Merge pull request #70 from pim-n/feature-unc-plotting

Feature unc plotting

src/pg_rad/data/field_efficiencies/LU_NaIR.csv

@@ -1,3 +1,5 @@
 energy_keV,field_efficiency_m2
 0,0
-662,0.0216
+661.657,0.0261
+1173.228,0.0203
+1332.492,0.0166

src/pg_rad/plotting/result_plotter.py

@@ -98,13 +98,26 @@ class ResultPlotter:
     def _draw_counts(self, ax):
         x = self.count_rate_res.distance[1:]
         y = self.count_rate_res.integrated_counts[1:]
+
+        yerr = np.sqrt(y)
+        peak_idx = y.argmax()
+
+        ax.fill_between(
+            x,
+            y - yerr,
+            y + yerr,
+            color='red',
+            alpha=0.15,
+        )
+
         ax.plot(
-            x, y, color='r', linestyle='--',
-            alpha=0.2, label=f'max(counts) = {y.max():.2f}'
+            x, y,
+            color='r', linestyle='--', alpha=0.2,
+            label=rf'max(counts)={y[peak_idx]:.2f} $\pm$ {yerr[peak_idx]:.2f}'
         )
         ax.legend(handlelength=0, handletextpad=0, fancybox=True)
         ax.scatter(x, y, color='r', marker='x')
-        ax.set_title('Integrated counts')
+        ax.set_title('Counts')
         ax.set_xlabel('Arc length s [m]')
         ax.set_ylabel('N')
 

src/pg_rad/utils/export.py

@@ -47,7 +47,6 @@ def save_results(sim: SimulationOutput, folder_name: str) -> None:
     csv_name = generate_csv_name(sim)
     df.to_csv(f"{folder_name}/{csv_name}.csv", index=False)
     param_dict = generate_sim_param_dict(sim)
-    print(type(param_dict['detector']['is_isotropic']))
     with open(f"{folder_name}/parameters.json", 'w') as f:
         json.dump(param_dict, f, cls=NumpyEncoder)
     logger.info(f"Simulation output saved to {folder_name}!")