CrossMass.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standard imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import HpAlgorithms\n",
    "import HpTrainingFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mc=pd.read_hdf('/eos/user/j/jglatzer/L2Output_R20_Nov_v2_pandaspython3/pandas_ge6jge4b.h5', 'INC_ge6jge4b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare classifier and training frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "split into test and training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=HpAlgorithms.getGradientBDTClassifier()#options={\"n_estimators\":10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "htf=HpTrainingFrame.HpTrainingFrame(df_mc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calibrate ROC AUC to Cross section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit results: [ 5.84983620e-03 -4.16702246e-03  1.22938606e-03 -1.36789294e-05]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv('aucvsxslimit.txt', sep=\"\\t\", header=None)\n",
    "data.columns=['mass','xslimit','oneminusauc']\n",
    "z = np.polyfit(data.oneminusauc[5:], data.xslimit[5:], 3)\n",
    "p = np.poly1d(z)\n",
    "\n",
    "p1 = plt.plot(data.oneminusauc[5:], data.xslimit[5:], '.', data.oneminusauc[5:], p(data.oneminusauc[5:]), '-')\n",
    "for line in range(5,data.shape[0]):\n",
    "    plt.text(data.oneminusauc[line], data.xslimit[line]+0.000002, data.mass[line], horizontalalignment='left', size='medium', color='black', weight='semibold')\n",
    "\n",
    "plt.gcf().subplots_adjust(left=0.2)\n",
    "plt.xlabel(\"1-ROC AUC(6j4b)\")\n",
    "plt.ylabel(\"95%CL $\\sigma$ * efficiency\")\n",
    "print \"Fit results:\", z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate the cross mass score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training for m(H+)= 200\n",
      "Evaluating for m(H+)= 200  ->  0.3290841140322679\n",
      "Evaluating for m(H+)= 225  ->  0.38970884616572443\n",
      "Evaluating for m(H+)= 250  ->  0.43424471983088697\n",
      "Evaluating for m(H+)= 275  ->  0.46823732698993825\n",
      "Evaluating for m(H+)= 300  ->  0.5004735878084714\n",
      "Evaluating for m(H+)= 350  ->  0.5356872094330141\n",
      "Evaluating for m(H+)= 400  ->  0.5621110848237549\n",
      "Evaluating for m(H+)= 500  ->  0.6166156283891369\n",
      "Evaluating for m(H+)= 600  ->  0.6391848910563508\n",
      "Evaluating for m(H+)= 700  ->  0.6630102109233188\n",
      "Evaluating for m(H+)= 800  ->  0.6824311193432019\n",
      "Evaluating for m(H+)= 900  ->  0.6989353158794472\n",
      "Evaluating for m(H+)= 1000  ->  0.7118059930123791\n",
      "Evaluating for m(H+)= 1200  ->  0.7347691941862875\n",
      "Evaluating for m(H+)= 1400  ->  0.7643910297991211\n",
      "Evaluating for m(H+)= 1600  ->  0.778269778152973\n",
      "Evaluating for m(H+)= 1800  ->  0.798298532598569\n",
      "Evaluating for m(H+)= 2000  ->  0.8191778264083365\n",
      "Training for m(H+)= 225\n",
      "Evaluating for m(H+)= 200  ->  0.3621513777781562\n",
      "Evaluating for m(H+)= 225  ->  0.3566890794845421\n",
      "Evaluating for m(H+)= 250  ->  0.37479892196250786\n",
      "Evaluating for m(H+)= 275  ->  0.40442651861980805\n",
      "Evaluating for m(H+)= 300  ->  0.44121131893615906\n",
      "Evaluating for m(H+)= 350  ->  0.48960056385633055\n",
      "Evaluating for m(H+)= 400  ->  0.528402042633173\n",
      "Evaluating for m(H+)= 500  ->  0.5969963293668108\n",
      "Evaluating for m(H+)= 600  ->  0.6439819989774545\n",
      "Evaluating for m(H+)= 700  ->  0.6808182990935931\n",
      "Evaluating for m(H+)= 800  ->  0.6989928005787843\n",
      "Evaluating for m(H+)= 900  ->  0.7117896958347527\n",
      "Evaluating for m(H+)= 1000  ->  0.7177732004858526\n",
      "Evaluating for m(H+)= 1200  ->  0.7362042602466915\n",
      "Evaluating for m(H+)= 1400  ->  0.7599322730860683\n",
      "Evaluating for m(H+)= 1600  ->  0.76891514688743\n",
      "Evaluating for m(H+)= 1800  ->  0.7821409611151726\n",
      "Evaluating for m(H+)= 2000  ->  0.7964630666297676\n",
      "Training for m(H+)= 250\n",
      "Evaluating for m(H+)= 200  ->  0.4118691393904935\n",
      "Evaluating for m(H+)= 225  ->  0.3655318001294494\n",
      "Evaluating for m(H+)= 250  ->  0.3602662115193581\n",
      "Evaluating for m(H+)= 275  ->  0.362171366594055\n",
      "Evaluating for m(H+)= 300  ->  0.38480947910870533\n",
      "Evaluating for m(H+)= 350  ->  0.4256603280693628\n",
      "Evaluating for m(H+)= 400  ->  0.4715277501974957\n",
      "Evaluating for m(H+)= 500  ->  0.5233819923219309\n",
      "Evaluating for m(H+)= 600  ->  0.5794565322053593\n",
      "Evaluating for m(H+)= 700  ->  0.6211069387072436\n",
      "Evaluating for m(H+)= 800  ->  0.6490173570711284\n",
      "Evaluating for m(H+)= 900  ->  0.6787390929473456\n",
      "Evaluating for m(H+)= 1000  ->  0.6958928550546748\n",
      "Evaluating for m(H+)= 1200  ->  0.7409823829132665\n",
      "Evaluating for m(H+)= 1400  ->  0.777233842582794\n",
      "Evaluating for m(H+)= 1600  ->  0.8026593825850806\n",
      "Evaluating for m(H+)= 1800  ->  0.8218417344656048\n",
      "Evaluating for m(H+)= 2000  ->  0.8418563550640681\n",
      "Training for m(H+)= 275\n",
      "Evaluating for m(H+)= 200  ->  0.4380331365047794\n",
      "Evaluating for m(H+)= 225  ->  0.3835504795115785\n",
      "Evaluating for m(H+)= 250  ->  0.3630112846811686\n",
      "Evaluating for m(H+)= 275  ->  0.34498428916467183\n",
      "Evaluating for m(H+)= 300  ->  0.34167429314175046\n",
      "Evaluating for m(H+)= 350  ->  0.3715761188348976\n",
      "Evaluating for m(H+)= 400  ->  0.39980651415635904\n",
      "Evaluating for m(H+)= 500  ->  0.43995568728098167\n",
      "Evaluating for m(H+)= 600  ->  0.49339852343885915\n",
      "Evaluating for m(H+)= 700  ->  0.5330040014697099\n",
      "Evaluating for m(H+)= 800  ->  0.5594753428801462\n",
      "Evaluating for m(H+)= 900  ->  0.5897270933519434\n",
      "Evaluating for m(H+)= 1000  ->  0.6006394262089396\n",
      "Evaluating for m(H+)= 1200  ->  0.6499394109535934\n",
      "Evaluating for m(H+)= 1400  ->  0.695233949326075\n",
      "Evaluating for m(H+)= 1600  ->  0.7365357185853143\n",
      "Evaluating for m(H+)= 1800  ->  0.7695574892209833\n",
      "Evaluating for m(H+)= 2000  ->  0.8008711543630772\n",
      "Training for m(H+)= 300\n",
      "Evaluating for m(H+)= 200  ->  0.45179162766055836\n",
      "Evaluating for m(H+)= 225  ->  0.40139869029507613\n",
      "Evaluating for m(H+)= 250  ->  0.37992898214417936\n",
      "Evaluating for m(H+)= 275  ->  0.3483103198259889\n",
      "Evaluating for m(H+)= 300  ->  0.33375748714257425\n",
      "Evaluating for m(H+)= 350  ->  0.3392704445111522\n",
      "Evaluating for m(H+)= 400  ->  0.3634610996317946\n",
      "Evaluating for m(H+)= 500  ->  0.3968750469052119\n",
      "Evaluating for m(H+)= 600  ->  0.44613299651231486\n",
      "Evaluating for m(H+)= 700  ->  0.48015265180547306\n",
      "Evaluating for m(H+)= 800  ->  0.5104076052078875\n",
      "Evaluating for m(H+)= 900  ->  0.5404069247267208\n",
      "Evaluating for m(H+)= 1000  ->  0.5599086535748563\n",
      "Evaluating for m(H+)= 1200  ->  0.6083123958436072\n",
      "Evaluating for m(H+)= 1400  ->  0.6519598719444692\n",
      "Evaluating for m(H+)= 1600  ->  0.6916197578618873\n",
      "Evaluating for m(H+)= 1800  ->  0.7174178950546866\n",
      "Evaluating for m(H+)= 2000  ->  0.7455637557110317\n",
      "Training for m(H+)= 350\n",
      "Evaluating for m(H+)= 200  ->  0.4838113944811706\n",
      "Evaluating for m(H+)= 225  ->  0.4461348092637537\n",
      "Evaluating for m(H+)= 250  ->  0.42162495411060286\n",
      "Evaluating for m(H+)= 275  ->  0.38079379224463383\n",
      "Evaluating for m(H+)= 300  ->  0.3470027524658891\n",
      "Evaluating for m(H+)= 350  ->  0.32019286312547357\n",
      "Evaluating for m(H+)= 400  ->  0.3175685329152155\n",
      "Evaluating for m(H+)= 500  ->  0.31981166450847465\n",
