def test_save_figure(tmpdir):
base = str(tmpdir)
series = TimeSeries(numpy.random.normal(loc=0, scale=1, size=24*60),
sample_rate=60, unit='Mpc', name='X1:TEST')
fig = series.plot()
tsplot = plot.save_figure(fig, os.path.join(base, 'test.png'))
assert tsplot == os.path.join(base, 'test.png')
# no-directory should raise warning
with pytest.warns(UserWarning) as record:
noneplot = plot.save_figure(fig, os.path.join('tgpflk', 'test.png'))
assert noneplot is None
assert len(record.list) == 1
assert 'Error saving' in str(record.list[0].message)
# remove base directory
shutil.rmtree(base, ignore_errors=True)
h1spec = h1.spectrogram(30, fftlength=4)
l1spec = l1.spectrogram(30, fftlength=4)
# To calculate the inspiral range variation, we need to create a
# :class:`~gwpy.timeseries.TimeSeries` in which to store the values, then
# loop over each PSD bin in the spectrogram, calculating the
# :func:`gwpy.astro.inspiral_range` for each one:
import numpy
from gwpy.astro import inspiral_range
h1range = TimeSeries(numpy.zeros(len(h1spec)),
dt=h1spec.dt, t0=h1spec.t0, unit='Mpc')
l1range = h1range.copy()
for i in range(h1range.size):
h1range[i] = inspiral_range(h1spec[i], fmin=10)
l1range[i] = inspiral_range(l1spec[i], fmin=10)
# We can now easily plot the timeseries to see the variation in LIGO
# sensitivity over the hour or so including GW150914:
plot = h1range.plot(label='LIGO-Hanford', color='gwpy:ligo-hanford',
figsize=(12, 5))
ax = plot.gca()
ax.plot(l1range, label='LIGO-Livingston', color='gwpy:ligo-livingston')
ax.set_ylabel('Angle-averaged sensitive distance [Mpc]')
ax.set_title('LIGO sensitivity to BNS around GW150914')
ax.set_epoch(1126259462) # <- set 0 on plot to GW150914
ax.legend()
plot.show()
I would like to study the gravitational wave strain time-series around the time of an interesting simulated signal during the last science run (S6).
These data are public, so we can load them directly from the web.
"""
__author__ = "Duncan Macleod <*****@*****.**>"
__currentmodule__ = 'gwpy.timeseries'
# First: import everything we need (and nothing we don't need)
from urllib2 import urlopen
from numpy import asarray
from gwpy.timeseries import TimeSeries
# Next, download the data as a string of text
data = urlopen(
'http://www.ligo.org/science/GW100916/L-strain_hp30-968654552-10.txt'
).read()
# We can now parse the text as a list of floats, and generate a `TimeSeries`
# by supplying the necessary metadata
ts = TimeSeries(asarray(data.splitlines(), dtype=float),
epoch=968654552,
sample_rate=16384,
unit='strain')
# Finally, we can make a plot:
plot = ts.plot()
plot.set_title('LIGO Livingston Observatory data for GW100916')
plot.set_ylabel('Gravitational-wave strain amplitude')
plot.show()
from urllib2 import urlopen
from numpy import asarray
from gwpy.timeseries import TimeSeries
data = urlopen('http://www.ligo.org/science/GW100916/'
'L-strain_hp30-968654552-10.txt').read()
ts = TimeSeries(asarray(data.splitlines(), dtype=float),
epoch=968654552, sample_rate=16384)
plot = ts.plot()
plot.set_ylabel('Gravitational-wave strain amplitude')
plot.set_title('LIGO Livingston Observatory data for GW100916')
plot.show()
l1spec = l1.spectrogram(30, fftlength=4)
# To calculate the inspiral range variation, we need to create a
# :class:`~gwpy.timeseries.TimeSeries` in which to store the values, then
# loop over each PSD bin in the spectrogram, calculating the
# :func:`gwpy.astro.inspiral_range` for each one:
import numpy
