def read(cls, subsys, filename):
"""
read from a file
Parameters
----------
subsys : `str`
Channel subsystem
filename : `str`
File to read from
"""
# open file
if isinstance(filename, str):
f = h5py.File(filename, 'r')
else:
f = filename
chandict = {subsys: f[subsys].keys()}
darm_channel = f['psd1'].keys()[0].split()[0].strip()
ss = PEMSubsystem(subsys, darm_channel, chandict)
psd1 = FrequencySeries.read(f['psd1'][f['psd1'].keys()[0]])
for channel in chandict[subsys]:
N = int(f[subsys][channel]['metadata'][0, 1])
st = int(f[subsys][channel]['metadata'][1, 1])
et = int(f[subsys][channel]['metadata'][2, 1])
csd12 = FrequencySeries.read(f[subsys][channel]['csd mean'])
psd2 = FrequencySeries.read(f[subsys][channel]['%s mean' %
channel])
ss[channel] = PEMCoherenceSegment(darm_channel, channel, csd12,
psd1, psd2, N, st, et)
ss.failed_channels = f['failed_channels'][:]
f.close()
return ss
def mybode(*args, **kwargs):
freq = kwargs.pop('freq', None)
kwargs['omega'] = freq * 2.0 * np.pi
mag, phase, omega = bode(*args, **kwargs)
phase = degwrap(phase)
mag = FrequencySeries(mag, frequencies=freq)
phase = FrequencySeries(phase, frequencies=freq)
return mag, phase
def test_read_ligolw(self):
with tempfile.NamedTemporaryFile(mode='w+') as fobj:
fobj.write(LIGO_LW_ARRAY)
array = FrequencySeries.read(
fobj, 'psd', match={'channel': 'X1:TEST-CHANNEL_1'})
utils.assert_array_equal(array, list(range(1, 11)) / units.Hz)
utils.assert_array_equal(array.frequencies,
list(range(10)) * units.Hz)
assert numpy.isclose(array.epoch.gps, 1000000000) # precision gah!
assert array.unit == units.Hz**-1
array2 = FrequencySeries.read(
fobj, 'psd', match={'channel': 'X1:TEST-CHANNEL_2'})
assert array2.epoch is None
# assert errors
with pytest.raises(ValueError):
FrequencySeries.read(fobj, 'blah')
with pytest.raises(ValueError):
FrequencySeries.read(fobj, 'psd')
with pytest.raises(ValueError):
FrequencySeries.read(fobj,
'psd',
match={
'channel': 'X1:TEST-CHANNEL_1',
'blah': 'blah'
})
def create_matrix_from_file(coh_file, channels):
"""
Creates coherence matrix from data that's in a file.
Used typically as helper function for plotting
Parameters
----------
coh_file : str
File containing coherence data
channels : list (str)
channels to plot
Returns
-------
coh_matrix : Spectrogram object
coherence matrix in form of spectrogram object
returns automatically in terms of coherence SNRr -
coherence * N.
(not actually a spectrogram, though)
frequencies : numpy array
numpy array of frequencies associated with coherence matrix
labels : list (str)
labels for coherence matrix
N : int
Number of time segment used to create coherence spectra
"""
labels = []
counter = 0
f = h5py.File(coh_file, 'r')
# get number of averages
N = f['info'].value
channels = f['psd2s'].keys()
failed_channels = f['failed_channels'].value
print failed_channels
First = 1
for channel in channels:
if First:
# initialize matrix!
darm_psd = FrequencySeries.from_hdf5(
f['psd1'][f['psd1'].keys()[0]])
First = 0
coh_matrix = np.zeros((darm_psd.size, len(channels)))
if channel in failed_channels:
continue
data = FrequencySeries.from_hdf5(f['coherences'][channel])
labels.append(channel[3:-3].replace('_', '-'))
coh_matrix[:data.size, counter] = data
counter += 1
coh_matrix = Spectrogram(coh_matrix * N)
return coh_matrix, darm_psd.frequencies.value, labels, N
def _percentile(axis, pctl=50, unit='um', suffix='', _dir='', **kwargs):
fname = './data2/{3}/{0}_{1}.hdf5'.format(axis, pctl, suffix, _dir)
model = kwargs.pop('model', None)
_asd = FrequencySeries.read(fname)**0.5
if model == '120QA':
amp = 10**(30.0 / 20.0)
elif model == 'compact':
amp = 10**(45.0 / 20.0)
seis = Trillium(model)
v2vel = seis.v2vel
c2v = 20.0 / 2**15
_asd = v2vel(_asd) * c2v / amp * 1e6
if unit == 'um':
asd = _asd / (2.0 * np.pi * _asd.frequencies.value)
