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Remove accidental HEAD rebase comment
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@tomvothecoder
tomvothecoder committed Oct 2, 2024
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Showing 1 changed file with 146 additions and 154 deletions.
300 changes: 146 additions & 154 deletions xcdat/dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,68 +52,64 @@ def open_dataset(
) -> xr.Dataset:
"""Wraps ``xarray.open_dataset()`` with post-processing options.
Parameters
----------
path : 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).
data_var: Optional[str], optional
The key of the non-bounds data variable to keep in the Dataset,
alongside any existing bounds data variables, by default None.
<<<<<<< HEAD
add_bounds: List[CFAxisKey] | None
=======
add_bounds: List[CFAxisKey] | Tuple[CFAxisKey, ...] | None
>>>>>>> 2401905 (Fix ruff linting errors)
List of CF axes to try to add bounds for (if missing), by default
("X", "Y"). Set to None to not add any missing bounds. Please note that
bounds are required for many xCDAT features.
* This parameter calls :py:func:`xarray.Dataset.bounds.add_missing_bounds`
* Supported CF axes include "X", "Y", "Z", and "T"
* By default, missing "T" bounds are generated using the time frequency
of the coordinates. If desired, refer to
:py:func:`xarray.Dataset.bounds.add_time_bounds` if you require more
granular configuration for how "T" bounds are generated.
decode_times: bool, optional
If True, attempt to decode times encoded in the standard NetCDF
datetime format into cftime.datetime objects. Otherwise, leave them
encoded as numbers. This keyword may not be supported by all the
backends, by default True.
center_times: bool, optional
If True, attempt to center time coordinates using the midpoint between
its upper and lower bounds. Otherwise, use the provided time
coordinates, by default False.
lon_orient: Optional[Tuple[float, float]], optional
The orientation to use for the Dataset's longitude axis (if it exists).
Either `(-180, 180)` or `(0, 360)`, by default None. Supported options
include:
* None: use the current orientation (if the longitude axis exists)
* (-180, 180): represents [-180, 180) in math notation
* (0, 360): represents [0, 360) in math notation
**kwargs : Dict[str, Any]
Additional arguments passed on to ``xarray.open_dataset``. Refer to the
[1]_ xarray docs for accepted keyword arguments.
Returns
-------
xr.Dataset
Dataset after applying operations.
Notes
-----
``xarray.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.
References
----------
.. [1] https://xarray.pydata.org/en/stable/generated/xarray.open_dataset.html
Parameters
----------
path : 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).
data_var: Optional[str], optional
The key of the non-bounds data variable to keep in the Dataset,
alongside any existing bounds data variables, by default None.
add_bounds: List[CFAxisKey] | Tuple[CFAxisKey, ...] | None
List of CF axes to try to add bounds for (if missing), by default
("X", "Y"). Set to None to not add any missing bounds. Please note that
bounds are required for many xCDAT features.
* This parameter calls :py:func:`xarray.Dataset.bounds.add_missing_bounds`
* Supported CF axes include "X", "Y", "Z", and "T"
* By default, missing "T" bounds are generated using the time frequency
of the coordinates. If desired, refer to
:py:func:`xarray.Dataset.bounds.add_time_bounds` if you require more
granular configuration for how "T" bounds are generated.
decode_times: bool, optional
If True, attempt to decode times encoded in the standard NetCDF
datetime format into cftime.datetime objects. Otherwise, leave them
encoded as numbers. This keyword may not be supported by all the
backends, by default True.
center_times: bool, optional
If True, attempt to center time coordinates using the midpoint between
its upper and lower bounds. Otherwise, use the provided time
coordinates, by default False.
lon_orient: Optional[Tuple[float, float]], optional
The orientation to use for the Dataset's longitude axis (if it exists).
Either `(-180, 180)` or `(0, 360)`, by default None. Supported options
include:
* None: use the current orientation (if the longitude axis exists)
* (-180, 180): represents [-180, 180) in math notation
* (0, 360): represents [0, 360) in math notation
**kwargs : Dict[str, Any]
Additional arguments passed on to ``xarray.open_dataset``. Refer to the
[1]_ xarray docs for accepted keyword arguments.
Returns
-------
xr.Dataset
Dataset after applying operations.
Notes
-----
``xarray.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.
References
----------
.. [1] https://xarray.pydata.org/en/stable/generated/xarray.open_dataset.html
"""
ds = xr.open_dataset(path, decode_times=False, **kwargs) # type: ignore

Expand Down Expand Up @@ -141,98 +137,94 @@ def open_mfdataset(
) -> xr.Dataset:
"""Wraps ``xarray.open_mfdataset()`` with post-processing options.
Parameters
----------
paths : str | NestedSequence[str | os.PathLike]
Paths to dataset files. Paths can be given as strings or as pathlib.Path
objects. Supported options include:
* Directory path (e.g., ``"path/to/files"``), which is converted
to a string glob of `*.nc` files
* String glob (e.g., ``"path/to/files/*.nc"``), which is expanded
to a 1-dimensional list of file paths
* File path to dataset (e.g., ``"path/to/files/file1.nc"``)
* List of file paths (e.g., ``["path/to/files/file1.nc", ...]``).
If concatenation along more than one dimension is desired, then
``paths`` must be a nested list-of-lists (see [2]_
``xarray.combine_nested`` for details).
