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Introduction to Python for Computational Science and Engineering [pdf]

southampton.ac.uk

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Re: Introduction to Python for Computational Science and Engineering [pdf]

#11

Post by water42 »

mynegation wrote: post #2

This is a very good guide. I thought I knew python ecosystem well and I found something new for myself ('visual' package for 3D illustrations). I am wondering if there are guides for the "reverse direction": I already know how to program, but I want to learn new scientific domain that is interesting to me: e.g. material science, climate modeling, etc. Something like Rosalind[1] does for bioinformatics. [1] http://ros…

Woah, Rosalind looks awesome- like project euler for bioinformatics. Thanks for sharing the link.

Re: Introduction to Python for Computational Science and Engineering [pdf]

#12

Post by godelski »

drej wrote: post #4

> As Python 2.x is still the default Python on many system and there are a fair number of research codes out there based on Python 2, we will use Python 2.x in this book. This is so unfortunate. Scientific computing is riddled with technical debt and starting with Python 2 today is fairly irresponsible. If you're already invested in Python 2 and have code/training written up, fine. But if you're learning it just now,…

In a lot of scientific computing backwards compatibility is essential. Knowing Fortran in that world is still helpful, even if you only have a basic understanding it helps with common packages like LAPACK and ARPACK. In science the science is first and coding second. Coding is just the tool used to get the job done. Most people in the community adopted python2 because it was easier to use than C/Fortran in a lot of cases where speed wasn't critical. I can not tell you how much easier it is to read scientific python code than scientific C or Fortran code. So even though a lot of main packages are supported by 3 a lot of university and custom software isn't. Someone hands you code and asks you to improve on it, or make this model work, you don't rewrite the entire thing, you use what already works and build from there.

Re: Introduction to Python for Computational Science and Engineering [pdf]

#13

Post by godelski »

rishabguha wrote: post #9
drej wrote: post #4

> As Python 2.x is still the default Python on many system and there are a fair number of research codes out there based on Python 2, we will use Python 2.x in this book. This is so unfortunate. Scientific computing is riddled with technical debt and starting with Python 2 today is fairly irresponsible. If you're already invested in Python 2 and have code/training written up, fine. But if you're learning it just now,…

IMO, Python 3's support for matrix multiplication using the @ operator is itself worth the cost of admission. Much of technical computing is just implementing algorithms that use linear algebra extensively, and if you're coming from matlab littering your code with dot(dot(X,Y),Z) is a real pain

I actually see np.dot(np.dot(x,y),z) as easier to read and remember. It is more explicit in what it is doing. Being explicit is a good thing.

Re: Introduction to Python for Computational Science and Engineering [pdf]

#14

Post by dman »

rishabguha wrote: post #9
drej wrote: post #4

> As Python 2.x is still the default Python on many system and there are a fair number of research codes out there based on Python 2, we will use Python 2.x in this book. This is so unfortunate. Scientific computing is riddled with technical debt and starting with Python 2 today is fairly irresponsible. If you're already invested in Python 2 and have code/training written up, fine. But if you're learning it just now,…

IMO, Python 3's support for matrix multiplication using the @ operator is itself worth the cost of admission. Much of technical computing is just implementing algorithms that use linear algebra extensively, and if you're coming from matlab littering your code with dot(dot(X,Y),Z) is a real pain

Does @ work with numpy?

Re: Introduction to Python for Computational Science and Engineering [pdf]

#15

Post by Normal_gaussian »

I'm dubious about the worth of this book as I lived with several soton Engineering students as they went through the associated course (whilst I did CS as part of the other 'Engineering' faculty, ECS), though a lot may have been due to lack of engagement with this particular course (copying and memorisation were sufficient to get you through it). I was always so frustrated that my friends couldn't benefit from some of the great lecturers in my own faculty.

That said I know a few Engineering students there now who are awesome programmers (though Aero Engineers at heart, so I can't hire them :( ).

