{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Scientific Computing Lecture 04: Arrays, I/O, Plotting\n",
    "** J.R. Gladden, University of Mississippi, Spring 2018 **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "\n",
    "**Topics:**\n",
    "- Vectorization of fucntions with arrays\n",
    "- File I/O with arrays\n",
    "- Array manipulation\n",
    "- Data visualization modules\n",
    "- First steps with matplotlib (pylab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Arrays are a new data type (object) like lists, but MUCH more efficient for mathematical operations.  Some differences is that arrays are restricted to numeric data types.  Arrays are NOT a built in object and have to be defined in an imported module. The industry standard now is a module called ** numpy **."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Generating arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import numpy as np ## Common way to import\n",
    "a1=np.zeros(100) #creates an array of 100 zeros (floating)\n",
    "I1=np.eye(10) # 10x10 array with I_{i=j}=1. & 0 otherwise.\n",
    "a2=np.array( [1,2,3,4,5 ] ) #converts a list to array\n",
    "x=np.linspace(0,10,100) #array of 100 floats b/t 0 & 10\n",
    "x2=np.arange(0,100,0.1) #array b/t 0 & 10 in steps of 0.1 \n",
    "xy=np.array([ [1,1],[2,4],[3,9],[4,16]] ) #2D array\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xy[1,0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Slicing arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "x=xy[:,0] # x becomes the 1st column of xy  (‘:’ means all)\n",
    "y=xy[:,1] # First index is row #, second is column #\n",
    "xy[2,1] # returns -> 9 (3rd row, 2nd column)\n",
    "xy[0,1] = 4.0 # reassigns this element \n",
    "xy2 = xy # just makes xy accessible by another name\n",
    "xycopy = xy.copy() #makes a 2nd independent array \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Manipulating arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.00000000e+00,   1.01010101e-01,   2.02020202e-01,\n",
       "         3.03030303e-01,   4.04040404e-01,   5.05050505e-01,\n",
       "         6.06060606e-01,   7.07070707e-01,   8.08080808e-01,\n",
       "         9.09090909e-01,   1.01010101e+00,   1.11111111e+00,\n",
       "         1.21212121e+00,   1.31313131e+00,   1.41414141e+00,\n",
       "         1.51515152e+00,   1.61616162e+00,   1.71717172e+00,\n",
       "         1.81818182e+00,   1.91919192e+00,   2.02020202e+00,\n",
       "         2.12121212e+00,   2.22222222e+00,   2.32323232e+00,\n",
       "         2.42424242e+00,   2.52525253e+00,   2.62626263e+00,\n",
       "         2.72727273e+00,   2.82828283e+00,   2.92929293e+00,\n",
       "         3.03030303e+00,   3.13131313e+00,   3.23232323e+00,\n",
       "         3.33333333e+00,   3.43434343e+00,   3.53535354e+00,\n",
       "         3.63636364e+00,   3.73737374e+00,   3.83838384e+00,\n",
       "         3.93939394e+00,   4.04040404e+00,   4.14141414e+00,\n",
       "         4.24242424e+00,   4.34343434e+00,   4.44444444e+00,\n",
       "         4.54545455e+00,   4.64646465e+00,   4.74747475e+00,\n",
       "         4.84848485e+00,   4.94949495e+00,   5.05050505e+00,\n",
       "         5.15151515e+00,   5.25252525e+00,   5.35353535e+00,\n",
       "         5.45454545e+00,   5.55555556e+00,   5.65656566e+00,\n",
       "         5.75757576e+00,   5.85858586e+00,   5.95959596e+00,\n",
       "         6.06060606e+00,   6.16161616e+00,   6.26262626e+00,\n",
       "         6.36363636e+00,   6.46464646e+00,   6.56565657e+00,\n",
       "         6.66666667e+00,   6.76767677e+00,   6.86868687e+00,\n",
       "         6.96969697e+00,   7.07070707e+00,   7.17171717e+00,\n",
       "         7.27272727e+00,   7.37373737e+00,   7.47474747e+00,\n",
       "         7.57575758e+00,   7.67676768e+00,   7.77777778e+00,\n",
       "         7.87878788e+00,   7.97979798e+00,   8.08080808e+00,\n",
       "         8.18181818e+00,   8.28282828e+00,   8.38383838e+00,\n",
       "         8.48484848e+00,   8.58585859e+00,   8.68686869e+00,\n",
       "         8.78787879e+00,   8.88888889e+00,   8.98989899e+00,\n",
       "         9.09090909e+00,   9.19191919e+00,   9.29292929e+00,\n",
       "         9.39393939e+00,   9.49494949e+00,   9.59595960e+00,\n",
       "         9.69696970e+00,   9.79797980e+00,   9.89898990e+00,\n",
       "         1.00000000e+01,   0.00000000e+00,   9.11502583e-02,\n",
       "         1.63945851e-01,   2.20401487e-01,   2.62464743e-01,\n",
       "         2.91992415e-01,   3.10732878e-01,   3.20313668e-01,\n",
       "         3.22233562e-01,   3.17858491e-01,   3.08420641e-01,\n",
       "         2.95020188e-01,   2.78629158e-01,   2.60096933e-01,\n",
       "         2.40157022e-01,   2.19434721e-01,   1.98455383e-01,\n",
       "         1.77653000e-01,   1.57378914e-01,   1.37910456e-01,\n",
       "         1.19459364e-01,   1.02179882e-01,   8.61764256e-02,\n",
       "         7.15107753e-02,   5.82087333e-02,   4.62662298e-02,\n",
       "         3.56548618e-02,   2.63268685e-02,   1.82195529e-02,\n",
       "         1.12591706e-02,   5.36431082e-03,   4.48800435e-04,\n",
       "        -3.57583540e-03,  -6.79832024e-03,  -9.30594431e-03,\n",
       "        -1.11832166e-02,  -1.25108147e-02,  -1.33647975e-02,\n",
       "        -1.38160475e-02,  -1.39299095e-02,  -1.37659988e-02,\n",
       "        -1.33781496e-02,  -1.28144790e-02,  -1.21175458e-02,\n",
       "        -1.13245819e-02,  -1.04677801e-02,  -9.57462192e-03,\n",
       "        -8.66823244e-03,  -7.76775085e-03,  -6.88870634e-03,\n",
       "        -6.04339206e-03,  -5.24123032e-03,  -4.48912389e-03,\n",
       "        -3.79178961e-03,  -3.15207116e-03,  -2.57122934e-03,\n",
       "        -2.04920828e-03,  -1.58487737e-03,  -1.17624867e-03,\n",
       "        -8.20670417e-04,  -5.14997385e-04,  -2.55739136e-04,\n",
       "        -3.91875674e-05,   1.38474883e-04,   2.81084792e-04,\n",
       "         3.92423508e-04,   4.76157503e-04,   5.35791756e-04,\n",
       "         5.74634644e-04,   5.95772878e-04,   6.02055115e-04,\n",
       "         5.96082957e-04,   5.80208158e-04,   5.56534944e-04,\n",
       "         5.26926479e-04,   4.93014592e-04,   4.56211996e-04,\n",
       "         4.17726308e-04,   3.78575291e-04,   3.39602811e-04,\n",
       "         3.01495078e-04,   2.64796829e-04,   2.29927159e-04,\n",
       "         1.97194771e-04,   1.66812477e-04,   1.38910812e-04,\n",
       "         1.13550678e-04,   9.07349548e-05,   7.04190636e-05,\n",
       "         5.25204690e-05,   3.69271446e-05,   2.35050343e-05,\n",
       "         1.21045543e-05,   2.56619294e-06,  -5.27473113e-06,\n",
       "        -1.15840857e-05,  -1.65256483e-05,  -2.02584678e-05,\n",
       "        -2.29347955e-05,  -2.46985202e-05])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.linspace(0,10,100)\n",
    "y=np.sin(x)*np.exp(-x)\n",
    "xy=np.array([x,y])\n",
    "xy.shape   #returns tuple (2,100) – 2 rows, 100 columns\n",
    "xyT=xy.transpose() # inverts rows and columns\n",
    "xyT.shape #returns tuple (100,2)\n",
    "y.max() #returns max value in y (y.min() also)\n",
    "y.argmax() #return the index of the max value.\n",
    "x.dot(y) #returns dot product of 2 1D arrays\n",
    "x.tolist() # converts the array to a list object\n",
    "xy.flatten() #turn multidim array into 1D array\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Clipping Arrays###\n",
