[英]SciPy lfilter for arbitrary filter, with initial conditions applied along any axis of N-D array
This python numpy/Scipy question SciPy lfilter with initial conditions applied along any axis of ND array has an answer that works well when the operation axis!=0
, or when the length of the initial condition vector zi
remains 1, ie for a first-order filter.这个 python numpy/Scipy 问题SciPy 沿 ND 数组的任何轴应用初始条件的 lfilter有一个答案,当操作
axis!=0
时,或者当初始条件向量zi
的长度保持为 1 时,即对于第一个 -订单过滤器。 It fails as soon as a higher order filter is defined to operate on an input axis
assigned to the first axis of signal
, where signal
is 2D.一旦高阶滤波器被定义为在分配给
signal
的第一轴的输入axis
运行,它就会失败,其中signal
是二维的。
For example:例如:
import numpy as np
from scipy.signal import (lfilter, lfilter_zi, butter)
def apply_filter(B, A, signal, axis=-1):
# apply filter, setting proper initial state (doesn't assume rest)
filtered, zf = lfilter(B, A, signal,
zi= lfilter_zi(B, A) * np.take(signal, [0], axis=axis), axis=axis)
return filtered
B, A = butter(1, 0.5)
x = np.random.randn(12, 50)
apply_filter(B, A, x, axis=1) # works without error
apply_filter(B, A, x, axis=0) # works without error
B, A = butter(2, 0.5)
x = np.random.randn(12, 50)
apply_filter(B, A, x, axis=1) # works without error
apply_filter(B, A, x, axis=0) # raises error
raises加薪
ValueError: operands could not be broadcast together with shapes (2,) (1,50)
How could the solution be made more general to apply to an arbitrary filter length for axis=0
?如何使解决方案更通用以适用于
axis=0
的任意滤波器长度?
Your error came from the shape of your arrays when *
these two array.当
*
这两个数组时,您的错误来自 arrays 的形状。 Simple example:简单示例:
>>> np.random.rand(2) * np.random.rand(1,10)
----> 1 np.random.rand(2) * np.random.rand(1,10)
ValueError: operands could not be broadcast together with shapes (2,) (1,10)
>>> np.random.rand(2)[:,None] * np.random.rand(1,10)
array([[0.05905608, 0.30028617, 0.12495555, 0.28012041, 0.15031258,
0.05166653, 0.2035891 , 0.01499304, 0.31749996, 0.3146938 ],
[0.06860488, 0.34883958, 0.14515967, 0.3254132 , 0.17461669,
0.06002052, 0.23650752, 0.01741727, 0.36883668, 0.36557679]])
For handling different axis in your function you can write the function like the below:为了处理 function 中的不同轴,您可以像下面这样编写 function:
def apply_filter(B, A, signal, axis=-1):
tmp = lfilter_zi(B, A) if axis==1 else lfilter_zi(B, A)[:,None]
filtered, zf = lfilter(B, A, signal, zi = tmp * np.take(signal, [0], axis=axis), axis=axis)
return filtered
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