Gather, join & save dataframes

src/ert/run_models/everest_run_model.py

@@ -36,6 +36,7 @@
 from ert.ensemble_evaluator import EvaluatorServerConfig
 from ert.storage import open_storage
 from everest.config import EverestConfig
+from everest.everest_storage import EverestStorage
 from everest.optimizer.everest2ropt import everest2ropt
 from everest.simulator import Simulator
 from everest.simulator.everest_to_ert import everest_to_ert_config
@@ -401,6 +402,14 @@ def run_experiment(
         # Initialize the ropt optimizer:
         optimizer = self._create_optimizer(simulator)
 
+        self.ever_storage = EverestStorage(
+            simulator,
+            optimizer,
+            Path(self.everest_config.optimization_output_dir)
+            / "dakota"
+            / "OPT_DEFAULT.out",
+        )
+
         # The SqliteStorage object is used to store optimization results from
         # Seba in an sqlite database. It reacts directly to events emitted by
         # Seba and is not called by Everest directly. The stored results are

src/ert/storage/local_ensemble.py

@@ -17,6 +17,7 @@
 
 from ert.config.gen_kw_config import GenKwConfig
 from ert.storage.mode import BaseMode, Mode, require_write
+from everest.everest_storage import JoinedBatchDataFrames
 
 from .realization_storage_state import RealizationStorageState
 
@@ -502,6 +503,10 @@ def _find_state(realization: int) -> RealizationStorageState:
 
         return [_find_state(i) for i in range(self.ensemble_size)]
 
+    def save_optimization_results(self, data: JoinedBatchDataFrames):
+        os.mkdir(self.mount_point / "optimization_results")
+        data.write_to_folder(self.mount_point / "optimization_results")
+
     def get_summary_keyset(self) -> List[str]:
         """
         Find the first folder with summary data then load the