def max_score(self):
xc_score = 0
xc_distance = 0
xc_type = None
xc_points = []
for v1 in self._groups[0].vertices:
for v2 in self._groups[1].vertices:
for v3 in self._groups[2].vertices:
legs = [
distance(v1, v2),
distance(v2, v3),
distance(v3, v1)
]
dist = sum(legs)
if self.is_closed(tol=0.05 * dist):
if min(legs) / dist > 0.28: # TODO check coefficient ?
current_score = 0.0014 * dist
current_type = 'FAI triangle'
else:
current_score = 0.0012 * dist
current_type = 'flat triangle'
else:
current_score = 0.001 * dist
current_type = '3 points'
if current_score > xc_score:
xc_score = current_score
xc_type = current_type
xc_distance = dist
xc_points = [v1, v2, v3]
return xc_points, xc_distance, xc_type, xc_score
def __init__(self, lat, lon, p1=None, p2=None, radius=None):
self.lat = lat
self.lon = lon
if radius is not None:
self.radius = radius
self.start_angle = -180
self.end_angle = 180
else:
# why these two distances are not necessary equal is a mystery
self.radius = max(distance(self, p1), distance(self, p2))
# FIXME maybe angles are ordered and should not be exchanged, check clockwise argument
p1_heading = heading(self, p1)
p2_heading = heading(self, p2)
self.start_angle = min(p1_heading, p2_heading)
self.end_angle = max(p1_heading, p2_heading)
def tasklen(angles, position, waypoints):
dist = 0
for theta, wp in zip(angles, waypoints):
proj = destination(wp, wp.radius, theta)
dist += distance(proj, position)
position = proj
return dist
def is_closed(self, tol=2000):
if self._closed:
return True
for p1 in self._before:
for p2 in self._after:
if distance(p1, p2) < tol:
self._closed = True
return True
return False
def optimize(position, waypoints, prev_opti=None):
x0 = np.zeros(len(waypoints)) if prev_opti is None else prev_opti
result = minimize(tasklen,
x0,
args=(position, waypoints),
tol=OPTIMIZER_PRECISION)
distances = []
fast_waypoints = [Turnpoint(position.lat, position.lon)]
for theta, wp in zip(result.x, waypoints):
proj = destination(wp, wp.radius, theta)
distances.append(distance(proj, fast_waypoints[-1]))
fast_waypoints.append(Turnpoint(proj[0], proj[1]))
return Opti(sum(distances), distances, fast_waypoints, result.x)
def __init__(self, task):
# try to base64 decode the task
if not os.path.isfile(task):
try:
task = base64.b64decode(task)
task = json.loads(task)
except TypeError:
logging.debug(f'Task is not base64')
else:
with open(task, 'r') as f:
task = json.load(f)
# try to parse with every implemented format, raise if no match
for task_format in [
taskparse.XCTask, taskparse.PWCATask, taskparse.RawTask,
taskparse.pygclibTask
]:
try:
task = task_format(task)
break
except (KeyError, TypeError):
logging.debug(f'Task format does not fit into {task_format}')
if not hasattr(task, 'takeoff'):
raise NotImplementedError('Task format not recognized')
self.__dict__.update(task.__dict__)
# if the goal is a line, we need a to compute its validation with the following technique
# we compute the heading between the goal and the first previous turnpoint which has a different center
# during validation, we compute the heading between the pilot and the goal center
# when the absolute difference between these two headings is greater than 90~ish° (and is inside the goal radius), the goal is validated
# not only does this allow us to validate goal line, but it also helps drawing goal lines in TaskCreator by directly having the line normal direction
if self.goal_style == 'LINE':
index_last_turnpoint = -2
while distance(self.turnpoints[index_last_turnpoint],
self.turnpoints[-1]) < 1:
index_last_turnpoint -= 1
self.last_leg_heading = heading(
self.turnpoints[index_last_turnpoint], self.turnpoints[-1])
self.opti = optimize(self.takeoff, self.turnpoints)
def triangle_length(points, flight):
total_distance = distance(flight[points[0]], flight[points[1]])
total_distance += distance(flight[points[1]], flight[points[2]])
total_distance += distance(flight[points[2]], flight[points[0]])
return -total_distance
def __eq__(self, other):
return distance(*self.bounds) == distance(*other.bounds)
def __gt__(self, other):
return distance(*self.bounds) > distance(*other.bounds)
def __lt__(self, other):
return distance(*self.bounds) < distance(*other.bounds)
def __contains__(self, point):
return (distance(self, point) < self.radius) and (
self.start_angle <= heading(self, point) <= self.end_angle)
def __contains__(self, point):
return distance(self, point) < self.radius
def close_enough(self, wpt):
# TODO delete when proper task validation
return abs(distance(self, wpt) -
wpt.radius) < 10 + wpt.radius * TOLERANCE
