/
api.py
1337 lines (1158 loc) · 48 KB
/
api.py
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import os.path
import warnings
from glob import glob
from io import BytesIO
from numbers import Number
from pathlib import Path
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Callable,
Dict,
Hashable,
Iterable,
Mapping,
Tuple,
Union,
)
import numpy as np
from .. import DataArray, Dataset, auto_combine, backends, coding, conventions
from ..core import indexing
from ..core.combine import (
_infer_concat_order_from_positions,
_nested_combine,
combine_by_coords,
)
from ..core.utils import close_on_error, is_grib_path, is_remote_uri
from .common import AbstractDataStore, ArrayWriter
from .locks import _get_scheduler
if TYPE_CHECKING:
try:
from dask.delayed import Delayed
except ImportError:
Delayed = None
DATAARRAY_NAME = "__xarray_dataarray_name__"
DATAARRAY_VARIABLE = "__xarray_dataarray_variable__"
def _get_default_engine_remote_uri():
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_grib():
msgs = []
try:
import Nio # noqa: F401
msgs += ["set engine='pynio' to access GRIB files with PyNIO"]
except ImportError: # pragma: no cover
pass
try:
import cfgrib # noqa: F401
msgs += ["set engine='cfgrib' to access GRIB files with cfgrib"]
except ImportError: # pragma: no cover
pass
if msgs:
raise ValueError(" or\n".join(msgs))
else:
raise ValueError("PyNIO or cfgrib is required for accessing " "GRIB files")
def _get_default_engine_gz():
try:
import scipy # noqa: F401
engine = "scipy"
except ImportError: # pragma: no cover
raise ValueError("scipy is required for accessing .gz files")
return engine
def _get_default_engine_netcdf():
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_engine_from_magic_number(filename_or_obj):
# check byte header to determine file type
if isinstance(filename_or_obj, bytes):
magic_number = filename_or_obj[:8]
else:
if filename_or_obj.tell() != 0:
raise ValueError(
"file-like object read/write pointer not at zero "
"please close and reopen, or use a context "
"manager"
)
magic_number = filename_or_obj.read(8)
filename_or_obj.seek(0)
if magic_number.startswith(b"CDF"):
engine = "scipy"
elif magic_number.startswith(b"\211HDF\r\n\032\n"):
engine = "h5netcdf"
if isinstance(filename_or_obj, bytes):
raise ValueError(
"can't open netCDF4/HDF5 as bytes "
"try passing a path or file-like object"
)
else:
if isinstance(filename_or_obj, bytes) and len(filename_or_obj) > 80:
filename_or_obj = filename_or_obj[:80] + b"..."
raise ValueError(
"{} is not a valid netCDF file "
"did you mean to pass a string for a path instead?".format(filename_or_obj)
)
return engine
def _get_default_engine(path, allow_remote=False):
if allow_remote and is_remote_uri(path):
engine = _get_default_engine_remote_uri()
elif is_grib_path(path):
engine = _get_default_engine_grib()
elif path.endswith(".gz"):
engine = _get_default_engine_gz()
else:
engine = _get_default_engine_netcdf()
return engine
def _normalize_path(path):
if is_remote_uri(path):
return path
else:
return os.path.abspath(os.path.expanduser(path))
def _validate_dataset_names(dataset):
"""DataArray.name and Dataset keys must be a string or None"""
def check_name(name):
if isinstance(name, str):
if not name:
raise ValueError(
"Invalid name for DataArray or Dataset key: "
"string must be length 1 or greater for "
"serialization to netCDF files"
)
elif name is not None:
raise TypeError(
"DataArray.name 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):
"""`attrs` must have a string key and a value which is either: a number,
a string, an ndarray or a list/tuple of numbers/strings.
