gistfile1.txt

{
 "metadata": {
  "name": "",
  "signature": "sha256:8f9e8b1dbcf53f6b2ac76f34bdff851edd2215c9bc8fcd57bf9d69bf7ba5bf33"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Write linear model to generate gene expression data from SNPs\n",
      "%pylab inline\n",
      "\n",
      "import numpy as np\n",
      "import GPy\n",
      "import pylab as pb\n",
      "#Columns are samples\n",
      "X = np.genfromtxt('ALLsnpspanama.csv', delimiter=',')[1:,1:]\n",
      "#Y = WX + N  , W is sparse\n",
      "print X.shape\n",
      "#Use one SNP, MKT1\n",
      "\n",
      "\n",
      "#parse SNPs\n",
      "#(8000,13000)\n",
      "ng=50\n",
      "W=np.zeros((ng,13314))\n",
      "W[:,[1000]]=5\n",
      "W[:,[9000]]=4\n",
      "W[:,[7000]]=3\n",
      "W[:,[13000]]=2\n",
      "W[:,[5]]=1\n",
      "\n",
      "Y=np.dot(W,X) + np.random.normal(size=(ng,182))\n",
      "\n",
      "print W[0, 1000]\n",
      "print W[0, 999]\n",
      "print np.dot(W,X).shape\n",
      "print np.random.normal(size=(1,182)).shape\n",
      "pb.matshow(Y)\n",
      "pb.colorbar()\n",
      "print Y.shape\n",
      "pb.matshow(X[1000,:].reshape((1,182)))\n",
      "pb.colorbar()\n",
      "pb.matshow(X[453,:].reshape((1,182)))\n",
      "pb.colorbar()\n",
      "#fe=open(\"ALLexprpanama.csv\",\"r\")\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        "(13314, 182)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "5.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.0\n",
        "(50, 182)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(1, 182)\n",
        "(50, 182)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['step']\n",
        "`%matplotlib` prevents importing * from pylab and numpy\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0x45b0488>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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T3ZJwbGltc6dxy5MlpWcjgIwEtxyn7bga5ziLrZfVEnzYjAzehSgJHnM32/dR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\nHmAP9oZhGIZhGIZRD/i/VDT6r3Meu/YAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x54a3c90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x4456bd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x4460b90>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Do GPy ML based regression\n",
      "#Start with no SNPs, add SNPs by greedy forward selection - add best SNP at each step, is there a maxima?\n",
      "#Y ~ N(0, s^2 XX^T + e^2 I )\n",
      "\n",
      "X=X.transpose()\n",
      "Y=Y.transpose()\n",
      "print X.shape\n",
      "#Y=Y.reshape((182,1))\n",
      "print Y.shape\n",
      "chosenSNPS=[]\n",
      "X2=np.zeros((182,0))\n",
      "print X2.shape\n",
      "print X2.dtype\n",
      "k = GPy.kern.linear(input_dim=0)\n",
      "m = GPy.models.GPRegression(X2,Y,k)\n",
      "m.optimize(messages=False)\n",
      "#m['.*variance']=1\n",
      "#m['linear_variance']=10000\n",
      "bestll=m.log_likelihood()\n",
      "print m\n",
      "print bestll\n",
      "#provides initial parameters for 0 SNPS\n",
      "oldparams=m['.*variance']\n",
      "for step in range(100):\n",
      "    hasbeenadded=False\n",
      "    #bestll=np.inf\n",
      "    bestSNP=np.nan\n",
      "    X2=np.hstack([X2, np.zeros((182, 1))])\n",
      "    tempbestll=bestll\n",
      "    for SNP in np.array(list(set(range(X.shape[1])).difference(set(chosenSNPS)))):\n",
      "        X2[:,-1]=X[:,SNP]\n",
      "        m.X=X2\n",
      "        m.kern.X=X2\n",
      "        m.input_dim=step+1\n",
      "        m.kern.input_dim=step+1\n",
      "        #m['.*variance']=1\n",
      "        #m['linear_variance']=10000\n",
      "        #m.optimize(messages=False)\n",
      "        #print m\n",
      "        m.update_likelihood_approximation()\n",
      "        #print m.log_likelihood()\n",
      "        if m.log_likelihood() > tempbestll:\n",
      "            hasbeenadded=True\n",
      "            tempbestll=m.log_likelihood()\n",
      "            bestSNP=SNP\n",
      "            #print SNP\n",
      "    if hasbeenadded==False:\n",
      "        print \"converged: \" + str(len(chosenSNPS)) + \"SNPs\" \n",
      "        X2=X2[:,:-1]\n",
