Lectures/Day_2/01_Numpy/IntroNumPy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<CENTER>\n",
    "<H1>\n",
    "NASA Langley Research Center <BR>\n",
    "Spring 2017 Python Training <br>\n",
    "</H1>\n",
    "</CENTER>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy and Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference Documents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<OL>\n",
    "<LI> <A HREF=\"http://wiki.scipy.org/Tentative_NumPy_Tutorial\">Tentative Numpy Tutorial</A>\n",
    "<LI> <A HREF=\"http://docs.scipy.org/doc/numpy/reference\">NumPy Reference</A>\n",
    "<LI> <A HREF=\"http://mathesaurus.sourceforge.net/matlab-numpy.html\">NumPy for MATLAB Users</A>\n",
    "<LI> <A HREF=\"http://mathesaurus.sourceforge.net/r-numpy.html\">NumPy for R (and S-Plus) Users</A>\n",
    "<LI> <A HREF=\"http://people.duke.edu/~ccc14/pcfb/numerics.html\">NumPy and Matplotlib (Practical Cumputing for Biologists)</A>\n",
    "</OL>"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "In case you prefer a video:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "#YouTubeVideo(\"http://www.youtube.com/watch?v=3Fp1zn5ao2M\")\n",
    "YouTubeVideo(\"3Fp1zn5ao2M\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is Numpy?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<UL>\n",
    "<LI> Efficient array computing in Python.\n",
    "<LI> Allows the creation of arrays.\n",
    "<LI> Allows efficient indexing/slicing of arrays\n",
    "<LI> Provides mathematical functions that operate on an entire array.\n",
    "<LI> The critical thing to know is that Python <B> for </B> loops are very slow!\n",
    "     One should try to use array-operations as much as possible.\n",
    "</UL>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making Numpy Arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we want to import the appropriate modules into our name space (note this is done automatically with the \"--pylab\" flag."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<UL>\n",
    "<LI> The primary building block of the numpy module is the class \"ndarray\". \n",
    "<LI> A ndarray object represents a multidimensional, homogeneous array of fixed-sized items. \n",
    "<LI> An associated date-type object describes the format of each element in the array. \n",
    "<LI> An ndarray object is (almost) never instantiated directly, but instead using a method that returns an instance of the class.\n",
    "</UL>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image at 0x105f66990>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import Image \n",
    "Image(filename='npArray.png') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Array vs. List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "myList = [1, 2, 3, 5]\n",
    "arr = np.array([1, 2, 3, 5])\n",
    "print arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Elements of a one-dimensional array are accessed with the same syntax as a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "myList[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "arr[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "arr[2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Excercise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do you access the final element in the arr array?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first difference to note between lists and arrays is that arrays are homogeneous; <P>\n",
    "i.e. all elements of an array must be of the same type. <P>\n",
    "In contrast, lists can contain elements of arbitrary type. <P>\n",
    "For example, we can change the last element in our list above to be a string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "myList[-1] ='adding a string'\n",
    "myList"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But the same can not be done with an array, as we get an error message:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "arr[-1] ='adding a string'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Array memory representation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The information about the type of an array is contained in its dtype attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = np.array([[1, 2], [3, 4]], dtype=np.uint8)\n",
    "print x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "[b for b in bytes(x.data)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "arr = np.array([10, 20, 123123])\n",
    "arr.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once an array has been created, its dtype is fixed and it can only store elements of the same type. <P>\n",
    "For this example where the dtype is integer, if we store a floating point number it will be automatically converted into an integer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "arr[-1] = 1.234\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why is a homogeneous data type required for arrays? Speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = range(50000)\n",
    "y = np.arange(50000)\n",
    "\n",
    "%timeit [e**2  for e in x]\n",
    "%timeit y**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we created an array from an existing list; <P>\n",
    "now let us now see other ways in which we can create arrays, which we'll illustrate next. <P>\n",
    "A common need is to have an array initialized with a constant value, <P>\n",
    "and very often this value is 0 or 1 (suitable as starting value for additive and multiplicative loops respectively); <P>\n",
    "zeros creates arrays of all zeros, with any desired dtype:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "b = np.ones((3,2))\n",
    "print b\n",
    "print b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "c = np.zeros((1,3), int)\n",
    "print c\n",
    "print type(c)\n",
    "print c.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d = np.zeros(3, complex)\n",
    "print d\n",
    "print d.dtype\n",
    "id = np.eye(5)\n",
    "print id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then there are the linspace and logspace functions to create linearly and logarithmically-spaced grids, respectively, with a fixed number of points and including both ends of the specified interval.<P>\n",
    "\n",
    "linspace(a, b, n) generates n uniformly spaced coordinates, starting with a and ending with b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = np.linspace(-5, 5, 11) # start, stop, num\n",
    "print x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = np.r_[-5:5:11j]        # same as linspace(-1, 1, 11)\n",
