xarray.open_dataarray (original) (raw)

xarray.open_dataarray(filename_or_obj, *, engine=None, chunks=None, cache=None, decode_cf=None, mask_and_scale=None, decode_times=None, decode_timedelta=None, use_cftime=None, concat_characters=None, decode_coords=None, drop_variables=None, inline_array=False, chunked_array_type=None, from_array_kwargs=None, backend_kwargs=None, **kwargs)[source]#

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", "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, default: None) – If provided, used to load the data into dask arrays.
  • chunks='auto' will use dask auto chunking taking into account the engine preferred chunks.
  • chunks=None skips using dask, which is generally faster for small arrays.
  • chunks=-1 loads the data with dask using a single chunk for all arrays.
  • chunks={} loads the data with dask using engine preferred chunks if exposed by the backend, otherwise with a single chunk for all arrays.
    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 chunksargument 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 ormissing_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. This keyword may not be supported by all the backends.
• decode_times (bool, CFDatetimeCoder or dict-like, optional) – If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, use coders.CFDatetimeCoder or leave them encoded as numbers. Pass a mapping, e.g. {"my_variable": False}, to toggle this feature per-variable individually. 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_times; ifdecode_times is a coders.CFDatetimeCoder instance, this takes the form of a coders.CFTimedeltaCoder instance with a matching time_unit. 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 tonp.datetime64[ns] objects; if this is not possible, decode times tocftime.datetime objects. If True, always decode times tocftime.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.
  Deprecated since version 2025.01.1: Please pass a coders.CFDatetimeCoder instance initialized with use_cftime to the decode_times kwarg instead.
• 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.
    Only existing variables can be set as coordinates. Missing variables will be silently ignored.
• 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. Withinline_array=True, Dask will instead inline the array directly in the values of the task graph. See dask.array.from_array().
• chunked_array_type (str, optional) – Which chunked array type to coerce the underlying data array to. Defaults to ‘dask’ if installed, else whatever is registered via the ChunkManagerEnetryPoint system. Experimental API that should not be relied upon.
• from_array_kwargs (dict) – Additional keyword arguments passed on to the ChunkManagerEntrypoint.from_array method used to create chunked arrays, via whichever chunk manager is specified through the chunked_array_type kwarg. For example if dask.array.Array() objects are used for chunking, additional kwargs will be passed to dask.array.from_array(). Experimental API that should not be relied upon.
• 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”.
    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.