perform_fit

sofia_redux.toolkit.splines.spline_utils.perform_fit(coordinates, knots, coefficients, degrees, panel_mapping, panel_steps, knot_steps, nk1, spline_mapping, n_knots)

Evaluate a given spline at multiple coordinates.

Parameters:

coordinatesnumpy.ndarray (float)

The coordinates at which to evaluate the spline of shape (n_dimensions, n).

knotsnumpy.ndarray (float)

The knots in each dimension of shape (n_dimensions, max_knot_estimate). Must be monotonically increasing for each dimension.

coefficientsnumpy.ndarray (float)

The spline coefficients of shape (n_coefficients,).

degreesnumpy.ndarray (int)

The degrees of the spline in each dimension (n_dimensions,).

panel_mappingnumpy.ndarray (int)

An array containing the panel mapping (flat to n-D) indices. This is created by passing the panel shape (n_knots - (2 * degrees) - 1) into flat_index_mapping(). Should be an array of shape (n_dimensions, n_panels).

panel_stepsnumpy.ndarray (int)

The flat index mapping steps in panel-space of shape (n_dimensions,). These are returned by passing the shape Spline.panel_shape into flat_index_mapping().

knot_stepsnumpy.ndarray (int)

The flat index mapping steps in knot-space of shape (n_dimensions,). These are returned by passing the shape (n_knots - degrees - 1) into flat_index_mapping().

nk1numpy.ndarray (int)

An array of shape (n_dimensions,) containing the values n_knots - k1 where n_knots are the number of knots in each dimension, and k1 are the spline degrees + 1 in each dimension.

spline_mappingnumpy.ndarray (int)

An array containing the spline mapping (flat to n-D) indices. This is created by passing the spline shape (degrees + 1) into flat_index_mapping(). Should be an array of shape (n_dimensions, n_spline_coefficients).

n_knotsnumpy.ndarray (int)

The number of knots in each dimension (n_dimensions,).

Returns:

fitted_valuesnumpy.ndarray (float)

The spline evaluated at coordinates of shape (n,).