def fetch(self, channels, t0, duration, fs, nproc=4):
""" Fetch data """
# if channels is a file
if isinstance(channels, str):
channels = open(channels).read().splitlines()
target_channel = channels[0]
# get data and resample
data = TimeSeriesDict.get(channels,
t0,
t0 + duration,
nproc=nproc,
allow_tape=True)
data = data.resample(fs)
# sorted by channel name
data = OrderedDict(sorted(data.items()))
# reset attributes
self.data = []
self.channels = []
for chan, ts in data.items():
self.data.append(ts.value)
self.channels.append(chan)
self.data = np.stack(self.data)
self.channels = np.stack(self.channels)
self.t0 = t0
self.fs = fs
self.target_idx = np.where(self.channels == target_channel)[0][0]
pyplot.ion()
# Before anything else, we import the objects we will need:
from gwpy.time import tconvert
from gwpy.timeseries import TimeSeriesDict
from gwpy.plotter import BodePlot
# and set the times of our query, and the channels we want:
start = tconvert('May 27 2014 04:00')
end = start + 1800
gndchannel = 'L1:ISI-GND_STS_ITMY_Z_DQ'
hpichannel = 'L1:HPI-ITMY_BLND_L4C_Z_IN1_DQ'
# We can call the :meth:`~TimeSeriesDict.get` method of the `TimeSeriesDict`
# to retrieve all data in a single operation:
data = TimeSeriesDict.get([gndchannel, hpichannel], start, end, verbose=True)
gnd = data[gndchannel]
hpi = data[hpichannel]
# Next, we can call the :meth:`~TimeSeries.average_fft` method to calculate
# an averages, complex-valued FFT for each `TimeSeries`:
gndfft = gnd.average_fft(100, 50, window='hamming')
hpifft = hpi.average_fft(100, 50, window='hamming')
# Finally, we can divide one by the other to get the transfer function
# (up to the lower Nyquist)
size = min(gndfft.size, hpifft.size)
tf = hpifft[:size] / gndfft[:size]
# The `~gwpy.plotter.BodePlot` knows how to separate a complex-valued
# `~gwpy.spectrum.Spectrum` into magnitude and phase:
from gwpy.timeseries import TimeSeriesDict alldata = TimeSeriesDict.get(['H1:PSL-PWR_PMC_TRANS_OUT16','H1:IMC-PWR_IN_OUT16'], 'Feb 1 00:00', 'Feb 1 02:00')
(`~gwpy.spectrum.Spectrum`) giving a time-averaged measure of coherence.
The `TimeSeries` method :meth:`~TimeSeries.coherence_spectrogram` performs the
same coherence calculation every ``stride``, giving a time-varying coherence
measure.
"""
__author__ = "Duncan Macleod <*****@*****.**>"
__currentmodule__ = 'gwpy.timeseries'
# First, we import the `TimeSeriesDict`
from gwpy.timeseries import TimeSeriesDict
# and then :meth:`~TimeSeriesDict.get` both data sets:
data = TimeSeriesDict.get(['L1:LSC-SRCL_IN1_DQ', 'L1:LSC-CARM_IN1_DQ'],
'Feb 13 2015', 'Feb 13 2015 00:15')
# We can then use the :meth:`~TimeSeries.coherence_spectrogram` method
# of one `TimeSeries` to calcululate the time-varying coherence with
# respect to the other, using a 0.5-second FFT length, with a
# 0.45-second (90%) overlap, with a 8-second stride:
coh = data['L1:LSC-SRCL_IN1_DQ'].coherence_spectrogram(
data['L1:LSC-CARM_IN1_DQ'], 8, 0.5, 0.45)
# Finally, we can :meth:`~gwpy.spectrogram.Spectrogram.plot` the
# resulting data
plot = coh.plot()
ax = plot.gca()
ax.set_ylabel('Frequency [Hz]')
ax.set_yscale('log')
ax.set_ylim(10, 8000)
(`~gwpy.spectrum.Spectrum`) giving a time-averaged measure of coherence.
The `TimeSeries` method :meth:`~TimeSeries.coherence_spectrogram` performs the
same coherence calculation every ``stride``, giving a time-varying coherence
measure.
