def test_invoke_as_part_of_workflow(self):
resource = get_resource('workflows/three_ops.json')
workflow = Workflow.load(resource)
step = WorkflowStep(workflow, resource, node_id='jojo_87')
workflow = Workflow(
OpMetaInfo('contains_jojo_87',
has_monitor=True,
inputs=OrderedDict(x={}),
outputs=OrderedDict(y={})))
workflow.add_step(step)
step.inputs.p.source = workflow.inputs.x
workflow.outputs.y.source = step.outputs.q
value_cache = ValueCache()
workflow.inputs.x.value = 4
workflow.invoke(context=dict(value_cache=value_cache))
output_value = workflow.outputs.y.value
self.assertEqual(output_value, 2 * (4 + 1) + 3 * (2 * (4 + 1)))
self.assertEqual(
value_cache, {
'jojo_87._child': {
'op1': {
'y': 5
},
'op2': {
'b': 10
},
'op3': {
'w': 40
}
}
})
def test_invoke_as_part_of_workflow(self):
resource = get_resource('workflows/three_ops.json')
workflow = Workflow.load(resource)
step = WorkflowStep(workflow, resource, node_id='jojo_87')
workflow = Workflow(OpMetaInfo('contains_jojo_87',
has_monitor=True,
inputs=OrderedDict(x={}),
outputs=OrderedDict(y={})))
workflow.add_step(step)
step.inputs.p.source = workflow.inputs.x
workflow.outputs.y.source = step.outputs.q
value_cache = ValueCache()
workflow.inputs.x.value = 4
workflow.invoke(context=dict(value_cache=value_cache))
output_value = workflow.outputs.y.value
self.assertEqual(output_value, 2 * (4 + 1) + 3 * (2 * (4 + 1)))
self.assertEqual(value_cache, {'jojo_87._child': {'op1': {'y': 5}, 'op2': {'b': 10}, 'op3': {'w': 40}}})
def test_invoke_with_context_inputs(self):
def some_op(context, workflow, workflow_id, step, step_id, invalid):
return dict(context=context,
workflow=workflow,
workflow_id=workflow_id,
step=step,
step_id=step_id,
invalid=invalid)
from cate.core.op import OP_REGISTRY
try:
op_reg = OP_REGISTRY.add_op(some_op)
op_reg.op_meta_info.inputs['context']['context'] = True
op_reg.op_meta_info.inputs['workflow']['context'] = 'workflow'
op_reg.op_meta_info.inputs['workflow_id'][
'context'] = 'workflow.id'
op_reg.op_meta_info.inputs['step']['context'] = 'step'
op_reg.op_meta_info.inputs['step_id']['context'] = 'step.id'
op_reg.op_meta_info.inputs['invalid']['context'] = 'gnarz[8]'
step = OpStep(op_reg, node_id='test_step')
workflow = Workflow(OpMetaInfo('test_workflow'))
workflow.add_step(step)
workflow.invoke()
output = step.outputs['return'].value
self.assertIsInstance(output, dict)
self.assertIsInstance(output.get('context'), dict)
self.assertIs(output.get('workflow'), workflow)
self.assertEqual(output.get('workflow_id'), 'test_workflow')
self.assertIs(output.get('step'), step)
self.assertEqual(output.get('step_id'), 'test_step')
self.assertEqual(output.get('invalid', 1), None)
finally:
OP_REGISTRY.remove_op(some_op)
def test_invoke_with_context_inputs(self):
def some_op(context, workflow, workflow_id, step, step_id, invalid):
return dict(context=context,
workflow=workflow,
workflow_id=workflow_id,
step=step,
step_id=step_id,
invalid=invalid)
from cate.core.op import OP_REGISTRY
try:
op_reg = OP_REGISTRY.add_op(some_op)
op_reg.op_meta_info.inputs['context']['context'] = True
op_reg.op_meta_info.inputs['workflow']['context'] = 'workflow'
op_reg.op_meta_info.inputs['workflow_id']['context'] = 'workflow.id'
op_reg.op_meta_info.inputs['step']['context'] = 'step'
op_reg.op_meta_info.inputs['step_id']['context'] = 'step.id'
op_reg.op_meta_info.inputs['invalid']['context'] = 'gnarz[8]'
step = OpStep(op_reg, node_id='test_step')
workflow = Workflow(OpMetaInfo('test_workflow'))
workflow.add_step(step)
workflow.invoke()
output = step.outputs['return'].value
self.assertIsInstance(output, dict)
self.assertIsInstance(output.get('context'), dict)
self.assertIs(output.get('workflow'), workflow)
self.assertEqual(output.get('workflow_id'), 'test_workflow')
self.assertIs(output.get('step'), step)
self.assertEqual(output.get('step_id'), 'test_step')
self.assertEqual(output.get('invalid', 1), None)
finally:
OP_REGISTRY.remove_op(some_op)
def test_to_json_dict(self):
def dataset_op() -> xr.Dataset:
periods = 5
temperature_data = (15 + 8 * np.random.randn(periods, 2, 2)).round(decimals=1)
temperature_attrs = {'a': np.array([1, 2, 3]), 'comment': 'hot', '_FillValue': np.nan}
precipitation_data = (10 * np.random.rand(periods, 2, 2)).round(decimals=1)
precipitation_attrs = {'x': True, 'comment': 'wet', '_FillValue': -1.0}
ds = xr.Dataset(
data_vars={
'temperature': (('time', 'lat', 'lon'), temperature_data, temperature_attrs),
