def test_hist2dplot():
np.random.seed(0)
xedges = np.arange(0, 11.5, 1.5)
yedges = [0, 2, 3, 4, 6, 7]
x = np.random.normal(5, 1.5, 100)
y = np.random.normal(4, 1, 100)
H, xedges, yedges = np.histogram2d(x, y, bins=(xedges, yedges))
fig, ax = plt.subplots()
hep.hist2dplot(H, xedges, yedges, labels=True)
return fig
def test_hist2dplot_cbar_subplots():
np.random.seed(0)
xedges = np.arange(0, 11.5, 1.5)
yedges = [0, 2, 3, 4, 6, 7]
x = np.random.normal(5, 1.5, 100)
y = np.random.normal(4, 1, 100)
H, xedges, yedges = np.histogram2d(x, y, bins=(xedges, yedges))
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))
hep.hist2dplot(H, xedges, yedges, labels=True, cbar=True, ax=ax1)
hep.hist2dplot(H * 2, xedges, yedges, labels=True, cbar=True, ax=ax2)
return fig
def test_inputs_uproot():
import uproot4
from skhep_testdata import data_path
fname = data_path("uproot-hepdata-example.root")
f = uproot4.open(fname)
fig, axs = plt.subplots(1, 2, figsize=(14, 5))
TH1, TH2 = f["hpx"], f["hpxpy"]
hep.histplot(TH1, ax=axs[0])
hep.hist2dplot(TH2, ax=axs[1], cbar=False)
return fig
def test_inputs_bh():
import boost_histogram as bh
hist2d = bh.Histogram(bh.axis.Regular(10, 0.0, 1.0),
bh.axis.Regular(10, 0, 1))
hist2d.fill(np.random.normal(0.5, 0.2, 1000),
np.random.normal(0.5, 0.2, 1000))
fig, axs = plt.subplots(1, 2, figsize=(14, 5))
hep.histplot(hist2d.project(0), ax=axs[0])
hep.hist2dplot(hist2d, labels=True, cbar=False, ax=axs[1])
return fig
def create_plot2d(hist, save_name):
import matplotlib.pyplot as plt
import mplhep as hep
plt.style.use(hep.style.CMS)
# plot
ax = plt.gca()
hep.hist2dplot(hist, ax=ax)
ax.set_xlabel(hist.axes[0].metadata, loc='right')
ax.set_ylabel(hist.axes[1].metadata, loc='top')
fig = ax.get_figure()
fig.savefig(save_name)
ax.clear()
fig.clear()
def plotMatrix(biasMatrix, funcs, funcDegs, outdir):
hep.set_style(hep.style.CMS)
fig, ax = plt.subplots()
ax.set_xticklabels(funcs)
ax.set_yticklabels(funcs)
hep.hist2dplot(biasMatrix, labels=True, vmin=-1, vmax=1, cmap='RdBu_r')
ax.set_xlabel('gen function', ha='right', x=1)
ax.set_ylabel('fit function', ha='right', y=1)
#ax.set_title('MC')
if not os.path.isdir(outdir): os.makedirs(outdir)
for ext in ['png', 'pdf']:
fig.savefig(
os.path.join(
outdir, f'{args.dataOrMC}_' +
'_'.join([f'{f}{d}' for f, d in funcDegs.items()]) +
f'_r{args.rInjected}_{args.signal}.{ext}'))
def test_hist2dplot_custom_labels():
np.random.seed(0)
xedges = np.arange(0, 11.5, 1.5)
yedges = [0, 2, 3, 4, 6, 7]
x = np.random.normal(5, 1.5, 100)
y = np.random.normal(4, 1, 100)
H, xedges, yedges = np.histogram2d(x, y, bins=(xedges, yedges))
fig, ax = plt.subplots()
@np.vectorize
def _fmt(x):
return f"${x:.2f}$"
hep.hist2dplot(H, xedges, yedges, labels=_fmt(H).T)
return fig
def make2Dplot(numx, numy, denx, deny, tagger="", outname="", masks=[]):
numx = numx.flatten()
numy = numy.flatten()
denx = denx.flatten()
deny = deny.flatten()
if masks != []:
numx[masks[0].flatten()] = -1
numy[masks[0].flatten()] = -1
denx[masks[1].flatten()] = -1
deny[masks[1].flatten()] = -1
histnum, binx, biny = np.histogram2d(numx,
numy,
range=[(0, 1), (0, 1)],
bins=(50, 10))
histden, binx, biny = np.histogram2d(denx,
deny,
range=[(0, 1), (0, 1)],
bins=(50, 10))
numm1 = numx[numx < 0].size
denm1 = denx[denx < 0].size
if batch:
pickle.dump(
[histnum, numx.size, histden, denx.size, numm1, denm1],
open(
"%s/%s_%s.pkl" %
(outdir, outname, fname.split('/')[-1].rstrip(".root")), "wb"))
binfl = "%s/bin.pkl" % outdir
if not os.path.isfile(binfl):
pickle.dump([binx, biny], open(binfl, "wb"))
else:
fig, ax = plt.subplots()
histnum = histnum / numx.size
histden = histden / denx.size
ratio = np.where(histden > 0, histnum / histden, 1.)
