def plot(path, leg_title, ecms, xmin, xmax, ymin, ymax, bins, charm):
try:
f_data = TFile(path[0])
t_data = f_data.Get('save')
entries_data = t_data.GetEntries()
logging.info('data entries :' + str(entries_data))
except:
logging.error(path[0] + ' is invalid!')
sys.exit()
try:
f_side1 = TFile(path[1])
t_side1 = f_side1.Get('save')
entries_side1 = t_side1.GetEntries()
logging.info('data(side1) entries :' + str(entries_side1))
except:
logging.error(path[1] + ' is invalid!')
sys.exit()
try:
f_side2 = TFile(path[2])
t_side2 = f_side2.Get('save')
entries_side2 = t_side2.GetEntries()
logging.info('data(side2) entries :' + str(entries_side2))
except:
logging.error(path[2] + ' is invalid!')
sys.exit()
try:
f_side3 = TFile(path[3])
t_side3 = f_side3.Get('save')
entries_side3 = t_side3.GetEntries()
logging.info('data(side3) entries :' + str(entries_side3))
except:
logging.error(path[3] + ' is invalid!')
sys.exit()
try:
f_side4 = TFile(path[4])
t_side4 = f_side4.Get('save')
entries_side4 = t_side4.GetEntries()
logging.info('data(side4) entries :' + str(entries_side4))
except:
logging.error(path[4] + ' is invalid!')
sys.exit()
set_pub_style()
set_prelim_style()
# from ROOT import gStyle
# colors = array('i', 7*[0])
# for i in range(7): colors[i] = 18 - i
# gStyle.SetPalette(7, colors)
gStyle.SetPalette(112)
mbc = TCanvas('mbc', 'mbc', 800, 600)
if charm == 1:
xtitle = 'RM(D_{miss}^{-}) (GeV/c^{2})'
ytitle = 'RM(#pi_{d}^{+}#pi_{d}^{-}) (GeV/c^{2})'
else:
xtitle = 'RM(D_{miss}^{+}) (GeV/c^{2})'
ytitle = 'RM(#pi_{d}^{+}#pi_{d}^{-}) (GeV/c^{2})'
h_data = TH2F('scatter_data', 'scatter plot of M(Dpi) and M(Dmisspi)',
bins, xmin, xmax, bins, ymin, ymax)
format_data_hist(h_data)
name_axis(h_data, xtitle, ytitle)
fill(t_data, h_data, charm)
h_side1 = TH2F('scatter_side1', 'scatter plot of M(Dpi) and M(Dmisspi)',
bins, xmin, xmax, bins, ymin, ymax)
fill(t_side1, h_side1, charm)
h_side2 = TH2F('scatter_side2', 'scatter plot of M(Dpi) and M(Dmisspi)',
bins, xmin, xmax, bins, ymin, ymax)
fill(t_side2, h_side2, charm)
h_side3 = TH2F('scatter_side3', 'scatter plot of M(Dpi) and M(Dmisspi)',
bins, xmin, xmax, bins, ymin, ymax)
fill(t_side3, h_side3, charm)
h_side4 = TH2F('scatter_side4', 'scatter plot of M(Dpi) and M(Dmisspi)',
bins, xmin, xmax, bins, ymin, ymax)
fill(t_side4, h_side4, charm)
h_side1.Add(h_side2)
h_side1.Scale(0.5)
h_side3.Add(h_side4)
h_side3.Scale(0.25)
h_side1.Add(h_side3, -1)
h_data.Add(h_side1, -1)
h_data.Draw('COLZ')
# h_data.Draw('box')
pt = TPaveText(0.55, 0.7, 0.75, 0.85, "BRNDC")
set_pavetext(pt)
pt.Draw()
# pt.AddText(leg_title)
if charm == 1: pt.AddText(leg_title + ' D_{tag}^{+}')
if charm == -1: pt.AddText(leg_title + ' D_{tag}^{-}')
if not os.path.exists('./figs/'):
os.makedirs('./figs/')
mbc.SaveAs('./figs/scatter_rm_Dmiss_rm_pipi_' + str(ecms) + '_' +
str(charm) + '.pdf')
raw_input('Press <Enter> to end...')
def fit(path, shape_path, ecms, mode, patch):
try:
f_data = TFile(path[0])
t_data = f_data.Get('save')
entries_data = t_data.GetEntries()
logging.info('Entries :'+str(entries_data))
except:
logging.error('File paths are invalid!')
set_pub_style()
set_prelim_style()
mbc = TCanvas('mbc', 'mbc', 1000, 700)
pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 1.0)
pad.Draw()
xmin = 1.75
xmax = 1.95
xbins = 100
if mode == 'data_after' and (ecms == 4245 or ecms == 4280 or ecms == 4310 or ecms == 4575): xbins = 50
if mode == 'data_before' and (ecms == 4245 or ecms == 4280 or ecms == 4310 or ecms == 4575): xbins = 50
rm_Dpipi = RooRealVar('rm_Dpipi', 'rm_Dpipi', xmin, xmax)
data = RooDataSet('data', 'dataset', t_data, RooArgSet(rm_Dpipi))
is_OK = 2
chi2_ndf = 999.
