/
NoiseAnalysis.py
executable file
·208 lines (185 loc) · 12.7 KB
/
NoiseAnalysis.py
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#!/usr/bin/env python
import ROOT as ro
import numpy as np
import ipdb, os
from ConfigParser import ConfigParser
from TransparentGrid import TransparentGrid
from optparse import OptionParser
from Utils import *
color_index = 10000
class NoiseAnalysis:
def __init__(self, trans_grid, numstrips, clustersize):
self.window_shift = 3
self.delta_ev = 100
self.min_adc_noise, self.max_adc_noise, self.delta_adc_noise = -322.25, 322.25, 0.5
self.min_snr_noise, self.max_snr_noise, self.delta_snr_noise = -32.225, 32.225, 0.05
self.trash = []
self.w = 0
self.trans_grid = trans_grid
self.num_strips = numstrips
self.cluster_size = clustersize
self.suffix = GetSuffixDictionary(self.trans_grid)
self.noise_cuts = self.trans_grid.cuts_man.noise_cuts
self.noise_friend_cuts = self.trans_grid.cuts_man.noise_friend_cuts
self.noise_nc_cuts = self.trans_grid.cuts_man.noise_nc_cuts
self.noise_nc_friend_cuts = self.trans_grid.cuts_man.noise_nc_friend_cuts
self.in_transp_cluster = self.trans_grid.cuts_man.AndCuts([self.trans_grid.cuts_man.transp_ev, self.trans_grid.cuts_man.in_transp_cluster])
self.noise_varz = self.trans_grid.noise_varz
self.noise_friend_varz = self.trans_grid.noise_friend_varz
def PosCanvas(self, canvas_name):
self.w = PositionCanvas(self.trans_grid, canvas_name, self.w, self.window_shift)
def OverlayNoiseDistribution(self, histo, cells='all', isFriend=False):
suffix = self.suffix[cells]
hname = histo.GetName().split('h_')[1]
typ = 'adc' if 'adc' in hname.lower() else 'snr'
noise_name0 = 'signal_noise_{s}_{t}'.format(s=suffix, t=typ) if not isFriend else 'signal_noise_buffer_{b}_{s}_{t}'.format(s=suffix, t=typ, b=self.trans_grid.noise_friend_buffer)
if not noise_name0 in self.trans_grid.histo.keys():
self.PlotNoiseNotInCluster(cells)
elif not self.trans_grid.histo[noise_name0]:
del self.trans_grid.histo[noise_name0]
self.PlotNoiseNotInCluster(cells)
noise_name_new = noise_name0 + '_' + hname
nbins = histo.GetNbinsX()
xbins = np.zeros(nbins, 'float64')
histo.GetXaxis().GetLowEdge(xbins)
xbins = np.append(xbins, 2 * xbins[-1] - xbins[-2])
self.trans_grid.histo[noise_name_new] = self.trans_grid.histo[noise_name0].Rebin(nbins, 'h_' + noise_name_new, xbins)
if self.trans_grid.histo[noise_name_new]:
self.trans_grid.histo[noise_name_new].SetTitle(noise_name0 + '(scaled)')
scale = histo.GetMaximum() / self.trans_grid.histo[noise_name_new].GetMaximum()
self.trans_grid.histo[noise_name_new].Scale(scale)
self.trans_grid.histo[noise_name_new].SetLineColor(ro.kGray + 1)
self.trans_grid.histo[noise_name_new].SetStats(0)
self.trans_grid.canvas[hname].cd()
if self.trans_grid.histo[noise_name_new].GetFunction('f_gaus_' + noise_name0):
self.trans_grid.histo[noise_name_new].GetFunction('f_gaus_' + noise_name0).SetBit(ro.TF1.kNotDraw)
