Doc/notebooks/3_NumpyIntro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Numpy\n",
    "<span style=\"color:blue; font-family:Georgia; font-size:1.5em;\">Ahmed Ammar and Hassen Ghalila</span>\n",
    "\n",
    "<span style=\"color:blue; font-family:Georgia; font-size:1em;\">Tunis Faculty of Sciences, University of Tunis El Manar- Tunisia</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed May 17 22:25:11 2017\n"
     ]
    }
   ],
   "source": [
    "# This gives the last run:\n",
    "import time\n",
    "print(time.ctime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# The meaning of this command line is in MatplotlibIntro\n",
    "%pylab inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See <A HREF=\"4_MatplotlibIntro.ipynb\" target=\"_blank\">MatplotlibIntro</A> file for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "The library **numpy** is used in almost all Python numerical calculations. It provides all kind of data structures vectors, matrix and higher dimension data structures. It's impemented in C and Fortran whith great performances for vectorized calculations. To use it you need to import the package by for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the **numpy** package the terminology used for vectors, matix and others is **array**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating *numpy* arrays\n",
    "\n",
    "There are several ways to initialize new arrays with **numpy** such as for example:\n",
    "\n",
    "* Using lists or tuples\n",
    "* Using functions dedicated to the generation of arrays such as **arange**, **linspace**, etc.\n",
    "* Reading data from files\n",
    "\n",
    "### Lists\n",
    "\n",
    "For example, we can use the function **numpy.array** to create vectors or matrices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vector: the argument of array is simply a Python list\n",
    "v = array([1,2,3,4])\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Matrix: the argument of the function is a nested list\n",
    "M = array([[1, 2], [3, 4]])\n",
    "\n",
    "M"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *v* and *M* objects defined above are both of type **ndarray** provided by **numpy** module. We can get their type by using the **type** function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(numpy.ndarray, numpy.ndarray)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(v), type(M)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get informations about their forms by using the **ndarray.shape** module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get informations about the size of the array by using **ndarray.size** module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get similar informations by using the functions **numpy.shape** and **numpy.size**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size(M)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what is the difference between Python list and **numpy.ndarray** ? Why not use directly lists to perform calculations? The answer is :\n",
    "\n",
    "* List are of general form, they can containt any king of elements, in numpy arrays all the elements must have the same type. \n",
    "* List are dynamically typed and can be appended to very efficiently, consequently they don't support mathematical functions. In other hand, Numpy arrays are statically typed and homogeneous. The type of element is fixed when it is created.\n",
    "* In practice, numpy arrays are faster for vectorial functions than mapping functions to lists\n",
    "* Numpy arrays have many array related methods (´argmin´, ´min´, ´sort´, etc)\n",
    "\n",
    "We can get the data type of an array by using the **dtype** property:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A wrong type assignement in a numpy array will generate an error ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: 'hello'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-a09d72434238>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"hello\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'hello'"
     ]
    }
   ],
   "source": [
    "M[0,0] = \"hello\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can explicitely define the type of the elements of the array when we create it by using the keyword **dtype**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.+0.j,  2.+0.j],\n",
       "       [ 3.+0.j,  4.+0.j]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = array([[1, 2], [3, 4]], dtype=complex)\n",
    "\n",
    "M"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The common data types that can be used with **dtype** are: **int**, **float**, **complex**, **bool**, **object**, etc. We can also define the bit size of the data by the keywords : **int64**, **int16**, **float128**, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Functions to generate matrices\n",
    "\n",
    "For large arrays it is difficult to initialize them manually by using Python lists. Instead of that we can use some of the many functions provided by **numpy**. The most common are:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### arange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a range\n",
    "x = arange(0, 10, 1) # Arguments: start, stop, step\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -1.00000000e+00,  -9.00000000e-01,  -8.00000000e-01,\n",
       "        -7.00000000e-01,  -6.00000000e-01,  -5.00000000e-01,\n",
       "        -4.00000000e-01,  -3.00000000e-01,  -2.00000000e-01,\n",
       "        -1.00000000e-01,  -2.22044605e-16,   1.00000000e-01,\n",
       "         2.00000000e-01,   3.00000000e-01,   4.00000000e-01,\n",
