def DrawMATPLOTLIB(self, histos, scales, ref, irelhisto, filenamePy,
outputnames):
# Is there any legend?
legendmode = False
if len(self.main.datasets) > 1:
legendmode = True
# Type of histogram
frequencyhisto = True
for histo in histos:
if histo.__class__.__name__ != 'HistogramFrequency':
frequencyhisto = False
break
logxhisto = True
for histo in histos:
if histo.__class__.__name__ != 'HistogramLogX':
logxhisto = False
break
# Stacking or superimposing histos ?
stackmode = False
if ref.stack==StackingMethodType.STACK or \
( ref.stack==StackingMethodType.AUTO and \
self.main.stack==StackingMethodType.STACK ):
stackmode = True
# Open the file in write-mode
try:
outputPy = file(filenamePy, 'w')
except:
logging.getLogger('MA5').error('Impossible to write the file: ' +
filenamePy)
return False
# File header
function_name = filenamePy[:-3]
function_name = function_name.split('/')[-1]
outputPy.write('def ' + function_name + '():\n')
outputPy.write('\n')
# Import Libraries
outputPy.write(' # Library import\n')
outputPy.write(' import numpy\n')
outputPy.write(' import matplotlib\n')
# outputPy.write(" matplotlib.use('Agg')\n")
outputPy.write(' import matplotlib.pyplot as plt\n')
outputPy.write(' import matplotlib.gridspec as gridspec\n')
outputPy.write('\n')
# Matplotlib & numpy version
outputPy.write(' # Library version\n')
outputPy.write(' matplotlib_version = matplotlib.__version__\n')
outputPy.write(' numpy_version = numpy.__version__\n')
outputPy.write('\n')
# Binning
# Loop over datasets and histos
xnbin = histos[0].nbins
xmin = histos[0].xmin
xmax = histos[0].xmax
outputPy.write(' # Histo binning\n')
if logxhisto:
outputPy.write(' xBinning = [')
for bin in range(1, xnbin + 2):
if bin != 1:
outputPy.write(',')
outputPy.write(str(histos[0].GetBinLowEdge(bin)))
outputPy.write(']\n')
outputPy.write('\n')
else:
outputPy.write(' xBinning = numpy.linspace('+\
str(xmin)+','+str(xmax)+','+str(xnbin+1)+\
',endpoint=True)\n')
outputPy.write('\n')
# Data
outputPy.write(' # Creating data sequence: middle of each bin\n')
outputPy.write(' xData = numpy.array([')
for bin in range(0, xnbin):
if bin != 0:
outputPy.write(',')
outputPy.write(str(histos[0].GetBinMean(bin)))
outputPy.write('])\n\n')
# Loop over datasets and histos
ntot = 0
for ind in range(0, len(histos)):
# Ntot
# Creating a new histo
histoname = 'y' + histos[ind].name + '_' + str(ind)
outputPy.write(' # Creating weights for histo: ' + histoname +
'\n')
outputPy.write(' ' + histoname + '_weights = numpy.array([')
for bin in range(1, xnbin + 1):
ntot += histos[ind].summary.array[bin - 1] * scales[ind]
if bin != 1:
outputPy.write(',')
outputPy.write(
str(histos[ind].summary.array[bin - 1] * scales[ind]))
outputPy.write('])\n\n')
# Canvas
outputPy.write(' # Creating a new Canvas\n')
dpi = 80
height = 500
widthx = 700
if legendmode:
widthx = 1000
outputPy.write(' fig = plt.figure(figsize=('+\
str(widthx/dpi)+','+str(height/dpi)+\
'),dpi='+str(dpi)+')\n')
if not legendmode:
outputPy.write(' frame = gridspec.GridSpec(1,1)\n')
else:
outputPy.write(' frame = gridspec.GridSpec(1,1,right=0.7)\n')
# subplot argument: nrows, ncols, plot_number
# outputPy.write(' pad = fig.add_subplot(111)\n')
outputPy.write(' pad = fig.add_subplot(frame[0])\n')
outputPy.write('\n')
# Stack
outputPy.write(' # Creating a new Stack\n')
for ind in range(len(histos) - 1, -1, -1):
myweight = 'y' + histos[ind].name + '_' + str(ind) + '_weights'
mytitle = '"' + PlotFlow.NiceTitleMatplotlib(
self.main.datasets[ind].title) + '"'
mytitle = mytitle.replace('_', '\_')
if not stackmode:
myweights = 'y' + histos[ind].name + '_' + str(
ind) + '_weights'
else:
myweights = ''
for ind2 in range(0, ind + 1):
if ind2 >= 1:
myweights += '+'
myweights += 'y' + histos[ind2].name + '_' + str(
ind2) + '_weights'
# reset
linecolor = 0
linestyle = 0
backcolor = 0
backstyle = 0
linewidth = 1
# Setting AUTO settings
if len(histos) == 1:
linecolor1 = [9]
linecolor = linecolor1[ind]
if stackmode:
backstyle1 = [3004]
backstyle = backstyle1[ind]
backcolor = linecolor1[ind]
elif len(histos) == 2:
linecolor2 = [9, 46]
linecolor = linecolor2[ind]
if stackmode:
backstyle2 = [3004, 3005]
backstyle = backstyle2[ind]
backcolor = linecolor2[ind]
elif len(histos) == 3:
linecolor3 = [9, 46, 8]
linecolor = linecolor3[ind]
if stackmode:
backstyle3 = [3004, 3005, 3006]
backstyle = backstyle3[ind]
backcolor = linecolor3[ind]
elif len(histos) == 4:
linecolor4 = [9, 46, 8, 4]
linecolor = linecolor4[ind]
if stackmode:
backstyle4 = [3004, 3005, 3006, 3007]
backstyle = backstyle4[ind]
backcolor = linecolor4[ind]
elif len(histos) == 5:
linecolor5 = [9, 46, 8, 4, 6]
linecolor = linecolor5[ind]
if stackmode:
backstyle5 = [3004, 3005, 3006, 3007, 3013]
backstyle = backstyle5[ind]
backcolor = linecolor5[ind]
elif len(histos) == 6:
linecolor6 = [9, 46, 8, 4, 6, 2]
linecolor = linecolor6[ind]
if stackmode:
backstyle6 = [3004, 3005, 3006, 3007, 3013, 3017]
backstyle = backstyle6[ind]
backcolor = linecolor6[ind]
elif len(histos) == 7:
linecolor7 = [9, 46, 8, 4, 6, 2, 7]
linecolor = linecolor7[ind]
if stackmode:
backstyle7 = [3004, 3005, 3006, 3007, 3013, 3017, 3022]
backstyle = backstyle7[ind]
backcolor = linecolor7[ind]
elif len(histos) == 8:
linecolor8 = [9, 46, 8, 4, 6, 2, 7, 3]
linecolor = linecolor8[ind]
if stackmode:
backstyle8 = [
3004, 3005, 3006, 3007, 3013, 3017, 3022, 3315
]
backstyle = backstyle8[ind]
backcolor = linecolor8[ind]
elif len(histos) == 9:
linecolor9 = [9, 46, 8, 4, 6, 2, 7, 3, 42]
linecolor = linecolor9[ind]
if stackmode:
backstyle9 = [
3004, 3005, 3006, 3007, 3013, 3017, 3022, 3315, 3351
]
backstyle = backstyle9[ind]
backcolor = linecolor9[ind]
elif len(histos) == 10:
linecolor10 = [9, 46, 8, 4, 6, 2, 7, 3, 42, 48]
linecolor = linecolor10[ind]
if stackmode:
backstyle10 = [
3004, 3005, 3006, 3007, 3013, 3017, 3022, 3315, 3351,
3481
]
backstyle = backstyle10[ind]
backcolor = linecolor10[ind]
else:
linecolor = self.color
self.color += 1
# lineStyle
linestyle = LineStyleType.convert2code(
self.main.datasets[ind].linestyle)
# linewidth
linewidth = self.main.datasets[ind].linewidth
# background color
if self.main.datasets[ind].backcolor != ColorType.AUTO:
backcolor=ColorType.convert2root( \
self.main.datasets[ind].backcolor,\
self.main.datasets[ind].backshade)
# background style
if self.main.datasets[ind].backstyle != BackStyleType.AUTO:
backstyle=BackStyleType.convert2matplotlib( \
self.main.datasets[ind].backstyle)
mylinecolor = '"' + madanalysis.enumeration.color_hex.color_hex[
linecolor] + '"'
mybackcolor = '"' + madanalysis.enumeration.color_hex.color_hex[
backcolor] + '"'
filledmode = '"stepfilled"'
rWidth = 1.