'precipitation': (('time', 'lat', 'lon'), precipitation_data, precipitation_attrs)
},
coords={
'lon': np.array([12, 13]),
'lat': np.array([50, 51]),
'time': pd.date_range('2014-09-06', periods=periods)
},
attrs={
'history': 'a b c'
})
return ds
def scalar_dataset_op() -> xr.Dataset:
ds = xr.Dataset(
data_vars={
'temperature': (('time', 'lat', 'lon'), [[[15.2]]]),
'precipitation': (('time', 'lat', 'lon'), [[[10.1]]])
},
coords={
'lon': [12.],
'lat': [50.],
'time': [pd.to_datetime('2014-09-06')],
},
attrs={
'history': 'a b c'
})
return ds
def empty_dataset_op() -> xr.Dataset:
ds = xr.Dataset(
data_vars={
'temperature': (('time', 'lat', 'lon'), np.ndarray(shape=(0, 0, 0), dtype=np.float32)),
'precipitation': (('time', 'lat', 'lon'), np.ndarray(shape=(0, 0, 0), dtype=np.float32))
},
coords={
'lon': np.ndarray(shape=(0,), dtype=np.float32),
'lat': np.ndarray(shape=(0,), dtype=np.float32),
'time': np.ndarray(shape=(0,), dtype=np.datetime64),
},
attrs={
'history': 'a b c'
})
return ds
def data_frame_op() -> pd.DataFrame:
data = {'A': [1, 2, 3, np.nan, 4, 9, np.nan, np.nan, 1, 0, 4, 6],
'B': [5, 6, 8, 7, 5, 5, 5, 9, 1, 2, 7, 6]}
time = pd.date_range('2000-01-01', freq='MS', periods=12)
return pd.DataFrame(data=data, index=time, dtype=float, columns=['A', 'B'])
def scalar_data_frame_op() -> pd.DataFrame:
data = {'A': [1.3],
'B': [5.9]}
return pd.DataFrame(data=data, dtype=float, columns=['A', 'B'])
def empty_data_frame_op() -> pd.DataFrame:
data = {'A': [],
'B': []}
return pd.DataFrame(data=data, dtype=float, columns=['A', 'B'])
def geo_data_frame_op() -> gpd.GeoDataFrame:
data = {'name': ['A', 'B', 'C'],
'lat': [45, 46, 47.5],
'lon': [-120, -121.2, -122.9]}
df = pd.DataFrame(data, columns=['name', 'lat', 'lon'])
geometry = [Point(xy) for xy in zip(df['lon'], df['lat'])]
return gpd.GeoDataFrame(df, geometry=geometry)
def scalar_geo_data_frame_op() -> gpd.GeoDataFrame:
data = {'name': [2000 * 'A'],
'lat': [45],
'lon': [-120]}
df = pd.DataFrame(data, columns=['name', 'lat', 'lon'])
geometry = [Point(xy) for xy in zip(df['lon'], df['lat'])]
return gpd.GeoDataFrame(df, geometry=geometry)
def int_op() -> int:
return 394852
def str_op() -> str:
return 'Hi!'
from cate.core.op import OP_REGISTRY
try:
OP_REGISTRY.add_op(dataset_op)
OP_REGISTRY.add_op(data_frame_op)
OP_REGISTRY.add_op(geo_data_frame_op)
OP_REGISTRY.add_op(scalar_dataset_op)
OP_REGISTRY.add_op(scalar_data_frame_op)
OP_REGISTRY.add_op(scalar_geo_data_frame_op)
OP_REGISTRY.add_op(empty_dataset_op)
OP_REGISTRY.add_op(empty_data_frame_op)
OP_REGISTRY.add_op(int_op)
OP_REGISTRY.add_op(str_op)
workflow = Workflow(OpMetaInfo('workspace_workflow', header=dict(description='Test!')))
workflow.add_step(OpStep(dataset_op, node_id='ds'))
workflow.add_step(OpStep(data_frame_op, node_id='df'))
workflow.add_step(OpStep(geo_data_frame_op, node_id='gdf'))
workflow.add_step(OpStep(scalar_dataset_op, node_id='scalar_ds'))
workflow.add_step(OpStep(scalar_data_frame_op, node_id='scalar_df'))
workflow.add_step(OpStep(scalar_geo_data_frame_op, node_id='scalar_gdf'))
workflow.add_step(OpStep(empty_dataset_op, node_id='empty_ds'))
workflow.add_step(OpStep(empty_data_frame_op, node_id='empty_df'))
workflow.add_step(OpStep(int_op, node_id='i'))
workflow.add_step(OpStep(str_op, node_id='s'))
ws = Workspace('/path', workflow)
ws.execute_workflow()
d_ws = ws.to_json_dict()
# import pprint
# pprint.pprint(d_ws)
d_wf = d_ws.get('workflow')
self.assertIsNotNone(d_wf)
l_res = d_ws.get('resources')
self.assertIsNotNone(l_res)
self.assertEqual(len(l_res), 10)
res_ds = l_res[0]
self.assertEqual(res_ds.get('name'), 'ds')
self.assertEqual(res_ds.get('dataType'), 'xarray.core.dataset.Dataset')
self.assertEqual(res_ds.get('dimSizes'), dict(lat=2, lon=2, time=5))
self.assertEqual(res_ds.get('attributes'), {'history': 'a b c'})
res_ds_vars = res_ds.get('variables')
self.assertIsNotNone(res_ds_vars)
self.assertEqual(len(res_ds_vars), 2)
res_ds_var_1 = res_ds_vars[0]
self.assertEqual(res_ds_var_1.get('name'), 'precipitation')
self.assertEqual(res_ds_var_1.get('dataType'), 'float64')
self.assertEqual(res_ds_var_1.get('numDims'), 3)
self.assertEqual(res_ds_var_1.get('shape'), (5, 2, 2))
self.assertEqual(res_ds_var_1.get('chunkSizes'), None)
self.assertEqual(res_ds_var_1.get('isYFlipped'), True)
self.assertEqual(res_ds_var_1.get('isFeatureAttribute'), None)
self.assertEqual(res_ds_var_1.get('attributes'), dict(x=True, comment='wet', _FillValue=-1.))
