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plotters.py
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plotters.py
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"""
Definines and instantiate the plotter classes.
These are the classes where the analysis logic is implemented.
The plotter classes need to implement a standard interface:
init:
accept one or more DFCollection (or TPSet) and a corresponding list of selections.
book_histos:
activates the relevant DFCollection instances and books the HistoClasses defined in the
`l1THistos` module for each combination of the object and selections.
The naming convention of the histograms uses the namse of the objects,
the name of the selection and the name of the gen-matched object (if any).
fill_histos:
actually implements the analysis logic, running the selections on the input
collections and filling the histograms.
Several collections of plotters are also instantiated. Which one will actually be run
is steered via the configuration file.
"""
from __future__ import print_function
from __future__ import absolute_import
import ROOT
import pandas as pd
import numpy as np
from . import l1THistos as histos
from . import utils as utils
from . import clusterTools as clAlgo
from . import selections as selections
from . import calibrations as calib
# import collections as collections
ROOT.gROOT.ProcessLine('#include "src/fastfilling.h"')
class Test(object):
def __init__(self, name):
self.name = name
def __repr__(self):
return "<TEST class, {}>".format(self.name)
class BasePlotter(object):
def __init__(self, data_set, data_selections, gen_set=None, gen_selections=None):
self.data_set = data_set
self.data_selections = data_selections
self.gen_set = gen_set
self.gen_selections = gen_selections
@property
def tp_set(self):
return self.data_set
@property
def tp_selections(self):
return self.data_selections
def get_histo_primitives(self):
histo_primitives = pd.DataFrame(columns=['data', 'data_sel', 'gen_sel', 'data_label', 'data_sel_label', 'gen_sel_label'])
gen_sel_names = ['nomatch']
gen_sel_labels = ['']
if self.gen_selections is not None:
gen_sel_names = [sel.name for sel in self.gen_selections]
gen_sel_labels = [sel.label for sel in self.gen_selections]
for data_sel in self.data_selections:
for idx, gen_sel_name in enumerate(gen_sel_names):
histo_primitives = histo_primitives.append({'data': self.data_set.name,
'data_sel': data_sel.name,
'gen_sel': gen_sel_name,
'data_label': self.data_set.label,
'data_sel_label': data_sel.label,
'gen_sel_label': gen_sel_labels[idx]},
ignore_index=True)
return histo_primitives
def __repr__(self):
return '<{}, ds: {}, ds_sel: {}, g: {}, g_sel: {} >'.format(
self.__class__.__name__,
self.data_set,
self.data_selections,
self.gen_set,
self.gen_selections)
# def change_genpart_selection(self, newselection):
# """Allow customization of gen selection per sample."""
# if self.gen_selections is not None:
# self.gen_selections = newselection
#
# def __repr__(self):
# if self.gen_selections is not None:
# return '<{}, tp: {}, gen_sel: {}>'.format(self.__class__.__name__,
# self.data_set.name,
# self.gen_selections[0].selection)
# else:
# return '<{}, tp: {}>'.format(self.__class__.__name__,
# self.data_set.name)
class RatePlotter(BasePlotter):
def __init__(self, tp_set, tp_selections=[selections.Selection('all')]):
self.h_rate = {}
super(RatePlotter, self).__init__(tp_set, tp_selections)
def book_histos(self):
self.tp_set.activate()
tp_name = self.tp_set.name
for selection in self.tp_selections:
self.h_rate[selection.name] = histos.RateHistos(name='{}_{}'.format(tp_name,
selection.name))
def fill_histos(self, debug=0):
# print '------------------'
# print self.tp_set.name
for selection in self.tp_selections:
sel_clusters = self.tp_set.query(selection)
self.h_rate[selection.name].fill_many(sel_clusters.loc[sel_clusters['pt'].groupby(level='entry', group_keys=False).nlargest(n=1).index, ['pt']])
self.h_rate[selection.name].fill_norm(self.tp_set.new_read_nentries)
def fill_histos_event(self, idx, debug=0):
if self.data_set.new_read:
self.fill_histos(debug)
class BaseRateCounter(BasePlotter):
def __init__(self, HistoClass, tp_set, tp_selections=[selections.Selection('all')]):
self.HistoClass = HistoClass
self.h_rate = {}
super(BaseRateCounter, self).__init__(tp_set, tp_selections)
