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histo_utilities.py
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import numpy as np
import ROOT as rt
import root_numpy as rtnp
import matplotlib.pyplot as plt
from array import array
std_color_list = [1, 2, 4, 8, 6, 28, 43, 7, 25, 36, 30, 40, 42, 49, 46, 38, 32, 800, 600, 900, 870, 840]
def find_intersect(graph1,graph2):
mass = list(np.arange(0,3,0.001))
diff = []
for m in mass:
diff.append(graph1.Eval(m)-graph2.Eval(m))
diff = np.sign(np.array(diff))
for i in range(len(diff)-1):
if not diff[i] == diff[i+1]:
return mass[i]
def find_intersectInt(graph1,graph2):
mass = list(np.arange(0,3000))
diff = []
for m in mass:
diff.append(graph1.Eval(m)-graph2.Eval(m))
diff = np.sign(np.array(diff))
for i in range(len(diff)-1):
if not diff[i] == diff[i+1]:
return mass[i]
def quantile(a, p, weight=None, f=None):
if a.shape[0] == 0:
return None, None
if weight is None:
q = np.percentile(a, 100*p).astype(np.float64)
weight = np.full_like(a, 1.0/a.shape[0], np.float64)
else:
i_sort = np.argsort(a)
a = a[i_sort]
weight = weight[i_sort]
cum_sum = np.cumsum(weight, dtype=np.float128)
idx_q = np.argmax(cum_sum>p*cum_sum[-1])
q = a[idx_q]
weight /= np.sum(weight)
if not f==None:
f_q = f(q)
else:
h = create_TH1D(a, binning=[None, np.percentile(a, 2), np.percentile(a, 98)], weights=weight)
f_q = h.GetBinContent(h.FindBin(q))/h.GetBinWidth(h.FindBin(q))
if f_q == 0:
f_q = 1e-3
print ('[ERROR]: Failed to estimate pdf')
sigma_q = np.sqrt(p*(1-p)/(a.shape[0]*f_q**2))
return q, sigma_q
def EstimateDispersion(aux, w=None):
q_up, e_up = quantile(aux, 0.15, weight=w)
q_dwn, e_dwn = quantile(aux, 0.85, weight=w)
if q_up == None or q_dwn == None:
print ('[WARNING] Quantile estimation failed')
print (aux.shape)
print (q_up, q_dwn)
disp_est = 0.5*np.abs(q_up - q_dwn)
disp_unc = 0.5*np.hypot(e_up, e_dwn)
return disp_est, disp_unc
def create_TH1D(x, name='h', title=None, binning=[None, None, None], weights=None, h2clone=None, axis_title = ['',''], bin_list=False):
if title is None:
title = name
if h2clone == None:
if binning[1] is None:
binning[1] = min(x)
if binning[2] is None:
if ((np.percentile(x, 95)-np.percentile(x, 50))<0.2*(max(x)-np.percentile(x, 95))):
binning[2] = np.percentile(x, 90)
else:
binning[2] = max(x)
if binning[0] is None:
bin_w = 4*(np.percentile(x,75) - np.percentile(x,25))/(len(x))**(1./3.)
if bin_w == 0:
bin_w = 0.5*np.std(x)
if bin_w == 0:
bin_w = 1
binning[0] = int((binning[2] - binning[1])/bin_w) + 5
if len(binning) > 3 or bin_list:
h = rt.TH1D(name, title, len(binning)-1, array('f',binning))
else:
h = rt.TH1D(name, title, binning[0], binning[1], binning[2])
else:
h = h2clone.Clone(name)
h.SetTitle(title)
h.Reset()
rtnp.fill_hist(h, x, weights=weights)
h.SetXTitle(axis_title[0])
h.SetYTitle(axis_title[1])
h.binning = binning
return h
def create_prof1D(x, y, name='h', title=None, binning=[None, None, None], h2clone=None, axis_title = ['',''], opt=''):
if title is None:
title = name
if h2clone == None:
if binning[1] is None:
binning[1] = min(x)
if binning[2] is None:
if ((np.percentile(x, 95)-np.percentile(x, 50))<0.2*(max(x)-np.percentile(x, 95))):
binning[2] = np.percentile(x, 90)
else:
binning[2] = max(x)
if binning[0] is None:
bin_w = 4*(np.percentile(x,75) - np.percentile(x,25))/(len(x))**(1./3.)
