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# 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. See convolve.
`convolve2d`(in1, in2[, mode, boundary, fillvalue]) | 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) | 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 a 2-dimensional array.
`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 x 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`(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 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, vertex_zero]) | 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*t).
`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.

## 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

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