def book_histos(self):
self.tp_set.activate()
tp_name = self.tp_set.name
for selection in self.tp_selections:
self.h_rate[selection.name] = self.HistoClass(
name='{}_{}'.format(tp_name, selection.name))
def fill_histos(self, debug=0):
# print('------------------')
# print(f'L1 Obj: {self.tp_set.name}')
for selection in self.tp_selections:
# print(f' . sel: {selection}')
# print(f' . # of read entries: {self.tp_set.new_read_nentries}')
sel_clusters = self.tp_set.query(selection)
self.h_rate[selection.name].fill(sel_clusters)
self.h_rate[selection.name].fill_norm(self.tp_set.new_read_nentries)
def fill_histos_event(self, idx, debug=0):
if self.data_set.new_read:
self.fill_histos(debug)
class RateCounter(BaseRateCounter):
def __init__(self, tp_set, tp_selections=[selections.Selection('all')]):
self.h_rate = {}
super(RateCounter, self).__init__(histos.SingleObjRateHistoCounter, tp_set, tp_selections)
class DoubleObjRateCounter(BaseRateCounter):
def __init__(self, tp_set, tp_selections=[selections.Selection('all')]):
self.h_rate = {}
super(DoubleObjRateCounter, self).__init__(histos.DoubleObjRateHistoCounter, tp_set, tp_selections)
class HGCCl3DRatePlotter(BasePlotter):
def __init__(self, tp_set, tp_selections=[selections.Selection('all')]):
self.h_rate = {}
super(HGCCl3DRatePlotter, self).__init__(tp_set, tp_selections)
def book_histos(self):
self.tp_set.activate()
tp_name = self.tp_set.name
for selection in self.tp_selections:
self.h_rate[selection.name] = histos.RateHistos(name='{}_{}'.format(tp_name,
selection.name))
def fill_histos(self, debug=0):
# print '------------------'
# print self.tp_set.name
for selection in self.tp_selections:
sel_clusters = self.tp_set.query(selection)
self.h_rate[selection.name].fill_many(sel_clusters.loc[sel_clusters['pt_em'].groupby(level='entry', group_keys=False).nlargest(n=1).index, ['pt_em']])
self.h_rate[selection.name].fill_norm(self.tp_set.new_read_nentries)
def fill_histos_event(self, idx, debug=0):
if self.data_set.new_read:
self.fill_histos(debug)
class GenericDataFrameLazyPlotter(BasePlotter):
def __init__(self, HistoClass, data_set, selections=[selections.Selection('all')]):
self.HistoClass = HistoClass
self.h_set = {}
super(GenericDataFrameLazyPlotter, self).__init__(data_set, selections)
def book_histos(self):
self.data_set.activate()
data_name = self.data_set.name
for selection in self.data_selections:
self.h_set[selection.name] = self.HistoClass(name='{}_{}_nomatch'.format(data_name,
selection.name))
def fill_histos(self, debug=0):
# if not (idx, 0) in self.data_set.df.index:
# return
dataframe = self.data_set.df
if dataframe.empty:
return
filler = histos.HistoLazyFiller(dataframe)
# print (self.data_set.name)
# print (self.data_set.df.columns)
for data_sel in self.data_selections:
if data_sel.all:
filler.add_selection(data_sel.name, np.full(dataframe.shape[0], True, dtype=bool))
else:
filler.add_selection(data_sel.name, dataframe.eval(data_sel.selection).values)
self.h_set[data_sel.name].fill_lazy(filler, data_sel.name)
filler.fill()
def fill_histos_event(self, idx, debug=0):
if self.data_set.new_read:
self.fill_histos(debug)
if hasattr(self.HistoClass, 'fill_event'):
for selection in self.data_selections:
objects = self.data_set.query_event(selection, idx)
self.h_set[selection.name].fill_event(objects)
class GenericDataFramePlotter(BasePlotter):
def __init__(self, HistoClass, data_set, selections=[selections.Selection('all')]):
self.HistoClass = HistoClass
self.h_set = {}
super(GenericDataFramePlotter, self).__init__(data_set, selections)
def book_histos(self):
self.data_set.activate()
data_name = self.data_set.name
for selection in self.data_selections:
self.h_set[selection.name] = self.HistoClass(name='{}_{}_nomatch'.format(data_name,
selection.name))
def fill_histos(self, debug=0):
if self.data_set.df.empty:
return
for data_sel in self.data_selections:
data = self.data_set.query(data_sel)
self.h_set[data_sel.name].fill(data)
def fill_histos_event(self, idx, debug=0):
if self.data_set.new_read:
self.fill_histos(debug)
class GenPlotter(GenericDataFrameLazyPlotter):
def __init__(self, gen_set, gen_selections=[selections.Selection('all')]):
super(GenPlotter, self).__init__(
histos.GenParticleHistos,
gen_set,
selections.multiply_selections(
gen_selections,
[selections.Selection('', '', 'gen > 0')]))
