1565 lines
62 KiB
Python
1565 lines
62 KiB
Python
#!/usr/bin/env python3
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"""Spectral analysis tool for dependency DAGs.
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Reads a GraphViz DOT file (produced by the depgraph tool) and applies spectral
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graph theory (Laplacian eigenvalues, Fiedler vectors) to derive quantitative
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complexity metrics and visual analysis of codebase structural coupling.
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Usage:
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python spectral_analysis.py deps.dot [-o OUTPUT_DIR] [--no-plots] [--json]
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"""
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from __future__ import annotations
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import argparse
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import json
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import math
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import os
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import re
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import sys
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from dataclasses import dataclass, field
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from typing import Any
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import numpy as np
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from scipy import sparse
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# ─── Data Structures ──────────────────────────────────────────────────────────
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@dataclass
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class Node:
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name: str
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module: str
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@dataclass
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class Edge:
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source: str
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target: str
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label: str
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edge_type: str # "field" or "trait_impl"
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cross_module: bool
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@dataclass
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class DependencyGraph:
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nodes: list[Node] = field(default_factory=list)
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edges: list[Edge] = field(default_factory=list)
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modules: list[str] = field(default_factory=list) # ordered module names
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node_to_module: dict[str, str] = field(default_factory=dict)
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@dataclass
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class SpectralResults:
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eigenvalues: np.ndarray
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eigenvectors: np.ndarray
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fiedler_value: float
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fiedler_vector: np.ndarray
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adjacency: np.ndarray
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adjacency_sym: np.ndarray
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laplacian: np.ndarray
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node_names: list[str]
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node_modules: list[str]
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@dataclass
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class ModuleCouplingResult:
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module_names: list[str]
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coupling_matrix: np.ndarray # directed
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cross_module_edges: int
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total_edges: int
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@dataclass
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class ComplexityMetrics:
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algebraic_connectivity: float
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normalized_algebraic_connectivity: float
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spectral_entropy: float
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normalized_spectral_entropy: float
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edge_density: float
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cross_module_ratio: float
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spectral_radius: float
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normalized_spectral_radius: float
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cci: float
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n_nodes: int
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n_edges: int
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n_modules: int
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connected_components: int
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@dataclass
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class StructuralProperties:
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avg_degree: float
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max_fan_in: int
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max_fan_in_node: str
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max_fan_out: int
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max_fan_out_node: str
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dag_depth: int
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clustering_coeff: float
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module_cohesion: dict[str, float]
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avg_module_cohesion: float
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avg_module_size: float
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# ─── DOT Parser ───────────────────────────────────────────────────────────────
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def parse_dot(text: str) -> DependencyGraph:
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"""Parse a depgraph-generated DOT file into a DependencyGraph.
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Uses a line-by-line state machine to extract:
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- subgraph cluster_<module> blocks -> nodes with module membership
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- A -> B [label="...", style=..., ...] -> edges with classification
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"""
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graph = DependencyGraph()
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current_module: str | None = None
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module_order: list[str] = []
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seen_nodes: set[str] = set()
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for line in text.splitlines():
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stripped = line.strip()
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# Entering a subgraph cluster
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m = re.match(r'subgraph\s+cluster_(\w+)\s*\{', stripped)
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if m:
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current_module = m.group(1)
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if current_module not in module_order:
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module_order.append(current_module)
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continue
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# Closing brace - exit current subgraph if we're in one
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if stripped == '}' and current_module is not None:
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current_module = None
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continue
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# Node definition inside a subgraph: NodeName [label="...", ...]
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if current_module is not None:
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node_match = re.match(r'(\w+)\s*\[', stripped)
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if node_match:
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node_name = node_match.group(1)
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# Skip DOT keywords
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if node_name in ('label', 'style', 'node', 'edge', 'graph',
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'subgraph', 'digraph', 'rankdir', 'fontname',
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'fontsize', 'labelloc', 'compound', 'newrank',
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'splines', 'fillcolor', 'color'):
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continue
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if node_name not in seen_nodes:
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seen_nodes.add(node_name)
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graph.nodes.append(Node(name=node_name, module=current_module))
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graph.node_to_module[node_name] = current_module
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continue
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# Edge definition: A -> B [label="...", style=..., ...]
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edge_match = re.match(
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r'(\w+)\s*->\s*(\w+)\s*\[(.+)\];', stripped
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)
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if edge_match:
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src = edge_match.group(1)
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tgt = edge_match.group(2)
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attrs_str = edge_match.group(3)
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# Extract label
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label_match = re.search(r'label="([^"]*)"', attrs_str)
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label = label_match.group(1) if label_match else ""
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# Classify edge type
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style_match = re.search(r'style=(\w+)', attrs_str)
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style = style_match.group(1) if style_match else "solid"
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edge_type = "trait_impl" if style == "dotted" else "field"
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# Determine cross-module status
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src_mod = graph.node_to_module.get(src)
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tgt_mod = graph.node_to_module.get(tgt)
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cross = src_mod is not None and tgt_mod is not None and src_mod != tgt_mod
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graph.edges.append(Edge(
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source=src, target=tgt, label=label,
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edge_type=edge_type, cross_module=cross,
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))
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continue
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graph.modules = module_order
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return graph
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# ─── Matrix Construction ──────────────────────────────────────────────────────
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def get_node_ordering(graph: DependencyGraph) -> list[str]:
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"""Order nodes by module order, then alphabetical within module."""
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module_index = {m: i for i, m in enumerate(graph.modules)}
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return sorted(
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[n.name for n in graph.nodes],
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key=lambda name: (
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module_index.get(graph.node_to_module.get(name, ""), 999),
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name,
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),
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)
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def build_adjacency(graph: DependencyGraph, node_order: list[str]) -> np.ndarray:
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"""Build directed binary adjacency matrix."""
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n = len(node_order)
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idx = {name: i for i, name in enumerate(node_order)}
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A = np.zeros((n, n), dtype=float)
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for edge in graph.edges:
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i = idx.get(edge.source)
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j = idx.get(edge.target)
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if i is not None and j is not None:
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A[i, j] = 1.0
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return A
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def symmetrize(A: np.ndarray) -> np.ndarray:
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"""OR-symmetrize: A_sym[i,j] = 1 if A[i,j] or A[j,i]."""
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return np.clip(A + A.T, 0, 1)
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def build_laplacian(A_sym: np.ndarray) -> np.ndarray:
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"""Build graph Laplacian L = D - A_sym."""
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D = np.diag(A_sym.sum(axis=1))
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return D - A_sym
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# ─── Spectral Analysis ────────────────────────────────────────────────────────
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def compute_spectral(graph: DependencyGraph) -> SpectralResults:
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"""Compute full spectral analysis of the dependency graph."""
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node_order = get_node_ordering(graph)
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n = len(node_order)
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A = build_adjacency(graph, node_order)
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A_sym = symmetrize(A)
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L = build_laplacian(A_sym)
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if n == 0:
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return SpectralResults(
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eigenvalues=np.array([]),
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eigenvectors=np.array([[]]),
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fiedler_value=0.0,
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fiedler_vector=np.array([]),
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adjacency=A, adjacency_sym=A_sym, laplacian=L,
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node_names=node_order,
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node_modules=[graph.node_to_module.get(name, "") for name in node_order],
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)
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eigenvalues, eigenvectors = np.linalg.eigh(L)
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# Clean up near-zero eigenvalues
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eigenvalues = np.where(np.abs(eigenvalues) < 1e-10, 0.0, eigenvalues)
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if n == 1:
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fiedler_value = 0.0
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fiedler_vector = np.array([0.0])
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elif n >= 2:
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fiedler_value = float(eigenvalues[1])
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fiedler_vector = eigenvectors[:, 1]
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else:
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fiedler_value = 0.0
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fiedler_vector = np.array([])
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return SpectralResults(
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eigenvalues=eigenvalues,
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eigenvectors=eigenvectors,
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fiedler_value=fiedler_value,
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fiedler_vector=fiedler_vector,
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adjacency=A,
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adjacency_sym=A_sym,
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laplacian=L,
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node_names=node_order,
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node_modules=[graph.node_to_module.get(name, "") for name in node_order],
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)
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# ─── Module Coupling ──────────────────────────────────────────────────────────
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def compute_module_coupling(graph: DependencyGraph) -> ModuleCouplingResult:
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"""Compute directed module-level coupling matrix."""
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modules = graph.modules
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n = len(modules)
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mod_idx = {m: i for i, m in enumerate(modules)}
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M = np.zeros((n, n), dtype=float)
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cross = 0
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total = len(graph.edges)
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for edge in graph.edges:
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src_mod = graph.node_to_module.get(edge.source)
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tgt_mod = graph.node_to_module.get(edge.target)
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if src_mod is not None and tgt_mod is not None:
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i = mod_idx.get(src_mod)
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j = mod_idx.get(tgt_mod)
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if i is not None and j is not None:
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M[i, j] += 1.0
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if src_mod != tgt_mod:
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cross += 1
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return ModuleCouplingResult(
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module_names=modules,
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coupling_matrix=M,
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cross_module_edges=cross,
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total_edges=total,
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)
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# ─── Complexity Metrics ───────────────────────────────────────────────────────
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def count_connected_components(A_sym: np.ndarray) -> int:
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"""Count connected components using BFS on the symmetrized adjacency."""
