def showDecay():
tree1 = TTree()
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan1.dat',
'idX/i:vL/F:vH:A:D/i:R:W')
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan2_mod.dat'
)
# p1 = TProfile('p1','p1;#DeltaU [V];Prob',50,0.12,0.2)
# tree1.Draw("W:(vH-vL)>>p1","","profE")
tree1.Draw("W*0.0001:(vH-vL)>>p1", "", "profs")
p1 = gPad.GetPrimitive('p1')
p1.GetYaxis().SetTitle('<Width> [us]')
# p1.GetYaxis().SetTitle('<Raising time> [us]')
p1.GetXaxis().SetTitle('#Delta U [V]')
# fun1 = TF1('fun1','0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))',0.05,0.3)
# fun1.SetParameter(0,0.155);
# fun1.SetParameter(1,0.005);
#
# p1.Fit(fun1)
# fun1a = p1.GetFunction('fun1')
# fun1a.SetLineColor(2)
#
# p1.Draw("Esame")
#
# v0 = fun1a.GetParameter(0)
# e0 = fun1a.GetParError(0)
# v1 = fun1a.GetParameter(1)
# e1 = fun1a.GetParError(1)
#
# print v0, v1
#
# fUnit = 1000.
# lt = TLatex()
# lt.DrawLatexNDC(0.185,0.89,'#mu = {0:.1f} #pm {1:.1f} mV'.format(v0*fUnit, e0*fUnit))
# lt.DrawLatexNDC(0.185,0.84,'#sigma = {0:.1f} #pm {1:.1f} mV'.format(v1*fUnit, e1*fUnit))
#
# print 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1)
# fun2 = TF1('gaus1','TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1))
# fun2.SetLineColor(4)
# fun2.SetLineStyle(2)
# fun2.Draw('same')
#
# lg = TLegend(0.7,0.4, 0.95, 0.5)
# lg.SetFillStyle(0)
# lg.AddEntry(p1,'Measurement','p')
# lg.AddEntry(fun1a,'Fit','l')
# lg.AddEntry(fun2,'Gaus','l')
# lg.Draw()
waitRootCmdX()
def test2():
add_fit_menu()
t150 = TTree()
t150.ReadFile('Jan05a_150mV.dat')
t150.ReadFile('Jan05a_50mV.dat')
# t150.ReadFile('Jan05a_100mV.dat')
t150.ReadFile('tt2.dat')
t150.ReadFile('Jan05a_250mV.dat')
t150.Draw('A','ch==12')
waitRootCmdX()
def compareX():
# fl = ['Jan05a_100mV.dat', 'Jan08a_100mV_r30p0us.dat','Jan08a_100mV_r40p0us.dat','Jan08a_100mV_r50p0us.dat']
# fl = ['Jan05a_100mV.dat', 'Jan08a_100mV_r30p0us.dat','Jan08a_100mV_r40p0us.dat','Jan08a_100mV_r50p0us.dat']
fl = ['data/fpgaLin/Jan22a_C2_100mV_f{0:d}.dat'.format(x) for x in [100,200,500,1000]]
fs = [TTree() for f in fl]
opt = ''
chx = TTree()
for f in fl: chx.ReadFile(f)
chx.Draw('A','ch==12','axis')
opt = 'same'
for i in range(len(fl)):
fs[i].ReadFile(fl[i])
fs[i].SetFillStyle(0)
fs[i].SetLineColor(i+1)
fs[i].Draw('A','ch==12',opt)
waitRootCmdX()
def test1(fname='tt2.dat'):
add_fit_menu()
t1 = TTree()
t1.ReadFile(fname)
t1.Show(0)
for i in range(18):
c1.cd(i+1)
lt.DrawLatexNDC(0.2,0.4,'Ch={0:d}'.format(i))
t1.Draw('A','ch=={0:d}'.format(i))
# t1.Draw('A>>h1','ch=={0:d}'.format(i),'goff')
# h1 = gDirectory.Get('h1')
# h1.SetName('ch'+str(i))
#
# c1.cd(i)
# h1.Draw()
# if i>1: break
c1.cd()
waitRootCmdX()
def inspectCh(ds, chs):
t = TTree()
t.SetFillColor(0)
t.SetFillStyle(0)
for d in ds:
t.ReadFile(d[1])
cv1 = TCanvas()
cv1.Divide(2,4)
icv = 1
for ch in chs:
cv1.cd(icv)
t.Draw('A','ch=={0:d}'.format(ch))
hx = gPad.GetPrimitive('htemp')
hx.GetXaxis().SetTitle('U_{Out} [V]')
hx.SetName('h{0:d}'.format(ch))
lt.DrawLatexNDC(0.7,0.9,"Ch {0:d}".format(ch))
icv += 1
cv1.cd(0)
waitRootCmdX()
tree = TTree("tree_name", "tree title")
nevts = 100
Nmax = 10
nParticles = array( 'i', [ 0 ] )
pt = array( 'd', Nmax*[ 0. ] )
tree.Branch( 'nParticles', nParticles, 'nParticles/I' )
tree.Branch( 'pt', pt, 'pt[nParticles]/D' )
for i in xrange(nevts):
nParticles[0] = int(gRandom.Uniform()*10)
for j in range(nParticles[0]):
pt[j] = gRandom.Gaus(20,2)
tree.Fill()
tree.Draw("pt[0] >> h(100,0,10)")
hist = gDirectory.Get("h")
print gDirectory.GetName()
print gDirectory.ls()
print hist
print type(hist)
file.Write()
file.Close()
def main():
print "\n>>> Hello, world!"
writeTree()
def showENC():
fname1 = '/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan1.dat'
tree1 = TTree()
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan1.dat',
'idX/i:vL/F:vH:A:D/i:R:W')
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan2_mod.dat'
)
tree1.Show(500)
p1 = TProfile('p1', 'p1;#DeltaU [V];Prob', 50, 0.12, 0.2)
tree1.Draw("D:(vH-vL)>>p1", "", "profE")
### change it to tgraph
g1 = TGraphErrors()
for i in range(p1.GetNbinsX() + 2):
N = p1.GetBinEntries(i)
if N > 0:
print i, N, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(
i), p1.GetBinError(i)
n = g1.GetN()
g1.SetPoint(n, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(i))
g1.SetPointError(n, 0, p1.GetBinError(i))
# g1.SetMarkerColor(3)
# g1.SetLineColor(3)
p1.Draw("axis")
g1.Draw('Psame')
fun1 = TF1('fun1', '0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))',
0.05, 0.3)
fun1.SetParameter(0, 0.155)
fun1.SetParameter(1, 0.005)
g1.Fit(fun1)
fun1a = g1.GetFunction('fun1')
# p1.Fit(fun1)
# fun1a = p1.GetFunction('fun1')
fun1a.SetLineColor(2)
# p1.Draw("Esame")
v0 = fun1a.GetParameter(0)
e0 = fun1a.GetParError(0)
v1 = fun1a.GetParameter(1)
e1 = fun1a.GetParError(1)
print v0, v1
fUnit = 1000.
lt = TLatex()
lt.DrawLatexNDC(
0.185, 0.89,
'#mu = {0:.1f} #pm {1:.1f} mV'.format(v0 * fUnit, e0 * fUnit))
lt.DrawLatexNDC(
0.185, 0.84,
'#sigma = {0:.1f} #pm {1:.1f} mV'.format(v1 * fUnit, e1 * fUnit))
print 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1)
fun2 = TF1('gaus1', 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1))
fun2.SetLineColor(4)
fun2.SetLineStyle(2)
fun2.Draw('same')
lg = TLegend(0.7, 0.4, 0.95, 0.5)
lg.SetFillStyle(0)
lg.AddEntry(p1, 'Measurement', 'p')
lg.AddEntry(fun1a, 'Fit', 'l')
lg.AddEntry(fun2, 'Gaus', 'l')
lg.Draw()
waitRootCmdX()
class Run(Analysis):
""" Run class containing all the information for a single run. """
NTelPlanes = 4
def __init__(self, number=None, testcampaign=None, load_tree=True, verbose=None):
"""
:param number: if None is provided it creates a dummy run
:param testcampaign: if None is provided ...
:param load_tree: load the ROOT TTree
:param verbose: turn on more output
"""
# Basics
super(Run, self).__init__(testcampaign, verbose=verbose, pickle_dir='Run')
self.Number = number
# Directories / Test Campaign
self.IrradiationFile = join(self.Dir, self.MainConfig.get('MISC', 'irradiation file'))
# Configuration & Root Files
self.Config = self.load_run_config()
self.RootFileDir = self.load_rootfile_dirname()
self.RootFilePath = self.load_rootfile_path()
# Run Info
self.InfoFile = join(self.TCDir, 'run_log.json')
self.Info = self.load_run_info()
self.RootFile = None
self.Tree = TTree()
self.TreeName = self.Config.get('BASIC', 'treename')
self.DUTs = [self.dut(i + 1, self.Info) for i in range(self.get_n_diamonds())] if self.Number is not None else None
# Settings
self.Plane = Plane()
self.TriggerPlanes = self.load_trigger_planes()
# General Information
self.Flux = self.get_flux()
self.Type = self.get_type()
# Times
self.LogStart = self.load_log_start()
self.LogEnd = self.load_log_stop()
self.Duration = self.LogEnd - self.LogStart
self.Converter = Converter(self)
if self.set_run(number, load_tree):
# tree info
self.TimeOffset = None
self.Time = self.load_time_vec()
self.StartEvent = 0
self.NEvents = int(self.Tree.GetEntries())
self.EndEvent = self.NEvents - 1
self.StartTime = self.get_time_at_event(self.StartEvent)
self.EndTime = self.get_time_at_event(self.EndEvent)
self.TotalTime = self.load_total_time()
self.TotalMinutes = self.TotalTime / 60000.
