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API Documentation

1. Introduction

This document provides comprehensive documentation for the API of the Advanced Image Sensor Interface project (v2.0.0). It covers all supported protocols, advanced image processing, multi-sensor synchronization, calibration, and buffer management interfaces.

Table of Contents

  1. Protocol Drivers
  2. Enhanced Sensor Interface
  3. Multi-Sensor Synchronization
  4. Image Processing
  5. Buffer Management
  6. Power Management
  7. Calibration
  8. Configuration Management

Protocol Drivers

MIPI CSI-2 Driver

Class: MIPIDriver

from advanced_image_sensor_interface.sensor_interface.protocol.mipi import MIPIDriver, MIPIConfig

config = MIPIConfig(
    lanes=4,
    data_rate_mbps=2500,
    pixel_format="RAW12",
    resolution=(1920, 1080),
    frame_rate=60
)
driver = MIPIDriver(config)

Methods:

  • send_data(data: bytes) -> bool: Send data over MIPI interface
  • receive_data(size: int) -> Optional[bytes]: Receive data from MIPI interface
  • get_status() -> Dict[str, Any]: Get driver status and statistics
  • reset() -> bool: Reset the MIPI interface

CoaXPress Driver

Class: CoaXPressDriver

from advanced_image_sensor_interface.sensor_interface.protocol.coaxpress import (
    CoaXPressDriver, CoaXPressConfig
)

config = CoaXPressConfig(
    speed_grade="CXP-6",
    connections=2,
    packet_size=8192,
    power_over_coax=True
)
driver = CoaXPressDriver(config)

Methods:

  • connect() -> bool: Establish CoaXPress connection
  • disconnect() -> bool: Disconnect from device
  • send_command(command: bytes) -> bool: Send control command
  • capture_frame() -> Optional[np.ndarray]: Capture single frame
  • start_streaming() -> bool: Start continuous streaming
  • stop_streaming() -> bool: Stop streaming

GigE Vision Driver

Class: GigEDriver

from advanced_image_sensor_interface.sensor_interface.protocol.gige import (
    GigEDriver, GigEConfig
)

config = GigEConfig(
    ip_address="192.168.1.100",
    packet_size=1500,
    pixel_format="BayerRG8",
    resolution=(1920, 1200)
)
driver = GigEDriver(config)

Methods:

  • discover_devices() -> List[Dict]: Discover GigE Vision devices
  • connect(device_info: Dict) -> bool: Connect to specific device
  • set_parameter(name: str, value: Any) -> bool: Set camera parameter
  • get_parameter(name: str) -> Any: Get camera parameter
  • capture_frame() -> Optional[np.ndarray]: Capture single frame

USB3 Vision Driver

Class: USB3Driver

from advanced_image_sensor_interface.sensor_interface.protocol.usb3 import (
    USB3Driver, USB3Config
)

config = USB3Config(
    device_id="USB3Vision_Device",
    transfer_size=1048576,
    pixel_format="BayerGR8"
)
driver = USB3Driver(config)

Methods:

  • enumerate_devices() -> List[Dict]: Enumerate USB3 Vision devices
  • open_device(device_id: str) -> bool: Open specific device
  • close_device() -> bool: Close device connection
  • start_acquisition() -> bool: Start image acquisition
  • stop_acquisition() -> bool: Stop image acquisition

Enhanced Sensor Interface

Class: EnhancedSensorInterface

The main interface for advanced sensor operations with multi-protocol support.

from advanced_image_sensor_interface.sensor_interface.enhanced_sensor import (
    EnhancedSensorInterface, SensorConfiguration
)

config = SensorConfiguration(
    resolution=SensorResolution.UHD_4K,
    frame_rate=30.0,
    bit_depth=12,
    enable_hdr=True,
    enable_gpu_acceleration=True
)

sensor = EnhancedSensorInterface(config)

Methods:

