Add plot script for e/pi separation performance

• update the json output from epcut and ML so rejection power and uncertainties can be directly read from them
• add a json output for run_benchmark.py to collect all run information
• add a script reading the run info files and plot the e/pi separation power curve

benchmarks/imaging_shower_ML/run_benchmark.py

@@ -275,8 +275,7 @@ if __name__ == '__main__':
 
     # TODO: collect the two-step results (e/p cut and ML)
 
-    # save run information
-    # NOTE: runs with the same outdir will be combined (overwrite if nametag is the same)
+    # save run information, combine runs with the same run_type
     run_data = {args.run_type: {args.ntag: kwargs}}
     try:
         with open(os.path.join(args.outdir, 'result.json'), 'r') as f: