def test_seasonal(self):
"""
Test aggregation to a seasonal dataset
"""
# daily -> seasonal
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31')})
ds = adjust_temporal_attrs(ds)
ex = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 5])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 5])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('1999-12-01', freq='QS-DEC', periods=5)})
ex.first.attrs['cell_methods'] = 'time: mean within years'
ex.second.attrs['cell_methods'] = 'time: mean within years'
m = ConsoleMonitor()
actual = temporal_aggregation(ds, output_resolution='season', monitor=m)
self.assertTrue(actual.broadcast_equals(ex))
# monthly -> seasonal
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', freq='MS', periods=12)})
ds = adjust_temporal_attrs(ds)
actual = temporal_aggregation(ds, output_resolution='season', monitor=m)
self.assertTrue(actual.broadcast_equals(ex))
def test_seasonal(self):
"""
Test aggregation to a seasonal dataset
"""
# daily -> seasonal
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31')})
ds = adjust_temporal_attrs(ds)
ex = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 5])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 5])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('1999-12-01', freq='QS-DEC', periods=5)})
ex.first.attrs['cell_methods'] = 'time: mean within years'
ex.second.attrs['cell_methods'] = 'time: mean within years'
m = ConsoleMonitor()
actual = temporal_aggregation(ds, output_resolution='season', monitor=m)
self.assertTrue(actual.broadcast_equals(ex))
# monthly -> seasonal
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', freq='MS', periods=12)})
ds = adjust_temporal_attrs(ds)
actual = temporal_aggregation(ds, output_resolution='season', monitor=m)
self.assertTrue(actual.broadcast_equals(ex))
def test_validation(self):
"""
Test input validation
"""
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 24])),
'lat':
np.linspace(-88, 88, 45),
'lon':
np.linspace(-178, 178, 90)
})
ds = adjust_temporal_attrs(ds)
with self.assertRaises(ValueError) as err:
temporal_aggregation(ds)
self.assertIn('normalize', str(err.exception))
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 24])),
'lat':
np.linspace(-88, 88, 45),
'lon':
np.linspace(-178, 178, 90),
'time':
pd.date_range('2000-01-01', freq='MS', periods=24)
})
ds = adjust_temporal_attrs(ds)
with self.assertRaises(ValueError) as err:
temporal_aggregation(ds)
self.assertIn('daily dataset', str(err.exception))
def test_custom(self):
"""
Test aggergating to custom temporal resolutions
"""
# daily -> 8 days
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31')})
ds = adjust_temporal_attrs(ds)
ex = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 46])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 46])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31', freq='8D')})
ex.first.attrs['cell_methods'] = 'time: mean within years'
ex.second.attrs['cell_methods'] = 'time: mean within years'
m = ConsoleMonitor()
actual = temporal_aggregation(ds, custom_resolution='8D', monitor=m)
self.assertTrue(actual.broadcast_equals(ex))
# daily -> weekly
ex = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 53])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 53])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31', freq='W')})
ex.first.attrs['cell_methods'] = 'time: mean within years'
ex.second.attrs['cell_methods'] = 'time: mean within years'
actual = temporal_aggregation(ds, custom_resolution='W', monitor=m)
self.assertTrue(actual.broadcast_equals(ex))
# monthly -> 4 month seasons
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', freq='MS', periods=12)})
ds = adjust_temporal_attrs(ds)
ex = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 4])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 4])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', freq='4M', periods=4)})
ex.first.attrs['cell_methods'] = 'time: mean within years'
ex.second.attrs['cell_methods'] = 'time: mean within years'
actual = temporal_aggregation(ds, custom_resolution='4M', monitor=m)
print(actual)
self.assertTrue(actual.broadcast_equals(ex))
def test_custom(self):
"""
Test aggergating to custom temporal resolutions
"""
# daily -> 8 days
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31')})
ds = adjust_temporal_attrs(ds)
ex = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 46])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 46])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31', freq='8D')})
ex.first.attrs['cell_methods'] = 'time: mean within years'
ex.second.attrs['cell_methods'] = 'time: mean within years'
m = ConsoleMonitor()
actual = temporal_aggregation(ds, custom_resolution='8D', monitor=m)
self.assertTrue(actual.broadcast_equals(ex))
# daily -> weekly
ex = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 53])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 53])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31', freq='W')})
