`scipy.signal._signaltools:order_filter`

source: /scipy/signal/_signaltools.py :1521

Signature

def order_filter ( a , domain , rank )

Summary

Perform an order filter on an N-D array.

Extended Summary

Perform an order filter on the array in. The domain argument acts as a mask centered over each pixel. The non-zero elements of domain are used to select elements surrounding each input pixel which are placed in a list. The list is sorted, and the output for that pixel is the element corresponding to rank in the sorted list.

Parameters

a : ndarray: The N-dimensional input array.
domain : array_like: A mask array with the same number of dimensions as a. Each dimension should have an odd number of elements.
rank : int: A non-negative integer which selects the element from the sorted list (0 corresponds to the smallest element, 1 is the next smallest element, etc.).

Returns

out : ndarray: The results of the order filter in an array with the same shape as a.

Notes

Array API Standard Support

order_filter has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ✅                   
PyTorch               ✅                     ⛔                   
JAX                   ⚠️ no JIT             ⛔                   
Dask                  ⚠️ computes graph     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Examples

import numpy as np
from scipy import signal
x = np.arange(25).reshape(5, 5)
domain = np.identity(3)
x

✓

signal.order_filter(x, domain, 0)
signal.order_filter(x, domain, 2)