{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Generate the estimated gravitational waveform and calculate the signal-to-noise for GW151226 ###" ] }, { "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) 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/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: 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: 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: 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: 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: 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 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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": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 2, "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, taper_timeseries\n", "from pycbc.psd import welch, interpolate\n", "\n", "# Read the Hanford data and remove low frequency content\n", "h1 = Merger(\"GW151226\").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": 3, "metadata": {}, "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 GW151226\n", "# Make a template from the public parameters from the LOSC and filter \n", "# the data to get the phase difference.\n", "z = 0.09\n", "m1 = 14.2 * (1 + z) \n", "m2 = 7.5 * (1 + z)\n", "s1 = s2 = 0.2\n", "\n", "# The frequency to start generating the waveform\n", "fstart = 15.0\n", "hp, hc = get_td_waveform(approximant=\"SEOBNRv2\",\n", " mass1=m1, mass2=m2, spin1z=s1, spin2z=s2,\n", " f_lower=fstart,\n", " delta_t=h1.delta_t)\n", "hp = taper_timeseries(hp, tapermethod='start')\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", "pylab.plot(hp.sample_times, hp)\n", "pylab.xlabel('Time (s)')\n", "pylab.ylabel('Strain')\n", "\n", "hp.resize(len(h1))\n", "hp.roll(int(hp.start_time * hp.sample_rate))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Frequency (Hz)')" ] }, "execution_count": 4, "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": 11, "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.figure()\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": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2020-06-27 14:34:13-- https://losc.ligo.org/s/events/GW151226/L-L1_LOSC_4_V2-1135136334-32.gwf\n", "Resolving losc.ligo.org (losc.ligo.org)... 131.215.113.73\n", "Connecting to losc.ligo.org (losc.ligo.org)|131.215.113.73|:443... connected.\n", "HTTP request sent, awaiting response... 302 Found\n", "Location: https://www.gw-openscience.org/s/events/GW151226/L-L1_LOSC_4_V2-1135136334-32.gwf [following]\n", "--2020-06-27 14:34:14-- https://www.gw-openscience.org/s/events/GW151226/L-L1_LOSC_4_V2-1135136334-32.gwf\n", "Resolving www.gw-openscience.org (www.gw-openscience.org)... 131.215.113.73\n", "Connecting to www.gw-openscience.org (www.gw-openscience.org)|131.215.113.73|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 1013363 (990K)\n", "Saving to: ‘L-L1_LOSC_4_V2-1135136334-32.gwf’\n", "\n", "L-L1_LOSC_4_V2-1135 100%[===================>] 989.61K 616KB/s in 1.6s \n", "\n", "2020-06-27 14:34:17 (616 KB/s) - ‘L-L1_LOSC_4_V2-1135136334-32.gwf’ saved [1013363/1013363]\n", "\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from pycbc.frame import read_frame\n", "# Now let's see what the SNR timeseries looks like in the other detector in Livingston.\n", "# Get the data for L1 from the LOSC site\n", "!wget -O L-L1_LOSC_4_V2-1135136334-32.gwf https://losc.ligo.org/s/events/GW151226/L-L1_LOSC_4_V2-1135136334-32.gwf\n", "\n", "# Read the Lvingston data and remove low frequency content\n", "l1 = read_frame('L-L1_LOSC_4_V2-1135136334-32.gwf', 'L1:LOSC-STRAIN')\n", "l1 = highpass_fir(l1, 15, 16)\n", "l1_psd = interpolate(welch(l1), 1.0 / l1.duration)\n", "l1_snr = matched_filter(hp, l1, psd=l1_psd, low_frequency_cutoff=30.0)\n", "l1_snr = l1_snr[len(l1_snr) // 4: len(l1_snr)* 3 // 4]\n", "\n", "# Let's look closer around the L1 peak\n", "time = snr.sample_times[snr.abs_arg_max()]\n", "snr = snr.time_slice(time-.1, time+.1)\n", "l1_snr = l1_snr.time_slice(time-.1, time+.1)\n", "\n", "# There is a SNR peak near the same time!\n", "pylab.figure()\n", "pylab.plot(snr.sample_times, abs(snr), label='Hanford')\n", "pylab.plot(l1_snr.sample_times, abs(l1_snr), label='Livingston')\n", "pylab.ylabel('signal-to-noise')\n", "pylab.xlabel('GPS Time (s)')\n", "pylab.legend()\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 }