hep.hist2dplot(ratio, binx, biny, vmin=0.5, vmax=1.5)
ax.set_ylabel("%s CvsB" % tagger)
ax.set_xlabel("%s CvsL" % tagger)
hep.cms.label(ax=ax)
# hep.r_align()
plt.savefig(outname + ".png")
def test_uproot_versions(fig_test, fig_ref):
import uproot
import uproot4
from skhep_testdata import data_path
fname = data_path("uproot-hepdata-example.root")
f4 = uproot4.open(fname)
f3 = uproot.open(fname)
fig_test.set_size_inches(14, 5)
fig_ref.set_size_inches(14, 5)
test_axs = fig_test.subplots(1, 2)
TH1u4, TH2u4 = f4["hpx"], f4["hpxpy"]
hep.histplot(TH1u4, ax=test_axs[0])
hep.hist2dplot(TH2u4, ax=test_axs[1], cbar=False)
ref_axs = fig_ref.subplots(1, 2)
TH1u3, TH2u3 = f3["hpx"], f3["hpxpy"]
hep.histplot(TH1u3, ax=ref_axs[0])
hep.hist2dplot(TH2u3, ax=ref_axs[1], cbar=False)
def plotTF_ratio(in_ratio, mask, region):
fig, ax = plt.subplots()
H = np.ma.masked_where(in_ratio * mask <= 0.01, in_ratio * mask)
zmin, zmax = np.floor(10 * np.min(TFres)) / 10, np.ceil(
10 * np.max(TFres)) / 10
zmin, zmax = zmin + 0.001, zmax - 0.001
clim = np.max([.3, np.min([abs(zmin - 1), abs(zmax - 1)])])
ptbins = np.array([450, 500, 550, 600, 675, 800, 1200])
msdbins = np.linspace(40, 201, 24)
hep.hist2dplot(H.T,
msdbins,
ptbins,
vmin=1 - clim,
vmax=1 + clim,
cmap='RdBu_r',
cbar=False)
cax = hep.make_square_add_cbar(ax, pad=0.2, size=0.5)
if abs(1 - zmin) > .3 and abs(1 - zmax) > .3:
c_extend = 'both'
elif abs(1 - zmin) > .3:
c_extend = 'min'
elif abs(1 - zmax) > .3:
c_extend = 'max'
else:
c_extend = 'neither'
cbar = fig.colorbar(ax.get_children()[0], cax=cax, extend=c_extend)
ax.set_xticks(np.arange(40, 220, 20))
ax.tick_params(axis='y', which='minor', left=False, right=False)
ax.invert_yaxis()
ax.set_title('{} QCD Ratio'.format(region), pad=15, fontsize=26)
ax.set_xlabel(r'Jet $\mathrm{m_{SD}}$', ha='right', x=1)
ax.set_ylabel(r'Jet $\mathrm{p_{T}}$', ha='right', y=1)
cbar.set_label(r'(Pass QCD) / (Fail QCD * eff)', ha='right', y=1)
fig.savefig('{}/{}{}.png'.format(args.output_folder, "TF_ratio_", region),
bbox_inches="tight")
def plotTF_ratio(in_ratio, mask, region, args=None, zrange=None):
fig, ax = plt.subplots()
H = np.ma.masked_where(in_ratio * mask <= 0.01, in_ratio * mask)
zmin, zmax = np.nanmin(H), np.nanmax(H)
if zrange is None:
# Scale clim to fit range up to a max of 0.6
clim = np.max([.3, np.min([0.6, 1 - zmin, zmax - 1])])
else:
clim = zrange
ptbins = np.array([450, 500, 550, 600, 675, 800, 1200])
msdbins = np.linspace(40, 201, 24)
hep.hist2dplot(H.T,
msdbins,
ptbins,
vmin=1 - clim,
vmax=1 + clim,
cmap='RdBu_r',
cbar=False)
cax = hep.make_square_add_cbar(ax, pad=0.2, size=0.5)
if abs(1 - zmin) > .3 and abs(1 - zmax) > .3:
c_extend = 'both'
elif abs(1 - zmin) > .3:
c_extend = 'min'
elif abs(1 - zmax) > .3:
c_extend = 'max'
else:
c_extend = 'neither'
cbar = fig.colorbar(ax.get_children()[0], cax=cax, extend=c_extend)
