Overview
The Refactron class is the main entry point for all code analysis and refactoring operations.
Constructor
from refactron import Refactron
from refactron.core.config import RefactronConfig
# Default configuration
refactron = Refactron()
# Custom configuration
config = RefactronConfig(
max_function_complexity=15,
enabled_analyzers=["security", "complexity"]
)
refactron = Refactron(config)
config
RefactronConfig
default:"None"
Configuration object. If None, uses default configuration.
Methods
analyze()
Analyze a file or directory for issues.
def analyze(target: Union[str, Path]) -> AnalysisResult
Path to file or directory to analyze
AnalysisResult containing detected issues and metrics
Example:
refactron = Refactron()
result = refactron.analyze("myproject/")
print(f"Files analyzed: {result.summary['files_analyzed']}")
print(f"Total issues: {result.summary['total_issues']}")
for issue in result.issues:
print(f"{issue.level.value}: {issue.message}")
refactor()
Refactor a file or directory with intelligent transformations.
def refactor(
target: Union[str, Path],
preview: bool = True,
operation_types: Optional[List[str]] = None
) -> RefactorResult
Path to file or directory to refactor
If True, show changes without applying them
Specific refactoring types to apply. None = all types.Available types:
extract_constantadd_docstringsimplify_conditionalsreduce_parameters
RefactorResult containing operations and status
Example:
# Preview all refactorings
result = refactron.refactor("myfile.py", preview=True)
result.show_diff()
# Apply specific refactorings
result = refactron.refactor(
"myfile.py",
preview=False,
operation_types=["extract_constant", "add_docstring"]
)
if result.success:
print(f"Applied {len(result.operations)} refactorings")
print(f"Backup: {result.backup_path}")
record_feedback()
Record developer feedback on a refactoring suggestion.
def record_feedback(
operation_id: str,
action: str,
reason: Optional[str] = None,
operation: Optional[RefactoringOperation] = None
) -> None
Unique identifier for the refactoring operation
Feedback action: "accepted", "rejected", or "ignored"
Optional reason for the feedback
operation
RefactoringOperation
default:"None"
The refactoring operation object (for pattern learning)
result = refactron.refactor("myfile.py", preview=True)
for op in result.operations:
refactron.record_feedback(
operation_id=op.operation_id,
action="accepted",
reason="Improved readability",
operation=op
)
get_python_files()
Get all Python files in a directory, respecting exclude patterns.
def get_python_files(directory: Path) -> List[Path]
Directory to search for Python files
from pathlib import Path
refactron = Refactron()
files = refactron.get_python_files(Path("myproject"))
print(f"Found {len(files)} Python files")
for file in files:
print(file)
Get performance statistics from optimization components.
def get_performance_stats() -> Dict[str, Any]
stats = refactron.get_performance_stats()
print(f"AST cache hits: {stats['ast_cache']['hits']}")
print(f"AST cache misses: {stats['ast_cache']['misses']}")
print(f"Parallel jobs: {stats['parallel']['max_workers']}")
clear_caches()
Clear all performance-related caches.
def clear_caches() -> None
# Clear caches after major code changes
refactron.clear_caches()
detect_project_root ()
Detect project root by looking for common markers.
def detect_project_root(file_path: Path) -> Path
File path to start searching from
from pathlib import Path
refactron = Refactron()
root = refactron.detect_project_root(Path("src/module/file.py"))
print(f"Project root: {root}")
Properties
config
Access current configuration.
config = refactron.config
print(f"Max complexity: {config.max_function_complexity}")
analyzers
List of enabled analyzers.
analyzers = refactron.analyzers
print(f"Enabled analyzers: {[a.__class__.__name__ for a in analyzers]}")
refactorers
List of enabled refactorers.
refactorers = refactron.refactorers
print(f"Enabled refactorers: {[r.__class__.__name__ for r in refactorers]}")
Complete Example
from refactron import Refactron
from refactron.core.config import RefactronConfig
from pathlib import Path
# Configure Refactron
config = RefactronConfig(
max_function_complexity=15,
max_function_length=100,
enabled_analyzers=["security", "complexity", "code_smell"],
enabled_refactorers=["extract_constant", "add_docstring"],
enable_pattern_learning=True
)
# Initialize
refactron = Refactron(config)
# Analyze project
print("Analyzing project...")
analysis = refactron.analyze("myproject/")
# Print summary
print(f"\nAnalysis Summary:")
print(f" Files analyzed: {analysis.summary['files_analyzed']}")
print(f" Total issues: {analysis.summary['total_issues']}")
# Print critical issues
critical = [i for i in analysis.issues if i.level.value == "CRITICAL"]
if critical:
print(f"\nCritical Issues ({len(critical)}):")
for issue in critical:
print(f" {issue.file_path}:{issue.line_number} - {issue.message}")
# Refactor with preview
print("\nGenerating refactoring suggestions...")
result = refactron.refactor("myproject/", preview=True)
# Review and apply specific operations
for op in result.operations:
print(f"\nOperation: {op.operation_type}")
print(f" File: {op.file_path}:{op.line_number}")
print(f" Risk: {op.risk_score}")
print(f" Description: {op.description}")
# Record feedback
refactron.record_feedback(
operation_id=op.operation_id,
action="accepted",
reason="Approved after review",
operation=op
)
# Get performance stats
stats = refactron.get_performance_stats()
print(f"\nPerformance:")
print(f" AST cache hit rate: {stats['ast_cache']['hit_rate']:.1%}")
print("\nRefactoring complete!")
Next Steps
