Advanced Coding Tutorials: Cython
Section 1: Introduction +
Welcome to our advanced coding tutorials. Here, you'll dive deep into programming concepts, best practices, and more.
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Section 1: The Global Interpreter Lock (GIL)+
The GIL is a mutex in CPython that protects access to Python objects, preventing multiple native threads from executing Python bytecode simultaneously within a single process. While it simplifies memory management, it limits parallelism for CPU-bound tasks in multi-threaded applications. I/O-bound threads still benefit as the GIL is released during blocking I/O calls.
import time
import threading
# A CPU-bound task (counting)
def cpu_bound_task(count_to):
count = 0
while count < count_to:
count += 1
COUNT = 100_000_000 # A large number to make the task take time
# --- Run Sequentially ---
start_seq = time.time()
cpu_bound_task(COUNT)
cpu_bound_task(COUNT)
end_seq = time.time()
print(f"Sequential execution time: {end_seq - start_seq:.4f} seconds")
# --- Run with Threads ---
thread1 = threading.Thread(target=cpu_bound_task, args=(COUNT,))
thread2 = threading.Thread(target=cpu_bound_task, args=(COUNT,))
start_thread = time.time()
thread1.start()
thread2.start()
thread1.join() # Wait for thread 1 to finish
thread2.join() # Wait for thread 2 to finish
end_thread = time.time()
print(f"Threaded execution time: {end_thread - start_thread:.4f} seconds")
# Note: Due to the GIL, the threaded execution time for this CPU-bound task
# will likely be similar to or even slightly longer than the sequential time,
# not significantly faster as one might expect from true parallelism.
Section 2: Introduction to Cython+
Cython is a superset of Python that allows writing C extensions. It lets you compile Python-like code (with optional static type declarations) into efficient C code. This is particularly useful for speeding up CPU-bound bottlenecks in Python applications or for interfacing directly with C/C++ libraries without writing complex C API code manually.
# --- Example Cython code (save as example.pyx) ---
# This file needs to be compiled using a setup.py script
# cdef allows defining C variables for speed
# cpdef creates both C and Python accessible versions
# cython: language_level=3 # Specify Python 3 semantics
cpdef int sum_of_squares_cython(int n):
# Declare C integer type for loop variable and result
cdef int i, total = 0
for i in range(n):
total += i * i
return total
# --- Example setup.py (save as setup.py) ---
# from setuptools import setup
# from Cython.Build import cythonize
#
# setup(
# ext_modules = cythonize("example.pyx")
# )
# Run: python setup.py build_ext --inplace
# --- Example Python usage (after compiling) ---
# import example # Import the compiled module
# import time
#
# N = 10000
#
# start = time.time()
# result = example.sum_of_squares_cython(N)
# end = time.time()
# print(f"Cython result: {result}, Time: {end - start:.6f}s")
#
# # Compare with pure Python (usually slower)
# def sum_of_squares_python(n):
# total = 0
# for i in range(n):
# total += i * i
# return total
#
# start_py = time.time()
# result_py = sum_of_squares_python(N)
# end_py = time.time()
# print(f"Python result: {result_py}, Time: {end_py - start_py:.6f}s")
# Placeholder message as direct execution isn't possible here:
print("Cython code needs compilation. See comments for structure.")
Section 3: CPython Memory Management+
CPython primarily uses reference counting for memory management. Each object keeps track of how many references point to it. When the count drops to zero, the object's memory is deallocated. A cyclic garbage collector runs periodically to detect and break reference cycles (objects referring to each other) that reference counting alone cannot handle.