if bin_w == 0:
bin_w = 0.5*np.std(x)
if bin_w == 0:
bin_w = 1
binning[0] = int((binning[2] - binning[1])/bin_w) + 5
h = rt.TH1D(name, title, binning[0], binning[1], binning[2])
else:
h = h2clone.Clone(name)
h.SetTitle(title)
h.Reset()
for i in range(1, binning[0]+1):
xl = h.GetBinCenter(i) - h.GetBinWidth(i)/2.
xu = h.GetBinCenter(i) + h.GetBinWidth(i)/2.
sel = np.logical_and(x<xu, x>xl)
aux = y[sel]
if len(aux) > 0:
if 'Res' in opt:
q_up, e_up = quantile(aux, 0.15)
q_dwn, e_dwn = quantile(aux, 0.85)
if q_up == None or q_dwn == None:
print ('[WARNING] Quantile estimation failed')
print (aux.shape)
print (q_up, q_dwn)
return
disp_est = 0.5*np.abs(q_up - q_dwn)
h.SetBinContent(i, disp_est)
disp_unc = 0.5*np.hypot(e_up, e_dwn)
h.SetBinError(i, disp_unc)
else:
h.SetBinContent(i, np.mean(aux))
if 's' in opt:
q_up, e_up = quantile(aux, 0.15)
q_dwn, e_dwn = quantile(aux, 0.85)
if q_up == None or q_dwn == None:
print ('[WARNING] Quantile estimation failed')
print (aux.shape)
print (q_up, q_dwn)
return
disp_est = 0.5*np.abs(q_up - q_dwn)
h.SetBinError(i, disp_est)
else:
h.SetBinError(i, np.std(aux)/np.sqrt(aux.shape[0]))
h.SetXTitle(axis_title[0])
h.SetYTitle(axis_title[1])
h.binning = binning
return h
def create_TH2D(sample, name='h', title=None, binning=[None, None, None, None, None, None], weights=None, axis_title = ['','', '']):
if title is None:
title = name
if (sample.shape[0] == 0):
for i in range(len(binning)):
if binning[i] == None:
binning[i] = 1
else:
if binning[1] is None:
binning[1] = min(sample[:,0])
if binning[2] is None:
binning[2] = max(sample[:,0])
if binning[0] is None:
bin_w = 4*(np.percentile(sample[:,0],75) - np.percentile(sample[:,0],25))/(len(sample[:,0]))**(1./3.)
if bin_w == 0:
bin_w = 0.5*np.std(sample[:,0])
if bin_w == 0:
bin_w = 1
binning[0] = int((binning[2] - binning[1])/bin_w)
if binning[4] is None:
binning[4] = min(sample[:,1])
if binning[5] == None:
binning[5] = max(sample[:,1])
if binning[3] == None:
bin_w = 4*(np.percentile(sample[:,1],75) - np.percentile(sample[:,1],25))/(len(sample[:,1]))**(1./3.)