res_ds_var_2 = res_ds_vars[1]
self.assertEqual(res_ds_var_2.get('name'), 'temperature')
self.assertEqual(res_ds_var_2.get('dataType'), 'float64')
self.assertEqual(res_ds_var_2.get('numDims'), 3)
self.assertEqual(res_ds_var_2.get('shape'), (5, 2, 2))
self.assertEqual(res_ds_var_2.get('chunkSizes'), None)
self.assertEqual(res_ds_var_2.get('isYFlipped'), True)
self.assertEqual(res_ds_var_2.get('isFeatureAttribute'), None)
self.assertEqual(res_ds_var_2.get('attributes'), dict(a=[1, 2, 3], comment='hot', _FillValue=np.nan))
res_df = l_res[1]
self.assertEqual(res_df.get('name'), 'df')
self.assertEqual(res_df.get('dataType'), 'pandas.core.frame.DataFrame')
self.assertEqual(res_df.get('attributes'), {'num_rows': 12, 'num_columns': 2})
res_df_vars = res_df.get('variables')
self.assertIsNotNone(res_df_vars)
self.assertEqual(len(res_df_vars), 2)
res_df_var_1 = res_df_vars[0]
self.assertEqual(res_df_var_1.get('name'), 'A')
self.assertEqual(res_df_var_1.get('dataType'), 'float64')
self.assertEqual(res_df_var_1.get('numDims'), 1)
self.assertEqual(res_df_var_1.get('shape'), (12,))
self.assertEqual(res_df_var_1.get('isYFlipped'), None)
self.assertEqual(res_df_var_1.get('isFeatureAttribute'), True)
self.assertIsNone(res_df_var_1.get('attributes'))
res_df_var_2 = res_df_vars[1]
self.assertEqual(res_df_var_2.get('name'), 'B')
self.assertEqual(res_df_var_2.get('dataType'), 'float64')
self.assertEqual(res_df_var_2.get('numDims'), 1)
self.assertEqual(res_df_var_2.get('shape'), (12,))
self.assertEqual(res_df_var_2.get('isYFlipped'), None)
self.assertEqual(res_df_var_2.get('isFeatureAttribute'), True)
self.assertIsNone(res_df_var_2.get('attributes'))
res_gdf = l_res[2]
self.assertEqual(res_gdf.get('name'), 'gdf')
self.assertEqual(res_gdf.get('dataType'), 'geopandas.geodataframe.GeoDataFrame')
self.assertEqual(res_gdf.get('attributes'), {'num_rows': 3, 'num_columns': 4, 'geom_type': 'Point'})
res_gdf_vars = res_gdf.get('variables')
self.assertIsNotNone(res_gdf_vars)
self.assertEqual(len(res_gdf_vars), 4)
res_gdf_var_1 = res_gdf_vars[0]
self.assertEqual(res_gdf_var_1.get('name'), 'name')
self.assertEqual(res_gdf_var_1.get('dataType'), 'object')
self.assertEqual(res_gdf_var_1.get('numDims'), 1)
self.assertEqual(res_gdf_var_1.get('shape'), (3,))
self.assertEqual(res_gdf_var_1.get('isYFlipped'), None)
self.assertEqual(res_gdf_var_1.get('isFeatureAttribute'), True)
self.assertIsNone(res_gdf_var_1.get('attributes'))
res_gdf_var_2 = res_gdf_vars[1]
self.assertEqual(res_gdf_var_2.get('name'), 'lat')
self.assertEqual(res_gdf_var_2.get('dataType'), 'float64')
self.assertEqual(res_gdf_var_2.get('numDims'), 1)
self.assertEqual(res_gdf_var_2.get('shape'), (3,))
self.assertEqual(res_gdf_var_2.get('isYFlipped'), None)
self.assertEqual(res_gdf_var_2.get('isFeatureAttribute'), True)
self.assertIsNone(res_gdf_var_2.get('attributes'))
res_gdf_var_3 = res_gdf_vars[2]
self.assertEqual(res_gdf_var_3.get('name'), 'lon')
self.assertEqual(res_gdf_var_3.get('dataType'), 'float64')
self.assertEqual(res_gdf_var_3.get('numDims'), 1)
self.assertEqual(res_gdf_var_3.get('shape'), (3,))
self.assertEqual(res_gdf_var_3.get('isYFlipped'), None)
self.assertEqual(res_gdf_var_3.get('isFeatureAttribute'), True)
self.assertIsNone(res_gdf_var_3.get('attributes'))
res_gdf_var_4 = res_gdf_vars[3]
self.assertEqual(res_gdf_var_4.get('name'), 'geometry')
self.assertEqual(res_gdf_var_4.get('dataType'), 'geometry')
self.assertEqual(res_gdf_var_4.get('numDims'), 1)
self.assertEqual(res_gdf_var_4.get('shape'), (3,))
self.assertEqual(res_gdf_var_4.get('isYFlipped'), None)
self.assertEqual(res_gdf_var_4.get('isFeatureAttribute'), True)
self.assertIsNone(res_gdf_var_4.get('attributes'))
res_scalar_ds = l_res[3]
res_scalar_ds_vars = res_scalar_ds.get('variables')
self.assertIsNotNone(res_scalar_ds_vars)
self.assertEqual(len(res_scalar_ds_vars), 2)
