{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Introduction to Python prgramming"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Thanks to J.R. Johansson for some examples https://github.com/jrjohansson/scientific-python-lectures"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Hints: \n",
      "\n",
      "**Shift-Enter** : run cell\n",
      "\n",
      "**Ctrl-m h** : show keyboard shortcuts\n",
      "\n",
      "If something goes wrong restart IPtython kernel from menu **Kernel -> Restart**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Basic python"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Hello world!\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Hello world!\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "2+2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "4"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = math.cos(2 * math.pi)\n",
      "print x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1.0\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "\n",
      "print(dir(math))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['__doc__', '__name__', '__package__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'hypot', 'isinf', 'isnan', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'modf', 'pi', 'pow', 'radians', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc']\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "help(math.log)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Help on built-in function log in module math:\n",
        "\n",
        "log(...)\n",
        "    log(x[, base])\n",
        "    \n",
        "    Return the logarithm of x to the given base.\n",
        "    If the base not specified, returns the natural logarithm (base e) of x.\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "math.log? # to close help press ESC button"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "math.log(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "2.302585092994046"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Numpy - multidimensional data arrays"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the numpy package the terminology used for vectors, matrices and higher-dimensional data sets is array."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#1D array\n",
      "v = array([1.0,2,3,4])\n",
      "print 'V shape =',v.shape\n",
      "print v"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "V shape = (4,)\n",
        "[ 1.  2.  3.  4.]\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#2D array\n",
      "M = array([[1.1, 2], [3, 4]])\n",
      "print 'M shape =',M.shape\n",
      "print M"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "M shape = (2, 2)\n",
        "[[ 1.1  2. ]\n",
        " [ 3.   4. ]]\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M.dtype"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "dtype('float64')"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#complex 2D array\n",
      "Mc = array([[1, 2], [3, 4]], dtype=complex)\n",
      "\n",
      "Mc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "array([[ 1.+0.j,  2.+0.j],\n",
        "       [ 3.+0.j,  4.+0.j]])"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#array indexing starting from 0 (instead 1 in Matlab) \n",
      "M[0,0] = pi/2\n",
      "M"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "array([[ 1.57079633,  2.        ],\n",
        "       [ 3.        ,  4.        ]])"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "2*M"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "array([[ 3.14159265,  4.        ],\n",
        "       [ 6.        ,  8.        ]])"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sin(M)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "array([[ 1.        ,  0.90929743],\n",
        "       [ 0.14112001, -0.7568025 ]])"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# using linspace, both end points ARE included\n",
      "print linspace(0, 10, 25)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[  0.           0.41666667   0.83333333   1.25         1.66666667\n",
        "   2.08333333   2.5          2.91666667   3.33333333   3.75         4.16666667\n",
        "   4.58333333   5.           5.41666667   5.83333333   6.25         6.66666667\n",
        "   7.08333333   7.5          7.91666667   8.33333333   8.75         9.16666667\n",
        "   9.58333333  10.        ]\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy import random\n",
      "# uniform random numbers in [0,1]\n",
      "random.rand(5,5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "array([[ 0.18635853,  0.59065389,  0.16282443,  0.15621812,  0.02380626],\n",
        "       [ 0.32957591,  0.95074041,  0.99227521,  0.90509676,  0.00363596],\n",
        "       [ 0.09479192,  0.23989856,  0.01910765,  0.78669724,  0.01033156],\n",
        "       [ 0.86864125,  0.44958662,  0.55159224,  0.03632517,  0.71992203],\n",
        "       [ 0.03922948,  0.4708347 ,  0.37057454,  0.8662272 ,  0.45103257]])"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# using linspace, both end points ARE included\n",
      "print linspace(0, 10, 25)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[  0.           0.41666667   0.83333333   1.25         1.66666667\n",
        "   2.08333333   2.5          2.91666667   3.33333333   3.75         4.16666667\n",
        "   4.58333333   5.           5.41666667   5.83333333   6.25         6.66666667\n",
        "   7.08333333   7.5          7.91666667   8.33333333   8.75         9.16666667\n",
        "   9.58333333  10.        ]\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# a diagonal matrix\n",
      "diag([1,2,3])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "array([[1, 0, 0],\n",
        "       [0, 2, 0],\n",
        "       [0, 0, 3]])"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zeros((3,3))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "array([[ 0.,  0.,  0.],\n",
        "       [ 0.,  0.,  0.],\n",
        "       [ 0.,  0.,  0.]])"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ones((3,3))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "array([[ 1.,  1.,  1.],\n",
        "       [ 1.,  1.,  1.],\n",
        "       [ 1.,  1.,  1.]])"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numpy.lookfor('save file')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Search results for 'save file'\n",
        "------------------------------\n",
        "numpy.save\n",
        "    Save an array to a binary file in NumPy ``.npy`` format.\n",
        "numpy.savez\n",
        "    Save several arrays into a single file in uncompressed ``.npz`` format.\n",
        "numpy.savetxt\n",
        "    Save an array to a text file.\n",
        "numpy.savez_compressed\n",
        "    Save several arrays into a single file in compressed ``.npz`` format.\n",
        "numpy.numarray.save\n",
        "    saves variables from a numpy session to a file.  Variables\n",
        "numpy.ma.mvoid.tofile\n",
        "    Save a masked array to a file in binary format.\n",
        "numpy.ma.MaskedArray.tofile\n",
        "    Save a masked array to a file in binary format.\n",
        "numpy.fromfile\n",
        "    Construct an array from data in a text or binary file.\n",
        "numpy.load\n",
        "    Load an array(s) or pickled objects from .npy, .npz, or pickled files.\n",
        "numpy.memmap\n",
        "    Create a memory-map to an array stored in a *binary* file on disk.\n",
        "numpy.core.load\n",
        "    Wrapper around cPickle.load which accepts either a file-like object or"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numpy.savetxt?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Save data to ASCII file\n",
      "numpy.savetxt('my.dat',M)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#execute command as system shell\n",
      "!cat my.dat"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1.570796326794896558e+00 2.000000000000000000e+00\r\n",
        "3.000000000000000000e+00 4.000000000000000000e+00\r\n"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#load data from ASCII file\n",
      "f = numpy.loadtxt('my.dat')\n",
      "f"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "array([[ 1.57079633,  2.        ],\n",
        "       [ 3.        ,  4.        ]])"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy.io\n",
      "scipy.io.savemat('my.mat', {'M':M})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Manipulating arrays, slicing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# v is a vector, and has only one dimension, taking one index\n",
      "print v[0] #1.0\n",
      "# M is a matrix, or a 2 dimensional array, taking two indices \n",
      "print M[1,1] #4.0\n",
      "\n",
      "print M[:,1] # column 1\n",
      "print M[1,:] # raw 1\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1.0\n",
        "4.0\n",
        "[ 2.  4.]\n",
        "[ 3.  4.]\n"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = array([1,2,3,4,5])\n",
      "print A[1:3] #\n",
      "A[1:3] = [-2,-3]\n",
      "print A\n",
      "print A[::] # lower, upper, step all take the default values\n",
      "print A[::2] # step is 2, lower and upper defaults to the beginning and end of the array\n",
      "print A[:3] # first three elements"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Scipy and matplotlib"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#\n",
      "# The scipy.special module includes a large number of Bessel-functions\n",