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n = A_sym.shape[0]
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if n == 0:
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return 0
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visited = set()
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components = 0
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for start in range(n):
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if start in visited:
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continue
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components += 1
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queue = [start]
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visited.add(start)
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while queue:
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node = queue.pop(0)
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for neighbor in range(n):
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if A_sym[node, neighbor] > 0 and neighbor not in visited:
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visited.add(neighbor)
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queue.append(neighbor)
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return components
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def compute_spectral_entropy(eigenvalues: np.ndarray) -> float:
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"""Compute spectral entropy from positive Laplacian eigenvalues.
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H(lambda) = -sum(p_i * log2(p_i)) where p_i = lambda_i / sum(lambdas)
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over positive eigenvalues.
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"""
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positive = eigenvalues[eigenvalues > 1e-10]
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if len(positive) == 0:
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return 0.0
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p = positive / positive.sum()
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# Avoid log(0)
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p = p[p > 0]
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return float(-np.sum(p * np.log2(p)))
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def compute_complexity_metrics(
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spectral: SpectralResults,
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coupling: ModuleCouplingResult,
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) -> ComplexityMetrics:
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"""Compute the Connectome Complexity Index (CCI) and all sub-metrics."""
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n = len(spectral.node_names)
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n_edges = int(spectral.adjacency.sum()) # directed edge count
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n_modules = len(coupling.module_names)
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components = count_connected_components(spectral.adjacency_sym)
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if n <= 1:
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return ComplexityMetrics(
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algebraic_connectivity=0.0,
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normalized_algebraic_connectivity=0.0,
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spectral_entropy=0.0,
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normalized_spectral_entropy=0.0,
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edge_density=0.0,
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cross_module_ratio=0.0,
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spectral_radius=0.0,
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normalized_spectral_radius=0.0,
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cci=0.0,
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n_nodes=n,
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n_edges=n_edges,
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n_modules=n_modules,
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connected_components=components,
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)
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# Sub-metric 1: Normalized algebraic connectivity (lambda_2 / n)
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algebraic_connectivity = spectral.fiedler_value
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norm_alg_conn = algebraic_connectivity / n
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# Sub-metric 2: Spectral entropy
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raw_entropy = compute_spectral_entropy(spectral.eigenvalues)
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positive_count = int(np.sum(spectral.eigenvalues > 1e-10))
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max_entropy = math.log2(positive_count) if positive_count > 1 else 1.0
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norm_entropy = raw_entropy / max_entropy if max_entropy > 0 else 0.0
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# Sub-metric 3: Edge density |E| / (n*(n-1))
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edge_density = n_edges / (n * (n - 1)) if n > 1 else 0.0
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# Sub-metric 4: Cross-module coupling ratio
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cross_ratio = (coupling.cross_module_edges / coupling.total_edges
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if coupling.total_edges > 0 else 0.0)
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# Sub-metric 5: Normalized spectral radius (max eigenvalue of A_sym / (n-1))
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if spectral.adjacency_sym.shape[0] > 0:
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eig_A = np.linalg.eigvalsh(spectral.adjacency_sym)
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spectral_radius = float(np.max(np.abs(eig_A)))
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else:
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spectral_radius = 0.0
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norm_spec_radius = spectral_radius / (n - 1) if n > 1 else 0.0
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# CCI = weighted sum
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cci = (
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0.25 * norm_alg_conn
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+ 0.25 * norm_entropy
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+ 0.15 * edge_density
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+ 0.20 * cross_ratio
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+ 0.15 * norm_spec_radius
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)
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return ComplexityMetrics(
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algebraic_connectivity=algebraic_connectivity,
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normalized_algebraic_connectivity=norm_alg_conn,
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spectral_entropy=raw_entropy,
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normalized_spectral_entropy=norm_entropy,
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edge_density=edge_density,
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cross_module_ratio=cross_ratio,
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spectral_radius=spectral_radius,
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normalized_spectral_radius=norm_spec_radius,
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cci=cci,
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n_nodes=n,
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n_edges=n_edges,
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n_modules=n_modules,
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connected_components=components,
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)
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# ─── Structural Properties ───────────────────────────────────────────────────
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def _compute_dag_depth(A: np.ndarray) -> int:
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"""Longest directed path in the graph."""
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n = A.shape[0]
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if n == 0:
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return 0
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UNVISITED, VISITING, DONE = 0, 1, 2
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state = [UNVISITED] * n
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depth = [0] * n
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def dfs(node: int) -> int:
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if state[node] == DONE:
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return depth[node]
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if state[node] == VISITING:
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return 0 # cycle — treat as leaf
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state[node] = VISITING
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best = 0
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for j in range(n):
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if A[node, j] > 0:
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best = max(best, 1 + dfs(j))
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state[node] = DONE
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depth[node] = best
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return best
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return max(dfs(i) for i in range(n))
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def _compute_clustering_coefficient(A_sym: np.ndarray) -> float:
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"""Global clustering coefficient (transitivity) on the undirected graph.
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Uses the matrix identity: C = trace(A³) / (||A²||₁ - trace(A²))
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where ||·||₁ is the sum of all elements.
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"""
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n = A_sym.shape[0]
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if n < 3:
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return 0.0
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A2 = A_sym @ A_sym
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A3 = A2 @ A_sym
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numerator = np.trace(A3)
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denominator = A2.sum() - np.trace(A2)
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if denominator == 0:
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return 0.0
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return float(numerator / denominator)
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def compute_structural_properties(
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graph: DependencyGraph,
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spectral: SpectralResults,
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) -> StructuralProperties:
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"""Compute graph-theoretic structural properties."""
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n = len(graph.nodes)
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n_edges = len(graph.edges)
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node_names = spectral.node_names
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A = spectral.adjacency
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avg_degree = n_edges / n if n > 0 else 0.0
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in_degrees = A.sum(axis=0)
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out_degrees = A.sum(axis=1)
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if n > 0:
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fi_idx = int(np.argmax(in_degrees))
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fo_idx = int(np.argmax(out_degrees))
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max_fan_in = int(in_degrees[fi_idx])
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max_fan_out = int(out_degrees[fo_idx])
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max_fan_in_node = node_names[fi_idx]
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max_fan_out_node = node_names[fo_idx]
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else:
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max_fan_in = max_fan_out = 0
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max_fan_in_node = max_fan_out_node = ""
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dag_depth = _compute_dag_depth(A)
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clustering_coeff = _compute_clustering_coefficient(spectral.adjacency_sym)
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# Per-module cohesion: intra-edges / max-possible-intra-edges
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module_cohesion: dict[str, float] = {}
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module_sizes: dict[str, int] = {}
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for mod in graph.modules:
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mod_nodes = [i for i, name in enumerate(node_names)
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if graph.node_to_module.get(name) == mod]
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k = len(mod_nodes)
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module_sizes[mod] = k
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if k <= 1:
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module_cohesion[mod] = float("nan")
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continue
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max_possible = k * (k - 1)
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actual = sum(1 for i in mod_nodes for j in mod_nodes
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if i != j and A[i, j] > 0)
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module_cohesion[mod] = actual / max_possible
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|
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valid = [v for v in module_cohesion.values() if not math.isnan(v)]
|
|
avg_cohesion = sum(valid) / len(valid) if valid else 0.0
|
|
|
|
sizes = list(module_sizes.values())
|
|
avg_size = sum(sizes) / len(sizes) if sizes else 0.0
|
|
|
|
return StructuralProperties(
|
|
avg_degree=avg_degree,
|
|
max_fan_in=max_fan_in,
|
|
max_fan_in_node=max_fan_in_node,
|
|
max_fan_out=max_fan_out,
|
|
max_fan_out_node=max_fan_out_node,
|
|
dag_depth=dag_depth,
|
|
clustering_coeff=clustering_coeff,
|
|
module_cohesion=module_cohesion,
|
|
avg_module_cohesion=avg_cohesion,
|
|
avg_module_size=avg_size,
|
|
)
|
|
|
|
|
|
# ─── Full Pipeline ────────────────────────────────────────────────────────────
|
|
|
|
@dataclass
|
|
class AnalysisResult:
|
|
graph: DependencyGraph
|
|
spectral: SpectralResults
|
|
coupling: ModuleCouplingResult
|
|
metrics: ComplexityMetrics
|
|
structural: StructuralProperties
|
|
|
|
|
|
def run_analysis(graph: DependencyGraph) -> AnalysisResult:
|
|
"""Run the full spectral analysis pipeline on a DependencyGraph."""
|
|
spectral = compute_spectral(graph)
|
|
coupling = compute_module_coupling(graph)
|
|
metrics = compute_complexity_metrics(spectral, coupling)
|
|
structural = compute_structural_properties(graph, spectral)
|
|
return AnalysisResult(
|
|
graph=graph,
|
|
spectral=spectral,
|
|
coupling=coupling,
|
|
metrics=metrics,
|
|
structural=structural,
|
|
)
|
|
|
|
|
|
# ─── Text Report ──────────────────────────────────────────────────────────────
|
|
|
|
def generate_report(result: AnalysisResult) -> str:
|
|
"""Generate a text report of the spectral analysis."""
