[SciPy-Dev] Custom __repr__?

[Andrew Nelson]

On Fri, 14 May 2021 at 11:40, Robert Kern <robert.kern at gmail.com> wrote:

>
> You'll run into more such objects. Having a round-trippable `__repr__()`
> isn't all that common. Even `ndarray` will trip you up
> with its summarization. And that's not even considering what kind of
> imports you need to have done in order for even the eval()able
> representations to actually evaluate. You will probably need to incorporate
> a subsystem in your code generation to get eval()able string
> representations of the objects you care about.
>
You can often bound the effort a bit more than what the class author would
> need to do since you likely only deal with a subset of functionality (do
> you need eval()able datetime64 arrays? Probably not. Do you
> need array parameter arguments to the distributions or just scalars?
> Maybe). You'll need that to make sure the imports are there in any case.
>

I have __repr__ for all the major classes I need to care about, and I know
which imports I need for the code generation (they're pretty much fixed). I
just use the name of the object in the __repr__ (norm), rather than the
name of the object with the full import path (scipy.stats.norm). In my case
I do need to use 'simple' numpy arrays in the code generation, but they're
directly loaded from datafiles rather than having a __repr__.  I don't need
datetime64 arrays.

My requirements would be to have a __repr__ for frozen rv_continuous
distributions with scalar shape parameters. I hadn't appreciated before
that rv_continuous could be frozen with scale/loc/shape parameters that
were array rather than scalar. I agree that it's a major hassle to get a
__repr__ of a numpy array. Perhaps Pamphiles suggestion of
```
f"{dist.dist.name}(*{dist.args!r}, **{dist.kwds!r})"
```

would work as a good general __repr__, with more specific implementations
as required. This would have the specific proviso that roundtripping would
work for scalar cases, but not for array scale/loc/shape cases.
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