[julia-users] Function to get Index of nonzero entries in sparse vector
Hello, I have a sparse vector, is there any function that returns an array with the index of its nonzero entires? E.g I have A 1048576x1 sparse matrix with 2 Float64 entries: [1 , 1] = 0.5 [32801 , 1] = 0.5 I want a function(A) -- [1,32801] Thanks! (I thought this wold be basic, but I couldn't find info in the documentation or the forums, sorry if I missed it)
[julia-users] Developping and debugging a module
Hello, I am developing a module. I load it using using module. Then I call a function from the module to test it out. If I get an error I fix it in module.jl but Julia is still using the older unfixed version of the module. Running using module again does not help so I have to quit Julia and start over to reflect the changes. I guess there is a smarter way ? :). Thanks in advance. Jan
[julia-users] Developping and debugging a module
If you include() the source file for the module, it will be replaced. Then you just have to make sure that you don't have any references to stuff in the old module that stick around. If you always reference things in the module via dot notation you should be fine. Another way, which I usually use, is to create a module for my test code as well, which I reload via include() after reloading the module (or that reloads the module before using it).
[julia-users] Re: Developping and debugging a module
There seems to an answer in the Julia FAQ but nevertheless any tips are welcome. Thanks, Jan Dňa piatok, 10. októbra 2014 11:04:34 UTC+2 Ján Dolinský napísal(-a): Hello, I am developing a module. I load it using using module. Then I call a function from the module to test it out. If I get an error I fix it in module.jl but Julia is still using the older unfixed version of the module. Running using module again does not help so I have to quit Julia and start over to reflect the changes. I guess there is a smarter way ? :). Thanks in advance. Jan
[julia-users] Re: Developping and debugging a module
Hello Toivo, Thanks for the tip. This is also the recommended way I found later in FAQ. Thanks a lot. Jan Dňa piatok, 10. októbra 2014 11:27:14 UTC+2 Toivo Henningsson napísal(-a): If you include() the source file for the module, it will be replaced. Then you just have to make sure that you don't have any references to stuff in the old module that stick around. If you always reference things in the module via dot notation you should be fine. Another way, which I usually use, is to create a module for my test code as well, which I reload via include() after reloading the module (or that reloads the module before using it).
[julia-users] Re: Installing Julia Studio properly on Windows 7 - Step by Step Instructions
Thanks Iain. So then how could I fix it? On Wednesday, November 27, 2013 8:17:32 AM UTC, Dominik Holenstein wrote: I needed several attempts until I could start working with Julia Studio on Windows 7. Most of the issues I faced were related to the package management and that Julia Studio creates the package folder for Julia automatically if you don't set the JULIA_PKGDIR path. This may work in most environments but it didn't on the notebook I use at work. These are the steps I had to follow to make Julia Studio (v. 0.43) running properly on Windows 7: - Set the JULIA_PKGDIR variable - Create a folder named for example *Julia Packages* on your computer. - I added it to the *My Documents* folder - I tried to create this folder in the Google Drive folder but this did not work properly - Go to Control Panel - System - Advanced System Settings - Environment Variables - Under *User variables* click *New...* and add this: - Variable Name: JULIA_PKGDIR - Variable Value: [the path to your *Julia Packages* folder] - Download and install Julia Studio: http://forio.com/julia/downloads/ - Start Julia Studio - No packages are installed. This is correct - If you are behind a firewall you may have to run this command in the console first: - run(`git config --global url.https://.insteadOf git://`) - Initialize the package repository now by running this command in the console: - Pkg.status() - Now you can start adding packages to your environment - http://docs.julialang.org/en/release-0.2/manual/packages/#adding-and-removing-packages - Done + Enjoy! Is this useful for you? Inputs, corrections, comments and questions are much appreciated. Regards, Dominik
Re: [julia-users] How to define rounding macros depending on type
On Friday, 10 October 2014 04:12:37 UTC+1, David P. Sanders wrote: I believe (but please correct me if I'm wrong) that I do need a macro, since I use it with whole expressions to ensure the correct rounding, for example (simplifying) something like @round_down( min(a*b, c*d) ) You can use with_rounding for this: with_rounding(() - min(a*b,c*d), T, RoundDown) or equivalently, with do syntax: with_rounding(T, RoundDown) do min(a*b,c*d) end -Simon