class TkElePlotter(GenericDataFrameLazyPlotter):
def __init__(self, tkeg_set, tkeg_selections=[selections.Selection('all')]):
super(TkElePlotter, self).__init__(histos.TkEleHistos, tkeg_set, tkeg_selections)
class TkEmPlotter(GenericDataFrameLazyPlotter):
def __init__(self, tkeg_set, tkeg_selections=[selections.Selection('all')]):
super(TkEmPlotter, self).__init__(histos.TkEmHistos, tkeg_set, tkeg_selections)
class TkEGPlotter(GenericDataFramePlotter):
def __init__(self, tkeg_set, tkeg_selections=[selections.Selection('all')]):
super(TkEGPlotter, self).__init__(histos.TkEGHistos, tkeg_set, tkeg_selections)
class TrackPlotter(GenericDataFramePlotter):
def __init__(self, trk_set, track_selections=[selections.Selection('all')]):
super(TrackPlotter, self).__init__(histos.TrackHistos, trk_set, track_selections)
class EGPlotter(GenericDataFrameLazyPlotter):
def __init__(self, eg_set, eg_selections=[selections.Selection('all')]):
super(EGPlotter, self).__init__(histos.EGHistos, eg_set, eg_selections)
class TTPlotter(GenericDataFramePlotter):
def __init__(self, tt_set, tt_selections=[selections.Selection('all')]):
super(TTPlotter, self).__init__(histos.TriggerTowerHistos, tt_set, tt_selections)
class DecTkPlotter(GenericDataFrameLazyPlotter):
def __init__(self, tk_set, tk_selections=[selections.Selection('all')]):
super(DecTkPlotter, self).__init__(histos.DecTkHistos, tk_set, tk_selections)
class Cl3DPlotter(GenericDataFrameLazyPlotter):
def __init__(self, data_set, data_selections=[selections.Selection('all')]):
super(Cl3DPlotter, self).__init__(histos.Cluster3DHistos, data_set, data_selections)
class TPPlotter(BasePlotter):
def __init__(self, tp_set, tp_selections=[selections.Selection('all')]):
# self.tp_set = tp_set
# self.tp_selections = tp_selections
self.h_tpset = {}
super(TPPlotter, self).__init__(tp_set, tp_selections)
def book_histos(self):
self.tp_set.activate()
tp_name = self.tp_set.name
for selection in self.tp_selections:
self.h_tpset[selection.name] = histos.HistoSetClusters(name='{}_{}_nomatch'.format(tp_name, selection.name))
def fill_histos(self, debug=0):
# FIXME: migrate to the new query caching system
for tp_sel in self.tp_selections:
tcs = self.tp_set.tc_df
cl2Ds = self.tp_set.cl2d_df
cl3Ds = self.tp_set.cl3d_df
if not tp_sel.all:
cl3Ds = self.tp_set.cl3d_df.query(tp_sel.selection)
# debug = 4
# utils.debugPrintOut(debug, '{}_{}'.format(self.tp_set.name, 'TCs'), tcs, tcs[:3])
# utils.debugPrintOut(debug, '{}_{}'.format(self.tp_set.name, 'CL2D'), cl2Ds, cl2Ds[:3])
# utils.debugPrintOut(debug, '{}_{}'.format(self.tp_set.name, 'CL3D'), cl3Ds, cl3Ds[:3])
if not cl3Ds.empty and not cl2Ds.empty and not tcs.empty:
self.h_tpset[tp_sel.name].fill(tcs, cl2Ds, cl3Ds)
class TPGenMatchPlotter(BasePlotter):
def __init__(self, tp_set, gen_set,
tp_selections=[selections.Selection('all')],
gen_selections=[selections.Selection('all')],
extended_range=False):
# self.tp_set = tp_set
# self.tp_selections = tp_selections
# self.gen_set = gen_set
# self.gen_selections = gen_selections
self.h_tpset = {}
self.h_resoset = {}
self.h_effset = {}
self.h_conecluster = {}
self.extended_range = extended_range
super(TPGenMatchPlotter, self).__init__(
tp_set,
tp_selections,
gen_set,
selections.multiply_selections(
gen_selections,
[selections.Selection('', '', 'gen > 0')]))
def plot3DClusterMatch(self,
genParticles,
trigger3DClusters,
triggerClusters,
triggerCells,
histoGen,
histoGenMatched,
histoTCMatch,
histoClMatch,
histo3DClMatch,
histoReso,
histoResoCone,
histoReso2D,
histoConeClusters,
algoname,
debug):
def computeIsolation(all3DClusters, idx_best_match, idx_incone, dr):
ret = pd.DataFrame()
# print 'index best match: {}'.format(idx_best_match)
# print 'indexes all in cone: {}'.format(idx_incone)
components = all3DClusters[(all3DClusters.index.isin(idx_incone)) & ~(all3DClusters.index == idx_best_match)]
# print 'components indexes: {}'.format(components.index)
compindr = components[np.sqrt((components.eta-all3DClusters.loc[idx_best_match].eta)**2 + (components.phi-all3DClusters.loc[idx_best_match].phi)**2) < dr]
if not compindr.empty:
# print 'components indexes in dr: {}'.format(compindr.index)
ret['energy'] = [compindr.energy.sum()]
ret['eta'] = [np.sum(compindr.eta*compindr.energy)/compindr.energy.sum()]
ret['pt'] = [(ret.energy/np.cosh(ret.eta)).values[0]]
else:
ret['energy'] = [0.]
ret['eta'] = [0.]
ret['pt'] = [0.]
return ret
def sumClustersInCone(all3DClusters, idx_incone, debug=0):
components = all3DClusters[all3DClusters.index.isin(idx_incone)]
ret = clAlgo.sum3DClusters(components)
if debug > 0:
print('-------- in cone:')
print(components.sort_values(by='pt', ascending=False))
print(' - Cone sum:')
print(ret)
return ret
best_match_indexes = {}
if not trigger3DClusters.empty:
best_match_indexes, allmatches = utils.match_etaphi(genParticles[['eta', 'phi']],
trigger3DClusters[['eta', 'phi']],
trigger3DClusters['pt'],
deltaR=0.1)
# print ('------ best match: ')
# print (best_match_indexes)
# print ('------ all matches:')
# print (allmatches)
# allmatched2Dclusters = list()
# matchedClustersAll = pd.DataFrame()
if histoGen is not None:
histoGen.fill(genParticles)
for idx, genParticle in genParticles.iterrows():
if idx in best_match_indexes.keys():
# print ('-----------------------')
# print(genParticle)
matched3DCluster = trigger3DClusters.loc[[best_match_indexes[idx]]]
# print (matched3DCluster)
# allMatches = trigger3DClusters.iloc[allmatches[idx]]
# print ('--')
# print (allMatches)
# print (matched3DCluster.clusters.item())
# print (type(matched3DCluster.clusters.item()))
# matchedClusters = triggerClusters[ [x in matched3DCluster.clusters.item() for x in triggerClusters.id]]
matchedClusters = triggerClusters[triggerClusters.id.isin(matched3DCluster.clusters.item())]
# print (matchedClusters)
matchedTriggerCells = triggerCells[triggerCells.id.isin(np.concatenate(matchedClusters.cells.values))]
# allmatched2Dclusters. append(matchedClusters)
if False:
if 'energyCentral' not in matched3DCluster.columns:
calib_factor = 1.084
matched3DCluster['energyCentral'] = [matchedClusters[(matchedClusters.layer > 9) & (matchedClusters.layer < 21)].energy.sum()*calib_factor]
if False:
iso_df = computeIsolation(trigger3DClusters,
idx_best_match=best_match_indexes[idx],
idx_incone=allmatches[idx], dr=0.2)
matched3DCluster['iso0p2'] = iso_df.energy
matched3DCluster['isoRel0p2'] = iso_df.pt/matched3DCluster.pt
# fill the plots
histoTCMatch.fill(matchedTriggerCells)
histoClMatch.fill(matchedClusters)
histo3DClMatch.fill(matched3DCluster)
# print matchedClusters
# print matchedClusters.layer.unique()
for layer in matchedClusters.layer.unique():
if histoReso2D is not None:
histoReso2D.fill(reference=genParticle,
target=clAlgo.build2D(matchedClusters[matchedClusters.layer == layer]))
if False:
histoReso2D.fill(reference=genParticle, target=matchedClusters)
histoReso.fill(reference=genParticle, target=matched3DCluster)
if False:
# now we fill the reso plot for all the clusters in the cone
clustersInCone = sumClustersInCone(trigger3DClusters, allmatches[idx])
def fill_cluster_incone_histos(cl3ds,
idx_allmatches,
idx_bestmatch,
charge,
h_clustersInCone,
debug=0):
if debug > 4:
print('- best match: {}, all matches: {}'.format(idx_bestmatch,
idx_allmatches))
bestcl = cl3ds.loc[idx_bestmatch]
h_clustersInCone.fill_n(len(idx_allmatches)-1)
for idx in idx_allmatches:
if idx == idx_bestmatch:
continue
clincone = cl3ds.loc[idx]
h_clustersInCone.fill(reference=bestcl,
target=clincone,
charge=charge)
# print genParticle
# print genParticle.pid/abs(genParticle.pid)
fill_cluster_incone_histos(trigger3DClusters,
allmatches[idx],
best_match_indexes[idx],
genParticle.pid/abs(genParticle.pid),
histoConeClusters)
# print ('----- in cone sum:')
# print (clustersInCone)
# histoResoCone.fill(reference=genParticle, target=clustersInCone.iloc[0])
if histoGenMatched is not None:
histoGenMatched.fill(genParticles.loc[[idx]])
if debug >= 6:
print(('--- Dump match for algo {} ---------------'.format(algoname)))
print(('GEN particle: idx: {}'.format(idx)))
print(genParticle)
print('Matched to 3D cluster:')
print(matched3DCluster)
print('Matched 2D clusters:')
print(matchedClusters)
