from __future__ import annotations import os from functools import partial from glob import glob from io import BytesIO from numbers import Number from typing import ( TYPE_CHECKING, Any, Callable, Dict, Final, Hashable, Iterable, Literal, Mapping, MutableMapping, Sequence, Type, Union, cast, overload, ) import numpy as np from xarray import backends, conventions from xarray.backends import plugins from xarray.backends.common import AbstractDataStore, ArrayWriter, _normalize_path from xarray.backends.locks import _get_scheduler from xarray.core import indexing from xarray.core.combine import ( _infer_concat_order_from_positions, _nested_combine, combine_by_coords, ) from xarray.core.dataarray import DataArray from xarray.core.dataset import Dataset, _get_chunk, _maybe_chunk from xarray.core.indexes import Index from xarray.core.utils import is_remote_uri if TYPE_CHECKING: try: from dask.delayed import Delayed except ImportError: Delayed = None # type: ignore from io import BufferedIOBase from xarray.backends.common import BackendEntrypoint from xarray.core.types import ( CombineAttrsOptions, CompatOptions, JoinOptions, NestedSequence, ) T_NetcdfEngine = Literal["netcdf4", "scipy", "h5netcdf"] T_Engine = Union[ T_NetcdfEngine, Literal["pydap", "pynio", "pseudonetcdf", "cfgrib", "zarr"], Type[BackendEntrypoint], str, # no nice typing support for custom backends None, ] T_Chunks = Union[int, Dict[Any, Any], Literal["auto"], None] T_NetcdfTypes = Literal[ "NETCDF4", "NETCDF4_CLASSIC", "NETCDF3_64BIT", "NETCDF3_CLASSIC" ] DATAARRAY_NAME = "__xarray_dataarray_name__" DATAARRAY_VARIABLE = "__xarray_dataarray_variable__" ENGINES = { "netcdf4": backends.NetCDF4DataStore.open, "scipy": backends.ScipyDataStore, "pydap": backends.PydapDataStore.open, "h5netcdf": backends.H5NetCDFStore.open, "pynio": backends.NioDataStore, "pseudonetcdf": backends.PseudoNetCDFDataStore.open, "cfgrib": backends.CfGribDataStore, "zarr": backends.ZarrStore.open_group, } def _get_default_engine_remote_uri() -> Literal["netcdf4", "pydap"]: engine: Literal["netcdf4", "pydap"] try: import netCDF4 # noqa: F401 engine = "netcdf4" except ImportError: # pragma: no cover try: import pydap # noqa: F401 engine = "pydap" except ImportError: raise ValueError( "netCDF4 or pydap is required for accessing " "remote datasets via OPeNDAP" ) return engine def _get_default_engine_gz() -> Literal["scipy"]: try: import scipy # noqa: F401 engine: Final = "scipy" except ImportError: # pragma: no cover raise ValueError("scipy is required for accessing .gz files") return engine def _get_default_engine_netcdf() -> Literal["netcdf4", "scipy"]: engine: Literal["netcdf4", "scipy"] try: import netCDF4 # noqa: F401 engine = "netcdf4" except ImportError: # pragma: no cover try: import scipy.io.netcdf # noqa: F401 engine = "scipy" except ImportError: raise ValueError( "cannot read or write netCDF files without " "netCDF4-python or scipy installed" ) return engine def _get_default_engine(path: str, allow_remote: bool = False) -> T_NetcdfEngine: if allow_remote and is_remote_uri(path): return _get_default_engine_remote_uri() # type: ignore[return-value] elif path.endswith(".gz"): return _get_default_engine_gz() else: return _get_default_engine_netcdf() def _validate_dataset_names(dataset: Dataset) -> None: """DataArray.name and Dataset keys must be a string or None""" def check_name(name: Hashable): if isinstance(name, str): if not name: raise ValueError( f"Invalid name {name!r} for DataArray or Dataset key: " "string must be length 1 or greater for " "serialization to netCDF files" ) elif name is not None: raise TypeError( f"Invalid name {name!r} for DataArray or Dataset key: " "must be either a string or None for serialization to netCDF " "files" ) for k in dataset.variables: check_name(k) def _validate_attrs(dataset, invalid_netcdf=False): """`attrs` must have a string key and a value which is either: a number, a string, an ndarray, a list/tuple of numbers/strings, or a numpy.bool_. Notes ----- A numpy.bool_ is only allowed when using the h5netcdf engine with `invalid_netcdf=True`. """ valid_types = (str, Number, np.ndarray, np.number, list, tuple) if invalid_netcdf: valid_types += (np.bool_,) def check_attr(name, value, valid_types): if isinstance(name, str): if not name: raise ValueError( f"Invalid name for attr {name!r}: string must be " "length 1 or greater for serialization to " "netCDF files" ) else: raise TypeError( f"Invalid name for attr: {name!r} must be a string for " "serialization to netCDF files" ) if not isinstance(value, valid_types): raise TypeError( f"Invalid value for attr {name!r}: {value!r}. For serialization to " "netCDF files, its value must be of one of the following types: " f"{', '.join([vtype.__name__ for vtype in valid_types])}" ) # Check attrs on the dataset itself for k, v in dataset.attrs.items(): check_attr(k, v, valid_types) # Check attrs on each variable within the dataset for variable in dataset.variables.values(): for k, v in variable.attrs.items(): check_attr(k, v, valid_types) def _resolve_decoders_kwargs(decode_cf, open_backend_dataset_parameters, **decoders): for d in list(decoders): if decode_cf is False and d in open_backend_dataset_parameters: decoders[d] = False if decoders[d] is None: decoders.pop(d) return decoders def _get_mtime(filename_or_obj): # if passed an actual file path, augment the token with # the file modification time mtime = None try: path = os.fspath(filename_or_obj) except TypeError: path = None if path and not is_remote_uri(path): mtime = os.path.getmtime(os.path.expanduser(filename_or_obj)) return mtime def _protect_dataset_variables_inplace(dataset, cache): for name, variable in dataset.variables.items(): if name not in dataset._indexes: # no need to protect IndexVariable objects data = indexing.CopyOnWriteArray(variable._data) if cache: data = indexing.MemoryCachedArray(data) variable.data = data def _finalize_store(write, store): """Finalize this store by explicitly syncing and closing""" del write # ensure writing is done first store.close() def _multi_file_closer(closers): for closer in closers: closer() def load_dataset(filename_or_obj, **kwargs) -> Dataset: """Open, load into memory, and close a Dataset from a file or file-like object. This is a thin wrapper around :py:meth:`~xarray.open_dataset`. It differs from `open_dataset` in that it loads the Dataset into memory, closes the file, and returns the Dataset. In contrast, `open_dataset` keeps the file handle open and lazy loads its contents. All parameters are passed directly to `open_dataset`. See that documentation for further details. Returns ------- dataset : Dataset The newly created Dataset. See Also -------- open_dataset """ if "cache" in kwargs: raise TypeError("cache has no effect in this context") with open_dataset(filename_or_obj, **kwargs) as ds: return ds.load() def load_dataarray(filename_or_obj, **kwargs): """Open, load into memory, and close a DataArray from a file or file-like object containing a single data variable. This is a thin wrapper around :py:meth:`~xarray.open_dataarray`. It differs from `open_dataarray` in that it loads the Dataset into memory, closes the file, and returns the Dataset. In contrast, `open_dataarray` keeps the file handle open and lazy loads its contents. All parameters are passed directly to `open_dataarray`. See that documentation for further details. Returns ------- datarray : DataArray The newly created DataArray. See Also -------- open_dataarray """ if "cache" in kwargs: raise TypeError("cache has no effect in this context") with open_dataarray(filename_or_obj, **kwargs) as da: return da.load() def _chunk_ds( backend_ds, filename_or_obj, engine, chunks, overwrite_encoded_chunks, inline_array, **extra_tokens, ): from dask.base import tokenize mtime = _get_mtime(filename_or_obj) token = tokenize(filename_or_obj, mtime, engine, chunks, **extra_tokens) name_prefix = f"open_dataset-{token}" variables = {} for name, var in backend_ds.variables.items(): var_chunks = _get_chunk(var, chunks) variables[name] = _maybe_chunk( name, var, var_chunks, overwrite_encoded_chunks=overwrite_encoded_chunks, name_prefix=name_prefix, token=token, inline_array=inline_array, ) return backend_ds._replace(variables) def _dataset_from_backend_dataset( backend_ds, filename_or_obj, engine, chunks, cache, overwrite_encoded_chunks, inline_array, **extra_tokens, ): if not isinstance(chunks, (int, dict)) and chunks not in {None, "auto"}: raise ValueError( f"chunks must be an int, dict, 'auto', or None. Instead found {chunks}." ) _protect_dataset_variables_inplace(backend_ds, cache) if chunks is None: ds = backend_ds else: ds = _chunk_ds( backend_ds, filename_or_obj, engine, chunks, overwrite_encoded_chunks, inline_array, **extra_tokens, ) ds.set_close(backend_ds._close) # Ensure source filename always stored in dataset object if "source" not in ds.encoding and isinstance(filename_or_obj, (str, os.PathLike)): ds.encoding["source"] = _normalize_path(filename_or_obj) return ds def open_dataset( filename_or_obj: str | os.PathLike[Any] | BufferedIOBase | AbstractDataStore, *, engine: T_Engine = None, chunks: T_Chunks = None, cache: bool | None = None, decode_cf: bool | None = None, mask_and_scale: bool | None = None, decode_times: bool | None = None, decode_timedelta: bool | None = None, use_cftime: bool | None = None, concat_characters: bool | None = None, decode_coords: Literal["coordinates", "all"] | bool | None = None, drop_variables: str | Iterable[str] | None = None, inline_array: bool = False, backend_kwargs: dict[str, Any] | None = None, **kwargs, ) -> Dataset: """Open and decode a dataset from a file or file-like object. Parameters ---------- filename_or_obj : str, Path, file-like or DataStore Strings and Path objects are interpreted as a path to a netCDF file or an OpenDAP URL and opened with python-netCDF4, unless the filename ends with .gz, in which case the file is gunzipped and opened with scipy.io.netcdf (only netCDF3 supported). Byte-strings or file-like objects are opened by scipy.io.netcdf (netCDF3) or h5py (netCDF4/HDF). engine : {"netcdf4", "scipy", "pydap", "h5netcdf", "pynio", "cfgrib", \ "pseudonetcdf", "zarr", None}, installed backend \ or subclass of xarray.backends.BackendEntrypoint, optional Engine to use when reading files. If not provided, the default engine is chosen based on available dependencies, with a preference for "netcdf4". A custom backend class (a subclass of ``BackendEntrypoint``) can also be used. chunks : int, dict, 'auto' or None, optional If chunks is provided, it is used to load the new dataset into dask arrays. ``chunks=-1`` loads the dataset with dask using a single chunk for all arrays. ``chunks={}`` loads the dataset with dask using engine preferred chunks if exposed by the backend, otherwise with a single chunk for all arrays. In order to reproduce the default behavior of ``xr.open_zarr(...)`` use ``xr.open_dataset(..., engine='zarr', chunks={})``. ``chunks='auto'`` will use dask ``auto`` chunking taking into account the engine preferred chunks. See dask chunking for more details. cache : bool, optional If True, cache data loaded from the underlying datastore in memory as NumPy arrays when accessed to avoid reading from the underlying data- store multiple times. Defaults to True unless you specify the `chunks` argument to use dask, in which case it defaults to False. Does not change the behavior of coordinates corresponding to dimensions, which always load their data from disk into a ``pandas.Index``. decode_cf : bool, optional Whether to decode these variables, assuming they were saved according to CF conventions. mask_and_scale : bool, optional If True, replace array values equal to `_FillValue` with NA and scale values according to the formula `original_values * scale_factor + add_offset`, where `_FillValue`, `scale_factor` and `add_offset` are taken from variable attributes (if they exist). If the `_FillValue` or `missing_value` attribute contains multiple values a warning will be issued and all array values matching one of the multiple values will be replaced by NA. mask_and_scale defaults to True except for the pseudonetcdf backend. This keyword may not be supported by all the backends. decode_times : bool, optional If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers. This keyword may not be supported by all the backends. decode_timedelta : bool, optional If True, decode variables and coordinates with time units in {"days", "hours", "minutes", "seconds", "milliseconds", "microseconds"} into timedelta objects. If False, leave them encoded as numbers. If None (default), assume the same value of decode_time. This keyword may not be supported by all the backends. use_cftime: bool, optional Only relevant if encoded dates come from a standard calendar (e.g. "gregorian", "proleptic_gregorian", "standard", or not specified). If None (default), attempt to decode times to ``np.datetime64[ns]`` objects; if this is not possible, decode times to ``cftime.datetime`` objects. If True, always decode times to ``cftime.datetime`` objects, regardless of whether or not