-
Notifications
You must be signed in to change notification settings - Fork 2
/
analysis.lyso6.falloff.py
183 lines (141 loc) · 6.9 KB
/
analysis.lyso6.falloff.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
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
#!/usr/bin/env python
import numpy as np,plot,auger,subprocess,tableio,dump,math
from matplotlib.ticker import AutoMinorLocator
#OPT: quickly get sorted rundirs
# zb autogen | sort -k1.13 -r
#OPT: fix seed
#np.random.seed(65983247)
np.random.seed(983452324)
addnoise=True #True for BGO, false for LYSO
precolli=False #gaan we niet meer doen
pgexit = True #if so, then pgprod_ratio must be set.
resultstable=[["typ","nprim","fopmu","fopsigma","fow","contrast","detyieldmu","detyieldsigma","detcount/prod"]]
if precolli:
resultstable[0].extend(["collieffmu","collieffsigma"])
def megaplot(ctsets,studyname,emisfops=None,labels=["$10^9$","$10^8$","$10^7$","$10^6$"],axlabel='Primaries [nr]'):
if len(ctsets) == 4:
f, ((ax1,ax2),(ax3,ax4)) = plot.subplots(nrows=2, ncols=2, sharex=False, sharey=False)
auger.plot_all_ranges_CTONLY(ax1,ctsets[0])
auger.plot_all_ranges_CTONLY(ax2,ctsets[1])
auger.plot_all_ranges_CTONLY(ax3,ctsets[2])
#auger.plot_all_ranges_CTONLY(ax4,ctsets[3])
if not 'Primaries' in axlabel:
ax1.set_title(labels[0])
ax2.set_title(labels[1])
ax3.set_title(labels[2])
#ax4.set_title(labels[3])
f.subplots_adjust(hspace=.5)
ax1.set_xlabel('')
ax2.set_xlabel('')
ax2.set_ylabel('')
#ax4.set_ylabel('')
f.savefig(studyname+'-'+typ+'-FOP.pdf', bbox_inches='tight')
plot.close('all')
#############################################################################################
# print 'FOP shift distributions'
# from mpl_toolkits.mplot3d import Axes3D
# import matplotlib.pyplot as plt
# fig = plt.figure()
# ax1 = plt.axes(projection='3d')
# ax1.view_init(30, -50)
# for i,ctset in enumerate(ctsets):
# auger.plotfodiffdist(ax1,ctset,i,emisfops,labels,axlabel)
# if not emisfops == None:
# fopshifts=[]
# for fopset in emisfops:
# fopshifts.append( fopset[-1]-fopset[0] )
# ax1.set_xlim3d(np.mean(fopshifts)-20,np.mean(fopshifts)+20)
# if emisfops is not None and len(emisfops) == 1:
# ax1.set_title(studyname+', $Shift_{em}$ = '+str(emisfops[0][-1]-emisfops[0][0]), y=1.08)
# #plt.tight_layout(rect = [-0.1, 0.0, 1.0, 1.1])#L,B,R,T
# fig.savefig(studyname+'-'+typ+'-FOP-shift.pdf')#, bbox_inches='tight')
# plt.close('all')
#############################################################################################
# print 'FOP FOW Contrast DE averages over 10e9 ctset'
# removed 2nd deriv from plot
f, (ax1,ax2) = plot.subplots(nrows=2, ncols=1, sharex=False, sharey=False)
x=ctsets[0]['ct']['x'] # 0 should be one with nprim=1e9
y=ctsets[0]['ct']['av']
if 'iba' in ctsets[0]['name']: # ctset['name'] == typ
mm=4/0.8
if 'ipnl' in ctsets[0]['name']:
mm=8
falloff_pos,g_fwhm,contrast = auger.get_fop_fow_contrast(x,y,plot='wut',ax=ax1,ax2=ax2,smooth=0.2,filename=ctsets[0]['ct']['path'],contrast_divisor=ctsets[0]['nprim']*len(ctsets[0]['ct']['files'])*mm,fitlines=False)
# some cosmetics
maxyrounded = int(math.ceil(max(y) / 100.0)) * 100
ticks = np.arange(0, maxyrounded, 100)
if len(ticks)>10:
ticks = np.arange(0, maxyrounded+100, 200)
if len(ticks)>10:
ticks = np.arange(0, maxyrounded+200, 400)
ax1.set_yticks(ticks)
minor_locator = AutoMinorLocator(2)
minor_locator1 = AutoMinorLocator(2)
ax1.xaxis.set_minor_locator(minor_locator)
ax2.xaxis.set_minor_locator(minor_locator)
ax2.yaxis.set_minor_locator(minor_locator1)
print "NPRIM", ctsets[0]['nprim'],"NJOBS",len(ctsets[0]['ct']['files']),"MM",mm
#gebruiken deze falloff_pos niet. we doen contrast en fow over de average van 50 batches wegens smoothe curve. daardoor geen sigma
f.savefig(studyname+'-'+typ+'-FOW.pdf', bbox_inches='tight')
plot.close('all')
#############################################################################################
# results table
print 'Computing pgprod ratios...'
