New Perlin run with twice the map sizes.

scripts/spatial_maps/orientation_maps/orientation_map_generator_pypet.py

@@ -49,7 +49,7 @@ def create_seeded_maps_in_scale_range():
     traj.f_add_parameter('corr_len', 200.0, comment='Correlation length')
     traj.f_add_parameter('seed', 1, comment='Seed for random')
 
-    correlation_length_range = np.linspace(1.0, 800.0, 12, endpoint=True).tolist()
+    correlation_length_range = np.linspace(1.0, 650.0, 24, endpoint=True).tolist()
     # correlation_length_range = [200.0, 400.0, 800.0]
     seed_range = range(10)
     # seed_range = [1]

scripts/spatial_network/orientation_map/paper_figures_orientation_map.py

@@ -1120,7 +1120,7 @@ def filter_run_names_by_par_dict(traj, par_dict):
     return run_name_list
 
 
-def plot_exc_and_inh_hdi_over_simplex_grid_scale(traj, plot_run_names):
+def plot_exc_and_inh_hdi_over_simplex_grid_scale(traj, plot_run_names, cut_off_dist):
     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()]
@@ -1133,13 +1133,28 @@ def plot_exc_and_inh_hdi_over_simplex_grid_scale(traj, plot_run_names):
 
     for run_name, corr_len, seed, label in zip(plot_run_names, corr_len_expl, seed_expl, label_expl):
         ex_tunings = traj.results.runs[run_name].ex_tunings
-        head_direction_indices = traj.results[run_name].head_direction_indices
-        # TODO: Actual correlation lengths
-        # actual_corr_len = get_correlation_length(ex_tunings.reshape((30,30)), 450, 30)
-        exc_hdi_frame[corr_len, seed, label] = np.mean(head_direction_indices)
+        inh_hdis = []
+        exc_hdis = []
+
+        inh_head_direction_indices = traj.results.runs[run_name].inh_head_direction_indices
+        inh_axonal_cloud = traj.results.runs[run_name].inhibitory_axonal_cloud_array
+        inh_cut_off_ids = (inh_axonal_cloud[:, 0] >= cut_off_dist) & \
+                          (inh_axonal_cloud[:, 0] <= traj.parameters.map.sheet_size - cut_off_dist) & \
+                          (inh_axonal_cloud[:, 1] >= cut_off_dist) & \
+                          (inh_axonal_cloud[:, 1] <= traj.parameters.map.sheet_size - cut_off_dist)
+        inh_hdis.append(sorted(inh_head_direction_indices[inh_cut_off_ids]))
+
+        exc_head_direction_indices = traj.results.runs[run_name].head_direction_indices
+        ex_positions = traj.results.runs[run_name].ex_positions
+        exc_cut_off_ids = (ex_positions[:, 0] >= cut_off_dist) & \
+                          (ex_positions[:, 0] <= traj.parameters.map.sheet_size - cut_off_dist) & \
+                          (ex_positions[:, 1] >= cut_off_dist) & \
+                          (ex_positions[:, 1] <= traj.parameters.map.sheet_size - cut_off_dist)
+        exc_hdis.append(sorted(exc_head_direction_indices[exc_cut_off_ids]))
 
-        inh_head_direction_indices = traj.results[run_name].inh_head_direction_indices
-        inh_hdi_frame[corr_len, seed, label] = np.mean(inh_head_direction_indices)
+        exc_hdi_frame[corr_len, seed, label] = np.mean(exc_hdis)
+
+        inh_hdi_frame[corr_len, seed, label] = np.mean(inh_hdis)
 
     # TODO: Standard deviation also for the population
     exc_hdi_n_and_seed_mean = exc_hdi_frame.groupby(level=[0, 2]).mean()
@@ -1225,7 +1240,7 @@ def plot_exc_and_inh_hdi_over_simplex_grid_scale(traj, plot_run_names):
         plt.savefig(FIGURE_SAVE_PATH + 'F_hdi_over_grid_scale.png')
         plt.close(fig)
 
-def plot_exc_and_inh_hdi_over_fit_corr_len(traj, plot_run_names):
+def plot_exc_and_inh_hdi_over_fit_corr_len(traj, plot_run_names, cut_off_dist):
     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()]
@@ -1238,13 +1253,29 @@ def plot_exc_and_inh_hdi_over_fit_corr_len(traj, plot_run_names):
 
     for run_name, corr_len, seed, label in zip(plot_run_names, corr_len_expl, seed_expl, label_expl):
         ex_tunings = traj.results.runs[run_name].ex_tunings
-        head_direction_indices = traj.results[run_name].head_direction_indices
-        # TODO: Actual correlation lengths
-        # actual_corr_len = get_correlation_length(ex_tunings.reshape((30,30)), 450, 30)
-        exc_hdi_frame[corr_len, seed, label] = np.mean(head_direction_indices)
 
