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analysis.art2.pc.py
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analysis.art2.pc.py
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#!/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.
fopdistcenter = 50 #50 since we moved the mps, for iiiba set back to zero
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,fopdistcenter)
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')