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Array Programming with NumPy

nature.com

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Re: Array Programming with NumPy

#81

Post by cycomanic »

westurner wrote: post #53
clickok wrote: post #15

Earlier quoted context omitted.

It would be nice if citing repositories were easier-- either for generating a reference for my own code or acknowledging when I've used someone else's code in my research. There's tons of math and physics blogs that contain useful results that the author wanted to make available but didn't manage to incorporate into a paper. I wonder if there'd be any interest in a sort of GitHub for proofs? It could even use git, si…

You can get a free DOI for and archive a tag of a Git repo with FigShare or Zenodo. If you have repo2docker REES dependency scripts (requirements.txt, environment.yml, postInstall,) in your repo, a BinderHub like https://mybinder.org can build and cache a container image and launch a (free) instance in a k8s cloud. Journals haven't yet integrated with BinderHub. Putting the suggested citation and DOI URI/URL in your…

The issue is that none of the citation count engines (Google scholar, scopus, Web of Science...) count citations on those DOIs. So for a researcher who needs to somehow demonstrate impact through citation counts, it does not really help unfortunately.

Re: Array Programming with NumPy

#82

Post by kergonath »

coliveira wrote: post #58
Random_ernest wrote: post #46

Earlier quoted context omitted.

I recall a story where a friend was unable to publish a paper in which he wrote an alternative to a very commonly used commercial tool (that virtually everybody used) with roughly 10 times better performance. He open sourced it and all, it was extremely useful, but there was no new methodology, it was simply very well implemented. At a talk of his it lead to a very heated discussion where an older professor accused h…

It is obvious that we need good software, however from the point of view of science the old professor may have reason. If you are receiving a grant, you're not being paid to write software, in the same way that an engineer is not paid to write novels. As useful as the software may be, the person in question should be spending time on research (by definition new subjects), not writing again an existing software.

Writing software is sometimes necessary to achieve the objectives of a grant (even though this is not necessarily explicit). It’s not writing software that’s a problem, it’s reinventing the wheel; you should not focus on “scientists should not write software”, because that is obviously far from the truth.

For a scientist, writing useful software is a good way to get exposure, build a reputation and get citations. It’s an opportunity to do some different kind of problem solving than usual. It’s also a way of understanding how the software really work (which assumptions are built in, which methods are used, and how does it affect the software’s results?). This does help improve the quality of subsequent results.

A grant typically (there are exceptions, of course) lists things that are going to be studied. How the studying is done is typically down to the people doing the work. It certainly isn’t for grumpy old professors who hear a talk at a conference to judge.

Re: Array Programming with NumPy

#83

Post by cycomanic »

edgyquant wrote: post #49
srean wrote: post #32

Earlier quoted context omitted.

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.

I just don't like the BASIC derived syntax of Julia (and Ruby.) I wish there was a language that was typed, had python like classes, subroutines and lambdas but JS like anonymous functions that was fast like Julia or at least close to numpy in number crunching without needing a module written in C.

Have a look at Nim, I was presently surprised when I recently tried it out. Now if there was just a better way of integrating with numpy it would be my goto language for writing computation intensive modules for python.

Re: Array Programming with NumPy

#84

Post by yqx »

uryga wrote: post #62
ktpsns wrote: post #56

Earlier quoted context omitted.

R ( https://en.wikipedia.org/wiki/R_(programming_language) ) is kind of the successof of S. In certain communities (not only statistics, but for instance also biogenetics), there is quite some concurrency between R and (scientific) Python for data science. For my understanding, the numpy syntax most closely resembles what would be possible in matlab. And matlab again seems to have roots from Fortran. Thanks to that,…

> there is quite some concurrency i think you meant "competition" here :) (in polish, my native language, it's "konkurencja", but it's a "false friend of the translator" ; i'm guessing you're in a similar boat)

Similar false friends in Dutch: Concurrentie

Re: Array Programming with NumPy

#85

Post by joaogui1 »

Mikhail_K wrote: post #77

Numpy array syntax inconsistent to the degree it can be considered broken. For instance, to take elements number 4,3,2 of an array in Python, one writes A[3:0:-1], but for the elements 3,2,1 one has to write A[2::-1], because someone decided that making A[-1] refer to the last element of an array is "intuitive", and so A[2:-1:-1] will return empty slice. Now if you want to use array comprehensions, the first case loo…

You don't need the matrix type in Numpy, it's just syntax sugar over a 2-dimensional array

Re: Array Programming with NumPy

#86

Post by tgb »

Mikhail_K wrote: post #77

Numpy array syntax inconsistent to the degree it can be considered broken. For instance, to take elements number 4,3,2 of an array in Python, one writes A[3:0:-1], but for the elements 3,2,1 one has to write A[2::-1], because someone decided that making A[-1] refer to the last element of an array is "intuitive", and so A[2:-1:-1] will return empty slice. Now if you want to use array comprehensions, the first case loo…

The matrix class is now officially deprecated, FYI, so just use array everywhere.

