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Ah, just the right time to publish about numpy - right when everyone is moving over to Julia because of numpy's warts.
Interestingly, one of the Julia creators is one of the named reviewers.
Earlier quoted context omitted.
Theoretically Julia is better for scientific computing, the only issue is its package ecosystem isn't as mature as Python's. But it's growing incredibly fast and there have already been libraries available for a few years that would be really impractical to write and maintain at such a level in C++ for Python. I assume that for science at least Julia will catch on a lot.
> Theoretically Julia is better for scientific computing, the only issue is its package ecosystem isn't as mature as Python's. That's not a small issue. The ecosystem is probably the reason people choose NumPy over MATLAB, for example. NumPy is not inherently superior to MATLAB, and most academicians that adopted NumPy in the 2000's already had a MATLAB license, so cost was not a concern either.
Don't underestimate the impact this has on getting funding or even just tenure/etc recognition for working on numpy. I'm in industry these days, but coming from the academic side, it's _really_ hard to get recognized for building the underlying infrastructure that tons of people use. I've built and maintained libraries that are used in a ton of publications, but was always told my work was "utterly and completely use…
> my work was "utterly and completely useless I can never understand the arrogance of folks who would say something like this
It’s hard to tell why people behave like that. I presume it’s because many people have a great fear of originality and require social validation.
For some reason this struck me as inappropriate for the outlet. It's a nice piece as an introduction to array programming with numpy, but seemed out of place to me.
Yeah, this is a review article. Nature is not the right spot for it.
For some reason this struck me as inappropriate for the outlet. It's a nice piece as an introduction to array programming with numpy, but seemed out of place to me.
While NumPy may be old news to the programming community, I have experienced a surge of programming capability into wet bio labs that was not there a decade ago. This kind of article heralds its adoption to mainstream biology. It's now well known enough to interest biologists in general!
At first I was like, NumPy's fame -- owing to the rise of Python in scientific computing and data science circles -- has gone far beyond that of most things ever published in academic journals, so this hardly seems necessary.
But I see your point: Nature has always been held in high regard among natural scientists, and though most recently minted natural scientists have at least a passing familiarity with Python, the generations of scientists before them probably don't. Nature at least has enough of a cachet to grab their attention.
Plus having a publication in Nature does open doors. Travis Oliphant and some of the already-famous co-authors probably don't need these doors opened, but I'm sure others on that list would benefit.
Earlier quoted context omitted.
> Theoretically Julia is better for scientific computing, the only issue is its package ecosystem isn't as mature as Python's. That's not a small issue. The ecosystem is probably the reason people choose NumPy over MATLAB, for example. NumPy is not inherently superior to MATLAB, and most academicians that adopted NumPy in the 2000's already had a MATLAB license, so cost was not a concern either.
Well that could have been said (and was said) about Numpy/Scipy when it started, "oh R has so many more packages, what numpy can do I can do in MATLAB ...", yet here we are.
Don't underestimate the impact this has on getting funding or even just tenure/etc recognition for working on numpy. I'm in industry these days, but coming from the academic side, it's _really_ hard to get recognized for building the underlying infrastructure that tons of people use. I've built and maintained libraries that are used in a ton of publications, but was always told my work was "utterly and completely use…
> my work was "utterly and completely useless I can never understand the arrogance of folks who would say something like this
Earlier quoted context omitted.
I'm interested in Julia, but if numpy has warts, I suppose Julia has not yet had time to even grow its skin.
Theoretically Julia is better for scientific computing, the only issue is its package ecosystem isn't as mature as Python's. But it's growing incredibly fast and there have already been libraries available for a few years that would be really impractical to write and maintain at such a level in C++ for Python. I assume that for science at least Julia will catch on a lot.
using PyCall
np = pyimport("numpy")
res = np.fft.fft(rand(ComplexF64, 10))
You just called numpy fft from Julia.