"""
__author__ = "Duncan Macleod <*****@*****.**>"
__currentmodule__ = 'gwpy.timeseries'
# First, we import the `TimeSeriesDict`
from gwpy.timeseries import TimeSeriesDict
# and then :meth:`~TimeSeriesDict.get` both data sets:
data = TimeSeriesDict.get(['L1:LSC-SRCL_IN1_DQ', 'L1:LSC-CARM_IN1_DQ'],
'Feb 13 2015', 'Feb 13 2015 00:15')
# We can then use the :meth:`~TimeSeries.coherence_spectrogram` method
# of one `TimeSeries` to calcululate the time-varying coherence with
# respect to the other, using a 0.5-second FFT length, with a
# 0.45-second (90%) overlap, with a 8-second stride:
coh = data['L1:LSC-SRCL_IN1_DQ'].coherence_spectrogram(
data['L1:LSC-CARM_IN1_DQ'], 8, 0.5, 0.45)
# Finally, we can :meth:`~gwpy.spectrogram.Spectrogram.plot` the
# resulting data
plot = coh.plot()
ax = plot.gca()
ax.set_ylabel('Frequency [Hz]')
ax.set_yscale('log')
ax.set_ylim(10, 8000)
# and one for plotting the data:
from gwpy.plotter import TimeSeriesPlot
# Next we define the channels we want, namely the 0.03Hz-1Hz ground motion
# band-limited RMS channels (1-second average trends).
# We do this using string-replacement so we can substitute the interferometer
# prefix easily when we need to:
channels = [
'%s:ISI-BS_ST1_SENSCOR_GND_STS_X_BLRMS_30M_100M.mean,s-trend',
'%s:ISI-BS_ST1_SENSCOR_GND_STS_Y_BLRMS_30M_100M.mean,s-trend',
'%s:ISI-BS_ST1_SENSCOR_GND_STS_Z_BLRMS_30M_100M.mean,s-trend',
]
# At last we can :meth:`~TimeSeriesDict.get` 12 hours of data for each
# interferometer:
lho = TimeSeriesDict.get([c % 'H1' for c in channels], 'Feb 13 2015 16:00',
'Feb 14 2015 04:00')
llo = TimeSeriesDict.get([c % 'L1' for c in channels], 'Feb 13 2015 16:00',
'Feb 14 2015 04:00')
# Next we can plot the data, with a separate `~gwpy.plotter.Axes` for each
# instrument:
plot = TimeSeriesPlot(lho, llo)
ax1, ax2 = plot.axes
for ifo, ax in zip(('Hanford', 'Livingston'), (ax1, ax2)):
ax.legend(['X', 'Y', 'Z'])
ax.set_yscale('log')
ax.text(1.01,
0.5,
ifo,
ha='left',
va='center',
continue
### Get triggers
print("Collecting triggers for labelling")
trig_files = idq.get_all_files_in_range(trigger_dir,
seg[0],
seg[1],
pad=0,
suffix='.trg')
trigger_dict = event.loadkwm(trig_files)
trigger_dict.include([[seg[0], seg[1]]])
if trigger_dict[gwchannel]:
trigger_dict.apply_signif_threshold(threshold=signif_threshold,
channels=[gwchannel])
darmtrg = trigger_dict.get_triggers_from_channel(gwchannel)
auxdata = TimeSeriesDict.get(
channels, seg[0], seg[1], frametype='L1_R',
verbose=True) #Generate a dictionary for each
for key, value in auxdata.iteritems():
#print(value)
value.whiten(fftlength, overlap) #Whiten the data
if value.sample_rate.value == samplerate: #Convert all channels to the same samplingrate
continue
else:
auxdata[key] = auxdata[key].resample(samplerate)
assert auxdata[key].sample_rate.value == 1024.
