### Implement RL Training Loop with ROAR_PY and CARLA

Source: https://context7.com/augcog/roar_py/llms.txt

This Python code demonstrates setting up a CARLA simulation environment using ROAR_PY, spawning a vehicle with a camera sensor, retrieving Gymnasium-compatible action and observation spaces, and executing a basic training loop. The loop involves receiving observations, sampling a random action, applying the action, and stepping the simulation. It is designed for training RL agents and requires CARLA and ROAR_PY to be installed.

```python
import roar_py_interface
import numpy as np
import asyncio
import carla
import roar_py_carla

async def rl_training_loop():
    # Setup environment
    carla_client = carla.Client('localhost', 2000)
    carla_client.set_timeout(5.0)
    roar_py_instance = roar_py_carla.RoarPyCarlaInstance(carla_client)
    carla_world = roar_py_instance.world
    carla_world.set_asynchronous(False)
    carla_world.set_control_steps(0.05, 0.01)

    # Spawn vehicle with sensors
    vehicle = carla_world.spawn_vehicle(
        "vehicle.tesla.model3",
        carla_world.spawn_points[0][0],
        carla_world.spawn_points[0][1]
    )

    camera = vehicle.attach_camera_sensor(
        roar_py_interface.RoarPyCameraSensorDataRGB,
        np.array([-2.0, 0.0, 1.5]),
        np.array([0, 0, 0]),
        image_width=84,
        image_height=84
    )

    # Get Gymnasium spaces
    action_space = vehicle.get_action_spec()
    observation_space = vehicle.get_gym_observation_spec()

    print(f"Action space: {action_space}")
    print(f"Observation space: {observation_space}")

    # Training loop
    for episode in range(100):
        # Reset (in real use, reset vehicle position)
        await carla_world.step()

        for step in range(1000):
            # Get observation
            obs = await vehicle.receive_observation()
            gym_obs = vehicle.convert_obs_to_gym_obs(obs)

            # Sample action (replace with your RL agent)
            action = {
                "throttle": np.array([0.5]),
                "steer": np.array([np.random.uniform(-0.3, 0.3)]),
                "brake": np.array([0.0]),
                "hand_brake": np.array([0]),
                "reverse": np.array([0])
            }

            # Apply action
            await vehicle.apply_action(action)
            await carla_world.step()

            # Calculate reward, check done, etc.
            # Your RL training logic here

    roar_py_instance.close()

if __name__ == '__main__':
    asyncio.run(rl_training_loop())

```

--------------------------------

### Get and Use CARLA Spawn Points

Source: https://context7.com/augcog/roar_py/llms.txt

Retrieve all available spawn points on the current CARLA map and use them to place vehicles. Spawn points are provided as location and rotation tuples. Random selection is demonstrated for vehicle placement.

```python
# Get all spawn points
spawn_points = carla_world.spawn_points
print(f"Available spawn points: {len(spawn_points)}")

# Each spawn point is a tuple of (location, rotation)
for i, (location, rotation) in enumerate(spawn_points[:5]):
    print(f"Spawn {i}:")
    print(f"  Location: {location}")  # np.ndarray [x, y, z]
    print(f"  Rotation: {rotation}")  # np.ndarray [roll, pitch, yaw] in radians

# Spawn at random point
random_idx = np.random.randint(len(spawn_points))
spawn_loc, spawn_rot = spawn_points[random_idx]

vehicle = carla_world.spawn_vehicle(
    "vehicle.tesla.model3",
    spawn_loc + np.array([0, 0, 2.0]),  # Add 2m height for safety
    spawn_rot
)
```

--------------------------------

### Generate Native Waypoints using Global Route Planner (Python)

Source: https://github.com/augcog/roar_py/blob/main/utilities/transform_waypoints.ipynb