  • initialize() -> bool: Initialize sensor interface
  • capture_frame(sensor_id: int = 0) -> Optional[np.ndarray]: Capture single frame
  • capture_synchronized_frames() -> Optional[Dict[int, np.ndarray]]: Capture from all sensors
  • start_streaming() -> bool: Start continuous streaming
  • stop_streaming() -> bool: Stop streaming
  • get_sensor_status(sensor_id: Optional[int] = None) -> Dict: Get sensor status
  • set_exposure(exposure_us: float) -> bool: Set exposure time
  • set_gain(gain_db: float) -> bool: Set analog gain
  • enable_hdr(enable: bool) -> bool: Enable/disable HDR processing

Multi-Sensor Synchronization

Class: MultiSensorSync

Provides hardware and software synchronization for multiple sensors.

from advanced_image_sensor_interface.sensor_interface.multi_sensor_sync import (
    MultiSensorSync, SyncConfiguration
)

config = SyncConfiguration(
    sync_mode=SyncMode.HARDWARE,
    master_sensor_id=0,
    sync_tolerance_us=100,
    hardware_sync_pin=18
)

sync_manager = MultiSensorSync(config)

Methods:

  • add_sensor(sensor_id: int, sensor_config: Dict) -> bool: Add sensor to sync group
  • remove_sensor(sensor_id: int) -> bool: Remove sensor from sync group
  • start_synchronization() -> bool: Start synchronized operation
  • stop_synchronization() -> bool: Stop synchronized operation
  • capture_synchronized_frames() -> Optional[Dict[int, Tuple[np.ndarray, float]]]: Synchronized capture
  • get_sync_statistics() -> Dict: Get synchronization statistics
  • calibrate_timing() -> bool: Calibrate sensor timing offsets

Image Processing

HDR Processing

Class: HDRProcessor

from advanced_image_sensor_interface.sensor_interface.hdr_processing import (
    HDRProcessor, HDRParameters
)

params = HDRParameters(
    tone_mapping_method=ToneMappingMethod.REINHARD,
    exposure_fusion_method=ExposureFusionMethod.MERTENS,
    gamma=2.2,
    key_value=0.18
)

hdr_processor = HDRProcessor(params)

Methods:

  • process_hdr_sequence(images: List[np.ndarray], exposures: List[float]) -> np.ndarray: Process HDR sequence
  • tone_map(hdr_image: np.ndarray) -> np.ndarray: Apply tone mapping
  • exposure_fusion(images: List[np.ndarray]) -> np.ndarray: Fuse multiple exposures
  • estimate_camera_response(images: List[np.ndarray], exposures: List[float]) -> np.ndarray: Estimate response curve

RAW Processing

Class: RAWProcessor

from advanced_image_sensor_interface.sensor_interface.raw_processing import (
    RAWProcessor, RAWParameters
)

params = RAWParameters(
    bayer_pattern=BayerPattern.RGGB,
    demosaic_method=DemosaicMethod.MALVAR,
    white_balance_method=WhiteBalanceMethod.GRAY_WORLD,
    color_matrix=np.eye(3)
)

raw_processor = RAWProcessor(params)

Methods:

  • process_raw_image(raw_data: np.ndarray, metadata: Optional[Dict] = None) -> np.ndarray: Process RAW to RGB
  • demosaic(raw_data: np.ndarray) -> np.ndarray: Demosaic Bayer pattern
  • white_balance(image: np.ndarray) -> np.ndarray: Apply white balance
  • color_correction(image: np.ndarray) -> np.ndarray: Apply color correction
  • gamma_correction(image: np.ndarray, gamma: float = 2.2) -> np.ndarray: Apply gamma correction

GPU Acceleration

Class: GPUAccelerator

from advanced_image_sensor_interface.sensor_interface.gpu_acceleration import (
    GPUAccelerator, GPUConfiguration
)

config = GPUConfiguration(
    backend=GPUBackend.CUDA,
    device_id=0,
    memory_pool_size_mb=512,
    enable_profiling=True
)

gpu_accel = GPUAccelerator(config)

Methods:

  • process_batch(images: List[np.ndarray], operation: str, **kwargs) -> List[np.ndarray]: Process image batch
  • gaussian_blur(image: np.ndarray, sigma: float = 1.0) -> np.ndarray: GPU-accelerated blur
  • edge_detection(image: np.ndarray) -> np.ndarray: GPU-accelerated edge detection
  • histogram_equalization(image: np.ndarray) -> np.ndarray: GPU-accelerated histogram equalization
  • get_performance_stats() -> Dict: Get GPU performance statistics

Buffer Management

Class: BufferManager

Advanced buffer management with memory pooling and statistics.

from advanced_image_sensor_interface.utils.buffer_manager import (
    BufferManager, get_buffer_manager
)

# Get global buffer manager
buffer_manager = get_buffer_manager(
    pool_size=100,
    max_buffer_size=8*1024*1024,  # 8MB
    enable_statistics=True
)

Methods:

  • allocate_buffer(size: int) -> Optional[bytearray]: Allocate buffer from pool
  • deallocate_buffer(buffer: bytearray) -> bool: Return buffer to pool
  • get_stats() -> Optional[BufferStats]: Get buffer usage statistics
  • clear_pool() -> bool: Clear all buffers from pool
  • resize_pool(new_size: int) -> bool: Resize buffer pool

Class: AsyncBufferManager

Asynchronous buffer operations for high-performance applications.

from advanced_image_sensor_interface.utils.buffer_manager import AsyncBufferManager

async_manager = AsyncBufferManager(
    pool_size=50,
    max_buffer_size=4*1024*1024
)

Methods:

  • async allocate_buffer_async(size: int) -> Optional[bytearray]: Async buffer allocation
  • async deallocate_buffer_async(buffer: bytearray) -> bool: Async buffer deallocation
  • async get_stats_async() -> Optional[BufferStats]: Async statistics retrieval

Context Manager: ManagedBuffer

Automatic buffer lifecycle management.

from advanced_image_sensor_interface.utils.buffer_manager import ManagedBuffer

# Automatic buffer management
with ManagedBuffer(size=1024*1024) as buffer:
    # Use buffer for operations
    buffer[:1000] = data
    # Buffer automatically returned to pool when exiting context

Power Management

Class: AdvancedPowerManager

Advanced power management with multiple power states and thermal monitoring.

from advanced_image_sensor_interface.sensor_interface.advanced_power_management import (
    AdvancedPowerManager, PowerConfiguration
)

config = PowerConfiguration(
    enable_thermal_monitoring=True,
    thermal_threshold_c=75.0,
    enable_battery_monitoring=True,
    power_optimization_mode=PowerOptimizationMode.BALANCED
)

power_manager = AdvancedPowerManager(config)

Methods:

  • transition_to_state(state: PowerState) -> bool: Transition to power state
  • get_power_metrics() -> PowerMetrics: Get current power metrics
  • set_component_power(component: str, enabled: bool) -> bool: Control component power
  • optimize_for_workload(workload: WorkloadType) -> bool: Optimize for specific workload
  • get_thermal_status() -> Dict: Get thermal monitoring status
  • get_battery_status() -> Dict: Get battery status (if available)

Calibration

Camera Calibration

Class: CameraCalibrator

from advanced_image_sensor_interface.utils.calibration import CameraCalibrator

calibrator = CameraCalibrator()

Methods:

  • calibrate_camera(images: List[np.ndarray], config: Dict) -> CalibrationResult: Calibrate single camera
  • calibrate_stereo(left_images: List[np.ndarray], right_images: List[np.ndarray], config: Dict) -> StereoCalibrationResult: Calibrate stereo pair
  • assess_calibration_quality(result: CalibrationResult) -> QualityMetrics: Assess calibration quality
  • undistort_image(image: np.ndarray, camera_matrix: np.ndarray, distortion_coeffs: np.ndarray) -> np.ndarray: Remove distortion

