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I have defined a class where its __ge__ method returns an instance of itself, and whose __bool__ method is not allowed to be invoked (similar to a Pandas Series).

Why is X.__bool__ invoked during np.int8(0) <= x, but not for any of the other examples? Who is invoking it? I have read the Data Model docs but I haven’t found my answer there.

I am trying to dynamically select directory from the computer where files are stored. Currently there is a sas dataset named 'sample' stored under my win10 PC desktop.

I would like to print out the first 10 observations of the sample dataset I read into, but it is not working without showing any error.

I haven’t entirely given up on the idea of validators moonlighting as oracles for off-chain computation…based on this extensive discussion: https://gov.near.org/t/off-chain-computation-framework/1400/6

So far from studying Sputnik’s code, I have figured out the mechanics of how to upload a blob to a smart contract. Let's say that a blob represents a storage-less contract, having only stateless functions that act only on input to the function, and return those inputs modified.

Now I’m missing the piece of how Validators can download and execute the blob. As mentioned by Ilya in the link above, the NearSDK would be able to interpret the blob (if the blob is essentially a compiled contract), but it needs to be a modified version of the SDK...

Think of this like sandbox mode…blob cannot modify state of any other contract, but can read state (forget about the internet access part for now). Results of the blob execution are then fed back to a smart contract, where they have to match the results of every other validator who executed the blob. This can be done by hash comparison (rather than looping through the results individually), so it’s not an expensive comparison, especially because it’s all or nothing.

Question: how can a Validator download the blob and execute it via a sandboxed SDK, and post the result via the regular SDK to the blockchain? I am missing a lot of architectural context…and this is bringing me to the edge of giving on the idea. Please help prevent that from happening!

I have a task that requires functions to read, write and display information, from and to .txt files.
I have completed the fist part of the code.

I am struggling to figure out this part of the question:

1. Add an option to generate reports to the main menu of the application.
The menu for the admin user should now look like this:

I am trying to scrape the table on google colab from the following web page: https://247sports.com/college/penn-state/Sport/Football/AllTimeRecruits/

Below is the python script I am trying to use...

I am new to Couchbase and have been trying this for several hours. Have looked through the documentation but haven’t been able to locate this specific scenario, My bucket has documents:

I’m loving the expanded CSS support in SVG2. It’s great not having to rewrite attributes over and over. So I’ve been converting some code in a project from SVG attributes to CSS. Most of this has worked just fine.

When it comes to transforms, things can seem tricky if you are comfy with how CSS transforms work in HTML. (This is especially true for rotate() transformations, which is the focus of this question.) That’s because SVG doesn’t have the “automatic flow” that HTML does.

In other words, when you have a bunch of HTML elements, one after another, they will automatically lay themselves out according to the box model.

There is no such “automatic” or “default” layout in SVG. As a result, SVG transforms default to being calculated from the origin. (That’s 0,0 in user coordinates).

For most elements, there’s a simple solution: the awesome CSS property transform-box. In most cases, using the following CSS will allow you to transform SVG elements in pretty much the same way as HTML elements:

I have a very large dataset in SAS (> 6million rows). I'm trying to read that to R. For this purpose, I'm using "read_sas" from the "haven" library in R.

However, due to its extremely large size, I'd like to split the data into subsets (e.g., 12 subsets each having 500000 rows), and then read each subset into R. I was wondering if there is any possible way to address this issue. Any input is highly appreciated!

Our team intends to use JavaScript library observablehq/plot from an app written in C# and running on .NET 5. As a proof of concept, we were able to successfully use that library running in a separate process on Node.js. The downside of this approach is that we would need to implement all data serialization and inter-process communication ourselves, and we would need to bundle Node.js in our app installer. We are looking for an alternative approach that would not require Node.js, but instead would directly host V8 JavaScript engine in our .NET app. Currently, we are considering using ClearScript package for that. While we were able to successfully run simple JavaScript code using it, we discovered that observablehq/plot and its dependencies use some APIs that are present in Node.js, but are apparently unavailable in ClearScript out-of-the-box (it seems that Node.js implements some of those APIs in JavaScript, and some in C++). Making those implementations available to ClearScript running in .NET app looks like a significant task whose cost I cannot estimate at this point.

I would like to ask if anyone had to solve the same or similar problem, and what approach would you recommend? Is there an easy way to make all necessary APIs available to observablehq/plot running on ClearScript? Should I consider hosting Chromium/Edge browser engine in the .NET app instead? I haven’t yet tried this, but I assume it is possible because my understanding (correct me if I’m wrong) is that WebView2 control can be used in WPF apps running on .NET, and it is implemented using Chromium and V8 JavaScript engine. Is there any other approach you can recommend?

Update: The app GUI is built on Avalonia and SkiaSharp. It is possible that eventually we will choose another charting library, perhaps, one not written in JavaScript. But my task at hand is specifically to investigate whether and how we can use JavaScript libraries in our app, taking observablehq/plot as an example, and investigate its features and performance characteristics.