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/config.yml

@@ -8,6 +8,6 @@ ml_shower:epi_separation:
       if [[ ${DETECTOR} =~ athena
          || ${DETECTOR} =~ ecce && ${DETECTOR_CONFIG} =~ imaging ]] ; then
         pip3 install -r benchmarks/imaging_shower_ML/requirements.txt
-        python3 benchmarks/imaging_shower_ML/run_benchmark.py -t imcal_epi -n 1000 --pmin 1.8 --pmax 2.2 --nocut-samples
+        python3 benchmarks/imaging_shower_ML/run_benchmark.py -t imcal_epi -n 1000 --p-min 1.8 --p-max 2.2
       fi