      "# Here we will use the functions jn and yn, which are the Bessel functions \n",
      "# of the first and second kind and real-valued order. We also include the \n",
      "# function jn_zeros and yn_zeros that gives the zeroes of the functions jn\n",
      "# and yn.\n",
      "#\n",
      "from scipy.special import jn, yn, jn_zeros, yn_zeros"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = linspace(0, 10, 100) # define 100 points in gange from 0 to 10\n",
      "\n",
      "for n in range(4): # cycle from 0 to 4 (0,1,2,3)\n",
      "    pylab.plot(x, jn(n, x), label=r\"$J_%d(x)$\" % n) #plot the n-th bessel function for all x points\n",
      "    pylab.hold(True) # keep drwing on this canvas\n",
      "pylab.legend(); # show legend"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "<matplotlib.legend.Legend at 0x49ee610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NFIICg/YPwgc9PsDc/nO1dp6KyMtjwTZ+fkC5iXePHgHvvcc2NP/8E2jeXCe2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\n36jpqgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x49ee650>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Ordinary differential equations (ODEs)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's consider a physical example: The double compound pendulum, described in some detail here: http://en.wikipedia.org/wiki/Double_pendulum"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "Image(url='http://upload.wikimedia.org/wikipedia/commons/c/c9/Double-compound-pendulum-dimensioned.svg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<img src=\"http://upload.wikimedia.org/wikipedia/commons/c/c9/Double-compound-pendulum-dimensioned.svg\"/>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<IPython.core.display.Image at 0x4317910>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The equations of motion of the pendulum are given on the wiki page:\n",
      "\n",
      "${\\dot \\theta_1} = \\frac{6}{m\\ell^2} \\frac{ 2 p_{\\theta_1} - 3 \\cos(\\theta_1-\\theta_2) p_{\\theta_2}}{16 - 9 \\cos^2(\\theta_1-\\theta_2)}$\n",
      "\n",
      "${\\dot \\theta_2} = \\frac{6}{m\\ell^2} \\frac{ 8 p_{\\theta_2} - 3 \\cos(\\theta_1-\\theta_2) p_{\\theta_1}}{16 - 9 \\cos^2(\\theta_1-\\theta_2)}.$\n",
      "\n",
      "${\\dot p_{\\theta_1}} = -\\frac{1}{2} m \\ell^2 \\left [ {\\dot \\theta_1} {\\dot \\theta_2} \\sin (\\theta_1-\\theta_2) + 3 \\frac{g}{\\ell} \\sin \\theta_1 \\right ]$\n",
      "\n",
      "${\\dot p_{\\theta_2}} = -\\frac{1}{2} m \\ell^2 \\left [ -{\\dot \\theta_1} {\\dot \\theta_2} \\sin (\\theta_1-\\theta_2) +  \\frac{g}{\\ell} \\sin \\theta_2 \\right]$\n",
      "\n",
      "To make the Python code simpler to follow, let's introduce new variable names and the vector notation: $x = [\\theta_1, \\theta_2, p_{\\theta_1}, p_{\\theta_2}]$\n",
      "\n",
      "${\\dot x_1} = \\frac{6}{m\\ell^2} \\frac{ 2 x_3 - 3 \\cos(x_1-x_2) x_4}{16 - 9 \\cos^2(x_1-x_2)}$\n",
      "\n",
      "${\\dot x_2} = \\frac{6}{m\\ell^2} \\frac{ 8 x_4 - 3 \\cos(x_1-x_2) x_3}{16 - 9 \\cos^2(x_1-x_2)}$\n",
      "\n",
      "${\\dot x_3} = -\\frac{1}{2} m \\ell^2 \\left [ {\\dot x_1} {\\dot x_2} \\sin (x_1-x_2) + 3 \\frac{g}{\\ell} \\sin x_1 \\right ]$\n",
      "\n",
      "${\\dot x_4} = -\\frac{1}{2} m \\ell^2 \\left [ -{\\dot x_1} {\\dot x_2} \\sin (x_1-x_2) +  \\frac{g}{\\ell} \\sin x_2 \\right]$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.integrate import odeint,ode"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = 9.82\n",
      "L = 0.5\n",
      "m = 0.1\n",
      "\n",
      "def dx(x, t):\n",
      "    \"\"\"\n",
      "    The right-hand side of the pendulum ODE\n",
      "    \"\"\"\n",
      "    x1, x2, x3, x4 = x[0], x[1], x[2], x[3]\n",
      "    \n",
      "    dx1 = 6.0/(m*L**2) * (2 * x3 - 3 * cos(x1-x2) * x4)/(16 - 9 * cos(x1-x2)**2)\n",
      "    dx2 = 6.0/(m*L**2) * (8 * x4 - 3 * cos(x1-x2) * x3)/(16 - 9 * cos(x1-x2)**2)\n",
      "    dx3 = -0.5 * m * L**2 * ( dx1 * dx2 * sin(x1-x2) + 3 * (g/L) * sin(x1))\n",
      "    dx4 = -0.5 * m * L**2 * (-dx1 * dx2 * sin(x1-x2) + (g/L) * sin(x2))\n",
      "    \n",
      "    return [dx1, dx2, dx3, dx4]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# choose an initial state\n",
      "x0 = [pi/4, pi/2, 0, 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# time coodinate to solve the ODE for: from 0 to 10 seconds\n",
      "t = linspace(0, 10, 250)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# solve the ODE problem\n",
      "x = odeint(dx, x0, t)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plot the angles as a function of time\n",
      "\n",
      "pylab.figure(figsize=(12,4))\n",
      "pylab.subplot(1,2,1)\n",
      "pylab.plot(t, x[:, 0], 'r', label=\"theta1\")\n",
      "pylab.hold(True)\n",
      "pylab.plot(t, x[:, 1], 'b', label=\"theta2\")\n",
      "\n",
      "\n",
      "x1 = + L * sin(x[:, 0])\n",
      "y1 = - L * cos(x[:, 0])\n",
      "\n",
      "x2 = x1 + L * sin(x[:, 1])\n",
      "y2 = y1 - L * cos(x[:, 1])\n",
      "\n",
      "pylab.subplot(1,2,2)\n",
      "pylab.plot(x1, y1, 'r', label=\"pendulum1\")\n",
      "pylab.plot(x2, y2, 'b', label=\"pendulum2\")\n",
      "pylab.ylim([-1, 0])\n",
      "pylab.xlim([1, -1])\n",
      "pylab.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "<matplotlib.legend.Legend at 0x594ea50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ayrA3Eq0m4eloJNrPj0r2qYELF6xv+c2Hs3SYS8ZTHXv2mEahdZQEDkKMWykA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kgTEfXvL2577hxRmzbGHx9HHasS0V5e2iPR9+8XfRHNGBfRRZG6deE5ljOGRKLj4ojc3Qt3f4zB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\nh8PhcAyEi2gOh8PhcDgcDsdA/g9FcAMJG03+6gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x564e810>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import clear_output\n",
      "import time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Some animations\n",
      "fig, ax = subplots(figsize=(4,4))\n",
      "\n",
      "for t_idx, tt in enumerate(t[:200]):\n",
      "\n",
      "    x1 = + L * sin(x[t_idx, 0])\n",
      "    y1 = - L * cos(x[t_idx, 0])\n",
      "\n",
      "    x2 = x1 + L * sin(x[t_idx, 1])\n",
      "    y2 = y1 - L * cos(x[t_idx, 1])\n",
      "    \n",
      "    ax.cla()    \n",
      "    ax.plot([0, x1], [0, y1], 'r.-')\n",
      "    ax.plot([x1, x2], [y1, y2], 'b.-')\n",
      "    ax.set_ylim([-1.5, 0.5])\n",
      "    ax.set_xlim([1, -1])\n",
      "\n",
      "    display(fig)\n",
      "    clear_output()\n",
      "    \n",
      "    time.sleep(0.02)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x5a69990>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "matplotlib - 2D and 3D plotting in Python"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pylab import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 53
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "MATLAB-like API"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#variables for drawings\n",
      "x = linspace(0, 5, 10)\n",
      "y = x ** 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#looks like MATALB\n",
      "figure()\n",
      "plot(x, y, 'r')\n",
      "xlabel('x')\n",
      "ylabel('y')\n",
      "title('title')\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Gr776ynRZItWmFbkiPnrmmWc4dOgQBw8epFGjRjz55JOmSxKpNoW+iI+OHj3K\nzTffTO3atVm7dq2j93qR0KXhHREf7dmzh/LycsrKyjh48KDpckQuijp9ER8NGDCAu+66i2+//Zai\noiJmzJhhuiSRaqtpugCRUDBv3jwuv/xyhg0bRmVlJV26dMHj8ZCUlGS6NJFqUacvIuIgGtMXEXEQ\nhb6IiIMo9EVEHEShLyLiIAp9EREHUeiLiDiIQl9ExEEU+iIiDvL/Lx/MoHiEH50AAAAASUVORK5C\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5652950>"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Subplots\n",
      "subplot(1,2,1)\n",
      "plot(x, y, 'r--')\n",
      "subplot(1,2,2)\n",
      "plot(y, x, 'g*-');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vry/t27fHaDTSqVMnS2VybC+9BH/8I7i7q04ibmP48OF4enrSrl27Kn+PwWAg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\nQtg5KeRCCGHnpJALIYSd+x+KWiAMhq55PQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x596e890>"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#plot in plot\n",
      "fig = plt.figure()\n",
      "\n",
      "axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes [x0,y0,dx,dy]\n",
      "axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes\n",
      "\n",
      "# main figure\n",
      "axes1.plot(x, y, 'r')\n",
      "axes1.set_xlabel('x')\n",
      "axes1.set_ylabel('y')\n",
      "axes1.set_title('title')\n",
      "\n",
      "# insert\n",
      "axes2.plot(y, x, 'g')\n",
      "axes2.set_xlabel('y')\n",
      "axes2.set_ylabel('x')\n",
      "axes2.set_title('insert title');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9O9/z7ix3pwXprmanFTvtksUuOcBeWSzx0Ufw/POwejXcd5/VacQJKhCF2Lx5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\nESmQCoSIiBTo/wMthehj0BeQMgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6058890>"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Latex in legend\n",
      "fig, ax = plt.subplots()\n",
      "\n",
      "ax.plot(x, x**2, label=r\"$y = \\alpha^2$\")\n",
      "ax.plot(x, x**3, label=r\"$y = \\alpha^3$\")\n",
      "ax.set_xlabel(r'$\\alpha$', fontsize=18)\n",
      "ax.set_ylabel(r'$y$', fontsize=18)\n",
      "ax.set_title('title')\n",
      "ax.legend(loc=2); # upper left corner"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "<matplotlib.legend.Legend at 0x5a6c4d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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b1beQ8HICnMycxC6JRMKWBRE1IAgCgk8FY1TwKEyynYRfF/3KoNBxbFkQUT3Z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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5a5d9d0>"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# custom grid\n",
