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Julia 1.0

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Re: Julia 1.0

#321

Post by ChrisRackauckas »

jimbokun wrote: post #305
ChrisRackauckas wrote: post #153

Earlier quoted context omitted.

For scientific computing, showing the package ecosystem is the most important thing. When you look at this thread, people are asking about dataframes and differential equations. Julia's site reflects this: yes there are things like Pandas, and for plotting, etc.

So show code samples using dataframes and differential equations.

Using the IterableTables interface, the DifferentialEquations.jl solutions are presented as tables and directly convert to dataframes:

http://docs.juliadiffeq.org/latest/features/io.html#Tabular-...

f_2dlinear = (du,u,p,t) -> du.=1.01u

prob = ODEProblem(f_2dlinear,rand(2,2),(0.0,1.0))

sol1 =solve(prob,Euler();dt=1//2^(4))

using DataFrames

df = DataFrame(sol1)

# Result 17×5 DataFrames.DataFrame │ Row │ timestamp │ value 1 │ value 2 │ value 3 │ value 4 │ ├─────┼───────────┼──────────┼──────────┼──────────┼──────────┤ │ 1 │ 0.0 │ 0.110435 │ 0.569561 │ 0.918336 │ 0.508044 │ │ 2 │ 0.0625 │ 0.117406 │ 0.605515 │ 0.976306 │ 0.540114 │ ...

Re: Julia 1.0

#322

Post by elcritch »

ontouchstart wrote: post #238
StefanKarpinski wrote: post #190

Earlier quoted context omitted.

I can understand how one can reasonably hypothesize that. In some sense you can see Julia as one big social experiment in what happens when you do everything possible to blur the line between users and developers of a scientific (and, of course, also general) programming language by solving the two language problem and making the language for users and developers one and the same. ... and it's been wildly successful.…

I was watching live stream last night from the East coast and very impressed by the achievement by you scientists and grad students since 2012. https://arxiv.org/abs/1209.5145 As a former physicist, I have been through the Numerical Recipes/F77 stage myself two decades ago. In the age of Big Data and Machine Learning, there are many ways to harvest the creativities in people who are not trained as professional engine…

That’s a great viewpoint, imho! Scientists in most fields have to write code nowadays, better to make it easier for them to hack together novel and useful tools.

Re: Julia 1.0

#323

Post by simonbyrne »

jacobolus wrote: post #315
simonbyrne wrote: post #312

Earlier quoted context omitted.

What language defines % as mod and not remainder?

According to Wikipedia: Perl, Python, Lua, Ruby, Tcl, R (as %%), Smalltalk (as \\), various other languages under some alternate name, sometimes with the two variants given different names. The other (“remainder”) version which takes its sign from the first argument is pretty much worthless in practice. IMO it doesn’t need any name at all. But what it definitely doesn’t need is a shorter and more convenient name than…

I guess because remainder is simpler to implement? (anecdotally, the assembly generated by @code_native in Julia is much shorter for remainder)

In my opinion neither are used sufficiently often to justify taking up a valuable ASCII symbol (same with xor).

Re: Julia 1.0

#324

Post by jacobolus »

simonbyrne wrote: post #323
jacobolus wrote: post #315

Earlier quoted context omitted.

According to Wikipedia: Perl, Python, Lua, Ruby, Tcl, R (as %%), Smalltalk (as \\), various other languages under some alternate name, sometimes with the two variants given different names. The other (“remainder”) version which takes its sign from the first argument is pretty much worthless in practice. IMO it doesn’t need any name at all. But what it definitely doesn’t need is a shorter and more convenient name than…

I guess because remainder is simpler to implement? (anecdotally, the assembly generated by @code_native in Julia is much shorter for remainder) In my opinion neither are used sufficiently often to justify taking up a valuable ASCII symbol (same with xor).

This depends on what the behavior is of the division operator. People should use floor division, then implementing modulo is easy.

I don’t know what is available as chip instructions, I could certainly believe hardware designers made the wrong choice.

Re: Julia 1.0

#325

Post by simonbyrne »

simonbyrne wrote: post #323
jacobolus wrote: post #315

Earlier quoted context omitted.

According to Wikipedia: Perl, Python, Lua, Ruby, Tcl, R (as %%), Smalltalk (as \\), various other languages under some alternate name, sometimes with the two variants given different names. The other (“remainder”) version which takes its sign from the first argument is pretty much worthless in practice. IMO it doesn’t need any name at all. But what it definitely doesn’t need is a shorter and more convenient name than…

I guess because remainder is simpler to implement? (anecdotally, the assembly generated by @code_native in Julia is much shorter for remainder) In my opinion neither are used sufficiently often to justify taking up a valuable ASCII symbol (same with xor).

