5c-WH-Compute-FisherInfo-Distributions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MadMiner version: 0.4.10\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "madminer_src_path = \"/home/shomiller/madminer\"\n",
    "sys.path.append(madminer_src_path)\n",
    "\n",
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "import logging\n",
    "\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy.optimize import curve_fit\n",
    "% matplotlib inline\n",
    "from matplotlib.patches import Patch\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "from madminer.fisherinformation import FisherInformation\n",
    "from madminer.fisherinformation import project_information, profile_information\n",
    "\n",
    "from madminer.sampling import SampleAugmenter\n",
    "\n",
    "from madminer.plotting import plot_distribution_of_information\n",
    "\n",
    "from pandas import DataFrame\n",
    "\n",
    "import madminer.__version__\n",
    "print( 'MadMiner version: {}'.format(madminer.__version__) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# MadMiner output\n",
    "logging.basicConfig(\n",
    "    format='%(asctime)-5.5s %(name)-20.20s %(levelname)-7.7s %(message)s',\n",
    "    datefmt='%H:%M',\n",
    "    level=logging.INFO\n",
    ")\n",
    "\n",
    "# Output of all other modules (e.g. matplotlib)\n",
    "for key in logging.Logger.manager.loggerDict:\n",
    "    if \"madminer\" not in key:\n",
    "        logging.getLogger(key).setLevel(logging.WARNING)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Formatting Options for Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['text.usetex'] = True\n",
    "\n",
    "mpl.rcParams['figure.figsize'] = (8,6)\n",
    "mpl.rcParams['axes.labelsize'] = 20\n",
    "mpl.rcParams['xtick.labelsize'] = 16\n",
    "mpl.rcParams['ytick.labelsize'] = 16\n",
    "\n",
    "mpl.rcParams['font.size'] = 14\n",
    "mpl.rcParams['legend.fontsize'] = 14\n",
    "\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "mpl.rcParams['font.style'] = 'normal'\n",
    "mpl.rcParams['font.serif'] = 'Times New Roman'\n",
    "\n",
    "mpl.rcParams['savefig.bbox'] = 'tight'\n",
    "\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "mpl.rcParams['axes.formatter.use_mathtext'] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Data Files:\n",
    "\n",
    "Saving the paths for all our lhe data files, for easy use later on.\n",
    "\n",
    "Note, lhedata are saved in the format:\n",
    "`data/{channel}_lhedata_{observables}.h5`\n",
    "while the `SALLY` models are saved in:\n",
    "`models/sally_ensemble_{channel}_{observables}/`\n",
    "\n",
    "here `channel` can be e.g, `wph_mu_wbkgs` or `wmh_e_wbkgs` (or take out the backgrounds for the background free runs), while `observables` can be `full`, `met`, `ptw` or `2d`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Signal + Backgrounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "lhedatafile_wph_mu_wbkgs_met = 'data/met/wph_mu_wbkgs_lhedata_met.h5'\n",
    "lhedatafile_wph_e_wbkgs_met = 'data/met/wph_e_wbkgs_lhedata_met.h5'\n",
    "lhedatafile_wmh_mu_wbkgs_met = 'data/met/wmh_mu_wbkgs_lhedata_met.h5'\n",
    "lhedatafile_wmh_e_wbkgs_met = 'data/met/wmh_e_wbkgs_lhedata_met.h5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Signal Only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "lhedatafile_wph_mu_met = 'data/met/signal/wph_mu_smeftsim_lhedata_met.h5'\n",
    "lhedatafile_wph_e_met = 'data/met/signal/wph_e_smeftsim_lhedata_met.h5'\n",
    "lhedatafile_wmh_mu_met = 'data/met/signal/wmh_mu_smeftsim_lhedata_met.h5'\n",
    "lhedatafile_wmh_e_met = 'data/met/signal/wmh_e_smeftsim_lhedata_met.h5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Set up Fisher Info objects to compute histograms of F.I. in each file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### F.I. in MET (Signal + Background)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "20:26 madminer.analysis    INFO    Loading data from data/met/wph_mu_wbkgs_lhedata_met.h5\n",
      "20:26 madminer.analysis    INFO    Found 4 parameters\n",
      "20:26 madminer.analysis    INFO    Did not find nuisance parameters\n",
      "20:26 madminer.analysis    INFO    Found 15 benchmarks, of which 15 physical\n",
      "20:26 madminer.analysis    INFO    Found 48 observables\n",
      "20:26 madminer.analysis    INFO    Found 5000000 events\n",
      "20:26 madminer.analysis    INFO      1000000 signal events sampled from benchmark sm\n",
      "20:26 madminer.analysis    INFO      4000000 background events\n",
      "20:26 madminer.analysis    INFO    Found morphing setup with 15 components\n",
      "20:26 madminer.analysis    INFO    Did not find nuisance morphing setup\n",
      "20:26 madminer.analysis    INFO    Loading data from data/met/wph_e_wbkgs_lhedata_met.h5\n",
      "20:26 madminer.analysis    INFO    Found 4 parameters\n",
      "20:26 madminer.analysis    INFO    Did not find nuisance parameters\n",
      "20:26 madminer.analysis    INFO    Found 15 benchmarks, of which 15 physical\n",
      "20:26 madminer.analysis    INFO    Found 48 observables\n",
      "20:26 madminer.analysis    INFO    Found 5000000 events\n",
      "20:26 madminer.analysis    INFO      1000000 signal events sampled from benchmark sm\n",
      "20:26 madminer.analysis    INFO      4000000 background events\n",
      "20:26 madminer.analysis    INFO    Found morphing setup with 15 components\n",
      "20:26 madminer.analysis    INFO    Did not find nuisance morphing setup\n",
      "20:26 madminer.analysis    INFO    Loading data from data/met/wmh_mu_wbkgs_lhedata_met.h5\n",
      "20:26 madminer.analysis    INFO    Found 4 parameters\n",
      "20:26 madminer.analysis    INFO    Did not find nuisance parameters\n",
      "20:26 madminer.analysis    INFO    Found 15 benchmarks, of which 15 physical\n",
      "20:26 madminer.analysis    INFO    Found 48 observables\n",
      "20:26 madminer.analysis    INFO    Found 5000000 events\n",
      "20:26 madminer.analysis    INFO      1000000 signal events sampled from benchmark sm\n",
      "20:26 madminer.analysis    INFO      4000000 background events\n",
      "20:26 madminer.analysis    INFO    Found morphing setup with 15 components\n",
      "20:26 madminer.analysis    INFO    Did not find nuisance morphing setup\n",
      "20:26 madminer.analysis    INFO    Loading data from data/met/wmh_e_wbkgs_lhedata_met.h5\n",
      "20:26 madminer.analysis    INFO    Found 4 parameters\n",
      "20:26 madminer.analysis    INFO    Did not find nuisance parameters\n",
      "20:26 madminer.analysis    INFO    Found 15 benchmarks, of which 15 physical\n",
      "20:26 madminer.analysis    INFO    Found 48 observables\n",
      "20:26 madminer.analysis    INFO    Found 5000000 events\n",
      "20:26 madminer.analysis    INFO      1000000 signal events sampled from benchmark sm\n",
      "20:26 madminer.analysis    INFO      4000000 background events\n",
      "20:26 madminer.analysis    INFO    Found morphing setup with 15 components\n",
      "20:26 madminer.analysis    INFO    Did not find nuisance morphing setup\n"
     ]
    }
   ],
   "source": [
    "fisher_wph_mu_wbkgs_met = FisherInformation(lhedatafile_wph_mu_wbkgs_met, include_nuisance_parameters=False)\n",
    "\n",
    "fisher_wph_e_wbkgs_met = FisherInformation(lhedatafile_wph_e_wbkgs_met, include_nuisance_parameters=False)\n",
    "\n",
    "fisher_wmh_mu_wbkgs_met = FisherInformation(lhedatafile_wmh_mu_wbkgs_met, include_nuisance_parameters=False)\n",
    "\n",
    "fisher_wmh_e_wbkgs_met = FisherInformation(lhedatafile_wmh_e_wbkgs_met, include_nuisance_parameters=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### F.I. in MET info (Signal Only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "20:26 madminer.analysis    INFO    Loading data from data/met/signal/wph_mu_smeftsim_lhedata_met.h5\n",
      "20:26 madminer.analysis    INFO    Found 4 parameters\n",
      "20:26 madminer.analysis    INFO    Found 33 nuisance parameters\n",
      "20:26 madminer.analysis    INFO    Found 54 benchmarks, of which 15 physical\n",
      "20:26 madminer.analysis    INFO    Found 48 observables\n",
      "20:26 madminer.analysis    INFO    Found 1000000 events\n",
      "20:26 madminer.analysis    INFO      1000000 signal events sampled from benchmark sm\n",
      "20:26 madminer.analysis    INFO    Found morphing setup with 15 components\n",
      "20:26 madminer.analysis    INFO    Found nuisance morphing setup\n",
      "20:26 madminer.analysis    INFO    Loading data from data/met/signal/wph_e_smeftsim_lhedata_met.h5\n",
      "20:26 madminer.analysis    INFO    Found 4 parameters\n",
      "20:26 madminer.analysis    INFO    Found 33 nuisance parameters\n",
      "20:26 madminer.analysis    INFO    Found 54 benchmarks, of which 15 physical\n",
      "20:26 madminer.analysis    INFO    Found 48 observables\n",
      "20:26 madminer.analysis    INFO    Found 1000000 events\n",
      "20:26 madminer.analysis    INFO      1000000 signal events sampled from benchmark sm\n",
      "20:26 madminer.analysis    INFO    Found morphing setup with 15 components\n",
      "20:26 madminer.analysis    INFO    Found nuisance morphing setup\n",
      "20:26 madminer.analysis    INFO    Loading data from data/met/signal/wmh_mu_smeftsim_lhedata_met.h5\n",
      "20:26 madminer.analysis    INFO    Found 4 parameters\n",
      "20:26 madminer.analysis    INFO    Found 33 nuisance parameters\n",
      "20:26 madminer.analysis    INFO    Found 54 benchmarks, of which 15 physical\n",
      "20:26 madminer.analysis    INFO    Found 48 observables\n",
      "20:26 madminer.analysis    INFO    Found 1000000 events\n",
      "20:26 madminer.analysis    INFO      1000000 signal events sampled from benchmark sm\n",
      "20:26 madminer.analysis    INFO    Found morphing setup with 15 components\n",
      "20:26 madminer.analysis    INFO    Found nuisance morphing setup\n",
      "20:26 madminer.analysis    INFO    Loading data from data/met/signal/wmh_e_smeftsim_lhedata_met.h5\n",
      "20:26 madminer.analysis    INFO    Found 4 parameters\n",
      "20:26 madminer.analysis    INFO    Found 33 nuisance parameters\n",
      "20:26 madminer.analysis    INFO    Found 54 benchmarks, of which 15 physical\n",
      "20:26 madminer.analysis    INFO    Found 48 observables\n",
      "20:26 madminer.analysis    INFO    Found 1000000 events\n",
      "20:26 madminer.analysis    INFO      1000000 signal events sampled from benchmark sm\n",
      "20:26 madminer.analysis    INFO    Found morphing setup with 15 components\n",
      "20:26 madminer.analysis    INFO    Found nuisance morphing setup\n"
     ]
    }
   ],
   "source": [
    "fisher_wph_mu_met = FisherInformation(lhedatafile_wph_mu_met, include_nuisance_parameters=True)\n",
    "\n",
    "fisher_wph_e_met = FisherInformation(lhedatafile_wph_e_met, include_nuisance_parameters=True)\n",
    "\n",
    "fisher_wmh_mu_met = FisherInformation(lhedatafile_wmh_mu_met, include_nuisance_parameters=True)\n",
    "\n",
    "fisher_wmh_e_met = FisherInformation(lhedatafile_wmh_e_met, include_nuisance_parameters=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 1E. Rotating the Information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The coefficients $c_{H\\square}$ and $c_{HD}$ always enter in the combination $(c_{H\\square} - \\frac{1}{4}c_{HD})$. This implies there is an inherent flat direction, along the axis $c_{H\\square} + 4 c_{HD}$. \n",
    "\n",
    "Before analyzing the information, we want to rotate to a basis where this flat direction is manifest. Then we can profile (or project -- the results should be approximately the same) out the flat direction easily and have a $3 \\times 3$ fisher information matrix to work with in the basis $c_{H\\square} - \\frac{1}{4} c_{HD}$, $c_{HW}$, and $c_{Hq}^{(3)}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Function for Rotating:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Function for rotating the fisher information, including the covariance matrix.\n",
    "\n",
    "By default, it takes the rotation angle to be `np.arctan(4)`, which rotates out the flat direction and leaves the direction $c_{H\\square} - \\frac{1}{4} c_{HD}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def rotate(\n",
    "    fisher_info, covariance=None, include_flat_direction=False, include_nuisance_params=False, rotation_angle=None, axis1=0, axis2=1,\n",
    "):\n",
    "\n",
    "    # specify an angle (default = arctan(4), which rotates out the flat direction in cHD and cHbox)\n",
    "    if rotation_angle == None:\n",
    "        this_angle = np.arctan(4)\n",
    "    else: \n",
    "        this_angle = rotation_angle\n",
    "    \n",
    "    #define the rotation matrix\n",
    "    dimension = len(fisher_info)\n",
    "    this_rotation_matrix = np.zeros((dimension, dimension))\n",
    "    np.fill_diagonal(this_rotation_matrix, 1)\n",
    "    this_rotation_matrix[axis1,axis1] = np.cos(this_angle)\n",
    "    this_rotation_matrix[axis1,axis2] = -1.*np.sin(this_angle)\n",
    "    this_rotation_matrix[axis2,axis1] =  1.*np.sin(this_angle)\n",
    "    this_rotation_matrix[axis2,axis2] = np.cos(this_angle)\n",
    "    \n",
    "    #get the rotated fisher information\n",
    "    rotated_fisher_info = np.einsum('ki,lj,kl->ij', this_rotation_matrix, this_rotation_matrix, fisher_info)\n",
    "    \n",
    "    #get the rotated covariance of the fisher information\n",
    "    if covariance is None:\n",
    "        rotated_covariance = covariance \n",
    "    else:\n",
    "        rotated_covariance = np.einsum('mi,nj,ok,pl,mnop->ijkl', \n",
    "                                this_rotation_matrix, this_rotation_matrix, this_rotation_matrix, this_rotation_matrix, covariance)\n",
    "\n",
    "    # If not desired, project out the nuisance parameters\n",
    "    if not include_nuisance_params:\n",
    "        if covariance is None:\n",
    "            rotated_fisher_info = project_information(rotated_fisher_info, [0,1,2,3])\n",
    "        else:\n",
    "            rotated_fisher_info, rotated_covariance = project_information(rotated_fisher_info, [0,1,2,3], covariance=rotated_covariance)\n",
    "        \n",
    "    #return either the three physical directions or include the flat direction\n",
    "    if include_flat_direction:\n",
    "        if covariance is None:\n",
    "            return rotated_fisher_info\n",
    "        else:\n",
    "            return rotated_fisher_info, rotated_covariance\n",
    "    else:\n",
    "        return project_information(rotated_fisher_info, np.arange(1,len(rotated_fisher_info)), covariance=rotated_covariance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Distributions of Information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4A. Computing the Binned Information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Paths to Model Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_path_wph_mu = 'models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met'\n",
    "model_path_wph_e = 'models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met'\n",
    "model_path_wmh_mu = 'models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met'\n",
    "model_path_wmh_e = 'models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### $p_{T,W}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "16:53 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "16:53 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "16:53 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "16:53 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "16:53 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "16:53 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "17:00 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:00 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:01 madminer.ml          INFO    Starting score evaluation\n",
      "17:02 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "17:02 madminer.ml          INFO    Starting score evaluation\n",
      "17:02 madminer.ml          INFO    Starting score evaluation\n",
      "17:02 madminer.ml          INFO    Starting score evaluation\n",
      "17:02 madminer.ml          INFO    Starting score evaluation\n",
      "17:02 madminer.ml          INFO    Starting score evaluation\n",
      "17:02 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "17:02 madminer.ml          INFO    Starting score evaluation\n",
      "17:02 madminer.ml          INFO    Starting score evaluation\n",
      "17:02 madminer.ml          INFO    Starting score evaluation\n",
      "17:02 madminer.ml          INFO    Starting score evaluation\n",
      "17:02 madminer.ml          INFO    Starting score evaluation\n",
      "/home/shomiller/.conda/envs/my_root/lib/python2.7/site-packages/madminer-0.4.9-py2.7.egg/madminer/fisherinformation.py:1166: RuntimeWarning: divide by zero encountered in true_divide\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_mu_ptw, w_in_histo_wph_mu_ptw, fi_histo_rate_wph_mu_ptw, fi_histo_wph_mu_ptw = fisher_wph_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_w',\n",
    "    nbins=20,\n",
    "    histrange=(0,1000),\n",
    "    model_file=model_path_wph_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17:11 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "17:11 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "17:11 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "17:11 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "17:11 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "17:11 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "17:17 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "17:17 madminer.ml          INFO    Starting score evaluation\n",
      "17:17 madminer.ml          INFO    Starting score evaluation\n",
      "17:17 madminer.ml          INFO    Starting score evaluation\n",
      "17:17 madminer.ml          INFO    Starting score evaluation\n",
      "17:17 madminer.ml          INFO    Starting score evaluation\n",
      "17:17 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "17:17 madminer.ml          INFO    Starting score evaluation\n",
      "17:17 madminer.ml          INFO    Starting score evaluation\n",
      "17:17 madminer.ml          INFO    Starting score evaluation\n",
      "17:17 madminer.ml          INFO    Starting score evaluation\n",
      "17:17 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:18 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n",
      "17:19 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_e_ptw, w_in_histo_wph_e_ptw, fi_histo_rate_wph_e_ptw, fi_histo_wph_e_ptw = fisher_wph_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_w',\n",
    "    nbins=20,\n",
    "    histrange=(0,1000),\n",
    "    model_file=model_path_wph_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17:20 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "17:20 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "17:20 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "17:20 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "17:20 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "17:20 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "17:25 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "17:25 madminer.ml          INFO    Starting score evaluation\n",
      "17:25 madminer.ml          INFO    Starting score evaluation\n",
      "17:25 madminer.ml          INFO    Starting score evaluation\n",
      "17:25 madminer.ml          INFO    Starting score evaluation\n",
      "17:25 madminer.ml          INFO    Starting score evaluation\n",
      "17:25 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "17:25 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:26 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n",
      "17:27 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_mu_ptw, w_in_histo_wmh_mu_ptw, fi_histo_rate_wmh_mu_ptw, fi_histo_wmh_mu_ptw = fisher_wmh_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_w',\n",
    "    nbins=20,\n",
    "    histrange=(0,1000),\n",
    "    model_file=model_path_wmh_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17:27 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "17:27 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "17:27 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "17:27 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "17:27 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "17:27 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "17:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:33 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "17:34 madminer.ml          INFO    Starting score evaluation\n",
      "17:35 madminer.ml          INFO    Starting score evaluation\n",
      "17:35 madminer.ml          INFO    Starting score evaluation\n",
      "17:35 madminer.ml          INFO    Starting score evaluation\n",
      "17:35 madminer.ml          INFO    Starting score evaluation\n",
      "17:35 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "17:35 madminer.ml          INFO    Starting score evaluation\n",
      "17:35 madminer.ml          INFO    Starting score evaluation\n",
      "17:35 madminer.ml          INFO    Starting score evaluation\n",
      "17:35 madminer.ml          INFO    Starting score evaluation\n",
      "17:35 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_e_ptw, w_in_histo_wmh_e_ptw, fi_histo_rate_wmh_e_ptw, fi_histo_wmh_e_ptw = fisher_wmh_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_w',\n",
    "    nbins=20,\n",
    "    histrange=(0,1000),\n",
    "    model_file=model_path_wmh_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_ptw_outfile = './fisher_info/dists/fi_histo_ptw.npz'\n",
    "\n",
    "fi_histo_ptw_list = [\n",
    "    bins_histo_wph_mu_ptw,\n",
    "    w_in_histo_wph_mu_ptw,\n",
    "    fi_histo_rate_wph_mu_ptw,\n",
    "    fi_histo_wph_mu_ptw,\n",
    "    bins_histo_wph_e_ptw,\n",
    "    w_in_histo_wph_e_ptw,\n",
    "    fi_histo_rate_wph_e_ptw,\n",
    "    fi_histo_wph_e_ptw,\n",
    "    bins_histo_wmh_mu_ptw,\n",
    "    w_in_histo_wmh_mu_ptw,\n",
    "    fi_histo_rate_wmh_mu_ptw,\n",
    "    fi_histo_wmh_mu_ptw,\n",
    "    bins_histo_wmh_e_ptw,\n",
    "    w_in_histo_wmh_e_ptw,\n",
    "    fi_histo_rate_wmh_e_ptw,\n",
    "    fi_histo_wmh_e_ptw,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.savez(fi_histo_ptw_outfile, fi_histo_ptw_list, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_rate_ptw = [ fi_histo_rate_wph_mu_ptw[i]\n",
    "                     + fi_histo_rate_wph_e_ptw[i] \n",
    "                     + fi_histo_rate_wmh_mu_ptw[i] \n",
    "                     + fi_histo_rate_wmh_e_ptw[i] \n",
    "                     for i in range(0, len(fi_histo_rate_wph_mu_ptw)) ]\n",
    "\n",
    "fi_histo_ptw = [ fi_histo_wph_mu_ptw[i]\n",
    "                     + fi_histo_wph_e_ptw[i] \n",
    "                     + fi_histo_wmh_mu_ptw[i] \n",
    "                     + fi_histo_wmh_e_ptw[i] \n",
    "                     for i in range(0, len(fi_histo_wph_mu_ptw)) ]\n",
    "\n",
    "fi_histo_rate_ptw_rot = [ rotate(fi_histo_rate_ptw[i]) for i in range(0, len(fi_histo_rate_ptw)) ]\n",
    "fi_histo_ptw_rot = [ rotate(fi_histo_ptw[i]) for i in range(0, len(fi_histo_ptw)) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $m_{T,\\mathrm{tot}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17:39 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "17:39 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "17:39 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "17:39 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "17:39 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "17:39 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "17:45 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "17:45 madminer.ml          INFO    Starting score evaluation\n",
      "17:45 madminer.ml          INFO    Starting score evaluation\n",
      "17:45 madminer.ml          INFO    Starting score evaluation\n",
      "17:45 madminer.ml          INFO    Starting score evaluation\n",
      "17:45 madminer.ml          INFO    Starting score evaluation\n",
      "17:45 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "17:45 madminer.ml          INFO    Starting score evaluation\n",
      "17:45 madminer.ml          INFO    Starting score evaluation\n",
      "17:45 madminer.ml          INFO    Starting score evaluation\n",
      "17:45 madminer.ml          INFO    Starting score evaluation\n",