Re: Introduction to Python for Computational Science and Engineering [pdf]

#16

Post by noobermin »

godelski wrote: post #13
rishabguha wrote: post #9

Earlier quoted context omitted.

IMO, Python 3's support for matrix multiplication using the @ operator is itself worth the cost of admission. Much of technical computing is just implementing algorithms that use linear algebra extensively, and if you're coming from matlab littering your code with dot(dot(X,Y),Z) is a real pain

I actually see np.dot(np.dot(x,y),z) as easier to read and remember. It is more explicit in what it is doing. Being explicit is a good thing.

   a + 4*(x-y) + b**5
vs

   (+ a (* 4 (- x y )) (expt b 5 ))
Sometimes, as arithmetic with lisp shows, conciseness is better. Also, it isn't being more explicit than it is being more verbose.

Re: Introduction to Python for Computational Science and Engineering [pdf]

#17

Post by [deleted] »

godelski wrote: post #13
rishabguha wrote: post #9

Earlier quoted context omitted.

IMO, Python 3's support for matrix multiplication using the @ operator is itself worth the cost of admission. Much of technical computing is just implementing algorithms that use linear algebra extensively, and if you're coming from matlab littering your code with dot(dot(X,Y),Z) is a real pain

I actually see np.dot(np.dot(x,y),z) as easier to read and remember. It is more explicit in what it is doing. Being explicit is a good thing.

[deleted]

Re: Introduction to Python for Computational Science and Engineering [pdf]

#18

Post by drej »

godelski wrote: post #12
drej wrote: post #4

> As Python 2.x is still the default Python on many system and there are a fair number of research codes out there based on Python 2, we will use Python 2.x in this book. This is so unfortunate. Scientific computing is riddled with technical debt and starting with Python 2 today is fairly irresponsible. If you're already invested in Python 2 and have code/training written up, fine. But if you're learning it just now,…

In a lot of scientific computing backwards compatibility is essential. Knowing Fortran in that world is still helpful, even if you only have a basic understanding it helps with common packages like LAPACK and ARPACK. In science the science is first and coding second. Coding is just the tool used to get the job done. Most people in the community adopted python2 because it was easier to use than C/Fortran in a lot of c…

I very much understand the bit that science is first and code second. I live by the very much same principle at work and we actually have a lot of (terrible) Fortran and C/C++ code around. And we're trying to adopt Python for large parts of of it.

But I'm not sure I follow your argument. I'm not talking about rewriting existing code. I'm talking about building new things. And they should be built in Python 3, because that's the standard that's meant to be used and supported in the future. Learning Py3 doesn't mean you can't maintain existing Python 2 code. It just means that whatever new thing you write is in a future proof setting.

(Btw. As much as people hate on Fortran, there's one thing that beats most other languages - take a 20 or 30 year old codebase and you're not unlikely to compile it and run it on a modern machine. We can't count on that with Python, but Py3 gives us a slightly better chance.)

Re: Introduction to Python for Computational Science and Engineering [pdf]

#19

Post by a3n »

I'm very sympathetic to "use python2." I still use python2.

But we should all keep in mind the "no shit really this time" EOL for python is 2020. http://legacy.python.org/dev/peps/pep-0373/

Even if you say "well, it'll just be forked," you don't really know how many forks and pain there will be. Maybe python2 will be like LibreOffice, or maybe it will be like OpenOffice.

Like it or not, python3 is the future.

Re: Introduction to Python for Computational Science and Engineering [pdf]

#20

Post by jmbond »

dman wrote: post #14
rishabguha wrote: post #9

Earlier quoted context omitted.

IMO, Python 3's support for matrix multiplication using the @ operator is itself worth the cost of admission. Much of technical computing is just implementing algorithms that use linear algebra extensively, and if you're coming from matlab littering your code with dot(dot(X,Y),Z) is a real pain

Does @ work with numpy?

Yes. See Notes section in the docs for numpy.matmul [1]

[1] https://docs.scipy.org/doc/numpy/reference/generated/numpy.m...

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