Signal processing (scipy.signal)¶
Convolution¶
| 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 |
B-splines¶
| 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. |
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 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. |
Waveforms¶
| 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. |
