LRP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "# TensorFlow and tf.keras\n",
    "# import tensorflow as tf\n",
    "#from tensorflow import keras\n",
    "#from tensorflow.keras import layers\n",
    "import pandas as pd\n",
    "\n",
    "import keras\n",
    "from keras import layers\n",
    "import keras.backend\n",
    "from keras.layers import Input, Conv2D, MaxPool2D, Flatten, Dense, BatchNormalization, Dropout\n",
    "import keras.models\n",
    "import keras.utils\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "from matplotlib.colors import Normalize\n",
    "import math\n",
    "import time\n",
    "import h5py\n",
    "import sklearn\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = 32\n",
    "# training data : 10,000 signal samples and 10,000 bkg samples\n",
    "traindata_sig = np.load('data/jetImages_signal.npz')\n",
    "traindata_bkg = np.load('data/jetImages_bkg.npz')\n",
    "trainimages = np.concatenate((traindata_sig['jetImages'], traindata_bkg['jetImages']),axis=0)\n",
    "trainlabels = np.concatenate((traindata_sig['labels'],traindata_bkg['labels']),axis=0)\n",
    "train_zlabels = np.concatenate((traindata_sig['zlab'],traindata_bkg['zlab']),axis=0)\n",
    "train_thetalabels = np.concatenate((traindata_sig['thetalab'],traindata_bkg['thetalab']),axis=0)\n",
    "\n",
    "# testing data : 10,000 signal samples and 10,000 bkg samples\n",
    "testdata_sig = np.load('data/jetImages_signal_test.npz')\n",
    "testdata_bkg = np.load('data/jetImages_bkg_test.npz')\n",
    "testimages = np.concatenate((testdata_sig['jetImages'], testdata_bkg['jetImages']),axis=0)\n",
    "testlabels = np.concatenate((testdata_sig['labels'], testdata_bkg['labels']),axis=0)\n",
    "test_zlabels = np.concatenate((traindata_sig['zlab'],traindata_bkg['zlab']),axis=0)\n",
    "test_thetalabels = np.concatenate((traindata_sig['thetalab'],traindata_bkg['thetalab']),axis=0)\n",
    "\n",
    "n_test = len(testlabels)\n",
    "n_train = len(trainlabels)\n",
    "trainimages = trainimages.reshape(n_train, grid, grid, 1)\n",
    "testimages = testimages.reshape(n_test, grid, grid, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot input signal and background\n",
    "sig_trainimages = np.zeros_like(trainimages[1])\n",
    "bkg_trainimages = np.zeros_like(trainimages[1])\n",
    "for i in range(len(trainimages)):\n",
    "    if testlabels[i] == 1:\n",
    "        sig_trainimages += trainimages[i]\n",
    "    else:\n",
    "        bkg_trainimages += trainimages[i]\n",
    "#signal\n",
    "display_sig = sig_trainimages.reshape(grid,grid)\n",
    "plt.grid(False)\n",
    "plt.imshow(display_sig, interpolation='nearest', origin='low', cmap = 'jet', norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#background\n",
    "display_bkg = bkg_trainimages.reshape(grid,grid)\n",
    "plt.grid(False)\n",
    "plt.imshow(display_bkg, interpolation='nearest', origin='low', cmap = 'jet', norm=LogNorm())\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 22, 22, 32)        3904      \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 20, 20, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 10, 10, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 8, 8, 32)          9248      \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 6, 6, 32)          9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 3, 3, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 288)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 32)                9248      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                2112      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 43,073\n",
      "Trainable params: 43,073\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "20000/20000 [==============================] - 10s 512us/step\n",
      "[0.05206185774156647, 0.011907283318233484, 0.05206185774156647, 0.98545]\n"
     ]
    }
   ],
   "source": [
    "# !ls training\n",
    "best_model = keras.models.load_model('model/CNN_test.h5')\n",
    "best_model.summary()\n",
    "results = best_model.evaluate(testimages, testlabels)\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20000, 1) (20000, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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ANUAd4C0RueAhs6o6RVUbqGqD7A/TGBPokpOTWbhwYcp83bp1Wb16NYsXL7YkkQ38mSjigPJe8+Vwag7eHgI+V8cOYBdwrR9jMsbkMTt27KBFixa0atWK2bNnpyxv0KBByuB+5tL4M1GsA6qLSGVPB/U9OM1M3vYALQBEpCxQA9jpx5iMMXlEUlISo0ePpnbt2ixdupQyZcoQFGRX/PuD3/ooVDVRRB4FFgDBwDRV3SIivT3rJwEvANNF5CecpqqnVfWQv2IyxuQNmzdvplu3bqxbtw6A+++/nzFjxlC6dGmXI8ub/Hpntqr+F/hvqmWTvKZ/A1r6MwZjTN4yf/582rVrR0JCAuXKlWPy5MncdtttboeVp9kQHsaYgHLDDTdQrlw5WrduzauvvkqJEhdc/2KyWUAmCuufMib/OHXqFKNGjeLJJ5+kWLFiFCtWjI0bN1K8eHG3Q8s3AjJRGGPyh2+++YaHH36YnTt3cvjwYcaNGwdgSSKH2SUCxphc5+jRozz88MO0aNGCnTt3EhUVRdeuXd0OK9+yRGGMyVXmzJlDREQEU6dOpWDBgrz44ousW7eO+vVtVAa3WNOTMSbXWLduHe3bO0PCNW3alOjoaGrWrOlyVMYShTEm12jYsCHdunUjKiqKvn37Ehwc7HZIBmt6Msa4aO/evbRv354ffvghZVl0dDT9+vWzJJGLBGSNwi6PNSawJScnM3nyZJ5++mlOnDjB8ePH+fbbb90Oy6QjIBOFMSZw/fLLLzz88MMsW7YMgA4dOjBhwgSXozIZsaYnY0yOSExM5PXXXycqKoply5ZRtmxZZs2axeeff87VV1/tdngmA5YojDE54sCBA7z44ovEx8fTtWtXYmNjueuuu9wOy/jAmp6MMX7z999/ExISQnBwMGFhYUyePJlSpUrRunVrt0MzF8FqFMYYv1i1ahV169blrbfeSlnWpUsXSxIByBKFMSZbnTx5kv79+3P99dezdetWZsyYQXJystthmUsQkInCLo81JndatGgRtWrVYuzYsQQFBTF48GBWrFhhT54LcNZHYYy5ZKdOnaJfv35MmzYNgDp16jBt2jTq1q3rcmQmO1iaN8ZcssKFC7N582YKFSrEK6+8wtq1ay1J5CGZ1ihERID7gCqqOkJEKgBXqepav0dnjMm1fv/9dwCuuuoqgoODmTFjBgA1atRwMyzjB77UKN4GmgJdPPMnALuN0ph8SlV59913CQ8Pp1evXqgq4CQISxJ5ky99FI1VtZ6IbABQ1SMiUtDPcRljcqFff/2VXr16sWDBAsC5T+Kvv/6iaNGiLkdm/MmXGkWCiAQDCiAiZQC71s2YfCQ5OZkJEyYQGRnJggULKFWqFO+++y7z5s2zJJEP+FKjGAd8AVwpIi8BnYDn/BpVJuzyWGNyTlJSErfeeitLly4FoFOnTrz11luULVvW3cBMjsk0UajqByKyHmgBCHCnqm71e2TGmFwhODiYRo0a8fPPPzNhwgQ6duzodkgmh8nZjqh0C4i8p6oPZLYsp4g00AULYmjZ0o2jG5M/bNiwgaNHj3LzzTcDcPr0aeLj4ylVqpTLkZmsEpH1qtogK9v60kcRkepgwYA95dyYPCg+Pp5nnnmGhg0bcv/993Ps2DEAihQpYkkiH0u36UlEBgPPAEVE5DhOsxPAGWBKDsRmjMlBK1asoHv37mzbtg0R4V//+hchITZ4g8mgRqGqr6hqcWCkqpZQ1eKeV2lVHZyDMRpj/OjEiRM89thj3HjjjWzbto2aNWuyYsUKxowZY1c0GcC3zuzBIlIKqA4U9lq+zJ+BGWNyRtu2bVm2bBkhISEMGjSIZ599lkKFCrkdlslFfBnCowfwOFAO+BFoAqwCbvFvaMaYnDB48GBOnTpFdHQ0UVFRbodjciFfOrMfBxoCv6rqzUBd4KBfo8qE3UdhTNbNmjWLF154IWW+devWrF271pKESZcvPVXxqhovIohIIVX9WURsQBdjAsz+/ft59NFH+fzzzxER2rVrl5Ic7HkRJiO+JIo4EbkMmA0sEpEjwG/+DcsYk11UlenTpzNgwACOHj1K8eLFef3116lVq5bboZkA4UtndgfP5DAR+RYoCcz3a1TGmGyxa9cuevbsyeLFiwFo06YNkydPpnz58i5HZgJJhvVNEQkSkc1n51X1O1Wdo6pnfNm5iLQWkW0iskNEBqVTprmI/CgiW0Tku4sL3xiTkeeee47FixdTunRp3n//fb7++mtLEuaiZVijUNVkEdkoIhVUdc/F7NhzB/cE4J9AHLBOROaoaqxXmctwnnfRWlX3iMiVF/8WjDHekpKSCA4OBmDUqFEpT5278kr79zJZ40sP1tXAFhFZIiJzzr582K4RsENVd3pqIB8B7VOVuRf4/GwSUtU/LiZ4Y8w5CQkJvPjii9x0000kJiYCztPnoqOjLUmYS+JLZ/bwLO47DNjrNR8HNE5V5h9AARFZChQHxqrqjNQ7EpGeQE9nrr5dHmtMKuvXr6dbt25s2rQJgEWLFtGmTRuXozJ5hS+d2VntN0jr4zz1ULUhOAMMtgCKAKtEZLWq/pIqhil4xpcSaZDxcLfG5COnT59m2LBhjBo1iuTkZKpUqcI777zDLbfY/bAm+/hzxK84wLvXrBwXXlYbBxxS1VPAKRFZBkQBv2CMydDy5cvp3r0727dvJygoiAEDBjBixAgbn8lkO3/eZbMOqC4ilT3P2L4HSN238SVwo4iEiEgoTtOUPRTJGB/89NNPbN++nYiICFauXMno0aMtSRi/8KlGISJFgAqqus3XHatqoog8CiwAgoFpqrpFRHp71k9S1a0iMh/YhPMc7qmqujn9vRqTv8XFxVGuXDkAevfuTYECBejatSsFCxZ0OTKTl/nyhLs7gFFAQVWtLCJ1gBGq2i4nArwwnga6aFEMt97qxtGNccehQ4fo378/s2fPZsuWLVSsWNHtkEyA8fcT7obhXOp6FEBVfwQqZeVgxpiLo6p8/PHHhIeH88EHH5CcnMwPP/zgdlgmn/ElUSSq6jG/R3IR7PJYkx/89ttvdOjQgXvuuYeDBw/SvHlzNm3aRIcOHTLf2Jhs5Eui2Cwi9wLBIlJdRMYDK/0clzH52pw5cwgPD+fLL7+kRIkSTJ48mSVLllCtWjW3QzP5kC+J4jEgAvgbmAkcA/r7Myhj8rtKlSpx6tQp2rZty5YtW+jZs6cNBW5c48tVTzVUdQgwxN/BGJNfJSUlMXfuXNq3b4+IULt2bTZs2EBERARiba3GZb58RXlDRH4WkRdEJMLvERmTz2zZsoXrr7+eDh06MGvWrJTlkZGRliRMrpBpovA8/rQ5zuNPp4jITyLyrL8DMyavO3PmDCNGjKBu3bqsWbOGsLAwSpQo4XZYxlzAp0ZPVf1dVccBvYEfgef9GpUxedy6deto0KABQ4cOJSEhgV69erFlyxZatWrldmjGXCDTPgoRqQl0BjoBh3GGC3/Sz3EZk2d99dVXtG/fnuTkZKpWrcrUqVNp3ry522EZky5fOrP/A3wItFTVXPGsbGu2NYHslltuoVq1arRr147hw4cTGhrqdkjGZMiXYcab5EQgxuRVx44d4+WXX2bIkCGUKFGC0NBQNm7cSOHChd0OzRifpJsoROQTVb1bRH7i/OdICKCqWtvv0RkT4L766it69+7Nvn37OHnyJBMmTACwJGECSkY1isc9P9vmRCDG5CUHDx7k8ccf58MPPwSgcePG9OnTx+WojMmadK96UtX9nsk+qvqr9wuwv3hj0qCqzJw5k5o1a/Lhhx8SGhrKm2++yYoVK4iIsNuQTGDy5fLYf6axzB7Ga0wa1q5dy3333cfhw4dp0aIFP/30E/379yc4ONjt0IzJsoz6KB7BqTlUEZFNXquKAyv8HZgxgehsE1O9evXo1q2b3Vlt8oSM+ihmAvOAV4BBXstPqOqffo0qE/a/Z3KLHTt20Lt3b15++WUaNWoEkNJhbUxekVHTk6rqbqAvcMLrhYhc7v/QjMm9EhMTGTVqFLVq1WLJkiUMHjzY7ZCM8ZvMahRtgfU4l8d6f49XoIof4zIm19q0aRPdu3cnJiYGgAceeIA333zT5aiM8Z90E4WqtvX8rJxz4RiTe/3999+8/PLLvPzyyyQmJlK+fHkmT55MmzZ2bYfJ2zK96klErheRop7p+0XkDRGp4P/QjMld/vzzT8aOHUtiYiJ9+/Zly5YtliRMvuDLWE8TgSgRiQIGAtHAe0AzfwZmTG5w6tQpChUqREhICFdffTXR0dFceeWV3HjjjW6HZkyO8eU+ikRVVaA9MFZVx+JcImtMnrZkyRJq1arF2LFjU5bdddddliRMvuNLojghIoOBB4CvRSQYKODfsDJml8cafzp69Cg9evTg1ltvZdeuXXz22WckJye7HZYxrvElUXQG/ga6qervQBgw0q9RGeOSL7/8kvDwcKKjoylYsCAvvfQS3333HUFBPj3jy5g8yZdhxn8XkQ+AhiLSFlirqjP8H5oxOefkyZP06NGDjz/+GICmTZsSHR1NzZo1XY7MGPf5ctXT3cBa4F/A3cAaEenk78CMyUmhoaHExcVRtGhRxo0bx/Llyy1JGOPhy1VPQ4CGqvoHgIiUARYDs/wZmDH+tnfvXoKCgggLCyMoKIh3332X4OBgKlWq5HZoxuQqvjS8Bp1NEh6HfdzOmFwpOTmZiRMnEh4ezsMPP4xzUR9UrVrVkoQxafClRjFfRBbgPDcbnM7t//ovJGP855dffqFHjx4sX74cgCJFinD69Gl7brUxGci0ZqCq/wdMBmoDUcAUVX3a34EZk50SExN5/fXXiYqKYvny5ZQtW5ZZs2bx2WefWZIwJhO+1CgAVgJJQDKwzn/h+MbuozAXIzExkRtuuIE1a9YA0LVrV9544w0uv9wGQTbGF75c9dQD56qnDkAnYLWIdPN3YMZkl5CQEFq0aEGFChWYP38+06dPtyRhzEWQsx156RYQ2QZcp6qHPfOlgZWqWiMH4ksjnga6dGkMzWykKZOBlStXcvLkSVq2bAlAfHw8CQkJFC9uo8+Y/ElE1qtqg6xs68vVS3F4HljkcQLYm5WDGeNvJ0+e5PHHH+eGG26ga9euHDlyBIDChQtbkjAmi3xJFPtwbrIbJiJDgdXADhEZICIDMtpQRFqLyDYR2SEigzIo11BEkuxGPnMpFi5cSGRkJOPGjSMoKIhu3bpRpEgRt8MyJuD50pn9P8/rrC89PzP8euYZPHAC8E+cWsk6EZmjqrFplHsNWOBr0MZ4O3LkCAMGDGD69OkA1K1bl2nTplGnTh13AzMmj/BlrKfhWdx3I2CHqu4EEJGPcIYqj01V7jHgM6BhFo9j8rm2bduycuVKChUqxLBhw3jyyScpUMDVAY6NyVP8eYd1GOf3ZcR5lqUQkTCcq6kmZbQjEekpIjEiEuPMZ3OkJqANHz6cG2+8kY0bNzJo0CBLEsZkM38mirQ+zlNfYjUGeFpVkzLakapOUdUGWe2xN3mHqvLuu+/y7LPPpiy79dZb+e6776hRw5UL8YzJ83y94S4r4oDyXvPlgN9SlWkAfCROFeEK4DYRSVTV2X6MywSo3bt306tXLxYuXAjA3XffTe3atQEQq2Ya4ze+3HD3DxFZIiKbPfO1ReTZzLbDuYO7uohUFpGCwD3AHO8CqlpZVSupaiWc0Wj7WJIwqSUnJzN+/HgiIyNZuHAhl19+OTNmzKBWrVpuh2ZMvuBL09M7wGAgAUBVN+F86GdIVROBR3GuZtoKfKKqW0Skt4j0znrIJj/5+eefuemmm+jXrx+nTp3i7rvvJjY2lgceeMBqEcbkEF+ankJVdW2qf8pEX3auqv8l1Uizqppmx7WqPujLPk3+8tJLL7FixQquuuoqJk6cyJ133ul2SMbkO74kikMiUhVPR7Tnprj9fo3K5GsJCQkpVy698cYblCpViuHDh1OqVCmXIzMmf/Kl6akvzjDj14rIPqA/8Ihfo8qEtTjkTadPn2bw4ME0bdqUhIQEAMqUKcO4ceMsSRjjIl9uuNsJ3CoiRXGedncis22MuVjff/893bt355dffkFE+Pbbb1MG9DPGuCvTRCEiz6eaB0BVR/gpJpOPnDhxgltnjOIAABsMSURBVMGDBzNhwgQAatasSXR0NE2bNnU5MmPMWb70UZzymi4MtMW5ismYS7Jo0SJ69OjBnj17CAkJYfDgwQwZMoRChQq5HZoxxosvTU+jvedFZBSp7ocwJit2797Nnj17qF+/PtHR0URFRbkdkjEmDVm5MzsUqJLdgZi8T1XZuXMnVatWBaBHjx6EhobSuXNnQkL8OUiAMeZS+HJn9k8issnz2gJsA8b6PzSTl+zfv5+77rqLWrVqsXPnTsDp77rvvvssSRiTy/nyH9rWazoROOC569o1dnls4FBVpk+fzoABAzh69CjFixcnNjaWKlWsUmpMoMgwUYhIEPC1qkbmUDwmD9m1axc9e/Zk8eLFANx+++1MmjSJcuXKuRyZMeZiZNj0pKrJwEYRqZBD8Zg8YtasWURGRrJ48WJKly7NBx98wNy5cy1JGBOAfGl6uhrYIiJr8bpUVlXb+S0qE/DCw8NJTEzknnvuYezYsVx55ZVuh2SMySJfEkVWH4Vq8pGEhAQ+/fRTunTpgogQHh7Oli1bqFatmtuhGWMukS+J4jZVfdp7gYi8Bnznn5BMoImJiaF79+5s2rQJEaFLly4AliSMySN8GRTwn2ksa5PdgZjAc/r0aQYOHEjjxo3ZtGkTVapU4ZprrnE7LGNMNku3RiEijwB9gCoisslrVXFghb8DM7nbd999R48ePdixYwdBQUEMGDCAF154gdDQULdDM8Zks4yanmYC84BXgEFey0+o6p9+jSoTdh+Fu2bPnk2HDh0AiIiIIDo6msaNG7sclTHGX9JNFKp6DDgGdMm5cEwgaNWqFZGRkXTq1InBgwdTsGBBt0MyxviRL30UJp87dOgQjz76KEePHgWgSJEi/PDDDwwdOtSShDH5gA2yY9Klqnz88cc89thjHDp0iKSkJCZOnAiQ8qhSY0zeZ4nCpGnfvn306dOHOXOcEeVvvvlmnnrqKZejMsa4wZqezHlUlXfeeYfw8HDmzJlDiRIlmDJlCkuWLEkZHtwYk79YjcKcZ82aNfTs2ROAO+64g4kTJxIWFuZyVMYYNwVkorDLY7OXqqY8C71JkyYMGDCAhg0b0rlz55Tlxpj8y5qe8rnNmzdz3XXXsWrVqpRlo0eP5p577rEkYYwBLFHkW2fOnGH48OHUq1eP1atXM3ToULdDMsbkUgHZ9GQuzdq1a+nevTubN28G4JFHHuHVV191OSpjTG5lNYp85K+//uKpp56iadOmbN68mWrVqrF06VLefvttSpQo4XZ4xphcyhJFPnLixAmmTZsGwMCBA9m0aRPNmjVzOSpjTG5nTU953LFjxwgNDaVAgQKULVuWd999l6uvvpoGDRq4HZoxJkAEZI3CLsbxzdy5cwkPD2f06NEpy+644w5LEsaYixKQicJk7ODBg9x77720a9eO3377jfnz55OcnOx2WMaYAGWJIg9RVWbOnEnNmjX58MMPCQ0N5c0332TJkiUEBdmv2hiTNdZHkUccP36ce++9l6+//hqAFi1aMGXKFKpUqeJyZMaYQOfXr5ki0lpEtonIDhEZlMb6+0Rkk+e1UkSi/BlPXlasWDFOnDhByZIliY6OZtGiRZYkjDHZwm81ChEJBiYA/wTigHUiMkdVY72K7QKaqeoREWkDTAHsmZo+2r59O4UKFaJChQoEBQUxY8YMChQowDXXXON2aMaYPMSfNYpGwA5V3amqZ4CPgPbeBVR1paoe8cyuBsr5MZ48IzExkZEjR1K7dm169OiBqgJQsWJFSxLGmGznz0QRBuz1mo/zLEtPd2BeWitEpKeIxIhITDbGF5A2bdpE06ZNGThwIPHx8Vx11VXEx8e7HZYxJg/zZ6JI624HTbOgyM04ieLptNar6hRVbaCqDZzy2RZjwPj77795/vnnqV+/PjExMZQvX57//ve/zJgxgyJFirgdnjEmD/PnVU9xQHmv+XLAb6kLiUhtYCrQRlUP+zGegJWYmEiTJk348ccfAejbty+vvPIKxYsXdzkyY0x+4M9EsQ6oLiKVgX3APcC93gVEpALwOfCAqv7ix1gCWkhICO3bt+evv/5i6tSp3HjjjW6HZIzJR+RsR6hfdi5yGzAGCAamqepLItIbQFUnichU4C7gV88miWebl9LfZwNdtSqGJk38FnausGTJEuLj47n99tsB5/kRSUlJ1sxkjMkSEVmf2edrutv6M1H4Q15PFEePHuXJJ59k2rRplClThq1bt1K6dGm3wzLGBLhLSRR2Z3YuMnv2bPr06cP+/fspWLAg/fv3t+dEmByVkJBAXFycXUkXwAoXLky5cuUoUKBAtu3TEkUucODAAR577DE+/fRTAK677jqmTp1KzZo1XY7M5DdxcXEUL16cSpUq2TPTA5CqcvjwYeLi4qhcuXK27TcgR4rLS3+/qkr79u359NNPKVq0KOPGjWP58uWWJIwr4uPjKV26tCWJACUilC5dOttrhAGZKPISEeG1116jVatWbN68mccee8xGejWusiQR2Pzx+7OmpxyWnJzMpEmT2LVrFyNHjgSgWbNm9khSY0yuZV9dc9C2bdto1qwZffv2ZdSoUfz0009uh2SMSeXnn3+madOmFCpUiFGjRqVbbteuXTRu3Jjq1avTuXNnzpw5AzjNyf369aNatWrUrl2bH374IWWb+fPnU6NGDapVq8arr77q9/eSXSxR5IDExEReffVVoqKi+P777ylbtiyzZs2iVq1abodmjEnl8ssvZ9y4cTz11FMZlnv66ad54okn2L59O6VKlSI6OhqAefPmsX37drZv386UKVN45JFHAEhKSqJv377MmzeP2NhYPvzwQ2JjYzM6RK5hicLPfvzxRxo3bszgwYP5+++/efDBB4mNjeWuu+5yOzRjMiTin1dm7rzzTurXr09ERARTpkxJWV6sWLGU6VmzZvHggw8CzlWDHTp0ICoqiqioKFauXHlJ7/vKK6+kYcOGGV5eqqp88803dOrUCYCuXbsye/ZsAL788kv+/e9/IyI0adKEo0ePsn//ftauXUu1atWoUqUKBQsW5J577uHLL7+8pFhzivVR+NmYMWP44YcfqFixIlOmTKFly5Zuh2RMrjZt2jQuv/xyTp8+TcOGDbnrrrsyvOm0X79+NGvWjC+++IKkpCROnjx5QZnOnTuzbdu2C5YPGDCAf//73xcd4+HDh7nssssICXE+QsuVK8e+ffsA2LdvH+XLnxvm7uy6tJavWbPmoo/thoBMFLn9ooz4+HgKFy4MwOjRo7nqqqt49tlnz/tGZExu59agDePGjeOLL74AYO/evWzfvj3DRPHNN98wY8YMAIKDgylZsuQFZT7++ONsjTGtES3OXm2U3rqMtsntAjJR5FYnT57kmWeeYenSpcTExFCwYEFKly4dUJ1Wxrhp6dKlLF68mFWrVhEaGkrz5s1T7gnw/lC92PsEsrtGccUVV3D06FESExMJCQkhLi4u5aFh5cqVY+/ec4/iObvuzJkzaS4PBNZHkU0WLlxIZGQk48ePJzY2luXLl7sdkjEB59ixY5QqVYrQ0FB+/vlnVq9enbKubNmybN26leTk5JQaB0CLFi2YOHEi4HQYHz9+/IL9fvzxx/z4448XvLKSJMBJWjfffDOzZs0C4N1336V9e+cBnu3atWPGjBmoKqtXr6ZkyZJcffXVNGzYkO3bt7Nr1y7OnDnDRx99RLt27bJ0/BynqgH1gvq6Zo3mGocPH9YHH3xQcR7KpHXr1tUNGza4HZYxWRIbG+vq8ePj47V169Zaq1Yt7dSpkzZr1ky//fZbVVX99NNPtUqVKtqsWTPt27evdu3aVVVVf//9d23Xrp1GRkZqVFSUrly58pJi2L9/v4aFhWnx4sW1ZMmSGhYWpseOHVNV1TZt2ui+fftUVfV///ufNmzYUKtWraqdOnXS+Ph4VVVNTk7WPn36aJUqVTQyMlLXrVuXsu+vv/5aq1evrlWqVNEXX3zxkuLMSFq/RyBGs/i5G5Cjx65ZE0OjRm5HAnPnzuXhhx/mwIEDFCpUiOHDh/Pkk0+mdHAZE2i2bt1qw8fkAWn9Hm30WJccPnyYAwcOcMMNNzB16lRq1KjhdkjGGJPtLFFcBFUlNjaWiIgIwLl2umTJkrRv397GZzLG5Fn26eaj3bt307p1axo0aMD27dsBp0OrQ4cOliSMMXlaQH7C5eSlx8nJyYwfP57IyEgWLlxIaGgoO3fuzLkAjDHGZdb0lIGtW7fSo0ePlCEB7r77bsaNG0fZsmVdjswYY3KOJYp0zJw5k4ceeogzZ85w1VVXMXHiRO688063wzLGmBwXkE1POaF+/foEBQXRvXt3YmNjLUkYE6AqVarEoUOH8tyxcpIlCo/Tp08THR2dMh5LjRo1+OWXX5g6dSqlSpVyOTpj8h9VJTk52e0wDJYoAFi+fDl16tShR48evP/++ynLvUd6NCY/EpF0X95DgE+ZMiXDsr7avXs3NWvWpE+fPtSrV4+9e/fyyCOP0KBBAyIiIhg6dGhK2UqVKjF06FDq1atHrVq1+PnnnwHn/qaWLVtSt25devXqdd5gfG+88QaRkZFERkYyZsyYlGNee+219OjRg8jISO677z4WL17M9ddfT/Xq1Vm7du0FcSYlJfHUU09Rq1Ytateuzfjx41PWjR8//oKY1q5dy3XXXUfdunW57rrrUsadmj59Oh07dqR169ZUr16dgQMHpuxn/vz51KtXj6ioKFq0aAHAqVOn6NatGw0bNqRu3bo5N0x5Vm/pdusF9XXt2izc056G48ePa58+fVKG36hZs6auWrUqe3ZuTABKPfTD2f+NtF6TJ09OKTd58uQMy/pq165dKiLn/R8ePnxYVVUTExO1WbNmunHjRlVVrVixoo4bN05VVSdMmKDdu3dXVdXHHntMhw8frqqqX331lQJ68OBBjYmJ0cjISD158qSeOHFCw8PD9YcfftBdu3ZpcHCwbtq0SZOSkrRevXr60EMPaXJyss6ePVvbt29/QZxvv/22duzYURMSEs6LMb2Yjh07llJ20aJF2rFjR1VV/c9//qOVK1fWo0eP6unTp7VChQq6Z88e/eOPP7RcuXK6c+fO8/Y/ePBgfe+991RV9ciRI1q9enU9efLkBfFl9xAeAdmZnR2Xx86bN49evXqxd+9eQkJCGDx4MEOGDKFQoUKXvnNj8gj1cYifnj170rNnz2w5ZsWKFWnSpEnK/CeffMKUKVNITExk//79xMbGUrt2bQA6duwIOH2Kn3/+OQDLli1Lmb799ttTmo6///57OnToQNGiRVO2Xb58Oe3ataNy5copT5yMiIigRYsWiAi1atVi9+7dF8S4ePFievfunTJcz+WXX56yLq2Yjh07RteuXdm+fTsiQkJCQkr5Fi1apAyNHh4ezq+//sqRI0e46aabqFy58nn7X7hwIXPmzEl5RGt8fDx79uzx+7ArAZkoLtWsWbP417/+BTi/zGnTpqX84Rlj3HX2gxyc51KPGjWKdevWUapUKR588MHzhhg/+8UuODiYxMTElOVpNXdllPS8vyAGBQWlzAcFBZ23X+99pdekllZMzz33HDfffDNffPEFu3fvpnnz5mke++w26e1fVfnss89yfLigfNlHcccdd1C/fn1GjhzJ6tWrLUkYk0sdP36cokWLUrJkSQ4cOMC8efMy3eamm27igw8+AJyWgyNHjqQsnz17Nn/99RenTp3iiy++4MYbb8xSXC1btmTSpEkpieDPP//MsPyxY8cICwsDnH6JzDRt2pTvvvuOXbt2nbf/Vq1aMX78+JSkt2HDhizFf7HyRaL47bff6NatW8rJLlSoEGvWrOGpp56ykV6NycWioqKoW7cuERERdOvWjeuvvz7TbYYOHcqyZcuoV68eCxcupEKFCgDUq1ePBx98kEaNGtG4cWN69OhB3bp1sxRXjx49qFChArVr1yYqKoqZM2dmWH7gwIEMHjyY66+/nqSkpEz3X6ZMGaZMmULHjh2Jioqic+fOgFMzSUhIoHbt2kRGRvLcc89lKf6LFZDDjK9bF0MDHwbLVVWmTZvGk08+ybFjx+jVqxeTJk3yf5DGBCgbZjxvsGHGfbRz50569uzJkiVLAKdTa8iQIS5HZYwxgSfPNT0lJSUxZswYatWqxZIlSyhdujQffPABc+fOtfsijDEmCwKyRpHR5bHr1q3jiSeeAKBLly6MHTuWMmXK5FBkxgS+jK7oMbmfP7oTAjJRpJacnJzyTIgmTZowZMgQGjduzB133OFyZMYElsKFC3P48GFKly5tySIAqSqHDx+mcOHC2brfgOzMjomJoX59Zz4mJoYePXowfvz4LF/qZoxxJCQkEBcXd969CiawFC5cmHLlylGgQIHzlufLzuzTp08zdOhQRo8eTXJyMq+88oolCmMuUYECBVLuBjbmLL92ZotIaxHZJiI7RGRQGutFRMZ51m8SkXq+7Hf9+u+oXbs2I0eOBODJJ59k1qxZ2Ry9McYY8GPTk4gEA78A/wTigHVAF1WN9SpzG/AYcBvQGBirqo0z3u+VCgcBiIyMJDo6mkaNGvnlPRhjTF5xKU1P/qxRNAJ2qOpOVT0DfAS0T1WmPTDDM7jhauAyEbk6490eISSkAMOGDWP9+vWWJIwxxs/82UcRBuz1mo/DqTVkViYM2O9dSER6AmeHpvw7MZHNw4YNY9iwYdkacAC6Ash7j9PKGjsX59i5OMfOxTlZHknQn4kirWvrUrdz+VIGVZ0CTAEQkZisVp/yGjsX59i5OMfOxTl2Ls4RkZisbuvPpqc4wPtW6HLAb1koY4wxxkX+TBTrgOoiUllECgL3AHNSlZkD/Ntz9VMT4Jiq7k+9I2OMMe7xW9OTqiaKyKPAAiAYmKaqW0Skt2f9JOC/OFc87QD+Ah7yYddTMi+Sb9i5OMfOxTl2Ls6xc3FOls9FwN2ZbYwxJmfludFjjTHGZC9LFMYYYzKUaxOFv4b/CEQ+nIv7POdgk4isFJEoN+LMCZmdC69yDUUkSUQ65WR8OcmXcyEizUXkRxHZIiLf5XSMOcWH/5GSIjJXRDZ6zoUv/aEBR0SmicgfIrI5nfVZ+9xU1Vz3wun8/h9QBSgIbATCU5W5DZiHcy9GE2CN23G7eC6uA0p5ptvk53PhVe4bnIslOrkdt4t/F5cBsUAFz/yVbsft4rl4BnjNM10G+BMo6HbsfjgXNwH1gM3prM/S52ZurVH4afiPgJTpuVDVlap6xDO7Gud+lLzIl78LcMYP+wz4IyeDy2G+nIt7gc9VdQ+AqubV8+HLuVCguDgP2SiGkygSczZM/1PVZTjvLT1Z+tzMrYkivaE9LrZMXnCx77M7zjeGvCjTcyEiYUAHYFIOxuUGX/4u/gGUEpGlIrJeRP6dY9HlLF/OxVtATZwben8CHlfV5JwJL1fJ0udmbn0eRbYN/5EH+Pw+ReRmnERxg18jco8v52IM8LSqJuXxJ7T5ci5CgPpAC6AIsEpEVqvqL/4OLof5ci5aAT8CtwBVgUUislxVj/s7uFwmS5+buTVR2PAf5/j0PkWkNjAVaKOqh3Motpzmy7loAHzkSRJXALeJSKKqzs6ZEHOMr/8jh1T1FHBKRJYBUTjD/+clvpyLh4BX1Wmo3yEiu4BrgbU5E2KukaXPzdza9GTDf5yT6bkQkQrA58ADefDbordMz4WqVlbVSqpaCZgF9MmDSQJ8+x/5ErhRREJEJBRn9OatORxnTvDlXOzBqVkhImVxRlLdmaNR5g5Z+tzMlTUK9d/wHwHHx3PxPFAaeNvzTTpR8+CImT6ei3zBl3OhqltFZD6wCUgGpqpqmpdNBjIf/y5eAKaLyE84zS9Pq2qeG35cRD4EmgNXiEgcMBQoAJf2uWlDeBhjjMlQbm16MsYYk0tYojDGGJMhSxTGGGMyZInCGGNMhixRGGOMyZAlCpOriUg/EdkqIh9kUKa5iHyVk3GlR0TanR29VETuFJFwr3UjROTWHIyluYhcl1PHM3lXrryPwhgvfXDuNt/ldiC+UNU5nLvZ607gK5wRXFHV57P7eCISoqrpDW7XHDgJrMzu45r8xWoUJtcSkUk4Q0fPEZEnRKSR53kbGzw/a6SxTTPP8xd+9JQr7ln+fyKyzjMG//B0jndSREaLyA8iskREyniW1xGR1Z5tvxCRUp7l/UQk1rP8I8+yB0XkLc83+XbASE8sVUVkuoh0EpE2IvKJ13Gbi8hcz3RLEVnlieFTESmWRpxLReRlcZ4v8biI3CEiazzvd7GIlBWRSkBv4AnP8W8UkTIi8pnnPKwTkesv4ddj8hO3x0+3l70yegG7gSs80yWAEM/0rcBnnunmwFee6bnA9Z7pYji15pY4D5YXnC9HXwE3pXEsBe7zTD8PvOWZ3gQ080yPAMZ4pn8DCnmmL/P8fNBru+l4PQ/j7Lwnpj1AUc/yicD9OGNTLfNa/jTwfBpxLgXe9povxbmbZ3sAoz3Tw4CnvMrNBG7wTFcAtrr9+7VXYLys6ckEkpLAuyJSHedDvUAaZVYAb3j6ND5X1TgRaYmTLDZ4yhQDquN8KHtLBj72TL8PfC4iJXGSwNmnw70LfOqZ3gR8ICKzAZ/Hk1JnyIn5wB0iMgu4HRgINAPCgRWeoVgKAqvS2c3HXtPlgI/Fea5AQSC9ZrpbgXA5N6puCREprqonfI3d5E+WKEwgeQH4VlU7eJpWlqYuoKqvisjXOOPZrPZ0HgvwiqpOvsjjZTa+ze04TxRrBzwnIhEXse+Pgb44D5lZp6onxPkEX6SqXXzY/pTX9HjgDVWdIyLNcWoSaQkCmqrq6YuI0xjrozABpSSwzzP9YFoFRKSqqv6kqq8BMThDSS8Aup1t7xeRMBG5Mo3Ng3CahsB5Otz3qnoMOCIiN3qWPwB8JyJBQHlV/RanNnAZTk3F2wmgeDrvZSnOIysf5lztYDVwvYhU88QZKiL/SGd7b97npWsGx18IPHp2RkTq+LBvYyxRmIDyOvCKiKzAGSU0Lf1FZLOIbAROA/NUdSFO+/wqz+ihs0j7A/wUECEi63EecDPCs7wrTqf0JqCOZ3kw8L5nfxuAN1X1aKr9fQT8n6eTuar3ClVNwukraeP5iaoexEmAH3qOtRon0WVmGPCpiCwHvEdEnQt0ONuZDfQDGng632NxOruNyZSNHmuMh4icVNULrjIyJr+zGoUxxpgMWY3CGGNMhqxGYYwxJkOWKIwxxmTIEoUxxpgMWaIwxhiTIUsUxhhjMvT/HUVFl5rEx4IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC\n",
    "predict = best_model.predict(testimages)\n",
    "print(testlabels.shape, predict.shape)\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "fpr, tpr, thresholds = roc_curve(testlabels, predict)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "plt.plot(fpr, tpr, lw=2, color='blue', label='auc = %.3f' % (roc_auc))\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='k', label='random chance')\n",
