fixups

src/ert/run_models/everest_run_model.py

@@ -3,6 +3,7 @@
 import copy
 import datetime
 import functools
+import json
 import logging
 import os
 import queue
@@ -300,10 +301,10 @@ def __init__(
         optimization_callback: OptimizerCallback,
         display_all_jobs: bool = True,
     ):
-        self._add_defaults(everest_config)
+        everest_config = self._add_defaults(everest_config)
 
-        Path(everest_config.log_dir).mkdir(parents=True)
-        Path(everest_config.optimization_output_dir).mkdir(parents=True)
+        Path(everest_config.log_dir).mkdir(parents=True, exist_ok=True)
+        Path(everest_config.optimization_output_dir).mkdir(parents=True, exist_ok=True)
 
         self.ropt_config = everest2ropt(everest_config)
         self.everest_config = everest_config
@@ -341,7 +342,7 @@ def __init__(
         self.num_retries_per_iter = 0  # OK?
 
     @staticmethod
-    def _add_defaults(config: EverestConfig):
+    def _add_defaults(config: EverestConfig) -> EverestConfig:
         """This function exists as a temporary mechanism to default configurations that
         needs to be global in the sense that they should carry over both to ropt and ERT.
         When the proper mechanism for this is implemented this code
@@ -372,24 +373,23 @@ def create(
         simulation_callback: Optional[SimulationCallback] = None,
         optimization_callback: Optional[OptimizerCallback] = None,
         random_seed: Optional[int] = None,
-    ):
-        if simulation_callback is None:
-
-            def simulation_callback():
-                return None
+    ) -> EverestRunModel:
+        def default_simulation_callback(
+            simulation_status: SimulationStatus | None, event: str
+        ) -> str | None:
+            return None
 
-        if optimization_callback is None:
-
-            def optimization_callback():
-                return None
+        def default_optimization_callback() -> str | None:
+            return None
 
         ert_config = everest_to_ert_config(ever_config)
         return cls(
             random_seed=random_seed,
             config=ert_config,
             everest_config=ever_config,
-            simulation_callback=simulation_callback,
-            optimization_callback=optimization_callback,
+            simulation_callback=simulation_callback or default_simulation_callback,
+            optimization_callback=optimization_callback
+            or default_optimization_callback,
         )
 
     def run_experiment(
@@ -561,10 +561,9 @@ def description(cls) -> str:
         return "Run batches "
 
     @property
-    def result(self):
+    def result(self) -> Optional[seba_sqlite.sqlite_storage.OptimalResult]:
         return self._result
 
-    def __repr__(self):
-        return "EverestRunModel(config={})" % json.dumps(
-            self.everest_config, sort_keys=True, indent=2
-        )
+    def __repr__(self) -> str:
+        config_json = json.dumps(self.everest_config, sort_keys=True, indent=2)
+        return f"EverestRunModel(config={config_json})"

tests/everest/test_everserver.py

@@ -109,7 +109,7 @@ def test_everserver_status_failure(_1, copy_math_func_test_data_to_tmp):
 )
 @patch("everest.detached.jobs.everserver._everserver_thread")
 @patch(
-    "everest.detached.jobs.everserver.start_optimization",
+    "ert.run_models.everest_run_model.run_experiment",
     side_effect=partial(check_status, status=ServerStatus.running),
 )
 @patch("everest.detached.jobs.everserver.validate_export", return_value=([], False))

tests/everest/test_objective_type.py

@@ -21,4 +21,4 @@ def test_mathfunc_stddev(
     assert x1 == pytest.approx(0.5, abs=0.025)
     assert x2 == pytest.approx(0.5, abs=0.025)
 
-    assert workflow.result.total_objective < 0.0
+    assert run_model.result.total_objective < 0.0

tests/everest/test_templating.py

@@ -6,6 +6,7 @@
 from ruamel.yaml import YAML
 
 import everest
+from ert.run_models.everest_run_model import EverestRunModel
 from everest.config import EverestConfig
 
 TMPL_CONFIG_FILE = "config.yml"
@@ -125,10 +126,13 @@ def test_render_executable(copy_template_test_data_to_tmp):
 
 
 @pytest.mark.integration_test
-def test_install_template(copy_template_test_data_to_tmp):
+def test_install_template(
+    copy_template_test_data_to_tmp, evaluator_server_config_generator
+):
     config = EverestConfig.load_file(TMPL_CONFIG_FILE)
-    workflow = everest.suite._EverestWorkflow(config)
-    workflow.start_optimization()
+    run_model = EverestRunModel.create(config)
+    evaluator_server_config = evaluator_server_config_generator(run_model)
+    run_model.run_experiment(evaluator_server_config)
 
 
 def test_well_order_template(change_to_tmpdir):
@@ -164,7 +168,9 @@ def test_well_order_template(change_to_tmpdir):
 
 
 @pytest.mark.integration_test
-def test_user_specified_data_n_template(copy_math_func_test_data_to_tmp):
+def test_user_specified_data_n_template(
+    copy_math_func_test_data_to_tmp, evaluator_server_config_generator
+):
     """
     Ensure that a user specifying a data resource and an installed_template
     with "extra_data", the results of that template will be passed to the
@@ -212,10 +218,9 @@ def test_user_specified_data_n_template(copy_math_func_test_data_to_tmp):
 
     config = EverestConfig.with_defaults(**updated_config_dict)
 
-    workflow = everest.suite._EverestWorkflow(config)
-    assert workflow is not None
-
-    workflow.start_optimization()
+    run_model = EverestRunModel.create(config)
+    evaluator_server_config = evaluator_server_config_generator(run_model)
+    run_model.run_experiment(evaluator_server_config)
 
     # The data should have been loaded and passed through template to file.
     expected_file = os.path.join(

tests/everest/test_workflows.py

@@ -38,7 +38,9 @@ def test_state_modifier_workflow_run(
 ) -> None:
     cwd = copy_testdata_tmpdir("open_shut_state_modifier")
 
-    run_model = EverestRunModel.create(config)
+    run_model = EverestRunModel.create(
+        EverestConfig.load_file(f"everest/model/{config}.yml")
+    )
     evaluator_server_config = evaluator_server_config_generator(run_model)
     run_model.run_experiment(evaluator_server_config)