More_Python.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#More (I)Python, numpy, matplotlib, astropy..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "from __future__ import print_function, division"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Ipython..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def inverse(input):\n",
      "    print(\"starting\")\n",
      "    x = 2*input\n",
      "    y = 1/x\n",
      "    print(\"done\")\n",
      "    return y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#%pdb"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###list comprehensions\n",
      "\n",
      "Convenient means of defining lists"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "inverse(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "starting\n",
        "done\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "0.5"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[item**2 for item in range(10)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[item**2 for item in range(10) if item % 2 == 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "[0, 4, 16, 36, 64]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names = ['Bill', 'Sally', 'Bozo', 'Sue', 'Kathryn']\n",
      "[name + ' the great' for name in names if len(name) == 4]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "['Bill the great', 'Bozo the great']"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###String-related"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# printing with no new line\n",
      "print(\"hello there\",end='') # in the python 2 style print it would be: print \"hello there\",\n",
      "print(\" hello again\") "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "hello there hello again\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "##Getting interactive input\n",
      "value = raw_input('enter a positive integer')\n",
      "print(value*2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "enter a positive integer22\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2222\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# but what if I really want to multiply the value above as an integer\n",
      "2 * int(value)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "44"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\",\".join(names) # If consistent and logical, very unintuitive to newcomers!"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "'Bill,Sally,Bozo,Sue,Kathryn'"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# str() vs repr()\n",
      "# First is how something usually would be printed, second is often how it would be created in \n",
      "# python source code. (sort of)\n",
      "import numpy as np\n",
      "a = np.arange(5)\n",
      "b = 2\n",
      "c = \"2\"\n",
      "print(str(a), str(b), str(c))\n",
      "print(a, b, c)\n",
      "print(repr(a), repr(b), repr(str(b)), repr(c)) # what you see if you just type the value at the command line"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0 1 2 3 4] 2 2\n",
        "[0 1 2 3 4] 2 2\n",
        "array([0, 1, 2, 3, 4]) 2 '2' '2'\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = 'abcd Efgh'\n",
      "print(s.upper(),'\\n', s.lower(),'\\n', s.capitalize())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ABCD EFGH \n",
        " abcd efgh \n",
        " Abcd efgh\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(s.find('cd'), s.find('zz'))\n",
      "print(s.startswith('ab'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2 -1\n",
        "True\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##File access"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open('data/ascii/sextractor.dat') # get a file handle\n",
      "for line in f: print(line)\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "# 1 NUMBER        Galaxy ID number\n",
        "\n",
        "# 2 FLUX_ISO   \n",
        "\n",
        "# 3 FLUXERR_ISO   \n",
        "\n",
        "# 4 VALUES        Note column 5 is missing\n",
        "\n",
        "# 6 FLAG\n",
        "\n",
        "1 0.02580616000000000 0.03974229000000000 1.6770000000000000 0.2710000000000000 0\n",
        "\n",
        "2 5.72769100000000009 0.20643300000000001 2.6250000000000000 2.5219999999999998 0\n",
        "\n",
        "3 88.31933999999999685 0.59369850000000002 5.9249999999999998 4.7140000000000004 0\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open('data/ascii/sextractor.dat') # get a file handle\n",
      "lines = f.readlines()\n",
      "lines"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "['# 1 NUMBER        Galaxy ID number\\n',\n",
        " '# 2 FLUX_ISO   \\n',\n",
        " '# 3 FLUXERR_ISO   \\n',\n",
        " '# 4 VALUES        Note column 5 is missing\\n',\n",
        " '# 6 FLAG\\n',\n",
        " '1 0.02580616000000000 0.03974229000000000 1.6770000000000000 0.2710000000000000 0\\n',\n",
        " '2 5.72769100000000009 0.20643300000000001 2.6250000000000000 2.5219999999999998 0\\n',\n",
