The filter type, either 'iir' or 'fir' **kwargs The minimum attenuation in the stopband (dB) type str, optional, default: 'iir' The maximum loss in the passband (dB) gstop float, optional, default: 30 (low, high) edge-frequencies of stop band gpass float, optional, default: 2 Sampling rate of target data fstop tuple of float, optional Upper corner frequency of pass band sample_rate float Lower corner frequency of pass band fhigh float bandpass ( fhigh, sample_rate, fstop = None, gpass = 2, gstop = 30, type = 'iir', ** kwargs ) ¶ĭesign a band-pass filter for the given cutoff frequencies Parameters ¶ flow float 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:ĭesign a low-pass filter for the given cutoff frequencyĭesign a high-pass filter for the given cutoff frequencyĭesign a band-pass filter for the given cutoff frequenciesĭesign a ZPK notch filter for the given frequency and sampling rateĬoncatenate a list of zero-pole-gain (ZPK) filtersĮach of these will return filter coefficients that can be passed directly into zpk (default for analogue filters) or filter (default for digital filters).įor 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.Ĭalculate the frequency-coherence between this TimeSeries and another.Ĭalculate the coherence spectrogram between this TimeSeries and other.įor a worked example of how to compare channels like this, see the example Calculating the coherence between two channels. Scan a TimeSeries using the multi-Q transform and return an interpolated high-resolution spectrogramĬalculate the Rayleigh FrequencySeries for this TimeSeries.Ĭalculate the Rayleigh statistic spectrogram of this TimeSeriesįor a worked example of how to load data and calculate the Amplitude Spectral Density FrequencySeries, see the example Calculating and plotting a FrequencySeries. Frequency-domain filtering ¶Īdditionally, the TimeSeries object includes a number of instance methods to generate frequency-domain information for some data. Whiten this TimeSeries using inverse spectrum truncationįilter this TimeSeries with an IIR or FIR filterĮach 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).įor 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. The TimeSeries object comes with a number of instance methods that should make filtering data trivial for a number of common use cases.įilter this TimeSeries with a high-pass filter.įilter this TimeSeries with a Butterworth low-pass filter.įilter this TimeSeries with a band-pass filter.įilter this TimeSeries by applying a zero-pole-gain filter See () for more detailed documentation on the PSDĮstimation method used. TimeSeries.spectrogram2(fftlength)Ĭalculate the non-averaged power Spectrogram of this TimeSeries TimeSeries.spectrogram(stride)Ĭalculate the average power spectrogram of this TimeSeries using the specified average spectrum method. TimeSeries.asd()Ĭalculate the ASD FrequencySeries of this TimeSeries TimeSeries.psd()Ĭalculate the PSD FrequencySeries for this TimeSeries Method keyword argument to any of the relevant TimeSeries 'welch' - mean average of overlapping periodogramsĮach of these can be specified by passing the function name as the 'median' - median average of overlapping periodograms 'bartlett' - mean average of non-overlapping periodograms GWpy provides wrappers of power spectral density (PSD) estimation methodsįrom scipy.signal to simplify calculating a Is a common way of investigating the frequency-domain content of a time-domain GWpy provides a suite of functions to simplify and extend the excellent digital signal processing suite in scipy.signal. In a wide-array of applications, the original data recorded from a digital system must be manipulated in order to extract the greatest amount of information. Signal processing Signal processing Signal processing.
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