def plot(template, name):
plt.clf()
#ax = hist.plot2d(template, xaxis = "jet_rho", patch_opts={"vmin":0.5, "vmax":0.99})#,xoverflow='all',yoverflow='all')
ax = hist.plot2d(template,
xaxis="jet_rho",
patch_opts={
"vmin": 0.5,
"vmax": 1.
}) #,xoverflow='all',yoverflow='all')
cmstext = plt.text(0.0,
1.,
"CMS",
fontsize=12,
horizontalalignment='left',
verticalalignment='bottom',
fontweight='bold',
transform=ax.transAxes)
addtext = plt.text(0.11,
1.,
"Simulation Preliminary",
fontsize=10,
horizontalalignment='left',
verticalalignment='bottom',
style='italic',
transform=ax.transAxes)
plt.ylim(200, 1200)
plt.xlim(-5.5, -2)
plt.savefig('plots/' + name + '.pdf')
plt.savefig('plots/' + name + '.png')
def make_2d_hist(h,
xaxis,
title=None,
zscale='linear',
text_opts=None,
**kwargs):
"""
more kwargs see `coffea.hist.plot2d`
(https://github.com/CoffeaTeam/coffea/blob/master/coffea/hist/plot.py#L388)
"""
import matplotlib.pyplot as plt
from coffea import hist
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
if zscale == 'log':
from matplotlib.colors import LogNorm
hist.plot2d(h,
xaxis,
ax=ax,
patch_opts=dict(norm=LogNorm()),
text_opts=text_opts)
else:
hist.plot2d(h, xaxis, ax=ax, text_opts=text_opts)
if 'xlabel' in kwargs: ax.set_xlabel(kwargs['xlabel'])
if 'ylabel' in kwargs: ax.set_ylabel(kwargs['ylabel'])
ax.set_xlabel(ax.get_xlabel(), x=1, ha='right')
ax.set_ylabel(ax.get_ylabel(), y=1, ha="right")
if 'xlim' in kwargs: ax.set_xlim(*kwargs['xlim'])
if 'ylim' in kwargs: ax.set_ylim(*kwargs['ylim'])
ax.set_title(title, x=0.0, ha="left")
ax.text(1,
1,
'59.74/fb (13TeV)',
ha='right',
va='bottom',
transform=ax.transAxes)
return fig, ax
def responsematrix():
for btype in ["Bu", "Bs", "Bd"]:
if btype == "Bu":
dataset_bname = "Bu2KJpsi2KMuMu"
histogram_bname = "BuToKMuMu"
axis_bname = "BToKMuMu"
elif btype == "Bs":
dataset_bname = "Bs2PhiJpsi2KKMuMu"
histogram_bname = "BsToKKMuMu"
axis_bname = "BsToKKMuMu"
elif btype == "Bd":
dataset_bname = "Bd2KstarJpsi2KPiMuMu"
histogram_bname = "BdToKPiMuMu"
axis_bname = "BdToKPiMuMu"
for selection in ["tag", "probe", "inclusive"]:
if selection == "inclusive":
hist2d = histograms[btype][
f"Truth{histogram_bname}_truthpt_recopt"].integrate(
"dataset", ([f"{dataset_bname}_inclusive"])).integrate(
"selection", "inclusive")
elif selection == "tag":
hist2d = histograms[btype][
f"Truth{histogram_bname}_truthpt_recopt"].integrate(
"dataset", ([f"{dataset_bname}_inclusive"])).integrate(
"selection", "matched_tag")
elif selection == "probe":
hist2d = histograms[btype][
f"Truth{histogram_bname}_truthpt_recopt"].integrate(
"dataset",
([f"{dataset_bname}_probefilter"])).integrate(
"selection", "matched_probe")
fig, ax = plt.subplots(1, 1, figsize=(
10, 7)) #, gridspec_kw={"height_ratios": (3, 1)}, sharex=True)
hist2d = hist2d.rebin(f"reco_pt", coarse_reco_pt_axis)