Iterates through the sparse waypoints (`dat_np_sparse`) to generate a series of native CARLA waypoints. It creates `carla.Location` objects for start and end points and uses the `GlobalRoutePlanner` to trace routes between them. Handles potential `nx.NetworkXNoPath` exceptions by skipping the pair.

```python
for i in range(len(dat_np_sparse)):
    curr_start = dat_np_sparse[i]
    curr_end = dat_np_sparse[(i+1)%len(dat_np_sparse)]
    loc_1 = carla.Location(curr_start[0], curr_start[1], curr_start[2])
    loc_2 = carla.Location(curr_end[0], curr_end[1], curr_end[2])
    try:
        native_ws += grp.trace_route(loc_1, loc_2)
    except nx.NetworkXNoPath:
        continue
```

--------------------------------

### Initialize CARLA and ROAR Py Instances (Python)

Source: https://github.com/augcog/roar_py/blob/main/utilities/transform_waypoints.ipynb

Sets up connections to a CARLA simulator and initializes the ROAR Py interface. It establishes a CARLA client connection, creates a ROAR Py CARLA instance, obtains the CARLA world object, and initializes a `GlobalRoutePlanner`.

```python
import carla
import roar_py_carla
import roar_py_interface
import networkx as nx

carla_client = carla.Client('localhost', 2000)
carla_client.set_timeout(5.0)
roar_py_instance = roar_py_carla.RoarPyCarlaInstance(carla_client)
roar_py_world = roar_py_instance.world
grp = GlobalRoutePlanner(roar_py_world._native_carla_map, 2.0)
native_ws = []
```

--------------------------------

### Initialize ROAR-Py and CARLA Client

Source: https://github.com/augcog/roar_py/blob/main/utilities/trace_waypoints.ipynb

Establishes a connection to the CARLA server and initializes the ROAR-Py CARLA instance. It configures asynchronous mode and control step timings for the simulation world.

```python
import roar_py_carla
import roar_py_interface
import carla
import numpy as np
from typing import List, Tuple
import transforms3d as tr3d

carla_client = carla.Client('localhost', 2000)
carla_client.set_timeout(15.0)
roar_py_instance = roar_py_carla.RoarPyCarlaInstance(carla_client)
roar_py_world = roar_py_instance.world
roar_py_world.set_asynchronous(True)
roar_py_world.set_control_steps(0.00, 0.005)
```

--------------------------------

### Initialize Carla Instance and World in ROAR_PY (Python)

Source: https://context7.com/augcog/roar_py/llms.txt

Connects to a Carla simulator instance and configures the simulation world. It sets up the ROAR_PY-Carla integration, configures synchronous/asynchronous modes, and cleans up previous actors. Requires `carla`, `roar_py_carla`, and `asyncio`.

```python
import carla
import roar_py_carla
import asyncio

async def main():
    # Connect to Carla server
    carla_client = carla.Client('localhost', 2000)
    carla_client.set_timeout(5.0)

    # Create ROAR_PY instance
    roar_py_instance = roar_py_carla.RoarPyCarlaInstance(carla_client)

    # Get world and configure simulation mode
    carla_world = roar_py_instance.world
    carla_world.set_asynchronous(True)  # or False for synchronous mode
    carla_world.set_control_steps(0.0, 0.005)  # timestep, substep

    # Step the world
    await carla_world.step()

    # Clean up unregistered actors from previous runs
    roar_py_instance.clean_actors_not_registered()

    try:
        # Your simulation code here
        pass
    finally:
        roar_py_instance.close()

if __name__ == '__main__':
    asyncio.run(main())
```