while True:
# signal
f_shape = TFile(shape_path, 'READ')
h_shape = f_shape.Get('h_hist')
h_signal = RooDataHist('h_shape', 'h_shape', RooArgList(rm_Dpipi), h_shape)
pdf_signal = RooHistPdf('pdf_signal', 'pdf_signal', RooArgSet(rm_Dpipi), h_signal, 0)
mean_low, mean_up, sigma_up = param_rm_Dpipi(ecms)
if not (mode == 'data_before' or mode == 'data_after'): mean_low, mean_up, sigma_up = mean_low*(1 + random.uniform(-1, 1)), mean_up*(1 + random.uniform(-1, 1)), sigma_up*(1 + random.uniform(-1, 1))
if sigma_up > 0.001: sigma_up += 0.001
mean = RooRealVar('mean', 'mean of gaussian', 0., mean_low, mean_up)
sigma = RooRealVar('sigma', 'sigma of gaussian', 0.001, 0, sigma_up)
f_param = open('./txts/param_' + str(ecms) + '_' + patch + '.txt', 'r')
lines_param = f_param.readlines()
for line_param in lines_param:
rs_param = line_param.rstrip('\n')
rs_param = filter(None, rs_param.split(" "))
ndf = float(float(rs_param[0]))
a_val = float(float(rs_param[1]))
if ndf == 6:
b_val = float(float(rs_param[2]))
mean_val = float(float(rs_param[3]))
sigma_val = float(float(rs_param[4]))
else:
mean_val = float(float(rs_param[2]))
sigma_val = float(float(rs_param[3]))
if mode == 'data_before' or mode == 'data_after':
mean = RooRealVar('mean', 'mean of gaussian', mean_val)
sigma = RooRealVar('sigma', 'sigma of gaussian', sigma_val)
gauss = RooGaussian('gauss', 'gaussian', rm_Dpipi, mean, sigma)
rm_Dpipi.setBins(xbins, 'cache')
sigpdf = RooFFTConvPdf('sigpdf', 'sigpdf', rm_Dpipi, pdf_signal, gauss)
# background
if ndf == 6:
a = RooRealVar('a', 'a', 0., -99., 99.)
b = RooRealVar('b', 'b', 0., -99., 99.)
if ecms == 4440:
a = RooRealVar('a', 'a', 0., -1., 1.)
b = RooRealVar('b', 'b', 0., -1., 1.)
if ecms == 4470:
a = RooRealVar('a', 'a', 0., -1., 1.)
b = RooRealVar('b', 'b', 0., -1., 1.)
if ecms == 4260:
a = RooRealVar('a', 'a', 0., -1., 1.)
b = RooRealVar('b', 'b', 0., -1., 1.)
if ecms == 4190:
a = RooRealVar('a', 'a', 0., -9., 9.)
b = RooRealVar('b', 'b', 0., -9., 9.)
bkgpdf = RooChebychev('bkgpdf', 'bkgpdf', rm_Dpipi, RooArgList(a, b))
if ndf == 5:
a = RooRealVar('a', 'a', 0., -99., 99.)
bkgpdf = RooChebychev('bkgpdf', 'bkgpdf', rm_Dpipi, RooArgList(a))
# event number
nsig = RooRealVar('nsig', 'nsig', 100, -500000, 500000)
nbkg = RooRealVar('nbkg', 'nbkg', 80, 0, 500000)
# fit model
model = RooAddPdf('model', 'sigpdf + bkgpdf', RooArgList(sigpdf, bkgpdf), RooArgList(nsig, nbkg))
results = model.fitTo(data, RooFit.Save())
is_OK = int(results.covQual())
status = int(results.status())
# plot results
xframe = rm_Dpipi.frame(RooFit.Bins(xbins), RooFit.Range(xmin, xmax))
data.plotOn(xframe)
model.plotOn(xframe, RooFit.LineWidth(5))
model.plotOn(xframe, RooFit.Components('sigpdf'), RooFit.LineColor(kRed), RooFit.LineWidth(5), RooFit.LineStyle(9))
model.plotOn(xframe, RooFit.Components('bkgpdf'), RooFit.LineColor(kGreen), RooFit.LineWidth(5), RooFit.LineStyle(6))
data.plotOn(xframe)
xtitle = 'RM(D^{+}#pi^{+}_{d}#pi^{-}_{d}) (GeV/c^{2})'
content = (xmax - xmin)/xbins * 1000
ytitle = 'Events/%.1f MeV/c^{2}'%content
format_data_hist(xframe)
name_axis(xframe, xtitle, ytitle)
xframe.Draw()
