From 9c3b8bb929746680f99e20c9c1641daad68b1c5b Mon Sep 17 00:00:00 2001 From: Sangyu Xu Date: Wed, 1 Feb 2023 11:57:32 +0800 Subject: [PATCH 1/8] Revert "typo" This reverts commit 9c4e55a9ec659790e47f88af3fc9536c4aecaacc. --- README.md | 4 +- nbs/index.ipynb | 4 +- nbs/myplot.svg | 2510 +++++++++++++++++++++++------------------------ 3 files changed, 1259 insertions(+), 1259 deletions(-) diff --git a/README.md b/README.md index 8581ce6..cf9077f 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,7 @@ Duke-NUS Medical School The goal of this class is to introduce biomedical scientists to data analysis with Python notebooks. There are two parts to the session: a lecture on the key issues in data analysis and a hands-on tutorial. -Please prepare for class as follows: +Please do the following preparations before class. 1. Read the [Introduction](https://github.com/sangyu/moda/blob/main/nbs/01_Introduction.ipynb) @@ -29,4 +29,4 @@ Please prepare for class as follows: If we have more time in class, you will also be introduced to our estimation statistics package [DABEST -Introduction](https://github.com/sangyu/moda/blob/main/nbs/04_Dabest_introduction.ipynb). +Introduction](https://github.com/sangyu/moda/blob/main/nbs/04_Dabest_introduction.ipynb) diff --git a/nbs/index.ipynb b/nbs/index.ipynb index de836d5..5f52dd4 100644 --- a/nbs/index.ipynb +++ b/nbs/index.ipynb @@ -30,13 +30,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The goal of this class is to introduce biomedical scientists to data analysis with Python notebooks. There are two parts to the session: a lecture on the key issues in data analysis and a hands-on tutorial. Please prepare for class as follows:\n", + "The goal of this class is to introduce biomedical scientists to data analysis with Python notebooks. There are two parts to the session: a lecture on the key issues in data analysis and a hands-on tutorial. Please do the following preparations before class.\n", "\n", "1. Read the [Introduction](https://github.com/sangyu/moda/blob/main/nbs/01_Introduction.ipynb) and install the prerequisite software on your laptop.\n", "2. Go through a [Quick Tour of the Notebook](https://github.com/sangyu/moda/blob/main/nbs/02_Quick_tour_of_the_Notebook.ipynb) to familiarise yoruself to the jupyter notebook environment.\n", "3. Bring your laptop to class, and we will go through the examples in [Data Analysis with Jupyter](http://localhost:8890/notebooks/nbs/03_Data_Analysis_with_Jupyter.ipynb)\n", "\n", - "If we have more time in class, you will also be introduced to our estimation statistics package [DABEST Introduction](https://github.com/sangyu/moda/blob/main/nbs/04_Dabest_introduction.ipynb).\n" + "If we have more time in class, you will also be introduced to our estimation statistics package [DABEST Introduction](https://github.com/sangyu/moda/blob/main/nbs/04_Dabest_introduction.ipynb)\n" ] } ], diff --git a/nbs/myplot.svg b/nbs/myplot.svg index 22f9b68..3320223 100644 --- a/nbs/myplot.svg +++ b/nbs/myplot.svg @@ -6,7 +6,7 @@ - 2023-02-01T11:24:23.006339 + 2023-02-01T11:20:59.843343 image/svg+xml @@ -43,10 +43,10 @@ z +" id="m7d28ab93c6" style="stroke:#262626;stroke-width:1.25;"/> - + @@ -214,7 +214,7 @@ z - + @@ -308,7 +308,7 @@ z - + @@ -432,10 +432,10 @@ z +" id="m13c854b597" style="stroke:#262626;stroke-width:1.25;"/> - 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- + + - - + + - - + + @@ -3603,16 +3603,16 @@ z - + - + - + - + From bbf6a221c2189a676335157811089fa5d8e9b4d9 Mon Sep 17 00:00:00 2001 From: Sangyu Xu Date: Wed, 1 Feb 2023 11:57:37 +0800 Subject: [PATCH 2/8] Revert "changed title" This reverts commit f06045fed9d077aa3fa43c0763186f7a2784b23d. --- README.md | 2 +- nbs/index.ipynb | 2 +- nbs/myplot.svg | 2510 +++++++++++++++++++++++------------------------ 3 files changed, 1257 insertions(+), 1257 deletions(-) diff --git a/README.md b/README.md index cf9077f..c9ebc72 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -Modern Data Analysis +moda (Modern Data Analysis) ================ diff --git a/nbs/index.ipynb b/nbs/index.ipynb index 5f52dd4..b43db30 100644 --- a/nbs/index.ipynb +++ b/nbs/index.ipynb @@ -7,7 +7,7 @@ "---\n", "description: A data analysis tutorial for biomedical scientists (v23.2)\n", "output-file: index.html\n", - "title: Modern Data Analysis\n", + "title: moda (Modern Data Analysis) \n", "\n", "---\n", "\n" diff --git a/nbs/myplot.svg b/nbs/myplot.svg index 3320223..0fb4323 100644 --- a/nbs/myplot.svg +++ b/nbs/myplot.svg @@ -6,7 +6,7 @@ - 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- + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + @@ -3603,16 +3603,16 @@ z - + - + - + - + From 70d0576d0826e05cf0ab5585210430fa5f47cae0 Mon Sep 17 00:00:00 2001 From: Sangyu Xu Date: Wed, 1 Feb 2023 11:57:51 +0800 Subject: [PATCH 3/8] Revert "moved version number" This reverts commit 78b2c47a83432afcdf2aa255649a7cc82400856f. --- nbs/01_Introduction.ipynb | 4 +++- nbs/02_Quick_tour_of_the_Notebook.ipynb | 4 +++- nbs/03_Data_Analysis_with_Jupyter.ipynb | 4 +++- nbs/04_Dabest_introduction.ipynb | 4 +++- nbs/index.ipynb | 4 ++-- 5 files changed, 14 insertions(+), 6 deletions(-) diff --git a/nbs/01_Introduction.ipynb b/nbs/01_Introduction.ipynb index 889199f..04c9567 100644 --- a/nbs/01_Introduction.ipynb +++ b/nbs/01_Introduction.ipynb @@ -19,7 +19,9 @@ "source": [ "by [Joses Ho](https://twitter.com/jacuzzijo), [Sangyu\n", "Xu](https://xusangyu.com/), and [Adam\n", - "Claridge-Chang](http://www.claridgechang.net/)\n" + "Claridge-Chang](http://www.claridgechang.net/)\n", + "\n", + "Version 23.2" ] }, { diff --git a/nbs/02_Quick_tour_of_the_Notebook.ipynb b/nbs/02_Quick_tour_of_the_Notebook.ipynb index 46cc705..b95bc30 100644 --- a/nbs/02_Quick_tour_of_the_Notebook.ipynb +++ b/nbs/02_Quick_tour_of_the_Notebook.ipynb @@ -18,7 +18,9 @@ "source": [ "by [Joses Ho](https://twitter.com/jacuzzijo), [Sangyu\n", "Xu](https://xusangyu.com/), and [Adam\n", - "Claridge-Chang](http://www.claridgechang.net/)\n" + "Claridge-Chang](http://www.claridgechang.net/)\n", + "\n", + "Version 23.2" ] }, { diff --git a/nbs/03_Data_Analysis_with_Jupyter.ipynb b/nbs/03_Data_Analysis_with_Jupyter.ipynb index 53a7d92..f9d208d 100755 --- a/nbs/03_Data_Analysis_with_Jupyter.ipynb +++ b/nbs/03_Data_Analysis_with_Jupyter.ipynb @@ -17,7 +17,9 @@ "source": [ "By [Joses Ho](https://twitter.com/jacuzzijo), [Sangyu\n", "Xu](https://xusangyu.com/), and [Adam\n", - "Claridge-Chang](http://www.claridgechang.net/)\n" + "Claridge-Chang](http://www.claridgechang.net/)\n", + "\n", + "Version 23.2" ] }, { diff --git a/nbs/04_Dabest_introduction.ipynb b/nbs/04_Dabest_introduction.ipynb index ca34560..55a244f 100644 --- a/nbs/04_Dabest_introduction.ipynb +++ b/nbs/04_Dabest_introduction.ipynb @@ -17,7 +17,9 @@ "source": [ "By [Joses Ho](https://twitter.com/jacuzzijo), [Sangyu\n", "Xu](https://xusangyu.com/), and [Adam\n", - "Claridge-Chang](http://www.claridgechang.net/)\n" + "Claridge-Chang](http://www.claridgechang.net/)\n", + "\n", + "Version 23.2" ] }, { diff --git a/nbs/index.ipynb b/nbs/index.ipynb index b43db30..a13d478 100644 --- a/nbs/index.ipynb +++ b/nbs/index.ipynb @@ -5,9 +5,9 @@ "metadata": {}, "source": [ "---\n", - "description: A data analysis tutorial for biomedical scientists (v23.2)\n", + "description: A data analysis tutorial for biomedical scientists.\n", "output-file: index.html\n", - "title: moda (Modern Data Analysis) \n", + "title: moda (Modern Data Analysis)\n", "\n", "---\n", "\n" From 264c5dfc274fe3ec86015a244cdc92221763883c Mon Sep 17 00:00:00 2001 From: Sangyu Xu Date: Wed, 1 Feb 2023 11:57:56 +0800 Subject: [PATCH 4/8] Revert "Update with version number" This reverts commit a5053dc4516f15d816fb6a1b0e48c11826bdb05e. --- nbs/01_Introduction.ipynb | 4 +- nbs/02_Quick_tour_of_the_Notebook.ipynb | 4 +- nbs/03_Data_Analysis_with_Jupyter.ipynb | 4 +- nbs/04_Dabest_introduction.ipynb | 11 - nbs/index.ipynb | 2 +- nbs/myplot.svg | 2510 +++++++++++------------ 6 files changed, 1259 insertions(+), 1276 deletions(-) diff --git a/nbs/01_Introduction.ipynb b/nbs/01_Introduction.ipynb index 04c9567..c3898bb 100644 --- a/nbs/01_Introduction.ipynb +++ b/nbs/01_Introduction.ipynb @@ -19,9 +19,7 @@ "source": [ "by [Joses Ho](https://twitter.com/jacuzzijo), [Sangyu\n", "Xu](https://xusangyu.com/), and [Adam\n", - "Claridge-Chang](http://www.claridgechang.net/)\n", - "\n", - "Version 23.2" + "Claridge-Chang](http://www.claridgechang.net/)" ] }, { diff --git a/nbs/02_Quick_tour_of_the_Notebook.ipynb b/nbs/02_Quick_tour_of_the_Notebook.ipynb index b95bc30..0835ef5 100644 --- a/nbs/02_Quick_tour_of_the_Notebook.ipynb +++ b/nbs/02_Quick_tour_of_the_Notebook.ipynb @@ -18,9 +18,7 @@ "source": [ "by [Joses Ho](https://twitter.com/jacuzzijo), [Sangyu\n", "Xu](https://xusangyu.com/), and [Adam\n", - "Claridge-Chang](http://www.claridgechang.net/)\n", - "\n", - "Version 23.2" + "Claridge-Chang](http://www.claridgechang.net/)" ] }, { diff --git a/nbs/03_Data_Analysis_with_Jupyter.ipynb b/nbs/03_Data_Analysis_with_Jupyter.ipynb index f9d208d..48a84b5 100755 --- a/nbs/03_Data_Analysis_with_Jupyter.ipynb +++ b/nbs/03_Data_Analysis_with_Jupyter.ipynb @@ -17,9 +17,7 @@ "source": [ "By [Joses Ho](https://twitter.com/jacuzzijo), [Sangyu\n", "Xu](https://xusangyu.com/), and [Adam\n", - "Claridge-Chang](http://www.claridgechang.net/)\n", - "\n", - "Version 23.2" + "Claridge-Chang](http://www.claridgechang.net/)" ] }, { diff --git a/nbs/04_Dabest_introduction.ipynb b/nbs/04_Dabest_introduction.ipynb index 55a244f..3f6a9df 100644 --- a/nbs/04_Dabest_introduction.ipynb +++ b/nbs/04_Dabest_introduction.ipynb @@ -11,17 +11,6 @@ "---\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By [Joses Ho](https://twitter.com/jacuzzijo), [Sangyu\n", - "Xu](https://xusangyu.com/), and [Adam\n", - "Claridge-Chang](http://www.claridgechang.net/)\n", - "\n", - "Version 23.2" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/nbs/index.ipynb b/nbs/index.ipynb index a13d478..18a5c00 100644 --- a/nbs/index.ipynb +++ b/nbs/index.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "---\n", - "description: A data analysis tutorial for biomedical scientists.\n", + "description: A tutorial for introducing biomedical scientists to data analysis with Python notebooks.\n", "output-file: index.html\n", "title: moda (Modern Data Analysis)\n", "\n", diff --git a/nbs/myplot.svg b/nbs/myplot.svg index 0fb4323..e79ead5 100644 --- a/nbs/myplot.svg +++ b/nbs/myplot.svg @@ -6,7 +6,7 @@ - 2023-02-01T11:15:36.983026 + 2023-02-01T11:05:12.042962 image/svg+xml @@ -43,10 +43,10 @@ z +" id="m38b04cbe5f" style="stroke:#262626;stroke-width:1.25;"/> - + @@ -214,7 +214,7 @@ z - + @@ -308,7 +308,7 @@ z - + @@ -432,10 +432,10 @@ z +" id="ma54062e97c" style="stroke:#262626;stroke-width:1.25;"/> - + @@ -475,7 +475,7 @@ z - + @@ -515,7 +515,7 @@ z - + @@ -549,7 +549,7 @@ z - + @@ -597,7 +597,7 @@ z - + @@ -652,7 +652,7 @@ z - + @@ -815,157 +815,157 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="C0_0_5aefee402c"/> +" id="C0_0_b7df7a9159"/> - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - 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- + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + @@ -2139,7 +2139,7 @@ z - + @@ -2157,7 +2157,7 @@ z - + @@ -2179,7 +2179,7 @@ z - + @@ -2214,7 +2214,7 @@ z - + @@ -2227,7 +2227,7 @@ z - + @@ -2240,7 +2240,7 @@ z - + @@ -2253,7 +2253,7 @@ z - + @@ -2266,7 +2266,7 @@ z - + @@ -2279,7 +2279,7 @@ z - + @@ -2348,157 +2348,157 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="C6_0_8d1603cf25"/> +" id="C6_0_9ead9f085d"/> - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + @@ -2513,157 +2513,157 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="C7_0_e3fb385399"/> +" id="C7_0_882a1a559e"/> - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + @@ -2678,157 +2678,157 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="C8_0_352a0ea3d8"/> +" id="C8_0_2d49657909"/> - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + @@ -2874,7 +2874,7 @@ z - + @@ -2892,7 +2892,7 @@ z - + @@ -2914,7 +2914,7 @@ z - + @@ -2949,7 +2949,7 @@ z - + @@ -2962,7 +2962,7 @@ z - + @@ -2975,7 +2975,7 @@ z - + @@ -2988,7 +2988,7 @@ z - + @@ -3001,7 +3001,7 @@ z - + @@ -3014,7 +3014,7 @@ z - + @@ -3082,157 +3082,157 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="C9_0_8a4a7f1d58"/> +" id="C9_0_bf0e374b65"/> - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + @@ -3247,157 +3247,157 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="Ca_0_5cfec01bc4"/> +" id="Ca_0_3cedba51af"/> - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + @@ -3412,157 +3412,157 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="Cb_0_b795bd1c51"/> +" id="Cb_0_cb7ac4ce12"/> - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + @@ -3603,16 +3603,16 @@ z - + - + - + - + From b23e7ca446674d25a8472a45cd6cdf4c53964fa5 Mon Sep 17 00:00:00 2001 From: Sangyu Xu Date: Wed, 1 Feb 2023 13:31:17 +0800 Subject: [PATCH 5/8] Fixed Overwrite --- README.md | 15 +- nbs/01_Introduction.ipynb | 18 +- nbs/02_Quick_tour_of_the_Notebook.ipynb | 13 +- nbs/03_Data_Analysis_with_Jupyter.ipynb | 1539 ------ ...ata_Analysis_with_Jupyter_and_Python.ipynb | 1659 +++++++ nbs/04_Dabest_introduction.ipynb | 21 +- nbs/index.ipynb | 19 +- nbs/myplot.png | Bin 305055 -> 234968 bytes nbs/myplot.svg | 4350 ++++++++--------- nbs/sidebar.yml | 4 +- 10 files changed, 3858 insertions(+), 3780 deletions(-) delete mode 100755 nbs/03_Data_Analysis_with_Jupyter.ipynb create mode 100644 nbs/03_Data_Analysis_with_Jupyter_and_Python.ipynb diff --git a/README.md b/README.md index c9ebc72..09737dc 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -moda (Modern Data Analysis) +Modern Data Analysis ================ @@ -17,16 +17,15 @@ analysis with Python notebooks. There are two parts to the session: a lecture on the key issues in data analysis and a hands-on tutorial. Please do the following preparations before class. -1. Read the - [Introduction](https://github.com/sangyu/moda/blob/main/nbs/01_Introduction.ipynb) - and install the prerequisite software on your laptop. +1. Read the [Introduction](introduction.html) and install the + prerequisite software on your laptop. 2. Go through a [Quick Tour of the - Notebook](https://github.com/sangyu/moda/blob/main/nbs/02_Quick_tour_of_the_Notebook.ipynb) - to familiarise yoruself to the jupyter notebook environment. + Notebook](quick_tour_of_the_notebook.html) to familiarise yoruself + to the jupyter notebook environment. 3. Bring your laptop to class, and we will go through the examples in [Data Analysis with - Jupyter](http://localhost:8890/notebooks/nbs/03_Data_Analysis_with_Jupyter.ipynb) + Jupyter](data_analysis_with_jupyter_and_python.html) If we have more time in class, you will also be introduced to our estimation statistics package [DABEST -Introduction](https://github.com/sangyu/moda/blob/main/nbs/04_Dabest_introduction.ipynb) +Introduction](dabest_introduction.html) diff --git a/nbs/01_Introduction.ipynb b/nbs/01_Introduction.ipynb index c3898bb..1196a35 100644 --- a/nbs/01_Introduction.ipynb +++ b/nbs/01_Introduction.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "---\n", - "output-file: 01_introduction.html\n", + "output-file: introduction.html\n", "title: 01. Introduction\n", "\n", "---" @@ -17,7 +17,7 @@ "id": "e97fe986", "metadata": {}, "source": [ - "by [Joses Ho](https://twitter.com/jacuzzijo), [Sangyu\n", + "[Joses Ho](https://twitter.com/jacuzzijo), [Sangyu\n", "Xu](https://xusangyu.com/), and [Adam\n", "Claridge-Chang](http://www.claridgechang.net/)" ] @@ -47,7 +47,7 @@ "\n", "1. You'll need to get set up with a [version-control](https://en.wikipedia.org/wiki/Version_control) system. Go to [GitHub](https://github.com/) and get an account. Download and install [GitHub Desktop](https://desktop.github.com/).\n", "\n", - "2. Retrieve the course materials from GitHub. Go to the course repository (\"repo\") at https://github.com/ACCLAB/moda. Click the green Code button and then select Open with GitHub Desktop.\"clone You will be prompted to select a directory for the local repository. If you are using a PC it can be something like \"C:\\\\Users\\YOURUSERNAME\\Documents\\GitHub\\moda\". If you are using a mac, it can be \"//Users/YOURUSERNAME/Documents/GitHub/moda\".\"clone\n", + "2. Retrieve the course materials from GitHub. Go to the course repository (\"repo\") at https://github.com/ACCLAB/moda. Click the green Code button and then select Open with GitHub Desktop.\"clonemoda.png\" You will be prompted to select a directory for the local repository. If you are using a PC it can be something like \"C:\\\\Users\\YOURUSERNAME\\Documents\\GitHub\\moda\". If you are using a mac, it can be \"//Users/YOURUSERNAME/Documents/GitHub/moda\".\"clonedir.png\"\n", "\n", "3. To get set up with Python and [Jupyter](https://en.wikipedia.org/wiki/Project_Jupyter) notebooks, install the [Anaconda\n", " Distribution](https://www.anaconda.com/download/) on your laptop." @@ -58,9 +58,9 @@ "id": "666c283a-337a-4e42-a71d-da7a4b70f7ea", "metadata": {}, "source": [ - "4. Open Anaconda Navigator and launch [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/user/interface.html) by clicking on it.\"cloneJupyterLab will open in a browser tab.\n", + "4. Open Anaconda Navigator and launch [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/user/interface.html) by clicking on it.\"launchjupyterlab.png\"JupyterLab will open in a browser tab.\n", "\n", - "5. In the File Browser panel in JupyterLab, navigate to the folder where you cloned the course repo (refer to step 2). Double click on 'nbs'. You should see a list of notebook files. Open \"02_Quick_tour_of_the_Notebook.ipynb\" by double-clicking on the icon shown in the JupyterLab browser window.\"clone\n", + "5. In the File Browser panel in JupyterLab, navigate to the folder where you cloned the course repo (refer to step 2). Double click on 'nbs'. You should see a list of notebook files. Open \"02_Quick_tour_of_the_Notebook.ipynb\" by double-clicking on the icon shown in the JupyterLab browser window.\"opennotebook.png\"\n", "\n", "6. Work through the notebook. Familiarize yourself with basic Python,\n", " and with working in the JupyterLab environment.\n", @@ -195,7 +195,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2df926e4", + "id": "66d8d30a", "metadata": {}, "outputs": [ { @@ -203,7 +203,7 @@ "text/html": [ "The social reasons to learn programming also apply to research programming.\n", "\n" ], "text/plain": [ @@ -218,14 +218,14 @@ "%%HTML\n", "The social reasons to learn programming also apply to research programming.\n", "" ] }, { "cell_type": "code", "execution_count": null, - "id": "3d2dbe91-0f99-4047-9d62-ba7052867fa2", + "id": "cef28733", "metadata": {}, "outputs": [], "source": [] diff --git a/nbs/02_Quick_tour_of_the_Notebook.ipynb b/nbs/02_Quick_tour_of_the_Notebook.ipynb index 0835ef5..91c2a16 100644 --- a/nbs/02_Quick_tour_of_the_Notebook.ipynb +++ b/nbs/02_Quick_tour_of_the_Notebook.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "---\n", - "output-file: 02_quick_tour_of_the_notebook.html\n", + "output-file: quick_tour_of_the_notebook.html\n", "title: 02. A Quick Tour of The Notebook\n", "\n", "---\n", @@ -21,15 +21,6 @@ "Claridge-Chang](http://www.claridgechang.net/)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -113,7 +104,7 @@ "\n", "One of the most useful things about IPython notebook is its tab completion. \n", "\n", - "Try this: remove the \"`#`\" in the cell below and click just after `read_csv(` in the cell below and press Shift+Tab." + "Try this: remove the \"`#`\" in the cell below (called uncommenting) and click just after `read_csv(` in the cell below and press Shift+Tab. " ] }, { diff --git a/nbs/03_Data_Analysis_with_Jupyter.ipynb b/nbs/03_Data_Analysis_with_Jupyter.ipynb deleted file mode 100755 index 48a84b5..0000000 --- a/nbs/03_Data_Analysis_with_Jupyter.ipynb +++ /dev/null @@ -1,1539 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "output-file: 03_data_analysis_with_jupyter_and_python.html\n", - "title: 03. Data Analysis with Jupyter and Python\n", - "\n", - "---\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By [Joses Ho](https://twitter.com/jacuzzijo), [Sangyu\n", - "Xu](https://xusangyu.com/), and [Adam\n", - "Claridge-Chang](http://www.claridgechang.net/)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sns\n", - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot settings\n", - "sns.set(style='ticks', font_scale=1.2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "exploration_times = pd.read_csv(\"exploration_times.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "\n", - "
\n", - "\n", - ">The data set consists of 50 samples from each of three species of Iris (*iris setosa*, *iris virginica* and *iris versicolor*). Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters.\n", - "\n", - "\"Parts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Read iris data from the sheet with pandas\n", - "iris = pd.read_csv(\"https://www.bitly.com/dukenusda\")\n", - "\n", - "## If you're using Windows, you need to use:\n", - "# iris = pd.read_csv('C://Users//whho//Downloads//IrisData - iris.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You have created a new object known as a pandas `DataFrame`, with the contents of the CSV. Think of it as a spreadsheet, but with a lot more useful features for data analysis.\n", - "It has several methods we can use to handle, analyse, and plot the data.\n", - "\n", - "We can peak at the data using the `.head()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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45.03.61.40.2setosa
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" - ], - "text/plain": [ - " sepal_length sepal_width petal_length petal_width species\n", - "0 5.1 3.5 1.4 0.2 setosa\n", - "1 4.9 3.0 1.4 0.2 setosa\n", - "2 4.7 3.2 1.3 0.2 setosa\n", - "3 4.6 3.1 1.5 0.2 setosa\n", - "4 5.0 3.6 1.4 0.2 setosa" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "iris.head() # Gives us the first 5 rows of the dataframe.\n", - "# iris.head(10) # Gives us the first 5 rows of the dataframe.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get a summary of the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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sepal_lengthsepal_widthpetal_lengthpetal_width
count150.000000150.000000150.000000150.000000
mean5.8433333.0573333.7580001.199333
std0.8280660.4358661.7652980.762238
min4.3000002.0000001.0000000.100000
25%5.1000002.8000001.6000000.300000
50%5.8000003.0000004.3500001.300000
75%6.4000003.3000005.1000001.800000
max7.9000004.4000006.9000002.500000
\n", - "
" - ], - "text/plain": [ - " sepal_length sepal_width petal_length petal_width\n", - "count 150.000000 150.000000 150.000000 150.000000\n", - "mean 5.843333 3.057333 3.758000 1.199333\n", - "std 0.828066 0.435866 1.765298 0.762238\n", - "min 4.300000 2.000000 1.000000 0.100000\n", - "25% 5.100000 2.800000 1.600000 0.300000\n", - "50% 5.800000 3.000000 4.350000 1.300000\n", - "75% 6.400000 3.300000 5.100000 1.800000\n", - "max 7.900000 4.400000 6.900000 2.500000" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "iris.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see what is in the `species` column." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['setosa', 'versicolor', 'virginica'], dtype=object)" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "iris.species.unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the old-fashioned bar chart" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
\n", - "
" - ], - "text/plain": [ - " sepal_length sepal_width petal_length petal_width species\n", - "0 5.1 3.5 1.4 0.2 setosa\n", - "1 4.9 3.0 1.4 0.2 setosa\n", - "2 4.7 3.2 1.3 0.2 setosa\n", - "3 4.6 3.1 1.5 0.2 setosa\n", - "4 5.0 3.6 1.4 0.2 setosa" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "iris.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax1 = sns.barplot(data = iris, \n", - " x = 'species', \n", - " y = 'petal_width')\n", - "\n", - "# Axes should always be labelled.\n", - "# ax1.set(xlabel='Species', ylabel='Mean Sepal length (cm)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot a swarmplot, which shows all the data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\xusy\\.conda\\envs\\nbdevdabestdev\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 14.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n", - " warnings.warn(msg, UserWarning)\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax2 = sns.swarmplot(data = iris, \n", - " x = 'species', \n", - " y = 'petal_length',\n", - " hue = 'species')\n", - "\n", - "# ax2.set(xlabel='Species', ylabel='Petal length (cm)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The split-apply-combine workflow" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All your scientific experiments follow a very simple analysis workflow: *split-apply-combine*\n", - "\n", - "You do an experiment on 2 or more groups, apply some summary function to each group, and then aggregate the results.\n", - "\n", - "
\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
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34.63.11.50.2setosa
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" - ], - "text/plain": [ - " sepal_length sepal_width petal_length petal_width species\n", - "0 5.1 3.5 1.4 0.2 setosa\n", - "1 4.9 3.0 1.4 0.2 setosa\n", - "2 4.7 3.2 1.3 0.2 setosa\n", - "3 4.6 3.1 1.5 0.2 setosa\n", - "4 5.0 3.6 1.4 0.2 setosa" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "iris.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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versicolor5.9362.7704.2601.326
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" - ], - "text/plain": [ - " sepal_length sepal_width petal_length petal_width\n", - "species \n", - "setosa 5.006 3.428 1.462 0.246\n", - "versicolor 5.936 2.770 4.260 1.326\n", - "virginica 6.588 2.974 5.552 2.026" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "iris.groupby('species').mean()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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sepal_lengthsepal_widthpetal_lengthpetal_width
species
setosa0.0498500.0536080.0245600.014904
versicolor0.0729980.0443780.0664550.027966
virginica0.0899270.0456080.0780500.038841
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" - ], - "text/plain": [ - " sepal_length sepal_width petal_length petal_width\n", - "species \n", - "setosa 0.049850 0.053608 0.024560 0.014904\n", - "versicolor 0.072998 0.044378 0.066455 0.027966\n", - "virginica 0.089927 0.045608 0.078050 0.038841" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "iris.groupby('species').sem()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The plotting package `seaborn` does this automatically for you." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I commented out the below cell because it was giving an error." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# # `catplot` is short for \"categorical plot\", \n", - "# # where either the x-axis or y-axis consists of categories.\n", - "\n", - "# ax3 = sns.catplot(data=iris, \n", - "# kind='bar', # there are several types of plots.\n", - "# errorbar='sd', # plot the error bars as ± standard deviations.\n", - "# col='species' # plot each species as its own column.\n", - "# )\n", - "# ax3.set_axis_labels(\"\", \"Length (cm)\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# %debug" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You should quickly notice that the plot isn't as informative as we want it to be.\n", - "\n", - "The current plot only allows us to investigate the relationships the four metrics within species.\n", - "\n", - "Ideally, we want to directly compare metrics between species. \n", - "\n", - "To do so, we need to _reshape_ the data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The Long-form vs the Wide-form of your data\n", - "\n", - "Our iris dataframe is in the wide-form (below right) and we want to turn it into the long-form. In the original iris dataframe, the data is organised by unit (flower, in rows) and the columns contain a mixtrue of variables (sepal length, sepal width etc). In a long-form dataframe, each columne is a variable, and each row is an observation. (Please read Hadley Wickham's https://vita.had.co.nz/papers/tidy-data.pdf to learn more about tidiness of datasets.) \n", - "
\n", - "\n", - "
Michael Waskom, Seaborn Tutorial 2022
\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "iris_tidy = pd.melt(iris.reset_index(), \n", - " id_vars=['index','species'], \n", - " var_name='metric', \n", - " value_name='cm')\n", - "iris_tidy = iris_tidy.rename(columns = {'index': 'ID'})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
..................
1456.73.05.22.3virginica
1466.32.55.01.9virginica
1476.53.05.22.0virginica
1486.23.45.42.3virginica
1495.93.05.11.8virginica
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IDspeciesmetriccm
00setosasepal_length5.1
11setosasepal_length4.9
22setosasepal_length4.7
33setosasepal_length4.6
44setosasepal_length5.0
...............
