def init_random_node_start_location(n, axis):
"""
initializes n cars at n random nodes and sets their destinations as a culdesac
:param n: int
:param axis: list: x_range, y_range of road network
:return state: dict
"""
# TODO: combine this function with other car initialization functions using flags
nodes = nav.find_nodes(n)
culdesacs = nav.find_culdesacs()
cars_data = []
for i in range(n):
if i < n - 1:
origin = nodes[i]
destination = culdesacs[i % len(culdesacs)]
try:
path = nav.get_init_path(origin, destination)
route = nav.get_route(origin, destination)
except NetworkXNoPath:
print('No path between {} and {}.'.format(origin, destination))
continue
x, y = nav.get_position_of_node(origin)
car = {'object': 'car',
'x': x,
'y': y,
'vx': 0,
'vy': 0,
'route-time': 0,
'origin': origin,
'destination': destination,
'route': route,
'xpath': [path[i][0] for i in range(len(path))],
'ypath': [path[i][1] for i in range(len(path))],
'distance-to-car': 0,
'distance-to-node': 0,
'distance-to-red-light': 0}
cars_data.append(car)
cars = pd.DataFrame(cars_data)
# determine binning and assign bins to cars
xbins, ybins = np.arange(axis[0], axis[1], 200), np.arange(axis[2], axis[3], 200)
x_indices, y_indices = np.digitize(cars['x'], xbins), np.digitize(cars['y'], ybins)
cars['xbin'], cars['ybin'] = pd.Series(x_indices), pd.Series(y_indices)
print('Number of cars: {}'.format(len(cars)))
return cars
def init_custom_agent(n=1, fig_axis=axis, car_id=None, alternate_route=None):
"""
This function initializes a singular car with custom origin and destination
:param n: int
:param fig_axis: list
:param car_id: None or int
:param alternate_route: None or list
:return cars_frame: DataFrame
"""
origin = 53085387
dest = 53082621
path = nav.get_init_path(origin, dest)
route = nav.get_route(origin, dest)
x, y = nav.get_position_of_node(origin)
car = {'object': 'car',
'x': x,
'y': y,
'vx': 0,
'vy': 0,
'route-time': 0,
'origin': origin,
'destination': dest,
'route': route,
'xpath': np.array([path[i][0] for i in range(len(path))]),
'ypath': np.array([path[i][1] for i in range(len(path))]),
'distance-to-car': 0,
'distance-to-node': 0,
'distance-to-red-light': 0}
cars_data = [car]
if alternate_route:
cars_data[car_id]['route'], cars_data[car_id]['xpath'], cars_data[car_id]['ypath'] = alternate_route
cars_frame = pd.DataFrame(cars_data)
# determine binning and assign bins to cars
cars_frame['xbin'], cars_frame['ybin'] = models.determine_bins(fig_axis, cars_frame)
return cars_frame
def init_culdesac_start_location(n, axis, car_id=None, alternate_route=None):
"""
initializes N cars into N culdesacs
Parameters
__________
:param n: int
:param axis: list of x and y ranges
:param car_id: None or int: optional, int if you wish to prescribe an alternate route for car
:param alternate_route: list: optional, list of alternate route nodes for provided car
Returns
_______
:return cars: dataframe
"""
# TODO: combine this function with other car initialization functions using flags
culdesacs = nav.find_culdesacs()
if n > len(culdesacs):
raise ValueError('Number of cars greater than culdesacs to place them. '
'Choose a number less than {}'.format(len(culdesacs)))
cars_data = []
for i in range(n):
# i = 17 # TEMP SETTING
origin = culdesacs[i]
destination = culdesacs[i + 1]
""" TEMP SETTINGS FOR ONE-CAR-ONE-ROUTE STUDY """
# destination = 53028190
""" TEMP SETTINGS FOR ONE-CAR-ONE-ROUTE STUDY """
try:
path = nav.get_init_path(origin, destination)
route = nav.get_route(origin, destination)
except NetworkXNoPath:
print('No path between {} and {}.'.format(origin, destination))
continue
position = nav.get_position_of_node(origin)
car = {'object': 'car',
'x': position[0],
'y': position[1],
'vx': 0,
'vy': 0,
'route-time': 0,
'origin': origin,
'destination': destination,
'route': route,
'xpath': [path[i][0] for i in range(len(path))],
'ypath': [path[i][1] for i in range(len(path))],
'distance-to-car': 0,
'distance-to-node': 0,
'distance-to-red-light': 0}
cars_data.append(car)
if alternate_route:
cars_data[car_id]['route'], cars_data[car_id]['xpath'], cars_data[car_id]['ypath'] = alternate_route
cars = pd.DataFrame(cars_data)
# determine binning and assign bins to cars
cars['xbin'], cars['ybin'] = models.determine_bins(axis, cars)
# print('Number of cars: {}'.format(len(cars)))
return cars