def complex_histo_draw(file_name, val_1_col, err_1_col, val_2_col, err_2_col,
val_3_col, err_3_col, val_4_col, err_4_col):
#from csv to numpy-array
data = numpy.genfromtxt(file_name, delimiter=',')
#binning
y_bins = [2.25, 2.75, 3.25, 3.75, 4.25]
pt_bins = [3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20]
length_x = numpy.size(data, 0)
#histogram for all bins
hist_os = h2_axes(y_bins, pt_bins)
#histograms with ratios & yeilds
hist_y1 = h1_axis(pt_bins)
hist_y2 = h1_axis(pt_bins)
hist_y3 = h1_axis(pt_bins)
hist_y4 = h1_axis(pt_bins)
hist_array = [hist_y1, hist_y2, hist_y3, hist_y4]
#filling loop
pt_bin = 1
y_bin = 1
for j in range(length_x - 1):
if pt_bin == len(pt_bins):
y_bin += 1
pt_bin = 1
bin_VE_1 = VE(data[j + 1, val_1_col], data[j + 1, err_1_col]**2)
bin_VE_2 = VE(data[j + 1, val_2_col], data[j + 1, err_2_col]**2)
bin_VE_3 = VE(data[j + 1, val_3_col], data[j + 1, err_3_col]**2)
bin_VE_4 = VE(data[j + 1, val_4_col], data[j + 1, err_4_col]**2)
#fill histogram with bins values
bin_val = bin_VE_2 * bin_VE_4 / (bin_VE_1 * bin_VE_3)
#1. uncomment if one need to non-round-values
hist_os.SetBinContent(y_bin, pt_bin, bin_val.value())
hist_os.SetBinError(y_bin, pt_bin, bin_val.error())
#2. uncomment if one need to round-values
#hist_os.SetBinContent(y_bin, pt_bin, round(bin_val.value()))
#hist_os.SetBinError(y_bin, pt_bin, round(bin_val.error()))
#stuff for changing bin's colors which determine by Z axis range in 2d hist
#hist_os.SetMinimum(0.)
#hist_os.SetMaximum(0.08)
#fill histogram with R-values
hist_array[y_bin - 1].SetBinContent(pt_bin, 100 * (bin_val.value()))
hist_array[y_bin - 1].SetBinError(pt_bin, 100 * (bin_val.error()))
pt_bin += 1
return hist_os, hist_array[0], hist_array[1], hist_array[2], hist_array[3]
def complex_histo_draw(file_name, val_1_col, err_1_col, val_2_col, err_2_col, corr_1_val): #from csv to numpy-array data = numpy.genfromtxt(file_name, delimiter = ',') #binning #for statistic y_bins_1 = [2.25, 2.75, 3.25, 3.75, 4.25] pt_bins_1 = [ 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20 ] #for systematic y_bins_2 = [2.25, 2.75, 3.25, 3.75, 4.25] pt_bins_2 = [ 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20 ] length_x = numpy.size(data, 0) #histograms with ratios & yeilds #stat hist_array_1 = [h1_axis(pt_bins_1), h1_axis(pt_bins_1), h1_axis(pt_bins_1), h1_axis(pt_bins_1)] #syst hist_array_2 = [h1_axis(pt_bins_2), h1_axis(pt_bins_2), h1_axis(pt_bins_2), h1_axis(pt_bins_2)] #filling loop pt_bin = 1 y_bin = 1 for j in range(length_x - 1): if pt_bin == len(pt_bins_1): y_bin += 1 pt_bin = 1 bin_VE_1 = VE(data[j + 1, val_1_col]*data[j+1, corr_1_val], (data[j + 1, val_1_col]*data[j + 1, err_1_col])**2) #R + syst bin_VE_2 = VE(data[j + 1, val_2_col]*data[j+1, corr_1_val], (data[j + 1, val_2_col]*data[j + 1, err_2_col])**2) #R + stat #fill histogram with bins values #syst bin_val_1 = bin_VE_1 #stat bin_val_2 = bin_VE_2 #fill histogram with R-values hist_array_1[y_bin - 1].SetBinContent(pt_bin, bin_val_1.value()) hist_array_1[y_bin - 1].SetBinError(pt_bin, bin_val_1.error()) hist_array_2[y_bin - 1].SetBinContent(pt_bin, bin_val_2.value()) hist_array_2[y_bin - 1].SetBinError(pt_bin, bin_val_2.error()) pt_bin += 1 return hist_array_1[0], hist_array_2[0]
def prepare_data():
#
seed = 1234567890
random.seed(seed)
logger.info('Test *RANDOM* data will be generated/seed=%s' % seed)
## prepare "data" histograms:
# 1) 2D histograms
ix, iy = 30, 30
hdata = h2_axes([xmax / ix * i for i in range(ix + 1)],
[ymax / iy * i for i in range(iy + 1)])
# 2) non-equal binning 1D histograms for x-component
hxdata = h1_axis([i for i in range(5)] + [5 + i * 0.2 for i in range(50)] +
[15 + i for i in range(6)])
# 2) equal binning 1D histograms for y-component
hydata = h1_axis([i * 0.5 for i in range(31)])
assert hdata.xmax() == xmax, 'XMAX is invalid!'
