def main():
'''
Main entry point of execution.
'''
## Mitigate numerical noise paying lower performance.
if debuglevel == 0:
ROOT.RooAbsReal.defaultIntegratorConfig().setEpsAbs(1e-9)
ROOT.RooAbsReal.defaultIntegratorConfig().setEpsRel(1e-9)
## Assemble the job name
name = '_'.join([em, src, cat])
f = ModeAndEffSigmaFitter(name, debuglevel)
f.run()
## Store RooFit objects in a rootfile
w = ROOT.RooWorkspace('w')
for item in [f.data, f.data_half_odd, f.data_half_even, f.model]:
w.Import(item)
w.Import(f.fit_data, f.name + '_fit_data')
w.Import(f.train_data, f.name + '_train_data')
w.Import(f.fit_result, f.name + '_fit_result')
w.writeToFile(output_filename)
## Store canvases in a rootfile
outfile = ROOT.TFile.Open(output_filename, 'UPDATE')
outfile.mkdir('Canvases').cd()
for c in canvases.canvases:
if c:
c.Write(c.GetName())
## Make the plots
canvases.make_plots('eps png C'.split())
def main():
'''
Main entry point of execution.
'''
## Mitigate numerical noise paying lower performance.
if debuglevel == 0:
ROOT.RooAbsReal.defaultIntegratorConfig().setEpsAbs(1e-9)
ROOT.RooAbsReal.defaultIntegratorConfig().setEpsRel(1e-9)
## Assemble the job name
name = '_'.join([em, src, cat])
f = ModeAndEffSigmaFitter(name, debuglevel)
f.run()
## Store RooFit objects in a rootfile
w = ROOT.RooWorkspace('w')
for item in [f.data, f.data_half_odd, f.data_half_even, f.model]:
w.Import(item)
w.Import(f.fit_data, f.name + '_fit_data')
w.Import(f.train_data, f.name + '_train_data')
w.Import(f.fit_result, f.name + '_fit_result')
w.writeToFile(output_filename)
## Store canvases in a rootfile
outfile = ROOT.TFile.Open(output_filename, 'UPDATE')
outfile.mkdir('Canvases').cd()
for c in canvases.canvases:
if c:
c.Write(c.GetName())
## Make the plots
canvases.make_plots('eps png C'.split())
def build_animation():
'''
Creates an animated gif out of the canvases.
'''
canvases.make_plots(['gif'])
mc_files, data_files = [], []
## Create lists of files with the animation snapshots
for c in canvases.canvases:
if 'MC' in c.GetName():
mc_files.append(c.GetName() + '.gif')
else:
data_files.append(c.GetName() + '.gif')
## Repeat the first snapshot 3xtimes and the 2nd 2xtimes.
for files in [mc_files, data_files]:
files = 3 * files[:1] + 2 * files[1:2] + mc_files[2:]
options = ' --delay=100 --loop'
command = 'gifsicle %s \\\n %s > %s_Animation_MC.gif' % (
options, '\\\n '.join(mc_files), base_name
)
print command
commands.getoutput(command)
command = 'gifsicle %s \\\n %s > %s_Animation_Data.gif' % (
options, '\\\n '.join(data_files), base_name
)
print command
commands.getoutput(command)
def make_pdf_plots():
'''
Creates pdf files out of the canvases.
'''
canvases.make_plots(['eps'])
## Create lists of files with the animation snapshots
for c in canvases.canvases:
command = "ps2pdf -dEPSCrop %s.eps" % c.GetName()
print command
commands.getoutput(command)
def save_and_cleanup(outdir='plots'):
## Save plots
if not os.path.exists(outdir):
print "Creating folder `%s'" % outdir
os.mkdir(outdir)
else:
## TODO: remove outdir
pass
canvases.make_plots(['png', 'root'], outdir)
canvases.make_pdf_from_eps(outdir)
## Store corrections
for extractor in extractors:
raw = extractor.raw.name.split('-')[0]
target = extractor.target.name.split('-')[0]
out_file_name = '_'.join([raw, 'to', target, 'qqcorrections.root'])
extractor.write_corrector_to_file(outdir + '/' + out_file_name)
## Cleanup
trees.close_files()
def save_and_cleanup(outdir = 'plots'):
## Save plots
if not os.path.exists(outdir):
print "Creating folder `%s'" % outdir
os.mkdir(outdir)
else:
## TODO: remove outdir
pass
canvases.make_plots(['png', 'root'], outdir)
canvases.make_pdf_from_eps(outdir)
## Store corrections
for extractor in extractors:
raw = extractor.raw .name.split('-')[0]
target = extractor.target.name.split('-')[0]
out_file_name = '_'.join([raw, 'to', target, 'qqcorrections.root'])
extractor.write_corrector_to_file(outdir + '/' + out_file_name)
## Cleanup
trees.close_files()
def main():
'''
Main entry point of execution.