self.trans_grid.histo[noise_name_new].Draw('same')
ro.gPad.Update()
def PlotNoise1D(self, varzdic, name, cut, typ='adc'):
temp_cut_noise = cut
temph = ro.TH1F('temph0', 'temph0', int(RoundInt((self.max_adc_noise - self.min_adc_noise) / float(self.delta_adc_noise))), self.min_adc_noise, self.max_adc_noise)
self.trans_grid.trans_tree.Draw('{v}>>temph0'.format(v=varzdic['adc']), temp_cut_noise, 'goff')
mean, sigma = temph.GetMean(), temph.GetRMS()
temph.Delete()
if sigma > 0:
self.min_snr_noise, self.max_snr_noise, self.delta_snr_noise = (ni / float(sigma) for ni in [self.min_adc_noise, self.max_adc_noise, self.delta_adc_noise])
if typ == 'snr':
self.trans_grid.DrawHisto1D(name + '_snr', self.min_snr_noise, self.max_snr_noise, self.delta_snr_noise, varzdic['snr'], varname='Signal not in cluster [SNR]', cuts=temp_cut_noise, option='e hist')
self.trans_grid.FitGaus(name + '_snr')
self.trans_grid.histo[name + '_snr'].GetXaxis().SetRangeUser(-3.2, 3.2)
self.PosCanvas(name + '_snr')
else:
self.trans_grid.DrawHisto1D(name + '_adc', self.min_adc_noise, self.max_adc_noise, self.delta_adc_noise, varzdic['adc'], varname='Signal not in cluster [ADC]', cuts=temp_cut_noise, option='e hist')
self.trans_grid.FitGaus(name + '_adc')
self.trans_grid.histo[name + '_adc'].GetXaxis().SetRangeUser(-32, 32)
self.PosCanvas(name + '_adc')
def PlotNoiseNotInCluster(self, cells='all', typ='adc', doNC=False, isFriend=False):
suffix = self.suffix[cells]
if doNC:
nameh = 'signal_noise_NC_chs_{c}'.format(c=suffix) if not isFriend else 'signal_noise_NC_chs_buffer_{b}_{c}'.format(c=suffix, b=self.trans_grid.noise_friend_buffer)
else:
nameh = 'signal_noise_{c}'.format(c=suffix) if not isFriend else 'signal_noise_buffer_{b}_{c}'.format(c=suffix, b=self.trans_grid.noise_friend_buffer)
temp_cut_noise = self.noise_cuts[cells] if not isFriend and not doNC else self.noise_friend_cuts[cells] if isFriend and not doNC else self.noise_nc_cuts[cells] if not isFriend and doNC else self.noise_nc_friend_cuts[cells]
var = self.noise_varz if not isFriend else self.noise_friend_varz
self.PlotNoise1D(var, nameh, temp_cut_noise, typ=typ)
def DoCommonMode(self, isFriend=False):
minev, maxev = self.trans_grid.trans_tree.GetMinimum('event'), self.trans_grid.trans_tree.GetMaximum('event')
var = 'cmn' if not isFriend else 'pedTree.cmn'
hname = 'cm_event_profile' if not isFriend else 'cm_event_profile_buffer_{v}'.format(v=RoundInt(self.trans_grid.noise_friend_buffer))
self.trans_grid.DrawProfile1D(hname, minev + self.delta_ev / 2.0, maxev - self.delta_ev / 2.0, self.delta_ev, 'event', 'event', var, 'cm [ADC]', self.trans_grid.cuts_man.transp_ev)
self.PosCanvas(hname)
hname = 'cm_histo' if not isFriend else 'cm_histo_buffer_{v}'.format(v=RoundInt(self.trans_grid.noise_friend_buffer))
self.trans_grid.DrawHisto1D(hname, -31, 31, 2, var, 'cm [ADC]', self.trans_grid.cuts_man.transp_ev)
self.PosCanvas(hname)
