removing hardcoded gen_specs.out, removing hardcoded persis_info.nwor…

…kers, use gen_specs.get("out") so if it isnt provided, the dtype discovery process commences

libensemble/gen_classes/aposmm.py

@@ -30,8 +30,7 @@ def __init__(
             ]
             gen_specs["persis_in"] = ["x", "f", "local_pt", "sim_id", "sim_ended", "x_on_cube", "local_min"]
         if not persis_info:
-            persis_info = add_unique_random_streams({}, 4, seed=4321)[1]
-            persis_info["nworkers"] = 4
+            persis_info = add_unique_random_streams({}, 2, seed=4321)[1]
         super().__init__(History, persis_info, gen_specs, libE_info, **kwargs)
         self.all_local_minima = []
         self.results_idx = 0

libensemble/gen_classes/sampling.py

@@ -34,7 +34,6 @@ class UniformSample(SampleBase):
     def __init__(self, _=[], persis_info={}, gen_specs={}, libE_info=None, **kwargs):
         super().__init__(_, persis_info, gen_specs, libE_info, **kwargs)
         self._get_user_params(self.gen_specs["user"])
-        self.gen_specs["out"] = [("x", float, (self.n,))]
 
     def ask_numpy(self, n_trials):
         H_o = np.zeros(n_trials, dtype=self.gen_specs["out"])
@@ -60,7 +59,6 @@ def __init__(self, _, persis_info, gen_specs, libE_info=None, **kwargs):
         self.gen_specs = gen_specs
         self.persis_info = persis_info
         self._get_user_params(self.gen_specs["user"])
-        self.gen_specs["out"] = [("x", float, (self.n,))]
 
     def ask(self, n_trials):
         H_o = []

libensemble/generators.py

@@ -99,7 +99,6 @@ def __init__(
             self.gen_specs["user"] = kwargs
         if not persis_info:
             self.persis_info = add_unique_random_streams({}, 4, seed=4321)[1]
-            self.persis_info["nworkers"] = 4
         else:
             self.persis_info = persis_info
 

libensemble/utils/runners.py

@@ -108,7 +108,7 @@ def __init__(self, specs):
 
     def _get_points_updates(self, batch_size: int) -> (npt.NDArray, npt.NDArray):
         # no ask_updates on external gens
-        return (list_dicts_to_np(self.gen.ask(batch_size), dtype=self.specs["out"]), None)
+        return (list_dicts_to_np(self.gen.ask(batch_size), dtype=self.specs.get("out")), None)
 
     def _convert_tell(self, x: npt.NDArray) -> list:
         self.gen.tell(np_to_list_dicts(x))
@@ -142,7 +142,7 @@ def _persistent_result(self, calc_in, persis_info, libE_info):
             if self.gen.thread is None:
                 self.gen.setup()  # maybe we're reusing a live gen from a previous run
         # libE gens will hit the following line, but list_dicts_to_np will passthrough if the output is a numpy array
-        H_out = list_dicts_to_np(self._get_initial_ask(libE_info), dtype=self.specs["out"])
+        H_out = list_dicts_to_np(self._get_initial_ask(libE_info), dtype=self.specs.get("out"))
         tag, Work, H_in = self.ps.send_recv(H_out)  # evaluate the initial sample
         final_H_in = self._start_generator_loop(tag, Work, H_in)
         return self.gen.final_tell(final_H_in), FINISHED_PERSISTENT_GEN_TAG