ax.set_xticks(np.arange(40, 220, 20))
ax.tick_params(axis='y', which='minor', left=False, right=False)
ax.invert_yaxis()
ax.set_title('{} QCD Ratio'.format(region), pad=15, fontsize=26)
ax.set_xlabel(r'Jet $\mathrm{m_{SD}}$', ha='right', x=1)
ax.set_ylabel(r'Jet $\mathrm{p_{T}}$', ha='right', y=1)
cbar.set_label(r'(Pass QCD) / (Fail QCD * eff)', ha='right', y=1)
return ax
def test_binned_from_unbinned_2D():
zfit.run.set_graph_mode(True)
n = 100000
mu = zfit.Parameter("mu", 1, 0, 19)
sigma = zfit.Parameter("sigma", 6, 0, 120)
obsx = zfit.Space("x", (-5, 10))
obsy = zfit.Space("y", (-50, 100))
gaussx = zfit.pdf.Gauss(mu=mu, sigma=sigma, obs=obsx)
muy = mu + 3
sigmay = sigma * 20
gaussy = zfit.pdf.Gauss(mu=muy, sigma=sigmay, obs=obsy)
gauss2D = zfit.pdf.ProductPDF([gaussx, gaussy])
axisx = zfit.binned.VariableBinning(
np.concatenate([np.linspace(-5, 5, 43), np.linspace(5, 10, 30)[1:]], axis=0),
name="x",
)
axisxhist = hist.axis.Variable(
np.concatenate([np.linspace(-5, 5, 43), np.linspace(5, 10, 30)[1:]], axis=0),
name="x",
)
axisy = zfit.binned.RegularBinning(15, -50, 100, name="y")
axisyhist = hist.axis.Regular(15, -50, 100, name="y")
obs_binnedx = zfit.Space(["x"], binning=axisx)
obs_binnedy = zfit.Space("y", binning=axisy)
obs_binned = obs_binnedx * obs_binnedy
gauss_binned = BinnedFromUnbinnedPDF(pdf=gauss2D, space=obs_binned, extended=n)
values = gauss_binned.rel_counts(obs_binned) # TODO: good test?
start = time.time()
ntrial = 10
for _ in range(ntrial):
values = gauss_binned.rel_counts(obs_binned)
print(f"Time taken {(time.time() - start) / ntrial}")
hist2d = hist.Hist(axisxhist, axisyhist)
nruns = 5
npoints = 5_000_000
for _ in range(nruns):
normal2d = np.random.normal(
[float(mu), float(muy)], [float(sigma), float(sigmay)], size=(npoints, 2)
)
hist2d.fill(*normal2d.T, threads=4)
diff = np.abs(values * hist2d.sum() - hist2d.counts()) - 6.5 * np.sqrt(
hist2d.counts()
) # 5 sigma for 1000 bins
print(diff)
np.testing.assert_array_less(diff, 0)
sample = gauss_binned.sample(n, limits=obs_binned)
hist_sampled = sample.to_hist()
hist_pdf = gauss_binned.to_hist()
max_error = hist_sampled.values() * 6**2 # 6 sigma away
np.testing.assert_array_less(
(hist_sampled.values() - hist_pdf.values()) ** 2, max_error
)
plt.figure()
plt.title("Gauss 2D binned sampled.")
mplhep.hist2dplot(hist_sampled)
pytest.zfit_savefig()
plt.figure()
plt.title("Gauss 2D binned plot, irregular (x<4.5 larger bins than x>4.5) binning.")
mplhep.hist2dplot(hist_pdf)
pytest.zfit_savefig()
htot = f_gen['totxsecs'][:]
h = f_gen['xsecs'][:]
htot_PDF = f_gen['totxsecs_LHEPdfWeight'][:]
h_PDF = f_gen['xsecs_LHEPdfWeight'][:]
yBins = f_gen['edges_totxsecs_0'][:]
qtBins = f_gen['edges_totxsecs_1'][:]
factors = np.array([[20. / 3., 1. / 10], [5., 0.], [20., 0.], [4., 0.],
[4., 0.], [5., 0.], [5., 0.], [4., 0.], [1., 0.]])