Multi-Camera Calibration

Class: MultiCameraCalibrator

from advanced_image_sensor_interface.utils.calibration import MultiCameraCalibrator

multi_calibrator = MultiCameraCalibrator()

Methods:

  • calibrate_camera_array(image_sets: List[List[np.ndarray]], config: Dict) -> ArrayCalibrationResult: Calibrate camera array
  • compute_relative_poses(calibration_results: List[CalibrationResult]) -> List[Pose]: Compute relative camera poses
  • validate_calibration(result: ArrayCalibrationResult, test_images: List[List[np.ndarray]]) -> ValidationResult: Validate calibration

Configuration Management

Class: ConfigurationManager

Centralized configuration management with environment support.

from advanced_image_sensor_interface.config.constants import get_config

# Get configuration for current environment
config = get_config()

# Access configuration values
mipi_config = config.mipi
power_config = config.power
processing_config = config.processing

Configuration Sections:

  • mipi: MIPI CSI-2 protocol settings
  • coaxpress: CoaXPress protocol settings
  • gige: GigE Vision protocol settings
  • usb3: USB3 Vision protocol settings
  • power: Power management settings
  • processing: Image processing settings
  • synchronization: Multi-sensor sync settings
  • calibration: Calibration parameters
  • gpu: GPU acceleration settings

Environment-Specific Configuration

from advanced_image_sensor_interface.config.constants import get_config

# Get configuration for specific environment
dev_config = get_config(environment="development")
test_config = get_config(environment="testing")
prod_config = get_config(environment="production")

Available Environments:

  • development: Development settings with debug enabled
  • testing: Testing configuration with mock backends
  • production: Production settings optimized for performance

Error Handling

Exception Hierarchy

from advanced_image_sensor_interface.exceptions import (
    SensorInterfaceError,
    ProtocolError,
    CalibrationError,
    BufferError,
    PowerManagementError
)

try:
    sensor.capture_frame()
except ProtocolError as e:
    print(f"Protocol error: {e}")
except SensorInterfaceError as e:
    print(f"Sensor error: {e}")

Validation Results

Many methods return validation results with detailed error information:

from advanced_image_sensor_interface.sensor_interface.security import ValidationResult

result = validator.validate_image_data(image)
if not result.is_valid:
    print(f"Validation failed: {result.error_message}")
    for warning in result.warnings:
        print(f"Warning: {warning}")

Performance Monitoring

Performance Metrics

from advanced_image_sensor_interface.utils.performance_metrics import (
    calculate_snr,
    calculate_dynamic_range,
    calculate_color_accuracy
)

# Calculate image quality metrics
snr = calculate_snr(image, noise_image)
dynamic_range = calculate_dynamic_range(image)
color_accuracy = calculate_color_accuracy(image, reference_image)

Profiling and Benchmarking

from advanced_image_sensor_interface.utils.profiling import ProfileManager

with ProfileManager("image_processing") as profiler:
    processed_image = processor.process_image(raw_image)
    
# Get profiling results
stats = profiler.get_statistics()
print(f"Processing time: {stats.total_time:.3f}s")
print(f"Memory usage: {stats.peak_memory_mb:.1f}MB")

This comprehensive API documentation covers all major components and features of the Advanced Image Sensor Interface v2.1.0.

Retrieves the current status of the MIPI driver.

  • Returns: A dictionary containing status information:
    • lanes: Number of MIPI lanes
    • data_rate: Data rate in Gbps per lane
    • channel: MIPI channel number
    • error_rate: Current error rate
    • throughput: Current throughput
    • total_data_sent: Total bytes sent
    • total_time: Total time spent sending data
optimize_performance
optimize_performance() -> None

Optimizes the driver performance for increased data transfer rates. This increases the data rate by 40% and reduces the error rate by 50%.

