def main():
log.basicConfig(level=log.INFO) # Set logging level
PDF.set_default_mpl_format()
input_dir = "/home/jakob/DESY/MountPoints/DUST/TGCAnalysis/SampleProduction/NewMCProduction/4f_WW_sl/PrEWInput"
output_dir = input_dir + "/shape_checks/WWShapePlots"
log.info("Looking in dir: {}".format(input_dir))
for file_path in tqdm(IOSH.find_files(input_dir, ".csv"), desc="files"):
if "tau" in file_path:
continue # Skipping tau distributions, not use right now
plot_WW_distr(file_path, output_dir)
def main():
log.basicConfig(level=log.INFO) # Set logging level
PDF.set_default_mpl_format()
input_dir = "/home/jakob/DESY/MountPoints/DUST/TGCAnalysis/SampleProduction"+\
"/NewMCProduction/2f_Z_l/PrEWInput/MuAcc_costheta_0.9925"
output_dir = input_dir + "/shape_checks/2fShapePlots"
log.info("Looking in dir: {}".format(input_dir))
for file_path in tqdm(IOSH.find_files(input_dir, ".csv"), desc="files"):
if not "2f_mu" in file_path:
continue # Only draw mumu distributions
plot_mumu_distr(file_path, output_dir)
import Plotting.Naming as PN
import Shape.ShapeFunctions as SSF
import Shape.ShapeTesting as SST
log.basicConfig(level=log.INFO) # Set logging level
PDF.set_default_mpl_format()
MCLumi = 5000 # MC Statistics is 5ab^-1
input_dir = "/home/jakob/DESY/MountPoints/DUST/TGCAnalysis/SampleProduction/NewMCProduction/2f_Z_l/PrEWInput/MuAcc_costheta_0.9925"
# input_dir = "/home/jakob/DESY/MountPoints/DUST/TGCAnalysis/SampleProduction/NewMCProduction/2f_Z_l/PrEWInput/MuAcc_costheta_0.9925/TrueAngle"
output_dir = input_dir + "/shape_checks"
IOSH.create_dir(output_dir)
log.info("Looking in dir: {}".format(input_dir))
for file_path in IOSH.find_files(input_dir, ".csv"):
# Read the input file
base_name = os.path.basename(file_path).replace(".csv","")
log.info("Reading file: {}.csv".format(base_name))
reader = IOR.Reader(file_path)
# Get the pandas dataframe for the cut histograms
angle = "costh_f_star_true" if "TrueAngle" in input_dir else "costh_f_star"
df = reader["Data"]
bin_vals = np.array(df["Cross sections"])
bin_middles = np.array(df["BinCenters:{}".format(angle)])
edges_min = np.array(df["BinLow:{}".format(angle)])
edges_max = np.array(df["BinUp:{}".format(angle)])
bin_width = edges_max[0] - edges_min[0]
# Rescale to MC Lumi