visualizing_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'inflammation-01.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-b7944b43814c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadtxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'inflammation-01.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'l'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mloadtxt\u001b[0;34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin)\u001b[0m\n\u001b[1;32m    858\u001b[0m                 \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'U'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    859\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 860\u001b[0;31m                 \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    861\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    862\u001b[0m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'inflammation-01.csv'"
     ]
    }
   ],
   "source": [
    "data = numpy.loadtxt(fname='inflammation-01.csv', delimiter='l')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  1. ...,  3.  0.  0.]\n",
      " [ 0.  1.  2. ...,  1.  0.  1.]\n",
      " [ 0.  1.  1. ...,  2.  1.  1.]\n",
      " ..., \n",
      " [ 0.  1.  1. ...,  1.  1.  1.]\n",
      " [ 0.  0.  0. ...,  0.  2.  0.]\n",
      " [ 0.  0.  1. ...,  1.  1.  0.]]\n"
     ]
    }
   ],
   "source": [
    "print (data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  1.  3.  1.  2.  4.  7.  8.  3.]\n",
      " [ 0.  1.  2.  1.  2.  1.  3.  2.  2.  6.]\n",
      " [ 0.  1.  1.  3.  3.  2.  6.  2.  5.  9.]\n",
      " [ 0.  0.  2.  0.  4.  2.  2.  1.  6.  7.]]\n"
     ]
    }
   ],
   "source": [
    "print(data[0:4, 0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  1.  2.  2.  4.  2.  1.  6.  4.]\n",
      " [ 0.  0.  2.  2.  4.  2.  2.  5.  5.  8.]\n",
      " [ 0.  0.  1.  2.  3.  1.  2.  3.  5.  3.]\n",
      " [ 0.  0.  0.  3.  1.  5.  6.  5.  5.  8.]\n",
      " [ 0.  1.  1.  2.  1.  3.  5.  3.  5.  8.]]\n"
     ]
    }
   ],
   "source": [
    "print(data[5:10, 0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "small is:\n",
      "[[ 2.  3.  0.  0.]\n",
      " [ 1.  1.  0.  1.]\n",
      " [ 2.  2.  1.  1.]]\n"
     ]
    }
   ],
   "source": [
    "small = data[:3, 36:]\n",
    "print('small is:')\n",
    "print(small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doubledata = data * 2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original:\n",
      "[[ 2.  3.  0.  0.]\n",
      " [ 1.  1.  0.  1.]\n",
      " [ 2.  2.  1.  1.]]\n",
      "doubledata:\n",
      "[[ 4.  6.  0.  0.]\n",
      " [ 2.  2.  0.  2.]\n",
      " [ 4.  4.  2.  2.]]\n"
     ]
    }
   ],
   "source": [
    "print('original:')\n",
    "print(data[:3, 36:])\n",
    "print('doubledata:')\n",
    "print(doubledata[:3, 36:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tripledata = doubledata + data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tripledata:\n",
      "[[ 6.  9.  0.  0.]\n",
      " [ 3.  3.  0.  3.]\n",
      " [ 6.  6.  3.  3.]]\n"
     ]
    }
   ],
   "source": [
    "print('tripledata:')\n",
    "print(tripledata[:3, 36:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.14875\n"
     ]
    }
   ],
   "source": [