print('matched cells:')
print(matchedTriggerCells)
print(('3D cluster energy: {}'.format(matched3DCluster.energy.sum())))
print(('3D cluster pt: {}'.format(matched3DCluster.pt.sum())))
calib_factor = 1.084
print(('sum 2D cluster energy: {}'.format(matchedClusters.energy.sum()*calib_factor)))
# print ('sum 2D cluster pt: {}'.format(matchedClusters.pt.sum()*calib_factor))
print(('sum TC energy: {}'.format(matchedTriggerCells.energy.sum())))
print('Sum of matched clusters in cone:')
print(clustersInCone)
else:
if debug >= 5:
print(('==== Warning no match found for algo {}, idx {} ======================'.format(algoname,
idx)))
if debug >= 2:
print(genParticle)
print(trigger3DClusters)
# if len(allmatched2Dclusters) != 0:
# matchedClustersAll = pd.concat(allmatched2Dclusters)
# return matchedClustersAll
def book_histos(self):
self.gen_set.activate()
self.tp_set.activate()
for tp_sel in self.tp_selections:
for gen_sel in self.gen_selections:
histo_name = '{}_{}_{}'.format(self.tp_set.name,
tp_sel.name,
gen_sel.name)
self.h_tpset[histo_name] = histos.HistoSetClusters(histo_name)
self.h_resoset[histo_name] = histos.HistoSetReso(histo_name)
self.h_effset[histo_name] = histos.HistoSetEff(histo_name, extended_range=self.extended_range)
self.h_conecluster[histo_name] = histos.ClusterConeHistos(histo_name)
def fill_histos(self, debug=0):
for tp_sel in self.data_selections:
tcs = self.tp_set.tc_df
cl2Ds = self.tp_set.cl2d_df
cl3Ds = self.tp_set.cl3ds.query(tp_sel)
for gen_sel in self.gen_selections:
genReference = self.gen_set.query(gen_sel)
histo_name = '{}_{}_{}'.format(self.tp_set.name, tp_sel.name, gen_sel.name)
h_tpset_match = self.h_tpset[histo_name]
h_resoset = self.h_resoset[histo_name]
h_genseleff = self.h_effset[histo_name]
h_conecl = self.h_conecluster[histo_name]
# print 'TPsel: {}, GENsel: {}'.format(tp_sel.name, gen_sel.name)
# print cl3Ds
# print cl2Ds
# print tcs
self.plot3DClusterMatch(genReference,
cl3Ds,
cl2Ds,
tcs,
h_genseleff.h_den,
h_genseleff.h_num,
h_tpset_match.htc,
h_tpset_match.hcl2d,
h_tpset_match.hcl3d,
h_resoset.hreso,
h_resoset.hresoCone,
h_resoset.hreso2D,
h_conecl,
self.tp_set.name,
debug)
def __repr__(self):
return '<{} tps: {}, tps_s: {}, gen:{}, gen_s:{}> '.format(self.__class__.__name__,
self.tp_set.name,
[sel.name for sel in self.tp_selections],
self.gen_set.name,
[sel.name for sel in self.gen_selections])
class GenericGenMatchPlotter(BasePlotter):
def __init__(self, ObjectHistoClass, ResoHistoClass,
data_set, gen_set,
data_selections=[selections.Selection('all')],
gen_selections=[selections.Selection('all')],
gen_eta_phi_columns=['exeta', 'exphi']):
self.ObjectHistoClass = ObjectHistoClass
self.ResoHistoClass = ResoHistoClass
# self.data_set = data_set
# self.data_selections = data_selections
# self.gen_set = gen_set
# self.gen_selections = gen_selections
self.h_dataset = {}
self.h_resoset = {}
self.h_effset = {}
self.gen_eta_phi_columns = gen_eta_phi_columns
super(GenericGenMatchPlotter, self).__init__(
data_set,
data_selections,
gen_set,
selections.multiply_selections(
gen_selections,
[selections.Selection('', '', 'gen > 0')]))
# print self
# print gen_selections
def plotObjectMatch(self,
genParticles,
objects,
h_gen,
h_gen_matched,
h_object_matched,
h_reso,
algoname,
debug):
gen_filler = histos.HistoLazyFiller(genParticles)
den_sel = np.full(genParticles.shape[0], True, dtype=bool)
num_sel = np.full(genParticles.shape[0], False, dtype=bool)
# fill histo with all selected GEN particles before any match
gen_filler.add_selection('den', den_sel)
if h_gen:
h_gen.fill_lazy(gen_filler, 'den')
best_match_indexes = {}
positional = True
if not objects.empty:
obj_filler = histos.HistoLazyFiller(objects)
obj_ismatch = np.full(objects.shape[0], False, dtype=bool)
best_match_indexes, allmatches = utils.match_etaphi(
genParticles[self.gen_eta_phi_columns],
