D has integration with Python/Matplotlib too =P http://pyd.readthedocs.org http://d.readthedocs.org/en/latest/examples.html#plotting-wi...

It looks like you need to copy your D array to a newly-allocated Numpy ndarray before you can pass it to Python. So there's no binary PyArrayObject interoperability between D & Python (right?). Copying large N-D arrays all the time sounds slow...

(That Matplotlib example uses the function `d_to_python_numpy_ndarray` in the PyD project, which I found defined here: https://github.com/ariovistus/pyd/blob/master/infrastructure... . It clearly allocates a new Numpy array: https://github.com/ariovistus/pyd/blob/master/infrastructure... )

Also, I couldn't find any examples of invoking D functions from Python. In fact, I could only find mentions on the D mailing list of people reporting that they couldn't get it to work: http://forum.dlang.org/post/[email protected]...

By compiling (transpiling) to C, Nim really does have an unfair advantage in the interoperability challenge...

ndslice was merged to DLang master repo today. It is not a problem to fix PyD. (compiling to C is crispy)

EDIT: Exposing-d-functions-to-python http://pyd.readthedocs.org/en/latest/functions.html#exposing...

Yes, Julia is amazing! In the same time, if you want to write a package for Julia you _may_ need to use C/C++. D is going to have integration with Julia in 2016 ;)

D already have good integration with Python. You may want to read this article http://d.readthedocs.org/en/latest/examples.html#plotting-wi... (it may be a little bit outdated).

Julia is really fast in 95% cases, but 5% still "make the weather". The pairwise summation is an example. I will post benchmarks D vs Julia next week ;)

I would say the biggest benefit of D is static typing. In Julia you can run a simulation and discover only after half an hour that you misspelled a function

Make sure you use the devect macro or parallel accelerator if you're going to use vector iced code :)