def test_registered(self):
"""
Test nominal execution of ONI Index calculation, as a registered
operation.
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(index.oni))
dataset = 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': ([datetime(2001, x, 1) for x in range(1, 13)]
+ [datetime(2002, x, 1) for x in range(1, 13)])})
lta = 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': [x for x in range(1, 13)]})
lta = 2 * lta
expected_time = ([datetime(2001, x, 1) for x in range(2, 13)]
+ [datetime(2002, x, 1) for x in range(1, 12)])
expected = pd.DataFrame(data=(np.ones([22]) * -1),
columns=['ONI Index'],
index=expected_time)
with create_tmp_file() as tmp_file:
lta.to_netcdf(tmp_file)
actual = reg_op(ds=dataset, var='first', file=tmp_file)
self.assertTrue(expected.equals(actual))
def test_registered(self):
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(
index.enso_nino34))
dataset = 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': ([datetime(2001, x, 1) for x in range(1, 13)] +
[datetime(2002, x, 1) for x in range(1, 13)])
})
lta = 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': [x for x in range(1, 13)]
})
lta = 2 * lta
expected_time = ([datetime(2001, x, 1) for x in range(3, 13)] +
[datetime(2002, x, 1) for x in range(1, 11)])
expected = pd.DataFrame(data=(np.ones([20]) * -1),
columns=['ENSO N3.4 Index'],
index=expected_time)
with create_tmp_file() as tmp_file:
lta.to_netcdf(tmp_file)
actual = reg_op(ds=dataset, var='first', file=tmp_file)
self.assertTrue(expected.equals(actual))
def test_registered_compute(self):
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(arithmetics.compute))
first = np.ones([45, 90, 3])
second = np.ones([45, 90, 3])
dataset = xr.Dataset({
'first': (['lat', 'lon', 'time'], first),
'second': (['lat', 'lon', 'time'], second),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90)})
actual = reg_op(ds=dataset,
expr="6 * first - 3 * second",
var="third")
expected = xr.Dataset({
'third': (['lat', 'lon', 'time'], 6 * first - 3 * second),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90)})
assert_dataset_equal(expected, actual)
actual = reg_op(ds=dataset,
expr="6 * first - 3 * second",
var="third",
copy=True)
expected = xr.Dataset({
'first': (['lat', 'lon', 'time'], first),
'second': (['lat', 'lon', 'time'], second),
'third': (['lat', 'lon', 'time'], 6 * first - 3 * second),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90)})
assert_dataset_equal(expected, actual)
def test_registered_compute_with_context(self):
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(arithmetics.compute))
first = np.ones([45, 90, 3])
second = np.ones([45, 90, 3])
lon = np.linspace(-178, 178, 90)
lat = np.linspace(-88, 88, 45)
res_1 = xr.Dataset({
'first': (['lat', 'lon', 'time'], first),
'lat': lat,
'lon': lon
})
res_2 = xr.Dataset({
'second': (['lat', 'lon', 'time'], second),
'lat': lat,
'lon': lon
})
# Note, if executed from a workflow, _ctx will be set by the framework
_ctx = dict(value_cache=dict(res_1=res_1, res_2=res_2))
actual = reg_op(ds=None,
expr="6 * res_1.first - 3 * res_2.second",
var="third",
_ctx=_ctx)
expected = xr.Dataset({
'third': (['lat', 'lon', 'time'], 6 * first - 3 * second),
'lat': lat,
'lon': lon})
assert_dataset_equal(expected, actual)
def test_registered(self):
"""
Test nominal execution through the operations registry.
