model 3.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pybrain"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pybrain.tools.shortcuts import buildNetwork"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pybrain.supervised.trainers import BackpropTrainer"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pybrain.datasets import SupervisedDataSet"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nlayers = 4\n",
      "module_size=20 #cm\n",
      "module_cell = 100 #per edge\n",
      "distance_between_layers = 5 #cm\n",
      "ncells=module_cell*module_cell*nlayers\n",
      "nhidden=10\n",
      "net = buildNetwork(ncells, nhidden, ncells)\n",
      "ds = SupervisedDataSet(ncells, ncells)\n",
      "trainer = BackpropTrainer(net, ds)\n",
      "maximum_hight = nlayers * distance_between_layers\n",
      "theta_max = math.atan(0.5 * module_size / maximum_hight)\n",
      "theta_min = -1 * theta_max\n",
      "min_R = nlayers * distance_between_layers / 2.0\n",
      "max_R = 50\n",
      "y = []\n",
      "y.append(0)\n",
      "y_prime = []\n",
      "y_prime.append(0)\n",
      "z = []\n",
      "z.append(0)\n",
      "x = []\n",
      "x.append(0)\n",
      "for i in range (nlayers) :\n",
      "    y.append((i+1) * distance_between_layers)\n",
      "    y_prime.append((i+1) * distance_between_layers)\n",
      "    z.append(0)\n",
      "    x.append(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "spent = []\n",
      "list_error = []\n",
      "sampleInput = []\n",
      "for i in range (ncells) :\n",
      "    sampleInput.append (1)\n",
      "sampleOutput = []\n",
      "for i in range (ncells) :\n",
      "    sampleOutput.append (1)\n",
      "min_ntracks = 200\n",
      "max_ntracks = 1500\n",
      "nsamples = 1000\n",
      "training_ratio = 0.8\n",
      "ntraining = int (training_ratio * nsamples)\n",
      "testing_ratio = 0.2\n",
      "ntesting = int(testing_ratio * nsamples)\n",
      "def sample_creater () :\n",
      "    for i in range (ncells) :\n",
      "        sampleInput [i] = 1\n",
      "    for i in range (ncells) :\n",
      "        sampleOutput [i] = 1\n",
      "    ntracks = int( uniform(min_ntracks, max_ntracks ) ) #should be a better model than flat\n",
      "    for track in range (ntracks) :\n",
      "        theta = uniform (theta_min,theta_max)\n",
      "        x[2] = uniform (-1*module_size/2.0,module_size/2.0)\n",
      "        x[4] = uniform (-1*module_size/2.0,module_size/2.0)\n",
      "        for i in range (4) :\n",
      "            y_prime[i+1] = y[i+1] / cos(theta)\n",
      "            z[i+1] = y[i+1] * tan(theta)\n",
      "        x_c = 0.5 * (((x[2]**2 + y_prime[2]**2) * y_prime[4]/y_prime[2] ) - x[4]**2 - y_prime[4]**2 )/ (x[2] * y_prime[4]/y_prime[2] - x[4])\n",