      "17:45 madminer.ml          INFO    Starting score evaluation\n",
      "17:45 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "17:45 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:46 madminer.ml          INFO    Starting score evaluation\n",
      "17:47 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "17:47 madminer.ml          INFO    Starting score evaluation\n",
      "17:47 madminer.ml          INFO    Starting score evaluation\n",
      "17:47 madminer.ml          INFO    Starting score evaluation\n",
      "17:47 madminer.ml          INFO    Starting score evaluation\n",
      "17:47 madminer.ml          INFO    Starting score evaluation\n",
      "17:47 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "17:47 madminer.ml          INFO    Starting score evaluation\n",
      "17:47 madminer.ml          INFO    Starting score evaluation\n",
      "17:47 madminer.ml          INFO    Starting score evaluation\n",
      "17:47 madminer.ml          INFO    Starting score evaluation\n",
      "17:47 madminer.ml          INFO    Starting score evaluation\n",
      "17:47 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "17:47 madminer.ml          INFO    Starting score evaluation\n",
      "17:47 madminer.ml          INFO    Starting score evaluation\n",
      "17:47 madminer.ml          INFO    Starting score evaluation\n",
      "17:47 madminer.ml          INFO    Starting score evaluation\n",
      "17:47 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_mu_mttot, w_in_histo_wph_mu_mttot, fi_histo_rate_wph_mu_mttot, fi_histo_wph_mu_mttot = fisher_wph_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='mt_tot',\n",
    "    nbins=20,\n",
    "    histrange=(200,2200),\n",
    "    model_file=model_path_wph_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17:47 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "17:47 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "17:47 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "17:47 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "17:47 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "17:47 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "17:53 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "17:53 madminer.ml          INFO    Starting score evaluation\n",
      "17:53 madminer.ml          INFO    Starting score evaluation\n",
      "17:53 madminer.ml          INFO    Starting score evaluation\n",
      "17:53 madminer.ml          INFO    Starting score evaluation\n",
      "17:53 madminer.ml          INFO    Starting score evaluation\n",
      "17:53 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "17:53 madminer.ml          INFO    Starting score evaluation\n",
      "17:53 madminer.ml          INFO    Starting score evaluation\n",
      "17:53 madminer.ml          INFO    Starting score evaluation\n",
      "17:53 madminer.ml          INFO    Starting score evaluation\n",
      "17:53 madminer.ml          INFO    Starting score evaluation\n",
      "17:53 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:54 madminer.ml          INFO    Starting score evaluation\n",
      "17:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "17:55 madminer.ml          INFO    Starting score evaluation\n",
      "17:55 madminer.ml          INFO    Starting score evaluation\n",
      "17:55 madminer.ml          INFO    Starting score evaluation\n",
      "17:55 madminer.ml          INFO    Starting score evaluation\n",
      "17:55 madminer.ml          INFO    Starting score evaluation\n",
      "17:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "17:55 madminer.ml          INFO    Starting score evaluation\n",
      "17:55 madminer.ml          INFO    Starting score evaluation\n",
      "17:55 madminer.ml          INFO    Starting score evaluation\n",
      "17:55 madminer.ml          INFO    Starting score evaluation\n",
      "17:55 madminer.ml          INFO    Starting score evaluation\n",
      "17:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "17:55 madminer.ml          INFO    Starting score evaluation\n",
      "17:55 madminer.ml          INFO    Starting score evaluation\n",
      "17:55 madminer.ml          INFO    Starting score evaluation\n",
      "17:55 madminer.ml          INFO    Starting score evaluation\n",
      "17:55 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_e_mttot, w_in_histo_wph_e_mttot, fi_histo_rate_wph_e_mttot, fi_histo_wph_e_mttot = fisher_wph_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='mt_tot',\n",
    "    nbins=20,\n",
    "    histrange=(200,2200),\n",
    "    model_file=model_path_wph_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17:55 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "17:55 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "17:55 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "17:55 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "17:55 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "17:55 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "18:01 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "18:01 madminer.ml          INFO    Starting score evaluation\n",
      "18:01 madminer.ml          INFO    Starting score evaluation\n",
      "18:01 madminer.ml          INFO    Starting score evaluation\n",
      "18:01 madminer.ml          INFO    Starting score evaluation\n",
      "18:01 madminer.ml          INFO    Starting score evaluation\n",
      "18:01 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "18:01 madminer.ml          INFO    Starting score evaluation\n",
      "18:01 madminer.ml          INFO    Starting score evaluation\n",
      "18:01 madminer.ml          INFO    Starting score evaluation\n",
      "18:01 madminer.ml          INFO    Starting score evaluation\n",
      "18:01 madminer.ml          INFO    Starting score evaluation\n",
      "18:01 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "18:01 madminer.ml          INFO    Starting score evaluation\n",
      "18:01 madminer.ml          INFO    Starting score evaluation\n",
      "18:01 madminer.ml          INFO    Starting score evaluation\n",
      "18:01 madminer.ml          INFO    Starting score evaluation\n",
      "18:01 madminer.ml          INFO    Starting score evaluation\n",
      "18:01 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:02 madminer.ml          INFO    Starting score evaluation\n",
      "18:03 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "18:03 madminer.ml          INFO    Starting score evaluation\n",
      "18:03 madminer.ml          INFO    Starting score evaluation\n",
      "18:03 madminer.ml          INFO    Starting score evaluation\n",
      "18:03 madminer.ml          INFO    Starting score evaluation\n",
      "18:03 madminer.ml          INFO    Starting score evaluation\n",
      "18:03 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "18:03 madminer.ml          INFO    Starting score evaluation\n",
      "18:03 madminer.ml          INFO    Starting score evaluation\n",
      "18:03 madminer.ml          INFO    Starting score evaluation\n",
      "18:03 madminer.ml          INFO    Starting score evaluation\n",
      "18:03 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_mu_mttot, w_in_histo_wmh_mu_mttot, fi_histo_rate_wmh_mu_mttot, fi_histo_wmh_mu_mttot = fisher_wmh_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='mt_tot',\n",
    "    nbins=20,\n",
    "    histrange=(200,2200),\n",
    "    model_file=model_path_wmh_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "18:03 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "18:03 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "18:03 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "18:03 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "18:03 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "18:03 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "18:12 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "18:12 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.ml          INFO    Starting score evaluation\n",
      "18:13 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n",
      "18:14 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_e_mttot, w_in_histo_wmh_e_mttot, fi_histo_rate_wmh_e_mttot, fi_histo_wmh_e_mttot = fisher_wmh_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='mt_tot',\n",
    "    nbins=20,\n",
    "    histrange=(200,2200),\n",
    "    model_file=model_path_wmh_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_histo_mttot_outfile = './fisher_info/dists/fi_histo_mttot.npz'\n",
    "\n",
    "fi_histo_mttot_list = [\n",
    "    bins_histo_wph_mu_mttot,\n",
    "    w_in_histo_wph_mu_mttot,\n",
    "    fi_histo_rate_wph_mu_mttot,\n",
    "    fi_histo_wph_mu_mttot,\n",
    "    bins_histo_wph_e_mttot,\n",
    "    w_in_histo_wph_e_mttot,\n",
    "    fi_histo_rate_wph_e_mttot,\n",
    "    fi_histo_wph_e_mttot,\n",
    "    bins_histo_wmh_mu_mttot,\n",
    "    w_in_histo_wmh_mu_mttot,\n",
    "    fi_histo_rate_wmh_mu_mttot,\n",
    "    fi_histo_wmh_mu_mttot,\n",
    "    bins_histo_wmh_e_mttot,\n",
    "    w_in_histo_wmh_e_mttot,\n",
    "    fi_histo_rate_wmh_e_mttot,\n",
    "    fi_histo_wmh_e_mttot,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savez(fi_histo_mttot_outfile, fi_histo_mttot_list, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_histo_rate_mttot = [ fi_histo_rate_wph_mu_mttot[i]\n",
    "                     + fi_histo_rate_wph_e_mttot[i] \n",
    "                     + fi_histo_rate_wmh_mu_mttot[i] \n",
    "                     + fi_histo_rate_wmh_e_mttot[i] \n",
    "                     for i in range(0, len(fi_histo_rate_wph_mu_mttot)) ]\n",
    "\n",
    "fi_histo_mttot = [ fi_histo_wph_mu_mttot[i]\n",
    "                     + fi_histo_wph_e_mttot[i] \n",
    "                     + fi_histo_wmh_mu_mttot[i] \n",
    "                     + fi_histo_wmh_e_mttot[i] \n",
    "                     for i in range(0, len(fi_histo_wph_mu_mttot)) ]\n",
    "\n",
    "fi_histo_rate_mttot_rot = [ rotate(fi_histo_rate_mttot[i]) for i in range(0, len(fi_histo_rate_mttot)) ]\n",
    "fi_histo_mttot_rot = [ rotate(fi_histo_mttot[i]) for i in range(0, len(fi_histo_mttot)) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### $m_{T,W}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "23:47 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "23:47 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "23:47 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "23:47 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "23:47 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "23:47 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "23:52 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "23:52 madminer.ml          INFO    Starting score evaluation\n",
      "23:52 madminer.ml          INFO    Starting score evaluation\n",
      "23:52 madminer.ml          INFO    Starting score evaluation\n",
      "23:52 madminer.ml          INFO    Starting score evaluation\n",
      "23:52 madminer.ml          INFO    Starting score evaluation\n",
      "23:52 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "23:52 madminer.ml          INFO    Starting score evaluation\n",
      "23:52 madminer.ml          INFO    Starting score evaluation\n",
      "23:52 madminer.ml          INFO    Starting score evaluation\n",
      "23:52 madminer.ml          INFO    Starting score evaluation\n",
      "23:52 madminer.ml          INFO    Starting score evaluation\n",
      "23:52 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "23:52 madminer.ml          INFO    Starting score evaluation\n",
      "23:52 madminer.ml          INFO    Starting score evaluation\n",
      "23:52 madminer.ml          INFO    Starting score evaluation\n",
      "23:52 madminer.ml          INFO    Starting score evaluation\n",
      "23:52 madminer.ml          INFO    Starting score evaluation\n",
      "23:52 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:53 madminer.ml          INFO    Starting score evaluation\n",
      "23:54 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "23:54 madminer.ml          INFO    Starting score evaluation\n",
      "23:54 madminer.ml          INFO    Starting score evaluation\n",
      "23:54 madminer.ml          INFO    Starting score evaluation\n",
      "23:54 madminer.ml          INFO    Starting score evaluation\n",
      "23:54 madminer.ml          INFO    Starting score evaluation\n",
      "23:54 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "23:54 madminer.ml          INFO    Starting score evaluation\n",
      "23:54 madminer.ml          INFO    Starting score evaluation\n",
      "23:54 madminer.ml          INFO    Starting score evaluation\n",
      "23:54 madminer.ml          INFO    Starting score evaluation\n",
      "23:54 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_mu_mtlv, w_in_histo_wph_mu_mtlv, fi_histo_rate_wph_mu_mtlv, fi_histo_wph_mu_mtlv = fisher_wph_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='mt_lv',\n",
    "    nbins=20,\n",
    "    histrange=(0,1000),\n",
    "    model_file=model_path_wph_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "23:54 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "23:54 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "23:54 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "23:54 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "23:54 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "23:54 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "23:59 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "23:59 madminer.ml          INFO    Starting score evaluation\n",
      "23:59 madminer.ml          INFO    Starting score evaluation\n",
      "23:59 madminer.ml          INFO    Starting score evaluation\n",
      "23:59 madminer.ml          INFO    Starting score evaluation\n",
      "23:59 madminer.ml          INFO    Starting score evaluation\n",
      "23:59 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "23:59 madminer.ml          INFO    Starting score evaluation\n",
      "23:59 madminer.ml          INFO    Starting score evaluation\n",
      "23:59 madminer.ml          INFO    Starting score evaluation\n",
      "23:59 madminer.ml          INFO    Starting score evaluation\n",
      "23:59 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:00 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n",
      "00:01 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_e_mtlv, w_in_histo_wph_e_mtlv, fi_histo_rate_wph_e_mtlv, fi_histo_wph_e_mtlv = fisher_wph_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='mt_lv',\n",
    "    nbins=20,\n",
    "    histrange=(0,1000),\n",
    "    model_file=model_path_wph_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "00:01 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "00:01 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "00:01 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "00:01 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "00:01 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "00:01 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "00:06 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "00:06 madminer.ml          INFO    Starting score evaluation\n",
      "00:06 madminer.ml          INFO    Starting score evaluation\n",
      "00:06 madminer.ml          INFO    Starting score evaluation\n",
      "00:06 madminer.ml          INFO    Starting score evaluation\n",
      "00:06 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:07 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "00:07 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n",
      "00:08 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_mu_mtlv, w_in_histo_wmh_mu_mtlv, fi_histo_rate_wmh_mu_mtlv, fi_histo_wmh_mu_mtlv = fisher_wmh_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='mt_lv',\n",
    "    nbins=20,\n",
    "    histrange=(0,1000),\n",
    "    model_file=model_path_wmh_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "00:08 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "00:08 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "00:08 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "00:08 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "00:08 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "00:08 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "00:14 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.ml          INFO    Starting score evaluation\n",
      "00:14 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:15 madminer.ml          INFO    Starting score evaluation\n",
      "00:16 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "00:16 madminer.ml          INFO    Starting score evaluation\n",
      "00:16 madminer.ml          INFO    Starting score evaluation\n",
      "00:16 madminer.ml          INFO    Starting score evaluation\n",
      "00:16 madminer.ml          INFO    Starting score evaluation\n",
      "00:16 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_e_mtlv, w_in_histo_wmh_e_mtlv, fi_histo_rate_wmh_e_mtlv, fi_histo_wmh_e_mtlv = fisher_wmh_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='mt_lv',\n",
    "    nbins=20,\n",
    "    histrange=(0,1000),\n",
    "    model_file=model_path_wmh_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_mtlv_outfile = './fisher_info/dists/fi_histo_mtlv.npz'\n",
    "\n",
    "fi_histo_mtlv_list = [\n",
    "    bins_histo_wph_mu_mtlv,\n",
    "    w_in_histo_wph_mu_mtlv,\n",
    "    fi_histo_rate_wph_mu_mtlv,\n",
    "    fi_histo_wph_mu_mtlv,\n",
    "    bins_histo_wph_e_mtlv,\n",
    "    w_in_histo_wph_e_mtlv,\n",
    "    fi_histo_rate_wph_e_mtlv,\n",
    "    fi_histo_wph_e_mtlv,\n",
    "    bins_histo_wmh_mu_mtlv,\n",
    "    w_in_histo_wmh_mu_mtlv,\n",
    "    fi_histo_rate_wmh_mu_mtlv,\n",
    "    fi_histo_wmh_mu_mtlv,\n",
    "    bins_histo_wmh_e_mtlv,\n",
    "    w_in_histo_wmh_e_mtlv,\n",
    "    fi_histo_rate_wmh_e_mtlv,\n",
    "    fi_histo_wmh_e_mtlv,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.savez(fi_histo_mtlv_outfile, fi_histo_mtlv_list, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_rate_mtlv = [ fi_histo_rate_wph_mu_mtlv[i]\n",
    "                     + fi_histo_rate_wph_e_mtlv[i] \n",
    "                     + fi_histo_rate_wmh_mu_mtlv[i] \n",
    "                     + fi_histo_rate_wmh_e_mtlv[i] \n",
    "                     for i in range(0, len(fi_histo_rate_wph_mu_mtlv)) ]\n",
    "\n",
    "fi_histo_mtlv = [ fi_histo_wph_mu_mtlv[i]\n",
    "                     + fi_histo_wph_e_mtlv[i] \n",
    "                     + fi_histo_wmh_mu_mtlv[i] \n",
    "                     + fi_histo_wmh_e_mtlv[i] \n",
    "                     for i in range(0, len(fi_histo_wph_mu_mtlv)) ]\n",
    "\n",
    "fi_histo_rate_mtlv_rot = [ rotate(fi_histo_rate_mtlv[i]) for i in range(0, len(fi_histo_rate_mtlv)) ]\n",
    "fi_histo_mtlv_rot = [ rotate(fi_histo_mtlv[i]) for i in range(0, len(fi_histo_mtlv)) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### $\\eta_H$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "21:49 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "21:49 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "21:49 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "21:49 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "21:49 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "21:49 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "21:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:55 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.ml          INFO    Starting score evaluation\n",
      "21:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "21:57 madminer.ml          INFO    Starting score evaluation\n",
      "21:57 madminer.ml          INFO    Starting score evaluation\n",
      "21:57 madminer.ml          INFO    Starting score evaluation\n",
      "21:57 madminer.ml          INFO    Starting score evaluation\n",
      "21:57 madminer.ml          INFO    Starting score evaluation\n",
      "21:57 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "21:57 madminer.ml          INFO    Starting score evaluation\n",
      "21:57 madminer.ml          INFO    Starting score evaluation\n",
      "21:57 madminer.ml          INFO    Starting score evaluation\n",
      "21:57 madminer.ml          INFO    Starting score evaluation\n",
      "21:57 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_mu_etah, w_in_histo_wph_mu_etah, fi_histo_rate_wph_mu_etah, fi_histo_wph_mu_etah = fisher_wph_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='eta_h',\n",
    "    nbins=20,\n",
    "    histrange=(-5.,5.),\n",
    "    model_file=model_path_wph_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "21:57 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "21:57 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "21:57 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "21:57 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "21:57 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "21:57 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "22:02 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "22:02 madminer.ml          INFO    Starting score evaluation\n",
      "22:02 madminer.ml          INFO    Starting score evaluation\n",
      "22:02 madminer.ml          INFO    Starting score evaluation\n",
      "22:02 madminer.ml          INFO    Starting score evaluation\n",
      "22:02 madminer.ml          INFO    Starting score evaluation\n",
      "22:02 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.ml          INFO    Starting score evaluation\n",
      "22:03 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n",
      "22:04 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_e_etah, w_in_histo_wph_e_etah, fi_histo_rate_wph_e_etah, fi_histo_wph_e_etah = fisher_wph_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='eta_h',\n",
    "    nbins=20,\n",
    "    histrange=(-5.,5.),\n",
    "    model_file=model_path_wph_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "22:04 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "22:04 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "22:04 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "22:04 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "22:04 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "22:04 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "22:10 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "22:10 madminer.ml          INFO    Starting score evaluation\n",
      "22:10 madminer.ml          INFO    Starting score evaluation\n",
      "22:10 madminer.ml          INFO    Starting score evaluation\n",
      "22:10 madminer.ml          INFO    Starting score evaluation\n",
      "22:10 madminer.ml          INFO    Starting score evaluation\n",
      "22:10 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "22:10 madminer.ml          INFO    Starting score evaluation\n",
      "22:10 madminer.ml          INFO    Starting score evaluation\n",
      "22:10 madminer.ml          INFO    Starting score evaluation\n",
      "22:10 madminer.ml          INFO    Starting score evaluation\n",
      "22:10 madminer.ml          INFO    Starting score evaluation\n",
      "22:10 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "22:10 madminer.ml          INFO    Starting score evaluation\n",
      "22:10 madminer.ml          INFO    Starting score evaluation\n",
      "22:10 madminer.ml          INFO    Starting score evaluation\n",
      "22:10 madminer.ml          INFO    Starting score evaluation\n",
      "22:10 madminer.ml          INFO    Starting score evaluation\n",
      "22:10 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:11 madminer.ml          INFO    Starting score evaluation\n",
      "22:12 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "22:12 madminer.ml          INFO    Starting score evaluation\n",
      "22:12 madminer.ml          INFO    Starting score evaluation\n",
      "22:12 madminer.ml          INFO    Starting score evaluation\n",
      "22:12 madminer.ml          INFO    Starting score evaluation\n",
      "22:12 madminer.ml          INFO    Starting score evaluation\n",
      "22:12 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "22:12 madminer.ml          INFO    Starting score evaluation\n",
      "22:12 madminer.ml          INFO    Starting score evaluation\n",
      "22:12 madminer.ml          INFO    Starting score evaluation\n",
      "22:12 madminer.ml          INFO    Starting score evaluation\n",
