def simulate_reco_event(evt_id: int,
hits: pd.DataFrame,
particles: pd.DataFrame,
errmat_p_r: errmat,
errmat_p_phi: errmat,
errmat_p_z: errmat,
errmat_p_t: errmat,
errmat_c_r: errmat,
errmat_c_phi: errmat,
errmat_c_z: errmat,
errmat_c_t: errmat,
true_e_threshold: float = 0.) -> pd.DataFrame:
"""
Simulate the reconstructed coordinates for 1 coincidence from true GEANT4 dataframes.
Notice that the time binning must be provided in ps.
"""
evt_parts = particles[particles.event_id == evt_id]
evt_hits = hits[hits.event_id == evt_id]
energy = evt_hits.energy.sum()
if energy < true_e_threshold:
events = pd.DataFrame({
'event_id': [float(evt_id)],
'true_energy': [energy],
'true_r1': [0.],
'true_phi1': [0.],
'true_z1': [0.],
'true_t1': [0.],
'true_r2': [0.],
'true_phi2': [0.],
'true_z2': [0.],
'true_t2': [0.],
'phot_like1': [0.],
'phot_like2': [0.],
'reco_r1': [0.],
'reco_phi1': [0.],
'reco_z1': [0.],
'reco_t1': [0.],
'reco_r2': [0.],
'reco_phi2': [0.],
'reco_z2': [0.],
'reco_t2': [0.]
})
return events
pos1, pos2, t1, t2, phot1, phot2 = rf.find_first_interactions_in_active(
evt_parts, evt_hits)
if len(pos1) == 0 or len(pos2) == 0:
events = pd.DataFrame({
'event_id': [float(evt_id)],
'true_energy': [energy],
'true_r1': [0.],
'true_phi1': [0.],
'true_z1': [0.],
'true_t1': [0.],
'true_r2': [0.],
'true_phi2': [0.],
'true_z2': [0.],
'true_t2': [0.],
'phot_like1': [0.],
'phot_like2': [0.],
'reco_r1': [0.],
'reco_phi1': [0.],
'reco_z1': [0.],
'reco_t1': [0.],
'reco_r2': [0.],
'reco_phi2': [0.],
'reco_z2': [0.],
'reco_t2': [0.]
})
return events
t1 = t1 / units.ps
t2 = t2 / units.ps
# Transform in cylindrical coordinates
cyl_pos = rf.from_cartesian_to_cyl(np.array([pos1, pos2]))
r1 = cyl_pos[0, 0]
phi1 = cyl_pos[0, 1]
z1 = cyl_pos[0, 2]
r2 = cyl_pos[1, 0]
phi2 = cyl_pos[1, 1]
z2 = cyl_pos[1, 2]
# Get all errors.
if phot1:
er1 = errmat_p_r.get_random_error(r1)
ephi1 = errmat_p_phi.get_random_error(phi1)
ez1 = errmat_p_z.get_random_error(z1)
et1 = errmat_p_t.get_random_error(t1)
else:
er1 = errmat_c_r.get_random_error(r1)
ephi1 = errmat_c_phi.get_random_error(phi1)
ez1 = errmat_c_z.get_random_error(z1)
et1 = errmat_c_t.get_random_error(t1)
if phot2:
er2 = errmat_p_r.get_random_error(r2)
ephi2 = errmat_p_phi.get_random_error(phi2)
ez2 = errmat_p_z.get_random_error(z2)
et2 = errmat_p_t.get_random_error(t2)
else:
er2 = errmat_c_r.get_random_error(r2)
ephi2 = errmat_c_phi.get_random_error(phi2)
ez2 = errmat_c_z.get_random_error(z2)
et2 = errmat_c_t.get_random_error(t2)