|
|
s = result.spectral
|
|
m = result.metrics
|
|
c = result.coupling
|
|
p = result.structural
|
|
lines: list[str] = []
|
|
|
|
def w(text: str = "") -> None:
|
|
lines.append(text)
|
|
|
|
w("=" * 72)
|
|
w(" SPECTRAL ANALYSIS REPORT — Dependency DAG")
|
|
w("=" * 72)
|
|
w()
|
|
|
|
# Graph summary
|
|
w("GRAPH SUMMARY")
|
|
w("-" * 40)
|
|
w(f" Nodes: {m.n_nodes}")
|
|
w(f" Directed edges: {m.n_edges}")
|
|
w(f" Modules: {m.n_modules}")
|
|
w(f" Connected components: {m.connected_components}")
|
|
w(f" Modules: {', '.join(c.module_names)}")
|
|
w()
|
|
|
|
# Structural properties
|
|
w("STRUCTURAL PROPERTIES")
|
|
w("-" * 40)
|
|
w(f" Edges/node (avg degree): {p.avg_degree:.2f}")
|
|
w(f" Max fan-in: {p.max_fan_in:<4d} ({p.max_fan_in_node})")
|
|
w(f" Max fan-out: {p.max_fan_out:<4d} ({p.max_fan_out_node})")
|
|
w(f" DAG depth: {p.dag_depth}")
|
|
w(f" Clustering coefficient: {p.clustering_coeff:.4f}")
|
|
w()
|
|
|
|
# Module cohesion
|
|
w("MODULE COHESION")
|
|
w("-" * 40)
|
|
w(f" {'Module':<16s} {'Size':>5s} {'Cohesion':>8s}")
|
|
for mod in c.module_names:
|
|
coh = p.module_cohesion.get(mod, float("nan"))
|
|
size = sum(1 for n in result.graph.nodes if n.module == mod)
|
|
coh_str = f"{coh:.3f}" if not math.isnan(coh) else " —"
|
|
w(f" {mod:<16s} {size:>5d} {coh_str:>8s}")
|
|
w(f" {'─' * 32}")
|
|
w(f" {'Average cohesion:':<22s} {p.avg_module_cohesion:8.3f}")
|
|
w(f" {'Avg module size:':<22s} {p.avg_module_size:8.1f}")
|
|
w()
|
|
|
|
# Module coupling
|
|
w("MODULE COUPLING MATRIX (directed edge counts)")
|
|
w("-" * 40)
|
|
header = " " + " " * 14 + "".join(f"{name:>10s}" for name in c.module_names)
|
|
w(header)
|
|
for i, row_name in enumerate(c.module_names):
|
|
row = f" {row_name:12s} " + "".join(
|
|
f"{int(c.coupling_matrix[i, j]):10d}" for j in range(len(c.module_names))
|
|
)
|
|
w(row)
|
|
w()
|
|
w(f" Cross-module edges: {c.cross_module_edges} / {c.total_edges} "
|
|
f"({m.cross_module_ratio:.1%})")
|
|
w()
|
|
|
|
# Complexity metrics
|
|
w("CONNECTOME COMPLEXITY INDEX (CCI)")
|
|
w("-" * 40)
|
|
w(f" {'Sub-metric':<40s} {'Raw':>10s} {'Normalized':>10s} {'Weight':>8s} {'Contrib':>8s}")
|
|
w(f" {'─' * 40} {'─' * 10} {'─' * 10} {'─' * 8} {'─' * 8}")
|
|
|
|
rows = [
|
|
("Algebraic connectivity (lambda_2/n)",
|
|
f"{m.algebraic_connectivity:.4f}", f"{m.normalized_algebraic_connectivity:.4f}",
|
|
"0.25", f"{0.25 * m.normalized_algebraic_connectivity:.4f}"),
|
|
("Spectral entropy (H/log2(k))",
|
|
f"{m.spectral_entropy:.4f}", f"{m.normalized_spectral_entropy:.4f}",
|
|
"0.25", f"{0.25 * m.normalized_spectral_entropy:.4f}"),
|
|
("Edge density (|E|/n(n-1))",
|
|
f"{m.edge_density:.4f}", f"{m.edge_density:.4f}",
|
|
"0.15", f"{0.15 * m.edge_density:.4f}"),
|
|
("Cross-module coupling ratio",
|
|
f"{m.cross_module_ratio:.4f}", f"{m.cross_module_ratio:.4f}",
|
|
"0.20", f"{0.20 * m.cross_module_ratio:.4f}"),
|
|
("Spectral radius (rho/(n-1))",
|
|
f"{m.spectral_radius:.4f}", f"{m.normalized_spectral_radius:.4f}",
|
|
"0.15", f"{0.15 * m.normalized_spectral_radius:.4f}"),
|
|
]
|
|
for label, raw, norm, weight, contrib in rows:
|
|
w(f" {label:<40s} {raw:>10s} {norm:>10s} {weight:>8s} {contrib:>8s}")
|
|
w(f" {'─' * 40} {'─' * 10} {'─' * 10} {'─' * 8} {'─' * 8}")
|
|
w(f" {'CCI (weighted sum)':<40s} {'':>10s} {'':>10s} {'1.00':>8s} {m.cci:8.4f}")
|
|
w()
|
|
|
|
# Interpretation
|
|
if m.cci < 0.3:
|
|
interp = "LOW complexity — well-decomposed architecture"
|
|
elif m.cci < 0.6:
|
|
interp = "MODERATE complexity — typical well-structured codebase"
|
|
else:
|
|
interp = "HIGH complexity — consider reviewing module boundaries"
|
|
w(f" Interpretation: {interp}")
|
|
w()
|
|
w("=" * 72)
|
|
|
|
return "\n".join(lines)
|
|
|
|
|
|
# ─── Dashboard Visualization ─────────────────────────────────────────────────
|
|
|
|
# Module border colors from the depgraph palette (used as the accent color).
|
|
# These rotate by discovery-order index; the palette has 8 entries.
|
|
_PALETTE_BORDER = [
|
|
"#1565c0", # 0 — blue
|
|
"#c62828", # 1 — red
|
|
"#e65100", # 2 — orange
|
|
"#7b1fa2", # 3 — purple
|
|
"#2e7d32", # 4 — green
|
|
"#f9a825", # 5 — yellow
|
|
"#00838f", # 6 — teal
|
|
"#d84315", # 7 — deep orange
|
|
]
|
|
|
|
# Module index assigned at analysis time (populated by generate_dashboard_html)
|
|
_module_index: dict[str, int] = {}
|
|
|
|
|
|
def get_module_color(module: str) -> str:
|
|
idx = _module_index.get(module)
|
|
if idx is not None:
|
|
return _PALETTE_BORDER[idx % len(_PALETTE_BORDER)]
|
|
return "#9e9e9e"
|
|
|
|
|
|
def generate_dashboard(result: AnalysisResult, output_path: str) -> None:
|
|
"""Generate spectral dashboard PNG (16x12, 150 DPI, dark theme)."""
