Lec 12 - Array Processing.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:619530f5418fcc0cf4751eac9112d61c4952bce617eb8a1ce2159bd61ba925f9"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Set array for one side of grid\n",
      "points = np.arange(-5,5,0.01)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Create the grid\n",
      "dx,dy=np.meshgrid(points,points)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Show what one side looks like\n",
      "dx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "array([[-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99],\n",
        "       [-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99],\n",
        "       [-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99],\n",
        "       ..., \n",
        "       [-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99],\n",
        "       [-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99],\n",
        "       [-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99]])"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Evaluating Function\n",
      "z = (np.sin(dx) + np.sin(dy))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Lets take a look at the z result\n",
      "z"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "array([[  1.91784855e+00,   1.92063718e+00,   1.92332964e+00, ...,\n",
        "         -8.07710558e-03,  -5.48108704e-03,  -2.78862876e-03],\n",
        "       [  1.92063718e+00,   1.92342581e+00,   1.92611827e+00, ...,\n",
        "         -5.28847682e-03,  -2.69245827e-03,  -5.85087534e-14],\n",
        "       [  1.92332964e+00,   1.92611827e+00,   1.92881072e+00, ...,\n",
        "         -2.59601854e-03,  -5.63993297e-14,   2.69245827e-03],\n",
        "       ..., \n",
        "       [ -8.07710558e-03,  -5.28847682e-03,  -2.59601854e-03, ...,\n",
        "         -1.93400276e+00,  -1.93140674e+00,  -1.92871428e+00],\n",
        "       [ -5.48108704e-03,  -2.69245827e-03,  -5.63993297e-14, ...,\n",
        "         -1.93140674e+00,  -1.92881072e+00,  -1.92611827e+00],\n",
        "       [ -2.78862876e-03,  -5.85087534e-14,   2.69245827e-03, ...,\n",
        "         -1.92871428e+00,  -1.92611827e+00,  -1.92342581e+00]])"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Plot out the 2d array\n",
      "plt.imshow(z)\n",
      "\n",
      "#Plot with a colorbar\n",
      "plt.colorbar()\n",
      "\n",
      "#Give the plot a title\n",
      "plt.title(\"Plot for sin(x)+sin(y)\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.text.Text at 0xe5897f0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAT8AAAEKCAYAAABkEVK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHyJeV8rwSxF+AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xe205080>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Lets learn how to use the numpy where\n",
      "\n",
      "#First the slow way to do things\n",
      "\n",
      "A = np.array([1,2,3,4])\n",
      "\n",
      "B= np.array([100,200,300,400])\n",
      "\n",
      "#Now a boolean array\n",
      "condition = np.array([True,True,False,False])\n",
      "\n",
      "#Using a list comprehension\n",
      "answer = [(A_val if cond else B_val) for A_val,B_val,cond in zip(A,B,condition)]\n",
      "\n",
      "#Show the answer\n",
      "answer\n",
      "\n",
      "#Problems include speed issues and multi-dimensional array issues"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "[1, 2, 300, 400]"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Now using numpy.where\n",
      "\n",
      "answer2 = np.where(condition,A,B)\n",
      "\n",
      "#Show\n",
      "answer2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "array([  1,   2, 300, 400])"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Can use np.where  on 2d for manipulation\n",
      "\n",
      "from numpy.random import randn\n",
      "\n",
      "arr = randn(5,5)\n",
      "\n",
      "#Show arr\n",
      "arr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "array([[-0.73634048, -1.51965619,  1.22297383, -0.79751068,  1.53059327],\n",
        "       [-0.47915242, -0.2784561 ,  0.04653599, -0.40005163,  0.87191881],\n",
        "       [-0.85121555,  0.57870514, -1.06428997, -0.39276083, -0.14220729],\n",
        "       [-0.06358242, -0.15793119,  0.74910386,  0.96455735, -0.18257646],\n",
        "       [ 0.34020976,  2.31433917,  0.71924504, -1.03610407, -0.12509066]])"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Where array is less than zero, make that value zero, otherwise leave it as the array value\n",
      "np.where(arr < 0,0,arr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "array([[ 0.        ,  0.        ,  1.22297383,  0.        ,  1.53059327],\n",
        "       [ 0.        ,  0.        ,  0.04653599,  0.        ,  0.87191881],\n",
        "       [ 0.        ,  0.57870514,  0.        ,  0.        ,  0.        ],\n",
        "       [ 0.        ,  0.        ,  0.74910386,  0.96455735,  0.        ],\n",
        "       [ 0.34020976,  2.31433917,  0.71924504,  0.        ,  0.        ]])"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Other Statistical Processing\n",
      "arr = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
      "\n",
      "arr\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "array([[1, 2, 3],\n",
        "       [4, 5, 6],\n",
        "       [7, 8, 9]])"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#SUM\n",
      "arr.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "45"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Can also do along an axis (we shold expect a 3 diff between the columns)\n",
      "arr.sum(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "array([12, 15, 18])"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Mean\n",
      "arr.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "5.0"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Standard Deviation\n",
      "arr.std()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "2.5819888974716112"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Variance\n",
      "arr.var()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "6.666666666666667"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Also any and all for processing boolean arrays\n",
      "\n",
      "bool_arr = np.array([True,False,True])\n",
      "\n",
      "#For any True\n",
      "bool_arr.any()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# For all True\n",
      "bool_arr.all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Finally sort array\n",
      "\n",
      "#Create a random array\n",
      "arr = randn(5)\n",
      "#show\n",
      "arr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "array([ 0.13206284, -0.2571765 ,  1.66549674, -0.15187365,  1.25231422])"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Sort it\n",
      "arr.sort()\n",
      "#show\n",
      "arr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "array([-0.2571765 , -0.15187365,  0.13206284,  1.25231422,  1.66549674])"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Lets learn about unique\n",
      "countries = np.array(['France', 'Germany', 'USA', 'Russia','USA','Mexico','Germany'])\n",
      "\n",
      "np.unique(countries)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "array(['France', 'Germany', 'Mexico', 'Russia', 'USA'], \n",
        "      dtype='|S7')"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# in1d test values in one array\n",
      "np.in1d(['France','USA','Sweden'],countries)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "array([ True,  True, False], dtype=bool)"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}