-
Notifications
You must be signed in to change notification settings - Fork 0
/
ledAnalysis.py
131 lines (112 loc) · 5.41 KB
/
ledAnalysis.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
import ROOT as R
import math as m
import numpy as np
import time
import os
from array import array
R.gROOT.SetBatch(1)
ledRuns = []
ledRuns.append("SinglePEAnalysis/LED-20190705-SCAN-1_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190705-SCAN-2_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190708-SCAN-1_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190709-SCAN-1_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190709-SCAN-2_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190709-SCAN-3_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190709-SCAN-4_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190710-SCAN-1_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190710-SCAN-2_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190710-SCAN-3_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190711-SCAN-1_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190712-SCAN-1_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190712-SCAN-2_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190712-SCAN-3_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190715-SCAN-1_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190715-SCAN-2_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190716-SCAN-1_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190716-SCAN-2_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190717-SCAN-1_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190719-SCAN-1_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190722-SCAN-1_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190722-SCAN-2_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190722-SCAN-4_simul_out.root")
ledRuns.append("SinglePEAnalysis/LED-20190724-SCAN-1_simul_out.root")
def Map(tf):
"""
Maps objets as dict[obj_name][0] using a TFile (tf) and TObject to browse.
"""
m = {}
for k in tf.GetListOfKeys():
n = k.GetName()
m[n] = tf.Get(n)
return m
singlePEfits = {}
for r in ledRuns:
histos=Map(R.TFile(r))
if 'PMT_0' in histos.keys():
singlePEfits[r]=histos
x,mu,mu_err,gain,gain_err,ped,ped_err = array('d'),array('d'),array('d'),array('d'),array('d'),array('d'),array('d')
histos = {}
histos['mu']=R.TH1F('mu','mu',200,0.,2.)
histos['gain']=R.TH1F('gain','gain',200,10,30)
histos['ped']=R.TH1F('ped','ped',200,0,100)
histos['gainVsMu']=R.TH2F('gainVsMu','gainVsMu',200,0.,2.,200,10,30)
histos['gainVsPed']=R.TH2F('gainVsPed','gainVsPed',200,0.,100.,200,10,30)
goodFits=[]
for r in singlePEfits.keys():
# if ( abs(singlePEfits[r]['PMT_0'].GetParameter(4)-35)<0.01 or abs(singlePEfits[r]['PMT_0'].GetParameter(1) - 0.1)<0.001):
if ( abs(singlePEfits[r]['PMT_0'].GetParameter(4)-35)<0.01):
print "Bad Run (Fit not converging): "+r
continue
if (singlePEfits[r]['PMT_4'].GetParameter(1) < 1.):
print "Bad Run (LED amplitude): "+r
continue
goodFits.append(r)
histos['gainFrame']=R.TH2F("gainFrame","gainFrame",len(goodFits),0,len(goodFits),10,10,20)
histos['gainFrame'].SetStats(0)
histos['gainFrame'].GetYaxis().SetTitle("Single PE Charge [ADC]")
histos['pedFrame']=R.TH2F("pedFrame","pedFrame",len(goodFits),0,len(goodFits),10,0,100)
histos['pedFrame'].SetStats(0)
histos['pedFrame'].GetYaxis().SetTitle("Pedestal [ADC]")
histos['muFrame']=R.TH2F("muFrame","muFrame",len(goodFits),0,len(goodFits),10,0,2)
histos['muFrame'].SetStats(0)
histos['muFrame'].GetYaxis().SetTitle("Average number of pe @ 4.180V")
goodFits.sort()
for i,r in enumerate(goodFits):
label=r.split('SinglePEAnalysis/LED-')[1].split('_simul_out.root')[0].replace('-SCAN','')
x.append(i+0.5)
mu.append(singlePEfits[r]['PMT_0'].GetParameter(1))
mu_err.append(singlePEfits[r]['PMT_0'].GetParError(1))
histos['mu'].Fill(singlePEfits[r]['PMT_0'].GetParameter(1))
gain.append(singlePEfits[r]['PMT_0'].GetParameter(2))
gain_err.append(singlePEfits[r]['PMT_0'].GetParError(2))
histos['gain'].Fill(singlePEfits[r]['PMT_0'].GetParameter(2))
ped.append(singlePEfits[r]['PMT_0'].GetParameter(4))
ped_err.append(singlePEfits[r]['PMT_0'].GetParError(4))
histos['ped'].Fill(singlePEfits[r]['PMT_0'].GetParameter(4))
histos['gainVsMu'].Fill(singlePEfits[r]['PMT_0'].GetParameter(1),singlePEfits[r]['PMT_0'].GetParameter(2))
histos['gainVsPed'].Fill(singlePEfits[r]['PMT_0'].GetParameter(4),singlePEfits[r]['PMT_0'].GetParameter(2))
for h in ['gainFrame','pedFrame','muFrame']:
histos[h].GetXaxis().SetBinLabel(i+1,label)
histos['gainGraph']=R.TGraph(len(x),x,gain)
histos['gainGraph'].SetName("gainGraph")
histos['muGraph']=R.TGraph(len(x),x,mu)
histos['muGraph'].SetName("muGraph")
histos['pedGraph']=R.TGraph(len(x),x,ped)
histos['pedGraph'].SetName("pedGraph")
c1=R.TCanvas("c1","c1",800,600)
R.gStyle.SetOptTitle(0)
text=R.TLatex()
text.SetTextSize(0.03)
for h in ['gain','ped','mu']:
histos[h+'Frame'].Draw()
histos[h+'Graph'].SetMarkerColor(R.kBlack)
histos[h+'Graph'].SetMarkerStyle(20)
histos[h+'Graph'].SetMarkerSize(1.2)
histos[h+'Graph'].Draw("PSAME")
text.DrawLatexNDC(0.12,0.91,h+" vs RunID")
c1.SaveAs("ledAnalysis/"+h+"Graph.pdf")
out=R.TFile("ledAnalysis/ledAnalysis.root","RECREATE")
for h,histo in histos.items():
histo.Write()
out.Write()
out.Close()