factors = factors[np.newaxis, np.newaxis, ...]
h = (h / htot[..., np.newaxis] + factors[..., 1]) * factors[..., 0]
fig, ax1 = plt.subplots()
hep.hist2dplot(h[..., 4], yBins, qtBins)
plt.tight_layout()
plt.savefig("testprefit/A0_{}.png".format(era))
plt.clf()
fig, ax1 = plt.subplots()
hep.hist2dplot(htot, yBins, qtBins)
plt.tight_layout()
plt.savefig("testprefit/htot_{}.png".format(era))
plt.clf()
factors = factors[..., np.newaxis]
h_PDF = h_PDF.reshape(len(yBins) - 1, len(qtBins) - 1, 9, 103)
h_PDF = (h_PDF / htot_PDF[:, :, np.newaxis, :] +
factors[:, :, :, 1, ...]) * factors[:, :, :, 0, ...]
def plot_phi_reso(big_df,
pid,
v0,
msk_true,
msk_pred,
msk_both,
bins,
target='target',
outpath='./'):
plt.figure(figsize=(4, 4))
ax = plt.axes()
hist = np.histogram2d(v0[msk_both, 0],
v0[msk_both, 1],
bins=(bins["phi_val"], bins["phi_val"]))
mplhep.hist2dplot(hist[0], hist[1], hist[2], cmap="Blues", cbar=False)
plt.xlabel(bins["true_val"] + " " + bins["phi_xlabel"])
plt.ylabel(bins["pred_val"] + " " + bins["phi_xlabel"])
cms_label()
sample_label(ax)
particle_label(ax, pid)
plt.plot([bins["phi_val"][0], bins["phi_val"][-1]],
[bins["phi_val"][0], bins["phi_val"][-1]],
color="black",
ls="--",
lw=0.5)
plt.savefig(osp.join(outpath, "phi_2d_pid{}.pdf".format(pid)),
bbox_inches="tight")
plt.figure(figsize=(4, 4))
ax = plt.axes()
plt.hist(v0[msk_true, 0],
bins=bins["phi_val"],
density=1.0,
histtype="step",
lw=2,
label=bins["true_val"])
plt.hist(v0[msk_pred, 1],
bins=bins["phi_val"],
density=1.0,
histtype="step",
lw=2,
label=bins["pred_val"])
plt.xlabel(bins["phi_xlabel"])
plt.ylabel("number of particles\n(normalized, a.u.)")
plt.legend(frameon=False)
cms_label()
sample_label(ax)
particle_label(ax, pid)
plt.savefig(osp.join(outpath, "phi_hist_pid{}.pdf".format(pid)),
bbox_inches="tight")
ax.set_ylim(ax.get_ylim()[0], 1.5 * ax.get_ylim()[1])
res = (v0[msk_both, 1] - v0[msk_both, 0])
res[np.isnan(res)] = -1
plt.figure(figsize=(4, 4))
ax = plt.axes()
ax.text(0.98,
0.98,
"avg. $\Delta \phi$\n$%.2f \pm %.2f$" %
(np.mean(res), np.std(res)),
transform=ax.transAxes,
ha="right",
va="top")
plt.hist(res, bins=bins["phi_res"], density=1.0)
plt.xlabel("$\Delta \phi$")
plt.ylabel("number of particles\n(normalized, a.u.)")
