def test_misfit_preprocessor_with_scaling(test_data_root):
test_data_dir = os.path.join(test_data_root, "snake_oil")
shutil.copytree(test_data_dir, "test_data")
os.chdir(os.path.join("test_data"))
res_config = ResConfig("snake_oil.ert")
ert = EnKFMain(res_config)
config = {
"workflow": {
"type": "custom_scale",
"clustering": {
"fcluster": {
"threshold": 1.0
},
},
}
}
config_file = "my_config_file.yaml"
with open(config_file, "w") as f:
yaml.dump(config, f)
misfit_preprocessor.MisfitPreprocessorJob(ert).run(config_file)
# assert that this arbitrarily chosen cluster gets scaled as expected
obs = ert.getObservations()["FOPR"]
for index in [13, 14, 15, 16, 17, 18, 19, 20]:
assert obs.getNode(index).getStdScaling() == 2.8284271247461903
for index in (38, 39, 40, 41, 42, 43, 44):
assert obs.getNode(index).getStdScaling() == 2.6457513110645907
def test_misfit_preprocessor_passing_scaling_parameters(
monkeypatch, test_data_root):
run_mock = Mock()
scal_job = Mock(return_value=Mock(run=run_mock))
monkeypatch.setattr(
misfit_preprocessor,
"CorrelatedObservationsScalingJob",
scal_job,
)
test_data_dir = os.path.join(test_data_root, "snake_oil")
shutil.copytree(test_data_dir, "test_data")
os.chdir(os.path.join("test_data"))
res_config = ResConfig("snake_oil.ert")
ert = EnKFMain(res_config)
config = {
"workflow": {
"type": "custom_scale",
"pca": {
"threshold": 0.5
},
},
}
config_file = "my_config_file.yaml"
with open(config_file, "w") as f:
yaml.dump(config, f)
misfit_preprocessor.MisfitPreprocessorJob(ert).run(config_file)
for scaling_config in list(run_mock.call_args)[0][0]:
assert 0.5 == scaling_config["CALCULATE_KEYS"]["threshold"]
def test_misfit_preprocessor_invalid_config(test_data_root):
test_data_dir = os.path.join(test_data_root, "snake_oil")
shutil.copytree(test_data_dir, "test_data")
os.chdir(os.path.join("test_data"))
res_config = ResConfig("snake_oil.ert")
ert = EnKFMain(res_config)
config = {
"unknown_key": [],
"workflow": {
"type": "custom_scale",
"clustering": {
"threshold": 1.0
}
},
}
config_file = "my_config_file.yaml"
with open(config_file, "w") as f:
yaml.dump(config, f)
expected_err_msg = (
"Invalid configuration of misfit preprocessor\n"
" - extra fields not permitted (workflow.clustering.threshold)\n"
# There are two clustering functions, and this is invalid in both
" - extra fields not permitted (workflow.clustering.threshold)\n"
" - extra fields not permitted (unknown_key)\n")
job = misfit_preprocessor.MisfitPreprocessorJob(ert)
with pytest.raises(
semeio.workflows.misfit_preprocessor.ValidationError) as err:
job.run(config_file)
assert str(err.value) == expected_err_msg
def test_misfit_preprocessor_invalid_config(test_data_root):
test_data_dir = os.path.join(test_data_root, "snake_oil")
shutil.copytree(test_data_dir, "test_data")
os.chdir(os.path.join("test_data"))
res_config = ResConfig("snake_oil.ert")
ert = EnKFMain(res_config)
config = {
"unknown_key": [],
"clustering": {
"method": "spearman_correlation",
"spearman_correlation": {
"fcluster": {
"threshold": 1.0
}
},
},
}
config_file = "my_config_file.yaml"
with open(config_file, "w") as f:
yaml.dump(config, f)
job = misfit_preprocessor.MisfitPreprocessorJob(ert)
with pytest.raises(
semeio.workflows.misfit_preprocessor.ValidationError) as ve:
job.run(config_file)
expected_err_msg = (
"Invalid configuration of misfit preprocessor\n"
" - Unknown key: unknown_key (root level)\n"
" - Unknown key: threshold (clustering.spearman_correlation.fcluster)\n"
)
assert expected_err_msg == str(ve.value)
def test_misfit_preprocessor_with_scaling(test_data_root):
test_data_dir = os.path.join(test_data_root, "snake_oil")
shutil.copytree(test_data_dir, "test_data")
os.chdir(os.path.join("test_data"))
res_config = ResConfig("snake_oil.ert")
ert = EnKFMain(res_config)
config = {
"clustering": {
"method": "spearman_correlation",
"spearman_correlation": {
"fcluster": {
"t": 1.0
}
},
}
}
config_file = "my_config_file.yaml"
with open(config_file, "w") as f:
yaml.dump(config, f)
