使用 Raspberry Pi 构建用于车辆的自定义数据记录器：集成 BNO 和 ELM 传感器

VDDM/
│
├── fusion/
│   ├── __init__.py
│   ├── sensor_fusion.py   # Handles data fusion, collects data from sensors
│   └── sensors/
│       ├── __init__.py
│       ├── bno055_measurement.py   # Script for BNO055 sensor data collection
│       └── elm327_measurement.py   # Script for ELM327 OBD2 data collection
│
├── signalprocessing/
│   ├── __init__.py
│   └── filter.py           # Contains filtering algorithms (e.g., Butterworth filter)
│
├── config/
│   ├── config_manager.py    # Loads configuration from YAML
│   └── measurement_system_config.yaml   # Configuration file for sensors and system settings
│
├── utils/
│   ├── tools.py             # Utility functions (e.g., wait functions)
│   └── visualize_data.py    # Functions to format and display sensor data
│
├── measurement/
│   ├── __init__.py
│   └── measurement_control.py   # Controls the measurement process
│
├── gui/
│   ├── __init__.py
│   └── main_gui.py          # GUI setup for starting/stopping measurement
│
├── main.py                  # Entry point for starting the system
└── requirements.txt         # Project dependencies

main.py
  └── main_gui.py
        ├── Starts GUI interface and buttons
        └── measurement_control.py
              ├── Initializes sensor measurements
              ├── Controls start/stop of measurements
              └── sensor_fusion.py
                    ├── Manages sensor data collection and fusion
                    ├── Uses:
                    │    ├── bno055_measurement.py  (BNO055 sensor data collection)
                    │    └── elm327_measurement.py  (ELM327 OBD2 data collection)
                    └── Processes data with:
                         ├── filter.py  (Applies Butterworth low-pass filter)
                         └── visualize_data.py  (Formats and visualizes sensor data)

# sensor_fusion.py
import os
import sys
import importlib
from time import perf_counter
from collections import defaultdict
import pandas as pd
import datetime
import asyncio
import numpy as np



parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.append(parent_dir)

from config import config_manager
from utils.tools import wait_process
from utils.visualize_data import format_sensor_fusion_data
from signalprocessing.filter import butterlowpass

config_path = os.path.join(parent_dir, 'config', 'measurement_system_config.yaml')


class SensorFactory:
    @staticmethod
    def create_sensor(sensor_type, config):
        try:
            # Import the appropriate sensor module based on sensor_type
            module = importlib.import_module(f"fusion.sensors.{sensor_type}_measurement")
            # Get a sensor class
            sensor_class = getattr(module, f"{sensor_type.upper()}")
            # Create a sensor instance and return
            return sensor_class(config)
        except (ImportError, AttributeError) as e:
            print(f"Error creating sensor {sensor_type}: {e}")
            return None


class Sensors:
    def __init__(self, config):
        self.config = config_manager.load_config(config_path)
        self.sensor_list = tuple(self.config.sensors.keys())
        self.sensor_instances = {}
        self.is_running = False


        self.SAMPLING_FREQUENCY_HZ = config.sampling_frequency_hz
        self.SAMPLING_TIME = 1 / self.SAMPLING_FREQUENCY_HZ
        self.SAVE_DATA_DIR = config.save_data_dir
        self.SAVE_BUF_CSVDATA_PATH = self.SAVE_DATA_DIR + "/" + "measurement_raw_data.csv"
        self.SEQUENCE_LENGTH = config.sequence_length # Windows size [s]
        # Buffer size is determined by the relation of sequence length and sampling frequency
        # Buffer secures data for SEQUENCE_LENGTH[s]
        self.MAX_DATA_BUF_LEN = self.SEQUENCE_LENGTH * self.SAMPLING_FREQUENCY_HZ
        self.FPASS = config.filter_params.fpass
        self.FSTOP = config.filter_params.fstop
        self.GPASS = config.filter_params.gpass
        self.GSTOP = config.filter_params.gstop
        self.is_filter = config.filter_params.is_filter
        self.is_show_real_time_data = config.is_show_real_time_data
        self.TIMEZONE = config.timezone
        self.all_data_columns_list = ()
        for sensor_name in self.sensor_list:
            self.all_data_columns_list += tuple(self.config["sensors"][sensor_name]["data_columns"])            




        self.data_buffer = pd.DataFrame()  # data buffer

        for sensor_type in self.sensor_list:
            sensor_config = self.config.sensors[sensor_type]
            sensor_instance = SensorFactory.create_sensor(sensor_type, sensor_config)
            if sensor_instance:
                self.sensor_instances[sensor_type] = sensor_instance


        if os.path.exists(self.SAVE_BUF_CSVDATA_PATH):
            os.remove(self.SAVE_BUF_CSVDATA_PATH)
            print(f"File  '{self.SAVE_BUF_CSVDATA_PATH}' was deleted for initialization")                


    def get_sensor(self, sensor_type):
        """
        Retrieve the sensor instance corresponding to the specified sensor type.

        Args:
            sensor_type (str): The type of the sensor to retrieve.

        Returns:
            object: The sensor instance corresponding to the specified sensor type.
                    Returns None if the sensor type does not exist.
        """
        return self.sensor_instances.get(sensor_type)

    def collect_data(self):
        """
        Collect data from all sensors.

        This method iterates over all sensor instances and collects data from each sensor.
        The collected data is stored in a dictionary where the keys are sensor types and
        the values are the data collected from the corresponding sensors.

        Returns:
            dict: A dictionary containing the collected data from all sensors.
                The keys are sensor types and the values are the data from each sensor.

        Raises:
            Exception: If an error occurs while collecting data from any sensor, the exception
                    is caught and printed.
        """
        data = {}
        try:
            for sensor_type, sensor in self.sensor_instances.items():
                # get data from sensors
                data[sensor_type] = sensor.get_data_from_sensor()
            return data
        except Exception as e:
            print(e)



    def on_change_start_measurement(self):
        """
        Start the measurement process.

        This method sets the is_running flag to True, indicating that the measurement
        process should start.
        """
        self.is_running = True

    def on_change_stop_measurement(self):
        """
        Stop the measurement process.

        This method sets the is_running flag to False, indicating that the measurement
        process should stop.
        """
        self.is_running = False


    def filtering(self, df, labellist):
        """
        Apply a low-pass filter to the specified columns in the DataFrame.

        This method applies a Butterworth low-pass filter to each column specified
        in the labellist. The "Time" column should be excluded from the labellist
        as it is not needed for the computation.

        Args:
            df (pd.DataFrame): The input DataFrame containing the data to be filtered.
            labellist (list of str): A list of column names to be filtered. The "Time"
                                     column should not be included in this list.

        Returns:
            pd.DataFrame: A new DataFrame with the filtered data.
        """
        filtered_df = df.copy()
        for labelname in labellist:
            # Ensure the column is converted to a numpy array
            x = df[labelname].to_numpy()
            filtered_df[labelname] = butterlowpass(
                x=x,  # Correctly pass the numpy array as 'x'
                fpass=self.FPASS,
                fstop=self.FSTOP,
                gpass=self.GPASS,
                gstop=self.GSTOP,
                fs=self.SAMPLING_FREQUENCY_HZ,
                dt=self.SAMPLING_TIME,
                checkflag=False,
                labelname=labelname
            )
        return filtered_df

    def convert_dictdata(self, current_time, sensor_data_dict):
        """
        Convert nested dictionary data from multiple sensors into a single DataFrame.

        This method converts nested dictionary data obtained from multiple sensors
        into a single dictionary and then converts it into a pandas DataFrame. The
        current_time information is associated with the data.

        Args:
            current_time (float): The current time at which the data was obtained.
            sensor_data_dict (dict): A nested dictionary containing data from multiple sensors.

        Returns:
            pd.DataFrame: A DataFrame containing the converted data with the current time information.
        """
        converted_data = {'Time': current_time}
        for sensor, data in sensor_data_dict.items():
            converted_data.update(data)

        converted_data = pd.DataFrame([converted_data])

        return converted_data



    async def update_data_buffer(self, dict_data):
        """
        Add data from sensors to the buffer and save it if necessary.

