reffft = ref.average_fft(fft, ol, window='hanning')
comfft = com.average_fft(fft, ol, window='hanning')
size = min(reffft.size, comfft.size)
tf = comfft[:size] / reffft[:size]
plot = BodePlot(tf)
plot.maxes.set_title('Transfer function')
#coh = ref.coherence_spectrogram(com,stride,fftlength=fft,overlap=ol)
# The time axis of coherencegram seems buggy in this version. Temporal fix is needed.
#coh.dx = stride
#cohplot=coh.plot(figsize = (12, 8),vmin=0.,vmax=1.)
#ax = cohplot.gca()
ax = plot.gca()
#ax.set_ylabel('Frequency [Hz]')
#ax.set_yscale('log')
#ax.set_title(latexrefchname + ' ' + latexchname)
#if fmin < 0:
# fmin = 0.8/fft
#ax.set_ylim(fmin,fmax)
#cohplot.add_colorbar(cmap='YlGnBu_r',label='Coherence')
fname = outdir + '/' + refchannel + '_' + channel + '_TF_' + gpsstart + '_' + gpsend + '_' + index + '.png'
plot.savefig(fname, dpi=dpi)
plot.clf()
plot.close()
def main(channel, start, end):
data = TimeSeries.read(dumped_gwf_fmt.format(start=start,
end=end,
chname=channel),
channel,
verbose=True,
nproc=8)
# Make Filter
dnumden_120qa = trillium.tf_120qa(analog=True,
sample_rate=2048 / 2,
Hz=False,
normalize=False)
#
filters = [dnumden_120qa]
#plot_dfilt(*dnumden_120qa)
# Plot Bodeplot
plot = BodePlot(*filters,
frequencies=numpy.logspace(-3, 3, 1e5),
dB=False,
sample_rate=2048 / 2,
unwrap=False,
analog=True,
title='filter')
axes = plot.get_axes()
labels = ['Trillium120QA', 'TrilliumCompact']
for i, ax in enumerate(axes):
ax.legend(labels, loc='lower left')
#axes[0].set_yscale('log')
axes[0].set_ylim(1e-3, 1e8)
#axes[0].set_ylim(-40,2)
#axes[-1].set_xlim(5e-3,3e0)
axes[-1].set_ylim(-200, 200)
plot.savefig('Bodeplot_Trillium120QA.png')
plot.close()
exit()
# Filtering
data_calib = data.filter(zpk_trillium120qa, filtfilt=True, analog=False)
# crop
data_calib = data_low.crop(*data_calib.span.contract(1))
# Plot TimeSeries
from gwpy.plot import Plot
data_set = [data_calib]
plot = Plot(*data_set,
separate=True,
sharex=True,
sharey=True,
color='gwpy:ligo-livingston',
figsize=[10, 10])
# Add text, and save figure
title = channel[3:].replace('_', ' ')
labels = [
'No filt', 'High (300mHz-)', 'Mid (50mHz-300mHz)', 'Low (-50mHz)'
]
if data.unit == ' ':
yaxis_label = 'Count'
else:
yaxis_label = data.unit
axes = plot.get_axes()
for i, ax in enumerate(axes):
ax.legend([labels[i]], loc='upper left')
plot.text(0.04,
0.5,
yaxis_label,
va='center',
rotation='vertical',
fontsize=18)
axes[0].set_title(title, fontsize=16)
axes[-1].set_xscale('Hours', epoch=start)
plot.savefig(timeseriesplot_fname_fmt.format(channel=channel))
plot.close()
# Plot ASD
fftlen = 2**7
specgram = data.spectrogram2(fftlength=fftlen, overlap=2,
window='hanning')**(1 / 2.)
median = specgram.percentile(50)
low = specgram.percentile(5)
high = specgram.percentile(95)
plot = Plot()
ylabel_fmt = r'{yaxis_label} [{yaxis_label}/\rtHz]'
ax = plot.gca(
xscale='log',
xlim=(1e-3, 10),
xlabel='Frequency [Hz]',
yscale='log', #ylim=(3e-24, 2e-20),
ylabel=ylabel_fmt.format(yaxis_label=yaxis_label))
ax.plot_mmm(median, low, high, color='gwpy:ligo-livingston')
ax.set_title(title, fontsize=16)
plot.savefig(asdplot_fname_fmt.format(channel=channel))
plot.close()
# Plot Spectrogram
specgram = data.spectrogram(fftlen * 2, fftlength=fftlen,
overlap=.5)**(1 / 2.)
plot = specgram.imshow(norm='log')
ax = plot.gca()
ax.set_yscale('log')
ax.set_ylim(1e-3, 10)
ax.set_title(title, fontsize=16)
ax.colorbar(label=ylabel_fmt.format(yaxis_label=yaxis_label))
plot.savefig(spectrogramplot_fname_fmt.format(channel=channel))