      "Evaluating for m(H+)= 600  ->  0.3589684645976432\n",
      "Evaluating for m(H+)= 700  ->  0.38821093751815405\n",
      "Evaluating for m(H+)= 800  ->  0.4136743002562979\n",
      "Evaluating for m(H+)= 900  ->  0.44146810034539785\n",
      "Evaluating for m(H+)= 1000  ->  0.453657078522381\n",
      "Evaluating for m(H+)= 1200  ->  0.4949492187548554\n",
      "Evaluating for m(H+)= 1400  ->  0.547454399704849\n",
      "Evaluating for m(H+)= 1600  ->  0.5965718124920328\n",
      "Evaluating for m(H+)= 1800  ->  0.6379015947283588\n",
      "Evaluating for m(H+)= 2000  ->  0.6779317624864529\n",
      "Training for m(H+)= 400\n",
      "Evaluating for m(H+)= 200  ->  0.50520456444321\n",
      "Evaluating for m(H+)= 225  ->  0.48022191747097676\n",
      "Evaluating for m(H+)= 250  ->  0.45367627191889737\n",
      "Evaluating for m(H+)= 275  ->  0.4139175919349378\n",
      "Evaluating for m(H+)= 300  ->  0.37666591418873074\n",
      "Evaluating for m(H+)= 350  ->  0.3234485317603183\n",
      "Evaluating for m(H+)= 400  ->  0.29225728165594855\n",
      "Evaluating for m(H+)= 500  ->  0.2654380470635441\n",
      "Evaluating for m(H+)= 600  ->  0.28832986742177036\n",
      "Evaluating for m(H+)= 700  ->  0.3135212179301555\n",
      "Evaluating for m(H+)= 800  ->  0.34403032325508887\n",
      "Evaluating for m(H+)= 900  ->  0.3654710805324971\n",
      "Evaluating for m(H+)= 1000  ->  0.3882448249777861\n",
      "Evaluating for m(H+)= 1200  ->  0.43484334036176175\n",
      "Evaluating for m(H+)= 1400  ->  0.48407774638032963\n",
      "Evaluating for m(H+)= 1600  ->  0.5200899905105794\n",
      "Evaluating for m(H+)= 1800  ->  0.5547899673580649\n",
      "Evaluating for m(H+)= 2000  ->  0.5794573777613131\n",
      "Training for m(H+)= 500\n",
      "Evaluating for m(H+)= 200  ->  0.5326505339066595\n",
      "Evaluating for m(H+)= 225  ->  0.5298635037285702\n",
      "Evaluating for m(H+)= 250  ->  0.5104019930132889\n",
      "Evaluating for m(H+)= 275  ->  0.4766967629860067\n",
      "Evaluating for m(H+)= 300  ->  0.4358632508003666\n",
      "Evaluating for m(H+)= 350  ->  0.37064154823393747\n",
      "Evaluating for m(H+)= 400  ->  0.3152521057714419\n",
      "Evaluating for m(H+)= 500  ->  0.23146437865390979\n",
      "Evaluating for m(H+)= 600  ->  0.2082292758365476\n",
      "Evaluating for m(H+)= 700  ->  0.20323960643182393\n",
      "Evaluating for m(H+)= 800  ->  0.21829320290028875\n",
      "Evaluating for m(H+)= 900  ->  0.23273638506820882\n",
      "Evaluating for m(H+)= 1000  ->  0.24887987011519552\n",
      "Evaluating for m(H+)= 1200  ->  0.2840004061904865\n",
      "Evaluating for m(H+)= 1400  ->  0.33474986497565984\n",
      "Evaluating for m(H+)= 1600  ->  0.37748374400161244\n",
      "Evaluating for m(H+)= 1800  ->  0.41001375586835265\n",
      "Evaluating for m(H+)= 2000  ->  0.44210556339519524\n",
      "Training for m(H+)= 600\n",
      "Evaluating for m(H+)= 200  ->  0.5417151454404066\n",
      "Evaluating for m(H+)= 225  ->  0.5464412704936509\n",
      "Evaluating for m(H+)= 250  ->  0.5334712965659252\n",
      "Evaluating for m(H+)= 275  ->  0.5106902620241821\n",
      "Evaluating for m(H+)= 300  ->  0.47166520962236025\n",
      "Evaluating for m(H+)= 350  ->  0.40977274012110987\n",
      "Evaluating for m(H+)= 400  ->  0.3521504865346443\n",
      "Evaluating for m(H+)= 500  ->  0.2488992073515537\n",
      "Evaluating for m(H+)= 600  ->  0.18697956512088676\n",
      "Evaluating for m(H+)= 700  ->  0.15498174384536434\n",
      "Evaluating for m(H+)= 800  ->  0.1467108225807271\n",
      "Evaluating for m(H+)= 900  ->  0.14902343614098557\n",
      "Evaluating for m(H+)= 1000  ->  0.1573845393780562\n",
      "Evaluating for m(H+)= 1200  ->  0.17800447574914013\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating for m(H+)= 1400  ->  0.2136211541664783\n",
      "Evaluating for m(H+)= 1600  ->  0.24704836972316124\n",
      "Evaluating for m(H+)= 1800  ->  0.2720413648847759\n",
      "Evaluating for m(H+)= 2000  ->  0.2933406336677751\n",
      "Training for m(H+)= 700\n",
      "Evaluating for m(H+)= 200  ->  0.5475697279499445\n",
      "Evaluating for m(H+)= 225  ->  0.5568945836443148\n",
      "Evaluating for m(H+)= 250  ->  0.5454849297350552\n",
      "Evaluating for m(H+)= 275  ->  0.5305802730591251\n",
      "Evaluating for m(H+)= 300  ->  0.48950185339378083\n",
      "Evaluating for m(H+)= 350  ->  0.43635382852626436\n",
      "Evaluating for m(H+)= 400  ->  0.38071730804227366\n",
      "Evaluating for m(H+)= 500  ->  0.2735917226021829\n",
      "Evaluating for m(H+)= 600  ->  0.19668600822271765\n",
      "Evaluating for m(H+)= 700  ->  0.14718158177504637\n",
      "Evaluating for m(H+)= 800  ->  0.12348539434224393\n",
      "Evaluating for m(H+)= 900  ->  0.11403254964546261\n",
      "Evaluating for m(H+)= 1000  ->  0.1133717379626008\n",
      "Evaluating for m(H+)= 1200  ->  0.12099995518016604\n",
      "Evaluating for m(H+)= 1400  ->  0.14230635748180465\n",
      "Evaluating for m(H+)= 1600  ->  0.15893532110485487\n",
      "Evaluating for m(H+)= 1800  ->  0.17304555687102985\n",
      "Evaluating for m(H+)= 2000  ->  0.18386696922541812\n",
      "Training for m(H+)= 800\n",
      "Evaluating for m(H+)= 200  ->  0.5509145351725918\n",
      "Evaluating for m(H+)= 225  ->  0.5606436735738912\n",
      "Evaluating for m(H+)= 250  ->  0.5483211160023519\n",
      "Evaluating for m(H+)= 275  ->  0.538714936265871\n",
      "Evaluating for m(H+)= 300  ->  0.5011607618078732\n",
      "Evaluating for m(H+)= 350  ->  0.457746687300446\n",
      "Evaluating for m(H+)= 400  ->  0.4063706785926259\n",
      "Evaluating for m(H+)= 500  ->  0.3004288322550044\n",
      "Evaluating for m(H+)= 600  ->  0.2148923320180034\n",
      "Evaluating for m(H+)= 700  ->  0.15589577441441205\n",
      "Evaluating for m(H+)= 800  ->  0.11895855659790655\n",
      "Evaluating for m(H+)= 900  ->  0.09704768092433991\n",
      "Evaluating for m(H+)= 1000  ->  0.09160048516236452\n",
      "Evaluating for m(H+)= 1200  ->  0.08811449824107054\n",
      "Evaluating for m(H+)= 1400  ->  0.10185201195328564\n",
      "Evaluating for m(H+)= 1600  ->  0.11452627758779499\n",
      "Evaluating for m(H+)= 1800  ->  0.12680810125172826\n",
      "Evaluating for m(H+)= 2000  ->  0.13666361694448637\n",
      "Training for m(H+)= 900\n",
      "Evaluating for m(H+)= 200  ->  0.5548495745158755\n",
      "Evaluating for m(H+)= 225  ->  0.560561057600057\n",
      "Evaluating for m(H+)= 250  ->  0.5503472239359255\n",
      "Evaluating for m(H+)= 275  ->  0.5410463449775765\n",
      "Evaluating for m(H+)= 300  ->  0.5028425791401052\n",
      "Evaluating for m(H+)= 350  ->  0.46322601027268706\n",
      "Evaluating for m(H+)= 400  ->  0.4134993862333738\n",
      "Evaluating for m(H+)= 500  ->  0.3117844829027807\n",
      "Evaluating for m(H+)= 600  ->  0.22540581884697175\n",
      "Evaluating for m(H+)= 700  ->  0.1648282192428907\n",
      "Evaluating for m(H+)= 800  ->  0.12080313202681547\n",
      "Evaluating for m(H+)= 900  ->  0.09204235231427149\n",
      "Evaluating for m(H+)= 1000  ->  0.080711483441532\n",
      "Evaluating for m(H+)= 1200  ->  0.06679563056344806\n",
      "Evaluating for m(H+)= 1400  ->  0.07170345679736101\n",
      "Evaluating for m(H+)= 1600  ->  0.07849600451061212\n",
      "Evaluating for m(H+)= 1800  ->  0.08563265812813803\n",
      "Evaluating for m(H+)= 2000  ->  0.09177334736328424\n",
      "Training for m(H+)= 1000\n",
      "Evaluating for m(H+)= 200  ->  0.5561323719662619\n",
      "Evaluating for m(H+)= 225  ->  0.5608051470045823\n",
      "Evaluating for m(H+)= 250  ->  0.5485776076061539\n",
      "Evaluating for m(H+)= 275  ->  0.5395615745243296\n",
      "Evaluating for m(H+)= 300  ->  0.5050552337154017\n",
      "Evaluating for m(H+)= 350  ->  0.4701797873391075\n",
      "Evaluating for m(H+)= 400  ->  0.42583827205247615\n",
      "Evaluating for m(H+)= 500  ->  0.331935405817818\n",