-        inh_head_direction_indices = traj.results[run_name].inh_head_direction_indices
-        inh_hdi_frame[corr_len, seed, label] = np.mean(inh_head_direction_indices)
+        inh_hdis = []
+        exc_hdis = []
+
+        inh_head_direction_indices = traj.results.runs[run_name].inh_head_direction_indices
+        inh_axonal_cloud = traj.results.runs[run_name].inhibitory_axonal_cloud_array
+        inh_cut_off_ids = (inh_axonal_cloud[:, 0] >= cut_off_dist) & \
+                          (inh_axonal_cloud[:, 0] <= traj.parameters.map.sheet_size - cut_off_dist) & \
+                          (inh_axonal_cloud[:, 1] >= cut_off_dist) & \
+                          (inh_axonal_cloud[:, 1] <= traj.parameters.map.sheet_size - cut_off_dist)
+        inh_hdis.append(sorted(inh_head_direction_indices[inh_cut_off_ids]))
+
+        exc_head_direction_indices = traj.results.runs[run_name].head_direction_indices
+        ex_positions = traj.results.runs[run_name].ex_positions
+        exc_cut_off_ids = (ex_positions[:, 0] >= cut_off_dist) & \
+                          (ex_positions[:, 0] <= traj.parameters.map.sheet_size - cut_off_dist) & \
+                          (ex_positions[:, 1] >= cut_off_dist) & \
+                          (ex_positions[:, 1] <= traj.parameters.map.sheet_size - cut_off_dist)
+        exc_hdis.append(sorted(exc_head_direction_indices[exc_cut_off_ids]))
+
+        exc_hdi_frame[corr_len, seed, label] = np.mean(exc_hdis)
+
+        inh_hdi_frame[corr_len, seed, label] = np.mean(inh_hdis)
 
     # TODO: Standard deviation also for the population
     exc_hdi_n_and_seed_mean = exc_hdi_frame.groupby(level=[0, 2]).mean()
@@ -1548,10 +1579,10 @@ if __name__ == "__main__":
     map_seed = 1
     exemplary_head_direction = 0
 
-    corr_len_fit_dict = correlation_length_fit_dict(traj, map_type='pinwheel', load=True)
-    plt.plot(corr_len_fit_dict.keys(),corr_len_fit_dict.values())
-    plt.show()
-    abbrechen
+    # corr_len_fit_dict = correlation_length_fit_dict(traj, map_type='pinwheel', load=True)
+    # plt.plot(corr_len_fit_dict.keys(),corr_len_fit_dict.values())
+    # plt.show()
+    # abbrechen
 
 
     print("## Map specifications")
@@ -1645,9 +1676,9 @@ if __name__ == "__main__":
 
     plot_orientation_maps_diff_scales_with_ellipse(traj)
 
-    plot_exc_and_inh_hdi_over_simplex_grid_scale(traj, traj.f_get_run_names())
+    plot_exc_and_inh_hdi_over_simplex_grid_scale(traj, traj.f_get_run_names(), cut_off_dist=100.)
 
-    plot_exc_and_inh_hdi_over_fit_corr_len(traj, traj.f_get_run_names())
+    plot_exc_and_inh_hdi_over_fit_corr_len(traj, traj.f_get_run_names(), cut_off_dist=100.)
 
     if not save_figs:
         plt.show()

scripts/spatial_network/perlin/paper_figures_spatial_head_direction_network_perlin_map.py

