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Binning explanation for 2D observables #20
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Working on RunCoefficients.py and RunPlotCoefficients.py |
RunCoefficients and RunPlotCoefficients updated for 2D measurements.
Name of the 2D variable: '(name_var_1) vs (name_var_2)' The obs_bins variable is a dictionary whose index is the number of the bin, each entry of the dictionary is a list [obs_bins_low, obs_bins_high, obs_bins_low_2nd, obs_bins_high_2nd]. The first two entries are the lower and upper bound of the first variable. The second two entries are the lower and upper bound of the second variable Working on RunTemplates and plot_templates |
RunTemplates and plot_templates updated for 2d measurements. Working on "fit" scripts |
"fit" scripts updated for 2d measurements. To do: Implement nBins method in other scripts as well to optimize the framework Working on producePlots |
When ready, PR [to this branch] for testing before final release in the framework. |
As a first benchmark for 2D measurements try to produce binned pTH in nJets bins measurement.
In principle it's still a "1D" measurement, where we use as gen/reco observables a cut on both pTH and nJets.
This measurement is interesting because of its many possible interpretations.
E.g. see Fig. 10 in ATLAS differential paper.
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