7 ms·

Isn't this solved by julia? I think scientific community should use a more functional language rather than language like python tbh

montalbano 8y ago
Though I'll still use Python for non-scientific programming, I've switched to Julia for all my scientific programming needs in my day job.
KronenR 8y ago
Give me an ecosystem like Python has and I won't switch anyways because for when that happens I will have more experience with Python, which is much more important.
rfeather 8y ago
Could you elaborate more on the advantages of a functional language?
- cbcoutinho 8y ago
  Scientific work should strive to be functional by definition (identical input == identical output), so I could see why a programing language should reflect that.
  - ianamartin 8y ago
    That's a pretty broad statement. And it's a goal that a lot of scientific work simply doesn't allow for. Any stochastic process will have variability for a given set of inputs. I think there are more fields within the umbrella of Science where purely functional programming isn't an achievable ideal--let alone a desirable one--than there are where this is a good fit. You can make the argument that all programming should be functional on its own if you want, and I'm open to that. But this it's pretty sketchy to me to just shoehorn all scientific work into a category of work that should definitionally be functional. There are huge numbers of scientists who do not agree with that at all.
    - dagw 8y ago
      Any stochastic process will have variability for a given set of inputs If you're modelling stochastic processes it's important to be able to set the random seed you can reproduce your simulations. So for a given set of inputs you should get the same output, given that one of the inputs is your seed.
      - dr_zoidberg 8y ago
        Until you change how you consume randomness, and the order isn't the same anymore.
    - gnufx 8y ago
      What are these fields in which you can't use functional programming and it isn't desirable? (It's true there should be more engineering done on implementations of scientific stuff.) I've seen how unnatural imperative programming was to physicists who hadn't been exposed to it.
  - gnufx 8y ago
    While I agree on a functional outlook, you're going to have a hard time generally making efficient, deterministic parallel programs, particularly distributed ones.
poster123 8y ago
Or by modern Fortran, which has had array operations since the 1990 standard? There are functional elements such as PURE functions and the FORALL construct.
- ataspinar 8y ago
  "Fortran is the only language that has an International Standards Body that sees that sees scientific programmers (and by extensions HPC programmers) as their target audience." https://www.youtube.com/watch?v=4Gp5YJfinOA https://www.youtube.com/watch?v=4Gp5YJfinOA
- Sean1708 8y ago
  I've always felt that it was a bit of a shame that Fortran is falling by the wayside nowadays, a lot of universities are teaching C++ (or even C) instead now and it just isn't as pleasant to use for scientific computing work.
hprotagonist 8y ago
just as soon as someone who knows C/C++ ports numpy and scipy and pandas. and gensim and nltk and sounddevice. and tensorflow and scikit-learn and keras....
- ChrisRackauckas 8y ago
  That was done quite awhile ago? Julia now has a bunch of unique stuff Python doesn't have because the basics are already done.
  - joshuamorton 8y ago
    Such as?
    - ChrisRackauckas 8y ago
      The iterative linear solvers from IterativeSolvers.jl along with the preconditioner ecosystem is more expansive and uses genericness to have a lot more functionality (it's all able to be used with matrix-free operators, GPUs and Xeon Phis, arbitrary precision number choices along with complex, quaternions, etc.). The differential equations solvers from DifferentialEquations.jl covers a lot more domains than the stuff you'll find in SciPy+PyDSTool (SDEs, DAEs, DAEs, semi-linear ODEs via exponential integrators, IMEX, etc.). The dynamical systems library DynamicalSystems.jl is one of a kind. QuantumOptics.jl is not only faster than QuTIP but it also covers more areas like stochastic Schrodinger. And JuMP for mathematical programming (optimization) is also very good in comparison to Pyomo. Scientific computing's core is linear algebra, optimization, and diffeqs and right there you have the basics plus some widespread applications. I agree that Python has a library advantage in data science + ML. R has a library advantage in the area of statistics. But Julia has quite a few advantages in the core math areas of scientific computing and algorithm development. There is headway being made into DS+ML as well. Julia's pandas/dataframe equivalent is JuliaDB which adds out-of-core and online stats functionality, so it's more at the level of pandas+dask. Flux.jl is still in its early stages but it's quite a unique ML framework which can directly incorporate any Julia function at any level, and then has some working experiments with compiling to things like JS and XLA. But in the broad view of things, every language has SciPy+NumPy pretty satisfactory (ex: Julia's Base library has most of it, the top 20 packages cover the rest), but from there all have tradeoffs in what areas the community is specializing in.
knlji 8y ago
Maybe. It's currently just a safer bet to learn and use Python. Easier to get a job after you fail getting your next grant. I have so far seen zero Julia job ads. Hell, I see more e.g. Haskell and Fortran job ads than Julia.
- montalbano 8y ago
  or learn both and have the convenience+speed of Julia for scientific work? Speaking from my experience here as a bioengineering PhD student. The Julia learning curve is low enough for an experienced Python/scipy user to switch over fairly swiftly. edit: also, I would be very surprised if you had seen any Julia job ads, v1.0 hasn't been released yet. Doesn't mean it can't make my scientific life easier in the meanwhile.
  - overkalix 8y ago
    I don't use Julia and I only use Python occasionally, but I'd rather go for Cython than Julia...
- ChrisRackauckas 8y ago
  Julia gives you such a competitive advantage that you can easily position yourself into a great career. It worked out really well for me. Of course YMMV, but having a much greater productivity and software quality never hurts.
moolcool 8y ago
Python has good support for a lot of functional concepts
- UncleEntity 8y ago
  Except tail-call optimization which the BDFL refuses to consider since it would break "there should be one -- and preferably only one -- obvious way to do it" regarding loops.
- eigenspace 8y ago
  Julia has much better support for functional programming.
xapata 8y ago
You want multi-line lambdas and macros? Anything else?
ocschwar 8y ago
Julia is FOrtran-indexed, and thus anathema to my religion.
- sampo 8y ago
  In Fortran you can choose the starting index for each array separately. Even negative integers work. (But it's true that idiomatic Fortran starts indexing from 1.)
  - ChrisRackauckas 8y ago
    And the same is true in Julia.
evrydayhustling 8y ago
In research programming, you often spend as much or more time in data acquisition and munging than implementing core algorithms. Plus, more than in production code, the requirements change as you explore different applications and approach. And, because it's not production code, you have more opportunity to explore outputs at different stages to review function. It's effectively continual prototyping. All of these things play to python's main strengths: huge community with connectors to every API and format, plus ability to conveniently integrate code at several levels of complexity & maturity as you prototype.
- ChrisRackauckas 8y ago
  Those are strengths that are shared with Julia, which is exactly why the question needs to be asked.
- kwertzzz 8y ago
  I am giving a class on numerical methods and one student choose to use python (while my example code was in Julia). It was a pain to see how this student was constantly shooting himself in the foot due some particular behaviors of python and numpy. For instance the student did not expect that a list comprehension iterating over a numpy vector returns just a python vector. Also the fact that the index ranges the last value is excluded let to several bugs. The exercise involved a time dependent matrix and the student choosed to represent it as a 3d Array, but then he needed to constantly convert slices as a matrix to use matrix multiplication (maybe this is now better solved with python 3 and the @ operator). So in short for doing linear algebra, Julia is really more convenient to use.
  - targafarian 8y ago
    This comment just sounds like "I know tool X well, somebody else doesn't know tool Y well, and therefore tool X is better." To do matrix multiplication on many matrices "stacked" together in one step, use numpy.matmul: https://docs.scipy.org/doc/numpy/reference/generated/numpy.matmul.html https://docs.scipy.org/doc/numpy/reference/generated/numpy.m... (and so there's no need to slice up the array, convert to matrix, etc.) Note that the Numpy devs are trying to (if they haven't already) get rid of the "matrix" class and just use arrays, but of course dealing with legacy code is always an issue. Once that's out of the way, people won't be distracted by "matrices" to do matrix operations, and hopefully they'll see you can do matrix operations on arrays directly. (And yes, in Python 3 you can use the @ syntax to the same effect.)
Derbasti 8y ago
I keep trying out Julia every year or so. And it has come a long way. Gone are the days of crashes, missing documentation, and terrible error messages. And yet, for my particular area (audio signal processing), Julia is just objectively worse than Python in expressivity, library support, and even speed. But I'll keep trying. Maybe next year.

montalbano 8y ago

Though I'll still use Python for non-scientific programming, I've switched to Julia for all my scientific programming needs in my day job.

