def test_update(self):
config = self.createTestPath("local/snake_oil/snake_oil.ert")
with ErtTestContext("update_test", config) as context:
ert = context.getErt()
es_update = ESUpdate(ert)
fsm = ert.getEnkfFsManager()
sim_fs = fsm.getFileSystem("default_0")
target_fs = fsm.getFileSystem("target")
mask = BoolVector(initial_size=ert.getEnsembleSize(), default_value=True)
run_context = ErtRunContext.ensemble_smoother_update(sim_fs, target_fs)
es_update.smootherUpdate(run_context)
conf = ert.ensembleConfig()["SNAKE_OIL_PARAM"]
sim_node = EnkfNode(conf)
target_node = EnkfNode(conf)
node_id = NodeId(0, 0)
sim_node.load(sim_fs, node_id)
target_node.load(target_fs, node_id)
sim_gen_kw = sim_node.asGenKw()
target_gen_kw = target_node.asGenKw()
# Test that an update has actually taken place
for index in range(len(sim_gen_kw)):
self.assertNotEqual(sim_gen_kw[index], target_gen_kw[index])
def run(self):
target = self._run_widget.target_case()
source = self._run_widget.source_case()
ert = ERT.ert
fs_manager = ert.getEnkfFsManager()
es_update = ESUpdate(ert)
target_fs = fs_manager.getFileSystem(target)
source_fs = fs_manager.getFileSystem(source)
run_context = ErtRunContext.ensemble_smoother_update(
source_fs,
target_fs,
)
success = es_update.smootherUpdate(run_context)
if not success:
msg = QMessageBox()
msg.setIcon(QMessageBox.Warning)
msg.setWindowTitle("Run Analysis")
msg.setText("Unable to run analysis for case '%s'." % source)
msg.setStandardButtons(QMessageBox.Ok)
msg.exec_()
return
ERT.ertChanged.emit()
self._dialog.accept()
def test_update_code1(self):
with ErtTestContext("row_scaling", self.config_file) as tc:
main = tc.getErt()
local_config = main.getLocalConfig()
local_config.clear()
obs = local_config.createObsdata("OBSSET_LOCAL")
obs.addNode("WBHP0")
obs.addNode("WWCT0")
ministep = local_config.createMinistep("MINISTEP_LOCAL")
ministep.addActiveData("PORO")
ministep.attachObsset(obs)
updatestep = local_config.getUpdatestep()
updatestep.attachMinistep(ministep)
row_scaling = ministep.row_scaling("PORO")
ens_config = main.ensembleConfig()
poro_config = ens_config["PORO"]
field_config = poro_config.getFieldModelConfig()
# -------------------------------------------------------------------------------------
grid = main.eclConfig().getGrid()
obs_pos = grid.get_xyz(ijk=(5, 5, 1))
length_scale = (2, 1, 0.50)
row_scaling.assign(field_config.get_data_size(),
GaussianDecay(obs_pos, length_scale, grid))
# -------------------------------------------------------------------------------------
init_fs = init_data(main)
target_fs = main.getEnkfFsManager().getFileSystem("target")
es_update = ESUpdate(main)
run_context = ErtRunContext.ensemble_smoother_update(
init_fs, target_fs)
es_update.smootherUpdate(run_context)
def _run_ministep(ert, obs_group, data_parameters, prior_name, target_name,
output_path):
local_config = ert.getLocalConfig()
# Reset internal local config structure, in order to make your own
local_config.clear()
# A ministep is used to link betwen data and observations.
# Make more ministeps to condition different groups together
ministep = local_config.createMinistep("MINISTEP")
# Add all dataset to localize
data_all = local_config.createDataset("DATASET")
for data in data_parameters:
data_all.addNode(data)
# Add all obs to be used in this updating scheme
obsdata = local_config.createObsdata("OBS")
for obs in obs_group:
obsdata.addNode(obs)
# Attach the created dataset and obsset to the ministep
ministep.attachDataset(data_all)
ministep.attachObsset(obsdata)
# Then attach the ministep to the update step
local_config.getUpdatestep().attachMinistep(ministep)
# Perform update analysis
ert.analysisConfig().set_log_path(output_path)
run_context = ErtRunContext.ensemble_smoother_update(
ert.getEnkfFsManager().getFileSystem(prior_name),
ert.getEnkfFsManager().getFileSystem(target_name),
)
ESUpdate(ert).smootherUpdate(run_context)
def test_update_workflow(self):
with ErtTestContext("row_scaling", self.config_file) as tc:
main = tc.getErt()
workflow_list = main.getWorkflowList()
workflow = workflow_list["ROW_SCALING_WORKFLOW1"]
self.assertTrue(workflow.run(main))
init_fs = init_data(main)
target_fs = main.getEnkfFsManager().getFileSystem("target")
es_update = ESUpdate(main)
run_context = ErtRunContext.ensemble_smoother_update(init_fs, target_fs)
es_update.smootherUpdate(run_context)
def analyse(ert, target, source):
"""Runs analysis using target and source cases. Returns whether or not
the analysis was successful."""
