{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using heron's verification interface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heron includes a growing suite of tools for verifiying its models against other models and alternative waveform catalogues.\n",
    "This notebook contains an overview of the process for comparing on of the models with the most up to date version of the Georgia Tech waveform catalogue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import heron.testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from elk.catalogue import NRCatalogue, PPCatalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from heron.models.georgebased import HeronHodlr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from heron.models.torchbased import HeronCUDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to load the model itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = HeronCUDA()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then load the NR catalogue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_root = \"/home/daniel/data/gravitational-waves/gt-waveforms/\"\n",
    "samples_catalogue = NRCatalogue(\"GeorgiaTech\", catalogue_root = data_root)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matches between every waveform from the catalogue can then be calculated against the output of the model using the `nrcat_match` function from `heron.testing`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/daniel/repositories/elk/elk/waveform.py:224: H5pyDeprecationWarning: The default file mode will change to 'r' (read-only) in h5py 3.0. To suppress this warning, pass the mode you need to h5py.File(), or set the global default h5.get_config().default_file_mode, or set the environment variable H5PY_DEFAULT_READONLY=1. Available modes are: 'r', 'r+', 'w', 'w-'/'x', 'a'. See the docs for details.\n",
      "  waveform = h5py.File(self.data_file)\n",
      "/home/daniel/.virtualenvs/gaston/sandbox/local/lib/python3.7/site-packages/pycbc/filter/matchedfilter.py:1249: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  norm = (4.0 * stilde.delta_f) / sqrt( h_norm)\n",
      "/home/daniel/.virtualenvs/gaston/sandbox/local/lib/python3.7/site-packages/pycbc/filter/matchedfilter.py:1364: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  return maxsnr * snr_norm / sqrt(v2_norm), max_id\n"
     ]
    }
   ],
   "source": [
    "matches = heron.testing.nrcat_match(model, samples_catalogue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family [\"'URW Bookman L'\"] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family [\"'URW Bookman L'\"] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
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AAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1,1, dpi=300)\n",
    "ax.hist(1-np.array(list(matches.values())), alpha=0.8, density=True, \n",
    "        bins=np.logspace(-3,0,25), \n",
    "        histtype=\"step\",\n",
    "        color=\"black\",\n",
    "           lw=2\n",
    "       );\n",
    "\n",
    "ax.set_xscale(\"log\")\n",
    "\n",
    "ax.set_xlabel(\"Mismatch\")\n",
    "ax.set_ylabel(\"Instances\")\n",
    "\n",
    "ax.set_title(\"HeronCUDA - Georgia Tech [Apr 2020] Mismatches\")\n",
    "\n",
    "f.savefig(\"mistmatches-cuda.png\")"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}