Loading minke/mdctools.py +4 −2 Original line number Diff line number Diff line Loading @@ -105,9 +105,11 @@ class MDCSet(): lw.appendChild(sim) # This needs to be given the proper metadata once the package has the maturity to # write something sensible. procrow = process.register_to_xmldoc(xmldoc, "pyburst_binj", {}) procrow = process.register_to_xmldoc(xmldoc, "minke_burst_mdc", {}) for waveform in self.waveforms: sim.append(waveform._row(sim)) waveform_row = waveform._row(sim) waveform_row.process_id = procrow.process_id sim.append() # Write out the xml and gzip it. utils.write_filename(xmldoc, filename, gz=True) Loading minke/sources.py +355 −10 Original line number Diff line number Diff line Loading @@ -4,7 +4,7 @@ from optparse import OptionParser, Option, OptionGroup from scipy import random import numpy np = numpy from glue.ligolw import lsctables from glue.ligolw import utils from glue.ligolw import ligolw Loading @@ -14,9 +14,16 @@ from glue.lal import LIGOTimeGPS as GPS from glue.ligolw.utils import process from pylal.antenna import response import scipy.signal as signal import scipy.interpolate as interp import os.path import lal import lalburst import lalsimulation lalsim = lalsimulation from minke.distribution import * Loading Loading @@ -78,8 +85,11 @@ class Waveform(object): Produce a plot of the injection. """ self._generate() plt.plot(self.hp.data.data, label="+ polarisation") plt.plot(self.hx.data.data, label="x polarisation") f, ax = plt.subplots(1,2) times = np.arange(0, self.hp.deltaT*len(self.hp.data.data), self.hp.deltaT) ax[0].plot(times, self.hp.data.data, label="+ polarisation") ax[0].plot(times, self.hx.data.data, label="x polarisation") ax[1].plot(self.hp.data.data, self.hx.data.data) def _generate(self, rate=16384.0): """ Loading Loading @@ -107,7 +117,6 @@ class Waveform(object): for a in lsctables.SimBurstTable.validcolumns.keys(): try: setattr(self.swig_row, a, getattr( row, a )) print getattr(row,a) except AttributeError: continue except TypeError: continue Loading @@ -121,13 +130,19 @@ class Waveform(object): hp0, hx0 = lalburst.GenerateSimBurst(self.swig_row, 1.0/rate) self.hp, self.hx, self.hp0, self.hx0 = hp, hx, hp0, hx0 def _row(self, sim=None): def _row(self, sim=None, slide_id=1): """ Produce a simburst table row for this waveform. Todo ---- We also need to add the process_id back in. Parameters ---------- sim : table The table which the row should be made for. If this is left empty the table is assumed to be a sim_burst_table. slide_id : int The timeslide id. Defaults to 1. """ if not sim: sim = self.sim self.row = sim.RowType() Loading @@ -144,7 +159,7 @@ class Waveform(object): self.row.waveform_number = random.randint(0,int(2**32)-1) ### !! This needs to be updated. self.row.process_id = "process:process_id:0" #procrow.process_id self.row.time_slide_id = ilwd.ilwdchar("time_slide:time_slide_id:%d" % 1)#options.time_slide_id) self.row.time_slide_id = ilwd.ilwdchar("time_slide:time_slide_id:%d" % slide_id) return self.row Loading Loading @@ -209,7 +224,7 @@ class Gaussian(Waveform): Parameters ---------- duration : float or list The duration, in milliseconds, of the Gaussian waveform. The duration, in seconds, of the Gaussian waveform. hrss : float or list The strain magnitude of the injection. Loading Loading @@ -243,3 +258,333 @@ class Gaussian(Waveform): self.time = time self.params['pol_ellipse_e'] = 1.0 self.params['pol_ellipse_angle'] = 0 class WhiteNoiseBurst(Waveform): """ A class to represent a WNB injection. """ waveform = "BTLWNB" def __init__(self, duration, bandwidth, frequency, hrss, time, polarisation, sky_dist=uniform_sky, seed=0): """ A class to represent a white-noise burst ad-hoc waveform. Parameters ---------- duration : float or list The duration, in seconds, of the WNB. bandwidth : float or list The bandwidth, in hertz, of the WNB. frequency : float or list The frequency, in hertz, of the WNB. hrss : float or list The strain magnitude of the injection. If a float is provided then the hrss will be fixed, if a list is provided then this will be the minimum and maximum hrss. polarisation : str {'linear', 'elliptical', 'circular'} The type of polarisation of the waveform. time : float or list The time period over which the injection should