fs_manager = ert.getEnkfFsManager()
es_update = ESUpdate(ert)
target_fs = fs_manager.getFileSystem(target)
source_fs = fs_manager.getFileSystem(source)
run_context = ErtRunContext.ensemble_smoother_update(
source_fs,
target_fs,
)
es_update.smootherUpdate(run_context)
def test_update_report(setup_case, snapshot):
"""
Note that this is now a snapshot test, so there is no guarantee that the
snapshots are correct, they are just documenting the current behavior.
"""
res_config = setup_case("local/snake_oil", "snake_oil.ert")
ert = EnKFMain(res_config)
es_update = ESUpdate(ert)
fsm = ert.getEnkfFsManager()
sim_fs = fsm.getFileSystem("default_0")
target_fs = fsm.getFileSystem("target")
run_context = ErtRunContext.ensemble_smoother_update(sim_fs, target_fs)
es_update.smootherUpdate(run_context)
log_file = Path(ert.analysisConfig().get_log_path()) / "deprecated"
snapshot.assert_match(log_file.read_text("utf-8"), "update_log")
def test_snapshot_alpha(setup_case, alpha, expected):
"""
Note that this is now a snapshot test, so there is no guarantee that the
snapshots are correct, they are just documenting the current behavior.
"""
res_config = setup_case("local/snake_oil", "snake_oil.ert")
obs_file = Path("observations") / "observations.txt"
with obs_file.open(mode="w") as fin:
fin.write(
"""
SUMMARY_OBSERVATION LOW_STD
{
VALUE = 10;
ERROR = 0.1;
DATE = 2015-06-23;
KEY = FOPR;
};
SUMMARY_OBSERVATION HIGH_STD
{
VALUE = 10;
ERROR = 1.0;
DATE = 2015-06-23;
KEY = FOPR;
};
SUMMARY_OBSERVATION EXTREMELY_HIGH_STD
{
VALUE = 10;
ERROR = 10.0;
DATE = 2015-06-23;
KEY = FOPR;
};
"""
)
ert = EnKFMain(res_config)
es_update = ESUpdate(ert)
ert.analysisConfig().selectModule("IES_ENKF")
fsm = ert.getEnkfFsManager()
sim_fs = fsm.getFileSystem("default_0")
target_fs = fsm.getFileSystem("target")
run_context = ErtRunContext.ensemble_smoother_update(sim_fs, target_fs)
ert.analysisConfig().setEnkfAlpha(alpha)
es_update.smootherUpdate(run_context)
result_snapshot = ert.update_snapshots[run_context.get_id()]
assert result_snapshot.alpha == alpha
assert result_snapshot.ministep_snapshots["ALL_ACTIVE"].obs_status == expected
def test_large_case(self):
with open("config", "w") as fp:
fp.write(
"""NUM_REALIZATIONS 10
GRID CASE.EGRID
FIELD PORO PARAMETER poro.grdecl INIT_FILES:fields/poro%d.grdecl
SUMMARY WBHP
OBS_CONFIG observations.txt
TIME_MAP timemap.txt
"""
)
for f in ["timemap.txt", "observations.txt"]:
src_file = self.createTestPath(os.path.join("local/row_scaling", f))
shutil.copy(src_file, "./")
# The grid size must be greater than 250000 (the default matrix size in
# enkf_main_update())
grid = EclGridGenerator.create_rectangular((70, 70, 70), (1, 1, 1))
grid.save_EGRID("CASE.EGRID")
res_config = ResConfig(user_config_file="config")
main = EnKFMain(res_config)
init_fs = init_data(main)
# Configure the local updates
local_config = main.getLocalConfig()
local_config.clear()
local_data = local_config.createDataset("LOCAL")
local_data.addNode("PORO")
obs = local_config.createObsdata("OBSSET_LOCAL")
obs.addNode("WBHP0")
