Hi all,
A number of core projects (NumPy, SciPy, Matplotlib, Pandas, scikit-learn)
got together and put in a proposal to NSF for a large 5 year grant, and it
was unfortunately just rejected. We now published the proposal, which may
be of interest:
Hi Ralf,
The rejection is disappointing, for sure. Some good ammo for next time
might be the recommendations in this report from the US National
Academies of Science, Engineering, and Medicine:
http://sites.nationalacademies.org/SSB/CurrentProjects/SSB_178892
Looks like a good fit. Thanks.
On Thu, Apr 18, 2019 at 11:17 AM Eric Wieser
wrote:
> One option here would be to use masked arrays:
>
> arr = np.ma.zeros(3, dtype=bool)
> arr[0] = True
> arr[1] = False
> arr[2] = np.ma.masked
>
> giving
>
> masked_array(data=[True, False, --],
>
On Sun, Apr 14, 2019 at 6:42 PM Ralf Gommers wrote:
>
>
> On Tue, Apr 2, 2019 at 10:45 AM Ralf Gommers
> wrote:
>
>> Hi all,
>>
>> NumFOCUS has applied as an umbrella org for the inaugural Google Summer
>> of Docs, and we're participating. We need 1-2 ideas and work them out very
>> well (ideas
Very much second Joe's recommendations - especially trying NASA - which has
an amazing track record of open data also in astronomy (and a history of
open source analysis tools, as well as the "Astrophysics Data System").
-- Marten
___
NumPy-Discussion
Great, thanks Evgeni. And no worries about time, we have enough mentors who
volunteered, just need a backup admin name for the application.
Cheers,
Ralf
On Thu, Apr 18, 2019 at 11:19 PM Evgeni Burovski
wrote:
> Hi Ralf,
>
> If all this needs is a name, you can add mine. How much time I can
Hi Ralf,
If all this needs is a name, you can add mine. How much time I can put into
it though is... not much, I'm afraid.
Cheers,
Evgeni
чт, 18 апр. 2019 г., 23:51 Ralf Gommers ralf.gomm...@gmail.com:
>
>
> On Sun, Apr 14, 2019 at 6:42 PM Ralf Gommers
> wrote:
>
>>
>>
>> On Tue, Apr 2, 2019
Hi Stuart,
On Thu, 18 Apr 2019 09:12:31 -0700, Stuart Reynolds wrote:
> Is there an efficient way to represent bool arrays with null entries?
You can use the bool dtype:
In [5]: x = np.array([True, False, True])
Thanks to the work of Kevin Sheppard, Robert Kern and others, the branch
to merge randomgen https://github.com/bashtage/randomgen into numpy is
ready for final review.
The branch is here https://github.com/numpy/numpy/pull/13163. It is
fully backward compatible: numpy.random.mtrand,
Is there an efficient way to represent bool arrays with null entries?
Tools like pandas push you very hard into 64 bit float representations -
64 bits where 3 will suffice is neither efficient for memory, nor
(consequently), speed.
What I’m hoping for is that there’s a structure that is
Hi Ralf,
I'm sorry to hear the proposal did not pass the first round, but, having
looked at it briefly (about as much time as I would have spent had I been
on the panel), I have to admit I am not surprised: it is nice but nice is
not enough for a competition like this.
Compared to what will have
Thanks. I’m aware of bool arrays.
I think the tricky part of what I’m looking for is NULLability and
interoperability with code the deals with billable data (float arrays).
Currently the options seem to be float arrays, or custom operations that
carry (unabstracted) categorical array data
One option here would be to use masked arrays:
arr = np.ma.zeros(3, dtype=bool)
arr[0] = True
arr[1] = False
arr[2] = np.ma.masked
giving
masked_array(data=[True, False, --],
mask=[False, False, True],
fill_value=True)
On Thu, 18 Apr 2019 at 10:51, Stuart Reynolds wrote:
Matti, Kevin and Robert -- thanks for putting this together! I am very
excited about these long awaited improvements to numpy.random.
I have a number of concerns about the user facing API, starting with the
names "Random Generator" and "Base Random Number Generator," which I
suspect will be a
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