assert hdata.ymax() == ymax, 'YMAX is invalid!'
assert hxdata.xmax() == xmax, 'XMAX is invalid!'
assert hydata.xmax() == ymax, 'XMAX is invalid!'
with ROOT.TFile.Open(testdata, 'recreate') as mc_file:
mc_file.cd()
datatree = ROOT.TTree('DATA_tree', 'data-tree')
datatree.SetDirectory(mc_file)
from array import array
xvar = array('f', [0])
yvar = array('f', [0])
datatree.Branch('x', xvar, 'x/F')
datatree.Branch('y', yvar, 'y/F')
for i in range(N1):
x, y = -1, -1
while not 0 <= x < xmax or not 0 <= y < ymax:
v1 = random.gauss(0, 3)
v2 = random.gauss(0, 2)
x = 5 + v1 + v2
y = 12 + v1 - v2
hdata.Fill(x, y)
hxdata.Fill(x)
hydata.Fill(y)
xvar[0] = x
yvar[0] = y
datatree.Fill()
for i in range(N1):
x, y = -1, -1
while not 0 <= x < xmax or not 0 <= y < ymax:
v1 = random.gauss(0, 3)
v2 = random.gauss(0, 2)
x = 15 + v1
y = 4 + v2
hdata.Fill(x, y)
hxdata.Fill(x)
hydata.Fill(y)
xvar[0] = x
yvar[0] = y
datatree.Fill()
for i in range(N1):
x, y = -1, -1
while not 0 <= x < xmax or not 0 <= y < ymax:
v1 = random.gauss(0, 3)
v2 = random.gauss(0, 2)
x = 15 + v1 - v2
y = 12 + v1 + v2
hdata.Fill(x, y)
hxdata.Fill(x)
hydata.Fill(y)
xvar[0] = x
yvar[0] = y
datatree.Fill()
for i in range(0, 4 * N1):
x = random.uniform(0, xmax)
y = random.uniform(0, ymax)
hdata.Fill(x, y)
hxdata.Fill(x)
hydata.Fill(y)
xvar[0] = x
yvar[0] = y
datatree.Fill()
datatree.Write()
## write the histogram
mc_file[tag_data] = hdata
mc_file[tag_datax] = hxdata
mc_file[tag_datay] = hydata
mctree = ROOT.TTree(tag_mc, 'mc-tree')
mctree.SetDirectory(mc_file)
from array import array
xvar = array('f', [0.0])
yvar = array('f', [0.0])
mctree.Branch('x', xvar, 'x/F')
mctree.Branch('y', yvar, 'y/F')
for i in range(2 * N2):
xv = random.uniform(0, xmax)
yv = random.uniform(0, ymax)
xvar[0] = xv
yvar[0] = yv
mctree.Fill()
fx = lambda x: (x / 10.0 - 1)**2
fy = lambda y: (y / 7.5 - 1)**2
for i in range(N2):
while True:
xv = random.uniform(0, xmax)
if random.uniform(0, 1) < fx(xv): break
while True:
yv = random.uniform(0, ymax)
if random.uniform(0, 1) < fy(yv): break
xvar[0] = xv
yvar[0] = yv
mctree.Fill()
mctree.Write()
mc_file.ls()
def test_basic_1D() :
logger.info ( 'Test for very basic operations with 1D-histograms')
h1 = ROOT.TH1D ( hID() , '' , 10 , 0 , 1 )
