def test_rebin_1d(self):
hist = _get_hist(1)
# only checking the basic case here, since basically only the return
# statement is different from the 3d case
re_hist = hu.rebin(hist, [('x', 2)])
self.assertEqual(hu._get_nbins(hist), (10,))
self.assertEqual(hu._get_nbins(re_hist), (2,))
def test_uniform_rebinning(self):
hist = _get_hist(1)
targ_bin = np.linspace(0, 1, 6)
# since hist has 10 bin, rebin(hist, 5) should return the same histogram
# as the one when we rebin it according to a custom binning
npt.assert_allclose(hu.get_array(hu.rebin_1d_binning(hist, targ_bin)),
hu.get_array(hu.rebin(hist, [(0, 5)])))
def test_rebin_3d(self):
hist = _get_hist(3)
# some more possibilities here
re_hist = hu.rebin(hist, [(0, 2), (1, 5), (2, 6)])
self.assertEqual(hu._get_nbins(hist), (10, 20, 30))
self.assertEqual(hu._get_nbins(re_hist), (2, 5, 6))
# check that char indices work
re_hist = hu.rebin(hist, [('x', 5), ('Y', 2), ('z', 3)])
self.assertEqual(hu._get_nbins(hist), (10, 20, 30))
self.assertEqual(hu._get_nbins(re_hist), (5, 2, 3))
# check that in principle also mixed indices work
re_hist = hu.rebin(hist, [('x', 5), ('Y', 2), (2, 3)])
self.assertEqual(hu._get_nbins(hist), (10, 20, 30))
self.assertEqual(hu._get_nbins(re_hist), (5, 2, 3))
def test_rebin_4d(self):
hist = _get_hist(4)
# NOTE: only very limited possibilities to do an actual rebinning
# here
re_hist = hu.rebin(hist, [(0, 1), (2, 2)])
# check original hist is unchanged
self.assertEqual(hu._get_nbins(hist), (2, 3, 4, 5))
self.assertEqual(hu._get_nbins(re_hist), (1, 3, 2, 5))
def get_scaled_ppd(hfile, var, nbins=None):
"""
Get the ppd scaled to unity
"""
ppd = hfile.Get('ppd_1d_{}'.format(var))
if nbins is not None:
ppd = rebin(ppd, [(0, nbins)])
ppd.Scale(1 / ppd.Integral())
return ppd
def make_debug_plot(comb_ppd, inputfiles, var):
"""
Make a plot overlaying the combined ppd and the input ppds
"""
can = PLOT_FUNCTIONS[var](rebin(comb_ppd, [(0, 200)]))
set_color(can.pltables[1], 1) # make the combined ppd black
ppds = [get_scaled_ppd(f, var, 200) for f in inputfiles]
mkplot(ppds,
can=can,
drawOpt='samehist',
attr=default_attributes(linewidth=1))
return can
def make_ppd_comp_plot(nom_file, var_files, var):
"""
Make a plot comparing the full ppds directly (shifting all of them by the
median of the nominal ppd).
"""
n_bins = {'dlth': 100, 'dlph': 400, 'dkappa': 400}
nom_ppd = get_scaled_ppd(nom_file, var)
nom_med = get_quantiles(nom_ppd, 0.5)
nom_ppd = shift_by_median(rebin(nom_ppd, [(0, n_bins[var])]), nom_med)
nom_ppd.Scale(1.0 / nom_ppd.Integral())
var_ppds = [
get_scaled_ppd(f, var, n_bins[var]) for f in var_files.values()
]
var_ppds = [shift_by_median(p, nom_med) for p in var_ppds]
[p.Scale(1.0 / p.Integral()) for p in var_ppds]
xran = {'dlth': [-2, 2], 'dlph': [-0.5, 0.5], 'dkappa': [-0.5, 0.5]}
leg = create_legend(0.69, 0.88, 0.94, len(var_ppds) + 1)
label = '{0} - #bar{0}_{{nominal}}'.format(YLABELS.get(var))
can = mkplot(nom_ppd,
attr=[{
'color': 1,
'fillalpha': (1, 0.25)
}],
drawOpt='hist',
yLabel='PPD [a.u.]',
xLabel=label,
xRange=xran[var],
yRange=[0, None],
leg=leg,
legEntries=['nominal'],
legOpt='F')
mkplot(var_ppds,
can=can,
drawOpt='histsame',
attr=default_attributes(linewidth=2),
leg=leg,
legEntries=var_files.keys(),
legOpt='L')
return can
def _make_dlth_plot(hfile, shift=None):
"""
Make the dlth plot
"""
ppd = get_scaled_ppd(hfile, 'dlth')
ppd = rebin(shift_by_median(ppd, shift), [(0, 200)])
ppdmax = get_y_max(ppd)
can = mkplot(ppd,
xLabel=YLABELS['dlth'],
xRange=[-2, 2],
drawOpt='hist', yLabel='PPD [a.u.]')
mkplot([r.TLine(v, 0, v, ppdmax * 1.1) for v in [-1.6, 1.3333]], can=can,
drawOpt='same', attr=[{'color': 12, 'line': 7, 'width': 2}])
# ltx = setup_latex()
# put_on_latex(ltx, [
# (0.65, 0.8, '+{:.2f}'.format(np.diff(get_quantiles(ppd, [0.5, 0.84]))[0])),
# (0.40, 0.8, '#minus{:.2f}'.format(np.diff(get_quantiles(ppd, [0.16, 0.5]))[0]))
# ], ndc=True)
make_nice(can)
return can
def make_lth_plot(ppd_files):
"""Make the plots for the lth1 ppds"""
ppds = [get_scaled_ppd(f, 'lth') for f in ppd_files.values()]
quantiles = [get_quantiles(p, 0.1) for p in ppds]
ppds = [rebin(p, [(0, 100)]) for p in ppds] # for plotting
leg = setup_legend(0.18, 0.7, 0.48, 0.84)
leg.SetTextSize(0.0425)
leg.SetFillColor(0)
leg.SetFillStyle(1001)
# Draw boundaries first to not draw it over the legend in the end
can = mkplot(r.TLine(-1./3., 0, -1./3., get_y_max(ppds) * 1.1),
attr=BOUND_ATTR,
xRange=[-1, 1], xLabel='#lambda_{#vartheta}^{#chi_{c1}}',
yRange=[0, get_y_max(ppds) * 1.1], yLabel='PPD [a.u.]')
can = mkplot(ppds, drawOpt='hist same', can=can,
leg=leg, legOpt='L',
legEntries=['{} < p_{{T}} < {} GeV'.format(*p) for p in ppd_files.keys()])
can.pltables[0].GetYaxis().SetNdivisions(505)
can.pltables[0].GetXaxis().SetNdivisions(505)
mkplot([r.TLine(q, 0, q, 0.01) for q in quantiles], can=can, drawOpt='same')
arrow_attr = default_attributes(open_markers=False)
for att in arrow_attr:
att['fill'] = att['color']
att['width'] = 2
mkplot([
r.TArrow(q, v, q + 0.125, v, 0.025, '<|') for q, v in zip(quantiles, [0.0015, 0.003, 0.0045])
],
can=can, drawOpt='same <|', attr=arrow_attr)
return add_info(can, 'left')