Audio processing in Python.ipynb

Python Basics.ipynb

Python instead of Matlab.ipynb

{
 "metadata": {
  "name": "Python instead of Matlab"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Python instead of Matlab\n",
      "\n",
      "Python itself does not know matrices or linear algebra\n",
      "Python itself does not contain any signal processing tools\n",
      "Python itself has no plotting capabilities"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Matrices and linear algebra are available through Numpy:\n",
      "import numpy\n",
      "\n",
      "time = numpy.linspace(0, 1, 16)\n",
      "signal = numpy.sin(time * 2*3.14) # Python does not know pi yet!\n",
      "print(time)\n",
      "print(signal)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.          0.06666667  0.13333333  0.2         0.26666667  0.33333333\n",
        "  0.4         0.46666667  0.53333333  0.6         0.66666667  0.73333333\n",
        "  0.8         0.86666667  0.93333333  1.        ]\n",
        "[ 0.          0.40654264  0.74286057  0.95085946  0.99461032  0.8665558\n",
        "  0.58881556  0.20936545 -0.20624968 -0.586238   -0.86496168 -0.99427502\n",
        " -0.95184088 -0.74498919 -0.40945077 -0.0031853 ]\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Signal processing is possible through Scipy:\n",
      "import scipy\n",
      "spectrum = scipy.fft(signal)\n",
      "print(spectrum)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[-0.00158071+0.j          1.48576840-7.53643784j -0.28555269+0.6909811j\n",
        " -0.23438689+0.35116959j -0.22074392+0.22089335j -0.21515600+0.14383301j\n",
        " -0.21248557+0.0880493j  -0.21122677+0.0420304j  -0.21085240+0.j\n",
        " -0.21122677-0.0420304j  -0.21248557-0.0880493j  -0.21515600-0.14383301j\n",
        " -0.22074392-0.22089335j -0.23438689-0.35116959j -0.28555269-0.6909811j\n",
        "  1.48576840+7.53643784j]\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plotting is provided by Matplotlib:\n",
      "import matplotlib.pyplot\n",
      "matplotlib.pyplot.plot(spectrum)\n",
      "matplotlib.pyplot.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/opt/local/Library/Frameworks/Python.framework/Versions/3.2/lib/python3.2/site-packages/numpy/core/numeric.py:236: ComplexWarning: Casting complex values to real discards the imaginary part\n",
        "  return array(a, dtype, copy=False, order=order)\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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MfVVz9DVclUtE/nCkbwBV0zU1HOkTkT8MfQOobu9Mnw40N/O0iUQ0VG+vPGtWdrbqSsJj\nutBX3d6ZNEmeOvHOHXU1EJH5tLQAaWlAfLzqSsJjqtBvb5efpmlpautgi4eIhouF1g5gstC/fl3t\nHH0NQ5+IhmPoG0B1P1/D0Cei4WLhEAyAyUJfdT9fw1W5RDQcR/oGUD1dU8ORPhENx9A3ANs7RGRW\nsbAaF2Do+8RVuUQ0HEf6OhPCPD19jvSJ6ElCMPR1194ur81wGrK0NKCrSx7Xn4ioo0NeT52qtg49\nmCb0VR9H/0k2mzwcA0f7RATExmkSNaYJfbO0djRs8RCRJlZaO4CJQt8s0zU1DH0i0jD0DWCWmTsa\nhj4RaWJlNS7A0PeLq3KJSMORvgHY0ycis2LoG8BsI32GPhFpYmU1LmCi0LfbgeRk1VUM4qpcItJw\npG8AM43yATlP/+ZNoL9fdSVEpFJPD9DaCmRmqq5EHwx9P8aPl3953LqluhIiUqm5GcjIkN2IWBBW\n6NfU1KCwsBAFBQXYtWuXz21ef/11FBQUoKSkBJcvX/a7LzPN0dewr09EsdTaAcII/b6+Prz66quo\nqanBlStX8PHHH+O///3vkG2qq6tx7do11NfX47333sO2bdv87s9sI32AoU9EDP0BtbW1yM/PR25u\nLuLj47FmzRocP358yDZVVVXYsGEDAGDx4sVob29HS0uLz/0x9InIjGIt9EPuUnm9XuTk5Azcdzqd\nuHjxYsBtGhsbkenjG5Gamu2oq5O3y8rKUFZWFmppumHoE5FZVuO6XC64XK6w9xNy6NuCPNycECKo\nn9u1azumTAm1GmM4ncC5c6qrICKVvF6guFh1FSMHw5WVlSHtJ+T2jsPhgMfjGbjv8XjgHPZxOHyb\nxsZGOPz8nWS2wAc40iei2GvvhBz6CxcuRH19PdxuN7q7u3H06FFUVFQM2aaiogKHDx8GAFy4cAHJ\nyck+WztmxdAnolgL/ZDbO3a7Hfv27cOKFSvQ19eHzZs3Y9asWThw4AAAYOvWrVi5ciWqq6uRn5+P\nxMREfPjhh7oVHgkMfSJrEyK2DsEAADYxvOmuogibbUTv3wyEABIT5QKtyZNVV0NEkdbWJtcQ3bun\nupKRQs1N06zINSObjaN9IiuLtdYOwNAPiKFPZF0MfQti6BNZF0Pfghj6RNbF0Lcghj6RdTH0LYjn\nyiWyLrMcgkFPDP0AONInsi6O9C2IoU9kXbEY+lycFUBPj1yg1dUVO2fOIaLAHj+WxwR79AiIM+Hw\nmIuzDBIfD6SlAX5OA0BEMaqpCcjKMmfghyPG/nOMwRYPkfXEYmsHYOgHhaFPZD0MfQtj6BNZD0Pf\nwhj6RNbD0Lcwhj6R9TD0LYyrcomsJxZX4wIM/aBwpE9kPbE60ufirCB0dMj/+Z2dqishokgQApg0\nCbh7V16bUai5yTWmQZg6VV53dAzejiZCyJXFjx/L1YWPHvm+Pdrzjx8DfX1Ab6+8+Lo91uf7+oD+\nfnkRYvD28PujPedrW+19oNe1xmbT79pmk4t+tMuT90N9btw4ebHb5UW77esxf7eHPzZhgrxMnCgv\nY7k9YYJc3Kj9d0eT1lYgIcG8gR8Ohn6QHA55guSiosi/dm+vHHG0tspLW9vQ6+G3790bGd52u/83\nZjCPjR8v9zFxoj5hYrcPBlWoIedr2ycvgH7Xen6QPHkJ5YPO13PaB2goH76jPd/eHtzgwN9jvb0j\nf6eSk4HUVLnSPS3N923tOiVFzeFPYu1k6E9i6AdJ6+vrGfoNDcCFC8Dt274DXLu+f1/+8g9/Q2i3\nS0qGPp6cPPgG095wsbaUnKJDX9/gX4qPHgEPH8pByfDfdY8H+OqrkY+3twOTJ/v/UEhLA9LTge9+\nF3jqKf3qjtV+PsDQD5oeX+Z2dACffQacPCkvnZ3AkiXy+B6pqUBBAfDMMyNDPSmJoU3Radw42SZJ\nSAjt5/v7Bz8kfA2M/vc/4MwZ4Gc/k4Od5cvlpaxMHiwtVAx9Cin0+/qAS5cGQ/7LL+WIZPly4Ngx\noLiYYU40mrg4+VduSsro2/X3A//6l3yf7d0LrFsHlJYOfggsWCA/gILF0Cc4HMCVK4G3c7sHQ/70\naTnPd/ly4He/k6P6WPxiiEi1uDhg3jx5eeMNeSj0s2fl+3DTJqC5GVi6VL4Xy8sDt4K8XmDRosjU\nHmkM/SA5HMCnn458XGvZfPqp/AW7d0/+UlVUAO++C0yfHvlaiawuIQH44Q/lBZAhfuqUfI/+9rfy\nL4fRWkFeL7BqVcTLjgjO0w/SF18A27YBFy+ObNk888zgLxBbNkTm1t8vvzTW3sO1tcD8+YN/BSxY\nIFtDhw/LvxzMKtTcZOgHqakJyMuTZ9FyOAZDfsmS0L+kIiL1HjwAzp0b/BBobpaTLLxeOTPIrBj6\nBhMC+OQTYO5ctmyIYpnXC/z738CKFaorGR1Dn4jIQniOXCIiCoihT0RkIQx9IiILYegTEVkIQ5+I\nyEIY+kREFsLQJyKyEIY+EZGFMPSJiCyEoU9EZCEMfSIiC2HoExFZCEOfiMhCGPpERBbC0CcishCG\nPhGRhTD0iYgshKEfJJfLpbqEoLBOfbFOfbFO9UIO/ba2NpSXl2PmzJlYvnw52tvbfW6Xm5uLuXPn\norS0FN/5zndCLlS1aPklYJ36Yp36Yp3qhRz6O3fuRHl5Oa5evYqlS5di586dPrez2WxwuVy4fPky\namtrQy6UiIjCF3LoV1VVYcOGDQCADRs24G9/+5vfbXnScyIic7CJEBM5JSUFd+/eBSBDPTU1deD+\nk771rW8hKSkJ48aNw9atW7Fly5aRRdhsoZRARGRpocS3fbQny8vLcfPmzRGPv/POO0Pu22w2v8H9\nz3/+E9nZ2bh9+zbKy8tRWFiIJUuWDNmGfwkQEUXGqKH/6aef+n0uMzMTN2/eRFZWFpqbm5GRkeFz\nu+zsbABAeno6nn/+edTW1o4IfSIiioyQe/oVFRU4dOgQAODQoUNYtWrViG26urrQ2dkJAHjw4AFO\nnjyJ4uLiUF+SiIjCFHJPv62tDatXr8aNGzeQm5uLv/zlL0hOTkZTUxO2bNmCf/zjH/jmm2/wwgsv\nAAB6e3vxk5/8BG+99Zau/wFERDQGQrETJ06Ib3/72yI/P1/s3LlTdTk+3bhxQ5SVlYmioiIxe/Zs\nsXfvXtUl+dXb2yvmzZsnfvSjH6kuxa+7d++KF198URQWFopZs2aJzz//XHVJPu3YsUMUFRWJOXPm\niLVr14pHjx6pLkkIIcTGjRtFRkaGmDNnzsBjra2tYtmyZaKgoECUl5eLu3fvKqxQ8lXnr3/9a1FY\nWCjmzp0rnn/+edHe3q6wQslXnZo9e/YIm80mWltbFVQ2yF+N7777rigsLBSzZ88Wb7zxRlD7Uhr6\nvb294umnnxYNDQ2iu7tblJSUiCtXrqgsyafm5mZx+fJlIYQQnZ2dYubMmaasUwgh/vjHP4p169aJ\nH//4x6pL8Wv9+vXigw8+EEII0dPTY4o3/nANDQ0iLy9vIOhXr14tPvroI8VVSWfPnhV1dXVDAuA3\nv/mN2LVrlxBCiJ07d4o333xTVXkDfNV58uRJ0dfXJ4QQ4s033zRtnULIwd6KFStEbm6u8tD3VePp\n06fFsmXLRHd3txBCiFu3bgW1L6WHYaitrUV+fj5yc3MRHx+PNWvW4Pjx4ypL8ikrKwvz5s0DAEye\nPBmzZs1CU1OT4qpGamxsRHV1NX7605+adkbUvXv3cO7cOWzatAkAYLfbkZSUpLiqkaZOnYr4+Hh0\ndXWht7cXXV1dcDgcqssCACxZsgQpKSlDHhvLuplI8VVneXk54uJk7CxevBiNjY0qShvCV50A8Mtf\n/hJ/+MMfFFQ0kq8a9+/fj7feegvx8fEA5GSZYCgNfa/Xi5ycnIH7TqcTXq9XYUWBud1uXL58GYsX\nL1Zdygi/+MUvsHv37oE3lRk1NDQgPT0dGzduxPz587FlyxZ0dXWpLmuE1NRU/OpXv8KMGTMwffp0\nJCcnY9myZarL8qulpQWZmZkA5My6lpYWxRUFdvDgQaxcuVJ1GT4dP34cTqcTc+fOVV2KX/X19Th7\n9iyeeeYZlJWV4dKlS0H9nNJ0iLZFWffv38dLL72EvXv3YvLkyarLGeLvf/87MjIyUFpaatpRPiC/\n0K+rq8Mrr7yCuro6JCYm+j2Eh0pff/01/vSnP8HtdqOpqQn379/Hn//8Z9VlBWW0dTNm8c4772D8\n+PFYt26d6lJG6Orqwo4dO1BZWTnwmBnfU729vbh79y4uXLiA3bt3Y/Xq1UH9nNLQdzgc8Hg8A/c9\nHg+cTqfCivzr6enBiy++iJdfftnn9FTVzp8/j6qqKuTl5WHt2rU4ffo01q9fr7qsEZxOJ5xOJxYt\nWgQAeOmll1BXV6e4qpEuXbqE733ve0hLS4PdbscLL7yA8+fPqy7LL23dDIBR182YwUcffYTq6mrT\nfoh+/fXXcLvdKCkpQV5eHhobG7FgwQLcunVLdWlDOJ3OgdmRixYtQlxcHFpbWwP+nNLQX7hwIerr\n6+F2u9Hd3Y2jR4+ioqJCZUk+CSGwefNmFBUV4ec//7nqcnzasWMHPB4PGhoacOTIEfzgBz/A4cOH\nVZc1QlZWFnJycnD16lUAwKlTpzB79mzFVY1UWFiICxcu4OHDhxBC4NSpUygqKlJdll/BrJsxg5qa\nGuzevRvHjx/HxIkTVZfjU3FxMVpaWtDQ0ICGhgY4nU7U1dWZ7oN01apVOH36NADg6tWr6O7uRlpa\nWuAf1Ptb5rGqrq4WM2fOFE8//bTYsWOH6nJ8OnfunLDZbKKkpETMmzdPzJs3T5w4cUJ1WX65XC5T\nz9758ssvxcKFC001bc+XXbt2DUzZXL9+/cAsCdXWrFkjsrOzRXx8vHA6neLgwYOitbVVLF261FRT\nNofX+cEHH4j8/HwxY8aMgffRtm3bVJc5UOf48eMH/j2flJeXp3z2jq8au7u7xcsvvyzmzJkj5s+f\nLz777LOg9hXy4iwiIoo+5p3mQUREumPoExFZCEOfiMhCGPpERBbC0CcishCGPhGRhfw/Rvwsh5vt\nuR4AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Pylab: Numpy, Scipy and Matplotlib in one convenient package"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%reset\n",
      "\n",
      "# Import all the good stuff:\n",
      "from pylab import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now most things work like in Matlab\n",
      "time = linspace(0, 1, 64)\n",
      "signal = sin(time * 2*pi)\n",
      "spectrum = fft(signal)\n",
      "signal_again = ifft(spectrum)\n",
      "\n",
      "figure()\n",
      "plot(time, signal, 'ro')\n",
      "title('signal')\n",
      "\n",
      "figure()\n",
      "plot(time, signal_again, 'bx')\n",
      "title('ifft(fft(signal))')\n",
      "\n",
      "show() # except for show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      },
      {
       "output_type": "display_data",
       "png": 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8/PLLUg9TETyJm5K9PXra06/F/+QD+ZkOHTqEP/zhD6jV6NKD7lyLlStX4sUX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\nMPATEekMAz8Rkc4w8BMR6cz/A14icQxny2BWAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Partly with extended syntax\n",
      "figure()\n",
      "plot(signal, color='lightgray', linewidth=5, label='original signal')\n",
      "plot(signal_again, color='#AA1100', label='fft$^{-1}$(fft(signal))')\n",
      "legend()\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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EQiFKF9yMLiOLrPm/Q6vVUlRU1C3uY0RLrL/3RWyQ6Z1jrLKyEr/fT+POr3Cuf5Wc39+D\nSq2OjIK7O4PBQE5OTrg09G/vwLXpTdzbtxIIBCgrK5OkJUQckaR/FG63G5vNhuL3U37fnWRe/zu0\nZitJSUmkp6dHO7xjxmKxkJycjDbVTM4tCyhfeAfB+jpcLldkq7wQIvZJ0j+CUCgUWZ5Z89yT6HLy\nSDnz3Ehdne46j9+apu9Zo9GQPHIUSaf8nMpH7gPCuxn9fn+UIxRCtIUk/SOoqqrC5/Ph+X4P9pdX\nkP3bP4Zr02Rlodfrox3eMafT6cjNzQUg67pbaPh8G3Ufv08oFJLVPELECUn6h9HY2EhNTQ1KMEj5\nfX8i46rr0WVktVqToydJTU0lNTUVdWIi2TfdSeXDfyXkacTpdEbqpAghYpck/VYoihKZ1rG99Bxq\nozGy67anTeu0JicnJzzNc/JIjIOPp2bZY0B4mkcaaQgR2yTpt6K2thaPx4OvtJja5U+Qc8uCSG/Z\nhISEaIcXdVqtNlJxMHP+LTjWvYR333f4fD5qamqiHJ0Q4kgk6f+I3+8Pd8FSFCoe/itpF1+BPq+A\nhIQEKTTWjMViCW/aSssg/fJrqPj73SiKQnV1tdTeF61yOp28/PLLkdo3Ijok6f9IZWUloVCI+o/e\nw19RhnXaxQAyrfMjKpUqclPXcsHM8Lz+m2tRFIXy8nJZu9+N7Nq1i+HDh5OSksLixYsPedxWqamp\nnHjiifh8vlZfv/3221m0aFGHr/vj8xzO0KFDef/999scf3s1b7t46qmn8s0337R4vbXnupJspWym\noaEBh8NByOulcvH9ZP/2DlRaHVarFaPRGO3wYk5CQgLp6enU1NSQffOdFN/6K0w/H0094HK5Yro3\nsGi7+++/n7POOovPPvsMgCuvvLLF48LCQp566inOPPPMdl+jurqa5cuXh0t6t/G6rV27tfMczrEq\nf9x8sHjzzTdz5513snr16iM+15VkpH9Q0wgVwLZqOYa+/Ug+eSQajSYuu2AdK5mZmeh0OowDhpAy\n+pxIi8Xy8nK5qdtN7N+/n8GDBx/28Y9LAVRXV/PCCy+0+PfOO+8c8RpLly5l/PjxLVouHu26rV27\ntfPEkokTJ/Luu++26J/b2nNdSokBsRCGzWZTvvzyS2X7OxuVlwrNyrY31ytffvmlUlNTE+3QYp7T\n6VS+/PJL5bOPP1JeLspQtq5aqXz55ZdKZWVltEOLebHw3j+SMWPGKBqNRklISFCSk5OVM888s8Xj\n2bNnK2q1WjEajUpycrLywAMPHPF8e/fuVRYsWHDI82eeeaby3HPPtem6JpNJ2b17tzJnzpwW177/\n/vsPOY+iKMrChQuVvLw8xWQyKQMGDFA2bdqkKIqi9O7dW3n77bcjx23btk0ZPny4YjKZlOnTpysz\nZsxQ/vjHP0aO/dvf/qYcf/zxSmpqqjJz5kzF4/FEvvbee+9VjjvuOMVkMimDBw9WXnnllchrhYWF\nyjvvvBN5PHbsWOWZZ55pEWNrzzV3uPdJe94/MtInXFCtaXNR1b8fxnLBTPS5BRgMhm5XI78rpKSk\nYDKZ0CSbSL/iOqqWPICiKNTU1MhO3Ti3adMmRo0axZIlS6irq+Odd95p8fj555+nV69evPbaa9TV\n1XHzzTcf9lz19fW89NJLbNu27ZCplS+//JIBAwa06boul4t+/fqxfPnyFte+5ZZbDjnPrl27WLJk\nCZ9++ikul4u33norUi65eXMTn8/HlClTuOKKK7Db7cyePZtXX321xdTMqlWrePPNN9m7dy9ffPEF\nS5cujbxWVFTEhx9+iMvl4k9/+hNz5sw57Mh90KBBLZqqH+65riJJn/DO22AwSMPn22j4YhtpF18B\nhPtmys3btsnOzgbAfP4UgnUu6j98l1AoRFVVVZQjE51B+dGN+R8/bovk5GRuuukm1q5de0g7SIfD\ngclk6vB1f3wejUaD1+vl66/DfSB69epF3759D/m6LVu2EAwGuf7669FoNEyZMoVTTjkl8rpKpeKG\nG24gOzsbi8XCxIkTW9xbmDZtWuT/gRkzZtCvXz+2bt3aaowmkwmHw3HU57pKj7+R6/V6w3Xyg0Eq\nFt1L1nU3oTYmkpKSQlJSUrTDixsGg4G0tDRqa2vJvO5mKh7+C8mnjcJut8uN8A5YZe2cQcd0W8dW\nUx2uVWBnsVgs1NXVdfi6Pz5PUVERDz/8MAsWLODrr79m3LhxPPjgg+Tk5LT4urKyskMavhcUFLR4\n3JTUAYxGI2VlZZHHy5Yt46GHHmLfvn1A+FPN4fasuFyuQ5outfZcV+nxSb/pI5jjjVdRJyZjGjMO\nlUp1yJtCHF1GRgZ2u53kU36OPr8X9ldXYp1+CRUVFRQWFsqnpnboaLI+Fjrj93r88ceza9cuTjzx\nxA5du7XzzJ49m9mzZ1NXV8e8efO49dZbWbZsWYuvy8nJiezCb3LgwAGKioqOet39+/dz9dVXs2nT\nJkaOHIlKpWLEiBGH/VSyc+dOLr300qM+11V69PROQ0MDLpeLkKeRmqf/RdavborsvNXpdNEOL+5o\ntdrISqes626mZvkTBF1O3G631OWJc0eaZsnKymrTEskjOf/883nvvfd+0nVbu/aPz7N79242bdqE\n1+vFYDCQkJDQam/nkSPDK/UWL15MIBBgzZo1/Pe//z1svM3jcLvdqFQq0tPTCYVCPP3004ddDurx\neNi+fTtjx4494nNdqccmfUVRIqN82+rnMA49AeOgYWi12h5VJ7+zWa1W9Ho9hsLjMJ1xNjXPPApA\nRUWFbNiKY0eaZrn99tu55557sFgsPPjgg+06/6WXXsr69evxeDxtvm5r1/7xebxeL7fffjsZGRnk\n5ORQU1PT6o5gvV7Pyy+/zJNPPonFYuG5555jwoQJh1362fwm8ODBg7npppsYOXIk2dnZfPXVV5x+\n+umtft26desYM2ZMi6mi1p7rSj22XWJdXR379+8n4HTw/ZyJFP7zWfQFvcnNzZUVOx3kdDopLi4m\nYK/l+0snR362OTk5PbpCaWukXeIP/vCHP5CZmcmNN94YE+c59dRTue6665g7d26HztPcaaedxlNP\nPdViv0Frz/1YZ7ZL7JFJX1EUvv32W7xeL5VLHiDk9ZLz2z+i1+vp16+fzD13kKIo7N27l4aGBmqe\nexLPzi/Jv+dhtFot/fv3R63usR8wDyFJP3a8//779O/fn/T0dJ577jmuu+46vv/++0hxwWiSHrkd\n5HQ68Xq9+CvKcL6xhoy51wDh+UFJ+B2nUqkiH1Wt0y6mcedXNO78kkAgQG1tbZSjE6J1TbV9LBYL\nDz30EKtXr46JhN/ZetxIPxQKsWfPHvx+P2V/+QO67BwyrpyP0Wikb9++kvQ70YEDB3C5XNjXrKLu\n/Y30+vtjaDQa+vfv3+rNtJ5IRvqiLWSk3wE2mw2/34/nu93Ub/0Q66zLgPAaXEn4natplGQePxlf\naTHuzz4lGAxKzX0hoqhHJf1gMEh1dTUA1Y8+TPolV6NJSsZkMslGrC5gMBgwm82otDoyLr+O6sf/\ngaIo1NbWEggEoh2eED1Sj0r6NpstXG7hi+1493+PedJ0gG45bxcrMjMzUalUpJx9PsE6F+4tHxIK\nhWS0L0SU9Jik33xaofqpf5J+ydWo9XrMZrO0QOxCer0ei8WCSqMh48r5VD/xCEooRG1trRRjEyIK\nekzSr62tPVhU7VP8FaWknjsRQFogHgMZGRmoVCpM/3cWqFXUvf92pLWiEOLY6nDS37BhAwMHDqRf\nv37cd999h7y+efNmUlNTGTFiBCNGjOCee+7p6CV/smAwGFkqWP3UP0m/dB4qrQ6z2RyzzRa6E51O\nR1paWrjExS+vp/rJJSjBIHa7/bCt84QQXaNDBdeCwSDz58/n7bffJi8vj5NPPplJkyYxaNCgFsed\nccYZrF27tkOBdkTTKN+947/4qypIPWcCgHTEOobS09Ox2WwknfILNKmP49z4OuZzJ1FdXX1IdcOe\nxGKxyKoxcVSdWYGzQ0l/69atFBUVUVhYCMCsWbNYs2bNIUk/muuQm+byFUWh5qklpM+dh0qrxWKx\noNfroxZXT9NU06iqqoqMK+dT/sACUs8+H4fDQUZGRo/9XdhstmiHIHqYDiX90tLSFjWn8/Pz+eST\nT1oco1Kp+OijjzjhhBPIy8vjb3/7W6s1JhYsWBD579GjRzN69OiOhBZRW1tLKBSiYftWArU1pJ49\nHpC5/GhoqrefOPwktNYMXO9uIHXsBGpqasjNzY12eELEvM2bN7N58+YOnaNDSb8tH0t/9rOfUVxc\nTGJiIm+88QaTJ09m9+7dhxzXPOl3luaj/Oqn/ymj/CjTaDSkpaVRVVVF+tx5VP7jXlLOPA+73S7l\nrIVogx8PiO+6666ffI4O3cjNy8ujuLg48ri4uJj8/PwWx5hMJhITEwE477zz8Pv9x+wjbWSUv20L\nQbuNlLPOi9TLF9GRlpaGWq0m6aTTUCeZqHvv7Ug/XSFE1+tQ0j/ppJPYs2cP+/btw+fz8cILLzBp\n0qQWx1RWVkbm9Ldu3YqiKMekdHGLUf5T/yT9svAo32w2yyg/ippG+yqVioy511Cz7FGUUChSHkMI\n0bU6lPS1Wi2LFy9m3LhxDB48mJkzZzJo0CAeffRRHn003Dxj9erVDBs2jOHDh/PrX/+alStXdkrg\nR2Oz2cKj/M8+Jeiwk3KmjPJjRWS0f9rpqHQ66j98N1KeQQjRtbpllc1QKMTu3bsJBAIcuOlqUs48\nD/P4KVgslh69PDCWVFRUUFNTQ90Hm6hZ+m8Kn3ghUoFTq+3xrZuFaBOpsnmQ3W4nEAjQuPMrvPv3\nRtblSxvE2JGeno5KpSL5F6NRggHqt3xA6GB5BiFE1+l2Sb/5TcHa554gbdZlqHQ6UlNTZfdtDNFq\ntVitVlRqNelz51Gz9N+RKZ5gMBjt8ITotrpd0nc4HPj9frz7vqPhyx2YJ1wIyLr8WNQ02jedMZZQ\ngxv3fz+W0b4QXaxbJf3mRbxqn3sS67Q5qBOMmEwmqaQZg3Q6XbgMgVpN2sVXUvv8k8APS22FEJ2v\nWyV9l8uFz+fDV1ZC/ZYPsEyeCcgoP5Y13WdJPfs8fKXFNO78iuDBYmxCiM7XbZJ+i1H+yqWYJ01H\nY0ohKSkpsjlMxB69Xk9qaioqrY60GZdS+/xTAJE9FkKIztVtkn59fT0ejwd/bTWud97AOu1iQEb5\n8aDpd2SecCENn2/De2Avfr8fp9MZ5ciE6H66TdJvWrFje3E5qeMmorWkYTQapfdtHEhISCA5ORm1\nMRHLlJnYVj4DyGhfiK7QLZJ+Y2MjbrebYH0djtdeIm3GpcAPHZtE7Gsa7VumzMb13kb8NVV4PB7q\n6+ujHJkQ3Uu3SPpNc/mOtatIPnUUuuxcDAYDJpMpypGJtkpMTMRoNKI1W0gdNxHbqmcBpBCbEJ0s\n7pO+z+fD5XKh+P3YVj9H2uzLACJFvUR8aF4XKW3GpThff5lgnQu3201DQ0OUoxOi+4j7pN80EnS+\n/TqGPseR0G8g2oPVNEV8MZlMGAwGdNm5JI/8P+xrXgSQBupCdKK4TvqBQAC73Y6iKNhWPoN11uXA\nD1UcRXxRqVSRdfvW2ZdjX/0cIa+Huro6vF5vlKMTonuI68xos9lQFAX3lg9Bqw035lCrj0m9ftE1\nUlNT0Wq1JPTtR0L/QTjfeg1ASjMI0UniNuk3r9FSu/Jp0mbNRaVSYbFY0Gg0UY5OtJdarf5htD9z\nLrYXl6GEQpHKqUKIjonbpO9wOAgGgzT+72t8ZSWkjBkHhKd2RHyzWCyo1WoSf3YKan0C9Z98GJ7C\nO0ZtNoXozuIy6bcon7ziaazT56DShssnSyvE+KfRaMKF2FQqrLMuxfZCeLOWFGITouPiMunX1dUd\nLKxWTMP2TzBPmApIk5TupOkTW8qYcfiK99O4+xuCwSAOhyPKkQkR3+Iy6UdKLqx6FvOEqWgSk0hO\nTsZoNEY5MtFZmhdis067GNvKZUB4tC+lGYRov7hL+g0NDTQ0NBCsc+F86zUsUy8CZJTfHTX9Ts0T\nplL/yQf4qyrwer1SmkGIDoi7pN+0Ysfx2kskj/w/dOmZGAwGKazWDTUVzNOYUjCfdwG21c8BUppB\niI6Iq6STQpmJAAAgAElEQVTfVG5XCYRLLlhnXAL80HZPdD9Nc/vWaXNwrn+FoLset9tNY2NjlCMT\nIj7FVdJvGuW73nsbfV4Bxv6D0Wg0pKamRjky0VVMJhN6vR5ddi5JJ43E8drLgIz2hWivuEn6wWAw\nsgPX9sIyrAfLJ0vJhe6tRWmGmXOxrX4WJRDA5XLh9/ujHJ0Q8SdusqXD4SAUCtH41WcE610k//yM\n8DpuKbnQ7ZnNZjQaDcZBQ9Fl5VD3wTuyWUuIdoqLpK8oSmRqx/bCMqzT5qBSqzGbzWi12ihHJ7pa\n83pK1ulzIrX2bTabbNYS4ieKi6TfYjPW559iPu8CQEou9CRWqxWVSoXpF2MI1FTRuPNL2awlRDvE\nRdKPbMZa/Tzm8ReiNiaSnJxMQkJClCMTx4pOpzu4WUuLZerFkdG+bNYS4qeJ+aTf2NgY3oxVX4fz\nzbVYLpwNyCi/J2r6nZvHT6H+kw8jm7XcbneUIxMifsR80o9sxnr9FZJPPR1dZjYGg4Hk5OQoRyaO\nNaPRSGJiIppkE6nnTMD+6guALN8U4qeI6aQfCATCm7GCQewvP491+hxA+t/2ZJHlm1MvxrHuJUKe\nRurr66WzlhBtFNNJv2ldfv1/NqO1pmEcNAyNRiP9b3swk8mETqdDn98L49DhON+UzlpC/BQxm/Sb\nd8ayrX4W6/RwyYWmBhuiZ1KpVD+UZpg+B9vq5ZHOWsFgMMrRCRH7YjZ7Op1OgsEgnj3/w1dajOn/\nzgLkBq5o1llrxMmotDrcn34sm7WEaKOYTPotNmOtfhbLlFmRzlg6nS7K0Yloa9FZa8Yl2F5cDvww\nHSiEOLyYTPoNDQ14PB4C9lrqPngX88RpgIzyxQ8inbXOPA/Pnp149+/F7/fjcrmiHJkQsS0mk37T\nKN++dhUpY85Bm2qOLNcTAsKdtUwmE2qDAcvE6dhffh6QG7pCHE3MJX2fz4fL5ULx+3G88kKkM5aM\n8sWPRTZrTZ6Bc+N6gnUuGhoapNa+EEcQc0m/6Wac69030fcpIqFvP7RaLSkpKVGOTMSapKQkDAYD\nuvRMkk87Hcf6VwEZ7QtxJDGV9EOh0A8181c/G9mMZbVaZZmmOESL5ZvTLsb+8vMowSBOp5NAIBDl\n6ISITTGVSSM187/+nGCdi+TTRknNfHFEkVr7g49HY7ZS//F7snxTiCOImaTffJmmffVzWC+8SGrm\ni6NSq9VYLBYgPNpvap4utfaFaF3MJH23243X68VfVUH9fz8i9WDNfBnli6OJLN8cfQ6+/XvxfLeb\nwMGWikKIlmIm6UdG+WtWkXrOBDTJJpKSkjAajVGOTMQ6nU5HSkoKKp0O8+QZ2F+S5ZtCHE7MJP26\nujpCXi+O11ZHaubLKF+0VdNo3zJpOq7NGwk47JFeDEKIH8RM0gdwvfMGCQMGYygojIzehGiLxMRE\nEhIS0FrSMJ0+BsdrLwEy2hfix2Im6SuKgu2l57BOvRj4oSeqEG3RfPmmZdpF2F9ZiXKwH4Pf749y\ndELEjg4n/Q0bNjBw4ED69evHfffd1+oxN9xwA/369eOEE05gx44drR7T+MV2FI+HpJN/jkqliqzI\nEKKtUlNTw8s3+w9Gl5NH3YebAGT5puhU8V7Ur0NJPxgMMn/+fDZs2MA333zDihUr2LlzZ4tj1q9f\nz7fffsuePXt47LHHuPbaa1s9l+2l57FMlWWaov3UanXkPpB16kXYVodv6MryTdGZKisr+f7778Nd\n/eLwD0CHkv7WrVspKiqisDA8Bz9r1izWrFnT4pi1a9cyd+5cAE499VQcDgeVlZWHnKth+yeknjsJ\nkDo7ov2akr5p1Jn4y0vw7N5JMBiU5ZuiU4QONuxpaGiguLiY3bt3U11dHVcNfDo0nC4tLaWgoCDy\nOD8/n08++eSox5SUlJCVldXiuDXpvUla+gx6vZ7JkyczevTojoQmeiidLtx3wel0YpkyC9vLz5N7\n293U1NSQmpoq94lEhzgcDoLBIL6SAyhKCAoKqaqqOmbT0Zs3b2bz5s0dOkeHkn5b/wf68Ueg1r7u\nxnvuRZ/fi169esmqHdEhaWlpOJ1OzBOm8t1FEwhc8xs8ZiuNjY1Snlu0W/OqAdVP/5OEfoMwzCok\nNTX1mE1Hjx49usWA+K677vrJ5+jQ9E5eXh7FxcWRx8XFxeTn5x/xmJKSEvLy8g45lz6/FzqdDpPJ\n1JGQhMBoNGI0GtGaLZjOOAvHOlm+KTquoaEhXDWgtpr6LR9gHj8FiL/p6A4l/ZNOOok9e/awb98+\nfD4fL7zwApMmTWpxzKRJk1i2bBkAW7ZswWw2HzK10yQtLU0+fosOa1F988KLsL/6AkrAL8s3RYc0\nDRoca1eRctZ5aEwpJCYmxl3VgA59JtFqtSxevJhx48YRDAa58sorGTRoEI8++igA8+bN4/zzz2f9\n+vUUFRWRlJTE008/3eq5CgsL4+6HJ2JXSkoKWq2WhH4D0efmU/f+JlLOHIfNZjvsoEOIw2lq7hTy\n+bCveZHeDz8JxN8oH0ClxMCaI5VKFZdLn0Rsq6qqoqqqCtd7b2N7cRmFS5ah0WgYMGCA9GcQP0lF\nRQU1NTU431yH86119Pr7Y2i1WgYMGBDV2Yn25E5554tuy2KxoFKpMP1iNP6qChp3fU3wYJMVIdrq\nx82dLAerBsTrdLQkfdFtRapvarVYp8zCvvqH6pvyyVK01Q/Nnb4gWF8Xae4Ur1UDJOmLbi3SPH3C\nVOr+s5mArRaPxyPVN0WbtGju9NJzWC+cHfdVAyTpi26taXWFJiWVlDPPwb52FSDLN0XbRJo7VVdS\nv/U/pJ43GYjvsu+S9EW3F1m+OfViHK++gOL343K58Pl8UY5MxLrIKP/VF0kdOx5Nsikul2k2J0lf\ndHtNOyYNfYrQ9ynCtflNQKpviiPz+XytNneKx2WazUnSF92eSqX6ofrmtIuxrXoORVGw2+1SfVMc\nVtMo3/XOGyT0H4yhV59u0dxJkr7oEZqa8iSfNoqgyxFeiREM4nA4oh2aiEHBYBC73R5Zpmmd1n2a\nO0nSFz2CVqsNV9nUaLBOvQj7S88BsnxTtK5pmWbD59tQ/L5u1dxJkr7oMZrmYlPPm0z91v/gr67E\n6/XidrujHJmIJc2XadpWLcc69eK4X6bZnCR90WMYjUYSExPRJJtIHTse+6svALJ8U7RUV1eHz+fD\nV1ZC4xfbSR03EYj/G7hNJOmLHiXSPP3C2TjWvUTI66Gurg6v1xvlyESsiCzTfGUFqedNRm1MJDk5\nmYSEhChH1jkk6YseJSUlBZ1Oh6FXH4wDh+DauB6Q0b4I83g8uN1ugg1unG+swdpNlmk2J0lf9CjN\na+1bpl+CbfWzKIoSaYMneramP/7ODWtIHHEKuuxc9Ho9ycnJUY6s80jSFz2OxWJBrVaTdNJpoCg0\nbNsSaXgteq5AIIDD4UAJhbCvfj6yTDNeq2kejiR90eNoNBrMZnN4Cd70OdhWPQvI8s2ermldvvuT\n/6BOTMJ4/M9QH1y1051I0hc9UmT55tjxNO78Cm/xPvwHa/KInqfFMs3Vy7FMuziyLl+j0UQ5us4l\nSV/0SAaDAZPJhNqQgHnitBa19kXP43Q6CQQCePd+i/e7PaSceS7QvW7gNpGkL3qsyA3dKTNxvv06\nwTonDQ0NUmu/h1EUhZqaGgBsq57FPGUmar2elJQU9Hp9lKPrfJL0RY+VlJSEwWBAl55J8sj/w/Ha\ny4CM9nuahoYGPB4PAXstrs0bsVwwA+ieo3yQpC96MJVKRXp6OgDW6XOwvfQ8SiCA0+nE7/dHOTpx\nrDSN8u1rVpEy5hy0Zmtk93Z3JElf9GipqaloNBqMA4agy86j7oN3AKm131N4vd5IzXz7qyuxTp8D\ndL9lms1J0hc9mlqt/qHW/vQ52F5cDoSTvtTa7/5a1MzvNwhD4XGRiqzdlSR90eM11Ug3nT6GgK2W\nhq8+j9RTF91XUz8FRVGwvbgM6/RLgO49ygdJ+kKg0+l+qLU/4xJsLzwDyGat7q7p05z70y2gKCSd\nPLLFJ7/uSpK+EBC5oWs+bzINn/0XX2lxpEeq6H5a1swPj/JVKhVms7nbbcb6MUn6QgAJCQkkJyej\nTkzEPGEqtlXhuf2mlR2ie4lsxtr3PZ7dO0kZOx7ovss0m5OkL8RBTaN9y9SLcL71OkGXbNbqjlps\nxlq9HMsFM1Af3KFtMBiiHF3Xk6QvxEFJSUkkJCSgS8/E9IvR2NeuAmS039243e4fNmNtegvLBTOB\nH/7od3eS9IU4qHmtfevMS7G/9DzKwSJsPp8vytGJzhLZjPXyClLOHIfWmtatN2P9mCR9IZpJTU1F\nq9WSUDQAQ98inO9IZ63uxOPxUF9fT6ixAfuaVaTNnAuER/ndeZlmc5L0hWhGrVY3G+3PxbbyGRRF\nwW63S2etbqBplO9Y/yqJx49AX9Ab/cHiaj2FJH0hfsRqtYY7a538cwDc//2YUCgkpRninN/vx+l0\nogQC4c1Ysy4Huv9mrB+TpC/EjzTvrGWdeSm2lU8D4SkeKc0Qv5o229W9/zba9EwSh56ARqPBYrFE\nO7RjSpK+EK1oWsmRevZ4vPv30rjr60gPVRF/gsEgNpstvClrxdOkzQ6P8ps+1fUkPeu7FaKN9Hp9\nuDSDTod1xqXUPh8e7dfU1Ehphjhkt9sJhUI07PgvIU8jyT8/o8VqrZ5Ekr4QhxEpzTBxKg3bP8FX\ncgCfzyd9dONMKBSK3MCtXfk01plzUanVWCwWtFptlKM79iTpC3EYRqOR5ORkNIlJWC6YSe3KpQBU\nV1fLaD+ONJVc8Hy/B+/u/5E6dgLQM0outEaSvhBHkJGRAYBl2kW43n2TQG0NHo8Ht