      "Evaluating for m(H+)= 600  ->  0.24574034426621194\n",
      "Evaluating for m(H+)= 700  ->  0.18193308158560928\n",
      "Evaluating for m(H+)= 800  ->  0.13271999654766076\n",
      "Evaluating for m(H+)= 900  ->  0.0983101667777162\n",
      "Evaluating for m(H+)= 1000  ->  0.08130798049109356\n",
      "Evaluating for m(H+)= 1200  ->  0.057672642527554885\n",
      "Evaluating for m(H+)= 1400  ->  0.054130274697860914\n",
      "Evaluating for m(H+)= 1600  ->  0.05424841681900561\n",
      "Evaluating for m(H+)= 1800  ->  0.056946810622021116\n",
      "Evaluating for m(H+)= 2000  ->  0.058453652902205055\n",
      "Training for m(H+)= 1200\n",
      "Evaluating for m(H+)= 200  ->  0.5506664770995693\n",
      "Evaluating for m(H+)= 225  ->  0.5614050591404676\n",
      "Evaluating for m(H+)= 250  ->  0.5489489446051472\n",
      "Evaluating for m(H+)= 275  ->  0.5497479046244215\n",
      "Evaluating for m(H+)= 300  ->  0.5175456778629308\n",
      "Evaluating for m(H+)= 350  ->  0.48539759258809045\n",
      "Evaluating for m(H+)= 400  ->  0.4477690936291534\n",
      "Evaluating for m(H+)= 500  ->  0.3544565132257653\n",
      "Evaluating for m(H+)= 600  ->  0.26663353762471587\n",
      "Evaluating for m(H+)= 700  ->  0.2007221359167788\n",
      "Evaluating for m(H+)= 800  ->  0.14758719738144876\n",
      "Evaluating for m(H+)= 900  ->  0.10823641709984255\n",
      "Evaluating for m(H+)= 1000  ->  0.08704756381144474\n",
      "Evaluating for m(H+)= 1200  ->  0.05375183241729298\n",
      "Evaluating for m(H+)= 1400  ->  0.041737271199676296\n",
      "Evaluating for m(H+)= 1600  ->  0.03581106560668612\n",
      "Evaluating for m(H+)= 1800  ->  0.03400275359114935\n",
      "Evaluating for m(H+)= 2000  ->  0.032115661431693976\n",
      "Training for m(H+)= 1400\n",
      "Evaluating for m(H+)= 200  ->  0.5395848793070656\n",
      "Evaluating for m(H+)= 225  ->  0.5445836638411478\n",
      "Evaluating for m(H+)= 250  ->  0.5377483781389132\n",
      "Evaluating for m(H+)= 275  ->  0.5320399009923944\n",
      "Evaluating for m(H+)= 300  ->  0.5017287574463418\n",
      "Evaluating for m(H+)= 350  ->  0.4770895633855611\n",
      "Evaluating for m(H+)= 400  ->  0.44608472876682237\n",
      "Evaluating for m(H+)= 500  ->  0.3682605970866235\n",
      "Evaluating for m(H+)= 600  ->  0.2845748389660545\n",
      "Evaluating for m(H+)= 700  ->  0.21797958025642217\n",
      "Evaluating for m(H+)= 800  ->  0.1596488187462346\n",
      "Evaluating for m(H+)= 900  ->  0.11873550843461245\n",
      "Evaluating for m(H+)= 1000  ->  0.09333098130766948\n",
      "Evaluating for m(H+)= 1200  ->  0.055577560241299784\n",
      "Evaluating for m(H+)= 1400  ->  0.039379673243207236\n",
      "Evaluating for m(H+)= 1600  ->  0.029690040209890523\n",
      "Evaluating for m(H+)= 1800  ->  0.02546229019099866\n",
      "Evaluating for m(H+)= 2000  ->  0.021364885091870733\n",
      "Training for m(H+)= 1600\n",
      "Evaluating for m(H+)= 200  ->  0.5271891512775186\n",
      "Evaluating for m(H+)= 225  ->  0.5354979540211995\n",
      "Evaluating for m(H+)= 250  ->  0.5253479130285074\n",
      "Evaluating for m(H+)= 275  ->  0.5197899697403523\n",
      "Evaluating for m(H+)= 300  ->  0.4878448553900686\n",
      "Evaluating for m(H+)= 350  ->  0.46484222421919896\n",
      "Evaluating for m(H+)= 400  ->  0.43667736352475217\n",
      "Evaluating for m(H+)= 500  ->  0.3665424357879574\n",
      "Evaluating for m(H+)= 600  ->  0.2938227357962844\n",
      "Evaluating for m(H+)= 700  ->  0.23109128374310595\n",
      "Evaluating for m(H+)= 800  ->  0.17131122713621272\n",
      "Evaluating for m(H+)= 900  ->  0.12675639724929466\n",
      "Evaluating for m(H+)= 1000  ->  0.10067946211556755\n",
      "Evaluating for m(H+)= 1200  ->  0.05894490546674924\n",
      "Evaluating for m(H+)= 1400  ->  0.04061002771219391\n",
      "Evaluating for m(H+)= 1600  ->  0.028361924073472045\n",
      "Evaluating for m(H+)= 1800  ->  0.02302533788886818\n",
      "Evaluating for m(H+)= 2000  ->  0.018059683007026095\n",
      "Training for m(H+)= 1800\n",
      "Evaluating for m(H+)= 200  ->  0.530016474268266\n",
      "Evaluating for m(H+)= 225  ->  0.5361324662177163\n",
      "Evaluating for m(H+)= 250  ->  0.5265678482071411\n",
      "Evaluating for m(H+)= 275  ->  0.5232762373056703\n",
      "Evaluating for m(H+)= 300  ->  0.49622571884000777\n",
      "Evaluating for m(H+)= 350  ->  0.4750945102182764\n",
      "Evaluating for m(H+)= 400  ->  0.45352808419642177\n",
      "Evaluating for m(H+)= 500  ->  0.38745916615086773\n",
      "Evaluating for m(H+)= 600  ->  0.31310105367231944\n",
      "Evaluating for m(H+)= 700  ->  0.2446622267372418\n",
      "Evaluating for m(H+)= 800  ->  0.18226288129025903\n",
      "Evaluating for m(H+)= 900  ->  0.13301371921441163\n",
      "Evaluating for m(H+)= 1000  ->  0.10573555409686819\n",
      "Evaluating for m(H+)= 1200  ->  0.0621055884695082\n",
      "Evaluating for m(H+)= 1400  ->  0.04189473297302426\n",
      "Evaluating for m(H+)= 1600  ->  0.028229589834227187\n",
      "Evaluating for m(H+)= 1800  ->  0.022254109364992614\n",
      "Evaluating for m(H+)= 2000  ->  0.016547949875282963\n",
      "Training for m(H+)= 2000\n",
      "Evaluating for m(H+)= 200  ->  0.5355703316508813\n",
      "Evaluating for m(H+)= 225  ->  0.5401740376668094\n",
      "Evaluating for m(H+)= 250  ->  0.5341783945822405\n",
      "Evaluating for m(H+)= 275  ->  0.529011207147906\n",
      "Evaluating for m(H+)= 300  ->  0.5068791436960798\n",
      "Evaluating for m(H+)= 350  ->  0.4890448858449752\n",
      "Evaluating for m(H+)= 400  ->  0.46710135243890893\n",
      "Evaluating for m(H+)= 500  ->  0.40788970500677213\n",
      "Evaluating for m(H+)= 600  ->  0.327673868712298\n",
      "Evaluating for m(H+)= 700  ->  0.2579071516150543\n",
      "Evaluating for m(H+)= 800  ->  0.19217549991072447\n",
      "Evaluating for m(H+)= 900  ->  0.1411718423213476\n",
      "Evaluating for m(H+)= 1000  ->  0.11260235956965425\n",
      "Evaluating for m(H+)= 1200  ->  0.06613388546329091\n",
      "Evaluating for m(H+)= 1400  ->  0.04432437765118713\n",
      "Evaluating for m(H+)= 1600  ->  0.029416229070112987\n",
      "Evaluating for m(H+)= 1800  ->  0.022133350387720574\n",
      "Evaluating for m(H+)= 2000  ->  0.016057116782868586\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from HpMLUtils import WeightedStandardScaler\n",
    "\n",
    "hpmasses=[200,225,250,275,300,350,400,500,600,700,800,900,1000,1200,1400,1600,1800,2000]\n",
    "\n",
    "def calculateCrossMassScore(clf, htf,hpmasses, normalizeInputs=False):\n",
    "    \"\"\" calculate result of training for one mass point and evaluation on other mass point\n",
    "        clf: classifier\n",
    "        htf: HpTrainingFrame\n",
    "        hpmasses, list of H+ masses\n",
    "        normalizeInputs: if true the features are normalised to average 0 and variance 1 before fitting\n",
    "    \"\"\"\n",
    "\n",
    "    X_train={}\n",
    "    X_test={}\n",
    "    X_eval={}\n",
    "    y_train={}\n",
    "    y_test={}\n",
    "    y_eval={}\n",
    "    w_train={}\n",
    "    w_test={}\n",
    "    w_eval={}\n",
    "    \n",
    "    if normalizeInputs:\n",
    "        wss=WeightedStandardScaler()\n",
    "    \n",
    "    for hpmass in hpmasses:\n",
    "        X_train[hpmass], X_test[hpmass], X_eval[hpmass], y_train[hpmass], y_test[hpmass],y_eval[hpmass], w_train[hpmass], w_test[hpmass], w_eval[hpmass]=htf.prepare(hpmass=hpmass)\n",
    "        if normalizeInputs:\n",
    "            wss.fit(X_train[hpmass])\n",
    "            X_train[hpmass]=wss.transform(X_train[hpmass])\n",
    "            X_test[hpmass]=wss.transform(X_test[hpmass])\n",
    "    \n",
    "    scorematrix=np.zeros((len(hpmasses),len(hpmasses)))\n",
    "    \n",
    "    for i,hpmass in enumerate(hpmasses):\n",
    "        print \"Training for m(H+)=\", hpmass\n",
    " \n",
    "        clf.fit(X_train[hpmass], y_train[hpmass], sample_weight=w_train[hpmass])\n",
