{ "cells": [ { "cell_type": "markdown", "id": "725b74cb", "metadata": {}, "source": [ "# SNR Threshold Finder Example" ] }, { "cell_type": "code", "execution_count": 29, "id": "be898794", "metadata": {}, "outputs": [], "source": [ "import h5py\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from gwsnr.threshold import SNRThresholdFinder\n", "\n", "np.random.seed(1234)" ] }, { "cell_type": "markdown", "id": "d9536d6f", "metadata": {}, "source": [ "## With injection catalog as hf5 file " ] }, { "cell_type": "code", "execution_count": 4, "id": "019a8ec7", "metadata": {}, "outputs": [], "source": [ "# Example if you have the injection catalog file locally\n", "# you can download the file for O4 injections from zenodo:\n", "# !wget https://zenodo.org/records/16740117/files/samples-rpo4a_v2_20250503133839UTC-1366933504-23846400.hdf?download=1\n", "\n", "# file_name = 'samples-rpo4a_v2_20250503133839UTC-1366933504-23846400.hdf'\n", "\n", "# test = SNRThresholdFinder(\n", "# catalog_file = file_name,\n", "# # # below are all default values. You can omit them if you want. \n", "# # npool=4,\n", "# # original_detection_statistic = dict(\n", "# # key_name='gstlal_far',\n", "# # threshold=1, # 1 per year\n", "# # parameter=None, # you can provide parameter values (np.ndarray) here if needed, if you don't want to use the catalog\n", "# # ),\n", "# # projected_detection_statistic = dict(\n", "# # key_name='observed_snr_net',\n", "# # threshold=None, # to be determined\n", "# # threshold_search_bounds=(6, 12),\n", "# # parameter=None, # you can provide parameter values (np.ndarray) here if needed, if you don't want to use the catalog\n", "# # ),\n", "# # parameters_to_fit = dict(\n", "# # key_name = 'z',\n", "# # parameter=None, # you can provide parameter values (np.ndarray) here if needed, if you don't want to use the catalog\n", "# # ),\n", "# # sample_size=20000,\n", "# # selection_range = dict(\n", "# # key_name = 'mass1_source',\n", "# # range = (30, 60), # in solar masses\n", "# # parameter = None, # you can provide parameter values (np.ndarray) here if needed, if you don't want to use the catalog\n", "# # ),\n", "# # sample_size=20000,\n", "# # multiprocessing_verbose=True,\n", "# )" ] }, { "cell_type": "code", "execution_count": 5, "id": "d1252a04", "metadata": {}, "outputs": [], "source": [ "# best_thr, del_H, H, H_true, snr_thrs = test.find_threshold(iteration=10, print_output=True, no_multiprocessing=True)" ] }, { "cell_type": "markdown", "id": "231fdf9a", "metadata": {}, "source": [ "## With injection catalog parameters as numpy arrays" ] }, { "cell_type": "code", "execution_count": 45, "id": "aa8223be", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2025-10-28 18:07:10-- https://raw.githubusercontent.com/hemantaph/gwsnr/refs/heads/main/tests/unit/injection_data_bbh.json\n", "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.108.133, ...\n", "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 10458366 (10.0M) [text/plain]\n", "Saving to: ‘injection_data_bbh.json’\n", "\n", "injection_data_bbh. 100%[===================>] 9.97M 14.6MB/s in 0.7s \n", "\n", "2025-10-28 18:07:12 (14.6 MB/s) - ‘injection_data_bbh.json’ saved [10458366/10458366]\n", "\n" ] } ], "source": [ "# injection_data_bbh.json is the reduced data extracted from the above hdf file for testing purpose\n", "! wget https://raw.githubusercontent.com/hemantaph/gwsnr/refs/heads/main/tests/unit/injection_data_bbh.json" ] }, { "cell_type": "code", "execution_count": 46, "id": "b3149d68", "metadata": {}, "outputs": [], "source": [ "from gwsnr.utils import get_param_from_json\n", "params = get_param_from_json('injection_data_bbh.json')\n", "\n", "gstlal_far = params['gstlal_far']\n", "observed_snr_net = params['observed_snr_net']\n", "z = params['z']\n", "mass1_source = params['mass1_source']" ] }, { "cell_type": "code", "execution_count": 47, "id": "53ce455f", "metadata": {}, "outputs": [], "source": [ "test = SNRThresholdFinder(\n", " catalog_file = None,\n", " # below are all default values. You can omit them if you want. \n", " npool=4,\n", " original_detection_statistic = dict(\n", " key_name='gstlal_far',\n", " threshold=1, # 1 per year\n", " parameter=gstlal_far,\n", " ),\n", " projected_detection_statistic = dict(\n", " key_name='observed_snr_net',\n", " threshold=None, # to be determined\n", " threshold_search_bounds=(6, 12),\n", " parameter=observed_snr_net,\n", " ),\n", " parameters_to_fit = dict(\n", " key_name = 'z',\n", " parameter=z,\n", " ),\n", " sample_size=20000,\n", " selection_range = dict(\n", " key_name = 'mass1_source',\n", " range = (30, 60), # in solar masses\n", " parameter = mass1_source,\n", " ),\n", ")" ] }, { "cell_type": "code", "execution_count": 48, "id": "dff200ed", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████████████████████████| 10/10 [00:11<00:00, 1.12s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best SNR threshold: 9.38\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "best_thr, del_H, H, H_true, snr_thrs = test.find_threshold(iteration=10, print_output=True, no_multiprocessing=True)" ] }, { "cell_type": "code", "execution_count": 49, "id": "33ee19a9", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.interpolate import UnivariateSpline\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "spline = UnivariateSpline(snr_thrs, del_H, s=0)\n", "snr_fine = np.linspace(snr_thrs.min(), snr_thrs.max(), 100)\n", "del_H_fine = spline(snr_fine)\n", "plt.plot(snr_fine, del_H_fine, 'b-', alpha=0.7, label='Spline interpolation')\n", "plt.plot(snr_thrs, del_H, 'ro', markersize=6, label='Data points')\n", "plt.axvline(best_thr, color='gray', linestyle='--', alpha=0.7, label=f'Best thr: {best_thr:.2f}')\n", "plt.xlabel('net SNR_obs Threshold (L1+H1)')\n", "plt.ylabel('Cross-Entropy Difference (ΔH)')\n", "plt.grid(alpha=0.4) \n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "a4c8e435", "metadata": {}, "source": [ "### Visualizing how well the detectable population matches when using SNRth and FAR cutoffs as detection criteria" ] }, { "cell_type": "code", "execution_count": 21, "id": "66993eeb", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "snr_th = best_thr\n", "plt.hist(z[observed_snr_net>=snr_th], bins=50, density=True, alpha=0.5, color='C0', histtype='step', label=f'SNR>{best_thr:.2f}')\n", "far_th = 1\n", "plt.hist(z[gstlal_far= best_thr) / len(observed_snr_net)\n", "# fraction_above_gstlal_far = np.sum(gstlal_far < 1) / len(observed_snr_net)\n", "\n", "# print(f\"Fraction above SNR threshold {best_thr:.2f}: {fraction_above_snr_thresh:.6f}\")\n", "# print(f\"Fraction above GstLAL FAR threshold 1 per year: {fraction_above_gstlal_far:.6f}\")" ] } ], "metadata": { "kernelspec": { "display_name": "gwsnr", "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": "3.10.18" } }, "nbformat": 4, "nbformat_minor": 5 }