Delete analyze_temportal_patterns.py
Browse files- analyze_temportal_patterns.py +0 -466
analyze_temportal_patterns.py
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from collections import Counter
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import json
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from datetime import datetime
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import statistics
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from typing import Any, Dict, List, Tuple
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from analyze_repository_structure import RELEVANT_EXTENSIONS
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from pathlib import Path
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from prompt_analyzer import create_handler
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import subprocess
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import os
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def analyze_temporal_patterns(
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sources_data: Dict[str, Any], report_data: Dict[str, Any]
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) -> Dict[str, Any]:
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"""Analyzes temporal patterns using both LLM and statistical analysis"""
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commits = report_data.get("commits", {})
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# Setup LLM Prompting
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handler = create_handler()
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combined_results = {}
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# Get commit timestamps for activity analysis
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commit_times = [
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datetime.fromisoformat(
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commit["commit"]["author"]["date"].replace("Z", "+00:00")
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)
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for repo_commits in commits.values()
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for commit in repo_commits
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]
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# Get best targets and their commit contents
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temporal_best_targets = _select_best_targets(sources_data, commits)
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commit_contents = _get_commit_contents(temporal_best_targets, sources_data)
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# Save commit contents for inspection
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inspection_data = {
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"temporal_targets": temporal_best_targets,
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"commit_contents": commit_contents,
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}
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inspection_path = Path("out") / "temporal_analysis_contents.json"
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try:
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with open(inspection_path, "w", encoding="utf-8") as f:
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json.dump(inspection_data, f, indent=2)
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print(f"Saved temporal analysis data to {inspection_path}")
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except Exception as e:
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print(f"Error saving inspection data: {str(e)}")
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for repo_name, repo_data in sources_data.items():
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if repo_name not in temporal_best_targets:
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continue
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print(f"\nAnalyzing temporal patterns for repository: {repo_name}")
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# Get code changes for this repository
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repo_changes = commit_contents.get(repo_name, [])
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if not repo_changes:
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continue
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# Analyze code style evolution using LLM with actual code changes
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prompt = f"""
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TEMPORAL ANALYSIS
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Analyze the temporal evolution of this codebase with focus on developer behavior patterns and code evolution.
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Repository: {repo_name}
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Code Evolution Data:
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{json.dumps(repo_changes, indent=2)}
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Generate detailed temporal analysis JSON:
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{{
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"evolution_patterns": {{
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"code_quality": {{
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"progression": string,
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"refactoring_patterns": [
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{{
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"pattern": string,
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"frequency": string,
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"motivation": string
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}}
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],
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"complexity_trends": {{
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"direction": string,
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"significant_changes": [string],
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"trigger_patterns": [string]
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}}
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}},
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"development_cycles": {{
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"commit_patterns": {{
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"frequency": {{
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"pattern": string,
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"active_hours": [string],
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"timezone_confidence": {{
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"zone": string,
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"confidence": number,
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"evidence": [string]
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}}
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}},
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"burst_patterns": [
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{{
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"pattern": string,
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"typical_duration": string,
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"characteristics": [string]
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}}
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]
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}},
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"feature_development": {{
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"typical_cycle": string,
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"iteration_patterns": [string],
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"testing_integration": string
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}}
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}},
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"communication_patterns": {{
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"pr_characteristics": {{
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"detail_level": string,
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"discussion_style": string,
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"iteration_patterns": string
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}},
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"documentation_evolution": {{
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"frequency": string,
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"detail_trends": string,
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"update_patterns": string
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}}
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}}
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}},
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"architectural_evolution": {{
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"major_changes": [
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{{
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"change": string,
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"motivation": string,
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"impact": string
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}}
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],
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"improvement_patterns": {{
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"refactoring_types": [string],
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"optimization_focus": [string],
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"maintenance_patterns": string
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}},
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"technical_debt": {{
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"accumulation_patterns": [string],
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"resolution_approaches": string,
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"prevention_strategies": string
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}}
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}}
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}}
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Requirements:
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1. Focus on developer behavior patterns
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2. Track evolution of coding style
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3. Identify clear timezone patterns
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4. Detail burst activity characteristics
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5. Analyze code quality progression
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"""
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try:
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result = handler.generate_json_response(prompt)
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if result:
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combined_results[repo_name] = result
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except Exception as e:
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print(f"Error analyze_temporal_patterns {repo_name}: {str(e)}")
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combined_results[repo_name] = {"error": str(e)}
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return {
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"commit_style_metrics": combined_results,
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"activity_patterns": _analyze_activity_patterns(commit_times),
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}
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def _clean_diff(diff_output: str) -> str:
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"""Clean up diff output to focus on actual changes"""
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lines = diff_output.split("\n")
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cleaned_lines = []
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skip_next = False
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for line in lines:
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# Skip git-specific headers
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if (
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line.startswith("diff --git")
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or line.startswith("index ")
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or line.startswith("new file mode ")
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or line.startswith("deleted file mode ")
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):
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continue
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# Keep file markers but clean them up
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if line.startswith("--- ") or line.startswith("+++ "):
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# Convert /dev/null to clearer marker
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if "/dev/null" in line:
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continue
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# Keep just the filename
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cleaned_lines.append(line.split("/")[-1])
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continue
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# Keep actual diff content
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if (
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line.startswith("@@ ")
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or line.startswith("+")
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or line.startswith("-")
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or line.startswith(" ")
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):
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cleaned_lines.append(line)
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return "\n".join(cleaned_lines)
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def _get_commit_contents(
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target_repos: List[str], sources_data: Dict[str, Any], max_diff_lines: int = 100
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) -> Dict[str, List[Dict[str, Any]]]:
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"""
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Retrieves commit contents focusing on core files and limiting diff sizes.