    "    \n",
    "        for j,hpmass2 in enumerate(hpmasses):\n",
    "            print \"Evaluating for m(H+)=\", hpmass2,\" -> \",\n",
    "            score=1.-roc_auc_score(y_test[hpmass2], clf.decision_function(X_test[hpmass2]), sample_weight=w_test[hpmass2])\n",
    "            scorematrix[i,j]=score\n",
    "            print score\n",
    "    return scorematrix\n",
    "\n",
    "scorematrix=calculateCrossMassScore(clf,htf,hpmasses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from array import array\n",
    "\n",
    "def plotCrossMassScore(scorematrix1, scorematrix2=None, convertToXSec=False):\n",
    "    # score matrices can be read from txt file\n",
    "    # scorematrix1_orig=np.loadtxt(\"scorematrixnew_normal.txt\")\n",
    "    # scorematrix2_orig=np.loadtxt(\"scorematrixnew.txt\")\n",
    "\n",
    "    if convertToXSec:\n",
    "        #z=array(\"f\",[ 5.84983620e-03, -4.16702246e-03,  1.22938606e-03, -1.36789294e-05])\n",
    "        f=np.poly1d(z)\n",
    "        this_scorematrix1=f(scorematrix1[5:,5:].copy()) #do not take first 5 values as conversion to xsec is not reliable there\n",
    "        if not scorematrix2 is None:\n",
    "            this_scorematrix2=f(scorematrix2[5:,5:].copy())\n",
    "    else:\n",
    "        this_scorematrix1=scorematrix1.copy()\n",
    "        if not scorematrix2 is None:\n",
    "            this_scorematrix2=scorematrix2.copy()\n",
    "\n",
    "    title=\"Normalized input variables\"\n",
    "\n",
    "    hpmassesorig=[200,225,250,275,300,350,400,500,600,700,800,900,1000,1200,1400,1600,1800,2000]\n",
    "    if convertToXSec:\n",
    "        hpmasses=hpmassesorig[5:]\n",
    "    else:\n",
    "        hpmasses=hpmassesorig\n",
    "        \n",
    "    #first plot: compare the results for both methods if evaluated and trained at same mass point (expect same result)\n",
    "    if not scorematrix2 is None:\n",
    "        diag1=[scorematrix1[j,j] for j in range(0,scorematrix1.shape[0])]\n",
    "        diag2=[scorematrix2[j,j] for j in range(0,scorematrix2.shape[0])]\n",
    "\n",
    "        fig, ax = plt.subplots()\n",
    "        plt.plot(hpmassesorig, diag1,\"-\", label=\"normalised\")\n",
    "        plt.plot(hpmassesorig, diag2,\"--\", label=\"default\")\n",
    "        plt.legend(loc=\"upper right\")\n",
    "        plt.xlabel(\"H+ mass for evaluation\")\n",
    "        plt.ylabel(\"ROC AUC\")\n",
    "        plt.show()\n",
    "\n",
    "    #second plot: one line per training and evaluating it for multiple mass points\n",
    "    for j in range(this_scorematrix1.shape[1]): #j=index for evaluation\n",
    "        diag1=this_scorematrix1[j,j]\n",
    "        #print \"Diag:\", j, diag1,\n",
    "        if not scorematrix2 is None: \n",
    "            diag2=this_scorematrix2[j,j]\n",
    "            #print diag2\n",
    "        #else:\n",
    "            #print\n",
    "        for i in range(this_scorematrix1.shape[0]): #i=index for training\n",
    "            before=this_scorematrix1[i,j]\n",
    "            this_scorematrix1[i,j]=(this_scorematrix1[i,j]/diag1-1.)*100 #take difference in % to diagonal value, i.e. trained and evaluated at same mass point (=ideal case)\n",
    "            if not scorematrix2 is None:\n",
    "                this_scorematrix2[i,j]=(this_scorematrix2[i,j]/diag2-1.)*100\n",
    "            #print i,j, before, this_scorematrix1[i,j], diag1\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    ax.set_ylim(0, 25)\n",
    "    color=iter(plt.cm.rainbow(np.linspace(0,1,this_scorematrix1.shape[1])))\n",
    "    for j in range(this_scorematrix1.shape[1]):\n",
    "        if j %2==0:\n",
    "            c=next(color)\n",
    "            plt.plot(hpmasses[max(0,j-4):j+4], this_scorematrix1[max(0,j-4):j+4,j],\"-\",c=c, label=\"m(H+)=\"+str(hpmasses[j])+\" GeV\") #take only +/-4 mass points for curve to unclutter plot\n",
    "            plt.plot(hpmasses[max(0,j-4):j+4], this_scorematrix1[max(0,j-4):j+4,j],\"*\",c=c)\n",
    "            if not scorematrix2 is None:\n",
    "                plt.plot(hpmasses[max(0,j-4):j+4], this_scorematrix2[max(0,j-4):j+4,j],\"--\",c=c)#, label=\"m(H+)=\"+str(hpmasses[j])+\" GeV\")\n",
    "                #plt.plot(hpmasses[max(0,j-4):j+4], this_scorematrix2[max(0,j-4):j+4,j],\"-\",c=c, label=\"m(H+)=\"+str(hpmasses[j])+\" GeV\")\n",
    "                plt.plot(hpmasses[max(0,j-4):j+4], this_scorematrix2[max(0,j-4):j+4,j],\"*\",c=c)\n",
    "\n",
    "    plt.xlabel(\"H+ mass for evaluation\")\n",
    "    if convertToXSec:\n",
    "        plt.ylabel(\"% difference to 95% excluded XS at ideal mass for training\")\n",
    "    else:\n",
    "        plt.ylabel(\"% difference to ROC at ideal mass for training\")\n",
    "\n",
    "    plt.legend(loc=\"upper right\",title=\"Training mass\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotCrossMassScore(scorematrix)\n",
    "plotCrossMassScore(scorematrix,convertToXSec=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trying with normalised inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training for m(H+)= 200\n",
      "Evaluating for m(H+)= 200  ->  0.3290841140322679\n",
      "Evaluating for m(H+)= 225  ->  0.3873821792854871\n",
      "Evaluating for m(H+)= 250  ->  0.4318800657988413\n",
      "Evaluating for m(H+)= 275  ->  0.46330519784697755\n",
      "Evaluating for m(H+)= 300  ->  0.49680413243107646\n",
      "Evaluating for m(H+)= 350  ->  0.5331916864339244\n",
      "Evaluating for m(H+)= 400  ->  0.558982730238242\n",
      "Evaluating for m(H+)= 500  ->  0.6150087897111629\n",
      "Evaluating for m(H+)= 600  ->  0.6390448225852752\n",
      "Evaluating for m(H+)= 700  ->  0.6680924037524176\n",
      "Evaluating for m(H+)= 800  ->  0.6875924125187132\n",
      "Evaluating for m(H+)= 900  ->  0.7062862014855821\n",
      "Evaluating for m(H+)= 1000  ->  0.7158300400215436\n",
      "Evaluating for m(H+)= 1200  ->  0.7400665447170158\n",
      "Evaluating for m(H+)= 1400  ->  0.7644402259547682\n",
      "Evaluating for m(H+)= 1600  ->  0.7722602483783488\n",
      "Evaluating for m(H+)= 1800  ->  0.7843603551417064\n",
      "Evaluating for m(H+)= 2000  ->  0.802808520697938\n",
      "Training for m(H+)= 225\n",
      "Evaluating for m(H+)= 200  ->  0.3616495594393757\n",
      "Evaluating for m(H+)= 225  ->  0.3566890349158516\n",
      "Evaluating for m(H+)= 250  ->  0.37525662485968236\n",
      "Evaluating for m(H+)= 275  ->  0.40207242682113675\n",
      "Evaluating for m(H+)= 300  ->  0.4398621158337852\n",
      "Evaluating for m(H+)= 350  ->  0.4875673916993698\n",
      "Evaluating for m(H+)= 400  ->  0.5261309558068535\n",
      "Evaluating for m(H+)= 500  ->  0.595486663603869\n",
      "Evaluating for m(H+)= 600  ->  0.6454043662798269\n",
      "Evaluating for m(H+)= 700  ->  0.687750610510916\n",
      "Evaluating for m(H+)= 800  ->  0.7079786568422646\n",
      "Evaluating for m(H+)= 900  ->  0.7281947968942197\n",
      "Evaluating for m(H+)= 1000  ->  0.7406901504952925\n",
      "Evaluating for m(H+)= 1200  ->  0.7665861590539355\n",
      "Evaluating for m(H+)= 1400  ->  0.7928331324654633\n",
      "Evaluating for m(H+)= 1600  ->  0.795468939043924\n",
      "Evaluating for m(H+)= 1800  ->  0.8157016451608637\n",
      "Evaluating for m(H+)= 2000  ->  0.8300687037307674\n",
      "Training for m(H+)= 250\n",
      "Evaluating for m(H+)= 200  ->  0.40940562277672965\n",
      "Evaluating for m(H+)= 225  ->  0.3646617492882567\n",
      "Evaluating for m(H+)= 250  ->  0.36026558938865405\n",
      "Evaluating for m(H+)= 275  ->  0.36189451000700734\n",
      "Evaluating for m(H+)= 300  ->  0.383484449292247\n",
      "Evaluating for m(H+)= 350  ->  0.4235820392114219\n",
      "Evaluating for m(H+)= 400  ->  0.4703222520333089\n",
      "Evaluating for m(H+)= 500  ->  0.5221616011056076\n",
      "Evaluating for m(H+)= 600  ->  0.5821038559231797\n",
      "Evaluating for m(H+)= 700  ->  0.627845923957248\n",