they can be represented using ``np.datetime64[ns]`` objects. If False, always decode times to ``np.datetime64[ns]`` objects; if this is not possible raise an error. This keyword may not be supported by all the backends. concat_characters : bool, optional If True, concatenate along the last dimension of character arrays to form string arrays. Dimensions will only be concatenated over (and removed) if they have no corresponding variable and if they are only used as the last dimension of character arrays. This keyword may not be supported by all the backends. decode_coords : bool or {"coordinates", "all"}, optional Controls which variables are set as coordinate variables: - "coordinates" or True: Set variables referred to in the ``'coordinates'`` attribute of the datasets or individual variables as coordinate variables. - "all": Set variables referred to in ``'grid_mapping'``, ``'bounds'`` and other attributes as coordinate variables. drop_variables: str or iterable of str, optional A variable or list of variables to exclude from being parsed from the dataset. This may be useful to drop variables with problems or inconsistent values. inline_array: bool, default: False How to include the array in the dask task graph. By default(``inline_array=False``) the array is included in a task by itself, and each chunk refers to that task by its key. With ``inline_array=True``, Dask will instead inline the array directly in the values of the task graph. See :py:func:`dask.array.from_array`. backend_kwargs: dict Additional keyword arguments passed on to the engine open function, equivalent to `**kwargs`. **kwargs: dict Additional keyword arguments passed on to the engine open function. For example: - 'group': path to the netCDF4 group in the given file to open given as a str,supported by "netcdf4", "h5netcdf", "zarr". - 'lock': resource lock to use when reading data from disk. Only relevant when using dask or another form of parallelism. By default, appropriate locks are chosen to safely read and write files with the currently active dask scheduler. Supported by "netcdf4", "h5netcdf", "scipy", "pynio", "pseudonetcdf", "cfgrib". See engine open function for kwargs accepted by each specific engine. Returns ------- dataset : Dataset The newly created dataset. Notes ----- ``open_dataset`` opens the file with read-only access. When you modify values of a Dataset, even one linked to files on disk, only the in-memory copy you are manipulating in xarray is modified: the original file on disk is never touched. See Also -------- open_mfdataset """ if cache is None: cache = chunks is None if backend_kwargs is not None: kwargs.update(backend_kwargs) if engine is None: engine = plugins.guess_engine(filename_or_obj) backend = plugins.get_backend(engine) decoders = _resolve_decoders_kwargs( decode_cf, open_backend_dataset_parameters=backend.open_dataset_parameters, mask_and_scale=mask_and_scale, decode_times=decode_times, decode_timedelta=decode_timedelta, concat_characters=concat_characters, use_cftime=use_cftime, decode_coords=decode_coords, ) overwrite_encoded_chunks = kwargs.pop("overwrite_encoded_chunks", None) backend_ds = backend.open_dataset( filename_or_obj, drop_variables=drop_variables, **decoders, **kwargs, ) ds = _dataset_from_backend_dataset( backend_ds, filename_or_obj, engine, chunks, cache, overwrite_encoded_chunks, inline_array, drop_variables=drop_variables, **decoders, **kwargs, ) return ds def open_dataarray( filename_or_obj: str | os.PathLike[Any] | BufferedIOBase | AbstractDataStore, *, engine: T_Engine = None, chunks: T_Chunks = None, cache: bool | None = None, decode_cf: bool | None = None, mask_and_scale: bool | None = None, decode_times: bool | None = None, decode_timedelta: bool | None = None, use_cftime: bool | None = None, concat_characters: bool | None = None, decode_coords: Literal["coordinates", "all"] | bool | None = None, drop_variables: str | Iterable[str] | None = None, inline_array: bool = False, backend_kwargs: dict[str, Any] | None = None, **kwargs, ) -> DataArray: """Open an DataArray from a file or file-like object containing a single data variable. This is designed to read netCDF files with only one data variable. If multiple variables are present then a ValueError is raised. Parameters ---------- filename_or_obj : str, Path, file-like or DataStore Strings and Path objects are interpreted as a path to a netCDF file or an OpenDAP URL and opened with python-netCDF4, unless the filename ends with .gz, in which case the file is gunzipped and opened with scipy.io.netcdf (only netCDF3 supported). Byte-strings or file-like objects are opened by scipy.io.netcdf (netCDF3) or h5py (netCDF4/HDF). engine : {"netcdf4", "scipy", "pydap", "h5netcdf", "pynio", "cfgrib", \ "pseudonetcdf", "zarr", None}, installed backend \ or subclass of xarray.backends.BackendEntrypoint, optional Engine to use when reading files. If not provided, the default engine is chosen based on available dependencies, with a preference for "netcdf4". chunks : int, dict, 'auto' or None, optional If chunks is provided, it is used to load the new dataset into dask arrays. ``chunks=-1`` loads the dataset with dask using a single chunk for all arrays. `chunks={}`` loads the dataset with dask using engine preferred chunks if exposed by the backend, otherwise with a single chunk for all arrays. ``chunks='auto'`` will use dask ``auto`` chunking taking into account the engine preferred chunks. See dask chunking for more details. cache : bool, optional If True, cache data loaded from the underlying datastore in memory as NumPy arrays when accessed to avoid reading from the underlying data- store multiple times. Defaults to True unless you specify the `chunks` argument to use dask, in which case it defaults to False. Does not change the behavior of coordinates corresponding to dimensions, which always load their data from disk into a ``pandas.Index``. decode_cf : bool, optional Whether to decode these variables, assuming they were saved according to CF conventions. mask_and_scale : bool, optional If True, replace array values equal to `_FillValue` with NA and scale values according to the formula `original_values * scale_factor + add_offset`, where `_FillValue`, `scale_factor` and `add_offset` are taken from variable attributes (if they exist). If the `_FillValue` or `missing_value` attribute contains multiple values a warning will be issued and all array values matching one of the multiple values will be replaced by NA. mask_and_scale defaults to True except for the pseudonetcdf