        " '3 88.31933999999999685 0.59369850000000002 5.9249999999999998 4.7140000000000004 0\\n']"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# writing a file\n",
      "outtext = ''.join(lines)\n",
      "outtext"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "'# 1 NUMBER        Galaxy ID number\\n# 2 FLUX_ISO   \\n# 3 FLUXERR_ISO   \\n# 4 VALUES        Note column 5 is missing\\n# 6 FLAG\\n1 0.02580616000000000 0.03974229000000000 1.6770000000000000 0.2710000000000000 0\\n2 5.72769100000000009 0.20643300000000001 2.6250000000000000 2.5219999999999998 0\\n3 88.31933999999999685 0.59369850000000002 5.9249999999999998 4.7140000000000004 0\\n'"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "outfile = open('test.dat','w')\n",
      "outfile.write(outtext)\n",
      "outfile.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Astropy_Basics.ipynb\r\n",
        "More_Python.ipynb\r\n",
        "Python_Basics.ipynb\r\n",
        "README.md\r\n",
        "Untitled0.ipynb\r\n",
        "\u001b[34mdata\u001b[m\u001b[m/\r\n",
        "joinnb.py\r\n",
        "lat_background_model_slice.fits\r\n",
        "lat_background_model_slice_allhdus.fits\r\n",
        "random_array.fits\r\n",
        "random_array2.fits\r\n",
        "random_array3.fits\r\n",
        "test.dat\r\n",
        "test.fits\r\n",
        "test.vot\r\n",
        "test2.dat\r\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Useful Libraries\n",
      "\n",
      "- `re`: regular expressions [not covered here]\n",
      "- `sys`: system level information\n",
      "- `os`: access to OS-level services\n",
      "- `os.path`: platform independent path and filename manipulation\n",
      "- `shutil`: higher level file/directory operations\n",
      "- `glob`: file wildcard utilities\n",
      "- `time`: time functions\n",
      "- `datetime`: date/time functions\n",
      "- `argparse`: tools for parsing command line options\n",
      "- `StringIO`: reading and writing from strings as though they were files\n",
      "- `pdb`: debugger [not covered here"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "print(sys.path)\n",
      "print(sys.version) \n",
      "# also contains sysout sysin pointers"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
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'/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_lib.wcs-0.0-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.ami-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.assign_wcs-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.bias_drift-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.dark_current-0.2-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.dq_init-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.emission-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.extract_1d-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.extract_2d-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.flatfield-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.fringe-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.ipc-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.jump-0.2-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.lastframe-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.linearity-0.3-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.persistence-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.photom-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.pipeline-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.ramp_fitting-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.resample-0.1a0-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.reset-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.saturation-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.straylight-0.2-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_pipeline.wfs_combine-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_tools.csv_tools-0.6-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_tools.fits_generator-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_tools.jwpsf-3.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_tools.spectools-0.1-py2.7.egg', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/jwst_tools.associations-0.6-py2.7.egg', '/Users/perry/ssbvirt/ssbx-osx/lib/python27.zip', '/Users/perry/ssbvirt/ssbx-osx/lib/python2.7', '/Users/perry/ssbvirt/ssbx-osx/lib/python2.7/plat-darwin', '/Users/perry/ssbvirt/ssbx-osx/lib/python2.7/plat-mac', '/Users/perry/ssbvirt/ssbx-osx/lib/python2.7/plat-mac/lib-scriptpackages', '/Users/perry/ssbvirt/ssbx-osx/lib/python2.7/lib-tk', '/Users/perry/ssbvirt/ssbx-osx/lib/python2.7/lib-old', '/Users/perry/ssbvirt/ssbx-osx/lib/python2.7/lib-dynload', '/usr/stsci/ssbx/python/lib/python2.7', '/usr/stsci/ssbx/python/lib/python2.7/plat-darwin', '/usr/stsci/ssbx/python/lib/python2.7/lib-tk', '/usr/stsci/ssbx/python/lib/python2.7/plat-mac', '/usr/stsci/ssbx/python/lib/python2.7/plat-mac/lib-scriptpackages', '/Users/perry/ssbvirt/ssbx-osx/lib/python2.7/site-packages', '/usr/stsci/ssbx/python/lib/python2.7/site-packages', '/usr/stsci/ssbx/python/lib/python2.7/site-packages/IPython/extensions']\n",
        "2.7.5 (default, Mar 13 2015, 01:50:03) \n",
        "[GCC 4.2.1 (Apple Inc. build 5664)]\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "print(os.environ)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'IDL_DIR': '/Applications/exelis/idl84', 'UR_TMP': '/Users/perry/.ureka_tmp/ssbx', 'hbin': '/usr/stsci/ssbx/iraf//unix/bin.macosx/', 'INFOPATH': '/sw/share/info:/sw/info:/usr/share/info', 'VIRTUAL_ENV': '/Users/perry/ssbvirt/ssbx-osx', 'SHELL': '/bin/tcsh', 'XDG_DATA_DIRS': '/sw/share', 'GDFONTPATH': '/Library/Fonts', 'HSI_LFLAGS': '', 'GNOME_DISABLE_CRASH_DIALOG': '1', 'MANPATH': '/usr/stsci/ssbx/share/man:/usr/stsci/ssbx/variants/common/man:/usr/stsci/ssbx/variants/common/man:/usr/stsci/ssbx/variants/common/share/man:/usr/stsci/ssbx/python/man:/usr/stsci/ssbx/python/share/man::/sw/share/man:/usr/share/man:/usr/local/share/man:/opt/X11/share/man:/usr/texbin/man:/Applications/Xcode.app/Contents/Developer/usr/share/man:/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/share/man:/sw/lib/perl5/5.16.2/man', 'XDG_CONFIG_HOME': '/Users/perry/.config', 'VENDOR': 'apple', 'XDG_RUNTIME_DIR': '/var/folders/m1/8vfhq2315vlcqkbn6bhh4wgh0003y2/C/', 'RANLIB': 'ranlib', 'HSI_FF': '-I/usr/stsci/ssbx/iraf//include/ -O -arch i386 -m32 -DBLD_KERNEL -mmacosx-version-min=10.4', 'DBUS_SESSION_BUS_ADDRESS': 'launchd:env=DBUS_FINK_SESSION_BUS_SOCKET', 'F2C': '/usr/stsci/ssbx/iraf//unix/bin.macosx//f2c.e', 'IMTOOLRC': '/usr/stsci/ssbx/iraf/dev/imtoolrc', 'hostid': 'unix', 'HSI_OSLIBS': '', 'HSI_LIBS': '/usr/stsci/ssbx/iraf//unix/hlib/libboot.a /usr/stsci/ssbx/iraf//lib/libsys.a /usr/stsci/ssbx/iraf//lib/libvops.a /usr/stsci/ssbx/iraf//unix/hlib/libos.a ', 'MACHTYPE': 'x86_64', 'GROUP': 'STSCI\\\\science', 'XDG_DATA_HOME': '/Users/perry/.local/share', 'USER': 'perry', 'QTDIR': '/sw/lib/qt3', 'hlib': '/usr/stsci/ssbx/iraf//unix/hlib/', 'tmp': '/tmp/', 'HSI_CF': '-I/usr/stsci/ssbx/iraf//include/ -O -DMACOSX -w -Wunused -arch i386 -m32 -mmacosx-version-min=10.4', 'SHLVL': '1', 'GNUPLOT_DEFAULT_GDFONT': 'Arial', 'FERMI_DIR': '/Applications/ST_sci/heasoft-6.15.1/i386-apple-darwin13.0.2', 'TERM_SESSION_ID': 'D09F90D8-889F-4B0D-9557-A9DFEB9CDE4B', 'HSI_XF': '-I/usr/stsci/ssbx/iraf//include/ -Inolibc -/DMACOSX -w -/Wunused -/m32 -/arch -//i386 -/mmacosx-version-min=10.4', 'XAPPLRESDIR': '/sw/etc/app-defaults/', 'iraf': '/usr/stsci/ssbx/iraf/', 'host': '/usr/stsci/ssbx/iraf//unix/', 'PKG_CONFIG_PATH': '/usr/stsci/ssbx/python/lib/pkgconfig', 'TMPDIR': '/var/folders/m1/8vfhq2315vlcqkbn6bhh4wgh0003y2/T/', 'PERL5LIB': '/sw/lib/perl5:/sw/lib/perl5/darwin', 'XDG_CONFIG_DIRS': '/sw/etc/xdg', 'HSI_F77LIBS': '', 'GDK_USE_XFT': '1', 'IDL_PATH': '.:+/Applications/exelis/local/astron:+/Applications/exelis/idl84/lib', 'EXELIS_DIR': '/Applications/exelis', 'GDL_PATH': '+/sw/share/gnudatalanguage/lib', 'UR_VARIANT': 'common', 'PAGER': 'cat', 'HEADAS': '/Applications/ST_sci/heasoft-6.15.1/i386-apple-darwin13.0.2', 'REMOTEHOST': '', 'TERM_PROGRAM_VERSION': '326', 'UR_DIR': '/usr/stsci/ssbx', 'HOME': '/Users/perry', 'UR_DIR_PKG': '/usr/stsci/ssbx/variants/common/', 'DISPLAY': '/tmp/launch-sGTuMd/org.macosforge.xquartz:0', 'TERM_PROGRAM': 'Apple_Terminal', 'LANG': 'en_US.UTF-8', 'Apple_PubSub_Socket_Render': '/tmp/launch-OITUtg/Render', 'GVBINDIR': '/usr/stsci/ssbx/python/lib/graphviz', 'ac_cv_path_INTLTOOL_PERL': '/usr/bin/perl', 'OSTYPE': 'darwin', 'UR_CPU': 'x86', 'CC': 'cc', 'HOST': 'beeblebrox.home', 'GIT_PAGER': 'cat', 'IRAFARCH': 'macosx', 'F77': '/usr/stsci/ssbx/iraf//unix/hlib//f77.sh', 'UR_BITS': '64', '__CF_USER_TEXT_ENCODING': '0xFC2:0:0', '__CHECKFIX1436934': '1', 'HOSTTYPE': 'intel-mac', 'LOGNAME': 'perry', 'PATH': '/Users/perry/ssbvirt/ssbx-osx/bin:/usr/stsci/ssbx/variants/common/bin:/usr/stsci/ssbx/bin:/usr/stsci/ssbx/python/bin:/Users/perry/bin:/Applications/ST_sci/SM/bin:/sw/bin:/sw/sbin:/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/bin:/opt/X11/bin:/usr/texbin:/usr/X11R6/bin', 'TERM': 'xterm-color', 'UR_OS': 'osx-9', 'SSH_AUTH_SOCK': '/tmp/launch-sSf0Et/Listeners', 'XDG_CACHE_HOME': '/Users/perry/.cache', 'XML_CATALOG_FILES': '/sw/etc/xml/catalog', 'MACH': 'macosx', 'PGPLOT_DIR': '/sw/lib/pgplot', 'HSI_LF': '-I/usr/stsci/ssbx/iraf//include/ -arch i386 -m32 -mmacosx-version-min=10.4', 'CLICOLOR': '1', 'PWD': '/Users/perry/dropbox/gitfork/UserTraining2015', 'SGML_CATALOG_FILES': '/sw/etc/sgml/catalog'}\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Contains functions to run commands, change directories, get environment, get uid, etc\n",
      "print(os.system('ls -l')) #works in terminal window, but not in notebook\n",
      "import subprocess\n",
      "print(subprocess.check_output(['ls','-l'])) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0\n",
        "total 6256\n",