      "\n",
      "fig, axes = plt.subplots(1, 2, figsize=(10,3))\n",
      "\n",
      "# default grid appearance\n",
      "axes[0].plot(x, x**2, x, x**3, lw=2)\n",
      "axes[0].grid(True)\n",
      "\n",
      "# custom grid appearance\n",
      "axes[1].plot(x, x**2, x, x**3, lw=2)\n",
      "axes[1].grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bEjbQ1KMpkQ9HMrTTULXDqhOyNp8QGhGTHEPYV2Gcyz/HCP8RRP8lut40UkK7\nNPplyxrTSx6gzVyyC7MZvmI4GxI20KpRKzZP2mxVI6XFXGqLNFN2uHZaAWcmudSeyGOR3L3ybnKL\nc3mo20Osm7iOJh5Nbng/reUh9EcvL3Z6yQO0l0tabhp3fnUnO1N24tvMlx3TdtCnXR+r7qu1XGqT\nNFNC1KKI+AgeWPMARaYinr7tab65/xs8XD3UDksIIW7o5MWTDFw+kD8y/uCWVrewc9pOgloHqR2W\nJkkzZQe9fNMKJJfa8MFvHzAlcgomxcS/Bv+LT+79BFcXV6vvr5U8hBD1z8GMg9yx7A5OXjzJbW1v\nY/vU7XRo3kHtsDTL5m/zCSGqpigKr2x+hbd2vAXAhyM+5Ln+z6kclRBCWGdXyi5G/t9IsguzGeI3\nhMiHI/Fs4Kl2WJomZ6bsoKcxLZKLY5jMJp7+6Wne2vEWrgZXvh77tc2NlJ6eEyGEc4hKiOKur+8i\nuzCbsUFj+fnRn6WRsoKcmRLCQYqMRfzlh7/w3ZHvaOjWkDXj1zD6ltFqhyVEtapau8wZ6SUPUDeX\nbw9+y6S1kzCajUwLmcbnoz/HzcX2NkFPz8uNyDxTQjjA5eLL3L/6fqJPRtOsQTN+nPgjgzsOVjss\nzdDTca+nXIQotWTvEmb+PBMFhecHPM874e9Uu9B3fVOrM6ALISwu5F9g5P+NZHfqbm5ucjNRj0YR\n2iZU7bCEEOKGFEXhjW1v8FrMawAsGLaAFwe+qHJUzkfGTNlBT2NaJBfbpOakMvirwexO3U3H5h3Z\nMXWHwxopPT0nQgjtMStmZkfN5rWY13AxuPDFqC+kkbKRnJkSwkYnLpwgfEU4py6dIvimYDY+tpF2\nzdqpHZYQQtxQiamEaeumsfKPlXi4evDN/d8wPni82mE5LRkzJYQN9qft5+5v7iYzL5N+7frx0yM/\n0apxK7XD0iw9Hfd6ykXUTwUlBUz47wTWH19PE/cmrH14LXd1vkvtsDRL1uYTohZsO7WNsIgwMvMy\nCe8czqZJm6SREk5JL8t96CUPqP1csguzGbFyBOuPWxZa3zx5c601Unp6Xm5Emik76GlMi+RinR//\n/JERK0eQU5TDg8EP8uPEH2nq0bRWHktPz4nQJr282OklD6jdXDIuZxD2VRjbT2+nnWc7tk/dTt92\nfWvt8fT0vNyIXc2UyWQiNDSU0aMtc+lkZWURHh5OYGAgw4cPJzs72yFBCqEFKw6sYNzqcRQaC3mq\n91N8+8A9DFQyAAAfHElEQVS3NHBroHZYwkZSv0R9kpydzMDlAzmQcYDAVoHsnLaT4JuC1Q5LN+xq\nphYtWkRwcHDZXBQLFiwgPDyc48ePM2zYMBYsWOCQILVKT2unSS7Xtyh2EZPWTsKkmHh54Mt8OvLT\nGq2zZws9PSdaVN/rl6g/DmUe4o5ld5CQlcCtbW5l+9TtdPTqqHZYumJzM3XmzBl+/vlnnnjiibKB\nWevWrWPy5MkATJ48mbVr1zomSiFUoigKr255ldm/zAbg/eHv8+awN2UyOycn9UvUF7FnYhm8fDBn\nc89yZ8c72TJ5Czc3uVntsHTH5mZqzpw5vPvuu7i4XN1FRkYG3t7eAHh7e5ORkWF/hBqmpzEtkktl\nZsXMzJ9n8sa2N3A1uLL8vuXMHTDXIfu2hp6eE62R+iXqg42JGxn29TAuFl5kzC1jiHosimYNmqkd\nli7ZNM/U+vXrufnmmwkNDa224BsMhmrfvU+ZMgW/K4v2eHl5ERISUvaRRun+5HrdXi+llXjsuR4f\nH2/3/m4fdDuT105m1Y+rcHd157u/f8d9QfdpIj9nuF76c7IGR6DaW78ApkyZV/ZzSEgYISFh+PlV\nvRZZcnLVA3G1sH16OpT+F2ghHlu3T0+33KaVeOzZ/tr72Lr/LUlbeHP7O5jMfRgRcDfv932ehm6V\nX/JrM18/P/X/P23ZPiYmpqw2WDt00qZ5pl5++WVWrFiBm5sbhYWF5OTkcP/997N3715iYmLw8fEh\nLS2NIUOGcOzYsYoPKHO0CI3LK85j/HfjiUqIwtPDk3UT1xHmF6Z2WE5NS8e9PfULtJWLEFX5fN/n\nPP3T0ygozOk/h/eGv4eLQb68bytrjnm7J+3cunUr7733Hj/++CMvvPACrVq14sUXX2TBggVkZ2dX\nGsQphUho2bm8c4xdPZZdKbto3bg1UY9G0bttb7XDcnpaPe5rWr9Au7kIYVbMvLX9Lf615V8AvDn0\nTf4x8B8yxtNOdTZpZ+kT9dJLLxEdHU1gYCCbN2/mpZdecsTuNau6jwicUX3PRVEU1hxeQ/CSYHal\n7KJD8w7smLpD1UZKT8+JltXX+iX0JTErkaERQ/nXln9hwMBnIz/j5UEvSyNVR+xem+/OO+/kzjvv\nBKBly5Zs2rTJ7qCEqEsZlzOY8fMMvj/6PQBDOw0lYmwEvs18VY5M1DapX8LZmRUzH+/+mH/8+g8K\njAXc3ORmvhz9JaNvGa12aPWKrM0n6i1FUVh1aBUzN8wkqyALTw9P3hv+Hk/e+qS8m3MwPR33espF\nOLfjF44zLXIaO1N2AvBoj0dZdPciWd7Kwaw55u0+MyWEM0rLTePpn54m8s9IAIb7D2fp6KV0aN5B\n5ciEqDvVfQPO2eglD7AuF5PZxIexH/LKllcoNBbi09SHz0d9zphbxtRFiFbT0/NyIzK83w56GtNS\nX3JRFIUVB1bQbUk3Iv+MpFmDZnw5+kuiHo3SXCOlp+dEaJMGZ66wiV7ygBvncuz8MQYuH8jz0c9T\naCxkUq9JHJ5xWHONFOjrebkROTMl6o3UnFSeWv8UP534CYB7Au7hi9FfyNgoIYTmGc1GPvjtA17d\n8ipFpiLaebbj81GfMzJwpNqhCaSZsoue1k7Tcy6KovBV/FfM+WUOl4ou0bxBcxbdvYhJvSZpemyU\nnp4TIYTtDmceZmrkVPae3QvAtJBpvD/ifbwaeqkcmSglzZTQtZRLKUxfP52ohCgARgWO4vNRn9PW\ns63KkQkhxPUZzUbe2fkOr299nWJTMb7NfFk6eil3B9ytdmjiGjJmyg56GtOit1wURWHp70vptqQb\nUQlRtGjYghXjVrDu4XVO00jp6TkRQtTMwYyD9P+yP//c/E+KTcVMv3U6h2cclkZKo+TMlNCd9Mvp\njFg5guiT0QCMDRrLpyM/xaepj8qRCaEtevmmlV7yAGjXvoQ3ti7gjW1vUGIuoWPzjnw55kvu6nyX\n2qHVmJ6elxuReaaEbpgVM1/8/gV/j/47l4sv06pRKxbfu5iHuj2k6bFR9YGejns95SK0JT49nqmR\nU4lPjwdgxm0zWHDXAjwbeKocWf0m80yJeiPpYhKPr3ucLclbABgfPJ7F9yzGu6m3ypEJIcT1FZuK\neXPbm7y14y2MZiOdvDrxnzH/YUinIWqHJqwkY6bsoKcxLc6ai1kxs3jPYnp82oMtyVu4qfFNvNbh\nNb578Dunb6Sc9TkRQlgvLi2OPkv78O9t/8ZoNvJs32f54+k/pJFyMnJmSjitxKxEpq2bxrZT2wB4\nuPvDfHT3Rxzee1jlyER5xcVqRyCE9hQZi3hj2xss2LEAk2LCv4U/y+5bxuCOg9UOTZRj7Sf6MmZK\nOJ1rF/b0buLNpyM/ZVzXcWqHJoCzZ+G3365efv8dior0c9xLDRP22pu6l6mRUzl87jAGDMzuP5v/\nN/T/0di9sdqh1Xv5+bBv39X6FRsLGRkyZkrojCzsqS3FxRAfX7F5On1a7aiEtfSydpqz5FFoLGRe\nzDze3fUuZsVMYKtAlo1Zxh0d7ijbxllysYbWc1EUS4zl69eBA2A01nxf0kzZISYmRjezVGs9l2sX\n9mzTtA2fjfqsyvWotJ6LtbSYR1pa5bNOhYUVt/H0hP79YcAAy6VfP2jZUp14xfVp/cXOWs6QR+yZ\nWKZGTuXY+WO4GFx4fsDz/HvIv2nk3qjCds6Qi7W0lktBQcWzTr/9BhkZFbdxcYGQkKv1a8AA6NLl\nxvu2qZlKSUlh0qRJZGZmYjAYmD59OrNmzSIrK4uHHnqIU6dO4efnx5o1a/DykunuhX2OnT/G1Mip\nxJ6JBWByr8ksHLGQFo1aqByZvpWUVD7rdOpU5e2CgiwFp7SBCg4GV9e6j7cmpIaJulJQUsCrW17l\ng9gPMCtmurbuyvL7ltPPt5/aoemaoljqVfn6FR9f+axTq1YVG6c+faBp05o/nk1jptLT00lPTyck\nJITLly/Tu3dv1q5dy/Lly2ndujUvvPACb7/9NhcvXmTBggUVH1DGGwgrnb50mo92f8TiPYvLFvb8\nYvQX3NvlXrVD06X09IqFZ9++qs869etX87NOWjvupYZZxMSAxk5+2kSLeRQaC/n24Le8teMtErIS\ncDG48MLtL/Ba2Gs0dGtY7f20mIut6jKXggLLmfLyNSw9veI2Li7Qo0fF5ikgAG40DWGtzTPl4+OD\nj49lNummTZvStWtXUlNTWbduHVu3bgVg8uTJhIWFVSpEQtzIntQ9fPDbB/z3yH8xKSZAFvZ0tJIS\ny9iA8oUnObnydoGBloJz++3Oc9bJGlLDRG05l3eOT/d9yid7PyEzLxOAbjd1Y/l9y+nTro/K0emD\noljGZl571qmkpOJ2LVtWPuvkWUvzn9o9Zio5OZn9+/fTr18/MjIy8Pa2zO3j7e1NxrUfRuqMFse0\n2ErtXExmE2uPreWD2A/YlbILADcXNx7p/ghz+s/htra3Wb0vtXNxFEflYTTCsWMQF2e5/P675VJQ\nUHG7pk2hb9+rhad/f8spcL2rzzVMOM7hzMN8GPshK/5YQZGpCIAQnxDm9p/LQ90fwsPVQ+UIndfZ\ns1frV1wc7N1rua08Fxfo2bPyWKe6WvzCrmbq8uXLPPDAAyxatAjPa9o9g8FQ7RIeU6ZMwe/KqDQv\nLy9CQkLKXjRKJyqU63V7vVRdP/5PG39iQ8IGfir5ieTsZEiCJh5NmDlhJjP7ziQhLoHLxy9DW+vj\ni4+PV/3/U63r0dExnDoFBkMYcXGwZUsMiYlQXGz5PcRc+TeMLl2gU6cYunWDyZPD6N4dtm93TDyl\nPydXdbpLQ2yvYfPKfg4JCSMkJAw/v6oH2yYnV33WTwvbp6dbPorRSjy2bp+eXv1g59qMR1EUVmzb\nzuKN69mbuufKrQMY0H4As0aM4aE7+lX6G7rR/q99DGf4/69uez+/mv5/wq5dsGUL/PknnDgBx4/D\nxYuV79+ypeUNX1AQtG0LXbtC43IzS3h4VN1IWRNPTExMWS3Lzq68bVVsnmeqpKSEUaNGcc899zB7\n9mwAgoKCiImJwcfHh7S0NIYMGcKxY8cqPqCOxhsI253KPsVHuz/iy/1fklOUA4B/C39m95/NlJAp\nNPWwYQRgPVNYCAcPXj3bFBdnuV7VJJmdOsGtt1ouvXtbLq1b112sWjzupYYJWxUaC/nmj29YGLuQ\nw+cskwQ3cmvE1JCpPNf/OQJbBaocofYpCpw8WfGMeVwcXLhQedvmza/Wr9IaFhhYd2edam3MlKIo\nPP744wQHB5cVIYAxY8YQERHBiy++SEREBGPHjrVl90LHYs/EsjB2If878r+y8VCDOw5mbv+5jAoc\nhauLDgbk1IK8PPjjj6sFJy4ODh+uej6UwMCKhSc0VKYmuJbUMGGLjMsZfLrvU5bsXcK5/HMAtPVs\ny7N9n2V67+m0bCQHWlXMZstZpvJNU1wcXLpUedtWrSzNUvka1rlz3TVOtrLpzNSOHTsYPHgwPXv2\nLDuFOX/+fPr27cuECRM4ffp0tV8r1tO7Or2MzYHazcVoNvLD0R9YGLuQ3878BljGQz3U7SHm9J9D\n77a9Hfp4zv685ORYBlOuWRPDpUuWj+uOHbMUpPJcXCynuMsXnpAQaNZMnbivR2vHvdQwUROHMg+x\n8LeFfHPwm7LxULe2uZU5/ecwodsEGQ9VTlVjNOPj4fLlytt6e189U15aw9q3117jVGtnpgYOHIj5\n2sp+xaZNm2zZpdChnKIc/hP3HxbtXsSpS5YJilo0bMFTvZ9iZt+ZtGvWTuUI1aUokJlp+Whu//6r\nhefEicrburlZvtJb/jR3z57QpEndx60HUsPEjSiKwi+Jv7AwdiEbEzcCYMDAfbfcx5z+cxjccXC1\nY+rqi/x8OHrU0iyVNk8HDlT+cgtYmqTy9evWW6FNm7qPubbI2nzC4ZKzky3joeK+JLc4F4CAlgHM\n6T+Hyb0m08Sj/nUAWVmWj+UOHbJcSn+uanyAh4elUSp/mrtHD2hY/dQ0mqen415PuYjKCkoKWPnH\nSj7c/SFHzh0BoLF7Y6aFTGNWv1l0aWXFdNg6U1RkGRBeWrdK/z15suqFgDt1qni2KTQUbr657uN2\nlFo7MyVEVX5L+Y0PYj/g+6PfY1Ys7/rD/MKY238uIwNH4mJwUTnC2peTA0eOVCw4hw9blmGpSvPm\n0K2b5eO50uITHGxpqISobVpb7sNWjsgj43IGS/YuYcm+JZzPPw9AO892ZeOh6mrFBTWfE6MREhIq\n16/jx8Fkqry9mxvccsvVs+a9e1sapxZX/qv08vdlDWmm7ODsY3PKszUXo9nI90e/Z2HswrLlXtxc\n3Hi0x6PM6T+H0DahDo70xurieSk9vX3tO7XqFvlt0sTSJHXvbmmeune3XNq2rX58gJ7+voQ26eXF\nzp48DmYcZGGsZTxUscnyVdjebXozd8BcHgx+EHdXd4fFaY26eE7MZkhKqly/jh2r+tvALi6WOZtK\n61ZpDevS5fpv/PTy92UNaaaETS4VXuLLuC/5aM9HnL5k6SBaNGzBX2/7K8/0eUY346GKiy2nt699\np5aYWPXp7QYNLPOdlBab0n87drQUJCGE+syKmV8SfuGD2A/YdNIyRs6AgbFBY5nbfy4DOwzUxXgo\nRYEzZyrWr0OHLG8E8/Orvo+fX+X6FRQEjRpVvb2wkGbKDno6a2BNLoqicCjzEMv2L+PL/V9yudjy\n9YzAVoHM7jebSb0maWI8lC3PS3a25fR26SRxpYXnxImqpx9wc7NMQVD+LFO3buDvb/mdI+jp70sI\nLcjMy+SHoz/w4e4POXbeMn9YE/cmTAu1jIcKaBmgcoS2KS62nGk6ccJyOXr0agOVk1P1fdq2rdgw\ndetmOXteW8ut6J00U+K6zuaeJToxmuiT0Ww6uYmMvKvLawzxG8LcAXO5t8u9TjEeKifnasN07eX8\n+arvYzBYFsK89uO5wEAZ1ySE1hWUFLD99HaiE6PZlLSJ+PT4st/5NvNlVt9ZPHHrE3U2HsoeJSWW\nj83K163SepacXHnqlFKtW1f+eK5bt6vjmoRjSDNlBz2NaSnN5XLxZbYmbyX6pKWBKv02S6m2nm25\nJ+AeZvadSYhPiErRVu/yZVi1KobmzcMqNUyZmdXfr1EjS9PUpYvlUjq+KSio4hIFdUlPf19C1AWz\nYiY+Pb7sDeCO0zvK5oUCaOjWkEEdBjE1ZCrjg8fX+XioGzEaITUVoqIqNksnTljOPFU1CBwsb/r8\n/Cy1KyDAUrdKGyhn/hadM5Fmqp4zmo3sO7uPr+O/5rXk1/gt5TdKzFeX3m7i3oQwvzDCO4cT7h9O\n19ZdVR9LkJ9f/Rmm9PTq79ewoeVjuNKGqfQSEGA55S1jmkR9o4fBwaeyT7H7ciyf/fcHfk36teyb\neGAZB3Vrm1st9atzOHd0uIOGburOMWIyWb6ocu3ZpdKGqaSk6vsZDNChw9WaVb6Gde5sGa+pNXr4\n+7KWzDNVzyiKQuLFxLJ3bpuTNnOp6Oqc/i4GF/q07VPWPPX37V/ns/uazZCRYSk4p05VPrV97Wrh\n5Xl4VN8w+fpKw6QWPR33esrFGV0qvMSW5C1lNexEVsVZbjs071DWPA3rPIzWjetwEcorcnKu1q/T\npys2TCdPVv2NuVK+vpWbpdKGSQaBq0PmmRIAXMi/wOakzWUf3SVnJ1f4fUDLAMI7h3NX57sY4jek\n1scP5OdbCsy1l9LCk5JS/bszAHd3S2G5tlnq0sUyy66rLO8nhG6UmErYnbq7rHnak7qnbF1PgGYN\nmjG001Du6nQX4f7hdGnZpVbPnhuNlnnjqqpdpZeq1pwrr23bqhsmf3/1hhUI+0gzZQetjmkpMhax\nM2VnWfGJS4tD4WpX3bJRS4Z1GlZ29snPy8+SS9cwux/bbLaMTaquyJw+Xf1g7/Jat7ac0u7Y0fJv\n+cLTocP1vzGn1eelpvSShxA1oSgKx84fK3vzF5McU/bNYbDMYzew/cCys0992vXBzcVxL2W5udev\nX2fOVD92qVSjRlfrV/v2Fc+WBwTIMlB6JM2UDiiKwsHMg2XN07ZT2ygwXl0cycPVgzva31HWPIX6\nhOLqYtvpm8uXLR+zVVVkTp2ynFW63ilssHwU1769pdiUXkqbpg4dLL+Td2dC1B+ZeZlsOrnJ0kAl\nRpOam1rh90Gtg8qapzC/MDwb2Pb9/ZKSq0MIqjsznp194/34+FRdu0ovrVppb7FeUbtkzJQTMZqN\npFxKIfFiIglZCSRmJZJwMYFdKbvIzKv4VbWe3j3LProb3HEwjd2r707y8iwFJiPDMoD7ev9WN9Fb\nea1aVV1gSgvPzTfL2KX6Rk/HvZ5yqUuKonCh4IKlbmUlkHgxkcSLiRxIP8CBjAMVtr2p8U3c1fmu\nsjeAvs18q92v0Qjnzt24dmVkVL0W5rVKzypVVbs6dLCMadLiYG9Re6w55qWZ0phCYyFJF5MqNUyJ\nWYkkZydX+KZdeW0921YYdNnc1adSIamuyFy+XOUuq9SokWWl7+relbVvL6ewRWV6Ou71lIujl/sw\nK2bO5p6t0DCVNU5ZiRW+7FJeQ7eGDO44uKyGBbfuwYXzLla9ybtwoerVCKri4mJ5M1d6ZryqN32t\nW6t7VklPS7DoJRdppmqZrWNacopyKr47K9cwnck5U2F807VaN2iLt3sALfGnmdGfRoUBeFzsQVFq\nVzIzDGVFprpZb6vSoAE0bx6Dn18Y3t6WU9hV/evtbZkdV+unr/Uy1kgveYC+jns95RITAzX9Eysx\nlXDq0qmyN3vlG6aTF09SaCys9r6NXT1p0yCA1q7+eJkCaFzkj0duIK7pfTmX1rCsQTp/vvpJKK9l\nMFgWDG/f/vq1y8fHctZc619QseU50Sq95KLKt/mioqKYPXs2JpOJJ554ghdffNHRD6EZ8fHxVb7Y\nKYrCufxzZQ1TQlYixzITOHEhkeRLiVwsPlftPg2KKw3y/XDL8cd8wZ/i9ABM5/whKwCyO3G+pDFW\njN/G3b3qYlLVv82bw6JF8cyeXTkXZ1Td8+Js9JKHs6lfNazqhj2/JL+sUUrMSuTPcwn8eS6Rk9kJ\npOWfrvBtumu5F9+Me64/XPTHmBFAccaV+pXlT35+axIxkGhFbK1bW1fDWreGxYtjdFS/9PMmSk+5\n3IhDmymTycTMmTPZtGkT7dq1o0+fPowZM4auXbs68mFqjdkMl/ONZGbnci4nl/O5uWRdtlwu5ueS\nXZBLTqHlklucy9FfNrH8Yjz5plwKzbkUKrkUk0OeWwomt9zqH6ikIVz0h6wrRabcz8qlDhSZ3Skq\nt7mHh2Xq/xb+4OV15ecrFy8vSzG5tsi0aFGzM0jZ1oy6dBJ6yUUveTgTZ65higLFxQqZ2XmW+pWT\ny4XS+pVnqV+XCnLJKcoltyiXyyW5HP7uIJ9f+IwCs6WGFZFLgUsGRR5p13kgA1zqcKVuBVSqYyXF\nnpQfjGAwXKlbPlXXr5YtKzdIN91keUNorfj4GCDMtv84jZFcnJNDm6k9e/YQEBCA35UPSR9++GEi\nIyMrFaJlv+zFaDJjNJot/5qv/Gy2XDeZFMu/V66X//nqv0qF28xmy/1N5S5ms4LRbCLfeJl8Y66l\nYCi5FCm5lBgsF6NrLia3XMxuueCRC+7Vn6KuRIFUl71Q1WDqwuYVCo1Hnj/NjAG0MvhzU6M2tGzh\nYiksXSsXl2uvN2qk/Y/WhNADa2vY0g27q61hpfXL8nMVdavc70zl6pbZfKXuKVd/Z1bMFBoLyTfl\nUmCy1LAiJZdicilxsVxMrpb6pXjkgsdlMNTkI8iOZLltqnyzyR0udiprklyy/WlaYhlecJN7J1o1\nb2CpVb7Qokf1tatFC2jWTL5wIvTPoc1Uamoq7du3L7vu6+vL7t27K233eGxfRz7sVS5U3djUhGLA\nUOyJi9ETV6MnbmZP3M2eeOBJA4MnDQ2eNHL1pImbJ8lZ6+jP03g28KRZQ0+aN/TEq5EnHVu2pX3r\nlrRsaSgrKlpfFDc5OVntEBxGL7noJQ9nYm0Nm76nv+Mf3MDVimzPknEljXApsdQwN1Mz3BVPPBRP\nGlypYY1cPWl8pYYlKPsY6DafZg08ad7IUr9u9mxFwM2+tG7pWtYYNW4sb+iEuB6HNlPWzDrr7+9P\n4jxrPjFXi4JCDiZyMAE3mDKJtQf31kVQdSIiIkLtEBxGL7noJQ9/f3+1Q7CKPmpYAWYKMJOJEbjR\nufbVf6yqi6BqXUTE62qH4DCSi7ZYU78c2ky1a9eOlJSUsuspKSn4+lacHyQhIcGRDymEEA4jNUwI\nYQuHfpJ92223ceLECZKTkykuLmb16tWMGTPGkQ8hhBC1RmqYEMIWDj0z5ebmxuLFixkxYgQmk4nH\nH3/cKb4FI4QQIDVMCGGbOp+0UwghhBBCT+r0C6tRUVEEBQXRpUsX3n777bp8aIeaNm0a3t7e9OjR\nQ+1Q7JaSksKQIUPo1q0b3bt356OPPlI7JJsUFhbSr18/QkJCCA4O5h//+IfaIdnNZDIRGhrK6NGj\n1Q7FLn5+fvTs2ZPQ0FD69q2lb/LWAalf2qOX+gX6q2H1rn4pdcRoNCr+/v5KUlKSUlxcrPTq1Us5\ncuRIXT28Q23btk2Ji4tTunfvrnYodktLS1P279+vKIqi5ObmKoGBgU77vOTl5SmKoiglJSVKv379\nlO3bt6sckX3ef/995ZFHHlFGjx6tdih28fPzUy5cuKB2GHaR+qVNeqpfiqKvGlbf6lednZkqPxme\nu7t72WR4zmjQoEG0aNFC7TAcwsfHh5CQEACaNm1K165dOXv2rMpR2aZx48YAFBcXYzKZaNmypcoR\n2e7MmTP8/PPPPPHEE7pYB87Zc5D6pU16ql+gnxpWH+tXnTVTVU2Gl5qaWlcPL6yQnJzM/v376dev\nn9qh2MRsNhMSEoK3tzdDhgwhODhY7ZBsNmfOHN59911cdDB1tMFg4K677uK2225j6dKlaodjE6lf\n2ufs9Qv0U8PqY/2qs0ytmQxPqOfy5cuMHz+eRYsW0bRpU7XDsYmLiwvx8fGcOXOGbdu2ERMTo3ZI\nNlm/fj0333wzoaGhunhXt3PnTvbv38+GDRv45JNP2L59u9oh1ZjUL23TQ/0CfdSw+lq/6qyZsmYy\nPKGOkpISHnjgAR577DHGjh2rdjh2a968OSNHjmTfvn1qh2KTXbt2sW7dOjp16sTEiRPZvHkzkyZN\nUjssm7Vp0waAm266iXHjxrFnzx6VI6o5qV/apbf6Bc5dw+pr/aqzZkomw9MmRVF4/PHHCQ4OZvbs\n2WqHY7Pz58+TnZ0NQEFBAdHR0YSGhqoclW3eeustUlJSSEpKYtWqVQwdOpSvv/5a7bBskp+fT25u\nLgB5eXls3LjRKb9FJvVLm/RSv0A/Nay+1q86a6bKT4YXHBzMQw895LST4U2cOJHbb7+d48eP0759\ne5YvX652SDbbuXMnK1euZMuWLYSGhhIaGkpUVJTaYdVYWloaQ4cOJSQkhH79+jF69GiGDRumdlgO\n4cwfMWVkZDBo0KCy52XUqFEMHz5c7bBqTOqXNumlfoF+a1h9qV8yaacQQgghhB2cf6i9EEIIIYSK\npJkSQgghhLCDNFNCCCGEEHaQZkoIIYQQwg7STAkhhBBC2EGaKSGEEEIIO0gzJYQQQghhh/8P/AFa\nW61k8o0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x607a6d0>"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Double axis\n",
      "fig, ax1 = plt.subplots()\n",
      "\n",
      "ax1.plot(x, x**2, lw=2, color=\"blue\")\n",
      "ax1.set_ylabel(r\"area $(m^2)$\", fontsize=18, color=\"blue\")\n",
      "for label in ax1.get_yticklabels():\n",
      "    label.set_color(\"blue\")\n",
      "    \n",
      "ax2 = ax1.twinx()\n",
      "ax2.plot(x, x**3, lw=2, color=\"red\")\n",
      "ax2.set_ylabel(r\"volume $(m^3)$\", fontsize=18, color=\"red\")\n",
      "for label in ax2.get_yticklabels():\n",
      "    label.set_color(\"red\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TEvjvsnQpg4yfeDXVmZjoOZrK/SkuK+uvJwwD7r8feP996wKfiFhj6lSgb1/O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\nCBmtpQVoBwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6391550>"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n = array([0,1,2,3,4,5])\n",
      "xx = np.linspace(-0.75, 1., 100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# some more visualization modes\n",
      "fig, axes = plt.subplots(1, 4, figsize=(12,3))\n",
      "\n",
      "axes[0].scatter(xx, xx + 0.25*randn(len(xx)))\n",
      "\n",
      "axes[1].step(n, n**2, lw=2)\n",
      "\n",
      "axes[2].bar(n, n**2, align=\"center\", width=0.5, alpha=0.5)\n",
      "\n",
      "axes[3].fill_between(x, x**2, x**3, color=\"green\", alpha=0.5);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nd+/XiYtzxWTqDvgBHQAnYBywC3v7WURGni51q1mYYwzJuC06L70Emzebp+/i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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x62b9350>"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# polar plot using add_axes and polar projection\n",
      "fig = plt.figure()\n",
      "ax = fig.add_axes([0.0, 0.0, .6, .6], polar=True)\n",
      "t = linspace(0, 2 * pi, 100)\n",
      "ax.plot(t, t, color='blue', lw=3);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wRX3gOI75pty+fZsOHTokqL/iYiI/v9qH3M6OaNky/ffHajhw4IBR0muvXau+\nX6fLULt9+/Y6VkZtf7vy8nLq3bu3RgPRvw1TGk7MqnDvvvuuyRPlXLhwwaj5OXRRWUkUGlr7gNvb\nEz3iUKKGXC43erasR+E4ogED1EfaR3nrrbeodevWepu/ZTIZjRgxgpYuXVpvWdYRSyaTMT/YrAd0\nyuVy5jrdu3c3ieHErM7L9+7dQ7du3fQuT0T4/fffmfro06ePUZ1aNcFxHG7evIXp0wHVkKytW4Ep\nU7TXi4mJQUpKSr3tX7lyRS85bt26hd27d/PxcNeuXcOxY0cxd25tmR07AJXwPFRUVGDYsGE4duyY\nzrYjIiJQUlICIsLbb78NX19ffPLJJzrriMViLF26VC/Za0hJScGJEyeY6uzZswfV1dV6l7ezs+NT\nL+hLy5Ytce/ePaY6emF0FdaBj48P0/6LQqEQnAJbX6qqqpg9S3Jzc+nzz6PV1mzGtHwfOXJE41u/\nqKhIYyp41X0usVhMHKfckwMqCSA6fLi2bFxcHOXl5ZFIJNI5whUWFlJJSQnFxsaSnZ0dBQQE8Mc5\n6ZpB/FtODPr4448FbbTXh1kVrmnTpibfwGZduN++fZvi4uKY6iQnEzk51SrbG2+wr9lMzSefEAH7\nCLhL335b9/v6FO6/zoIFC2jWrFlGb9dsU8qKigpUV1ejRYsWJuuDiLB48WKmOt26dUP//v31Lq9Q\nAG+9BdSE2Pn7A2vWALqyMBBRvVM4TXAch8WLFwtKoRAYCAAvAuiC27crcPLkSeY2ADBnOSMiFBYW\nMtUpLCxk7ufBgwdM5bOzs6FQKPQu7+7uztyHPphN4XJycuDh4cGUH2TNmjVqAZb1YWdnh2+++UaI\neHqzdm054uOTAAAODsCffwJNmuiuI5PJ4OrqytyXvb09+vfvj7CwMJ3lNCmkandFRSUoLCyEVCpl\nloH1BQYAO3fuZCp/5coVpKenM9VhDRQ9f/48SktL9S7v7u6OjIwMpj70wuhjphbOnj1LPXv2ZKoj\n5BRUVu7evat32dJSIheXbAJuEUAap2qWQJOv4t69tVPesWOV5zCobgHYppS6uXHjBnXr1s3o7Zp9\nhGOhdevWTOWrqqqYpg2AMgOVvvz6K1BU5AGgO9q2Bb76iqkrJjZv3qx2XVpaiq1bt2osq2kEvH5d\nAmAjAKBdO6B9+/b8KDtp0iT069cPycnJaNeuXZ2+bAAeHh5qGcOMhtFVWAtLly6ljz76yKR9bNq0\nyWQnkJb1HIgcAAAgAElEQVSWqsemaQiE1khRURFFRkYy9ycSiep8xpLgpm9fOQFFBBCpnsAkJLU6\n6wlFpaWlzO5X+p4yW0NWVhbT+XfV1dVMmbw4jqPHH39c8GEx2tCpcJo2SC9cuEB9+vShwMBACg4O\nVvMJnD9/Pvn4+FDXrl3VMipFRESQq6sr9e7dm0m4FStWMJU3Jb/8UqNsJ6hzZyJ9oz7EYrHJtzYe\nnVKePq3ubaK657tp0yYqKipiap/1lNFbt24xW343bNjAVD48PJzpBSSVSuukuagPNze3Oi++o0eP\nUteuXcnHx4c/6Sc1NZX69OlDQ4YMqfdFo1Phzp49S5cuXVJTuEGDBtGxY8eISHm4RVhYGBEpnYwD\nAgJIJpORSCQib29v3k3o5Zdfpk8//ZSeeuoppn04Vu8AVoqKivQaERUKoo4dax7iWGJ8NpipzzXt\nxo0btHfvXv66RuE4jqNZs+ZQu3Ycr3BvvmlKSf/ddO7cWW2NL5fLydvbm0QiEclkMgoICKBbt27R\nrFmzKC0tjU6dOkWrVq3S2abONVxoaChatmxZZ25bY+15+PAh2rRpAwAIDw/HpEmT4OjoCC8vL/j4\n+PAZezmOg1Qqhb29PR577DG9p7utWrVimh6zZNsFlKnXcnNz6y13+jQgEil/btFiACZPZuqGieLi\nYqxevVpnGT8/Pzz//PP8dVhYGDgOiIy0w86dXyEzU2kJbt4cal4nNthwcHCAXC7nry9evAgfHx94\neXnB0dERr7zyCsLDw+Hg4ACxWAyxWKwxFaAqzEaThQsX4vPPP0f79u3xxRdfYMGCBQCU+xxt27bl\ny7Vt25bfx5g2bRr27t2LJk2aoHPnzqxd6k19D+qjdO/eHQEBAfWW27at9ufXXwf0zPINANi0aROT\nTC1btsT06dPrLefg4IDr14ExY+QYMEAOd3dg3DjgwQOHf75XunX98z5Ug9UEX1RUBLFYzFSHtQ/W\n8g8fPsTDhw8F9bFqFdC9OzB5MnD0qO46qgr34MEDtGvXjr+uecZnzJiBGTNmYNOmTZiiy7cPAhTu\n7bffxooVK5CRkYGlS5di6tSpWsvW7LkNGzYM48ePx/jx4/Xuh4iwcuVKJtn+7//+j6m8PlRXAxER\ntdc+Pmx+f0OGDGEqb2dnh0aNGulVtqICOHIkCvHxA1FQUMl/7uwMREYCo0Zprnfq1CkmmVJSUpj3\npFhfNKwypaWl4f79+0x1/vrrLwBAQgJw5w6wcyfwz2lWGqmurlbz2dS2h9y2bVvExMTg0KFDaFLP\npiyzwl28eBEvvPACAGDChAn8OV1t2rRBZmYmXy4rK4ufbgJKBSoqKkJMTIzaRq22azs7O0yaNEnv\n8kKu8/LyEBERobP8qlUxKClRXisH8LtM/aWlpTHLFx0drVd5pdPOfQBSAGfRqhXw6afAhg0xcHLS\n3kdycjKTTFVVVcjPz2f6HVRHAn3Ku7u7M5V/+PAhevXqpXf5mJgYZGdnAwBiY2MAKL/v1Ut7+See\neEItI/Sjz3hmZqbarE4v6ls4PrpBGhQUxAdPnjx5koKDg4mo1mgilUrp/v371KlTJ7XF/4cffsiU\nDUoIrIe6X7lypd6I42+/rbX4/ZMk2KTcu3dPb2taRQXRwYNES5dG0507+ltONW056CI/P9/kEfRC\nz5FjpbKSqFGj2nuqK7bZx8eHrl+/zl9XV1dTp06d+GDbGqMJCzoV7pVXXiEPDw9ydHSktm3b0qZN\nmyghIYHPrPvUU0+pJcCZN28eeXt7U9euXXlLZg2ffvop/frrr0zCsfLLL78YvU3VeDcBaS5px44d\nTHs5+gTQisViOn36tMa6h1VDA4zEvn37TJ5HlJVLly4J2nNNSKi9n5076y7r5eVFt2vSq/1DVFQU\ndenShby9vWn+/PnM/Ztt4/uLL75gVghLZ2eurlZPWZCbS8zBrbm5uUYfHUQikdaH7cKFCxY/T+DB\ngwcmTxR169YtpsgTjuNILpfTunW193PiRN11vL291dLFGwOzuXY1bdoUd+7cYarz6quvmkgaJQqF\nQqdMqalAjb9vmzaAmxuYz+R2c3PD448/ziyXroMnvby84Obmxl+rrj/69u2r00F80aJFIDLtobdO\nTk5qazh9ePSc8Pro3r17nS2r+tpfuXIlVD35goJ01yktLUXTpk2Z5KoPsymch4cHs2nZxcWFqXxV\nVRVTmIe9vT2uXbum9fsbN2p/9vdX/v/0008zySQEIsK+ffvUPquoqMAff/yhdxvl5eUafS9nzJjB\nFLEhk8mYTfbOzs7MxoT169czlWelefPmmDlzJk6frv1MV1SWTCZDaWmpoCgPnRh1vNTBkSNHaOTI\nkSbt48yZM3T+/HmjtVfrzqXMdCwUTVHarMhkMuZ1lDFcylJTU5ndtMyBkAM3RKLa+9mkCZGuWOX0\n9HRyd3cXLqAWzDrCsXpfL1u2jKn8wIEDERISwlRHF/9YkQHUbAkAJSUlalM4fXjttdcMlsXR0ZF5\nxFc1tZ84cULQVLJTp05MAboAsH37duZ+WGH9W1RXV+PkydrfPzQUeEyH01NOTg67yV8PzKZwnp6e\nzJunb7zxhsnXGzdv3tSakEZV4Tw9lf+3aNGCeR3n7OwsSDaO4zBy5Mh6T3Ct7wVw584dXLhwgWkq\naQjBwcFM5YuKipgCjQEwv1h/++03HD9ezl8PHaq7fHZ2NjxrbroRMZvCubq6oqKigmmN1bJlS+aH\npLi4mLm8Nheh8tr7g5r1uZ2dHYLqW21rQCqVqrkJ6YO9vT0iIyPh5OTE3B8A/mXVrVs3zJ49m7n+\nxYsXmY0ZANC1a1em8jt27DD5y+D99z/C6dPN+Ov6FO7u3btqhiljYTaFs7e3h6urK/Ly8kzaz59/\n/slUPjQ0VOvCuLLWW4rJf1ITd+/e5V2L6kM1iFbV2fvIkSNIS0urU15TAGpVVRUWLlzIX9c41bIE\n6EqlUqNb6TTx0UcfMSnc7du3cffuXaY+Tp8Gat7F7drVb6HMzs5W85QyFmbNS+np6cm71+hDVVUV\nli9fztTHzJkzWcXSimoKENX5vkwmY7IYAkDPnj3x7LPP6lX2119/1Zh/ZPjw4WrT2fj4eLUIiQMH\nDvAJfJycnPCVhpD0mJgYxMfH6yVHaGgokyIQEfP9EkKTJk2YrYc7dtT+nSZM0J30CVBahU0xpTRr\nmrwxY8Ywn0pi6kzFRErrpqbNYlUvk0cP42GJHjYVV69epdLSUj4eLisryyib3nK5XFA7HMcxp0F8\n8OCBSc/UJiIqLZVQkyYr+Hupz578sGHDTHL0sFlHuKCgIOapwJNPPslUnoiQlZXFVMfe3l7jiKI6\njVTNXgxAsAXr8uXLuH37dp3PKyoqUKk6h9WDnj17olmz2nVJmzZtmEYkbensNmzYoOasrC92dnZM\nm9GAcsQ19fotPr4xJJKPACitzfXZW4gISUlJeoVusWJWhQsODsatW7eY6xGDpdLOzg5H6wtyeoQB\nAwZoNEw88UTtz6oGlBpY0m3X4O/vrzEINzIyktkxoIb60uhpIzY2VuPLadq0acwGg8rKSkF/j8mT\nJzN54hQVFSEqKoqpD9U99YkTAft6nvqMjAw4ODg0/CllVlYWtWrVimm6cuTIEZOdRlkf779fO6XU\n5Na5cuVK5mQ5/1Z27NjBnGxICKWlpUxOy1lZRPb2+fx9fMQXWSP79++nUaNGGSCldsxuNLGzs1OL\nKaqPkSNHonfv3iaUSsmFCxfqBDSqGqk0JeH98MMPDcokvWvXLuYptiZYN+I1ce7cOfzvf/8TXH/S\npEnMVr24uDjmfpo1a8Y0+m7cCHDcLgBAWBigz1kySUlJJnvmzKpwdnZ2CA4ORmJiot51GjVqJGiO\nf/XqVaby3bt3rzO1UfW/ZQwu1gu5XG4Ws7s+lJaW4r333jNrn6xrVlbk8prppHL99v77+tU7deoU\n8+a93phk3NTB999/T19//TVTHYlEQnJ9oyv/Yf/+/UzlNXHhQu2Usnt37eVUUwI2NAw9XTY9PZ0S\nExONJI1uEhMTNWaZ1sa2bernnutzzgvHceTs7Gyy6bFZRzgA6N27N5KSkpjqnDx5knnqxZI/RRVS\nMdD4+dV+npysvi+nSvPmzfU2GFRUVGjcAJ87d64go4MhVFVVYZtqhiQo3cnCw8OZ2ujevbuxRdOI\nr68v+vXrp1dZjgPmzQMA5RlvM2bo9p2sITMzE46OjqYxmADmH+GysrKoWbNmFg+S1MZPP/2kdu3j\nU/uWjI83vP0HDx5oPDPBkHPVWN76+nD58mWjtvcoiYmJRo3q0MS+fTX3bSs1bao8GlofTGkwIbLA\nCNemTRu4ubnh0qVLJu8rMjKS2fn522+/VbseMKD25zNndNfNz8+vtz9PT0+NZyaoJqs5efIkzp07\nV7+wAoiNja336KpA5VlXOsnJyRG8BuvQoQOzUaKiokLve0mkmo/zNXz4Ya0vbH1s2LABgwcPZpKN\nCZOpsg5mzZpF33//PVOdhw8fMseDXb9+3WAvhs2ba0e4ESN0l42Li9M4OkgkElq/fj1Tv6p588vK\nypjqqqJQKOjatWuC6paUlNCWLVs0frdjxw6TJxZSZcmSJXqfJbBnT+09a9yYSN+M6AqFgtzc3Cg5\nOdkASXVjEYU7e/YsBQYGMtXJz883iauNJqqrq3lDQFpa7c177DHdWZ60oVAoDDr5dePGjWrnAaSl\npWmdknMcR0lJSfx1eXl5nYROLBgzeRDHcSbfq6usJPLyIgI4As4QyyGmFy9eNMkRVaqYfUoJKNMU\niEQipvg4V1dXjNKW2dTIODg48CnQO3QAajx8ZLL6M/XWoBrWYm9vz+zypMrUqVPVYurOnj2rFuoz\nderUOhmC6Z/p15NPPomRI0cK7vvRQE9Doj1EIpEgTyMWli8HlAEVEjRr5giW8zkjIiIwduxYE0n2\nDyZVZx28/vrr9R58YAwkEonBh6P/9FPtKPfii/WX5ziOlixZQj/++KNZjEPGNppo4vr167Rq1SqD\nRkshVFVVqaVi1EVuLlHTprX3ivXx6tChg8nTSVhM4fbt20cj6lsUaeDkyZPMdQxZx8nlcrp5s/Ym\nOjrqvyZg3Tu0dizx+6SmptbJDamNV19V3zdlOD6O0tLSyNXV1eS/o0WmlAAwYsQI/P3338wRxfWd\nTqIJQyKm58+fD19f4KmnlJ9VVwNbtmivo7qX1qhRI8jlcj4dfENELpfzqddrzjxg3S9MTExUS9/O\nQqdOndBND3+syEigNpVKBL7/vgAODvr3ExkZidGjR+t9roNgTKrO9TB06FDatGmTWfrKzs4WdPpn\nzZRw48bat2enTtrfnkuWLKmTaZk1eSwrppxS5ufn1xlhjh07RhcuXNC7DbFYbNKpdUkJkadn7f15\n/vk05jaGDBlCBw4cMIF06lhU4bZu3Wry1Hk15OTk0KlTpwTXF4uJWrasvak7dwprxxQPnikUzpCN\neGMgFotp5cqVepWdOrX2vri5EbEaVu/fv0/Nmzc3+vHCmrCowlVWVpKrqytzOukLFy5QvDHcPhjY\nsWMHffLJA/7G9uxJVKM7YrFY772yBQsWmDzC2VAuXLhAUVFRepXVlfty7969JJPJBMnAcZxeCqC6\n5wYQ7drFvjf45Zdf0meffSZETGYsqnBEyjMH3mQ8F1ehUAiaHhpCZWUlZWVVUZMmqjdX+d3evXs1\numv9F9i/f79WpWM9WYaV5GR1q+S4cYW0du1apjZqXvqm3OxWxeIKl5qaSi1btiSJRGKW/q5cuWLQ\nBvoXX9TeYC8v5UarUOLj441iZjfGlHLRokVmyR9THxzH6eVnWVlJFBiovq4W8g5es2YNDRs2TICk\nwrC4whERjRo1ijZv3sxcT6jXgiHKXVzMUZMm/yNATMB1WrBAcFN1yNd3v+ERhCicTCYz+qh8/vx5\nEolEBp2cU1RUVG99jiN6+211DyAV5xomAgIC6NChQ8IqC8AqFO7w4cPUp08f5nq7d+9Wc3kyF4sW\nlRCQQEAuOTkppzbGYMeOHVRQUGCcxuohLi5O7bBBY3Ds2DG6ffs2SfUJPDMA1TMfAKLVq0mQQezS\npUvUrl07vX00jYFVKJxcLqe2bdvS2bNnzdanRCIRbLWUyVSnMxwNHEhkbKNedXU1zZkzh7dq6nNQ\n46Oolk9JSaGtW7caVUZjwnGcXjOPR40kU6YQKRQcfyovC++++y7NnTtXiLiCsQqFI1Ja7yZNmmTW\nPv/++2+m8mKxmD9h9NIlInv7cgIWEUBk4sNdqaSkhJYuXcpfFxQU0IoVK4hIOaUsKChQM6Pn5uYy\nGxCEcvv2bbUIe7lcTvv27WNq4/jx4/VGjp87p35A5sCBREIDFvLy8qhp06ZGOWGIBatRuIcPH5Kr\nqyvduHGDue6WLVvMsoeSn5+vljHqq69qvNKJHByIGPXXYGpGMHP4UupCKpXWGX2NPV1NSiJq0aJW\n2bp0ISoqIsHTwZkzZ9L06dONKqM+WI3CESm9NEaPHs1cr7Cw0KDYrMuXLwvakJZKifr0qXkIZOTm\nlkBmWoJZnOrqaqOE2ujzd79yhcjZuVbZWrUiundPWXfOnDnMfaanp5Ozs7PZRzciK1O4yspK8vDw\nMPum9tmzZ7UaKyorK2n16tVa64pENW9ejoBjNGCAYVsFDYW4uDi9nIoLCgq0BrFyHFdvRMW1a0Qu\nLrXK1rKlcjqv2gYrr776Kn3zzTfM9YyBVSkckTLYMjQ0VNAf8vr164I9G7TBcVy9m+wREUR2drUP\nxaRJRBUV5tM6c00phRhuiEhnslxd7f39t7qyNW9OZGiCsBs3blDLli3N7jhRg8WiBbTx+uuvo7Cw\nkDldOaAMHL13757gvhUKRZ0zv+3s7Oo9gPG554BFi2qvd+4EnntuCwoLiwTLYo2sXbsWBQUFzPUe\nTZZLyhc9AGjNORoRAQwZAhT98yds1gw4cQLo3RuQSCTYvXs3sxwA8M033+Cbb75hPlTTaFhEzevh\n4MGD1KlTJ4s40B48eFCvqc6jcBzR9OnqJuvPPlN+LpPJrDZLWX0YU+6kpCQ6dOgQbdq0iUQikdZy\nv/1GZG+vvma7eLH2e7FYzJTuvIZz585Ru3btLOrLapUKx3Ec9e3bl/78809B9RUKhcF/VCHKXl1N\nNGGCutJ9+inR7dt3ac+ePQbJYwmysrJo3bp1Rm1ToVBoVWKplGjGDPW/X6dORIy+7RrhOI78/f1p\n48aNhjdmAFapcETKRbmnp6cgT5LCwkL6/fffmetVVVXxD0NhYaGgzVSZjGj8ePWH5pVX1PeLrl+/\nbtTIYmOu4fLy8oy+DiZSKprqWu5Rq3JmJtFTT6n/3YKDlWkTahCJRJSeni6o/z/++IN69OhhVq8S\nTVitwhERffzxx/SiPklEjMSKFSvUwmyuXLkiqB2ptK7ShYXVxmn9/fffRs1eZUyF+/PPP0ksFhut\nvRpOnDhBN2/e5K+PHDnCpxQ8cYKodWv1v9eECUSP+lKfOXNG0PZPVlYWubq6mjzBrT5YtcKJxWLq\n0KEDHTx4UHAbllo7yeVEH3yg/hC1b6++FlGWk9NPP/1kMTkjIyPp4qNCmQmxuO7fqFEjpdeOsf4c\nHMfR0KFD6YcffjBOgwZi1QpHpNwjEzq1JFJupusKDhWLxfW2XVhYSMs1HRBXDxxHtHCh+gP12GNK\nZ1ttD1Rubq7OfT9DuXjxotnye964cUNrxq24OCJv75q/SwYBylFO0yxedWRkZfPmzeTn52dyh2p9\nsXqFI1JOLV999VVBdesbOcLDw/UKUzFkBAoPV+4hqSreyJFEGRn193Xv3j21jeOsrCw1H9Dy8nI1\ng0xubq5aPNn169fVvjen5ffevXt11qr5+UTvvKO+bwkcoGeeKSRNjh+VlZUUHh4uqH9rmkrW0CAU\nrmZquW3bNoPaMdaB80KSAt27RxQQoK50zZoRrVunnH7qS1lZGaWmpvLXmZmZak7NRUVFat+bG7FY\nrNEwUV2tPEX20RdPs2ZEW7YYbwpZA8dxNGrUKKuZStbQIBSOSLlg9vDwEJx6WyqV8qEYYrFY7+Si\nmjh//rwg5ZVIlHtz6m93ol69iGJjBYtjVWzcuFEtrbtcrky41K2b+u8MEI0ZU3eUj/3nD3Hnzh2D\ntnaWLVtGvr6+VjOVrKHBKBwR0eeff07Dhw8XbNqtUZIrV64YLdo5MzOTuU5srPoxWKpZnQWeu2FR\nNL18FAqi3buJfH3r/p4+PkTalpGnTp0iuVxOBw4cEDz9vXLlCrVq1cqqppI1NCiFq66upqFDh9JL\nL71kUDsymcxo3gYnTpzQ6TWhjYoKoh9+IHJyqvtAjh/PljLAkuE5FRUVtHDhQv764UOiZcuIOneu\n+3s1bUq0YIHwGDZ9yM/PJy8vL9qxY4fpOjGABqVwRMo1ire3N3MOFLFYzG8v5ObmavVgNwSxWMzs\nFJuWRjRxYt2HEyDq1095bG59D6glFE51VOM45XbH9OlETzxR9/d48kmib75Rxq/p4ty5c/yMQSaT\n0e7du5lkkkql5OvrS7Nnz2b+fcxFg1M4ImX6NVdXV6ZkNSUlJSZPZVdcXCzYhSspiej55zUrXqtW\nyv2qmBg2A4upOHjwIF2+fJlu3iT69lvN0+Ma7/6vvtI/MeuNGzfUFPnu3btMcr3//vs0evRoiyex\n1UWDVDgi5Yatq6srZWizretJWloacyJafTl9+jTz/uGVK8rwHkdHzQ+xuzvRtGnK3B5GPLqtXjIz\nM6m0lOjgQaL331f6OGqSDyDy91daX03gsKKVNWvWUPfu3alUyAF+ZqTBKhyRMg9Kr169tKZXkEql\ntGzZMp1tyGQykwW85ubmCo4qzs0lmjdP6Z2i7cG2syMKCiJ67rlo+v13ZayYsRzhS0uJEhKUnvuT\nJuWQl1cENWqkXZZmzYjeeIMoOprNxL9hw4Z6/SNzcnJ0JkA6cOAAubi4mOzFaUzsiP4JTGqAEBGm\nTJkCmUyGXbt21Tn5hIhQUVGBJ5980kISqssyd+5cfP3110wntCgUQFwcsGcPsG8fkJ+vqVQMgDAA\ngJ0d4OkJeHkBHTsC7u5AixZA8+bKfw4OtWqiUABlZcqYs+JioLAQuH8fSE0FCgoWAvgEgPaTh554\nAnj2WWDSJGDUKEDIIUVEpDUmThWxWKzxPt69exeDBg3Ctm3bMGzYMHYBzI0ltd0YSCQSGjJkCA0f\nPpyfuwvd4M7LyzOpW5Uqubm5zIYfuZzozBmi774jevpp0jnisP+LIyBN5VqhdUSdPVs5kgnd4kpK\nShKc5flRL5y2bdua7QQmY9DgFY5IaR3s168fvffee8RxHP3000+CF87mdCJWnQrfvHmT2YOltJTo\n+HGlv+bEiUrjhWrgpu5/1wi4qXJ9jwCpms9nt27KdhctUiqYsZZHp06dEnx/zp8/T1FRUSQSiah1\n69a0Zs0a4whlJhr0lFKVsrIyjBgxAn379sWyZctgb29Y9ggiws6dOzFp0iS9pjyGQkQoLS3l0xEk\nJCQgLy8PY8aMAaD8/ezs7NC0adM6dWNiYhAWFgZAeQ55VhaQnCzD/fvVKC9/Ag8fArduRUMstkOr\nVmGwtwckknQ4OTnDxaUpnJ0BFxfA2Rlo3x7w9gbatAGMeTZhdXW1oMM0NZGRkYEhQ4ZgxowZ+PTT\nT43SptmwrL4bl5KSEurTpw+NGjXKKKbhu3fvWk1qhGvXrqkZd+Li4uj48eNEpNyHU70mUo4E5s5+\npg2pVEq//PKLUdpKTU2ljh070pIlS4zSnrmxKoXLyMigsLAw8vX1JT8/Pz4kZtasWdStWzfq2bMn\nvfDCC/zmskgkIicnJwoMDKTAwECaPn06lZaWUv/+/Wno0KHUo0cPeuedd4wiW2JiotWf62ZtGOtl\nVVlZSX379qVu3bpRo0aNaPjw4UREtGfPHvL19SV7e3tKUnHN0fRc1BAREUE9e/Y02nPBilUpXE5O\nDu//Vl5eTl26dKFbt27RiRMn+BHryy+/pC+//JKIlH9Yf3//Ou2IxWJq3bo1vfTSSzR79mxB2Zwf\nRSQSMW/E/pc5c+YM/fXXX0ZrLz4+ntq0aUPr1q2jkJAQio2Npdu3b9Pdu3cpLCysjsJpei6IiF5+\n+WVSKBT03XffGeW5YMWq0uS5u7sjMDAQAPDkk0+ie/fuyM7OxvDhw/k1WUhICLKysnS288QTT2DA\ngAFQKBRYt24diouLDZbNy8sLXbp0AaA0USckJBjcprGIiYmxtAgAAI7j+J8HDhxoNDP977//jnHj\nxmHZsmWYMmUKFAoFnJ2d0a1bN/6esMgolUohkUjw2GOPGUU+FqxK4VRJS0vD5cuXERISovb5pk2b\nMGrUKP5aJBIhKCgIYWFhiIuL4z+fPn060tLS4O/vj0mTJuHChQtGk61x48aorKw0Wnv/BogI8+bN\nU1M6Q+E4Dt999x3mzJmDY8eOYe7cuXBzc8PgwYPh6+urs66252LatGkIDQ1Fo0aN0LlzZ6PJqjdm\nH1P1oLy8nHr37l0nl8ncuXNp/Pjx/LVUKuVjr5KSkqhdu3Ya0ymEh4eTi4tLvV4nQomKivpPTjcr\nKytN5p9aVlZG48aNo/79+6vloHz48CGFhISoOWw/OqXU97mwBFY3wlVXV+PFF1/ElClT8Pzzz/Of\n//HHH4iKisL27dv5zx577DG0bNkSANCrVy94e3sjJSWlTptjx47FmTNnsGzZMsyaNQsKhcKoMg8f\nPhytW7c2apsNgfj4eFRUVBi93fv378Pf3x8uLi44ffo03Nzc+O+aN2+O0aNHIzExUWt9fZ8Li2Bp\njVeF4zh67bXX6JNPPlH7/OjRo+Tr61vnwI2CggI+Z0Zqaiq1adNGZx77wsJCGjJkCD3zzDMmPTl1\n3rx5Bp3mw4q5wnOysrJM7tVx+vRpcnNzo+XLl/NWzoKCAv6+SiQSCg0NpZMnT/J1wsLC1M6WY30u\nzALr+R0AAAbhSURBVIlVKVxsbCzZ2dlRQEAAb9KNiooiHx8fat++fR0z7759+8jPz48CAwOpV69e\n/GGJupDJZDRz5kxycXExS/aqwsJCk8TeqWIqheM4Ts2SV11dbbLQl4qKCnrzzTfJ3d29jnXz2rVr\nFBQURAEBAdSjRw9atGgRESmdltu2bUtOTk7k5uZGzzzzDBEJey7MhVUpnDk5deoUeXl50Ztvvmny\nt59qjo+0tDS6deuWSfszBNUkQAqFgvbu3Wvyzf+4uDjy8fGhl156yWxnnFsKq1vDmYshQ4bg2rVr\naNKkCTp37ozw8HCT9VWznqj5WXUNeefOHdy/f99kfddHSUkJxGIxf71z505+XWZvb48JEyaYzLVN\nIpHgk08+wUsvvYRffvkFe/bsQatWrUzSl9VgaY23Bsw52j1Kfn6+Wlq7Y8eOUUJCAlMbLFPKs2fP\nqsWNHTp0SNBJNIYSFxdHHTp0+E+MaqrYFO4fysvL6YMPPiB3d3davXq1xXwoOY5TS+0WGRmppoCH\nDh1SMxAcPnyY1q9fz18fPHhQzURekw6hhuzsbIu6qBUVFdHMmTPJw8OD9u/fbzE5LIVN4R4hOjqa\nAgMDKTg4WM0SZq1UV1cb9SQeUyEWi2nevHnUrFkzevfdd/9To5oqNoXTgEKhoF27dpGPjw8FBAQw\nT/Fs1CKTyWj16tXk6elJEydO/E86CKhiUzgdyGQyWrNmDXl4eFj1w2LJvJTaqHlpeXl50eDBg20v\nrX/4z1op9cHR0RHTp09HSkoKAgMD8fTTT2PcuHG4ceOGpUWzWqqrq7F27Vr06dMHS5YswYYNG3D6\n9GkEBwdbWjSr4F8T8W0OioqKsGLFCmzYsAE+Pj549913MXHiRIt4nVsbWVlZWL9+PdavX4+OHTvi\n888/xwsvvGCWaPkGhaWH2IaITCajvXv3Uv/+/cnNzY2+/fZbg/NjNkQ4jqO//vqLwsLCqEWLFvTh\nhx9aJMasIWFTOAO5desWffTRR9SyZUsKDg6myMhIs5vdzb2GS09Ppx9//JG6dOlCPXr0oN9++81q\nvPGtHduU0kiIxWJs374d27Ztw7Vr1xAaGoqRI0filVdegaurq0n7Vk0iZAqICJcvX0ZkZCQiIiKQ\nlpaGZ599Fu+//z769+9vmzYyYFM4E1BYWIioqCgcPHgQp0+fhr+/P4YPH44JEybAz8+vQTygUqkU\n0dHRiIiIQEREBBwcHDBhwgSMHTsW/fr1g4ODg6VFbJDYFM7ESKVSxMTE4NChQ/yD26dPH3Tu3Bn9\n+/fH008/DRcXF4vKyHEcUlJSkJSUxP9LSEhAUFAQxo4di7Fjx6Jr164N4kVh7dgUzowQEZKTk/kH\nOikpCVevXoWzszM6deqEp556CgMHDkT79u3h4eGB5s2b6/WQ6zullMvlyM/PR05ODs6cOYO0tDRc\nu3YNly5dQrNmzRASEoLg4GD07t0bwcHBcHZ2NsJvbUMVm8JZGNXRJTExEVevXkVWVhYePHgAjuPg\n4eEBDw8PODg4oHPnzvD29kaLFi0gFovRvHlzNGnSBHfu3IGLiwsaN24Me3t7SKVSJCYmguM4FBcX\nIzs7G+np6SgrK4OLiws8PT3Rvn179O3bl1cwS4+y/xVsCmfFiMVi5OTkICcnB9nZ2fz/ZWVlkEql\n4DgOCoWCD/dxcnKCo6MjHB0d4erqCk9PT3h4ePD/u7m52dZeFsamcDZsmBGba5cNG2bEpnA2bJgR\nm8JZKZmZmRg8eDD8/Pzg7++PFStWAABefvllBAUFISgoCB07dkRQUBBfZ8GCBejcuTO6deuGEydO\n8J9HRkYiICAA7777rtl/DxuPYH7nFhv6oO2cBVU+//xz+vnnn4lIeb5cQEAAyWQyEolE5O3tzUet\nWzqfvo1abCOclaLtnIUaiAh79uzBpEmTAADh4eGYNGkSHB0d4eXlBR8fHz69u6Xz6duoxaZwDQBN\n5yzExsbCzc0N3t7eAIDs7Gy0bduW/75t27Z48OABACvIp2+Dx7YpY+WIxWJMmDABy5cvVztUfufO\nnZg8ebLOujVeKsOGDdOZGtyG+bApnBWj7ZwFuVyOgwcP4tKlS/xnbdq0QWZmJn+dlZWFNm3amFVe\nG/Vjm1JaKUSEt99+G76+vvjkk0/Uvjt58iS6d+8OT09P/rOxY8di165dkMlkEIlESElJQd++fc0t\nto16sI1wVkp8fDy2bduGnj178qb/BQsW4JlnnsHu3bt5Y0kNvr6+mDhxInx9feHg4IA1a9bYvPut\nEJtrlw0bZsQ2pbRhw4zYFM6GDTNiUzgbNsyITeFs2DAjNoWzYcOM2BTOhg0zYlM4GzbMiE3hbNgw\nI/8PQO6AMjxPa1kAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6bbbb90>"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# A histogram\n",
