import typing
import dask
import fsspec
import pandas as pd
import pydantic
import xarray as xr
from intake.source.base import DataSource, Schema
from .cat import Aggregation, DataFormat
from .utils import OPTIONS
class ESMDataSourceError(Exception):
pass
def _get_xarray_open_kwargs(data_format, xarray_open_kwargs=None, storage_options=None):
xarray_open_kwargs = (xarray_open_kwargs or {}).copy()
_default_open_kwargs = {
'engine': 'zarr' if data_format in {'zarr', 'reference'} else 'netcdf4',
'chunks': {},
'backend_kwargs': {},
}
xarray_open_kwargs = (
{**_default_open_kwargs, **xarray_open_kwargs}
if xarray_open_kwargs
else _default_open_kwargs
)
if (
xarray_open_kwargs['engine'] == 'zarr'
and 'storage_options' not in xarray_open_kwargs['backend_kwargs']
):
xarray_open_kwargs['backend_kwargs']['storage_options'] = {} or storage_options
return xarray_open_kwargs
@dask.delayed
def _open_dataset(
urlpath,
varname,
*,
xarray_open_kwargs=None,
preprocess=None,
requested_variables=None,
additional_attrs=None,
expand_dims=None,
data_format=None,
storage_options=None,
):
storage_options = storage_options or xarray_open_kwargs.get('backend_kwargs', {}).get(
'storage_options', {}
)
# Support kerchunk datasets, setting the file object (fo) and urlpath
if data_format == 'reference':
xarray_open_kwargs['backend_kwargs']['storage_options']['fo'] = urlpath
xarray_open_kwargs['backend_kwargs']['consolidated'] = False
urlpath = 'reference://'
if xarray_open_kwargs['engine'] in 'zarr' or data_format == 'opendap':
url = urlpath
elif fsspec.utils.can_be_local(urlpath):
url = fsspec.open_local(urlpath, **storage_options)
else:
url = fsspec.open(urlpath, **storage_options).open()
# Handle multi-file datasets with `xr.open_mfdataset()`
if (isinstance(url, str) and '*' in url) or isinstance(url, list):
# How should we handle concat_dim, and other xr.open_mfdataset kwargs?
xarray_open_kwargs.update(preprocess=preprocess)
xarray_open_kwargs.update(parallel=True)
ds = xr.open_mfdataset(url, **xarray_open_kwargs)
else:
ds = xr.open_dataset(url, **xarray_open_kwargs)
if preprocess is not None:
ds = preprocess(ds)
if varname and isinstance(varname, str):
varname = [varname]
if requested_variables:
if isinstance(requested_variables, str):
requested_variables = [requested_variables]
variable_intersection = set(requested_variables).intersection(set(varname))
variables = [variable for variable in variable_intersection if variable in ds.data_vars]
scalar_variables = [v for v in ds.data_vars if len(ds[v].dims) == 0]
ds = ds.set_coords(scalar_variables)
ds = ds[variables]
ds.attrs[OPTIONS['vars_key']] = variables
elif varname:
ds.attrs[OPTIONS['vars_key']] = varname
ds = _expand_dims(expand_dims, ds)
ds = _update_attrs(additional_attrs=additional_attrs, ds=ds)
return ds
def _update_attrs(*, additional_attrs, ds):
additional_attrs = additional_attrs or {}
if additional_attrs:
additional_attrs = {
f"{OPTIONS['attrs_prefix']}:{key}": f'{value}'
if isinstance(value, str) or not hasattr(value, '__iter__')
else ','.join(value)
for key, value in additional_attrs.items()
}
ds.attrs = {**ds.attrs, **additional_attrs}
return ds
def _expand_dims(expand_dims, ds):
if expand_dims:
for variable in ds.attrs[OPTIONS['vars_key']]:
ds[variable] = ds[variable].expand_dims(**expand_dims)
return ds
[docs]class ESMDataSource(DataSource):
version = '1.0'
container = 'xarray'
name = 'esm_datasource'
partition_access = True
[docs] @pydantic.validate_call
def __init__(
self,
key: pydantic.StrictStr,
records: list[dict[str, typing.Any]],
path_column_name: pydantic.StrictStr,
data_format: DataFormat | None,
format_column_name: pydantic.StrictStr | None,
*,
variable_column_name: pydantic.StrictStr | None = None,
aggregations: list[Aggregation] | None = None,
requested_variables: list[str] | None = None,
preprocess: typing.Callable | None = None,
storage_options: dict[str, typing.Any] | None = None,
xarray_open_kwargs: dict[str, typing.Any] | None = None,
xarray_combine_by_coords_kwargs: dict[str, typing.Any] | None = None,
intake_kwargs: dict[str, typing.Any] | None = None,
):
"""An intake compatible Data Source for ESM data.