@@ -554,12 +554,16 @@ def plot_polar_plot_excitatory(traj, plot_run_names, selected_neuron_idx,
 
     line_styles = ['dotted', 'solid', 'dashed']
     colors = ['r', 'lightsalmon', 'grey']
+    labels = ['polar   ', 'circ.    ', 'no inh.']
     line_widths = [1.5, 1.5, 1]
     zorders = [10, 2, 1]
 
     max_rate = 0.0
+    ax.plot([], [], color='white',label='            HDI')
+
     for run_idx, run_name in enumerate(plot_run_names):
-        label = traj.derived_parameters.runs[run_name].morphology.morph_label
+        # label = traj.derived_parameters.runs[run_name].morphology.morph_label
+        label = labels[run_idx]
         hdi = traj.results.runs[run_name].head_direction_indices[selected_neuron_idx]
 
         tuning_vectors = traj.results.runs[run_name].tuning_vectors
@@ -580,7 +584,7 @@ def plot_polar_plot_excitatory(traj, plot_run_names, selected_neuron_idx,
     ax.set_thetagrids([0, 90, 180, 270], labels=[])
     ax.xaxis.grid(linewidth=0.4)
     ax.yaxis.grid(linewidth=0.4)
-    leg = ax.legend(loc="lower right", bbox_to_anchor=(1.15, -0.15), handlelength=1, fontsize="medium")
+    leg = ax.legend(loc="lower right", bbox_to_anchor=(1.15, -0.2), handlelength=1, fontsize="medium")
     leg.get_frame().set_linewidth(0.0)
     ax.axes.spines["polar"].set_visible(False)
     if save_figs:
@@ -603,13 +607,19 @@ def plot_polar_plot_inhibitory(traj, plot_run_names, selected_neuron_idx, fignam
 
     line_styles = ['dotted', 'solid']
     colors = ['b', 'lightblue']
+    labels = ['polar ', 'circ.  ']
+
     line_widths = [1.5, 1.5]
     zorders = [10, 2]
 
+    ax.plot([], [], color='white',label='          HDI')
+    hdis = []
     for run_idx, run_name in enumerate(plot_run_names[:2]):
         # ax = axes[max_hdi_idx, run_idx]
-        label = traj.derived_parameters.runs[run_name].morphology.morph_label
+        # label = traj.derived_parameters.runs[run_name].morphology.morph_label
+        label = labels[run_idx]
         hdi = traj.results.runs[run_name].inh_head_direction_indices[selected_neuron_idx]
+        hdis.append(hdi)
 
         tuning_vectors = traj.results.runs[run_name].inh_tuning_vectors
         rate_plot = [np.linalg.norm(v) for v in tuning_vectors[plot_n_idx]]
@@ -628,6 +638,9 @@ def plot_polar_plot_inhibitory(traj, plot_run_names, selected_neuron_idx, fignam
     ax.yaxis.grid(linewidth=0.4)
     leg = ax.legend(loc="lower right", bbox_to_anchor=(1.15, -0.15), handlelength=1, fontsize="medium")
     leg.get_frame().set_linewidth(0.0)
+
+    # hdi_box = ax.text(1.15, -0.15, 'HDI \n{:.2f} \n{:.2f}'.format(hdis[0], hdis[1]))
+
     ax.axes.spines["polar"].set_visible(False)
 
     if save_figs:
@@ -1121,7 +1134,7 @@ def filter_run_names_by_par_dict(traj, par_dict):
     return run_name_list
 
 
-def plot_exc_and_inh_hdi_over_simplex_grid_scale(traj, plot_run_names):
+def plot_exc_and_inh_hdi_over_simplex_grid_scale(traj, plot_run_names, cut_off_dist):
     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()]
@@ -1134,13 +1147,28 @@ def plot_exc_and_inh_hdi_over_simplex_grid_scale(traj, plot_run_names):
 
     for run_name, corr_len, seed, label in zip(plot_run_names, corr_len_expl, seed_expl, label_expl):
         ex_tunings = traj.results.runs[run_name].ex_tunings
-        head_direction_indices = traj.results[run_name].head_direction_indices
-        # TODO: Actual correlation lengths
-        # actual_corr_len = get_correlation_length(ex_tunings.reshape((30,30)), 450, 30)
-        exc_hdi_frame[corr_len, seed, label] = np.mean(head_direction_indices)
+        inh_hdis = []
+        exc_hdis = []
+
+        inh_head_direction_indices = traj.results.runs[run_name].inh_head_direction_indices
+        inh_axonal_cloud = traj.results.runs[run_name].inhibitory_axonal_cloud_array
+        inh_cut_off_ids = (inh_axonal_cloud[:, 0] >= cut_off_dist) & \
+                          (inh_axonal_cloud[:, 0] <= traj.parameters.map.sheet_size - cut_off_dist) & \
+                          (inh_axonal_cloud[:, 1] >= cut_off_dist) & \
+                          (inh_axonal_cloud[:, 1] <= traj.parameters.map.sheet_size - cut_off_dist)
+        inh_hdis.append(sorted(inh_head_direction_indices[inh_cut_off_ids]))
 