    "print(numpy.mean(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thu Sep 27 17:36:37 2018\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "print(time.ctime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum inflammation: 20.0\n",
      "minimum inflammation: 0.0\n",
      "standard deviation: 4.61383319712\n"
     ]
    }
   ],
   "source": [
    "maxval, minval, stdval = numpy.max(data), numpy.min(data), numpy.std(data)\n",
    "\n",
    "print('maximum inflammation:', maxval)\n",
    "print('minimum inflammation:', minval)\n",
    "print('standard deviation:', stdval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum inflammation for patient 0: 18.0\n"
     ]
    }
   ],
   "source": [
    "patient_0 = data[0, :]\n",
    "print('maximum inflammation for patient 0:', patient_0.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum inflammation for patient 2: 19.0\n"
     ]
    }
   ],
   "source": [
    "print('maximum inflammation for patient 2:', numpy.max(data[2, :]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.           0.45         1.11666667   1.75         2.43333333   3.15\n",
      "   3.8          3.88333333   5.23333333   5.51666667   5.95         5.9\n",
      "   8.35         7.73333333   8.36666667   9.5          9.58333333\n",
      "  10.63333333  11.56666667  12.35        13.25        11.96666667\n",
      "  11.03333333  10.16666667  10.           8.66666667   9.15         7.25\n",
      "   7.33333333   6.58333333   6.06666667   5.95         5.11666667   3.6\n",
      "   3.3          3.56666667   2.48333333   1.5          1.13333333\n",
      "   0.56666667]\n"
     ]
    }
   ],
   "source": [
    "print(numpy.mean(data, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40,)\n"
     ]
    }
   ],
   "source": [
    "print(numpy.mean(data, axis=0).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.45   5.425  6.1    5.9    5.55   6.225  5.975  6.65   6.625  6.525\n",
      "  6.775  5.8    6.225  5.75   5.225  6.3    6.55   5.7    5.85   6.55\n",
      "  5.775  5.825  6.175  6.1    5.8    6.425  6.05   6.025  6.175  6.55\n",
      "  6.175  6.35   6.725  6.125  7.075  5.725  5.925  6.15   6.075  5.75\n",
      "  5.975  5.725  6.3    5.9    6.75   5.925  7.225  6.15   5.95   6.275  5.7\n",
      "  6.1    6.825  5.975  6.725  5.7    6.25   6.4    7.05   5.9  ]\n"
     ]
    }
   ],
   "source": [
    "print(numpy.mean(data, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ua1Vn6/e5ems4h85a3Qde6uq4wpwZwmfCLnPurd5WGcIXqpbF6HEq/F7KRSnp\nlqo32BXbSCS2sY1EUjSex7z62ALuWcIZGDKij2s0c58ZGd3H5j6ZAMtMfVQXmZaSRtbcBvJNjC0z\ng+fx62f8p1qT3sSR136g1ra9oiP2OiqCCTKuVTUADK8J1yyONjxqR/T1rGKdjpmXXl42DfKxUY8D\ntHeYv888c66Ao/vM82gc0djGNhKJbWwjkRSNS72ENHZcBk1eD5oRQVez3j12iTY7bZkfTFbShQ4A\nk5cGzcimQOlaBrRO5TbNrKNlfxgu7n7DXaEd9UWpLWrtgrf+Sc0f+nzI5fjHv8xTazPG6fe5MhXc\n/KlabbqU2njXifoehDNzxr0c5tzLhj9rmeXPhUL3x/Tt7G2WTBx2xTYSiW1sI5HYxjYSSdG41POK\nDwqN3VPYqtR6Y9ZEZ54DwPEPBS266Zbxaq1ibbCPd1Tof/OyehIAlJ0bQi9PJ337x3vOUfPZoke6\nzJgBgFWzQtHzBdBw1s73Tw5hrKyppQsd0CEBozZF26aljR0AqhdTKK8og8QZR+kGfSxnzUikr4F9\nFmyRl+t5+8LCVo0jGdvYRiIpGnNfXKQf/3Sxu1YminIBSzZRyQg1rg0tkQm4AACSIuU/DibH7Xfr\n1+T6z3EJuyUi8u7AFL3WVql7LtbXhnNvelNnzFx5w8tq/syG0MpaFtAEdA+akW9qIdA2QcsNmdQs\n3fRAvvQo36gL/khkRk1efWwy7XKRyt5gV2wjkdjGNhKJbWwjkRRNDxpGFi5k9zprNKnf2E3OBRjl\nYznTu6xNuuq1iYz1b8d64Yq+V2drs5tamsl+duYv1dqL03WPQ8njjeequcrUOZmOXaGPnSzc5qUv\n6q+58cwQVsufj+yDCQCTl4Z7DXahs2lVhqPqT0B/7j1lqSOuB42Z+4wjmUJbdWwD0ApgP4Au7/1p\nzrlRAJ4AMBnANgBf8973fTSLYfSCQ7liX+C9nymyF+4A8FK2udJL2blhFAUFpYZlr9inee93ir+9\nC+B8732Dc64SwErv/Qlxz3MoqWFx/f04a12GsXJfR866lrZWDkVVr5HS2pNTsWR/8t215ZFrgLYb\nMzIdbO/9uo0P30uw/V7C2lj2iKz7pn4v6dXaPi5h97u8X2CX+nHPRNut4z53rirAIRP9WZTSA/iD\nc+5159wN2b9VeO8bACD7/3HdPdB60BgDQaFWkXO999udc+MALHfOberxEVmsB40xEBS0sb3327P/\nb3LOPY3buAsBAAAHjklEQVRMJ4NG51ylkCJNfXli8ueIzX2cQROXAc29G+VP5FE7dDa5LB6Tl6Wz\nW7vYS3eHj27GA/Eu4NWXBLc517z+3aLg+n7jgX9Ta/Pr5qi5jOAbsoHlhL5maNOcNlVKEx9nnnMF\ngM5hQYpwVn+cpOAAibg+QSwteb03FNIOL+WcG3ZwDOBvAbwN4FlkmioBfdBcyTD6kkKu2BUAns40\n40UJgMe898ucc+sAPOmcuw5AHYB5Mc9hGP1KIe3wtgKY0c3fPwbw+fxHGMbAUzRhq6BM5RLhVmUt\nx2YwWfCQzX2lZDWUan3nLG3uk6/Dr8H4+8ITc+F3LlI5LB00Lru+j/9KXW583HPXq7X/OWepmr9S\nG8JfOTOe6WgI4QIHGvXnt2PeJ5Fr+SZQqatJj1PP+PSIENog73sYrtYVZ+7Lq3dlLnXjSMY2tpFI\nbGMbiaRowlZLqJ+2dKkPIfc6azSph2UfRyC/ALmsBDV5qe6VKMNYB1Voey67octE73J2ua5EdJ/H\nEupHXpUK763qZP0+Fy24Qs1lDanOGdqdnXpIV5jaMS80SSqhwu8j14c594vkkFsJ93ZnH4H8Hjgk\nIra5Ukyl3d5iV2wjkdjGNhJJ0RTMORxk1BtHp7GLXbqFufDk8JogE/ZAm8G4l03Td6PNgak1+jU7\n0+H8vvPVp9Xae/vGRj4PbnpbTaVLPTouMZ+utJYQLdPC+2yaw4WB9LEySjAuuhAgiRFTWYClZVxR\nyt7uEbtiG4nENraRSGxjG4mkaFzq7DqVoYwy5BHID61smxBCUcvIhc66UGaxywKVgNbjLTP1v/l3\nb9TmvhPuCea0+gvjzVXzLludGz9815fVWvv8ltz40kkb1dqWhdPVfIgIMe2APp9S0tGDZIUpWpOa\nm4ttxmXFdIzQobwcviBDG+JCEjhM1UHPrRKUYURgG9tIJEVr7mP5IeHsFlmUsuHqk9Qam/+qRNSZ\naoNMVC/WP6VcLEZm4nC/w7N/9Kqa/+rVs3LjaTfrVtHnjX4/N1738bFq7Wg6tvkPoRDlt6/9jVp7\n7KYvqPnIa8P7/Kidij4uC5KLvYkMe3I1+nuQRSpTdZ/QscF8GvfdAoDvAzOwXbGNRGIb20gktrGN\nRFI00X2MjA7jaD7uIfjxF0M0HWtq1salu4Pe5MLlMlqNH8dRcCV3R2vY5+u0mU6a1Lamx6i1n01d\nnBsvWTpbrbErfO4Vf86NFyy7XJ/PBTpqsOutiYhClrKJy+IHgE7hu09t1+fDnx9/RupYYXYdSmtx\nxZF6i12xjURiG9tIJLaxjURSNC51Js7GLQu9M+xubz9dZ4R0DAt2WS6y2JUOOpWrRAHUN1wUkGyd\np3X97OpaNd+yPmS3bE9rhfn1W28L5z5Nv2Zqhk7JvmbsmvAaF9eotSe36zqN214JGT78POVLg/2Z\ni98fSGn7vS7G2dN1MHxGZT2EuEp4H8hsKi5KWSh2xTYSiW1sI5EUjbmPpYf8eWJ5MXSbdslK1zgX\n12Fkcq9M7AWAqY+28uE5uJDMoIogPwZRsuz2Cu2Grr8kmNTKx+voueYdwb3N5rSFn1mi5vNfui43\n5qg8lhsdFUFSlFJGT8eIUJSS21oP3/CRmsuW3eWbdZ1tju6ToQ4c3SeL4uyn6D7+7vvC/GdXbCOR\n2MY2EoltbCORFNSDps9ezLmPALwPYAyAnT0c3p/Y+cRTTOdzrPc+JrU/Q79u7NyLOvdaIQ1y+gs7\nn3iK7XwKwaSIkUhsYxuJZKA29qIBet0o7HziKbbz6ZEB0diG8WljUsRIJP26sZ1zFzvn3nXObXHO\nDUjvdefcL5xzTc65t8XfRjnnljvnarL/75tqmYWdzzHOuRXOuXecc39xzt06kOfknBvqnHvVObch\nez73ZP9+nHNubfZ8nnDOlfX0XAOK975f/kOm2FMtgGpk4hs3AJjeX68vzmMugFkA3hZ/uw/AHdnx\nHQAW9uP5VAKYlR0PA7AZwPSBOicADkA6Oy4FsBbAWQCeBHBV9u8/BfDf+vu7O6T30Y9f4NkAfi/m\ndwK4c0DeNDCZNva7ACrFRnt3wL6QTCPYi4rhnAAcDWA9gDORcdCUdPddFuN//SlFJgD4QMzrs38r\nBiq89w0AkP0/d9/oF5xzkwGcgsxVcsDOyTk32Dn3JjJtxJcj80vb4r0/GLJXTN9dt/TnxuaUFIAb\ngB/BOOfSAJ4C8C3vfXRlyH7Ae7/fez8TwEQAZwA4qbvD+vesDo3+3Nj1AI4R84kADr8bfN/Q6Jyr\nBIDs/5v688Wdc6XIbOpfee8P1i4b0HMCAO99C4CVyGjskc65g0HgxfTddUt/bux1AKZm767LAFwF\n4Nl+fP04ngVwTXZ8DTI6t19wmSb1Pwfwjvf+RwN9Ts65sc65kdnxUQAuBPAOgBUAvtrf59Nr+vlm\n5FJk7vprAdw9EDcVAB4H0ACgE5lfkesAjAbwEoCa7P9H9eP5zEbmZ/0tAG9m/7t0oM4JwMkA3sie\nz9sA/nf279UAXgWwBcASAEMG4vsr9D/zPBqJxDyPRiKxjW0kEtvYRiKxjW0kEtvYRiKxjW0kEtvY\nRiKxjW0kkv8PCTzocSsisZsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0428236a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot\n",
    "image = matplotlib.pyplot.imshow(data)\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NRZXMeSGLmU+uIHvvIX51zgC+vGcK5w2N58Vv9nj0AtTizHGlzMxMk5WV5bTt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\nHkILXSmlPMT/Az4LM1rYddlTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f04261140b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ave_inflammation = numpy.mean(data, axis=0)\n",
