def score_eclipse(eclipse: Dict, first_contact: Union[None, FirstContactTime],
type: ExpectedEclipseType, phase_timing: Union[None, PhaseTiming],
location: Union[None, EclipsePosition]) -> float:
scores = [LunarEclipseQuery.eclipse_core_score(eclipse, type)]
weights = [0.5]
if location is not None:
assert eclipse['angle'] is not None
scores.append(AngularSeparationQuery.separation_score(location.target_angle, location.tolerance,
location.target_position, eclipse['angle'],
eclipse['position']))
weights.append(0.25)
if first_contact is not None:
if type == ExpectedEclipseType.UNKNOWN:
# If the eclipse is a prediction then allow a higher time tolerance
scores.append(LunarEclipseQuery.eclipse_time_of_day_score(eclipse, first_contact,
HIGH_TIME_TOLERANCE))
else:
scores.append(LunarEclipseQuery.eclipse_time_of_day_score(eclipse, first_contact,
REGULAR_TIME_TOLERANCE))
weights.append(0.25)
if phase_timing is not None:
scores.append(LunarEclipseQuery.eclipse_phase_length_score(eclipse, phase_timing))
weights.append(0.25)
score = np.average(scores, weights=weights)
assert 0 <= score <= 1
return score
def plot_separation_score(angle: float, tolerance: float, dest: str):
range = (angle + tolerance) * 1.5
xs = np.arange(0, range, 0.01)
ys = list(
map(
lambda x: AngularSeparationQuery.separation_score(
target_angle=angle,
tolerance=tolerance,
target_position=None,
actual=x,
actual_position=EclipticPosition.BEHIND.value,
), xs))
f, ax1 = plt.subplots()
ax1.set_xlabel('Angular Separation°')
ax1.set_ylabel('Score')
ax1.plot(xs, ys)
if angle != 0:
ax1.axvline(x=angle, color='r', label="Target Angle")
ax1.axvline(x=angle + tolerance, color='g', label="Upper Bound")
if angle - tolerance > 0:
ax1.axvline(x=angle - tolerance, color='b', label="Lower Bound")
ax1.legend()
plt.savefig(dest)