        This method adds the provided sensor data to the internal buffer. If the buffer
        exceeds the specified maximum length, the oldest data is saved to a CSV file
        and removed from the buffer.

        Args:
            dict_data (dict): The data from sensors to be added to the buffer.
        """

        # Add data to the buffer
        self.data_buffer = pd.concat([self.data_buffer, dict_data], ignore_index=True)

        # If the buffer exceeds the specified length, save the oldest data
        if len(self.data_buffer) > self.MAX_DATA_BUF_LEN:
            # Save the oldest data to a CSV file
            old_data = self.data_buffer.head(self.MAX_DATA_BUF_LEN)

            await self.save_data(old_data, self.SAVE_BUF_CSVDATA_PATH)

            # Update the buffer
            self.data_buffer = self.data_buffer.tail(len(self.data_buffer) - self.MAX_DATA_BUF_LEN)        



    async def save_data_async(self, df, path):
        """
        Save the DataFrame to a CSV file asynchronously.

        This method uses asyncio.to_thread to run the synchronous to_csv method
        in a separate thread, allowing it to be handled asynchronously.

        Args:
            df (pd.DataFrame): The DataFrame to be saved.
            path (str): The file path where the DataFrame should be saved.
        """
        if not os.path.isfile(path):
            await asyncio.to_thread(df.to_csv, path, sep=',', encoding='utf-8', index=False, header=True, mode='w')
        else:
            await asyncio.to_thread(df.to_csv, path, sep=',', encoding='utf-8', index=False, header=False, mode='a')

    async def save_data(self, df, path):
        """
        Save the DataFrame to a CSV file asynchronously.

        This method calls save_data_async to save the DataFrame to a CSV file
        asynchronously.

        Args:
            df (pd.DataFrame): The DataFrame to be saved.
            path (str): The file path where the DataFrame should be saved.
        """
        await self.save_data_async(df, path)



    async def finish_measurement_and_save_data(self):
        """
        Finish the measurement process and save the data.

        This method finalizes the measurement process by saving the buffered data
        to a CSV file. It also applies filtering if specified and saves the filtered
        data to a separate CSV file. The method handles time zone settings and
        generates a timestamp for the file names.

        The buffered data is saved to a temporary CSV file, which is then read back
        and saved to a final file path with a timestamp. If filtering is enabled,
        the filtered data is also saved. The temporary CSV file is deleted after
        the data is saved.

        Raises:
            Exception: If an error occurs during the file operations.
        """
        t_delta = datetime.timedelta(hours=9)
        TIMEZONE = datetime.timezone(t_delta, self.TIMEZONE)# You have to set your timezone
        now = datetime.datetime.now(TIMEZONE)
        timestamp = now.strftime('%Y%m%d%H%M%S')
        final_file_path = self.SAVE_BUF_CSVDATA_PATH.replace(self.SAVE_BUF_CSVDATA_PATH.split('/')[-1], 
                                                   timestamp + "/" + timestamp + '_' + 
                                                   self.SAVE_BUF_CSVDATA_PATH.split('/')[-1])
        await self.save_data_async(self.data_buffer, self.SAVE_BUF_CSVDATA_PATH)
        raw_df = pd.read_csv(self.SAVE_BUF_CSVDATA_PATH, header=0)
        os.makedirs(self.SAVE_DATA_DIR + "/" + timestamp, exist_ok=True)
        raw_df.to_csv(final_file_path, sep=',', encoding='utf-8', index=False, header=True)


        if self.is_filter:
            filt_df = self.filtering(df=raw_df, labellist=raw_df.columns[1:])
            filt_df.to_csv(final_file_path.replace('_raw_data.csv', '_filt_data.csv'), sep=',', encoding='utf-8', index=False, header=True)

        if os.path.exists(self.SAVE_BUF_CSVDATA_PATH):
            os.remove(self.SAVE_BUF_CSVDATA_PATH)
            print(f"File  '{self.SAVE_BUF_CSVDATA_PATH}' was deleted")
        else:
            print(f"File '{self.SAVE_BUF_CSVDATA_PATH}' is not existed")






async def sensor_fusion_main():
    """
    Main function for sensor fusion.

