PythonReference.ipynb

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    "# Jupyter Notebook and Python Reference \n",
    "- Press the <mark>HOME</mark> button to return to the top this page.\n",
    "<a id = \"TOC\"></a>\n",
    "## Table of contents\n",
    "Here's how to make a [TOC](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/table_of_contents_ipython.ipynb)\n",
    "\n",
    "- [Notes from Comp116 TOC](#comptoc)\n",
    "- [Zen of Python](#Zen)\n",
    "- [Helpful resources](#handr)\n",
    "- [Building and installing python modules](#install)\n",
    "- [IPython Notebook](#ipython)\n",
    "    - [file links](#ipythonlinks)\n",
    "- [Markdown](#markdown)\n",
    "- [LaTeX in IPython](#latex)\n",
    "- [Types](#section2)\n",
    "- [Lists](#lists)\n",
    "- [Bools](#bools)\n",
    "- __[Arrays](#arrays)__\n",
    "    - [Creating arrays](#arrays)\n",
    "    - [Accessing Array Elements](#accessing array elements)\n",
    "    - [Changing Array Shapes](#changing array shape)\n",
    "    - [Array Functions](#array functions)\n",
    "    - [`np.ind1`](#indl)\n",
    "- [Formatting Matplotlib](#matplotlib)\n",
    "- [Simple Plotting](#plotting)\n",
    "- [Loops](#loops)\n",
    "- [Dicts](#dicts)\n",
    "- [Sets](#sets)\n",
    "- [Get some user input](#input)\n",
    "- [IPython Blocks](#blocks)\n",
    "- [Pickle](#pickle)\n",
    "- [Writing data to a file](#writing)\n",
    "- [Importing data from the web](#url)\n",
    "- [Pandas](#pandas)\n",
    "- [Mechanize](#mech)\n",
    "- [Biopython](#bio)\n",
    "- [Random module](#rand)\n",
    "- [Webcolors](#webcolors)\n",
    "    - [CSS3](#CSS)\n",
    "- [Time](#time)\n",
    "- [re](#re)\n",
    "- [Classes](#class)\n",
    "- [Java](#java)\n",
    "    - [Jython](#jython)\n",
    "- [Modules](#module)\n",
    "- [Misc.](#misc)\n",
    "- [Github](#github)\n",
    "- [Pandoc](#pandoc)\n",
    "- [FTP stuff](#ftp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [List of Jupyter Notebook Kernals](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id= \"comptoc\"></a>\n",
    "# __Comp116 Notes table of contents:__\n",
    "\n",
    "These are the class notes taken by Dr. Bishop, from his [Comp116](https://www.cs.unc.edu/Courses/comp116-s14/posts/2014/01/21/you-can-read-our-class-notes-here/) class at UNC Chapel Hill.\n",
    "\n",
    "- 0114- [Basics](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0114.ipynb?create=1)\n",
    "- 0116- [Sequences](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0116.ipynb?create=1)\n",
    "- 0121- [np.loadtxt, arrays](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0121.ipynb?create=1)\n",
    "\n",
    "- 0123- [Numpy functions](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0123.ipynb?create=1)\n",
    "\n",
    "- 0128- [Beginning A2](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0128.ipynb?create=1)\n",
    "\n",
    "- 0130- [Arrays](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0130.ipynb?create=1)\n",
    "\n",
    "- 0204- [Temperature conversions with functions](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0204.ipynb?create=1)\n",
    "\n",
    "- 0206- [Pylab stuff 0206- converting F to C, Dew point, messing around with boolean vectors](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0206.ipynb?create=1)\n",
    "\n",
    "- 0220- [np.logical, arrays (A3)-sh(im) function, AND, OR & NOT](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0220.ipynb?create=1)\n",
    "\n",
    "- 0225- [Beginning A4- LOOPS, reading and slinging up a file](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0225.ipynb?create=1)\n",
    "\n",
    "- 0227- [Distances, np.where, and A4](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0227.ipynb?create=1)\n",
    "\n",
    "- 0304- [Finding max without np.max writing a max function, writing a recursive factorial function.](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0304.ipynb?create=1)\n",
    "\n",
    "- 0306- [Counting number of occurences in an array-  loops, sub-loops and print functions for debugging](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0304.ipynb?create=1)\n",
    "\n",
    "- 0318- [Data Analysis](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0318.ipynb?create=1)\n",
    "    - google csv file on the class height data, making a random permuatation to test the hypothesis.\n",
    "\n",
    "- 0320- [Pallindromes](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0320.ipynb?create=1)\n",
    "    - working with strings and pallindromes in loops, graphing with loops.\n",
    "\n",
    "- 0325- [Max so far(in an array)](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0401.ipynb?create=1)\n",
    "\n",
    "- 0401- [Midterm 2 review](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0401.ipynb?create=1)\n",
    "\n",
    "- 0408- [Dictionary and Set (A6)](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0408.ipynb?create=1)\n",
    "    - working with dictionaries and a little bit of working with sets\n",
    "\n",
    "- 0410- [Jumble Words](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0410.ipynb?create=1)\n",
    "    - working with files and the strip function, user interface and word jumble all in the context of dictionaies\n",
    "\n",
    "- 0415- [Fitting Equations to Data](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0415.ipynb?create=1)\n",
    "\n",
    "- 0417- [Exponential Smoothing](http://nbviewer.ipython.org/urls/www.cs.unc.edu/Courses/comp116-s14/media/Notes0417.ipynb?create=1)\n",
    "\n"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='Zen'></a>\n",
    "## The Zen of Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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     "text": [
      "The Zen of Python, by Tim Peters\n",
      "\n",
      "Beautiful is better than ugly.\n",
      "Explicit is better than implicit.\n",
      "Simple is better than complex.\n",
      "Complex is better than complicated.\n",
      "Flat is better than nested.\n",
      "Sparse is better than dense.\n",
      "Readability counts.\n",
      "Special cases aren't special enough to break the rules.\n",
      "Although practicality beats purity.\n",
      "Errors should never pass silently.\n",
      "Unless explicitly silenced.\n",
      "In the face of ambiguity, refuse the temptation to guess.\n",
      "There should be one-- and preferably only one --obvious way to do it.\n",
      "Although that way may not be obvious at first unless you're Dutch.\n",
      "Now is better than never.\n",
      "Although never is often better than *right* now.\n",
      "If the implementation is hard to explain, it's a bad idea.\n",
      "If the implementation is easy to explain, it may be a good idea.\n",
      "Namespaces are one honking great idea -- let's do more of those!\n"
     ]
    }
   ],
   "source": [
    "import this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id = \"handr\"></a>\n",
    "# Helpful resources for beginners:\n",
    "- this is a list of some helpful recources\n",
    "- [StackOverflow](http://stackoverflow.com/)\n",
    "- [Python Standard Library](https://docs.python.org/2/contents.html)\n",
    "- [Informal introduction to python](https://docs.python.org/2/tutorial/introduction.html)\n",
    "    - there is alot of good stuff in here if you are not used to python it makes it very very simple to understand and use.\n",
    "- [Intermediate Ipython stuff](http://nbviewer.ipython.org/github/NelisW/ComputationalRadiometry/blob/master/01-IPythonHintsAndTips.ipynb)\n",
    "- remember that you can hit TAB after any module object and ipy will give you a list of its functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.pi  # hit tab then hit \"p\" and you should see it pop up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- you can also type `dir(module)`,where `module` is any module,to find the functionlist.\n",
    "- you can also you `dir(module.method)`, where `method` is any method or function,to find out about the functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#import pandas as pd\n",
    "#dir(pd)#uncomment this line if you wanna see"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "%quickref\n",
    "?numpy.*sum*#can use this to find any thing inside of a module"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='install'></a>\n",
    "# [Building and installing python modules](https://docs.python.org/2/install/)\n",
    "\n",
    "- in windows make sure that you have extracted the zip file\n",
    "\n",
    "`cd Downloads\\modulex`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "   \n",
    "   \n",
    "<a id = \"ipython\"></a>\n",
    "# IPython notebook\n",
    "- [IPython](http://ipython.org/)\n",
    "\n",
    "- [Citing Ipython ](http://ipython.org/citing.html)\n",
    "\n",
    "- [IPython Notebook Convert](http://ipython.org/ipython-doc/1/interactive/nbconvert.html)\n",
    "\n",
    "    - cd C:\\Users\\drapja\\Documents\\IPython Notebooks\n",
    "    - ipython nbconvert Untitled0.ipynb --to html\n",
    "\n",
    "- [Ipython Notebook Gallery](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks)\n",
    "\n",
    "- [IPython extensions](http://ipython.org/ipython-doc/dev/config/extensions/index.html)\n",
    "\n",
    "- [IPython extension index](https://github.com/ipython/ipython/wiki/Extensions-Index)\n",
    "\n",
    "- [Theming in ipython](http://www.damian.oquanta.info/posts/48-themes-for-your-ipython-notebook.html)\n",
    "\n",
    "- [IPython REDDIT](http://www.reddit.com/r/IPython)\n",
    "\n",
    "- [separate nbs](http://stackoverflow.com/a/20024051/3727854)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Mining the Social Web](http://nbviewer.ipython.org/github/ptwobrussell/Mining-the-Social-Web-2nd-Edition/blob/master/ipynb/Chapter%208%20-%20Mining%20the%20Semantically%20Marked-Up%20Web.ipynb)\n",
    "\n",
    "- [Ploting market data](http://nbviewer.ipython.org/gist/twiecki/3962843)\n",
    "\n",
    "- [Fortran and C in IPython notebook](http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-6A-Fortran-and-C.ipynb)\n",
    "\n",
    "- [Plotly](http://nbviewer.ipython.org/github/plotly/python-user-guide/blob/master/Index.ipynb)\n",
    "\n",
    "- [Plotly on Heatmaps and Histograms](https://plot.ly/python/heatmaps-contours-and-2dhistograms-tutorial/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "    \n",
    "<a id = 'ipythonlinks'></a>\n",
    "### Here is how to generate a list of links to all of the files in a directory;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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       "&nbsp;&nbsp;<a href='./PandasRef.ipynb' target='_blank'>PandasRef.ipynb</a><br>\n",
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       "&nbsp;&nbsp;<a href='./PythonRef_BeautifulSoup_usecase_naming_a_github.ipynb' target='_blank'>PythonRef_BeautifulSoup_usecase_naming_a_github.ipynb</a><br>\n",
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       "  PythonRef_assign_varibles.ipynb\n",
       "  PythonRef_BeautifulSoup_usecase_naming_a_github.ipynb\n",
       "  PythonRef_bokeh_interactive_plots.ipynb\n",
       "  PythonRef_Bytes_Unicode_and_Strings.ipynb\n",
       "  PythonRef_Classes.ipynb\n",
       "  PythonRef_conda_envs.ipynb\n",
       "  PythonRef_Conditionals.ipynb\n",
       "  PythonRef_cx_Oracle.ipynb\n",
       "  PythonRef_GUI_and_plotting.ipynb\n",
       "  PythonRef_Lambda_functions_incomplete.ipynb\n",
       "  PythonRef_Matplotlib.ipynb\n",
       "  PythonRef_Matplotlib_colormap_fail.ipynb\n",
       "  PythonRef_Mouse_control.ipynb\n",
       "  PythonRef_py27_kernel.ipynb\n",
       "  PythonRef_py34_kernel.ipynb\n",
       "  PythonRef_py34_with_pyside.ipynb\n",
       "  PythonRef_Py35_PyQt5.ipynb\n",
       "  PythonRef_PyQt_and_Pyside_compatibility.ipynb\n",
       "  PythonRef_static_methods.ipynb\n",
       "  PythonRef_Types_Bool.ipynb\n",
       "  Python_Ref_Decorators.ipynb\n",
       "  Python_Ref_Lists_&_Tuples.ipynb\n",
       "  Python_Ref_Types_sets.ipynb\n",
       "  Strings and Regular Expressions.ipynb\n",
       "  XlsxWriter and Pandas.ipynb\n",
       ".\\.ipynb_checkpoints/\n",
       "  Generators and Recusion-checkpoint.ipynb\n",
       "  Jupyter builtin magic commands and variable explorer-checkpoint.ipynb\n",
       "  PandasRef-checkpoint.ipynb\n",
       "  PythonReference-checkpoint.ipynb\n",
       "  PythonRef_assign_varibles-checkpoint.ipynb\n",
       "  PythonRef_Bytes_Unicode_and_Strings-checkpoint.ipynb\n",
       "  PythonRef_Classes-checkpoint.ipynb\n",
       "  PythonRef_conda_envs-checkpoint.ipynb\n",
       "  PythonRef_Conditionals-checkpoint.ipynb\n",
       "  PythonRef_cx_Oracle-checkpoint.ipynb\n",
       "  PythonRef_GUI_and_plotting-checkpoint.ipynb\n",
       "  PythonRef_py27_kernel-checkpoint.ipynb\n",
       "  PythonRef_py34_kernel-checkpoint.ipynb\n",
       "  PythonRef_py34_with_pyside-checkpoint.ipynb\n",
       "  PythonRef_Py35_PyQt5-checkpoint.ipynb\n",
       "  PythonRef_PyQt_and_Pyside_compatibility-checkpoint.ipynb\n",
       "  PythonRef_static_methods-checkpoint.ipynb\n",
       "  PythonRef_Types_Bool-checkpoint.ipynb\n",
       "  Python_Ref_Decorators-checkpoint.ipynb\n",
       "  Python_Ref_Lists_&_Tuples-checkpoint.ipynb\n",
       "  Strings and Regular Expressions-checkpoint.ipynb\n",
       "  XlsxWriter and Pandas-checkpoint.ipynb"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import FileLink, FileLinks\n",
    "FileLinks(\".\", included_suffixes=[\".ipynb\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = \"markdown\"></a> \n",
    "---\n",
    "# Markdown\n",
    "- [Mark Down](http://daringfireball.net/projects/markdown/syntax)   \n",
    "- [Markdown cheatsheet](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet)\n",
    "- [PHP Markdown Extra](https://michelf.ca/projects/php-markdown/extra/#fn-output)\n",
