WIENER.ipynb

{
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  "name": "",
  "signature": "sha256:349255c9de8aeeaefa3bb1ce1c1cd117c123f202a9b4f28800766f61eaba9b00"
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  {
   "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 ERpy\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": "code",
     "collapsed": false,
     "input": [
      "X0 = ER.sunspot_X;\n",
      "(ETA1, AV1) = ER.REMAV(100, X0[0:100]);\n",
      "(ETA2, AV2) = ER.REMAV(100, X0[100:200]);\n",
      "(ETA3, AV3) = ER.REMAV(100, X0[200:300]);\n",
      "print(ETA1[:10])\n",
      "print(ETA2[:10])\n",
      "print(ETA3[:10])\n",
      "X1 = hstack((ETA1, ETA2, ETA3))\n",
      "X1 = ER.MATRAN(100, 3, X1)\n",
      "print(X1[:10])\n",
      "print(X1[-10:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[  54.06   35.06   19.06  -11.94  -15.94  -39.94  -26.94   45.06  107.06\n",
        "   79.06]\n",
        "[ 91.57  49.57 -60.43 -53.43 -63.43 -63.43 -51.43  22.57  38.57 -43.43]\n",
        "[-31.97 -35.97 -31.97  99.03 -34.97 -97.97  23.03 -97.97  15.03 -70.97]\n",
        "[ 54.06  91.57 -31.97  35.06  49.57 -35.97  19.06 -60.43 -31.97 -11.94]\n",
        "[-33.97 -39.94  25.57 -16.97  -9.94  38.57  64.03  27.06  46.57  39.03]\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imp.reload(ER)\n",
      "N = 3\n",
      "LX = 99\n",
      "M = 3\n",
      "X = X1[:-3].copy()\n",
      "LZ = 99\n",
      "LR = 3\n",
      "LW = 0\n",
      "Z = X1[3:].copy()\n",
      "FLOOR = 0.0\n",
      "LF = 1\n",
      "F = empty(M * N * LR)\n",
      "E = empty(LR)\n",
      "LY = LX + LF - 1\n",
      "Y = empty(M * LY)\n",
      "S = empty(N * N * (5 * LR + 6) + M * N * (LR + 2) + 2 * M * M)\n",
      "(LF, F, E, LY, Y, S) = ER.WIENER_1(N, LX, X, M, LZ, Z, LR, LW, FLOOR, LF, F, E, LY, Y, S)\n",
      "print(numpy.round(F, 2))\n",
      "%time ER.WIENER_1(N, LX, X, M, LZ, Z, LR, LW, FLOOR, LF, F, E, LY, Y, S)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 1.38  0.06 -0.17  0.03  0.83 -0.03  0.02  0.06 -0.12 -0.77 -0.   -0.22\n",
        " -0.02 -0.23  0.19 -0.03  0.05 -0.07  0.05 -0.06  0.    0.06  0.11  0.22\n",
        " -0.01 -0.17 -0.02]\n",
        "CPU times: user 27.5 ms, sys: 5.74 ms, total: 33.2 ms\n",
        "Wall time: 29.2 ms\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "(3, array([  1.38404899e+00,   5.93240498e-02,  -1.71114808e-01,\n",
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        "         -3.18012562e-02,   4.85773979e-02,  -6.68406090e-02,\n",
        "          5.18970795e-02,  -5.80693211e-02,   5.66708127e-04,\n",
        "          5.65153055e-02,   1.08462075e-01,   2.17539037e-01,\n",
        "         -1.36975236e-02,  -1.73100464e-01,  -1.54069790e-02]), array([ 0.60412419,  0.53887706,  0.51738142]), 101, array([ 77.51732801,  76.89792287,  -8.43349986, ...,   0.        ,\n",
        "          0.        ,   0.        ]), array([  1.37643396e+05,   8.09023958e+04,  -1.15313318e+04,\n",
        "          8.09023958e+04,   2.62199745e+05,   1.18866629e+04,\n",
        "         -1.15313318e+04,   1.18866629e+04,   1.96319569e+05,\n",
        "          1.11852753e+05,   5.78486016e+04,  -2.06941136e+04,\n",
        "          7.91593016e+04,   1.83356890e+05,   1.88327458e+04,\n",
        "          3.72820640e+03,   2.06834358e+04,  -5.53039180e+03,\n",