The other point is that remainder makes much more sense for floating point numbers, as it's exact. mod on the other hand is not, and is fiendishly difficult to get "right" (if that is even possible).

Re: Julia 1.0

#326

Post by KenoFischer »

civility wrote: post #317
KenoFischer wrote: post #306

Earlier quoted context omitted.

I can understand why you might get the impression, but I'd encourage you to try out julia and see that we're really quite good at using index-agnostic abstractions, so most code doesn't care what your arrays are indexed with. If a certain set of indices make sense in your domain (0-based for FFTs as you say, symmetric indices about the original for image filters, 1-based for just regular lists of things, etc), just u…

I'll (cautiously) take your word that this works transparently when I supply arrays as arguments to a library function. However, what does the library function return to me as arrays it allocates? What if I do an outer product of two arrays with different base indices? Whatever your answer, I suspect it is more cognitive overhead to remember than "always 1 based" or "always 0 based".

> However, what does the library function return to me as arrays it allocates?

Depends what the library function does of course. If it's shape preserving (e.g. if it's a map over the array), it'll generally preserve the axes of the input array.

> What if I do an outer product of two arrays with different base indices?

You'll get an offset array indexed by the product of the axes of the input:

    julia> A = OffsetArray(1:3, 0:2)
    OffsetArray(::UnitRange{Int64}, 0:2) with eltype Int64 with indices 0:2:
     1
     2
     3

    julia> B = 4:6
    4:6

    julia> A*B'
    OffsetArray(::Array{Int64,2}, 0:2, 1:3) with eltype Int64 with indices 0:2×1:3:
      4   5   6
      8  10  12
     12  15  18
> Whatever your answer, I suspect it is more cognitive overhead to remember than "always 1 based" or "always 0 based".

Sure, but the real point here is that in most situations, you don't actually care what the axes are, because you use the higher level abstractions (e.g. iteration over elements or the index space, linear algebra operations, broadcasts, etc.), which all know how to deal with arbitrary axes. The only time you should ever really have to think about what your axes are is if there's some practical relevance to your problem (OffsetArrays are used heavily in the images stack).

Re: Julia 1.0

#328

Post by simonbyrne »

jacobolus wrote: post #324
simonbyrne wrote: post #323

Earlier quoted context omitted.

I guess because remainder is simpler to implement? (anecdotally, the assembly generated by @code_native in Julia is much shorter for remainder) In my opinion neither are used sufficiently often to justify taking up a valuable ASCII symbol (same with xor).

This depends on what the behavior is of the division operator. People should use floor division, then implementing modulo is easy. I don’t know what is available as chip instructions, I could certainly believe hardware designers made the wrong choice.

I take the opinion that people should have the option to round division (and compute remainders) however they want: down, up, to zero, to nearest (with different options for breaking ties).

https://github.com/JuliaLang/julia/issues/9283

Re: Julia 1.0

#329

Post by jacobolus »

simonbyrne wrote: post #325
simonbyrne wrote: post #323

Earlier quoted context omitted.

I guess because remainder is simpler to implement? (anecdotally, the assembly generated by @code_native in Julia is much shorter for remainder) In my opinion neither are used sufficiently often to justify taking up a valuable ASCII symbol (same with xor).

The other point is that remainder makes much more sense for floating point numbers, as it's exact. mod on the other hand is not, and is fiendishly difficult to get "right" (if that is even possible).

In what context do you use it? I use a “mod” operator all the time for floating point calculations, and have never come across a need for the other one.

As one simple example, it is frequently useful to map floats into the range [0, 2π), but I have never once wanted to map positive floats into the range [0, 2π) while negative floats get mapped into (–2π, 0] by the same code.

Re: Julia 1.0

#330

Post by elcritch »

ChrisRackauckas wrote: post #38
amrrs wrote: post #12

I use both R and Python in my work but when we move our models to production it's not real time, just a batch execution like once in a day. I'd like to hear from anyone who uses Julia in their actual job/work. Is it worth learning Julia, hoping to use it in work some day?

We are using Julia to develop a clinical trial simulator for drug dosage prediction and personalized medicine applications as a joint MIT, University of Maryland Baltimore, and JuliaComputing project. It will be open sourced in January, and at the same time we will be starting a clinical trial to test the methods in a real-world setting.

Definitely post a link! Are you doing stochastic modeling, and if so how do you structure the input data? I’d love to use similar techniques for other data modeling.
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