def get_skel_extremes(vox_size, energy_type, strict_vox_size, hitc):
voxels = plf.voxelize_hits(hitc.hits, vox_size, strict_vox_size,
energy_type)
tracks = plf.make_track_graphs(voxels)
df = pd.DataFrame(columns=[
'event', 'trackID', 'energy', 'skel_extr1_x', 'skel_extr1_y',
'skel_extr1_z', 'skel_extr2_x', 'skel_extr2_y', 'skel_extr2_z'
])
if (len(voxels) == 0):
return df
vox_size_x = voxels[0].size[0]
vox_size_y = voxels[0].size[1]
vox_size_z = voxels[0].size[2]
def get_track_energy(track):
return sum([vox.Ehits for vox in track.nodes()])
#sort tracks in energy
tracks = sorted(tracks, key=get_track_energy, reverse=True)
track_hits = []
for c, t in enumerate(tracks, 0):
tID = c
energy = get_track_energy(t)
extr1, extr2 = plf.find_extrema(t)
extr1_pos = extr1.XYZ
extr2_pos = extr2.XYZ
list_of_vars = [
hitc.event, tID, energy, extr1_pos[0], extr1_pos[1], extr1_pos[2],
extr2_pos[0], extr2_pos[1], extr2_pos[2]
]
df.loc[c] = list_of_vars
try:
types_dict
except NameError:
types_dict = dict(zip(df.columns, [type(x) for x in list_of_vars]))
#change dtype of columns to match type of variables
df = df.apply(lambda x: x.astype(types_dict[x.name]))
return df
def create_tracks_with_skel_extremes(vox_size, energy_type, strict_vox_size,
blob_radius, df_extr, hitc):
voxels = plf.voxelize_hits(hitc.hits, vox_size, strict_vox_size,
energy_type)
tracks = plf.make_track_graphs(voxels)
df = pd.DataFrame(columns=[
'event', 'trackID', 'energy', 'length', 'numb_of_voxels',
'numb_of_hits', 'numb_of_tracks', 'x_min', 'y_min', 'z_min', 'x_max',
'y_max', 'z_max', 'r_max', 'x_ave', 'y_ave', 'z_ave', 'extreme1_x',
'extreme1_y', 'extreme1_z', 'extreme2_x', 'extreme2_y', 'extreme2_z',
'blob1_x', 'blob1_y', 'blob1_z', 'blob2_x', 'blob2_y', 'blob2_z',
'eblob1', 'eblob2', 'ovlp_blob_energy', 'vox_size_x', 'vox_size_y',
'vox_size_z'
])
if (len(voxels) == 0):
return df
vox_size_x = voxels[0].size[0]
vox_size_y = voxels[0].size[1]
vox_size_z = voxels[0].size[2]
def get_track_energy(track):
return sum([vox.Ehits for vox in track.nodes()])
#sort tracks in energy
tracks = sorted(tracks, key=get_track_energy, reverse=True)
track_hits = []
for c, t in enumerate(tracks, 0):
tID = c
energy = get_track_energy(t)
length = plf.length(t)
numb_of_hits = len([h for vox in t.nodes() for h in vox.hits])
numb_of_voxels = len(t.nodes())
numb_of_tracks = len(tracks)
min_x = min([h.X for v in t.nodes() for h in v.hits])
max_x = max([h.X for v in t.nodes() for h in v.hits])
min_y = min([h.Y for v in t.nodes() for h in v.hits])
max_y = max([h.Y for v in t.nodes() for h in v.hits])
min_z = min([h.Z for v in t.nodes() for h in v.hits])
max_z = max([h.Z for v in t.nodes() for h in v.hits])