      "22:12 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_mu_etah, w_in_histo_wmh_mu_etah, fi_histo_rate_wmh_mu_etah, fi_histo_wmh_mu_etah = fisher_wmh_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='eta_h',\n",
    "    nbins=20,\n",
    "    histrange=(-5.,5.),\n",
    "    model_file=model_path_wmh_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "22:12 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "22:12 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "22:12 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "22:12 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "22:12 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "22:12 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "22:17 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "22:17 madminer.ml          INFO    Starting score evaluation\n",
      "22:17 madminer.ml          INFO    Starting score evaluation\n",
      "22:17 madminer.ml          INFO    Starting score evaluation\n",
      "22:17 madminer.ml          INFO    Starting score evaluation\n",
      "22:17 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:18 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n",
      "22:19 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_e_etah, w_in_histo_wmh_e_etah, fi_histo_rate_wmh_e_etah, fi_histo_wmh_e_etah = fisher_wmh_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='eta_h',\n",
    "    nbins=20,\n",
    "    histrange=(-5.,5.),\n",
    "    model_file=model_path_wmh_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_etah_outfile = './fisher_info/dists/fi_histo_etah.npz'\n",
    "\n",
    "fi_histo_etah_list = [\n",
    "    bins_histo_wph_mu_etah,\n",
    "    w_in_histo_wph_mu_etah,\n",
    "    fi_histo_rate_wph_mu_etah,\n",
    "    fi_histo_wph_mu_etah,\n",
    "    bins_histo_wph_e_etah,\n",
    "    w_in_histo_wph_e_etah,\n",
    "    fi_histo_rate_wph_e_etah,\n",
    "    fi_histo_wph_e_etah,\n",
    "    bins_histo_wmh_mu_etah,\n",
    "    w_in_histo_wmh_mu_etah,\n",
    "    fi_histo_rate_wmh_mu_etah,\n",
    "    fi_histo_wmh_mu_etah,\n",
    "    bins_histo_wmh_e_etah,\n",
    "    w_in_histo_wmh_e_etah,\n",
    "    fi_histo_rate_wmh_e_etah,\n",
    "    fi_histo_wmh_e_etah,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.savez(fi_histo_etah_outfile, fi_histo_etah_list, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_rate_etah = [ fi_histo_rate_wph_mu_etah[i]\n",
    "                     + fi_histo_rate_wph_e_etah[i] \n",
    "                     + fi_histo_rate_wmh_mu_etah[i] \n",
    "                     + fi_histo_rate_wmh_e_etah[i] \n",
    "                     for i in range(0, len(fi_histo_rate_wph_mu_etah)) ]\n",
    "\n",
    "fi_histo_etah = [ fi_histo_wph_mu_etah[i]\n",
    "                     + fi_histo_wph_e_etah[i] \n",
    "                     + fi_histo_wmh_mu_etah[i] \n",
    "                     + fi_histo_wmh_e_etah[i] \n",
    "                     for i in range(0, len(fi_histo_wph_mu_etah)) ]\n",
    "\n",
    "fi_histo_rate_etah_rot = [ rotate(fi_histo_rate_etah[i]) for i in range(0, len(fi_histo_rate_etah)) ]\n",
    "fi_histo_etah_rot = [ rotate(fi_histo_etah[i]) for i in range(0, len(fi_histo_etah)) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### $\\Delta \\phi_{\\ell \\nu}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "22:19 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "22:19 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "22:19 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "22:19 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "22:19 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "22:19 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "22:25 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.ml          INFO    Starting score evaluation\n",
      "22:25 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:26 madminer.ml          INFO    Starting score evaluation\n",
      "22:27 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "22:27 madminer.ml          INFO    Starting score evaluation\n",
      "22:27 madminer.ml          INFO    Starting score evaluation\n",
      "22:27 madminer.ml          INFO    Starting score evaluation\n",
      "22:27 madminer.ml          INFO    Starting score evaluation\n",
      "22:27 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_mu_dphilv, w_in_histo_wph_mu_dphilv, fi_histo_rate_wph_mu_dphilv, fi_histo_wph_mu_dphilv = fisher_wph_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='dphi_lv',\n",
    "    nbins=20,\n",
    "    histrange=(-3.2,3.2),\n",
    "    model_file=model_path_wph_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "22:27 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "22:27 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "22:27 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "22:27 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "22:27 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "22:27 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "22:32 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "22:32 madminer.ml          INFO    Starting score evaluation\n",
      "22:32 madminer.ml          INFO    Starting score evaluation\n",
      "22:32 madminer.ml          INFO    Starting score evaluation\n",
      "22:32 madminer.ml          INFO    Starting score evaluation\n",
      "22:32 madminer.ml          INFO    Starting score evaluation\n",
      "22:32 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "22:32 madminer.ml          INFO    Starting score evaluation\n",
      "22:32 madminer.ml          INFO    Starting score evaluation\n",
      "22:32 madminer.ml          INFO    Starting score evaluation\n",
      "22:32 madminer.ml          INFO    Starting score evaluation\n",
      "22:32 madminer.ml          INFO    Starting score evaluation\n",
      "22:32 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "22:32 madminer.ml          INFO    Starting score evaluation\n",
      "22:32 madminer.ml          INFO    Starting score evaluation\n",
      "22:32 madminer.ml          INFO    Starting score evaluation\n",
      "22:32 madminer.ml          INFO    Starting score evaluation\n",
      "22:32 madminer.ml          INFO    Starting score evaluation\n",
      "22:32 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:33 madminer.ml          INFO    Starting score evaluation\n",
      "22:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "22:34 madminer.ml          INFO    Starting score evaluation\n",
      "22:34 madminer.ml          INFO    Starting score evaluation\n",
      "22:34 madminer.ml          INFO    Starting score evaluation\n",
      "22:34 madminer.ml          INFO    Starting score evaluation\n",
      "22:34 madminer.ml          INFO    Starting score evaluation\n",
      "22:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "22:34 madminer.ml          INFO    Starting score evaluation\n",
      "22:34 madminer.ml          INFO    Starting score evaluation\n",
      "22:34 madminer.ml          INFO    Starting score evaluation\n",
      "22:34 madminer.ml          INFO    Starting score evaluation\n",
      "22:34 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_e_dphilv, w_in_histo_wph_e_dphilv, fi_histo_rate_wph_e_dphilv, fi_histo_wph_e_dphilv = fisher_wph_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='dphi_lv',\n",
    "    nbins=20,\n",
    "    histrange=(-3.2,3.2),\n",
    "    model_file=model_path_wph_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "22:34 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "22:34 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "22:34 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "22:34 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "22:34 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "22:34 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "22:39 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "22:39 madminer.ml          INFO    Starting score evaluation\n",
      "22:39 madminer.ml          INFO    Starting score evaluation\n",
      "22:39 madminer.ml          INFO    Starting score evaluation\n",
      "22:39 madminer.ml          INFO    Starting score evaluation\n",
      "22:39 madminer.ml          INFO    Starting score evaluation\n",
      "22:39 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "22:39 madminer.ml          INFO    Starting score evaluation\n",
      "22:39 madminer.ml          INFO    Starting score evaluation\n",
      "22:39 madminer.ml          INFO    Starting score evaluation\n",
      "22:39 madminer.ml          INFO    Starting score evaluation\n",
      "22:39 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:40 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "22:40 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n",
      "22:41 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_mu_dphilv, w_in_histo_wmh_mu_dphilv, fi_histo_rate_wmh_mu_dphilv, fi_histo_wmh_mu_dphilv = fisher_wmh_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='dphi_lv',\n",
    "    nbins=20,\n",
    "    histrange=(-3.2,3.2),\n",
    "    model_file=model_path_wmh_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "22:41 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "22:41 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "22:41 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "22:41 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "22:41 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "22:41 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "22:46 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "22:46 madminer.ml          INFO    Starting score evaluation\n",
      "22:46 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.ml          INFO    Starting score evaluation\n",
      "22:47 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n",
      "22:48 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_e_dphilv, w_in_histo_wmh_e_dphilv, fi_histo_rate_wmh_e_dphilv, fi_histo_wmh_e_dphilv = fisher_wmh_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='dphi_lv',\n",
    "    nbins=20,\n",
    "    histrange=(-3.2,3.2),\n",
    "    model_file=model_path_wmh_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_dphilv_outfile = './fisher_info/dists/fi_histo_dphilv.npz'\n",
    "\n",
    "fi_histo_dphilv_list = [\n",
    "    bins_histo_wph_mu_dphilv,\n",
    "    w_in_histo_wph_mu_dphilv,\n",
    "    fi_histo_rate_wph_mu_dphilv,\n",
    "    fi_histo_wph_mu_dphilv,\n",
    "    bins_histo_wph_e_dphilv,\n",
    "    w_in_histo_wph_e_dphilv,\n",
    "    fi_histo_rate_wph_e_dphilv,\n",
    "    fi_histo_wph_e_dphilv,\n",
    "    bins_histo_wmh_mu_dphilv,\n",
    "    w_in_histo_wmh_mu_dphilv,\n",
    "    fi_histo_rate_wmh_mu_dphilv,\n",
    "    fi_histo_wmh_mu_dphilv,\n",
    "    bins_histo_wmh_e_dphilv,\n",
    "    w_in_histo_wmh_e_dphilv,\n",
    "    fi_histo_rate_wmh_e_dphilv,\n",
    "    fi_histo_wmh_e_dphilv,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.savez(fi_histo_dphilv_outfile, fi_histo_dphilv_list, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_rate_dphilv = [ fi_histo_rate_wph_mu_dphilv[i]\n",
    "                     + fi_histo_rate_wph_e_dphilv[i] \n",
    "                     + fi_histo_rate_wmh_mu_dphilv[i] \n",
    "                     + fi_histo_rate_wmh_e_dphilv[i] \n",
    "                     for i in range(0, len(fi_histo_rate_wph_mu_dphilv)) ]\n",
    "\n",
    "fi_histo_dphilv = [ fi_histo_wph_mu_dphilv[i]\n",
    "                     + fi_histo_wph_e_dphilv[i] \n",
    "                     + fi_histo_wmh_mu_dphilv[i] \n",
    "                     + fi_histo_wmh_e_dphilv[i] \n",
    "                     for i in range(0, len(fi_histo_wph_mu_dphilv)) ]\n",
    "\n",
    "fi_histo_rate_dphilv_rot = [ rotate(fi_histo_rate_dphilv[i]) for i in range(0, len(fi_histo_rate_dphilv)) ]\n",
    "fi_histo_dphilv_rot = [ rotate(fi_histo_dphilv[i]) for i in range(0, len(fi_histo_dphilv)) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### $\\Delta R_{\\ell b_1}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "22:48 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "22:48 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "22:48 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "22:48 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "22:48 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "22:48 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "22:54 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:54 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:55 madminer.ml          INFO    Starting score evaluation\n",
      "22:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "22:56 madminer.ml          INFO    Starting score evaluation\n",
      "22:56 madminer.ml          INFO    Starting score evaluation\n",
      "22:56 madminer.ml          INFO    Starting score evaluation\n",
      "22:56 madminer.ml          INFO    Starting score evaluation\n",
      "22:56 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_mu_drlb1, w_in_histo_wph_mu_drlb1, fi_histo_rate_wph_mu_drlb1, fi_histo_wph_mu_drlb1 = fisher_wph_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='dR_lb1',\n",
    "    nbins=20,\n",
    "    histrange=(0.,5.),\n",
    "    model_file=model_path_wph_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "22:56 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "22:56 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "22:56 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "22:56 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "22:56 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "22:56 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "23:01 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "23:01 madminer.ml          INFO    Starting score evaluation\n",
      "23:01 madminer.ml          INFO    Starting score evaluation\n",
      "23:01 madminer.ml          INFO    Starting score evaluation\n",
      "23:01 madminer.ml          INFO    Starting score evaluation\n",
      "23:01 madminer.ml          INFO    Starting score evaluation\n",
      "23:01 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "23:01 madminer.ml          INFO    Starting score evaluation\n",
      "23:01 madminer.ml          INFO    Starting score evaluation\n",
      "23:01 madminer.ml          INFO    Starting score evaluation\n",
      "23:01 madminer.ml          INFO    Starting score evaluation\n",
      "23:01 madminer.ml          INFO    Starting score evaluation\n",
      "23:01 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "23:01 madminer.ml          INFO    Starting score evaluation\n",
      "23:01 madminer.ml          INFO    Starting score evaluation\n",
      "23:01 madminer.ml          INFO    Starting score evaluation\n",
      "23:01 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:02 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "23:02 madminer.ml          INFO    Starting score evaluation\n",
      "23:03 madminer.ml          INFO    Starting score evaluation\n",
      "23:03 madminer.ml          INFO    Starting score evaluation\n",
      "23:03 madminer.ml          INFO    Starting score evaluation\n",
      "23:03 madminer.ml          INFO    Starting score evaluation\n",
      "23:03 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "23:03 madminer.ml          INFO    Starting score evaluation\n",
      "23:03 madminer.ml          INFO    Starting score evaluation\n",
      "23:03 madminer.ml          INFO    Starting score evaluation\n",
      "23:03 madminer.ml          INFO    Starting score evaluation\n",
      "23:03 madminer.ml          INFO    Starting score evaluation\n",
      "23:03 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "23:03 madminer.ml          INFO    Starting score evaluation\n",
      "23:03 madminer.ml          INFO    Starting score evaluation\n",
      "23:03 madminer.ml          INFO    Starting score evaluation\n",
      "23:03 madminer.ml          INFO    Starting score evaluation\n",
      "23:03 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_e_drlb1, w_in_histo_wph_e_drlb1, fi_histo_rate_wph_e_drlb1, fi_histo_wph_e_drlb1 = fisher_wph_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='dR_lb1',\n",
    "    nbins=20,\n",
    "    histrange=(0.,5.),\n",
    "    model_file=model_path_wph_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "23:03 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "23:03 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "23:03 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "23:03 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "23:03 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "23:03 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "23:08 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "23:08 madminer.ml          INFO    Starting score evaluation\n",
      "23:08 madminer.ml          INFO    Starting score evaluation\n",
      "23:08 madminer.ml          INFO    Starting score evaluation\n",
      "23:08 madminer.ml          INFO    Starting score evaluation\n",
      "23:08 madminer.ml          INFO    Starting score evaluation\n",
      "23:08 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.ml          INFO    Starting score evaluation\n",
      "23:09 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n",
      "23:10 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_mu_drlb1, w_in_histo_wmh_mu_drlb1, fi_histo_rate_wmh_mu_drlb1, fi_histo_wmh_mu_drlb1 = fisher_wmh_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='dR_lb1',\n",
    "    nbins=20,\n",
    "    histrange=(0.,5.),\n",
    "    model_file=model_path_wmh_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "23:10 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "23:10 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "23:10 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "23:10 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "23:10 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "23:10 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "23:15 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:16 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:17 madminer.ml          INFO    Starting score evaluation\n",
      "23:18 madminer.ml          INFO    Starting score evaluation\n",
      "23:18 madminer.ml          INFO    Starting score evaluation\n",
      "23:18 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_e_drlb1, w_in_histo_wmh_e_drlb1, fi_histo_rate_wmh_e_drlb1, fi_histo_wmh_e_drlb1 = fisher_wmh_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='dR_lb1',\n",
    "    nbins=20,\n",
    "    histrange=(0.,5.),\n",
    "    model_file=model_path_wmh_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_drlb1_outfile = './fisher_info/dists/fi_histo_drlb1.npz'\n",
    "\n",
    "fi_histo_drlb1_list = [\n",
    "    bins_histo_wph_mu_drlb1,\n",
    "    w_in_histo_wph_mu_drlb1,\n",
    "    fi_histo_rate_wph_mu_drlb1,\n",
    "    fi_histo_wph_mu_drlb1,\n",
    "    bins_histo_wph_e_drlb1,\n",
    "    w_in_histo_wph_e_drlb1,\n",
    "    fi_histo_rate_wph_e_drlb1,\n",
    "    fi_histo_wph_e_drlb1,\n",
    "    bins_histo_wmh_mu_drlb1,\n",
    "    w_in_histo_wmh_mu_drlb1,\n",
    "    fi_histo_rate_wmh_mu_drlb1,\n",
    "    fi_histo_wmh_mu_drlb1,\n",
    "    bins_histo_wmh_e_drlb1,\n",
    "    w_in_histo_wmh_e_drlb1,\n",
    "    fi_histo_rate_wmh_e_drlb1,\n",
    "    fi_histo_wmh_e_drlb1,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.savez(fi_histo_drlb1_outfile, fi_histo_drlb1_list, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_rate_drlb1 = [ fi_histo_rate_wph_mu_drlb1[i]\n",
    "                     + fi_histo_rate_wph_e_drlb1[i] \n",
    "                     + fi_histo_rate_wmh_mu_drlb1[i] \n",
    "                     + fi_histo_rate_wmh_e_drlb1[i] \n",
    "                     for i in range(0, len(fi_histo_rate_wph_mu_drlb1)) ]\n",
    "\n",
    "fi_histo_drlb1 = [ fi_histo_wph_mu_drlb1[i]\n",
    "                     + fi_histo_wph_e_drlb1[i] \n",
    "                     + fi_histo_wmh_mu_drlb1[i] \n",
    "                     + fi_histo_wmh_e_drlb1[i] \n",
    "                     for i in range(0, len(fi_histo_wph_mu_drlb1)) ]\n",
    "\n",
    "fi_histo_rate_drlb1_rot = [ rotate(fi_histo_rate_drlb1[i]) for i in range(0, len(fi_histo_rate_drlb1)) ]\n",
    "fi_histo_drlb1_rot = [ rotate(fi_histo_drlb1[i]) for i in range(0, len(fi_histo_drlb1)) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### $m_{b\\bar{b}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "23:18 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "23:18 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "23:18 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "23:18 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "23:18 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "23:18 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "23:23 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "23:23 madminer.ml          INFO    Starting score evaluation\n",
      "23:23 madminer.ml          INFO    Starting score evaluation\n",
      "23:23 madminer.ml          INFO    Starting score evaluation\n",
      "23:23 madminer.ml          INFO    Starting score evaluation\n",
      "23:23 madminer.ml          INFO    Starting score evaluation\n",
      "23:23 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "23:23 madminer.ml          INFO    Starting score evaluation\n",
      "23:23 madminer.ml          INFO    Starting score evaluation\n",
      "23:23 madminer.ml          INFO    Starting score evaluation\n",
      "23:23 madminer.ml          INFO    Starting score evaluation\n",
      "23:23 madminer.ml          INFO    Starting score evaluation\n",
      "23:23 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "23:23 madminer.ml          INFO    Starting score evaluation\n",
      "23:23 madminer.ml          INFO    Starting score evaluation\n",
      "23:23 madminer.ml          INFO    Starting score evaluation\n",
      "23:23 madminer.ml          INFO    Starting score evaluation\n",
      "23:23 madminer.ml          INFO    Starting score evaluation\n",
      "23:23 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:24 madminer.ml          INFO    Starting score evaluation\n",
      "23:25 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "23:25 madminer.ml          INFO    Starting score evaluation\n",
      "23:25 madminer.ml          INFO    Starting score evaluation\n",
      "23:25 madminer.ml          INFO    Starting score evaluation\n",
      "23:25 madminer.ml          INFO    Starting score evaluation\n",
      "23:25 madminer.ml          INFO    Starting score evaluation\n",
      "23:25 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "23:25 madminer.ml          INFO    Starting score evaluation\n",
      "23:25 madminer.ml          INFO    Starting score evaluation\n",
      "23:25 madminer.ml          INFO    Starting score evaluation\n",
      "23:25 madminer.ml          INFO    Starting score evaluation\n",
      "23:25 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_mu_mbb, w_in_histo_wph_mu_mbb, fi_histo_rate_wph_mu_mbb, fi_histo_wph_mu_mbb = fisher_wph_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='m_bb',\n",
    "    nbins=20,\n",
    "    histrange=(80,160),\n",
    "    model_file=model_path_wph_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "23:25 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "23:25 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "23:25 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "23:25 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "23:25 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "23:25 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "23:30 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "23:30 madminer.ml          INFO    Starting score evaluation\n",
      "23:30 madminer.ml          INFO    Starting score evaluation\n",
      "23:30 madminer.ml          INFO    Starting score evaluation\n",
      "23:30 madminer.ml          INFO    Starting score evaluation\n",
      "23:30 madminer.ml          INFO    Starting score evaluation\n",
      "23:30 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "23:30 madminer.ml          INFO    Starting score evaluation\n",
      "23:30 madminer.ml          INFO    Starting score evaluation\n",
      "23:30 madminer.ml          INFO    Starting score evaluation\n",
      "23:30 madminer.ml          INFO    Starting score evaluation\n",