# self.trans_grid.DrawProfile2D('adc_channel_event_profile', minev + self.delta_ev / 2.0, maxev - self.delta_ev / 2.0, self.delta_ev, 'event', 0, 127, 1, 'VA channel', 'event', 'diaChannels', 'diaChADC', 'ADC', self.trans_grid.cuts_man.transp_ev)
# self.PosCanvas('adc_channel_event_profile')
# self.trans_grid.DrawProfile2D('signal_channel_event_profile', minev + self.delta_ev / 2.0, maxev - self.delta_ev / 2.0, self.delta_ev, 'event', 0, 127, 1, 'VA channel', 'event', 'diaChannels', 'diaChSignal', 'Signal [ADC]', self.trans_grid.cuts_man.transp_ev)
# self.PosCanvas('signal_channel_event_profile')
# self.trans_grid.DrawProfile2D('noise_cmc_channel_event_profile_all', minev + self.delta_ev / 2.0, maxev - self.delta_ev / 2.0, self.delta_ev, 'event', 0, 127, 1, 'VA channel', 'event', 'diaChannels', 'diaChPedSigmaCmc', 'Noise [ADC]', self.trans_grid.cuts_man.transp_ev)
# self.PosCanvas('noise_cmc_channel_event_profile_all')
def DoStrips2DHistograms(self, typ='adc', isFriend=False):
minch, maxch, deltach, xname, xvar = -0.5, 127.5, 1, 'VA channel', 'diaChannels'
minch_plot, maxch_plot = int(self.trans_grid.ch_ini - np.ceil((self.cluster_size - 1) / 2.0)), int(self.trans_grid.ch_end + np.ceil((self.cluster_size - 1) / 2.0))
def DrawHistogram(name, zmin, zmax, yname, yvar, cuts, typ='adc'):
histo_limits = Get1DLimits(zmin, zmax, self.delta_adc_noise) if typ == 'adc' else Get1DLimits(zmin, zmax, self.delta_snr_noise)
deltay = self.delta_adc_noise if typ == 'adc' else self.delta_snr_noise
miny_plot, maxy_plot = (-50, 50) if typ == 'adc' else (-5, 5)
self.trans_grid.DrawHisto2D(name, minch, maxch, deltach, xname, histo_limits['min'], histo_limits['max'], deltay, yname, xvar, yvar, cuts)
self.trans_grid.histo[name].GetXaxis().SetRangeUser(minch_plot, maxch_plot)
self.trans_grid.histo[name].GetYaxis().SetRangeUser(miny_plot, maxy_plot)
self.PosCanvas(name)
tempcuts = self.trans_grid.cuts_man.AndCuts([self.trans_grid.cuts_man.not_in_transp_cluster, self.trans_grid.cuts_man.valid_ped_sigma])
# tempcuts = self.trans_grid.cuts_man.ConcatenateCuts(self.trans_grid.cuts_man.not_in_cluster, self.trans_grid.cuts_man.valid_ped_sigma)
minz, maxz = (self.min_adc_noise, self.max_adc_noise) if typ == 'adc' else (self.min_adc_noise / 10., self.max_adc_noise / 10.)
hname = 'noise_Vs_channel_{t}'.format(t=typ) if not isFriend else 'noise_buffer_{v}_Vs_channel_{t}'.format(t=typ, v=self.trans_grid.noise_friend_buffer)
var = self.noise_varz['adc'] if typ == 'adc' and not isFriend else self.noise_friend_varz['adc'] if typ == 'adc' and isFriend else self.noise_friend_varz['snr'] if typ == 'snr' and isFriend else self.noise_varz['snr']
DrawHistogram(hname, minz, maxz, 'signal noise [{t}]'.format(t=typ.upper()), var, tempcuts, typ)
def DoPedestalEventHistograms(self, isFriend=False):
tempcuts = self.in_transp_cluster
minev, maxev = self.trans_grid.trans_tree.GetMinimum('event'), self.trans_grid.trans_tree.GetMaximum('event')
deltaev = 100.
