Python Median Filter for 1D numpy array

Question

I have a numpy.array with a dimension dim_array. I'm looking forward to obtain a median filter like scipy.signal.medfilt(data, window_len).

This in fact doesn't work with numpy.array may be because the dimension is (dim_array, 1) and not (dim_array, ).

How to obtain such filter?

Next, another question, how can I obtain other filter, i.e., min, max, mean?

Culicid

Perhaps you just want to reshape/flatten your (N,1) array to (N,). – Photosynthesis

So maybe I have to do so: data = np.reshape(data, len(data)) – Culicid

Better data.flatten(order='C'); this work! Thanks @John Zwinck. Now for the second question, how can I obtain these other filters? – Culicid

Answer 1

Based on this post, we could create sliding windows to get a 2D array of such windows being set as rows in it. These windows would merely be views into the data array, so no memory consumption and thus would be pretty efficient. Then, we would simply use those ufuncs along each row axis=1.

Thus, for example sliding-median` could be computed like so -

np.median(strided_app(data, window_len,1),axis=1)

For the other ufuncs, just use the respective ufunc names there : np.min, np.max & np.mean. Please note this is meant to give a generic solution to use ufunc supported functionality.

For the best performance, one must still look into specific functions that are built for those purposes. For the four requested functions, we have the builtins, like so -

Median : scipy.signal.medfilt.

Max : scipy.ndimage.filters.maximum_filter1d.

Min : scipy.ndimage.filters.minimum_filter1d.

Mean : scipy.ndimage.filters.uniform_filter1d

Disinclination

Thanks again @Disinclination – Culicid

Answer 2

The fact that applying of a median filter with the window size 1 will not change the array gives us a freedom to apply the median filter row-wise or column-wise.

For example, this code

from scipy.ndimage import median_filter
import numpy as np

arr = np.array([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]])
median_filter(arr, size=3, cval=0, mode='constant')
#with cval=0, mode='constant' we set that input array is extended with zeros 
#when window overlaps edges, just for visibility and ease of calculation

outputs an expected filtered with window (3, 3) array

array([[0., 2., 0.],
       [2., 5., 3.],
       [0., 5., 0.]])

because median_filter automatically extends the size to all dimensions, so the same effect we can get with:

median_filter(arr, size=(3, 3), cval=0, mode='constant')

Now, we can also apply median_filter row-wise with setting 1 to the first element of size

median_filter(arr, size=(1, 3), cval=0, mode='constant')

Output:

array([[1., 2., 2.],
       [4., 5., 5.],
       [7., 8., 8.]])

And column-wise with the same logic

median_filter(arr, size=(3, 1), cval=0, mode='constant')

Output:

array([[1., 2., 3.],
       [4., 5., 6.],
       [4., 5., 6.]])

Uyekawa