|
|
import matplotlib
|
|
matplotlib.use("Agg")
|
|
import matplotlib.pyplot as plt
|
|
from matplotlib.gridspec import GridSpec
|
|
|
|
# Ensure module palette indices are populated
|
|
_module_index.clear()
|
|
for i, mod in enumerate(result.graph.modules):
|
|
_module_index[mod] = i
|
|
|
|
s = result.spectral
|
|
m = result.metrics
|
|
c = result.coupling
|
|
|
|
# Dark theme
|
|
plt.rcParams.update({
|
|
"figure.facecolor": "#1a1a2e",
|
|
"axes.facecolor": "#16213e",
|
|
"axes.edgecolor": "#e0e0e0",
|
|
"axes.labelcolor": "#e0e0e0",
|
|
"text.color": "#e0e0e0",
|
|
"xtick.color": "#e0e0e0",
|
|
"ytick.color": "#e0e0e0",
|
|
"grid.color": "#2a2a4a",
|
|
"grid.alpha": 0.5,
|
|
})
|
|
|
|
fig = plt.figure(figsize=(16, 12), dpi=150)
|
|
gs = GridSpec(2, 2, figure=fig, hspace=0.35, wspace=0.3,
|
|
left=0.07, right=0.95, top=0.92, bottom=0.06)
|
|
|
|
fig.suptitle("Spectral Analysis Dashboard — Dependency DAG",
|
|
fontsize=16, fontweight="bold", color="#e0e0e0")
|
|
|
|
p = result.structural
|
|
|
|
# ── Top-left: Structural properties ──
|
|
ax1 = fig.add_subplot(gs[0, 0])
|
|
ax1.axis("off")
|
|
ax1.set_title("Structural Properties", fontsize=12, fontweight="bold")
|
|
props = [
|
|
("Edges/node (avg degree)", f"{p.avg_degree:.2f}"),
|
|
("Max fan-in", f"{p.max_fan_in} ({p.max_fan_in_node})"),
|
|
("Max fan-out", f"{p.max_fan_out} ({p.max_fan_out_node})"),
|
|
("DAG depth", f"{p.dag_depth}"),
|
|
("Clustering coefficient", f"{p.clustering_coeff:.4f}"),
|
|
("Avg module size", f"{p.avg_module_size:.1f}"),
|
|
("Avg module cohesion", f"{p.avg_module_cohesion:.3f}"),
|
|
]
|
|
y = 0.88
|
|
for label, value in props:
|
|
ax1.text(0.05, y, label, transform=ax1.transAxes, fontsize=10,
|
|
color="#aaa", fontfamily="monospace", va="top")
|
|
ax1.text(0.95, y, value, transform=ax1.transAxes, fontsize=10,
|
|
fontweight="bold", color="#e0e0e0", fontfamily="monospace",
|
|
va="top", ha="right")
|
|
y -= 0.12
|
|
|
|
# ── Top-right: Module cohesion ──
|
|
ax2 = fig.add_subplot(gs[0, 1])
|
|
cohesion_mods = [mod for mod in c.module_names
|
|
if not math.isnan(p.module_cohesion.get(mod, float("nan")))]
|
|
if cohesion_mods:
|
|
cohesion_vals = [p.module_cohesion[mod] for mod in cohesion_mods]
|
|
bar_colors = [get_module_color(mod) for mod in cohesion_mods]
|
|
bars = ax2.barh(range(len(cohesion_mods)), cohesion_vals,
|
|
color=bar_colors, edgecolor="none", height=0.6)
|
|
ax2.set_yticks(range(len(cohesion_mods)))
|
|
ax2.set_yticklabels(cohesion_mods, fontsize=9)
|
|
ax2.set_xlim(0, 1.05)
|
|
ax2.set_xlabel("Cohesion (intra-edges / max possible)")
|
|
ax2.axvline(x=p.avg_module_cohesion, color="#ff4444", linewidth=1.5,
|
|
linestyle="--", alpha=0.7, label=f"avg = {p.avg_module_cohesion:.3f}")
|
|
ax2.legend(fontsize=9, loc="lower right",
|
|
facecolor="#16213e", edgecolor="#444")
|
|
ax2.grid(True, axis="x", alpha=0.3)
|
|
else:
|
|
ax2.text(0.5, 0.5, "No modules with 2+ types",
|
|
ha="center", va="center", fontsize=14, transform=ax2.transAxes)
|
|
ax2.set_title("Module Cohesion", fontsize=12, fontweight="bold")
|
|
|
|
# ── Bottom-left: Module coupling heatmap ──
|
|
ax3 = fig.add_subplot(gs[1, 0])
|
|
if len(c.module_names) > 0:
|
|
im = ax3.imshow(c.coupling_matrix, cmap="YlOrRd", aspect="auto")
|
|
ax3.set_xticks(range(len(c.module_names)))
|
|
ax3.set_xticklabels(c.module_names, rotation=45, ha="right", fontsize=8)
|
|
ax3.set_yticks(range(len(c.module_names)))
|
|
ax3.set_yticklabels(c.module_names, fontsize=8)
|
|
ax3.set_title("Module Coupling (directed edge counts)", fontsize=12,
|
|
fontweight="bold")
|
|
ax3.set_xlabel("Target module")
|
|
ax3.set_ylabel("Source module")
|
|
|
|
# Annotate cells
|
|
for i in range(len(c.module_names)):
|
|
for j in range(len(c.module_names)):
|
|
val = int(c.coupling_matrix[i, j])
|
|
if val > 0:
|
|
text_color = "white" if val > c.coupling_matrix.max() * 0.6 else "black"
|
|
ax3.text(j, i, str(val), ha="center", va="center",
|
|
fontsize=8, color=text_color, fontweight="bold")
|
|
|
|
plt.colorbar(im, ax=ax3, shrink=0.8)
|
|
else:
|
|
ax3.text(0.5, 0.5, "No modules", ha="center", va="center",
|
|
fontsize=14, transform=ax3.transAxes)
|
|
ax3.set_title("Module Coupling", fontsize=12, fontweight="bold")
|
|
|
|
# ── Bottom-right: Metrics panel ──
|
|
ax4 = fig.add_subplot(gs[1, 1])
|
|
ax4.axis("off")
|
|
|
|
# CCI interpretation
|
|
if m.cci < 0.3:
|
|
cci_color = "#4caf50"
|
|
cci_label = "LOW"
|
|
elif m.cci < 0.6:
|
|
cci_color = "#ff9800"
|
|
cci_label = "MODERATE"
|
|
else:
|
|
cci_color = "#f44336"
|
|
cci_label = "HIGH"
|
|
|
|
text_lines = [
|
|
("GRAPH", "", False),
|
|
(f" Nodes: {m.n_nodes} Edges: {m.n_edges} "
|
|
f"Modules: {m.n_modules} Components: {m.connected_components}", "", False),
|
|
("", "", False),
|
|
("SPECTRAL METRICS", "", False),
|
|
(f" Algebraic connectivity (lambda_2): {m.algebraic_connectivity:.4f}", "", False),
|
|
(f" Normalized (lambda_2/n): {m.normalized_algebraic_connectivity:.4f}", "", False),
|
|
(f" Spectral entropy: {m.spectral_entropy:.4f}", "", False),
|
|
(f" Normalized entropy: {m.normalized_spectral_entropy:.4f}", "", False),
|
|
(f" Spectral radius: {m.spectral_radius:.4f}", "", False),
|
|
(f" Normalized radius: {m.normalized_spectral_radius:.4f}", "", False),
|
|
("", "", False),
|
|
("COUPLING METRICS", "", False),
|
|
(f" Edge density: {m.edge_density:.4f}", "", False),
|
|
(f" Cross-module ratio: {m.cross_module_ratio:.1%}", "", False),
|
|
("", "", False),
|
|
(f" CCI = {m.cci:.4f} [{cci_label}]", cci_color, True),
|
|
]
|
|
|
|
y = 0.95
|
|
for text, color, bold in text_lines:
|
|
if not text:
|
|
y -= 0.04
|
|
continue
|
|
fontsize = 11 if bold else 9
|
|
weight = "bold" if bold else "normal"
|
|
c_val = color if color else "#e0e0e0"
|
|
ax4.text(0.05, y, text, transform=ax4.transAxes, fontsize=fontsize,
|
|
fontweight=weight, color=c_val, fontfamily="monospace",
|
|
verticalalignment="top")
|
|
y -= 0.055
|
|
|
|
ax4.set_title("Complexity Metrics", fontsize=12, fontweight="bold")
|
|
|
|
plt.savefig(output_path, dpi=150, facecolor=fig.get_facecolor(),
|
|
edgecolor="none", bbox_inches="tight")
|
|
plt.close(fig)
|
|
|
|
|
|
# ─── Interactive HTML Dashboard ───────────────────────────────────────────────
|
|
|
|
def generate_dashboard_html(
|
|
result: AnalysisResult, output_path: str, *, dot_source: str = ""
|
|
) -> None:
|
|
"""Generate an interactive HTML dashboard with GraphViz DAG + spectral panels."""