## clone it!
h2 = h1.clone()
## set content for certain bins:
h1[1] = 1.55
h1[2] = VE(1,0.2**2)
## get content from certaom bins:
a = h1[3]
h1 += VE(3,0.5**2)
## add a function
h1 += lambda x : VE(25*x*x,25*x*x)
## Divide by function
h1 /= lambda x : float(1+x*x)
## multiply by constant
h1 *= 15.0
## loop over bins using index
for i in h1 :
logger.info( "bin# %2d, content %s" % ( i , h1[i] ) )
## loop over bins i nreverse order
for i in reversed( h1 ) :
logger.info( "bin# %2d, content %s" % ( i , h1[i] ) )
## iterate over all
for item in h1.items() :
logger.info ( "items: %s" % str ( item ) )
## interpolate
for x in range(10) :
x = random.uniform(0,1)
logger.info ( 'h1(%.3f) = %s[d] %s[0] %s[1] %s[2] %s[3] ' % ( x ,
h1(x) ,
h1(x, interpolate=0) ,
h1(x, interpolate=1) ,
h1(x, interpolate=2) ,
h1(x, interpolate=3) ) )
## running sum
h1_d = h1.sumv() ## default
h1_i = h1.sumv( increasing = True ) ## ditto
h1_r = h1.sumv( increasing = False ) ## ditto
## histogram efficiney of cuts
eff_i = h1.effic( increasing = True )
eff_r = h1.effic( increasing = False )
## efficiency of certaine value
e1_i = h1.efficiency( 0.3 , increasing = True )
e2_i = h1.efficiency( 0.3 , increasing = False )
## smear the histogram
h0 = ROOT.TH1F( hID() , '', 400 , -1 , 1 )
for i in range( 1000 ) : h0.Fill( random.gauss(0,0.10 ) )
hs = h0.smear ( sigma = 0.10 )
logger.info ( 'Original RMS %20s , smeared(0.10) %-20s' % ( h0.rms() , hs.rms() ) )
## specific transformation:
## "precision"
hp = h1.precision()
## "B/S"
hb2s = h1.b2s()
## rescale histo
hs1 = h1.scale(1)
hs2 = h1.rescale_bins(1)
## sample
hr = h1.sample()
## "figure of merit" to maximize precision:
fm2 = h1.fom_2()
fm2 = h1.FoM_2()
## rebin template:
h_tmpl = h1_axis ( [ 0 , 0.1 , 0.5 , 0.55, 0.80 , 0.95 , 1.0 ] )
## rebin as "numbers"
h1_n = h1.rebinNumbers ( h_tmpl )
## rebin as "function"
h1_f = h1.rebinFunction ( h_tmpl )
## slice for the histogram
hs = h1[4:30]
## accumulate
a1 = h1.accumulate( low = 1 , high = 10 )
a2 = h1.accumulate( xmin = 0.1 , xmax = 0.30 )
## shift
hs = h1.shift ( -0.3 )
## other operations
h2 = h1**2
h2 = h1*h1
h2 = h1*2
h2 = h1/2
h2 = 2*h1
h2 = abs(h1)
from ostap.math.math_ve import sin, exp
h2 = sin ( h1 )
h2 = exp ( h1 )
## shift for bins
hs = h1 >> 4
hs = h1 << 5
## integration (taking into account bin-width)
i1 = h1.integrate()
i2 = h1.integrate( lowx = 1 , highx = 15 )
i2 = h1.integrate( xmin = 0.1 , xmax = 0.6 )
## statistics
for i in range ( 0 , 5 ) :
logger.info ( " Moment (%d): %-20s" % ( i , h1.moment ( i ) ) )
## statistics
for i in range ( 0 , 5 ) :
logger.info ( "Central moment (%d): %-20s" % ( i , h1.centralMoment ( i ) ) )
logger.info ( " Mean : %-20s" % h1.mean () )
logger.info ( " RMS : %-20s" % h1.rms () )
logger.info ( " Skewness : %-20s" % h1.skewness () )
logger.info ( " Kurtosis : %-20s" % h1.kurtosis () )
logger.info ( " Stat: %s" % h1. stat() )
logger.info ( " WStat: %s" % h1.wstat() )