--------------------------------

### Create CARLA Actor with Custom Sensors

Source: https://context7.com/augcog/roar_py/llms.txt

Spawn a vehicle and attach various custom sensors, including RGB cameras, collision detectors, accelerometers, and gyroscopes. The code demonstrates how to configure sensor parameters and retrieve sensor data.

```python
# Spawn vehicle
vehicle = carla_world.spawn_vehicle(
    "vehicle.audi.a2",
    spawn_points[0][0] + np.array([0, 0, 1]),
    spawn_points[0][1],
    name="custom_vehicle"
)

# Attach front camera
front_camera = vehicle.attach_camera_sensor(
    roar_py_interface.RoarPyCameraSensorDataRGB,
    np.array([2.5, 0.0, 1.0]),  # Front bumper
    np.array([0, 0, 0]),
    image_width=800,
    image_height=600,
    name="front_cam"
)

# Attach rear camera
rear_camera = vehicle.attach_camera_sensor(
    roar_py_interface.RoarPyCameraSensorDataRGB,
    np.array([-2.5, 0.0, 1.0]),  # Rear
    np.array([0, 0, np.pi]),  # 180 degree rotation
    image_width=800,
    image_height=600,
    name="rear_cam"
)

# Attach collision sensor
collision_sensor = vehicle.attach_collision_sensor(name="collision_detector")

# Attach IMU sensors
accel_sensor = vehicle.attach_accelerometer_sensor(name="accelerometer")
gyro_sensor = vehicle.attach_gyroscope_sensor(name="gyroscope")

# Get all sensors
all_sensors = list(vehicle.get_sensors())
print(f"Total sensors: {len(all_sensors)}")

# Collect all observations
observations = await vehicle.receive_observation()
front_image = observations["front_cam"]
rear_image = observations["rear_cam"]
collision_data = observations["collision_detector"]
accel_data = observations["accelerometer"]
gyro_data = observations["gyroscope"]
```

--------------------------------

### Configure CARLA World Timestep

Source: https://context7.com/augcog/roar_py/llms.txt

Set the CARLA world to synchronous or asynchronous mode with configurable timesteps. This allows precise control over physics simulation speed and frequency. Asynchronous mode uses variable timesteps, while synchronous mode uses fixed timesteps.

```python
# Synchronous mode with fixed timestep
carla_world.set_asynchronous(False)
carla_world.set_control_steps(
    control_timestep=0.05,      # 50ms per tick (20 FPS)
    control_substimestep=0.01   # 10ms physics substeps
)

# Variable timestep (asynchronous mode)
carla_world.set_asynchronous(True)
carla_world.set_control_steps(0.0, 0.005)

# Step the world and get elapsed time
dt = await carla_world.step()
print(f"Time delta: {dt} seconds")
print(f"Total elapsed: {carla_world.last_tick_elapsed_seconds} seconds")

# Check mode
is_async = carla_world.is_asynchronous
timestep = carla_world.control_timestep
```

--------------------------------

### Import Libraries for Waypoint Processing (Python)

Source: https://github.com/augcog/roar_py/blob/main/utilities/transform_waypoints.ipynb

Imports necessary libraries including NumPy for numerical operations, Pandas for data manipulation, and specific modules from ROAR Py and CARLA for interacting with the simulation environment.

```python
import numpy as np
import pandas as pd
from roar_py_carla.carla_agents.navigation.global_route_planner import GlobalRoutePlanner
```