be made. If a list is given they should be the start and end times, and the waveform will be produced at some random point in that time range. If a float is given then the injection will be made at that specific time. sky_dist : func The function describing the sky distribution which the injections should be made over. Defaults to a uniform sky. seed : float The random seed used to make the injection time of the waveform. The default seed is 0. To Do ----- Add ability to create a WNB by giving the EGW rather than the strain. Notes ----- See http://software.ligo.org/docs/lalsuite/lalsimulation/group___l_a_l_sim_burst__h.html#ga0419dc37e5b83f18cd3bb34722ddac54 for what this calls "under the hood" in LALSuite. There are some important considerations here with respect to the differing sample rates used at LIGO and VIRGO, and so when creating the WNB it's important that the burst is created at a single sampel rate, and then resampled appropriately, so that the same waveform is used. """ self._clear_params() self.sky_dist = sky_dist self.params['hrss'] = hrss self.params['frequency'] = frequency # We need a minimum window size so that the whole burst can be contained within it, # so expand the duration if it's too small. min_len = np.sqrt( 4 * (np.pi**(-2) / bandwidth**2) ) if duration < min_len: self.params['duration'] = min_len + 1e-5 else: self.params['duration'] = duration self.params['bandwidth'] = bandwidth self.time = time self.polarisation = polarisation self.params['pol_ellipse_e'], self.params['pol_ellipse_angle'] = 0.0, 0.0#self.parse_polarisation(self.polarisation) self.params['egw_over_rsquared'] = hrss**2 * np.pi**2 * frequency**2 * lal.C_SI / lal.G_SI / lal.MSUN_SI * lal.PC_SI**2 class Supernova(Waveform): """ A superclass to handle the spherial harmonic decompositions which all supernova waveforms require. """ waveform = "Supernova" # We shouldn't ever use this anyway def construct_Hlm(self, Ixx, Ixy, Ixz, Iyy, Iyz, Izz, l=2, m=2): """ Construct the expansion parameters Hlm from T1000553. Returns the expansion parameters for l=2, m=m """ if l!=2: print "l!=2 not supported" sys.exit() if abs(m)>2: print "Only l=2 supported, |m| must be <=2" sys.exit() if m==-2: Hlm = np.sqrt(4*lal.PI/5) * (Ixx - Iyy + 2*1j*Ixy) elif m==-1: Hlm = np.sqrt(16*lal.PI/5) * (Ixx + 1j*Iyz) elif m==0: Hlm = np.sqrt(32*lal.PI/15) * (Izz - 0.5*(Ixx + Iyy)) elif m==1: Hlm = np.sqrt(16*lal.PI/5) * (-1*Ixx + 1j*Iyz) elif m==2: Hlm = np.sqrt(4*lal.PI/5) * (Ixx - Iyy - 2*1j*Ixy) return Hlm def interpolate(self, x_old, y_old, x_new): """ Convenience funtion to avoid repeated code """ interpolator = interp.interp1d(x_old, y_old) return interpolator(x_new) def decompose(self, numrel_file, sample_rate = 16384.0, step_back = 0.01, distance = 10e-3): """ Produce the spherial harmonic decompositions of a numerical waveform. Parameters ---------- numrel_file : str The location of the numerical relativity waveform file. sample_rate : float The sample rate of the NR file. Defaults to 16384.0 Hz. step_back : float The amount of time, in seconds, of the data which should be included before the peak amplitude. Defaults to 0.01 sec. distance : float The distance, in megaparsecs, from the observer at which the NR waveforms were simulated. Defaults to 10 kpc (i.e. 10e-3 Mpc). Returns ------- decomposition : ndarray The l=2 mode spherical decompositions of the waveform. """ # Load the times from the file data = np.loadtxt(numrel_file) data = data.T times = data[0] times -= times[0] # Load the I components from the file Ixx, Ixy, Ixz, Iyy, Iyz, Izz = data[5:] # Make the new time vector for the requried sample rate target_times = np.arange(0, times[-1], 1.0/sample_rate) # Prepare the output matrix output = np.zeros((len(target_times), 11)) # Add the times in to the first column of said matrix output[:, 0] = target_times for i, m in enumerate([-2,-1,0,1,2]): Hlm = self.construct_Hlm(Ixx, Ixy, Ixz, Iyy, Iyz, Izz, l=2, m=m) # # Resample to uniform spacing at 16384 kHz # Hlm_real = self.interpolate(times, Hlm.real, target_times) Hlm_imag = self.interpolate(times, Hlm.imag, target_times) # # Make the output, and rescale it into dimensionless strain values # output[:,2*(i+1)-1] = Hlm_real * np.sqrt(lal.G_SI / lal.C_SI**4) #/lal.MRSUN_SI / ( distance * lal.PC_SI * 1e6) output[:,2*(i+1)] = -Hlm_imag * np.sqrt(lal.G_SI / lal.C_SI**4)#/lal.MRSUN_SI / ( distance * lal.PC_SI * 1e6) return output class Scheidegger2010(Supernova): """ The Scheidegger2010 waveform. """ waveform = "Scheidegger+10" def __init__(self, phi, incl, time, sky_dist=uniform_sky, filepath="R0E1CA.txt", decomposed_path=None): """ Parameters ---------- phi : float The internal phi parameter of the supernova injection. incl : float The internal inclination parameter of the supernova injection. time : float or list The time period over which the injection should be made. If a list is given they should be the start and end times, and the waveform will be produced at some random point in that time range. If a float is given then the injection will be made at that specific time. sky_dist : func The function describing the sky distribution which the injections should be made over. Defaults to a uniform sky. filepath : str The filepath to the numerical relativity waveform. decomposed_path : str The location where the decomposed waveform file should be stored. Optional. """ self._clear_params() self.time = time self.params['phi'] = phi self.params['incl'] = incl self.sky_dist = sky_dist if not decomposed_path : decomposed_path = filepath+".dec" if not os.path.isfile(decomposed_path) : decomposed = self.decompose(filepath, sample_rate = 16384.0, step_back = 0.01, distance = 10e-3) np.savetxt(decomposed_path, decomposed, header="# time (2,-2) (2,-1) (2,0) (2,1) (2,2)", fmt='%.8e') self.params['numrel_data'] = decomposed_path class Dimmelmeier08(Supernova): """ The Dimmelmeier08 waveform. """ waveform = "Dimmelmeier+08" def __init__(self, time, sky_dist=uniform_sky, filepath="signal_s15a2o05_ls.dat", decomposed_path=None, ): """ Parameters ---------- time : float or list The time period over which the injection should be made. If a list is given they should be the start and end times, and the waveform will be produced at some random point in that time range. If a float is given then the injection will be made at that specific time. sky_dist : func The function describing the sky distribution which the injections should be made over. Defaults to a uniform sky. filepath : str The filepath to the numerical relativity waveform. decomposed_path : str The location where the decomposed waveform file should be stored. Optional. """ self._clear_params() self.time = time self.sky_dist = sky_dist if not decomposed_path : decomposed_path = filepath+".dec" if not os.path.isfile(decomposed_path) : decomposed = self.decompose(filepath, sample_rate = 16384.0, step_back = 0.01, distance = 10e-3) np.savetxt(decomposed_path, decomposed, header="# time (2,-2) (2,-1) (2,0) (2,1) (2,2)", fmt='%.8e') self.params['numrel_data'] = decomposed_path def decompose(self, numrel_file, sample_rate = 16384.0, step_back = 0.01, distance = 10e-3): """ Produce the spherial harmonic decompositions of the Dimmelmeier numerical waveform. This is a special case since it is axisymmetric. Parameters ---------- numrel_file : str The location of the numerical relativity waveform file. sample_rate : float The sample rate of the NR file. Defaults to 16384.0 Hz. step_back : float The amount of time, in seconds, of the data which should be included before the peak amplitude. Defaults to 0.01 sec. distance : float The distance, in megaparsecs, from the observer at which the NR waveforms were simulated. Defaults to 10 kpc (i.e. 10e-3 Mpc). Returns ------- decomposition : ndarray The l=2 mode spherical decompositions of the waveform. """ # Load the times from the file data = np.loadtxt(numrel_file) data = data.T times = data[0] times -= times[0] # Load the hp components strain = data[1] # Make the new time vector for the requried sample rate target_times = np.arange(0, times[-1], 1.0/sample_rate) # Prepare the output matrix output = np.zeros((len(target_times), 11)) # Add the times in to the first column of said matrix output[:, 0] = target_times # # Resample to uniform spacing at 16384 kHz # strain_new = self.interpolate(times, strain, target_times) # # Make the output, and rescale it into dimensionless strain values # output[:,5] = strain_new # * np.sqrt(lal.G_SI / lal.C_SI**4) #/lal.MRSUN_SI / ( distance * lal.PC_SI * 1e6) return output Loading
