def calc_source_features_sim(
source_x,
source_y,
source_zd,
source_az,
pointing_position_zd,
pointing_position_az,
cog_x,
cog_y,
delta,
):
result = calc_source_features_common(
source_x,
source_y,
source_zd,
source_az,
pointing_position_zd,
pointing_position_az,
)
source_x, source_y = horizontal_to_camera(
az=source_az,
zd=source_zd,
az_pointing=pointing_position_az,
zd_pointing=pointing_position_zd,
)
true_disp, true_sign = calc_true_disp(
source_x,
source_y,
cog_x,
cog_y,
delta,
)
result['true_disp'] = true_disp * true_sign
return result
def calc_source_features_sim(
source_x,
source_y,
source_zd,
source_az,
pointing_position_zd,
pointing_position_az,
cog_x,
cog_y,
delta,
):
result = calc_source_features_common(
source_x,
source_y,
source_zd,
source_az,
pointing_position_zd,
pointing_position_az,
)
source_x, source_y = horizontal_to_camera(
az=source_az,
zd=source_zd,
az_pointing=pointing_position_az,
zd_pointing=pointing_position_zd,
)
true_disp, true_sign = calc_true_disp(
source_x, source_y,
cog_x, cog_y,
delta,
)
result['true_disp'] = true_disp * true_sign
return result
def calc_delta_delta(event, mask):
# if 'source_position_zd' not in df.columns:
frame = AltAz(location=LOCATION,
obstime=to_astropy_time(
pd.to_datetime(event.observation_info.time)))
crab_altaz = crab.transform_to(frame)
source_position_zd_phs = crab_altaz.zen.deg
source_position_az_phs = crab_altaz.az.deg
source_x, source_y = horizontal_to_camera(
source_position_zd_phs,
source_position_az_phs,
event.zd,
event.az,
)
# safe x, y and t components of Photons. shape = (#photons,3)
xyt = event.photon_stream.point_cloud
lol = event.photon_stream.list_of_lists
x, y, t = xyt.T
x = np.rad2deg(x) / camera_distance_mm_to_deg(1)
y = np.rad2deg(y) / camera_distance_mm_to_deg(1)
cleaned_photons = np.zeros(len(x), dtype=bool)
for i in range(len(lol)):
if mask[i]:
for j in range(len(lol[i])):
cleaned_photons[i] = True
cog_x = np.mean(x[cleaned_photons])
cog_y = np.mean(y[cleaned_photons])
true_delta = np.arctan2(cog_y - source_y, cog_x - source_x)
delta = calc_delta(phs2image(event.photon_stream.list_of_lists), mask)
delta_delta = true_delta - delta
if delta_delta < -np.pi / 2:
delta_delta += 2 * np.pi
# if delta_delta < -90:
# delta_delta += 360
return delta_delta
def test_there_and_back_again_horizontal():
from fact.coordinates import camera_to_horizontal, horizontal_to_camera
df = pd.DataFrame({
'az_tracking': [0, 90, 270],
'zd_tracking': [0, 10, 20],
'timestamp':
pd.date_range('2017-10-01 22:00Z', periods=3, freq='10min'),
'x': [-100, 0, 150],
'y': [0, 100, 0],
})
zd, az = camera_to_horizontal(df.x, df.y, df.zd_tracking, df.az_tracking)
x, y = horizontal_to_camera(zd, az, df.zd_tracking, df.az_tracking)
assert np.allclose(x, df.x)
assert np.allclose(y, df.y)
def load_training_data_position(N=-1, path_gamma='./data/gamma_images.hdf5'):
'''
Loads gamma dl2 data and use the source position of the true source position in x y camera coordinates.
'''
df_gammas, images_gammas = read_rows(path_gamma, N=N)
X = scale_images(images_gammas)
# az_offset_between_magnetic_and_geographic_north = -0.12217305 # aboout -7 degrees
# az_offset_between_corsika_and_ceres = - np.pi + az_offset_between_magnetic_and_geographic_north
source_az = np.rad2deg(-np.pi + df_gammas.corsika_phi + -0.12217305)
source_zd = np.rad2deg(df_gammas.corsika_theta)
pointing_az = df_gammas.az
pointing_zd = df_gammas.zd
x, y = horizontal_to_camera(source_zd, source_az, pointing_zd, pointing_az)
Y = np.vstack([x, y]).T
return X, Y
def calc_source_features_sim(
prediction_x,
prediction_y,
source_zd,
source_az,
pointing_position_zd,
pointing_position_az,
cog_x,
cog_y,
delta,
project_disp=False,
):
result = calc_source_features_common(
prediction_x,
prediction_y,
source_zd,
source_az,
pointing_position_zd,
pointing_position_az,
)
source_x, source_y = horizontal_to_camera(
az=source_az,
zd=source_zd,
az_pointing=pointing_position_az,
zd_pointing=pointing_position_zd,
)
true_disp, true_sign = calc_true_disp(
source_x,
source_y,
cog_x,
cog_y,
delta,
project_disp=project_disp,
)
result["true_disp"] = true_disp * true_sign
return result
if args.threshold:
phs = phs.query(f'gamma_prediction >= {args.threshold}').copy()
if 'source_position_zd' not in df.columns:
frame = AltAz(location=LOCATION,
obstime=to_astropy_time(pd.to_datetime(df['timestamp'])))
crab = SkyCoord.from_name('Crab Nebula')
crab_altaz = crab.transform_to(frame)
df['source_position_zd_phs'] = crab_altaz.zen.deg
df['source_position_az_phs'] = crab_altaz.az.deg
df['source_x'], df['source_y'] = horizontal_to_camera(
df['source_position_zd'],
df['source_position_az'],
df['pointing_position_zd_phs'],
df['pointing_position_az_phs'],
)
df['true_delta'] = np.arctan2(df['cog_y_phs'] - df['source_y'],
df['cog_x_phs'] - df['source_x'])
df['delta_delta'] = df['true_delta'] - df['delta_phs']
df['delta_delta'][df['delta_delta'] < -np.pi / 2] += 2 * np.pi
mask = df['disp_prediction_phs'] < 0
plt.hist(df.loc[mask, 'delta_delta'],
bins=np.linspace(-np.pi / 2, 3 / 2 * np.pi, 101),
alpha=0.3)
plt.hist(df.loc[~mask, 'delta_delta'],
bins=np.linspace(-np.pi / 2, 3 / 2 * np.pi, 101),