class-material/basic_matplotlib_plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# this 'magic' statement turns on inline plots\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# numpy = numerical routines in python.\n",
    "import numpy as np\n",
    "# IMPLICIT: import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'module' object has no attribute 'ran'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-52-829c560fff7f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# start typing - type np.ran , then hit TAB\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mran\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'module' object has no attribute 'ran'"
     ]
    }
   ],
   "source": [
    "# I remember there is a routine in numpy to sample \n",
    "# from a normal distribution but I'm fuzzy on the details\n",
    "\n",
    "# start typing - type np.ran , then hit TAB\n",
    "np.ran"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5990154808013614"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ok so it's called np.random.normal\n",
    "# but what arguments does it expect?\n",
    "# type np.random.normal( ), put the cursor between the () and hit shfit-TAB\n",
    "np.random.normal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ok, loc --> mean\n",
    "#     scale --> stdev\n",
    "#     size --> how many values to sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# let's make up a 'dataset'\n",
    "x = np.random.normal( loc=0, scale=10, size=100 )\n",
    "y = np.random.normal( loc=20, scale=2, size=100 )\n",
    "z = 3*y + x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x:  [  6.69853385  -2.50874381  -8.44707205  10.31223915   1.6966581\n",
      " -14.33105211  10.38990066   5.66658846 -15.03857279  -5.16532647]\n",
      " \n",
      "y:  [ 19.82088259  14.23417709  18.67634032  22.83080509  22.66793983\n",
      "  20.55595857  23.22643503  20.63968742  19.931432    23.951746  ]\n"
     ]
    }
   ],
   "source": [
    "# let's look at some of these\n",
    "print 'x: ', x[0:10]\n",
    "print ' '\n",
    "print 'y: ',y[0:10]\n",
    "\n",
    "# makes sense - X ~ N(0,100), Y ~ N(20,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x mean:  0.0820478565308\n",
      "x std:  9.9856477737\n",
      "  \n",
      "y mean:  20.1839502205\n",
      "y std:  2.28637028247\n",
      "  \n",
      "z mean:  60.633898518\n",
      "z std:  11.6794827397\n"
     ]
    }
   ],
   "source": [
    "# numpy as lots of routines to manipulate arrays, e.g., taking mean\n",
    "print 'x mean: ',np.mean(x)\n",
    "print 'x std: ',np.std(x)\n",
    "print '  '\n",
    "print 'y mean: ',np.mean(y)\n",
    "print 'y std: ',np.std(y)\n",
    "print '  '\n",
    "print 'z mean: ',np.mean(z)\n",
    "print 'z std: ',np.std(z)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x4b21750>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Z8GngJUlLoJMOqHZQYYRatbLfVSN31y7a4JD+8ShK7aVe2nOoKmtvSLl20NeBB4DD3P3F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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x46f0e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first plot: scatterplot\n",
    "plt.scatter( x, y )\n",
    "plt.title('x versus y')\n",
    "plt.xlabel(' X ~ N(0,100)')\n",
    "plt.ylabel(' Y ~ N(20,4)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x4af8c50>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5MjIyknQSYqX8ZZvyly+xBQF3f9rd7w/fvwTcB+wAHAJcFR52NXBoXGlIq3b/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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4b3a8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first plot: scatterplot\n",
    "plt.scatter( x, z )\n",
    "plt.title('x versus z')\n",
    "plt.xlabel(' X ~ N(0,100); Y ~ N(20,4)')\n",
    "plt.ylabel(' Z = 3*Y+X ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x4d7fb50>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KeDKPGCqsni8lLenMqs33A/8CiGQs6tlTZOYA4Dzgd8DLgZuADwD3RcRmVcf9oXq7ar+D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\nJfb/ASIpeWXxV7XBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4d67dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# you can change color\n",
    "plt.scatter( x, z, color='red' )\n",
    "plt.title('x versus z')\n",
    "plt.xlabel(' X ~ N(0,100); Y ~ N(20,4)')\n",
    "plt.ylabel(' Z = 3*Y+X ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x5943990>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JP5P083Sg2dMyn50v6UZJP5U0Z5D57JSkT0n6Rfr6pqRdM599MN2/XtIrB5nP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5914190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# you can change the marker style\n",
    "#  -->  http://matplotlib.org/api/markers_api.html#module-matplotlib.markers\n",