I guess the generic way to write that would be A[bottom:top+1][::-1]. But the blame there goes to Python, not numpy, since the same is true of lists.

Re: Array Programming with NumPy

#87

Post by throwaway-wroc »

wjnc wrote: post #75
throwaway-wroc wrote: post #61

Earlier quoted context omitted.

props to your work and similar to numpy, i assume it has been immensely useful for loads of people. but 'building the underlying infrastructure that tons of people use' is not science. in my department we had to fail a phd student because 90% of his work was just implementing bunch of existing methods as a python library. useful, yes; science, no. wasn't his fault, had a shitty supervisor, but making useful tools is…

That's quite the wrong way of approaching science. The scientific method is based on building on the shoulders of giants. Those giants aren't the professors in the direct vicinity nor are they only the papers you cite. The whole of the process is science and if we need further specialization for building better tools (hey, maths and statistics are scientific tools as well) I would classify that as science to a large…

> The scientific method is based on building on the shoulders of giants. > The whole of the process is science

no. scientific research is proposing a useful model of an observable phenomenon. this is what you train for during a phd, at least in natural/life sciences: you learn how to test a hypothesis, not an easy skill.

refactoring code or transforming bunch of C++ into a python library is useful, but it's not science.

> What could be useful is openness in used tools and software and a way of getting citation counts for software used. It's nothing more than a table. That way the hotness of publication could start to flow for the underlying tools.

agreed 100%

Re: Array Programming with NumPy

#88

Post by throwaway-wroc »

bitxbit wrote: post #80
throwaway-wroc wrote: post #61

Earlier quoted context omitted.

props to your work and similar to numpy, i assume it has been immensely useful for loads of people. but 'building the underlying infrastructure that tons of people use' is not science. in my department we had to fail a phd student because 90% of his work was just implementing bunch of existing methods as a python library. useful, yes; science, no. wasn't his fault, had a shitty supervisor, but making useful tools is…

I am really sorry but most “science” coming out of even the top institutions these days are sadly uninspired garbage regurgitations.

most of anything is 'uninspired garbage'. not sure what it's to do whether a particular phd should be awarded.

no-one is proposing that numpy isn't useful or people developing / maintaining tools aren't doing gods work. they have my endless gratitude and try to donate regularly.

however, phd training in my field -- natural/life sciences -- has a specific remit: you learn how to build and test a hypothesis, from start to end. optimising libraries is emphatically not it. as a scientist you should care whether you have a useful model that explains something about the world. this is orthogonal to how neatly you have implemented your linear algebra in python.

Re: Array Programming with NumPy

#89

Post by mantap »

throwaway-wroc wrote: post #87
wjnc wrote: post #75

Earlier quoted context omitted.

That's quite the wrong way of approaching science. The scientific method is based on building on the shoulders of giants. Those giants aren't the professors in the direct vicinity nor are they only the papers you cite. The whole of the process is science and if we need further specialization for building better tools (hey, maths and statistics are scientific tools as well) I would classify that as science to a large…

> The scientific method is based on building on the shoulders of giants. > The whole of the process is science no. scientific research is proposing a useful model of an observable phenomenon. this is what you train for during a phd, at least in natural/life sciences: you learn how to test a hypothesis, not an easy skill. refactoring code or transforming bunch of C++ into a python library is useful, but it's not scien…

Maybe we should take all this "not science" software away from the scientists and see how much science they can do without it.

If you write code that allows science to be done that couldn't be done otherwise then that is science. As a high profile example, a large amount of specialist software was developed for the LHC to allow it to process all the events coming from the detectors.

It sounds like the refactoring here was not really that useful in the first place.

Re: Array Programming with NumPy

#90

Post by throwaway-wroc »

mantap wrote: post #89
throwaway-wroc wrote: post #87

Earlier quoted context omitted.

> The scientific method is based on building on the shoulders of giants. > The whole of the process is science no. scientific research is proposing a useful model of an observable phenomenon. this is what you train for during a phd, at least in natural/life sciences: you learn how to test a hypothesis, not an easy skill. refactoring code or transforming bunch of C++ into a python library is useful, but it's not scien…

Maybe we should take all this "not science" software away from the scientists and see how much science they can do without it. If you write code that allows science to be done that couldn't be done otherwise then that is science. As a high profile example, a large amount of specialist software was developed for the LHC to allow it to process all the events coming from the detectors. It sounds like the refactoring her…

yes, in 2020 you mostly cannot do science without software, electricity, desks and chairs and buildings, printers, pick your own irreplaceable tool. yet building these things to enable research is emphatically not itself scientific research.

doing a phd -> training to be a scientist.

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