scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
medfilt2d(input, kernel_size=3)
Median filter two 2-dimensional arrays.
Description:
Inputs:
Apply a median filter to the input array using a local window-size given by kernel_size (must be
odd).
in – An 2 dimensional input array. kernel_size – A scalar or an length-2 list giving the size of the
Outputs: (out,)
median filter window in each dimension. Elements of kernel_size should be odd. If
kernel_size is a scalar, then this scalar is used as the size in each dimension.
out – An array the same size as input containing the median filtered
result.
wiener(im, mysize=None, noise=None)
Perform a Wiener filter on an N-dimensional array.
Description:
Inputs:
Apply a Wiener filter to the N-dimensional array in.
in – an N-dimensional array. kernel_size – A scalar or an N-length list giving the size of the
Wiener filter window in each dimension. Elements of kernel_size should be odd. If
kernel_size is a scalar, then this scalar is used as the size in each dimension.
noise – The noise-power to use. If None, then noise is estimated as
the average of the local variance of the input.
Outputs: (out,)
out – Wiener filtered result with the same shape as in.
symiirorder1()
symiirorder1(input, c0, z1 {, precision}) -> output
Description:
Inputs:
Output:
Implement a smoothing IIR filter with mirror-symmetric boundary conditions using a cascade of
first-order sections. The second section uses a reversed sequence. This implements a system with
the following transfer function and mirror-symmetric boundary conditions.
c0
H(z) = ———————
(1-z1/z) (1 - z1 z)
The resulting signal will have mirror symmetric boundary conditions as well.
input – the input signal. c0, z1 – parameters in the transfer function. precision – specifies the
precision for calculating initial conditions
of the recursive filter based on mirror-symmetric input.
330 Chapter 3. Reference