if bin_w == 0:
bin_w = 0.5*np.std(sample[:,1])
if bin_w == 0:
bin_w = 1
binning[3] = int((binning[5] - binning[4])/bin_w)
if len(binning)==6:
h = rt.TH2D(name, title, binning[0], binning[1], binning[2], binning[3], binning[4], binning[5])
else:
h = rt.TH2D(name, title, binning[-2]-1, array('f',binning[:binning[-2]]), binning[-1]-1, array('f', binning[binning[-2]:-2]));
#for i in range(len(sample)):
# if weights is None:
# h.Fill(sample[i,0],sample[i,1])
# else:
# h.Fill(sample[i,0],sample[i,1],weights[i])
rtnp.fill_hist(h, sample, weights=weights)
h.SetXTitle(axis_title[0])
h.SetYTitle(axis_title[1])
h.SetZTitle(axis_title[2])
h.binning = binning
return h
def rootTH2_to_np(h, cut = None, Norm = False):
nx = h.GetNbinsX()
ny = h.GetNbinsY()
arr = np.zeros((ny, nx))
pos = np.zeros((ny, nx, 2))
for ix in range(nx):
for iy in range(ny):
x = h.GetXaxis().GetBinCenter( ix+1 );
y = h.GetYaxis().GetBinCenter( iy+1 );
z = h.GetBinContent(h.GetBin(ix+1, iy+1))
if cut == None:
arr[iy, ix] = z
else:
arr[iy, ix] = z if z > cut else 0
pos[iy, ix] = [x,y]
return arr, pos
def make_ratio_pEff(h_list_in, title = "", label = "", fit = False, in_tags = None, ratio_bounds = [0.1, 4], ratio_index = 0, logy = False,draw_opt = ['hist','E1'], text = ""):
h_list = []
if in_tags == None:
tag = []
else:
tag = in_tags
for i, h in enumerate(h_list_in):
h_list.append(h.Clone('h{}aux{}'.format(i, label)))
if in_tags == None:
tag.append(h.GetTitle())
c_out = rt.TCanvas("c_out_ratio"+label, "c_out_ratio"+label, 800, 800)
pad1 = rt.TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.SetBottomMargin(0.03)
pad1.SetLeftMargin(0.15)
# pad1.SetGridx()
if logy:
pad1.SetLogy()
pad1.Draw()
pad1.cd()
leg = rt.TLegend(0.68, 0.7, 0.9, 0.9)
leg = rt.TLegend(0.2, 0.6, 0.5, 0.9)
#leg = rt.TLegend(0.7, 0.2, 0.9, 0.4)
leg.SetBorderSize(0)
leg.SetTextSize(0.045)
leg.SetFillStyle(0)
c_out.cd(1)
for i, h in enumerate(h_list):
if i == 0:
h.Draw('AP')
else:
h.Draw("Psame")
leg.AddEntry(h, tag[i], "lep")
leg.Draw("same")
c_out.Draw()
#h_list[0].GetPaintedGraph().GetHistogram().GetXaxis().SetLabelSize(0)
h_list[0].GetPaintedGraph().GetHistogram().GetXaxis().SetLabelSize(0)
h_list[0].GetPaintedGraph().GetHistogram().GetXaxis().SetTitle("")
h_list[0].GetPaintedGraph().GetHistogram().GetXaxis().SetLimits(h_list_in[0].GetCopyTotalHisto().GetXaxis().GetXmin(),h_list_in[0].GetCopyTotalHisto().GetXaxis().GetXmax())
h_list[0].GetPaintedGraph().GetHistogram().GetYaxis().SetRangeUser(0, 1.05*max(map(lambda x: x.GetPaintedGraph().GetHistogram().GetMaximum(), h_list)))
h_list[0].GetPaintedGraph().GetHistogram().GetYaxis().SetRangeUser(0, 1.6)
if logy:
h_list[0].GetPaintedGraph().GetHistogram().GetYaxis().SetRangeUser(10e-7, 150*max(map(lambda x: x.GetPaintedGraph().GetHistogram().GetMaximum(), h_list)))
