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 h2(file_name, xvar, x_bins, yvar, y_bins, zvar):
"Create 2d histo"
h2 = h2_axes(x_bins, y_bins)
print("\nCreating 2D-histo:")
print("FILE : " + file_name)
print("HEADER : " + header(file_name))
print(" x_var : " + xvar)
print(" y_var : " + yvar)
print(" z_var : " + zvar)
xidx, yidx, zidx = find_vars(file_name, xvar, yvar, zvar)
with open(file_name, "r") as f:
for line in f:
if line[0:2] == " |":
wl = line[1:-1].split("|")
for w in wl:
x_min = float(wl[xidx].split(",")[0])
y_min = float(wl[yidx].split(",")[0])
z_val = float(wl[zidx].split("+-")[0])
z_err = float(wl[zidx].split("+-")[1])
h2[get_2d_bin(x_min, x_bins, y_min,
y_bins)] = VE(z_val, z_err**2)
return h2
def test_basic_2D () :
logger.info ( 'Test for very basic operations with 2D-histograms')
h2 = h2_axes ( [1,2,3,4,5,6,7] , [1,2,3,4,5] )
h2 += lambda x,y: VE(1,1)+VE(x*x+2*y*y,x*x+2*y*y)
## access the content
logger.info ( "h[%1d, %1d]=%s" % ( 2 , 3 , h2[2,3] ) )
logger.info ( "h[%1d][%1d]=%s" % ( 2 , 3 , h2[2][3] ) )
logger.info ( "h[%1d](%.2f)=%s" % ( 2 , 3.01 , h2[2](3.01 ) ) )
logger.info ( "h(%.2f,%.2f)=%s" % ( 2.01 , 3.01 , h2(2.01,3.01 ) ) )
## get the 1st X-slice
hx = h2.sliceX ( 1 )
## get the slice in 1,4,5 X-bins
hx = h2.sliceX ( [1,4,5] )
## get the 2nd Y-slice
hy = h2.sliceY ( 2 )
## get the slice in 1,3,5 Y-bins
hy = h2.sliceY ( [1,3,4] )
## projection in X
hx = h2.projX()
## projection in Y
hy = h2.projY()
logger.info ( ' minmax %20s' % str( h2. minmax() ) )
logger.info ( 'x-minmax %20s' % str( h2.xminmax() ) )
logger.info ( 'y-minmax %20s' % str( h2.yminmax() ) )
logger.info ( 'z-minmax %20s' % str( h2.zminmax() ) )
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 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