objects[['eta', 'phi']],
objects['pt'],
deltaR=0.1,
return_positional=positional)
# print (objects)
# print (best_match_indexes)
# print (best_match_iloc)
for idx in list(best_match_indexes.keys()):
if positional:
obj_matched = objects.iloc[[best_match_indexes[idx]]]
obj_ismatch[best_match_indexes[idx]] = True
else:
obj_matched = objects.loc[[best_match_indexes[idx]]]
obj_ismatch[objects.index.get_loc(best_match_indexes[idx])] = True
# h_object_matched.fill(obj_matched)
num_sel[genParticles.index.get_loc(idx)] = True
h_reso.fill(reference=genParticles.loc[idx], target=obj_matched)
if hasattr(h_reso, 'fill_nMatch'):
h_reso.fill_nMatch(len(allmatches[idx]))
# print('GEN')
# print(gen_matched)
# print('ALL matches: {}'.format(len(allmatches[idx])))
# print (objects.loc[allmatches[idx]])
# print('--------')
# print('best match index: {}'.format(best_match_indexes[idx]))
# print(obj_matched)
# print('best match iloc: {}'.format(best_match_iloc[idx]))
# print(objects.iloc[[best_match_iloc[idx]]])
# print ("--")
# if h_gen_matched is not None:
# h_gen_matched.fill(gen_matched)
obj_filler.add_selection('match', obj_ismatch)
h_object_matched.fill_lazy(obj_filler, 'match')
obj_filler.fill()
if h_gen_matched is not None:
gen_filler.add_selection('num', num_sel)
h_gen_matched.fill_lazy(gen_filler, 'num')
gen_filler.fill()
def book_histos(self):
self.gen_set.activate()
self.data_set.activate()
# print(f'# data sel: {len(self.data_selections)} x # gen sel: {len(self.gen_selections)} = {len(self.data_selections)*len(self.gen_selections)}')
for tp_sel in self.data_selections:
for gen_sel in self.gen_selections:
histo_name = '{}_{}_{}'.format(self.data_set.name, tp_sel.name, gen_sel.name)
self.h_dataset[histo_name] = self.ObjectHistoClass(histo_name)
self.h_resoset[histo_name] = self.ResoHistoClass(histo_name)
self.h_effset[histo_name] = histos.HistoSetEff(histo_name)
def fill_histos(self, debug=0):
pass
def fill_histos_event(self, idx, debug=0):
for tp_sel in self.data_selections:
objects = self.data_set.query_event(tp_sel, idx)
for gen_sel in self.gen_selections:
genReference = self.gen_set.query_event(gen_sel, idx)
if genReference.empty:
continue
histo_name = '{}_{}_{}'.format(self.data_set.name, tp_sel.name, gen_sel.name)
# print (histo_name)
# print (f'# data: {objects.shape[0]}')
# print (f'# gen: {genReference.shape[0]}')
# print (genReference)
h_obj_match = self.h_dataset[histo_name]
h_resoset = self.h_resoset[histo_name]
h_genseleff = self.h_effset[histo_name]
# print ('TPsel: {}, GENsel: {}'.format(tp_sel.name, gen_sel.name))
self.plotObjectMatch(genReference,
objects,
h_genseleff.h_den,
h_genseleff.h_num,
h_obj_match,
h_resoset,
self.data_set.name,
debug)
# print (f'Filling histof for data: {self.data_set.name}, entry: {idx}')
# print ("# of queries: {}".format(qcounter))
class TrackGenMatchPlotter(GenericGenMatchPlotter):
def __init__(self, data_set, gen_set,
data_selections=[selections.Selection('all')],
gen_selections=[selections.Selection('all')]):
super(TrackGenMatchPlotter, self).__init__(histos.TrackHistos, histos.TrackResoHistos,
data_set, gen_set,
data_selections, gen_selections,
gen_eta_phi_columns=['eta', 'phi'])
class DecTrackGenMatchPlotter(GenericGenMatchPlotter):
def __init__(self, data_set, gen_set,
data_selections=[selections.Selection('all')],
gen_selections=[selections.Selection('all')]):
super(DecTrackGenMatchPlotter, self).__init__(
histos.DecTkHistos, histos.DecTkResoHistos,
data_set, gen_set,
data_selections, gen_selections,
gen_eta_phi_columns=['eta', 'phi'])
class Cl3DGenMatchPlotter(GenericGenMatchPlotter):
def __init__(self, data_set, gen_set,
data_selections=[selections.Selection('all')],
gen_selections=[selections.Selection('all')]):
super(Cl3DGenMatchPlotter, self).__init__(histos.Cluster3DHistos, histos.ResoHistos,
