diff --git a/2021_05/noise_test_05.ipynb b/2021_05/noise_test_05.ipynb deleted file mode 100644 index 48b37ffa5348f44cfce4de00a70e11e5f8fdf884..0000000000000000000000000000000000000000 --- a/2021_05/noise_test_05.ipynb +++ /dev/null @@ -1,653 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import minke.sources\n", - "import lalsimulation\n", - "import lal\n", - "import numpy as np\n", - "import minke.distribution\n", - "import matplotlib.pyplot as plt\n", - "import bilby\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(100)\n", - "parameters = dict(\n", - " mass_1=81.9, mass_2=70.91, a_1=0., a_2=0., tilt_1=0., tilt_2=0.,\n", - " phi_12=0., phi_jl=0., luminosity_distance=1931.77, theta_jn=1.51, psi=1.54,\n", - " phase=0., geocent_time=1126259642.5, ra=3.89, dec=-0.94)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "13:33 bilby INFO : Waveform generator initiated with\n", - " frequency_domain_source_model: bilby.gw.source.lal_binary_black_hole\n", - " time_domain_source_model: None\n", - " parameter_conversion: bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters\n" - ] - } - ], - "source": [ - "duration =1\n", - "sampling_frequency = 256\n", - "waveform_arguments= dict(waveform_approximant='IMRPhenomPv2',\n", - " reference_frequency=50., minimum_frequency=20.)\n", - "waveform_generator = bilby.gw.WaveformGenerator(\n", - " duration=duration, sampling_frequency=sampling_frequency,\n", - " frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,\n", - " parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters,\n", - " waveform_arguments=waveform_arguments)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/weichangfeng/.local/lib/python3.8/site-packages/bilby/gw/detector/psd.py:356: RuntimeWarning: invalid value encountered in multiply\n", - " frequency_domain_strain = self.__power_spectral_density_interpolated(frequencies) ** 0.5 * white_noise\n" - ] - } - ], - "source": [ - "ifos_list = ['H1','L1','V1']\n", - "ifos = bilby.gw.detector.InterferometerList(ifos_list)\n", - "ifos.set_strain_data_from_power_spectral_densities(\n", - " sampling_frequency=sampling_frequency, duration=duration,\n", - " start_time=parameters['geocent_time'] - 0.5)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Detector noise" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(ifos[0].time_domain_strain)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Signal with detector noise" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "13:33 bilby INFO : Injected signal in H1:\n", - "13:33 bilby INFO : optimal SNR = 13.17\n", - "13:33 bilby INFO : matched filter SNR = 14.09-0.64j\n", - "13:33 bilby INFO : mass_1 = 81.9\n", - "13:33 bilby INFO : mass_2 = 70.91\n", - "13:33 bilby INFO : a_1 = 0.0\n", - "13:33 bilby INFO : a_2 = 0.0\n", - "13:33 bilby INFO : tilt_1 = 0.0\n", - "13:33 bilby INFO : tilt_2 = 0.0\n", - "13:33 bilby INFO : phi_12 = 0.0\n", - "13:33 bilby INFO : phi_jl = 0.0\n", - "13:33 bilby INFO : luminosity_distance = 1931.77\n", - "13:33 bilby INFO : theta_jn = 1.51\n", - "13:33 bilby INFO : psi = 1.54\n", - "13:33 bilby INFO : phase = 0.0\n", - "13:33 bilby INFO : geocent_time = 1126259642.5\n", - "13:33 bilby INFO : ra = 3.89\n", - "13:33 bilby INFO : dec = -0.94\n", - "13:33 bilby INFO : Injected signal in L1:\n", - "13:33 bilby INFO : optimal SNR = 11.18\n", - "13:33 bilby INFO : matched filter SNR = 11.16+1.00j\n", - "13:33 bilby INFO : mass_1 = 81.9\n", - "13:33 bilby INFO : mass_2 = 70.91\n", - "13:33 bilby INFO : a_1 = 0.0\n", - "13:33 bilby INFO : a_2 = 0.0\n", - "13:33 bilby INFO : tilt_1 = 0.0\n", - "13:33 bilby INFO : tilt_2 = 0.0\n", - "13:33 bilby INFO : phi_12 = 0.0\n", - "13:33 bilby INFO : phi_jl = 0.0\n", - "13:33 bilby INFO : luminosity_distance = 1931.77\n", - "13:33 bilby INFO : theta_jn = 1.51\n", - "13:33 bilby INFO : psi = 1.54\n", - "13:33 bilby INFO : phase = 0.0\n", - "13:33 bilby INFO : geocent_time = 1126259642.5\n", - "13:33 bilby INFO : ra = 3.89\n", - "13:33 bilby INFO : dec = -0.94\n", - "13:33 bilby INFO : Injected signal in V1:\n", - "13:33 bilby INFO : optimal SNR = 5.26\n", - "13:33 bilby INFO : matched filter SNR = 7.70+0.66j\n", - "13:33 bilby INFO : mass_1 = 81.9\n", - "13:33 bilby INFO : mass_2 = 70.91\n", - "13:33 bilby INFO : a_1 = 0.0\n", - "13:33 bilby INFO : a_2 = 0.0\n", - "13:33 bilby INFO : tilt_1 = 0.0\n", - "13:33 bilby INFO : tilt_2 = 0.0\n", - "13:33 bilby