backend. This keyword may not be supported by all the backends. decode_times : bool, optional If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers. This keyword may not be supported by all the backends. decode_timedelta : bool, optional If True, decode variables and coordinates with time units in {"days", "hours", "minutes", "seconds", "milliseconds", "microseconds"} into timedelta objects. If False, leave them encoded as numbers. If None (default), assume the same value of decode_time. This keyword may not be supported by all the backends. use_cftime: bool, optional Only relevant if encoded dates come from a standard calendar (e.g. "gregorian", "proleptic_gregorian", "standard", or not specified). If None (default), attempt to decode times to ``np.datetime64[ns]`` objects; if this is not possible, decode times to ``cftime.datetime`` objects. If True, always decode times to ``cftime.datetime`` objects, regardless of whether or not they can be represented using ``np.datetime64[ns]`` objects. If False, always decode times to ``np.datetime64[ns]`` objects; if this is not possible raise an error. This keyword may not be supported by all the backends. concat_characters : bool, optional If True, concatenate along the last dimension of character arrays to form string arrays. Dimensions will only be concatenated over (and removed) if they have no corresponding variable and if they are only used as the last dimension of character arrays. This keyword may not be supported by all the backends. decode_coords : bool or {"coordinates", "all"}, optional Controls which variables are set as coordinate variables: - "coordinates" or True: Set variables referred to in the ``'coordinates'`` attribute of the datasets or individual variables as coordinate variables. - "all": Set variables referred to in ``'grid_mapping'``, ``'bounds'`` and other attributes as coordinate variables. drop_variables: str or iterable of str, optional A variable or list of variables to exclude from being parsed from the dataset. This may be useful to drop variables with problems or inconsistent values. inline_array: bool, default: False How to include the array in the dask task graph. By default(``inline_array=False``) the array is included in a task by itself, and each chunk refers to that task by its key. With ``inline_array=True``, Dask will instead inline the array directly in the values of the task graph. See :py:func:`dask.array.from_array`. backend_kwargs: dict Additional keyword arguments passed on to the engine open function, equivalent to `**kwargs`. **kwargs: dict Additional keyword arguments passed on to the engine open function. For example: - 'group': path to the netCDF4 group in the given file to open given as a str,supported by "netcdf4", "h5netcdf", "zarr". - 'lock': resource lock to use when reading data from disk. Only relevant when using dask or another form of parallelism. By default, appropriate locks are chosen to safely read and write files with the currently active dask scheduler. Supported by "netcdf4", "h5netcdf", "scipy", "pynio", "pseudonetcdf", "cfgrib". See engine open function for kwargs accepted by each specific engine. Notes ----- This is designed to be fully compatible with `DataArray.to_netcdf`. Saving using `DataArray.to_netcdf` and then loading with this function will produce an identical result. All parameters are passed directly to `xarray.open_dataset`. See that documentation for further details. See also -------- open_dataset """ dataset = open_dataset( filename_or_obj, decode_cf=decode_cf, mask_and_scale=mask_and_scale, decode_times=decode_times, concat_characters=concat_characters, decode_coords=decode_coords, engine=engine, chunks=chunks, cache=cache, drop_variables=drop_variables, inline_array=inline_array, backend_kwargs=backend_kwargs, use_cftime=use_cftime, decode_timedelta=decode_timedelta, **kwargs, ) if len(dataset.data_vars) != 1: raise ValueError( "Given file dataset contains more than one data " "variable. Please read with xarray.open_dataset and " "then select the variable you want." ) else: (data_array,) = dataset.data_vars.values() data_array.set_close(dataset._close) # Reset names if they were changed during saving # to ensure that we can 'roundtrip' perfectly if DATAARRAY_NAME in dataset.attrs: data_array.name = dataset.attrs[DATAARRAY_NAME] del dataset.attrs[DATAARRAY_NAME] if data_array.name == DATAARRAY_VARIABLE: data_array.name = None return data_array def open_mfdataset( paths: str | NestedSequence[str | os.PathLike], chunks: T_Chunks = None, concat_dim: str | DataArray | Index | Sequence[str] | Sequence[DataArray] | Sequence[Index] | None = None, compat: CompatOptions = "no_conflicts", preprocess: Callable[[Dataset], Dataset] | None = None, engine: T_Engine = None, data_vars: Literal["all", "minimal", "different"] | list[str] = "all", coords="different", combine: Literal["by_coords", "nested"] = "by_coords", parallel: bool = False, join: JoinOptions = "outer", attrs_file: str | os.PathLike | None = None, combine_attrs: CombineAttrsOptions = "override", **kwargs, ) -> Dataset: """Open multiple files as a single dataset. If combine='by_coords' then the function ``combine_by_coords`` is used to combine the datasets into one before returning the result, and if combine='nested' then ``combine_nested`` is used. The filepaths must be structured according to which combining function is used, the details of which are given in the documentation for ``combine_by_coords`` and ``combine_nested``. By default ``combine='by_coords'`` will be used. Requires dask to be installed. See documentation for details on dask [1]_. Global attributes from the ``attrs_file`` are used for the combined dataset. Parameters ---------- paths : str or nested sequence of paths Either a string glob in the form ``"path/to/my/files/*.nc"`` or an explicit list of files to open. Paths can be given as strings or as pathlib Paths. If concatenation along more than one dimension is desired, then ``paths`` must be a nested list-of-lists (see ``combine_nested`` for details). (A string glob will be expanded to a 1-dimensional list.) chunks : int, dict, 'auto' or None, optional Dictionary with keys given by dimension names and values given by chunk sizes. In general, these should divide the dimensions of each dataset. If int, chunk each dimension by ``chunks``. By default, chunks will be chosen to load entire input files into memory at once. This has a major impact on performance: please see the full documentation for more details [2]_. concat_dim : str, DataArray, Index or a Sequence of these or None, optional Dimensions to concatenate files along. You only need to provide this argument if ``combine='nested'``, and if any of the dimensions along which you want to concatenate is not a dimension in the original datasets, e.g., if you want to stack a collection of 2D arrays along a third dimension. Set ``concat_dim=[..., None, ...]