Signal processing (scipy.signal)


convolve(in1, in2[, mode, old_behavior]) Convolve two N-dimensional arrays.
correlate(in1, in2[, mode, old_behavior]) Cross-correlate two N-dimensional arrays.
fftconvolve(in1, in2[, mode]) Convolve two N-dimensional arrays using FFT. See convolve.
convolve2d(in1, in2[, mode, boundary, ...]) Convolve two 2-dimensional arrays.
correlate2d(in1, in2[, mode, boundary, ...]) Cross-correlate two 2-dimensional arrays.
sepfir2d sepfir2d(input, hrow, hcol) -> output


bspline(x, n) bspline(x,n): B-spline basis function of order n.
gauss_spline(x, n) Gaussian approximation to B-spline basis function of order n.
cspline1d(signal[, lamb]) Compute cubic spline coefficients for rank-1 array.
qspline1d(signal[, lamb]) Compute quadratic spline coefficients for rank-1 array.
cspline2d cspline2d(input {, lambda, precision}) -> ck
qspline2d qspline2d(input {, lambda, precision}) -> qk
spline_filter(Iin[, lmbda]) Smoothing spline (cubic) filtering of a rank-2 array.


order_filter(a, domain, rank) Perform an order filter on an N-dimensional array.
medfilt(volume[, kernel_size]) Perform a median filter on an N-dimensional array.
medfilt2d(input[, kernel_size]) Median filter two 2-dimensional arrays.
wiener(im[, mysize, noise]) Perform a Wiener filter on an N-dimensional array.
symiirorder1 symiirorder1(input, c0, z1 {, precision}) -> output
symiirorder2 symiirorder2(input, r, omega {, precision}) -> output
lfilter(b, a, x[, axis, zi]) Filter data along one-dimension with an IIR or FIR filter.
lfiltic(b, a, y[, x]) Construct initial conditions for lfilter
deconvolve(signal, divisor) Deconvolves divisor out of signal.
hilbert(x[, N, axis]) Compute the analytic signal.
get_window(window, Nx[, fftbins]) Return a window of length Nx and type window.
decimate(x, q[, n, ftype, axis]) downsample the signal x by an integer factor q, using an order n filter
detrend(data[, axis, type, bp]) Remove linear trend along axis from data.
resample(x, num[, t, axis, window]) Resample to num samples using Fourier method along the given axis.

Filter design

bilinear(b, a[, fs]) Return a digital filter from an analog filter using the bilinear transform.
firwin(N, cutoff[, width, window]) FIR Filter Design using windowed ideal filter method.
freqs(b, a[, worN, plot]) Compute frequency response of analog filter.
freqz(b[, a, worN, whole, plot]) Compute the frequency response of a digital filter.
iirdesign(wp, ws, gpass, gstop[, analog, ...]) Complete IIR digital and analog filter design.
iirfilter(N, Wn[, rp, rs, btype, analog, ...]) IIR digital and analog filter design given order and critical points.
kaiserord(ripple, width) Design a Kaiser window to limit ripple and width of transition region.
remez(numtaps, bands, desired[, weight, Hz, ...]) Calculate the minimax optimal filter using Remez exchange algorithm.
unique_roots(p[, tol, rtype]) Determine the unique roots and their multiplicities in two lists
residue(b, a[, tol, rtype]) Compute partial-fraction expansion of b(s) / a(s).
residuez(b, a[, tol, rtype]) Compute partial-fraction expansion of b(z) / a(z).
invres(r, p, k[, tol, rtype]) Compute b(s) and a(s) from partial fraction expansion: r,p,k

