examples/sample_functions.py

import math
import random
from typing import List, Tuple

def calculate_factorial(n: int) -> int:
    """Calculate factorial of a number recursively."""
    if n <= 1:
        return 1
    return n * calculate_factorial(n - 1)

def fibonacci_sequence(n: int) -> List[int]:
    """Generate fibonacci sequence up to n terms."""
    if n <= 0:
        return []
    elif n == 1:
        return [0]
    elif n == 2:
        return [0, 1]
    
    sequence = [0, 1]
    for i in range(2, n):
        sequence.append(sequence[i-1] + sequence[i-2])
    return sequence

def is_prime(num: int) -> bool:
    """Check if a number is prime."""
    if num < 2:
        return False
    if num == 2:
        return True
    if num % 2 == 0:
        return False
    
    for i in range(3, int(math.sqrt(num)) + 1, 2):
        if num % i == 0:
            return False
    return True

def find_primes_in_range(start: int, end: int) -> List[int]:
    """Find all prime numbers in a given range."""
    primes = []
    for num in range(start, end + 1):
        if is_prime(num):
            primes.append(num)
    return primes

def calculate_statistics(numbers: List[float]) -> dict:
    """Calculate basic statistics for a list of numbers."""
    if not numbers:
        return {"error": "Empty list provided"}
    
    n = len(numbers)
    mean = sum(numbers) / n
    sorted_nums = sorted(numbers)
    
    # Calculate median
    if n % 2 == 0:
        median = (sorted_nums[n//2 - 1] + sorted_nums[n//2]) / 2
    else:
        median = sorted_nums[n//2]
    
    # Calculate variance and standard deviation
    variance = sum((x - mean) ** 2 for x in numbers) / n
    std_dev = math.sqrt(variance)
    
    return {
        "count": n,
        "mean": mean,
        "median": median,
        "min": min(numbers),
        "max": max(numbers),
        "variance": variance,
        "std_deviation": std_dev
    }

def monte_carlo_pi(iterations: int) -> float:
    """Estimate Pi using Monte Carlo method."""
    inside_circle = 0
    
    for _ in range(iterations):
        x, y = random.random(), random.random()
        if x*x + y*y <= 1:
            inside_circle += 1
    
    return 4 * inside_circle / iterations

def process_data_pipeline(data: List[int]) -> dict:
    """Complex data processing pipeline demonstrating function calls."""
    # Step 1: Filter for positive numbers
    positive_data = []
    for x in data:
        if x > 0:
            positive_data.append(x)
    
    # Step 2: Find primes in the data
    primes_in_data = []
    for x in positive_data:
        if is_prime(x):
            primes_in_data.append(x)
    
    # Step 3: Calculate statistics
    positive_data_floats = []
    for x in positive_data:
        positive_data_floats.append(float(x))
    stats = calculate_statistics(positive_data_floats)
    
    # Step 4: Generate fibonacci sequence up to max value
    max_val = max(positive_data) if positive_data else 0
    fib_count = min(max_val, 20)  # Limit to reasonable size
    fib_sequence = fibonacci_sequence(fib_count)
    
    # Step 5: Calculate factorials for small numbers
    small_numbers = []
    for x in positive_data:
        if x <= 10:
            small_numbers.append(x)
    factorials = {}
    for x in small_numbers:
        factorials[x] = calculate_factorial(x)
    
    return {
        "original_count": len(data),
        "positive_count": len(positive_data),
        "primes_found": primes_in_data,
        "statistics": stats,
        "fibonacci_sequence": fib_sequence,
        "factorials": factorials,
        "pi_estimate": monte_carlo_pi(1000)
    }

def main():
    """Main function demonstrating the pipeline."""
    sample_data = [1, 2, 3, 5, 8, 13, 21, -1, 0, 17, 19, 23]
    result = process_data_pipeline(sample_data)
    print(f"Processing complete: {len(result)} metrics calculated")
    return result

if __name__ == "__main__":
    main()