    This function initializes the sensor fusion process, starts the measurement loop,
    collects data from multiple sensors, updates the data buffer, and handles real-time
    data display. It also calculates and prints the sampling delay and reliability rate
    upon termination.

    The main loop runs until the measurement process is stopped, either by an exception
    or a keyboard interrupt.

    Raises:
        Exception: If an error occurs during the measurement process, it is caught and printed.
        KeyboardInterrupt: If a keyboard interrupt occurs, the measurement process is stopped
                           and the data is saved.

    """
    print("Start sensor fusion main")
    config = config_manager.load_config(config_path)
    sensors = Sensors(config["master"])
    print("Called an instance of Sensors class")

    # sensors.start_all_measurements()
    sampling_counter = 0
    current_time = 0
    #sensors.is_running = True
    sensors.on_change_start_measurement()

    try:
        main_loop_start_time = None
        while sensors.is_running:
            iteration_start_time = perf_counter() # Start time of each iteration

            if main_loop_start_time is None:
                    main_loop_start_time = iteration_start_time  # initialize main loop start time

            current_time = perf_counter() - main_loop_start_time # Current time

            data = sensors.collect_data() # Get data from sensors                                        
            sampling_counter += 1 # Num of sampling

            converted_data = sensors.convert_dictdata(current_time, data) # Convert data to dataframe format
            # Update the data buffer. If it reaches the buffer limit, write the data to a CSV file.
            await sensors.update_data_buffer(converted_data)
            # Display data in real time. This process is executed on additional thread.
            if sensors.is_show_real_time_data:
                formatted_data = format_sensor_fusion_data(data, sensors.all_data_columns_list)    
                print("--------------------------------------------------------------------")
                print("Current Time is: {:.3f}".format(current_time))
                print(formatted_data)

            # Wait based on the sampling interval and execution time to maintain the sampling frequency.
            iteration_end_time = perf_counter()
            iteration_duration = iteration_end_time - iteration_start_time 
            print("Iteration duration is: {0} [s]".format(iteration_duration))
            sleep_time = max(0, sensors.SAMPLING_TIME - iteration_duration)
            if sleep_time > 0:
                wait_process(sleep_time)

    except Exception as e:
        print(e)

    except KeyboardInterrupt:
        sensors.on_change_stop_measurement()
        print("KeyboardInterrupt")
        await sensors.finish_measurement_and_save_data()

    finally:
        print("finish")
         # Compute delay of sampling
        main_loop_end_time = perf_counter() - main_loop_start_time
        print("Program terminated")
        print("main loop is ended. current time is: {:.3f}".format(current_time))
        print("main loop is ended. end time is: {:.3f}".format(main_loop_end_time))
        print("sampling num is: {}".format(sampling_counter))



        # Compute ideal sampliing time
        ideal_time = ((sampling_counter - 1) / sensors.SAMPLING_FREQUENCY_HZ)
        # Cpmpute a delay
        delay_time = current_time - ideal_time
        # reliability rate
        sampling_reliability_rate = (delay_time / (sampling_counter / sensors.SAMPLING_FREQUENCY_HZ)) * 100


        print("sampling delay is: {:.3f} s".format(delay_time))
        print("sampling delay rate is: {:.3f} %".format(sampling_reliability_rate))


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

# bno055_measurement.py
import time
import numpy as np
import adafruit_bno055
import board
import os
import sys

parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
sys.path.append(parent_dir)

from config.config_manager import load_config
config_path = os.path.join(parent_dir, 'config', 'measurement_system_config.yaml')


class BNO055:
    def __init__(self, config):
        self.COLUMNS = config.data_columns    
        self.SAMPLING_FREQUENCY_HZ = config.sampling_frequency_hz
        self.SAMPLING_TIME = 1 / self.SAMPLING_FREQUENCY_HZ
        self.SAVE_DATA_DIR = config.save_data_dir
        self.SEQUENCE_LENGTH = config.sequence_length
        self.FPASS = config.filter_params.fpass
        self.FSTOP = config.filter_params.fstop
        self.GPASS = config.filter_params.gpass
        self.GSTOP = config.filter_params.gstop
        self.Isfilter = config.filter_params.is_filter