2.2 Class: MIPIConfig

Constructor

MIPIConfig(lanes: int, data_rate: float, channel: int)
  • lanes: Number of data lanes (1-4).
  • data_rate: Data rate in Gbps per lane.
  • channel: MIPI channel number.

3. Signal Processing API

3.1 Class: SignalProcessor

Constructor

SignalProcessor(config: SignalConfig)
  • config: An instance of SignalConfig containing the processor configuration.

Methods

process_frame
process_frame(frame: np.ndarray) -> np.ndarray

Processes a single frame of image data.

  • frame: Input frame as a numpy array.
  • Returns: Processed frame as a numpy array.
  • Raises ValueError if frame is not a numpy array or has an invalid shape.
_apply_noise_reduction
_apply_noise_reduction(frame: np.ndarray) -> np.ndarray

Applies noise reduction to the frame using a Gaussian blur approach.

  • frame: Input frame as a numpy array.
  • Returns: Noise-reduced frame as a numpy array.
_apply_dynamic_range_expansion
_apply_dynamic_range_expansion(frame: np.ndarray) -> np.ndarray

Applies dynamic range expansion to the frame.

  • frame: Input frame as a numpy array.
  • Returns: Frame with expanded dynamic range.
_apply_color_correction
_apply_color_correction(frame: np.ndarray) -> np.ndarray

Applies color correction to the frame using the color correction matrix.

  • frame: Input frame as a numpy array.
  • Returns: Color-corrected frame as a numpy array.
optimize_performance
optimize_performance() -> None

Optimizes the signal processing pipeline for increased speed by reducing processing time by 20% and improving noise reduction by 10%.

3.2 Class: SignalConfig

Constructor

SignalConfig(bit_depth: int, noise_reduction_strength: float, color_correction_matrix: np.ndarray)
  • bit_depth: Bit depth of the image data.
  • noise_reduction_strength: Strength of noise reduction (0.0 - 1.0).
  • color_correction_matrix: 3x3 color correction matrix.

4. Power Management API

4.1 Class: PowerManager

Constructor

PowerManager(config: PowerConfig)
  • config: An instance of PowerConfig containing the power management configuration.
  • Raises ValueError if any configuration parameter is invalid (e.g., voltage_main ≤ 0, voltage_io ≤ 0, or current_limit ≤ 0).

Methods

set_voltage
set_voltage(rail: str, voltage: float) -> bool

Sets the voltage for a specific power rail.

  • rail: Power rail identifier ('main' or 'io').
  • voltage: Desired voltage in volts.
  • Returns: True if successful, False otherwise.
  • Raises ValueError if rail is not 'main' or 'io'.
  • Raises Exception if power consumption exceeds limits.
get_power_status
get_power_status() -> Dict[str, Any]

Retrieves the current power status.

  • Returns: A dictionary containing power status information:
    • voltage_main: Main voltage in volts
    • voltage_io: I/O voltage in volts
    • current_main: Main current in amperes
    • current_io: I/O current in amperes
    • power_consumption: Total power consumption in watts
    • temperature: System temperature in degrees Celsius
    • noise_level: Noise level in power delivery
optimize_noise_reduction
optimize_noise_reduction() -> None

Optimizes power delivery for reduced signal noise. This reduces noise level by 30%.

4.2 Class: PowerConfig

Constructor

PowerConfig(voltage_main: float, voltage_io: float, current_limit: float)
  • voltage_main: Main voltage in volts.
  • voltage_io: I/O voltage in volts.
  • current_limit: Current limit in amperes.

5. Utility Functions

5.1 Performance Metrics

calculate_snr

calculate_snr(signal: np.ndarray, noise: np.ndarray) -> float

Calculates the Signal-to-Noise Ratio.

  • signal: Clean signal or reference image.
  • noise: Noise component or difference between noisy and clean signal.
  • Returns: SNR in decibels.
  • Raises ValueError if the shapes of signal and noise do not match.

calculate_dynamic_range

calculate_dynamic_range(image: np.ndarray) -> float

Calculates the dynamic range of an image.

