From b4138e0fd46108bc6dba792dbc6e91eca63c5394 Mon Sep 17 00:00:00 2001 From: LudovicoAlt Date: Tue, 20 Aug 2024 17:08:07 +0200 Subject: [PATCH 1/6] Added Time/Frequency Series functionality --- .gitignore | 1 + GWFish/modules/utilities.py | 128 +++++++- TutorialTimeFrequencySeries.ipynb | 469 ++++++++++++++++++++++++++++++ 3 files changed, 597 insertions(+), 1 deletion(-) create mode 100644 TutorialTimeFrequencySeries.ipynb diff --git a/.gitignore b/.gitignore index 59854cb3..9a9331e1 100644 --- a/.gitignore +++ b/.gitignore @@ -6,3 +6,4 @@ dist/ .hypothesis/* docs/source/detectors_autogen.inc docs/source/figures/* +23_gwstrain_trim.dat diff --git a/GWFish/modules/utilities.py b/GWFish/modules/utilities.py index 6fd2db41..345ee866 100644 --- a/GWFish/modules/utilities.py +++ b/GWFish/modules/utilities.py @@ -3,6 +3,7 @@ import pandas as pd import yaml from pathlib import Path +from lal import CreateREAL8TimeSeries, CreateREAL8Vector, DimensionlessUnit DEFAULT_CONFIG = Path(__file__).parent.parent / 'detectors.yaml' PSD_PATH = Path(__file__).parent.parent / 'detector_psd' @@ -165,4 +166,129 @@ def get_snr(parameters, network, waveform_model): return pd.DataFrame.from_dict(snrs, orient='index') - \ No newline at end of file +def make_fft_from_time_series(time_series_input, df, dt, title="Ines_Ludo"): + ''' + Returns the FFT done through the lal library given a time series. Also returns the frequency array. + + Parameters + ---------- + time_series_input : array + Time series data + df : float + Frequency step + dt : float + Time step + title : str, optional + Title of the time series + + Returns + ------- + tuple + FFT of the time series and the frequency array + ''' + dims = len(time_series_input) + time_series = CreateREAL8Vector(dims) + time_series.data = time_series_input + ts = CreateREAL8TimeSeries(title, 1, 0, dt, DimensionlessUnit, dims) + ts.data = time_series + fft_dat = gw.fft.fft_lal_timeseries(ts, df).data.data + freq_range = np.linspace( 0, df * len(fft_dat), len(fft_dat) ) + + return fft_dat, freq_range + +def _fd_phase_correction_and_output_format_from_stain_series(f_, hp, hc, geo_time = 1395964818): + ''' + Prepares the polarizations for GWFish projection function. Combining + the functions "_fd_phase_correction_geocent_time", "_fd_gwfish_output_format" as in LALFD_Waveform class from waveforms.py module. + + Parameters + ---------- + f_ : array + Frequency array + hp : array + Plus polarization + hc : array + Cross polarization + geo_time : int, optional + Geocentric time + + Returns + ------- + array + Polarizations in form (hp, hc) + ''' + phi_in = np.exp( 1.j * (2 * f_ * np.pi * geo_time) ).T[0] + fft_dat_plus = phi_in*np.conjugate( hp ) + fft_dat_cross = phi_in*np.conjugate( hc ) + + # GW Fish format for hfp and hfc + hfp = fft_dat_plus[:, np.newaxis] + hfc = fft_dat_cross[:, np.newaxis] + polarizations = np.hstack((hfp, hfc)) + + return polarizations + +def get_SNR_components(params, polarizations, detector, timevector, f_new, long_wavelength_approx = True): + ''' + Given a set of parameters, polarizations, detector, timevector and frequency array, returns the SNR associated to the signal + + Parameters + ---------- + params : dict + Parameters of the event, needs to include ra, dec, psi + polarizations : array + Array containing the (hp, hc) polarizations + detector : gw.Detector + Detector object + timevector : array + Time vector + f_new : array + Frequency array on which to evaluate the signal + long_wavelength_approx : bool, optional + Whether to use the long wavelength approximation or not + + Returns + ------- + float + Total signal-to-Noise Ratio + ''' + args = (params, detector, polarizations, timevector) + signal = gw.detection.projection(*args, long_wavelength_approx = long_wavelength_approx) + component_SNRs = gw.detection.SNR(detector, signal, frequencyvector=np.squeeze(f_new)) + out_SNR = np.sqrt(np.sum(component_SNRs**2)) + + return out_SNR + +def get_SNR_from_strains(f_in, hp, hc, detector, params, geo_time = 1395964818, long_wavelength_approx = True): + ''' + Given a set of parameters, polarizations, detector, timevector and frequency array, returns the SNR associated to the signal + + Parameters + ---------- + f_in : array + Frequency array on which to evaluate the signal + hp : array + Plus polarization without geocentric time phase corrections + hc : array + Cross polarization without geocentric time phase corrections + detector : gw.Detector + Detector object + params : dict + Parameters of the event, needs to include ra, dec, psi + geo_time : int, optional + Geocentric time + long_wavelength_approx : bool, optional + Whether to use the long wavelength approximation or not + + Returns + ------- + float + Total signal-to-Noise Ratio + ''' + detector.frequencyvector = f_in + + polarizations = _fd_phase_correction_and_output_format_from_stain_series(f_in, hp, hc) + timevector = np.ones( len(f_in) ) * geo_time + SNR = get_SNR_components(params, polarizations, detector, timevector, f_in, long_wavelength_approx) + + return SNR \ No newline at end of file diff --git a/TutorialTimeFrequencySeries.ipynb b/TutorialTimeFrequencySeries.ipynb new file mode 100644 index 00000000..21213123 --- /dev/null +++ b/TutorialTimeFrequencySeries.ipynb @@ -0,0 +1,469 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GWFish : Frequency/Time Series\n", + "\n", + "Quick tutorial to show how to use Frequency/Time series within GWFish\n", + "\n", + "Assumes you have already read the [gwfish_tutoial.ipynb](./gwfish_tutorial.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ludo/miniconda3/lib/python3.10/site-packages/lalsimulation/lalsimulation.py:8: UserWarning: Wswiglal-redir-stdio:\n", + "\n", + "SWIGLAL standard output/error redirection is enabled in IPython.\n", + "This may lead to performance penalties. To disable locally, use:\n", + "\n", + "with lal.no_swig_redirect_standard_output_error():\n", + " ...\n", + "\n", + "To disable globally, use:\n", + "\n", + "lal.swig_redirect_standard_output_error(True)\n", + "\n", + "Note however that this will likely lead to error messages from\n", + "LAL functions being either misdirected or lost when called from\n", + "Jupyter notebooks.\n", + "\n", + "To suppress this warning, use:\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", \"Wswiglal-redir-stdio\")\n", + "import lal\n", + "\n", + " import lal\n" + ] + } + ], + "source": [ + "from GWFish import detection\n", + "from GWFish.modules import utilities as util\n", + "\n", + "import math, h5py\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pathlib import Path\n", + "\n", + "import astropy.constants as const\n", + "from astropy.cosmology import Planck18" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyzing the Frequency Series" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To illustrate how to use GWFish to calculate SNR/horizons we will use GW strain available from [here](https://www.astro.princeton.edu/~burrows/gw.3d.new/). You can either manually download those files or execute the next cell to automatically download the file (\"23_gwstrain_trim.dat\")." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File downloaded successfully\n" + ] + } + ], + "source": [ + "import requests\n", + "#download from the URL, http\n", + "link = \"https://www.astro.princeton.edu/~burrows/gw.3d.new/data/\"\n", + "filename = \"23_gwstrain_trim.dat\"\n", + "\n", + "response = requests.get(link + filename)\n", + "\n", + "if response.status_code == 200:\n", + " with open(filename, 'wb') as f:\n", + " f.write(response.content)\n", + " print(\"File downloaded successfully\")\n", + "else:\n", + " print(\"Failed to download the file\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then look at the downloaded data and its fourier transform. Here we assume that we have a file with 3 columns one for time, h_plus and h_cross." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_5103/3960507349.py:20: UserWarning: Attempt to set non-positive xlim on a log-scaled axis will be ignored.