      "        break\n",
      "    X2[:,-1]=X[:,bestSNP]\n",
      "\n",
      "    #print \"X2\" + str(X2.shape)\n",
      "    k = GPy.kern.linear(input_dim=step+1)\n",
      "    tempm = GPy.models.GPRegression(X2,Y,k)\n",
      "    tempm.optimize(messages=False)\n",
      "    print tempm.log_likelihood()\n",
      "    if tempm.log_likelihood()>bestll:\n",
      "        bestll=tempm.log_likelihood()\n",
      "        m=tempm\n",
      "        oldparams=m['.*variance']\n",
      "        chosenSNPS.append(bestSNP)\n",
      "    else:\n",
      "        print \"converged: \" + str(len(chosenSNPS)) + \"SNPs\" \n",
      "        X2=X2[:,:-1]\n",
      "        break\n",
      "    #print X2.shape\n",
      "    \n",
      "pb.matshow(X2[:,:].transpose())\n",
      "pb.colorbar()\n",
      "pb.show()\n",
      "\n",
      "#m.plot()\n",
      "#print m\n",
      "\n",
      "#print m.predict(np.array([1]))\n",
      "#print m.predict(np.array([2]))\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(182, 13314)\n",
        "(182, 50)\n",
        "(182, 0)\n",
        "float64\n",
        "\n",
        "Log-likelihood: -3.204e+04\n",
        "\n",
        "       Name        |   Value   |  Constraints  |  Ties  |  prior  \n",
        "------------------------------------------------------------------\n",
        "  linear_variance  |  1.0000   |     (+ve)     |        |         \n",
        "  noise_variance   |  66.9786  |     (+ve)     |        |         \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-32042.2361747\n",
        "-26785.1594263"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-23648.756253"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-21001.299449"
       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-18513.0858872"
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-15453.0123087"
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       "output_type": "stream",
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       "text": [
        "\n",
        "-14631.1852232"
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       "text": [
        "\n",
        "-13993.1987934"
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       "text": [
        "\n",
        "-13964.8796738"
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        "\n",
        "-13948.6350845"
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        "\n",
        "-13936.4794245"
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       "stream": "stdout",
       "text": [
        "\n",
        "-13926.1135083"
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        "\n",
        "-13918.6647884"
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        "\n",
        "-13909.8027906"
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        "\n",
        "-13893.4356149"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-13891.9986395"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "converged: 18SNPs"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x4467dd0>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print chosenSNPS\n",
      "print m\n",
      "pb.matshow(X[:,chosenSNPS].transpose())\n",
      "pb.colorbar()\n",
      "pb.show()\n",
      "pb.matshow(X[:,1000].reshape(1,182))\n",
      "pb.colorbar()\n",
      "pb.show()\n",
      "#print m.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[987, 9008, 7000, 998, 12992, 0, 8996, 999, 8998, 1000, 7001, 1001, 9000, 1002, 9001, 7002, 12993, 7003]\n",
        "\n",
        "Log-likelihood: -1.398e+04\n",
        "\n",
        "       Name        |  Value   |  Constraints  |  Ties  |  prior  \n",
        "-----------------------------------------------------------------\n",
        "  linear_variance  |  2.1460  |     (+ve)     |        |         \n",
        "  noise_variance   |  1.0260  |     (+ve)     |        |         \n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bcxfyVcrXKZNirsGfq65C2M3/kc7aONioMp0PxZwHZZfbeR/KaVx5qc83J18L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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x54c1090>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x4467790>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Compute the biased estimator of HSIC.\n",