        self.IsStart = False
        self.IsStop = True
        self.Is_show_real_time_data = config.is_show_real_time_data 

        i2c_instance = board.I2C() # Create i2c instance
        self.bno055_sensor = adafruit_bno055.BNO055_I2C(i2c_instance) # create BNO055_I2C instance

    def calibration(self):
        print("Start calibration!")
        while not self.bno055_sensor.calibrated:
            print('SYS: {0}, Gyro: {1}, Accel: {2}, Mag: {3}'.format(*(self.bno055_sensor.calibration_status)))
            time.sleep(1)


    def calcEulerfromQuaternion(self, _w, _x, _y, _z):
        """
        Calculate Euler angles (roll, pitch, yaw) from quaternion components.

        This method converts quaternion components (_w, _x, _y, _z) into Euler angles
        (roll, pitch, yaw) in degrees. If any of the quaternion components are None,
        it returns (0.0, 0.0, 0.0) and prints an error message.

        Args:
            _w (float): The w component of the quaternion.
            _x (float): The x component of the quaternion.
            _y (float): The y component of the quaternion.
            _z (float): The z component of the quaternion.

        Returns:
            tuple: A tuple containing the roll, pitch, and yaw angles in degrees.
                If an error occurs, it returns (0.0, 0.0, 0.0) and prints an error message.
        """
        if None in (_w, _x, _y, _z):
            print(f"Error: One or more quaternion values are None: {_w}, {_x}, {_y}, {_z}")
            return 0.0, 0.0, 0.0

        try:
            sqw = _w ** 2
            sqx = _x ** 2
            sqy = _y ** 2
            sqz = _z ** 2
            COEF_EULER2DEG = 57.2957795131

            # Yaw
            term1 = 2.0 * (_x * _y + _z * _w)
            term2 = sqx - sqy - sqz + sqw
            yaw = np.arctan2(term1, term2)

            # Pitch
            term1 = -2.0 * (_x * _z - _y * _w)
            term2 = sqx + sqy + sqz + sqw
            pitch = np.arcsin(term1 / term2) if -1 <= term1 / term2 <= 1 else 0.0

            # Roll
            term1 = 2.0 * (_y * _z + _x * _w)
            term2 = -sqx - sqy + sqz + sqw
            roll = np.arctan2(term1, term2)

            return COEF_EULER2DEG * roll, COEF_EULER2DEG * pitch, COEF_EULER2DEG * yaw

        except Exception as e:
            print(f"Error in calcEulerfromQuaternion: {e}")
            return 0.0, 0.0, 0.0

    def get_data_from_sensor(self):
        """
        Retrieve data from the BNO055 sensor and return it as a dictionary.

        This method collects various sensor readings from the BNO055 sensor, including
        Euler angles, gyroscope data, linear acceleration, quaternion, magnetic field,
        and calibration status. It then constructs a dictionary with these values and
        returns only the columns specified in self.COLUMNS.

        Returns:
            dict: A dictionary containing the sensor data. Only the columns specified
                in self.COLUMNS are included in the returned dictionary.
        """
        # Get data
        euler_z, euler_y, euler_x = [val for val in self.bno055_sensor.euler]  # X: yaw, Y: pitch, Z: roll
        gyro_x, gyro_y, gyro_z = [val for val in self.bno055_sensor.gyro]  # Gyro[rad/s]
        linear_accel_x, linear_accel_y, linear_accel_z = [val for val in self.bno055_sensor.linear_acceleration]  # Linear acceleration[m/s^2]
        quaternion_1, quaternion_2, quaternion_3, quaternion_4 = [val for val in self.bno055_sensor.quaternion]  # Quaternion
        quat_roll, quat_pitch, quat_yaw = self.calcEulerfromQuaternion(quaternion_1, quaternion_2, quaternion_3, quaternion_4)  # Cal Euler angle from quaternion
        magnetic_x, magnetic_y, magnetic_z = [val for val in self.bno055_sensor.magnetic]  # Magnetic field
        calibstat_sys, calibstat_gyro, calibstat_accel, calibstat_mag = [val for val in self.bno055_sensor.calibration_status]  # Status of calibration