      "        y_c = 0.5 * (x[2]**2 + y_prime[2]**2 - 2 * x[2] * x_c) / y_prime[2]\n",
      "        R = sqrt(x_c**2 + y_c**2)\n",
      "        if R <= max_R :\n",
      "            normalized_R = (R - min_R) / (max_R - min_R)\n",
      "            if x[2] > 0.5 * x[4] :\n",
      "                x[1] = x_c + sqrt(R**2 - (y_prime[1] - y_c)**2)\n",
      "                x[3] = x_c + sqrt(R**2 - (y_prime[3] - y_c)**2)\n",
      "            else :\n",
      "                x[1] = x_c - sqrt(R**2 - (y_prime[1] - y_c)**2)\n",
      "                x[3] = x_c - sqrt(R**2 - (y_prime[3] - y_c)**2)\n",
      "            acceptance = 1\n",
      "            x_edge = (-1) * module_size / 2.0\n",
      "            z_edge = (-1) * module_size / 2.0\n",
      "            for i in range (1,nlayers + 1) :\n",
      "                if x[i] > x_edge and x[i] < -1 * x_edge :\n",
      "                    continue\n",
      "                else :\n",
      "                    acceptance = 0\n",
      "            for layer in range (1,nlayers+1) :\n",
      "                index_hit1 = int( (x[i] - x_edge ) / module_size * module_cell )\n",
      "                index_hit2 = int( (z[i] - z_edge ) / module_size * module_cell )\n",
      "                hit = module_cell**2 * (layer - 1) + index_hit2 * module_cell + index_hit1\n",
      "                if acceptance == 1 :\n",
      "                    sampleInput[hit] = 2\n",
      "                    sampleOutput[hit] = 1 + normalized_R\n",
      "    ds.addSample(sampleInput,sampleOutput)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for train_counter in range (9) :\n",
      "    start = time.mktime(time.gmtime())\n",
      "    ds.clear()\n",
      "    for sample in range (1,ntraining + 1) :\n",
      "        sample_creater ()\n",
      "    trainer.train()\n",
      "    errorsquare = 0\n",
      "    sample_sum_error = 0\n",
      "    error = 0\n",
      "    for sample in range (1,ntesting + 1) :\n",
      "        sample_creater ()\n",
      "        learningOutput = net.activate (sampleInput)\n",
      "        for i in range (ncells) :\n",
      "            errorsquare += (sampleOutput[i] - learningOutput[i])**2\n",
      "        sample_sum_error += sqrt(errorsquare)/ncells\n",
      "    error = sample_sum_error / ntesting\n",
      "    list_error.append (error)\n",
      "    print error\n",
      "    stop = time.mktime (time.gmtime())\n",
      "    spent.append (stop - start)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00347713001298\n",