scalar_values = {res_scalar_ds_vars[0].get('name'): res_scalar_ds_vars[0].get('value'),
res_scalar_ds_vars[1].get('name'): res_scalar_ds_vars[1].get('value')}
self.assertEqual(scalar_values, {'temperature': 15.2, 'precipitation': 10.1})
res_scalar_df = l_res[4]
res_scalar_df_vars = res_scalar_df.get('variables')
self.assertIsNotNone(res_scalar_df_vars)
self.assertEqual(len(res_scalar_df_vars), 2)
scalar_values = {res_scalar_df_vars[0].get('name'): res_scalar_df_vars[0].get('value'),
res_scalar_df_vars[1].get('name'): res_scalar_df_vars[1].get('value')}
self.assertEqual(scalar_values, {'A': 1.3, 'B': 5.9})
res_scalar_gdf = l_res[5]
res_scalar_gdf_vars = res_scalar_gdf.get('variables')
self.assertIsNotNone(res_scalar_gdf_vars)
self.assertEqual(len(res_scalar_gdf_vars), 4)
scalar_values = {res_scalar_gdf_vars[0].get('name'): res_scalar_gdf_vars[0].get('value'),
res_scalar_gdf_vars[1].get('name'): res_scalar_gdf_vars[1].get('value'),
res_scalar_gdf_vars[2].get('name'): res_scalar_gdf_vars[2].get('value'),
res_scalar_gdf_vars[3].get('name'): res_scalar_gdf_vars[3].get('value')}
self.assertEqual(scalar_values, {'name': (1000 * 'A') + '...',
'lat': 45,
'lon': -120,
'geometry': 'POINT (-120 45)'})
res_empty_ds = l_res[6]
res_empty_ds_vars = res_empty_ds.get('variables')
self.assertIsNotNone(res_empty_ds_vars)
self.assertEqual(len(res_empty_ds_vars), 2)
scalar_values = {res_empty_ds_vars[0].get('name'): res_empty_ds_vars[0].get('value'),
res_empty_ds_vars[1].get('name'): res_empty_ds_vars[1].get('value')}
self.assertEqual(scalar_values, {'temperature': None, 'precipitation': None})
res_empty_df = l_res[7]
res_empty_df_vars = res_empty_df.get('variables')
self.assertIsNotNone(res_empty_df_vars)
self.assertEqual(len(res_empty_df_vars), 2)
scalar_values = {res_empty_df_vars[0].get('name'): res_empty_df_vars[0].get('value'),
res_empty_df_vars[1].get('name'): res_empty_df_vars[1].get('value')}
self.assertEqual(scalar_values, {'A': None, 'B': None})
res_int = l_res[8]
self.assertEqual(res_int.get('name'), 'i')
self.assertEqual(res_int.get('dataType'), 'int')
self.assertIsNone(res_int.get('attributes'))
self.assertIsNone(res_int.get('variables'))
res_str = l_res[9]
self.assertEqual(res_str.get('name'), 's')
self.assertEqual(res_str.get('dataType'), 'str')
self.assertIsNone(res_str.get('attributes'))
self.assertIsNone(res_str.get('variables'))
finally:
OP_REGISTRY.remove_op(dataset_op)
OP_REGISTRY.remove_op(data_frame_op)
OP_REGISTRY.remove_op(geo_data_frame_op)
OP_REGISTRY.remove_op(scalar_dataset_op)
OP_REGISTRY.remove_op(scalar_data_frame_op)
OP_REGISTRY.remove_op(scalar_geo_data_frame_op)
OP_REGISTRY.remove_op(empty_dataset_op)
OP_REGISTRY.remove_op(empty_data_frame_op)
OP_REGISTRY.remove_op(int_op)
OP_REGISTRY.remove_op(str_op)
def test_to_json_dict(self):
def dataset_op() -> xr.Dataset:
periods = 5
temperature_data = (15 + 8 * np.random.randn(periods, 2, 2)).round(
decimals=1)
temperature_attrs = {
'a': np.array([1, 2, 3]),
'comment': 'hot',
'_FillValue': np.nan
}
precipitation_data = (10 * np.random.rand(periods, 2, 2)).round(
decimals=1)
precipitation_attrs = {
'x': True,
'comment': 'wet',
'_FillValue': -1.0
}
ds = xr.Dataset(data_vars={
'temperature':
(('time', 'lat', 'lon'), temperature_data, temperature_attrs),
'precipitation': (('time', 'lat', 'lon'), precipitation_data,
precipitation_attrs)
},
coords={
'lon':
np.array([12, 13]),
'lat':
np.array([50, 51]),
'time':
pd.date_range('2014-09-06', periods=periods)
},
attrs={'history': 'a b c'})
return ds
def data_frame_op() -> pd.DataFrame:
data = {
'A': [1, 2, 3, np.nan, 4, 9, np.nan, np.nan, 1, 0, 4, 6],
'B': [5, 6, 8, 7, 5, 5, 5, 9, 1, 2, 7, 6]
}
time = pd.date_range('2000-01-01', freq='MS', periods=12)
return pd.DataFrame(data=data, index=time, dtype=float)
def int_op() -> int:
return 394852
def str_op() -> str:
return 'Hi!'