|
|
s = result.spectral
|
|
m = result.metrics
|
|
c = result.coupling
|
|
|
|
p = result.structural
|
|
|
|
# Prepare data as JSON for embedding
|
|
structural_data = {
|
|
"avg_degree": round(p.avg_degree, 2),
|
|
"max_fan_in": p.max_fan_in,
|
|
"max_fan_in_node": p.max_fan_in_node,
|
|
"max_fan_out": p.max_fan_out,
|
|
"max_fan_out_node": p.max_fan_out_node,
|
|
"dag_depth": p.dag_depth,
|
|
"clustering_coeff": round(p.clustering_coeff, 4),
|
|
"avg_module_cohesion": round(p.avg_module_cohesion, 3),
|
|
"avg_module_size": round(p.avg_module_size, 1),
|
|
}
|
|
|
|
cohesion_data = []
|
|
for mod in c.module_names:
|
|
coh = p.module_cohesion.get(mod, float("nan"))
|
|
if not math.isnan(coh):
|
|
cohesion_data.append({
|
|
"module": mod,
|
|
"cohesion": round(coh, 3),
|
|
"size": sum(1 for n in result.graph.nodes if n.module == mod),
|
|
})
|
|
|
|
coupling_data = {
|
|
"modules": c.module_names,
|
|
"matrix": c.coupling_matrix.tolist(),
|
|
}
|
|
|
|
# Module colors — populate index from discovery order so palette rotates
|
|
_module_index.clear()
|
|
for i, mod in enumerate(result.graph.modules):
|
|
_module_index[mod] = i
|
|
all_modules = list(dict.fromkeys(n.module for n in result.graph.nodes))
|
|
module_colors_json = {mod: get_module_color(mod) for mod in all_modules}
|
|
|
|
# CCI interpretation
|
|
if m.cci < 0.3:
|
|
cci_color = "#4caf50"
|
|
cci_label = "LOW"
|
|
cci_desc = "well-decomposed architecture"
|
|
elif m.cci < 0.6:
|
|
cci_color = "#ff9800"
|
|
cci_label = "MODERATE"
|
|
cci_desc = "typical well-structured codebase"
|
|
else:
|
|
cci_color = "#f44336"
|
|
cci_label = "HIGH"
|
|
cci_desc = "consider reviewing module boundaries"
|
|
|
|
metrics_json = {
|
|
"n_nodes": m.n_nodes,
|
|
"n_edges": m.n_edges,
|
|
"n_modules": m.n_modules,
|
|
"connected_components": m.connected_components,
|
|
"algebraic_connectivity": round(m.algebraic_connectivity, 4),
|
|
"normalized_algebraic_connectivity": round(m.normalized_algebraic_connectivity, 4),
|
|
"spectral_entropy": round(m.spectral_entropy, 4),
|
|
"normalized_spectral_entropy": round(m.normalized_spectral_entropy, 4),
|
|
"edge_density": round(m.edge_density, 4),
|
|
"cross_module_ratio": round(m.cross_module_ratio, 4),
|
|
"spectral_radius": round(m.spectral_radius, 4),
|
|
"normalized_spectral_radius": round(m.normalized_spectral_radius, 4),
|
|
"cci": round(m.cci, 4),
|
|
"cci_label": cci_label,
|
|
"cci_color": cci_color,
|
|
"cci_desc": cci_desc,
|
|
}
|
|
|
|
data_blob = json.dumps({
|
|
"structural": structural_data,
|
|
"cohesion": cohesion_data,
|
|
"coupling": coupling_data,
|
|
"metrics": metrics_json,
|
|
"module_colors": module_colors_json,
|
|
})
|
|
|
|
# Escape DOT source for embedding in a JS template literal
|
|
dot_escaped = (dot_source
|
|
.replace("\\", "\\\\")
|
|
.replace("`", "\\`")
|
|
.replace("${", "\\${"))
|
|
|
|
html = _DASHBOARD_HTML_TEMPLATE.replace("__DATA_BLOB__", data_blob)
|
|
html = html.replace("__DOT_BLOB__", dot_escaped)
|
|
|
|
with open(output_path, "w") as f:
|
|
f.write(html)
|
|
|
|
|
|
_DASHBOARD_HTML_TEMPLATE = r"""<!DOCTYPE html>
|
|
<html><head>
|
|
<meta charset="utf-8">
|
|
<title>swactor — dependency analysis</title>
|
|
<style>
|
|
* { margin:0; padding:0; box-sizing:border-box; }
|
|
body { background:#1a1a2e; color:#e0e0e0; font-family:system-ui,-apple-system,sans-serif; overflow:hidden; }
|
|
|
|
/* ─── Tab bar ───────────────────────────────────────────────────────────── */
|
|
.tab-bar { display:flex; align-items:center; height:42px; background:#12122a;
|
|
border-bottom:1px solid #2a2a5a; padding:0 16px; gap:8px; }
|
|
.tab-bar .title { font-size:14px; font-weight:700; letter-spacing:0.5px; margin-right:18px;
|
|
color:#8ab4f8; white-space:nowrap; }
|
|
.tab { background:none; border:none; color:#888; font-size:13px; padding:8px 16px;
|
|
cursor:pointer; border-bottom:2px solid transparent; transition:color 0.15s; }
|
|
.tab:hover { color:#ccc; }
|
|
.tab.active { color:#e0e0e0; border-bottom-color:#4fc3f7; }
|
|
|
|
/* ─── Tab content ───────────────────────────────────────────────────────── */
|
|
.tab-content { display:none; }
|
|
.tab-content.active { display:block; }
|
|
|
|
/* ─── DAG tab ───────────────────────────────────────────────────────────── */
|
|
#tab-dag { height:calc(100vh - 42px); overflow:hidden; position:relative; }
|
|
#dag-viewport { width:100%; height:100%; cursor:grab; }
|
|
#dag-viewport:active { cursor:grabbing; }
|
|
#dag-viewport svg { display:block; }
|
|
#dag-controls { position:absolute; top:12px; left:12px; z-index:10;
|
|
background:rgba(30,30,60,0.9); border-radius:8px; padding:10px 14px;
|
|
color:#ccc; font-size:13px; backdrop-filter:blur(8px); }
|
|
#dag-controls button { background:#333; color:#fff; border:1px solid #555;
|
|
border-radius:4px; padding:4px 10px; cursor:pointer; margin:0 3px; }
|
|
#dag-controls button:hover { background:#555; }
|
|
#dag-loading { position:absolute; top:50%; left:50%; transform:translate(-50%,-50%);
|
|
color:#ccc; font-size:18px; }
|
|
|
|
/* ─── Spectral tab ──────────────────────────────────────────────────────── */
|
|
#tab-spectral { overflow-y:auto; max-height:calc(100vh - 42px); }
|
|
|
|
.grid { display:grid; grid-template-columns:1fr 1fr; grid-template-rows:auto auto;
|
|
gap:16px; padding:16px 20px 20px; max-width:1600px; margin:0 auto; }
|
|
|
|
.panel { background:#16213e; border-radius:10px; border:1px solid #2a2a5a;
|
|
padding:16px; position:relative; min-height:100px; }
|
|
.panel h2 { font-size:14px; font-weight:600; margin-bottom:10px; color:#8ab4f8;
|
|
display:flex; align-items:center; gap:8px; }
|
|
.panel h2 .icon { font-size:16px; }
|
|
.panel svg { width:100%; display:block; }
|
|
|
|
.tooltip { position:fixed; background:rgba(22,33,62,0.96); border:1px solid #4fc3f7;
|
|
border-radius:6px; padding:8px 12px; font-size:12px; pointer-events:none;
|
|
z-index:100; backdrop-filter:blur(8px); max-width:300px;
|
|
box-shadow:0 4px 20px rgba(0,0,0,0.4); display:none; }
|
|
.tooltip .tt-label { font-weight:600; color:#4fc3f7; }
|
|
.tooltip .tt-val { color:#e0e0e0; }
|
|
|
|
svg text { user-select:none; }
|
|
|
|
/* Metrics panel */
|
|
.metrics-grid { display:grid; grid-template-columns:1fr 1fr; gap:8px 20px; }
|
|
.metric-item { display:flex; justify-content:space-between; font-size:12px;
|
|
padding:4px 8px; border-radius:4px; }
|
|
.metric-item:hover { background:rgba(79,195,247,0.08); }
|
|
.metric-label { opacity:0.7; }
|
|
.metric-value { font-weight:600; font-family:'SF Mono',monospace; }
|
|
.cci-box { grid-column:1/-1; text-align:center; margin-top:10px; padding:14px;
|
|
border-radius:8px; background:rgba(0,0,0,0.25); border:1px solid #333; }
|
|
.cci-score { font-size:32px; font-weight:700; }
|
|
.cci-label { font-size:14px; margin-top:2px; }
|
|
.cci-desc { font-size:11px; opacity:0.6; margin-top:4px; }
|
|
|
|
.sub-header { font-size:11px; font-weight:600; text-transform:uppercase;
|
|
letter-spacing:1px; opacity:0.4; margin:8px 0 4px; grid-column:1/-1; }
|
|
|
|
/* Heatmap */
|
|
.hm-cell { cursor:pointer; transition:opacity 0.15s; }
|
|
.hm-cell:hover { opacity:0.8; stroke:#4fc3f7; stroke-width:2; }
|
|
|
|
/* Cohesion / heatmap bars */
|
|
.fi-bar { cursor:pointer; transition:opacity 0.15s; }
|
|
.fi-bar:hover { opacity:0.85; }
|
|
</style>
|
|
</head>
|
|
<body>
|
|
|
|
<div class="tab-bar">
|
|
<div class="title">swactor — dependency analysis</div>
|
|
<button class="tab active" data-tab="spectral">Spectral Analysis</button>
|
|
<button class="tab" data-tab="dag">Dependency DAG</button>
|
|
</div>
|
|
|
|