logger.info ( " XStat: %s" % h1.xstat() )
logger.info ( ' minmax %20s' % str( h1. minmax() ) )
logger.info ( 'x-minmax %20s' % str( h1.xminmax() ) )
logger.info ( 'y-minmax %20s' % str( h1.yminmax() ) )
def complex_histo_draw(file_name, epidlc_val, epidsc_val, se1_val, se1_err,
se0_val, se0_err, se2_val):
#from csv to numpy-array
data = numpy.genfromtxt(file_name, delimiter=',')
srtformat = "{:7.5f}"
#binning
y_bins = [2.25, 2.75, 3.25, 3.75, 4.25]
pt_bins = [3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20]
length_x = numpy.size(data, 0)
#histogram for all bins
hist_os = h2_axes(y_bins, pt_bins)
#histograms with ratios & yeilds
hist_y1 = h1_axis(pt_bins)
hist_y2 = h1_axis(pt_bins)
hist_y3 = h1_axis(pt_bins)
hist_y4 = h1_axis(pt_bins)
hist_array = [hist_y1, hist_y2, hist_y3, hist_y4]
#filling loop
pt_bin = 1
y_bin = 1
for j in range(length_x - 1):
if pt_bin == len(pt_bins):
y_bin += 1
pt_bin = 1
print(str("\hline"))
print("\multicolumn{6}{c}{$y\in$~(" + str(data[j + 1, 1]) + "," +
str(data[j + 1, 2]) + ")}" + "\\" + "\\")
print(str("\hline"))
r = data[j + 1, 13] / data[j + 1, 14]
r_i = data[j + 1, 18] / data[j + 1, 19]
r_s = data[j + 1, 20] / data[j + 1, 21]
s1 = abs(r - r_s)
s2 = abs(r - r_i)
s3 = data[j + 1, 25]
summ = (s1**2 + s2**2 + s3**2)**0.5
bin_VE_1 = VE(summ, 0)
#fill histogram with bins values
bin_val = bin_VE_1
#1. uncomment if one need to non-round-values
hist_os.SetBinContent(y_bin, pt_bin, bin_val.value())
hist_os.SetBinError(y_bin, pt_bin, bin_val.error())
print("(" + str(data[j + 1, 3]) + "," + str(data[j + 1, 4]) + ") & " +
srtformat.format(r) + " & " + srtformat.format(s1) + " & " +
srtformat.format(s2) + " & " + srtformat.format(s3) + " & " +
srtformat.format(summ) + "\\" + "\\")
#2. uncomment if one need to round-values
#hist_os.SetBinContent(y_bin, pt_bin, round(bin_val.value()))
#hist_os.SetBinError(y_bin, pt_bin, round(bin_val.error()))
#hist style
#remove stat box
hist_os.SetStats(0)
#set z range
hist_os.SetMinimum(0.)
hist_os.SetMaximum(0.06)
pt_bin += 1
print("\n")
return hist_os
# ## histos for gaussian distributions # h1g = ROOT.TH1D ( hID() , '' , 40 , -5 , 5 ) ; h1g.Sumw2() h2g = ROOT.TH1D ( hID() , '' , 40 , -5 , 5 ) ; h2g.Sumw2() h3g = ROOT.TH1D ( hID() , '' , 20 , -5 , 5 ) ; h3g.Sumw2() bins = [ -5 ] ## random.seed(10) for i in range(0, 15 ) : bins.append ( random.uniform ( -5 , 5 ) ) bins += [ 5 ] bins.sort() h4g = h1_axis ( bins ) # ## histos for uniform distributions # h1u = h1g.clone() h2u = h2g.clone() h3u = h3g.clone() h4u = h4g.clone() # ## histos for exponential distributions # h1e = h1g.clone() h2e = h2g.clone() h3e = h3g.clone()
from ostap.histos.histos import h1_axis
from ostap.math.ve import VE
csv_file = open("325.csv","r")
pt_bins = [1,2,3,4,5,6,7,8]
hR_5tev = h1_axis(pt_bins)
hR_7tev = h1_axis(pt_bins)
hR_13tev = h1_axis(pt_bins)
hR_5tev.blue()
hR_13tev.red()
bin=0
for line in csv_file:
bin+=1