      "23:30 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:31 madminer.ml          INFO    Starting score evaluation\n",
      "23:32 madminer.ml          INFO    Starting score evaluation\n",
      "23:32 madminer.ml          INFO    Starting score evaluation\n",
      "23:32 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "23:32 madminer.ml          INFO    Starting score evaluation\n",
      "23:32 madminer.ml          INFO    Starting score evaluation\n",
      "23:32 madminer.ml          INFO    Starting score evaluation\n",
      "23:32 madminer.ml          INFO    Starting score evaluation\n",
      "23:32 madminer.ml          INFO    Starting score evaluation\n",
      "23:32 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "23:32 madminer.ml          INFO    Starting score evaluation\n",
      "23:32 madminer.ml          INFO    Starting score evaluation\n",
      "23:32 madminer.ml          INFO    Starting score evaluation\n",
      "23:32 madminer.ml          INFO    Starting score evaluation\n",
      "23:32 madminer.ml          INFO    Starting score evaluation\n",
      "23:32 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "23:32 madminer.ml          INFO    Starting score evaluation\n",
      "23:32 madminer.ml          INFO    Starting score evaluation\n",
      "23:32 madminer.ml          INFO    Starting score evaluation\n",
      "23:32 madminer.ml          INFO    Starting score evaluation\n",
      "23:32 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_e_mbb, w_in_histo_wph_e_mbb, fi_histo_rate_wph_e_mbb, fi_histo_wph_e_mbb = fisher_wph_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='m_bb',\n",
    "    nbins=20,\n",
    "    histrange=(80,160),\n",
    "    model_file=model_path_wph_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "23:32 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "23:32 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "23:32 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "23:32 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "23:32 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "23:32 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "23:37 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "23:37 madminer.ml          INFO    Starting score evaluation\n",
      "23:37 madminer.ml          INFO    Starting score evaluation\n",
      "23:37 madminer.ml          INFO    Starting score evaluation\n",
      "23:37 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:38 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "23:38 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n",
      "23:39 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_mu_mbb, w_in_histo_wmh_mu_mbb, fi_histo_rate_wmh_mu_mbb, fi_histo_wmh_mu_mbb = fisher_wmh_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='m_bb',\n",
    "    nbins=20,\n",
    "    histrange=(80,160),\n",
    "    model_file=model_path_wmh_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "23:39 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "23:39 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "23:39 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "23:39 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "23:39 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "23:39 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "23:45 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.ml          INFO    Starting score evaluation\n",
      "23:45 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.ml          INFO    Starting score evaluation\n",
      "23:46 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "23:47 madminer.ml          INFO    Starting score evaluation\n",
      "23:47 madminer.ml          INFO    Starting score evaluation\n",
      "23:47 madminer.ml          INFO    Starting score evaluation\n",
      "23:47 madminer.ml          INFO    Starting score evaluation\n",
      "23:47 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_e_mbb, w_in_histo_wmh_e_mbb, fi_histo_rate_wmh_e_mbb, fi_histo_wmh_e_mbb = fisher_wmh_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='m_bb',\n",
    "    nbins=20,\n",
    "    histrange=(80,160),\n",
    "    model_file=model_path_wmh_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_mbb_outfile = './fisher_info/dists/fi_histo_mbb.npz'\n",
    "\n",
    "fi_histo_mbb_list = [\n",
    "    bins_histo_wph_mu_mbb,\n",
    "    w_in_histo_wph_mu_mbb,\n",
    "    fi_histo_rate_wph_mu_mbb,\n",
    "    fi_histo_wph_mu_mbb,\n",
    "    bins_histo_wph_e_mbb,\n",
    "    w_in_histo_wph_e_mbb,\n",
    "    fi_histo_rate_wph_e_mbb,\n",
    "    fi_histo_wph_e_mbb,\n",
    "    bins_histo_wmh_mu_mbb,\n",
    "    w_in_histo_wmh_mu_mbb,\n",
    "    fi_histo_rate_wmh_mu_mbb,\n",
    "    fi_histo_wmh_mu_mbb,\n",
    "    bins_histo_wmh_e_mbb,\n",
    "    w_in_histo_wmh_e_mbb,\n",
    "    fi_histo_rate_wmh_e_mbb,\n",
    "    fi_histo_wmh_e_mbb,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.savez(fi_histo_mbb_outfile, fi_histo_mbb_list, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_rate_mbb = [ fi_histo_rate_wph_mu_mbb[i]\n",
    "                     + fi_histo_rate_wph_e_mbb[i] \n",
    "                     + fi_histo_rate_wmh_mu_mbb[i] \n",
    "                     + fi_histo_rate_wmh_e_mbb[i] \n",
    "                     for i in range(0, len(fi_histo_rate_wph_mu_mbb)) ]\n",
    "\n",
    "fi_histo_mbb = [ fi_histo_wph_mu_mbb[i]\n",
    "                     + fi_histo_wph_e_mbb[i] \n",
    "                     + fi_histo_wmh_mu_mbb[i] \n",
    "                     + fi_histo_wmh_e_mbb[i] \n",
    "                     for i in range(0, len(fi_histo_wph_mu_mbb)) ]\n",
    "\n",
    "fi_histo_rate_mbb_rot = [ rotate(fi_histo_rate_mbb[i]) for i in range(0, len(fi_histo_rate_mbb)) ]\n",
    "fi_histo_mbb_rot = [ rotate(fi_histo_mbb[i]) for i in range(0, len(fi_histo_mbb)) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### $p_{T,b_1}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:28 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "07:28 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "07:28 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "07:28 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "07:28 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "07:28 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "07:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "07:33 madminer.ml          INFO    Starting score evaluation\n",
      "07:33 madminer.ml          INFO    Starting score evaluation\n",
      "07:33 madminer.ml          INFO    Starting score evaluation\n",
      "07:33 madminer.ml          INFO    Starting score evaluation\n",
      "07:33 madminer.ml          INFO    Starting score evaluation\n",
      "07:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "07:33 madminer.ml          INFO    Starting score evaluation\n",
      "07:33 madminer.ml          INFO    Starting score evaluation\n",
      "07:33 madminer.ml          INFO    Starting score evaluation\n",
      "07:33 madminer.ml          INFO    Starting score evaluation\n",
      "07:33 madminer.ml          INFO    Starting score evaluation\n",
      "07:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "07:33 madminer.ml          INFO    Starting score evaluation\n",
      "07:33 madminer.ml          INFO    Starting score evaluation\n",
      "07:33 madminer.ml          INFO    Starting score evaluation\n",
      "07:33 madminer.ml          INFO    Starting score evaluation\n",
      "07:33 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:34 madminer.ml          INFO    Starting score evaluation\n",
      "07:35 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "07:35 madminer.ml          INFO    Starting score evaluation\n",
      "07:35 madminer.ml          INFO    Starting score evaluation\n",
      "07:35 madminer.ml          INFO    Starting score evaluation\n",
      "07:35 madminer.ml          INFO    Starting score evaluation\n",
      "07:35 madminer.ml          INFO    Starting score evaluation\n",
      "07:35 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "07:35 madminer.ml          INFO    Starting score evaluation\n",
      "07:35 madminer.ml          INFO    Starting score evaluation\n",
      "07:35 madminer.ml          INFO    Starting score evaluation\n",
      "07:35 madminer.ml          INFO    Starting score evaluation\n",
      "07:35 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_mu_ptb1, w_in_histo_wph_mu_ptb1, fi_histo_rate_wph_mu_ptb1, fi_histo_wph_mu_ptb1 = fisher_wph_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_b1',\n",
    "    nbins=20,\n",
    "    histrange=(0,800),\n",
    "    model_file=model_path_wph_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:35 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "07:35 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "07:35 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "07:35 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "07:35 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "07:35 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "07:40 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "07:40 madminer.ml          INFO    Starting score evaluation\n",
      "07:40 madminer.ml          INFO    Starting score evaluation\n",
      "07:40 madminer.ml          INFO    Starting score evaluation\n",
      "07:40 madminer.ml          INFO    Starting score evaluation\n",
      "07:40 madminer.ml          INFO    Starting score evaluation\n",
      "07:40 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.ml          INFO    Starting score evaluation\n",
      "07:41 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n",
      "07:42 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_e_ptb1, w_in_histo_wph_e_ptb1, fi_histo_rate_wph_e_ptb1, fi_histo_wph_e_ptb1 = fisher_wph_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_b1',\n",
    "    nbins=20,\n",
    "    histrange=(0,800),\n",
    "    model_file=model_path_wph_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:42 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "07:42 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "07:42 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "07:42 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "07:42 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "07:42 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "07:48 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.ml          INFO    Starting score evaluation\n",
      "07:48 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:49 madminer.ml          INFO    Starting score evaluation\n",
      "07:50 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "07:50 madminer.ml          INFO    Starting score evaluation\n",
      "07:50 madminer.ml          INFO    Starting score evaluation\n",
      "07:50 madminer.ml          INFO    Starting score evaluation\n",
      "07:50 madminer.ml          INFO    Starting score evaluation\n",
      "07:50 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_mu_ptb1, w_in_histo_wmh_mu_ptb1, fi_histo_rate_wmh_mu_ptb1, fi_histo_wmh_mu_ptb1 = fisher_wmh_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_b1',\n",
    "    nbins=20,\n",
    "    histrange=(0,800),\n",
    "    model_file=model_path_wmh_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:50 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "07:50 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "07:50 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "07:50 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "07:50 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "07:50 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "07:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "07:55 madminer.ml          INFO    Starting score evaluation\n",
      "07:55 madminer.ml          INFO    Starting score evaluation\n",
      "07:55 madminer.ml          INFO    Starting score evaluation\n",
      "07:55 madminer.ml          INFO    Starting score evaluation\n",
      "07:55 madminer.ml          INFO    Starting score evaluation\n",
      "07:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:56 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n",
      "07:57 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_e_ptb1, w_in_histo_wmh_e_ptb1, fi_histo_rate_wmh_e_ptb1, fi_histo_wmh_e_ptb1 = fisher_wmh_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_b1',\n",
    "    nbins=20,\n",
    "    histrange=(0,800),\n",
    "    model_file=model_path_wmh_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_ptb1_outfile = './fisher_info/dists/fi_histo_ptb1.npz'\n",
    "\n",
    "fi_histo_ptb1_list = [\n",
    "    bins_histo_wph_mu_ptb1,\n",
    "    w_in_histo_wph_mu_ptb1,\n",
    "    fi_histo_rate_wph_mu_ptb1,\n",
    "    fi_histo_wph_mu_ptb1,\n",
    "    bins_histo_wph_e_ptb1,\n",
    "    w_in_histo_wph_e_ptb1,\n",
    "    fi_histo_rate_wph_e_ptb1,\n",
    "    fi_histo_wph_e_ptb1,\n",
    "    bins_histo_wmh_mu_ptb1,\n",
    "    w_in_histo_wmh_mu_ptb1,\n",
    "    fi_histo_rate_wmh_mu_ptb1,\n",
    "    fi_histo_wmh_mu_ptb1,\n",
    "    bins_histo_wmh_e_ptb1,\n",
    "    w_in_histo_wmh_e_ptb1,\n",
    "    fi_histo_rate_wmh_e_ptb1,\n",
    "    fi_histo_wmh_e_ptb1,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.savez(fi_histo_ptb1_outfile, fi_histo_ptb1_list, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_rate_ptb1 = [ fi_histo_rate_wph_mu_ptb1[i]\n",
    "                     + fi_histo_rate_wph_e_ptb1[i] \n",
    "                     + fi_histo_rate_wmh_mu_ptb1[i] \n",
    "                     + fi_histo_rate_wmh_e_ptb1[i] \n",
    "                     for i in range(0, len(fi_histo_rate_wph_mu_ptb1)) ]\n",
    "\n",
    "fi_histo_ptb1 = [ fi_histo_wph_mu_ptb1[i]\n",
    "                     + fi_histo_wph_e_ptb1[i] \n",
    "                     + fi_histo_wmh_mu_ptb1[i] \n",
    "                     + fi_histo_wmh_e_ptb1[i] \n",
    "                     for i in range(0, len(fi_histo_wph_mu_ptb1)) ]\n",
    "\n",
    "fi_histo_rate_ptb1_rot = [ rotate(fi_histo_rate_ptb1[i]) for i in range(0, len(fi_histo_rate_ptb1)) ]\n",
    "fi_histo_ptb1_rot = [ rotate(fi_histo_ptb1[i]) for i in range(0, len(fi_histo_ptb1)) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### $p_{T,b_2}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:57 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "07:57 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "07:57 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "07:57 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "07:57 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "07:57 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "08:03 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:03 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:04 madminer.ml          INFO    Starting score evaluation\n",
      "08:05 madminer.ml          INFO    Starting score evaluation\n",
      "08:05 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "08:05 madminer.ml          INFO    Starting score evaluation\n",
      "08:05 madminer.ml          INFO    Starting score evaluation\n",
      "08:05 madminer.ml          INFO    Starting score evaluation\n",
      "08:05 madminer.ml          INFO    Starting score evaluation\n",
      "08:05 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_mu_ptb2, w_in_histo_wph_mu_ptb2, fi_histo_rate_wph_mu_ptb2, fi_histo_wph_mu_ptb2 = fisher_wph_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_b2',\n",
    "    nbins=20,\n",
    "    histrange=(0,800),\n",
    "    model_file=model_path_wph_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "08:05 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "08:05 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "08:05 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "08:05 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "08:05 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "08:05 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "08:10 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "08:10 madminer.ml          INFO    Starting score evaluation\n",
      "08:10 madminer.ml          INFO    Starting score evaluation\n",
      "08:10 madminer.ml          INFO    Starting score evaluation\n",
      "08:10 madminer.ml          INFO    Starting score evaluation\n",
      "08:10 madminer.ml          INFO    Starting score evaluation\n",
      "08:10 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "08:10 madminer.ml          INFO    Starting score evaluation\n",
      "08:10 madminer.ml          INFO    Starting score evaluation\n",
      "08:10 madminer.ml          INFO    Starting score evaluation\n",
      "08:10 madminer.ml          INFO    Starting score evaluation\n",
      "08:10 madminer.ml          INFO    Starting score evaluation\n",
      "08:10 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.ml          INFO    Starting score evaluation\n",
      "08:11 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "08:12 madminer.ml          INFO    Starting score evaluation\n",
      "08:12 madminer.ml          INFO    Starting score evaluation\n",
      "08:12 madminer.ml          INFO    Starting score evaluation\n",
      "08:12 madminer.ml          INFO    Starting score evaluation\n",
      "08:12 madminer.ml          INFO    Starting score evaluation\n",
      "08:12 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "08:12 madminer.ml          INFO    Starting score evaluation\n",
      "08:12 madminer.ml          INFO    Starting score evaluation\n",
      "08:12 madminer.ml          INFO    Starting score evaluation\n",
      "08:12 madminer.ml          INFO    Starting score evaluation\n",
      "08:12 madminer.ml          INFO    Starting score evaluation\n",
      "08:12 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "08:12 madminer.ml          INFO    Starting score evaluation\n",
      "08:12 madminer.ml          INFO    Starting score evaluation\n",
      "08:12 madminer.ml          INFO    Starting score evaluation\n",
      "08:12 madminer.ml          INFO    Starting score evaluation\n",
      "08:12 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_e_ptb2, w_in_histo_wph_e_ptb2, fi_histo_rate_wph_e_ptb2, fi_histo_wph_e_ptb2 = fisher_wph_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_b2',\n",
    "    nbins=20,\n",
    "    histrange=(0,800),\n",
    "    model_file=model_path_wph_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "08:12 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "08:12 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "08:12 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "08:12 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "08:12 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "08:12 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "08:17 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:18 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:19 madminer.ml          INFO    Starting score evaluation\n",
      "08:20 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_mu_ptb2, w_in_histo_wmh_mu_ptb2, fi_histo_rate_wmh_mu_ptb2, fi_histo_wmh_mu_ptb2 = fisher_wmh_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_b2',\n",
    "    nbins=20,\n",
    "    histrange=(0,800),\n",
    "    model_file=model_path_wmh_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "08:20 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "08:20 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "08:20 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "08:20 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "08:20 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "08:20 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "08:25 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "08:25 madminer.ml          INFO    Starting score evaluation\n",
      "08:25 madminer.ml          INFO    Starting score evaluation\n",
      "08:25 madminer.ml          INFO    Starting score evaluation\n",
      "08:25 madminer.ml          INFO    Starting score evaluation\n",
      "08:25 madminer.ml          INFO    Starting score evaluation\n",
      "08:25 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "08:25 madminer.ml          INFO    Starting score evaluation\n",
      "08:25 madminer.ml          INFO    Starting score evaluation\n",
      "08:25 madminer.ml          INFO    Starting score evaluation\n",
      "08:25 madminer.ml          INFO    Starting score evaluation\n",
      "08:25 madminer.ml          INFO    Starting score evaluation\n",
      "08:25 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "08:25 madminer.ml          INFO    Starting score evaluation\n",
      "08:25 madminer.ml          INFO    Starting score evaluation\n",
      "08:25 madminer.ml          INFO    Starting score evaluation\n",
      "08:25 madminer.ml          INFO    Starting score evaluation\n",
      "08:25 madminer.ml          INFO    Starting score evaluation\n",
      "08:25 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:26 madminer.ml          INFO    Starting score evaluation\n",
      "08:27 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "08:27 madminer.ml          INFO    Starting score evaluation\n",
      "08:27 madminer.ml          INFO    Starting score evaluation\n",
      "08:27 madminer.ml          INFO    Starting score evaluation\n",
      "08:27 madminer.ml          INFO    Starting score evaluation\n",
      "08:27 madminer.ml          INFO    Starting score evaluation\n",
      "08:27 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "08:27 madminer.ml          INFO    Starting score evaluation\n",
      "08:27 madminer.ml          INFO    Starting score evaluation\n",
      "08:27 madminer.ml          INFO    Starting score evaluation\n",
      "08:27 madminer.ml          INFO    Starting score evaluation\n",
      "08:27 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_e_ptb2, w_in_histo_wmh_e_ptb2, fi_histo_rate_wmh_e_ptb2, fi_histo_wmh_e_ptb2 = fisher_wmh_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_b2',\n",
    "    nbins=20,\n",
    "    histrange=(0,800),\n",
    "    model_file=model_path_wmh_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_ptb2_outfile = './fisher_info/dists/fi_histo_ptb2.npz'\n",
    "\n",
    "fi_histo_ptb2_list = [\n",
    "    bins_histo_wph_mu_ptb2,\n",
    "    w_in_histo_wph_mu_ptb2,\n",
    "    fi_histo_rate_wph_mu_ptb2,\n",
    "    fi_histo_wph_mu_ptb2,\n",
    "    bins_histo_wph_e_ptb2,\n",
    "    w_in_histo_wph_e_ptb2,\n",
    "    fi_histo_rate_wph_e_ptb2,\n",
    "    fi_histo_wph_e_ptb2,\n",
    "    bins_histo_wmh_mu_ptb2,\n",
    "    w_in_histo_wmh_mu_ptb2,\n",
    "    fi_histo_rate_wmh_mu_ptb2,\n",
    "    fi_histo_wmh_mu_ptb2,\n",
    "    bins_histo_wmh_e_ptb2,\n",
    "    w_in_histo_wmh_e_ptb2,\n",
    "    fi_histo_rate_wmh_e_ptb2,\n",
    "    fi_histo_wmh_e_ptb2,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.savez(fi_histo_ptb2_outfile, fi_histo_ptb2_list, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_rate_ptb2 = [ fi_histo_rate_wph_mu_ptb2[i]\n",
    "                     + fi_histo_rate_wph_e_ptb2[i] \n",
    "                     + fi_histo_rate_wmh_mu_ptb2[i] \n",
    "                     + fi_histo_rate_wmh_e_ptb2[i] \n",
    "                     for i in range(0, len(fi_histo_rate_wph_mu_ptb2)) ]\n",
    "\n",
    "fi_histo_ptb2 = [ fi_histo_wph_mu_ptb2[i]\n",
    "                     + fi_histo_wph_e_ptb2[i] \n",
    "                     + fi_histo_wmh_mu_ptb2[i] \n",
    "                     + fi_histo_wmh_e_ptb2[i] \n",
    "                     for i in range(0, len(fi_histo_wph_mu_ptb2)) ]\n",
    "\n",
    "fi_histo_rate_ptb2_rot = [ rotate(fi_histo_rate_ptb2[i]) for i in range(0, len(fi_histo_rate_ptb2)) ]\n",
    "fi_histo_ptb2_rot = [ rotate(fi_histo_ptb2[i]) for i in range(0, len(fi_histo_ptb2)) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### MET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "08:27 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "08:27 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "08:27 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "08:27 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "08:27 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "08:27 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "08:32 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "08:32 madminer.ml          INFO    Starting score evaluation\n",
      "08:32 madminer.ml          INFO    Starting score evaluation\n",