      "Evaluating for m(H+)= 800  ->  0.6610312468242622\n",
      "Evaluating for m(H+)= 900  ->  0.6923959743903756\n",
      "Evaluating for m(H+)= 1000  ->  0.7088990690410717\n",
      "Evaluating for m(H+)= 1200  ->  0.7483836236290038\n",
      "Evaluating for m(H+)= 1400  ->  0.7765963973986143\n",
      "Evaluating for m(H+)= 1600  ->  0.7943858937725862\n",
      "Evaluating for m(H+)= 1800  ->  0.8092112722245073\n",
      "Evaluating for m(H+)= 2000  ->  0.8250123723091993\n",
      "Training for m(H+)= 275\n",
      "Evaluating for m(H+)= 200  ->  0.4363108807175281\n",
      "Evaluating for m(H+)= 225  ->  0.38403369452704106\n",
      "Evaluating for m(H+)= 250  ->  0.3635580344406242\n",
      "Evaluating for m(H+)= 275  ->  0.3449842510509099\n",
      "Evaluating for m(H+)= 300  ->  0.3426712808234087\n",
      "Evaluating for m(H+)= 350  ->  0.37125080331888827\n",
      "Evaluating for m(H+)= 400  ->  0.39678569745439274\n",
      "Evaluating for m(H+)= 500  ->  0.4374413221484911\n",
      "Evaluating for m(H+)= 600  ->  0.4950537986453708\n",
      "Evaluating for m(H+)= 700  ->  0.5366461804244254\n",
      "Evaluating for m(H+)= 800  ->  0.5708489619036696\n",
      "Evaluating for m(H+)= 900  ->  0.6034769221623064\n",
      "Evaluating for m(H+)= 1000  ->  0.6167459755149385\n",
      "Evaluating for m(H+)= 1200  ->  0.662205126109241\n",
      "Evaluating for m(H+)= 1400  ->  0.6919028581016546\n",
      "Evaluating for m(H+)= 1600  ->  0.7304172977948978\n",
      "Evaluating for m(H+)= 1800  ->  0.745779450066614\n",
      "Evaluating for m(H+)= 2000  ->  0.7658934547291925\n",
      "Training for m(H+)= 300\n",
      "Evaluating for m(H+)= 200  ->  0.45064528742245846\n",
      "Evaluating for m(H+)= 225  ->  0.4019662991775915\n",
      "Evaluating for m(H+)= 250  ->  0.3798556793436084\n",
      "Evaluating for m(H+)= 275  ->  0.3486071417334299\n",
      "Evaluating for m(H+)= 300  ->  0.3337575136079013\n",
      "Evaluating for m(H+)= 350  ->  0.3389720045180843\n",
      "Evaluating for m(H+)= 400  ->  0.3601601700743643\n",
      "Evaluating for m(H+)= 500  ->  0.3919280647742517\n",
      "Evaluating for m(H+)= 600  ->  0.4445569645619394\n",
      "Evaluating for m(H+)= 700  ->  0.4801851401263534\n",
      "Evaluating for m(H+)= 800  ->  0.5146723480620955\n",
      "Evaluating for m(H+)= 900  ->  0.5501424400777699\n",
      "Evaluating for m(H+)= 1000  ->  0.5678928144162593\n",
      "Evaluating for m(H+)= 1200  ->  0.6199598466910974\n",
      "Evaluating for m(H+)= 1400  ->  0.659391216297116\n",
      "Evaluating for m(H+)= 1600  ->  0.7006611710936659\n",
      "Evaluating for m(H+)= 1800  ->  0.7218390971818772\n",
      "Evaluating for m(H+)= 2000  ->  0.7498558949126825\n",
      "Training for m(H+)= 350\n",
      "Evaluating for m(H+)= 200  ->  0.4826144074744374\n",
      "Evaluating for m(H+)= 225  ->  0.4442478711852338\n",
      "Evaluating for m(H+)= 250  ->  0.4204663085181508\n",
      "Evaluating for m(H+)= 275  ->  0.3794379784971059\n",
      "Evaluating for m(H+)= 300  ->  0.34679690552400366\n",
      "Evaluating for m(H+)= 350  ->  0.3201930082314486\n",
      "Evaluating for m(H+)= 400  ->  0.3149925534412915\n",
      "Evaluating for m(H+)= 500  ->  0.31295072753012554\n",
      "Evaluating for m(H+)= 600  ->  0.3497571645692409\n",
      "Evaluating for m(H+)= 700  ->  0.37981332678229607\n",
      "Evaluating for m(H+)= 800  ->  0.4052727285123411\n",
      "Evaluating for m(H+)= 900  ->  0.4383773998731033\n",
      "Evaluating for m(H+)= 1000  ->  0.4542866742054592\n",
      "Evaluating for m(H+)= 1200  ->  0.4941223789768353\n",
      "Evaluating for m(H+)= 1400  ->  0.5291452927872183\n",
      "Evaluating for m(H+)= 1600  ->  0.582806900174624\n",
      "Evaluating for m(H+)= 1800  ->  0.6086756010088952\n",
      "Evaluating for m(H+)= 2000  ->  0.6478174366133953\n",
      "Training for m(H+)= 400\n",
      "Evaluating for m(H+)= 200  ->  0.502839025988086\n",
      "Evaluating for m(H+)= 225  ->  0.47981760447302635\n",
      "Evaluating for m(H+)= 250  ->  0.4511510358505407\n",
      "Evaluating for m(H+)= 275  ->  0.4114985261891201\n",
      "Evaluating for m(H+)= 300  ->  0.3742144715699014\n",
      "Evaluating for m(H+)= 350  ->  0.32278492130660175\n",
      "Evaluating for m(H+)= 400  ->  0.29225728165594855\n",
      "Evaluating for m(H+)= 500  ->  0.26357695716965635\n",
      "Evaluating for m(H+)= 600  ->  0.2821774549737579\n",
      "Evaluating for m(H+)= 700  ->  0.3021096237758344\n",
      "Evaluating for m(H+)= 800  ->  0.32841980790822634\n",
      "Evaluating for m(H+)= 900  ->  0.3480068860352664\n",
      "Evaluating for m(H+)= 1000  ->  0.3643732328098197\n",
      "Evaluating for m(H+)= 1200  ->  0.40248827683373345\n",
      "Evaluating for m(H+)= 1400  ->  0.4316707783613708\n",
      "Evaluating for m(H+)= 1600  ->  0.4932704376550532\n",
      "Evaluating for m(H+)= 1800  ->  0.5146564028697247\n",
      "Evaluating for m(H+)= 2000  ->  0.5612882103440968\n",
      "Training for m(H+)= 500\n",
      "Evaluating for m(H+)= 200  ->  0.5317242773459425\n",
      "Evaluating for m(H+)= 225  ->  0.5271349152084761\n",
      "Evaluating for m(H+)= 250  ->  0.5065630597627837\n",
      "Evaluating for m(H+)= 275  ->  0.4719782198318683\n",
      "Evaluating for m(H+)= 300  ->  0.4314014932436947\n",
      "Evaluating for m(H+)= 350  ->  0.3659952316366417\n",
      "Evaluating for m(H+)= 400  ->  0.3134954787908184\n",
      "Evaluating for m(H+)= 500  ->  0.2314644503870149\n",
      "Evaluating for m(H+)= 600  ->  0.20547866104192114\n",
      "Evaluating for m(H+)= 700  ->  0.19527887006033384\n",
      "Evaluating for m(H+)= 800  ->  0.20330976546523905\n",
      "Evaluating for m(H+)= 900  ->  0.2135553769154086\n",
      "Evaluating for m(H+)= 1000  ->  0.2224947178839206\n",
      "Evaluating for m(H+)= 1200  ->  0.24045411958607021\n",
      "Evaluating for m(H+)= 1400  ->  0.246820712198914\n",
      "Evaluating for m(H+)= 1600  ->  0.29845254897440754\n",
      "Evaluating for m(H+)= 1800  ->  0.29286826768701835\n",
      "Evaluating for m(H+)= 2000  ->  0.3309073496701176\n",
      "Training for m(H+)= 600\n",
      "Evaluating for m(H+)= 200  ->  0.5420727057425652\n",
      "Evaluating for m(H+)= 225  ->  0.5452884818718111\n",
      "Evaluating for m(H+)= 250  ->  0.5302622970347728\n",
      "Evaluating for m(H+)= 275  ->  0.5068635310609722\n",
      "Evaluating for m(H+)= 300  ->  0.4654713150548374\n",
      "Evaluating for m(H+)= 350  ->  0.40501243763046757\n",
      "Evaluating for m(H+)= 400  ->  0.3477347735281521\n",
      "Evaluating for m(H+)= 500  ->  0.24628724849712136\n",
      "Evaluating for m(H+)= 600  ->  0.18697940339510088\n",
      "Evaluating for m(H+)= 700  ->  0.15311117726396906\n",
      "Evaluating for m(H+)= 800  ->  0.14014140450673485\n",
      "Evaluating for m(H+)= 900  ->  0.13715476028285523\n",
      "Evaluating for m(H+)= 1000  ->  0.1378148321102407\n",
      "Evaluating for m(H+)= 1200  ->  0.13943599381938054\n",
      "Evaluating for m(H+)= 1400  ->  0.13513571090045662\n",
      "Evaluating for m(H+)= 1600  ->  0.16244861827875623\n",
      "Evaluating for m(H+)= 1800  ->  0.14771159800071176\n",
      "Evaluating for m(H+)= 2000  ->  0.1626869452979831\n",
      "Training for m(H+)= 700\n",
      "Evaluating for m(H+)= 200  ->  0.5464882945636143\n",
      "Evaluating for m(H+)= 225  ->  0.5552917071909937\n",
      "Evaluating for m(H+)= 250  ->  0.5413082980057312\n",
      "Evaluating for m(H+)= 275  ->  0.5245696704036327\n",
      "Evaluating for m(H+)= 300  ->  0.48345853402677874\n",
      "Evaluating for m(H+)= 350  ->  0.4285151586171261\n",
      "Evaluating for m(H+)= 400  ->  0.3734979680131766\n",
      "Evaluating for m(H+)= 500  ->  0.2683919838114378\n",
      "Evaluating for m(H+)= 600  ->  0.19469667986411565\n",
      "Evaluating for m(H+)= 700  ->  0.14718159571172995\n",
      "Evaluating for m(H+)= 800  ->  0.12165065628356819\n",