pgprod_1mev = dump.count_ekine_in_phasespace("/home/brent/phd/art2_lyso_box/stage2_box15_docker/output/pgprod-worldframe.root",1.)
pgprod_3mev = dump.count_ekine_in_phasespace("/home/brent/phd/art2_lyso_box/stage2_box15_docker/output/pgprod-worldframe.root",3.)
print 'done.'
for ctset in ctsets:
res=[typ,ctset['nprim'],ctset['ct']['fopmu'],ctset['ct']['fopsigma'],g_fwhm,contrast,ctset['detyieldmu'],ctset['detyieldsigma'],ctset['detcount']]
if ctset['nprim'] == 10**9:
if pgexit and '3' in typ:
res[-1]=res[-1]/pgprod_3mev
elif pgexit and '1' in typ:
res[-1]=res[-1]/pgprod_1mev
else:
res[-1]=''
if precolli:
res.extend([ctset['precollidetyieldmu'],ctset['precollidetyieldsigma']])
resultstable.append(res)
#############################################################################################
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
print 'FOP distributions'
fig = plt.figure()
ax1 = plt.axes(projection='3d')
ax1.view_init(30, -50)
for i,ctset in enumerate(ctsets):
auger.plotfodist_CTONLY(ax1,ctset,i,emisfops,labels,axlabel)
if emisfops is not None and len(emisfops) == 1:
ax1.set_title(studyname+', $CT_{FOP_{em}}$ = '+str(emisfops[0][0])[:5]+', $RPCT_{FOP_{em}}$ = '+str(emisfops[0][1])[:5], y=1.08)
#plt.legend()#shadow = True,frameon = True,fancybox = True,ncol = 1,fontsize = 'x-small',loc = 'lower right')
#plt.tight_layout(rect = [-0.1, 0.0, 1.0, 1.1])#L,B,R,T
plt.savefig(studyname+'-'+typ+'-FOP-dist.pdf')#, bbox_inches='tight')
plt.close('all')
# typs=['ipnl-auger-tof-1.root','iba-auger-tof-1.root','ipnl-auger-notof-1.root','iba-auger-notof-1.root','ipnl-auger-tof-3.root','iba-auger-tof-3.root','ipnl-auger-notof-3.root','iba-auger-notof-3.root']
typs=[
'ipnl-auger-tof-1.root',
'iba-auger-tof-1.root',
'ipnl-auger-notof-1.root',
'iba-auger-notof-1.root',
'ipnl-auger-tof-3.root',
'iba-auger-tof-3.root',
'ipnl-auger-notof-3.root',
'iba-auger-notof-3.root']
# typs=['ipnl-auger-tof-1.root','iba-auger-tof-1.root','ipnl-auger-notof-3.root','iba-auger-notof-3.root']
dirs = subprocess.check_output(['find . -iname "*autogen*" | sort -k1.13'],shell=True).split('\n')[:-1]
numprots = [1e9,1e8,1e7]#,1e6]
for typ in typs:
ctsetsets = []
for line,numprot in zip(dirs,[item for item in numprots for i in range(len(typs)/len(numprots))]):
for haha in ['iba','ipnl']:
if (haha+'lyso' in line or haha+'bgo' in line) and haha+'-' in typ:
print (haha,line,typ,numprot)
ctsetsets.append( auger.getctset(numprot,line[2:10],line[2:10],typ,addnoise=addnoise,precolli=precolli) )
if haha+'zinv' in line and haha+'-' in typ:
print (haha,line,typ,numprot)
ctsetsets.append( auger.getctset(numprot,line[2:10],line[2:10],typ,addnoise=addnoise,precolli=precolli) )
assert(len(ctsetsets)==3)
megaplot(ctsetsets,'PMMA_phantom')
print 'Mean detection yield in',typ,'study over',sum([ctset['totnprim'] for ctset in ctsetsets]),'primaries in',sum([ctset['nreal'] for ctset in ctsetsets]),'realisations:',sum([ctset['detyieldmu'] for ctset in ctsetsets])
tableio.print2d(resultstable)
tableio.write(resultstable,'resultstable.tsv')