       "         5.00000000e-01,   6.00000000e-01,   7.00000000e-01,\n",
       "         8.00000000e-01,   9.00000000e-01])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = arange(-1, 1, 0.1)\n",
    "\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### linspace et logspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.        ,   0.41666667,   0.83333333,   1.25      ,\n",
       "         1.66666667,   2.08333333,   2.5       ,   2.91666667,\n",
       "         3.33333333,   3.75      ,   4.16666667,   4.58333333,\n",
       "         5.        ,   5.41666667,   5.83333333,   6.25      ,\n",
       "         6.66666667,   7.08333333,   7.5       ,   7.91666667,\n",
       "         8.33333333,   8.75      ,   9.16666667,   9.58333333,  10.        ])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By using linspace, the two extrem points are used\n",
    "linspace(0, 10, 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.00000000e+00,   3.03773178e+00,   9.22781435e+00,\n",
       "         2.80316249e+01,   8.51525577e+01,   2.58670631e+02,\n",
       "         7.85771994e+02,   2.38696456e+03,   7.25095809e+03,\n",
       "         2.20264658e+04])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logspace(0, 10, 10, base=e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### mgrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x, y = mgrid[0:5, 0:5] # Similar to meshgrid in MATLAB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [2, 2, 2, 2, 2],\n",
       "       [3, 3, 3, 3, 3],\n",
       "       [4, 4, 4, 4, 4]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4],\n",
       "       [0, 1, 2, 3, 4],\n",
       "       [0, 1, 2, 3, 4],\n",
       "       [0, 1, 2, 3, 4],\n",
       "       [0, 1, 2, 3, 4]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### random data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.30217342,  0.17469325,  0.13748698,  0.59256396,  0.84727111],\n",
       "       [ 0.03885383,  0.39597383,  0.23501532,  0.01876195,  0.72151725],\n",
       "       [ 0.48115762,  0.65082568,  0.18980343,  0.40965788,  0.09004404],\n",
       "       [ 0.72036623,  0.6173244 ,  0.38741751,  0.51735635,  0.67081911],\n",
       "       [ 0.48094799,  0.1079393 ,  0.98895197,  0.58791055,  0.58175316]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Uniform random number in [0,1]\n",
    "random.rand(5,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.01139707, -1.03336181,  1.02696659,  0.1717184 ,  0.27175062],\n",
       "       [-0.36927631,  0.17178256, -1.39735975,  0.72859722,  0.06422201],\n",
       "       [ 0.41465608, -1.16840465, -0.12940491,  0.34972675,  1.07765169],\n",
       "       [-0.72994423, -1.17789234, -0.15645569, -0.24297149,  0.70644477],\n",
       "       [-0.04562984, -0.2674874 , -1.48753472,  0.46260047, -0.1098033 ]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalized randomly distributed numbers\n",
    "random.randn(5,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### diag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0],\n",
       "       [0, 2, 0],\n",
       "       [0, 0, 3]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Diagonal matrix\n",
    "diag([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, 0],\n",
       "       [0, 0, 2, 0],\n",
       "       [0, 0, 0, 3],\n",
       "       [0, 0, 0, 0]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Diagonal matrix with shift from the main diagonal\n",
    "diag([1,2,3], k=1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### zeros et ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeros((3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ones((3,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## File I / O\n",
    "\n",
    "\n",
    "### Data separated by commas (CSV)\n",
    "Among the datafiles, a common format are data separated by commas such as in *CSV* files. Reading data from these files and assign them in numpy array we can use the **numpy.genfromtxt** function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1800  1  1    -6.1    -6.1    -6.1 1\r\n",
      "1800  1  2   -15.4   -15.4   -15.4 1\r\n",
      "1800  1  3   -15.0   -15.0   -15.0 1\r\n",
      "1800  1  4   -19.3   -19.3   -19.3 1\r\n",
      "1800  1  5   -16.8   -16.8   -16.8 1\r\n",
      "1800  1  6   -11.4   -11.4   -11.4 1\r\n",
      "1800  1  7    -7.6    -7.6    -7.6 1\r\n",
      "1800  1  8    -7.1    -7.1    -7.1 1\r\n",
      "1800  1  9   -10.1   -10.1   -10.1 1\r\n",
      "1800  1 10    -9.5    -9.5    -9.5 1\r\n"
     ]
    }
   ],
   "source": [
    "!head data/stockholm_td_adj.dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = genfromtxt('data/stockholm_td_adj.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77431, 7)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HMe8zhKmje8oCC7jLmjE0qQxCrA+MSZO5rpPOGwaNcyqmoFo2apLJHQAHKfom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\n7Urk6kJokrh775VFBbLney7ftNtvj5f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aooLTUvsRDTMem6z2oIP4shJwe4wvWuhPflL/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DExswy4Cz/GdjI3LhlLRKYP5ZVX3DPuYmZNNwALJXQWKeX5/OlP/d/ljtq1odV3FpAx4Jhj+q8R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\n/C+XZaokRHmj0QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d2380f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize=(14,4))\n",
    "ax.plot(data[:,0]+data[:,1]/12.0+data[:,2]/365, data[:,5])\n",
    "ax.axis('tight')\n",
    "ax.set_title('tempeatures in Stockholm')\n",
    "ax.set_xlabel('year')\n",
    "ax.set_ylabel('tempature (C)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}