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output – filtered signal.
symiirorder2()
symiirorder2(input, r, omega {, precision}) -> output
Description:
Inputs:
Output:
SciPy Reference Guide, Release 0.8.dev
Implement a smoothing IIR filter with mirror-symmetric boundary conditions using a cascade of
second-order sections. The second section uses a reversed sequence. This implements the following
transfer function:
cs^2
H(z) = —————————————
(1 - a2/z - a3/z^2) (1 - a2 z - a3 z^2 )
where a2 = (2 r cos omega)
a3 = - r^2 cs = 1 - 2 r cos omega + r^2
input – the input signal. r, omega – parameters in the transfer function. precision – specifies the
precision for calculating initial conditions
of the recursive filter based on mirror-symmetric input.
output – filtered signal.
lfilter(b, a, x, axis=-1, zi=None)
Filter data along one-dimension with an IIR or FIR filter.
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 Notes).
Parameters
b : array_like
The numerator coefficient vector in a 1-D sequence.
a : array_like
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 : array_like
axis : int
An N-dimensional input array.
The axis of the input data array along which to apply the linear filter. The filter is
applied to each subarray along this axis (Default = -1)
zi : array_like (optional)
Returns
y : array
Initial conditions for the filter delays. It is a vector (or array of vectors for an Ndimensional
input) of length max(len(a),len(b))-1. If zi=None or is not given then
initial rest is assumed. SEE signal.lfiltic for more information.
The output of the digital filter.
3.13. Signal processing (scipy.signal) 331