{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Generate the estimated gravitational waveform and calculate the signal-to-noise for GW150914 with the Hanford Data ###" ] }, { "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: 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: 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: 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: 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: 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: 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: 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": [ { "name": "stdout", "output_type": "stream", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", "100 14 100 14 0 0 37 0 --:--:-- --:--:-- --:--:-- 37\n" ] } ], "source": [ "#download the data\n", "!curl -O -J https://raw.githubusercontent.com/ligo-cbc/binder/master/H-H1_LOSC_4_V2-1126259446-32.gwf" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import pylab\n", "# Import the functions we need for later!\n", "from pycbc.catalog import Merger\n", "from pycbc.filter import highpass_fir, matched_filter\n", "from pycbc.waveform import get_td_waveform\n", "from pycbc.psd import welch, interpolate\n", "\n", "# Read the Hanford data and remove low frequency content\n", "h1 = Merger(\"GW150914\").strain(\"H1\")\n", "h1 = highpass_fir(h1, 15, 16)\n", "\n", "# Can you spot where the signal is beforehand?\n", "pylab.plot(h1.sample_times, h1)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Generate a waveform similar to GW150914 \n", "# Change the parameters and see what happens to the waveform\n", "# and the resulting SNR.\n", "\n", "# Mass in Solar masses. \n", "m1 = 35.2\n", "m2 = 34.0\n", "\n", "# The intrinsic spin of each black hole\n", "s1z = -0.228\n", "s2z = -0.003\n", "\n", "# The frequency to start generating the waveform\n", "fstart = 15.0\n", "\n", "hp, hc = get_td_waveform(approximant=\"SEOBNRv2\",\n", " mass1=m1, spin1z=s1z,\n", " mass2=m2, spin2z=s2z,\n", " f_lower=fstart,\n", " delta_t=h1.delta_t)\n", "pylab.plot(hp.sample_times, hp)\n", "pylab.xlabel('Time (s)')\n", "pylab.ylabel('Strain')\n", "\n", "# Move the waveform so that the merge is about at the end\n", "# This means that an SNR spike later on in the data lines up with this point\n", "hp.resize(len(h1))\n", "hp.roll(int(hp.start_time * hp.sample_rate))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Frequency (Hz)')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Estimate the noise spectrum\n", "# Normally we use more data to estimate the psd, but this is illustrative\n", "psd = interpolate(welch(h1), 1.0 / h1.duration)\n", "\n", "pylab.loglog(psd.sample_frequencies, psd)\n", "pylab.xlim(20, 1024)\n", "pylab.xlabel('Frequency (Hz)')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Calculate the complex Signal-to-noise. This is a complex vector\n", "# because the signal could have any phase.\n", "snr = matched_filter(hp, h1, psd=psd, low_frequency_cutoff=30.0)\n", "\n", "# Remove regions corrupted by filter wraparound\n", "snr = snr[len(snr) // 4: len(snr) * 3 // 4]\n", "\n", "# Now you should be able to spot where the signal is!\n", "pylab.plot(snr.sample_times, abs(snr))\n", "pylab.ylabel('signal-to-noise')\n", "pylab.xlabel('GPS Time (s)')\n", "pylab.show()" ] }, { "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 }