595145virginicapetal_width2.3
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598148virginicapetal_width2.3
599149virginicapetal_width1.8
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" - ], - "text/plain": [ - " ID species metric cm\n", - "0 0 setosa sepal_length 5.1\n", - "1 1 setosa sepal_length 4.9\n", - "2 2 setosa sepal_length 4.7\n", - "3 3 setosa sepal_length 4.6\n", - "4 4 setosa sepal_length 5.0\n", - ".. ... ... ... ...\n", - "595 145 virginica petal_width 2.3\n", - "596 146 virginica petal_width 1.9\n", - "597 147 virginica petal_width 2.0\n", - "598 148 virginica petal_width 2.3\n", - "599 149 virginica petal_width 1.8\n", - "\n", - "[600 rows x 4 columns]" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "iris_tidy" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax4 = sns.catplot(data=iris_tidy, \n", - " x='metric', \n", - " y='cm', \n", - " hue='species',\n", - " kind='bar', \n", - " ci='sd',\n", - " aspect=1.5\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\xusy\\.conda\\envs\\nbdevdabestdev\\lib\\site-packages\\seaborn\\categorical.py:3750: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n", - " warnings.warn(msg, UserWarning)\n", - "C:\\Users\\xusy\\.conda\\envs\\nbdevdabestdev\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 9.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n", - " warnings.warn(msg, UserWarning)\n" - ] - }, - { - "data": { - "image/png": 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UFzkq0KxQXEw0bw79+zuMpdOn4c034fXXpeVFRVHBOnVEq6lHD2mLkZnpckhFNbB7N4waJWrt06bJWNlp2PgALOkOW58BS6nrfpX9oExKch2zWMS72L+/eKVAeg0+PAGufx3e/URCehWPXfH47u4S5j0XjhwRgdWePWHSJMnBKyoSfadu3eDhhyX/SqGoqdhqAampqbZGjRrZUlNTq3sqCkXtYsgQm00eTfIaMsRm8/eXv728bLaRI/Xr27Wr7hkriotttqgo/efy1Vc222+DbbYvcLzWjXXdNzXVZmvQQPYxGGy2hx+22crKbLbLL3ccq2tXxz1gfy1ZYrN17KgfGzlS7hGw2fz8ZJvdu222mBgZMxpttldfPbdrslhstkaN9MefNMlmu+UW13MqFDUUFZJTKC5WSkvB21uvtRMWJr3ANm4Ub1Tv3qL67Mzhw5L7oqgeli6Fvn31Y8OGwvAfAafP0jMcrs5y3d9sFtX3qCiRm7CzZYt4lvbuheuu0+9zww16+QmQ8O2KFXJ/tG4tml0gFXFr10oyub1y7u/YvFnUwJ3p0UOS0Z29Sm5uDskLhaKGoUJyCkVt5Y8/RIupSxfHw+7oUXn4tWkDL7/sGoJLSIBnnpGKp//9z3W9v7+IDiqqj/h4V4OhfgPwqfBZ+cbDxvGwsDWsuxNKs2X8ww/l833gAYcxvGiRtMh54IHKmy8nJUn1nDN160q47IEH4OmnRSHcapUQ7/jxIpK6f79s++230qC3b18x+ECMoSFDJHduyRJXXaZ69eTlTEKCMpYUNZfqdnGdCyokp1BUICPDZvP21oczFi+22VJSXEMc9vBLWJjNNmiQfn3XrjZbbKwjRDdlSnVfmcJms9meftpmc3OTz6VFC5vt+HGbLfUHm+1rfwnHfRtusy0bpA/R/TpQPj/nzzciwmbbtMlmM5kcY0aj3BcGgyx36WKznT5ts02f7rin6ta12bp31x9r9Gib7bXX9GMNGthsK1Y4jgU2m4eHzbZ1q80WHKzf9sYbbTZ3d/m7YUOb7dAhm+2332y2kBAZCwy02RYtqt73XaH4C5QOk0JRG/n5Z33/LhDNnIrhtV27pEpuzx5o1szVo7RypYRE9uyRX/uqJUrN4LnnJAn/xAkJnRoMwBAYdgzy9kBQM5gbrd8nYxH8WKFhclaWSE2YzY4xq1XCbUeOQF6eo8Jt9Gjp+3bwoKz39dUf64cfHB4lO/v3w9Sp+rBvWZl4uXJy9NuWl8u9mJYmcgRGo3iU0tKkf11ysus5FYoahArJKRS1kcoqn1q0cA2rxMfDnXfC9dfDuHHQoIHr+qeflpyWW25xfSAqqo+oKGlj4hyicvcXVW83L/CvcA/41YOGFbSVjEZo39712OXlUoV33XXw/vsydvCgVLFdf72E4pzznwAaNZKXM97errlJUPk5GzWScG+rVnolcG9vaNtWGUuKmk91u7jOBRWSUygq4cEHHWGbyy+32YqKpJrKHoJLSnKtfBo4UMIt9hDdiBH69cnJNpvVWt1XpjgXTq632ebWPROiC7PZMn6x2U6edHzmnp4224QJUqF2660SNjMYbLYrr5R1zp/73Lk2W7Nm+rHhw2228HD5OybGZlu71mY7etRma95cxnx9bbZPP7XZSktttquvdoT77r5b7qEXX5TwHNhs3brZbDk51f2OKRT/CVUlp1DUZk6ckNBcXJxjrLBQQh+xseDjo98+JET0dvbvlxBc27YiPOjMwYOuybiKmonVDPn7xbvk5ukYP3AAQkMhKMgxlpkpVXK//y7eJWeuu06a6zrTuLFUtx06JJ5Jk1MGx759ouHl7+8YS0uTxO7ISMfYqVOiBabuJ8VFgArJKRS1iYULpQKuXj2pggsP1xtLO3bA0KHSY278eOn35UxSkuSq9O4tLTQqPshCQqQBb0WmTJEck+Rk6UmmqBkYTRCQBC+8IvlA7dtLk93ERL2xdGgmbOgNm/pAwB7X47RtK5ITziQnwtprYFdvWHMDlJwQqYr77pP764orpBLOTt26Yiztfgt+bAA/tYDC35WxpLhoqFIPU1ZWFs8++yxr167F09OTkSNHcv/99//tfsrDpFAgLU/q1ZOHlp3PP3d4C6xWyRM5cMCxfsQIWLVKPE4NG8r+P//sWN+2rXgdNm2SB+ann4rB5cyqVY5mrXbWrZOmrYrq55NP4I47HMs+PiIvERoqy6f+hEVt0Wk4bb0N3vlC7qVhw+CLL8TQuvVW8Vq2aAEPeYFxrWOfusNgSVN48UXHWHS06HbZJQPSfoDlVzrWG9xg8F7wq5APpVDUQqrUwzR27FjCw8NZtWoVX3/9Nd9//z3z5s2ryikoFLWXFSv0xhLIQ87O4cN6YwmkSu7wYdHe2bMH1q/Xr9+wQQyitDQxqioaS+DQ1fm7MUX14HwPgLQbWbXKsZy5FJ2xBHB9jFTQHT8O330njZmvuELug7Q0+HOD3lgCyPzF9Vzp6bBzp34bZ2wWOL7sX12WQlHTqDKDacuWLaSmpvLkk0/i6elJbGwsM2fOpEOHDlU1BYWidtOypauon6+veAOCg+G111xFJxs0kH5hTZqIMVSxSWpSEtx2m6zv3l1CLDYbPPushFcaNKi8v1dllVGK6qF1a/2y0Qiev8OcKPihPpSedN3HXAS/tYHlTWHLU/KZb94s6ttNmsCoG8G/iX6fkFau5woIEA9XaKjcW6mVKNUEq3tFcXFQZSG5L774gkWLFtG6dWvmzJmDp6cn119/Pbfeeuvf7qtCcgrFGd57D558UhK7r75acpqcDZqbbhLvT1qaNDTNy5OWGHb69pWQy5YtEqJLSREPg534eHjpJVELt2M0iuTA55+LwXbfffDqqxf+WhXnRnGxfO7ffSeyEo9cC3EfOm1ggMTb4fAMMYwSRsHBaYDVsUnH6dDvOUn4t/PAMOi5A/L3QlAKdPkazOFw7bXiaapTBwYNgs8+c+zj6wULhkP6LJE+aPYkNHn0Ar8BCkXVUGXCladPn2bjxo20b9+epUuXcvDgQcaMGUN4eDiDBw/WtsvLyyMvL0+3b6bqoK5QCPfeK7pK5eWSd/TNN/r16ekiSJifD56eonHjzMaNUrmUmysP1+bN9euPHIGfftKPWa1ifL33nhhMXl7n/bIU/wFvb7kPCgrkM9/6COjyum0Q0hraviMGU+ocODhFf4xDC/XGEsCCnfDGbig/DR5BjvElS8QQ9/WFkSP1+xSWQNH1MOIjMJj0lXsKRS2nykJyHh4e+Pn5ce+99+Lh4UHjxo0ZPnw4S5Ys0W03ffp0+vTpo3uNqlgCq1Bcynh4yMOqWTNX2YDoaAnRhYaKJ6BJhbBKixZSIRcaKj2+KgoRRkVJBVRF2reXB7Mylmoufn6SfB1aSZrDT3sgIgbCouDrdUCF0G5sT6lyc6ZlEvzcGb4LhV96QeERKM+HP66BBaGwsCk0CdXv4+4u4VqTrzKWFBcdVWYw1a9fn+LiYsrKyrQxi8Xist1NN93E0qVLda8vvviiqqapUNQegoKkuik2Vrq8jxwJixeLrpLFIq0skpLESALo2lX+XbZMvEbr1omezsCB4jlq1AhmzZLw2733inEUGiptLpKTzzoNRQ0j/hpo/CC4eYNHMASNg/vfcmgi/e9d8Pk/8AwDoyc0HAsNxshnb1eQHzAArjwC2WvAZoWs32DtHbD1GTj6DdjM0qKlybdw4w2i0RQVJZITdepU6+UrFBeKKsthKi0tpX///vTt25dHH32UQ4cOcfPNN/Pss88yYMCAv9xX5TCdI8XF8pBT3b4vDkpKxJtkPIffNWazhNMqtj5p2lQMqLw8SdANDpZwnDPFxfLAM5lcx93dXccVNQ9zseQMOf/fLy0Wnaa334ZHHtFv//TT8PRTYC0Ddycvpc0moT1fb5jlrt/H5A+BTcWIcmbgZvBsKN89zveq1SzGlpvH+bhChaLaqTIPk6enJ59//jmpqal069aNMWPGMGbMmL81lhTnQEaGhFF8fOSB+fvv1T0jxX+hsBCGD5ewW0wMfP313+9jMknCdsUfFI0bi9EUGAgdOrhWObVpIw86Z6PIYpHGr4GBUnX37rv//ZoUF4bSbFh2GXzjAz8kQPpi8R6OGweBwaKtdeSI634J2TA3Ar4LgrW3i3Hz++/y/REQAH37Q0CFeyW8K0R01Y95hMG9L8s+derAjBkyvv0l+DYYvg2EPx/SN+dVKGopqjXKxcCoUfDll47l6Gj5klSegdrJ00/DCy84lj09peqtohJzZaxbJ8bOrl3SeX7bNti927G+WzcxrH/7TUQrP/vMtZHvZ5/BmDH6sW3bJGdKUbNYdxfs/9ix7BECpW/ADRWqjx96SKocLRa450ZoOFG/vs370OtF+fFl5/bBMCIXstdBRA/o+Bm4B8H6uyH1O/BvALs7woOfOPYxmWDLbNg8TH/8bt9B7FXn44oVimpDPVFrA+XlElIJrZBgmZUlD9ENG/Tj6enyxRcbW3VzVJw/Kn6epaUSVmvZ0rXyrahI7o/AQFlu316MptxcSQKuWCW3fbvkstjvHecQyqlT4tWqeH6Q6jplMNU8TlX4rMpOwfZKhCIbNICj+wAbpP0IqyusT1uhN5YAlu+Fj3eKjpNXhGO880zIflu8Sp9dq9/HbIY9C13Pn70BogaCtVRfcadQ1CJUL7mazuzZ4jEKCxNRuRMn5KGXnCzCgg0buooRNmzoGppR1B4qVqkFBEhJf2iohMgmTJBxey+5kBDRTSovhwULxFAOD4eePV1DcO3ayf0SGSn9xlavFgmCQYPk+JGREtJxxs1NRC0VNY/ICveKdwx0rODdMRohfK1Uu30bAhmLpeTfmfpXuOa/dWoK8xrCnEiY3wRyd8j3T48e8n0UHa3vVwfivWx1PS5VeKXZMCdCzv/HSLCU/NsrViiqDRWSq8nk58uXUkGBY+yuuyQ8snKlY6xxY/Es/PSTPAwnTVLegNqM2SxJul9+KYZv585iMDnz7beS5+TMe++JQnd2tmNsyBDxUG3YIIZYerq+bUZSklTXOYcATSZ48EGpwAsIgOefF5FMRc3DXAQb74e07yGgEbR5B0LawBtvyP3g4wP3XglBFYRGkx+B9AVQlgsN7oTmT8kPsf/7P2ngPGgQDFoD5U6CTuFd4bum8LFTCNDXV/rYzZolxvZrr4my/MHpsOMlsJZD/PWw82X9+VtPhMbjL8x7olBcIFRIriZz8KDeWAJRaN6+XT+2e7eMublV3dwU5w+LRXLO4uIc1WoTJ8L994v3qLIG1RV7egGsWaM3lkDuoT//lF5yCQmueVB79ogApjNmsyiC33efJIQHBzvW5eZKGDA6+p9fp+K/U17u+CyNRjD5QIfJ0PRx8IoE05kQ7EMPwT03gNFDRCorfMS4eUG/P8Tg8jnzWTZrBksXQXEGeNeFrytUt+Vsga0VpGAKC0Vl/MUHwD1AXgD1b4KYKySZ/MRy1+vI2eI6plDUcFRIribTpIlUSTkzYID8gnOmTx9lLNVWNm6E+vUlPBYXJw12s7NFMyk+Xn61V3QCe3pKV3n3CmXfw4e7ClEmJ0uILjFRQi4Vezf26CE6TM4EBsJbb4lRFBkJTzwh4888I8sxMZJQXlT0369fce4sW6b/LLdsgcJUWNgKfqwHc6Pg8JdgKZOw19xoCaflbMUlRFZ0VNZ9HwO/DRZZgtTvZZ8f68OilhDWSb9PVCXfPXVj4Ph98H0szKkDu96U+3XDvXL8uVFw5EyblIrHUihqGSokV9PZsgUefhj275ewyMsvS6ju/vth+XIJxb3zjojGKWofHTpIkradpCTpGv/mm44xk0mMllmzJGfk+eflwfXTT/Dcc1IQcPfdUkq+ezc88IBUyV1xBcyfD4cPO47VsaOcw14lZ793XnhBqqiioyXv6e239fP8/HN9fzkQL9h4FVapEmw2MZQOHXKMdesGrybA4ZmOMZM/tHgZNt6r37/Zs3Dse7BZIHY4bHtGv77lBNj1quQa2YkeCCY/R5Vc67fA4Cf34nffidF2Rz0om+zYx2CEDlNhzU364yc/JuKX5TmQOAaSH/r374VCUU2okFxNp0UL+Pln/VhICHz6qTwcGzZ0rYT6Kw4fFld+XNx5nabiX7Jrl355717YuVM/ZjZLO5NRo8Df36GkfPnlUjlXVORI2G3cWAyrI0fEczVpkv5Y+/aJ3s7u3bKPvbXKM89ILlNYmPxdEeecOTsV56m4cJSU6I0lkPc/r1g/Zs6H7PWu+/tEQfcfREjy5CrX9Tl/6o0lgIJDcNmfkL8PAho7BChffx2euFlCeWtvh1Snfc52fKMRBlQszVMoahcqJFcbWbVKXPMtWkh4pEI/vkopLxcPVb16Euq58UbXaihF1XPFFfrlgQMl3OVMeDg8+aSE22JiJCEbxKMUGytGc9++kk8yc6Z4jFJS5NW5s/5YHTrI52+/dxYtksqntm0dIeDCQv0+Xl7S8Ldi37qK81RcOLy9JfTuzODBkifkjH9DqHejfszoAccWwg/xEro7/KW0TXEm/joIaqEfC2wuIbuFLeCHWDixWvrJLWgCPzWT8J3JV7+PRzA0uKNCFZ4BoivMU6GohaiQXG2kTRtJ5LWTmCghu7/i88/FSHLmhx+kikpRfeTlwWOPSXi1QwepMgoNFekAe5VcgwYSOnNmyhTJY3LmtdfgpZfkmHYuv1wMLnuV3Jo1ep2l+HjJfXIOARqN8MorImkREABPPSUSBcuXSzgwN1cMqNtvP9/vhuKvyMqS6kn7Z/nKK+DjBduflyo5/0bQ4hUIaAiHZsLeSeDmA5E9Yduz+mM1fRJOrobyXKmSa3C7GEObHoXTOyBmEBz5BgqdvFohbaQ1yqEZjjE3L2j2FBz9VpLOU16A0LZw7CfY+apUySXdBwkV9JoUilqICslVN9u2Sb8wZ7XlI0fkV3/r1g5hwZwcCd+0bOlqHB06JGGZLVvE22CvhLLZYPNmSeKtzKD6OyNLceEJCIAPPnAdf+QRR/+vO+5wXV+ZuKS9Z5wzaWkiD7Bzp9w7FUOxR49KmM4Zq1U8ThX7j3XvXnl1nqJqiIiAadNcx1Oel5cz9W50eJp2vem6j9EE7T+G8jwIaSVjvvHQ9j3I3w9BKbBrgn6f/P3SrNcZSwnEDJYqPWdiLpeXQnERoUJy1UVxsYRRUlIk72TECCkvf+QRCZu1ayfrjh+XX/oxMdCli4Rg7F3n7fToIV6mzp1lu88+Ey+AvXdYYqIYZs6VdO7uruEgRc1kWAUhQn9/0cvxrRAOuekmaNVKP5ac7Lh37JpOzgwa5KrnFBnpup2i9hIzGIxOFZUGN6mcm9cAFrWGRe2h7DQc+Ay+rwtLOsu68ApipbFXubY38W8kXieF4hJAeZiqi88/h6VLHcvffguTJztUnEEE5CZMkG2LzyR3njol3qeHH3ZUye3cCZmZsr6sTKqk0tJgvVPy55w5EnZZtEgMp4ceci1BV9RMBg4UL9HkyVIl98QTYggtXSpVk/YquT59YN48CaHZq+Q++MBR/p+TA8eOwaOPOqrkXnhBdJaKihxVcs88I3lLiouDgEbQcyHsekOq5OoMgM1OVWqn1sOet8UTZS2TsZJMCGwGje51VMk1f8YhD5D6Hfg1gJTnpDJOobgEUAZTdXH0qOtYRUFKkHBbVpZ+LC1N8l769pUwS8VWGnl5cOCA67FiYlwr7hS1g+uvl5czHTpIHpozMTGS3wSi8P300/r1qanwagXVZ5CcpDvvPH/zVdQs6vQRUUmbBQoOu67P3y8Vds6UpEOXLyBnM4S0FZFMgOQH5aVQXGKonwbVxdVX60Nk3t6irFxRT+mGG1xDZx06SHhlwAAJ0VXsJdetG9x8s34sOFi2V1w6eHrC0KH6sWuuqZ65KKoPazn82g8Wt4efO8Ged8A9yGkDAyTeKq1PnAlsJoKUywbIv+mLqnLWCkWNQ3mYqouWLWHxYtHJcXeXUvFGjUQj5+WXJXdp9GjJX+nTB158USrjevfWh+jKykQjZ+JERy+5J5+UxO8ffnCEcR591LVRpuLiZ/p0ua/slVUPP1zdM1JUNalzINMpWT97DTR/VvSVynJFBiCyF3SbCztelCq56EGw83VHiM5SBJsegujLquMKFIoagZIVqImsXy8GU58+DlHKQ4ek51eXLpK/kpPj2N5kktyUlStFS8e54k6hOHxYjO3OnR2il4pLh91vw58VFNlTXoDI3lB+GiL7OEQp8/bA6Z0Q3gXmxoDN7NjHIwSGVxC3VCguIVRIrqZx002SyD14sEgEHDki3cEbNJAwXny8hNyc6dtXVJ2vukoq7l5/vXrmrqh5TJkiVZL2e2f+/OqekaKqib1KLzBp9ITMZbCkC/x2uQhRFh8Xj9L8xrDiKvgxUQwpZyoKYioUlxjKw1ST2LJFQnXO/N//SYWUs0epZUsRobRXyS1eLH/b8faWRHE/v6qYtaKmYrWKRMDJk46xZs1EYkJxaXFqE+x9F6wWaaq7Yax+fZPHJLfJ4tRqJaIHRPV3VMk1ulf0mxSKSxR19/9btm0TUciePSXxGiAjQ0q9k5NFjbsiBQWSZxQSIuE2g0G0l5YskXYUlfWEO3HCVYwwOxuuvFLylNq3l95hzhQXy/HO1WD6/XeZ+2WXnXueU2XXWlQk1+fvD/36OUQ3FVXLqY1weheEdBc9LmdOnpTw7h9/iKxAcnK1TFFxnig4BCf+kCq2wDOfZVmuJGj7REPEGS2loGZQ9yrACuYi1+OUHNcbSwBlObKPT6yE6OzGUskJyPgZ/BMhrKPrscxFkL5QvFp1+oHRTXrMHf8VSk9B9OXgrn7MKWofymD6N0yY4FBBdncXjSM/P9HLKSmR8SeekERtO0ePQqdOkJ4uywMHSlJ2nz6wYoWMJSSIaKW9yabBALfdJjlKX3zhOFb79pLIa7HINsOH670GgwaJZ+FcuO46h8EVGioP0saN/3qf33+X+dsTzx9/HO69Fzp2lBAiiCH5yy/6SkDFhWfLE7DjZfnbzRuu7gtfO1U3de4soV6LRZYnTYJ77qn6eSr+O0dnw8rrRCoAoO37kpf0S1dHI93466DDp7CkmzTYBWmk6xUFJRmybDBBg7vEaEr/yXF8/yQJ12ETsctOn4Nfffi1D5gLZJtG46CtU9uekixY3NHRUiWiO/T6BZYPgYwz96F3NPRfA76xF+RtUSguFFUakvv222955pln8PDw0MaefvpphlVUMq5AjQrJlZaKZ6egwDHWurW0H1m2zDHm4SGJ23aPzSOP6EUpQarhHq/QUuDRR8UIOn5cJAV695Zzvv++o0rupZfg4EHHPmFh0mts4UKpkrv3XlcV6MrYtk3UxJ25/XaprPsr+vbVi266u4tY5muv6bdbuFC8VoqqoSwH5kRKGbmdkF6we4ijSm7CBNizx2l9iHgxlTew9jG/sSRp2/EIgbrD4OBn+u1SXoStT+rHmr8gfeTKc6H+bRDeCcyFsOe9M1Vyl8PGcVDqFM71awBBTSHNSfvLYIShqeLNAtj+Imx9Sn+uFi/Dlgrfc8mPQKsK3xcKRQ2nSj1MO3fu5JZbbuGhhx76+41rKuXlDs+Knbw8MXKcKSsTT9GGDRKyO33a9VjZlVScWCzS6iQzU/JNQPR0unSRkF2nTpBfQWCuoEBaqZw+LQaT3VjKyYHvvxej7YorxLApKxM16Pz8yr1QeXnw66/SX+yyyyRRGER1fPlyCeNUDBGWl+tzrJyPpTh/FBXJ5wmir1TRKDYX640lAArgxi4wyBPCO8LTldw7+/aJsZ+cLPeeonZQXuH/l7lQqt4qUlrJ94zBIGG2slwIPONRNvlCeGfwCJJGu+YC/T7mPNdz2qzStDf9J/CuI8c7l/NXPI5CUQuoUoNpx44djB49uipPef7x84NRo2CGU8fuu+4SD5Nz9/YePaSarbBQlvv1E4Ol/MwDrX598SZ99ZUjTOftLd3k33hDlh9+GFatkoek3RNlNEr+0pw5jnP16ydyAuYzJcCPPAJjx0rozq4S3rWrhMh69YLVq2UsMlIekrt2OY5dWChhQhBDbfFiUYcePVqa+YKEAJ3brgweLMnp06eLNwxEWPNy1XzzvJGXJ5+n3TvUqBGsWyf3nR2faIgZAsd+dIz5xotgIQAGuOYqeOs7x/p+/aB5c8d9OX68aHopaj4N7oJtzziWE2+Viri0OWLIgDTRTX4YDs9wGC7uAZA21+F12vI/GLAW9r7vaLhrdIeoy+DYPP35ApvCcSdPemh7qbQrz5Xl8G4SCrbnQ3nHSEJ52lwoOOg4duKt5/WtUCiqgioLyVksFtq0aUP79u3ZuXMn3t7ejBgxgttvvx2Dk3cmLy+PvAqeiczMTEaNGlUzQnIgD5fPPpOk7379pGQbYMECKdtOTpa8pG+/1e/37bfipQkJkd5fERGin/TRR2KotG0rxpgzt98uRpVzCDA5WcQpf/9dVL8//dRhBIEYZvfcA2+/rT/WCy9InzFnHnxQ5pOeDv37i1Cm1epYf9llEv7bu9cxFhAAM2dKyC05Webo7S1VftOmSdL3XXdJXzLF+eGTT+COO/RjH34o77Mz5mI48IkkfUcPhNU36n/N+zeCwufEo9SunXyOzhWWJpOEg0NCLty1KM4fh7+CrN8gpB3Uv0USrE+sgsNfgk8MNLwLPIIlOXz/x5Lv5N8Y1o3RHyfpftj3vt5DGdYZEm+D7LVSJRd/nXimMn8RMUy/RAnfHZyqP1bnLyF7vXisGt4l8yg+Dvs/kqTv+jdBSOsL/c4oFOedKvMwnTp1imbNmnHllVcyadIkDhw4wNixY/H19WWUk5Ewffp0Jk2aVFXT+ne4u7s+qECSrQcNkr+d85ns+PuLJk5IiBgdIOGyxEQxmCrLOyoqkjCaMyUlsk9amvxr9+rYsVgcDVedqRjKAzGOEhPBx0eSvp2NJZDwY8UQZFmZJH0PGaIfb9EC3nrL9RyK/07FzwAkBDt9unyuI0aIx9DNSx5k1jLwiXMoNduxFEPf+pCSCqGJ8ElJhfUWh7dJUfNJuE5ezhTFwMoG0map0ZlqNM9QuS9sVjBV0ljZXARWs37MUiL7lGbLv/Yftj5xsuyXCDlbXI/lE+M6J+9Iad6rUNRiqlWH6bPPPmP58uVMnz5dG6sVHqZzYckSMSrs1Uht28L+/Y4y7w4dJFeoY0dHhVtYmCgx25vwuruLF+mLLyTp287w4Xrv1dVXw3dOYZZRoyTc16GD40HbqBGsXSvzsDfm9fWVpG+7d8rHR7wOv/8uywYDzJ4tuVjOLTXuv18ZRlVNVpaEzuwh1rAwMZB27JDl0FDJl8t6VTwJIJVN0YP0IbrY4ZDqdO8cuRoed7p3rrnGVaZCUXvYuBG6d3f8YOrfH+bPhoWtoeDM/3vvuqLsbQ+RuflISG7HK3DkS8exYq+GVKd7o92H4qH87TKHJ6pOP/Fw2ZeDW8Nl6yUZXKG4yKgyD9O+fftYuHAh48aN08bKy8vx9PTUbRcQEECA3ftSm+nXT/KRZs+GuDjYuVMeaHbWrpWqMmc5gJMnRUZgzBhJ+r7uOjFoOnSQcvCNGyW/qKJ365df5GWvkrvhBjG2/vxT+s4FBcGtt8q/a9aI+nN+vghgDh/uOE5RkeQeTZ4sIbihQyX3CURqYNkyMbiuvfYCvWmKsxIRIZ/n1KniBYyO1ufMZWfDB29Dq08cYzaLeAc6fwWn1ku/sA3j9MdNXAxLf4GFiyS8eqNSc67VvPOO3rv888+wYAIUHXCMFadJ5ZybpyRp17sRApKg0zSo01fCbFGXwR9X64+94xXJYXIO22UugZ4LIet38KoDibcoY0lx0VJlBlNAQABTp06lTp06DB8+nJ07dzJz5kyeqphTczHRtq28QEr9K1JZ6MNmE8+Pry94nXGdGwyy7Ocn/9q9VnYsFskhsu9n1z7y8nKMubvLmLu7LFutkq9SGc4PYjtXXCEvRfUREyO5awBz57qut1gcifkaVsklMfmdaY9RIeRqs0IzbwjzhUBfcFMPu1pNxZA6gPksIVaTrxg/RvuPVqP+XrFVvFcsDs0nZwKSVFNexSVBlYbkVq9ezYQJEzh06BDBwcHcdtttuvyls1GjdJj+LTt2SPjNnrzdsqUk27Zr56h8Cg6WMv7Nm2XZy0uSx2fNgjffdByrYkjuqqvkAWr/KG+7DR56SKqq7HlLzZpJ6K19e0dVXECAo5M9SFWcPZFcUbMpLYVWrRyfZWCgfI4nXoBD9gpOA9QdCmnfO/arGGape+UZXZ0z9069m8TToKidrFkjorH2vMbu3WHJ9/BTSyg6KmNedcA9CPJ3y7LJHwaskwq5g1POHMggmk5pTtW4rd8S4+j3wQ7DKfYq6OZ0PykUFzGql1xVcuiQGD8hIZJn5OcnOU1ffCFJ34mJ+hAZSDn/N984FMRBGvF+/LHDuHnmGX24z81NKqo+/FB/rMcfF7FMZ+6/X3Jj0tMlcTgp6XxeseJCkpcnIdf8fAnfxsVJr7DU7yBvF9TpL6rMzi0vfOOh4zQpDQ9tJ6rgJ50qLA1GGJYBXhFVfjmK88SePZIKEBMj4XNvbyg5CYc/F0PHMxzW3KTfp+HdZ6ronLxKIe2g+bOOKrk6vWU8Z4sY2X6JED9SZAIUFzVpaWns3r2bvn37nnV9nz59mDdvHo0aNari2VUdqjVKVVKvHvzvf/qx8nJ54BUWuobaQMJmFVWY3dxkH/urYvsRg6HyliSVheC8vCS/SVE7KMuBA59JeXa9G0RvyxmbBcz5UJ4vlU+GiveG25l1BfIvFUNwBnkgZv4iPcQSb5HwTGGqo3w88VbwqcU/XC52kpIcoVs7XmHQ+H75+9hPLrtgMCH3gpPBZHCDmMvl5UxwC3kpLhn+97//0axZs7MaTFFRUfzxxx8EBwdX8cyqFmUwVScFBRIiO3xYln19xWO0dq0s+/nBfffJL8UXXpAxg0HCa1de6TjO0KFiVNnzF+6+W/b78ks4dUrG2raFxx4Tle9Nm2QsNNRV20dRc7GUwc+dIe9MKGXPW9B/NQS3dGyz+gbpMQaw+03XEFxwK+nrZSdmiDwY7SGWqIHw20DH8tGvoes3sKi1o03Gvvfh8m3KC1VbieovzXpPnfFKe4RA0n2ADfaekXQxuEGTR6ttiorahZubG+Hh4dU9jQuOyvCsTubNcxhLIF6m7t0lbPf221JZl5ICzz8vvdtef10Utv/4Q3+cJUvECJowAX76SSplEhMlb+q99xzihN7esu/06dJ0dccO8XopageZSxzGEohOzv5PHcvFmXC0glhqURr0WQYtX5c8ley1FY75M1y2AVq9AT0W6I0ngBN/wK439D3FSrLgyNfn77oUVYvRBH2XQ6eZ0OY9GLQD/BOhzbvQ8ydoNQEGbobYK6t7pop/SVJSEgsWLGDIkCE0b96c6667jrS0NJ577jlat25N9+7d+d7eZgkoKCjgqaeeon379nTo0IFx48Zx/PhxAB577DHWrVvHlClT6N27t3b8t99+m86dOzNkyBCOHj1KUlISe88IHOfm5vLYY4/Rvn172rdvz8MPP0x+ZTqAtQzlYapOvL1dx4xG6e2VmSkq4LGx0vJk3z7RcYqLc93P01Pyo/btk6a/vXpJqC0jQ8aCgkTk0NtbtJZqe3uaSxW3Su4XgxF2vCoJvVGXnTF4nAQI3bwgfx8U7JcwWsVjGD2lF1j+PgnLuOllPgBJCj6XuShqDyZvCek6YzCIOnz0wOqZk+K88uabb/Lyyy8TGBjI3XffzVVXXcU111zDt99+y5dffskzzzxDv3798PX15emnnyYrK4tPP/0ULy8v3n//fcaMGcPcuXN54oknOHz4MI0bN9bJAs2bN4/p06djNpsxVkgb+b//+z+Kior4+OOP8fLy4vHHH+e5557jDXvbr1qKMpiqk8svlyo5e1+2mBjpEbdvnyx/9JF4lmbOlFYsIBpJw4fDkSOOqri+ffUhuuXLJcG7UyeHSvj06VJRVZmRpqgdRPaU5NusM8KinuFw/Dc4fUbLa99HZ3rJnekmb/SQEvF1Z8Ku+ydD3ashfz9aVVyd/rD8Ssc5wrvLPvbGq/HXQuPxcOQryD/THicgGeKvuXDXqVAo/jPXX389HTt2BKBnz5789ttvPPDAAxgMBm6++WZmzpxJWloaPj4+LFiwgOXLlxN5piH7hAkT6NChAytWrKBXr164u7vj7e1NiFPLpGuuuYaGDRsCkvRtZ9++faxfv54ffviBxo2lsfPzzz/PihUrqurSLxjKYKpOPDwkRDZvnoTjgoP17UYsFqmGmz1bv9/69bB1q0gOtG8vMgLOzJkjXiXnlipHjsCiRdIrTlE7MRih9y+QvkCSvn3jpQpOwyavvr9LL7mInrAwRX+M7NUwaLsYXSHtYP3d+vUnlsPATdILzCcOovrJeQduOiNPYIS6Q8Dkc0EvVaFQ/Dfi4+O1v729vYmJidH6ttoFo8vKykg/0/z9ssv0WlrFxcUcOnSIXr16VXr82NjYSsf379+Pu7s7SU4V182bN6d58+b//mJqCMpgqm48PBzNezdudF0fGCjJ4KdPO8YCAmDlSjGYysqkR13FYwYFuR7rYlBQv1g58o20MAloDEnjpKP8ybVw4FOpUksaB371Jf+k7lDZJ2+v63FMPpC1QmQF3ANkX2fD2T0QTqyErD8kB8rkp9/f6CHHPfGHVMmFtJEKq4IDMgYQ1ByCmkou0+63ofgYxF8P0QMuyFujqCLMhbDnHTi9E6Ivh4Trq3tGiv+AqUJVdMWwmR2LxYK7uztz587VDCo7gYGBZz2+l11YuQLu7u4ux7lYUAZTTaJNG70oZXg4PPigtCW5/34ZM5kkodveHuWrryS05+Xl0Gr63/+kvco330Bqqoz16wdnEvYUNYx9H+k9PceXQes34ZdujjYUh7+EwXvBI8ixXUAj6VBvL/f3CIaCI3DkTC+4w19A3AhH1ZzBTQwye4juyJeS9+Tm7dBqihkCK53CbcfmQbfv4edO8kAFOPS5VMn9frk8XEHEMrv/IN4nRe3kj5GQfkZy4PAXUHICGt9XvXNSXHDq169PeXk5xcXFJCcnA1BUVMTDDz/M7bffTsuWLf/R8erVq0dZWRn79+/XQnbr1q3j4YcfZsmSJXh4eJzvS6gyVJVcTeObb+C336RSbv9+Eam87z5pyDtjhoxVjAX/8ov0fps5U/qNPfOM5EPt2iXhvCVLJBx3kVr9tZ4DU/TLx3+Fve/re3aVnoBj81337TgF+v0Bnb+Uyqfs1fr1+Qdg0E7oNAMG74eTq/TrM3+BQbuh0+dw2Ua9yCVA7laRJ7AbSyA6T7vecBhLduyGm6L2UXzcYSzZOTil8m0VFxX169end+/ePPLII2zYsIEDBw7w6KOPsmXLFurXrw+Ar68vR44c0Srn/orExES6du3Kk08+yfbt29mxYwevvvoqnTp1qtXGEigPU40j9/RpVhUWUmAw0CIjg6SAAMlv+vZbMYZOnZJO9dnZjp1CQkRO4KefRIqgXj0Jya1aJQZTUJBU113ECqy1Gs8w/bLRA7yiXLcrPQWrzlQ4Jt0rSt1F6eJBKjwK1jKperOWOvbxCBGpgVMbRBrAMwxKnL70PIIhYxFk/ASnkyWMp8NQuUilTyX5CxWvQ1F7MPnqPY2gPs9LiNdee41XXnmFe+65h7KyMlq0aMGUKVMIOJPGcd111/Hoo48yZMgQVq9e/TdHk6TxF154gdGjR+Pu7k7//v157LHHLvRlXHBUa5QahNlsZtKkSZx2yle67rrraPT44/Cdk/jgsGGwYIHkpri5SS8558TwPn3Ey9Szp0PMMjxcvFMqj6nmcXIdLOsH5Xmy3Px5aDQWlnR16C5FdJftrGfCrm5ecNlmWDEU8vY4jhV3jYhNgsgBhHUQL5KduldC+kIxqgxGiB0OR79xrA9pB0VHJD8JoNG90PJVWNoLstfJWFgn6PMrbBwP+z+SMa860G8F+Dc4n++MoirZ+TpsfgywSW5br0UQ3qW6Z6VQ1BiUh6kGceTIEZ2xBLBt0yYaVexMv26dCF6uWSN5T5dXaF2wdKmE5Jw7l584AYsXS784Rc0irD0MPSKVa/5JECiluFy+VfKZTL7S7y1ruWMfSwnsfVdvLIGEy67YLYZWaAf4vsIPjJOr5FwnV0FIa72kAMCp9RKiy98LvnGOFhj9V4uEgcEoxpvBCO0/hAZ3SNJ3ZG9VOVfbafKIGNSnd8pn7Bnyt7soFJcSymCqQfhXrHYD/AIDJeR20klpOSpK9JjszXcjI0W1W9vJT0JwFYmOvgCzVpwXPIIc1W92jO7SxgKg4LDrPn71AQOaphJIiG3vJJEViD7oGoLzihI9pqzfpD2GZ4V2Bm4+4BMNgRWaMBuMjuarzoS0AlqdyxUqagMBjeSlUChcUEnfNYiIiAjatm2rLQcHB9O5SxeYOBHcz3QE9/ODOnXg2Wdh2TJ49VVRB7c3PXRzk7H775eec3ZGjYIuyr1ea4kbIV4cOxE9odH/iVfAjk8s5G4Xg+n4Utj0gGxnPJNoafIV7aZtT0ti+a7XRRXcM1TWG9ygxcvgXomyt0KhUFziqBymGsjJkycpKCggLi7OoZ2RmQnbtolQZUIC5OY6dnBzkyTw9etFgsD+HlkssHq1aDldBKJhCkRQ0maTMJ6d/P1QlCrJ2fMqeAeCWkCvxVLtFtoO5jXU94UzGOGqk5DzJ/g3At/KxegUCoXiUkeF5GogYWFhhIU5KlSKior4dcMGMjMzSTAY6BMXh8HZYKpbV4yivn31B3Jzg65dq2bSigtPxs9Szg/Q+AGIvkxymLY+I73k6l4lXiRnCQDvKNj+vFTJRfQUL5SzweQdLeHAOs6K4cDhWZLQbfKHZk9AWMcLfXUKhUJRo1EGUy1gzpw5HDhwAIBjx47hd+21dDx6VLxMvr7w3nvVO0HFhef0TvhtkKOx7vFfof86qZIrOtPH6eRqSBglMgPWMjGGzIWw7wNZn70Ooq+AwsNQliPGVZv3XPW5Mn+BVdc5lrOWwZCD4BVxwS9ToVAoair/yGBKTU3lwIEDlDm3WjhD//79z9ukFA7Kyso0Y8nOSg8POqalST+5Jk3Eu6S4uDk2z2EsAdgscOBjh7FkpzQbrkyTViYBTeHbCvdG9hpZn7MFApuARyX3TmqFqkxzoXi3Kna3VygUikuIczaYPv30U958800MBoNLjxqDwaAMpguEu7s7/v7+5Ofna2NBQUHMX7aMI0eOUDcri379+uHjo0q6L2r8KtE3Cm4FBpPekPIKh/VjpZdc9BXSQLfoiGO9bz3Y9LCjSq7VBKmU2/2mtDzxjoLAZNdzKX0lhUJxiXPOBtNnn33G+PHjGTNmzFmb+J0LeXl5DBkyhHHjxnHVVVf96+NcKhgMBq644grmzJlDaWkp/v7+eHp6svFMo157gvioUaOqeaaKC0rdKyH+OjjylSzHXQOJY8BcBFv+JyG4oBbSsDf/TFPe0zsgbiRknIbyXBGX9Ap3hOhO74TiDNlm08MylrtFQneRvUQDymCEhv+ncpgUCkWtwWKxkJWVRVRUJR0T/gPnbPnYbDb69ev3n4wlgGeeeeac+tEoHDRq1IgHHniAu+66i/vuu49Ue0PdM+zfvx+rs0il4uLD6AZdvhTRyaGHoessMJog+QEYli794rrPdRhLdvJ2wbBjIoJ55VE4uUa/PnMJHPtRP1Z2Cpo+AYP3Sfiu7TsX9NIUCkXtZs6cOQwdOvTvNwSSkpLYtWvXBZ3PAw88wOLFiwFYu3atTq7nv3DO1s+oUaOYPHlypflL58rcuXMpKCigkepp9o/x8PAgMjISNzc3wsP1YoMhISEsXLiQt99+my+//JJs5z5ziouHQ1/A74PldWimjJ1cByuuht+vgH0finClM77xsHo0/HYFrL0d/Bvq1/slQmBT/ZjBDbJWyDF/HwrHzjRlTV8MizvA/Mawa+KFuUaF4mLEaoUvvoC2bUVouG1bWVY/dC8Ip06duiDHPeeQ3GWXXcYNN9zA/PnzCQ8Px1Chsmbp0qV/uX9qaiqTJk1i1qxZjBkz5t/NVgHAoEGD+Prrrzl9+jR+fn5ERkayYcMGAE6fPk1ubi5jx46t5lkqzivZG2D1jWiq3qtHS37SH1dLojfArglQb7QkiJflQGAz6Qln7wF3aDpEDZA8psJDEqLr8Km0Pzm5WlqzuPlA/LWw/TnHuVdcCX1XwvIhEvoD2PSgaDbFqVY7CsVfYrVKv89ffpFG6gBZWXDnndJU/bvvRHz4vJ7Syquvvsr8+fOx2Ww0adKEZ555hri4OL755hs+++wzTp06RUpKCs8++yyxsbGkpaUxcOBAxo0bx6effoqbmxs333wzd9xxBwAZGRm8+OKL7Nixg1OnTpGUlMQLL7xA48aN//U8MzMzeeGFF9iwYQP+/v7ccMMN3HzzzQA89thj+Pj4sG/fPrZv305CQgJPPvkkbdq0AeCrr77io48+wmw2c+WVV7Jw4UJeeeUVfvnlFzZs2MCmTZs4evQoAwYMwGq18vbbb2upLaNHj+aee+75x/M9Z4Pp4YcfJiYmhssvvxxvb+9/dBKLxcLDDz/Mo48+6uIdqUheXh55eXm6sczMzH90voudqKgoxo0bx+nTpwkICODDDz/UrT9x4gSnT58mUFXPXTxkLkHXAgUkSbu0gjexOFNCdMWZ4F0Hvq7wfzV7PVx9AgqPiNCl8YyCfN/fpOLOPRA2PaLfx1oOB6c4jCU7GYuVwaRQ/B1ffaU3luwUFsKSJTBrFlx//Xk95ZIlS1i+fDkLFy7Ex8eHp59+mo8//pgePXrw7rvv8vHHH9OwYUOmTp3K7bffzvz58wGpyt68eTNLly7l2LFj3HLLLcTExDBo0CCeeOIJ6tevz9tvv43FYuGJJ55g4sSJTJ48+V/N0WKxcNddd9G2bVuWL19ORkYGd955J0FBQVx55ZWARKWmT59OUlISzz33HK+++iqzZ89m9erVvPnmm0yZMoWkpCReeeUVjh07BsATTzzB7t276dOnDzfffDNr166lsLCQ0tJSfv31V/78809Gjx7NgAEDaNDgnxWznLPBdPDgQX744Qfq1av3j04A8MEHH1CvXr1zqqSbPn06kyZN+sfnuJQoKChgwYIFHDlyhJiYGEJCQnRhOF9fX1asWMGuXbsICgpiwIABxFXWW05Rewhq4ToW0QMOzwRrqWPMuw782h/ydkLMYAnB5e9zrA9oDH+MkEa6oe2g3YciZrn5MTj8uWg3RXR3PVedviJk+XdzUigUet56y9VYslNYKK2vzrPB5O/vT1ZWFj/88AO9evXipZdewmg0MmbMGEaPHk3TphKGv+OOO5g5cyZr164lPj4egMcffxw/Pz+SkpIYMWIE8+fPZ9CgQbz00ksEBQUBkJ6eTmBgoIvkzT9h+/btHD16lG+//RaTyURCQgK33HILs2bN0gymXr16kZKSAkhk5YEHHgDgxx9/ZOjQodq6Rx99lNmzZ5/1XCaTifHjx2MymWjfvj1hYWGkpaVdOIOpefPmHDp06F8ZTAsWLCArK4slS5YAUFhYyHPPPcfWrVt59tlnddvedNNNDBs2TDeWmZmpqsCcmDdvHnv3SnLv/v37iY6OJjo6mvT0dPz9/alXr55WRVdUVMSsWbMYP3487vZ+dIraR/RASBoP+94HbNBwLNQbBVjhz/GSqF2nn1S2FZ0pCjg4DWKHi4eo8LDkKpl8IXWOrM9YDKtGSZXc7jdlrOQ45O2VsdRvwegFzZ6EuKuh5esSqrMUS5Vegzuq/n1QKGobFYp0XEhL++v1/4LOnTvz9NNPM2vWLF599VViY2N59NFHSU9P54MPPtB5hcrLy0lPTyc+Ph6TyURMTIy2rk6dOqxatQqAw4cPM2HCBDIyMkhMTMTT05P/0lnt2LFjFBcX07GjowLXarVqRhlAaGio9rfJZMJisQCQlZVFhw4dtHXe3t66/Sri7e2Nh4eHtuzh4YHZbD7r9mfjnA2mwYMH8/jjjzNo0CDi4uJctJj+yqBZtGiRbnno0KHcdNNNlcoKBAQEEBAQcK7TuiQ5fPiwbjk9PZ2nnnqKkpISvLy8mDlzpm59cXExx48fvyT68F20GAzQZiKkPC/L7n7yb/3RknNkLhDpgB8T9fvl7YIhByR05xUO34bo159cDR6h+jFLoUgWtJ8sjXtNZ8J6TR6Whr/WssoFLxUKhSuxsZKzdDYuwPdyamoqTZo0YdasWeTn5/Pll19y//3306pVK0aPHs21116rbXvgwAGio6PJzs7GbDaTnZ2tGSrp6elERUVRXl7O2LFjefHFFxk0aBAA06ZNY+7cuZWe/1yIiIggNDSUP/74Qxs7deoUJSUlf7tvnTp1dKk6JSUl5Dq3C7tAnLPBNHnyZHx8fFi2bJk2ZjAYsNlsGAwG5QE6T+zevZuff/6ZwsJCUlJSuOyyyzh16hQ//vgjGRkZ1KtXj4iICNKcfpVEREQwe/Zs9u3bR0REhEuemLu7O5s3b2bmzJn4+PjQt29fzSWrqGXYDSVn3DzALQRMfuAVKV4iO/4N4ZfukrsU2QsCm8OJ5Y71gc0gtD2kz3eMGT0gKKVyo8jkDfyzHEZFzWPr1q38+uuvlJSU0KZNG/r27UtGRgbz588nKyuLhg0bMmTIkH+cr6qohPHjJcG7srCcry+cCTOdT9asWcMnn3zCtGnTiIqKIjAwEH9/f4YNG8aHH35Iu3btqF+/PvPnz+d///sf8+fP15wgb7zxBs8++ywHDx5k9uzZvPzyy5SVlVFaWoqXlxcAO3bsYMaMGdryvyElJQU/Pz8++OADxowZQ0FBAWPHjiUhIYFXX331L/cdNmwYd999N8OGDSMpKYm33npL5zHy8PCgoKDgX8/tbJyzwfTzzz8zadIkIiMjue466TM1YsQIevTowd133/2PTvrDDz/8s1leIhQWFvLtt99qbscNGzYQEhLC5s2byTrzC2X//v3Ur19fC8FFREQQHBzM7t27AalkKCoqIikpiT179uDv70/Dhg21EF1ZWRlz5swhNjZWefIuNtw8oPOXsHaMVMHV6Qv5B+D0NlmfsRgie4uBlL1OQnSdZkBAkohcpn4LnmHQeiJ4R1bvtSguGDk5OXz//fdaOGXVqlWEhYXx+++/c/r0aUB+uHl5eZ2zto7iL7juOpg92zXx29cX+vUDJ2/P+eLqq6/m0KFDjBw5ksLCQurVq8e7775LmzZtyMvLY+zYsWRlZREXF8f7779PQkKC9iM8KCiI3r174+7uzn333Ufv3r0BeP7553nuued45JFHiImJ4ZprruHDDz+k8Gz5WX+Dh4cHkydP5uWXX6Zbt24YDAb69u3L448//rf7tm3blnHjxnH33XdjtVoZMWIEJpNJSzsZPHgwzz//vPYenC8MtnMMQr788sssXbqU559/ni5dugDw/fffM2nSJAYNGsT48ePP26QqkpaWRp8+fVi6dOlFGVay2WzYbDYOHDjAl19+qVtnN3yc8fX15aGHHtJCcJMmTXLRXrr//vvx9vbG3d2duXPnsm3bNt36kSNHkpxcSQsMRe3HZpNEcJsNvqnQMscjGIafgrI88KhgMFtKpWrOcH5LnBU1i61bt7qEUpo1a8b27dt1Y6Ghofzf//1fVU7t4sVqlWq4iRMlZ6luXfEsXXvteZcU+LfYn7Pr16+v8T+mDx48iLu7O7GxsYCknbRs2ZJFixb9qzzrc+WcPUwLFixg0qRJtGrVShu78sorqVu3LuPHj7+gBtPFzNq1a/ntt98wm82kpKTg5uameZgA4uLiyMnJ0TxMICG4zz77jLS0NOrUqUNYWJjOYAoICGDRokXs2bOHgIAAEhP1eS1Go1GX2Ke4yDAYwO2Mqzy4JeRsdqwLbgU/d4GTq6TKrdMMCJZKE9w8q3qmimqgbt26WjqFnXr16pGamqp5mADtYaQ4DxiNUgl3nqvhLlV27drFBx98wIwZMwgMDOSjjz4iNjaWhISEC3reczZtS0pKKm3wGhgYeEFihZcCx48fZ9GiRZSUlGA2m/nzzz9p06YNgYGBmEwmWrduTYcOHbjqqquIjo7GYDBQv359zGaz5j7NzMwkNzeXRo0aYTAYiIyMJD4+nt27d2Oz2Th9+jTbtm2jdevWmEwmAgICGDZsWI3/BaE4T3T+QkJwBiNE9pFGvSel6oXcLbD6huqdn6LKCQkJYciQIfj7++Pu7k7Hjh1p1aoVw4cPJzIyEoPBQFJSEv369avuqSpqGTNmzKBVq1ZnfW3atOm8nOfyyy+nV69eDB06lPbt2/Pnn3/y4Ycfughqn2/OOSQ3duxYysrKeOONN7Tyvby8PB577DEsFgsff/zxBZvkxRqS+/PPP5k3b55urH379vTv3x+z2Yynp+MXv81mo6SkBG9vb1555RWXFjVPPfUUZWVleHp6MmPGDJdKuttuu+2ieu8U/wCbTSroPIKlSq4sR7/+mmKHR0pxSWOz2XTJvQqFwsE5h+SefPJJbrnlFrp37655O9LT04mLi+ODDz64kHO8aImLi3NxjVssFt544w1KSkpITk5m2LBhZGZmMnfuXHJycqhTpw7R0dE6gyg6OpqpU6eSlpZGQECAi1vSy8uLyEiVxHtJcnKdaC0V7JcQXHBrOO7Uxii0vTKWFIBDnDgvL4/Y2FiGDx+uPNEKhRPn7GECqbBatWoVBw4cwN3dnYSEBLp27YrxAietXaweJpAEzN9++43y8nJSUlJYvXq1zoDq1asXW7Zs0TUTjI+Px9PTU1P6dnNzY98+h5qzr68vSUlJ7Ny5k6CgIC677DJNxVVxiTE/GfJ2O5bDuoBnKGT9BiFtRWvJP/GsuysuDSwWC2+99Zau4qlZs2ZcffXV1TgrhaJmcc4eJpAywJ49e9KzZ88LNJ3ag8VioaCggICAAF3cNC8vD19fX9zc3M7pOCkpKSQlJWGxWDh27JimqmonPT3dpfPyyZMnefDBB8nLy8Pf39/Fw1dYWEj37t0ZPHjwv7y6s5OXl4ePj49OuLSoqAij0ajc+DUFSxmUnhDDyNlYAhGyvPqk9I3zjgLjP/oKUFykFBQUuJSHZ2RkVNNsFIqaifq2/BccPHiQuXPnUlBQQFhYGNdccw0mk4mvv/6azMxMfHx8GDx48Dl1cV66dCmrV6/GYrHQuHFj3N3dKS8v19Y3aNCAgoICrbEgSPPd9957j5ycHAICAjSVVjthYWHn3ZWen5/PrFmzSE9Px8vLi0GDBpGcnMwPP/zAtm3bcHNzo2PHjvTt2/e8nlfxD0n9HtaNEWXv4JbiRTq1wbE+tB3MayQhOu8Y6PIVRHSrrtkqaggBAQGEhobqvkfq169fjTNSKGoeNUMAohZhs9n44YcftMrAkydPsmjRIn755RdNqr2oqIgff/zxb3vVpKWl8ccff2gyArt376ZNmzbExMQQFBRE9+7dadOmDcOHD6dRo0b4+/uTkpJCUVEROTmSuJuXl8eJEydo3bo1/v7+JCYmcs0115z3aoFff/2V9PR0QCom582bx6ZNmzR9J4vFwsqVKzl69Oh5Pa/iH2ApgbW3ibEEIifgHQUxQ6SpbsIoKDstxhJA8THZXnHJYzAYuPbaa0lMTMTf35/WrVurHz8KRQWUh+kfUlJSQl5enm7sxIkTuoo2ECGtgoICLBYLvr6+unBVXl4eBoNBp61kx2q1MmrUKIqLiwkJkb5fQUFBjBgxgpycHEJDQ3nttdd0+2RnZ3P33XdrHifnJoO5ubm4u7vj6+urjRUWFlJeXq5rVlhWVkZeXh6hoaGasWW1WsnOziYoKMhlrmVlZbr2LHbs6rGKaqA4XZrwOlNwCPOATdq9Y5wTpl+fv08EK5UG0yVPWFgYI0eOrPR74NSpUwQGBqoG3ooaR0lJiRbtudAog+kf4u3tTd26dXXGQoMGDfDy8uLEiRPaWGhoqBaiM5lM9O3bl3bt2vH9999rXpnk5GSMRiNWq1Xbr7S0lDfffBOLxUJsbCzXXXcdaWlpzJ07l+LiYgICAqhbty4HDx7U9rFXKp46dQpPT08GDx5Mw4YN+frrrzl48CBGo5FOnTrRt29ffv75Z9asWYPNZqNBgwaMHDmS3bt3M3/+fMrKyggNDeW6667DbDbz1Vdfcfr0aby8vEhMTNQ8TCBGXPPmzdmyZYs2ZjQalRu/OvGtBwHJkqd0hhyfznwycSLFxcX4+/tzV0ovfLLmOPaJ7K2MJQUg/cHmzZtHaWkpISEhXH/99VgsFr766ityc3O1VinnkmqguLSYM2cO06dPP29tzz766CP27t3LxIkTXdZVVCS/4YYbuOuuu+jbt+95n0dFlMH0LxgxYgQ///wzmZmZ1K9fn759+2I0GrHZbOzdu5fw8HDc3d01w8hsNvPzzz9jMpl0LUp27dpF9+7dOXToEOXl5TRp0oRff/1VW5+amsqqVavYunUrxcXFgHinAgMDadWqFUePHiUmJobCwkItMby0tJQFCxbQuXNnzaiyWq2sXLmS8PBwVq9erR1///79rFu3jhUrVmi6TtnZ2SxdupSSkhJN9bekpITDhw/TpUsX9uzZQ0hICP369SMsLIwhQ4awbt063N3d6datm+YVU1QDBgP0+BE2PQx5u7BFX8EXK6IoLpbwcX5+Pt8d7cWNzcLg+G+S39T6zeqds6JGYDabmT9/PqWlpYB0jV+yZAlms1nrAl9SUsL8+fNp1KjRBa+Mvtiw2Wxs27aNNWvWkJeXR0BAAB07dqR58+YXXGyxNnLXXXed87YVi6IuJMpg+hcEBAQwfPhwl/H+/fvTv39/AD7//HPdOqvVqvPQ2PHw8OCqq67SQmIVOXnypMt4Tk4ON954I1lZWYSHh/PJJ5/o1hcXF3P8+HEqUlkI7fjx49qXpJ3s7GxKSkp0Y4WFhXTt2tUlr8Gu4KqoJgqPQHkBBDWVZf8G0F36hFnMZrLnvaTbPONkEeWtJpGVlUVYWJgjlGyzifK3ZwT4RFflFSiqkYKCAvLz8/Hx8XH5P5+dne2Sh1lYWEhRURH5+fkEBAToQv3OZGVl4eHhoQv7X6rYbDbN228v6CksLGT+/Pns2rWLkSNHnnejyWq18uqrrzJ//nxsNhtNmjThmWeeIS4ujm+++YbPPvuMU6dOkZKSwrPPPktsbCxpaWkMHDiQcePG8emnn+Lm5sbNN9/MHXfcAUjV5IsvvsiOHTs4deoUSUlJvPDCC//I43jPPfeQnJys9SgcOHAgrVu35qWX5Hvq1ltvZeDAgWRmZmrtT6xWK2+//TZff/01JpOJa665Rne89PR0HnjgAe69915CQ0MpLS3lmWeeYcmSJRiNRu677z5GjBhxXt5X9TPhApGUlKRbtidSOv/HMBgMpKam8s477/Dhhx+yfPlyl9L85ORkl2aC0dHRvPXWW3z66ae8+eabRERE6NZHRUXRvHlz3Zi7uzvt2rVzyUFISUlxEbVMSkpymX98fLySDahprLsTfqgHPzWDJd2hPF+32mQy0aBBA91Y3bp1tXtn4sSJ0ti5+DgsbAELW8EPcbD1maq8CkU1sXLlSt566y0mT57MzJkzXb5HKvseiIqKYsqUKUyePJmJEyeydu1a3fqysjKmTZvGhx9+yDvvvMOPP/54wa+jprNt2zadsWSnvLycAwcOuDQ9Ph8sWbKE5cuXs3DhQpYvX05ERAQff/wxP//8M++++y4TJ05k5cqVtG/fnttvv10zjMvKyti8eTNLly5l6tSpTJs2jQULFgDwxBNPEBUVxZIlS1i3bh1xcXGVhsz+ip49e2rSOcePHyc9PV27h4qKiti4caOLbNFXX33F/PnzmT17NosXL2bHjh3auvfff5/o6GgmTpzI7bffDsChQ4dITExk5cqVPPTQQzz77LPk5+u/G/8tysN0gWjbti1ms5kdO3YQGBhIz549CQ8P59prr2X16tVaX7ilSx2qy6mpqXTu3Jns7GwKCwtJSUkhJSWFxMREli5dSmZmJgkJCRw8eFAL0ZWVlXHs2DG6d+/Ovn37iIiIoHfv3gQEBHDllVeyceNGvLy86NatGxEREdxwww2sWLGC0tJS2rZtS4MGDQgPD+fXX3/lxIkTNGrUiG7dumGz2fDw8ODgwYNERkbSp0+f6norFZWR9Qfsn+xYPrFClpMf1G02bNgwli5dSkZGBgkJCRw6dEh37/z000806n4UQ+6ZULHNAttfgPo3gZ/KR7tYKSgo4Ndff9XyJ7Ozs2nWrBlRUVFkZWXRsGFDunfvjs1mw93dnQMHDhAZGUlpaSm7dkmOnNVqZcmSJaSkpODt7Q1Iu6cjR45o59m0aRMpKSkXvClqTWbNmjUuxpKd8vJyVq9e7fID97/i7+9PVlYWP/zwA7169eKll17CaDQyZswYRo8eTdOm4pG+4447mDlzJmvXrtXEjR9//HH8/PxISkpixIgRzJ8/n0GDBvHSSy9pHsP09HQCAwM5cODAP5pXz549ee655ygoKGDVqlX079+fpUuXkp6ezq5du0hKSiI8PFy3z08//cQNN9ygFRPdf//9/Pbbb2c9R1RUFKNHjwak59yjjz5KZmYm/v7+/2iulaEMpguEwWCgU6dOdOrUSTfeqFEjGjVqBFBpI0KLxUL//v0pKCjQVM19fX3p06cPJ06cICoqij///FO3T15eHp07d6ZevXqEhoZqN0ZKSgqhoaF4eXlpFQRxcXH079+f0tJSYmJiAGmgPGzYMJe5qLLimoddTDCq5LDryoJDnDp1Srt3jEYjPj4+9OnTh6ysLKKionRJ+iB5TbaCw+gDAjYoOKwMplqMzWYjPT0dd3d3necoOzuboqIiDAaDrtgE5Bf+lVdeSXZ2ttZBAKBbt24kJiYSFhbGN998o9vHYrFw+vRpTpw4gZ+fn5bv5ExlY5cSlaVa/JP1/4bOnTvz9NNPM2vWLF599VViY2N59NFHSU9P54MPPmDyZMePrfLyctLT04mPj8dkMmnPBYA6depoHqHDhw8zYcIEMjIySExMxNPTk3/QKASA8PBwkpKSWLduHatWraJz587k5uaydu1aNm3aVOkP85MnT1KnTh1t+e+6fQQGBmp/2yvG/07i51xRBlM10rBhQxehyvz8fN577z1AKu1uuukmDh8+zA8//IDFYsHT05O4uDhdK5SEhATeffddTXF74MCBNG3alBkzZmjaUC1atODKK69kzpw5WuK53RJXobaaj71ayf6LLrl+GCM8/TGY7a5mA+uPx/PTT3LvhISEcNNNN5GamsrcuXOxWCx4eHiQkJDA3r17teM2atQIY3wDOPa942Te0RDepYquTHG+KS0t5fPPP9dyFps0acLw4cNZsGABGzduBERCICgoSGfM+Pr68vbbb2O1WvHx8WHUqFFYrVa++OILSkpKMBqNNGnShNTUVG2fkJAQZs+erSXeJicn6+bi4eHhEha+1AgICHBRUa+4/nyTmppKkyZNmDVrFvn5+Xz55Zfcf//9tGrVitGjR3Pttddq2x44cEATPzabzWRnZxMaGgqIJykqKory8nLGjh3Liy++yKBBgwCYNm0ac+fO/cdz69WrFytXrmTt2rU88MAD5OTksGbNGtauXeuSjwsQERGhE26uLD+3qlA5TNWIn58fN910E8nJyTRo0ICBAweyc+dObX12djarVq1i8eLFmrhlaWkphYWFdOvWjfj4eM2DVVRUBDjc5GvWrNGMJYAtW7awbt06XZVeRkYGGzY4qUAraiw7d+7Uub93HTzJ/vhPIG4ERA0gr+V0ftpYpK0/deoUq1atYtGiRdq9Yy8s6N69O/Hx8XTs2FE8iwnXQ8epENkHEm6EPsuU1EAtZtOmTboCj507d7J+/XrNWAL51d6wYUNatmxJQkICl112GXv27NG8TkVFRfz666/88ssvWjK41Wpl3759DBgwgISEBFq1akV8fLyuSmnXrl0MHDiQBg0akJyczE033YSfn18VXXnNpGPHjmfVr3J3d3eJQpwP1qxZw//93/+Rnp6On58fgYGB+Pv7M2zYMKZOncqBAwew2WzMmzePoUOH6oyQN954Qwu9zp49myuvvJKysjJKS0u1H9c7duxgxowZZw01/hU9e/Zk/vz5eHl5ERUVRadOnVi8eDEmk4mGDRu6bH/VVVfx+eefc+DAAQoLC3n77bd1693d3c9bjtLfoTxMF4ji4mJSU1OJiIjQ4r4Wi4XDhw/j7e1NdLRUIsXExDBy5EiASuPBp0+fdvl1kp+fT4cOHYiJiSE6OpqZM2fq1peVlVXqBndue2AnLy+P48ePk5+fT0JCgq5HnKLmUJnb/nh5DD713nKqcjyoW3+2e6d9+/bExMRQp04drUrOHHcDh61d8ff3JzJAXwSgOL9YLBaOHDmCl5eX9j1wPqns4XHy5EmXsbKyMrp06UJubi5hYWEsWrRItz4vL88llFFaWkpycjKhoaEEBwdXmksSGhpK+/bt//X8S0pKOHr0KOHh4QQHB//r49QUmjdvzs6dO10Sv93d3UlMTKRZs2bn/ZxXX301hw4dYuTIkRQWFlKvXj3effdd2rRpQ15eHmPHjtVEht9//30SEhI0IzsoKIjevXvj7u7OfffdR+/evQF4/vnnee6553jkkUeIiYnhmmuu4cMPP/xL71llNG3aFA8PDzp27AhA48aN8fLy0s5TkWHDhpGVlcWNN96IxWLhxhtvZMmSJdr6q666iueee45Dhw5d8Fw5g+2fBiGrAbtQ1dKlS/82flkTOHLkCF9++SVlZWUYDAYGDBhA06ZNmTp1qvZrrLJO4Gazmffee0/3cLz22mvZvHkzu3c7mqg2btyYffv2YbFYMBqNNG7cWOeZSkhIoEePHkyfPl0b8/b25o477uDjjz/WfjEaDAYaNWoklVKIa/jmm2++KL6kLjZOnTrFhx9+qD3A3NzciIuL49ChQ4A8pMrLy3X3zsiRI9m+fbvu3khKSuLAgQOYzWaMRiNDhgwhPj6eqVOnavu2adOGK664ogqv7tKhsLCQqVOnaj9emjRpct5Knu2kp6fz6aefavklnp6e3HnnnXz22We6h1tycrKWwO3r60tQUJAu9NG7d2/MZjPLly/XxmJjY7U8KJCHn3PVUkBAAPfee++//uF19OhRvvjiC00XbsCAAdqDtTZjs9nYvn07q1ev1nSYOnXqRLNmzWqMDlNFQUiFK1VqMC1btoyJEyeSlpZGaGgoY8aM0cVSz0ZtM5imTp2q66nm4eFBmzZtdKKRIJoTsbGxurGcnBxWrlxJQUEBLVq0IDk5mbKyMv744w8yMjKoV68ef/75p85b5OvrS/fu3dm/fz/h4eF07doVb29v9u3bx6ZNm/D09KRz586Eh4eTlZXF6tWrKS0tJTExkfnz5+vO37ZtWy1GrahZpKWlsW7dOmw2G3Xr1nXxCHTq1ImysjIKCgpISUmhSZMmlJWVsXLlStLT00lISGDLli06RXpfX1+Sk5NdQrNjx451qVZR/HeWLVumM0AAbrnllvPeTujQoUNs2LBBC/lERkaSnZ3NypUrKS4uplGjRi4l/02aNCEoKIgTJ07QsGFD2rZtC8DatWs5ePAgERERnDx5UvuBBaLuf8UVV7Bnzx78/Pzo0qXLf/rBNX36dA4fPqwte3h48NBDD6mWLFWAMpj+niqLv2RlZTFu3DgmTZpEjx492LFjB9dddx3NmzfXShwvFiq6KO0PsYrk5+ezd+9e3NzcqFevHkajkeDgYFJSUigoKCAxMRGQL42UlBQiIiKIi4vjjz/+0B2nuLiYxo0b4+PjQ3h4uFbim5CQgMVi0VXJhYeHk5KSQmlpqa7nnB37L0fFf+P48eOcOHGCevXqaeJ++fn5HDlyhMjIyEqNkbKyMvbv34+fn1+lD9Do6Git/NguDeCM1WqlRYsW5Ofn6+6dXm3qQr3jEB7nYrQXFxdX6lKv7D6w2WwcOXKEkpISEhMTtYdYVlYWWVlZumt1Ji8vj6NHj1KnTp0q6fdUk6nsvbZ/D9hbC9lVtI8ePap9D9hDp9nZ2WRkZBAbG6tVAxUVFXHo0CFCQkKIiooCxBNUVlamq5ILDQ2lRYsWFBUVVfo5lZaWkpKSot23ds9H06ZN8fPzIzIyUtPksWO1WomMjMTT0xM/Pz/NWLJarRw6dAiLxUJiYqJWcZeenk5OTg7169fXvqdyc3NJTU0lJibG5XuyrKxMuw5FzWfGjBm89dZbZ10/ZcqUWi10XGUGU0REBKtXr8bPzw+r1Upubi5ubm5nVYqtzbRs2VKnr9SoUSPatGnD9u3bNTe5n58fy5Yt03IL6taty0033cT333+vubh9fX257bbb2LdvHwsXLgTkF13Dhg11v/Lq16/Pe++9p4VrevXqRatWrfjss8+09iYNGjTguuuu44svvtBapgQGBhIcHExOTo52rBYtWlyot+WSYfny5SxbtgwQ8chRo0ZhNpuZNWuWloDdr18/OnfurO1z6tQppkyZoj1QK4Zsy8vLmTp1qiYrEBERgY+Pj2bYGI1GsrOzmTJlCiD3zi233ELoqdmwfixgA4OJlvXfYeU2x0O7WbNmNG3aVAvNgFQ+VfR82mw2vvzyS/bv3w/IvXPbbbexadMml2t1ziPYs2cP33zzjZZMPHDgwP+U31LbadGiBX/++af2PeDr68tvv/2mfQ/ExMRw88038+OPP2oFGr6+vtx6660cPHhQM1iMRiMjRozAz8+PmTNnaiGsLl260LlzZ6ZMmaJ5oePj4xk9ejSzZ8/WQvt+fn6EhYXpcps8PT356KOPAMdnaa/OtN+3TZo00V1PZGQkX375pe6+HTp0KNOnT9dyYsLDw7n11ltZtmwZ69at08510003kZ2dzZw5c7DZbBgMBpKTk3Vzatiw4UX5jKiJ1K1bV/dc+TeMHj1a00C6GKnSDF8/Pz+Ki4s1Ucfbb7/dJUkrLy/PJcHVudqrNtClSxd8fX05cOAAERERdOzYEQ8PD2688UY2b96Mt7c3Xl5e/P7779o+aWlprFmzRpcPUFhYyOrVq9m6das2ZrVaycnJYeDAgRw5coSYmBj27NmjS860C1PajSWQvnGrV6/WNe09ffo0HTt2xGAwUFBQQPPmzSutUlCcO6WlpbqQi9ls5vfff6e8vFx76AD8/vvvtG/fXsv1WL16tc77sH37drp06aLpj2zfvl0zlkC8Or179yY/P5+ysjISEhJ0DScLCwtZvWoVV5Q+AZyJutvM9PF4n8DLp3H48GGio6Pp0KEDJpOJ66+/nm3btuHn50enTp1ceoUdPnxYM5ZA7p3Vq1ezfv163bX+9ttv3HzzzdrYsmXLdHo/y5Yto23btpdsL7LY2FhGjx7N5s2b8fT0xNvbW/c9cOzYMdasWaOrZi0sLGTVqlW6XDSr1cqvv/5KUFCQZiyB3Edubm66kP2RI0dYvXq1Lg+yoKCAxo0b07hxY3Jzc11CdGe7b/fv389VV13F3r17CQ4OpqCgQKcnt337diIjI3VVeidOnGDt2rWasQTy/8SeZmA3Hu0ezKFDh2rpBReigkyh+LdUeUmUp6cnmzZtYs+ePdxxxx3Ex8frkh6nT5/OpEmTqnpa5xWDwVBpj7V69eppbU4qqy45W2ikYulmaWkp9evXx83Njbp16+oMKpAvu8pCKpUd32azMWDAgL+9JsW5UfEBA1L1U1lbBHsoIjg42KWfH8iPhxMnTuDu7l5pCM5oNJKYmEhpaSk+Pj4u60tKisGir5gylOdSr1493NzciI6O1gw2exNnf39/rQzcbDazd+9ebDabi8ghSDivYhVVSUkJx48fJy0tjfj4eJf+ZGVlZVqxwqVKQkKC9kOxYj4TVP7/tLi42OUeKSkpcXl/rVZrpftXlhJgsVioX78+ubm5REZGunyWlX2+5eXlREVFYTabCQ4OdhHRPdu5KptTZfMvLS2lRYsWtGzZ0mV7haK6qXKDyWg04uHhQfPmzRk5ciRLly7VGUw33XSTi+p0ZmYmo0aNquqpXlBSUlJYtWqV9iD19vamc+fO7Nu3T/t1aDAYaNOmDe7u7mzevFnbNzo6mvfff19bbtKkiU5Ho3HjxrRr146tW7dqD7qAgAC6dOnC9u3btbJjNzc39cV0nrG3FHB2bbdu3Zry8nJdKWxCQgKTJ0/WPv+GDRtiMBi0X9shISHMnz9f+6yioqLw9PTUHpqenp7s3LlTa+js5+dHaGio7t5p1boNnLwJDnyqnfe4/xA+/uAD7TwDBgwgMTGRKVOmaA+v5ORkhg0bxqeffkpWVhYAwcHB+Pv7a/MxGo20a9eO4uJi3bWGhoZqYR37sZw9nSkpKSofxYnmzZuzcuVKzUvk7e1Np06dOHDggJacb/8esP/YtNO6dWuCgoJ0QpL16tWjQ4cObNmyRTN2fH196dy5M3v27NHC7waDgfz8fGbMmAFIrlt8fLyurUmbNm3+8X0bFhZGly5d2Lp1q2bku7u707FjRzIzM3Vzbd26NRkZGaxcuVIba9WqVY2pGlMoKlJlVXLr1q3j1VdfZc6cOdrYa6+9RnZ2Nq+//vpf7lvbquTOlaysLDZu3Iibmxvt2rUjODiYwsJC1q9fr1U6xcXFYbFY2Lhxo1Ylt2zZMp3Okre3N1dccYXWS65t27a4u7uTnp7Opk2b8PLyol27dgQEBJCXl8e6desoKyujZcuWF0QH5lKnvLycDRs2kJWVRaNGjTT14+3bt3PgwAHq1KnD/v37dSEuEBmAAwcO4OfnR3l5udaSwM7ll1/OqVOnsNlshIeHu1Q4durUCQ8PDy28Gh8fD1YzHPgEstdDZC8mLcglO9shNOjl5UXjxo11BjmIuFxFL2jPnj0xm82UlJTQqlUroqOjddfasGFD5s+fr/OG2TVdDh48SFRUFG3atNESgBXCiRMn2LhxI0ajkbZt2xISEkJRURHr1q3TfZYWi4U///yT9PR06tWrR/PmzTEYDOzdu5fdu3cTGhpKu3bt8PDwIDMzk02bNmEymWjXrh1BQUEUFBSwbt06ioqKqF+/PrNnz9bNIzk5mdjY2LPet5GRkRw4cOAv79t27drh6+tLTk4O69evx2Kx0KZNGyIiIigpKWH9+vXk5OTQpEkTGjRogM1mY/PmzRw9epS6devSqlWrS9r7qKjZVJmHKTk5mePHjzN16lRGjx7Nli1b+O6772p9+O2/EBERwcCBA3Vjvr6+Lt2a3dzcdImyP//8s259eXk5ycnJLgmZ0dHRLgZRQECA6hF3gTmbem+zZs00kbrKkit9fX2JiIjAz89PlwNix2QyaRVPlT1UbDYbkZGR+Pj4OEq7jSaOeg4inVbEe8VTVvaVbp/y8nJdDoydykKEBoOByMhISkpKtAotd3d3IiMjMRgMhIWFVRp6bN68+XlvLnoxER4ezmWXXaYb8/Ly0hL77Z+lm5sbkZGRWCwWwsPDNU9MWFgYkZGRhISEaN67wMBAIiIicHd315Km/fz8NHHAytpLmM1mIiIiMBgMuirOiIgICgoKiIyM1LXVcV5fsSWKr6+vNld7ibqnpyeRkZF4eHhorTfOlr6gUNREqlSHaceOHbz44ovs3buXqKgoxo0bR//+/f92v4vVw/Rv+f3333W//jt27KjykGoZO3fu1P3Cr1u3LllZWZrxUrduXY4fP64ZIAEBARiNRs2zGBQUhM1m08JdJpOJqKgoLeTh4eHBLbfcwr59+/j111+18ziLFQK0b9+e5ORkZsyYoYVVoqKiuP766/noo4+03BMvLy8CAwO1B62Pjw9jxoxh7dq1rF27FnAVQgXxSvXo0eP8vXGXCPZWECCf5a233sqePXu0ikSAoUOHEhAQwBdffKGF3lu1akWvXr2YPHmylksUGRnJmDFjXMQknfXiKn52BoOBa665BrPZzHfffafdG40bN9YljycmJnLDDTfojltWVsYnn3yiVbsFBARwxx13sHjxYi2Z3c3NjdGjR593/SnFf+f48eM647s2kZaWdkFtBKX0XUvZuXOnViVnd80rahdHjhxh165dhISEkJ6ezpYtW3Trhw8fTkZGBh4eHri5ufHLL7/o1vfp0wer1UpZWRlRUVF8++23uvUtWrRg165dOg9SSEgIffv21arkUlJSMBgMpKens23bNvz9/WndujVeXl6cPn2aTZs2YbVaCQkJ0VXhgRhbGzZs0CWEx8TE0L59e44dO0Z8fLyL11Px96Snp7s0IW3ZsiU7d+50+SyDgoJ0la8godmKelvDhw930bsrKytj06ZN5ObmkpiYyFdffaX7LOvWrYvZbNZVKbu5uXHttdeyf/9+QkJCaNWqlcuDdcuWLXz//fe6sa5du7roxyUlJZ2TcLGi6jh58iT9+vXj999//1vxyrVr13LPPfecUz/S3r178/jjj1/Q6Mbrr79OeXk5TzzxxAUT4VSNw2opTZo0UQ+jWk58fLzkGYHLAwbQdMrc3d0rDZuBhD5MJlOlrSgsFotLdZvFYsHX11d72Q1tLy8vbcx+LA8PD3x8fLDZbJWGAM1mMxV/b1mtVlJSUkhJSfn7N0BRKRWrLEHe68o+y8qqFyvbv6SkRFOJb9asGb6+vri5ueHj40NZWRmenp6VfpYVj2Wz2bR7xcfHR8tHKygoYPv27bi5uVU6p4rVdmeb50WLzQqHv4Ldb0FRKvjEQuPxkHAdGGpOzlZJSUmtFS8+deoU/v7+F/QcymBSKGoA7dq1Y/v27dpDJDIykh9++EGrXAsNDcXPz08Ls/j6+rJlyxYt7OHp6UmdOnU0b4A97y0gIECXPB4dHc3UqVO15e7du9OoUSOmTZumPdQ2b97MNddcw+TJk7UQoK+vr64Kz94802q16hLGL4a+X9VN3bp1qVu3rpbH5ubmRocOHfD399d5jjp27EhQUJCulUhycjIdO3Zk69at2r0TFBTEihUrtPDtypUrufPOO/nxxx+1nCS7yrhzA/AOHTpgNpuZN2+eNpaYmMjUqVM1o2jXrl0MGDCAyZMna+HboKAgrcAEHJV/2dnZ7Nu3D5CQX4cOHc7r+1ZjsVlhxVWQ8QtYzsgrlGbBujsh9Vvo9t15N5rS0tIYOHAg48aN49NPP8XNzY2bb76ZO+64g9LSUiZOnMiiRYswm8306dOHxx57DB8fH00st0ePHkyZMoXExERefPFFNmzYQHZ2NnFxcTz++OP/SR/r9OnTvPrqqyxfvhyTycTQoUMZN24cJpOJ9957j8OHD1NUVMSaNWuIiorigQce0DxTP//8M2+++Sa5ubn06dOHgwcPMnLkSE6fPs28efMwGAykpqby5JNPAhLa/u6778jJyWHo0KE89dRT/6moQBlMCkUNICYmhjvvvJMdO3bg5+dHTk6OztDJzs5m8ODBFBcXY7PZ8PX11QkNlpaWkpCQQJs2bSgoKKBp06aEh4drD197L7mKlXWrV68mJydH5wE4fPgwK1eu1FViFhYW0q5dO7y8vCgpKaF58+aEhIQwePBg6tevr1XJqZyU/47BYODGG29k69atus8yJiaG2NhY7bO0t78ZM2YMe/bsITQ0lGbNmuHm5sZdd93F1q1bNW+hc6FIfn4+q1at0iVwW61WTCYTV111lctnGRISolXJ2UO0dnbu3ElgYKBOZyk3N5eBAwdiNpuxWCy0aNGCgIAARo4cybZt28jJySE5OVlr43LRc/grvbFkx1IIGUvgyCxIuP68n7asrIzNmzezdOlSjh07xi233EJMTAybNm1i165dfPfdd3h6evLYY4/x4osv8vLLL/Pdd9/Rp08fLST31FNPUVxczIIFC3B3d+fNN9/kxRdfdGmR80949NFH8fDwYPHixRQVFTFu3Dg+/vhj7rnnHgAWLlzIBx98wLvvvsuHH37Is88+S58+fTh8+DAPPfQQ7733Hp07d+azzz7ju+++Y+TIkdxyyy3s2bMHf39/LSQHcODAARYsWMCxY8e4+uqr/3NOpTKYFIoaQnh4uFYh6ZyobedsApJ27Ho4VqtVt53NZtNe/wUPDw8Xr4DRaFQVcBcADw8PrfmtHXvrkIoVaTExMcTExOjGAgMD6datGwAbN250Of7Zch6tVqvLveJ8/5xrrqSXl5dLWNZkMl2a1XC733I1luxYCmH3xAtiMAE8/vjjmjbciBEjmD9/PqtWrWLGjBlaX8cHH3yQQYMG8eyzz7rsf9999+Hh4YGHhwfp6ekEBARo2mz/hpMnT7Js2TJWrlyJn58ffn5+3HPPPTz++OOawdS8eXPte3DIkCG8//77FBYWsmDBAjp37qwZPHfeeSdffvnlX57v/vvvx8vLi8TERBo3blxp9fE/QRlMCkUNpHXr1mzYsEHTNAoNDWXZsmXaL3kfHx9dLzBvb28OHjyoVbGtXLmSm2++mV27dmlhnJUrV5KcnKzzHHXq1ImkpCR27dqleZnq1auniZzat/X391d5SbWUpk2b8scff+g+y06dOnHy5EmtKs7NzY3y8nItl27lypUMGzaM8vJynVeyQYMGGI1GzSBv2rQpnTp1YuvWrdq9GRoaSuPGjavuAms6Ral/s/6/PcTPhslk0hnSderUYd68eZSUlHDrrbfqjF+TycSxY8dcEvizsrJ4+eWX2bdvHwkJCYSFhf2nH152oV1nGQ2bzUZ5ebkmZWKXnLDPC8SQz8rKIjIyUltnMBi01lFnwy5/AiKBUlH25J+iDCaFogYSFBTE2LFj2b59Ox4eHhQXF+uq5IqKiujUqZOWtBscHMysWbO09VarlXXr1ulKwEG+sG699VatSs4e1hk7diw7d+7E39+fJk2aYDKZuPPOO9m+fTtWq5VmzZpV2n5FUfPx8vLizjvvZNu2bVrSt4+PDyNGjGD37t3k5uYSHx+vNW62s2bNGpdk7YMHD3Lrrbdy6NAhQkJCaNy4MUajkbvvvpvt27djMplo1qwZHh4eVXmJNRufWMlZOuv6C1P5bTabyc7O1gyQ9PR0kpOTycrKYvbs2dSvXx+Q0F1qaipxcXG6fpUA48eP5+qrr2bGjBkYjUaWLFmiyYj8GyIiIjAajaxYsQJvb29ACgays7Px9PT8y33r1KmjqyS22WyV6oldSGpOer5CodDh5+dHx44dad269Vm/TPLz88nLy6u0CsnNzc0lwdHNzY38/HztZfcUFBUVaWP2Y5WWlpKXl0d+fr5Lzy9F9bF7925++ukn/vzzT+3zS09PZ/Hixfzxxx8OpfXSMjh8DA6m4WWVwoL27dtrhq8b0DQglC6RsYS5e7qE20wmk4squ9FopLi4WLvv7EUKJSUl2pgmelpuhqMZsD8V8s8SkroUaDwe3HwrX+fmC40fuGCnfuONNygtLWXXrl3Mnj2bq6++miFDhvDGG2+Qk5NDWVkZr732GnfddReAZujaWyAVFBTg5eWF0WjkyJEjfPDBB//JS1OnTh3at2/Pq6++SmFhIQUFBfzvf//TkrT/isGDB7NmzRpWrFiB2Wxm+vTpOskLe5eDC4nyMCkUtYDmzZuzevVqTp2StiZBQUGsX79eq0QymUzExcVpQoQeHh506tSJsLAwli5dqh0nMjJSJ5hp/9U5c+ZMzdW+fft2Ro0axeTJk7US4/Xr13P33XfrXNyKqmf16tW6BO60tDRatWrFtGnTNONp69at3HXbGIx/7oKyMw+39Cxo0wS8vRwH27oP8uQB4wW0b96CNVukV53RaKRr166YzWa+/fZb7d5o0KABX3zxhXaIffv2MWjQICZPnqxJX/z555+MvftuvHcchKISx/lbJEGg34V4W2o2CddB6mzXxG83X4jqB/EXTovK3prI3d2d++67j969e9O+fXsmTpzI0KFDKSoqokWLFnzyySe4ubkRHh5Or169uPzyy3nnnXd46aWXePnll3nrrbeIiIjg2muvZcKECbqegP+UN998k1deeYV+/fphNpvp2LEjb7/99t/uFxsbyyuvvMIzzzxDQUEBAwYMIDo6WgsjDhw4kPvvv5/rrruOCRMm/Ov5/RVKuFKhqCWUlZWxa9cu7eFVUUiyQ4cOJCQkUFBQQFJSkqZJkpqaqlVWffHFF9qvRxCPU1JSEjt37tQdq2PHjqxZs0Y31qdPH7p27XohLk1xjrz77rtaA10Qw6Zp06aagradGwcNoX5hBZ2j+ChIOJPTUlAEG/WfOSGBHPIzkZWVRYMGDbRQzokTJzh48CCRkZEsX76cQ4cO6XZr164d69ev141df/lgGhZVKFCIDIXG9f7hFV8k2KxSDbd7ouQs+dQVz1L8tRdEh+lCCTdWJ+np6RQVFdGgQQNtrHPnzrz++utV9r2kPEwKRS0hLy9PS5p07vVlx2g0kpGRQX5+PmFhYfj7+2OxWMjIyCAjIwNPT0+X3BJ3d/dK8028vLxcxlReSvVT8TMwmUyVfi4enp5QWEGA0AYcTAOzGYIqeYgaDdQzeVMvMAKsjvBcuM1IeGAEmLxYUyEp2GAwVHqvuLm7AxX6EV7KTZcNRqmEu0DVcJcCWVlZjB07lq+//pq6devy9ddfa03kqwplMCkUtYC8vDw+/fRTLT/E3d2dmJgYjh07Bki/rj179mghu02bNnHDDTewY8cONm2SMMuWLVto3Lgxp06d0rxUPXv2pH79+uzevVvLU2rSpAmdO3dm165dWlJlaGioqpKrAfTs2ZPZs2dr4bfu3btr/QHt4dNGjRpRt2lj2Lwb8s8YTV6ecPwklJ4J0WWchJAAOCUhXdyMknN08EzF1vFsaGAGq9VpDLrWrcfBQ4e0PJY2bdrQsWNHtm/frnm+oqOjiWuWDLsPQbaIZeJugroRF/bNUVQLv/zyCw8//PBZ17/00ktcfvnl//k8LVu25I477uDGG2/k9OnT1K9fn48++gg/v6oL86qQnEJRC6iYuwLSnykyMpKysjK8vLx0uSUghs/u3bt1mkx+fn7ccsstHDlyhOjoaK1Mt7i4mL179+Lv70+9evUwGAxYLBb27duH1WqlUaNGlbZfUVQ9OTk5HD58mDp16mjijyUlJezduxdfX1/q168vCdxWqxhEVisYgJ36nnPUCZMwWUkp+PvChh369b7esm+x3lNU0KIB+w8fJiQkRBO3LC8vZ+/evZhMJho0aCDJ4jYb5OSJIRYaCOr+UdRy1B1cC7Faraxfv15rvtuhQwf1MLvIsZfgVmTfvn2UlZVRr55rboi3tzeenp6OqqkzY/v37+fw4cMUFhYSGhqKyWQiNTWV/fv34+fnR1hYGAEBAbi5uSk9nerEbIbU45I4HRooBo7NRnBxOcEeAVBmA4sF3NzwKiolxd0PbCapjvPyFEPnVC5YbZUnWxsMkJ0r22MAo0G2tWMyicHkHFpzM+IXEOASBnEvLqWpm48cs6RMjK3SMjh1WgwmdxOEBMp8045DQTEE+0NUuOyjUNQC1FO2FrJkyRItIXfXrl2cPHmSoUOHVvOsFBeSpk2bsn79ei2HKTIyktWrV2vG0LZt22jYsKHWq8vX15fOnTsTFRWlCQ8ajUZCQ0NZuHAhIPfOiRMnaNKkiU7Dac+ePfzf//3ff+q5pDgPbD8Ap88k6J/MAbPFUapvHysohqgw2LbPsd/JXGiZBJt2i4ECkHUKgvwh98zxPNwhN8/hPTqRA2HBckwAoxESosVg2rHfYUjFR8s6ZwqLYfMe8SgBZOVA2yYyVlrmOH/zhhIKtJ/jZI6ECOvpVcoVipqKMphqIc7iXSBlxIMHD1YPuIsYd3d3brvtNg4dOoTNZqOwsFBTZQYRcQsODua2224jPz+fxMREPDw8CAkJoV69emRkZBAbG8vkyZN1x92+fbuLrkpOTg5Hjx4lISGhCq5MUSklpQ5jyc7xbDGYnDmZgwulZeKZsjhVydlsEOAnRpDd47N5j34/iwXaNoWiYgj0F6MKoEOKGFp+3uBTiacz65TDWLIfJ/W4w1iy42wsOV+TMpgUtQRlMNVCfH19dWEWX19fZSxdAhiNRk2Zu2JpN4hEwKZNmygoKMBqtdK0aVOtAWd6ejo5OTn4+vrqGqV6e3tXmjRZlYmUikowubmGyDxMEr5yNkRMbuDp7rq/11kqGjOzHTlFlZ0z86R4jMrKIToCq83G2o0bOHjwIBEREXTr1k2q4k7myLHc3aCy6snKzu/pLucwOxlyHpXMXaGooSiDqRbSr18/Zs+ejdlsxmg00q9fv+qekqKKSUhIoHHjxlrrk/DwcLZv365pLO3duxeDwcCOHTs0jaUDBw6QlJTEqVOntHunf//+xMXFsWfPHk0Es23btlpjTkU1YTKJZpK9Qs3kJstmC2zffya3CKhfV3KDsnMldwgk16lupCRc55ypgvP1FvFIu7GSnQvhwRKKAzFciksdyzl5YLby24FdrFixAoD9+/eTlZXFqIFXwI4DTnN1E+9TwZkfccEBcv6iEjHAQAyo2Doyj71HZMxoVN4lRa1CVcnVUoqKijh27Bh16tTRBAoVlx7p6emUlpZitVr5/PPPdesaN27Mnj17dM0yfX19GTt2rMu9YzabOXLkCP7+/kREqPLvGkNxqRgegX5imIB4iPIKwNfH4cmxWuF0gYTa/Jx6/uUViJeq3Aw7D+iPHRkKcXXE0PL2hHXb9et9vHh39a86oUyAJ4aPwnQyV79tk0Q5t9EgoT87BUVy7kA/R+5TSZloRAX4yT6KGsXx48cJCQlxacRbk7BYLGRlZWlVolWFiuPUUnx8fGjYsKEyli5xoqOjqVevHkFBQS7rAgMD8fXV97AKCAio9N4xmUwkJiYqY6kmUVoGqRnySs+SPCGrFY4dh6OZkJrpyGk6kSPJ4EczJKRmJ8BPkr29KulF6GaU4xzNkERxtwqPA08PF5VoLy8vjN6VHMvLU87jbCwVFjvmlCX6YJSbIS1TznvsuMNTpqgRnDx5kssuu0yX8nE21q5dS9u2bc/buX/88UeuvfbsbWKSkpLYtWsXAA888ACLFy++IPP4K6rUvF+5ciVvvvkmhw8fJjQ0lNtuu+0v3yCFQnFuhIaG0rlzZ1atWqUtd+nShbi4OObOnYvZbMbT05P+/ftX80wV58z2/eKhAfEe2c54io5lyVheARSXSGn+bqecttx86NBcr6zt7wPR4ZB+QpZ9vc9ICpQ7jh8RIoaXzSaen3p16RvSly+//JLi4mLc3NwYMGAAxtg6ou9kN8yiw+X4zlgssGWPw6DLzZf5ZJxwhAnzCiRE2CDuvL1lNRabTYzGtDPJ8J4eEraMCKlRsgolJSWaAGpVM2TIEIYMGXJO29oFequaKjOYMjIyuPfee3nttdfo06cP27dvZ8yYMcTExNCtW7eqmoZCcdHSr18/2rZtS0FBATExMRiNRpo0aUJCQgInTpwgKipKtTepLRSXOowlOydyXKvkcvJcW46Um8VACQ3SjzeMl4d0uVnapGzerV9fVg4dUyQEGOALRiN1/X0YP3486enphIWFOTyWbZpAfqEYVt6urVHIzXeda9Yph7HkfE0Xu8Fks0nOV06ew6NWbpZcrhM50DTxvBtNaWlpDBw4kHHjxvHpp5/i5ubGzTffzB133EFpaSkTJ05k0aJFmM1m+vTpw2OPPYaPjw9XX301AD169GDKlCkkJiby4osvsmHDBrKzs4mLi+Pxxx+nU6dO5zyXl19+mfz8fF555RUAxowZg9lsZtq0aQA89dRThISEEB8fz/Tp07UemdOmTeOzzz6jtLSUW2+9VTveSy+9xIYNG9i0aRNHjx5lwIABWK1W3n77bebMmUNpaSmjR4/mnnvuOU/vpoMqC8kdO3aMK664gn79+mE0GklJSaF9+/b8+eefVTUFheKiJzg4mNjYWK1qsqioiF9++YWFCxfy66+/ah3lFTUcD5NriMzbU1667dxdx0CkATbtEi+Ps0dnf6q8The4PqQ93OFAKhw4CkfS5eFuteKeepz4kwX4pmY5KvQyTp451lE5Lsh5tuyR8xZVEtLx9nStiqts7hcbdkOxYvjRapXxrAvjLbFXyC5dupSpU6cybdo0FixYwIQJE9i+fTvfffcdixYtIjs7mxdffBGA7777DoDff/+dVq1aMWHCBIqLi1mwYAEbN26ka9eu2rbnSq9evTTPd1lZGVu3bmXLli3ad9Hy5cvp06ePbp/ffvuN999/nw8//JAVK1aQlpamrXviiSdo27YtDz30EE8//TQAhYWFlJaW8uuvv/Lee+/x3nvvsX///n/3xv0FVeZhatu2rS7OmJuby4YNG5TgokJxAZkzZw4HDkiy7/HjxyktLVX/52oDbm7iedl3RJK2PT2kosxilVBdWbkYVA3izghS5jn6xkWEwMFjjmOdLoA2ybB1n0ObKb8QIkNEZNJmAx8v8WgVST9BCorFC2W1OkKABcUSAqwbKfPSjl8IrZJEPNNeYJBXKPOwGwN+PlIlF+Ar4UOLVYynxNgL9hbWGNL+IlfLapX1kaEX5NSPP/44fn5+JCUlMWLECObPn8+qVauYMWOGVgn74IMPMmjQIJ599lmX/e+77z48PDzw8PAgPT2dgIAAsrKy/tEc7F7vAwcOkJ2dTaNGjcjNzWXLli1ag/DmzZvrDJyffvqJIUOG0KxZMwAeeeQRZs+efdZzmEwmxo8fj8lkon379oSFhZGWlkaDBg3+0Vz/jmopUcjPz+fuu++mRYsWLpZlXl6eVt5sJzMzsyqnp1BcFJjNZs1YsrNnz56zbK2ocdQJk7BaSZmU7ds9Qh2aS/6Qt5ejcq51ExkzuckD2BmbDY6d0AtZAmCQEFxZuey3dpt+dXau64M+r9AhPWDHYpHjVyy49nCX45stkjMFoibeMUAML19vV9Xwi5GKAp7/dP2/xGQyERPjkG2oU6cO8+bNo6SkhFtvvVX6DTpte+zYMZfKuKysLF5++WX27dtHQkICYWFh/NPCend3d7p06cKqVavIzs6mY8eOnDx5krVr1+Lu7k7v3r11cwFJPnc2dgICAlwKEJzx9vbWpRt4eHhgNpvPuv2/pcoNpkOHDjF27FgaNGjAG2+84SK4OH36dCZNmlTV06q55G6HTY9AwQGIvQpSngdzAfz5IGQth9AO0HoieEdW90wVNQw3NzeCgoLIzc3VxpS+Ug0g7bgkP5tMorwdHCA9146ki+clOhyiz1QruptcS++NRmmWWxG7UVJZTlGAr5zTGTejeHtKSsWQqSgq6e0lBlOJ0wPdw13OU9Foquz43l7iGasYdTO56edvtYpH7FSueLrqx8q/6VmSpO5mlJYsIZWIbdYGPD1c87kqrr8AmM1msrOzCQ0V71V6ejrJyclkZWUxe/Zs6tevD0iYLDU1lbi4ODIyMnTHGD9+PFdffTUzZszAaDSyZMkS1q5d+4/n0qtXLxYvXszp06d58MEHyc7OZubMmdhsNm6//XaX7SMiIrQ2UCAhN7vGXHVSpeb9+vXrGTlyJH379uXdd9/F09M1fn3TTTexdOlS3atiF/ZLBqsZfrscMhZC/l7Y+SrsfA3W3QUHp4oRdeRLWD26umeqqIEYDAYGDx6Mj49UMAUEBDBw4MBqntUlzolTkidUVCK5P9v3iddm+375t7AY9h0V786/pU4ohAU5lmMixFsVH+3wUgX5i9GTc6afXGqmGCT2BHJvTxHFTIx1SBKY3CRxPCZS9gc5Xny0HD/GSZIiNEjmcS4cTheJgeJSyD4t78XJHHkfCosd709JLc2/qxt5dk+a0SjrLxBvvPEGpaWl7Nq1i9mzZ3P11VczZMgQ3njjDXJycigrK+O1117jrrvuAtC8NHbjpKCgQKQkjEaOHDnCBx984NJK6Vzo0aMH69ev58CBA7Ro0YIOHTqwdetW9uzZU2kC+bBhw5g3bx6bNm2irKyMiRMn6jxbHh4eFBQU/Ju35D9RZR6mo0ePcueddzJ+/HhuvPHGs273d663S4rTO6AoVT+WvhByK7jOM38GqwWMFaplFJc89evXZ/z48Zw+fZrg4GDVQqe6OVWhSsxqg8xKwlmnTrtWuZ0rRiM0bSChHoPBkWidEC1GjcUiYbhNlVTJdUoRqQFvT4dx1b6ZGDOeHo5E9BZJ4plyc3N4wBrEQVyUXMs/8ZqcOq1fLi5x9WDZbJKnVacWekjtcg0VE7+NRvEuRoRcsFMHBQXRu3dv3N3due++++jduzft27dn4sSJDB06lKKiIlq0aMEnn3yCm5sb4eHh9OrVi8svv5x33nmHl156iZdffpm33nqLiIgIrr32WiZMmEBqaurfn9yJkJAQGjZsiK+vL+7u7gQFBdGgQQOioqIqdZx06NCB//3vfzzwwAPk5eUxYsQIndbc4MGDef755zl06BAjR478r2/TOVNlSt+vvPIK06ZN037t2rn++ut5+OGH/3LfS1bpuzwP5kaD2dH7iwZ3iMF0crVjLCAZrthZ9fNT1HxKSuWXen4hBAVAwzilrlydpB0XD5MzyfVh10H9WEyEeKDKyiEiVBK+S8tcP0ujEQ6mivCkt6cYLX4+cp5jWWfCWVEQHiKGyaFjEh4KD4H04/pedREhcr6iEvE2NYgVQ2V/qjzsfb3l+N6ecCRD2p64m2RuIYHiPTuSIWHFmHCoW0cSyfcfFYMrNEg8VlarXEdunoTmDIhnyY7JTYy7/f/f3nmHR1Wmf/87NZMyaZPeQyChhBISQEBdQBRFaTZwMbasBfBVV1dFV2VRwWXVVVBAfzZEUWClKMKCiwI2QIqKBEiA0JKQ3tvU8/5x5+ScMzMhoDBp9+e6cpF55pznPKdcOV/u6nSdBvdx74rsDHi4DpP4ztyzZw8bIC4iHvvL+dRTT+Gpp57y1OG6Bjp/YOi7wN5ZgKUCCBkB9H8eaCoGvr8ZqD0K+MQBw95r75UyHZXDeeTSAOiFBoHaWDDtQ1QoZa2VVZLYiY+kl2ZDE1X0dgjkTjtbKomZM0X0gi0pd72XBi+pGKXFSq6rnrFKUXYoDxikA7KPSXPmF5G1pqyS4pYC/EigiW6vorLm5r8OoLhcmv/QcXrRn2qOLzFbaN4BKXQckeP5tLZjZ2SlCEpJDDXJetZVVFNAu58PiSudFkiOJwFWXU/nqVYBsZGdVywBJIrCTZcsG47xDPxfzY5OwjQgdgpgqQS8I2jMOxy4IQdoPAsYwtkVx7jH4ZBesCLOhQMZz6JWU6FCq42EgBgzlBAFxIaToKmtJ4uRnIpq9/fSueWJ2eLqzgJI9DicnAkOBzB8IAkmh8M1S66y1jVLrr7RNb7KIQDFZa7HLK10zQCrrHGNRaprBC5Po3XotFK8T98egDVOeZ2YDsfWrVvP6SWaN28exo8f78EVXTpYMHU0Cr4Efn6crEiJdwBprwD1J4Gf7gMq9gHhY4Ch75Bo8olqfR5BAA48Axz7P0AXAAycB8RPBU5+Ahx4ltx9vWYA/ed2qNL8zAVQXE4Bs3Y7tcdIiFLeS7Waso3E2jqAsjEr0364c4tqNIAG5PpSqZRxTUZfssw430tvL2VFcK2GArKdiyEGBVCxSTlaDbD/MAmY0EBakzyby+hD7jW56PHSU784Z0Hnbv5AIwk9eeadnw8dRy7cfQzN5+5GFLH7+HcRExPjsRIiY8eOxc8//+yRY7U3HAHakWgqBb6/Bag5QhalnIXA0SXAD9OohICtHijYAOx9sO25TnwEZM8HzGWUTffjdKBoO7AzE6jLo/GDLwCnPr3kp8VcAhqapJRwq40anJa6qRjcO1GqpuznQ1lOTMfGS09uKW2zWAgJIutTitO9TI4HEqKljDW9ju53RAi5/lQqimFKjAZCgyjmSazbFBpEgru+kQR3UTkFIIvB2gF+lCXXM1ZyhRm8gD6JFF8lBiprNTRvaBAdR6Om40aG0jp6J0pB54FG2qZXnCTcvb3ovBimE8Dy/WIhCK6WGndj56J8D2BvUo4VfQNU/qIcK/227WOK27SM24HTKwHBycRe8i2Q8Gf36znfc7rQ82T+ODVuUmqr6ihAWH4/jL7AkFTJ3SGH71vHJSKE4l0cDsny4t98L+0OSfgAlLFms0tiBSBh3COWAqpFF1dUc3kBQSCh5Oy6s1ipKKbzszK4j+v8fXqQYFOppPnjIim+SYCUTWcKBC4LcFqzjnrROc/JMB0ctjD9Ucp2Axv7A59qgG3jyUrUWAxsu5bGNg0gIXQ+BA8G1E69lkJHAgF9lWMB/YH/XQl8qga2DAOqD5PQ2nknsMoL+DwBUDunaqqAmMn0rxzTMNd1VGUDm4fQ+reOAupPA5Zq4LubgJU6YEMKULyN/vD+8hTwHyOwJhTI4YKjHsNdAKwKwK4DwHf7gZwT9LKtb+4p9uMvwC85ZJGy2ShQ99t9wE+/cVxTR0XlJnZHpVKKJRGtxlV4aNSu9X/UaprTx9u1V52XDtibTc/Kb0cBeb0dt/NrWpnfaexC1swwHRiPlRX4I3TYsgIOO7AhCaiX9VVKuJ2sOXJXl28iMPEYoDoPfXpyJfDzY4C5lOYashSoOQzsvAuo+hUI+xPNX/q9tE9wBhA9EfjtOWlMbQASbwdOfkzZdgPmAT3/Ahx9m2KYbLVA0n1A+muu6/pvmtKqFXktYOwF5L4hjXmZgPSFwI+3K/e9dj8QnNb2eTJ/nMJS4GQBuVTCTJTZJCcxmmJZ6mWNUIP9AUNzFWURnZZaWHCNpu5FeRWl/DdZyKVW3VzGQCTcRC41hmEAdHWXnKWm2TysAbSXICW16axSLAFA2S4SNHLqTwBNJSRcNAalQLGb6bNoWUqYBsTdQhYjXfOagwYB438hK48+AFhtVM5fsRfwciro5mgiV1v6QkCtB9TNt7rX/UBSFuCwAFpZALAgAPYGQKVxdQGW7aKYJznmcqBwi+s1Kd91YYLJWgfo/JRjtuZ1QKDr1RkRs4vkIkQQ6Md5TO52EbHbaTv5/8AdDvosjkWF0o8gkJXIWTBV1ynFEkCZVhanNg1WG9XJEVtrnM95tLbm8z0P5zkZz2MKpPR9u53cY85ZcmJWnrv7yjDdkK4pmBwO4KuhlFUmEjEWGPEpYLiIlWK9owC/HhRELRJ2OVme6k9IY35JwK57gLObAe9IYMgSsgjtfxQ4upTETJ8ngf7PAnnLKEvOUgHE3gJc9gEVqtx1B1CTA5iGAsHpQMkOaX7TZUDYlTS/iNYXOP4+8M01gNYPGPACkPIgcOR14Ld/kDhKvIvWUrID2J1F4i9sFBA4kKxZLed0BeDXk4SZiCEMiJ4AnPxIdkFUQOjl53ftqg8BP/yZjhPYHxjxCeAbB/yYCRR8QXOp1EDSX4CMxZ2ndIIg0P/azzbXsYmPoi7thaXAiXyK5QgLpviP2nrgyElyk/n7URo1QLWTqusAgx5ISaDvjp4CiivI3ZEQrWxDoVKRi06tVqaBBxrJYiDPogow0rzyMZ1WCiaWc7IAOFMMQKD4lx4xlB117DTNGxxAQcBmK625vpFEV58eFDx85ARZMfQ6Ch4ODQby8qkNBlQU75IY7XpcxjOUVsruZXPAtzwjzscA7DlICQZ+PnRffTrpf2AY5iLQNV1yPz8JHP6X63jSvcCw/7u4i6v4GdjzADXJjboOGPp/AARg973A2S0kBvx6KF10WiOJgF1OPeCuWAt8fysgyCwAA+eTiKrNlcbCRgEar+bmu0OBYe9SAct9DwMnVwA+MUDEGCB3sXL+Kz8Hvp2kHMtYAhycS2UMRKInkWCr2EvHGvYuWcf2zADy15F7LuNNiq/67QUgdxGg8Qb6zyHr1fnw1QhltfLgIUD4aPf3bdj7QNLd5zdve1NUBuScVI71SwKyjyvHkmKA/BLlCyokiESWPCVcrwPiIlyrHg/pR3EocsqrqGCgxQqEB1NV5SYLkHsSqG0gAZUcT7EjuadI/Ph4AT3jKStKTmUNcCBXOdY7Acg9rRRlMeEk7mplNYKMvjRfvuyZUquBlHjgsOw/EgAwIJmysxjPYrMBOw8o72VYMLUlqW8CTAEkgOVlDAKMwKAUz6+VYToIXdPCVPqD+/GKfWT9MZdKRSBFmkoAfZAy6NpSRZ/l7jxbA+Aw07YAuZ+u/oFcVN6yJopXrqXgby8T8PUo5bFstUDxN67rK9ysFEsABZXXOr24ag4BU4pI5BjCJBff0KVkSdIZqTq4y/z/dR0r/U4plgCg7igw/je6JvLrNOIj1+vU/1kg5f8BKq3StWZrBOyNgJesT5LDTq4973Cl9Q8AKveRKHNHxb72EUwOB7kq9Lq2txWRW25EnHtlAVTF2LmoX129axCsxUqCxJnaBqC5UWZLQK0pkMSH3SFlOfkYKIvKalOeR58eNLfWKXDXZqM1uDsPd4UMaxtct62tdw38dThc+6iJ27oTTL/n2jPnT0OT6720WIG0PvSs6LSUFCCnzqlwJsN0M7pmEEHMFPfjxmTgiwRgXSRlttXkAg35lGm2NhxYHwvkbwDsFgpmXmOi7K/fXqD9D/2LPn9mIkuQvYnieD6PA9ZFAP9NB+pOAnUnKHB6XQTweTzgk6BchyEMiLvZaXEqCtLWOBUWjLyGgrrlBA0GNvah8/iiJ4kqSzXwzThgbSidi/OtVeuAxEwSNoprNYksYHL8+wJf9KD5v+xL7rPGIuCrkTT3uijg9BrAYQN23Q2sCaHj/vos7X9kIbA2jMa/nUIis3gbXYt1EcCmgeRGlBM+hqxi7mht/FJSVkUZZzt/peJ+zuKmNcSaOHLCTK5CyBTg6t4I9KcfOd5e5PqSo1KRCPvxF/oR22AUlgI//tqc5ZRLgqO6jmJTdv5K7pX6xubGq4dpbNcBcs0IArnQfmieU25ZaDmPIKk2kEiQ0fWcg/xdx7Qa901Gnc8XoHYd4rX/+QKuPXP++Pq43ku9XvnMG53+Frm7VwzTjeiaLjmABM2ZtVR3SOMFJGSSi6zhtLRN5DgKlj65QhrTB1P1633/TznfyNXAD05dkdNeAQ6/rLTQxN5IQd/5n0tjhkhqb3JmLYmTwf8GQoYBOW8AOa+TmOn3DAmmoq3kUhQrfQ98kSp975kJVOwnYVGXB1TIShUE9KWYqEP/lMbUOqD3Y3Ru+kCyPMVMojUcmNNc6fsBoN9TFCO19yGyXEXdQK6+umPSXGF/Aow9geOynnU6fyDtZeCn+52u0yoqtAnZYzVgHnDsLaBB5lYKH0NzlP4AhAynWCpDGPDzE8CJZRQMrwsAev8V6PsEPIrdAez6VVmhOCyYrDLnw5kiWePTKNq3vIoan9psQEQo9RBrNFNsUn2j1OwUKoorqaiWmp36elPvrrNlJDyC/Ztji2T0TiTBIycukopZNpqlsQAjiTB5gLhGA/SIpoaocmIjSLwIoMKJUWEkwI439wcLDaK4JquN9q2po3grscFvXj6JMS89uSADjJSdd6aYSiDERbp2n7fbyVVkl117zta6NMjvZUgguYLlz7wo1Gvr6d71imOLH9Ot6bqCyRlLNfBZoHLMO5IEU5VTdkj8dODUCuVYzwfopS8nbipwepVyzNiLBJM8EBwgF5q9gUSB3MXXWERWH3kwuqUasFZTELSI3UKCwy8R+E8gufXkRF4HnHVyuV21jdaj9ZFciABgriB3mY8s4NbWSFl/hihgtVNsjD6oOejbqZ5U4p3AiQ+VYz3vB469rRyLuQnIX6Mc84kFJju9oDsKDU1kjZHj600F/MwWqngstxg1mgG9VplJZLaQq0teANBmIzEmVlMGyC3iPKcgUCC4Xq90bYlz5hdTZW85ESGuWXKmAGUXeIAEl7cXudLkhJukJqsiveLpRQooX5R2O4kkeR8zcc3O16bJTNdAfm3E1HX5nDY7XR+HA9iTrVyHnw8VOmQuHU0WYPcB5ZiPgQplMgwDoKvGMLlDH0BuoPJd0ljkta6CydgLiLtVKZjUOgpmPrFMWYk79kagOhuolr1cI8eRYDq6VBoL6AfsuJ5icbR+lOqfmEmFJk+tpBikng8AGW8Ah1+hekr2Jgq4vnIdFb78cTrFXvkmAiGXAUX/k+aPGEsB53LBpA8GshcARZtp/X2eBAa+QIUmj7wKOKyU5TZyFVD4JbD7PsBaRdYq0zCgfLfyOhmdBJNvPBA/TSmYVNrm6/QRiUORmIlAwyllll3kuFZuVAfA24t+5JYZHwO5K6w2yjLr15PEx8FjZCHSyFpEHM4jl55KRdlsSbFkITp1loRFkD8FglfVklXIZqfjpfYEoKKikg1NNH9KAm1/KI+sTiqVe9dWRAhZc+xOFgLRLScfMzgJJoOeMtjkgkkFWt/R5rIZYcFk5SkqowB0h4NcNqm9SBRlHychpNcBfZPofA4eI+uEWk3XIDKEAuLF40SGkCgrKCHrm8NBAePO2VrB7Aq65Bj0rn0HORifYRR0H8EEAJevAvb/lTLbIsYCg1+hGkUOK6Wy+/cBBr8K+KdQFtjRJZTR1n8OYMoA/rSB3FmWSqDnfUD8rZTiv/9REk2R1wGDFqDFHSVmyakNkiXKVkcB2Q6LlDkn2IGjiynj7Zcnpf1LtgOHXwVOLCexBFC5Au8osu6IWXLpr5PlylIhZckFDAByF9I+DiuQ/SLg31vptivYQMc9OI/EEkDxSlHXUy2o8r1A+ChyIWp8yCp1pjlLLu0VILAfZQXmNGfJpT4DmIYAozZR419zGdDjbqDHHUD4n4B9f6UaT5HXkDuvo6JSkXg5nk8vEFMAiRGxMWmThdxmXnqpzpHdTuLCapMakwoCWYN8fahJrkhlDWXIFRRLLpBGMx1PpZJeWjY7ZbPFhEuB44JAgiMhitakUlEWXYAf0L8XlS6wWMliFBlKgeDHz5BwCfQn15hYfqCsioRNUixZ0HrFk8tMraJmqnIrVkkFuWWOnZaawtY2kBCUFzy0WOk6BBilzDmHg/YDlKLsbBm58MQYLID2iQgBLBagwUwWrvhzNJlmLh6pPeleiFlyiR2oSDDDdAC6j0uuPfnflZSNJqfHPUDe+8oxd26/mMlA/nrlmCEcmHSKKoAbk6UClIJAgscQBvz6d+D4O07zzwCOLVWOJUxXxnABQEAqcL2Tm7I7Y7cD3zt149ZpSTA5Z4iFBbt2io8MBc6WKsdCg1x7eRn0ZG1sbGp72z49yBqjgtI1ZrGSaJMXobTbSZD5GKSMOLGfmJde6TZsbKJtSiooBklOhImatMoJNJJgkv8ZUalIwFU5uY3duQ0jQ1y73JsCyHLFMAzTgeiaWXIdjegblJ9944Eed0HR102lJauVPNYIoIy/sFHKMdNQyuj7bxqwLho4+xWVMPhvGrApFVgf7drEV+MD9JpBliA5CdPJCqZY74QLPMEujkbjmvVlCqQXuxxvA1l25KhUVI3bOc0+NMi1H5y7Of18KMtOjlpNwuOn3ygD7nAeCZZTheQ23JsN7DtE4qm8ioKo9x2i72rqSBTtOdg89itl19kdVHfpp4PSds5EhFK/McWaA2jdbY3pdSS4nIkMdW0K7LwvwzBMB6B7ueTai96PkQvu9GeUJTdwPhDQGxi5sjlLTg/0nU01nUZ/Bfw2h4LBE+8gd1bkOHLVVewDIq4CirdLLjprFRWUjJ4gVed2WCkGa9ACOqY+EEh9FgjqD4z6L3DwBQoa7/kAxT759wF+mU0Wq+gbgNQ57XOdOjJ9epDFpa65AGRitGStKasi601iNImm3olSllxcJIme/skkaGw2srSEBpM7Ki+fLD1B/lLVa7FsgK83jRm8qODk2VJKBTf6uneXyd1+dQ2UrSePa7LaKP5IHp/lEMgNY7crm/CWVZHbr7KGxFhMeLPbL5nijcxmOofocCCiubt9TR3g70uuHK2GXHGlFYCXF52HrzfFN+U3Z8nFRNC5DGie02IlC11k6CW6iQzDML8fdsl1FswVJGiCBpFVySrLflKpKX6qcKNyn7E7qGUK03GxWptbT/hKViiHg2J5vA3u07hPFlAAuRx3bj93WXI6bXPQt1MRwtay5KLciBezhX6MvtxtnmGYbgNbmDoDp1YDu+6ioGt9ELUkKdwkfR89iTLR5ILJO9q1OCTTsSgqo6BuQSAhk9qLBMhvuWQNUqko887Z4mIKUgom0e1XXqXsNh8aTPkD8krjIYGugsnHQFYvuWBSq13dgwAd92QB/W7QAwNS3PehYxiG6WK0i2A6cOAA7r//fuzcubPtjbs7Djuw7yESSwBl6DWVAn2fBIp3UAHM/nOpbIK9CTj5CWXJpT4HaPTnnptpPxwOcoWJBl6rjTLcVCopG08QKHMuzKSMgRLT+QuKafuYcHL7DWh2+4lZcuEmEj0nC6nzfKCRMs7UKtqvrJKsWAlRJKL6JUmuxNgIZb0ogKxKolgCKFvw9FkqfcAwDNPF8ahgEgQBn332GRYsWODJw3ZuHE3Uv01OYwHQ53EKBg9KI7EEUOXuXg94fInM78DuUFZVBkiQOLu47HZy29WYScCI7VSC/SlOSAWKhQIoRig+igST2ExXq6U4qroGcqGJwisqlLb3NkhZdqZAsnSp1cqA9LoGEnHOgesA1WBiGIbpBnhUMC1atAg7duzAjBkzsHTp0rZ3YKgqePQEqhMlYhpGWXL2RgoYH74ciJ/afmtkLhydliw+8tT70GASTKdkwdtGH+DnI5KrLS6S6i4dyCWrEUDz9O9F1qjCZnFt0FPT3eo6KhYpCCSE+iVRXNSBXMmSlRBNBTZ/zZHKJJgCqDhn7kmplIBBT+JKLpLcFdFkGIbpgnhUME2bNg0PP/wwdu/e3eo2NTU1qKlRdjUvKiq61Evr2Iz4CMieT1ly4VdRIUvRReewUOFMFkydj75J5NISs+Riwmlcq6G4Ix9vKuAor8p9+ixZempkMUhVtSSUCmWWyCaLlCUnuv0cDsrK8/aSxBLQLNAEZU2p8mpy+cnrLjVZpHiqJjOJJedecAzDMF0Ujwqm8PDwNrf58MMP8eabb3pgNZ0InT8wSFah+7BTlWxzKcU6qTVgOhE6LVXZdiYmXBJPB3Jdv2+yuI41unGNiUUsncecXWuCcP5zOhzcCJdhmG5Jh8uSu/POOzFlyhTFWFFREaZPn95OK+qAJGYCOQulz/F/ZrHUVQk3Kesj+XqT+6yoTLIcadTN7VNqlO6y8BCpard8Pm8vpYXK6EtzFpdLc2o1VCeprEqZeedcmJNhGKab0OEEk7+/P/z9uenjOUl7BfBNAEp2UDxT77+294qYS0W4ieKaSisphig2gmKQBqVQhW6xwa+3gcbOFDUXgDRRCYFgf3Lt1TZnyUWH0T4ajZQlFxtB1q6ByTSnRk0FKb29gEG9gfwislRFhHBDVoZhui0dTjAx54FaC/R+hH6Yrk9YsGtwtb+flB0n4qUHesYpx9RqID7SdU6x7ICcACP9yPH2ogKWDMMw3RzuJccwDMMwDNMG7SKYhg0bhr1797bHoRmGYRiGYS4YtjAxDMMwDMO0AQsmhmEYhmGYNmDBxDAMwzAM0wacJddZqKimSsyBRtfsKIZhGIZhLiksmDoDJwqoJYZISgK3pGAYhmEYD8IuuY6OwwHkFyvH5OKJYRiGYZhLDluYOgWC61BFNXWiN/pSRWeGYRiGYS4ZLJg6Omo1EBWmtDL5egO/HZU+x0UCidGeXxvDMAzDdBNYMHUGkmIp2Lu2AQgyAofylN8XFAMJUdQjjGEYhmGYiw4Lpj+C3Q6cLQPMFiA0GPD3pW7vpZVATR315QoNom2ra6nzu8GLArY1aqDRTF3n1Soa89K3fixTIP0AboTRBQgli5WOabMDESZqzOpwUKf6+ibAFCA1WC2vAiprAD8fqQkswzAMw3RDWDD9EQ7kAjX19Ht+MTAgmWKLRPdZQQkQH0WCI/uYtF9ZJWW67TtEogugLvEZ/ahrfFvERQJHT8k+R5yfmLE7gJ+PAE1maX2D+wCnCknkAWSt6p0I2GzAsTPSvjV1QHJC28dgGIZhmC4IC6bfS229JJZECkuAihrXMT8f5VhVLYkqUSwBZPkpqwQiQ9s+dlQozVldS0Hfgca29wFIzIliCSDLUkGxJJZECkpIMMkpKifXoEZzfsdiGIZhmC5E1xVMDY3kLlOpSIR4e13c+dVuKjKo1fTjcLiOOaNxM2Z3AHn5tH9kKAV32+zA2VJy34UEAsEB5PZrbAIamgCtBgjwo/OsqgVKKwC9jgLFdVrarrCU5vc2uFmzhvYVZJl4apXrmlUqz7nkzBYSmnYHuSqdBSfDMAzDeJiuKZgazcD+w/TCBUg4DelHQuJi4etN8UmidUajBmIjqAr3sdPSdglRgI+BYoFEIRURAsREACUVQJOFxnwM5Bqz2aU1p/cFck9S+QCAhFOfHkB9o1SLqaiMKoCbApWZc2WVQL+edB3EOdVqEh91DfRZrwNiwykESnQjqlTk8rM7gEPHpfniItwLv4uNzU5rtljpc2EpuQ1ZNDEMwzDtSNcUTCXlklgCyL1UVklWl4tJnx5ARA1ZREyBJED8fMjiU1NP//p607ZDU8klZvAiF5pKRTFLZVVk0bHYlEJLdJeJYknkbCkJJsVYmSQwROoaSQTZZG4/h4NEXnwkjYcEAlotudpCAinoO9if1ggAQ1LJauXn7bl2LBVVynMRBApIZ8HEMAzDtCNdUzBp3Z2WilqMNJlJNIQE0cu4sJREib8vxQap1ZQdVlJBWWsx4SSE6hok15YYQ2S1kaAwW2gbUyCJkqpaEkyCQJYjlYpinqpqAYOF9tVpyaVWXUvfu3MZugsA12rox2pzHTuf/QXQOux2ctEF+JF4qqql9WjUJJgEga5LVS0JTl8f+q6yhgSMTkvXxktP+xWU0LlHhVJclc1Ggq3RTNc6tPl6ny2jOf19SMCq1SQkS5pdiaLAdD5nhmEYhmlHuqZgKqtyHcs7Ddib43RKKigTrLaBrDgAxf40NFKMULbMFVVeBaT2pOwy0aVWUk7usoPHSCyIc/ZLopf/2TJpziYzCaQjJ6Q5K6vp+L8cARzNa9I4ucu8DUB0OImZghJpm7hIEiGHZbWYEqPJAlRWLQVrh5tI0JRVSXP6GID8IsnqVFwBpPUmIVlZI52HzUbbnCxsPg/QHJGhlBnYcp0rgYEpwM8yt19xueRKFIPiSyooK7ChCThTJF2b+kYSUwdlGYRqN3FSFVWUbcgwDMMw7UTXFExVNa5jdqf2ImLsj2KsnKxFchqayFIiD+S2N/d3E8WSfE7nLLmiMld3Um1D85yyNdkdVBfJO5qOFRxA1peecUCYCWhqAoICyLJj9CWLmNgaxac5mHtYKh3fS0c1oAASRKIYstmVwk0QKLi60nnN5a5ZcqWVcKn31GRxdfsJAo05ZxCeLaMAdDnFFYDVrhxzON0nAKhpcB1jGIZhGA/SNQWTCm7brynQaelH/rLXaQG9m+KR7gpKuhvT6WgOeQyOeJzz2d8hkDXGIdA+AUaaq7SCrEoOgQLGHQ4SMFW1ZMGKiZDcZaUVdA5eXoBBT7FMJRU0v7+v6zH1OrLqyIWKTktuwiaZeNRoAP15noeXzjXzTq8FbFpXV6K7OZ3hepkMwzBMO9M1BVOfHq7tQ8KCgJLmjDatllw8jU20nSDQC75HDFlsKqol0RMVRq6xsiqKQwJom+hwwGwlCw3QnHEWQQHdOSeVcxq8gMpayWoTGyHNKVq5/H2BkwWScCmrpOywnBMkegByDzoEcmWJx62oJktXSKDynCurgdRewK85knWstILWV1VLn70N5LbTaMgtB9DvCVFk8Tp4TNq3RzTFaJVVSVa4sGDav6JamtPXm8YESJl8Wg1db7OF3J2ikOoRQ6KwXHa9TQH0WU6veNd7zDAMwzAeRCUIQlu2mIvGkSNHMGfOHOTk5CA2Nhbz5s3DgAED2twvPz8fV111Fb7++mvExMSc38HMZmD/ERItaSlkcalvJItMoFEqwGi2kBAy+krWEntzELSXXnKnCQIFaAP0khdrEtU10Bzu5vT3k0oZiHMavKTAZkGgMZUKqG9QVtYGgMgQKR5KJMCPzkNuGVOpKLvNWWhEhUqB6iKJ0bRWm53+FUsFNDTXdQr0k4LmrVZy+/n6SEHpDgeJP32za1CkupbEXKDs2tQ3kmUs0CgFblusVDXcz5csYACJs6oa5fXed4gEZlpv91Y/hmEYhvEgHrMwWSwWzJw5E3fccQc+/vhjfPXVV8jKysK2bdvg53cJUta9vIDhA5Vjvt6uWVheele3kkYj9W0TUamAQH/X4/j5uMYoXcicYt82m1MsD0DiysW1pSe3lnx7vda9a8xdoUq9zn2JAB+DFAslotNRULYctZqsQM6IMVNy3F1vvZs5NWrXa5Pe13U+hmEYhmknPFCJkPjpp59gtVpx1113QafT4frrr0fPnj2xadMmTy2hY2MKUAoRf18gOoxqJonodUBCJNVNEi1DKhV9jo2QLDYAucWiQpVtUwKN5EZjGIZhGOaC8JiF6dixY0hKSlKM9ejRA7m5uYqxmpoa1NQos7aKioou+fraHZWKYo7qGsjtZfSlsfgoKhHQaCZ3nFoN+HgDwwdQtp2vt+T2G5JK2WleOsm6NDBFGXvFMAzDMMwF4zHB1NDQAINB6fLx9vZGY6OyavWHH36IN99801PL6ni4q2ht8JKqb4totZI7T0Stdt+Il4USwzAMw/whPCaYfHx8YDabFWONjY3w8VEKhDvvvBNTpkxRjBUVFWH69OmXfI0MwzAMwzDu8JhgSkpKwrJlyxRjeXl5mDx5smLM398f/v5ugqsZhmEYhmHaCY8FfQ8bNgyCIGDZsmWwWq3YuHEjcnJycPXVV3tqCQzDMAzDML8LjwkmvV6Pd955B1u2bMHQoUPx1ltvYfHixQgO5qwthmEYhmE6Nh6t9J2cnIxPP/3Uk4dkGIZhGIb5w3jMwsQwDMMwDNNZ6RS95Ox2qmrdLeoxMQzDMB2SiIgIaLWd4rXJXAI6xZ0vLaV+aFxagGEYhmkvLqifKdPl8Gjz3d9LU1MTDh48iNDQUGjEBrdMS32qFStWICIior2Xw3RQ+Dlhzhd+Vs4NW5i6N53izhsMBmRkZLT3MjosERER/L8epk34OWHOF35WGMYVDvpmGIZhGIZpAxZMDMMwDMMwbcCCiWEYhmEYpg1YMHVi/P398eCDD3LvPeac8HPCnC/8rDBM63SKLDmGYRiGYZj2hC1MDMMwDMMwbcCCqZPQ1NSEsrKy9l4GwzAMw3RLWDB1Em6//Xb88ssv7b0MphORlpaGnJyc371/fn4+UlJSUFNTcxFXxVwq3nrrLTz66KO/a9+//OUvWLFiRZvbffHFF5g2bdrvOgbDdHY4hqmTMGbMGDz99NMYO3Zsey+F6Sbk5+fjqquuwp49ezgImGGYbg9bmDyMw+HA/PnzMWLECAwfPhxZWVk4ffo0AGD16tUYN24chgwZgqysLJw5cwYAMGvWLBQWFuLRRx/FO++8AwD46KOPMHbsWGRkZCAzMxNHjhxpc36LxYIXX3wR11xzDQYNGoSrr74aGzdubIerwDz22GOYN29ey2e73Y4RI0Zg9+7drT4H+fn5SEtLwzPPPIOMjAysXLkSv/76K2666SZkZGTg2muvxbvvvtsyZ0pKCg4fPgwAyMvLw913343Bgwdj9OjR+OSTT1q2a+1ZcubHH3/ETTfdhMGDB2PChAnYvHlzy3djxozBc889h8suuwxPPPHERb1WjJLWnp3MzEzMnDkTAPDGG2/gvvvuw4QJEzBy5EhUVFRg7969mDhxIjIyMjBr1izMmjULb7zxBgAgMzMTy5Yta/n9tddew5QpUzB48GD8+c9/xvHjxwEAa9euxaRJk1qOvWbNGowbNw5paWm47bbbkJubCwA4e/YsZs2ahVGjRmHAgAG45ZZbWn2uGKbTIDAeZfPmzcK4ceOEqqoqwWKxCLNnzxaefvppYcuWLcLIkSOFgwcPCmazWXjrrbeEcePGCVarVRAEQRg9erTwv//9TxAEQVi1apUwcuRIITs7W7BYLMK7774rjBgxQqiurm51fkEQhKVLlwpTp04VqqqqBLvdLixfvlwYPHhwyzEYz/Hdd98JI0eOFGw2myAIgrBjxw5h9OjRwubNm1t9Ds6cOSMkJycLr776qmA2m4X6+nrhhhtuEFasWCEIgiAcOXJESEtLE06fPi0IgiAkJycLhw4dEsxmszB69GjhlVdeEcxms3D48GEhPT1d2Ldv3zmfJfF41dXVQm5urpCamips3LhRsFqtwo8//igMGjRI2Lt3ryAI9HxmZmYKDQ0NQk1NTftc1G5Ca8/OwoULhRkzZgiCIAiLFi0SUlNThcOHDws1NTVCZWWlkJGRIaxevVqwWq3C+vXrheTkZGHRokWCIAjC7bffLnzwwQctv1955ZVCXl6eUFtbK9xzzz3Cww8/LAiCIKxZs0aYOHFiyzrS0tKEPXv2CHa7XXjzzTeFa665RhAEQbj77ruFF154QbBYLEJjY6Pw6KOPCvfee68HrxLDXHzYwuRhjEYjSkpK8Pnnn6OoqAjz5s3DvHnzsHr1atxxxx3o168f9Ho97rvvPtTV1WH37t0uc6xfvx533HEH+vbtC51Oh6ysLBiNRmzfvr3V+QFg2rRpWLJkCfz9/VFSUgJvb2/U1dWhsbHR05eh2zNixAio1eqW+7thwwZMmDAB//nPf9p8DiZMmAC9Xg8fHx/4+flh+/bt+P7775GQkIC9e/ciNjZWcaz9+/ejpqYGDz/8MPR6PXr37o0VK1agR48e53yW5GzcuBHDhg3D+PHjodVqMXz4cEyYMAHr1q1r2WbcuHHw9vaG0Wi8dBeOafXZUalUiu2Sk5PRu3fvlvsZERGBW265BVqtFpMmTUJaWlqrx5g4cSISExPh5+eHcePGtVip5WzYsAGTJk1CRkYG1Go17r33XvzrX/+Cw+HAvHnz8NhjjwEACgsLERAQgJKSkot4FRjG87Bg8jAjRozAc889h02bNmHcuHG47rrr8M0336CwsBBLlixBRkYGMjIyMGTIENTW1qKwsNBljvLyckRHRyvGoqOjUVRU1Or8AFBXV4fZs2fjsssuw8yZM7Fz504AgMBhbB5HrVZj4sSJ2LBhAxobG7F161ZMnjz5vJ6DsLCwlt9ff/11mEwmzJ49G0OGDMFTTz2Furo6xbHKy8sRGhqq6LKekpKCwMDAcz5LcioqKhAVFaUYi4mJwdmzZ92ui7l0tPbsOCO/H8XFxQgPD1d8HxkZ2eoxTCZTy+9arRZ2u91lm7KyMsUcer0eAwcOhFqtxsmTJzF9+nRceeWVeO6553Dq1Cn+O8N0elgweZgzZ86gb9++WLlyJXbv3o0bb7wRjzzyCEJDQzF79mzs3bu35Wft2rW44YYbXOaIiopCQUGBYiw/Px8mk6nV+a1WK+bMmYOYmBj88MMPWLt2LbKysjx12owbpkyZgq1bt+Krr75CcnIyEhMTERYW1uZzIFoSbDYb8vLy8Pzzz+P777/HypUr8dtvv2H58uWK44SHh6OsrEzx0lu7di127dp1zmdJTmRkpMt2Z86cQUhIiMu6mEuPu2fHGfn9iIiIcBHBzp8vlPDwcBQXF7d8tlqtWLBgAWprazFz5kxkZWVh586d+Pjjj3HFFVf8oWMxTEeABZOH2bVrFx588EEUFhbCz88PAQEBMBqNmDJlCj744AMcP34cgiC0mLvFP0g6nQ61tbUAgMmTJ2P58uU4fPgwrFYr3nvvPVRUVGDUqFGtzq/ValFbWwsvLy+o1WqUlJTg1VdfBUB/6BjPk5SUhPj4eLz++ustgbSTJ08+53MgR6PR4Omnn8ayZctgt9sREREBtVqNgIAAxXYDBgxAUFAQlixZAqvVipycHCxYsAA6ne6cz5Kc8ePHY+/evdi0aRPsdjt27tzZ4gpiPI+7Z+dcXHXVVSgtLcVnn30Gm82GzZs3Y//+/X9oDRMmTMAXX3yBAwcOwG634/3338e2bdugUqlgNpthMBgAANnZ2Vi+fDn/nWE6Pdq2N2EuJjfddBNOnDiBW2+9FfX19UhMTMSiRYuQnp6OmpoazJw5EyUlJYiLi8PixYu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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# f, ax = plt.subplots(1, figsize=(3,3))\n", - "\n", - "ax5 = sns.catplot(data=iris_tidy, \n", - " kind='swarm', \n", - " x='species', y='cm', hue='metric',\n", - " size=4.5,\n", - " aspect=1.5,\n", - " #ci='sd',\n", - " palette=['red','grey','orange','pink'],\n", - " \n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Scatterplot and linear regression line\n", - "\n", - "Next to the categorical plot, the scatter plot is a very useful visualization tool for biological experiments. Often we want to know how one variable is correlated with another, we can then use a scatterplot to easily take a quick look." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Draw a scatteplot of petal width versus length with a simple linear regression line\n", - "ax6 = sns.regplot(data=iris, \n", - " ci=95,\n", - " x=\"sepal_width\", \n", - " y=\"petal_length\")\n", - "\n", - "\n", - "# ax6.set(xlabel='Sepal width (cm)', ylabel='Sepal length (cm)')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "for s in iris.species.unique():\n", - " ax7 = sns.regplot(data=iris.loc[iris.species == s], \n", - " ci=95,\n", - " x=\"sepal_width\", \n", - " y=\"petal_length\", label = s)\n", - " ax7.legend()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Seaborn allows you to do that more systematically with pairplot\n", - "\n", - "This is like doing a scatter plot for each pair of the variables in one go. On the diagonal, distributions of values within each species group are plotted for each variable. \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig = sns.pairplot(iris, hue=\"species\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dimension Reduction with Principle Component Analysis\n", - "We have 4 dimensions we measured the irises on, what if we want a more concise way of describing the data? We can try to find 2 dimensions along which the data has the most variance. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn import decomposition\n", - "from sklearn import datasets\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "iris_pca = datasets.load_iris()\n", - "X = iris_pca.data\n", - "y = iris_pca.target\n", - "\n", - "pca = decomposition.PCA(n_components=2)\n", - "pca.fit(X)\n", - "X = pca.transform(X)\n", - "\n", - "\n", - "f, ax8 = plt.subplots(1, figsize = (5, 5))\n", - "sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=[iris_pca.target_names[i] for i in y], alpha = 0.7)\n", - "ax8.set_xlabel('PC1')\n", - "ax8.set_ylabel('PC2')\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Towards Publication-Ready Plots\n", - "Try to achieve as much of the final figure requirements as possible via code" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "all_metrics = iris_tidy.metric.unique()\n", - "\n", - "all_metrics\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y_titles = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n", - "letters = ['A', 'B', 'C', 'D']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "f, ax = plt.subplots(2, 2, figsize=(10, 10))\n", - "\n", - "all_axes = ax.flatten()\n", - "\n", - "for i, metric in enumerate(all_metrics):\n", - " \n", - " current_axes = all_axes[i]\n", - " \n", - " sns.swarmplot(data=iris, size = 3.5, \n", - " x='species', y=metric, hue = 'species',\n", - " ax=current_axes)\n", - "\n", - " current_axes.set(ylabel=y_titles[i])\n", - "\n", - " \n", - " current_axes.set_ylim(0, 10)\n", - " current_axes.get_ylim \n", - " current_axes.get_legend().remove()\n", - " current_axes.text(-1, 10.5, letters[i], fontsize = 25, fontweight = 'semibold')\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "f.savefig(\"myplot.svg\")\n", - "f.savefig(\"myplot.png\", dpi = 300)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/nbs/03_Data_Analysis_with_Jupyter_and_Python.ipynb b/nbs/03_Data_Analysis_with_Jupyter_and_Python.ipynb new file mode 100644 index 0000000..20659c2 --- /dev/null +++ b/nbs/03_Data_Analysis_with_Jupyter_and_Python.ipynb @@ -0,0 +1,1659 @@ +{ + "cells": [ + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "---\n", + "output-file: data_analysis_with_jupyter_and_python.html\n", + "title: 03. Data Analysis with Jupyter and Python\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Joses Ho](https://twitter.com/jacuzzijo), [Sangyu\n", + "Xu](https://xusangyu.com/), and [Adam\n", + "Claridge-Chang](http://www.claridgechang.net/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can already interact with the data files in your file directory with using the [pandas.read_csv function](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "exploration_times = pd.read_csv(\"exploration_times.csv\") # have pandas read the csv file \"exploration_times.csv\" and store the data into the variable 'exploration_times'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " WT CCR5 KO\n", + "0 3.478549 4.983642\n", + "1 2.234100 3.502187\n", + "2 1.724468 2.469726\n", + "3 1.723056 2.210396\n", + "4 0.965881 2.508039\n", + "5 0.726249 1.465072\n", + "6 0.490557 1.202951\n", + "7 0.217963 1.474133\n", + "8 0.479953 0.976419\n", + "9 -0.053350 0.752514\n", + "10 0.505003 0.742008\n", + "11 0.256178 1.004063" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exploration_times # display the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading data\n", + "\n", + "Let's load in an example dataset. We shall load the [iris flower dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set).\n", + "\n", + ">The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by the British statistician and biologist [Ronald Fisher](https://en.wikipedia.org/wiki/Ronald_Fisher) in 1936. It is sometimes called Anderson's Iris data set because [Edgar Anderson](https://en.wikipedia.org/wiki/Edgar_Anderson) collected the data to quantify the morphologic variation of Iris flowers of three related species. Two of the three species were collected in the Gaspé Peninsula \"all from the same pasture, and picked on the same day and measured at the same time by the same person with the same apparatus\".\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + ">The data set consists of 50 samples from each of three species of Iris (*iris setosa*, *iris virginica* and *iris versicolor*). Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters.\n", + "\n", + "