      "n = np.random.randn(100000)\n",
      "fig, axes = plt.subplots(1, 2, figsize=(12,4))\n",
      "\n",
      "axes[0].hist(n)\n",
      "axes[0].set_title(\"Default histogram\")\n",
      "axes[0].set_xlim((min(n), max(n)))\n",
      "\n",
      "axes[1].hist(n, cumulative=True, bins=50)\n",
      "axes[1].set_title(\"Cumulative detailed histogram\")\n",
      "axes[1].set_xlim((min(n), max(n)));"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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q1Ci2bdvWpfUTEfEmJc4iIt3E1q1b+dvf/sbGjRv54osvOHDgAHl5eWRlZZGU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\ncpC9tgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x69d94d0>"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(2, 3)\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zhvX279+PdevW4aOPPsq6ZrlviHRCXsz2CZjPDPOSn3LNi9maJZcZO39faOdN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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6057550>"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "ax1 = plt.subplot2grid((3,3), (0,0), colspan=3)\n",
      "ax2 = plt.subplot2grid((3,3), (1,0), colspan=2)\n",
      "ax3 = plt.subplot2grid((3,3), (1,2), rowspan=2)\n",
      "ax4 = plt.subplot2grid((3,3), (2,0))\n",
      "ax5 = plt.subplot2grid((3,3), (2,1))\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sI0eOoLy8HEajEW/fvsXJkyfdihNFD+YMEWmFrY4IgHz7R7Z4yJWM+0fGmOg9\ntjoiIqKIwQJFRERSYoEiIiIpsUAREZGU/G4WC8zMkcjNzYXBYOBcFWLOEJEm/G4WOzY2hsLCQty+\nfRupqamw2+1ITEx0XxHvsJFaMJvFKskZ5ovcZNw/MsZE74WkWeyVK1ewc+dOZ7eA+YoTRQ/mDBFp\nxWOBmq/x59OnT12W6evrw8uXL1FUVASz2YxLly4FJlIKC8wZItKKx04SShp/OhwOdHV1oampCa9f\nv0ZBQQHy8/ORmZnptix7q8kjUL3VtMwZ5os82IuPQsHvZrFpaWlITExEXFwc4uLisHHjRnR3d3st\nUBRacw/4x44d02RcLXOG+SKPQOULkScev+Izm83o6+vDb7/9hjdv3uDq1asoLS11WeYvf/kLfvnl\nF0xPT+P169e4f/8+srOzAxo0yYs5Q0Ra8VigZjf+zM7OxmeffeZs/Pmu+WdWVhaKi4uRk5ODvLw8\nWCwWHmyiGHOG1PI2LeHy5cswGo3IyclBYWEhbDZbCKKkUGCzWAIg3/6RLR5yFcxpCa2trcjOzkZ8\nfDysViuqqqrQ1tYWsJgoMNgslojCipJpCQUFBYiPjwcA5OXlYXh4OBShUgiwQBFRyCiZljDbuXPn\nUFJSEozQSAIe7+IjIgokJdMS3rl79y7Onz+Pe/fuBTAikokmvfgAoKOjAzExMbh586amAVL4Yc6Q\nUkqmJQCAzWaDxWLBrVu3sGzZsgXHq6qqcr44byu0mpubXfaHT4QHU1NTQqfTicHBQfHmzRthNBpF\nT0/PvMsVFRWJHTt2iOvXr887lpdVUYhptX+0yhnmi9y02j8Oh0OsWrVKDA4OisnJyXnz5cmTJ0Kn\n04nW1tagxESB4cv+8bsXHwCcPn0au3btwooVK3yrkhQxmDOkhpJpCcePH8fo6Cj27duH3NxcrF+/\nPsRRU7B4vAY13wXM+/fvuy1TX1+Pf//73+jo6FD1nTJFHuYMqbV9+3Zs377d5b99+eWXzn9/9913\n+O6774IdFknA4xmUkgNHRUUFampqnPe4C85DiGrMGSLSit+9+Do7O1FWVgYAsNvtaGxsRGxsrFt7\nG4DNP2USqOafWuYM80UebBZLoeCxk8TU1BT0ej2ampqQnJyM9evXu83ynq28vByffPIJPv30U/cV\ncZa31LTaP1rlDPNFbjLuHxljovd82T8ez6BmX8Ccnp7G3r17nRcwAdfviYkA5gwRaYe9+AiAfPtH\ntnjIlYxlJpGoAAAGTElEQVT7R8aY6D324iMioojBAkVERFJigSIiIimxQBERkZQUFSg+8ZLUYL4Q\nkSa8NetT0vyzpaVFjI2NCSGEaGxsFHl5eZo0CqTg0Wr/MF+ig4z7R8aY6D1f9o/XMyg+8ZLUYL4Q\nkVa8Fig+8ZLUYL4QkVa8PlGXT7wkNZgvRKQVrwVK7RMvrVbrgk+8ZPNPeYSyWSzAfAk3bBZLIeHt\nIpVWT7xUsCoKIa32D/MlOsi4f2SMid7zZf94PYNS0vxz9hMvASA2Nhbt7e2BqqkkMeYLEWmFzWIJ\ngHz7R7Z4yJWM+0fGmOg9NoslIqKIwQJFRERSYoEiIiIpsUAREZGUvBYob40/AeDgwYPIzMyE0WjE\ngwcPNA+SwgtzhtRgvtCCPN2DrqTx548//ii2b98uhBCira1t3safvt4D783du3elHzMcYhQiuM1i\nleRMtOZLIMaMhnzRMqbZwmFfhMuYvuwfj2dQShp/3rp1C7t37wYw0/hzbGwMIyMjgailbgIxs13r\nMcMhRi3JnDPhsi/CIQe1InO+AOGxL8JlTF94LFBKGn/Otwy7U0cv5gypwXwhTzwWKKWNP8WcyVdq\nGoZSZGHOkBrMF/LI0/d/ra2tYtu2bc6fq6urRU1NjcsyX375pairq3P+rNfrxfPnz93G0ul0AgBf\nkr50Op3q74cDmTPMF7lfsuULc0b+ly8547FAKWn8OfsCZmtr64IXMCk6MGdIDeYLeeKxWaySxp8l\nJSVoaGhARkYGlixZggsXLngakiIcc4bUYL6QJ0FrFktERKSG5p0kAjHpztuYly9fhtFoRE5ODgoL\nC2Gz2fyOEQA6OjoQExODmzdv+h0jMHPrZm5uLgwGg6KH73kb0263o7i4GCaTCQaDARcvXvQ43p49\ne5CUlIQ1a9YsuEywJ0SGQ74ojRNQnjPMF99Ea74oHTPickbL7wu1nHSnZsyWlhYxNjYmhBCisbHR\n45hKxnu3XFFRkdixY4e4fv263zGOjo6K7OxsMTQ0JIQQ4sWLF36PWVlZKQ4fPuwcLyEhQTgcjgXH\n/Pnnn0VXV5cwGAzz/l7tvvFXOOSL0jHfLackZ5gvvonWfFE6ZiTmjKZnUIGYdKdkzIKCAsTHxzvH\n9DRHQsl4AHD69Gns2rULK1as0OR9X7lyBTt37nQ+/jwxMdHvMVeuXInx8XEAwPj4OJYvX46YmIUv\nK27YsGHBx6sDwZ8QGQ75onRMQHnOMF98E635onTMSMwZTQtUICbdKRlztnPnzqGkpMTvGOvr651P\nfPU250LJmH19fXj58iWKiopgNptx6dIlv8e0WCx49OgRkpOTYTQacerUKY9jehPsCZHhkC9q4lSa\nM8wX7dYXDfmidMxIzBmvj3xXIxCT7tRMyLt79y7Onz+Pe/fu+TVeRUUFampqnE+AnBuvL2M6HA50\ndXWhqakJr1+/RkFBAfLz85GZmenzmNXV1TCZTGhubsbAwAC2bt2K7u5ufPjhh17/34Wo2Tf+Cod8\nUTqmmpxhvvgmWvNF6ZiRmDOaFqiUlBQMDQ05fx4aGnKebi60zPDwMFJSUvwaEwBsNhssFgusVqvH\nU0wl43V2dqKsrAzAzEXCxsZGxMbGorS01Ocx09LSkJiYiLi4OMTFxWHjxo3o7u5eMHmUjNnS0oKj\nR48CAHQ6HdLT0/H48WOYzeYF378naveNv8IhX5SOqSZnmC++idZ8UTpmROaMxytUKgVi0p2SMZ88\neSJ0Op1obW3VJMbZvvjiC3Hjxg2/x+zt7RVbtmwRU1NT4tWrV8JgMIhHjx75NeZXX30lqqqqhBBC\nPH/+XKSkpIg//vjDY6yDg4OKLmAGY0JkOOSL0jFn85YzzBffRGu+KB0zEnNG8/70DQ0N4v/+7/+E\nTqcT1dXVQgghvv32W/Htt986l9m/f7/Q6XQiJydHdHZ2+j3m3r17RUJCgjCZTMJkMol169b5HeM7\nSpJH6Zj/+Mc/RHZ2tjAYDOLUqVN+j/nixQvx8ccfi5ycHGEwGMTly5c9jldWViZWrlwpYmNjRWpq\nqjh37pzf+8Zf4ZAvSuN8R0nOMF98E635onTMSMsZTtQlIiIp8ZHvREQkJRYoIiKSEgsUERFJiQWK\niIikxAJFRERSYoEiIiIpsUAREZGUWKCIiEhK/w/T7RUxKBZyMwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x607ad90>"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "alpha = 0.7\n",
      "phi_ext = 2 * pi * 0.5\n",
      "\n",
      "def flux_qubit_potential(phi_m, phi_p):\n",
      "    return 2 + alpha - 2 * cos(phi_p)*cos(phi_m) - alpha * cos(phi_ext - 2*phi_p)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "phi_m = linspace(0, 2*pi, 100)\n",
      "phi_p = linspace(0, 2*pi, 100)\n",
      "X,Y = meshgrid(phi_p, phi_m)\n",
      "Z = flux_qubit_potential(X, Y).T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots()\n",
      "\n",
      "im = imshow(Z, vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])\n",
      "#pylab.set_cmap('jet')\n",
      "pylab.set_cmap('RdBu')\n",
      "#pylab.set_cmap('bone')\n",
      "\n",
      "im.set_interpolation('bilinear')\n",
      "\n",
      "cb = fig.colorbar(im, ax=ax)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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j0fMHg0iv+9PT0uM4hk1skkjfBXv3MmpYhDMu3SeNqyr4O7j1e9zZlzoeOvO6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\n11KWspRtKUvwWspSlrIt5f8Bvhl66OdlDasAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x638ded0>"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots()\n",
      "\n",
      "cnt = contour(Z, cmap=cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3RMRd13VmzZrFs88+S0tLCytWrODhhx9mzpxXAy0PPvggxWKRr371q8RiMWbN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\nHR2dDEQXdx0dHZ0M5P8B0xUl8c3V82UAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7d11c10>"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "3D figures"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from mpl_toolkits.mplot3d.axes3d import Axes3D"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(14,6))\n",
      "\n",
      "# `ax` is a 3D-aware axis instance, because of the projection='3d' keyword argument to add_subplot\n",
      "ax = fig.add_subplot(1, 2, 1, projection='3d')\n",
      "\n",
      "p = ax.plot_surface(X, Y, Z, rstride=4, cstride=4, linewidth=0)\n",
      "\n",
      "# surface_plot with color grading and color bar\n",
      "ax = fig.add_subplot(1, 2, 2, projection='3d')\n",
      "p = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=False)\n",
      "cb = fig.colorbar(p, shrink=0.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8,6))\n",
      "\n",
      "ax = fig.add_subplot(1, 1, 1, projection='3d')\n",
      "\n",
      "p = ax.plot_wireframe(X, Y, Z, rstride=4, cstride=4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8,6))\n",
      "\n",
      "ax = fig.add_subplot(1,1,1, projection='3d')\n",
      "\n",
      "ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)\n",
      "cset = ax.contour(X, Y, Z, zdir='z', offset=-pi, cmap=cm.coolwarm)\n",
      "cset = ax.contour(X, Y, Z, zdir='x', offset=-pi, cmap=cm.coolwarm)\n",
      "cset = ax.contour(X, Y, Z, zdir='y', offset=3*pi, cmap=cm.coolwarm)\n",
      "\n",
      "ax.set_xlim3d(-pi, 2*pi);\n",
      "ax.set_ylim3d(0, 3*pi);\n",
      "ax.set_zlim3d(-pi, 2*pi);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Drawing Matplotlib logo with Matplotlib\n",
      "Thanks to Tony Yu <tsyu80@gmail.com> for the logo design\n",
      "\"\"\"\n",
      "\n",
      "import numpy as np\n",
      "import matplotlib as mpl\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib.cm as cm\n",
      "\n",
      "mpl.rcParams['xtick.labelsize'] = 10\n",
      "mpl.rcParams['ytick.labelsize'] = 12\n",
      "mpl.rcParams['axes.edgecolor'] = 'gray'\n",
      "\n",
      "\n",
      "axalpha = 0.05\n",
      "#figcolor = '#EFEFEF'\n",
      "figcolor = 'white'\n",
      "dpi = 80\n",
      "fig = plt.figure(figsize=(6, 1.1),dpi=dpi)\n",
      "fig.figurePatch.set_edgecolor(figcolor)\n",
      "fig.figurePatch.set_facecolor(figcolor)\n",
      "\n",
      "\n",
      "def add_math_background():\n",
