cryoswath.l3 module

cryoswath.l3.append_basin_group(ds: DataArray | Dataset, basin_gdf: GeoDataFrame = None) → Dataset

cryoswath.l3.append_basin_id(ds: DataArray | Dataset, basin_gdf: GeoDataFrame = None) → Dataset

cryoswath.l3.append_elevation_reference(geospatial_ds: Dataset | DataArray, ref_elev_name: str = 'ref_elev') → Dataset

cryoswath.l3.build_dataset(region_of_interest: str | ~shapely.geometry.polygon.Polygon, start_datetime: str | ~pandas._libs.tslibs.timestamps.Timestamp, end_datetime: str | ~pandas._libs.tslibs.timestamps.Timestamp, *, l2_type: str = 'swath', max_elev_diff: float = 150, timestep_months: int = 1, window_ntimesteps: int = 3, spatial_res_meter: float = 500, agg_func_and_meta: tuple[callable, dict] = (<function med_iqr_cnt>, {'_count': 'i8', '_iqr': 'f8', '_median': 'f8'}), cache_filename: str = None, cache_filename_extra: str = None, crs: ~pyproj.crs.crs.CRS | int = None, reprocess: bool = False, **l2_from_id_kwargs)

cryoswath.l3.build_path(region_of_interest, timestep_months, spatial_res_meter, aggregation_period=None)

cryoswath.l3.fill_missing_coords(l3_data, minx: int = 90000000.0, miny: int = 90000000.0, maxx: int = -90000000.0, maxy: int = -90000000.0) → Dataset

cryoswath.l3.fill_voids(ds: Dataset, main_var: str, error: str, *, elev: str = 'ref_elev', per: tuple[str] = ('basin', 'basin_group'), basin_shapes: GeoDataFrame = None, outlier_limit: float = 5, outlier_replace: bool = False, outlier_iterations: int = 1) → Dataset

cryoswath.l3.interpolate_hypsometrically_poly3(df)

cryoswath.l3.med_iqr_cnt(data)