def test_select_all(self):
ds1 = create_highroc_dataset()
# noinspection PyTypeChecker
ds2 = select_vars(ds1, None)
self.assertIs(ds2, ds1)
ds2 = select_vars(ds1, ds1.data_vars.keys())
self.assertIs(ds2, ds1)
def select_vars(self, var_names: Sequence[str] = None):
"""
Select data variable from given *dataset* and create new dataset.
:param var_names: The names of data variables to select.
:return: A new dataset. It is empty, if *var_names* is empty. It is *dataset*, if *var_names* is None.
"""
return select_vars(self._dataset, var_names)
def test_select_variables_for_some(self):
ds1 = create_highroc_dataset()
self.assertEqual(36, len(ds1.data_vars))
ds2 = select_vars(ds1, ['conc_chl', 'c2rcc_flags', 'rtoa_10'])
self.assertEqual(3, len(ds2.data_vars))
def test_select_none(self):
ds1 = create_highroc_dataset()
ds2 = select_vars(ds1, [])
self.assertEqual(0, len(ds2.data_vars))
ds2 = select_vars(ds1, ['bibo'])
self.assertEqual(0, len(ds2.data_vars))
def step3(input_slice):
extra_vars = input_processor.get_extra_vars(input_slice)
selected_variables = set(
[var_name for var_name, _ in output_variables])
selected_variables.update(extra_vars or set())
return select_vars(input_slice, selected_variables)
def get_time_series(cube: xr.Dataset,
geometry: GeometryLike = None,
var_names: Sequence[str] = None,
start_date: Date = None,
end_date: Date = None,
include_count: bool = False,
include_stdev: bool = False,
use_groupby: bool = False,
cube_asserted: bool = False) -> Optional[xr.Dataset]:
"""
Get a time series dataset from a data *cube*.
*geometry* may be provided as a (shapely) geometry object, a valid GeoJSON object, a valid WKT string,
a sequence of box coordinates (x1, y1, x2, y2), or point coordinates (x, y). If *geometry* covers an area,
i.e. is not a point, the function aggregates the variables to compute a mean value and if desired,
the number of valid observations and the standard deviation.
*start_date* and *end_date* may be provided as a numpy.datetime64 or an ISO datetime string.
Returns a time-series dataset whose data variables have a time dimension but no longer have spatial dimensions,
hence the resulting dataset's variables will only have N-2 dimensions.
A global attribute ``max_number_of_observations`` will be set to the maximum number of observations
that could have been made in each time step.
If the given *geometry* does not overlap the cube's boundaries, or if not output variables remain,
the function returns ``None``.
:param cube: The xcube dataset
:param geometry: Optional geometry
:param var_names: Optional sequence of names of variables to be included.
:param start_date: Optional start date.
:param end_date: Optional end date.
:param include_count: Whether to include the number of valid observations for each time step.
Ignored if geometry is a point.
:param include_stdev: Whether to include standard deviation for each time step.
Ignored if geometry is a point.
:param use_groupby: Use group-by operation. May increase or decrease runtime performance and/or memory consumption.
:param cube_asserted: If False, *cube* will be verified, otherwise it is expected to be a valid cube.
:return: A new dataset with time-series for each variable.