    "- [Rich Display system](http://nbviewer.ipython.org/github/ipython/ipython/blob/1.x/examples/notebooks/Part%205%20-%20Rich%20Display%20System.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Here is how to change the color of fonts\n",
    "\n",
    "$\\rightarrow$  Check this out you can white out text\n",
    "\n",
    "$\\rightarrow$\n",
    "\n",
    "- `<font color = #FFFFFF>You can change the color of the fonts. Check the .pynb file and could cick on this cell for the secret message</font>`      \n",
    "\n",
    "- <font color = #FFFFFF>SEE this is all whited out out. That is if you have not changed your css default file like I have.</font>  \n",
    "\n",
    "- <font color = #20dfdf>HEY this is a kind of electric blue color. Try highlighing the bullet point above to reveal the secret message.</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id = 'latex'></a>\n",
    "# LaTeX in ipython\n",
    "\n",
    "- This is how to write stuff in latex.\n",
    "- Here how this looks `$x_{0}^2+y_{0}^2=z_{0}^2$` ;\n",
    "\n",
    "$x_{0}^2+y_{0}^2=z_{0}^2$\n",
    "\n",
    "- You can center that by adding an extra set of dollar signs;\n",
    "\n",
    "$$x_{0}^2+y_{0}^2=z_{0}^2$$\n",
    "\n",
    "\n",
    "\n",
    "## Greek symbols \n",
    "These can be found at ['The Macdonald Polynomial Web Page'](http://garsia.math.yorku.ca/MPWP/LATEXmath/node2.html)\n",
    "- `$\\alpha$` $\\alpha$\n",
    "- `$\\varepsilon$` $\\varepsilon$\n",
    "- `$\\Phi$` $\\Phi$\n",
    "- `$\\Sigma$` $\\Sigma$\n",
    "- `$\\Delta$` $\\Delta$\n",
    "\n",
    "<a id = 'arrowsymbols'></a>\n",
    "## Arrow Symbols\n",
    "I found these on the ['The Macdonald Polynomial Web Page'](http://garsia.math.yorku.ca/MPWP/LATEXmath/node9.html).\n",
    "- `$\\leftarrow$` $\\leftarrow$\n",
    "- `$\\longleftarrow$` $\\longleftarrow$\n",
    "- `$\\uparrow$` $\\uparrow$\n",
    "- `$\\Leftarrow$` $\\Leftarrow$\n",
    "- `$\\Longleftarrow$` $\\Longleftarrow$\n",
    "- `$\\leftrightarrow$` $\\leftrightarrow$\n",
    "- `$\\Updownarrow$` $\\Updownarrow$\n",
    "- `$\\mapsto$` $\\mapsto$\n",
    "- `$\\hookleftarrow$` $\\hookleftarrow$\n",
    "- `$\\leftharpoondown$` $\\leftharpoondown$\n",
    "- `$\\swarrow$` $\\swarrow$\n",
    "- `$\\leftrightharpoon$` $\\leftrightharpoons$\n",
    "\n",
    "<a id = 'miscmarkdown'></a>\n",
    "## Misc. Markdown\n",
    "I found these on the ['The Macdonald Polynomial Web Page'](http://garsia.math.yorku.ca/MPWP/LATEXmath/node8.html).\n",
    "\n",
    "- `$\\because$` $\\because$\n",
    "- `$\\therefore$` $\\therefore$\n",
    "- `$\\thicksim$` $\\thicksim$\n",
    "- `$\\thickapprox$` $\\thickapprox$\n",
    "- `$\\partial$` $\\partial$\n",
    "- `$\\ell$` $\\ell$\n",
    "- `$\\prime$` $\\prime$\n",
    "- `$\\infty$` $\\infty$\n",
    "- `$\\triangle$` $\\triangle$\n",
    "- `$\\wp$` $\\wp$\n",
    "\n",
    "## Math mode accents\n",
    "- `$\\hat{a}$` $\\hat{a}$\n",
    "- `$\\acute{a}$` $\\acute{a}$\n",
    "- `$\\bar{bar}$` $\\bar{a}$\n",
    "- `$\\dot{a}$` $\\dot{a}$\n",
    "- `$\\breve{a}$` $\\breve{a}$\n",
    "- `$\\vec{a}` $\\vec{a}$\n",
    "- `$\\tilde{a}$` $\\tilde{a}$\n",
    "\n",
    "## LaTeX math constructs\n",
    "- `$\\overrightarrow{hello!}$` $\\overrightarrow{hello!}$\n",
    "- `$\\frac{hello!}{there}$` $\\frac{hello!}{there}$\n",
    "- `$\\sqrt[n]{123}$` $\\sqrt[n]{123}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id = 'section2'></a>\n",
    "## Types\n",
    "\n",
    "-[Python built-in types -- Python docs](https://docs.python.org/2/library/stdtypes.html)\n",
    "\n",
    "**int**: integers, positive and negative\n",
    "\n",
    "    1 2 3 -42\n",
    "    \n",
    "**float**: like real numbers but not quite.\n",
    "\n",
    "    1.1 0.01 -3.14 6.022e23\n",
    "    \n",
    "**str**: string, a sequence of characters\n",
    "\n",
    "    \"a string\" 'another string' 'Z'\n",
    "    \n",
    "**list**: a sequence of values of any type\n",
    "\n",
    "    [1, 2] ['hello', 17]\n",
    "    \n",
    "**tuple**: a sequence of values of any type. Like a list but you can't change the values\n",
    "\n",
    "    (1, 2) (1.5, 1, 'abc')\n",
    "    \n",
    "**bool**: a Boolean value, True or False\n",
    "\n",
    "    True False 0 1\n",
    "    \n",
    "**array**: vector, matrix, or higher dimensioned collection of numbers\n",
    "\n",
    "    array([1, 2, 3]) array([0.1, 1.5, 2.3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id = 'lists'></a>\n",
    "# List operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "L= [\"I\",\"said\",\"hey\",\"there\",\"world\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['I', 'said', 'hey', 'there', 'world', 'YO', 'YO', 'YO']"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L= L.__add__(3*[\"YO\"])\n",
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['I', 'said', 'hey', 'there', 'world', 'YO', 'YO', 'YO', 'see']"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L.append(\"see\")\n",
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "print L.count(\"YO\")\n",
    "print L.index(\"YO\")#gives the first instance of \"YO\" as int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['I', 'said', 'hey', 'there', 'world', 'YO', 'YO', 'YO']"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L.pop()#pop removes the last term from the list\n",
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['I', 'said', 'hey', 'there', 'world', 'YO', 'YO', 'YO', 'yes!']"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L.extend([\"yes!\"])#extend is like __add__ or append\n",
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['I', 'said', 'hey', 'there', 'world', 'YO', 'YO', 'yes!']"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L.remove(\"YO\")\n",
    "L\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['yes!', 'world', 'there', 'hey', 'said', 'I']"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L.reverse()\n",
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "L.sort()\n",
    "L.remove(\"YO\")\n",
    "L"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ###A really quick way to remove repeats from a list is to just make that list a [set](#sets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'g', 'p', 'r', 'y'}"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K = [\"p\",\"p\",\"p\",\"p\",\"r\",\"g\",\"y\"]\n",
    "K = set(K)\n",
    "K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id=\"bools\"></a>\n",
    "# Bools\n",
    "\n",
    "- ###AND & OR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ORing is the same as adding in boolean algebra\n",
    "False or True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool(False + True)#see....\n",
    "#think about it if True is 1 and False is 0 then 1+0=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ANDing is the same thing as multiplying in boolean algebra\n",
    "False and True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool(True * False)#do you get it now?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True * True * False + True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one is usually greater than zero\n"
     ]
    }
   ],
   "source": [
    "#anyways this can be worked out to give you a conditional that can have multiple terms\n",
    "if 1>0 or 1<0:\n",
    "    print(\"one is usually greater than zero\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# Functions\n",
    "\n",
    "In the function descriptions below, I will sometimes refer to variables. I'll initialize them below and I will use a naming rule to help you remember their types. Of course, in Python, the types of variables is **not** determined by their name but using a convention makes code easier to read.\n",
    "\n",
    "* i, j, k will be integer variables\n",
    "* x, y, z will be floats\n",
    "* A, B, C will be one-dimensional arrays of floats\n",
    "* M, N will be two-dimensioanl arrays of floats\n",
    "* s, t will be strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np # we need to import the numpy module before we can use it\n",
    "import pylab # so we can plot\n",
    "\n",
    "i = 1\n",
    "j = 7\n",
    "k = 42\n",
    "\n",
    "x = 1.0\n",
    "y = 3.14\n",
    "z = 0.42\n",
    "\n",
    "A = np.arange(10.0)\n",
    "B = np.arange(14.0, 34, 2)\n",
    "C = np.array([-1.0, 2.0, -3.0, 4, -5, 6, -7, 8, -9.1, 10])\n",
    "\n",
    "M = np.array([[1, 2], [3, 4], [5, 6]]) # 3 rows of 2 columns\n",
    "N = np.ones((3, 2)) # also 3 by 2\n",
    "\n",
    "\n",
    "s = 'Hello world!'\n",
    "t = 'z'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='numpy'></a>\n",
    "# Numpy and Scipy\n",
    "## A halfassed investigation\n",
    "- [Scipy for the matlab user](http://wiki.scipy.org/NumPy_for_Matlab_Users)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id = \"arrays\"></a>\n",
    "\n",
    "# ARRAYS!\n",
    "- _\"START,STOP,STEP...\"_\n",
    "\n",
    "## Creating arrays\n",
    " \n",
    "`np.arange(10.0)` will return an array of 10 floats counting from 0 to 9 inclusive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`np.arange(7, 21, 3)` returns an array that starts with 7, counts up to but not including 21, and counts by 3's. The pattern to remember with those arguments is <mark>start, stop, step</mark>. Also remember in virtually every case, the end of the range, `stop`, is not included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  7.,  10.,  13.,  16.,  19.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(7.0, 21, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`np.arange(7, 21)` is just like above but steps by 1. That is a common pattern. If you omit the step it defaults to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  7.,   8.,   9.,  10.,  11.,  12.,  13.,  14.,  15.,  16.,  17.,\n",
       "        18.,  19.,  20.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(7., 21)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also create arrays from lists. This allows us to enter any numbers we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 19.1 , -42.5 ,  73.1 ,   0.  ,   3.14])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([19.1, -42.5, 73.1, 0.0, 3.14])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two-dimensional arrays are just as easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 42,  33],\n",
       "       [-19,   5],\n",
       "       [  0, 101]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[42, 33], [-19, 5], [0, 101]]) # 3 rows of 2 columms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create arrays full of zeros or ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((2,3)) # 2 rows of 3 columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also create arrays that are `like` others. This is often useful for initializing an array to use as a result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.] [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n"
     ]
    }
   ],
   "source": [
    "D = np.zeros_like(C)\n",
    "E = np.ones_like(C)\n",
    "print D, E"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Reading arrays\n",
    "\n",
    "We can read an array from a file with one line per row and spaces between numbers. To read data from a .csv (comma separated values) file, add sep=',' to the arguments. We can also read a _comma separated values_ file by specifying `delimiter=\",\"` as the second argument to `np.loadtxt`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rain = np.loadtxt('A2/depth.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also give `loadtxt` an optional delimiter character to use. For example, read a CSV (comma separated value) file like this: `foo = np.loadtxt('A5/foo.csv', delimiter=',')`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<a id='accessing array elements'></a>\n",
    "##Accessing array elements\n",
    "\n",
    "Let's start by looking at the contents of B."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 14.,  16.,  18.,  20.,  22.,  24.,  26.,  28.,  30.,  32.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first element in the array has index 0 because we love counting from 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the length of an array using the `len` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last element can be accessed with len(B)-1 or, for convenience, simply -1. If we use a negative index we are telling it to count backward from the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B[9]  # because B is of length 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B[-1]  # I don't care about the length, just give me the last one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also pull out a _slice_ of an array using the notation start\\:end. For example to get the elements with index 3, 4, and 5, we can write B[3:6]. Why 6? Read [Dijkstra's paper](https://www.cs.unc.edu/Courses/comp116-s14/media/EWD831.PDF) again!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 20.,  22.,  24.])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B[3:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can leave off the second number to get all the elements up to the end. Note we still need the colon to indicate we want a slice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 20.,  22.,  24.,  26.,  28.,  30.,  32.])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B[3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also leave off the first number to get all elements from the beginning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 14.,  16.,  18.,  20.,  22.,  24.])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following the pattern we learned above, we can also adjust the step size by adding another color and number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 18.,  24.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B[2:8:3] # will get the elements at index 2 and 5 but not 8. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For arrays of more than one dimension we can use multiple subscripts separated by commas. First look at the values in the array `M`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M[2,1] # row 2, column 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get an entire row or column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can acess whole colmuns very easily by assigning multiple variables at the same time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R=\n",
