Append paramater_config from design matrix to existing parameters bef…

…ore running experiment
equinor · Oct 22, 2024 · 349abd0 · 349abd0
1 parent 1ad1fa0
commit 349abd0
Show file tree

Hide file tree

Showing 2 changed files with 14 additions and 24 deletions.
diff --git a/src/ert/enkf_main.py b/src/ert/enkf_main.py
@@ -174,7 +174,8 @@ def sample_prior(
     in the case of GEN_KW we sample the data and store it, and if INIT_FILES
     are used without FORWARD_INIT we load files and store them. If FORWARD_INIT
     is set the state is set to INITIALIZED, but no parameters are saved to storage
-    until after the forward model has completed.
+    until after the forward model has completed. If parameters come from the DESIGN_MATRIX_GROUP
+    they are handled separately via save_design_matrix_to_ensemble.
     """
     random_seed = _seed_sequence(random_seed)
     t = time.perf_counter()
@@ -183,7 +184,7 @@ def sample_prior(
         parameters = list(parameter_configs.keys())
     for parameter in parameters:
         config_node = parameter_configs[parameter]
-        if config_node.forward_init:
+        if config_node.forward_init or config_node.name == DESIGN_MATRIX_GROUP:
             continue
         for realization_nr in active_realizations:
             ds = config_node.sample_or_load(

diff --git a/src/ert/run_models/ensemble_experiment.py b/src/ert/run_models/ensemble_experiment.py
@@ -62,8 +62,9 @@ def run_experiment(
         restart: bool = False,
     ) -> None:
         self.log_at_startup()
-        # If design matrix is present, we substitute the experiment parameters
-        # with those in the design matrix
+        # If design matrix is present, we append design matrix parameters
+        # to the experiment parameters and set new active realizations
+        parameters_config = self.ert_config.ensemble_config.parameter_configuration
         if self.ert_config.analysis_config.design_matrix is not None:
             if (
                 self.ert_config.analysis_config.design_matrix.parameter_configuration
@@ -74,18 +75,19 @@ def run_experiment(
                 self.ert_config.analysis_config.design_matrix.parameter_configuration
                 is not None
             )
-            parameters_config = [
+            parameters_config.append(
                 self.ert_config.analysis_config.design_matrix.parameter_configuration[
                     DESIGN_MATRIX_GROUP
                 ]
-            ]
+            )
             assert (
                 self.ert_config.analysis_config.design_matrix.active_realizations
                 is not None
             )
             self.active_realizations = (
                 self.ert_config.analysis_config.design_matrix.active_realizations
             )
+        if not restart:
             self.experiment = self._storage.create_experiment(
                 name=self.experiment_name,
                 parameters=parameters_config,
@@ -97,18 +99,6 @@ def run_experiment(
                 name=self.ensemble_name,
                 ensemble_size=self.ensemble_size,
             )
-        elif not restart:
-            self.experiment = self._storage.create_experiment(
-                name=self.experiment_name,
-                parameters=self.ert_config.ensemble_config.parameter_configuration,
-                observations=self.ert_config.observations,
-                responses=self.ert_config.ensemble_config.response_configuration,
-            )
-            self.ensemble = self._storage.create_ensemble(
-                self.experiment,
-                name=self.ensemble_name,
-                ensemble_size=self.ensemble_size,
-            )
         else:
             self.active_realizations = self._create_mask_from_failed_realizations()
 
@@ -133,12 +123,11 @@ def run_experiment(
                 self.ensemble,
                 np.where(self.active_realizations)[0],
             )
-        else:
-            sample_prior(
-                self.ensemble,
-                np.where(self.active_realizations)[0],
-                random_seed=self.random_seed,
-            )
+        sample_prior(
+            self.ensemble,
+            np.where(self.active_realizations)[0],
+            random_seed=self.random_seed,
+        )
 
         self._evaluate_and_postprocess(
             run_args,