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 classify_etc(loadfile, savefile, v):
"""
1. Moments algorithm
2. HybridTrack algorithm
3. Classify
"""
progressflag = not file_vars()[0]
pn = 'pix10_5noise15'
HTname = 'python HT v1.52'
MTname = 'moments v1.0'
vprint(v, 1, 'Starting {} at {} with {}% mem usage'.format(
loadfile, time.ctime(), psutil.virtual_memory().percent))
pyobj_to_h5 = {}
try:
with h5py.File(loadfile, 'a', driver='core') as f, h5py.File(
savefile, 'a', driver='core') as h5save:
#
n = 0
if progressflag:
pbar = progressbar.ProgressBar(
widgets=[progressbar.Percentage(), ' ',
progressbar.Bar(), ' ',
progressbar.ETA()], maxval=len(f))
pbar.start()
keylist = f.keys()
keylist.sort()
for ind in keylist:
vprint(v, 3, 'Beginning track {} in {}'.format(ind, loadfile))
if int(ind) % 50 == 0:
vprint(v, 2,
'Beginning track {} in {}'.format(ind, loadfile))
# evt is for the G4Track object
evt = f[ind]
# shouldn't get a KeyError because we are looping over ind
# trk is for the Track object
try:
trk = evt[pn]
except KeyError:
vprint(v, 1,
'**Missing key {} in {}{}, skipping'.format(
pn, loadfile, evt.name))
continue
trkpath = ind + '/' + pn
errorcode = 0
n += 1
if n > 50:
# testing
# vprint(v, 1, 'Finished 50 files, exiting')
# break
pass
# load G4Track
vprint(v, 3, 'Loading G4Track {} in {}'.format(ind, loadfile))
try:
this_g4track = G4Track.from_dth5(evt)
except trackio.InterfaceError:
vprint(v, 2, 'InterfaceError in G4Track at {}, {}'.format(
loadfile, ind))
continue
# load Track
vprint(v, 3, 'Loading Track {} in {}'.format(ind, loadfile))
try:
this_track = Track.from_dth5(trk, g4track=this_g4track)
except trackio.InterfaceError:
vprint(v, 2, 'InterfaceError in Track at {}, {}'.format(
loadfile, ind))
continue
# check for error codes - Track.from_dth5 may return an int
if isinstance(this_track, int):
this_save = h5save.create_group(trkpath)
this_save.attrs.create(
'errorcode', this_track, shape=np.shape(this_track))
continue
# run moments algorithm
vprint(v, 3, 'Running moments on track {} in {}'.format(
ind, loadfile))
try:
mom = tm.MomentsReconstruction(this_track.image)
mom.reconstruct()
except ht.NoEndsFound:
mom.alpha = None
errorcode = 4
this_save = h5save.create_group('mom_' + trkpath)
this_save.attrs.create(
'errorcode', errorcode, shape=np.shape(errorcode))
else:
# write into savefile
trackio.write_object_to_hdf5(
mom, h5save, 'mom_' + trkpath, pyobj_to_h5=pyobj_to_h5)
# write into track object
if mom.alpha:
this_track.add_algorithm(
MTname,
alpha_deg=mom.alpha * 180 / np.pi,
beta_deg=np.nan, info=None)
else:
this_track.add_algorithm(
MTname,
alpha_deg=np.nan,
beta_deg=np.nan, info=None)
# run HT algorithm (v1.52)
vprint(v, 3, 'Running HT on track {} in {}'.format(
ind, loadfile))
try:
_, HTinfo = ht.reconstruct(this_track)
except (ht.InfiniteLoop, ht.NoEndsFound):
# write empty into track object
this_track.add_algorithm(
HTname,
alpha_deg=np.nan,
beta_deg=np.nan,
info=None)
else:
# trim memory usage
if hasattr(HTinfo, 'ridge'):
if HTinfo.ridge:
for ridgept in HTinfo.ridge:
ridgept.cuts = None
ridgept.best_cut = None
# write into track object
this_track.add_algorithm(
HTname,
alpha_deg=HTinfo.alpha_deg,
beta_deg=HTinfo.beta_deg,
info=HTinfo)
# write py HDF5 format into savefile (including alg outputs)
try:
trackio.write_object_to_hdf5(
this_track, h5save, trkpath,
pyobj_to_h5=pyobj_to_h5)
except trackio.InterfaceError:
vprint(v, 1, 'InterfaceError writing to file at ' +
'{}, {}'.format(savefile, ind))
continue
# run classifier
vprint(v, 3, 'Running MC classifier on track {} in {}'.format(
ind, loadfile))
classifier = cl.Classifier(this_track.g4track)
try:
classifier.mc_classify()
except cl.TrackTooShortError:
classifier.error = 'TrackTooShortError'
else:
vprint(v, 3,
'Running ends classifier on track {} in {}'.format(
ind, loadfile))
try:
classifier.end_classify(this_track, mom=mom)
except tp.G4TrackTooBigError:
classifier.error = 'G4TrackTooBigError'
except cl.NoEnds:
classifier.error = 'NoEnds'
# write into savefile
vprint(v, 3, 'Writing classifier into {} for track {}'.format(
savefile, ind))
trackio.write_object_to_hdf5(
classifier, h5save, 'cl_' + trkpath,
pyobj_to_h5=pyobj_to_h5)
if progressflag:
pbar.update(n)
if progressflag:
pbar.finish()
# f gets closed here
except NotImplementedError:
pass
def test_Track():
"""
"""
def test_Track_read(track):
# test Track data format
from etrack.io import trackio
import os
filebase = ''.join(
chr(i) for i in np.random.randint(97, 122, size=(8, )))
filename = '.'.join([filebase, 'h5'])
with h5py.File(filename, 'a') as h5file:
trackio.write_object_to_hdf5(track, h5file, 'track')
with h5py.File(filename, 'r') as h5file:
track2 = trackio.read_object_from_hdf5(h5file['track'])
assert track2['is_modeled'] == track.is_modeled
assert track2['pixel_size_um'] == track.pixel_size_um
assert track2['noise_ev'] == track.noise_ev
assert track2['label'] == track.label
assert track2['energy_kev'] == track.energy_kev
assert np.all(track2['image'] == track.image)
assert track2['algorithms']['python HT v1.5']['alpha_deg'] == 120.5
assert track2['algorithms']['python HT v1.5']['beta_deg'] == 43.5
test_Track_from_pydict(track, track2)
test_Track_from_hdf5()
os.remove(filename)
def test_Track_from_pydict(track, track2):
"""
track is the original generated Track object
track2 is the pydict from file
Returns the track object constructed from pydict.