      "[[ 1.  0.  0.]\n",
      " [ 0.  0.  0.]\n",
      " [ 0.  0.  1.]]\n",
      "\n",
      "A= [ 1.  0.  0.]\n",
      "\n",
      "M= [ 0.  0.  0.]\n",
      "\n",
      "Z= [ 0.  0.  1.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "R = np.zeros((3,3))\n",
    "R[0,0] = 1\n",
    "R[2,2] = 1\n",
    "print 'R=\\n',R\n",
    "A,M,Z = R\n",
    "print '\\nA=',A\n",
    "print '\\nM=',M\n",
    "print '\\nZ=',Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  0.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id='changing array shape'></a>\n",
    "## Changing array shape\n",
    "\n",
    "- We can change the shape of an array using the np.reshape function. (We can also assign to the shape property but that seems more  confusing to beginners so let's focus on using np.reshape)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T = np.arange(12)  # create an array with 12 elements\n",
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12,)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.shape # look at its shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10, 11]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2 = np.reshape(T, (2, 6))  # create a new array\n",
    "T2 # has 2 rows of 6 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  4,  5],\n",
       "       [ 6,  7,  8],\n",
       "       [ 9, 10, 11]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T3 = np.reshape(T, (-1, 3)) # let numpy figure out how many rows it should have for 3 columns\n",
    "T3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  4,  5],\n",
       "       [ 6,  7,  8],\n",
       "       [ 9, 10, 11]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T4 = np.reshape(T, (4, -1)) # let numpy figure out how many columns it should have\n",
    "T4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also transpose(make the rows the columns) arrays with the .T command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F=\n",
      "[[ 1.  0.  0.]\n",
      " [ 1.  0.  0.]\n",
      " [ 1.  0.  0.]]\n",
      "F.T=\n",
      "[[ 1.  1.  1.]\n",
      " [ 0.  0.  0.]\n",
      " [ 0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "F = np.zeros((3,3))\n",
    "F[:,0] = 1\n",
    "print 'F=\\n',F\n",
    "print 'F.T=\\n',F.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---  \n",
    "\n",
    "<a id='array functions'></a>\n",
    "## Array functions\n",
    "\n",
    "Numpy has a wide variety of functions that we can apply to arrays. Of course we can combine them with the usual arithmetic operators as long as we assure they have compatible shapes (more on this later)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 14.,  17.,  20.,  23.,  26.,  29.,  32.,  35.,  38.,  41.])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A+B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-14.        ,   9.        ,  -4.        ,   8.        ,\n",
       "        -0.4       ,   9.        ,   2.28571429,  10.5       ,\n",
       "         4.7032967 ,  12.2       ])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A+B/C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`np.min(A)`, `np.max(B)` will give us the minimum or maximum value in the array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-9.0999999999999996"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(C)  # notice the result isn't exactly -9.1. That is approximation error. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`np.mean(B)` computes the mean (average) of the values in the array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`np.std(C)` computes the starndard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.0719843474291775"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(C+B)  # I can apply these to the results of expressions as well as variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the above can be used with the `axis` argument to operate along that axis only. axis=0 will remove the row dimension because the row index is the first of the indexes. Likewise, axis=1 removes the column dimension because it is second."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2] [1 3 5]\n"
     ]
    }
   ],
   "source": [
    "print np.min(M, axis=0), np.min(M, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also compute cumulative sums."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1. ,  1. , -2. ,  2. , -3. ,  3. , -4. ,  4. , -5.1,  4.9])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3. ,  -5. ,   7. ,  -9. ,  11. , -13. ,  15. , -17.1,  19.1])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diff(C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can count the number of non-zero (typically True) items in an array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(C > 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can limit the values in an array using the clip command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-10,  20,  30],\n",
       "       [ 15,  17,  25],\n",
       "       [ 10,  13,  15],\n",
       "       [ 24, 100,  95]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A1=np.array([[-10,20,30],[15,17,25],[10,13,15],[24,100,95]])\n",
    "A1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0, 20, 20],\n",
       "       [15, 17, 20],\n",
       "       [10, 13, 15],\n",
       "       [20, 20, 20]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B1=np.clip(A1,0,20)\n",
    "B1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can find `NaN` (Not a Number) values with the np.isnan(A) function which returns a Boolean vector with value True if the number was NaN and False if not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`np.interp(x, xp, yp)` Returns the one-dimensional piecewise linear interpolant in the data defined by xp and yp. For example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 200.,  250.])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.interp(np.array([2, 4]), np.array([1, 3, 5]), np.array([100, 300, 200]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"indl\"></a>\n",
    "## `np.ind1`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`numpy.in1d(ar1, ar2, assume_unique=False, invert=False)[source]`\n",
    "\n",
    "Test whether each element of a 1-D array is also present in a second array.\n",
    "\n",
    "Returns a boolean array the same length as ar1 that is True where an element of ar1 is in ar2 and False otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True, False,  True, False, False, False], dtype=bool)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "test = np.array([0, 1, 2, 5, 0, 3, 3, 5])\n",
    "states = [0, 2]\n",
    "mask = np.in1d(test, states)\n",
    "mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id ='matplotlib'></a>\n",
    "#Formating Matplotlib\n",
    "\n",
    "\n",
    "-[Matplotlib tutorial](http://matplotlib.org/users/pyplot_tutorial.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xa1ba2b0>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GqFGj2LFjB926dYvNKyoqYtCgQXz88cdceuml1KhRg+eff56srCzGjh3Lcccd\nVxa3K0mSDkBODtxxB9x5J1x8cdjVSP8T92C8bds27rvvPjIzMxk6dOg+502aNIlIJMLjjz9O3bp1\nAWjSpAm33HILr7zyCh06dAAgEokwfvx4MjMzyc7OBqB9+/ZEo1EmTpzI+eefT7X/7gCem5vLBx98\nQHZ2Nq1btwagbdu29OjRgwkTJjDEcyUlSQrV8uVw+eVBb/F//1iXyo24P3z3+uuvs3nzZq6++moA\nvvnmG4qKikrNmzt3LpmZmbFQDNC8eXPq16/P7NmzY2NLly5ly5YtdOzYscT3X3jhhezYsYOFCxfG\nxnJzc8nIyIiFYoD09HTOOuss5s+fz65du+J1m5Ik6QDl5cEFF0CDBvD00x73rPIn7v9KLlmyhB/8\n4Afk5eVxxRVX0L59ezp06MDw4cMpLCwEgjaKzZs307hx41Lf36RJE1avXh37es2aNQCl5jZq1IiU\nlBQ++uijEnP31kvcuHFjIpEI69ati8s9SpKkA1NYCF26wLZt8MILHves8inuwXj9+vXs3r2bIUOG\n8Itf/IK7776bdu3a8cILL/DAAw8AsHHjRgAyMjJKfX9GRgZbtmyJre7m5+dTqVKlUg/kpaWlkZ6e\nXqJvOT8/n9q1a5e6ZvFYfn5+fG5SkiTtt2gU+vULDvJ47jk44YSwK5L2Lu49xjt27CASiXDBBRdw\n3XXXAXDmmWeya9cuXnjhBXr16kUkEgGgcuXKpb6/eCwSiZCamkokEiEtLW2vn5WWlhZbhQYoLCzc\n69w9rylJkg6v4cPhiSfgqafgl78Muxpp3+IejItD6NnfOuj817/+NS+88AIffPABP/rRjwBKhNpi\nxWNV/rvLd5UqVdi5c+deP6uwsLBEuN7X3G9fc1+ysrJKrUx369atxM4XkiRp/02fDjffDLfdBldc\nEXY1qigmT57M5MmTS4wVFBSU+efGPRjXqVOHtWvXUqtWrRLjxV9v2bIl1tpQ3FKxp40bN1KjRo3Y\ndm21a9emqKiIgoKCEqF1586dfP3119SpUyc2Vrt27RKtFcWKWyj21maxp+HDh9OsWbP9uU1JkvQ9\n3n8funWD88+He+8NuxpVJHtbmHznnXdo3rx5mX5u3HuMTz75ZCB4wG5PxYG1Zs2a1KlTh5o1a7Jy\n5cpS379y5UpOOumk2NfFv/723FWrVhGNRmnYsGFsrGHDhqxevZpoNFpi7ooVK6hatSrHH3/8IdyZ\nJEnaX3k/cA6CAAAgAElEQVR5QSBu0AAmTXIHClUMcf/XtG3btgC89NJLJcanT59OamoqP/vZzwBo\n1aoVixYtKhGglyxZwvr162nTpk1srFmzZlSvXp2cnJwS18vJyaFq1apkZmbGxtq0acOmTZuYM2dO\nbKygoIDc3FwyMzNLHBoiSZLKRiTyvx0ocnKgevWwK5L2T9yT4kknnUS7du14+eWX2b17Nz/96U/5\n97//zZw5c7j88stjO1F0796d3NxcsrKy6NKlC9u3b2fKlCmceOKJtGvXLna9ypUr06tXL0aOHEl2\ndjYtWrRg2bJlzJw5k969e8cO94AgGD/77LMMGzaMtWvXxk6+i0aj9OzZM963KkmSviUahb59gx0o\nZs1yBwpVLGWyhHrjjTdSr149XnnlFebNm8cxxxzDgAED6NKlS2xO3bp1GTFiBKNHj2bcuHFUrlyZ\nM844g/79+5da2e3YsSOpqalMnTqVBQsWcPTRR5e6HkClSpW4//77GTNmDNOmTSMSidC0aVMGDx5M\n/fr1y+JWJUnSHh54INh9YtIkd6BQxZMya9as6PdPS2wffvghffv2ZcmSJT58J0nSQZo2LWihuOsu\nGDo07GqUaIofvhs7dmzsmbZ4sxVekiQdsiVLoHt3uOQSyM4Ouxrp4BiMJUnSIfn8c7jgAvjJT2DC\nBEhJCbsi6eAYjCVJ0kHbti3Ylu2II+D55+HII8OuSDp47l8mSZIOSlFR0D6xejXMnw/HHBN2RdKh\nMRhLkqSDctttwSpxTg789KdhVyMdOoOxJEk6YOPGwUMPwYgR0KFD2NVI8WGPsSRJOiAzZkD//jBg\nANxwQ9jVSPFjMJYkSftt+XK4+GL4zW+C1WJ3oFAiMRhLkqT98tVXQdvEj38MU6ZAqg2ZSjAGY0mS\n9L22bw/2Ki4shBdfhOrVw65Iij//ridJkr5TURFccUXQRjFnDhx/fNgVSWXDYCxJkr7T4MEwbVrw\nat487GqksmMwliRJ+zRmDAwbBsOHw4UXhl2NVLbsMZYkSXv10kvBlmzXXw8DB4ZdjVT2DMaSJKmU\npUuha1do3z5YLXZbNiUDg7EkSSph3bpgW7YmTWDyZDjiiLArkg4Pg7EkSYr5+utglTgtLdiW7aij\nwq5IOnx8+E6SJAGwc2fQPvHZZzB/PhxzTNgVSYeXwViSJBGNQv/+8Prr8MorcOqpYVckHX4GY0mS\nxD33wF//Ck8+CWefHXY1UjjsMZYkKck99RTcdRfcfTdceWXY1UjhMRhLkpTEXnsNevcOXkOGhF2N\nFC6DsSRJSerdd6FLFzjnHBg92r2KJYOxJElJaN06+N3v4KSTYOrUYHs2KdkZjCVJSjKbNwehOC0N\npk+H6tXDrkgqH9yVQpKkJBKJQOfOsH49LFgAxx4bdkVS+WEwliQpSRQVwVVXBYF4xgxo2jTsiqTy\nxWAsSVKSuPVWmDIl6Clu3TrsaqTyx2AsSVISGD4cHn4YHnkELroo7Gqk8smH7yRJSnBTpsCNNwYr\nxtdfH3Y1UvllMJYkKYHNng1XXAGXXw733Rd2NVL5ZjCWJClBvfceXHghtGoFTzwBlfxTX/pO/ici\nSVIC+uwzaNcOGjSAadOgcuWwK5LKP4OxJEkJJj8ffvvb4ACPl16CGjXCrkiqGNyVQpKkBLJ9O3To\nAHl5MH++B3hIB8JgLElSgti1Cy65JOgtnjULTj457IqkisVgLElSAohGoW9feOUVePFFaNEi7Iqk\nisdgLElSAhgyJNh5YuJEOO+8sKuRKiYfvpMkqYIbNQruvRcefBC6dw+7GqniMhhLklSBPfMMDBwY\nnGx3881hVyNVbAZjSZIqqBkz/neq3YMPhl2NVPEZjCVJqoAWL4bOneHccz3VTooX/zOSJKmCWbEC\nfvc7OO00+Mc/goM8JB06g7EkSRXIZ5/Bb34DP/xhsC3bD34QdkVS4jAYS5JUQWzYEGzFdsQR8Oqr\nUKtW2BVJicV9jCVJqgC2boX27SE/Pzjq+Yc/DLsiKfEYjCVJKuciEejUKegtnj0bGjUKuyIpMRmM\nJUkqx3btgssug7lzg+OemzULuyIpcRmMJUkqp6JR6NsXnn8epk2Ds84KuyIpsRmMJUkqh6JRuOWW\nYI/ip5+GCy4IuyIp8bkrhSRJ5dD998PDD8PIkdCjR9jVSMnBYCxJUjkzZgzcfjtkZ8MNN4RdjZQ8\nDMaSJJUjzzwD/fsHgfiuu8KuRkouBmNJksqJF18M2ia6d4fhwyElJeyKpORiMJYkqRyYNQsuugjO\nPz944K6Sf0JLh53/2UmSFLLFi4NdJ1q3hsmTIdU9o6RQGIwlSQrRe+9Bu3bw05/Cc89BlSphVyQl\nL4OxJEkhWbMGzj0XTjgBpk+Ho44KuyIpuRmMJUkKwbp1cM45UKsWzJgBNWuGXZEkg7EkSYfZV18F\noRjgtdegbt1w65EUsL1fkqTDKD8/CMVbt8KcOVC/ftgVSSpmMJYk6TApKIDzzgtWjHNzoWHDsCuS\ntCeDsSRJh8G2bdC+PXz0EcyeDU2bhl2RpG8zGEuSVMZ27ICOHeHdd2HmTDjttLArkrQ3BmNJkspQ\nYWFwot2CBfDKK9CyZdgVSdoXg7EkSWVk1y64/PJg54kXXghOtpNUfhmMJUkqA7t3w5VXwr/+Bf/8\nJ/zmN2FXJOn7GIwlSYqzoiLo0weeeSZ4dewYdkWS9ofBWJKkOIpGYcAAePJJmDgRLr447Iok7S+D\nsSRJcRKNQlYWjBkD48cH/cWSKg6PhJYkKQ6iURg0CEaOhMceg169wq5I0oEyGEuSFAd/+AMMGwYj\nRsC114ZdjaSDYTCWJOkQ/fGPwev++2HgwLCrkXSwDMaSJB2C++6Du+6Ce+6B224LuxpJh8JgLEnS\nQRo2DG6/HbKz4Y47wq5G0qEyGEuSdBD+/OdghfjOO4P+YkkVn8FYkqQDNHIk3HQTDB4MQ4eGXY2k\neDEYS5J0AB59FH7/e7j1VvjTnyAlJeyKJMWLwViSpP302GNw3XVw443BDhSGYimxGIwlSdoPo0dD\n//7BavFDDxmKpURkMJYk6XuMHg0DBgTHPf/5z4ZiKVEZjCVJ+g6PPvq/UPzww4ZiKZGllvUHTJo0\niSeeeIIf//jHPPHEEyXeW7t2LY8++ijLly8nLS2Nli1bMmDAANLT00tdZ/r06UydOpUvv/ySevXq\n0blzZzp16lRq3tatWxkzZgzz5s0jEonQtGlT+vXrR6NGjcrsHiVJienRR//XU2z7hJT4ynTFOC8v\nj7/97W9UrVqVlG/9bpKXl8fAgQP54osv6NOnD127dmXx4sXcfPPN7Nq1q8TcnJwcHn74YRo0aMAN\nN9zAKaecwqhRo5g8eXKJeUVFRQwaNIg33niDzp0707dvXzZt2kRWVhaff/55Wd6qJCnBGIql5FOm\nK8aPPfYYp556Krt376agoKDEe5MmTSISifD4449Tt25dAJo0acItt9zCK6+8QocOHQCIRCKMHz+e\nzMxMsrOzAWjfvj3RaJSJEydy/vnnU61aNQByc3P54IMPyM7OpnXr1gC0bduWHj16MGHCBIYMGVKW\ntytJShB/+Qtcf72hWEo2ZbZi/O677zJnzhwGDBhANBottWI8d+5cMjMzY6EYoHnz5tSvX5/Zs2fH\nxpYuXcqWLVvo2LFjie+/8MIL2bFjBwsXLoyN5ebmkpGREQvFAOnp6Zx11lnMnz+/1Eq0JEnfNnx4\nEIpvuslQLCWbMgnGu3fv5pFHHqF9+/Y0aNCg1Pt5eXls3ryZxo0bl3qvSZMmrF69Ovb1mjVrAErN\nbdSoESkpKXz00Ucl5u6tl7hx48ZEIhHWrVt30PckSUp8Dz4YrBIPGhT82lAsJZcyCcY5OTn85z//\noVevXnt9f+PGjQBkZGSUei8jI4MtW7bEVnfz8/OpVKlSqQfy0tLSSE9PZ8OGDbGx/Px8ateuXeqa\nxWP5+fkHd0OSpIR3333BaXZDhsC99xqKpWQU92BcUFDAk08+yRVXXLHX3SUg6BsGqFy5cqn3iseK\n50QiEdLS0vZ6nbS0NAoLC2NfFxYW7nXut68pSdKe7r4bbr8dsrPhj380FEvJKu4P3z3xxBOkp6fT\nuXPnfc6pUqUKQIlQW6x4rHhOlSpV2Llz516vU1hYWCJc72vut6+5L1lZWaXCfLdu3ejWrdt3fp8k\nqWKKRuEPfwjC8B//GKwWSwrf5MmTS+0+9u2NHMpCXIPx+vXrefHFF7nuuuvIy8uLjRcWFrJz506+\n/PJLjjrqqFgLRXFLxZ42btxIjRo1SE0NSqtduzZFRUUUFBSUCK07d+7k66+/pk6dOrGx2rVrl2it\nKFbcQrG3Nos9DR8+nGbNmh3AHUuSKqpoNFglvv/+4HXbbWFXJKnY3hYm33nnHZo3b16mnxvXYLxh\nwwai0SijRo1i1KhRpd6/7LLL6NKlCwMGDKBmzZqsXLmy1JyVK1dy0kknxb4u/vXKlStp2bJlbHzV\nqlVEo1EaNmwYG2vYsCHvvfdeqV0wVqxYQdWqVTn++OPjcp+SpIotGg0eshsxIth54qabwq5IUnkQ\n12DcoEED7r777hKhNBqN8sQTT/DNN99w3XXX8cMf/hCAVq1aMWPGDPLy8mJbti1ZsoT169dz8cUX\nx76/WbNmVK9enZycnBLBOCcnh6pVq5KZmRkba9OmDXPmzGHOnDm0adMGCJbdc3NzyczMjK1CS5KS\nV1FRcHDHY48F+xUPGBB2RZLKi7gmxfT0dM4888xS4//85z8B+NWvfhUb6969O7m5uWRlZdGlSxe2\nb9/OlClTOPHEE2nXrl1sXuXKlenVqxcjR44kOzubFi1asGzZMmbOnEnv3r1jh3tAEIyfffZZhg0b\nxtq1a6lRowbPP/880WiUnj17xvNWJUkV0O7d0KcPPPkkPP449O4ddkWSypPDsoSakpJS6oCPunXr\nMmLECEaPHs24ceOoXLkyZ5xxBv379y+1stuxY0dSU1OZOnUqCxYs4Oijj2bAgAF06dKlxLxKlSpx\n//33M2bMGKZNm0YkEqFp06YMHjyY+vXrl/l9SpLKr1274Mor4Zln4OmnoXv3sCuSVN6kzJo1Kxp2\nEWH78MMP6du3L0uWLPHhO0lKQIWFcNll8Pzz8Pe/wx4de5IqiOKH78aOHcvJJ59cJp9h060kKaHt\n2BEE4Rkz4J//hI4dw65IUnllMJYkJaxt24IgPH9+sFr829+GXZGk8sxgLElKSAUF0L49vPsuvPIK\n/HezIknaJ4OxJCnhbNgA550HH38MM