Signal processing¶

Fourier methods¶

The TimeSeries object comes with a number of Fourier methods to calculate a FrequencySeries or Spectrogram by calculating and averaging FFTs.

See FFT routines for GWpy for more details.

Time-domain filtering¶

The TimeSeries object comes with a number of instance methods that should make filtering data trivial for a number of common use cases. Available methods include:

Each of the above methods eventually calls out to TimeSeries.filter() to apply a digital linear filter, normally via cascaded second-order-sections (requires scipy >= 0.16).

For a worked example of how to filter LIGO data to discover a gravitational-wave signal, see the example Filtering a TimeSeries to detect gravitational waves.

Frequency-domain filtering¶

Additionally, the TimeSeries object includes a number of instance methods to generate frequency-domain information for some data. Available methods include:

For a worked example of how to load data and calculate the Amplitude Spectral Density FrequencySeries, see the example Calculating and plotting a FrequencySeries.

Filter design¶

The gwpy.signal provides a number of filter design methods which, when combined with the BodePlot visualisation, can be used to create a number of common filters:

Each of these will return filter coefficients that can be passed directly into zpk (default for analogue filters) or filter (default for digital filters).

For a worked example of how to filter LIGO data to discover a gravitational-wave signal, see the example Filtering a TimeSeries to detect gravitational waves.

Cross-channel correlations:

For a worked example of how to compare channels like this, see the example Calculating the coherence between two channels.

Reference/API¶

filter_design.bandpass(flow, fhigh, sample_rate, fstop=None, gpass=2, gstop=30, type='iir', **kwargs)[source]¶

Design a band-pass filter for the given cutoff frequencies

Parameters:

flow : float lower corner frequency of pass band fhigh : float upper corner frequency of pass band sample_rate : float sampling rate of target data fstop : tuple of float, optional (low, high) edge-frequencies of stop band gpass : float, optional, default: 2 the maximum loss in the passband (dB) gstop : float, optional, default: 30 the minimum attenuation in the stopband (dB) type : str, optional, default: 'iir' the filter type, either 'iir' or 'fir' **kwargs other keyword arguments are passed directly to iirdesign() or firwin()

Returns:

filter the formatted filter. the output format for an IIR filter depends on the input arguments, default is a tuple of (zeros, poles, gain)

Notes

By default a digital filter is returned, meaning the zeros and poles are given in the Z-domain in units of radians/sample.

Examples

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 BodePlot:

>>> from gwpy.plotter import BodePlot
>>> plot = BodePlot(bp, sample_rate=4096)
>>> plot.show()

(png)

filter_design.lowpass(frequency, sample_rate, fstop=None, gpass=2, gstop=30, type='iir', **kwargs)[source]¶

Design a low-pass filter for the given cutoff frequency

Parameters:

frequency : float corner frequency of low-pass filter (Hertz) sample_rate : float sampling rate of target data (Hertz) fstop : float, optional edge-frequency of stop-band (Hertz) gpass : float, optional, default: 2 the maximum loss in the passband (dB) gstop : float, optional, default: 30 the minimum attenuation in the stopband (dB) type : str, optional, default: 'iir' the filter type, either 'iir' or 'fir' **kwargs other keyword arguments are passed directly to iirdesign() or firwin()

Returns:

filter the formatted filter. the output format for an IIR filter depends on the input arguments, default is a tuple of (zeros, poles, gain)

Notes

By default a digital filter is returned, meaning the zeros and poles are given in the Z-domain in units of radians/sample.

Examples

To create a low-pass filter at 1000 Hz for 4096 Hz-sampled data:

>>> from gwpy.signal.filter_design import lowpass
>>> lp = lowpass(1000, 4096)

To view the filter, you can use the BodePlot:

>>> from gwpy.plotter import BodePlot
>>> plot = BodePlot(lp, sample_rate=4096)
>>> plot.show()

(png)

filter_design.highpass(frequency, sample_rate, fstop=None, gpass=2, gstop=30, type='iir', **kwargs)[source]¶

Design a high-pass filter for the given cutoff frequency

Parameters:

frequency : float corner frequency of high-pass filter sample_rate : float sampling rate of target data fstop : float, optional edge-frequency of stop-band gpass : float, optional, default: 2 the maximum loss in the passband (dB) gstop : float, optional, default: 30 the minimum attenuation in the stopband (dB) type : str, optional, default: 'iir' the filter type, either 'iir' or 'fir' **kwargs other keyword arguments are passed directly to iirdesign() or firwin()

Returns:

filter the formatted filter. the output format for an IIR filter depends on the input arguments, default is a tuple of (zeros, poles, gain)

Notes

By default a digital filter is returned, meaning the zeros and poles are given in the Z-domain in units of radians/sample.

Examples

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 BodePlot:

>>> from gwpy.plotter import BodePlot
>>> plot = BodePlot(hp, sample_rate=4096)
>>> plot.show()

(png)

filter_design.notch(frequency, sample_rate, type='iir', **kwargs)[source]¶

Design a ZPK notch filter for the given frequency and sampling rate

Parameters:	frequency : float, Quantity frequency (default in Hertz) at which to apply the notch sample_rate : float, Quantity number of samples per second for TimeSeries to which this notch filter will be applied type : str, optional, default: ‘iir’ type of filter to apply, currently only ‘iir’ is supported **kwargs other keyword arguments to pass to scipy.signal.iirdesign
Returns:	zpk : tuple of complex or float the filter components in digital zero-pole-gain format

See also

scipy.signal.iirdesign

for details on the IIR filter design method

Notes

By default a digital filter is returned, meaning the zeros and poles are given in the Z-domain in units of radians/sample.

Examples

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 BodePlot:

>>> from gwpy.plotter import BodePlot
>>> plot = BodePlot(n, sample_rate=4096)
>>> plot.show()

(png)

filter_design.concatenate_zpks(*zpks)[source]¶

Concatenate a list of zero-pole-gain (ZPK) filters

Parameters:	*zpks one or more zero-pole-gain format, each one should be a 3-tuple containing an array of zeros, an array of poles, and a gain float
Returns:	zeros : numpy.ndarray the concatenated array of zeros poles : numpy.ndarray the concatenated array of poles gain : float the overall gain

Examples

Create a lowpass and a highpass filter, and combine them:

>>> from gwpy.signal.filter_design import (
...     highpass, lowpass, concatenate_zpks)
>>> hp = highpass(100, 4096)
>>> lp = lowpass(1000, 4096)
>>> zpk = concatenate_zpks(hp, lp)

Plot the filter:

>>> from gwpy.plotter import BodePlot
>>> plot = BodePlot(zpk, sample_rate=4096)
>>> plot.show()

(png)