import sys
import gc # Garbage Collector interface
# --- Reference Counting Example ---
a = [1, 2, 3] # Object [1, 2, 3] created, ref count = 1 (a)
print(f"Initial ref count for a: {sys.getrefcount(a) - 1}") # -1 because getrefcount itself adds a temporary ref
b = a # Ref count = 2 (a, b)
print(f"Ref count after b = a: {sys.getrefcount(a) - 1}")
del b # Ref count = 1 (a)
print(f"Ref count after del b: {sys.getrefcount(a) - 1}")
del a # Ref count = 0, object [1, 2, 3] is deallocated
# print(sys.getrefcount(a)) # This would raise NameError as 'a' is deleted
# --- Cyclic Reference Example (handled by GC) ---
class Node:
def __init__(self, name):
self.name = name
self.neighbor = None
print(f"Node '{self.name}' created.")
def __del__(self):
# This is called when the object is about to be destroyed
print(f"Node '{self.name}' being destroyed.")
# Create a cycle
node1 = Node("A")
node2 = Node("B")
node1.neighbor = node2 # node1 references node2
node2.neighbor = node1 # node2 references node1
# Check initial reference counts (will be 2 due to self + neighbor)
print(f"Ref count node1: {sys.getrefcount(node1) - 1}")
print(f"Ref count node2: {sys.getrefcount(node2) - 1}")
# Remove external references
del node1
del node2
# At this point, the nodes still reference each other (ref count > 0)
# They are unreachable but not deallocated by ref counting alone.
print("\nNodes deleted from namespace, but cycle exists.")
print("Running garbage collection manually...")
collected_count = gc.collect() # Manually run the cyclic GC
print(f"Garbage collector collected {collected_count} objects.")
# Now the __del__ methods should be called as the GC breaks the cycle.
Section 4: Abstract Base Classes (ABCs)+
Abstract Base Classes (ABCs) from the `abc` module define interfaces or contracts that subclasses must implement. They use the `@abstractmethod` decorator to specify methods that must be overridden. This enforces a common structure across different implementations, promoting better code organization and polymorphism.
from abc import ABC, abstractmethod
# Define an Abstract Base Class
class Vehicle(ABC):
@abstractmethod
def start_engine(self):
"""Abstract method to start the vehicle's engine."""
pass
@abstractmethod
def stop_engine(self):
"""Abstract method to stop the vehicle's engine."""
pass
def honk(self):
"""A concrete method available to all subclasses."""
print("Generic honk!")
# Concrete subclass implementing the abstract methods
class Car(Vehicle):
def start_engine(self):
print("Car engine started with a key turn.")
def stop_engine(self):
print("Car engine stopped.")
# Can override concrete methods too
def honk(self):
print("Beep beep!")
# Another concrete subclass
class Motorcycle(Vehicle):
def start_engine(self):
print("Motorcycle engine started with a kick.")
def stop_engine(self):
print("Motorcycle engine stopped.")
# Usage
my_car = Car()
my_moto = Motorcycle()
vehicles = [my_car, my_moto]
for v in vehicles:
v.start_engine()
v.honk()
v.stop_engine()
print("-" * 10)
# Attempting to instantiate the ABC directly will fail
try:
generic_vehicle = Vehicle()
except TypeError as e:
print(f"Error trying to instantiate ABC: {e}")
# Attempting to instantiate a subclass that *doesn't* implement
# all abstract methods will also fail:
# class BrokenCar(Vehicle):
# def start_engine(self): pass
# try:
# bad_car = BrokenCar() # Raises TypeError
# except TypeError as e:
# print(f"Error with incomplete subclass: {e}")
Section 5: Threading vs Multiprocessing+
Python's `threading` module allows concurrent execution within a single process, sharing memory but limited by the GIL for CPU-bound tasks. The `multiprocessing` module bypasses the GIL by creating separate processes, each with its own memory space, enabling true parallelism for CPU-bound work but incurring higher communication overhead.
