Gradually | experimental slide show plug-in using the canvas element

I'm trying to make a basic command line tool using command line arguments (starting simple and gradually building up). I am using Ruby and its OptionParser class to do this. I have the following code:

consider the following example: https://codesandbox.io/s/nuxt-playground-forked-rld4j?file=/pages/index.vue

I tried to make a minimal example that involves my general use case. That's the reason for the odd format of the data. Type 000 or 111 and you can see how it gradually searches through the data.

Basically it generates a lot of data (I actually want to have more than that) but you already notice a drop in performance. Now I thought I could start improving the performance by debouncing my watcher. You can see that in line 58 in the above example. It's commented out because. You can comment line 57 out and add the debouncing to see that it doesn't work.

Here's the code of the above example:

I am trying to incrementally build a rolling "minimum" column which gradually increases in value from a minimum value up to halfway between the initial minimum and maximum values, but by group, and over a number of days in a Pandas DataFrame. The maximum value should stay the same over time. Picture a control chart, where the upper bound remains a flat line, and the lower bound linearly rises up to end halfway between the initial min and max bounds.

Here is code that does what I want in vanilla Python (without the grouping).

I'm having difficulty applying the countpattern function from e1071 package. I aim to find binary patterns and count them. My data consists of a large matrix (1117200 elements, 9.6 MB) with 114 columns and 9800 rows. When applied the function, I keep receiving the following error message:

Error in matrix(0, 2^nvar, nvar) : invalid 'nrow' value (too large or NA)

I was testing the function increasing gradually the number of columns from my data, and it worked until use ~19 columns (just a small part of my 114 columns in total). More than that, it produced an Error.

So, the solution might be to find a more efficient function/algorithm than this function to find the binary patterns. However, before moving on, I wanted to ask if there is a way to contour this situation using the countpattern function?

Thanks for your time!

As requested by @slamballais, a data samples is presented as following,

data_sample <- rbind(c(1,1,1,0,1,0,1,1,0,1,0), c(1,0,0,1,1,1,9,1,0,0,1), c(1,0,0,0,0,1,0,1,1,0,0), c(0,1,1,0,0,0,0,0,1,1,1), c(1,1,1,0,0,1,1,0,1,1,0))

I have a Python 3.6 data processing task that involves pre-loading a large dict for looking up dates by ID for use in a subsequent step by a pool of sub-processes managed by the multiprocessing module. This process was eating up most if not all of the memory on the box, so one optimisation I applied was to 'intern' the string dates being stored in the dict. This reduced the memory footprint of the dict by several GBs as I expected it would, but it also had another unexpected effect.

Before applying interning, the sub-processes would gradually eat more and more memory as they executed, which I believe was down to them having to copy the dict gradually from global memory across to the sub-processes' individual allocated memory (this is running on Linux and so benefits from the copy-on-write behaviour of fork()). Even though I'm not updating the dict in the sub-processes, it looks like read-only access can still trigger copy-on-write through reference counting.

I was only expecting the interning to reduce the memory footprint of the dict, but in fact it stopped the memory usage gradually increasing over the sub-processes lifetime as well.

Here's a minimal example I was able to build that replicates the behaviour, although it requires a large file to load in and populate the dict with and a sufficient amount of repetition in the values to make sure that interning provides a benefit.

So I have been experimenting with adding darkmode to my website and it has been going very well so far, I have a 0.3s ease transition for the theme change, so the change isn't very abrupt. I used this JS code to change the color of the address bar in chrome for mobile

I am very new to the world of Python and have started to learn the coding gradually. I am actually trying to implement all my SAS codes in Python to see how they work. One of my code involves using the macros. The code looks something like this

As part of a load test in Gatling, I download a huge file (about 4GB).

When doing so, I can observe the memory usage of Gatling gradually increase, until it hits 2GB, when it stops the download until it times out.

As I don't care about the response body (as long as it's being downloaded), I'd like to discard it.

How is this possible?

I'm not sure a code example is useful, but this is the calling exec:

I'm quite new at this, but I'm attempting to create a fixed distance brush in P5 where the size of the brush gets bigger/wider overtime (using a timer)

This is what the code looks like right now