  • image: Input image.
  • Returns: Dynamic range in decibels. Returns 0 if minimum value is 0.

calculate_color_accuracy

calculate_color_accuracy(reference_colors: np.ndarray, measured_colors: np.ndarray) -> Tuple[float, np.ndarray]

Calculates color accuracy using a simplified Delta E formula.

  • reference_colors: Array of reference RGB colors.
  • measured_colors: Array of measured RGB colors.
  • Returns: Tuple of mean Delta E value and array of Delta E values for each color.
  • Raises ValueError if the shapes of reference_colors and measured_colors do not match.

6. Example Usage

6.1 Basic Usage with Error Handling

import numpy as np
from advanced_image_sensor_interface import (
    MIPIDriver, MIPIConfig,
    SignalProcessor, SignalConfig,
    PowerManager, PowerConfig,
    calculate_snr, calculate_dynamic_range, calculate_color_accuracy
)

def main():
    try:
        # Define frame parameters explicitly
        frame_size = (3840, 2160, 3)  # 4K RGB frame
        frame_bytes = frame_size[0] * frame_size[1] * frame_size[2]
        
        # Initialize MIPI Driver with error handling
        mipi_config = MIPIConfig(lanes=4, data_rate=2.5, channel=0)
        mipi_driver = MIPIDriver(mipi_config)
        print("✓ MIPI Driver initialized successfully")

        # Initialize Signal Processor with proper color correction matrix
        color_correction_matrix = np.array([
            [1.2, -0.1, -0.1],
            [-0.1, 1.2, -0.1], 
            [-0.1, -0.1, 1.2]
        ])
        signal_config = SignalConfig(
            bit_depth=12, 
            noise_reduction_strength=0.5, 
            color_correction_matrix=color_correction_matrix
        )
        signal_processor = SignalProcessor(signal_config)
        print("✓ Signal Processor initialized successfully")

        # Initialize Power Manager with validation
        power_config = PowerConfig(voltage_main=1.8, voltage_io=3.3, current_limit=1.0)
        power_manager = PowerManager(power_config)
        print("✓ Power Manager initialized successfully")

        # Generate synthetic test frame (in real usage, this comes from sensor)
        test_frame = np.random.randint(0, 4095, frame_size, dtype=np.uint16)
        print(f"✓ Generated test frame: {test_frame.shape}")

        # Process the frame with error handling
        try:
            processed_frame = signal_processor.process_frame(test_frame)
            print(f"✓ Frame processed successfully: {processed_frame.shape}")
        except ValueError as e:
            print(f"✗ Frame processing failed: {e}")
            return

        # Optimize performance
        mipi_driver.optimize_performance()
        signal_processor.optimize_performance()
        power_manager.optimize_noise_reduction()
        print("✓ Performance optimization completed")

        # Get system status with error handling
        try:
            mipi_status = mipi_driver.get_status()
            power_status = power_manager.get_power_status()
            
            print(f"MIPI Status: {mipi_status}")
            print(f"Power Status: {power_status}")
        except Exception as e:
            print(f"✗ Status retrieval failed: {e}")

        # Calculate performance metrics with proper reference data
        try:
            # Create noise component for SNR calculation
            noise_component = test_frame.astype(np.float32) - processed_frame.astype(np.float32)
            snr = calculate_snr(processed_frame.astype(np.float32), noise_component)
            
            dynamic_range = calculate_dynamic_range(processed_frame)
            
            # Create reference colors for color accuracy test
            reference_colors = np.array([[255, 0, 0], [0, 255, 0], [0, 0, 255]], dtype=np.uint8)
            measured_colors = np.array([[250, 5, 5], [5, 250, 5], [5, 5, 250]], dtype=np.uint8)
            color_accuracy, delta_e_values = calculate_color_accuracy(reference_colors, measured_colors)
            
            print(f"SNR: {snr:.2f} dB")
            print(f"Dynamic Range: {dynamic_range:.2f} dB")
            print(f"Color Accuracy (Mean Delta E): {color_accuracy:.2f}")
            
        except ValueError as e:
            print(f"✗ Performance metrics calculation failed: {e}")
        except Exception as e:
            print(f"✗ Unexpected error in metrics: {e}")

    except ValueError as e:
        print(f"✗ Configuration error: {e}")
    except Exception as e:
        print(f"✗ Unexpected error: {e}")

if __name__ == "__main__":
    main()

