run_pso_earm.ipynb

{
 "metadata": {
  "name": "run_pso_earm"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import operator\nimport random\nimport scipy.optimize\nimport numpy\nimport pylab\nfrom deap import base\nfrom deap import creator\nfrom deap import tools\nimport pysb.integrate\nimport pysb.util\nimport numpy as np\n%matplotlib inline\nimport matplotlib.pyplot as plt\nimport os\nimport inspect\nimport multiprocessing\nimport multiprocessing as mp\nfrom earm.lopez_embedded import model",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "obs_names = ['mBid', 'aSmac', 'cPARP']\ndata_filename = '/home/pinojc/git/earm/earm/experimental_data.npy'\nydata_norm = numpy.load(data_filename)\nexp_var = 0.2\ntspan = np.linspace(0,5.5 * 3600,len(ydata_norm)*10)  # 5.5 hours, in seconds",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "rate_params = model.parameters_rules()\nparam_values = np.array([p.value for p in model.parameters])\nrate_mask = np.array([p in rate_params for p in model.parameters])\n# Build a boolean mask for those params against the entire param list\nk_ids = [p.value for p in model.parameters_rules()]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "solver = pysb.integrate.Solver(model, tspan, integrator='vode',rtol=1e-8, atol=1e-8)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 113
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def likelihood(x):\n\n    Y=np.copy(x)\n    param_values[rate_mask] = 10 ** Y\n    solver.run(param_values)\n    ysim_array = extract_records(solver.yobs, obs_names)\n    ysim_norm = normalize(ysim_array)\n    err = numpy.sum((ydata_norm - ysim_norm[::10]) ** 2 / (2 * exp_var ** 2))\n    return err,",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 114
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "The experimental data is normalized from [0,1] so we define a function that will normalized our simulated trajectories. We also define a function to extract the obserables from the simulations."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def normalize(trajectories):\n    \"\"\"Rescale a matrix of model trajectories to 0-1\"\"\"\n    ymin = trajectories.min(0)\n    ymax = trajectories.max(0)\n    return (trajectories - ymin) / (ymax - ymin)\n\ndef extract_records(recarray, names):\n    \"\"\"Convert a record-type array and list of names into a float array\"\"\"\n    return numpy.vstack([recarray[name] for name in names]).T",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 115
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "To start the optimization we will use values that were used to create the model. We will search parameter space in log space and set upper and lower bounds to 1 order of magnitude up and down."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "nominal_values = np.array([p.value for p in model.parameters])\nxnominal = np.log10(nominal_values[rate_mask])\nbounds_radius = 1\nlb = xnominal - bounds_radius\nub = xnominal + bounds_radius",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 116
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "To use the particle swarm algorithm we need to first generate a population and define an update function. Below is a basic example of the general PSO modified from Deap."