self.Duration = timedelta(seconds=self.TotalTime)
self.LogEnd = self.LogStart + self.Duration # overwrite if we know exact duration
self.NPlanes = self.load_n_planes()
self.TInit = time() - self.InitTime
def __str__(self):
return f'{self.__class__.__name__} {self.Number}{self.evt_str} ({self.TCString})'
def __repr__(self):
return self.__str__()
def __call__(self, number, load_tree=False):
self.set_run(number, load_tree)
return self
def __gt__(self, other):
return self.Number > (other.Number if isinstance(other, Run) else other)
@property
def evt_str(self):
return f' with {make_ev_str(self.Info["events"])} ev' if 'events' in self.Info else f' with {make_ev_str(self.NEvents)} ev' if self.Tree.Hash() else ''
def set_run(self, number, load_tree):
if number is None:
return False
if number < 0 and type(number) is not int:
critical('incorrect run number')
self.Number = number
self.load_run_info()
self.Flux = self.get_flux()
# check for conversion
if load_tree:
self.Converter.convert_run()
self.load_rootfile()
else:
return False
if not self.rootfile_is_valid():
self.Converter.convert_run()
self.load_rootfile()
return True
def get_type(self):
return self.Config.get('BASIC', 'type') if self.Number is not None else None
def set_estimate(self, n=None):
self.Tree.SetEstimate(choose(n, -1))
def is_volt_scan(self):
return any(name in self.Info['runtype'] for name in ['voltage', 'hv'])
# ----------------------------------------
# region INIT
@property
def dut(self):
return DUT
def load_rootfile(self, prnt=True):
self.info('Loading information for rootfile: {file}'.format(file=basename(self.RootFilePath)), endl=False, prnt=prnt)
self.RootFile = TFile(self.RootFilePath)
self.Tree = self.RootFile.Get(self.TreeName)
return self.Tree
def load_run_config(self):
base_file_name = join(get_base_dir(), 'config', self.TCString, 'RunConfig.ini')
if not file_exists(base_file_name):
critical('RunConfig.ini does not exist for {0}! Please create it in config/{0}!'.format(self.TCString))
parser = Config(base_file_name) # first read the main config file with general information for all splits
if parser.has_section('SPLIT') and self.Number is not None:
split_runs = [0] + loads(parser.get('SPLIT', 'runs')) + [inf]
config_nr = next(i for i in range(1, len(split_runs)) if split_runs[i - 1] <= self.Number < split_runs[i])
parser.read(join(get_base_dir(), 'config', self.TCString, 'RunConfig{nr}.ini'.format(nr=config_nr))) # add the content of the split config
return parser
@staticmethod
def make_root_filename(run):
return f'TrackedRun{run:0>3}.root'
def make_root_subdir(self):
return join('root', 'pads' if self.get_type() == 'pad' else self.get_type())
def load_rootfile_path(self, run=None):
run = choose(run, self.Number)
return None if run is None else join(self.RootFileDir, self.make_root_filename(run))
def load_rootfile_dirname(self):
return ensure_dir(join(self.TCDir, self.make_root_subdir())) if self.Number is not None else None
def load_trigger_planes(self):
return array(self.Config.get_list('BASIC', 'trigger planes', [1, 2]))
def get_n_diamonds(self, run_number=None):
run_info = self.load_run_info(run_number)
return len([key for key in run_info if key.startswith('dia') and key[-1].isdigit()])
def load_dut_numbers(self):
return [i + 1 for i in range(len([key for key in self.Info.keys() if key.startswith('dia') and key[-1].isdigit()]))]
def load_dut_type(self):
dut_type = self.Config.get('BASIC', 'type') if self.Number is not None else None
if dut_type not in ['pixel', 'pad', None]:
critical("The DUT type {0} has to be either 'pixel' or 'pad'".format(dut_type))
return dut_type
def load_default_info(self):
with open(join(self.Dir, 'Runinfos', 'defaultInfo.json')) as f:
return load(f)
def load_run_info_file(self):
if not file_exists(self.InfoFile):
critical('Run Log File: "{f}" does not exist!'.format(f=self.InfoFile))
with open(self.InfoFile) as f:
return load(f)
def load_run_info(self, run_number=None):
data = self.load_run_info_file()
run_number = self.Number if run_number is None else run_number
if run_number is not None:
run_info = data.get(str(run_number))
if run_info is None: # abort if the run is still not found
critical('Run {} not found in json run log file!'.format(run_number))
self.Info = run_info
self.Info['masked pixels'] = [0] * 4
self.translate_diamond_names()
return run_info
else:
self.Info = self.load_default_info()
return self.Info
def load_dut_names(self):
return [self.Info['dia{nr}'.format(nr=i)] for i in range(1, self.get_n_diamonds() + 1)]
def load_biases(self):
return [int(self.Info['dia{nr}hv'.format(nr=i)]) for i in range(1, self.get_n_diamonds() + 1)]
def load_log_start(self):
return conv_log_time(self.Info['starttime0'])
def load_log_stop(self):
return conv_log_time(self.Info['endtime'])
def load_total_time(self):
return (self.Time[-1] - self.Time[0]) / 1000
def load_n_planes(self):
if self.has_branch('cluster_col'):
self.Tree.Draw('@cluster_col.size()', '', 'goff', 1)
return int(self.Tree.GetV1()[0])
else:
return 4
def load_time_vec(self):
t = get_time_vec(self.Tree)
t0 = datetime.fromtimestamp(t[0] / 1000) if t[0] < 1e12 else None
self.TimeOffset = None if t0 is None or t0.year > 2000 and t0.day == self.LogStart.day else t[0] - time_stamp(self.LogStart) * 1000
return t if self.TimeOffset is None else t - self.TimeOffset
def load_plane_efficiency(self, plane):
return self.load_plane_efficiencies()[plane - 1]
def load_plane_efficiencies(self):
return [ufloat(e, .03) for e in self.Config.get_list('BASIC', 'plane efficiencies', default=[.95, .95])]
# endregion INIT
# ----------------------------------------
# ----------------------------------------
# region MASK
def load_mask_file_path(self):
mask_dir = self.MainConfig.get('MAIN', 'maskfile directory') if self.MainConfig.has_option('MAIN', 'maskfile directory') else join(self.DataDir, self.TCDir, 'masks')
if not dir_exists(mask_dir):
warning('Mask file directory does not exist ({})!'.format(mask_dir))
return join(mask_dir, basename(self.Info['maskfile']))
def load_mask(self, plane=None):
mask_file = self.load_mask_file_path()
if basename(mask_file).lower() in ['no mask', 'none', 'none!', ''] or self.Number is None:
return
try:
data = genfromtxt(mask_file, [('id', 'U10'), ('pl', 'i'), ('x', 'i'), ('y', 'i')])
if 'cornBot' not in data['id']:
warning('Invalid mask file: "{}". Not taking any mask!'.format(mask_file))
mask = [[data[where((data['pl'] == pl) & (data['id'] == n))][0][i] for n in ['cornBot', 'cornTop'] for i in [2, 3]] for pl in sorted(set(data['pl']))]
mask = [[max(1, m[0]), max(1, m[1]), min(self.Plane.NCols - 2, m[2]), min(self.Plane.NRows - 2, m[3])] for m in mask] # outer pixels are ignored
return mask if plane is None else mask[plane - 1] if plane - 1 < len(mask) else None
except Exception as err:
warning(err)
warning('Could not read mask file... not taking any mask!')
def get_mask_dim(self, plane=1, mm=True):
return Plane.get_mask_dim(self.load_mask(plane), mm)
def get_mask_dims(self, mm=True):
return array([self.get_mask_dim(pl, mm) for pl in [1, 2]])
def get_unmasked_area(self, plane):
return None if self.Number is None else Plane.get_area(self.load_mask(plane))
def find_for_in_comment(self):
for name in ['for1', 'for2']:
if name not in self.Info:
for cmt in self.Info['comments'].split('\r\n'):
cmt = cmt.replace(':', '')
cmt = cmt.split(' ')
if str(cmt[0].lower()) == name:
self.Info[name] = int(cmt[1])
return 'for1' in self.Info
# endregion MASK
# ----------------------------------------
# ----------------------------------------
# region HELPERS
def translate_diamond_names(self):
for key, value in [(key, value) for key, value in self.Info.items() if key.startswith('dia') and key[-1].isdigit()]:
self.Info[key] = self.translate_dia(value)
def register_new_dut(self):
if input('Do you want to add a new diamond? [y,n] ').lower() in ['y', 'yes']:
dut_type = int(input('Enter the DUT type (1 for pCVD, 2 for scCVD, 3 for silicon): ')) - 1
dut_name = input('Enter the name of the DUT (no "_"): ')
alias = input(f'Enter the alias (no "_", for default {dut_name.lower()} press enter): ')
self.add_alias(alias, dut_name, dut_type)
self.add_dut_info(dut_name)
return True
else:
return False
@staticmethod
def add_alias(alias, dut_name, dut_type):
alias_file = join(Dir, 'config', 'DiamondAliases.ini')
with open(alias_file, 'r+') as f:
lines = [line.strip(' \n') for line in f.readlines()]
i0 = lines.index(['# pCVD', '# scCVD', '# Silicon'][dut_type])
i = next(i for i, line in enumerate(lines[i0:], i0) if line.strip() == '')
lines.insert(i, f'{(alias if alias else dut_name).lower()} = {dut_name}')
f.seek(0)
f.writelines([f'{line}\n' for line in lines])
info(f'added entry: {(alias if alias else dut_name).lower()} = {dut_name} in {alias_file}')
def add_dut_info(self, dut_name):
dia_info_file = join(Dir, 'Runinfos', 'dia_info.json')
data = load_json(dia_info_file)
if dut_name in data:
return warning('The entered DUT name already exists!')
tc = get_input(f'Enter the beam test [YYYYMM]', self.TCString)
data[dut_name] = {'irradiation': {tc: get_input(f'Enter the irradiation for {tc}', '0')},
'boardnumber': {tc: get_input(f'Enter the board number for {tc}')},
'thickness': get_input('Enter the thickness'),
'size': get_input('Enter the lateral size ([x, y])'),
'manufacturer': get_input('Enter the manufacturer')}
with open(dia_info_file, 'w') as f:
dump(data, f, indent=2)
info(f'added {dut_name} to {dia_info_file}')
def translate_dia(self, dia):
name, suf = dia.split('_')[0].lower(), '_'.join(dia.split('_')[1:])
if name not in Config(join(self.Dir, 'config', 'DiamondAliases.ini')).options('ALIASES'):
warning(f'{dia} was not found in config/DiamondAliases.ini!')
if not self.register_new_dut():
critical(f'unknown diamond {dia}')
parser = Config(join(self.Dir, 'config', 'DiamondAliases.ini'))
return '_'.join([parser.get('ALIASES', name)] + ([suf] if suf else []))
def reload_run_config(self, run_number):
self.Number = run_number
self.Config = self.load_run_config()
self.Info = self.load_run_info()
self.RootFileDir = self.load_rootfile_dirname()
self.RootFilePath = self.load_rootfile_path()
return self.Config
def rootfile_is_valid(self, file_path=None):
tfile = self.RootFile if file_path is None else TFile(file_path)
ttree = self.Tree if file_path is None else tfile.Get(self.TreeName)
is_valid = not tfile.IsZombie() and tfile.ClassName() == 'TFile' and ttree and ttree.ClassName() == 'TTree'
if not is_valid:
warning('Invalid TFile or TTree! Deleting file {}'.format(tfile.GetName()))
remove_file(tfile.GetName())
return is_valid
def calculate_plane_flux(self, plane=1, corr=True):
"""estimate the flux [kHz/cm²] through a trigger plane based on Poisson statistics."""
rate, eff, area = self.Info[f'for{plane}'], self.load_plane_efficiency(plane), self.get_unmasked_area(plane)
return -log(1 - rate / Plane.Frequency) * Plane.Frequency / area / 1000 / (eff if corr else ufloat(1, .05)) # count zero hits of Poisson
def find_n_events(self, n, cut, start=0):
evt_numbers = self.get_tree_vec(var='Entry$', cut=cut, nentries=self.NEvents, firstentry=start)
return int(evt_numbers[:n][-1] + 1 - start)
def get_max_run(self):
return int(max(self.load_run_info_file(), key=int))
# endregion HELPERS
# ----------------------------------------
# ----------------------------------------
# region GET
def get_flux(self, plane=None, corr=True):
if self.Number is None:
return
if not self.find_for_in_comment():
# warning('no plane rates in the data...')
return self.Info['measuredflux'] / (mean(self.load_plane_efficiencies()) if corr else 1)
return self.get_mean_flux(corr) if plane is None else self.calculate_plane_flux(plane, corr)
def get_mean_flux(self, corr=True):
return mean([self.get_flux(pl, corr) for pl in [1, 2]])
def get_time(self):
return ufloat(time_stamp(self.LogStart + self.Duration / 2), self.Duration.seconds / 2)
def get_channel_name(self, channel):
self.Tree.GetEntry()
return self.Tree.sensor_name[channel]
def get_time_at_event(self, event):
""" For negative event numbers it will return the time stamp at the startevent. """
return self.Time[min(event, self.EndEvent)] / 1000.
def get_event_at_time(self, seconds, rel=True):
""" Returns the event nunmber at time dt from beginning of the run. Accuracy: +- 1 Event """
if seconds - (0 if rel else self.StartTime) >= self.TotalTime or seconds == -1: # return time of last event if input is too large
return self.NEvents - 1
return where(self.Time <= 1000 * (seconds + (self.StartTime if rel else 0)))[0][-1]
def get_tree_vec(self, var, cut='', dtype=None, nentries=None, firstentry=0):
return get_tree_vec(self.Tree, var, cut, dtype, nentries, firstentry)
def get_tree_tuple(self):
return (self.Tree, self.RootFile) if self.Tree is not None else False
def get_time_vec(self):
return self.Time if hasattr(self, 'Time') else None
def get_bias_strings(self):
return [str(b) for b in self.load_biases()]
@save_pickle('HR', suf_args=0)
def get_high_rate_run(self, high=True):
from src.run_selection import RunSelector
return int(RunSelector(testcampaign=self.TCString).get_high_rate_run(self.Number, high))
def get_low_rate_run(self):
return self.get_high_rate_run(high=False)
# endregion GET
# ----------------------------------------
# ----------------------------------------
# region SHOW
def show_info(self):
print('Run information for', self)
for key, value in sorted(self.Info.items()):
print(f'{key:<13}: {value}')
def has_branch(self, name):
return bool(self.Tree.GetBranch(name))
def info(self, msg, endl=True, blank_lines=0, prnt=True):
return info(msg, endl, prnt=self.Verbose and prnt, blank_lines=blank_lines)
def add_to_info(self, t, txt='Done', prnt=True):
return add_to_info(t, txt, prnt=self.Verbose and prnt)
def showENC(self):
tree1 = TTree()
header = 'idX/i:vL/F:vH:A:D/i:R:W'
first = True
for f in self.dataFiles:
if first: tree1.ReadFile(f, header)
else: tree1.ReadFile(f)
p1 = TProfile('p1', 'p1;#DeltaU [V];Prob', self.bins[0],
tree1.GetMinimum('vH-vL') * 0.8,
tree1.GetMaximum('vH-vL') * 1.2)
tree1.Draw("D:(vH-vL)>>p1", "", "profE")
### change it to tgraph
g1 = TGraphErrors()
for i in range(p1.GetNbinsX() + 2):
N = p1.GetBinEntries(i)
if N > 0:
print i, N, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(
i), p1.GetBinError(i)
n = g1.GetN()
g1.SetPoint(n,
p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(i))
g1.SetPointError(n, 0, p1.GetBinError(i))
p1.Draw("axis")
g1.Draw('Psame')
fun1 = TF1('fun1', '0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))',
0.05, 0.3)
fun1.SetParameter(0, 0.155)
fun1.SetParameter(1, 0.005)
g1.Fit(fun1)
fun1a = g1.GetFunction('fun1')
fun1a.SetLineColor(2)
v0 = fun1a.GetParameter(0)
e0 = fun1a.GetParError(0)
v1 = fun1a.GetParameter(1)
e1 = fun1a.GetParError(1)
print v0, v1
fUnit = 1000.
self.lt.DrawLatexNDC(
0.185, 0.89,
'#mu = {0:.1f} #pm {1:.1f} mV'.format(v0 * fUnit, e0 * fUnit))
self.lt.DrawLatexNDC(
0.185, 0.84,
'#sigma = {0:.1f} #pm {1:.1f} mV'.format(v1 * fUnit, e1 * fUnit))
if self.Info:
self.lt.DrawLatexNDC(0.185, 0.6, self.Info)
print 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1)
fun2 = TF1('gaus1', 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1))
fun2.SetLineColor(4)
fun2.SetLineStyle(2)
fun2.Draw('same')
lg = TLegend(0.7, 0.4, 0.95, 0.5)
lg.SetFillStyle(0)
lg.AddEntry(p1, 'Measurement', 'p')
lg.AddEntry(fun1a, 'Fit', 'l')
lg.AddEntry(fun2, 'Gaus', 'l')
lg.Draw()
waitRootCmdX()
from ROOT import TTree, TRandom3, TCanvas
from array import array
mass_tree = TTree("mass_tree", "Tree containing invariant mass points")
random_generator = TRandom3()
maxNevents = 1000
mass = array("f", [0.])