Matlab-style IIR filter design

butter(N, Wn[, btype, analog, output]) Butterworth digital and analog filter design.
buttord(wp, ws, gpass, gstop[, analog]) Butterworth filter order selection.
cheby1(N, rp, Wn[, btype, analog, output]) Chebyshev type I digital and analog filter design.
cheb1ord(wp, ws, gpass, gstop[, analog]) Chebyshev type I filter order selection.
cheby2(N, rs, Wn[, btype, analog, output]) Chebyshev type I digital and analog filter design.
cheb2ord(wp, ws, gpass, gstop[, analog]) Chebyshev type II filter order selection.
ellip(N, rp, rs, Wn[, btype, analog, output]) Elliptic (Cauer) digital and analog filter design.
ellipord(wp, ws, gpass, gstop[, analog]) Elliptic (Cauer) filter order selection.
bessel(N, Wn[, btype, analog, output]) Bessel digital and analog filter design.

Linear Systems

lti(*args, **kwords) Linear Time Invariant class which simplifies representation.
lsim(system, U, T[, X0, interp]) Simulate output of a continuous-time linear system.
lsim2(system, **kwargs[, U, T, X0]) Simulate output of a continuous-time linear system, by using
impulse(system[, X0, T, N]) Impulse response of continuous-time system.
impulse2(system, **kwargs[, X0, T, N]) Impulse response of a single-input continuous-time linear system.
step(system[, X0, T, N]) Step response of continuous-time system.
step2(system, **kwargs[, X0, T, N]) Step response of continuous-time system.

LTI Representations

tf2zpk(b, a) Return zero, pole, gain (z,p,k) representation from a numerator, denominator representation of a linear filter.
zpk2tf(z, p, k) Return polynomial transfer function representation from zeros
tf2ss(num, den) Transfer function to state-space representation.
ss2tf(A, B, C, D[, input]) State-space to transfer function.
zpk2ss(z, p, k) Zero-pole-gain representation to state-space representation
ss2zpk(A, B, C, D[, input]) State-space representation to zero-pole-gain representation.


chirp(t, f0, t1, f1[, method, phi, ...]) Frequency-swept cosine generator.
gausspulse(t[, fc, bw, bwr, tpr, retquad, ...]) Return a gaussian modulated sinusoid: exp(-a t^2) exp(1j*2*pi*fc).
sawtooth(t[, width]) Return a periodic sawtooth waveform.
square(t[, duty]) Return a periodic square-wave waveform.
sweep_poly(t, poly[, phi]) Frequency-swept cosine generator, with a time-dependent frequency specified as a polynomial.

Window functions

get_window(window, Nx[, fftbins]) Return a window of length Nx and type window.
barthann(M[, sym]) Return the M-point modified Bartlett-Hann window.
bartlett(M[, sym]) The M-point Bartlett window.
blackman(M[, sym]) The M-point Blackman window.
blackmanharris(M[, sym]) The M-point minimum 4-term Blackman-Harris window.
bohman(M[, sym]) The M-point Bohman window.
boxcar(M[, sym]) The M-point boxcar window.
chebwin(M, at[, sym]) Dolph-Chebyshev window.
flattop(M[, sym]) The M-point Flat top window.
gaussian(M, std[, sym]) Return a Gaussian window of length M with standard-deviation std.
general_gaussian(M, p, sig[, sym]) Return a window with a generalized Gaussian shape.
hamming(M[, sym]) The M-point Hamming window.
hann(M[, sym]) The M-point Hanning window.
kaiser(M, beta[, sym]) Return a Kaiser window of length M with shape parameter beta.
nuttall(M[, sym]) A minimum 4-term Blackman-Harris window according to Nuttall.
parzen(M[, sym]) The M-point Parzen window.
slepian(M, width[, sym]) Return the M-point slepian window.
triang(M[, sym]) The M-point triangular window.


cascade(hk[, J]) (x,phi,psi) at dyadic points K/2**J from filter coefficients.
daub(p) The coefficients for the FIR low-pass filter producing Daubechies wavelets.
morlet(M[, w, s, complete]) Complex Morlet wavelet.
qmf(hk) Return high-pass qmf filter from low-pass