2fCHrt9StI+GYwlSQnliy/gnHMgLw9mz4bTTgu7IkkVhcFY\nkpQw1q6Fs88OHribOxcaNw67IkkVSaWwC5AkKR4+/BBatQqOezYUSzoYBmNJUoX3738HobhaNZgz\nBxo0CLsiSRWRwViSVKEtWABnnQU/+lEQio87LuyKJFVUBmNJUoU1Ywacey787Gfw+utQp07YFUmq\nyAzGkqQK6dlnoUMHaNsWXn4ZatQIuyJJFZ3BWJJU4UyYAF27wkUXwXPPwZFHhl2RpERgMJYkVSjD\nh0OvXnDNNTBxIqSlhV2RpERhMJYkVQjRKNxxB9x4IwwaBKNHwxFHhF2VpETiAR+SpHJv927o3x/G\njYOHHoKbbgq7IkmJyGAsSSrXIhHo3j3oJZ4wAa66KuyKJCUqg7EkqdzasgU6dYJ584JdKDp2DLsi\nSYnMYCxJKpc2bIDf/Q5WroRXX4U2bcKuSFKiMxhLksqdtWvhvPNg40bIzYWf/zzsiiQlA3elkCSV\nK++9B7/8JRQWwvz5hmJJh4/BWJJUbsydC61aQb16sGABNGoUdkWSkonBWJJULjz/PPzmN9CsWdA+\nccwxYVckKdkYjCVJofvrX6FzZ+jQAV5+GWrUCLsiScnIYCxJCk00CvfcA336QL9+8MwzUKVK2FVJ\nSlbuSiFJCsWuXXDddTB2LPzxj8FxzykpYVclKZkZjCVJh9327dCtG0yf7ml2ksoPg7Ek6bDasAHO\nPz/Ylu2FF6Bdu7ArkqSAwViSdNh88gn89reweTPMng2nnx52RZL0Pz58J0k6LN55BzIzoago2KPY\nUCypvDEYS5LK3MsvQ+vWcMIJQShu2DDsiiSpNIOxJKlMPf540FP861/DG29A3bphVyRJe2cwliSV\niWg02ILtmmvg2mvhuefgqKPCrkqS9s2H7yRJcVdYCL16wd/+Bg8+CDfd5B7Fkso/g7EkKa42bw6O\nd54/PzjJ7pJLwq5IkvaPwViSFDeffgrt28MXX8DMmdCqVdgVSdL+s8dYkhQXb74JZ5wB33wT7Dxh\nKJZU0RiMJUmHbNo0OOssOPFEWLwYmjQJuyJJOnAGY0nSQYtG4aGH4KKLgi3ZXn/d7dgkVVwGY0nS\nQdm1C/r1g1tugUGDYPJkOPLIsKuSpIPnw3eSpAP29dfQtWuwQjx+fLA1myRVdAZjSdIB+fRT6NAB\n1q8Pjno+55ywK5Kk+LCVQpK03xYuhF/8Ith5YuFCQ7GkxGIwliTtl7//Hdq2DXacWLwYmjYNuyJJ\nii+DsSTpO0Wj8Ic/wOWXw6WXwmuvQZ06YVclSfFnj7EkaZ+++QZ69oQpU+C+++C22yAlJeyqJKls\nGIwlSXv1f/8HF14Iy5fDs89C585hVyRJZctgLEkq5e23oWPHYHV43jxo1izsiiSp7NljLEkqYcoU\naNUKjj8e3nrLUCwpeRiMJUkAFBXBXXcFD9h16QKzZ8Oxx4ZdlSQdPrZSSJLYtg2uuAKee86H7CQl\nL4OxJCW5Tz8NHrL76CP417/gggvCrkiSwmEwlqQklpsLF10E1avDggXwk5+EXZEkhcceY0lKQtEo\njB4dHOn8058GD9kZiiUlO4OxJCWZwkK49loYMCB4vfoq1K4ddlWSFD5bKSQpiXz1VbDjxFtvwRNP\nBKfaSZICBmNJShJvvRWcXrdrV7AVW2Zm2BVJUvliK4UkJYEJE4JDO447LjjVzlAsSaUZjCUpgRUW\nBn3EvXpBjx7BLhTHHRd2VZJUPtlKIUkJ6ssvg63Y3nwTxo6Fa64JuyJJKt8MxpKUgBYtCh6yi0aD\nVWJbJyTp+9lKIUkJJBoNVofbtIEf/xiWLDEUS9L+MhhLUoL45pugl/jaa6FPH5g1C449NuyqJKni\nsJVCkhLAJ58EW7GtWgVPPx08aCdJOjAGY0mq4F5+GS6/HGrVgoUL4bTTwq5IkiomWykkqYIqKoKh\nQ6F9e/jlL4P9iQ3FknTwXDGWpAooPx+6d4dXXw3C8R13QCWXOiTpkBiMJamCWbwYLr4Ytm8P2ijO\nOy/siiQpMbi+IEkVRDQKf/nL/452XrrUUCxJ8WQwlqQKYOtWuOwyuP566N8/OLTj+OPDrkqSEout\nFJJUzr3/fnC08/r1MGUKdO0adkWSlJhcMZakcuzJJ6FFCzjiiGDXCUOxJJUdg7EklUPbtkHPnsGr\nWzd4801o3DjsqiQpsdlKIUnlzAcfBLtOfPopPPUUXHFF2BVJUnJwxViSypGnnw5aJwDeestQLEmH\nk8FYksqBbdugVy+48kq45JKgdeKUU8KuSpKSi60UkhSyd9+FSy+Fzz4LHra78sqwK5Kk5OSKsSSF\nJBqF0aOhZUuoUgXeecdQLElhMhhLUgg2bQr2Jh4wAHr3hkWL3HVCksJmK4UkHWYLFgRbsG3ZAtOm\nQadOYVckSQJXjCXpsNm9G+65B1q3hvr14d//NhRLUnniirEkHQaffQbdu8P8+TBkCNx5J6T6O7Ak\nlSv+tixJZeyf/4Q+faB6dZg9G1q1CrsiSdLexD0Yr1y5kldffZWlS5fy1VdfkZ6eTtOmTbn66qup\nX79+iblr167l0UcfZfny5aSlpdGyZUsGDBhAenp6qetOnz6dqVOn8uWXX1KvXj06d+5Mp738f5Bb\nt25lzJgxzJs3j0gkQtOmTenXrx+NGjWK961K0nfatg1+/3v461+DB+3GjYNatcKuSpK0L3HvMZ48\neTLz5s3j9NNP5/rrr6dDhw4sW7aMa665hk8++SQ2Ly8vj4EDB/LFF1/Qp08funbtyuLFi7n55pvZ\ntWtXiWvm5OTw8MMP06BBA2644QZOOeUURo0axeTJk0vMKyoqYtCgQbzxxht07tyZvn37smnTJrKy\nsvj888/jfauStE9LlkDz5vD3v8P48TB1qqFYksq7uK8Yd+3alSZNmnDEEUfExtq2bUuvXr2YPHky\nt99+OwCTJk0iEonw+OOPU7duXQCaNGnCLbfcwiuvvEKHDh0AiEQijB8/nszMTLKzswFo37490WiU\niRMncv7551OtWjUAcnNz+eCDD8jOzqZ169axz+7RowcTJkxgyJAh8b5dSSph924YNgzuugt++tNg\nb2K3YZOkiiHuK8annnpqiVAMcNxxx3HCCSfw2Wefxcbmzp1LZmZmLBQDNG/enPr16zN79uzY2NKl\nS9myZQsdO3Yscc0LL7yQHTt2sHDhwthYbm4uGRkZsVAMkJ6ezllnncX8+fNLrURLUjx9+im0bQt3\n3AG33goLFxqKJakiOSzbtUWjUTZt2hTrHc7Ly2Pz5s003sufGE2aNGH16tWxr9esWQNQam6jRo1I\nSUnho48+KjF3b73EjRs3JhKJsG7durjcjyTtKRqFSZPgtNOC3Sdyc+FPf4LKlcOuTJJ0IA5LMJ45\ncyb5+fm0bdsWgI0bNwKQkZFRam5GRgZbtmyJre7m5+dTqVKlUg/kpaWlkZ6ezoYNG2Jj+fn51K5d\nu9Q1i8fy8/Pjc0OS9F+bNgWHdfToARdcAO++664TklRRlfl2bZ999hkjR47k1FNP5bzzzgOCvmGA\nyntZTikei0QipKamEolESEtL2+u109LSKCwsjH1dWFi417l7XlOS4mXGDOjZE7Zvh8mT4dJLw65I\nknQoyjQYb9y4kcGDB1OtWjWGDh1KSkoKAFWqVAEoEWqLFY8Vz6lSpQo7d+7c6/ULCwtLhOt9zf32\nNfclKyur1Mp0t27d6Nat23d+n6Tksm1b0EM8ejScey488URwkp0kKT4mT55cavexgoKCMv/cMgvG\nW7du5bbbbmPbtm088sgjJdomin9d3FKxp40bN1KjRg1S/3skVO3atSkqKqKgoKBEaN25cydff/01\nderUiY3Vrl27RGtFseIWir21Wexp+PDhNGvW7ADuUlKyWbQIrrgC1q+Hv/wF+veH//6dX5IUJ3tb\nmHznnXdo3rx5mX5umfQYFxYWcscdd/D5559z77338qMf/ajE+3Xr1qVmzZqsXLmy1PeuXLmSk046\nKfZ18a+/PXfVqlVEo1EaNmwYG2vYsCGrV68mGo2WmLtixQqqVq3K8ccff8j3Jik5FRYGRzn/6leQ\nkQH//jcMGGAolqREEvdgvHv3boYOHcqKFSvIzs7mlFNO2eu8Vq1asWjRIvLy8mJjS5YsYf369bRp\n0yY21qxZM6pXr05OTk6J78/JyaFq1apkZmbGxtq0acOmTZuYM2dObKygoIDc3FwyMzNjq9CSdCDe\nfRdatoQHHoChQ2HePDj55LCrkiTFW9yT4mOPPcbChQvJzMykoKCA1157rcT75557LgDdu3cnNzeX\nrKwsunTpwvbt25kyZQonnngi7dq1i82vXLkyvXr1YuTIkWRnZ9OiRQuWLVvGzJkz6d27d+xwDwiC\n8bPPPsuwYcNYu3YtNWrU4PnnnycajdKzZ89436qkBLdzZxCG774bmjSBxYvBbitJSlxxD8YfffQR\nKSkpLFy4sMThGwApKSmxYFy3bl1GjBjB6NGjGTduHJUrV+aMM86gf//+pVZ2O3bsSGpqKlOnTmXB\nggUcffTRDBgwgC5dupSYV6lSJe6//37GjBnDtGnTiEQiNG3alMGDB1PfJ2MkHYD334crrwxaJgYN\ngjvvhO95fleSVMGlzJo1K/r90xLbhx9+SN++fVmyZIkP30lJbvdueOih4Ejnhg3hqaegRYuwq5Ik\nFT98N3bsWE4uo362w3LAhyRVBB98EDxcN3gwDBwI77xjKJakZPL/7d17dFTV+cbxbxISkIJJCAiR\ncI/cURFamqCNgCw0gEBUFItKg6VUtJUqSBErYItVW6nl4kJKuCpgRVyRmyQSwVJYXAWEBARBTCHk\nxiUJkJNk5vfH/mUghNoESE7mzPNZ66zJnJk4b3xzedizz94KxiLi84qKYNo06NoVzpwxF9e9+SbU\nqWN3ZSIiUp20TIOI+LS9e83udV99BePGweTJCsQiIr5KI8Yi4pMsy4Tgbt3Mx1u3wp//rFAsIuLL\nNGIsIj5n2zZ4+mlITYWJE82hFSdEREQjxiLiMwoK4He/g6goCAyE7dvNhh0KxSIiAhoxFhEfsX49\n/OpXcOqU2bTj+edBm2GKiMjlNGIsIo6Wk2M26ujXD1q3hn374MUXFYpFRKQ8/WkQEUdyu+GDD2Ds\nWLMc27x5ZvUJPz+7KxMRkZpKI8Yi4jhHjpgR4uHDoVcvs3FHfLxCsYiI/DAFYxFxDMuC11+Hzp3h\n0CFYvRqWL4fwcLsrExERb6CpFCLiCJs3m4vr0tLMyhOvvgo/+pHdVYmIiDfRiLGIeLWcHBg1Cu6+\n2wThHTvMds4KxSIiUlkaMRYRr+RywcKFMH68ubhu5kwYPRoCAuyuTEREvJVGjEXE6+zbBzEx5oK6\nfv3M9IkxYxSKRUTk+igYi4jXyM+HceOga1fIzoYNG2DJEmjSxO7KRETECTSVQkRqPLcbPvwQXnjB\nzCmeOtVs0hEUZHdlIiLiJBoxFpEabf9+6NMHHnsMfvITSE2FiRMVikVE5MZTMBaRGuncObPs2h13\nQHo6rFsHH38MLVvaXZmIiDiVplKISI3icpl5w+PHQ14e/PGPZlvn2rXtrkxERJxOI8YiUmNs2wbR\n0fDUU/Czn5nVJiZMUCgWEZHqoWAsIrY7eRJGjIAePeDiRfjiC3OxXbNmdlcmIiK+RMFYRGxTWAhv\nvI5EkSgAABUYSURBVAFt28KqVfDuu7Bzp1mjWEREpLppjrGIVDu3Gz75xKxJfOyY2Zxj8mQIDbW7\nMhER8WUaMRaRarVrF/TqBXFxEBkJe/bAO+8oFIuIiP0UjEWkWpw4Ab/4BXTvDpmZsGaNWYKtUye7\nKxMRETE0lUJEqlRBAfz1r2Yu8U03wcyZMGoU1NJvHxERqWH0p0lEqkRJCSxYAH/4A2RlwW9+A5Mm\nQUiI3ZWJiIhcnaZSiMgN5XabaRJ33AFPP23WIz54EP7yF4ViERGp2RSMReSG2bUL7rsP+veHhg3N\nhh1Ll0KrVnZXJiIi8r8pGIvIdTtyBB5/HLp1M5t1JCZCSgr8+Md2VyYiIlJxCsYics0yMuDZZ6F9\ne9i4Ed57D/buhYEDwc/P7upEREQqRxffiUilnTsHb70F06dDYCD88Y/w3HNQt67dlYmIiFw7BWMR\nqbALF8y2zdOmmWXYfvtbeOklbc4hIiLOoGAsIv+TZcG8eWZkODMT4uPNMmxNm9pdmYiIyI2jOcYi\n8l8VF5u1iNu1gzFjoHdvSEuDOXMUikVExHkUjEWkHJcLli+Hzp3NNs7dusG+fbB4MbRpY3d1IiIi\nVUPBWEQ8XC745z/h9tvhscegdWvYsQM++gg6dbK7OhERkaqlYCwiuFwm/N5xBwwdChERsGWL2cGu\nWze7qxMREakeCsYiPszlgo8/hq5d4ZFHIDwc/v1vWLcOfvpTu6sTERGpXgrGIj6opMRs1Xz77fDQ\nQ9CoEfzrX7B+PURF2V2diIiIPRSMRXxIUREsXAgdO5otnJs1g82bITkZeva0uzoRERF7KRiL+IDC\nQpg71yy7NmIEdOgA27bB2rUQHW13dSIiIjWDNvgQcbD8fLPm8Ntvw8mT8PDDsHKluchOREREylIw\nFnGg7GyYMcMceXnwxBMwfjy0b293ZSIiIjWXgrGIgxw/DtOnw3vvmfu//CW88IKZSywiIiI/TMFY\nxAG++gr+8hdYtgzq1zdh+De/gYYN7a5MRETEeygYi3gptxuSkuCtt8yqEi1awF//CiNHQr16dlcn\nIiLifRSMRbyMZcHy5SYE79ljNuf44AOzQUct/USLiIhcMy3XJuIlsrPhT3+Cli3hySehSRMzUrxz\nJwwbplAsIiJyvfSnVKSGS02Fv/0NFi0y9594Ap5/3mzSISIiIjeOgrFIDeRymc03ZsyAzz6D8HCY\nNAl+9StdUCciIlJVFIxFapAzZ2D+fJg1C44cge7dzUjxo49CUJDd1YmIiDibgrFIDbB/P8ycCYsX\nm4vrHnkEliyBHj3Az8/u6kRERHyDgrGITSwLPv4Y3n0XNm0yF9ONGwejRpmpEyIiIlK9FIxFqtmx\nY2ZnunnzIDMTYmLMxhxDhmi6hIiIiJ0UjEWqQXExrF4Nc+fCmjVmd7qnnoLRo7W6hIiISE2hYCxS\nhY4eNSPDCQlw8iR062ZGi4cNgx/9yO7qRERE5HIKxiI3WGEhJCaa0eGkJLj5Zvj5z+GXvzS71ImI\niEjNpGAscoPs3m2WWnv/fcjNhehoc/+RRzQ6LCIi4g0UjEWuQ3a2CcLz58OePdC4McTHwy9+obnD\nIiIi3kbBWKSSCgvNBXSLF8OqVeB2w8CB8NprcP/9EBhod4UiIiJyLRSMRSrA7YatW80udMuXw+nT\nZr7wG2+Y+cO33GJ3hSIiInK9FIxFfsDBg7B0qZkucfgwRESYDTieeAI6dbK7OhEREbmRFIxFrvD9\n92bDjaVLzQV1N98McXEwZw7cey/4+9tdoYiIiFQFBWMRICPDbM+8bBl8+SXUrm3mDU+aBLGxUKeO\n3RWKiIhIVVMwFp9VGob/+U/YtAn8/OC++2DhQhg82IwUi4iIiO9QMBafcuIEfPJJ2TDcp4/ZjW7w\nYAgLs7tCERERsYuCsTjeN9/AypXm2LoVatVSGBYREZHyFIzFcVwu2LXLbMu8ciV8/TXcdJNZY3jR\nIhgwAEJD7a5SREREahoFY3GECxfg88/h00/NcfIkhISYC+imToV+/aBuXburFBERkZpMwVi81vff\nw9q1sHo1JCWZcBwZCcOGmUDcs6d2oRMREZGKUzAWr2FZsHmz2Y557VrYvx8CAiA6GqZMMWG4XTtz\nQZ2IiIhIZSkYS43ldpvd5pKSYP16M1UiPx+aNDHzhV99Ffr2NVMmRERERK6XgrHUKLm5sGGDCcJJ\nSXDsmFlFIioKJkyABx6AO+/U7nMiIiJy4ykYi63OnjU7zaWkmEC8Z48ZKW7fHh580IwIx8RA/fp2\nVyoiIiJOp2As1ersWTNPeNMmE4Z37DDLq0VEQO/e8Pzz5rZZM7srFREREV+jYCxV6tQpMyK8aZO5\nLR0RbtIE7r0Xnn4aevWCNm100ZyIiIjYS8FYbpjiYrOZxpYt8O9/m9sjR8xjrVvDz34Gzz1nbhWE\nRUREpKZRMJZrduIEbNsG27ebrZa3bTOrRtSqBV27mh3moqLgnnvg1lvtrlZERETkhykYS4VkZcHu\n3WZO8PbtJgSfOGEeCw+Hn/wEXnnFBOHu3c0WzCIiIiLeRMFYynC7IT3dhOBduy7dpqebx4ODTfB9\n6in48Y9NIG7a1N6aRURERG4ERwZjy7KYP38+SUlJ5Ofn07p1a0aOHEm3bt3sLq1GOXfOzAnetw/2\n7r10e/asebxhQ7jrLhg+3EyNuOsuM1dYawiLiIiIEzkyGL/xxhts2rSJhx9+mIiICNauXcuECRN4\n++236dKli93lVbucHEhNhQMHLt0eOHBpFDggwKwb3KWL2UCjSxeziUbTps69QG7p0qUMGzbM7jKk\nmqjfvkX99i3qt9xIjgvGqamppKSkMHr0aIYOHQpA3759iY+PZ86cOcycOdPmCqvGuXPw7bdw6JA5\nvvnm0se5ueY5/v4QGQkdO8ITT0CHDnD77SYU165tb/3VTb9IfYv67VvUb9+ifsuN5LhgvHHjRgIC\nAhg4cKDnXFBQELGxsfzjH/8gKyuLRo0a2VjhtTl/Hr7/3hzHjpkQXHocPQrZ2Zee26ABtG1rjv79\nzW3HjnDbbb4XgEVEREQqynHB+PDhw0RERHDTFcsitGvXzvN4TQrGbrcZ0T150qzycOLEpY9Lg/Dx\n42WDr5+f2RmudWvo3Nlsndyqlbnfti2Ehdn39YiIiIh4K8cF45ycHMKukgxLz+Xk5FTZa7vdcOGC\nCbpXHjk5ZsmzzExzXP6xZV1Zq1kCrVkzswJEXBw0b27uN29utk8OCqqyL0NERETEJzkuGFuWRWBg\nYLnzQf+fJAsLC6/6OQAJCamsWgVFRZcOy4KLF8sfFy5AQYHZ0KKg4NJRUnL1uurVM1McQkPNbcuW\nZpWH0FBo1OjSERb2w6H3zBlzyPU5e/Ysu3btsrsMqSbqt29Rv32L+u07UlNTgUu5rSo4LhgHBQVR\nVFRU7nzp/8TaV5lkm5GRAcCsWcOrrK78fHMcP15lLyGVpOX7fIv67VvUb9+ifvuWjIwMOnfuXCX/\nbccF47CwMLIvn5D7/0qnUFxtmkX37t15+eWXadKkiWdkWURERERqDsuyyMjIoHv37lX2Go4LxpGR\nkXz11VecP3+eunXres6XDr9HRkaW+5yQkBDuu+++aqtRRERERCqvqkaKSzluD7OYmBhcLherVq3y\nnLMsi3Xr1tGxY8catSKFiIiIiNQcjhsx7tChAzExMcydO5fTp09z66238tlnn5GZmclLL71kd3ki\nIiIiUkP5paSkuO0u4kazLIuEhASSk5PJy8ujTZs2xMfHV+mcFBERERHxbo4MxiIiIiIileW4OcYi\nIiIiItfCcXOMK8qyLObPn09SUhL5+fm0bt2akSNHai1EL5eWlsZnn33G7t27OXXqFMHBwXTo0IGR\nI0cSERFR5rnfffcds2bN4uuvvyYwMJAePXowZswYgoODbapeboQlS5aQkJBAy5YtSUhIKPOYeu4M\nhw4dYuHChXz99ddYlkV4eDgDBgwgLi7O8xz12hkOHjzIokWLOHjwIAUFBdxyyy306dOHRx99tMy+\nBOq3d7lw4QLLli0jNTWVtLQ08vPzGT9+PPfff3+551amt6tXr+bDDz8kIyODW265hbi4OIYMGVKp\n2gJGjBgx+Rq/Lq82bdo01q5dy8CBA+nbty9Hjhxh+fLldO3alcaNG9tdnlyjGTNmsHPnTqKjo7n/\n/vtp1qwZGzduZOXKlURHRxMaGgpAVlYWzzzzDJZlMXz4cNq2bcv69evZvHkzsbGx+PvrzRRvlJWV\nxdSpUwkMDKR+/foMGjSozGPquffbvn07L774IvXq1WPQoEH07NmTkJAQzp8/7xnYUK+d4dtvv2XM\nmDFYlsXgwYO55557cLvdrFixgqNHj9K7d29A/fZGOTk5vPrqq5SUlNCiRQsyMjK4++67yy2pW5ne\nJiYmMn36dO68807i4uJwuVwsXbqUoKAgunTpUuHafHLEODU1lZSUFEaPHs3QoUMB6Nu3L/Hx8cyZ\nM4eZM2faXKFcq6FDh9K+fXsCAgI853r16kV8fDxLly5l4sSJgBlVLCwsZO7cuZ4l/Nq3b8+4ceNY\nt24dAwYMsKV+uT7vvvsunTp1oqSkhLNnz5Z5TD33fgUFBbz++utERUUxZcqU//o89doZNmzYQHFx\nMa+//jotWrQAoH///rjdbtavX09+fj716tVTv71QWFgYK1asIDQ0lIMHD/LrX//6qs+raG8LCwuZ\nN28eUVFRTJ48Gbj0vbJ48WIGDhxIvXr1KlSbT/4zauPGjQQEBDBw4EDPuaCgIGJjYzlw4ABZWVk2\nVifXo1OnTmVCMUDTpk1p0aIFxy/bj/vLL78kKiqqzLrW3bp1IyIigi+++KK6ypUbaM+ePWzatIkx\nY8bgdrvx8/Mr87h67v0+//xzzpw5w8iRIwHzdqzL5Sr3PPXaGUqnSoSEhJQ536BBA/z9/QkMDATU\nb28UGBjoeQf3h1S0t7t37yYvL6/Mu4QAgwcP5uLFi2zZsqXCtflkMD58+DARERHcdNNNZc63a9fO\n87g4h9vt5vTp0575SFlZWZw5c8bT78u1b9+eb775prpLlOtUUlLC3//+d/r370+rVq3KPa6eO8PO\nnTupW7cuWVlZPPnkk/Tv358BAwYwffp0LMsC1GsneeCBBwgNDeWtt97i8OHDZGZmsmHDBhITE4mL\ni6N27drqt4NVprelue3K59522234+flx5MiRCr+uT06lyMnJISwsrNz50nM5OTnVXZJUoeTkZHJy\ncjyjTLm5uYAZdbhSgwYNyMvLo7i4mFq1fPLHwyslJiaSmZlJfHz8VR9Xz50hPT2dkpISJk2aRP/+\n/Rk1ahS7d+9m5cqV5Ofn88orr6jXDtKwYUNmzJjBhAkTGDVqlOf88OHDPT/r6rdzVaa3OTk5+Pv7\nl7sgLzAwkODgYLKzsyv8uj75nWJZluctmMsFBQUBZq6KOMPx48d555136NSpE/369QMu9be035e7\n/HtAv0i9w9mzZ1mwYAFPPvnkf70CXT13hosXL1JYWMiDDz7Is88+C8Ddd99NcXExn376KfHx8eq1\ng+Tm5jJhwgQAXnjhBYKDg9myZQvvv/8+oaGhDBkyRP12sMr0trCw8Kq5Dkw4Ln1HqSJ88jslKCiI\noqKicudL/8ddvgSMeK/c3Fx+//vfU69ePaZMmeKZc1ra36v9oOh7wPskJCQQHBxcZqmuK6nnzlD6\nx7BPnz5lzvfu3ZtPP/2UAwcO0Lx5c0C9doLFixeTlZXF4sWLadiwIWD+IeRyuXjvvffo06ePfrYd\nrDK9rV279lVzXelzrxau/xufnGMcFhZ21WH10ikUV5tmId4lPz+fl156iYKCAt58880yb8WUflz6\nNs3lcnNzufnmmzW64CXS09NZtWoVQ4YMISsri4yMDDIyMrAsi6KiIjIyMsjLy1PPHaI0HF150U7p\n/by8PM/vb/Xa++3bt4/bbrvN0/dS0dHRFBYWcvjwYf1sO1hlehsWFobL5Sq3GlFRURHnzp0r9z30\nQ3wyGEdGRpKens758+fLnE9NTfU8Lt7Lsixefvll/vOf/zBt2jTPCFKpRo0aERISQlpaWrnPTUtL\nU/+9SHZ2Nm63mxkzZvD44497jrS0NNLT03n88cdZtGiReu4Qbdu2BSi3clDpQEdISAgNGzZUrx2i\nuLj4qquOFBcXA+aiW/1sO1dlelv68ZXPPXjwIG63mzZt2lT4dX0yGMfExOByuVi1apXnnGVZrFu3\njo4dO5ZZFkS8S0lJCVOmTCE1NZXJkyfTsWPHqz7vnnvuYevWrWX+wO7cuZP09HRiYmKqq1y5Tq1a\ntWLq1Km89tprnmPq1Km0bNmSxo0b89prrxEbGwuo507Qq1cvANasWVPm/OrVq6lVqxZ33nknoF47\nRdu2bTl06BDp6ellzm/YsAF/f39P2FG/nauivb3rrruoX78+iYmJZT4/MTGROnXqEBUVVeHX9Mmd\n7xo1asSxY8f45JNPuHDhAidPnmT27NkcP36ciRMnauc7LzZ79mySkpLo0aMH4eHhfPvtt2WO0l+k\nkZGRrFmzhpSUFPz8/Ni1axezZs0iIiKCsWPHaqckL1GnTh2aN29e7khJScHlcvHcc8953mZXz71f\ngwYNyMzMZP369Xz33XecPn2aZcuWsXHjRoYNG0bPnj0B9dopbr31VtauXUtycjKWZXH06FEWLFjA\n1q1biY2N9cw1V7+908qVK9mxYwd79uzh0KFD+Pv7k56ezt69e4mMjCQoKKjCvQ0ICKBu3bp89NFH\nHD16lIKCAlasWEFycjIjRozw7IpZEX4pKSnuqvqiazLLskhISCA5OZm8vDzatGlDfHw83bt3t7s0\nuQ5jx45l7969uN3lv639/Pz4/PPPPfePHTvG7Nmz2bdvH0FBQfTo0YNnnnmm3GLy4n3Gjh3LuXPn\nmDdvXpnz6rn3KykpYcmSJaxbt46cnByaNGnCoEGDeOihh8o8T712htTUVBYsWMD+/fuxLIvw8HD6\n9evHY489Vibwqt/eZ9iwYZw6dQrAc3F86eZMH3zwgWeQsjK9Xb16NR9++CEnT56kcePGDB48uNzv\nhv/FZ4OxiIiIiMjl9P6CiIiIiAgKxiIiIiIigIKxiIiIiAigYCwiIiIiAigYi4iIiIgACsYiIiIi\nIoCCsYiIiIgIoGAsIiIiIgIoGIuIiIiIAArGIiIiIiKAgrGIiIiICKBgLCIiIiICwP8BB8jGKNNh\nV9kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa029b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#This function sets the matlibplot back to its defaults.\n",
    "matplotlib.pyplot.rcdefaults()\n",
    "pylab.plot(arange(1,100)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Q1ATWNALrnQ8nxw9NA+pTLKm784l+cGfBS8BRzofvj78PpxFNHEiV8+EH4o8T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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9bd8a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.plot(arange(10)**2)\n",
    "ax.set_xlabel('X-axis')\n",
    "ax.set_ylabel('Y-axis')\n",
    "\n",
    "#ax.spines['bottom'].set_color('lightblue')\n",
    "#ax.spines['top'].set_color('lightblue')\n",
    "\n",
    "#Change the color of the X-axis \n",
    "ax.xaxis.label.set_color('lightblue')\n",
    "ax.tick_params(axis='x', colors='lightblue')\n",
    "\n",