if mode == 'data_before' or mode == 'data_after' or mode == 'DDPIPI' or mode == 'psipp' or mode == 'D1_2420':
fr = model.fitTo(data, RooFit.Extended(kTRUE), RooFit.Save(kTRUE))
curve = xframe.getObject(1)
histo = xframe.getObject(0)
chi2_tot, nbin, ytot, avg, eyl, eyh = 0, 0, 0, 0, 0, 0
x = array('d', 999*[0])
y = array('d', 999*[0])
for i in xrange(xbins):
histo.GetPoint(i, x, y)
exl = histo.GetEXlow()[i]
exh = histo.GetEXhigh()[i]
avg += curve.average(x[0] - exl, x[0] + exh)
ytot += y[0]
eyl += histo.GetEYlow()[i] * histo.GetEYlow()[i]
eyh += histo.GetEYhigh()[i] * histo.GetEYhigh()[i]
if ytot >= 7:
if ytot > avg:
pull = (ytot - avg)/sqrt(eyl)
else:
pull = (ytot - avg)/sqrt(eyh)
chi2_tot += pull * pull
nbin += 1
ytot, avg, eyl, eyh = 0, 0, 0, 0
if mode == 'data_before' or mode == 'data_after':
if ecms == 4245 or ecms == 4310: pt = TPaveText(0.6, 0.7, 0.75, 0.85, "BRNDC")
else: pt = TPaveText(0.17, 0.15, 0.3, 0.25, "BRNDC")
else: pt = TPaveText(0.17, 0.7, 0.3, 0.85, "BRNDC")
set_pavetext(pt)
pt.Draw()
if ecms == 4230: pt_title = '(b)'
elif ecms == 4420: pt_title = '(d)'
elif ecms == 4680: pt_title = '(f)'
else: pt_title = str(ecms) + ' MeV: '
pt.AddText(pt_title)
n_param = results.floatParsFinal().getSize()
pt_title = '#chi^{2}/ndf = '
# pt.AddText(pt_title)
pt_title = str(round(chi2_tot, 2)) + '/' + str(nbin - n_param -1) + '=' + str(round(chi2_tot/(nbin - n_param -1), 2))
chi2_ndf = chi2_tot/(nbin - n_param -1)
# pt.AddText(pt_title)
print 'chi2 vs ndf = ' + str(round(chi2_tot/(nbin - n_param -1), 2))
if not os.path.exists('./figs/'):
os.makedirs('./figs/')
mbc.SaveAs('./figs/fit_rm_Dpipi_' + str(ecms) + '_' + mode + '_sideband.pdf')
if not os.path.exists('./txts/'):
os.makedirs('./txts/')
if mode == 'data_after': path_sig = './txts/data_sideband_events_'+ str(ecms) +'_' + patch + '.txt'
if not mode == 'data_after': path_sig = './txts/' + mode + '_sideband_events_'+ str(ecms) +'_' + patch + '.txt'
f_sig = open(path_sig, 'w')
out = str(nsig.getVal()) + ' ' + str(nsig.getError()) + '\n'
f_sig.write(out)
f_sig.close()
if mode == 'data_before': is_OK = -1
if mode == 'data_after': is_OK = -1
if mode == 'D1_2420': is_OK = -1
if mode == 'psipp': is_OK = -1
if mode == 'DDPIPI': is_OK = -1
if mode == 'DDPI': is_OK = -1
if is_OK == -1: break
if (is_OK == 3 and status == 0 and chi2_ndf < 1.8 and ecms < 4221): break
if (is_OK == 3 and status == 0 and chi2_ndf < 1.5 and ecms > 4221): break
raw_input('enter anything to end...')
def fit(path, shape_path, ecms, mode, patch):
try:
f_data = TFile(path[0])
t_data = f_data.Get('save')
entries_data = t_data.GetEntries()
logging.info('Entries :' + str(entries_data))
except:
logging.error('File paths are invalid!')
set_pub_style()
set_prelim_style()
mbc = TCanvas('mbc', 'mbc', 1000, 700)
pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 1.0)
# set_pad_style(pad)
pad.Draw()
xmin = 1.75
xmax = 1.95
xbins = 100
if mode == 'data' and (ecms == 4245 or ecms == 4280 or ecms == 4310
or ecms == 4575):
xbins = 50
rm_Dpipi = RooRealVar('rm_Dpipi', 'rm_Dpipi', xmin, xmax)
data = RooDataSet('data', 'dataset', t_data, RooArgSet(rm_Dpipi))
is_OK = 2
chi2_ndf = 999.