`` explicitly to disable concatenation along a particular dimension. Default is None, which for a 1D list of filepaths is equivalent to opening the files separately and then merging them with ``xarray.merge``. combine : {"by_coords", "nested"}, optional Whether ``xarray.combine_by_coords`` or ``xarray.combine_nested`` is used to combine all the data. Default is to use ``xarray.combine_by_coords``. compat : {"identical", "equals", "broadcast_equals", \ "no_conflicts", "override"}, default: "no_conflicts" String indicating how to compare variables of the same name for potential conflicts when merging: * "broadcast_equals": all values must be equal when variables are broadcast against each other to ensure common dimensions. * "equals": all values and dimensions must be the same. * "identical": all values, dimensions and attributes must be the same. * "no_conflicts": only values which are not null in both datasets must be equal. The returned dataset then contains the combination of all non-null values. * "override": skip comparing and pick variable from first dataset preprocess : callable, optional If provided, call this function on each dataset prior to concatenation. You can find the file-name from which each dataset was loaded in ``ds.encoding["source"]``. engine : {"netcdf4", "scipy", "pydap", "h5netcdf", "pynio", "cfgrib", \ "pseudonetcdf", "zarr", None}, installed backend \ or subclass of xarray.backends.BackendEntrypoint, optional Engine to use when reading files. If not provided, the default engine is chosen based on available dependencies, with a preference for "netcdf4". data_vars : {"minimal", "different", "all"} or list of str, default: "all" These data variables will be concatenated together: * "minimal": Only data variables in which the dimension already appears are included. * "different": Data variables which are not equal (ignoring attributes) across all datasets are also concatenated (as well as all for which dimension already appears). Beware: this option may load the data payload of data variables into memory if they are not already loaded. * "all": All data variables will be concatenated. * list of str: The listed data variables will be concatenated, in addition to the "minimal" data variables. coords : {"minimal", "different", "all"} or list of str, optional These coordinate variables will be concatenated together: * "minimal": Only coordinates in which the dimension already appears are included. * "different": Coordinates which are not equal (ignoring attributes) across all datasets are also concatenated (as well as all for which dimension already appears). Beware: this option may load the data payload of coordinate variables into memory if they are not already loaded. * "all": All coordinate variables will be concatenated, except those corresponding to other dimensions. * list of str: The listed coordinate variables will be concatenated, in addition the "minimal" coordinates. parallel : bool, default: False If True, the open and preprocess steps of this function will be performed in parallel using ``dask.delayed``. Default is False. join : {"outer", "inner", "left", "right", "exact", "override"}, default: "outer" String indicating how to combine differing indexes (excluding concat_dim) in objects - "outer": use the union of object indexes - "inner": use the intersection of object indexes - "left": use indexes from the first object with each dimension - "right": use indexes from the last object with each dimension - "exact": instead of aligning, raise `ValueError` when indexes to be aligned are not equal - "override": if indexes are of same size, rewrite indexes to be those of the first object with that dimension. Indexes for the same dimension must have the same size in all objects. attrs_file : str or path-like, optional Path of the file used to read global attributes from. By default global attributes are read from the first file provided, with wildcard matches sorted by filename. combine_attrs : {"drop", "identical", "no_conflicts", "drop_conflicts", \ "override"} or callable, default: "override" A callable or a string indicating how to combine attrs of the objects being merged: - "drop": empty attrs on returned Dataset. - "identical": all attrs must be the same on every object. - "no_conflicts": attrs from all objects are combined, any that have the same name must also have the same value. - "drop_conflicts": attrs from all objects are combined, any that have the same name but different values are dropped. - "override": skip comparing and copy attrs from the first dataset to the result. If a callable, it must expect a sequence of ``attrs`` dicts and a context object as its only parameters. **kwargs : optional Additional arguments passed on to :py:func:`xarray.open_dataset`. Returns ------- xarray.Dataset Notes ----- ``open_mfdataset`` opens files with read-only access. When you modify values of a Dataset, even one linked to files on disk, only the in-memory copy you are manipulating in xarray is modified: the original file on disk is never touched. See Also -------- combine_by_coords combine_nested open_dataset Examples -------- A user might want to pass additional arguments into ``preprocess`` when applying some operation to many individual files that are being opened. One route to do this is through the use of ``functools.partial``. >>> from functools import partial >>> def _preprocess(x, lon_bnds, lat_bnds): ... return x.sel(lon=slice(*lon_bnds), lat=slice(*lat_bnds)) ... >>> lon_bnds, lat_bnds = (-110, -105), (40, 45) >>> partial_func = partial(_preprocess, lon_bnds=lon_bnds, lat_bnds=lat_bnds) >>> ds = xr.open_mfdataset( ... "file_*.nc", concat_dim="time", preprocess=partial_func ... ) # doctest: +SKIP References ---------- .. [1] https://docs.xarray.dev/en/stable/dask.html .. [2] https://docs.xarray.dev/en/stable/dask.html#chunking-and-performance """ if isinstance(paths, str): if is_remote_uri(paths) and engine == "zarr": try: from fsspec.core import get_fs_token_paths except ImportError as e: raise ImportError( "The use of remote URLs for opening zarr requires the package fsspec" ) from e fs, _, _ = get_fs_token_paths( paths, mode="rb", storage_options=kwargs.get("backend_kwargs", {}).get( "storage_options", {} ), expand=False, ) tmp_paths = fs.glob(fs._strip_protocol(paths)) # finds directories paths = [fs.get_mapper(path) for path in tmp_paths] elif is_remote_uri(paths): raise ValueError( "cannot do wild-card matching for paths that are remote URLs " f"unless engine='zarr' is specified. Got paths: {paths}. " "Instead, supply paths as an explicit list of strings." ) else: paths = sorted(glob(_normalize_path(paths))) elif isinstance(paths, os.PathLike): paths = [os.fspath(paths)] else: paths = [os.fspath(p) if isinstance(p, os.PathLike) else p for p in paths] if not paths: raise OSError("no files to open") if combine == "nested": if isinstance(concat_dim, (str, DataArray)) or concat_dim is None: concat_dim = [concat_dim] # type: ignore[assignment] # This creates a flat list which is easier to iterate over, whilst # encoding the originally-supplied structure as "ids". # The "ids" are not used at all if combine='by_coords`. combined_ids_paths = _infer_concat_order_from_positions(paths) ids, paths = ( list(combined_ids_paths.keys()), list(combined_ids_paths.values()), ) elif combine == "by_coords" and concat_dim is not None: raise ValueError( "When combine='by_coords', passing a value for `concat_dim` has no " "effect. To manually combine along a specific dimension you should " "instead specify combine='nested' along with a value for `concat_dim`.", ) open_kwargs = dict(engine=engine, chunks=chunks or {}, **kwargs) if parallel: import dask # wrap the open_dataset, getattr, and preprocess with delayed open_ = dask.delayed(open_dataset) getattr_ = dask.delayed(getattr) if preprocess is not None: preprocess = dask.delayed(preprocess) else: open_ = open_dataset getattr_ = getattr datasets = [open_(p, **open_kwargs) for p in paths] closers = [getattr_(ds, "_close") for ds in datasets] if preprocess is not None: datasets = [preprocess(ds) for ds in datasets] if parallel: # calling compute here will return the datasets/file_objs lists, # the underlying datasets will still be stored as dask arrays datasets, closers = dask.compute(datasets, closers) # Combine all datasets, closing them in case of a ValueError try: if combine == "nested": # Combined nested list by successive concat and merge operations # along each dimension, using structure given by "ids" combined = _nested_combine( datasets, concat_dims=concat_dim, compat=compat, data_vars=data_vars, coords=coords, ids=ids, join=join, combine_attrs=combine_attrs, ) elif combine == "by_coords": # Redo ordering from coordinates, ignoring how they were ordered # previously combined = combine_by_coords( datasets, compat=compat, data_vars=data_vars, coords=coords, join=join, combine_attrs=combine_attrs, ) else: raise ValueError( "{} is an invalid option for the keyword argument" " ``combine``".format(combine) ) except ValueError: for ds in datasets: ds.close() raise combined.set_close(partial(_multi_file_closer, closers)) # read global attributes from the attrs_file or from the first dataset if attrs_file is not None: if isinstance(attrs_file, os.PathLike): attrs_file = cast(str, os.fspath(attrs_file)) combined.attrs = datasets[paths.index(attrs_file)].attrs return combined WRITEABLE_STORES: dict[T_NetcdfEngine, Callable] = { "netcdf4": backends.NetCDF4DataStore.open, "scipy": backends.ScipyDataStore, "h5netcdf": backends.H5NetCDFStore.open, } # multifile=True returns writer and datastore @overload def to_netcdf( dataset: Dataset, path_or_file: str | os.PathLike | None = None, mode: Literal["w", "a"] = "w", format: T_NetcdfTypes | None = None, group: str | None = None, engine: T_NetcdfEngine | None = None, encoding: Mapping[Hashable, Mapping[str, Any]] | None = None, unlimited_dims: Iterable[Hashable] | None = None, compute: bool = True, *, multifile: Literal[True], invalid_netcdf: bool = False, ) -> tuple[ArrayWriter, AbstractDataStore]: ... # path=None writes to bytes @overload def to_netcdf( dataset: Dataset, path_or_file: None = None, mode: Literal["w", "a"] = "w", format: T_NetcdfTypes | None = None, group: str | None = None, engine: T_NetcdfEngine | None = None, encoding: Mapping[Hashable, Mapping[str, Any]] | None = None, unlimited_dims: Iterable[Hashable] | None = None, compute: bool = True, multifile: Literal[False] = False, invalid_netcdf: bool = False, ) -> bytes: ... # compute=False returns dask.Delayed @overload def to_netcdf( dataset: Dataset, path_or_file: str | os.PathLike, mode: Literal["w", "a"] = "w", format: T_NetcdfTypes | None = None, group: str | None = None, engine: T_NetcdfEngine | None = None, encoding: Mapping[Hashable, Mapping[str, Any]] | None = None, unlimited_dims: Iterable[Hashable] | None = None, *, compute: Literal[False], multifile: Literal[False] = False, invalid_netcdf: bool = False, ) -> Delayed: ... # default return None @overload def to_netcdf( dataset: Dataset, path_or_file: str | os.PathLike, mode: Literal["w", "a"] = "w", format: T_NetcdfTypes | None = None, group: str | None = None, engine: T_NetcdfEngine | None = None, encoding: Mapping[Hashable, Mapping[str, Any]] | None = None, unlimited_dims: Iterable[Hashable] | None = None, compute: Literal[True] = True, multifile: Literal[False] = False, invalid_netcdf: bool = False, ) -> None: ... def to_netcdf( dataset: Dataset, path_or_file: str | os.PathLike | None = None, mode: Literal["w", "a"] = "w", format: T_NetcdfTypes | None = None, group: str | None = None, engine: T_NetcdfEngine | None = None, encoding: Mapping[Hashable, Mapping[str, Any]] | None = None, unlimited_dims: Iterable[Hashable] | None = None, compute: bool = True, multifile: bool = False, invalid_netcdf: bool = False, ) -> tuple[ArrayWriter, AbstractDataStore] | bytes | Delayed | None: """This function creates an appropriate datastore for writing a dataset to disk as a netCDF file See `Dataset.to_netcdf` for full API docs. The ``multifile`` argument is only for the private use of save_mfdataset. """ if isinstance(path_or_file, os.PathLike): path_or_file = os.fspath(path_or_file) if encoding is None: encoding = {} if path_or_file is None: if engine is None: engine = "scipy" elif engine != "scipy": raise ValueError( "invalid engine for creating bytes with " f"to_netcdf: {engine!r}. Only the default engine " "or engine='scipy' is supported" ) if not compute: raise NotImplementedError( "to_netcdf() with compute=False is not yet implemented when " "returning bytes" ) elif isinstance(path_or_file, str): if engine is None: engine = _get_default_engine(path_or_file) path_or_file = _normalize_path(path_or_file) else: # file-like object engine = "scipy" # validate Dataset keys, DataArray names, and attr keys/values _validate_dataset_names(dataset) _validate_attrs(dataset, invalid_netcdf=invalid_netcdf and engine == "h5netcdf") try: store_open = WRITEABLE_STORES[engine] except KeyError: raise ValueError(f"unrecognized engine for to_netcdf: {engine!r}") if format is not None: format = format.upper() # type: ignore[assignment] # handle scheduler specific logic scheduler = _get_scheduler() have_chunks = any(v.chunks is not None for v in dataset.variables.values()) autoclose = have_chunks and scheduler in ["distributed", "multiprocessing"] if autoclose and engine == "scipy": raise NotImplementedError( f"Writing netCDF files with the {engine} backend " f"is not currently supported with dask's {scheduler} scheduler" ) target = path_or_file if path_or_file is not None else BytesIO() kwargs = dict(autoclose=True) if autoclose else {} if invalid_netcdf: if engine == "h5netcdf": kwargs["invalid_netcdf"] = invalid_netcdf else: raise ValueError( f"unrecognized option 'invalid_netcdf' for engine {engine}" ) store = store_open(target, mode, format, group, **kwargs) if unlimited_dims is None: unlimited_dims = dataset.encoding.get("unlimited_dims", None) if unlimited_dims is not None: if isinstance(unlimited_dims, str) or not isinstance(unlimited_dims, Iterable): unlimited_dims = [unlimited_dims] else: unlimited_dims = list(unlimited_dims) writer = ArrayWriter() # TODO: figure out how to refactor this logic (here and in save_mfdataset) # to avoid this mess of conditionals try: # TODO: allow this work (setting up the file for writing array data) # to be parallelized with dask dump_to_store( dataset, store, writer, encoding=encoding, unlimited_dims=unlimited_dims ) if autoclose: store.close() if multifile: return writer, store writes = writer.sync(compute=compute) if isinstance(target, BytesIO): store.sync() return target.getvalue() finally: if not multifile and compute: store.close() if not compute: import dask return dask.delayed(_finalize_store)(writes, store) return None def dump_to_store( dataset, store, writer=None, encoder=None, encoding=None, unlimited_dims=None ): """Store dataset contents to a backends.*DataStore object.""" if writer is None: writer = ArrayWriter() if encoding is None: encoding = {} variables, attrs = conventions.encode_dataset_coordinates(dataset) check_encoding = set() for k, enc in encoding.items(): # no need to shallow copy the variable again; that already happened # in encode_dataset_coordinates variables[k].encoding = enc check_encoding.add(k) if encoder: variables, attrs = encoder(variables, attrs) store.store(variables, attrs, check_encoding, writer, unlimited_dims=unlimited_dims) def save_mfdataset( datasets, paths, mode="w", format=None, groups=None, engine=None, compute=True, **kwargs, ): """Write multiple datasets to disk as netCDF files simultaneously. This function is intended for use with datasets consisting of dask.array objects, in which case it can write the multiple datasets to disk simultaneously using a shared thread pool. When not using dask, it is no different than calling ``to_netcdf`` repeatedly. Parameters ---------- datasets : list of Dataset List of datasets to save. paths : list of str or list of path-like objects List of paths to which to save each corresponding dataset. mode : {"w", "a"}, optional Write ("w") or append ("a") mode. If mode="w", any existing file at these locations will be overwritten. format : {"NETCDF4", "NETCDF4_CLASSIC", "NETCDF3_64BIT", \ "NETCDF3_CLASSIC"}, optional **kwargs : additional arguments are passed along to ``to_netcdf`` File format for the resulting netCDF file: * NETCDF4: Data is stored in an HDF5 file, using netCDF4 API features. * NETCDF4_CLASSIC: Data is stored in an HDF5 file, using only netCDF 3 compatible API features. * NETCDF3_64BIT: 64-bit offset version of the netCDF 3 file format, which fully supports 2+ GB files, but is only compatible with clients linked against netCDF version 3.6.0 or later. * NETCDF3_CLASSIC: The classic netCDF 3 file format. It does not handle 2+ GB files very well. All formats are supported by the netCDF4-python library. scipy.io.netcdf only supports the last two formats. The default format is NETCDF4 if you are saving a file to disk and have the netCDF4-python library available. Otherwise, xarray falls back to using scipy to write netCDF files and defaults to the NETCDF3_64BIT format (scipy does not support netCDF4). groups : list of str, optional Paths to the netCDF4 group in each corresponding file to which to save datasets (only works for format="NETCDF4"). The groups will be created if necessary. engine : {"netcdf4", "scipy", "h5netcdf"}, optional Engine to use when writing netCDF files. If not provided, the default engine is chosen based on available dependencies, with a preference for "netcdf4" if writing to a file on disk. See `Dataset.to_netcdf` for additional information. compute : bool If true compute immediately, otherwise return a ``dask.delayed.Delayed`` object that can be computed later. Examples -------- Save a dataset into one netCDF per year of data: >>> ds = xr.Dataset( ... {"a": ("time", np.linspace(0, 1, 48))}, ... coords={"time": pd.date_range("2010-01-01", freq="M", periods=48)}, ... ) >>> ds Dimensions: (time: 48) Coordinates: * time (time) datetime64[ns] 2010-01-31 2010-02-28 ... 2013-12-31 Data variables: a (time) float64 0.0 0.02128 0.04255 0.06383 ... 0.9574 0.9787 1.0 >>> years, datasets = zip(*ds.groupby("time.year")) >>> paths = [f"{y}.nc" for y in years] >>> xr.save_mfdataset(datasets, paths) """ if mode == "w" and len(set(paths)) < len(paths): raise ValueError( "cannot use mode='w' when writing multiple datasets to the same path" ) for obj in datasets: if not isinstance(obj, Dataset): raise TypeError( "save_mfdataset only supports writing Dataset " f"objects, received type {type(obj)}" ) if groups is None: groups = [None] * len(datasets) if len({len(datasets), len(paths), len(groups)}) > 1: raise ValueError( "must supply lists of the same length for the " "datasets, paths and groups arguments to " "save_mfdataset" ) writers, stores = zip( *[ to_netcdf( ds, path, mode, format, group, engine, compute=compute, multifile=True, **kwargs, ) for ds, path, group in zip(datasets, paths, groups) ] ) try: writes = [w.sync(compute=compute) for w in writers] finally: if compute: for store in stores: store.close() if not compute: import dask return dask.delayed( [dask.delayed(_finalize_store)(w, s) for w, s in