CovarianceMatrix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "import glob\n",
    "import os,sys\n",
    "import csv\n",
    "import tqdm\n",
    "import itertools\n",
    "import pandas as pd\n",
    "%matplotlib notebook\n",
    "from scipy import special\n",
    "from scipy import optimize\n",
    "from scipy.optimize import curve_fit as scipy\n",
    "import scipy.integrate as integrate\n",
    "import scipy.special as special\n",
    "from scipy.special import erf\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.special import gamma, factorial\n",
    "import mpmath\n",
    "from mpmath import *\n",
    "import cmath\n",
    "from scipy.special import gamma\n",
    "# Needed for math\n",
    "import numpy as np\n",
    "import scipy\n",
    "import scipy.stats\n",
    "import os\n",
    "# Needed for plotting\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from cycler import cycler\n",
    "\n",
    "# Set some nice plotting defaults\n",
    "matplotlib.rcParams[\"figure.figsize\"] = (7, 5)\n",
    "matplotlib.rcParams[\"savefig.dpi\"] = 200\n",
    "matplotlib.rcParams[\"font.size\"] = 18\n",
    "matplotlib.rcParams[\"font.family\"] = \"serif\"\n",
    "matplotlib.rcParams[\"font.serif\"] = [\n",
    "    \"Computer Modern\",\n",
    "    \"Latin Modern Roman\",\n",
    "    \"Bitstream Vera Serif\",\n",
    "]\n",
    "matplotlib.rcParams[\"font.sans-serif\"] = [\n",
    "    \"Computer Modern Sans serif\",\n",
    "    \"Helvetica\",\n",
    "    \"Avant Garde\",\n",
    "]\n",
    "matplotlib.rcParams[\"text.usetex\"] = True\n",
    "matplotlib.rcParams[\"axes.linewidth\"] = 1.0\n",
    "matplotlib.rcParams[\"font.weight\"] = \"bold\"\n",
    "matplotlib.rcParams[\"axes.prop_cycle\"] = cycler(\n",
    "    \"color\", [\"#29A2C6\", \"#FF6D31\", \"#73B66B\", \"#EF597B\", \"#333333\", \"#FFCB18\"]\n",
    ")\n",
    "matplotlib.rcParams[\"axes.grid\"] = False\n",
    "matplotlib.rcParams[\"patch.linewidth\"] = 2\n",
    "matplotlib.rcParams[\"lines.linewidth\"] = 2\n",
    "matplotlib.rcParams[\"xtick.labelsize\"] = \"medium\"\n",
    "matplotlib.rcParams[\"ytick.labelsize\"] = \"medium\"\n",
    "matplotlib.rcParams[\"xtick.minor.visible\"] = True\n",
    "matplotlib.rcParams[\"ytick.minor.visible\"] = True\n",
    "matplotlib.rcParams[\"xtick.major.size\"] = 8\n",
    "matplotlib.rcParams[\"xtick.minor.size\"] = 4\n",
    "matplotlib.rcParams[\"ytick.major.size\"] = 8\n",
    "matplotlib.rcParams[\"ytick.minor.size\"] = 4\n",
    "matplotlib.rcParams[\"xtick.major.width\"] = 1.2\n",
    "matplotlib.rcParams[\"xtick.minor.width\"] = 1.2\n",
    "matplotlib.rcParams[\"ytick.major.width\"] = 1.2\n",
    "matplotlib.rcParams[\"ytick.minor.width\"] = 1.2\n",
    "matplotlib.rcParams[\"legend.frameon\"] = False\n",
    "matplotlib.rcParams[\"legend.fontsize\"] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filereader(file):\n",
    "    data = pd.read_csv('%s' % file, sep = \"  \", header = None)\n",
    "    df = pd.DataFrame(data)\n",
    "    df.columns = ['energy','event']\n",
    "    df_x = np.array(df['event'])\n",
    "    df_e = np.array(df['energy'])\n",
    "    split_line = file.split('_w')\n",
    "    w = split_line[1]\n",
    "    return df_e, df_x, w\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def xs_per_mode(mode, true_energy):\n",
    "    mode, true_energy = mode, true_energy\n",
    "    \n",
    "    path = '/Users/darcynewmark/Desktop/LANL/Post-Bacc/Theory/'\n",
    "    os.chdir(path)\n",
    "    for file in sorted(os.listdir(path)):\n",
    "        if '_nu_w' in file:\n",
    "            df_e, df_x, w = filereader(file)\n",
    "            \n",