datapath = '/home/kyle.rose/RNN/runs/%d/version%d/Lockseg%d_%d-%d_Data' % (
run, version, (i + 10), seg[0], seg[1])
if not os.path.exists(os.path.dirname(datapath)):
try:
os.makedirs(os.path.dirname(datapath))
pyplot.ion()
# Before anything else, we import the objects we will need:
from gwpy.time import tconvert
from gwpy.timeseries import TimeSeriesDict
from gwpy.plot import BodePlot
# and set the times of our query, and the channels we want:
start = tconvert('May 27 2014 04:00')
end = start + 1800
gndchannel = 'L1:ISI-GND_STS_ITMY_Z_DQ'
hpichannel = 'L1:HPI-ITMY_BLND_L4C_Z_IN1_DQ'
# We can call the :meth:`~TimeSeriesDict.get` method of the `TimeSeriesDict`
# to retrieve all data in a single operation:
data = TimeSeriesDict.get([gndchannel, hpichannel], start, end, verbose=True)
gnd = data[gndchannel]
hpi = data[hpichannel]
# Next, we can call the :meth:`~TimeSeries.average_fft` method to calculate
# an averages, complex-valued FFT for each `TimeSeries`:
gndfft = gnd.average_fft(100, 50, window='hamming')
hpifft = hpi.average_fft(100, 50, window='hamming')
# Finally, we can divide one by the other to get the transfer function
# (up to the lower Nyquist)
size = min(gndfft.size, hpifft.size)
tf = hpifft[:size] / gndfft[:size]
# The `~gwpy.plot.BodePlot` knows how to separate a complex-valued
# `~gwpy.frequencyseries.FrequencySeries` into magnitude and phase:
from gwpy.timeseries import TimeSeriesDict
data = TimeSeriesDict.get(
[
"H1:ISI-GND_STS_ITMY_Z_BLRMS_30M_100M.rms,s-trend",
"H1:ISI-GND_STS_ETMY_Z_BLRMS_30M_100M.rms,s-trend"
],
"July 22 2021 12:00",
"July 22 2021 14:00",
)
plot = data.plot(ylabel="Ground motion [nm/s]")
plot.show()
# and one for plotting the data:
from gwpy.plot import Plot
# Next we define the channels we want, namely the 0.03Hz-1Hz ground motion
# band-limited RMS channels (1-second average trends).
# We do this using string-replacement so we can substitute the interferometer
# prefix easily when we need to:
channels = [
'{ifo}:ISI-BS_ST1_SENSCOR_GND_STS_X_BLRMS_30M_100M.mean,s-trend',
'{ifo}:ISI-BS_ST1_SENSCOR_GND_STS_Y_BLRMS_30M_100M.mean,s-trend',
'{ifo}:ISI-BS_ST1_SENSCOR_GND_STS_Z_BLRMS_30M_100M.mean,s-trend',
]
# At last we can :meth:`~TimeSeriesDict.get` 12 hours of data for each
# interferometer:
lho = TimeSeriesDict.get([c.format(ifo='H1') for c in channels],
'Feb 13 2015 16:00', 'Feb 14 2015 04:00')
llo = TimeSeriesDict.get([c.format(ifo='L1') for c in channels],
'Feb 13 2015 16:00', 'Feb 14 2015 04:00')
# Next we can plot the data, with a separate `~gwpy.plot.Axes` for each
# instrument:
plot = Plot(lho, llo, figsize=(12, 6), sharex=True, yscale='log')
ax1, ax2 = plot.axes
for ifo, ax in zip(('Hanford', 'Livingston'), (ax1, ax2)):
ax.legend(['X', 'Y', 'Z'])
ax.text(1.01, 0.5, ifo, ha='left', va='center', transform=ax.transAxes,
fontsize=18)
ax1.set_ylabel('$1-3$\,Hz motion [nm/s]', y=-0.1)
ax2.set_ylabel('')
ax1.set_title('Magnitude 7.1 earthquake impact on LIGO')
plot.show()
def timesseriesdict(start, end):
return TimeSeriesDict.get([args.channel1, args.channel2],
start,
end,
verbose=True,
frametype='H1_R')
#grabbing individual data from TimeSeriesDict
c1_b = data[args.channel1]
c2_b = data[args.channel2]
plt_type = 'TimeSeries'
c1_b_name = c1.name
c2_b_name = c2.name
c = c1_b_name.replace("_", "\_")
d = c2_b_name.replace("_", "\_")
plot_2(c1_b, c2_b, EQ_before, EQ_after, c, d, plt_type, args.date_str + ", time(sec)" , "velocity (nm/s)", "total" + plt_type + str(EQ_before) + "\_" + str(EQ_after))
'''
dict_data = []
for start, end in time_segs:
data = TimeSeriesDict.get([args.channel1, args.channel2],
start,
end,
verbose=True,
frametype='H1_R')
#print data
dict_data.append(data)
print dict_data[0][args.channel1]
print dict_data[1][args.channel1]
print time_segs[0][0]
#plotting before, during, after the EQ
#plt_type=ASD, BLRMS, TimeSeries, AVG_FFT
#data = TimeSeriesDict.get([args.channel1, args.channel2], start, end, verbose=True, frametype='H1_R')
#grabbing individual data from TimeSeriesDict
c1_b = dict_data[0][args.channel1]
c2_b = dict_data[0][args.channel2]
c1_d = dict_data[1][args.channel1]
