{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate the estimated gravitational waveform and calculate the signal-to-noise for LVT151012 ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pycbc in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages/PyCBC-9rc6331-py3.7-linux-x86_64.egg (9rc6331)\n",
      "Requirement already satisfied: lalsuite in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (6.70)\n",
      "Requirement already satisfied: ligo-common in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (1.0.3)\n",
      "Requirement already satisfied: numpy>=1.16.0 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (1.19.0)\n",
      "Requirement already satisfied: Mako>=1.0.1 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (1.1.3)\n",
      "Requirement already satisfied: cython>=0.29 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (0.29.20)\n",
      "Requirement already satisfied: decorator>=3.4.2 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (4.4.2)\n",
      "Requirement already satisfied: matplotlib>=1.5.1 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (3.2.2)\n",
      "Requirement already satisfied: pillow in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (7.1.2)\n",
      "Requirement already satisfied: h5py>=2.5 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (2.10.0)\n",
      "Requirement already satisfied: jinja2 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (2.11.2)\n",
      "Requirement already satisfied: mpld3>=0.3 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (0.5.1)\n",
      "Requirement already satisfied: lscsoft-glue>=1.59.3 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (2.0.0)\n",
      "Requirement already satisfied: emcee==2.2.1 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (2.2.1)\n",
      "Requirement already satisfied: requests>=1.2.1 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (2.24.0)\n",
      "Requirement already satisfied: beautifulsoup4>=4.6.0 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (4.9.1)\n",
      "Requirement already satisfied: six>=1.10.0 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (1.15.0)\n",
      "Requirement already satisfied: ligo-segments in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (1.2.0)\n",
      "Requirement already satisfied: tqdm in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (4.46.1)\n",
      "Requirement already satisfied: gwdatafind in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (1.0.4)\n",
      "Requirement already satisfied: astropy>=2.0.3 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (4.0.1.post1)\n",
      "Requirement already satisfied: scipy>=0.16.0 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pycbc) (1.5.0)\n",
      "Requirement already satisfied: python-dateutil in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from lalsuite) (2.8.1)\n",
      "Requirement already satisfied: MarkupSafe>=0.9.2 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from Mako>=1.0.1->pycbc) (1.1.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from matplotlib>=1.5.1->pycbc) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from matplotlib>=1.5.1->pycbc) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from matplotlib>=1.5.1->pycbc) (2.4.7)\n",
      "Requirement already satisfied: pyOpenSSL in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from lscsoft-glue>=1.59.3->pycbc) (19.1.0)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from requests>=1.2.1->pycbc) (1.25.9)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from requests>=1.2.1->pycbc) (2.9)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from requests>=1.2.1->pycbc) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from requests>=1.2.1->pycbc) (2020.6.20)\n",
      "Requirement already satisfied: soupsieve>1.2 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from beautifulsoup4>=4.6.0->pycbc) (2.0.1)\n",
      "Requirement already satisfied: cryptography>=2.8 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from pyOpenSSL->lscsoft-glue>=1.59.3->pycbc) (2.9.2)\n",
      "Requirement already satisfied: cffi!=1.11.3,>=1.8 in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from cryptography>=2.8->pyOpenSSL->lscsoft-glue>=1.59.3->pycbc) (1.14.0)\n",
      "Requirement already satisfied: pycparser in /home/ahnitz/projects/PyCBC-Tutorials/env/lib/python3.7/site-packages (from cffi!=1.11.3,>=1.8->cryptography>=2.8->pyOpenSSL->lscsoft-glue>=1.59.3->pycbc) (2.20)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install pycbc ligo-common --no-cache-dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a template from the public parameters from the LOSC and filter \n",
    "# the data to get the phase difference.\n",
    "from pycbc.waveform import get_fd_waveform\n",
    "z = 0.20\n",
    "m1 = 23 * (1 + z) \n",
    "m2 = 13 * (1 + z)\n",
    "time = 1128678900.44\n",
    "hp, hc = get_fd_waveform(approximant=\"IMRPhenomD\",\n",
    "                         mass1=m1, mass2=m2, delta_f=1.0/32, f_lower=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the SNR in both the L1 and H1 gravitational-wave detectors\n",
    "# See that there is a peak in the SNR near the same time.\n",
    "%matplotlib inline\n",
    "import pylab\n",
    "from pycbc.filter import highpass_fir\n",
    "from pycbc.filter import matched_filter\n",
    "from pycbc.psd import interpolate, welch\n",
    "from pycbc.catalog import Merger\n",
    "\n",
    "for ifo in ['L1', 'H1']:\n",
    "    # Read in the data into a TimeSeries\n",
    "    ts = highpass_fir(Merger(\"GW151012\").strain(ifo), 15, 16)\n",
    "\n",
    "    # Estimate the noise spectrum\n",
    "    psd = interpolate(welch(ts), 1.0 / ts.duration)\n",
    "    \n",
    "    # Calculate the signal-to-noise\n",
    "    hp.resize(len(ts) / 2 + 1)\n",
    "    snr = matched_filter(hp, ts, psd=psd, low_frequency_cutoff=30.0)\n",
    "\n",
    "    # Choose the +-100ms around the event.\n",
    "    i = int((time - snr.start_time) / snr.delta_t)\n",
    "    snr = snr[int(i - snr.sample_rate * .1):int(i + snr.sample_rate * .1)]\n",
    "    \n",
    "    pylab.plot(snr.sample_times, abs(snr))"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}