Env: added support for registering new populations

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "miv-simulator"
-version = "0.2.0"
+version = "0.2.1"
 description = "Mind-In-Vitro simulator"
 authors = []
 dependencies = [

src/miv_simulator/env.py

@@ -889,6 +889,26 @@ def load_celltypes(self) -> None:
                                 weights_dict["closure"] = clos
                         synapses_dict["weights"] = weights_dicts
 
+    def register_population(self, population_name, population_cell_distribution):
+        if population_name in self.Populations:
+            return None
+
+        max_pop_enum = 0
+        pop_offset = 0
+        for this_pop_name, this_pop_enum in self.Populations.items():
+            max_pop_enum = max(this_pop_enum, max_pop_enum)
+            pop_offset += self.celltypes[this_pop_name]["num"]
+
+        pop_id = max_pop_enum + 1
+        self.Populations[population_name] = pop_id
+        cell_distribution = {}
+        if "Cell Distribution" in self.geometry:
+            cell_distribution = self.geometry["Cell Distribution"]
+        else:
+            self.geometry["Cell Distribution"] = population_cell_distribution
+        cell_distribution[population_name] = population_cell_distribution
+        return {"population_id": pop_id, "population_start_gid": pop_offset}
+
     def clear(self):
         self.gidset = set()
         self.gjlist = []

src/miv_simulator/input_spike_trains.py

@@ -93,6 +93,9 @@ def generate_input_spike_trains(
         logger.info(f"{comm.size} ranks have been allocated")
 
     population_name = population.name
+    start_gid = 0
+    if hasattr(population, "start_gid"):
+        start_gid = population.start_gid
 
     soma_positions_dict = None
     if coords_path is not None:
@@ -186,6 +189,8 @@ def generate_input_spike_trains(
     feature_items = list(population.features.items())
     n_iter = comm.allreduce(len(feature_items), op=MPI.MAX)
 
+    logger.info(f"n_iter = {n_iter} feature_items = {feature_items}")
+
     if not dry_run and rank == 0:
         if output_path is None:
             raise RuntimeError("generate_input_spike_trains: missing output_path")
@@ -198,6 +203,7 @@ def generate_input_spike_trains(
     for iter_count in range(n_iter):
         if iter_count < len(feature_items):
             gid, input_feature = feature_items[iter_count]
+            gid += start_gid
         else:
             gid, input_feature = None, None
         if gid is not None:
@@ -218,13 +224,11 @@ def generate_input_spike_trains(
 
             # Get spike response
             response = input_feature.get_response(processed_signal)
+            if isinstance(response, list):
+                response = np.concatenate(np.concatenate(response, dtype=np.float32))
 
             if len(response) > 0:
-                spikes_attr_dict[gid] = {
-                    output_spike_train_attr_name: np.concatenate(
-                        response, dtype=np.float32
-                    )
-                }
+                spikes_attr_dict[gid] = {output_spike_train_attr_name: response}
 
             gid_count += 1
         if (iter_count > 0 and iter_count % write_every == 0) or (