Update simulation parameters and improve analysis plotting

- Changed master_seed in experiment_stabilized_groundstate.py
- Specified exact versions for dependencies in humam.yml
- Enhanced raster statistics plotting in analysis.py
- Updated display name in humam_tutorial.ipynb
- Fixed NaN handling in connectivity.py

experiments/experiment_stabilized_groundstate.py

@@ -4,7 +4,7 @@
 # Overwrite values of src/default_sim_params.py
 sim_params = {
     't_sim': 12500.0,
-    'master_seed': 2106
+    'master_seed': 2903
 }
 
 # Parameters for the analysis

humam.yml

@@ -1,22 +1,19 @@
 name: humam
-channels:
-  - conda-forge
-  - defaults
 dependencies:
-  - nest-simulator
-  - joblib
-  - xlrd
-  - matplotlib
-  - pyyaml
-  - numpy
-  - seaborn
-  - python
-  - pandas
-  - scipy
-  - ipykernel
-  - pip
+  - nest-simulator=3.8
+  - joblib=1.4.2
+  - xlrd=2.0.1
+  - matplotlib=3.9.2
+  - pyyaml=6.0.2
+  - numpy=2.1.3
+  - seaborn=0.13.2
+  - python=3.12.7
+  - pandas=2.2.3
+  - scipy=1.14.1
+  - ipykernel=6.29.5
+  - pip=24.3.1
   - pip:
-    - nnmt
-    - dicthash
-    - networkx
-    - snakemake
+    - nnmt==1.3.0
+    - dicthash==0.0.2
+    - networkx==3.4.2
+    - snakemake==8.25.3

humam_tutorial.ipynb

@@ -623,7 +623,7 @@
     "areas_to_plot = ['caudalanteriorcingulate', 'pericalcarine', 'fusiform']\n",
     "\n",
     "# Plot the raster plot for the selected areas\n",
-    "ana.plot_raster_statistics()"
+    "ana.plot_raster_statistics(raster_areas=areas_to_plot)"
    ]
   },
   {
@@ -705,7 +705,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "humam-nest3",
+   "display_name": "humam_nest3",
    "language": "python",
    "name": "python3"
   },

src/analysis.py

@@ -147,6 +147,9 @@ def fullAnalysis(self):
             self.plotRasterArea(area)
         print('{} Plotting BOLD connectivities'.format(datetime.now().time()))
         self.plotBOLDConnectivity()
+        print('{} Plotting {}'.format(datetime.now().time(), 'Raster statistics'))
+        self.plot_raster_statistics(save_fig=True)
+        plt.close('all')
 
     @timeit
     def meanFiringRate(self):
@@ -311,14 +314,14 @@ def plot_instantaneous_firing_rate(self, save_fig=False):
         rate_hist_areas_df = pd.DataFrame(rate_hist_areas.tolist(), index=rate_hist_areas.index)
 
         # Plot the heatmap with an orange-yellow color palette
+        plt.style.use('default')
         plt.figure(figsize=(12, 5))
         sns.heatmap(rate_hist_areas_df, cmap='YlOrBr', cbar_kws={'label': 'Spikes/s'}, yticklabels=rate_hist_areas_df.index)
         plt.xlabel('Time (ms)')
         plt.ylabel('Area')
         plt.title('Instantaneous firing rate over simulated areas')
         plt.xticks(rotation=0)  # Rotate x-axis labels to make the times horizontal
         plt.xlim(self.sim_dict['t_sim']-500, self.sim_dict['t_sim'])
-        plt.tight_layout()
 
         # Save the plot if save_fig is True
         if save_fig:
@@ -358,6 +361,7 @@ def plot_average_rate_per_pop(self, save_fig=False):
         mask = mean_rates_df.isna()
 
         # Plot the heatmap with external grid
+        plt.style.use('default')
         plt.figure(figsize=(12, 4.5))
         sns.heatmap(mean_rates_df, cmap='YlOrBr', fmt=".2f", mask=mask, cbar_kws={'label': 'Spikes/s'})
         plt.title('Time-averaged firing rate over simulated populations')
@@ -1099,13 +1103,13 @@ def calculateFuncionalConnectivityCorrelations(self):
         # (df_sim_fc_syn)
         df_sim_fc_syn = synaptic_currents[synaptic_currents.index >= tmin].corr()
         if exclude_diagonal:
-            np.fill_diagonal(df_sim_fc_syn.values, np.NaN)
+            np.fill_diagonal(df_sim_fc_syn.values, np.nan)
 
         # Read in simulated functional connectivity based on calculated BOLD
         # signal
         df_sim_fc_bold = self.BOLD_correlation
         if exclude_diagonal:
-            np.fill_diagonal(df_sim_fc_bold.values, np.NaN)
+            np.fill_diagonal(df_sim_fc_bold.values, np.nan)
 
         # Sort
         df_sim_fc_syn = df_sim_fc_syn.sort_index(axis=0).sort_index(axis=1)
@@ -1199,8 +1203,8 @@ def calculateFuncionalConnectivityCorrelations(self):
                 # Correlation with itself is trivially 1, set those values to
                 # nan
                 if exclude_diagonal:
-                    np.fill_diagonal(lh_fc.values, np.NaN)
-                    np.fill_diagonal(rh_fc.values, np.NaN)
+                    np.fill_diagonal(lh_fc.values, np.nan)
+                    np.fill_diagonal(rh_fc.values, np.nan)
 
                 # Sort and put into dictionary
                 lh_fc = lh_fc.sort_index(axis=0).sort_index(axis=1)
@@ -1667,7 +1671,6 @@ def plot_raster_statistics(self, save_fig=False, raster_areas=None):
                     upper.append(upper_whisker)
             print('label:', label, 'lowest whisker:', round(min(lower), 1))
             print('label:', label, 'highest whisker:', round(max(upper), 1))
-            ax.set_yticks(ind)
             ax.set_yticklabels(names)
         ax_rates.set_xlim(0)
         ax_rates.set_xlabel('Firing rate (spikes/s)')
@@ -1679,7 +1682,7 @@ def plot_raster_statistics(self, save_fig=False, raster_areas=None):
         if save_fig:
             extension = self.ana_dict['extension']
             fig.savefig(os.path.join(self.plot_folder, f'figure_spike_statistics.{extension}'))
-        plt.show()
+            plt.close(fig)
 
     def plot_all_binned_spike_rates_area(self):
         """

src/data_preprocessing/connectivity.py

@@ -218,7 +218,7 @@ def tmp(val, SLN_FF, SLN_FB):
                 return 'lat'
             else:
                 return 'FF'
-        d = self.SLN.map(lambda x: tmp(x, SLN_FF, SLN_FB))
+        d = self.SLN.applymap(lambda x: tmp(x, SLN_FF, SLN_FB))
         return d
 
     def populationSpecificSynapseNumbers(self, SLN_FF, SLN_FB, Z_i, lmbda):