      "Evaluating for m(H+)= 900  ->  0.10894927906196705\n",
      "Evaluating for m(H+)= 1000  ->  0.10342128892260372\n",
      "Evaluating for m(H+)= 1200  ->  0.09584201233794121\n",
      "Evaluating for m(H+)= 1400  ->  0.0890657766397317\n",
      "Evaluating for m(H+)= 1600  ->  0.10430202003792488\n",
      "Evaluating for m(H+)= 1800  ->  0.08992771020459278\n",
      "Evaluating for m(H+)= 2000  ->  0.09615561682186324\n",
      "Training for m(H+)= 800\n",
      "Evaluating for m(H+)= 200  ->  0.5510448967603252\n",
      "Evaluating for m(H+)= 225  ->  0.5602233810430406\n",
      "Evaluating for m(H+)= 250  ->  0.5474563103662722\n",
      "Evaluating for m(H+)= 275  ->  0.5357457956785473\n",
      "Evaluating for m(H+)= 300  ->  0.497769956287769\n",
      "Evaluating for m(H+)= 350  ->  0.4518119070578688\n",
      "Evaluating for m(H+)= 400  ->  0.3992326239026033\n",
      "Evaluating for m(H+)= 500  ->  0.29344968656048565\n",
      "Evaluating for m(H+)= 600  ->  0.21007345702933133\n",
      "Evaluating for m(H+)= 700  ->  0.15467533211717488\n",
      "Evaluating for m(H+)= 800  ->  0.11850478985860757\n",
      "Evaluating for m(H+)= 900  ->  0.09574391905644386\n",
      "Evaluating for m(H+)= 1000  ->  0.0870218819224966\n",
      "Evaluating for m(H+)= 1200  ->  0.07320757903084063\n",
      "Evaluating for m(H+)= 1400  ->  0.06446623741891921\n",
      "Evaluating for m(H+)= 1600  ->  0.07163386928406124\n",
      "Evaluating for m(H+)= 1800  ->  0.05984192721166193\n",
      "Evaluating for m(H+)= 2000  ->  0.06144355161857029\n",
      "Training for m(H+)= 900\n",
      "Evaluating for m(H+)= 200  ->  0.5547022669713608\n",
      "Evaluating for m(H+)= 225  ->  0.5628775414441515\n",
      "Evaluating for m(H+)= 250  ->  0.5509095489215191\n",
      "Evaluating for m(H+)= 275  ->  0.5409535519478134\n",
      "Evaluating for m(H+)= 300  ->  0.5013299948978638\n",
      "Evaluating for m(H+)= 350  ->  0.4579432053830277\n",
      "Evaluating for m(H+)= 400  ->  0.4069188303956326\n",
      "Evaluating for m(H+)= 500  ->  0.3042336568468941\n",
      "Evaluating for m(H+)= 600  ->  0.21980472859003086\n",
      "Evaluating for m(H+)= 700  ->  0.1618870894826705\n",
      "Evaluating for m(H+)= 800  ->  0.12033821688150415\n",
      "Evaluating for m(H+)= 900  ->  0.0920423717631147\n",
      "Evaluating for m(H+)= 1000  ->  0.08016417172544998\n",
      "Evaluating for m(H+)= 1200  ->  0.059339377991048714\n",
      "Evaluating for m(H+)= 1400  ->  0.04967656849242785\n",
      "Evaluating for m(H+)= 1600  ->  0.051548603916036884\n",
      "Evaluating for m(H+)= 1800  ->  0.04189986231777376\n",
      "Evaluating for m(H+)= 2000  ->  0.04165874862918428\n",
      "Training for m(H+)= 1000\n",
      "Evaluating for m(H+)= 200  ->  0.5582380933446067\n",
      "Evaluating for m(H+)= 225  ->  0.565213712504884\n",
      "Evaluating for m(H+)= 250  ->  0.5517095113835573\n",
      "Evaluating for m(H+)= 275  ->  0.5426122593641406\n",
      "Evaluating for m(H+)= 300  ->  0.5055480733208506\n",
      "Evaluating for m(H+)= 350  ->  0.4666316259560025\n",
      "Evaluating for m(H+)= 400  ->  0.42128475737080584\n",
      "Evaluating for m(H+)= 500  ->  0.32288248650363427\n",
      "Evaluating for m(H+)= 600  ->  0.2368171760770421\n",
      "Evaluating for m(H+)= 700  ->  0.17641950163620645\n",
      "Evaluating for m(H+)= 800  ->  0.13010535906611476\n",
      "Evaluating for m(H+)= 900  ->  0.09732505616840748\n",
      "Evaluating for m(H+)= 1000  ->  0.08130798049109356\n",
      "Evaluating for m(H+)= 1200  ->  0.05501212739524464\n",
      "Evaluating for m(H+)= 1400  ->  0.04305599998006793\n",
      "Evaluating for m(H+)= 1600  ->  0.039347681668432166\n",
      "Evaluating for m(H+)= 1800  ->  0.0311312435805563\n",
      "Evaluating for m(H+)= 2000  ->  0.028067538303978123\n",
      "Training for m(H+)= 1200\n",
      "Evaluating for m(H+)= 200  ->  0.555027583312755\n",
      "Evaluating for m(H+)= 225  ->  0.5655223211263565\n",
      "Evaluating for m(H+)= 250  ->  0.5557720574828837\n",
      "Evaluating for m(H+)= 275  ->  0.5527636894102508\n",
      "Evaluating for m(H+)= 300  ->  0.5146266867910713\n",
      "Evaluating for m(H+)= 350  ->  0.4779575179221164\n",
      "Evaluating for m(H+)= 400  ->  0.4360327780593971\n",
      "Evaluating for m(H+)= 500  ->  0.340897958040279\n",
      "Evaluating for m(H+)= 600  ->  0.25539620814261965\n",
      "Evaluating for m(H+)= 700  ->  0.19309730254800428\n",
      "Evaluating for m(H+)= 800  ->  0.1422878570170013\n",
      "Evaluating for m(H+)= 900  ->  0.10492225712943837\n",
      "Evaluating for m(H+)= 1000  ->  0.08555774130481308\n",
      "Evaluating for m(H+)= 1200  ->  0.053751816573470834\n",
      "Evaluating for m(H+)= 1400  ->  0.03927184400440109\n",
      "Evaluating for m(H+)= 1600  ->  0.030802165463425846\n",
      "Evaluating for m(H+)= 1800  ->  0.024256498885388944\n",
      "Evaluating for m(H+)= 2000  ->  0.019622255760070928\n",
      "Training for m(H+)= 1400\n",
      "Evaluating for m(H+)= 200  ->  0.5494444345479422\n",
      "Evaluating for m(H+)= 225  ->  0.5563282690106148\n",
      "Evaluating for m(H+)= 250  ->  0.5470170120548985\n",
      "Evaluating for m(H+)= 275  ->  0.5410116474197713\n",
      "Evaluating for m(H+)= 300  ->  0.506512280022463\n",
      "Evaluating for m(H+)= 350  ->  0.4750524326611434\n",
      "Evaluating for m(H+)= 400  ->  0.436290890580071\n",
      "Evaluating for m(H+)= 500  ->  0.3478849106114239\n",
      "Evaluating for m(H+)= 600  ->  0.2636692952571529\n",
      "Evaluating for m(H+)= 700  ->  0.20117236895420065\n",
      "Evaluating for m(H+)= 800  ->  0.1477083474374865\n",
      "Evaluating for m(H+)= 900  ->  0.10875787671046577\n",
      "Evaluating for m(H+)= 1000  ->  0.08789714126449222\n",
      "Evaluating for m(H+)= 1200  ->  0.055010667696506865\n",
      "Evaluating for m(H+)= 1400  ->  0.03937969045541201\n",
      "Evaluating for m(H+)= 1600  ->  0.029493548859129026\n",
      "Evaluating for m(H+)= 1800  ->  0.022634280744709412\n",
      "Evaluating for m(H+)= 2000  ->  0.0175376572787016\n",
      "Training for m(H+)= 1600\n",
      "Evaluating for m(H+)= 200  ->  0.5404437304201916\n",
      "Evaluating for m(H+)= 225  ->  0.5491637294343271\n",
      "Evaluating for m(H+)= 250  ->  0.5388048400994849\n",
      "Evaluating for m(H+)= 275  ->  0.5334165004670378\n",
      "Evaluating for m(H+)= 300  ->  0.49734358468357787\n",
      "Evaluating for m(H+)= 350  ->  0.4683459994463377\n",
      "Evaluating for m(H+)= 400  ->  0.4357921590124134\n",
      "Evaluating for m(H+)= 500  ->  0.35525977008384024\n",
      "Evaluating for m(H+)= 600  ->  0.27701192901351557\n",
      "Evaluating for m(H+)= 700  ->  0.21322493707610413\n",
      "Evaluating for m(H+)= 800  ->  0.15802126081668932\n",
      "Evaluating for m(H+)= 900  ->  0.11758483596831637\n",
      "Evaluating for m(H+)= 1000  ->  0.09382355705117573\n",
      "Evaluating for m(H+)= 1200  ->  0.05708686024997278\n",
      "Evaluating for m(H+)= 1400  ->  0.04085308275080379\n",
      "Evaluating for m(H+)= 1600  ->  0.028361919809960057\n",
      "Evaluating for m(H+)= 1800  ->  0.02212755084798501\n",
      "Evaluating for m(H+)= 2000  ->  0.01654067481043109\n",
      "Training for m(H+)= 1800\n",
      "Evaluating for m(H+)= 200  ->  0.5489630422536482\n",
      "Evaluating for m(H+)= 225  ->  0.5577795046633942\n",
      "Evaluating for m(H+)= 250  ->  0.547147350730704\n",
      "Evaluating for m(H+)= 275  ->  0.5434158374607423\n",
      "Evaluating for m(H+)= 300  ->  0.5097158837473525\n",
      "Evaluating for m(H+)= 350  ->  0.48212002763456874\n",
      "Evaluating for m(H+)= 400  ->  0.44740335886053284\n",
      "Evaluating for m(H+)= 500  ->  0.36442182365752007\n",
      "Evaluating for m(H+)= 600  ->  0.2792949292465322\n",
      "Evaluating for m(H+)= 700  ->  0.21603437177310503\n",
      "Evaluating for m(H+)= 800  ->  0.1598337329059627\n",
