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)
integrate.cumulative_simpson(obj, coord)	Cumulatively integrate y(x) using the composite Simpson's 1/3 rule.

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.coherence(darray, other_darray[, fs, ...])	Calculate the magnitude squared coherence as \(|CSD|^2 / (PSD_1 * PSD_2)\)
signal.spectrogram(darray[, fs, seglen, ...])	Calculate the spectrogram using crossspectrogram applied to the same data
signal.hilbert(darray[, N, dim])	Compute the analytic signal, using the Hilbert transform.

Digital filters

signal.sosfilt(sos, darray[, dim, zi])	Filter data along one dimension using cascaded second-order sections.
signal.sosfiltfilt(sos, darray[, dim, ...])	A forward-backward digital filter using cascaded second-order sections.
signal.savgol_filter(darray, window_length, ...)	Apply a Savitzky-Golay filter to an array.
signal.decimate(darray[, q, target_fs, n, ...])	Downsample the signal after applying an anti-aliasing filter.