      "# @param x The data.\n",
      "# @param y The labels.\n",
      "# @param kernelx The kernel on the data, default to linear kernel.\n",
      "# @param kernely The kernel on the labels, default to linear kernel.\n",
      "#\n",
      "\n",
      "def BiasedHSIC(x, y, kernelx, kernely):\n",
      "    #nx = kernelx.input_dim\n",
      "    #ny = kernely.input_dim\n",
      "    #print x.shape\n",
      "    nx=float(x.shape[0])\n",
      "    ny=float(y.shape[0])\n",
      "    \n",
      "    assert nx == ny, \\\n",
      "           \"Argument 1 and 2 have different number of data points\"\n",
      "\n",
      "    kMat=kernelx.K(x)\n",
      "    #if len(nx) > 1:\n",
      "    #    kMat = kernelx.Dot(x, x)\n",
      "    #else:\n",
      "    #    kMat = numpy.outerproduct(x, x)\n",
      "\n",
      "    #print kMat\n",
      "    hlhMat = ComputeHLH(y, kernely)\n",
      "    #print hlhMat\n",
      "    return numpy.sum(numpy.sum(kMat * hlhMat)) / ((nx-1)*(nx-1))\n",
      "        \n",
      "## Compute HLH give the labels.\n",
      "# @param y The labels.\n",
      "# @param kernely The kernel on the labels, default to linear kernel.\n",
      "#\n",
      "def ComputeHLH(y, kernely):\n",
      "    #ny = kernely.input_dim\n",
      "    ny=float(y.shape[0])\n",
      "    #if len(ny) > 1:\n",
      "    #    lMat = kernely.Dot(y, y)\n",
      "    #else:\n",
      "    #    lMat = numpy.outerproduct(y, y)\n",
      "    #print y.shape\n",
      "    lMat=kernely.K(y)\n",
      "    #print lMat\n",
      "    sL = numpy.sum(lMat, axis=1)\n",
      "    ssL = numpy.sum(sL)\n",
      "    # hlhMat\n",
      "    return lMat - numpy.add.outer(sL, sL)/ny + ssL/(ny*ny)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def calcHSIC(x,y,kernx,kerny):\n",
      "    kernx=kernelx2.K(x)\n",
      "    kerny=kernely2.K(y)\n",
      "    m=x.shape[0]\n",
      "    H=np.eye(m)-(m**(-1))\n",
      "    print H.shape\n",
      "    HSIC=((m-1)**(-2))*np.trace(np.dot(kernx,np.dot(H, np.dot(kerny, H))))\n",
      "    return HSIC"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Do GPy ML based regression\n",
      "#Start with no SNPs, add SNPs by greedy forward selection - add best SNP at each step, is there a maxima?\n",
      "#Y ~ N(0, s^2 XX^T + e^2 I )\n",
      "\n",
      "#X=X.transpose()\n",
      "#Y=Y.transpose()\n",
      "#print X.shape\n",
      "#print Y.shape\n",
      "chosenSNPS2=[]\n",
      "X2=np.zeros((182,0))\n",
      "#print X2.shape\n",
      "#print X2.dtype\n",
      "\n",
      "Y=Y.transpose()\n",
      "#X=X.transpose()\n",
      "print X2.shape\n",
      "print Y.shape\n",
      "k2 = GPy.kern.linear(input_dim=0)\n",
      "m2 = GPy.models.GPRegression(X2,Y,k2)\n",
      "m2.optimize(messages=False)\n",
      "#m['.*variance']=1\n",
      "#m['linear_variance']=10000\n",
      "bestll=m2.log_likelihood()\n",
      "X=X.transpose()\n",
      "oldHSIC=0\n",
      "#print m\n",
      "#print bestll\n",
      "#provides initial parameters for 0 SNPS\n",
      "oldparams=m['.*variance']\n",
      "kernelx2 = GPy.kern.linear(input_dim=ng)\n",
      "kernely2 = GPy.kern.linear(input_dim=X.shape[1])\n",
      "\n",
      "#print X.shape\n",
      "for step in range(500):\n",
      "    hasbeenadded=False\n",
      "    #bestll=np.inf\n",
      "    bestSNP=np.nan\n",
      "    X2=np.hstack([X2, np.zeros((182, 1))])\n",
      "    tempbestHSIC=oldHSIC\n",
      "\n",
      "    for SNP in np.array(list(set(range(X.shape[1])).difference(set(chosenSNPS2)))):\n",
      "        X2[:,-1]=X[:,SNP]\n",
      "        m2.X=X2\n",
      "        m2.kern.X=X2\n",
      "        m2.input_dim=step+1\n",
      "        m2.kern.input_dim=step+1\n",
      "\n",
      "        m2.update_likelihood_approximation()\n",
      " \n",
      "        m2._Xoffset = np.zeros((1, m2.input_dim))\n",
      "        m2._Xscale = np.ones((1, m2.input_dim))\n",
      "\n",
      "        residuals=m2.predict(X2)[0]\n",
      "\n",
      "        newHSIC=BiasedHSIC(residuals, X, kernelx2, kernely2)\n",
      "        if newHSIC > tempbestHSIC:\n",
