Using D and std.ndslice as a Numpy Replacement

He covered this, specifically out that Numpy has an advantage in the number of libraries and other resources available for it.

> because everyone else in the world knows and uses them;

No, everyone working in some specialized niches does. And those niches don't even scratch the surface of the number of people who regularly do simple operations on multi-dimensional arrays but who don't need most of what you list.

And sometimes people even in your niche that does depend on the other stuff run into things that are just way too slow, that they have to rewrite in a lower level language.

There are plenty of people for whom this will be very useful without having all the support infrastructure around Numpy in place.

> I can't think of any reason I'd ever reach for D.

D is for people who wants a "C++ done right", where it is implicit that you want a C-like language, and share ideals of C++ such as to whatever extent possible not paying for what you don't use.

Specifically in this case, you'd reach for D if you need multi-dimensional arrays and either prefer a C++-like language over Python or your Python code is too slow and you prefer to avoid a lower level language like C.

That may not be for you, and that's fine. Just like Numpy isn't nearly universal for numerical computing.

> Yes, my np.mean() might be slower than your map(). I almost always don't care.

I find this an amusing objection... I did my MSc thesis on methods for reducing error rates in OCR, and included methods that used various nearest-neighbour variations and statistical methods on bitmap slices and similar.

I did it mainly in Ruby (with some inline C where absolutely necessary for performance - if I remember correctly it was about a dozen lines of code in C) because I cared far more about readable code than fast code, and Python would in my eyes have been a double loss: not the speed of C, yet still ugly and annoying to work with in my opinion.

Seeing the D code examples, I might very well have picked D for it today.

https://github.com/DlangScience

[x] Plotting: http://code.dlang.org/packages/plot2kill

[+] Optimization: Nothing directly for the purpose, but the intermediate steps are done. And a little bit of work from http://code.dlang.org/packages/atmosphere

[x] Probability Distributions: http://dlangscience.github.io/dstats/api/dstats/random.html and http://dlang.org/phobos/std_mathspecial.html

[-] Machine Learning: Not that I'm aware of, but I vaguely recall some work being done on this

[-] Financial calculations: I couldn't find anything, but I'd be surprised if this wasn't implemented already.

[x] Masked Arrays: Accomplished via language features in combination with ndslice

[x] Structured Arrays: Absolutely. Standard library support as well as a number of 3rd-party libs can do this.

[-] SVD

[?] QR: Afraid I'm not sure what you're referring to.

[+] Cholesky Decomposition: Trivial to implement

[x] Eigenvalues/Eigenvectors: Yes. SciD provides Eigens as well as two other third party packages. https://dlangscience.github.io/scid/api/scid/linalg/eigenval...

[x] Least Squares (linear): Trivial to implement, but also implemented by a few of the 2-d & 3-d graphics libraries.

[x] Least Squares (nonlinear): Nothing, but we both know that this is easily done by applying a function to your independent variable to linearize it in many cases.

[-] Levenberg Marquardt

[x] Matrix Inverse: Provided by SciD

[?] pseudoinverses: I'm afraid my math background doesn't cover that, and I couldn't find anything with a similar naming in the package repository

[x] Integration: SciD provides integration

[-] Range Kutta

[-] Interpolation: Sort of. A few of the graphics libraries have this, but it's asking a bit much to include a graphics library to do interpolation.

[-] Bsplines

[-] fft convoles

[?] Multidimensional images

[?] KDTrees: Probably in one of the 3d graphics libs

[+] Symbolic equation solvers: Some of this was done for Tango back in the day, and it has been ported, but the project looks fairly dead. https://github.com/opticron/libdmathexpr/blob/master/source/...

[x] Merge/join of data sets: Can be done efficiently and easily with core language features

Additionally, anything written for C can be trivially wrapped and used in D. An example is http://scimath.com/, which completes much of what you mentioned.

Are they in standard libraries? SciD needs more time Fully Documented? D tends to encourage good API documentation, and the algorithms are largely ports of "69 year old, fully debugged code".

I think you're point about "reliably email to anyone across the world" is an overstatement. Python is popular, not ubiquitous. D is obviously much less popular. Can anyone run the code? Yes. For them to modify it, they need to learn the language.

You might not care about how long computations take, but I know back at my old university there were dozens of researchers complaining about the resources that had to be spent on supercomputer time, and the annoying amount of time that many calculations involving things like molecular configurations can take. Speeding this up saves money and makes the research less painful.

"No one is ever going to use D for serious number crunching without the infrastructure in place" - Yeah, that's totally true. The infrastructure is a WIP. There's nothing wrong with that.

Your post comes across to me as being rather cynical - but why not be supportive of the good work that's being done?

If you don't like D and don't want to use it move on to the next item on the front page.

I mean this is a terrible benchmark. Comparing to pure python, the numpy code is about 4-5x faster on my machine, whereas in reasonable real world benchmarks, numpy is hundreds of times faster than pure python.

For reference my benchmark was

    %timeit [sum(row)/len(row) for row in mat]

These results are as valuable as the ones pypy gave me:

    pypy -mtimeit -s "[sum(row)/len(row) for row in [[range(100000)[i*j] for i in range(100)] for j in range(1000)]]"  
    1000000000 loops, best of 3: 0.00101 usec per loop

In other words, because the code returns a python array of length 1000, what you're benchmarking is python's speed of array instantiation vs. Ds mathematical speed. Its obvious that D will win. The only fair way to benchmark the mathematics here is if you compare the D implementation of O(n) algorithm that returns a single value, or an O(n^2) algorithm that returns a 1d array or single value etc. Otherwise the python overhead of creating O(n) objects will overshadow the actual mathematical calculations on those O(n) objects.