mass_tree.Branch('mass', mass, 'mass/F')
for i in range(maxNevents):
mass[0] = random_generator.BreitWigner(125., 0.004)
mass_tree.Fill()
print mass
c1 = TCanvas('c1', 'Tree Data Structure')
mass_tree.Draw("mass", "mass < 140. && mass > 110.")
c1.SaveAs("smear_mass.gif")
# events.Fill()
for i in range(h_zvm.GetNbinsX()):
uncM[0] = h_zvm.GetXaxis().GetBinCenter(i + 1)
for j in range(h_zvm.GetNbinsY()):
uncVZ[0] = h_zvm.GetYaxis().GetBinCenter(j + 1)
for k in range(int(h_zvm.GetBinContent(i + 1, j + 1))):
events.Fill()
events.Print()
outrootfile.Write()
#outrootfile.Close()
#events = inFile.Get("cut")
#events.Print()
events.Draw("uncVZ:{0}>>hnew(100,0,0.1,100,-60,60)".format(massVar), "",
"colz")
gDirectory.Get("hnew").SetTitle("vertexing data")
gDirectory.Get("hnew").GetXaxis().SetTitle("mass [GeV]")
gDirectory.Get("hnew").GetYaxis().SetTitle("vertex z [mm]")
c.Print(remainder[0] + ".pdf")
gStyle.SetOptStat(1111)
effFile = remainder[2]
#acceptanceFile = TFile(remainder[2])
#tailsFile = TFile(remainder[3])
#radfracFile = TFile(remainder[4])
#fitfunc = TF1("fitfunc","[0]*exp( ((x-[1])<[3])*(-0.5*(x-[1])^2/[2]^2) + ((x-[1])>=[3])*(-0.5*[3]^2/[2]^2-(x-[1]-[3])/[4]))",-50,50)
#fitfunc.SetParName(0,"Amplitude")
#fitfunc.SetParName(1,"Mean")
#fitfunc.SetParName(2,"Sigma")
if (line or noline) and not one_run:
data += ":runnumber"
elif vsbin:
data +=":labels"
elif one_run:
data += ":luminum"
canv = TCanvas("PlotCalib","plotCalib",0,0,1600,800)
if opt2d:
hhh = TH2D("hhh","hhh",modmax-modmin+1,modmin-0.5,modmax+0.5,chanmax-chanmin+1,chanmin-0.5,chanmax+0.5)
if vsbin: tree.Draw(data,cut_cond,"goff",15)
else: tree.Draw(data,cut_cond,"goff")
if (not opt2d and tree.GetSelectedRows() <= 1) or tree.GetSelectedRows() <= 0:
print "Not enough points to plot"
sys.exit(2)
if vsbin:
if tree.GetSelectedRows() >= 15:
print "Maximum number of bins is 15"
if line or noline:
gr = TGraph(tree.GetSelectedRows(),tree.GetV2(),tree.GetV1())
gr.SetTitle(modulename +" " + " Channel "+ str(chan_n) +" " + gainvalue + titsuff)
gr.SetMarkerStyle(20)
def main(argv):
startT = time.clock()
inDir, outDir = ".", "."
inFile, inFileName, outFileName = TFile(), "", ""
gatTree = TTree()
dsNum, subNum, runNum, theCut, inFileName = -1, -1, -1, "", ""
saveLAT, savePacket, saveWave = False, False, False
if len(argv) == 0: return
for i, opt in enumerate(argv):
if opt == "-f":
# dsNum, subNum, inDir, outDir = int(argv[i+1]), int(argv[i+2]), str(argv[i+3]), str(argv[i+4])
inDir, inFileName, outDir = str(argv[i + 1]), str(
argv[i + 2]), str(argv[i + 3])
# inFileName = "waveSkimDS{}_{}.root".format(dsNum, subNum)
if opt == "-r":
inDir, inFileName, outDir = str(argv[i + 1]), str(
argv[i + 2]), str(argv[i + 3])
# dsNum, runNum, inDir, outDir = int(argv[i+1]), int(argv[i+2]), str(argv[i+3]), str(argv[i+4])
# inFileName = "waveSkimDS{}_run{}.root".format(dsNum, runNum)
# inFileName = inDir + inFile
print("Scanning File: {}".format(inFileName))
inFile = TFile("%s/%s" % (inDir, inFileName))
gatTree = inFile.Get("skimTree")
theCut = inFile.Get("theCut").GetTitle()
# Make files smaller for tests
# theCut += " && sumEHL > 236 && sumEHL < 240 && mHL==2 && trapENFCal < 5"
# Select only pulsers
# theCut += " && EventDC1Bits > 0"
print "Using cut:\n", theCut
gatTree.Draw(">>elist", theCut, "entrylist")
elist = gDirectory.Get("elist")
gatTree.SetEntryList(elist)
nList = elist.GetN()
print "Found", gatTree.GetEntries(), "input entries."
print "Found", nList, "entries passing cuts."
# Gimmicky but works... this bypasses creating the branches...
gatTree.GetEntry(0)
# Mess of various branches
channel = std.vector("int")()
trapENFCal = std.vector("double")()
trapENM = std.vector("double")()
# Create map of branches to put into dataframe
# This map is only for branches that we want to keep!
keepMapBase = {
'trapENFCal': gatTree.trapENFCal,
'trapENM': gatTree.trapENM,
"channel": gatTree.channel,
"run": gatTree.run,
"mHL": gatTree.mHL
}
# Combine dictionaries, if keepMapLAT is empty it won't add any branches
keepMap = dict(keepMapBase)
# keepMap.update(keepMapLAT)
dataList = []
print 'Writing to: ', '%s/proc%s.h5' % (outDir, inFileName.split('.')[0])
iList, removeNBeg, removeNEnd = -1, 500, 500
# Loop over events
while True:
iList += 1
if iList >= nList: break
# if iList >= 5000: break
entry = gatTree.GetEntryNumber(iList)
gatTree.LoadTree(entry)
gatTree.GetEntry(entry)
nChans = gatTree.channel.size()
numPass = gatTree.Draw("channel", theCut, "GOFF", 1, iList)
chans = gatTree.GetV1()
chanList = list(set(int(chans[n]) for n in xrange(numPass)))
hitList = (iH for iH in xrange(nChans)
if gatTree.channel.at(iH) in chanList
) # a 'generator expression'
for iH in hitList:
dataMap = {}
wf = gatTree.MGTWaveforms.at(iH)
signal = wl.processWaveform(wf, removeNBeg, removeNEnd)
wave = np.array(signal.GetWaveRaw(), dtype=np.int16)
for key, branch in keepMap.items():
# Save branches that aren't vector<Template> (so far only run and mHL)
if key == 'run' or key == 'mHL': dataMap[key] = int(branch)
elif key == 'channel': dataMap[key] = int(branch.at(iH))
else: dataMap[key] = float(branch.at(iH))
dataMap['waveform'] = wave.tolist()
dataList.append(dataMap)
if iList % 5000 == 0 and iList != 0:
print "%d / %d entries saved (%.2f %% done), time: %s" % (
iList, nList, 100 *
(float(iList) / nList), time.strftime('%X %x %Z'))
df = pd.DataFrame.from_dict(dataList)
print(df.head())
print(df.info())
print(np.unique(df.channel))
print(np.unique(df.mHL))
print(np.unique(df.run))
# Suppress stupid warning
warnings.filterwarnings(action="ignore",
module="pandas",
message="^\nyour performance")
# Chunk write like a sucker
chunksize = 50000
start = 0
end = chunksize - 1
i = 0
# for i in len(df):
while end < df.shape[0]:
# chunk = df.iloc[(i*chunksize):min((i+1)*chunksize,len(df))]
chunk = df.iloc[start:end]
try:
chunk.to_hdf('{}/proc{}_{}.h5'.format(outDir,
inFileName.split('.')[0], i),
key='skimTree',
data_columns=[
'trapENFCal', 'trapENM', 'channel', 'mHL',
'waveform'
],
format='fixed',
mode='w',
complevel=9)
except (Exception) as e:
print e
print chunk
print chunk.info()
start += chunksize
end += chunksize
i += 1
# df.to_hdf('%s/proc%s.h5' % (outDir,inFileName.split('.')[0]), key="skimTree", data_columns=['trapENFCal', 'trapENM','channel','mHL','waveform'], format='fixed', mode='w', complevel=9)
stopT = time.clock()
print("Stopped:", time.strftime('%X %x %Z'), "\nProcess time (min):",
(stopT - startT) / 60)
print(float(nList) / ((stopT - startT) / 60.), "entries per minute.")
outdir = dirname.rstrip("/")
try:
os.makedirs(outdir)
except:
pass
ofname = outdir + "/" + fname.rstrip(".txt") + ".root"
#output file and tree
outfile = TFile(ofname, "RECREATE")
tree = TTree("tree", "tree of " + fname)
outfile.cd()
#create tree from text file
desc = "MUEFFECTIVE:EFFICIENCY" #this gives names and types of variables
tree.ReadFile(ifname, desc) # delimiter = space
#tree.ReadFile(ifname,desc,",") # delimiter = comma
c1 = TCanvas("c1", "c1", 500, 500)
tree.Draw(
"EFFICIENCY:MUEFFECTIVE>>MvsE") # draws MuE vs Eff and saves to histogram
c1.SaveAs("MvsE.root")
#save output tree
tree.Write()
outfile.Close()
#print message
print "Created tree in root file: %s" % ofname
def main(argv):
print("=======================================")
print("LAT started:",time.strftime('%X %x %Z'))
startT = time.clock()
# gROOT.ProcessLine("gErrorIgnoreLevel = 3001;") # suppress ROOT error messages
global batMode
intMode, batMode, rangeMode, fileMode, gatMode, singleMode, pathMode, cutMode = False, False, False, False, False, False, False, False
dontUseTCuts = False
dsNum, subNum, runNum, plotNum = -1, -1, -1, 1
pathToInput, pathToOutput, manualInput, manualOutput, customPar = ".", ".", "", "", ""
if len(argv)==0: return
for i,opt in enumerate(argv):
if opt == "-r":
rangeMode, dsNum, subNum = True, int(argv[i+1]), int(argv[i+2])
print("Scanning DS-%d sub-range %d" % (dsNum, subNum))
if opt == "-p":
pathMode, manualInput, manualOutput = True, argv[i+1], argv[i+2]
print("Manually set input/output files:\nInput: %s\nOutput: %s" % (manualInput, manualOutput))
if opt == "-d":
pathToInput, pathToOutput = argv[i+1], argv[i+2]
print("Custom paths: Input %s, Output %s" % (pathToInput,pathToOutput))
if opt == "-f":
fileMode, dsNum, runNum = True, int(argv[i+1]), int(argv[i+2])
print("Scanning DS-%d, run %d" % (dsNum, runNum))
if opt == "-g":
gatMode, runNum = True, int(argv[i+1])
print("GATDataSet mode. Scanning run %d" % (runNum))
if opt == "-s":
singleMode, pathToInput = True, argv[i+1]
print("Single file mode. Scanning {}".format(pathToInput))
if opt == "-i":
intMode, plotNum = True, int(argv[i+1])
print("Interactive mode selected. Use \"p\" for previous and \"q\" to exit.")
if opt == "-x":
dontUseTCuts = True
print("DC TCuts deactivated. Retaining all events ...")
if opt == "-c":
cutMode, customPar = True, str(argv[i+1])
print("Using custom cut parameter: {}".format(customPar))
if opt == "-b":
batMode = True
import matplotlib
# if os.environ.get('DISPLAY','') == '':
# print('No display found. Using non-interactive Agg backend')
matplotlib.use('Agg')
print("Batch mode selected. A new file will be created.")
import matplotlib.pyplot as plt
from matplotlib import gridspec
import matplotlib.ticker as mtick
plt.style.use('pltTalks.mplstyle')
from matplotlib.colors import LogNorm, Normalize
# File I/O
inFile, outFile, bltFile = TFile(), TFile(), TFile()
gatTree, bltTree, out = TTree(), TTree(), TTree()
theCut, inPath, outPath = "", "", ""
# Set input and output files
if rangeMode:
inPath = "%s/waveSkimDS%d_%d.root" % (pathToInput, dsNum, subNum)
outPath = "%s/latSkimDS%d_%d.root" % (pathToOutput, dsNum, subNum)
if fileMode:
inPath = "%s/waveSkimDS%d_run%d.root" % (pathToInput, dsNum, runNum)
outPath = "%s/latSkimDS%d_run%d.root" % (pathToOutput, dsNum, runNum)
if pathMode:
inPath, outPath = manualInput, manualOutput
if gatMode:
ds = GATDataSet()
gatPath = ds.GetPathToRun(runNum,GATDataSet.kGatified)
bltPath = ds.GetPathToRun(runNum,GATDataSet.kBuilt)
outPath = "%s/lat_run%d.root" % (pathToOutput, runNum)
if pathMode and gatMode:
outPath = manualOutput
# Initialize trees
if rangeMode or fileMode or pathMode:
inFile = TFile(inPath)
gatTree = inFile.Get("skimTree")
print(gatTree.GetEntries(),"entries in input tree.")
elif gatMode:
inFile = TFile(gatPath)
bltFile = TFile(bltPath)
gatTree = inFile.Get("mjdTree")
bltTree = bltFile.Get("MGTree")
gatTree.AddFriend(bltTree)
if singleMode:
inFile = TFile(pathToInput)
gatTree = inFile.Get("skimTree")
# apply cut to tree
if (rangeMode or fileMode or pathMode) and not dontUseTCuts:
try:
theCut = inFile.Get("theCut").GetTitle()
except ReferenceError:
theCut = ""
if cutMode:
# theCut += customPar
# theCut = "(channel==672 || channel==674) && mH==2" # sync chan: 672, extp chan: 674
# theCut += " && fitSlo < 10"
# theCut = "trapENFCal > 1 && trapENFCal < 10 && riseNoise > 2"
theCut = "trapENFCal > 20 && trapENFCal < 100 && riseNoise > 2"
print("WARNING: Custom cut in use! : ",theCut)
gatTree.Draw(">>elist", theCut, "entrylist")
elist = gDirectory.Get("elist")
gatTree.SetEntryList(elist)
nList = elist.GetN()
print("Using cut:\n",theCut)
print("Found",gatTree.GetEntries(),"input entries.")
print("Found",nList,"entries passing cuts.")
