Solving NumPy NonCContiguousWarning: A non-contiguous array was created on a strided slice

Overview

Working with NumPy, a fundamental library for scientific computing in Python, sometimes developers encounter warnings that can be perplexing. One such warning is the NonCContiguousWarning, which indicates that an operation has created a non-contiguous subset of an array. This tutorial discusses the reasons behind such warnings and provides practical solutions for them.

Understanding Non-Contiguous Arrays

Arrays in NumPy can be either contiguous or non-contiguous in memory. Contiguous arrays have evenly spaced elements in memory, facilitating efficient operations. Non-contiguous arrays, however, have gaps or strides between elements, which can lead to inefficiencies in certain operations. This disparity usually arises when creating a slice of an array.

Solutions to Solve NumPy NonCContiguousWarning

Solution 1: Use copy()

Creating a separate, contiguous array from a non-contiguous one can be done using the copy() method.

1. Identify the operation that triggers the warning.
2. After the slice or strided operation, chain the copy() method.

Example:

import numpy as np
# Example code that may cause a NonCContiguousWarning
original_array = np.arange(10)[::2]
contiguous_array = original_array.copy()

Note: Using copy() can solve the problem but at the cost of increased memory usage as a separate array is created.

Solution 2: Utilize Contiguous Slicing

Modify your slicing operations to preserve the contiguity of the array.

1. Review your slicing operations.
2. Make slicing operations contiguous when possible.

Example:

import numpy as np
original_array = np.arange(10)
even_indices = np.arange(0, 10, 2)
contiguous_array = original_array[even_indices]

Absolute indexing using an array of indices usually returns a contiguous array; hence, it avoids the warning and can improve performance.

Solution 3: Rely on built-in NumPy Functions

Some NumPy functions can output contiguous arrays even when working with non-contiguous data.

• Discover built-in functions that can handle the operation.
• Replace custom strided slices with these functions when appropriate.

Example:

import numpy as np
original_array = np.arange(10)
# Instead of a strided slice like original_array[::2], use np.take
contiguous_array = np.take(original_array, np.arange(0, 10, 2))

Note: This may not always be available for all types of operations but is beneficial when applicable as it typically retains the benefits of vectorized operations.

Conclusion

In conclusion, the NonCContiguousWarning in NumPy emphasizes the need to optimize array contiguity for performance gains. The solutions detailed in this tutorial help overcome this warning by creating contiguous arrays explicitly, modifying slicing operations, and using proper NumPy functions. While there are trade-offs in memory usage and performance depending on the solution chosen, understanding the nature of the problem is key to writing efficient numerical computations with NumPy.