def check_wrong_end(self, track, mom=None, HT=None, maxdist=3):
"""
See if the algorithm's end segment was correct or not.
"""
if mom is None and HT is None:
raise ValueError(
'Requires either a moments object or a HybridTrack object')
g4xfull, g4yfull = tp.get_image_xy(track)
g4x, g4y = g4xfull[0], g4yfull[0]
# could throw an AttributeError if there were errors in the algorithm
if mom:
algx, algy = mom.start_coordinates
elif HT:
algx, algy = HT.start_coordinates
else:
raise ValueError('bad value in moments or HybridTrack object')
dist = np.sqrt((algx - g4x)**2 + (algy - g4y)**2)
self.end_distance = dist
if dist > maxdist:
self.wrong_end = True
else:
self.wrong_end = False
self.g4xy = g4x, g4y
self.algxy = algx, algy
def check_wrong_end(self, track, mom=None, HT=None, maxdist=3):
"""
See if the algorithm's end segment was correct or not.
"""
if mom is None and HT is None:
raise ValueError(
'Requires either a moments object or a HybridTrack object')
g4xfull, g4yfull = tp.get_image_xy(track)
g4x, g4y = g4xfull[0], g4yfull[0]
# could throw an AttributeError if there were errors in the algorithm
if mom:
algx, algy = mom.start_coordinates
elif HT:
algx, algy = HT.start_coordinates
else:
raise ValueError('bad value in moments or HybridTrack object')
dist = np.sqrt((algx - g4x)**2 + (algy - g4y)**2)
self.end_distance = dist
if dist > maxdist:
self.wrong_end = True
else:
self.wrong_end = False
self.g4xy = g4x, g4y
self.algxy = algx, algy
def show_event(ind, track, mom, classifier):
"""
Plot stuff for one event.
geant4 is cyan
ridge-following is light green
moments is magenta
"""
if track.g4track.energy_tot_kev < 100:
print('Low energy event: {} keV'.format(track.g4track.energy_tot_kev))
return None
mom.reconstruct()
try:
_, HTinfo = ht.reconstruct(track)
except ht.HybridTrackError:
HTinfo = None
# geant4 position
g4x, g4y = tp.get_image_xy(track)
g4x0 = np.array([g4x[0], g4y[0]])
# geant4 direction (radians)
g4_alpha_deg = track.g4track.alpha_deg
g4_alpha_rad = g4_alpha_deg * np.pi / 180.0
# moments
mom_arc, mom_ep = tp.get_arc2(mom)
mom_alpha_rad = mom.alpha
reject_flag, reject_reason = mom_reject(mom)
mom_da = ev.AlphaUncertainty.delta_alpha(
g4_alpha_deg, mom_alpha_rad * 180.0 / np.pi)
# ridge-following
# HTinfo = track.algorithms['python HT v1.52'].info
ridge_ep = (HTinfo.ridge[HTinfo.measurement_start_pt].coordinates_pix
- np.array([1, 1]))
ridge_alpha_rad = (track.algorithms['python HT v1.52'].alpha_deg
* np.pi / 180.0)
ridge_da = ev.AlphaUncertainty.delta_alpha(
g4_alpha_deg, ridge_alpha_rad * 180.0 / np.pi)
titlebase = (
r'{}' + '\n' +
r'E={:.0f}keV, $\beta$={:.0f}{deg}, ' +
r'scattering {:.1f}{deg}, E_end={:.1f}keV' + '\n' +
r'Rejection: {}' + '\n')
titlestr = titlebase.format(
ind,
track.g4track.energy_tot_kev,
track.g4track.beta_deg,
classifier.total_scatter_angle * 180 / np.pi,
mom.end_energy,
reject_reason,
deg=DEG,)
if np.abs(classifier.g4track.beta_deg) > 60:
titlestr += '[Beta > 60{}]'.format(DEG)
if classifier.early_scatter:
titlestr += ' [Early scatter in 25um]'
if classifier.overlap:
titlestr += ' [Overlapping]'
if classifier.wrong_end:
titlestr += ' [Wrong end]'
# PLOTS
fig = plt.figure()
# top left: ridge-following, zoomed
plt.subplot(2, 2, 1)
# g4
plot_track(track.image)
plt.plot(g4y, g4x, '.c')
tp.plot_arrow(g4x0, g4_alpha_rad, color='c')
# ridge
ridge = HTinfo.ridge[HTinfo.measurement_start_pt:HTinfo.measurement_end_pt]
tp.plot_ridgepoints(
plt.gca(), ridge, fmtstring='.', offset=[1, 1], color=[0, 1, 0])