    "print a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = np.arange(-5, 5, 1, float)   # upper limit 5 is not included!!\n",
    "print x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new array of 11 elements logarithmically spaced from 1 to 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing with other arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we saw how to index arrays with single numbers and slices, just like Python lists. \n",
    "But arrays allow for a more sophisticated kind of indexing which is very powerful: \n",
    "you can index an array with another array, and in particular with an array of boolean values. \n",
    "This is particluarly useful to extract information from an array that matches a certain condition.\n",
    "<P>\n",
    "Consider for example that in the array uni we want to replace all values above 0 with the value 10. We can do so by first finding the mask that indicates where this condition is true or false:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(0)  # <-- seed value, do not have to specify, but useful for reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rng.normal(loc=0, scale=1, size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "uni = rng.uniform(-10, 10, size=5)  # 5 random numbers, picked from a uniform distribution between -10 and 10\n",
    "print uni"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mask = uni > 0\n",
    "mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have this mask, we can use it to either read those values or to reset them to 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print 'Array:', uni\n",
    "print 'Masked array:', uni[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "uni[mask] = 10\n",
    "print uni"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Changing Array Dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = np.array([0, 1.2, 4, -9.1, 5, 8])\n",
    "print a.shape\n",
    "a.shape = (2,3) # turn a into a 2x3 matrix\n",
    "print a.size\n",
    "a.shape = (a.size,) # turn a into a vector of length 6 again\n",
    "print a.shape\n",
    "a = a.reshape(2,3) # same effect as setting a.shape\n",
    "print a.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Array Initialization from a Python Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def myfunc(i, j):\n",
    "    return (i+1)*(j+4-i)\n",
    "\n",
    "# make 3x6 array where a[i,j] = myfunc(i,j):\n",
    "a = fromfunction(myfunc, (3,6))\n",
    "print a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Array Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = np.linspace(-1, 1, 6)\n",
    "a[2:4] = -1        # set a[2] and a[3] equal to -1\n",
    "a[-1]  = a[0]      # set last element equal to first one\n",
    "a[:]   = 0         # set all elements of a equal to 0\n",
    "a.fill(0)          # set all elements of a equal to 0\n",
    "\n",
    "i = 1\n",
    "j = 2\n",
    "k = 2\n",
    "a.shape = (2,3)    # turn a into a 2x3 matrix\n",
    "print a[0,1]       # print element (0,1)\n",
    "a[i,j] = 10        # assignment to element (i,j)\n",
    "a[i][j] = 10       # equivalent syntax (slower)\n",
    "print a[:,k]       # print column with index k\n",
    "print a[1,:]       # print second row\n",
    "a[:,:] = 0         # set all elements of a equal to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = np.linspace(0, 29, 30)\n",
    "a.shape = (5,6)\n",
    "print a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print a[1:3,:-1:2]   # a[i,j] for i=1,2 and j=0,2,4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print a[::3,2:-1:2]   # a[i,j] for i=0,3 and j=2,4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Array Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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UQHRc7JQ4t/hjCXdJSLJMylBqWTpLRlnmg2y6nKLcx0OX5NXllxSuKmorflXKV9ZTXlUp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\nfgqIuycQCISfgv8D7vMy45WLbckAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image at 0x1058d5490>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import Image \n",
    "Image(filename='ArraySlicing.png') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slices Refer the Array Data\n",
    "<UL>\n",
    "<LI> With a as list, a[:] makes a copy of the data\t\n",
    "<LI> With a as array, a[:] is a reference to the data\n",
    "</UL>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print a\n",
    "b = a[1,:]      # extract 2nd column of a\n",
    "print a[1,1]\n",
    "b[1] = 2\n",
    "print a[1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Take a copy to avoid referencing via slices:\n",
    "b = a[1,:].copy()\n",
    "print a[1,1]\n",
    "b[1] = 7777     # b and a are two different arrays now\n",
    "print a[1,1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Array Computations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "b = 3*a - 1    # a is array, b becomes array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above operation generates a temporary array:\n",
    "<OL>\n",
    "<LI> tb = 3*a\n",
    "<LI> b = tb - 1\n",
    "</OL>\n",
    "As far as possible, we want to avoid the creation\n",
    "  of temporary arrays to limit the memory usage and\n",
    "  to decrease the computational time associated with\n",
    "  with array computations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RVhAxEnUVixLP3H1boktyRhotwye7R85ZPkwhTbFEqU75ucp71YU9WHWOvTIaZpoBWuna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\n2FJkKHoaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image at 0x105f669d0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import Image \n",
    "Image(filename='Broadcasting.png') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In-Place Array Arithmetics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "b = a\n",
    "b *= 3  # or multiply(b, 3, b)\n",
    "b -= 1  # or subtract(b, 1, b)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "In-place operations:\n",
    "    \n",
    "   a *= 3.0     # multiply a's elements by 3\n",
    "   a -= 1.0     # subtract 1 from each element\n",
    "   a /= 3.0     # divide each element by 3\n",
    "   a += 1.0     # add 1 to each element\n",
    "   a **= 2.0    # square all elements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Math Functions and Array Arguments"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "   # let b be an array\n",
    "   c = sin(b)    \n",
    "   c = arcsin(c) \n",
    "   c = sinh(b)\n",
    "   # same functions for the cos and tan families\n",
    "   c = b**2.5  # power function\n",
    "   c = log(b)\n",