"""
reg_op = OP_REGISTRY.get_op(
object_to_qualified_name(anomaly.anomaly_internal))
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': [datetime(2000, x, 1) for x in range(1, 13)]
})
expected = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.zeros([45, 90, 12])),
'second': (['lat', 'lon', 'time'], np.zeros([45, 90, 12])),
'lat':
np.linspace(-88, 88, 45),
'lon':
np.linspace(-178, 178, 90),
'time': [datetime(2000, x, 1) for x in range(1, 13)]
})
actual = reg_op(ds=ds,
time_range='2000-01-01, 2000-04-01',
region='-50, -50, 50, 50')
assert_dataset_equal(expected, actual)
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_registered(self):
"""
Test the operation when invoked through the OP_REGISTRY
"""
reg_op = OP_REGISTRY.get_op(
object_to_qualified_name(arithmetics.ds_arithmetics))
dataset = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 3])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 3])),
'lat':
np.linspace(-88, 88, 45),
'lon':
np.linspace(-178, 178, 90)
})
expected = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 3])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 3])),
'lat':
np.linspace(-88, 88, 45),
'lon':
np.linspace(-178, 178, 90)
})
actual = reg_op(ds=dataset, op='+2, -2, *3, /3, *4')
assert_dataset_equal(expected * 4, actual)
def test_registered(self):
"""
Test if it runs as an operation registered in the op registry.
"""
reg_op = OP_REGISTRY.get_op(
object_to_qualified_name(subset.subset_temporal_index))
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': [
'2000-01-01', '2000-02-01', '2000-03-01', '2000-04-01',
'2000-05-01', '2000-06-01'
]
})
actual = reg_op(ds=dataset, time_ind_min=2, time_ind_max=4)
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': ['2000-03-01', '2000-04-01', '2000-05-01']
})
assert_dataset_equal(expected, actual)
def test_registered(self):
"""
Test if it runs as an operation registered in the op registry.
"""
reg_op = OP_REGISTRY.get_op(
object_to_qualified_name(subset.subset_temporal))
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 = reg_op(ds=dataset, time_range='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_registered(self):
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(index.enso_nino34))
dataset = 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': ([datetime(2001, x, 1) for x in range(1, 13)]
+ [datetime(2002, x, 1) for x in range(1, 13)])})
lta = 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': [x for x in range(1, 13)]})
lta = 2 * lta
expected_time = ([datetime(2001, x, 1) for x in range(3, 13)]
+ [datetime(2002, x, 1) for x in range(1, 11)])
expected = pd.DataFrame(data=(np.ones([20]) * -1),
columns=['ENSO N3.4 Index'],
index=expected_time)
with create_tmp_file() as tmp_file:
lta.to_netcdf(tmp_file)
actual = reg_op(ds=dataset, var='first', file=tmp_file)
self.assertTrue(expected.equals(actual))
def test_registered(self):
"""
Test nominal execution of ONI Index calculation, as a registered
operation.
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(index.oni))
dataset = 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': ([datetime(2001, x, 1) for x in range(1, 13)] +
[datetime(2002, x, 1) for x in range(1, 13)])})
lta = 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': [x for x in range(1, 13)]})
lta = 2 * lta
expected_time = ([datetime(2001, x, 1) for x in range(2, 13)] +
[datetime(2002, x, 1) for x in range(1, 12)])
expected = pd.DataFrame(data=(np.ones([22]) * -1),
columns=['ONI Index'],
index=expected_time)
with create_tmp_file() as tmp_file:
lta.to_netcdf(tmp_file)
actual = reg_op(ds=dataset, var='first', file=tmp_file)
self.assertTrue(expected.equals(actual))
def test_existing_method(self):
op = OP_REGISTRY.get_op('cate.ops.timeseries.tseries_point', True)
op_args, op_kwargs = main._parse_op_args(['ds=@ds', 'point=12.2,54.3', 'var=temperature', 'method=bfill'],
input_props=op.op_meta_info.inputs)
self.assertEqual(op_args, [])
self.assertEqual(op_kwargs, OrderedDict([('ds', dict(source='ds')),