KronenR 8y ago

Give me an ecosystem like Python has and I won't switch anyways because for when that happens I will have more experience with Python, which is much more important.

rfeather 8y ago

Could you elaborate more on the advantages of a functional language?

cbcoutinho 8y ago
Scientific work should strive to be functional by definition (identical input == identical output), so I could see why a programing language should reflect that.
- ianamartin 8y ago
  That's a pretty broad statement. And it's a goal that a lot of scientific work simply doesn't allow for. Any stochastic process will have variability for a given set of inputs. I think there are more fields within the umbrella of Science where purely functional programming isn't an achievable ideal--let alone a desirable one--than there are where this is a good fit. You can make the argument that all programming should be functional on its own if you want, and I'm open to that. But this it's pretty sketchy to me to just shoehorn all scientific work into a category of work that should definitionally be functional. There are huge numbers of scientists who do not agree with that at all.
  - dagw 8y ago
    Any stochastic process will have variability for a given set of inputs If you're modelling stochastic processes it's important to be able to set the random seed you can reproduce your simulations. So for a given set of inputs you should get the same output, given that one of the inputs is your seed.
    - dr_zoidberg 8y ago
      Until you change how you consume randomness, and the order isn't the same anymore.
  - gnufx 8y ago
    What are these fields in which you can't use functional programming and it isn't desirable? (It's true there should be more engineering done on implementations of scientific stuff.) I've seen how unnatural imperative programming was to physicists who hadn't been exposed to it.
- gnufx 8y ago
  While I agree on a functional outlook, you're going to have a hard time generally making efficient, deterministic parallel programs, particularly distributed ones.

poster123 8y ago

Or by modern Fortran, which has had array operations since the 1990 standard? There are functional elements such as PURE functions and the FORALL construct.

ataspinar 8y ago
"Fortran is the only language that has an International Standards Body that sees that sees scientific programmers (and by extensions HPC programmers) as their target audience." https://www.youtube.com/watch?v=4Gp5YJfinOA https://www.youtube.com/watch?v=4Gp5YJfinOA
Sean1708 8y ago
I've always felt that it was a bit of a shame that Fortran is falling by the wayside nowadays, a lot of universities are teaching C++ (or even C) instead now and it just isn't as pleasant to use for scientific computing work.

hprotagonist 8y ago

just as soon as someone who knows C/C++ ports numpy and scipy and pandas. and gensim and nltk and sounddevice. and tensorflow and scikit-learn and keras....

ChrisRackauckas 8y ago
That was done quite awhile ago? Julia now has a bunch of unique stuff Python doesn't have because the basics are already done.
- joshuamorton 8y ago
  Such as?
  - ChrisRackauckas 8y ago
    The iterative linear solvers from IterativeSolvers.jl along with the preconditioner ecosystem is more expansive and uses genericness to have a lot more functionality (it's all able to be used with matrix-free operators, GPUs and Xeon Phis, arbitrary precision number choices along with complex, quaternions, etc.). The differential equations solvers from DifferentialEquations.jl covers a lot more domains than the stuff you'll find in SciPy+PyDSTool (SDEs, DAEs, DAEs, semi-linear ODEs via exponential integrators, IMEX, etc.). The dynamical systems library DynamicalSystems.jl is one of a kind. QuantumOptics.jl is not only faster than QuTIP but it also covers more areas like stochastic Schrodinger. And JuMP for mathematical programming (optimization) is also very good in comparison to Pyomo. Scientific computing's core is linear algebra, optimization, and diffeqs and right there you have the basics plus some widespread applications. I agree that Python has a library advantage in data science + ML. R has a library advantage in the area of statistics. But Julia has quite a few advantages in the core math areas of scientific computing and algorithm development. There is headway being made into DS+ML as well. Julia's pandas/dataframe equivalent is JuliaDB which adds out-of-core and online stats functionality, so it's more at the level of pandas+dask. Flux.jl is still in its early stages but it's quite a unique ML framework which can directly incorporate any Julia function at any level, and then has some working experiments with compiling to things like JS and XLA. But in the broad view of things, every language has SciPy+NumPy pretty satisfactory (ex: Julia's Base library has most of it, the top 20 packages cover the rest), but from there all have tradeoffs in what areas the community is specializing in.

