fitterTesting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check runner "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import runner\n",
    "import fittingAlgorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "yamlFile = \"inputParams.yaml\"\n",
    "varDict = fittingAlgorithm.YamlToParamDict(yamlFile)\n",
    "varDict['kon'] = 5.0\n",
    "varDict['koff'] = 1.0\n",
    "newDict = runner.simulation(varDict=varDict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb636f637f0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb637451710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = newDict['data']\n",
    "plt.plot(data['t'],data['Nai'],label=\"nai\")\n",
    "plt.plot(data['t'],data['Cai'],label=\"cai\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CHeck main IO routines "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import analyze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataSub = analyze.GetData(data,\"Nai\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = analyze.ProcessDataArray(dataSub,\"mean\",[8,10],None,\"Nai\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.000145963697871\n"
     ]
    }
   ],
   "source": [
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check sampler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING: In debug mode.\n",
      "Fixing random seed\n",
      "\n",
      "timeRange:  [0.0, 3.0]\n",
      "Generating single-variable output list\n",
      "WHY is this wrapper needed\n",
      "iter 1  out of 10\n",
      "Inputs:  kon 5.0\n",
      "Using 3 cores for 3 jobs\n",
      "Should probably rescale sigma by the tolerated error vs current error and only for selected params \n",
      "Multi-threading\n",
      "Worker bee 32285, Job 2 \n",
      "Worker bee 32284, Job 1 \n",
      "Worker bee 32283, Job 0 \n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636ec1e48>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636ec1f28>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636d8a080>}\n",
      "result:  0.14305658358422646 truthValue:  0.1\n",
      "result:  0.0030206064429950195 truthValue:  0.1\n",
      "result:  0.0008873128386769079 truthValue:  0.1\n",
      "myDataFrame: \n",
      "           Container    jobID jobNum       kon   fitness\n",
      "0_32283     0.143057  0_32283      0  0.227328  0.656175\n",
      "1_32284   0.00302061  1_32284      1   20.9052  0.984781\n",
      "2_32285  0.000887313  2_32285      2   71.7086  0.995554\n",
      "jobFitnesses:  [ 0.65617516  0.98478116  0.99555355]\n",
      "jobIndex:  0\n",
      "bestVarDict:  {'kon': 0.22732770417919937}\n",
      "currentFitness 0.656175156374\n",
      "iter 1  out of 10\n",
      "\n",
      "######\n",
      "\n",
      "iter 2  out of 10\n",
      "Inputs:  kon 0.227327704179\n",
      "Using 3 cores for 3 jobs\n",
      "Should probably rescale sigma by the tolerated error vs current error and only for selected params \n",
      "Multi-threading\n",
      "Worker bee 32301, Job 0 \n",
      "Worker bee 32302, Job 1 \n",
      "Worker bee 32303, Job 2 \n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636dbb358>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636dbb400>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636dbb4a8>}\n",
      "result:  0.20870983130784126 truthValue:  0.1\n",
      "result:  0.14385260059896776 truthValue:  0.1\n",
      "result:  0.08813800010543807 truthValue:  0.1\n",
      "myDataFrame: \n",
      "        Container    jobID jobNum        kon   fitness\n",
      "0_32301   0.20871  0_32301      0  0.0907491  1.042640\n",
      "1_32302  0.143853  1_32302      1    0.22491  0.662213\n",
      "2_32303  0.088138  2_32303      2   0.502078  0.344413\n",
      "jobFitnesses:  [ 1.04264007  0.66221296  0.34441254]\n",
      "jobIndex:  2\n",
      "bestVarDict:  {'kon': 0.50207786856689474}\n",
      "currentFitness 0.344412541795\n",
      "iter 2  out of 10\n",
      "\n",
      "######\n",
      "\n",
      "iter 3  out of 10\n",
      "Inputs:  kon 0.502077868567\n",
      "Using 3 cores for 3 jobs\n",
      "Should probably rescale sigma by the tolerated error vs current error and only for selected params \n",
      "Multi-threading\n",
      "Worker bee 32322, Job 2 \n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636dc3160>}\n",
      "Worker bee 32320, Job 0 \n",
      "Worker bee 32321, Job 1 \n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636dbbfd0>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636dc30b8>}\n",
      "result:  0.08792358558055036 truthValue:  0.1\n",
      "result:  0.08280946942479896 truthValue:  0.1\n",
      "result:  0.07546479804715149 truthValue:  0.1\n",
      "myDataFrame: \n",
      "         Container    jobID jobNum       kon   fitness\n",
      "0_32320  0.0879236  0_32320      0  0.503833  0.347511\n",
      "1_32321  0.0828095  1_32321      1  0.548408  0.414615\n",
      "2_32322  0.0754648  2_32322      2  0.623066  0.495330\n",
      "jobFitnesses:  [ 0.34751136  0.41461465  0.49533021]\n",
      "jobIndex:  0\n",
      "Old draw is better starting point, not overwriting starting point\n",
      "Rejected 1 in a row (of 3) \n",
      "iter 3  out of 10\n",
      "\n",
      "######\n",
      "\n",
      "iter 4  out of 10\n",
      "Inputs:  kon 0.502077868567\n",
      "Using 3 cores for 3 jobs\n",
      "Should probably rescale sigma by the tolerated error vs current error and only for selected params \n",
      "Multi-threading\n",
      "Worker bee 32339, Job 1 \n",
      "Worker bee 32338, Job 0 \n",
      "Worker bee 32340, Job 2 \n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636dc30f0>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636dc3518>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636dc3ac8>}\n",
      "result:  0.0938049499091055 truthValue:  0.1\n",
      "result:  0.0749846640185074 truthValue:  0.1\n",
      "result:  0.054998989422320434 truthValue:  0.1\n",
      "myDataFrame: \n",
      "         Container    jobID jobNum       kon   fitness\n",
      "0_32338  0.0938049  0_32338      0  0.458623  0.248899\n",
      "1_32339  0.0749847  1_32339      1  0.628459  0.500153\n",
      "2_32340   0.054999  2_32340      2  0.936835  0.670828\n",
      "jobFitnesses:  [ 0.24889858  0.50015334  0.67082793]\n",
      "jobIndex:  0\n",
      "bestVarDict:  {'kon': 0.45862295153224386}\n",
      "currentFitness 0.248898575546\n",
      "iter 4  out of 10\n",
      "\n",
      "######\n",
      "\n",
      "iter 5  out of 10\n",
      "Inputs:  kon 0.458622951532\n",
      "Using 3 cores for 3 jobs\n",
      "Should probably rescale sigma by the tolerated error vs current error and only for selected params \n",
      "Multi-threading\n",
      "Worker bee 32356, Job 0 \n",
      "Worker bee 32357, Job 1 \n",
      "Worker bee 32358, Job 2 \n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e49748>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e49828>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e49198>}\n",
      "result:  0.11522437326445713 truthValue:  0.1\n",
      "result:  0.08905955685881388 truthValue:  0.1\n",
      "result:  0.07360860548146415 truthValue:  0.1\n",
      "myDataFrame: \n",
      "         Container    jobID jobNum       kon   fitness\n",
      "0_32356   0.115224  0_32356      0  0.333346  0.390184\n",
      "1_32357  0.0890596  1_32357      1  0.494631  0.330763\n",
      "2_32358  0.0736086  2_32358      2  0.644306  0.513726\n",
      "jobFitnesses:  [ 0.39018423  0.33076341  0.51372555]\n",