      "08:32 madminer.ml          INFO    Starting score evaluation\n",
      "08:32 madminer.ml          INFO    Starting score evaluation\n",
      "08:32 madminer.ml          INFO    Starting score evaluation\n",
      "08:32 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:33 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n",
      "08:34 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_mu_met, w_in_histo_wph_mu_met, fi_histo_rate_wph_mu_met, fi_histo_wph_mu_met = fisher_wph_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='met',\n",
    "    nbins=20,\n",
    "    histrange=(0,800),\n",
    "    model_file=model_path_wph_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "08:34 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "08:34 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "08:34 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "08:34 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "08:34 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "08:34 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "08:40 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:40 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.ml          INFO    Starting score evaluation\n",
      "08:41 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "08:42 madminer.ml          INFO    Starting score evaluation\n",
      "08:42 madminer.ml          INFO    Starting score evaluation\n",
      "08:42 madminer.ml          INFO    Starting score evaluation\n",
      "08:42 madminer.ml          INFO    Starting score evaluation\n",
      "08:42 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_e_met, w_in_histo_wph_e_met, fi_histo_rate_wph_e_met, fi_histo_wph_e_met = fisher_wph_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='met',\n",
    "    nbins=20,\n",
    "    histrange=(0,800),\n",
    "    model_file=model_path_wph_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "08:42 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "08:42 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "08:42 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "08:42 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "08:42 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "08:42 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "08:47 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "08:47 madminer.ml          INFO    Starting score evaluation\n",
      "08:47 madminer.ml          INFO    Starting score evaluation\n",
      "08:47 madminer.ml          INFO    Starting score evaluation\n",
      "08:47 madminer.ml          INFO    Starting score evaluation\n",
      "08:47 madminer.ml          INFO    Starting score evaluation\n",
      "08:47 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "08:47 madminer.ml          INFO    Starting score evaluation\n",
      "08:47 madminer.ml          INFO    Starting score evaluation\n",
      "08:47 madminer.ml          INFO    Starting score evaluation\n",
      "08:47 madminer.ml          INFO    Starting score evaluation\n",
      "08:47 madminer.ml          INFO    Starting score evaluation\n",
      "08:47 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.ml          INFO    Starting score evaluation\n",
      "08:48 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "08:49 madminer.ml          INFO    Starting score evaluation\n",
      "08:49 madminer.ml          INFO    Starting score evaluation\n",
      "08:49 madminer.ml          INFO    Starting score evaluation\n",
      "08:49 madminer.ml          INFO    Starting score evaluation\n",
      "08:49 madminer.ml          INFO    Starting score evaluation\n",
      "08:49 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "08:49 madminer.ml          INFO    Starting score evaluation\n",
      "08:49 madminer.ml          INFO    Starting score evaluation\n",
      "08:49 madminer.ml          INFO    Starting score evaluation\n",
      "08:49 madminer.ml          INFO    Starting score evaluation\n",
      "08:49 madminer.ml          INFO    Starting score evaluation\n",
      "08:49 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "08:49 madminer.ml          INFO    Starting score evaluation\n",
      "08:49 madminer.ml          INFO    Starting score evaluation\n",
      "08:49 madminer.ml          INFO    Starting score evaluation\n",
      "08:49 madminer.ml          INFO    Starting score evaluation\n",
      "08:49 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_mu_met, w_in_histo_wmh_mu_met, fi_histo_rate_wmh_mu_met, fi_histo_wmh_mu_met = fisher_wmh_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='met',\n",
    "    nbins=20,\n",
    "    histrange=(0,800),\n",
    "    model_file=model_path_wmh_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "08:49 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "08:49 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "08:49 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "08:49 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "08:49 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "08:49 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "08:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.ml          INFO    Starting score evaluation\n",
      "08:55 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.ml          INFO    Starting score evaluation\n",
      "08:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "08:57 madminer.ml          INFO    Starting score evaluation\n",
      "08:57 madminer.ml          INFO    Starting score evaluation\n",
      "08:57 madminer.ml          INFO    Starting score evaluation\n",
      "08:57 madminer.ml          INFO    Starting score evaluation\n",
      "08:57 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_e_met, w_in_histo_wmh_e_met, fi_histo_rate_wmh_e_met, fi_histo_wmh_e_met = fisher_wmh_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='met',\n",
    "    nbins=20,\n",
    "    histrange=(0,800),\n",
    "    model_file=model_path_wmh_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_met_outfile = './fisher_info/dists/fi_histo_met.npz'\n",
    "\n",
    "fi_histo_met_list = [\n",
    "    bins_histo_wph_mu_met,\n",
    "    w_in_histo_wph_mu_met,\n",
    "    fi_histo_rate_wph_mu_met,\n",
    "    fi_histo_wph_mu_met,\n",
    "    bins_histo_wph_e_met,\n",
    "    w_in_histo_wph_e_met,\n",
    "    fi_histo_rate_wph_e_met,\n",
    "    fi_histo_wph_e_met,\n",
    "    bins_histo_wmh_mu_met,\n",
    "    w_in_histo_wmh_mu_met,\n",
    "    fi_histo_rate_wmh_mu_met,\n",
    "    fi_histo_wmh_mu_met,\n",
    "    bins_histo_wmh_e_met,\n",
    "    w_in_histo_wmh_e_met,\n",
    "    fi_histo_rate_wmh_e_met,\n",
    "    fi_histo_wmh_e_met,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.savez(fi_histo_met_outfile, fi_histo_met_list, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_rate_met = [ fi_histo_rate_wph_mu_met[i]\n",
    "                     + fi_histo_rate_wph_e_met[i] \n",
    "                     + fi_histo_rate_wmh_mu_met[i] \n",
    "                     + fi_histo_rate_wmh_e_met[i] \n",
    "                     for i in range(0, len(fi_histo_rate_wph_mu_met)) ]\n",
    "\n",
    "fi_histo_met = [ fi_histo_wph_mu_met[i]\n",
    "                     + fi_histo_wph_e_met[i] \n",
    "                     + fi_histo_wmh_mu_met[i] \n",
    "                     + fi_histo_wmh_e_met[i] \n",
    "                     for i in range(0, len(fi_histo_wph_mu_met)) ]\n",
    "\n",
    "fi_histo_rate_met_rot = [ rotate(fi_histo_rate_met[i]) for i in range(0, len(fi_histo_rate_met)) ]\n",
    "fi_histo_met_rot = [ rotate(fi_histo_met[i]) for i in range(0, len(fi_histo_met)) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### $p_{T,\\ell}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "08:57 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "08:57 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "08:57 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "08:57 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "08:57 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "08:57 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "09:02 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "09:02 madminer.ml          INFO    Starting score evaluation\n",
      "09:02 madminer.ml          INFO    Starting score evaluation\n",
      "09:02 madminer.ml          INFO    Starting score evaluation\n",
      "09:02 madminer.ml          INFO    Starting score evaluation\n",
      "09:02 madminer.ml          INFO    Starting score evaluation\n",
      "09:02 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "09:02 madminer.ml          INFO    Starting score evaluation\n",
      "09:02 madminer.ml          INFO    Starting score evaluation\n",
      "09:02 madminer.ml          INFO    Starting score evaluation\n",
      "09:02 madminer.ml          INFO    Starting score evaluation\n",
      "09:02 madminer.ml          INFO    Starting score evaluation\n",
      "09:02 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "09:02 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:03 madminer.ml          INFO    Starting score evaluation\n",
      "09:04 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "09:04 madminer.ml          INFO    Starting score evaluation\n",
      "09:04 madminer.ml          INFO    Starting score evaluation\n",
      "09:04 madminer.ml          INFO    Starting score evaluation\n",
      "09:04 madminer.ml          INFO    Starting score evaluation\n",
      "09:04 madminer.ml          INFO    Starting score evaluation\n",
      "09:04 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "09:04 madminer.ml          INFO    Starting score evaluation\n",
      "09:04 madminer.ml          INFO    Starting score evaluation\n",
      "09:04 madminer.ml          INFO    Starting score evaluation\n",
      "09:04 madminer.ml          INFO    Starting score evaluation\n",
      "09:04 madminer.ml          INFO    Starting score evaluation\n",
      "09:04 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "09:04 madminer.ml          INFO    Starting score evaluation\n",
      "09:04 madminer.ml          INFO    Starting score evaluation\n",
      "09:04 madminer.ml          INFO    Starting score evaluation\n",
      "09:04 madminer.ml          INFO    Starting score evaluation\n",
      "09:04 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_mu_ptl, w_in_histo_wph_mu_ptl, fi_histo_rate_wph_mu_ptl, fi_histo_wph_mu_ptl = fisher_wph_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_l',\n",
    "    nbins=20,\n",
    "    histrange=(0,800),\n",
    "    model_file=model_path_wph_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "09:04 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "09:04 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "09:04 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "09:04 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "09:04 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "09:04 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "09:10 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "09:10 madminer.ml          INFO    Starting score evaluation\n",
      "09:10 madminer.ml          INFO    Starting score evaluation\n",
      "09:10 madminer.ml          INFO    Starting score evaluation\n",
      "09:10 madminer.ml          INFO    Starting score evaluation\n",
      "09:10 madminer.ml          INFO    Starting score evaluation\n",
      "09:10 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "09:10 madminer.ml          INFO    Starting score evaluation\n",
      "09:10 madminer.ml          INFO    Starting score evaluation\n",
      "09:10 madminer.ml          INFO    Starting score evaluation\n",
      "09:10 madminer.ml          INFO    Starting score evaluation\n",
      "09:10 madminer.ml          INFO    Starting score evaluation\n",
      "09:10 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "09:10 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:11 madminer.ml          INFO    Starting score evaluation\n",
      "09:12 madminer.ml          INFO    Starting score evaluation\n",
      "09:12 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "09:12 madminer.ml          INFO    Starting score evaluation\n",
      "09:12 madminer.ml          INFO    Starting score evaluation\n",
      "09:12 madminer.ml          INFO    Starting score evaluation\n",
      "09:12 madminer.ml          INFO    Starting score evaluation\n",
      "09:12 madminer.ml          INFO    Starting score evaluation\n",
      "09:12 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "09:12 madminer.ml          INFO    Starting score evaluation\n",
      "09:12 madminer.ml          INFO    Starting score evaluation\n",
      "09:12 madminer.ml          INFO    Starting score evaluation\n",
      "09:12 madminer.ml          INFO    Starting score evaluation\n",
      "09:12 madminer.ml          INFO    Starting score evaluation\n",
      "09:12 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "09:12 madminer.ml          INFO    Starting score evaluation\n",
      "09:12 madminer.ml          INFO    Starting score evaluation\n",
      "09:12 madminer.ml          INFO    Starting score evaluation\n",
      "09:12 madminer.ml          INFO    Starting score evaluation\n",
      "09:12 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_e_ptl, w_in_histo_wph_e_ptl, fi_histo_rate_wph_e_ptl, fi_histo_wph_e_ptl = fisher_wph_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_l',\n",
    "    nbins=20,\n",
    "    histrange=(0,800),\n",
    "    model_file=model_path_wph_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "09:12 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "09:12 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "09:12 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "09:12 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "09:12 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "09:12 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "09:18 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "09:18 madminer.ml          INFO    Starting score evaluation\n",
      "09:18 madminer.ml          INFO    Starting score evaluation\n",
      "09:18 madminer.ml          INFO    Starting score evaluation\n",
      "09:18 madminer.ml          INFO    Starting score evaluation\n",
      "09:18 madminer.ml          INFO    Starting score evaluation\n",
      "09:18 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "09:18 madminer.ml          INFO    Starting score evaluation\n",
      "09:18 madminer.ml          INFO    Starting score evaluation\n",
      "09:18 madminer.ml          INFO    Starting score evaluation\n",
      "09:18 madminer.ml          INFO    Starting score evaluation\n",
      "09:18 madminer.ml          INFO    Starting score evaluation\n",
      "09:18 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "09:18 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:19 madminer.ml          INFO    Starting score evaluation\n",
      "09:20 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "09:20 madminer.ml          INFO    Starting score evaluation\n",
      "09:20 madminer.ml          INFO    Starting score evaluation\n",
      "09:20 madminer.ml          INFO    Starting score evaluation\n",
      "09:20 madminer.ml          INFO    Starting score evaluation\n",
      "09:20 madminer.ml          INFO    Starting score evaluation\n",
      "09:20 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "09:20 madminer.ml          INFO    Starting score evaluation\n",
      "09:20 madminer.ml          INFO    Starting score evaluation\n",
      "09:20 madminer.ml          INFO    Starting score evaluation\n",
      "09:20 madminer.ml          INFO    Starting score evaluation\n",
      "09:20 madminer.ml          INFO    Starting score evaluation\n",
      "09:20 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "09:20 madminer.ml          INFO    Starting score evaluation\n",
      "09:20 madminer.ml          INFO    Starting score evaluation\n",
      "09:20 madminer.ml          INFO    Starting score evaluation\n",
      "09:20 madminer.ml          INFO    Starting score evaluation\n",
      "09:20 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_mu_ptl, w_in_histo_wmh_mu_ptl, fi_histo_rate_wmh_mu_ptl, fi_histo_wmh_mu_ptl = fisher_wmh_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_l',\n",
    "    nbins=20,\n",
    "    histrange=(0,800),\n",
    "    model_file=model_path_wmh_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "09:20 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "09:20 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "09:20 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "09:20 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "09:20 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "09:20 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "09:26 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.ml          INFO    Starting score evaluation\n",
      "09:26 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.ml          INFO    Starting score evaluation\n",
      "09:27 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "09:28 madminer.ml          INFO    Starting score evaluation\n",
      "09:28 madminer.ml          INFO    Starting score evaluation\n",
      "09:28 madminer.ml          INFO    Starting score evaluation\n",
      "09:28 madminer.ml          INFO    Starting score evaluation\n",
      "09:28 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_e_ptl, w_in_histo_wmh_e_ptl, fi_histo_rate_wmh_e_ptl, fi_histo_wmh_e_ptl = fisher_wmh_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_l',\n",
    "    nbins=20,\n",
    "    histrange=(0,800),\n",
    "    model_file=model_path_wmh_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_ptl_outfile = './fisher_info/dists/fi_histo_ptl.npz'\n",
    "\n",
    "fi_histo_ptl_list = [\n",
    "    bins_histo_wph_mu_ptl,\n",
    "    w_in_histo_wph_mu_ptl,\n",
    "    fi_histo_rate_wph_mu_ptl,\n",
    "    fi_histo_wph_mu_ptl,\n",
    "    bins_histo_wph_e_ptl,\n",
    "    w_in_histo_wph_e_ptl,\n",
    "    fi_histo_rate_wph_e_ptl,\n",
    "    fi_histo_wph_e_ptl,\n",
    "    bins_histo_wmh_mu_ptl,\n",
    "    w_in_histo_wmh_mu_ptl,\n",
    "    fi_histo_rate_wmh_mu_ptl,\n",
    "    fi_histo_wmh_mu_ptl,\n",
    "    bins_histo_wmh_e_ptl,\n",
    "    w_in_histo_wmh_e_ptl,\n",
    "    fi_histo_rate_wmh_e_ptl,\n",
    "    fi_histo_wmh_e_ptl,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.savez(fi_histo_ptl_outfile, fi_histo_ptl_list, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_rate_ptl = [ fi_histo_rate_wph_mu_ptl[i]\n",
    "                     + fi_histo_rate_wph_e_ptl[i] \n",
    "                     + fi_histo_rate_wmh_mu_ptl[i] \n",
    "                     + fi_histo_rate_wmh_e_ptl[i] \n",
    "                     for i in range(0, len(fi_histo_rate_wph_mu_ptl)) ]\n",
    "\n",
    "fi_histo_ptl = [ fi_histo_wph_mu_ptl[i]\n",
    "                     + fi_histo_wph_e_ptl[i] \n",
    "                     + fi_histo_wmh_mu_ptl[i] \n",
    "                     + fi_histo_wmh_e_ptl[i] \n",
    "                     for i in range(0, len(fi_histo_wph_mu_ptl)) ]\n",
    "\n",
    "fi_histo_rate_ptl_rot = [ rotate(fi_histo_rate_ptl[i]) for i in range(0, len(fi_histo_rate_ptl)) ]\n",
    "fi_histo_ptl_rot = [ rotate(fi_histo_ptl[i]) for i in range(0, len(fi_histo_ptl)) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### $p_{T,b\\bar{b}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "09:28 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "09:28 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "09:28 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "09:28 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "09:28 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "09:28 madminer.ml          INFO    Loading model from models/met/ensemble_wph_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "09:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "09:33 madminer.ml          INFO    Starting score evaluation\n",
      "09:33 madminer.ml          INFO    Starting score evaluation\n",
      "09:33 madminer.ml          INFO    Starting score evaluation\n",
      "09:33 madminer.ml          INFO    Starting score evaluation\n",
      "09:33 madminer.ml          INFO    Starting score evaluation\n",
      "09:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:34 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n",
      "09:35 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_mu_ptbb, w_in_histo_wph_mu_ptbb, fi_histo_rate_wph_mu_ptbb, fi_histo_wph_mu_ptbb = fisher_wph_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_h',\n",
    "    nbins=20,\n",
    "    histrange=(0,1000),\n",
    "    model_file=model_path_wph_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "09:35 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "09:35 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "09:35 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "09:35 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "09:35 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "09:35 madminer.ml          INFO    Loading model from models/met/ensemble_wph_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "09:41 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "09:41 madminer.ml          INFO    Starting score evaluation\n",
      "09:41 madminer.ml          INFO    Starting score evaluation\n",
      "09:41 madminer.ml          INFO    Starting score evaluation\n",
      "09:41 madminer.ml          INFO    Starting score evaluation\n",
      "09:41 madminer.ml          INFO    Starting score evaluation\n",
      "09:41 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "09:41 madminer.ml          INFO    Starting score evaluation\n",
      "09:41 madminer.ml          INFO    Starting score evaluation\n",
      "09:41 madminer.ml          INFO    Starting score evaluation\n",
      "09:41 madminer.ml          INFO    Starting score evaluation\n",
      "09:41 madminer.ml          INFO    Starting score evaluation\n",
      "09:41 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "09:41 madminer.ml          INFO    Starting score evaluation\n",
      "09:41 madminer.ml          INFO    Starting score evaluation\n",
      "09:41 madminer.ml          INFO    Starting score evaluation\n",
      "09:41 madminer.ml          INFO    Starting score evaluation\n",
      "09:41 madminer.ml          INFO    Starting score evaluation\n",
      "09:41 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.ml          INFO    Starting score evaluation\n",
      "09:42 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "09:43 madminer.ml          INFO    Starting score evaluation\n",
      "09:43 madminer.ml          INFO    Starting score evaluation\n",
      "09:43 madminer.ml          INFO    Starting score evaluation\n",
      "09:43 madminer.ml          INFO    Starting score evaluation\n",
      "09:43 madminer.ml          INFO    Starting score evaluation\n",
      "09:43 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "09:43 madminer.ml          INFO    Starting score evaluation\n",
      "09:43 madminer.ml          INFO    Starting score evaluation\n",
      "09:43 madminer.ml          INFO    Starting score evaluation\n",
      "09:43 madminer.ml          INFO    Starting score evaluation\n",
      "09:43 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_e_ptbb, w_in_histo_wph_e_ptbb, fi_histo_rate_wph_e_ptbb, fi_histo_wph_e_ptbb = fisher_wph_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_h',\n",
    "    nbins=20,\n",
    "    histrange=(0,1000),\n",
    "    model_file=model_path_wph_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "09:43 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "09:43 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "09:43 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "09:43 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "09:43 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "09:43 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_mu_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "09:48 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "09:48 madminer.ml          INFO    Starting score evaluation\n",
      "09:48 madminer.ml          INFO    Starting score evaluation\n",
      "09:48 madminer.ml          INFO    Starting score evaluation\n",
      "09:48 madminer.ml          INFO    Starting score evaluation\n",
