<jupyter_start><jupyter_text>----------L1---------------____________________+++++++++++=======<jupyter_code>from gwpy.timeseries import TimeSeries
ldata = TimeSeries.fetch_open_data('L1', *segment, verbose=True)
print(ldata)
%matplotlib inline
plot = ldata.plot()
fft = ldata.fft()
print(fft)
from scipy.signal import get_window
window = get_window('hann', ldata.size)
lwin = ldata * window
fftamp = lwin.fft().abs()
plot = fftamp.plot(xscale="log", yscale="log")
plot.show(warn=False)
asd = ldata.asd(fftlength=4, method="median")
plot = asd.plot()
plot.show(warn=False)
ax = plot.gca()
ax.set_xlim(10, 1400)
ax.set_ylim(1e-25, 1e-19)
plot
ldata2 = TimeSeries.fetch_open_data('L1', int(gps)-512, int(gps)+512, verbose=True)
lasd2 = ldata2.asd(fftlength=4, method="median")
plot = lasd2.plot()
ax = plot.gca()
ax.set_xlim(10, 1400)
ax.set_ylim(1e-25, 1e-19)
plot.show(warn=False)
# get Hanford data
hdata2 = TimeSeries.fetch_open_data('H1', int(gps)-512, int(gps)+512, verbose=True)
hasd2 = hdata2.asd(fftlength=4, method="median")

# get Virgo data
vdata2 = TimeSeries.fetch_open_data('V1', int(gps)-512, int(gps)+512, verbose=True)
vasd2 = vdata2.asd(fftlength=4, method="median")

# and plot using standard colours
ax.plot(hasd2, label='LIGO-Hanford', color='gwpy:ligo-hanford')
ax.plot(vasd2, label='Virgo', color='gwpy:virgo')

# update the Livingston line to use standard colour, and have a label
lline = ax.lines[0]
lline.set_color('gwpy:ligo-livingston')  # change colour of Livingston data
lline.set_label('LIGO-Livingston')

ax.set_ylabel(r'Strain noise [$1/\sqrt{\mathrm{Hz}}$]')
ax.legend()
plot<jupyter_output>Fetched 1 URLs from www.gw-openscience.org for [1239081750 .. 1239082774))
Reading data... [Done]
Fetched 1 URLs from www.gw-openscience.org for [1239081750 .. 1239082774))
Reading data... [Done]<jupyter_text>V1 ________________---------------------------------------------------------------<jupyter_code>from gwpy.timeseries import TimeSeries
vdata = TimeSeries.fetch_open_data('V1', *segment, verbose=True)
print(vdata)
%matplotlib inline
plot = vdata.plot()<jupyter_output><empty_output><jupyter_text>---------------------H1---------------------================================<jupyter_code>from gwpy.timeseries import TimeSeries
hdata = TimeSeries.fetch_open_data('H1', *segment, verbose=True)
print(hdata)
from scipy.signal import get_window
window = get_window('hann', hdata.size)
hwin = hdata * window
fftamp = lwin.fft().abs()
plot = fftamp.plot(xscale="log", yscale="log")
plot.show(warn=False)
%matplotlib inline
plot = hdata.plot()
from gwpy.timeseries import TimeSeries
from gwosc.datasets import event_gps
import pylab as plt

gps = event_gps('GW190412')
ldata = TimeSeries.fetch_open_data('L1', int(gps)-512, int(gps)+512, cache=True)
lasd = ldata.asd(fftlength=4, method="median")

gps2 = event_gps('GW150914')
ldata2 = TimeSeries.fetch_open_data('L1', int(gps2)-512, int(gps2)+512, cache=True)
lasd2 = ldata2.asd(fftlength=4, method="median")

plt.loglog(lasd, label = 'GW190412')
plt.loglog(lasd2,label = 'GW150914')
plt.legend()
plt.xlim(10,2000)<jupyter_output><empty_output>
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