import time
import threading
import multiprocessing
import os
# Shared function (can be CPU or I/O bound depending on content)
def worker_function(worker_id, duration):
pid = os.getpid()
thread_id = threading.get_ident()
print(f"Worker {worker_id}: START - PID {pid}, Thread {thread_id}")
time.sleep(duration) # Simulate work (I/O bound)
# For CPU-bound, replace sleep with heavy computation
print(f"Worker {worker_id}: END - PID {pid}, Thread {thread_id}")
return f"Result from worker {worker_id}"
NUM_WORKERS = 3
DURATION = 1
# --- Using Threading ---
print("--- Starting with Threading ---")
threads = []
start_th = time.time()
for i in range(NUM_WORKERS):
t = threading.Thread(target=worker_function, args=(f"T{i}", DURATION))
threads.append(t)
t.start()
for t in threads:
t.join() # Wait for threads to complete
end_th = time.time()
print(f"Threading finished in {end_th - start_th:.4f}s\n")
# Note: All threads run within the same PID.
# --- Using Multiprocessing ---
# Required for multiprocessing on some platforms (like Windows)
if __name__ == "__main__": # Guard clause
print("--- Starting with Multiprocessing ---")
processes = []
start_mp = time.time()
# Use a Pool for easier management (optional)
# with multiprocessing.Pool(processes=NUM_WORKERS) as pool:
# results = pool.starmap(worker_function, [(f"P{i}", DURATION) for i in range(NUM_WORKERS)])
# Manual process creation:
for i in range(NUM_WORKERS):
p = multiprocessing.Process(target=worker_function, args=(f"P{i}", DURATION))
processes.append(p)
p.start()
results = [] # Placeholder if not using Pool.join() implicitly handles results
for p in processes:
p.join() # Wait for processes to complete
# results.append(p.exitcode) # Can get exit code
end_mp = time.time()
print(f"Multiprocessing finished in {end_mp - start_mp:.4f}s")
# Note: Each process will have a different PID.
# Key takeaway: For I/O-bound tasks (like time.sleep), both can offer concurrency.
# For CPU-bound tasks, multiprocessing typically offers true parallelism (speedup),
# while threading is limited by the GIL in CPython.
Section 6: Functional Programming Tools (`functools`)+
Python supports functional programming paradigms. The `functools` module provides higher-order functions and operations on callable objects. Key tools include `partial` for freezing function arguments, `reduce` for cumulative operations, `lru_cache` for memoization, and `wraps` for creating well-behaved decorators.
import functools
import time
import operator
# --- functools.partial ---
def power(base, exponent):
return base ** exponent
# Create specialized functions by freezing arguments
square = functools.partial(power, exponent=2)
cube = functools.partial(power, exponent=3)
print(f"Square of 5: {square(5)}") # Output: 25
print(f"Cube of 3: {cube(3)}") # Output: 27
# --- functools.reduce ---
# Performs cumulative operations on a sequence
numbers = [1, 2, 3, 4, 5]
product = functools.reduce(operator.mul, numbers) # Multiply all numbers
# Equivalent to (((1*2)*3)*4)*5
print(f"Product of {numbers}: {product}") # Output: 120
# --- functools.lru_cache ---
# Memoization decorator - caches results of expensive function calls
@functools.lru_cache(maxsize=128) # Least Recently Used cache
def slow_fibonacci(n):
"""Calculates Fibonacci recursively (inefficient without cache)."""
if n < 2:
return n
# print(f"Calculating fib({n})") # Uncomment to see cache effect
time.sleep(0.01) # Simulate slowness
return slow_fibonacci(n-1) + slow_fibonacci(n-2)
start_cache = time.time()
result_fib = slow_fibonacci(15) # Calculation happens
end_cache = time.time()
print(f"Fib(15) = {result_fib}. First call time: {end_cache - start_cache:.4f}s")
start_cache2 = time.time()
result_fib2 = slow_fibonacci(15) # Result retrieved from cache instantly
end_cache2 = time.time()
print(f"Fib(15) = {result_fib2}. Second call time: {end_cache2 - start_cache2:.4f}s")
# --- functools.wraps ---
# (Already demonstrated in Section 2: Advanced Decorators)
# Ensures decorators preserve metadata of the original function.
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