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "\ndef generate(size, speedmin, speedmax):\n    \n    random_pertubation = (random.uniform(-1., 1.) for _ in range(size))\n    start_position = map(operator.add,np.log10(k_ids),random_pertubation)\n    part = creator.Particle(start_position)\n    part.speed = [random.uniform(speedmin, speedmax) for _ in range(size)]\n    part.smin = speedmin\n    part.smax = speedmax\n    return part\n\ndef updateParticle(particle_position, best, phi1, phi2):\n\n    u1 = (random.uniform(0, phi1) for _ in range(len(particle_position)))\n    u2 = (random.uniform(0, phi2) for _ in range(len(particle_position)))\n    v_u1 = map(operator.mul, u1, map(operator.sub, particle_position.best, particle_position))\n    v_u2 = map(operator.mul, u2, map(operator.sub, best, particle_position))\n    part.speed = list(map(operator.add, part.speed, map(operator.add, v_u1, v_u2)))\n    for i, speed in enumerate(particle_position.speed):\n        if speed < particle_position.smin:\n            particle_position.speed[i] = particle_position.smin\n        elif speed > particle_position.smax:\n            particle_position.speed[i] =  part.smax\n    part[:] = list(map(operator.add, particle_position, particle_position.speed))\n    for i, pos in enumerate(particle_position):\n        if pos < lb[i]:\n            part[i] = lb[i]\n        elif pos > ub[i]:\n            part[i] =  ub[i]\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 117
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Now we use Deap to create the optimization protocol. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "toolbox = base.Toolbox()\ncreator.create(\"FitnessMin\", base.Fitness,weights=(-1.00,))\ncreator.create(\"Particle\", list, fitness=creator.FitnessMin, \\\n    speed=list,smin=list, smax=list, best=None)\ntoolbox.register(\"particle\", generate, size=np.shape(rate_params)[0],\\\n                 speedmin=-.1,speedmax=.1)\ntoolbox.register(\"population\", tools.initRepeat, list, toolbox.particle)\ntoolbox.register(\"update\", updateParticle, phi1=2, phi2=2)\ntoolbox.register(\"evaluate\", likelihood)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 118
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Deap also has tools to keep track of some statistics."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "stats = tools.Statistics(lambda ind: ind.fitness.values)\nstats.register(\"avg\", numpy.mean)\nstats.register(\"std\", numpy.std)\nstats.register(\"min\", numpy.min)\nstats.register(\"max\", numpy.max)\nlogbook = tools.Logbook()\nlogbook.header = [\"gen\", \"evals\"] + stats.fields",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 119
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Here we create a function to display a given parameter set to the experimental data."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def display(x):\n    Y=np.copy(x)\n    param_values = np.array([p.value for p in model.parameters])\n    rate_mask = np.array([p in rate_params for p in model.parameters])\n    param_values[rate_mask] = 10 ** Y\n    solver.run(param_values)\n    ysim_array = extract_records(solver.yobs, obs_names)\n    ysim_norm = normalize(ysim_array)\n    count=1\n    plt.figure(figsize=(8,6),dpi=200)\n    for j,obs_name in enumerate(obs_names):\n      plt.subplot(3,1,count)\n      plt.plot(solver.tspan,ysim_norm[:,j])\n      plt.plot(solver.tspan[::10],ydata_norm[:,j],'-x')\n      plt.title(str(obs_name))\n      count+=1\n    plt.ylabel('concentration')\n    plt.xlabel('time (s)')\n    plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)\n    plt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 120
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "This example uses parallel computing so we must tell all processers the information we need which is a list of the parameters for each iteration and a dictonary to hold the results. This way we send the largest part of data to each processor once. This way we can send the processor a key and its places the result into a dictonary. This can be done others but this is a just a quick example."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def init(sample,dictionary):\n    global Sample\n    global Dictionary\n    Sample,Dictionary = sample,dictionary",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 121
