Add RHO2013 and fix combiner

compton_combiner.py

@@ -4,6 +4,7 @@
 import argparse
 from configparser import ConfigParser
 from datetime import datetime, timedelta, timezone
+import logging
 import os
 import sqlite3
 from typing import Union, Tuple, Optional
@@ -18,8 +19,8 @@ import pandas as pd
 from tqdm import tqdm
 
 SEASONS = {
-    'name': ['HIGH2017', 'RHO2018', 'HIGH2019', 'LOW2020', 'HIGH2020', 'HIGH2021', 'NNBAR2021'],
-    'start_run': [36872, 48938, 70014, 85224, 89973, 98116, 107342, None],
+    'name': ['RHO2013', 'BRK2013/16', 'HIGH2017', 'RHO2018', 'HIGH2019', 'LOW2020', 'HIGH2020', 'HIGH2021', 'NNBAR2021'],
+    'start_run': [18809, 32076, 36872, 48938, 70014, 85224, 89973, 98116, 107342, None],
 }
 
 class RunsDBHandler():
@@ -278,6 +279,12 @@ def calculate_point(comb_df: pd.DataFrame, runs_df: pd.DataFrame, compton_df: pd
         min_run_time, max_run_time = min(min_Yruntime, runs_df_with_bads_df.starttime.min()), max(max_Yruntime, runs_df_with_bads_df.stoptime.max())
         
         compton_meas = compton_df.query('((begintime>=@min_run_time)&(begintime<=@max_run_time))|((endtime>=@min_run_time)&(endtime<=@max_run_time))').copy()
+        
+        if len(compton_meas) == 0:
+            # no compton measurements
+            raise Exception("No measurement in this point. Pass it.")
+            
+        
         res_df = pd.DataFrame(list(map(lambda x: {
             'compton_start': x[1]['begintime'],
             'compton_stop': x[1]['endtime'],
@@ -286,6 +293,7 @@ def calculate_point(comb_df: pd.DataFrame, runs_df: pd.DataFrame, compton_df: pd
             'spread_mean': float(x[1]['data'][2]),
             'spread_std': float(x[1]['data'][3]),
         }, compton_meas.iterrows())))
+        
         res_df = res_df.query(f'abs(e_mean -{comb_df.iloc[0].at["elabel"]})<5')
         
         if len(res_df) == 0:
@@ -306,20 +314,23 @@ def calculate_point(comb_df: pd.DataFrame, runs_df: pd.DataFrame, compton_df: pd
         
     
     df = comb_df.loc[~comb_df.compton_start.isna()].copy()
+    # df.spread_mean = np.where(df.spread_mean < 1e-3, 1e-3, df.spread_mean)
     df.spread_std = np.where(df.spread_std < 1e-4, 1e-4, df.spread_std)
     
     df = df[df.e_std > 0]
     mean_energy = np.sum(df.e_mean*df.luminosity/(df.e_std**2))/np.sum(df.luminosity/(df.e_std**2))
     # std_energy = np.sqrt(1/np.sum((df.luminosity/df.luminosity.mean())/df.e_std**2))
     
-    good_criterion = np.abs((df.e_mean - mean_energy)/np.sqrt(df.e_mean.std()**2 + df.e_std**2)) < 5
-    #print(df[~good_criterion].elabel.value_counts())
+    good_criterion = np.abs((df.e_mean - mean_energy)/np.sqrt(df.e_mean.std(ddof=0)**2 + df.e_std**2)) < 5
+    # print('vals', np.abs((df.e_mean - mean_energy)/np.sqrt(df.e_mean.std()**2 + df.e_std**2)) )
+    # print(df[~good_criterion].elabel.value_counts())
     df = df[good_criterion]
     
-    m = Minuit(Likelihood(df.e_mean, df.e_std, df.luminosity), mean=df.e_mean.mean(), sigma=df.e_mean.std())
+    m = Minuit(Likelihood(df.e_mean, df.e_std, df.luminosity), mean=df.e_mean.mean(), sigma=df.e_mean.std(ddof=0))
     m.errordef = 0.5 
     m.limits['sigma'] = (0, None)
     m.migrad();
+    # print(m.migrad())
     sys_err = m.values['sigma']
     mean_en = m.values['mean']
     
@@ -394,7 +405,10 @@ def process_combined(combined_df: pd.DataFrame, runs_df: pd.DataFrame, compton_d
     result_df = pd.DataFrame(columns=['energy_point', 'first_run', 'last_run', 'mean_energy', 'mean_energy_stat_err', 'mean_energy_sys_err', 'mean_spread', 'mean_spread_stat_err', 'used_lum', 'comment'])
     
     for i, table in tqdm(combined_df.groupby('point_idx', dropna=False)):
-        res_dict, good_df = calculate_point(table, runs_df, compton_df, rdb)
+        try:
+            res_dict, good_df = calculate_point(table, runs_df, compton_df, rdb)
+        except Exception:
+            continue
         result_df = result_df.append(res_dict, ignore_index=True)
         
         if pics_folder is not None:
@@ -441,10 +455,10 @@ def final_table_to_clbrdb(df: pd.DataFrame, clbrdb: CalibrdbHandler, runs_df: pd
     clbrdb.insert(df_clbrdb, 'Misc', 'RunHeader', 'Compton_run_avg', 'Default', comment = season)
     clbrdb.commit()
     
-def save_csv(df: pd.DataFrame, filepath: str):
+def save_csv(df: pd.DataFrame, filepath: str, update_current: bool = True):
     """Saves csv file. Updates current file in filepath if exists"""
     
-    if os.path.isfile(filepath):
+    if (os.path.isfile(filepath) and update_current):
         df_current = pd.read_csv(filepath)
         df_current = df_current.append(df, ignore_index=True)
         df_current = df_current.drop_duplicates(subset=['energy_point', 'first_run'], keep='last')