        "-rw-r--r--@  1 perry  STSCI\\science   311605 May  6 08:24 Astropy_Basics.ipynb\n",
        "-rw-r--r--@  1 perry  STSCI\\science    16354 May  6 08:32 More_Python.ipynb\n",
        "-rw-r--r--@  1 perry  STSCI\\science    56393 May  6 08:09 Python_Basics.ipynb\n",
        "-rw-r--r--@  1 perry  STSCI\\science      334 May  1 13:32 README.md\n",
        "-rw-r--r--@  1 perry  STSCI\\science     2630 May  4 13:30 Untitled0.ipynb\n",
        "drwxr-xr-x@ 12 perry  STSCI\\science      408 Apr 28 11:17 data\n",
        "-rw-r--r--@  1 perry  STSCI\\science      446 May  1 08:19 joinnb.py\n",
        "-rw-r--r--@  1 perry  STSCI\\science  1039680 May  6 08:11 lat_background_model_slice.fits\n",
        "-rw-r--r--@  1 perry  STSCI\\science  1045440 May  6 08:11 lat_background_model_slice_allhdus.fits\n",
        "-rw-r--r--@  1 perry  STSCI\\science   135360 May  6 08:11 random_array.fits\n",
        "-rw-r--r--@  1 perry  STSCI\\science   138240 May  6 08:12 random_array2.fits\n",
        "-rw-r--r--@  1 perry  STSCI\\science   406080 May  6 08:12 random_array3.fits\n",
        "-rw-r--r--@  1 perry  STSCI\\science      369 May  6 08:33 test.dat\n",
        "-rw-r--r--@  1 perry  STSCI\\science     8640 May  6 08:11 test.fits\n",
        "-rw-r--r--@  1 perry  STSCI\\science     1403 May  6 08:11 test.vot\n",
        "-rw-r--r--@  1 perry  STSCI\\science      369 May  5 20:54 test2.dat\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# or just this\n",
      "!ls -l"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "total 6256\r\n",
        "-rw-r--r--@  1 perry  STSCI\\science   311605 May  6 08:24 Astropy_Basics.ipynb\r\n",
        "-rw-r--r--@  1 perry  STSCI\\science    16354 May  6 08:32 More_Python.ipynb\r\n",
        "-rw-r--r--@  1 perry  STSCI\\science    56393 May  6 08:09 Python_Basics.ipynb\r\n",
        "-rw-r--r--@  1 perry  STSCI\\science      334 May  1 13:32 README.md\r\n",
        "-rw-r--r--@  1 perry  STSCI\\science     2630 May  4 13:30 Untitled0.ipynb\r\n",
        "drwxr-xr-x@ 12 perry  STSCI\\science      408 Apr 28 11:17 \u001b[34mdata\u001b[m\u001b[m\r\n",
        "-rw-r--r--@  1 perry  STSCI\\science      446 May  1 08:19 joinnb.py\r\n",
        "-rw-r--r--@  1 perry  STSCI\\science  1039680 May  6 08:11 lat_background_model_slice.fits\r\n",
        "-rw-r--r--@  1 perry  STSCI\\science  1045440 May  6 08:11 lat_background_model_slice_allhdus.fits\r\n",
        "-rw-r--r--@  1 perry  STSCI\\science   135360 May  6 08:11 random_array.fits\r\n",
        "-rw-r--r--@  1 perry  STSCI\\science   138240 May  6 08:12 random_array2.fits\r\n",
        "-rw-r--r--@  1 perry  STSCI\\science   406080 May  6 08:12 random_array3.fits\r\n",
        "-rw-r--r--@  1 perry  STSCI\\science      369 May  6 08:33 test.dat\r\n",
        "-rw-r--r--@  1 perry  STSCI\\science     8640 May  6 08:11 test.fits\r\n",
        "-rw-r--r--@  1 perry  STSCI\\science     1403 May  6 08:11 test.vot\r\n",
        "-rw-r--r--@  1 perry  STSCI\\science      369 May  5 20:54 test2.dat\r\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os.path\n",
      "os.path.join('mydir','data.dat') # handles platform variances like windows '\\'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "'mydir/data.dat'"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "os.path.splitext(os.path.split('/a/b/c/file.txt')[1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "('file', '.txt')"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import shutil\n",
      "shutil.copyfile('test.dat','test2.dat')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# has copytree(), rmtree(), move(), make_archive()..."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import glob\n",
      "fl = glob.glob('*.fits')\n",
      "print(fl)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['lat_background_model_slice.fits', 'lat_background_model_slice_allhdus.fits', 'random_array.fits', 'random_array2.fits', 'random_array3.fits', 'test.fits']\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "t = time.time()\n",
      "print(t)\n",
      "tt = time.localtime(t)\n",
      "print(tt)\n",
      "print(time.strftime('%y, %m, %d', tt))\n",
      "time.sleep(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1430915614.24\n",
        "time.struct_time(tm_year=2015, tm_mon=5, tm_mday=6, tm_hour=8, tm_min=33, tm_sec=34, tm_wday=2, tm_yday=126, tm_isdst=1)\n",
        "15, 05, 06\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "more numpy"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###array creation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.zeros((10,10))\n",
      "a = np.ones((10,10))\n",
      "a = np.arange(10)\n",
      "a = np.linspace(1,10,100)\n",
      "a = np.array([1,2,6,9.2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###broadcasting"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.arange(10)\n",
      "a.shape=(2,5)\n",
      "print(a)\n",
      "b = np.arange(5)\n",