      "jobIndex:  1\n",
      "Old draw is better starting point, not overwriting starting point\n",
      "Rejected 1 in a row (of 3) \n",
      "iter 5  out of 10\n",
      "\n",
      "######\n",
      "\n",
      "iter 6  out of 10\n",
      "Inputs:  kon 0.458622951532\n",
      "Using 3 cores for 3 jobs\n",
      "Should probably rescale sigma by the tolerated error vs current error and only for selected params \n",
      "Multi-threading\n",
      "Worker bee 32374, Job 0 \n",
      "Worker bee 32376, Job 2 \n",
      "Worker bee 32375, Job 1 \n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e50400>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e50550>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e504a8>}\n",
      "result:  0.1097004783168216 truthValue:  0.1\n",
      "result:  0.09200027607963197 truthValue:  0.1\n",
      "result:  0.08793476234771273 truthValue:  0.1\n",
      "myDataFrame: \n",
      "         Container    jobID jobNum       kon   fitness\n",
      "0_32374     0.1097  0_32374      0  0.360912  0.311456\n",
      "1_32375  0.0920003  1_32375      1  0.471875  0.282838\n",
      "2_32376  0.0879348  2_32376      2  0.503741  0.347351\n",
      "jobFitnesses:  [ 0.31145591  0.28283783  0.34735051]\n",
      "jobIndex:  1\n",
      "Old draw is better starting point, not overwriting starting point\n",
      "Rejected 2 in a row (of 3) \n",
      "iter 6  out of 10\n",
      "\n",
      "######\n",
      "\n",
      "iter 7  out of 10\n",
      "Inputs:  kon 0.458622951532\n",
      "Using 3 cores for 3 jobs\n",
      "Should probably rescale sigma by the tolerated error vs current error and only for selected params \n",
      "Multi-threading\n",
      "Worker bee 32394, Job 2 \n",
      "Worker bee 32392, Job 0 \n",
      "Worker bee 32393, Job 1 \n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e50208>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e50358>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e502b0>}\n",
      "result:  0.1115118804024799 truthValue:  0.1\n",
      "result:  0.10328855233097517 truthValue:  0.1\n",
      "result:  0.08158619321899423 truthValue:  0.1\n",
      "myDataFrame: \n",
      "         Container    jobID jobNum       kon   fitness\n",
      "0_32392   0.111512  0_32392      0  0.351567  0.339292\n",
      "1_32393   0.103289  1_32393      1  0.396664  0.181344\n",
      "2_32394  0.0815862  2_32394      2  0.559905  0.429113\n",
      "jobFitnesses:  [ 0.33929162  0.18134366  0.42911312]\n",
      "jobIndex:  1\n",
      "bestVarDict:  {'kon': 0.39666445988523164}\n",
      "currentFitness 0.181343660793\n",
      "iter 7  out of 10\n",
      "\n",
      "######\n",
      "\n",
      "iter 8  out of 10\n",
      "Inputs:  kon 0.396664459885\n",
      "Using 3 cores for 3 jobs\n",
      "Should probably rescale sigma by the tolerated error vs current error and only for selected params \n",
      "Multi-threading\n",
      "Worker bee 32413, Job 2 \n",
      "Worker bee 32411, Job 0 \n",
      "Worker bee 32412, Job 1 \n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e5a9e8>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e5a898>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e5a940>}\n",
      "result:  0.11350943253569477 truthValue:  0.1\n",
      "result:  0.10176783826624117 truthValue:  0.1\n",
      "result:  0.09007243446027582 truthValue:  0.1\n",
      "myDataFrame: \n",
      "         Container    jobID jobNum       kon   fitness\n",
      "0_32411   0.113509  0_32411      0  0.341612  0.367552\n",
      "1_32412   0.101768  1_32412      1  0.405814  0.132960\n",
      "2_32413  0.0900724  2_32413      2  0.486624  0.315080\n",
      "jobFitnesses:  [ 0.3675518   0.13296008  0.3150804 ]\n",
      "jobIndex:  1\n",
      "bestVarDict:  {'kon': 0.40581365603375558}\n",
      "currentFitness 0.132960079206\n",
      "iter 8  out of 10\n",
      "\n",
      "######\n",
      "\n",
      "iter 9  out of 10\n",
      "Inputs:  kon 0.405813656034\n",
      "Using 3 cores for 3 jobs\n",
      "Should probably rescale sigma by the tolerated error vs current error and only for selected params \n",
      "Multi-threading\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Worker bee 32431, Job 0 \n",
      "Worker bee 32433, Job 2 \n",
      "Worker bee 32432, Job 1 \n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e64048>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e646d8>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e645f8>}\n",
      "result:  0.1014081294597548 truthValue:  0.1\n",
      "result:  0.0977286245746162 truthValue:  0.1\n",
      "result:  0.09578608775774798 truthValue:  0.1\n",
      "myDataFrame: \n",
      "         Container    jobID jobNum       kon   fitness\n",
      "0_32431   0.101408  0_32431      0  0.408018  0.118665\n",
      "1_32432  0.0977286  1_32432      1  0.431514  0.150711\n",
      "2_32433  0.0957861  2_32433      2  0.444655  0.205278\n",
      "jobFitnesses:  [ 0.11866463  0.15071083  0.20527816]\n",
      "jobIndex:  0\n",
      "bestVarDict:  {'kon': 0.40801842400626342}\n",
      "currentFitness 0.118664630777\n",
      "iter 9  out of 10\n",
      "\n",
      "######\n",
      "\n",
      "iter 10  out of 10\n",
      "Inputs:  kon 0.408018424006\n",
      "Using 3 cores for 3 jobs\n",
      "Should probably rescale sigma by the tolerated error vs current error and only for selected params \n",
      "Multi-threading\n",
      "Worker bee 32462, Job 1 \n",
      "Worker bee 32461, Job 0 \n",
      "Worker bee 32464, Job 2 \n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e642b0>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e64128>}\n",
      "outputList:  {'Container': <fittingAlgorithm.OutputObj object at 0x7fb636e64470>}\n",
      "result:  0.10203601094221008 truthValue:  0.1\n",
      "result:  0.09999578823660063 truthValue:  0.1\n",
      "result:  0.09820285327060875 truthValue:  0.1\n",
      "myDataFrame: \n",
      "         Container    jobID jobNum       kon   fitness\n",
      "0_32461   0.102036  0_32461      0   0.40418  0.142689\n",
      "1_32462  0.0999958  1_32462      1   0.41683  0.006490\n",
      "2_32464  0.0982029  2_32464      2  0.428386  0.134058\n",
      "jobFitnesses:  [ 0.14268886  0.00648981  0.1340577 ]\n",
      "jobIndex:  1\n",
      "bestVarDict:  {'kon': 0.41683033826796417}\n",
      "currentFitness 0.00648981001215\n",
      "iter 10  out of 10\n",
      "\n",
      "######\n",
      "\n",
      "Best fit parameters {'kon': 0.41683033826796417}\n",
      "Leaving!!\n",
      "Commented out Demo\n",
      "PASS!!\n"
     ]
    }
   ],
   "source": [
    "fittingAlgorithm.validation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Spot check "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'kon': 1.0, 'koff': 1.0, 'bMax': 5.0, 'scale': 1.0}\n"
     ]
    }
   ],
   "source": [
    "yamlFile = \"inputParams.yaml\"\n",
    "varDict = fittingAlgorithm.YamlToParamDict(yamlFile)\n",
    "#varDict['kon'] = 5.0\n",
    "#varDict['koff'] = 1.0\n",
    "print(varDict)\n",
    "bestDict = runner.simulation(varDict=varDict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb636e3fa20>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb636e3f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = newDict['data']\n",
    "#plt.plot(data['t'],data['Nai'])\n",
    "plt.plot(data['t'],data['Cai'])\n",
    "data = bestDict['data']\n",