h_list[0].GetPaintedGraph().GetHistogram().GetYaxis().SetTitleOffset(1.0)
h_list[0].GetPaintedGraph().GetHistogram().GetYaxis().SetTitleSize(0.06)
h_list[0].GetPaintedGraph().GetHistogram().GetYaxis().SetLabelSize(0.05)
h_list[0].GetPaintedGraph().GetHistogram().SetTitle(title)
#h_list[0].GetPaintedGraph().GetHistogram().DrawCopy('hist')
h_list[0].GetPaintedGraph().GetHistogram().SetFillColor(h_list_in[0].GetLineColor())
h_list[0].GetPaintedGraph().GetHistogram().SetFillStyle(3002)
h_list[0].GetPaintedGraph().GetHistogram().SetLineColor(h_list_in[0].GetLineColor())
h_list[0].GetPaintedGraph().GetHistogram().SetLineWidth(2)
h_list[0].GetPaintedGraph().GetHistogram().SetMarkerColor(h_list_in[0].GetLineColor())
h_list[0].GetPaintedGraph().GetHistogram().SetMarkerSize(2)
c_out.cd()
pad2 = rt.TPad("pad2", "pad2", 0, 0, 1, 0.3)
pad2.SetTopMargin(0.03)
pad2.SetBottomMargin(0.25)
pad2.SetLeftMargin(0.15)
# pad2.SetGrid()
pad2.Draw()
pad2.cd()
band = h_list_in[ratio_index].GetCopyTotalHisto()
for j in range(band.GetXaxis().GetNbins()):
band.SetBinContent(j+1, 1.0)
if h_list[0].GetEfficiency(j+1) == 0:
band.SetBinError(j+1, 0.0)
else:
band.SetBinError(j+1, h_list_in[ratio_index].GetEfficiencyErrorUp(j+1)/h_list_in[ratio_index].GetEfficiency(j+1))
#print(j, h_list_in[0].GetBinError(j+1)/h_list_in[0].GetBinContent(j+1))
band.SetFillColor(h_list_in[ratio_index].GetLineColor())
band.SetFillStyle(3002)
band.SetLineColor(h_list_in[ratio_index].GetLineColor())
band.GetYaxis().SetTitleOffset(0.6)
band.GetYaxis().SetRangeUser(ratio_bounds[0], ratio_bounds[1])
band.GetYaxis().SetTitleSize(0.1)
band.GetYaxis().SetLabelSize(0.12)
band.GetYaxis().SetNdivisions(506)
band.GetXaxis().SetTitleOffset(0.95)
band.GetXaxis().SetTitleSize(0.12)
band.GetXaxis().SetLabelSize(0.12)
band.GetXaxis().SetTickSize(0.07)
band.SetYTitle('Ratio with {}'.format(tag[ratio_index]))
#band.SetYTitle('Ratio with nominal')
band.SetTitle("")
band.DrawCopy('E2')
ln = rt.TLine(band.GetXaxis().GetXmin(), 1, band.GetXaxis().GetXmax(), 1)
ln.SetLineWidth(3)
ln.SetLineColor(h_list_in[ratio_index].GetLineColor())
ln.DrawLine(band.GetXaxis().GetXmin(), 1, band.GetXaxis().GetXmax(), 1)
for i, h in enumerate(h_list):
if i == ratio_index:
continue
else:
nom = h.GetCopyPassedHisto()
denom = h_list[ratio_index].GetCopyPassedHisto()
nom.SetLineColor(h.GetLineColor())
#set relative error of ratio to be the relative error of data
for j in range(nom.GetXaxis().GetNbins()+1):
nom.SetBinContent(j+1,h.GetEfficiency(j+1))
denom.SetBinContent(j+1,h_list[ratio_index].GetEfficiency(j+1))
nom.SetBinError(j+1,max(h.GetEfficiencyErrorLow(j+1), h.GetEfficiencyErrorUp(j+1)))
denom.SetBinError(j+1,max(h_list[ratio_index].GetEfficiencyErrorLow(j+1), h_list[ratio_index].GetEfficiencyErrorUp(j+1)))