while True:
# signal
f_shape = TFile(shape_path, 'READ')
h_shape = f_shape.Get('h_hist')
h_signal = RooDataHist('h_shape', 'h_shape', RooArgList(rm_Dpipi),
h_shape)
pdf_signal = RooHistPdf('pdf_signal', 'pdf_signal',
RooArgSet(rm_Dpipi), h_signal, 0)
mean_low, mean_up, sigma_up = param_rm_Dpipi(ecms)
if not mode == 'data':
mean_low, mean_up, sigma_up = mean_low * (1 + random.uniform(
-1, 1)), mean_up * (1 + random.uniform(-1, 1)), sigma_up * (
1 + random.uniform(-1, 1))
if sigma_up > 0.001: sigma_up += 0.001
mean = RooRealVar('mean', 'mean of gaussian', 0., mean_low, mean_up)
sigma = RooRealVar('sigma', 'sigma of gaussian', 0.001, 0, sigma_up)
if mode == 'data' and (ecms == 4245 or ecms == 4390 or ecms == 4575
or ecms == 4946 or ecms == 4237 or ecms == 4220
or ecms == 4210 or ecms == 4200
or ecms == 4190):
if ecms == 4190:
f_param = open('./txts/param_4230_' + patch + '.txt', 'r')
if ecms == 4200:
f_param = open('./txts/param_4230_' + patch + '.txt', 'r')
if ecms == 4210:
f_param = open('./txts/param_4230_' + patch + '.txt', 'r')
if ecms == 4220:
f_param = open('./txts/param_4230_' + patch + '.txt', 'r')
if ecms == 4237:
f_param = open('./txts/param_4230_' + patch + '.txt', 'r')
if ecms == 4245:
f_param = open('./txts/param_4246_' + patch + '.txt', 'r')
if ecms == 4390:
f_param = open('./txts/param_4380_' + patch + '.txt', 'r')
if ecms == 4575 or ecms == 4530:
f_param = open('./txts/param_4440_' + patch + '.txt', 'r')
if ecms == 4946:
f_param = open('./txts/param_4914_' + patch + '.txt', 'r')
lines_param = f_param.readlines()
for line_param in lines_param:
rs_param = line_param.rstrip('\n')
rs_param = filter(None, rs_param.split(" "))
ndf = float(float(rs_param[0]))
a_val = float(float(rs_param[1]))
if ndf == 6:
b_val = float(float(rs_param[2]))
mean_val = float(float(rs_param[3]))
sigma_val = float(float(rs_param[4]))
else:
mean_val = float(float(rs_param[2]))
sigma_val = float(float(rs_param[3]))
mean = RooRealVar('mean', 'mean of gaussian', mean_val)
sigma = RooRealVar('sigma', 'sigma of gaussian', sigma_val)
gauss = RooGaussian('gauss', 'gaussian', rm_Dpipi, mean, sigma)
rm_Dpipi.setBins(xbins, 'cache')
sigpdf = RooFFTConvPdf('sigpdf', 'sigpdf', rm_Dpipi, pdf_signal, gauss)
# background
a = RooRealVar('a', 'a', 0, -99, 99)
b = RooRealVar('b', 'b', 0, -99, 99)
if ecms == 4780:
a = RooRealVar('a', 'a', 0, -1, 1)
b = RooRealVar('b', 'b', 0, -1, 1)
if ecms == 4700:
a = RooRealVar('a', 'a', 0, -1, 1)
b = RooRealVar('b', 'b', 0, -1, 1)
if ecms == 4640:
a = RooRealVar('a', 'a', 0, -1, 1)
b = RooRealVar('b', 'b', 0, -1, 1)
if ecms == 4600:
a = RooRealVar('a', 'a', 0, -1, 1)
b = RooRealVar('b', 'b', 0, -1, 1)
if ecms == 4440:
a = RooRealVar('a', 'a', 0, -9, 9)
b = RooRealVar('b', 'b', 0, -9, 9)
if ecms == 4400:
a = RooRealVar('a', 'a', 0, -1, 1)
b = RooRealVar('b', 'b', 0, -1, 1)
if ecms == 4380:
a = RooRealVar('a', 'a', 0, -9, 9)
b = RooRealVar('b', 'b', 0, -9, 9)
c = RooRealVar('c', 'c', 0, -99, 99)
d = RooRealVar('c', 'c', 0, -99, 99)
bkgpdf = RooChebychev('bkgpdf', 'bkgpdf', rm_Dpipi, RooArgList(a, b))
n_free = 6
if ecms == 4237 or ecms == 4245 or ecms == 4270 or ecms == 4280 or ecms == 4310 or ecms == 4360 or ecms == 4390:
bkgpdf = RooChebychev('bkgpdf', 'bkgpdf', rm_Dpipi, RooArgList(a))
n_free = 5
if ecms == 4290 or ecms == 4315 or ecms == 4340 or ecms == 4575 or ecms == 4620 or ecms == 4740 or ecms == 4750 or ecms == 4780 or ecms == 4840:
bkgpdf = RooChebychev('bkgpdf', 'bkgpdf', rm_Dpipi, RooArgList(a))