ex.first.attrs['cell_methods'] = 'time: mean within years'
ex.second.attrs['cell_methods'] = 'time: mean within years'
actual = temporal_aggregation(ds, custom_resolution='W', monitor=m)
self.assertTrue(actual.broadcast_equals(ex))
# monthly -> 4 month seasons
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', freq='MS', periods=12)})
ds = adjust_temporal_attrs(ds)
ex = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 4])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 4])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', freq='4M', periods=4)})
ex.first.attrs['cell_methods'] = 'time: mean within years'
ex.second.attrs['cell_methods'] = 'time: mean within years'
actual = temporal_aggregation(ds, custom_resolution='4M', monitor=m)
print(actual)
self.assertTrue(actual.broadcast_equals(ex))
def test_validation(self):
"""
Test input validation
"""
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 24])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90)})
ds = adjust_temporal_attrs(ds)
with self.assertRaises(ValueError) as err:
temporal_aggregation(ds)
self.assertIn('normalize', str(err.exception))
# invalid custom resolution
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31')})
ds = adjust_temporal_attrs(ds)
with self.assertRaises(ValueError) as err:
temporal_aggregation(ds, custom_resolution='SDFG')
self.assertIn('Invalid custom resolution', str(err.exception))
# unexpected input resolution string
ds.attrs['time_coverage_resolution'] = 'P2M1D'
with self.assertRaises(ValueError) as err:
temporal_aggregation(ds)
self.assertIn('Could not interpret', str(err.exception))
# output resolution finer than input
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', freq='MS', periods=12)})
ds = adjust_temporal_attrs(ds)
with self.assertRaises(ValueError) as err:
temporal_aggregation(ds, custom_resolution='W')
self.assertIn('output resolution is smaller', str(err.exception))
# output and input resolutions are the same
with self.assertRaises(ValueError) as err:
temporal_aggregation(ds)
self.assertIn('already at the requested', str(err.exception))
def test_validation(self):
"""
Test input validation
"""
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 24])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90)})
ds = adjust_temporal_attrs(ds)
with self.assertRaises(ValueError) as err:
temporal_aggregation(ds)
self.assertIn('normalize', str(err.exception))
# invalid custom resolution
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 366])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31')})
ds = adjust_temporal_attrs(ds)
with self.assertRaises(ValueError) as err:
temporal_aggregation(ds, custom_resolution='SDFG')
self.assertIn('Invalid custom resolution', str(err.exception))
# unexpected input resolution string
ds.attrs['time_coverage_resolution'] = 'P2M1D'
with self.assertRaises(ValueError) as err:
temporal_aggregation(ds)
self.assertIn('Could not interpret', str(err.exception))
# output resolution finer than input
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', freq='MS', periods=12)})
ds = adjust_temporal_attrs(ds)
with self.assertRaises(ValueError) as err:
temporal_aggregation(ds, custom_resolution='W')
self.assertIn('output resolution is smaller', str(err.exception))
# output and input resolutions are the same
with self.assertRaises(ValueError) as err:
temporal_aggregation(ds)
self.assertIn('already at the requested', str(err.exception))
def test_8days(self):
"""
Test nominal execution with a 8 Days frequency input dataset
"""
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 46])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 46])),
'lat':
np.linspace(-88, 88, 45),
'lon':
np.linspace(-178, 178, 90),
'time':
pd.date_range('2000-01-01', '2000-12-31', freq='8D')
})
ds = adjust_temporal_attrs(ds)
ex = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'lat':
np.linspace(-88, 88, 45),
'lon':
np.linspace(-178, 178, 90),
'time':
pd.date_range('2000-01-01', freq='MS', periods=12)
})
ex.first.attrs['cell_methods'] = 'time: mean within years'
ex.second.attrs['cell_methods'] = 'time: mean within years'
m = ConsoleMonitor()
actual = temporal_aggregation(ds, monitor=m)
self.assertTrue(actual.broadcast_equals(ex))
def test_8days(self):
"""
Test nominal execution with a 8 Days frequency input dataset
"""
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 46])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 46])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31', freq='8D')})
ds = adjust_temporal_attrs(ds)
ex = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 12])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', freq='MS', periods=12)})
ex.first.attrs['cell_methods'] = 'time: mean within years'
ex.second.attrs['cell_methods'] = 'time: mean within years'
m = ConsoleMonitor()
actual = temporal_aggregation(ds, monitor=m)
self.assertTrue(actual.broadcast_equals(ex))