'''import json
import os
from typing import List, Dict
import pandas as pd
import sys

# Append the parent directory to the system path to allow importing from core
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

class WeakPointDetector:
    def __init__(self, intersection_pressure_path: str):
        # self.lane_queue_path = lane_queue_path
        # self.lane_pressure_path = lane_pressure_path
        self.intersection_pressure_path = intersection_pressure_path
        self.intersections = set()
        # self.queue_data = {}
        # self.lane_pressure_data = {}
        self.intersection_pressure_data = {}
    
    def parse_log_file(self, file_path: str) -> List[Dict[str, float]]:
        """
        Parses a log file and returns a list of dictionaries containing the data
        from the specified epoch type (e.g., "TEST") for the last epoch.
        """
        data = []
        with open(file_path, 'r') as file:
            lines = file.readlines()
        
        # Filter lines for the specified epoch type
        #filtered_lines = [line for line in lines if line.startswith(f"colight\tTEST")] #colight\ttest\
        filtered_lines = [line for line in lines if line.split()[1].lower() == "test"]
        print(filtered_lines)
        # Get the last epoch
        last_epoch_num = None
        last_epoch_lines = []
        for line in reversed(filtered_lines):
            parts = line.strip().split('\t')
            if len(parts) >= 4:
                epoch_num = parts[2]
                if last_epoch_num is None:
                    last_epoch_num = epoch_num
                if epoch_num == last_epoch_num:
                    last_epoch_lines.insert(0, line)  # Maintain original order
                else:
                    break
        
        for line in last_epoch_lines:
            parts = line.strip().split('\t')
            if len(parts) >= 4:
                json_str = parts[3]
                try:
                    json_data = json.loads(json_str)
                    data.append(json_data)
                    # Extract intersection IDs
                    for key in json_data.keys():
                        intersection_id = key.split('_')[0] if '_' in key else key
                        self.intersections.add(intersection_id)
                except json.JSONDecodeError:
                    print(f"Failed to decode JSON for line: {line}")
        return data
    
    def aggregate_data(self):
        """
        Aggregates intersection pressure data from the specified epoch type
        (e.g., "TEST") for the last epoch.
        """
        # Aggregate Lane Queue Data
        '''
        lane_queue = self.parse_log_file(self.lane_queue_path)
        for entry in lane_queue:
            for key, value in entry.items():
                intersection_id = key.split('_')[0] if '_' in key else key
                self.queue_data[intersection_id] = self.queue_data.get(intersection_id, 0) + value
        
        # Aggregate Lane Pressure Data
        lane_pressure = self.parse_log_file(self.lane_pressure_path)
        for entry in lane_pressure:
            for key, value in entry.items():
                intersection_id = key.split('_')[0] if '_' in key else key
                self.lane_pressure_data[intersection_id] = self.lane_pressure_data.get(intersection_id, 0) + abs(value)
        '''
        
        # Aggregate Intersection Pressure Data
        intersection_pressure = self.parse_log_file(self.intersection_pressure_path)
        for entry in intersection_pressure:
            for key, value in entry.items():
                self.intersection_pressure_data[key] = self.intersection_pressure_data.get(key, 0) + abs(value)
    
    def compute_performance_metric(self) -> pd.DataFrame:
        """
        Computes a performance metric for each intersection.
        The metric can be a combination of queue lengths and pressure values.
        """
        '''
        data = []
        for intersection in self.intersections:
            queue = self.queue_data.get(intersection, 0)
            lane_pressure = self.lane_pressure_data.get(intersection, 0)
            intersection_pressure = self.intersection_pressure_data.get(intersection, 0)
            # Define a simple metric: sum of queue and pressures
            w = [1, 1, 1]
            metric = w[1]*queue + w[2]*lane_pressure + w[3]*intersection_pressure
            data.append({
                'Intersection': intersection,
                'Queue_Length': queue,
                'Lane_Pressure': lane_pressure,
                'Intersection_Pressure': intersection_pressure,
                'Performance_Metric': metric
            })
        '''
        data = []
        for intersection in self.intersections:
            intersection_pressure = self.intersection_pressure_data.get(intersection, 0)
            # Define a simple metric: intersection pressure
            metric = intersection_pressure
            data.append({
                'Intersection': intersection,
                'Intersection_Pressure': intersection_pressure,
                'Performance_Metric': metric
            })
        df = pd.DataFrame(data)
        return df
    
    def identify_weak_points(self, threshold: float = 0.35) -> pd.DataFrame:
        """
        Identifies the top percentage of intersections that are the weakest.
        """
        self.aggregate_data()
        df = self.compute_performance_metric()

        # Sort by Performance_Metric descendingly
        # df_sorted = df.sort_values(by='Performance_Metric', ascending=False)
        # Determine the cutoff index
        #cutoff = int(len(df_sorted) * (top_percentage / 100))
        #weak_points = df_sorted.head(cutoff)
        weak_points = df[df['Performance_Metric'] > threshold].sort_values(by='Performance_Metric', ascending=False)
        return weak_points

if __name__ == "__main__":
    # Define paths to the log files
    # lane_queue_log = "workspace/data/output_data/tsc/cityflow_colight/cityflow_grid4x4/test/logger/2024_10_01-06_42_53_LANE_QUEUE.log"
    # lane_pressure_log = "workspace/data/output_data/tsc/cityflow_colight/cityflow_grid4x4/test/logger/2024_10_01-06_42_53_LANE_PRESSURE.log"
    intersection_pressure_log = "workspace/data/output_data/tsc/cityflow_colight/cityflow_grid4x4/test/logger/2024_10_01-08_40_50_PRESSURE.log"
    
    detector = WeakPointDetector(intersection_pressure_log)
    weak_points_df = detector.identify_weak_points(threshold=0.5)
    
    # Save the weak points to a CSV file
    output_path = "workspace/data/output_data/tsc/cityflow_colight/cityflow_grid4x4/test/logger/weak_points.csv"
    weak_points_df.to_csv(output_path, index=False)
    print(f"Weak points saved to {output_path}")'''

is there a way or a formula or a AI approach for 'identify_weak_points', if there is i would like to use an AI approach like anomoly detection. give me clear detailed with clean code how to anomoly detection.