    "plt.plot(data['t'],data['Cai'],label=\"best\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting against non-scalar data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fittingAlgorithm as fA\n",
    "from fittingAlgorithm import OutputObj\n",
    "\n",
    "# file with default parameters for model\n",
    "yamlVarFile = \"inputParams.yaml\"\n",
    "fixedParamDict = fA.YamlToParamDict(yamlVarFile)\n",
    "\n",
    "# parameters you want to vary \n",
    "variedParamList= { \n",
    "  # paramDict[myVariedParam] = [variedParamTruthVal, 0.2] # for log normal\n",
    "  #\"kon\":  [0.4,0.2],\n",
    "  \"scale\":  [1,0.2],\n",
    "} \n",
    "\n",
    "# list of observables to be scored by GA \n",
    "outputList = { \n",
    "    #\"Cai\":OutputObj(\"Cai\",\"mean\",[8,10], # in [s]\n",
    "    # 0.1),          # value you want \n",
    "    \"Nai\":OutputObj(\"Nai\",\"val_vs_time\",[  0, 2],\n",
    "    [1,0.5,0.15],timeInterpolations=[  0,1,2]) # check that interpolated values at 0, 100, 200 are 1, 0.5 ... \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(outputList[\"Nai\"].timeInterpolations))\n",
    "print(type(outputList[\"Nai\"].truthValue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timeRange:  [0.0, 30.0]\n",
      "WHY is this wrapper needed\n",
      "iter 1  out of 2\n",
      "Inputs:  scale 1\n",
      "Using 3 cores for 3 jobs\n",
      "Should probably rescale sigma by the tolerated error vs current error and only for selected params \n",
      "Multi-threading\n",
      "Worker bee 3500, Job 0 \n",
      "Worker bee 3501, Job 1 \n",
      "Worker bee 3502, Job 2 \n",
      "outputList:  {'Nai': <fittingAlgorithm.OutputObj object at 0x7fb636e332b0>}\n",
      "outputList:  {'Nai': <fittingAlgorithm.OutputObj object at 0x7fb636e336a0>}\n",
      "outputList:  {'Nai': <fittingAlgorithm.OutputObj object at 0x7fb636e331d0>}\n",
      "result:  [ 1.          0.40752799  0.16727598] truthValue:  [ 1.    0.5   0.15]\n",
      "result:  [ 1.          0.34662286  0.12116992] truthValue:  [ 1.    0.5   0.15]\n",
      "result:  [ 1.          0.31595781  0.10075346] truthValue:  [ 1.    0.5   0.15]\n",
      "myDataFrame: \n",
      "                                          Nai   jobID jobNum     scale  \\\n",
      "0_3500  [1.0, 0.407527988827, 0.167275984054]  0_3500      0  0.897649   \n",
      "1_3501  [1.0, 0.346622856254, 0.121169923601]  1_3501      1   1.05952   \n",
      "2_3502   [1.0, 0.31595781257, 0.100753463704]  2_3502      2   1.15215   \n",
      "\n",
      "         fitness  \n",
      "0_3500  0.288779  \n",
      "1_3501  0.371951  \n",
      "2_3502  0.410963  \n",
      "jobFitnesses:  [ 0.28877902  0.37195106  0.41096277]\n",
      "jobIndex:  0\n",
      "bestVarDict:  {'scale': 0.89764930647476393}\n",
      "currentFitness 0.288779017234\n",
      "iter 1  out of 2\n",
      "\n",
      "######\n",
      "\n",
      "iter 2  out of 2\n",
      "Inputs:  scale 0.897649306475\n",
      "Using 3 cores for 3 jobs\n",
      "Should probably rescale sigma by the tolerated error vs current error and only for selected params \n",
      "Multi-threading\n",
      "Worker bee 3521, Job 1 \n",
      "Worker bee 3520, Job 0 \n",
      "Worker bee 3522, Job 2 \n",
      "outputList:  {'Nai': <fittingAlgorithm.OutputObj object at 0x7fb636e33fd0>}\n",
      "outputList:  {'Nai': <fittingAlgorithm.OutputObj object at 0x7fb636e3e0b8>}\n",
      "outputList:  {'Nai': <fittingAlgorithm.OutputObj object at 0x7fb636e3e160>}\n",
      "result:  [ 1.          0.39468749  0.15694105] truthValue:  [ 1.    0.5   0.15]\n",
      "result:  [ 1.          0.35427052  0.12655367] truthValue:  [ 1.    0.5   0.15]\n",
      "result:  [ 1.          0.25779187  0.0671809 ] truthValue:  [ 1.    0.5   0.15]\n",
      "myDataFrame: \n",
      "                                           Nai   jobID jobNum     scale  \\\n",
      "0_3520   [1.0, 0.394687493369, 0.156941050771]  0_3520      0  0.929665   \n",
      "1_3521   [1.0, 0.354270519815, 0.126553671793]  1_3521      1    1.0377   \n",
      "2_3522  [1.0, 0.257791873823, 0.0671809026664]  2_3522      2   1.35561   \n",
      "\n",
      "         fitness  \n",
      "0_3520  0.305877  \n",
      "1_3521  0.361730  \n",
      "2_3522  0.476360  \n",
      "jobFitnesses:  [ 0.30587653  0.36172954  0.47636   ]\n",
      "jobIndex:  0\n",
      "Old draw is better starting point, not overwriting starting point\n",
      "Rejected 1 in a row (of 3) \n",
      "iter 2  out of 2\n",
      "\n",
      "######\n",
      "\n",
      "Best fit parameters {'scale': 0.89764930647476393}\n",
      "Leaving!!\n",
      "Commented out Demo\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'allDraws': [array([ 0.89764931,  1.05952304,  1.15215258]),\n",
       "  array([ 0.92966489,  1.03769935,  1.35561103])],\n",
       " 'bestDraws': [{'scale': 0.89764930647476393}, {'scale': 0.89764930647476393}],\n",
       " 'bestFitDict': {'scale': 0.89764930647476393},\n",
       " 'bestFitness': 0.28877901723442673,\n",
       " 'outputList': {'Nai': <fittingAlgorithm.OutputObj at 0x7fb636e1bf28>}}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fA.run(\n",
    "    yamlVarFile=\"inputParams.yaml\",\n",
    "    variedParamDict = variedParamList,\n",
    "    outputList = outputList,\n",
    "    numRandomDraws = 3,\n",
    "    numIters = 2\n",
    "    \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = runner.Runner()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "  'Cai': array([ 0.3       ,  0.28544794,  0.27178129,  0.25894266,  0.2468788 ,\n",
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       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437,\n",
       "          0.05215437,  0.05215437,  0.05215437,  0.05215437,  0.05215437]),\n",
       "  'Nai': array([  1.00000000e+00,   9.90039924e-01,   9.80179050e-01,\n",
       "           9.70416392e-01,   9.60750970e-01,   9.51181817e-01,\n",
       "           9.41707973e-01,   9.32328489e-01,   9.23042426e-01,\n",
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       "           8.10413871e-01,   8.02342087e-01,   7.94350698e-01,\n",
       "           7.86438904e-01,   7.78605912e-01,   7.70850938e-01,\n",
       "           7.63173204e-01,   7.55571940e-01,   7.48046386e-01,\n",
       "           7.40595786e-01,   7.33219395e-01,   7.25916474e-01,\n",
       "           7.18686290e-01,   7.11528120e-01,   7.04441245e-01,\n",
       "           6.97424957e-01,   6.90478551e-01,   6.83601331e-01,\n",
       "           6.76792610e-01,   6.70051703e-01,   6.63377937e-01,\n",
       "           6.56770642e-01,   6.50229156e-01,   6.43752824e-01,\n",
       "           6.37340996e-01,   6.30993031e-01,   6.24708292e-01,\n",
       "           6.18486150e-01,   6.12325981e-01,   6.06227167e-01,\n",
       "           6.00189098e-01,   5.94211168e-01,   5.88292780e-01,\n",
       "           5.82433339e-01,   5.76632258e-01,   5.70888956e-01,\n",
       "           5.65202859e-01,   5.59573395e-01,   5.54000001e-01,\n",
       "           5.48482119e-01,   5.43019195e-01,   5.37610682e-01,\n",
       "           5.32256038e-01,   5.26954727e-01,   5.21706218e-01,\n",
       "           5.16509984e-01,   5.11365505e-01,   5.06272265e-01,\n",
       "           5.01229755e-01,   4.96237468e-01,   4.91294905e-01,\n",
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       "           4.72012056e-01,   4.67310780e-01,   4.62656329e-01,\n",
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       "           4.31347676e-01,   4.27051420e-01,   4.22797955e-01,\n",