      "09:48 madminer.ml          INFO    Starting score evaluation\n",
      "09:48 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "09:48 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.ml          INFO    Starting score evaluation\n",
      "09:49 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n",
      "09:50 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_mu_ptbb, w_in_histo_wmh_mu_ptbb, fi_histo_rate_wmh_mu_ptbb, fi_histo_wmh_mu_ptbb = fisher_wmh_mu_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_h',\n",
    "    nbins=20,\n",
    "    histrange=(0,1000),\n",
    "    model_file=model_path_wmh_mu,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "09:50 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "09:50 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_0\n",
      "09:50 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_1\n",
      "09:50 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_2\n",
      "09:50 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_3\n",
      "09:50 madminer.ml          INFO    Loading model from models/met/ensemble_wmh_e_wbkgs_sigsystonly_lhedata_met/estimator_4\n",
      "09:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 10\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 10\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 3 / 10\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 4 / 10\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.ml          INFO    Starting score evaluation\n",
      "09:56 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 5 / 10\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 6 / 10\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 7 / 10\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 8 / 10\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 9 / 10\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:57 madminer.ml          INFO    Starting score evaluation\n",
      "09:58 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 10 / 10\n",
      "09:58 madminer.ml          INFO    Starting score evaluation\n",
      "09:58 madminer.ml          INFO    Starting score evaluation\n",
      "09:58 madminer.ml          INFO    Starting score evaluation\n",
      "09:58 madminer.ml          INFO    Starting score evaluation\n",
      "09:58 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_e_ptbb, w_in_histo_wmh_e_ptbb, fi_histo_rate_wmh_e_ptbb, fi_histo_wmh_e_ptbb = fisher_wmh_e_wbkgs_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_h',\n",
    "    nbins=20,\n",
    "    histrange=(0,1000),\n",
    "    model_file=model_path_wmh_e,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_ptbb_outfile = './fisher_info/dists/fi_histo_ptbb.npz'\n",
    "\n",
    "fi_histo_ptbb_list = [\n",
    "    bins_histo_wph_mu_ptbb,\n",
    "    w_in_histo_wph_mu_ptbb,\n",
    "    fi_histo_rate_wph_mu_ptbb,\n",
    "    fi_histo_wph_mu_ptbb,\n",
    "    bins_histo_wph_e_ptbb,\n",
    "    w_in_histo_wph_e_ptbb,\n",
    "    fi_histo_rate_wph_e_ptbb,\n",
    "    fi_histo_wph_e_ptbb,\n",
    "    bins_histo_wmh_mu_ptbb,\n",
    "    w_in_histo_wmh_mu_ptbb,\n",
    "    fi_histo_rate_wmh_mu_ptbb,\n",
    "    fi_histo_wmh_mu_ptbb,\n",
    "    bins_histo_wmh_e_ptbb,\n",
    "    w_in_histo_wmh_e_ptbb,\n",
    "    fi_histo_rate_wmh_e_ptbb,\n",
    "    fi_histo_wmh_e_ptbb,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.savez(fi_histo_ptbb_outfile, fi_histo_ptbb_list, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_rate_ptbb = [ fi_histo_rate_wph_mu_ptbb[i]\n",
    "                     + fi_histo_rate_wph_e_ptbb[i] \n",
    "                     + fi_histo_rate_wmh_mu_ptbb[i] \n",
    "                     + fi_histo_rate_wmh_e_ptbb[i] \n",
    "                     for i in range(0, len(fi_histo_rate_wph_mu_ptbb)) ]\n",
    "\n",
    "fi_histo_ptbb = [ fi_histo_wph_mu_ptbb[i]\n",
    "                     + fi_histo_wph_e_ptbb[i] \n",
    "                     + fi_histo_wmh_mu_ptbb[i] \n",
    "                     + fi_histo_wmh_e_ptbb[i] \n",
    "                     for i in range(0, len(fi_histo_wph_mu_ptbb)) ]\n",
    "\n",
    "fi_histo_rate_ptbb_rot = [ rotate(fi_histo_rate_ptbb[i]) for i in range(0, len(fi_histo_rate_ptbb)) ]\n",
    "fi_histo_ptbb_rot = [ rotate(fi_histo_ptbb[i]) for i in range(0, len(fi_histo_ptbb)) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4B. Plot Distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "#### Get the Signal and Background Distributions (for Plotting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17:35 madminer.analysis    INFO    Loading data from data/met/signal/wph_mu_smeftsim_lhedata_met.h5\n",
      "17:35 madminer.analysis    INFO    Found 4 parameters\n",
      "17:35 madminer.analysis    INFO    Found 33 nuisance parameters\n",
      "17:35 madminer.analysis    INFO    Found 54 benchmarks, of which 15 physical\n",
      "17:35 madminer.analysis    INFO    Found 48 observables\n",
      "17:35 madminer.analysis    INFO    Found 1000000 events\n",
      "17:35 madminer.analysis    INFO      1000000 signal events sampled from benchmark sm\n",
      "17:35 madminer.analysis    INFO    Found morphing setup with 15 components\n",
      "17:35 madminer.analysis    INFO    Found nuisance morphing setup\n",
      "17:35 madminer.analysis    INFO    Loading data from data/met/wph_mu_backgrounds_only_lhedata_met.h5\n",
      "17:35 madminer.analysis    INFO    Found 4 parameters\n",
      "17:35 madminer.analysis    INFO    Found 33 nuisance parameters\n",
      "17:35 madminer.analysis    INFO    Found 54 benchmarks, of which 15 physical\n",
      "17:35 madminer.analysis    INFO    Found 48 observables\n",
      "17:35 madminer.analysis    INFO    Found 4000000 events\n",
      "17:35 madminer.analysis    INFO      4000000 background events\n",
      "17:35 madminer.analysis    INFO    Found morphing setup with 15 components\n",
      "17:35 madminer.analysis    INFO    Found nuisance morphing setup\n",
      "/home/shomiller/.conda/envs/my_root/lib/python2.7/site-packages/madminer-0.4.9-py2.7.egg/madminer/analysis.py:811: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    }
   ],
   "source": [
    "sa_sig_wph_mu = SampleAugmenter('data/met/signal/wph_mu_smeftsim_lhedata_met.h5')\n",
    "x_weighted_sig_wph_mu, weights_sig_wph_mu = sa_sig_wph_mu.weighted_events(theta=[0.,0.,0.,0.])\n",
    "\n",
    "sa_bkgs_wph_mu = SampleAugmenter('data/met/wph_mu_backgrounds_only_lhedata_met.h5')\n",
    "x_weighted_bkg_wph_mu, weights_bkg_wph_mu = sa_bkgs_wph_mu.weighted_events(theta=[0.,0.,0.,0.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17:35 madminer.analysis    INFO    Loading data from data/met/signal/wph_e_smeftsim_lhedata_met.h5\n",
      "17:35 madminer.analysis    INFO    Found 4 parameters\n",
      "17:35 madminer.analysis    INFO    Found 33 nuisance parameters\n",
      "17:35 madminer.analysis    INFO    Found 54 benchmarks, of which 15 physical\n",
      "17:35 madminer.analysis    INFO    Found 48 observables\n",
      "17:35 madminer.analysis    INFO    Found 1000000 events\n",
      "17:35 madminer.analysis    INFO      1000000 signal events sampled from benchmark sm\n",
      "17:35 madminer.analysis    INFO    Found morphing setup with 15 components\n",
      "17:35 madminer.analysis    INFO    Found nuisance morphing setup\n",
      "17:36 madminer.analysis    INFO    Loading data from data/met/wph_e_backgrounds_only_lhedata_met.h5\n",
      "17:36 madminer.analysis    INFO    Found 4 parameters\n",
      "17:36 madminer.analysis    INFO    Found 33 nuisance parameters\n",
      "17:36 madminer.analysis    INFO    Found 54 benchmarks, of which 15 physical\n",
      "17:36 madminer.analysis    INFO    Found 48 observables\n",
      "17:36 madminer.analysis    INFO    Found 4000000 events\n",
      "17:36 madminer.analysis    INFO      4000000 background events\n",
      "17:36 madminer.analysis    INFO    Found morphing setup with 15 components\n",
      "17:36 madminer.analysis    INFO    Found nuisance morphing setup\n"
     ]
    }
   ],
   "source": [
    "sa_sig_wph_e = SampleAugmenter('data/met/signal/wph_e_smeftsim_lhedata_met.h5')\n",
    "x_weighted_sig_wph_e, weights_sig_wph_e = sa_sig_wph_e.weighted_events(theta=[0.,0.,0.,0.])\n",
    "\n",
    "sa_bkgs_wph_e = SampleAugmenter('data/met/wph_e_backgrounds_only_lhedata_met.h5')\n",
    "x_weighted_bkg_wph_e, weights_bkg_wph_e = sa_bkgs_wph_e.weighted_events(theta=[0.,0.,0.,0.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17:36 madminer.analysis    INFO    Loading data from data/met/signal/wmh_mu_smeftsim_lhedata_met.h5\n",
      "17:36 madminer.analysis    INFO    Found 4 parameters\n",
      "17:36 madminer.analysis    INFO    Found 33 nuisance parameters\n",
      "17:36 madminer.analysis    INFO    Found 54 benchmarks, of which 15 physical\n",
      "17:36 madminer.analysis    INFO    Found 48 observables\n",
      "17:36 madminer.analysis    INFO    Found 1000000 events\n",
      "17:36 madminer.analysis    INFO      1000000 signal events sampled from benchmark sm\n",
      "17:36 madminer.analysis    INFO    Found morphing setup with 15 components\n",
      "17:36 madminer.analysis    INFO    Found nuisance morphing setup\n",
      "17:36 madminer.analysis    INFO    Loading data from data/met/wmh_mu_backgrounds_only_lhedata_met.h5\n",
      "17:36 madminer.analysis    INFO    Found 4 parameters\n",
      "17:36 madminer.analysis    INFO    Found 33 nuisance parameters\n",
      "17:36 madminer.analysis    INFO    Found 54 benchmarks, of which 15 physical\n",
      "17:36 madminer.analysis    INFO    Found 48 observables\n",
      "17:36 madminer.analysis    INFO    Found 4000000 events\n",
      "17:36 madminer.analysis    INFO      4000000 background events\n",
      "17:36 madminer.analysis    INFO    Found morphing setup with 15 components\n",
      "17:36 madminer.analysis    INFO    Found nuisance morphing setup\n"
     ]
    }
   ],
   "source": [
    "sa_sig_wmh_mu = SampleAugmenter('data/met/signal/wmh_mu_smeftsim_lhedata_met.h5')\n",
    "x_weighted_sig_wmh_mu, weights_sig_wmh_mu = sa_sig_wmh_mu.weighted_events(theta=[0.,0.,0.,0.])\n",
    "\n",
    "sa_bkgs_wmh_mu = SampleAugmenter('data/met/wmh_mu_backgrounds_only_lhedata_met.h5')\n",
    "x_weighted_bkg_wmh_mu, weights_bkg_wmh_mu = sa_bkgs_wmh_mu.weighted_events(theta=[0.,0.,0.,0.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17:36 madminer.analysis    INFO    Loading data from data/met/signal/wmh_e_smeftsim_lhedata_met.h5\n",
      "17:36 madminer.analysis    INFO    Found 4 parameters\n",
      "17:36 madminer.analysis    INFO    Found 33 nuisance parameters\n",
      "17:36 madminer.analysis    INFO    Found 54 benchmarks, of which 15 physical\n",
      "17:36 madminer.analysis    INFO    Found 48 observables\n",
      "17:36 madminer.analysis    INFO    Found 1000000 events\n",
      "17:36 madminer.analysis    INFO      1000000 signal events sampled from benchmark sm\n",
      "17:36 madminer.analysis    INFO    Found morphing setup with 15 components\n",
      "17:36 madminer.analysis    INFO    Found nuisance morphing setup\n",
      "17:37 madminer.analysis    INFO    Loading data from data/met/wmh_e_backgrounds_only_lhedata_met.h5\n",
      "17:37 madminer.analysis    INFO    Found 4 parameters\n",
      "17:37 madminer.analysis    INFO    Found 33 nuisance parameters\n",
      "17:37 madminer.analysis    INFO    Found 54 benchmarks, of which 15 physical\n",
      "17:37 madminer.analysis    INFO    Found 48 observables\n",
      "17:37 madminer.analysis    INFO    Found 4000000 events\n",
      "17:37 madminer.analysis    INFO      4000000 background events\n",
      "17:37 madminer.analysis    INFO    Found morphing setup with 15 components\n",
      "17:37 madminer.analysis    INFO    Found nuisance morphing setup\n"
     ]
    }
   ],
   "source": [
    "sa_sig_wmh_e = SampleAugmenter('data/met/signal/wmh_e_smeftsim_lhedata_met.h5')\n",
    "x_weighted_sig_wmh_e, weights_sig_wmh_e = sa_sig_wmh_e.weighted_events(theta=[0.,0.,0.,0.])\n",
    "\n",
    "sa_bkgs_wmh_e = SampleAugmenter('data/met/wmh_e_backgrounds_only_lhedata_met.h5')\n",
    "x_weighted_bkg_wmh_e, weights_bkg_wmh_e = sa_bkgs_wmh_e.weighted_events(theta=[0.,0.,0.,0.])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Combine the different channels to plot distributions together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "x_weighted_sig = np.concatenate((x_weighted_sig_wph_mu, x_weighted_sig_wph_e, x_weighted_sig_wmh_mu, x_weighted_sig_wmh_e))\n",
    "weights_sig = np.concatenate((weights_sig_wph_mu, weights_sig_wph_e, weights_sig_wmh_mu, weights_sig_wmh_e))\n",
    "x_weighted_bkg = np.concatenate((x_weighted_bkg_wph_mu, x_weighted_bkg_wph_e, x_weighted_bkg_wmh_mu, x_weighted_bkg_wmh_e))\n",
    "weights_bkg = np.concatenate((weights_bkg_wph_mu, weights_bkg_wph_e, weights_bkg_wmh_mu, weights_bkg_wmh_e))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "#### Plot Elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "color_sig = 'gold'\n",
    "color_bkg = '#DCDCDC'\n",
    "\n",
    "fi_dist_color1 = 'blue'\n",
    "fi_dist_linestyle1 = 'solid'\n",
    "fi_dist_color2 = 'red'\n",
    "fi_dist_linestyle2 = 'dotted'\n",
    "fi_dist_color3 = 'green'\n",
    "fi_dist_linestyle3 = 'dashed'\n",
    "\n",
    "fi_dist_linewidth = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "legend_elements = [\n",
    "        Line2D([0],[0],color=fi_dist_color1, lw=fi_dist_linewidth,ls=fi_dist_linestyle1,\n",
    "           label=r'$\\tilde{C}_{HD}$'),\n",
    "        Line2D([0],[0],color=fi_dist_color2, lw=fi_dist_linewidth,ls=fi_dist_linestyle2,\n",
    "           label=r'$C_{HW}$'),\n",
    "        Line2D([0],[0],color=fi_dist_color3, lw=fi_dist_linewidth,ls=fi_dist_linestyle3,\n",
    "           label=r'$C_{Hq}^{(3)}$'),\n",
    "        Patch(facecolor=color_sig, label='SM Signal'),\n",
    "        Patch(facecolor=color_bkg, label='Background')\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Function for Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def distribution_plot(bins, observable, xlabel, fi_list, outfilename, log_y_axis=True, include_legend=True, legend_loc='upper right', my_xrange=None):\n",
    "\n",
    "#    bins_ptw = bins_histo_wph_mu_ptw\n",
    "    xvals = [ (bins[i] + bins[i+1]) / 2 for i in range(0, len(bins) - 1)]\n",
    "    xvals = [ bins[0] - 1e-9 ] + xvals + [bins[len(bins) - 1] + 1e-9]\n",
    "    \n",
    "    if my_xrange is not None:\n",
    "        thisrange = my_xrange\n",
    "    else:\n",
    "        thisrange = (bins[0], bins[-1])\n",
    "\n",
    "    index = sa_bkgs_wph_mu.observables.keys().index(observable)\n",
    "    #x_weighted_bkg[:,thisindex]\n",
    "\n",
    "    fig = plt.figure(figsize=(8,6))\n",
    "    ax1 = plt.subplot(111)\n",
    "    fig.subplots_adjust(left=0.1667, right=0.8333, bottom=0.17, top=0.97)\n",
    "\n",
    "    ax1.set_ylabel('Events (arb.)', color='black',fontsize=22)\n",
    "    ax1.set_xlabel(xlabel, color='black',fontsize=22)\n",
    "    \n",
    "    ax1.set_xlim(my_xrange)\n",
    "    \n",
    "    ax1.hist(\n",
    "        [ x_weighted_bkg[:,index], x_weighted_sig[:,index] ],\n",
    "        weights = [ weights_bkg, weights_sig ],\n",
    "        bins = bins,\n",
    "        range = thisrange,\n",
    "        histtype='stepfilled',\n",
    "        stacked=True,\n",
    "        color=[color_bkg, color_sig],\n",
    "        alpha=1.0\n",
    "    )\n",
    "\n",
    "    if log_y_axis:\n",
    "        ax1.set_yscale('log')\n",
    "\n",
    "    ## Second Axis for F.I.\n",
    "\n",
    "    ax2 = ax1.twinx()\n",
    "    ax2.set_ylabel(r'Normalized Distribution of F.I.', color='black',fontsize=22)\n",
    "\n",
    "    fi1_norm = sum([fi[0,0] for fi in fi_list])\n",
    "    fi2_norm = sum([fi[1,1] for fi in fi_list])\n",
    "    fi3_norm = sum([fi[2,2] for fi in fi_list])\n",
    "\n",
    "    ax2.hist(\n",
    "        xvals,\n",
    "        weights = [ 1./(fi1_norm)*fi[0,0] for fi in fi_list ],\n",
    "        bins = bins,\n",
    "        range = thisrange,\n",
    "        histtype = 'step',\n",
    "        color = fi_dist_color1,\n",
    "        linewidth = fi_dist_linewidth,\n",
    "        linestyle = fi_dist_linestyle1,\n",
    "        label=r'$c_{H\\boxdot} - \\frac{1}{4}c_{HD}$'\n",
    "    )\n",
    "\n",
    "    ax2.hist(\n",
    "        xvals,\n",
    "        weights = [ 1./(fi2_norm)*fi[1,1] for fi in fi_list ],\n",
    "        bins = bins,\n",
    "        range = thisrange,\n",
    "        histtype = 'step',\n",
    "        color = fi_dist_color2,\n",
    "        linewidth = fi_dist_linewidth,\n",
    "        linestyle = fi_dist_linestyle2,\n",
    "        label=r'$c_{HW}$'\n",
    "    )\n",
    "\n",
    "    ax2.hist(\n",
    "        xvals,\n",
    "        weights = [ 1./(fi3_norm)*fi[2,2] for fi in fi_list ],\n",
    "        bins = bins,\n",
    "        range = thisrange,\n",
    "        histtype = 'step',\n",
    "        color = fi_dist_color3,\n",
    "        linewidth = fi_dist_linewidth,\n",
    "        linestyle = fi_dist_linestyle3,\n",
    "        label=r'$c_{Hq}^{(3)}$'\n",
    "    )\n",
    "\n",
    "    if include_legend:\n",
    "        ax2.legend(handles=legend_elements, loc=legend_loc, fontsize=16, frameon=False)\n",
    "        \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('plots/dists/{}.pdf'.format(outfilename))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Make the Plots:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### $p_{T,W}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ulwMAbG9vN+SuXLt2DbFYrHZPIpFANput/bna5evU/7nGMQBS1esikgWwDuAV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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f710f3590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(bins_histo_wph_mu_ptw, 'pt_w', r'$p_{T,W}$ (GeV)', fi_histo_ptw_rot, 'info_dist_ptw')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ty6qPl0gkUK1WG56Px+NIJBKIxWL18SsV5+dub4CVSqXqwUYsFusrU9L8/biP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\nUMQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f68744d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(bins_histo_wph_mu_mttot, 'mt_tot',r'$m_{T,\\mathrm{tot}}$ (GeV)', fi_histo_mttot_rot, 'info_dist_mttot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AVVhByob92JGAdSjTnwP47/YkV7yua1E8vYdDOJR+H+/+5Cf1c5Y+dfo0Dqys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\ndBjAEBERUen8H+5P7eT7c411AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f6adf74d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(bins_histo_wph_mu_mtlv, 'mt_lv', r'$m_{T,W}$ (GeV)', fi_histo_mtlv_rot, 'info_dist_mtlv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wTbQfISriDIC7z5tZPPt+I4vAXjPbGB7XOk69o8m+KnSMWqRJd4QlJKV3l3Yr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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f6ae72510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(bins_histo_wph_mu_etah, 'eta_h', r'$\\eta_{H}$', fi_histo_etah_rot, 'info_dist_etah', log_y_axis=False, include_legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/flQSVWrTmjowpdFfZN4EASCFXqWd5A1gkocuHZf0VnORGkk3Ze00rpVtKJ6a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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f68740090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(bins_histo_wph_mu_dphilv, 'dphi_lv', r'$\\Delta \\phi_{\\ell\\nu}$', fi_histo_dphilv_rot, 'info_dist_dphilv', log_y_axis=False, include_legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YzSBlfv7ev9umaSKdTrcl7rpyuRzK5TIqlQqq1Wrb8REA7r7QxHnhsy+EvQSi/RBHx20h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\ngNohgAEAALVDAAMAAGrn/5dcWT1esh/xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f6ae0f190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(bins_histo_wph_mu_drlb1, 'dR_lb1', r'$\\Delta R_{\\ell b_1}$', fi_histo_drlb1_rot, 'info_dist_drlb1', log_y_axis=False, legend_loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Q82pUROQTbb6fr/vsy8UZGCJaMzcOOr/kfbWj4yDqQemaWZQ5mByTQqPGqloE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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f68786390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(bins_histo_wph_mu_mbb, 'm_bb', r'$m_{b\\bar{b}}$', fi_histo_mbb_rot, 'info_dist_mbb', log_y_axis=False, legend_loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7uHr1KnK5HPb391EsFr1anI2NDTx8+LDnl7NcLodqtYr19XVsbm7i7t272Nzc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\nEamc/wMTD8ejVfav1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f6ae57ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(bins_histo_wph_mu_ptb1, 'pt_b1', r'$p_{T,b}^{\\mathrm{lead}}$', fi_histo_ptb1_rot, 'info_dist_ptb1', log_y_axis=False, legend_loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BdHGuQgCTqiUkEbkHc9R2FgBUtTlmbAZABYAHYMWOr8WdL+3etUtIX0+oiPcRT0Eiohly\nXRcAsLa2Nuc7oYskIt8CkFXVma/xpaYPjIjUATiq2rKBxoqIjDuta1tV91S1aQOXooj0w/wY8xER\nUUL8Lc7jdgo1m4M/UzqOg6WlJdRqtf5S0sbGBjY2NvqHPFLqXYNJHPSJyDcnvUlEXo/6QWnKwFSG\nMihtADUArRHjSyJyHMiquDBnLPjPo86Xann07KNktg1uvfxjSmQ+IpodeXfUsTJA43YDlYL577jZ\na6L6qDpyrN5/WTJQaBbgfBIeFGzlt9DcNP+P6J30UFguhI4bJ5PJIJfLwXGc0B1Dnued6ePi173U\n6/WBearVKs89Whz/r6ruD11bmeJ9dQC/EeWDUhHAiEjYv8xTAOOqtDZUNRjl5QA8OMd8qdaDScMe\nI5ki3ib8/8kxgCGi2Wg0GqhWqygWi2cKdTudzpnaGMdxziw5dTod7O8Pfz+kFBMReQ8maeB3JsyJ\nyFcmvC/y9+dUFPGKSBFAQ1VXAtdyMF3bllR17KldNmDZV9XCeedLaxFvT8xPQJmjZHrxPl41P+FU\nFrcsiIgWgOM42N3dxfr6ej+IWVtbC82oNJtNtNvt/snTruui0Wjg+Ph4YL5ut4tcLod2uz2QraGX\n5lnEKyJbMAc6+im2HIaWlYZkANxU1c9G+ZxUZGBgbn64J7R/yEUWL6O4AbaQ9w7MH9RW3Pls7UwF\nAF599dWIt34x1uwS0lFCRbxVu4TE/AsRzVI+nz/TRXeUdruN7e3tfnDTbDYHtkt7nodarYZ2uw3P\n89BoNGZyz3Q+dgmpnzYTkd9T1W+Me4+IRP7pPC1FvGEBih+AjDytS1U9W8S7AWA/UMQbaT47x5qq\nrr3ySlpiOiKiqyXYhRcwAY3fpRcAHj582H+90+kMvEbRicg9ESmJSCW4CWbE2JIdnxORjP94yo+a\nJoKtTR4yKC0BzCnOVqdmABOkhL3BZl+CGvZXrPmumus4wXWczPs2iIgAmOWi4R1LwwHN8fFxP2hp\nt9tsZncOMXbqZmEKbY8BfATAHapDHUlVHwc+93URecv+ej0w5qOoX0MqAhhVdXA2a5IF0Akbb2tc\nnocEMRCRTNT5FsFTLOMplhOb7wQ3cIIbic1HRBSX67r9WhZ/+3Sz2YTruv1+MgBQrVbhOA5arRY6\nnQ7PQzqfiqoGvye2AYzewmYsAVhR1SVVjbSj1wYuD2G+N3fsr+ci8oci8vkoc/XnTEMRL9CPBp/4\nfyghz3MA8qrasoFLXVWrgfcfAICqlqeZb5S0FvEmfZTA51b/CQBgWZmFIaLF4nketra2pq6tuYrG\nFfHajS+PVXVp6FpPVUP37ItIJW4zWBF5A2YZ6QBAx8+22O/rJZg61luq+ldR5k1NwYeq1vz1OJiK\n5eOhYKMI80W2VNUTkYbttAvYxjnBvi9TzLdQlvEUAPBH+JtE5rthl4/SEb4SEU3HL+Slc8nibD2o\nB/RXMUaVblQC78up6t6Un7cFoKyqL4IX7RLUnoi0AGzbX1NLTQYmLdKagbEJGBwdJbMLaXXV7DDn\nXz8R0eUjIv8fgD8NXGr6GRT7g/3+UAYmA+A5zBLRmdoWEckFr4tIAyZjMzErIyJbIc3tIo8ZlpoM\nDBERESXmU1UddRBV5J2/IUFNG6aod5plpWl+VH5jijEDGMAsiEa/Y0syqdMu/NbgvbHjiIjo0om0\nUzeQnQk2gvVgyjOmsSQir4+qcbG7kX52yrn6GMAsiIrtCXScUABTAA9GIyK6ilTVEZGoO3X3hoKb\nSd11g5oAWvaIgcd+IGMDlzswu59uTTlXHwOYBVGxLW6SKl0roAuA+RcioiuqKSKlwOaWDbzspTaw\n89dunHk29P4ypvyWpKovRKRq5/+BiASXlBwAd6LuQAJYxHsGi3iJiGjRTXMWkt3J68JkU7xgQa7d\ncVS2ne79ZaQKzNLRCqZoSzLiM28C8LsTOnEa2PXnYgAziAEMEREtunke5nhRuIS0IG7j0D76uUTm\nu48d+2hnzCgiIqJ0YgZmSFozMEl34l1ZXTUP+PdPRHTpMANDqXGI2wCSyr8AO7hvfyei1PtJaHf3\ni/PFeD/oeJ6H3d1drKys9J8Xi0Wcnp6iWCzCdV00Gg00m02sra2h3W6HztNsNlGtVlEqlVCtVkce\n4jjp8wBznhIANBqN0DmS5jgOHjx4gGvXruHevXuT30BTYwCzIN62S0hHSKYG5l0buuwkMhsR0Vlb\nW1vY39/vnzLteR5u3bqF7W3TMT6Xy6Fer+PatWuo1WpwXXfsAY3BueJ8HgCUy2Vks9lRUyQun8+j\n2+3i4OCAAUzCUnEaNRERXS6e58FxnIGAI5PJDAQTweulUgmt1tlNLY7jYG1tVEPZ6J9XLBaRz+eH\n3z5TPDV7NhjAXFF59JBnFxgimpFMJgPXdeE4g00zS6VSaBalWq2GLuucnp5OFQBM83me56HT6ZwZ\nQ4uJS0gLQmGLeJFMEW8P/k80LOIlotm4d+8eCoUCKpUKNjY2UCwWkclkQmtY/FoVx3FiZ0gmfd7p\n6SkajQY8z+vX2/h1OOvr63jy5Em/fgYwmZNarYZcLtevnWm321hZWUGlUumPcxwHruv2X69Wqxee\n5bmKmIG5ono2B0NENCv1eh2NRgPdbhflchlLS0v9QCBMpVIZyMJ0Op2RBbtxPi8YiPjK5TLu3r2L\nUqmEjY0NHBwcoFKpoFKpoFgsYnt7G47jIJfL9Z8Pz7G1tQXAZHtqtRpu3YrcFf/KE5G3or6HAcyC\nECgkwWzJGnpY4xISEc1YpVJBr9eDquLg4ADNZhPNZvgBxtVqdeRrs/g8AP3gBDABTrfbHXjdX37y\nxwSXo3yPHz9GqVQaGOdnZGgyeyZS5JNyGMAQEVHi/KLaoFKphEajgYODg9D35HI55PN5NJtNeJ4X\nabdQnM/zx3Q65gzDTqeDO3funBkzbueT/3qn00Gz2QwtRL7qROR1EfmRiPxt2C+Y4wmmT7VZDGCI\niChxp6en/cAgaG1tbWxA4BfzdrvdSHUkcT/v7t27OD09RavVQjabjdUfZmNjA57noVKp9DMxNOB9\nAAcA1gCsjvj1YdRJGcAsiA+wiQ+wmdh8T7GMp1hObD4iomG7u7tnllIePHhwpoYkuBxTqVTgOM7A\ntaQ/L+jJkyf9wCNO8OE4Drrd7sB7/XtnNqavrar7qvqhqn4U8ssFsBt1Uu5CWhCbeAQAOMb3E5lv\nGZ8kMg8RUZhsNot6vQ7HcdDpdOB5Hp49e4b19fV+Ya7ruqjX62g2m3j27Bnq9ToAs5vIH9PpdPpZ\nkVqthnK5HFrYO83nOY6Der2ObreLZrOJSqWClZUVLC0t9Zer/ELfUqnUH+84Dvb29lCpVLC7u9u/\nl1qthnw+jzt37mBvbw+5XA6ZTAaNRgP1eh3lcrk/R6fTwd7e3lVtZnc6aYCq/iDqpDwLaUhaz0La\nFNOJ9/tHyRwm8E9WPwcAOFFmYYjoavLb/G9vbyOTycDzPLiu2+/ou8hbodN0FpKI3AJwrKofjxnz\nTVX9XpR5mYFZEI/s8tH3EzpK4BMuHxHRFdfpdLC+vt6vkclkMsjn81N1/qVIFEBVRDIAejibkckC\nqAKIFMAwAzMkrRkYexg1jo6SCWBWV02zJv71E9FV5m+xzmazOD09hed5yOVyC1+