SHAPE SPIKE SPIKER SHAPER.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:f1977b60f47e637d002fbfea1ed98edf9c6495f40ec6ed784527a635b20d803e"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "import sys\n",
      "# sys.path.append(\"/Users/becqg/Documents/code/Robinson\")\n",
      "sys.path.append(\"C:\\Users\\Guillaume\\Documents\\projets\\code\\Robinson\")\n",
      "import ER\n",
      "import imp"
     ],
     "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": 1,
     "metadata": {},
     "source": [
      "SHAPE"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imp.reload(ER)\n",
      "LB = 2\n",
      "B = numpy.array([-1.25, 1.0])\n",
      "LD = 5\n",
      "D = numpy.array([2.0, 1.0, 0.0, 1.0, 2.0])\n",
      "LA = 2\n",
      "A = numpy.empty(LA)\n",
      "LC = 3\n",
      "C = numpy.empty(LC)\n",
      "ASE = 0\n",
      "SPACE = numpy.empty(3*LA)\n",
      "(A, LC, C, ASE, SPACE) = ER.SHAPE(LB, B, LD, D, LA, A, LC, C, ASE, SPACE)\n",
      "print(A)\n",
      "print(LC)\n",
      "print(C)\n",
      "print(ASE)\n",
      "print(SPACE)\n",
      "plot(B, '.-', label=\"B\")\n",
      "plot(D, '.-', label=\"D\")\n",
      "plot(A, '.-', label=\"A\")\n",
      "plot(C, '.-', label=\"C\")\n",
      "legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[-1.08040593 -1.01483216]\n",
        "3\n",
        "[ 1.35050742  0.18813427 -1.01483216]\n",
        "0.711085089774\n",
        "[ 2.5625     -1.25       -1.5        -1.25        1.          0.48780488]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "<matplotlib.legend.Legend at 0x102239d10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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O+X5P6/f8mJ/PiMxMouvUYd4VV1A7BO8FKBYMewRo7X3lKZ32sbH9+6FXL+d0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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f28b790>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LB = 2\n",
      "B = numpy.array([1.0, -1.25])\n",
      "LD = 5\n",
      "D = numpy.array([2.0, 1.0, 0.0, 1.0, 2.0])\n",
      "LA = 2\n",
      "A = numpy.empty(LA)\n",
      "LC = 3\n",
      "C = numpy.empty(LC)\n",
      "ASE = 0\n",
      "SPACE = numpy.empty(3*LA)\n",
      "(A, LC, C, ASE, SPACE) = ER.SHAPE(LB, B, LD, D, LA, A, LC, C, ASE, SPACE)\n",
      "print(A)\n",
      "print(LC)\n",
      "print(C)\n",
      "print(ASE)\n",
      "print(SPACE)\n",
      "plot(B, '.-', label=\"B\")\n",
      "plot(D, '.-', label=\"D\")\n",
      "plot(A, '.-', label=\"A\")\n",
      "plot(C, '.-', label=\"C\")\n",
      "legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.63387978  0.69945355]\n",
        "3\n",
        "[ 0.63387978 -0.09289617 -0.87431694]\n",
        "0.882513661202\n",
        "[ 2.5625     -1.25        0.75        1.          1.          0.48780488]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<matplotlib.legend.Legend at 0x10efd4dd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RUHxaSWvvK2/TaR83tGkDgwbB6NFVdwwRoWfDhmyKj6d/RAS9f/qJAVu38nNu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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x102221950>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LB = 3\n",
      "B = numpy.array([1.5625, -1.7677, 1.0])\n",
      "LD = 5\n",
      "D = numpy.array([2.0, 1.0, 0.0, 1.0, 2.0])\n",
      "LA = 2\n",
      "A = numpy.empty(LA)\n",
      "LC = 4\n",
      "C = numpy.empty(LC)\n",
      "ASE = 0\n",
      "SPACE = numpy.empty(3*LA)\n",
      "(A, LC, C, ASE, SPACE) = ER.SHAPE(LB, B, LD, D, LA, A, LC, C, ASE, SPACE)\n",
      "print(A)\n",
      "print(LC)\n",
      "print(C)\n",
      "print(ASE)\n",
      "print(SPACE)\n",
      "plot(B, '.-', label=\"B\")\n",