       "           4.18586855e-01,   4.14417698e-01,   4.10290066e-01,\n",
       "           4.06203546e-01,   4.02157727e-01,   3.98152206e-01,\n",
       "           3.94186579e-01,   3.90260451e-01,   3.86373427e-01,\n",
       "           3.82525118e-01,   3.78715138e-01,   3.74943106e-01,\n",
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       "           3.60226953e-01,   3.56639065e-01,   3.53086913e-01,\n",
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       "           3.10003661e-01,   3.06916001e-01,   3.03859094e-01,\n",
       "           3.00832634e-01,   2.97836318e-01,   2.94869846e-01,\n",
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       "           2.83296490e-01,   2.80474835e-01,   2.77681285e-01,\n",
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       "           2.36586487e-01,   2.34230068e-01,   2.31897118e-01,\n",
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       "           2.16204290e-01,   2.14050879e-01,   2.11918916e-01,\n",
       "           2.09808187e-01,   2.07718482e-01,   2.05649590e-01,\n",
       "           2.03601304e-01,   2.01573419e-01,   1.99565733e-01,\n",
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       "           1.91732971e-01,   1.89823296e-01,   1.87932642e-01,\n",
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       "           1.70031466e-01,   1.68337939e-01,   1.66661281e-01,\n",
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       "           1.60119987e-01,   1.58525180e-01,   1.56946257e-01,\n",
       "           1.55383060e-01,   1.53835433e-01,   1.52303220e-01,\n",
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       "           1.33719360e-01,   1.32387505e-01,   1.31068915e-01,\n",
       "           1.29763459e-01,   1.28471005e-01,   1.27191424e-01,\n",
       "           1.25924588e-01,   1.24670369e-01,   1.23428643e-01,\n",
       "           1.22199284e-01,   1.20982170e-01,   1.19777178e-01,\n",
       "           1.18584188e-01,   1.17403081e-01,   1.16233737e-01,\n",
       "           1.15076040e-01,   1.13929874e-01,   1.12795124e-01,\n",
       "           1.11671676e-01,   1.10559417e-01,   1.09458237e-01,\n",
       "           1.08368025e-01,   1.07288671e-01,   1.06220067e-01,\n",
       "           1.05162107e-01,   1.04114685e-01,   1.03077695e-01,\n",
       "           1.02051033e-01,   1.01034597e-01,   1.00028284e-01,\n",
       "           9.90319951e-02,   9.80456289e-02,   9.70690871e-02,\n",
       "           9.61022717e-02,   9.51450858e-02,   9.41974336e-02,\n",
       "           9.32592199e-02,   9.23303510e-02,   9.14107336e-02,\n",
       "           9.05002758e-02,   8.95988862e-02,   8.87064746e-02,\n",
       "           8.78229514e-02,   8.69482282e-02,   8.60822172e-02,\n",
       "           8.52248317e-02,   8.43759859e-02,   8.35355947e-02,\n",
       "           8.27035738e-02,   8.18798400e-02,   8.10643106e-02,\n",
       "           8.02569040e-02,   7.94575391e-02,   7.86661360e-02,\n",
       "           7.78826152e-02,   7.71068985e-02,   7.63389079e-02,\n",
       "           7.55785666e-02,   7.48257984e-02,   7.40805278e-02,\n",
       "           7.33426802e-02,   7.26121815e-02,   7.18889586e-02,\n",
       "           7.11729391e-02,   7.04640512e-02,   6.97622239e-02,\n",
       "           6.90673869e-02,   6.83794706e-02,   6.76984059e-02,\n",
       "           6.70241246e-02,   6.63565592e-02,   6.56956428e-02,\n",
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       "           6.31171528e-02,   6.24885012e-02,   6.18661110e-02,\n",
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       "           5.11510201e-02,   5.06415516e-02,   5.01371574e-02,\n",
       "           4.96377869e-02,   4.91433900e-02,   4.86539174e-02,\n",
       "           4.81693200e-02,   4.76895492e-02,   4.72145571e-02,\n",
       "           4.67442961e-02,   4.62787192e-02,   4.58177795e-02,\n",
       "           4.53614308e-02,   4.49096274e-02,   4.44623239e-02,\n",
       "           4.40194755e-02,   4.35810379e-02,   4.31469671e-02,\n",
       "           4.27172197e-02,   4.22917526e-02,   4.18705232e-02,\n",
       "           4.14534893e-02,   4.10406091e-02,   4.06318413e-02,\n",
       "           4.02271450e-02,   3.98264796e-02,   3.94298049e-02,\n",
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       "           3.78822234e-02,   3.75049136e-02,   3.71313617e-02,\n",
       "           3.67615305e-02,   3.63953828e-02,   3.60328820e-02,\n",
       "           3.56739917e-02,   3.53186760e-02,   3.49668993e-02,\n",
       "           3.46186263e-02,   3.42738222e-02,   3.39324523e-02,\n",
       "           3.35944824e-02,   3.32598787e-02,   3.29286077e-02,\n",
       "           3.26006362e-02,   3.22759313e-02,   3.19544604e-02,\n",
       "           3.16361914e-02,   3.13210924e-02,   3.10091319e-02,\n",
       "           3.07002785e-02,   3.03945013e-02,   3.00917698e-02,\n",
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       "           2.72254336e-02,   2.69542662e-02,   2.66857996e-02,\n",
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       "           2.48799312e-02,   2.46321251e-02,   2.43867872e-02,\n",
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       "           1.40622074e-02,   1.39221465e-02,   1.37834807e-02,\n",
       "           1.36461961e-02,   1.35102789e-02,   1.33757154e-02,\n",
       "           1.32424922e-02,   1.31105958e-02,   1.29800130e-02,\n",
       "           1.28507309e-02,   1.27227365e-02,   1.25960170e-02,\n",
       "           1.24705596e-02,   1.23463519e-02,   1.22233811e-02,\n",
       "           1.21016351e-02,   1.19811018e-02,   1.18617689e-02,\n",
       "           1.17436246e-02,   1.16266572e-02,   1.15108548e-02,\n",
       "           1.13962057e-02,   1.12826985e-02,   1.11703218e-02,\n",
       "           1.10590643e-02,   1.09489151e-02,   1.08398629e-02,\n",
       "           1.07318970e-02,   1.06250065e-02,   1.05191805e-02,\n",
       "           1.04144086e-02,   1.03106801e-02,   1.02079848e-02,\n",
       "           1.01063123e-02,   1.00056526e-02,   9.90599552e-03,\n",
       "           9.80733100e-03,   9.70964914e-03,   9.61294017e-03,\n",
       "           9.51719441e-03,   9.42240228e-03,   9.32855431e-03,\n",
       "           9.23564114e-03,   9.14365344e-03,   9.05258190e-03,\n",
       "           8.96241740e-03,   8.87315092e-03,   8.78477352e-03,\n",
       "           8.69727637e-03,   8.61065073e-03,   8.52488796e-03,\n",
       "           8.43997940e-03,   8.35591650e-03,   8.27269085e-03,\n",
       "           8.19029410e-03,   8.10871801e-03,   8.02795444e-03,\n",
       "           7.94799532e-03,   7.86883282e-03,   7.79045854e-03,\n",
       "           7.71286487e-03,   7.63604404e-03,   7.55998833e-03,\n",
       "           7.48469015e-03,   7.41014196e-03,   7.33633632e-03,\n",
       "           7.26326583e-03,   7.19092304e-03,   7.11930069e-03,\n",
       "           7.04839163e-03,   6.97818876e-03,   6.90868504e-03,\n",
       "           6.83987355e-03,   6.77174739e-03,   6.70429976e-03,\n",
       "           6.63752393e-03,   6.57141322e-03,   6.50596104e-03,\n",
       "           6.44116084e-03,   6.37700616e-03,   6.31349060e-03,\n",
       "           6.25060778e-03,   6.18835141e-03,   6.12671524e-03,\n",
       "           6.06569310e-03,   6.00527886e-03,   5.94546647e-03,\n",
       "           5.88624992e-03,   5.82762327e-03,   5.76958060e-03,\n",
       "           5.71211608e-03,   5.65522390e-03,   5.59889832e-03,\n",