n_free = 5
# event number
nsig = RooRealVar('nsig', 'nsig', 100, -500000, 500000)
nbkg = RooRealVar('nbkg', 'nbkg', 80, 0, 500000)
# fit model
model = RooAddPdf('model', 'sigpdf + bkgpdf',
RooArgList(sigpdf, bkgpdf), RooArgList(nsig, nbkg))
if mode == 'none_sig':
model = RooAddPdf('model', 'bkgpdf', RooArgList(bkgpdf),
RooArgList(nbkg))
results = model.fitTo(data, RooFit.Save())
is_OK = int(results.covQual())
status = int(results.status())
# plot results
xframe = rm_Dpipi.frame(RooFit.Bins(xbins), RooFit.Range(xmin, xmax))
data.plotOn(xframe)
model.plotOn(xframe, RooFit.LineWidth(5))
if not mode == 'none_sig':
model.plotOn(xframe, RooFit.Components('sigpdf'),
RooFit.LineColor(kRed), RooFit.LineWidth(5),
RooFit.LineStyle(9))
model.plotOn(xframe, RooFit.Components('bkgpdf'),
RooFit.LineColor(kGreen), RooFit.LineWidth(5),
RooFit.LineStyle(6))
data.plotOn(xframe)
xtitle = 'RM(D^{+}#pi^{+}_{d}#pi^{-}_{d}) (GeV/c^{2})'
content = (xmax - xmin) / xbins * 1000
ytitle = 'Events/%.1f MeV/c^{2}' % content
format_data_hist(xframe)
name_axis(xframe, xtitle, ytitle)
xframe.Draw()
if mode == 'data' or mode == 'DDPIPI' or mode == 'psipp' or mode == 'D1_2420' or mode == 'MC':
fr = model.fitTo(data, RooFit.Extended(kTRUE), RooFit.Save(kTRUE))
curve = xframe.getObject(1)
histo = xframe.getObject(0)
chi2_tot, nbin, ytot, avg, eyl, eyh = 0, 0, 0, 0, 0, 0
x = array('d', 999 * [0])
y = array('d', 999 * [0])
for i in xrange(xbins):
histo.GetPoint(i, x, y)
exl = histo.GetEXlow()[i]
exh = histo.GetEXhigh()[i]
avg += curve.average(x[0] - exl, x[0] + exh)
ytot += y[0]
eyl += histo.GetEYlow()[i] * histo.GetEYlow()[i]
eyh += histo.GetEYhigh()[i] * histo.GetEYhigh()[i]
if ytot >= 7:
if ytot > avg:
pull = (ytot - avg) / sqrt(eyl)
else:
pull = (ytot - avg) / sqrt(eyh)
chi2_tot += pull * pull
nbin += 1
ytot, avg, eyl, eyh = 0, 0, 0, 0
if mode == 'data':
if ecms == 4245 or ecms == 4310:
pt = TPaveText(0.6, 0.7, 0.75, 0.85, "BRNDC")
else:
pt = TPaveText(0.17, 0.15, 0.3, 0.25, "BRNDC")
else:
pt = TPaveText(0.17, 0.7, 0.3, 0.85, "BRNDC")
set_pavetext(pt)
pt.Draw()
if ecms == 4230: pt_title = '(a)'
elif ecms == 4420: pt_title = '(c)'
elif ecms == 4680: pt_title = '(e)'
else: pt_title = str(ecms) + ' MeV: '
pt.AddText(pt_title)
n_param = results.floatParsFinal().getSize()
pt_title = '#chi^{2}/ndf = '
# pt.AddText(pt_title)
pt_title = str(round(
chi2_tot, 2)) + '/' + str(nbin - n_param - 1) + '=' + str(
round(chi2_tot / (nbin - n_param - 1), 2))
chi2_ndf = chi2_tot / (nbin - n_param - 1)
# pt.AddText(pt_title)
print 'chi2 vs ndf = ' + str(
round(chi2_tot / (nbin - n_param - 1), 2))
if not os.path.exists('./figs/'):
os.makedirs('./figs/')
mbc.SaveAs('./figs/fit_rm_Dpipi_' + str(ecms) + '_' + mode + '.pdf')
if not os.path.exists('./txts/'):
os.makedirs('./txts/')
path_sig = './txts/' + mode + '_events_' + str(
ecms) + '_' + patch + '.txt'
f_sig = open(path_sig, 'w')
out = str(nsig.getVal()) + ' ' + str(nsig.getError()) + '\n'
f_sig.write(out)
f_sig.close()
if mode == 'data':
path_param = './txts/param_' + str(ecms) + '_' + patch + '.txt'
f_param = open(path_param, 'w')
param = str(n_free) + ' '
param += str(a.getVal()) + ' '
if n_free == 6:
param += str(b.getVal()) + ' '
param += str(mean.getVal()) + ' '
param += str(sigma.getVal()) + ' '
f_param.write(param)
f_param.close()
if mode == 'data' or mode == 'D1_2420' or mode == 'DDPIPI' or mode == 'psipp' or mode == 'MC':
signal_low = 1.86965 - window(ecms) / 2.
signal_up = 1.86965 + window(ecms) / 2.