      "plot(D, '.-', label=\"D\")\n",
      "plot(A, '.-', label=\"A\")\n",
      "plot(C, '.-', label=\"C\")\n",
      "legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.90810727  1.01672395]\n",
        "4\n",
        "[ 1.4189176  -0.01663004 -0.88915567  1.01672395]\n",
        "0.616207087368\n",
        "[ 6.56616954 -4.52973125  1.3573      2.5625      1.          0.68985901]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "<matplotlib.legend.Legend at 0x10f013750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TvBwmMxPat7eWKm7Z0u5o7LN6NfzpT9aNXncHuRzJz+fZtDTmHDrE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\n/JVSKgT9P6HBxwiHM6LcAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10efbffd0>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LB = 3\n",
      "B = numpy.array([1.0, -1.7677, 1.56250])\n",
      "LD = 5\n",
      "D = numpy.array([2.0, 1.0, 0.0, 1.0, 2.0])\n",
      "LA = 2\n",
      "A = numpy.empty(LA)\n",
      "LC = 4\n",
      "C = numpy.empty(LC)\n",
      "ASE = 0\n",
      "SPACE = numpy.empty(3*LA)\n",
      "(A, LC, C, ASE, SPACE) = ER.SHAPE(LB, B, LD, D, LA, A, LC, C, ASE, SPACE)\n",
      "print(A)\n",
      "print(LC)\n",
      "print(C)\n",
      "print(ASE)\n",
      "print(SPACE)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.58119532  0.7912008 ]\n",
        "4\n",
        "[ 0.58119532 -0.23617816 -0.49048797  1.23625125]\n",
        "0.783753627953\n",
        "[ 6.56616954 -4.52973125  0.2323      2.5625      1.          0.68985901]\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LB = 5\n",
      "B = numpy.array([2.0, 1.0, 0.0, -1.0, -2.0])\n",
      "LD = 5\n",
      "D = numpy.array([2.0, 1.0, 0.0, 1.0, 2.0])\n",
      "LA = 2\n",
      "A = numpy.empty(LA)\n",
      "LC = 6\n",
      "C = numpy.empty(LC)\n",
      "ASE = 0\n",
      "SPACE = numpy.empty(3*LA)\n",
      "(A, LC, C, ASE, SPACE) = ER.SHAPE(LB, B, LD, D, LA, A, LC, C, ASE, SPACE)\n",
      "print(A)\n",
      "print(LC)\n",
      "print(C)\n",
      "print(ASE)\n",
      "print(SPACE)\n",
      "plot(B, '.-', label=\"B\")\n",
      "plot(D, '.-', label=\"D\")\n",
      "plot(A, '.-', label=\"A\")\n",
      "plot(C, '.-', label=\"C\")\n",
      "legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.  0.]\n",
        "6\n",
        "[ 0.  0.  0.  0.  0.  0.]\n",
        "1.0\n",
        "[ 10.    4.    0.    0.    1.   -0.4]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "<matplotlib.legend.Legend at 0x10f047ed0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dP0jx81FApbuPTb6fCzS5+7wUY/WPgohIGjq7vDLtJQlmNha4BbgwVZFPehEo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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10efdf210>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "S = numpy.empty(3)\n",
      "I = ER.IMPULS(3, S, 0)\n",
      "print(I)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 1.  0.  0.]\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imp.reload(ER)\n",
      "LB = 5\n",
      "B = numpy.array([2.0, 1, 0, 1, 2])\n",
      "LA = 8\n",
      "A = numpy.empty((LA, ))\n",
      "INDEX = 0\n",
      "ASE = 0\n",
      "S = numpy.empty((2 * (LA + LB - 1) + 3 * LA, ))\n",
      "(A, INDEX, ASE, S) = ER.SPIKE(LB, B, LA, A, INDEX, ASE, S)\n",
      "print(numpy.round(A, 2))\n",
      "print(INDEX)\n",
      "print(ASE)\n",
      "print(S)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "12\n",
        "[ 0.36 -0.15  0.08 -0.15 -0.11  0.09 -0.04  0.11]\n",
        "0\n",
        "0.286100877654\n",
        "[  2.86100878e-01   3.50219657e-01   3.51498807e-01   3.59083486e-01\n",
        "   3.04580577e-01   3.48516596e-01   3.48516596e-01   3.04580577e-01\n",