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Now with cleaner diff output.
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"""
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commit_contents = {}
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# Extract username from the first repository's path structure
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username = None
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for repo in sources_data.values():
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if repo.get('structure', {}).get('name', ''):
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# Extract username from the repository name (format: username_reponame.git)
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username = repo['structure']['name'].split('_')[0]
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break
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if not username:
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raise ValueError("Could not determine username from repository structure")
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for repo_name in target_repos:
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# Store the full repo path but don't overwrite repo_name
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repo_path_name = sources_data[repo_name]['structure'].get('name', '')
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if not repo_path_name:
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print(f"Warning: No path found for repository {repo_name}")
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continue
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# Construct correct path using extracted username
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repo_path = f"out/{username}/{repo_path_name}"
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# Get core files from sources_data using original repo_name
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core_files = sources_data[repo_name].get("samples", {}).get("core_files", {})
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if not core_files:
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continue
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try:
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commits = []
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for file_path, _ in core_files.items():
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try:
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# Get commit history for this file
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commit_history = subprocess.check_output(
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[
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"git",
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"log",
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"--format=%H %ad",
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"--date=iso",
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"--reverse",
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"--",
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file_path,
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],
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cwd=repo_path,
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text=True,
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).splitlines()
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# Process key commits
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commits_to_process = []
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if len(commit_history) > 0:
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commits_to_process.append(commit_history[0]) # First commit
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if len(commit_history) > 4:
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# Add some middle commits, evenly spaced
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middle_idx = len(commit_history) // 2
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commits_to_process.append(commit_history[middle_idx])
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if len(commit_history) > 1:
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commits_to_process.append(commit_history[-1]) # Last commit
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prev_content = None
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for commit_info in commits_to_process:
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sha, date = commit_info.split(" ", 1)
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try:
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# Get the diff for this commit
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diff_output = subprocess.check_output(
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["git", "show", "--format=", sha, "--", file_path],
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cwd=repo_path,
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text=True,
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stderr=subprocess.PIPE,
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)
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# Skip if diff is too large
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diff_lines = diff_output.splitlines()
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if len(diff_lines) > max_diff_lines:
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continue
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# Clean up the diff
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clean_diff = _clean_diff(diff_output)
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if not clean_diff.strip():
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continue
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# Get actual file content at this commit for first and last commit only
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if prev_content is None: # First commit
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file_content = subprocess.check_output(
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["git", "show", f"{sha}:{file_path}"],
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cwd=repo_path,
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text=True,
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stderr=subprocess.PIPE,
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)
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prev_content = file_content
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elif commit_info == commits_to_process[-1]: # Last commit
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file_content = subprocess.check_output(
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["git", "show", f"{sha}:{file_path}"],
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cwd=repo_path,
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text=True,
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stderr=subprocess.PIPE,
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)
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else:
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file_content = None
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commit_data = {
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"sha": sha,
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"date": date,
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"file": file_path,
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"changes": clean_diff,
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}
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if file_content:
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commit_data["content"] = file_content
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commits.append(commit_data)
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except subprocess.CalledProcessError:
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continue
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| 332 |
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except subprocess.CalledProcessError:
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continue
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| 334 |
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| 335 |
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if commits:
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| 336 |
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# Sort commits by date
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commits.sort(key=lambda x: x["date"])
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| 338 |
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# Group commits by file for better analysis
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| 340 |
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files_commits = {}
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| 341 |
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for commit in commits:
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| 342 |
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file_path = commit["file"]
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| 343 |
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if file_path not in files_commits:
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| 344 |
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files_commits[file_path] = []
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| 345 |
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files_commits[file_path].append(commit)
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| 346 |
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| 347 |
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commit_contents[repo_name] = {
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| 348 |