# Compute reconstructed quantities.
r1_reco = r1 - er1
r2_reco = r2 - er2
phi1_reco = phi1 - ephi1
phi2_reco = phi2 - ephi2
z1_reco = z1 - ez1
z2_reco = z2 - ez2
t1_reco = t1 - et1
t2_reco = t2 - et2
event_ids = [float(evt_id)]
energies = [energy]
true_r1 = [r1]
true_phi1 = [phi1]
true_z1 = [z1]
true_t1 = [t1]
true_r2 = [r2]
true_phi2 = [phi2]
true_z2 = [z2]
true_t2 = [t2]
phot_like1 = [float(phot1)]
phot_like2 = [float(phot2)]
reco_r1 = [r1_reco]
reco_phi1 = [phi1_reco]
reco_z1 = [z1_reco]
reco_t1 = [t1_reco]
reco_r2 = [r2_reco]
reco_phi2 = [phi2_reco]
reco_z2 = [z2_reco]
reco_t2 = [t2_reco]
events = pd.DataFrame({
'event_id': event_ids,
'true_energy': energies,
'true_r1': true_r1,
'true_phi1': true_phi1,
'true_z1': true_z1,
'true_t1': true_t1,
'true_r2': true_r2,
'true_phi2': true_phi2,
'true_z2': true_z2,
'true_t2': true_t2,
'phot_like1': phot_like1,
'phot_like2': phot_like2,
'reco_r1': reco_r1,
'reco_phi1': reco_phi1,
'reco_z1': reco_z1,
'reco_t1': reco_t1,
'reco_r2': reco_r2,
'reco_phi2': reco_phi2,
'reco_z2': reco_z2,
'reco_t2': reco_t2
})
return events
for evt in events[:]:
### Select photoelectric events only
evt_parts = particles[particles.event_id == evt]
evt_hits = hits[hits.event_id == evt]
select, true_pos = mcf.select_photoelectric(evt_parts, evt_hits)
if not select: continue
sns_resp = sel_df[sel_df.event_id == evt]
if len(sns_resp) == 0: continue
_, _, pos1, pos2, q1, q2 = rf.assign_sipms_to_gammas(
sns_resp, true_pos, DataSiPM_idx)
if len(pos1) > 0:
pos_phi = rf.from_cartesian_to_cyl(np.array(pos1))[:, 1]
_, var_phi = rf.phi_mean_var(pos_phi, q1)
pos_z = np.array(pos1)[:, 2]
mean_z = np.average(pos_z, weights=q1)
var_z = np.average((pos_z - mean_z)**2, weights=q1)
reco_cart = np.average(pos1, weights=q1, axis=0)
var_phi1.append(var_phi)
var_z1.append(var_z)
touched_sipms1.append(len(pos1))
r = np.sqrt(true_pos[0][0]**2 + true_pos[0][1]**2)
true_r1.append(r)
def simulate_reco_event(evt_id: int, hits: pd.DataFrame, particles: pd.DataFrame,
errmat_p_r: errmat, errmat_p_phi: errmat3d, errmat_p_z: errmat3d,
errmat_p_t: errmat, errmat_c_r: errmat, errmat_c_phi: errmat3d,
errmat_c_z: errmat3d, errmat_c_t: errmat,
true_e_threshold: float = 0., photo_range: float = 1.,
only_phot: bool = False) -> pd.DataFrame:
"""
Simulate the reconstructed coordinates for 1 coincidence from true GEANT4 dataframes.
"""
evt_parts = particles[particles.event_id == evt_id]
evt_hits = hits [hits .event_id == evt_id]
energy = evt_hits.energy.sum()
if energy < true_e_threshold:
return None
pos1, pos2, t1, t2, phot1, phot2 = rf.find_first_interactions_in_active(evt_parts, evt_hits, photo_range)
no_reco_positions = len(pos1) == 0 or len(pos2) == 0
no_phot_interactions = not phot1 or not phot2
if no_reco_positions or (only_phot and no_phot_interactions):
return None
t1 = t1 / units.ps
t2 = t2 / units.ps
# Transform in cylindrical coordinates
(r1, phi1, z1), (r2, phi2, z2) = rf.from_cartesian_to_cyl(np.array([pos1, pos2]))
# Get all errors.
if phot1:
er1, ephi1, ez1, et1 = get_reco_interaction(r1, phi1, z1, t1, errmat_p_r, errmat_p_phi, errmat_p_z, errmat_p_t)
else:
er1, ephi1, ez1, et1 = get_reco_interaction(r1, phi1, z1, t1, errmat_c_r, errmat_c_phi, errmat_c_z, errmat_c_t)
if phot2:
er2, ephi2, ez2, et2 = get_reco_interaction(r2, phi2, z2, t2, errmat_p_r, errmat_p_phi, errmat_p_z, errmat_p_t)
else:
er2, ephi2, ez2, et2 = get_reco_interaction(r2, phi2, z2, t2, errmat_c_r, errmat_c_phi, errmat_c_z, errmat_c_t)
if er1 == None or ephi1 == None or ez1 == None or et1 == None or er2 == None or ephi2 == None or ez2 == None or et2 == None:
return None
# Compute reconstructed quantities.
r1_reco = r1 - er1
r2_reco = r2 - er2
phi1_reco = phi1 - ephi1
phi2_reco = phi2 - ephi2
z1_reco = z1 - ez1
z2_reco = z2 - ez2
t1_reco = t1 - et1
t2_reco = t2 - et2
events = pd.DataFrame({'event_id': float(evt_id),
'true_energy': energy,
'true_r1': r1,
'true_phi1': phi1,
'true_z1': z1,
'true_t1': t1,
'true_r2': r2,
'true_phi2': phi2,
'true_z2': z2,
'true_t2': t2,
'phot_like1': float(phot1),
'phot_like2': float(phot2),
'reco_r1': r1_reco,
'reco_phi1': phi1_reco,
'reco_z1': z1_reco,
'reco_t1': t1_reco,
'reco_r2': r2_reco,
'reco_phi2': phi2_reco,
'reco_z2': z2_reco,
'reco_t2': t2_reco}, index=[0])
return events
pos2 = np.array(pos2)
## Calculate R
r1 = r2 = None
sel1_r = q1 > thr_r
q1r = q1[sel1_r]
pos1r = pos1[sel1_r]
sel2_r = q2 > thr_r
q2r = q2[sel2_r]
pos2r = pos2[sel2_r]
if len(pos1r) == 0 or len(pos2r) == 0:
c1 += 1
continue
pos1_phi = rf.from_cartesian_to_cyl(np.array(pos1r))[:, 1]
diff_sign = min(pos1_phi) < 0 < max(pos1_phi)
if diff_sign & (np.abs(np.min(pos1_phi)) > np.pi / 2.):
pos1_phi[pos1_phi < 0] = np.pi + np.pi + pos1_phi[pos1_phi < 0]
mean_phi = np.average(pos1_phi, weights=q1r)
var_phi1 = np.average((pos1_phi - mean_phi)**2, weights=q1r)
r1 = Rpos(np.sqrt(var_phi1)).value
pos2_phi = rf.from_cartesian_to_cyl(np.array(pos2r))[:, 1]
diff_sign = min(pos2_phi) < 0 < max(pos2_phi)
if diff_sign & (np.abs(np.min(pos2_phi)) > np.pi / 2.):
pos2_phi[pos2_phi < 0] = np.pi + np.pi + pos2_phi[pos2_phi < 0]
mean_phi = np.average(pos2_phi, weights=q2r)
var_phi2 = np.average((pos2_phi - mean_phi)**2, weights=q2r)
r2 = Rpos(np.sqrt(var_phi2)).value
def build_r_map(input_file: str, output_file: str, threshold: float):
"""
This function extracts the true radial position
and the variance of the SiPM positions
in phi and z for each gamma interaction.
"""
DataSiPM = db.DataSiPMsim_only('petalo', 0) # full body PET
DataSiPM_idx = DataSiPM.set_index('SensorID')
try:
sns_response = pd.read_hdf(input_file, 'MC/sns_response')
except ValueError:
print(f'File {input_file} not found')
exit()
except OSError:
print(f'File {input_file} not found')
exit()
except KeyError:
print(f'No object named MC/sns_response in file {input_file}')
exit()
print(f'Analyzing file {input_file}')
sel_df = rf.find_SiPMs_over_threshold(sns_response, threshold)
particles = pd.read_hdf(input_file, 'MC/particles')
hits = pd.read_hdf(input_file, 'MC/hits')
events = particles.event_id.unique()
true_r1, true_r2 = [], []
var_phi1, var_phi2 = [], []
var_z1, var_z2 = [], []
touched_sipms1, touched_sipms2 = [], []
for evt in events:
### Select photoelectric events only
evt_parts = particles[particles.event_id == evt]
evt_hits = hits[hits.event_id == evt]
select, true_pos = mcf.select_photoelectric(evt_parts, evt_hits)
if not select: continue
sns_resp = sel_df[sel_df.event_id == evt]
if len(sns_resp) == 0: continue
_, _, pos1, pos2, q1, q2 = rf.assign_sipms_to_gammas(
sns_resp, true_pos, DataSiPM_idx)
if len(pos1) > 0:
pos_phi = rf.from_cartesian_to_cyl(np.array(pos1))[:, 1]
_, var_phi = rf.phi_mean_var(pos_phi, q1)
pos_z = np.array(pos1)[:, 2]
mean_z = np.average(pos_z, weights=q1)
var_z = np.average((pos_z - mean_z)**2, weights=q1)
r = np.sqrt(true_pos[0][0]**2 + true_pos[0][1]**2)
var_phi1.append(var_phi)
var_z1.append(var_z)
touched_sipms1.append(len(pos1))
true_r1.append(r)
else:
var_phi1.append(1.e9)
var_z1.append(1.e9)
touched_sipms1.append(1.e9)
true_r1.append(1.e9)
if len(pos2) > 0:
pos_phi = rf.from_cartesian_to_cyl(np.array(pos2))[:, 1]
_, var_phi = rf.phi_mean_var(pos_phi, q2)
pos_z = np.array(pos2)[:, 2]
mean_z = np.average(pos_z, weights=q2)
var_z = np.average((pos_z - mean_z)**2, weights=q2)
r = np.sqrt(true_pos[1][0]**2 + true_pos[1][1]**2)
var_phi2.append(var_phi)
var_z2.append(var_z)
touched_sipms2.append(len(pos2))
true_r2.append(r)
else:
var_phi2.append(1.e9)
var_z2.append(1.e9)
touched_sipms2.append(1.e9)
true_r2.append(1.e9)
a_true_r1 = np.array(true_r1)
a_true_r2 = np.array(true_r2)
a_var_phi1 = np.array(var_phi1)
a_var_phi2 = np.array(var_phi2)
a_var_z1 = np.array(var_z1)
a_var_z2 = np.array(var_z2)
a_touched_sipms1 = np.array(touched_sipms1)
a_touched_sipms2 = np.array(touched_sipms2)
np.savez(output_file,
a_true_r1=a_true_r1,
a_true_r2=a_true_r2,
a_var_phi1=a_var_phi1,
a_var_phi2=a_var_phi2,
a_var_z1=a_var_z1,
a_var_z2=a_var_z2,
a_touched_sipms1=a_touched_sipms1,
a_touched_sipms2=a_touched_sipms2)