    "#Change the color of the Y-axis\n",
    "ax.yaxis.label.set_color('lightblue')\n",
    "ax.tick_params(axis='y', colors='lightblue')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/f6nH1w4nFDMz6wgnlBJKvQ039fhG0odSJql/f6nH1460f9lmZjZqnFBKKPU23NTjcx9K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\nJxQzM+sIJxQzM+uI/w96T3GIulz1PAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb625d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mu, sigma = 100, 15\n",
    "x = mu + sigma * np.random.randn(10000)\n",
    "\n",
    "# the histogram of the data\n",
    "n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)\n",
    "\n",
    "\n",
    "plt.xlabel('Smarts')\n",
    "plt.ylabel('Probability')\n",
    "plt.title('Histogram of IQ')\n",
    "\n",
    "plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
    "plt.axis([40, 160, 0, 0.03])\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plotting\"></a>\n",
    "##Simple plotting\n",
    "\n",
    "We can plot a graph of x vs y using\n",
    "\n",
    "    plot(x, y)\n",
    "    \n",
    "If y has several columns, we'll get several lines on our graph. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xb795f28>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sgjzcT0REKmlQ3SfNbCKwbRWfusrdn0nzHq3d/Wsz+w0w0cwWuPu0TIOKiEjt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb33a748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0.0, 2*np.pi, 0.01)\n",
    "y = 1.1 * np.sin(x)\n",
    "pylab.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can omit x, and it will use integers for the x axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xa026320>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TOolI6kvoDsDMKgIfA2cDXwIfAN2cc6vyHHMe0Mc5d56ZtQRGOedaFfBe7je/cSxfrrVd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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9caec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create bar charts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 10 artists>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C1AHAEKIOAIYQdQAwhKgDgCFEHQAM+R9tyRQpFBL6oAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x289dbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.bar(np.arange(len(C)), C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot multiple curves on the same graph. Below the first colum is the blue line and the second column is green."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2db8610>,\n",
       " <matplotlib.lines.Line2D at 0x2db8890>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hKJXAhg30KevyZbGtqRmoM2tkHNLNLDGS37WLJmb46O60TNsKtxujseWwOWP2Tl6ITdfy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\ng2HBMCfPYDAYFgxz8gwGg2HB/H9fza41EatAmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ca2150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.random.randn(20, 2) # 20 rows of 2 columns of mrandom numbers\n",
    "pylab.plot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = \"loops\"></a>\n",
    "# Loops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexes are useful, but lists really shine when you start looping.\n",
    "\n",
    "Loops let you do something for each item in a list. They are kind of like if statements because they have an indented block.\n",
    "\n",
    "They look like this:\n",
    "\n",
    "    `for item in list:\n",
    "        print(item) # Do any action per item in the list`\n",
    "\n",
    "\"for\" and \"in\" are required. \"list\" can be any variable or literal which is like a list. \"item\" is the name you want to give each item of the list in the indented block as you iterate through. We call each step where item has a new value an iteration.\n",
    "\n",
    "Let's see it in action with our list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eric Idle\n",
      "Albert O'Connor\n",
      "John Cleese\n"
     ]
    }
   ],
   "source": [
    "your_name = \"Albert O'Connor\"\n",
    "awesome_people = [\"Eric Idle\", your_name]\n",
    "awesome_people.append(\"John Cleese\")\n",
    "\n",
    "for person in awesome_people:\n",
    "    print(person)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can also use loops with the builtin function `range()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 squared is 0\n",
      "1 squared is 1\n",
      "2 squared is 4\n",
      "3 squared is 9\n",
      "4 squared is 16\n",
      "5 squared is 25\n",
      "6 squared is 36\n",
      "7 squared is 49\n",
      "8 squared is 64\n",
      "9 squared is 81\n"
     ]
    }
   ],
   "source": [
    "for number in range(0,10):\n",
    "    print(\"{0} squared is {1}\".format(number, number*number))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `enumerate()` can save some time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 4, 3, 8, 8, 4, 7, 4, 3, 9, 7, 5])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L = np.random.randint(3,10,12)\n",
    "for i in enumerate(L):\n",
    "    print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 squared is 0\n",
      "1 squared is 1\n",
      "2 squared is 4\n",
      "3 squared is 9\n",
      "4 squared is 16\n",
      "5 squared is 25\n",
      "6 squared is 36\n",
      "7 squared is 49\n",
      "8 squared is 64\n",
      "9 squared is 81\n"
     ]
    }
   ],
   "source": [
    "for number in range(0,10):\n",
    "    print(\"{0} squared is {1}\".format(number, number*number))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [writing a simple factorial](https://www.youtube.com/watch?v=ZyYp1V84Xqc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter a non-negative integer to take the factorial of:3\n",
      "1\n",
      "2\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "#writing a simple factorial\n",
    "#this will take the input from the user\n",
    "number = input(\"enter a non-negative integer to take the factorial of:\")\n",
    "product = 1\n",
    "for i in range(number):#so this effectively give this index of the number\n",
    "    product = product *(i+1)\n",
    "    'the for loop will increment through the list'\n",
    "    print product\n",
    "    'prints the product'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [stepping through the for loop](https://www.youtube.com/watch?v=dn9XjHz33O8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [recursive factorial](https://www.youtube.com/watch?v=o920mj0NbhE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#recursive factorial\n",
    "def factorial(number):\n",
    "    if number <=1:\n",
    "        'this is our base case'\n",
    "        return 1\n",
    "    else:\n",
    "        return number*factorial(number-1)\n",
    "        \"here the function is reffering to itself \"\n",
    "        #this is actually kind of amazing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factorial(5)\n",
    "#see we can run the program and it finds it recursively\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [fibonacci iterative method](https://www.youtube.com/watch?v=94O_3QCvfqI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#fibonacci iterative method\n",
    "def fibbonacci(n):\n",
    "    terms = [0,1]\n",
    "    'the zeroth term is 0 and the first term is one'\n",
    "    i = 2\n",
    "    #we start i at 2 b/c 0 and 1 are delt with\n",
    "    while i<=n:\n",
    "        terms.append(terms[i-1]+terms[i-2])\n",
    "        i = i +1\n",
    "        'must have this step to break out of the loop'\n",
    "    return terms[n]\n",
    "#I should see what the book has to say about all of this\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "21\n"
     ]
    }
   ],
   "source": [
    "print fibbonacci(0)\n",
    "#lets just try it at zero\n",
    "print fibbonacci(1)\n",
    "#and 1\n",
    "print fibbonacci(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [recursive fibbonacci](https://www.youtube.com/watch?v=urPVT1lymzU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#write a recursive fibbonacci function\n",
    "#you really have to think about the base cases in these functions\n",
    "def fibbonacciR(n):\n",
    "    if n == 0:\n",
    "        return 0\n",
    "    elif n==1:\n",
    "        return 1\n",
    "    else:\n",
    "        return (fibbonacciR(n-1) + fibbonacciR(n-2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "21\n"
     ]
    }
   ],
   "source": [
    "print(fibbonacciR(0))\n",
    "print(fibbonacciR(8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=tightloops></a>\n",
    "## Loops in one line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['02CC', '02FF', '02FC']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex = \"3300033\"\n",
    "rgb = (hex[0:2], hex[2:4], hex[4:6])\n",
    "comp = ['02%X' % (255 - int(a, 16)) for a in rgb]\n",
    "comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['New', 'way', 'to', 'iterate.']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rit = \"New way to iterate.\".split()\n",
    "[\"\" + i for i in rit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('New', 0, 0), ('way', 1, 1), ('to', 2, 2), ('iterate.', 3, 3)]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rit = \"New way to iterate.\".split()\n",
    "X = [\"\" + i for i in rit]\n",
    "tupz = [(rit[i], i, i) for i in range(len(rit))]\n",
    "tupz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>      New</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>      way</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>       to</td>\n",
       "      <td> 2</td>\n",
       "      <td> 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> iterate.</td>\n",
       "      <td> 3</td>\n",
       "      <td> 3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0  1  2\n",
       "0       New  0  0\n",
       "1       way  1  1\n",
       "2        to  2  2\n",
       "3  iterate.  3  3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import DataFrame as df\n",
    "df(tupz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old\n",
      "way\n",
      "to\n",
      "iterate.\n"
     ]
    }
   ],
   "source": [
    "rit = \"Old way to iterate.\".split()\n",
    "for i in rit:\n",
    "    print \"\" + i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'You',\n",
       " 'can',\n",
       " 'change',\n",
       " 'data',\n",
       " 'interate',\n",
       " 'that',\n",
       " 'the',\n",
       " 'through',\n",
       " 'types',\n",
       " 'you'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rit = \"You can change the data types that you interate through\".split()\n",
    "{\"\"+ i for i in rit}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['08', '09', '07', '07', '08', '04', '02', '04', '05', '06', '02']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nipsy = \"89778424562\"\n",
    "[\"0%X\"%int(a) for a in nipsy]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "def funzo(input):\n",
    "    [print(i+2) for i in arange(input)]\n",
    "funzo(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id=\"dicts\"></a>\n",
    "# Dicts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#dir({})\n",
    "d={}\n",
    "d[\"the answer\"] = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[42]\n",
      "['the answer']\n"
     ]
    }
   ],
   "source": [
    "print d.values()\n",
    "print d.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 10, 'b': 10, 'c': 10, 'd': 10, 'e': 10}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D = {}\n",
    "for i in \"abcde\":\n",
    "    D[i] = 10\n",
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#d = D\n",
    "#d.clear()#clears the dict\n",
    "#d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'b': 10, 'c': 10, 'd': 10, 'e': 10}"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.pop(\"a\")#pop drops the key \n",
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.has_key(\"a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We still have 'b' though\n",
      "AND we can use has_key as a conditional.\n"
     ]
    }
   ],
   "source": [
    "if D.has_key(\"b\"):\n",
    "    print \"We still have 'b' though\\nAND we can use has_key as a conditional.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#dir({})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('c', 10)\n",
      "('b', 10)\n",
      "('e', 10)\n",
      "('d', 10)\n"
     ]
    }
   ],
   "source": [
    "#we can use iteritems to iterate through the items\n",
    "for i in D.iteritems():\n",
    "    print i\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "10\n",
      "10\n",
      "10\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "for i in D.itervalues():\n",
    "    print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 2, 'b': 10, 'c': 10, 'd': 10, 'e': 10}"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.update({\"a\":2})\n",
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['a', 'c', 'b', 'e', 'd'])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.viewkeys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id=\"sets\"></a>\n",
    "# Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'hello', 'there'}"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = set()#create a new set\n",
    "s.add('hello')#here we add an element\n",
    "s.add('there')\n",
    "# print the set\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'hello', 'world'}"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can also compute set unions and intersections\n",
    "s2 = set(['hello', 'world'])\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set(['world', 'there', 'hello'])\n"
     ]
    }
   ],
   "source": [
    "#print 'union s with s2', s | s2\n",
    "print s|s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set(['hello'])\n"
     ]
    }
   ],
   "source": [
    "print s&s2#we can also get the intersection of two sets by using the and symbol "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set(['o'])\n",
      "set([])\n"
     ]
    }
   ],
   "source": [
    "print set(\"yo\")&set(\"no\")\n",
