Update TOA and TOT scans

ana/tot/s-curves.py

@@ -45,7 +45,7 @@ def toa_conv(df):
             ax[1].plot(ch_df['calib'], toa_conv(ch_df))
     plt.show()
 
-quit()
+#quit()
 
 if (args.plot_adc):
     fig, ax = plt.subplots(1,1)
@@ -73,6 +73,7 @@ def toa_conv(df):
 fig_toa, ax_toa = plt.subplots(1, 2, sharey=True)
 fig_tot, ax_tot = plt.subplots(1, 2, sharey=True)
 fig_adc, ax_adc = plt.subplots(1, 1)
+fig_ch, ax_ch = plt.subplots(8,9, sharex=True)
 
 # Efficiency calculation
 def efficiency(vals):
@@ -110,7 +111,7 @@ def condition_max(vals):
 max_non_saturated = 0
 for ch_id, ch_df in ch_group:
     if ch_id == 0:
-        ax_toa[0].plot(ch_df['calib'], ch_df['toa_eff'], label=f'channels')
+        ax_toa[0].plot(ch_df['calib'], ch_df['toa_eff'], label=f'channels', linewidth = 0.2)
         ax_tot[0].plot(ch_df['calib'], ch_df['tot_eff'], label=f'channels')
         ax_toa[1].plot(ch_df['max_adc'], ch_df['toa_eff'], label=f'channels')
         ax_tot[1].plot(ch_df['max_adc'], ch_df['tot_eff'], label=f'channels')
@@ -125,6 +126,7 @@ def condition_max(vals):
     max_val = temp_df['max_adc'].max()
     if (max_val > max_non_saturated):
         max_non_saturated = max_val
+    ax_ch[ch_id // 9, ch_id % 9].plot(ch_df['calib'], ch_df['toa_eff'], label=ch_id)
 
 ax_adc.axhline(y = max_non_saturated, linestyle='--', color='b', label=f'max non-saturated at {max_non_saturated}')
 
@@ -144,6 +146,7 @@ def condition_max(vals):
 fig_toa.savefig("toa_s_curve.png", dpi=400)
 fig_tot.savefig("tot_s_curve.png", dpi=400)
 fig_adc.savefig("adc_linearity.png", dpi=400)
+#fig_ch.savefig("toa_s_curve_channel.png", dpi=400)
 plt.show()
 #fig_toa.savefig('toa_efficiency.png', dpi=400, bbox_inches='tight')
 #fig_tot.savefig('tot_efficiency.png', dpi=400, bbox_inches='tight')

app/tool/algorithm/global_pedestal_level.cxx

@@ -26,8 +26,10 @@ void DataFitter::sort_and_append(std::vector<int>& inv_vrefs,
   };
   std::vector<DerivPoint> slope_points;
 
+  // derivs should be around -1.4 in the linear region. These upper and lower
+  // bounds sort outliers.
   double flat_threshold = 0.1;
-  double linear_threshold = 10;
+  double linear_threshold = 5;
   std::vector<double> LH_derivs;
   std::vector<double> LH_stdevs;
   std::vector<double> RH_derivs;
@@ -125,8 +127,8 @@ int DataFitter::linear_fit(int& target) {
   pflib_log(info) << "Fitting Data";
 
   // preform a linear fit
-  int x_sum = 0;
-  int y_sum = 0;
+  double x_sum = 0;
+  double y_sum = 0;
 
   for (const auto& p : linear_) {
     x_sum += p.x_;
@@ -246,7 +248,7 @@ global_pedestal_level(Target* tgt) {
     }
   }
 
-  int target_adc = 200;
+  int target_adc = 150;
 
   DecodeAndBuffer buffer{nevents, tgt->nrocs() * 2};