9sd5chEW7Qo\nubBiKZapF6E2GEhJSekRJRdaI0lfiCNITEzEaDSiNVtJPXs8ttXPAuHRvoh9LpcLr9eLr7yU+o/f\nwzK5Z5VcaI0kfSGOQKVSRUb71pmX4li3mmB9HW63m8bGxihHJ45EUZTIH2fbyqWYJ01HY0ohKSmp\nx5RcaI0kfSGOwmQyodfr0efmk3TK6TgOFmKT0X5sq6+vDxdWq63B+fb6SP/bpj/iPZUkfSGOovlo\nP+3iK7CtepaQ1xuZOhCxKTLKX7Wc1HMmoLWEC6slJSVFObLokqQvRBtECrEd1x9DvwE4N6wFZLQf\nq9xuNw0NDQRdThyvvUTarMuA8Ci/J5VcaI0kfSHaQK1WR27+pV9yNbXPP4kS8ONwOKTscgyKjPJf\nXkHy6WPQZeVgONgopaeTpC9EG1ksFjQaDYnDRqDLyce58XVARvuxprGxMVw+uaEB+0vPk3bRFYCM\n8ptI0heijTQazQ+j/bnzqF3+BMrBejwy2o8dTX+E7etWk/izkzH06oNOpyM1NTXKkcWGdid9m83G\n2LFj6d+/P+ecc85hC1EVFhZy/PHHM2LECE455ZR2BypELGgq0JU4/CQ01jRc725osQFIRJf34A32\nkNeL7YVnSJ9zFSCj/ObanfQXLlzI2LFj2b17N2eddRYLFy5s9TiVSsXmzZvZsWMHW7dubXegQsSC\nptG+SqUi/dJ51Cx7HCUUwm634/f7ox1ej1dVVQWAc8MaDEX9Seg3EK1Wi9lsjnJksaPdSX/t2rXM\nnRtuNTZ37lxeffXVwx4rdUpEd5KWlnawycpI1IlJ1L23UUb7McDj8eB0Ogn5fNQsf5yMy64Fwrtv\ne1r55CNpd4m5yspKsrKyAMjKyqKysrLV41QqFWeffTYajYZ58+Zx1VVXtXrcggULIv89evRoRo8e\n3d7QhOhSGo2GtLQ0qqurSb/sGqoffRjTGWOx2WxkZGT0yMqNsaBpLt/5+ssY+hZhHHw8Go0Gq9Ua\n5cg6z+bNm9m8eXOHzqFSjjAMHzt2LBUVFYc8/5e//IW5c+dit9sjz1mt1lbbyZWXl5OTk0N1dTVj\nx47lkUceYdSoUS2DUKnk04CIK4FAgN27dxMMBtl31SzS587DNOpM0tPTyc7OjnZ4PY7H4+Hbb78l\n5PXy3UXjyb/nIYyDhpGdnd2t6+y0J3cecUiycePGw76WlZVFRUUF2dnZlJeXk5mZ2epxOTk5QPhG\nypQpU9i6deshSV+IeKPVarFardTU1JB+6dXUPPMoyaePwWazkZ6eLqP9Y6xpLt/x+kskFA3AOGhY\n5HckWmr3RNekSZN45plnAHjmmWeYPHnyIcc0NDRQV1cHhHfIvfXWWwwbNqy9lxQipjQ11E4+fQxK\nwE/9R+8RCoVk3f4x5vF4Iit2ap97ivTLrwNkLv9w2v0Tue2229i4cSP9+/dn06ZN3HbbbQCUlZUx\nfvx4ACoqKhg1ahTDhw/n1FNPZcKECZxzzjmdE7kQUabT6bBarajUajJ+OZ/qJx5BCYWw2WyykucY\niozy160mof8gjAOHyCj/CI44p3/MgpA5fRGnAoEAu3btIhQKse+ai0mbeSkpZ56LxWIhLy8v2uF1\nez/M5Xv4btb55N+3GGP/weTk5PSIzljtyZ3y2UeIDtBqtZFpnoxfXk/1k0tQAgHsdrtU4DwGIqP8\ntatIGDQUY//BaLVaLBZLlCOLXZL0heigjIyM8Lr9k05Dm5aO8811wA8JSXSNhoaG8Fy+p5Ha558m\n4+BcftPvQ7ROfjJCdFDzXboZV91AzTP/JuTz4XQ68Xg80Q6vW1IUJbI3yPbS8xiHDo/svpVR/pFJ\n0heiE6SlpUUqcOoL++J4bTUgo/2uUl9fj9vtJuhyYlv5DBlX3wBAZmamjPKPQn46QnQCjUYT6a6V\n8cvrqV3+BCFPIy6XS3rpdrLmo/yaZ5/ANHoshoJC9Hq9jPLbQJK+EJ3EarWi1Wox9h+McdgI7C+v\nADhsiRLRPk3TZv7KcpzrXyH9smuA8IZRqaR5dJL0hegkarU6sjM944pfUbvyGYJ1Turr6yObFEXH\nhEKhyB/R6qeWYJk8C11aBkajkZSUlChHFx8k6QvRiSwWC3q9HkNhX0z/dyY1yx4HwhsVZS9KxzVt\nfPN8t5v6LR9gnX0ZANnZ2TLKbyNJ+kJ0IpVKFak+m3HFfJwb1uArLcbr9bYoUCh+umAwGClxUf34\nP0ifcxWapGSSk5NJSkqKcnTxQ5K+EJ0sJSWFxMREtNY0rDMuperfDwHhuf1gMBjl6OJXTU0NwWCQ\nhs8/xbv3W8wXzACQqqY/kSR9ITqZSqWKJCLrjEto/N9XNHy+rcVIVfw0Pp+PmpoaFEWh6t8PkfHL\n+aj1esxmMwkJCdEOL65I0heiCyQmJpKamorakEDm1TdSueRvKKEQtbW10kS9HZruibjefh0lECDl\nrPNRqVSHLekuDk+SvhBdpGkJYcpZ5wHgemd9izXmom3q6+txuVwEG9xU/fthsm78PSq1mrS0NPR6\nfbTDizuS9IXoInq9PlyMTa0ma/4tVD26iJCnEafTSUNDQ7TDiwuKolBeXg5A7fLHSfrZqSQOPQGt\nVhvZDCd+Gkn6QnShjIyMcHmG43+GcfDx2F5cDoT7TsgSzqOrra3F6/XiK96P47WXyLjm10D45q1G\no4lydPFJkr4QXUij0USWcGZe82tsq5bjryjD4/G02lNa/CAQCERqF1Uuvp+0i66IbMRKTU2NcnTx\nS5K+EF3MYrFgMBjQ5xZgnT6HikULgfASTumwdXiVlZWEQiHqPn4fX8kBrNPmAJCbmysbsTpAkr4Q\nXUylUkW6aFlnXY6veC91H24iFApRUVER5ehiU0NDA3a7nZDPR9Uj95N1/e9Q6XRYLBaMRmO0w4tr\nkvSFOAYSExOxWCyo9Xqyf/NHKhYtJNTQgNPplLo8P6IoCmVlZQDYVi1H36uQ5NNGoVarI1Nlov0k\n6QtxjGRlZaHRaEg68VQSTziR6mf+DUB5eTmhUCjK0cWOmpoaPB4PvrJibCuXkjX/d0D456fVaqMc\nXfyTpC/EMaLVaiM7dbOuuxnnG2vwfLcbn88nO3UP8ng8VFVVhZdq3n8XaRdfiT6/FwkJCVit1miH\n1y1I0hfiGDKbzZG6PBlX/oqKB+9BCYUio9ueTFEUSktLURQFx+svE2qoj9y8zcvLk5u3nUSSvhDH\nkEqlIjc3FwDzxGkogQCO119ukfB6KpvNRmNjI/7qSqofXUTOrX9GpdWSnp4uN287kSR9IY6xhISE\ncCN1tZqc3y2g+rF/4CsvpbGxscdO8/h8PiorK1EUhYoH78EyZSYJx/VHr9dLfZ1OJklfiCjIzMxE\nr9eTcFx/0mZfRvm9f0QJhaiqqupxJRqaVuuEQiHq3n0TX2kxaXOuAsLTOtLovHPJT1OIKFCr1eTn\n5wNgnTkXJRTCtupZAEpKSnrUah6bzUZ9fT0Bh52Kfywk99a7UOv1WK1WaY7SBSTpCxEliYmJZGRk\noNJoyP39PdQ++wTevd9Gpjp6gsbGxkjZ5Iq/303q2edjHBIuqCZr8ruGJH0hoigjI4OEhAT0uQVk\nzLuRsnt+j+L3U1tbS319fbTD61LBYJD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      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Or even with an object oriented version\n",
      "fig, [ax1, ax2] = subplots(1, 2)\n",
      "ax1.plot(real(spectrum), label='$\\Re\\{$spectrum$\\}$')\n",
      "ax1.legend(loc='upper center')\n",
      "ax2.plot(imag(spectrum), label='$\\Im\\{$spectrum$\\}$')\n",
      "ax2.legend(loc='upper center')\n",
      "fig.set_figwidth(17)\n",
      "fig.savefig('spectrum.png')\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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BIG4lubm5iomJUXR0tCRp5syZys7O1tChQ30+5pYt0ne+467Af8010g03uB8x\nMe47rVx9tdS7d+3HVVe5f+/CwigUCAD+ZtWMeMAF4mTEAcA6ysvdlx7BGoqKihQVFeV97XA4tG/f\nvnr7/fCHGd77ySYlJSkpKanRY/773+5A/P/9P6mwUProoyuPrVul8+cbflRUuG+b17Wr+3fQ8wgL\nc99Sz/PsWe7Sxb3cpcuVh+d1SMiVR93XISHuYL/ua8+6ms911zf2qLldanifhtbXXNfQcmPPLV3X\nkue6y768p6OX/fF5Hd2+trw3EH5mrf3daM0x2vLcluWa61rzaOjvQ92/JXX3CXSlpVJkpL9b0TpO\np1NOp7NNxyAQBwD4DVPTrcXWwhHhsGEZmjevZcf0ZFK6dHFnwK+/Xho/vmXvra52B+Q1H5cvu6e7\n1312udyPy5drL1dXX3m4XLWXDePKa8+yZ73nUXNbQ+sbe1RXu/vQ0Hvqrq+7rqHlxp5buq4lz3WX\nfXlPRy/74/M6un1teW8g/Mxa+7vRmmO05bktyzXX+fJo6O9D3b8lnr8RnsC8sUfNk4c1TzTWPenY\n0AnKsLDaJzI9j+7d3TOP6s5Guvpq99/oqCj3cT1KSqQRIxRU6p4kzszMbPUxCMQBAH5DIG4tkZGR\nKigo8L4uKCiQw+Got19JScuP2ZZrC0NC3JfENXZZHAAEs5pBekMnCeuePGzscfly7YfnJGVl5ZWT\nmDWXL12SLlyQTp+Wjh+/MhPpiy+kkyfdM5muu+7KpUS5udKUKf7+aXW+gArEqZoOANZCIG4tY8aM\nUX5+vk6ePKlBgwZp06ZN2rBhQ739WhOIl5S4B3IAgNpqTl0PJOXl0okTVy4j6tZNGjvW363qfAEV\niJMRBwBrIRC3ltDQUK1evVqTJk2Sy+XSvHnzGizU1lkZcQBA5+veXRo61P2wsoALxKmaDgDWQdV0\n60lJSVFKSkqT+7Q2I27FarsAgOAWUBMVyIgDgLVQNR0NISMOADA7AnEAgN8wNR0NKS1t3b4E4gCA\nYEMgDgDwGwJxNISp6QAAswuoQJyq6QBgLQTiaEhLA/GqKuniRff9aQEACCZtCsRzcnIUHx+v2NhY\nrVixosF9nE6nEhMTNXz48Fo3PW8IGXEAsBYCcTTk4kV3kN2csjKpb9/AuzUPAADN8blqusvl0qJF\ni7Rjxw5FRkbq5ptvVlpaWq3bkJSVlen73/++/v73v8vhcOjs2bNNHpOq6QBgLRUVUv/+/m4FAk14\nuPva7wFzkwJhAAAcmklEQVQDmt6P68MBAMHK53PIubm5iomJUXR0tMLCwjRz5kxlZ2fX2ueVV17R\n9OnT5XA4JEn9+vVr8phkxAHAWqiajoZERLRsejrXhwMAgpXPGfGioiJFRUV5XzscDu3bt6/WPvn5\n+aqqqtKdd96p8+fP65FHHtGsWbPqHSsjI0OSdPCgdOONSZKS6u1DIA4A5hNMU9OdTqecTqe/m2EJ\nEREtq5xORhwAEKx8DsRtNluz+1RVVenAgQPauXOnLl68qFtvvVW33HKLYmNja+3nCcTfe0+66aaG\nj0UgDgDmE0yBeFJSUq1aJ5mZmf5rjMmREQcAmJ3PgXhkZKQKCgq8rwsKCrxT0D2ioqLUr18/9ejR\nQz169NAdd9yh999/v14g7kHVdACwlmAKxNF5WhqIkxEHAAQrn68RHzNmjPLz83Xy5ElVVlZq06ZN\nSktLq7XPPffco7ffflsul0sXL17Uvn37lJCQ0OgxuUYcAKylooJAHPWFh7c8I04gDgAIRj5nxEND\nQ7V69WpNmjRJLpdL8+bN09ChQ7V27VpJ0vz58xUfH6+77rpLI0eOVEhIiB588MFmA3GqpgOAdVCs\nDQ1pTUa8RrkaAACChs+BuCSlpKQoJSWl1rr58+fXev3oo4/q0UcfbdHxyIgDgLUwNR0NiYiQ8vOb\n36+0VBo5suPbAwBAe/N5anpHIBAHAGshEEdDWlo1nWJtAIBgFTSBOMXaAMB8CMTREIq1AQDMLqAC\n8aaqppMRBwDzIRBHQ7h9GQDA7AIqEG9qQBYWJrlc7gcAwByomo6GtLRqOhlxAECwCphA3DCaHpDZ\nbFROBwCzoWo6GkJGHABgdgETiFdWurPeIU20iOnpAGAuTE1HQ8LDpbIyqbq68X0uXXI/N3bbUwAA\nAlnABOItGYwRiAOAuRCIm8+f/vQnDRs2TF26dNGBAwdqbVu+fLliY2MVHx+vbdu2NXqM0FCpVy/p\n/PnGP4dsOAAgmAVVIE7ldAAwj8uX3ZclhYb6uyVoTyNGjNBrr72mO+64o9b6vLw8bdq0SXl5ecrJ\nydHChQtV3UTKu7np6VwfDgAIZgETiDdVMd2DjDgAmIfnBKzN5u+WoD3Fx8crLi6u3vrs7Gylp6cr\nLCxM0dHRiomJUW5ubqPHaa5gG4E4ACCYBUweoqVT0ynWBgDmQMV0azl16pRuueUW72uHw6GioqIG\n983IyFBZmbRqlTR3bpKSkpLq7cPUdACAvzidTjmdzjYdI6AC8eYKrpARBwDzoGJ68EpOTtaZM2fq\nrV+2bJlSU1NbfBxbI9MhMjIylJcn3X231EAMLomMOADAf5KSap8kzszMbPUxAioQZ2o6AFgHhdqC\n1/bt21v9nsjISBUUFHhfFxYWKjIystH9m7tGnIw4ACCYBcw14gTiAGAtBOLmZxiGdzktLU0bN25U\nZWWlTpw4ofz8fI0dO7bR90ZEuLPejSEjDgAIZkEViFM1HQDMg0DcnF577TVFRUVp7969uvvuu5WS\nkiJJSkhI0IwZM5SQkKCUlBStWbOm0anpEhlxAIC5BczUdKqmA4C1EIib09SpUzV16tQGty1dulRL\nly5t0XHCw6W8vMa3kxEHAASzNmXEc3JyFB8fr9jYWK1YsaLR/d577z2Fhobqr3/9a6P7UDUdAKyl\nooJibWgcGXEAgJn5HIi7XC4tWrRIOTk5ysvL04YNG3T06NEG93vsscd011131bpWrC6qpgOAtZAR\nR1OaC8TJiAMAgpnPgXhubq5iYmIUHR2tsLAwzZw5U9nZ2fX2++1vf6t7771X/fv3b/J4FGsDAGsh\nEEdTWpIRJxAHAAQrn68RLyoqUlRUlPe1w+HQvn376u2TnZ2tXbt26b333mvyfqF790pffCE5nbXv\nyVYTgTgAmEewBeJOp1NOp9PfzbCMllRNZ2o6ACBY+RyIN1Xp1GPx4sXKysqSzWaTYRiNTk3PyMjQ\n8uXSuXNSIzG4JKqmA4CZBFsgnpRU+0RxZmam/xpjAeHhjWfEq6ulsjKpb9/ObRMAAO3F50A8MjJS\nBQUF3tcFBQVyOBy19tm/f79mzpwpSTp79qzefPNNhYWFKS0trd7xWlo1/YsvfG0xACCQBFsgjs7V\no4dkGO7xQd0aMufPSz17SmFh/mkbAABt5XMgPmbMGOXn5+vkyZMaNGiQNm3apA0bNtTa5+OPP/Yu\nz5kzR6mpqQ0G4ZJ7QHbNNU1/Zvfu0mef+dpiAEAgoWo6mmKzXblOPDKy9jYKtQEAgp3PgXhoaKhW\nr16tSZMmyeVyad68eRo6dKjWrl0rSZo/f36rjkfVdACwFjLiaE5jgTi3LgMABDufA3FJSklJUUpK\nSq11jQXgzz//fJPHomo6AFgLgTia01jldDLiAIBg5/Pty9obgTgAWAuBOJrTWCBORhwAEOyCKhCn\najoAmAeBOJoTHt7wLczIiAMAgl3ABOItrZpOIA4A5kAgjuaQEQcAmFXABOItnZpeUdE57QEAdCyq\npqM5XCMOADCrgArEqZoOANZBRhzNIRAHAJhVQAXiTE0HAOsgEEdzmJoOADArAnEAgF8QiKM5ZMQB\nAGYVVIE4VdMBwDwIxNGcxqqmkxEHAAS7gAnEW1o1nWJtAGAOFRUE4mgaGXEAgFkFTCDO1HQAsJby\ncqqmo2lcIw4AMKuACsSpmg4A1sHUdHP67//+bw0dOlSjRo3StGnT9MUXX3i3LV++XLGxsYqPj9e2\nbduaPdbVV0tffilVVV1ZV1UlXbwo9e7dEa0HAKBzBFQg3tJrxA2jc9oEAOg4BOLmNHHiRH3wwQd6\n//33FRcXp+XLl0uS8vLytGnTJuXl5SknJ0cLFy5UdXV1k8cKCZH69pXKyq6sKytzrwsJmBEMAACt\nFxBfY5cvu59DQ5veLzTU/cXr2R8AELwIxM0pOTlZIf+JkseNG6fCwkJJUnZ2ttLT0xUWFqbo6GjF\nxMQoNze32eOFh9eens714QAAM2gm9O0crRmMebLiYWEd2yYAQMciEDe/devWKT09XZJ06tQp3XLL\nLd5tDodDRUVFDb4vIyPDuxwamqTS0iTv65ISAnEAgH85nU45nc42HSMgAvGWVEz38FRO59owAAhu\nFRUUawtWycnJOnPmTL31y5YtU2pqqiTpySefVNeuXXXfffc1ehybzdbg+pqB+L599TPiFGoDAPhT\nUlKSkpKSvK8zMzNbfYyACMRbkxWhYBsABD/DoGp6MNu+fXuT21944QVt3bpVO3fu9K6LjIxUQUGB\n93VhYaEiIyOb/ay6ldOZmg4AMIM2XyOek5Oj+Ph4xcbGasWKFfW2v/zyyxo1apRGjhypr33tazp8\n+HC9fVpSMd2DQBwAgl9VldSlS/O1QRB8cnJy9NRTTyk7O1vda5xlT0tL08aNG1VZWakTJ04oPz9f\nY8eObfZ4dQNxbl0GADCDNg2BXC6XFi1apB07digyMlI333yz0tLSNHToUO8+Q4YM0VtvvaU+ffoo\nJydH3/ve97R3795axyEjDgDWwvXh5vXQQw+psrJSycnJkqRbb71Va9asUUJCgmbMmKGEhASFhoZq\nzZo1jU5Nr4mMOADAjNoUiOfm5iomJkbR0dGSpJkzZyo7O7tWIH7rrbd6l2tWT62JQBwArIVA3Lzy\n8/Mb3bZ06VItXbq0VccLD5c+/vjK65ISKSrK19YBABAY2hSIFxUVKarGt6HD4dC+ffsa3f+5557T\n5MmT663/3e8y9NlnUkZG/Qvf6/JUTQcABK9gDMTbo0IqWi8iQtq//8rr0lJp5Ej/tQcAgPbQpkC8\nJVPKPHbv3q1169bpnXfeqbdt5swMFRS4A/HmeKqmAwCCVzBWTG+PCqloPa4RBwCYUZsC8boVUAsK\nCuRwOOrtd/jwYT344IPKyclReAMXdjE1HQCsJRgz4vAPrhEHAJhRm6qmjxkzRvn5+Tp58qQqKyu1\nadMmpaWl1drn008/1bRp0/TSSy8pJiamweMQiAOAtRCIo6XIiAMAzKhNGfHQ0FCtXr1akyZNksvl\n0rx58zR06FCtXbtWkjR//nz97Gc/U2lpqRYsWCBJCgsLU25ubq3jcPsyALAWAnG0FBlxAIAZtfkO\nrikpKUpJSam1bv78+d7lZ599Vs8++2yTxyAjDgDWQiCOlgoPdwffhiHZbATiAABzaHMg3h5aMyCj\najoABD8CcbRUWJh71tz58+5lw2j5LDoAAAJVQATily61LiNO1XQACG7BWDUd/uOZnt61qzsb3oqb\ntgAAEJACIhBnajoAWAsZcbRGzUCcQm0AADMImEC8d++W7UsgDgDBj0AcreEJxLt14/pwAIA5tOn2\nZe2FqukAYC0E4miN8HB3IM6tywAAZhEwGXGmpgOAdRCIozU8GfHu3cmIAwDMIegCcaqmA0DwKy+n\nWBtaLiLCfduybt3IiAMAzCEgAnGqpgOAtVRUkBFHy0VESP/+NxlxAIB5BMw14kxNBwDrYGo6WsMz\nNb2khEAcAGAOAZERJxAHAGshEEdreALxHj2Ymg4