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        "         -9.52051128e-02,   1.11925086e-01,  -9.98750984e-02,\n",
        "         -3.63023342e-02,  -2.24500910e-01,   3.63544645e-02,\n",
        "          1.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
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        "         -6.15923677e-02,  -6.47436466e-02,   5.14080013e-02,\n",
        "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
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        "          0.00000000e+00,   1.00000000e+00,   0.00000000e+00,\n",
        "          0.00000000e+00,   0.00000000e+00,   1.00000000e+00,\n",
        "         -8.26839543e-01,  -2.09871053e-01,   2.83034117e-02,\n",
        "          3.20063254e-02,  -6.31371383e-01,  -8.92146974e-02,\n",
        "          5.49059074e-02,  -7.00283717e-02,   3.52345561e-02,\n",
        "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
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        "          5.86679565e+03,   6.85731567e+03,   1.74417797e+05,\n",
        "          2.53830042e+04,   1.37954510e+04,  -1.18959967e+04,\n",
        "          1.37954510e+04,   1.13758639e+05,   2.79865989e+03,\n",
        "         -1.18959967e+04,   2.79865989e+03,   1.92361906e+05,\n",
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        "         -9.52051128e-02,   1.11925086e-01,  -9.98750984e-02,\n",
        "         -3.63023342e-02,  -2.24500910e-01,   3.63544645e-02,\n",
        "          1.11583776e+05,   5.73856958e+04,  -2.10820718e+04,\n",
        "          8.02030058e+04,   1.85153095e+05,   2.03381329e+04,\n",
        "          5.46085820e+03,   2.36653129e+04,  -3.03130090e+03,\n",
        "          5.92392728e+04,   3.48704216e+04,  -1.49596136e+04,\n",
        "          6.15817416e+04,   1.09748180e+05,   4.94824958e+04,\n",
        "          8.30796640e+03,   2.79713458e+04,  -4.83514180e+03,\n",
        "          9.58652920e+03,   2.11318474e+04,  -3.89149540e+03,\n",
        "          4.25123774e+04,   7.61273753e+04,   5.36559687e+04,\n",
        "          7.36841460e+03,  -1.30821513e+04,   1.11832730e+03,\n",
        "          5.18970795e-02,  -5.80693211e-02,   5.66708127e-04,\n",
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        "         -3.09408212e+03,  -3.23035942e+04,  -2.36163963e+03,\n",
        "          1.35453156e+05,   7.72123058e+04,  -8.74688180e+03,\n",
        "          7.72123058e+04,   2.55983445e+05,   1.66317829e+04,\n",
        "         -8.74688180e+03,   1.66317829e+04,   1.96820829e+05,\n",
        "          1.12941738e+05,   6.19701195e+04,  -1.49065798e+04,\n",
        "          6.19701195e+04,   1.42983000e+05,   9.50471619e+03,\n",
        "         -1.49065798e+04,   9.50471619e+03,   2.79792263e+04]))"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imp.reload(ER)\n",
      "N = 2\n",
      "LX = 2\n",
      "M = 2\n",
      "X = array([1, 11, 2, 12, 3, 13, 4, 14])\n",
      "LZ = 2\n",
      "LR = 2\n",
      "LW = 0\n",
      "Z = array([2, 12, 3, 13, 4, 14, 5, 15])\n",
      "FLOOR = 0.0\n",
      "LF = 1\n",
      "F = empty(M * N * LR)\n",
      "E = empty(LR)\n",
      "LY = LX + LF - 1\n",
      "Y = empty(M * LY)\n",
      "S = empty(N * N * (5 * LR + 6) + M * N * (LR + 2) + 2 * M * M)\n",