ministep = local_config.createMinistep("MINISTEP_LOCAL")
ministep.attachDataset(local_data)
ministep.attachObsset(obs)
updatestep = local_config.getUpdatestep()
updatestep.attachMinistep(ministep)
# Apply the row scaling
row_scaling = local_data.row_scaling("PORO")
ens_config = main.ensembleConfig()
poro_config = ens_config["PORO"]
field_config = poro_config.getFieldModelConfig()
grid = main.eclConfig().getGrid()
row_scaling.assign(field_config.get_data_size(), ScalingTest(grid))
es_update = ESUpdate(main)
update_fs = main.getEnkfFsManager().getFileSystem("target2")
run_context = ErtRunContext.ensemble_smoother_update(init_fs, update_fs)
es_update.smootherUpdate(run_context)
def test_update(setup_case, module, expected_gen_kw):
"""
Note that this is now a snapshot test, so there is no guarantee that the
snapshots are correct, they are just documenting the current behavior.
"""
res_config = setup_case("local/snake_oil", "snake_oil.ert")
ert = EnKFMain(res_config)
es_update = ESUpdate(ert)
ert.analysisConfig().selectModule(module)
fsm = ert.getEnkfFsManager()
sim_fs = fsm.getFileSystem("default_0")
target_fs = fsm.getFileSystem("target")
run_context = ErtRunContext.ensemble_smoother_update(sim_fs, target_fs)
es_update.smootherUpdate(run_context)
conf = ert.ensembleConfig()["SNAKE_OIL_PARAM"]
sim_node = EnkfNode(conf)
target_node = EnkfNode(conf)
node_id = NodeId(0, 0)
sim_node.load(sim_fs, node_id)
target_node.load(target_fs, node_id)
sim_gen_kw = list(sim_node.asGenKw())
target_gen_kw = list(target_node.asGenKw())
assert sim_gen_kw != target_gen_kw
assert sim_gen_kw == pytest.approx(
[
-1.3035319087841115,
0.8222709205428339,
-1.1400029486153482,
0.7477534046493867,
-0.10400064074767973,
-1.7223242794585338,
0.0761604027734105,
0.4039137216428462,
0.10001691562080614,
0.09549338450036506,
]
)
assert target_gen_kw == pytest.approx(expected_gen_kw)
def test_attach_obs_data_to_ministep(self):
config = self.createTestPath("local/snake_oil/snake_oil.ert")
expected_keys = {
"WPR_DIFF_1",
"WOPR_OP1_108",
"FOPR",
"WOPR_OP1_144",
"WOPR_OP1_190",
"WOPR_OP1_9",
"WOPR_OP1_36",
"WOPR_OP1_72",
}
with ErtTestContext("obs_data_ministep_test", config) as context:
ert = context.getErt()
es_update = ESUpdate(ert)
fsm = ert.getEnkfFsManager()
sim_fs = fsm.getFileSystem("default_0")
target_fs = fsm.getFileSystem("target")
run_context = ErtRunContext.ensemble_smoother_update(
sim_fs, target_fs)
es_update.smootherUpdate(run_context)
update_step = ert.getLocalConfig().getUpdatestep()
ministep = update_step[len(update_step) - 1]
obs_data = ministep.get_obs_data()
self.assertEqual(len(expected_keys), obs_data.get_num_blocks())
observed_obs_keys = set()
for block_num in range(obs_data.get_num_blocks()):
block = obs_data.get_block(block_num)
obs_key = block.get_obs_key()
observed_obs_keys.add(obs_key)
for i in range(len(block)):
self.assertTrue(block.is_active(i))
self.assertSetEqual(expected_keys, observed_obs_keys)
def run(self):
target = self._run_widget.target_case()
source = self._run_widget.source_case()