from gwpy.astro import inspiral_range
h1range = TimeSeries(numpy.zeros(len(h1spec)),
dt=h1spec.dt,
t0=h1spec.t0,
unit='Mpc')
l1range = h1range.copy()
for i in range(h1range.size):
h1range[i] = inspiral_range(h1spec[i], fmin=10)
l1range[i] = inspiral_range(l1spec[i], fmin=10)
# We can now easily plot the timeseries to see the variation in LIGO
# sensitivity over the hour or so including GW150914:
from gwpy.plotter.colors import GW_OBSERVATORY_COLORS as GWO_COLORS
plot = h1range.plot(label='LIGO-Hanford', color=GWO_COLORS['H1'])
ax = plot.gca()
ax.plot(l1range, label='LIGO-Livingston', color=GWO_COLORS['L1'])
ax.set_ylabel('Angle-averaged sensitive distance [Mpc]')
ax.set_title('LIGO sensitivity to BNS around GW150914')
ax.set_epoch(1126259462) # <- set 0 on plot to GW150914
ax.legend()
plot.show()
from gwpy.timeseries import TimeSeries # To create an array of random numbers, sampled at 100 Hz, in units of # 'metres': from numpy import random series = TimeSeries(random.random(1000), sample_rate=100, unit='m') # which can then be simply visualised via plot = series.plot() plot.show()
h1spec = h1.spectrogram(30, fftlength=4)
l1spec = l1.spectrogram(30, fftlength=4)
# To calculate the inspiral range variation, we need to create a
# :class:`~gwpy.timeseries.TimeSeries` in which to store the values, then
# loop over each PSD bin in the spectrogram, calculating the
# :func:`gwpy.astro.inspiral_range` for each one:
import numpy
from gwpy.astro import inspiral_range
h1range = TimeSeries(numpy.zeros(len(h1spec)),
dt=h1spec.dt, t0=h1spec.t0, unit='Mpc')
l1range = h1range.copy()
for i in range(h1range.size):
h1range[i] = inspiral_range(h1spec[i], fmin=10)
l1range[i] = inspiral_range(l1spec[i], fmin=10)
# We can now easily plot the timeseries to see the variation in LIGO
# sensitivity over the hour or so including GW150914:
from gwpy.plotter.colors import GW_OBSERVATORY_COLORS as GWO_COLORS
plot = h1range.plot(label='LIGO-Hanford', color=GWO_COLORS['H1'])
ax = plot.gca()
ax.plot(l1range, label='LIGO-Livingston', color=GWO_COLORS['L1'])
ax.set_ylabel('Angle-averaged sensitive distance [Mpc]')
ax.set_title('LIGO sensitivity to BNS around GW150914')
ax.set_epoch(1126259462) # <- set 0 on plot to GW150914
ax.legend()
plot.show()
list(map("%s:%s".__mod__, front_end_channel_list),
start=start,
end=end))
if plot_additional_hoft:
additional_hoft_data = TimeSeriesDict.read(
options.additional_hoft_frames_cache,
list(map("%s:%s".__mod__, additional_channel_list)),
start=start,
end=end)
print(map("%s:%s".__mod__, front_end_channel_list))
segs = DataQualityFlag.query('%s:DMT-CALIBRATED:1' % ifo, start, end)
for n, channel in enumerate(channels):
plot = TimeSeries.plot(data["%s:%s" % (ifo, channel)])
ax = plot.gca()
if plot_front_end:
ax.plot(front_end_data["%s:%s" % (ifo, front_end_channels[n])])
if plot_additional_hoft:
ax.plot(additional_hoft_data["%s:%s" % (ifo, additional_channels[n])])
ax.set_ylabel('Correction value')
plot.gca().legend()
#title = item
#title = title.replace('_', '\_')
ax.set_title(channel.replace('_', '\_'))
if 'F_S_SQUARED' in channel:
ax.set_ylim(-100, 100)
elif 'SRC_Q_INVERSE' in channel:
ax.set_ylim(-2, 2)
#plot.add_state_segments(segs, plotargs=dict(label='Calibrated'))