# Output: In batch mode (-b) only, create an output file+tree & append new branches.
if batMode and not intMode:
outFile = TFile(outPath, "RECREATE")
print("Attempting tree copy to",outPath)
out = gatTree.CopyTree("")
out.Write()
print("Wrote",out.GetEntries(),"entries.")
cutUsed = TNamed("theCut",theCut)
cutUsed.Write()
waveS1, waveS2 = std.vector("double")(), std.vector("double")()
waveS3, waveS4, waveS5 = std.vector("double")(), std.vector("double")(), std.vector("double")()
bcMax, bcMin = std.vector("double")(), std.vector("double")()
bandMax, bandTime = std.vector("double")(), std.vector("double")()
den10, den50, den90 = std.vector("double")(), std.vector("double")(), std.vector("double")()
oppie = std.vector("double")()
fitMu, fitAmp, fitSlo = std.vector("double")(), std.vector("double")(), std.vector("double")()
fitTau, fitBL = std.vector("double")(), std.vector("double")()
matchMax, matchWidth, matchTime = std.vector("double")(), std.vector("double")(), std.vector("double")()
pol0, pol1, pol2, pol3 = std.vector("double")(), std.vector("double")(), std.vector("double")(), std.vector("double")()
fails, fitChi2, fitLL = std.vector("int")(), std.vector("double")(), std.vector("double")()
riseNoise = std.vector("double")()
t0_SLE, t0_ALE, lat, latF = std.vector("double")(), std.vector("double")(), std.vector("double")(), std.vector("double")()
latAF, latFC, latAFC = std.vector("double")(), std.vector("double")(), std.vector("double")()
nMS = std.vector("int")()
tE50, latE50, wfStd = std.vector("double")(), std.vector("double")(), std.vector("double")()
wfAvgBL, wfRMSBL = std.vector("double")(), std.vector("double")()
fitErr = std.vector("int")()
# It's not possible to put the "out.Branch" call into a class initializer (waveLibs::latBranch). You suck, ROOT.
b1, b2 = out.Branch("waveS1",waveS1), out.Branch("waveS2",waveS2)
b3, b4, b5 = out.Branch("waveS3",waveS3), out.Branch("waveS4",waveS4), out.Branch("waveS5",waveS5)
b7, b8 = out.Branch("bcMax",bcMax), out.Branch("bcMin",bcMin)
b9, b10 = out.Branch("bandMax",bandMax), out.Branch("bandTime",bandTime)
b11, b12, b13 = out.Branch("den10",den10), out.Branch("den50",den50), out.Branch("den90",den90)
b14 = out.Branch("oppie",oppie)
b15, b16, b17 = out.Branch("fitMu", fitMu), out.Branch("fitAmp", fitAmp), out.Branch("fitSlo", fitSlo)
b18, b19 = out.Branch("fitTau",fitTau), out.Branch("fitBL",fitBL)
b20, b21, b22 = out.Branch("matchMax", matchMax), out.Branch("matchWidth", matchWidth), out.Branch("matchTime", matchTime)
b23, b24, b25, b26 = out.Branch("pol0", pol0), out.Branch("pol1", pol1), out.Branch("pol2", pol2), out.Branch("pol3", pol3)
b27, b28, b29 = out.Branch("fails",fails), out.Branch("fitChi2",fitChi2), out.Branch("fitLL",fitLL)
b30 = out.Branch("riseNoise",riseNoise)
b31, b32, b33, b34 = out.Branch("t0_SLE",t0_SLE), out.Branch("t0_ALE",t0_ALE), out.Branch("lat",lat), out.Branch("latF",latF)
b35, b36, b37 = out.Branch("latAF",latAF), out.Branch("latFC",latFC), out.Branch("latAFC",latAFC)
b38 = out.Branch("nMS",nMS)
b39, b40, b41 = out.Branch("tE50", tE50), out.Branch("latE50", latE50), out.Branch("wfStd", wfStd)
b42, b43 = out.Branch("wfAvgBL", wfAvgBL), out.Branch("wfRMSBL", wfRMSBL)
b44 = out.Branch("fitErr",fitErr)
# make a dictionary that can be iterated over (avoids code repetition in the loop)
brDict = {
"waveS1":[waveS1, b1], "waveS2":[waveS2, b2],
"waveS3":[waveS3, b3], "waveS4":[waveS4, b4], "waveS5":[waveS5, b5],
"bcMax":[bcMax, b7], "bcMin":[bcMin, b8],
"bandMax":[bandMax, b9], "bandTime":[bandTime, b10],
"den10":[den10, b11], "den50":[den50, b12], "den90":[den90, b13],
"oppie":[oppie, b14],
"fitMu":[fitMu, b15], "fitAmp":[fitAmp, b16], "fitSlo":[fitSlo, b17],
"fitTau":[fitTau, b18], "fitBL":[fitBL,b19],
"matchMax":[matchMax, b20], "matchWidth":[matchWidth, b21], "matchTime":[matchTime, b22],
"pol0":[pol0, b23], "pol1":[pol1, b24], "pol2":[pol2, b25], "pol3":[pol3, b26],
"fails":[fails,b27], "fitChi2":[fitChi2,b28], "fitLL":[fitLL,b29],
"riseNoise":[riseNoise,b30],
"t0_SLE":[t0_SLE,b31], "t0_ALE":[t0_ALE,b32], "lat":[lat,b33], "latF":[latF,b34],
"latAF":[latAF,b35], "latFC":[latFC,b36], "latAFC":[latAFC,b37],
"nMS":[nMS,b38], "tE50":[tE50,b39], "latE50":[latE50,b40], "wfStd":[wfStd,b41],
"wfAvgBL":[wfAvgBL,b42], "wfRMSBL":[wfRMSBL,b43],
"fitErr":[fitErr,b44]
}
# Make a figure (-i option: select different plots)
# fig = plt.figure(figsize=(12,9), facecolor='w')
fig = plt.figure()
if plotNum==0 or plotNum==7 or plotNum==8:
p0 = plt.subplot(111) # 0-raw waveform, 7-new trap filters
elif plotNum==1 or plotNum==2:
p0 = plt.subplot(211) # 1-wavelet, 2-time points, bandpass filters, tail slope
p1 = plt.subplot(212)
elif plotNum==3:
p0 = plt.subplot2grid((2,5), (0,0), colspan=3) # oppie / freq-domain matched filter
p1 = plt.subplot2grid((2,5), (0,3), colspan=2)
p2 = plt.subplot2grid((2,5), (1,0), colspan=3)
elif plotNum==4:
p0 = plt.subplot(111) # time-domain matched filter
elif plotNum==5:
p0 = plt.subplot(111) # bandpass / bandTime
elif plotNum==6:
p0 = plt.subplot2grid((6,10), (0,0), colspan=10, rowspan=3) # waveform fit
p1 = plt.subplot2grid((6,10), (3,0), colspan=10, rowspan=1) # residual
p2 = plt.subplot2grid((6,10), (4,0), colspan=2, rowspan=2) # traces
p3 = plt.subplot2grid((6,10), (4,2), colspan=2, rowspan=2)
p4 = plt.subplot2grid((6,10), (4,4), colspan=2, rowspan=2)
p5 = plt.subplot2grid((6,10), (4,6), colspan=2, rowspan=2)
p6 = plt.subplot2grid((6,10), (4,8), colspan=2, rowspan=2)
elif plotNum==9:
p0 = plt.subplot2grid((5,1), (0,0)) # 9- wpt on wf fit residual
p1 = plt.subplot2grid((5,1), (1,0), rowspan=2)
p2 = plt.subplot2grid((5,1), (3,0), rowspan=2)
if not batMode: plt.show(block=False)
# Load a fast signal template - used w/ the freq-domain matched filter
# print("Generating signal template ...")
tSamp, tR, tZ, tAmp, tST, tSlo = 5000, 0, 15, 100, 2500, 10
# tOrig, tOrigTS = wl.MakeSiggenWaveform(tSamp,tR,tZ,tAmp,tST,tSlo) # Damn you to hell, PDSF
templateFile = np.load("%s/data/lat_template.npz" % os.environ['LATDIR'])
if dsNum==2 or dsNum==6: templateFile = np.load("%s/data/lat_ds2template.npz" % os.environ['LATDIR'])
tOrig, tOrigTS = templateFile['arr_0'], templateFile['arr_1']
# Load stuff from DS1 forced acq. runs
npzfile = np.load("%s/data/fft_forcedAcqDS1.npz" % os.environ['LATDIR'])
noise_asd, noise_xFreq, avgPwrSpec, xPwrSpec, data_forceAcq, data_fft = npzfile['arr_0'],npzfile['arr_1'],npzfile['arr_2'],npzfile['arr_3'],npzfile['arr_4'],npzfile['arr_5']
# Loop over events
print("Starting event loop ...")
iList = -1
while True:
iList += 1
if intMode==True and iList != 0:
value = input()
if value=='q': break # quit
if value=='p': iList -= 2 # go to previous
if (value.isdigit()):
iList = int(value) # go to entry number
elif intMode==False and batMode==False:
plt.pause(0.00001) # rapid-draw mode
if iList >= nList: break # bail out, goose!
entry = gatTree.GetEntryNumber(iList);
gatTree.LoadTree(entry)
gatTree.GetEntry(entry)
nChans = gatTree.channel.size()
event = MGTEvent()
if gatMode: event = bltTree.event
# Reset all branch vectors
# NOTE: The events sometimes contain 'straggler' hits that do not pass the
# given TCut. This line sets ALL the new parameters to -88888 by default.
# If you see this value in a plot, then you must be including hits that
# passed the cut in wave-skim but did not pass the (different?) cut in LAT.
for key in brDict: brDict[key][0].assign(nChans,-88888)
brDict["fails"][0].assign(nChans,0) # set error code to 'true' by default
errorCode = [0,0,0,0]
# Loop over hits passing cuts
numPass = gatTree.Draw("channel",theCut,"GOFF",1,iList)
chans = gatTree.GetV1()
chanList = list(set(int(chans[n]) for n in range(numPass)))
hitList = (iH for iH in range(nChans) if gatTree.channel.at(iH) in chanList) # a 'generator expression'
for iH in hitList:
# ------------------------------------------------------------------------
# Waveform processing
# load data
run = gatTree.run
chan = gatTree.channel.at(iH)
dataENFCal = gatTree.trapENFCal.at(iH)
dataENM = gatTree.trapENM.at(iH)
dataTSMax = gatTree.trapENMSample.at(iH)*10. - 4000
wf = MGTWaveform()
iEvent = 0
if gatMode:
wf = event.GetWaveform(iH)
iEvent = entry
else:
wf = gatTree.MGTWaveforms.at(iH)
iEvent = gatTree.iEvent
# print("%d: run %d chan %d trapENFCal %.2f" % (iList, run, chan, dataENFCal))
# be absolutely sure you're matching the right waveform to this hit
if wf.GetID() != chan:
print("ERROR -- Vector matching failed. iList %d run %d iEvent %d" % (iList,run,iEvent))
