Pulse: fix save hilbert timing + figsizing

simulations/11_pulsed_timing.py

@@ -199,9 +199,9 @@ def read_time_residuals_cache(cache_fname, template_dt, antenna_dt, snr_sigma_fa
             if len(ret.shape) > 2:
                 return ret[0,:], ret[1,:], ret[2,:]
             elif len(ret.shape) > 1:
-                return ret[0,:], ret[1,:], np.array([np.nan]*len(ret))
+                return ret[0,:], ret[1,:], np.array([np.nan]*len(ret[0]))
             else:
-                return ret[:], np.array([np.nan]*len(ret)), np.array([np.nan]*len(ret))
+                return ret[:], np.array([np.nan]*len(ret[0])), np.array([np.nan]*len(ret[0]))
     except (KeyError, FileNotFoundError):
         return np.array([]), np.array([]), np.array([])
 
@@ -215,7 +215,8 @@ def write_time_residuals_cache(cache_fname, data, template_dt, antenna_dt, noise
         if ds_name in pgroup2.keys():
             del pgroup2[ds_name]
 
-        ds = pgroup2.create_dataset(ds_name, (3, len(time_residuals)), dtype='f', maxshape=(None))
+
+        ds = pgroup2.create_dataset(ds_name, (3, len(data[0])), dtype='f', maxshape=(None))
         ds[0] = data[0]
         ds[1] = data[1]
         ds[2] = data[2]
@@ -472,7 +473,7 @@ def get_time_residuals_for_template(
 
     # Were new time residuals calculated?
     # Add them to the cache file
-    if len(time_residuals) > 1:
+    if len(time_residuals) >= 1:
         # merge cached and calculated time residuals
         time_residuals = np.concatenate((cached_time_residuals, time_residuals), axis=None)
         snrs = np.concatenate( (cached_snrs, snrs), axis=None)
@@ -495,6 +496,12 @@ if __name__ == "__main__":
     if os.name == 'posix' and "DISPLAY" not in os.environ:
         matplotlib.use('Agg')
 
+
+    if False:
+        plt.rc('font', size=25)
+
+    figsize = (12,12)
+
     bp_freq = (30e-3, 80e-3) # GHz
     interp_template_dt = 5e-5 # ns
     template_length = 200 # ns
@@ -521,6 +528,9 @@ if __name__ == "__main__":
     use_cache = True
     write_cache = None # Leave None for default action
 
+    wrong_peak_condition_multiple = 2
+    wrong_peak_condition = lambda t_res: abs(t_res) > antenna_dt*wrong_peak_condition_multiple
+
     #
     # Interpolation Template
     # to create an 'analog' sampled antenna
@@ -580,7 +590,6 @@ if __name__ == "__main__":
             print()# separating tqdm
             print()# separating tqdm
 
-            wrong_peak_condition = lambda t_res: abs(t_res) > antenna_dt*4
             mask = wrong_peak_condition(time_residuals)
 
             # Save directly to large data array
@@ -677,10 +686,11 @@ if __name__ == "__main__":
     # SNR time accuracy plot
     #
     if True:
+        enable_threshold_markers = [False, False, True, True]
         threshold_markers = ['^', 'v', '8', 'X'] # make sure to have filled markers here
         mask_thresholds = np.array([np.inf, N_residuals*0.5, N_residuals*0.1, 1, 0])
 
-        fig, ax = plt.subplots()
+        fig, ax = plt.subplots(figsize=figsize)
         ax.set_title(f"Template matching SNR vs time accuracy")
         ax.set_xlabel("Signal to Noise Factor")
         ax.set_ylabel("Time Accuracy [ns]")
@@ -706,13 +716,14 @@ if __name__ == "__main__":
                 # calculate absolute deviation from the mean
                 residual_mean_deviation = np.sqrt( (time_residuals - mean_residual)**2 )
 
-                snr_std = np.std(snrs)
-                time_accuracy_std = np.std(residual_mean_deviation)
+                snr_std = np.std(snrs[valid_mask])
+                time_accuracy_std = np.std(residual_mean_deviation[valid_mask])
 
                 scatter_kwargs = dict(
                         ls='none',
-                        marker='.',
-                        alpha=0.3,
+                        marker='o',
+                        alpha=0.2,
+                        ms=1,
                         zorder=1.8,
                 )
 
@@ -780,7 +791,8 @@ if __name__ == "__main__":
             # limit y-axis upper limit to 1e1
             if True:
                 this_lim = 1e1
-                ax.set_ylim([None, this_lim])
+                if ax.get_ylim()[1] >= this_lim:
+                    ax.set_ylim([None, this_lim])
 
             # require y-axis lower limit to be at least 1e-1
             if True:
@@ -796,10 +808,11 @@ if __name__ == "__main__":
                 if low_ylims <= this_lim:
                     ax.set_ylim([this_lim, None])
 
-        if True: # require y-axis lower limit to be at least 1e-1
-            low_ylims = ax.get_ylim()[0]
-            if low_ylims >= 1e-1:
-                ax.set_ylim([1e-1, None])
+            # require x-axis lower limit to be under 1e0
+            if True:
+                this_lim = 1e0
+                if ax.get_xlim()[0] >= this_lim:
+                    ax.set_xlim([this_lim, None])
 
         fig.tight_layout()
         if len(template_dts) == 1: