G.1_intro_to_matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff720bf8c50>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff741d8da58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pyplot plots a list of numbers, given as a single argument, as y values against their indices (x-axis)\n",
    "\n",
    "plt.plot(list(range(10)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff71e3d92b0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AkCSNKpN/c7IAkCSNGpN/87IAkCSNCpN/c7MAkCQ1nMm/+VkASJIayuTfGiwAJEkNY/Jv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\nkqQO9P8BYplNituo9GcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff720b6c048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# here we use list comprehension to generate the list of y values\n",
    "# note that the x- and y-axes are not on the same scale\n",
    "\n",
    "plt.plot([x**2 for x in range(10)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fab8279ea20>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ssjRGhm4rPOCALW8rfPDBYuyAgUBS2QwF0igbeVvha17jbYWSmpPdB9Io8bZC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\nFEiSpIr/D2me9Od5M457AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab827c2a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# if we give pyplot two lists, the first is taken as the x values, and the second as the y values\n",
    "# the lists must be of the same length\n",
    "\n",
    "plt.plot([2*x for x in range(10)],[x**2 for x in range(10)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 18.0, 0.0, 90.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qf08OZ59M1Ov9MS47j3iNL4rBd/NExE8AjwHLMnPDuON/DLwlM5dNan8Z8Pmm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\nAgx8SZIqwMCXJKkC/j9X1znozSkemQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff71e4010f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# when the axes scales are unequal, the plot distorts the shape of the curve\n",
    "# to set the axes to the same scale, we use the plt.axis() method, passing the string 'equal' as argument\n",
    "# note that matplotlib returns the tuple (xmin, xmax, ymin, ymax)\n",
    "\n",
    "plt.plot([2*x for x in range(10)],[x**2 for x in range(10)])\n",
    "plt.axis('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f225b922160>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NciWwrl3ee8zMzOy53Gg0aDQagz5dz+bPfly3DtasgU2bYMWKsY8hSQtqNps0m82e7jPQ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\nddRDSH0GKiQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f225b90c048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the matplotlib inline magic allows for multiple curves to render on the same plot\n",
    "\n",
    "plt.plot(list(range(10)))\n",
