ssiog/metrics_collector.py [52:75]:
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def analyze_metrics(path, timestamp_filter=True):
    """
    Analyzes metrics from CSV files in a Google Cloud Storage bucket or local filesystem.

    Args:
        path (str): The path to the bucket or local containing CSV files, e.g., "gs://my-bucket/path/to/files/*.csv", "/local/*.csv"

    Returns:
        A pandas DataFrame containing the combined latency data, or None if no files are found. Also, timebased filtering which selects
        common entry among all the CSV files if timestamp_filter is set to True.
    """
    try:
        if path.startswith("gs://"): # if gcs path
            fs = gcsfs.GCSFileSystem()
        else: # otherwise assume it a local path
            fs = fsspec.filesystem("local")
        
        # Find all CSV files in the path using glob-like pattern matching.
        csv_files = list(fs.glob(path))
        if not csv_files:
            return None
        
        logger.info(f"Total number of CSV files: {len(csv_files)}")
        systemMemory = get_system_memory()
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ssiog/metrics_per_minute_collector.py [76:93]:
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def analyze_metrics(path, timestamp_filter=True):
    """
    Analyzes metrics from CSV files in a Google Cloud Storage bucket or local filesystem.
    """

    try:
        if path.startswith("gs://"):  # if GCS path
            fs = gcsfs.GCSFileSystem()
        else:  # otherwise assume it's a local path
            fs = fsspec.filesystem("local")
        
        # Find all CSV files in the path using glob-like pattern matching.
        csv_files = list(fs.glob(path))
        if not csv_files:
            return None
        
        logger.info(f"Total number of CSV files: {len(csv_files)}")
        systemMemory = get_system_memory()
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