return
# Let's start the show - grab a waveform.
# Remove first 4 samples when we have multisampling
# Remove last 2 samples to get rid of the ADC spike at the end of all wf's.
truncLo, truncHi = 0, 2
if dsNum==6 or dsNum==2: truncLo = 4
signal = wl.processWaveform(wf,truncLo,truncHi)
data = signal.GetWaveRaw()
data_blSub = signal.GetWaveBLSub()
dataTS = signal.GetTS()
dataBL,dataNoise = signal.GetBaseNoise()
# wavelet packet transform
wp = pywt.WaveletPacket(data_blSub, 'db2', 'symmetric', maxlevel=4)
nodes = wp.get_level(4, order='freq')
wpCoeff = np.array([n.data for n in nodes],'d')
wpCoeff = abs(wpCoeff)
# wavelet parameters
# First get length of wavelet on the time axis, the scale axis will always be the same
# due to the number of levels in the wavelet
wpLength = len(wpCoeff[1,:])
waveS1[iH] = np.sum(wpCoeff[0:1,1:wpLength//4+1]) # python3 : floor division (//) returns an int
waveS2[iH] = np.sum(wpCoeff[0:1,wpLength//4+1:wpLength//2+1])
waveS3[iH] = np.sum(wpCoeff[0:1,wpLength//2+1:3*wpLength//4+1])
waveS4[iH] = np.sum(wpCoeff[0:1,3*wpLength//4+1:-1])
waveS5[iH] = np.sum(wpCoeff[2:-1,1:-1])
S6 = np.sum(wpCoeff[2:9,1:wpLength//4+1])
S7 = np.sum(wpCoeff[2:9,wpLength//4+1:wpLength//2+1])
S8 = np.sum(wpCoeff[2:9,wpLength//2+1:3*wpLength//4+1])
S9 = np.sum(wpCoeff[2:9,3*wpLength//4+1:-1])
S10 = np.sum(wpCoeff[9:,1:wpLength//4+1])
S11 = np.sum(wpCoeff[9:,wpLength//4+1:wpLength//2+1])
S12 = np.sum(wpCoeff[9:,wpLength//2+1:3*wpLength//4+1])
S13 = np.sum(wpCoeff[9:,3*wpLength//4+1:-1])
sumList = [S6, S7, S8, S9, S10, S11, S12, S13]
bcMax[iH] = np.max(sumList)
bcMin[iH] = 1. if np.min(sumList) < 1 else np.min(sumList)
# reconstruct waveform w/ only lowest frequency.
new_wp = pywt.WaveletPacket(data=None, wavelet='db2', mode='symmetric')
new_wp['aaa'] = wp['aaa'].data
data_wlDenoised = new_wp.reconstruct(update=False)
# resize in a smart way
diff = len(data_wlDenoised) - len(data_blSub)
if diff > 0: data_wlDenoised = data_wlDenoised[diff:]
# waveform high/lowpass filters - parameters are a little arbitrary
B1,A1 = butter(2, [1e5/(1e8/2),1e6/(1e8/2)], btype='bandpass')
data_bPass = lfilter(B1, A1, data_blSub)
# used in the multisite tagger
B2, A2 = butter(1, 0.08)
data_filt = filtfilt(B2, A2, data_blSub)
data_filtDeriv = wl.wfDerivative(data_filt)
filtAmp = np.amax(data_filtDeriv) # scale the max to match the amplitude
data_filtDeriv = data_filtDeriv * (dataENM / filtAmp)
B3, A3 = butter(2,1e6/(1e8/2), btype='lowpass')
data_lPass = lfilter(B3, A3, data_blSub)
idx = np.where((dataTS > dataTS[0]+100) & (dataTS < dataTS[-1]-100))
windowingOffset = dataTS[idx][0] - dataTS[0]
bandMax[iH] = np.amax(data_bPass[idx])
bandTime[iH] = dataTS[ np.argmax(data_bPass[idx])] - windowingOffset
# timepoints of low-pass waveforms
tpc = MGWFTimePointCalculator();
tpc.AddPoint(.2)
tpc.AddPoint(.5)
tpc.AddPoint(.9)
mgtLowPass = wl.MGTWFFromNpArray(data_lPass)
tpc.FindTimePoints(mgtLowPass)
den10[iH] = tpc.GetFromStartRiseTime(0)*10
den50[iH] = tpc.GetFromStartRiseTime(1)*10
den90[iH] = tpc.GetFromStartRiseTime(2)*10
# ================ xgauss waveform fitting ================
amp, mu, sig, tau, bl = dataENM, dataTSMax, 600., -72000., dataBL
floats = np.asarray([amp, mu, sig, tau, bl])
temp = xgModelWF(dataTS, floats)
if not batMode: MakeTracesGlobal()
# get the noise of the denoised wf
denoisedNoise,_,_ = wl.baselineParameters(data_wlDenoised)
# NOTE: fit is to wavelet-denoised data, BECAUSE there are no HF components in the model,
# AND we'll still calculate fitChi2 w/r/t the data, not the denoised data.
# datas = [dataTS, data, dataNoise] # fit data
datas = [dataTS, data_wlDenoised + dataBL, denoisedNoise] # fit wavelet-denoised data w/ Bl added back in
# Set bounds - A,mu,sig,tau,bl.
# bnd = ((None,None),(None,None),(None,None),(None,None),(None,None)) # often gets caught at sig=0
bnd = ((None,None),(None,None),(2.,None),(-72001.,-71999.),(None,None)) # gets caught much less often.
# L-BGFS-B with numerical gradient.
start = time.clock()
result = op.minimize(lnLike, floats, args=datas, method="L-BFGS-B", options=None, bounds=bnd)
fitSpeed = time.clock() - start
fitErr[iH] = 0
if not result["success"]:
# print("fit fail: ", result["message"])
fitErr[iH] = 1
errorCode[0] = 1
amp, mu, sig, tau, bl = result["x"]
# save parameters
fitMu[iH], fitAmp[iH], fitSlo[iH], fitTau[iH], fitBL[iH] = mu, amp, sig, tau, bl
floats = np.asarray([amp, mu, sig, tau, bl])
fit = xgModelWF(dataTS, floats)
# print("%d/%d iH %d e %-10.2f fs %-8.2f f %d" % (iList, nList, iH, dataENFCal, fitSlo[iH], fitErr[iH]))
# log-likelihood of this fit
fitLL[iH] = result["fun"]
# chi-square of this fit
# Textbook is (observed - expected)^2 / expected,
# but we'll follow MGWFCalculateChiSquare.cc and do (observed - expected)^2 / NDF.
# NOTE: we're doing the chi2 against the DATA, though the FIT is to the DENOISED DATA.
fitChi2[iH] = np.sum(np.square(data-fit)) / (len(data)-1)/dataNoise
# get wavelet coeff's for rising edge only. normalize to bcMin
# view this w/ plot 1
# find the window of rising edge
fit_blSub = fit - bl
fitMaxTime = dataTS[np.argmax(fit_blSub)]
fitStartTime = dataTS[0]
idx = np.where(fit_blSub < 0.1)
if len(dataTS[idx] > 0): fitStartTime = dataTS[idx][-1]
fitRiseTime50 = (fitMaxTime + fitStartTime)/2.
# bcMin is 32 samples long in the x-direction.
# if we make the window half as wide, it'll have the same # of coeff's as bcMin.
# this is still 'cheating' since we're not summing over the same rows.
numXRows = wpCoeff.shape[1]
wpCtrRise = int((fitRiseTime50 - dataTS[0]) / (dataTS[-1] - dataTS[0]) * numXRows)
wpLoRise = wpCtrRise - 8
if wpLoRise < 0: wpLoRise = 0
wpHiRise = wpCtrRise + 8
if wpHiRise > numXRows: wpHiRise = numXRows
# sum all HF wavelet components for this edge.
riseNoise[iH] = np.sum(wpCoeff[2:-1,wpLoRise:wpHiRise]) / bcMin[iH]
# print("%d %d %d %d e %-5.2f bmax %-6.2f bmin %-6.2f mu %-5.2f a %-5.2f s %-5.2f bl %-5.2f rn %.2f" % (run,iList,iH,chan,dataENFCal,bcMax[iH],bcMin[iH],fitMu[iH],fitAmp[iH],fitSlo[iH],fitBL[iH],riseNoise[iH]))
# =========================================================
# optimal matched filter (freq. domain)
# we use the pysiggen fast template (not the fit result) to keep this independent of the wf fitter.
# pull in the template, shift it, and make sure it's the same length as the data
guessTS = tOrigTS - 15000.
idx = np.where((guessTS > -5) & (guessTS < dataTS[-1]))
guessTS, guess = guessTS[idx], tOrig[idx]
if len(guess)!=len(data):
if len(guess)>len(data):
guess, guessTS = guess[0:len(data)], guessTS[0:len(data)]
else:
guess = np.pad(guess, (0,len(data)-len(guess)), 'edge')
guessTS = np.pad(guessTS, (0,len(data)-len(guessTS)), 'edge')
data_fft = np.fft.fft(data_blSub) # can also try taking fft of the low-pass data
temp_fft = np.fft.fft(guess)
datafreq = np.fft.fftfreq(data.size) * 1e8
power_vec = np.interp(datafreq, noise_xFreq, noise_asd) # load power spectra from file
# Apply the filter
optimal = data_fft * temp_fft.conjugate() / power_vec
optimal_time = 2 * np.fft.ifft(optimal)
# Normalize the output
df = np.abs(datafreq[1] - datafreq[0]) # freq. bin size
sigmasq = 2 * (temp_fft * temp_fft.conjugate() / power_vec).sum() * df
sigma = np.sqrt(np.abs(sigmasq))
SNR = abs(optimal_time) / (sigma)
oppie[iH] = np.amax(SNR)
# time-domain matched filter. use the baseline-subtracted wf as data, and fit_blSub too.
# make a longer best-fit waveform s/t it can be shifted L/R.
matchTS = np.append(dataTS, np.arange(dataTS[-1], dataTS[-1] + 20000, 10)) # add 2000 samples
match = xgModelWF(matchTS, [amp, mu+10000., sig, tau, bl]) # shift mu accordingly
match = match[::-1] - bl # time flip and subtract off bl
# line up the max of the 'match' (flipped wf) with the max of the best-fit wf
# this kills the 1-1 matching between matchTS and dataTS (each TS has some offset)
matchMaxTime = matchTS[np.argmax(match)]
matchTS = matchTS + (fitMaxTime - matchMaxTime)
# resize match, matchTS to have same # samples as data, dataTS.
# this is the only case we really care about
# ("too early" and "too late" also happen, but the shift is larger than the trigger walk, making it unphysical)
if matchTS[0] <= dataTS[0] and matchTS[-1] >= dataTS[-1]:
idx = np.where((matchTS >= dataTS[0]) & (matchTS <= dataTS[-1]))
match, matchTS = match[idx], matchTS[idx]
sizeDiff = len(dataTS)-len(matchTS)
if sizeDiff < 0:
match, matchTS = match[:sizeDiff], matchTS[:sizeDiff]
elif sizeDiff > 0:
match = np.hstack((match, np.zeros(sizeDiff)))
matchTS = np.hstack((matchTS, dataTS[-1*sizeDiff:]))
if len(match) != len(data):
print("FIXME: match filter array manip is still broken.")
# compute match filter parameters
matchMax[iH], matchWidth[iH], matchTime[iH] = -888, -888, -888
if len(match)==len(data):
smoothMF = gaussian_filter(match * data_blSub, sigma=5.)
matchMax[iH] = np.amax(smoothMF)
matchTime[iH] = matchTS[ np.argmax(smoothMF) ]
idx = np.where(smoothMF > matchMax[iH]/2.)
if len(matchTS[idx]>1):
matchWidth[iH] = matchTS[idx][-1] - matchTS[idx][0]
# Fit tail slope to polynomial. Guard against fit fails
idx = np.where(dataTS >= fitMaxTime)
tail, tailTS = data[idx], dataTS[idx]
popt1,popt2 = 0,0
try:
popt1,_ = op.curve_fit(wl.tailModelPol, tailTS, tail)
pol0[iH], pol1[iH], pol2[iH], pol3[iH] = popt1[0], popt1[1], popt1[2], popt1[3]
except:
# print("curve_fit tailModelPol failed, run %i event %i channel %i" % (run, iList, chan))
errorCode[2] = 1
pass
# =========================================================
# new trap filters.
# params: t0_SLE, t0_ALE, lat, latF, latAF, latFC, latAFC
# calculate trapezoids
# standard trapezoid - prone to walking, less sensitive to noise. use to find energy
eTrap = wl.trapFilter(data_blSub, 400, 250, 7200.)
eTrapTS = np.arange(0, len(eTrap)*10., 10)
eTrapInterp = interpolate.interp1d(eTrapTS, eTrap)
# short trapezoid - triggers more quickly, sensitive to noise. use to find t0
sTrap = wl.trapFilter(data_blSub, 100, 150, 7200.)
sTrapTS = np.arange(0, len(sTrap)*10., 10)
# asymmetric trapezoid - used to find the t0 only
aTrap = wl.asymTrapFilter(data_blSub, 4, 10, 200, True) # (0.04us, 0.1us, 2.0us)
aTrapTS = np.arange(0, len(aTrap)*10., 10)
# find leading edges (t0 times)
# limit the range from 0 to 10us, and use an ADC threshold of 1.0 as suggested by DCR
t0_SLE[iH],_ = wl.walkBackT0(sTrap, eTrapTS[-1]+7000-4000-2000, 1., 0, 1000) # (in ns) finds leading edge from short trap
t0_ALE[iH],_ = wl.walkBackT0(aTrap, eTrapTS[-1]+7000-4000-2000, 1., 0, 1000) # (in ns) finds leading edge from asymmetric trap
# standard energy trapezoid w/ a baseline padded waveform
data_pad = np.pad(data_blSub,(200,0),'symmetric')
pTrap = wl.trapFilter(data_pad, 400, 250, 7200.)