-        inh_head_direction_indices = traj.results[run_name].inh_head_direction_indices
-        inh_hdi_frame[corr_len, seed, label] = np.mean(inh_head_direction_indices)
+        exc_head_direction_indices = traj.results.runs[run_name].head_direction_indices
+        ex_positions = traj.results.runs[run_name].ex_positions
+        exc_cut_off_ids = (ex_positions[:, 0] >= cut_off_dist) & \
+                          (ex_positions[:, 0] <= traj.parameters.map.sheet_size - cut_off_dist) & \
+                          (ex_positions[:, 1] >= cut_off_dist) & \
+                          (ex_positions[:, 1] <= traj.parameters.map.sheet_size - cut_off_dist)
+        exc_hdis.append(sorted(exc_head_direction_indices[exc_cut_off_ids]))
+
+        exc_hdi_frame[corr_len, seed, label] = np.mean(exc_hdis)
+
+        inh_hdi_frame[corr_len, seed, label] = np.mean(inh_hdis)
 
     # TODO: Standard deviation also for the population
     exc_hdi_n_and_seed_mean = exc_hdi_frame.groupby(level=[0, 2]).mean()
@@ -1209,7 +1237,7 @@ def plot_exc_and_inh_hdi_over_simplex_grid_scale(traj, plot_run_names):
                          inh_hdi_mean + inh_hdi_std, alpha=0.3, color=inh_col)
 
     ax.set_xlabel('simplex grid scale')
-    ax.set_ylabel('head direction index')
+    ax.set_ylabel('HDI')
     ax.axvline(get_closest_correlation_length(traj, 200.0) * np.sqrt(2), color='k', linewidth=0.5, zorder=0)
     ax.set_ylim(0.0, 1.0)
     # ax.set_xlim(0.0, np.max(corr_len_range))
@@ -1226,7 +1254,7 @@ def plot_exc_and_inh_hdi_over_simplex_grid_scale(traj, plot_run_names):
         plt.savefig(FIGURE_SAVE_PATH + 'F_hdi_over_grid_scale.png')
         plt.close(fig)
 
-def plot_exc_and_inh_hdi_over_fit_corr_len(traj, plot_run_names):
+def plot_exc_and_inh_hdi_over_fit_corr_len(traj, plot_run_names, cut_off_dist):
     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()]
@@ -1239,13 +1267,29 @@ def plot_exc_and_inh_hdi_over_fit_corr_len(traj, plot_run_names):
 
     for run_name, corr_len, seed, label in zip(plot_run_names, corr_len_expl, seed_expl, label_expl):
         ex_tunings = traj.results.runs[run_name].ex_tunings
-        head_direction_indices = traj.results[run_name].head_direction_indices
-        # TODO: Actual correlation lengths
-        # actual_corr_len = get_correlation_length(ex_tunings.reshape((30,30)), 450, 30)
-        exc_hdi_frame[corr_len, seed, label] = np.mean(head_direction_indices)
 
-        inh_head_direction_indices = traj.results[run_name].inh_head_direction_indices
-        inh_hdi_frame[corr_len, seed, label] = np.mean(inh_head_direction_indices)
+        inh_hdis = []
+        exc_hdis = []
+
+        inh_head_direction_indices = traj.results.runs[run_name].inh_head_direction_indices
+        inh_axonal_cloud = traj.results.runs[run_name].inhibitory_axonal_cloud_array
+        inh_cut_off_ids = (inh_axonal_cloud[:, 0] >= cut_off_dist) & \
+                          (inh_axonal_cloud[:, 0] <= traj.parameters.map.sheet_size - cut_off_dist) & \
+                          (inh_axonal_cloud[:, 1] >= cut_off_dist) & \
+                          (inh_axonal_cloud[:, 1] <= traj.parameters.map.sheet_size - cut_off_dist)
+        inh_hdis.append(sorted(inh_head_direction_indices[inh_cut_off_ids]))
+
+        exc_head_direction_indices = traj.results.runs[run_name].head_direction_indices
+        ex_positions = traj.results.runs[run_name].ex_positions
+        exc_cut_off_ids = (ex_positions[:, 0] >= cut_off_dist) & \
+                          (ex_positions[:, 0] <= traj.parameters.map.sheet_size - cut_off_dist) & \
+                          (ex_positions[:, 1] >= cut_off_dist) & \
+                          (ex_positions[:, 1] <= traj.parameters.map.sheet_size - cut_off_dist)
+        exc_hdis.append(sorted(exc_head_direction_indices[exc_cut_off_ids]))
+
+        exc_hdi_frame[corr_len, seed, label] = np.mean(exc_hdis)
+
+        inh_hdi_frame[corr_len, seed, label] = np.mean(inh_hdis)
 