cms_label()
sample_label(ax)
particle_label(ax, pid)
plt.savefig(osp.join(outpath, "phi_ratio_pid{}.pdf".format(pid)),
bbox_inches="tight")
#efficiency vs fake rate
plt.figure(figsize=(4, 4))
ax = plt.axes()
big_df["bins_{}_phi".format(target)] = np.searchsorted(
bins["phi_val"], big_df["{}_phi".format(target)])
big_df["bins_pred_phi"] = np.searchsorted(bins["phi_val"],
big_df["pred_phi"])
vals_eff = big_df[(big_df["{}_pid".format(target)] == pid)].groupby(
"bins_{}_phi".format(target))["pred_pid"].apply(get_eff, pid)
vals_fake = big_df[(big_df["pred_pid"] == pid)].groupby("bins_pred_phi")[
"{}_pid".format(target)].apply(get_fake, pid)
out_eff = np.zeros((len(bins["phi_val"]), 2))
out_fake = np.zeros((len(bins["phi_val"]), 2))
for ib in range(len(bins["phi_val"])):
if ib in vals_eff.keys():
out_eff[ib, 0] = vals_eff[ib][0]
out_eff[ib, 1] = vals_eff[ib][1]
if ib in vals_fake.keys():
out_fake[ib, 0] = vals_fake[ib][0]
out_fake[ib, 1] = vals_fake[ib][1]
cms_label()
sample_label(ax)
particle_label(ax, pid)
plt.errorbar(bins["phi_val"],
out_eff[:, 0],
out_eff[:, 1],
marker=".",
lw=0,
elinewidth=1.0,
color="green",
label="efficiency")
plt.ylabel("efficiency\nN(pred|true) / N(true)")
ax.set_ylim(0, 1.5)
plt.xlabel(bins["phi_xlabel"])
ax2 = ax.twinx()
col = "red"
plt.errorbar(bins["phi_val"],
out_fake[:, 0],
out_fake[:, 1],
marker=".",
lw=0,
elinewidth=1.0,
color=col,
label="fake rate")
plt.ylabel("fake rate\nN(true|pred) / N(pred)")
plt.xlabel(bins["phi_xlabel"])
ax2.set_ylim(0, 1.5)
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines + lines2, labels + labels2, loc=0, frameon=False)
plt.savefig(osp.join(outpath, "phi_eff_fake_pid{}.pdf".format(pid)),
bbox_inches="tight")
htot = f_gen['totxsecs'][:]
h = f_gen['xsecs'][:]
htot_PDF = f_gen['totxsecs_LHEPdfWeight'][:]
h_PDF = f_gen['xsecs_LHEPdfWeight'][:]
yBins = f_gen['edges_totxsecs_0'][:]
qtBins = f_gen['edges_totxsecs_1'][:]
factors = np.array([[20. / 3., 1. / 10], [5., 0.], [20., 0.], [4., 0.],
[4., 0.], [5., 0.], [5., 0.], [4., 0.], [1., 0.]])
factors = factors[np.newaxis, np.newaxis, ...]
h = (h / htot[..., np.newaxis] + factors[..., 1]) * factors[..., 0]
fig, ax1 = plt.subplots()
hep.hist2dplot(h[..., 4], yBins, qtBins)
plt.tight_layout()
plt.savefig("testprefitWminus/A0_{}.png".format(era))
plt.clf()
fig, ax1 = plt.subplots()
hep.hist2dplot(htot, yBins, qtBins)
plt.tight_layout()
plt.savefig("testprefitWminus/htot_{}.png".format(era))
plt.clf()
factors = factors[..., np.newaxis]
h_PDF = h_PDF.reshape(len(yBins) - 1, len(qtBins) - 1, 9, 103)
h_PDF = (h_PDF / htot_PDF[:, :, np.newaxis, :] +
factors[:, :, :, 1, ...]) * factors[:, :, :, 0, ...]
import h5py
import numpy as np
import matplotlib.pyplot as plt
import mplhep as hep
import ROOT
from root_numpy import array2hist
from array import array
plt.style.use([hep.style.ROOT, hep.style.firamath])
#hep.cms.label(loc=0)
hep.cms.text('Simulation')
qtBins = ROOT.vector('float')(
[0., 4., 8., 12., 16., 20., 24., 28., 32., 40., 60., 100., 200.])