\n", + " ax2.set_xlim(min(freq_range), max(freq_range))\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t, hp, hc = np.loadtxt(\"23_gwstrain_trim.dat\").T\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(2, 1)\n", + "\n", + "ax1.plot(t, hp)\n", + "ax1.set_ylabel(r\"h$_+$$\\cdot$D [cm]\")\n", + "ax1.set_title(r\"GW Strain Supernova progenitor 23M$_\\odot$ @ 10kpc\")\n", + "ax1.set_xlim(min(t), max(t))\n", + "ax1.set_xlabel(\"Time [s]\")\n", + "\n", + "dt = np.mean(np.diff(t)) #note the time step is not exactly constant but it is fine for this example\n", + "df = 1 / (max(t) - min(t)) \n", + "hp_f, freq_range = util.make_fft_from_time_series(hp, df, dt)\n", + "\n", + "ax2.plot(freq_range, abs(hp_f))\n", + "ax2.set_ylabel(r\"$\\tilde{h}_+\\cdot$D [cm]\")\n", + "ax2.set_xscale('log')\n", + "ax2.set_yscale('log')\n", + "ax2.set_xlabel(\"Frequency [Hz]\")\n", + "ax2.set_xlim(min(freq_range), max(freq_range))\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now proceed to analyze the signal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. We start by selecting a detector" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "detector = detection.Detector(\"ET\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. We prepare the signal with proper scaling/units, note that GWFish needs an \"augmented\" frequency vector (meaning it has an extra axis here denoted by the ```None``` value)\n", + "\n", + "**_NOTE:_** If you already have a frequency series at hand you may skip ```util.make_fft_form_time_series``` step" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "t, hp, hc = np.loadtxt(\"23_gwstrain_trim.dat\").T\n", + "\n", + "kpc_to_cm = 3.086e21 # cm/kpc\n", + "D = 10 * kpc_to_cm\n", + "\n", + "dt = np.mean(np.diff(t)) #the time step is not quite constant for this particular dataset, resampling would be necessary but it gives close enough results to be illustrative\n", + "df = 1 / (max(t) - min(t))\n", + "hc_f, freq_range = util.make_fft_from_time_series(hc, df, dt) \n", + "hp_f, _ = util.make_fft_from_time_series(hp, df, dt) \n", + "\n", + "hc_f_10kpc = hc_f/D\n", + "hp_f_10kpc = hp_f/D\n", + "\n", + "f_in = freq_range[:, None]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also need to selected a certain number of parameters that are needed to evaluate the SNR. The parameter explanation can be found [here](https://gwfish.readthedocs.io/en/latest/reference/parameters_units.html). For a input Frequency series only 3 parameters will affect the SNR. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "params = {\n", + " \"ra\" : math.radians(200.405),\n", + " \"dec\" : math.radians(-12.008),\n", + " \"psi\" : np.pi*0.3,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "we can also quickly check the detector PSD vs the strains:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f_et, psd_et = np.loadtxt('GWFish/detector_psd/ET_psd.txt').T\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n", + "\n", + "ax.plot(f_et, (psd_et)**0.5, label=\"ET PSD\")\n", + "ax.plot(freq_range, (freq_range)**0.5*abs(hp_f_10kpc), label=r\"$\\tilde{h}_+$\")\n", + "ax.set_xscale('log')\n", + "ax.set_yscale('log')\n", + "ax.set_xlabel(\"Frequency [Hz]\")\n", + "ax.set_xlim(min(f_et), max(f_et))\n", + "ax.set_ylim([1e-25, 1e-20])\n", + "ax.set_ylabel(r\"$\\tilde{h}_+$\")\n", + "ax.legend()\n", + "\n", + "#second ax with same x but showing the ration\n", + "psd_et_new = detector.components[0].Sn(freq_range) #interpolate the PSD to the freq_range\n", + "ax2 = ax.twinx()\n", + "ratio_snr = (freq_range)**0.5*abs(hp_f_10kpc) / (psd_et_new)**0.5 \n", + "ax2.plot(freq_range, ratio_snr, color='red', label=\"SNR\", alpha=0.5, zorder=-10)\n", + "ax2.set_ylabel(\"SNR\")\n", + "ax2.set_ylim([0, max(ratio_snr)])\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. With a prepared Signal we can evaluate an associated SNR" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SNR : 75.09\n" + ] + } + ], + "source": [ + "component_SNRs = util.get_SNR_from_strains(f_in, hp_f_10kpc, hc_f_10kpc, detector, params)\n", + "out_SNR = np.sqrt(np.sum(component_SNRs**2))\n", + "print(f\"SNR : {out_SNR:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# What about Reshift ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For signals far enough away we need to take into account also the shift in frequency in frequency, obviously for this specific signal (CC-SN) the considered distances are usually ``` D < 1 Mpc ``` so redshift effects are negligible. But for completeness the procedure is as follows :" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SNR : 75.09\n" + ] + } + ], + "source": [ + "from astropy.coordinates import Distance\n", + "from astropy import units as u\n", + "\n", + "redshift = Distance(10, u.kpc).z #get redshift at the distance we care for\n", + "\n", + "f_in = freq_range[:, None] / (1+redshift) #redshift the frequency, the signal in itself should not change just shift\n", + "\n", + "component_SNRs = util.get_SNR_from_strains(f_in, hp_f_10kpc, hc_f_10kpc, detector, params)\n", + "out_SNR = np.sqrt(np.sum(component_SNRs**2))\n", + "print(f\"SNR : {out_SNR:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clearly at 10 kpc we do not see any significant redshift. So we can do a quick check for (slightly) higher redshifts " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Redshift @ 20 Mpc : 4.50e-03 redshift\n", + "SNR no z : 3.755e-02\n", + "SNR z : 3.750e-02\n", + "SNR ratio : 1.00119\n" + ] + } + ], + "source": [ + "redshift = Distance(20, u.Mpc).z #get redshift at the distance we care for\n", + "\n", + "kpc_10_to_20_mpc = 2000\n", + "\n", + "hp_f_20Mpc = hp_f_10kpc / kpc_10_to_20_mpc\n", + "hc_f_20Mpc = hc_f_10kpc / kpc_10_to_20_mpc\n", + "\n", + "f_in_noz = freq_range[:, None]\n", + "f_in_z = freq_range[:, None] / (1+redshift)\n", + "\n", + "component_SNRs_noz = util.get_SNR_from_strains(f_in_noz, hp_f_20Mpc, hc_f_20Mpc, detector, params)\n", + "component_SNRs_z = util.get_SNR_from_strains(f_in_z, hp_f_20Mpc, hc_f_20Mpc, detector, params)\n", + "\n", + "out_SNR_noz = np.sqrt(np.sum(component_SNRs_noz**2))\n", + "out_SNR_z = np.sqrt(np.sum(component_SNRs_z**2))\n", + "\n", + "print(f\"Redshift @ 20 Mpc : {redshift:.2e}\")\n", + "print(f\"SNR no z : {out_SNR_noz:.3e}\")\n", + "print(f\"SNR z : {out_SNR_z:.3e}\")\n", + "print(f\"SNR ratio : {out_SNR_noz/out_SNR_z:.5f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# What about high frequencies ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "GWFish mainly works under the long waveform approximation. However, this can be turned off for a more accurate SNR determination:\n", + "\n", + "**_NOTE:_** explicitly remove f = 0 (if your signal includes it) as without the approximation there are 1/f terms that diverge. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SNR : 74.39\n" + ] + } + ], + "source": [ + "f_in = freq_range[:, None]\n", + "\n", + "f_max = 0\n", + "condition = freq_range > f_max\n", + "\n", + "f_masked = freq_range[condition][:, None]\n", + "hp_f_masked = hp_f_10kpc[condition]\n", + "hc_f_masked = hc_f_10kpc[condition]\n", + "\n", + "component_SNRs = util.get_SNR_from_strains(f_masked, hp_f_masked, hc_f_masked, detector, params, long_wavelength_approx=False)\n", + "out_SNR = np.sqrt(np.sum(component_SNRs**2))\n", + "print(f\"SNR : {out_SNR:.2f}\")" + ] + } + ], + "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": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 20b37c834aac400e566cc32c072995eea9635ca9 Mon Sep 17 00:00:00 2001 From: LudovicoAlt Date: Wed, 28 Aug 2024 10:13:59 +0200 Subject: [PATCH 2/6] Merge branch 'main' of https://github.com/LudoDe/GWFish_SN From e4bc96a701d223648d19a75708bd86a52194325a Mon Sep 17 00:00:00 2001 From: LudovicoAlt Date: Wed, 28 Aug 2024 10:18:11 +0200 Subject: [PATCH 3/6] removed get_SNR_components, included functionality in geT_snr --- GWFish/modules/utilities.py | 86 ++++++++++++++++++------------- TutorialTimeFrequencySeries.ipynb | 48 ++++++++--------- 2 files changed, 76 insertions(+), 58 deletions(-) diff --git a/GWFish/modules/utilities.py b/GWFish/modules/utilities.py index 345ee866..f66fb229 100644 --- a/GWFish/modules/utilities.py +++ b/GWFish/modules/utilities.py @@ -228,40 +228,53 @@ def _fd_phase_correction_and_output_format_from_stain_series(f_, hp, hc, geo_tim return polarizations -def get_SNR_components(params, polarizations, detector, timevector, f_new, long_wavelength_approx = True): - ''' - Given a set of parameters, polarizations, detector, timevector and frequency array, returns the SNR associated to the signal +def get_snr(parameters, network, waveform_model = None, series_data = None, long_wavelength_approx = True): + + #a routine that only activates if the series_data is provided + if series_data: + polarizations, timevector, f_new = series_data + snrs_series = {} + for detector in network.detectors: + detector.frequencyvector = f_new + args = (parameters, detector, polarizations, timevector) + signal = gw.detection.projection(*args, long_wavelength_approx = long_wavelength_approx) + component_SNRs = gw.detection.SNR(detector, signal, frequencyvector=np.squeeze(f_new)) + out_SNR = np.sqrt(np.sum(component_SNRs**2)) + snrs_series[detector.name] = out_SNR - Parameters - ---------- - params : dict - Parameters of the event, needs to include ra, dec, psi - polarizations : array - Array containing the (hp, hc) polarizations - detector : gw.Detector - Detector object - timevector : array - Time vector - f_new : array - Frequency array on which to evaluate the signal - long_wavelength_approx : bool, optional - Whether to use the long wavelength approximation or not + out_SNR = np.sqrt(np.sum([snrs_series[detector.name]**2 for detector in network.detectors])) + return out_SNR - Returns - ------- - float - Total signal-to-Noise Ratio - ''' - args = (params, detector, polarizations, timevector) - signal = gw.detection.projection(*args, long_wavelength_approx = long_wavelength_approx) - component_SNRs = gw.detection.SNR(detector, signal, frequencyvector=np.squeeze(f_new)) - out_SNR = np.sqrt(np.sum(component_SNRs**2)) + waveform_class = gw.waveforms.LALFD_Waveform + + nsignals = len(parameters) - return out_SNR + # The SNR is then computed by taking the norm of the signal projected onto the detector + # and dividing by the noise of the detector + snrs = {} + for i in range(nsignals): + snr = {} + for detector in network.detectors: + data_params = { + 'frequencyvector': detector.frequencyvector, + 'f_ref': 50. + } + waveform_obj = waveform_class(waveform_model, parameters.iloc[i], data_params) + wave = waveform_obj() + t_of_f = waveform_obj.t_of_f + signal = gw.detection.projection(parameters.iloc[i], detector, wave, t_of_f) + + snr[detector.name] = np.sqrt(np.sum(gw.detection.SNR(detector, signal)**2)) + + snr['network'] = np.sqrt(np.sum([snr[detector.name]**2 for detector in network.detectors])) + snrs['event_' + str(i)] = snr + + return pd.DataFrame.from_dict(snrs, orient='index') + +def get_SNR_from_strains(f_in, hp, hc, network, params, geo_time = 1395964818, long_wavelength_approx = True): -def get_SNR_from_strains(f_in, hp, hc, detector, params, geo_time = 1395964818, long_wavelength_approx = True): ''' - Given a set of parameters, polarizations, detector, timevector and frequency array, returns the SNR associated to the signal + Given a set of parameters, polarizations, network, timevector and frequency array, returns the SNR associated to the signal Parameters ---------- @@ -271,8 +284,8 @@ def get_SNR_from_strains(f_in, hp, hc, detector, params, geo_time = 1395964818, Plus polarization without geocentric time phase corrections hc : array Cross polarization without geocentric time phase corrections - detector : gw.Detector - Detector object + network : gw.detection.DetectorNetwork + Detector Network object params : dict Parameters of the event, needs to include ra, dec, psi geo_time : int, optional @@ -285,10 +298,13 @@ def get_SNR_from_strains(f_in, hp, hc, detector, params, geo_time = 1395964818, float Total signal-to-Noise Ratio ''' - detector.frequencyvector = f_in - + polarizations = _fd_phase_correction_and_output_format_from_stain_series(f_in, hp, hc) timevector = np.ones( len(f_in) ) * geo_time - SNR = get_SNR_components(params, polarizations, detector, timevector, f_in, long_wavelength_approx) - + + series_data = (polarizations, timevector, f_in) + + # SNR = get_snr(params, polarizations, detector, timevector, f_in, long_wavelength_approx) + SNR = get_snr(params, network, series_data = series_data, long_wavelength_approx = long_wavelength_approx) + return SNR \ No newline at end of file diff --git a/TutorialTimeFrequencySeries.ipynb b/TutorialTimeFrequencySeries.ipynb index 21213123..8519c9fa 100644 --- a/TutorialTimeFrequencySeries.ipynb +++ b/TutorialTimeFrequencySeries.ipynb @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -118,14 +118,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_5103/3960507349.py:20: UserWarning: Attempt to set non-positive xlim on a log-scaled axis will be ignored.\n", + "/tmp/ipykernel_9162/3960507349.py:20: UserWarning: Attempt to set non-positive xlim on a log-scaled axis will be ignored.\n", " ax2.set_xlim(min(freq_range), max(freq_range))\n" ] }, @@ -176,16 +176,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "1. We start by selecting a detector" + "1. We start by selecting detectors" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "detector = detection.Detector(\"ET\")" + "detector = detection.Detector(\"ET\") #used for the PSD later\n", + "detectors = ['ET', 'LHO', 'VIR'] #Justput only one detector in the array if you want to use only one detector\n", + "network = detection.Network(detector_ids = detectors)" ] }, { @@ -199,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -228,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -248,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -297,19 +299,19 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "SNR : 75.09\n" + "SNR : 76.26\n" ] } ], "source": [ - "component_SNRs = util.get_SNR_from_strains(f_in, hp_f_10kpc, hc_f_10kpc, detector, params)\n", + "component_SNRs = util.get_SNR_from_strains(f_in, hp_f_10kpc, hc_f_10kpc, network, params)\n", "out_SNR = np.sqrt(np.sum(component_SNRs**2))\n", "print(f\"SNR : {out_SNR:.2f}\")" ] @@ -330,14 +332,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "SNR : 75.09\n" + "SNR : 76.26\n" ] } ], @@ -349,7 +351,7 @@ "\n", "f_in = freq_range[:, None] / (1+redshift) #redshift the frequency, the signal in itself should not change just shift\n", "\n", - "component_SNRs = util.get_SNR_from_strains(f_in, hp_f_10kpc, hc_f_10kpc, detector, params)\n", + "component_SNRs = util.get_SNR_from_strains(f_in, hp_f_10kpc, hc_f_10kpc, network, params)\n", "out_SNR = np.sqrt(np.sum(component_SNRs**2))\n", "print(f\"SNR : {out_SNR:.2f}\")" ] @@ -363,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -371,8 +373,8 @@ "output_type": "stream", "text": [ "Redshift @ 20 Mpc : 4.50e-03 redshift\n", - "SNR no z : 3.755e-02\n", - "SNR z : 3.750e-02\n", + "SNR no z : 3.813e-02\n", + "SNR z : 3.809e-02\n", "SNR ratio : 1.00119\n" ] } @@ -388,8 +390,8 @@ "f_in_noz = freq_range[:, None]\n", "f_in_z = freq_range[:, None] / (1+redshift)\n", "\n", - "component_SNRs_noz = util.get_SNR_from_strains(f_in_noz, hp_f_20Mpc, hc_f_20Mpc, detector, params)\n", - "component_SNRs_z = util.get_SNR_from_strains(f_in_z, hp_f_20Mpc, hc_f_20Mpc, detector, params)\n", + "component_SNRs_noz = util.get_SNR_from_strains(f_in_noz, hp_f_20Mpc, hc_f_20Mpc, network, params)\n", + "component_SNRs_z = util.get_SNR_from_strains(f_in_z, hp_f_20Mpc, hc_f_20Mpc, network, params)\n", "\n", "out_SNR_noz = np.sqrt(np.sum(component_SNRs_noz**2))\n", "out_SNR_z = np.sqrt(np.sum(component_SNRs_z**2))\n", @@ -418,14 +420,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "SNR : 74.39\n" + "SNR : 75.58\n" ] } ], @@ -439,7 +441,7 @@ "hp_f_masked = hp_f_10kpc[condition]\n", "hc_f_masked = hc_f_10kpc[condition]\n", "\n", - "component_SNRs = util.get_SNR_from_strains(f_masked, hp_f_masked, hc_f_masked, detector, params, long_wavelength_approx=False)\n", + "component_SNRs = util.get_SNR_from_strains(f_masked, hp_f_masked, hc_f_masked, network, params, long_wavelength_approx=False)\n", "out_SNR = np.sqrt(np.sum(component_SNRs**2))\n", "print(f\"SNR : {out_SNR:.2f}\")" ] From c044309b45c4c7dbc04e14c520811d52fa352133 Mon Sep 17 00:00:00 2001 From: LudovicoAlt Date: Fri, 13 Sep 2024 11:43:56 +0200 Subject: [PATCH 4/6] Modified SNR function names removed if statement from get_snr function (reverted to previous functionality) renamed get_SNR_from_strains to get_SNR_from_series --- .gitignore | 6 +- GWFish/modules/utilities.py | 36 ++-- GWFish/modules/waveforms.py | 66 +++++++ TutorialTimeFrequencySeries.ipynb | 293 +++++++++++++++++++++++++++--- 4 files changed, 354 insertions(+), 47 deletions(-) diff --git a/.gitignore b/.gitignore index 9a9331e1..58792687 100644 --- a/.gitignore +++ b/.gitignore @@ -6,4 +6,8 @@ dist/ .hypothesis/* docs/source/detectors_autogen.inc docs/source/figures/* -23_gwstrain_trim.dat +*_gwstrain_trim.dat +Merger_data.ipynb +spectrum_data.hdf5 +*eatmap* + diff --git a/GWFish/modules/utilities.py b/GWFish/modules/utilities.py index f66fb229..05e9dd6f 100644 --- a/GWFish/modules/utilities.py +++ b/GWFish/modules/utilities.py @@ -228,23 +228,8 @@ def _fd_phase_correction_and_output_format_from_stain_series(f_, hp, hc, geo_tim return polarizations -def get_snr(parameters, network, waveform_model = None, series_data = None, long_wavelength_approx = True): +def get_snr(parameters, network, waveform_model = None): - #a routine that only activates if the series_data is provided - if series_data: - polarizations, timevector, f_new = series_data - snrs_series = {} - for detector in network.detectors: - detector.frequencyvector = f_new - args = (parameters, detector, polarizations, timevector) - signal = gw.detection.projection(*args, long_wavelength_approx = long_wavelength_approx) - component_SNRs = gw.detection.SNR(detector, signal, frequencyvector=np.squeeze(f_new)) - out_SNR = np.sqrt(np.sum(component_SNRs**2)) - snrs_series[detector.name] = out_SNR - - out_SNR = np.sqrt(np.sum([snrs_series[detector.name]**2 for detector in network.detectors])) - return out_SNR - waveform_class = gw.waveforms.LALFD_Waveform nsignals = len(parameters) @@ -271,7 +256,7 @@ def get_snr(parameters, network, waveform_model = None, series_data = None, long return pd.DataFrame.from_dict(snrs, orient='index') -def get_SNR_from_strains(f_in, hp, hc, network, params, geo_time = 1395964818, long_wavelength_approx = True): +def get_SNR_from_series(f_in, hp, hc, network, parameters, geo_time = 1395964818, long_wavelength_approx = True): ''' Given a set of parameters, polarizations, network, timevector and frequency array, returns the SNR associated to the signal @@ -305,6 +290,17 @@ def get_SNR_from_strains(f_in, hp, hc, network, params, geo_time = 1395964818, l series_data = (polarizations, timevector, f_in) # SNR = get_snr(params, polarizations, detector, timevector, f_in, long_wavelength_approx) - SNR = get_snr(params, network, series_data = series_data, long_wavelength_approx = long_wavelength_approx) - - return SNR \ No newline at end of file + + polarizations, timevector, f_new = series_data + + snrs_series = {} + for detector in network.detectors: + detector.frequencyvector = f_new + args = (parameters, detector, polarizations, timevector) + signal = gw.detection.projection(*args, long_wavelength_approx = long_wavelength_approx) + component_SNRs = gw.detection.SNR(detector, signal, frequencyvector=np.squeeze(f_new)) + out_SNR = np.sqrt(np.sum(component_SNRs**2)) + snrs_series[detector.name] = out_SNR + + out_SNR = np.sqrt(np.sum([snrs_series[detector.name]**2 for detector in network.detectors])) + return out_SNR \ No newline at end of file diff --git a/GWFish/modules/waveforms.py b/GWFish/modules/waveforms.py index 77609eb2..79c1a512 100644 --- a/GWFish/modules/waveforms.py +++ b/GWFish/modules/waveforms.py @@ -1016,3 +1016,69 @@ def plot(self, output_folder='./'): plt.savefig(output_folder + 'psi_phenomD_zoomed.png') plt.close() + +class Ludo_Waveform(LALFD_Waveform): + """ + """ + + def __init__(self, f, hp_func, hc_func, parameters): + self.hf_plus_func = hp_func + self.hf_cross_func = hc_func + + self.gw_params = parameters + self.frequencyvector = f + + def _update_frequency_range_indices(self): + self.idx_low = int(self.f_min / self.delta_f) + self.idx_high = int(self.f_max / self.delta_f) + + def _lal_fd_strain_adjust_frequency_range(self): + """ Frequency array starts from zero, so we need to mask some frequencies """ + self._update_frequency_range_indices() + self.hf_cross_out = self._lal_hf_cross.data.data[self.idx_low:self.idx_high+1] + self.hf_plus_out = self._lal_hf_plus.data.data[self.idx_low:self.idx_high+1] + + def _lal_fd_phase_correction_by_epoch_and_df(self): + """ This correction is also done in Bilby after calling SimInspiralFD """ + # BORIS: weird Bilby correction + dt = 1. / self.delta_f + (self._lal_hf_plus.epoch.gpsSeconds + + self._lal_hf_plus.epoch.gpsNanoSeconds * 1e-9) + self.hf_plus_out *= np.exp( + -1j * 2 * np.pi * dt * self.frequencyvector) + self.hf_cross_out *= np.exp( + -1j * 2 * np.pi * dt * self.frequencyvector) + + def _hf_postproccessing_SimInspiralFD(self): + self._lal_fd_strain_adjust_frequency_range() + self._lal_fd_phase_correction_by_epoch_and_df() + + def _hf_postproccessing_SimInspiralCFDWS(self): + self.hf_plus_out, self.hf_cross_out = self._lal_hf_plus.data.data, self._lal_hf_cross.data.data + + def _fd_phase_correction_geocent_time(self): + """ Add initial 2pi*f*tc - phic - pi/4 to phase """ + phi_in = np.exp(1.j*(2*self.frequencyvector*np.pi*self.gw_params['geocent_time'])) + + hfp = phi_in * np.conjugate(self.hf_plus_out) # it's already multiplied by the phase + hfc = phi_in * np.conjugate(self.hf_cross_out) + + return hfp, hfc + + def _fd_gwfish_output_format(self, hfp, hfc): + + hfp = hfp[:, np.newaxis] + hfc = hfc[:, np.newaxis] + + polarizations = np.hstack((hfp, hfc)) + + return polarizations + + def calculate_frequency_domain_strain(self): + + self.hf_plus_out = self.hf_plus_func(self.frequencyvector) + self.hf_cross_out = self.hf_cross_func(self.frequencyvector) + + hfp, hfc = self._fd_phase_correction_geocent_time() + polarizations = self._fd_gwfish_output_format(hfp, hfc) + + self._frequency_domain_strain = polarizations \ No newline at end of file diff --git a/TutorialTimeFrequencySeries.ipynb b/TutorialTimeFrequencySeries.ipynb index 8519c9fa..e38c3051 100644 --- a/TutorialTimeFrequencySeries.ipynb +++ b/TutorialTimeFrequencySeries.ipynb @@ -82,14 +82,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "File downloaded successfully\n" + "File already exists\n" ] } ], @@ -99,14 +99,17 @@ "link = \"https://www.astro.princeton.edu/~burrows/gw.3d.new/data/\"\n", "filename = \"23_gwstrain_trim.dat\"\n", "\n", - "response = requests.get(link + filename)\n", - "\n", - "if response.status_code == 200:\n", - " with open(filename, 'wb') as f:\n", - " f.write(response.content)\n", - " print(\"File downloaded successfully\")\n", - "else:\n", - " print(\"Failed to download the file\")" + "if Path(filename).exists():\n", + " print(\"File already exists\")\n", + "else :\n", + " response = requests.get(link + filename)\n", + " print(f\"Downloading {filename} from {link}\")\n", + " if response.status_code == 200:\n", + " with open(filename, 'wb') as f:\n", + " f.write(response.content)\n", + " print(\"File downloaded successfully\")\n", + " else:\n", + " print(\"Failed to download the file\")" ] }, { @@ -118,14 +121,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_9162/3960507349.py:20: UserWarning: Attempt to set non-positive xlim on a log-scaled axis will be ignored.\n", + "/tmp/ipykernel_14439/3960507349.py:20: UserWarning: Attempt to set non-positive xlim on a log-scaled axis will be ignored.\n", " ax2.set_xlim(min(freq_range), max(freq_range))\n" ] }, @@ -181,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -196,12 +199,12 @@ "source": [ "2. We prepare the signal with proper scaling/units, note that GWFish needs an \"augmented\" frequency vector (meaning it has an extra axis here denoted by the ```None``` value)\n", "\n", - "**_NOTE:_** If you already have a frequency series at hand you may skip ```util.make_fft_form_time_series``` step" + "
Tip: If you already have a frequency series at hand you may skip util.make_fft_form_time_series
" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -230,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -238,6 +241,7 @@ " \"ra\" : math.radians(200.405),\n", " \"dec\" : math.radians(-12.008),\n", " \"psi\" : np.pi*0.3,\n", + " 'geocent_time': 1187008882.4\n", "}" ] }, @@ -250,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -299,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -332,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -351,7 +355,7 @@ "\n", "f_in = freq_range[:, None] / (1+redshift) #redshift the frequency, the signal in itself should not change just shift\n", "\n", - "component_SNRs = util.get_SNR_from_strains(f_in, hp_f_10kpc, hc_f_10kpc, network, params)\n", + "component_SNRs = util.get_SNR_from_series(f_in, hp_f_10kpc, hc_f_10kpc, network, params)\n", "out_SNR = np.sqrt(np.sum(component_SNRs**2))\n", "print(f\"SNR : {out_SNR:.2f}\")" ] @@ -365,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -390,8 +394,8 @@ "f_in_noz = freq_range[:, None]\n", "f_in_z = freq_range[:, None] / (1+redshift)\n", "\n", - "component_SNRs_noz = util.get_SNR_from_strains(f_in_noz, hp_f_20Mpc, hc_f_20Mpc, network, params)\n", - "component_SNRs_z = util.get_SNR_from_strains(f_in_z, hp_f_20Mpc, hc_f_20Mpc, network, params)\n", + "component_SNRs_noz = util.get_SNR_from_series(f_in_noz, hp_f_20Mpc, hc_f_20Mpc, network, params)\n", + "component_SNRs_z = util.get_SNR_from_series(f_in_z, hp_f_20Mpc, hc_f_20Mpc, network, params)\n", "\n", "out_SNR_noz = np.sqrt(np.sum(component_SNRs_noz**2))\n", "out_SNR_z = np.sqrt(np.sum(component_SNRs_z**2))\n", @@ -415,12 +419,12 @@ "source": [ "GWFish mainly works under the long waveform approximation. However, this can be turned off for a more accurate SNR determination:\n", "\n", - "**_NOTE:_** explicitly remove f = 0 (if your signal includes it) as without the approximation there are 1/f terms that diverge. " + "
Tip: If your signal includes it, explicitly remove f = 0 as without the approximation there are 1/f terms that diverge.
" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -441,10 +445,247 @@ "hp_f_masked = hp_f_10kpc[condition]\n", "hc_f_masked = hc_f_10kpc[condition]\n", "\n", - "component_SNRs = util.get_SNR_from_strains(f_masked, hp_f_masked, hc_f_masked, network, params, long_wavelength_approx=False)\n", + "component_SNRs = util.get_SNR_from_series(f_masked, hp_f_masked, hc_f_masked, network, params, long_wavelength_approx=False)\n", + "out_SNR = np.sqrt(np.sum(component_SNRs**2))\n", + "print(f\"SNR : {out_SNR:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Functional Approximations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you would like the change the frequency interval on which you evaluate your strain, you can approximate it and turn it into a \"function\"." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.interpolate import interp1d\n", + "\n", + "t, hp, hc = np.loadtxt(\"23_gwstrain_trim.dat\").T\n", + "\n", + "kpc_to_cm = 3.086e21 # cm/kpc\n", + "D = 10 * kpc_to_cm\n", + "\n", + "dt = np.mean(np.diff(t)) \n", + "df = 1 / (max(t) - min(t))\n", + "hc_f, freq_range = util.make_fft_from_time_series(hc, df, dt) \n", + "hp_f, _ = util.make_fft_from_time_series(hp, df, dt) \n", + "\n", + "hc_f_10kpc = hc_f/D\n", + "hp_f_10kpc = hp_f/D\n", + "\n", + "hp_f_interp = interp1d(freq_range, hp_f_10kpc, kind='cubic', fill_value='extrapolate')\n", + "hc_f_interp = interp1d(freq_range, hc_f_10kpc, kind='cubic', fill_value='extrapolate')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SNR : 76.26\n" + ] + } + ], + "source": [ + "from GWFish.modules import waveforms as wv\n", + "\n", + "waves = wv.Ludo_Waveform( freq_range, hp_f_interp, hc_f_interp, params)\n", + "waves.calculate_frequency_domain_strain()\n", + "\n", + "f_in = freq_range[:, None]\n", + "hfp, hfc = waves.frequency_domain_strain.T\n", + "\n", + "component_SNRs = util.get_SNR_from_series(f_in, hfp, hfc, network, params)\n", "out_SNR = np.sqrt(np.sum(component_SNRs**2))\n", "print(f\"SNR : {out_SNR:.2f}\")" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What about Parameters ? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Maybe there is some dependence of your signal to one or more parameters. While supernova signals are probably the worst example of this as they are mainly stochastic, here we can see how to take multiple simulations / series to generate a functional form that can also be used to estimate parameters. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File 11_gwstrain_trim.dat already exists\n", + "File 15.01_gwstrain_trim.dat already exists\n", + "File 23_gwstrain_trim.dat already exists\n" + ] + } + ], + "source": [ + "import requests\n", + "\n", + "link = \"https://www.astro.princeton.edu/~burrows/gw.3d.new/data/\"\n", + "files = [\"11\", \"15.01\", \"23\"]\n", + "\n", + "for f in files:\n", + " filename = f + \"_gwstrain_trim.dat\"\n", + " if Path(filename).exists():\n", + " print(f\"File {filename} already exists\")\n", + " else :\n", + " response = requests.get(link + filename)\n", + " print(f\"Downloading {filename} from {link}\")\n", + " if response.status_code == 200:\n", + " with open(filename, 'wb') as f:\n", + " f.write(response.content)\n", + " print(\"File downloaded successfully\")\n", + " else:\n", + " print(\"Failed to download the file\")" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "times = []\n", + "strains_p = []\n", + "strains_c = []\n", + "\n", + "masses = [11, 15.01, 23]\n", + "\n", + "strains_f_p = []\n", + "strains_f_c = []\n", + "freqs_file = []\n", + "\n", + "strains_p_interp = []\n", + "strains_c_interp = []\n", + "\n", + "for f in files:\n", + "\n", + " t, hp, hc = np.loadtxt(f + \"_gwstrain_trim.dat\").T\n", + "\n", + " dt = np.mean(np.diff(t)) \n", + " df = 1 / (max(t) - min(t))\n", + " hc_f, freq_range = util.make_fft_from_time_series(hc, df, dt) \n", + " hp_f, _ = util.make_fft_from_time_series(hp, df, dt) \n", + "\n", + " hc_f_10kpc = hc_f/D\n", + " hp_f_10kpc = hp_f/D\n", + "\n", + " hp_f_interp = interp1d(freq_range, hp_f_10kpc, fill_value='extrapolate')\n", + " hc_f_interp = interp1d(freq_range, hc_f_10kpc, fill_value='extrapolate')\n", + "\n", + " strains_p_interp.append(hp_f_interp)\n", + " strains_c_interp.append(hc_f_interp)\n", + "\n", + " freqs_file.append(freq_range)\n", + " strains_f_p.append(hp_f_10kpc)\n", + " strains_f_c.append(hc_f_10kpc)\n", + " \n", + " plt.plot(freq_range, abs(hp_f_10kpc), label=f+r\"M$_\\odot$\")\n", + "\n", + "plt.legend()\n", + "plt.yscale('log')\n", + "plt.xscale('log')\n", + "plt.xlim(0.1, max(freq_range))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.1, 10000.0)" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.interpolate import RegularGridInterpolator\n", + "\n", + "#interp the data in frequency and mass\n", + "\n", + "freq_interpolation = np.logspace(-1, 4, 1000)\n", + "\n", + "masses = [11, 15.01, 23]\n", + "freqs = np.logspace(-1, 4, 1000)\n", + "ref = np.ones_like(freqs)\n", + "\n", + "mass_grid, freq_grid = np.meshgrid(masses, freq_interpolation, indexing='ij')\n", + "\n", + "hp_2D_interp = np.array( [ hp(freq_interpolation) for hp in strains_p_interp] ) # (num_masses, num_freqs)\n", + "hc_2D_interp = np.array( [ hc(freq_interpolation) for hc in strains_c_interp] ) # (num_masses, num_freqs)\n", + "\n", + "# Create the interpolator\n", + "interpolator_hp = RegularGridInterpolator((masses, freq_interpolation), hp_2D_interp)\n", + "\n", + "# Define the new masses for interpolation (from 11 to 23 in steps of 1)\n", + "new_masses = [12, 18]\n", + "\n", + "plt.plot(freqs_file[0], abs(strains_f_p[0]), label=\"11M$_\\odot$ data\", color='black', linestyle='--', alpha=0.5)\n", + "\n", + "for k in range(len(new_masses)):\n", + " interpolated_values = np.array( [interpolator_hp([new_masses[k], freq]) for freq in freq_interpolation] )\n", + " plt.plot(freq_interpolation, abs(interpolated_values), label=f\"{new_masses[k]}M$_\\odot$ interp\")\n", + "\n", + "plt.legend()\n", + "plt.yscale('log')\n", + "plt.xscale('log')\n", + "plt.xlim(0.1, 1e4)" + ] } ], "metadata": { From e42e24e47f2b8a7c6b1703c7ae6b3ad33b3e93e5 Mon Sep 17 00:00:00 2001 From: LudovicoAlt Date: Fri, 13 Sep 2024 11:47:23 +0200 Subject: [PATCH 5/6] renamed tutorial notebook to match gwfish_tutorial naming scheme Updated Tutorial for series SNR name --- TutorialTimeFrequencySeries.ipynb | 712 ------------------------------ series_snr_tutorial.ipynb | 541 +++++++++++++++++++++++ 2 files changed, 541 insertions(+), 712 deletions(-) delete mode 100644 TutorialTimeFrequencySeries.ipynb create mode 100644 series_snr_tutorial.ipynb diff --git a/TutorialTimeFrequencySeries.ipynb b/TutorialTimeFrequencySeries.ipynb deleted file mode 100644 index e38c3051..00000000 --- a/TutorialTimeFrequencySeries.ipynb +++ /dev/null @@ -1,712 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# GWFish : Frequency/Time Series\n", - "\n", - "Quick tutorial to show how to use Frequency/Time series within GWFish\n", - "\n", - "Assumes you have already read the [gwfish_tutoial.ipynb](./gwfish_tutorial.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import packages" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ludo/miniconda3/lib/python3.10/site-packages/lalsimulation/lalsimulation.py:8: UserWarning: Wswiglal-redir-stdio:\n", - "\n", - "SWIGLAL standard output/error redirection is enabled in IPython.\n", - "This may lead to performance penalties. To disable locally, use:\n", - "\n", - "with lal.no_swig_redirect_standard_output_error():\n", - " ...\n", - "\n", - "To disable globally, use:\n", - "\n", - "lal.swig_redirect_standard_output_error(True)\n", - "\n", - "Note however that this will likely lead to error messages from\n", - "LAL functions being either misdirected or lost when called from\n", - "Jupyter notebooks.\n", - "\n", - "To suppress this warning, use:\n", - "\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\", \"Wswiglal-redir-stdio\")\n", - "import lal\n", - "\n", - " import lal\n" - ] - } - ], - "source": [ - "from GWFish import detection\n", - "from GWFish.modules import utilities as util\n", - "\n", - "import math, h5py\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from pathlib import Path\n", - "\n", - "import astropy.constants as const\n", - "from astropy.cosmology import Planck18" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analyzing the Frequency Series" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To illustrate how to use GWFish to calculate SNR/horizons we will use GW strain available from [here](https://www.astro.princeton.edu/~burrows/gw.3d.new/). You can either manually download those files or execute the next cell to automatically download the file (\"23_gwstrain_trim.dat\")." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File already exists\n" - ] - } - ], - "source": [ - "import requests\n", - "#download from the URL, http\n", - "link = \"https://www.astro.princeton.edu/~burrows/gw.3d.new/data/\"\n", - "filename = \"23_gwstrain_trim.dat\"\n", - "\n", - "if Path(filename).exists():\n", - " print(\"File already exists\")\n", - "else :\n", - " response = requests.get(link + filename)\n", - " print(f\"Downloading {filename} from {link}\")\n", - " if response.status_code == 200:\n", - " with open(filename, 'wb') as f:\n", - " f.write(response.content)\n", - " print(\"File downloaded successfully\")\n", - " else:\n", - " print(\"Failed to download the file\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then look at the downloaded data and its fourier transform. Here we assume that we have a file with 3 columns one for time, h_plus and h_cross." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_14439/3960507349.py:20: UserWarning: Attempt to set non-positive xlim on a log-scaled axis will be ignored.\n", - " ax2.set_xlim(min(freq_range), max(freq_range))\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "t, hp, hc = np.loadtxt(\"23_gwstrain_trim.dat\").T\n", - "\n", - "fig, (ax1, ax2) = plt.subplots(2, 1)\n", - "\n", - "ax1.plot(t, hp)\n", - "ax1.set_ylabel(r\"h$_+$$\\cdot$D [cm]\")\n", - "ax1.set_title(r\"GW Strain Supernova progenitor 23M$_\\odot$ @ 10kpc\")\n", - "ax1.set_xlim(min(t), max(t))\n", - "ax1.set_xlabel(\"Time [s]\")\n", - "\n", - "dt = np.mean(np.diff(t)) #note the time step is not exactly constant but it is fine for this example\n", - "df = 1 / (max(t) - min(t)) \n", - "hp_f, freq_range = util.make_fft_from_time_series(hp, df, dt)\n", - "\n", - "ax2.plot(freq_range, abs(hp_f))\n", - "ax2.set_ylabel(r\"$\\tilde{h}_+\\cdot$D [cm]\")\n", - "ax2.set_xscale('log')\n", - "ax2.set_yscale('log')\n", - "ax2.set_xlabel(\"Frequency [Hz]\")\n", - "ax2.set_xlim(min(freq_range), max(freq_range))\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now proceed to analyze the signal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. We start by selecting detectors" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "detector = detection.Detector(\"ET\") #used for the PSD later\n", - "detectors = ['ET', 'LHO', 'VIR'] #Justput only one detector in the array if you want to use only one detector\n", - "network = detection.Network(detector_ids = detectors)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2. We prepare the signal with proper scaling/units, note that GWFish needs an \"augmented\" frequency vector (meaning it has an extra axis here denoted by the ```None``` value)\n", - "\n", - "
Tip: If you already have a frequency series at hand you may skip util.make_fft_form_time_series
" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "t, hp, hc = np.loadtxt(\"23_gwstrain_trim.dat\").T\n", - "\n", - "kpc_to_cm = 3.086e21 # cm/kpc\n", - "D = 10 * kpc_to_cm\n", - "\n", - "dt = np.mean(np.diff(t)) #the time step is not quite constant for this particular dataset, resampling would be necessary but it gives close enough results to be illustrative\n", - "df = 1 / (max(t) - min(t))\n", - "hc_f, freq_range = util.make_fft_from_time_series(hc, df, dt) \n", - "hp_f, _ = util.make_fft_from_time_series(hp, df, dt) \n", - "\n", - "hc_f_10kpc = hc_f/D\n", - "hp_f_10kpc = hp_f/D\n", - "\n", - "f_in = freq_range[:, None]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We also need to selected a certain number of parameters that are needed to evaluate the SNR. The parameter explanation can be found [here](https://gwfish.readthedocs.io/en/latest/reference/parameters_units.html). For a input Frequency series only 3 parameters will affect the SNR. " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "params = {\n", - " \"ra\" : math.radians(200.405),\n", - " \"dec\" : math.radians(-12.008),\n", - " \"psi\" : np.pi*0.3,\n", - " 'geocent_time': 1187008882.4\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "we can also quickly check the detector PSD vs the strains:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_et, psd_et = np.loadtxt('GWFish/detector_psd/ET_psd.txt').T\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n", - "\n", - "ax.plot(f_et, (psd_et)**0.5, label=\"ET PSD\")\n", - "ax.plot(freq_range, (freq_range)**0.5*abs(hp_f_10kpc), label=r\"$\\tilde{h}_+$\")\n", - "ax.set_xscale('log')\n", - "ax.set_yscale('log')\n", - "ax.set_xlabel(\"Frequency [Hz]\")\n", - "ax.set_xlim(min(f_et), max(f_et))\n", - "ax.set_ylim([1e-25, 1e-20])\n", - "ax.set_ylabel(r\"$\\tilde{h}_+$\")\n", - "ax.legend()\n", - "\n", - "#second ax with same x but showing the ration\n", - "psd_et_new = detector.components[0].Sn(freq_range) #interpolate the PSD to the freq_range\n", - "ax2 = ax.twinx()\n", - "ratio_snr = (freq_range)**0.5*abs(hp_f_10kpc) / (psd_et_new)**0.5 \n", - "ax2.plot(freq_range, ratio_snr, color='red', label=\"SNR\", alpha=0.5, zorder=-10)\n", - "ax2.set_ylabel(\"SNR\")\n", - "ax2.set_ylim([0, max(ratio_snr)])\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "3. With a prepared Signal we can evaluate an associated SNR" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SNR : 76.26\n" - ] - } - ], - "source": [ - "component_SNRs = util.get_SNR_from_strains(f_in, hp_f_10kpc, hc_f_10kpc, network, params)\n", - "out_SNR = np.sqrt(np.sum(component_SNRs**2))\n", - "print(f\"SNR : {out_SNR:.2f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# What about Reshift ?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For signals far enough away we need to take into account also the shift in frequency in frequency, obviously for this specific signal (CC-SN) the considered distances are usually ``` D < 1 Mpc ``` so redshift effects are negligible. But for completeness the procedure is as follows :" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SNR : 76.26\n" - ] - } - ], - "source": [ - "from astropy.coordinates import Distance\n", - "from astropy import units as u\n", - "\n", - "redshift = Distance(10, u.kpc).z #get redshift at the distance we care for\n", - "\n", - "f_in = freq_range[:, None] / (1+redshift) #redshift the frequency, the signal in itself should not change just shift\n", - "\n", - "component_SNRs = util.get_SNR_from_series(f_in, hp_f_10kpc, hc_f_10kpc, network, params)\n", - "out_SNR = np.sqrt(np.sum(component_SNRs**2))\n", - "print(f\"SNR : {out_SNR:.2f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Clearly at 10 kpc we do not see any significant redshift. So we can do a quick check for (slightly) higher redshifts " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Redshift @ 20 Mpc : 4.50e-03 redshift\n", - "SNR no z : 3.813e-02\n", - "SNR z : 3.809e-02\n", - "SNR ratio : 1.00119\n" - ] - } - ], - "source": [ - "redshift = Distance(20, u.Mpc).z #get redshift at the distance we care for\n", - "\n", - "kpc_10_to_20_mpc = 2000\n", - "\n", - "hp_f_20Mpc = hp_f_10kpc / kpc_10_to_20_mpc\n", - "hc_f_20Mpc = hc_f_10kpc / kpc_10_to_20_mpc\n", - "\n", - "f_in_noz = freq_range[:, None]\n", - "f_in_z = freq_range[:, None] / (1+redshift)\n", - "\n", - "component_SNRs_noz = util.get_SNR_from_series(f_in_noz, hp_f_20Mpc, hc_f_20Mpc, network, params)\n", - "component_SNRs_z = util.get_SNR_from_series(f_in_z, hp_f_20Mpc, hc_f_20Mpc, network, params)\n", - "\n", - "out_SNR_noz = np.sqrt(np.sum(component_SNRs_noz**2))\n", - "out_SNR_z = np.sqrt(np.sum(component_SNRs_z**2))\n", - "\n", - "print(f\"Redshift @ 20 Mpc : {redshift:.2e}\")\n", - "print(f\"SNR no z : {out_SNR_noz:.3e}\")\n", - "print(f\"SNR z : {out_SNR_z:.3e}\")\n", - "print(f\"SNR ratio : {out_SNR_noz/out_SNR_z:.5f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# What about high frequencies ?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "GWFish mainly works under the long waveform approximation. However, this can be turned off for a more accurate SNR determination:\n", - "\n", - "
Tip: If your signal includes it, explicitly remove f = 0 as without the approximation there are 1/f terms that diverge.
" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SNR : 75.58\n" - ] - } - ], - "source": [ - "f_in = freq_range[:, None]\n", - "\n", - "f_max = 0\n", - "condition = freq_range > f_max\n", - "\n", - "f_masked = freq_range[condition][:, None]\n", - "hp_f_masked = hp_f_10kpc[condition]\n", - "hc_f_masked = hc_f_10kpc[condition]\n", - "\n", - "component_SNRs = util.get_SNR_from_series(f_masked, hp_f_masked, hc_f_masked, network, params, long_wavelength_approx=False)\n", - "out_SNR = np.sqrt(np.sum(component_SNRs**2))\n", - "print(f\"SNR : {out_SNR:.2f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Functional Approximations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you would like the change the frequency interval on which you evaluate your strain, you can approximate it and turn it into a \"function\"." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.interpolate import interp1d\n", - "\n", - "t, hp, hc = np.loadtxt(\"23_gwstrain_trim.dat\").T\n", - "\n", - "kpc_to_cm = 3.086e21 # cm/kpc\n", - "D = 10 * kpc_to_cm\n", - "\n", - "dt = np.mean(np.diff(t)) \n", - "df = 1 / (max(t) - min(t))\n", - "hc_f, freq_range = util.make_fft_from_time_series(hc, df, dt) \n", - "hp_f, _ = util.make_fft_from_time_series(hp, df, dt) \n", - "\n", - "hc_f_10kpc = hc_f/D\n", - "hp_f_10kpc = hp_f/D\n", - "\n", - "hp_f_interp = interp1d(freq_range, hp_f_10kpc, kind='cubic', fill_value='extrapolate')\n", - "hc_f_interp = interp1d(freq_range, hc_f_10kpc, kind='cubic', fill_value='extrapolate')" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SNR : 76.26\n" - ] - } - ], - "source": [ - "from GWFish.modules import waveforms as wv\n", - "\n", - "waves = wv.Ludo_Waveform( freq_range, hp_f_interp, hc_f_interp, params)\n", - "waves.calculate_frequency_domain_strain()\n", - "\n", - "f_in = freq_range[:, None]\n", - "hfp, hfc = waves.frequency_domain_strain.T\n", - "\n", - "component_SNRs = util.get_SNR_from_series(f_in, hfp, hfc, network, params)\n", - "out_SNR = np.sqrt(np.sum(component_SNRs**2))\n", - "print(f\"SNR : {out_SNR:.2f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What about Parameters ? " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Maybe there is some dependence of your signal to one or more parameters. While supernova signals are probably the worst example of this as they are mainly stochastic, here we can see how to take multiple simulations / series to generate a functional form that can also be used to estimate parameters. " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File 11_gwstrain_trim.dat already exists\n", - "File 15.01_gwstrain_trim.dat already exists\n", - "File 23_gwstrain_trim.dat already exists\n" - ] - } - ], - "source": [ - "import requests\n", - "\n", - "link = \"https://www.astro.princeton.edu/~burrows/gw.3d.new/data/\"\n", - "files = [\"11\", \"15.01\", \"23\"]\n", - "\n", - "for f in files:\n", - " filename = f + \"_gwstrain_trim.dat\"\n", - " if Path(filename).exists():\n", - " print(f\"File {filename} already exists\")\n", - " else :\n", - " response = requests.get(link + filename)\n", - " print(f\"Downloading {filename} from {link}\")\n", - " if response.status_code == 200:\n", - " with open(filename, 'wb') as f:\n", - " f.write(response.content)\n", - " print(\"File downloaded successfully\")\n", - " else:\n", - " print(\"Failed to download the file\")" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "times = []\n", - "strains_p = []\n", - "strains_c = []\n", - "\n", - "masses = [11, 15.01, 23]\n", - "\n", - "strains_f_p = []\n", - "strains_f_c = []\n", - "freqs_file = []\n", - "\n", - "strains_p_interp = []\n", - "strains_c_interp = []\n", - "\n", - "for f in files:\n", - "\n", - " t, hp, hc = np.loadtxt(f + \"_gwstrain_trim.dat\").T\n", - "\n", - " dt = np.mean(np.diff(t)) \n", - " df = 1 / (max(t) - min(t))\n", - " hc_f, freq_range = util.make_fft_from_time_series(hc, df, dt) \n", - " hp_f, _ = util.make_fft_from_time_series(hp, df, dt) \n", - "\n", - " hc_f_10kpc = hc_f/D\n", - " hp_f_10kpc = hp_f/D\n", - "\n", - " hp_f_interp = interp1d(freq_range, hp_f_10kpc, fill_value='extrapolate')\n", - " hc_f_interp = interp1d(freq_range, hc_f_10kpc, fill_value='extrapolate')\n", - "\n", - " strains_p_interp.append(hp_f_interp)\n", - " strains_c_interp.append(hc_f_interp)\n", - "\n", - " freqs_file.append(freq_range)\n", - " strains_f_p.append(hp_f_10kpc)\n", - " strains_f_c.append(hc_f_10kpc)\n", - " \n", - " plt.plot(freq_range, abs(hp_f_10kpc), label=f+r\"M$_\\odot$\")\n", - "\n", - "plt.legend()\n", - "plt.yscale('log')\n", - "plt.xscale('log')\n", - "plt.xlim(0.1, max(freq_range))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.1, 10000.0)" - ] - }, - "execution_count": 131, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from scipy.interpolate import RegularGridInterpolator\n", - "\n", - "#interp the data in frequency and mass\n", - "\n", - "freq_interpolation = np.logspace(-1, 4, 1000)\n", - "\n", - "masses = [11, 15.01, 23]\n", - "freqs = np.logspace(-1, 4, 1000)\n", - "ref = np.ones_like(freqs)\n", - "\n", - "mass_grid, freq_grid = np.meshgrid(masses, freq_interpolation, indexing='ij')\n", - "\n", - "hp_2D_interp = np.array( [ hp(freq_interpolation) for hp in strains_p_interp] ) # (num_masses, num_freqs)\n", - "hc_2D_interp = np.array( [ hc(freq_interpolation) for hc in strains_c_interp] ) # (num_masses, num_freqs)\n", - "\n", - "# Create the interpolator\n", - "interpolator_hp = RegularGridInterpolator((masses, freq_interpolation), hp_2D_interp)\n", - "\n", - "# Define the new masses for interpolation (from 11 to 23 in steps of 1)\n", - "new_masses = [12, 18]\n", - "\n", - "plt.plot(freqs_file[0], abs(strains_f_p[0]), label=\"11M$_\\odot$ data\", color='black', linestyle='--', alpha=0.5)\n", - "\n", - "for k in range(len(new_masses)):\n", - " interpolated_values = np.array( [interpolator_hp([new_masses[k], freq]) for freq in freq_interpolation] )\n", - " plt.plot(freq_interpolation, abs(interpolated_values), label=f\"{new_masses[k]}M$_\\odot$ interp\")\n", - "\n", - "plt.legend()\n", - "plt.yscale('log')\n", - "plt.xscale('log')\n", - "plt.xlim(0.1, 1e4)" - ] - } - ], - "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": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/series_snr_tutorial.ipynb b/series_snr_tutorial.ipynb new file mode 100644 index 00000000..7c43c9a2 --- /dev/null +++ b/series_snr_tutorial.ipynb @@ -0,0 +1,541 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GWFish : Frequency/Time Series\n", + "\n", + "Quick tutorial to show how to use Frequency/Time series within GWFish\n", + "\n", + "Assumes you have already read the [gwfish_tutoial.ipynb](./gwfish_tutorial.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ludo/miniconda3/lib/python3.10/site-packages/lalsimulation/lalsimulation.py:8: UserWarning: Wswiglal-redir-stdio:\n", + "\n", + "SWIGLAL standard output/error redirection is enabled in IPython.\n", + "This may lead to performance penalties. To disable locally, use:\n", + "\n", + "with lal.no_swig_redirect_standard_output_error():\n", + " ...\n", + "\n", + "To disable globally, use:\n", + "\n", + "lal.swig_redirect_standard_output_error(True)\n", + "\n", + "Note however that this will likely lead to error messages from\n", + "LAL functions being either misdirected or lost when called from\n", + "Jupyter notebooks.\n", + "\n", + "To suppress this warning, use:\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", \"Wswiglal-redir-stdio\")\n", + "import lal\n", + "\n", + " import lal\n" + ] + } + ], + "source": [ + "from GWFish import detection\n", + "from GWFish.modules import utilities as util\n", + "\n", + "import math, h5py\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pathlib import Path\n", + "\n", + "import astropy.constants as const\n", + "from astropy.cosmology import Planck18" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyzing the Frequency Series" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To illustrate how to use GWFish to calculate SNR/horizons we will use GW strain available from [here](https://www.astro.princeton.edu/~burrows/gw.3d.new/). You can either manually download those files or execute the next cell to automatically download the file (\"23_gwstrain_trim.dat\")." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File already exists\n" + ] + } + ], + "source": [ + "import requests\n", + "#download from the URL, http\n", + "link = \"https://www.astro.princeton.edu/~burrows/gw.3d.new/data/\"\n", + "filename = \"23_gwstrain_trim.dat\"\n", + "\n", + "if Path(filename).exists():\n", + " print(\"File already exists\")\n", + "else :\n", + " response = requests.get(link + filename)\n", + " print(f\"Downloading {filename} from {link}\")\n", + " if response.status_code == 200:\n", + " with open(filename, 'wb') as f:\n", + " f.write(response.content)\n", + " print(\"File downloaded successfully\")\n", + " else:\n", + " print(\"Failed to download the file\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then look at the downloaded data and its fourier transform. Here we assume that we have a file with 3 columns one for time, h_plus and h_cross." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_14439/3960507349.py:20: UserWarning: Attempt to set non-positive xlim on a log-scaled axis will be ignored.\n", + " ax2.set_xlim(min(freq_range), max(freq_range))\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t, hp, hc = np.loadtxt(\"23_gwstrain_trim.dat\").T\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(2, 1)\n", + "\n", + "ax1.plot(t, hp)\n", + "ax1.set_ylabel(r\"h$_+$$\\cdot$D [cm]\")\n", + "ax1.set_title(r\"GW Strain Supernova progenitor 23M$_\\odot$ @ 10kpc\")\n", + "ax1.set_xlim(min(t), max(t))\n", + "ax1.set_xlabel(\"Time [s]\")\n", + "\n", + "dt = np.mean(np.diff(t)) #note the time step is not exactly constant but it is fine for this example\n", + "df = 1 / (max(t) - min(t)) \n", + "hp_f, freq_range = util.make_fft_from_time_series(hp, df, dt)\n", + "\n", + "ax2.plot(freq_range, abs(hp_f))\n", + "ax2.set_ylabel(r\"$\\tilde{h}_+\\cdot$D [cm]\")\n", + "ax2.set_xscale('log')\n", + "ax2.set_yscale('log')\n", + "ax2.set_xlabel(\"Frequency [Hz]\")\n", + "ax2.set_xlim(min(freq_range), max(freq_range))\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now proceed to analyze the signal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. We start by selecting detectors" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "detector = detection.Detector(\"ET\") #used for the PSD later\n", + "detectors = ['ET', 'LHO', 'VIR'] #Justput only one detector in the array if you want to use only one detector\n", + "network = detection.Network(detector_ids = detectors)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. We prepare the signal with proper scaling/units, note that GWFish needs an \"augmented\" frequency vector (meaning it has an extra axis here denoted by the ```None``` value)\n", + "\n", + "