data_set, gen_set,
data_selections, gen_selections)
class EGGenMatchPtWPSPlotter(GenericGenMatchPlotter):
def __init__(self, data_set, gen_set, gen_selections):
super(EGGenMatchPtWPSPlotter, self).__init__(
histos.EGHistos, histos.EGResoHistos,
data_set, gen_set,
[], gen_selections)
def book_histos(self):
calib_mgr = calib.CalibManager()
rate_pt_wps = calib_mgr.get_pt_wps()
self.data_selections = calib.rate_pt_wps_selections(
rate_pt_wps, self.data_set.name)
GenericGenMatchPlotter.book_histos(self)
class HGCCl3DGenMatchPtWPSPlotter(GenericGenMatchPlotter):
def __init__(self, data_set, gen_set, gen_selections):
super(HGCCl3DGenMatchPtWPSPlotter, self).__init__(
histos.Cluster3DHistos, histos.ResoHistos,
data_set, gen_set,
[], gen_selections)
def book_histos(self):
calib_mgr = calib.CalibManager()
rate_pt_wps = calib_mgr.get_pt_wps()
self.data_selections = calib.rate_pt_wps_selections(
rate_pt_wps, self.data_set.name, 'pt_em')
GenericGenMatchPlotter.book_histos(self)
class EGGenMatchPlotter(GenericGenMatchPlotter):
def __init__(self, data_set, gen_set,
data_selections=[selections.Selection('all')],
gen_selections=[selections.Selection('all')]):
super(EGGenMatchPlotter, self).__init__(histos.EGHistos, histos.EGResoHistos,
data_set, gen_set,
data_selections, gen_selections)
class TkEGGenMatchPlotter(GenericGenMatchPlotter):
def __init__(self, data_set, gen_set,
data_selections=[selections.Selection('all')],
gen_selections=[selections.Selection('all')]):
super(TkEGGenMatchPlotter, self).__init__(histos.TkEGHistos, histos.EGResoHistos,
data_set, gen_set,
data_selections, gen_selections)
class ResoNtupleMatchPlotter(BasePlotter):
def __init__(self, data_set, gen_set,
data_selections=[selections.Selection('all')], gen_selections=[selections.Selection('all')]):
self.h_calibration = {}
super(ResoNtupleMatchPlotter, self).__init__(
data_set,
data_selections,
gen_set,
selections.multiply_selections(
gen_selections,
[selections.Selection('', '', 'gen > 0')]))
# print self
# print gen_selections
def plotObjectMatch(self,
genParticles,
objects,
h_calibration,
algoname,
debug):
best_match_indexes = {}
if not objects.empty:
best_match_indexes, allmatches = utils.match_etaphi(genParticles[['exeta', 'exphi']],
objects[['eta', 'phi']],
objects['pt'],
deltaR=0.1)
for idx, genParticle in genParticles.iterrows():
if idx in best_match_indexes.keys():
# print ('-----------------------')
# print(genParticle)
obj_matched = objects.loc[[best_match_indexes[idx]]]
# print obj_matched
# print obj_matched.clusters
# print obj_matched.clusters[0]
# print algoname
# print obj_matched[['energy', 'layer_energy']]
h_calibration.fill(reference=genParticle, target=obj_matched)
if debug >= 4:
print(('--- Dump match for algo {} ---------------'.format(algoname)))
print(('GEN particle: idx: {}'.format(idx)))
print(genParticle)
print('Matched to track object:')
print(obj_matched)
else:
if debug >= 5:
print(('==== Warning no match found for algo {}, idx {} ======================'.format(algoname, idx)))
print(genParticle)
print(objects)
def book_histos(self):
self.gen_set.activate()
self.data_set.activate()
for tp_sel in self.data_selections:
for gen_sel in self.gen_selections:
histo_name = '{}_{}_{}'.format(self.data_set.name, tp_sel.name, gen_sel.name)
self.h_calibration[histo_name] = histos.ResoTuples(histo_name)
def fill_histos(self, debug=0):
for tp_sel in self.data_selections:
objects = self.data_set.query(tp_sel)
for gen_sel in self.gen_selections:
genReference = self.gen_set.query(gen_sel)
histo_name = '{}_{}_{}'.format(self.data_set.name, tp_sel.name, gen_sel.name)
h_calib = self.h_calibration[histo_name]
# print 'TPsel: {}, GENsel: {}'.format(tp_sel.name, gen_sel.name)
self.plotObjectMatch(genReference,
objects,
h_calib,
self.data_set.name,
debug)
class CalibrationPlotter(BasePlotter):