def compute_all(channels,
start,
stop,
history=timedelta(hours=2),
filename=DEFAULT_FILENAME,
**kwargs):
# set up duration (minute-trend data has dt=1min, so reject intervals not on the minute).
duration = (stop - start).total_seconds() / 60
assert (stop - start).total_seconds() / 60 == (stop -
start).total_seconds() // 60
duration = int(duration)
logger.info(
f'Clustering data from {start} to {stop} ({duration} minutes).')
# download data using TimeSeries.get(), including history of point at t0.
logger.debug(
f'Initiating download from {start} to {stop} with history={history}...'
)
dl = TimeSeriesDict.get(channels,
start=to_gps(start - history),
end=to_gps(stop))
logger.info(f'Downloaded from {start} to {stop} with history={history}.')
if exists('input.npy'):
input_data = np.load('input.npy')
logger.info('Loaded input matrix.')
else:
# generate input matrix of the form [sample1;...;sampleN] with sampleK = [feature1,...,featureN]
# for sklearn.cluster algorithms. This is the slow part of the function, so a progress bar is shown.
logger.debug(f'Initiating input matrix generation...')
with Progress('building input', (duration * 60)) as progress:
input_data = stack([
concatenate([
progress(dl[channel].crop,
t,
start=to_gps(start + timedelta(seconds=t) -
history),
end=to_gps(start + timedelta(seconds=t))).value
for channel in channels
]) for t in range(0, int(duration * 60), 60)
])
# verify input matrix dimensions.
assert input_data.shape == (duration,
int(
len(channels) *
history.total_seconds() / 60))
np.save('input.npy', input_data)
logger.info('Completed input matrix generation.')
params = {
'quantile': .3,
'eps': .3,
'damping': .9,
'preference': -200,
'n_neighbors': 10,
'n_clusters': 15,
'min_samples': 20,
'xi': 0.05,
'min_cluster_size': 0.1
}
if exists('X.npy'):
X = np.load('X.npy')
logger.info('Loaded X')
else:
# normalize dataset for easier parameter selection
X = StandardScaler().fit_transform(input_data)
np.save('X.npy', X)
logger.info('Generated X')
if exists('bandwidth.npy'):
bandwidth = np.load('bandwidth.npy')
logger.info('Loaded bandwidth')
else:
# estimate bandwidth for mean shift
bandwidth = cluster.estimate_bandwidth(X, quantile=params['quantile'])
np.save('bandwidth.npy', bandwidth)
logger.info('Generated bandwidth')
if exists('connectivity.npy'):
connectivity = np.load('connectivity.npy', allow_pickle=True)
logger.info('Loaded connectivity')
else:
# connectivity matrix for structured Ward
connectivity = kneighbors_graph(X,
n_neighbors=params['n_neighbors'],
include_self=False)
# make connectivity symmetric
connectivity = 0.5 * (connectivity + connectivity.T)
np.save('connectivity.npy', connectivity)
logger.info('Generated connectivity')
ms = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)
two_means = cluster.MiniBatchKMeans(n_clusters=params['n_clusters'])
ward = cluster.AgglomerativeClustering(n_clusters=params['n_clusters'],
linkage='ward',
connectivity=connectivity)
spectral = cluster.SpectralClustering(n_clusters=params['n_clusters'],
eigen_solver='arpack',
affinity="nearest_neighbors")
dbscan = cluster.DBSCAN(eps=params['eps'])
optics = cluster.OPTICS(min_samples=params['min_samples'],
xi=params['xi'],
min_cluster_size=params['min_cluster_size'])
affinity_propagation = cluster.AffinityPropagation(
damping=params['damping'], preference=params['preference'])
average_linkage = cluster.AgglomerativeClustering(
linkage="average",
affinity="cityblock",
n_clusters=params['n_clusters'],
connectivity=connectivity)
birch = cluster.Birch(n_clusters=params['n_clusters'])
