def test_highpass(self):
iir = filter_design.highpass(100, 1024)
utils.assert_zpk_equal(iir, HIGHPASS_IIR_100HZ)
fir = filter_design.highpass(100, 1024, type='fir')
utils.assert_allclose(fir, HIGHPASS_FIR_100HZ)
# To create a high-pass filter at 100 Hz for 4096 Hz-sampled data: from gwpy.signal.filter_design import highpass hp = highpass(100, 4096) # To view the filter, you can use the `~gwpy.plotter.BodePlot`: from gwpy.plotter import BodePlot plot = BodePlot(hp, 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()
def test_highpass(self):
iir = filter_design.highpass(100, 1024)
utils.assert_zpk_equal(iir, HIGHPASS_IIR_100HZ)
fir = filter_design.highpass(100, 1024, type='fir')
utils.assert_allclose(fir, HIGHPASS_FIR_100HZ)
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()