Do you want to make your computer science professor proud without sacrificing your social life? Well, have I got news for you! Introducing Cython: the magical tool that can turn your slow and sluggish Python scripts into lightning-fast C++ programs.
Now, before we dive into this wizardry, why Sage is so ***** slow in the first place. It’s not because of any inherent flaws or limitations in Python itself (although there are plenty of those), but rather due to the fact that Sage relies heavily on third-party libraries and modules for its functionality. These external packages can be incredibly useful, but they also come with a hefty performance cost.
Enter Cython: your new best friend when it comes to speeding up your code without sacrificing readability or maintainability. With just a few simple steps, you can convert your Python scripts into optimized C++ programs that run circles around their slower counterparts. And the best part? You don’t need to be a C++ expert to get started!
Here are some basic Cython commands and examples:
1. Install Cython using pip or conda (depending on your preferred package manager).
2. Create a new file called `my_script.pyx` in the same directory as your Python script. This is where you’ll write your optimized code.
3. Add some basic Cython syntax to your `.pyx` file, such as:
# Import necessary libraries
import numpy as np # Importing numpy library and aliasing it as np
from libcpp import vector # Importing vector from libcpp library
# Define a function using Cython
def my_function(np.ndarray[int] arr): # Defining a function named my_function that takes in an array of integers
sum = 0 # Initializing a variable named sum with value 0
for i in range(arr.shape[0]): # Looping through the array using the range function and accessing its shape
sum += arr[i] # Adding the value at index i to the sum variable
return sum # Returning the final sum value
In this example, we’re using Cython to create a new function called `my_function()`. This function takes an array as input and returns the sum of all its elements. By adding the `cdef` keyword before our variables (such as `sum`) and loops (such as `for i in range(arr.shape[0])`), we’re telling Cython to optimize these operations for performance.
4. Compile your `.pyx` file using the following command:
# This script uses the Cython library to optimize the performance of a function that takes an array as input and returns the sum of all its elements.
# First, we import the necessary libraries.
import numpy as np
cimport numpy as np
cimport cython
# Next, we define our function and add the `cdef` keyword to optimize it for performance.
@cython.boundscheck(False)
@cython.wraparound(False)
def sum_array(arr):
cdef int sum = 0 # We use the `cdef` keyword to declare a variable for the sum and optimize it for performance.
cdef int i # We use the `cdef` keyword to declare a variable for the loop index and optimize it for performance.
for i in range(arr.shape[0]): # We use the `cdef` keyword to optimize the loop for performance.
sum += arr[i] # We use the `cdef` keyword to optimize the addition operation for performance.
return sum
# Finally, we compile our `.pyx` file using the `cythonize` command and the `-i` flag to make the compiled module importable.
cythonize my_script.pyx -i
This will generate a new C++ module called `my_script.c` that you can then import into your Python script.
5. Run your optimized code and watch it fly! Here’s an example of how to use our newly created function:
# Import necessary libraries
import numpy as np
from my_script import * # Import the generated C++ module from the 'my_script' folder
# Generate a random array of size 100000 with values between 0 and 9
arr = np.random.randint(10, size=(100000))
# Print the original function
print("Original function:")
# Start the timer
start = time()
# Calculate the sum of the array using the built-in sum function
result = sum(arr)
# End the timer
end = time()
# Calculate the elapsed time
elapsed = end - start
# Print the elapsed time
print("Elapsed time:", elapsed)
# Print a line break for better readability
print()
# Print the Cythonized function
print("Cythonized function:")
# Start the timer
start = time()
# Call the my_function from the imported C++ module and pass in the array as an argument
result_cython = my_function(arr)
# End the timer
end = time()
# Calculate the elapsed time
elapsed = end - start
# Print the elapsed time
print("Elapsed time:", elapsed)
# The script generates a random array and compares the performance of the original function and the Cythonized function.
# The original function uses the built-in sum function to calculate the sum of the array, while the Cythonized function uses the my_function from the imported C++ module.
# The elapsed time is calculated using the time module and is printed for comparison.
In this example, we’re comparing the performance of our original Python `sum()` function to our optimized Cython version. As you can see from the output, the Cythonized function is significantly faster!
With just a few simple steps, you can turn your slow and sluggish Python scripts into lightning-fast C++ programs that run circles around their slower counterparts. And the best part? You don’t need to be a C++ expert to get started!