        "0.00345636206674"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.00344758132749"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.00342763753665"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.0034031415722"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.00341177139845"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.00334120228605"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.00352358932629"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.0033410591114"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trackbins = 10000\n",
      "track_quantity1 = []\n",
      "for i in range (trackbins + 1) :\n",
      "    track_quantity1.append(0)\n",
      "track_quantity2 = []\n",
      "for i in range (trackbins + 1) :\n",
      "    track_quantity2.append(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def tracking1 (vector) :\n",
      "    for i in range (trackbins) :\n",
      "        track_quantity1[i] = 0\n",
      "    for i in range (len(vector)) :\n",
      "        if int((vector[i] - 1) * trackbins) > 1 :\n",
      "            track_quantity1[int((vector[i] - 1) * trackbins)] += 1\n",
      "def tracking2 (vector) :\n",
      "    for i in range (trackbins) :\n",
      "        track_quantity2[i] = 0\n",
      "    for i in range (len(vector)) :\n",
      "        if int((vector[i] - 1) * trackbins) > 1 :\n",
      "            track_quantity2[int((vector[i] - 1) * trackbins)] += 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tracking1(sampleOutput)\n",
      "plt.plot([val for val in track_quantity1 if val < 10])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f91bf5ac8d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f91bf6c97d0>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tracking2(learningOutput)\n",
      "plt.plot([val for val in track_quantity2 if val < 10])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f91bf679d90>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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o91gvGvKa5torydPpDzEcDTyF/vDBWvpLY9amfg4sJ9S/DWweeLyZh/6v0ozuIs/HgY8M\n/El5T5IndM8/kf48fvj5dpnvmaz19noe8NtJbgMuAX4T+BvgCQNljmKhx/Ft4KkA3Z/XRwD38Mjt\ns9bcDtxRVdd0j3cAzwbuXmfnxXOBL1fVd7th2E8AL2D9nhcwuWx4uPKDPfqHtZxQf/CDSV3ovQb4\nzDL2tyolCfB+4Iaq2jbw1KeBc7r1c7rH89tf3732ZGB+jPA/gDOSPLb7M/0M4PMzOISJqKq3V9Xm\nqjoGeC3wn1V1LvDVJK/qih3YDvPt80rgK1W1r9v+e0nmxxNPAP57ZgcyIVV1O3Bvkmd0m04HbqT/\nO7Buzgv6F/VOTfKY7nfldOAm1ul50ZlINnTn2P6BWTKvH9jXI1vmld/foj9+dgPwtpW+Ej2NBXg+\n/THk64Cd3XIG8Hjgc/QvnP47cPjAa/6e/oWea4GTB7af17XVDcAbV/rYltEmL2Rh9ssxwFeA3cCl\nwKO67QcDH+u2fxk4euD1b+/aYA/wspU+nmW0w4n0OzfXd79sv7gezwtgC3ALsLc7Bw5ZL+cF/b9a\nvwP8lH7v+rxJngP0ZwXt7F7z3lHq5IePJKkh3s5OkhpiqEtSQwx1SWqIoS5JDTHUJakhhrokNcRQ\nl6SGGOqS1JD/B8UAIbcI2ag8AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f91bf56ab90>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "track_quantity3 = []\n",
      "for i in range (trackbins + 1) :\n",
      "    track_quantity3.append (0)\n",
      "for i in range (trackbins) :\n",
      "    track_quantity3[i]  = track_quantity1[i] - track_quantity2[i]\n",
