Pulse: snr plot multiple template_dt curves

simulations/11_pulsed_timing.py

@@ -401,11 +401,14 @@ if __name__ == "__main__":
         matplotlib.use('Agg')
 
     bp_freq = (30e-3, 80e-3) # GHz
-    template_dt = 5e-2 # ns
     interp_template_dt = 5e-5 # ns
     template_length = 200 # ns
 
+    antenna_dt = 2 # ns
+    antenna_timelength = 1024 # ns
+
     N_residuals = 50*3 if len(sys.argv) < 2 else int(sys.argv[1])
+    template_dts = np.array([antenna_dt, 5e-1, 5e-2]) # ns
     snr_factors = np.concatenate( # 1/noise_amplitude factor
             (
                 #[0.25, 0.5, 0.75],
@@ -415,8 +418,6 @@ if __name__ == "__main__":
             ),
             axis=None, dtype=float)
 
-    antenna_dt = 2 # ns
-    antenna_timelength = 1024 # ns
 
     cut_wrong_peak_matches = True
     normalise_noise = False
@@ -454,19 +455,21 @@ if __name__ == "__main__":
         if True:
             plt.close(fig)
 
-    #
-    # Create the template
-    # This is sampled at a lower samplerate than the interpolation template
-    #
-    template, _ = create_template(dt=template_dt, timelength=template_length, bp_freq=bp_freq, name='Template')
 
     #
     # Find time accuracies as a function of signal strength
     #
-    time_accuracies = np.zeros(len(snr_factors))
-    mask_counts = np.zeros(len(snr_factors))
+    time_accuracies = np.zeros((len(template_dts), len(snr_factors)))
+    mask_counts = np.zeros_like(time_accuracies)
+    for l, template_dt in tqdm(enumerate(template_dts)):
+
+        # Create the template
+        # This is sampled at a lower samplerate than the interpolation template
+        template, _ = create_template(dt=template_dt, timelength=template_length, bp_freq=bp_freq, name='Template')
+
         for k, snr_sigma_factor in tqdm(enumerate(snr_factors)):
 
+            # get the time residuals
             time_residuals = get_time_residuals_for_template(
                     N_residuals, template, interpolation_template=interp_template,
                     antenna_dt=antenna_dt, antenna_timelength=antenna_timelength,
@@ -492,11 +495,10 @@ if __name__ == "__main__":
                         time_residuals = time_residuals[~mask]
 
                         if not mask_count:
-                        print("Continuing")
                             continue
 
-                time_accuracies[k] = np.std(time_residuals)
-                mask_counts[k] = mask_count
+                    time_accuracies[l, k] = np.std(time_residuals)
+                    mask_counts[l, k] = mask_count
 
                     hist_kwargs = dict(bins='sqrt', density=False, alpha=0.8, histtype='step')
                     fig, ax = plt.subplots()
@@ -567,10 +569,11 @@ if __name__ == "__main__":
         ax.set_title(f"Template matching SNR vs time accuracy")
         ax.set_xlabel("Signal to Noise Factor")
         ax.set_ylabel("Time Accuracy [ns]")
+        ax.grid()
 
         ax.legend(title="\n".join([
             f"N={N_residuals}",
-            f"template_dt={template_dt:0.1e}ns",
+            #f"template_dt={template_dt:0.1e}ns",
             f"antenna_dt={antenna_dt:0.1e}ns",
             ]))
 
@@ -578,28 +581,36 @@ if __name__ == "__main__":
             ax.set_xscale('log')
             ax.set_yscale('log')
 
-        # plot the values
-        l = None
+        # plot the values per template_dt slice
+        template_dt_colors = [None]*len(template_dts)
+        for k, template_dt in enumerate(template_dts):
+
+            # indicate masking values
             for j, mask_threshold in enumerate(pairwise([np.inf, 250, 50, 1, 0])):
                 kwargs = dict(
                         ls='none',
                         marker=['^', 'v','8', 'o',][j],
-                    color=None if l is None else l[0].get_color(),
+                        color= None if template_dt_colors[k] is None else template_dt_colors[k]
                         )
-            mask = mask_counts >= mask_threshold[1]
-            mask &= mask_counts < mask_threshold[0]
+                mask = mask_counts[k] >= mask_threshold[1]
+                mask &= mask_counts[k] < mask_threshold[0]
 
-            l = ax.plot(snr_factors[mask], time_accuracies[mask], **kwargs)
+                l = ax.plot(snr_factors[mask], time_accuracies[k][mask], **kwargs)
+                template_dt_colors[k] = l[0].get_color()
 
-        if True: # limit y-axis to 1e1
-            ax.set_ylim([None, 1e1])
+            # indicate threshold
+            if True:
+                ax.axhline(template_dt/np.sqrt(12), ls='--', alpha=0.7, color=template_dt_colors[k], label=f'Template dt:{template_dt:0.1e}ns')
 
 
         # Set horizontal line at 1 ns
         if True:
             ax.axhline(1, ls='--', alpha=0.8, color='g')
-            ax.grid()
-            ax.axhline(template_dt/np.sqrt(12), ls='--', alpha=0.7, color='b')
+
+        ax.legend()
+
+        if True: # limit y-axis to 1e1
+            ax.set_ylim([None, 1e1])
 
         fig.tight_layout()
         fig.savefig(f"figures/11_time_res_vs_snr_tdt{template_dt:0.1e}.pdf")