    "ave_plot = matplotlib.pyplot.plot(ave_inflammation)\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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G2UYN/gpYu2UHl00opFUj5RNFslHT+rUZMzSX4q07uWpS+mUbNfgP0u49e7li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\nIpJl/h9PVgkwxEcLEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f04260a32b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_plot = matplotlib.pyplot.plot(numpy.max(data, axis=0))\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EjonIYyIyKSJFEfmUH4XR3nGneHGNEtpOPpdBvaG4NM3hkiBr54q7BuC3VPUMgPcA+E0R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\ncBMRhQyDm4goZP4/wP15vn6XgdcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0428505f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_plot = matplotlib.pyplot.plot(numpy.min(data, axis=0))\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4t9a608TGCOBlEXkfWIZ/D/IzDtcUN4Lt2rwFtoJ8ME9GGkNzM20FOSCkGZeI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f04260bfef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy\n",
    "import matplotlib.pyplot\n",
    "\n",
    "data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')\n",
    "\n",
    "fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))\n",
    "\n",
    "axes1 = fig.add_subplot(1, 3, 1)\n",
    "axes2 = fig.add_subplot(1, 3, 2)\n",
    "axes3 = fig.add_subplot(1, 3, 3)\n",
    "\n",
    "axes1.set_ylabel('average')\n",
    "axes1.plot(numpy.mean(data, axis=0))\n",
    "\n",
    "axes2.set_ylabel('max')\n",
    "axes2.plot(numpy.max(data, axis=0))\n",
    "\n",
    "axes3.set_ylabel('min')\n",
    "axes3.plot(numpy.min(data, axis=0))\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mass = 47.5\n",
    "age = 122\n",
    "mass = mass * 2.0\n",
    "age = age - 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95.0\n"
     ]
    }
   ],
   "source": [
    "print (mass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102\n"
     ]
    }
   ],
   "source": [
    "print (age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hopper Grace\n"
     ]
    }
   ],
   "source": [
    "first, second = 'Grace', 'Hopper'\n",
    "third, fourth = second, first\n",
    "print(third,fourth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first three chracters: oxy\n",
      "last three characters: gen\n"
     ]
    }
   ],
   "source": [
    "element = 'oxygen'\n",
    "print('first three chracters:', element[0:3])\n",
    "print('last three characters:', element[3:6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A = \n",
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n",
      "B = \n",
      "[[1 2 3 1 2 3]\n",
      " [4 5 6 4 5 6]\n",
      " [7 8 9 7 8 9]]\n",
      "C = \n",
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]\n",
      " [1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "\n",
    "A = numpy.array([[1,2,3], [4,5,6], [7, 8, 9]])\n",
    "print('A = ')\n",
    "print(A)\n",
    "\n",
    "B = numpy.hstack([A, A])\n",
    "print('B = ')\n",
    "print(B)\n",
    "\n",
    "C = numpy.vstack([A, A])\n",
    "print('C = ')\n",
    "print(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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