        data_dict = {
            "linear_accel_x": linear_accel_x,
            "linear_accel_y": linear_accel_y,
            "linear_accel_z": linear_accel_z,
            "gyro_x": gyro_x,
            "gyro_y": gyro_y,
            "gyro_z": gyro_z,
            "euler_x": euler_x,
            "euler_y": euler_y,
            "euler_z": euler_z,
            "quat_roll": quat_roll,
            "quat_pitch": quat_pitch,
            "quat_yaw": quat_yaw,
            "quaternion_1": quaternion_1,
            "quaternion_2": quaternion_2,
            "quaternion_3": quaternion_3,
            "quaternion_4": quaternion_4,
            "magnetic_x": magnetic_x,
            "magnetic_y": magnetic_y,
            "magnetic_z": magnetic_z,
            "calibstat_sys": calibstat_sys,
            "calibstat_gyro": calibstat_gyro,
            "calibstat_accel": calibstat_accel,
            "calibstat_mag": calibstat_mag
        }

        return {column: data_dict[column] for column in self.COLUMNS if column in data_dict}


def format_sensor_data(data, labels):
    """
    Format sensor data into a string for display.

    This method takes a dictionary of sensor data and a list of labels, and formats
    the data into a string where each label is followed by its corresponding value.
    If a value is None, it is replaced with the string "None". Each label-value pair
    is separated by " / ".

    Args:
        data (dict): The sensor data to format.
        labels (list of str): The list of labels to include in the formatted string.

    Returns:
        str: A formatted string containing the sensor data.
    """
    formatted_str = ""
    if isinstance(data, dict):
        for label in labels:
            value = data.get(label, None)
            if value is None:
                value = "None"
            else:
                value = f"{value:.4f}"
            formatted_str += f"{label}: {value} / "
    return formatted_str.rstrip(" / ")

def test_main():
    """
    Main function for testing sensor data collection and display.

    This function initializes the BNO055 sensor, starts a loop to collect data,
    formats the data for display, and prints it in real-time. It also calculates
    and prints the sampling delay and reliability rate upon termination.

    The main loop runs until interrupted by the user.

    Raises:
        KeyboardInterrupt: If a keyboard interrupt occurs, the loop is terminated
                           and the final statistics are printed.
    """
    from utils.tools import wait_process
    from time import perf_counter
    import matplotlib.pyplot as plt

    print("Main start")

    config = load_config(config_path)
    meas_bno055 = BNO055(config.sensors['bno055'])

    start_time = perf_counter()
    sampling_counter = 0

    try:
        main_loop_start_time = perf_counter()
        while True:
            iteration_start_time = perf_counter()

            # Data acquisition process
            data = meas_bno055.get_data_from_sensor()
            current_time = perf_counter() - start_time
            sampling_counter += 1

            if meas_bno055.Is_show_real_time_data:
                formatted_data = format_sensor_data(data, meas_bno055.COLUMNS)
                # current time    
                print("--------------------------------------------------------------------")
                print("Current Time is: {:.3f}".format(current_time))
                print(formatted_data)

            # Wait to meet the sampling frequency based on the sampling interval and execution time
            elapsed_time = perf_counter() - iteration_start_time
            sleep_time = meas_bno055.SAMPLING_TIME - elapsed_time
            if sleep_time > 0:
                wait_process(sleep_time)

    except KeyboardInterrupt:
        print("Interrupted by user")

    finally:
        # Calculate the sampling delay from the number of samples and the current time
        main_loop_end_time = perf_counter() - main_loop_start_time
        print("Program terminated")
        print("main loop is ended. current time is: {:.3f}".format(current_time))
        print("main loop is ended. end time is: {:.3f}".format(main_loop_end_time))
        print("sampling num is: {}".format(sampling_counter)) # Since it is 0-based, the number of samples is current_time + 1

        # Calculate the ideal sampling time
        ideal_time = ((sampling_counter - 1) / meas_bno055.SAMPLING_FREQUENCY_HZ)
        # Calculate the delay
        delay_time = current_time - ideal_time
        # The reliability rate is the delay divided by the sampling time
        sampling_reliability_rate = (delay_time / (sampling_counter / meas_bno055.SAMPLING_FREQUENCY_HZ)) * 100

        print("sampling delay is: {:.3f} s".format(delay_time))
        print("sampling delay rate is: {:.3f} %".format(sampling_reliability_rate))




if __name__ == '__main__':
    test_main()

