02_quick_introduction_to_numpy_and_matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy\n",
    "Numpy is at the core of scientific computing in python. It provides containers for numerical arrays in computing memory and basic to advanced array manipulation routines, such as pointwise operations, but also matrix multiplications and decompositions. It is designed for efficient **array-oriented computing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Vendor:  Continuum Analytics, Inc.\n",
      "Package: mkl\n",
      "Message: trial mode expires in 26 days\n"
     ]
    }
   ],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.05199373 -1.27031501 -2.01749499  0.46799571 -1.20234413]\n",
      " [-0.66353157  0.44764524  0.65576448  0.38230002 -0.48073244]\n",
      " [ 0.9325603   0.01567161  1.48827167 -0.16287019 -1.58845233]]\n"
     ]
    }
   ],
   "source": [
    "X = np.random.randn(3, 5)\n",
    "print X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like a list of lists. And indeed, if we use a single index into the array, we will obtain rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.05199373, -1.27031501, -2.01749499,  0.46799571, -1.20234413])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.2703150105949943"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.05199373, -1.27031501])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0, 0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.05199373, -1.27031501, -2.01749499,  0.46799571, -1.20234413])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0, 0:None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.05199373, -1.27031501, -2.01749499,  0.46799571, -1.20234413])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.05199373, -0.66353157,  0.9325603 ])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pointwise operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = np.arange(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    1,    4,    9,   16,   25,   36,   49,   64,   81,  100,\n",
       "        121,  144,  169,  196,  225,  256,  289,  324,  361,  400,  441,\n",
       "        484,  529,  576,  625,  676,  729,  784,  841,  900,  961, 1024,\n",
       "       1089, 1156, 1225, 1296, 1369, 1444, 1521, 1600, 1681, 1764, 1849,\n",
       "       1936, 2025, 2116, 2209, 2304, 2401, 2500, 2601, 2704, 2809, 2916,\n",
       "       3025, 3136, 3249, 3364, 3481, 3600, 3721, 3844, 3969, 4096, 4225,\n",
       "       4356, 4489, 4624, 4761, 4900, 5041, 5184, 5329, 5476, 5625, 5776,\n",
       "       5929, 6084, 6241, 6400, 6561, 6724, 6889, 7056, 7225, 7396, 7569,\n",
       "       7744, 7921, 8100, 8281, 8464, 8649, 8836, 9025, 9216, 9409, 9604,\n",
       "       9801])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   2,   4,   6,   8,  10,  12,  14,  16,  18,  20,  22,  24,\n",
       "        26,  28,  30,  32,  34,  36,  38,  40,  42,  44,  46,  48,  50,\n",
       "        52,  54,  56,  58,  60,  62,  64,  66,  68,  70,  72,  74,  76,\n",
       "        78,  80,  82,  84,  86,  88,  90,  92,  94,  96,  98, 100, 102,\n",
       "       104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128,\n",
       "       130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154,\n",
       "       156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180,\n",
       "       182, 184, 186, 188, 190, 192, 194, 196, 198])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 * a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,\n",
       "        27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,\n",
       "        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,\n",
       "        53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,\n",
       "        66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,\n",
       "        79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,\n",
       "        92,  93,  94,  95,  96,  97,  98,  99, 100])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.zeros_like(a)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[0] = 1\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
       "       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using lists for this would be much slower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "L = range(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 5.67 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "10000 loops, best of 3: 10.8 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit [i ** 2 for i in L]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 47.78 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "100000 loops, best of 3: 714 ns per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit a ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix multiplication\n",
    "Contrary to matlab defaults, the **\"*\"**-operator is pointwise multiplication (in matlab it is matrix multiplication). Matrix multiplication is done with `np.dot`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y = np.random.randn(5, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[-0.05199373, -1.27031501, -2.01749499,  0.46799571, -1.20234413],\n",
       "        [-0.66353157,  0.44764524,  0.65576448,  0.38230002, -0.48073244],\n",
       "        [ 0.9325603 ,  0.01567161,  1.48827167, -0.16287019, -1.58845233]]),\n",
       " array([[ 2.01596208,  0.21241268, -1.02793462, -0.68280268],\n",
       "        [-0.12565588,  0.63898147, -0.60971913, -0.12127546],\n",
       "        [ 1.50399573, -0.49686611, -1.16951062, -0.08914925],\n",
       "        [-0.34710659, -1.68030252,  1.14634621,  1.81467219],\n",
       "        [ 0.64284392, -0.5112657 , -2.53507266,  0.42444107]]))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Z = np.dot(X, Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3, 5), (5, 4), (3, 4))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, Y.shape, Z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Z = X.dot(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How numpy arrays represent data\n",
