def makemask(self):
for i in range(1,len(self.mask)):
mask = copy.deepcopy(self.mask)
mask[i][-1] = "gray=1001"
#tableio.print2d(mask)
maskfile = self.maskfile.replace("000",str(i))
maskimagefile = self.maskimagefile.replace("000",str(i))
tableio.write(mask,maskfile)
print "Your maskfile was saved as",maskfile
call(["rtkdrawgeometricphantom","--phantomfile",maskfile,"--dimension",self.sizeinvox,"--spacing",self.spacingstr,"--output",maskimagefile])
call(["clitkCropImage","-i",maskimagefile,"-o",maskimagefile,"--BG=-1000"])
call(["clitkBinarizeImage","-i",maskimagefile,"-o",maskimagefile,"-l","0.5","-u","1.5"]) #the intensity egion that is 1, and not 0
#call(["clitkImageConvert","-i",maskimagefile,"-o",maskimagefile,"-c"])
print "Your mask was saved as",maskimagefile
def makemask(self):
for i in range(1, len(self.mask)):
mask = copy.deepcopy(self.mask)
mask[i][-1] = "gray=1001"
#tableio.print2d(mask)
maskfile = self.maskfile.replace("000", str(i))
maskimagefile = self.maskimagefile.replace("000", str(i))
tableio.write(mask, maskfile)
print "Your maskfile was saved as", maskfile
call([
"rtkdrawgeometricphantom", "--phantomfile", maskfile,
"--dimension", self.sizeinvox, "--spacing", self.spacingstr,
"--output", maskimagefile
])
call([
"clitkCropImage", "-i", maskimagefile, "-o", maskimagefile,
"--BG=-1000"
])
call([
"clitkBinarizeImage", "-i", maskimagefile, "-o", maskimagefile,
"-l", "0.5", "-u", "1.5"
]) #the intensity egion that is 1, and not 0
#call(["clitkImageConvert","-i",maskimagefile,"-o",maskimagefile,"-c"])
print "Your mask was saved as", maskimagefile
for haha in ['iba', 'ipnl']:
if (haha + 'lyso' in line
or haha + 'bgo' in line) and haha + '-' in typ:
print(haha, line, typ)
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)
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')
def writedensityfile(self,filename): tableio.write(self.density,filename)
def writephantomfile(self,filename): tableio.write(self.phantom,filename)
def writephantomfile(self, filename):
tableio.write(self.phantom, filename)
def writedensityfile(self, filename):
tableio.write(self.density, filename)
parser.add_argument('--dump', action='store_true')
parser.add_argument('--scatter', action='store_true')
parser.add_argument('files', nargs='*') # This is it!!
args = parser.parse_args()
if len(args.files)<1:
print "no files given or found. exiting..."
quit()
print "openening",args.files
rtplan = rtplan.rtplan(args.files,norm2nprim=(not args.nonorm),MSWtoprotons=(not args.nomu),noproc=(args.noproc))
outname = args.files[0][:-4]+('nonorm' if args.nonorm else '')+('nomu' if args.nomu else '')+('noproc' if args.noproc else '')+('scatter' if args.scatter else '')+'-plot.pdf'
if args.dump:
write(rtplan.spots,outname+'spots.txt')
write(rtplan.layers,outname+'layers.txt')
write(rtplan.fields,outname+'fields.txt')
spotdata=rtplan.getspotdata()
layerdata=rtplan.getlayerdata()
MSW=[]
for spot in rtplan.spots:
MSW.append(spot[-1])
#spot_weight_mean = np.median(MSW)
spot_weight_mean = np.mean(MSW)
nrfields=len(rtplan.fields)
'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)/4)]):
for haha in ['iba','ipnl']:
if haha+'lyso' in line and haha+'-' in typ:
print (haha,line,typ)
ctsetsets.append( auger.getctset(numprot,line[2:10],line[2:10],typ,addnoise=addnoise) )
if haha+'zinv' in line and haha+'-' in typ:
print (haha,line,typ)
ctsetsets.append( auger.getctset(numprot,line[2:10],line[2:10],typ,addnoise=addnoise) )
assert(len(ctsetsets)==4)
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')