knlji 8y ago

Maybe. It's currently just a safer bet to learn and use Python. Easier to get a job after you fail getting your next grant. I have so far seen zero Julia job ads. Hell, I see more e.g. Haskell and Fortran job ads than Julia.

montalbano 8y ago
or learn both and have the convenience+speed of Julia for scientific work? Speaking from my experience here as a bioengineering PhD student. The Julia learning curve is low enough for an experienced Python/scipy user to switch over fairly swiftly. edit: also, I would be very surprised if you had seen any Julia job ads, v1.0 hasn't been released yet. Doesn't mean it can't make my scientific life easier in the meanwhile.
- overkalix 8y ago
  I don't use Julia and I only use Python occasionally, but I'd rather go for Cython than Julia...
ChrisRackauckas 8y ago
Julia gives you such a competitive advantage that you can easily position yourself into a great career. It worked out really well for me. Of course YMMV, but having a much greater productivity and software quality never hurts.

moolcool 8y ago

Python has good support for a lot of functional concepts

UncleEntity 8y ago
Except tail-call optimization which the BDFL refuses to consider since it would break "there should be one -- and preferably only one -- obvious way to do it" regarding loops.
eigenspace 8y ago
Julia has much better support for functional programming.

xapata 8y ago

You want multi-line lambdas and macros? Anything else?

ocschwar 8y ago

Julia is FOrtran-indexed, and thus anathema to my religion.

sampo 8y ago
In Fortran you can choose the starting index for each array separately. Even negative integers work. (But it's true that idiomatic Fortran starts indexing from 1.)
- ChrisRackauckas 8y ago
  And the same is true in Julia.

evrydayhustling 8y ago

In research programming, you often spend as much or more time in data acquisition and munging than implementing core algorithms. Plus, more than in production code, the requirements change as you explore different applications and approach. And, because it's not production code, you have more opportunity to explore outputs at different stages to review function. It's effectively continual prototyping. All of these things play to python's main strengths: huge community with connectors to every API and format, plus ability to conveniently integrate code at several levels of complexity & maturity as you prototype.

ChrisRackauckas 8y ago
Those are strengths that are shared with Julia, which is exactly why the question needs to be asked.
kwertzzz 8y ago
I am giving a class on numerical methods and one student choose to use python (while my example code was in Julia). It was a pain to see how this student was constantly shooting himself in the foot due some particular behaviors of python and numpy. For instance the student did not expect that a list comprehension iterating over a numpy vector returns just a python vector. Also the fact that the index ranges the last value is excluded let to several bugs. The exercise involved a time dependent matrix and the student choosed to represent it as a 3d Array, but then he needed to constantly convert slices as a matrix to use matrix multiplication (maybe this is now better solved with python 3 and the @ operator). So in short for doing linear algebra, Julia is really more convenient to use.
- targafarian 8y ago
  This comment just sounds like "I know tool X well, somebody else doesn't know tool Y well, and therefore tool X is better." To do matrix multiplication on many matrices "stacked" together in one step, use numpy.matmul: https://docs.scipy.org/doc/numpy/reference/generated/numpy.matmul.html https://docs.scipy.org/doc/numpy/reference/generated/numpy.m... (and so there's no need to slice up the array, convert to matrix, etc.) Note that the Numpy devs are trying to (if they haven't already) get rid of the "matrix" class and just use arrays, but of course dealing with legacy code is always an issue. Once that's out of the way, people won't be distracted by "matrices" to do matrix operations, and hopefully they'll see you can do matrix operations on arrays directly. (And yes, in Python 3 you can use the @ syntax to the same effect.)

Derbasti 8y ago

I keep trying out Julia every year or so. And it has come a long way. Gone are the days of crashes, missing documentation, and terrible error messages. And yet, for my particular area (audio signal processing), Julia is just objectively worse than Python in expressivity, library support, and even speed. But I'll keep trying. Maybe next year.