    "plt.xlim([0, 1.0])\n",
    "plt.ylim([0, 1.0])\n",
    "plt.xlabel('false positive rate')\n",
    "plt.ylabel('true positive rate')\n",
    "plt.title('receiver operating curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import innvestigate\n",
    "import innvestigate.utils as iutils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/math_grad.py:102: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Deprecated in favor of operator or tf.math.divide.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating an analyzer\n",
    "lrp_analyzer = innvestigate.create_analyzer(\"lrp.alpha_1_beta_0\", best_model)\n",
    "\n",
    "# Applying the analyzer\n",
    "display = trainimages[1].reshape(1,grid,grid,1) #the reshape is this way is because the input is expected to be 4 dimensions\n",
    "analysis = lrp_analyzer.analyze(display)\n",
    "\n",
    "# Displaying one result\n",
    "plt.imshow(analysis.reshape(grid, grid), cmap='RdBu', origin = 'low', interpolation='nearest')\n",
    "plt.colorbar()\n",
    "plt.clim(-0.6, 0.6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([predict[:int(len(predict)/2)].flatten(), predict[int(len(predict)/2):].flatten()] ,\n",
    "         color = ['blue', 'red'], histtype = 'step', label = ['signal', 'background'])\n",
    "plt.yscale('log')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(predict.flatten(), train_zlabels.flatten())\n",
    "plt.show()\n",
    "\n",
    "plt.hist2d(predict.flatten(), train_thetalabels.flatten())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " time = \n",
      " 134.3303999900818\n"
     ]
    }
   ],
   "source": [
    "#plot output signal and background\n",
    "sig_analysis_true = np.zeros_like(analysis)\n",
    "bkg_analysis_true = np.zeros_like(analysis)\n",
    "sig_analysis_pred = np.zeros_like(analysis)\n",
    "bkg_analysis_pred = np.zeros_like(analysis)\n",
    "sig_inefficiency = np.zeros_like(analysis)\n",
    "bkg_inefficiency = np.zeros_like(analysis)\n",
    "\n",
    "toc = time.time()\n",
    "\n",
    "display = trainimages.reshape(len(trainimages), 1, grid, grid, 1) \n",
    "analysis = [lrp_analyzer.analyze(display[i]) for i in range(len(trainimages))]\n",
    "\n",
    "tic = time.time()\n",
    "\n",
    "print('\\n time = \\n', tic-toc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "signal inefficiency =  269 \n",
      "background inefficiency =  22\n",
      "\n",
      "time =\n",
      " 0.9098672866821289\n",
      "signal efficiency =  9731 \n",
      "background efficiency =  9978\n"
     ]
    }
   ],
   "source": [
    "toc = time.time()\n",
    "\n",
    "for i in range(len(trainimages)):\n",
    "    if trainlabels[i] == 1:\n",
    "        sig_analysis_true += analysis[i]  \n",
    "        \n",
    "    else:\n",
    "        bkg_analysis_true += analysis[i]\n",
    "\n",
    "sig_analysis_true /= len(trainimages)/2\n",
    "bkg_analysis_true /= len(trainimages)/2\n",
    "        \n",
    "sig_anal_num = 0   \n",
    "sig_inef_num = 0\n",
    "bkg_anal_num = 0\n",
    "bkg_inef_num = 0\n",
    "weird = 0\n",
    "sig_zlab = np.array([0.])\n",
    "sig_thetalab = np.array([0.])\n",
    "bkg_zlab = np.array([0.])\n",
    "bkg_thetalab = np.array([0.])\n",
    "\n",
    "sig_ineff_zlab = np.array([0.])\n",
    "sig_ineff_thetalab = np.array([0.])\n",
    "bkg_ineff_zlab = np.array([0.])\n",
    "bkg_ineff_thetalab = np.array([0.])\n",
    "\n",
    "for i in range(len(trainimages)):\n",
    "    if i < int(len(predict)/2) and predict[i] > 0.50:\n",
    "        sig_analysis_pred += analysis[i]\n",
    "        sig_anal_num += 1\n",
    "        sig_zlab = np.append(sig_zlab, train_zlabels[i])\n",
    "        sig_thetalab = np.append(sig_thetalab, train_thetalabels[i])\n",
    "    elif i < int(len(predict)/2) and predict[i] < 0.50:\n",
    "        sig_inefficiency += analysis[i]\n",
    "        sig_inef_num += 1\n",
    "        sig_ineff_zlab = np.append(sig_ineff_zlab, train_zlabels[i])\n",
    "        sig_ineff_thetalab = np.append(sig_ineff_thetalab, train_thetalabels[i])\n",
    "    elif i >= int(len(predict)/2) and predict[i] < 0.50:\n",
    "        bkg_analysis_pred += analysis[i]\n",
    "        bkg_anal_num += 1\n",
    "        bkg_zlab = np.append(bkg_zlab, train_zlabels[i])\n",
    "        bkg_thetalab = np.append(bkg_thetalab, train_thetalabels[i])\n",
    "    elif i >= int(len(predict)/2) and predict[i] > 0.50:\n",
    "        bkg_inefficiency += analysis[i]\n",
    "        bkg_inef_num += 1\n",
    "        bkg_ineff_zlab = np.append(bkg_ineff_zlab, train_zlabels[i])\n",
    "        bkg_ineff_thetalab = np.append(bkg_ineff_thetalab, train_thetalabels[i])\n",
    "        \n",
    "#normalizing relevance scores\n",
    "sig_analysis_pred /= sig_anal_num\n",
    "sig_inefficiency /= sig_inef_num\n",
    "bkg_analysis_pred /= bkg_anal_num\n",
    "bkg_inefficiency /= bkg_inef_num\n",
    "\n",
    "tic = time.time()\n",
    "print('signal inefficiency = ', sig_inef_num, '\\nbackground inefficiency = ', bkg_inef_num)\n",
    "print('\\ntime =\\n', tic-toc)\n",
    "print('signal efficiency = ', sig_anal_num, '\\nbackground efficiency = ', bkg_anal_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the colormap and centre the colorbar\n",
    "class MidpointNormalize(Normalize):\n",
    "    \"\"\"Normalise the colorbar so that diverging bars work there way either side from a prescribed midpoint value)\"\"\"\n",
    "    def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):\n",
    "        self.midpoint = midpoint\n",
    "        Normalize.__init__(self, vmin, vmax, clip)\n",
    "        \n",
    "    def __call__(self, value, clip=None):\n",
    "        x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]\n",