('point', dict(value=(12.2, 54.3))),
('var', dict(value='temperature')),
('method', dict(value='bfill'))]))
def test_existing_method(self):
op = OP_REGISTRY.get_op('cate.ops.timeseries.tseries_point', True)
op_args, op_kwargs = main._parse_op_args(['ds=@ds', 'point=12.2,54.3', 'var=temperature', 'method=bfill'],
input_props=op.op_meta_info.inputs)
self.assertEqual(op_args, [])
self.assertEqual(op_kwargs, OrderedDict([('ds', dict(source='ds')),
('point', dict(value=(12.2, 54.3))),
('var', dict(value='temperature')),
('method', dict(value='bfill'))]))
def test_registered(self):
"""
Test nominal execution as a registered operation
"""
op = OP_REGISTRY.get_op(object_to_qualified_name(identity))
self.assertEqual(op(value=True), True)
self.assertEqual(op(value=42), 42)
self.assertEqual(op(value=3.14), 3.14)
self.assertEqual(op(value='ha'), 'ha')
self.assertEqual(op(value=[3, 4, 5]), [3, 4, 5])
def test_registered(self):
"""
Test nominal execution as a registered operation
"""
op = OP_REGISTRY.get_op(object_to_qualified_name(literal))
self.assertEqual(op(value='True'), True)
self.assertEqual(op(value='42'), 42)
self.assertEqual(op(value='3.14'), 3.14)
self.assertEqual(op(value='"ha"'), 'ha')
self.assertEqual(op(value='[3,4,5]'), [3, 4, 5])
def test_registered(self):
"""
Test nominal execution as a registered operation
"""
op = OP_REGISTRY.get_op(object_to_qualified_name(identity))
self.assertEqual(op(value=True), True)
self.assertEqual(op(value=42), 42)
self.assertEqual(op(value=3.14), 3.14)
self.assertEqual(op(value='ha'), 'ha')
self.assertEqual(op(value=[3, 4, 5]), [3, 4, 5])
def test_registered(self):
"""
Test nominal execution as a registered operation
"""
op = OP_REGISTRY.get_op(object_to_qualified_name(literal))
self.assertEqual(op(value='True'), True)
self.assertEqual(op(value='42'), 42)
self.assertEqual(op(value='3.14'), 3.14)
self.assertEqual(op(value='"ha"'), 'ha')
self.assertEqual(op(value='[3,4,5]'), [3, 4, 5])
def test_registered(self):
"""
Test as a registered operation
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(harmonize))
dataset = xr.Dataset(
{'first': (['latitude', 'longitude'], [[1, 2, 3], [2, 3, 4]])})
expected = xr.Dataset(
{'first': (['lat', 'lon'], [[1, 2, 3], [2, 3, 4]])})
actual = reg_op(ds=dataset)
assertDatasetEqual(actual, expected)
def test_registered(self):
"""
Test nominal execution of the function as a registered operation.
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(plot))
# Test plot
dataset = xr.Dataset({'first': np.random.rand(10)})
with create_tmp_file('remove_me', 'jpg') as tmp_file:
reg_op(ds=dataset, var='first', file=tmp_file)
self.assertTrue(os.path.isfile(tmp_file))
def test_registered(self):
"""
Test nominal execution of the function as a registered operation.
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(plot))
# Test plot
dataset = xr.Dataset({
'first': np.random.rand(10)})
with create_tmp_file('remove_me', 'jpg') as tmp_file:
reg_op(ds=dataset, var='first', file=tmp_file)
self.assertTrue(os.path.isfile(tmp_file))
def test_registered(self):
"""
Test as a registered operation
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(normalize))
dataset = xr.Dataset({'first': (['latitude',
'longitude'], [[1, 2, 3],
[2, 3, 4]])})
expected = xr.Dataset({'first': (['lat', 'lon'], [[1, 2, 3],
[2, 3, 4]])})
actual = reg_op(ds=dataset)
assertDatasetEqual(actual, expected)
def test_registered(self):
"""
Test the method when run as a registered operation
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(plot_data_frame))
data = {'A': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
'B': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]}
df = pd.DataFrame(data=data, index=pd.date_range('2000-01-01',
periods=10))
with create_tmp_file('remove_me', 'png') as tmp_file:
reg_op(df=df, file=tmp_file)
self.assertTrue(os.path.isfile(tmp_file))
def test_registered(self):
"""
Test nominal execution of the function as a registered operation.