#asd.write('./LongTerm_{0}_{1}_DISP.txt'.format(axis,pctl),format='txt')
elif unit == 'm':
asd = _asd / (2.0 * np.pi * _asd.frequencies.value) * 1e-6
asd.write('./{0}_{1}_DISP.txt'.format(axis, pctl), format='txt')
elif unit == 'um/sec':
asd = _asd
asd.write('./{0}_{1}_VELO.txt'.format(axis, pctl), format='txt')
elif unit == 'um/sec/sec':
asd = _asd * (2.0 * np.pi * _asd.frequencies.value)
#asd.write('./LongTerm_{0}_{1}_ACC.txt'.format(axis,pctl),format='txt')
elif unit == 'm/sec/sec':
asd = _asd * (2.0 * np.pi * _asd.frequencies.value) * 1e-6
else:
raise ValueError('!!1', unit)
return asd
def comb_finder(self, offsets, comb_widths, cutoff=None):
"""
Parameters
----------
offsets : `int`
comb_widths : `int`
cutoff : `bool`, optional
Returns
-------
:py:class:`CoherenceCombMatrix` object
"""
comb_mat = np.zeros((len(offsets), len(comb_widths)))
likelihood_mat = np.zeros((len(offsets), len(comb_widths)))
f0 = self.psd1.frequencies[0].value
df = self.psd1.df.value
coh = FrequencySeries(self.get_coh(cutoff=cutoff),
frequencies=self.psd1.frequencies)
for ii, offset in enumerate(offsets):
start_idx = max(0, ((offset - f0) / df))
for jj, width in enumerate(comb_widths):
mask = np.zeros(self.psd1.size, dtype=bool)
comb_idx_width = width / np.float(df)
mask[start_idx::comb_idx_width] = True
comb_mat[ii, jj] = self.sum_coh(mask, cutoff=cutoff)
likelihood_mat[ii, jj] = stats.gamma.logpdf(
self.N * self.sum_coh(mask, cutoff=cutoff),
np.sum(np.int_(mask))) * self.N
return CoherenceCombMatrix(comb_mat, likelihood_mat, offsets,
comb_widths, coh)
def _get_extended_metadata(h5group):
"""Extract the extended metadata for a PyCBC table in HDF5
This method packs non-table-column datasets in the given h5group into
a metadata `dict`
Returns
-------
meta : `dict`
the metadata dict
"""
meta = dict()
# get PSD
try:
psd = h5group['psd']
except KeyError:
pass
else:
from gwpy.frequencyseries import FrequencySeries
meta['psd'] = FrequencySeries(
psd[:], f0=0, df=psd.attrs['delta_f'], name='pycbc_live')
# get everything else
for key in META_COLUMNS - {'psd'}:
try:
value = h5group[key][:]
except KeyError:
pass
else:
meta[key] = value
return meta
def load_strain_data(self, cache=True):
"""
Load the strain data for the event
If it doesn't exist already, then it will be downloaded
into the strain_data folder
INPUTS
------
cache: bool
if True, then store local copy of strain data
passed to pesummary StrainData.fetch_open_data
"""
self.data = {}
self.data_w = {}
for IFO in self.IFOs:
# download public strain data
print("Downloading strain data for " + IFO)
self.data[IFO] = StrainData.fetch_open_data(IFO,
self.t_start,
self.t_end,
cache=cache)
# unpack PSD into FrequencySeries ASD object
fS = np.array(self.psd[IFO])
asd = FrequencySeries(np.sqrt(fS[:, 1]), frequencies=fS[:, 0])
# whitened strain
self.data_w[IFO] = self.data[IFO].whiten(asd=asd)
def tf(sys,omega):
mag, phase, omega = matlab.bode(sys,omega,Plot=False)
mag = np.squeeze(mag)
phase = np.squeeze(phase)
G = mag*np.exp(1j*phase)
freq = omega/(2.0*np.pi)
hoge = FrequencySeries(G,frequencies=freq)
return hoge
def inner_product(data1,data2):
assert data1.duration==data2.duration
srate1=data1.sample_rate.value
srate2=data2.sample_rate.value
fdata1=rfft(sig.hann(len(data1))*data1.detrend().value)
fdata2=rfft(sig.hann(len(data2))*data2.detrend().value)
max_idx=min(len(fdata1),len(fdata2))
return FrequencySeries(fdata1[:max_idx]*fdata2.conjugate()[:max_idx],
df=1./data1.duration)
def read(fname):
try:
data = FrequencySeries.read(fname, format='hdf5')
except IOError as ioe:
print(ioe)
exit()
else:
pass
return data
def avg_freq_bins(fdata,new_df):
nyquist=(len(fdata)-1)*fdata.df.value
new_flen=1+int(nyquist/float(new_df))
binlen=int(new_df/float(fdata.df.value))
win=sig.hann(2*binlen)
win/=np.sum(win)
result=np.zeros(new_flen,dtype=fdata.dtype)