\n", + "\"versicolor\"\n", + "
An iris versicolor. (Photo by Danielle Langlois. Marking by Vijay Kotu and Bala Deshpande. Licensed under Creative Commons)
\n", + "
\n", + "
\n", + "\"setosa\"\n", + "
An iris setosa. (Photo by Денис Анисимов. Public Domain)
\n", + "
\n", + "
\n", + "\"virginica\"\n", + "
An iris virginica. (Photo by Eric Hunt. Licensed under Creative Commons)
\n", + "
\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read iris data from the sheet with pandas\n", + "iris = pd.read_csv(\"https://www.bitly.com/dukenusda\")\n", + "\n", + "## Or, since we have a copy of the file in the downloaded directory:\n", + "# iris = pd.read_csv('iris.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You have created a new object known as a pandas `DataFrame`, with the contents of the CSV. Think of it as a spreadsheet, but with a lot more useful features for data analysis.\n", + "It has several methods we can use to handle, analyse, and plot the data.\n", + "\n", + "We can peak at the data using the `.head()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
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24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris.head() # Gives us the first 5 rows of the dataframe.\n", + "# iris.head(10) # Gives us the first 5 rows of the dataframe.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get a summary of the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_width
count150.000000150.000000150.000000150.000000
mean5.8433333.0573333.7580001.199333
std0.8280660.4358661.7652980.762238
min4.3000002.0000001.0000000.100000
25%5.1000002.8000001.6000000.300000
50%5.8000003.0000004.3500001.300000
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max7.9000004.4000006.9000002.500000
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width\n", + "count 150.000000 150.000000 150.000000 150.000000\n", + "mean 5.843333 3.057333 3.758000 1.199333\n", + "std 0.828066 0.435866 1.765298 0.762238\n", + "min 4.300000 2.000000 1.000000 0.100000\n", + "25% 5.100000 2.800000 1.600000 0.300000\n", + "50% 5.800000 3.000000 4.350000 1.300000\n", + "75% 6.400000 3.300000 5.100000 1.800000\n", + "max 7.900000 4.400000 6.900000 2.500000" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see what is in the `species` column." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['setosa', 'versicolor', 'virginica'], dtype=object)" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris.species.unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the old-fashioned bar chart" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax1 = sns.barplot(data = iris, \n", + " x = 'species', \n", + " y = 'petal_width')\n", + "\n", + "# Axes should always be labelled.\n", + "# ax1.set(xlabel='Species', ylabel='Mean Sepal length (cm)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot a swarmplot, which shows all the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\xusy\\.conda\\envs\\nbdevdabestdev\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 14.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n", + " warnings.warn(msg, UserWarning)\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax2 = sns.swarmplot(data = iris, \n", + " x = 'species', \n", + " y = 'petal_length',\n", + " hue = 'species')\n", + "\n", + "# ax2.set(xlabel='Species', ylabel='Petal length (cm)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The split-apply-combine workflow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All your scientific experiments follow a very simple analysis workflow: *split-apply-combine*\n", + "\n", + "You do an experiment on 2 or more groups, apply some summary function to each group, and then aggregate the results.\n", + "\n", + "
\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width\n", + "species \n", + "setosa 5.006 3.428 1.462 0.246\n", + "versicolor 5.936 2.770 4.260 1.326\n", + "virginica 6.588 2.974 5.552 2.026" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris.groupby('species').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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versicolor0.0729980.0443780.0664550.027966
virginica0.0899270.0456080.0780500.038841
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width\n", + "species \n", + "setosa 0.049850 0.053608 0.024560 0.014904\n", + "versicolor 0.072998 0.044378 0.066455 0.027966\n", + "virginica 0.089927 0.045608 0.078050 0.038841" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris.groupby('species').sem()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plotting package `seaborn` does this automatically for you." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# `catplot` is short for \"categorical plot\", \n", + "# where either the x-axis or y-axis consists of categories.\n", + "\n", + "ax3 = sns.catplot(data=iris, \n", + " kind='bar', # there are several types of plots.\n", + "# errorbar='sd', # plot the error bars as ± standard deviation, use this line if you have seaborn 0.12.x\n", + "# ci='sd', # plot the error bars as ± standard deviations, use this line if you have seaborn 0.11.x.\n", + " col='species' # plot each species as its own column.\n", + " )\n", + "ax3.set_axis_labels(\"\", \"Length (cm)\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should quickly notice that the plot isn't as informative as we want it to be.\n", + "\n", + "The current plot only allows us to investigate the relationships the four metrics within species.\n", + "\n", + "Ideally, we want to directly compare metrics between species. \n", + "\n", + "To do so, we need to _reshape_ the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Long-form vs the Wide-form of your data\n", + "\n", + "Our iris dataframe is in the wide-form (below right) and we want to turn it into the long-form. In the original iris dataframe, the data is organised by unit (flower, in rows) and the columns contain a mixtrue of variables (sepal length, sepal width etc). In a long-form dataframe, each columne is a variable, and each row is an observation. (Please read Hadley Wickham's https://vita.had.co.nz/papers/tidy-data.pdf to learn more about tidiness of datasets.) \n", + "
\n", + "\n", + "
Michael Waskom, Seaborn Tutorial 2022
\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "iris_tidy = pd.melt(iris.reset_index(), \n", + " id_vars=['index','species'], \n", + " var_name='metric', \n", + " value_name='cm')\n", + "iris_tidy = iris_tidy.rename(columns = {'index': 'ID'})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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595145virginicapetal_width2.3
596146virginicapetal_width1.9
597147virginicapetal_width2.0
598148virginicapetal_width2.3
599149virginicapetal_width1.8
\n", + "

600 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " ID species metric cm\n", + "0 0 setosa sepal_length 5.1\n", + "1 1 setosa sepal_length 4.9\n", + "2 2 setosa sepal_length 4.7\n", + "3 3 setosa sepal_length 4.6\n", + "4 4 setosa sepal_length 5.0\n", + ".. ... ... ... ...\n", + "595 145 virginica petal_width 2.3\n", + "596 146 virginica petal_width 1.9\n", + "597 147 virginica petal_width 2.0\n", + "598 148 virginica petal_width 2.3\n", + "599 149 virginica petal_width 1.8\n", + "\n", + "[600 rows x 4 columns]" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris_tidy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax4 = sns.catplot(data=iris_tidy, \n", + " x='metric', \n", + " y='cm', \n", + " hue='species',\n", + " kind='bar', \n", + " ci='sd',\n", + " aspect=1.5\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\xusy\\.conda\\envs\\nbdevdabestdev\\lib\\site-packages\\seaborn\\categorical.py:3750: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n", + " warnings.warn(msg, UserWarning)\n", + "C:\\Users\\xusy\\.conda\\envs\\nbdevdabestdev\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 8.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n", + " warnings.warn(msg, UserWarning)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# f, ax = plt.subplots(1, figsize=(3,3))\n", + "\n", + "ax5 = sns.catplot(data=iris_tidy, \n", + " kind='swarm', \n", + " x='species', y='cm', hue='metric',\n", + " size=4.5,\n", + " aspect=1.5,\n", + "# errorbar='sd', # plot the error bars as ± standard deviation, use this line if you have seaborn 0.12.x\n", + "# ci='sd', # plot the error bars as ± standard deviations, use this line if you have seaborn 0.11.x.\n", + " palette=['red','grey','orange','pink'],\n", + " \n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scatterplot and linear regression line\n", + "\n", + "Next to the categorical plot, the scatter plot is a very useful visualization tool for biological experiments. Often we want to know how one variable is correlated with another, we can then use a scatterplot to easily take a quick look." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Up/C2ysRDp9C9mgQhE/I143+H1np9MRv92596g/7xAFVOK/3jAW5/6g1eOTAwZbtql40Pn7eEn958Hl++6kzWNFcBcPD4ON/47T4+dNdL3P2HA/SOpB9oNhM/euEQP3jpLfzhCJaYpPKt372RVUklWfqKVGkt0s2hkymVDivjwQgOq4HTZuCwGowHI0mT4C2udeMLTb35zOahk8k+glDoiNQzB+79YydWw4zcVJjvVkNx7x87Z9zeYig2nl7PHTe28Z2PtnP5mYuwWRQj/jA/e6WTD9/zMv/08MnXCPjF1iMYCiwxScUSk1Tu+sMB/KHszK6vXtsIgNaTr8T2mcgkH38mTJw7nfBKbJ9GJh464tUjlCL5MPwaeEIptVUpdcv0D5VStyiltiiltvT19WXciSJ3QU7dIz6ctqmnymkz6BmZfQH3tEWV/N2Vq/jZzefziQtOodZtI6rhmX19fPHe7XzuJx08sbuHYDj9hVpfKML02C6lwBtfAB71Ez7JBeBv3tDOdeubJmb4FkNx3fomvnlDe9J9vnjZadx66QpcNgvhqCmN3Hrpiqx79YwFI7TUOLFaFBGtsVoULTXOpJLSxlUN3Hb1ahoqnQz7QjRUOmdNvZDJPoJQ6OTDnfNCrfVRpVQD8Ful1F6t9bPxD7XWdwF3gZmrJ9NOXHYLS+rcjAcijAXDBLI04wVoqnLRPx7AlaDP+0NRGqtcc/6OOo+dT7wtXiOgj80dXbx+bJR9x8b419+8HqsR0Mz71jWxIMni5HTiuWPCOoomtsCpmBjnmD+MNxCh2mWjxp15BPA161vpGQlOuEBes7511n3Wttawurl6Yp+1rTUZ9Z2KxbVuDh4fm9IWCEdZtnD23Erp/KFJcJVQauR8xq+1Php77wXuB87NVV9Wi1npqqXGxeI6d9aKnt9wzmLCUY0vFEFjvoejmhvOWZz2d9ksBu86cxH//ZE2/uPG9bzjdLNGwKA3xA9feosb736Zf3n0Nfb2jMz6XRcsr5twZYRJl8bEUoJRrRn0Bukc8DHiD834PanI1AUyH/nlL1heR9/Y1FKKfWPJSylK3ntBMMmp4VdKeZRSlfGfgcuBXbnsM47NYlDjttNa62ZxnZsFFQ7cditGBrPec5fXceulK1ngcTDqD7PA4+DWS1dOePVkglKK1c3V/P1VZ/Kzm8/nI+ctocppJRzVPPlaL5//yTb+7Kfb+P3e3qRyzfGxEJUOy4TIpYBKh4XjYyca+HgEcLpFYArZBfLFAwM0VNqxWwyiGuwWg4ZKOy9OW3TP97gEodDJtdSzCLg/JjFYgZ9qrX+T4z5PwGYxqHYZVLtsaK3xh6L4QhF8ocicJaFzl9edlKFPRX2lg89ctIyPnreEp/b2ct+2Lg70jbOne4Q9j4ywsMJu1gg4q4ka92SpyO4RH43VzinrGxqdcu0hXgTGZTeLwDisqT1tMnWBDITCHDw+TlSbrpwLPLaM1jFmG5t9WgpruyV5KcVMXTMLtR6BIGRKTg2/1voAsC6XfaSLUgqX3TKRViESk3C8wTD+oOmBMl84bBauPKuJK9Y0suPIMJs7unjhzeMcHwvy3ecO8cMX3+KyMxaxqa2FUxsqTmrtwReM0BX0UeGwUuuxJy0+n1GSMq3pS3jqiGroGwvRWpNdd84Ku4X9feNYlJpSSnFFvWfG7TM5lsR8/Iny0G0gxl8oWsrendNiKCocVhoqnSxZ4Ka11s0CT+ayUDZQSrF+cQ23XbOaH3/mPD5wdiseh4VQRPPYrh5u/tFWvvTz7axpqSIUiZ7U2kM8Avj4WGBGSSkTd8aBcdPoT4+ojbdnixNKKapp7dMo1Hz8gpBvJEnbNOxWA7vVoJrMZaFs0ljt5HMbT+WTb1vKE3vMGgGHB7zsODLMjiPD1LhtGEox7AvRXO2eElE8V7TWjPhCjPrDVDqt1Lgmq4Blkis+EIliURBJcJ2xKLM9FelKKumWUswk4VrnoBeLggN9YxN9LKywizwkFDVi+FOQShbyBSN5rZLlslu4Zr3p7rn1rUE2d3Tx8sEBhrwhhgjhtBqct9zFouq5uYLOROINoMpppcZtN4PR0nRndFgNvMGIOQFXgDZvAm5b8gfMTCSVuHSTWBozXrIwWR/xhGtLYq6wv+roYm1rTdI+Kh1W3ugdw2IoLIYiHDXlpJUNyV1GRR4SCp2yl3rSIVEWOmWBh+YaV15TJINZI+CcpXV8ddNZ/PDT53BdWwsumwV/OMrDO7r51Pe38De/2slLBzKvEaC1ZtgX4vCAl/6xQNo3uDqXOZ/QxKJ9p7XPRCaSSrrSTSZ9pBsdnGk/gpBPZMZ/EjhtFpw2C7VAdOJpIIIvGMnLInFrrZsvXLqCT1+4lN/s7uH+bV0cHfKz5a1Btrw1SGuti2vXt3DFmkVTFjTnSvwGMOoPU+Uyy0CmytEzgWFQ5TAYCUyegyqHgTKSzzMykVTSlW4y8eqJRwdPkZMqHCkTzkliN6HQScsaKKXeBixN3E9r/cMsj6koMQyFx2HFE0sQFgibNwBfKII/FM1JauY4HoeV69tbuXZ9Cy8f7GdzRxcdh4c4MujjP3+/n+89f5B3r2nkurYWWmrmHm0cJ5pQB3guN4BKh5WeYT8Oq4FS5qzfG9K0JEmeFt8nE0klHekmE6+edOWkTPsRhHwyZ6lHKfUj4OvARcA5sVdRZtvMBw6rhRq3naZqF0sXuGmsdlLtsmUlkjgZFkPxtlMX8vUPrOO7n9jAVWubJjJWbu7o4uPffYX/c/8utr6VWY2A+A2gc8DLwHgwqQSUiTySD0klX0naJLGbUOikM+PfAJypczl1LWBOxksjbpTiM8D4IrEvC7JQsjoByxZ6+It3ncZNFy3j0Ve7eWD7UXpHA7x4oJ8XD/SzdIGbTe0tXHbGorTXKBKfAKpjTwBGwhPAWDBCrdtK/3hoSgBXKnkkk306B70EQ5EpgWILPfakgWKZeChl4gmUST/iBSTkk3QM/y6gEejO0VgKlmx7acQXiSsSZCFvIMJ4MJxWdGu8ToDVUFPqBNzKZDqJKpeNG85dwgc2LOa5/cfZ3NHFq13DHOr38o3fvsHdfzjIe89q4ur1zTRWJZcvZiKeB2jEb94AqpzmDaDCbqFn2I/NmJR6Br1hVtQn9zjKZB8F9I4FJ1z4tTZ/X1ybXM5K10MpE0+gdPsRLyAh38xq+JVSD2M+eFcCe5RSrwCB+Oda66tzN7zCIFFSAHDbrXiDYe589kBW/jEdVgsOq4Xa2Gx1LBBmzB+e9UkgsU4ATGbrvPePnSf48lsMxdtPq+ftp9XzxrFRNm/r4qm9vYz6w9z7x05+saWTi1Ys5Lr2Fta2VKeVyTMS1QyMBxmOPQFMkBi9pZMHVsEMwVhz2Of4WCC+2Yzt2SDX1z5ffQhCInOZ8X8956MocPKZ48VuNaiz2qnz2PEFI4wGQngDkRldM7tHfFRNKzU4lzoBg+Mhjg0HqHHZsBgG48Ewo/4wz75xnGffOM6K+go2tbdw6aqGtNYk4jeAIV+IxioHA+NBQlE9a2AVpB+MBRCMaAwg8fZoxNqTke41yYeHjngBCflm1v9qrfUzWutngPfEf05sy/0Q559Myu9lIwWwy26JxQy4qa90nKDFN1W58IemPhXMlqsnsYxkjduGzaLw2C3csGExZzRVArC/b4z/9/jr3HDXS/zP8wfTnkE3VrlQStFa52ZlQyXLFnqwWoxZvWesFoPl9RWsaqxieX3FrPs4LAZRpmZsiMbaZyKTa5KP0otS3lHIN+m4mLxrhrYrszWQQma+c7wopah02miucdFa66bWbdYZyKROwExlJG0Wg709o/zXh9v5rw+38c5VDVgMxZAvxI9fOsyNd7/MPz/yGq91z14jABLqFwQjhCIRRvwh/KEoN6covZjJOa7zmLPkaY5AE+3TyUeQWCaIF5CQb2Y1/EqpzymlXgVOV0rtTHgdBHbmfojzTybl9zoHvVOyZkJ2Ht/tVoNaj1ln4PoNrfzf95xBfeXc6wTMVkbyjKYq/s97z+BnN5/Hx85fQo3LRiSq+d3eXv70p9v405928LvXegmlyLszvX5BndvBF96xguUNFfQnSQa3cVUDZy+p5lC/l11HRzjU7+XsJdWppTGlqK+wEXcoMhTUVySvNJbJNclH6UUp7yjkGzWbd6ZSqhqoBb4K/F3CR6Na65krXmTIhg0b9JYtW7L5lfPGjXe9dEIQTzzw52e3nJ/1/oLhKOOB8KyeQX/x8x0npHL2hSIs8Dj4xodOzKAdDEd5am8vmzu62N83WeZwgcfO1eubuWptE7UJNQLmglKmV5MpNZk3oTue3MftT+3HUKYBj2rzlapWb7rnON/XRBDyhVJqq9Z6znFVc9H4h7XWh4A/BUYTXiilZn6mFvL++J74JLC4zkwt7bJbTpj9pisP2a0GV6xp5M6PtfOtD63jkpULMRT0jwf53vOHuOGul/jab/ayv3dsxv1nQmvNqD9E54CX3hE/gXCEe547iKHAahgYyoi9wz3PHUz6PemeY5FUBMFk1hn/xIZKHQIWA4OY62g1mD79vcDNWuutJzuYUprxw6QHyVyDeHJBNKrxhiKM+cOmsdd6IuirZ8RHY0LQ11zpGfHz0PajPPJqN6P+Sa+bs1qqub69hQtXLJxbTp8ELvvGM1gNMJQxcbOK6ijhKLz5L8l9CNI9x4VwTQQh26Q7408ngOs3wP1a68djHV0OXAH8Avhv4Lx0BloOpBsslAuMhGCxSFQzHgyzcVUD5526YM5pG2aKDr7lkuV87IJTeHLPMTZv6+Ktfi+vdg3zatcwDZUOrl3fzHvOaqLKNbeHQpfNgi8YQRNFY84sDMVE7qNk7DwyxO6jw4wHIwz7Quw8MpS1wCpBKFXSmfFvmX5HibcppbZrrdef7GBKbcZfyGg9NZtossXaxOhgp83AH4oSjuopi8haazoOD7G5o4uXDvRPeNc4rAaXn7mIa9taWLZw5nKIcf7lkT08ubfvhPZr1jVy+41nz7hPJusCglCK5HLGP6CU+lvg3tjvHwIGlVIWpsbQCEXA9PxB8cXh0WkRw3OJDlZKcfYptZx9Si1dgz7u397Fb3b14A1GeHhnNw/v7ObsJTVsam/lvOV1M5a0PD4WosppYdQfmZjxVzotvDXg5/hYYEpVsDiJ6wJgGv9wNMo9zx0Uwy8IKUjH8H8Y+AfgAcz/y+dibRbgg1kfmZBXzJKTdmpjEcNjgTDeYJjuER8WBZ2DAUKRKDaLQa3bljQ6uKXWxZ+9YwVrm6u5+7mD9Iz4iUQ1Ww8PsfXwEM01Tq5ra+GK1Y1TZJzuER+Lqpw0Vk3eFDSanmHfRFUwj8NCtcuGw2rehMaDEQw0gXAErUEps8RjqsRugiCkYfi11seBLyT5eH92hiMUApPlJh3UuGzs7x3DMBRGLE/+sZEApyxILt28cmCAO/9wAKuhWLbAxZAvzIjfdDM9OuTnv37/Jv/z3CGuWNPIdW3NtNa6aapyneBmmhiFrLVmzG/mMHLYLFQ6rdgNhS+sJ1P7aAjNUuJREIQ0DL9S6jTgrzixEMul2R+WUCgoTClH6VjutLiAn2JtaLo8VOe247JZ8NitnNZYyRO7e/CFIty/rYsHtnVx3vI62pfU8OguM/Fr4lrCTG6mgVjh+yqXFd9o8IQkbckidwVBMElH6vkl8B3gHiCtZ+nYOsAWoEtrfVU6+xYKmSRcy0eO9VyPa3rpQZtFsajCgS+c/E8gmTw05Avy55et5DMXLeXRV3t4YHsXx0YCvHRggJcODNBQ6UBrGPaFaK52z+5mqhS1bitD3vDEusACjzWtzKKCUI6kY/jDWutvZ9jPrcBrQFWG+88rmeRLz0eO9XyMK1npwdZKJ801Lkb8IcYDkSmuoR67lbf6x5PKQ5VOGx86ZzHvP7uVF97s5/5tR9jeOUzvqJkMrsJh5bTGCpYsSJ2kLC4P1VdM1hHwhSI0VDoZC4TxzBDAJghCeknaHlZKfV4p1aSUqou/ZttJKdUKvBfzSaEoySS5VzaTtM3nuFJFuzptseyhdW4WVDhwxPX5JGUUp8tDFkNx8cqFfOOD67n7Y2dz5ZpGbBbFWCDML7Yc4aPffZkvP7ibHZ1DM8YcJItC/sDZrfSO+Dk84KV/LJAyr5AglCPpzPg/EXv/64Q2DcwW7/4t4G8wC7mcgFLqFuAWgCVLlqQxnPyRSb70fOVxtyg40Dc2kcN+YYU9q+PauKqBs7cf4aGdPUSiGouhuHpt45SnA8NQEyUYA+EIvnCURVUOBr2hBKnHjjeUXB46taGCv3736dx88TIeiZWK7B8L8tz+4zy3/zjL6z1samvhnasaJm4w5y6v41ZWJo1CjkQ1w74Qw74QLruFSqdNngIEgfS8epLn1E2CUuoqoFdrvVUptTHJ994F3AVmAFe6feSDuNyRmNxrtnzpmeyTLpUOK2/0jmExFJaYpNI15GdlQ0XSfdId1x1P7uOhnT2mv7xVEdXw0M4eli3cN6OvvMNqYekCD8dGfCxb6CAS1RPBYg2e5GUU49S47XzkvFP40IbFPPuGWSpyT/cIB/rG+foT+7j7Dwe5am0TV69rpr7SwbnL6+aUbiJe39hiKDyxSOZ0aw0LQqkwZ6lHKeVWSv1fpdRdsd9Xxgx7Ki4Ero7l+bkXuFQp9eOMRztPZJLcKx8JwXQSSSVVNHa648o0eVo4atYStlkU4WiUqIYPn5e8TsB0rBaDS1c18J8fbuO/P9LGZWc0YDUUw74QP3n5MB++52W+8us97D46POfUE2A+BYz4Qhwd8nG438vxsQA+8fsXyox0Ujb8HNgKfFxrvUYp5QJenGuqhtiM/69SefUUcsqGTJJ7ZZpAbK4eOhd97SksiinlChdW2Ilq+MPfJveyTWdcp/7vRyeSp8XJNHna20+vxxs0C7NkYmz7xwJmJPCOowx6QxPtpzdWcn17C28/rX4izXO6WAxzvaPCYY3FMAhC8ZBuyoa0c/UopbZprdtibTu01icmcZ95/40UseHPNYneNvG0CKGITlmQIx/55df+4+P4QpGJtAhgpkVw2Szs/Md3Z/y9wXCUYV+IsUA4rRl7fN+nX+/lvo4u3khIB13nsXP1uiauWttMnSe9GgGJiBwkFBtZz8efQDA2y9exjk4F5lyMVWv9dLH68OeDQi0LeNNFy4hqYnJNdEK2uSlFGcW5YLca1Fc6WFzrosZtn3Jjmcu+l69u5Dsfbef2D63n7afVYygYGA/y/Rfe4sa7X+JfH9vLvmOjGY0tUQ7qHPAy5A3OWDVMEIqVdLx6/gEzNfNipdRPMPX7T+ZiUOVIJh46G1c1cBvkNL98fAH3nucOMh6M4LFbuOmiZVlLgma1GNR57NS6bWnLQEopzmqt5qzWao6N+Hlw+1EefbWbEX+YJ/Yc44k9x1jTXMWm9lYuXpl+jQCAUCTKwHiQgfGgeAYJJcOcpR4ApdQC4HzMIMmXYvl7skY5Sz1XfPMZ9veNY1EKpUyX94jWrKj38JsvvX2+h5dX/KEIIzEZKJN9f/daL5u3dXHw+PhEe32Fg2vWN/PetU1Uz7FGQDIMpXDbLVQ4rWbRerkJCPNM1tMyK6XapzV1x96XKKWWaK070hmgMDMTxkPFXgCasjQqTpsFp81CTQbrAE6bhfeubeI9ZzWyrXOI+zu6eOHNfvrGAtzz3EF+9NJbXHbGIja1z14jIBlRrRkLhBkLhGU9QChK5iL1/HuKzzQgSdqywGggPCUnjt1i0FjlyGjWWyrE1wHqPPaJ1MyJtQJSoZSifUkt7UtqOTrk44HtXTz2ag/jwQiPvNrNI69207akhk1tLZy/fEFGMhBMrgeM+EJYDIXLbsFtN58EMv1OQcg1sxp+rfU75vJFSql3aa1/e/JDKk+S5cRpqHSm2Ku0SObOajEUtR47NRmsAwA017j4/MYVfOpty3h8dw+bt3VxZNDHtsNDbDs8RFO1k2vbWrhyTSMVs5R6TEUkOpk6WimzYpnbbsVjt5xQREYQ5pO0NP6UX6RUh9Z6uiyUFuWs8WfizllKpHv8oUiUUX+YUX+ISDS9v+Go1mw5NMjmjiO8cmhwot1pM3j36kaua2thSV32IqzN7zbTUrsdloxjDQQhGTnz459DxxP+/ZlSzoYfMgsSKxUyjUnQWjPiDzPsDc1ZBkrkcL+X+7d38fjuHvyhyf3PXVrLpvZWNiytnbFU5MngsFnw2C14HFa5CQhZYT4N/7zN+POR976UKMTzddHXnqLGZZuymK21mWQtVRRy4rYj/jAjvlBG2TjH/GEe293DA9u66B72T7QvrnWxqb2Fy89szElEr91qUOmw4XGIHCRkTtkZ/nKXSNKlUM9XNqOQxwNhhn0h/CmygSYjEtW8dKCfzdu62HZ4aKLd47DwnjVNXLO+meYaV9rfOxccNgsVdqvcBIS0yWXk7mwcyuJ3zZl85L0vJQr1fGUzCtnjsNJc46Kl1kWFM72KXBZDceGKhfz7B9Zxz8fP5j1nNWK3GowHIvxy6xE+9t1X+PsHdtFxeDDtVBOzEQhF6B8PcHjAS/ewj1F/iGia6xeCMBfm4se/KdXnWuvNsfeU2+WKfOS9LyUK9XzlIgrZYbXQUGkh7I4yksFC8PL6Cv7q8tO5+eLlPLKzmwe3H6VvLMDzb/bz/Jv9LF/o4bq2Fi47Y7JGQLaIp5HuV0HcDguVDpskjxOyxlx8196X4jMNbM7SWDIiH3nvS4lCPl8bVzXkRG5KTAuRyTpAtcvGh89bwgc3tPLc/n42dxxh19ERDhwf599/u4+7/3CAq9Y2cc36FuorZ685kA5RPekiajUMXHYLHodFIoaFkyJrGn82EI0/98j5Mhn1hxjyZrYQDLDv2CibO7r4/eu9hCLm/5Ch4JKV9Wxqb2F1c1VODbOhJiOG5UlAyOnirlLqvcBqYCKqSGt9W1ojTMHJevUUmhtkut4z+fK2KdTzNR+MB8IM+UIEMlgIBjMj6K93HuWhHd0MjAcn2k9fVMl17S1sPK0euzW3C7VWw8DtsEjaiDIml/n4vwO4gXdgFk5/P/CK1vozmQx0JkrJjz/dmbXMxOcXXzDCkC+YcTWuUCTK06/3sbmji9cT0kHXum28b20zV68/uRoBcyUuB7nsFkkbUUbk0vDv1FqvTXivADZrrS/PdLDTKSXDn657Yj6KqgizczKZQcGMJ9jTPcLmji6e2ddHfC3Zaig2nl7P9e2tnN5YmcURp8Zps+CO3QgcVnkaKFWynp0zAV/s3auUagb6gZOrxlHCpOs9U6jeNuVGYmbQIV+QMX96NwClFKubq1ndXM2fjAZ4cHsXv95p1gh48rVennytlzObqri+vYWLVy7Mub++PxQx4xnGJ58G3LGnAUOeBsqWdAz/r5VSNcC/AR2YHj335GJQpUC63jOF7G1TjtitBg2VTmpcUYa8wYyeAOorHdx08XI+dv4p/G5vL5s7ujhwfJw93SPseWRkskbAWU1Uu0+uRsBcCEejjPqjjPpDKKVwWA3csWyiuV6HEAqLdKQeh9Y6EP8Zc4HXH2/LBqUk9YjGX1oEwhGGvCHGTyJNttaaHUeGua/jCC/s7yf+n2e3Gly2qoHr2ls4NSE7az6xWQwqHGYSOZGEio9cavwnpGTIRpqGRErJ8EP63jPibVP4ZOMGANA97OOBbUd5dFc344HJBeX1i6vZ1NbKBadmXiPgZInfBDwOeRIoFrJu+JVSjUAL8GPgw0zWh6oCvqO1XpXhWE+g1Ay/ULpk6wbgC0Z4Ys8x7t/WxeGByfWcxion17Y1c+WaRiqduZeBkmGzGHgcZmEZp82QoLECJReG/xOYRdU3AIlWeQT4QTxlQzYQwy8UG4FwhGFv5l5AcaJas/WtQTZ3dPHywYGJdqfV4PLVjWxqa2HJgvld7zGUwmkzF4Zddos8DRQQuZR6rtda35fmYJzAs4ADcyH5V1rrf0i2vRh+oVjJ1g0AoHPAy/3bunh89zF8CYFlG06pZVN7C+cuq8t6jYBMsBoGTrshpSYLgFwa/kbgn4FmrfWVSqkzgQu01t9NsY8CPFrrMaWUDXgOuFVr/dJM24vhT59CzK1fzmRLAgIYC4T5za4e7p9WI6C11sW161u4Ys2iKV5g840j9jTgtlskgjjP5NLwPwZ8D/g/Wut1SikrsE1rfdYc93djGv7Paa1fnmkbMfzpIZ5AhUswFgcwHoicdPrmeI2A+7d10ZFYI8Bu4Yo1jVzb1kJLjmoEZMqELBSLGRBZKLfk0vD/UWt9TmKJRaXUdq31+ln2swBbgRXAf2mt/zbZtmL400OifQufcCTKoDfEqD+Ule87eHyc+7d18ds9xwiEzQRzCrjg1AVsamuhbUlNQS7AiiyUW3IZuTuulFqAGbiFUup8YHi2nbTWEWB9LPjrfqXUGq31roQB3wLcArBkyZI0hiNItG/hY7UY1Fc6qHbZMg4ES2TZQg9/8a7TuOmiZTz6ajcPbD9K72iAF97s54U3+1m6wM2m9hYuO2NRQckt4WiUMX90IhLabp28CYi3UP5JZ8bfDvwHZnbO3UA98H6t9c45d6bUPwDjWuuvz/S5zPjTQ2b8xUcwHGXEH2LUH85KBa9IVPP8/uPc19HFq12T87Aqp5X3nGWWilxU5UzxDfOPUqZUKd5CmZNLqccJ/BnwbmAUeBH4D621P8U+9UBIaz2klHIBTwBf01r/eqbtxfCnh2j8xUskqhnxhRj2hYhmqSbGG8dG2byti6f2Tq0RcNHKhWxqa+GsluqimFmLLJQ+uTT8v8D03f9JrOlGoFZr/YEU+6wFfgBYMOv7/iJV/n4x/Okj0b7FTTSqGfGbN4B0ykKmYmA8aJaK3HF0So2AFQ0VbGpr4dJVDUU1qxZvodnJpeHfobVeN1vbySCGXyhXIlHNkDfISJYkIDBrBDyzz6wRsLdnskZAjcvGVeuauHpdMwsrslsqMtcYSpk3ALsFt82S8+ymxUIuDf/3MVM0vBT7/TzgE1rrz2cy0JkQwy+UO+FIlAFv+umgZ+O17hHui9UIiD9ZWAzFxtPMUpFnNFVltb98YbNMTTVdDFJWLsil4X8NOB04HGtaArwGRAGttV6b5lhPQAy/IJgEwhEGxjOvCJaM42MBHtpxlId3dDPsm3QxPaOpkk1trbz9tNzXCMgVhlIT1cfK7Wkgl4b/lFSfa63fmmunyRDDLwhT8QbDDIwHCYYzKwqfjGA4ylOxGgH7+8Ym2hdU2LlmXTNXrW2ixp37UpG5pJxcRnNabD3XiOEXhJkZC4QZ8mb/BqC15tWuYTZ3dPHc/uMTpSJtFsU7Vy1iU3sLKxrmp0ZANklMMOe0GyVXc0AMvyCUMN5gmEFviEAouxIQQM+In4e2H+WRV7sZTVhjWNtazab2Fi48dWHJuFbGXUbj8QPFLguJ4ReEMsAXjDDoDZr1dLP93aEIT+45xuZtXbzVPxkFvqjKwTXrW3jvWfNbIyAXJC4SO63FV49YDL8glBG+YIQBbzAnTwBaazoOD3FfxxFePjAwUSrSaTV415mLuK69haULPFnvd76J1yN22SxFU4pSDL8glCG5WgSO0zXo4/5tXfxmdw/eBE+js0+pZVNbC+ctL4waAbnAaphPAx5H4bqMiuEXhDJmLBBmcDxIKJKbG8B4IMzju81SkV1Dvon25hon17W1cMXqRjyOwqkRkG3iAWSmLGQtmDUPMfyCUOZorRnxm15A2UoDMZ2o1rxycIDNHV1seWtwot1tt3DF6kaua2uhpbawagRkG6UUTpuB22ad9+RyYvgFQQAm8wANebOXCG4mDvXHagTsPoY/oUbAecvr2NTWwtmn1BakPJJtbBZj4mkg35KQGH5BEKYQiWqGY5lAc/n/PuoP8eirPTywvYtjI4GJ9lMWuLmurYV3nbkIV5kkWZtINR3zFLLl2F1UDL8gCDMSrwY2FsheIriZiEQ1L7zZz+aOI+w4MlkjoMJh5T1nmaUiGwu8RkC2yXVOITH8giCkJBiOMugNZqUg/Gy82TvG5m1dPPnasSk1Ai5cYdYIWNtaHDUCskni00C26hGL4RcEYU74Q2YiuFwEgU1nyBvk17EaAf1jkzUCTq33sKm9lXcWWY2AbBJ/GljgsWd8ExTDLwhCWuQyDcR0wpEoz75xnM0dR9jTPVkjoNpl46q1Zo2A+sriqhGQLZYu8GQcMSyGXxCEjMhlFPBMvNY9wv3bunj69T7CCTUCLlm5kE3tLZzZVFVWMpAYfkEQ5o3RmAtoroLAptM/FuDhHd08vPMog97JGgGnN1ZyfXsLbz+tPudeMYWAGH5BEOYVrU0X0FzHACQSDEd5+vVeftXRxf7ehBoBHjtXr2vmqnVN1BZ5jYBUiOEXBKEgiEQ1g94go1msBTwbWmt2dY2weVsXf3ijb0qNgEtXNbCprYWViyrzMpZ8IoZfEISCIp8uoIkcG/Hz4Aw1As5qqWJTeysXrSidGgFi+AVBKEjy6QI6vd8nX+tlc8cRDiXUCGiodHDN+mbec1YT1a7irhEghl8QhIIm11lAk6G1ZtvhITZv6+LFN/snagQ4rAaXnWGWily2sDhrBJSM4VdKLQZ+CDQCUeAurfXtybYXwy8IxcN8LAAn0jXk48HtXTz2ag/jCTUC2pbUsKmthfOXLygqGaiUDH8T0KS17lBKVQJbgWu11ntm2l4MvyAUH/OxAJyINzhZI+DI4GSNgKbqWI2ANY1UFEGNgJIx/Cd0ptSDwH9qrX870+di+AWheAmGowyMB/EG87sAHCeqNX88ZNYI+OOhyRoBLpuFd69exHVtLSyuc8/L2OZCSRp+pdRS4FlgjdZ6JKH9FuAWgCVLlpz91ltv5WU8glBIPL23lzufPUDnoJfFtW4+e8lyNq5qmO9hZcTju7q585kDdA37aKpyccM5izl3eV1ex3C438v927p4fE8P/tDkOsS5y+q4vt2sEVBopSJLzvArpSqAZ4B/1lpvTradzPiFcuTpvb18+aHd2Cxm1kZfKEIoornt6tVFZ/wTj8VuMRgPhglFNLdeujLvxh9gzB/msV3dPLD9KN3D/on2JXVurmtr5vIzG3HZC6NGQD4Nf87joJVSNuA+4CepjL4glCt3PnsAm0XhtltRyny3WRR3PntgvoeWNonHYrUYVDltOKwG927pnJfxVDitfGDDYn746XP5yjWrWb+4BoDDA15u/91+PnjXi3z76TfpHval/qISI6crHsrMsPRd4DWt9Tdy2ZcgFCudg15qpvmgu2wWjgx6k+xRuEw/FqUUFQ4rx0f91LjtjPjmxwPIYiguXLGQC1cs5M2+Me7v6OLJvb2MByL8cusR7us4wgWnLuD69lbWlUGNgFzP+C8EPgZcqpTaHnu9J8d9CkJRsbjWjW9aQJQvFKG1tnAXIpOR7FgW13mo89hZXOem2mWbV8N6an0Ff/Xu0/n5zedz00XLWFhhJ6rh+f39/MUvdnDzj7by6KvdectSOh9IAJcgzDOlqvGnOpZIVDPkDTIyTy6giYQjUZ7bf5z7OrrYfXTC74Qqp5Wr1jZxzfqWvNQIKLnF3bkihl8oV+JePUcGvbQWuVdPOseSrzrAc+X1nlE2b+vi93t7J2oEGAouWVnPpvYWVjfnrkZAPg1/4Uc1CEIRkql75vybvuwxl2OxWgzqKx3s6Bzk208f4MiQd95cQMGsAfC/rlzFZy9ZzsM7jvLQDrNGwNP7+nh6Xx+nLapgU3srG0+rL+pSkTLjF4Qsk650U45ST7J9HFaD8UCY4Dy6gCYSDEd5el8fmzuOsO/YZI2AWreNq9c18751zdR5slMjoKTcOQWh3EjXPbNU3TnneiyJ+1gMgyqXHafN4Bfz5AKaiN1qcPmZi/j2R9q544b1vOP0egwFg94QP3jxLW646yW++theXu8Znf3LCgiReoSiJl1JJRMJJt190nXP7Bz0EgiFOXh8nKg2NeUFHhvBcOrMl4UY7ds56MWi4EDfGMFIFLvFYGGFPaVr6kzny2O30jcWYEGFgyFvkEh0fpUJpRRrWqpZ01LNn4wGeHB7F7/e2c2IP8xv9xzjt3uOsbq5ik1tLVy8ciHWAi8VWdijE4QUxCWC3lE/NS4bvaN+vvzQbp7e25uV7TPdJ233TK3pGwtNVJqKaugbC6Vc7MxkXPmgwm6ha8hPOKKxKEU4ouka8uNJER2b6nxVu2wsrnVT67YXTIqF+koHN128nJ/fcj5/+a7TWB5LA7376AhfeeQ1PnLPK/z05cMMJ9QPLjTE8AtFSz4klUz2+ewlywlFNN6g6anijaUt+Owly2fcfmDcNBAq4ZXYnq1x5YMJj5dpB5PKE2a282UYitpYDECls3CKrThsFt67tom7P3423/jgOi5csQAF9I0FuOe5g3zo7pf4+uOv82bf2KzflW9E6hGKlkwklVFvkOHA5Oyy2mFJKalkElW7cVUDt8GcXRoDsWIm0+f3gRRFTjKRVDLhS/d28NDOHiJRjcVQXL22kW/e0J50+9FAmDq3jePjwQnZaqHHzliKko1zPV8WQ1Ff6aDKZWVwPJR2FtBXDgxw7x876R7JbvI4pRTrF9ewfnEN3cM+Hth2lEd3dTMeiPDorh4e3dXD+sXVXNfWyttOPbFGQHxcfWN+ltR58iLZieEXipbFtW56R/247ZN/xqkkFX8wPMXoAwwHItisyQ1Iun3E2biqYe7/vMkknRRST6XDyhu9Y1gMhcVQhKOmpLKyoWJufc6BL93bwf3buyd+j0R17PeOpMa/0mHljWE/NouBUuYhDHhDrGxIHQCVzvlyWC00VlvwBsMMjAdnXQsB07je/tQbWA1FldNK/3iA2596g1vJrudQU7WLz208lU++bSlP7Olhc0cXnYM+tncOs71zmMYqJ9e2NfOeNU1UOK1TxpUo2d0GOTX+IvUIRUvakkoSzTVZeyZ9ZEKydctU65kT+r9OeCW2Z4GHdvYAoNTkK7F9vsYVx2230lrrZmGlA6uR2pTd+8dOrIbpYqow362G4t4/5sZzyGW3cM36Fr73qXP42vVncd4y8+bSM+LnO88c4IN3vcjtT77B9184NDmuPEp2MuMXCoZ0vVQ2rmqg5Zn9vHhwsujGBctqk+6TiYHduKqB9x8Z4p7nDjIejOCxW7jpomVZ9QRK1n0qUzkWjOCxGSfIVoklCGfijif3nXAsX7zstBm3TeZJk8rDZiwYoaXGyfGx4IQE1VjhmHVcJ0OV00alw8qIL8yQb2YPoO4RH1XOqebOaTPoGUmdlfNk5SFDKc5ZWsc5S+voHPDywPaj/GZXD75QhAd3HAXAbbdQ67Jhs5g5jPKRoE9m/EJBkImXypfu7Zhi9AFePDjIl+7tmHH7uLY6fQabqi7r03t7+VVHF/WVDs5orKS+0sGvOrqy6gmUbN0zlROLwpSpEtdQp8tY07njyX3c/tR+fKEIVsOUrG5/aj93PLlvxu2TnZdU52txrRurxWB5fQWrGqtYXl+B1WLkPOGcUopqt+kBVOexnzDGpirXlIIsAP5QlMYqV9LvjMsw/eOBKfLQKwcGMhrj4jo3X7h0BT//7Pl8fuOpNFU7AfAGI3QN+3mjd5yxQDgvCfrE8AsFQSZeKulKEVevbQRM3Tn+SmzP1rjS3cdIMoFO1g5wfCxgHgtTFJWJ9pm457mDGAqshoGhjNi72T4TmZyvfEhjqTAMRY3bfsIN4IZzFhOOanyhCBrzPRzV3HDO4qTflSt5qMJh5f1nt/LDT5/Lx88/BXvM5z8YiRKORPNyvkTqySGFGGBTqGTiPRN/pJ8uHyeTIswFyfS8VDoHvQRDkSnBVQs99ux6AhlqZr0pxcw6GJn5GJO1A4wHzZn+9C6SyTCZnK90PZriZPt/JX4DqHLaGPKFOP/UBYBpzHtGfDTOQbbJVB6aKxZD8ckLl3JmUxXfe+EQPcO+vCXoE8OfIxLzj+Rztb5YycR7JgN7yTdvaOebN8x9XAroHQtOyClam78vrk0uEaR7LB67JSa/TFrlcDSKy5Y86MlAM5O5NlKsDMT7STw/UU3K4Kp0zxek6dFEbv9XDENR57FT7bLhcVjT0uebqlz0jwemXIfZ5KFMOHd5HecurzupXD3pIlJPjijUAJtCJROJoM49czBPsvZMyERSSfdYbrpoGVFtGvuojsbezfZkGEm8WJK1Z9pPPsjH/0o8BqCl1oUzxQ01kUzkoWJBZvw5opTK6eWDTCQCp91KdTh6gmeLy569P+tgRGMAicKOQWpJJd1jiXvVzNXbBiAc1VgUJA7DopjIIZ+tfvJBJv8rmUpDDquF5hoX4wEzBiCUIkju3OV13MrKtOShYkEMf47INPCnnElXIlhc66bXarAk4Rx7g2EaKp1ZG5PDYuCNRkh8AI8C7lmScKV7LF+87LS0DHBctrGnIQ9l0k8+SPd/JRvSkMdhxeOwMuoPMeQNJb0BxGWYUkOknhwx394N5UA+znGdx5yJTpd64u3zRaHKNpmQ7nXMpjRU6bTRWutiQYUjpZtqqSGGP0dsXNXAbVevpqHSybAvREOlsygLaxQyeTnHSlFfYZtYEDUU1FfMb7FwMGfut166ApfNQjhqSiO3Xrqi4GbzcyHd69g56D3hyeZkZFSl1EQW0DpP4WQBzSUi9eSQdB/3hczJVbb2uAzRWD0pO2RbTsqUQpRtMiWd/5VcyahxF9BKp61gCsHnCpnxC0VLPnLSi2RXeOT6mlgMxYIKB621LjyO0pwb59TwK6X+RynVq5Talct+hPIkH26AItkVHvm6JjaLwaIqJ03VrqIurD4Tub6dfR/4T+CHOe5HKEPy5TJbSpJdqUST5/OauOwWWu1uhn2hgigDmQ1yehvTWj8LZJbRSBBmIe0Sh2VOoZZrLBbiC8DVrvlf3D9ZSuv5RSgrRH9PD4kmP3mMmP7fUuOasrhcbMy74VdK3aKU2qKU2tLX1zffwxGKCNHf0yPbbpDljN1q0FjtpLHaiW2WYL5CZN5vWVrru4C7ADZs2FD84pmQV0pJf881Ek2efdx2K65aCyO+MIPeINEicf8svluVIAgZIdJYbpgoAlPnptI5vxHdcyXX7pw/A14ETldKHVFKfSaX/QmCkByRxnJLJhlA54ucSj1a6xtz+f2CIKSHSGO5J50MoPPFvGv8giAIpYjHYcVttzDiDxec/78YfkEQhBwRTwBX6bAyWED5f2RxVxAEIccUmv+/GH5BEIQ8Eff/X1Q1v/7/83/rEQRBKDPi+v+QN8SQL5R3+UcMvyAIwjyglKLWY6fCaWVgPJjXvsXwC4IgzCPx9M/5RDR+QRCEMkMMvyAIQpkhhl8QBKHMEMMvCIJQZojhFwRBKDPE8AuCIJQZYvgFQRDKDDH8giAIZYYYfkEQhDJDFUKK0DhKqT7grZP4ioXA8SwNp9go52OH8j5+OfbyJX78p2it6+e6U0EZ/pNFKbVFa71hvscxH5TzsUN5H78ce3keO2R+/CL1CIIglBli+AVBEMqMUjP8d833AOaRcj52KO/jl2MvXzI6/pLS+AVBEITZKbUZvyAIgjALYvgFQRDKjKIy/EqpxUqp3yulXlNK7VZK3TrDNkopdYdSar9SaqdSqn0+xpoL5nj8G5VSw0qp7bHXl+djrNlGKeVUSr2ilNoRO/Z/mmGbUr72czn+krz2cZRSFqXUNqXUr2f4rGSvPcx67Glf92IrvRgG/lJr3aGUqgS2KqV+q7Xek7DNlcDK2Os84Nux91JgLscP8Aet9VXzML5cEgAu1VqPKaVswHNKqce01i8lbFPK134uxw+lee3j3Aq8BlTN8FkpX3tIfeyQ5nUvqhm/1rpba90R+3kU80S0TNvsGuCH2uQloEYp1ZTnoeaEOR5/SRK7nmOxX22x13TPhFK+9nM5/pJFKdUKvBe4J8kmJXvt53DsaVNUhj8RpdRSoA14edpHLUBnwu9HKEHjmOL4AS6ISQKPKaVW53dkuSP2uLsd6AV+q7Uuq2s/h+OHEr32wLeAvwGiST4v5Wv/LVIfO6R53YvS8CulKoD7gD/XWo9M/3iGXUpqZjTL8Xdg5u1YB/wH8ECeh5cztNYRrfV6oBU4Vym1ZtomJX3t53D8JXntlVJXAb1a662pNpuhreiv/RyPPe3rXnSGP6Zv3gf8RGu9eYZNjgCLE35vBY7mY2z5YLbj11qPxCUBrfWjgE0ptTDPw8wpWush4GngimkflfS1j5Ps+Ev42l8IXK2UOgTcC1yqlPrxtG1K9drPeuyZXPeiMvxKKQV8F3hNa/2NJJs9BHw8tsp/PjCste7O2yBzyFyOXynVGNsOpdS5mNe4P3+jzA1KqXqlVE3sZxdwGbB32malfO1nPf5SvfZa6/+ltW7VWi8FbgCe0lp/dNpmJXnt53LsmVz3YvPquRD4GPBqTOsE+N/AEgCt9XeAR4H3APsBL/Cp/A8zZ8zl+N8PfE4pFQZ8wA26NMKzm4AfKKUsmH/Yv9Ba/1op9SdQFtd+Lsdfqtd+Rsro2p/AyV53SdkgCIJQZhSV1CMIgiCcPGL4BUEQygwx/IIgCGWGGH5BEIQyQwy/IAhCmSGGXxAEocwQwy8I04iluT0h/W0a+29QSt2R5LNDSqmFSqkapdTns9WnIKSDGH5ByDJa6y1a6y/OslkN8PlZthGEnCCGXyhKlFIepdQjsYyEu5RSH1JKna2UekYptVUp9Xg8La9S6mml1LeUUi/Etj031n5urG1b7P30Ofb9amzGrpRS/Uqpj8faf6SUuixx9q6UWqCUeiLWx51MJhP7V+BUZRbO+LdYW4VS6ldKqb1KqZ/Ew/AFIduI4ReKlSuAo1rrdVrrNcBvMDMTvl9rfTbwP8A/J2zv0Vq/DXOW/T+xtr3AJVrrNuDLwL/Mse/nMdNnrAYOABfH2s8HphdG+QfguVgfDxFLrwH8HfCm1nq91vqvY21twJ8DZwLLY30IQtYptlw9ghDnVeDrSqmvAb8GBoE1wG9jE2ULkJik62cAWutnlVJVsYRnlZj5b1ZipvC1zbHvPwCXAG9hVnq6RSnVAgzEKmQlbnsJsCnW9yNKqcEU3/uK1voIQCwX01LguTmOSRDmjMz4haJEa70POBvzBvBV4Hpgd2wGvV5rfZbW+vLEXaZ/BfAV4PexJ4b3Ac45dv8s5iz/Ysz0yH2YibL+kGy4c/zeQMLPEWRiJuQIMfxCUaKUaga8WusfA1/HrK9ar5S6IPa5TU2tRPShWPtFmCl7h4FqoCv2+Sfn2rfWuhNYCKzUWh/AnJX/FTMb/meBj8T6vhKojbWPYj5xCELekRmFUKycBfybUioKhIDPYRajv0MpVY35t/0tYHds+0Gl1AuYxao/HWv7f5hSz18AT6XZ/8uYchKYBv+rzCzL/BPwM6VUB/AMcBhAa92vlHpeKbULeAx4JM3+BSFjJC2zUPIopZ4G/kprvWW+xyIIhYBIPYIgCGWGzPgFIQlKqU8Bt05rfl5r/afzMR5ByBZi+AVBEMoMkXoEQRDKDDH8giAIZYYYfkEQhDJDDL8gCEKZ8f8DR7yP+T/9VlMAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Draw a scatteplot of petal width versus length with a simple linear regression line\n", + "ax6 = sns.regplot(data=iris, \n", + " ci=95,\n", + " x=\"sepal_width\", \n", + " y=\"petal_length\")\n", + "\n", + "\n", + "# ax6.set(xlabel='Sepal width (cm)', ylabel='Sepal length (cm)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "for s in iris.species.unique():\n", + " ax7 = sns.regplot(data=iris.loc[iris.species == s], \n", + " ci=95,\n", + " x=\"sepal_width\", \n", + " y=\"petal_length\", label = s)\n", + " ax7.legend()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Seaborn allows you to do that more systematically with pairplot\n", + "\n", + "This is like doing a scatter plot for each pair of the variables in one go. On the diagonal, distributions of values within each species group are plotted for each variable. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = sns.pairplot(iris, hue=\"species\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dimension Reduction with Principle Component Analysis\n", + "We have 4 dimensions we measured the irises on, what if we want a more concise way of describing the data? We can try to find 2 dimensions along which the data has the most variance. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'PC2')" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn import decomposition\n", + "from sklearn import datasets\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "iris_pca = datasets.load_iris()\n", + "X = iris_pca.data\n", + "y = iris_pca.target\n", + "\n", + "pca = decomposition.PCA(n_components=2)\n", + "pca.fit(X)\n", + "X = pca.transform(X)\n", + "\n", + "\n", + "f, ax8 = plt.subplots(1, figsize = (5, 5))\n", + "sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=[iris_pca.target_names[i] for i in y], alpha = 0.7)\n", + "ax8.set_xlabel('PC1')\n", + "ax8.set_ylabel('PC2')\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Towards Publication-Ready Plots\n", + "Try to achieve as much of the final figure requirements as possible via code" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['sepal_length', 'sepal_width', 'petal_length', 'petal_width'],\n", + " dtype=object)" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_metrics = iris_tidy.metric.unique()\n", + "\n", + "all_metrics\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y_titles = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n", + "letters = ['A', 'B', 'C', 'D']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\xusy\\.conda\\envs\\nbdevdabestdev\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 20.