Mm7IMjB+wjNpb\nngFwU1U/G2VeZmAWxBb8PgbJNEhqwO8ieb6eC0REiyzYUZdmxlXVsWktEXkYdVJmYIakNQPjp2CO\nj5I5SmBlddU84N8/EdGlk7IMzE1V/ei8Y4YxA7MgmjCtqpNqUF1Bw85LREQ0O8HAxHbd9bMxXVX9\nq+Ex02IGZkhaMzCsgSEiommlKQMD9AOX9wEEi4sUQAdAddwOpVHYyI6IiIhmRkTeANAC0Aawoqqf\nUdXPAPgCgMcADmyAEwmXkBbEdZwkOt9tHNpHyXX3JSIiCrEFoKyqL4IXbQfePRFpAdi2v6bGAGZB\nnOAGAOAYyRTxHuJt+4hrSERENFMvhoOXIFV1RSTy8d0MYBbECa4nOt8hbgNg/oWIiGZump+U34g6\nKQOYBXHDLiEdJdSJ9227hMT8C1H6HR8n8999XCsrK3P9/KvG87yxJ2gvoCURed3fcTTM1r/8bNRJ\nGcAQEVHiXNdFo9HA3t4e8vk87t6923/tyZMnqFaroYcyRtHpdNBut7GysnIpGtJ5noetrS20Wi1c\nsh3CTQAtEXkPwGM/kLGByx2YYwQidwmJtY1aRN4EUARwDUAOpg2wB3O+wTFMu+DOqGgrzbiNmojS\nZpEzMIVCAdVqdSDAcF0XKysr6PV65z4wsVarwXVdHBwcnGueNBGRcwcwKdxGfRNAAyZ2CH5xDoA7\ncfrATJ2BEZG3AHwDwJdg9m27ALr2sS8DcyjTLwL4VyKiAB5Me8KkiNyz82YBQFXH9lmz4wFgHcAT\nVd0LvFaCCa5aMIFVBUDLVj0vnC4K9lHkbsuhFNJ/RER0kXK5HIrFInZ3d88deKyvr8N1F/J/61eK\nDVC+LCI5mDgCAJw4gYtvYgAjIl+C2dp0DGBXVT+cdnK79/uOiDwA0FbV98eMrdsxHf+5iJRUtTVq\nvKrWAs97Nmr1g5gsgLr95QHYWtTgBQAKcAAgoQoYIqL5cl134Q9MHK5VuYS1K4mz34fPfC8WkXfG\nxQhhxgYwIvJVAGuqeifaLRp229Q+gH0RuSUiu6o6ap93JRiQwDS8qcFkUIbvKwPg2dDlBkywshe4\ntgQgu8iBi6+ALoCk8i+A2MwL8y9EdNH8E6C3t19+O3Acp59JabfbqFarZ5aXarUa1tfX+8+HAyDP\n83Dz5k3kcjnU63UUi8V+Lc76+jqePHkysByWy+VQq9WwtraGjY0NPHjwABsbG6hUKvA8D81mE7lc\nDoAJuO7dM0n/TqeDWs18u+r1egO1K8+fP0cmk+mPyeVyqFar/a8rrF4n+HUtegBkS0y8YGddEfnK\nFG+twXTqndrIAMZmXlxV/UGUCUdR1ccAHodFWSIStgh6CrNWFiYLoC4iwSWhU5glrOBnejDZl4Xn\n9JeQmIMhosXS6/XQapmfRZ88eQLXddFutwe+WW9tbWF7exulUgn5fB6FQgHPnz/vv14oFLC/v98P\napaWlvrBhe/09BTb29v9QAMAyuVy/32ZTAb1eh3tdrv/erVaRb1eR71eH5jv1q1bePz4cf8eO50O\nqtUqGo0GisUitre3sbu7C8AEHQcHBxC/WBHoj/GDmFwuh7W1NSwtLQ0EMBsbG6jX6/2v6xIsh/0Y\n5hvVeuCa/z1/1BeXAXAz6geNDGCiLBVFMSJFlIUJQII8wGRbbCASnMMVkcJQZmUDg/U4EJFKYN5c\nsEaGiIguRqFQ6GdLSqUSPM/DrVu3+gELgIFgIZj1yOVy/exMMCPT6/WQy+X63/Adx8GDBw9Qr9cH\nPttxnP58uVwO3W534PVsNotcLodMJtOfv9Mx30qCAVaxWOwHG5lMZqpMyfDX4z/3l5ocx0G32x34\nuoaDsgV0C2cTB66qroUN9olI5AWGtJyF5Bf/BvmBx/B1AICqOv5ju6Tkb8XydVS1qaotW0ezYgOa\nM0SkIiJdEel++umnsb+IWbqPHdzHTmLzfYBNfMA2dkQ0B5lMBtVqFeVyeeBap9NBs9nsZ2t83W73\nzDf24HM/EGg2m/C8we+dpVKpH5B0Oh3cuXO2ImI4GHEcB9ns2W89mUzmTAA0ybhAJ+zrWnSq+mFI\nYe40W6Rrk4cMmkkAIyJvicg7Ed4Stszj/+sZzsyEOQBwK5iRCal78WtqzrCBzpqqrr3ySjpb4+zg\nXezg3cTm28QjbOJRYvMREUXhBwh+wLGxsQHP81CpVM7UtgQzLWGKxSIqlQq2t7extbU18Nrdu3dx\nenqKVquFbDaLRqMx8d5yuRxOT89+6/E8L9GAY9LXdVmMO0YgMCbybqRZZWC+jWjR1Jn6Ff/58PLR\nMLt7qT6ckRERtZkZnwezrXoh7dgcTFI2bQ6GiGgednd3USwWB5ZSgoGLH9i0Wi0Ui0Xkcrl+JgUw\ny0uO4wzMee/ePTiOM5DBefLkST8omnbXk7/MFQwuWq0WSqXSwHJUMNszfC/T8L+u4HvjzJN2trB3\n+NpNEdk/uCFxAAAgAElEQVQSkXemLPI9YybpBlX9csTxjogMBypZDNW0DLO9XoJbr/OBQGZvKPjJ\nYXQBUeq9a5ePvp5QEe8jLh8RLYxFbOXv7/5xHAeNRqP/zf7Zs2dwHAfFYrFfr5LP53Hnzh3s7e31\n61EajQbq9Xp/menx48f9pnXZbBaZTKafUel2uygUCqhUKshkMiiXy6jX67h37x5WVlawtLTUz/j4\nu4JKpVL/3rrdLvb29vrvB0yNze7uLtbX13F6egrP8wZ61uRyOZRKJTSbzf79ZDIZ1Go11Ot1uK6L\ner0Ox3H6c/tFv7VarV/c+/jxY+zu7sJ1XWQymf6fk198vOi7kqwqgN8IXrAZl33/eZxt1LE68c6C\nzaQ88fu+hDzPAcgHnhdhghK/8CcLoOpvxRaRe0ON7doAGqP6yvjYiZeI6HLwC3u3t7f7wYHrutja\n2hrY0XQZpakTr4i8p6q/MWHMN6dteus7dwbGnmXgVxd34x4foKo1EbkX6KB7PBRsFAGUYc5TyMDU\ntACm/4svOL5pO/V6AFYwRfCSZnn07KNkovEt+E2OF//8ECKiMJ1OB+vr6/0shr/TaG1t7IYYOicR\nuYXBNmM5EfllABIyPAOz5TpyiUfsDIyIfB7mgKZbgZtSmGWfiqr+ZayJ5yytGRg/BXN8dJTIdCur\nq+YBUzBEdIn5TfOy2Wx/Kchf/rnM5pmBsece5QHcBVCCiQ1GFfKewpSCjM3QhH5OzMMcb8JkQOow\n5yG5qvrCXi8D2AJQ4GGOyemJaWSXOUqmF+/jVbP2XBl/3BQRES2gaQKYqOcPDr23oarVKcbdA3Az\nToAyce6YAcy4IwH8viy1cWPSKq0BDGtgiIhoWpMCmLDzBxGoO50wdx1AUVULU4x9A2ZV5rvT3/10\n4m6jHtvJx+7+WdgdP0RERJdcxQ9erDYGm8GGshtqpqaqL6YJXuJspY4bwEzzczt/tk+x6zjBdZzM\n+zaIiOiCxTh/MKiIl5tokrqfmwhsqZ5W3AAmrJJ42PPJQ2haT7GMp1hObL4T3MAJbiQ2HxERLYyx\n5w+OepNtXxKpEFNEbonI3477BeAIwFLEr2H8NmrbPS/sLKLnIvIAJgob/kPIAshE3c9N4y3jEwDJ\nnUV9gut2XiIiumImnT84qgN+RlW94KnbUzgF8CHMBp+w+1gH8AaAyDtKJvWBeR9mK9SoepaRBTwi\nsjKLquOrahlPAQB/hL9JZL4bdvmI63xERJfSKyISrFdtBnYZRT5/UERKMXupuQB2x5x19KGd/x2Y\nmGNqkwIYF0A5ziFLlKxP+rmSpHIwRER0iX2qqqM69kU6f9AW7o49l3AUe5DjD6YYGimtA0wOYMZF\nTURERLRgYpw/mIfppusX/64DyNgeLy1VTWLXceQFgbEBjKp+GHbdHh+ARWxUt6ga/Zb/UQ75Hq3b\nX/3rjR1HRESXUnNoWWgDgaN5gucPDi8diUgFQC543uAotg/MrQnD1mEyQLM/zFFEfgTTafda5Den\nXFob2fEoASIimlbETrw5AF6wE68NUsqqujH0ngpMQe4agF2Y2pqRy0v2XKS2/ZywcacAfhRn40/c\nAGYLwEO7tnWppDWAqYj5d1U7mhTITufOqvl31JvcSJGIiBZMWk6jtj1eaqr6jcTnjhnAfBVAT1U/\nHjNm4vHZaZTWAIZHCRAR0bTSEsAAJoiZRT3tpCLeUY4BlETkmn0cVsAzTUc/IiIiusSCwYutofV3\nR3XPU0sbN4D5sf09dL84TDXzGzHnphC3cWgf/Vwi893Hjn20M2YUERHR+YnI52Ga1d3Cyy3TKiId\nmHOZ/jLynDGXkLpj9pf7Yx6q6p3Ik89ZWpeQWMRLRETTStsSEkwhbx3mMGhXVV/Y62UAWzAbgyJl\nY+JmYLamGLMbc24KcYjbAJLKvwA7uG9/JyIimqmKqq4OX7RLS3si0gSwbX9NLVYGZuRkZr/3HZil\npeeq+uMJb0mdtGZgWMRLRETTSlkG5quqOrYbr4hsqWqkE6njnkYdSlVfqOq+vdGwg5uIiIjoapnm\nR+VkO/GOIyJfAVCFPX166OUsIh65TRcr3+/Ayz4wREQ0U9Occ/Q86qSxAhjbWW8Ppu2wC9MG+Il9\nOQcAqvrdOHNTOLV//8dIpoi319/FxjUkIiI6PxF5Ey9PtQ56LiIPYAp5h3cvZwFk4nTijZuBKQUL\nckREVfUPggNE5B1VjXSuAV2cHsyZXMy/EBFRQt6HOfhx1OGOI7/liMhK1Oa3cQMYZ+j5NRF5fWgL\nVOSjsWk0sZmSIyRTxLtml5CYfyEiooS4MOcnJd51N0xSRbwPcXb7ExvZERERXR27FxW8APEDmK6I\n3BKRvxCRb9pDHQsi8p6IvCUi78DUxRAREdEVoKofxn2vjRsiibWEpKof2g56TQCP7eUygBaADkw1\ncTLHJhMA4ANs2kffT2S+p1i2j04SmY+IiMgW8nrBw57truVJajA1NNN/VpKN7C6DtDay41ECREQ0\nrXk1shORUwDHqro+dA0YXdybAXBTVT8b5bNi94Ghi7WJDwAklX8BlvEUAPMvRESUqFsAvKFr7jTn\nJ0b9oJEZGBF5U1X/JOqEEz9Q5K00HzGQ1gwMjxIgIqJppewogTdsrey4MTejFgCPLOJV1T8Rke/Y\n9axzE5HXRWQXo1NIREREdPmoiLw+dkCM3Utjl5BU9dsi8i0RqQJoxMnI2Ju+C6AIYCvqcdlkbKFp\nHyVTG91AxT5qjh1HRER0Ti2YJnbXkpx0qiJee8p03d5AB6YdsBusMg6MfR3mOIEigC8DWAJQS/Oy\nUVBal5BYxEtERNNK2RLSFoCHk5aRopqqiNd+6DfsjXwJNqMiIhm8PMxRYQp3TmE69XYBVKOkhUTk\nHswSU9Z+7tj0gB0P2LOYVHXvPPOlWRNbAJLbm15Bw85LREQ0U6cwyYyRAYyIvBf1KIHUbKMWkTqA\ntqp2As+fqGpr1HhVrQWe9wA88IOYqPP50pqBYREvERFNK2UZmDdhVmWuAThGeC1sQ1W/EGneFAUw\nz1V1KfC8CLP0tBEyNgOgEsy4iEgFQN2fI8p8QQxgiIho0aUsgPH7wAyfRO3LAnhjIfvAiEg+5PIp\nTMQWJgugLiItVXUD4zMx50u96wl3bLmNQ/toc+w4IiKic5pJH5hUBDAwAclwZOYBJtuiqgNNcVTV\nFZFCIHgBgA2YAuPI8y2CE9wAABwjmSLeQ7xtHzEFQ0REM7U1xZjdqJOmJYDxi4GD/AAki7Nd/aCq\njv/YLindgdklFXk+u/xUAYBXX301+t1fgBNcT3S+Q9wGwPwLERHN1jSHPMY5CDItAUxYRsQPQEat\nmQUdALgVyMhEms/uTmoCpgZmis+7cDfsEtIRkqmBedsuIaXyiyUioksjrLO/PRC6CPNt6FRV/yDq\nvCM78V6wfv1KQAYAJi332N1F9WBG5jzzERERUaKqwxdU9SNV3VfV91X1D0TknaiTpiKAscHHcGCR\nxcuallAiUsLgVun8eeYjIiKiuRhOOkyUliUkAGiKSCnQp2UDsN3WAIhIDkDef91ui84C6AQa6t2F\naaI3cb5F0+2X90Qu1A6lkP4jIiKipIjILQx+c8mJyC8D/W88QRmYZrS5yJ+TZB8Ye4xANuyIgSnf\n73fOzQHwgp1zbaFtWVU3bMDyPGSKlqqWp5lvlLT2geFRAkRENK159oGx9S15mKRCCSaYGdWF9xRm\nJSVSF14gZgAjIt8B8CWYbEdDVT8WkT+ECRQe4+X5Rx9HnnzO0hrAFKQHAHh4FDnLFoqN7IiILq+0\nNLKziYSbcQKUSeIuIT2BCVw+AvoBTS7YBlhEvgnge+e/RQIAp7+ElMwuJCIiogvQgG1TkrS4RbxL\nQ4c0lnC2vmTqQxyJiIjo8lHVF6r63bDXROR1Efl83LnjZmD6wYld67qJszt8uDiRoPvYsY++nsh8\nH/Rb2B2OHUdERHQek8pORCRW2UncAOaNwOMSgI+Gm9TAnDpJCdnBuwCA44QCmE08SmQeIiKiCWZS\ndhI3gHlhP0wA1GGCGH8X0gaAbwMoj347RbWD+wCSyr8Am/gAAPMvREQ0c2FlJ783NCZy2UnsbdSB\nNsCdQFT1rcCQ56r6fqzJ5yitu5DsLmocHSVTxMtdSEREl1dadiEBpi+Mqj62j28COAJQCK7ciMhX\noh4nELuRnQ1a9oeuhRbqEBER0ZU1k7KTWAHMrA5motHy6NlHyfSB2YLf028mu9uIiIh8Myk7idvI\n7r1JTWlE5B0uISWInXiJiGhKaVpCAmZTdjLLs5CSSRUQAKCHPIDk/lCb2ALA/AsR0VUVOG4nCwDj\njtuxR/hUYA5KXrHja9N+1qSykzinUU+VgQk5mKkG4DuYcDCTqt6NekPzltYMDIt4iYhoWpMyMCJS\nhzmDqBN4/iRwAPKZ8cGARUR6MFujm0Pj3oQ5e/DjwLWvTHHL9eC26mlMG8BcyMFMacAAhoiIFt0U\nAcxzVV0KPC/CNJPbGDH+GCbIaNrnBwAQPEDZXj8FcKyq60PXAJPtCZOBOS/ps5O/spemWkKyqZ+P\nAPxglgcz0cW5jhP7aHmu90FERBdLRPIhl09halRG2VDVYACSA/AgZNwtmGWmIFdV1ybc08Nxr4eJ\ncxZSA6OjqODNvBVjbhrhKZbxNMFg4wQ3cIIbic1HREQLIwsTsAR5QL/W5Yxg8OIHQKq6FzLuw6Gm\ndYAJaiaZup7GF7mIV1VfABjb78VujaoB+HHU+SncMj4BkNxZ1Ce4buclIqIrJgNbuBvgBzRZnM2g\nAOgHN3dgtjxvRfg8tUmNjJ3bHT73KCTomShuH5jXAbQwOqoS8DDHRC3jKQDgj/A3icx3wy4h8S+J\niOhSekVEuoHnzUDBbViA4gc0w5mZPlX1ADQBNEWkJyJniniDbPHuNgB/yaofG4iIC+A7qvq/T/XV\nhIi7jfp9AAcwWZawPwgBEHk9i0b7pJ8rSSoHQ0REl9inY+pOTnG2K0cG6AcpZ4hIZui1hv0VGsCI\nyI9gampaMPGACxMv+NmfNQD7IlIFUFTVv5rmiwqKG8C0VXV/3AAR2Y05NxEREc2IqjoiMhyoZAF0\nwsbbHUptEVkaDnBCAhs/ePFgDnEctWN5X0RqMJ15H8O0X4kkThEvMCbF5FPVH8Scm0I0UEEjwbZz\nXRTQRSGx+YiIaKE0RaQUeL4Bk1EBAIhILvB6F2YJyhsa3woJXr4K0wfmzpjgBYDJ9qhqFcBHM2tk\nd+ZNprHd8XARztCYb6rq9yJPPmdp7QPDowSIiGha0xwlEOjEm4MJOpqB1yoAyn5fGLvzyN9mfQ0I\n78QrIn9o3xdpSUhE/lBV/2mU98RdQlIAVVuR3MPZjEwWQBXAwgUwaVWxgXHkfWYjFGBqu3oTxhER\n0eUUtg068FoTgfoWVXUAOFNM+yJOPQtGN8cdKW4A47cadhG+bpUBcDPm3BRi3y4f1RIq4nW4fERE\nRMmbWGKS1PviBjAz6apHRERECy1uXULk98UNYMqThyS22kEAbuPQPvq5ROa7jx37aGfMKCIionSK\nVcTbf7NpaLcG4FRV/8Ree9N/vIhYxEtERItumiLeGX3uEwD3YPrBRVEPHgA5jbgZGIjId2Bu8jlM\nkxr/cMcXi7oDKc0OcRtAUvkXYAf37e9ERESJKcD0k4kawET+aTruNupvwWyj2lXVFyLy1eG+LyLy\njqq+H3nyOUtrBsYmYHB0lEwR7+rqCgAmYIiILqM5ZmC6mK7MZOBtAB5Oqq0dFjcD46lq8EDHsG+D\nz2POTURERIupE+dgRhEJ7QI8TtwA5tkUY7iNOsXy/Q4w3E5NRETJUNVvX9T74h4l8ItDzwfWukTk\n8wBWY85NIRQCjbykOFoPa+ghUraOiIgoNeJmYBq20vh3AHyIl8djfx5m7asC/mifaj17ujn/koiI\naBHFCmBU9SN7BHYTQB6AivSzAx0AX47TSjhwLkPWfk7oMd1D7ykBWB8+k8Fez8F0DT6FCapaqupG\nva80EFtmdJRQJ941u4TEGl4iIlpEsQIY2+vFAbAmIjcB++M84MQp3rFz1gG0VbXjPxeRkqq2Rowv\n2s/dgAl6hmVhjumuwxzrvbWowQsRERENiruEtA97BpINWGIFLUMqQ1mUNkw339AAxgY6HRG5BnP2\nUpglAFkGLkRERJdL3CLegog8EJG3krgJe0z3sFO8PLo7FlX1Lkvw8gE28QE2E5vvKZbxFMuJzUdE\nRHSR4mZgaqr6XRG5KSJbMKUUHVX9OOZ8WZw9idIDABHJqKoXZ1IRqQTmzY07OjztNvEIAHCM7ycy\n3zI+SWQeIiKieYhbxPtd+/tHMMtJEJFbIrIB0yOmE7GINwNbuBvgBx5Z2GAmok4w+yIiDRGpTFMY\nnEab+AAAEgpfgGU8BQCcJDQfERHRRYq7hHSGqj5W1X2YnjAfich7Ed4eFqD4Ac1wZmba+xleOvJr\nahbSI2ziUYJLSJ9gGZ9wCYmIiFIgTklKIgGMiHxeRHZF5BnM1up9AFGWa05xthA3A5g6lhj3kxER\nFZHgnB7Mtuqw8RUR6YpI99NPP436cURERHQ+jahviLuN+gGAdwDcBfANAF+C2S10R1UfR51PVR0R\nGQ5UsjA9ZeLaGwp+cgjfbu33m2kC5jDHc3zmzGzBX/m6lch8DVTso4VcUSMiopQRkR8hqW9SU4hb\nxFsGUALgAGjYpaPzag71fdlAICITkRyA/Ki+MEGq6tls0PA9L+wSUhNVAMAxjhKZrwL/r4wBDBER\nJeYbOJt8yACoAjjA2bKQFQB3APxe1A8S1egJBxE5BVCI27RuzLx+J94czInXzcBrFQBlVd2wz/Mw\n26yrMNmaXZjCXce+noHpvuvB/AE9mSb4ee211/SnP/1pkl9WIppSwb/d/CP8ceEnoa//fPbn8cNf\n/WH/+ervjz6K6rf+4W/hL3/l75h5tTJyHBERLSYR+WtVfe2CP3MrLKEhIu+o6vsT3jtxzLC4GZjd\npIMXABi3zTm4zGOfOzAZoND32OWjhd02PayKJkw8Fh7ARLVvl5CYfyEioiSMWY15PsXbpxkzIFYG\n5swkIp8/Rw+YVElrBsY/auroKJmzkFbvPgb+px3gvwvvB6P3X/67KDQLcD5xQsdt5bfQ3GQYRESU\nJvPIwIwytwyMPWE6B7MM8wbMgYgfDw1bsj1gVmDOJzoGcKyq34tyMzTaz17/j/bRF5KZcLOazDxW\n78QcDllY5vnWREQ0YFVE/q6q/mXYiyLyOoDRdQ8jTMzAiMjfwhTkVKfNsojIAYCvqOpno97QvKU2\nA/OuScEcfS2ZIt6VVftvJYEMHPDy/oKZGyIimo80ZWAAQESOYIp423i5IzgPc65iCaauNkoD3Klq\nYASmeHbqiVW1LCL/LcqN0MU6xG0ASLA1HhER0UhrMGWXNZjjhwATXxwAWIsavADTBTBRjwXwhRdN\nUCq8jUMAL/8VERERzYrdWHMHAETkS/bah+eZc5pOvAPN30TkWyLyIxH5WxH5C9uB951J7yMiIqKr\nS0Ret0cGqB+8iMibceebJoAZ+CFdVb+rql8G8AIm7bM9onI41hlGREREdLmIyHdg+rK1AAR3kbwQ\nkW/GmXOaAEZGXO+o6os4H0rzpxDoyL9aIiKiZIjIt+zDJVUdOCZIVT9S1e+NWMkZa5oamFFlEsOt\n+qd9HxEREV0dnqp+N/A8LD6I3MhumgBmTUR+GWczMbkR133FqDdDo32x8W/Ng68lM5/Yfz9JRZnd\nrW5CMxER0SUzKeEBADejTjpNAFOASfeEBSobY97HDEyCfvKJH7kk04k3aWxgR0REI/wigD8IPB+I\nJ2zD3MiN7KYJYByYk5yjEAAPo94MERERXToNEXkC4HcAfAib4LCBSxnmoL/IPwVP2wcm8sGNIsI1\nhQTlN/2/22Tiwg/6LewOE5mvcmgPh+S5SEREFKCqH4lIFaaRXR6AivSTMB0AX47Tby6RwxwvEx4l\nEA+PEiAiSo+0HSXgE5GbMEEMADhxEiS+aTIwdAlt4gMASeVfiIiIJrMBS+ygJYgBzBX1iKcgERHR\nBRCRvwBQUtX/e+j6FoAMgLsAXlfVvxdl3mka2RERERHF9TEAR0R+J3hRVfdtd/81xIhHGMBcUVto\nYgssuCUiopn7PQBfAPBle4bi/xAyphV1Ui4hXVHN/lEUlbneBxERzYeI3IM5eDkLAKo69qdaOx4A\n1gE8UdW9KT9KVdWFaYx7D8CHIvIdVf1XgTGRm5wxgFkQXzz5XKLzNbEFILnwJX89P3kQERGlgojU\nAbRVteM/F5GSqoZmQkSkrqq1wPOeiGDKICbrP1DVPRFpAXgoImWE1MZM/TVwG/Wg1G6jtlvmj46S\n6cS7uroCILFd1ERElCKTtlGLyHNVXQo8LwKoqeqZDvsikgFQCQYrIlIBUA/OMeazHqjq3ZDr9wB8\nB0AdwDNV/d6kuYJYA0NERHSFiEhYyvwUo88wzAKoi0huaHxmyo8si8iuiLwpIq/7F21AtArgyzBB\nTCRcQrqiruPEPlqe630QEdGFy8IEIEEeYLItquoFX1BVV0QKto7FtwHTRXcaS/YzM/b3ftddO2fB\nbqmOhBmYRbEj5ldCTnADJ7gxeLFQMGtVvV7gc3fMtZ2dl9d6PXOt8PLoCnlX+t14iYgo1fxAIsgP\naIavAwBU1fEf2yWlO0B/N8hYqvpCVT9S1Q9V9eNRw6aZK4gZmCvsBNeZfyEiupxeGTqTsBnYZeSF\njPcDl+HMTJgDALeGMjIAABF5E4AXDFRE5CtTzFkD8P4U415+Fot4B6W2iDfhs5CSLuINy740bjdQ\nKdhDHntNVB+NDtaDZygVmgU4nzhnrhMR0XTGFfHaGpieqsq4ayPeO7B7KeT1UwDHqro+dA0wW7bD\nZADcVNXPjvvsYczAEBERXSGq6ojIcBYmiwk1LSJSwuDW63xwacm6hbMZHtd22x0398PJdz70HmZg\nBjEDkw483ZqIKL4ptlHXYZrRtUY8zwHIB54XAeQA+IFGFkA12BtmzGe9oaovJoy5GfVkahbxEhER\nXTE28MiJSMn2YzkeamJXhC3StUW7bQANAM/tr2OYgGaazxobvFhfjXD7ALiEREmp2J6+TZ6vRES0\nCMZ10bUFv0372AMwcZupiLyF6XvDDKsCiNTIjgHMgvilw39uHnxtvvcx0v6++Z0BDBHRVfVtAGsY\nXaw7SgbAzagfxgBmQfxx7/fto2SOEkhco5HodFv5yD2NiIhovjwAhai1LACLeBOR2iJenoVERERT\nmlTEO6PPjFyIe573pioDE/Vob/ueEoD1sEroOPOl1T8o/Av76F/O9T6IiIjCRA1AROQNmI6+pzCF\nwYsZwMQ42rsIIA9zHkNYN8BI86Xdn27+G/sopQHM4aH5fXMzkel6J+Y4g8JyYcJIIiJaRHZ30j4A\niMh7AH4c5f2pCWBgjuoOZlHaMK2FQwMOG5h0ROQawqueI81H5/T22+b3hNak1vZNzyP2gSEiWnz2\nOIEqXh7qGJTFy/4yU0tFABPjaO8Lne9cfnJFDji8fXved0BERCkkIrcA7MH0kXEBrAN4Yl/OAYCq\nfjfqvKkIYBDxaO85zEeT+EtIREREg0qquuo/ERFV1T8IDhCRd1Q10mGOaenEG/lo7wuej4iIiOIZ\nPi/pmoi8PnQt8nJFWgKY8x7tfa75RKQiIl0R6X766acxPo6IiIim9BDA9tC1N6JOkpYA5hRni3oy\nQL+F8UznU9Wmqq6p6torr6RlVW3BiLxsVkNERPRSV0RuichfiMg37e6jgoi8JyJvicg7MHUxkaTi\nu3Xco70var5U2LG7cb6WbCfepGIO7hUiIqIwqvqhiNyEOVvpsb1chtkV3IHpAXMr6rypCGCs5lCf\nlg2YimUAZ4/2Pu98F+X4Z44u+iPnQmwIk1Qg093qJjQTERHNm21y993A8xcw35djS9VRAoHOuTkA\nXrBzrohUAJRVdcM+z+Plcd9ZALsAOqrqTDPfKEkfJXB8nGzr/6SOEgCAlZWVxObyMzkp+udERHRl\nzeMogfMQkddV9a8ivSdNAUwapDWA+ad7piPtH97rJTIfwACGiOiyWsAA5oGq3o3ynjQtIdEYx8sv\n5n0LY30A/wiBZPrBVA4rAIDm5sIeX0VERJYt1N1AeOd8IEajWQYwlIhNPEp0vn1nHwADGCKiRSci\n34E5u9AB8CxsCIC1qPMygKFEbOIDAEnlX4iI6BJ5pqpfHjdARCI3mWUAQ4l4hGROoSYioktnYjGo\nqn476qRpaWRHREREl9O1SQNE5K2okzIDQ4nYgl+rUpnrfRARUbqo6r4t4nUBdIe3S9tzkWoAfhxl\nXgYwC+KXel80D7423/sYpYmqfcQAhoiIXhKRzwO4A7PTSOVsC3hBjD6oDGAWxB8f/rl58L8le5RA\nUprYApBc+JK/nk9oJiIimrM9AAcwWZaw8w0F5oDHSNjIbkiSjexmcbYhO/ESEdEkaWpkJyJbqro/\nYcxXVfUHUeZlBmZB/Oz1/2gffWGu90FERBTR6aQBUYMXgAHMhUgia7L6+7/iz3buuWbhOk7so+XE\n5pR3R6ewGrcbqBRst95eE9VH1ZFj9T7TQkREc+SJyOdV9eNRA0Tkm6r6vSiTMoChRJzghn3EYIGI\niAYogJKIrADo4WxGJgtzMHOkAIY1MENmUQOTTAZm1cz1teQyMEnWwJyIybws68mEkURENGspq4Hx\nAxZ3xJAMgJuq+tko8zIDQ4m4YZeQ0hgOF5rmJO9eJbmTvImIaGquqo4960hEIu9CYgBDl57ziTPv\nWyAiusrKU4ypRZ2URwkQERHRLD2z3XZHUtWPok7KDAwloouCfcRlGiIiGtACUMAUZyJFwQzMDCkE\njUBv2s/+1/+KldVV/N1//I8Hxv33v/ZrWFldxat/9mf9a0u/+7tYWV3F0u/+LgDg0d/7Hh79vUgF\n2g5hnNAAABWqSURBVBeqAAcFcKmGiIjOOACQS3pSBjAzVsHY5oNT++Iv/jq++Iu/nshcs1BAFwV0\n530bRESUPqcAlsYNEJH3ok7KbdRDktxGXRFzQnPt6FYi8yXtqhwl4DfEY0M7IroqUraN+k2Ygxyv\nAThG+HbqhqpGajXPGpgZ2rfLRzWcvw/Mv/6//jUA4Lf/0W+fe66rZiu/Ne9bICK6yn5sfx91pEAW\nwBtRJ2UAsyAeHD0AkN4A5j527KOdMaPmo7nZnPctEBFdZewDs2hu49A++rm53sdF2MG7/UdEREQB\n06TBd6NOygBmhg7xNgDgOKUHMCZpB/ft7+nTOzFbuwvLhQkjiYgoaar6of/Y9oPxszFdVf2r4THT\nYgAzQ4e4DeAq5F+Ad23osjPXuwi3tm/+W2ERLxHRSyJyD6agNgsAqjpxvV1ESgDWVTVS51wbuLwP\noBS4rCLSAVAdd1L1KNxGPUNv4xBv95eRiIiI0kFE6gAcVW3ZwGXFBiejxhdtwFOFOXwxyme9AdPM\nrg1gRVU/o6qfAfAFAI8BHEzq1BuGAQwlIo8e8uzCS0S0KCqq2gk8b8MEJ6FUtaOqe0CsjqVbAMqq\nuh88MkBVXTvnXQDbUSflEtKC+Pnsz8/7Fsbq9Zc0uUxDRJRmIpIPuXwK06tlFl6o6otRL6qqKyJh\nvWHGYgAzQwrTQC2JIt4f/uoPzz3HLPVg/ntgmSwRUeplcbYniwcAIpJRVS/hz5vmJ1v2gaH5WLPL\nR8y/EBGlXga2cDfAD2iysMFMgpZE5HV/x9EwW//ys1EnZQ3MDAkUwm/pRESULmEBih/QjOqWex5N\nAC0R+Z+Dxboi8rqIvANTyPs7USdlBmZBrP7+KgDg6GuXv6dM0rpbPGSSiK6cV0Qk+D+/ZmCb9CnO\n7iTKAMAMlo+gqi9EpAqgAeAHIhL8yd4BcGdUdmYcBjCUiKdYto9O5nofYdjAjoiuoE9Hte9XVUdE\nhgOVLIBO2Pgk2N1HXxaRmwD8ImInuCspqlQFMFGb6owbb/ez52D2np8CqABoqWrkSue4PsCmffT9\ni/rIuVnGJ/O+BSIiml5TREqq2rLPN2AyJAAAEckByAdeT4QNWGIHLUGpqYGJ0VRn0vgsgDrM0d0f\nwRwmdWHBCwBs4hE28egiP3JulvEUy3g679sIVTmsoHJYmfdtEBGlhu2kmxORkk0GHA8FK0UE+sKI\nSN6OKwG4IyL3wrZji8h7s773/meppqPIVESeq+pS4HkRQE1VN+KMF5EKgIcAslECl9dee01/+tOf\nxv0yBmyK6cL7/aPzHyYwixqYlZWVxOYSs2McKfnnNEDeNTfHowSI6KoQkb9W1dfm8Ln/DSbIiZww\nUNU/iTI+FRmYqE11ph2vqt5FZ12CHtkcDBER0RWSBSATfgHme3bP/orcRC8tNTBRm+pMNd5mYfxx\nOduymGagAX+JZuJZYHPjZ2Ly1/PoVXpnrodp3G6gUjBfW7PXRPXRy07bzOgQEZ3RUtX3xw0YOtjR\nP8wxcl1MWgKYqE11phnfCWZfRKQhIpWwwmAb6FQA4NVXX431BYTZ6n8zv3XuuX7rH/7WueeYpQr2\n7aP0BjBERDRbqnpn3Osi8k2Y+tQXMOcj/SDuZ6WiBsbWrxwM1bTkYApwl4YzMFHH29dLAOqqOrbw\nI8kaGL8w5Pgonb1bkqyBqYgJXJp6+YtlWVNDRGk3rxqYUUTkTQAHAFYA7Knqt887Z1oyMFGb6owd\nLyIZAM8xGMx4MNuqL0wTWwCSyL+k375dQmL+hYiIfHa5qA6zyvEYwJfP0/slKBUBTNSmOlOO3xsK\nfnKIURV9HlX77fwIx+ee69/9xb8DAPyzL/yzc89FREQ0a/aYgDpM0e6d8ywXhUnFLiSrOdTH5UxT\nnaHXR463gcuzofnLAGrJ3vLF+c3/9Jv4zf/0m/O+jZFu4xC3cTjv2yAiojkTkTdF5AlMUn5fVbNJ\nBy9ASmpgfIHOujkA3lBn3QpMwc/GlOMzMCkrD2bN7ck0HQWTrIFZFtNW/4+O/ubcc6W9D0yqG8EQ\nEV0xc+wD8x7M917/jKOplotE5IGq3o30WWkKYNIgrUW8aQ9gDsX0u9lUZmGIiOZtzo3sWjArItMG\nGCsAfk9VPxvls1JRA3NZneD6vG/hwrxtl48YDhMRXWkdAPfwslndNARmW3UkDGBm6IY9mTmJIl5K\nj0LTnG4dbIZHREQAzBmFH0d8z0ciEnkTKwMYooicT5x53wIRUSrF7e8S530MYCgR2s8WchGJiIhm\njwHMDHVRsI8ennuuJIt3iYiIFh0DmBkqwCw1XIUKGLGZF+ZfiIjoIqSpkd2lU0AXBXTnfRsXa3PT\nbB8/DGynbjbNtUrgnKSTE3NteXnw/YWCud5jgSwREY3GDMwMOf0lpPPnYH7tP/waAOCHv/rDc89F\nRES06BjALIj/fPqf530L0zkMaWRXqQxmXwCTeQlrorgAmZet/Na8b4GI6MpjADND97FjH319nrex\nmHZ2Bn9PkeYmz9wmIpo3HiUwhEcJxJP4UUg8W4mIKLZ5HSVwkZiBmaEd3AfA/Ess9+/P+w5G6p2Y\nZa7CcmHCSCIimhVmYIYkmYHxkwhHR+cv4r1yGZgUk3fNF6v3r8AXS0QL6SpkYLiNmoiIiBYOl5Bm\nKA9/R03m3HPdXb177jkugkQ5f3QM/8+up1ymISKis7iENCStRbyzMIslpKT0z1ZK4b9PLiERUdpd\nhSUkZmBmqIc8vlb5c/zE1q8Mu7t6F7/9j34bAPBnz/4Mv/5//PrIuf79r/x7/MK1X5jJfSYh6Tij\nJ3kAAPMvREQUhgHMDK2hB/Mt2Jn3rSycNbuElOYcR++k19+JVDmsYN/ZDx2Xv55Hr/KyQZ+fwRnG\njA4R0fQYwMxaszfVLqRfuPYLPHGaiIhoSqyBGZLWbdSzkGQNTNK4LZuIKD7WwND57PhLBcysRPUU\n/inVJ3O9j4vQuN2Y9y0QES0cBjCUSsv4ZN63cGEqhcrkQURENIABDKXSMp4CuAr5FyIiioMBDKXS\nJ/0lpMuv2TOnWzMTQ0Q0PQYwRHNWfVQFwACGiCgKBjCUSg3438ybc70PIiJKJwYwlEoV+E3hGMAQ\nEdFZDGBm6JcO/7l58LX53sdIP0n4AKMvJtfHpAKztbgJAIeHwNtvA7dvm8e+sGYxm5vAo0fABx+Y\nxwDQbALV6tVoKkNEdEUwgJmhP+79vn2UzkZ2abZvl5ASyb/s7CQxCxERpQgDGEq/zc3w7EnYtWCG\nxnf7dvL3REREc8UAZob+QeFf2Ef/cq73McrxzyTbIXgWBxNIIqtcJo+jLKchIro0GMDM0J9u/hv7\nKJ0BDKUDz0AiIoqOAQylUpL1tsvi9/NNd3O8QrMA5xMn9LWt/BaamyaF1DvpYW1/beQ83a0uCsuF\nmdwjEVFafGbeNxAkIvdEpCQiFRGZ2NVr0vio89HldIIbOMGNwYuFglmf6vVeXtvZMdeCRb+9nrlW\nYEBARJdL0t9zL1pqMjAiUgfQVtWO/1xESqraijM+6nx0uZ3gOm4E6mm6AAoACmuAn/O4D2AHwM67\nwLvvmmv/f3v3r9RGlsVx/HuqJtlyIjPROFuxTyDwE4wIncE42dTiDaCczUQu5g0QTzBjModmn8DA\nEyxMNN5oDMnsBrtVZ4M+LS5NAxJupHvF71OlwupuSX3U7e6j+3cAnAAnp7Aer69nyn7h3c7UdDI6\nuX8jYO3FmqqdROSrdH3PXQTzTMbGMLMLd3+ePB8Cu+6+8ZDtZ32/2rNnz/zPP//8ymjiM3+q7nj/\n/Hu3jWVztbr6GM14v143DYGvOC3jz2Rk9CG6oL9Sq2WRp8rM/u3uz+5Y3+k9dxGyqEIys0HL4i/A\n8CHbz/p+stzcu3284Hde8DtmMLYRmDGyMWZVsvTKPoAZH+zVZJkZk3+kyz7YKzCrXlMbj6uVo6SE\n9vPnatmLRjuelqqwg9MDDk4PEBFp0/U9d1FyqUJaofoyUpcAZtZz98tZtn/A+0kHzs66HbBv9b9/\n6+7NOhwl+LFmyq5Lit5QdfweH8B25CHfAZ+Bz//i3qowfoz3++lqw8FnGI8HrBOJzo+3F0vtf4Dj\nk/1qMMG1MbzavnVb/xGM+G5Ha/DilkbIJ7D+4Q3bjOG7E9i+oxHyPoz+dcwpa/BqBGvtyZhiUkzL\nFNOcq4U7vecu6p6aRRWSmW0CB43iqR5wAay6+/ks21M1XZjl/UYwmT1wAPynw/C+Af7X4fstgmLI\nxzLEsQwxwHLEoRjy0XUcfyH5XQOM3avRsLq+5za3n5dcSmDasreV+NvM+qbZfqb3i4P6KA0GzOzY\n3W9P+QugGPKxDHEsQwywHHEohnzMOY6u77kLkUUbGKovoNdY1gO4pWjqvu1nfT8REZGnout77kJk\nkcC4+yk3M7wV4Ogh28/6fiIiIk9F1/fcRckigQnjqGerbQD79RMz6zfW37n9FOvnZRn6siqGfCxD\nHMsQAyxHHIohH/OOo+t77txl0Yi3ZmY7wDnQBy7rBkexbgRspX3O79p+mvUiIiJPVdf33HnLKoGR\n+Yps+qW777asq0/UFZg0dJ56vTxtZrbv7tuNZTqnRKQzufRCWjo5X4xjBMUBVRHgje5vpU3TEN81\nwEvgk7v/3LI+2xtndEccUdUxr8Y+7Da2yTqGxr7sAesty7I/pyKp7wOHVA0XR8Bh2k20hGMR59Rb\n4FPsx3G0Y5hqHzOJYR/Yu6uLbiFx7HDVfqRX2vUpa+6uR8cPYA8YNp5vLnq/btnP/ZblF43nQ6qb\ny1Tr5x1D4/kJsDPtscjhWN0Sw6ikGJLP7sfnn5R4TlElLB6Pi+b3WMKxoOodcpI83wHelxRDfO5Z\ncizSx6iUONJrUTwflHZ9yvmx8B1YxkcuF+Mp9vNGAhP/wZr7PwB8mvVz3v9eywVilO5fCTfOuFCn\nCcv7xg0n+xga3/+wcQMt6ZwaxXnVv2V99seCqmHlqLGsV1IMSRwDqqS4fuwl67OPg0YiH8uK/L+d\n4yOnXkhLIdc5I2bwVdM0PPreXbcC7JlZP1k2Ga+goDmzNvx6sXCfqui/pBjqqslfW1aVdE7h7pfe\nUm1R0LEY0eje6jFWRykxxHHfc/dTdz+P4zEE3sX6IuIAvpjZ+/o8jirKX+LfpcSQLbWB6V6Wc0bM\noL6hpOp4VqZYP7f43P3czNYaN5sNri7eRcyZ5dfbVwxiWV1PXkQMoeful3Zz+u9izimY9L6oP79f\n0rFIkvl+nEsrXG93kX0MMEm4Jp8VsZwnn19EHMA28BH4zczeUcVQt+sqJYZsqQSme/ddjHPX6TQN\nj82vN0zsAT9QXTTg62+cc2Nmvbhx7lHN51grIoZ7GtyWdE4dufvY3Q8jntU4LlDGsZiURkYM9dw3\ne7G4hBjabHs08A5FxBE/Tvap5l3do+poUCsihpwpgeleThfjhyh5mob3wPdJiUYxN86othh7NebC\nQXLTzD6G+NV/17Ev5pxqqTr6CNQ9wrI/FsnnHCfLjqga8kIZMVwTVZPNqe6LiCMSx6P4f70BjMzs\nfawuIoacqQqpe9lcjB/C3U/N7M5pGu5avyhxodhLS2S451iYWRbHqqU4eD8eY8qIYcBVlQVUvzJ7\n0f3zsJRzyq5m132efHeXXJVqlHAsLls+L62WKCGGpm2i3Ugi+ziS6uDT+HtkZn8FfotNso8hdyqB\n6ZhnOmfEjEqZpgGYNIxLxxBJLxx33jjvWj8P8evyoq2xaiQ22ccQVRU/1w+qUovLeF6XaJRyTv3c\nuDn0ibGSCjkW58Blo2H75KZXQgwtNmmMV1VIHCvAH+mCOLem2sdMYsiaEpjHkcvFuJWZDeLX8Sbw\ng5ntpC3evRpErW9mm7HdWdq+4b718xQJwApwHO1I+sDrZJPcb5zHwLhx09ygKrmol+Uew0RUfW1R\nnR87dWJWwjkV3/cfjcVbXFUhQRnH4h3Xe6q8prwYgGu90NpKHLKOI35QbaTLIp40Gcs6htxpKoFH\nYpnNGbGMkiL/pkN330q2y3rOrEge6xvOt3DnSLxZxrAs7OaoyJ+aiVQJx8KuRqcGrvVqS9dnHUPs\nR49qYMe1tmqT3OOIH1TbVIlxXb030z4uOoacKYERERGR4qgKSURERIqjBEZERESKowRGREREiqME\nRkRERIqjBEZERESKowRGRLJjZkMzO0vm8BERuUYJjIhkJwYBU/IiIrdSAiMiIiLFUQIjIgvTnAOq\nbU4oEZE2SmBE5FHFnEjDmOfofSwbmtkZcBDP+2b2EfhH4+W9eN2wfp85776IZEoJjIg8mpjc8dTd\nj2JOoXpm52ttXGIW5d2Wt+jHbNdHMZ/PXmOmZRF5opTAiMhjOgf2zWwUice7ZN2XKV+fOqI90RGR\nJ0YJjIg8mihp2QW2gEmV0Vf4g2pWXhF54pTAiMijMbPNqALaAJ7Hsq9px/ItN0tlROQJ+mbROyAi\n5YnqoG2q0pB3VEnFKFa/BN65+ynw0szO3f3U3S+joW7tEkh7Ha03nsPN0pYhVWmOiDxxSmBE5CG2\n3X3XzHaA98Chu+8CmNmAqjfRc6pqo/W04W1UK+HuR2a2ZWabseoL0DeznWiwC7CbrH8JvIkGvyLy\nxJm7L3ofRKQwZjZy97GZ7QM/uPvzxnoHtqLnkYhI59QGRkRmEqUpv8bT9eTf9fq6GkiNbUXk0SiB\nEZGZuPt5tGfpAQPgY2OT9fh7Ot89E5GnRG1gROSh6kTlqLF8I/4eAyQNd+u/b6ka/f5C1atoCPyS\ntHshXrdJNdjdfnOdiIgSGBF5qA3g3N0vG8s3qRr11ss/pgmImb0FduvGvFEldaNrtbsfmtk2KskR\nkRZqxCsiD2JmJ1QJzFaybEhV0rLq7udRinJUJzORrJy5uyWv6QO96Hbd/AxPtxURqakERkRmlrR/\naS7bBzaSrs6njRKaIS0lKrckL4O2bUVEQAmMiDxM3f7ljZntUQ3x/5Kq6/Qk6WgZs2WNaBtzxza1\nIfAlSnFWYttxB/suIktACYyIPETd/uWU2UpJhkw/GeMGVfuZQwAzOwOUwIgIoDYwIvIA0f7l2N23\nZ3hND7gAnrc0/G3b/iIdIC8Gx5vqtSKy/DQOjIjM5I7xX+4zpL3XUttn9KmmFkifo+RFRGpKYERk\namY2Ak7i6duYC2laG0xf3dRvbLuJqo9EJKE2MCIytWhE+9BEYkg1MF2r6HW0EuPDNBv2vga+f+Dn\nisgSUgIjIo8qSmlWqUpVtsysd8vIuq+BEVU7l3Mz+xQlPj2q3k2qPhKRCTXiFZFsmNmwHqFXROQu\nagMjIjnp3b+JiIhKYEQkE0lPo9sGthMRmVACIyIiIsVRFZKIiIgURwmMiIiIFEcJjIiIiBRHCYyI\niIgURwmMiIiIFEcJjIiIiBRHCYyIiIgU5//DkOOhYSVT6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f6add2a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(bins_histo_wph_mu_ptb2, 'pt_b2', r'$p_{T,b}^{\\mathrm{sub}}$', fi_histo_ptb2_rot, 'info_dist_ptb2', log_y_axis=False, legend_loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lcsjlctjcND90b29v92yPLpVMDWh/Aa9/L8WnqhURuRnqoHvU17ulCBNoNFTVE5Ga7dwL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\nXNnZer9vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f6adec210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(bins_histo_wph_mu_met, 'met', r'$\\mathrm{MET}$', fi_histo_met_rot, 'info_dist_met', log_y_axis=False, legend_loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tVvsCDNd1sbGxgV6vd+Xzhmq1GlzXxeHh4ZXmSRMRuXIAs0hLSCKyM8FRApeO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\nCmBERESkcv4PKbscbQFrtR4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f689c2c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(bins_histo_wph_mu_ptl, 'pt_l', r'$p_{T,\\ell}$', fi_histo_ptl_rot, 'info_dist_ptl', log_y_axis=False, legend_loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3H3kEv15b68tcdu/cCW2sidBGIqJ+sG2nY/zMzEzEM6FWItIuQqzC6YbbThJAQURKqmo3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\nyZy/Acnx+9IP2KkzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f6ac11090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(bins_histo_wph_mu_ptbb, 'pt_h', r'$p_{T,b\\bar{b}}$', fi_histo_ptbb_rot, 'info_dist_ptbb', log_y_axis=True, legend_loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Alternative Distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We're also going to plot a few other things:\n",
    "* The distributions of the _kinematic only_ information (with backgrounds)\n",
    "* The same, but _without_ backgrounds\n",
    "* The distribution of the diagonalized information\n",
    "\n",
    "The kinematic only information is actually just the full information - the rate information, so this is trivial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Distribution of Kinematic Only Information (with Backgrounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "bins_ptw = bins_histo_wph_mu_ptw\n",
    "xvals = [ (bins_ptw[i] + bins_ptw[i+1]) / 2 for i in range(0, len(bins_ptw) - 1)]\n",
    "xvals = [ bins_ptw[0] - 1e-9 ] + xvals + [bins_ptw[len(bins_ptw) - 1] + 1e-9]\n",
    "thisrange = (bins_ptw[0], bins_ptw[-1])\n",
    "\n",
    "ptw_index = sa_bkgs_wph_mu.observables.keys().index('pt_w')\n",
    "#x_weighted_bkg[:,thisindex]\n",
    "\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax1 = plt.subplot(111)\n",
    "fig.subplots_adjust(left=0.1667, right=0.8333, bottom=0.17, top=0.97)\n",
    "\n",
    "ax1.set_ylabel('Events (arb.)', color='black',fontsize=18)\n",
    "ax1.set_xlabel(r'$p_{T,W}$ (GeV)', color='black',fontsize=18)\n",
    "\n",
    "ax1.hist(\n",
    "    [ x_weighted_bkg[:,ptw_index], x_weighted_sig[:,ptw_index] ],\n",
    "    weights = [ weights_bkg, weights_sig ],\n",
    "    bins = bins_ptw,\n",
    "    histtype='stepfilled',\n",
    "    stacked=True,\n",
    "    color=[color_bkg, color_sig],\n",
    "    alpha=1.0\n",
    ")\n",
    "\n",
    "ax1.set_yscale('log')\n",
    "\n",
    "## Second Axis for F.I.\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel(r'Normalized Distribution of F.I.', color='black',fontsize=18)\n",
    "\n",
    "fi1_norm = sum([fi_histo_ptw_rot[i][0,0] - fi_histo_rate_ptw_rot[i][0,0] for i in range(len(fi_histo_ptw_rot))])\n",
