def car_obstacles(frontview, cars):
"""
Determines if there are any other_cars within the car's bin and then calculates the distance to the
nearest car in the same bin
Parameters
__________
:param frontview: object: FrontView object
:param cars: dataframe:
Returns
_______
:return distance: list: double or False (returns False if no car obstacle found)
"""
x_space, y_space = models.upcoming_linspace(frontview)
if x_space.any() and y_space.any():
other_cars = cars.drop(frontview.car.name)
obstacles = (frontview.car['xbin'] == other_cars['xbin']) & (frontview.car['ybin'] == other_cars['ybin'])
if obstacles.any():
nearby_cars = other_cars[obstacles]
for car in nearby_cars.iterrows():
car_within_xlinspace = np.isclose(x_space, car[1]['x'], rtol=1.0e-6).any()
car_within_ylinspace = np.isclose(y_space, car[1]['y'], rtol=1.0e-6).any()
if car_within_xlinspace and car_within_ylinspace:
first_x, first_y = car[1]['x'], car[1]['y']
vector = (first_x - frontview.car['x'], first_y - frontview.car['y'])
distance = models.magnitude(vector)
return distance
else:
return False
else:
return False
else:
return False
def distance_to_node(self):
"""
Determines the distance to the most immediate node
:return distance: double
"""
next_node = np.array(self.upcoming_node_position())
distance_vector = next_node - self.position
distance = models.magnitude(distance_vector)
return distance
def light_obstacles(frontview, lights):
"""
Determines the distance to red traffic lights. If light is green, returns False
Parameters
__________
:param frontview: object: FrontView object
:param lights: dataframe:
Returns
_______
:return distance: list: double for False (returns False if no red light is found)
"""
x_space, y_space = models.upcoming_linspace(frontview)
if x_space.any() and y_space.any():
obstacles = (frontview.car['xbin'] == lights['xbin']) & (
frontview.car['ybin'] == lights['ybin'])
if obstacles.any():
nearby_lights = lights[obstacles]
for light in nearby_lights.iterrows():
light_within_xlinspace = np.isclose(x_space[1:],
light[1]['x'],
rtol=1.0e-6).any()
light_within_ylinspace = np.isclose(y_space[1:],
light[1]['y'],
rtol=1.0e-6).any()
if light_within_xlinspace and light_within_ylinspace:
car_vector = [
light[1]['x'] - frontview.car['x'],
light[1]['y'] - frontview.car['y']
]
face_values = light[1]['go-values']
face_vectors = [(light[1]['out-xvectors'][i],
light[1]['out-yvectors'][i])
for i in range(light[1]['degree'])]
for value, vector in zip(face_values, face_vectors):
if not value and models.determine_anti_parallel_vectors(
car_vector, vector):
distance = models.magnitude(car_vector)
return distance
else:
continue
else:
return False
else:
return False
else:
return False