def setUp(self):
self.acc_map = create_histogram(np.random.uniform(0, 1, (10000, 2)),
(20, 0, 1, 10, 0, 1))
self.acc_map.Scale(1.0 / self.acc_map.Integral())
self.acc_map3 = create_histogram(np.random.uniform(0, 1, (100000, 3)),
(10, 0, 1, 10, 0, 1, 5, 0, 1))
self.acc_map3.Scale(1.0 / self.acc_map3.Integral())
def make_pt_eta_plot(plotvar, cuts, cut_plot_set, savename):
"""
Make pt-eta plot (2D) for passed variables and overlay the passed cuts
"""
pt_eta_hist = (50, 0, 20, 24, 0, 2.4)
pt_eta = create_histogram(plotvar, pt_eta_hist)
pt_eta.SetXTitle('p_{T}')
pt_eta.SetYTitle('|#eta|')
can = r.TCanvas(create_random_str(), 'c', 600, 600)
can.cd()
pt_eta.Draw('colz')
# lines = {c: [] for c in cuts} # keep the TLines alive long enough for saving
lines = []
for cut in cuts.values():
lines.append(draw_line(cut()))
for i, cut in enumerate(lines):
plot_on_canvas(can, cut, attr=(cut_plot_set[i], ))
leg = r.TLegend(0.5, 0.3, 0.8, 0.4)
leg.SetTextColor(0)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
for i, cut in enumerate(cuts.keys()):
leg.AddEntry(lines[i][0], cut, 'l')
leg.Draw()
can.Update()
# can.SaveAs(savename)
return can, pt_eta, lines # for ipython so that they survive
def _get_hist(ndim):
"""Create a histogram of a given dimensionality"""
if ndim == 3:
return hu.create_histogram(np.random.uniform(0, 1, (10000, 3)),
(10, 0, 1, 20, 0, 1, 30, 0, 1))
if ndim == 2:
return hu.create_histogram(np.random.uniform(0, 1, (10000, 2)),
(10, 0, 1, 20, 0, 1))
if ndim == 1:
return hu.create_histogram(np.random.uniform(0, 1, 10000),
(10, 0, 1))
if ndim >= 4:
hist = r.THnD('hist4d', '', ndim, np.arange(2, ndim + 2, 1, dtype='i4'),
np.zeros(ndim), np.ones(ndim))
fill_vals = np.random.uniform(0, 1, (10000, ndim))
for i in xrange(len(fill_vals)):
hist.Fill(fill_vals[i, :])
hist.Sumw2()
return hist
def create_mass_hist(dfr, costh_bin=None):
"""
Create a mass histogram in a given costh_bin (or integrated if None)
"""
if costh_bin is not None:
plot_data = dfr[get_bin_cut_df(dfr, lambda d: d.costh_HX.abs(),
*costh_bin)]
else:
plot_data = dfr
return create_histogram(plot_data.chicMass, (75, 3.325, 3.725))
def create_hist(dfr, variable, hist_sett, get_weights=None):
"""
Create a histogram from the passed dataframe
"""
weights = None
if get_weights is not None and hasattr(get_weights, '__call__'):
weights = get_weights(dfr)
if hasattr(variable, '__call__'):
var = variable(dfr)
else:
var = dfr.loc[:, variable]
hist = create_histogram(var, hist_sett, weights=weights)
return hist
def main(args):
"""Main"""
# While loading also disregard the events with JpsiRap > 1.2 which have been
# accidentally stored for parts of the generations
data = get_dataframe(args.toymcfile,
columns=['costh_HX', '*eff_sm'],
where='TMath::Abs(JpsiRap) < 1.2')
# per-event efficiency as product of photon x muon efficiencies
eff = lambda d: d.gamma_eff_sm * 0.01 * d.lepP_eff_sm * d.lepN_eff_sm
costh_binning = read_costh_binning(args.binningfile)
hist = create_histogram(data.costh_HX.abs(),
costh_binning,
weights=eff(data),
name=args.name,
x_axis='|cos#vartheta^{HX}|')
outfile = r.TFile.Open(args.output, 'update')
hist.Write()
outfile.Close()
def create_store_dist(outfile,
df,
variable,
hist_sett,
get_weights=None,
**kwargs):
"""
Create the distribution and store it to the output file
"""
weights = None
if get_weights is not None:
weights = get_weights(df)
if hasattr(variable, '__call__'):
var = variable(df)
else:
var = df.loc[:, variable]
hist = create_histogram(var, hist_sett, weights=weights, **kwargs)
outfile.cd()
hist.Write()