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(plot_line))
# Test plot
dataset = xr.Dataset({
'first': (['time'], np.random.rand(10)),
'second': (['time'], np.random.rand(10)),
'time': pd.date_range('2000-01-01', periods=10)})
with create_tmp_file('remove_me', 'jpg') as tmp_file:
reg_op(ds=dataset, var_names=['first', 'second'], file=tmp_file)
self.assertTrue(os.path.isfile(tmp_file))
def test_registered(self):
"""
Test the method when run as a registered operation
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(plot_data_frame))
data = {'A': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
'B': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]}
df = pd.DataFrame(data=data, index=pd.date_range('2000-01-01',
periods=10))
with create_tmp_file('remove_me', 'png') as tmp_file:
reg_op(df=df, file=tmp_file)
self.assertTrue(os.path.isfile(tmp_file))
def test_registered(self):
"""
Test nominal execution of the function as a registered operation.
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(plot_map))
dataset = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.random.rand(5, 10, 2)),
'second': (['lat', 'lon', 'time'], np.random.rand(5, 10, 2)),
'lat': np.linspace(-89.5, 89.5, 5),
'lon': np.linspace(-179.5, 179.5, 10),
'time': pd.date_range('2000-01-01', periods=2)})
with create_tmp_file('remove_me', 'png') as tmp_file:
reg_op(ds=dataset, file=tmp_file)
self.assertTrue(os.path.isfile(tmp_file))
def test_registered(self):
"""
Test nominal execution of the function as a registered operation.
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(plot_map))
dataset = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.random.rand(5, 10, 2)),
'second': (['lat', 'lon', 'time'], np.random.rand(5, 10, 2)),
'lat': np.linspace(-89.5, 89.5, 5),
'lon': np.linspace(-179.5, 179.5, 10),
'time': pd.date_range('2000-01-01', periods=2)})
with create_tmp_file('remove_me', 'png') as tmp_file:
reg_op(ds=dataset, file=tmp_file)
self.assertTrue(os.path.isfile(tmp_file))
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_registered(self):
"""
Test nominal execution of the function as a registered operation.
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(plot_line))
# Test plot
dataset = xr.Dataset({
'first': (['time'], np.random.rand(10)),
'second': (['time'], np.random.rand(10)),
'time': pd.date_range('2000-01-01', periods=10)
})
with create_tmp_file('remove_me', 'jpg') as tmp_file:
reg_op(ds=dataset, var_names=['first', 'second'], file=tmp_file)
self.assertTrue(os.path.isfile(tmp_file))
def test_registered(self):
"""
Test operation when run as a registered operation
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(pandas_fillna))
# Test na filling using a given method
data = {'A': [1, 2, 3, np.nan, 4, 9, np.nan, np.nan, 1, 0, 4, 6],
'B': [5, 6, 8, 7, 5, np.nan, np.nan, np.nan, 1, 2, 7, 6]}
expected = {'A': [1, 2, 3, 3, 4, 9, 9, 9, 1, 0, 4, 6],
'B': [5, 6, 8, 7, 5, 5, 5, 5, 1, 2, 7, 6]}
time = pd.date_range('2000-01-01', freq='MS', periods=12, tz=timezone.utc)
expected = pd.DataFrame(data=expected, index=time, dtype=float)
df = pd.DataFrame(data=data, index=time, dtype=float)
actual = reg_op(df=df, method='ffill')
self.assertTrue(actual.equals(expected))
def test_registered(self):
"""
Test if it runs as an operation registered in the op registry.