--------------------------------

### Implement Waypoint Following Controller in Python

Source: https://context7.com/augcog/roar_py/llms.txt

Implements a proportional controller for autonomous vehicle waypoint following. It includes functions for normalizing radian angles and filtering waypoints to find the closest one to the vehicle's current location. The controller calculates steering and throttle based on the vehicle's speed and heading towards the target waypoint.

```python
def normalize_rad(rad: float):
    return (rad + np.pi) % (2 * np.pi) - np.pi

def filter_waypoints(location: np.ndarray, current_idx: int, waypoints: list) -> int:
    """Find closest waypoint to current location"""
    def dist_to_waypoint(waypoint):
        return np.linalg.norm(location[:2] - waypoint.location[:2])

    for i in range(current_idx, len(waypoints) + current_idx):
        if dist_to_waypoint(waypoints[i % len(waypoints)]) < 3:
            return i % len(waypoints)
    return current_idx

# Main control loop
current_waypoint_idx = 10
waypoints = carla_world.maneuverable_waypoints

while True:
    await carla_world.step()

    # Get vehicle state
    vehicle_location = vehicle.get_3d_location()
    vehicle_rotation = vehicle.get_roll_pitch_yaw()
    vehicle_velocity = vehicle.get_linear_3d_velocity()

    # Find target waypoint
    current_waypoint_idx = filter_waypoints(
        vehicle_location, current_waypoint_idx, waypoints
    )
    target_waypoint = waypoints[(current_waypoint_idx + 3) % len(waypoints)]

    # Calculate steering angle
    vector_to_waypoint = (target_waypoint.location - vehicle_location)[:2]
    heading_to_waypoint = np.arctan2(vector_to_waypoint[1], vector_to_waypoint[0])
    delta_heading = normalize_rad(heading_to_waypoint - vehicle_rotation[2])

    # Proportional steering controller
    speed = np.linalg.norm(vehicle_velocity)
    steer_control = -8.0 / np.sqrt(speed) * delta_heading / np.pi if speed > 1e-2 else -np.sign(delta_heading)
    steer_control = np.clip(steer_control, -1.0, 1.0)

    # Speed controller targeting 20 m/s
    throttle_control = 0.05 * (20 - speed)

    # Apply control
    control = {
        "throttle": np.clip(throttle_control, 0.0, 1.0),
        "steer": steer_control,
        "brake": np.clip(-throttle_control, 0.0, 1.0),
        "hand_brake": 0.0,
        "reverse": 0
    }
    await vehicle.apply_action(control)
```

--------------------------------

### Spawn Vehicle in Carla World using ROAR_PY (Python)

Source: https://context7.com/augcog/roar_py/llms.txt

Spawns a vehicle actor in the Carla simulation world at a specified location and rotation. It allows for customization of the vehicle blueprint, color, and automatic gear shifting. Requires `numpy` and access to `carla_world` object.

```python
import numpy as np

# Get spawn points from the map
spawn_points = carla_world.spawn_points
spawn_point, spawn_rpy = spawn_points[1]

# Spawn vehicle with automatic gear shifting
vehicle = carla_world.spawn_vehicle(
    "vehicle.tesla.model3",  # Blueprint ID
    spawn_point + np.array([0, 0, 2.0]),  # Location [x, y, z]
    spawn_rpy,  # Rotation [roll, pitch, yaw] in radians
    auto_gear=True,  # Enable automatic gear shifting
    name="my_vehicle",
    rgba=np.array([255, 0, 0, 255])  # Optional: Red color
)

# Access vehicle properties
bounding_box = vehicle.bounding_box
print(f"Vehicle extent: {bounding_box.extent}")
print(f"Vehicle location: {vehicle.get_3d_location()}")
print(f"Vehicle velocity: {vehicle.get_linear_3d_velocity()}")
print(f"Vehicle rotation: {vehicle.get_roll_pitch_yaw()}")
```

--------------------------------

### Load and Reload CARLA Maps

Source: https://context7.com/augcog/roar_py/llms.txt

Manage CARLA maps by listing available maps, loading specific ones, or reloading the current map. This operation destroys all existing actors. It also retrieves CARLA client and server version information.

```python
# Get available maps
available_maps = roar_py_instance.get_available_maps()
print("Available maps:", available_maps)

# Load a specific map (destroys all actors)
roar_py_instance.load_world("Town05", reset_settings=True)

# Reload current map
roar_py_instance.reload_world(reset_settings=True)

# Get map name
map_name = carla_world.map_name
print(f"Current map: {map_name}")

# Get version info
client_version = roar_py_instance.get_client_version()
server_version = roar_py_instance.get_server_version()
print(f"Client: {client_version}, Server: {server_version}")
```

--------------------------------

### Apply Vehicle Control Actions with ROAR_PY (Python)

Source: https://context7.com/augcog/roar_py/llms.txt

Applies control commands (throttle, steer, brake, gear) to a vehicle actor using a Gymnasium-compatible action dictionary. This enables interaction with the simulated vehicle. Requires `numpy` and the `vehicle` object.

```python
# Define control action
control = {
    "throttle": np.array([0.5]),  # 0.0 to 1.0
    "steer": np.array([0.0]),     # -1.0 to 1.0
    "brake": np.array([0.0]),     # 0.0 to 1.0
    "hand_brake": np.array([0]),  # 0 or 1
    "reverse": np.array([0])      # 0 or 1
}

# For manual gear vehicles, add:
# control["target_gear"] = 3

# Apply action asynchronously
await vehicle.apply_action(control)

# Get vehicle action specification (Gymnasium Space)
action_spec = vehicle.get_action_spec()
print(action_spec)  # Dict space with throttle, steer, brake, etc.
```