      "a*b\n",
      "c = np.arange(2)\n",
      "a*c"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[0 1 2 3 4]\n",
        " [5 6 7 8 9]]\n"
       ]
      },
      {
       "ename": "ValueError",
       "evalue": "operands could not be broadcast together with shapes (2,5) (2,) ",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-34-3d63b1e84bf7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,5) (2,) "
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a*c[:, np.newaxis]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.arange(10.)* 2\n",
      "y = x**2\n",
      "import numpy.random as ran\n",
      "xx = ran.uniform(0., 18., size=(100,)) # 100 random numbers between 0 and 18\n",
      "xind = np.searchsorted(x, xx)-1 # indices where x is next value below that in xx\n",
      "xfract = (xx - x[xind])/(x[xind+1]-x[xind]) # fractional distance between x points \n",
      "yy = y[xind] + xfract*(y[xind+1]-y[xind]) # the interpolated values for xx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import astropy.io.fits as fits\n",
      "from matplotlib import pyplot as plt\n",
      "im = fits.getdata('data/pix.fits')\n",
      "plt.imshow(im,vmax=500)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "<matplotlib.image.AxesImage at 0x108b2b850>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fv/tTDd7PhW8X0Tg4IZZitiLK4db8GtqX+9dQREAyMk8jWDdAKQE7rgKR86T5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mGvuAQrm68iKlXIllnNNq3yatqwm40aAOW88yipoJoh1fI0fL/L29RGJo8EsN6Cf0nrwen1Ci/k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\nMyPqYLgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107472050>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y, x = np.indices((512,512)) # first determine radii of all pixels\n",
      "plt.imshow(y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "<matplotlib.image.AxesImage at 0x108becc10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAQcAAAD7CAYAAABnjFFxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngNX/AbZYAGjqt1ZPAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107444d90>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r = np.sqrt((x-257.)**2+(y-258)**2)\n",
      "plt.imshow(r)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "<matplotlib.image.AxesImage at 0x108d7db10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/8WnGBlYCVJMQDBjtiU4S1mwg0T/tSY5LC0eWjEgeiSk+Qw8I244x7frIMbE9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s2cXIY2AaB//d1UWci3LynaZWwygaUFJz4B26RFbzqWfZs84BDmmjlpZU3KPgUMDGkRy/wLLmWE\nmFsatO6M3Ok0KFimoB52vY/3YLEI3m7VZWM/Zh57lCMDhymWF906LSsO6/SSCMVtDgtX8vY1asVs\nQtrz1QlGu4wFQLgNcCoB01AjGjnDDx7BJ0TvEd0wP/nlNpVkKTEZSweWl+a2fD8BjBah6Cd54ahF\nqheK5YOMyiynK5kVdn5DwcPtM0DpInv3RCx1dc2T6EdlSls5iwCO/gAeEziqETpw4M7fp1ezp6Bz\nIQKm4ifENL/lep4yXsZHMBhos1HXWenLa7JAD6jS0YhdUqu3HdPKqlwSua3lyMBhnMr4hcBegeU5\nAK0DbgMO7S2sGY2Jljlkqa+EtNPSwqt95JgyL0QFiE0C0gD4lBFTRBwyvE8lu9KXKERvULZJXgQI\nmrQoUCEQIrd3qStxDslh6NlD7y9wsnfZ10qZ7hOkrre0RKY+c2KNQbAv0UuMflxFzx56QBCmwKDA\n+zIohBThc54jEXNCUyK2oJOOpNPz5xoLOMx03Fa3re2uxzRAKt8yzKHKilMoM0FD/+OSUTreYYOq\noOr0MXQEgo/N+3GIk5c1IHDIM6NFP+oxfQbcUCIaYUqIA+aQZ/axMIk5NIn5WS6dneWEhCNltidh\nC+xNlFPkQVfLtGleHow6Qd8WL9i/6JRpYGlMMvhZPgMfZ+k12ElQvD+HKzG3rZPPkq/gMvoXzFjR\niGgsiwG4JimsjqsjHJavYHkJlnG5tt3yHCowjLcKOIwVHLwDvCf/QcsAHm0pxer4a4XlhCU52HyU\n9gFdJ5/Zw4aOwf8EQFJtz1IjAkMAcgUJnxKCT0jBwQ8BPmQEV0KPHLqcKhAUkIhzfTEh/QwSOqwJ\ncOdvk8eujcqUdStlem0gFhd5kvO6LgwEel1kAtD7DGsJUPqtV300Q5iBkUqd45yzUORDLyE6lsAS\nwuq4a8yC2YVettY1C9jmMWxjHGs5D8IYIhAjcO1WCWVOsYJDBYYNKkAAy7ETh5VtkkJAwfIatK+g\n5UWiT2EPQbXlTwEIAYk6/Zz4EUMAUgaiL3LDpQjvE3JwiKFENoBltKKYhpwiPdRTEQkRIcZiAY8W\nseDJY3lUZiP9HM68MZJCSmMEwhDK0beNymzLPDJzGebsX6HXy44uFyJVLyGlyhQaKHjpkNrRT8Y2\nBo0EGwg0gzjMoFyTHhZA7MIkDB8ijxUYJmDt5XNr5eiYAwpi+9r5nauBAWELTO+4rJmPVihTRxW4\ncIfXUkKYQqbjchuWFxk9QASjzrd6nwpbykPJjUghI8ciN3xIyM7BhTx38PYkbWMmRG5I5AIoIMKZ\nkLu+m8KWFLtPKrtWLGmhQ5u8vk1O9F5Dn+OgX7grxxpiXa7yoSwXUBBG13V6izlsyzbUbbZJEF63\n0pqlw7PhuU1KHAYodVsei3wfp/a5TzlacJDEklpyBk7tEqI8DDC2tdMGoxSOmnA9A4H+lH8BS8AQ\nSioMgtd9+e6bVCmfB3xMSCEhBV+XPaL3neQoX6GlVxfs6g0+kQwD2uAqlhc6TCkdSoMBA8jaKM3+\n8vWJTlZd297kQDmHdTlRtvWvxtOjOGU4d8glm1GMRh/zHIHwFZgdA4DOG9A+Q0bfATkqoYFiF2BY\nW7dSorexiF0AIzZAkH/TrRKtmFD7ovwAKBJjmqrMXwtJWsUCBx2Z4CL9YBsYsM+gJYS0kw4/oAeI\nRNsTtRVGJG094EIZzBV8oX0xJSQPpJgQvEPyHnFovoSEM1ueQ6pgENFGYkpdi1rI/A0tlNn7Dpn2\nk0ujL7/tOaSuLS8LKKxNFefm/XmEZh+90CM0OXw5hzBzkQ0FEFKVbCVPIdTr71hCMDDwP0s2HBa5\n4P21vNBgwR2e22kw0HW7AgOFQFOVEeMEjCMwpjJJ9T7lSJmDlJALqo+0cR4OdD3yl5/uUhgIOJ8h\nUTuuk2XJvZDjsoyQv2PJiaC2i7zg7bUnuVoXQrkO2QHRZ8RQw5+pyI1UZUf0/Qty9YhMoIUpdf4C\nZ0XqunZJdp8BShdmOJZRufQY2tk0QLDSpwVoaiZjikDOCCmVofOxjzygLs8ALv+YDbDhyN6DxQzW\nzEgrQrEmHw5jE9q8NDr9KqsgMEpT8RjG6jfECgx7+pFHDw7zpNNEeXKuIU6QSTlXqnWLNViRCdku\nrMCSFyItIm1f8xnk7zFbYPBw1J7XmVlwqnbd7sYS/vS+3ORTADCWMCjGiDj4mVEAE6Zgj8jsR2Ky\nBzGh9xl6T0KXfdKprXTpVjep9dbWwzYmm+dQAMFVMACKl+BSkQ2hHnqoh5xDknKdGSB0BILr9H68\nbskGYQ/7hDW3AQR3evYedHvOjlSAkabGFq6NRUrI6dwyzOEa+mBERtHfUgQU5hPcJjG0zzAYbTVj\nAPpJZRgoBCS2+QzCBHQIU8DEU9ug1if0TAPoQaQaoS4AmwnIxChcLp0jhgS44lVk7zC4kjcxuBEt\nmmGPxBwgCVWurkuO5lI2DPUX2sYirPClFB3GLOeSVplE5z3U0a4CCIUdlDCky0U2gD7nkKR0bmDJ\nEjRDsNgDg8FobIuqjmXGNv/BMhgndTzNHnY1JUcCBvIYRrR33jRXaLdyZODASNbdOsbDZ/YgDiv8\nxJeix0XwCXAadKLtOjTJfgOzhaDasZzw1F5YggCIpIBLO6+WRXLI6M9QPAn4oqGzA0LMSA7ILiKF\n0nFDcMjOIfgCGABmCVK+Wj/Qyprc5duRFFJ0x+ftyxGasS4DPtUJX6qhCEjIN88hSKDmJggYAI0B\nMFMQohJpeTLaMlhYTGEX38EChjVwYLkxrtRxx0/YGRhESszRCTQ5If/2KUcuKwBDKWiJkUlirEUm\nQHX6U0Om5S3I8diM5L/HsoGf8pIYxczBG+tsTjpal2VuJ8vMJFCOOcsOlHAoAMSYkR2Qp4wUAKDI\nD8jhBpIU3pcX9VBZkxXXM0JzTVowEEiRcCNQZEJbTrQsbetxuKMD7XfR2zULsGSGxRYmdUxeX/MR\ntDTZV0Zsq+Pj7ikluPkt5zmskQGHXmIAwCl3yMlqP0GK0HygdWqm8tloJ8vc6Tk7kqWD/G05LgOD\nlhRRrTtqy8tADxLyKXKkgoar64Mr0gMAUqzMYkoVKMpydkD2JaqRHZACjcSsaBx9LysGjIDbJhr6\n4ox3MPqUZjYwb6usoLDBJhNKXWOJoV5Tx9dX7ouo1tfYQ1Tb+VNvm9Q2ZgeWX6G9Ct1+F2lx2Lox\nRmIfKXENPUjsU47Uc7Ae/F2SpOFBmGFOSzYAvY/AnVrXS6fW27TRqDMirYQn6bxykwXaFtCkj8gI\nbsdGpbAGARvxQWQ7Z2oKUPg6TiXUyzMB2RewAIDkUlkHAFcAI/mGpKEuZ+cWoLAWrlyr8+ITpNzq\ncgEBV4mBl89M63VbBwgs+fhfVNsZFHhfiz1oALHkhF7XSU9aTqz5DlpGyLKuW/MlrMxKBQzXqpyw\npASziH3KkYLDWtnJgwD6rEVZ54Pob8csWQMHAwIzh6Q+dT4Dg4J0ZJYMvM5Pfg5rOtpftkdjmUEj\n1k85Jl+YEfNQ+HlIvKvEJDSwAIBYe2aek07qJfF9O17WRftB3vjNXK5MgNadZnHc2bPaJsv8W8SV\nTw0CFqisAYBmAxarsCSEAAPLD+0ZrEkKXrZSrjWwTEuPQYBBSwlZvqU8BwaHbVaCwyFhTqcaWweR\ndb4RLc/BSo7Kxj8OgzIjcdRGAID9CR0KZRAhX2Hu8Lws7Ue0X43DrU7VQS3XY7ixfr2h/8rwBRC+\nE8XpRxZ3ellPRh3vp7ext2DJC+ns2o/g/bit5UWsyQhmHNvqrsd72NaugsY2j8FiDgwQ+5Qjz5Bk\nGcH9ZlFnSYxUzLlFY8tn0B0ZxrKOSrDPINtFAvAJc+Yj61sxGNln0KxBswMBC2Yaa96DfIr8cNQG\nVK+X5fsqcJ19yhsxUxw/8bkktT2uLCdju2YGDCr8pLdAQUsPfrJvkxeWJzGpbWvsQcuLw/wH7S2o\n+jwCOe3mMTAwcN0+5XhGK9bqCCBSAjaDMZqTmQDLBfYNgF5WJCy9A1nXMoKjDgwWHJHwtC43oiUX\nuB37CxTG7OqAJWsQUJi11pZlhx4QPBYAMbfzav2won9ELS2ysV0zBgsQNCgA6yDBHRtozGBNTujl\nCUtw0FIiogcI9VRf9R0sENHHsMzHWj+Prqyf28KVGhhuuWgFM8adwQFFYsQEnJIcgBrFcAwAQA8I\na/+4nbAIi0FwvTAEuSmlDUsKTqJiBiE3Nyc/6QiFZgvcjuWHMAzeTzo1ywstNWCsazBYK2umbzK2\n66LBQu83rSxH+syqfk1qMCgw1U9qWQOElhmW77BNclgG5Zp0YB9B9tOZkXWfHLEYXbktXKkjniw3\n9infEXC4dOkS3vve9yLGiJ/7uZ/DU089tWhjRSukaDdc183ThNeSM+Tl18tOz8vMDDjBCWgdWo+j\n0NJBRzCEOeiRl7LOnToa7ViCsP8g56IlhHT2Nckgf4/lxYjGUqT9GjvYJim0d3BYWZMWUW07TE4w\nkGgzUssFXc9AsgYUertmB7yugSOqbbpuxTfotuvQJy2nqTEFLSU0O5jUNi0z9v35bjg4xBjxC7/w\nC/jEJz6B06dP4wd/8Afxtre9Dd///d/ftdsplGnUzaqBfvicq8zIZRBXd5OsLQN9OFDLBgKBy1eB\n86fRRzCEQTAr4A7P60Etr8kJ7S3oCMWk6hkAyI+4/Dng/Dk6NssICxA0a/Ary4cVBmTNJjRjq20u\n/x/g/H+nfTQI8G+W1D/tOQgoWO0tOaE/tZww5MXlLwPnX0v1HN6Mat2SGWtSw5AbaQTGWIzHmNCN\nruQgxpqUeBbAvegBYp9yw8HhmWeewRve8AacOXMGAPCud70LTz/99AIctp3oGmtgO0Ea5qlcuLwp\n+21QjUrrvRTMFPjmWUuTrv8uvwScP1Bt5AbkMRMe/Q3JoUwGDXm6Sxsd+tSfFjPY4jVc/t/A+QfQ\nd/5tPoMce81b2AUg1qSF5TXI9ljB4UFVr5mExRasOmYDwBIQpI6lAy+nle0MDv8MnP9vsEHE8h+0\nAcnLOu9B/IUJiFN54HFEImbbcJyMbdcA/F8Ar8MxYg5Xr17FnXfeOa/fcccd+Ju/+ZtFu71CmXXZ\nfBhlYFNvrJzr+mD4EOIPyI7iL/CnxTKEwl/D0rQUNqBToRkcsqqTJ7zlK2zzHCKWeQ1r8iIB+Bb6\nX3dbzsdI23YxH/cp1h3JQCDnKiVTPS9Hap9W6izPYZsfYXkM0sbyFWQ/bSZaEoLrNJvQrIG251SA\n4dpY/IVZUqBhCecvrHkLmlnILbxPueHg4BZjrO0ishf0uQYK8hvp10pwP86xgUPOQMpFZngNEHwg\n7TMk1Zaf9iN6YOA0aWudO7ejOl4GembAgKLrdLiSgWKiOn4qaeDg7zrSuUC1kXI9eQ/M1DQFTKpe\n/qaA02HeAwOCZhPac9BgoJmFJTksOcF1+omvgUEzBQYGzS40METMM0THVIBB5n20mIJOcOLtI/1Z\nLTv2KTccHE6fPo0rV67M61euXMEdd9yxaPc/b/QfBsrVuIb9IXKH8v4v3PhjfifL+z961Gewe3n/\n/zrqM9ivvP9zR30Gu5env419Xc7GaJlvo0zThHvvvRd/8Rd/ge/7vu/Dww8/jD/4gz9YeA4n5aSc\nlONdbjhzGIYBv/M7v4PHH38cMUb87M/+7AkwnJSTcguWG84cTspJOSn/f5Tv0FCb9XLp0iXcd999\nuPvuu/HBD37wZv/5RXnPe96Dg4MDPPjgg/O2b3zjG3jsscdwzz334C1veQv+9V//da67cOEC7r77\nbtx33334sz/7s5t+vleuXMGP/MiP4I1vfCMeeOABfOhDHzq25/ytb30LjzzyCB566CHcf//9+JVf\n+ZVje65cYow4d+4c3vrWtx778z1z5gx+4Ad+AOfOncPDDz98Y88338QyTVO+66678vPPP5+vXbuW\nz549m5977rmbeQqL8ld/9Vf57/7u7/IDDzwwb3vf+96XP/jBD+acc7548WJ+6hGJe9gAAAP8SURB\nVKmncs45f/7zn89nz57N165dy88//3y+6667cozxpp7viy++mJ999tmcc87/8R//ke+555783HPP\nHdtzfuWVV3LOOY/jmB955JH86U9/+tieq5Tf+q3fyu9+97vzW9/61pzz8b4fzpw5k7/+9a93227U\n+d5UcPjsZz+bH3/88Xn9woUL+cKFCzfzFMzy/PPPd+Bw77335pdeeinnXDrjvffem3PO+QMf+EC+\nePHi3O7xxx/Pf/3Xf31zT1aVn/iJn8h//ud/fuzP+ZVXXslvetOb8j/+4z8e63O9cuVKfvTRR/Mn\nP/nJ/OM//uM55+N9P5w5cyZ/7Wtf67bdqPO9qbLCSpC6evXqzTyFncrLL7+Mg4MDAMDBwQFefvll\nAMBXvvKVLix71Of/pS99Cc8++yweeeSRY3vOKSU89NBDODg4mOXQcT1XAPjFX/xF/OZv/iY8TZl3\nnM/XOYcf/dEfxZve9Cb83u/93g0935s6KnPXBKnjVJxzW8/7qL7TN7/5TbzjHe/Ab//2b+O7vuu7\nFud0XM7Ze4+///u/x7/927/h8ccfx1/+5V8uzuW4nOsf/dEf4bbbbsO5c+dw+fLl1fM5LucLAJ/5\nzGdw++2346tf/Soee+wx3HfffYvzud7zvanMYdcEqaMuBwcHeOmllwAAL774Im677TYAy/N/4YUX\ncPr06Zt+fuM44h3veAeefPJJvP3tb78lzvm7v/u78WM/9mP427/922N7rp/97GfxsY99DK973evw\nxBNP4JOf/CSefPLJY3u+AHD77bcDAL73e78XP/mTP4lnnnnmxp3vd0AGrZZxHPPrX//6/Pzzz+dX\nX331WBiSOS89h/e9732zNrtw4cLC0Hn11VfzF7/4xfz6178+p5Ru6rmmlPKTTz6Z3/ve93bbj+M5\nf/WrX83/8i//knPO+T//8z/zm9/85vyJT3ziWJ6rLpcvX549h+N6vq+88kr+93//95xzzt/85jfz\nD//wD+c//dM/vWHne1PBIeecP/7xj+d77rkn33XXXfkDH/jAzf7zi/Kud70r33777Xmz2eQ77rgj\nf/jDH85f//rX86OPPprvvvvu/Nhjj803eM45//qv/3q+66678r333psvXbp008/305/+dHbO5bNn\nz+aHHnooP/TQQ/lP/uRPjuU5f+5zn8vnzp3LZ8+ezQ8++GD+jd/4jZxzPpbnqsvly5fnaMVxPd8v\nfvGL+ezZs/ns2bP5jW9849yfbtT5niRBnZSTclLMctOToE7KSTkpt0Y5AYeTclJOillOwOGknJST\nYpYTcDgpJ+WkmOUEHE7KSTkpZjkBh5NyUk6KWU7A4aSclJNilhNwOCkn5aSY5f8BDH7sbfJ0AqYA\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108bd5e50>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ind = np.argsort(r.flat) # get sorted indices (could use sort\n",