"""
def check_attr(name, value):
if isinstance(name, str):
if not name:
raise ValueError(
"Invalid name for attr: string must be "
"length 1 or greater for serialization to "
"netCDF files"
)
else:
raise TypeError(
"Invalid name for attr: {} must be a string for "
"serialization to netCDF files".format(name)
)
if not isinstance(value, (str, Number, np.ndarray, np.number, list, tuple)):
raise TypeError(
"Invalid value for attr: {} must be a number, "
"a string, an ndarray or a list/tuple of "
"numbers/strings for serialization to netCDF "
"files".format(value)
)
# Check attrs on the dataset itself
for k, v in dataset.attrs.items():
check_attr(k, v)
# 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)
def _protect_dataset_variables_inplace(dataset, cache):
for name, variable in dataset.variables.items():
if name not in variable.dims:
# 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 load_dataset(filename_or_obj, **kwargs):
"""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 open_dataset(
filename_or_obj,
group=None,
decode_cf=True,
mask_and_scale=None,
decode_times=True,
autoclose=None,
concat_characters=True,
decode_coords=True,
engine=None,
chunks=None,
lock=None,
cache=None,
drop_variables=None,
backend_kwargs=None,
use_cftime=None,
):
"""Open and decode a dataset from a file or file-like object.
Parameters
----------
filename_or_obj : str, Path, file or xarray.backends.*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).
group : str, optional
Path to the netCDF4 group in the given file to open (only works for
netCDF4 files).
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.
decode_times : bool, optional
If True, decode times encoded in the standard NetCDF datetime format
into datetime objects. Otherwise, leave them encoded as numbers.
autoclose : bool, optional
If True, automatically close files to avoid OS Error of too many files
being open. However, this option doesn't work with streams, e.g.,
BytesIO.
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.
decode_coords : bool, optional
If True, decode the 'coordinates' attribute to identify coordinates in
the resulting dataset.
engine : {'netcdf4', 'scipy', 'pydap', 'h5netcdf', 'pynio', 'cfgrib', \
'pseudonetcdf'}, 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 or dict, optional
If chunks is provided, it used to load the new dataset into dask
arrays. ``chunks={}`` loads the dataset with dask using a single
chunk for all arrays.
lock : False or duck threading.Lock, optional
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.
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``.
drop_variables: string or iterable, 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.
backend_kwargs: dictionary, optional
A dictionary of keyword arguments to pass on to the backend. This
may be useful when backend options would improve performance or
allow user control of dataset processing.
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.
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
"""
engines = [
None,
"netcdf4",
"scipy",
"pydap",
"h5netcdf",
"pynio",
"cfgrib",
"pseudonetcdf",
]
if engine not in engines:
raise ValueError(
"unrecognized engine for open_dataset: {}\n"
"must be one of: {}".format(engine, engines)
)
if autoclose is not None:
warnings.warn(
"The autoclose argument is no longer used by "
"xarray.open_dataset() and is now ignored; it will be removed in "
"a future version of xarray. If necessary, you can control the "
"maximum number of simultaneous open files with "
"xarray.set_options(file_cache_maxsize=...).",
FutureWarning,
stacklevel=2,
)
if mask_and_scale is None:
mask_and_scale = not engine == "pseudonetcdf"
if not decode_cf:
mask_and_scale = False
decode_times = False
concat_characters = False
decode_coords = False
if cache is None:
cache = chunks is None
if backend_kwargs is None:
backend_kwargs = {}
def maybe_decode_store(store, lock=False):
ds = conventions.decode_cf(
store,
mask_and_scale=mask_and_scale,
decode_times=decode_times,
concat_characters=concat_characters,
decode_coords=decode_coords,
drop_variables=drop_variables,
use_cftime=use_cftime,
)
_protect_dataset_variables_inplace(ds, cache)
if chunks is not None:
from dask.base import tokenize
# if passed an actual file path, augment the token with
# the file modification time
if isinstance(filename_or_obj, str) and not is_remote_uri(filename_or_obj):
mtime = os.path.getmtime(filename_or_obj)
else:
mtime = None
token = tokenize(
filename_or_obj,
mtime,
group,
decode_cf,
mask_and_scale,
decode_times,
concat_characters,
decode_coords,
engine,
chunks,
drop_variables,
use_cftime,
)
name_prefix = "open_dataset-%s" % token
ds2 = ds.chunk(chunks, name_prefix=name_prefix, token=token)