    "fi2_norm = sum([fi_histo_ptw_rot[i][1,1] - fi_histo_rate_ptw_rot[i][1,1] for i in range(len(fi_histo_ptw_rot))])\n",
    "fi3_norm = sum([fi_histo_ptw_rot[i][2,2] - fi_histo_rate_ptw_rot[i][2,2] for i in range(len(fi_histo_ptw_rot))])\n",
    "\n",
    "ax2.hist(\n",
    "    xvals,\n",
    "    weights = [ 1./(fi1_norm)*(fi_histo_ptw_rot[i][0,0] - fi_histo_rate_ptw_rot[i][0,0]) for i in range(len(fi_histo_ptw_rot)) ],\n",
    "    bins = bins_ptw,\n",
    "    range = thisrange,\n",
    "    histtype = 'step',\n",
    "    color = fi_dist_color1,\n",
    "    linewidth = fi_dist_linewidth,\n",
    "    linestyle = fi_dist_linestyle1,\n",
    "    label=r'$c_{H\\boxdot} - \\frac{1}{4}c_{HD}$'\n",
    ")\n",
    "\n",
    "ax2.hist(\n",
    "    xvals,\n",
    "    weights = [ 1./(fi2_norm)*(fi_histo_ptw_rot[i][1,1] - fi_histo_rate_ptw_rot[i][1,1]) for i in range(len(fi_histo_ptw_rot)) ],\n",
    "    bins = bins_ptw,\n",
    "    range = thisrange,\n",
    "    histtype = 'step',\n",
    "    color = fi_dist_color2,\n",
    "    linewidth = fi_dist_linewidth,\n",
    "    linestyle = fi_dist_linestyle2,\n",
    "    label=r'$c_{HW}$'\n",
    ")\n",
    "\n",
    "ax2.hist(\n",
    "    xvals,\n",
    "    weights = [ 1./(fi3_norm)*(fi_histo_ptw_rot[i][2,2] - fi_histo_rate_ptw_rot[i][2,2]) for i in range(len(fi_histo_ptw_rot)) ],\n",
    "    bins = bins_ptw,\n",
    "    range = thisrange,\n",
    "    histtype = 'step',\n",
    "    color = fi_dist_color3,\n",
    "    linewidth = fi_dist_linewidth,\n",
    "    linestyle = fi_dist_linestyle3,\n",
    "    label=r'$c_{Hq}^{(3)}$'\n",
    ")\n",
    "\n",
    "ax2.legend(loc='upper right',fontsize=16,frameon=False)\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig('plots/info_dist_ptw.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Binned Information w/o Backgrounds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### $p_{T,W}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "18:29 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "18:29 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_mu_smeftsim_met/estimator_0\n",
      "18:29 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_mu_smeftsim_met/estimator_1\n",
      "18:29 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_mu_smeftsim_met/estimator_2\n",
      "18:29 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_mu_smeftsim_met/estimator_3\n",
      "18:29 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_mu_smeftsim_met/estimator_4\n",
      "18:30 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 2\n",
      "18:30 madminer.ml          INFO    Starting score evaluation\n",
      "18:30 madminer.ml          INFO    Starting score evaluation\n",
      "18:30 madminer.ml          INFO    Starting score evaluation\n",
      "18:30 madminer.ml          INFO    Starting score evaluation\n",
      "18:30 madminer.ml          INFO    Starting score evaluation\n",
      "18:30 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 2\n",
      "18:31 madminer.ml          INFO    Starting score evaluation\n",
      "18:31 madminer.ml          INFO    Starting score evaluation\n",
      "18:31 madminer.ml          INFO    Starting score evaluation\n",
      "18:31 madminer.ml          INFO    Starting score evaluation\n",
      "18:31 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_mu_nobkgs_ptw, w_in_histo_wph_mu_nobkgs_ptw, fi_histo_rate_wph_mu_nobkgs_ptw, fi_histo_wph_mu_nobkgs_ptw = fisher_wph_mu_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_w',\n",
    "    nbins=20,\n",
    "    histrange=(0,1000),\n",
    "    model_file='models/met/sally_ensemble_wph_mu_smeftsim_met',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "18:31 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "18:31 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_e_smeftsim_met/estimator_0\n",
      "18:31 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_e_smeftsim_met/estimator_1\n",
      "18:31 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_e_smeftsim_met/estimator_2\n",
      "18:31 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_e_smeftsim_met/estimator_3\n",
      "18:31 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_e_smeftsim_met/estimator_4\n",
      "18:32 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 2\n",
      "18:32 madminer.ml          INFO    Starting score evaluation\n",
      "18:32 madminer.ml          INFO    Starting score evaluation\n",
      "18:32 madminer.ml          INFO    Starting score evaluation\n",
      "18:32 madminer.ml          INFO    Starting score evaluation\n",
      "18:32 madminer.ml          INFO    Starting score evaluation\n",
      "18:32 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 2\n",
      "18:32 madminer.ml          INFO    Starting score evaluation\n",
      "18:32 madminer.ml          INFO    Starting score evaluation\n",
      "18:32 madminer.ml          INFO    Starting score evaluation\n",
      "18:32 madminer.ml          INFO    Starting score evaluation\n",
      "18:32 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_e_nobkgs_ptw, w_in_histo_wph_e_nobkgs_ptw, fi_histo_rate_wph_e_nobkgs_ptw, fi_histo_wph_e_nobkgs_ptw = fisher_wph_e_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_w',\n",
    "    nbins=20,\n",
    "    histrange=(0,1000),\n",
    "    model_file='models/met/sally_ensemble_wph_e_smeftsim_met',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "18:33 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "18:33 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_mu_smeftsim_met/estimator_0\n",
      "18:33 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_mu_smeftsim_met/estimator_1\n",
      "18:33 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_mu_smeftsim_met/estimator_2\n",
      "18:33 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_mu_smeftsim_met/estimator_3\n",
      "18:33 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_mu_smeftsim_met/estimator_4\n",
      "18:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 2\n",
      "18:34 madminer.ml          INFO    Starting score evaluation\n",
      "18:34 madminer.ml          INFO    Starting score evaluation\n",
      "18:34 madminer.ml          INFO    Starting score evaluation\n",
      "18:34 madminer.ml          INFO    Starting score evaluation\n",
      "18:34 madminer.ml          INFO    Starting score evaluation\n",
      "18:34 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 2\n",
      "18:34 madminer.ml          INFO    Starting score evaluation\n",
      "18:34 madminer.ml          INFO    Starting score evaluation\n",
      "18:34 madminer.ml          INFO    Starting score evaluation\n",
      "18:34 madminer.ml          INFO    Starting score evaluation\n",
      "18:34 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_mu_nobkgs_ptw, w_in_histo_wmh_mu_nobkgs_ptw, fi_histo_rate_wmh_mu_nobkgs_ptw, fi_histo_wmh_mu_nobkgs_ptw = fisher_wmh_mu_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_w',\n",
    "    nbins=20,\n",
    "    histrange=(0,1000),\n",
    "    model_file='models/met/sally_ensemble_wmh_mu_smeftsim_met',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "18:34 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "18:34 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_e_smeftsim_met/estimator_0\n",
      "18:34 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_e_smeftsim_met/estimator_1\n",
      "18:34 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_e_smeftsim_met/estimator_2\n",
      "18:34 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_e_smeftsim_met/estimator_3\n",
      "18:34 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_e_smeftsim_met/estimator_4\n",
      "18:36 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 2\n",
      "18:36 madminer.ml          INFO    Starting score evaluation\n",
      "18:36 madminer.ml          INFO    Starting score evaluation\n",
      "18:36 madminer.ml          INFO    Starting score evaluation\n",
      "18:36 madminer.ml          INFO    Starting score evaluation\n",
      "18:36 madminer.ml          INFO    Starting score evaluation\n",
      "18:36 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 2\n",
      "18:36 madminer.ml          INFO    Starting score evaluation\n",
      "18:36 madminer.ml          INFO    Starting score evaluation\n",
      "18:36 madminer.ml          INFO    Starting score evaluation\n",
      "18:36 madminer.ml          INFO    Starting score evaluation\n",
      "18:36 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_e_nobkgs_ptw, w_in_histo_wmh_e_nobkgs_ptw, fi_histo_rate_wmh_e_nobkgs_ptw, fi_histo_wmh_e_nobkgs_ptw = fisher_wmh_e_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='pt_w',\n",
    "    nbins=20,\n",
    "    histrange=(0,1000),\n",
    "    model_file='models/met/sally_ensemble_wmh_e_smeftsim_met',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_rate_nobkgs_ptw = [ fi_histo_rate_wph_mu_nobkgs_ptw[i]\n",
    "                     + fi_histo_rate_wph_e_nobkgs_ptw[i] \n",
    "                     + fi_histo_rate_wmh_mu_nobkgs_ptw[i] \n",
    "                     + fi_histo_rate_wmh_e_nobkgs_ptw[i] \n",
    "                     for i in range(0, len(fi_histo_rate_wph_mu_nobkgs_ptw)) ]\n",
    "\n",
    "fi_histo_nobkgs_ptw = [ fi_histo_wph_mu_nobkgs_ptw[i]\n",
    "                     + fi_histo_wph_e_nobkgs_ptw[i] \n",
    "                     + fi_histo_wmh_mu_nobkgs_ptw[i] \n",
    "                     + fi_histo_wmh_e_nobkgs_ptw[i] \n",
    "                     for i in range(0, len(fi_histo_wph_mu_nobkgs_ptw)) ]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_rate_nobkgs_ptw_rot = [ rotate(fi) for fi in fi_histo_rate_nobkgs_ptw ]\n",
    "fi_histo_nobkgs_ptw_rot = [ rotate(fi) for fi in fi_histo_nobkgs_ptw ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fi_histo_nobkgs_ptw_outfile = './fisher_info/fi_histo_nobkgs_ptw.npz'\n",
    "\n",
    "fi_histo_nobkgs_ptw_list = [\n",
    "    bins_histo_wph_mu_nobkgs_ptw,\n",
    "    w_in_histo_wph_mu_nobkgs_ptw,\n",
    "    fi_histo_rate_wph_mu_nobkgs_ptw,\n",
    "    fi_histo_wph_mu_nobkgs_ptw,\n",
    "    bins_histo_wph_e_nobkgs_ptw,\n",
    "    w_in_histo_wph_e_nobkgs_ptw,\n",
    "    fi_histo_rate_wph_e_nobkgs_ptw,\n",
    "    fi_histo_wph_e_nobkgs_ptw,\n",
    "    bins_histo_wmh_mu_nobkgs_ptw,\n",
    "    w_in_histo_wmh_mu_nobkgs_ptw,\n",
    "    fi_histo_rate_wmh_mu_nobkgs_ptw,\n",
    "    fi_histo_wmh_mu_nobkgs_ptw,\n",
    "    bins_histo_wmh_e_nobkgs_ptw,\n",
    "    w_in_histo_wmh_e_nobkgs_ptw,\n",
    "    fi_histo_rate_wmh_e_nobkgs_ptw,\n",
    "    fi_histo_wmh_e_nobkgs_ptw,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.savez(fi_histo_nobkgs_ptw_outfile, fi_histo_nobkgs_ptw_list, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $m_{T,\\mathrm{tot}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "20:28 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "20:28 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_mu_smeftsim_met/estimator_0\n",
      "20:28 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_mu_smeftsim_met/estimator_1\n",
      "20:28 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_mu_smeftsim_met/estimator_2\n",
      "20:28 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_mu_smeftsim_met/estimator_3\n",
      "20:28 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_mu_smeftsim_met/estimator_4\n",
      "20:29 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 2\n",
      "20:29 madminer.ml          INFO    Starting score evaluation\n",
      "20:29 madminer.ml          INFO    Starting score evaluation\n",
      "20:29 madminer.ml          INFO    Starting score evaluation\n",
      "20:29 madminer.ml          INFO    Starting score evaluation\n",
      "20:29 madminer.ml          INFO    Starting score evaluation\n",
      "20:29 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 2\n",
      "20:29 madminer.ml          INFO    Starting score evaluation\n",
      "20:29 madminer.ml          INFO    Starting score evaluation\n",
      "20:29 madminer.ml          INFO    Starting score evaluation\n",
      "20:29 madminer.ml          INFO    Starting score evaluation\n",
      "20:29 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_mu_nobkgs_mttot, w_in_histo_wph_mu_nobkgs_mttot, fi_histo_rate_wph_mu_nobkgs_mttot, fi_histo_wph_mu_nobkgs_mttot = fisher_wph_mu_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='mt_tot',\n",
    "    nbins=20,\n",
    "    histrange=(200,2200),\n",
    "    model_file='models/met/sally_ensemble_wph_mu_smeftsim_met',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "20:29 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "20:29 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_e_smeftsim_met/estimator_0\n",
      "20:29 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_e_smeftsim_met/estimator_1\n",
      "20:29 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_e_smeftsim_met/estimator_2\n",
      "20:29 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_e_smeftsim_met/estimator_3\n",
      "20:29 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wph_e_smeftsim_met/estimator_4\n",
      "20:30 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 2\n",
      "20:30 madminer.ml          INFO    Starting score evaluation\n",
      "20:30 madminer.ml          INFO    Starting score evaluation\n",
      "20:30 madminer.ml          INFO    Starting score evaluation\n",
      "20:30 madminer.ml          INFO    Starting score evaluation\n",
      "20:30 madminer.ml          INFO    Starting score evaluation\n",
      "20:30 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 2\n",
      "20:30 madminer.ml          INFO    Starting score evaluation\n",
      "20:30 madminer.ml          INFO    Starting score evaluation\n",
      "20:30 madminer.ml          INFO    Starting score evaluation\n",
      "20:31 madminer.ml          INFO    Starting score evaluation\n",
      "20:31 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wph_e_nobkgs_mttot, w_in_histo_wph_e_nobkgs_mttot, fi_histo_rate_wph_e_nobkgs_mttot, fi_histo_wph_e_nobkgs_mttot = fisher_wph_e_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='mt_tot',\n",
    "    nbins=20,\n",
    "    histrange=(200,2200),\n",
    "    model_file='models/met/sally_ensemble_wph_e_smeftsim_met',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "20:31 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "20:31 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_mu_smeftsim_met/estimator_0\n",
      "20:31 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_mu_smeftsim_met/estimator_1\n",
      "20:31 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_mu_smeftsim_met/estimator_2\n",
      "20:31 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_mu_smeftsim_met/estimator_3\n",
      "20:31 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_mu_smeftsim_met/estimator_4\n",
      "20:32 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 2\n",
      "20:32 madminer.ml          INFO    Starting score evaluation\n",
      "20:32 madminer.ml          INFO    Starting score evaluation\n",
      "20:32 madminer.ml          INFO    Starting score evaluation\n",
      "20:32 madminer.ml          INFO    Starting score evaluation\n",
      "20:32 madminer.ml          INFO    Starting score evaluation\n",
      "20:32 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 2\n",
      "20:32 madminer.ml          INFO    Starting score evaluation\n",
      "20:32 madminer.ml          INFO    Starting score evaluation\n",
      "20:32 madminer.ml          INFO    Starting score evaluation\n",
      "20:32 madminer.ml          INFO    Starting score evaluation\n",
      "20:32 madminer.ml          INFO    Starting score evaluation\n",
      "/home/shomiller/.conda/envs/my_root/lib/python2.7/site-packages/madminer-0.4.10-py2.7.egg/madminer/fisherinformation.py:1166: RuntimeWarning: divide by zero encountered in true_divide\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_mu_nobkgs_mttot, w_in_histo_wmh_mu_nobkgs_mttot, fi_histo_rate_wmh_mu_nobkgs_mttot, fi_histo_wmh_mu_nobkgs_mttot = fisher_wmh_mu_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='mt_tot',\n",
    "    nbins=20,\n",
    "    histrange=(200,2200),\n",
    "    model_file='models/met/sally_ensemble_wmh_mu_smeftsim_met',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "20:32 madminer.ml          INFO    Found score ensemble with 5 estimators\n",
      "20:32 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_e_smeftsim_met/estimator_0\n",
      "20:32 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_e_smeftsim_met/estimator_1\n",
      "20:32 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_e_smeftsim_met/estimator_2\n",
      "20:32 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_e_smeftsim_met/estimator_3\n",
      "20:32 madminer.ml          INFO    Loading model from models/met/sally_ensemble_wmh_e_smeftsim_met/estimator_4\n",
      "20:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 1 / 2\n",
      "20:33 madminer.ml          INFO    Starting score evaluation\n",
      "20:33 madminer.ml          INFO    Starting score evaluation\n",
      "20:33 madminer.ml          INFO    Starting score evaluation\n",
      "20:33 madminer.ml          INFO    Starting score evaluation\n",
      "20:33 madminer.ml          INFO    Starting score evaluation\n",
      "20:33 madminer.fisherinfor INFO    Evaluating kinematic Fisher information on batch 2 / 2\n",
      "20:33 madminer.ml          INFO    Starting score evaluation\n",
      "20:33 madminer.ml          INFO    Starting score evaluation\n",
      "20:33 madminer.ml          INFO    Starting score evaluation\n",
      "20:33 madminer.ml          INFO    Starting score evaluation\n",
      "20:34 madminer.ml          INFO    Starting score evaluation\n"
     ]
    }
   ],
   "source": [
    "bins_histo_wmh_e_nobkgs_mttot, w_in_histo_wmh_e_nobkgs_mttot, fi_histo_rate_wmh_e_nobkgs_mttot, fi_histo_wmh_e_nobkgs_mttot = fisher_wmh_e_met.histogram_of_fisher_information(\n",
    "    theta=[0.,0.,0.,0.],\n",
    "    luminosity=100*1000.,\n",
    "    observable='mt_tot',\n",
    "    nbins=20,\n",
    "    histrange=(200,2200),\n",
    "    model_file='models/met/sally_ensemble_wmh_e_smeftsim_met',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_histo_rate_nobkgs_mttot = [ fi_histo_rate_wph_mu_nobkgs_mttot[i]\n",
    "                     + fi_histo_rate_wph_e_nobkgs_mttot[i] \n",
    "                     + fi_histo_rate_wmh_mu_nobkgs_mttot[i] \n",
    "                     + fi_histo_rate_wmh_e_nobkgs_mttot[i] \n",
    "                     for i in range(0, len(fi_histo_rate_wph_mu_nobkgs_mttot)) ]\n",
    "\n",
    "fi_histo_nobkgs_mttot = [ fi_histo_wph_mu_nobkgs_mttot[i]\n",
    "                     + fi_histo_wph_e_nobkgs_mttot[i] \n",
    "                     + fi_histo_wmh_mu_nobkgs_mttot[i] \n",
    "                     + fi_histo_wmh_e_nobkgs_mttot[i] \n",
    "                     for i in range(0, len(fi_histo_wph_mu_nobkgs_mttot)) ]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_histo_rate_nobkgs_mttot_rot = [ rotate(fi) for fi in fi_histo_rate_nobkgs_mttot ]\n",
    "fi_histo_nobkgs_mttot_rot = [ rotate(fi) for fi in fi_histo_nobkgs_mttot ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_histo_nobkgs_mttot_outfile = './fisher_info/fi_histo_nobkgs_mttot.npz'\n",
    "\n",
    "fi_histo_nobkgs_mttot_list = [\n",
    "    bins_histo_wph_mu_nobkgs_mttot,\n",
    "    w_in_histo_wph_mu_nobkgs_mttot,\n",
    "    fi_histo_rate_wph_mu_nobkgs_mttot,\n",
    "    fi_histo_wph_mu_nobkgs_mttot,\n",
    "    bins_histo_wph_e_nobkgs_mttot,\n",
    "    w_in_histo_wph_e_nobkgs_mttot,\n",
    "    fi_histo_rate_wph_e_nobkgs_mttot,\n",
    "    fi_histo_wph_e_nobkgs_mttot,\n",
    "    bins_histo_wmh_mu_nobkgs_mttot,\n",
    "    w_in_histo_wmh_mu_nobkgs_mttot,\n",
    "    fi_histo_rate_wmh_mu_nobkgs_mttot,\n",
    "    fi_histo_wmh_mu_nobkgs_mttot,\n",
    "    bins_histo_wmh_e_nobkgs_mttot,\n",
    "    w_in_histo_wmh_e_nobkgs_mttot,\n",
    "    fi_histo_rate_wmh_e_nobkgs_mttot,\n",
    "    fi_histo_wmh_e_nobkgs_mttot,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savez(fi_histo_nobkgs_mttot_outfile, fi_histo_nobkgs_mttot_list, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "#### Plot of the Distribution (Full Info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "legend_elements_sig = [\n",
    "        Line2D([0],[0],color=fi_dist_color1, lw=fi_dist_linewidth,ls=fi_dist_linestyle1,\n",
    "           label=r'$\\tilde{C}_{HD}$'),\n",
    "        Line2D([0],[0],color=fi_dist_color2, lw=fi_dist_linewidth,ls=fi_dist_linestyle2,\n",
    "           label=r'$C_{HW}$'),\n",
    "        Line2D([0],[0],color=fi_dist_color3, lw=fi_dist_linewidth,ls=fi_dist_linestyle3,\n",
    "           label=r'$C_{Hq}^{(3)}$'),\n",
    "        Patch(facecolor=color_sig, label='SM Signal'),\n",
    "#        Patch(facecolor=color_bkg, label='Background')\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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N3vbhtiIO9meY1cb4pudHpdkHJs32/2liJ14iWqZ2u416vY5+vx8ZcFQqFdRqNRSLRXS7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\n3Y/OuUsz+wdJfw2HUGYZFr0PG3qfLoQWmBVyFV0/ogrC4MB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f68844190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins_ptw = bins_histo_wph_mu_nobkgs_ptw\n",
    "xvals = [ (bins_ptw[i] + bins_ptw[i+1]) / 2 for i in range(0, len(bins_ptw) - 1)]\n",
    "xvals = [ bins_ptw[0] - 1e-9 ] + xvals + [bins_ptw[len(bins_ptw) - 1] + 1e-9]\n",
    "thisrange = (bins_ptw[0], bins_ptw[-1])\n",
    "\n",
    "ptw_index = sa_bkgs_wph_mu.observables.keys().index('pt_w')\n",
    "#x_weighted_bkg[:,thisindex]\n",
    "\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax1 = plt.subplot(111)\n",
    "fig.subplots_adjust(left=0.1667, right=0.8333, bottom=0.17, top=0.97)\n",
    "\n",
    "ax1.set_ylabel('Events (arb.)', color='black',fontsize=22)\n",
    "ax1.set_xlabel(r'$p_{T,W}$ (GeV)', color='black',fontsize=22)\n",
    "\n",
    "ax1.hist(\n",
    "    [ x_weighted_sig[:,ptw_index] ],\n",
    "    weights = [ 300.*1000.*weights_sig ],\n",
    "    bins = bins_ptw,\n",
    "    histtype='stepfilled',\n",
    "    stacked=True,\n",
    "    color=[color_sig],\n",
    "    alpha=1.0\n",
    ")\n",
    "\n",
    "#ax1.set_yscale('log')\n",
    "\n",
    "## Second Axis for F.I.\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel(r'Normalized Distribution of F.I.', color='black',fontsize=22)\n",
    "\n",
    "fi1_norm = sum([fi[0,0] for fi in fi_histo_nobkgs_ptw_rot])\n",
    "fi2_norm = sum([fi[1,1] for fi in fi_histo_nobkgs_ptw_rot])\n",
    "fi3_norm = sum([fi[2,2] for fi in fi_histo_nobkgs_ptw_rot])\n",
    "\n",
    "ax2.hist(\n",
    "    xvals,\n",
    "    weights = [ 1./(fi1_norm)*fi[0,0] for fi in fi_histo_nobkgs_ptw_rot ],\n",
    "    bins = bins_ptw,\n",
    "    range = thisrange,\n",
    "    histtype = 'step',\n",
    "    color = fi_dist_color1,\n",
    "    linewidth = fi_dist_linewidth,\n",
    "    linestyle = fi_dist_linestyle1,\n",
    "    label=r'$c_{H\\boxdot} - \\frac{1}{4}c_{HD}$'\n",
    ")\n",
    "\n",
    "ax2.hist(\n",
    "    xvals,\n",
    "    weights = [ 1./(fi2_norm)*fi[1,1] for fi in fi_histo_nobkgs_ptw_rot ],\n",
    "    bins = bins_ptw,\n",
    "    range = thisrange,\n",
    "    histtype = 'step',\n",
    "    color = fi_dist_color2,\n",
    "    linewidth = fi_dist_linewidth,\n",
    "    linestyle = fi_dist_linestyle2,\n",
    "    label=r'$c_{HW}$'\n",
    ")\n",
    "\n",
    "ax2.hist(\n",
    "    xvals,\n",
    "    weights = [ 1./(fi3_norm)*fi[2,2] for fi in fi_histo_nobkgs_ptw_rot ],\n",
    "    bins = bins_ptw,\n",
    "    range = thisrange,\n",
    "    histtype = 'step',\n",
    "    color = fi_dist_color3,\n",
    "    linewidth = fi_dist_linewidth,\n",
    "    linestyle = fi_dist_linestyle3,\n",
    "    label=r'$c_{Hq}^{(3)}$'\n",
    ")\n",
    "\n",
    "ax2.legend(handles=legend_elements_sig, loc='upper right',fontsize=16,frameon=False)\n",
    "\n",
    "plt.text(600,ax2.get_ylim()[1]*0.50, r'Full Info',fontsize=22)\n",
    "plt.text(600,ax2.get_ylim()[1]*0.43, r'(No Backgrounds)',fontsize=22)\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig('plots/info_dist_nobkgs_ptw.pdf')\n",
    "plt.show()"
   ]
  }
 ],
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