API reference

This page provides an auto-generated summary of xr-scipy’s API. For more details and examples, refer to the relevant chapters in the main part of the documentation.

integrate

integrate.trapezoid(obj, coord)	Integrate along the given coordinate using the composite trapezoidal rule.
integrate.simpson(obj, coord[, even])	Integrate y(x) using samples along the given coordinate and the composite
integrate.romb(obj, coord[, show])	romb(obj, coord, show=False)
integrate.cumulative_trapezoid(obj, coord)	cumulative_trapezoid(obj, coord)

fft

fft.fft(x, coord[, n, norm])	Compute the 1-D discrete Fourier Transform.
fft.ifft(x, coord[, n, norm])	Compute the 1-D inverse discrete Fourier Transform.
fft.rfft(x, coord[, n, norm])	Compute the 1-D discrete Fourier Transform for real input.
fft.irfft(x, coord[, n, norm])	Computes the inverse of rfft.
fft.fftn(x, coord[, n, norm])	Compute the N-D discrete Fourier Transform.
fft.ifftn(x, coord[, n, norm])	Compute the N-D inverse discrete Fourier Transform.
fft.rfftn(x, coord[, n, norm])	Compute the N-D discrete Fourier Transform for real input.
fft.irfftn(x, coord[, n, norm])	Computes the inverse of rfftn
fft.hfft(x, coord[, n, norm])	Compute the FFT of a signal that has Hermitian symmetry, i.e., a real
fft.ihfft(x, coord[, n, norm])	ihfft(x, coord, n=None, norm=None)

fftpack

fftpack.fft(x, coord[, n])	Return discrete Fourier transform of real or complex sequence.
fftpack.ifft(x, coord[, n])	Return discrete inverse Fourier transform of real or complex sequence.
fftpack.rfft(x, coord[, n])	rfft(x, coord, n=None)
fftpack.irfft(x, coord[, n])	Return inverse discrete Fourier transform of real sequence x.
fftpack.dct(x, coord[, type, n, norm])	dct(x, coord, type=2, n=None, norm=None)
fftpack.idct(x, coord[, type, n, norm])	idct(x, coord, type=2, n=None, norm=None)
fftpack.dst(x, coord[, type, n, norm])	dst(x, coord, type=2, n=None, norm=None)
fftpack.idst(x, coord[, type, n, norm])	idst(x, coord, type=2, n=None, norm=None)

Spectral (FFT) analysis

signal.csd(darray, other_darray[, fs, ...])	Estimate the cross power spectral density, Pxy, using Welch's method.
signal.extra.psd(darray[, fs, seglen, ...])	Calculate the power spectral density.
signal.coherence(darray, other_darray[, fs, ...])	Calculate the coherence as <CSD> / sqrt(<PSD1> * <PSD2>)
signal.extra.xcorrelation(darray, other_darray)	Calculate the crosscorrelation.
signal.extra.crossspectrogram(darray, ...[, ...])	Calculate the cross spectrogram.
signal.spectrogram(darray[, fs, seglen, ...])	Calculate the spectrogram using crossspectrogram applied to the same data
signal.extra.coherogram(darray, other_darray)	Calculate the coherogram
signal.hilbert(darray[, N, dim])	Compute the analytic signal, using the Hilbert transform.

Digital filters

signal.extra.frequency_filter(darray, f_crit)	Applies given frequency filter to a darray.
signal.extra.lowpass(darray, f_cutoff, ...)	Applies lowpass filter to a darray.
signal.extra.highpass(darray, f_cutoff, ...)	Applies highpass filter to a darray.
signal.extra.bandpass(darray, f_low, f_high, ...)	Applies bandpass filter to a darray.
signal.extra.bandstop(darray, f_low, f_high, ...)	Applies bandstop filter to a darray.
signal.decimate(darray[, q, target_fs, dim])	Decimate signal by given (int) factor or to closest possible target_fs along the specified dimension.
signal.savgol_filter(darray, window_length, ...)	Apply a Savitzky-Golay filter to an array.