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"core_files": list(core_files.keys()),
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| 349 |
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"evolution": {
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| 350 |
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"commit_count": len(commits),
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| 351 |
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"commits_by_file": files_commits,
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},
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}
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print(f"Processed {len(commits)} commits for {repo_name} core files")
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except Exception as e:
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| 358 |
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print(f"Error analyzing repository {repo_name}: {str(e)}")
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| 359 |
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continue
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| 360 |
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| 361 |
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return commit_contents
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| 362 |
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| 363 |
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def _select_best_targets(
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| 364 |
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sources_data: Dict[str, Any], commits: Dict[str, Any]
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| 365 |
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) -> List[str]:
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| 366 |
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"""Selects repositories with sufficient history for analysis"""
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| 367 |
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targets = []
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| 368 |
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| 369 |
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for repo_name, repo_data in sources_data.items():
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| 370 |
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if (
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| 371 |
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len(commits.get(repo_name, [])) < 5
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or repo_data["file_stats"]["file_count"] < 10
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):
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continue
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| 375 |
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targets.append(repo_name)
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| 376 |
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| 377 |
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return targets
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| 378 |
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| 379 |
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| 380 |
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def _analyze_activity_patterns(commit_times: List[datetime]) -> Dict[str, Any]:
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| 381 |
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"""Analyzes commit timing patterns"""
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| 382 |
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if not commit_times:
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| 383 |
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return {
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| 384 |
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"frequency": {
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| 385 |
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"commits_per_day": 0,
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| 386 |
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"active_hours": [],
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| 387 |
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"timezone_hint": "unknown",
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| 388 |
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},
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| 389 |
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"burst_patterns": {
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| 390 |
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"intensity": "low",
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| 391 |
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"average_duration": "n/a",
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| 392 |
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"frequency": "sporadic",
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},
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}
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| 396 |
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# Sort commit times
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| 397 |
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commit_times.sort()
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| 398 |
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| 399 |
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# Calculate commits per day
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| 400 |
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days_span = (commit_times[-1] - commit_times[0]).days or 1
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| 401 |
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commits_per_day = round(len(commit_times) / days_span, 2)
|
| 402 |
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| 403 |
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# Analyze active hours
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| 404 |
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hours = Counter([t.hour for t in commit_times])
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| 405 |
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active_hours = [
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| 406 |
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f"{h:02d}-{(h+1):02d}"
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| 407 |
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for h, c in hours.most_common(3)
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| 408 |
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if c > len(commit_times) * 0.1
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| 409 |
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]
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| 410 |
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| 411 |
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# Estimate timezone from most active hours
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| 412 |
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# NOTE: Unclear should show the closest timezone
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| 413 |
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peak_hour = max(hours.items(), key=lambda x: x[1])[0]
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| 414 |
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if 4 <= peak_hour <= 8:
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| 415 |
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tz_hint = "UTC+8 to UTC+10"
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| 416 |
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elif 8 <= peak_hour <= 12:
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| 417 |
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tz_hint = "UTC+0 to UTC+2"
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| 418 |
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elif 12 <= peak_hour <= 16:
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| 419 |
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tz_hint = "UTC-6 to UTC-4"
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| 420 |
-
elif 16 <= peak_hour <= 20:
|
| 421 |
-
tz_hint = "UTC-12 to UTC-8"
|
| 422 |
-
else:
|
| 423 |
-
tz_hint = "unclear"
|
| 424 |
-
|
| 425 |
-
# Analyze burst patterns
|
| 426 |
-
time_diffs = []
|
| 427 |
-
for i in range(1, len(commit_times)):
|
| 428 |
-
diff = (commit_times[i] - commit_times[i - 1]).total_seconds() / 3600
|
| 429 |
-
time_diffs.append(diff)
|
| 430 |
-
|
| 431 |
-
if time_diffs:
|
| 432 |
-
avg_diff = statistics.mean(time_diffs)
|
| 433 |
-
if avg_diff < 1:
|
| 434 |
-
intensity = "high"
|
| 435 |
-
elif avg_diff < 4:
|
| 436 |
-
intensity = "moderate"
|
| 437 |
-
else:
|
| 438 |
-
intensity = "low"
|
| 439 |
-
|
| 440 |
-
burst_duration = (
|
| 441 |
-
"few hours"
|
| 442 |
-
if avg_diff < 4
|
| 443 |
-
else "day-length" if avg_diff < 24 else "multi-day"
|
| 444 |
-
)
|
| 445 |
-
burst_frequency = (
|
| 446 |
-
"frequent"
|
| 447 |
-
if commits_per_day > 3
|
| 448 |
-
else "regular" if commits_per_day > 1 else "sporadic"
|
| 449 |
-
)
|
| 450 |
-
else:
|
| 451 |
-
intensity = "low"
|
| 452 |
-
burst_duration = "n/a"
|
| 453 |
-
burst_frequency = "sporadic"
|
| 454 |
-
|
| 455 |
-
return {
|
| 456 |
-
"frequency": {
|
| 457 |
-
"commits_per_day": commits_per_day,
|
| 458 |
-
"active_hours": active_hours,
|
| 459 |
-
"timezone_hint": tz_hint,
|
| 460 |
-
},
|
| 461 |
-
"burst_patterns": {
|
| 462 |
-
"intensity": intensity,
|
| 463 |
-
"average_duration": burst_duration,
|
| 464 |
-
"frequency": burst_frequency,
|
| 465 |
-
},
|
| 466 |
-
}
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