If A is a dense ndarray, and B is a sparse matrix. And I do:

    A bool_op B

The ndarray calls B.__bool__() for some reason, and I have not figured out how to set __bool__ to work appropriately for all bool ops. In my last PR I set __bool__ to raise a ValueError, like ndarrays do. And this is ok for A == B and A != B. In these cases, the sparse matrix B handles the operation, like it should. With __bool__ set to True or False, the ndarray tries to handle the operation and fails.

But with A < B, A > B, the ValueError in bool is raised. So I'm not sure what to do.

Any suggestions? I'm currently looking for the rich comparison implementation for ndarrays. 

Dear experts,

1. For incorrect arguments of a distribution, rvs() raises a ValueError, but
pdf(), pmf() and their relatives return a magic badarg value:

>>> from scipy.stats import norm
>>> norm._argcheck(0, -1)
False
>>> norm.pdf(1, loc=0, scale=-1)
nan
>>> norm.rvs(loc=0, scale=-1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/br/virtualenvs/scipy-dev/local/lib/python2.7/site-packages/scipy/stats/distributions.py", line 617, in rvs
    raise ValueError("Domain error in arguments.")
ValueError: Domain error in arguments.

Is there the rationale behind this? I'd naively expect a pdf to raise an error as well --- or is there a use case where the current behavior is preferrable?


2. For frozen distributions, is there a reason not to check the arguments at freezing time:
>>> from scipy.stats import norm
>>> n = norm(0, -1)
# ...long long time afterwards...
>>> n.pdf([1, 2, 3])
array([ nan,  nan,  nan])

Thanks,

Zhenya

All of these seem to be in sparse, some are depreciation warnings, like:

DeprecationWarning: Implicitly casting between incompatible kinds. In a future numpy release, this will raise an error. Use casting="unsafe" if this is intentional.

and the others are TypeErrors:

But for operations like ==, which should return True for all the zero entries, the binopt routines do not apply the binopt when both elements are zero. So I think the best way to implement == would just be by negating the != result in python. Since it will return a very dense matrix, it will be slow anyway.

What sort of syntax would be best for implementing negation? It should only be applicable if the sparse matrix is dtype=bool. Should it be a method, or a function in sparse?

Thanks

Thoughts?

While fixing some screw ups in a pull request, I rebased my feature branch on the latest master.  Can I push this back to the pull request on github?  To test, I tried pushing it with the '-n' option, and got:

To https://WarrenWeckesser <at> github.com/WarrenWeckesser/scipy.git
 ! [rejected]        bug-stats-pareto-skewness -> bug-stats-pareto-skewness (non-fast-forward)
error: failed to push some refs to 'https://WarrenWeckesser <at> github.com/WarrenWeckesser/scipy.git'
hint: Updates were rejected because the tip of your current branch is behind
hint: its remote counterpart. Merge the remote changes (e.g. 'git pull')
hint: before pushing again.
hint: See the 'Note about fast-forwards' in 'git push --help' for details.

Asked on #scipy, but nobody responded.

Many thanks,

Kenny