zip(writes, stores)] ) def _validate_region(ds, region): if not isinstance(region, dict): raise TypeError(f"``region`` must be a dict, got {type(region)}") for k, v in region.items(): if k not in ds.dims: raise ValueError( f"all keys in ``region`` are not in Dataset dimensions, got " f"{list(region)} and {list(ds.dims)}" ) if not isinstance(v, slice): raise TypeError( "all values in ``region`` must be slice objects, got " f"region={region}" ) if v.step not in {1, None}: raise ValueError( "step on all slices in ``region`` must be 1 or None, got " f"region={region}" ) non_matching_vars = [ k for k, v in ds.variables.items() if not set(region).intersection(v.dims) ] if non_matching_vars: raise ValueError( f"when setting `region` explicitly in to_zarr(), all " f"variables in the dataset to write must have at least " f"one dimension in common with the region's dimensions " f"{list(region.keys())}, but that is not " f"the case for some variables here. To drop these variables " f"from this dataset before exporting to zarr, write: " f".drop_vars({non_matching_vars!r})" ) def _validate_datatypes_for_zarr_append(zstore, dataset): """If variable exists in the store, confirm dtype of the data to append is compatible with existing dtype. """ existing_vars = zstore.get_variables() def check_dtype(vname, var): if ( vname not in existing_vars or np.issubdtype(var.dtype, np.number) or np.issubdtype(var.dtype, np.datetime64) or np.issubdtype(var.dtype, np.bool_) or var.dtype == object ): # We can skip dtype equality checks under two conditions: (1) if the var to append is # new to the dataset, because in this case there is no existing var to compare it to; # or (2) if var to append's dtype is known to be easy-to-append, because in this case # we can be confident appending won't cause problems. Examples of dtypes which are not # easy-to-append include length-specified strings of type `|S*` or ` backends.ZarrStore: ... # compute=False returns dask.Delayed @overload def to_zarr( dataset: Dataset, store: MutableMapping | str | os.PathLike[str] | None = None, chunk_store: MutableMapping | str | os.PathLike | None = None, mode: Literal["w", "w-", "a", "r+", None] = None, synchronizer=None, group: str | None = None, encoding: Mapping | None = None, *, compute: Literal[False], consolidated: bool | None = None, append_dim: Hashable | None = None, region: Mapping[str, slice] | None = None, safe_chunks: bool = True, storage_options: dict[str, str] | None = None, zarr_version: int | None = None, ) -> Delayed: ... def to_zarr( dataset: Dataset, store: MutableMapping | str | os.PathLike[str] | None = None, chunk_store: MutableMapping | str | os.PathLike | None = None, mode: Literal["w", "w-", "a", "r+", None] = None, synchronizer=None, group: str | None = None, encoding: Mapping | None = None, compute: bool = True, consolidated: bool | None = None, append_dim: Hashable | None = None, region: Mapping[str, slice] | None = None, safe_chunks: bool = True, storage_options: dict[str, str] | None = None, zarr_version: int | None = None, ) -> backends.ZarrStore | Delayed: """This function creates an appropriate datastore for writing a dataset to a zarr ztore See `Dataset.to_zarr` for full API docs. """ # Load empty arrays to avoid bug saving zero length dimensions (Issue #5741) for v in dataset.variables.values(): if v.size == 0: v.load() # expand str and path-like arguments store = _normalize_path(store) chunk_store = _normalize_path(chunk_store) if storage_options is None: mapper = store chunk_mapper = chunk_store else: from fsspec import get_mapper if not isinstance(store, str): raise ValueError( f"store must be a string to use storage_options. Got {type(store)}" ) mapper = get_mapper(store, **storage_options) if chunk_store is not None: chunk_mapper = get_mapper(chunk_store, **storage_options) else: chunk_mapper = chunk_store if encoding is None: encoding = {} if mode is None: if append_dim is not None: mode = "a" elif region is not None: mode = "r+" else: mode = "w-" if mode != "a" and append_dim is not None: raise ValueError("cannot set append_dim unless mode='a' or mode=None") if mode not in ["a", "r+"] and region is not None: raise ValueError("cannot set region unless mode='a', mode='r+' or mode=None") if mode not in ["w", "w-", "a", "r+"]: raise ValueError( "The only supported options for mode are 'w', " f"'w-', 'a' and 'r+', but mode={mode!r}" ) # validate Dataset keys, DataArray names _validate_dataset_names(dataset) if region is not None: _validate_region(dataset, region) if append_dim is not None and append_dim in region: raise ValueError( f"cannot list the same dimension in both ``append_dim`` and " f"``region`` with to_zarr(), got {append_dim} in both" ) if zarr_version is None: # default to 2 if store doesn't specify it's version (e.g. a path) zarr_version = int(getattr(store, "_store_version", 2)) if consolidated is None and zarr_version > 2: consolidated = False if mode == "r+": already_consolidated = consolidated consolidate_on_close = False else: already_consolidated = False consolidate_on_close = consolidated or consolidated is None zstore = backends.ZarrStore.open_group( store=mapper, mode=mode, synchronizer=synchronizer, group=group, consolidated=already_consolidated, consolidate_on_close=consolidate_on_close, chunk_store=chunk_mapper, append_dim=append_dim, write_region=region, safe_chunks=safe_chunks, stacklevel=4, # for Dataset.to_zarr() zarr_version=zarr_version, ) if mode in ["a", "r+"]: _validate_datatypes_for_zarr_append(zstore, dataset) if append_dim is not None: existing_dims = zstore.get_dimensions() if append_dim not in existing_dims: raise ValueError( f"append_dim={append_dim!r} does not match any existing " f"dataset dimensions {existing_dims}" ) existing_var_names = set(zstore.zarr_group.array_keys()) for var_name in existing_var_names: if var_name in encoding.keys(): raise ValueError( f"variable {var_name!r} already exists, but encoding was provided" ) if mode == "r+": new_names = [k for k in dataset.variables if k not in existing_var_names] if new_names: raise ValueError( f"dataset contains non-pre-existing variables {new_names}, " "which is not allowed in ``xarray.Dataset.to_zarr()`` with " "mode='r+'. To allow writing new variables, set mode='a'." ) writer = ArrayWriter() # TODO: figure out how to properly handle unlimited_dims dump_to_store(dataset, zstore, writer, encoding=encoding) writes = writer.sync(compute=compute) if compute: _finalize_store(writes, zstore) else: import dask return dask.delayed(_finalize_store)(writes, zstore) return zstore