Dataframe used for hdi over correlation length

scripts/spatial_network/figures_spatial_head_direction_network_orientation_map.py

@@ -208,51 +208,40 @@ def plot_single_cell_analysis(traj, plot_run_names):
 
 def plot_hdi_over_corr_len(traj, plot_run_names):
 
-    fig, ax = plt.subplots(1, 1)
 
     corr_len_expl = traj.f_get('correlation_length').f_get_range()
     seed_expl = traj.f_get('seed').f_get_range()
     label_expl = [traj.derived_parameters.runs[run_name].morphology.morph_label for run_name in traj.f_get_run_names()]
-    # seed_range = sorted(set(seed_expl))
-    # corr_len_range = sorted(set(corr_len_expl))
-    # label_range = set(label_expl)
-    # print(seed_range)
-    # print(corr_len_range)
-    # print(label_range)
-
-    # index = pd.MultiIndex.from_tuples(zip(corr_len_expl, seed_expl, label_expl), names=['corr_len', 'seed', 'label'])
-    hdi_frame = pd.DataFrame(index=[corr_len_expl, seed_expl, label_expl])
+    label_range = set(label_expl)
 
-    # label_range = [p.f_get() for p in traj.f_get_derived_parameters().values()]
+    hdi_frame = pd.Series(index=[corr_len_expl, seed_expl, label_expl])
+    hdi_frame.index.names = ["corr_len", "seed", "label"]
 
     for run_name, corr_len, seed, label in zip(plot_run_names, corr_len_expl, seed_expl, label_expl):
-        print(run_name, corr_len, seed, label)
         ex_tunings = traj.results.runs[run_name].ex_tunings
         head_direction_indices = traj.results[run_name].head_direction_indices
-        print(head_direction_indices.shape)
         hdi_frame[corr_len, seed, label] = np.mean(head_direction_indices)
 
+    # TODO: Standart deviation also for the population
+    hdi_exc_n_and_seed_mean = hdi_frame.groupby(level=[0, 2]).mean()
+    hdi_exc_n_and_seed_std_dev = hdi_frame.groupby(level=[0, 2]).std()
+
+
+    fig, ax = plt.subplots(1, 1)
+
+    for label in label_range:
+        hdi_mean = hdi_exc_n_and_seed_mean[:, label]
+        hdi_std = hdi_exc_n_and_seed_std_dev[:, label]
+        corr_len_range = hdi_mean.keys().to_numpy()
+
+
+        ax.plot(corr_len_range, hdi_mean, label=label, marker='o')
+        plt.fill_between(corr_len_range, hdi_mean - hdi_std,
+                         hdi_mean + hdi_std, alpha=0.4)
+    ax.set_xlabel('Correlation length')
+    ax.set_ylabel('Head Direction Index')
+    ax.legend()
 
-        # label = traj.derived_parameters.runs[run_name].morphology.morph_label
-        #
-        # ex_tunings = traj.results.runs[run_name].ex_tunings
-        #
-        #
-        # ex_tunings_plt = np.array(ex_tunings)
-        # sort_ids = ex_tunings_plt.argsort()
-        # ex_tunings_plt = ex_tunings_plt[sort_ids]
-        #
-        # head_direction_indices = traj.results[run_name].head_direction_indices
-        #
-        # hdi_plt = head_direction_indices
-        # hdi_plt = hdi_plt[sort_ids]
-        # ax.scatter(ex_tunings_plt, hdi_plt, label=label, alpha=0.3)
-
-    # ax.legend()
-    # ax.set_xlabel("Angles (rad)")
-    # ax.set_ylabel("head direction index")
-    # ax.set_title('hdi over input tuning', fontsize=16)
-    print(hdi_frame)
 
 
 def filter_run_indices_by_par_dict(traj, par_dict):

scripts/spatial_network/run_entropy_maximisation_orientation_map.py

@@ -171,7 +171,7 @@ if __name__ == "__main__":
     traj.f_add_parameter_group("simulation")
     traj.f_add_parameter("simulation.entropy_maximisation.steps", 30, comment="Steps for entropy maximisation")
     traj.f_add_parameter("simulation.dt", 0.1, comment="Network simulation time step in ms")
-    traj.f_add_parameter("simulation.duration", 10, comment="Network simulation duration in ms")
+    traj.f_add_parameter("simulation.duration", 600, comment="Network simulation duration in ms")
 
     ellipsoid_parameter_exploration = {
         "morphology.long_axis": [100.0],