        "   3.59083486e-01   3.51498807e-01   3.50219657e-01   2.86100878e-01\n",
        "   7.13899122e-01   5.08211818e-02  -1.07337361e-03   1.26608960e-01\n",
        "   1.97385807e-01  -1.48977398e-01   1.22575930e-02  -2.31430660e-01\n",
        "  -7.18177786e-03   1.46439491e-01   3.02379914e-02   2.19902553e-01\n",
        "   1.00000000e+01   4.00000000e+00   1.00000000e+00   4.00000000e+00\n",
        "   4.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
        "   2.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
        "   0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
        "   1.00000000e+00  -4.28811816e-01   2.12902371e-01  -4.29102656e-01\n",
        "  -2.94552990e-01   2.60955824e-01  -1.11659032e-01   3.08030289e-01]\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imp.reload(ER)\n",
      "LB = 5\n",
      "B = numpy.array([2.0, 1, 0, -1, -2])\n",
      "LA = 8\n",
      "A = numpy.empty((LA, ))\n",
      "INDEX = 0\n",
      "ASE = 0\n",
      "S = numpy.empty((2 * (LA + LB - 1) + 3 * LA, ))\n",
      "(A, INDEX, ASE, S) = ER.SPIKE(LB, B, LA, A, INDEX, ASE, S)\n",
      "print(numpy.round(A, 2))\n",
      "print(INDEX)\n",
      "print(ASE)\n",
      "print(S)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "12\n",
        "[ 0.36 -0.16  0.07  0.1   0.11 -0.12  0.11  0.03]\n",
        "0\n",
        "0.286273055126\n",
        "[  0.28627306   0.2889933    0.38069828   0.38787973   0.32764258\n",
        "   0.32851306   0.32851306   0.32764258   0.38787973   0.38069828\n",
        "   0.2889933    0.28627306   0.71372694   0.03168676  -0.01562576\n",
        "  -0.09072931  -0.22506502   0.13640639  -0.12919361  -0.13866502\n",
        "  -0.08752931   0.11557424  -0.25631324  -0.05427306  10.           4.          -1.\n",
        "  -4.          -4.           0.           0.           0.           2.           0.\n",
        "   0.           0.           0.           0.           0.           0.           1.\n",
        "  -0.45560381   0.20590871   0.26992516   0.32189781  -0.3224799\n",
        "   0.32109859   0.07604176]\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "SPIKER"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imp.reload(ER)\n",
      "H = 1\n",
      "LF = 2\n",
      "F = numpy.array([1, 0.9, 0.5])\n",
      "R = numpy.array([1, 0.4, 0.1])\n",
      "A = numpy.array([1, 2, 3.])\n",
      "F = ER.SIDE(H, LF, F, A, R)\n",
      "print(F)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "('A', array([ 1.,  2.,  3.]), 'R', array([ 1. ,  0.4,  0.1]))\n",
        "[ 0.73333333  0.66666667  0.5       ]\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imp.reload(ER)\n",
      "LB = 7\n",
      "B = numpy.array([50., -65, 28, 68, 6, -9, -2])\n",
      "LA = 5\n",
      "A = numpy.empty(LA)\n",
      "SPACE = numpy.empty(3*LA)\n",
      "LC = 11\n",
      "C = numpy.zeros(LC)\n",
      "INDEX = 0.\n",
      "ERRORS = numpy.zeros(LC)\n",
      "(A, LC, C, INDEX, ERRORS) = ER.SPIKER(LB, B, LA, A, LC, C, INDEX, ERRORS, SPACE)\n",
      "print(\"A\", A)\n",
      "print(\"C\", C)\n",
      "print(\"INDEX\", INDEX)\n",
      "print(\"ERRORS\", ERRORS)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "('A', array([ 0.0036256 ,  0.00613757,  0.0051449 , -0.0023822 ,  0.00343802]))\n",
        "('C', array([ 0.18127979,  0.07121472, -0.04018015, -0.03513607,  0.90990932,\n",
        "        0.06387563, -0.09734468,  0.16091294,  0.03177808, -0.02617779,\n",
        "       -0.00687604]))\n",
        "('INDEX', 4)\n",
        "('ERRORS', array([ 0.75242232,  0.50181613,  0.44040022,  0.20884637,  0.09009068,\n",
        "        0.18396038,  0.29772128,  0.54482154,  0.98980966,  0.99050756,\n",
