parser.add_argument('-o',
'--outpath',
action='store',
default='.',
dest='outpath',
help='Directory for storing output plots')
parser.add_argument('-t',
'--selection',
action='store',
default='events',
dest='selection',
help='Treename used for making histograms')
results = parser.parse_args()
outpath = results.outpath
files = util.file2list(results.listOfFiles)
histograms = util.file2list(results.listOfHists)
selection = results.selection
betterColors = hpt.betterColors()['linecolors']
variables = hpl.variable_labels()
sample_labels = hpl.sample_labels()
metadata = util.loadMetadata(
"config/sampleMetaData.txt") # dictionary of metadata; key=primary dataset
#numberOfHists = 0
# Access data -- assumes you are plotting histograms from multiple sources in one figure
for hi, histogram in enumerate(histograms):
histogramName = histogram[2:].replace(
"_" + selection, "") # turn "h_jet_pt_events" into "jet_pt"
vb = util.VERBOSE()
## Set configuration options ##
if len(sys.argv) < 2:
vb.HELP()
sys.exit(1)
vb.level = config['verbose_level']
vb.initialize()
## Set output directory
output_dir = "nMCMC{0}_".format(config.nMCMC)
output_dir += "nThin{0}_".format(config.nThin)
hep_data_name = config.hep_data.split('/')[-1].split('.')[0]
listOfSystematics = util.file2list(config['listOfSystematics'])
## Setup Deep Learning class
fbu = Unfolding()
fbu.nMCMC = config.nMCMC
fbu.nThin = config.nThin
fbu.monitoring = config.monitoring
fbu.output_dir = output
#fbu.stat_only = config['stat_only']
#data files
if not os.path.isdir(output_dir):
vb.INFO("RUN : '{0}' does not exist ".format(output))
vb.INFO("RUN : Creating the directory. ")
"eos root://cmseos.fnal.gov/ mkdir -p {0}".format(eos_path_full))
if os_err:
print "RUNBATCH :: INFO : Attemp to make directory {0}".format(
eos_path_full)
print "RUNBATCH :: INFO : Message = {0}".format(os_err)
else:
print "RUNBATCH :: INFO : Created directory {0}".format(eos_path_full)
# define the directory to write the output
if cfg['output_dir'] == 'eos':
batch.output_dir = 'root://cmseos.fnal.gov/' + eos_path
else:
batch.output_dir = cfg['output_dir']
## Submit
batch.execute()
## Metadata so that we know what the samples are weeks later!
metadata
git_branches = commands.getoutput("git branch").split(
) # ' cwoala-dev\n* hadtop-plotter\n master\n ttbarAC_skim_v0.5'
git_branch = git_branches[git_branches.index(
"*")] # the current branch follows the '*'
outputdir = batch.output_dir
inputfiles = util.file2list(batch.file)[0].split(
"/"
) # get the input directory from the first input file (assumes all input files are in the same directory)
inputdir = "/".join(inputfiles[:-1])
## THE END ##
name = metadata[
pd].sampleType # compare primary dataset with metadatafile
for h in histograms:
try:
h_temp = getattr(f, h)
h_temp.SetDirectory(0)
hists[h].Add(h_temp)
except KeyError:
hists[h] = getattr(f, h) # retrieve the histogram
hists[h].SetDirectory(0)
return {"hists": hists, "primaryDataset": pd, "name": name}
ttbar_files = util.file2list("config/samples_cyminiana/listOfTtbarFiles.txt")
ttbar_files += util.file2list(
"config/samples_cyminiana/listOfTtbarExtFiles.txt")
signal1_files = util.file2list(
"config/samples_cyminiana/listOfWp1500NarTp1200NarLHFiles.txt")
signal2_files = util.file2list(
"config/samples_cyminiana/listOfWp2500NarTp1200NarLHFiles.txt")
outpath = 'plots/b2g-workshop'
x_labels = hpl.variable_labels()
sample_labels = hpl.sample_labels()
selections = ['mujets', 'ejets']
histograms = util.file2list("config/listOfHists_noReco.txt")
histograms = [i.format(sel) for sel in selections for i in histograms]
default='share/listOfSytsDataMC.txt',
help='Name of file that contains detector systematics to plot')
parser.add_argument('-o',
'--outpath',
action='store',
default='plots/datamc/',
dest='outpath',
help='Directory for storing output plots')
results = parser.parse_args()
detectorSystematics = []
outpath = results.outpath
samples = hpl.sample_labels() # labels and binnings for samples
variables = hpl.variable_labels() # labels and binnings for variables
selections = ['ejets', 'mujets']
histograms = util.file2list(results.listOfHists)
histograms = [i.format(sel) for sel in selections for i in histograms]
# Load information
ttbar_files = util.file2list("config/samples_cyminiana/listOfTtbarFiles.txt")
#ttbar_files += util.file2list("config/samples_cyminiana/listOfTtbarExtFiles.txt")
#signal_files = util.file2list("config/samples_cyminiana/listOfWp1500NarTp1200NarLHFiles.txt")
#wjets
#zjets
#singletop
#diboson
ttbar_files = util.file2list(
"config/samples_cyminiana/listOfTtbarFiles.txt") + util.file2list(
"config/samples_cyminiana/listOfTtbarExtFiles.txt")
ejets_files = []
help='Name of file that contains root files to plot')
parser.add_argument('--hists',
action='store',
default=None,
dest='listOfHists',
help='Name of file that contains histograms to plot')
parser.add_argument('-o',
'--outpath',
action='store',
default=None,
dest='outpath',
help='Directory for storing output plots')
results = parser.parse_args()
outpath = results.outpath
files = util.file2list(results.listOfFiles) # ROOT files to read
histograms = util.file2list(
results.listOfEffs) # TEfficiencies/Histograms to plot
labels = hpl.variable_labels()
variable = 'jet_pt' # Setup to plot multiple efficiencies/hists as a function of one variable
betterColors = hpt.betterColors()['linecolors']
## Add the data from each file
## Assume the data is structured such that you want to plot
## multiple efficiencies from the same file in one plot
## -> change to your desired structure / plot
## e.g., to plot efficiencies from two sources (files) on 1 plot:
## switch order of file & hist loops
## To plot multiple kinds of variables on different plots,
## you'll need another loop