    "plt.scatter( x, z, marker='x', color='blue' )\n",
    "plt.title('x versus z')\n",
    "plt.xlabel(' X ~ N(0,100); Y ~ N(20,4)')\n",
    "plt.ylabel(' Z = 3*Y+X ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x5a41cd0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dxWSP9Voxeaej9nDxs8vVTTjLkZu81BHbHUzHYI2dxhiT5xIJ5JZa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x591cd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# multiple calls on one plot\n",
    "plt.scatter( x, z, marker='x', color='blue', label='Z versus X' )\n",
    "plt.scatter( x, y, marker='o', color='green', label='Y versus X' )\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7604150>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UHLtdsVN85ez29YQdPQ0NipcuIYvmQYRcEOKQzMzQV1/W1rYU/cLClo9SEy89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\nLuPUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x67b2210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# adjacent plots\n",
    "my_figure, my_axes = plt.subplots( 1, 2, sharey=True, sharex=True )\n",
    "my_axes[0].scatter( x, y, marker='x', color='green', label='Y versus X' )\n",
    "my_axes[1].scatter( x, z, marker='x', color='blue', label='Z versus X' )\n",
    "\n",
    "my_axes[0].legend()\n",
    "my_axes[1].legend()\n",
    "\n",
    "# what happens if you turn off sharex or sharey (set to =False)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# line plot\n",
    "# let's make a sorted version of the Y values\n",
    "ysorted = np.sort(y)\n",
    "# let's make an array of 0....|Y|  (i.e., ranks)\n",
    "a = np.arange(0, y.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x78ace50>]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6f51290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot( a, ysorted )\n",
    "# what happens if you has passed in y instead of ysorted?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kr02yv3t9N8kXp13TiWYf+gQk+QPggcCDgNuq6v1TLkkjSPKzwKeq6pwk9wO+BZxbVXdO\nuTRtQZIdwBeB91fV56Zdz4k0rlEu+mm/D/wT8CPgd6Zci0ZUVbcmOZrkF1iZUHejYT6XLgW+sGhh\nDgb6pJwCPAS4Pyut9B9ufLhmyEdZGWp7GvDxKdeiLUryCuCMqnrdtGuZBrtcJiDJ3wJXAI8FHlVV\nttLnRJKTgAOs/DB+XPkPZG4kOQf4E+BZVXXXlMuZClvoY5bk14GfVNWVXT/sPyQZVNVwyqVpBFV1\nd3cz7U7DfO78NrAT+FISgK9X1WumW9KJZQtdWiUrSfAN4KKq+va065G2wmGLUqcbXnoLMDTMNY9s\noUtSI2yhS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEb8L+ctg5hUVwYBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6f25d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plots\n",
    "_ = plt.boxplot( x=[ x,y,z ], labels=['x','y','z'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xada0a50>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2+6/37PXXnl67r/FaJq3/3P2fvLxPrdP6n3SsV6uvqnrUv/qxmGX5LPva/3HbvOXTapml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\nkhpiqEtSQ/4/vvSHLDHuQGsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x96cc310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xada0a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of many stripes\n",
    "_ = plt.hist( x=z,bins=100 )\n",
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa24dd0>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f27490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa24dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist( x=z,bins=100, edgecolor='none' )\n",
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a9add0>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x9aad050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x9aad210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x86a9350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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977I6HLh7wv27GI/j49eAu5L8fZLbknw+yVMYw+Oiqn4EfJRe1P8L2APcxngeF3tNdxwc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\nRhl4SWqUgZekRhl4SWqUgZekRv0fip8Vk4HH64wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9992e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a9add0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist( x=z, histtype='step', cumulative=True )\n",
    "plt.figure()\n",
    "_ = plt.hist( x=z, histtype='step', normed=True, cumulative=True )\n",
    "plt.figure()\n",
    "# narrower bins\n",
    "_ = plt.hist( x=z, bins=100, histtype='step', normed=True, cumulative=True )\n",
    "plt.figure()\n",
    "# custom bins\n",
    "_ = plt.hist( x=z, bins=[0,20,30,31,32,33,34,35,40,50,60,65,80,100], histtype='step', normed=True, cumulative=True )\n",
    "plt.figure()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x84e9ad0>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x84edb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of all three datasets\n",
    "_ = plt.hist( x=[x,y,z],bins=100, ec='none', label=['x','y','z'] )\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}