2 ms·

Another problem is that most people who wrote this code aren't programmers - they don't write clean code, no tests, etc. They don't really know those are import

Another problem is that most people who wrote this code aren't programmers - they don't write clean code, no tests, etc. They don't really know those are important. Sometimes the code that is used and updated for years looks like a dirty prototype. I don't know what can be done about it except hiring programmers to write code, which wouldn't be either easy or cheap

guitarbill 9y ago
Well, I guess one moral of the story is that doing image manipulation is easier in Python than Fortran, so it's again using the right tool for the job. And I think the push for Physicists to use Python if possible is good, as the learning curve is less steep. And once you have that tool at your disposal, you might use it more often (instead of Excel). I can see many projects being improved by providing a Python pre-processor that writes out e.g. a binary config file, the hard-code Fortran/C simulation code reads that, and spits out the simulation results, and then having a Python post-processor that does the pretty stuff at the end. Academically, it seems that pairing CS undergrads with Physics undergrads to do e.g. a molecular dynamics (MD) course would be cool. The Physics behind MD isn't too hard, and given the right parameters the programming part would be manageable. Then again, CS undergrads aren't necessarily great programmers either... Clean code and tests are overrated. Version control and a big eco-system is underrated. It's like maths, physicists don't understand maths, they just use it like a carpenter uses a nail without understanding metallurgy, or a programmer uses a CPU without understanding solid-state physics. And that's okay.
- sametmax 9y ago
  Why not using both ? You can reuse existing fortran code withing Python, and benefit from the prior experience and speed while enjoying the qualities of Python. There are several ways to bind Python and fortran together. E.G http://arogozhnikov.github.io/2015/11/29/using-fortran-from-python.html http://arogozhnikov.github.io/2015/11/29/using-fortran-from-...
- EvgeniyZh 9y ago
  If you don't have clean code and tests, vsc would allow you to switch between old and new bad, probably non working code. That's nice, but probably not what you'd want. If I'd need to choose one out of all, I'd choose tests. From my experience, it's nearly impossible to implement big system without tests. Maybe I'm just not good enough as a programmer
- cm2187 9y ago
  I would have thought a more natural candidate for scientists would be languages like Java or C# rather than python: you get both a language that is relatively easy to write, with no memory to manage, no hornet nest of pointers on pointers, nice debuggers, lots of 3rd party libraries, while a the same time getting the performance of static typing / running compiled code once the program has been JIT (which for long running code is a negligible cost). It's never going to be as fast as a C++ or Fortran, but you get a compromise between performance and ease of use, while I understand python is going the other extreme, i.e. all ease of use at the expense of performance. Doesn't matter for a simple simulation, but for some large data analysis projects, I assume it could make a big difference. But that doesn't seem the case. The choice seems to be between python and c++/fortran. Does anyone know why?
  - asfdsfggtfd 9y ago
    1. Nice bindings already exist for numerical libraries in python. It is more accurate in some ways to say that scientists are using a domain specific language based on numpy than they are using python. 2. The choice is either "fastest possible" or "I just don't care how long it takes" - for either development or run time. There is usually no middle ground. 3. Tooling - the scientist is most likely using a text editor not an IDE (especially when they start working). Fortran and python are both low enough on boilerplate to not need IDE support. 4. Abstraction level. Scientists in general don't bother care about abstraction level at all. Procedural programing's abstractions are usually more than enough for them (and may actually be the correct level for some numerical work - think cache misses vs. hits). 5. Some areas of science do use Java (check out imagej for example).

guitarbill 9y ago

Well, I guess one moral of the story is that doing image manipulation is easier in Python than Fortran, so it's again using the right tool for the job. And I think the push for Physicists to use Python if possible is good, as the learning curve is less steep. And once you have that tool at your disposal, you might use it more often (instead of Excel). I can see many projects being improved by providing a Python pre-processor that writes out e.g. a binary config file, the hard-code Fortran/C simulation code reads that, and spits out the simulation results, and then having a Python post-processor that does the pretty stuff at the end. Academically, it seems that pairing CS undergrads with Physics undergrads to do e.g. a molecular dynamics (MD) course would be cool. The Physics behind MD isn't too hard, and given the right parameters the programming part would be manageable. Then again, CS undergrads aren't necessarily great programmers either... Clean code and tests are overrated. Version control and a big eco-system is underrated. It's like maths, physicists don't understand maths, they just use it like a carpenter uses a nail without understanding metallurgy, or a programmer uses a CPU without understanding solid-state physics. And that's okay.

sametmax 9y ago
Why not using both ? You can reuse existing fortran code withing Python, and benefit from the prior experience and speed while enjoying the qualities of Python. There are several ways to bind Python and fortran together. E.G http://arogozhnikov.github.io/2015/11/29/using-fortran-from-python.html http://arogozhnikov.github.io/2015/11/29/using-fortran-from-...
EvgeniyZh 9y ago
If you don't have clean code and tests, vsc would allow you to switch between old and new bad, probably non working code. That's nice, but probably not what you'd want. If I'd need to choose one out of all, I'd choose tests. From my experience, it's nearly impossible to implement big system without tests. Maybe I'm just not good enough as a programmer
cm2187 9y ago
I would have thought a more natural candidate for scientists would be languages like Java or C# rather than python: you get both a language that is relatively easy to write, with no memory to manage, no hornet nest of pointers on pointers, nice debuggers, lots of 3rd party libraries, while a the same time getting the performance of static typing / running compiled code once the program has been JIT (which for long running code is a negligible cost). It's never going to be as fast as a C++ or Fortran, but you get a compromise between performance and ease of use, while I understand python is going the other extreme, i.e. all ease of use at the expense of performance. Doesn't matter for a simple simulation, but for some large data analysis projects, I assume it could make a big difference. But that doesn't seem the case. The choice seems to be between python and c++/fortran. Does anyone know why?
- asfdsfggtfd 9y ago
  1. Nice bindings already exist for numerical libraries in python. It is more accurate in some ways to say that scientists are using a domain specific language based on numpy than they are using python. 2. The choice is either "fastest possible" or "I just don't care how long it takes" - for either development or run time. There is usually no middle ground. 3. Tooling - the scientist is most likely using a text editor not an IDE (especially when they start working). Fortran and python are both low enough on boilerplate to not need IDE support. 4. Abstraction level. Scientists in general don't bother care about abstraction level at all. Procedural programing's abstractions are usually more than enough for them (and may actually be the correct level for some numerical work - think cache misses vs. hits). 5. Some areas of science do use Java (check out imagej for example).