       "           5.54313364e-03,   5.48792420e-03,   5.43326440e-03,\n",
       "           5.37914873e-03,   5.32557202e-03,   5.27252896e-03,\n",
       "           5.22001422e-03,   5.16802256e-03,   5.11654875e-03,\n",
       "           5.06558765e-03,   5.01513413e-03,   4.96518315e-03,\n",
       "           4.91572969e-03,   4.86676878e-03,   4.81829552e-03,\n",
       "           4.77030503e-03,   4.72279249e-03,   4.67575313e-03,\n",
       "           4.62918221e-03,   4.58307514e-03,   4.53742732e-03,\n",
       "           4.49223416e-03,   4.44749115e-03,   4.40319380e-03,\n",
       "           4.35933768e-03,   4.31591838e-03,   4.27293155e-03,\n",
       "           4.23037288e-03,   4.18823811e-03,   4.14652301e-03,\n",
       "           4.10522339e-03,   4.06433510e-03,   4.02385404e-03,\n",
       "           3.98377614e-03,   3.94409742e-03,   3.90481392e-03,\n",
       "           3.86592171e-03,   3.82741689e-03,   3.78929559e-03,\n",
       "           3.75155401e-03,   3.71418834e-03,   3.67719486e-03,\n",
       "           3.64056984e-03,   3.60430961e-03,   3.56841053e-03,\n",
       "           3.53286899e-03,   3.49768143e-03,   3.46284431e-03,\n",
       "           3.42835420e-03,   3.39420750e-03,   3.36040092e-03,\n",
       "           3.32693107e-03,   3.29379459e-03,   3.26098818e-03,\n",
       "           3.22850852e-03,   3.19635238e-03,   3.16451652e-03,\n",
       "           3.13299776e-03,   3.10179293e-03,   3.07089890e-03,\n",
       "           3.04031257e-03,   3.01003087e-03,   2.98005076e-03,\n",
       "           2.95036923e-03,   2.92098333e-03,   2.89189016e-03,\n",
       "           2.86308679e-03,   2.83457035e-03,   2.80633798e-03,\n",
       "           2.77838684e-03,   2.75071414e-03,   2.72331711e-03,\n",
       "           2.69619298e-03,   2.66933905e-03,   2.64275262e-03,\n",
       "           2.61643102e-03,   2.59037161e-03,   2.56457176e-03,\n",
       "           2.53902890e-03,   2.51374045e-03,   2.48870387e-03,\n",
       "           2.46391663e-03,   2.43937626e-03,   2.41508027e-03,\n",
       "           2.39102622e-03,   2.36721168e-03,   2.34363426e-03,\n",
       "           2.32029158e-03,   2.29718129e-03,   2.27430110e-03,\n",
       "           2.25164874e-03,   2.22922194e-03,   2.20701846e-03,\n",
       "           2.18503606e-03,   2.16327255e-03,   2.14172573e-03,\n",
       "           2.12039346e-03,   2.09927360e-03,   2.07836403e-03,\n",
       "           2.05766266e-03,   2.03716742e-03,   2.01687627e-03,\n",
       "           1.99678718e-03,   1.97689814e-03,   1.95720718e-03,\n",
       "           1.93771234e-03,   1.91841168e-03,   1.89930328e-03,\n",
       "           1.88038525e-03,   1.86165571e-03,   1.84311282e-03,\n",
       "           1.82475475e-03,   1.80657969e-03,   1.78858586e-03,\n",
       "           1.77077132e-03,   1.75313420e-03,   1.73567274e-03,\n",
       "           1.71838516e-03,   1.70126976e-03,   1.68432480e-03,\n",
       "           1.66754859e-03,   1.65093946e-03,   1.63449573e-03,\n",
       "           1.61821576e-03,   1.60209792e-03,   1.58614061e-03,\n",
       "           1.57034222e-03,   1.55470117e-03,   1.53921591e-03,\n",
       "           1.52388489e-03,   1.50870658e-03,   1.49367946e-03,\n",
       "           1.47880204e-03,   1.46407284e-03,   1.44949040e-03,\n",
       "           1.43505326e-03,   1.42076000e-03,   1.40660919e-03,\n",
       "           1.39259944e-03,   1.37872928e-03,   1.36499727e-03,\n",
       "           1.35140204e-03,   1.33794222e-03,   1.32461647e-03,\n",
       "           1.31142343e-03,   1.29836180e-03,   1.28543027e-03,\n",
       "           1.27262752e-03,   1.25995229e-03,   1.24740330e-03,\n",
       "           1.23497929e-03,   1.22267901e-03,   1.21050124e-03,\n",
       "           1.19844475e-03,   1.18650833e-03,   1.17469079e-03,\n",
       "           1.16299095e-03,   1.15140762e-03,   1.13993966e-03,\n",
       "           1.12858591e-03,   1.11734524e-03,   1.10621651e-03,\n",
       "           1.09519863e-03,   1.08429048e-03,   1.07349097e-03,\n",
       "           1.06279900e-03,   1.05221349e-03,   1.04173340e-03,\n",
       "           1.03135767e-03,   1.02108525e-03,   1.01091513e-03,\n",
       "           1.00084628e-03,   9.90877687e-04,   9.81008366e-04,\n",
       "           9.71237324e-04,   9.61563584e-04,   9.51986179e-04,\n",
       "           9.42504153e-04,   9.33116557e-04,   9.23822456e-04,\n",
       "           9.14620921e-04,   9.05511036e-04,   8.96491893e-04,\n",
       "           8.87562594e-04,   8.78722250e-04,   8.69969982e-04,\n",
       "           8.61304921e-04,   8.52726207e-04,   8.44232989e-04,\n",
       "           8.35824384e-04,   8.27499532e-04,   8.19257597e-04,\n",
       "           8.11097755e-04,   8.03019187e-04,   7.95021084e-04,\n",
       "           7.87102645e-04,   7.79263076e-04,   7.71501592e-04,\n",
       "           7.63817415e-04,   7.56209775e-04,   7.48677909e-04,\n",
       "           7.41221063e-04,   7.33838490e-04,   7.26529449e-04,\n",
       "           7.19293208e-04,   7.12129042e-04,   7.05036233e-04,\n",
       "           6.98014069e-04,   6.91061847e-04,   6.84178871e-04,\n",
       "           6.77364451e-04,   6.70617902e-04,   6.63938551e-04,\n",
       "           6.57325397e-04,   6.50778507e-04,   6.44296809e-04,\n",
       "           6.37879656e-04,   6.31526404e-04,   6.25236421e-04,\n",
       "           6.19009075e-04,   6.12843745e-04,   6.06739813e-04,\n",
       "           6.00696668e-04,   5.94713706e-04,   5.88790327e-04,\n",
       "           5.82925939e-04,   5.77119954e-04,   5.71371792e-04,\n",
       "           5.65680877e-04,   5.60046640e-04,   5.54468516e-04,\n",
       "           5.48945948e-04,   5.43478383e-04,   5.38065275e-04,\n",
       "           5.32706083e-04,   5.27400271e-04,   5.22147309e-04,\n",
       "           5.16946673e-04,   5.11797844e-04,   5.06700304e-04,\n",
       "           5.01653541e-04,   4.96657045e-04,   4.91710316e-04,\n",
       "           4.86812853e-04,   4.81964163e-04,   4.77163762e-04,\n",
       "           4.72411170e-04,   4.67705913e-04,   4.63047520e-04,\n",
       "           4.58435525e-04,   4.53869471e-04,   4.49348896e-04,\n",
       "           4.44873348e-04,   4.40442377e-04,   4.36055538e-04,\n",
       "           4.31712393e-04,   4.27412505e-04,   4.23155444e-04,\n",
       "           4.18940783e-04,   4.14768101e-04,   4.10636979e-04,\n",
       "           4.06547003e-04,   4.02497764e-04,   3.98488856e-04,\n",
       "           3.94519876e-04,   3.90590428e-04,   3.86700117e-04,\n",
       "           3.82848555e-04,   3.79035351e-04,   3.75260132e-04,\n",
       "           3.71522513e-04,   3.67822120e-04,   3.64158584e-04,\n",
       "           3.60531537e-04,   3.56940615e-04,   3.53385459e-04,\n",
       "           3.49865713e-04,   3.46381023e-04,   3.42931040e-04,\n",
       "           3.39515420e-04,   3.36133819e-04,   3.32785899e-04,\n",
       "           3.29471326e-04,   3.26189765e-04,   3.22940888e-04,\n",
       "           3.19724369e-04,   3.16539885e-04,   3.13387119e-04,\n",
       "           3.10265755e-04,   3.07175482e-04,   3.04115990e-04,\n",
       "           3.01086970e-04,   2.98088118e-04,   2.95119134e-04,\n",
       "           2.92179721e-04,   2.89269584e-04,   2.86388433e-04,\n",
       "           2.83535980e-04,   2.80711940e-04,   2.77916026e-04,\n",
       "           2.75147958e-04,   2.72407460e-04,   2.69694257e-04,\n",