gmm = mixture.GaussianMixture(n_components=params['n_clusters'],
covariance_type='full')
clustering_algorithms = (
('MiniBatchKMeans', two_means),
('AffinityPropagation', affinity_propagation), ('MeanShift', ms),
('SpectralClustering', spectral), ('DBSCAN', dbscan),
('OPTICS', optics), ('Birch', birch), ('GaussianMixture', gmm)
# ('Ward', ward),
# ('AgglomerativeClustering', average_linkage),
)
for name, algorithm in clustering_algorithms:
if exists(f'part-{name}-{filename}'):
labels = TimeSeries.read(f'part-{name}-{filename}',
f'{name}-labels')
logger.debug(f'LOADED {name}.')
else:
logger.debug(f'doing {name}...')
# catch warnings related to kneighbors_graph
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message="the number of connected components of the " +
"connectivity matrix is [0-9]{1,2}" +
" > 1. Completing it to avoid stopping the tree early.",
category=UserWarning)
warnings.filterwarnings(
"ignore",
message="Graph is not fully connected, spectral embedding"
+ " may not work as expected.",
category=UserWarning)
algorithm.fit(X)
if hasattr(algorithm, 'labels_'):
y_pred = algorithm.labels_.astype(np.int)
else:
y_pred = algorithm.predict(X)
# cast the output labels to a TimeSeries so that cropping is easy later on.
labels = TimeSeries(
y_pred,
times=dl[channels[0]].crop(start=to_gps(start),
end=to_gps(stop)).times,
name=f'{name}-labels')
labels.write(f'part-{name}-{filename}')
# put labels in data download dictionary for easy saving.
dl[labels.name] = labels
# write data download and labels to specified filename.
cache_file = abspath(filename)
if exists(cache_file):
remove(cache_file)
dl.write(cache_file)
logger.info(f'Wrote cache to {filename}')
# and one for plotting the data:
from gwpy.plot import Plot
# Next we define the channels we want, namely the 0.03Hz-1Hz ground motion
# band-limited RMS channels (1-second average trends).
# We do this using string-replacement so we can substitute the interferometer
# prefix easily when we need to:
channels = [
'{ifo}:ISI-BS_ST1_SENSCOR_GND_STS_X_BLRMS_30M_100M.mean,s-trend',
'{ifo}:ISI-BS_ST1_SENSCOR_GND_STS_Y_BLRMS_30M_100M.mean,s-trend',
'{ifo}:ISI-BS_ST1_SENSCOR_GND_STS_Z_BLRMS_30M_100M.mean,s-trend',
]
# At last we can :meth:`~TimeSeriesDict.get` 12 hours of data for each
# interferometer:
lho = TimeSeriesDict.get([c.format(ifo='H1') for c in channels],
'Feb 13 2015 16:00', 'Feb 14 2015 04:00')
llo = TimeSeriesDict.get([c.format(ifo='L1') for c in channels],
'Feb 13 2015 16:00', 'Feb 14 2015 04:00')
# Next we can plot the data, with a separate `~gwpy.plot.Axes` for each
# instrument:
plot = Plot(lho, llo, figsize=(12, 6), sharex=True, yscale='log')
ax1, ax2 = plot.axes
for ifo, ax in zip(('Hanford', 'Livingston'), (ax1, ax2)):
ax.legend(['X', 'Y', 'Z'])
ax.text(1.01,
0.5,
ifo,
ha='left',
va='center',
transform=ax.transAxes,
resultName = "./Results/{}_{}.txt".format(IFO,ID)
channels = [
'L1:ISI-GND_STS_ITMY_X_BLRMS_{}.mean,s-trend'.format(blrms_band),
'L1:ISI-GND_STS_ITMY_Y_BLRMS_{}.mean,s-trend'.format(blrms_band),
'L1:ISI-GND_STS_ITMY_Z_BLRMS_{}.mean,s-trend'.format(blrms_band),
'L1:ISI-GND_STS_ETMY_X_BLRMS_{}.mean,s-trend'.format(blrms_band),
'L1:ISI-GND_STS_ETMY_Y_BLRMS_{}.mean,s-trend'.format(blrms_band),
'L1:ISI-GND_STS_ETMY_Z_BLRMS_{}.mean,s-trend'.format(blrms_band),
'L1:ISI-GND_STS_ETMX_X_BLRMS_{}.mean,s-trend'.format(blrms_band),
'L1:ISI-GND_STS_ETMX_Y_BLRMS_{}.mean,s-trend'.format(blrms_band),
'L1:ISI-GND_STS_ETMX_Z_BLRMS_{}.mean,s-trend'.format(blrms_band),
]
print("Fetching data......")