<div class="tab-content" id="tab-dag">
|
|
<div id="dag-controls">
|
|
<button onclick="zoomIn()">+</button>
|
|
<button onclick="zoomOut()">−</button>
|
|
<button onclick="resetView()">fit</button>
|
|
<span style="margin-left:8px;opacity:0.6">scroll to zoom · drag to pan · click node to focus</span>
|
|
</div>
|
|
<div id="dag-viewport"></div>
|
|
<div id="dag-loading">Loading Graphviz…</div>
|
|
</div>
|
|
|
|
<div class="tab-content active" id="tab-spectral">
|
|
<div class="grid">
|
|
<div class="panel" id="panel-structural">
|
|
<h2><span class="icon">◉</span> Structural Properties</h2>
|
|
<div id="structural-content"></div>
|
|
</div>
|
|
|
|
<div class="panel" id="panel-cohesion">
|
|
<h2><span class="icon">▨</span> Module Cohesion</h2>
|
|
<svg id="svg-cohesion"></svg>
|
|
</div>
|
|
|
|
<div class="panel" id="panel-heatmap">
|
|
<h2><span class="icon">▦</span> Module Coupling (directed edge counts)</h2>
|
|
<svg id="svg-heatmap"></svg>
|
|
</div>
|
|
|
|
<div class="panel" id="panel-metrics">
|
|
<h2><span class="icon">∑</span> Complexity Metrics</h2>
|
|
<div id="metrics-content"></div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div class="tooltip" id="tooltip"></div>
|
|
|
|
<!-- ─── Script 1: synchronous — data + tab switching + spectral panels ─── -->
|
|
<script>
|
|
// ─── Data ──────────────────────────────────────────────────────────────────
|
|
const DATA = __DATA_BLOB__;
|
|
const { structural, cohesion, coupling, metrics, module_colors } = DATA;
|
|
|
|
// ─── Tab switching ─────────────────────────────────────────────────────────
|
|
document.querySelectorAll('.tab').forEach(btn => {
|
|
btn.addEventListener('click', () => {
|
|
document.querySelectorAll('.tab').forEach(b => b.classList.remove('active'));
|
|
document.querySelectorAll('.tab-content').forEach(c => c.classList.remove('active'));
|
|
btn.classList.add('active');
|
|
document.getElementById('tab-' + btn.dataset.tab).classList.add('active');
|
|
if (btn.dataset.tab === 'dag') {
|
|
window.dispatchEvent(new Event('dag-visible'));
|
|
}
|
|
});
|
|
});
|
|
|
|
// ─── Tooltip ───────────────────────────────────────────────────────────────
|
|
const TT = document.getElementById('tooltip');
|
|
function showTip(evt, html) {
|
|
TT.innerHTML = html;
|
|
TT.style.display = 'block';
|
|
const x = evt.clientX + 14, y = evt.clientY - 10;
|
|
TT.style.left = Math.min(x, window.innerWidth - TT.offsetWidth - 20) + 'px';
|
|
TT.style.top = Math.min(y, window.innerHeight - TT.offsetHeight - 20) + 'px';
|
|
}
|
|
function hideTip() { TT.style.display = 'none'; }
|
|
|
|
function modColor(mod) { return module_colors[mod] || '#9e9e9e'; }
|
|
|
|
// ─── Structural Properties ────────────────────────────────────────────────
|
|
(function() {
|
|
const c = document.getElementById('structural-content');
|
|
const s = structural;
|
|
c.innerHTML = `
|
|
<div class="metrics-grid">
|
|
<div class="sub-header">Density & Depth</div>
|
|
<div class="metric-item"><span class="metric-label">Edges/node (avg degree)</span><span class="metric-value">${s.avg_degree}</span></div>
|
|
<div class="metric-item"><span class="metric-label">DAG depth</span><span class="metric-value">${s.dag_depth}</span></div>
|
|
<div class="metric-item"><span class="metric-label">Clustering coefficient</span><span class="metric-value">${s.clustering_coeff}</span></div>
|
|
<div class="metric-item"><span class="metric-label">Avg module size</span><span class="metric-value">${s.avg_module_size}</span></div>
|
|
|
|
<div class="sub-header">Dependency Hotspots</div>
|
|
<div class="metric-item"><span class="metric-label">Max fan-in</span><span class="metric-value">${s.max_fan_in} ← ${s.max_fan_in_node}</span></div>
|
|
<div class="metric-item"><span class="metric-label">Max fan-out</span><span class="metric-value">${s.max_fan_out} → ${s.max_fan_out_node}</span></div>
|
|
|
|
<div class="sub-header">Cohesion</div>
|
|
<div class="metric-item"><span class="metric-label">Avg module cohesion</span><span class="metric-value">${s.avg_module_cohesion}</span></div>
|
|
<div class="metric-item"><span class="metric-label">Cross-module ratio</span><span class="metric-value">${(metrics.cross_module_ratio*100).toFixed(1)}%</span></div>
|
|
</div>
|
|
`;
|
|
})();
|
|
|
|
// ─── Module Cohesion ──────────────────────────────────────────────────────
|
|
(function() {
|
|
const svg = document.getElementById('svg-cohesion');
|
|
const n = cohesion.length;
|
|
if (n === 0) return;
|
|
const barH = Math.max(20, Math.min(36, 300/n));
|
|
const W = 560, H = Math.max(200, n*barH + 60), M = {t:10,r:30,b:30,l:120};
|
|
const w = W-M.l-M.r, h = H-M.t-M.b;
|
|
svg.setAttribute('viewBox', `0 0 ${W} ${H}`);
|
|
|
|
const xScale = v => M.l + v * w;
|
|
const yScale = i => M.t + (i/n) * h + barH/2;
|
|
|
|
// Background grid
|
|
for (const tick of [0.25, 0.5, 0.75, 1.0]) {
|
|
const x = xScale(tick);
|
|
const line = document.createElementNS('http://www.w3.org/2000/svg','line');
|
|
Object.entries({x1:x,x2:x,y1:M.t,y2:M.t+h,stroke:'#2a2a5a','stroke-width':0.5}).forEach(([k,v])=>line.setAttribute(k,v));
|
|
svg.appendChild(line);
|
|
const txt = document.createElementNS('http://www.w3.org/2000/svg','text');
|
|
txt.setAttribute('x', x); txt.setAttribute('y', H-8);
|
|
txt.setAttribute('text-anchor','middle'); txt.setAttribute('fill','#666'); txt.setAttribute('font-size','10');
|
|
txt.textContent = (tick*100).toFixed(0) + '%';
|
|
svg.appendChild(txt);
|
|
}
|
|
|
|
// Average line
|
|
const avgX = xScale(structural.avg_module_cohesion);
|
|
const avgLine = document.createElementNS('http://www.w3.org/2000/svg','line');
|
|
Object.entries({x1:avgX,x2:avgX,y1:M.t,y2:M.t+h,stroke:'#ff4444','stroke-width':1.5,'stroke-dasharray':'5,3','stroke-opacity':0.7}).forEach(([k,v])=>avgLine.setAttribute(k,v));
|
|
svg.appendChild(avgLine);
|
|
const avgLbl = document.createElementNS('http://www.w3.org/2000/svg','text');
|
|
avgLbl.setAttribute('x', avgX+4); avgLbl.setAttribute('y', M.t+10);
|
|
avgLbl.setAttribute('fill','#ff4444'); avgLbl.setAttribute('font-size','9'); avgLbl.setAttribute('opacity','0.8');
|
|
avgLbl.textContent = 'avg';
|
|
svg.appendChild(avgLbl);
|
|
|
|
cohesion.forEach((d, i) => {
|
|
const barW = Math.max(d.cohesion * w, 2);
|
|
const y = yScale(i) - barH*0.35;
|
|
const rect = document.createElementNS('http://www.w3.org/2000/svg','rect');
|
|
rect.setAttribute('x', M.l); rect.setAttribute('y', y);
|
|
rect.setAttribute('width', barW); rect.setAttribute('height', barH*0.7);
|
|
rect.setAttribute('rx', 3);
|
|
rect.setAttribute('fill', modColor(d.module));
|
|
rect.setAttribute('opacity', 0.85);
|
|
rect.classList.add('fi-bar');
|
|
rect.addEventListener('mousemove', e => showTip(e,
|
|
`<span class="tt-label">${d.module}</span><br>` +
|
|
`Types: <span class="tt-val">${d.size}</span><br>` +
|
|
`Cohesion: <span class="tt-val">${(d.cohesion*100).toFixed(1)}%</span>`
|
|
));
|
|
rect.addEventListener('mouseleave', hideTip);
|
|
svg.appendChild(rect);
|
|
|
|
// Value label on bar
|
|
const valTxt = document.createElementNS('http://www.w3.org/2000/svg','text');
|
|
valTxt.setAttribute('x', M.l + barW + 6); valTxt.setAttribute('y', yScale(i)+4);
|
|
valTxt.setAttribute('fill','#ccc'); valTxt.setAttribute('font-size','10'); valTxt.setAttribute('font-weight','600');
|
|
valTxt.textContent = (d.cohesion*100).toFixed(0) + '%';
|
|
svg.appendChild(valTxt);
|
|
|
|
// Module label
|
|
const txt = document.createElementNS('http://www.w3.org/2000/svg','text');
|
|
txt.setAttribute('x', M.l-8); txt.setAttribute('y', yScale(i)+4);
|
|
