lib.glob Namespace Reference

Classes
class	NTupleProc
class	NTupleGlob

Functions
def	_makedf
def	_loaddf
def	_process
def	_do_process

Function Documentation

def lib.glob._do_process (   rootf,   branches,   vars,   whens,   bins  )

private

Definition at line 84 of file glob.py.

def _do_process(rootf, branches, vars, whens, bins):
    hists = {}

    dfW = _makedf(rootf[names.folderW][names.tname].arrays(branches, library="pd"))
    dfE = _makedf(rootf[names.folderE][names.tname].arrays(branches, library="pd"))
    
    valW = [v(dfW) for v in vars]
    valE = [v(dfE) for v in vars]
    
    wW = [w(dfW) if w else None for w in whens]
    wE = [w(dfE) if w else None for w in whens]
    
    runs = dfW.meta.run.unique()
    
    hists["W"] = {}
    for r in runs:
        hists["W"][r] = {}
        for val, var in zip(valW, vars):
            hists["W"][r][var.name] = {}
            for w, when in zip(wW, whens):
                if w is None:
                    hist = np.histogram(val[dfW.meta.run == r], bins=bins)
                else:
                    hist = np.histogram(val[w & (dfW.meta.run == r)], bins=bins)

                hists["W"][r][var.name][when.name] = hist

    hists["E"] = {}
    for r in runs:
        hists["E"][r] = {}
        for val, var in zip(valE, vars):
            hists["E"][r][var.name] = {}
            for w, when in zip(wE, whens):
                if w is None:
                    hist = np.histogram(val[dfE.meta.run == r], bins=bins)
                else:
                    hist = np.histogram(val[w & (dfE.meta.run == r)], bins=bins)

                hists["E"][r][var.name][when.name] = hist

    return hists