savename = 'tm1_rec_example_onepc.eps'
else:
npcs = waveform_data.nwaves
savename = 'tm1_rec_example_allpcs.eps'
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Test Waveform
#
print "Building test waveform"
#
# Create test waveform
#
waveform = pwave.Waveform(eos=eos_example,
mass=mass_example,
viscosity=viscosity,
distance=50)
waveform.reproject_waveform()
waveform.compute_characteristics()
# Get this waveform's amplitude and SNR
targetAmp = waveform.hplus.max()
targetH = waveform.hplus.to_frequencyseries()
# Standardise
waveform_FD, target_fpeak, _ = \
ppca.condition_spectrum(waveform.hplus.data,
nsamples=nTsamples)
# Take FFT:
waveform_FD = ppca.unit_hrss(waveform_FD.data,
f.writelines("# rho_full rho_post Dhor Dsens Rate\n")
rho_min = 100
rho_max = 0
for w, wave in enumerate(waveform_data.waves):
print "SNR for %s, %s ,%s (%d of %d)" % (wave['eos'], wave['mass'],
wave['viscosity'], w + 1,
waveform_data.nwaves)
#
# Create test waveform
#
waveform = pwave.Waveform(eos=wave['eos'],
mass=wave['mass'],
viscosity=wave['viscosity'],
distance=reference_distance)
waveform.reproject_waveform()
hplus_padded = pycbc.types.TimeSeries(np.zeros(16384),
delta_t=waveform.hplus.delta_t)
hplus_padded.data[:len(waveform.hplus)] = np.copy(waveform.hplus.data)
Hplus = hplus_padded.to_frequencyseries()
#
# Construct PSD
#
psd = pwave.make_noise_curve(fmax=Hplus.sample_frequencies.max(),
delta_f=Hplus.delta_f,
noise_curve=noise_curve)
# 2*frequencies[abs(scales-0.5*max(scales)).argmin()]) # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Waveform Generation # eos = "tm1" mass = "135135" # # Create the list of dictionaries which comprises our catalogue # #waveform_data = pdata.WaveData(eos=eos, mass=mass, viscosity='lessvisc') waveform = pwave.Waveform(eos=eos, mass=mass, viscosity="lessvisc") waveform.reproject_waveform() Hplus = waveform.hplus.to_frequencyseries() # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Wavelet analysis # cwt_result = ppca.build_cwt(waveform.hplus, mother_freq=3, fmin=500) # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Plotting # fig, ax_cont, ax_ts, ax_fs = tripleplot(cwt_result)
def build_catalogues(waveform_data,
fshift_center,
nTsamples=16384,
delta_t=1. / 16384):
"""
Build the data matrix. waveform_data is the pmns_waveform_data.WaveData()
class which contains the list of dictionaries of waveform data
"""
nFsamples = nTsamples / 2 + 1
sample_times = np.arange(0, delta_t * nTsamples, delta_t)
# Preallocation
timedomain_cat = np.zeros(shape=(waveform_data.nwaves, len(sample_times)))
original_cat = np.zeros(shape=(waveform_data.nwaves, nFsamples),
dtype=complex)
fpeaks = np.zeros(waveform_data.nwaves)
# Preallocate for the TF maps
example_waveform = pmns_waveform.Waveform(
eos=waveform_data.waves[0]['eos'],
mass=waveform_data.waves[0]['mass'],
viscosity=waveform_data.waves[0]['viscosity'])
example_waveform.reproject_waveform()
example_map = example_tfmap(waveform=example_waveform, delta_t=delta_t)
mapdims = np.shape(example_map['map'])
times = example_map['times']
frequencies = example_map['frequencies']
scales = example_map['scales']
original_image_cat = np.zeros(shape=(waveform_data.nwaves, mapdims[0],
mapdims[1]))
align_image_cat = np.zeros(shape=(waveform_data.nwaves, mapdims[0],
mapdims[1]))
for w, wave in enumerate(waveform_data.waves):
print "Building waveform for %s, %s, %s (%d of %d)" % (
wave['eos'], wave['mass'], wave['viscosity'], w + 1,
waveform_data.nwaves)
#
# Create waveform instance: pmns_utils
#
waveform = pmns_waveform.Waveform(eos=wave['eos'],
mass=wave['mass'],
viscosity=wave['viscosity'])
waveform.reproject_waveform()
# Waveform conditioning
original_spectrum, fpeaks[w], timeseries = \
condition_spectrum(waveform.hplus.data, nsamples=nTsamples)
