`analytics/utils.py`

60 lines
#!/usr/bin/env python3
"""
Utility functions for analytics modules
"""

import json
import numpy as np
import pandas as pd


class NumpyEncoder(json.JSONEncoder):
    """JSON encoder that handles numpy types"""
    def default(self, obj):
        if isinstance(obj, np.integer):
            return int(obj)
        elif isinstance(obj, np.floating):
            return float(obj)
        elif isinstance(obj, np.ndarray):
            return obj.tolist()
        elif isinstance(obj, np.bool_):
            return bool(obj)
        elif pd.isna(obj):
            return None
        return super(NumpyEncoder, self).default(obj)


def save_json(data, filepath):
    """Save data to JSON with numpy type handling"""
    with open(filepath, 'w') as f:
        json.dump(data, f, indent=2, cls=NumpyEncoder)


def convert_numpy_types(obj):
    """Recursively convert numpy types to Python native types"""
    if isinstance(obj, dict):
        return {k: convert_numpy_types(v) for k, v in obj.items()}
    elif isinstance(obj, list):
        return [convert_numpy_types(item) for item in obj]
    elif isinstance(obj, np.integer):
        return int(obj)
    elif isinstance(obj, np.floating):
        # Handle NaN and Infinity
        val = float(obj)
        if np.isnan(val) or np.isinf(val):
            return None
        return val
    elif isinstance(obj, np.bool_):
        return bool(obj)
    elif isinstance(obj, np.ndarray):
        return obj.tolist()
    elif isinstance(obj, float):
        # Handle regular float NaN/Inf
        if np.isnan(obj) or np.isinf(obj):
            return None
        return obj
    elif pd.isna(obj):
        return None
    else:
        return obj