from cate.core.op import OP_REGISTRY
try:
OP_REGISTRY.add_op(dataset_op)
OP_REGISTRY.add_op(data_frame_op)
OP_REGISTRY.add_op(int_op)
OP_REGISTRY.add_op(str_op)
workflow = Workflow(
OpMetaInfo('workspace_workflow',
header=dict(description='Test!')))
workflow.add_step(OpStep(dataset_op, node_id='ds'))
workflow.add_step(OpStep(data_frame_op, node_id='df'))
workflow.add_step(OpStep(int_op, node_id='i'))
workflow.add_step(OpStep(str_op, node_id='s'))
ws = Workspace('/path', workflow)
ws.execute_workflow()
d_ws = ws.to_json_dict()
# import pprint
# pprint.pprint(d_ws)
d_wf = d_ws.get('workflow')
self.assertIsNotNone(d_wf)
l_res = d_ws.get('resources')
self.assertIsNotNone(l_res)
self.assertEqual(len(l_res), 4)
res_1 = l_res[0]
self.assertEqual(res_1.get('name'), 'ds')
self.assertEqual(res_1.get('dataType'),
'xarray.core.dataset.Dataset')
self.assertEqual(res_1.get('dimSizes'), dict(lat=2, lon=2, time=5))
self.assertEqual(res_1.get('attributes'), {'history': 'a b c'})
res_1_vars = res_1.get('variables')
self.assertIsNotNone(res_1_vars)
self.assertEqual(len(res_1_vars), 2)
var_1 = res_1_vars[0]
self.assertEqual(var_1.get('name'), 'precipitation')
self.assertEqual(var_1.get('dataType'), 'float64')
self.assertEqual(var_1.get('numDims'), 3)
self.assertEqual(var_1.get('shape'), (5, 2, 2))
self.assertEqual(var_1.get('chunkSizes'), None)
self.assertEqual(var_1.get('isYFlipped'), True)
self.assertEqual(var_1.get('isFeatureAttribute'), None)
self.assertEqual(var_1.get('attributes'),
dict(x=True, comment='wet', _FillValue=-1.))
var_2 = res_1_vars[1]
self.assertEqual(var_2.get('name'), 'temperature')
self.assertEqual(var_2.get('dataType'), 'float64')
self.assertEqual(var_2.get('numDims'), 3)
self.assertEqual(var_2.get('shape'), (5, 2, 2))
self.assertEqual(var_2.get('chunkSizes'), None)
self.assertEqual(var_2.get('isYFlipped'), True)
self.assertEqual(var_2.get('isFeatureAttribute'), None)
self.assertEqual(
var_2.get('attributes'),
dict(a=[1, 2, 3], comment='hot', _FillValue=np.nan))
res_2 = l_res[1]
self.assertEqual(res_2.get('name'), 'df')
self.assertEqual(res_2.get('dataType'),
'pandas.core.frame.DataFrame')
self.assertIsNone(res_2.get('attributes'))
res_2_vars = res_2.get('variables')
self.assertIsNotNone(res_2_vars)
self.assertEqual(len(res_2_vars), 2)
var_1 = res_2_vars[0]
self.assertEqual(var_1.get('name'), 'A')
self.assertEqual(var_1.get('dataType'), 'float64')
self.assertEqual(var_1.get('numDims'), 1)
self.assertEqual(var_1.get('shape'), (12, ))
self.assertEqual(var_1.get('isYFlipped'), None)
self.assertEqual(var_1.get('isFeatureAttribute'), None)
self.assertIsNone(var_1.get('attributes'))
var_2 = res_2_vars[1]
self.assertEqual(var_2.get('name'), 'B')
self.assertEqual(var_2.get('dataType'), 'float64')
self.assertEqual(var_2.get('numDims'), 1)
self.assertEqual(var_2.get('shape'), (12, ))
self.assertEqual(var_2.get('isYFlipped'), None)
self.assertEqual(var_2.get('isFeatureAttribute'), None)
self.assertIsNone(var_2.get('attributes'))
res_3 = l_res[2]
self.assertEqual(res_3.get('name'), 'i')
self.assertEqual(res_3.get('dataType'), 'int')
self.assertIsNone(res_3.get('attributes'))
self.assertIsNone(res_3.get('variables'))
res_4 = l_res[3]
self.assertEqual(res_4.get('name'), 's')
self.assertEqual(res_4.get('dataType'), 'str')
self.assertIsNone(res_4.get('attrs'))