# Populate time series catalogue
timedomain_cat[w, :] = np.copy(timeseries.data)
# Time-frequency maps
tfmap = build_cwt(
pycbc.types.TimeSeries(waveform.hplus.data, delta_t=delta_t))
original_image_cat[w, :, :] = tfmap['map']
aligned_tfmap = align_cwt(tfmap, fpeaks[w])
align_image_cat[w, :, :] = aligned_tfmap
del waveform
# Normalise to unit hrss
original_spectrum = unit_hrss(original_spectrum,
delta=original_spectrum.delta_f,
domain='frequency')
# Add to catalogue
original_frequencies = np.copy(original_spectrum.sample_frequencies)
original_cat[w, :] = np.copy(original_spectrum.data)
return (sample_times, original_frequencies, timedomain_cat, original_cat,
fpeaks, original_image_cat, align_image_cat, times, scales,
frequencies)
hp.data *= 1.4
Hp = hp.to_frequencyseries()
print max(hp)
#
# Generate own waveform
#
from pmns_utils import pmns_waveform as pwave
eos = "tm1"
mass = "135135"
total_mass = 2 * 1.35
mass1 = 1.35
mass2 = 1.35
waveform = pwave.Waveform(eos=eos,
mass=mass,
viscosity="lessvisc",
distance=100)
waveform.reproject_waveform()
waveform_tilde = waveform.hplus.to_frequencyseries()
from matplotlib import pyplot as pl
f, ax = pl.subplots(nrows=2, ncols=2)
ax[0][0].plot(hp.sample_times, hp)
ax[0][1].plot(waveform.hplus.sample_times, waveform.hplus)
ax[0][0].set_ylim(-1.5e-22, 1.5e-22)
ax[0][1].set_ylim(-1.5e-22, 1.5e-22)
ax[1][0].loglog(Hp.sample_frequencies, abs(Hp))
ax[1][1].loglog(waveform_tilde.sample_frequencies, abs(waveform_tilde))
pl.show()
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Set noise curves and example waveform
instruments = ['aLIGO', 'A+', 'Voyager', 'CE1', 'ET-D']
fmax = 4096
fmin = 10
delta_f = 0.5
# --- Example signal
eos = "tm1"
mass = "135135"
viscosity = "lessvisc"
waveform = pwave.Waveform(eos=eos, mass=mass, viscosity=viscosity, distance=50)
waveform.reproject_waveform()
waveform.compute_characteristics()
Hplus = waveform.hplus.to_frequencyseries()
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Build curves and plot on the fly
f, ax = pl.subplots()
ax.semilogy(Hplus.sample_frequencies,
2 * np.sqrt(Hplus.sample_frequencies) * abs(Hplus.data),
label='BNS @ 50 Mpc',
color='k')
lines = ["-", "--", "-.", ":"]
# Dump time-domain waveforms
#
#injections = {}
injections = []
names = []
for w, wave in enumerate(waveform_data.waves):
print "Projecting %s, %s ,%s (%d of %d)" % (wave['eos'], wave['mass'],
wave['viscosity'], w + 1,
waveform_data.nwaves)
# --- Fourier Domain Analysis --- #
# Get complex spectrum of this waveform
waveform = pwave.Waveform(eos=wave['eos'],
mass=wave['mass'],
viscosity=wave['viscosity'])
waveform.reproject_waveform()
name = wave['eos'] + '_' + wave['mass']
names.append(name)
#injections[name] = (pwave.taper_start(waveform.hplus),
# pwave.taper_start(waveform.hcross))
injections.append((pwave.taper_start(waveform.hplus),
pwave.taper_start(waveform.hcross)))
# Standardise
waveform_FD, target_fpeak, _ = ppca.condition_spectrum(waveform.hplus.data,
nsamples=nTsamples)
SNR = pycbc.filter.sigma(Hp, psd=psd, low_frequency_cutoff=1000)
print 'pycbc snr=', SNR
amp = wfutils.amplitude_from_polarizations(hp, hc)
#
# Generate own waveform
#
from pmns_utils import pmns_waveform as pwave
eos = "shen"
mass = "135135"
total_mass = 2 * 1.35
mass1 = 1.35
mass2 = 1.35
waveform = pwave.Waveform(eos=eos,
mass=mass,
viscosity="oldvisc",
distance=params['distance'])
waveform.reproject_waveform(theta=params['inclination'])
waveform_tilde = waveform.hplus.to_frequencyseries()
waveform.amp = wfutils.amplitude_from_polarizations(waveform.hplus,
waveform.hcross)
psd = pwave.make_noise_curve(fmax=waveform_tilde.sample_frequencies.max(),
delta_f=waveform_tilde.delta_f,
noise_curve='aLIGO')
SNR = pycbc.filter.sigma(waveform_tilde, psd=psd, low_frequency_cutoff=1000)
print 'my snr=', SNR
#