    "print set([\"yo\"])&set(\"no\")#however one must be careful to type bractets around any strings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id = \"input\"></a>\n",
    "# Get some user input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def getInput():\n",
    "    return raw_input()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YO\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'YO'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getInput()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id = \"blocks\"></a>\n",
    "# ipythonblocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blocksdd5a9911-8eb5-4eef-843b-504af3f12163 td {border: 1px solid white;}</style><table id=\"blocksdd5a9911-8eb5-4eef-843b-504af3f12163\" class=\"blockgrid\"><tbody><tr><td title=\"Index: [0, 0]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [0, 1]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [0, 2]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [0, 3]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [0, 4]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td></tr><tr><td title=\"Index: [1, 0]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [1, 1]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [1, 2]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [1, 3]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [1, 4]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td></tr><tr><td title=\"Index: [2, 0]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [2, 1]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [2, 2]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [2, 3]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [2, 4]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td></tr><tr><td title=\"Index: [3, 0]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [3, 1]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [3, 2]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [3, 3]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [3, 4]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td></tr><tr><td title=\"Index: [4, 0]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [4, 1]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [4, 2]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [4, 3]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [4, 4]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td></tr></tbody></table>"
      ],
      "text/plain": [
       "<ipythonblocks.ipythonblocks.BlockGrid at 0x3d32b70>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ipythonblocks import BlockGrid as bg\n",
    "grid =bg(5,5,fill= (123,230,123))#can manipulate the color by using fill\n",
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blocks632fff9c-b9b7-4797-8e65-5973f131164c td {border: 0px solid white;}</style><table id=\"blocks632fff9c-b9b7-4797-8e65-5973f131164c\" class=\"blockgrid\"><tbody><tr><td title=\"Index: [0, 0]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td></tr></tbody></table>"
      ],
      "text/plain": [
       "Block(123, 230, 123, size=20)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blocksd0d0973d-c3cd-49b4-8dd9-4fdace278e68 td {border: 1px solid white;}</style><table id=\"blocksd0d0973d-c3cd-49b4-8dd9-4fdace278e68\" class=\"blockgrid\"><tbody><tr><td title=\"Index: [0, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [0, 1]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [0, 2]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [0, 3]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [0, 4]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td></tr><tr><td title=\"Index: [1, 0]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [1, 1]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [1, 2]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [1, 3]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [1, 4]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td></tr><tr><td title=\"Index: [2, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [2, 1]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [2, 2]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [2, 3]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [2, 4]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td></tr><tr><td title=\"Index: [3, 0]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [3, 1]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [3, 2]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [3, 3]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [3, 4]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td></tr><tr><td title=\"Index: [4, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [4, 1]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [4, 2]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td><td title=\"Index: [4, 3]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [4, 4]&#10;Color: (123, 230, 123)\" style=\"width: 20px; height: 20px;background-color: rgb(123, 230, 123);\"></td></tr></tbody></table>"
      ],
      "text/plain": [
       "<ipythonblocks.ipythonblocks.BlockGrid at 0x4370978>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for block in grid:\n",
    "    if block.row % 2 == 0 and block.col%3==0:\n",
    "        block.red=0\n",
    "        block.blue=0\n",
    "        block.green=0\n",
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import webcolors\n",
    "from webcolors import hex_to_rgb as h2rgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#dir(webcolors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 223, 223)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2rgb(\"#20dfdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(143, 85, 54)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2rgb(\"#8f5536\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id = \"pickle\"></a>\n",
    "# Pickling your data\n",
    "- here I want to demonstrate how one can simply store and reload their data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "favorite_color = { \"lion\": \"yellow\", \"kitty\": \"red\" }\n",
    "pickle.dump(favorite_color, open( \"save.pkl\", \"wb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'kitty': 'red', 'lion': 'yellow'}"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "favcol = pickle.load(open(\"save.pkl\", \"rb\"))\n",
    "favcol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id=\"writing\"></a>\n",
    "# Writing data to a file\n",
    "\n",
    "- r Opens a file for reading. This is the default value if mode isn’t specified.\n",
    "- w Opens a file for writing, overwriting an existing file.\n",
    "- a Opens a file for writing in append mode; all write operations are performed at the end. If the file doesn’t exist, it is created.\n",
    "- r+ Opens a file for reading and updating. If the file doesn’t exist, an exception is raised.\n",
    "- w+ Creates a new file for writing and updating, overwriting an existing one.\n",
    "- a+ Opens a file for reading and writing in append mode. All write operations are performed at the end. If the file doesn’t exist, it is created."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id=\"url\"></a>\n",
    "# Loading data from the Internet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import urllib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'>AT3G25730.1|Arabidopsis_thaliana|RAV|AT3G25730.1'"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R = urllib.urlopen(\"http://planttfdb.cbi.pku.edu.cn/download_seq.php?sp=Ath\")\n",
    "R.read().split(\"\\n\")[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id = \"pandas\"></a>\n",
    "# Pandas\n",
    "- this is a breif primer on how to use pandas for other infor on pandas look to the other files with pandas in the title.\n",
    "- [Pandas -- Python doc ](https://pypi.python.org/pypi/pandas/0.7.3)\n",
    "- [Intro to Pandas notebook](http://nbviewer.ipython.org/gist/fonnesbeck/5850375)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import mechanize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The file is already in the ALL_ARATH folder.\n"
     ]
    }
   ],
   "source": [
    "path = \"ALL_ARATH/msb201166-s2.xls\"\n",
    "if os.path.exists(path):\n",
    "    print \"The file is already in the ALL_ARATH folder.\"\n",
    "else:\n",
    "    try:\n",
    "        br = mechanize.Browser(factory=mechanize.RobustFactory())\n",
    "        br.set_handle_robots(False)\n",
    "        br.addheaders = [('User-agent', 'Firefox')]\n",
    "        br.retrieve(\"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3202803/bin/msb201166-s2.xls\",path)[0]\n",
    "        print \"File added\"\n",
    "    except:\n",
    "        print \"Sorry but you will need to use the method below to download the master interactome file.\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "interactome = pd.ExcelFile(\"ALL_ARATH\\msb201166-s2.xls\")\n",
    "inter = interactome.parse(\"All Interactions\")\n",
    "#inter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"dfs\"></a>\n",
    "### Manipulating dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 0.114964</td>\n",
       "      <td> 0.070294</td>\n",
       "      <td> 0.897893</td>\n",
       "      <td> 0.376143</td>\n",
       "      <td> 0.366992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 0.612923</td>\n",
       "      <td> 0.898654</td>\n",
       "      <td> 0.074942</td>\n",
       "      <td> 0.024219</td>\n",
       "      <td> 0.240974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 0.102403</td>\n",
       "      <td> 0.220786</td>\n",
       "      <td> 0.848184</td>\n",
       "      <td> 0.093102</td>\n",
       "      <td> 0.130015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 0.095744</td>\n",
       "      <td> 0.522096</td>\n",
       "      <td> 0.444983</td>\n",
       "      <td> 0.061148</td>\n",
       "      <td> 0.747584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d         e\n",
       "0  0.114964  0.070294  0.897893  0.376143  0.366992\n",
       "1  0.612923  0.898654  0.074942  0.024219  0.240974\n",
       "2  0.102403  0.220786  0.848184  0.093102  0.130015\n",
       "3  0.095744  0.522096  0.444983  0.061148  0.747584"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here is how to make a dataframe of random numbers\n",
    "df = pd.DataFrame(np.random.rand(4,5), columns = list('abcde'))\n",
    "#df.ix[:,'b':]\n",
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 0.673857</td>\n",
       "      <td> 0.486491</td>\n",
       "      <td> 0.095341</td>\n",
       "      <td> 0.969702</td>\n",
       "      <td> 0.606079</td>\n",
       "      <td> 0.566294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 0.142393</td>\n",
       "      <td> 0.818200</td>\n",
       "      <td> 0.811911</td>\n",
       "      <td> 0.741636</td>\n",
       "      <td> 0.739120</td>\n",
       "      <td> 0.650652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 0.732082</td>\n",
       "      <td> 0.528871</td>\n",
       "      <td> 0.667710</td>\n",
       "      <td> 0.718694</td>\n",
       "      <td> 0.982376</td>\n",
       "      <td> 0.725947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 0.243188</td>\n",
       "      <td> 0.968778</td>\n",
       "      <td> 0.875107</td>\n",
       "      <td> 0.596253</td>\n",
       "      <td> 0.507893</td>\n",
       "      <td> 0.638244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 0.688845</td>\n",
       "      <td> 0.413349</td>\n",
       "      <td> 0.807936</td>\n",
       "      <td> 0.678651</td>\n",
       "      <td> 0.328136</td>\n",
       "      <td> 0.583384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> 0.145051</td>\n",
       "      <td> 0.194280</td>\n",
       "      <td> 0.695920</td>\n",
       "      <td> 0.615227</td>\n",
       "      <td> 0.290453</td>\n",
       "      <td> 0.388186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td> 0.122538</td>\n",
       "      <td> 0.552865</td>\n",
       "      <td> 0.238154</td>\n",
       "      <td> 0.873221</td>\n",
       "      <td> 0.362436</td>\n",
       "      <td> 0.429843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td> 0.454810</td>\n",
       "      <td> 0.766246</td>\n",
       "      <td> 0.224121</td>\n",
       "      <td> 0.493584</td>\n",
       "      <td> 0.416837</td>\n",
       "      <td> 0.471120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td> 0.896857</td>\n",
       "      <td> 0.448190</td>\n",
       "      <td> 0.985159</td>\n",
       "      <td> 0.569679</td>\n",
       "      <td> 0.545488</td>\n",
       "      <td> 0.689075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td> 0.872328</td>\n",
       "      <td> 0.015707</td>\n",
       "      <td> 0.752156</td>\n",
       "      <td> 0.928003</td>\n",
       "      <td> 0.216253</td>\n",
       "      <td> 0.556889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4      mean\n",
       "0  0.673857  0.486491  0.095341  0.969702  0.606079  0.566294\n",
       "1  0.142393  0.818200  0.811911  0.741636  0.739120  0.650652\n",
       "2  0.732082  0.528871  0.667710  0.718694  0.982376  0.725947\n",
       "3  0.243188  0.968778  0.875107  0.596253  0.507893  0.638244\n",
       "4  0.688845  0.413349  0.807936  0.678651  0.328136  0.583384\n",
       "5  0.145051  0.194280  0.695920  0.615227  0.290453  0.388186\n",
       "6  0.122538  0.552865  0.238154  0.873221  0.362436  0.429843\n",
       "7  0.454810  0.766246  0.224121  0.493584  0.416837  0.471120\n",
       "8  0.896857  0.448190  0.985159  0.569679  0.545488  0.689075\n",
       "9  0.872328  0.015707  0.752156  0.928003  0.216253  0.556889"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10, 5))#setup a dataframe of random values\n",
    "df['mean'] = df.mean(1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0L, 1L, 2L, 3L, 4L, 'mean']"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = df.columns.tolist()\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mean', 0L, 1L, 2L, 3L, 4L]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = cols[-1:] + cols[:-1]\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 0.566294</td>\n",