      "Evaluating for m(H+)= 900  ->  0.11803034203840901\n",
      "Evaluating for m(H+)= 1000  ->  0.09497265870996707\n",
      "Evaluating for m(H+)= 1200  ->  0.058092817786159046\n",
      "Evaluating for m(H+)= 1400  ->  0.04135476928249693\n",
      "Evaluating for m(H+)= 1600  ->  0.02861641817075422\n",
      "Evaluating for m(H+)= 1800  ->  0.022254169404680613\n",
      "Evaluating for m(H+)= 2000  ->  0.016373246016820997\n",
      "Training for m(H+)= 2000\n",
      "Evaluating for m(H+)= 200  ->  0.5461525537200647\n",
      "Evaluating for m(H+)= 225  ->  0.5540389354822822\n",
      "Evaluating for m(H+)= 250  ->  0.5509014533275765\n",
      "Evaluating for m(H+)= 275  ->  0.5452247263399559\n",
      "Evaluating for m(H+)= 300  ->  0.5189772407150242\n",
      "Evaluating for m(H+)= 350  ->  0.49582722967880466\n",
      "Evaluating for m(H+)= 400  ->  0.4601910037407694\n",
      "Evaluating for m(H+)= 500  ->  0.3809461707259765\n",
      "Evaluating for m(H+)= 600  ->  0.29088351172919535\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating for m(H+)= 700  ->  0.22444670421987523\n",
      "Evaluating for m(H+)= 800  ->  0.1671849855788281\n",
      "Evaluating for m(H+)= 900  ->  0.12480724812755606\n",
      "Evaluating for m(H+)= 1000  ->  0.09925587537247371\n",
      "Evaluating for m(H+)= 1200  ->  0.06098089966246023\n",
      "Evaluating for m(H+)= 1400  ->  0.042655857519142026\n",
      "Evaluating for m(H+)= 1600  ->  0.02922366056997261\n",
      "Evaluating for m(H+)= 1800  ->  0.022549141316753563\n",
      "Evaluating for m(H+)= 2000  ->  0.016057123890930947\n"
     ]
    }
   ],
   "source": [
    "scorematrix_normalised=calculateCrossMassScore(clf,htf,hpmasses,normalizeInputs=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EfisWqloATABmAyuA6aqaISKTRWSEM9kEEckQkXTgLmCsM28GMB3IBD4HblPVWrtTP2XIVawK7kSbZU+Tl3vY7TjGGPMrdj+LALH8hxl0++pa5iY9wJmX3+V2HGNMHWH3s6hhkgYM5/8aP8jda5M5WlDkdhxjjDmJFYsAIR4P/S68ms37j/Lx4q1uxzHGmJNYsQgggzrGcVvsIjp/drmdGWWMCShWLAKIiDA4OYHuRStI//w1t+MYY8wJViwCTMqQq9joaU3s4mfsqm5jTMCwYhFgPEFB7O51OwlFW1jy5VtuxzHGGMCKRUDqOew6tkgLIuY/hRbZmVHGGPdZsQhAQcHBbOhzP3/JHcO3q2pen1fGmNrHikWAOvOC0WxulMrT36y1+10YY1xnxSJAhQR5uH1AE4Zve5qMn/7jdhxjTB1nxSKAjUpNZHjwPOT7R92OYoyp46xYBLCw+g1Y1+E6ko4uZeW8/7odxxhTh1mxCHDdR97JXhqR/41tXRhj3GPFIsCFN4xkZeLv6JG3gDXpP7gdxxhTR1mxqAG6jfoDH3Aery/a63YUY0wdZcWiBoiIjGbTgEd4e3UQq3YcdDuOMaYOsmJRQ4zvn0Cv0C0s+9COXRhjqp8VixoiqkEok1ou49Kdz7BlzRK34xhj6hgrFjVIh5H3cIxgtn/6d7ejGGPqGCsWNUhss1akNxlFz72z2bZxldtxjDF1iF+LhYgME5FVIrJWRO4pYfxdIpIpIktF5CsRaeMzrlBE0p3HTH/mrEkSR/4JBbZ88r9uRzHG1CF+KxYiEgQ8B1wIdAWuFJGuxSZbDKSqanfgfcD36G2uqqY4jxH+ylnTNI1vx7y4y1iwK4idB/LcjmOMqSP8uWXRF1irqutV9SgwDRjpO4GqfqOqR5ynPwPxfsxTa7S58kn+WXgZU75f73YUY0wd4c9i0RLY4vM8y2krzfXAZz7Pw0QkTUR+FpFR/ghYU7WOCWdkj+Zsnvcxe3ZtdTuOMaYO8GexkBLaSrwxg4hcA6QCj/k0t1bVVOAq4EkRaVfCfDc6BSUtO7tu3STojl4hvOh5lFUfP+J2FGNMHeDPYpEFtPJ5Hg9sKz6RiAwB7gVGqGr+8XZV3eb8XQ98C/QsPq+qTlHVVFVNjYuLq9r0AS6hQzcWR5xD8tbp7N9TtwqlMab6+bNYLAA6iEiiiIQCY4CTzmoSkZ7Ai3gLxS6f9igRqecMxwIDgEw/Zq2Roob9mYaSS+bHj5U/sTHGnAa/FQtVLQAmALOBFcB0Vc0QkckicvzspseAhsB7xU6R7QKkicgS4Bvg76pqxaKYtt3OYHF4f7pufotDB6yTQWOM/0htub9zamqqpqWluR2j2q1e9C0NZ4xnTp+nueyS4W7HMcbUMCKy0Dk+XCa7gruG69hrEH9u9RZ/Tw8j71ih23GMMbWUFYta4NbBXdh/6Aiff/2121GMMbWUFYtaoG9iNG80fpn+c28kP+9I+TMYY0wlWbGoJSL6X0cT9rDkP8+7HcUYUwtZsaglug0cyergjsRnvMixo/nlz2CMMZVgxaKWEI+H3AF/pIXuJO3t+92OY4ypZaxY1CI9zr2chRHnIhu+Z9X2/W7HMcbUIlYsapnE617l9uAH+MP7yygoLHI7jjGmlii1WIhIXAn3n0BEuopI3eqIqQaJjormwVE92LZ1M//96FW34xhjaomytiyeAWJLaI8BnvJPHFMVLu7enKebfMKQZXezacVCt+MYY2qBsopFe1X9vnijqv4AdPdfJFMVOl/9GIelPvkf3ExhwTG34xhjariyikVEGeNCqjqIqVoxTeNZ1+cBOhasZsG/H3I7jjGmhiurWKwVkYuKN4rIhYDdz7MG6H3h9SwOH0DPtf9i8+p0t+MYY2qw4DLGTQQ+FZErgOM7vlOBM4FL/B3MnD7xeGh17Qt89sIdzPhiGy+370GQp6QbGBpjTNlK3bJQ1TVAMvAdkOA8vgO6q+rq6ghnTl9ss9boyGf5Zovy2k8b3I5jjKmhytqywLnN6WvVlMX4yaiUlsxbuIguX17LlubP06p9stuRjDE1TFnXWRwUkQM+j/0isk5EXhaRmOoMaU6PiPD7i7qTLOs5+O7NFBXafS+MMZVT1m6oCFVt5POIxHvMIgN4odoSmioR1yKBVSl/ouux5cyf/ojbcYwxNUyluvtQ1b2q+k+gnZ/yGD9KHXEbS8L60H3lk2xdv8LtOMaYGqTSfUOJSAjlHOswgUk8Hppd8yKFeFj73n0UFdWO+68bY/yvrGMWvynhcT3wKfB+RRYuIsNEZJWIrBWRe0oYf5eIZIrIUhH5SkTa+IwbKyJrnMfYU3lz5teaxrdj3oCXuGnv1bw9f7PbcYwxNURZWwjDiz1XIAd4SlU/LW/BIhIEPAecD2QBC0Rkpqpm+ky2GEhV1SMicgvwKDBaRKKBB/AeI1FgoTPv3oq+MVO6wedfQp8t8/nnrHTOi4eW8W3Kn8kYU6eVWixUdXxp40Skj6ouKGfZfYG1qrremWcaMBI4USxU9Ruf6X8GrnGGLwC+UNU9zrxfAMOAf5fzmqYCRIS//6Yb+568ib1TI2lx9zeIx3qrN8aUrsLfEE7X5JNFZA1QkRs9twS2+DzPctpKcz3wWWXmFZEbRSRNRNKys7MrEMkc1zKqAYe6XUO3/HTmf/BPt+MYYwJcmcVCRNqIyD0isgSYCtwKnK+qqRVYdkn9SpR4RFVErsG7y+mxysyrqlNUNVVVU+Pi7BYbldX3t3exvF4KScsfY8fmNW7HMcYEsLIOcM8BZuHtYfYyVe0NHFTVjRVcdhbQyud5PLCthNcZAtwLjHCuGK/wvOb0iMdD9JgX8VDErnduQovsznrGmJKVtWWRjbeb8qbA8Z/tlTnXcgHQQUQSRSQUGAPM9J1ARHoCL+ItFLt8Rs0GhopIlIhEAUOdNlPFWiR2ZlnXuzh25AAf/5xZ/gzGmDqprAPcI0UkEvgt8FcRaQ80FpG+qjq/vAWraoGITMD7JR8EvKqqGSIyGUhT1Zl4dzs1BN4TEYDNqjpCVfeIyEN4Cw7A5OMHu03V63PZJK7eN5Dls7fSL6kdzSPrux3JGBNgRLViGwsi0gQYDVwJtFLVVuXMUq1SU1M1LS3N7Rg11qacw1z15H+YGDOPy+543M6OMqaOEJGFFTkOXeFvBFXdparPqGp/YOBppTMBp01MAx5PWs/l+14hbea/3I5jjAkwp/TzUVU3VXUQ474zLv8jmSHd6JT+v3Z2lDHmJLavwZzgCQoi8sopeLSIfVOv5djR/PJnMsbUCVYszElatk1iZd+H6Hwsk7lT73c7jjEmQJR1ncWjInJzCe03iYjdEKEWS734Bma0uIuJa1L4frVdGW+MKXvLYjDeayCKewm42D9xTKAYOu4+Ypu2YNK7aWRn73A7jjHGZWUVi3pawnm1qlpEyd1xmFqkfmgQz13ZkycKHmbny6MpLChwO5IxxkVlFYtcEelQvNFpy/VfJBMoOjRrRGiPy72dDU69z+04xhgXlVUs7gc+E5FxIpLsPMbjvfmRHfmsI1JH3U5aoyH03fgCmT9/7nYcY4xLSi0WqvoZMAo4F3jdeZwL/FZVZ1VHOOM+8Xjo/P9eZpunObGf38Le7O1uRzLGuKDMU2dVdbmqjgXOAc5W1WtVdVn1RDOBomGjKPJHvcxebcDjH82hol3EGGNqj/LuZ3GriGwGNgGbRWSTiNxaPdFMIGnfYwA/D53J2+vDeOXHDW7HMcZUs7Kus7gPuAQYpKoxqhqDdzfUhc44U8dc2z+RS7o0pv5/J7F60XduxzHGVKOytix+B/zm+D20AZzhK4Br/R3MBB4R4eHhHRkctISGn9zAgX05bkcyxlST8o5Z5JXQlgvYLdXqqMjoOPZd/AJNirJZ+/J4u7ueMXVEWcUiS0QGF28UkfMAOyWmDuvcZwgL2k2g16HvmP/BE27HMcZUg1LvlAfcAcwQkR+BhXhvqdoHGACMrIZsJoCdcfWDLH1sDu2XP8XKM66hc+tmbkcyxvhRWddZZADdgO+BBKCtM9zNGWfqME9QEC3Hv8ENIQ9z23srOXLUugMxpjYr95iFqr6qqr9X1btU9RXgmIhcXU35TACLaRrPH8ZcxPrdh3jz7TfdjmOM8aOyTp1tJCJ/EpFnReR88ZoAHD8jqlwiMkxEVonIWhG5p4TxZ4vIIhEpEJHLio0rFJF05zGzsm/MVI/+7WN5JnkjN2/6HxbMsNuxGlNblbVlMRXoBCwDbgC+AS4HRqpquccsRCQIeA64EOgKXCkiXYtNthkYB7xTwiJyVTXFeYwo7/WMe4ZdfiOZockkLXqQzavT3Y5jjPGDsopFW1Udp6ovAlfi/cK/QFUr+m3QF1irqutV9SgwjWIHxlV1o6ouxU7FrdGCQ0KJHTuVfAnl2LvjyMs97HYkY0wVK6tYHDs+oKqFQFZJ112UoSWwxed5ltNWUWEikiYiP4vIqErMZ1zQpGUim8/+B+0KN7DklQluxzHGVLGyTp3tISIHnGEB6jvPBVBVbVTOsku6QVJleqBrrarbRKQt8LWILFPVdSe9gMiNwI0ArVu3rsSijT/0OG80365L4/UNkexZtp0Lk5u7HckYU0XKOnU2SFUbOY8IVQ32GS6vUIB3S6KVz/N4YFtFg6nqNufveuBboGcJ00xR1VRVTY2Li6vooo0f9R//CHtbnsuk95eSnrnK7TjGmCpS5qmzp2kB0EFEEkUkFBgDVOisJhGJEpF6znAs3gsBM/2W1FSZ0GAPL17TmyvC5tPx3bPI/HGG25GMMVXAb8VCVQuACcBsYAUwXVUzRGSyiIwAEJE+IpKF9yyrF0Xk+MV+XYA0EVmC9yysv6uqFYsaollkGLeMG8uOoGa0++J6Mr6Z7nYkY8xpktpyI5vU1FRNS0tzO4bxkZO9g5wXLiaxYAOrBv6TbuePdTuSMaYYEVmoqqnlTefP3VCmjouJa0bcbbNZHdKJLj/eyU8/z3E7kjHmFFmxMH4VFR1L/O2f8c9Gkxg7cx+zllmHxcbURFYsjN9FRjbmptv+SEqrxrwybTpLP3jE7UjGmEqyYmGqRURYCG9c15fbGv1E92X/y/J/3we15HiZMXWBFQtTbRrUC+bMO6fyQ/gQuq16hsypv7eCYUwNYcXCVKv6YfXoM3Ea3zS8mK7rX2HFa7eC3ZrVmIBnxcJUu7DQEPrf+SZfRP6WjRvW8MoPa92OZIwpR1l9QxnjN/VCghl0+0v8z7SF/OezNUj+fq4b3BOC7CNpTCCyLQvjmpDgIJ68MpXfdo9hwI/jWPuvy9CCfLdjGWNKYMXCuCo4yMOjY85gZYtRtM/5hg3PjkSPHnE7ljGmGCsWxnVBHmH4jX/jw5aTSNj7M5ueuQTNP+h2LGOMDysWJiB4PMKo6+/lw4S/0OrAItKn3ERt6bfMmNrAioUJGB6P8Ntxd/Feh0e5cetF/PmjZRQWWcEwJhBYsTABRUQYffUNXHFuKtPnb+Tbp64jP3u927GMqfOsWJiAIyJMuqAzj5zbiN77ZnP4+cEc3LTE7VjG1GlWLEzAuuyCQSwe8m+OFgKvXcjuzG/djmRMnWXFwgS0c886h62XfsxubUTE9MvZOv9jtyMZUydZsTABr3dKD/KvncVy6cDds7aStnGP25GMqXOsWJgaoXO7tjSZ8AXbGiZx9cvz+Pn7z9yOZEydYsXC1BitYhrw/i39uTJmDf2+HkPmG3daF+fGVBO/FgsRGSYiq0RkrYjcU8L4s0VkkYgUiMhlxcaNFZE1zmOsP3OamiO6QSh/vOUmvmo4nK4bXmf5879DC4+5HcuYWs9vxUJEgoDngAuBrsCVItK12GSbgXHAO8XmjQYeAM4A+gIPiEiUv7KamiU8rB5nT3yD2XHj6bbrE1Y8NYqCvMNuxzKmVvPnlkVfYK2qrlfVo8A0YKTvBKq6UVWXAsXvfnMB8IWq7lHVvcAXwDA/ZjU1TEhwEENv/SdfJv6RTvt/4vnXXiXvWKHbsYyptfxZLFoCW3yeZzlt/p7X1BEiwpCx9zLzrI95YnNbrn55HvsO5body5hayZ/FQkpoq+jRyArNKyI3ikiaiKRlZ2dXKpypPS4dMojnrupq0qVMAAAW+UlEQVRFva0/s/+JPuzYkOF2JGNqHX8Wiyyglc/zeGBbVc6rqlNUNVVVU+Pi4k45qKn5Lkpuzp9GpBBRtI/QNy5k47I5bkcyplbxZ7FYAHQQkUQRCQXGADMrOO9sYKiIRDkHtoc6bcaUKrnveewb8