# create a clean mask of pixels above the threshold
cleanmask = tailcuts_clean(camgeom, image, picture_thresh=10, boundary_thresh=5)
# set all rejected pixels to zero
cleaned_image[~cleanmask] = 0
# Calulate hillas parameters
# It fails for empty pixels
try:
hillas_params[tel_id] = hillas_parameters(camgeom, cleaned_image)
except:
pass
if len(hillas_params) < 2:
continue
reco.get_great_circles(hillas_params, event.inst.subarray,
point_azimuth, point_altitude)
# fit the gamma's direction of origin
# return reconstructed direction (3 components) with errors on the values
reco_direction, reco_dir_err = reco.fit_origin_crosses()
# In case fit fails to get any real value
if np.isnan(reco_direction).any():
continue
# get angular offset between reconstructed shower direction and MC
# generated shower direction
off_angle = linalg.angle(reco_direction, shower_direction)
# Appending all estimated off angles
off_angles.append(off_angle.to(u.deg).value)
Imagecutflow.count("Hillas")
'''
get some more parameters and put them in an astropy.table.Table '''
sum_p = np.sum(pmt_signal_p)
sum_w = np.sum(pmt_signal_w)
sum_t = np.sum(pmt_signal_t)
Epsilon_intensity_w = abs(sum_w - sum_p) / sum_p
Epsilon_intensity_t = abs(sum_t - sum_p) / sum_p
alpha = {}
length = {}
width = {}
for k, h in hillas.items():
fit.get_great_circles({tel_id: h}, event.inst.subarray,
tel_phi, tel_theta)
c = fit.circles[tel_id]
alpha[k] = abs((angle(c.norm, shower_org) * u.rad) -
90 * u.deg).to(u.deg)
length[k] = h.length
width[k] = h.width
for k, signal in { # 'p': pmt_signal_p,
'w': pmt_signal_w
}.items():
h = hillas[k]
p1_x = h.cen_x
p1_y = h.cen_y