def load_hits_skipping_NN(DST_file_name, evt_number):
"""Return the Hits as PD DataFrames."""
dst = load_dst(DST_file_name,'RECO','Events')
dst_size = len(dst)
all_events = {}
event = dst.event.values
time = dst.time .values
npeak = dst.npeak.values
X = dst.X .values
Y = dst.Y .values
Z = dst.Z .values
Q = dst.Q .values
E = dst.E .values
for i in range(dst_size):
if event[i] == evt_number:
current_event = all_events.setdefault(event[i],
HitCollection(event[i], time[i] * 1e-3))
hit = Hit(npeak[i],
Cluster(Q[i], xy(X[i], Y[i]), xy(0, 0),
0, Z[i], E[i]), Z[i], E[i], xy(0, 0))
if(hit.Q != NN):
current_event.hits.append(hit)
good_events = {}
for event, hitc in all_events.items():
if len(hitc.hits) > 0:
good_events[event] = hitc
return good_events
base_vsize = float(sys.argv[3])
kind = str(sys.argv[4])
with tb.open_file(the_file) as h5in:
table = getattr(getattr(h5in.root, 'CHITS'), 'highTh').read()
hits_df = pd.DataFrame.from_records(table)
this_evt_df = hits_df[hits_df.event == evt_number]
the_hits = []
xs = this_evt_df.X
ys = this_evt_df.Y
zs = this_evt_df.Z
es = this_evt_df.Ec
for x, y, z, e in zip(xs, ys, zs, es):
h = Hit(0, Cluster(0, xy(x, y), xy(0, 0), 0), z, e * 1000, xy(0, 0))
the_hits.append(h)
voxels = voxelize_hits(the_hits, np.array([base_vsize, base_vsize,
base_vsize]), False)
vsizex = voxels[0].size[0]
vsizey = voxels[0].size[1]
vsizez = voxels[0].size[2]
min_corner_x = min(v.X for v in voxels) - vsizex / 2.
min_corner_y = min(v.Y for v in voxels) - vsizey / 2.
min_corner_z = min(v.Z for v in voxels) - vsizez / 2.
x = [np.round(v.X / vsizex) for v in voxels]
y = [np.round(v.Y / vsizey) for v in voxels]
if mc:
drift_velocity = 1
with tb.open_file(the_file) as h5in:
table = getattr(getattr(h5in.root, 'RECO'), 'Events').read()
hits_df = pd.DataFrame.from_records(table)
this_evt_df = hits_df[hits_df.event == evt_number]
the_hits = []
## exclude NN hits from plot
xs = this_evt_df[this_evt_df.Q >= 0].X
ys = this_evt_df[this_evt_df.Q >= 0].Y
zs = this_evt_df[this_evt_df.Q >= 0].Z
es = this_evt_df[this_evt_df.Q >= 0].E
for x, y, z, e in zip(xs, ys, zs, es):
h = Hit(0, Cluster(0, xy(x, y), xy(0, 0), 0), z * drift_velocity, e,
xy(0, 0))
the_hits.append(h)
voxels = voxelize_hits(the_hits, np.array([10., 10., 10]), False)
fig = plt.figure() #figsize=(20, 10))
ax = fig.add_subplot(111, projection='3d')
#ax = fig.gca(projection='3d')
#ax.set_aspect("equal")
energies = [v.E for v in voxels]
energies = np.array(energies)
min_energy = energies.min()
max_energy = energies.max()
print('Minimum energy = {}, maximum energy = {}'.format(
def convert_df_to_hits(df):
return [
Hit(0, Cluster(0, xy(h.X, h.Y), xy(0, 0), 0), h.Z, h.E, xy(0, 0))
for h in df.itertuples(index=False)
]
