def test_invalid_dtype(self):
# Test passing in a MJD dataset
dataset = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([180, 360, 6])),
'second': (['lat', 'lon', 'time'], np.ones([180, 360, 6])),
'lat':
np.linspace(-89.5, 89.5, 180),
'lon':
np.linspace(-179.5, 179.5, 360),
'time':
[2451544.5, 2451575.5, 2451604.5, 2451635.5, 2451665.5, 2451696.5]
})
with self.assertRaises(ValueError) as err:
subset.subset_temporal(dataset, '2000-01-10, 2000-04-01')
self.assertIn('type datetime', str(err.exception))
def anomaly_internal(ds: xr.Dataset,
time_range: TimeRangeLike.TYPE = None,
region: PolygonLike.TYPE = None,
monitor: Monitor = Monitor.NONE) -> xr.Dataset:
"""
Calculate anomaly using as reference data the mean of an optional region
and time slice from the given dataset. If no time slice/spatial region is
given, the operation will calculate anomaly using the mean of the whole
dataset as the reference.
This is done for each data array in the dataset.
:param ds: The dataset to calculate anomalies from
:param time_range: Time range to use for reference data
:param region: Spatial region to use for reference data
:param monitor: a progress monitor.
:return: The anomaly dataset
"""
ref = ds.copy()
if time_range:
time_range = TimeRangeLike.convert(time_range)
ref = subset_temporal(ref, time_range)
if region:
region = PolygonLike.convert(region)
ref = subset_spatial(ref, region)
with monitor.observing("Calculating anomaly"):
ref = ref.mean(keep_attrs=True, skipna=True)
diff = ds - ref
return diff
def test_invalid_dtype(self):
# Test passing in a MJD dataset
dataset = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([180, 360, 6])),
'second': (['lat', 'lon', 'time'], np.ones([180, 360, 6])),
'lat': np.linspace(-89.5, 89.5, 180),
'lon': np.linspace(-179.5, 179.5, 360),
'time': [2451544.5,
2451575.5,
2451604.5,
2451635.5,
2451665.5,
2451696.5]})
with self.assertRaises(ValueError) as err:
subset.subset_temporal(dataset, '2000-01-10, 2000-04-01')
self.assertIn('type datetime', str(err.exception))
def anomaly_internal(ds: xr.Dataset,
time_range: TimeRangeLike.TYPE = None,
region: PolygonLike.TYPE = None,
monitor: Monitor = Monitor.NONE) -> xr.Dataset:
"""
Calculate anomaly using as reference data the mean of an optional region
and time slice from the given dataset. If no time slice/spatial region is
given, the operation will calculate anomaly using the mean of the whole
dataset as the reference.
This is done for each data array in the dataset.
:param ds: The dataset to calculate anomalies from
:param time_range: Time range to use for reference data
:param region: Spatial region to use for reference data
:param monitor: a progress monitor.
:return: The anomaly dataset
"""
ref = ds.copy()
if time_range:
time_range = TimeRangeLike.convert(time_range)
ref = subset_temporal(ref, time_range)
if region:
region = PolygonLike.convert(region)
ref = subset_spatial(ref, region)
with monitor.observing("Calculating anomaly"):
ref = ref.mean(keep_attrs=True, skipna=True)
diff = ds - ref
return diff
def test_subset_temporal(self):
# Test general functionality
dataset = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([180, 360, 6])),
'second': (['lat', 'lon', 'time'], np.ones([180, 360, 6])),
'lat': np.linspace(-89.5, 89.5, 180),
'lon': np.linspace(-179.5, 179.5, 360),
'time': [datetime(2000, x, 1) for x in range(1, 7)]})
actual = subset.subset_temporal(dataset, '2000-01-10, 2000-04-01')
expected = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([180, 360, 3])),
'second': (['lat', 'lon', 'time'], np.ones([180, 360, 3])),
'lat': np.linspace(-89.5, 89.5, 180),
'lon': np.linspace(-179.5, 179.5, 360),
'time': [datetime(2000, x, 1) for x in range(2, 5)]})
assert_dataset_equal(expected, actual)
def test_subset_temporal(self):
# Test general functionality
dataset = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([180, 360, 6])),
'second': (['lat', 'lon', 'time'], np.ones([180, 360, 6])),
'lat':
np.linspace(-89.5, 89.5, 180),
'lon':
np.linspace(-179.5, 179.5, 360),
'time': [datetime(2000, x, 1) for x in range(1, 7)]
})
actual = subset.subset_temporal(dataset, '2000-01-10, 2000-04-01')
expected = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([180, 360, 3])),
'second': (['lat', 'lon', 'time'], np.ones([180, 360, 3])),
'lat':
np.linspace(-89.5, 89.5, 180),
'lon':
np.linspace(-179.5, 179.5, 360),
'time': [datetime(2000, x, 1) for x in range(2, 5)]
})
assert_dataset_equal(expected, actual)