# elm327_measurement.py

import obd
import os
import time
from collections import deque
import numpy as np
import pandas as pd
import datetime
import asyncio
import scipy
from scipy import signal
import matplotlib as plt
import sys
from collections import defaultdict
import random

parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
sys.path.append(parent_dir)

from config.config_manager import load_config
config_path = os.path.join(parent_dir, 'config', 'measurement_system_config.yaml')

class ELM327:
    def __init__(self, config):
        """
        Initialize the ELM327 class with configuration parameters.

        Args:
            config (dict): Configuration parameters for the ELM327.
        """
        self.COLUMNS = config.data_columns    
        self.SAMPLING_FREQUENCY_HZ = config.sampling_frequency_hz
        self.SAMPLING_TIME = 1 / self.SAMPLING_FREQUENCY_HZ
        self.SAVE_DATA_DIR = config.save_data_dir
        self.SEQUENCE_LENGTH = config.sequence_length
        self.FPASS = config.filter_params.fpass
        self.FSTOP = config.filter_params.fstop
        self.GPASS = config.filter_params.gpass
        self.GSTOP = config.filter_params.gstop
        self.Isfilter = config.filter_params.is_filter
        self.res = self.connect_to_elm327()

        self.is_offline = config.is_offline
        self.IsStart = False
        self.IsStop = True
        self.Is_show_real_time_data = config.is_show_real_time_data

    def initialize_BLE(self):
        """
        Initialize Bluetooth Low Energy (BLE) for ELM327 connection.
        """
        os.system('sudo hcitool scan')
        os.system('sudo hciconfig hci0 up')
        os.system('sudo rfcomm bind 0 8A:2A:D4:FF:38:F3')
        os.system('sudo rfcomm listen 0 1 &')

    def connect_to_elm327(self):
        """
        Establish a connection to the ELM327 device.

        Returns:
            res (obd.OBDStatus): The connection status of the ELM327 device.
        """
        res = None
        try:
            self.initialize_BLE()
            self.connection = obd.OBD()
            print(self.connection.status())
            res = self.connection.status()
            if res == obd.OBDStatus.CAR_CONNECTED:
                print("----------Connection establishment is successful!----------")
                return res
            else:
                print("----------Connection establishment failed!----------")
                print("End program. Please check settings of the computer and ELM327")
        except Exception as e:
            print("----------Exception!----------")
            print(e)
        finally:
            return res

    def get_data_from_sensor(self):
        """
        Retrieve data from the sensor.

        Returns:
            dict: A dictionary containing sensor data.
        """
        if self.is_offline:
            data = self.get_data_from_sensor_stub()
        else:
            # Retrieve data and save it in dictionary format
            data = {column: self.get_obd2_value(column) for column in self.COLUMNS}
        return data

    def get_obd2_value_debug(self, column):
        """
        Retrieve OBD-II value for a specific column with debug information.

        Args:
            column (str): The OBD-II command column.

        Returns:
            float or None: The value of the OBD-II command, or None if not available.
        """
        command = getattr(obd.commands, column, None)
        if command:
            response = self.connection.query(command)
            if response:
                print(f"Response for command '{command}': {response}")
                if response.value is not None:  # Check for None
                    print(f"Response value for command '{command}': {response.value}")
                    return response.value.magnitude
                else:
                    print(f"No value in response for command '{command}'")
            else:
                print(f"No response for command '{command}'")
        else:
            print(f"No command found for column '{column}'")
        return None

    def get_obd2_value(self, column):
        """
        Retrieve OBD-II value for a specific column.

        Args:
            column (str): The OBD-II command column.

        Returns:
            float or None: The value of the OBD-II command, or None if not available.
        """
        command = getattr(obd.commands, column, None)
        if command:
            response = self.connection.query(command)
            if response.value is not None:  # Check for None
                return response.value.magnitude
        return None

    def get_data_from_sensor_stub(self):
        """
        Generate stub data for the sensor.