      "(LF, F, E, LY, Y, S) = ER.WIENER_1(N, LX, X, M, LZ, Z, LR, LW, FLOOR, LF, F, E, LY, Y, S)\n",
      "print(F)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.9 -0.1  0.1  1.1  0.   0.   0.   0. ]\n"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<h2> WIENER_2 </h2>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imp.reload(ER)\n",
      "N = 3\n",
      "LX = 99\n",
      "M = 3\n",
      "X = X1[:-3].copy()\n",
      "LZ = 99\n",
      "LR = 3\n",
      "LW = 0\n",
      "Z = X1[3:].copy()\n",
      "FLOOR = 0.0\n",
      "LF = 10\n",
      "F = empty(M * N * LR)\n",
      "E = empty(LR)\n",
      "LY = LX + LF - 1\n",
      "Y = empty(M * LY)\n",
      "S = empty(N * N * (5 * LR + 6) + M * N * (LR + 2) + 2 * M * M)\n",
      "(LF, F, E, LY, Y, S) = ER.WIENER_2(N, LX, X, M, LZ, Z, LR, LW, FLOOR, LF, F, E, LY, Y, S)\n",
      "print(numpy.round(F, 2))\n",
      "ER.RITE(3, 3, 3, 3, numpy.round(F, 2))\n",
      "print(\"E\", numpy.round(E, 2))\n",
      "%time ER.WIENER_2(N, LX, X, M, LZ, Z, LR, LW, FLOOR, LF, F, E, LY, Y, S)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 1.38  0.06 -0.17  0.03  0.83 -0.03  0.02  0.06 -0.12 -0.77 -0.   -0.22\n",
        " -0.02 -0.23  0.19 -0.03  0.05 -0.07  0.05 -0.06  0.    0.06  0.11  0.22\n",
        " -0.01 -0.17 -0.02]\n",
        "[[1.38  0.03  0.02] [[-0.77  -0.02  -0.03] [[0.05  0.06  -0.01] \n",
        " [0.06  0.83  0.06]  [-0.0  -0.23  0.05]  [-0.06  0.11  -0.17] \n",
        " [-0.17  -0.03  -0.12]] [-0.22  0.19  -0.07]] [0.0  0.22  -0.02]]\n",
        "\n",
        "('E', array([ 0.6 ,  0.54,  0.52]))\n",
        "CPU times: user 27.1 ms, sys: 7.5 ms, total: 34.6 ms\n",
        "Wall time: 29.4 ms\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "(3, array([  1.38404899e+00,   5.93240498e-02,  -1.71114808e-01,\n",
        "          3.49283241e-02,   8.26741081e-01,  -3.31784718e-02,\n",
        "          1.57255878e-02,   6.29908053e-02,  -1.20585528e-01,\n",
        "         -7.72702563e-01,  -2.43382126e-03,  -2.16933247e-01,\n",
        "         -1.85696123e-02,  -2.32841882e-01,   1.91174169e-01,\n",
        "         -3.18012562e-02,   4.85773979e-02,  -6.68406090e-02,\n",
        "          5.18970795e-02,  -5.80693211e-02,   5.66708127e-04,\n",
        "          5.65153055e-02,   1.08462075e-01,   2.17539037e-01,\n",
        "         -1.36975236e-02,  -1.73100464e-01,  -1.54069790e-02]), array([ 0.60412419,  0.53887706,  0.51738142]), 101, array([  7.75173280e+001,   7.68979229e+001,  -8.43349986e+000,\n",
        "          7.23447142e+000,   1.77897544e+001,   4.60882099e+000,\n",
        "          5.31752419e+000,  -5.18910275e+001,   2.73169738e+001,\n",
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        "         -4.94239465e+001,  -7.01155143e+001,   1.26612421e+000,\n",
        "         -7.49830614e+000,  -3.24603669e+001,  -6.64312455e+000,\n",
        "          7.83366144e+001,   4.07190330e+001,  -1.44844659e+001,\n",
        "          1.13018158e+002,   2.10610305e+001,  -3.18816047e+001,\n",
        "          2.78261711e+001,  -2.74067834e+001,  -1.39405272e+001,\n",
        "          3.52995043e+000,   3.32442673e+001,  -2.12386698e+001,\n",
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        "          9.95374224e-001,   7.87200533e+001,   2.46887787e+001,\n",