    "        return np.ma.masked_array(np.interp(value, x, y), np.isnan(value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2BYuIn0n6W0n/ml7f8wbMd1kq93WpnJ/rm+UNqppp3KgqBD6p6vqFQdZJOkDSHRHxrZ7x71F1ofRPVO2HTzesY42kG1z1nvb3qq6j+HnD/OgusiWzbOnxL6o6nbhc0vsjoukG3X+vKhsutf2AqvfxiFSeG1QdPJ+XynOvBjS1SwePR6t6/+9R9dpnewk8S9KBaZ9/psV7+B5VTa1+oOrEV5uma+eruibn8YOqiLhR0gdUHajdoaqTnX8dsPzTVL2f96Zt363qHnLI00Typ8bA78+wIuIqVSdwPqTq7/G/6Mm/us3Od5GqFjoXuGpOeL2qjnXabON/qjr+OE/VPvyMqo5XZp2r6nvStrnorP+g6kTKLZK+ktZ/9pDr+KyqytG9kl4v6VUR8UjdjCPOkA+qqkBequpk31mqOq6R+v5eRMR6Vdcp/2Mq5wZV10gqIh5W1XHRG9O031Hz8ZQi4uuq9tteqq7hnPV3qQxbVH2mvtCwml9X9T14UNVn8R0R8YOm7U6r2Z7HMCRXP9lvkvTaiPjSpMszSuZmo8DEkC1Ltu399UTPfFvHvX2gBF3On8Wyva+qJvFPj4j7x7jd01R13vK6+eYFFmrafyEciu3fsr1Taq40e23MoJ/sAaAVsgXApJA/80sV5T9U1bPw2CqDwLgsn3QBCvMbqn7qn22KeGxEPNS8CADMi2wBMCnkTwNXHVjdoaqZ5ZoJFwdYEjQZBQAAAIApRZNRAAAAAJhSNBkFMMcqL4+ft7o1krRFj10SETSjAbDkhskmiXwCMD4lHzuNtUK4cuWusf++891DF8BS2Xjrrdqy5e55b0T+C4Vere1brfN0PbBy0QWbMLIJmKylyCaJfAKweNNw7DTWCuH+++6r9V/58jg3CaDH6iNf2HreGc+bfZUOXIZMNgGTtSTZJJFPABZtGo6daDIKYA6LC4wB5IdsApCrkvOJCiGAWjNtT8JndpYLQLe1ziaJfAIwVqUeO1EhBFCr1LNcALqNbAKQq1LziQohgDksD3edDgCMAdkEIFcl5xMVQgC1Sj3LBaDbyCYAuSo1n+Ytt+1tbV9l+1u2b7D9njR+F9uX2f5e+n/npS8ugHGwqnbwbYZW67PX2L7Z9gbbp9ZMf63t69LwVdvP6Zm20fa3bV9re33PeLIJmDLDZFObfFqKbErTyCdgyoz62Gmc2lRkfyHpxRHxHEmHSFpj+3mSTpV0eUQcIOny9BxAF1haZrca5l2VvUzShyUdJelAScfbPrBvth9I+s2IOFjSX0ta2zf9RRFxSESs7hlHNgHTZohsmi+fljCbJPIJmD4jPHaSlu6EVZ15K4RReTA93SYNIekYSeem8edKOrbFawNQgNmuk9sMLRwuaUNE3BIRD0u6QFV+PC4ivhoR96anV0raZ76Vkk3A9Bkmm1rk05JkU1qOfAKmzCiPnZb4hNUcrY7nbC+zfa2kOyVdFhFfl7RHRGyWpPT/7gOWPcn2etvr79pyd5vNAcjACJs97C3ptp7nm9K4QU6U9Pme5yHpUtvX2D6pd0ayCZg+I2wyumTZJJFPwDQa4bHTkp2wqi13m5ki4tGIOCRt6HDbB7XdQESsjYjVEbF6t5W7LrScAMZsiLNcK2cPXNLQf2BUF321d+Cx/SJVB11/0jP6+RFxmKqzZG+3/YLHV0I2AVNnyF8Im/JpybJJIp+AaTTCY6clPWHVb6heRiPiPttflrRG0h2294yIzbb3VHUGDEAHVBdGt77qecs8zRE2SVrV83wfSbfP2aZ9sKQzJR0VEY+fEo+I29P/d9q+SNVZsyt6lyWbgOkwZDZJzfm05NmUppNPwBQY8bHTQk5YHdkz+vkRcbvt3SVdZvs7ETEnn2a16WV0N9s7pcfbSXqppO9IWifphDTbCZI+O9+6AJRjhNcQXi3pANvPsL1C0nGq8uNxtveV9GlJr4+I7/aM3972jrOPJb1c0vXpOdkETKERXkO4JNmUxpFPwBQa4bHTsCesjhl0wkrS7Amrgdr8QrinpHPTxY0zki6MiM/Z/pqkC22fKOlWSa9usS4ABZjtOnkUImKr7ZMlXSJpmaSzI+IG229L08+Q9JeSdpX0T67Orm1NZ872kHRRGrdc0nkR8YW0arIJmDKFZJNEPgFTZ5T5pJ4TVpJ+pOqE1e8+aXsNJ6wkzUTEAz0nrP5j08bmrRBGxHWSDq0Zf7ekl8z7cgAUaXlta4WFiYiLJV3cN+6MnsdvlvTmmuVukfSc/vFpGtkETKHcsylNJ5+AKTSqfFriE1Y15QaAPiM+ywUAI0E2AcjVqPNpqU5Y1aFCCKBWyzbuADBWZBOAXJWaT1QIAczh9vfJAYCxIZsA5KrkfKJCCKDWzAiv0wGAUSGbAOSq1HyiQgigVqlnuQB0G9kEIFel5hMVQgBzWOW2gwfQXWQTgFyVnE9UCAHUKvUsF4BuI5sA5KrUfKJCCGAOy1ruQlMNQGeRTQByVXI+USEEUKvMSAPQdWQTgFyVmk9UCAHMwc2fAeSIbAKQq5LziQohgFqldp0MoNvIJgC5KjWfqBACmKPkm6sC6C6yCUCuSs4nKoQAapXadTKAbiObAOSq1HyiQgigVqEnuQB0HNkEIFel5hMVQgBzVBdGlxprALqKbAKQq5LziQohgFqlNnsA0G1kE4BclZpPVAgB1CrzHBeAriObAOSq1HyiQgiglgtt9gCg28gmALkqNZ+oEAKYwyr3LBeA7iKbAOSq5HyiQgigVqnt4AF0G9kEIFel5hMVQgC1Cm31AKDjyCYAuSo1n6gQAqjlYhs+AOgysglArkrNJyqEAOawym32AKC7yCYAuSo5n6gQAqg1U+ZJLgAdRzYByFWp+USFEEANF9vsAUCXkU0AclVuPlEhBDBHyV0nA+gusglArkrOp3mbutpeZftLtm+yfYPtd6Txp9n+ke1r03D00hcXwFi46imrzTCxIpJNwPQZIpvIJwBjVUA2DdLmF8Ktkt4ZEd+wvaOka2xflqZ9KCLev3TFAzApGeZVP7IJmEIFZJNEPgFTqZB8mmPeCmFEbJa0OT1+wPZNkvZe6oIBmKyZzGONbAKmU+7ZJJFPwLQqIZ/qDNU7qu39JR0q6etp1Mm2r7N9tu2dByxzku31ttffteXuRRUWwHh4iCEHZBMwHYbJJvIJwDiVlk29WlcIbe8g6VOSTomI+yWdLumZkg5RdRbsA3XLRcTaiFgdEat3W7nrCIoMYBxm3G6YNLIJmC5ts4l8AjBupWRTv1a9jNreRlWgfSIiPi1JEXFHz/SPSvrckpQQwESU0HUy2QRMnxKySSKfgGlUSj71a9PLqCWdJemmiPhgz/g9e2Z7paTrR188AJNg5X+Wi2wCps8w2UQ+ARinEo6dBmnzC+HzJb1e0rdtX5vGvVvS8bYPkRSSNkp665KUEMBEZJhX/cgmYAoVkE0S+QRMpULyaY42vYx+RfWv7+LRFwdALkYZarbXSPp7ScsknRkR7+2b/lpJf5KePijp9yPiW03Lkk3AdMo9myTyCZhWJeRTnaF6GQUwPdzy37zrsZdJ+rCkoyQdqOoM+YF9s/1A0m9GxMGS/lrS2iGWBTBF2mbTfPlENgEYtVKPnagQAqhltxtaOFzShoi4JSIelnSBpGN6Z4iIr0bEvenplZL2abssgOnSNpta5BPZBGCkSj12okIIYA6rCoc2Qwt7S7qt5/kmNd+g+URJn1/gsgA6bJhsapFPZBOAkSn52KnVbScATJ+ZlqewJK20vb7n+dqIWNvzvG5FUbci2y9SFWpHDrssgOkwRDZJzflENgEYqVKPnagQAqg1xCHXlohY3TB9k6RVPc/3kXT7nO3ZB0s6U9JREXH3MMsCmB5DdtrQlE9kE4CRKvXYiSajAObwEEMLV0s6wPYzbK+QdJykdU/anr2vpE9Len1EfHeYZQFMj2GyqUU+kU0ARqbkYyd+IQQwly0P1yxroIjYavtkSZeo6v747Ii4wfbb0vQzJP2lpF0l/VPa7taIWD1o2ZEUDEB5yCYAuSo4n6gQAqg1M8Kb6UTExeq7/1YKs9nHb5b05rbLApheZBOAXJWaT1QIAdTyKFMNAEaEbAKQq1LziQohgDms1vfJAYCxIZsA5KrkfKJCCGAuSzOFnuUC0GFkE4BcFZxPVAgB1Cr1LBeAbiObAOSq1HyiQgig1qh6ygKAUSKbAOSq1HyiQghgjpLbwQPoLrIJQK5KzicqhADmsjRTaqoB6C6yCUCuCs4nKoQAahWaaQA6jmwCkKtS84kKIYAaLrYdPIAuI5sA5KrcfKJCCGAOS/LMpEsBAE9GNgHIVcn5RIUQwFwF30sHQIeRTQByVXA+USEEUKvUZg8Auo1sApCrUvOJCiGAWoVmGoCOI5sA5KrUfKJCCGAOq9yukwF0F9kEIFcl5xMVQgBzudyzXAA6jGwCkKuC84kKIYBapbaDB9BtZBOAXJWaT1QIAdQqNNMAdBzZBCBXpeYTFUIAc1jlhhqA7iKbAOSq5Hya9/aJtlfZ/pLtm2zfYPsdafwuti+z/b30/85LX1wAY2FrZlm7YXJFJJuAqTNENpFPAMaqgGOnQeatEEraKumdEfGrkp4n6e22D5R0qqTLI+IASZen5wA6YsZuNUwQ2QRMobbZRD4BGLcCsqnWvBXCiNgcEd9Ijx+QdJOkvSUdI+ncNNu5ko5dqkICGK/ZZg9thkkhm4DpM0w2kU8AxqmEY6dBhrqG0Pb+kg6V9HVJe0TEZqkKPtu7j7x0ACampJ6yyCZgepSUTRL5BEyT0vJpVpsmo5Ik2ztI+pSkUyLi/iGWO8n2etvr79py90LKCGDcCjgD/3hRySZgegyRTeQTgLEqKJv6taoQ2t5GVaB9IiI+nUbfYXvPNH1PSXfWLRsRayNidUSs3m3lrqMoM4AxsN1qmHAZySZgyrTNJvIJwLiVkE112vQyaklnSbopIj7YM2mdpBPS4xMkfXb0xQMwKbmf5SKbgOlUwi+E5BMwnXLPpkHaXEP4fEmvl/Rt29emce+W9F5JF9o+UdKtkl69NEVETiJi4LQcz3hgYWxl2S1yH7IJjyObpkMh2SSRT+gRjz02cJpnWl+9hcwVlE9zzFshjIivqOo4p85LRlscAHnIs0lDL7IJmEb5Z5NEPgHTqYx8qjNUL6MApshMmaEGoOPIJgC5KjSfqBACqFfoWS4AHUc2AchVoflEhRDAXOa6KwAZIpsA5KrgfKJCCKBeoc0eAHQc2QQgV4XmExVCADUy7RcZwJQjmwDkqtx8okKIOZq6b1cM7jo5Gm5rWepP6NPKllzoWS5011JkUxNyKz9kE3LVmE8aPI1b5nRHyflEhRBALS/j3kgA8kM2AchVqflUZqkBLD273dBqVV5j+2bbG2yfWjP92ba/ZvsXtt/VN22j7W/bvtb2+hG9OgClaptNLfKJbAIwUoUeO/ELIYC57JFdGG17maQPS3qZpE2Srra9LiJu7JntHkl/IOnYAat5UURsGUmBAJSLbAKQq4LziV8IAdSy3Wpo4XBJGyLiloh4WNIFko7pnSEi7oyIqyU9MvpXAqBL2mZTi3wimwCMVKnHTlQIAdSbcbthfntLuq3n+aY0rq2QdKnta2yfNMRyALqobTbNn09kE4DRKvTYiSajAOayhuk6eWVf+/S1EbG2b239mrpj6/f8iLjd9u6SLrP9nYi4YojlAXTFcNkkNecT2QRgdAo+dqJC2GHNXSA3Ljh42taHB09bvmLwKun2vThu/5ZtiYjVDdM3SVrV83wfSbe3XXlE3J7+v9P2RaqaUXDQVbCcsqn+b27aXNNSZNPEDJFNUnM+kU0YSjw2+PY2jYnxyC8GT9tm24WskQzKVKnHTjQZBVBvdD1lXS3pANvPsL1C0nGS1rUrgre3vePsY0kvl3T9Al8RgC4YXS+jZBOA0Sr02IlfCAHMZY/sXjoRsdX2yZIukbRM0tkRcYPtt6XpZ9h+uqT1kp4m6THbp0g6UNJKSRelM6HLJZ0XEV8YScEAlIdsApCrgvOJCiGAeiPqOlmSIuJiSRf3jTuj5/GPVTWH6He/pOeMrCAAykc2AchVoflEhRDAHFWLBq5PAJAXsglArkrOJyqEAOqN8CwXAIwM2fTs9jcAABaQSURBVAQgV4XmExVCADVaX/QMAGNENgHIVbn5RIVwWkVD18mPPTp42oP3DZ624y6Dpy1r+Kg19NHb1D19qT/Ll4L9i4nIKZuabklBNk0M+xdLqfG2OE351OSnPxk8bcdlg6c15FPE4O+BZ7iJwKSUmk9UCAHMZRXb7AFAh5FNAHJVcD5RIQRQa1RdJwPAKJFNAHJVaj5RIQQwV/sbpwLA+JBNAHJVcD5RIQRQy4U2ewDQbWQTgFyVmk9UCAHUK/QsF4COI5sA5KrQfKJCCGCugi+MBtBhZBOAXBWcT/NWCG2fLekVku6MiIPSuNMkvUXSXWm2d0fExUtVyGnX3AXyArtHfvSRwdMaum///VW/PnDa6bddNXid2+80eNryFYOnNd2SYvBSxXb7m5MS9iH5NFlkUz2yaWmVsA/Jpslbknx65OeDpz304MBJjfl0+zcHr/MpTx08bZunDJzUeHeMhu9PCd+t3JW6D9t0hXOOpDU14z8UEYekgUADOsXVWa42w2SdI/IJmCJDZNNk8+kckU3AlCkim2rN+wthRFxhe/+lLwqArBRwlot8AqYQ2QQgVwXkU53F3CzjZNvX2T7b9s4jKxGAybOkmZl2Q57IJ6CLhsmmPPOJbAK6quBjp4WW6HRJz5R0iKTNkj4waEbbJ9leb3v9XVvuXuDmAIyXiw01tcwnsgko0RDZlF8+cewEdFq5x04LKlFE3BERj0bEY5I+KunwhnnXRsTqiFi928pdF1pOAOM2e4PV+YbMtM0nsgkoVNtsyiyfOHYCpkCB2SQtsEJoe8+ep6+UdP1oigMgC1axoUY+AR02TDZllk9kE9BxBR87tbntxPmSXihppe1Nkv5K0gttH6Kqd+2Nkt66hGWcek1d2DZ1b95Y31+2zeBpO+4ycNLpP7xy8HI7DF5Oyxo+ag3dt9M98gQVsH/Jp8kimwZNyv+7U7QC9i/ZNHkLzicvGzxtxXaDp80MzpLGW0tst8OC1tmUT86wSeLUKCCf6rTpZfT4mtFnLUFZAGTDWbZx70c+AdOGbAKQqzLyqc68FUIAU6rQs1wAOo5sApCrQvOJCiGAuWbbwQNATsgmALkqOJ+oEAKoYWlZw3UUADARZBOAXJWbT1QIAdQr9CwXgI4jmwDkqtB8okIIYK6Cmz0A6DCyCUCuCs4nKoSFW3i37w0f2Kau1p/6tMHTGrtvb9pemV+ezuN9wSKQTVgyvC/IUVPOrNi2YbmGW+0s8NY3EYNTltviLLFC9y8VQgBzWOY+RgCyQzYByFXJ+USFEEC9Qs9yAeg4sglArgrNJyqEAOYquB08gA4jmwDkquB8okIIoEa5XScD6DKyCUCuys0nKoQA6hV6lgtAx5FNAHJVaD5RIQQwV8HNHgB0GNkEIFcF5xMVwqm1wK7WZxb6U3iZX5CpVmiooXRkE+ZBNmEpNdyyoTmfmtb5WMNyTb1SLrAsmJxC84kKIYAalgrtOhlAl5FNAHJVbj5RIQRQr9CzXAA6jmwCkKtC86nMaiyApTXbDr7N0GZ19hrbN9veYPvUmunPtv0127+w/a5hlgUwRYbJphb5RDYBGJmCj534hRBAjdE1e7C9TNKHJb1M0iZJV9teFxE39sx2j6Q/kHTsApYFMDXIJgC5Kjef+IUQQL2ZmXbD/A6XtCEibomIhyVdIOmY3hki4s6IuFrSI8MuC2DKtM2m+fOJbAIwWoUeO1EhBDDXaJs97C3ptp7nm9K4pV4WQNeMtsko2QRgdAo+dqLJaOGisXvkxiUbJjV0j/zznw6etnybwdMaP/xlXoDbbUM1e1hpe33P87URsfbJK5uj7Qd3MctigsgmLI2hm2Q15RPZNKXc8L1fcHY91pBPW7c2TPvF4GnLVwye1lTOQjs2KV+5x05UCAHUa/8HZUtErG6YvknSqp7n+0i6veW6F7MsgC4a7mC3KZ/IJgCjVeixE01GAdQbXbOHqyUdYPsZtldIOk7SupalWMyyALpodE1GySYAo1XosRO/EAKYy5I8mvNFEbHV9smSLpG0TNLZEXGD7bel6WfYfrqk9ZKeJukx26dIOjAi7q9bdiQFA1AesglArgrOJyqEAGpYmhndNQgRcbGki/vGndHz+MeqmjS0WhbAtCKbAOSq3HyiQgig3ojOcgHASJFNAHJVaD5RIQQwly0tWzbpUgDAk5FNAHJVcD7NWyG0fbakV0i6MyIOSuN2kfTPkvaXtFHSayLi3qUrJhakqUvipmmPDu4e+bGv/K+B02ZedvzgdTaeMaHr5CwVsO/Jp0KRTViMAvY92dRRTbe+ebT/3uBPeOzrnx84bebIhvuFzzQcps8srOLRdFuNpttxoKVC92Gb3zXPkbSmb9ypki6PiAMkXZ6eA+gSz7QbJusckU/AdGmbTZPNp3NENgHTJ/9sqjVviSLiCkn39I0+RtK56fG5ko4dcbkATNrouk5eMuQTMIVGd9uJJUM2AVMq82waZKHXEO4REZslKSI22959hGUCMGm2NJPfGayWyCegq8gmALkqOJ+WvNS2T7K93vb6u7bcvdSbAzAqhZ7laotsAgpVwC+Ei0U+AYUqNJsWWiG8w/aekpT+v3PQjBGxNiJWR8Tq3VbuusDNARi7QtvBq2U+kU1Aocq4hrAOx05A15WZTQuuEK6TdEJ6fIKkz46mOACyMNt1cpshP+QT0FXDZFN++UQ2AV1W8LFTm9tOnC/phZJW2t4k6a8kvVfShbZPlHSrpFcvZSExZg3dKsfVXxu83Et/p2GdTd23tygTxi/DJg39yKd8NXVf3tTteSOyCRLZhEVrzKCmz9djDcs99ujg7X35/wxe7t819C20BJ91bi2xxArdv/NWCCNi0A2cXjLisgDIhrNs0tCPfAKmDdkEIFdl5FOdhfYyCqDLLGmmzLNcADqMbAKQq4LziQohgHqFNnsA0HFkE4BcFZpPVAgB1Cu02QOAjiObAOSq0HyiQghgLrvYZg8AOoxsApCrgvOJCiGAeoWe5QLQcWQTgFwVmk9UCLussR1zw7SZwfdH8b9/VcMqG74Ehbapnl5u/BwA81lwt+5LkU1Nn2WyqTBkExZvwbfFacqL5SsGL3bUMW2KVbNgU+Wi6fY95NpklJtPVAgBzFVwT1kAOoxsApCrgvOJCiGAeoU2ewDQcWQTgFwVmk9UCAHUoykdgByRTQByVWg+USEEUMPSTJlnuQB0GdkEIFfl5hMVQgBzWcWe5QLQYWQTgFwVnE9UCAHUK7QdPICOI5sA5KrQfKJCCKCGiz3LBaDLyCYAuSo3n6gQFm7B99JpukfNsm0GTpo58DcWtBz3KCyMJS0r8146yENW2TTT8KeObCoL2YSl1ngfwoa8WDY4L2YOe/Hg5VZs11SYhmmDt9eUv1hCBecTFUIANVxsswcAXUY2AchVuflEhRBAPc4wAsgR2QQgV4XmExVCAPUKPcsFoOPIJgC5KjSfqBACmMuWZso8ywWgw8gmALkqOJ+oEAKoV+hZLgAdRzYByFWh+USFEEC9QtvBA+g4sglArgrNJyqEHdbY7Xvjgg1d5m67w+BpMw3LNZSF7pFzZLnp/QQWgWzCwpFNWFqeGfwLz4JvmbPiqU0bnL9QdYuRTxkqN5+oEAKYyyq22QOADiObAOSq4HyiQgigRrn30gHQZWQTgFyVm09llhrA0ptxu6EF22ts32x7g+1Ta6bb9j+k6dfZPqxn2kbb37Z9re31I3yFAErUNpta5BPZBGCkCj124hdCAPVGdJbL9jJJH5b0MkmbJF1te11E3Ngz21GSDkjDEZJOT//PelFEbBlJgQCUjWwCkKtC84lfCAHMZVWdbbQZ5ne4pA0RcUtEPCzpAknH9M1zjKSPReVKSTvZ3nOkrwlA+YbJpvnziWwCMDoFHztRIQRQI7WDbzPMb29Jt/U835TGtZ0nJF1q+xrbJy3wBQHohCGyaf58IpsAjFC5x06LajJqe6OkByQ9KmlrRKxezPowPgvtrjiWDf7I0AVyx7R/P1f2tU9fGxFre9dUs0x/391N8zw/Im63vbuky2x/JyKumK9Q5FOZyCbMa7j3symfyCYMhXzCvAo9dhrFNYS0nwe6xpaWtb6XzpZ5Dmg2SVrV83wfSbe3nSciZv+/0/ZFqppRzHvQlZBPQJcMl01Scz6RTQBGp+BjJ5qMAqg3umYPV0s6wPYzbK+QdJykdX3zrJP0htRj1vMk/SQiNtve3vaOkmR7e0kvl3T96F4kgOKMrsko2QRgtAo9dlrsL4Sz7VND0kf6fupUKshJkk6SpH1XreqfDCBXI2rGEhFbbZ8s6RJJyySdHRE32H5bmn6GpIslHS1pg6SfSXpTWnwPSRelJjXLJZ0XEV9ou2k15BPZBBSq49kkkU9AsQrNp8VWCOdtn5qCbq0krT7s0P62rwCyNNqbq0bExaqCq3fcGT2PQ9Lba5a7RdJzFrjZxnwim4ASdT+b0vrJJ6A45ebTokrd2z5V0mz7VABdMLqukyeCfAI6anS3nZgIsgnosEKzacEVQtrPAx1mjbId/NiRT0BHDZNNGeYT2QR0WMHHTotpMrqY9vMoFN0jTwtLM/kF1hDIpylDNk0LsgnlIZ+mRbn5tOAK4SLbzwPIXMl/wMgnoLvIJgC5KjWfRnEfQgBdY0kzQ93rCwCWHtkEIFcF5xMVQgA1RttTFgCMBtkEIFfl5hMVQgD1Cm32AKDjyCYAuSo0n6gQAqhX6IXRADqObAKQq0LziQohgLkyvU8OgClHNgHIVcH5RIUQQL1C28ED6DiyCUCuCs0nKoQA6hV6lgtAx5FNAHJVaD5RIQRQo9yesgB0GdkEIFfl5hMVQgD1Cj3LBaDjyCYAuSo0n6gQAhigzFAD0HVkE4BclZlPVAgBzGUVe5YLQIeRTQByVXA+USEEUK/MTAPQdWQTgFwVmk9UCAEMUGiqAeg4sglArsrMJyqEAGqUe3NVAF1GNgHIVbn5RIUQQL1CQw1Ax5FNAHJVaD5RIQRQr9B76QDoOLIJQK4KzScqhAAGKPMsF4CuI5sA5KrMfKJCCGAul9sOHkCHkU0AclVwPlEhBFCv0FAD0HFkE4BcFZpPVAgBDFBmqAHoOrIJQK7KzCcqhABqudCzXAC6jWwCkKtS84kKIYB6hYYagI4jmwDkqtB8okIIoIZVarMHAF1GNgHIVbn5RIUQwFyWNFPmvXQAdBjZBCBXBecTFUIAA5R5lgtA15FNAHJVZj5RIQRQr9B28AA6jmwCkKtC82lRv2vaXmP7ZtsbbJ86qkIByIBbDpkin4COaptNmeYT2QR0WKHZtOAKoe1lkj4s6ShJB0o63vaBoyoYgEkq+4iLfAK6aphsyi+fyCagy8rNpsX8Qni4pA0RcUtEPCzpAknHjKZYACbObjfkiXwCuqptNuWZT2QT0GWFZtNiKoR7S7qt5/mmNO5JbJ9ke73t9XdtuXsRmwMwNlaxoZbMm09kE1CgYbIpz3zi2AnoqoKPnRZTIax7NTFnRMTaiFgdEat3W7nrIjYHYKw8025os6p5rplx5R/S9OtsH9Z22UGbrBn3pHwim4BCtc2mFvmUYzZJ5BNQrEKPnRZTIdwkaVXP830k3b6I9QHIxujOwLe8ZuYoSQek4SRJpw+xbB3yCeikIbJpnnwimwCMVrnHToupEF4t6QDbz7C9QtJxktYtYn0AsjKyC6PbXDNzjKSPReVKSTvZ3rPlsnXIJ6CzRtZxA9kEYMTKPHZa8H0II2Kr7ZMlXSJpmaSzI+KGpmWu+ea1W7z9Tj9MT1dK2rLQ7S+BnMpDWepRlnrDlGW/1msdXRv3umtmjmgxz94tl51j2Hzqyyap3Pd3qVGWwXIqT4llIZsGyPjYKaeySHmVh7LUK7Usnc+nRd2YPiIulnTxEPPvNvvY9vqIWL2Y7Y9STuWhLPUoS72lKMs137z2Em+/08qWs29re33P87URsbbneZtrZgbN0+p6mzrD5FNvNkndf38XirIMllN5ulyWIbNJas6n7LMpzZ/lsVNOZZHyKg9lqdf1spR87LSoCiGAboqINSNcXZtrZgbNs6LFsgCmBNkEIFcl59NiriEEgDbaXDOzTtIbUo9Zz5P0k4jY3HJZAFgIsglArsaaT5P8hXDt/LOMVU7loSz1KEu9nMoyx6BrZmy/LU0/Q1XzqaMlbZD0M0lvalp2DMXOaZ9Slno5lUXKqzyUpQWyadFyKouUV3koSz3K0tK488kRrZq8AwAAAAA6hiajAAAAADClJlIhtL3G9s22N9g+dRJl6CnLRtvftn1tX28/49r+2bbvtH19z7hdbF9m+3vp/50nWJbTbP8o7Z9rbR89hnKssv0l2zfZvsH2O9L4Se2XQeWZxL7Z1vZVtr+VyvKeNH4i+6ZrcsqmVJ6J5RPZNLAs2eQT2TRdcsonsmne8kz1sVNO2ZS2Sz7NY+xNRm0vk/RdSS9T1TvO1ZKOj4gbx1qQJ8qzUdLqiJjIfVFsv0DSg6puLHlQGvc+SfdExHtT6O8cEX8yobKcJunBiHj/Um+/pxx7StozIr5he0dJ10g6VtIbNZn9Mqg8r9H4940lbR8RD9reRtJXJL1D0qs0gX3TJbllUyrTRk0on8imgWXJJp/IpumRWz6RTfOW5zRN8bFTTtmUykM+zWMSvxAeLmlDRNwSEQ9LukDSMRMoRxYi4gpJ9/SNPkbSuenxuaq+RJMqy9hFxOaI+EZ6/ICkm1TdZHNS+2VQecYuKg+mp9ukITShfdMxZFMPsqleTvlENk0V8inJKZsayjN2ZFNjecineUyiQri3pNt6nm/SBD8kqj4Ql9q+xvZJEyxHrz1St7FK/+8+4fKcbPu61CxirD+n295f0qGSvq4M9ktfeaQJ7Bvby2xfK+lOSZdFRBb7pgNyyyYpv3zK7XM2sWyS8sonsqnzcssnsml+HDvNLYs0of1CPjWbRIXQNeMm2dXp8yPiMElHSXp7+ukfTzhd0jMlHSJps6QPjGvDtneQ9ClJp0TE/ePa7hDlmci+iYhHI+IQVTcaPdz2QePY7hTILZsk8qnJxLJJyiufyKapkFs+kU3NOHaqL8vE9gv51GwSFcJNklb1PN9H0u0TKIckKSJuT//fKekiVc0yJu2O1P56th32nZMqSETckb5Ej0n6qMa0f1Ib709J+kREfDqNnth+qSvPpPbNrIi4T9KXJa1RRp+ZgmWVTVKW+ZTN52yS37+c8olsmhpZ5RPZ1IxjpzyzKZWBfKoxiQrh1ZIOsP0M2yskHSdp3QTKIdvbp4tdZXt7SS+XdH3zUmOxTtIJ6fEJkj47qYLMflGSV2oM+ydd/HuWpJsi4oM9kyayXwaVZ0L7ZjfbO6XH20l6qaTvKKPPTMGyySYp23zK5nM2ie9f2m42+UQ2TZVs8olsmt+0HzvllE1pu+TTfCJi7IOko1X1lvV9SX82iTKkcvyypG+l4YZJlEXS+ap+Nn9E1RnAEyXtKulySd9L/+8ywbL8D0nflnSdqi/OnmMox5GqmsJcJ+naNBw9wf0yqDyT2DcHS/pm2ub1kv4yjZ/IvunakEs2pbJMNJ/IpoFlySafyKbpGnLJJ7KpVXmm+tgpp2xK5SGf5hnGftsJAAAAAEAeJnJjegAAAADA5FEhBAAAAIApRYUQAAAAAKYUFUIAAAAAmFJUCAEAAABgSlEhBAAAAIApRYUQAAAAAKYUFUIAAAAAmFL/P9ist3HJEHcFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sig_pred_max= np.amax(sig_analysis_pred)         \n",
    "sig_pred_min = np.amin(sig_analysis_pred)        \n",