        Returns:
            dict: A dictionary containing stub sensor data.
        """
        data_stub = {column: np.abs(np.random.randn()).astype(np.float32).item() for column in self.COLUMNS}        
        # Randomly insert None or 0.0
        if random.choice([True, False]):
            random_column = random.choice(self.COLUMNS)
            if random.choice([True, False]):
                data_stub[random_column] = None
            else:
                data_stub[random_column] = 0.0

        return data_stub

def format_data_for_display(data, labels):
    """
    Format sensor data for display.

    Args:
        data (dict): The sensor data to format.
        labels (list of str): The list of labels to include in the formatted string.

    Returns:
        str: A formatted string containing the sensor data.
    """
    formatted_str = ""
    for label, value in zip(labels, data.values()):
        if value is None:
            value = "None"
        else:
            value = f"{value:.4f}"
        formatted_str += f"{label}: {value} / "
    return formatted_str.rstrip(" / ")

def format_sensor_data(data, labels):
    """
    Format sensor data for display.

    Args:
        data (dict or list): The sensor data to format.
        labels (list of str): The list of labels to include in the formatted string.

    Returns:
        str: A formatted string containing the sensor data.
    """
    formatted_str = ""
    if isinstance(data, dict):
        for label in labels:
            value = data.get(label, None)
            if value is None:
                value = "None"
            else:
                value = f"{value:.4f}"
            formatted_str += f"{label}: {value} / "
    else:
        for label, value in zip(labels, data):
            if value is None:
                value = "None"
            else:
                value = f"{value:.4f}"
            formatted_str += f"{label}: {value} / "
    return formatted_str.rstrip(" / ")

def test_main():
    """
    Main function for testing sensor data collection and display.

    This function initializes the ELM327 sensor, starts a loop to collect data,
    formats the data for display, and prints it in real-time. It also calculates
    and prints the sampling delay and reliability rate upon termination.

    The main loop runs until interrupted by the user.

    Raises:
        KeyboardInterrupt: If a keyboard interrupt occurs, the loop is terminated
                           and the final statistics are printed.
    """
    from utils.tools import wait_process
    from time import perf_counter
    import matplotlib.pyplot as plt

    print("Main start")
    config = load_config(config_path)
    meas_elm327 = ELM327(config.sensors['elm327'])
    # res = meas_elm327.connect_to_elm327()

    start_time = perf_counter()
    sampling_counter = 0
    try:
        main_loop_start_time = perf_counter()
        while True:
            iteration_start_time = perf_counter()

            # Data acquisition process
            data = meas_elm327.get_data_from_sensor()

            current_time = perf_counter() - start_time
            sampling_counter += 1

            if meas_elm327.Is_show_real_time_data:
                formatted_data = format_sensor_data(data, meas_elm327.COLUMNS)
                print("--------------------------------------------------------------------")
                print("Current Time is: {:.3f}".format(current_time))
                print(formatted_data)

            # Wait to meet the sampling frequency based on the sampling interval and execution time
            elapsed_time = perf_counter() - iteration_start_time
            sleep_time = meas_elm327.SAMPLING_TIME - elapsed_time
            if sleep_time > 0:
                wait_process(sleep_time)

    except KeyboardInterrupt:
        print("Interrupted by user")

    finally:
        main_loop_end_time = perf_counter() - main_loop_start_time
        print("Program terminated")
        print("main loop is ended. current time is: {:.3f}".format(current_time))
        print("main loop is ended. end time is: {:.3f}".format(main_loop_end_time))
        print("sampling num is: {}".format(sampling_counter))

        # Calculate the ideal sampling time
        ideal_time = ((sampling_counter - 1) / meas_elm327.SAMPLING_FREQUENCY_HZ)
        # Calculate the delay
        delay_time = current_time - ideal_time
        # The reliability rate is the delay divided by the sampling time
        sampling_reliability_rate = (delay_time / (sampling_counter / meas_elm327.SAMPLING_FREQUENCY_HZ)) * 100

        print("sampling delay is: {:.3f} s".format(delay_time))
        print("sampling delay rate is: {:.3f} %".format(sampling_reliability_rate))

if __name__ == '__main__':
    test_main()