pTrapTS = np.linspace(0, len(pTrap)*10, len(pTrap))
pTrapInterp = interpolate.interp1d(pTrapTS, pTrap)
# calculate energy parameters
# standard amplitude. basically trapEM, but w/o NL correction if the input WF doesn't have it.
lat[iH] = np.amax(eTrap)
# Calculate DCR suggested amplitude, using the 50% to the left and right of the maximum point
t0_F50,t0fail1 = wl.walkBackT0(pTrap, thresh=lat[iH]*0.5, rmin=0, rmax=len(pTrap)-1)
t0_B50,t0fail2 = wl.walkBackT0(pTrap, thresh=lat[iH]*0.5, rmin=0, rmax=len(pTrap)-1, forward=True)
t0_E50 = (t0_F50 + t0_B50)/2.0
#TODO -- if it's necessary due to the trigger walk, we could potentially add a way to recursively increase the threshold until a timepoint is found, however it will still always fail for most noise events
if not t0fail1 or not t0fail2:
latE50[iH] = 0 # Set amplitude to 0 if one of the evaluations failed
else:
latE50[iH] = pTrapInterp(t0_E50) # Maybe I should call this latDCR50 to confuse people
tE50[iH] = t0_B50 - t0_F50 # Save the difference between the middle points, can be used as a cut later
# standard amplitude with t0 from the shorter traps
# If either fixed pickoff time (t0) is < 0, use the first sample as the amplitude (energy).
latF[iH] = eTrapInterp( np.amax([t0_SLE[iH]-7000+4000+2000, 0.]) ) # This should be ~trapEF
latAF[iH] = eTrapInterp( np.amax([t0_ALE[iH]-7000+4000+2000, 0.]) )
# amplitude from padded trapezoid, with t0 from short traps and a correction function
# function is under development. currently: f() = exp(p0 + p1*E), p0 ~ 7.8, p1 ~ -0.45 and -0.66
# functional walk back distance is *either* the minimum of the function value, or 5500 (standard value)
# t0_corr = -7000+6000+2000 # no correction
t0_corr = -7000+6000+2000 - np.amin([np.exp(7.8 - 0.45*lat[iH]),1000.])
t0A_corr = -7000+6000+2000 - np.amin([np.exp(7.8 - 0.66*lat[iH]),1000.])
latFC[iH] = pTrapInterp( np.amax([t0_SLE[iH] + t0_corr, 0.]) )
latAFC[iH] = pTrapInterp( np.amax([t0_ALE[iH] + t0A_corr, 0.]) )
# =========================================================
# the genius multisite event tagger - plot 8
# decide a threshold
dIdx = np.argmax(data_filtDeriv)
dMax = data_filtDeriv[dIdx]
dRMS,_,_ = wl.baselineParameters(data_filtDeriv)
# msThresh = np.amax([dMax * .2, dRMS * 5.])
# msThresh = dMax * .15
msThresh = 50. # I don't know. this seems like a good value
# run peak detect algorithm
maxtab,_ = wl.peakdet(data_filtDeriv, msThresh)
# profit
msList = []
for iMax in range(len(maxtab)):
idx = int(maxtab[iMax][0])
val = maxtab[iMax][1]
msList.append(dataTS[idx])
# print("%d idx %d TS %d val %.2f thresh %.2f" % (iList, idx, dataTS[idx], val, msThresh))
nMS[iH] = len(maxtab)
# =========================================================
# wfStd analysis
wfAvgBL[iH] = dataBL
wfRMSBL[iH] = dataNoise
wfStd[iH] = np.std(data[5:-5])
# ------------------------------------------------------------------------
# End waveform processing.
# Calculate error code
fails[iH] = 0
for i,j in enumerate(errorCode):
if j==1: fails[iH] += int(j)<<i
# print("fails:",fails[iH])
# Make plots!
if batMode: continue
if plotNum==0: # raw data
p0.cla()
p0.plot(dataTS,data,'b')
p0.set_title("Run %d Entry %d Channel %d ENFCal %.2f" % (run,iList,chan,dataENFCal))
p0.set_xlabel("Time (ns)", ha='right', x=1.)
p0.set_ylabel("Voltage (ADC)", ha='right', y=1.)
if plotNum==1: # wavelet plot
p0.cla()
p0.margins(x=0)
p0.plot(dataTS,data_blSub,color='blue',label='data (%.2f keV)' % dataENFCal)
p0.plot(dataTS,data_wlDenoised,color='cyan',label='denoised',alpha=0.7)
p0.axvline(fitRiseTime50,color='green',label='fit 50%',linewidth=2)
p0.plot(dataTS,fit_blSub,color='red',label='bestfit',linewidth=2)
# p0.set_title("Run %d Entry %d Channel %d ENFCal %.2f flo %.0f fhi %.0f fhi-flo %.0f" % (run,iList,chan,dataENFCal,fitStartTime,fitMaxTime,fitMaxTime-fitStartTime))
p0.legend(loc='best')
p0.set_xlabel("Time (ns)", ha='right', x=1.)
p0.set_ylabel("Voltage (ADC)", ha='right', y=1.)
p1.cla()
p1.imshow(wpCoeff, interpolation='nearest', aspect="auto", origin="lower",extent=[0, 1, 0, len(wpCoeff)],cmap='viridis')
p1.axvline(float(wpLoRise)/numXRows,color='orange',linewidth=2)
p1.axvline(float(wpHiRise)/numXRows,color='orange',linewidth=2)
# p1.set_title("waveS5 %.2f bcMax %.2f bcMin %.2f riseNoise %.2f" % (waveS5[iH], bcMax[iH], bcMin[iH], riseNoise[iH]))
# p1.set_xlabel("Time (%wf)", ha='right', x=1.)
p1.set_ylabel("WPT Coefficients", ha='right', y=1.)
if plotNum==2: # time points, bandpass filters, tail slope
p0.cla()
p0.plot(dataTS,data,color='blue',label='data')
p0.axvline(den10[iH],color='black',label='lpTP')
p0.axvline(den50[iH],color='black')
p0.axvline(den90[iH],color='black')
p0.plot(dataTS,fit,color='magenta',label='bestfit')
if errorCode[2]!=1: p0.plot(tailTS, wl.tailModelPol(tailTS, *popt1), color='orange',linewidth=2, label='tailPol')
p0.legend(loc='best')
p0.set_title("Run %d Entry %d Channel %d ENFCal %.2f" % (run,iEvent,chan,dataENFCal))
p1.cla()
p1.plot(dataTS,data_lPass,color='blue',label='lowpass')
p1.plot(dataTS,data_filtDeriv,color='green',label='filtDeriv')
p1.plot(dataTS,data_filt,color='black',label='filtfilt')
p1.plot(dataTS,data_bPass,color='red',label='bpass')
p1.axvline(bandTime[iH],color='orange',label='bandTime')
p1.legend(loc='best')
if plotNum==3: # freq-domain matched filter
p0.cla()
p0.plot(dataTS,data,'b')
p0.plot(dataTS,temp,'r')
p0.plot(dataTS,fit,color='cyan')
p0.set_title("Run %d Entry %d Channel %d ENFCal %.2f" % (run,iEvent,chan,dataENFCal))
data_asd, data_xFreq = plt.psd(data, Fs=1e8, NFFT=2048, pad_to=2048, visible=False)
temp_asd, temp_xFreq = plt.psd(temp, Fs=1e8, NFFT=2048, pad_to=2048, visible=False)
p1.cla()
p1.loglog(data_xFreq, np.sqrt(data_asd), 'b')
p1.loglog(noise_xFreq, np.sqrt(noise_asd), 'g')
p1.loglog(temp_xFreq, np.sqrt(temp_asd), 'r')
p1.set_xlabel('Frequency (Hz)')
p1.set_ylabel('ASD')
p1.grid('on')
p2.cla()
p2.plot(dataTS, SNR)
p2.set_title('oppie %.1f' % (oppie[iH]))
p2.set_xlabel('Offset time (s)')
p2.set_ylabel('SNR')
if plotNum==4: # time domain match filter plot
p0.cla()
p0.plot(dataTS,data_blSub,color='blue',label='data',alpha=0.7)
p0.plot(dataTS,fit_blSub,color='red',label='bestfit',linewidth=3)
p0.axvline(matchTime[iH],color='orange',label='matchTime',linewidth=2)
p0.plot(matchTS,smoothMF,color='magenta',label='smoothMF',linewidth=3)
p0.plot(matchTS,match,color='cyan',label='match',linewidth=3)
p0.set_xlabel('Time (s)')
p0.set_ylabel('Voltage (arb)')
p0.legend(loc='best')
p0.set_title("Run %d Entry %d Channel %d ENFCal %.2f matchMax %.2f matchTime %.2f matchWidth %.2f" % (run,iEvent,chan,dataENFCal,matchMax[iH],matchTime[iH],matchWidth[iH]))
if plotNum==5: # bandTime plot
p0.cla()
p0.plot(dataTS,data_blSub,color='blue',label='data',alpha=0.7)
p0.plot(dataTS,data_lPass,color='magenta',label='lowpass',linewidth=4)
p0.plot(dataTS,data_bPass,color='red',label='bpass',linewidth=4)
p0.axvline(bandTime[iH],color='orange',label='bandTime',linewidth=4)
p0.legend(loc='best')
p0.set_xlabel('Time (ns)')
p0.set_ylabel('ADC (arb)')
p0.set_title("Run %d Entry %d Channel %d ENFCal %.2f" % (run,iEvent,chan,dataENFCal))
if plotNum==6: # waveform fit plot
p0.cla()
p0.plot(dataTS,data,color='blue',label='data')
# p0.plot(dataTS,data_wlDenoised,color='cyan',label='wlDenoised',alpha=0.5)
p0.plot(dataTS,temp,color='orange',label='xgauss guess')
p0.plot(dataTS,fit,color='red',label='xgauss fit')
p0.set_title("Run %d evt %d chan %d trapENFCal %.1f trapENM %.1f deltaBL %.1f\n amp %.2f mu %.2f sig %.2f tau %.2f chi2 %.2f spd %.3f" % (run,iList,chan,dataENFCal,dataENM,dataBL-bl,amp,mu,sig,tau,fitChi2[iH],fitSpeed))
p0.legend(loc='best')
p1.cla()
p1.plot(dataTS,data-fit,color='blue',label='residual')
p1.legend(loc='best')
p2.cla()
p2.plot(ampTr[1:],label='amp',color='red')
p2.legend(loc='best')
p3.cla()
p3.plot(muTr[1:],label='mu',color='green')
p3.legend(loc='best')
p4.cla()
p4.plot(sigTr[1:],label='sig',color='blue')
p4.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1e'))
p4.legend(loc='best')
p5.cla()
p5.plot(tauTr[1:],label='tau',color='black')
p5.legend(loc='best')
p6.cla()
p6.plot(blTr[1:],label='bl',color='magenta')
p6.legend(loc='best')
print(gatTree.fitSlo.at(iH), sig)
if plotNum==7: # new traps plot
p0.cla()
p0.plot(dataTS, data_blSub, color='blue', label='data')
p0.plot(sTrapTS, sTrap, color='red', label='sTrap')
p0.axvline(t0_SLE[iH], color='red')
p0.plot(aTrapTS, aTrap, color='orange', label='aTrap')
p0.axvline(t0_ALE[iH], color='orange')
p0.plot(eTrapTS, eTrap, color='green', label='eTrap')
p0.axhline(lat[iH],color='green')
p0.plot(pTrapTS, pTrap, color='magenta', label='pTrap')
p0.axhline(latAFC[iH], color='magenta')
p0.axhline(latE50[iH], color='cyan')
p0.set_title("trapENFCal %.2f trapENM %.2f || latEM %.2f latEF %.2f latEAF %.2f latEFC %.2f latEAFC %.2f latE50 %.2f" % (dataENFCal,dataENM,lat[iH],latF[iH],latAF[iH],latFC[iH],latAFC[iH], latE50[iH]))
p0.legend(loc='best')
if plotNum==8: # multisite tag plot
p0.cla()
p0.plot(dataTS, data_blSub, color='blue', label='data')
p0.plot(dataTS, data_filtDeriv, color='red', label='filtDeriv')
for mse in msList: p0.axvline(mse, color='green')
p0.axhline(msThresh,color='red')
p0.legend()
if plotNum==9: # wavelet vs wf fit residual plot
# wavelet packet transform on wf fit residual
fitResid = data-fit
wpRes = pywt.WaveletPacket(fitResid, 'db2', 'symmetric', maxlevel=4)
nodesRes = wpRes.get_level(4, order='freq')
wpCoeffRes = np.array([n.data for n in nodesRes], 'd')
wpCoeffRes = abs(wpCoeffRes)
R6 = np.sum(wpCoeffRes[2:9,1:wpLength//4+1])
R7 = np.sum(wpCoeffRes[2:9,wpLength//4+1:wpLength//2+1])
R8 = np.sum(wpCoeffRes[2:9,wpLength//2+1:3*wpLength//4+1])
R9 = np.sum(wpCoeffRes[2:9,3*wpLength//4+1:-1])
R10 = np.sum(wpCoeffRes[9:,1:wpLength//4+1])
R11 = np.sum(wpCoeffRes[9:,wpLength//4+1:wpLength//2+1])
R12 = np.sum(wpCoeffRes[9:,wpLength//2+1:3*wpLength//4+1])
R13 = np.sum(wpCoeffRes[9:,3*wpLength//4+1:-1])
RsumList = [R6, R7, R8, R9, R10, R11, R12, R13]
bcMinRes = 1. if np.min(RsumList) < 1 else np.min(RsumList)
riseNoiseRes = np.sum(wpCoeffRes[2:-1,wpLoRise:wpHiRise]) / bcMinRes
rnCut = 1.1762 + 0.00116 * np.log(1 + np.exp((dataENFCal-7.312)/0.341))
p0.cla()
p0.margins(x=0)
p0.plot(dataTS,data_blSub,color='blue',label='data')
# p0.plot(dataTS,data_wlDenoised,color='cyan',label='denoised',alpha=0.7)
# p0.axvline(fitRiseTime50,color='green',label='fit 50%',linewidth=2)
p0.plot(dataTS,fit_blSub,color='red',label='bestfit',linewidth=2)
# p0.set_title("Run %d Entry %d Channel %d ENFCal %.2f flo %.0f fhi %.0f fhi-flo %.0f" % (run,iList,chan,dataENFCal,fitStartTime,fitMaxTime,fitMaxTime-fitStartTime))
# p0.legend(loc='best')
p0.set_title("Run %d Entry %d Channel %d ENFCal %.2f flo %.0f fhi %.0f fhi-flo %.0f approxFitE %.2f" % (run,iList,chan,dataENFCal,fitStartTime,fitMaxTime,fitMaxTime-fitStartTime,fitAmp[iH]*0.4))
p1.cla()
p1.plot(dataTS,fitResid,color='blue')
p2.cla()
p2.set_title("riseNoise %.2f rnCut %.2f riseNoiseRes %.2f bcMinRes %.2f bcMin %.2f max %.2f" % (riseNoise[iH],rnCut,riseNoiseRes,bcMinRes,bcMin[iH],wpCoeffRes.max()))
p2.imshow(wpCoeffRes, interpolation='nearest', aspect="auto", origin="lower",extent=[0, 1, 0, len(wpCoeff)],cmap='viridis')
plt.tight_layout()
plt.pause(0.000001)
# ------------------------------------------------------------------------
# End loop over hits, fill branches
if batMode:
for key in brDict:
brDict[key][1].Fill()
if iList%5000 == 0 and iList!=0:
out.Write("",TObject.kOverwrite)
print("%d / %d entries saved (%.2f %% done), time: %s" % (iList,nList,100*(float(iList)/nList),time.strftime('%X %x %Z')))
# End loop over events
if batMode and not intMode:
out.Write("",TObject.kOverwrite)
print("Wrote",out.GetBranch("channel").GetEntries(),"entries in the copied tree,")
print("and wrote",b1.GetEntries(),"entries in the new branches.")