nom.Divide(denom)
nom.DrawCopy('same'+draw_opt[i])
print(nom.GetBinContent(1),nom.GetBinError(1), nom.GetBinContent(2), nom.GetBinError(2))
pad2.Update()
c_out.pad1 = pad1
c_out.pad2 = pad2
c_out.h_list = h_list
c_out.leg = leg
return c_out
def make_ratio_plot(h_list_in, title = "", label = "", fit = False, in_tags = None, ratio_bounds = [0.1, 4], logy = False, ratio_index = 0, draw_opt = ['hist','E1'], text = ""):
h_list = []
if in_tags == None:
tag = []
else:
tag = in_tags
for i, h in enumerate(h_list_in):
h_list.append(h.Clone('h{}aux{}'.format(i, label)))
if in_tags == None:
tag.append(h.GetTitle())
c_out = rt.TCanvas("c_out_ratio"+label, "c_out_ratio"+label, 800, 800)
pad1 = rt.TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.SetBottomMargin(0.03)
pad1.SetLeftMargin(0.15)
pad1.SetRightMargin(0.04)# pad2.SetGrid()
if logy:
pad1.SetLogy()
pad1.Draw()
pad1.cd()
leg = rt.TLegend(0.5, 0.65, 0.9, 0.92)
leg = rt.TLegend(0.7, 0.65, 0.9, 0.92)
#leg = rt.TLegend(0.2, 0.7, 0.5, 0.9)
# leg = rt.TLegend(0.7, 0.2, 0.9, 0.4)
leg.SetBorderSize(0)
leg.SetTextSize(0.045)
leg.SetFillStyle(0)
c_out.cd(1)
for i, h in enumerate(h_list):
if i == 0:
h.GetXaxis().SetLabelSize(0)
h.GetXaxis().SetTitle("")
h.GetYaxis().SetRangeUser(0, 1.5*max(map(lambda x: x.GetMaximum(), h_list)))
if logy:
h.GetYaxis().SetRangeUser(10e-2, 2.5*max(map(lambda x: x.GetMaximum(), h_list)))
h.GetYaxis().SetTitleOffset(1.0)
h.GetYaxis().SetTitleSize(0.06)
h.GetYaxis().SetLabelSize(0.05)
h.SetTitle(title)
if ratio_index == 0:h.DrawCopy("hist")
h.SetFillColor(h_list_in[0].GetLineColor())
h.SetFillStyle(3002)
#h.SetStats(1)
h.SetLineColor(h_list_in[0].GetLineColor())
h.SetLineWidth(2)
h.SetMarkerColor(h_list_in[0].GetLineColor())
h.SetMarkerSize(2)
# if ratio_index == 0:
# # h.DrawCopy("hist")
# h.DrawCopy(draw_opt[i]+'same')
# else:h.DrawCopy(draw_opt[i])
if ratio_index == 0:h.DrawCopy(draw_opt[i]+"same")
else:h.DrawCopy(draw_opt[i])
if len(text)>0:
l = rt.TLatex()
l.SetTextSize(0.045)
if logy:l.DrawLatex((h.GetXaxis().GetXmax()-h.GetXaxis().GetXmin())*0.1+h.GetXaxis().GetXmin() , h.GetMaximum()/10, text)
else:l.DrawLatex((h.GetXaxis().GetXmax()-h.GetXaxis().GetXmin())*0.1+h.GetXaxis().GetXmin() , h.GetMaximum()*0.8, text)
else:
#h.DrawCopy(draw_opt[i]+"same")
h.Draw("E1 same")
leg.AddEntry(h, tag[i], "lep")
leg.Draw("same")
c_out.cd()
pad2 = rt.TPad("pad2", "pad2", 0, 0, 1, 0.3)
pad2.SetTopMargin(0.03)
pad2.SetBottomMargin(0.25)
pad2.SetLeftMargin(0.15)
pad2.SetRightMargin(0.04)# pad2.SetGrid()
pad2.Draw()
pad2.cd()
band = h_list_in[ratio_index].Clone('h_band')
for j in range(band.GetXaxis().GetNbins()):
band.SetBinContent(j+1, 1.0)
if h_list[ratio_index].GetBinContent(j+1) == 0:
band.SetBinError(j+1, 0.0)
else:
band.SetBinError(j+1, h_list_in[ratio_index].GetBinError(j+1)/h_list_in[ratio_index].GetBinContent(j+1))
#print(j, h_list_in[0].GetBinError(j+1)/h_list_in[0].GetBinContent(j+1))
band.SetFillColor(h_list_in[ratio_index].GetLineColor())
band.SetFillStyle(3002)
band.SetLineColor(h_list_in[ratio_index].GetLineColor())
#band.SetFillColorAlpha(0,0)
#band.SetLineColor(0)
band.GetYaxis().SetTitleOffset(0.5)
band.GetYaxis().SetRangeUser(ratio_bounds[0], ratio_bounds[1])
band.GetYaxis().SetTitleSize(0.11)
band.GetYaxis().SetLabelSize(0.12)
band.GetYaxis().SetNdivisions(506)
band.GetXaxis().SetTitleOffset(0.95)
band.GetXaxis().SetTitleSize(0.12)
band.GetXaxis().SetLabelSize(0.12)
band.GetXaxis().SetTickSize(0.07)
band.SetYTitle('Ratio with {}'.format(tag[ratio_index]))
band.SetTitle("")
band.DrawCopy('E2')
ln = rt.TLine(h.GetXaxis().GetXmin(), 1, h.GetXaxis().GetXmax(), 1)
ln.SetLineWidth(3)
ln.SetLineColor(h_list_in[ratio_index].GetLineColor())
ln.DrawLine(h.GetXaxis().GetXmin(), 1, h.GetXaxis().GetXmax(), 1)
for i, h in enumerate(h_list):
if i == ratio_index:
continue
else:
if fit:h.GetFunction("expo")
h.Divide(h_list[ratio_index])
# h.GetYaxis().SetTitleOffset(0.6)
# h.GetYaxis().SetRangeUser(ratio_bounds[0], ratio_bounds[1])
# h.GetYaxis().SetTitleSize(0.12)
# h.GetYaxis().SetLabelSize(0.12)
# h.GetYaxis().SetNdivisions(506)
# h.GetXaxis().SetTitleOffset(0.95)
# h.GetXaxis().SetTitleSize(0.12)
# h.GetXaxis().SetLabelSize(0.12)
# h.GetXaxis().SetTickSize(0.07)
# h.SetYTitle('Ratio with {}'.format(tag[0]))
# h.SetTitle("")
#set relative error of ratio to be the relative error of data
for j in range(h.GetXaxis().GetNbins()):
if h_list[i].GetBinContent(j+1) == 0:
h.SetBinError(j+1, 0.0)
else:
h.SetBinError(j+1, h_list_in[i].GetBinError(j+1)/h_list_in[i].GetBinContent(j+1)*h.GetBinContent(j+1))
h.Draw('same'+draw_opt[i])
pad2.Update()
c_out.pad1 = pad1
c_out.pad2 = pad2
c_out.h_list = h_list
c_out.leg = leg
return c_out
def binning2d(histo2D,min_event,direc):#returns an appropriate binning in x/y direction with at least min_event points in each binning
bins = [1]
i=0
if direc == 'x':
nbins = histo2D.GetNbinsX()
elif direc == 'y':
nbins = histo2D.GetNbinsY()
else:
return None
while i < nbins:
counter = 0
while counter < min_event:
if direc == 'x':
h1 = histo2D.ProjectionY("h"+str(i),i+1,i+1)
else:
h1 = histo2D.ProjectionX("h"+str(i),i+1,i+1)
counter += h1.Integral()
i+=1
if i >nbins:
break
bins.append(i)
bins = bins[:len(bins)-2]+[bins[len(bins)-1]]
if bins[1] == 1:
bins.pop(1)
return bins
def histo2D_projectionFit(histo2D, bins, gaus_thr, rebin, direc): #given the binning, plot the scale and resolution of the projection of 2D map
proj = []
if direc == 'x':
bw = histo2D.GetXaxis().GetBinWidth(1)
left_edge = histo2D.GetXaxis().GetBinLowEdge(1)
