86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
import math
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from pathlib import Path
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from typing import Dict, Any
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def analyze_file_metrics(path) -> Dict[str, Any]:
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"""Analyze a single file and return metrics dict.
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Returns:
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{"file": str(path), "name": filename, "avg_cc": float, "max_cc": int,
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"func_count": int, "mi": float, "language": str}
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"""
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p = Path(path)
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# lazy import lizard to avoid hard dependency until needed
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try:
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import lizard as lizard_mod
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except Exception:
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lizard_mod = None
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# If lizard is not available, attempt to approximate with simple counts
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if lizard_mod is None:
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# fallback: minimal values using line counts
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loc = 0
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try:
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with p.open('r', encoding='utf-8', errors='ignore') as fh:
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loc = sum(1 for _ in fh)
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except Exception:
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loc = 0
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return {
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"file": str(p),
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"name": p.name,
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"avg_cc": 0.0,
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"max_cc": 0,
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"func_count": 0,
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"mi": 0.0,
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"language": "unknown",
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}
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# use lizard when available
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res = lizard_mod.analyze_file(str(p))
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funcs = res.function_list if hasattr(res, 'function_list') else []
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cc_values = [getattr(f, 'cyclomatic_complexity', 0) for f in funcs]
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func_count = len(cc_values)
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avg_cc = float(sum(cc_values) / func_count) if func_count else 0.0
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max_cc = int(max(cc_values)) if cc_values else 0
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# Maintainability Index (approximate): use Coleman-Oman formula when possible.
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# MI = 171 - 5.2 * ln(HV) - 0.23 * CC - 16.2 * ln(LOC)
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loc = getattr(res, 'nloc', None) or 0
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if not loc:
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try:
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with p.open('r', encoding='utf-8', errors='ignore') as fh:
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loc = sum(1 for _ in fh)
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except Exception:
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loc = 0
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hv = None
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try:
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hv = getattr(res, 'halstead_volume', None)
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except Exception:
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hv = None
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mi_raw = 171.0
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try:
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if hv and hv > 0 and loc and loc > 0:
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mi_raw = 171 - 5.2 * math.log(max(1.0, hv)) - 0.23 * avg_cc - 16.2 * math.log(max(1.0, loc))
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elif loc and loc > 0:
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mi_raw = 171 - 0.23 * avg_cc - 16.2 * math.log(max(1.0, loc))
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else:
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mi_raw = 0.0
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mi = max(0.0, min(100.0, (mi_raw * 100.0 / 171.0)))
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except Exception:
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mi = 0.0
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# attempt to map extension to language
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ext = p.suffix
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language = ext.lower().lstrip('.') if ext else 'unknown'
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return {
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"file": str(p),
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"name": p.name,
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"avg_cc": round(avg_cc, 2),
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"max_cc": int(max_cc),
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"func_count": int(func_count),
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"mi": round(mi, 2),
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"language": language,
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}
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