sliding-window | unidimensional chunk allocation / free system | GPU library

Recently I came across some code that extracted (sliding-window style) a number of square patches from an RGB image (or set of them) of shape N x B x H x W. They did this as follows:

Proceeding from one of my earlier posts, I noticed that the operation np.add.at(A, indices, B) is a lot slower than A[indices] += B.

In Spring cloud Gateway request timeout for path in not working as expected.

I tried to specify global timeout, but its not working as expected.

I am trying to call a microservice A. In A microservice I have added an delay to 5 sec.

Now in path if I add response timeout as more than 5s, it should wait for the response, but currently circuit breaker is opened and currently its returning the fallback message in 1000ms

I have configured resilience4j circuit breaker in gateway.

I am not able to understand why response timeout for Path and global timeout not working.

application.yml

I have a 3D NumPy array a of shape (2, 9, 9) like this one:

In the following program, I am trying to find the maximum in window size of k in an array of length n. For reference, the question is from LeetCode.

For example, if the array is [2 5 3 1 2] and window size is 3, then I have windows as [2 5 3], [5 3 1], [3 1 2] and corresponding maximums are 5, 5, 3.

I am using std::deque for this purpose but it is giving runtime error, I don't think any pointer invalidations are happening, I cannot find any. Please help me, I am stuck for a long time on this.

Here's my code:

I tried solving Sliding window maximum using deque problem on Hackerrank(Deque-stl). I followed the algorithm given on this link. I didn't want to copy the solution so I tried coding my own solution. But my code is giving me "segmentation fault" and I don't understand what is wrong with my code. Can anyone please explain the fault in my code?

I need to produce a sliding window of millions of lines and to calculate the median of column 3. My data looks like this with column 1 always being the same, column 2 equaling the line number and column 3 being the information that I need the median for:

Spent a while this morning looking for a generalized question to point duplicates to for questions about as_strided and/or how to make generalized window functions. There seem to be a lot of questions on how to (safely) create patches, sliding windows, rolling windows, tiles, or views onto an array for machine learning, convolution, image processing and/or numerical integration.

I'm looking for a generalized function that can accept a window, step and axis parameter and return an as_strided view for over arbitrary dimensions. I will give my answer below, but I'm interested if anyone can make a more efficient method, as I'm not sure using np.squeeze() is the best method, I'm not sure my assert statements make the function safe enough to write to the resulting view, and I'm not sure how to handle the edge case of axis not being in ascending order.

DUE DILIGENCE

The most generalized function I can find is sklearn.feature_extraction.image.extract_patches written by @eickenberg (as well as the apparently equivalent skimage.util.view_as_windows), but those are not well documented on the net, and can't do windows over fewer axes than there are in the original array (for example, this question asks for a window of a certain size over just one axis). Also often questions want a numpy only answer.

@Divakar created a generalized numpy function for 1-d inputs here, but higher-dimension inputs require a bit more care. I've made a bare bones 2D window over 3d input method, but it's not very extensible.

I have adapted the sliding window generator function here (https://scipher.wordpress.com/2010/12/02/simple-sliding-window-iterator-in-python/) for my needs. It is my first experience with generator functions so I did a lot of background reading. Given my (still) limited experience, I'm soliciting advice for the following problem:

The code below does this: I use the sliding-window function to iterate over a 5,500-character string (DNA sequence with ~5,500 bp) in roughly 250-char windows with a step size of 1. For each chunk, I compare its GC content to a line in a 750-line file. (GC content is the percentage of the string elements that equal G or C).

However, for my downstream use I would really like to loop over these chunks randomly. From my Stack Overflow searching, I understand that it is not possible to shuffle a generator object, and that I cannot shuffle the windows inside the function because it actually searches the windows one at a time, returning to the function for the next chunk because of that "yield". (Please correct me if I've misunderstood).

Currently, my code looks something like this (using the generator function in the link above, of course):