--------------------------------

### Generate and Save Waypoints

Source: https://github.com/augcog/roar_py/blob/main/utilities/trace_waypoints.ipynb

Generates a list of waypoints from CARLA's lane data, converts them into ROAR-Py waypoint objects, and saves them to a compressed NPZ file. Asserts that the number of waypoints per lane is even.

```python
waypoint_list : List[roar_py_interface.RoarPyWaypoint] = []
trace_waypoint_laneid = 1
print("LaneIDS: ", roar_py_world.comprehensive_waypoints.keys())
for lane_id in [0, 1]:
    lane_waypoints = roar_py_world.comprehensive_waypoints[lane_id]
    assert len(lane_waypoints) % 2 == 0
    for i in range(len(lane_waypoints)//2):
        first_waypoint = lane_waypoints[2*i]
        second_waypoint = lane_waypoints[2*i+1]
        real_waypoint = roar_py_interface.RoarPyWaypoint.from_line_representation(
            first_waypoint.location,
            second_waypoint.location,
            first_waypoint.roll_pitch_yaw
        )
        # print(real_waypoint)
        waypoint_list.append(real_waypoint)


roar_py_instance.close()
np.savez_compressed("Monza.npz", **roar_py_interface.RoarPyWaypoint.save_waypoint_list(waypoint_list))
```

--------------------------------

### Generate and Save Spawn Points (Python)

Source: https://github.com/augcog/roar_py/blob/main/utilities/transform_waypoints.ipynb

Calls the `generate_spawn_points` function with the previously generated waypoints to obtain arrays of spawn point locations and rotations. It then prints the shape of the spawn point locations array and saves these arrays into a compressed NumPy `.npz` file named 'final_major_map_spawn_points.npz'.

```python
spawn_point_locations, spawn_rotations = generate_spawn_points(waypoints)
print(spawn_point_locations.shape)
np.savez_compressed("final_major_map_spawn_points.npz", locations=spawn_point_locations, rotations=spawn_rotations)
```

--------------------------------

### Visualize Waypoints with Debug Arrows

Source: https://github.com/augcog/roar_py/blob/main/utilities/trace_waypoints.ipynb

Iterates through a list of waypoints and draws debug arrows in the CARLA world to visualize the forward direction of each waypoint. Arrows are green and persist indefinitely.

```python
for waypoint in waypoint_list:
    origin_loc = waypoint.location
    forward_loc = waypoint.location + tr3d.euler.euler2mat(*waypoint.roll_pitch_yaw) @ np.array([0.5,0.0,0.0])
    roar_py_world.carla_world.debug.draw_arrow(
        roar_py_carla.location_to_carla(origin_loc),
        roar_py_carla.location_to_carla(forward_loc),
        thickness=0.1,
        arrow_size=0.1,
        color=carla.Color(0,255,0),
        life_time=-1.0
    )
```

--------------------------------

### Load and Access Maneuverable Waypoints in Python

Source: https://context7.com/augcog/roar_py/llms.txt

Retrieves pre-computed or dynamically traced waypoints for path planning and navigation. It allows access to waypoint properties such as location, roll-pitch-yaw, and lane width, and provides functionality to spawn a vehicle at a specific waypoint.

```python
# Get maneuverable waypoints (racing line or road center line)
waypoints = carla_world.maneuverable_waypoints

if waypoints is not None:
    print(f"Total waypoints: {len(waypoints)}")

    # Access waypoint properties
    for i, waypoint in enumerate(waypoints[:10]):
        location = waypoint.location  # np.ndarray [x, y, z]
        rpy = waypoint.roll_pitch_yaw  # np.ndarray [roll, pitch, yaw]
        lane_width = waypoint.lane_width  # float
        print(f"Waypoint {i}: pos={location}, rotation={rpy}, width={lane_width}")

    # Spawn vehicle at specific waypoint
    vehicle = carla_world.spawn_vehicle(
        "vehicle.audi.a2",
        waypoints[10].location + np.array([0, 0, 1]),
        waypoints[10].roll_pitch_yaw
    )
```

