Signal processing (scipy.signal)¶
Convolution¶
| convolve(in1, in2[, mode]) | Convolve two N-dimensional arrays. |
| correlate(in1, in2[, mode]) | Cross-correlate two N-dimensional arrays. |
| fftconvolve(in1, in2[, mode]) | Convolve two N-dimensional arrays using FFT. |
| convolve2d(in1, in2[, mode, boundary, fillvalue]) | Convolve two 2-dimensional arrays. |
| correlate2d(in1, in2[, mode, boundary, ...]) | Cross-correlate two 2-dimensional arrays. |
| sepfir2d((input, hrow, hcol) -> output) | Description: |
B-splines¶
| bspline(x, n) | B-spline basis function of order n. |
| cubic(x) | A cubic B-spline. |
| quadratic(x) | A quadratic B-spline. |
| 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((input {, lambda, precision}) -> ck) | Description: |
| qspline2d((input {, lambda, precision}) -> qk) | Description: |
| cspline1d_eval(cj, newx[, dx, x0]) | Evaluate a spline at the new set of points. |
| cspline1d_eval(cj, newx[, dx, x0]) | Evaluate a spline at the new set of points. |
| spline_filter(Iin[, lmbda]) | Smoothing spline (cubic) filtering of a rank-2 array. |
Filtering¶
| 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 a 2-dimensional array. |
| wiener(im[, mysize, noise]) | Perform a Wiener filter on an N-dimensional array. |
| symiirorder1((input, c0, z1 {, ...) | Implement a smoothing IIR filter with mirror-symmetric boundary conditions |
| symiirorder2((input, r, omega {, ...) | Implement a smoothing IIR filter with mirror-symmetric boundary conditions |
| 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. |
| lfilter_zi(b, a) | Compute an initial state zi for the lfilter function that corresponds to the steady state of the step response. |
| filtfilt(b, a, x[, axis, padtype, padlen]) | A forward-backward filter. |
| deconvolve(signal, divisor) | Deconvolves divisor out of signal. |
| hilbert(x[, N, axis]) | Compute the analytic signal, using the Hilbert transform. |
| get_window(window, Nx[, fftbins]) | Return a window. |
| decimate(x, q[, n, ftype, axis]) | Downsample the signal by using a filter. |
| detrend(data[, axis, type, bp]) | Remove linear trend along axis from data. |
| resample(x, num[, t, axis, window]) | Resample x to num samples using Fourier method along the given axis. |
Filter design¶
| bilinear(b, a[, fs]) | Return a digital filter from an analog one using a bilinear transform. |
| firwin(numtaps, cutoff[, width, window, ...]) | FIR filter design using the window method. |
| firwin2(numtaps, freq, gain[, nfreqs, ...]) | FIR filter design using the window 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. |
| kaiser_atten(numtaps, width) | Compute the attenuation of a Kaiser FIR filter. |
| kaiser_beta(a) | Compute the Kaiser parameter beta, given the attenuation a. |
| 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 the Remez exchange algorithm. |
| unique_roots(p[, tol, rtype]) | Determine unique roots and their multiplicities from a list of roots. |
| 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 II 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/Thomson digital and analog filter design. |
Continuous-Time Linear Systems¶
| freqresp(system[, w, n]) | Calculate the frequency response of a continuous-time system. |
| 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[, 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[, 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[, X0, T, N]) | Step response of continuous-time system. |
| bode(system[, w, n]) | Calculate Bode magnitude and phase data of a continuous-time system. |
Discrete-Time Linear Systems¶
| dlsim(system, u[, t, x0]) | Simulate output of a discrete-time linear system. |
| dimpulse(system[, x0, t, n]) | Impulse response of discrete-time system. |
| dstep(system[, x0, t, n]) | Step response of discrete-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. |
| cont2discrete(sys, dt[, method, alpha]) | Transform a continuous to a discrete state-space system. |
Waveforms¶
| chirp(t, f0, t1, f1[, method, phi, vertex_zero]) | Frequency-swept cosine generator. |
| gausspulse(t[, fc, bw, bwr, tpr, retquad, ...]) | Return a Gaussian modulated sinusoid: |
| sawtooth(t[, width]) | Return a periodic sawtooth or triangle waveform. |
| square(t[, duty]) | Return a periodic square-wave waveform. |
| sweep_poly(t, poly[, phi]) | Frequency-swept cosine generator, with a time-dependent frequency. |
Window functions¶
| get_window(window, Nx[, fftbins]) | Return a window. |
| barthann(M[, sym]) | Return a modified Bartlett-Hann window. |
| bartlett(M[, sym]) | Return a Bartlett window. |
| blackman(M[, sym]) | Return a Blackman window. |
| blackmanharris(M[, sym]) | Return a minimum 4-term Blackman-Harris window. |
| bohman(M[, sym]) | Return a Bohman window. |
| boxcar(M[, sym]) | Return a boxcar or rectangular window. |
| chebwin(M, at[, sym]) | Return a Dolph-Chebyshev window. |
| cosine(M[, sym]) | Return a window with a simple cosine shape. |
| flattop(M[, sym]) | Return a flat top window. |
| gaussian(M, std[, sym]) | Return a Gaussian window. |
| general_gaussian(M, p, sig[, sym]) | Return a window with a generalized Gaussian shape. |
| hamming(M[, sym]) | Return a Hamming window. |
| hann(M[, sym]) | Return a Hann window. |
| kaiser(M, beta[, sym]) | Return a Kaiser window. |
| nuttall(M[, sym]) | Return a minimum 4-term Blackman-Harris window according to Nuttall. |
| parzen(M[, sym]) | Return a Parzen window. |
| slepian(M, width[, sym]) | Return a digital Slepian (DPSS) window. |
| triang(M[, sym]) | Return a triangular window. |
Wavelets¶
| cascade(hk[, J]) | Return (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 |
| ricker(points, a) | Return a Ricker wavelet, also known as the “Mexican hat wavelet”. |
| cwt(data, wavelet, widths) | Continuous wavelet transform. |
Peak finding¶
| find_peaks_cwt(vector, widths[, wavelet, ...]) | Attempt to find the peaks in a 1-D array. |
| argrelmin(data[, axis, order, mode]) | Calculate the relative minima of data. |
| argrelmax(data[, axis, order, mode]) | Calculate the relative maxima of data. |
| argrelextrema(data, comparator[, axis, ...]) | Calculate the relative extrema of data. |
Spectral Analysis¶
| periodogram(x[, fs, window, nfft, detrend, ...]) | Estimate power spectral density using a periodogram. |
| welch(x[, fs, window, nperseg, noverlap, ...]) | Estimate power spectral density using Welch’s method. |
| lombscargle(x, y, freqs) | Computes the Lomb-Scargle periodogram. |