wn5hBD7wW9YMec/bkcyptbwW7FQ1QJgAt4v+RXAdFXNEJHJIjICQET6iEgWcDnwoohkOPPuAR7CW3AWAJOdNmPK1LZzT/T6/5LtiaPd7LF89t/Z5B61A9/GnC6pLTeYSU1N1bS0NLdjmACxb/dOPnvtb/w5ZyjhoSE82DKNLknd6XrmxXiCgtyOZ0zAEJGFqppa3nTB1RHGmOrWOLYpo3//NAkb9jBj0UbOWf4yTbbvZeeXMaxvcTEtzhpHmy693Y5pTI1hWxamTsg9fIiMb6cRsvxdko6kESxFvBJ+HdL/DkaktCC2YT23IxrjiopuWVixMHXO7h1bWPv167y+sx2f74xkYFAGkxp9QUG30SSdO4aw8IZuRzSm2lixMKYCVu88SMYXb9J/7T9oSg4HtT4ros6lQd/f0eWMC+z4hqn1rFgYUwmFBQWs+HkWuQvepuu+bzlMGJeFvczwXq0YnRJL66axbkc0xi/sALcxlRAUHEy3gSNg4AiOHNpPZtp8EtZH8tK3qxg95xIWNupG5JBJtO8xwO2oxrjCtiyMKcOu3Tms++BBkre9R0PJZWlYKp6zJpJ05sWIx24HY2q+im5Z2KfdmDI0iY3hzJueoXDicuYmTqBF3lq6fXENf3nqBT5fvoOiotrxY8uY8tiWhTGVkHfkEAs/f4M/r+nMpr153BX5LWd2iqfHxTcRWi/M7XjGVJod4DbGjwoKi/hs2XZa/2c0PQqWsYto1ncYR7fhd9Cwkd2ny9QcthvKGD8KDvIwPKUl3f/8PUsHvUp2aDz91jxB0RNJ/OedZ8k5lO92RGOqlBULY06DeDx0H/Rbkv78A6su+Yh1DXoyZVkRAx75mn+89xXbNq5yO6IxVcJOnTWminRKPQ9Sz+OJXQd58bv1tFr2AE2Wf0da5GBiLphEYtIZbkc05pTZMQtj/GRn1jo2fPIY3Xd8SLjkk16/H/XO/QNd+p7vdjRjTrBjFsa4rGl8O/rd8gLH7ljG3DY30yY3g/kzX+Sy5+fwVeYOigqL3I5oTIVZsTDGzyJjmnLm+EeoNymTkCH3sX1/Hi9OfYvND/cgbea/OHbUDoabwGe7oYypZscKi/j5qw+J/3kyiUWb2EEcGztdR48Rt1O/QYTb8UwdY7uhjAlQIUEezhp6GW3uTSf9rBfZG9KEfqseYdtj/Xj6y9XsO3LU7YjG/IptWRgTAFbMm823act4ZEsXGoYKz8V/ReeLbqVpfDu3o5lazq7gNqYGWrnjAJ9//gkTNkygCCE96gKaXng3bTqluB3N1FIBsRtKRIaJyCoRWSsi95Qwvp6IvOuMnyciCU57gojkiki683jBnzmNCRSdmzVi4rir2TX+ZxbHjSJ575e0emcQix+7mNnpG9ltV4Ybl/jtojwRCQKeA84HsoAFIjJTVTN9Jrse2Kuq7UVkDPAIMNoZt05V7eeUqZNaJHSixYRX2bNrK0tm/oPorC+4adpyIIOHG31E+4ijBCUOpHXP82nSMtHtuKYO8OcV3H2Btaq6HkBEpgEjAd9iMRJ40Bl+H3hWRMSPmYypUaKbtKTf/3uCo8cK+XD7Aeat30OrtIN03f0dETkzIA2ypBmZscM40G8SZyRGEx9VH/tvZKqaP4tFS2CLz/MsoHh/ByemUdUCEdkPxDjjEkVkMXAAuE9Vfyj+AiJyI3AjQOvWras2vTEBJDQkiF6to+jVOgoGvUthQQFrM35m9/Kvqbf1Z7Zk7+eh95bgoYj/1r+XfREdKGo9gGbdz6NV++52oyZz2vxZLEr6aVP8aHpp02wHWqtqjoj0Bj4WkSRVPXDShKpTgCngPcBdBZmNqRGCgoNp32Mg7XsMBKBHkTJw1yEWr97AgfkJJBxII3b5l7D8r+ymMf9peiuelNH0TWhMxyYReIKCXH4HpqbxZ7HIAlr5PI8HtpUyTZaIBAORwB71nqKVD6CqC0VkHdARsNOdjCmBxyN0ahZBp2bd4eyZaFERW9YtY/uSr/BsnsP8PfWZNSODM2QFL9b7JxvCu5Pfsh8xSeeRmHQGwSGhbr8FE+D8duqs8+W/GhgMbAUWAFepaobPNLcByap6s3OA+zeqeoWIxOEtGoUi0hb4wZluT2mvZ6fOGlM6VSVrby4r0+cQufQVWuxfTLxuB+CQ1ufhls/QqmNP+rcIpmubZnbXvzqkoqfO+m3LwjkGMQGYDQQBr6pqhohMBtJUdSbwCjBVRNYCe4AxzuxnA5NFpAAoBG4uq1AYY8omIrSKDqfVeUPgvCEA7Nq6gc2Lv6Rgw08sPhjFvz9fxb3Bb9Ex6CtWh3XhYNMzaNR5EO16nkNY/QYuvwPjNrsozxgDwO5D+aybNwtd+Slxe9JILNiIR5TtGs2dzd6mb9sYBkXn0D6xLY1jm7kd11QR17csjDE1S2zDesQOvhQGXwrA/j3ZbFj0JVu2biX/YBHPf7eO3wZPpLFnJzuIZUd4B/Kiu+BpezbNelxAfFR9PB47Zbe2si0LY0yFHMo7xob5n3J40yKCszOIPbSKVoVZTC8cxJ8KbiCinoe36j1CXqO2SPPuRLXrTatOvWwXVoCzLQtjTJVqGBZC8tmjgFEn2vKOHCI5ayf/ty+EDZuzCF2VT/vsT2mw+wNYBgXq4V/1r2dN4jV0bxJCavBa4jv3JSquuXtvxJwSKxbGmFMWFt6Qbh0b0g2gb2tgLkWFhWRtWMGuNQvIz1rC3sJuzF2XQ1Z6OuPrTYavYCcx7KjfniPRXcnvdiUJHbrROjrcdmMFMCsWxpgq5QkKIr59N+LbdwPgTOBeIGdPCssy2nNo02KCdy0n9tBqkrIWcMX6NizS3VwSuojb6s1if2Rn726sxF607tybsPCGrr4f42XHLIwxrsnLPcya7Dwydx6mYMUsem6ZSuuj62gouQAUqnB1xKs0jU/k7IZZdGxUQItOfYhpGu9y8trDjlkYYwJeWP0GJLduQHLrGOhzM3AzRYWFbN20ip2rF5C3dTkNg+JZsGEPZx2ZQnLQ9/A15BDJnuAm7K3Xkpkd/kbTiDCSCjOJq1dIw7jWRDdrQ6PGMdYnVhWyYmGMCSieoCBatu1Ky7ZdAejvtO/b3Y3lK+dxaNNiPNkrCcvbBfmH+M/S7ew7coypIY+SHLT8xHJyNZSMoC482vQRmjYK48K8WUSHFhLSuAX1Y1oR2aQ1Mc1b29laFWTFwhhTIzSObUrjgSNg4IiT2tOBvGOF5GztQOaO9eTmZHFs31Y4sJ2cAm+3JUuz9nHLoel0lY0nzTu3sCu3hPyVZo3CuOfYc4SHeChs2BxPo+bUi2pJ/RZdiGrdhZgG9Qiq4wffrVgYY2q8sJAgWiZ0pGVCx1+Nu9j5q0WL2b8vh707N3Fw12Zy92xlz7EwLgltzs4D+URv2Uazw1nE7N1LkHiP5X5QOJDfH7uVIA98U+/35AVFcLheE47Wb0JRRHPyW/QjJLE/TSPq0TTsGBGR0dX4rquXFQtjTJ0gHg+R0XFERsdBl1+O5158Ysh7y5yCY0fJzt7G3h2baJYfxEPEk73vEDtXpVAvbxdRuZuJOryYyN2HeXZNFo9/FUQjDrE07EYOaxh7PNEcCIkjN6wJ61pcQl7rQTRvICQUrKdx0zZEN42vkb382tlQxhhzCnIPH2TX/sNszwshJyebmJXvwMHthB7ZQXheNo0LdvN0wSjeKTiXzrKZz+vdA3jP8NojjdkXHMus2OvIaX42iWGH6JabRlh0Kxo1aUV084Rq20qp6NlQViyMMcZPioqUnMNHyd69i6PrfyJ/Txa6fxueQ9sJy9vFm0G/4YsjHeiVP5/XQh87ad7DGsZfwu9lR3RfeoduZkD+D0ij5oRGxdMgtpWzldKK4JCQ08pop84aY4zLPB4hLqIecRGtIHHMr8Y/7vzNPTKQrG2j2L9rM7k5WyjYtxUObqdBeFvyjhRyaNdyeh19m9BthSfNf9HRv5PTsAN9E2N45sqefn0vViyMMcZl9cPDT7rq/bh+J4YGUFT4ALt3b2ffjk0cyt5C/p4shkX0Y8shD3ER9fye0YqFMcbUAJ6gIGKbxhPrc/X6GdX5+tX4WsYYY2ooKxbGGGPKZcXCGGNMufxaLERkmIisEpG1InJPCePrici7zvh5IpLgM+5PTvsqEbnAnzmNMcaUzW/FQkSCgOeAC4GuwJUi0rXYZNcDe1W1PfBP4BFn3q7AGCAJGAb8y1meMcYYF/hzy6IvsFZV16vqUWAaMLLYNCOBN5zh94HBIiJO+zRVzVfVDcBaZ3nGGGNc4M9i0RLY4vM8y2krcRpVLQD2AzEVnNcYY0w18WexKKk/3+J9i5Q2TUXmRURuFJE0EUnLzs4+hYjGGGMqwp8X5WUBrXyexwPbSpkmS0SCgUhgTwXnRVWnAFMARCRbRDZVWfpfiwV2+3H5VaWm5ISak9VyVq2akhNqTtbTydmmIhP5s1gsADqISCKwFe8B66uKTTMTGAvMBS4DvlZVFZGZwDsi8gTQAugAzC/rxVQ1rorzn0RE0irS2ZbbakpOqDlZLWfVqik5oeZkrY6cfisWqlogIhOA2UAQ8KqqZojIZCBNVWcCrwBTRWQt3i2KMc68GSIyHcgECoDbVLWwxBcyxhjjd37tG0pVZwGzirXd7zOcB1xeyrwPAw/7M58xxpiKsSu4K26K2wEqqKbkhJqT1XJWrZqSE2pOVr/nrDU3PzLGGOM/tmVhjDGmXFYsABFpJSLfiMgKEckQkTud9gdFZKuIpDuPi3zmca3vKhHZKCLLnExpTlu0iHwhImucv1FOu4jI007WpSLSq5oydvJZb+kickBEJgbKOhWRV0Vkl4gs92mr9DoUkbHO9GtEZGw15XxMRFY6WT4SkcZOe4KI5Pqs2xd85untfGbWOu+lpGuZqjpnpf+tpZz+5PyU812fjBtFJN1pd3N9lvad5N5nVFXr/ANoDvRyhiOA1Xj7s3oQ+EMJ03cFlgD1gERgHRBUjXk3ArHF2h4F7nGG7wEecYYvAj7De6FjP2CeC+s3CNiB93zugFinwNlAL2D5qa5DIBpY7/yNcoajqiHnUCDYGX7EJ2eC73TFljMfONN5D58BF1ZDzkr9WzuPdUBbINSZpqu/cxYb/w/g/gBYn6V9J7n2GbUtC0BVt6vqImf4ILCCsrsXCcS+q3z72XoDGOXT/qZ6/Qw0FpHm1ZxtMLBOVcu6aLJa16mqfo/3dO3iGSqzDi8AvlDVPaq6F/gCb8eXfs2pqv9Vb/c4AD/jvWi1VE7WRqo6V73fIG/yy3vzW84ylPZvXZH+5PyW09k6uAL4d1nLqKb1Wdp3kmufUSsWxYi3m/SewDynaYKzWffq8U0+3O+7SoH/ishCEbnRaWuqqtvB+0EDmjjtbmcF7/Uzvv8BA3GdQuXXYSBkvg7vL8rjEkVksYh8JyJnOW0tnWzHVWfOyvxbu70+zwJ2quoanzbX12ex7yTXPqNWLHyISEPgA2Ciqh4AngfaASnAdrybqFDBvqv8aICq9sLb/fttInJ2GdO6mlVEQoERwHtOU6Cu07KcVh9m/iIi9+K9aPVtp2k70FpVewJ34e0FoRHu5azsv7Xbn4ErOflHjevrs4TvpFInLSVTlWW1YuEQkRC8/yhvq+qHAKq6U1ULVbUIeIlfdotUqO8qf1HVbc7fXcBHTq6dx3cvOX93BUJWvAVtkaruhMBdp47KrkPXMjsHKi8BrnZ2heDs1slxhhfi3f/f0cnpu6uqWnKewr+1m+szGPgN8O7xNrfXZ0nfSbj4GbViwYl9la8AK1T1CZ923337lwLHz6CYCYwR753+EqlA31VVmLWBiEQcH8Z7sHM5v/SzhfN3hk/Wa52zJfoB+49vxlaTk36tBeI69VHZdTgbGCoiUc4ulqFOm1+JyDDgbmCEqh7xaY8T5yZhItIW7zpc72Q9KCL9nM/6tT7vzZ85K/tvfaI/OWeLdIwzbXUYAqxU1RO7l9xcn6V9J+HmZ7Qqj+DX1AcwEO+m2VIg3XlcBEwFljntM4HmPvPci/eXxiqq+EyIcrK2xXuWyBIgA7jXaY8BvgLWOH+jnXbBe8fCdc57Sa3GrOFADhDp0xYQ6xRvAdsOHMP76+v6U1mHeI8ZrHUe46sp51q8+6GPf1ZfcKb9rfOZWAIsAob7LCcV75f1OuBZnAty/Zyz0v/Wzv+71c64e6tjfTrtrwM3F5vWzfVZ2neSa59Ru4LbGGNMuWw3lDHGmHJZsTDGGFMuKxbGGGPKZcXCGGNMuaxYGGOMKZcVC+MqETlU7Pk4EXnWrTyVJSKdxdsj6WIRaedyltdF5LJTnDdFTu4VdoT4oddXU3NZsTA1goh86/SRE2hGAe+rak9VXVfexM5FU4H4/y4F73n8AKjqTFX9u4t5TIAJxA+tMZUm3nsPrHR+Xa8WkbdFZIiI/CTefvz7OtP1FZE5zpbAHBHp5LQnich8ZythqYh0cK6W/1RElojIchEZXew1LwImAreIyDdO213OtMtFZKJPtlUi8ibeC7laFVtOb6ejuoUiMltEmotIFxGZ7zNNgogsdYbvF5EFzmtMca72Lb4+NopIrDOcKiLflvb+naulJwOjnfc/2ncLT0TaiMhXznr5SkRaO+2vi/ceCnNEZP2pbtWYGqKqr5C0hz0q8wAK+eUK1XRgM/BsCdN9CySUsZwEvJ3qJeP9EbQQeBXvla0jgY+d6Rrxy70ghgAfOMPP4O1nCbz3UqiP9wrel3xeI7KE130Q554NQG+8V882ABrivfq3p5OtCOhXwvwhwBwgznk+GnjVGU4H2jrDdwP3OcPRPvNPxbmyGO9VyJc5wxtx7nmC92rjb8t5/+N817vvc+ATYKwzfJ3PunwdbweRHrz3Wljr9ufJHv57BJdYQYypPrmqmnL8iYiMw/vlhoiMB+50RrUHZonIUWCDql5awrI2qOoyZ94M4CtVVRFZhvcLGyASeENEOuDtTiHEaZ8L3Csi8cCHqrrGme9xEXkE+I+q/lDOexkIfKSqh50MH+Lt9nomsEm99xkorhPQDfjC2UAIwtsdBcB0vPdX+DveInJ8y+ZcEfkj3u5UovEWpU/KyXZcae+/LGfi7WQPvMXpUZ9xH6u3o8BMEWlawQymBrLdUCZgqeprqpriFJM04CLneUmFAiDfZ7jI53kRnPhh9BDwjap2A4YDYc5rvYO3K/VcvEXpPFVdzS9bC38TkfvLiVzWrTUPlzFPxvH3qarJqjrUGfcucIWIdPRG1DUiEgb8C+8WRDLe3lzDSlhuAb/8//YdX+L7ryTfPoJ813mV3lrUBBYrFqauiQS2OsPjjjc6vYquV9Wn8fbk2V1EWgBHVPUt4DG8t+Msy/fAKBEJF2+PwJcC5W2NrALiRORMJ0eIiCQBqPeAeSHwF37pOvv4l/tu8d7roLTjBBvxFjrw7k47rsT3DxzEe/vOkszB2wMswNXAj2W+I1MrWbEwdc2jwP+JyGI4aTfsaGC5iKTj3S30Jt7jH/OdtgeAv5W1YPXeBvN1vN1tzwNeVtXF5cxzFO8X/iMisgTvcYr+PpO8C1yDd5cUqroP79bEcrxdTS8oZdF/BZ4SkTS8Bae89/8N0PX4Ae5iy7oDGO8cYP8dv+waNHWI9TprjDGmXLZlYYwxplxWLIwxxpTLioUxxphyWbEwxhhTLisWxhhjymXFwhhjTLmsWBhjjCmXFQtjjDHl+v85FgHUMuVSVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotCrossMassScore(scorematrix_normalised, scorematrix)\n",
    "plotCrossMassScore(scorematrix_normalised, scorematrix, convertToXSec=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "sparkconnect": {
   "bundled_options": [],
   "list_of_options": []
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}