Merge pull request #7 from alessandratrapani/full_cereb_100_trials

30 trials and full cerebellum

check_models.py

@@ -0,0 +1,59 @@
+import json
+
+with open("./demo_cerebellum.json", "r") as json_file:
+    demo_cerebellum_config = json.load(json_file)
+with open("./data/mouse_cerebellum_config_healthy.json", "r") as json_file:
+    mouse_cerebellum_config_healthy = json.load(json_file)
+#%%
+check_neurons_models = False
+demo_neuron_models = demo_cerebellum_config["cell_types"]
+mouse_healthy_neuron_models = mouse_cerebellum_config_healthy["simulations"][
+    "DCN_update"
+]["cell_models"]
+neuron_to_check = [
+    "basket_cell",
+    "dcn_cell_GABA",
+    "dcn_cell_Gly-I",
+    "dcn_cell_glut_large",
+    "golgi_cell",
+    "granule_cell",
+    "purkinje_cell",
+    "stellate_cell",
+    "io_cell",
+]
+if check_neurons_models:
+
+    for cell in neuron_to_check:
+        print(cell)
+
+        for property in mouse_healthy_neuron_models[cell]["parameters"].keys():
+            assert (
+                demo_neuron_models[cell]["parameters"][property]
+                == mouse_healthy_neuron_models[cell]["parameters"][property]
+            ), f"{cell}, {property}"
+        if cell == "io_cell":
+            for property in mouse_healthy_neuron_models[cell]["iaf_cond_alpha"].keys():
+                if property != "receptors":
+                    demo = demo_neuron_models[cell]["parameters"][property]
+                    ali = mouse_healthy_neuron_models[cell]["iaf_cond_alpha"][property]
+                    assert demo == ali, f"{cell}, {property}: demo={demo} ali={ali}"
+        else:
+            for property in mouse_healthy_neuron_models[cell][
+                "eglif_cond_alpha_multisyn"
+            ].keys():
+                if property != "receptors":
+                    demo = demo_neuron_models[cell]["parameters"][property]
+                    ali = mouse_healthy_neuron_models[cell][
+                        "eglif_cond_alpha_multisyn"
+                    ][property]
+                    assert demo == ali, f"{cell}, {property}: demo={demo} ali={ali}"
+#%%
+check_connections_model=True
+if check_connections_model:
+    demo_conn_models = demo_cerebellum_config["connection_models"]
+    mouse_healthy_conn_models = mouse_cerebellum_config_healthy["simulations"]["DCN_update"]["connection_models"]
+    conn_models_to_check = demo_conn_models.keys()
+    for conn in conn_models_to_check:
+        demo = [demo_conn_models[conn]["weight"], demo_conn_models[conn]["delay"]]
+        ali = [mouse_healthy_conn_models[conn]["connection"]["weight"], mouse_healthy_conn_models[conn]["connection"]["delay"]]
+        assert demo==ali, f"{conn}, weight: demo={demo[0]} ali={ali[0]}, delay: demo={demo[1]} ali={ali[1]}"

check_populations_ids.py

@@ -0,0 +1,53 @@
+#%%
+import h5py
+import dill
+
+
+def save_dict_to_hdf5(file_path, data_dict):
+    with h5py.File(file_path, 'w') as hdf_file:
+        recursively_save_dict_contents(hdf_file, '/', data_dict)
+
+def recursively_save_dict_contents(hdf_file, path, data_dict):
+    for key, item in data_dict.items():
+        if key!="param_vt" and key!="vt_num":
+            if isinstance(item, (dict)):
+                recursively_save_dict_contents(hdf_file, path + key + '/', item)
+            else:
+                hdf_file[path + key] = item
+
+
+data_path = "./data/"
+hdf5_file = "cerebellum_300x_200z.hdf5"
+scaffold = h5py.File(data_path + hdf5_file, "r")
+neuronal_populations_from_hdf5 = scaffold["cells"]["placement"]
+connectivity_from_hdf5 = scaffold["cells"]["connections"]
+
+#%%
+create_new = False
+if create_new:
+    network_geom_file = data_path + "geom_" + hdf5_file
+    network_connectivity_file = data_path + "conn_" + hdf5_file
+    neuronal_populations = dill.load(open(network_geom_file, "rb"))
+    connectivity = dill.load(open(network_connectivity_file, "rb"))
+
+    save_dict_to_hdf5(data_path + "new_geom_" + hdf5_file, neuronal_populations)
+    save_dict_to_hdf5(data_path + "new_conn_" + hdf5_file, connectivity)
+#%%
+check_connections=False
+if check_connections:
+    neuronal_populations = h5py.File(data_path + "new_geom_" + hdf5_file, "r")
+    connectivity = h5py.File(data_path + "new_conn_" + hdf5_file, "r")
+
+    for conn in connectivity:
+        if not conn=="io_to_vt":
+            print(conn)
+            id_pre = connectivity[conn]["id_pre"]
+            id_post = connectivity[conn]["id_post"]
+            differences = []
+            for i in range(len(connectivity_from_hdf5[conn])):
+                if id_pre[i] != int(connectivity_from_hdf5[conn][i][0])+1:
+                    differences.append([0,i])
+                if id_post[i] != int(connectivity_from_hdf5[conn][i][1])+1:
+                    differences.append([1,i])
+            print(differences)
+#%%