stopT = time.clock()
print("Stopped:",time.strftime('%X %x %Z'),"\nProcess time (min):",(stopT - startT)/60)
print(float(nList)/((stopT-startT)/60.),"entries per minute.")
'''RObjs['headerText'] = drawTitle(title)
#Create pad to hold all plots.
body = TPad("MainPad", "My main pad", 0, 0, 1, 0.9)
body.Draw()
halfSize = (int) (len(numReadsList)/2)
body.Divide(halfSize,halfSize)
i=1
for reads in numReadsList:
body.cd(i)
RObjs["graph%i" %i] = drawNext(reads)
i+=1
'''
constraints = "ConcurrentReads==%i" % 6
numPoints = allData.Draw("VelocityMBps:BufferSize", constraints, "goff")
v2 = allData.GetV2()
v1 = allData.GetV1()
print("not broken")
graph = TGraph(numPoints, v2, v1)
print("did we break yet?")
graph.SetTitle("%i Concurrent Reads" % reads)
graph.SetXTitle("Read Velocity (MB/s)")
graph.Draw("ap")
BG.SaveAs("%s.png" % data_file.split('.output')[0])
def test3():
ds = [(50, 'Jan05a_50mV.dat'), (100, 'Jan05a_100mV.dat'), (150, 'Jan05a_150mV.dat'), (250, 'Jan05a_250mV.dat'), (400, 'Jan05a_400mV.dat')]
chs = [12, 8, 1, 4, 6, 9, 13,15]
inspectCh(ds,chs)
return
# pars = array('d',[0]*3)
# errs = array('d',[0]*3)
# print(max(chs))
# return
nch = max(chs)+1
g1s = [None]*nch
g2s = [None]*nch
g1M = -1
g2M = -1
for d in ds:
tr = TTree()
tr.ReadFile(d[1])
for ch in chs:
gDirectory.DeleteAll('h1*')
tr.Draw('A>>h1','ch=={0:d}'.format(ch),'goff')
h1 = gDirectory.Get('h1')
h1.Fit('gaus')
fun1 = h1.GetFunction('gaus')
fun1.SetLineColor(4)
errs = fun1.GetParErrors()
pars = fun1.GetParameters()
# print(d[0],'--'*20)
# print(pars[0],errs[0])
# print(pars[1],errs[1])
# print(pars[2],errs[2])
if g1s[ch] is None:
g1s[ch] = TGraphErrors()
g2s[ch] = TGraphErrors()
g1 = g1s[ch]
g2 = g2s[ch]
n = g1.GetN()
g1.SetPoint(n, d[0]*7, pars[1])
g1.SetPointError(n, 0, errs[1])
g2.SetPoint(n, d[0]*7, pars[2]*d[0]*7/pars[1])
g2.SetPointError(n, 0, errs[2]*d[0]*7/pars[1])
if pars[1]+errs[1]>g1M: g1M = pars[1]+errs[1]
t = (pars[2]+errs[2])*d[0]*7/pars[1]
if t>g2M: g2M = t
opt = 'A'
h1x = None
for g1 in g1s:
if g1 is None: continue
g1.Draw('PL PMC PLC'+opt)
opt = ' same'
if h1x is None: h1x = g1.GetHistogram()
h1x.GetYaxis().SetRangeUser(0, g1M*1.1)
h1x.GetYaxis().SetTitle("U_{Out} [V]")
h1x.GetXaxis().SetTitle("N_{Sig} [e^{-}]")
waitRootCmdX()
opt = 'A'
h2x = None
for g2 in g2s:
if g2 is None: continue
g2.Draw('PL PMC PLC'+opt)
opt = ' same'
if h2x is None: h2x = g2.GetHistogram()
h2x.GetYaxis().SetRangeUser(0, g2M*1.1)
h2x.GetYaxis().SetTitle("ENC [e^{-}]")
h2x.GetXaxis().SetTitle("N_{Sig} [e^{-}]")
waitRootCmdX()
def MakeHistsToFit2(file, f_out_str):
E_arr = array('d', [])
E_arr_err = array('d', [])
effic_arr = array('d', [])
effic_arr_err = array('d', [])
c1 = TCanvas("c1", "c1", 1600, 900)
c1.SetGridx(1)
c1.SetGridy(1)
#Find which energy files exist, create file list and energy array
f_wogamma_list = []
curr_E_val = 0.1
while curr_E_val <= 3.0:
normfile = REL_DIR + "tree_pi+pi-p_nofit_" + str(curr_E_val) + ".root"
if (os.path.isfile(normfile)):
E_arr.append(curr_E_val)
E_arr_err.append(0)
f_wogamma_list.append(TFile.Open(normfile, 'read'))
# print "File found!!!"
# print "Was looking for file: " + normfile
# print "Current E val: " + str(curr_E_val)
# else:
# print "File not found!!!!!!!!!!!!!!!!!!!!!!!!!!!!"
# print "Was looking for file: " + normfile
# print "Current E val: " + str(curr_E_val)
curr_E_val += 0.05
#Get normalization factor
norm_arr = array('d', [])
for i in range(0, len(f_wogamma_list)):
my_tr = TTree()
my_tr = f_wogamma_list[i].Get("pi+pi-p_nofit_Tree")
norm_arr.append(my_tr.GetEntries())
# print "Energy: " + str(E_arr[i])
# print "Normalization: " + str(norm_arr[i])
#Create and fit histograms in larger file
f = TFile.Open(file, 'read')
my_tr = TTree()
my_tr = f.Get("gamma_pi+pi-p_nofit_Tree")
h_DeltaPhi_list = []
h_DeltaTheta_list = []
h_EGammaPostCuts_list = []
for i in range(0, len(E_arr)):
h_DeltaPhi_curr = TH1F()
my_tr.Draw("DeltaPhi>>h_DeltaPhi_curr(1000,-180.,180.)", "")
h_DeltaPhi_curr = gPad.GetPrimitive("h_DeltaPhi_curr")
h_DeltaPhi_curr.SetName("h_DeltaPhi_" + str(E_arr[i]))
h_DeltaPhi_list.append(h_DeltaPhi_curr)
h_DeltaTheta_curr = TH1F()
my_tr.Draw("DeltaTheta>>h_DeltaTheta_curr(1000,-25.,10.)", "")
h_DeltaTheta_curr = gPad.GetPrimitive("h_DeltaTheta_curr")
h_DeltaTheta_curr.SetName("h_DeltaTheta_" + str(E_arr[i]))
h_DeltaTheta_list.append(h_DeltaTheta_curr)
h_EGammaPostCuts_curr = TH1F()
E_cut_str = "abs(ThrownE-" + str(E_arr[i]) + ")<0.0001"
print "Cut string: " + E_cut_str
my_tr.Draw("FoundE>>h_EGammaPostCuts_curr(1000,0,4)",
"abs(DeltaPhi)<2.&&abs(DeltaTheta)<0.5&&" + E_cut_str)
h_EGammaPostCuts_curr = gPad.GetPrimitive("h_EGammaPostCuts_curr")
h_EGammaPostCuts_curr.SetName("h_EGammaPostCuts_" + str(E_arr[i]))
h_EGammaPostCuts_list.append(h_EGammaPostCuts_curr)
my_gaus_fit = TF1("my_gaus_fit", "gausn")
my_gaus_fit.SetParLimits(0, 0, 30000)
my_gaus_fit.SetParameter(1, E_arr[i])
my_gaus_fit.SetParLimits(1, E_arr[i] - 0.5, E_arr[i] + 0.1)
my_gaus_fit.SetParLimits(2, 0.02, 0.5)
my_gaus_fit.SetNpx(1000)
h_EGammaPostCuts_curr.Fit(my_gaus_fit, "Q", "", E_arr[i] - 0.3,
E_arr[i] + 0.2)
c1.SaveAs(".plots/ReactionFilterFit_pipip_" + str(E_arr[i]) + ".png")
effic = my_gaus_fit.GetParameter(
0) / h_EGammaPostCuts_curr.GetBinWidth(0) / norm_arr[i]
effic_err = my_gaus_fit.GetParError(
0) / h_EGammaPostCuts_curr.GetBinWidth(0) / norm_arr[i]
if (E_arr[i] < 0.2):
effic = h_EGammaPostCuts_curr.GetEntries() / norm_arr[i]
effic_err = sqrt(h_EGammaPostCuts_curr.GetEntries()) / norm_arr[i]
if (h_EGammaPostCuts_curr.GetEntries() < 100.):
effic = -1
effic_err = 0
print "File Entries: " + str(my_tr.GetEntries())
print "Histogram Entries: " + str(h_EGammaPostCuts_curr.GetEntries())
print "Gaussian yield: " + str(
my_gaus_fit.GetParameter(0) / h_EGammaPostCuts_curr.GetBinWidth(0))
print "Normalization: " + str(norm_arr[i])
print "Efficiency: " + str(effic)
effic_arr.append(effic)
effic_arr_err.append(effic_err)
gr_gauscore_effic = TGraphErrors(len(E_arr), E_arr, effic_arr, E_arr_err,
effic_arr_err)
gr_gauscore_effic.SetName("gr_gauscore_effic")
f_out = TFile(f_out_str, "RECREATE")
gr_gauscore_effic.Write()
for i in range(0, len(E_arr)):
h_DeltaPhi_list[i].Write()
for i in range(0, len(E_arr)):
h_DeltaTheta_list[i].Write()
for i in range(0, len(E_arr)):
h_EGammaPostCuts_list[i].Write()
f_out.Close()
# curr_E_val = 0.1
# curr_E_val = 2.0
# while curr_E_val <= 3.:
# while curr_E_val <= 2.2:
# print "Curr E thing " + str(curr_E_val)
# normfile = REL_DIR+"tree_pi+pi-p_nofit_"+str(curr_E_val)+".root"
# my_tr2 = TTree()
# my_tr2 = f_wogamma_list[i].Get("pi+pi-p_nofit_Tree")
# NormalizationFactor = my_tr2.GetEntries()
# curr_E_val+=0.05
return
def MakeHistsToFit(file, MIN_E, MAX_E, E_STEP, f_out_str):
E_arr = array('d', [])
c1 = TCanvas("c1", "c1", 1600, 900)
c1.SetGridx(1)
c1.SetGridy(1)
norm_arr = array('d', [])
curr_E_val = MIN_E
while curr_E_val <= MAX_E + 0.0001:
E_arr.append(curr_E_val)
curr_E_val += E_STEP
#Create and fit histograms in larger file
f = TFile.Open(file, 'read')
my_tr = TTree()
my_tr = f.Get(WITH_GAMMA_TREENAME)
h_DeltaPhi_list = [] #List of deltaPhi histograms to save
h_DeltaTheta_list = [] #List of deltaTheta histograms to save
h_EGammaPreCuts_list = [] #List of E_gamma histograms to save
h_EGammaPostCuts_list = [] #List of E_gamma histograms to save
h_EThrown_list = [] #List of E_gamma histograms to save
num_tree_entries = float(my_tr.GetEntries())
num_branch_entries = my_tr.Draw("ThrownE", "")
norm_scale_factor = num_branch_entries / num_tree_entries
# print "Num tree entries: " + str(num_tree_entries)
# print "Num branch entries: " + str(num_branch_entries)
print "Need to scale by: " + str(norm_scale_factor)
for i in range(0, len(E_arr)):
E_cut_str = ""
if (NORMALIZE_EXTERNAL):
E_cut_str = "abs(ThrownE-" + str(E_arr[i]) + ")<0.0001"
if (not NORMALIZE_EXTERNAL):
E_cut_str = str(E_arr[i] -
E_STEP / 2.) + "<ThrownE&&ThrownE<" + str(
E_arr[i] +
E_STEP / 2.) + "&&ThrownTheta<9&&ThrownTheta>4"
print "Cut string: " + E_cut_str
#Make DeltaPhi and DeltaTheta histograms
h_DeltaPhi_curr = TH1F()
my_tr.Draw("DeltaPhi>>h_DeltaPhi_curr(1000,-180.,180.)", E_cut_str)
h_DeltaPhi_curr = gPad.GetPrimitive("h_DeltaPhi_curr")
if (NORMALIZE_EXTERNAL):
h_DeltaPhi_curr.SetNameTitle("h_DeltaPhi_" + str(E_arr[i]),
"h_DeltaPhi_" + str(E_arr[i]))
if (not NORMALIZE_EXTERNAL):
h_DeltaPhi_curr.SetNameTitle("h_DeltaPhi_" + E_cut_str,
"h_DeltaPhi_" + E_cut_str)
h_DeltaPhi_list.append(h_DeltaPhi_curr)
print "h_DeltaPhi_curr hist entries:" + str(
h_DeltaPhi_curr.GetEntries())
h_DeltaTheta_curr = TH1F()