w = line[:-1].split(",")
hR_5tev [bin] = VE(float(w[ 3]),float(w[ 6])**2)
hR_7tev [bin] = VE(float(w[ 7]),float(w[10])**2)
hR_13tev[bin] = VE(float(w[11]),float(w[14])**2)
print(w)
hR_5tev.GetYaxis().SetRangeUser(0,4)
hR_5tev.GetXaxis().SetTitleSize(0.05)
hR_5tev.GetXaxis().SetTitle("p_{T}, GeV/c")
hR_5tev.GetYaxis().SetTitle("( d#sigma(D^{0})/dp_{T} ) / ( d#sigma(D*)/dp_{T} )")
hR_5tev.Draw("e1")
def draw_param(r_fit, w_fit, h_fit, N_BINS, var, W_max, name, XTitle, Prefix,
Type, var_Units):
var_list = []
for idx in range(N_BINS + 1):
var_list.append(var.getMin() + idx *
(var.getMax() - var.getMin()) / N_BINS)
h_pull = h1_axis(var_list)
for idx in range(1, N_BINS):
h_pull[idx] = VE(h_fit[idx - 1][3], 0**2)
h_pull.GetYaxis().SetRangeUser(-4, 4)
h_pull.GetYaxis().SetTitle("#Delta / #sigma")
h_pull.GetYaxis().SetTitle("#Delta / #sigma")
h_pull.GetYaxis().SetTitleSize(0.2)
h_pull.GetYaxis().SetTitleOffset(0.2)
h_pull.GetYaxis().SetLabelSize(0.1)
h_pull.GetXaxis().SetLabelSize(0.15)
h_pull.SetFillColor(34)
line_up = ROOT.TLine(var.getMin(), 2, var.getMax(), 2)
line_down = ROOT.TLine(var.getMin(), -2, var.getMax(), -2)
line_up.SetLineColor(2)
line_down.SetLineColor(2)
#-------------------------------------------------------------------
# Prepare for drawing
#-------------------------------------------------------------------
Y_title = 0.92 * W_max
Y_step = 0.05 * W_max
x_pos = var.getMin() + 0.05 * (var.getMax() - var.getMin())
percentage = " (" + "{:2.2f}".format(
100. * r_fit("S")[0].error() / r_fit("S")[0].value()) + "%)"
text_SIG = "SIGNAL = " + "{:5.0f}".format(r_fit("S")[0].value())
text_SIG += " #pm" + "{:4.0f}".format(r_fit("S")[0].error()) + percentage
text_BKG = "BKG = " + "{:5.0f}".format(r_fit("B")[0].value())
text_BKG += " #pm" + "{:4.0f}".format(r_fit("B")[0].error())
labels = []
labels.append(ROOT.TLatex(x_pos, Y_title - 0.0 * Y_step, text_SIG))
labels.append(ROOT.TLatex(x_pos, Y_title - 1.5 * Y_step, text_BKG))
for lb in labels:
lb.SetTextSize(0.04)
w_fit.GetXaxis().SetTitle(XTitle + ", " + var_Units)
w_fit.GetXaxis().SetTitleSize(0.05)
w_fit.GetXaxis().SetTitleOffset(0.9)
w_fit.GetYaxis().SetRangeUser(0.01, W_max)
w_fit.GetYaxis().SetTitleOffset(0.9)
w_fit.GetYaxis().SetTitle("Candidates per " +
str((var.getMax() - var.getMin()) / N_BINS) +
" " + var_Units)
w_fit.GetYaxis().SetLabelSize(0.025)
#-------------------------------------------------------------------
# Draw it!
#-------------------------------------------------------------------
canv = ROOT.TCanvas("canv", "canv", 700, 800)
canv.Divide(1, 2)
pad1 = canv.GetListOfPrimitives().FindObject("canv_1")
pad2 = canv.GetListOfPrimitives().FindObject("canv_2")
pad1.SetPad(0.00, 0.11, 1.00, 1.00)
pad2.SetPad(0.00, 0.00, 1.00, 0.10)
canv.cd(1)
w_fit.Draw()
for lb in labels:
lb.Draw()
canv.cd(2)
h_pull.Draw("HIST")
line_up.Draw()
line_down.Draw()
canv.Print(Prefix + "_" + name + "." + Type)
canv.Close()