    "            if int(w) == mode: ## if we're in the right mode\n",
    "                for i in range(len(df_e)): ## let's loop over energy\n",
    "                    if float(df_e[i]) == true_energy: #until we find desired true energy\n",
    "                        return df_x[i], df_x ## returning single xs value at given true_energy \n",
    "                                                ##and array of xs for mode \n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "## this is a function to make unweighted smearing matrix but not necessary since it doesn't change\n",
    "## I calculate unweighted smearing matrix once below\n",
    "\n",
    "# def entire_smear_unweighted():\n",
    "    \n",
    "#     empty = np.zeros((1, 200,200)) \n",
    "\n",
    "#     path_smear = '/Users/darcynewmark/theory/charged_current/Jan2022/flat_smearing/test'\n",
    "\n",
    "#     os.chdir(path_smear)\n",
    "#     for file in sorted(os.listdir(path_smear)):\n",
    "\n",
    "#         if 'smear_' in file:\n",
    "#             filename = str(file)\n",
    "\n",
    "#             w_of_file = filename.split('flat_w')\n",
    "#             gamma_energy = (w_of_file[1].split('.dat'))[0]\n",
    "#             gamma_index = float(gamma_energy)-1\n",
    "\n",
    "#             smear = pd.read_csv('%s' % file , sep = \",\", header = None) \n",
    "#             sm = pd.DataFrame(smear)\n",
    "#             sm[len(sm.columns)-1] = sm[len(sm.columns)-1].map(lambda x: (x.replace('{', '').replace('}:', '').replace('};', '')if type(x) == str else x ))\n",
    "#             sm = sm.iloc[:200, 2:]\n",
    "#             sm = sm.values.astype(float)\n",
    "\n",
    "#             sm = sm.reshape((1, sm.shape[0], sm.shape[1]))\n",
    "#             running_tally = np.concatenate([sm, empty])\n",
    "#             empty = running_tally\n",
    "\n",
    "#     m_3d = running_tally[:-1]\n",
    "#     m_3d = np.swapaxes(m_3d, 1, 2)\n",
    "    \n",
    "#     return m_3d\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "## make unweighted smearing matrix \n",
    "\n",
    "empty = np.zeros((1, 200,200)) \n",
    "\n",
    "path_smear = '/Users/darcynewmark/theory/charged_current/Jan2022/flat_smearing/test'\n",
    "\n",
    "os.chdir(path_smear)\n",
    "for file in sorted(os.listdir(path_smear)):\n",
    "\n",
    "    if 'smear_' in file:\n",
    "        filename = str(file)\n",
    "\n",
    "        w_of_file = filename.split('flat_w')\n",
    "        gamma_energy = (w_of_file[1].split('.dat'))[0]\n",
    "        gamma_index = float(gamma_energy)-1\n",
    "\n",
    "        smear = pd.read_csv('%s' % file , sep = \",\", header = None) \n",
    "        sm = pd.DataFrame(smear)\n",
    "        sm[len(sm.columns)-1] = sm[len(sm.columns)-1].map(lambda x: (x.replace('{', '').replace('}:', '').replace('};', '')if type(x) == str else x ))\n",
    "        sm = sm.iloc[:200, 2:]\n",
    "        sm = sm.values.astype(float)\n",
    "\n",
    "        sm = sm.reshape((1, sm.shape[0], sm.shape[1]))\n",
    "        running_tally = np.concatenate([sm, empty])\n",
    "        empty = running_tally\n",
    "\n",
    "m_3d = running_tally[:-1]\n",
    "m_3d = np.swapaxes(m_3d, 1, 2)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "def smear_factor(mode, reco_energy):\n",
    "    # want to return smear = smearing matrix elements at reco energy summed over true energy for given mode\n",
    "    \n",
    "    mode, reco_energy_of_interest = mode, reco_energy\n",
    "    \n",
    "    true_energy = np.linspace(0.5, 100, 200)\n",
    "    reco_energy = np.linspace(0.5, 100, 200)\n",
    "    reco_energy_of_interest_index = np.where(reco_energy == reco_energy_of_interest)[0][0]\n",