max_r = max([
np.sqrt(h.X * h.X + h.Y * h.Y) for v in t.nodes() for h in v.hits
])
pos = [h.pos for v in t.nodes() for h in v.hits]
e = [getattr(h, energy_type.value) for v in t.nodes() for h in v.hits]
ave_pos = np.average(pos, weights=e, axis=0)
# classic paolina extremes
extr1, extr2 = plf.find_extrema(t)
extr1_pos = extr1.XYZ
extr2_pos = extr2.XYZ
t_extr = df_extr[df_extr.trackID == tID]
if len(t_extr) == 0:
blob_pos1 = np.array([1.e6, 1.e6, 1.e6])
blob_pos2 = np.array([1.e6, 1.e6, 1.e6])
eblob1 = -1
eblob2 = -1
overlap = -1
else:
blob_pos1 = np.array([
t_extr.skel_extr1_x.values[0], t_extr.skel_extr1_y.values[0],
t_extr.skel_extr1_z.values[0]
])
blob_pos2 = np.array([
t_extr.skel_extr2_x.values[0], t_extr.skel_extr2_y.values[0],
t_extr.skel_extr2_z.values[0]
])
e_blob1, e_blob2, hits_blob1, hits_blob2, blob_pos1, blob_pos2 = blob_energies_hits(
t, blob_radius, blob_pos1, blob_pos2)
overlap = sum(
[h.E for h in set(hits_blob1).intersection(set(hits_blob2))])
list_of_vars = [
hitc.event, tID, energy, length, numb_of_voxels, numb_of_hits,
numb_of_tracks, min_x, min_y, min_z, max_x, max_y, max_z, max_r,
ave_pos[0], ave_pos[1], ave_pos[2], extr1_pos[0], extr1_pos[1],
extr1_pos[2], extr2_pos[0], extr2_pos[1], extr2_pos[2],
blob_pos1[0], blob_pos1[1], blob_pos1[2], blob_pos2[0],
blob_pos2[1], blob_pos2[2], e_blob1, e_blob2, overlap, vox_size_x,
vox_size_y, vox_size_z
]
df.loc[c] = list_of_vars
try:
types_dict
except NameError:
types_dict = dict(zip(df.columns, [type(x) for x in list_of_vars]))
#change dtype of columns to match type of variables
df = df.apply(lambda x: x.astype(types_dict[x.name]))
return df
etmax = sum([vox.E for vox in trks[itmax].nodes()])
# Construct the blobs.
eblobs = plf.blob_energies(trks[itmax],blob_radius)
iter_eblobs = iter(eblobs)
Eblob1, Eblob2 = next(iter_eblobs),next(iter_eblobs)
# Ensure blob1 always has higher energy.
if(Eblob2 > Eblob1):
eswap = Eblob1
Eblob1 = Eblob2
Eblob2 = eswap
# Get the extremes.
distances = plf.shortest_paths(trks[itmax])
a,b = plf.find_extrema(trks[itmax]) #(distances)
ltrk = distances[a][b]
print("Found {0} tracks of {1}; max containing {2} voxels; total of {3} voxels, distance = {4}".format(len(trks),len(hitc_cevt),len(trks[itmax]),len(voxels),distances[a][b]))
# Center the voxels.
xmin = np.min([vox.X for vox in voxels]); xmax = np.max([vox.X for vox in voxels])
ymin = np.min([vox.Y for vox in voxels]); ymax = np.max([vox.Y for vox in voxels])
zmin = np.min([vox.Z for vox in voxels]); zmax = np.max([vox.Z for vox in voxels])
x0 = ((xmin + xmax) / 2. - (rng / 2.))
y0 = ((ymin + ymax) / 2. - (rng / 2.))
z0 = ((zmin + zmax) / 2. - (rng / 2.))
