Pseudocode To Python Code

#!/usr/bin/env python3
"""
Brief description of what this module does.

Implements [algorithm name] with [key features].
"""

from typing import List, Dict, Optional, Tuple, Set, Any

def algorithm_name(param1: type1, param2: type2) -> return_type:
    """Brief description.

    Detailed explanation of the algorithm, including approach and complexity.

    Args:
        param1: Description with constraints
        param2: Description with constraints

    Returns:
        Description of return value

    Raises:
        ValueError: When input is invalid
        TypeError: When input type is wrong

    Examples:
        >>> algorithm_name([3, 1, 2])
        [1, 2, 3]
    """
    # Input validation
    if not param1:
        raise ValueError("param1 cannot be empty")

    # Main algorithm implementation
    # Use clear variable names and comments for complex logic
    result = implementation_here

    return result

def test_algorithm_name():
    """Test algorithm_name with various inputs."""
    # Test 1: Normal case
    assert algorithm_name([3, 1, 2]) == [1, 2, 3]

    # Test 2: Edge case - empty
    try:
        algorithm_name([])
        assert False, "Should raise ValueError"
    except ValueError:
        pass

    # Test 3: Edge case - single element
    assert algorithm_name([1]) == [1]

    # Test 4: Edge case - duplicates
    assert algorithm_name([2, 1, 2]) == [1, 2, 2]

    # Test 5: Edge case - already sorted
    assert algorithm_name([1, 2, 3]) == [1, 2, 3]

    print("✓ All tests passed!")

if __name__ == "__main__":
    # Run tests
    test_algorithm_name()

    # Example usage
    example_input = [3, 1, 4, 1, 5, 9, 2, 6]
    result = algorithm_name(example_input)
    print(f"Input: {example_input}")
    print(f"Output: {result}")

# Instead of loops
result = [transform(x) for x in items if condition(x)]

# Instead of manual loops
total = sum(items)
maximum = max(items)

for i, item in enumerate(items):
    process(i, item)

for a, b in zip(list1, list2):
    process(a, b)

def function(param):
    """Function with error handling."""
    # Validate input
    if param is None:
        raise ValueError("param cannot be None")

    if not isinstance(param, expected_type):
        raise TypeError(f"Expected {expected_type}, got {type(param)}")

    # Check constraints
    if param < 0:
        raise ValueError("param must be non-negative")

    # Implementation
    try:
        result = risky_operation(param)
    except SpecificError as e:
        # Handle or re-raise with context
        raise RuntimeError(f"Operation failed: {e}") from e

    return result

Pseudocode to Python Mapping Summary:
======================================

Algorithm: [Name]
Complexity: Time O(...), Space O(...)

Key Mappings:
1. FOR i FROM 1 TO n → for i in range(1, n + 1)
2. ARRAY[n] → list of size n
3. IF condition THEN → if condition:
4. SWAP(a, b) → a, b = b, a

Data Structures Used:
- Input: List[int]
- Auxiliary: Dict[str, int] for tracking

Functions Generated:
- main_function(): Main algorithm implementation
- helper_function(): Helper for [specific task]

Test Cases: 5 tests covering normal and edge cases

FOR i FROM 0 TO n-1
    FOR j FROM 0 TO n-i-1
        IF array[j] > array[j+1]
            SWAP array[j] and array[j+1]