minke/mdctools.py +4 −2 Original line number Diff line number Diff line Loading @@ -105,9 +105,11 @@ class MDCSet(): lw.appendChild(sim) # This needs to be given the proper metadata once the package has the maturity to # write something sensible. procrow = process.register_to_xmldoc(xmldoc, "pyburst_binj", {}) procrow = process.register_to_xmldoc(xmldoc, "minke_burst_mdc", {}) for waveform in self.waveforms: sim.append(waveform._row(sim)) waveform_row = waveform._row(sim) waveform_row.process_id = procrow.process_id sim.append() # Write out the xml and gzip it. utils.write_filename(xmldoc, filename, gz=True) Loading
minke/sources.py +355 −10 Original line number Diff line number Diff line Loading @@ -4,7 +4,7 @@ from optparse import OptionParser, Option, OptionGroup from scipy import random import numpy np = numpy from glue.ligolw import lsctables from glue.ligolw import utils from glue.ligolw import ligolw Loading @@ -14,9 +14,16 @@ from glue.lal import LIGOTimeGPS as GPS from glue.ligolw.utils import process from pylal.antenna import response import scipy.signal as signal import scipy.interpolate as interp import os.path import lal import lalburst import lalsimulation lalsim = lalsimulation from minke.distribution import * Loading Loading @@ -78,8 +85,11 @@ class Waveform(object): Produce a plot of the injection. """ self._generate() plt.plot(self.hp.data.data, label="+ polarisation") plt.plot(self.hx.data.data, label="x polarisation") f, ax = plt.subplots(1,2) times = np.arange(0, self.hp.deltaT*len(self.hp.data.data), self.hp.deltaT) ax[0].plot(times, self.hp.data.data, label="+ polarisation") ax[0].plot(times, self.hx.data.data, label="x polarisation") ax[1].plot(self.hp.data.data, self.hx.data.data) def _generate(self, rate=16384.0): """ Loading Loading @@ -107,7 +117,6 @@ class Waveform(object): for a in lsctables.SimBurstTable.validcolumns.keys(): try: setattr(self.swig_row, a, getattr( row, a )) print getattr(row,a) except AttributeError: continue except TypeError: continue Loading @@ -121,13 +130,19 @@ class Waveform(object): hp0, hx0 = lalburst.GenerateSimBurst(self.swig_row, 1.0/rate) self.hp, self.hx, self.hp0, self.hx0 = hp, hx, hp0, hx0 def _row(self, sim=None): def _row(self, sim=None, slide_id=1): """ Produce a simburst table row for this waveform. Todo ---- We also need to add the process_id back in. Parameters ---------- sim : table The table which the row should be made for. If this is left empty the table is assumed to be a sim_burst_table. slide_id : int The timeslide id. Defaults to 1. """ if not sim: sim = self.sim self.row = sim.RowType() Loading @@ -144,7 +159,7 @@ class Waveform(object): self.row.waveform_number = random.randint(0,int(2**32)-1) ### !! This needs to be updated. self.row.process_id = "process:process_id:0" #procrow.process_id self.row.time_slide_id = ilwd.ilwdchar("time_slide:time_slide_id:%d" % 1)#options.time_slide_id) self.row.time_slide_id = ilwd.ilwdchar("time_slide:time_slide_id:%d" % slide_id) return self.row Loading Loading @@ -209,7 +224,7 @@ class Gaussian(Waveform): Parameters ---------- duration : float or list The duration, in milliseconds, of the Gaussian waveform. The duration, in seconds, of the Gaussian waveform. hrss : float or list The strain magnitude of the injection. Loading Loading @@ -243,3 +258,333 @@ class Gaussian(Waveform): self.time = time self.params['pol_ellipse_e'] = 1.0 self.params['pol_ellipse_angle'] = 0 class WhiteNoiseBurst(Waveform): """ A class to represent a WNB injection. """ waveform = "BTLWNB" def __init__(self, duration, bandwidth, frequency, hrss, time, polarisation, sky_dist=uniform_sky, seed=0): """ A class to represent a white-noise burst ad-hoc waveform. Parameters ---------- duration : float or list The duration, in seconds, of the WNB. bandwidth : float or list The bandwidth, in hertz, of the WNB. frequency : float or list The frequency, in hertz, of the WNB. hrss : float or list The strain magnitude of the injection. If a float is provided then the hrss will be fixed, if a list is provided then this will be the minimum and maximum hrss. polarisation : str {'linear', 'elliptical', 'circular'} The type of polarisation of the waveform. time : float or list The time period over which the injection should