# Create the voxel array.
varr = np.zeros([vdim[0],vdim[1],vdim[2]])
# Iterate through the voxels, applying offsets.
tot_e = sum([hh.E for hh in hitc])
voxels = plf.voxelize_hits(hitc, vox_size)
trks = plf.make_track_graphs(voxels)
while True:
modified_voxels = 0
trks = plf.make_track_graphs(voxels)
vxl_size = voxels[0].size
for t in trks:
if len(t.nodes()) < 3:
continue
extr1, extr2 = plf.find_extrema(t)
if extr1.E < voxel_cut:
modified_voxels += 1
### remove voxel from list of voxels
voxels.remove(extr1)
### remove hits from the list of hits
pos1 = [h.pos for h in extr1.hits]
qs1 = [h.E for h in extr1.hits]
bary_extr1_pos = np.average(pos1, weights=qs1, axis=0)
for h in extr1.hits:
hitc.remove(h)
### find hit with minimum distance, only among neighbours
min_dist = 1e+06
min_hit = hitc[0]
def create_extract_track_blob_info(hitc):
df = pd.DataFrame(columns=list(types_dict_tracks.keys()))
if len(hitc.hits) > max_num_hits:
return df, hitc, True
#track_hits is a new Hitcollection object that contains hits belonging to tracks, and hits that couldnt be corrected
track_hitc = evm.HitCollection(hitc.event, hitc.time)
out_of_map = np.any(np.isnan([h.Ep for h in hitc.hits]))
if out_of_map:
#add nan hits to track_hits, the track_id will be -1
track_hitc.hits.extend([h for h in hitc.hits if np.isnan(h.Ep)])
hits_without_nan = [h for h in hitc.hits if np.isfinite(h.Ep)]
#create new Hitcollection object but keep the name hitc
hitc = evm.HitCollection(hitc.event, hitc.time)
hitc.hits = hits_without_nan
if len(hitc.hits) > 0:
voxels = plf.voxelize_hits(hitc.hits, vox_size, strict_vox_size,
evm.HitEnergy.Ep)
(mod_voxels, dropped_voxels) = plf.drop_end_point_voxels(
voxels, energy_threshold, min_voxels)
tracks = plf.make_track_graphs(mod_voxels)
for v in dropped_voxels:
track_hitc.hits.extend(v.hits)
vox_size_x = voxels[0].size[0]
vox_size_y = voxels[0].size[1]
vox_size_z = voxels[0].size[2]
del (voxels)
#sort tracks in energy
tracks = sorted(tracks, key=plf.get_track_energy, reverse=True)
track_hits = []
for c, t in enumerate(tracks, 0):
tID = c
energy = plf.get_track_energy(t)
length = plf.length(t)
numb_of_hits = len([h for vox in t.nodes() for h in vox.hits])
numb_of_voxels = len(t.nodes())
numb_of_tracks = len(tracks)
pos = [h.pos for v in t.nodes() for h in v.hits]
x, y, z = map(np.array, zip(*pos))
r = np.sqrt(x**2 + y**2)
e = [h.Ep for v in t.nodes() for h in v.hits]
ave_pos = np.average(pos, weights=e, axis=0)
ave_r = np.average(r, weights=e, axis=0)
extr1, extr2 = plf.find_extrema(t)
extr1_pos = extr1.XYZ
extr2_pos = extr2.XYZ
blob_pos1, blob_pos2 = plf.blob_centres(t, blob_radius)
e_blob1, e_blob2, hits_blob1, hits_blob2 = plf.blob_energies_and_hits(
t, blob_radius)
overlap = float(
sum(h.Ep for h in set(hits_blob1).intersection(
set(hits_blob2))))
list_of_vars = [
hitc.event, tID, energy, length, numb_of_voxels,
numb_of_hits, numb_of_tracks,
min(x),
min(y),
min(z),
min(r),
max(x),
max(y),
max(z),
max(r), *ave_pos, ave_r, *extr1_pos, *extr2_pos,
*blob_pos1, *blob_pos2, e_blob1, e_blob2, overlap,
vox_size_x, vox_size_y, vox_size_z
]
df.loc[c] = list_of_vars
for vox in t.nodes():
