D extensions to python, inline in an ipython/jupyter notebook

[Laeeth Isharc via Digitalmars-d-announce]

On Monday, 15 June 2015 at 17:28:52 UTC, John Colvin wrote:
> On Monday, 15 June 2015 at 17:11:26 UTC, Laeeth Isharc wrote:
>> On Monday, 15 June 2015 at 06:51:44 UTC, John Colvin wrote:
>>> [...]
>>
>> Yes - I had noticed same, but don't yet have the experience 
>> (and don't have available time for now) to do much about it.  
>> Was looking at Facebook torch to see how that fitted with 
>> bokeh and inotebook, but glad you actually did something to 
>> make it happen.
>
> I haven't looked at torch much, is it up to the hype?

To be direct: I don't know, as I haven't used it.  I was just 
interested in seeing how it fitted with ipython and bokeh in 
particular.

I liked your talk at dconf.  Looks like progress is being made on 
planting the seeds of a matrix implementation and computation 
library on top.  Having a way to use D code to explore data 
within an approach of rapid iteration will be useful too, and I 
think your work is a great start on this (seems like its value as 
it is is very much greater than you modestly suggest, and also I 
am amazed by how short the code is).

At the moment I am using a combination of the python interface to 
Bokeh and D code to do the work.  It would be great to port the 
bokeh server side stuff to D - I have made a start, but I haven't 
the space for a concerted effort.  The funny thing is there is 
nothing to the server side - all you do is have a shallow layer 
that translates the cumulative state from API calls to JSON; the 
hard work of drawing stuff is done in Javascript on the client.

Whether it's Bokeh or not, having some kind of visualisation 
solution that fits well with iteratively exploring data in D will 
be nice.  (What you have done means it's already enough to be 
very usable, but there may be benefits to moving the charting 
itself to D).

Perhaps not for scientific applications, but for finance: 
reporting is also something that is an important and rising theme 
in use cases.  Not just for accounting, but for example to 
understand portfolio risk in a richer way.  You have for a long 
time been able to hook up reportlab with D code using PyD, but 
this element of rapid iteration was not so easy - small frictions 
have large consequences when you don't have a big team.

So in any case, I really appreciate your work.

One more thing - any thoughts on best spreadsheet like interface 
either purely on browser or within ipython to display results and 
allow user to enter parameters?  My friend, Giles Thomas, wrote 
Resolver but that's too big and aimed at something different, and 
I don't know if it is being maintained properly.



Laeeth