def compute_track_line(silicon_hit_lines):
# Translate so we operate in a different coordinate system. Track begins at z = 0mm, Closest line at 50 000mm = 50m
translate_hit_lines(
silicon_hit_lines
) # Translated z. So the closest z to IP5 = [-/+] 50m = [+/-] 50 000 mm
# Constraints
seed_x0 = get_seed_x0(silicon_hit_lines)
bounds = get_bounds()
# Computing solution
global HIT_LINES
HIT_LINES = silicon_hit_lines
solution = least_squares(objective,
seed_x0,
bounds=bounds,
jac='2-point',
method='trf')
track_line = Line(params=solution.x) # solution.x = [x, y, dx, dy, dz]
# Normalization
track_line.normalize_line_vector() # |[dx, dy, dz]| = 1.0
normalize_point(
track_line
) # We assumed silicon_hit_lines starts from [+/-] 50m. Move point to real "z" of first line.
# We want track_line.z = FIRST_Z_FROM_IP
untranslate_hit_lines(silicon_hit_lines)
return track_line
def get_tracks_in_det_no(solution, hit_lines):
track_line = Line(params=solution.x)
silicon_id_list = TrackProviderDfUtility.get_silicon_id_list(hit_lines)
det_with_track = TrackProviderDfUtility.get_det_with_track_list(
silicon_id_list, track_line)
return len(det_with_track)
def compute_line(self, rp_id, rp_hits_df):
self.uv_skeleton.setup(rp_id, rp_hits_df, self.det_avg_geom_df,
self.rp_geom_df)
pt0 = self.uv_skeleton.get_pt0()
pt1 = self.uv_skeleton.get_pt1()
return Line(start=pt0, end=pt1, silicon_id=rp_id * 10)
def get_multitrack_line(self, honza_row):
event_id = honza_row[EVENT]
group_id = honza_row[GROUP]
multitrack_row = self.multitrack_df[
(self.multitrack_df[EVENT] == event_id)
& (self.multitrack_df[GROUP] == group_id)]
x = float(multitrack_row['x [mm]'])
y = float(multitrack_row['y [mm]'])
z = float(multitrack_row['z [mm]'])
dx = float(multitrack_row['dx'])
dy = float(multitrack_row['dy'])
dz = float(multitrack_row['dz'])
return Line(x, y, z, dx, dy, dz)
def create_lines(self, current_hits_df):
lines = []
for idx, hit_info in current_hits_df.iterrows():
silicon_id = compute_silicon_id(hit_info)
silicon_info = self.extract_silicon_info(silicon_id)
x = hit_line_x(hit_info, silicon_info)
y = hit_line_y(hit_info, silicon_info)
z = hit_line_z(silicon_info)
dx = hit_line_dx(silicon_info)
dy = hit_line_dy(silicon_info)
dz = hit_line_dz()
lines.append(Line(x, y, z, dx, dy, dz, silicon_id=silicon_id))
return lines
def get_silicon_hit_lines(hit_positions_df):
silicon_hit_lines = []
for idx, hit_position_info in hit_positions_df.iterrows():
silicon_id = get_silicon_id(hit_position_info)
silicon_info = get_silicon_info(silicon_id)
x = hit_line_x(hit_position_info, silicon_info)
y = hit_line_y(hit_position_info, silicon_info)
z = hit_line_z_in_mm(silicon_info)
dx = hit_line_dx(silicon_info)
dy = hit_line_dy(silicon_info)
dz = hit_line_dz()
silicon_hit_lines.append(Line(x, y, z, dx, dy, dz, silicon_id=silicon_id))
return silicon_hit_lines
def objective(params):
x, y, dx, dy, dz = params
line = Line(x=x, y=y, dx=dx, dy=dy, dz=dz) # Assume z = 0
return np.sum([line.distance(other)
for other in HIT_LINES]) # Sum of distances
def get_chi2(solution, hit_lines):
# We assume that our track begins on z = LOWEST_ABS_SILICON_Z
track = Line(params=solution.x,
z=HitLinesProviderConfig.LOWEST_ABS_SILICON_Z)
return sum([(track.distance(hit_line) / SIGMA)**2
for hit_line in hit_lines]) # SUM OF DISTANCES
def get_distances(solution, hit_lines):
# We assume that our track begins on z = LOWEST_ABS_SILICON_Z
track = Line(params=solution.x,
z=HitLinesProviderConfig.LOWEST_ABS_SILICON_Z)
return [track.distance(line) for line in hit_lines]