# All files / src orchestrator.ts

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85.71% Branches 12/14
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     import { scanFiles, readFileContent } from '@aiready/core';
    import type {
      ContextAnalysisResult,
      ContextAnalyzerOptions,
      FileClassification,
    } from './types';
    import { calculateEnhancedCohesion } from './metrics';
    import { analyzeIssues } from './issue-analyzer';

    import {
      buildDependencyGraph,
      detectCircularDependencies,
    } from './graph-builder';
    import { detectModuleClusters } from './cluster-detector';
    import { mapNodeToResult } from './mapper';

    /**
     * Calculate cohesion score (how related are exports in a file).
     * Legacy wrapper for backward compatibility with exact test expectations.
     *
     * @param exports - List of exported symbols
     * @param filePath - Path to the file being analyzed
     * @param options - Additional options for cohesion calculation
     * @returns Cohesion score between 0 and 1
     */
    export function calculateCohesion(
      exports: any[],
      filePath?: string,
      options?: any
    ): number {
      return calculateEnhancedCohesion(exports, filePath, options);
    }

    /**
     * Performs deep context analysis of a project.
     * Scans files, builds a dependency graph, calculates context budgets,
     * and identifies structural issues like high fragmentation or depth.
     *
     * @param options - Analysis parameters including root directory and focus areas
     * @returns Comprehensive analysis results with metrics and identified issues
     */
    export async function analyzeContext(
      options: ContextAnalyzerOptions
    ): Promise<ContextAnalysisResult[]> {
      const {
        maxDepth = 5,
        maxContextBudget = 25000,
        minCohesion = 0.6,
        maxFragmentation = 0.5,
        includeNodeModules = false,
        ...scanOptions
      } = options;

      const files = await scanFiles({
        ...scanOptions,
        exclude:
          includeNodeModules && scanOptions.exclude
            ? scanOptions.exclude.filter(
                (pattern) => pattern !== '**/node_modules/**'
              )
            : scanOptions.exclude,
      });

      const pythonFiles = files.filter((f) => f.toLowerCase().endsWith('.py'));
      const fileContents = await Promise.all(
        files.map(async (file) => ({
          file,
          content: await readFileContent(file),
        }))
      );

      const graph = await buildDependencyGraph(
        fileContents.filter((f) => !f.file.toLowerCase().endsWith('.py'))
      );

      let pythonResults: ContextAnalysisResult[] = [];
      if (pythonFiles.length > 0) {
        const { analyzePythonContext } = await import('./analyzers/python-context');
        const pythonMetrics = await analyzePythonContext(
          pythonFiles,
          scanOptions.rootDir || options.rootDir || '.'
        );

        pythonResults = pythonMetrics.map((metric) => {
          const { severity, issues, recommendations, potentialSavings } =
            analyzeIssues({
              file: metric.file,
              importDepth: metric.importDepth,
              contextBudget: metric.contextBudget,
              cohesionScore: metric.cohesion,
              fragmentationScore: 0,
              maxDepth,
              maxContextBudget,
              minCohesion,
              maxFragmentation,
              circularDeps: [],
            });

          return {
            file: metric.file,
            tokenCost: 0,
            linesOfCode: 0,
            importDepth: metric.importDepth,
            dependencyCount: 0,
            dependencyList: [],
            circularDeps: [],
            cohesionScore: metric.cohesion,
            domains: [],
            exportCount: 0,
            contextBudget: metric.contextBudget,
            fragmentationScore: 0,
            relatedFiles: [],
            fileClassification: 'unknown' as FileClassification,
            severity,
            issues,
            recommendations,
            potentialSavings,
          };
        });
      }

      const clusters = detectModuleClusters(graph);
      const allCircularDeps = detectCircularDependencies(graph);

      const results: ContextAnalysisResult[] = Array.from(graph.nodes.values()).map(
        (node) =>
          mapNodeToResult(node, graph, clusters, allCircularDeps, {
            maxDepth,
            maxContextBudget,
            minCohesion,
            maxFragmentation,
          })
      );

      return [...results, ...pythonResults];
    }