       "           2.67008077e-04,   2.64348653e-04,   2.61715328e-04,\n",
       "           2.59108787e-04,   2.56528146e-04,   2.53973166e-04,\n",
       "           2.51443602e-04,   2.48939215e-04,   2.46459763e-04,\n",
       "           2.44005003e-04,   2.41574695e-04,   2.39168607e-04,\n",
       "           2.36786503e-04,   2.34428143e-04,   2.32093289e-04,\n",
       "           2.29781702e-04,   2.27493142e-04,   2.25227370e-04,\n",
       "           2.22984148e-04,   2.20763236e-04,   2.18564429e-04,\n",
       "           2.16387554e-04,   2.14232389e-04,   2.12098712e-04,\n",
       "           2.09986303e-04,   2.07894940e-04,   2.05824401e-04,\n",
       "           2.03774466e-04,   2.01744913e-04,   1.99735528e-04,\n",
       "           1.97746171e-04,   1.95776655e-04,   1.93826779e-04,\n",
       "           1.91896342e-04,   1.89985142e-04,   1.88092978e-04,\n",
       "           1.86219649e-04,   1.84364954e-04,   1.82528691e-04,\n",
       "           1.80711121e-04,   1.78911098e-04,   1.77129079e-04,\n",
       "           1.75364868e-04,   1.73618270e-04,   1.71889093e-04,\n",
       "           1.70177145e-04,   1.68482237e-04,   1.66804184e-04,\n",
       "           1.65142806e-04,   1.63497973e-04,   1.61869521e-04,\n",
       "           1.60257283e-04,   1.58661095e-04,   1.57080793e-04,\n",
       "           1.55516216e-04,   1.53967208e-04,   1.52433613e-04,\n",
       "           1.50915279e-04,   1.49412055e-04,   1.47923794e-04,\n",
       "           1.46450350e-04,   1.44991582e-04,   1.43547348e-04,\n",
       "           1.42117511e-04,   1.40701937e-04,   1.39300491e-04,\n",
       "           1.37913024e-04,   1.36539355e-04,   1.35179344e-04,\n",
       "           1.33832856e-04,   1.32499759e-04,   1.31179919e-04,\n",
       "           1.29873208e-04,   1.28579498e-04,   1.27298662e-04,\n",
       "           1.26030577e-04,   1.24775120e-04,   1.23532171e-04,\n",
       "           1.22301612e-04,   1.21083325e-04,   1.19877196e-04,\n",
       "           1.18683111e-04,   1.17500961e-04,   1.16330636e-04,\n",
       "           1.15171988e-04,   1.14024878e-04,   1.12889189e-04,\n",
       "           1.11764807e-04,   1.10651620e-04,   1.09549516e-04,\n",
       "           1.08458385e-04,   1.07378118e-04,   1.06308608e-04,\n",
       "           1.05249747e-04,   1.04201432e-04,   1.03163557e-04,\n",
       "           1.02136020e-04,   1.01118721e-04,   1.00111558e-04,\n",
       "           9.91144330e-05,   9.81272485e-05,   9.71499066e-05,\n",
       "           9.61822970e-05,   9.52243192e-05,   9.42758771e-05,\n",
       "           9.33368757e-05,   9.24072211e-05,   9.14868205e-05,\n",
       "           9.05755822e-05,   8.96734155e-05,   8.87802309e-05,\n",
       "           8.78959399e-05,   8.70204550e-05,   8.61536900e-05,\n",
       "           8.52955595e-05,   8.44459796e-05,   8.36048670e-05,\n",
       "           8.27721399e-05,   8.19477172e-05,   8.11316350e-05,\n",
       "           8.03235269e-05,   7.95234697e-05,   7.87313820e-05,\n",
       "           7.79471834e-05,   7.71707949e-05,   7.64021380e-05,\n",
       "           7.56411357e-05,   7.48877117e-05,   7.41417909e-05,\n",
       "           7.34032990e-05,   7.26721629e-05,   7.19483103e-05,\n",
       "           7.12316700e-05,   7.05221716e-05,   6.98197459e-05,\n",
       "           6.91243242e-05,   6.84358393e-05,   6.77542128e-05,\n",
       "           6.70793652e-05,   6.64112292e-05,   6.57497380e-05,\n",
       "           6.50948259e-05,   6.44464277e-05,   6.38044790e-05,\n",
       "           6.31689162e-05,   6.25396762e-05,   6.19166969e-05,\n",
       "           6.12999165e-05,   6.06892742e-05,   6.00847097e-05,\n",
       "           5.94861633e-05,   5.88935761e-05,   5.83068896e-05,\n",
       "           5.77260461e-05,   5.71509884e-05,   5.65816600e-05,\n",
       "           5.60180046e-05,   5.54599670e-05,   5.49074921e-05,\n",
       "           5.43605256e-05,   5.38190135e-05,   5.32829026e-05,\n",
       "           5.27521399e-05,   5.22266730e-05,   5.17064501e-05,\n",
       "           5.11914196e-05,   5.06815305e-05,   5.01767323e-05,\n",
       "           4.96769748e-05,   4.91822083e-05,   4.86923833e-05,\n",
       "           4.82074510e-05,   4.77273657e-05,   4.72520823e-05,\n",
       "           4.67815536e-05,   4.63157326e-05,   4.58545728e-05,\n",
       "           4.53980280e-05]),\n",
       "  't': array([  0.        ,   0.01001001,   0.02002002,   0.03003003,\n",
       "           0.04004004,   0.05005005,   0.06006006,   0.07007007,\n",
       "           0.08008008,   0.09009009,   0.1001001 ,   0.11011011,\n",
       "           0.12012012,   0.13013013,   0.14014014,   0.15015015,\n",
       "           0.16016016,   0.17017017,   0.18018018,   0.19019019,\n",
       "           0.2002002 ,   0.21021021,   0.22022022,   0.23023023,\n",
       "           0.24024024,   0.25025025,   0.26026026,   0.27027027,\n",
       "           0.28028028,   0.29029029,   0.3003003 ,   0.31031031,\n",
       "           0.32032032,   0.33033033,   0.34034034,   0.35035035,\n",
       "           0.36036036,   0.37037037,   0.38038038,   0.39039039,\n",
       "           0.4004004 ,   0.41041041,   0.42042042,   0.43043043,\n",
       "           0.44044044,   0.45045045,   0.46046046,   0.47047047,\n",
       "           0.48048048,   0.49049049,   0.5005005 ,   0.51051051,\n",
       "           0.52052052,   0.53053053,   0.54054054,   0.55055055,\n",
       "           0.56056056,   0.57057057,   0.58058058,   0.59059059,\n",
       "           0.6006006 ,   0.61061061,   0.62062062,   0.63063063,\n",
       "           0.64064064,   0.65065065,   0.66066066,   0.67067067,\n",
       "           0.68068068,   0.69069069,   0.7007007 ,   0.71071071,\n",
       "           0.72072072,   0.73073073,   0.74074074,   0.75075075,\n",
       "           0.76076076,   0.77077077,   0.78078078,   0.79079079,\n",
       "           0.8008008 ,   0.81081081,   0.82082082,   0.83083083,\n",
       "           0.84084084,   0.85085085,   0.86086086,   0.87087087,\n",
       "           0.88088088,   0.89089089,   0.9009009 ,   0.91091091,\n",
       "           0.92092092,   0.93093093,   0.94094094,   0.95095095,\n",
       "           0.96096096,   0.97097097,   0.98098098,   0.99099099,\n",
       "           1.001001  ,   1.01101101,   1.02102102,   1.03103103,\n",
       "           1.04104104,   1.05105105,   1.06106106,   1.07107107,\n",
       "           1.08108108,   1.09109109,   1.1011011 ,   1.11111111,\n",
       "           1.12112112,   1.13113113,   1.14114114,   1.15115115,\n",
       "           1.16116116,   1.17117117,   1.18118118,   1.19119119,\n",
       "           1.2012012 ,   1.21121121,   1.22122122,   1.23123123,\n",
       "           1.24124124,   1.25125125,   1.26126126,   1.27127127,\n",
       "           1.28128128,   1.29129129,   1.3013013 ,   1.31131131,\n",
       "           1.32132132,   1.33133133,   1.34134134,   1.35135135,\n",
       "           1.36136136,   1.37137137,   1.38138138,   1.39139139,\n",
       "           1.4014014 ,   1.41141141,   1.42142142,   1.43143143,\n",
       "           1.44144144,   1.45145145,   1.46146146,   1.47147147,\n",
       "           1.48148148,   1.49149149,   1.5015015 ,   1.51151151,\n",
       "           1.52152152,   1.53153153,   1.54154154,   1.55155155,\n",
       "           1.56156156,   1.57157157,   1.58158158,   1.59159159,\n",
       "           1.6016016 ,   1.61161161,   1.62162162,   1.63163163,\n",