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def OBJ(block):\n    #print block\n    obj_values[block]=likelihood(sample[block])",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 122
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Finally, the main call of the function. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "if __name__ == '__main__':\n    print \"Model with rate parameters from literature\"\n    display(xnominal)\n    GEN = 100\n    num_particles = 50\n    pop = toolbox.population(n=num_particles)\n    best = creator.Particle(xnominal)\n    best.fitness.values = likelihood(xnominal)\n    best_values =[]\n    evals = []\n    print logbook.header\n    for g in range(1,GEN+1):\n        m = mp.Manager()\n        obj_values = m.dict()\n        sample = []\n        for p in pop:\n            sample.append(p)\n        p = mp.Pool(4,initializer = init, initargs=(sample,obj_values))\n        allblocks =range(len(pop))\n        p.imap_unordered(OBJ,allblocks)\n        p.close()\n        p.join()\n        count=0\n        \n        for part in pop:\n            part.fitness.values = obj_values[count]\n            count+=1\n            if g == 1:\n                part.best = creator.Particle(part)\n                part.best.fitness.values = part.fitness.values\n            elif part.best.fitness < part.fitness:\n                part.best = creator.Particle(part)\n                part.best.fitness.values = part.fitness.values\n            if best.fitness < part.fitness:\n                best = creator.Particle(part)\n                best.fitness.values = part.fitness.values\n        for part in pop:\n            toolbox.update(part, best)\n \n        logbook.record(gen=g, evals=len(pop), **stats.compile(pop))\n        print(logbook.stream),best.fitness.values\n        best_values.append(best.fitness.values)\n        evals.append(g*num_particles)\n    \n    \n    display(best)\n    plt.semilogy(evals,best_values)\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Model with rate parameters from literature\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAkYAAAG0CAYAAADXb+jjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": "<matplotlib.figure.Figure at 0x7f2bc41e3ad0>"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "['gen', 'evals', 'avg', 'std', 'min', 'max']\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "gen\tevals\tavg    \tstd    \tmin    \tmax    \n1  \t50   \t742.951\t366.535\t84.0364\t1421.18 (84.036425198305494,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "2  \t50   \t705.899\t378.642\t62.166 \t1394.56 (62.166028784675532,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "3  \t50   \t658.981\t386.996\t54.6732\t1359.11 (54.673155250020827,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "4  \t50   \t626.866\t373.081\t50.719 \t1333.72 (50.719045371273452,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "5  \t50   \t599.057\t356.032\t54.0023\t1270.07 (50.719045371273452,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "6  \t50   \t536.858\t332.873\t31.0943\t1187.06 (31.094311927765961,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "7  \t50   \t481.159\t295.664\t33.6935\t1090.76 (31.094311927765961,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "8  \t50   \t418.103\t263.273\t34.9992\t1014.81 (31.094311927765961,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "9  \t50   \t360.237\t234.124\t40.9727\t945.35  (31.094311927765961,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "10 \t50   \t311.334\t205.728\t26.5168\t849.782 (26.516805427402403,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "11 \t50   \t259.968\t181.837\t34.4358\t761.43  (26.516805427402403,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "12 \t50   \t193.721\t155.437\t27.0853\t716.951 (26.516805427402403,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "13 \t50   \t131.417\t134.265\t24.2073\t702.851 (24.207267078086321,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "14 \t50   \t92.8464\t113.805\t22.745 \t712.39  (22.744972550506549,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "15 \t50   \t92.1735\t104.275\t28.6927\t695.452 (22.744972550506549,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "16 \t50   \t79.9295\t82.3777\t20.9021\t535.475 (20.902092855490189,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "17 \t50   \t70.7902\t76.0781\t19.8333\t479.514 (19.833311958152521,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "18 \t50   \t61.1071\t67.2099\t19.1829\t445.073 (19.182924745495537,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "19 \t50   \t53.3352\t62.7478\t17.612 \t362.017 (17.611993055504769,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "20 \t50   \t55.0686\t46.2017\t17.6701\t282.47  (17.611993055504769,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "21 \t50   \t44.453 \t35.7018\t15.9747\t198.502 (15.974745660351955,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "22 \t50   \t51.7481\t33.9069\t15.4522\t162.913 (15.4521801921404,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "23 \t50   \t43.0882\t27.6897\t14.3945\t115.203 (14.394531626758022,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "24 \t50   \t47.6936\t38.1081\t14.55  \t161.624 (14.394531626758022,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "25 \t50   \t42.937 \t27.7873\t15.2146\t136.536 (14.394531626758022,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "26 \t50   \t38.1412\t32.7225\t14.7125\t198.204 (14.394531626758022,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "27 \t50   \t48.2586\t32.7757\t14.7136\t180.83  (14.394531626758022,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "28 \t50   \t36.6073\t31.8249\t13.5362\t172.946 (13.536167438877934,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "29 \t50   \t41.9529\t32.2795\t13.9357\t143.17  (13.536167438877934,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "30 \t50   \t36.3298\t24.3824\t13.6061\t113.799 (13.536167438877934,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "31 \t50   \t56.2498\t48.0113\t14.3007\t199.584 (13.536167438877934,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "32 \t50   \t49.0256\t43.6056\t14.0001\t248.512 (13.536167438877934,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "33 \t50   \t36.0175\t30.9173\t12.496 \t164.383 (12.495956729789459,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "34 \t50   \t43.1934\t37.1604\t12.9518\t216.321 (12.495956729789459,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "35 \t50   \t35.0903\t32.1712\t12.2967\t185.668 (12.296729983440564,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "36 \t50   \t37.5874\t27.7241\t11.5333\t117.324 (11.533341120818012,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "37 \t50   \t42.6975\t33.4247\t11.9493\t134.935 (11.533341120818012,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "38 \t50   \t35.7634\t34.6217\t11.6359\t172.696 (11.533341120818012,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "39 \t50   \t43.9148\t37.5712\t12.1129\t168.278 (11.533341120818012,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "40 \t50   \t34.4357\t24.5517\t11.9112\t132.817 (11.533341120818012,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "41 \t50   \t42.5988\t36.7946\t11.508 \t139.692 (11.507956068635158,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "42 \t50   \t47.7658\t44.0477\t11.9482\t230.97  (11.507956068635158,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "43 \t50   \t40.1931\t34.6282\t12.4952\t178.065 (11.507956068635158,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "44 \t50   \t46.4326\t41.1634\t11.0403\t194.552 (11.040267070809836,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "45 \t50   \t48.7886\t43.227 \t11.7484\t234.697 (11.040267070809836,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "46 \t50   \t39.4466\t28.3542\t10.3971\t120.619 (10.397068693411281,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "47 \t50   \t45.703 \t45.1588\t10.8874\t258.583 (10.397068693411281,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "48 \t50   \t42.8429\t37.0799\t10.9786\t167.461 (10.397068693411281,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "49 \t50   \t37.2563\t29.9787\t9.82406\t127.65  (9.8240578764630939,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "50 \t50   \t42.0476\t37.4148\t9.76877\t155.165 (9.7687733586488683,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "51 \t50   \t41.9346\t30.8132\t10.8687\t130.832 (9.7687733586488683,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "52 \t50   \t51.4157\t53.4808\t10.5597\t251.511 (9.7687733586488683,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "53 \t50   \t36.8983\t33.8156\t9.89235\t186.77  (9.7687733586488683,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "54 \t50   \t37.2506\t41.3339\t10.573 \t254.576 (9.7687733586488683,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "55 \t50   \t48.3755\t46.4659\t9.59473\t209.094 (9.5947281346850541,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "56 \t50   \t35.6365\t29.1981\t9.36293\t163.812 (9.362925077201357,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "57 \t50   \t42.4384\t41.2275\t10.0167\t210.95  (9.362925077201357,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "58 \t50   \t38.0305\t32.8513\t9.33123\t155.655 (9.3312311074301011,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "59 \t50   \t41.1148\t34.0509\t8.53302\t186.415 (8.5330163352744215,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "60 \t50   \t40.7748\t37.4014\t8.65532\t173.688 (8.5330163352744215,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "61 \t50   \t37.9223\t24.1542\t9.49883\t105.759 (8.5330163352744215,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "62 \t50   \t37.6598\t39.8116\t8.67155\t218.76  (8.5330163352744215,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "63 \t50   \t42.5995\t40.4758\t9.83327\t261.664 (8.5330163352744215,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "64 \t50   \t35.5424\t31.7745\t9.17296\t156.108 (8.5330163352744215,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "65 \t50   \t33.9829\t30.9282\t8.80141\t188.48  (8.5330163352744215,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "66 \t50   \t37.4401\t39.8607\t8.26565\t178.373 (8.2656549515708342,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "67 \t50   \t34.2368\t32.8045\t8.15858\t164.083 (8.1585849296942889,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "68 \t50   \t27.8636\t24.0018\t9.02778\t121.83  (8.1585849296942889,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "69 \t50   \t30.6146\t33.87  \t8.58097\t227.848 (8.1585849296942889,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "70 \t50   \t35.6363\t35.1534\t9.30545\t202.197 (8.1585849296942889,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "71 \t50   \t41.4506\t35.1634\t8.5151 \t170.552 (8.1585849296942889,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "72 \t50   \t35.0329\t29.8477\t7.87942\t114.653 (7.8794193276536255,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "73 \t50   \t35.7632\t35.4413\t8.25259\t170.894 (7.8794193276536255,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "74 \t50   \t24.2909\t16.9774\t7.79721\t103.84  (7.7972056545459676,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "75 \t50   \t40.9571\t34.3221\t8.26739\t128.387 (7.7972056545459676,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "76 \t50   \t31.1758\t27.5611\t7.50316\t115.185 (7.5031566199530557,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "77 \t50   \t35.6546\t32.6368\t7.10796\t122.856 (7.1079612070230125,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "78 \t50   \t39.0784\t37.3381\t7.95887\t182.387 (7.1079612070230125,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "79 \t50   \t42.5669\t36.8383\t8.49118\t173.994 (7.1079612070230125,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "80 \t50   \t42.7825\t39.4891\t8.25905\t189.811 (7.1079612070230125,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "81 \t50   \t41.4194\t38.8753\t7.52806\t210.208 (7.1079612070230125,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "82 \t50   \t40.1351\t32.3131\t8.21982\t125.476 (7.1079612070230125,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "83 \t50   \t37.6563\t31.4652\t7.72447\t129.245 (7.1079612070230125,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "84 \t50   \t45.5991\t34.5369\t7.39195\t159.293 (7.1079612070230125,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "85 \t50   \t46.7013\t40.5425\t7.81779\t167.859 (7.1079612070230125,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "86 \t50   \t39.467 \t34.5741\t7.05128\t134.064 (7.051278247901581,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "87 \t50   \t46.9959\t38.837 \t7.12671\t142.973 (7.051278247901581,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "88 \t50   \t38.5195\t28.8334\t8.42874\t140.253 (7.051278247901581,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "89 \t50   \t47.7542\t38.5897\t6.17251\t163.616 (6.1725112423965873,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "90 \t50   \t42.4482\t35.827 \t6.87564\t138.027 (6.1725112423965873,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "91 \t50   \t41.7122\t35.4562\t6.71005\t151.134 (6.1725112423965873,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "92 \t50   \t38.678 \t37.7508\t7.4965 \t178.537 (6.1725112423965873,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "93 \t50   \t41.724 \t35.705 \t6.16452\t142.644 (6.1645158857522331,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "94 \t50   \t42.316 \t39.9674\t5.82241\t182.341 (5.8224098581297277,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "95 \t50   \t46.4749\t37.3582\t7.60424\t146.606 (5.8224098581297277,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "96 \t50   \t39.4857\t33.0738\t6.0938 \t127.178 (5.8224098581297277,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "97 \t50   \t34.5733\t28.5338\t6.10486\t114.49  (5.8224098581297277,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "98 \t50   \t41.0986\t40.703 \t5.82831\t212.293 (5.8224098581297277,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "99 \t50   \t40.7405\t39.7336\t6.10985\t167.404 (5.8224098581297277,)\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "100\t50   \t33.0292\t26.2371\t5.64233\t117.304 (5.6423335034411002,)\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAkYAAAG0CAYAAADXb+jjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": "<matplotlib.figure.Figure at 