Parameters
----------
key: str
The key of the data source.
records: list of dict
A list of records, each of which is a dictionary
mapping column names to values.
path_column_name: str
The column name of the path.
data_format: DataFormat
The data format of the data.
variable_column_name: str, optional
The column name of the variable name.
aggregations: list of Aggregation, optional
A list of aggregations to apply to the data.
requested_variables: list of str, optional
A list of variables to load.
preprocess: callable, optional
A preprocessing function to apply to the data.
storage_options: dict, optional
fsspec parameters passed to the backend file-system such as Google Cloud Storage,
Amazon Web Service S3.
xarray_open_kwargs: dict, optional
Keyword arguments to pass to :py:func:`~xarray.open_dataset` function.
xarray_combine_by_coords_kwargs: dict, optional
Keyword arguments to pass to :py:func:`~xarray.combine_by_coords` function.
intake_kwargs: dict, optional
Additional keyword arguments are passed through to the :py:class:`~intake.source.base.DataSource` base class.
"""
intake_kwargs = intake_kwargs or {}
super().__init__(**intake_kwargs)
self.key = key
self.storage_options = storage_options or {}
self.preprocess = preprocess
self.requested_variables = requested_variables or []
self.path_column_name = path_column_name
self.variable_column_name = variable_column_name
self.aggregations = aggregations
self.df = pd.DataFrame.from_records(records)
self.xarray_open_kwargs = xarray_open_kwargs
self.xarray_combine_by_coords_kwargs = dict(combine_attrs='drop_conflicts')
if xarray_combine_by_coords_kwargs is None:
xarray_combine_by_coords_kwargs = {}
self.xarray_combine_by_coords_kwargs = {
**self.xarray_combine_by_coords_kwargs,
**xarray_combine_by_coords_kwargs,
}
self._ds = None
if data_format is not None:
self.df['_data_format_'] = data_format.value
else:
self.df = self.df.rename(columns={format_column_name: '_data_format_'})
def __repr__(self) -> str:
return f'<{type(self).__name__} (name: {self.key}, asset(s): {len(self.df)})>'
def _get_schema(self) -> Schema:
if self._ds is None:
self._open_dataset()
metadata: dict[str, typing.Any] = {'dims': {}, 'data_vars': {}, 'coords': ()}
self._schema = Schema(
datashape=None,
dtype=None,
shape=None,
npartitions=None,
extra_metadata=metadata,
)
return self._schema
def _open_dataset(self):
"""Open dataset with xarray"""
try:
datasets = [
_open_dataset(
record[self.path_column_name],
record[self.variable_column_name] if self.variable_column_name else None,
xarray_open_kwargs=_get_xarray_open_kwargs(
record['_data_format_'], self.xarray_open_kwargs, self.storage_options
),
preprocess=self.preprocess,
expand_dims={
agg.attribute_name: [record[agg.attribute_name]]
for agg in self.aggregations
if agg.type.value == 'join_new'
},
requested_variables=self.requested_variables,
data_format=record['_data_format_'],
additional_attrs=record[~record.isnull()].to_dict(),
storage_options=self.storage_options,
)
for _, record in self.df.iterrows()
]
datasets = dask.compute(*datasets)
if len(datasets) == 1:
self._ds = datasets[0]
else:
datasets = sorted(
datasets,
key=lambda ds: tuple(
f"{OPTIONS['attrs_prefix']}/{agg.attribute_name}"
for agg in self.aggregations
),
)
datasets = [
ds.set_coords(set(ds.variables) - set(ds.attrs[OPTIONS['vars_key']]))
for ds in datasets
]
self._ds = xr.combine_by_coords(datasets, **self.xarray_combine_by_coords_kwargs)
self._ds.attrs[OPTIONS['dataset_key']] = self.key
except Exception as exc:
raise ESMDataSourceError(
f"""Failed to load dataset with key='{self.key}'
You can use `cat['{self.key}'].df` to inspect the assets/files for this key.
"""
) from exc
[docs] def to_dask(self):
"""Return xarray object (which will have chunks)"""
self._load_metadata()
return self._ds
[docs] def close(self):
"""Delete open files from memory"""
self._ds = None
self._schema = None