def _plot_eff_array(array_dict, use_contour = True,
out_name = 'rejrej.pdf'):
eff_array = array_dict['eff']
eff_array = _maximize_efficiency(eff_array)
x_min, x_max = array_dict['x_range']
y_min, y_max = array_dict['y_range']
eff_array[np.nonzero(eff_array < 0.0)] = np.nan
fig = plt.figure()
gs = GridSpec(20,21)
ax = plt.subplot(gs[:,0:-1])
aspect = float(x_max - x_min) / (y_max - y_min)
im = ax.imshow(
eff_array,
origin = 'upper',
extent = (x_min,x_max, y_min, y_max),
aspect = aspect,
interpolation = 'nearest',
vmin = 0,
vmax = 1,
)
if use_contour:
im.set_cmap('Greys')
ct = ax.contour(
eff_array,
origin = 'upper',
extent = (x_min,x_max, y_min, y_max),
aspect = aspect,
linewidths = 2,
levels = np.arange(0.1,1.0,0.1),
)
im.get_cmap().set_bad(alpha=0)
ax.set_xticks([])
ax.set_yticks([])
ax.invert_yaxis()
cb_ax = plt.subplot(gs[:,-1])
plt.subplots_adjust(left = 0.25) # FIXME: use OO call here
# cb = plt.colorbar()
cb = Colorbar(ax = cb_ax, mappable = im)
cb.set_label('{} efficiency'.format(array_dict['signal']))
if use_contour: cb.add_lines(ct)
position = ax.get_position()
new_aspect = ax.get_aspect()
# ax.set_position(position)
ax_log = fig.add_subplot(111, frameon = False)
ax_log.set_xscale('log')
ax_log.set_yscale('log')
ax_log.axis((10**x_min, 10**x_max, 10**y_min, 10**y_max))
ax_log.set_aspect(aspect)
ax_log.set_xlabel('{} rejection'.format(array_dict['x_bg']))
ax_log.set_ylabel('{} rejection'.format(array_dict['y_bg']))
ax_log.grid(True)
ax_log.set_aspect(new_aspect)
ax_log.set_position(position)
plt.savefig(out_name, bbox_inches = 'tight')
plt.close()
def _overlay_rejrej(array_one, array_two,
out_name = 'rejrej.pdf',
do_rel = False,
do_contour = False,
do_abs_contour = False,
do_equal_contour = False,
z_range = None,
extra_cuts=[]):
if do_contour and do_abs_contour:
warnings.warn("can't do both abs_contour and contour, "
"won't use abs_contour")
do_abs_contour = False
array_one['eff'] = _maximize_efficiency(array_one['eff'])
array_two['eff'] = _maximize_efficiency(array_two['eff'])
arrays = array_one, array_two
# pretty sure the 'nonzero' calls here aren't needed (bool array returned
# by the inequality should work)
array_one['eff'][np.nonzero(array_one['eff'] <= 0.0)] = np.NaN
array_two['eff'][np.nonzero(array_two['eff'] <= 0.0)] = np.NaN
if do_rel:
old_warn_set = np.seterr(divide = 'ignore')
eff_array = array_one['eff'] / array_two['eff']
np.seterr(**old_warn_set)
else:
eff_array = array_one['eff'] - array_two['eff']
for a in arrays:
if not 'tagger' in a:
warnings.warn('no tagger name given',stacklevel = 2)
a['tagger'] = 'unknown'
if _get_rel_plot_alignment(array_one, array_two) > 1e-6:
ranges = [(p['x_range'], p['y_range']) for p in arrays]
raise ValueError("ranges {} don't match {}".format(*ranges))
if not _check_sig_bg_match(*arrays):
err = 'array mismatch ---'
for a in arrays:
err += 'sig: {signal}, bgx: {x_bg}, bgy: {y_bg} '.format(**a)
raise ValueError(err)
x_min, x_max = array_one['x_range']
