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 from_pydict(cls, read_dict, pydict_to_pyobj=None, reconstruct=False):
"""
Initialize a Classifier object from a pydict.
"""
if pydict_to_pyobj is None:
pydict_to_pyobj = {}
if id(read_dict) in pydict_to_pyobj:
return pydict_to_pyobj[id(read_dict)]
# first, reconstruct g4track (if needed)
if isinstance(read_dict['g4track'], G4Track):
# g4track is already a G4Track object (not sure how)
g4track = read_dict['g4track']
elif (isinstance(read_dict['g4track'], dict) and
id(read_dict['g4track']) in pydict_to_pyobj):
# g4track is in the pydict table
g4track = pydict_to_pyobj[id(read_dict['g4track'])]
elif isinstance(read_dict['g4track'], dict):
# g4track not in the pydict table. create and add it
g4track = G4Track.from_pydict(
read_dict['g4track'], pydict_to_pyobj=pydict_to_pyobj)
pydict_to_pyobj[id(read_dict['g4track'])] = g4track
else:
raise Exception("Unexpected or missing 'g4track' in Classifier")
new_obj = cls(g4track)
# add entry to pydict_to_pyobj
pydict_to_pyobj[id(read_dict)] = new_obj
# fill in outputs
if read_dict['error'] is not None:
new_obj.error = read_dict['error']
if read_dict['scatterlen_um'] is not None:
# mc_classify ran
new_obj.scatterlen_um = read_dict['scatterlen_um']
new_obj.overlapdist_um = read_dict['overlapdist_um']
new_obj.escaped = read_dict['escaped']
new_obj.scatter_type = read_dict['scatter_type']
new_obj.use2d_angle = read_dict['use2d_angle']
new_obj.use2d_dist = read_dict['use2d_dist']
new_obj.angle_threshold_deg = read_dict['angle_threshold_deg']
if read_dict['early_scatter'] is not None:
# no TrackTooShortError
new_obj.early_scatter = read_dict['early_scatter']
new_obj.total_scatter_angle = read_dict['total_scatter_angle']
new_obj.overlap = read_dict['overlap']
if read_dict['wrong_end'] is not None:
# end_classify ran
new_obj.wrong_end = read_dict['wrong_end']
new_obj.n_ends = read_dict['n_ends']
new_obj.max_end_energy = read_dict['max_end_energy']
new_obj.min_end_energy = read_dict['min_end_energy']
if reconstruct:
new_obj.mc_classify()
print(
'Reconstructing Classifier.end_classify() not implemented yet')
return new_obj
def from_pydict(cls, read_dict, pydict_to_pyobj=None, reconstruct=False):
"""
Initialize a Classifier object from a pydict.
"""
if pydict_to_pyobj is None:
pydict_to_pyobj = {}
if id(read_dict) in pydict_to_pyobj:
return pydict_to_pyobj[id(read_dict)]
# first, reconstruct g4track (if needed)
if isinstance(read_dict['g4track'], G4Track):
# g4track is already a G4Track object (not sure how)
g4track = read_dict['g4track']
elif (isinstance(read_dict['g4track'], dict) and
id(read_dict['g4track']) in pydict_to_pyobj):
# g4track is in the pydict table
g4track = pydict_to_pyobj[id(read_dict['g4track'])]
elif isinstance(read_dict['g4track'], dict):
# g4track not in the pydict table. create and add it
g4track = G4Track.from_pydict(
read_dict['g4track'], pydict_to_pyobj=pydict_to_pyobj)
pydict_to_pyobj[id(read_dict['g4track'])] = g4track
else:
raise Exception("Unexpected or missing 'g4track' in Classifier")
new_obj = cls(g4track)
# add entry to pydict_to_pyobj
pydict_to_pyobj[id(read_dict)] = new_obj
# fill in outputs
if read_dict['error'] is not None:
new_obj.error = read_dict['error']
if read_dict['scatterlen_um'] is not None:
# mc_classify ran
new_obj.scatterlen_um = read_dict['scatterlen_um']
new_obj.overlapdist_um = read_dict['overlapdist_um']
new_obj.escaped = read_dict['escaped']
new_obj.scatter_type = read_dict['scatter_type']
new_obj.use2d_angle = read_dict['use2d_angle']
new_obj.use2d_dist = read_dict['use2d_dist']
new_obj.angle_threshold_deg = read_dict['angle_threshold_deg']
if read_dict['early_scatter'] is not None:
# no TrackTooShortError
new_obj.early_scatter = read_dict['early_scatter']
new_obj.total_scatter_angle = read_dict['total_scatter_angle']
new_obj.overlap = read_dict['overlap']
if read_dict['wrong_end'] is not None:
# end_classify ran
new_obj.wrong_end = read_dict['wrong_end']
new_obj.n_ends = read_dict['n_ends']
new_obj.max_end_energy = read_dict['max_end_energy']
new_obj.min_end_energy = read_dict['min_end_energy']
if reconstruct:
new_obj.mc_classify()
print(
'Reconstructing Classifier.end_classify() not implemented yet')
return new_obj