6.2 Advanced Usage with Custom Parameters

def advanced_usage_example():
    """
    Advanced usage example showing custom configurations and batch processing
    """
    try:
        # High-performance 8K configuration
        frame_size = (7680, 4320, 3)  # 8K RGB frame
        
        # High-speed MIPI configuration
        mipi_config = MIPIConfig(lanes=8, data_rate=5.0, channel=0)
        mipi_driver = MIPIDriver(mipi_config)
        
        # Advanced signal processing configuration
        advanced_ccm = np.array([
            [1.5, -0.3, -0.2],
            [-0.2, 1.4, -0.2],
            [-0.1, -0.4, 1.5]
        ])
        signal_config = SignalConfig(
            bit_depth=16,
            noise_reduction_strength=0.8,
            color_correction_matrix=advanced_ccm
        )
        signal_processor = SignalProcessor(signal_config)
        
        # High-power configuration for 8K processing
        power_config = PowerConfig(voltage_main=2.5, voltage_io=3.3, current_limit=2.0)
        power_manager = PowerManager(power_config)
        
        # Batch process multiple frames
        num_frames = 10
        processed_frames = []
        
        for i in range(num_frames):
            # Generate test frame with varying characteristics
            test_frame = np.random.randint(0, 65535, frame_size, dtype=np.uint16)
            
            try:
                processed_frame = signal_processor.process_frame(test_frame)
                processed_frames.append(processed_frame)
                print(f"✓ Processed frame {i+1}/{num_frames}")
            except Exception as e:
                print(f"✗ Failed to process frame {i+1}: {e}")
                continue
        
        print(f"✓ Successfully processed {len(processed_frames)}/{num_frames} frames")
        
        # Performance analysis
        if processed_frames:
            avg_snr = np.mean([
                calculate_snr(frame.astype(np.float32), 
                            np.random.normal(0, 10, frame.shape).astype(np.float32))
                for frame in processed_frames[:3]  # Sample first 3 frames
            ])
            print(f"Average SNR: {avg_snr:.2f} dB")
        
    except Exception as e:
        print(f"✗ Advanced usage failed: {e}")

if __name__ == "__main__":
    advanced_usage_example()

7. Error Handling

All API functions use Python's built-in exception handling mechanism. Here are the common exceptions you might encounter:

  • ValueError: Raised when an invalid argument is passed to a function, or for invalid configurations.
  • Exception: Raised for general errors, such as power limit exceeded.
  • RuntimeError: Raised when an operation fails due to an unexpected condition.
  • IOError: Raised when a hardware-related operation fails.

Example of error handling:

try:
    mipi_driver.send_data(frame_data)
except ValueError as e:
    print(f"Invalid data format: {e}")
except Exception as e:
    print(f"Error occurred during data transfer: {e}")

8. Best Practices

  1. Initialization: Always initialize the MIPI Driver, Signal Processor, and Power Manager with appropriate configurations before use.

  2. Performance Optimization: Call the optimize_performance() methods after the system has been running for a while to adapt to current conditions.

  3. Error Checking: Always check the return values of methods like send_data() and set_voltage() to ensure operations were successful.

  4. Resource Management: Properly clean up resources when they're no longer needed.

  5. Concurrent Access: Implement proper synchronization mechanisms when accessing objects from multiple threads.

9. Version Compatibility

This API documentation is for version 1.1.0 of the Advanced Image Sensor Interface project. Future versions will maintain backwards compatibility for major version numbers (e.g., 1.x.x).