INFO : phi_12 = 0.0\n", - "13:33 bilby INFO : phi_jl = 0.0\n", - "13:33 bilby INFO : luminosity_distance = 1931.77\n", - "13:33 bilby INFO : theta_jn = 1.51\n", - "13:33 bilby INFO : psi = 1.54\n", - "13:33 bilby INFO : phase = 0.0\n", - "13:33 bilby INFO : geocent_time = 1126259642.5\n", - "13:33 bilby INFO : ra = 3.89\n", - "13:33 bilby INFO : dec = -0.94\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ifos.inject_signal(waveform_generator=waveform_generator,\n", - " parameters=parameters)\n", - "plt.plot(ifos[0].time_domain_strain)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "waveform_generator.parameters = parameters\n", - "Nt = 256\n", - "# extract waveform from bilby\n", - "freq_signal = waveform_generator.frequency_domain_strain()\n", - "bbh_noisefree_fd_h = ifos[0].get_detector_response(freq_signal, parameters) \n", - "bbh_noisefree_h = Nt*np.fft.irfft(bbh_noisefree_fd_h) \n", - "bbh_noisefree_fd_l = ifos[1].get_detector_response(freq_signal, parameters) \n", - "bbh_noisefree_l = Nt*np.fft.irfft(bbh_noisefree_fd_l) \n", - "bbh_noisefree_fd_v = ifos[2].get_detector_response(freq_signal, parameters) \n", - "bbh_noisefree_v = Nt*np.fft.irfft(bbh_noisefree_fd_v) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Signal" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(bbh_noisefree_h)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "ref_geocent_time=1126259642.5" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "13:33 bilby INFO : No prior given, using default BBH priors in /home/weichangfeng/.local/lib/python3.8/site-packages/bilby/gw/prior_files/precessing_spins_bbh.prior.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'mass_1': Uniform(minimum=5, maximum=160, name='mass_1', latex_label='$m_1$', unit='$M_{\\\\odot}$', boundary=None),\n", - " 'mass_2': Uniform(minimum=5, maximum=160, name='mass_2', latex_label='$m_2$', unit='$M_{\\\\odot}$', boundary=None),\n", - " 'luminosity_distance': Uniform(minimum=1000, maximum=5000, name='luminosity_distance', latex_label='$d_L$', unit='$Mpc_{\\\\odot}$', boundary=None),\n", - " 'dec': Cosine(name='dec', latex_label='$\\\\mathrm{DEC}$', unit=None, minimum=-1.5707963267948966, maximum=1.5707963267948966, boundary=None),\n", - " 'ra': Uniform(minimum=0, maximum=6.283185307179586, name='ra', latex_label='$\\\\mathrm{RA}$', unit=None, boundary='periodic'),\n", - " 'theta_jn': Sine(name='theta_jn', latex_label='$\\\\theta_{JN}$', unit=None, minimum=0, maximum=3.141592653589793, boundary=None),\n", - " 'psi': Uniform(minimum=0, maximum=3.141592653589793, name='psi', latex_label='$\\\\psi$', unit=None, boundary='periodic'),\n", - " 'phase': Uniform(minimum=0, maximum=6.283185307179586, name='phase', latex_label='$\\\\phi$', unit=None, boundary='periodic'),\n", - " 'a_1': Uniform(minimum=0, maximum=0.99, name='a_1', latex_label='$a_1$', unit=None, boundary=None),\n", - " 'a_2': Uniform(minimum=0, maximum=0.99, name='a_2', latex_label='$a_2$', unit=None, boundary=None),\n", - " 'tilt_1': Sine(name='tilt_1', latex_label='$\\\\theta_1$', unit=None, minimum=0, maximum=3.141592653589793, boundary=None),\n", - " 'tilt_2': Sine(name='tilt_2', latex_label='$\\\\theta_2$', unit=None, minimum=0, maximum=3.141592653589793, boundary=None),\n", - " 'phi_12': Uniform(minimum=0, maximum=6.283185307179586, name='phi_12', latex_label='$\\\\Delta\\\\phi$', unit=None, boundary='periodic'),\n", - " 'phi_jl': Uniform(minimum=0, maximum=6.283185307179586, name='phi_jl', latex_label='$\\\\phi_{JL}$', unit=None, boundary='periodic'),\n", - " 'geocent_time': Uniform(minimum=1126259641.5, maximum=1126259643.5, name='geocent_time', latex_label='$t_c$', unit='$s$', boundary=None)}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "priors = bilby.gw.prior.BBHPriorDict()\n", - "priors['mass_1'] = bilby.core.prior.Uniform(name='mass_1', minimum=5, maximum=160, unit='$M_{\\\\odot}$')\n", - "priors['mass_2'] = bilby.core.prior.Uniform(name='mass_2', minimum=5, maximum=160, unit='$M_{\\\\odot}$')\n", - "priors['luminosity_distance'] = bilby.core.prior.Uniform(name='luminosity_distance', minimum=1000, maximum=5000, unit='$Mpc_{\\\\odot}$')\n", - "priors['dec'] = bilby.core.prior.Cosine(name='dec')\n", - "priors['tilt_1'] = bilby.core.prior.Sine(name='tilt_1') \n", - "priors['tilt_2'] = bilby.core.prior.Sine(name='tilt_2') \n", - "priors['theta_jn'] = bilby.core.prior.Sine(name='theta_jn')\n", - "\n", - "priors.pop('chirp_mass')\n", - "priors.pop('mass_ratio')\n", - "\n", - "priors['geocent_time'] = bilby.core.prior.Uniform(\n", - " minimum=ref_geocent_time - 1,\n", - " maximum=ref_geocent_time + 1,\n", - " name='geocent_time', latex_label='$t_c$', unit='$s$')\n", - "\n", - "priors" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "likelihood = bilby.gw.GravitationalWaveTransient(\n", - " interferometers=ifos, waveform_generator=waveform_generator,priors=priors\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "outdir = 'outdir_BH_encounter'\n", - "label = 'test_bbh_051205'" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "13:33 bilby INFO : Running for label 'test_bbh_051205', output will be saved to 'outdir_BH_encounter'\n", - "13:33 bilby INFO : Using lal version 7.1.0\n", - "13:33 bilby INFO : Using lal git version Branch: None;Tag: lalsuite-v6.79;Id: 2439365b5a320735efaf7171b37738032dcf7b6b;;Builder: Unknown User <>;Repository status: UNCLEAN: Modified working tree\n", - "13:33 bilby INFO : Using lalsimulation version 2.3.0\n", - "13:33 bilby INFO : Using lalsimulation git version Branch: None;Tag: lalsuite-v6.79;Id: 2439365b5a320735efaf7171b37738032dcf7b6b;;Builder: Unknown User <>;Repository status: UNCLEAN: Modified working tree\n", - "13:33 bilby INFO : Search parameters:\n", - "13:33 bilby INFO : mass_1 = Uniform(minimum=5, maximum=160, name='mass_1', latex_label='$m_1$', unit='$M_{\\\\odot}$', boundary=None)\n", - "13:33 bilby INFO : mass_2 = Uniform(minimum=5, maximum=160, name='mass_2', latex_label='$m_2$', unit='$M_{\\\\odot}$', boundary=None)\n", - "13:33 bilby INFO : luminosity_distance = Uniform(minimum=1000, maximum=5000, name='luminosity_distance', latex_label='$d_L$', unit='$Mpc_{\\\\odot}$', boundary=None)\n", - "13:33 bilby INFO : dec = Cosine(name='dec', latex_label='$\\\\mathrm{DEC}$', unit=None, minimum=-1.5707963267948966, maximum=1.5707963267948966, boundary=None)\n", - "13:33 bilby INFO : ra = Uniform(minimum=0, maximum=6.283185307179586, name='ra', latex_label='$\\\\mathrm{RA}$', unit=None, boundary='periodic')\n", - "13:33 bilby INFO : theta_jn = Sine(name='theta_jn', latex_label='$\\\\theta_{JN}$', unit=None, minimum=0, maximum=3.141592653589793, boundary=None)\n", - "13:33 bilby INFO : psi = Uniform(minimum=0, maximum=3.141592653589793, name='psi', latex_label='$\\\\psi$', unit=None, boundary='periodic')\n", - "13:33 bilby INFO : phase = Uniform(minimum=0, maximum=6.283185307179586, name='phase', latex_label='$\\\\phi$', unit=None, boundary='periodic')\n", - "13:33 bilby INFO : a_1 = Uniform(minimum=0, maximum=0.99, name='a_1', latex_label='$a_1$', unit=None, boundary=None)\n", - "13:33 bilby INFO : a_2 = Uniform(minimum=0, maximum=0.99, name='a_2', latex_label='$a_2$', unit=None, boundary=None)\n", - "13:33 bilby INFO : tilt_1 = Sine(name='tilt_1', latex_label='$\\\\theta_1$', unit=None, minimum=0, maximum=3.141592653589793, boundary=None)\n", - "13:33 bilby INFO : tilt_2 = Sine(name='tilt_2', latex_label='$\\\\theta_2$', unit=None, minimum=0, maximum=3.141592653589793, boundary=None)\n", - "13:33 bilby INFO : phi_12 = Uniform(minimum=0, maximum=6.283185307179586, name='phi_12', latex_label='$\\\\Delta\\\\phi$', unit=None, boundary='periodic')\n", - "13:33 bilby INFO : phi_jl = Uniform(minimum=0, maximum=6.283185307179586, name='phi_jl', latex_label='$\\\\phi_{JL}$', unit=None, boundary='periodic')\n", - "13:33 bilby INFO : geocent_time = Uniform(minimum=1126259641.5, maximum=1126259643.5, name='geocent_time', latex_label='$t_c$', unit='$s$', boundary=None)\n", - "13:33 bilby INFO : Single likelihood evaluation took 1.239e-03 s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "0it [00:00, ?it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "13:33 bilby INFO : Using sampler Dynesty with kwargs {'bound': 'multi', 'sample': 'rwalk', 'verbose': True, 'periodic': None, 'reflective': None, 'check_point_delta_t': 600, 'nlive': 8000, 'first_update': None, 'walks': 100, 'npdim': None, 'rstate': None, 'queue_size': 1, 'pool': None, 'use_pool': None, 'live_points': None, 'logl_args': None, 'logl_kwargs': None, 'ptform_args': None, 'ptform_kwargs': None, 'enlarge': 1.5, 'bootstrap': None, 'vol_dec': 0.5, 'vol_check': 8.0, 'facc': 0.2, 'slices': 5, 'update_interval': 4800, 'print_func': >, 'dlogz': 0.1, 'maxiter': None, 'maxcall': None, 'logl_max': inf, 'add_live': True, 