for idx in range(1,new_flen-1):
result[idx]=np.sum(win*fdata.value[binlen*(idx-1):binlen*(idx+1)])
return FrequencySeries(result,df=new_df,channel=fdata.channel,
name=fdata.name,epoch=fdata.epoch)
def test_inject(self):
# create a timeseries out of an array of zeros
df, nyquist = 1, 2048
data = FrequencySeries(numpy.zeros(df*nyquist + 1), f0=0,
df=df, unit='')
# create a second timeseries to inject into the first
w_nyquist = 1024
sig = FrequencySeries(numpy.ones(df*w_nyquist + 1), f0=0,
df=df, unit='')
# test that we recover this waveform when we add it to data,
# and that the operation does not change the original data
new_data = data.inject(sig)
assert new_data.unit == data.unit
assert new_data.size == data.size
ind, = new_data.value.nonzero()
assert len(ind) == sig.size
utils.assert_allclose(new_data.value[ind], sig.value)
utils.assert_allclose(data.value, numpy.zeros(df*nyquist + 1))
def plot_spectrum(fd_psd):
'''
Plot power spectral density
'''
plot = FrequencySeriesPlot()
ax = plot.gca()
ax.plot(FrequencySeries(fd_psd, df=fd_psd.delta_f))
#plt.ylim(1e-10, 1e-3)
plt.xlim(0.1, 500)
plt.loglog()
plt.savefig("psd.png", dpi=300, transparent=True)
plt.close()
def test_add_arrays(self):
ts = TimeSeries([1, 2, 3, 4])
fs = FrequencySeries([1, 2, 3, 4])
fig = self.FIGURE_CLASS()
self.assertEqual(len(fig.axes), 0)
fig.add_timeseries(ts)
self.assertEqual(len(fig.axes), 1)
self.assertIsInstance(fig.axes[0], TimeSeriesAxes)
fig.add_frequencyseries(fs)
self.assertEqual(len(fig.axes), 2)
self.assertIsInstance(fig.axes[1], FrequencySeriesAxes)
fig.close()
def dataprod(cls, prod):
super(_TestFrequencyDomainProduct, cls).dataprod(prod)
fftlength = prod.args.secpfft
for ts in prod.timeseries:
nsamp = int(fftlength * 512 / 2.) + 1
fs = FrequencySeries(_random_data(nsamp),
x0=0,
dx=1 / fftlength,
channel=ts.channel,
name=ts.name)
prod.spectra.append(fs)
return prod
def to_gwpy_frequencyseries(self):
"""
Output the frequency series strain data as a :class:`gwpy.frequencyseries.FrequencySeries`.
"""
try:
from gwpy.frequencyseries import FrequencySeries
except ModuleNotFoundError:
raise ModuleNotFoundError(
"Cannot output strain data as gwpy FrequencySeries")
return FrequencySeries(self.frequency_domain_strain,
frequencies=self.frequency_array,
epoch=self.start_time,
channel=self.channel)
def test_add_arrays(self):
ts = TimeSeries([1, 2, 3, 4])
fs = FrequencySeries([1, 2, 3, 4])
fig = self.FIGURE_CLASS()
assert len(fig.axes) == 0
fig.add_timeseries(ts)
assert len(fig.axes) == 1
assert isinstance(fig.axes[0], TimeSeriesAxes)
fig.add_frequencyseries(fs)
assert len(fig.axes) == 2
assert isinstance(fig.axes[1], FrequencySeriesAxes)
fig.close()
def read_frequencyseries(filename):
"""Read a `~gwpy.frequencyseries.FrequencySeries` from a file
IF using HDF5, the filename can be given as a combined filename/path, i.e.
``test.hdf5/path/to/dataset``.
Parameters
----------
filename : `str`
path of file to read
Returns
-------
series : `~gwpy.frequencyseries.FrequencySeries`
the data as read
Raises
------
astropy.io.registry.IORegistryError
if the input format cannot be identified or is not registered
"""
# try and parse path in HDF5 file if given
try:
ext = HDF5_FILENAME.search(filename).groupdict()['ext']
except AttributeError: # no match
kwargs = {}
else:
kwargs = {'path': filename.rsplit(ext, 1)[1]}
# read file
try:
return FrequencySeries.read(filename, **kwargs)
except IORegistryError:
if filename.endswith('.gz'):
fmt = os.path.splitext(filename[:-3])[-1]
else:
fmt = os.path.splitext(filename)[-1]
return FrequencySeries.read(filename, format=fmt.lstrip('.'), **kwargs)
def read_frequencyseries(filename):
"""Read a `~gwpy.frequencyseries.FrequencySeries` from a file
IF using HDF5, the filename can be given as a combined filename/path, i.e.
``test.hdf5/path/to/dataset``.
