CGH0S7
85afe00fc5
- Implement multiprocessing-based parallel plotting - Add parallel_plot_helper.py for concurrent plot task execution - Use matplotlib 'Agg' backend for multiprocessing safety - Set OMP_NUM_THREADS=1 to prevent BLAS thread explosion - Use subprocess for binary data plots to avoid thread conflicts - Add fork bomb protection in main program This merge only includes plotting improvements and excludes MPI communication changes to preserve existing optimizations. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
759 lines
40 KiB
Python
Executable File
759 lines
40 KiB
Python
Executable File
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#################################################
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##
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## This file extracts gravitational-wave strain amplitude
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## from AMSS-NCKU numerical-relativity outputs and plots it.
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## Author: Xiaoqu
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## Dates: 2024/10/01 --- 2025/11/20
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##
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#################################################
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import numpy ## numpy for array operations
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import scipy ## scipy for interpolation and signal processing
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import math
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import matplotlib
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matplotlib.use('Agg') ## use non-interactive backend for multiprocessing safety
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import matplotlib.pyplot as plt ## matplotlib for plotting
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import os ## os for system/file operations
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import AMSS_NCKU_Input as input_data
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# plt.rcParams['text.usetex'] = True ## enable LaTeX fonts in plots
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####################################################################################
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####################################################################################
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## Compute Fourier transform for discrete time data in [t0, t1].
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## Adapted from deepseek examples.
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## Parameters:
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## time : time series [0, t0]
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## f : function value series
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## apply_window : apply windowing (recommended True)
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## zero_pad_factor : zero-padding factor (recommended 2-8)
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## Returns:
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## frequency : frequency axis (Hz)
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## frequency_spectrum : complex frequency spectrum
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def compute_frequency_spectrum(time, signal_f, apply_window=True, zero_pad_factor=4):
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## Sampling parameters
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N = len(time)
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dt = time[1] - time[0] ## sample interval
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fs = 1/dt ## sampling frequency
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omega_s = fs * 2.0 * math.pi ## sampling angular frequency
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## Data preprocessing
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f_detrended = signal_f - numpy.mean(signal_f) ## remove DC offset
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## Windowing (reduce spectral leakage)
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if apply_window:
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# window = scipy.signal.windows.hann(N) # Hann window
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window = scipy.signal.windows.tukey(N, alpha=0.1) # or Tukey window
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f_windowed = f_detrended * window
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else:
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f_windowed = f_detrended
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# Zero-padding (improve frequency resolution)
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M = zero_pad_factor * N
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f_padded = numpy.zeros(M)
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f_padded[:N] = f_windowed
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# Execute FFT to obtain complex frequency spectrum
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# Use numpy.fft.fft to perform the fast Fourier transform
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# Basic signature: numpy.fft.fft(a, n=None, axis=-1, norm=None)
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# a: input 1-D array to be transformed
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# n: transform length; if n>len(a) the input is zero-padded, if n<len(a) it is truncated
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# axis: axis along which to compute the FFT (default: last axis)
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# norm: normalization mode (None, 'ortho', etc.)
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# numpy.fft.fft returns a complex array; the corresponding frequency axis spans -fs/2 to fs/2
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frequency_spectrum = numpy.fft.fft(f_padded, norm='ortho')
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# frequency_spectrum = numpy.fft.fft(f_detrended, norm='ortho')
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# frequency_spectrum = numpy.fft.fft(f_windowed, norm='ortho')
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# Frequency axis generation
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# Use numpy.fft.fftfreq to build the discrete frequency axis:
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# numpy.fft.fftfreq(n, d=1.0)
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# n: signal length (number of samples)
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# d: sample spacing (time between samples)
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frequency = numpy.fft.fftfreq(M, dt)
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# frequency = numpy.fft.fftfreq(N, dt)
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# The above gives frequency in Hz; convert to angular frequency
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frequency_omega = 2.0 * math.pi * frequency
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# Amplitude correction (compensate for window loss)
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if apply_window:
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window_gain = numpy.mean(window) # window gain