ds2._file_obj = ds._file_obj
else:
ds2 = ds
return ds2
if isinstance(filename_or_obj, Path):
filename_or_obj = str(filename_or_obj)
if isinstance(filename_or_obj, AbstractDataStore):
store = filename_or_obj
elif isinstance(filename_or_obj, str):
filename_or_obj = _normalize_path(filename_or_obj)
if engine is None:
engine = _get_default_engine(filename_or_obj, allow_remote=True)
if engine == "netcdf4":
store = backends.NetCDF4DataStore.open(
filename_or_obj, group=group, lock=lock, **backend_kwargs
)
elif engine == "scipy":
store = backends.ScipyDataStore(filename_or_obj, **backend_kwargs)
elif engine == "pydap":
store = backends.PydapDataStore.open(filename_or_obj, **backend_kwargs)
elif engine == "h5netcdf":
store = backends.H5NetCDFStore(
filename_or_obj, group=group, lock=lock, **backend_kwargs
)
elif engine == "pynio":
store = backends.NioDataStore(filename_or_obj, lock=lock, **backend_kwargs)
elif engine == "pseudonetcdf":
store = backends.PseudoNetCDFDataStore.open(
filename_or_obj, lock=lock, **backend_kwargs
)
elif engine == "cfgrib":
store = backends.CfGribDataStore(
filename_or_obj, lock=lock, **backend_kwargs
)
else:
if engine not in [None, "scipy", "h5netcdf"]:
raise ValueError(
"can only read bytes or file-like objects "
"with engine='scipy' or 'h5netcdf'"
)
engine = _get_engine_from_magic_number(filename_or_obj)
if engine == "scipy":
store = backends.ScipyDataStore(filename_or_obj, **backend_kwargs)
elif engine == "h5netcdf":
store = backends.H5NetCDFStore(
filename_or_obj, group=group, lock=lock, **backend_kwargs
)
with close_on_error(store):
ds = maybe_decode_store(store)
# Ensure source filename always stored in dataset object (GH issue #2550)
if "source" not in ds.encoding:
if isinstance(filename_or_obj, str):
ds.encoding["source"] = filename_or_obj
return ds
def open_dataarray(
filename_or_obj,
group=None,
decode_cf=True,
mask_and_scale=None,
decode_times=True,
autoclose=None,
concat_characters=True,
decode_coords=True,
engine=None,
chunks=None,
lock=None,
cache=None,
drop_variables=None,
backend_kwargs=None,
use_cftime=None,
):
"""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 or xarray.backends.*DataStore
Strings and Paths 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).
group : str, optional
Path to the netCDF4 group in the given file to open (only works for
netCDF4 files).
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.
decode_times : bool, optional
If True, decode times encoded in the standard NetCDF datetime format
into datetime objects. Otherwise, leave them encoded as numbers.
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.
decode_coords : bool, optional
If True, decode the 'coordinates' attribute to identify coordinates in
the resulting dataset.
engine : {'netcdf4', 'scipy', 'pydap', 'h5netcdf', 'pynio', 'cfgrib'}, \
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 or dict, optional
If chunks is provided, it used to load the new dataset into dask
arrays.
lock : False or duck threading.Lock, optional
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.
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``.
drop_variables: string or iterable, 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.
backend_kwargs: dictionary, optional
A dictionary of keyword arguments to pass on to the backend. This
may be useful when backend options would improve performance or
allow user control of dataset processing.
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.
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,
group=group,
decode_cf=decode_cf,
mask_and_scale=mask_and_scale,
decode_times=decode_times,
autoclose=autoclose,
concat_characters=concat_characters,
decode_coords=decode_coords,
engine=engine,
chunks=chunks,
lock=lock,
cache=cache,
drop_variables=drop_variables,
backend_kwargs=backend_kwargs,
use_cftime=use_cftime,
)
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._file_obj = dataset._file_obj
# 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
class _MultiFileCloser:
__slots__ = ("file_objs",)
def __init__(self, file_objs):
self.file_objs = file_objs
def close(self):
for f in self.file_objs:
f.close()
def open_mfdataset(
paths,
chunks=None,
concat_dim="_not_supplied",
compat="no_conflicts",
preprocess=None,
engine=None,
lock=None,
data_vars="all",
coords="different",
combine="_old_auto",
autoclose=None,
parallel=False,
join="outer",
**kwargs,
):
"""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 the old (now deprecated)
``auto_combine`` will be used, please specify either ``combine='by_coords'`` or
``combine='nested'`` in future. Requires dask to be installed. See documentation for
details on dask [1]_. Attributes from the first dataset file are used for the
combined dataset.