       "           1.64164164,   1.65165165,   1.66166166,   1.67167167,\n",
       "           1.68168168,   1.69169169,   1.7017017 ,   1.71171171,\n",
       "           1.72172172,   1.73173173,   1.74174174,   1.75175175,\n",
       "           1.76176176,   1.77177177,   1.78178178,   1.79179179,\n",
       "           1.8018018 ,   1.81181181,   1.82182182,   1.83183183,\n",
       "           1.84184184,   1.85185185,   1.86186186,   1.87187187,\n",
       "           1.88188188,   1.89189189,   1.9019019 ,   1.91191191,\n",
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       "           2.44244244,   2.45245245,   2.46246246,   2.47247247,\n",
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       "           2.52252252,   2.53253253,   2.54254254,   2.55255255,\n",
       "           2.56256256,   2.57257257,   2.58258258,   2.59259259,\n",
       "           2.6026026 ,   2.61261261,   2.62262262,   2.63263263,\n",
       "           2.64264264,   2.65265265,   2.66266266,   2.67267267,\n",
       "           2.68268268,   2.69269269,   2.7027027 ,   2.71271271,\n",
       "           2.72272272,   2.73273273,   2.74274274,   2.75275275,\n",
       "           2.76276276,   2.77277277,   2.78278278,   2.79279279,\n",
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       "           2.84284284,   2.85285285,   2.86286286,   2.87287287,\n",
       "           2.88288288,   2.89289289,   2.9029029 ,   2.91291291,\n",
       "           2.92292292,   2.93293293,   2.94294294,   2.95295295,\n",
       "           2.96296296,   2.97297297,   2.98298298,   2.99299299,\n",
       "           3.003003  ,   3.01301301,   3.02302302,   3.03303303,\n",
       "           3.04304304,   3.05305305,   3.06306306,   3.07307307,\n",
       "           3.08308308,   3.09309309,   3.1031031 ,   3.11311311,\n",
       "           3.12312312,   3.13313313,   3.14314314,   3.15315315,\n",
       "           3.16316316,   3.17317317,   3.18318318,   3.19319319,\n",
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       "           3.24324324,   3.25325325,   3.26326326,   3.27327327,\n",
       "           3.28328328,   3.29329329,   3.3033033 ,   3.31331331,\n",
       "           3.32332332,   3.33333333,   3.34334334,   3.35335335,\n",
       "           3.36336336,   3.37337337,   3.38338338,   3.39339339,\n",
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       "           3.44344344,   3.45345345,   3.46346346,   3.47347347,\n",
       "           3.48348348,   3.49349349,   3.5035035 ,   3.51351351,\n",
       "           3.52352352,   3.53353353,   3.54354354,   3.55355355,\n",
       "           3.56356356,   3.57357357,   3.58358358,   3.59359359,\n",
       "           3.6036036 ,   3.61361361,   3.62362362,   3.63363363,\n",
       "           3.64364364,   3.65365365,   3.66366366,   3.67367367,\n",
       "           3.68368368,   3.69369369,   3.7037037 ,   3.71371371,\n",
       "           3.72372372,   3.73373373,   3.74374374,   3.75375375,\n",
       "           3.76376376,   3.77377377,   3.78378378,   3.79379379,\n",
       "           3.8038038 ,   3.81381381,   3.82382382,   3.83383383,\n",
       "           3.84384384,   3.85385385,   3.86386386,   3.87387387,\n",
       "           3.88388388,   3.89389389,   3.9039039 ,   3.91391391,\n",
       "           3.92392392,   3.93393393,   3.94394394,   3.95395395,\n",
       "           3.96396396,   3.97397397,   3.98398398,   3.99399399,\n",
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       "           4.04404404,   4.05405405,   4.06406406,   4.07407407,\n",
       "           4.08408408,   4.09409409,   4.1041041 ,   4.11411411,\n",
       "           4.12412412,   4.13413413,   4.14414414,   4.15415415,\n",
       "           4.16416416,   4.17417417,   4.18418418,   4.19419419,\n",
       "           4.2042042 ,   4.21421421,   4.22422422,   4.23423423,\n",
       "           4.24424424,   4.25425425,   4.26426426,   4.27427427,\n",
       "           4.28428428,   4.29429429,   4.3043043 ,   4.31431431,\n",
       "           4.32432432,   4.33433433,   4.34434434,   4.35435435,\n",
       "           4.36436436,   4.37437437,   4.38438438,   4.39439439,\n",
       "           4.4044044 ,   4.41441441,   4.42442442,   4.43443443,\n",
       "           4.44444444,   4.45445445,   4.46446446,   4.47447447,\n",
       "           4.48448448,   4.49449449,   4.5045045 ,   4.51451451,\n",
       "           4.52452452,   4.53453453,   4.54454454,   4.55455455,\n",
       "           4.56456456,   4.57457457,   4.58458458,   4.59459459,\n",
       "           4.6046046 ,   4.61461461,   4.62462462,   4.63463463,\n",
       "           4.64464464,   4.65465465,   4.66466466,   4.67467467,\n",
       "           4.68468468,   4.69469469,   4.7047047 ,   4.71471471,\n",
       "           4.72472472,   4.73473473,   4.74474474,   4.75475475,\n",
       "           4.76476476,   4.77477477,   4.78478478,   4.79479479,\n",
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       "           4.84484484,   4.85485485,   4.86486486,   4.87487487,\n",
       "           4.88488488,   4.89489489,   4.9049049 ,   4.91491491,\n",
       "           4.92492492,   4.93493493,   4.94494494,   4.95495495,\n",
       "           4.96496496,   4.97497497,   4.98498498,   4.99499499,\n",
       "           5.00500501,   5.01501502,   5.02502503,   5.03503504,\n",
       "           5.04504505,   5.05505506,   5.06506507,   5.07507508,\n",
       "           5.08508509,   5.0950951 ,   5.10510511,   5.11511512,\n",
       "           5.12512513,   5.13513514,   5.14514515,   5.15515516,\n",
       "           5.16516517,   5.17517518,   5.18518519,   5.1951952 ,\n",
       "           5.20520521,   5.21521522,   5.22522523,   5.23523524,\n",
       "           5.24524525,   5.25525526,   5.26526527,   5.27527528,\n",
       "           5.28528529,   5.2952953 ,   5.30530531,   5.31531532,\n",
       "           5.32532533,   5.33533534,   5.34534535,   5.35535536,\n",
       "           5.36536537,   5.37537538,   5.38538539,   5.3953954 ,\n",
       "           5.40540541,   5.41541542,   5.42542543,   5.43543544,\n",
       "           5.44544545,   5.45545546,   5.46546547,   5.47547548,\n",
       "           5.48548549,   5.4954955 ,   5.50550551,   5.51551552,\n",
       "           5.52552553,   5.53553554,   5.54554555,   5.55555556,\n",
       "           5.56556557,   5.57557558,   5.58558559,   5.5955956 ,\n",
       "           5.60560561,   5.61561562,   5.62562563,   5.63563564,\n",
       "           5.64564565,   5.65565566,   5.66566567,   5.67567568,\n",
       "           5.68568569,   5.6956957 ,   5.70570571,   5.71571572,\n",
       "           5.72572573,   5.73573574,   5.74574575,   5.75575576,\n",
       "           5.76576577,   5.77577578,   5.78578579,   5.7957958 ,\n",
       "           5.80580581,   5.81581582,   5.82582583,   5.83583584,\n",
       "           5.84584585,   5.85585586,   5.86586587,   5.87587588,\n",
       "           5.88588589,   5.8958959 ,   5.90590591,   5.91591592,\n",
       "           5.92592593,   5.93593594,   5.94594595,   5.95595596,\n",
       "           5.96596597,   5.97597598,   5.98598599,   5.995996  ,\n",
       "           6.00600601,   6.01601602,   6.02602603,   6.03603604,\n",
       "           6.04604605,   6.05605606,   6.06606607,   6.07607608,\n",
       "           6.08608609,   6.0960961 ,   6.10610611,   6.11611612,\n",
       "           6.12612613,   6.13613614,   6.14614615,   6.15615616,\n",