tp.plot_arrow(ridge_ep, ridge_alpha_rad, color=[0, 1, 0])
# general
xoff = mom.end_segment_offsets[1]
yoff = mom.end_segment_offsets[0]
plt.xlim((xoff - 1, xoff + mom.end_segment_image.shape[1] + 1))
plt.ylim((yoff - 1, yoff + mom.end_segment_image.shape[0] + 1))
plt.xlabel('y [pixels]')
plt.ylabel('x [pixels]')
plt.colorbar()
# bottom left: ridge-following, full
plt.subplot(2, 2, 3)
# g4
plot_track(track.image)
plt.plot(g4y, g4x, '.c')
tp.plot_arrow(g4x0, g4_alpha_rad, color='c')
# ridge
ridge = HTinfo.ridge[HTinfo.measurement_start_pt:HTinfo.measurement_end_pt]
tp.plot_ridgepoints(
plt.gca(), ridge, fmtstring='.', offset=[1, 1], color=[0, 1, 0])
tp.plot_arrow(ridge_ep, ridge_alpha_rad, color=[0, 1, 0])
# general
plt.xlim((0, track.image.shape[1] - 1))
plt.ylim((0, track.image.shape[0] - 1))
plt.xlabel('y [pixels]')
plt.ylabel('x [pixels]')
plt.colorbar()
plt.title(r'Ridge-following, $\Delta_\alpha$ = {:2.1f}{}'.format(
ridge_da, DEG))
# top right: moments, zoomed
plt.subplot(2, 2, 2)
# g4
plot_track(track.image)
plt.plot(g4y, g4x, '.c')
tp.plot_arrow(g4x0, g4_alpha_rad, color='c')
# moments
plt.plot(mom.box_y, mom.box_x, 'm')
if reject_flag:
plt.plot(mom_arc[1, :], mom_arc[0, :], 'm', lw=2.5, ls='dashed')
tp.plot_arrow(mom_ep, mom_alpha_rad, color='m', ls='dashed')
else:
plt.plot(mom_arc[1, :], mom_arc[0, :], 'm', lw=2.5)
tp.plot_arrow(mom_ep, mom_alpha_rad, color='m')
# general
xoff = mom.end_segment_offsets[1]
yoff = mom.end_segment_offsets[0]
plt.xlim((xoff - 1, xoff + mom.end_segment_image.shape[1] + 1))
plt.ylim((yoff - 1, yoff + mom.end_segment_image.shape[0] + 1))
plt.xlabel('y [pixels]')
plt.ylabel('x [pixels]')
plt.colorbar()
# bottom right: moments, full
plt.subplot(2, 2, 4)
# g4
plot_track(track.image)
plt.plot(g4y, g4x, '.c')
tp.plot_arrow(g4x0, g4_alpha_rad, color='c')
# moments
plt.plot(mom.box_y, mom.box_x, 'm')
if reject_flag:
plt.plot(mom_arc[1, :], mom_arc[0, :], 'm', lw=2.5, ls='dashed')
tp.plot_arrow(mom_ep, mom_alpha_rad, color='m', ls='dashed')
else:
plt.plot(mom_arc[1, :], mom_arc[0, :], 'm', lw=2.5)
tp.plot_arrow(mom_ep, mom_alpha_rad, color='m')
# general
plt.xlim((0, track.image.shape[1] - 1))
plt.ylim((0, track.image.shape[0] - 1))
plt.xlabel('y [pixels]')
plt.ylabel('x [pixels]')
plt.colorbar()
plt.title(r'Moments, $\Delta_\alpha$ = {:2.1f}{}'.format(mom_da, DEG))
fig.suptitle(titlestr)
figManager = plt.get_current_fig_manager()
figManager.window.showMaximized()
plt.show()
return fig
def show_event(ind, track, mom, classifier):
"""
Plot stuff for one event.
geant4 is cyan
ridge-following is light green
moments is magenta
"""
if track.g4track.energy_tot_kev < 100:
print('Low energy event: {} keV'.format(track.g4track.energy_tot_kev))
return None
mom.reconstruct()
try:
_, HTinfo = ht.reconstruct(track)
except ht.HybridTrackError:
HTinfo = None
# geant4 position
g4x, g4y = tp.get_image_xy(track)
g4x0 = np.array([g4x[0], g4y[0]])
# geant4 direction (radians)
g4_alpha_deg = track.g4track.alpha_deg
g4_alpha_rad = g4_alpha_deg * np.pi / 180.0
# moments
mom_arc, mom_ep = tp.get_arc2(mom)
mom_alpha_rad = mom.alpha
reject_flag, reject_reason = mom_reject(mom)
mom_da = ev.AlphaUncertainty.delta_alpha(g4_alpha_deg,
mom_alpha_rad * 180.0 / np.pi)
# ridge-following
# HTinfo = track.algorithms['python HT v1.52'].info
ridge_ep = (HTinfo.ridge[HTinfo.measurement_start_pt].coordinates_pix -
np.array([1, 1]))
ridge_alpha_rad = (track.algorithms['python HT v1.52'].alpha_deg * np.pi /
180.0)
