def test_dict(self):
self.assertIs(to_scalar({}), UNDEFINED)
self.assertIs(to_scalar({'a': 1}), UNDEFINED)
self.assertIs(to_scalar({'a': 1, 'b': 2}), UNDEFINED)
self.assertEqual(to_scalar({
'a': 1,
'b': 2
}, stringify=True), "{'a': 1, 'b': 2}")
def extract_point(ds: DatasetLike.TYPE,
point: PointLike.TYPE,
indexers: DictLike.TYPE = None,
tolerance_default: float = 0.01) -> Dict:
"""
Extract data at the given point location. The returned dict will contain scalar
values for all variables for which all dimension have been given in ``indexers``.
For the dimensions *lon* and *lat* a nearest neighbour lookup is performed.
All other dimensions must mach exact.
:param ds: Dataset or dataframe to subset
:param point: Geographic point given by longitude and latitude
:param indexers: Optional indexers into data array of *var*. The *indexers* is a dictionary
or a comma-separated string of key-value pairs that maps the variable's dimension names
to constant labels. e.g. "layer=4".
:param tolerance_default: The default longitude and latitude tolerance for the nearest neighbour lookup.
It will only be used, if it is not possible to deduce the resolution of the dataset.
:return: A dict with the scalar values of all variables and the variable names as keys.
"""
ds = DatasetLike.convert(ds)
point = PointLike.convert(point)
indexers = DictLike.convert(indexers) or {}
lon_lat_indexers = {'lon': point.x, 'lat': point.y}
tolerance = _get_tolerance(ds, tolerance_default)
variable_values = {}
var_names = sorted(ds.data_vars.keys())
for var_name in var_names:
if not var_name.endswith('_bnds'):
variable = ds.data_vars[var_name]
effective_indexers = {}
used_dims = {'lat', 'lon'}
for dim_name, dim_value in indexers.items():
if dim_name in variable.dims:
effective_indexers[dim_name] = dim_value
used_dims.add(dim_name)
if set(variable.dims) == used_dims:
try:
lon_lat_data = variable.sel(**effective_indexers)
except KeyError:
# if there is no exact match for the "additional" dims, skip this variable
continue
try:
point_data = lon_lat_data.sel(method='nearest',
tolerance=tolerance,
**lon_lat_indexers)
except KeyError:
# if there is no point within the given tolerance, return an empty dict
return {}
if not variable_values:
variable_values['lat'] = float(point_data.lat)
variable_values['lon'] = float(point_data.lon)
value = to_scalar(point_data.values, ndigits=3)
if value is not UNDEFINED:
variable_values[var_name] = value
return variable_values
def test_primitives(self):
self.assertEqual(to_scalar(3456), 3456)
self.assertEqual(to_scalar(34.56789), 34.56789)
self.assertEqual(to_scalar(34.56789, ndigits=1), 34.6)
self.assertEqual(to_scalar(True), True)
self.assertEqual(to_scalar("Oh!"), "Oh!")
self.assertEqual(to_scalar("Oh!Oh!", nchars=2), "Oh...")
self.assertEqual(to_scalar(None), None)
def test_primitives(self):
self.assertEqual(to_scalar(3456), 3456)
self.assertEqual(to_scalar(34.56789), 34.56789)
self.assertEqual(to_scalar(34.56789, ndigits=1), 34.6)
self.assertEqual(to_scalar(True), True)
self.assertEqual(to_scalar("Oh!"), "Oh!")
self.assertEqual(to_scalar("Oh!Oh!", nchars=2), "Oh...")
self.assertEqual(to_scalar(None), None)
def extract_point(ds: DatasetLike.TYPE,
point: PointLike.TYPE,
indexers: DictLike.TYPE = None,
tolerance_default: float = 0.01) -> Dict:
"""
Extract data at the given point location. The returned dict will contain scalar
values for all variables for which all dimension have been given in ``indexers``.
For the dimensions *lon* and *lat* a nearest neighbour lookup is performed.
All other dimensions must mach exact.