DATA = TimeSeriesDict.get([c for c in channels],
tstart,tstop)
for i in DATA:
LEN = len(DATA[i].value)
dataX = np.array(np.zeros([len(channels), LEN] ))
count = 0
for i in DATA:
if eqBandpass:
W = fftfreq(DATA[i].value.size, d=np.array(DATA[i].dt.value))
f_signal = rfft(np.array(DATA[i].value))
cut_f_signal = f_signal.copy()
cut_f_signal[(W<20.0e-3)] = 0
cut_f_signal[(W>100.0e-3)] = 0
cut_signal = irfft(cut_f_signal)
resultName = "./Results/{}_{}.txt".format(IFO, ID)
channels = [
'L1:ISI-GND_STS_ITMY_X_BLRMS_{}.mean,s-trend'.format(blrms_band),
'L1:ISI-GND_STS_ITMY_Y_BLRMS_{}.mean,s-trend'.format(blrms_band),
'L1:ISI-GND_STS_ITMY_Z_BLRMS_{}.mean,s-trend'.format(blrms_band),
'L1:ISI-GND_STS_ETMY_X_BLRMS_{}.mean,s-trend'.format(blrms_band),
'L1:ISI-GND_STS_ETMY_Y_BLRMS_{}.mean,s-trend'.format(blrms_band),
'L1:ISI-GND_STS_ETMY_Z_BLRMS_{}.mean,s-trend'.format(blrms_band),
'L1:ISI-GND_STS_ETMX_X_BLRMS_{}.mean,s-trend'.format(blrms_band),
'L1:ISI-GND_STS_ETMX_Y_BLRMS_{}.mean,s-trend'.format(blrms_band),
'L1:ISI-GND_STS_ETMX_Z_BLRMS_{}.mean,s-trend'.format(blrms_band),
]
print("Fetching data......")
DATA = TimeSeriesDict.get([c for c in channels], tstart, tstop)
for i in DATA:
LEN = len(DATA[i].value)
dataX = np.array(np.zeros([len(channels), LEN]))
count = 0
for i in DATA:
if eqBandpass:
W = fftfreq(DATA[i].value.size, d=np.array(DATA[i].dt.value))
f_signal = rfft(np.array(DATA[i].value))
cut_f_signal = f_signal.copy()
cut_f_signal[(W < 20.0e-3)] = 0
cut_f_signal[(W > 100.0e-3)] = 0
cut_signal = irfft(cut_f_signal)
of coherence.
The `TimeSeries` method :meth:`~TimeSeries.coherence_spectrogram` performs the
same coherence calculation every ``stride``, giving a time-varying coherence
measure.
