def __init__(self, options):
self.rebin = options.rebin
self.options = options
self.pedfile_fullres_name = options.pedfile_fullres_name
self.tmpname = options.tmpname
geometryPSet = open(
'modules_config/geometry_{det}.txt'.format(det=options.geometry),
'r')
geometryParams = eval(geometryPSet.read())
self.cg = cameraGeometry(geometryParams)
self.xmax = self.cg.npixx
if not os.path.exists(self.pedfile_fullres_name):
print("WARNING: pedestal file with full resolution ",
self.pedfile_fullres_name,
" not existing. First calculate them...")
self.calcPedestal(options, 1)
if not options.justPedestal:
print("Pulling pedestals...")
# first the one for clustering with rebin
ctools = cameraTools(self.cg)
# then the full resolution one
pedrf_fr = ROOT.TFile.Open(self.pedfile_fullres_name)
self.pedmap_fr = pedrf_fr.Get('pedmap').Clone()
self.pedmap_fr.SetDirectory(0)
self.pedarr_fr = hist2array(self.pedmap_fr).T
self.noisearr_fr = ctools.noisearray(self.pedmap_fr).T
pedrf_fr.Close()
if options.vignetteCorr:
self.vignmap = ctools.loadVignettingMap()
else:
self.vignmap = np.ones((self.xmax, self.xmax))
def __init__(self, options, shape, neighbor_window=6):
self.options = options
self.shape = shape
self.neighbor_window = neighbor_window
self.debug = options.debug_mode
self.calibrate = self.options.calibrate_clusters
# geometry
geometryPSet = open(
'modules_config/geometry_{det}.txt'.format(det=options.geometry),
'r')
geometryParams = eval(geometryPSet.read())
self.cg = cameraGeometry(geometryParams)
# supercluster energy calibration for the saturation effect
filePar = open('modules_config/energyCalibrator.txt', 'r')
params = eval(filePar.read())
self.calibrator = EnergyCalibrator(params, self.debug)
def __init__(self, hits, rebin, img_fr, img_fr_zs, geometry, debug=False):
self.hits = hits
self.rebin = rebin
self.debug = debug
self.x = hits[:, 0]
self.y = hits[:, 1]
if img_fr.any() and img_fr_zs.any():
self.hits_fr, self.hits_fr_zs = self.fullResHits(img_fr, img_fr_zs)
else:
print(
"WARNING! Cluster created without underlying image... Are you using it standalone?"
)
self.mean_point = np.array([np.mean(self.x), np.mean(self.y)])
self.EVs, self.theta = self.eigenvectors()
self.widths = {}
self.profiles = {}
self.shapes = {}
geometryPSet = open(
'modules_config/geometry_{det}.txt'.format(det=geometry), 'r')
geometryParams = eval(geometryPSet.read())
self.cg = cameraGeometry(geometryParams)
self.minDistKiller = self.cg.npixx
self.nMatchKiller = 0
self.nMatchKillerWeak = 0
def calcVignettingMap(self,
run,
pedfile,
outfile,
maxImages=1000,
rebin=12,
det='lime',
daq='midas'):
################ GEOMETRY ###
geometryPSet = open(
'modules_config/geometry_{det}.txt'.format(det=det), 'r')
geometryParams = eval(geometryPSet.read())
cg = cameraGeometry(geometryParams)
ctools = cameraTools(cg)
#############################
# pedestal map, full reso
pedrf_fr = ROOT.TFile.Open(pedfile)
pedmap_fr = pedrf_fr.Get('pedmap').Clone()
pedmap_fr.SetDirectory(0)
pedarr_fr = hist2array(pedmap_fr).T
noisearr_fr = ctools.noisearray(pedmap_fr).T
pedrf_fr.Close()
outname_base = os.path.basename(outfile).split('.')[0]
tf_out = ROOT.TFile.Open(outname_base + '.root', 'recreate')
N = cg.npixx
nx = int(N / rebin)
ny = int(N / rebin)
normmap = ROOT.TH2D('normmap', 'normmap', nx, 0, N, nx, 0, N)
mapsum = np.zeros((nx, nx))
USER = os.environ['USER']
if sw.checkfiletmp(int(run)):
infile = "/tmp/%s/histograms_Run%05d.root" % (USER, int(run))
else:
print('Downloading file: ' + sw.swift_root_file('Data', int(run)))
infile = sw.swift_download_root_file(
sw.swift_root_file('Data', int(run)), int(run))
tf_in = sw.swift_read_root_file(infile)
framesize = 216 if det == 'lime' else 0
#files = ["~/Work/data/cygnus/run03930.root","~/Work/data/cygnus/run03931.root","~/Work/data/cygnus/run03932.root"]
#for f in files:
tf_in = ROOT.TFile(f)
# first calculate the mean
for i, e in enumerate(tf_in.GetListOfKeys()):
iev = i if daq != 'midas' else i / 2 # when PMT is present
if maxImages > -1 and i < len(tf_in.GetListOfKeys()) - maxImages:
continue
name = e.GetName()
obj = e.ReadObj()
if not obj.InheritsFrom('TH2'): continue
print("Calc pixel sums with event: ", name)
arr = hist2array(obj)
# Upper Threshold full image
#img_cimax = np.where(arr < 300, arr, 0)
img_cimax = arr
img_fr_sub = ctools.pedsub(img_cimax, pedarr_fr)
img_fr_zs = ctools.zsfullres(img_fr_sub, noisearr_fr,
nsigma=1) * 1e-8
# for lime, remove the borders of the sensor
if det == 'lime':
#img_fr_zs[:framesize,:]=0
#img_fr_zs[-framesize:,:]=0
img_fr_zs[:, :framesize] = 0
img_fr_zs[:, -framesize:] = 0
img_rb_zs = ctools.arrrebin(img_fr_zs, rebin)
mapsum = np.add(mapsum, img_rb_zs)
print(mapsum)
# calc the normalized map wrt the center area
CA = 16
central_square = mapsum[int((N - CA) / rebin / 2):int((N + CA) /
rebin / 2),
int((N - CA) / rebin / 2):int((N + CA) /
rebin / 2)]
print(central_square)
norm = np.mean(central_square)
print("Now normalizing to the central area value = ", norm)
mapnorm = mapsum / float(norm)
if det == 'lime':
framesize_rb = int(framesize / rebin)
#mapnorm[:framesize_rb,:]=1
#mapnorm[-framesize_rb:,:]=1
mapnorm[:, :framesize_rb] = 1
mapnorm[:, -framesize_rb:] = 1
# now save in a persistent ROOT object. Threshold to 1
for ix in range(nx):
for iy in range(nx):
normmap.SetBinContent(ix + 1, iy + 1, min(mapnorm[ix, iy], 1.))
tf_in.Close()
tf_out.cd()
normmap.Write()
tf_out.Close()
print(
"Written the mean map with rebinning {rb}x{rb} into file {outf}.".
format(rb=rebin, outf=outfile))
