What are the Benefits of Efficient Moving Average Filtering Using Strides and when to Use it?

Efficient Moving Average Filtering Using Strides

In this article, we address the use of strides for constructing an efficient moving average filter. Strides provide a means to create views of existing arrays, allowing for optimized calculations without modifying the original data.

Existing Approach

The existing approach utilizes strides to generate an array representing a moving filter kernel. This kernel is then rolled vertically to capture the necessary values, and their sum is computed to arrive at the average.

Improved Approach

The improved approach employs "fancy" striding techniques to obtain the 9 values or aggregate of the kernel elements directly, delivering a more comprehensive solution. This can be implemented for N-dimensional arrays.

Memory Considerations

While strides enable efficient single-axis moving window operations, it is crucial to note potential memory implications when working with multidimensional arrays. Intermediate steps involving copying the array can lead to a significant increase in memory usage.

Specialized Functions

When dealing with multidimensional moving windows, specialized functions like those in scipy.ndimage are recommended over striding tricks. These functions offer efficient boundary handling, perform computations in-place, and excel in performance.

Demonstration

The code snippet below illustrates the rolling window function for a specific filter size:

<code class="python">filtsize = (3, 3)
a = np.zeros((10,10), dtype=np.float)
a[5:7,5] = 1

b = rolling_window(a, filtsize)
blurred = b.mean(axis=-1).mean(axis=-1)</code>

Conclusion

While strides offer a convenient approach for single-axis moving window operations, they are less effective for multidimensional arrays. Specialized functions like scipy.ndimage provide a more efficient and versatile solution for such scenarios.

The above is the detailed content of What are the Benefits of Efficient Moving Average Filtering Using Strides and when to Use it?. For more information, please follow other related articles on the PHP Chinese website!