hist2d = hist2d.rebin(f"truth_pt", coarse_truth_pt_axis)
hist.plot2d(hist2d, xaxis="coarse_reco_pt", ax=ax)
plt.tight_layout()
fig.savefig(
f"{figure_directory}/responsematrix_{btype}_{selection}.png")
def test_plot2d():
# histogram creation and manipulation
from coffea import hist
# matplotlib
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
lepton_kinematics = fill_lepton_kinematics()
# looking at lepton pt for all eta
muon_kinematics = lepton_kinematics.integrate("flavor", "muon")
fig, ax, primitives = hist.plot2d(muon_kinematics, "eta")
def test_plot2d():
# histogram creation and manipulation
from coffea import hist
# matplotlib
import matplotlib as mpl
import matplotlib.pyplot as plt
plt.switch_backend('agg')
lepton_kinematics = fill_lepton_kinematics()
# looking at lepton pt for all eta
muon_kinematics = lepton_kinematics.integrate("flavor", "muon")
ax = hist.plot2d(muon_kinematics, "eta")
return ax.figure
def plot_iso_2d(output, isotype, njet='both', issignal=False):
"""
make 2D plot with each leptonjet isolation value on XY axis. 2mu2e left/4mu right.
:param hist.Hist output: histogram
:param str isotype: all05/nopu05/dbeta
:param str njet: both/sr/cr
:param bool issignal: `output` from signal?
:return: (fig, axes) / dictionary of (fig, ax)
:rtype: tuple / dict
"""
if issignal:
res = {}
for d in output['ljpfiso'].identifiers('dataset'):
res[str(d)] = plt.subplots(1, 1, figsize=(7, 5))
fig, ax = res[str(d)]
histo = output['ljpfiso'].integrate('dataset', d).integrate(
'isotype', isotype)
chan_ = str(d).split('/')[0]
if njet == 'both':
hist.plot2d(
histo.integrate('channel', chan_).sum('njet'),
xaxis='lj0iso',
ax=ax,
)
elif njet == "sr":
hist.plot2d(
histo.integrate('channel',
chan_).integrate('njet', slice(0, 4)),
xaxis='lj0iso',
ax=ax,
)
elif njet == 'cr':
hist.plot2d(
histo.integrate('channel',
chan_).integrate('njet', slice(4, 10)),
xaxis='lj0iso',
ax=ax,
)
ax.set_title(f'[{chan_}] leptonjet isolation ({isotype})',
x=0,
ha="left")
ax.text(1,
1,
'59.74/fb (13TeV)',
ha='right',
va='bottom',
transform=ax.transAxes)
if chan_ == '2mu2e':
ax.set_xlabel('EGM-type lj isolation value', x=1, ha='right')
ax.set_ylabel('Mu-type lj isolation value', y=1, ha='right')
if chan_ == '4mu':
ax.set_xlabel('Mu-type lj0 isolation value', x=1, ha='right')
ax.set_ylabel('Mu-type lj1 isolation value', y=1, ha='right')
return res
else:
fig, axes = plt.subplots(1, 2, figsize=(14, 5))
histo = output['ljpfiso_cat'].sum('cat').integrate('isotype', isotype)
if njet == "both":
hist.plot2d(histo.integrate('channel', '2mu2e').sum('njet'),
xaxis='lj0iso',
ax=axes[0],
patch_opts=dict(norm=LogNorm()))
hist.plot2d(histo.integrate('channel', '4mu').sum('njet'),
xaxis='lj0iso',
ax=axes[1],
patch_opts=dict(norm=LogNorm()))
elif njet == "sr":
hist.plot2d(histo.integrate('channel', '2mu2e').integrate(
