Delete src/drone_data_collection.py with huggingface_hub

src/drone_data_collection.py

@@ -1,68 +0,0 @@
-import numpy as np
-import os
-import time
-
-try:
-    from gym_pybullet_drones.envs.VelocityAviary import VelocityAviary
-    from gym_pybullet_drones.utils.Logger import Logger
-    from gym_pybullet_drones.utils.utils import sync, str2bool
-except ImportError:
-    print("gym_pybullet_drones is not installed or available.")
-    print("Please make sure the environment is set up correctly.")
-    exit(1)
-
-def collect_data(num_steps=8000, save_path="drone_dataset.npz"):
-    """
-    Collects flight data for ESN training.
-    We will fly in a circle with varying height to generate dynamic states.
-    Inputs: 12D state (x,y,z, r,p,y, vx,vy,vz, wx,wy,wz)
-    Targets: 4D velocity ref (vx_ref, vy_ref, vz_ref, yaw_rate_ref)
-    """
-    BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-    if not os.path.isabs(save_path):
-        save_path = os.path.join(BASE_DIR, save_path)
-    INIT_XYZS = np.array([[0, 0, .5]])
-    INIT_RPYS = np.array([[0, 0, 0]])
-    
-    env = VelocityAviary(
-        gui=False,
-        record=False,
-        initial_xyzs=INIT_XYZS,
-        initial_rpys=INIT_RPYS
-    )
-    
-    states = []
-    targets = []
-    
-    obs, info = env.reset()
-    state = obs[0]
-    
-    for i in range(num_steps):
-        # Generate some target velocities for a figure-8 or circle
-        t = i / 100.0
-        vx_ref = 0.5 * np.cos(t)
-        vy_ref = 0.5 * np.sin(t)
-        vz_ref = 0.1 * np.sin(t/2.0)
-        yaw_rate_ref = 0.2 * np.cos(t)
-        
-        target_action = np.array([vx_ref, vy_ref, vz_ref, yaw_rate_ref])
-        
-        # Save state and target
-        states.append(state)
-        targets.append(target_action)
-        
-        # Step env (action is the target velocity)
-        obs, reward, terminated, truncated, info = env.step(np.array([target_action]))
-        state = obs[0]
-        
-    env.close()
-    
-    states = np.array(states)
-    targets = np.array(targets)
-    
-    # Save the data
-    np.savez(save_path, states=states, targets=targets)
-    print(f"Data saved to {save_path}. Collected {len(states)} steps.")
-
-if __name__ == "__main__":
-    collect_data(8000, "drone_dataset.npz")