| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185 | ;;;;;;;; General configuration options.;;    PlotStyle:  Either a global matplotlib style name, or a style file in the base dir.;;    Variable:   Variable to decompose.;;    Weight:     Event weight. This is REQUIRED.;;    Scale:      Scale factor to apply to the data;;;;    Lumi:       The luminosity when converted events -> cross-sections;;    LumiUnc:    Uncertainty on the luminosity; range is from 0 to 1;;    XSecUnits:  Units to display on limit plots.  This is cosmetic;;;                  units should correspond to whatever units are used;;                  for 'Lumi'.;;;;;;[General]PlotStyle:       plot.mplstyleVariable:                  MyyWeight:                 weightScale:                       1Lumi:                      0.0LumiUnc:                  0.02XSecUnits:              Events;;;;;;;; Input files. Format is;;    DataSet : n;; to keep every n'th event.;;;;;;[InputFiles]Test:                        1;;;;;;;; Cuts to apply.  The format is;;    'name:  condition';; where condition can be a Python snippet referring to any variable in the dataset.;;;;;;[Cuts]Mass:                 Myy > 60;;;;;;;; Parameters for the decomposition.  Note that most are fixed,;;   but Alpha and Lambda are free and will be adjusted during;;   optimization.;;;; Nbasis:      number of moments when calculating moments, yields, covariances;; Nxfrm:       number of moments used as inputs to transformation;; Ncheck:      max number of moments to consider as possible background;;;; x0:          Minimum mass cut.  There should be a corresponding cut in 'Cuts' above;;                in order to guarantee consistency.;;;; Nthread:     Number of threads to use for array operations.;;;;;;[ExpDecompFactory]Nthread:                     4Nbasis:                   4096Nxfrm:                     256Ncheck:                     24x0:                       60.0;;;;;;; Scan ranges for the hyperparameters 'Alpha' and 'Lambda'. These use;; the syntax:;;      (start) : (stop) : (# fine steps) : (# coarse steps);; The search region is delineated by 'start' and 'stop'.  Full decompositions;; are performed on the coarse grid, and transformed decompositions are;; produced on the fine grid.  The best set of parameters are used as the;; initial point for a final optimization.  The final ':' and last parameter;; may be omitted, in which case they default to 1.;;;;;[HyperParameterScan]Alpha:       0.45 : 0.85 : 49 : 5Lambda:       120 :   40 : 81 : 5;;;;;;;; Specification of signal models. 'func' is a function that describes;; the signal shape, which takes argument 'x'.  Other parameters can be;; specified in curly braces like {parameter}, and then later set as;; configuration keys like:;;;;      parameter: 0.256;;;; The configuration blocks 'ParametricSignal: <name>' inherit from;; 'ParametricSignalDefault'.  Any keys not specified in or overridden;; by 'ParametricSignal: <name>' will be retrieved from 'ParametricSignalDefault'.;;;; If the ParametricSignal block DOES NOT have a 'Scan' key specified, it is;; included in all decomposition, including during the hyperparameter;; optimization.  If 'Scan' is specified, it should be a list or ndarray;; containing a list of masses to consider.  The masses are exposed as;; {Mass} when the block is evaluated, so the functions and their;; parameters can be written as functions of {Mass} when necessary.;;;;;;[ParametricSignalDefault];;;;;;; Plots.  Specify as many as you would like.  Each plot is specified in a block;; with header;;;; [Plot : filename.pdf];;;; The plot is saved in '<basedir>/Output/filename.pdf'.  Like the signal blocks,;; all plot blocks inherit from 'PlotDefault'.;;;; There are several plot types available, specified by the 'Type' key:;;    Estimators:  Draw signal shapes and the corresponding estimators. Use the;;                 'signals' key to specifiy which signals to draw, and the 'Draw';;                 key to specify what to draw (signals, estimators, or residuals).;;;;    Moments:     Line / bar plot of the norm-squared of the moments for the data;;                 and specified signal models.  The data is always drawn; signals;;                 can be selectively specified using the 'Draw' key, just like;;                 above. 'Style' can be set to either 'line' or 'bar'.;;;;    Fit:         Draw a histogram of the mass spectrum together with the decomposition.;;                 The key 'Bins' should contain a list or ndarray of bin edges, in;;                 order.  The plot will be drawn from the lower edge to the upper;;                 edge, ignoring events outside the range.;;;;    Scan:        Draw a p-value and limit plot for the mass scans specified in;;                 'Scans'.  You're allowed to choose any signal model above that;;                 had a 'Scan' parameter specified.;;;; All of these have various keys available to adjust the cosmetic features of;; the plot (titles, linear vs. log, etc.).  See below for examples.  The plot;; code lives in Plots/Plots.py, so consult that for a full list of parameters.;;;;;[PlotDefault]XLabel:    $M_{\gamma\gamma}$ (GeV)LogX:      FalseLogY:      TrueStyle:     errorbarCMap:      copperResidual:  TrueResYLim:   (-1.0, 1.0);;;;;;; Compare all the data moments to the Higgs signal and estimator.;;;;;[Plot: moments.pdf]Type:       MomentsTitle:      Moment Comparison - Data vs. HiggsXLabel:     "Moment \#"Draw:       ("Signal", "Estimator")Style:      lineRange:      (1, 2048)LogX:       FalseLogY:       True;;;;;;; Draw the first 128 data moments.;;;;;[Plot: low_moments.pdf]Type:       MomentsTitle:      Data MomentsXLabel:     "Moment \#"Style:      barRange:      (1, 128)LogX:       FalseLogY:       True;;;;;;; Make some plots of the fits;;;;;[Plot: full.pdf]Type:      FitTitle:     Full SpectrumYLabel:    Events / GeVBins:      np.logspace(np.log10(60), np.log10(3060), 201)LogX:      TrueYLim:      (1e-5, 1e5)[Plot: lowmass.pdf]Type:      FitTitle:     Low-Mass RegionYLabel:    Events / GeVBins:      np.linspace(60, 160, 101)LogY:      False
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