    "plt.plot([2*x for x in range(10)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f225b90c4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to render multiple curves on the same plot\n",
    "# the pyplot hold() method must be passed the boolean argument True\n",
    "# and once we've declared our plots, we use the pyplot show() method to show the plot\n",
    "\n",
    "plt.hold(True)\n",
    "plt.plot(list(range(10)))\n",
    "plt.plot([2*x for x in range(10)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f225c50f0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NciWwrl3ee8zMzOy53Gg0aDQagz5dz+bPfly3DtasgU2bYMWKsY8hSQtqNps0m82e7jPQ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\nddRDSH0GKiQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f225b8edfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# likewise, we can render each curve on its own plot\n",
    "\n",
    "plt.plot(list(range(10)))\n",
    "plt.show()\n",
    "plt.plot([2*x for x in range(10)])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EEtbWBsuXhwk+cWLsNOXFzKqAccCJM+XHmtmFwAvu/nPgUJ/XdwMH3P2npU0qkp9ly2D/\n/jD/hw6Nnab0CjlynwKMQZ27pEDv7v3GG2OnKUsTge8Tlt6dsAwP4UTZuf28Pv/38URKrLUVVq+G\nW26B8eNjp4kj7+Lu7t/l5BvZiJSt3t372WfHTlN+3P0R8rgkdqD32UXKxdGj4ez4Sy6Bz38+dpp4\ndG95qVjq3kWyZ+lSOHAAmpthSIYPQ1XcpSKpexfJnpYW2LAB1q+HceNip4lLxV0q0nXXqXsXyZJD\nh2DuXJg6FRYsiJ0mPhV3qTjNzXDnnereRbJk8eKwYrdxI/T/lIRsUXGXinLoUHiUq7p3kezYvh22\nbIHNm2HMmNhpykPBD44RKUfq3kWy5eDB8ITH6dNh1qzYacqHjtylYqh7F8kWd6irC/82Nqqh703F\nXSqCuneR7Nm6NTT127bB6NGx05QXLctL6ql7F8meri5YuBBmzoQZM2KnKT86cpfUU/cuki3u4cTZ\nqipYty52mvKk4i6ppu5dJHsaG8MNa3bsgJEjY6cpT1qWl9RS9y6SPZ2dsGRJOMdm2rTYacqXjtwl\ntdS9i2RLTw/MmQOjRsGtt8ZOU95U3CWV1L2LZM/atbBrF+zcCcOHx05T3rQsL6mj7l0kezo6oKEB\n6uth8uTYacqfjtwlddasUfcukiXd3TB7NowdCytWxE6TDirukirq3kWyZ+VK2LsXdu+GYcNip0kH\nLctLapzo3s8/X927SFa0tcHy5aGpnzgxdpr00JG7pIa6d5FsOXYsNPQTJoTiLmdOxV1SQd27SPYs\nWwb794f5P3Ro7DTpouIuZU/du0j2tLbC6tWwahWMHx87TfqouEvZU/cuki1Hj8LVV8OkSXDttbHT\npJOKu5Q1de8i2bN0KRw4AM3NMGRI7DTplPfZ8mb2J2a2xcyeN7OXzWyvmVUXI5xkm7r34jOzS83s\nQTN71syOm9nlvb72OjO7xcx+ZGZHc6/ZbGZviplZKltLC2zYEG5QNW5c7DTplVdxN7M3AruA3wKX\nARcA1wKHko8mWXeie9+8Wd17EVUBe4DPAt7na68HLgK+BPwl8FHgbcC3SxlQsuPwYZg7F6ZMgbq6\n2GnSLd9l+S8Az7j7vF7b/jPBPCLAq937+vXq3ovJ3R8GHgYwM+vztd8QmvjfM7OFwL+Z2Zvdvatk\nQSUTFi0KK3Z33w1n6S4sg5Lvj+8jwONm9k0ze87M2s1s3mm/SyQPJ7r3qVNhwYLYaaSPNxKO8A/H\nDiKVZft22LIFbr8dxoyJnSb98i3uY4EFwH8AHwA2ALeb2aykg0l2nejeN26Ek48lJSYzOwdYBXzd\n3Y/GziOV4+DB8ITH6dNhlqpJIvJdlj8L+KG7L8t9vtfMxgN1wJZEk0kmnejeN29W915OzOx1wH2E\no/bPRo4jFcQ9vL/uDo2NauiTkm9x/yWwr8+2fcCVp/qm+vp6RowYcdK22tpaamtr89y9VDJ1769q\namqiqanppG1HjhyJkqVXYR8DvO9Mj9o17+VMbN0amvpt22D06Nhp4kl6zpt73xNkT/Fis63Am939\nPb22rQEudvd39/P6aqCtra2N6mpdLScDc4cZM+DRR+GJJ7I9yQfS3t5OTU0NQI27tyc9vpkdB65w\n9wd7bTtR2McC73X3F85gHM17OSNdXeH+FR/+MNx7b+w05Wcwcz7fI/c1wC4zux74JvBOYB7wmTzH\nETmJuvc4zKwKGAecWAwda2YXAi8QVuruJ1wO92HgbDM78V/nBXfvLnVeqRzu8OlPQ1UVrFsXO03l\nyau4u/vjZvZRwkk1y4CngMXu/o1ihJNs6OqChQth5sxw9C4lNRH4PuG9dAdW57ZvJlzf/pHc9j25\n7Zb7/L3Av5Y0qVSUxsZwyeuOHTByZOw0lSfv28+6+0PAQ0XIIhmk7j0ud3+EU181o6uNJXGdnbBk\nCcyfD9OmxU5TmXRveYlK3btItvT0wJw5MGoU3HZb7DSVS8VdolH3LpI9a9fCrl2wcycMHx47TeXS\nkptEoe5dJHs6OqChAerrYfLk2Gkqm47cJQp17yLZ0t0dnvI4diwsXx47TeVTcZeSU/cukj0rV8Ke\nPbB7N5x7buw0lU/L8lJSvbv3FStipxGRUmhrC0frDQ0wcWLsNNmgI3cpqd7d+7BhsdOISLEdOwaz\nZ8OECaG4S2mouEvJqHsXyZ5ly2D//jD/hw6NnSY7VNylJNS9i2RPayusXg2rVoV7yEvpqLhLSah7\nF8mWo0fD+TWTJsG118ZOkz0q7lJ06t5FsmfpUjhwAJqbYciQ2GmyR8Vdikrdu0j2tLTAhg2wfj2M\nGxc7TTapuEtRqXsXyZbDh2HuXJgyBerqYqfJLhV3KRp17yLZs2gRvPgi3H03nKU7qUSj4i5Foe5d\nJHu2b4ctW2DTJhgzJnaabFNfJUWh7l0kWw4ehGuugenTw2WvEpeO3CVx6t5FssUdFiwI/zY2glns\nRKLiLolS9y6SPVu3wgMPwLZtMHp07DQCWpaXBKl7F8meri5YuBBmzoQZM2KnkRN05C6JUfcuki3u\nMG8eVFXBunWx00hvKu6SCHXvItnT2BjuYbFjB4wcGTuN9KZleRk0d/j0p9W9i2RJZycsWQLz58O0\nabHTSF86cpdBa2wMN6xR9y6SDT09MGcOjBoFt90WO430R8VdBkXdu0j2rF0Lu3bBzp0wfHjsNNKf\nvJblzezvzOx4n48nixVOypu69/Qzs0vN7EEzezY3ny/v5zV/b2a/MLOXzey7ZqabCWdYRwc0NEB9\nPUyeHDuNDKSQ99yfAEYDf5z7eHeiiSQ1TnTvmzape0+xKmAP8FnA+37RzP4WWAjMB94BvAQ0m9nQ\nUoaU8tDdHZ7yOHYsLF8eO42cSiHL8r9z918lnkRSRd17ZXD3h4GHAcz6vTPBYuBmd/+fudfMBp4D\nrgC+WaqcUh5WroQ9e+Cxx+Dcc2OnkVMp5Mj9rbklvE4zu9fMdIPRjFH3ng1m9meE1bl/ObHN3X8D\n/BswKVYuiaOtLcz3G26Aiy+OnUZOJ98j993AHOA/gDcBXwT+1czGu/tLyUaTcqXuPTP+mLBU/1yf\n7c/lviYZcexYaOgnTIAbb4ydRs5EXsXd3Zt7ffqEmf0Q+E/gb4B/Guj76uvrGTFixEnbamtrqa2t\nzWf3UgbUvZdGU1MTTU1NJ207cuRIpDSF0byvHDfdBD/9KTz+OAzV2RZFkfScN/fXnEOT3wChwH/X\n3Rv6+Vo10NbW1kZ1dfWg9iPxHTsGNTVwzjmwe7cmeam1t7dTU1MDUOPu7UmPb2bHgSvc/cHc538G\ndAIXufuPer1uJ/Dv7l4/wDia9xWktTWcV7NqFSxdGjtNtgxmzg/qDnVmdh4wDvjlYMaRdLjpJti/\nH+65R4U9C9z9KeAA8P4T28zsDcA7gR/EyiWlc/RoWI6fNAmuvTZ2GslHXsvyZnYr8B3CUvx/Ab4E\ndANNp/o+Sb/W1nAt+6pVMH587DSSFDOrIjToJ86UH2tmFwIvuPvPgbXAjWa2H3gauBnoAr4dIa6U\n2NKlcOBAuH/8kCGx00g+8j2h7s3A14E/AH4FtAJ/5e6/TjqYlA917xVtIvB9wolzDqzObd8MzHX3\nL5vZ64FG4I3Ao8AH3f3/xAgrpdPSAhs2wPr1ME63LUqdfE+o05kwGaTuvXK5+yOc5u05d/8i4coY\nyYjDh2HuXJgyBerqYqeRQuje8nJK6t5FsmfRInjxRbj7bjhLzw5NJRV3GZC6d5Hs2b4dtmwJt5Ue\no1uUpZZ6MhmQuneRbDl4EK65BqZPh9mzY6eRwdCRu/RL3btItrjDggXh38ZG6PdJA5IaKu7yGure\nRbJn61Z44AG47z4YPTp2GhksLbbKSdS9i2RPVxcsXAhXXQUf+1jsNJIEHbnLSdS9i2SLO8ybB1VV\ncMcdsdNIUlTc5ffUvYtkz113hXtYPPQQjBwZO40kRcvyAqh7F8mizs5w18n58+GDH4ydRpKkI3cB\nXu3ed+xQ9y6SBT098KlPwahR4bkRUllU3OWk7n3atNhpRKQU1q4ND4TauROGD4+dRpKmZfmMU/cu\nkj1PPgkNDVBfH57VLpVHR+4Zp+5dJFu6u8P9K8aOheXLY6eRYlFxz7CODnXvIlmzciXs2QOPPQbn\nnhs7jRSLluUzqrs7PKNd3btIdrS1hfl+ww1w8cWx00gx6cg9o9S9i2TLsWNhOX7CBLjxxthppNhU\n3DNI3btI9tx0E+zfH+b/0KGx00ixqbhnjLp3kexpbQ1Xw6xaBePHx04jpaDinjHq3kWy5ejRcH7N\npEnhfhaSDSruGaLuXSR7li6FAwfCHSiHDImdRkpFxT0j1L2LZE9LC2zYEJ4XMW5c7DRSSiruGaHu\nXSRbDh+GuXNhyhRYsCB2Gik1FfcMONG9r1+v7l0kKxYvhhdfhLvvhrN0R5PMGdR/cjP7gpkdN7Ov\nJBVIktW7e6+ri51G0sjMzjKzm83sZ2b2spntNzNda1HGvvUtuOceuP12GDMmdhqJoeAjdzO7GJgP\n7E0ujiRN3bsk4AvANcBs4