    "sig_ineff_max= np.amax(sig_inefficiency)\n",
    "sig_ineff_min= np.amin(sig_inefficiency)\n",
    "sig_true_max= np.amax(sig_analysis_true)\n",
    "sig_true_min = np.amin(sig_analysis_true)\n",
    "\n",
    "# Displaying the signal results\n",
    "ax = plt.figure(figsize = (15,15))\n",
    "ax.add_subplot(131)\n",
    "im = plt.imshow(sig_analysis_true.reshape(grid,grid), origin = 'low',cmap='Reds', interpolation='nearest', clim=(sig_true_min, sig_true_max))\n",
    "plt.colorbar(im,fraction=0.046, pad=0.04)\n",
    "plt.title('Sig heatmap for true vals')\n",
    "ax.add_subplot(132)\n",
    "im = plt.imshow(sig_analysis_pred.reshape(grid,grid), origin = 'low', cmap='Reds', interpolation='nearest', clim=(sig_pred_min, sig_pred_max))\n",
    "plt.colorbar(im,fraction=0.046, pad=0.04)\n",
    "plt.title('Sig heatmap for predicted vals')\n",
    "ax.add_subplot(133)\n",
    "im = plt.imshow(sig_inefficiency.reshape(grid,grid), origin = 'low', cmap='Reds', interpolation='nearest', clim=(sig_ineff_min, sig_ineff_max))\n",
    "plt.colorbar(im,fraction=0.046, pad=0.04)\n",
    "plt.title('Sig inefficiency for predicted vals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bkg_pred_max= np.amax(bkg_analysis_pred)\n",
    "bkg_pred_min = np.amin(bkg_analysis_pred)\n",
    "bkg_ineff_max= np.amax(bkg_inefficiency)\n",
    "bkg_ineff_min= np.amin(bkg_inefficiency)\n",
    "bkg_true_max= np.amax(bkg_analysis_true)\n",
    "bkg_true_min = np.amin(bkg_analysis_true)\n",
    "\n",
    "# Displaying the background results\n",
    "ax = plt.figure(figsize = (15,15))\n",
    "ax.add_subplot(131)\n",
    "im_ = plt.imshow(bkg_analysis_true.reshape(grid,grid), origin = 'low',cmap='Reds', interpolation='nearest', clim=(bkg_true_min, bkg_true_max))\n",
    "plt.colorbar(im_,fraction=0.046, pad=0.04)\n",
    "plt.title('Bkg heatmap for true vals')\n",
    "ax.add_subplot(132)\n",
    "im_ = plt.imshow(bkg_analysis_pred.reshape(grid,grid), origin = 'low',cmap='Reds', interpolation='nearest', clim=(bkg_pred_min, bkg_pred_max))\n",
    "plt.colorbar(im_,fraction=0.046, pad=0.04)\n",
    "plt.title('Bkg heatmap for predicted vals')\n",
    "ax.add_subplot(133)\n",
    "im = plt.imshow(bkg_inefficiency.reshape(grid,grid), origin = 'low', cmap='Reds', interpolation='nearest', clim=(bkg_ineff_min, bkg_ineff_max))\n",
    "plt.colorbar(im,fraction=0.046, pad=0.04)\n",
    "plt.title('Bkg ineff. heatmap for pred vals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1D histogram of $\\theta$ and z for signal and bkg\n",
    "\n",
    "Z histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xbins = np.arange(0.5, 1.0, 5e-3)\n",
    "plt.hist([sig_ineff_zlab[1:], bkg_ineff_zlab[1:]] , xbins, \n",
    "         color = ['blue', 'red'], histtype = 'step', label = ['signal ineff', 'background ineff'])\n",
    "# plt.yscale('log')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD4CAYAAAAAczaOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAdVElEQVR4nO3de5BU5bnv8e8viKJRFGU0yOAMpiCKgAQHMF6JGsHLjrIrJnA0aIwOolSSOknKy0nUmLKSc7Y5qcQdTSRS0ZTB60bUqMcbSgy6YbijSEQZcIRSRKOiQgSe80evGZqxh+np7um5rN+nqmtWv+v2vPTwzNvvete7FBGYmVk6fK6jAzAzs/Jx0jczSxEnfTOzFHHSNzNLESd9M7MU2aOjA2hN3759o7q6uqPDMDPrUhYuXPhORFQ0L+/0Sb+6upq6urqODsPMrEuRtDZXubt3zMxSxEnfzCxFnPTNzFKk0/fpm1nhPv30UxoaGtiyZUtHh2LtpFevXlRWVtKzZ8+8tnfSN+vGGhoa2G+//aiurkZSR4djJRYRbNq0iYaGBgYOHJjXPu7eMevGtmzZwkEHHeSE301J4qCDDmrTNzknfbNuzgm/e2vr5+ukb2aWIk76ZmlSXQ1S6V6t3C1fX1/P0KFDiw772Wef5eyzzy76OO3hoosu4v777+8y5+nWST/799szOZgBa9dCROlea3Pe9NmpRAQ7duzo6DBy2rZtW9nP2a2Tfvbvdxf43TTrlrZt28aFF17I8OHD+cY3vsHHH38MwA033MCoUaMYOnQotbW1ND7Fb/Xq1Zx22mkcffTRjBw5ktdee22X4y1YsIAvf/nLvP7662zcuJGvfe1rjBw5kilTplBVVcU777xDfX09Rx55JJdffjkjR47kjTfeYObMmQwbNoyhQ4dy5ZVXNh1v3333bVq+//77ueiii4BMy/p73/sexx13HIcffnhTKzsimDZtGkOGDOGss87i7bffzlnvsWPH8oMf/IDjjjuOoUOHMn/+fACuv/56amtrOf3005k8eTLbt2/nxz/+MaNGjWL48OH84Q9/aNN52qpbJ30z63irVq2itraWZcuW0bt3b2655RYApk2bxoIFC1ixYgWffPIJjzzyCADnn38+V1xxBUuXLmXevHn069ev6Vjz5s3jsssuY/bs2Rx++OH87Gc/45RTTmHRokVMmDCBdevW7XLeyZMns3jxYnr27MmVV17JM888w5IlS1iwYAEPPvhgq7Fv2LCB559/nkceeYSrrroKgFmzZrFq1SqWL1/O9OnTmTdvXov7f/TRR8ybN49bbrmFiy++uKl84cKFzJ49m7/85S/cfvvt7L///ixYsIAFCxYwffp01qxZ06bztIWTvpm1qwEDBnD88ccDcMEFF/D8888DMGfOHMaMGcOwYcN45plneOmll/jwww958803mTBhApC58WifffYBYOXKldTW1vLwww9z2GGHAfD8888zceJEAMaPH0+fPn2azltVVcWxxx4LZL4djB07loqKCvbYYw/OP/985s6d22rs5557Lp/73OcYMmQIb731FgBz585l0qRJ9OjRg0MPPZRTTjmlxf0nTZoEwEknncQHH3zAP//5TwC+/vWvs/feewPwxBNPcOeddzJixAjGjBnDpk2bePXVV9t0nrbwzVlm1q6aDymUxJYtW7j88supq6tjwIABXH/99WzZsqWpiyeXfv36sWXLFhYvXsyhhx4KsNvtP//5zzct72677Piaj3ffa6+9ch4j32GSueqeK7abb76ZcePG7bLto48+2i7Dbd3SN7N2tW7dOl544QUAZs6cyQknnNCUXPv27cvmzZub+st79+5NZWVlU9fL1q1bm64BHHDAAfz1r3/lmmuu4dlnnwXghBNO4N577wUyLeb33nsvZwxjxozhueee45133mH79u3MnDmTk08+GYBDDjmElStXsmPHDmbNmtVqfU466STuvvtutm/fzoYNG5gzZ06L295zzz1A5hvJ/vvvz/777/+ZbcaNG8ett97Kp59+CsA//vEPPvroozadpy3c0jdLk6qqzHC2Uh6vFUceeSR33HEHU6ZMYdCgQUydOpV99tmHSy+9lGHDhlFdXc2oUaOatv/zn//MlClTuPbaa+nZsyf33Xdf07pDDjmEhx9+mDPOOIMZM2Zw3XXXMWnSJO655x5OPvlk+vXrx3777cfmzZt3iaFfv3784he/4Ktf/SoRwZlnnsk555wDwC9/+UvOPvtsBgwYwNChQz+zb3MTJkzgmWeeYdiwYQwePLjpj0cuffr04bjjjuODDz5gxowZObe55JJLqK+vZ+TIkUQEFRUVPPjgg206T1tod197OoOampoo9CEqUmbkTvNls7RYuXIlRx55ZEeH0W62bt1Kjx492GOPPXjhhReYOnUqS5Ys6eiwgMzonZtuuomampp2P1euz1nSwoj4zMnd0jezLmvdunV885vfZMeOHey5555Mnz69o0Pq9FKT9LO/1VZVQX19h4ZjZiUwaNAgFi9e3NFh5NR43aGzSU3Sz07ynn/KzNKq1dE7kmZIelvSiqyyeyQtSV71kpYk5dWSPsla9/usfY6RtFzSakm/laf+MzMru3xa+n8C/hO4s7EgIr7VuCzpV8D7Wdu/FhEjchznVqAWeBF4FBgPPNb2kM3MrFCttvQjYi7wbq51SWv9m8DM3R1DUj+gd0S8EJnhQncC57Y9XDMzK0axN2edCLwVEa9mlQ2UtFjSc5JOTMr6Aw1Z2zQkZTlJqpVUJ6lu48aNRYZoZo3KPLNyiy655BJefvnlUlYN2HXytPZUrvO0h2Iv5E5i11b+BuCwiNgk6RjgQUlHAbn671scNR8RtwG3QWacfpExmlmicebZUin0ytwf//jH0gVRItu3b6dHjx4dHUa7K7ilL2kP4N+BexrLImJrRGxKlhcCrwGDybTsK7N2rwTWF3puM+saPvroI8466yyOPvpohg4d2jQtwdixY2m86fL2229n8ODBjB07lksvvZRp06YBLU9tvHnzZk499VRGjhzJsGHDmD179m5jqK+v54gjjsg5vXN1dTU33HADJ5xwAvfddx+vvfYa48eP55hjjuHEE0/klVdeAWDNmjV85StfYdSoUfz0pz9tl3+rcimme+c04JWIaOq2kVQhqUeyfDgwCHg9IjYAH0o6NrkOMBnY/SdlZl3e448/zqGHHsrSpUtZsWIF48eP32X9+vXr+fnPf86LL77Ik08+2ZRkG+Wa2rhXr17MmjWLRYsWMWfOHH74wx/udkI1aHl658bjNc7WWVtby80338zChQu56aabuPzyywH4/ve/z9SpU1mwYAFf+MIXSvFP02HyGbI5E3gB+JKkBknfTVZN5LMXcE8ClklaCtwPXBYRjReBpwJ/BFaT+QbgkTtm3dywYcN46qmnuPLKK/nb3/72mQnH5s+fz8knn8yBBx5Iz549Oe+883ZZn2tq44jgmmuuYfjw4Zx22mm8+eabTeta0tL0zgDf+lZmMOLmzZuZN28e5513HiNGjGDKlCls2LABgL///e9N0yR/+9vfLuJfpOO12qcfEZNaKL8oR9kDwAMtbF8HFP+wTDPrMgYPHszChQt59NFHufrqqzn99NO59tprm9a31kLPNbXxXXfdxcaNG1m4cCE9e/akurr6M1MiN9fSFMewc5rjHTt2cMABB7Q4d093ubXIUyubWbtZv349++yzDxdccAE/+tGPWLRo0S7rR48ezXPPPcd7773Htm3beOCBnG3GXbz//vscfPDB9OzZkzlz5rA2j2eh5preubnevXszcODAplk9I4KlS5cCcPzxx3P33XcDmT86XZmTvlmKNM5BVapXazMrL1++nNGjRzNixAhuvPFGfvKTn+yyvn///lxzzTWMGTOG0047jSFDhuSccz7b+eefT11dHTU1Ndx1110cccQRrda7cXrn4cOH8+677zJ16tSc2911113cfvvtHH300Rx11FFNF4l/85vf8Lvf/Y5Ro0bx/vvv59y3q0jN1Mr5lJt1N11hauXNmzez7777sm3bNiZMmMDFF1/c9LjEUqivr+fss89mxYoVrW/cRXlqZTPrMq6//nqeeuoptmzZwumnn86556b4Zv1ly+Bf/8os77knDB9e8lM46ZtZh7rpppva9fjV1dVdp5X/r39B40NXCuzhaI379M26uc7ehWvFaevn66Rv1o316tWLTZs2OfF3UxHBpk2b6NWrV977uHvHrBurrKykoaEBT1zYRbzzDqxc+dnl3ejVqxeVlZWtbtfISd+sG+vZsycDBw7s6DAsX0OG7BxamL1cQu7eMTNLESd9M7MUcdI3M0sRJ30zsxRx0jczSxEnfTOzFHHSNzNLESd9M7MUSWXSz55TvLq6o6MxMyufVN6RW1+/c7mbPAHNzCwv+TwYfYaktyWtyCq7XtKbkpYkrzOz1l0tabWkVZLGZZUfI2l5su636i4PnDQz60Ly6d75EzA+R/mvI2JE8noUQNIQYCJwVLLPLZJ6JNvfCtQCg5JXrmOamVk7ajXpR8Rc4N08j3cOcHdEbI2INcBqYLSkfkDviHghMnO83gmk+PE4ZmYdo5gLudMkLUu6f/okZf2BN7K2aUjK+ifLzctzklQrqU5SnaeENTMrnUKT/q3AF4ERwAbgV0l5rn762E15ThFxW0TURERNRUVFgSGamVlzBSX9iHgrIrZHxA5gOjA6WdUADMjatBJYn5RX5ig3M7MyKijpJ330jSYAjSN7HgImStpL0kAyF2znR8QG4ENJxyajdiYDs4uI28zMCtDqOH1JM4GxQF9JDcB1wFhJI8h00dQDUwAi4iVJ9wIvA9uAKyJie3KoqWRGAu0NPJa8zMysjNTZH5hcU1MTdXV1Be0rtf60sXy2MTMri+yEVGRykrQwImqal6dyGgYzs7Ry0jczSxEnfTOzFHHSNzNLESd9M7MUcdI3M0sRJ30zs45UXb3zqU5VVe1+ulQ+RMXMrNNYu7asNwu5pW9mliJO+mZmKeKkb2aWIqlP+lVVO6+hVFd3dDRmZu0r9Rdy6+t3LvtR7WbW3aW+pW9mliZO+mZmKeKkb2aWIk76ZmYp4qRvZpYirSZ9STMkvS1pRVbZf0h6RdIySbMkHZCUV0v6RNKS5PX7rH2OkbRc0mpJv00ekG5mZmWUT0v/T8D4ZmVPAkMjYjjwD+DqrHWvRcSI5HVZVvmtQC0wKHk1P6aZmbWzVpN+RMwF3m1W9kREbEvevghU7u4YkvoBvSPihcg8if1O4NzCQjYzs0KVok//YuCxrPcDJS2W9JykE5Oy/kBD1jYNSVlOkmol1Umq27hxYwlCNDMzKDLpS/pfwDbgrqRoA3BYRHwZ+J/AXyT1BnL137c4l2hE3BYRNRFRU1FRUUyIZmaWpeBpGCRdCJwNnJp02RARW4GtyfJCSa8Bg8m07LO7gCqB9YWe28zMClNQS1/SeOBK4OsR8XFWeYWkHsny4WQu2L4eERuADyUdm4zamQzMLjp6MzNrk1Zb+pJmAmOBvpIagOvIjNbZC3gyGXn5YjJS5yTgBknbgO3AZRHReBF4KpmRQHuTuQaQfR3AzMzKQFHGx3QVoqamJurq6graV2rbU8jaur2ZWdFaSjxFJiRJCyOipnm578g1M0sRJ30zsxRx0jczSxEnfTOzFHHSNzNLESd9M7MUcdI3M0sRJ30zsxRx0jczSxEnfTOzFHHSNzNLESd9M7MUcdI3M0sRJ30zsxRx0s9SVZWZzVSC6uqOjsbMrPQKflxid1Rfv3NZuZ7qa2bWxbmlb2aWIk76ZmYp4qRvZpYirSZ9STMkvS1pRVbZgZKelPRq8rNP1rqrJa2WtErSuKzyYyQtT9b9VnKvuZmlVHX1zlEjVVVlPXU+Lf0/AeOblV0FPB0Rg4Cnk/dIGgJMBI5K9rlFUo9kn1uBWmBQ8mp+TDOzdFi7NvPQ84hdR5CUQatJPyLmAu82Kz4HuCNZvgM4N6v87ojYGhFrgNXAaEn9gN4R8UJEBHBn1j5mZlYmhfbpHxIRGwCSnwcn5f2BN7K2a0jK+ifLzctzklQrqU5S3caNGwsM0czMmiv1hdxc/fSxm/KcIuK2iKiJiJqKioqSBWdmlnaFJv23ki4bkp9vJ+UNwICs7SqB9Ul5ZY5yMzMro0KT/kPAhcnyhcDsrPKJkvaSNJDMBdv5SRfQh5KOTUbtTM7ax8zMyqTVaRgkzQTGAn0lNQDXAb8E7pX0XWAdcB5ARLwk6V7gZWAbcEVEbE8ONZXMSKC9gceSl5mZlZEyg2k6r5qamqirqytoXykzIqrc+5qZ7VY+CabIJCRpYUTUNC/3HblmZinipG9mliJO+mZmKeKkb2aWIk76ZmYp4qRvZpYiTvpmZinipG9mliJO+mZmKeKkb2aWIk76ZmYp4qRvZpYiTvpmZinipG9mliJO+mZmKeKkb2ZWDtXVmTnyJaiq6rAwWn1ylpmZlcDatZ3iyUxu6ZuZpUjBSV/SlyQtyXp9IOkHkq6X9GZW+ZlZ+1wtabWkVZLGlaYK7aOqauc3serqjo7GzKw0Cu7eiYhVwAgAST2AN4FZwHeAX0fETdnbSxoCTASOAg4FnpI0OOvB6Z1Kff3OZanDwjAzK6lSde+cCrwWEWt3s805wN0RsTUi1gCrgdElOr+ZmeWhVEl/IjAz6/00ScskzZDUJynrD7yRtU1DUvYZkmol1Umq27hxY4lCNDOzopO+pD2BrwP3JUW3Al8k0/WzAfhV46Y5ds95KTsibouImoioqaioKDZEMzNLlKKlfwawKCLeAoiItyJie0TsAKazswunARiQtV8lsL4E5zczszyVIulPIqtrR1K/rHUTgBXJ8kPAREl7SRoIDALml+D8ZmaWp6JuzpK0D/A1YEpW8f+RNIJM101947qIeEnSvcDLwDbgis46csfMrLsqKulHxMfAQc3Kvr2b7W8EbizmnGZmVjjfkWtmliJO+mZmKeKkb2aWIk76ZmYp4qRvZpYiTvpmZinipG9mliJO+mZmKeKkb2aWIk76ZmbtpZM8DD2bH4xuZtZeOsnD0LO5pW9mliJO+mZmKeKkb2aWIk76ZmYp4qRvZpYiTvpmZinipG9mliJO+mZmKVJU0pdUL2m5pCWS6pKyAyU9KenV5GefrO2vlrRa0ipJ44oNvlyqqnbeVFdd3dHRmJkVrhQt/a9GxIiIqEneXwU8HRGDgKeT90gaAkwEjgLGA7dI6lGC87e7+vrMTXURmRvszMy6qvbo3jkHuCNZvgM4N6v87ojYGhFrgNXA6HY4v5mZtaDYpB/AE5IWSqpNyg6JiA0Ayc+Dk/L+wBtZ+zYkZZ8hqVZSnaS6jRs3FhmimZk1KjbpHx8RI4EzgCsknbSbbZWjLOdMRBFxW0TURERNRUVFkSGamZVRJ5xZM1tRs2xGxPrk59uSZpHprnlLUr+I2CCpH/B2snkDMCBr90pgfTHnNzPrdDrhzJrZCm7pS/q8pP0al4HTgRXAQ8CFyWYXArOT5YeAiZL2kjQQGATML/T8ZmbWdsW09A8BZklqPM5fIuJxSQuAeyV9F1gHnAcQES9Juhd4GdgGXBER24uK3szM2qTgpB8RrwNH5yjfBJzawj43AjcWek4zMyuO78g1M0sRJ30zsxRx0jczSxEnfTOzFHHSNzNLESd9M7MUcdI3M0sRJ30zsxRx0jczSxEnfTOzFHHSNzMrViefTjlbUVMrp1Hj83Kz39fXd1g4ZtYZdPLplLM56bdR8wSvXI+GMTPrpNy9Y2aWIk76ZmaF6EL9+NncvWNmVogu1I+fzS19M7MUcdI3M0uRYh6MPkDSHEkrJb0k6ftJ+fWS3pS0JHmdmbXP1ZJWS1olaVwpKmBmZvkrpk9/G/DDiFgkaT9goaQnk3W/joibsjeWNASYCBwFHAo8JWmwH45uZlY+Bbf0I2JDRCxKlj8EVgL9d7PLOcDdEbE1ItYAq4HRhZ7fzMzariR9+pKqgS8D/50UTZO0TNIMSX2Ssv7AG1m7NdDCHwlJtZLqJNVt3LixFCGamRklSPqS9gUeAH4QER8AtwJfBEYAG4BfNW6aY/ec450i4raIqImImoqKimJDNDOzRFFJX1JPMgn/roj4L4CIeCsitkfEDmA6O7twGoABWbtXAuuLOb+ZWVl10RuyshUzekfA7cDKiPi/WeX9sjabAKxIlh8CJkraS9JAYBAwv9Dzm5mVXeMNWRFddqbFYkbvHA98G1guaUlSdg0wSdIIMl039cAUgIh4SdK9wMtkRv5c4ZE7ZmblVXDSj4jnyd1P/+hu9rkRuLHQc3ZG2VMte5plM+vsPPdOkbKTvKdZNuuGqqsz3TrQZfvxsznpm5ntThedWK0lnnvHzCxFnPTNzFLESd/MLEWc9M3MUsRJ38wsW/Zdt134ztuWePSOmVm2bjZapzm39M3MUsRJv4Qa786VMt8Qzcw6G3fvlJDvzjXrorrZXbe746RvZtbN+/GzuXvHzCxFnPTNLJ26wQNRCuHuHTNLpxR16WRzS9/MLEWc9NuJh2+adRLN77BNYZdONnfvtBMP3zTrJFLajdMSt/TNrHtwiz4vZU/6ksZLWiVptaSryn3+juCuHrMyaGzRN3/5wdW7KGvSl9QD+B1wBjAEmCRpSDlj6Aj19Tt//8B/AMzarKVWvFv0bVbulv5oYHVEvB4R/wLuBs4pcwwdqqU/ANkv/zGwLiWfhLy7X/B89ofcrXi36Nus3Bdy+wNvZL1vAMY030hSLVCbvN0saVWB5+sr8U6B+3aYtWuLuvjbF7penUsgjfXumnUu5Bd81326Zr0LUVydc371KXfSz/VJf+ayekTcBtxW9MmkuoioKfY4XUka6wzprHca6wzprHcp61zu7p0GYEDW+0pgfZljMDNLrXIn/QXAIEkDJe0JTAQeKnMMZmapVdbunYjYJmka8P+AHsCMiHipHU9ZdBdRF5TGOkM6653GOkM6612yOit8p5qZWWr4jlwzsxRx0jczS5FukfRbm9pB0lhJ70takryu7Yg4Symf6SySei+R9JKk58odY3vI47P+cdbnvELSdkkHdkSspZJHnfeX9LCkpcln/Z2OiLOU8qhzH0mzJC2TNF/S0I6Is5QkzZD0tqQVLayXpN8m/ybLJI0s6EQR0aVfZC4IvwYcDuwJLAWGNNtmLPBIR8da5jofALwMHJa8P7ij4y5HvZtt/2/AMx0ddxk+62uA/50sVwDvAnt2dOztXOf/AK5Llo8Anu7ouEtQ75OAkcCKFtafCTxG5n6nY4H/LuQ83aGln8apHfKp8/8A/isi1gFExNtljrE9tPWzngTMLEtk7SefOgewnyQB+5JJ+tvKG2ZJ5VPnIcDTABHxClAt6ZDyhllaETGXzGfXknOAOyPjReAASf3aep7ukPRzTe3QP8d2X0m+/j4m6ajyhNZu8qnzYKCPpGclLZQ0uWzRtZ98P2sk7QOMBx4oQ1ztKZ86/ydwJJkbHZcD34+IHeUJr13kU+elwL8DSBpNZsqByrJE13Hy/v3fne7wEJV8pnZYBFRFxGZJZwIPAoPaPbL2k0+d9wCOAU4F9gZekPRiRPyjvYNrR3lN45H4N+DvEbG7llNXkE+dxwFLgFOALwJPSvpbRHzQ3sG1k3zq/EvgN5KWkPlDt5iu/e0mH235/W9Rd2jptzq1Q0R8EBGbk+VHgZ6S+pYvxJLLZzqLBuDxiPgoIt4B5gJHlym+9tKWaTwm0vW7diC/On+HTFdeRMRqYA2Zfu6uKt//09+JiBHAZDLXMtaUL8QOUZJpbLpD0m91agdJX0j6Oxu/Cn4O2FT2SEsnn+ksZgMnStoj6eoYA6wsc5ylltc0HpL2B04m82/Q1eVT53VkvtGR9Gt/CXi9rFGWVj7/pw9I1gFcAsztwt9s8vUQMDkZxXMs8H5EbGjrQbp89060MLWDpMuS9b8HvgFMlbQN+ASYGMnl8K4onzpHxEpJjwPLgB3AHyMi51CwriLPzxpgAvBERHzUQaGWTJ51/jnwJ0nLyXQBXJl8u+uS8qzzkcCdkraTGaX23Q4LuEQkzSQz0rCvpAbgOqAnNNX5UTIjeFYDH5P5htf283Th3GdmZm3UHbp3zMwsT076ZmYp4qRvZpYiTvpmZinipG9mliJO+mZmKeKkb2aWIv8fgCK/EOkgp2wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([sig_zlab[1:], bkg_zlab[1:]] , xbins, \n",
    "         color = ['b', 'r'], histtype = 'step', label = ['signal pred', 'background pred'])\n",
    "# plt.yscale('log')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\theta$ histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}