credit.regrid ============= .. py:module:: credit.regrid .. autoapi-nested-parse:: This scripts contains functions that performs nearest, bilinear, and conservative interpolation on xarray.Datasets. The original version of this script is available at WeatherBench2. Note: only rectalinear grids (one dimensional lat/lon coordinates) are supported. Reference - WeatherBench2 regridding: https://github.com/google-research/weatherbench2/blob/main/weatherbench2/regridding.py Example usage # ================================================================================== # import credit.regrid as regrid # --------------------- # # prepare grids # target grid lon_1deg = np.arange(0, 360, 1) lat_1deg = np.arange(-90, 91, 1) target_grid = regrid.Grid.from_degrees(lon_1deg, lat_1deg) # input grid (flip 90 --> -90 to -90 --> 90) lon_025deg = ds_static['longitude'].values lat_025deg = ds_static['latitude'].values[::-1] source_grid = regrid.Grid.from_degrees(lon_025deg, lat_025deg) # --------------------- # # define regridder regridder = regrid.ConservativeRegridder(source=source_grid, target=target_grid) # --------------------- # # clear old chunking and interpolate data ds_static = ds_static.chunk({'longitude': -1, 'latitude': -1}) ds_static_1deg = regridder.regrid_dataset(ds_static) # --------------------- # # ... some xarray operations to preserve the order of dims ... # # assign coordinates lon_1deg = np.arange(0, 360, 1) lat_1deg = np.arange(-90, 91, 1) ds_static_1deg = ds_static_1deg.assign_coords({ 'latitude': lat_1deg, 'longitude': lon_1deg }) # flip latitude from -90 --> 90 to 90 --> -90 ds_static_1deg = ds_static_1deg.isel(latitude=slice(None, None, -1)) Attributes ---------- .. autoapisummary:: credit.regrid.Array Classes ------- .. autoapisummary:: credit.regrid.Grid credit.regrid.Regridder credit.regrid.NearestRegridder credit.regrid.BilinearRegridder credit.regrid.ConservativeRegridder Functions --------- .. autoapisummary:: credit.regrid.nearest_neighbor_indices credit.regrid._assert_increasing credit.regrid._latitude_cell_bounds credit.regrid._latitude_overlap credit.regrid._conservative_latitude_weights credit.regrid._align_phase_with credit.regrid._periodic_upper_bounds credit.regrid._periodic_lower_bounds credit.regrid._periodic_overlap credit.regrid._longitude_overlap credit.regrid._conservative_longitude_weights Module Contents --------------- .. py:data:: Array .. py:class:: Grid Representation of a rectilinear grid. .. py:attribute:: lon :type: numpy.ndarray .. py:attribute:: lat :type: numpy.ndarray .. py:method:: from_degrees(lon: numpy.ndarray, lat: numpy.ndarray) -> Grid :classmethod: .. py:property:: shape :type: tuple[int, int] .. py:method:: _to_tuple() -> tuple[tuple[float, Ellipsis], tuple[float, Ellipsis]] .. py:method:: __eq__(other) .. py:method:: __hash__() .. py:class:: Regridder Base class for regridding. .. py:attribute:: source :type: Grid .. py:attribute:: target :type: Grid .. py:method:: regrid_array(field: Array) -> numpy.ndarray :abstractmethod: Regrid an array with dimensions (..., lon, lat) from source to target. .. py:method:: regrid_dataset(dataset: xarray.Dataset) -> xarray.Dataset Regrid an xarray.Dataset from source to target. .. py:function:: nearest_neighbor_indices(source_grid: Grid, target_grid: Grid) -> numpy.ndarray Returns Haversine nearest neighbor indices from source_grid to target_grid. .. py:class:: NearestRegridder Bases: :py:obj:`Regridder` Regrid with nearest neighbor interpolation. .. py:method:: indices() The interpolation indices associated with source_grid. .. py:method:: _nearest_neighbor_2d(array: Array) -> numpy.ndarray 2D nearest neighbor interpolation using BallTree. .. py:method:: regrid_array(field: Array) -> numpy.ndarray Regrid an array with dimensions (..., lon, lat) from source to target. .. py:class:: BilinearRegridder Bases: :py:obj:`Regridder` Regrid with bilinear interpolation. .. py:method:: regrid_array(field: Array) -> numpy.ndarray Regrid an array with dimensions (..., lon, lat) from source to target. .. py:function:: _assert_increasing(x: numpy.ndarray) -> None .. py:function:: _latitude_cell_bounds(x: Array) -> numpy.ndarray .. py:function:: _latitude_overlap(source_points: Array, target_points: Array) -> numpy.ndarray Calculate the area overlap as a function of latitude. .. py:function:: _conservative_latitude_weights(source_points: Array, target_points: Array) -> numpy.ndarray Create a weight matrix for conservative regridding along latitude. :param source_points: 1D latitude coordinates in radians for centers of source cells. :param target_points: 1D latitude coordinates in radians for centers of target cells. :returns: NumPy array with shape (target_size, source_size). Rows sum to 1. .. py:function:: _align_phase_with(x, target, period) Align the phase of a periodic number to match another. .. py:function:: _periodic_upper_bounds(x, period) .. py:function:: _periodic_lower_bounds(x, period) .. py:function:: _periodic_overlap(x0, x1, y0, y1, period) Calculate the overlap between two intervals considering periodicity. .. py:function:: _longitude_overlap(first_points: Array, second_points: Array, period: float = 2 * np.pi) -> numpy.ndarray Calculate the area overlap as a function of longitude. .. py:function:: _conservative_longitude_weights(source_points: numpy.ndarray, target_points: numpy.ndarray) -> numpy.ndarray Create a weight matrix for conservative regridding along longitude. :param source_points: 1D longitude coordinates in radians for centers of source cells. :param target_points: 1D longitude coordinates in radians for centers of target cells. :returns: NumPy array with shape (target_size, source_size). Rows sum to 1. .. py:class:: ConservativeRegridder Bases: :py:obj:`Regridder` Regrid with linear conservative regridding. .. py:method:: _mean(field: Array) -> numpy.ndarray Computes cell-averages of field on the target grid. .. py:method:: _nanmean(field: Array) -> numpy.ndarray Compute cell-averages skipping NaNs like np.nanmean. .. py:method:: regrid_array(field: Array) -> numpy.ndarray Regrid an array with dimensions (..., lon, lat) from source to target.