self.assertIsNone(res_4.get('variables'))
finally:
OP_REGISTRY.remove_op(dataset_op)
OP_REGISTRY.remove_op(data_frame_op)
OP_REGISTRY.remove_op(int_op)
OP_REGISTRY.remove_op(str_op)
def test_to_json_dict(self):
def dataset_op() -> xr.Dataset:
periods = 5
temperature_data = (15 + 8 * np.random.randn(periods, 2, 2)).round(decimals=1)
temperature_attrs = {'a': np.array([1, 2, 3]), 'comment': 'hot', '_FillValue': np.nan}
precipitation_data = (10 * np.random.rand(periods, 2, 2)).round(decimals=1)
precipitation_attrs = {'x': True, 'comment': 'wet', '_FillValue': -1.0}
ds = xr.Dataset(
data_vars={
'temperature': (('time', 'lat', 'lon'), temperature_data, temperature_attrs),
'precipitation': (('time', 'lat', 'lon'), precipitation_data, precipitation_attrs)
},
coords={
'lon': np.array([12, 13]),
'lat': np.array([50, 51]),
'time': pd.date_range('2014-09-06', periods=periods)
},
attrs={
'history': 'a b c'
})
return ds
def scalar_dataset_op() -> xr.Dataset:
ds = xr.Dataset(
data_vars={
'temperature': (('time', 'lat', 'lon'), [[[15.2]]]),
'precipitation': (('time', 'lat', 'lon'), [[[10.1]]])
},
coords={
'lon': [12.],
'lat': [50.],
'time': [pd.to_datetime('2014-09-06')],
},
attrs={
'history': 'a b c'
})
return ds
def empty_dataset_op() -> xr.Dataset:
ds = xr.Dataset(
data_vars={
'temperature': (('time', 'lat', 'lon'), np.ndarray(shape=(0, 0, 0), dtype=np.float32)),
'precipitation': (('time', 'lat', 'lon'), np.ndarray(shape=(0, 0, 0), dtype=np.float32))
},
coords={
'lon': np.ndarray(shape=(0,), dtype=np.float32),
'lat': np.ndarray(shape=(0,), dtype=np.float32),
'time': np.ndarray(shape=(0,), dtype=np.datetime64),
},
attrs={
'history': 'a b c'
})
return ds
def data_frame_op() -> pd.DataFrame:
data = {'A': [1, 2, 3, np.nan, 4, 9, np.nan, np.nan, 1, 0, 4, 6],
'B': [5, 6, 8, 7, 5, 5, 5, 9, 1, 2, 7, 6]}
time = pd.date_range('2000-01-01', freq='MS', periods=12)
return pd.DataFrame(data=data, index=time, dtype=float, columns=['A', 'B'])
def scalar_data_frame_op() -> pd.DataFrame:
data = {'A': [1.3],
'B': [5.9]}
return pd.DataFrame(data=data, dtype=float, columns=['A', 'B'])
def empty_data_frame_op() -> pd.DataFrame:
data = {'A': [],
'B': []}
return pd.DataFrame(data=data, dtype=float, columns=['A', 'B'])
def geo_data_frame_op() -> gpd.GeoDataFrame:
data = {'name': ['A', 'B', 'C'],
'lat': [45, 46, 47.5],
'lon': [-120, -121.2, -122.9]}
df = pd.DataFrame(data, columns=['name', 'lat', 'lon'])
geometry = [Point(xy) for xy in zip(df['lon'], df['lat'])]
return gpd.GeoDataFrame(df, geometry=geometry)
def scalar_geo_data_frame_op() -> gpd.GeoDataFrame:
data = {'name': [2000 * 'A'],
'lat': [45],
'lon': [-120]}
df = pd.DataFrame(data, columns=['name', 'lat', 'lon'])
geometry = [Point(xy) for xy in zip(df['lon'], df['lat'])]
return gpd.GeoDataFrame(df, geometry=geometry)
def int_op() -> int:
return 394852
def str_op() -> str:
return 'Hi!'
from cate.core.op import OP_REGISTRY
try:
OP_REGISTRY.add_op(dataset_op)
OP_REGISTRY.add_op(data_frame_op)
OP_REGISTRY.add_op(geo_data_frame_op)
OP_REGISTRY.add_op(scalar_dataset_op)
OP_REGISTRY.add_op(scalar_data_frame_op)
OP_REGISTRY.add_op(scalar_geo_data_frame_op)
OP_REGISTRY.add_op(empty_dataset_op)
OP_REGISTRY.add_op(empty_data_frame_op)
OP_REGISTRY.add_op(int_op)
OP_REGISTRY.add_op(str_op)
workflow = Workflow(OpMetaInfo('workspace_workflow', header=dict(description='Test!')))