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(subset.subset_spatial))
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)})
actual = reg_op(ds=dataset, region="-20, -10, 20, 10")
expected = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([22, 42, 6])),
'second': (['lat', 'lon', 'time'], np.ones([22, 42, 6])),
'lat': np.linspace(-10.5, 10.5, 22),
'lon': np.linspace(-20.5, 20.5, 42)})
assert_dataset_equal(expected, actual)
def test_registered(self):
"""
Test operation when run as a registered operation
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(pandas_fillna))
# Test na filling using a given method
data = {'A': [1, 2, 3, np.nan, 4, 9, np.nan, np.nan, 1, 0, 4, 6],
'B': [5, 6, 8, 7, 5, np.nan, np.nan, np.nan, 1, 2, 7, 6]}
expected = {'A': [1, 2, 3, 3, 4, 9, 9, 9, 1, 0, 4, 6],
'B': [5, 6, 8, 7, 5, 5, 5, 5, 1, 2, 7, 6]}
time = pd.date_range('2000-01-01', freq='MS', periods=12)
expected = pd.DataFrame(data=expected, index=time, dtype=float)
df = pd.DataFrame(data=data, index=time, dtype=float)
actual = reg_op(df=df, method='ffill')
self.assertTrue(actual.equals(expected))
def run_op(self,
op_name: str,
op_args: List[str],
validate_args=False,
monitor=Monitor.NONE):
assert op_name
assert op_args
op = OP_REGISTRY.get_op(op_name)
if not op:
raise WorkspaceError('Unknown operation "%s"' % op_name)
with monitor.starting("Running operation '%s'" % op_name, 2):
self.workflow.invoke(self.resource_cache,
monitor=monitor.child(work=1))
op_kwargs = self._parse_op_args(op, op_args, self.resource_cache,
validate_args)
op(monitor=monitor.child(work=1), **op_kwargs)
def test_registered(self):
"""
Test the operation when invoked from the OP_REGISTRY
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(arithmetics.diff))
dataset = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 3])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 3])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90)})
expected = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([45, 90, 3])),
'second': (['lat', 'lon', 'time'], np.ones([45, 90, 3])),
'lat': np.linspace(-88, 88, 45),
'lon': np.linspace(-178, 178, 90)})
actual = reg_op(ds=dataset, ds2=dataset * 2)
assert_dataset_equal(expected * -1, actual)
def test_registered(self):
"""
Test execution as a registered operation
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(sel))
ds = new_ds()
sel_ds = reg_op(ds=ds, time='2014-09-06')
self.assertEqual(set(sel_ds.coords.keys()), {'lon', 'lat', 'time', 'reference_time'})
self.assertEqual(sel_ds.dims['lon'], 4)
self.assertEqual(sel_ds.dims['lat'], 2)
self.assertNotIn('time', sel_ds.dims)
sel_ds = reg_op(ds=ds, point=(34.51, 10.25))
self.assertEqual(set(sel_ds.coords.keys()), {'lon', 'lat', 'time', 'reference_time'})
self.assertNotIn('lon', sel_ds.dims)
self.assertNotIn('lat', sel_ds.dims)
self.assertEqual(sel_ds.dims['time'], 10)
def test_registered(self):
"""
Test if it runs as an operation registered in the op registry.
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(subset.subset_temporal))
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 = reg_op(ds=dataset, time_range='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_registered(self):
"""
Test execution as a registered operation
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(sel))
ds = new_ds()
sel_ds = reg_op(ds=ds, time='2014-09-06')
self.assertEqual(set(sel_ds.coords.keys()), {'lon', 'lat', 'time', 'reference_time'})
self.assertEqual(sel_ds.dims['lon'], 4)
self.assertEqual(sel_ds.dims['lat'], 2)
self.assertNotIn('time', sel_ds.dims)
sel_ds = reg_op(ds=ds, point=(34.51, 10.25))
self.assertEqual(set(sel_ds.coords.keys()), {'lon', 'lat', 'time', 'reference_time'})
self.assertNotIn('lon', sel_ds.dims)
self.assertNotIn('lat', sel_ds.dims)
self.assertEqual(sel_ds.dims['time'], 10)
def test_registered(self):
"""
Test nominal execution as a registered operation
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(from_dataframe))
time = pd.date_range('2000-01-01', periods=10)
data = {'A': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
'B': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
'time': time}
df = pd.DataFrame(data)
df = df.set_index('time')
expected = xr.Dataset({
'A': (['time'], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
'B': (['time'], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
'time': time})
actual = reg_op(df=df)
assert_dataset_equal(expected, actual)