ert = ERT.ert
fs_manager = ert.getEnkfFsManager()
es_update = ESUpdate(ert)
target_fs = fs_manager.getFileSystem(target)
source_fs = fs_manager.getFileSystem(source)
run_context = ErtRunContext.ensemble_smoother_update( source_fs, target_fs, )
success = es_update.smootherUpdate( run_context )
if not success:
msg = QMessageBox()
msg.setIcon(QMessageBox.Warning)
msg.setWindowTitle("Run Analysis")
msg.setText("Unable to run analysis for case '%s'." % source)
msg.setStandardButtons(QMessageBox.Ok)
msg.exec_()
return
ERT.ertChanged.emit()
self._dialog.accept()
def test_reuse_ALL_ACTIVE(self):
random_seed = "ABCDEFGHIJK0123456"
with ErtTestContext("row_scaling", self.config_file) as tc:
main = tc.getErt()
grid = main.eclConfig().getGrid()
init_fs = init_data(main)
es_update = ESUpdate(main)
update_fs1 = main.getEnkfFsManager().getFileSystem("target1")
run_context = ErtRunContext.ensemble_smoother_update(init_fs, update_fs1)
rng = main.rng()
rng.setState(random_seed)
# Normal update without any local configuration
es_update.smootherUpdate(run_context)
local_config = main.getLocalConfig()
local_config.clear_active()
with self.assertRaises(KeyError):
obs_data = local_config.copyObsdata("NO_SUCH_OBS", "my_obs")
local_data = local_config.createDataset("LOCAL")
local_data.addNode("PORO")
obs_data = local_config.copyObsdata("ALL_OBS", "my_obs")
ministep = local_config.createMinistep("MINISTEP_LOCAL")
ministep.attachDataset(local_data)
ministep.attachObsset(obs_data)
updatestep = local_config.getUpdatestep()
updatestep.attachMinistep(ministep)
update_fs2 = main.getEnkfFsManager().getFileSystem("target2")
run_context = ErtRunContext.ensemble_smoother_update(init_fs, update_fs2)
rng.setState(random_seed)
# Local update with reused ALL_OBS observation configuration
es_update.smootherUpdate(run_context)
del obs_data["WBHP0"]
ministep2 = local_config.createMinistep("MINISTEP_LOCAL2")
obs_data2 = local_config.createObsdata("OBSDATA2")
obs_data2.addNode("WBHP0")
ministep2.attachDataset(local_data)
ministep2.attachObsset(obs_data2)
updatestep.attachMinistep(ministep2)
update_fs3 = main.getEnkfFsManager().getFileSystem("target3")
run_context = ErtRunContext.ensemble_smoother_update(init_fs, update_fs3)
# Local update with two ministeps - where one observation has been removed from the first
es_update.smootherUpdate(run_context)
ens_config = main.ensembleConfig()
poro_config = ens_config["PORO"]
update_node1 = EnkfNode(poro_config)
update_node2 = EnkfNode(poro_config)
update_node3 = EnkfNode(poro_config)
for iens in range(main.getEnsembleSize()):
node_id = NodeId(0, iens)
update_node1.load(update_fs1, node_id)
update_node2.load(update_fs2, node_id)
update_node3.load(update_fs3, node_id)
field1 = update_node1.asField()
field2 = update_node2.asField()
field3 = update_node3.asField()
for k in range(grid.nz):
for j in range(grid.ny):
for i in range(grid.nx):
assert field1.ijk_get_double(
i, j, k
) == field2.ijk_get_double(i, j, k)
f1 = field1.ijk_get_double(i, j, k)
f3 = field3.ijk_get_double(i, j, k)
