plot_utils.py

import numpy as np

def NormOne(a):
    return np.ones_like(a)/len(a)

def Eff(df,var,query,acceptance,bin_edges,absval=False):
    bin_centers = 0.5*(bin_edges[1:]+bin_edges[:-1])
    bins = []
    bin_eff = []
    bin_err = []
    for i in range(len(bin_centers)):
        binmin = bin_edges[i]
        binmax = bin_edges[i+1]
        bincut = '%s > %f and %s < %f'%(var,binmin,var,binmax)
        if (absval == True):
            bincut = '(%s > %f and %s < %f) or (%s > -%f and %s < -%f)'%(var,binmin,var,binmax,var,binmax,var,binmin)
        if (acceptance != ''): bincut += ' and %s'%acceptance
        df_tmp =  df.query(bincut) # cut on bin range for desired var.
        df_sub = df_tmp.query(query) # apply constrain 
        if (df_tmp.shape[0] == 0): continue
        eff = df_sub.shape[0] / float( df_tmp.shape[0] )
        err = np.sqrt( eff*(1-eff)/df_tmp.shape[0] )
        bin_eff.append( eff )
        bin_err.append( err )
        bins.append(bin_centers[i])
    return np.array(bins),np.array(bin_eff),np.array(bin_err)