def __init__(self, data_set, gen_set,
data_selections=[selections.Selection('all')], gen_selections=[selections.Selection('all')]):
self.h_calibration = {}
super(CalibrationPlotter, self).__init__(
data_set,
data_selections,
gen_set,
selections.multiply_selections(
gen_selections,
[selections.Selection('', '', 'gen > 0')]))
# print self
# print gen_selections
def plotObjectMatch(self,
genParticles,
objects,
tcs,
h_calibration,
algoname,
debug):
best_match_indexes = {}
if not objects.empty:
best_match_indexes, allmatches = utils.match_etaphi(genParticles[['exeta', 'exphi']],
objects[['eta', 'phi']],
objects['pt'],
deltaR=0.1)
for idx, genParticle in genParticles.iterrows():
if idx in best_match_indexes.keys():
# print ('-----------------------')
# print(genParticle)
obj_matched = objects.loc[[best_match_indexes[idx]]]
# print obj_matched
# print obj_matched.clusters
# print obj_matched.clusters[0]
components = tcs[tcs.id.isin(obj_matched.iloc[0].clusters)].copy()
if 'layer_energy' not in obj_matched.columns:
layer_energy = []
for layer in range(1, 29, 2):
# components[components.layer == layer].energy.sum()
layer_energy.append(components[components.layer == layer].energy.sum())
obj_matched['layer_energy'] = [layer_energy]
# print algoname
# print obj_matched[['energy', 'layer_energy']]
h_calibration.fill(reference=genParticle, target=obj_matched)
if debug >= 4:
print(('--- Dump match for algo {} ---------------'.format(algoname)))
print(('GEN particle: idx: {}'.format(idx)))
print(genParticle)
print('Matched to track object:')
print(obj_matched)
else:
if debug >= 5:
print(('==== Warning no match found for algo {}, idx {} ======================'.format(algoname, idx)))
print(genParticle)
print(objects)
def book_histos(self):
self.gen_set.activate()
self.data_set.activate()
for tp_sel in self.data_selections:
for gen_sel in self.gen_selections:
histo_name = '{}_{}_{}'.format(self.data_set.name, tp_sel.name, gen_sel.name)
self.h_calibration[histo_name] = histos.CalibrationHistos(histo_name)
def fill_histos(self, debug=0):
for tp_sel in self.data_selections:
objects = self.data_set.query(tp_sel)
for gen_sel in self.gen_selections:
genReference = self.gen_set.query(gen_sel)
histo_name = '{}_{}_{}'.format(self.data_set.name, tp_sel.name, gen_sel.name)
h_calib = self.h_calibration[histo_name]
# print 'TPsel: {}, GENsel: {}'.format(tp_sel.name, gen_sel.name)
self.plotObjectMatch(genReference,
objects,
self.data_set.tcs.df,
h_calib,
self.data_set.name,
debug)
class TTGenMatchPlotter:
def __init__(self, tt_set, gen_set,
tt_selections=[selections.Selection('all')], gen_selections=[selections.Selection('all')]):
self.tt_set = tt_set
self.tt_selections = tt_selections
self.gen_set = gen_set
self.gen_selections = gen_selections
self.h_tt = {}
self.h_reso_tt = {}
self.h_reso_ttcl = {}
def book_histos(self):
self.tt_set.activate()
self.gen_set.activate()
for tp_sel in self.tt_selections:
for gen_sel in self.gen_selections:
histo_name = '{}_{}'.format(tp_sel.name, gen_sel.name)
self.h_tt[histo_name] = histos.TriggerTowerHistos('{}_{}'.format(self.tt_set.name, histo_name))
self.h_reso_tt[histo_name] = histos.TriggerTowerResoHistos('{}_{}'.format(self.tt_set.name, histo_name))
self.h_reso_ttcl[histo_name] = histos.TriggerTowerResoHistos('{}Cl_{}'.format(self.tt_set.name, histo_name))
def fill_histos(self, debug=0):
triggerTowers_all = self.tt_set.df
genParts_all = self.gen_set.df[(self.gen_set.df.gen > 0)]
for tp_sel in self.tt_selections:
triggerTowers = triggerTowers_all
if not tp_sel.all:
triggerTowers = triggerTowers_all.query(tp_sel.selection)
for gen_sel in self.gen_selections:
histo_name = '{}_{}'.format(tp_sel.name, gen_sel.name)
genReference = genParts_all
if not gen_sel.all:
genReference = genParts_all.query(gen_sel.selection)
self.plotTriggerTowerMatch(genReference,