# 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:
plot = BodePlot(tf)
plot.maxes.set_title(
r'L1 ITMY ground $\rightarrow$ HPI transfer function')
plot.maxes.set_ylim(-55, 50)
plot.show()
#if fft < com.dt.value:
# fft=2*com.dt.value
# ol=fft/2. # overlap in FFTs.
# stride=2*fft
# print("Given fft/stride was bad against the sampling rate. Automatically set to:")
# print("fft="+str(fft))
# print("ol="+str(ol))
# print("stride="+str(stride))
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
# To create a low-pass filter at 1000 Hz for 4096 Hz-sampled data: from gwpy.signal.filter_design import notch n = notch(100, 4096) # To view the filter, you can use the `~gwpy.plot.BodePlot`: from gwpy.plot import BodePlot plot = BodePlot(n, sample_rate=4096) plot.show()
def main(start, end, chname):
data = TimeSeries.read(dumped_gwf_fmt.format(start=start,
end=end,
chname=channel),
channel,
verbose=True,
nproc=8)
# Filtering
from gwpy.signal import filter_design
from gwpy.plot import BodePlot
import numpy
bp_high = filter_design.highpass(
0.3,
data.sample_rate,
analog=True,
ftype='butter',
gpass=2,
gstop=30 #,fstop()
)
bp_mid = filter_design.bandpass(
0.05,
0.3,
data.sample_rate,
analog=True,
ftype='butter',
gpass=2,
gstop=30 #,fstop=(0.01,0.5)
)
bp_low = filter_design.lowpass(
0.05,
data.sample_rate,
analog=True,
ftype='butter',
gpass=2,
gstop=30 #, fstop=2
)
filters = [bp_high, bp_mid, bp_low]
plot = BodePlot(*filters,
analog=True,
frequencies=numpy.logspace(-3, 1, 1e5),
dB=False,
unwrap=False,
title='filter')
axes = plot.get_axes()
axes[0].set_yscale('log')
axes[0].set_ylim(1e-4, 2e0)
axes[-1].set_xlim(1e-2, 1e0)
axes[-1].set_ylim(-180, 180)
plot.savefig('Bodeplot_BandPass.png')
plot.close()
data_high = data.filter(bp_high, filtfilt=True)
data_high = data_high.crop(*data_high.span.contract(1))
data_mid = data.filter(bp_mid, filtfilt=True)
data_mid = data_mid.crop(*data_mid.span.contract(1))
data_low = data.filter(bp_low, filtfilt=True)
data_low = data_low.crop(*data_low.span.contract(1))
# Plot TimeSeries
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
from gwpy.plot import Plot
data_set = [data, data_high, data_mid, data_low]
plot = Plot(*data_set,
separate=True,
sharex=True,
sharey=True,
color='gwpy:ligo-livingston',
figsize=[10, 10])
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=16)
#plot.text(0.5, 0.93, title, va='center',ha='center',rotation='horizontal',fontsize=16)
axes[0].set_title(title, fontsize=16)
axes[-1].set_xscale('Hours', epoch=start)
plot.savefig(timeseriesplot_fname_fmt.format(channel=channel))
plot.close()
# To create a band-pass filter for 100-1000 Hz for 4096 Hz-sampled data: from gwpy.signal.filter_design import bandpass bp = bandpass(100, 1000, 4096) # To view the filter, you can use the `~gwpy.plot.BodePlot`: from gwpy.plot import BodePlot plot = BodePlot(bp, sample_rate=4096) plot.show()
from gwpy.signal.filter_design import bandpass from gwpy.plot import BodePlot zpk = bandpass(40, 1000, 4096, analog=True) plot = BodePlot(zpk, analog=True, title='40-1000\,Hz bandpass filter') plot.show()
def main(channel,start,end):
data = TimeSeries.read(
dumped_gwf_fmt.format(start=start,end=end,chname=channel),
channel, verbose=True ,nproc=8)