:param ds: Dataset or dataframe to subset
:param point: Geographic point given by longitude and latitude
:param indexers: Optional indexers into data array of *var*. The *indexers* is a dictionary
or a comma-separated string of key-value pairs that maps the variable's dimension names
to constant labels. e.g. "layer=4".
:param tolerance_default: The default longitude and latitude tolerance for the nearest neighbour lookup.
It will only be used, if it is not possible to deduce the resolution of the dataset.
:return: A dict with the scalar values of all variables and the variable names as keys.
"""
ds = DatasetLike.convert(ds)
point = PointLike.convert(point)
indexers = DictLike.convert(indexers) or {}
lon_lat_indexers = {'lon': point.x, 'lat': point.y}
tolerance = _get_tolerance(ds, tolerance_default)
variable_values = {}
var_names = sorted(ds.data_vars.keys())
for var_name in var_names:
if not var_name.endswith('_bnds'):
variable = ds.data_vars[var_name]
effective_indexers = {}
used_dims = {'lat', 'lon'}
for dim_name, dim_value in indexers.items():
if dim_name in variable.dims:
effective_indexers[dim_name] = dim_value
used_dims.add(dim_name)
if set(variable.dims) == used_dims:
try:
lon_lat_data = variable.sel(**effective_indexers)
except KeyError:
# if there is no exact match for the "additional" dims, skip this variable
continue
try:
point_data = lon_lat_data.sel(method='nearest', tolerance=tolerance, **lon_lat_indexers)
except KeyError:
# if there is no point within the given tolerance, return an empty dict
return {}
if not variable_values:
variable_values['lat'] = float(point_data.lat)
variable_values['lon'] = float(point_data.lon)
value = to_scalar(point_data.values, ndigits=3)
if value is not UNDEFINED:
variable_values[var_name] = value
return variable_values
def test_pandas(self):
try:
import pandas as pd
self.assertIs(to_scalar(pd.Series(np.array([]))), UNDEFINED)
self.assertEqual(to_scalar(pd.Series(np.array([234]))), 234)
self.assertIs(to_scalar(pd.Series(np.array([234, 567]))),
UNDEFINED)
self.assertEqual(to_scalar(pd.Series(np.array([234.567]))),
234.567)
self.assertEqual(
to_scalar(pd.Series(np.array([234.567, 567.234]))), UNDEFINED)
self.assertEqual(
to_scalar(pd.Series(np.array([234.567])), ndigits=2), 234.57)
self.assertEqual(to_scalar(pd.Series(np.array([True]))), True)
self.assertIs(to_scalar(pd.Series(np.array([True, False]))),
UNDEFINED)
self.assertIs(to_scalar(pd.Series([])), UNDEFINED)
self.assertEqual(to_scalar(pd.Series(234)), 234)
self.assertEqual(to_scalar(pd.Series([234])), 234)
self.assertIs(to_scalar(pd.Series([234, 567])), UNDEFINED)
self.assertEqual(to_scalar(pd.Series(234.567)), 234.567)
self.assertEqual(to_scalar(pd.Series([234.567])), 234.567)
self.assertEqual(to_scalar(pd.Series([234.567, 567.234])),
UNDEFINED)
self.assertEqual(to_scalar(pd.Series([234.567]), ndigits=2),
234.57)
self.assertEqual(to_scalar(pd.Series([True])), True)
self.assertIs(to_scalar(pd.Series([True, False])), UNDEFINED)
except ImportError:
pass
def test_xarrays(self):
try:
import xarray as xr
self.assertIs(to_scalar(xr.DataArray(np.array([]))), UNDEFINED)