0x7f2b18cb8f10>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEBCAYAAAB7Wx7VAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFjRJREFUeJzt3XtwlNX9x/FPLiAXCd5Aw4DaEryMkgJWIQkJSWkBaxFF\nQxVIG5qCUWtRUm1t7Y9AtcVYx7FVqUYnVKwXOoS01CuoywghGVGBtlogEYoOiFBtWA0kmD2/P05J\nvIBsspt99tnzfs08A7ubPPvNIXxycs55zpNkjDECACS8ZK8LAADEBoEPAI4g8AHAEQQ+ADiCwAcA\nRxD4AOCIqAb+iy++qDlz5mjmzJnavHlzNE8NAIhQajRPduDAAT300EPauHGjXnjhBWVmZkbz9ACA\nCCRF+8Krjz/+WDfccIMqKip0yimnRPPUAIAIhD2kU19fr4KCAklSKBRSaWmpsrOzVVBQoMbGRknS\nvn37dMMNN2jhwoWEPQDEmbACv6KiQrNnz1ZLS4skqaamRq2traqtrdWiRYtUVlYmSSorK9OePXt0\n6623avny5d1XNQCg08Iaw8/IyFB1dbWKiookSWvXrtWkSZMkSaNHj9aGDRskSX/84x+7qUwAQKTC\n6uFPnTpVqakdPxuCwaDS0tLaH6ekpCgUCkW/OgBA1HRplU5aWpqCwWD741AopOTk8KYDMjIy2sf8\nAQDhGTp0qBoaGiI6R5fW4efk5OiZZ56RJNXV1XVq+WVjY6OMMRzGaP78+Z7XEC8HbUFb0BZffkSj\no9ypHn5SUpIk6fLLL9eqVauUk5MjSaqqqoq4EABA9wo78M8880zV1tZKssG/ePHibisKABB97KXj\nofz8fK9LiBu0RQfaogNtEV1Rv9L2mG+YlKT58+crPz+ff0wAOIZAIKBAIKAFCxYo0rj2JPBj/JYA\n4HvRyE7PhnS2bJG4GBcAYsezwP/gA+n22716dwBwj2dDOi0t0kknSe+9J/XrF8sKAMB/fDukU15e\nrvXrAxo5Uqqv96ICAPCHQCCg8vLyqJzL00nbn/5U6tNHmj8/lhUAgP/4tod/WE6O9L9ruQAA3czT\nHv7evVJGhp3ATUmJZRUA4C++7+EPGCCddpr0j394WQUAuMHzrRUY1gGA2PA88LOzpXXrvK4CABKf\nZ8syA4GAJNvDJ/AB4MgSZlmmJIVCdiz/73+XBg2KZSUA4B++n7SVpORkKSuLcXwA6G6eB77EsA4A\nxELcBD49fADoXp6P4UtSc7N06qlSY6M0cGAsqwEAf0iIMXzJ7qdTXCzdcYfXlQBA4vJ8WeZhv/yl\n9Nhj0vbtXlQEAPEpoZZlftqCBdK2bTb4AQAdojGkE1eBHwxKw4ZJzz0njRgRy6oAIL4lzBj+Yf36\nSbfdJt16q9eVAEDiiavAl6Q5c6StW6Wnn/a6EgBILHEX+D17SlVV0qxZ0oYNXlcDAIkj7gJfkvLy\npMpKafJkacsWr6sBgMSQ6nUBRzNlir0T1sSJ0tq10uDBXlcEAP4Wt4Ev2WGdDz+UzjjD7qop2Yu0\n3nlHOukkb2sDAL+JmwuvjmbePOmTT2zgh0LSeecxzAPAHQl74VU4pk+XLr5YKiqKYlEAEOcSbh1+\nOIYNkxoavK4CAPzHd4GfkUHgA0BX+DLwt23zugoA8B9fBj49fADoPN8F/imnSG1tdo0+ACB8vgv8\npCR6+QDQFb4LfInAB4CuIPABwBG+DHzW4gNA58X91gpHQg8fgCuc3lpBkt57T8rMlN5/P0pFAUCc\nc3JrBUk69VSpuVlqavK6EgDwD18GPkszAaDzfBn4EoEPAJ1F4AOAIwh8AHCEbwOftfgA0Dm+DXx6\n+ADQOb4N/PR0uyzzo4+8rgQA/MG3gZ+cLA0dSi8fAMKV6nUBkcjIkFassFfeHkt2tpSW1v01AUC8\n8uXWCoc9/rj06KPH/rj335fOO09aujQqbwsAMReN7PR14Ifrww+lr35VevNNO/YPAH7j2710It0t\ns7NOPFG66ipp8eKYvSUARIXzu2V2xVtvSfn50r//LfXqFfO3B4CI+LaH74Vzz5VGjJCefNLrSgDA\nG84EviTNnSvde6/kwS8YAOA5pwJ/0iS7j/4rr3hdCQDEnjNj+Ifdf7/08MNSXt6RX586VRo3LrY1\nAcCxRCM7fX3hVVeUlEipqdLBg198rblZKiyU1q+3V/ECQCJxLvB79ZKuuebor/ftK02bJq1bx2oe\nAInFuSGdYzHGBv6AAdIDD3hdDQBYLMvsBklJdox/1SrpiSe8rgYAose5IZ1w9O8v/fnP0re+JfXu\nLV12mdcVAUDkCPyjGDFCevZZafJku/nanDleVwQAkWEM/xgaGqSJE6Xvfc8ehw0aJB13nHd1AXAL\nu2XGyHvvSTNmSG+/bR8fOiQNHiwFAqzkARAbBL5HjJG++12pTx+pqspO9AJAd2KVjkeSkmzQb9ok\n3XOP19UAQHiYtO2ivn2lmhppzBh7N62JE72uCAC+HD38CJxxhrRsmTRzpl3GCQDxjDH8KHj9dbsH\nzyWXSL/9rdSzp9cVAUg0jOHHiVGjpNdek955Rxo7Vtq40euKAOCLnLinbSyccIJUXS3NmmV7+t/5\njt11EwAiwT1t49zBg9KSJdKiRdI3vylVVrJ0E0BkWIcf55qbpdxcafp0qazM62oA+BmB7wM7d9ql\nm0uWSBMmeF0NAL9i0tYHTj9deuopqahIamz0uhoALqOHHyOLF0u//rV0zjn2cXKytHChNHq0t3UB\n8AeGdHzEGKmuTvroI/t4yxbp7rvtEs7+/b2tDUD8I/B9rrRU+vhjaelSrysBEO8Yw/e5u++WXn2V\nWykCiA16+B577TXp4ott8J9xhtfVAIhX9PATwAUXSLfcIuXl2Yndgwe9rghAoiLw48BPfiI9+aT0\n9NPS0KHS735nJ3kBIJoY0okzGzdK11xjl2veey9bMgCwWKWToJqa7B4848ZJd91F6ANgDD9h9e8v\nPf+89OKL0i9+wfAOgOighx/H9u2Txo+XGho638s/5xxpxQppyJDuqQ1AbDGk44C2NunAgc5/3uLF\n0u9/byeChw+Pfl0AYovAx5d6/HHpxhvtfXfz872uBkAkCHwc00svSVde2bGHT2eUlEgPPMCkMRAP\nCHyEpa3NHp3R3GznD664Qvr5z7unLgDhi0Z2pkapFsSxlBR7dEbPntLKlVJWlvSVr0hXX909tQGI\nHQIfRzVokPS3v9me/sknd0z+JidLAwcy1AP4DUM6OKZVq6Qf/lBqbbWPDx2yYZ+dLY0dK02bxsZv\nQHdjDB+eefddad06KRCQ/vIXu/xz5EivqwISF4GPuFBdbW/msny5lJvrdTVAYiLwETdWr5amT7c3\ndTnrrM5/fo8eUu/eUq9e9u9Hmh9ISpLS05k7gJsIfMSV+nq71fPhsf5wGWPnBQ4etFcVHzp05I9r\naZHOPlu6/35pxIjI6wX8JG4D/6WXXtITTzyhysrKL74hgY8uCoWkRx6RbrvNThSXlUnHHWdf691b\nOuEEb+sDulNc7pbZ2NiojRs36iC3bkKUJSdLs2dLb75pfwvIypJGjbLHkCHSww97XSEQ37ptSKeo\nqEhLly794hvSw0c32LZN+va3bc//V7+yPxyARBLzHn59fb0KCgokSaFQSKWlpcrOzlZBQYEaGxsj\nKgSIxLBhUm2t9PLL0syZ0htv2LuHff7YutXrSgHvhH2lbUVFhR577DEdf/zxkqSamhq1traqtrZW\n9fX1KisrU01NTbcVChzLgAH2pjE/+pE0a9aRP2b3bvsD4c47pVSuM4djwh7Sqa6uVmZmpoqKirR+\n/XrNmzdPY8aM0bRp0yRJgwcP1rvvvnvsN2RIBx764AM77JOaam8cz0Qv/CKmm6dNnTpVO3bsaH8c\nDAaVlpbW/jglJUWhUEjJYQyelpeXt/89Pz9f+WzWjhg56STpuefsCp/Ro+2fAwdKp5xiw7+za/xT\nUqSMDH5bQPQFAgEFAoGonrPL36ZpaWkKBoPtj8MNe+mzgQ/EWmqqdO+90hNP2CGgvXvt0dTU+XO1\ntNhbUebm2k3mLrnEzicAkfp8Z3jBggURn7PLgZ+Tk6OVK1eqsLBQdXV1yszMjLgYIJauvjo62z7v\n3Wsni198UVq0SDr9dGnGDBv+vXp1fFzv3lK/fnbracALnVqWuWPHDk2fPl21