    "This allows you to select only part of an array based on some Boolean (T/F) test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.00000000e+00   3.24699469e-01   6.14212713e-01   8.37166478e-01\n",
      "   9.69400266e-01   9.96584493e-01   9.15773327e-01   7.35723911e-01\n",
      "   4.75947393e-01   1.64594590e-01  -1.64594590e-01  -4.75947393e-01\n",
      "  -7.35723911e-01  -9.15773327e-01  -9.96584493e-01  -9.69400266e-01\n",
      "  -8.37166478e-01  -6.14212713e-01  -3.24699469e-01  -2.44929360e-16]\n"
     ]
    }
   ],
   "source": [
    "x=np.linspace(0,2*np.pi,20)\n",
    "y=np.sin(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.32469947,  0.61421271,  0.83716648,  0.96940027,  0.99658449,\n",
       "        0.91577333,  0.73572391,  0.47594739,  0.16459459])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[y>0]  # This will return another array with only the + values of y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.          0.32469947  0.61421271  0.83716648  0.96940027  0.99658449\n",
      "  0.91577333  0.73572391  0.47594739  0.16459459  0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.          0.        ]\n"
     ]
    }
   ],
   "source": [
    "y[y<0]=0  # This sets all the - values equal to 0\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False  True  True  True  True  True  True  True  True  True False False\n",
      " False False False False False False False False]\n"
     ]
    }
   ],
   "source": [
    "booleanArray = y >0   # This returns an array of Booleans according to the test\n",
    "print(booleanArray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Arrays are MUCH faster than lists. I will explain the technical reasons for this later. Here is a test where we square 100,000 numbers in a list and an array and compare the speed.  We'll take advantage of the %time magic in iPython."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to square the list:\n",
      "CPU times: user 2.49 s, sys: 194 ms, total: 2.68 s\n",
      "Wall time: 2.68 s\n",
      "==============================\n",
      "Time to square the array:\n",
      "CPU times: user 18.6 ms, sys: 2.5 ms, total: 21.1 ms\n",
      "Wall time: 20.6 ms\n"
     ]
    }
   ],
   "source": [
    "length = 10**7\n",
    "xList = range(length)\n",
    "xListSquared=[]\n",
    "print(\"Time to square the list:\")\n",
    "%time  for i in range(length):  xListSquared.append(xList[i]**2)\n",
    "xArray=np.linspace(0,length,length)\n",
    "print('='*30)\n",
    "print(\"Time to square the array:\")\n",
    "%time xArraySquared = xArray**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### File I/O with Numpy###\n",
    "Reading data in (or writing data out) to files is much easier with numpy arrays using np.loadtxt() and np.savetxt(). Files need to be ASCII text files. There are many options: \n",
    "* Specify character that separates numbers\n",
    "* Specify character for comments\n",
    "* Specify number of lines to skip if there is a header\n",
    "\n",
    "In this example, there is a file in this directory called 'data.dat' with 2 columns of numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def squareit(x):\n",
    "\treturn x**2\n",
    "data = np.loadtxt('oldfile.dat')\n",
    "x=data[:,0] # Select the 1st column for x values\n",
    "y=data[:,1] # Select the 2nd column as y values\n",
    "#Pass an array to a funct and it returns an array\n",
    "y2= squareit(y) # OR can just use y2=x**2\n",
    "\n",
    "#This will create a new text file in the current directory with new data in it.\n",
    "np.savetxt('newfile.dat',(x,y2)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.,   4.,   9.,  36.,  64.])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Remote Files\n",