def test_registered(self):
"""
Test nominal execution as a registered operation
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(from_dataframe))
time = pd.date_range('2000-01-01', periods=10, tz=timezone.utc)
data = {'A': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
'B': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
'time': time}
df = pd.DataFrame(data)
df = df.set_index('time')
expected = xr.Dataset({
'A': (['time'], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
'B': (['time'], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
'time': time})
actual = reg_op(df=df)
assert_dataset_equal(expected, actual)
def test_registered(self):
"""
Test the operation when it is invoked through the operation registry
"""
reg_op = OP_REGISTRY.get_op(
object_to_qualified_name(anomaly.anomaly_external))
ref = 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)
})
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': [datetime(2000, x, 1) for x in range(1, 13)] +
[datetime(2001, x, 1) for x in range(1, 13)]
})
expected = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.zeros([45, 90, 24])),
'second': (['lat', 'lon', 'time'], np.zeros([45, 90, 24])),
'lat':
np.linspace(-88, 88, 45),
'lon':
np.linspace(-178, 178, 90),
'time': [datetime(2000, x, 1) for x in range(1, 13)] +
[datetime(2001, x, 1) for x in range(1, 13)]
})
with create_tmp_file() as tmp_file:
ref.to_netcdf(tmp_file, 'w')
actual = reg_op(ds=ds, file=tmp_file)
assert_dataset_equal(actual, expected)
def registered(self):
"""
Test tseries_point as a registered operation
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(tseries_mean))
dataset = xr.Dataset({
'abs': (['lat', 'lon', 'time'], np.ones([4, 8, 6])),
'bbs': (['lat', 'lon', 'time'], np.ones([4, 8, 6])),
'lat': np.linspace(-67.5, 67.5, 4),
'lon': np.linspace(-157.5, 157.5, 8),
'time': ['2000-01-01', '2000-02-01', '2000-03-01', '2000-04-01',
'2000-05-01', '2000-06-01']})
actual = reg_op(ds=dataset, var='*bs')
expected = xr.Dataset({
'abs': (['time'], np.ones([6])),
'bbs': (['time'], np.ones([6])),
'abs_std': (['time'], np.zeros([6])),
'bbs_std': (['time'], np.zeros([6])),
'lat': np.linspace(-67.5, 67.5, 4),
'lon': np.linspace(-157.5, 157.5, 8),
'time': ['2000-01-01', '2000-02-01', '2000-03-01', '2000-04-01',
'2000-05-01', '2000-06-01']})
assertDatasetEqual(expected, actual)
def test_registered(self):
"""
Test if it runs as an operation registered in the op registry.
"""
reg_op = OP_REGISTRY.get_op(
object_to_qualified_name(subset.subset_spatial))
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)
})
actual = reg_op(ds=dataset, region="-20, -10, 20, 10")
expected = xr.Dataset({
'first': (['lat', 'lon', 'time'], np.ones([20, 40, 6])),
'second': (['lat', 'lon', 'time'], np.ones([20, 40, 6])),
'lat':
np.linspace(-9.5, 9.5, 20),
'lon':
np.linspace(-19.5, 19.5, 40)
})
assert_dataset_equal(expected, actual)
def test_registered(self):
"""
Test if it runs as an operation registered in the op registry.
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(subset.subset_temporal_index))
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': ['2000-01-01',
'2000-02-01',
'2000-03-01',
'2000-04-01',
'2000-05-01',
'2000-06-01']})
actual = reg_op(ds=dataset, time_ind_min=2, time_ind_max=4)
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': ['2000-03-01', '2000-04-01', '2000-05-01']})
assert_dataset_equal(expected, actual)
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 write_op(self):
return OP_REGISTRY.get_op('write_text')
def read_op(self):
return OP_REGISTRY.get_op('read_json')
def write_op(self):
return OP_REGISTRY.get_op('write_json')
def read_op(self):
return OP_REGISTRY.get_op('read_netcdf')
def write_op(self):
return OP_REGISTRY.get_op('write_netcdf4')
def test_registered(self):
"""
Test registered operation execution execution
"""
reg_op = OP_REGISTRY.get_op(object_to_qualified_name(coregister))
ds_fine = xr.Dataset({
'first': (['time', 'lat', 'lon'], np.array([np.eye(4, 8), np.eye(4, 8)])),
'second': (['time', 'lat', 'lon'], np.array([np.eye(4, 8), np.eye(4, 8)])),
'lat': np.linspace(-67.5, 67.5, 4),
'lon': np.linspace(-157.5, 157.5, 8),
'time': np.array([1, 2])})
ds_coarse = xr.Dataset({
'first': (['time', 'lat', 'lon'], np.array([np.eye(3, 6), np.eye(3, 6)])),
'second': (['time', 'lat', 'lon'], np.array([np.eye(3, 6), np.eye(3, 6)])),
'lat': np.linspace(-60, 60, 3),
'lon': np.linspace(-150, 150, 6),
'time': np.array([1, 2])})