workflow.add_step(OpStep(dataset_op, node_id='ds'))
workflow.add_step(OpStep(data_frame_op, node_id='df'))
workflow.add_step(OpStep(geo_data_frame_op, node_id='gdf'))
workflow.add_step(OpStep(scalar_dataset_op, node_id='scalar_ds'))
workflow.add_step(OpStep(scalar_data_frame_op, node_id='scalar_df'))
workflow.add_step(OpStep(scalar_geo_data_frame_op, node_id='scalar_gdf'))
workflow.add_step(OpStep(empty_dataset_op, node_id='empty_ds'))
workflow.add_step(OpStep(empty_data_frame_op, node_id='empty_df'))
workflow.add_step(OpStep(int_op, node_id='i'))
workflow.add_step(OpStep(str_op, node_id='s'))
ws = Workspace('/path', workflow)
ws.execute_workflow()
d_ws = ws.to_json_dict()
# import pprint
# pprint.pprint(d_ws)
d_wf = d_ws.get('workflow')
self.assertIsNotNone(d_wf)
l_res = d_ws.get('resources')
self.assertIsNotNone(l_res)
self.assertEqual(len(l_res), 10)
res_ds = l_res[0]
self.assertEqual(res_ds.get('name'), 'ds')
self.assertEqual(res_ds.get('dataType'), 'xarray.core.dataset.Dataset')
self.assertEqual(res_ds.get('dimSizes'), dict(lat=2, lon=2, time=5))
self.assertEqual(res_ds.get('attributes'), {'history': 'a b c'})
res_ds_vars = res_ds.get('variables')
self.assertIsNotNone(res_ds_vars)
self.assertEqual(len(res_ds_vars), 2)
res_ds_var_1 = res_ds_vars[0]
self.assertEqual(res_ds_var_1.get('name'), 'precipitation')
self.assertEqual(res_ds_var_1.get('dataType'), 'float64')
self.assertEqual(res_ds_var_1.get('numDims'), 3)
self.assertEqual(res_ds_var_1.get('shape'), (5, 2, 2))
self.assertEqual(res_ds_var_1.get('chunkSizes'), None)
self.assertEqual(res_ds_var_1.get('isYFlipped'), True)
self.assertEqual(res_ds_var_1.get('isFeatureAttribute'), None)
self.assertEqual(res_ds_var_1.get('attributes'), dict(x=True, comment='wet', _FillValue=-1.))
res_ds_var_2 = res_ds_vars[1]
self.assertEqual(res_ds_var_2.get('name'), 'temperature')
self.assertEqual(res_ds_var_2.get('dataType'), 'float64')
self.assertEqual(res_ds_var_2.get('numDims'), 3)
self.assertEqual(res_ds_var_2.get('shape'), (5, 2, 2))
self.assertEqual(res_ds_var_2.get('chunkSizes'), None)
self.assertEqual(res_ds_var_2.get('isYFlipped'), True)
self.assertEqual(res_ds_var_2.get('isFeatureAttribute'), None)
self.assertEqual(res_ds_var_2.get('attributes'), dict(a=[1, 2, 3], comment='hot', _FillValue=np.nan))
res_df = l_res[1]
self.assertEqual(res_df.get('name'), 'df')
self.assertEqual(res_df.get('dataType'), 'pandas.core.frame.DataFrame')
self.assertEqual(res_df.get('attributes'), {'num_rows': 12, 'num_columns': 2})
res_df_vars = res_df.get('variables')
self.assertIsNotNone(res_df_vars)
self.assertEqual(len(res_df_vars), 2)
res_df_var_1 = res_df_vars[0]
self.assertEqual(res_df_var_1.get('name'), 'A')
self.assertEqual(res_df_var_1.get('dataType'), 'float64')
self.assertEqual(res_df_var_1.get('numDims'), 1)
self.assertEqual(res_df_var_1.get('shape'), (12,))
self.assertEqual(res_df_var_1.get('isYFlipped'), None)
self.assertEqual(res_df_var_1.get('isFeatureAttribute'), True)
self.assertIsNone(res_df_var_1.get('attributes'))
res_df_var_2 = res_df_vars[1]
self.assertEqual(res_df_var_2.get('name'), 'B')
self.assertEqual(res_df_var_2.get('dataType'), 'float64')
self.assertEqual(res_df_var_2.get('numDims'), 1)
self.assertEqual(res_df_var_2.get('shape'), (12,))
self.assertEqual(res_df_var_2.get('isYFlipped'), None)
self.assertEqual(res_df_var_2.get('isFeatureAttribute'), True)
self.assertIsNone(res_df_var_2.get('attributes'))
res_gdf = l_res[2]
self.assertEqual(res_gdf.get('name'), 'gdf')
self.assertEqual(res_gdf.get('dataType'), 'geopandas.geodataframe.GeoDataFrame')
self.assertEqual(res_gdf.get('attributes'), {'num_rows': 3, 'num_columns': 4, 'geom_type': 'Point'})
res_gdf_vars = res_gdf.get('variables')
self.assertIsNotNone(res_gdf_vars)
self.assertEqual(len(res_gdf_vars), 4)
res_gdf_var_1 = res_gdf_vars[0]
self.assertEqual(res_gdf_var_1.get('name'), 'name')
self.assertEqual(res_gdf_var_1.get('dataType'), 'object')
self.assertEqual(res_gdf_var_1.get('numDims'), 1)
self.assertEqual(res_gdf_var_1.get('shape'), (3,))
self.assertEqual(res_gdf_var_1.get('isYFlipped'), None)
self.assertEqual(res_gdf_var_1.get('isFeatureAttribute'), True)
self.assertIsNone(res_gdf_var_1.get('attributes'))