     # TODO: Standard deviation also for the population
     exc_hdi_n_and_seed_mean = exc_hdi_frame.groupby(level=[0, 2]).mean()
@@ -1270,7 +1314,7 @@ def plot_exc_and_inh_hdi_over_fit_corr_len(traj, plot_run_names):
     # linestyles = ['solid', 'dashed', 'solid']
     # markers = [verts, '', 'o']
 
-    corr_len_fit_dict = correlation_length_fit_dict(traj, load=True)
+    corr_len_fit_dict = correlation_length_fit_dict(traj, map_type='perlin', load=True)
 
     width = 2 * panel_size
     height = 1.2 * panel_size
@@ -1315,7 +1359,7 @@ def plot_exc_and_inh_hdi_over_fit_corr_len(traj, plot_run_names):
                          inh_hdi_mean + inh_hdi_std, alpha=0.3, color=inh_col)
 
     ax.set_xlabel('correlation length')
-    ax.set_ylabel('head direction index')
+    ax.set_ylabel('HDI')
     ax.axvline(corr_len_fit_dict[get_closest_correlation_length(traj, 200.0)], color='k', linewidth=0.5, zorder=0)
     ax.set_ylim(0.0, 1.0)
     # ax.set_xlim(0.0, np.max(corr_len_range))
@@ -1382,7 +1426,7 @@ def plot_spatial_hdi_map(traj, plot_run_names):
         print('Mean {}-HDI = {}'.format(label, np.mean(head_direction_indices)))
         c = plot_hdi_in_space(ax, positions, head_direction_indices, max_val)
         ax.set_title(label)
-        fig.colorbar(c, ax=ax, label="head direction index")
+        fig.colorbar(c, ax=ax, label="HDI")
     fig.suptitle('spatial HDI map', fontsize=16)
 
     if save_figs:
@@ -1407,7 +1451,7 @@ def plot_exc_spatial_hdi_map(traj, plot_run_names):
         print('Mean {}-HDI = {}'.format(label, np.mean(head_direction_indices)))
         c = plot_hdi_in_space(ax, positions, head_direction_indices, max_val)
         ax.set_title(label)
-        fig.colorbar(c, ax=ax, label="head direction index")
+        fig.colorbar(c, ax=ax, label="HDI")
     fig.suptitle('spatial exc. HDI map', fontsize=16)
 
     if save_figs:
@@ -1433,7 +1477,7 @@ def plot_inh_spatial_hdi_map(traj, plot_run_names):
         print('Mean {}-HDI = {}'.format(label, np.mean(head_direction_indices)))
         c = plot_hdi_in_space(ax, positions, head_direction_indices, max_val)
         ax.set_title(label)
-        fig.colorbar(c, ax=ax, label="head direction index")
+        fig.colorbar(c, ax=ax, label="HDI")
     fig.suptitle('spatial inh. HDI map', fontsize=16)
 
     if save_figs:
@@ -1549,7 +1593,7 @@ if __name__ == "__main__":
     map_seed = 1
     exemplary_head_direction = 0
 
-    # corr_len_fit_dict = correlation_length_fit_dict(traj, load=False)
+    # corr_len_fit_dict = correlation_length_fit_dict(traj, map_type='perlin', load=True)
     # plt.plot(corr_len_fit_dict.keys(),corr_len_fit_dict.values())
     # plt.show()
     # abbrechen
@@ -1646,9 +1690,9 @@ if __name__ == "__main__":
 
     plot_orientation_maps_diff_scales_with_ellipse(traj)
 
-    plot_exc_and_inh_hdi_over_simplex_grid_scale(traj, traj.f_get_run_names())
+    plot_exc_and_inh_hdi_over_simplex_grid_scale(traj, traj.f_get_run_names(), cut_off_dist=100.)
 
-    plot_exc_and_inh_hdi_over_fit_corr_len(traj, traj.f_get_run_names())
+    plot_exc_and_inh_hdi_over_fit_corr_len(traj, traj.f_get_run_names(), cut_off_dist=100.)
 
     if not save_figs:
         plt.show()