    "**Contiguous** arrays represent data as one long ravelled line in memory. It uses the **shape**, **stride** and **dtype** attributes to represent N-dimensional arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "A = np.arange(30).reshape(5, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10, 11],\n",
       "       [12, 13, 14, 15, 16, 17],\n",
       "       [18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 6)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48, 8)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.strides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "B = A.view()\n",
    "B.shape = 30,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100,   1,   2,   3,   4,   5],\n",
       "       [  6,   7,   8,   9,  10,  11],\n",
       "       [ 12,  13,  14,  15,  16,  17],\n",
       "       [ 18,  19,  20,  21,  22,  23],\n",
       "       [ 24,  25,  26,  27,  28,  29]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B[0] = 100\n",
    "A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the last lines we infer that `B` points to the same data as `A`. As a matter of fact, we can obtain the exact memory address as follows (but this is not useful unless interacting with C code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29905264"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.ctypes.data  # memory address of first data point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8, 40), (48, 8))"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C = np.asfortranarray(A)\n",
    "C.strides, A.strides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "incompatible shape for a non-contiguous array",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-62-8c7476c882fe>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mD\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mC\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mD\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m30\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: incompatible shape for a non-contiguous array"
     ]
    }
   ],
   "source": [
    "D = C.view()\n",
    "D.shape = 30,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy.lib.stride_tricks import as_strided"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "D = as_strided(C, (30,), (8,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[100,   1,   2,   3,   4,   5],\n",
       "        [  6,   7,   8,   9,  10,  11],\n",
       "        [ 12,  13,  14,  15,  16,  17],\n",
       "        [ 18,  19,  20,  21,  22,  23],\n",
       "        [ 24,  25,  26,  27,  28,  29]]),\n",
       " array([100,   6,  12,  18,  24,   1,   7,  13,  19,  25,   2,   8,  14,\n",
       "         20,  26,   3,   9,  15,  21,  27,   4,  10,  16,  22,  28,   5,\n",
       "         11,  17,  23,  29]))"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C, D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Matplotlib\n",
    "Another important part of machine learning is visualization of data.  The most common\n",
    "tool for this in Python is `matplotlib`.  It is an extremely flexible package, but\n",
    "we will go over some basics here.\n",
    "\n",
    "First, something special to IPython notebook.  We can turn on the \"IPython inline\" mode,\n",
    "which will make plots show up inline in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Here we import the plotting functions\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f96a981fa50>]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OubN0oq4/9PvhN2rXIrgL+AOwbe4sltVOwLy0kZh1p3ZDPLUr+hLLAhNx0e/V2RS/PK29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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f96a9b80a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a line\n",
    "x = np.linspace(0, 10, 100)\n",
    "plt.plot(x, np.sin(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f96a9560290>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0mSc/L6jxY0f325IQ5Ys0OigbadMTabG+XPNj4cNtwwdNWkjtbRaL/T5R8fdQ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\ngBNCyEChgBNCyEChgBNCyED5/+/pBJsw0Cf2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f96a98bcd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter-plot points\n",
    "x = np.random.normal(size=500)\n",
    "y = np.random.normal(size=500)\n",
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 100)\n"
     ]
    }
   ],
   "source": [
    "# showing images\n",
    "x = np.linspace(1, 12, 100)\n",
    "y = x[:, np.newaxis]\n",
    "\n",
    "im = y * np.sin(x) * np.cos(y)\n",
    "print im.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f96a9433950>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IFfgsCZ2TYjNRTW/Q0Vd7HmmO485fm4gAqkOoiUPNQN8AkPBePo+RS3e+0wlr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\n/wD0Lm0/riJuFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f96a98c8910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# imshow - note that origin is at the top-left by default!\n",
    "plt.imshow(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet instance at 0x7f96a93e87e8>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uHOhGoezj0IN/JkX7XoYzrwHWj0WvckMcDtkaJQnTnRMwf/z5nT3sPY1m45fK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\n4QHbOedy4v8BB2fmDoIBWHIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f96a946c250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Contour plot - note that origin here is at the bottom-left by default!\n",
    "plt.contour(im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many, many more plot types available.  One useful way to explore these is by\n",
    "looking at the matplotlib gallery: http://matplotlib.org/gallery.html\n",
    "\n",
    "You can test these examples out easily in the notebook: simply copy the ``Source Code``\n",
    "link on each page, and put it in a notebook using the ``%load`` magic.\n",
    "For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %load http://matplotlib.org/mpl_examples/pylab_examples/ellipse_collection.py"
   ]
  },
  {
   "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": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}