Alright, Cython Memoryviews the coolest thing since sliced bread (or maybe even better than that). If you’re a Python programmer who loves speed but hates performance bottlenecks, this is for you!
So what exactly are memoryviews? Well, they’re basically fancy pointers to arrays. But instead of just pointing at the data like regular C-style pointers do, memoryviews let us access that data in a more Pythonic way. And by “Pythonic,” we mean without all those ***** indexing and slicing headaches.
Here’s an example: say you have a big ol’ NumPy array called `arr` with some fancy shape like (3, 4, 5). If you want to add up the values in that array using regular Python code, it might look something like this:
# Define a variable "total" and set its initial value to 0
total = 0
# Loop through the range of the product of the shape of the NumPy array "arr"
# The range function returns a sequence of numbers starting from 0 up to the product of the shape of "arr"
# The product function from NumPy returns the total number of elements in the array
for i in range(np.product(arr.shape)):
# Add the value of the current element in "arr" to the "total" variable
# The "i" variable represents the index of the current element in the array
total += arr[i]
But if we use memoryviews instead, we can do the same thing with much less overhead and way fewer lines of code! Here’s how:
# Importing necessary libraries
import numpy as np # Importing numpy library and assigning it an alias "np"
from cython.view import array as cvarray # Importing array function from cython.view library and assigning it an alias "cvarray"
# Creating a 3D array using numpy
arr = np.arange(np.product(arr.shape), dtype=np.int32).reshape((3, 4, 5)) # Using numpy's arange function to create an array with values from 0 to the product of the shape of arr, with data type int32. Reshaping the array to have 3 dimensions with 3 rows, 4 columns, and 5 elements in each row and column.
# Creating a memoryview of the array
arr_view = cvarray(arr) # Using the cvarray function to create a memoryview of the array "arr"
# Calculating the sum of all elements in the array
total = 0 # Initializing a variable "total" with value 0
for i in range(len(arr_view)): # Looping through the length of the memoryview "arr_view"
total += arr_view[i] # Adding the value at index i of the memoryview to the variable "total"
# Printing the total sum
print(total) # Printing the final sum of all elements in the array
Notice how we’re using a `cvarray` instead of the regular NumPy array. This is because memoryviews are optimized for speed and can handle C-style arrays as well! And by using a simple loop to iterate over the view, we avoid all those ***** indexing headaches that come with working with NumPy arrays directly.
Memoryviews also work great with Cython arrays which are basically like regular Python lists but faster and more memory-efficient. Here’s an example:
# Importing necessary libraries
import numpy as np # Importing numpy library and aliasing it as "np"
from cython.view import array as cvarray # Importing "array" function from "cython.view" library and aliasing it as "cvarray"
from libc.stdlib cimport malloc, free # Importing "malloc" and "free" functions from "libc.stdlib" library
# Defining a new type "CyArray_t" which is a pointer to an integer
ctypedef int* CyArray_t
# Defining a function to create a Cython array
def create_cyarray(int n): # Function name "create_cyarray" with parameter "n" of type integer
arr = (int*)malloc(n * sizeof(int)) # Allocating memory for the array using "malloc" function
for i in range(n): # Looping through the array
arr[i] = i + 100 # Assigning values to the array
return cvarray(arr, len(arr), <CyArray_t> arr) # Returning a Cython array using "cvarray" function with parameters "arr" (array), "len(arr)" (length of array), and "<CyArray_t> arr" (type of array)
# Defining a function to sum the elements of a Cython array
def sum_cyarray(cvarray input_view not None): # Function name "sum_cyarray" with parameter "input_view" of type Cython array
total = 0 # Initializing a variable "total" to store the sum
for i in range(len(input_view)): # Looping through the array
total += input_view[i] # Adding each element to the total
return total # Returning the total sum
In this example, we’re using a Cython array to create some data and then passing it into our `sum_cyarray()` function. By using memoryviews instead of regular NumPy arrays or C-style pointers, we can avoid all those ***** indexing headaches while still getting the speed benefits that come with working directly with memory!
If you’re a Python programmer who loves speed but hates performance bottlenecks, this is for you. Give it a try !