    "    \n",
    "    M = np.zeros((200, 200))\n",
    "    df_x = np.asarray(xs_per_mode(mode, 10.0)[1]) ##true energy doesnt matter since returning array of xs\n",
    "#     m_3d = entire_smear_unweighted()\n",
    "    \n",
    "#     for i in range(len(true_energy)):\n",
    "#         for j in range(len(reco_energy)):\n",
    "#             numerator = (df_x[i]*m_3d[:, i , j]).sum()\n",
    "#             denominator = (df_x[i].sum())\n",
    "\n",
    "#             if numerator == 0:\n",
    "#                 M[i, j] = 0.0\n",
    "\n",
    "#             else:\n",
    "#                 M[i, j] = numerator/denominator\n",
    "\n",
    "    for alpha, beta in itertools.product(true_energy, reco_energy ):\n",
    "    \n",
    "        index_i = np.where(true_energy == alpha)[0][0]\n",
    "        index_j = np.where(reco_energy == beta)[0][0]\n",
    "        \n",
    "        numerator = (df_x[index_i]*m_3d[:, index_i , index_j]).sum()\n",
    "        denominator = (df_x[index_i].sum())\n",
    "        \n",
    "        if numerator == 0:\n",
    "            M[index_i, index_j] = 0.0\n",
    "        \n",
    "        else:\n",
    "            M[index_i, index_j] = numerator/denominator\n",
    "    \n",
    "                \n",
    "    ### M is now smearing matrix for specified mode, still need to sum over true energy at specified reco energy\n",
    "    smear = M[:, int(reco_energy_of_interest_index)].sum()\n",
    "    \n",
    "    return smear\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "### function to calculate covariance between bin 0 and bin 1\n",
    "def cov_bin0_to_bin1(true_energy0, reco_energy0,true_energy1, reco_energy1 ):\n",
    "    true_energy0, reco_energy0,true_energy1, reco_energy1 = true_energy0, reco_energy0,true_energy1, reco_energy1 \n",
    "    \n",
    "    w = np.linspace(1, 14, 14)\n",
    "    sub_cov = 0\n",
    "    total_cov =  []\n",
    "\n",
    "    \n",
    "#     for i in range(14):  ## looping over 14 modes\n",
    "#         sub_cov = xs_per_mode(i+1, true_energy0)[0] * xs_per_mode(i+1, true_energy1)[0] * (0.15)**2 * smear_factor(i+1, reco_energy0) * smear_factor(i+1, reco_energy1)\n",
    "#         total_cov += sub_cov\n",
    "\n",
    "    for mode in w:\n",
    "        sub_cov = xs_per_mode(mode, true_energy0)[0] * xs_per_mode(mode, true_energy1)[0] * (0.15)**2 * smear_factor(mode, reco_energy0) * smear_factor(mode, reco_energy1)\n",
    "        total_cov.append(sub_cov)\n",
    "        \n",
    "    return (sum(total_cov))\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "### function to calculate variance on diagonal\n",
    "def var_bin0_to_bin1(true_energy0, reco_energy0,true_energy1, reco_energy1 ):\n",
    "    true_energy0, reco_energy0,true_energy1, reco_energy1 = true_energy0, reco_energy0,true_energy1, reco_energy1 \n",
    "    \n",
    "    sub_var = 0\n",
    "    total_var =  []\n",
    "    \n",
    "#     for i in range(14):  ## looping over 14 modes\n",
    "#         sub_var = xs_per_mode(i+1, true_energy0)[0] * xs_per_mode(i+1, true_energy1)[0] * ((0.15)**2 +(0.40)**2) * smear_factor(i+1, reco_energy0) * smear_factor(i+1, reco_energy1)\n",
    "#         total_var += sub_var\n",
    "\n",
    "    for mode in w:\n",
    "        sub_var = xs_per_mode(mode, true_energy0)[0] * xs_per_mode(mode, true_energy1)[0] * ((0.15)**2 +(0.40)**2) * smear_factor(mode, reco_energy0) * smear_factor(mode, reco_energy1)\n",
    "        total_var.append(sub_var)\n",
    "    \n",
    "    return (sum(total_var))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6310195705016566e-05\n",
      "4.016071185956437e-05\n"
     ]
    }