y_min, y_max = array_one['y_range']
fig = plt.figure()
gs = GridSpec(20,21)
ax = plt.subplot(gs[:,0:-1])
aspect = float(x_max - x_min) / (y_max - y_min)
# cmap = mp.colors.Colormap('rainbow')
rel_min = np.min(eff_array[np.isfinite(eff_array)])
rel_max = np.max(eff_array[np.isfinite(eff_array)])
if z_range:
rel_min, rel_max = z_range
if do_abs_contour:
contour_array = array_one['eff']
signal = array_one['signal']
contour_range = (0,0.5) if signal == 'charm' else (0.5,1)
else:
contour_array = eff_array
contour_range = (rel_min, rel_max)
contour_order, c_lines = _get_contour_order_and_lines(contour_range)
print 'plot range: {: .2f}--{:.2f}'.format(
rel_min, rel_max)
im = ax.imshow(
eff_array,
origin = 'upper',
extent = (x_min,x_max, y_min, y_max),
aspect = aspect,
interpolation = 'nearest',
vmin = rel_min,
vmax = rel_max,
)
if len(c_lines) == 0:
do_contour = False
if do_contour or do_abs_contour:
ct = ax.contour(
contour_array,
origin = 'upper',
extent = (x_min,x_max, y_min, y_max),
aspect = aspect,
linewidths = 2,
levels = c_lines,
colors = 'k',
)
plt.clabel(ct, fontsize=12, inline=1,
fmt = '%.{}f'.format(-contour_order + 1 ))
equal_contour_level = 1.0 if do_rel else 0.0
if do_equal_contour:
equal_ct = ax.contour(
eff_array,
origin = 'upper',
extent = (x_min,x_max, y_min, y_max),
aspect = aspect,
linewidths = 2,
levels = [equal_contour_level],
colors = 'r',
)
plt.clabel(equal_ct, fontsize=9, inline=True,
fmt={equal_contour_level:'equal'})
im.get_cmap().set_bad(alpha=0)
ax.set_xticks([])
ax.set_yticks([])
ax.invert_yaxis()
cb_ax = plt.subplot(gs[:,-1])
plt.subplots_adjust(left = 0.25) # FIXME: use OO call here
# cb = plt.colorbar()
cb = Colorbar(ax = cb_ax, mappable = im)
taggers = [_simplify_tagger_name(x) for x in arrays]
flav_char = _flav_to_char[array_one['signal']]
sig_label = '$\epsilon_\mathrm{{ {} }}$'.format(flav_char)
cb_lab_string = '{} $/$ {} {s}' if do_rel else '{} $-$ {} {s}'
cb.set_label(cb_lab_string.format(*taggers, s = sig_label ))
if do_contour:
cb.add_lines(ct)
if do_equal_contour:
if rel_min < equal_contour_level < rel_max:
cb.add_lines(equal_ct)
position = ax.get_position()
new_aspect = ax.get_aspect()
# ax.set_position(position)
ax_log = fig.add_subplot(111, frameon = False)
ax_log.set_xscale('log')
ax_log.set_yscale('log')
ax_log.axis((10**x_min, 10**x_max, 10**y_min, 10**y_max))
ax_log.set_aspect(aspect)
ax_log.set_xlabel('{} rejection'.format(array_one['x_bg']))
ax_log.set_ylabel('{} rejection'.format(array_one['y_bg']))
ax_log.grid(True)
cuts_to_display = array_one['cuts_to_display']
for cut in extra_cuts:
cut.point_type = 'wo'
cuts_to_display.append(cut)
for cut in cuts_to_display:
x, y, z = cut.xyz
# print cut.xyz
ax_log.plot([x],[y],cut.point_type)
fmt_dict = dict(cut1=cut.cut1, cut2=cut.cut2, eff=z)
ax_log.annotate(cut.plot_string.format(**fmt_dict), (x,y),
**cut.ann_opts)
ax_log.set_aspect(new_aspect)
ax_log.set_position(position)
plt.savefig(out_name, bbox_inches = 'tight')
plt.close()