Parameters
----------
filename : `str`
path of file to read
Returns
-------
series : `~gwpy.frequencyseries.FrequencySeries`
the data as read
Raises
------
astropy.io.registry.IORegistryError
if the input format cannot be identified or is not registered
"""
# try and parse path in HDF5 file if given
try:
ext = HDF5_FILENAME.search(filename).groupdict()['ext']
except AttributeError: # no match
kwargs = {}
else:
kwargs = {'path': filename.rsplit(ext, 1)[1]}
# read file
try:
return FrequencySeries.read(filename, **kwargs)
except IORegistryError as e:
if filename.endswith('.gz'):
fmt = os.path.splitext(filename[:-3])[-1]
else:
fmt = os.path.splitext(filename)[-1]
return FrequencySeries.read(filename, format=fmt.lstrip('.'), **kwargs)
def test_inject(self):
# create a timeseries out of an array of zeros
df, nyquist = 1, 2048
data = FrequencySeries(numpy.zeros(df * nyquist + 1),
f0=0,
df=df,
unit='')
# create a second timeseries to inject into the first
w_nyquist = 1024
sig = FrequencySeries(numpy.ones(df * w_nyquist + 1),
f0=0,
df=df,
unit='')
# test that we recover this waveform when we add it to data,
# and that the operation does not change the original data
new_data = data.inject(sig)
assert new_data.unit == data.unit
assert new_data.size == data.size
ind, = new_data.value.nonzero()
assert len(ind) == sig.size
utils.assert_allclose(new_data.value[ind], sig.value)
utils.assert_allclose(data.value, numpy.zeros(df * nyquist + 1))
def kagra_seis(axis='X', pctl=90):
if axis in ['X', 'Y', 'Z']:
prefix = '/Users/miyo/Git/miyopy/miyopy/seismodel/JGW-T1910436-v5/'
fname = 'LongTerm_{axis}_{pctl}_VELO.txt'.format(axis=axis, pctl=pctl)
vel_asd = FrequencySeries.read(prefix + fname)
return vel_asd
elif axis == 'H':
vel_x = kagra_seis('X', pctl)
vel_y = kagra_seis('Y', pctl)
vel_h = (vel_x**2 + vel_y**2)**(1. / 2)
return vel_h
elif axis == 'V':
return kagra_seis('Z', pctl)
else:
raise ValueError('hoge')
def _percentile(axis, pctl=50, unit='um/sec', **kwargs):
_asd = FrequencySeries.read('./data2/LongTerm_{0}_{1}.hdf5'.format(
axis, pctl))**0.5
amp = 10**(30.0 / 20.0)
c2v = 20.0 / 2**15
_asd = v2vel(_asd) * c2v / amp * 1e6
if unit == 'um':
asd = _asd / (2.0 * np.pi * _asd.frequencies.value)
#asd.write('./LongTerm_{0}_{1}_DISP.txt'.format(axis,pctl),format='txt')
elif unit == 'um/sec':
asd = _asd
#asd.write('./LongTerm_{0}_{1}_VELO.txt'.format(axis,pctl),format='txt')
else:
raise ValueError('!!1')
return asd
def to_pycbc_frequencyseries(self):
"""
Output the frequency series strain data as a :class:`pycbc.types.frequencyseries.FrequencySeries`.
"""
try:
from pycbc.types.frequencyseries import FrequencySeries
from lal import LIGOTimeGPS
except ImportError:
raise ImportError(
"Cannot output strain data as PyCBC FrequencySeries")
return FrequencySeries(self.frequency_domain_strain,
delta_f=1 / self.duration,
epoch=LIGOTimeGPS(self.start_time))
def plot_spectra(clusters,
channel,
unit='cts',
xlog=True,
legend=None,
xlim=None,
**kwargs):
from glob import glob
from gwpy.frequencyseries import FrequencySeries
from gwpy.plot import Plot
title = channel
psds = {}
for cluster in clusters:
for filename in glob('*.hdf5'):
try:
psds[cluster] = FrequencySeries.read(filename,
f'{cluster}-{channel}')
print(f'found in {filename}')
break
except KeyError:
continue
else:
raise KeyError(f'Could not find Nº{cluster}')
if legend is None:
legend = clusters
# plot the group in one figure.
plt = Plot(*(psds[cluster] for cluster in psds),
separate=False,
sharex=True,
zorder=1,
**kwargs)
if xlim is not None:
plt.gca().set_xlim(xlim)
plt.gca().set_ylim((1e-48, 1e-37))
# modify the figure as a whole.
# plt.add_segments_bar(dq, label='')
# plt.gca().set_color_cycle(['red', 'green', 'blue', 'yellow'])
if xlog:
plt.gca().set_xscale('log')
plt.gca().set_yscale('log')
plt.gca().set_ylabel(f'Power Spectral Density [{unit}^2/Hz]')
plt.suptitle(title)
plt.legend(legend, prop={'size': 15})