yBins = ROOT.vector('float')([0, 0.4, 0.8, 1.2, 1.6, 2.0, 2.4, 3.0, 6.0])
ptBins = ROOT.vector('float')([25. + i * 0.5 for i in range(61)])
etaBins = ROOT.vector('float')([-2.4 + i * 0.1 for i in range(49)])
fewk = h5py.File('PLOTS/SignalTemplates.hdf5', mode='r+')
helxsecs = ["L", "I", "T", "A", "P", "UL"]
for hel in helxsecs:
h = np.array(fewk['xsecs_{}'.format(hel)][:].reshape(
(len(etaBins) - 1, len(ptBins) - 1, len(yBins) - 1, len(qtBins) - 1),
order='F'),
order='C')
hep.hist2dplot(np.sum(h, axis=(0, 1))[:, :], yBins, qtBins)
plt.savefig('PLOTS/xsecs_{}.png'.format(hel))
plt.clf()
#plt.show()
def run(self, d):
file_in = '/scratchnvme/emanca/wproperties-analysis/config/valeriosbins_{}/WPlusJetsToMuNu_costheta.hdf5'.format(
self.era)
file_in_0J = '/scratchnvme/emanca/wproperties-analysis/config/alternatesample_{}/WJetsToLNu_0J_helweights.hdf5'.format(
self.era)
file_in_1J = '/scratchnvme/emanca/wproperties-analysis/config/alternatesample_{}/WJetsToLNu_1J_helweights.hdf5'.format(
self.era)
file_in_2J = '/scratchnvme/emanca/wproperties-analysis/config/alternatesample_{}/WJetsToLNu_2J_helweights.hdf5'.format(
self.era)
f = h5py.File(file_in, mode='r+')
f0J = h5py.File(file_in_0J, mode='r+')
f1J = h5py.File(file_in_1J, mode='r+')
f2J = h5py.File(file_in_2J, mode='r+')
costheta_powheg = f['costheta'][:]
costheta_aMC = f0J['costheta'][:] + f1J['costheta'][:] + f2J[
'costheta'][:]
cosThetaBins = np.array(
[round(-1. + 2. * i / 100, 2) for i in range(101)])
yBins = np.array([0., 0.4, 0.8, 1.2, 1.6, 2.0, 2.4, 3.0, 10.0])
fig, ax1 = plt.subplots()
ax1.set_title("costheta powheg", fontsize=18)
hep.hist2dplot(costheta_powheg, yBins, cosThetaBins)
plt.tight_layout()
plt.savefig("costheta_powheg_{}".format(self.era))
plt.clf()
fig, ax1 = plt.subplots()
ax1.set_title("costheta aMC@NLO", fontsize=18)
hep.hist2dplot(costheta_aMC, yBins, cosThetaBins)
plt.tight_layout()
plt.savefig("costheta_aMC{}".format(self.era))
plt.clf()
h = costheta_aMC / costheta_powheg
# print(h)
if self.era == "preVFP":
@ROOT.Numba.Declare(["float", "float"], "float")
def getWeight_preVFP(y, costheta):
biny = np.digitize(np.array([y]), yBins)[0] - 1
bincostheta = np.digitize(np.array([costheta]),
cosThetaBins)[0] - 1
if not (y == 4.717765 and costheta == 1.):
return h[biny, bincostheta]
else:
return 1.
self.d = d
self.d = self.d.Define(
"ycosthetaweight",
"Numba::getWeight_preVFP(Vrap_preFSR_abs,CStheta_preFSR)"
) #.Filter("ycosthetaweight>2.")
else:
@ROOT.Numba.Declare(["float", "float"], "float")
def getWeight_postVFP(y, costheta):
biny = np.digitize(np.array([y]), yBins)[0] - 1
bincostheta = np.digitize(np.array([costheta]),
cosThetaBins)[0] - 1
if not (y == 4.717765 and costheta == 1.):
return h[biny, bincostheta]
else:
return 1.
self.d = d
self.d = self.d.Define(
"ycosthetaweight",
"Numba::getWeight_postVFP(Vrap_preFSR_abs,CStheta_preFSR)"
) #.Filter("ycosthetaweight>2.")
# node = self.d.AsNumpy()
# print('getWeight({},{}) = {}'.format(node['Vrap_preFSR_abs'],node['CStheta_preFSR'], node['ycosthetaweight']))
return self.d
def plotTF(rootFilePath, nptbins):
outdir = makeOutdir(rootFilePath)
plt.style.use([hep.cms.style.ROOT, {'font.size': 24}])
plt.switch_backend('agg')
rootfile = rt.TFile(rootFilePath)
ptdeg = rootFilePath.split('polyDegs')[1][0]
rhodeg = rootFilePath.split('polyDegs')[1][1]
tf, dataRatio = [], []
for ibin in range(2): #[1,0]:
msd_hist = {}
data_hist = {}
for region in ['Pass', 'Fail']:
SorB = 's'
p = f'ptbin{ibin}{region}_msd_fit_{SorB}'
b_suffix = '_model_bonly__' if SorB == 'b' else ''
hist_name = f'pdf_binptbin{ibin}{region}_{b_suffix}Norm[msd]'
msd_hist[region] = TH1fromRooObj(
rootfile.Get(p).getCurve(hist_name), returnHistArray=True)
data_hist[region] = TH1fromRooObj(
rootfile.Get(p).getHist(f'h_ptbin{ibin}{region}'),
returnHistArray=True)
tf.append(msd_hist['Pass'][:, 1] / msd_hist['Fail'][:, 1])