ElgIrDJzA67+x1Rk8lr/OpX4QmP06eHy14lmwr6c29m5wH3AvOAw4km\nksSoe5eETAK+7e4Pu/sz7v4A0AK8I3Iu6cM9rNC5Q2MjmMVOJLEUeiy3HviOu38vyTCSnIMH1b1L\nYn4AvN/M3gpgZhcC7wIeippKXmPrVnjggXCOzejRsdNITHkvy5vZJ4CLCEtzUobcw9mx6t4lIauA\nNwA/MbMewkFBg7t/I24s6a2rCxYuhKuugo99LHYaiS2v4m5mbwbWAlPcvbs4kWSwTnTv992n7l0S\n8XHgKuAThPfcLwK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V3Z8s9f4PHw4Pg5o6FRYsKPXeRdKjJMW9vr6eESNGnLSttraW2tra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\nIhXm/wfUbXDqnylTEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb0364a3ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we can also make subplots\n",
    "# first we create an instance of the plt.figure() object\n",
    "fig = plt.figure()\n",
    "# then we add each plot to it as a subplot\n",
    "# the add_subplot() method argument specifies: num_rows, num_cols, plot_number # 3 numbers 211 specify num-rows, num-col and plot _number \n",
    "ax1 = fig.add_subplot(121) # plot number 1\n",
    "ax1.plot(list(range(10)))\n",
    "ax2 = fig.add_subplot(122) # plot number 2\n",
    "ax2.plot([2*x for x in range(10)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DydxSRps3w9lnK5lbspP5BK1kbimrKVPC3erGj8+7EimrTFscSuaWNBSxxbF8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\nIlJQmqBFRArq/wMt3qFN8zLVrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f225c633d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "# then we add each plot to it as a subplot\n",
    "# the add_subplot() method argument specifies: num_rows, num_cols, plot_number\n",
    "ax1 = fig.add_subplot(121) # plot number 1\n",
    "ax1.plot(list(range(10)))\n",
    "ax2 = fig.add_subplot(122) # plot number 2\n",
    "ax2.plot([2*x for x in range(10)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xyHVdDaTVJaP9SP0y90m6pxh3GnAOcKWkEygOOwWIiLmSrgTmku5Od7J3B8ys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\nmRngBsHMzApuEMzMDID/DyTfxnKoJle6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f225b5c6be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's return to multiple curves on one plot\n",
    "# we can specify the colors of our curves\n",
    "# we can add a grid\n",
    "# we can label our curves and axes, title our plot, and provide a legend\n",
    "# Hold set to True, when we try to printing out in multiple curves, only the last curves showing up. \n",
    "plt.hold(True)\n",
    "\n",
    "plt.plot(list(range(10)), label='x', color='magenta') # label is shown in legend\n",
    "plt.plot([2**x for x in range(10)], label='x**2', color='cyan') # label is shown in legend\n",
    "\n",
    "plt.grid(True)\n",
    "\n",
    "plt.xlabel('indices')\n",
    "plt.ylabel('values')\n",
    "plt.title('linear v. quadratic growth')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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