Update README

Author: jbsauvan

README.md

@@ -56,24 +56,20 @@ This script runs all the steps needed for the egamma isolation:
 * Train multiple quantile regressions to derive isolation cuts for several efficiency working points. These cuts are derived as a function of `|ieta|` and `rho`. The trainings will be launched on batch.
 * Apply the `ntt` to `rho` mapping to the isolation regression and save results as 2D histograms (`|ieta|`, `ntt`) for all the working points.   
 * Produce efficiencies of all the working points vs eta and pt of the offline electron, and npv, rho
+* Combine different working points according to a predefined efficiency shape depending on `ieta` and `et`
+
+Features available but not used in this version:
 * Find the optimal inclusive working point, in terms of background rejection and signal efficiency
-* Find the optimal working point in bins of |ieta|
+* Find the optimal working point in bins of `|ieta|`
 
 The optimization of the working points is done by looking at the efficiency gradient for signal and background. The optimal working point is chosen as the point where the background gradient becomes smaller or equal to the signal gradient. This means that cutting harder than this point will kill signal more (or equally) than it kills background.   
 
 ```
 Usage: python egamma_isolation.py [options]
 
 Options:
-  -h, --help              show this help message and exit
-  --inputfile=INPUT_FILE  Input file
-  --tree=TREE_NAME        Tree in the input file
-  --outputdir=OUTPUT_DIR  Output directory
-  --name=NAME             Name used for the results
-  --test                  Flag to test regression on a test sample
-  --inputs=INPUTS         List of input variables of the form "var1,var2,..."
-  --pileupref=PILEUP_REF  Reference variable used for pile-up
-  --target=TARGET         Target variable
+  -h, --help            show this help message and exit
+  --cfg=PARAMETER_FILE  Python file containing the definition of parameters
 ```