Source code for PyMca5.PyMcaMath.PyMcaSciPy.signal.median
try:
from . import mediantools
except ImportError:
from PyMca5.PyMcaSciPy.signal import mediantools
from numpy import asarray
[docs]def medfilt2d(input_data, kernel_size=None, conditional=0):
"""Median filter for 2-dimensional arrays.
Description:
Apply a median filter to the input array using a local window-size
given by kernel_size (must be odd).
Inputs:
in -- An 2 dimensional input array.
kernel_size -- A scalar or an length-2 list giving the size of the
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.
conditional -- If different from 0 implements a conditional median filter.
Outputs: (out,)
out -- An array the same size as input containing the median filtered
result.
"""
image = asarray(input_data)
if kernel_size is None:
kernel_size = [3] * 2
kernel_size = asarray(kernel_size)
if len(kernel_size.shape) == 0:
kernel_size = [kernel_size.item()] * 2
kernel_size = asarray(kernel_size)
for size in kernel_size:
if (size % 2) != 1:
raise ValueError("Each element of kernel_size should be odd.")
return mediantools._medfilt2d(image, kernel_size, conditional)
[docs]def medfilt1d(input_data, kernel_size=None, conditional=0):
"""Median filter 1-dimensional arrays.
Description:
Apply a median filter to the input array using a local window-size
given by kernel_size (must be odd).
Inputs:
in -- An 1-dimensional input array.
kernel_size -- A scalar or an length-2 list giving the size of the
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.
conditional -- If different from 0 implements a conditional median filter.
Outputs: (out,)
out -- An array the same size as input containing the median filtered
result.
"""
image = asarray(input_data)
oldShape = image.shape
image.shape = -1, 1
if kernel_size is None:
kernel_size = [3, 1]
kernel_size = asarray(kernel_size)
if len(kernel_size.shape) == 0:
kernel_size = [kernel_size.item(), 1]
kernel_size = asarray(kernel_size)
for size in kernel_size:
if (size % 2) != 1:
image.shape = oldShape
raise ValueError("Kernel_size should be odd.")
output = mediantools._medfilt2d(image, kernel_size, conditional)
output.shape = oldShape
image.shape = oldShape
return output