import numpy as np

# Constants
MATRIX_SIZE = 83  # 83x83 matrix
BUFFER_SIZE = MATRIX_SIZE * MATRIX_SIZE  # 6889 elements
AZIMUTH_RANGE = (-41.8, 41.8)  # Azimuth range
ELEVATION_RANGE = (41.8, -41.8)  # Elevation range

def generate_angle_grid():
    """Generate azimuth and elevation matrices."""
    azimuth_values = np.linspace(AZIMUTH_RANGE[0], AZIMUTH_RANGE[1], MATRIX_SIZE)
    elevation_values = np.linspace(ELEVATION_RANGE[0], ELEVATION_RANGE[1], MATRIX_SIZE)
    
    azimuth_matrix, elevation_matrix = np.meshgrid(azimuth_values, elevation_values)
    return azimuth_matrix, elevation_matrix

def process_radar_data(buffer):
    """Convert buffer into an 83x83 matrix, find max value position, and get azimuth/elevation."""
    if len(buffer) != BUFFER_SIZE:
        raise ValueError(f"Invalid buffer size! Expected {BUFFER_SIZE}, got {len(buffer)}")
    
    # Reshape buffer into [83][83] matrix
    matrix = np.array(buffer).reshape((MATRIX_SIZE, MATRIX_SIZE))
    
    # Find position of the max value
    max_index = np.unravel_index(np.argmax(matrix), matrix.shape)
    
    # Generate azimuth and elevation mapping
    azimuth_matrix, elevation_matrix = generate_angle_grid()
    
    # Get azimuth and elevation for max value position
    max_azimuth = azimuth_matrix[max_index]
    max_elevation = elevation_matrix[max_index]
    
    return matrix, max_index, max_azimuth, max_elevation

# Example: Simulated buffer with random values (Replace with actual RADAR data)
np.random.seed(42)  # For reproducibility
buffer = np.random.rand(BUFFER_SIZE)  # Simulated intensity values

# Process the buffer
matrix, max_pos, max_azimuth, max_elevation = process_radar_data(buffer)

# Output results
print(f"Max value found at matrix position: {max_pos}")
print(f"Estimated Target Angles -> Azimuth: {max_azimuth:.2f}°, Elevation: {max_elevation:.2f}°")