rm_Dpipi.setRange('srange', signal_low, signal_up)
rm_Dpipi.setRange('allrange', xmin, xmax)
nsrange = sigpdf.createIntegral(
RooArgSet(rm_Dpipi), RooFit.NormSet(RooArgSet(rm_Dpipi)),
RooFit.Range('srange'))
nallrange = sigpdf.createIntegral(
RooArgSet(rm_Dpipi), RooFit.NormSet(RooArgSet(rm_Dpipi)),
RooFit.Range('allrange'))
n_signal = nsrange.getVal() / nallrange.getVal() * (nsig.getVal())
n_all = nsig.getVal()
n_signal_err = nsrange.getVal() / nallrange.getVal() * (
nsig.getError())
n_all_err = nsig.getError()
factor = n_signal / n_all
factor_err = sqrt(abs(factor * (1 - factor) / n_all))
print 'factor = n(signal) / n(all) = ' + str(round(
factor, 4)) + '+/-' + str(round(factor_err, 4))
path_factor = './txts/factor_rm_Dpipi_' + str(
ecms) + '_' + mode + '.txt'
f_factor = open(path_factor, 'w')
out = str(round(factor, 4)) + ' ' + str(round(factor_err,
4)) + '\n'
f_factor.write(out)
f_factor.close()
if mode == 'data' or mode == 'none_sig':
path_out = './txts/significance_likelihood_total_' + str(
ecms) + '.txt'
f_out = open(path_out, 'a')
# -log(L) minimum
sig_out = str(results.minNll()) + '\n'
f_out.write(sig_out)
f_out.close()
if mode == 'data' or mode == 'none_sig': is_OK = -1
if is_OK == -1: break
if (is_OK == 3 and status == 0 and chi2_ndf < 3. and ecms > 4221):
break
if (is_OK == 3 and status == 0 and chi2_ndf < 5. and ecms < 4221):
break
raw_input('enter anything to end...')
def fit(path, shape_path, ecms, mode, patch):
try:
f_data = TFile(path[0])
f_X_3842 = TFile(shape_path)
t_data = f_data.Get('save')
t_X_3842 = f_X_3842.Get('save')
entries_data = t_data.GetEntries()
entries_X_3842 = t_X_3842.GetEntries()
logging.info('Entries(data) :' + str(entries_data))
logging.info('Entries(X_3842) :' + str(entries_X_3842))
except:
logging.error('File paths are invalid!')
set_pub_style()
set_prelim_style()
mbc = TCanvas('mbc', 'mbc', 1000, 700)
pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 1.0)
pad.Draw()
xmin = 3.79
xmax = 3.89
xbins = 50
rm_pipi = RooRealVar('rm_pipi', 'rm_pipi', xmin, xmax)
data = RooDataSet('data', 'dataset', t_data, RooArgSet(rm_pipi))
chi2_ndf = 999.
# signal
cut = ''
h_X_3842_rm_pipi = TH1F('h_X_3842_rm_pipi', '', xbins, xmin, xmax)
t_X_3842.Project('h_X_3842_rm_pipi', 'rm_pipi', cut)
hist_X_3842_rm_pipi = RooDataHist('hist_X_3842_rm_pipi',
'hist_X_3842_rm_pipi',
RooArgList(rm_pipi), h_X_3842_rm_pipi)
pdf_signal = RooHistPdf('pdf_X_3842_rm_pipi', 'pdf_X_3842_rm_pipi',
RooArgSet(rm_pipi), hist_X_3842_rm_pipi, 1)
mean_low, mean_up, sigma_up = param_rm_pipi(ecms)
mean = RooRealVar('mean', 'mean of gaussian', 0., mean_low, mean_up)
sigma = RooRealVar('sigma', 'sigma of gaussian', 0.001, 0, sigma_up)
if (mode == 'data' and ecms != 4680) or mode == 'total':
f_param = open('../../X_3842/txts/param_4680_' + patch + '.txt', 'r')
lines_param = f_param.readlines()
for line_param in lines_param:
rs_param = line_param.rstrip('\n')
rs_param = filter(None, rs_param.split(" "))
ndf = float(float(rs_param[0]))
a_val = float(float(rs_param[1]))
if ndf == 6:
b_val = float(float(rs_param[2]))
mean_val = float(float(rs_param[3]))
sigma_val = float(float(rs_param[4]))
else:
mean_val = float(float(rs_param[2]))
sigma_val = float(float(rs_param[3]))
mean = RooRealVar('mean', 'mean of gaussian', mean_val)
sigma = RooRealVar('sigma', 'sigma of gaussian', sigma_val)
gauss = RooGaussian('gauss', 'gaussian', rm_pipi, mean, sigma)
rm_pipi.setBins(xbins, 'cache')
sigpdf = RooFFTConvPdf('sigpdf', 'sigpdf', rm_pipi, pdf_signal, gauss)
# background
a = RooRealVar('a', 'a', 0, -99, 99)
b = RooRealVar('b', 'b', 0, -99, 99)
c = RooRealVar('c', 'c', 0, -99, 99)
d = RooRealVar('c', 'c', 0, -99, 99)
bkgpdf = RooChebychev('bkgpdf', 'bkgpdf', rm_pipi, RooArgList(a, b))