'print_progress': True, 'save_bounds': False, 'n_effective': None, 'maxmcmc': 20000, 'nact': 50}\n", - "13:33 bilby INFO : Checkpoint every check_point_delta_t = 1800s\n", - "13:33 bilby INFO : Using dynesty version 1.0.1\n", - "13:33 bilby INFO : Using the bilby-implemented rwalk sample method with ACT estimated walks\n", - "13:33 bilby INFO : Reading resume file outdir_BH_encounter/test_bbh_051205_resume.pickle\n", - "13:33 bilby INFO : Resume file successfully loaded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "150585it [00:20, 31893.63it/s, bound:8652 nc: 1 ncall:4.4e+07 eff:0.4% logz-ratio=164.80+/-0.62 dlogz:0.069>0.1] " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "13:36 bilby INFO : Written checkpoint file outdir_BH_encounter/test_bbh_051205_resume.pickle\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "151162it [33:00, 3.52s/it, bound:8726 nc:377 ncall:4.4e+07 eff:0.3% logz-ratio=154.89+/-0.07 dlogz:16.187>0.1] " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "14:09 bilby INFO : Written checkpoint file outdir_BH_encounter/test_bbh_051205_resume.pickle\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "151615it [1:06:42, 20.79s/it, bound:8793 nc:1235 ncall:4.5e+07 eff:0.3% logz-ratio=155.11+/-0.07 dlogz:16.648>0.1]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "14:44 bilby INFO : Written checkpoint file outdir_BH_encounter/test_bbh_051205_resume.pickle\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "152270it [1:41:00, 1.05s/it, bound:8874 nc:600 ncall:4.5e+07 eff:0.3% logz-ratio=155.41+/-0.07 dlogz:16.266>0.1] " - ] - } - ], - "source": [ - "\n", - "# Run sampler. In this case we're going to use the `dynesty` sampler\n", - "result = bilby.run_sampler(\n", - " likelihood=likelihood, priors=priors, label=label,outdir=outdir,sampler='dynesty', \n", - " nlive=8000, sample='rwalk',walks =100,nact=50,check_point_delta_t=1800,check_point_plot=True,maxmcmc=20000)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# Make a corner plot.\n", - "result.plot_corner()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Output posterior samples for VItamin analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Use Bilby to generate BBH waveform\n", - "def generate_whitened_bbh_waveform(parameters,ifos_list):\n", - " \n", - " whitened_signal = np.zeros((3,256))\n", - " for k in range(len(ifos_list)):\n", - " \n", - " signal_fd = ifos[k].get_detector_response(freq_signal, parameters) \n", - " \n", - " whitened_signal_fd = signal_fd/ifos[k].amplitude_spectral_density_array\n", - " \n", - " whitened_signal_td = np.sqrt(2.0*Nt)*np.fft.irfft(whitened_signal_fd)\n", - " \n", - " whitened_signal[k] = whitened_signal_td + 1.0*np.random.randn(256) \n", - " \n", - " return whitened_signal" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "whitened_signal = generate_whitened_bbh_waveform(parameters,ifos_list)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Whitened signal" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(whitened_signal[0])\n", - "plt.title('whitened Korean BH encounter waveform')\n", - "plt.xlabel('times')\n", - "plt.ylabel('waveform')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import h5py\n", - "\n", - "f = h5py.File('./data_0.h5py','r')\n", - "name_list=[]\n", - "#shape_list=[]\n", - "#type_list=[]\n", - "data_set_list=[]\n", - "\n", - "for name in f:\n", - " name_list.append(name)\n", - " #shape_list.append(f2[f'{name}'].shape)\n", - " #type_list.append(f2[f'{name}'].dtype)\n", - " data_set_list.append(f[f'{name}'])\n", - " #print(y)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with h5py.File('data_2021051305.h5py','w') as f1:\n", - " \n", - " for k in range(len(name_list)):\n", - " d = f1.create_dataset(name_list[k],data=data_set_list[k])\n", - " \n", - " del f1['y_data_noisy']\n", - " \n", - " \n", - " d2 = f1.create_dataset('y_data_noisy',data = whitened_signal)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = result.posterior['mass_1'].values\n", - "b = result.posterior['mass_2'].values\n", - "c = result.posterior['luminosity_distance'].values\n", - "np.savetxt('BBH_m1_posterior_samples_05.txt',a,fmt='%.8f') \n", - "np.savetxt('BBH_m2_posterior_samples_05.txt',b,fmt='%.8f') \n", - "np.savetxt('BBH_d_posterior_samples_05.txt',c,fmt='%.8f') " - ] - }, - { - "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": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/2021_05/noise_test_06.ipynb