<<<<<<< HEAD
add_bounds: List[CFAxisKey] | None
=======
add_bounds: List[CFAxisKey] | Tuple[CFAxisKey, ...] | None
>>>>>>> 2401905 (Fix ruff linting errors)
List of CF axes to try to add bounds for (if missing), by default
("X", "Y"). Set to None to not add any missing bounds. Please note that
bounds are required for many xCDAT features.
* This parameter calls :py:func:`xarray.Dataset.bounds.add_missing_bounds`
* Supported CF axes include "X", "Y", "Z", and "T"
* By default, missing "T" bounds are generated using the time frequency
of the coordinates. If desired, refer to
:py:func:`xarray.Dataset.bounds.add_time_bounds` if you require more
granular configuration for how "T" bounds are generated.
data_var: Optional[str], optional
The key of the data variable to keep in the Dataset, by default None.
decode_times: bool, optional
If True, attempt to decode times encoded in the standard NetCDF
datetime format into cftime.datetime objects. Otherwise, leave them
encoded as numbers. This keyword may not be supported by all the
backends, by default True.
center_times: bool, optional
If True, attempt to center time coordinates using the midpoint between
its upper and lower bounds. Otherwise, use the provided time
coordinates, by default False.
lon_orient: Optional[Tuple[float, float]], optional
The orientation to use for the Dataset's longitude axis (if it exists),
by default None. Supported options include:
* None: use the current orientation (if the longitude axis exists)
* (-180, 180): represents [-180, 180) in math notation
* (0, 360): represents [0, 360) in math notation
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, the default value.
* "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.
The ``data_vars`` kwarg defaults to ``"minimal"``, which concatenates
data variables in a manner where only data variables in which the
dimension already appears are included. For example, the time dimension
will not be concatenated to the dimensions of non-time data variables
such as "lat_bnds" or "lon_bnds". ``data_vars="minimal"`` is required for
some xCDAT functions, including spatial averaging where a reduction is
performed using the lat/lon bounds.
preprocess : Optional[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"]``.
**kwargs : Dict[str, Any]
Additional arguments passed on to ``xarray.open_mfdataset``. Refer to
the [3]_ xarray docs for accepted keyword arguments.
Returns
-------
xr.Dataset
The Dataset.
Notes
-----
``xarray.open_mfdataset`` 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.
References
----------
.. [2] https://docs.xarray.dev/en/stable/generated/xarray.combine_nested.html
.. [3] https://xarray.pydata.org/en/stable/generated/xarray.open_mfdataset.html
Parameters
----------
paths : str | NestedSequence[str | os.PathLike]
Paths to dataset files. Paths can be given as strings or as pathlib.Path
objects. Supported options include:
* Directory path (e.g., ``"path/to/files"``), which is converted
to a string glob of `*.nc` files
* String glob (e.g., ``"path/to/files/*.nc"``), which is expanded
to a 1-dimensional list of file paths
* File path to dataset (e.g., ``"path/to/files/file1.nc"``)
* List of file paths (e.g., ``["path/to/files/file1.nc", ...]``).
If concatenation along more than one dimension is desired, then
``paths`` must be a nested list-of-lists (see [2]_
``xarray.combine_nested`` for details).
add_bounds: List[CFAxisKey] | Tuple[CFAxisKey, ...] | None
List of CF axes to try to add bounds for (if missing), by default
("X", "Y"). Set to None to not add any missing bounds. Please note that
bounds are required for many xCDAT features.
* This parameter calls :py:func:`xarray.Dataset.bounds.add_missing_bounds`
* Supported CF axes include "X", "Y", "Z", and "T"
* By default, missing "T" bounds are generated using the time frequency
of the coordinates. If desired, refer to
:py:func:`xarray.Dataset.bounds.add_time_bounds` if you require more
granular configuration for how "T" bounds are generated.
data_var: Optional[str], optional
The key of the data variable to keep in the Dataset, by default None.
decode_times: bool, optional
If True, attempt to decode times encoded in the standard NetCDF
datetime format into cftime.datetime objects. Otherwise, leave them
encoded as numbers. This keyword may not be supported by all the
backends, by default True.
center_times: bool, optional
If True, attempt to center time coordinates using the midpoint between
its upper and lower bounds. Otherwise, use the provided time
coordinates, by default False.
lon_orient: Optional[Tuple[float, float]], optional
The orientation to use for the Dataset's longitude axis (if it exists),
by default None. Supported options include:
* None: use the current orientation (if the longitude axis exists)
* (-180, 180): represents [-180, 180) in math notation
* (0, 360): represents [0, 360) in math notation
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, the default value.
* "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.
The ``data_vars`` kwarg defaults to ``"minimal"``, which concatenates
data variables in a manner where only data variables in which the
dimension already appears are included. For example, the time dimension
will not be concatenated to the dimensions of non-time data variables
such as "lat_bnds" or "lon_bnds". ``data_vars="minimal"`` is required for
some xCDAT functions, including spatial averaging where a reduction is
performed using the lat/lon bounds.
preprocess : Optional[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"]``.
**kwargs : Dict[str, Any]
Additional arguments passed on to ``xarray.open_mfdataset``. Refer to
the [3]_ xarray docs for accepted keyword arguments.
Returns
-------
xr.Dataset
The Dataset.
Notes
-----
``xarray.open_mfdataset`` 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.
References
----------
.. [2] https://docs.xarray.dev/en/stable/generated/xarray.combine_nested.html
.. [3] https://xarray.pydata.org/en/stable/generated/xarray.open_mfdataset.html
"""
if isinstance(paths, str) or isinstance(paths, pathlib.Path):
if os.path.isdir(paths):
Expand Down

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