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frequency_spectrum /= window_gain
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## return frequency_omega, frequency_spectrum
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return frequency, frequency_omega, frequency_spectrum
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## Window types, properties and typical use cases:
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## Hann : wide main lobe, fast sidelobe decay -> black-hole quasi-periodic oscillations (QPO)
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## Tukey : adjustable flat-top region -> gravitational-wave chirp signals
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## Blackman: optimal sidelobe suppression -> high dynamic-range data
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####################################################################################
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####################################################################################
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def frequency_filter_integration(omega, frequency_spectrum, omega0):
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'''
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## modifiy the part |omega| < omega0
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omega_filter = numpy.where(omega < omega0, omega0, omega)
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'''
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## Replace region |omega| < omega0 with signed omega0
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# Build replacements: positive omegas -> +omega0, negative -> -omega0
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replacements = numpy.where(omega >= 0, omega0, -omega0)
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# Apply replacement where abs(omega) < omega0
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omega_filter = numpy.where( numpy.abs(omega) < omega0, replacements, omega )
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## Integrand for frequency-domain integration
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## Note: convolution in time corresponds to multiplication in frequency
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frequency_integration = - frequency_spectrum / (omega_filter)**2
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return frequency_integration
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####################################################################################
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####################################################################################
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## This function replaces |omega| < omega0 with signed omega0
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def omega_filter(omega, omega0):
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## Replace region |omega| < omega0 with signed omega0
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# Build replacements: positive omegas -> +omega0, negative -> -omega0
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replacements = numpy.where(omega >= 0, omega0, -omega0)
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# Apply replacements where |omega| < omega0
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# omega_filter = numpy.where(mask, replacements, omega)
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omega_filter = numpy.where( numpy.abs(omega) < omega0, replacements, omega )
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return omega_filter
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####################################################################################
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####################################################################################
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## Inverse Fourier transform utility
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## Inputs:
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## omega : frequency axis (Hz)
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## F_omega : complex frequency-domain data
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## sampling_factor : sampling multiplier for reconstruction
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## original_zero_pad_factor : original zero-padding factor used in forward FFT
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## Returns:
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## t : time axis
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## reconstructed : reconstructed time-domain signal
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def inverse_fourier_transform(omega, F_omega, sampling_factor=2, original_zero_pad_factor=4):
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# Compute sampling parameters
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N = len(F_omega)
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domega = omega[1] - omega[0] # frequency resolution
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# Determine sampling rate
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# To avoid aliasing, ensure sampling rate >= 2 * max frequency (Nyquist)
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if sampling_factor > 2:
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sampling_rate_omega = sampling_factor * omega.max()
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else:
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sampling_rate_omega = 2.0 * omega.max()
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# The input omega is angular frequency; convert to ordinary frequency
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## dt = 1.0 / sampling_rate_omega
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frequency = omega / (2.0*math.pi)
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dt = 2 * math.pi / sampling_rate_omega
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'''
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# DC component check
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if not numpy.isclose(omega[0], 0):
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warnings.warn("Frequency-domain data does not include zero-frequency component; DC offset may result")
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'''
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# Perform inverse FFT (use same normalization as forward transform)
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reconstructed_signal = numpy.fft.ifft(F_omega, norm='ortho')
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# Note: numpy.fft.ifft already handles normalization for 'ortho'
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## Build time axis
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## If zero-padding was used originally, recover the unpadded length
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if (original_zero_pad_factor > 1):
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N0 = N // original_zero_pad_factor
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t = numpy.arange(0, N0*dt, dt)
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reconstructed_signal2 = reconstructed_signal[:N0]