# Due to the randomness in the sampling process,
# which becomes different when the update steps is
# split in two ministeps we can not enforce
# equality here.
diff = abs(f1 - f3)
assert diff < 0.01
def test_2ministep(self):
with ErtTestContext("row_scaling", self.config_file) as tc:
main = tc.getErt()
init_fs = init_data(main)
update_fs1 = main.getEnkfFsManager().getFileSystem("target1")
# The first smoother update without row scaling
es_update = ESUpdate(main)
run_context = ErtRunContext.ensemble_smoother_update(init_fs, update_fs1)
rng = main.rng()
es_update.smootherUpdate(run_context)
# Configure the local updates
local_config = main.getLocalConfig()
local_config.clear()
obs = local_config.createObsdata("OBSSET_LOCAL")
obs.addNode("WBHP0")
ministep1 = local_config.createMinistep("MINISTEP1")
local_data1 = local_config.createDataset("LOCAL1")
local_data1.addNode("PORO")
row_scaling1 = local_data1.row_scaling("PORO")
ministep1.attachDataset(local_data1)
ministep1.attachObsset(obs)
ministep2 = local_config.createMinistep("MINISTEP2")
local_data2 = local_config.createDataset("LOCAL2")
local_data2.addNode("PORO")
row_scaling2 = local_data2.row_scaling("PORO")
ministep2.attachDataset(local_data2)
ministep2.attachObsset(obs)
updatestep = local_config.getUpdatestep()
updatestep.attachMinistep(ministep1)
updatestep.attachMinistep(ministep2)
# Apply the row scaling
ens_config = main.ensembleConfig()
poro_config = ens_config["PORO"]
field_config = poro_config.getFieldModelConfig()
grid = main.eclConfig().getGrid()
row_scaling1.assign(field_config.get_data_size(), SelectLayer(0, grid))
row_scaling2.assign(field_config.get_data_size(), SelectLayer(1, grid))
update_fs2 = main.getEnkfFsManager().getFileSystem("target2")
es_update = ESUpdate(main)
run_context = ErtRunContext.ensemble_smoother_update(init_fs, update_fs2)
es_update.smootherUpdate(run_context)
init_node = EnkfNode(poro_config)
node1 = EnkfNode(poro_config)
node2 = EnkfNode(poro_config)
for iens in range(main.getEnsembleSize()):
node_id = NodeId(0, iens)
init_node.load(init_fs, node_id)
node1.load(update_fs1, node_id)
node2.load(update_fs2, node_id)
init_field = init_node.asField()
field1 = node1.asField()
field2 = node2.asField()
for iv, v1, v2 in zip(init_field, field1, field2):
assert iv != v1
def test_row_scaling_using_assign_vector(self):
random_seed = "ABCDEFGHIJK0123456"
with ErtTestContext("row_scaling", self.config_file) as tc:
main = tc.getErt()
init_fs = init_data(main)
update_fs1 = main.getEnkfFsManager().getFileSystem("target1")
# The first smoother update without row scaling
es_update = ESUpdate(main)
run_context = ErtRunContext.ensemble_smoother_update(init_fs, update_fs1)
rng = main.rng()
rng.setState(random_seed)
es_update.smootherUpdate(run_context)
# Configure the local updates
local_config = main.getLocalConfig()
local_config.clear()
local_data = local_config.createDataset("LOCAL")
local_data.addNode("PORO")
obs = local_config.createObsdata("OBSSET_LOCAL")
obs.addNode("WWCT0")
obs.addNode("WBHP0")
ministep = local_config.createMinistep("MINISTEP_LOCAL")
ministep.attachDataset(local_data)
ministep.attachObsset(obs)
updatestep = local_config.getUpdatestep()
updatestep.attachMinistep(ministep)
# Apply the row scaling
row_scaling = local_data.row_scaling("PORO")
ens_config = main.ensembleConfig()
poro_config = ens_config["PORO"]
field_config = poro_config.getFieldModelConfig()
grid = main.eclConfig().getGrid()
scaling = ScalingTest(grid)
scaling_vector = np.ndarray(
[field_config.get_data_size()], dtype=np.float32
)
for i in range(field_config.get_data_size()):
scaling_vector[i] = scaling(i)
row_scaling.assign_vector(scaling_vector)
# Second update with row scaling
update_fs2 = main.getEnkfFsManager().getFileSystem("target2")
es_update = ESUpdate(main)
run_context = ErtRunContext.ensemble_smoother_update(init_fs, update_fs2)
rng.setState(random_seed)
es_update.smootherUpdate(run_context)
# Fetch the three values initial, update without row scaling and
# update with row scaling and verify that the row scaling has been
# correctly applied.
init_node = EnkfNode(poro_config)
update_node1 = EnkfNode(poro_config)
update_node2 = EnkfNode(poro_config)
for iens in range(main.getEnsembleSize()):
node_id = NodeId(0, iens)
init_node.load(init_fs, node_id)
update_node1.load(update_fs1, node_id)
update_node2.load(update_fs2, node_id)
assert_field_update(
grid,
init_node.asField(),
update_node1.asField(),
update_node2.asField(),
)
