sup3r.utilities.regridder.WindRegridder

class WindRegridder(source_meta, target_meta, cache_pattern=None, leaf_size=4, k_neighbors=4, n_chunks=100, max_distance=None, max_workers=None)[source]

Bases: Regridder

Class to regrid windspeed and winddirection. Includes methods for converting windspeed and winddirection to U and V and inverting after interpolation

Get weights and indices used to map from source grid to target grid

Parameters:

source_meta (pd.DataFrame) – Set of coordinates for source grid
target_meta (pd.DataFrame) – Set of coordinates for target grid
cache_pattern (str | None) – Pattern for cached indices and distances for ball tree. Will load these if provided. Should be of the form ‘./{array_name}.pkl’ where array_name will be replaced with either ‘indices’ or ‘distances’.
leaf_size (int, optional) – leaf size for BallTree
k_neighbors (int, optional) – number of nearest neighbors to use for interpolation
n_chunks (int) – Number of spatial chunks to use for tree queries. The total number of points in the target_meta will be split into n_chunks and the points in each chunk will be queried at the same time.
max_distance (float | None) – Max distance to new grid points from original points before filling with nans.
max_workers (int | None) – Max number of workers to use for running all tree queries needed to building full set of indices and distances for each target_meta coordinate.

Methods

`cache_all_queries`()	Cache indices and distances from ball tree query
`get_all_queries`([max_workers])	Query ball tree for all coordinates in the target_meta and store results
`get_source_uv`(index_chunk, height, source_files)	Get u/v wind components from windspeed and winddirection
`get_source_values`(index_chunk, feature, ...)	Get values to use for interpolation from h5 source files
`get_spatial_chunk`(s_slice)	Get list of coordinates in target_meta specified by the given spatial slice
`init_queries`()	Initialize arrays for tree queries and either load query cache or perform all queries
`interpolate`(distance_chunk, values)	Interpolate to new coordinates based on distances from those coordinates and the values of the points at those distances
`invert_uv`(u, v)	Get u/v wind components from windspeed and winddirection
`load_cache`()	Load cached indices and distances from ball tree query
`query_tree`(s_slice)	Get indices and distances for points specified by the given spatial slice
`regrid_coordinates`(index_chunk, ...)	Regrid wind fields at given height for the requested coordinate index
`run`(source_meta, target_meta[, ...])	Query tree for every point in target_meta to get full set of indices and distances for the neighboring points in the source_meta.
`save_query`(s_slice)	Save tree query for coordinates specified by given spatial slice

Attributes

`MAX_DISTANCE`
`MIN_DISTANCE`
`cache_exists`	Check if cache exists before building tree.
`dist_mask`	Mask for points too far from original grid
`distance_file`	Get name of cache distances file
`distances`	Get distances for all tree queries.
`index_file`	Get name of cache indices file
`indices`	Get indices for all tree queries.
`tree`	Build ball tree from source_meta
`weights`	Get weights used for regridding

classmethod get_source_values(index_chunk, feature, source_files)[source]

Get values to use for interpolation from h5 source files

Parameters:

index_chunk (ndarray) – Chunk of the full array of indices where indices[i] gives the list of coordinate indices in the source data to be used for interpolation for the i-th coordinate in the target data. (temporal, n_points, k_neighbors)
feature (str) – Name of feature to interpolate
source_files (list) – List of paths to source files

Returns:

ndarray – Array of values to use for interpolation with shape (temporal, n_points, k_neighbors)

classmethod get_source_uv(index_chunk, height, source_files)[source]

Get u/v wind components from windspeed and winddirection

Parameters:

index_chunk (ndarray) – Chunk of the full array of indices where indices[i] gives the list of coordinate indices in the source data to be used for interpolation for the i-th coordinate in the target data. (temporal, n_points, k_neighbors)
height (int) – Wind height level
source_files (list) – List of paths to h5 source files

Returns:

u (ndarray) – Array of zonal wind values to use for interpolation with shape (temporal, n_points, k_neighbors)
v (ndarray) – Array of meridional wind values to use for interpolation with shape (temporal, n_points, k_neighbors)

classmethod invert_uv(u, v)[source]

Get u/v wind components from windspeed and winddirection

Parameters:

u (ndarray) – Array of interpolated zonal wind values with shape (temporal, n_points)
v (ndarray) – Array of interpolated meridional wind values with shape (temporal, n_points)