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## If no zero-padding
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else:
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t = numpy.arange(0, N*dt, dt)
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reconstructed_signal2 = reconstructed_signal
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# Handle real signals
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if numpy.allclose(numpy.imag(reconstructed_signal2), 0):
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reconstructed_signal3 = numpy.real(reconstructed_signal2)
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return t[:len(reconstructed_signal3)], reconstructed_signal3
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####################################################################################
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####################################################################################
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# Instantaneous frequency estimation using analytic signal (Hilbert transform)
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def instantaneous_frequency(signal, sampling_rate):
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"""
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Compute instantaneous frequency of a signal.
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:param signal: input time-domain sampled signal
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:param sampling_rate: sampling rate
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:return: time array and instantaneous frequency array
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"""
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analytic_signal = scipy.signal.hilbert(signal)
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phase = numpy.unwrap(numpy.angle(analytic_signal))
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time = numpy.arange(len(signal)) / sampling_rate
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frequency = numpy.gradient(phase, time) / (2 * numpy.pi)
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return time, frequency
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def get_frequency_at_t1(signal, sampling_rate, t1):
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"""
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Get instantaneous frequency at time t1
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:param signal: input time-domain sampled signal
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:param sampling_rate: sampling rate
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:param t1: target time
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:return: instantaneous frequency at t1
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"""
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time, freq = instantaneous_frequency(signal, sampling_rate)
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index = numpy.argmin(numpy.abs(time - t1))
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return freq[index]
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####################################################################################
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####################################################################################
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## Function to plot gravitational-wave waveform h
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## Inputs:
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## outdir path to data directory
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## figure_outdir path to figure output directory
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## detector_number_i detector index
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## total_mass total system mass
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def generate_gravitational_wave_amplitude_plot( outdir, figure_outdir, detector_number_i ):
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# build file path
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file0 = os.path.join(outdir, "bssn_psi4.dat")
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if ( detector_number_i == 0 ):
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print()
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print("Plotting the gravitational-wave strain amplitude h")
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print()
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print("The corresponding data file is", file0)
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print()
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print()
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print( "Plotting gravitational-wave data for detector no.", detector_number_i )
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# read entire data file, assume whitespace-delimited floats
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data = numpy.loadtxt(file0)
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# extract columns from psi4 file
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time = data[:,0]
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psi4_l2m2m_real = data[:,1]
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psi4_l2m2m_imaginary = data[:,2]
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psi4_l2m1m_real = data[:,3]
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psi4_l2m1m_imaginary = data[:,4]
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psi4_l2m0_real = data[:,5]
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psi4_l2m0_imaginary = data[:,6]
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psi4_l2m1_real = data[:,7]
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psi4_l2m1_imaginary = data[:,8]
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psi4_l2m2_real = data[:,9]
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psi4_l2m2_imaginary = data[:,10]
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# Note: file0 is just a filename; no file.open() was used, so nothing to close
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# file0.close()
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# Use integer division to compute length per detector
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length = len(time) // input_data.Detector_Number
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time2 = numpy.zeros( (input_data.Detector_Number, length) )
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psi4_l2m2m_real2 = numpy.zeros( (input_data.Detector_Number, length) )
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psi4_l2m2m_imaginary2 = numpy.zeros( (input_data.Detector_Number, length) )
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psi4_l2m1m_real2 = numpy.zeros( (input_data.Detector_Number, length) )
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psi4_l2m1m_imaginary2 = numpy.zeros( (input_data.Detector_Number, length) )
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psi4_l2m0_real2 = numpy.zeros( (input_data.Detector_Number, length) )
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psi4_l2m0_imaginary2 = numpy.zeros( (input_data.Detector_Number, length) )
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psi4_l2m1_real2 = numpy.zeros( (input_data.Detector_Number, length) )
