grid_process.grid_interp_utils#

Module with utilities for spatial interpolation of gridded datasets

Functions#

create_dst_grid() → xarray.Dataset)

Create a destination grid on which data can be interpolated. The output

create_dst_grid_from_src(→ xarray.Dataset)

Creates a destination grid on which data can be interpolated from a source grid

Module Contents#

create_dst_grid(dst_grid_crs: Any, bottomleft: tuple[float, float], topright: tuple[float, float] | None = None, nrowcol: tuple[int, int] | None = None, resolution: float | None = None, add_latlon: bool = False, dtype: numpy.dtype = np.dtype('float32')) → xarray.Dataset[source]#

Create a destination grid on which data can be interpolated. The output can be directly used with interp2grid(). Two out of the three optional input arguments topright, nrowcol, resolution must be provided.

Parameters:

  • dst_grid_crs (string, int, dict or a CRS object) – Anything accepted by pyproj.crs.CRS.from_user_input: https://pyproj4.github.io/pyproj/dev/api/crs/crs.html#pyproj.crs.CRS.from_user_input (e.g. EPSG:4326)

  • bottomleft (tuple of floats) – (y, x) coordinates of the center of the bottom left corner pixel

  • topright (tuple of floats, optional) – (y, x) coordinates of the center of the top right corner pixel. If provided with nrowcol, this is the exact coordinates. If provided with resolution this is a closed upper bound. The exact coordinates of the top right corner pixel will then be a multiple of the resolution with respect to the bottom left corner.

  • nrowcol (tuple of int, optional) – number of rows (y) and columns (x) respectively

  • resolution (float, optional) – resolution of the destination grid in the units of the grid_mapping

  • add_latlon (bool) – If True, add latitude and longitude 2d arrays as coordinates

  • dtype (numpy.dtype) – The dtype in which spatial coordinates are returned

Returns:

dst_grid – dataset with spatial horizontal coordinates and an associated crs attribute

Return type:

xarray.Dataset

See also

interp2grid()

create_dst_grid_from_src(src_grid: xarray.Dataset, dst_grid_crs: Any, add_latlon: bool = False) → xarray.Dataset[source]#

Creates a destination grid on which data can be interpolated from a source grid in a different Coordinate Reference System. Domain and sizes are preserved. The output can be directly used with interp2grid().

Parameters:

  • src_grid (xarray Dataset) – The source grid from which a new destination grid with same sizes and domain is derived. Supported data models include:

    Typical spatial dimension and coordinate names are supported, and can be extended via environment variables:

    • Y coordinate: export GRID_PROCESS__data_model__grid__y (e.g., ‘[“y”, “latitude”, “lat”, “dummy_y”]’)

    • X coordinate: export GRID_PROCESS__data_model__grid__x (e.g., ‘[“x”, “longitude”, “lon”, “dummy_x”]’)

    • Unstructured dimension: export GRID_PROCESS__data_model__grid__unstructured_dim (e.g., ‘[“dummy_cell”]’)

    • When the spatial coordinates variables have a CF compliant standard_name attribute, arbitrary names can be used directly.

    Spatial CRS must be referenced via one of:

    • Global attribute ‘crs’ (e.g., “EPSG:2056” or anything accepted by pyproj.CRS.from_user_input())

    • Global attribute ‘grid_mapping’ (internal grid database convention)

    • CF-convention grid mapping variable

    • If coordinates are named ‘latitude’/’longitude’ or ‘lat’/’lon’, WGS84 (EPSG:4326) is assumed.

  • dst_grid_crs (string, int, dict or a CRS object) – Anything accepted by pyproj.crs.CRS.from_user_input: https://pyproj4.github.io/pyproj/dev/api/crs/crs.html#pyproj.crs.CRS.from_user_input (e.g. EPSG:4326)

  • add_latlon (bool) – If True, add latitude and longitude 2d arrays as coordinates

Returns:

dst_grid – dataset with spatial horizontal coordinates and an associated crs attribute

Return type:

xarray.Dataset

See also

interp2grid()