--------------------------------

### Display DataFrame Content (Python)

Source: https://github.com/augcog/roar_py/blob/main/utilities/transform_waypoints.ipynb

Prints the content of the Pandas DataFrame `dat` to the console. This is useful for inspecting the loaded waypoint data and understanding its structure before further processing.

```python
print(dat)
```

--------------------------------

### Attach LiDAR Sensor to Vehicle in Python

Source: https://context7.com/augcog/roar_py/llms.txt

Attaches a LiDAR sensor to a vehicle to capture 3D point cloud data. Configuration options include location, number of lasers, maximum distance, and field of view. It allows for receiving and processing point cloud data and converting it to a Gymnasium observation.

```python
lidar = carla_world.attach_lidar_sensor(
    location=np.array([0.0, 0.0, 2.5]),  # Relative to vehicle
    roll_pitch_yaw=np.array([0.0, 0.0, 0.0]),
    num_lasers=32,
    max_distance=50.0,
    points_per_second=56000,
    rotation_frequency=10.0,
    upper_fov=10.0,
    lower_fov=-30.0,
    horizontal_fov=360.0,
    control_timestep=0.0,
    name="lidar_sensor",
    bind_to=vehicle
)

# Receive LiDAR data
lidar_data = await lidar.receive_observation()

# Access point cloud: shape (N, 4) where columns are [x, y, z, intensity]
points = lidar_data.lidar_points_data
print(f"Number of points: {points.shape[0]}")
print(f"Channels: {lidar_data.channels}")
print(f"Horizontal angle: {lidar_data.horizontal_angle}")

# Convert to Gymnasium observation
gym_obs = lidar_data.convert_obs_to_gym_obs()
```

--------------------------------

### Convert CARLA Waypoints to ROAR Py Waypoints (Python)

Source: https://github.com/augcog/roar_py/blob/main/utilities/transform_waypoints.ipynb

Transforms the generated native CARLA waypoints (`native_ws`) into ROAR Py `RoarPyWaypoint` objects. This involves converting CARLA transforms to ROAR Py format, adjusting orientation, and creating new `RoarPyWaypoint` instances with associated lane width.

```python
real_ws = []
for native_ww in native_ws:
    w = native_ww[0]
    transform_w = roar_py_carla.transform_from_carla(w.transform)
    transform_w[1][2] += np.pi/2
    real_w = roar_py_interface.RoarPyWaypoint(
        transform_w[0],
        transform_w[1],
        w.lane_width
    )
    real_ws.append(real_w)
```

--------------------------------

### Define Function to Generate Spawn Points (Python)

Source: https://github.com/augcog/roar_py/blob/main/utilities/transform_waypoints.ipynb

Defines a Python function `generate_spawn_points` that takes a list of `RoarPyWaypoint` objects. It selects a subset of these waypoints (e.g., every 10th waypoint) to serve as spawn points, extracting their locations and rotations. These are then stacked into NumPy arrays.

```python
from roar_py_interface import RoarPyWaypoint
from typing import List, Tuple
from roar_py_carla.utils import *
import carla

def generate_spawn_points(waypoints : List[RoarPyWaypoint]) -> Tuple[np.ndarray, np.ndarray]:
    spawn_point_locations = []
    spawn_rotations = []
    for spawn_point in waypoints[::len(waypoints)//10]:
        spawn_point_locations.append(spawn_point.location)
        spawn_rotations.append(spawn_point.roll_pitch_yaw)
    return np.stack(spawn_point_locations, axis=0), np.stack(spawn_rotations, axis=0)
```