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psi4_l2m1_imaginary2 = numpy.zeros( (input_data.Detector_Number, length) )
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psi4_l2m2_real2 = numpy.zeros( (input_data.Detector_Number, length) )
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psi4_l2m2_imaginary2 = numpy.zeros( (input_data.Detector_Number, length) )
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# Split data into per-detector-radius series
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for i in range(input_data.Detector_Number):
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for j in range(length):
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time2[i,j] = time[ j*input_data.Detector_Number + i ]
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psi4_l2m2m_real2[i,j] = psi4_l2m2m_real[ j*input_data.Detector_Number + i ]
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psi4_l2m2m_imaginary2[i,j] = psi4_l2m2m_imaginary[ j*input_data.Detector_Number + i ]
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psi4_l2m1m_real2[i,j] = psi4_l2m1m_real[ j*input_data.Detector_Number + i ]
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psi4_l2m1m_imaginary2[i,j] = psi4_l2m1m_imaginary[ j*input_data.Detector_Number + i ]
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psi4_l2m0_real2[i,j] = psi4_l2m0_real[ j*input_data.Detector_Number + i ]
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psi4_l2m0_imaginary2[i,j] = psi4_l2m0_imaginary[ j*input_data.Detector_Number + i ]
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psi4_l2m1_real2[i,j] = psi4_l2m1_real[ j*input_data.Detector_Number + i ]
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psi4_l2m1_imaginary2[i,j] = psi4_l2m1_imaginary[ j*input_data.Detector_Number + i ]
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psi4_l2m2_real2[i,j] = psi4_l2m2_real[ j*input_data.Detector_Number + i ]
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psi4_l2m2_imaginary2[i,j] = psi4_l2m2_imaginary[ j*input_data.Detector_Number + i ]
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## Compute discrete Fourier transforms of Psi4 data
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## l=2 m=-2 spectrum
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psi4_l2m2m_real_frequency, psi4_l2m2m_real_omega, psi4_l2m2m_real_omega_spectrem \
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= compute_frequency_spectrum( time2[detector_number_i], psi4_l2m2m_real2[detector_number_i], apply_window=True, zero_pad_factor=4 )
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psi4_l2m2m_imaginary_frequency, psi4_l2m2m_imaginary_omega, psi4_l2m2m_imaginary_omega_spectrem \
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= compute_frequency_spectrum( time2[detector_number_i], psi4_l2m2m_imaginary2[detector_number_i], apply_window=True, zero_pad_factor=4 )
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## l=2 m=-1 spectrum
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psi4_l2m1m_real_frequency, psi4_l2m1m_real_omega, psi4_l2m1m_real_omega_spectrem \
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= compute_frequency_spectrum( time2[detector_number_i], psi4_l2m1m_real2[detector_number_i], apply_window=True, zero_pad_factor=4 )
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psi4_l2m1m_imaginary_frequency, psi4_l2m1m_imaginary_omega, psi4_l2m1m_imaginary_omega_spectrem \
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= compute_frequency_spectrum( time2[detector_number_i], psi4_l2m1m_imaginary2[detector_number_i], apply_window=True, zero_pad_factor=4 )
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## l=2 m=0 spectrum
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psi4_l2m0_real_frequency, psi4_l2m0_real_omega, psi4_l2m0_real_omega_spectrem \
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= compute_frequency_spectrum( time2[detector_number_i], psi4_l2m0_real2[detector_number_i], apply_window=True, zero_pad_factor=4 )
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psi4_l2m0_imaginary_frequency, psi4_l2m0_imaginary_omega, psi4_l2m0_imaginary_omega_spectrem \
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= compute_frequency_spectrum( time2[detector_number_i], psi4_l2m0_imaginary2[detector_number_i], apply_window=True, zero_pad_factor=4 )
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## l=2 m=1 spectrum
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psi4_l2m1_real_frequency, psi4_l2m1_real_omega, psi4_l2m1_real_omega_spectrem \
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= compute_frequency_spectrum( time2[detector_number_i], psi4_l2m1_real2[detector_number_i], apply_window=True, zero_pad_factor=4 )
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psi4_l2m1_imaginary_frequency, psi4_l2m1_imaginary_omega, psi4_l2m1_imaginary_omega_spectrem \
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= compute_frequency_spectrum( time2[detector_number_i], psi4_l2m1_imaginary2[detector_number_i], apply_window=True, zero_pad_factor=4 )
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## l=2 m=2 spectrum
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psi4_l2m2_real_frequency, psi4_l2m2_real_omega, psi4_l2m2_real_omega_spectrem \
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= compute_frequency_spectrum( time2[detector_number_i], psi4_l2m2_real2[detector_number_i], apply_window=True, zero_pad_factor=4 )
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psi4_l2m2_imaginary_frequency, psi4_l2m2_imaginary_omega, psi4_l2m2_imaginary_omega_spectrem \
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= compute_frequency_spectrum( time2[detector_number_i], psi4_l2m2_imaginary2[detector_number_i], apply_window=True, zero_pad_factor=4 )
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# Compute detector distance from input parameters
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Detector_Interval = ( input_data.Detector_Rmax - input_data.Detector_Rmin ) / ( input_data.Detector_Number - 1 )
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Detector_Distance_R = input_data.Detector_Rmax - Detector_Interval * detector_number_i
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#################################################
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## Set minimum cutoff frequency for frequency-domain integration
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## Create output file to record frequency-domain cutoff values
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file_cut_path = os.path.join( figure_outdir, "frequency_cut.txt" )
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file_cut = open( file_cut_path, "w" )
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## Compute total mass of the system and output
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total_mass = 0.0
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puncture_mass = numpy.zeros( input_data.puncture_number )
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## For 'Ansorg-TwoPuncture' initial data: normalize masses of the first two black holes
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if ( input_data.Initial_Data_Method == "Ansorg-TwoPuncture" ):
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mass_ratio_Q = input_data.parameter_BH[0,0] / input_data.parameter_BH[1,0]
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BBH_M1 = mass_ratio_Q / ( 1.0 + mass_ratio_Q )
