convolve

astropy.convolution.convolve(array, kernel, boundary=u'fill', fill_value=0.0, normalize_kernel=False)

Convolve an array with a kernel.

This routine differs from scipy.ndimage.filters.convolve because it includes a special treatment for NaN values. Rather than including NaN``s in the convolution calculation, which causes large ``NaN holes in the convolved image, NaN values are replaced with interpolated values using the kernel as an interpolation function.

Parameters:

array : numpy.ndarray

The array to convolve. This should be a 1, 2, or 3-dimensional array or a list or a set of nested lists representing a 1, 2, or 3-dimensional array.

kernel : numpy.ndarray or Kernel

The convolution kernel. The number of dimensions should match those for the array, and the dimensions should be odd in all directions.

boundary : str, optional

A flag indicating how to handle boundaries:

• None

  Set the result values to zero where the kernel extends beyond the edge of the array (default).

• ‘fill’

  Set values outside the array boundary to fill_value.

• ‘wrap’

  Periodic boundary that wrap to the other side of array.

• ‘extend’

  Set values outside the array to the nearest array value.

fill_value : float, optional

The value to use outside the array when using boundary=’fill’

normalize_kernel : bool, optional

Whether to normalize the kernel prior to convolving

Returns:

result : numpy.ndarray

An array with the same dimensions and as the input array, convolved with kernel. The data type depends on the input array type. If array is a floating point type, then the return array keeps the same data type, otherwise the type is numpy.float.

Notes

Masked arrays are not supported at this time. The convolution is always done at numpy.float precision.