ridge_da = ev.AlphaUncertainty.delta_alpha(g4_alpha_deg,
ridge_alpha_rad * 180.0 / np.pi)
titlebase = (r'{}' + '\n' + r'E={:.0f}keV, $\beta$={:.0f}{deg}, ' +
r'scattering {:.1f}{deg}, E_end={:.1f}keV' + '\n' +
r'Rejection: {}' + '\n')
titlestr = titlebase.format(
ind,
track.g4track.energy_tot_kev,
track.g4track.beta_deg,
classifier.total_scatter_angle * 180 / np.pi,
mom.end_energy,
reject_reason,
deg=DEG,
)
if np.abs(classifier.g4track.beta_deg) > 60:
titlestr += '[Beta > 60{}]'.format(DEG)
if classifier.early_scatter:
titlestr += ' [Early scatter in 25um]'
if classifier.overlap:
titlestr += ' [Overlapping]'
if classifier.wrong_end:
titlestr += ' [Wrong end]'
# PLOTS
fig = plt.figure()
# top left: ridge-following, zoomed
plt.subplot(2, 2, 1)
# g4
plot_track(track.image)
plt.plot(g4y, g4x, '.c')
tp.plot_arrow(g4x0, g4_alpha_rad, color='c')
# ridge
ridge = HTinfo.ridge[HTinfo.measurement_start_pt:HTinfo.measurement_end_pt]
tp.plot_ridgepoints(plt.gca(),
ridge,
fmtstring='.',
offset=[1, 1],
color=[0, 1, 0])
tp.plot_arrow(ridge_ep, ridge_alpha_rad, color=[0, 1, 0])
# general
xoff = mom.end_segment_offsets[1]
yoff = mom.end_segment_offsets[0]
plt.xlim((xoff - 1, xoff + mom.end_segment_image.shape[1] + 1))
plt.ylim((yoff - 1, yoff + mom.end_segment_image.shape[0] + 1))
plt.xlabel('y [pixels]')
plt.ylabel('x [pixels]')
plt.colorbar()
# bottom left: ridge-following, full
plt.subplot(2, 2, 3)
# g4
plot_track(track.image)
plt.plot(g4y, g4x, '.c')
tp.plot_arrow(g4x0, g4_alpha_rad, color='c')
# ridge
ridge = HTinfo.ridge[HTinfo.measurement_start_pt:HTinfo.measurement_end_pt]
tp.plot_ridgepoints(plt.gca(),
ridge,
fmtstring='.',
offset=[1, 1],
color=[0, 1, 0])
tp.plot_arrow(ridge_ep, ridge_alpha_rad, color=[0, 1, 0])
# general
plt.xlim((0, track.image.shape[1] - 1))
plt.ylim((0, track.image.shape[0] - 1))
plt.xlabel('y [pixels]')
plt.ylabel('x [pixels]')
plt.colorbar()
plt.title(r'Ridge-following, $\Delta_\alpha$ = {:2.1f}{}'.format(
ridge_da, DEG))
# top right: moments, zoomed
plt.subplot(2, 2, 2)
# g4
plot_track(track.image)
plt.plot(g4y, g4x, '.c')
tp.plot_arrow(g4x0, g4_alpha_rad, color='c')
# moments
plt.plot(mom.box_y, mom.box_x, 'm')
if reject_flag:
plt.plot(mom_arc[1, :], mom_arc[0, :], 'm', lw=2.5, ls='dashed')
tp.plot_arrow(mom_ep, mom_alpha_rad, color='m', ls='dashed')
else:
plt.plot(mom_arc[1, :], mom_arc[0, :], 'm', lw=2.5)
tp.plot_arrow(mom_ep, mom_alpha_rad, color='m')
# general
xoff = mom.end_segment_offsets[1]
yoff = mom.end_segment_offsets[0]
plt.xlim((xoff - 1, xoff + mom.end_segment_image.shape[1] + 1))
plt.ylim((yoff - 1, yoff + mom.end_segment_image.shape[0] + 1))
plt.xlabel('y [pixels]')
plt.ylabel('x [pixels]')
plt.colorbar()
# bottom right: moments, full
plt.subplot(2, 2, 4)
# g4
plot_track(track.image)
plt.plot(g4y, g4x, '.c')
tp.plot_arrow(g4x0, g4_alpha_rad, color='c')
# moments
plt.plot(mom.box_y, mom.box_x, 'm')
if reject_flag:
plt.plot(mom_arc[1, :], mom_arc[0, :], 'm', lw=2.5, ls='dashed')
tp.plot_arrow(mom_ep, mom_alpha_rad, color='m', ls='dashed')
else:
plt.plot(mom_arc[1, :], mom_arc[0, :], 'm', lw=2.5)
tp.plot_arrow(mom_ep, mom_alpha_rad, color='m')
# general
plt.xlim((0, track.image.shape[1] - 1))
plt.ylim((0, track.image.shape[0] - 1))
plt.xlabel('y [pixels]')
plt.ylabel('x [pixels]')
plt.colorbar()
plt.title(r'Moments, $\Delta_\alpha$ = {:2.1f}{}'.format(mom_da, DEG))
fig.suptitle(titlestr)
figManager = plt.get_current_fig_manager()
figManager.window.showMaximized()
plt.show()
return fig
