def remove_doubles(group, tree, only_1cluster=False):
""" find the tracks which are closer to the cluster in case there are more than 1 track per cluster. """
s = array(group['Clusters']['Size'])
cut = s > 0
nt = get_tree_vec(tree, var='evt_ntracks', dtype='u1')[cut] # number of tracks
nc = get_tree_vec(tree, var='evt_nclusters', dtype='u1')[cut] # number of clusters
tu, tv = array(group['Tracks']['U'])[cut], array(group['Tracks']['V'])[cut]
u, v = array(group['Clusters']['U']), array(group['Clusters']['V'])
r = sqrt((tu - u) ** 2 + (tv - v) ** 2)
good = nt == 1
c2 = (nt > 1) & (nc == 2) # selector for one cluster for two tracks, if nc == 2 and nt > 1 there is always a double
r2 = r[c2][::2] < r[c2][1::2]
r2c = concatenate(array([r2, invert(r2)]).T) # merge with zipper method
good[c2] = r2c
# more than 1 cluster
if not only_1cluster:
for nt_i in arange(2, 5):
for nc_i in arange(4, 7, 2):
c = (nt == nt_i) & (nc == nc_i)
ri = array(split(r[c], arange(nt_i, r[c].size, nt_i)))
good[c] = concatenate(ri) == min(ri, axis=1).repeat(nt_i)
# write changes to the file
s0 = s[cut]
s0[invert(good)] = 0
s[cut] = s0 # set all sizes of the removed clusters to 0
group['Clusters']['Size'][...] = s
for name in ['U', 'V', 'X', 'Y', 'Charge']:
data = array(group['Clusters'][name])[good]
del group['Clusters'][name]
group['Clusters'].create_dataset(name, data=data)
def add_clusters(self, group, tree):
gr = group.create_group('Clusters')
# tracking
t = self.TrackFile.Get(group.name.strip('/')).Get('Clusters')
t.SetEstimate(-1)
gr.create_dataset('N', data=get_tree_vec(t, 'NClusters', dtype='u1'))
# matching
branches = [f'clu_{n}' for n in ['size', 'col', 'row', 'u', 'v']]
data = get_tree_vec(tree, branches, dtype=['u2'] + ['f2'] * len(branches))
cluster_size = data[0]
gr.create_dataset('Size', data=cluster_size)
for i, name in enumerate(['X', 'Y', 'U', 'V'], 1):
gr.create_dataset(name, data=data[i][cluster_size > 0]) # filter out the nan events
def add_trigger_info(self, group):
f = TFile(self.ROOTFileName)
tree = f.Get(group.name.strip('/')).Get('Hits')
tree.SetEstimate(tree.GetEntries())
data = get_tree_vec(tree, ['TriggerPhase', 'TriggerCount'], dtype='u1')
for i, name in enumerate(['TriggerPhase', 'TriggerCount']):
group.create_dataset(name, data=data[i])
def get_hits(self, match_tree, group, dut_nr):
t = info('getting hit charges for DUT Plane {} ...'.format(dut_nr), endl=False)
calibration = self.get_calibration(dut_nr)
x, y, adc = get_tree_vec(match_tree, ['hit_col', 'hit_row', 'hit_value'], dtype=['u2', 'u2', 'u1'])
hits = array([x, y, calibration(x, y, adc)]).T
group.create_dataset('CalChiSquare', data=calibration.get_chi2s())
add_to_info(t)
return hits
def add_track_vars(self):
info('add track information ...')
g = self.F.create_group('Tracks')
track_types = {'Chi2': 'f2', 'Dof': 'u1', 'X': 'f2', 'Y': 'f2', 'SlopeX': 'f2', 'SlopeY': 'f2'}
match_types = {'evt_frame': 'u4', 'evt_ntracks': 'u1', 'trk_size': 'f2'}
self.PBar.start(len(track_types) + len(match_types), counter=True, t='s')
# from tracking tree
t_tree = self.TrackFile.Get('Tracks')
t_tree.SetEstimate(t_tree.GetEntries())
g.create_dataset('NTracks', data=get_tree_vec(t_tree, var='NTracks', dtype='u1'))
t_tree.SetEstimate(sum(g['NTracks']))
for n, t in track_types.items():
g.create_dataset(n, data=get_tree_vec(t_tree, n, dtype=t))
self.PBar.update()
# from match tree
m_tree = self.MatchFile.Get('C0').Get('tracks_clusters_matched') # same for all plane dirs
m_tree.SetEstimate(m_tree.GetEntries())
for n, t in match_types.items():
g.create_dataset(n.replace('trk_', '').title().replace('_', ''), data=get_tree_vec(m_tree, n, dtype=t))
self.PBar.update()
def add_plane(self, i):
group = self.F.create_group(f'Plane{i}')
i_dut = i - self.NTelPlanes
pl_tag = f'C{i_dut}' if i_dut >= 0 else f'M{i}'
# mask
m_tree = self.MatchFile.Get(pl_tag).Get('masked_pixels')
m_tree.SetEstimate(m_tree.GetEntries())
group.create_dataset('Mask', data=get_tree_vec(m_tree, ['col', 'row'], dtype='u2'))
# cluster
tree = self.MatchFile.Get(pl_tag).Get('tracks_clusters_matched')
tree.SetEstimate(-1)
self.add_tracks(group, tree)
self.add_clusters(group, tree)
if i_dut >= 0:
self.add_cluster_charge(tree, group, i_dut)
self.remove_doubles(group, tree)
# trigger info
self.add_trigger_info(group)
def add_tracks(self, group, tree):
gr = group.create_group('Tracks')
data = get_tree_vec(tree, [f'trk_{n}' for n in ['u', 'v', 'col', 'row', 'du', 'dv']], dtype='f2')
for i, name in enumerate(['U', 'V', 'X', 'Y', 'dU', 'dV']):
gr.create_dataset(name, data=data[i])
def add_time_stamp(self):
tree = self.TrackFile.Get('Event')
tree.SetEstimate(tree.GetEntries())
t_vec = get_tree_vec(self.TrackFile.Get('Event'), var='TriggerTime', dtype='u8')
g = self.F.create_group('Event')
g.create_dataset('Time', data=((t_vec - t_vec[0]) / 1e9).astype('f4')) # convert time vec to seconds
def make_cuts(self):
[t.SetEstimate(t.GetEntries() * 10) for t in self.P]
n = [get_tree_vec(t, 'NHits', dtype='i2') for t in self.P[:6]]
c = all([i == 1 for i in n], axis=0)
return [c.repeat(i) for i in n]