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        "          5.91713902e+001,  -6.13356799e+001,   1.86052989e+001,\n",
        "          3.99994865e+001,  -2.45366474e+001,  -2.13579470e+001,\n",
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        "         -8.11283850e+000,  -3.99400201e+001,  -3.95334938e+001,\n",
        "         -4.92952976e-001,  -4.91936796e+001,  -2.79642270e+001,\n",
        "         -1.36586348e+001,  -5.21369072e+001,  -1.61297837e+001,\n",
        "         -8.23685295e+000,  -1.15777662e+001,  -1.72843029e+001,\n",
        "         -3.56692730e+001,  -3.54770360e+001,  -8.14341725e+000,\n",
        "         -2.46427015e+001,  -3.60020376e+001,  -2.62705949e+000,\n",
        "         -3.75665599e+001,  -2.04456562e+001,  -4.10695622e+000,\n",
        "         -3.30248301e+001,  -3.86411567e+001,   2.15324472e+000,\n",
        "         -2.56600184e+001,  -1.74227183e+001,   3.80910542e-001,\n",
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        "          3.95252517e-323,   3.95252517e-323,   3.95252517e-323,\n",
        "          2.12199580e-314,   2.12358761e-314,   1.06099790e-313,\n",
        "          1.06099790e-313,   1.06099790e-313,   1.06099790e-313,\n",
        "          1.06099790e-313,   2.12199579e-314,   2.17591698e-314,\n",
        "          2.17591701e-314,   3.95252517e-323,   3.95252517e-323,\n",
        "          3.95252517e-323,   4.94065646e-324,   2.17591707e-314]))"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(3): \n",
      "    print(Y[i+3::3].size)\n",
      "    subplot(3, 1, i+1)\n",
      "    plot(arange(99), X[i::3], '.-')\n",
      "    plot(arange(1, 108), Y[i+3::3], '.-')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "107\n",
        "107"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "107"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qHPS6eHcht/AchqCMOrPdns0/S35xPr7OfowdW7+1qDLKImKf2TWeI4Oj2HGg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\nwMAAhjIwMDAwMADw//cVm2VWx58vAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e5b17d0>"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imp.reload(ER)\n",
      "N = 3\n",
      "LX = 99\n",
      "M = 3\n",
      "X = X1[:-3].copy()\n",
      "LZ = 99\n",
      "LR = 3\n",
      "LW = 10\n",
      "Z = X1[3:].copy()\n",
      "FLOOR = 0.0\n",
      "LF = 10\n",
      "F = empty(M * N * LR)\n",
      "E = empty(LR)\n",
      "LY = LX + LF - 1\n",
      "Y = empty(M * LY)\n",
      "S = empty(N * N * (5 * LR + 6) + M * N * (LR + 2) + 2 * M * M)\n",
      "\n",
      "LW = 0\n",
      "(LF, F, E, LY, Y, S) = ER.WIENER_2(N, LX, X, M, LZ, Z, LR, LW, FLOOR, LF, F, E, LY, Y, S)\n",
      "print(\"LW, LF, E\", LW, LF, E)\n",
      "\n",
      "LW = 10\n",
      "(LF, F, E, LY, Y, S) = ER.WIENER_2(N, LX, X, M, LZ, Z, LR, LW, FLOOR, LF, F, E, LY, Y, S)\n",
      "print(\"LW, LF, E\", LW, LF, E)\n",
      "\n",
      "LW = 20\n",
      "(LF, F, E, LY, Y, S) = ER.WIENER_2(N, LX, X, M, LZ, Z, LR, LW, FLOOR, LF, F, E, LY, Y, S)\n",
      "print(\"LW, LF, E\", LW, LF, E)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "('LW, LF, E', 0, 3, array([ 0.60412419,  0.53887706,  0.51738142]))\n",
        "('LW, LF, E', 10, 3, array([ 0.60412419,  0.56404364,  0.55239124]))\n",
        "('LW, LF, E', 20, 3, array([ 0.60412419,  0.55292823,  0.53687181]))"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}