2. Filtering a TimeSeries with a ZPK filter¶

Several data streams read from the LIGO detectors are whitened before being recorded to prevent numerical errors when using single-precision data storage. In this example we read such channel and undo the whitening to show the physical content of these data.

First, we import the TimeSeries and get() the data:

from gwpy.timeseries import TimeSeries
white = TimeSeries.get(
    'L1:OAF-CAL_DARM_DQ', 'March 2 2015 12:00', 'March 2 2015 12:30')

Now, we can re-calibrate these data into displacement units by first applying a highpass filter to remove the low-frequency noise, and then applying our de-whitening filter in ZPK format with five zeros at 100 Hz and five poles at 1 Hz (giving an overall DC gain of 10 ^-10:

hp = white.highpass(4)
displacement = hp.zpk([100]*5, [1]*5, 1e-10)

We can visualise the impact of the whitening by calculating the ASD FrequencySeries before and after the filter,

whiteasd = white.asd(8, 4)
dispasd = displacement.asd(8, 4)

and plotting:

from gwpy.plot import Plot
plot = Plot(whiteasd, dispasd, separate=True, sharex=True,
            xscale='log', yscale='log')

Here we have passed the two spectra in order, then separate=True to display them on separate Axes, sharex=True to tie the XAxis of each of the Axes together.

Finally, we prettify our plot with some limits, and some labels:

plot.axes[0].set_ylabel('ASD [whitened]')
plot.axes[1].set_ylabel(r'ASD [m/$\sqrt{\mathrm{Hz}}$]')
plot.axes[1].set_xlabel('Frequency [Hz]')
plot.axes[1].set_ylim(1e-20, 1e-15)
plot.axes[1].set_xlim(5, 4000)
plot.show()

(png)