Tip: If you already have a frequency series at hand you may skip util.make_fft_form_time_series
" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "t, hp, hc = np.loadtxt(\"23_gwstrain_trim.dat\").T\n", + "\n", + "kpc_to_cm = 3.086e21 # cm/kpc\n", + "D = 10 * kpc_to_cm\n", + "\n", + "dt = np.mean(np.diff(t)) #the time step is not quite constant for this particular dataset, resampling would be necessary but it gives close enough results to be illustrative\n", + "df = 1 / (max(t) - min(t))\n", + "hc_f, freq_range = util.make_fft_from_time_series(hc, df, dt) \n", + "hp_f, _ = util.make_fft_from_time_series(hp, df, dt) \n", + "\n", + "hc_f_10kpc = hc_f/D\n", + "hp_f_10kpc = hp_f/D\n", + "\n", + "f_in = freq_range[:, None]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also need to selected a certain number of parameters that are needed to evaluate the SNR. The parameter explanation can be found [here](https://gwfish.readthedocs.io/en/latest/reference/parameters_units.html). For a input Frequency series only 3 parameters will affect the SNR. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "params = {\n", + " \"ra\" : math.radians(200.405),\n", + " \"dec\" : math.radians(-12.008),\n", + " \"psi\" : np.pi*0.3,\n", + " 'geocent_time': 1187008882.4\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "we can also quickly check the detector PSD vs the strains:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f_et, psd_et = np.loadtxt('GWFish/detector_psd/ET_psd.txt').T\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n", + "\n", + "ax.plot(f_et, (psd_et)**0.5, label=\"ET PSD\")\n", + "ax.plot(freq_range, (freq_range)**0.5*abs(hp_f_10kpc), label=r\"$\\tilde{h}_+$\")\n", + "ax.set_xscale('log')\n", + "ax.set_yscale('log')\n", + "ax.set_xlabel(\"Frequency [Hz]\")\n", + "ax.set_xlim(min(f_et), max(f_et))\n", + "ax.set_ylim([1e-25, 1e-20])\n", + "ax.set_ylabel(r\"$\\tilde{h}_+$\")\n", + "ax.legend()\n", + "\n", + "#second ax with same x but showing the ration\n", + "psd_et_new = detector.components[0].Sn(freq_range) #interpolate the PSD to the freq_range\n", + "ax2 = ax.twinx()\n", + "ratio_snr = (freq_range)**0.5*abs(hp_f_10kpc) / (psd_et_new)**0.5 \n", + "ax2.plot(freq_range, ratio_snr, color='red', label=\"SNR\", alpha=0.5, zorder=-10)\n", + "ax2.set_ylabel(\"SNR\")\n", + "ax2.set_ylim([0, max(ratio_snr)])\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. With a prepared Signal we can evaluate an associated SNR" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SNR : 76.26\n" + ] + } + ], + "source": [ + "component_SNRs = util.get_SNR_from_strains(f_in, hp_f_10kpc, hc_f_10kpc, network, params)\n", + "out_SNR = np.sqrt(np.sum(component_SNRs**2))\n", + "print(f\"SNR : {out_SNR:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# What about Reshift ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For signals far enough away we need to take into account also the shift in frequency in frequency, obviously for this specific signal (CC-SN) the considered distances are usually ``` D < 1 Mpc ``` so redshift effects are negligible. But for completeness the procedure is as follows :" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SNR : 76.26\n" + ] + } + ], + "source": [ + "from astropy.coordinates import Distance\n", + "from astropy import units as u\n", + "\n", + "redshift = Distance(10, u.kpc).z #get redshift at the distance we care for\n", + "\n", + "f_in = freq_range[:, None] / (1+redshift) #redshift the frequency, the signal in itself should not change just shift\n", + "\n", + "component_SNRs = util.get_SNR_from_series(f_in, hp_f_10kpc, hc_f_10kpc, network, params)\n", + "out_SNR = np.sqrt(np.sum(component_SNRs**2))\n", + "print(f\"SNR : {out_SNR:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clearly at 10 kpc we do not see any significant redshift. So we can do a quick check for (slightly) higher redshifts " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Redshift @ 20 Mpc : 4.50e-03 redshift\n", + "SNR no z : 3.813e-02\n", + "SNR z : 3.809e-02\n", + "SNR ratio : 1.00119\n" + ] + } + ], + "source": [ + "redshift = Distance(20, u.Mpc).z #get redshift at the distance we care for\n", + "\n", + "kpc_10_to_20_mpc = 2000\n", + "\n", + "hp_f_20Mpc = hp_f_10kpc / kpc_10_to_20_mpc\n", + "hc_f_20Mpc = hc_f_10kpc / kpc_10_to_20_mpc\n", + "\n", + "f_in_noz = freq_range[:, None]\n", + "f_in_z = freq_range[:, None] / (1+redshift)\n", + "\n", + "component_SNRs_noz = util.get_SNR_from_series(f_in_noz, hp_f_20Mpc, hc_f_20Mpc, network, params)\n", + "component_SNRs_z = util.get_SNR_from_series(f_in_z, hp_f_20Mpc, hc_f_20Mpc, network, params)\n", + "\n", + "out_SNR_noz = np.sqrt(np.sum(component_SNRs_noz**2))\n", + "out_SNR_z = np.sqrt(np.sum(component_SNRs_z**2))\n", + "\n", + "print(f\"Redshift @ 20 Mpc : {redshift:.2e}\")\n", + "print(f\"SNR no z : {out_SNR_noz:.3e}\")\n", + "print(f\"SNR z : {out_SNR_z:.3e}\")\n", + "print(f\"SNR ratio : {out_SNR_noz/out_SNR_z:.5f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# What about high frequencies ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "GWFish mainly works under the long waveform approximation. However, this can be turned off for a more accurate SNR determination:\n", + "\n", + "
Tip: If your signal includes it, explicitly remove f = 0 as without the approximation there are 1/f terms that diverge.
" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SNR : 75.58\n" + ] + } + ], + "source": [ + "f_in = freq_range[:, None]\n", + "\n", + "f_max = 0\n", + "condition = freq_range > f_max\n", + "\n", + "f_masked = freq_range[condition][:, None]\n", + "hp_f_masked = hp_f_10kpc[condition]\n", + "hc_f_masked = hc_f_10kpc[condition]\n", + "\n", + "component_SNRs = util.get_SNR_from_series(f_masked, hp_f_masked, hc_f_masked, network, params, long_wavelength_approx=False)\n", + "out_SNR = np.sqrt(np.sum(component_SNRs**2))\n", + "print(f\"SNR : {out_SNR:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Functional Approximations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you would like the change the frequency interval on which you evaluate your strain, you can approximate it and turn it into a \"function\"." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.interpolate import interp1d\n", + "\n", + "t, hp, hc = np.loadtxt(\"23_gwstrain_trim.dat\").T\n", + "\n", + "kpc_to_cm = 3.086e21 # cm/kpc\n", + "D = 10 * kpc_to_cm\n", + "\n", + "dt = np.mean(np.diff(t)) \n", + "df = 1 / (max(t) - min(t))\n", + "hc_f, freq_range = util.make_fft_from_time_series(hc, df, dt) \n", + "hp_f, _ = util.make_fft_from_time_series(hp, df, dt) \n", + "\n", + "hc_f_10kpc = hc_f/D\n", + "hp_f_10kpc = hp_f/D\n", + "\n", + "hp_f_interp = interp1d(freq_range, hp_f_10kpc, kind='cubic', fill_value='extrapolate')\n", + "hc_f_interp = interp1d(freq_range, hc_f_10kpc, kind='cubic', fill_value='extrapolate')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SNR : 76.26\n" + ] + } + ], + "source": [ + "from GWFish.modules import waveforms as wv\n", + "\n", + "waves = wv.Ludo_Waveform( freq_range, hp_f_interp, hc_f_interp, params)\n", + "waves.calculate_frequency_domain_strain()\n", + "\n", + "f_in = freq_range[:, None]\n", + "hfp, hfc = waves.frequency_domain_strain.T\n", + "\n", + "component_SNRs = util.get_SNR_from_series(f_in, hfp, hfc, network, params)\n", + "out_SNR = np.sqrt(np.sum(component_SNRs**2))\n", + "print(f\"SNR : {out_SNR:.2f}\")" + ] + } + ], + "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": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 9becca2444584bbc396c62a045cb90c47756ad56 Mon Sep 17 00:00:00 2001 From: LudoDe Date: Fri, 13 Dec 2024 16:06:48 +0100 Subject: [PATCH 6/6] completed updates --- .gitignore | 4 ++-- GWFish/detectors.yaml | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.gitignore b/.gitignore index 58792687..e6388e30 100644 --- a/.gitignore +++ b/.gitignore @@ -7,7 +7,7 @@ dist/ docs/source/detectors_autogen.inc docs/source/figures/* *_gwstrain_trim.dat -Merger_data.ipynb -spectrum_data.hdf5 +Merger_data*.ipynb +*.hdf5 *eatmap* diff --git a/GWFish/detectors.yaml b/GWFish/detectors.yaml index 68dd9200..26b0fac7 100644 --- a/GWFish/detectors.yaml +++ b/GWFish/detectors.yaml @@ -3,7 +3,7 @@ ET: lon: (9 + 25. / 60) * np.pi / 180. opening_angle: np.pi / 3. azimuth: 70.5674 * np.pi / 180. - psd_data: ET_psd.txt + psd_data: ET_full.txt duty_factor: 0.85 detector_class: earthDelta plotrange: 3, 1000, 1e-25, 1e-20 @@ -33,7 +33,7 @@ CE1: lon: -119.4 * np.pi / 180. opening_angle: np.pi / 2. azimuth: 126. * np.pi / 180. - psd_data: CE1_psd.txt + psd_data: CE20km_full.txt duty_factor: 0.85 detector_class: earthL plotrange: 10, 1000, 1e-25, 1e-20 @@ -48,7 +48,7 @@ CE2: lon: -119.4 * np.pi / 180. opening_angle: np.pi / 2. azimuth: 126. * np.pi / 180. - psd_data: CE2_psd.txt + psd_data: CE40km_full.txt duty_factor: 0.85 detector_class: earthL plotrange: 10, 1000, 1e-25, 1e-20