convolve¶

astropy.convolution.
convolve
(array, kernel, boundary='fill', fill_value=0.0, nan_treatment='interpolate', normalize_kernel=True, mask=None, preserve_nan=False, normalization_zero_tol=1e08)[source]¶ Convolve an array with a kernel.
This routine differs from
scipy.ndimage.convolve
because it includes a special treatment forNaN
values. Rather than includingNaN
values in the array in the convolution calculation, which causes largeNaN
holes in the convolved array,NaN
values are replaced with interpolated values using the kernel as an interpolation function.Parameters:  array :
NDData
ornumpy.ndarray
or array_like The array to convolve. This should be a 1, 2, or 3dimensional array or a list or a set of nested lists representing a 1, 2, or 3dimensional array. If an
NDData
, themask
of theNDData
will be used as themask
argument. kernel :
numpy.ndarray
orKernel
The convolution kernel. The number of dimensions should match those for the array, and the dimensions should be odd in all directions. If a masked array, the masked values will be replaced by
fill_value
. 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.
 ‘fill’
 Set values outside the array boundary to
fill_value
(default).
 ‘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 to have a sum of one.
 nan_treatment : {‘interpolate’, ‘fill’}
interpolate will result in renormalization of the kernel at each position ignoring (pixels that are NaN in the image) in both the image and the kernel. ‘fill’ will replace the NaN pixels with a fixed numerical value (default zero, see
fill_value
) prior to convolution Note that if the kernel has a sum equal to zero, NaN interpolation is not possible and will raise an exception. preserve_nan : bool
After performing convolution, should pixels that were originally NaN again become NaN?
 mask :
None
ornumpy.ndarray
A “mask” array. Shape must match
array
, and anything that is masked (i.e., not 0/False
) will be set to NaN for the convolution. IfNone
, no masking will be performed unlessarray
is a masked array. Ifmask
is notNone
andarray
is a masked array, a pixel is masked of it is masked in eithermask
orarray.mask
. normalization_zero_tol: float, optional
The absolute tolerance on whether the kernel is different than zero. If the kernel sums to zero to within this precision, it cannot be normalized. Default is “1e8”.
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
For masked arrays, masked values are treated as NaNs. The convolution is always done at
numpy.float
precision. array :