    "   c = exp(b)\n",
    "   c = sqrt(b)\n",
    "    \n",
    "   # a is an array\n",
    "   a.clip(min=3, max=12)  \t# clip elements\n",
    "   a.mean(); mean(a)     \t# mean value\n",
    "   a.var();  var(a)       \t# variance\n",
    "   a.std();  std(a)        \t# standard deviation\n",
    "   median(a)\n",
    "   cov(x,y)              \t# covariance\n",
    "   trapz(a)              \t# Trapezoidal integration\n",
    "   diff(a)               \t# finite differences (da/dx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NumPy Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x1 = np.array([1, 2, 3], float)\n",
    "x2 = np.matrix(x)               # or just mat(x)\n",
    "print x2                        # row vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x3 = mat(x).transpose()          # column vector\n",
    "print x3\n",
    "type(x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "A = np.eye(10)                    # identity matrix\n",
    "print A\n",
    "B = np.mat(A)                    # turn array to matrix\n",
    "print B\n",
    "y2 = x2*B                     # vector-matrix product\n",
    "print y2\n",
    "y3 = B*x3                     # matrix-vector product\n",
    "print y3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = np.array([[1,2],[3,4]])\n",
    "print \"a = \", a\n",
    "m = np.mat(a)\n",
    "print \"m = \", m\n",
    "print \"a[0] = \", a[0]\n",
    "print \"m[0] = \", m[0]\n",
    "print \"a*a  = \", a*a\n",
    "print \"m*m  = \", m*m\n",
    "print \"dot  = \", dot(a, a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Universal Functions and Loops"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Universal functions run much faster than for loops, which should be avoided whenever possible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def mult1(a,b):\n",
    "    return a * b\n",
    "\n",
    "def mult2(a,b):\n",
    "    c = np.empty(a.shape)\n",
    "    for i in range(a.shape[0]):\n",
    "        for j in range(a.shape[1]):\n",
    "            c[i,j] = a[i,j] * b[i,j]\n",
    "    return c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = np.random.random((800,800))\n",
    "b = np.random.random((800,800))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "timeit mult1(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "timeit mult2(a,b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading and writing arrays to disk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy lets you read and write arrays into files in a number of ways. In order to use these tools well, it is critical to understand the difference between a text and a binary file containing numerical data. \n",
    "In a text file, the number\n",
    "&pi;\n",
    "could be written as \"3.141592653589793\", for example: a string of digits that a human can read, with in this case 15 decimal digits. In contrast, that same number written to a binary file would be encoded as 8 characters (bytes) that are not readable by a human but which contain the exact same data that the variable pi had in the computer's memory. <P>\n",
    "\n",
    "The tradeoffs between the two modes are thus:\n",
    "<UL>\n",
    "<LI> <B>Text mode</B>: occupies more space, precision can be lost (if not all digits are written to disk), but is readable and editable by hand with a text editor. Can only be used for one- and two-dimensional arrays.\n",
    "<LI> <B>Binary mode</B>: compact and exact representation of the data in memory, can't be read or edited by hand. Arrays of any size and dimensionality can be saved and read without loss of information.\n",
    "</UL>\n",
    "\n",
    "First, let's see how to read and write arrays in text mode. The np.savetxt function saves an array to a text file, with options to control the precision, separators and even adding a header:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "arr = np.arange(10).reshape(2, 5)\n",
    "print arr                            \n",
    "np.savetxt('test.out', arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!cat test.out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this same type of file can then be read with the matching np.loadtxt function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "arr2 = np.loadtxt('test.out')\n",
    "print arr2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use the function np.genfromtxt that deals with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "arr3 = np.genfromtxt('test.out', missing_values='0.000000000000000000e+00', usemask=True)\n",
    "print arr3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For binary data, Numpy provides the np.save and np.savez routines. \n",
    "The first saves a single array to a file with .npy extension, \n",
    "while the latter can be used to save a group of arrays into a single file with .npz extension. \n",
    "The files created with these routines can then be read with the np.load function.\n",
    "<P>\n",
    "Let us first see how to use the simpler np.save function to save a single array:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.save('test.npy', arr)\n",
    "# Now we read this back\n",
    "arr_loaded = np.load('test.npy')\n",
    "\n",
    "print arr\n",
    "print arr_loaded\n",
    "\n",
    "print arr_loaded.dtype\n",
    "\n",
    "# Let's see if any element is non-zero in the difference.\n",
    "# A value of True would be a problem.\n",
    "print'Any differences?', np.any(arr - arr_loaded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us see how the np.savez_compressed function works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.savez_compressed('test.npz', first=arr, second=arr2)\n",
    "arrays = np.load('test.npz')\n",
    "arrays.files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The object returned by np.load from an .npz file works like a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a=arrays['first']\n",
    "b=arrays['second']\n",
    "print 'a = ', a\n",
    "print 'b = ', b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This .npz format is a very convenient way to package compactly and without loss of information, into a single file, a group of related arrays that pertain to a specific problem. At some point, however, the complexity of your dataset may be such that the optimal approach is to use one of the standard formats in scientific data processing that have been designed to handle complex datasets, such as NetCDF or HDF5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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