--------------------------------

### Receive Multi-Sensor Observations in Python

Source: https://context7.com/augcog/roar_py/llms.txt

Enables the collection of observations from multiple sensors (camera, collision, location, velocimeter) attached to an actor in a single asynchronous call. It facilitates accessing individual sensor data and retrieving the Gymnasium observation specification for all sensors.

```python
# Attach multiple sensors
camera = vehicle.attach_camera_sensor(
    roar_py_interface.RoarPyCameraSensorDataRGB,
    np.array([-2.0, 0.0, 1.5]),
    np.array([0, 0, 0])
)

collision_sensor = vehicle.attach_collision_sensor(
    name="collision"
)

location_sensor = vehicle.attach_location_in_world_sensor(
    name="gps"
)

velocimeter = vehicle.attach_velocimeter_sensor(
    name="speed"
)

# Receive all observations at once
await carla_world.step()
observations = await vehicle.receive_observation()

# Access individual sensor data
camera_obs = observations["camera"]
collision_obs = observations["collision"]
location_obs = observations["gps"]
velocity_obs = observations["speed"]

# Get Gymnasium observation specification
obs_spec = vehicle.get_gym_observation_spec()
print(obs_spec)  # Dict space containing all sensor specs
```

--------------------------------

### Convert CARLA World Coordinates to Geolocation

Source: https://context7.com/augcog/roar_py/llms.txt

Transform 3D world coordinates obtained from a vehicle's location into GPS latitude and longitude. This is useful for integrating simulator data with real-world geographic systems or for GPS sensor simulation.

```python
# Get vehicle location in world coordinates
vehicle_location = vehicle.get_3d_location()  # [x, y, z]

# Convert to geolocation
geolocation = carla_world.get_geolocation(vehicle_location)
latitude = geolocation[0]   # degrees
longitude = geolocation[1]  # degrees
altitude = geolocation[2]   # meters

print(f"GPS: {latitude:.6f}, {longitude:.6f}, {altitude:.2f}m")

# Useful for GPS sensor simulation or geographic visualization
```

--------------------------------

### Load Waypoint Data from CSV (Python)

Source: https://github.com/augcog/roar_py/blob/main/utilities/transform_waypoints.ipynb

Reads waypoint data from a 'new_right_side_waypoints.txt' file into a Pandas DataFrame. The file is expected to have no header row. The data is assumed to be in a format readable by `pd.read_csv`.

```python
dat = pd.read_csv("new_right_side_waypoints.txt", header=None)
```

--------------------------------

### Attach Camera Sensor to Vehicle in ROAR_PY (Python)

Source: https://context7.com/augcog/roar_py/llms.txt

Attaches a camera sensor (RGB, Depth, Grayscale, Segmentation) to a vehicle, specifying its relative position, rotation, and camera parameters (FOV, resolution). It allows receiving and processing sensor data, including converting it to PIL images or NumPy arrays. Requires `roar_py_interface` and the `vehicle` object.

```python
import roar_py_interface

# Attach RGB camera relative to vehicle
camera = vehicle.attach_camera_sensor(
    roar_py_interface.RoarPyCameraSensorDataRGB,  # Data type
    np.array([-2.0 * vehicle.bounding_box.extent[0], 0.0, 3.0 * vehicle.bounding_box.extent[2]]),  # Relative position
    np.array([0, 10/180.0*np.pi, 0]),  # Relative rotation [roll, pitch, yaw]
    fov=90.0,
    image_width=1024,
    image_height=768
)

# Receive camera observation
await carla_world.step()
camera_data = await camera.receive_observation()

# Access image data
pil_image = camera_data.get_image()  # PIL Image
numpy_array = camera_data.to_gym()    # numpy array (H, W, 3)
image_size = camera_data.get_size()   # (width, height)

# Save image
pil_image.save("capture.png")

# Other camera types:
# RoarPyCameraSensorDataGreyscale - Grayscale images
# RoarPyCameraSensorDataDepth - Depth maps
# RoarPyCameraSensorDataSemanticSegmentation - Segmentation masks
```