       "           6.16616617,   6.17617618,   6.18618619,   6.1961962 ,\n",
       "           6.20620621,   6.21621622,   6.22622623,   6.23623624,\n",
       "           6.24624625,   6.25625626,   6.26626627,   6.27627628,\n",
       "           6.28628629,   6.2962963 ,   6.30630631,   6.31631632,\n",
       "           6.32632633,   6.33633634,   6.34634635,   6.35635636,\n",
       "           6.36636637,   6.37637638,   6.38638639,   6.3963964 ,\n",
       "           6.40640641,   6.41641642,   6.42642643,   6.43643644,\n",
       "           6.44644645,   6.45645646,   6.46646647,   6.47647648,\n",
       "           6.48648649,   6.4964965 ,   6.50650651,   6.51651652,\n",
       "           6.52652653,   6.53653654,   6.54654655,   6.55655656,\n",
       "           6.56656657,   6.57657658,   6.58658659,   6.5965966 ,\n",
       "           6.60660661,   6.61661662,   6.62662663,   6.63663664,\n",
       "           6.64664665,   6.65665666,   6.66666667,   6.67667668,\n",
       "           6.68668669,   6.6966967 ,   6.70670671,   6.71671672,\n",
       "           6.72672673,   6.73673674,   6.74674675,   6.75675676,\n",
       "           6.76676677,   6.77677678,   6.78678679,   6.7967968 ,\n",
       "           6.80680681,   6.81681682,   6.82682683,   6.83683684,\n",
       "           6.84684685,   6.85685686,   6.86686687,   6.87687688,\n",
       "           6.88688689,   6.8968969 ,   6.90690691,   6.91691692,\n",
       "           6.92692693,   6.93693694,   6.94694695,   6.95695696,\n",
       "           6.96696697,   6.97697698,   6.98698699,   6.996997  ,\n",
       "           7.00700701,   7.01701702,   7.02702703,   7.03703704,\n",
       "           7.04704705,   7.05705706,   7.06706707,   7.07707708,\n",
       "           7.08708709,   7.0970971 ,   7.10710711,   7.11711712,\n",
       "           7.12712713,   7.13713714,   7.14714715,   7.15715716,\n",
       "           7.16716717,   7.17717718,   7.18718719,   7.1971972 ,\n",
       "           7.20720721,   7.21721722,   7.22722723,   7.23723724,\n",
       "           7.24724725,   7.25725726,   7.26726727,   7.27727728,\n",
       "           7.28728729,   7.2972973 ,   7.30730731,   7.31731732,\n",
       "           7.32732733,   7.33733734,   7.34734735,   7.35735736,\n",
       "           7.36736737,   7.37737738,   7.38738739,   7.3973974 ,\n",
       "           7.40740741,   7.41741742,   7.42742743,   7.43743744,\n",
       "           7.44744745,   7.45745746,   7.46746747,   7.47747748,\n",
       "           7.48748749,   7.4974975 ,   7.50750751,   7.51751752,\n",
       "           7.52752753,   7.53753754,   7.54754755,   7.55755756,\n",
       "           7.56756757,   7.57757758,   7.58758759,   7.5975976 ,\n",
       "           7.60760761,   7.61761762,   7.62762763,   7.63763764,\n",
       "           7.64764765,   7.65765766,   7.66766767,   7.67767768,\n",
       "           7.68768769,   7.6976977 ,   7.70770771,   7.71771772,\n",
       "           7.72772773,   7.73773774,   7.74774775,   7.75775776,\n",
       "           7.76776777,   7.77777778,   7.78778779,   7.7977978 ,\n",
       "           7.80780781,   7.81781782,   7.82782783,   7.83783784,\n",
       "           7.84784785,   7.85785786,   7.86786787,   7.87787788,\n",
       "           7.88788789,   7.8978979 ,   7.90790791,   7.91791792,\n",
       "           7.92792793,   7.93793794,   7.94794795,   7.95795796,\n",
       "           7.96796797,   7.97797798,   7.98798799,   7.997998  ,\n",
       "           8.00800801,   8.01801802,   8.02802803,   8.03803804,\n",
       "           8.04804805,   8.05805806,   8.06806807,   8.07807808,\n",
       "           8.08808809,   8.0980981 ,   8.10810811,   8.11811812,\n",
       "           8.12812813,   8.13813814,   8.14814815,   8.15815816,\n",
       "           8.16816817,   8.17817818,   8.18818819,   8.1981982 ,\n",
       "           8.20820821,   8.21821822,   8.22822823,   8.23823824,\n",
       "           8.24824825,   8.25825826,   8.26826827,   8.27827828,\n",
       "           8.28828829,   8.2982983 ,   8.30830831,   8.31831832,\n",
       "           8.32832833,   8.33833834,   8.34834835,   8.35835836,\n",
       "           8.36836837,   8.37837838,   8.38838839,   8.3983984 ,\n",
       "           8.40840841,   8.41841842,   8.42842843,   8.43843844,\n",
       "           8.44844845,   8.45845846,   8.46846847,   8.47847848,\n",
       "           8.48848849,   8.4984985 ,   8.50850851,   8.51851852,\n",
       "           8.52852853,   8.53853854,   8.54854855,   8.55855856,\n",
       "           8.56856857,   8.57857858,   8.58858859,   8.5985986 ,\n",
       "           8.60860861,   8.61861862,   8.62862863,   8.63863864,\n",
       "           8.64864865,   8.65865866,   8.66866867,   8.67867868,\n",
       "           8.68868869,   8.6986987 ,   8.70870871,   8.71871872,\n",
       "           8.72872873,   8.73873874,   8.74874875,   8.75875876,\n",
       "           8.76876877,   8.77877878,   8.78878879,   8.7987988 ,\n",
       "           8.80880881,   8.81881882,   8.82882883,   8.83883884,\n",
       "           8.84884885,   8.85885886,   8.86886887,   8.87887888,\n",
       "           8.88888889,   8.8988989 ,   8.90890891,   8.91891892,\n",
       "           8.92892893,   8.93893894,   8.94894895,   8.95895896,\n",
       "           8.96896897,   8.97897898,   8.98898899,   8.998999  ,\n",
       "           9.00900901,   9.01901902,   9.02902903,   9.03903904,\n",
       "           9.04904905,   9.05905906,   9.06906907,   9.07907908,\n",
       "           9.08908909,   9.0990991 ,   9.10910911,   9.11911912,\n",
       "           9.12912913,   9.13913914,   9.14914915,   9.15915916,\n",
       "           9.16916917,   9.17917918,   9.18918919,   9.1991992 ,\n",
       "           9.20920921,   9.21921922,   9.22922923,   9.23923924,\n",
       "           9.24924925,   9.25925926,   9.26926927,   9.27927928,\n",
       "           9.28928929,   9.2992993 ,   9.30930931,   9.31931932,\n",
       "           9.32932933,   9.33933934,   9.34934935,   9.35935936,\n",
       "           9.36936937,   9.37937938,   9.38938939,   9.3993994 ,\n",
       "           9.40940941,   9.41941942,   9.42942943,   9.43943944,\n",
       "           9.44944945,   9.45945946,   9.46946947,   9.47947948,\n",
       "           9.48948949,   9.4994995 ,   9.50950951,   9.51951952,\n",
       "           9.52952953,   9.53953954,   9.54954955,   9.55955956,\n",
       "           9.56956957,   9.57957958,   9.58958959,   9.5995996 ,\n",
       "           9.60960961,   9.61961962,   9.62962963,   9.63963964,\n",
       "           9.64964965,   9.65965966,   9.66966967,   9.67967968,\n",
       "           9.68968969,   9.6996997 ,   9.70970971,   9.71971972,\n",
       "           9.72972973,   9.73973974,   9.74974975,   9.75975976,\n",
       "           9.76976977,   9.77977978,   9.78978979,   9.7997998 ,\n",
       "           9.80980981,   9.81981982,   9.82982983,   9.83983984,\n",
       "           9.84984985,   9.85985986,   9.86986987,   9.87987988,\n",
       "           9.88988989,   9.8998999 ,   9.90990991,   9.91991992,\n",
       "           9.92992993,   9.93993994,   9.94994995,   9.95995996,\n",
       "           9.96996997,   9.97997998,   9.98998999,  10.        ])}}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.simulate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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