elif direc == 'y':
bw = histo2D.GetYaxis().GetBinWidth(1)
left_edge = histo2D.GetYaxis().GetBinLowEdge(1)
else:
return None
x_bins = [(x-1)*bw+left_edge for x in bins]
res = rt.TH1F('res','res', len(bins)-1,array( 'f', x_bins))
res_eff = rt.TH1F('res_eff','res_eff', len(bins)-1,array( 'f', x_bins))
scale = rt.TH1F('scale','scale',len(bins)-1,array( 'f', x_bins))
scale_eff = rt.TH1F('scale_eff','scale_eff',len(bins)-1,array( 'f', x_bins))
for i in range(len(bins)-1):
key = 'bin'+str(i)
proj.append(histo2D.ProjectionX("h"+str(i),bins[i],bins[i+1]-1))
if direc == 'x':
y_low = histo2D.GetXaxis().GetBinLowEdge(bins[i])
y_up = histo2D.GetXaxis().GetBinLowEdge(bins[i+1]-1)+histo2D.GetXaxis().GetBinWidth(1)
else:
y_low = histo2D.GetYaxis().GetBinLowEdge(bins[i])
y_up = histo2D.GetYaxis().GetBinLowEdge(bins[i+1]-1)+histo2D.GetYaxis().GetBinWidth(1)
for i in range(len(proj)):
h1 = proj[i]
hnew = h1.Clone("hnew")
hnew = hnew.Rebin(rebin)
SS = rt.TSpectrum()
n_pks = SS.Search(hnew, 0.1, "", 0.5)
x_pos = SS.GetPositionX()
y_pos = SS.GetPositionY()
pks = []
if n_pks == 0:
hnew.SetLineColor(4)
# hnew.Rebin(2)
hnew.GetXaxis().SetRangeUser(-5,5)
hnew.DrawCopy()
print("npks == 0",i,hnew.Integral(),n_pks)
break
n_pk = hnew.FindBin(x_pos[0])
thr = gaus_thr* hnew.GetBinContent(n_pk)
n_low = n_pk
while hnew.GetBinContent(n_low) > thr:
n_low -= 1
x_low = hnew.GetBinCenter(n_low)
n_up = n_pk
while hnew.GetBinContent(n_up) > thr:
n_up += 1
x_up = hnew.GetBinCenter(n_up)
r = hnew.Fit('gaus', 'LRSQ+', '', x_low, x_up)
r = r.Get()
scale.SetBinContent(i+1,r.Parameter(1))
scale.SetBinError(i+1,r.ParError(1))
if abs(r.Parameter(1)-hnew.GetMean())>1:
print("scale fit warning", i,r.Parameter(1),hnew.GetMean())
n_pk = h1.FindBin(r.Parameter(1))
thr = 0.68 * h1.Integral()
counter = h1.GetBinContent(n_pk)*1.0
n_low = n_pk
n_up = n_pk
flag = True
while counter < thr:
if flag:
n_low -= 1
counter += h1.GetBinContent(n_low)
else:
n_up += 1
counter += h1.GetBinContent(n_up)
flag = not flag
sigma_eff = (n_up - n_low) *h1.GetBinWidth(1)
res.SetBinContent(i+1,sigma_eff)
res.SetBinError(i+1,h1.GetBinWidth(1))
res_eff.SetBinContent(i+1,r.Parameter(2))
res_eff.SetBinError(i+1,r.ParError(2))
return scale, scale_eff, res, res_eff
def create_TGraph(x,y,ex=[],ey=[], axis_title = ['','']):
x = array("d", x)
y = array("d", y)
ex = array("d", ex)
ey = array("d", ey)
if not len(x) == len(y):
print("length of x and y are not equal!")
if not len(ex)==len(ey):
print("length of ex and ey are not equal!")
if len(ex)>0 and not len(x)==len(ex):
print("leng of ex and x are not equal!")
if len(ex)==0:gr = rt.TGraph(len(x),x,y)
else: gr = rt.TGraphErrors(len(x),x,y,ex,ey)
if len(axis_title) == 2:
gr.GetXaxis().SetTitle(axis_title[0])
gr.GetYaxis().SetTitle(axis_title[1])
return gr