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BBH_M2 = 1.0 / ( 1.0 + mass_ratio_Q )
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for k in range( input_data.puncture_number ):
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if ( k == 0 ):
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puncture_mass[k] = BBH_M1
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elif( k == 1 ):
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puncture_mass[k] = BBH_M2
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else:
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puncture_mass[k] = input_data.parameter_BH[k,0]
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total_mass += puncture_mass[k]
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## For other initial-data methods: read puncture masses from input
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else:
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for k in range( input_data.puncture_number ):
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puncture_mass[k] = input_data.parameter_BH[k,0]
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total_mass += puncture_mass[k]
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## Output total mass
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print( file=file_cut )
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for k in range( input_data.puncture_number ):
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print( f" mass[{k}] = {puncture_mass[k]} ", file=file_cut )
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print( file=file_cut )
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print( f" total mass = {total_mass} ", file=file_cut )
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print( file=file_cut )
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## Compute and output pairwise puncture distances
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puncture_distance = numpy.zeros( (input_data.puncture_number, input_data.puncture_number) )
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puncture_position = input_data.position_BH
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## Compute pairwise puncture separations
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for k1 in range(input_data.puncture_number):
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for k2 in range(input_data.puncture_number):
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if (k1 != k2):
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puncture_distance[k1,k2] = ( ( puncture_position[k1,0] - puncture_position[k2,0] )**2 \
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+ ( puncture_position[k1,1] - puncture_position[k2,1] )**2 \
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+ ( puncture_position[k1,2] - puncture_position[k2,2] )**2 )**(0.5)
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print( f" puncture distance r[{k1,k2}] = {puncture_distance[k1,k2]} ", file=file_cut )
|
|
print( file=file_cut )
|
|
## If k1 == k2, avoid zero-distance artifacts on the diagonal
|
|
else:
|
|
puncture_distance[k1,k2] = ( ( puncture_position[0,0] - puncture_position[0,0] )**2 \
|
|
+ ( puncture_position[0,1] - puncture_position[0,1] )**2 \
|
|
+ ( puncture_position[0,2] - puncture_position[0,2] )**2 )**(0.5)
|
|
|
|
print( file=file_cut )
|
|
|
|
## Estimate orbital periods and frequencies using Newtonian approximation
|
|
orbital_period = numpy.zeros( (input_data.puncture_number, input_data.puncture_number) )
|
|
orbital_frequency = numpy.zeros( (input_data.puncture_number, input_data.puncture_number) )
|
|
|
|
## Estimate maximum orbital frequency using Newtonian approximation
|
|
frequency_max = ( numpy.max(puncture_distance) / numpy.min( puncture_mass ) )**(0.5)
|
|
|
|
## Estimate orbital period and frequency for each pair using Newtonian mechanics
|
|
for k1 in range(input_data.puncture_number):
|
|
for k2 in range(input_data.puncture_number):
|
|
if (k1 != k2):
|
|
orbital_period[k1,k2] = 2.0 * math.pi * ( puncture_distance[k1,k2]**3 / ( puncture_mass[k1] + puncture_mass[k2] ) )**(0.5)
|
|
orbital_frequency[k1,k2] = 1.0 / orbital_period[k1,k2]
|
|
print( f" orbital period estimate: T_orbital[{k1,k2}] = {orbital_period[k1,k2]} ", file=file_cut )
|
|
print( f" orbital frequency estimate: f_orbital[{k1,k2}] = {orbital_frequency[k1,k2]} ", file=file_cut )
|
|
print( file=file_cut )
|
|
else:
|
|
orbital_frequency[k1,k2] = frequency_max
|
|
orbital_period[k1,k2] = 1.0 / orbital_frequency[k1,k2]
|
|
|
|
print( file=file_cut )
|
|
|
|
## Set minimum frequency cutoff based on orbital estimate
|
|
orbital_frequency_min = numpy.min( orbital_frequency )
|
|
gravitational_frequency_min = 2.0 * orbital_frequency_min ## GW frequency ~ 2 * orbital frequency for quadrupole
|
|
print( " Orbital frequency estimate: f_orbital_min =", orbital_frequency_min, file=file_cut )
|
|
print( " Gravitational Wave frequency estimate: f_GW_min =", orbital_frequency_min, file=file_cut )
|
|
print( file=file_cut )
|
|
|
|
## Set minimum frequency cutoff based on orbital estimate
|
|
frequency_cut = gravitational_frequency_min
|
|
omega_cut = 2.0 * math.pi * frequency_cut
|
|
print( " Frequency Cut estimate: frequency_cut =", frequency_cut, file=file_cut )
|
|
print( " Omega Cut estimate: omega_cut =", omega_cut, file=file_cut )
|
|
print( file=file_cut )
|
|
|
|
## Manual cutoff setting (deprecated)
|
|
## omega_cut = 2.0 * math.pi / 100.0
|
|
|
|
#################################################
|
|
|
|
## Set tortoise coordinate (r*) for waveform retarded-time correction
|
|
tortoise_R = Detector_Distance_R + 2.0 * total_mass * math.log( Detector_Distance_R / (2.0*total_mass) - 1.0)
|
|
|
|
## For more than two punctures, tortoise coordinate is ambiguous; use detector radius
|
|
if ( input_data.puncture_number > 2 ):
|
|
tortoise_R = Detector_Distance_R
|
|
|
|
## Set cutoff based on instantaneous frequency of the Psi4 signal
|
|
## Abandoned due to large errors
|
|
'''
|
|
## Set initial time
|
|
t1 = tortoise_R
|
|
|
|
## Compute instantaneous frequency of Psi4 signals
|
|
## instantaneous_frequency_psi4_l2m2_real = instantaneous_frequency( psi4_l2m2_real2[detector_number_i], len(psi4_l2m2_real2[detector_number_i]) )
|
|
instantaneous_frequency_psi4_l2m2m_real = get_frequency_at_t1( psi4_l2m2m_real2[detector_number_i], len(psi4_l2m2m_real2[detector_number_i]), t1 ) / (2.0*math.pi)
|
|
instantaneous_frequency_psi4_l2m1m_real = get_frequency_at_t1( psi4_l2m1m_real2[detector_number_i], len(psi4_l2m1m_real2[detector_number_i]), t1 ) / (2.0*math.pi)
|
|
instantaneous_frequency_psi4_l2m0_real = get_frequency_at_t1( psi4_l2m0_real2[detector_number_i], len(psi4_l2m0_real2[detector_number_i]), t1 ) / (2.0*math.pi)
|
|
instantaneous_frequency_psi4_l2m1_real = get_frequency_at_t1( psi4_l2m1_real2[detector_number_i], len(psi4_l2m1_real2[detector_number_i]), t1 ) / (2.0*math.pi)
|
|
instantaneous_frequency_psi4_l2m2_real = get_frequency_at_t1( psi4_l2m2_real2[detector_number_i], len(psi4_l2m2_real2[detector_number_i]), t1 ) / (2.0*math.pi)
|
|
print( f" Instantaneous frequency at t - r* = 0, l=2 m=-2 psi4_real = {instantaneous_frequency_psi4_l2m2m_real:.2f} 1/M" )
|
|
print( f" Instantaneous frequency at t - r* = 0, l=2 m=-1 psi4_real = {instantaneous_frequency_psi4_l2m1m_real:.2f} 1/M" )
|
|
print( f" Instantaneous frequency at t - r* = 0, l=2 m=0 psi4_real = {instantaneous_frequency_psi4_l2m0_real:.2f} 1/M" )