for hit in vox.hits:
hit.track_id = tID
track_hits.append(hit)
#change dtype of columns to match type of variables
df = df.apply(lambda x: x.astype(types_dict_tracks[x.name]))
track_hitc.hits.extend(track_hits)
return df, mod_voxels, track_hitc, out_of_map
def create_extract_track_blob_info(hitc):
voxels = plf.voxelize_hits(hitc.hits, vox_size, strict_vox_size,
energy_type)
tracks = plf.make_track_graphs(voxels)
df = pd.DataFrame(columns=[
'event', 'trackID', 'energy', 'length', 'numb_of_voxels',
'numb_of_hits', 'numb_of_tracks', 'x_min', 'y_min', 'z_min',
'x_max', 'y_max', 'z_max', 'r_max', 'x_ave', 'y_ave', 'z_ave',
'extreme1_x', 'extreme1_y', 'extreme1_z', 'extreme2_x',
'extreme2_y', 'extreme2_z', 'blob1_x', 'blob1_y', 'blob1_z',
'blob2_x', 'blob2_y', 'blob2_z', 'eblob1', 'eblob2',
'ovlp_blob_energy', 'vox_size_x', 'vox_size_y', 'vox_size_z'
])
if (len(voxels) == 0):
return df, None
vox_size_x = voxels[0].size[0]
vox_size_y = voxels[0].size[1]
vox_size_z = voxels[0].size[2]
def get_track_energy(track):
return sum([vox.Ehits for vox in track.nodes()])
#sort tracks in energy
tracks = sorted(tracks, key=get_track_energy, reverse=True)
track_hits = []
for c, t in enumerate(tracks, 0):
tID = c
energy = get_track_energy(t)
length = plf.length(t)
numb_of_hits = len([h for vox in t.nodes() for h in vox.hits])
numb_of_voxels = len(t.nodes())
numb_of_tracks = len(tracks)
min_x = min([h.X for v in t.nodes() for h in v.hits])
max_x = max([h.X for v in t.nodes() for h in v.hits])
min_y = min([h.Y for v in t.nodes() for h in v.hits])
max_y = max([h.Y for v in t.nodes() for h in v.hits])
min_z = min([h.Z for v in t.nodes() for h in v.hits])
max_z = max([h.Z for v in t.nodes() for h in v.hits])
max_r = max([
np.sqrt(h.X * h.X + h.Y * h.Y) for v in t.nodes()
for h in v.hits
])
pos = [h.pos for v in t.nodes() for h in v.hits]
e = [
getattr(h, energy_type.value) for v in t.nodes()
for h in v.hits
]
ave_pos = np.average(pos, weights=e, axis=0)
extr1, extr2 = plf.find_extrema(t)
extr1_pos = extr1.XYZ
extr2_pos = extr2.XYZ
blob_pos1, blob_pos2 = plf.blob_centres(t, blob_radius)
e_blob1, e_blob2, hits_blob1, hits_blob2 = plf.blob_energies_and_hits(
t, blob_radius)
overlap = False
overlap = sum(
[h.E for h in set(hits_blob1).intersection(hits_blob2)])
list_of_vars = [
hitc.event, tID, energy, length, numb_of_voxels, numb_of_hits,
numb_of_tracks, min_x, min_y, min_z, max_x, max_y, max_z,
max_r, ave_pos[0], ave_pos[1], ave_pos[2], extr1_pos[0],
extr1_pos[1], extr1_pos[2], extr2_pos[0], extr2_pos[1],
extr2_pos[2], blob_pos1[0], blob_pos1[1], blob_pos1[2],
blob_pos2[0], blob_pos2[1], blob_pos2[2], e_blob1, e_blob2,
overlap, vox_size_x, vox_size_y, vox_size_z
]
df.loc[c] = list_of_vars
try:
types_dict
except NameError:
types_dict = dict(
zip(df.columns, [type(x) for x in list_of_vars]))
for vox in t.nodes():
for hit in vox.hits:
hit.track_id = tID
track_hits.append(hit)
track_hitc = evm.HitCollection(hitc.event, hitc.time)
track_hitc.hits = track_hits
#change dtype of columns to match type of variables
df = df.apply(lambda x: x.astype(types_dict[x.name]))
return df, track_hitc
