Start PHI2024

* Update compton_combiner.py
  * Set start run for PHI2024
  * Round some values in tables to gain stability
* Add PHI2024 results

compton_combiner.py

@@ -20,8 +20,8 @@ import pandas as pd
 from tqdm import tqdm
 
 SEASONS = {
-    'name': ['PHIOMEGA2012', 'RHO2013', 'BRK2013/16', 'HIGH2017', 'RHO2018', 'HIGH2019', 'LOW2020', 'HIGH2020', 'HIGH2021', 'NNBAR2022', 'HIGH2023'],
-    'start_run': [17405, 18809, 32076, 36872, 48938, 70014, 85224, 89973, 98116, 107342, 131913, None],
+    'name': ['PHIOMEGA2012', 'RHO2013', 'BRK2013/16', 'HIGH2017', 'RHO2018', 'HIGH2019', 'LOW2020', 'HIGH2020', 'HIGH2021', 'NNBAR2022', 'HIGH2023', 'PHI2024'],
+    'start_run': [17405, 18809, 32076, 36872, 48938, 70014, 85224, 89973, 98116, 107342, 131913, 163012, None],
 }
 MANUAL_RUNS_SPLIT = [0, 150159, np.inf] # list[runs] to manually split point with the same energy
 
@@ -309,10 +309,10 @@ def ultimate_averager(df: pd.DataFrame) -> dict:
     std_spread = np.sqrt(1/np.sum((df.luminosity/df.luminosity.mean())/df.spread_std**2))
     return {
         'mean_energy': mean_en, 
-        'mean_energy_stat_err': m.errors['mean'], 
-        'mean_energy_sys_err': sys_err, 
+        'mean_energy_stat_err': round(m.errors['mean'], 5), 
+        'mean_energy_sys_err': round(sys_err, 5), 
         'mean_spread': mean_spread,
-        'mean_spread_stat_err': std_spread,
+        'mean_spread_stat_err': round(std_spread, 5),
     }
 
     
@@ -409,7 +409,7 @@ def calculate_point(comb_df: pd.DataFrame, runs_df: pd.DataFrame, compton_df: pd
         'mean_energy_sys_err': averages['mean_energy_sys_err'], 
         'mean_spread': averages['mean_spread'],
         'mean_spread_stat_err': averages['mean_spread_stat_err'], 
-        'used_lum': df.luminosity.sum()/comb_df.luminosity_total.sum(), 
+        'used_lum': round(df.luminosity.sum()/comb_df.luminosity_total.sum(), 4), 
         'comment': '',
     }