Thanks for the feedback, everybody! Appreciated.

It is definitely a good idea to guard that line "What should really be in MPL, and what not".

I remember my situation as a user, when I first had to plot those ellipses.

I thought "MPL gives me boxplots and histograms - I'll have a look whether I can also get a confidence ellipse, here."

Just like with scatter() or hist() I expected to put data in and get back a plot.

Not analysis, but mere visualisation.

And this basically is what confidence_ellipse() does. (Maybe I should then not return the matrix with the pearson coefficients, for clarity.)

In the function, numpy.cov() is the only thing that looks a bit like data analysis, from my perspective.

The rest of the code is addressing that non-trivial geometric problem to get the ellipse right.

And I think that MPL is also supposed to help users with geometrical problems, given it is about standard plotting functionality like histograms, boxplots and confidence ellipses?

hist() for instance uses numpy.histogram() to estimate the right number of bins. But only to get the plot right, not for analysis. And hist() is not to be considered data-aware, particularly.

The statsmodels module on the other hand has a lot of data-awareness and no geometry/plot awareness.

confidence_ellipse() would not really fit in there, I think.

Please don't get me wrong: I am not arguing. I just want to show you where my idea came from: "Hey this would be handy to have in MPL".

The MPS gallery is a great place to show the "howto", though. For the time being, I can submit an example there.

If the devel-community changes her mind, she knows how to find me :-)

Kind regards, Carsten

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