my_tr.Draw("DeltaTheta>>h_DeltaTheta_curr(1000,-25.,10.)", E_cut_str)
h_DeltaTheta_curr = gPad.GetPrimitive("h_DeltaTheta_curr")
if (NORMALIZE_EXTERNAL):
h_DeltaTheta_curr.SetNameTitle("h_DeltaTheta_" + str(E_arr[i]),
"h_DeltaTheta_" + str(E_arr[i]))
if (not NORMALIZE_EXTERNAL):
h_DeltaTheta_curr.SetNameTitle("h_DeltaTheta_" + E_cut_str,
"h_DeltaTheta_" + E_cut_str)
h_DeltaTheta_list.append(h_DeltaTheta_curr)
print "h_DeltaTheta_curr hist entries:" + str(
h_DeltaTheta_curr.GetEntries())
h_EGammaPreCuts_curr = TH1F()
my_tr.Draw(
"FoundE>>h_EGammaPreCuts_curr(1000,0," + str(MAX_E + 1) + ")",
E_cut_str)
h_EGammaPreCuts_curr = gPad.GetPrimitive("h_EGammaPreCuts_curr")
if (NORMALIZE_EXTERNAL):
h_EGammaPreCuts_curr.SetNameTitle(
"h_EGammaPreCuts_" + str(E_arr[i]),
"h_EGammaPreCuts_" + str(E_arr[i]))
if (not NORMALIZE_EXTERNAL):
h_EGammaPreCuts_curr.SetNameTitle("h_EGammaPreCuts_" + E_cut_str,
"h_EGammaPreCuts_" + E_cut_str)
h_EGammaPreCuts_list.append(h_EGammaPreCuts_curr)
print "h_EGammaPreCuts_curr hist entries:" + str(
h_EGammaPreCuts_curr.GetEntries())
h_EGammaPostCuts_curr = TH1F()
my_tr.Draw(
"FoundE>>h_EGammaPostCuts_curr(1000,0," + str(MAX_E + 1) + ")",
"abs(DeltaPhi)<" + DELTA_PHI_CUT + "&&abs(DeltaTheta)<" +
DELTA_THETA_CUT + "&&" + E_cut_str)
h_EGammaPostCuts_curr = gPad.GetPrimitive("h_EGammaPostCuts_curr")
if (NORMALIZE_EXTERNAL):
h_EGammaPostCuts_curr.SetNameTitle(
"h_EGammaPostCuts_" + str(E_arr[i]),
"h_EGammaPostCuts_" + str(E_arr[i]))
if (not NORMALIZE_EXTERNAL):
h_EGammaPostCuts_curr.SetNameTitle("h_EGammaPostCuts_" + E_cut_str,
"h_EGammaPostCuts_" + E_cut_str)
h_EGammaPostCuts_curr.SetTitle("E_{#gamma} thrown = " + str(E_arr[i]))
h_EGammaPostCuts_curr.GetXaxis().SetTitle("E_{#gamma}")
h_EGammaPostCuts_curr.GetYaxis().SetTitle("Yield")
h_EGammaPostCuts_list.append(h_EGammaPostCuts_curr)
print "h_EGammaPostCuts_curr hist entries:" + str(
h_EGammaPostCuts_curr.GetEntries())
h_EThrown_curr = TH1F()
# my_tr.Draw("ThrownE>>h_EThrown_curr(1000,0,10",E_cut_str)
my_tr.Draw("MCWeight>>h_EThrown_curr(1000,-10,10", E_cut_str)
h_EThrown_curr = gPad.GetPrimitive("h_EThrown_curr")
if (NORMALIZE_EXTERNAL):
h_EThrown_curr.SetNameTitle("h_EThrown_" + str(E_arr[i]),
"h_EThrown_" + str(E_arr[i]))
if (not NORMALIZE_EXTERNAL):
h_EThrown_curr.SetNameTitle("h_EThrown_" + E_cut_str,
"h_EThrown_" + E_cut_str)
h_EThrown_curr.SetTitle("E_{#gamma} thrown = " + str(E_arr[i]))
h_EThrown_curr.GetXaxis().SetTitle("E_{#gamma}")
h_EThrown_curr.GetYaxis().SetTitle("Yield")
h_EThrown_list.append(h_EThrown_curr)
print "h_EThrown_curr hist entries:" + str(h_EThrown_curr.GetEntries())
#Save to file
f_out = TFile(f_out_str, "RECREATE")
# gr_gauscore_effic.Write()
for i in range(0, len(E_arr)):
h_EGammaPostCuts_list[i].Write()
for i in range(0, len(E_arr)):
h_DeltaPhi_list[i].Write()
for i in range(0, len(E_arr)):
h_DeltaTheta_list[i].Write()
for i in range(0, len(E_arr)):
h_EGammaPreCuts_list[i].Write()
for i in range(0, len(E_arr)):
h_EThrown_list[i].Write()
f_out.Close()
return norm_scale_factor
cx = 1600
cy = 800
#if label is not None:
# cy = int(1.05*cy)
canv = TCanvas("PlotCalib", "plotCalib", 0, 0, cx, cy)
if opt2d:
hhh = TH2D("hhh", "hhh", modmax - modmin + 1, modmin - 0.5, modmax + 0.5,
chanmax - chanmin + 1, chanmin - 0.5, chanmax + 0.5)
if not many and not opt2d:
print("Plotting", data)
print("With cut", cut_cond)
if vsbin:
tree.Draw(data, cut_cond, "goff", 15)
else:
tree.Draw(data, cut_cond, "goff")
if tree.GetSelectedRows() <= 0:
print("Not enough points to plot")
sys.exit(2)
if vsbin:
if tree.GetSelectedRows() >= 15:
print("Maximum number of bins is 15")
if line or noline:
if many:
first = True
color = 1
histogram.SetXTitle(
"Invariant mass of #mu^+mu^- pair(GeV/C^2)#rightarrow")
histogram.SetYTitle(ytitle)
histogram.GetYaxis().SetTitleOffset(1.4)
histogram.GetYaxis().CenterTitle()
histogram.GetXaxis().CenterTitle()
# master_canvas.append(canvas)
#histograms.append(histogram)
#master_canvas[i].cd()
canvas.cd()
tree.Draw(
"fRecPair_ParentMass>>histogram", cut[i],
"e") #need to define the cut parameter which will be on cos theta
histogram.SetDirectory(0)
#tail_func.FixParameter(1,tail_func_parammeters[1])
#tail_func.FixParameter(2,tail_func_parammeters[2])
#tail_func.FixParameter(3,tail_func_parammeters[3])
#par 3 = jpsi n and par 9 = psin
#par
#sig_func.FixParameter()
#histogram.Fit(ft.tail_func,"NQR")
#histogram.Fit(ft.sig_func,"NQR+")
#ft.comb_func.SetParameters(ft.sig_func.GetParameter(0),ft.sig_func.GetParameter(1),ft.sig_func.GetParameter(2),ft.sig_func.GetParameter(3),ft.tail_func.GetParameter(0),ft.tail_func.GetParameter(1),ft.tail_func.GetParameter(6),ft.tail_func.GetParameter(3))
# for comb function n1 = par3 +n2 = par9 a1 = par 2 a2 = par 10
def main(argv):
"""
Matplotlib animation tutorial:
http://jakevdp.github.io/blog/2012/08/18/matplotlib-animation-tutorial/
Requires ffmpeg. (brew install ffmpeg)
"""
chans = [1176, 672, 648, 660]
chan = chans[0]
dummyTree = ROOT.TChain("skimTree")
# dummyTree.Add("/global/homes/w/wisecg/project/cal/lat/latSkimDS5_run2549*.root")
# dummyTree.Add("/global/homes/w/wisecg/project/cal/lat/latSkimDS5_run2550*.root")
dummyTree.Add(
"/global/homes/w/wisecg/project/bkg/waves/waveSkimDS6_*.root")
print("Found %d entries." % (dummyTree.GetEntries()))
theCut = "trapENFCal > 2000 && trapENFCal < 2200 && channel==%d && avse>-1" % chan
gatTree = TTree()
gatTree = dummyTree.CopyTree(theCut)
outFile = "../plots/movie-5c-%d.mp4" % chan
# Print cut and events passing cut
print("Using cut:\n", theCut, "\n")
gatTree.Draw(">>elist", theCut, "entrylist")
elist = gDirectory.Get("elist")
gatTree.SetEntryList(elist)
nList = elist.GetN()
print("Found", nList, "entries passing cuts.")
nWFLimit = 1000 if nList > 1000 else nList
# First set up the figure, the axis, and the plot element we want to animate
fig = plt.figure(figsize=(20, 10), facecolor='w')
fig.set_size_inches(20, 10, True)
a1 = plt.subplot(111)
a1.set_xlabel("Time (ns)")
a1.set_ylabel("ADC")
p1, = a1.plot(np.ones(1), np.ones(1), color='blue')
# initialization function: plot the background of each frame
def init():
p1.set_data([], [])
return p1,
# animation function. This is called sequentially (it's the loop over events.)
def animate(iList):
entry = gatTree.GetEntryNumber(iList)
gatTree.LoadTree(entry)
gatTree.GetEntry(entry)
nChans = gatTree.channel.size()
numPass = gatTree.Draw("channel", theCut, "GOFF", 1, iList)
chans = gatTree.GetV1()
chanList = list(set(int(chans[n]) for n in range(numPass)))
# Loop over hits passing cuts
hitList = (iH for iH in range(nChans)
if gatTree.channel.at(iH) in chanList
) # a 'generator expression'
for iH in hitList:
wf = gatTree.MGTWaveforms.at(iH)
iEvent = entry
run = gatTree.run
chan = gatTree.channel.at(iH)
energy = gatTree.trapENFCal.at(iH)
dcr = gatTree.dcr99.at(iH)
# fs = gatTree.fitSlo.at(iH)
# rn = gatTree.riseNoise.at(iH)
signal = wl.processWaveform(wf)
waveRaw = signal.GetWaveRaw()
waveTS = signal.GetTS()
baseline = np.sum(waveRaw[:50]) / 50
# fill the figure
p1.set_ydata(waveRaw)
p1.set_xdata(waveTS)
# plt.title("Run %d Ch %d Entry %d trapENFCal %.1f fitSlo %.2f riseNoise %.2f" % (run,chan,iList,energy, fs, rn))
plt.title("Run %d Ch %d Entry %d trapENFCal %.1f dcr99 %.8f" %
(run, chan, iList, energy, dcr))
# dynamically scale the axes
xmin, xmax = np.amin(waveTS), np.amax(waveTS)
# a1.set_xlim([xmin,xmax])
a1.set_xlim([9000, xmax])
# if energy > 1000:
# a1.set_ylim(0,1000)
# else:
ymin, ymax = np.amin(waveRaw), np.amax(waveRaw)
# a1.set_ylim([ymin-abs(0.1*ymin),ymax+abs(0.1*ymax)])
a1.set_ylim([ymax - abs(0.35 * ymax), ymax + abs(0.2 * ymax)])
# a1.set_ylim([5000,6000])
# print(progress)
# print("%d / %d Run %d nCh %d chan %d trapE %.1f samp %d" % (iList,nList,run,nChans,chan,energy,wf.GetLength()))
if iList % 500 == 0 and iList != 0:
print("%d / %d entries saved (%.2f %% done)." %
(iList, nList, 100 * (float(iList) / nList)))
return p1,
# call the animator. blit=True means only re-draw the parts that have changed.
# anim = animation.FuncAnimation(fig, animate, init_func=init, frames=elist.GetN(), interval=0, blit=True)
anim = animation.FuncAnimation(fig,
animate,
init_func=init,
frames=nWFLimit,
interval=0,
blit=True)
# save the animation as an mp4. This requires ffmpeg or mencoder to be
# installed. The extra_args ensure that the x264 codec is used, so that
# the video can be embedded in html5. You may need to adjust this for
# your system: for more information, see
# http://matplotlib.sourceforge.net/api/animation_api.html
anim.save(outFile, fps=20) #, extra_args=['-vcodec', 'libx264'])