      "plt.plot([val for val in track_quantity3 if val < 10 and val > -10])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f91bf88c750>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f91bf6980d0>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(sampleInput)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f91bf6b17d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f91bf7cad50>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(sampleOutput)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f91bf4deb50>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VyZsutfEXIW1CIcA/eifrGwXnbF+bFcUWzgf+U0+N/40zQKbJivp9wIBssj/5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\nhiGBXxAEoWFI4BcEQWgY/x8HavrF1YIn2AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f91bf694590>"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(learningOutput)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f91bf41b710>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AGCIirayNwo3l0xOfQhKHjG4F9gMws62BLnefY2YfAf4BjHf3iQlMR0REUlR1\nD8HMJgDDgH5mNhsYAfQFcPcx7j7RzHY0sw5gGXBw+NW9ge2Bdc3s/8JhB7n7Uwn/BhERSYC5e/Wx\n0gzAzPNOOYiISCSGuxefv62LrlQWERFACUFEREJKCCIiAighiIhISAlBREQAJQQREQkpIYiICKCE\nICIiISUEEREBlBBERCSkhCAiIoASgoiIhJQQREQEUEIQEZGQEoKIiABKCCIiElJCEBERQAlBRERC\nSggiIgIoIYiISEgJQUREACUEEREJKSGIiAgQISGY2Vgzm29m0yuMM8rMOsxsipkNzRt+u5m9aWY3\nJRWwiIikI8oewjhg13IfmtlwYCN3HwIcGo6fcxZwQF0RiohIQ1RNCO5+P/BmhVF2A64Mx50K9DGz\ngeH7e4DFCcQpIiIpS+IcwkBgdt77znCYiIj0In0SKseK3nttXx+Z97otfIiISLf28JGeJBJCJzAI\neDR8PzAclhMhOYxMIAwRkVbWRuHG8umJTyGJQ0a3AvsBmNnWQJe7z8n7vHjvQUREmlDVPQQzmwAM\nA/qZ2WxgBNAXwN3HuPtEM9vRzDqAZcDBed+9H/gCsHb43UPc/c4UfoeIiNTJ3Gs83J90AGZe8ykH\nEZFVnuHuiR6B0ZXKIiICKCGIiEhICUFERAAlBBERCSkhiIgIoIQgIiIhJQQREQGUEEREJKSEICIi\ngBKCiIiElBBERARQQhARkZASgoiIAEoIIiISUkIQERFACUFEREJKCCIiAighiIhISAlBREQAJQQR\nEQkpIYiICKCEICIioYoJwczGmtl8M5teYZxRZtZhZlPMbGje8IPC4R1mdmCSQTfCySdHH/dLX0ov\nDsnOlltmHYEkrdR/esop9X2/lVTbQxgH7FruQzMbDmzk7kOAQ8PxMbMBwKnAtuHjNDNbr5bA7rkn\n2ninn95z2HbblR9/1iyYNKn7/WqrwfDhwevPf757+B57FH+zHYC11grevfMOvPACnHBC6RiiOOmk\neN8r57bb2pkxo+fwDTaIXsamm/Yctvnmhe/PPDN4Pv986OyEPffs/uzHPy5d7ppr5r9rZ401Cqf3\n6qtwxx1w6aXRYy1n223rLwPg/PPbueiinsNvvjmZ8nMuuSR4njcPliwp/Ozaa3uOP2xY8ZD2gncn\nnghz5sDxx8Nzz8WL6e67u1///Ofxysh32mlw113tJT97551oZZx9dm3TnDsXnnoqWMZzDjsseN5h\nh+5h2280MIwOAAALAUlEQVRf/M32gnfPPw+vvAI/+Qlcd11tMeRMnhzve+W8/Xay5X3I3Ss+gMHA\n9DKf/RUYnvf+aWAgcCBwYd7wi4D9y5Thjz3mfttt7tD96OgInu+8M3h+4gn3F14oHAfc772357CH\nHgqe58wJnr/8Zff11w9e57z/fvB+4cLg9SOPuC9fHgxbb71gnPwy29pG+B57uM+a5b50qRe4/vqe\nMfzkJz2H3Xij++zZ7rfeWvh7/ud/3CdMCF5//vPup5zS/Z1rrulZzmGHuV96qfvaaxcOHzFihLu7\nX3WV+2c+0z18gw3cd945eH3qqd31cN55Pcv+r//qOez224PnlSuD50cecR80yH3q1O46uOSS7nJf\nesm9szOoKwh+7+jR+WWO8EWL3A84wL2rq2d9rrNOzxjWWCN43mST7mFPPOH+9NPuDz8c/I877dQ9\nfNdd3T/7Wfd993Vfa61g+Lnnuo8dW1juEUe477WX+/PPB