b/2021_05/noise_test_06.ipynb deleted file mode 100644 index e070762bcda8803c39488f47a04729399afac6a9..0000000000000000000000000000000000000000 --- a/2021_05/noise_test_06.ipynb +++ /dev/null @@ -1,653 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import minke.sources\n", - "import lalsimulation\n", - "import lal\n", - "import numpy as np\n", - "import minke.distribution\n", - "import matplotlib.pyplot as plt\n", - "import bilby\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(100)\n", - "parameters = dict(\n", - " mass_1=81.9, mass_2=70.91, a_1=0., a_2=0., tilt_1=0., tilt_2=0.,\n", - " phi_12=0., phi_jl=0., luminosity_distance=1931.77, theta_jn=1.51, psi=1.54,\n", - " phase=0., geocent_time=1126259642.5, ra=3.89, dec=-0.94)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "13:33 bilby INFO : Waveform generator initiated with\n", - " frequency_domain_source_model: bilby.gw.source.lal_binary_black_hole\n", - " time_domain_source_model: None\n", - " parameter_conversion: bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters\n" - ] - } - ], - "source": [ - "duration =1\n", - "sampling_frequency = 256\n", - "waveform_arguments= dict(waveform_approximant='IMRPhenomPv2',\n", - " reference_frequency=50., minimum_frequency=20.)\n", - "waveform_generator = bilby.gw.WaveformGenerator(\n", - " duration=duration, sampling_frequency=sampling_frequency,\n", - " frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,\n", - " parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters,\n", - " waveform_arguments=waveform_arguments)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/weichangfeng/.local/lib/python3.8/site-packages/bilby/gw/detector/psd.py:356: RuntimeWarning: invalid value encountered in multiply\n", - " frequency_domain_strain = self.__power_spectral_density_interpolated(frequencies) ** 0.5 * white_noise\n" - ] - } - ], - "source": [ - "ifos_list = ['H1','L1','V1']\n", - "ifos = bilby.gw.detector.InterferometerList(ifos_list)\n", - "ifos.set_strain_data_from_power_spectral_densities(\n", - " sampling_frequency=sampling_frequency, duration=duration,\n", - " start_time=parameters['geocent_time'] - 0.5)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Detector noise" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(ifos[0].time_domain_strain)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Signal with detector noise" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "13:33 bilby INFO : Injected signal in H1:\n", - "13:33 bilby INFO : optimal SNR = 13.17\n", - "13:33 bilby INFO : matched filter SNR = 14.09-0.64j\n", - "13:33 bilby INFO : mass_1 = 81.9\n", - "13:33 bilby INFO : mass_2 = 70.91\n", - "13:33 bilby INFO : a_1 = 0.0\n", - "13:33 bilby INFO : a_2 = 0.0\n", - "13:33 bilby INFO : tilt_1 = 0.0\n", - "13:33 bilby INFO : tilt_2 = 0.0\n", - "13:33 bilby INFO : phi_12 = 0.0\n", - "13:33 bilby INFO : phi_jl = 0.0\n", - "13:33 bilby INFO : luminosity_distance = 1931.77\n", - "13:33 bilby INFO : theta_jn = 1.51\n", - "13:33 bilby INFO : psi = 1.54\n", - "13:33 bilby INFO : phase = 0.0\n", - "13:33 bilby INFO : geocent_time = 1126259642.5\n", - "13:33 bilby INFO : ra = 3.89\n", - "13:33 bilby INFO : dec = -0.94\n", - "13:33 bilby INFO : Injected signal in L1:\n", - "13:33 bilby INFO : optimal SNR = 11.18\n", - "13:33 bilby INFO : matched filter SNR = 11.16+1.00j\n", - "13:33 bilby INFO : mass_1 = 81.9\n", - "13:33 bilby INFO : mass_2 = 70.91\n", - "13:33 bilby INFO : a_1 = 0.0\n", - "13:33 bilby INFO : a_2 = 0.0\n", - "13:33 bilby INFO : tilt_1 = 0.0\n", - "13:33 bilby INFO : tilt_2 = 0.0\n", - "13:33 bilby INFO : phi_12 = 0.0\n", - "13:33 bilby INFO : phi_jl = 0.0\n", - "13:33 bilby INFO : luminosity_distance = 1931.77\n", - "13:33 bilby INFO : theta_jn = 1.51\n", - "13:33 bilby INFO : psi = 1.54\n", - "13:33 bilby INFO : phase = 0.0\n", - "13:33 bilby INFO : geocent_time = 1126259642.5\n", - "13:33 bilby INFO : ra = 3.89\n", - "13:33 bilby INFO : dec = -0.94\n", - "13:33 bilby INFO : Injected signal in V1:\n", - "13:33 bilby INFO : optimal SNR = 5.26\n", - "13:33 bilby INFO : matched filter SNR = 7.70+0.66j\n", - "13:33 bilby INFO : mass_1 = 81.9\n", - "13:33 bilby INFO : mass_2 = 70.91\n", - "13:33 bilby INFO : a_1 = 0.0\n", - "13:33 bilby INFO : a_2 = 0.0\n", - "13:33 bilby INFO : tilt_1 = 0.0\n", - "13:33 bilby INFO : tilt_2 = 0.0\n", - "13:33 bilby INFO : phi_12 = 0.0\n", - "13:33 bilby INFO : phi_jl = 0.0\n", - "13:33 bilby INFO : luminosity_distance = 1931.77\n", - "13:33 bilby INFO : theta_jn = 1.51\n", - "13:33 bilby INFO : psi = 1.54\n", - "13:33 bilby INFO : phase = 0.0\n", - "13:33 bilby INFO : geocent_time = 1126259642.5\n", - "13:33 bilby INFO : ra = 3.89\n", - "13:33 bilby INFO : dec = -0.94\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ifos.inject_signal(waveform_generator=waveform_generator,\n", - " parameters=parameters)\n", - "plt.plot(ifos[0].time_domain_strain)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "waveform_generator.parameters = parameters\n", - "Nt = 256\n", - "# extract waveform from bilby\n", - "freq_signal = waveform_generator.frequency_domain_strain()\n", - "bbh_noisefree_fd_h = ifos[0].get_detector_response(freq_signal, parameters) \n", - "bbh_noisefree_h = Nt*np.fft.irfft(bbh_noisefree_fd_h) \n", - "bbh_noisefree_fd_l = ifos[1].get_detector_response(freq_signal, parameters) \n", - "bbh_noisefree_l = Nt*np.fft.irfft(bbh_noisefree_fd_l) \n", - "bbh_noisefree_fd_v = ifos[2].get_detector_response(freq_signal, parameters) \n", - "bbh_noisefree_v = Nt*np.fft.irfft(bbh_noisefree_fd_v) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Signal" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(bbh_noisefree_h)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "ref_geocent_time=1126259642.5" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "13:33 bilby INFO : No prior given, using default BBH priors in /home/weichangfeng/.local/lib/python3.8/site-packages/bilby/gw/prior_files/precessing_spins_bbh.prior.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'mass_1': Uniform(minimum=5, maximum=160, name='mass_1', latex_label='$m_1$', unit='$M_{\\\\odot}$', boundary=None),\n", - " 'mass_2': Uniform(minimum=5, maximum=160, name='mass_2', latex_label='$m_2$', unit='$M_{\\\\odot}$', boundary=None),\n", - " 'luminosity_distance': Uniform(minimum=1000, maximum=5000, name='luminosity_distance', latex_label='$d_L$', unit='$Mpc_{\\\\odot}$', boundary=None),\n", - " 'dec': Cosine(name='dec', latex_label='$\\\\mathrm{DEC}$', unit=None, minimum=-1.5707963267948966, maximum=1.5707963267948966, boundary=None),\n", - " 'ra': Uniform(minimum=0, maximum=6.283185307179586, name='ra', latex_label='$\\\\mathrm{RA}$', unit=None, boundary='periodic'),\n", - " 'theta_jn': Sine(name='theta_jn', latex_label='$\\\\theta_{JN}$', unit=None, minimum=0, maximum=3.141592653589793, boundary=None),\n", - " 'psi': Uniform(minimum=0, maximum=3.141592653589793, name='psi', latex_label='$\\\\psi$', unit=None, boundary='periodic'),\n", - " 'phase': Uniform(minimum=0, maximum=6.283185307179586, name='phase', latex_label='$\\\\phi$', unit=None, boundary='periodic'),\n", - " 'a_1': Uniform(minimum=0, maximum=0.99, name='a_1', latex_label='$a_1$', unit=None, boundary=None),\n", - " 'a_2': Uniform(minimum=0, maximum=0.99, name='a_2', latex_label='$a_2$', unit=None, boundary=None),\n", - " 'tilt_1': Sine(name='tilt_1', latex_label='$\\\\theta_1$', unit=None, minimum=0, maximum=3.141592653589793, boundary=None),\n", - " 'tilt_2': Sine(name='tilt_2', latex_label='$\\\\theta_2$', unit=None, minimum=0, maximum=3.141592653589793, boundary=None),\n", - " 'phi_12': Uniform(minimum=0, maximum=6.283185307179586, name='phi_12', latex_label='$\\\\Delta\\\\phi$', unit=None, boundary='periodic'),\n", - " 'phi_jl': Uniform(minimum=0, maximum=6.283185307179586, name='phi_jl', latex_label='$\\\\phi_{JL}$', unit=None, boundary='periodic'),\n", - " 'geocent_time': Uniform(minimum=1126259641.5, maximum=1126259643.5, name='geocent_time', latex_label='$t_c$', unit='$s$', boundary=None)}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "priors = bilby.gw.prior.BBHPriorDict()\n", - "priors['mass_1'] = bilby.core.prior.Uniform(name='mass_1', minimum=5, maximum=160, unit='$M_{\\\\odot}$')\n", - "priors['mass_2'] = bilby.core.prior.Uniform(name='mass_2', minimum=5, maximum=160, unit='$M_{\\\\odot}$')\n", - "priors['luminosity_distance'] = bilby.core.prior.Uniform(name='luminosity_distance', minimum=1000, maximum=5000, unit='$Mpc_{\\\\odot}$')\n", - "priors['dec'] = bilby.core.prior.Cosine(name='dec')\n", - "priors['tilt_1'] = bilby.core.prior.Sine(name='tilt_1') \n", - "priors['tilt_2'] = bilby.core.prior.Sine(name='tilt_2') \n", - "priors['theta_jn'] = bilby.core.prior.Sine(name='theta_jn')\n", - "\n", - "priors.pop('chirp_mass')\n", - "priors.pop('mass_ratio')\n", - "\n", - "priors['geocent_time'] = bilby.core.prior.Uniform(\n", - " minimum=ref_geocent_time - 1,\n", - " maximum=ref_geocent_time + 1,\n", - " name='geocent_time', latex_label='$t_c$', unit='$s$')\n", - "\n", - "priors" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "likelihood = bilby.gw.GravitationalWaveTransient(\n", - " interferometers=ifos, waveform_generator=waveform_generator,priors=priors\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "outdir = 'outdir_BH_encounter'\n", - "label = 'test_bbh_051206'" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "13:33 bilby INFO : Running for label 'test_bbh_051206', output will be saved to 'outdir_BH_encounter'\n", - "13:33 bilby INFO : Using lal version 7.1.0\n", - "13:33 bilby INFO : Using lal git version Branch: None;Tag: lalsuite-v6.79;Id: 2439365b5a320735efaf7171b37738032dcf7b6b;;Builder: Unknown User <>;Repository status: UNCLEAN: Modified working tree\n", - "13:33 bilby INFO : Using lalsimulation version 2.3.0\n", - "13:33 bilby INFO : Using lalsimulation git version Branch: None;Tag: lalsuite-v6.79;Id: 2439365b5a320735efaf7171b37738032dcf7b6b;;Builder: Unknown User <>;Repository status: UNCLEAN: Modified working tree\n", - "13:33 bilby INFO : Search parameters:\n", - "13:33 bilby INFO : mass_1 = Uniform(minimum=5, maximum=160, name='mass_1', latex_label='$m_1$', unit='$M_{\\\\odot}$', boundary=None)\n", - "13:33 bilby INFO : mass_2 = Uniform(minimum=5, maximum=160, name='mass_2', latex_label='$m_2$', unit='$M_{\\\\odot}$', boundary=None)\n", - "13:33 bilby INFO : luminosity_distance = Uniform(minimum=1000, maximum=5000, name='luminosity_distance', latex_label='$d_L$', unit='$Mpc_{\\\\odot}$', boundary=None)\n", - "13:33 bilby INFO : dec = Cosine(name='dec', latex_label='$\\\\mathrm{DEC}$', unit=None, minimum=-1.5707963267948966, maximum=1.5707963267948966, boundary=None)\n", - "13:33 bilby INFO : ra = Uniform(minimum=0, maximum=6.283185307179586, name='ra', latex_label='$\\\\mathrm{RA}$', unit=None, boundary='periodic')\n", - "13:33 bilby INFO : theta_jn = Sine(name='theta_jn', latex_label='$\\\\theta_{JN}$', unit=None, minimum=0, maximum=3.141592653589793, boundary=None)\n", - "13:33 bilby INFO : psi = Uniform(minimum=0, maximum=3.141592653589793, name='psi', latex_label='$\\\\psi$', unit=None, boundary='periodic')\n", - "13:33 bilby INFO : phase = Uniform(minimum=0, maximum=6.283185307179586, name='phase', latex_label='$\\\\phi$', unit=None, boundary='periodic')\n", - "13:33 bilby INFO : a_1 = Uniform(minimum=0, maximum=0.99, name='a_1', latex_label='$a_1$', unit=None, boundary=None)\n", - "13:33 bilby INFO : a_2 = Uniform(minimum=0, maximum=0.99, name='a_2', latex_label='$a_2$', unit=None, boundary=None)\n", - "13:33 bilby INFO : tilt_1 = Sine(name='tilt_1', latex_label='$\\\\theta_1$', unit=None, minimum=0, maximum=3.141592653589793, boundary=None)\n", - "13:33 bilby INFO : tilt_2 = Sine(name='tilt_2', latex_label='$\\\\theta_2$', unit=None, minimum=0, maximum=3.141592653589793, boundary=None)\n", - "13:33 bilby INFO : phi_12 = Uniform(minimum=0, maximum=6.283185307179586, name='phi_12', latex_label='$\\\\Delta\\\\phi$', unit=None, boundary='periodic')\n", - "13:33 bilby INFO : phi_jl = Uniform(minimum=0, maximum=6.283185307179586, name='phi_jl', latex_label='$\\\\phi_{JL}$', unit=None, boundary='periodic')\n", - "13:33 bilby INFO : geocent_time = Uniform(minimum=1126259641.5, maximum=1126259643.5, name='geocent_time', latex_label='$t_c$', unit='$s$', boundary=None)\n", - "13:33 bilby INFO : Single likelihood evaluation took 3.178e-03 s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "0it [00:00, ?it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "13:33 bilby INFO : Using sampler Dynesty with kwargs {'bound': 'multi', 'sample': 'rwalk', 'verbose': True, 'periodic': None, 'reflective': None, 'check_point_delta_t': 600, 'nlive': 8000, 'first_update': None, 'walks': 100, 'npdim': None, 'rstate': None, 'queue_size': 1, 'pool': None, 'use_pool': None, 'live_points': None, 'logl_args': None, 'logl_kwargs': None, 'ptform_args': None, 'ptform_kwargs': None, 'enlarge': 1.5, 'bootstrap': None, 'vol_dec': 0.5, 'vol_check': 8.0, 'facc': 0.2, 'slices': 5, 'update_interval': 4800, 'print_func': >, 'dlogz': 0.1, 'maxiter': None, 'maxcall': None, 'logl_max': inf, 'add_live': True, 'print_progress': True, 