"""
__author__ = "Duncan Macleod <*****@*****.**>"
__currentmodule__ = "gwpy.timeseries"
# First, we import the `TimeSeriesDict`
from gwpy.timeseries import TimeSeriesDict
# and then :meth:`~TimeSeriesDict.get` both data sets:
data = TimeSeriesDict.get(["L1:LSC-SRCL_IN1_DQ", "L1:LSC-CARM_IN1_DQ"], "Feb 13 2015", "Feb 13 2015 00:15")
# We can then use the :meth:`~TimeSeries.coherence_spectrogram` method
# of one `TimeSeries` to calcululate the time-varying coherence with
# respect to the other, using a 0.5-second FFT length, with a
# 0.45-second (90%) overlap, with a 8-second stride:
coh = data["L1:LSC-SRCL_IN1_DQ"].coherence_spectrogram(data["L1:LSC-CARM_IN1_DQ"], 8, 0.5, 0.45)
# Finally, we can :meth:`~gwpy.spectrogram.Spectrogram.plot` the
# resulting data
plot = coh.plot()
ax = plot.gca()
ax.set_ylabel("Frequency [Hz]")
ax.set_yscale("log")
ax.set_ylim(10, 8000)
ax.set_title("Coherence between SRCL and CARM for L1")
same coherence calculation every ``stride``, giving a time-varying coherence
measure.
"""
__author__ = "Duncan Macleod <*****@*****.**>"
__currentmodule__ = 'gwpy.timeseries'
# First, we import the `TimeSeriesDict`
from gwpy.timeseries import TimeSeriesDict
# and then :meth:`~TimeSeriesDict.get` the data for the strain output
# (``H1:GDS-CALIB_STRAIN``) and the PSL periscope accelerometer
# (``H1:PEM-CS_ACC_PSL_PERISCOPE_X_DQ``):
data = TimeSeriesDict.get(['H1:GDS-CALIB_STRAIN',
'H1:PEM-CS_ACC_PSL_PERISCOPE_X_DQ'],
1126260017, 1126260617)
hoft = data['H1:GDS-CALIB_STRAIN']
acc = data['H1:PEM-CS_ACC_PSL_PERISCOPE_X_DQ']
# We can then calculate the :meth:`~TimeSeries.coherence` of one
# `TimeSeries` with respect to the other, using an 2-second Fourier
# transform length, with a 1-second (50%) overlap:
coh = hoft.coherence_spectrogram(acc, 10, fftlength=.5, overlap=.25)
# Finally, we can :meth:`~gwpy.spectrogram.Spectrogram.plot` the
# resulting data
plot = coh.plot()
ax = plot.gca()
ax.set_ylabel('Frequency [Hz]')
ax.set_yscale('log')
# and one for plotting the data:
from gwpy.plotter import TimeSeriesPlot
# Next we define the channels we want, namely the 0.03Hz-1Hz ground motion
# band-limited RMS channels (1-second average trends).
# We do this using string-replacement so we can substitute the interferometer
# prefix easily when we need to:
channels = [
'%s:ISI-BS_ST1_SENSCOR_GND_STS_X_BLRMS_30M_100M.mean,s-trend',
'%s:ISI-BS_ST1_SENSCOR_GND_STS_Y_BLRMS_30M_100M.mean,s-trend',
'%s:ISI-BS_ST1_SENSCOR_GND_STS_Z_BLRMS_30M_100M.mean,s-trend',
]
# At last we can :meth:`~TimeSeriesDict.get` 12 hours of data for each
# interferometer:
lho = TimeSeriesDict.get([c % 'H1' for c in channels],
'Feb 13 2015 16:00', 'Feb 14 2015 04:00', verbose=True)
llo = TimeSeriesDict.get([c % 'L1' for c in channels],
'Feb 13 2015 16:00', 'Feb 14 2015 04:00', verbose=True)
# Next we can plot the data, with a separate `~gwpy.plotter.Axes` for each
# instrument:
plot = TimeSeriesPlot(lho, llo)
for ifo, ax in zip(['H1', 'L1'], plot.axes):
ax.legend(['X', 'Y', 'Z'])
ax.yaxis.set_label_position('right')
ax.set_ylabel(ifo, rotation=0, va='center', ha='left')
ax.set_yscale('log')
plot.text(0.1, 0.5, '$1-3$\,Hz motion [nm/s]', rotation=90, fontsize=24,
ha='center', va='center')
plot.axes[0].set_title('Magnitude 7.1 earthquake impact on LIGO', fontsize=24)
plot.show()