Returns:

ws (ndarray) – Array of interpolated windspeed values with shape (temporal, n_points)
wd (ndarray) – Array of winddirection values with shape (temporal, n_points)

classmethod regrid_coordinates(index_chunk, distance_chunk, height, source_files)[source]

Regrid wind fields at given height for the requested coordinate index

Parameters:

index_chunk (ndarray) – Chunk of the full array of indices where indices[i] gives the list of coordinate indices in the source data to be used for interpolation for the i-th coordinate in the target data. (temporal, n_points, k_neighbors)
distance_chunk (ndarray) – Chunk of the full array of distances where distances[i] gives the list of distances to the source coordinates to be used for interpolation for the i-th coordinate in the target data. (temporal, n_points, k_neighbors)
height (int) – Wind height level
source_files (list) – List of paths to h5 source files

Returns:

ws (ndarray) – Array of interpolated windspeed values with shape (temporal, n_points)
wd (ndarray) – Array of winddirection values with shape (temporal, n_points)

__call__(data)

Regrid given spatiotemporal data over entire grid

Parameters:: data (ndarray) – Spatiotemporal data to regrid to target_meta. Data can be flattened in the spatial dimension to match the target_meta or be in a 2D spatial grid, e.g.: (spatial, temporal) or (spatial_1, spatial_2, temporal)
Returns:: out (ndarray) – Flattened regridded spatiotemporal data (spatial, temporal)

cache_all_queries(): Cache indices and distances from ball tree query

property cache_exists: Check if cache exists before building tree.

property dist_mask

Mask for points too far from original grid

Returns:: mask (ndarray) – Bool array for points outside original grid extent

property distance_file: Get name of cache distances file

property distances: Get distances for all tree queries.

get_all_queries(max_workers=None): Query ball tree for all coordinates in the target_meta and store results

get_spatial_chunk(s_slice)

Get list of coordinates in target_meta specified by the given spatial slice

Parameters:: s_slice (slice) – slice specifying which spatial indices in the target grid should be selected. This selects n_points from the target grid
Returns:: ndarray – Array of n_points in target_meta selected by s_slice.

property index_file: Get name of cache indices file

property indices: Get indices for all tree queries.

init_queries(): Initialize arrays for tree queries and either load query cache or perform all queries

classmethod interpolate(distance_chunk, values)

Interpolate to new coordinates based on distances from those coordinates and the values of the points at those distances

Parameters:

distance_chunk (ndarray) – Chunk of the full array of distances where distances[i] gives the list of k_neighbors distances to the source coordinates to be used for interpolation for the i-th coordinate in the target data. (n_points, k_neighbors)
values (ndarray) – Array of values corresponding to the point distances with shape (temporal, n_points, k_neighbors)

Returns:

ndarray – Time series of values at interpolated points with shape (temporal, n_points)

load_cache(): Load cached indices and distances from ball tree query

query_tree(s_slice)

Get indices and distances for points specified by the given spatial slice

Parameters:

s_slice (slice) – slice specifying which spatial indices in the target grid should be selected. This selects n_points from the target grid

Returns:

distances (ndarray) – Array of distances for neighboring points for each point selected by s_slice. (n_ponts, k_neighbors)
indices (ndarray) – Array of indices for neighboring points for each point selected by s_slice. (n_ponts, k_neighbors)

classmethod run(source_meta, target_meta, cache_pattern=None, leaf_size=4, k_neighbors=4, n_chunks=100, max_workers=None)

Query tree for every point in target_meta to get full set of indices and distances for the neighboring points in the source_meta.

Parameters:

source_meta (pd.DataFrame) – Set of coordinates for source grid
target_meta (pd.DataFrame) – Set of coordinates for target grid
cache_pattern (str | None) – Pattern for cached indices and distances for ball tree. Will load these if provided. Should be of the form ‘./{array_name}.pkl’ where array_name will be replaced with either ‘indices’ or ‘distances’.
leaf_size (int, optional) – leaf size for BallTree
k_neighbors (int, optional) – number of nearest neighbors to use for interpolation
n_chunks (int) – Number of spatial chunks to use for tree queries. The total number of points in the target_meta will be split into n_chunks and the points in each chunk will be queried at the same time.
max_workers (int | None) – Max number of workers to use for running all tree queries needed to building full set of indices and distances for each target_meta coordinate.

save_query(s_slice): Save tree query for coordinates specified by given spatial slice

property tree: Build ball tree from source_meta

property weights: Get weights used for regridding