py-complexity（py 复杂度）

Python Complexity Reduction

Reduce code complexity to improve maintainability and understandability.

Effective use of context windows.

Objectives

1. Measure cyclomatic and cognitive complexity
2. Identify overly complex functions and modules
3. Identify overly long files (no code files >500 lines, unless unavoidable)
4. Apply refactoring patterns to reduce complexity
5. Track complexity improvements over time
6. Enforce complexity thresholds in CI/CD

Required Tools

Add to [dependency-groups] dev: "radon", "lizard", "xenon", "wily"

• radon: Cyclomatic complexity & maintainability index
• lizard: Cognitive complexity (better for readability)
• xenon: CI/CD threshold enforcement
• wily: Track trends across git history

Permissions: Run py-quality-setup first to configure .claude/settings.local.json with all needed tool permissions.

Discovery Phase

Measure Complexity

# Key commands
radon cc . -n C              # Functions with complexity ≥11
lizard -C 15 .               # Cognitive complexity warnings
radon mi . -n B              # Maintainability index <65
wily build .                 # Initialize tracking (one-time)
wily diff HEAD~10            # Compare trends
scc --by-file --ci           # Optional: code statistics (called 'sccount' on openSUSE)

Thresholds

• Cyclomatic: Refactor at ≥C (11+)
• Cognitive: Refactor at >15
• Maintainability: Refactor at <65
• Lines per code file: Split if significantly >500, unless unavoidable

Manual Pattern Detection

Use Explore agent to find these complexity-increasing patterns:

Magic numbers and strings:

• Numeric literals (except 0, 1, -1) scattered in code
• String literals used as keys, thresholds, or configuration
• Search: grep -rE '\b[0-9]{2,}\b' --include='*.py' for multi-digit numbers
• Look for: fee calculations, timeout values, buffer sizes, regex patterns

Repetitive field operations:

• Multiple similar if-statements checking/setting object fields
• Pattern: if obj.field: obj.field = func(obj.field)
• Search: Functions with 5+ lines doing similar operations on different fields
• Candidates: configuration loading, validation, serialization, field clearing

Repeated complex type definitions:

• Same complex type annotation used in multiple places
• Example pattern: Literal["a", "b", "c"] | None repeated across functions/classes
• Search: grep -r 'Literal\[' --include='*.py' then look for duplicates
• Also: Complex union types, nested generics used multiple times
• Candidates: Function parameters, return types, class attributes, cast() calls

Long if/elif chains:

• 5+ conditional branches doing similar operations
• Can often be replaced with lookup tables or polymorphism

Deeply nested code:

• 4+ levels of indentation
• Multiple nested loops or conditionals

Refactoring Patterns

Extract Function

Break complex functions into focused sub-functions:

# BEFORE - 20+ lines, complexity: 15
def process_order(order: dict) -> bool:
    # Validation, payment, confirmation logic all mixed

# AFTER - Each function <5 lines, complexity: <5
def process_order(order: dict) -> bool:
    return is_valid_order(order) and process_payment(order) and complete_order(order)

Guard Clauses

Replace nested conditions with early returns:

# BEFORE - Deep nesting
if user:
    if user.get("active"):
        if user.get("verified"):
            return True
return False

# AFTER - Guard clauses
if not user or not user.get("active") or not user.get("verified"):
    return False
return True

Lookup Tables

Replace if/elif chains with dictionaries:

# BEFORE - Nested conditionals (complexity: 7)
if customer == "gold" and total > 1000: return 0.20
elif customer == "gold": return 0.15
# ... more branches

# AFTER - Lookup table (complexity: 2)
RATES = {("gold", "high"): 0.20, ("gold", "low"): 0.15, ...}
return RATES.get((customer, tier), 0.0)

Extract Magic Numbers and Strings

Extract scattered literals to configuration classes:

# BEFORE - Magic numbers throughout code
if amount < 100: base_fee = 2.50
elif amount < 1000: base_fee = 5.00
if account_type == "premium": return base_fee * 0.5

# AFTER - Configuration class
class FeeConfig:
    TIER_SMALL = 100
    FEE_SMALL = 2.50
    PREMIUM_DISCOUNT = 0.5

if amount < FeeConfig.TIER_SMALL:
    base_fee = FeeConfig.FEE_SMALL

Benefits: Single source of truth, easy to change, self-documenting, can load from environment

Replace Repetitive Field Operations

Replace repetitive if-statements with loops over field names:

# BEFORE - 15 similar if-statements (complexity: 9)
if config.email.smtp_host:
    config.email.smtp_host = substitute_secret(config.email.smtp_host, secrets)
if config.email.smtp_user:
    config.email.smtp_user = substitute_secret(config.email.smtp_user, secrets)
# ... 13 more similar lines

# AFTER - Loop over field list (complexity: 3)
email_fields = ["smtp_host", "smtp_user", "smtp_password", "smtp_from"]
for field in email_fields:
    if value := getattr(config.email, field, None):
        setattr(config.email, field, substitute_secret(value, secrets))

Pattern: Use getattr/setattr loops for: validation, field clearing, transformation, serialization

Extract Repeated Complex Type Definitions

Replace repeated complex type annotations with TypeAlias:

# BEFORE - Type repeated 8 times across files
cache_mode: Literal["use", "only", "refresh"] | None
# ... used in 8 different functions, classes

# AFTER - Define once, use everywhere
CacheMode = Literal["use", "only", "refresh"]
CacheModeOptional = CacheMode | None

cache_mode: CacheModeOptional

Placement:

• Module-level: types used within one module
• types.py: project-wide types
• __init__.py: package-wide exports

Benefits: DRY, semantic names, easier to change, reduced typos

Verification Checklist

• radon cc. -n C reports no functions with complexity ≥C (11+)
• lizard -C 15. reports no cognitive complexity warnings
• radon mi. -n B reports no modules with maintainability index <65
• No code files >500 lines (unless unavoidable)
• wily build. initialized for tracking
• All tests pass after refactoring
• Code coverage maintained or improved

Examples

Example: Complexity reduction workflow

1. Measure: radon cc . -n C; lizard -CCN 15 .
2. Found: handlers.py:process_data (complexity D: 25)
3. Apply patterns: Extract functions, guard clauses, lookup tables
4. Result: 4 functions with complexity A-B
5. Track: wily diff HEAD~1 shows 20-point reduction

Example: Apply specialized patterns

# Magic numbers: Search with grep, extract to config class
if amount < 100: fee = 2.50  # BEFORE
if amount < FeeConfig.TIER_SMALL: fee = FeeConfig.FEE_SMALL  # AFTER

# Repetitive fields: Replace 15 if-statements with loop
for field in ["smtp_host", "smtp_user", ...]:
    if value := getattr(config, field, None):
        setattr(config, field, process(value))

# Complex types: Extract repeated Literal types to TypeAlias
CacheMode = Literal["use", "only", "refresh"]  # Used 8 times → defined once

Related Skills

• Prerequisites: py-quality-setup (tool configuration), py-test-quality (safety net before refactoring)
• Prior cleanup: py-code-health (remove dead code first to reduce noise)
• Enforcement: py-git-hooks (add complexity checks to pre-commit)