'''
hist = get_gaussian_histogram()
make_canvases(hist)
make_more_canvases(hist)
## Make output graphics in different formats using ROOT for the conversions
canvases.make_plots('png C root eps'.split())
## Store canvases as pdf by creating eps and then using ps2pdf to convert
## them.
canvases.make_pdf_from_eps()
## The module holds references to the created canvases.
## Note that they did not get killed even though we do not
## explicitly keep references to them. They still show up on the screen.
## You can access them like this:
print 'List of canvases:'
pprint.pprint(canvases.canvases)
def main():
'''
Main entry point of execution.
'''
hist = get_gaussian_histogram()
make_canvases(hist)
make_more_canvases(hist)
## Make output graphics in different formats using ROOT for the conversions
canvases.make_plots('png C root eps'.split())
## Store canvases as pdf by creating eps and then using ps2pdf to convert
## them.
canvases.make_pdf_from_eps()
## The module holds references to the created canvases.
## Note that they did not get killed even though we do not
## explicitly keep references to them. They still show up on the screen.
## You can access them like this:
print 'List of canvases:'
pprint.pprint(canvases.canvases)
for icalcat in range(8):
cat = 'calcat%d' % icalcat
print '+++ ', em, src, cat
fitter = ModeAndEffSigmaFitter(name = '_'.join([em, src, cat]),
debuglevel = debuglevel)
fitter.run()
fitters.append(fitter)
## Store RooFit objects in a rootfile
w = ROOT.RooWorkspace('w')
for f in fitters:
for item in [f.data, f.data_half_odd, f.data_half_even, f.model]:
w.Import(item)
w.Import(f.fit_data, f.name + '_fit_data')
w.Import(f.train_data, f.name + '_train_data')
w.Import(f.fit_result, f.name + '_fit_result')
w.writeToFile(output_filename)
## Store canvases in a rootfile
outfile = ROOT.TFile.Open(output_filename, 'UPDATE')
outfile.mkdir('Canvases').cd()
for c in canvases.canvases:
if c:
c.Write(c.GetName())
canvases.make_plots('eps png C'.split())
if __name__ == '__main__':
import user
for icalcat in range(8):
cat = 'calcat%d' % icalcat
print '+++ ', em, src, cat
fitter = ModeAndEffSigmaFitter(name='_'.join([em, src, cat]),
debuglevel=debuglevel)
fitter.run()
fitters.append(fitter)
## Store RooFit objects in a rootfile
w = ROOT.RooWorkspace('w')
for f in fitters:
for item in [f.data, f.data_half_odd, f.data_half_even, f.model]:
w.Import(item)
w.Import(f.fit_data, f.name + '_fit_data')
w.Import(f.train_data, f.name + '_train_data')
w.Import(f.fit_result, f.name + '_fit_result')
w.writeToFile(output_filename)
## Store canvases in a rootfile
outfile = ROOT.TFile.Open(output_filename, 'UPDATE')
outfile.mkdir('Canvases').cd()
for c in canvases.canvases:
if c:
c.Write(c.GetName())
canvases.make_plots('eps png C'.split())
if __name__ == '__main__':
import user
if ytitle:
graph.GetYaxis().SetTitle(ytitle)
if yrange:
graph.GetYaxis().SetRangeUser(*yrange)
if color:
paint(graph, color)
return graph
## End of customize(graph)
##______________________________________________________________________________
def paint(graph, color):
'''
For the given graph, sets the color of the line and marker to the given one.
'''
graph.SetLineColor(color)
graph.SetMarkerColor(color)
return graph
## End of customize(graph)
##______________________________________________________________________________
if __name__ == '__main__':
main()
#pprint.pprint(graphs)
canvases.make_plots(['png'])
canvases.make_pdf_from_eps()
'''
if name:
graph.SetName(name)
if ytitle:
graph.GetYaxis().SetTitle(ytitle)
if yrange:
graph.GetYaxis().SetRangeUser(*yrange)
if color:
paint(graph, color)
return graph
## End of customize(graph)
##______________________________________________________________________________
def paint(graph, color):
'''
For the given graph, sets the color of the line and marker to the given one.
'''
graph.SetLineColor(color)
graph.SetMarkerColor(color)
return graph
## End of customize(graph)
##______________________________________________________________________________
if __name__ == '__main__':
main()
#pprint.pprint(graphs)
canvases.make_plots(['png'])
canvases.make_pdf_from_eps()
def save_plots(self):
canvases.make_plots(['root'])
canvases.make_pdf_from_eps()
def save_plots(self):
canvases.make_plots(['root'])
canvases.make_pdf_from_eps()