Parameters
----------
paths : str or sequence
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 ``manual_combine`` for details). (A string glob will
be expanded to a 1-dimensional list.)
chunks : int or dict, 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, or list of str, DataArray, Index or None, optional
Dimensions to concatenate files along. You only need to provide this argument
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.
combine : {'by_coords', 'nested'}, optional
Whether ``xarray.combine_by_coords`` or ``xarray.combine_nested`` is used to
combine all the data. If this argument is not provided, `xarray.auto_combine` is
used, but in the future this behavior will switch to use
`xarray.combine_by_coords` by default.
compat : {'identical', 'equals', 'broadcast_equals',
'no_conflicts', 'override'}, optional
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'}, \
optional
Engine to use when reading files. If not provided, the default engine
is chosen based on available dependencies, with a preference for
'netcdf4'.
lock : False or duck threading.Lock, optional
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.
data_vars : {'minimal', 'different', 'all' or list of str}, optional
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, optional
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'}, optional
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.
**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
auto_combine
open_dataset
References
----------
.. [1] http://xarray.pydata.org/en/stable/dask.html
.. [2] http://xarray.pydata.org/en/stable/dask.html#chunking-and-performance
"""
if isinstance(paths, str):
if is_remote_uri(paths):
raise ValueError(
"cannot do wild-card matching for paths that are remote URLs: "
"{!r}. Instead, supply paths as an explicit list of strings.".format(
paths
)
)
paths = sorted(glob(paths))
else:
paths = [str(p) if isinstance(p, Path) else p for p in paths]
if not paths:
raise OSError("no files to open")
# If combine='by_coords' then this is unnecessary, but quick.
# If combine='nested' then this creates a flat list which is easier to
# iterate over, while saving the originally-supplied structure as "ids"
if combine == "nested":
if str(concat_dim) == "_not_supplied":
raise ValueError("Must supply concat_dim when using " "combine='nested'")
else:
if isinstance(concat_dim, (str, DataArray)) or concat_dim is None:
concat_dim = [concat_dim]
combined_ids_paths = _infer_concat_order_from_positions(paths)
ids, paths = (list(combined_ids_paths.keys()), list(combined_ids_paths.values()))
open_kwargs = dict(
engine=engine, chunks=chunks or {}, lock=lock, autoclose=autoclose, **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]
file_objs = [getattr_(ds, "_file_obj") 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, file_objs = dask.compute(datasets, file_objs)
# Combine all datasets, closing them in case of a ValueError
try:
if combine == "_old_auto":
# Use the old auto_combine for now
# Remove this after deprecation cycle from #2616 is complete
basic_msg = dedent(
"""\
In xarray version 0.15 the default behaviour of `open_mfdataset`
will change. To retain the existing behavior, pass
combine='nested'. To use future default behavior, pass
combine='by_coords'. See
http://xarray.pydata.org/en/stable/combining.html#combining-multi
"""
)
warnings.warn(basic_msg, FutureWarning, stacklevel=2)
combined = auto_combine(
datasets,
concat_dim=concat_dim,
compat=compat,
data_vars=data_vars,
coords=coords,
join=join,
from_openmfds=True,
)
elif 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,
)
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
)
else:
raise ValueError(
"{} is an invalid option for the keyword argument"
" ``combine``".format(combine)
)
except ValueError:
for ds in datasets:
ds.close()
raise
combined._file_obj = _MultiFileCloser(file_objs)
combined.attrs = datasets[0].attrs
return combined
WRITEABLE_STORES: Dict[str, Callable] = {
"netcdf4": backends.NetCDF4DataStore.open,
"scipy": backends.ScipyDataStore,
"h5netcdf": backends.H5NetCDFStore,
}
def to_netcdf(
dataset: Dataset,
path_or_file=None,
mode: str = "w",
format: str = None,
group: str = None,
engine: str = None,
encoding: Mapping = None,
unlimited_dims: Iterable[Hashable] = None,
compute: bool = True,
multifile: bool = False,
invalid_netcdf: bool = False,
) -> Union[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, Path):
path_or_file = str(path_or_file)
if encoding is None:
encoding = {}