dataRatio.append(data_hist['Pass'][:, 1] / data_hist['Fail'][:, 1])
tf = np.array(tf)
dataRatio = np.array(dataRatio)
residuals = (tf - dataRatio) / dataRatio
msdbins = np.round(msd_hist['Pass'][:, 0])
msdbins = np.append(msdbins, msdbins[-1] + np.diff(msdbins)[0])
msdsampl = msdbins[:-1] + 0.5 * np.diff(msdbins)
pt_start = 200 if 'v06' in rootFilePath else 250
ptbins = np.array([pt_start, 300, 2000])
ptsampl = ptbins[:-1] + 0.3 * np.diff(ptbins)
sampling = np.meshgrid(msdsampl, ptsampl)
rho = 2 * np.log(sampling[0] / sampling[1])
rho_max = 0 if 'v06' in rootFilePath else -1.2
mask = (rho > rho_max) | (rho < -6)
for arr in [tf, residuals]:
fig, ax = plt.subplots()
fig.subplots_adjust(right=.85)
ax = hep.cms.cmslabel(data=args.isData,
paper=False,
year=args.year,
ax=ax,
loc=1)
ptbins[-1] = 400
vmin = np.floor(100 * min(arr[~mask])) / 100
vmax = np.ceil(100 * max(arr[~mask])) / 100
if arr is residuals:
colmap = 'RdBu_r'
if abs(vmin) > abs(vmax):
vmax = abs(vmin)
else:
vmin = -vmax
else:
colmap = 'inferno'
hist2d = hep.hist2dplot(arr.T,
msdbins,
ptbins,
vmin=vmin,
vmax=vmax,
cmap=colmap,
ax=ax)
for ibin in range(len(ptbins) - 1):
pt = ptbins[ibin]
ptnext = ptbins[ibin + 1]
ax.fill_between(msdbins,
np.full_like(msdbins, pt),
np.full_like(msdbins, ptnext),
where=np.append(mask[ibin], True),
facecolor='w',
edgecolor='k',
linewidth=0,
hatch='xx')
ax.set_title(
fr'{"" if args.isData else "MC "}TF{" residuals" if arr is residuals else ""}, $\deg( p_\mathrm{{T}}, \rho ) = ({ptdeg},{rhodeg})$',
pad=9,
fontsize=22,
loc='left')
#ax.set_title('2017',pad=9, fontsize=22, loc='right')
ax.set_xlabel(r'Jet $\mathrm{m_{SD}}$ [GeV]', ha='right', x=1)
ax.set_ylabel(r'Jet $\mathrm{p_{T}}$ [GeV]', ha='right', y=1)
ax.yaxis.set_ticks([pt_start, 300])
cax = fig.axes[1]
cax.set_ylabel('(TF - data)/data' if arr is residuals else 'TF',
ha='right',
y=1)
for ext in ['pdf', 'png']:
fig.savefig(
os.path.join(
outdir,
f'TF{"_residuals" if arr is residuals else ""}.{ext}'))
histConfig = json.load(config_file)
histograms = uproot.open(args.input_file)
# Create the plots
numberOfHists = len(histograms.keys())
for figureNumber, key in enumerate(histograms.keys()):
if(args.verbose):
print(key)
else:
print("[" + "H" * figureNumber + "h" * (numberOfHists - figureNumber) + "] %i" % int(figureNumber / numberOfHists * 100) + '%', end="\r")
plt.figure(figureNumber)
if re.search("_vs_", key):
hep.hist2dplot(histograms[key], color = histConfig[key]["color"])
else:
hep.histplot(histograms[key], color = histConfig[key]["color"], histtype = histConfig[key]["histtype"])
#plt.legend(fontsize = args.font_size, ncol = args.number_of_cols)
plt.ylabel(histConfig[key]["ylabel"])
plt.style.use(hep.style.CMS)
if args.cms_label:
hep.cms.label()
plt.xlabel(histConfig[key]["xlabel"])
plt.savefig(args.output_directory + "/" + histConfig[key]["subdir"] + "/" + re.sub(";1", "", key) + ".png")
plt.savefig(args.output_directory + "/" + histConfig[key]["subdir"] + "/" + re.sub(";1", "", key) + ".pdf")
plt.yscale("log")
P3w = np.outer(np.sqrt(1. - cosThetaBinsC * cosThetaBinsC), np.cos(phiBinsC))
P4w = np.outer(cosThetaBinsC, np.ones(len(phiBinsC)))
P5w = np.outer((1. - cosThetaBinsC * cosThetaBinsC), np.sin(2. * phiBinsC))
P6w = np.outer(
2. * cosThetaBinsC * np.sqrt(1. - cosThetaBinsC * cosThetaBinsC),
np.sin(phiBinsC))
P7w = np.outer(np.sqrt(1. - cosThetaBinsC * cosThetaBinsC), np.sin(phiBinsC))
P8w = np.outer(1 + cosThetaBinsC * cosThetaBinsC, np.ones(len(phiBinsC)))
wharmonics = [P0w, P1w, P2w, P3w, P4w, P5w, P6w, P7w]
hharmonics = []
totalxsec = np.sum(hFullAcc, axis=(2, 3))
fig, ax1 = plt.subplots()
ax1.set_title("total xsec", fontsize=18)
hep.hist2dplot(totalxsec, yBins, qtBins)
plt.savefig("total_xsec")
plt.clf()
factors = [(20. / 3., 1. / 10), (5., 0.), (20., 0.), (4., 0.), (4., 0.),
(5., 0.), (5., 0.), (4., 0.)]