    "You can also easily open files on a remote server using the urllib2 module. Handy for working with remote collaborators or grabbing constantly updated data such as from a data logger. Obviously have to be connected to the internet!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import urllib2 as url\n",
    "import numpy as np\n",
    "# This file actually exists on the Physics server.\n",
    "address='http://www.phy.olemiss.edu/~jgladden/sci_comp/handouts/data.dat'\n",
    "infile=url.urlopen(address)\n",
    "x,y = np.loadtxt(infile,unpack=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.,   4.,   9.,  36.,  64.])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Visualizing Data with matplotlib\n",
    "Finally we can talk about plotting!  Visualizing data is a very rich and broad field and we will discuss in more depth soon.  There are many choices in python, each with certain strengths. Matplotlib has become a very robust and standard library.  It easily creates very professional looking plots, similar to MatLab.\n",
    "\n",
    "Note the import method below.  That's pretty standard these days.  Since we are in iPython Notebook, we'll also use a \"magic\" which allows plots to show up in this web browser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HfB14EygEnjfGnBORR0TkEc9ue4AyoAT4GfDVGx072ZiUb+gC7mN3/HIzfQNG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\nKaXUEP8PXct5/W9iau8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15cb04f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x=np.linspace(0,4*np.pi)\n",
    "y=np.sin(x)\n",
    "plt.plot(x,y)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now let's get a bit fancier by plotting 2 curves together and adjusting the markers and colors.  And add a title and axes labels.  Code should be pretty self explanatory..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x112f04310>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6fs2Pkl/SKZQyJqs8FBHZH/glzufxBXBZHu69DNg5abxTMBe99wHALcBJqvrl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\nXjCFYhiGYeSF/w8OS3OWx2x5FwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112dfe2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y2=np.cos(x)\n",
    "plt.plot(x,y,'go',ms=8)\n",
    "plt.plot(x,y2,'r-', lw=4)\n",
    "plt.xlabel(\"Time (sec)\")\n",
    "plt.ylabel(\"Voltage (mV)\")\n",
    "plt.title(\"My first plot!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can make quick plots of data in a file with a single command - the plotfile() function.  Run plt.plotfile? to see how it works and what the options are!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M5AA+n3hdoqcsYIwxpgdCYWVDYwuf1u1haX3ktnzLHlo7up+AWwRy05L44uiB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\njDHGFf8fAfeUAN/dEiYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ff718d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plotfile('mydata.dat',(0,1,2),delimiter=' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a bit of code demonstrating **Mayavi**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy import pi, sin, cos, mgrid\n",
    "dphi, dtheta = pi/250.0, pi/250.0\n",
    "[phi,theta] = mgrid[0:pi+dphi*1.5:dphi,0:2*pi+dtheta*1.5:dtheta]\n",
    "m0 = 4; m1 = 3; m2 = 2; m3 = 3; m4 = 6; m5 = 2; m6 = 6; m7 = 4;\n",
    "r = sin(m0*phi)**m1 + cos(m2*phi)**m3 + sin(m4*theta)**m5 + cos(m6*theta)**m7\n",
    "x = r*sin(phi)*cos(theta)\n",
    "y = r*cos(phi)\n",
    "z = r*sin(phi)*sin(theta)\n",
    "\n",
    "# View it.\n",
    "from mayavi import mlab\n",
    "s = mlab.mesh(x, y, z)\n",
    "mlab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "plt.plotfile?"
   ]
  },
  {
   "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": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}