|
|
print( f" Instantaneous frequency at t - r* = 0, l=2 m=1 psi4_real = {instantaneous_frequency_psi4_l2m1_real:.2f} 1/M" )
|
|
print( f" Instantaneous frequency at t - r* = 0, l=2 m=2 psi4_real = {instantaneous_frequency_psi4_l2m2_real:.2f} 1/M" )
|
|
|
|
## Add frequency cutoffs based on instantaneous frequency
|
|
frequency_cut_l2m2m = abs( instantaneous_frequency_psi4_l2m2m_real ) * 1.5
|
|
frequency_cut_l2m1m = abs( instantaneous_frequency_psi4_l2m1m_real ) * 1.5
|
|
frequency_cut_l2m0 = abs( instantaneous_frequency_psi4_l2m0_real ) * 1.5
|
|
frequency_cut_l2m1 = abs( instantaneous_frequency_psi4_l2m1_real ) * 1.5
|
|
frequency_cut_l2m2 = abs( instantaneous_frequency_psi4_l2m2_real ) * 1.5
|
|
|
|
## Add frequency-domain filter conditions
|
|
omega_cut_l2m2m = 2.0 * math.pi / frequency_cut_l2m2m
|
|
omega_cut_l2m1m = 2.0 * math.pi / frequency_cut_l2m1m
|
|
omega_cut_l2m0 = 2.0 * math.pi / frequency_cut_l2m0
|
|
omega_cut_l2m1 = 2.0 * math.pi / frequency_cut_l2m1
|
|
omega_cut_l2m2 = 2.0 * math.pi / frequency_cut_l2m2
|
|
|
|
if (omega_cut_l2m0 < omega_cut_l2m2):
|
|
omega_cut_l2m0 = omega_cut_l2m2
|
|
if (omega_cut_l2m1 < omega_cut_l2m2):
|
|
omega_cut_l2m1 = omega_cut_l2m2
|
|
if (omega_cut_l2m1m < omega_cut_l2m2):
|
|
omega_cut_l2m1m = omega_cut_l2m2
|
|
if (omega_cut_l2m2m < omega_cut_l2m2):
|
|
omega_cut_l2m2m = omega_cut_l2m2
|
|
'''
|
|
|
|
## Obtain integrand for inverse Fourier transform
|
|
psi4_l2m2m_real_omega_integration = frequency_filter_integration( psi4_l2m2m_real_omega, psi4_l2m2m_real_omega_spectrem, omega_cut )
|
|
psi4_l2m2m_imaginary_omega_integration = frequency_filter_integration( psi4_l2m2m_imaginary_omega, psi4_l2m2m_imaginary_omega_spectrem, omega_cut )
|
|
psi4_l2m1m_real_omega_integration = frequency_filter_integration( psi4_l2m1m_real_omega, psi4_l2m1m_real_omega_spectrem, omega_cut )
|
|
psi4_l2m1m_imaginary_omega_integration = frequency_filter_integration( psi4_l2m1m_imaginary_omega, psi4_l2m1m_imaginary_omega_spectrem, omega_cut )
|
|
psi4_l2m0_real_omega_integration = frequency_filter_integration( psi4_l2m0_real_omega, psi4_l2m0_real_omega_spectrem, omega_cut )
|
|
psi4_l2m0_imaginary_omega_integration = frequency_filter_integration( psi4_l2m0_imaginary_omega, psi4_l2m0_imaginary_omega_spectrem, omega_cut )
|
|
psi4_l2m1_real_omega_integration = frequency_filter_integration( psi4_l2m1_real_omega, psi4_l2m1_real_omega_spectrem, omega_cut )
|
|
psi4_l2m1_imaginary_omega_integration = frequency_filter_integration( psi4_l2m1_imaginary_omega, psi4_l2m1_imaginary_omega_spectrem, omega_cut )
|
|
psi4_l2m2_real_omega_integration = frequency_filter_integration( psi4_l2m2_real_omega, psi4_l2m2_real_omega_spectrem, omega_cut )
|
|
psi4_l2m2_imaginary_omega_integration = frequency_filter_integration( psi4_l2m2_imaginary_omega, psi4_l2m2_imaginary_omega_spectrem, omega_cut )
|
|
|
|
## Perform inverse Fourier transform in frequency domain to obtain gravitational-wave strain amplitudes
|
|
## l=2 m=-2 amplitude
|
|
time_grid_h_plus_l2m2m, GW_h_plus_l2m2m \
|
|
= inverse_fourier_transform( psi4_l2m2m_real_omega, psi4_l2m2m_real_omega_integration, sampling_factor=2, original_zero_pad_factor=4 )
|
|
time_grid_h_cross_l2m2m, GW_h_cross_l2m2m \
|
|
= inverse_fourier_transform( psi4_l2m2m_imaginary_omega, psi4_l2m2m_imaginary_omega_integration, sampling_factor=2, original_zero_pad_factor=4 )
|
|
## l=2 m=-1 amplitude
|
|
time_grid_h_plus_l2m1m, GW_h_plus_l2m1m \
|
|
= inverse_fourier_transform( psi4_l2m1m_real_omega, psi4_l2m1m_real_omega_integration, sampling_factor=2, original_zero_pad_factor=4 )
|
|
time_grid_h_cross_l2m1m, GW_h_cross_l2m1m \
|
|
= inverse_fourier_transform( psi4_l2m1m_imaginary_omega, psi4_l2m1m_imaginary_omega_integration, sampling_factor=2, original_zero_pad_factor=4 )
|
|
## l=2 m=0 amplitude
|
|
time_grid_h_plus_l2m0, GW_h_plus_l2m0 \
|
|
= inverse_fourier_transform( psi4_l2m0_real_omega, psi4_l2m0_real_omega_integration, sampling_factor=2, original_zero_pad_factor=4 )
|
|
time_grid_h_cross_l2m0, GW_h_cross_l2m0 \
|
|
= inverse_fourier_transform( psi4_l2m0_imaginary_omega, psi4_l2m0_imaginary_omega_integration, sampling_factor=2, original_zero_pad_factor=4 )
|
|
## l=2 m=1 amplitude
|
|
time_grid_h_plus_l2m1, GW_h_plus_l2m1 \
|
|
= inverse_fourier_transform( psi4_l2m1_real_omega, psi4_l2m1_real_omega_integration, sampling_factor=2, original_zero_pad_factor=4 )
|
|
time_grid_h_cross_l2m1, GW_h_cross_l2m1 \
|
|
= inverse_fourier_transform( psi4_l2m1_imaginary_omega, psi4_l2m1_imaginary_omega_integration, sampling_factor=2, original_zero_pad_factor=4 )
|
|
## l=2 m=2 amplitude
|
|
time_grid_h_plus_l2m2, GW_h_plus_l2m2 \
|
|
= inverse_fourier_transform( psi4_l2m2_real_omega, psi4_l2m2_real_omega_integration, sampling_factor=2, original_zero_pad_factor=4 )
|
|
time_grid_h_cross_l2m2, GW_h_cross_l2m2 \
|
|
= inverse_fourier_transform( psi4_l2m2_imaginary_omega, psi4_l2m2_imaginary_omega_integration, sampling_factor=2, original_zero_pad_factor=4 )
|
|
|
|
|
|
# Construct time grids for computing gravitational-wave strain h
|
|
# time_max = max( time2[detector_number_i] )
|
|
# time_grid = numpy.linspace( tortoise_R, time_max, 2000 )
|
|
# time_grid_new = numpy.linspace( 0, time_max-tortoise_R, len(time_grid) ) ## subtract tortoise coordinate from times
|
|
# l=2 m=-2
|
|
time_grid_h_plus_l2m2m_new = time_grid_h_plus_l2m2m - tortoise_R
|
|
time_grid_h_cross_l2m2m_new = time_grid_h_cross_l2m2m - tortoise_R
|
|
# l=2 m=-1
|
|
time_grid_h_plus_l2m1m_new = time_grid_h_plus_l2m1m - tortoise_R
|
|
time_grid_h_cross_l2m1m_new = time_grid_h_cross_l2m1m - tortoise_R
|
|
# l=2 m=0
|
|
time_grid_h_plus_l2m0_new = time_grid_h_plus_l2m0 - tortoise_R
|
|
time_grid_h_cross_l2m0_new = time_grid_h_cross_l2m0 - tortoise_R
|
|
# l=2 m=1
|
|
time_grid_h_plus_l2m1_new = time_grid_h_plus_l2m1 - tortoise_R
|
|
time_grid_h_cross_l2m1_new = time_grid_h_cross_l2m1 - tortoise_R
|
|
# l=2 m=2
|
|
time_grid_h_plus_l2m2_new = time_grid_h_plus_l2m2 - tortoise_R
|
|
time_grid_h_cross_l2m2_new = time_grid_h_cross_l2m2 - tortoise_R
|
|
|
|
plt.figure( figsize=(8,8) ) ## figsize controls figure size
|
|
plt.title( f" Gravitational Wave h Detector Distance = { Detector_Distance_R } ", fontsize=18 ) ## fontsize controls text size
|
|
plt.plot( time_grid_h_plus_l2m0_new, GW_h_plus_l2m0, \
|
|
color='red', label="l=2 m=0 h+", linewidth=2 )
|
|
plt.plot( time_grid_h_cross_l2m0_new, GW_h_cross_l2m0, \
|
|
color='orange', label="l=2 m=0 hx", linestyle='--', linewidth=2 )
|
|
plt.plot( time_grid_h_plus_l2m1_new, GW_h_plus_l2m1, \
|
|
color='green', label="l=2 m=1 h+", linewidth=2 )
|
|
plt.plot( time_grid_h_cross_l2m1_new, GW_h_cross_l2m1, \
|
|
color='cyan', label="l=2 m=1 hx", linestyle='--', linewidth=2 )
|
|
plt.plot( time_grid_h_plus_l2m2_new, GW_h_plus_l2m2, \
|
|
color='black', label="l=2 m=2 h+", linewidth=2 )
|
|
plt.plot( time_grid_h_cross_l2m2_new, GW_h_cross_l2m2, \
|
|
color='gray', label="l=2 m=2 hx", linestyle='--', linewidth=2 )
|
|
if ( input_data.puncture_number > 2 ):
|
|
plt.xlabel( "T - R [M]", fontsize=16 )
|
|
else:
|
|
plt.xlabel( "T - R* [M]", fontsize=16 )
|
|
plt.ylabel( r"R*h", fontsize=16 )
|
|
plt.xlim( 0.0, max(time_grid_h_plus_l2m0_new) )
|
|
plt.legend( loc='upper right' )
|
|
plt.grid( color='gray', linestyle='--', linewidth=0.5 ) # show grid lines
|
|
plt.savefig( os.path.join(figure_outdir, "Gravitational_Wave_h_Detector_" + str(detector_number_i) + ".pdf") )
|
|
|
|
|
|
print( "Gravitational-wave plot for detector no.", detector_number_i, "finished.")