      "    ax = fig.add_axes([0., 0., 1., 1.])\n",
      "\n",
      "    text = []\n",
      "    text.append((r\"$W^{3\\beta}_{\\delta_1 \\rho_1 \\sigma_2} = U^{3\\beta}_{\\delta_1 \\rho_1} + \\frac{1}{8 \\pi 2} \\int^{\\alpha_2}_{\\alpha_2} d \\alpha^\\prime_2 \\left[\\frac{ U^{2\\beta}_{\\delta_1 \\rho_1} - \\alpha^\\prime_2U^{1\\beta}_{\\rho_1 \\sigma_2} }{U^{0\\beta}_{\\rho_1 \\sigma_2}}\\right]$\", (0.7, 0.2), 20))\n",
      "    text.append((r\"$\\frac{d\\rho}{d t} + \\rho \\vec{v}\\cdot\\nabla\\vec{v} = -\\nabla p + \\mu\\nabla^2 \\vec{v} + \\rho \\vec{g}$\",\n",
      "                (0.35, 0.9), 20))\n",
      "    text.append((r\"$\\int_{-\\infty}^\\infty e^{-x^2}dx=\\sqrt{\\pi}$\",\n",
      "                (0.15, 0.3), 25))\n",
      "    #text.append((r\"$E = mc^2 = \\sqrt{{m_0}^2c^4 + p^2c^2}$\",\n",
      "    #            (0.7, 0.42), 30))\n",
      "    text.append((r\"$F_G = G\\frac{m_1m_2}{r^2}$\",\n",
      "                (0.85, 0.7), 30))\n",
      "    for eq, (x, y), size in text:\n",
      "        ax.text(x, y, eq, ha='center', va='center', color=\"#11557c\", alpha=0.25,\n",
      "                transform=ax.transAxes, fontsize=size)\n",
      "    ax.set_axis_off()\n",
      "    return ax\n",
      "\n",
      "def add_matplotlib_text(ax):\n",
      "    ax.text(0.95, 0.5, 'matplotlib', color='#11557c', fontsize=65,\n",
      "               ha='right', va='center', alpha=1.0, transform=ax.transAxes)\n",
      "\n",
      "def add_polar_bar():\n",
      "    ax = fig.add_axes([0.025, 0.075, 0.2, 0.85], polar=True)\n",
      "\n",
      "\n",
      "    ax.axesPatch.set_alpha(axalpha)\n",
      "    ax.set_axisbelow(True)\n",
      "    N = 7\n",
      "    arc = 2. * np.pi\n",
      "    theta = np.arange(0.0, arc, arc/N)\n",
      "    radii = 10 * np.array([0.2, 0.6, 0.8, 0.7, 0.4, 0.5, 0.8])\n",
      "    width = np.pi / 4 * np.array([0.4, 0.4, 0.6, 0.8, 0.2, 0.5, 0.3])\n",
      "    bars = ax.bar(theta, radii, width=width, bottom=0.0)\n",
      "    for r, bar in zip(radii, bars):\n",
      "        bar.set_facecolor(cm.jet(r/10.))\n",
      "        bar.set_alpha(0.6)\n",
      "\n",
      "    for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
      "        label.set_visible(False)\n",
      "\n",
      "    for line in ax.get_ygridlines() + ax.get_xgridlines():\n",
      "        line.set_lw(0.8)\n",
      "        line.set_alpha(0.9)\n",
      "        line.set_ls('-')\n",
      "        line.set_color('0.5')\n",
      "\n",
      "    ax.set_yticks(np.arange(1, 9, 2))\n",
      "    ax.set_rmax(9)\n",
      "\n",
      "main_axes = add_math_background()\n",
      "add_polar_bar()\n",
      "add_matplotlib_text(main_axes)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7ANnbTDP48PH7YHf7QNP0MJ83DJsjJY7V5cXT7+7pM1KHeU/7yBDe3X8Gxxo6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\nJFAgEAgEAoFQlP8P/QPwLgvX0AcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x73d5fd0>"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Using Fortran and C code with Python"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%file hellofortran.f\n",
      "C File  hellofortran.f\n",
      "        subroutine hellofortran (n)\n",
      "        integer n\n",
      "       \n",
      "        do 100 i=0, n\n",
      "            print *, \"Fortran says hello\"\n",
      "100     continue\n",
      "        end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Overwriting hellofortran.f\n"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#compyle fortran module hellofortran.f\n",
      "!f2py -c -m hellofortran hellofortran.f"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[39mrunning build\u001b[0m\r\n",
        "\u001b[39mrunning config_cc\u001b[0m\r\n",
        "\u001b[39munifing config_cc, config, build_clib, build_ext, build commands --compiler options\u001b[0m\r\n",
        "\u001b[39mrunning config_fc\u001b[0m\r\n",
        "\u001b[39munifing config_fc, config, build_clib, build_ext, build commands --fcompiler options\u001b[0m\r\n",
        "\u001b[39mrunning build_src\u001b[0m\r\n",
        "\u001b[39mbuild_src\u001b[0m\r\n",
        "\u001b[39mbuilding extension \"hellofortran\" sources\u001b[0m\r\n",
        "\u001b[39mf2py options: []\u001b[0m\r\n",
        "\u001b[39mf2py:> /tmp/tmpjgAgLB/src.linux-x86_64-2.7/hellofortranmodule.c\u001b[0m\r\n",
        "\u001b[39mcreating /tmp/tmpjgAgLB\u001b[0m\r\n",
        "\u001b[39mcreating /tmp/tmpjgAgLB/src.linux-x86_64-2.7\u001b[0m\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Reading fortran codes...\r\n",
        "\tReading file 'hellofortran.f' (format:fix,strict)\r\n",
        "Post-processing...\r\n",
        "\tBlock: hellofortran\r\n",
        "\t\t\tBlock: hellofortran\r\n",
        "Post-processing (stage 2)...\r\n",
        "Building modules...\r\n",
        "\tBuilding module \"hellofortran\"...\r\n",
        "\t\tConstructing wrapper function \"hellofortran\"...\r\n",
        "\t\t  hellofortran(n)\r\n",
        "\tWrote C/API module \"hellofortran\" to file \"/tmp/tmpjgAgLB/src.linux-x86_64-2.7/hellofortranmodule.c\"\r\n",
        "\u001b[39m  adding '/tmp/tmpjgAgLB/src.linux-x86_64-2.7/fortranobject.c' to sources.\u001b[0m\r\n",
        "\u001b[39m  adding '/tmp/tmpjgAgLB/src.linux-x86_64-2.7' to include_dirs.\u001b[0m\r\n",
        "\u001b[39mcopying /usr/local/lib/python2.7/dist-packages/numpy/f2py/src/fortranobject.c -> /tmp/tmpjgAgLB/src.linux-x86_64-2.7\u001b[0m\r\n",
        "\u001b[39mcopying /usr/local/lib/python2.7/dist-packages/numpy/f2py/src/fortranobject.h -> /tmp/tmpjgAgLB/src.linux-x86_64-2.7\u001b[0m\r\n",
        "\u001b[39mbuild_src: building npy-pkg config files\u001b[0m\r\n",
        "\u001b[39mrunning build_ext\u001b[0m\r\n",
        "\u001b[39mcustomize UnixCCompiler\u001b[0m\r\n",
        "\u001b[39mcustomize UnixCCompiler using build_ext\u001b[0m\r\n",
        "\u001b[39mcustomize Gnu95FCompiler\u001b[0m\r\n",
        "\u001b[32mFound executable /usr/bin/gfortran\u001b[0m\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[39mcustomize Gnu95FCompiler\u001b[0m\r\n",
        "\u001b[39mcustomize Gnu95FCompiler using build_ext\u001b[0m\r\n",
        "\u001b[39mbuilding 'hellofortran' extension\u001b[0m\r\n",
        "\u001b[39mcompiling C sources\u001b[0m\r\n",
        "\u001b[39mC compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -fPIC\r\n",
        "\u001b[0m\r\n",
        "\u001b[39mcreating /tmp/tmpjgAgLB/tmp\u001b[0m\r\n",
        "\u001b[39mcreating /tmp/tmpjgAgLB/tmp/tmpjgAgLB\u001b[0m\r\n",
        "\u001b[39mcreating /tmp/tmpjgAgLB/tmp/tmpjgAgLB/src.linux-x86_64-2.7\u001b[0m\r\n",
        "\u001b[39mcompile options: '-I/tmp/tmpjgAgLB/src.linux-x86_64-2.7 -I/usr/local/lib/python2.7/dist-packages/numpy/core/include -I/usr/include/python2.7 -c'\u001b[0m\r\n",
        "\u001b[39mgcc: /tmp/tmpjgAgLB/src.linux-x86_64-2.7/fortranobject.c\u001b[0m\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "In file included from /usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarraytypes.h:1760:0,\r\n",
        "                 from /usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarrayobject.h:17,\r\n",
        "                 from /usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h:4,\r\n",
        "                 from /tmp/tmpjgAgLB/src.linux-x86_64-2.7/fortranobject.h:13,\r\n",
        "                 from /tmp/tmpjgAgLB/src.linux-x86_64-2.7/fortranobject.c:2:\r\n",
        "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning \"Using deprecated NumPy API, disable it by \" \"#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[39mgcc: /tmp/tmpjgAgLB/src.linux-x86_64-2.7/hellofortranmodule.c\u001b[0m\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "In file included from /usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarraytypes.h:1760:0,\r\n",
        "                 from /usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarrayobject.h:17,\r\n",
        "                 from /usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h:4,\r\n",
        "                 from /tmp/tmpjgAgLB/src.linux-x86_64-2.7/fortranobject.h:13,\r\n",
        "                 from /tmp/tmpjgAgLB/src.linux-x86_64-2.7/hellofortranmodule.c:17:\r\n",
        "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning \"Using deprecated NumPy API, disable it by \" \"#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[39mcompiling Fortran sources\u001b[0m\r\n",
        "\u001b[39mFortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops\r\n",
        "Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops\r\n",
        "Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops\u001b[0m\r\n",
        "\u001b[39mcompile options: '-I/tmp/tmpjgAgLB/src.linux-x86_64-2.7 -I/usr/local/lib/python2.7/dist-packages/numpy/core/include -I/usr/include/python2.7 -c'\u001b[0m\r\n",
        "\u001b[39mgfortran:f77: hellofortran.f\u001b[0m\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[39m/usr/bin/gfortran -Wall -Wall -shared /tmp/tmpjgAgLB/tmp/tmpjgAgLB/src.linux-x86_64-2.7/hellofortranmodule.o /tmp/tmpjgAgLB/tmp/tmpjgAgLB/src.linux-x86_64-2.7/fortranobject.o /tmp/tmpjgAgLB/hellofortran.o -lgfortran -o ./hellofortran.so\u001b[0m\r\n",
        "Removing build directory /tmp/tmpjgAgLB\r\n"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%file hello.py\n",
      "import hellofortran\n",
      "\n",
      "hellofortran.hellofortran(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Overwriting hello.py\n"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# run the script\n",