self.assertEqual(to_scalar(xr.DataArray(np.array(234))), 234)
self.assertEqual(to_scalar(xr.DataArray(np.array(234))), 234)
self.assertEqual(to_scalar(xr.DataArray(np.array([234]))), 234)
self.assertEqual(to_scalar(xr.DataArray(np.array([[234]]))), 234)
self.assertIs(to_scalar(xr.DataArray(np.array([234, 567]))),
UNDEFINED)
self.assertIs(to_scalar(xr.DataArray(np.array([[234], [567]]))),
UNDEFINED)
self.assertEqual(to_scalar(xr.DataArray(np.array(234.567))),
234.567)
self.assertEqual(to_scalar(xr.DataArray(np.array([234.567]))),
234.567)
self.assertEqual(to_scalar(xr.DataArray(np.array([[234.567]]))),
234.567)
self.assertEqual(
to_scalar(xr.DataArray(np.array([234.567, 567.234]))),
UNDEFINED)
self.assertEqual(
to_scalar(xr.DataArray(np.array([234.567])), ndigits=2),
234.57)
self.assertEqual(to_scalar(xr.DataArray(np.array(True))), True)
self.assertEqual(to_scalar(xr.DataArray(np.array([True]))), True)
self.assertIs(to_scalar(xr.DataArray(np.array([True, False]))),
UNDEFINED)
self.assertIs(to_scalar(xr.DataArray(np.array([[True], [False]]))),
UNDEFINED)
except ImportError:
pass
def test_ndarrays(self):
self.assertIs(to_scalar(np.array([])), UNDEFINED)
self.assertEqual(to_scalar(np.array(234)), 234)
self.assertEqual(to_scalar(np.array([234])), 234)
self.assertEqual(to_scalar(np.array([[234]])), 234)
self.assertIs(to_scalar(np.array([234, 567])), UNDEFINED)
self.assertIs(to_scalar(np.array([[234], [567]])), UNDEFINED)
self.assertEqual(to_scalar(np.array([234.567])), 234.567)
self.assertEqual(to_scalar(np.array(234.567)), 234.567)
self.assertEqual(to_scalar(np.array([[234.567]])), 234.567)
self.assertEqual(to_scalar(np.array([234.567, 567.234])), UNDEFINED)
self.assertEqual(to_scalar(np.array([234.567]), ndigits=2), 234.57)
self.assertEqual(to_scalar(np.array(True)), True)
self.assertEqual(to_scalar(np.array([True])), True)
self.assertIs(to_scalar(np.array([True, False])), UNDEFINED)
self.assertIs(to_scalar(np.array([[True], [False]])), UNDEFINED)
self.assertIs(to_scalar(np.array([None])), UNDEFINED)
def test_list(self):
self.assertIs(to_scalar([]), UNDEFINED)
self.assertEqual(to_scalar([1]), UNDEFINED)
self.assertIs(to_scalar([1, 2, 3]), UNDEFINED)
self.assertEqual(to_scalar(["Oh!"]), UNDEFINED)
self.assertEqual(to_scalar([1, 2, 3], stringify=True), '[1, 2, 3]')
def test_pandas(self):
try:
import pandas as pd
self.assertIs(to_scalar(pd.Series(np.array([]))), UNDEFINED)
self.assertEqual(to_scalar(pd.Series(np.array([234]))), 234)
self.assertIs(to_scalar(pd.Series(np.array([234, 567]))), UNDEFINED)
self.assertEqual(to_scalar(pd.Series(np.array([234.567]))), 234.567)
self.assertEqual(to_scalar(pd.Series(np.array([234.567, 567.234]))), UNDEFINED)
self.assertEqual(to_scalar(pd.Series(np.array([234.567])), ndigits=2), 234.57)
self.assertEqual(to_scalar(pd.Series(np.array([True]))), True)
self.assertIs(to_scalar(pd.Series(np.array([True, False]))), UNDEFINED)
self.assertIs(to_scalar(pd.Series([])), UNDEFINED)
self.assertEqual(to_scalar(pd.Series(234)), 234)
self.assertEqual(to_scalar(pd.Series([234])), 234)