'save_bounds': False, 'n_effective': None, 'maxmcmc': 20000, 'nact': 50}\n", - "13:33 bilby INFO : Checkpoint every check_point_delta_t = 1800s\n", - "13:33 bilby INFO : Using dynesty version 1.0.1\n", - "13:33 bilby INFO : Using the bilby-implemented rwalk sample method with ACT estimated walks\n", - "13:33 bilby INFO : Reading resume file outdir_BH_encounter/test_bbh_051206_resume.pickle\n", - "13:33 bilby INFO : Resume file successfully loaded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "157057it [00:20, 31889.55it/s, bound:9885 nc: 1 ncall:5.1e+07 eff:0.3% logz-ratio=164.28+/-0.66 dlogz:0.081>0.1] " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "13:36 bilby INFO : Written checkpoint file outdir_BH_encounter/test_bbh_051206_resume.pickle\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "157458it [33:01, 5.19s/it, bound:9957 nc:1100 ncall:5.1e+07 eff:0.3% logz-ratio=155.84+/-0.07 dlogz:14.906>0.1] " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "14:09 bilby INFO : Written checkpoint file outdir_BH_encounter/test_bbh_051206_resume.pickle\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "157813it [1:06:00, 10.52s/it, bound:10022 nc:1193 ncall:5.1e+07 eff:0.3% logz-ratio=155.98+/-0.07 dlogz:14.717>0.1]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "14:43 bilby INFO : Written checkpoint file outdir_BH_encounter/test_bbh_051206_resume.pickle\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "158283it [1:40:42, 1.61s/it, bound:10100 nc:844 ncall:5.2e+07 eff:0.3% logz-ratio=156.17+/-0.07 dlogz:14.470>0.1] " - ] - } - ], - "source": [ - "\n", - "# Run sampler. In this case we're going to use the `dynesty` sampler\n", - "result = bilby.run_sampler(\n", - " likelihood=likelihood, priors=priors, label=label,outdir=outdir,sampler='dynesty', \n", - " nlive=8000, sample='rwalk',walks =100,nact=50,check_point_delta_t=1800,check_point_plot=True,maxmcmc=20000)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# Make a corner plot.\n", - "result.plot_corner()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Output posterior samples for VItamin analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Use Bilby to generate BBH waveform\n", - "def generate_whitened_bbh_waveform(parameters,ifos_list):\n", - " \n", - " whitened_signal = np.zeros((3,256))\n", - " for k in range(len(ifos_list)):\n", - " \n", - " signal_fd = ifos[k].get_detector_response(freq_signal, parameters) \n", - " \n", - " whitened_signal_fd = signal_fd/ifos[k].amplitude_spectral_density_array\n", - " \n", - " whitened_signal_td = np.sqrt(2.0*Nt)*np.fft.irfft(whitened_signal_fd)\n", - " \n", - " whitened_signal[k] = whitened_signal_td + 1.0*np.random.randn(256) \n", - " \n", - " return whitened_signal" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "whitened_signal = generate_whitened_bbh_waveform(parameters,ifos_list)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Whitened signal" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(whitened_signal[0])\n", - "plt.title('whitened Korean BH encounter waveform')\n", - "plt.xlabel('times')\n", - "plt.ylabel('waveform')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import h5py\n", - "\n", - "f = h5py.File('./data_0.h5py','r')\n", - "name_list=[]\n", - "#shape_list=[]\n", - "#type_list=[]\n", - "data_set_list=[]\n", - "\n", - "for name in f:\n", - " name_list.append(name)\n", - " #shape_list.append(f2[f'{name}'].shape)\n", - " #type_list.append(f2[f'{name}'].dtype)\n", - " data_set_list.append(f[f'{name}'])\n", - " #print(y)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with h5py.File('data_2021051306.h5py','w') as f1:\n", - " \n", - " for k in range(len(name_list)):\n", - " d = f1.create_dataset(name_list[k],data=data_set_list[k])\n", - " \n", - " del f1['y_data_noisy']\n", - " \n", - " \n", - " d2 = f1.create_dataset('y_data_noisy',data = whitened_signal)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = result.posterior['mass_1'].values\n", - "b = result.posterior['mass_2'].values\n", - "c = result.posterior['luminosity_distance'].values\n", - "np.savetxt('BBH_m1_posterior_samples_06.txt',a,fmt='%.8f') \n", - "np.savetxt('BBH_m2_posterior_samples_06.txt',b,fmt='%.8f') \n", - "np.savetxt('BBH_d_posterior_samples_06.txt',c,fmt='%.8f') " - ] - }, - { - "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": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}