--------------------------------

### Manipulate and Subset NumPy Waypoint Array (Python)

Source: https://github.com/augcog/roar_py/blob/main/utilities/transform_waypoints.ipynb

Performs several operations on the NumPy array `dat_np`: prints its shape, reorders columns (0, 2, 1), and then selects every 50th row to create a sparse waypoint array `dat_np_sparse`. Finally, it prints the shape of the sparse array.

```python
print(dat_np.shape)
dat_np = dat_np[:,[0,2,1]]
dat_np_sparse = dat_np[::50,:]
print(dat_np_sparse.shape)
```

--------------------------------

### Save ROAR Py Waypoints to NPZ File (Python)

Source: https://github.com/augcog/roar_py/blob/main/utilities/transform_waypoints.ipynb

Saves the processed list of `RoarPyWaypoint` objects (`real_ws`) into a compressed NumPy `.npz` file named 'final_major_map_waypoints.npz'. It uses the `save_waypoint_list` method from `RoarPyWaypoint` for serialization.

```python
np.savez_compressed("final_major_map_waypoints.npz", **roar_py_interface.RoarPyWaypoint.save_waypoint_list(real_ws))
```

--------------------------------

### Generate and Save Waypoints using Custom Function (Python)

Source: https://github.com/augcog/roar_py/blob/main/utilities/transform_waypoints.ipynb

Calls the `generate_waypoints` function with processed data (`dat_np`) and saves the resulting waypoints to 'final_major_map_waypoints.npz'. It also prints the total number of generated waypoints and the location of the first waypoint.

```python
waypoints = generate_waypoints(dat_np, 2, 3.5)
print("final length",len(waypoints))

np.savez_compressed("final_major_map_waypoints.npz", **RoarPyWaypoint.save_waypoint_list(waypoints))
print(waypoints[0].location)
```

--------------------------------

### Define Function to Generate ROAR Py Waypoints (Python)

Source: https://github.com/augcog/roar_py/blob/main/utilities/transform_waypoints.ipynb

Defines a Python function `generate_waypoints` that takes a list of 3D locations (as NumPy arrays), a desired distance between waypoints, and a lane width. It processes the input locations to create a list of `RoarPyWaypoint` objects, calculating rotations based on the path's direction.

```python
from roar_py_interface import RoarPyWaypoint
from typing import List, Tuple
from roar_py_carla.utils import *
import carla

def generate_waypoints(locations : List[np.ndarray], distance_between_waypoints : float, lane_width : float):
    locations = [location_from_carla(carla.Location(x=location[0], y=location[1], z=location[2])) for location in locations]
    waypoint_locations = [locations[0]]
    waypoint_rotations = []
    last_location_idx = 0
    for i in range(1,len(locations)):
        next_location = locations[i]
        dist_to_last_location = np.linalg.norm(next_location - locations[last_location_idx])
        if dist_to_last_location > distance_between_waypoints:
            waypoint_locations.append(next_location)
            waypoint_rotations.append(np.arctan2(next_location[1] - locations[last_location_idx][1], next_location[0] - locations[last_location_idx][0]))
            last_location_idx = i
    waypoint_rotations.append(np.arctan2(locations[0][1] - waypoint_locations[-1][1], locations[0][0] - waypoint_locations[-1][0]))
    waypoints = []
    for i in range(len(waypoint_locations)):
        waypoints.append(RoarPyWaypoint(
            waypoint_locations[i],
            np.array([0,0,waypoint_rotations[i]]),
            lane_width
        ))
    return waypoints
```

--------------------------------

### Convert DataFrame to NumPy Array (Python)

Source: https://github.com/augcog/roar_py/blob/main/utilities/transform_waypoints.ipynb

Converts the Pandas DataFrame `dat` into a NumPy array named `dat_np`. This allows for efficient numerical operations and array manipulation.

```python
dat_np = np.asarray(dat)
```

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