self.assertIs(to_scalar(pd.Series([234, 567])), UNDEFINED)
self.assertEqual(to_scalar(pd.Series(234.567)), 234.567)
self.assertEqual(to_scalar(pd.Series([234.567])), 234.567)
self.assertEqual(to_scalar(pd.Series([234.567, 567.234])), UNDEFINED)
self.assertEqual(to_scalar(pd.Series([234.567]), ndigits=2), 234.57)
self.assertEqual(to_scalar(pd.Series([True])), True)
self.assertIs(to_scalar(pd.Series([True, False])), UNDEFINED)
except ImportError:
pass
def test_xarrays(self):
try:
import xarray as xr
self.assertIs(to_scalar(xr.DataArray(np.array([]))), UNDEFINED)
self.assertEqual(to_scalar(xr.DataArray(np.array(234))), 234)
self.assertEqual(to_scalar(xr.DataArray(np.array(234))), 234)
self.assertEqual(to_scalar(xr.DataArray(np.array([234]))), 234)
self.assertEqual(to_scalar(xr.DataArray(np.array([[234]]))), 234)
self.assertIs(to_scalar(xr.DataArray(np.array([234, 567]))), UNDEFINED)
self.assertIs(to_scalar(xr.DataArray(np.array([[234], [567]]))), UNDEFINED)
self.assertEqual(to_scalar(xr.DataArray(np.array(234.567))), 234.567)
self.assertEqual(to_scalar(xr.DataArray(np.array([234.567]))), 234.567)
self.assertEqual(to_scalar(xr.DataArray(np.array([[234.567]]))), 234.567)
self.assertEqual(to_scalar(xr.DataArray(np.array([234.567, 567.234]))), UNDEFINED)
self.assertEqual(to_scalar(xr.DataArray(np.array([234.567])), ndigits=2), 234.57)
self.assertEqual(to_scalar(xr.DataArray(np.array(True))), True)
self.assertEqual(to_scalar(xr.DataArray(np.array([True]))), True)
self.assertIs(to_scalar(xr.DataArray(np.array([True, False]))), UNDEFINED)
self.assertIs(to_scalar(xr.DataArray(np.array([[True], [False]]))), UNDEFINED)
except ImportError:
pass
def test_ndarrays(self):
self.assertIs(to_scalar(np.array([])), UNDEFINED)
self.assertEqual(to_scalar(np.array(234)), 234)
self.assertEqual(to_scalar(np.array([234])), 234)
self.assertEqual(to_scalar(np.array([[234]])), 234)
self.assertIs(to_scalar(np.array([234, 567])), UNDEFINED)
self.assertIs(to_scalar(np.array([[234], [567]])), UNDEFINED)
self.assertEqual(to_scalar(np.array([234.567])), 234.567)
self.assertEqual(to_scalar(np.array(234.567)), 234.567)
self.assertEqual(to_scalar(np.array([[234.567]])), 234.567)
self.assertEqual(to_scalar(np.array([234.567, 567.234])), UNDEFINED)
self.assertEqual(to_scalar(np.array([234.567]), ndigits=2), 234.57)
self.assertEqual(to_scalar(np.array(True)), True)
self.assertEqual(to_scalar(np.array([True])), True)
self.assertIs(to_scalar(np.array([True, False])), UNDEFINED)
self.assertIs(to_scalar(np.array([[True], [False]])), UNDEFINED)
self.assertIs(to_scalar(np.array([None])), UNDEFINED)
def test_dict(self):
self.assertIs(to_scalar({}), UNDEFINED)
self.assertIs(to_scalar({'a': 1}), UNDEFINED)
self.assertIs(to_scalar({'a': 1, 'b': 2}), UNDEFINED)
self.assertEqual(to_scalar({'a': 1, 'b': 2}, stringify=True), "{'a': 1, 'b': 2}")
def test_list(self):
self.assertIs(to_scalar([]), UNDEFINED)
self.assertEqual(to_scalar([1]), UNDEFINED)
self.assertIs(to_scalar([1, 2, 3]), UNDEFINED)
self.assertEqual(to_scalar(["Oh!"]), UNDEFINED)
self.assertEqual(to_scalar([1, 2, 3], stringify=True), '[1, 2, 3]')