res_gdf_var_2 = res_gdf_vars[1]
self.assertEqual(res_gdf_var_2.get('name'), 'lat')
self.assertEqual(res_gdf_var_2.get('dataType'), 'float64')
self.assertEqual(res_gdf_var_2.get('numDims'), 1)
self.assertEqual(res_gdf_var_2.get('shape'), (3,))
self.assertEqual(res_gdf_var_2.get('isYFlipped'), None)
self.assertEqual(res_gdf_var_2.get('isFeatureAttribute'), True)
self.assertIsNone(res_gdf_var_2.get('attributes'))
res_gdf_var_3 = res_gdf_vars[2]
self.assertEqual(res_gdf_var_3.get('name'), 'lon')
self.assertEqual(res_gdf_var_3.get('dataType'), 'float64')
self.assertEqual(res_gdf_var_3.get('numDims'), 1)
self.assertEqual(res_gdf_var_3.get('shape'), (3,))
self.assertEqual(res_gdf_var_3.get('isYFlipped'), None)
self.assertEqual(res_gdf_var_3.get('isFeatureAttribute'), True)
self.assertIsNone(res_gdf_var_3.get('attributes'))
res_gdf_var_4 = res_gdf_vars[3]
self.assertEqual(res_gdf_var_4.get('name'), 'geometry')
self.assertEqual(res_gdf_var_4.get('dataType'), 'object')
self.assertEqual(res_gdf_var_4.get('numDims'), 1)
self.assertEqual(res_gdf_var_4.get('shape'), (3,))
self.assertEqual(res_gdf_var_4.get('isYFlipped'), None)
self.assertEqual(res_gdf_var_4.get('isFeatureAttribute'), True)
self.assertIsNone(res_gdf_var_4.get('attributes'))
res_scalar_ds = l_res[3]
res_scalar_ds_vars = res_scalar_ds.get('variables')
self.assertIsNotNone(res_scalar_ds_vars)
self.assertEqual(len(res_scalar_ds_vars), 2)
scalar_values = {res_scalar_ds_vars[0].get('name'): res_scalar_ds_vars[0].get('value'),
res_scalar_ds_vars[1].get('name'): res_scalar_ds_vars[1].get('value')}
self.assertEqual(scalar_values, {'temperature': 15.2, 'precipitation': 10.1})
res_scalar_df = l_res[4]
res_scalar_df_vars = res_scalar_df.get('variables')
self.assertIsNotNone(res_scalar_df_vars)
self.assertEqual(len(res_scalar_df_vars), 2)
scalar_values = {res_scalar_df_vars[0].get('name'): res_scalar_df_vars[0].get('value'),
res_scalar_df_vars[1].get('name'): res_scalar_df_vars[1].get('value')}
self.assertEqual(scalar_values, {'A': 1.3, 'B': 5.9})
res_scalar_gdf = l_res[5]
res_scalar_gdf_vars = res_scalar_gdf.get('variables')
self.assertIsNotNone(res_scalar_gdf_vars)
self.assertEqual(len(res_scalar_gdf_vars), 4)
scalar_values = {res_scalar_gdf_vars[0].get('name'): res_scalar_gdf_vars[0].get('value'),
res_scalar_gdf_vars[1].get('name'): res_scalar_gdf_vars[1].get('value'),
res_scalar_gdf_vars[2].get('name'): res_scalar_gdf_vars[2].get('value'),
res_scalar_gdf_vars[3].get('name'): res_scalar_gdf_vars[3].get('value')}
self.assertEqual(scalar_values, {'name': (1000 * 'A') + '...',
'lat': 45,
'lon': -120,
'geometry': 'POINT (-120 45)'})
res_empty_ds = l_res[6]
res_empty_ds_vars = res_empty_ds.get('variables')
self.assertIsNotNone(res_empty_ds_vars)
self.assertEqual(len(res_empty_ds_vars), 2)
scalar_values = {res_empty_ds_vars[0].get('name'): res_empty_ds_vars[0].get('value'),
res_empty_ds_vars[1].get('name'): res_empty_ds_vars[1].get('value')}
self.assertEqual(scalar_values, {'temperature': None, 'precipitation': None})
res_empty_df = l_res[7]
res_empty_df_vars = res_empty_df.get('variables')
self.assertIsNotNone(res_empty_df_vars)
self.assertEqual(len(res_empty_df_vars), 2)
scalar_values = {res_empty_df_vars[0].get('name'): res_empty_df_vars[0].get('value'),
res_empty_df_vars[1].get('name'): res_empty_df_vars[1].get('value')}
self.assertEqual(scalar_values, {'A': None, 'B': None})
res_int = l_res[8]
self.assertEqual(res_int.get('name'), 'i')
self.assertEqual(res_int.get('dataType'), 'int')
self.assertIsNone(res_int.get('attributes'))
self.assertIsNone(res_int.get('variables'))
res_str = l_res[9]
self.assertEqual(res_str.get('name'), 's')
self.assertEqual(res_str.get('dataType'), 'str')
self.assertIsNone(res_str.get('attributes'))
self.assertIsNone(res_str.get('variables'))
finally:
OP_REGISTRY.remove_op(dataset_op)
OP_REGISTRY.remove_op(data_frame_op)
OP_REGISTRY.remove_op(geo_data_frame_op)
OP_REGISTRY.remove_op(scalar_dataset_op)
OP_REGISTRY.remove_op(scalar_data_frame_op)
OP_REGISTRY.remove_op(scalar_geo_data_frame_op)
OP_REGISTRY.remove_op(empty_dataset_op)
OP_REGISTRY.remove_op(empty_data_frame_op)
OP_REGISTRY.remove_op(int_op)
OP_REGISTRY.remove_op(str_op)