gwdetchar.scattering.core module

Utilities for analysing optical scattering

gwdetchar.scattering.core.get_blrms(series, flow=4.0, fhigh=10.0, stride=1, whiten=True, fftlength=4, overlap=2, **kwargs)

Compute the whitened, band-limited RMS of a TimeSeries

Parameters:

seriesTimeSeries

the input TimeSeries data

flowfloat, optional

lower limit (Hz) of the passband, default: 4.0

fhighfloat, optional

upper limit (Hz) of the passband, default: 10.0

stride: `float`, optional

RMS integration length (seconds), default: 1

whitenbool, optional

boolean switch to enable (True) or disable (False) whitening of the input, default: True

fftlengthfloat, optional

FFT integration length (seconds), default: 4

overlapfloat, optional

FFT overlap length (seconds), default: 2

**kwargsdict, optional

additional keyword arguments to TimeSeries.whiten

Returns:

wblrmsTimeSeries

whitened, band-limited RMS trends of the input TimeSeries

See also

gwpy.timeseries.TimeSeries.whiten

for the underlying whitening scheme

gwpy.timeseries.TimeSeries.rms

for the underlying root-mean-square (RMS) estimation method

gwdetchar.scattering.core.get_fringe_frequency(series, multiplier=2.0)

Predict scattering fringe frequency from the derivative of a timeseries

Parameters:

seriesTimeSeries

timeseries record of relative motion

multiplierfloat

harmonic number of fringe frequency

Returns:

fringefTimeSeries

timeseries record of fringe frequency

See also

scipy.signal.savgol_filter

for an implementation of the Savitzky-Golay filter

gwdetchar.scattering.core.get_segments(series, threshold, name=None, pad=0)

Generate data-quality segments by thresholding a TimeSeries

Parameters:

seriesTimeSeries

the input TimeSeries data

thresholdfloat

the threshold value for active data-quality segments

namestr, optional

name of the data-quality flag, defaults to series.name

padfloat, optional

length (seconds) by which to pad active segments, default: 0

Returns:

threshflagDataQualityFlag

the populated data-quality flag