ewatercycle.util module

ewatercycle.util.find_closest_point(grid_longitudes: Iterable[float], grid_latitudes: Iterable[float], point_longitude: float, point_latitude: float) → Tuple[int, int]

Find closest grid cell to a point based on Geographical distances.

Parameters

  • grid_longitudes – 1d array of model grid longitudes in degrees

  • grid_latitudes – 1d array of model grid latitudes in degrees

  • point_longitude – longitude in degrees of target coordinate

  • point_latitude – latitude in degrees of target coordinate

Returns

index of closest grid point in the original longitude array idx_lat: index of closest grid point in the original latitude array

Return type

idx_lon

ewatercycle.util.geographical_distances(point_longitude: float, point_latitude: float, lon_vectors: numpy.ndarray, lat_vectors: numpy.ndarray, radius=6373.0) → numpy.ndarray

It uses Spherical Earth projected to a plane formula: https://en.wikipedia.org/wiki/Geographical_distance

Parameters

  • point_longitude – longitude in degrees of target coordinate

  • point_latitude – latitude in degrees of target coordinate

  • lon_vectors – 1d array of longitudes in degrees

  • lat_vectors – 1d array of latitudes in degrees

  • radius – Radius of a sphere in km. Default is Earths approximate radius.

Returns

array of geographical distance of point to all vector members

Return type

distances

ewatercycle.util.get_time(time_iso: str) → datetime

Return a datetime in UTC.

Convert a date string in ISO format to a datetime and check if it is in UTC.

ewatercycle.util.get_extents(shapefile: Any, pad=0) → Dict[str, float]

Get lat/lon extents from shapefile and add padding.

Parameters

  • shapefile – Path to shapfile

  • pad – Optional padding

Returns

Dict with start_longitude, start_latitude, end_longitude, end_latitude

ewatercycle.util.fit_extents_to_grid(extents, step=0.1, offset=0.05, ndigits=2) → Dict[str, float]

Get lat/lon extents fitted to a grid.

Parameters

  • extents – Dict with start_longitude, start_latitude, end_longitude, end_latitude

  • step – Distance between to grid cells

  • offset – Offset to pad with after rounding extent to step.

  • ndigits – Number of digits to return

Returns

Dict with start_longitude, start_latitude, end_longitude, end_latitude

ewatercycle.util.data_files_from_recipe_output(recipe_output: esmvalcore.experimental.recipe_output.RecipeOutput) → Tuple[str, Dict[str, str]]

Get data files from a ESMVaLTool recipe output

Expects first diagnostic task to produce files with single var each.

Parameters

recipe_output – ESMVaLTool recipe output

Returns

Tuple with directory of files and a dict where key is cmor short name and value is relative path to NetCDF file

ewatercycle.util.to_absolute_path(input_path: str, parent: Optional[Path] = None, must_exist: bool = False, must_be_in_parent=True) → Path

Parse input string as pathlib.Path object.

Parameters

  • input_path – Input string path that can be a relative or absolute path.

  • parent – Optional parent path of the input path

  • must_exist – Optional argument to check if the input path exists.

  • must_be_in_parent – Optional argument to check if the input path is subpath of parent path

Returns

The input path that is an absolute path and a pathlib.Path object.

ewatercycle.util.reindex(source_file: str, var_name: str, mask_file: str, target_file: str)

Conform the input file onto the indexes of a mask file, writing the results to the target file.

Parameters

  • source_file – Input string path of the file that needs to be reindexed.

  • var_name – Variable name in the source_file dataset.

  • mask_file – Input string path of the mask file.

  • target_file – Output string path of the

  • reindexed. (file that is) –