern-net-on-mnist.ipynb)
+	 - [09.14 Theano 实例：卷积神经网络](09-theano/09.14-convolutional-net-on-mnist.ipynb)
+	 - [09.15 Theano tensor 模块：基础](09-theano/09.15-tensor-basics.ipynb)
+	 - [09.16 Theano tensor 模块：索引](09-theano/09.16-tensor-indexing.ipynb)
+	 - [09.17 Theano tensor 模块：操作符和逐元素操作](09-theano/09.17-tensor-operator-and-elementwise-operations.ipynb)
+	 - [09.18 Theano tensor 模块：nnet 子模块](09-theano/09.18-tensor-nnet-.ipynb)
+	 - [09.19 Theano tensor 模块：conv 子模块](09-theano/09.19-tensor-conv.ipynb)
+- [10. **有趣的第三方模块**](10-something-interesting)
+	 - [10.01 使用 basemap 画地图](10-something-interesting/10.01-maps-using-basemap.ipynb)
+	 - [10.02 使用 cartopy 画地图](10-something-interesting/10.02-maps-using-cartopy.ipynb)
+	 - [10.03 探索 NBA 数据](10-something-interesting/10.03-nba-data.ipynb)
+	 - [10.04 金庸的武侠世界](10-something-interesting/10.04-louis-cha's-kungfu-world.ipynb)
+- [11. **有用的工具**](11-useful-tools)
+	 - [11.01 pprint 模块：打印 Python 对象](11-useful-tools/11.01-pprint.ipynb)
+	 - [11.02 pickle, cPickle 模块：序列化 Python 对象](11-useful-tools/11.02-pickle-and-cPickle.ipynb)
+	 - [11.03 json 模块：处理 JSON 数据](11-useful-tools/11.03-json.ipynb)
+	 - [11.04 glob 模块：文件模式匹配](11-useful-tools/11.04-glob.ipynb)
+	 - [11.05 shutil 模块：高级文件操作](11-useful-tools/11.05-shutil.ipynb)
+	 - [11.06 gzip, zipfile, tarfile 模块：处理压缩文件](11-useful-tools/11.06-gzip,-zipfile,-tarfile.ipynb)
+	 - [11.07 logging 模块：记录日志](11-useful-tools/11.07-logging.ipynb)
+	 - [11.08 string 模块：字符串处理](11-useful-tools/11.08-string.ipynb)
+	 - [11.09 collections 模块：更多数据结构](11-useful-tools/11.09-collections.ipynb)
+	 - [11.10 requests 模块：HTTP for Human](11-useful-tools/11.10-requests.ipynb)
+- [12. **Pandas**](12-pandas)
+	 - [12.01 十分钟上手 Pandas](12-pandas/12.01-ten-minutes-to-pandas.ipynb)
+	 - [12.02 一维数据结构：Series](12-pandas/12.02-series-in-pandas.ipynb)
+	 - [12.03 二维数据结构：DataFrame](12-pandas/12.03-dataframe-in-pandas.ipynb)