regular_kernel_convolve

sofia_redux.scan.utilities.numba_functions.regular_kernel_convolve(data, kernel, kernel_reference_index=None, weight=None, valid=None)

Direct convolution of ND data with a given kernel.

Fast Numba implementation of direct kernel convolution for arbitrary dimensions. The actual convolution is performed on a point-by-point basis with point_aligned_smooth(), while the majority of this function relates to process reducing N-dimensions to a single dimension. Note that this is not suitable for 1-dimensional convolution.

Parameters:

datanumpy.ndarray (float)

The data to convolve.

kernelnumpy.ndarray (float)

The kernel to convolve the data with. Should have the same dimensionality as data.

kernel_reference_indexnumpy.ndarray (int), optional

The index marking the center of the kernel. If not provided, defaults to ceil((kernel.shape - 1) / 2).

weightnumpy.ndarray (float), optional

An optional weighting array.

validnumpy.ndarray (bool), optional

An optional validating array where False excludes a datum from inclusion in processing. Effectively the same as setting weight[i] = 0 for index i.

Returns:

convolved, smoothed_weightsnumpy.ndarray (float), numpy.ndarray (float)