for i, hw in enumerate(wharmonics):
htmp = np.einsum('ijkm,km->ij', hFullAcc, hw) / totalxsec
htmp = factors[i][0] * (htmp + factors[i][1])
hharmonics.append(htmp)
fig, ax1 = plt.subplots()
ax1.set_title("A{}".format(i), fontsize=18)
hep.hist2dplot(htmp, yBins, qtBins)
plt.savefig("A{}".format(i))
plt.cla()
bins_dxy = np.geomspace(0.01, 20., num=5)
track_pt = np.concatenate(df_bkg['track_pt'].to_numpy().flatten())
track_dxy = np.concatenate(df_bkg['track_dxy'].to_numpy().flatten())
weights = np.concatenate(df_bkg['weight'].to_numpy().flatten())
track_pt_data = np.concatenate(df_data['track_pt'].to_numpy().flatten())
track_dxy_data = np.concatenate(df_data['track_dxy'].to_numpy().flatten())
hist = histogram2d(track_pt, track_dxy, bins_pt, bins_dxy, weights=weights)
hist_data = histogram2d(track_pt_data, track_dxy_data, bins_pt, bins_dxy)
fig, ax = plt.subplots()
fig.suptitle(cut.title, fontsize=30)
hep.cms.label(loc=0)
hep.hist2dplot(hist_data, bins_pt, bins_dxy, labels=True)
ax.set_xticklabels(labels=bins_pt, rotation=45)
ax.set_xlabel(r'leading track $p_\mathrm{T}$ [GeV]')
ax.set_ylabel(r'leading track $|d_{xy}| [cm]$')
ax.set_xscale('log')
ax.set_yscale('log')
fig.tight_layout()
plt.show()
plt.savefig(f'tagger_efficiency/counts_numerator_data_{cut.label}.pdf')
hist_ratio = []
labels = []
for nums, denoms in zip(hist_data, hist):
row = []
label_row = []
def test_simple2d():
fig, ax = plt.subplots()
h = [[1, 3, 2], [1, 3, 2]]
hep.hist2dplot(h)
return fig
fig, ax = plt.subplots(figsize=(12, 12))
if norm:
histnum = histnum / numsize
histden = histden / densize
numm1 = numm1 / numsize
denm1 = denm1 / densize
# We decided to flip Num and Den, hence things got weird here.
ratio = histden / histnum
mn = np.nanmean(ratio)
print(mn)
ratio = np.where(histnum > 0, ratio, -10)
m1ratio = denm1 / numm1
if norm:
hep.hist2dplot(ratio, binx, biny, vmin=0.5, vmax=1.5, cmap=my_cmap)
else:
hep.hist2dplot(ratio,
binx,
biny,
vmin=max(mn / 2, 2 * mn - 1),
vmax=min(1, mn * 3 / 2),
cmap=my_cmap)
# pickle.dump([ratio,m1ratio],open(f.rstrip('*')+".pkl",'wb'))
ax.set_ylabel("%s CvsB" % tagger)
ax.set_xlabel("%s CvsL" % tagger)
zlabel = r"$\frac{\#\ %s\ jets\ with\ MUSTATUS\ muNORM}{\#\ %s\ jetsNORM}$" % (
flav, flav)
if "nomu" in f: zlabel = zlabel.replace("MUSTATUS", "no")