# Test that the coarse dataset has been resampled onto the grid
# of the finer dataset.
ds_coarse_resampled = reg_op(ds_master=ds_fine, ds_replica=ds_coarse)
expected = xr.Dataset({
'first': (['time', 'lat', 'lon'], np.array([[[1., 0.28571429, 0., 0., 0., 0., 0., 0.],
[0.33333333, 0.57142857, 0.38095238, 0., 0., 0., 0., 0.],
[0., 0.47619048, 0.52380952, 0.28571429, 0.04761905, 0., 0.,
0.],
[0., 0., 0.42857143, 0.85714286, 0.14285714, 0., 0., 0.]],
[[1., 0.28571429, 0., 0., 0., 0., 0., 0.],
[0.33333333, 0.57142857, 0.38095238, 0., 0., 0., 0., 0.],
[0., 0.47619048, 0.52380952, 0.28571429, 0.04761905, 0., 0.,
0.],
[0., 0., 0.42857143, 0.85714286, 0.14285714, 0., 0., 0.]]])),
'second': (['time', 'lat', 'lon'], np.array([[[1., 0.28571429, 0., 0., 0., 0., 0., 0.],
[0.33333333, 0.57142857, 0.38095238, 0., 0., 0., 0., 0.],
[0., 0.47619048, 0.52380952, 0.28571429, 0.04761905, 0., 0.,
0.],
[0., 0., 0.42857143, 0.85714286, 0.14285714, 0., 0., 0.]],
[[1., 0.28571429, 0., 0., 0., 0., 0., 0.],
[0.33333333, 0.57142857, 0.38095238, 0., 0., 0., 0., 0.],
[0., 0.47619048, 0.52380952, 0.28571429, 0.04761905, 0., 0.,
0.],
[0., 0., 0.42857143, 0.85714286, 0.14285714, 0., 0., 0.]]])),
'lat': np.linspace(-67.5, 67.5, 4),
'lon': np.linspace(-157.5, 157.5, 8),
'time': np.array([1, 2])})
assert_almost_equal(ds_coarse_resampled['first'].values, expected['first'].values)
# Test that the fine dataset has been resampled (aggregated)
# onto the grid of the coarse dataset.
ds_fine_resampled = reg_op(ds_master=ds_coarse, ds_replica=ds_fine)
expected = xr.Dataset({
'first': (['time', 'lat', 'lon'], np.array([[[0.625, 0.125, 0., 0., 0., 0.],
[0.125, 0.5, 0.125, 0., 0., 0.],
[0., 0.125, 0.625, 0., 0., 0.]],
[[0.625, 0.125, 0., 0., 0., 0.],
[0.125, 0.5, 0.125, 0., 0., 0.],
[0., 0.125, 0.625, 0., 0., 0.]]])),
'second': (['time', 'lat', 'lon'], np.array([[[0.625, 0.125, 0., 0., 0., 0.],
[0.125, 0.5, 0.125, 0., 0., 0.],
[0., 0.125, 0.625, 0., 0., 0.]],
[[0.625, 0.125, 0., 0., 0., 0.],
[0.125, 0.5, 0.125, 0., 0., 0.],
[0., 0.125, 0.625, 0., 0., 0.]]])),
'lat': np.linspace(-60, 60, 3),
'lon': np.linspace(-150, 150, 6),
'time': np.array([1, 2])})
assert_almost_equal(ds_fine_resampled['first'].values, expected['first'].values)
