point_aligned_smooth

sofia_redux.scan.utilities.numba_functions.point_aligned_smooth(flat_data, flat_kernel, flat_weight, flat_valid, data_index, kernel_indices, kernel_reference_index, data_shape, data_steps, validated, weighted)

Perform direct kernel convolution at a single point.

This low-level function is designed to return the convolved result and associated weight of data with a kernel at a given point, where all kernel points perfectly align with points in the data array.

Parameters:

flat_datanumpy.ndarray (float)

A flattened version of N-dimensional data (data.ravel()) of shape (n,).

flat_kernelnumpy.ndarray (float)

A flattened version of an N-dimensions kernel (kernel.ravel()) of shape (m,).

flat_weightnumpy.ndarray (float)

A flattened version of N-dimensional weights (weight.ravel()). If weighted is True, must be of shape (n,).

flat_validnumpy.ndarray (bool)

A flattened version of an N-dimensional validity array (valid.ravel()). If validated is True, must be of shape (n,).

data_indexnumpy.ndarray (int)

The N-D index on the original data array at which to determine the smoothed value. Must be of shape (n_dimensions,) and by in Numpy (y, x) order.

kernel_indicesnumpy.ndarray (int)

An array of shape (n_dimensions, kernel.size) as returned by spline_utils.flat_index_mapping(). This gives the N-dimensional kernel index for a flat kernel. I.e., kernel.ravel()[i] is the same as kernel[kernel_indices[i]]. Note that dimensions are ordered using the Numpy (y, x) convention.

kernel_reference_indexnumpy.ndarray (int)

The kernel reference index specifying the center of the kernel. Must be of shape (n_dimensions,) and use numpy dimensional ordering (y, x).

data_shapenumpy.ndarray (int)

The shape of data as an array of shape (n_dimensions,) in Numpy (y, x) order.

data_stepsnumpy.ndarray (int)

An array of shape (n_dimensions,) in Numpy (y, x) order giving the number of elements one would need to jump on a flattened data for a single increment along a given dimension on the ND-array. Please see spline_utils.flat_index_mapping() for further details.

weightedbool

If True, indicates that weighting is required and flat_weight should be of shape (data.size,). Otherwise, no weighting is required, and flat_weight may be of shape (0,).

validatedbool

If True, indicates that validity checking is required and flat_valid should be of shape (data.size,). Otherwise, flat_valid may be of shape (0,).

Returns:

smooth_value, smooth_weightfloat, float

The derived smooth value and associated weight by convolving data with kernel at data_index.