if not isFriend:
varz = 'diaChPedMeanCmc'
maxy = GetMaximumFromTree(self.trans_grid.trans_tree, 'diaChPedMeanCmc', tempcuts)
miny = GetMinimumFromTree(self.trans_grid.trans_tree, 'diaChPedMeanCmc', tempcuts)
nameh = 'pedestal_mean_vs_event'
else:
if not self.trans_grid.trans_tree.GetFriend('pedTree'):
print 'The transparent tree has no pedTree friend. Cannot do these plots'
return
optending = 'buffer_{v}'.format(v=int(RoundInt(self.trans_grid.trans_tree.GetMaximum('pedTree.slidingLength'))))
varz = 'pedTree.diaChPedMeanCmc'
maxy = GetMaximumFromTree(self.trans_grid.trans_tree, 'pedTree.diaChPedMeanCmc', tempcuts)
miny = GetMinimumFromTree(self.trans_grid.trans_tree, 'pedTree.diaChPedMeanCmc', tempcuts)
nameh = 'pedestal_mean_{s}_vs_event'.format(s=optending)
self.trans_grid.DrawHisto2D(nameh, minev, maxev, deltaev, 'event', miny, maxy, 1.0, 'pedestal mean cluster chs [ADC]', 'event', varz, tempcuts)
self.PosCanvas(nameh)
def DoNoiseVsEventStudies(self, cells='all', num_delta_ev=100, doNC=False, isFriend=False):
suffix = self.suffix[cells]
xmin, xmax, deltax = self.trans_grid.trans_tree.GetMinimum('event'), self.trans_grid.trans_tree.GetMaximum('event'), num_delta_ev * self.delta_ev
hlimitsx = Get1DLimits(xmin, xmax, deltax, oddbins=False)
deltay = self.delta_adc_noise * 4
hlimitsy = Get1DLimits(RoundInt(self.min_adc_noise / 10.0), RoundInt(self.max_adc_noise / 10.0), deltay)
if not isFriend:
nameh = 'signal_noise_Vs_event_{c}'.format(c=suffix) if not doNC else 'signal_noise_NC_chs_Vs_event_{c}'.format(c=suffix)
temp_cut_noise = self.noise_cuts[cells] if not doNC else self.noise_nc_cuts[cells]
varz = 'diaChSignal'
else:
if not self.trans_grid.trans_tree.GetFriend('pedTree'):
print 'The transparent tree has no pedTree friend. Cannot do these plots'
return
optending = 'buffer_{v}'.format(v=int(RoundInt(self.trans_grid.trans_tree.GetMaximum('pedTree.slidingLength'))))
nameh = 'signal_noise_{o}_Vs_event_{c}'.format(o=optending, c=suffix) if not doNC else 'signal_noise_NC_chs_{o}_Vs_event_{c}'.format(o=optending, c=suffix)
temp_cut_noise = self.noise_friend_cuts[cells] if not doNC else self.noise_nc_friend_cuts[cells]
varz = 'pedTree.diaChSignal'
self.trans_grid.DrawHisto2D(nameh, hlimitsx['min'], hlimitsx['max'], deltax, 'event', hlimitsy['min'], hlimitsy['max'], deltay, 'signal noise [ADC]', 'event', varz, temp_cut_noise)
self.PosCanvas(nameh)
tempArray = ro.TObjArray()
self.trans_grid.histo[nameh].FitSlicesY(0, 0, -1, 0, 'QNR', tempArray)
if not doNC:
nameh2 = 'signal_noise_fitted_sigma_Vs_event_{c}'.format(c=suffix) if not isFriend else 'signal_noise_buffer_{o}_Fitted_sigma_Vs_event_{c}'.format(o=self.trans_grid.noise_friend_buffer, c=suffix)
else:
nameh2 = 'signal_noise_NC_chs_fitted_sigma_Vs_event_{c}'.format(c=suffix) if not isFriend else 'signal_noise_NC_chs_buffer_{o}_Fitted_sigma_Vs_event_{c}'.format(o=self.trans_grid.noise_friend_buffer, c=suffix)
self.trans_grid.histo[nameh2] = tempArray[2]
self.trans_grid.histo[nameh2].SetNameTitle('h_' + nameh2, 'h_' + nameh2)
self.trans_grid.canvas[nameh2] = ro.TCanvas('c_' + nameh2, 'c_' + nameh2, 1)
self.trans_grid.histo[nameh2].Draw('e hist')
ro.gPad.Update()
SetDefault1DCanvasSettings(self.trans_grid.canvas[nameh2])
SetDefault1DStats(self.trans_grid.histo[nameh2], y1=0.15, y2=0.45, optstat=1000000001)
self.trans_grid.FitPol(nameh2, 1)
self.PosCanvas(nameh2)
def DoNoiseAnalysis(self, cells='all', typ='adc', isFriend=False):
if isFriend:
if not self.trans_grid.trans_tree.GetFriend('pedTree'):
print 'The transparent tree has no pedTree friend. Add it first in transparent grid class'
return
self.DoCommonMode(isFriend)
self.DoPedestalEventHistograms(isFriend)
self.DoStrips2DHistograms(typ, isFriend)
self.PlotNoiseNotInCluster(cells, typ, False, isFriend)
self.DoNoiseVsEventStudies(cells, isFriend=isFriend)
self.PlotNoiseNotInCluster('all', typ, True, isFriend)
self.DoNoiseVsEventStudies('all', doNC=True, isFriend=isFriend)
if __name__ == '__main__':
c = NoiseAnalysis(None, 0, 0)