AMIeACcSpmg4A1kEgjtbwVE3v2ZOMOADAHAIm\nECcjDgDWQSCO1vBkxMvLyYgDAMwh6AJxqqYDQPCjajpaw1M1vaKCjDgAwBwColgbVdMBwFqomm5e\nP/nJTzRq1CiNHj1aEyZMUEFBgXfb8uXLFRsbq/j4eG3btq3Fx6xZrI2MOADADAIiEGdqOgBYC1PT\nzetHP/qR3n//fR06dEhTpkxRZmamJCkvL0+bNm1SXl6ecnJytHDhQlVXV7fomD17Si6XOyvet29H\nth4AgM5BIA4A6HQE4ubVu3dv7/KFCxfUr18/SVJ2drbS09MVFham6OhoxcTEKDc3t0XHtNncU9J7\n9ZLCwjqk2QAAdKo2XSOek5OjxYsXy+Vy6YEHHtBjjz1Wb5+HH35Yb775pnr27KkXXnhBiYmJ9fah\najoAWAuBuLk98cQTevHFF9WjRw9vsH3q1Cndcsst3n0cDoeKiooafH9GRoZ3OSkpSUlJSYqIoK4A\nACAwOJ1OOZ3ONh3D50Dc5XJp0aJF2rFjhyIjI3XzzTcrLS1NQ4cO9e6zdetWffjhh8rPz9e+ffu0\nYMEC7d27t96xKNYGANZCIB7ckpOTdebMmXrrly1bptTUVD355JN68sknlZWVpcWLF+v5559v8Dg2\nm63B9TUDcY+ICKlr1zY1GwCAduE5SezhuQyrNXwOxHNzcxUTE6Po6GhJ0syZM5WdnV0rEN+8ebNm\nz54tSRo3bpzKyspUXFwsu91e61hVVS3/cqVYGwAEv4oKspvBbPv27S3a77777tPkyZMlSZGRkbUK\ntxUWFioyMrLFn0kgDgAwE58D8aKiIkVFRXlfOxwO7du3r9l9CgsL6wXiXbpkyHMSoe7Zhbq6dXMP\n4AzDfc0YACD4BGNGvD2moVlBfn6+YmNjJbmvC/dckpaWlqb77rtPP/zhD1VUVKT8/HyNHTu2xceN\niOD6cACAefgciDc2nawuwzCafd/Pfpahxx9v2eeGhLi/iCsryaYAQLAKxkC8PaahWcHjjz+uY8eO\nqUuXLrrhhhv0u9/9TpKUkJCgGTNmKCEhQaGhoVqzZk2LxxISgTgAwFx8DsTrTjErKCiQw+Focp/G\npqG1NAj38BRsIxAHgOAUjIE4WubPf/5zo9uWLl2qpUuX+nTcQYOkc+d8bRUAAIHF59uXjRkzRvn5\n+Tp58qQqKyu1adMmpaWl1donLS1Nf/zjHyVJe/fuVd++fetNS/cFldMBILgRiKO1HnlE+vGP/d0K\nAADah88Z8dDQUK1evVqTJk2Sy+XSvHnzNHToUK1du1aSNH/+fE2ePFlbt25VTEyMevXq1WjV1Nai\ncjoABK/qai4vQusxLR0AYCY2o+5F3J3dAJut3nXkzYmLk7ZscT8DAIJLebnUp0/w3wHDl+8vNI+f\nKwAg2Pjy3eVzRtyf2mNqusvlHgRWVrqXL1+u/1xdXfthGPWXDaP+ct2HVH+5Jc++LNfU2vVNac9j\ndeRx2lMgtqmzBOIdCdqrTY0dx5fjt/ZYNde3ZrmxdU0t132EhDT8OiSk4UeXLlJoaP3nsDB3Jju0\nDd8eTEsHAABWZ4pAvLxcOnhQ2rtX+vRTqaxM+uKL2s8XLriD7oqKK7c/69bNPagMC2t40NmlS/3B\nat3lhga4DT2k+sstefZluSZ/Bh3tdXx/CsQ2dbRAPAHR0Sd8OuPklK8n2Bpb19RyY4+6JwzrnmD0\nPFyuK4+aJycvX5aqqq5ksrt2df8d7dpV6tVL6tvXnemu+TxokDRunDRmjHTVVe73EYgDAACrC9pA\n/PXXpZdfdgffR45I8fHuwV5MjDRiRP3BYK9e7gGj59GlizWDLABoD55ZRZ6ZRRcuuE981j0JevKk\n+84Yhw9LsbHSLbdIQ4YQiAMAAGsLykD8ttukY8fcA7pf/lK66SapZ09/twoArKNLF/ffXc/f3uZu\niFFRIR065D55unevNH58x7cRAAAgUAVlsTYAAPyN76+Owc8VABBsfPnu8vk+4gAAAAAAoPUIxAEA\nAAAA6EQE4gAAAAAAdCICcQAAAAAAOhGBOAAAAAAAnYhAHAAAAACATkQgDgAAAABAJyIQBwAAAACg\nExGIAwAAAADQiQjEAQAAAADoRATifuJ0Ov3dBL+wYr+t2GfJmv22Yp8l6/YbaC9W/T9Ev63Din2W\nrNlvK/bZVz4H4iUlJUpOTlZcXJwmTpyosrKyevsUFBTozjvv1LBhwzR8+HA9/fTTbWqsmVj1l9SK\n/bZinyVr9tuKfZas22807X/+538UEhKikpIS77rly5crNjZW8fHx2rZtmx9bF1is+n+IfluHFfss\nWbPfVuyzr3wOxLOyspScnKzjx49rwoQJysrKqrdPWFiYfv3rX+uDDz7Q3r179cwzz+jo0aNtajAA\nAAhsBQUF2r59u66//nrvury8PG3atEl5eXnKycnRwoULVV1d7cdWAgDgPz4H4ps3b9bs2bMlSbNn\nz9brr79eb5+BAwdq9OjRkqSrrrpKQ4cO1alTp3z9SAAAEAR++MMfauXKlbXWZWdnKz09XWFhYYqO\njlZMTIxyc3P91EIAAPzLZhiG4csbw8PDVVpaKkkyDEMRERHe1w05efKkvv71r+uDDz7QVVdddaUB\nNpsvHw8AgN/5+BVqatnZ2XI6nfr1r3+twYMHa//+/YqIiNBDDz2kW265Rd/5znckSQ888IBSUlI0\nffr0Wu9nXAAACEatHROENrUxOTlZZ86cqbf+ySefrPXaZrM1+cV54cIF3XvvvVq1alWtIFxiEAMA\nQLBpanywfPnyWtd/N/U939DYgXEBAMAKmgzEt2/f3ug2u92uM2fOaODAgTp9+rQGDBjQ4H5VVVWa\nPn26vvvd72rKlCltay0AAPC7xsYHR44c0YkTJzRq1ChJUmFhoW666Sbt27dPkZGRKigo8O5bWFio\nyMjITmkvAACBxudrxNPS0rR+/XpJ0vr16xsMsg3D0Lx585SQkKDFixf73koAABDwhg8fruLiYp04\ncUInTpyQw+HQgQMHZLfblZaWpo0bN6qyslInTpxQfn6+xo4d6+8mAwDgFz4H4kuWLNH27dsVFxen\nXbt2acmSJZKkU6dO6e6775YkvfPOO3rppZe0e/duJSYmKjExUTk5Oe3TcgAAENBqTj1PSEjQjBkz\nlJCQoJSUFK1Zs4brwQEAluVzIB4REaEdO3bo+PHj2rZtm/r27StJGjRokP72t79Jkm677TZVV1fr\n0KFDOnjwoA4ePKi77rrLe4ycnBzFx8crNjZWK1asaGNXAtfcuXNlt9s1YsQI77qW3Ic92DV2H3kz\n9728vFzjxo3T6NGjlZCQoMcff1ySuftck8vlUmJiolJTUyWZv9/R0dEaOXKkEhMTvZk9s/dZksrK\nynTvvfdq6NChSkhI0L59+0zf72PHjnlPKCcmJqpPnz56+umnTd/vtvr4448VERHhfb106VJ9+OGH\n+te//qVJkybV259xgXl/l6w4JpCsPS6w2phAsua4gDGB72MCnwPxtnK5XFq0aJFycnKUl5enDRs2\nmPYe43PmzKk3E6Al92EPdo3dR97Mfe/evbt2796tQ4cO6fDhw9q9e7fefvttU/e5plWrVikhIcGb\n5TJ7v202m5xOpw4ePOi9DZPZ+yxJjzzyiCZPnqyjR4/q8OHDio+PN32/b7zxRu8J5f3796tnz56a\nOnWq6fvdmRgXmPt3yYpjAsna4wKrjQkka44LGBO0YUxg+Mm7775rTJo0yft6+fLlxvLly/3VnA53\n4sQJY/jw4d7XN954o3HmzBnDMAzj9OnTxo033uivpnWae+65x9i+fbtl+v7ll18aY8aMMY4cOWKJ\nPhcUFBgTJkwwdu3aZXzzm980DMP8v+fR0dHG2bNna60ze5/LysqMwYMH11tv9n7X9Pe//9247bbb\nDMOwVr87GuMCa/0uWW1MYBjWGhdYcUxgGNYbFzAmaNuYwG8Z8aKiIkVFRXlfOxwOFRUV+as5na64\nuFh2u12SuwJ9cXGxn1vUsU6ePKmDBw9q3Lhxpu97dXW1Ro8eLbvd7p2GZ/Y+S9IPfvADPfXUUwoJ\nufJnxez9ttls+sY3vqExY8boD3/4gyTz9/nEiRPq37+/5syZo6985St68MEH9eWXX5q+3zVt3LhR\n6enpksz/792ZGBdY53fJSmMCyZrjAiuOCSTrjQsYE7RtTOC3QJwCLVc0dx/2YHfhwgVNnz5dq1at\nUu/evWttM2PfQ0JCdOjQIRUWFuqtt97S7t27a203Y5+3bNmiAQMGKDExsdF7AJux3++8844OHjyo\nN998U88884z+8Y9/1Npuxj5fvnxZBw4c0MKFC3XgwAH16tWr3tQrM/bbo7KyUm+88Ya+9a1v1dtm\n5n53Bn52V5j5d8lqYwLJeuMCq44JJOuNCxgTtG1M4LdAvO79RAsKCuRwOPzVnE7nuQ+7pCbvwx7s\nPPeRnzVrlvcWd1bpe58+fXT33Xdr//79pu/zu+++q82bN2vw4MFKT0/Xrl27NGvWLNP3+9prr5Uk\n9e/fX1OnTlVubq7p++xwOORwOHTzzTdLku69914dOHBAAwcONHW/Pd58803ddNNN6t+/vyTr/D3r\nDIwLzP+7ZOUxgWSdcYFVxwSS9cYFjAnaNibwWyA+ZswY5efn6+TJk6qsrNSmTZuUlpbmr+Z0upbc\nhz3YGY3cR97MfT979qy3QuKlS5e0fft2JSYmmrrPkrRs2TIVFBToxIkT2rhxo8aPH68XX3zR1P2+\nePGizp8/L0n68ssvtW3bNo0YMcLUfZakgQMHKioqSsePH5ck7dixQ8OGDVNqaqqp++2xYcMG7xQ0\nydx/zzob4wJz/y5ZcUwgWXNcYMUxgWTNcQFjgjaOCTr06vVmbN261YiLizNuuOEGY9myZf5sSoea\nOXOmce211xphYWGGw+Ew1q1bZ3z++efGhAkTjNjYWCM5OdkoLS31dzPb3T/+8Q/DZrMZo0aNMkaP\nHm2MHj3aePPNN03d98OHDxuJiYnGqFGjjBEjRhgrV640DMMwdZ/rcjqdRmpqqmEY5u73xx9/bIwa\nNcoYNWqUMWzYMO/fMDP32ePQoUPGmDFjjJEjRxpTp041ysrKLNHvCxcuGNdcc41x7tw57zor9Lsz\nMS4w7++SFccEhsG4wCpjAsOw7riAMYHvYwKbYTRy8QYAAAAAAGh3fpuaDgAAAACAFRGIAwAAAADQ\niQjEAQAAAADoRATiAAAAAAB0IgJxAAAAAAA6EYE4AAAAAACd6P8DaeatjYMIwakAAAAASUVORK5C\nYII=\n"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Fundamental differences to Matlab\n",