txt.setAttribute('text-anchor','end'); txt.setAttribute('fill', modColor(d.module));
|
|
txt.setAttribute('font-size','11'); txt.setAttribute('font-weight','600');
|
|
txt.textContent = `${d.module} (${d.size})`;
|
|
svg.appendChild(txt);
|
|
});
|
|
})();
|
|
|
|
// ─── Module Coupling Heatmap ───────────────────────────────────────────────
|
|
(function() {
|
|
const mods = coupling.modules;
|
|
const mat = coupling.matrix;
|
|
const n = mods.length;
|
|
const svg = document.getElementById('svg-heatmap');
|
|
const cellSz = Math.min(55, 400/n);
|
|
const M = {t:10,r:60,b:80,l:100};
|
|
const W = M.l + n*cellSz + M.r, H = M.t + n*cellSz + M.b;
|
|
svg.setAttribute('viewBox', `0 0 ${W} ${H}`);
|
|
|
|
const maxVal = Math.max(...mat.flat(), 1);
|
|
|
|
// Color scale: 0=transparent dark, max=deep red
|
|
function heatColor(v) {
|
|
if (v === 0) return '#1a1a2e';
|
|
const t = v / maxVal;
|
|
const r = Math.round(40 + 215*t);
|
|
const g = Math.round(30 + 40*(1-t));
|
|
const b = Math.round(50*(1-t));
|
|
return `rgb(${r},${g},${b})`;
|
|
}
|
|
|
|
for (let i = 0; i < n; i++) {
|
|
// Row labels
|
|
const rl = document.createElementNS('http://www.w3.org/2000/svg','text');
|
|
rl.setAttribute('x', M.l-8); rl.setAttribute('y', M.t + i*cellSz + cellSz/2 + 4);
|
|
rl.setAttribute('text-anchor','end'); rl.setAttribute('fill', modColor(mods[i]));
|
|
rl.setAttribute('font-size','11'); rl.setAttribute('font-weight','600');
|
|
rl.textContent = mods[i];
|
|
svg.appendChild(rl);
|
|
|
|
// Column labels
|
|
const cl = document.createElementNS('http://www.w3.org/2000/svg','text');
|
|
cl.setAttribute('x', M.l + i*cellSz + cellSz/2);
|
|
cl.setAttribute('y', M.t + n*cellSz + 16);
|
|
cl.setAttribute('text-anchor','end'); cl.setAttribute('fill', modColor(mods[i]));
|
|
cl.setAttribute('font-size','11'); cl.setAttribute('font-weight','600');
|
|
cl.setAttribute('transform', `rotate(-45, ${M.l + i*cellSz + cellSz/2}, ${M.t + n*cellSz + 16})`);
|
|
cl.textContent = mods[i];
|
|
svg.appendChild(cl);
|
|
|
|
for (let j = 0; j < n; j++) {
|
|
const v = mat[i][j];
|
|
const rect = document.createElementNS('http://www.w3.org/2000/svg','rect');
|
|
rect.setAttribute('x', M.l + j*cellSz + 1);
|
|
rect.setAttribute('y', M.t + i*cellSz + 1);
|
|
rect.setAttribute('width', cellSz-2); rect.setAttribute('height', cellSz-2);
|
|
rect.setAttribute('rx', 3);
|
|
rect.setAttribute('fill', heatColor(v));
|
|
rect.classList.add('hm-cell');
|
|
rect.addEventListener('mousemove', e => showTip(e,
|
|
`<span class="tt-label">${mods[i]} → ${mods[j]}</span><br>` +
|
|
`Edges: <span class="tt-val">${v}</span>` +
|
|
(i !== j ? '<br><span style="opacity:0.6">cross-module</span>' : '<br><span style="opacity:0.6">intra-module</span>')
|
|
));
|
|
rect.addEventListener('mouseleave', hideTip);
|
|
svg.appendChild(rect);
|
|
|
|
// Cell text
|
|
if (v > 0) {
|
|
const txt = document.createElementNS('http://www.w3.org/2000/svg','text');
|
|
txt.setAttribute('x', M.l + j*cellSz + cellSz/2);
|
|
txt.setAttribute('y', M.t + i*cellSz + cellSz/2 + 4);
|
|
txt.setAttribute('text-anchor','middle'); txt.setAttribute('font-size','11');
|
|
txt.setAttribute('font-weight','700'); txt.setAttribute('pointer-events','none');
|
|
txt.setAttribute('fill', v > maxVal*0.5 ? '#fff' : '#ccc');
|
|
txt.textContent = v;
|
|
svg.appendChild(txt);
|
|
}
|
|
}
|
|
}
|
|
|
|
// Axis labels
|
|
const srcL = document.createElementNS('http://www.w3.org/2000/svg','text');
|
|
srcL.setAttribute('x', 10); srcL.setAttribute('y', M.t + n*cellSz/2);
|
|
srcL.setAttribute('text-anchor','middle'); srcL.setAttribute('fill','#666');
|
|
srcL.setAttribute('font-size','10');
|
|
srcL.setAttribute('transform', `rotate(-90,10,${M.t + n*cellSz/2})`);
|
|
srcL.textContent = 'source module';
|
|
svg.appendChild(srcL);
|
|
})();
|
|
|
|
// ─── Metrics Panel ─────────────────────────────────────────────────────────
|
|
(function() {
|
|
const c = document.getElementById('metrics-content');
|
|
const mm = metrics;
|
|
c.innerHTML = `
|
|
<div class="metrics-grid">
|
|
<div class="sub-header">Graph</div>
|
|
<div class="metric-item"><span class="metric-label">Nodes</span><span class="metric-value">${mm.n_nodes}</span></div>
|
|
<div class="metric-item"><span class="metric-label">Directed edges</span><span class="metric-value">${mm.n_edges}</span></div>
|
|
<div class="metric-item"><span class="metric-label">Modules</span><span class="metric-value">${mm.n_modules}</span></div>
|
|
<div class="metric-item"><span class="metric-label">Components</span><span class="metric-value">${mm.connected_components}</span></div>
|
|
|
|
<div class="sub-header">Spectral</div>
|
|
<div class="metric-item"><span class="metric-label">λ<sub>2</sub> (alg. connectivity)</span><span class="metric-value">${mm.algebraic_connectivity}</span></div>
|
|
<div class="metric-item"><span class="metric-label">λ<sub>2</sub>/n (normalized)</span><span class="metric-value">${mm.normalized_algebraic_connectivity}</span></div>
|
|
<div class="metric-item"><span class="metric-label">Spectral entropy</span><span class="metric-value">${mm.spectral_entropy}</span></div>
|
|
<div class="metric-item"><span class="metric-label">Norm. entropy</span><span class="metric-value">${mm.normalized_spectral_entropy}</span></div>
|
|
<div class="metric-item"><span class="metric-label">Spectral radius</span><span class="metric-value">${mm.spectral_radius}</span></div>
|
|
<div class="metric-item"><span class="metric-label">Norm. radius</span><span class="metric-value">${mm.normalized_spectral_radius}</span></div>
|
|
|
|
<div class="sub-header">Coupling</div>
|
|
<div class="metric-item"><span class="metric-label">Edge density</span><span class="metric-value">${mm.edge_density}</span></div>
|
|
<div class="metric-item"><span class="metric-label">Cross-module ratio</span><span class="metric-value">${(mm.cross_module_ratio*100).toFixed(1)}%</span></div>
|
|
|
|
<div class="cci-box">
|
|
<div class="cci-score" style="color:${mm.cci_color}">CCI = ${mm.cci}</div>
|
|
<div class="cci-label" style="color:${mm.cci_color}">${mm.cci_label}</div>
|
|
<div class="cci-desc">${mm.cci_desc}</div>
|
|
</div>
|
|
</div>
|
|
`;
|
|
})();
|
|
</script>
|
|
|
|
<!-- ─── Script 2: module — viz-js DAG rendering (async) ─────────────────── -->
|
|
<script type="module">
|
|
import { instance } from 'https://cdn.jsdelivr.net/npm/@viz-js/viz@3.11.0/lib/viz-standalone.mjs';
|
|
|
|
const DOT_SOURCE = `__DOT_BLOB__`;
|
|
|
|
const viz = await instance();
|
|
const svg = viz.renderSVGElement(DOT_SOURCE);
|
|
document.getElementById('dag-loading').remove();
|
|
|
|
const vp = document.getElementById('dag-viewport');
|
|
vp.appendChild(svg);
|
|
|
|
// ─── Dark-mode SVG recoloring ──────────────────────────────────────────────
|
|
svg.querySelectorAll('polygon[fill="white"]').forEach(el => el.setAttribute('fill','#1a1a2e'));
|
|
svg.querySelectorAll('.graph > text').forEach(el => el.setAttribute('fill','#e0e0e0'));
|
|
svg.querySelectorAll('.cluster > text').forEach(el => el.setAttribute('fill','#1a1a1a'));
|
|
svg.querySelectorAll('.edge text').forEach(el => el.setAttribute('fill','#ffb74d'));
|
|
svg.querySelectorAll('.node text').forEach(el => el.setAttribute('fill','#1a1a1a'));
|
|
|
|
// ─── Click-to-focus ────────────────────────────────────────────────────────
|
|
const edges = svg.querySelectorAll('.edge');
|
|
const nodes = svg.querySelectorAll('.node');
|
|
const clusterChrome = [];
|
|
svg.querySelectorAll('.cluster').forEach(c => {
|
|
c.querySelectorAll(':scope > path, :scope > polygon, :scope > text').forEach(el => clusterChrome.push(el));
|
|
});
|
|
|
