Ralf Gommers skrev:
Sure, I know. The docs I linked have improvements over svn. If you merge your changes, there'll be a conflict and those docs will not get merged soon - I was hoping to avoid that.
Oh, the docs, not the code... Would something like this be better? The Cython module doesn't need to have docs in it. from linear_filter import lfilter as _lfilter def lfilter(b, a, x, axis=-1, zi=None, direction=1, inplace=-1): """Filter data along one-dimension with an IIR or FIR filter. Description Filter a data sequence, x, using a digital filter. This works for many fundamental data types (including Object type). The filter is a direct form II transposed implementation of the standard difference equation (see "Algorithm"). Inputs: b -- The numerator coefficient vector in a 1-D sequence. a -- The denominator coefficient vector in a 1-D sequence. If a[0] is not 1, then both a and b are normalized by a[0]. x -- An N-dimensional input array. axis -- The axis of the input data array along which to apply the linear filter. The filter is applied to each subarray along this axis. If axis is -1, the filter is applied to x flattened. (*Default* = -1) zi -- Initial conditions for the filter delays. It is a vector (or array of vectors for an N-dimensional input) of length max(len(a),len(b)). If zi=None or is not given then initial rest is assumed. If axis is None, zi should be a vector of length K = max(M,N), where M=len(b)-1 and N=len(a)-1. If an axis is not None, zi should either be a vector of length K, or an array with same shape as x, except for zi.shape[axis] which should be K. If zi is a vector, the same initial conditions are applied to each vector along the specified axis. Default: zi=None (initial rest). direction -- Filtered in the reverse direction if -1. (*Default* = 1) inplace -- If possible, modification to x is done implace. (*Default* = -1) Outputs: (y, {zf}) y -- The output of the digital filter. zf -- If zi is None, this is not returned, otherwise, zf holds the final filter delay values. Algorithm: The filter function is implemented as a direct II transposed structure. This means that the filter implements a[0]*y[n] = b[0]*x[n] + b[1]*x[n-1] + ... + b[nb]*x[n-nb] - a[1]*y[n-1] - ... - a[na]*y[n-na] using the following difference equations: y[m] = b[0]*x[m] + z[0,m-1] z[0,m] = b[1]*x[m] + z[1,m-1] - a[1]*y[m] ... z[n-3,m] = b[n-2]*x[m] + z[n-2,m-1] - a[n-2]*y[m] z[n-2,m] = b[n-1]*x[m] - a[n-1]*y[m] where m is the output sample number and n=max(len(a),len(b)) is the model order. The rational transfer function describing this filter in the z-transform domain is -1 -nb b[0] + b[1]z + ... + b[nb] z Y(z) = ---------------------------------- X(z) -1 -na a[0] + a[1]z + ... + a[na] z """ if isscalar(a): a = [a] if axis < 0: axis = None if inplace < 0: inplace = None return _lfilter(b, a, x, axis=axis, zi=zi, inplace=inplace, direction=direction) Regards, S.M.