"""
track3 = Track.from_pydict(track2)
assert track3.is_modeled == track.is_modeled
assert track3.pixel_size_um == track.pixel_size_um
assert track3.noise_ev == track.noise_ev
assert track3.label == track.label
assert track3.energy_kev == track.energy_kev
assert np.all(track3.image == track.image)
assert track3.algorithms['python HT v1.5'].alpha_deg == 120.5
assert track3.algorithms['python HT v1.5'].beta_deg == 43.5
return track3
def test_Track_from_hdf5():
"""
as previously, but test the Track.from_hdf5() constructor.
"""
# TODO
print('test_Track_from_hdf5 not implemented yet!')
pass
def test_TrackExceptions():
"""
"""
image = hybridtrack.test_input()
try:
Track(image)
raise RuntimeError('Failed to raise error on Track instantiation')
except InputError:
pass
try:
Track(image, is_modeled=True, is_measured=True)
raise RuntimeError('Failed to raise error on Track instantiation')
except InputError:
pass
try:
Track(image, is_modeled=False, is_measured=False)
raise RuntimeError('Failed to raise error on Track instantiation')
except InputError:
pass
# test_Track() main
image = hybridtrack.test_input()
track = Track(image,
is_modeled=True,
pixel_size_um=10.5,
noise_ev=0.0,
label='MultiAngle',
energy_kev=np.sum(image))
options, info = hybridtrack.reconstruct(track)
track.add_algorithm('python HT v1.5', 120.5, 43.5, info=info)
test_Track_read(track)
test_TrackExceptions()
def test_Track():
"""
"""
def test_Track_read(track):
# test Track data format
from etrack.io import trackio
import os
filebase = ''.join(
chr(i) for i in np.random.randint(97, 122, size=(8,)))
filename = '.'.join([filebase, 'h5'])
with h5py.File(filename, 'a') as h5file:
trackio.write_object_to_hdf5(
track, h5file, 'track')
with h5py.File(filename, 'r') as h5file:
track2 = trackio.read_object_from_hdf5(
h5file['track'])
assert track2['is_modeled'] == track.is_modeled
assert track2['pixel_size_um'] == track.pixel_size_um
assert track2['noise_ev'] == track.noise_ev
assert track2['label'] == track.label
assert track2['energy_kev'] == track.energy_kev
assert np.all(track2['image'] == track.image)
assert track2['algorithms']['python HT v1.5']['alpha_deg'] == 120.5
assert track2['algorithms']['python HT v1.5']['beta_deg'] == 43.5
test_Track_from_pydict(track, track2)
test_Track_from_hdf5()
os.remove(filename)
def test_Track_from_pydict(track, track2):
"""
track is the original generated Track object
track2 is the pydict from file
Returns the track object constructed from pydict.
"""
track3 = Track.from_pydict(track2)
assert track3.is_modeled == track.is_modeled
assert track3.pixel_size_um == track.pixel_size_um
assert track3.noise_ev == track.noise_ev
assert track3.label == track.label
assert track3.energy_kev == track.energy_kev
assert np.all(track3.image == track.image)
assert track3.algorithms['python HT v1.5'].alpha_deg == 120.5
assert track3.algorithms['python HT v1.5'].beta_deg == 43.5
return track3
def test_Track_from_hdf5():
"""
as previously, but test the Track.from_hdf5() constructor.
"""
# TODO
print('test_Track_from_hdf5 not implemented yet!')
pass
def test_TrackExceptions():
"""
"""
image = hybridtrack.test_input()
try:
Track(image)
raise RuntimeError('Failed to raise error on Track instantiation')
except InputError:
pass
try:
Track(image, is_modeled=True, is_measured=True)
raise RuntimeError('Failed to raise error on Track instantiation')
except InputError:
pass
try:
Track(image, is_modeled=False, is_measured=False)
raise RuntimeError('Failed to raise error on Track instantiation')
except InputError:
pass
# test_Track() main
image = hybridtrack.test_input()
track = Track(image, is_modeled=True, pixel_size_um=10.5, noise_ev=0.0,
label='MultiAngle', energy_kev=np.sum(image))
options, info = hybridtrack.reconstruct(track)
track.add_algorithm('python HT v1.5', 120.5, 43.5, info=info)
test_Track_read(track)
test_TrackExceptions()
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