+/POKOwPpcvdx81qvA7N98cPOfH6R78\nj/njbbttz98Bwe/eZRf3P/85eD93buG86e7er5/7nnsGr9vbg3nzyCOD8ZYtKy5zhN9yi/sFFwT/\n0cqVhWUVT3/vvbtff+pTwfO++7o/80zwXz32WPd/De5LlgTPQ4YUzjNjx7pvtllh2Ucd5f6jHwVl\ngPtvfxs8jx8f1OdLL7n/61+F38kti7l6Hj3a/a23esbd3t5z2JVXuvft6z5tmvtqqwXD9tyzZ31C\nME5XVzD9/HoprqMttxzhI0cG66CFCwvLefHFaMv66NHBPHXxxe6PP949jXXXdX/ySfePf9x9wAD3\nceO6v3PTTT3LOflk9z/8wX2HHQqHB+Xh7pXX37U+qo9QOSHcAWyT9/524GvAScAv84b/CvhVmTI+\nrOy77nKfMcM9XK/58uXB87hx3a+32SaIepdd3GfODIa98or7Aw+4v/lmMCPmO+ecYAGO6txzgz/G\nvbvyd9/d/dRTR5T9zqxZwUz58Y+7H3989/DcjHL00cHCX2zZMvef/7znApyb9s9+Frz++tfdr746\nWAHn6sG9fEJwd1+xwn3SpGCGvP764HvLlrkvWuR+0UWF04Fghb5ggfvixcHzv/8drKhmzSqM61vf\nCuo5qkMOcX/nneD/yU1r331HVPzOqFHuW2wRJLJ77gmGdXUF3z38cPfPfc79tdd6fm/mTPd//rPn\n8BUrClcQ4P7ee+733Vc4Hrj/6U/B85gxhfX56qvBCuUHPwjqZ9my4H+bPDl4uLu//bZ/mLSeeir4\n3a++Gow/fXpQp++/3z29uXODeSOq2bPdDzooeH3ssd31+atfjaj0Nd95Z/ettnLv06fn73300SDe\nUsaMCX5Hsdx87e5+ww3BBte8ecEGTs7MmcE4F10UPC9aVFift9wSrDBzv3/FiuD5ggvcX389eH30\n0bkVdLCB4R6skGfOdH/jjZ7/39/+FmxARTVpUvCdXF1AsCFRaVl/9133j3wk2HDbZ59g2MqV3RsS\nI0eW/0+POSaYb4pBsF5zdz/wQPezzgqS8+LF3eP0poSwbd77uhJCFIsWBVv+jdDVFWwduRfOzFEt\nXdo9c9fq/vuD31rJ9OnB1vBpp7n/4x/xYpw7t3thTNvSpUFiihOne7DSyV+h1iKXWCq5+GL3+fOD\nledbb8WLM7eR0gi5uohbn6+8Em+6b70VzHeVfPBBsJJ89dVgz8a99jiXLg3+j0Z5993gOU59Ll/e\nc+Mpqocfdn/55crjzJgRbKiOG+d+xRXBsDQSggXllmdmg4Gb3H2LEp/9FbjN3f8evn8a+A6wU5go\njgyHXww85O5XlyijcgAiIlKSu1uS5fWp8/u3AvsDfzezrYEud59jZncDI8zsY+F4uwK/KVVA0j9I\nRETiqZgQzGwCMAzoZ2azgRFAXwB3H+PuE81sRzPrAJYBB4efzTWz3wGPhkX9xt3np/UjRESkflUP\nGYmIyKohsyuVzWxXM5tuZs+Y2S8zimGWmT1lZlPN7LFw2Lpmdmc4/A4z+0Te+A25CK/UBYFJxmVm\nXwl/c4eZXZBgjCPNrDMse6qZfTfvs5PC/3q6me2SN7zkfGBmm5jZw+Fn15pZ35hxDjKzf4XlPGtm\nJ4bDm60+y8XZVHVqZmuY2eNhLM+Z2XmVyjazj5rZdeHwB81s47jxJxTn5Wb2Ul59bhkOtyz+97Cc\n1cNybgrfZ1eXSZ+ljvIAPgq8DGxIcNjqcWBoBnG8DKxbNOxC4Njw9bHABeHr4cAN4euhwLTw9QDg\nBWDt8PECsF6dcW0fTmN6wnH1Dz97KlffwA3AfycU4wjg5yXG/Ur4H68e/ucvExx6LDsfADcBe4Wv\nzweOi1mX6wGbh6/XBp4DtmzC+iwXZzPW6Zrhcx/gEWDHcmUDxwPnh6/3Am6MG39CcY4D/qfEuJn8\n7+F3fw5cDfyz0v/UiLrMag9hW6DD3ee4+wrgOmD3jGIpPqm9G3Bl+PoquuPaPTfc3acCfcxsILAz\nQUurxe6+mKDp7c71BOTu9wNvphDXLma2EbBaOG5xWfXGCD3rMxfjte7e5e5zgA66r2LvMR+YWR/g\na+5+Qz0xhnHOd/enw9eLCRbiDWm++iwXJzRfnb4fvvwIwUpoQYWy8+v5n8A3zGy1WuNPME4oXZ8f\nxtnI/z2cxm7AZcFbW50M6zKrhDAQmJ33vjMc1mgO5A4bHBkO+7S7vwHg7q8D/cPhG1I65g3D18XD\nk5ZUXMXjz0k43p+Z2Qwzu8rM1s2LMUosueGfBl5POkYLmlB/FXiAJq7PvDjvDwc1VZ2a2WpmNg2Y\nD9xLsGFQruwPl3V3Xwm8QVDXtcZfd5zu3hF+9LuwPi8ys48Wx1kinjT/9/OAE4CV4fv+ZFiXWSWE\nZjmT/TV33xr4FnCwmX27yvjN2kS2WeK6GNgU+BLwIjAq23C6mdnawN+BY9y9Wu85mdVnGOf1BHG+\nSxPWqbuvdPetCFYuOwBt2UZUWnGcZtZGcMHsFwkOx61J0OdaTkP/dzP7HrAg3MvITTvTZTmrhNAJ\nDMp7P4jCTNYQ7r4gfF5IsLL4KrDQzPoBmNmn6d7NLI45l60b9VuSiqvU+PlbF7G5+4fXZQNjCOoz\nTowLgH5JxRielJsIXJ23K9509ZkX5zW5OJu1TsPY3gZuAT5ToexOYKPw960GfApYGCP+JOL8Wt4y\n/wFBX2xx6zOJ//0bwJ5m9jIwgeCC3j+SZV3GOQlS7wNYA5hFsEvTl+Bkx9YNjmEtYK3w9X8A9wF7\nUniy8ThglHefdPpH+Hpr4Mnw9QYEJ5o+Fj5epM6TymG5gyl/UrmuuOh5MqzHSbaYMfbPe30UMMkL\nT3r1CWfWWeH/XnY+oPDE2gWUOLEaMUYDxgPnFQ1vqvqsEGdT1SnBSuhj4es1gX8B3ytXNoUnQv+b\n7hOnNcefQJy7031C2IA/AedmvRyF3x9G0CNE2f+pEXXZsBVwiQr4LkHvqM8AJ2Uw/U2AJ4FpBC06\nfhMOXxe4M/yzJwOfyPvORQQnbKbkVyzBBXnPhI+DEohtAjAX+IAgox+cZFzhDDQ1/M6ohGI8hOCE\n15PADIKTbxvmjX9yGMfTwHeqzQfh//MwMB24FugbM85vEhyfnRb+5qkEV843W32WivO7zVanwBZh\nbNOAmcBplcomaOnyt3D4Q8DguPEnFOc94bBnwzjXyfJ/zytrGN0r+MzqUhemiYgIoFtoiohISAlB\nREQAJQQREQkpIYiICKCEICIiISUEEREBlBBERCSkhCAiIgD8P3efP7Xv0gaKAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f91bf501a90>"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range (0,100) :\n",
      "    plt.plot (i+1,list_error[i],'bo')\n",
      "    if list_error[i+1] != 0 :\n",
      "        continue"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "IndexError",
       "evalue": "list index out of range",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-49-dca49d24361f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlist_error\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'bo'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0mlist_error\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m         \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mIndexError\u001b[0m: list index out of range"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8FZhk+7ckvdP2P5XjXQx8BzivDI01t9dDnU9ERBxNErZHPI88ZI+jXAF1E41f7pOAlbY3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\n8v8Bn+4sCAvtfPcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f91bf8a1050>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range (0,100) :\n",
      "    plt.plot (i+1,spent[i],'bo')\n",
      "    if spent[i+1] != 0 :\n",
      "        continue"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "IndexError",
       "evalue": "list index out of range",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-50-bd0332451e2d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mspent\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'bo'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0mspent\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m         \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mIndexError\u001b[0m: list index out of range"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f91bf88c6d0>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#plt.plot(track_quantity (sampleOutput))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist([val for val in sampleOutput if val!=1], bins=10000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "(array([ 1.,  0.,  0., ...,  0.,  0.,  1.]),\n",
        " array([ 1.01040119,  1.01049955,  1.01059792, ...,  1.99385973,\n",
        "         1.9939581 ,  1.99405646]),\n",