tbUyxui6667T5s2b\nJUlVVVU6K4xr6hnDRyL75BN7l7E//cn+EAiF7PPG2KuIg0E7bDRnjnTffd7WCn+J2yttv/QNCXw4\nzBg7dJSdbecPSkq8rgh+4ds7XpWXlzNZCyclJdnJ4eXLpbw8e5Uw20rjy0Rz8pYePuCRp56y9wve\nsEE68USvq0G8Y0gH8Lkbb7Q3kvna16J3zr597YVn7CiaWAh8wOdaW6Vly+zW0NGya5f04IPS+edL\nt9wiFRSwt1AiIPABHFFLi10pdM890s6d9jeIESOkM8/sfPj36iUVFtob2cM7BD6AY/rPf6RNm+zm\ncTt3dv7z9+61Vydfe600b569WhmxxyodAMd08snSN75hj67avl264w57FfH553dsQdGnj33u7LPt\nFhOfvtBs+HAmo6OBVToAPLFzp/T22x2Pg0F7L4ItW6SGho7bU7a12ed/9jPp+us77kyGrmNIB0Dc\neustO2n85pv25vaXXeZ1Rf5G4AOIey+9ZG9NOXmydNddUo8eXlfkTwQ+AF/48EOpqMhuK7FsmZSe\n7nVF/kPgA/CNUEi6/XbpgQfsJG8spKZKgwbZHUyHDLGTzEdy6qnSxImxqamrWKUDwDeSk6X/+z/p\nyivtUs9YaG21F6Lt3Cm9/vrRL3B74QWppkYaMyY2dXUGq3QAIIoqK+3eRqtXe13J0UUjO7ngGoDz\nioulHTvsPQwSGYEPwHk9ekgLFki/+IW9Z0GiIvABQNJVV0n790vPPut1Jd2HwAcASSkp0sKF0m23\nddyaMtEwaQsA/2OMvf3ktm3h7SqanGyHg3r0kDIzpUce6b5dRVmHDwBR1toq/fe/4X1sW5vdP+iT\nT6TFi6UVK6SVK6Vzz41+XazDB4Ao69lTGjiw85931112J9Fx4+wyz6ysyOro29cerMMHgDhVW2uX\neYb7W8LRtLZK06dLN91kt6D2bQ8fABJVdra0dWvk59mzR7rvPnu+3NzIzyfRwweAuPbxx9LSpdK1\n1zJpCwBOYGsFAEDYCHwAcASBDwCOIPABwBGeBH55eXnULiQAgEQWCARUXl4elXOxSgcAfIBVOgCA\nsBH4AOAIAh8AHEHgA4AjCHwAcASBDwCOIPABwBEEPgA4gsAHAEewtQIAxDG2VgAAx7C1AgAgbAQ+\nADiCwAcARxD4AOAIAh8AHEHgA4AjCHwAcASBDwCOIPABwBEEPgA4gsAHAEcQ+ADgCHbLBIA4xm6Z\nAOAYdssEAISNwAcARxD4AOAIAh8AHEHgA4AjCHwAcASBDwCOIPABwBEEPgA4gsAHAEcQ+ADgCAIf\nABxB4AOAIwh8AHAEgQ8AjiDwAcARBD4AOIJbHAJAHOMWhwDgGG5xCAAIG4EPAI4g8AHAEQQ+ADiC\nwAcARxD4AOAIAh8AHEHgA4AjCHwAcASBDwCOIPABwBEEPgA4gsAHAEcQ+ADgCAIfABxB4AOAIwh8\nAHAEgQ8AjiDwAcARBD4AOILABwBHEPgA4AgCHwAcQeADgCMIfABwRFQDv7a2VsXFxSouLlZTU1M0\nTw0AiFBUA7+yslIPPfSQSkpK9NRTT0Xz1ACACEU18Nva2tSzZ0+lp6dr9+7d0Tw1ACBCYQd+fX29\nCgoKJEmhUEilpaXKzs5WQUGBGhsbJUl9+vRRa2urdu3apdNOO617Kk4ggUDA6xLiBm3RgbboQFtE\nV1iBX1FRodmzZ6ulpUWSVFNTo9bWVtXW1mrRokUqKyuTJM2ZM0fXXHONKisrVVRU1H1VJwi+mTvQ\nFh1oiw60RXSlhvNBGRkZqq6ubg/xtWvXatKkSZKk0aNHa8OGDZKkUaNGqaqqqptKBQBEIqwe/tSp\nU5Wa2vGzIRgMKi0trf1xSkqKQqFQ9KsDAESPCdP27dvNmDFjjDHGzJs3zyxbtqz9tcGDB4d7GjN0\n6FAjiYODg4OjE8fQoUPDztmjCWtI5/NycnK0cuVKFRYWqq6uTpmZmWF/bkNDQ1feEgAQoU4FflJS\nkiTp8ssv16pVq5STkyNJjNsDgA8kGWOM10UAALpfzPbSOdra/UT06WsWGhoaNHbsWOXl5em6667T\n4Z+vlZWVuvDCC5WVlaWnn35aknTgwAFdccUVysvL0yWXXKJ9+/Z59jVEw6FDh1RUVKS8vDyNHj1a\nK1eudLY92tra9IMf/EBjx45Vbm6u/vnPfzrbFoe9//77GjJkiLZu3ep0W4waNUoFBQUqKChQSUlJ\n97ZFxLMAYVq+fLmZNWuWMcaYuro6M2XKlFi9dUzdeeedZvjw4SYrK8sYY8zkyZPNmjVrjDHGlJaW\nmhUrVpjdu3eb4cOHm9bWVtPU1GSGDx9uWlpazN13320WLFhgjDHmySefNHPnzvXs64iGqqoqc9NN\nNxljjPnggw/MkCFDzKWXXupke9TU1JiSkhJjjDGBQMBceumlzraFMca0traayy67zJx99tnmX//6\nl7P/Tw4cOGBGjhz5mee6sy1i1sNft27dEdfuJ5rD1yyY//1Ufv3115WXlydJuvjii7V69Wq9+uqr\nysnJUY8ePZSWlqaMjAxt3rz5M200adIkrV692rOvIxoKCwu1cOFCSfY3vB49ejjbHlOmTNGDDz4o\nSdqxY4dOPPFEvfbaa062hSTdfPPNuvbaa5Weni7J3f8nmzZtUnNzsyZOnKjx48errq6uW9siZoG/\nf/9+J9buf/6aBfOpKZJ+/fqpqalJ+/fvV//+/Y/4/OE2Ovycn/Xt21fHH3+8gsGgCgsLdfvtt3/m\n39y19khJSVFxcbHmzp2rGTNmOPu9sWTJEg0YMEATJkyQZP+PuNoWffv21c0336znn39ef/jDHzRj\nxozPvB7ttujSssyuSEtLUzAYbH8cCoWUnJz42/F/+mvcv3+/TjjhhC+0RTAY/MLzh5/zu3feeUdT\np07V9ddfr6uvvlq33HJL+2sutseSJUu0Z88eXXTRRTp48GD78y61RVVVlZKSkrR69Wpt3LhR3//+\n97V37972111qi7POOksZGRmSpGHDhunkk0/WG2+80f56tNsiZombk5OjZ555RpI6vXbfz0aOHKk1\na9ZIkp599lnl5eXpoosu0iuvvKKWlhY1NTXprbfe0vnnn/+ZNjr8sX62Z88eTZgwQRUVFSouLpbk\nbnssXbpUv/nNbyRJvXv3VkpKir7+9a872RZr1qxRIBDQyy+/rBEjRujRRx/VpEmTnGyLqqqq9r3I\ndu3apWAwqAkTJnRfW0R9FuIoQqGQKS0tNdnZ2SY7O9ts2bIlVm8dc9u3b2+ftN26dasZN26cycrK\nMiUlJSYUChljjKmsrDQXXnihueCCC0x1dbUxxpjm5mZTWFhoxo4da8aPH2/27Nnj2dcQDT/+8Y9N\nenq6yc/Pbz82bdrkZHs0NzebadOmmby8PJOVlWX++te/Ov29cVh+fr7ZsmWLs21x6NAhM3PmTJOb\nm2tyc3PN+vXru7UtWIcPAI5I/EF0AIAkAh8AnEHgA4AjCHwAcASBDwCOIPABwBEEPgA4gsAHAEf8\nP/fxF6VxDXZYAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x7f2b18c20bd0>"
      }
     ],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 123
    }
   ],
   "metadata": {}
  }
 ]
}