      "\n",
      "### Arrays are indexed from 0"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = arange(10)\n",
      "print(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0 1 2 3 4 5 6 7 8 9]\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(a[0], a[9])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0 9\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(a[10]) # does not work!"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "IndexError",
       "evalue": "index out of bounds",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-13-a88eda7ab931>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# does not work!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;31mIndexError\u001b[0m: index out of bounds"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Ranges always *exclude* the last element"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = arange(10)\n",
      "print(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0 1 2 3 4 5 6 7 8 9]\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(a[5:7])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[5 6]\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(a[5:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[5 6 7 8 9]\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(a[:5])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0 1 2 3 4]\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(a[-3:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[7 8 9]\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(a[2:-2:2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[2 4 6]\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(a[::-1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[9 8 7 6 5 4 3 2 1 0]\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Ranges do not copy"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = arange(10)\n",
      "print(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0 1 2 3 4 5 6 7 8 9]\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = a[2:-2]\n",
      "b[2:-2] = 0\n",
      "print(b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[2 3 0 0 6 7]\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0 1 2 3 0 0 6 7 8 9]\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = arange(10)\n",
      "print(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0 1 2 3 4 5 6 7 8 9]\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c = array(a[2:-2])\n",
      "c[2:-2] = 0\n",
      "print(c)\n",
      "print(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[2 3 0 0 6 7]\n",
        "[0 1 2 3 4 5 6 7 8 9]\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Arrays are *strongly typed*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = arange(10, dtype=int8) # an 8 bit integer\n",
      "print(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0 1 2 3 4 5 6 7 8 9]\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(a * 32) # integer overflow, since MAX_INT8 == 128!!"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[   0   32   64   96 -128  -96  -64  -32    0   32]\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# wavread has a different name in Python:\n",
      "from scipy.io.wavfile import read as wavread\n",
      "\n",
      "fs, signal = wavread('Mann_short.wav')\n",
      "time = linspace(0, len(signal)/fs, len(signal))\n",
      "plot(time, signal)\n",
      "title('speech sample (%s)' % signal.dtype) # signal.dtype is typically int16!\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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l0IfuzkhI4qiv77rPtJQU7tmQEgi9EiW2o8RBJGOPIWU7U10t7UPyrhpgugIh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\ndmvWOH6fkZHm99qnTxe+jZk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      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# xcorr has a different name in Python:\n",
      "xcorr = lambda x, y: correlate(x, y, mode='full')\n",
      "    \n",
      "signal_short = signal[fs:2*fs] # also, it's dog slow.\n",
      "plot(xcorr(signal_short, signal_short))\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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/l00slZWVV7WQNWvW4L777gOgnIkMvZDvcrlgNBphMBjgcrlG1A+0aWhowNy5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\nap5Z7s0AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "signal_short = array(signal[fs:2*fs], dtype=float64)\n",
      "signal_short /= 2**15 # 2^15 == MAX_INT16\n",
      "plot(xcorr(signal_short, signal_short))\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Av/mN7JxSr/eMo9HI+v7ExP4tg6gvWPVE1A/PP+97DwNA3jcBkD11unifXL7Q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\n"
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}