misfit_preprocessor.MisfitPreprocessorJob(ert).run(config_file)
# assert that this arbitrarily chosen cluster gets scaled as expected
obs = ert.getObservations()["FOPR"]
for index in [13, 14, 15, 16, 17, 18, 19, 20]:
assert obs.getNode(index).getStdScaling() == 2.8284271247461903
def test_misfit_preprocessor_skip_clusters_yielding_empty_data_matrixes(
monkeypatch, test_data_root):
def raising_scaling_job(data):
if data == {
"CALCULATE_KEYS": {
"keys": [{
"index": [88, 89],
"key": "FOPR"
}]
}
}:
raise EmptyDatasetException("foo")
scaling_mock = Mock(return_value=Mock(
**{"run.side_effect": raising_scaling_job}))
monkeypatch.setattr(misfit_preprocessor,
"CorrelatedObservationsScalingJob", scaling_mock)
test_data_dir = os.path.join(test_data_root, "snake_oil")
shutil.copytree(test_data_dir, "test_data")
os.chdir(os.path.join("test_data"))
res_config = ResConfig("snake_oil.ert")
ert = EnKFMain(res_config)
config = {
"workflow": {
"type": "custom_scale",
"clustering": {
"fcluster": {
"threshold": 1.0
}
},
},
}
config_file = "my_config_file.yaml"
with open(config_file, "w") as f:
yaml.dump(config, f)
job = misfit_preprocessor.MisfitPreprocessorJob(ert)
try:
job.run(config_file)
except EmptyDatasetException:
pytest.fail(
"EmptyDatasetException was not handled by misfit preprocessor")
def test_misfit_preprocessor_main_entry_point_gen_data(monkeypatch,
test_data_root,
observation,
expected_nr_clusters):
run_mock = Mock()
scal_job = Mock(return_value=Mock(run=run_mock))
monkeypatch.setattr(
misfit_preprocessor,
"CorrelatedObservationsScalingJob",
scal_job,
)
test_data_dir = os.path.join(test_data_root, "snake_oil")
shutil.copytree(test_data_dir, "test_data")
os.chdir(os.path.join("test_data"))
res_config = ResConfig("snake_oil.ert")
ert = EnKFMain(res_config)
config = {
"observations": [observation],
"workflow": {
"type": "custom_scale",
"clustering": {
"fcluster": {
"threshold": 1.0
}
},
},
}
config_file = "my_config_file.yaml"
with open(config_file, "w") as f:
yaml.dump(config, f)
misfit_preprocessor.MisfitPreprocessorJob(ert).run(config_file)
# call_args represents the clusters, we expect the snake_oil
# observations to generate this amount of them
# call_args is a call object, which itself is a tuple of args and kwargs.
# In this case, we want args, and the first element of the arguments, which
# again is a tuple containing the configuration which is a list of configs.
assert (len(list(run_mock.call_args)[0][0]) == expected_nr_clusters
), "wrong number of clusters"
def test_misfit_preprocessor_main_entry_point_no_config(
monkeypatch, test_data_root):
run_mock = Mock()
scal_job = Mock(return_value=Mock(run=run_mock))
monkeypatch.setattr(
misfit_preprocessor,
"CorrelatedObservationsScalingJob",
scal_job,
)
test_data_dir = os.path.join(test_data_root, "snake_oil")
shutil.copytree(test_data_dir, "test_data")
os.chdir(os.path.join("test_data"))
res_config = ResConfig("snake_oil.ert")
ert = EnKFMain(res_config)
misfit_preprocessor.MisfitPreprocessorJob(ert).run()
assert len(run_mock.call_args[0][0]) > 1 # pylint: disable=unsubscriptable-object
def test_misfit_preprocessor_all_obs(test_data_root, monkeypatch):
from unittest.mock import MagicMock
from semeio.workflows.correlated_observations_scaling import cos
test_data_dir = os.path.join(test_data_root, "snake_oil")
shutil.copytree(test_data_dir, "test_data")
os.chdir(os.path.join("test_data"))
res_config = ResConfig("snake_oil.ert")
ert = EnKFMain(res_config)
monkeypatch.setattr(cos.ObservationScaleFactor, "get_scaling_factor",
MagicMock(return_value=1.234))
misfit_preprocessor.MisfitPreprocessorJob(ert).run()
scaling_factors = []
obs = ert.getObservations()
for key in [
"FOPR",
"WOPR_OP1_9",
"WOPR_OP1_36",
"WOPR_OP1_72",
"WOPR_OP1_108",
"WOPR_OP1_144",
"WOPR_OP1_190",
"WPR_DIFF_1",
]:
obs_vector = obs[key]
for index, node in enumerate(obs_vector):
scaling_factors.append((index, key, node.getStdScaling(index)))
for index, key, scaling_factor in scaling_factors:
assert scaling_factor == 1.234, f"{index}, {key}"
