def plot_WW_distr(infile, outdir, out_formats=["pdf", "png"]):
""" Plot the distribution that is stored in the given file.
"""
base_name = os.path.basename(infile).replace(".csv", "")
log.debug("Reading file: {}.csv".format(base_name))
reader = IOR.Reader(infile)
# Get the pandas dataframe for the cut histograms
df = reader["Data"]
# Get the data
angles = ("costh_Wminus_star", "costh_l_star", "phi_l_star")
x = np.array([df["BinCenters:{}".format(angle)] for angle in angles])
y = np.array(df["Cross sections"])
xmin = np.array(
[np.amin(df["BinLow:{}".format(angle)]) for angle in angles])
xmax = np.array(
[np.amax(df["BinUp:{}".format(angle)]) for angle in angles])
# Bin counting assumes that bins are not binned thinner than 1/1000 and that
# absolute values are of order 0.1-1
nbins = np.array(
[len(np.unique((x[d] * 10000.).astype(int))) for d in range(len(x))])
create_2D_projection_plot(x, y, xmin, xmax, nbins, angles, base_name,
outdir, out_formats)
for i in range(len(angles)):
create_1D_projection_plot(i, x, y, xmin, xmax, nbins, angles,
base_name, outdir, out_formats)
def plot_mumu_distr(infile, outdir, out_formats=["pdf", "png"]):
""" Plot the distribution that is stored in the given file.
"""
base_name = os.path.basename(infile).replace(".csv", "")
log.debug("Reading file: {}.csv".format(base_name))
reader = IOR.Reader(infile)
# Get the pandas dataframe for the cut histograms
df = reader["Data"]
# Get the data
x_name = "costh_f_star"
x = np.array(df["BinCenters:{}".format(x_name)])
y = np.array(df["Cross sections"])
xmin = np.array(np.amin(df["BinLow:{}".format(x_name)]))
xmax = np.array(np.amax(df["BinUp:{}".format(x_name)]))
# Bin counting assumes that bins are not binned thinner than 1/1000 and that
# absolute values are of order 0.1-1
nbins = len(np.unique((x * 10000.).astype(int)))
# Create the figure and plot
fig = plt.figure(figsize=(6.5, 5), tight_layout=True)
ax = plt.gca()
ax.hist(x=x,
weights=y,
bins=nbins,
range=(xmin, xmax),
ls="-",
lw=3,
histtype=u'step')
# Useful limits
ax.set_xlim(xmin, xmax)
# ax.set_ylim(0, 1.1*ax.get_ylim()[1])
# Useful labels
ax.set_xlabel("${}$".format(PN.observable_str(x_name, "mumu")))
ax.set_ylabel("$d\\sigma [$fb$]$")
# Mark which process it is
chirality = IOFH.find_chirality(base_name)
Z_direction = IOFH.find_Z_direction(base_name)
mass_label = IOFH.find_2f_mass_label(base_name)
process_str = "${}$".format(
PN.difermion_process_str("mu", chirality, mass_label, Z_direction))
ax.set_title(process_str)
# Save the plot in files
for out_format in out_formats:
format_dir = "{}/{}".format(outdir, out_format)
IOSH.create_dir(format_dir)
fig.savefig("{}/{}_{}.{}".format(format_dir, base_name, x_name,
out_format),
transparent=True,
bbox_inches='tight')
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
bin_vals *= MCLumi
# Perform fits to the distributions
fit_vals_ha, p_ha, cov_ha = SST.fit_1D(SSF.helicity_amplitudes, bin_vals, edges_min, edges_max, bounds=[[0,0],[np.inf,np.inf]])
output_dir = input_dir_nocor + "/ISR_shape_checks"
IOSH.create_dir(output_dir)
file_pairs = [
["2f_mu_81to101_BZ_250_eLpR.csv", "2f_mu_81to101_BZ_true_250_eLpR.csv"],
["2f_mu_81to101_BZ_250_eRpL.csv", "2f_mu_81to101_BZ_true_250_eRpL.csv"],
["2f_mu_81to101_FZ_250_eLpR.csv", "2f_mu_81to101_FZ_true_250_eLpR.csv"],
["2f_mu_81to101_FZ_250_eRpL.csv", "2f_mu_81to101_FZ_true_250_eRpL.csv"],
]
for file_pair in file_pairs:
# Read the input file
base_name = file_pair[0].replace(".csv", "")
log.info("Processing: {}".format(base_name))
reader_nocor = IOR.Reader(input_dir_nocor + "/" + file_pair[0])
reader_sicor = IOR.Reader(input_dir_sicor + "/" + file_pair[1])
# Get the pandas dataframe for the cut histograms
df_nocor = reader_nocor["Data"]
df_sicor = reader_sicor["Data"]
bin_vals_nocor = MCLumi * np.array(df_nocor["Cross sections"])
bin_vals_sicor = MCLumi * np.array(df_sicor["Cross sections"])
angle = "costh_f_star"
bin_middles = np.array(df_nocor["BinCenters:{}".format(angle)])
edges_min = np.array(df_nocor["BinLow:{}".format(angle)])
edges_max = np.array(df_nocor["BinUp:{}".format(angle)])
bin_width = edges_max[0] - edges_min[0]
# Perform fits to the distributions