#
# # Only correct if we have nonzero Q'.
# if(Qptot > 0):
# for hh in hqcoll:
# hh.energy = hh.E*(Qtot/Qptot)*XYQcorr(hh.X,hh.Y).value*LTQcorr(hh.Z,hh.X,hh.Y).value
# #else:
# # print("Not correcting for {} hits".format(len(hqcoll)))
for hh in hc.hits:
hecorr = hh.E*XYcorr(hh.X,hh.Y).value*LTcorr(hh.Z,hh.X,hh.Y).value**(ftlife) #/np.exp(-hh.Z/tlife)
hegcorr = hh.E*XYcorr(hh.X,hh.Y).value
hetcorr = hh.E*LTcorr(hh.Z,hh.X,hh.Y).value**(ftlife) #/np.exp(-hh.Z/tlife)
hegtglobalcorr = hh.E*XYcorr(hh.X,hh.Y).value/np.exp(-hh.Z/tlife)
hucorr = Hit(0,Cluster(0, xy(hh.X,hh.Y), xy(0,0), 0),hh.Z,hh.E)
hcorr = Hit(0,Cluster(0, xy(hh.X,hh.Y), xy(0,0), 0),hh.Z,hecorr)
hgcorr = Hit(0,Cluster(0, xy(hh.X,hh.Y), xy(0,0), 0),hh.Z,hegcorr)
htcorr = Hit(0,Cluster(0, xy(hh.X,hh.Y), xy(0,0), 0),hh.Z,hetcorr)
hgtglobalcorr = Hit(0,Cluster(0, xy(hh.X,hh.Y), xy(0,0), 0),hh.Z,hegtglobalcorr)
hc_ucorr.append(hucorr); hc_corr.append(hcorr); hc_gcorr.append(hgcorr); hc_tcorr.append(htcorr); hc_gtglobalcorr.append(hgtglobalcorr)
# Only save events with >= 2 hits.
if(len(hc_corr) >= 2):
hitc_uevt.append(hc_ucorr)
hitc_cevt.append(hc_corr)
hitc_gevt.append(hc_gcorr)
hitc_tevt.append(hc_tcorr)
hitc_gtglobalevt.append(hc_gtglobalcorr)
for ee, hc in good_hits.items():
bad_evt = False
hc_corr = []
hX = [hh.X for hh in hc.hits]
hY = [hh.Y for hh in hc.hits]
hZ = [hh.Z for hh in hc.hits]
hE = [hh.E for hh in hc.hits]
hEcorr = hE * LTcorrection(hZ, hX, hY).value * XYcorrection(hX, hY).value
for h, ec in zip(hc.hits, hEcorr):
if ec == 0:
bad_evt = True
break
hcorr = Hit(0, Cluster(0, xy(h.X,h.Y), xy(0,0), 0), h.Z*drift_velocity, ec, xy(h.Xpeak,h.Ypeak))
hc_corr.append(hcorr)
hits_evt[ee] = hc_corr
bad_event[ee] = bad_evt
times[ee] = hc.time
raw_energy[ee] = sum(hE)
events_in += len(hits_evt)
for nevt, hitc in hits_evt.items():
if bad_event[nevt]:
not_fid += 1
continue
def convert_df_to_hits(df):
return [
Hit(0, Cluster(0, xy(h.x, h.y), xy(0, 0), 0), h.z, h.energy, xy(0, 0))
for h in df.itertuples(index=False)
]
bad_event = {}
for ee, hc in good_hits.items():
bad_evt = False
hc_corr = []
raw_e = 0.
for hh in hc.hits:
raw_e += hh.E
# temporary - in the future no zeros in maps.
if XYcorrection(hh.X, hh.Y).value == 0:
bad_evt = True
break
hecorr = hh.E * LTcorrection(
hh.Z, hh.X, hh.Y).value * XYcorrection(hh.X, hh.Y).value
# here we convert time to space for z.
z = hh.Z * drift_velocity
hcorr = Hit(0, Cluster(0, xy(hh.X, hh.Y), xy(0, 0), 0), z, hecorr,
xy(0, 0))
hc_corr.append(hcorr)
hits_evt[ee] = hc_corr
raw_energy[ee] = raw_e
bad_event[ee] = bad_evt
events_in += len(hits_evt)
for nevt, hitc in hits_evt.items():
if bad_event[nevt]:
not_fid += 1
continue
vmaxX = -1e+06
vminX = 1e+06