# save to png.
plt.save(f'{title}.png')
def _gen_white_gaussian_noise(cls, station_names, psd_amp, sample_rate,
duration, segdur=None, seed=None):
if segdur is None:
segdur=duration
data = SeismometerArray()
psd = FrequencySeries(psd_amp * np.ones(100000), df=1./(segdur))
psd[0]=0
for station in station_names:
data[station] = Seismometer.initialize_all_good(duration=segdur)
# set data channels to white noise
data[station]['HHE'] = gaussian.noise_from_psd(duration,
sample_rate, psd, seed=seed, name=station, unit=u.m)
data[station]['HHN'] = gaussian.noise_from_psd(duration,
sample_rate, psd, seed=seed, name=station, unit=u.m)
data[station]['HHZ'] = gaussian.noise_from_psd(duration,
sample_rate, psd, seed=seed, name=station, unit=u.m)
return data
def _percentile(axis, pctl=50, unit='um/sec', **kwargs):
suffix = '_{0}_{1}'.format(start, end)
fname = fname_hdf5_longasd(axis, pctl, suffix=suffix, prefix='./tmp')
_asd = FrequencySeries.read(fname)**0.5
amp = 10**(30.0 / 20.0)
c2v = 20.0 / 2**15
_asd = v2vel(_asd) * c2v / amp * 1e6
if unit == 'um':
asd = _asd / (2.0 * np.pi * _asd.frequencies.value)
asd.write('./LongTerm_{0}_{1}_DISP.txt'.format(axis, pctl),
format='txt')
elif unit == 'um/sec':
asd = _asd
asd.write('./LongTerm_{0}_{1}_VELO.txt'.format(axis, pctl),
format='txt')
else:
raise ValueError('!!1')
return asd
def get_range_spectrum(channel, segments, config=None, cache=None, query=True,
nds=None, return_=True, nproc=1, datafind_error='raise',
frametype=None, stride=60, fftlength=None, overlap=None,
method=None, which='all', **rangekwargs):
"""Compute percentile spectra of the range integrand from a set of
spectrograms
"""
name = str(channel)
cmin = '%s.min' % name
cmax = '%s.max' % name
if name not in globalv.SPECTRUM:
speclist = get_range_spectrogram(
channel, segments, config=config, cache=cache, query=query,
nds=nds, return_=return_, nproc=nproc, frametype=frametype,
datafind_error=datafind_error, method=method, stride=stride,
fftlength=fftlength, overlap=overlap, **rangekwargs)
specgram = speclist.join(gap='ignore')
try: # store median spectrum
globalv.SPECTRUM[name] = specgram.percentile(50)
except (ValueError, IndexError):
unit = 'Mpc' if 'energy' in rangekwargs else 'Mpc^2 / Hz'
globalv.SPECTRUM[name] = FrequencySeries(
[], channel=channel, f0=0, df=1, unit=unit)
globalv.SPECTRUM[cmin] = globalv.SPECTRUM[name]
globalv.SPECTRUM[cmax] = globalv.SPECTRUM[name]
else: # store percentiles
globalv.SPECTRUM[cmin] = specgram.percentile(5)
globalv.SPECTRUM[cmax] = specgram.percentile(95)
if not return_:
return
if which == 'all':
return (globalv.SPECTRUM[name], globalv.SPECTRUM[cmin],
globalv.SPECTRUM[cmax])
if which == 'mean':
return globalv.SPECTRUM[name]
if which == 'min':
return globalv.SPECTRUM[cmin]
if which == 'max':
return globalv.SPECTRUM[cmax]
raise ValueError("Unrecognised value for `which`: %r" % which)
def vel2vel(fseries):
#c2v = 10.0/2**15
#degain = 10**(-30./20.)
z, p, k = trillium.zpk_120qa()
num, den = zpk2tf(z, p, k)
w, h = freqs(num, den, worN=np.logspace(-4, 5, 1e2))
mag = abs(h)
f = w / np.pi / 2.0
func = interp1d(f, mag)
_f = fseries.frequencies.value
if False:
plt.loglog(f, abs(mag), 'o-')
#plt.loglog(_f,_mag)
plt.savefig('hoge.png')
vel2v = func(_f[1:])
value = fseries.value[1:] / vel2v * 1202.5
f0 = fseries.f0
df = fseries.df
return FrequencySeries(value, df=df, f0=f0 + df)
def waveform(self, IFO=None, index=None, whiten=True):
"""
Generate waveform from a posterior sample
INPUTS
------
IFO: str
which instrument into which to project
e.g. 'H1', 'L1' or 'V1'
index: int
which individual posterior sample to use?
if None, then use the max posterior point
whiten: bool
if True, then whiten the signal
RETURNS
-------
model:
the model waveform
"""
ind = self.MAPindex if index is None else ind
dt = 1 / 2**np.ceil(np.log2(2 * max(self.f_high.values())))
# compute TD waveform
model = self.samples.td_waveform(self.approx,
dt,
self.f_low[IFO],
f_ref=self.f_ref,
ind=ind,
project=IFO)
model.dx = dt
if whiten:
# unpack PSD into FrequencySeries ASD object
fS = np.array(self.psd[IFO])
asd = FrequencySeries(np.sqrt(fS[:, 1]), frequencies=fS[:, 0])
# whiten the model
model = model.whiten(asd=asd)
return model
def save_asd(axis, available, percentile=50, **kwargs):
prefix = kwargs.pop('prefix', './data')
write = kwargs.pop('write', None)
write_gwf = kwargs.pop('write_gwf', None)
skip = kwargs.pop('skip', None)
asd_fmt = '{0}/{1}_{2:02d}_LongTerm.hdf5'.format(prefix, axis, percentile)
if os.path.exists(asd_fmt):
#log.debug(asd_fmt+' Read')
return FrequencySeries.read(asd_fmt, format='hdf5')
log.debug(asd_fmt + ' Saving {0:02d} percentile'.format(percentile))
fnamelist = [
prefix + '/{0}_{1}_{2}.hdf5'.format(axis, start, end)
for start, end in available
]
specgrams = Spectrogram.read(fnamelist[0], format='hdf5')
[specgrams.append(Spectrogram.read(fname,format='hdf5'),gap='ignore') \
for fname in fnamelist]
asd = specgrams.percentile(percentile)
asd.write(asd_fmt, format='hdf5', overwrite=True)
return asd
def test_read_ligolw(self):
with tempfile.NamedTemporaryFile(mode='w+') as fobj:
fobj.write(LIGO_LW_ARRAY)
array = FrequencySeries.read(
fobj, 'psd', match={'channel': 'X1:TEST-CHANNEL_1'})
utils.assert_array_equal(array, list(range(1, 11)) / units.Hz)
utils.assert_array_equal(array.frequencies,
list(range(10)) * units.Hz)
assert numpy.isclose(array.epoch.gps, 1000000000) # precision gah!
