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:
long_term_average(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', periods=24)
})
ds = adjust_temporal_attrs(ds)
with self.assertRaises(ValueError) as err:
long_term_average(ds)
self.assertIn('temporal aggregation', str(err.exception))
def test_general(self):
"""
Test creating a 'general' LTA dataset
"""
# Test seasonal
ds = 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)
})
ds = adjust_temporal_attrs(ds)
actual = long_term_average(ds)
self.assertEqual(actual['first'].attrs['cell_methods'],
'time: mean over years')
self.assertEqual(actual.dims, {
'time': 4,
'nv': 2,
'lat': 45,
'lon': 90
})
self.assertEqual(actual.time.attrs['climatology'],
'climatology_bounds')
# Test irregular seasons
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 6])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 6])),
'lat':
np.linspace(-88, 88, 45),
'lon':
np.linspace(-178, 178, 90),
'time':
pd.date_range('2000-01-01', freq='5M', periods=6)
})
ds = adjust_temporal_attrs(ds)
with self.assertRaises(ValueError) as err:
long_term_average(ds)
self.assertIn('inconsistent seasons', str(err.exception))
# Test 8D
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 137])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 137])),
'lat':
np.linspace(-88, 88, 45),
'lon':
np.linspace(-178, 178, 90),
'time':
pd.date_range('2000-01-01', '2002-12-31', freq='8D')
})
ds = adjust_temporal_attrs(ds)
with self.assertRaises(ValueError) as err:
long_term_average(ds)
self.assertIn('inconsistent seasons', 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_registered(self):
"""
Test registered operation execution
"""
reg_op = OP_REGISTRY.get_op(
object_to_qualified_name(temporal_aggregation))
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, 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'
actual = reg_op(ds=ds)
self.assertTrue(actual.broadcast_equals(ex))
def test_nominal(self):
"""
Test nominal execution
"""
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'], np.ones([45, 90])),
'second': (['lat', 'lon'], np.ones([45, 90])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31')})
m = ConsoleMonitor()
actual = reduce(ds, monitor=m)
self.assertTrue(actual.broadcast_equals(ex))
actual = reduce(ds, dim=['lat', 'time', 'there_is_no_spoon'], monitor=m)
ex = xr.Dataset({
'first': (['lon'], np.ones([90])),
'second': (['lon'], np.ones([90])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31')})
self.assertTrue(actual.broadcast_equals(ex))
def test_nominal(self):
"""
Test nominal execution
"""
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 24])),
'second': (['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)
# Test monitor
m = ConsoleMonitor()
actual = long_term_average(ds, monitor=m)
self.assertEqual(m._percentage, 100)
# Test CF attributes
self.assertEqual(actual['first'].attrs['cell_methods'],
'time: mean over years')
self.assertEqual(actual.dims, {'time': 12,
'nv': 2,
'lat': 45,
'lon': 90})
self.assertEqual(actual.time.attrs['climatology'],
'climatology_bounds')
# Test variable selection
actual = long_term_average(ds, var='first')
with self.assertRaises(KeyError):
actual['second']
def test_nominal(self):
"""
Test nominal execution
"""
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 24])),
'second': (['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)
# Test monitor
m = ConsoleMonitor()
actual = long_term_average(ds, monitor=m)
self.assertEqual(m._percentage, 100)
# Test CF attributes
self.assertEqual(actual['first'].attrs['cell_methods'],
'time: mean over years')
self.assertEqual(actual.dims, {'time': 12,
'nv': 2,
'lat': 45,
'lon': 90})
self.assertEqual(actual.time.attrs['climatology'],
'climatology_bounds')
# Test variable selection
actual = long_term_average(ds, var='first')
with self.assertRaises(KeyError):
actual['second']
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_nominal(self):
"""
Test nominal execution
"""
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'], np.ones([45, 90])),
'second': (['lat', 'lon'], np.ones([45, 90])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31')})
m = ConsoleMonitor()
actual = reduce(ds, monitor=m)
self.assertTrue(actual.broadcast_equals(ex))
actual = reduce(ds, dim=['lat', 'time', 'there_is_no_spoon'], monitor=m)
ex = xr.Dataset({
'first': (['lon'], np.ones([90])),
'second': (['lon'], np.ones([90])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2000-12-31')})
self.assertTrue(actual.broadcast_equals(ex))
def test_daily(self):
"""
Test creating a daily LTA dataset
"""
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 730])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 730])),
'lat':
np.linspace(-88, 88, 45),
'lon':
np.linspace(-178, 178, 90),
'time':
pd.date_range('2001-01-01', '2002-12-31')
})
ds = adjust_temporal_attrs(ds)
actual = long_term_average(ds)
# Test CF attributes
self.assertEqual(actual['first'].attrs['cell_methods'],
'time: mean over years')
self.assertEqual(actual.dims, {
'time': 365,
'nv': 2,
'lat': 45,
'lon': 90
})
self.assertEqual(actual.time.attrs['climatology'],
'climatology_bounds')
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_general(self):
"""
Test creating a 'general' LTA dataset
"""
# Test seasonal
ds = 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)})
ds = adjust_temporal_attrs(ds)
actual = long_term_average(ds)
self.assertEqual(actual['first'].attrs['cell_methods'],
'time: mean over years')
self.assertEqual(actual.dims, {'time': 4,
'nv': 2,
'lat': 45,
'lon': 90})
self.assertEqual(actual.time.attrs['climatology'],
'climatology_bounds')
# Test irregular seasons
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 6])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 6])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', freq='5M', periods=6)})
ds = adjust_temporal_attrs(ds)
with self.assertRaises(ValueError) as err:
long_term_average(ds)
self.assertIn('inconsistent seasons', str(err.exception))
# Test 8D
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 137])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 137])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2000-01-01', '2002-12-31', freq='8D')})
ds = adjust_temporal_attrs(ds)
with self.assertRaises(ValueError) as err:
long_term_average(ds)
self.assertIn('inconsistent seasons', 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:
long_term_average(ds)
self.assertIn('normalize', str(err.exception))
def test_registered(self):
"""
Test registered operation execution
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(long_term_average))
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 24])),
'second': (['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)
reg_op(ds=ds)
def test_registered(self):
"""
Test registered operation execution
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(long_term_average))
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 24])),
'second': (['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)
reg_op(ds=ds)
def test_daily(self):
"""
Test creating a daily LTA dataset
"""
ds = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 730])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 730])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90),
'time': pd.date_range('2001-01-01', '2002-12-31')})
ds = adjust_temporal_attrs(ds)
actual = long_term_average(ds)
# Test CF attributes
self.assertEqual(actual['first'].attrs['cell_methods'],
'time: mean over years')
self.assertEqual(actual.dims, {'time': 365,
'nv': 2,
'lat': 45,
'lon': 90})
self.assertEqual(actual.time.attrs['climatology'],
'climatology_bounds')
def test_registered(self):
"""
Test registered operation execution
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(temporal_aggregation))
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, 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'
actual = reg_op(ds=ds)
self.assertTrue(actual.broadcast_equals(ex))