be made. If a list is given they should be the start and end times, and the waveform will be produced at some random point in that time range. If a float is given then the injection will be made at that specific time. sky_dist : func The function describing the sky distribution which the injections should be made over. Defaults to a uniform sky. seed : float The random seed used to make the injection time of the waveform. The default seed is 0. To Do ----- Add ability to create a WNB by giving the EGW rather than the strain. Notes ----- See http://software.ligo.org/docs/lalsuite/lalsimulation/group___l_a_l_sim_burst__h.html#ga0419dc37e5b83f18cd3bb34722ddac54 for what this calls "under the hood" in LALSuite. There are some important considerations here with respect to the differing sample rates used at LIGO and VIRGO, and so when creating the WNB it's important that the burst is created at a single sampel rate, and then resampled appropriately, so that the same waveform is used. """ self._clear_params() self.sky_dist = sky_dist self.params['hrss'] = hrss self.params['frequency'] = frequency # We need a minimum window size so that the whole burst can be contained within it, # so expand the duration if it's too small. min_len = np.sqrt( 4 * (np.pi**(-2) / bandwidth**2) ) if duration < min_len: self.params['duration'] = min_len + 1e-5 else: self.params['duration'] = duration self.params['bandwidth'] = bandwidth self.time = time self.polarisation = polarisation self.params['pol_ellipse_e'], self.params['pol_ellipse_angle'] = 0.0, 0.0#self.parse_polarisation(self.polarisation) self.params['egw_over_rsquared'] = hrss**2 * np.pi**2 * frequency**2 * lal.C_SI / lal.G_SI / lal.MSUN_SI * lal.PC_SI**2 class Supernova(Waveform): """ A superclass to handle the spherial harmonic decompositions which all supernova waveforms require. """ waveform = "Supernova" # We shouldn't ever use this anyway def construct_Hlm(self, Ixx, Ixy, Ixz, Iyy, Iyz, Izz, l=2, m=2): """ Construct the expansion parameters Hlm from T1000553. Returns the expansion parameters for l=2, m=m """ if l!=2: print "l!=2 not supported" sys.exit() if abs(m)>2: print "Only l=2 supported, |m| must be <=2" sys.exit() if m==-2: Hlm = np.sqrt(4*lal.PI/5) * (Ixx - Iyy + 2*1j*Ixy) elif m==-1: Hlm = np.sqrt(16*lal.PI/5) * (Ixx + 1j*Iyz) elif m==0: Hlm = np.sqrt(32*lal.PI/15) * (Izz - 0.5*(Ixx + Iyy)) elif m==1: Hlm = np.sqrt(16*lal.PI/5) * (-1*Ixx + 1j*Iyz) elif m==2: Hlm = np.sqrt(4*lal.PI/5) * (Ixx - Iyy - 2*1j*Ixy) return Hlm def interpolate(self, x_old, y_old, x_new): """ Convenience funtion to avoid repeated code """ interpolator = interp.interp1d(x_old, y_old) return interpolator(x_new) def decompose(self, numrel_file, sample_rate = 16384.0, step_back = 0.01, distance = 10e-3): """ Produce the spherial harmonic decompositions of a numerical waveform. Parameters ---------- numrel_file : str The location of the numerical relativity waveform file. sample_rate : float The sample rate of the NR file. Defaults to 16384.0 Hz. step_back : float The amount of time, in seconds, of the data which should be included before the peak amplitude. Defaults to 0.01 sec. distance : float The distance, in megaparsecs, from the observer at which the NR waveforms were simulated. Defaults to 10 kpc (i.e. 10e-3 Mpc). Returns ------- decomposition : ndarray The l=2 mode spherical decompositions of the waveform. """ # Load the times from the file data = np.loadtxt(numrel_file) data = data.T times = data[0] times -= times[0] # Load the I components from the file Ixx, Ixy, Ixz, Iyy, Iyz, Izz = data[5:] # Make the new time vector for the requried sample rate target_times = np.arange(0, times[-1], 1.0/sample_rate) # Prepare the output matrix output = np.zeros((len(target_times), 11)) # Add the times in to the first column of said matrix output[:, 0] = target_times for i, m in enumerate([-2,-1,0,1,2]): Hlm = self.construct_Hlm(Ixx, Ixy, Ixz, Iyy, Iyz, Izz, l=2, m=m) # # Resample to uniform spacing at 16384 kHz # Hlm_real = self.interpolate(times, Hlm.real, target_times) Hlm_imag = self.interpolate(times, Hlm.imag, target_times) # # Make the output, and rescale it into dimensionless strain values # output[:,2*(i+1)-1] = Hlm_real * np.sqrt(lal.G_SI / lal.C_SI**4) #/lal.MRSUN_SI / ( distance * lal.PC_SI * 1e6) output[:,2*(i+1)] = -Hlm_imag * np.sqrt(lal.G_SI / lal.C_SI**4)#/lal.MRSUN_SI / ( distance * lal.PC_SI * 1e6) return output class Scheidegger2010(Supernova): """ The Scheidegger2010 waveform. """ waveform = "Scheidegger+10" def __init__(self, phi, incl, time, sky_dist=uniform_sky, filepath="R0E1CA.txt", decomposed_path=None): """ Parameters ---------- phi : float The internal phi parameter of the supernova injection. incl : float The internal inclination parameter of the supernova injection. time : float or list The time period over which the injection should be made. If a list is given they should be the start and end times, and the waveform will be produced at some random point in that time range. If a float is given then the injection will be made at that specific time. sky_dist : func The function describing the sky distribution which the injections should be made over. Defaults to a uniform sky. filepath : str The filepath to the numerical relativity waveform. decomposed_path : str The location where the decomposed waveform file should be stored. Optional. """ self._clear_params() self.time = time self.params['phi'] = phi self.params['incl'] = incl self.sky_dist = sky_dist if not decomposed_path : decomposed_path = filepath+".dec" if not os.path.isfile(decomposed_path) : decomposed = self.decompose(filepath, sample_rate = 16384.0, step_back = 0.01, distance = 10e-3) np.savetxt(decomposed_path, decomposed, header="# time (2,-2) (2,-1) (2,0) (2,1) (2,2)", fmt='%.8e') self.params['numrel_data'] = decomposed_path class Dimmelmeier08(Supernova): """ The Dimmelmeier08 waveform. """ waveform = "Dimmelmeier+08" def __init__(self, time, sky_dist=uniform_sky, filepath="signal_s15a2o05_ls.dat", decomposed_path=None, ): """ Parameters ---------- time : float or list The time period over which the injection should be made. If a list is given they should be the start and end times, and the waveform will be produced at some random point in that time range. If a float is given then the injection will be made at that specific time. sky_dist : func The function describing the sky distribution which the injections should be made over. Defaults to a uniform sky. filepath : str The filepath to the numerical relativity waveform. decomposed_path : str The location where the decomposed waveform file should be stored. Optional. """ self._clear_params() self.time = time self.sky_dist = sky_dist if not decomposed_path : decomposed_path = filepath+".dec" if not os.path.isfile(decomposed_path) : decomposed = self.decompose(filepath, sample_rate = 16384.0, step_back = 0.01, distance = 10e-3) np.savetxt(decomposed_path, decomposed, header="# time (2,-2) (2,-1) (2,0) (2,1) (2,2)", fmt='%.8e') self.params['numrel_data'] = decomposed_path def decompose(self, numrel_file, sample_rate = 16384.0, step_back = 0.01, distance = 10e-3): """ Produce the spherial harmonic decompositions of the Dimmelmeier numerical waveform. This is a special case since it is axisymmetric. Parameters ---------- numrel_file : str The location of the numerical relativity waveform file. sample_rate : float The sample rate of the NR file. Defaults to 16384.0 Hz. step_back : float The amount of time, in seconds, of the data which should be included before the peak amplitude. Defaults to 0.01 sec. distance : float The distance, in megaparsecs, from the observer at which the NR waveforms were simulated. Defaults to 10 kpc (i.e. 10e-3 Mpc). Returns ------- decomposition : ndarray The l=2 mode spherical decompositions of the waveform. """ # Load the times from the file data = np.loadtxt(numrel_file) data = data.T times = data[0] times -= times[0] # Load the hp components strain = data[1] # Make the new time vector for the requried sample rate target_times = np.arange(0, times[-1], 1.0/sample_rate) # Prepare the output matrix output = np.zeros((len(target_times), 11)) # Add the times in to the first column of said matrix output[:, 0] = target_times # # Resample to uniform spacing at 16384 kHz # strain_new = self.interpolate(times, strain, target_times) # # Make the output, and rescale it into dimensionless strain values # output[:,5] = strain_new # * np.sqrt(lal.G_SI / lal.C_SI**4) #/lal.MRSUN_SI / ( distance * lal.PC_SI * 1e6) return output