assert array.unit == units.Hz ** -1
array2 = FrequencySeries.read(
fobj, 'psd', match={'channel': 'X1:TEST-CHANNEL_2'})
assert array2.epoch is None
# assert errors
with pytest.raises(ValueError):
FrequencySeries.read(fobj, 'blah')
with pytest.raises(ValueError):
FrequencySeries.read(fobj, 'psd')
with pytest.raises(ValueError):
FrequencySeries.read(
fobj, 'psd',
match={'channel': 'X1:TEST-CHANNEL_1', 'blah': 'blah'})
def plot(request):
# if this is a POST request we need to process the form data
if request.method == 'GET':
# create a form instance and populate it with data from the request:
form = WaveformForm(request.GET)
# check whether it's valid:
if form.is_valid():
# This is where waveform generation code will go Chris P
# You parse the request for as such
f, hp, hx = gen_waveform(form.cleaned_data)
assert len(f) == len(hp), "{0}, {1}".format(len(f), len(hp))
hp = FrequencySeries(hp, frequencies=f)
hx = FrequencySeries(hx, frequencies=f)
asd = FrequencySeries(
gen_psd(form, f),
frequencies=f) if form.cleaned_data["psd"] != "None" else None
plot = hp.abs().plot(color='red',
label=r"$|h_+|(f)$",
yscale='log')
ax = plot.gca()
ax.plot(hx.abs(),
color='black',
linestyle='-.',
label=r"$|h_x|(f)$")
if asd is not None:
ax.plot(asd)
# For now just plot |h|
# FIXME: These will most likely overlap
ax.set_ylabel(r'GW strain [strain$/\sqrt{\mathrm{Hz}}$]')
plot.legend()
buf = io.BytesIO()
canvas = FigureCanvas(plot)
canvas.print_png(buf)
response = HttpResponse(buf.getvalue(), content_type='image/png')
pyplot.close(plot)
return response
def draw(self):
# initialise
(plot, axes) = self.init_plot()
ax = axes[0]
ax.grid(b=True, axis='y', which='major')
channel = self.channels[0]
# parse data arguments
sdform = self.pargs.pop('format')
clim = self.pargs.pop('clim')
clog = self.pargs.pop('logcolor')
clabel = self.pargs.pop('colorlabel')
rasterized = self.pargs.pop('rasterized', True)
ratio = self.ratio
# get cmap
if ratio in ['median', 'mean'] or (
isinstance(ratio, str) and os.path.isfile(ratio)):
self.pargs.setdefault('cmap', 'Spectral_r')
cmap = self.pargs.pop('cmap', None)
# get data
if self.state and not self.all_data:
valid = self.state.active
else:
valid = SegmentList([self.span])
if self.type == 'coherence-spectrogram':
specgrams = get_coherence_spectrogram(self.channels, valid,
query=False)
else:
specgrams = get_spectrogram(channel, valid, query=False,
format=sdform)
# calculate ratio spectrum
if len(specgrams) and (ratio in ['median', 'mean'] or
isinstance(ratio, int)):
try:
allspec = specgrams.join(gap='ignore')
except ValueError as e:
if 'units do not match' in str(e):
warnings.warn(str(e))
for spec in specgrams[1:]:
spec.unit = specgrams[0].unit
allspec = specgrams.join(gap='ignore')
else:
raise
if isinstance(ratio, int):
ratio = allspec.percentile(ratio)
else:
ratio = getattr(allspec, ratio)(axis=0)
elif isinstance(ratio, str) and os.path.isfile(ratio):
try:
ratio = FrequencySeries.read(ratio)
except IOError as e:
warnings.warn('IOError: %s' % str(e))
except Exception as e:
# hack for old versions of GWpy
# TODO: remove me when GWSumm requires GWpy > 0.1
if 'Format could not be identified' in str(e):
ratio = FrequencySeries.read(ratio, format='dat')
else:
raise
# allow channel data to set parameters
if getattr(channel, 'frequency_range', None) is not None:
self.pargs.setdefault('ylim', channel.frequency_range)
if isinstance(self.pargs['ylim'], Quantity):
self.pargs['ylim'] = self.pargs['ylim'].value
if (ratio is None and sdform in ['amplitude', 'asd'] and
hasattr(channel, 'asd_range') and clim is None):
clim = channel.asd_range
elif (ratio is None and hasattr(channel, 'psd_range') and
clim is None):
clim = channel.psd_range
# plot data
for specgram in specgrams:
# undo demodulation
specgram = undo_demodulation(specgram, channel,
self.pargs.get('ylim', None))
# calculate ratio
if ratio is not None:
specgram = specgram.ratio(ratio)
# plot
ax.plot_spectrogram(specgram, cmap=cmap, rasterized=rasterized)
# add colorbar
if len(specgrams) == 0:
ax.scatter([1], [1], c=[1], visible=False, cmap=cmap)
plot.add_colorbar(ax=ax, clim=clim, log=clog, label=clabel, cmap=cmap)
# customise and finalise
for key, val in self.pargs.iteritems():
if key == 'ratio':
continue
try:
getattr(ax, 'set_%s' % key)(val)
except AttributeError:
setattr(ax, key, val)
if self.state:
self.add_state_segments(ax)
return self.finalize()
def _draw(self):
"""Load all data, and generate this `SpectrumDataPlot`
"""
plot = self.plot = FrequencySeriesPlot(
figsize=self.pargs.pop('figsize', [12, 6]))
ax = plot.gca()
ax.grid(b=True, axis='both', which='both')
if self.state:
self.pargs.setdefault(
'suptitle',
'[%s-%s, state: %s]' % (self.span[0], self.span[1],
label_to_latex(str(self.state))))
suptitle = self.pargs.pop('suptitle', None)
if suptitle:
plot.suptitle(suptitle, y=0.993, va='top')
# get spectrum format: 'amplitude' or 'power'
sdform = self.pargs.pop('format')
if sdform == 'rayleigh':
method = 'rayleigh'
else:
method = None
use_percentiles = str(
self.pargs.pop('no-percentiles')).lower() == 'false'
# parse plotting arguments
plotargs = self.parse_plot_kwargs()
legendargs = self.parse_legend_kwargs()
# get reference arguments
refs = []
refkey = 'None'
for key in sorted(self.pargs.keys()):
if key == 'reference' or re.match('reference\d+\Z', key):
refs.append(dict())
refs[-1]['source'] = self.pargs.pop(key)
refkey = key
if re.match('%s[-_]' % refkey, key):
refs[-1][key[len(refkey)+1:]] = self.pargs.pop(key)
# add data
if self.type == 'coherence-spectrum':
iterator = zip(self.channels[0::2], self.channels[1::2], plotargs)
else:
iterator = zip(self.channels, plotargs)
for chantuple in iterator:
channel = chantuple[0]
channel2 = chantuple[1]
pargs = chantuple[-1]
if self.state and not self.all_data:
valid = self.state
else:
valid = SegmentList([self.span])
if self.type == 'coherence-spectrum':
data = get_coherence_spectrum([str(channel), str(channel2)],
valid, query=False)
else:
data = get_spectrum(str(channel), valid, query=False,
format=sdform, method=method)
# undo demodulation
for spec in data:
spec = undo_demodulation(spec, channel,
self.pargs.get('xlim', None))
# anticipate log problems
if self.pargs['logx']:
data = [s[1:] for s in data]
if self.pargs['logy']:
for sp in data:
sp.value[sp.value == 0] = 1e-100
if use_percentiles:
ax.plot_spectrum_mmm(*data, **pargs)
else:
pargs.pop('alpha', None)
ax.plot_spectrum(data[0], **pargs)
# allow channel data to set parameters
if getattr(channel, 'frequency_range', None) is not None:
self.pargs.setdefault('xlim', channel.frequency_range)
if isinstance(self.pargs['xlim'], Quantity):
self.pargs['xlim'] = self.pargs['xlim'].value
if (sdform in ['amplitude', 'asd'] and
hasattr(channel, 'asd_range')):
self.pargs.setdefault('ylim', channel.asd_range)
elif hasattr(channel, 'psd_range'):
self.pargs.setdefault('ylim', channel.psd_range)
# display references
for i, ref in enumerate(refs):
if 'source' in ref:
source = ref.pop('source')
try:
refspec = FrequencySeries.read(source)
except IOError as e:
warnings.warn('IOError: %s' % str(e))
except Exception as e:
# hack for old versions of GWpy
# TODO: remove me when GWSumm requires GWpy > 0.1
if 'Format could not be identified' in str(e):
refspec = FrequencySeries.read(source, format='dat')
else:
raise
else:
ref.setdefault('zorder', -len(refs) + 1)
if 'filter' in ref:
refspec = refspec.filter(*ref.pop('filter'))
if 'scale' in ref:
refspec *= ref.pop('scale', 1)
ax.plot(refspec, **ref)
# customise
hlines = list(self.pargs.pop('hline', []))
for key, val in self.pargs.iteritems():
try:
getattr(ax, 'set_%s' % key)(val)
except AttributeError:
setattr(ax, key, val)
# add horizontal lines to add
if hlines:
if not isinstance(hlines[-1], float):
lineparams = hlines.pop(-1)
else:
lineparams = {'color':'r', 'linestyle': '--'}
for yval in hlines:
try:
yval = float(yval)
except ValueError:
continue
else:
ax.plot(ax.get_xlim(), [yval, yval], **lineparams)
if len(self.channels) > 1 or ax.legend_ is not None:
plot.add_legend(ax=ax, **legendargs)
if not plot.colorbars:
plot.add_colorbar(ax=ax, visible=False)
return self.finalize()
def _draw(self):
"""Load all data, and generate this `SpectrumDataPlot`
"""
plot = self.plot = FrequencySeriesPlot(
figsize=self.pargs.pop('figsize', [12, 6]))
ax = plot.gca()
if self.state:
self.pargs.setdefault(
'suptitle',
'[%s-%s, state: %s]' % (self.span[0], self.span[1],
label_to_latex(str(self.state))))
suptitle = self.pargs.pop('suptitle', None)
if suptitle:
plot.suptitle(suptitle, y=0.993, va='top')
# parse plotting arguments
cmap = self.pargs.pop('cmap', None)
varargs = self.parse_variance_kwargs()
plotargs = self.parse_plot_kwargs()[0]
legendargs = self.parse_legend_kwargs()
# get reference arguments
refs = []
refkey = 'None'
for key in sorted(self.pargs.keys()):
if key == 'reference' or re.match('reference\d+\Z', key):
refs.append(dict())
refs[-1]['source'] = self.pargs.pop(key)
refkey = key
if re.match('%s[-_]' % refkey, key):
refs[-1][key[len(refkey)+1:]] = self.pargs.pop(key)
# get channel arguments
if hasattr(self.channels[0], 'asd_range'):
low, high = self.channels[0].asd_range
varargs.setdefault('low', low)
varargs.setdefault('high', high)
# calculate spectral variance and plot
# pad data request to over-fill plots (no gaps at the end)
if self.state and not self.all_data:
valid = self.state.active
else:
valid = SegmentList([self.span])
livetime = float(abs(valid))
if livetime:
plotargs.setdefault('vmin', 1/livetime)
plotargs.setdefault('vmax', 1.)
plotargs.pop('label')
specgram = get_spectrogram(self.channels[0], valid, query=False,
format='asd').join(gap='ignore')
if specgram.size:
asd = specgram.median(axis=0)
asd.name = None
variance = specgram.variance(**varargs)
# normalize the variance
variance /= livetime / specgram.dt.value
# undo demodulation
variance = undo_demodulation(variance, self.channels[0],
self.pargs.get('xlim', None))
# plot
ax.plot(asd, color='grey', linewidth=0.3)
m = ax.plot_variance(variance, cmap=cmap, **plotargs)
#else:
# ax.scatter([1], [1], c=[1], visible=False, vmin=plotargs['vmin'],
# vmax=plotargs['vmax'], cmap=plotargs['cmap'])
#plot.add_colorbar(ax=ax, log=True, label='Fractional time at amplitude')
# allow channel data to set parameters
if getattr(self.channels[0], 'frequency_range', None) is not None:
self.pargs.setdefault('xlim', self.channels[0].frequency_range)
if isinstance(self.pargs['xlim'], Quantity):
self.pargs['xlim'] = self.pargs['xlim'].value
if hasattr(self.channels[0], 'asd_range'):
self.pargs.setdefault('ylim', self.channels[0].asd_range)
# display references
for i, ref in enumerate(refs):
if 'source' in ref:
source = ref.pop('source')
try:
refspec = FrequencySeries.read(source)
except IOError as e:
warnings.warn('IOError: %s' % str(e))
except Exception as e:
# hack for old versions of GWpy
# TODO: remove me when GWSumm requires GWpy > 0.1
if 'Format could not be identified' in str(e):
refspec = FrequencySeries.read(source, format='dat')
else:
raise
else:
if 'filter' in ref:
refspec = refspec.filter(*ref.pop('filter'))
if 'scale' in ref:
refspec *= ref.pop('scale', 1)
ax.plot(refspec, **ref)
# customise
hlines = list(self.pargs.pop('hline', []))
self.apply_parameters(ax, **self.pargs)
# add horizontal lines to add
if hlines:
if not isinstance(hlines[-1], float):
lineparams = hlines.pop(-1)
else:
lineparams = {'color':'r', 'linestyle': '--'}
for yval in hlines:
try:
yval = float(yval)
except ValueError:
continue
else:
ax.plot(ax.get_xlim(), [yval, yval], **lineparams)
# set grid
ax.grid(b=True, axis='both', which='both')
if not plot.colorbars:
plot.add_colorbar(ax=ax, visible=False)
return self.finalize()