from gwpy.plot import Plot
plot = Plot(figsize=(12, 6))
ax = plot.gca()
for specgram in spectrograms:
ax.imshow(specgram)
ax.set_xscale('auto-gps', epoch='Sep 16 2010')
ax.set_xlim('Sep 16 2010', 'Sep 17 2010')
ax.set_ylim(40, 2000)
ax.set_yscale('log')
ax.set_ylabel('Frequency [Hz]')
ax.set_title('LIGO-Hanford strain data')
ax.colorbar(cmap='viridis', norm='log', clim=(1e-23, 1e-19),
label=r'Strain noise [1/\rtHz]')
plot.add_segments_bar(h1segs)
plot.show()
# Now, we can loop through the active segments of ``'H1_DATA'`` and fetch the
# strain `TimeSeries` for each segment, calculating a
# :class:`~gwpy.spectrogram.Spectrogram` for each segment.
from gwpy.timeseries import TimeSeries
spectrograms = []
for start, end in h1segs.active:
h1strain = TimeSeries.fetch_open_data('H1', start, end, verbose=True)
specgram = h1strain.spectrogram(30, fftlength=4)**(1 / 2.)
spectrograms.append(specgram)
# Finally, we can build a :meth:`~gwpy.spectrogram.Spectrogram.plot`:
from gwpy.plot import Plot
plot = Plot(figsize=(12, 6))
ax = plot.gca()
for specgram in spectrograms:
ax.imshow(specgram)
ax.set_xscale('auto-gps', epoch='Sep 16 2010')
ax.set_xlim('Sep 16 2010', 'Sep 17 2010')
ax.set_ylim(40, 2000)
ax.set_yscale('log')
ax.set_ylabel('Frequency [Hz]')
ax.set_title('LIGO-Hanford strain data')
ax.colorbar(cmap='viridis',
norm='log',
clim=(1e-23, 1e-19),
label=r'Strain noise [1/\rtHz]')
plot.add_segments_bar(h1segs)
plot.show()
def cluster_plotter(channels,
start,
stop,
prefix='.',
label='kmeans-labels',
groups=None,
filename=DEFAULT_FILENAME,
dqflag='L1:DMT-ANALYSIS_READY:1',
xscale=None,
unit=None,
progressbar=True,
**kwargs):
"""
Plots data with clusters labeled by color in the working directory, or a relative path given by prefix.
Requires a .hdf5 file produced with a clustering function defined in this module to be in the working directory.
**kwargs are forwarded to TimeSeries.plot().
:param prefix: relative path to output images.
:param label: name attribute of labels TimeSeries saved in filename.
:param groups: groups of channels to plot in the same figure. See the example.
:param dqflag: data quality flag for segments bar.
:param xscale: gps x-axis scale to use.
:param unit: override y-axis unit.
:param progressbar: show progress bar.
>>> from gwpy.time import tconvert, from_gps
>>> from datetime import timedelta
>>> from cluster import cluster_plotter
>>>
>>> channels = [f'L1:ISI-GND_STS_ETMX_Z_BLRMS_1_3.mean,m-trend', 'L1:ISI-GND_STS_ETMY_Z_BLRMS_1_3.mean,m-trend']
>>> groups = [[channels, ('ETMX', 'ETMY'), 'L1:ISI-GND_STS_BLRMS_1_3 Z-axis']] # plot on the same figure.
>>>
>>> stop = from_gps(60 * (int(tconvert('now')) // 60)) # gets nearest minute to now
>>> start = stop - timedelta(days=1) # cluster the past day
>>> cluster_plotter(channels, start, stop, filename='my_kmeans.hdf5', groups=groups)
"""
# some defaults.
if not kwargs:
kwargs['color'] = 'k'
kwargs['alpha'] = 0.3
if groups is None:
groups = channels
# read the data from the save file.
data = TimeSeriesDict.read(filename,
channels + [label],
start=to_gps(start),
end=to_gps(stop))
logger.info(f'Read {start} to {stop} from {filename}')
# get segments for the duration specified. Note that this may require doing `ligo-proxy-init -p`.
logger.debug(f'Getting segments for {dqflag} from {start} to {stop}...')
dq = DataQualityFlag.query(dqflag, to_gps(start), to_gps(stop))
logger.info(f'Got segments for {dqflag} from {start} to {stop}.')
# plotting is slow, so show a nice progress bar.
logger.debug('Initiating plotting routine...')
with Progress('plotting', len(channels),
quiet=not progressbar) as progress:
for p, (group, labels, title) in enumerate(groups):
# plot the group in one figure.
plt = Plot(*(data[channel] for channel in group),
separate=True,
sharex=True,
zorder=1,
**kwargs)
# modify the axes one by one.
axes = plt.get_axes()
for i, ax in enumerate(axes):
# namely, add a colored overlay that indicates clustering labels.
ax.scatter(data[group[i]].times,
data[group[i]].value,
c=[colors[j] for j in data[label]],
edgecolor='',
s=4,
zorder=2)
ax.set_ylabel(
f'{labels[i]} {data[group[i]].unit if unit is None else unit}'
)
setp(ax.get_xticklabels(), visible=False)
# modify the figure as a whole.
plt.add_segments_bar(dq, label='')
if xscale is not None:
plt.gca().set_xscale(xscale)
plt.suptitle(title)
# save to png.
progress(plt.save, p, get_path(title, 'png', prefix=prefix))
logger.info(f'Completed plotting for {start} to {stop} from {filename}')