# Filter
zpk_bp_high = filter_design.highpass(0.3, data.sample_rate,
ftype='butter',analog=False,
gpass=2,gstop=40,fstop=0.03
)
zpk_bp_mid = filter_design.bandpass(0.03, 0.3, data.sample_rate,
ftype='butter',analog=False,
gpass=2, gstop=40,fstop=(0.003,3)
)
zpk_bp_low = filter_design.lowpass(0.03, data.sample_rate,
ftype='butter',analog=False,
gpass=2, gstop=40, fstop=0.3
)
filters = [zpk_bp_high, zpk_bp_mid, zpk_bp_low]
# Plot Bodeplot
plot = BodePlot(*filters,
frequencies=numpy.logspace(-4,1,1e5),
dB=True,sample_rate=data.sample_rate,
unwrap=False,analog=False,
title='filter')
axes = plot.get_axes()
labels = ['High (300mHz-)','Mid (50mHz-300mHz)','Low (-50mHz)']
for i,ax in enumerate(axes):
ax.legend(labels,loc='lower left')
#axes[0].set_yscale('log')
#axes[0].set_ylim(1e-3,2e0)
axes[0].set_ylim(-40,2)
axes[-1].set_xlim(5e-3,3e0)
axes[-1].set_ylim(-200,200)
plot.savefig('Bodeplot_BandPass.png')
plot.close()
# Filtering
data_high = data.filter(zpk_bp_high, filtfilt=True,analog=False)
data_mid = data.filter(zpk_bp_mid, filtfilt=True,analog=False)
data_low = data.filter(zpk_bp_low, filtfilt=True,analog=False)
#
data_high = data_high.crop(*data_high.span.contract(1))
data_mid = data_mid.crop(*data_mid.span.contract(1))
data_low = data_low.crop(*data_low.span.contract(1))
# Plot TimeSeries
from gwpy.plot import Plot
data_set = [data,data_high, data_mid, data_low]
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()
from gwpy.signal.filter_design import bandpass
from gwpy.plot import BodePlot
zpk = bandpass(40, 1000, 4096, analog=False)
plot = BodePlot(zpk,
analog=False,
sample_rate=4096,
title='40-1000 Hz bandpass filter')
plot.show()
chunk_start += averaging_time
chunk_end += averaging_time
N += 1
hoft_tf_data = hoft_tf_data / N
hoft_tf = FrequencySeries(hoft_tf_data, f0=f0, df=df)
if options.analyze_calcs_hoft:
CALCS_tf_data = CALCS_tf_data / N
CALCS_tf = FrequencySeries(CALCS_tf_data, f0=f0, df=df)
if options.analyze_additional_hoft:
additional_hoft_tf_data = additional_hoft_tf_data / N
additional_hoft_tf = FrequencySeries(additional_hoft_tf_data, f0=f0, df=df)
# Make plot that compares all of the derived response functions and the model response
plot = BodePlot(response_fs, frequencies=freqs, dB = False, linewidth=2)
plot.add_frequencyseries(hoft_tf*3995.1, dB = False, color='#ee0000',linewidth=2)
if options.analyze_calcs_hoft:
plot.add_frequencyseries(CALCS_tf, dB=False, color='#4ba6ff', linewidth=2)
if options.analyze_additional_hoft:
plot.add_frequencyseries(additional_hoft_tf*3995.1, dB = False, color="#94ded7", linewidth=2)
plot.legend([r'Reference model response function', r'GDS h(t) derived response function', r'CALCS h(t) derived response function', r'DCS h(t) derived response function'], loc='upper right', fontsize='x-small')
# FIXME: Figure out how to make the legend and title appropriate and flexible
plot.maxes.set_yscale('log')
plot.paxes.set_yscale("linear")
plot.save('%s_%s_%s_all_tf.png' % (ifo, options.gps_start_time, options.gps_end_time))
# Make a plot that compares the ratios of each derived response to the model
ratio_hoft = hoft_tf / response_fs
if options.analyze_calcs_hoft:
ratio_CALCS = CALCS_tf / response_fs
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))