      "!python hello.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Fortran says hello\r\n",
        " Fortran says hello\r\n",
        " Fortran says hello\r\n",
        " Fortran says hello\r\n",
        " Fortran says hello\r\n",
        " Fortran says hello\r\n"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# simple python algorithm: example of a SLOW implementation\n",
      "# Why? Because the loop is implemented in python.\n",
      "def py_dcumsum(a):\n",
      "    b = empty_like(a)\n",
      "    b[0] = a[0]\n",
      "    for n in range(1,len(a)):\n",
      "        b[n] = b[n-1]+a[n]\n",
      "    return b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%file dcumsum.f\n",
      "c File dcumsum.f\n",
      "       subroutine dcumsum(a, b, n)\n",
      "       double precision a(n)\n",
      "       double precision b(n)\n",
      "       integer n\n",
      "cf2py  intent(in) :: a\n",
      "cf2py  intent(out) :: b\n",
      "cf2py  intent(hide) :: n\n",
      "\n",
      "       b(1) = a(1)\n",
      "       do 100 i=2, n\n",
      "           b(i) = b(i-1) + a(i)\n",
      "100    continue\n",
      "       end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Overwriting dcumsum.f\n"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!f2py -c dcumsum.f -m dcumsum"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[39mrunning build\u001b[0m\r\n",
        "\u001b[39mrunning config_cc\u001b[0m\r\n",
        "\u001b[39munifing config_cc, config, build_clib, build_ext, build commands --compiler options\u001b[0m\r\n",
        "\u001b[39mrunning config_fc\u001b[0m\r\n",
        "\u001b[39munifing config_fc, config, build_clib, build_ext, build commands --fcompiler options\u001b[0m\r\n",
        "\u001b[39mrunning build_src\u001b[0m\r\n",
        "\u001b[39mbuild_src\u001b[0m\r\n",
        "\u001b[39mbuilding extension \"dcumsum\" sources\u001b[0m\r\n",
        "\u001b[39mf2py options: []\u001b[0m\r\n",
        "\u001b[39mf2py:> /tmp/tmpyL1tIs/src.linux-x86_64-2.7/dcumsummodule.c\u001b[0m\r\n",
        "\u001b[39mcreating /tmp/tmpyL1tIs\u001b[0m\r\n",
        "\u001b[39mcreating /tmp/tmpyL1tIs/src.linux-x86_64-2.7\u001b[0m\r\n",
        "Reading fortran codes...\r\n",
        "\tReading file 'dcumsum.f' (format:fix,strict)\r\n",
        "Post-processing...\r\n",
        "\tBlock: dcumsum\r\n",
        "\t\t\tBlock: dcumsum\r\n",
        "Post-processing (stage 2)...\r\n",
        "Building modules...\r\n",
        "\tBuilding module \"dcumsum\"...\r\n",
        "\t\tConstructing wrapper function \"dcumsum\"...\r\n",
        "\t\t  b = dcumsum(a)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\tWrote C/API module \"dcumsum\" to file \"/tmp/tmpyL1tIs/src.linux-x86_64-2.7/dcumsummodule.c\"\r\n",
        "\u001b[39m  adding '/tmp/tmpyL1tIs/src.linux-x86_64-2.7/fortranobject.c' to sources.\u001b[0m\r\n",
        "\u001b[39m  adding '/tmp/tmpyL1tIs/src.linux-x86_64-2.7' to include_dirs.\u001b[0m\r\n",
        "\u001b[39mcopying /usr/local/lib/python2.7/dist-packages/numpy/f2py/src/fortranobject.c -> /tmp/tmpyL1tIs/src.linux-x86_64-2.7\u001b[0m\r\n",
        "\u001b[39mcopying /usr/local/lib/python2.7/dist-packages/numpy/f2py/src/fortranobject.h -> /tmp/tmpyL1tIs/src.linux-x86_64-2.7\u001b[0m\r\n",
        "\u001b[39mbuild_src: building npy-pkg config files\u001b[0m\r\n",
        "\u001b[39mrunning build_ext\u001b[0m\r\n",
        "\u001b[39mcustomize UnixCCompiler\u001b[0m\r\n",
        "\u001b[39mcustomize UnixCCompiler using build_ext\u001b[0m\r\n",
        "\u001b[39mcustomize Gnu95FCompiler\u001b[0m\r\n",
        "\u001b[32mFound executable /usr/bin/gfortran\u001b[0m\r\n",
        "\u001b[39mcustomize Gnu95FCompiler\u001b[0m\r\n",
        "\u001b[39mcustomize Gnu95FCompiler using build_ext\u001b[0m\r\n",
        "\u001b[39mbuilding 'dcumsum' extension\u001b[0m\r\n",
        "\u001b[39mcompiling C sources\u001b[0m\r\n",
        "\u001b[39mC compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -fPIC\r\n",
        "\u001b[0m\r\n",
        "\u001b[39mcreating /tmp/tmpyL1tIs/tmp\u001b[0m\r\n",
        "\u001b[39mcreating /tmp/tmpyL1tIs/tmp/tmpyL1tIs\u001b[0m\r\n",
        "\u001b[39mcreating /tmp/tmpyL1tIs/tmp/tmpyL1tIs/src.linux-x86_64-2.7\u001b[0m\r\n",
        "\u001b[39mcompile options: '-I/tmp/tmpyL1tIs/src.linux-x86_64-2.7 -I/usr/local/lib/python2.7/dist-packages/numpy/core/include -I/usr/include/python2.7 -c'\u001b[0m\r\n",
        "\u001b[39mgcc: /tmp/tmpyL1tIs/src.linux-x86_64-2.7/dcumsummodule.c\u001b[0m\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "In file included from /usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarraytypes.h:1760:0,\r\n",
        "                 from /usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarrayobject.h:17,\r\n",
        "                 from /usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h:4,\r\n",
        "                 from /tmp/tmpyL1tIs/src.linux-x86_64-2.7/fortranobject.h:13,\r\n",
        "                 from /tmp/tmpyL1tIs/src.linux-x86_64-2.7/dcumsummodule.c:18:\r\n",
        "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning \"Using deprecated NumPy API, disable it by \" \"#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\r\n",
        "/tmp/tmpyL1tIs/src.linux-x86_64-2.7/dcumsummodule.c:111:12: warning: \u2018f2py_size\u2019 defined but not used [-Wunused-function]\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[39mgcc: /tmp/tmpyL1tIs/src.linux-x86_64-2.7/fortranobject.c\u001b[0m\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "In file included from /usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarraytypes.h:1760:0,\r\n",
        "                 from /usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarrayobject.h:17,\r\n",
        "                 from /usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h:4,\r\n",
        "                 from /tmp/tmpyL1tIs/src.linux-x86_64-2.7/fortranobject.h:13,\r\n",
        "                 from /tmp/tmpyL1tIs/src.linux-x86_64-2.7/fortranobject.c:2:\r\n",
        "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning \"Using deprecated NumPy API, disable it by \" \"#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[39mcompiling Fortran sources\u001b[0m\r\n",
        "\u001b[39mFortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops\r\n",
        "Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops\r\n",
        "Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops\u001b[0m\r\n",
        "\u001b[39mcompile options: '-I/tmp/tmpyL1tIs/src.linux-x86_64-2.7 -I/usr/local/lib/python2.7/dist-packages/numpy/core/include -I/usr/include/python2.7 -c'\u001b[0m\r\n",
        "\u001b[39mgfortran:f77: dcumsum.f\u001b[0m\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[39m/usr/bin/gfortran -Wall -Wall -shared /tmp/tmpyL1tIs/tmp/tmpyL1tIs/src.linux-x86_64-2.7/dcumsummodule.o /tmp/tmpyL1tIs/tmp/tmpyL1tIs/src.linux-x86_64-2.7/fortranobject.o /tmp/tmpyL1tIs/dcumsum.o -lgfortran -o ./dcumsum.so\u001b[0m\r\n",
        "Removing build directory /tmp/tmpyL1tIs\r\n"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import dcumsum # import fortran module"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# test sequence for cumsum\n",
      "a = array([1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#pure python version\n",
      "py_dcumsum(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Fortran module\n",
      "dcumsum.dcumsum(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Numpy function\n",
      "cumsum(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = rand(10000) # test data for benchmarking"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit py_dcumsum(a) # pure python version"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100 loops, best of 3: 5.39 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit dcumsum.dcumsum(a) # fortran version"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100000 loops, best of 3: 12.2 \u00b5s per loop\n"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit a.cumsum() # numpy version"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Some other features"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The four most helpful commands, as well as their brief description, is shown to you in a banner, every time you start IPython:\n",
      "\n",
      "**?**\t- Introduction and overview of IPython\u2019s features.\n",
      "\n",
      "**%quickref** - \tQuick reference.\n",
      "\n",
      "**help**\t- Python\u2019s own help system.\n",
      "\n",
      "**object?**\t- Details about \u2018object\u2019, use \u2018object??\u2019 for extra details."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%quickref"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%whos"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Only for matplotbib >=1.3.0\n",
      "plt.xkcd()  # Yes...\n",
      "plt.plot(sin(linspace(0, 10)), label='sin')\n",
      "plt.title('Whoo Hoo!!!')\n",
      "plt.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#!ipython nbconvert --to html Tutorial.ipynb"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# this work only on ipython >=1.0\n",
      "!ipython nbconvert --to latex --post pdf --template book Tutorial.ipynb"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(url='http://imgs.xkcd.com/comics/python.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<img src=\"http://imgs.xkcd.com/comics/python.png\"/>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<IPython.core.display.Image at 0x43172d0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}