        " <a list of 10000 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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twJeqaiXJTwD/mOQFQz/xNiXJDwB/C7x5k/c4V3bu9JX7CWDP0PLu7rnTUpIL2Jhrf3VV\nTZu2aNmLgL9J8lXgKuDPk7x6yT0ty78Dn6iq/6mqh4BPsfGhfaejS9kIO6rqfuCrwPOX2tE2SXIm\ncAuwVlUfGVMyd3budLjfBqwCJLkIeKyqTu5wD08ISc4FbgVeW1X/uux+lqmqfryqnltVz2Vj3v26\nqvrYsvtakn8ALk3ypCRPBV4C/MuSe1qW+4FXAiR5JvDTwNeW2dB26H5a/WvgWFW9e5OyubOz12mZ\nJB8CXgY8I8lxYD9wJkBV/WVV3ZLk5UmOAo8Ar+tz+08k0/YF8PvADwM3dR+i+X9V9bNLandbzbAv\nThsznCNHknycjV+qnQm8t6q+tLSGt9EMx8UfAB9IcoyN6aq3n/pFc2MuYeMu/3uTHOmeextwLiye\nnd7EJEkN8n+zJ0kNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWrQ/wO2cZMm+T4HXAAA\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f1414578dd0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist([val for val in learningOutput if val> 1.15], bins=10000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "(array([ 1.,  0.,  0., ...,  0.,  0.,  1.]),\n",
        " array([ 1.15278845,  1.15280657,  1.15282469, ...,  1.33391409,\n",
        "         1.33393221,  1.33395032]),\n",
        " <a list of 10000 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f141440cb10>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x[2] = uniform (-1*module_size/2.0,module_size/2.0)\n",
      "x[4] = uniform (-1*module_size/2.0,module_size/2.0)\n",
      "for i in range (4) :\n",
      "    y_prime[i+1] = y[2] / cos(theta)\n",
      "x_c = 0.5 * ((x[4]**2 + y[4]**2) * (y[4]/y[2] - 1) ) / (x[2] * y[4]/y[2] - x[4])\n",
      "y_c = 0.5 * (x[2]**2 + y[2]**2 - 2 * x[2] * x_c) / y[2]\n",
      "R = sqrt(x_c**2 + y_c**2)\n",
      "if R <= max_R :\n",
      "    normalized_R = R / max_R\n",
      "    if x[2] > 0.5 * x[4] :\n",
      "        x[1] = x_c + sqrt(R**2 - (y_prime[1] - y_c)**2)\n",
      "        x[3] = x_c + sqrt(R**2 - (y_prime[3] - y_c)**2)\n",
      "    else :\n",
      "        x[1] = x_c - sqrt(R**2 - (y_prime[1] - y_c)**2)\n",
      "        x[3] = x_c - sqrt(R**2 - (y_prime[3] - y_c)**2)\n",
      "    accept = 1\n",
      "    for i in range (1,nlayers+1) :\n",
      "        if x[i] > x_edge and x[i] < -1 * x_edge :\n",
      "            continue\n",
      "        else :\n",
      "            accept = 0\n",
      "    for i in range (1,nlayers+1) :\n",
      "        x_edge = (-1) * module_size / 2.0\n",
      "        z_edge = (-1) * module_size / 2.0\n",
      "        index_hit1 = int( (x[i] - x_edge ) / module_size * module_cell )\n",
      "        index_hit2 = int( (z[i] - z_edge ) / module_size * module_cell )\n",
      "        hit = module_cell**2 * (layer - 1) + lindex_hit2 * module_cell + lindex_hit1\n",
      "        if accept == 1 :\n",
      "            sampleInput[hit] = 2\n",
      "            sampleOutput[hit] = 1 + ((track + 1) / float(ntracks))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}