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> Does D have code for: [...]

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.

>Are those all in standard libraries, fully documented, backed by 60 year old, fully debugge code (LAPACK, etc) ... yes, my np.mean() might be slower than your map(). I almost always don't care.

Hmm ... LAPACK itself is only 23 years old (1992). Its was implemented to be a faster replacement of the "standard" LINPACK library, by effectively exploiting the caches on modern processors.

For most serious users of numerical computing, performance is extremely important.

Also, I think you might be putting far too much faith in an arbitrary assortment of Python libraries. If you think that NumPy is just a thin wrapper on LAPACK, and you are using it in mission or life critical operations, then I think you have some code vetting to do.

Even if some of your chosen libraries do use LAPACK, and even if there is some code in LAPACK that is 60 years old, that does not suddenly mean that all of your chosen Python libraries (and your code on top of them) is all correct. Most serious users of many of the routines that you mention (e.g. "machine learning" and "financial calculations") implement and test these routines themselves in order to ensure their correctness. In very serious model vetting operations, it would be customary to even invest in two different independent implementations of the same routine.

> I reliably email to anyone across the world and they can immediately run and modify my code because it is such a standard?

I don't think "reliable" and "emailing scripts" normally go together. It would be more customary to use a source code control system in a high reliability environment. Personally I have found reproducing Python installations to be very tricky and I would take extreme care in any critical operations that assume two different Python installations produce the exactly the same results (high reliability, like some of the 60+year old codes run by NASA, would even require bug for bug reproducibility).

"Python/Numpy/Scipy/Pandas/Matplotlib" are not the last word on anything, theyt are not standardized as you imply, and they leave vast room for improvement in all dimensions (performance, correctness, reliability, productivity, etc).

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).

What would one need to use c or c++ to write a julia package? It's as fast as native code so no need to use multiple languages.

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 :)

A lot of that is up and coming.

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?

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

> [+] Cholesky Decomposition: Trivial to implement

How is it that you can judge Cholesky decomposition to be trivial to implement, while being unfamiliar with QR decomposition?

Cholesky decomposition is not trivial to implement well.

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

I had a similar experience to what's happening here myself. I worked on a project that had some very specific requirements for a high performance simulation (described by a differential equation). I worked on a very specialized piece of code for a long time to optimize this differential equation solver, eventually writing a JIT compiler to generate specialized code for each simulation.

I had a D advocate come along and rather vigorously argue that D can solve this problem, without really understanding it, and missing some key requirements.

This sort of comes off the same way, "a numpy replacement". numpy is big and does a lot of stuff. Like Cholesky decomposition, many of these things have trivial implementations, and then they have good implementations. The trivial implementations will take a few hours to write. The good implementations will take a few months, or even longer. Sometimes "not good" means slow, sometimes "not good" means numerical stability issues, etc. If you don't have the right background, these things may not even be obvious.

This is a bit of a pattern with D folks. Several times (this, my previous experience, and others), I've gotten the impression that a D fan comes along, invests a bit of time (a few weeks, months) in a particular domain, and starts making comparisons with other tools that they only scratch the surface of. It can rub people the wrong way.

edit: The symbolic expression reference in this post is another example. Computer algebra is an incredibly difficult thing to implement. I don't know of anything that is harder to implement well than a computer algebra system. Yet, you link a single file under 1000 lines as partially checking the box in your list for symbolic expression manipulation. D might be a highly productive, but it's not that productive.

> This is a bit of a pattern with D folks

Nothing D specific here. That's the pattern for every language, be it Haskell, Go, Common Lisp, JavaScript or Java ...

I should have provided a key. Where I listed a +, I basically meant that there was either work underway or it was something that could be done yourself without hindering a project too much. I did not intend to imply that symbolic expression was a solved problem, or anything of that sort.

As for QR decomposition, I am actually rather familiar with it, my brain simply decided to fart on the symbols QR without being followed by decomposition.

For Cholesky Decomposition, I'm sticking with "trivial to implement", and I'll even throw in "trivial to implement well": https://github.com/numpy/numpy/blob/v1.10.1/numpy/linalg/lin...

You asked if D had code for a list of math capabilities that you use on a regular basis. My answer says "In many cases, no, but there's work being done toward that goal." No one is saying "we have a feature-complete math library!". There's much more needed than what you listed, and even the things you listed have many items with zero work done toward them. I am not attempting to say "D can do all these things!", and I don't think the author of the article was either.