Populate gen_specs from generator

libensemble/generators.py

@@ -167,7 +167,18 @@ def _prep_fields(self, results: npt.NDArray) -> npt.NDArray:
             (name, results.dtype[name]) for name in results.dtype.names if name in self.gen_specs["persis_in"]
         ]
 
-        new_dtype = filtered_dtype + [("sim_ended", bool)]
+        # SH TODO - only add if not there and also is this not APOSMM specific?
+        # SH TODO - can remove as its added in aposmm persis_in (mirrored in gen_specs due to the _sync_gen_specs function)
+        # SH TODO - however as set to True below should only be APOSMM subclass.
+        # SH TODO - works in libE - but will need to check in Optimas - till then add if not there. But may not be needed.
+        # SH TODO - also test here if need the new dtype when using the merged gen_specs. *wrs now rply||
+
+        print(f'filtered_dtype: {filtered_dtype}')
+        if "sim_ended" not in [name for name, _ in filtered_dtype]:
+            new_dtype = filtered_dtype + [("sim_ended", bool)]
+        else:
+            new_dtype = filtered_dtype
+
         new_results = np.zeros(len(results), dtype=new_dtype)
 
         for field in new_results.dtype.names:
@@ -176,7 +187,7 @@ def _prep_fields(self, results: npt.NDArray) -> npt.NDArray:
             except ValueError:
                 continue
 
-        new_results["sim_ended"] = True
+        new_results["sim_ended"] = True  #SH TODO - APOSMM specific and only needed if was added here.
         return new_results
 
     def ingest(self, results: List[dict], tag: int = EVAL_GEN_TAG) -> None:
@@ -201,6 +212,8 @@ def ingest_numpy(self, results: npt.NDArray, tag: int = EVAL_GEN_TAG) -> None:
                 tag, np.copy(results)
             )  # SH for threads check - might need deepcopy due to dtype=object
         else:
+            print(f'\n=======self.running_gen_f: {self.running_gen_f} type ({type(self.running_gen_f)})\n')
+            import pdb; pdb.set_trace()
             self.running_gen_f.send(tag, None)
 
     def finalize(self) -> None:

libensemble/libE.py

@@ -137,7 +137,7 @@
 from libensemble.tools.alloc_support import AllocSupport
 from libensemble.tools.tools import _USER_SIM_ID_WARNING
 from libensemble.utils import launcher
-from libensemble.utils.misc import specs_dump
+from libensemble.utils.misc import specs_dump, sync_gen_specs
 from libensemble.utils.pydantic_bindings import libE_wrapper
 from libensemble.utils.timer import Timer
 from libensemble.version import __version__
@@ -241,6 +241,7 @@ def libE(
         for spec in [ensemble.sim_specs, ensemble.gen_specs, ensemble.alloc_specs, ensemble.libE_specs]
     ]
     exit_criteria = specs_dump(ensemble.exit_criteria, by_alias=True, exclude_none=True)
+    sync_gen_specs(gen_specs)
 
     # Extract platform info from settings or environment
     platform_info = get_platform(libE_specs)

libensemble/tests/regression_tests/test_asktell_aposmm_nlopt.py

@@ -69,21 +69,21 @@
         max_active_runs=workflow.nworkers,  # should this match nworkers always? practically?
     )
 
-    # SH TODO - dont want this stuff duplicated
+    # SH initial_batch_size conflicts with initial_sample_size
+    # with server type gen, deciding outside seems correct way.
     workflow.gen_specs = GenSpecs(
-        persis_in=["x", "x_on_cube", "sim_id", "local_min", "local_pt", "f"],
         generator=aposmm,
-        batch_size=5,
-        initial_batch_size=10,
-        user={"initial_sample_size": 100},
+        batch_size=5,  # SH what happens if not set - test this
+        initial_batch_size=10,  # SH what happens if not set - does it get from gen? - test this
+        # persis_in=["x", "x_on_cube", "sim_id", "local_min", "local_pt", "f"],
+        # user={"initial_sample_size": 100},
     )
 
     workflow.libE_specs.gen_on_manager = True
     workflow.add_random_streams()
 
-    H, _, _ = workflow.run()
-
     # Perform the run
+    H, _, _ = workflow.run()
 
     if workflow.is_manager:
         print("[Manager]:", H[np.where(H["local_min"])]["x"])

libensemble/utils/misc.py

@@ -10,6 +10,8 @@
 import numpy.typing as npt
 
 
+# SH TODO - some of these need docstrings.
+
 def extract_H_ranges(Work: dict) -> str:
     """Convert received H_rows into ranges for labeling"""
     work_H_rows = Work["libE_info"]["H_rows"]
@@ -272,3 +274,43 @@ def np_to_list_dicts(array: npt.NDArray, mapping: dict = {}, allow_arrays: bool
             entry["_id"] = entry.pop("sim_id")
 
     return out
+
+
+def _merge_fields(a, b):
+    """Merge dict or list fields from b into a."""
+    if isinstance(a, dict):
+        for k, v in b.items():
+            if k not in a:
+                a[k] = v
+    elif isinstance(a, list):
+        a.extend(x for x in b if x not in a)
+
+
+def _find_generator(gen_specs):
+    """Find the generator object in the gen_specs"""
+    from libensemble.generators import LibensembleGenerator
+    #SH TODO - import in here to avoid circular import. but i will move these to diff file in utils or 
+    #SH TODO - do within the gens as a generator function.
+    #SH TMP TEST - dont think optimas sould need but lets see.
+    generator = gen_specs.get("generator") or gen_specs.get("user").get('generator')
+    if generator and not isinstance(generator, LibensembleGenerator):
+        if hasattr(generator, 'gen') and isinstance(generator.gen, LibensembleGenerator):
+            generator = generator.gen
+
+    return generator
+
+
+def sync_gen_specs(gen_specs):
+    """Automatically populate gen_specs with values from generator if available."""
+    generator = _find_generator(gen_specs)
+    if not generator or not hasattr(generator, "gen_specs"):
+        return
+
+    for field_name, field_value in generator.gen_specs.items():
+        if isinstance(field_value, (dict, list)) and field_value:
+            if field_name not in gen_specs:
+                gen_specs[field_name] = field_value.copy()
+            else:
+                _merge_fields(gen_specs[field_name], field_value)
+        elif field_name not in gen_specs:
+            gen_specs[field_name] = field_value