|
const nodeByTitle = new Map();
|
|
nodes.forEach(n => {
|
|
const t = n.querySelector('title');
|
|
if (t) nodeByTitle.set(t.textContent.trim(), n);
|
|
});
|
|
|
|
const nodeToClusterEls = new Map();
|
|
svg.querySelectorAll('.cluster').forEach(cluster => {
|
|
const chrome = [...cluster.querySelectorAll(':scope > path, :scope > polygon, :scope > text')];
|
|
cluster.querySelectorAll('.node title').forEach(t => {
|
|
nodeToClusterEls.set(t.textContent.trim(), chrome);
|
|
});
|
|
});
|
|
|
|
const adj = new Map();
|
|
edges.forEach(edge => {
|
|
const t = edge.querySelector('title');
|
|
if (!t) return;
|
|
const parts = t.textContent.trim().split('->').map(s => s.trim());
|
|
if (parts.length !== 2) return;
|
|
const [src, dst] = parts;
|
|
if (!adj.has(src)) adj.set(src, { edges: [], neighbors: new Set() });
|
|
if (!adj.has(dst)) adj.set(dst, { edges: [], neighbors: new Set() });
|
|
adj.get(src).edges.push(edge);
|
|
adj.get(src).neighbors.add(dst);
|
|
adj.get(dst).edges.push(edge);
|
|
adj.get(dst).neighbors.add(src);
|
|
});
|
|
|
|
const DIM = 0.08;
|
|
let focused = null;
|
|
|
|
function clearFocus() {
|
|
focused = null;
|
|
nodes.forEach(n => n.style.opacity = '');
|
|
edges.forEach(e => e.style.opacity = '');
|
|
clusterChrome.forEach(el => el.style.opacity = '');
|
|
}
|
|
|
|
function focusNode(title) {
|
|
if (focused === title) { clearFocus(); return; }
|
|
focused = title;
|
|
const info = adj.get(title) || { edges: [], neighbors: new Set() };
|
|
const connected = new Set([title, ...info.neighbors]);
|
|
|
|
nodes.forEach(n => n.style.opacity = DIM);
|
|
edges.forEach(e => e.style.opacity = DIM);
|
|
clusterChrome.forEach(el => el.style.opacity = DIM);
|
|
|
|
connected.forEach(name => {
|
|
const el = nodeByTitle.get(name);
|
|
if (el) el.style.opacity = 1;
|
|
});
|
|
|
|
info.edges.forEach(e => e.style.opacity = 1);
|
|
|
|
const seen = new Set();
|
|
connected.forEach(name => {
|
|
const chrome = nodeToClusterEls.get(name);
|
|
if (chrome) chrome.forEach(el => {
|
|
if (!seen.has(el)) { seen.add(el); el.style.opacity = 1; }
|
|
});
|
|
});
|
|
}
|
|
|
|
nodes.forEach(node => {
|
|
node.style.cursor = 'pointer';
|
|
node.addEventListener('click', e => {
|
|
e.stopPropagation();
|
|
const t = node.querySelector('title');
|
|
if (t) focusNode(t.textContent.trim());
|
|
});
|
|
});
|
|
|
|
// ─── Pan & zoom ────────────────────────────────────────────────────────────
|
|
let scale = 1, tx = 0, ty = 0, dragging = false, didDrag = false, sx = 0, sy = 0;
|
|
function applyTransform() { svg.style.transform = `translate(${tx}px,${ty}px) scale(${scale})`; svg.style.transformOrigin = '0 0'; }
|
|
|
|
window.resetView = function() {
|
|
const vw = vp.clientWidth, vh = vp.clientHeight;
|
|
const bb = svg.getBBox();
|
|
scale = Math.min(vw / bb.width, vh / bb.height) * 0.92;
|
|
tx = (vw - bb.width * scale) / 2;
|
|
ty = (vh - bb.height * scale) / 2;
|
|
applyTransform();
|
|
};
|
|
let dagFitted = false;
|
|
window.addEventListener('dag-visible', () => {
|
|
if (!dagFitted) { dagFitted = true; requestAnimationFrame(resetView); }
|
|
});
|
|
|
|
window.zoomIn = function() { scale *= 1.3; applyTransform(); };
|
|
window.zoomOut = function() { scale *= 0.7; applyTransform(); };
|
|
|
|
vp.addEventListener('wheel', e => { e.preventDefault(); const f = e.deltaY < 0 ? 1.12 : 0.89; const rect = vp.getBoundingClientRect(); const mx = e.clientX - rect.left; const my = e.clientY - rect.top; tx = mx - f * (mx - tx); ty = my - f * (my - ty); scale *= f; applyTransform(); }, { passive:false });
|
|
vp.addEventListener('pointerdown', e => { dragging=true; didDrag=false; sx=e.clientX-tx; sy=e.clientY-ty; vp.setPointerCapture(e.pointerId); });
|
|
vp.addEventListener('pointermove', e => { if(!dragging) return; didDrag=true; tx=e.clientX-sx; ty=e.clientY-sy; applyTransform(); });
|
|
vp.addEventListener('pointerup', () => dragging=false);
|
|
vp.addEventListener('click', e => { if (!didDrag && !e.target.closest('.node')) clearFocus(); });
|
|
</script>
|
|
</body></html>
|
|
"""
|
|
|
|
|
|
# ─── JSON Output ──────────────────────────────────────────────────────────────
|
|
|
|
def metrics_to_dict(result: AnalysisResult) -> dict[str, Any]:
|
|
"""Convert analysis results to a JSON-serializable dict."""
|
|
m = result.metrics
|
|
c = result.coupling
|
|
p = result.structural
|
|
|
|
return {
|
|
"graph": {
|
|
"n_nodes": m.n_nodes,
|
|
"n_edges": m.n_edges,
|
|
"n_modules": m.n_modules,
|
|
"connected_components": m.connected_components,
|
|
"modules": c.module_names,
|
|
},
|
|
"structural": {
|
|
"avg_degree": p.avg_degree,
|
|
"max_fan_in": {"count": p.max_fan_in, "node": p.max_fan_in_node},
|
|
"max_fan_out": {"count": p.max_fan_out, "node": p.max_fan_out_node},
|
|
"dag_depth": p.dag_depth,
|
|
"clustering_coefficient": p.clustering_coeff,
|
|
"avg_module_size": p.avg_module_size,
|
|
},
|
|
"module_coupling": {
|
|
"module_names": c.module_names,
|
|
"coupling_matrix": c.coupling_matrix.tolist(),
|
|
"cross_module_edges": c.cross_module_edges,
|
|
"total_edges": c.total_edges,
|
|
},
|
|
"module_cohesion": {
|
|
mod: None if math.isnan(v) else v
|
|
for mod, v in p.module_cohesion.items()
|
|
},
|
|
"metrics": {
|
|
"algebraic_connectivity": m.algebraic_connectivity,
|
|
"spectral_entropy": m.spectral_entropy,
|
|
"edge_density": m.edge_density,
|
|
"cross_module_ratio": m.cross_module_ratio,
|
|
"spectral_radius": m.spectral_radius,
|
|
"avg_module_cohesion": p.avg_module_cohesion,
|
|
"cci": m.cci,
|
|
},
|
|
}
|
|
|
|
|
|
# ─── CLI ──────────────────────────────────────────────────────────────────────
|
|
|
|
def main() -> None:
|
|
parser = argparse.ArgumentParser(
|
|
description="Spectral analysis of dependency DAGs"
|
|
)
|
|
parser.add_argument("dot_file", help="Path to DOT file (from depgraph)")
|
|
parser.add_argument("-o", "--output-dir", default="docs/connectome",
|
|
help="Output directory (default: docs/connectome)")
|
|
parser.add_argument("--no-plots", action="store_true",
|
|
help="Text report only (no matplotlib dependency)")
|
|
parser.add_argument("--json", action="store_true",
|
|
help="Also output spectral_metrics.json")
|
|
args = parser.parse_args()
|
|
|
|
# Read and parse DOT
|
|
dot_text = open(args.dot_file).read()
|
|
graph = parse_dot(dot_text)
|
|
print(f"Parsed {len(graph.nodes)} nodes, {len(graph.edges)} edges, "
|
|
f"{len(graph.modules)} modules")
|
|
|
|
# Run analysis
|
|
result = run_analysis(graph)
|
|
|
|
# Ensure output directory exists
|
|
os.makedirs(args.output_dir, exist_ok=True)
|
|
|
|
# Generate report
|
|
report = generate_report(result)
|
|
print(report)
|
|
report_path = os.path.join(args.output_dir, "connectome_report.txt")
|
|
with open(report_path, "w") as f:
|
|
f.write(report)
|
|
print(f"\nReport saved to {report_path}")
|
|
|
|
# Generate interactive HTML dashboard
|
|
html_path = os.path.join(args.output_dir, "connectome_dashboard.html")
|
|
generate_dashboard_html(result, html_path, dot_source=dot_text)
|
|
print(f"Interactive dashboard saved to {html_path}")
|
|
|
|
# Generate static PNG dashboard
|
|
if not args.no_plots:
|
|
dashboard_path = os.path.join(args.output_dir, "connectome_dashboard.png")
|
|
generate_dashboard(result, dashboard_path)
|
|
print(f"Static dashboard saved to {dashboard_path}")
|
|
|
|
# Generate JSON
|
|
if args.json:
|
|
json_path = os.path.join(args.output_dir, "connectome_metrics.json")
|
|
with open(json_path, "w") as f:
|
|
json.dump(metrics_to_dict(result), f, indent=2)
|
|
print(f"JSON saved to {json_path}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|