def debug_plots(basename, rfile, state_prob, corr_w, mass):
"""
Make some debug plots and store them into a root file so that they can be
viewed later
"""
rfile.cd()
prob_h = hist1d(state_prob, min=0, max=1, name=basename + '_state_prob')
corr_h = hist1d(corr_w, log=True, name=basename + '_corr_w')
w_h = hist1d(state_prob * corr_w, log=True, name=basename + '_final_weight')
# Keep the 2d histograms consistent between different bins by setting ranges
corr_v_prob = hist2d(state_prob, corr_w, minx=0, maxx=1,
miny=1, maxy=1e5, logy=True, y_axis='corr_weight',
name=basename + '_corr_w_v_state_prob')
w_v_prob = hist2d(state_prob, corr_w * state_prob, minx=0, maxx=1,
miny=0.01, maxy=1e4, logy=True, y_axis='final_weight',
name=basename + '_final_weight_v_state_prob')
mass_h = hist1d(mass, min=3.2, max=3.75, weights=state_prob,
name=basename + '_mass_w_state_prob')
mass_h_w = hist1d(mass, min=3.2, max=3.75, weights=state_prob * corr_w,
name=basename + '_mass_w_final_weight')
rej_sp_h = hist1d(state_prob[corr_w == 0], min=0, max=1,
name=basename + '_state_prob_rej_ev')
for h in [prob_h, corr_h, w_h, corr_v_prob, w_v_prob, mass_h, mass_h_w,
rej_sp_h]:
h.Write()
def make_plot_good_fit(data, var_x, var_y, tf_x, tf_y):
"""Make a plot showing whether the fit was successful or not for each point
of the scan"""
# Remove the minimum since that is in any case valid
data = apply_selections(data, lambda d: d.llh != d.llh.min())
trans_f_x = globals()[tf_x]
trans_f_y = globals()[tf_y]
x_vals = trans_f_x(data.loc[:, var_x])
y_vals = trans_f_y(data.loc[:, var_y])
x_binning = get_variable_binning(x_vals)
y_binning = get_variable_binning(y_vals)
good_fit = data.goodFit > 0
hist = hist2d(x_vals[good_fit],
y_vals[good_fit],
x_hist_sett=(len(x_binning) - 1, x_binning),
y_hist_sett=(len(y_binning) - 1, y_binning))
can = mkplot(to_bw_hist(hist),
drawOpt='colz',
xRange=[x_binning[0], x_binning[-1]],
yRange=[y_binning[0], y_binning[-1]],
xLabel=get_var_name(var_x, tf_x),
yLabel=get_var_name(var_y, tf_y))
hist = can.pltables[1]
hist.GetZaxis().SetRangeUser(0, 10)
remove_color_bar(can)
return can
def ppd_2d(data, var1, vfunc1, var2, vfunc2, name_fmt, weights=None):
"""
Get the 2d ppd histogram for a given variable
"""
xran = XRANGES.get(var1)
yran = XRANGES.get(var2)
bounds = {
'minx': xran[0],
'maxx': xran[1],
'nbinsx': NBINS_2D,
'miny': yran[0],
'maxy': yran[1],
'nbinsy': NBINS_2D,
'name': name_fmt.format('_'.join(['2d', var1, var2]))
}
# TODO: Remove after unblinding
vals1, low1, high1 = get_var_shifted(data, vfunc1, name_fmt.format(var1), {
'min': bounds['minx'],
'max': bounds['maxx']
})
bounds['minx'] = low1
bounds['maxx'] = high1
vals2, low2, high2 = get_var_shifted(data, vfunc2, name_fmt.format(var2), {
'min': bounds['miny'],
'max': bounds['maxy']
})
bounds['miny'] = low2
bounds['maxy'] = high2
return hist2d(vals1, vals2, weights=weights, **bounds)
def make_2D_hist(dfr, varx, vary, selection=None, weight=None, **kwargs):
"""
Make the 2D histogram and return it
"""
logging.debug('Making histograms for {}:{}'.format(varx, vary))
dfr, weights = get_df_and_weights(dfr, selection, weight)
return hist2d(_get_var(dfr, varx),
_get_var(dfr, vary),
weights=weights,
**kwargs)
def get_2d_hist(data, varx, vary, nbinsx=100, nbinsy=100):
"""Get the 2d distribution of vary vs varx"""
# Get rid of very large outliers
xmin, xmax = quantile(data.loc[:, varx].values, [0.0001, 0.9999])
ymin, ymax = quantile(data.loc[:, vary].values, [0.0001, 0.9999])
return hist2d(data.loc[:, varx],
data.loc[:, vary],
minx=xmin,
maxx=xmax,
miny=ymin,
maxy=ymax,
nbinsx=nbinsx,
nbinsy=nbinsy)
def make_costh_phi_plot(data, frame):
"""Make costh-phi histogram"""
hist = hist2d(_get_var(data, 'costh_{}_fold'.format(frame), np.abs),
_get_var(data, 'phi_{}_fold'.format(frame)),
nbinsx=24, minx=0, maxx=1,
nbinsy=32, miny=0, maxy=90)
cth_lab = '|cos#vartheta^{{{}}}|'.format(frame)
phi_lab = '#varphi^{{{}}}_{{fold}}'.format(frame)
can = mkplot(hist, drawOpt='colz', xRange=[0, 1], xLabel=cth_lab,
yRange=[0, 90], yLabel=phi_lab)
can.pltables[0].SetNdivisions(505, 'X')
can.Update()
return can
def make_overlay_plot(pt_map, pt_data, **kwargs):
"""
Plot the coverage of the pt_data onto the
"""
amap_x, amap_y = get_binning(pt_map, 0), get_binning(pt_map, 1)
if np.min(amap_x) == 0:
costh = pt_data.costh_HX_fold.abs()
else:
costh = pt_data.costh_HX_fold
data_dist = hist2d(costh,
pt_data.phi_HX_fold,
x_hist_sett=(len(amap_x) - 1, amap_x),
y_hist_sett=(len(amap_y) - 1, amap_y))
coverage = get_array(data_dist) > 0
cov_graph = get_mask_graph(amap_x, amap_y, coverage)
can = mkplot(pt_map, **kwargs)
mkplot(cov_graph,
can=can,
drawOpt='sameE5',
attr=[{
'color': r.kRed,
'fillalpha': (r.kRed, 0),
'marker': 1
}])
mkplot([
r.TLine(v, np.min(amap_y), v, np.max(amap_y))
for v in [-0.625, -0.45, 0.45, 0.625]
],
attr=[{
'color': 12,
'line': 7,
'width': 2
}],
can=can,
drawOpt='same')
return can
def fill_2d(data, frame, weights):
"""2D histograms"""
return hist2d(_get_var(data, 'costh_' + frame),
_get_var(data, 'phi_' + frame),
hist_sett=(n_costh, -1, 1, n_phi, -180, 180),
weights=weights)