if backcolor == 0: #invisible
filledmode = '"step"'
mybackcolor = 'None'
# if frequencyhisto:
# filledmode='"bar"'
# rWidth=0.8
mylinewidth = self.main.datasets[ind].linewidth
mylinestyle = LineStyleType.convert2matplotlib(
self.main.datasets[ind].linestyle)
outputPy.write(' pad.hist('+\
'x=xData, '+\
'bins=xBinning, '+\
'weights='+myweights+',\\\n'+\
' label='+mytitle+', ')
if ntot != 0:
outputPy.write('histtype=' + filledmode + ', ')
outputPy.write( 'rwidth='+str(rWidth)+',\\\n'+\
' color='+mybackcolor+', '+\
'edgecolor='+mylinecolor+', '+\
'linewidth='+str(mylinewidth)+', '+\
'linestyle='+mylinestyle+',\\\n'+\
' bottom=None, '+\
'cumulative=False, normed=False, align="mid", orientation="vertical")\n\n')
outputPy.write('\n')
# Label
outputPy.write(' # Axis\n')
outputPy.write(" plt.rc('text',usetex=False)\n")
# X-axis
if ref.titleX == "":
axis_titleX = ref.GetXaxis_Matplotlib()
else:
axis_titleX = ref.titleX
axis_titleX = axis_titleX.replace('#DeltaR', '#Delta R')
axis_titleX = axis_titleX.replace('#', '\\')
outputPy.write(' plt.xlabel(r"' + axis_titleX + '",\\\n')
outputPy.write(' fontsize=16,color="black")\n')
# Y-axis
axis_titleY = ref.GetYaxis_Matplotlib()
# Scale to one ?
scale2one = False
if ref.stack==StackingMethodType.NORMALIZE2ONE or \
(self.main.stack==StackingMethodType.NORMALIZE2ONE and \
ref.stack==StackingMethodType.AUTO):
scale2one = True
if scale2one:
axis_titleY += " $(#mathrm{scaled}\ #mathrm{to}# #mathrm{one})$"
elif self.main.normalize == NormalizeType.LUMI or \
self.main.normalize == NormalizeType.LUMI_WEIGHT:
axis_titleY += " $(#mathcal{L}_{#mathrm{int}} = " + str(
self.main.lumi) + "# #mathrm{fb}^{-1})$ "
elif self.main.normalize == NormalizeType.NONE:
axis_titleY += " $(#mathrm{not}# #mathrm{normalized})$"
if ref.titleY != "":
axis_titleY = PlotFlow.NiceTitle(ref.titleY)
axis_titleY = axis_titleY.replace('#', '\\')
outputPy.write(' plt.ylabel(r"' + axis_titleY + '",\\\n')
outputPy.write(' fontsize=16,color="black")\n')
outputPy.write('\n')
# Tag Log/Linear
is_logx = False
if ref.logX and ntot != 0:
is_logx = True
is_logy = False
if ref.logY and ntot != 0:
is_logy = True
# Bound y
outputPy.write(' # Boundary of y-axis\n')
myweights = ''
if stackmode:
for ind in range(0, len(histos)):
if ind >= 1:
myweights += '+'
myweights += 'y' + histos[ind].name + '_' + str(
ind) + '_weights'
else:
myweights = 'numpy.array(['
for ind in range(0, len(histos)):
if ind >= 1:
myweights += ','
myweights += 'y' + histos[ind].name + '_' + str(
ind) + '_weights.max()'
myweights += '])'
outputPy.write(' ymax=(' + myweights + ').max()*1.1\n')
outputPy.write(' ')
if is_logy:
outputPy.write('#')
outputPy.write('ymin=0 # linear scale\n')
myweights = ''
if stackmode:
for ind in range(0, len(histos)):
if ind >= 1:
myweights += '+'
myweights += 'y' + histos[ind].name + '_' + str(
ind) + '_weights'
else:
myweights = 'numpy.array(['
for ind in range(0, len(histos)):
if ind >= 1:
myweights += ','
myweights += 'y' + histos[ind].name + '_' + str(
ind) + '_weights.min()'
myweights += ',1.])'