       "      <td> 0.673857</td>\n",
       "      <td> 0.486491</td>\n",
       "      <td> 0.095341</td>\n",
       "      <td> 0.969702</td>\n",
       "      <td> 0.606079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 0.650652</td>\n",
       "      <td> 0.142393</td>\n",
       "      <td> 0.818200</td>\n",
       "      <td> 0.811911</td>\n",
       "      <td> 0.741636</td>\n",
       "      <td> 0.739120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 0.725947</td>\n",
       "      <td> 0.732082</td>\n",
       "      <td> 0.528871</td>\n",
       "      <td> 0.667710</td>\n",
       "      <td> 0.718694</td>\n",
       "      <td> 0.982376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 0.638244</td>\n",
       "      <td> 0.243188</td>\n",
       "      <td> 0.968778</td>\n",
       "      <td> 0.875107</td>\n",
       "      <td> 0.596253</td>\n",
       "      <td> 0.507893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 0.583384</td>\n",
       "      <td> 0.688845</td>\n",
       "      <td> 0.413349</td>\n",
       "      <td> 0.807936</td>\n",
       "      <td> 0.678651</td>\n",
       "      <td> 0.328136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> 0.388186</td>\n",
       "      <td> 0.145051</td>\n",
       "      <td> 0.194280</td>\n",
       "      <td> 0.695920</td>\n",
       "      <td> 0.615227</td>\n",
       "      <td> 0.290453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td> 0.429843</td>\n",
       "      <td> 0.122538</td>\n",
       "      <td> 0.552865</td>\n",
       "      <td> 0.238154</td>\n",
       "      <td> 0.873221</td>\n",
       "      <td> 0.362436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td> 0.471120</td>\n",
       "      <td> 0.454810</td>\n",
       "      <td> 0.766246</td>\n",
       "      <td> 0.224121</td>\n",
       "      <td> 0.493584</td>\n",
       "      <td> 0.416837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td> 0.689075</td>\n",
       "      <td> 0.896857</td>\n",
       "      <td> 0.448190</td>\n",
       "      <td> 0.985159</td>\n",
       "      <td> 0.569679</td>\n",
       "      <td> 0.545488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td> 0.556889</td>\n",
       "      <td> 0.872328</td>\n",
       "      <td> 0.015707</td>\n",
       "      <td> 0.752156</td>\n",
       "      <td> 0.928003</td>\n",
       "      <td> 0.216253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean         0         1         2         3         4\n",
       "0  0.566294  0.673857  0.486491  0.095341  0.969702  0.606079\n",
       "1  0.650652  0.142393  0.818200  0.811911  0.741636  0.739120\n",
       "2  0.725947  0.732082  0.528871  0.667710  0.718694  0.982376\n",
       "3  0.638244  0.243188  0.968778  0.875107  0.596253  0.507893\n",
       "4  0.583384  0.688845  0.413349  0.807936  0.678651  0.328136\n",
       "5  0.388186  0.145051  0.194280  0.695920  0.615227  0.290453\n",
       "6  0.429843  0.122538  0.552865  0.238154  0.873221  0.362436\n",
       "7  0.471120  0.454810  0.766246  0.224121  0.493584  0.416837\n",
       "8  0.689075  0.896857  0.448190  0.985159  0.569679  0.545488\n",
       "9  0.556889  0.872328  0.015707  0.752156  0.928003  0.216253"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>mean</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 0.486491</td>\n",
       "      <td> 0.566294</td>\n",
       "      <td> 0.095341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 0.818200</td>\n",
       "      <td> 0.650652</td>\n",
       "      <td> 0.811911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 0.528871</td>\n",
       "      <td> 0.725947</td>\n",
       "      <td> 0.667710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 0.968778</td>\n",
       "      <td> 0.638244</td>\n",
       "      <td> 0.875107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 0.413349</td>\n",
       "      <td> 0.583384</td>\n",
       "      <td> 0.807936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> 0.194280</td>\n",
       "      <td> 0.388186</td>\n",
       "      <td> 0.695920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td> 0.552865</td>\n",
       "      <td> 0.429843</td>\n",
       "      <td> 0.238154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td> 0.766246</td>\n",
       "      <td> 0.471120</td>\n",
       "      <td> 0.224121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td> 0.448190</td>\n",
       "      <td> 0.689075</td>\n",
       "      <td> 0.985159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td> 0.015707</td>\n",
       "      <td> 0.556889</td>\n",
       "      <td> 0.752156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1      mean         2\n",
       "0  0.486491  0.566294  0.095341\n",
       "1  0.818200  0.650652  0.811911\n",
       "2  0.528871  0.725947  0.667710\n",
       "3  0.968778  0.638244  0.875107\n",
       "4  0.413349  0.583384  0.807936\n",
       "5  0.194280  0.388186  0.695920\n",
       "6  0.552865  0.429843  0.238154\n",
       "7  0.766246  0.471120  0.224121\n",
       "8  0.448190  0.689075  0.985159\n",
       "9  0.015707  0.556889  0.752156"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#columns can also be rearranged by writing a list and then indexing the dataframe\n",
    "cols = [1,\"mean\",2]\n",
    "df[cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id = \"mech\"></a>\n",
    "# Mechanize\n",
    "- mechanize cheatsheet](http://www.pythonforbeginners.com/cheatsheet/python-mechanize-cheat-sheet)\n",
    "- [another one](http://dreamrunner.org/wiki/public_html/Python/Python%20Mechanize%20Cheat%20Sheet%20.html)\n",
    "- [Mechanize guide](http://swizec.com/blog/scraping-with-mechanize-and-beautifulsoup/swizec/5039)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<response_seek_wrapper at 0xbb8a188L whose wrapped object = <closeable_response at 0xbb8a208L whose fp = <socket._fileobject object at 0x000000000B9DAE58>>>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import urllib\n",
    "import mechanize\n",
    "br = mechanize.Browser()\n",
    "br.open(\"http://www.cbs.dtu.dk/services/TMHMM/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<test POST http://www.cbs.dtu.dk/cgi-bin/search.cgi application/x-www-form-urlencoded\n",
      "  <TextControl(Words=)>\n",
      "  <ImageControl(<None>=Search)>>\n",
      "<POST http://www.cbs.dtu.dk/cgi-bin/webface2.fcgi multipart/form-data\n",
      "  <HiddenControl(configfile=/usr/opt/www/pub/CBS/services/TMHMM-2.0/TMHMM2CONV.cf) (readonly)>\n",
      "  <FileControl(seqfile=<No files added>)>\n",
      "  <TextareaControl(SEQ=)>\n",
      "  <RadioControl(outform=[*-noshort, -noplot, -short])>\n",
      "  <CheckboxControl(version=[-v1])>\n",
      "  <SubmitControl(<None>=Submit) (readonly)>\n",
      "  <IgnoreControl(<None>=<None>)>>\n"
     ]
    }
   ],
   "source": [
    "for i in br.forms():#Now we will print out each form contained in the page\n",
    "    print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "br.select_form(nr=1)\n",
    "br[\"SEQ\"] = \"MAAAAAAAAAAAAAAFHHTMSVVVVVVVVVVVV\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<test POST http://www.cbs.dtu.dk/cgi-bin/search.cgi application/x-www-form-urlencoded\n",
      "  <TextControl(Words=)>\n",
      "  <ImageControl(<None>=Search)>>\n",
      "<POST http://www.cbs.dtu.dk/cgi-bin/webface2.fcgi multipart/form-data\n",
      "  <HiddenControl(configfile=/usr/opt/www/pub/CBS/services/TMHMM-2.0/TMHMM2CONV.cf) (readonly)>\n",
      "  <FileControl(seqfile=<No files added>)>\n",
      "  <TextareaControl(SEQ=MAAAAAAAAAAAAAAFHHTMSVVVVVVVVVVVV)>\n",
      "  <RadioControl(outform=[*-noshort, -noplot, -short])>\n",
      "  <CheckboxControl(version=[-v1])>\n",
      "  <SubmitControl(<None>=Submit) (readonly)>\n",
      "  <IgnoreControl(<None>=<None>)>>\n"
     ]
    }
   ],
   "source": [
    "for i in br.forms():\n",
    "    print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "G = br.submit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<html>\\n<head>\\n<title>Job status of 54927CA10000221EA32D46F8</span></title></head>\\n<script type=\"text/javascript\" src=\"http://code.jquery.com/jquery-1.10.1.min.js\"></script>\\n<script type=\"text/javascript\" src=\"/js/webface.js\"></script>\\n<script type=\"text/javascript\">\\n$(document).ready(function(){\\n\\tlaunchcheck(\\'queued\\',\\'54927CA10000221EA32D46F8\\',\\'http://www.cbs.dtu.dk/cgi-bin/webface2.fcgi\\',20);\\n});\\n</script>\\n</head>\\n<body>\\n<!-- jobid: 54927CA10000221EA32D46F8 status: queued -->\\n<H1>Your job 54927CA10000221EA32D46F8 is <span name=\"status\">queued</span></H1>\\n<br>\\n\\n<form>\\n\\tSend me email when job finishes\\n\\t<input type=\"hidden\" name=\"jobid\" value=\"54927CA10000221EA32D46F8\">\\n\\t<input type=\"text\" name=\"email\" value=\"\">\\n\\t<input type=\"hidden\" name=\"wait\" value=\"20\">\\n\\t<input type=\"submit\" name=\"submit\" value=\"Send email\">\\n</form>\\n<div id=\"progress\"></div>\\n<noscript>This page should reload automatically. Otherwise <a href=\"http://www.cbs.dtu.dk//cgi-bin/webface2.fcgi?jobid=54927CA10000221EA32D46F8\">click here</a>\\n</noscript>\\n</body>\\n</html>\\n'"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If that does not work for you then try the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<POST http://web.expasy.org/cgi-bin/myristoylator/myristoylator.pl application/x-www-form-urlencoded\n",
      "  <TextareaControl(protein=)>\n",
      "  <SubmitControl(<None>=RUN PREDICTION) (readonly)>\n",
      "  <IgnoreControl(<None>=<None>)>>\n"
     ]
    }
   ],
   "source": [
    "import mechanize\n",
    "br = mechanize.Browser(factory=mechanize.RobustFactory())\n",
    "br.set_handle_robots(False)\n",
    "br.open(\"http://www.expasy.org/proteomics\")\n",
    "response1 = br.follow_link(text_regex=r\"Myristoylator\", nr=1)\n",
    "for i in br.forms():\n",
    "    print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http://www.w3.org/TR/html4/loose.dtd\">\\n<html lang=\"en-US\">\\n  <head>\\n    <meta http-equiv=\"Content-Type\" content=\"text/html;charset=iso-8859-1\"><!-- automatically added by Apache -->\\n    <link rel=\"shortcut icon\" href=\"http://web.expasy.org/favicon.ico\" ><!-- automatically added by Apache -->\\n    <link rel=\"stylesheet\" href=\"/css/sib_css/sib.css\" type=\"text/css\" media=\"screen\" charset=\"utf-8\" >\\n\\t   <link rel=\"stylesheet\" href=\"/css/sib_css/sib_print.css\" type=\"text/css\" media=\"print\" charset=\"utf-8\" >\\n           <!--[if lte IE 6]>\\n\\t   <link rel=\"stylesheet\" href=\"/css/sib_css/sib_ie6.css\" type=\"text/css\" media=\"screen\" title=\"\" charset=\"utf-8\">\\n           <![endif]-->\\n           <link rel=\"stylesheet\" href=\"/css/base.css\" type=\"text/css\"><!-- automatically added by Apache -->\\n<title>ExPASy - Myristoylator</title>\\n  </head>\\n  <body>\\n<!-- ********************************************************************************************** -->\\n<!-- START OF HEADER information on top of the page -->\\n<div id=\\'sib_top\\'><a name=\\'TOP\\'></a></div>\\n<div id=\\'sib_container\\'>\\n\\n<!-- TODO: Change id to \"sib_header\" or \"sib_header_small\" -->\\n<div id=\\'sib_header_small\\'>\\n\\n<!-- SIB-ExPASy -->\\n<div id=\"sib_expasy_logo\">\\n<a href=\"http://www.isb-sib.ch\" id=\"sib_link\" title=\"SIB Swiss Institute of Bioinformatics\"></a>\\n<a href=\"http://www.expasy.org\" id=\"expasy_link\" title=\"ExPASy - SIB Bioinformatics Resource Portal\"></a>\\n</div>\\n<!-- SIB-ExPASy END -->\\n\\n<div class=\\'sib_title_no_logo\\'>\\n<h1><a href=\\'http://web.expasy.org/myristoylator\\'>Myristoylator</a></h1>\\n</div>\\n\\n<! -- Additionnal resource header -->\\n<div id=\\'resource_header\\'></div>\\n\\n<div id=\\'sib_header_nav\\'>\\n<a href=\"http://web.expasy.org/myristoylator\" title=\"Home page\">Home</a>&nbsp;|&nbsp;<a style=\"font-weight: bold; color: rgb(204,0,0) !important;\" href=\"/contact\" title=\"Contact\">Contact</a></div>\\n</div>\\n<!-- END OF HEADER -->\\n<!-- ********************************************************************************************** -->\\n<div id=\\'sib_body\\'>\\n<!-- automatically added by Apache -->\\n<H1>\\n <a href=\"/myristoylator\">Myristoylator</a> </H1><P><HR>\\n<A HREF=\"/myristoylator/myristoylator-doc.html\">Documentation</A> is available.<p>The entered sequence is:<BR>\\n<PRE>\\nMGRSPCCEKA HTNKGAWTKE EDQRLVDYIR NHGEGCWRSL PKSAGLLRCG KSCRLRWINY \\nLRPDL</PRE>\\n\\n<br>This protein is predicted as <b>non-myristoylated</b>:<br><br><table class=\"proteomics2\"><tr><th></th><th>Positive</th><th>Negative</th></tr><tr><td nowrap align=\"center\">Average response of 25 neural networks</td><td align=\"center\">0.00778242</td><td align=\"center\">0.992308</td></tr><tr><td align=\"center\">Counters </td><td align=\"center\">0</td><td align=\"center\">25</td></tr></table>\\n<br><br>\\n<a href=\"/myristoylator/myristoylator-doc.html\"><img src=\"/images/others/help.gif\" alt=\"?\" width=\"12\" height=\"12\" border=\"0\"></a> Score = 0.00778242 - 0.992308 = -0.98452558 \\n<br><br><hr><!-- sib_body -->\\n<br clear=\"left\" />\\n</div>\\n<!-- ********************************************************************************************** -->\\n<!-- START OF FOOTER information at the bottom of the page -->\\n<div id = \\'sib_footer\\'>\\t\\t\\t\\n  <!-- This part of the footer is _left_ justified and should contain SIB on the very left -->\\t\\n  <div id = \\'sib_footer_content\\'> \\n    <a href = \"http://www.isb-sib.ch\">SIB Swiss Institute of Bioinformatics</a>&nbsp;|&nbsp;<a href = \"http://www.expasy.org/disclaimer.html\">Disclaimer</a>\\n\\n\\t<!-- This part of the footer is _right_ justified -->\\n\\t<div id = \"sib_footer_right\">\\t\\n      <a href=\"#TOP\" id = \"sib_footer_gototop\"><span style = \"padding-left: 10px\">Back to the Top</span></a>\\t\\t\\t\\t\\n    </div>\\n  </div>\\n</div>\\n<!-- END OF FOOTER -->\\n</div> <!-- sib_container -->\\n<script type=\"text/javascript\">\\n  var _gaq = _gaq || [];\\n  _gaq.push([\\'_setAccount\\', \\'UA-23625305-1\\']);\\n  _gaq.push([\\'_trackPageview\\']);\\n\\n  (function() {\\n    var ga = document.createElement(\\'script\\'); ga.type = \\'text/javascript\\'; ga.async = true;\\n    ga.src = (\\'https:\\' == document.location.protocol ? \\'https://ssl\\' : \\'http://www\\') + \\'.google-analytics.com/ga.js\\';\\n    var s = document.getElementsByTagName(\\'script\\')[0]; s.parentNode.insertBefore(ga, s);\\n  })();\\n\\n</script><!-- automatically added by Apache -->\\n\\n    </body>\\n</html>'"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc = \"MGRSPCCEKAHTNKGAWTKEEDQRLVDYIRNHGEGCWRSLPKSAGLLRCGKSCRLRWINYLRPDL\"\n",