      "# if not needed to arange image values too \n",
      "sr = r.flat[ind] # sorted radii\n",
      "sim = im.flat[ind] # image values sorted by radii\n",
      "ri = sr.astype(np.int16) # integer part of radii (bin size = 1)\n",
      "# Things get trickier here; nd numbers in same radius bin \n",
      "# by looking for where radius change value and determining distance between changes\n",
      "deltar = ri[1:] - ri[:-1] # assume all radii represented # (more work if not)\n",
      "rind = np.where(deltar)[0] # location of changed radius\n",
      "nr = rind[1:] - rind[:-1] # number in radius bin\n",
      "csim = np.cumsum(sim, dtype=np.float64) # cumulative sum to figure out sums for each radii bin \n",
      "tbin = csim[rind[1:]] - csim[rind[:-1]] # sum for image values in radius bins\n",
      "radialprofile = tbin/nr # the answer\n",
      "plt.semilogy(radialprofile)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "[<matplotlib.lines.Line2D at 0x108d19f50>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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t1ZWamooNGzbAbrejvLwcZWVlGDFihGZ1VldXN/958+bNzSuualmnUgozZ85E\nXFwc5s6d2/x9PV3TtmrU2/U8efJkczfIpUuX8P7772PYsGG6upbt1dkUooA+rufixYths9lQXl6O\nDRs24L777sPatWvddz09N87cuu3bt6tBgwapiIgItXjxYm+fvk1Hjx5VCQkJKiEhQQ0ePLi5tlOn\nTqmxY8dqOi0zPT1d9evXTwUGBiqLxaJWrVrVbl3PPfecioiIUNHR0So/P1+zOt944w2VmZmp4uPj\n1dChQ9XkyZNVTU2N5nUWFhYqk8mkEhISmqfj7dixQ1fXtLUat2/frrvr+fnnn6thw4aphIQEFR8f\nr1544QWlVPv/bvRUp96u5/WsVmvzLB13XU+TUlzjgIjICLinLRGRQTDwiYgMgoFPRGQQDHwiIoNg\n4BMRGQQDn4jIIBj4REQGwcAnIjKI/wfB7L2UZjhlJgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108d02f90>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = .4\n",
      "n = 1000000 # initial ensemble size\n",
      "n2heads = 0; n2tails = 0 # totals for each case\n",
      "import numpy.random as ran\n",
      "prev = ran.rand(n) < p # true if heads, false if not \n",
      "while n>0:\n",
      "    next = ran.rand(n) < p\n",
      "    notdone = (next != prev)\n",
      "    n2heads += np.sum(prev & next,dtype=np.int64) # careful about sums!\n",
      "    n2tails += np.sum(np.logical_not(prev | next),dtype=np.int64)\n",
      "    prev = next[notdone]\n",
      "    n = len(prev)\n",
      "    print(n)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "480867\n",
        "240103\n",
        "115234"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "57285\n",
        "27242\n",
        "13680\n",
        "6644\n",
        "3324\n",
        "1592\n",
        "792\n",
        "362\n",
        "175\n",
        "87\n",
        "34\n",
        "15\n",
        "10\n",
        "3\n",
        "2\n",
        "2\n",
        "0\n"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grid = np.zeros((500,500),np.float32)\n",
      "prevgrid = grid.copy()\n",
      "grid[200:300,200:300] = 100.\n",
      "done = False\n",
      "i=0\n",
      "while not done:\n",
      "    i +=1\n",
      "    prevgrid[:,:] = grid # just reuse arrays\n",
      "    # new value is average of 4 neighboring values\n",
      "    grid[1:-1,1:-1] = 0.25*(\n",
      "        grid[:-2,1:-1]\n",
      "      + grid[2:, 1:-1]\n",
      "      + grid[1:-1,:-2]\n",
      "      + grid[1:-1,2:])\n",
      "    grid[200:300,200:300] = 100.\n",
      "    diffmax = np.abs(grid - prevgrid).max()\n",
      "    print(i, diffmax)\n",
      "    if diffmax < 0.1: done = True"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 25.0\n",
        "2 12.5\n",
        "3 7.8125\n",
        "4 6.25\n",
        "5 4.6875\n",
        "6 4.02832\n",
        "7"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3.49121\n",
        "8 3.05481\n",
        "9 2.69775\n",
        "10 2.40269\n",
        "11 2.15626\n",
        "12 1.94833\n",
        "13"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1.82656\n",
        "14 1.71423\n",
        "15 1.61106\n",
        "16 1.51649\n",
        "17 1.42983\n",
        "18 1.3504\n",
        "19"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1.2775\n",
        "20 1.21053\n",
        "21 1.14888\n",
        "22 1.09204\n",
        "23 1.03954\n",
        "24 0.990955\n",
        "25"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.956783\n",
        "26 0.924164\n",
        "27 0.893057\n",
        "28 0.863405\n",
        "29 0.835148\n",
        "30 0.808222\n",
        "31"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.782564\n",
        "32 0.75811\n",
        "33 0.734795\n",
        "34 0.712559\n",
        "35 0.691338\n",
        "36 0.671085\n",
        "37"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.651741\n",
