slim4.rbf

class slim4.rbf.Csr(cols, csr_start, weights, index)

Bases: object

Compressed sparse row interpolation structure.

Parameters

colsnumpy.ndarray

Column indices of the coefficients.

csr_startnumpy.ndarray

CSR row pointer array.

weightsnumpy.ndarray

Interpolation weights.

indexnumpy.ndarray

Structured-grid (lon, lat) indices associated with the unique sampled points.

cols: ndarray

csr_start: ndarray

index: ndarray

weights: ndarray

class slim4.rbf.RbfInfo(pfrom, pto, weights, count)

Bases: object

add(other_rbf_info)

Return type:

None

count: ndarray

pfrom: ndarray

pto: ndarray

reorder()

Return type:

None

weights: ndarray

class slim4.rbf.RbfInterpolator2D(data_points, data_fields, time=None, is_lonlat=True)

Bases: object

compute_csr(mesh2d, radii, boundaries=None)

Compute Csr object based on the coordinates of the nodes of a 2D mesh

Parameters:

• object (mesh2d -- slim4.d2.Mesh)

• interpolation (radii -- List of radii to be used for the)

• boundaries. (boundaries -- Tag or list of tags of mesh) – If None, whole mesh (Default: None)

Return type:

None

csr: Csr | None = None

get_block()

Return type:

ndarray

get_data(time=None)

Get data corresponding to previously computed Csr object

Return type:

Tuple[Dict[str, ndarray], ndarray]

Parameters:

time:

array of times. If None, data is given at all data time (default: None)

Returns:

data_fields:

Dictionary of data fields

data_time:

array of corresponding times

interp_info: RbfInfo | None = None

slim4.rbf.apply_rbf_weights(rbf_info, data)

Return type:

ndarray

slim4.rbf.compute_rbf_weights(data_points, query_points, radii, allow_no_data=False)

Return type:

RbfInfo

slim4.rbf.compute_rbf_weights_radius(data_points, query_points, max_radius, n_max=20)

Return interpolation information to interpolate fields using radial basis functions.

Return type:

RbfInfo

slim4.rbf.empty_rbf_info(n)

Return type:

RbfInfo