|
|
print()
|
|
|
|
if ( detector_number_i == (input_data.Detector_Number-1) ):
|
|
print( "All gravitational-wave strain amplitude plots finished." )
|
|
print()
|
|
|
|
'''
|
|
# The following block performs direct time-domain integration of Psi4.
|
|
# This method was deprecated because of insufficient accuracy.
|
|
# h = int_{0}^{t} dt' int_{0}^{t"} Psi4(t") dt"
|
|
|
|
# Interpolate per-detector data to obtain smooth functions
|
|
# Use cubic spline interpolation
|
|
psi4_l2m2m_real2_interpolation = scipy.interpolate.interp1d( time2[detector_number_i], psi4_l2m2m_real2[detector_number_i], kind='cubic' )
|
|
psi4_l2m2m_imaginary2_interpolation = scipy.interpolate.interp1d( time2[detector_number_i], psi4_l2m2m_imaginary2[detector_number_i], kind='cubic' )
|
|
psi4_l2m1m_real2_interpolation = scipy.interpolate.interp1d( time2[detector_number_i], psi4_l2m1m_real2[detector_number_i], kind='cubic' )
|
|
psi4_l2m1m_imaginary2_interpolation = scipy.interpolate.interp1d( time2[detector_number_i], psi4_l2m1m_imaginary2[detector_number_i], kind='cubic' )
|
|
psi4_l2m0_real2_interpolation = scipy.interpolate.interp1d( time2[detector_number_i], psi4_l2m0_real2[detector_number_i], kind='cubic' )
|
|
psi4_l2m0_imaginary2_interpolation = scipy.interpolate.interp1d( time2[detector_number_i], psi4_l2m0_imaginary2[detector_number_i], kind='cubic' )
|
|
psi4_l2m1_real2_interpolation = scipy.interpolate.interp1d( time2[detector_number_i], psi4_l2m1_real2[detector_number_i], kind='cubic' )
|
|
psi4_l2m1_imaginary2_interpolation = scipy.interpolate.interp1d( time2[detector_number_i], psi4_l2m1_imaginary2[detector_number_i], kind='cubic' )
|
|
psi4_l2m2_real2_interpolation = scipy.interpolate.interp1d( time2[detector_number_i], psi4_l2m2_real2[detector_number_i], kind='cubic' )
|
|
psi4_l2m2_imaginary2_interpolation = scipy.interpolate.interp1d( time2[detector_number_i], psi4_l2m2_imaginary2[detector_number_i], kind='cubic' )
|
|
|
|
# Compute detector distance from input parameters
|
|
Detector_Interval = ( input_data.Detector_Rmax - input_data.Detector_Rmin ) / ( input_data.Detector_Number - 1 )
|
|
Detector_Distance_R = input_data.Detector_Rmax - Detector_Interval * detector_number_i
|
|
|
|
# Set tortoise coordinate
|
|
tortoise_R = Detector_Distance_R + 2.0 * total_mass * math.log( Detector_Distance_R / (2.0*total_mass) - 1.0)
|
|
|
|
# Construct time grid for gravitational-wave amplitude h
|
|
time_max = max( time2[detector_number_i] )
|
|
time_grid = numpy.linspace( tortoise_R, time_max, 2000 )
|
|
time_grid_new = numpy.linspace( 0, time_max-tortoise_R, len(time_grid) ) # subtract tortoise coordinate
|
|
|
|
GW_h_plus_l2m2m = numpy.zeros( len(time_grid) )
|
|
GW_h_cross_l2m2m = numpy.zeros( len(time_grid) )
|
|
GW_h_plus_l2m1m = numpy.zeros( len(time_grid) )
|
|
GW_h_cross_l2m1m = numpy.zeros( len(time_grid) )
|
|
GW_h_plus_l2m0 = numpy.zeros( len(time_grid) )
|
|
GW_h_cross_l2m0 = numpy.zeros( len(time_grid) )
|
|
GW_h_plus_l2m1 = numpy.zeros( len(time_grid) )
|
|
GW_h_cross_l2m1 = numpy.zeros( len(time_grid) )
|
|
GW_h_plus_l2m2 = numpy.zeros( len(time_grid) )
|
|
GW_h_cross_l2m2 = numpy.zeros( len(time_grid) )
|
|
|
|
# Solve for h by double numerical integration: h = int_{0}^{t} dt' int_{0}^{t"} Psi4(t") dt"
|
|
# The double integral can be reordered and simplified to h = int_{0}^{t} (t-t") Psi4(t") dt"
|
|
def GW_h_plus_l2m2m_integrand(t, tmax):
|
|
return psi4_l2m2m_real2_interpolation(t) * (tmax-t)
|
|
def GW_h_cross_l2m2m_integrand(t, tmax):
|
|
return psi4_l2m2m_imaginary2_interpolation(t) * (tmax-t)
|
|
def GW_h_plus_l2m1m_integrand(t, tmax):
|
|
return psi4_l2m1m_real2_interpolation(t) * (tmax-t)
|
|
def GW_h_cross_l2m1m_integrand(t, tmax):
|
|
return psi4_l2m1m_imaginary2_interpolation(t) * (tmax-t)
|
|
def GW_h_plus_l2m0_integrand(t, tmax):
|
|
return psi4_l2m0_real2_interpolation(t) * (tmax-t)
|
|
def GW_h_cross_l2m0_integrand(t, tmax):
|
|
return psi4_l2m0_imaginary2_interpolation(t) * (tmax-t)
|
|
def GW_h_plus_l2m1_integrand(t, tmax):
|
|
return psi4_l2m1_real2_interpolation(t) * (tmax-t)
|
|
def GW_h_cross_l2m1_integrand(t, tmax):
|
|
return psi4_l2m1_imaginary2_interpolation(t) * (tmax-t)
|
|
def GW_h_plus_l2m2_integrand(t, tmax):
|
|
return psi4_l2m2_real2_interpolation(t) * (tmax-t)
|
|
def GW_h_cross_l2m2_integrand(t, tmax):
|
|
return psi4_l2m2_imaginary2_interpolation(t) * (tmax-t)
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# Compute gravitational-wave strains h+ and hx
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# Redefine integrand with a lambda so it becomes a single-variable function for integration
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for j in range( len(time_grid) ):
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print( " j = ", j )
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GW_h_plus_l2m2m_integrand2 = lambda t: GW_h_plus_l2m2m_integrand(t, time_grid[j])
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## Note: scipy.integrate.quad returns a tuple (value, error)
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GW_h_plus_l2m2m[j], err0 = scipy.integrate.quad( GW_h_plus_l2m2m_integrand2, 0.0, time_grid[j], limit=600 )
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# epsabs=1e-8, # absolute tolerance
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# limit=600 ) # increase number of subintervals
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GW_h_cross_l2m2m_integrand2 = lambda t: GW_h_cross_l2m2m_integrand(t, time_grid[j])
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GW_h_cross_l2m2m[j], err0 = scipy.integrate.quad( GW_h_cross_l2m2m_integrand2, 0.0, time_grid[j], limit=600 )
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GW_h_plus_l2m1m_integrand2 = lambda t: GW_h_plus_l2m1m_integrand(t, time_grid[j])