        "        0.99960388]))\n"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "SHAPER"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imp.reload(ER)\n",
      "LB = 7\n",
      "B = numpy.array([50, -65, 28, 68, 6, -9, -2.0])\n",
      "LD = 5\n",
      "D = numpy.array([0.5, 0.8, 1.0, 0.8, 0.5])\n",
      "LA = 5\n",
      "A = numpy.empty(LA)\n",
      "LC = LA + LB - 1\n",
      "LCD = LC + LD - 1\n",
      "C = numpy.empty(LCD)\n",
      "INDEX = 0\n",
      "ERRORS = numpy.empty(LCD)\n",
      "S = numpy.empty(3 * LA)\n",
      "(A, LC, C, INDEX, ERRORS, S) = ER.SHAPER(LB, B, LD, D, LA, A, LC, C, INDEX, ERRORS, S)\n",
      "print(\"A\", A)\n",
      "print(\"LC\", LC)\n",
      "print(\"C\", C)\n",
      "print(\"INDEX\", INDEX)\n",
      "print(\"ERRORS\", ERRORS)\n",
      "print(\"S\", S)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "('LB', 7, 'B', array([ 50., -65.,  28.,  68.,   6.,  -9.,  -2.]), 'LD', 5, 'D', array([ 0.5,  0.8,  1. ,  0.8,  0.5]), 'LA', 5, 'A', array([  0.00000000e+000,   0.00000000e+000,   2.22952674e-314,\n",
        "         2.22954059e-314,   2.22955685e-314]), 'LC', 11, 'C', array([  0.00000000e+000,  -1.49166824e-154,   2.12422995e-314,\n",
        "         2.12358798e-314,   2.15765902e-314,   2.12358727e-314,\n",
        "         2.12358777e-314,   2.12358753e-314,   2.15766246e-314,\n",
        "         2.22948165e-314,   2.14006582e-314,   2.14006561e-314,\n",
        "         2.14006582e-314,   2.12595289e-314,   2.14202613e-314]), 'INDEX', 0, 'ERRORS', array([  0.00000000e+000,   2.31584192e+077,   2.12342047e-314,\n",
        "         2.23332812e-314,   2.12343414e-314,   2.23432050e-314,\n",
        "         2.23432052e-314,   2.23421541e-314,   2.12343412e-314,\n",
        "         2.23421539e-314,   2.12423420e-314,   2.12438194e-314,\n",
        "         2.12358753e-314,   2.15767028e-314,   2.22954047e-314]), 'S', array([  0.00000000e+000,  -1.49166824e-154,   2.23431884e-314,\n",
        "         2.12423540e-314,   2.23077597e-314,   2.12423479e-314,\n",
        "         2.23332816e-314,   2.12349960e-314,   2.23433376e-314,\n",
        "         2.23433643e-314,   2.23603647e-314,   2.23603676e-314,\n",
        "         2.23603614e-314,   2.23603496e-314,   2.23603662e-314]))\n",
        "('A', array([ 0.00595509,  0.01171018,  0.01337438,  0.01123284,  0.0060089 ]))\n",
        "('LC', 11)\n",
        "('C', array([ 0.29775463,  0.19842804,  0.0742997 ,  0.42513875,  0.77681577,\n",
        "        0.85006413,  0.8950266 ,  0.33221225, -0.09179092, -0.07654575,\n",
        "       -0.01201779]))\n",
        "('INDEX', 7)\n",
        "('ERRORS', array([ 1.        ,  0.9430037 ,  0.93983685,  0.93686295,  0.82800499,\n",
        "        0.61881635,  0.27428104,  0.15039171,  0.19931083,  0.51779723,\n",
        "        0.78541842,  0.9746531 ,  0.99606017,  0.99850888,  0.99996438]))\n",
        "('S', array([  1.22540000e+04,  -2.79400000e+03,  -3.47600000e+03,\n",
        "         2.62200000e+03,   8.29000000e+02,   2.82000000e+01,\n",
        "         6.62000000e+01,   5.82000000e+01,   5.84000000e+01,\n",
        "         3.14000000e+01,   1.00000000e+00,   2.92331904e-01,\n",
        "         3.18098084e-01,  -7.13408398e-02,  -5.62356470e-02]))\n"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(B / 60, label=\"B / 60\")\n",
      "plot(D, label=\"D\")\n",
      "plot(C, label=\"C\")\n",
      "plot(A * 60, label=\"A * 60\")\n",
      "legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "<matplotlib.legend.Legend at 0x10dac6bd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10db35850>"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}