def calc_random_source(pointing_zd, pointing_az, wobble_distance):
phi = np.random.uniform(0, 2 * np.pi, len(pointing_zd))
r = wobble_distance / camera_distance_mm_to_deg(1)
x = r * np.cos(phi)
y = r * np.sin(phi)
zd, az = camera_to_horizontal(x, y, pointing_zd, pointing_az)
return zd, az
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 calc_source_features_common(
prediction_x,
prediction_y,
source_zd,
source_az,
pointing_position_zd,
pointing_position_az,
):
result = {}
k_zd, k_az = "zd_prediction", "az_prediction"
result[k_zd], result[k_az] = camera_to_horizontal(
prediction_x,
prediction_y,
pointing_position_zd,
pointing_position_az,
)
result["theta_deg"] = calc_theta_camera(
prediction_x,
prediction_y,
source_zd=source_zd,
source_az=source_az,
zd_pointing=pointing_position_zd,
az_pointing=pointing_position_az,
)
theta_offs = calc_theta_offs_camera(
prediction_x,
prediction_y,
source_zd=source_zd,
source_az=source_az,
zd_pointing=pointing_position_zd,
az_pointing=pointing_position_az,
n_off=5,
)
for i, theta_off in enumerate(theta_offs, start=1):
result["theta_deg_off_{}".format(i)] = theta_off
return result