'njet', slice(0, 4)),
xaxis='lj0iso',
ax=axes[0],
patch_opts=dict(norm=LogNorm()))
hist.plot2d(histo.integrate('channel',
'4mu').integrate('njet', slice(0, 4)),
xaxis='lj0iso',
ax=axes[1],
patch_opts=dict(norm=LogNorm()))
elif njet == 'cr':
hist.plot2d(histo.integrate('channel',
'2mu2e').integrate('njet',
slice(4, 10),
overflow='over'),
xaxis='lj0iso',
ax=axes[0],
patch_opts=dict(norm=LogNorm()))
hist.plot2d(histo.integrate('channel',
'4mu').integrate('njet',
slice(4, 10),
overflow='over'),
xaxis='lj0iso',
ax=axes[1],
patch_opts=dict(norm=LogNorm()))
axes[0].set_title(f'[2mu2e] leptonJet isolation ({isotype})',
x=0.0,
ha="left")
axes[0].set_xlabel('EGM-type lj isolation value')
axes[0].set_ylabel('Mu-type lj isolation value')
axes[1].set_title(f'[4mu] leptonJet isolation ({isotype})',
x=0.0,
ha="left")
axes[1].set_xlabel('Mu-type lj0 isolation value')
axes[1].set_ylabel('Mu-type lj1 isolation value')
for ax in axes:
ax.text(1,
1,
'59.74/fb (13TeV)',
ha='right',
va='bottom',
transform=ax.transAxes)
ax.set_xlabel(ax.get_xlabel(), x=1.0, ha="right")
ax.set_ylabel(ax.get_ylabel(), y=1.0, ha="right")
return fig, axes
'node0_score',
'score',
data=[],
bins=score_bins,
log=False,
normalize=False,
axis_label=r'score',
shape=True,
ymax=0.35,
new_colors=my_colors,
new_labels=my_labels,
order=['TTW'],
signals=['topW_v3', 'topW_EFT_cp8', 'topW_EFT_mix'],
omit=[
'DoubleMuon', 'MuonEG', 'EGamma', 'diboson', 'ttbar', 'TTH', 'TTZ'
],
save=os.path.expandvars(plot_dir + '/SS/ML_node0_score_shape'),
)
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
ax = hist.plot2d(
output['chargeFlip_vs_nonprompt']['ttbar'].sum('n_ele').sum('dataset'),
xaxis='n1',
ax=ax,
text_opts={'format': '%.3g'},
patch_opts={},
)
ax.set_xlabel(r'$N_{charge flips}$')
ax.set_ylabel(r'$N_{nonprompt}$')
fig.savefig(plot_dir + '/SS/nChargeFlip_vs_nNonprompt_ttbar')
ymin = 1.
ymax = -1.
for dataset, values_arr in hy[(sels), :].values().items():
if np.max(values_arr) > ymax:
ymax = np.max(values_arr)
ax_y.set_ylim(1., ymax * 10.)
plt.tight_layout()
fig_y.savefig(
f"{figure_directory}/kinematic/data/{channel}_{side}_y_SR.png")
plt.close(fig_y)
# 2D plots
fig_pt_mass, ax_pt_mass = plt.subplots()
h_pt_mass = hists[f"{channel}_fit_pt_y_mass"].integrate(
"fit_y", y_slice).integrate("selection", (f"{side}_HLT_Mu9_IP6"))
hist.plot2d(h_pt_mass, xaxis="fit_mass")
fig_pt_mass.savefig(
f"{figure_directory}/kinematic/data/{channel}_{side}_pt_mass_SR.png"
)
plt.close(fig_pt_mass)
fig_pt_y, ax_pt_y = plt.subplots()
h_pt_y = hists[f"{channel}_fit_pt_y_mass"].integrate(
"fit_mass").integrate("selection", (f"{side}_HLT_Mu9_IP6"))
hist.plot2d(h_pt_y, xaxis="fit_y")
fig_pt_y.savefig(
f"{figure_directory}/kinematic/data/{channel}_{side}_pt_y_SR.png")
plt.close(fig_pt_y)
# pT subslices
for pt_subslice in [