        "38 0.633259\n",
        "39 0.615585\n",
        "40 0.598682\n",
        "41 0.585667\n",
        "42 0.573036\n",
        "43"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.560783\n",
        "44 0.548899\n",
        "45 0.537373\n",
        "46 0.526194\n",
        "47 0.515352\n",
        "48 0.504837\n",
        "49"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.494638\n",
        "50 0.484747\n",
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        "53 0.456802\n",
        "54 0.448036\n",
        "55"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.439522\n",
        "56 0.431255\n",
        "57 0.423229\n",
        "58 0.415436\n",
        "59 0.40786\n",
        "60 0.400505\n",
        "61"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.394524\n",
        "62 0.38867\n",
        "63 0.382938\n",
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        "65 0.371834\n",
        "66 0.366455\n",
        "67"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.361187\n",
        "68 0.35603\n",
        "69 0.350981\n",
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        "71 0.3412\n",
        "72 0.336458\n",
        "73"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.33182\n",
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       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
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       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
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       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
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       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
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       "stream": "stdout",
       "text": [
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       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
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       ]
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
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        "125 0.193689\n",
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       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.192163\n",
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       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
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       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
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       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.17004\n",
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       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.163261\n",
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       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
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       ]
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.118652\n",
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       ]
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       "stream": "stdout",
       "text": [
        " 0.115768\n",
        "210 0.115204\n",
        "211 0.114643\n",
        "212 0.11409\n",
        "213 0.113533\n",
        "214"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.112988\n",
        "215 0.112442\n",
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        "218 0.110832\n",
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        "220"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.109783\n",
        "221 0.10931\n",
        "222 0.108837\n",
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        "224 0.107906\n",
        "225 0.107447\n",
        "226"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.106987\n",
        "227 0.106531\n",
        "228 0.106079\n",
        "229 0.105629\n",
        "230 0.105179\n",
        "231"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.104736\n",
        "232 0.104294\n",
        "233 0.103855\n",
        "234 0.10342\n",
        "235 0.102987\n",
        "236"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.102556\n",
        "237 0.102127\n",
        "238 0.1017\n",
        "239 0.101278\n",
        "240 0.100859\n",
        "241 0.100441\n",
        "242"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.100027\n",
        "243 0.0996132\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.imshow(grid)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "<matplotlib.image.AxesImage at 0x10b868090>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RRx815eXlJhgMmoqKCvPaa6+ZEydOmPr6elNZWWkaGhrGPuzGGPOTn/zELFq0\nyCxZssR0dnYWpOb333/fOI5jVqxYYVauXGlWrlxpfve7303Zuv/yl7+Y2tpas2LFCrN8+XLz05/+\n1Bhjpmy9F+vq6hqbJZnKNR86dMisWLHCrFixwixdunTsGJuomrXSU0SsaaWniFhTYIiINQWGiFhT\nYIiINQWGiFhTYIiINQWGiFj7/0sSeW/YebZWAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108d06310>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}