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n", + " warnings.warn(msg, UserWarning)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "f, ax = plt.subplots(2, 2, figsize=(10, 10))\n", + "\n", + "all_axes = ax.flatten()\n", + "\n", + "for i, metric in enumerate(all_metrics):\n", + " \n", + " current_axes = all_axes[i]\n", + " \n", + " sns.swarmplot(data=iris, size = 3.5, \n", + " x='species', y=metric, hue = 'species',\n", + " ax=current_axes)\n", + "\n", + " current_axes.set(ylabel=y_titles[i])\n", + "\n", + " \n", + " current_axes.set_ylim(0, 10)\n", + " current_axes.get_ylim \n", + " current_axes.get_legend().remove()\n", + " current_axes.text(-1, 10.5, letters[i], fontsize = 25, fontweight = 'semibold')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "f.savefig(\"myplot.svg\")\n", + "f.savefig(\"myplot.png\", dpi = 300)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/nbs/04_Dabest_introduction.ipynb b/nbs/04_Dabest_introduction.ipynb index 3f6a9df..f0342a8 100644 --- a/nbs/04_Dabest_introduction.ipynb +++ b/nbs/04_Dabest_introduction.ipynb @@ -5,10 +5,21 @@ "metadata": {}, "source": [ "---\n", - "output-file: 04_dabest_introduction.html\n", - "title: 04. Dabest Introduction\n", + "output-file: dabest_introduction.html\n", + "title: 04. DABEST Introduction\n", "\n", - "---\n" + "---\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "DABEST is a package that performs [estimation statistics](https://www.estimationstats.com/#/) available on Python and R. With Jupyter Notebook you can try DABEST-Python. Before you are able to import the DABEST package, you need to install it. You can do so by going to your Anaconda Navigator>Environments Tab>base(root)>open Terminal. \n", + "\n", + "After this you can run\n", + ">pip install dabest\n" ] }, { @@ -155,8 +166,8 @@ "DABEST v0.3.9999\n", "================\n", " \n", - "Good morning!\n", - "The current time is Tue Jan 31 11:50:55 2023.\n", + "Good evening!\n", + "The current time is Wed Feb 1 12:40:28 2023.\n", "\n", "The unpaired mean difference between setosa and virginica is 1.58 [95%CI 1.38, 1.78].\n", "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n", diff --git a/nbs/index.ipynb b/nbs/index.ipynb index 18a5c00..3950dff 100644 --- a/nbs/index.ipynb +++ b/nbs/index.ipynb @@ -5,9 +5,9 @@ "metadata": {}, "source": [ "---\n", - "description: A tutorial for introducing biomedical scientists to data analysis with Python notebooks.\n", + "description: A data analysis tutorial for biomedical scientists. version 23.2\n", "output-file: index.html\n", - "title: moda (Modern Data Analysis)\n", + "title: Modern Data Analysis\n", "\n", "---\n", "\n" @@ -32,12 +32,19 @@ "source": [ "The goal of this class is to introduce biomedical scientists to data analysis with Python notebooks. There are two parts to the session: a lecture on the key issues in data analysis and a hands-on tutorial. Please do the following preparations before class.\n", "\n", - "1. Read the [Introduction](https://github.com/sangyu/moda/blob/main/nbs/01_Introduction.ipynb) and install the prerequisite software on your laptop.\n", - "2. Go through a [Quick Tour of the Notebook](https://github.com/sangyu/moda/blob/main/nbs/02_Quick_tour_of_the_Notebook.ipynb) to familiarise yoruself to the jupyter notebook environment.\n", - "3. Bring your laptop to class, and we will go through the examples in [Data Analysis with Jupyter](http://localhost:8890/notebooks/nbs/03_Data_Analysis_with_Jupyter.ipynb)\n", + "1. Read the [Introduction](introduction.html) and install the prerequisite software on your laptop.\n", + "2. Go through a [Quick Tour of the Notebook](quick_tour_of_the_notebook.html) to familiarise yoruself to the jupyter notebook environment.\n", + "3. Bring your laptop to class, and we will go through the examples in [Data Analysis with Jupyter](data_analysis_with_jupyter_and_python.html)\n", "\n", - "If we have more time in class, you will also be introduced to our estimation statistics package [DABEST Introduction](https://github.com/sangyu/moda/blob/main/nbs/04_Dabest_introduction.ipynb)\n" + "If we have more time in class, you will also be introduced to our estimation statistics package [DABEST Introduction](dabest_introduction.html)\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/nbs/myplot.png b/nbs/myplot.png index e03c305580a7bf355acdedf99c8d416a519c6c58..7cdda41eff398d7e4a2a9d25ad00b26f4eac3fcb 100644 GIT binary patch literal 234968 zcmeFYXH=6})HWPI0YyQFQ9znX02OH>y*U;z(i9B6iuB$=x{X1a2BbqQC=z<_MNuHM zCv_MQ-&*fl<5D!a&wb85yIgzk>m0*xYN{SSc;X-g0yzx3 zepMR+VXC0~VWbD&xE)*e1AobSUeou~ak2LFv2eG7XjpiWsdSJo=G=qg^4G(XCGag0rNSI-Hz 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0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="C7_0_882a1a559e"/> +" id="C7_0_bf145c3623"/> - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + @@ -2678,186 +2632,186 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="C8_0_2d49657909"/> +" id="C8_0_edb28c39aa"/> - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + 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style="fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;"/> @@ -3082,157 +3036,157 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="C9_0_bf0e374b65"/> +" id="C9_0_b8f75817f0"/> - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + @@ -3247,157 +3201,157 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="Ca_0_3cedba51af"/> +" id="Ca_0_da6c2f30ed"/> - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + @@ -3412,207 +3366,203 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="Cb_0_cb7ac4ce12"/> +" id="Cb_0_9df4833908"/> - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - + - +" id="DejaVuSans-Bold-44" transform="scale(0.015625)"/> - + - + - + - + - + diff --git a/nbs/sidebar.yml b/nbs/sidebar.yml index 9346ec7..7517c19 100644 --- a/nbs/sidebar.yml +++ b/nbs/sidebar.yml @@ -3,6 +3,6 @@ website: contents: - index.ipynb - 01_Introduction.ipynb - - 02_Quick_tour_of_the_Notebook.ipynb - - 03_Data_Analysis_with_Jupyter.ipynb + - 02_Quick_Tour_of_the_Notebook.ipynb + - 03_Data_Analysis_with_Jupyter_and_Python.ipynb - 04_Dabest_introduction.ipynb From c807eb169e9aa4454a1ba3c6c9bfe8a40a7f89f8 Mon Sep 17 00:00:00 2001 From: Sangyu Xu Date: Wed, 1 Feb 2023 13:51:03 +0800 Subject: [PATCH 6/8] Version 23.2 --- README.md | 10 +++++++--- nbs/01_Introduction.ipynb | 12 +----------- nbs/02_Quick_tour_of_the_Notebook.ipynb | 9 --------- ...03_Data_Analysis_with_Jupyter_and_Python.ipynb | 9 --------- nbs/index.ipynb | 15 ++++++++++++--- 5 files changed, 20 insertions(+), 35 deletions(-) diff --git a/README.md b/README.md index 09737dc..1acef57 100644 --- a/README.md +++ b/README.md @@ -3,12 +3,16 @@ Modern Data Analysis +By [Joses Ho](https://twitter.com/jacuzzijo), [Sangyu +Xu](https://xusangyu.com/), and [Adam +Claridge-Chang](http://www.claridgechang.net/) + **Part of GMS6812 2022: Foundations of Precision Medicine hands-on workshops (PhD programme in Clinical and Translational Sciences)** ------------------------------------------------------------------------ -9am - 1pm, Febuary 14, 2023 +9 am - 1 pm, Febuary 14, 2023 Duke-NUS Medical School @@ -24,8 +28,8 @@ Please do the following preparations before class. to the jupyter notebook environment. 3. Bring your laptop to class, and we will go through the examples in [Data Analysis with - Jupyter](data_analysis_with_jupyter_and_python.html) + Jupyter](data_analysis_with_jupyter_and_python.html). If we have more time in class, you will also be introduced to our estimation statistics package [DABEST -Introduction](dabest_introduction.html) +Introduction](dabest_introduction.html). diff --git a/nbs/01_Introduction.ipynb b/nbs/01_Introduction.ipynb index 1196a35..f72081f 100644 --- a/nbs/01_Introduction.ipynb +++ b/nbs/01_Introduction.ipynb @@ -12,16 +12,6 @@ "---" ] }, - { - "cell_type": "markdown", - "id": "e97fe986", - "metadata": {}, - "source": [ - "[Joses Ho](https://twitter.com/jacuzzijo), [Sangyu\n", - "Xu](https://xusangyu.com/), and [Adam\n", - "Claridge-Chang](http://www.claridgechang.net/)" - ] - }, { "cell_type": "markdown", "id": "3e5aa939", @@ -47,7 +37,7 @@ "\n", "1. You'll need to get set up with a [version-control](https://en.wikipedia.org/wiki/Version_control) system. Go to [GitHub](https://github.com/) and get an account. Download and install [GitHub Desktop](https://desktop.github.com/).\n", "\n", - "2. Retrieve the course materials from GitHub. Go to the course repository (\"repo\") at https://github.com/ACCLAB/moda. Click the green Code button and then select Open with GitHub Desktop.\"clonemoda.png\" You will be prompted to select a directory for the local repository. If you are using a PC it can be something like \"C:\\\\Users\\YOURUSERNAME\\Documents\\GitHub\\moda\". If you are using a mac, it can be \"//Users/YOURUSERNAME/Documents/GitHub/moda\".\"clonedir.png\"\n", + "2. Retrieve the course materials from GitHub. Go to the course repository (\"repo\") at https://github.com/ACCLAB/moda. Click the green Code button and then select Open with GitHub Desktop.\"clonemoda.png\" You will be prompted to select a directory for the local repository. If you are using a PC it can be something like this:
\"clonedir.png\"
If you are using a mac, it can be something like \"//Users/YOURUSERNAME/Documents/GitHub/moda\".\n", "\n", "3. To get set up with Python and [Jupyter](https://en.wikipedia.org/wiki/Project_Jupyter) notebooks, install the [Anaconda\n", " Distribution](https://www.anaconda.com/download/) on your laptop." diff --git a/nbs/02_Quick_tour_of_the_Notebook.ipynb b/nbs/02_Quick_tour_of_the_Notebook.ipynb index 91c2a16..61de3dd 100644 --- a/nbs/02_Quick_tour_of_the_Notebook.ipynb +++ b/nbs/02_Quick_tour_of_the_Notebook.ipynb @@ -12,15 +12,6 @@ "\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "by [Joses Ho](https://twitter.com/jacuzzijo), [Sangyu\n", - "Xu](https://xusangyu.com/), and [Adam\n", - "Claridge-Chang](http://www.claridgechang.net/)" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/nbs/03_Data_Analysis_with_Jupyter_and_Python.ipynb b/nbs/03_Data_Analysis_with_Jupyter_and_Python.ipynb index 20659c2..01e6403 100644 --- a/nbs/03_Data_Analysis_with_Jupyter_and_Python.ipynb +++ b/nbs/03_Data_Analysis_with_Jupyter_and_Python.ipynb @@ -11,15 +11,6 @@ "---" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Joses Ho](https://twitter.com/jacuzzijo), [Sangyu\n", - "Xu](https://xusangyu.com/), and [Adam\n", - "Claridge-Chang](http://www.claridgechang.net/)" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/nbs/index.ipynb b/nbs/index.ipynb index 3950dff..aa1acff 100644 --- a/nbs/index.ipynb +++ b/nbs/index.ipynb @@ -13,6 +13,15 @@ "\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By [Joses Ho](https://twitter.com/jacuzzijo), [Sangyu\n", + "Xu](https://xusangyu.com/), and [Adam\n", + "Claridge-Chang](http://www.claridgechang.net/)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -21,7 +30,7 @@ "\n", "__________________________\n", "\n", - "9am - 1pm, Febuary 14, 2023\n", + "9 am - 1 pm, Febuary 14, 2023\n", "\n", "Duke-NUS Medical School" ] @@ -34,9 +43,9 @@ "\n", "1. Read the [Introduction](introduction.html) and install the prerequisite software on your laptop.\n", "2. Go through a [Quick Tour of the Notebook](quick_tour_of_the_notebook.html) to familiarise yoruself to the jupyter notebook environment.\n", - "3. Bring your laptop to class, and we will go through the examples in [Data Analysis with Jupyter](data_analysis_with_jupyter_and_python.html)\n", + "3. Bring your laptop to class, and we will go through the examples in [Data Analysis with Jupyter](data_analysis_with_jupyter_and_python.html).\n", "\n", - "If we have more time in class, you will also be introduced to our estimation statistics package [DABEST Introduction](dabest_introduction.html)\n" + "If we have more time in class, you will also be introduced to our estimation statistics package [DABEST Introduction](dabest_introduction.html).\n" ] }, { From 972ded0fa0476a29e9eebe3ab36b7825d7715699 Mon Sep 17 00:00:00 2001 From: Sangyu Xu Date: Wed, 1 Feb 2023 13:53:26 +0800 Subject: [PATCH 7/8] Version 23.2 for GMS6812 removed moda python source code folder --- moda/__init__.py | 0 moda/_modidx.py | 8 - moda/moda.py | 7 - nbs/myplot.svg | 2510 +++++++++++++++++++++++----------------------- 4 files changed, 1255 insertions(+), 1270 deletions(-) delete mode 100644 moda/__init__.py delete mode 100644 moda/_modidx.py delete mode 100644 moda/moda.py diff --git a/moda/__init__.py b/moda/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/moda/_modidx.py b/moda/_modidx.py deleted file mode 100644 index e21df7d..0000000 --- a/moda/_modidx.py +++ /dev/null @@ -1,8 +0,0 @@ -# Autogenerated by nbdev - -d = { 'settings': { 'branch': 'main', - 'doc_baseurl': '/moda', - 'doc_host': 'https://ACCLAB.github.io', - 'git_url': 'https://github.com/ACCLAB/moda', - 'lib_path': 'moda'}, - 'syms': {'moda.moda': {}}} diff --git a/moda/moda.py b/moda/moda.py deleted file mode 100644 index d94b836..0000000 --- a/moda/moda.py +++ /dev/null @@ -1,7 +0,0 @@ -# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/02_Quick_tour_of_the_Notebook.ipynb. - -# %% auto 0 -__all__ = [] - -# %% ../nbs/02_Quick_tour_of_the_Notebook.ipynb 2 -import pandas as pd diff --git a/nbs/myplot.svg b/nbs/myplot.svg index 71da5a1..919a539 100644 --- a/nbs/myplot.svg +++ b/nbs/myplot.svg @@ -6,7 +6,7 @@ - 2023-02-01T13:28:31.436524 + 2023-02-01T13:50:52.522704 image/svg+xml @@ -46,10 +46,10 @@ z +" id="mff67d350af" style="stroke:#000000;stroke-width:0.8;"/> - + @@ -200,7 +200,7 @@ z - + @@ -292,7 +292,7 @@ z - + @@ -412,10 +412,10 @@ z +" id="m966cdea2ce" style="stroke:#000000;stroke-width:0.8;"/> - + @@ -451,7 +451,7 @@ z - + @@ -490,7 +490,7 @@ z - + @@ -524,7 +524,7 @@ z - + @@ -569,7 +569,7 @@ z - + @@ -623,7 +623,7 @@ z - + @@ -783,157 +783,157 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="C0_0_025d5f3c89"/> +" id="C0_0_81ea32e9ab"/> - - + + - - + + - - + + - - + + - - + + - - + + - 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DO NOT EDIT! File to edit: ../nbs/02_Quick_tour_of_the_Notebook.ipynb. + +# %% auto 0 +__all__ = [] + +# %% ../nbs/02_Quick_tour_of_the_Notebook.ipynb 2 +import pandas as pd diff --git a/nbs/myplot.svg b/nbs/myplot.svg index 919a539..71da5a1 100644 --- a/nbs/myplot.svg +++ b/nbs/myplot.svg @@ -6,7 +6,7 @@ - 2023-02-01T13:50:52.522704 + 2023-02-01T13:28:31.436524 image/svg+xml @@ -46,10 +46,10 @@ z +" id="m570929429a" style="stroke:#000000;stroke-width:0.8;"/> - + @@ -200,7 +200,7 @@ z - + @@ -292,7 +292,7 @@ z - + @@ -412,10 +412,10 @@ z +" id="mecc4d2f2be" style="stroke:#000000;stroke-width:0.8;"/> - + @@ -451,7 +451,7 @@ z - + @@ -490,7 +490,7 @@ z - + @@ -524,7 +524,7 @@ z - + @@ -569,7 +569,7 @@ z - + @@ -623,7 +623,7 @@ z - + @@ -783,157 +783,157 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="C0_0_81ea32e9ab"/> +" id="C0_0_025d5f3c89"/> - - + + - - + + - - + + - - + + - - + + - - + + - 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- + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + @@ -2467,157 +2467,157 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="C7_0_f2255e18cb"/> +" id="C7_0_bf145c3623"/> - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + @@ -2632,157 +2632,157 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="C8_0_f5b5957d7e"/> +" id="C8_0_edb28c39aa"/> - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + @@ -2831,7 +2831,7 @@ z - + @@ -2849,7 +2849,7 @@ z - + @@ -2871,7 +2871,7 @@ z - + @@ -2906,7 +2906,7 @@ z - + @@ -2919,7 +2919,7 @@ z - + @@ -2932,7 +2932,7 @@ z - + @@ -2945,7 +2945,7 @@ z - + @@ -2958,7 +2958,7 @@ z - + @@ -2971,7 +2971,7 @@ z - + @@ -3036,157 +3036,157 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="C9_0_65e86bbf6b"/> +" id="C9_0_b8f75817f0"/> - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + @@ -3201,157 +3201,157 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="Ca_0_e54ecf9f7d"/> +" id="Ca_0_da6c2f30ed"/> - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + @@ -3366,157 +3366,157 @@ C -1.565609 -0.909265 -1.75 -0.464105 -1.75 0 C -1.75 0.464105 -1.565609 0.909265 -1.237437 1.237437 C -0.909265 1.565609 -0.464105 1.75 0 1.75 z -" id="Cb_0_4d56d28164"/> +" id="Cb_0_9df4833908"/> - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + @@ -3553,16 +3553,16 @@ z - + - + - + - +

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