    "br.select_form(nr=0)\n",
    "br[u\"protein\"] = loc\n",
    "G = br.submit()\n",
    "R = G.read()\n",
    "R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Use the built in index function to find what you want in your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "non-myristoylated\n",
      "Score = 0.00778242 - 0.992308 = -0.98452558\n"
     ]
    }
   ],
   "source": [
    "s0 = R.index(\"Score\")\n",
    "\n",
    "s1 = R.index(\" \\n<br><br><hr><!-- sib_body -->\")\n",
    "\n",
    "G = R[s0:s1]\n",
    "\n",
    "r0 = R.index(\"predicted as\")\n",
    "\n",
    "r1 = R.index('</b>:<br><br><table class=\"proteomics2\"><tr><th></th><th>Positive')\n",
    "\n",
    "G1 = R[r0:r1]\n",
    "\n",
    "G1 = R[r0+16:r1]\n",
    "\n",
    "print G1+\"\\n\"+G"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using mechanize to download files online"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cooool.xls'"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "br = mechanize.Browser(factory=mechanize.RobustFactory())\n",
    "br.set_handle_robots(False)\n",
    "br.addheaders = [('User-agent', 'Firefox')]\n",
    "br.retrieve(\"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3202803/bin/msb201166-s2.xls\",\"cooool.xls\")[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id=\"bio\"></a>\n",
    "# BIOPYTHON\n",
    "- [biopython tutorial](http://biopython.org/DIST/docs/tutorial/Tutorial.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id = \"rand\"></a>\n",
    "# Random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random as rand\n",
    "\n",
    "rand.randint(0,10)#returns a random int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4863629132098982"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand.random()#returns random float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'whatever'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random as rand\n",
    "rand.choice(\"I can put whatever I want to in here\".split())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id=\"Webcolors\"></a>\n",
    "# Webcolors\n",
    "\n",
    "<a id = \"CSS\"></a>\n",
    "### [ipython blocks](http://nbviewer.jupyter.org/github/jiffyclub/ipythonblocks/blob/master/demos/ipythonblocks_demo.ipynb)\n",
    "### [Hexidecimal on wikipedia](https://en.wikipedia.org/wiki/Hexadecimal)\n",
    "### [CSS3 colors names and color codes](http://www.cssportal.com/css3-color-names/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blocks13739b74-0818-4b00-b15d-09bd16682d6f td {border: 1px solid white;}</style><table id=\"blocks13739b74-0818-4b00-b15d-09bd16682d6f\" class=\"blockgrid\"><tbody><tr><td title=\"Index: [0, 0]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [0, 1]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [0, 2]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [0, 3]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [0, 4]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td></tr><tr><td title=\"Index: [1, 0]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [1, 1]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [1, 2]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [1, 3]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [1, 4]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td></tr><tr><td title=\"Index: [2, 0]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [2, 1]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [2, 2]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [2, 3]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [2, 4]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td></tr><tr><td title=\"Index: [3, 0]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [3, 1]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [3, 2]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [3, 3]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [3, 4]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td></tr><tr><td title=\"Index: [4, 0]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [4, 1]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [4, 2]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [4, 3]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td><td title=\"Index: [4, 4]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td></tr></tbody></table>"
      ],
      "text/plain": [
       "<ipythonblocks.ipythonblocks.BlockGrid at 0x96a5e10>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import webcolors as wc\n",
    "\n",
    "my_color = wc.name_to_rgb(\"Salmon\")\n",
    "\n",
    "from ipythonblocks import BlockGrid as bg\n",
    "\n",
    "grid =bg(5,5,fill= my_color)#can manipulate the color by using fill\n",
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blocksd65187ea-e8ea-40de-a7e4-0a8d8da49592 td {border: 1px solid white;}</style><table id=\"blocksd65187ea-e8ea-40de-a7e4-0a8d8da49592\" class=\"blockgrid\"><tbody><tr><td title=\"Index: [0, 0]&#10;Color: (255, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(255, 0, 0);\"></td><td title=\"Index: [0, 1]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [0, 2]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [0, 3]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [0, 4]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [1, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [1, 1]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [1, 2]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [1, 3]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [1, 4]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [2, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [2, 1]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [2, 2]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [2, 3]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [2, 4]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [3, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [3, 1]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [3, 2]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [3, 3]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [3, 4]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [4, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [4, 1]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [4, 2]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [4, 3]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [4, 4]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [5, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [5, 1]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [5, 2]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [5, 3]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [5, 4]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [6, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [6, 1]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [6, 2]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [6, 3]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [6, 4]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [7, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [7, 1]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [7, 2]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [7, 3]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [7, 4]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [8, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [8, 1]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [8, 2]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [8, 3]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [8, 4]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [9, 0]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [9, 1]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [9, 2]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [9, 3]&#10;Color: (0, 0, 0)\" style=\"width: 20px; height: 20px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [9, 4]&#10;Color: (250, 128, 114)\" style=\"width: 20px; height: 20px;background-color: rgb(250, 128, 114);\"></td></tr></tbody></table>"
      ],
      "text/plain": [
       "<ipythonblocks.ipythonblocks.BlockGrid at 0x96ad470>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ipythonblocks import BlockGrid\n",
    "my_grid = BlockGrid(5,10)\n",
    "my_grid[0,0] = (255, 0, 0)\n",
    "my_grid[-1,-1] = my_color\n",
    "my_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id=\"time\"></a>\n",
    "# Time\n",
    "- Get the posix time for any given moment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1430374478.232946"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id= \"class\"></a>\n",
    "# Classes\n",
    "- [intro to python classes](http://introtopython.org/classes.html)\n",
    "- [classes and mtaclasses](http://nbviewer.ipython.org/github/jakevdp/jakevdp.github.com/blob/master/downloads/notebooks/MetaClasses.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id = \"java\"></a>\n",
    "## Java resources\n",
    "\n",
    "* [Java for Python programmers](http://interactivepython.org/courselib/static/java4python/index.html)\n",
    "* [From Python to Java](http://home.wlu.edu/~lambertk/pythontojava/index.htm)\n",
    "* [Python and Java side by side](http://pythonconquerstheuniverse.wordpress.com/2009/10/03/python-java-a-side-by-side-comparison/)\n",
    "* [Python to Java tutorial](http://www.csc.uvic.ca/~labspg/Reference/PytoJava/)\n",
    "* [Getting started with Eclipse](http://eclipsetutorial.sourceforge.net/totalbeginner.html)\n",
    "* [Videos from Dewan's offering of 401](https://www.youtube.com/playlist?list=PLH5XTBxCO2hx5lrFiPsQ7M3MIf4N1eV5s)\n",
    "\n",
    "GB recommends this one; [The Python Paradox](http://paulgraham.com/pypar.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id = \"jython\"></a>\n",
    "# Jython\n",
    "\n",
    "- [pretty good from the book on jython](http://www.jython.org/jythonbook/en/1.0/index.html#part-iii-developing-applications-with-jython)\n",
    "- [servlets](http://www.jython.org/jythonbook/en/1.0/SimpleWebApps.html#servlets)\n",
    "- []()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id = \"module\"></a>\n",
    "# Modules\n",
    "\n",
    "- [Modules -- Python doc](https://docs.python.org/2/tutorial/modules.html)\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "<a id=\"misc\"></a>\n",
    "# Misc.Info\n",
    "[FIXME: incorporate into helpful links]\n",
    "- 90px = 1in\n",
    "- [Calendar module](https://docs.python.org/2/library/calendar.html)\n",
    "- [Python for Biologists](http://pythonforbiologists.com/index.php/profiling-execution-time-in-python/)\n",
    "- [Bluecollar Bioinformatics](http://bcb.io/2009/01/10/extracting-protein-characteristics-for-an-interpro-domain/)\n",
    "- [deep learning reading list](http://deeplearning.net/reading-list/)\n",
    "\n",
    "- [Wikipedia on the Elliot Wave Priciple](http://en.wikipedia.org/wiki/Elliott_wave_principle)\n",
    "- back to [TOC](#TOC)\n",
    "\n",
    "---\n",
    "\n",
    "# Python docs\n",
    "[FIXME: incorporate into helpful links]\n",
    "- [graphviz Python doc](https://pypi.python.org/pypi/graphviz)\n",
    "- [time](https://docs.python.org/2/library/time.html)\n",
    "- [Classes](https://docs.python.org/2/tutorial/classes.html)\n",
    "- [re operations](https://docs.python.org/2/library/re.html#re.DOTALL)\n",
    "- [urlib](https://docs.python.org/2/library/urllib.html)\n",
    "- [random](https://docs.python.org/2/library/random.html)\n",
    "- [Pickle](https://docs.python.org/2/library/pickle.html)\n",
    "- [pptx](https://pypi.python.org/pypi/python-pptx)\n",
    "- [pptx doc](https://python-pptx.readthedocs.org/en/latest/index.html)\n",
    "\n",
    "---\n",
    "\n",
    "## links \n",
    "[FIXME: incorporate into helpful links]\n",
    "- [School of Data](http://schoolofdata.org/blog/)\n",
    "- [Allen Downing there is only one test](http://allendowney.blogspot.com/2011/05/there-is-only-one-test.html)\n",
    "- [Active State Python Recipies](http://code.activestate.com/recipes/langs/python/)\n",
    "- [Biopython](http://biopython.org/wiki/Main_Page)\n",
    "- [Orange computer learning](http://orange.biolab.si/)\n",
    "- [pybrain](http://pybrain.org/pages/features)\n",
    "\n",
    "---\n",
    "\n",
    "## Blogging with Ipython\n",
    "   - [Fenando Perez's original article on blogging with ipython](http://blog.fperez.org/2012/09/blogging-with-ipython-notebook.html) \n",
    "   - [jakevdp's github about ipy blogging](http://nbviewer.ipython.org/github/jakevdp/jakevdp.github.com/blob/master/downloads/notebooks/nb_in_octopress.ipynb)\n",
    "   - [jakevdp's github](http://jakevdp.github.io/)\n",
    "   - So if you wanna publish a notebook file you have to put that file in a github repository. Once that have been published to github then you can use [nbviewer](http://nbviewer.ipython.org/) to view it in the broswer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "\n",
    "# [MARKOV CHAIN IN IPYNOTEBOOK](http://nbviewer.ipython.org/gist/fonnesbeck/2352771)\n",
    "   - [here is the blog that it is from](http://nbipython.blogspot.com/2012/11/ipython-notebook-examples.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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