   ],
   "source": [
    "print(cov_bin0_to_bin1(5.0 ,5.0 ,5.5 ,5.5))\n",
    "print(var_bin0_to_bin1(5.0 ,5.0 ,5.0 ,5.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  3.  5.  7.  9. 11. 13. 15. 17. 19.]\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1000 [00:00<?, ?it/s]/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  \n",
      "100%|██████████| 1000/1000 [6:37:28<00:00, 12.71s/it]    \n",
      "\u001b[A\n",
      "\n",
      "100%|██████████| 1000/1000 [6:37:28<00:00, 23.85s/it]\n"
     ]
    }
   ],
   "source": [
    "### let's now calculate and plot the covariance matrix!\n",
    "\n",
    "# reco_energy = np.linspace(0.5, 100, 200)\n",
    "# true_energy = np.linspace(0.5, 100, 200)\n",
    "\n",
    "reco_energy = np.linspace(1, 19, 10)\n",
    "true_energy = np.linspace(1, 19, 10)\n",
    "\n",
    "\n",
    "C_3d = np.zeros((len(reco_energy), len(reco_energy), len(true_energy))) \n",
    "C = np.zeros((len(reco_energy), len(reco_energy))) ## cov is reco vs reco\n",
    "\n",
    "# for i in range(len(reco_energy)):\n",
    "#     for j in range(len(reco_energy)):\n",
    "#         for k in range(len(true_energy)):\n",
    "            \n",
    "#             if i == j: ## the variance\n",
    "#                 var = var_bin0_to_bin1( true_energy[k], reco_energy[i], true_energy[k], reco_energy[j])\n",
    "#                 C_3d[i, j, k] = var\n",
    "\n",
    "#             cov = cov_bin0_to_bin1( true_energy[k], reco_energy[i], true_energy[k], reco_energy[j])\n",
    "#             C_3d[i, j, k] = cov\n",
    "            \n",
    "            \n",
    "for alpha, beta, gamma in tqdm.tqdm(itertools.product(reco_energy, reco_energy, true_energy), total = 10*10*10, position=0, leave=True):\n",
    "    \n",
    "    index_i = np.where(reco_energy == alpha)[0][0]\n",
    "    index_j = np.where(reco_energy == beta)[0][0]\n",
    "    index_k = np.where(true_energy == gamma)[0][0]\n",
    "    \n",
    "    if alpha == beta:\n",
    "        var = var_bin0_to_bin1( gamma, alpha, gamma, beta)\n",
    "        C_3d[index_i, index_j, index_k] = var\n",
    "        \n",
    "    if alpha != beta:\n",
    "        cov = cov_bin0_to_bin1( gamma, alpha, gamma, beta)\n",
    "        C_3d[index_i, index_j, index_k] = cov\n",
    "    \n",
    "             \n",
    "for i in range(len(reco_energy)):\n",
    "    for j in range(len(reco_energy)):\n",
    "        \n",
    "        C[i, j] = C_3d[i, j, :].sum()\n",
    "\n",
    "## C is cov matrix! \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
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       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"840\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  if sys.path[0] == '':\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Set up the color scale\n",
    "cm = plt.get_cmap(\"viridis_r\")\n",
    "norm = matplotlib.colors.LogNorm(vmin=C.T[C.T > 0].min(), vmax=C.T.max())\n",
    "\n",
    "\n",
    "X = np.array([reco_energy] * (len(reco_energy)))\n",
    "Y = np.array([reco_energy] * (len(reco_energy))).T\n",
    "\n",
    "\n",
    "# Make the plot\n",
    "fig, ax = plt.subplots(figsize=(8.4, 6))\n",
    "mesh = ax.pcolormesh( X, Y, C.T, cmap=cm)\n",
    "\n",
    "\n",
    "\n",
    "# Change the plot style\n",
    "\n",
    "ax.set_xlabel(\"Reconstructed Neutrino Energy (MeV)\")\n",
    "ax.set_title(\"Covariance Matrix\")\n",
    "ax.set_ylabel(\"Reconstructed Neutrino Energy (MeV)\")\n",
    "cb = fig.colorbar(mesh, ax=ax)\n",
    "cb.ax.set_ylabel(\"Events\")\n",
    "cb.ax.minorticks_off()\n",
    "\n",
    "# Make everything nice\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}