Source code for xcdat.utils

import importlib
import json
from typing import Dict, List, Optional, Union

import xarray as xr
from dask.array.core import Array

[docs] def compare_datasets(ds1: xr.Dataset, ds2: xr.Dataset) -> Dict[str, List[str]]: """Compares the keys and values of two datasets. This utility function is especially useful for debugging tests that involve comparing two Dataset objects for being identical or equal. Checks include: - Unique keys - keys that exist only in one of the two datasets. - Non-identical - keys whose values have the same dimension, coordinates, values, name, attributes, and attributes on all coordinates. - Non-equal keys - keys whose values have the same dimension, coordinates, and values, but not necessarily the same attributes. Key values that are non-equal will also be non-identical. Parameters ---------- ds1 : xr.Dataset The first Dataset. ds2 : xr.Dataset The second Dataset. Returns ------- Dict[str, Union[List[str]]] A dictionary mapping unique, non-identical, and non-equal keys in both Datasets. """ results = { "unique_coords": list(ds1.coords.keys() ^ ds2.coords.keys()), "unique_data_vars": list(ds1.data_vars.keys() ^ ds2.data_vars.keys()), "nonidentical_coords": [], "nonidentical_data_vars": [], "nonequal_coords": [], "nonequal_data_vars": [], } ds_keys = { "coords": ds1.coords.keys() & ds2.coords.keys(), "data_vars": ds1.data_vars.keys() & ds2.data_vars.keys(), } for key_type, keys in ds_keys.items(): for key in keys: identical = ds1[key].identical(ds2[key]) equals = ds1[key].equals(ds2[key]) if not identical: results[f"nonidentical_{key_type}"].append(key) if not equals: results[f"nonequal_{key_type}"].append(key) return results
def str_to_bool(attr: str) -> bool: """Converts bool string to bool. netCDF files can only store attributes with a type of str, Number, ndarray, number, list, or tuple. xCDAT methods store boolean attributes as strings. This function will convert such attributes back to booleans. Parameters ---------- attr : str The boolean attribute as type str. Returns ------- bool The boolean attribute as type bool. """ if attr != "True" and attr != "False": raise ValueError( "The attribute is not a string representation of a Python" "bool ('True' or 'False')" ) bool_attr = json.loads(attr.lower()) return bool_attr def _has_module(modname: str) -> bool: # pragma: no cover """Checks if the specified module is installed in the Python environment. Parameters ---------- modname : str The name of the module. Returns ------- bool """ try: importlib.import_module(modname) has = True except ImportError: has = False return has def _if_multidim_dask_array_then_load( obj: Union[xr.DataArray, xr.Dataset] ) -> Optional[Union[xr.DataArray, xr.Dataset]]: """ If the underlying array for an xr.DataArray or xr.Dataset is a multidimensional, lazy Dask Array, load it into an in-memory NumPy array. This function must be called before manipulating values in a multidimensional Dask Array, which xarray does not support directly. Otherwise, it raises `NotImplementedError xarray can't set arrays with multiple array indices to dask yet`. Parameters ---------- obj : Union[xr.DataArray, xr.Dataset] The xr.DataArray or xr.Dataset. If the xarray object is chunked, the underlying array will be a Dask Array. """ if isinstance(, Array) and obj.ndim > 1: return obj.load() return None