smooth_values_at

sofia_redux.scan.utilities.numba_functions.smooth_values_at(data, kernel, indices, kernel_reference_index, knots, coefficients, degrees, panel_mapping, panel_steps, knot_steps, nk1, spline_mapping, weight=None, valid=None)

Return the values of data, smoothed by a kernel at the given indices.

A “smooth value” is one in which the convolution of the data with a kernel centered over a specific point is calculated and returned. This function is essentially a wrapper around smooth_value_at() to process multiple points in a single pass. This is a moderately low-level function, and requires a spline representation of the kernel to have been previously defined (see sofia_redux.toolkit.splines.spline.Spline for further details).

If the indices align perfectly with existing data indices (i.e., the difference between the indices and kernel_reference_index can be represented exactly as an integer), no spline representation is required as direct convolution with the kernel is possible.

Parameters:

datanumpy.ndarray (float)

The data from which to calculate smoothed values. Must be an array of arbitrary shape, but have n_dimensions.

kernelnumpy.ndarray (float)

The kernel to smooth with. Must be an array of arbitrary shape, but match the same number of dimensions as data.

indicesnumpy.ndarray (int)

The indices in relation to data for which to calculate smoothed values. Must be of shape (n_dimensions, n) and use numpy dimensional ordering (y, x).

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).

knotsnumpy.ndarray (float)

The knots as calculated by the Spline object on kernel. These should be of shape (n_dimensions, max_knots) where max_knots is the maximum possible number of knots for a spline representation of kernel over all dimensions. Dimensions should be ordered as (x, y).

coefficientsnumpy.ndarray (float)

The spline coefficients of shape (n_coefficients,).

degreesnumpy.ndarray (int)

The spline degrees for each dimension of shape (n_dimensions,).

Dimensions should be ordered as (x, y).

panel_mappingnumpy.ndarray (int)

The panel mapping translation for the spline fit. Should be of shape (n_dimensions, n_panels). Dimensions should be ordered as (x, y).

panel_stepsnumpy.ndarray (int)

The panel steps translation for the spline fit. Should be of shape (n_dimensions,). Dimensions should be ordered as (x, y).

knot_stepsnumpy.ndarray (int)

The spline knot steps translation for the spline fit. Should be of shape (n_dimensions,). Dimensions should be ordered as (x, y).

nk1numpy.ndarray (int)

Another spline mapping parameter which is equal to n_knots - degrees - 1 for the spline. Should be of shape (n_dimensions,). Dimensions should be ordered as (x, y).

spline_mappingnumpy.ndarray (int)

A spline mapping translation for the spline knots. Should be of shape (n_dimensions, max_knots). Dimensions should be ordered as (x, y).

weightnumpy.ndarray (float), optional

The optional data weights. Should have the same shape as data.

validnumpy.ndarray (bool), optional

An optional array that marks good data values as True, and all others that should not be used in the fit as False. Should be the same shape as data.

Returns:

smooth_values, smooth_weightsnumpy.ndarray, numpy.ndarray

The smoothed values and weights as determined by convolution of data with kernel, and possibly adjusted for position by a spline fit. Both will be of shape (n,) matching the number of indices passed in as an argument.

Notes

For the purposes of debugging, please remember that any spline parameters have their dimensionality expressed in (x, y, z, …) order, but all other parameters (such as indices) use native numpy ordering (…, z, y, x). If the parameters were calculated (as expected) via the spline class and other native sofia_scan functions, then no conversion is necessary. However, if any of these parameters were manually created, please be aware of this difference.