outputPy.write(' ')
if not is_logy:
outputPy.write('#')
outputPy.write('ymin=min([x for x in (' + myweights +
') if x])/100. # log scale\n')
outputPy.write(' plt.gca().set_ylim(ymin,ymax)\n')
outputPy.write('\n')
# X axis
outputPy.write(' # Log/Linear scale for X-axis\n')
# - Linear
outputPy.write(' ')
if is_logx:
outputPy.write('#')
outputPy.write('plt.gca().set_xscale("linear")\n')
# - Log
outputPy.write(' ')
if not is_logx:
outputPy.write('#')
outputPy.write('plt.gca().set_xscale("log",nonposx="clip")\n')
outputPy.write('\n')
# Y axis
outputPy.write(' # Log/Linear scale for Y-axis\n')
# - Linear
outputPy.write(' ')
if is_logy:
outputPy.write('#')
outputPy.write('plt.gca().set_yscale("linear")\n')
# - Log
outputPy.write(' ')
if not is_logy:
outputPy.write('#')
outputPy.write('plt.gca().set_yscale("log",nonposy="clip")\n')
outputPy.write('\n')
# Labels
if frequencyhisto:
outputPy.write(' # Labels for x-Axis\n')
outputPy.write(' xLabels = numpy.array([')
for bin in range(0, xnbin):
if bin >= 1:
outputPy.write(',')
outputPy.write(
'"' + str(histos[0].stringlabels[bin]).replace('_', '\_') +
'"')
outputPy.write('])\n')
outputPy.write(
' plt.xticks(xData, xLabels, rotation="vertical")\n')
outputPy.write('\n')
### BENJ: not necessary for getting the png and pdf files
# Draw
# outputPy.write(' # Draw\n')
# outputPy.write(' plt.show()\n')
# outputPy.write('\n')
# Legend
if legendmode:
# Reminder for 'loc'
# -'best' : 0, (only implemented for axes legends)
# -'upper right' : 1,
# -'upper left' : 2,
# -'lower left' : 3,
# -'lower right' : 4,
# -'right' : 5,
# -'center left' : 6,
# -'center right' : 7,
# -'lower center' : 8,
# -'upper center' : 9,
# -'center' : 10,
outputPy.write(' # Legend\n')
outputPy.write(' plt.legend(bbox_to_anchor=(1.05,1), loc=2,'+\
' borderaxespad=0.)\n')
outputPy.write('\n')
# Producing the image
outputPy.write(' # Saving the image\n')
for outputname in outputnames:
outputPy.write(" plt.savefig('" + outputname + "')\n")
outputPy.write('\n')
# Call the function
outputPy.write('# Running!\n')
outputPy.write("if __name__ == '__main__':\n")
outputPy.write(' ' + function_name + '()\n')
# Close the file
try:
outputPy.close()
except:
logging.getLogger('MA5').error('Impossible to close the file: ' +
outputPy)
return False
# Ok
return True