"""
if not cube_asserted:
assert_cube(cube)
geometry = convert_geometry(geometry)
dataset = select_vars(cube, var_names)
if len(dataset.data_vars) == 0:
return None
if start_date is not None or end_date is not None:
# noinspection PyTypeChecker
dataset = dataset.sel(time=slice(start_date, end_date))
if isinstance(geometry, shapely.geometry.Point):
bounds = get_dataset_geometry(dataset)
if not bounds.contains(geometry):
return None
dataset = dataset.sel(lon=geometry.x, lat=geometry.y, method='Nearest')
return dataset.assign_attrs(max_number_of_observations=1)
if geometry is not None:
dataset = mask_dataset_by_geometry(dataset,
geometry,
save_geometry_mask='__mask__')
if dataset is None:
return None
mask = dataset['__mask__']
max_number_of_observations = np.count_nonzero(mask)
dataset = dataset.drop('__mask__')
else:
max_number_of_observations = dataset.lat.size * dataset.lon.size
ds_count = None
ds_stdev = None
if use_groupby:
time_group = dataset.groupby('time')
ds_mean = time_group.mean(skipna=True, dim=xr.ALL_DIMS)
if include_count:
ds_count = time_group.count(dim=xr.ALL_DIMS)
if include_stdev:
ds_stdev = time_group.std(skipna=True, dim=xr.ALL_DIMS)
else:
ds_mean = dataset.mean(dim=('lat', 'lon'), skipna=True)
if include_count:
ds_count = dataset.count(dim=('lat', 'lon'))
if include_stdev:
ds_stdev = dataset.std(dim=('lat', 'lon'), skipna=True)
if ds_count is not None:
ds_count = ds_count.rename(
name_dict=dict({v: f"{v}_count"
for v in ds_count.data_vars}))
if ds_stdev is not None:
ds_stdev = ds_stdev.rename(
name_dict=dict({v: f"{v}_stdev"
for v in ds_stdev.data_vars}))
if ds_count is not None and ds_stdev is not None:
ts_dataset = xr.merge([ds_mean, ds_stdev, ds_count])
elif ds_count is not None:
ts_dataset = xr.merge([ds_mean, ds_count])
elif ds_stdev is not None:
ts_dataset = xr.merge([ds_mean, ds_stdev])
else:
ts_dataset = ds_mean
ts_dataset = ts_dataset.assign_attrs(
max_number_of_observations=max_number_of_observations)
return ts_dataset
def resample_in_time(cube: xr.Dataset,
frequency: str,
method: Union[str, Sequence[str]],
offset=None,
base: int = 0,
tolerance=None,
interp_kind=None,
time_chunk_size=None,
var_names: Sequence[str] = None,
metadata: Dict[str, Any] = None,
cube_asserted: bool = False) -> xr.Dataset:
"""
Resample a xcube dataset in the time dimension.
:param cube: The xcube dataset.
:param frequency: Temporal aggregation frequency. Use format "<count><offset>"
"where <offset> is one of 'H', 'D', 'W', 'M', 'Q', 'Y'.
:param method: Resampling method or sequence of resampling methods.
:param offset: Offset used to adjust the resampled time labels.
Uses same syntax as *frequency*.
:param base: For frequencies that evenly subdivide 1 day, the "origin" of the
aggregated intervals. For example, for '24H' frequency, base could range from 0 through 23.
:param time_chunk_size: If not None, the chunk size to be used for the "time" dimension.
:param var_names: Variable names to include.
:param tolerance: Time tolerance for selective upsampling methods. Defaults to *frequency*.
:param interp_kind: Kind of interpolation if *method* is 'interpolation'.
:param metadata: Output metadata.
:param cube_asserted: If False, *cube* will be verified, otherwise it is expected to be a valid cube.
:return: A new xcube dataset resampled in time.
"""
if not cube_asserted:
assert_cube(cube)
if var_names:
cube = select_vars(cube, var_names)
resampler = cube.resample(skipna=True,
closed='left',
label='left',
keep_attrs=True,
time=frequency,
loffset=offset,
base=base)
if isinstance(method, str):
methods = [method]
else:
methods = list(method)
resampled_cubes = []
for method in methods:
resampling_method = getattr(resampler, method)
kwargs = get_method_kwargs(method, frequency, interp_kind, tolerance)
resampled_cube = resampling_method(**kwargs)
resampled_cube = resampled_cube.rename(
{var_name: f'{var_name}_{method}' for var_name in resampled_cube.data_vars})
resampled_cubes.append(resampled_cube)
if len(resampled_cubes) == 1:
resampled_cube = resampled_cubes[0]
else:
resampled_cube = xr.merge(resampled_cubes)
# TODO: add time_bnds to resampled_ds
time_coverage_start = '%s' % cube.time[0]
time_coverage_end = '%s' % cube.time[-1]
resampled_cube.attrs.update(metadata or {})
# TODO: add other time_coverage_ attributes
resampled_cube.attrs.update(time_coverage_start=time_coverage_start,
time_coverage_end=time_coverage_end)
schema = CubeSchema.new(cube)
chunk_sizes = {schema.dims[i]: schema.chunks[i] for i in range(schema.ndim)}
if isinstance(time_chunk_size, int) and time_chunk_size >= 0:
chunk_sizes['time'] = time_chunk_size
return resampled_cube.chunk(chunk_sizes)