The autocorrelation is important to show that it's transformation to D-K plot will always give you the D-K affect for independent variables.

However, the focus on autocorrelation is not very illuminating. We can explain the behaviors found quite easily:

- If everyone's self-assessment score are (uniformally) random guesses, then the average self-assessment score for any quantile is 50%. Then of course those of lower quantile (less skilled) are overestimating.

- If self-assessment score vs actual score are dependent proportionally, then the average of each quantile is always at least it's quantile value. This is the D-K effect, which is weaker as the correlation grows.

-The opposite is true for disproportional relation.

So, the D-K plot is extremely sensitive to correlations and can easily over-exaggerate the weakest of correlations.

The more common experience with autocorrelations are with time series, but what the author said is correct even in that context. A time series autocorrelation relates the same time series function at different times. At the simplest you plot the arrays X vs X where X[i] = f(t[i]). You then may complicate it further by some transformation g(X) vs X (e.g., moving average).

Did you guys considered existing standards when you chose what to use for representing workflow definitions before choosing OpenFlow? For example, Common Workflow Language

We did and started with an existing standard but quickly, trying to fit to the standard was more complex than rolling our own.

Agreed we just created yet one more standard but the bit about the input transforms being full javascript expressions or the way we encoded suspend steps was impossible to retrofit.

Hover your mouse over those and you should get the absolute date. Some if not many are using time tags.

Amazing, how have I never seen this. Thank you !

It's not popular because you're mostly hearing from the science community who want more features in their array (vector/matrix/tensors).

Why would you want to use C-like arrays in Python anyways?

It is not deprecated. Calling setup.py directly is (i.e., `python setup.py sdist`)

Modern way of building a package is to use a build frontend. Most notably is the PyPA's build package.

    python -m build .

Most project managers like poetry has this built-in with it's `poetry build` command.

In this particular case. OP had a lot of knowledge which served as a crutch in this journey. The less you are aware of how setuptools used to work, the better.

Here's a packaging guide that answers most of your questions.

https://learn.scientific-python.org/development/guides/packa...

As a TLDR, you have many options 3rd party build tool (aka build backends). Each build tool have different *static* ways to specify compile options that is native to the language or generic (e.g., CMakeList,s Cargo.toml, 3rd party YAML. When it comes to dynamically specifying your extensions, setuptools is still the only option.

In diataxis, a tutorial serves your education and a how-to guide serves your work.

Following this standard, a tutorial may contain a simpler or more contrived example; it may contain things you'd never do in production.

Why? What's your goal visiting the W3C homepage?

It should be to learn who they are and what else the website provides. You're not there to read it's entire homepage. You're there to navigate.

The pages that need to be dense, like the standards document, are.