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GW_h_plus_l2m1m[j], err0 = scipy.integrate.quad( GW_h_plus_l2m1m_integrand2, 0.0, time_grid[j], limit=600 )
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GW_h_cross_l2m1m_integrand2 = lambda t: GW_h_cross_l2m1m_integrand(t, time_grid[j])
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GW_h_cross_l2m1m[j], err0 = scipy.integrate.quad( GW_h_cross_l2m1m_integrand2, 0.0, time_grid[j], limit=600 )
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GW_h_plus_l2m0_integrand2 = lambda t: GW_h_plus_l2m0_integrand(t, time_grid[j])
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GW_h_plus_l2m0[j], err0 = scipy.integrate.quad( GW_h_plus_l2m0_integrand2, 0.0, time_grid[j], limit=600 )
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GW_h_cross_l2m0_integrand2 = lambda t: GW_h_cross_l2m0_integrand(t, time_grid[j])
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GW_h_cross_l2m0[j], err0 = scipy.integrate.quad( GW_h_cross_l2m0_integrand2, 0.0, time_grid[j], limit=600 )
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GW_h_plus_l2m1_integrand2 = lambda t: GW_h_plus_l2m1_integrand(t, time_grid[j])
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GW_h_plus_l2m1[j], err0 = scipy.integrate.quad( GW_h_plus_l2m1_integrand2, 0.0, time_grid[j], limit=600 )
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GW_h_cross_l2m1_integrand2 = lambda t: GW_h_cross_l2m1_integrand(t, time_grid[j])
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GW_h_cross_l2m1[j], err0 = scipy.integrate.quad( GW_h_cross_l2m1_integrand2, 0.0, time_grid[j], limit=600 )
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GW_h_plus_l2m2_integrand2 = lambda t: GW_h_plus_l2m2_integrand(t, time_grid[j])
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GW_h_plus_l2m2[j], err0 = scipy.integrate.quad( GW_h_plus_l2m2_integrand2, 0.0, time_grid[j], limit=600 )
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GW_h_cross_l2m2_integrand2 = lambda t: GW_h_cross_l2m2_integrand(t, time_grid[j])
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GW_h_cross_l2m2[j], err0 = scipy.integrate.quad( GW_h_cross_l2m2_integrand2, 0.0, time_grid[j], limit=600 )
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# Computation of gravitational-wave amplitudes h+ and hx complete
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# Now perform plotting
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plt.figure( figsize=(8,8) ) ## figsize controls figure size
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plt.title( f" Gravitational Wave h Detector Distance = { Detector_Distance_R } ", fontsize=18 ) ## fontsize controls text size
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plt.plot( time_grid_new, GW_h_plus_l2m0, \
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color='red', label="l=2 m=0 h+", linewidth=2 )
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plt.plot( time_grid_new, GW_h_cross_l2m0, \
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color='orange', label="l=2 m=0 hx", linestyle='--', linewidth=2 )
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plt.plot( time_grid_new, GW_h_plus_l2m1, \
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color='green', label="l=2 m=1 h+", linewidth=2 )
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plt.plot( time_grid_new, GW_h_cross_l2m1, \
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color='cyan', label="l=2 m=1 hx", linestyle='--', linewidth=2 )
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plt.plot( time_grid_new, GW_h_plus_l2m2, \
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color='black', label="l=2 m=2 h+", linewidth=2 )
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plt.plot( time_grid_new, GW_h_cross_l2m2, \
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color='gray', label="l=2 m=2 hx", linestyle='--', linewidth=2 )
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plt.xlabel( "T [M]", fontsize=16 )
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plt.ylabel( r"R*h", fontsize=16 )
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plt.legend( loc='upper right' )
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plt.savefig( os.path.join(figure_outdir, "Gravitational_Wave_h_Detector_" + str(detector_number_i) + ".pdf") )
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print()
|
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print( "Gravitational-wave plot for detector no.", detector_number_i, "finished.")
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|
print( "Plotting of gravitational-wave strain amplitude h completed.")
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|
print()
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|
'''
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return
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####################################################################################
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####################################################################################
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## Standalone usage example
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'''
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## outdir = "./BBH_q=1"
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outdir = "./3BH"
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for i in range( input_data.Detector_Number ):
|
|
generate_gravitational_wave_amplitude_plot(outdir, outdir, i)
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'''
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####################################################################################
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