if ecms == 4680: n_free = 6
else: n_free = 4
# event number
nsig = RooRealVar('nsig', 'nsig', 100, -500000, 500000)
nbkg = RooRealVar('nbkg', 'nbkg', 80, 0, 500000)
# fit model
model = RooAddPdf('model', 'sigpdf + bkgpdf', RooArgList(sigpdf, bkgpdf),
RooArgList(nsig, nbkg))
if mode == 'none_sig':
model = RooAddPdf('model', 'bkgpdf', RooArgList(bkgpdf),
RooArgList(nbkg))
results = model.fitTo(data, RooFit.Save())
# plot results
xframe = rm_pipi.frame(RooFit.Bins(xbins), RooFit.Range(xmin, xmax))
data.plotOn(xframe)
model.plotOn(xframe, RooFit.LineWidth(5))
if not mode == 'none_sig':
model.plotOn(xframe, RooFit.Components('sigpdf'),
RooFit.LineColor(kRed), RooFit.LineWidth(5),
RooFit.LineStyle(9))
model.plotOn(xframe, RooFit.Components('bkgpdf'), RooFit.LineColor(kGreen),
RooFit.LineWidth(5), RooFit.LineStyle(6))
data.plotOn(xframe)
xtitle = 'RM(#pi^{+}_{0}#pi^{-}_{0}) (GeV/c^{2})'
content = (xmax - xmin) / xbins * 1000
ytitle = 'Events/%.1f MeV/c^{2}' % content
format_data_hist(xframe)
name_axis(xframe, xtitle, ytitle)
xframe.Draw()
if mode == 'data' or mode == 'total':
fr = model.fitTo(data, RooFit.Extended(kTRUE), RooFit.Save(kTRUE))
curve = xframe.getObject(1)
histo = xframe.getObject(0)
chi2_tot, nbin, ytot, avg, eyl, eyh = 0, 0, 0, 0, 0, 0
x = array('d', 999 * [0])
y = array('d', 999 * [0])
for i in xrange(xbins):
histo.GetPoint(i, x, y)
exl = histo.GetEXlow()[i]
exh = histo.GetEXhigh()[i]
avg += curve.average(x[0] - exl, x[0] + exh)
ytot += y[0]
eyl += histo.GetEYlow()[i] * histo.GetEYlow()[i]
eyh += histo.GetEYhigh()[i] * histo.GetEYhigh()[i]
if ytot >= 7:
if ytot > avg:
pull = (ytot - avg) / sqrt(eyl)
else:
pull = (ytot - avg) / sqrt(eyh)
chi2_tot += pull * pull
nbin += 1
ytot, avg, eyl, eyh = 0, 0, 0, 0
if ecms == 4914 or ecms == 4946 or ecms == 4620:
pt = TPaveText(0.17, 0.15, 0.3, 0.3, "BRNDC")
elif ecms < 4600:
pt = TPaveText(0.17, 0.15, 0.3, 0.2, "BRNDC")
else:
pt = TPaveText(0.17, 0.15, 0.3, 0.2, "BRNDC")
set_pavetext(pt)
pt.Draw()
if ecms != 'all':
if ecms == 4420:
pt_title = '(a)'
if ecms == 4680:
pt_title = '(b)'
else:
pt_title = '(c)'
pt.AddText(pt_title)
n_param = results.floatParsFinal().getSize()
pt_title = '#chi^{2}/ndf = '
# pt.AddText(pt_title)
pt_title = str(round(chi2_tot,
2)) + '/' + str(nbin - n_param - 1) + '=' + str(
round(chi2_tot / (nbin - n_param - 1), 2))
chi2_ndf = chi2_tot / (nbin - n_param - 1)
# pt.AddText(pt_title)
print 'chi2 vs ndf = ' + str(round(chi2_tot / (nbin - n_param - 1), 2))
if not os.path.exists('./figs/'):
os.makedirs('./figs/')
mbc.SaveAs('./figs/fit_rm_pipi_' + str(ecms) + '_' + mode + '.pdf')
if not os.path.exists('./txts/'):
os.makedirs('./txts/')
path_sig = './txts/' + mode + '_events_' + str(ecms) + '_' + patch + '.txt'
f_sig = open(path_sig, 'w')
out = str(nsig.getVal()) + ' ' + str(nsig.getError()) + '\n'
f_sig.write(out)
f_sig.close()
if mode == 'data':
path_param = './txts/param_' + str(ecms) + '_' + patch + '.txt'
f_param = open(path_param, 'w')
param = str(n_free) + ' '
param += str(a.getVal()) + ' '
param += str(b.getVal()) + ' '
param += str(mean.getVal()) + ' '
param += str(sigma.getVal()) + ' '
f_param.write(param)
f_param.close()
if mode == 'data' or mode == 'none_sig' or mode == 'total':
path_out = './txts/significance_likelihood_total_' + str(ecms) + '.txt'
f_out = open(path_out, 'a')
# -log(L) minimum
sig_out = str(results.minNll()) + '\n'
f_out.write(sig_out)
f_out.close()
raw_input('enter anything to end...')
def plot(path, leg_title, ecms, xmin, xmax, ymin, ymax, bins):
try:
f_data = TFile(path[0])
t_data = f_data.Get('save')
entries_data = t_data.GetEntries()
logging.info('data entries :'+str(entries_data))
except:
logging.error(path[0] + ' is invalid!')
