def test_inv_y(self):
ds1, ds2 = _get_inv_y_datasets()
bounds = get_dataset_geometry(ds1)
self.assertEqual(shapely.geometry.box(-25.0, -15.0, 15.0, 15.0),
bounds)
bounds = get_dataset_geometry(ds2)
self.assertEqual(shapely.geometry.box(-25.0, -15.0, 15.0, 15.0),
bounds)
def test_antimeridian(self):
ds1, ds2 = _get_antimeridian_datasets()
bounds = get_dataset_geometry(ds1)
self.assertEqual(
shapely.geometry.MultiPolygon(polygons=[
shapely.geometry.box(165.0, -15.0, 180.0, 15.0),
shapely.geometry.box(-180.0, -15.0, -155.0, 15.0)
]), bounds)
bounds = get_dataset_geometry(ds2)
self.assertEqual(
shapely.geometry.MultiPolygon(polygons=[
shapely.geometry.box(165.0, -15.0, 180.0, 15.0),
shapely.geometry.box(-180.0, -15.0, -155.0, 15.0)
]), bounds)
def find_dataset_places(ctx: ServiceContext,
place_group_id: str,
ds_id: str,
query_expr: Any = None,
comb_op: str = "and") -> GeoJsonFeatureCollection:
dataset = ctx.get_dataset(ds_id)
query_geometry = get_dataset_geometry(dataset)
return _find_places(ctx,
place_group_id,
query_geometry=query_geometry,
query_expr=query_expr,
comb_op=comb_op)
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_variables_subset(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 get_time_series(cube: xr.Dataset,
geometry: GeometryLike = None,
var_names: Sequence[str] = None,
start_date: Date = None,
end_date: Date = None,
agg_methods: Union[str, Sequence[str],
AbstractSet[str]] = AGG_MEAN,
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 agg_methods: Aggregation methods. May be single string or sequence of strings. Possible values are
'mean', 'median', 'min', 'max', 'std', 'count'. Defaults to 'mean'.
Ignored if geometry is a point.
:param include_count: Deprecated. Whether to include the number of valid observations for each time step.
Ignored if geometry is a point.
:param include_stdev: Deprecated. 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)
agg_methods = normalize_agg_methods(agg_methods)
if include_count:
warnings.warn("keyword argument 'include_count' has been deprecated, "
f"use 'agg_methods=[{AGG_COUNT!r}, ...]' instead")
agg_methods.add(AGG_COUNT)
if include_stdev:
warnings.warn("keyword argument 'include_stdev' has been deprecated, "
f"use 'agg_methods=[{AGG_STD!r}, ...]' instead")
agg_methods.add(AGG_STD)
dataset = select_variables_subset(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_vars(['__mask__'])
else:
max_number_of_observations = dataset.lat.size * dataset.lon.size
must_load = len(agg_methods) > 1 or any(
AGG_METHODS[agg_method] == MUST_LOAD for agg_method in agg_methods)
if must_load:
dataset.load()
agg_datasets = []
if use_groupby:
time_group = dataset.groupby('time')
for agg_method in agg_methods:
method = getattr(time_group, agg_method)
if agg_method == 'count':
agg_dataset = method(dim=xr.ALL_DIMS)
else:
agg_dataset = method(dim=xr.ALL_DIMS, skipna=True)
agg_datasets.append(agg_dataset)
else:
for agg_method in agg_methods:
method = getattr(dataset, agg_method)
if agg_method == 'count':
agg_dataset = method(dim=('lat', 'lon'))
else:
agg_dataset = method(dim=('lat', 'lon'), skipna=True)
agg_datasets.append(agg_dataset)
agg_datasets = [
agg_dataset.rename(name_dict=dict(
{v: f"{v}_{agg_method}"
for v in agg_dataset.data_vars}))
for agg_method, agg_dataset in zip(agg_methods, agg_datasets)
]
ts_dataset = xr.merge(agg_datasets)
ts_dataset = ts_dataset.assign_attrs(
max_number_of_observations=max_number_of_observations)
return ts_dataset