      "            hasbeenadded=True\n",
      "            #oldHSIC=newHSIC\n",
      "            tempbestHSIC=newHSIC\n",
      "            bestSNP=SNP\n",
      "    if hasbeenadded==False:\n",
      "        print \"converged: \" + str(len(chosenSNPS2)) + \"SNPs\" \n",
      "        X2=X2[:,:-1]\n",
      "        break\n",
      "    X2[:,-1]=X[:,bestSNP]\n",
      "\n",
      "    #print \"X2\" + str(X2.shape)\n",
      "\n",
      "    k2 = GPy.kern.linear(input_dim=step+1)\n",
      "\n",
      "    tempm2 = GPy.models.GPRegression(X2,Y,k2)\n",
      "    tempm2.optimize(messages=False)\n",
      "    residuals=tempm2.predict(X2)[0]\n",
      "\n",
      "    newHSIC=BiasedHSIC(residuals, X, kernelx2, kernely2)\n",
      "    if newHSIC > oldHSIC:\n",
      "        #hasbeenadded=True\n",
      "        oldHSIC=newHSIC\n",
      "        m2=tempm2\n",
      "        print newHSIC\n",
      "        print m2.log_likelihood()\n",
      "        oldparams=m2['.*variance']\n",
      "        chosenSNPS2.append(bestSNP)\n",
      "    else:\n",
      "        print \"converged: \" + str(len(chosenSNPS2)) + \"SNPs\" \n",
      "        X2=X2[:,:-1]\n",
      "        break\n",
      "    #print X2.shape\n",
      "    \n",
      "pb.matshow(X2[:,:].transpose())\n",
      "pb.colorbar()\n",
      "pb.show()\n",
      "\n",
      "#m.plot()\n",
      "#print m\n",
      "\n",
      "#print m.predict(np.array([1]))\n",
      "#print m.predict(np.array([2]))\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(182, 0)\n",
        "(182, 50)\n",
        "73807.1574834"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-27291.6014423\n",
        "80755.0494742"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-26583.7043046\n",
        "83778.2742419"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-25367.7363702\n",
        "84891.9952452"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-25273.4976681\n",
        "85944.0488984"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-25159.0613895\n",
        "86452.8193305"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-25188.5910357\n",
        "86804.256415"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-25179.4227757\n",
        "86989.462443"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-25205.6881412\n",
        "87058.1943556"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-25243.3532219\n",
        "87139.9944165"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-25236.11962\n",
        "87261.1705543"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-25235.909733\n",
        "87272.4179448"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-25255.5765661\n",
        "87314.9566523"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-25283.1588366\n",
        "87317.2709493"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-25281.2454194\n",
        "87332.380687"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "-25290.5269917\n",
        "converged: 15SNPs"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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       "text": [
        "<matplotlib.figure.Figure at 0x55b6b50>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print chosenSNPS2\n",
      "print m2\n",
      "pb.matshow(X[:,chosenSNPS2].transpose())\n",
      "pb.colorbar()\n",
      "pb.show()\n",
      "pb.matshow(X[:,1000].reshape(1,182))\n",
      "pb.colorbar()\n",
      "pb.show()\n",
      "#print m.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[979, 1023, 10865, 10935, 998, 10926, 1020, 10907, 10951, 999, 1021, 990, 966, 1000, 991]\n",
        "\n",
        "Log-likelihood: -2.460e+04\n",
        "\n",
        "       Name        |   Value   |  Constraints  |  Ties  |  prior  \n",
        "------------------------------------------------------------------\n",
        "  linear_variance  |  2.7051   |     (+ve)     |        |         \n",
        "  noise_variance   |  13.6821  |     (+ve)     |        |         \n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x4492610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x45c5590>"
       ]
      }
     ],
     "prompt_number": 15
    }
   ],
   "metadata": {}
  }
 ]
}