sys.exit()
set_pub_style()
set_prelim_style()
from ROOT import gStyle
colors = array('i', 7*[0])
for i in range(7): colors[i] = 18 - i
gStyle.SetPalette(7, colors)
mbc = TCanvas('mbc', 'mbc', 800, 600)
xtitle = 'M(K^{-}#pi^{+}#pi^{+}) (GeV)/c^{2}'
ytitle = 'RM(D^{+}#pi_{d}^{+}#pi_{d}^{-}) (GeV)/c^{2}'
h_data = TH2F('scatter_data', 'scatter plot of M(Kpipi) and Rm(Dpipi)', bins, xmin, xmax, bins, ymin, ymax)
format_data_hist(h_data)
name_axis(h_data, xtitle, ytitle)
fill(t_data, h_data)
h_data.SetMarkerSize(0.2)
# h_data.Draw('box')
h_data.Draw('COLZ')
x_move = width(ecms)
y_move = window(ecms)
x_offset = width(ecms)
y_offset = window(ecms)
x0 = 1.86965 - 5/2. * x_move
y0 = 1.86965 + 5/2. * y_move
mats = make_matrix(3, 3, (x0, y0), x_move, x_offset, y_move, y_offset)
VarsManager = locals()
for i, mat in enumerate(mats):
x_list = []
y_list = []
for point in mat:
x, y = point
x_list.append(x)
y_list.append(y)
x, y = array('d', x_list), array('d', y_list)
VarsManager['box_' + str(i)] = TPolyLine(5, x, y)
if i == 4: color = ROOT.kRed
else: color = ROOT.kBlue
VarsManager['box_' + str(i)].SetLineColor(color)
VarsManager['box_' + str(i)].SetLineWidth(4)
if i == 1 or i == 3 or i == 5 or i == 7: VarsManager['box_' + str(i)].SetLineStyle(9)
if i == 0 or i == 2 or i == 6 or i == 8: VarsManager['box_' + str(i)].SetLineStyle(2)
VarsManager['box_' + str(i)].Draw('same')
# if i == 0: left, right, bottom, top = position_convert(min(x), xmin, xmax) + 0.0045, position_convert(max(x), xmin, xmax) + 0.025, position_convert(min(y), ymin, ymax) + 0.0045, position_convert(max(y), ymin, ymax) + 0.025
# elif i == 1 or i == 2: left, right, bottom, top = position_convert(min(x), xmin, xmax), position_convert(max(x), xmin, xmax) + 0.025, position_convert(min(y), ymin, ymax), position_convert(max(y), ymin, ymax) + 0.025
# elif i == 3: left, right, bottom, top = position_convert(min(x), xmin, xmax) + 0.0045, position_convert(max(x), xmin, xmax), position_convert(min(y), ymin, ymax) + 0.0045, position_convert(max(y), ymin, ymax)
# elif i == 6: left, right, bottom, top = position_convert(min(x), xmin, xmax) + 0.0045, position_convert(max(x), xmin, xmax) - 0.025, position_convert(min(y), ymin, ymax) + 0.0045, position_convert(max(y), ymin, ymax) - 0.025
# elif i == 6 or i == 7 or i == 8: left, right, bottom, top = position_convert(min(x), xmin, xmax), position_convert(max(x), xmin, xmax) - 0.025, position_convert(min(y), ymin, ymax), position_convert(max(y), ymin, ymax) - 0.025
# else: left, right, bottom, top = position_convert(min(x), xmin, xmax), position_convert(max(x), xmin, xmax), position_convert(min(y), ymin, ymax), position_convert(max(y), ymin, ymax)
# VarsManager['pt_' + str(i)] = TPaveText(left, bottom, right, top, "BRNDC")
# set_pavetext(VarsManager['pt_' + str(i)])
# VarsManager['pt_' + str(i)].SetTextSize(0.06)
# VarsManager['pt_' + str(i)].Draw()
# if i == 0: VarsManager['pt_' + str(i)].AddText('(-1, 1)')
# if i == 1: VarsManager['pt_' + str(i)].AddText(' (0, 1) ')
# if i == 2: VarsManager['pt_' + str(i)].AddText(' (1, 1) ')
# if i == 3: VarsManager['pt_' + str(i)].AddText('(-1, 0)')
# if i == 4: VarsManager['pt_' + str(i)].AddText(' (0, 0) ')
# if i == 5: VarsManager['pt_' + str(i)].AddText(' (1, 0) ')
# if i == 6: VarsManager['pt_' + str(i)].AddText('(-1, -1)')
# if i == 7: VarsManager['pt_' + str(i)].AddText(' (0, -1)')
# if i == 8: VarsManager['pt_' + str(i)].AddText(' (1, -1)')
# VarsManager['pt_' + str(i)].SetTextColor(color)
pt = TPaveText(0.75, 0.77, 0.85, 0.87, "BRNDC")
set_pavetext(pt)
pt.Draw()
pt.AddText(leg_title)
if not os.path.exists('./figs/'):
os.makedirs('./figs/')
mbc.SaveAs('./figs/scatter_m_Kpipi_rm_Dpipi_'+str(ecms)+'.pdf')
raw_input('Press <Enter> to end...')