Re: [julia-users] Function to get Index of nonzero entries in sparse vector
On 10 October 2014 17:07, JVaz joanvazquezmol...@gmail.com wrote: I have a sparse vector, is there any function that returns an array with the index of its nonzero entires? E.g I have A 1048576x1 sparse matrix with 2 Float64 entries: [1 , 1] = 0.5 [32801 , 1] = 0.5 I want a function(A) -- [1,32801] The following should hold. A = sparse(zeros(4711)) idxs = rand(1:length(A), 17) A[idxs] = rand(17) @assert findnz(A)[1] == sort(idxs) A reference for the function. http://julia.readthedocs.org/en/release-0.3/stdlib/base/#Base.findnz (I thought this wold be basic, but I couldn't find info in the documentation or the forums, sorry if I missed it) No worries, what I usually do is use the page I linked and do a few text searches. Pontus
[julia-users] Re: Function to get Index of nonzero entries in sparse vector
Thanks Pontus! El viernes, 10 de octubre de 2014 17:07:25 UTC+9, JVaz escribió: Hello, I have a sparse vector, is there any function that returns an array with the index of its nonzero entires? E.g I have A 1048576x1 sparse matrix with 2 Float64 entries: [1 , 1] = 0.5 [32801 , 1] = 0.5 I want a function(A) -- [1,32801] Thanks! (I thought this wold be basic, but I couldn't find info in the documentation or the forums, sorry if I missed it)
[julia-users] Re: Loading data just once
I wrote this when I wanted to cache the results of matread. Your problem sounds like it could be similar. let matread_cache = (String = Any)[] global caching_matread function caching_matread(filename) if !haskey(matread_cache, filename) matread_cache[filename] = matread(filename) end return matread_cache[filename] end end Den torsdagen den 9:e oktober 2014 kl. 10:08:23 UTC+2 skrev cormu...@mac.com: A beginner's question... I'm writing a function that wants to load a set of data from a file, depending on an argument passed to the function (so a different argument requires a different set of data to be loaded). I'd like each set of data to be stored somehow in separate variables so that when the function is called again with the same argument, it doesn't have to load that particular data again (because it takes 5 seconds to load). I'm not sure whether this requires the use of global variables? I looked through the documents ( http://julia.readthedocs.org/en/latest/search/?q=global) but didn't gain enlightenment. :) I think I can test for the existence of a previously-defined variable using: if !isdefined(symbol(string(dataset))) dataset = include($(dataset).jl) end but I'm not convinced this works correctly, because this creates a variable inside the function...
Re: [julia-users] Re: Loading data just once
On 10 Oct 2014, at 12:46, Gunnar Farnebäck gun...@lysator.liu.se wrote: I wrote this when I wanted to cache the results of matread. Your problem sounds like it could be similar. let matread_cache = (String = Any)[] global caching_matread function caching_matread(filename) if !haskey(matread_cache, filename) matread_cache[filename] = matread(filename) end return matread_cache[filename] end end Looks pretty useful -- thanks!
Re: [julia-users] DataFrame groupby error
Frederico, the best way to solve this is to provide a reproducible example. If you file a bug report at the following with an example, there'll be less of a chance that this gets lost: https://github.com/JuliaStats/DataFrames.jl/issues/ On Wed, Oct 8, 2014 at 3:32 PM, Frederico Novaes frederico.nov...@gmail.com wrote: Hi, I have a DataFrame with 10 columns, say :c1 to :c10. I need to do a groupby, grouping by 4 of these columns. When I try, $ groupby(df,[:c1,:c3,:c4,:c7]) I get the following error: MemoryError() while loading In[13], in expression starting on line 1 in groupsort_indexer at /home/cloudopen/.julia/v0.3/DataArrays/src/grouping.jl:7 in groupsort_indexer at /home/cloudopen/.julia/v0.3/DataArrays/src/grouping.jl:5 in groupby at /home/cloudopen/.julia/v0.3/DataFrames/src/groupeddataframe/grouping.jl:44 Any help ? Thanks.
Re: [julia-users] How to define rounding macros depending on type
El viernes, 10 de octubre de 2014 05:15:48 UTC-5, Simon Byrne escribió: On Friday, 10 October 2014 04:12:37 UTC+1, David P. Sanders wrote: I believe (but please correct me if I'm wrong) that I do need a macro, since I use it with whole expressions to ensure the correct rounding, for example (simplifying) something like @round_down( min(a*b, c*d) ) You can use with_rounding for this: with_rounding(() - min(a*b,c*d), T, RoundDown) or equivalently, with do syntax: with_rounding(T, RoundDown) do min(a*b,c*d) end Thanks. I should have been more clear. I was previously using with_rounding, but in order to avoid this being littered through the code, I wanted to hide it somewhere, and this somewhere thus has to be a macro, in order to avoid the arithmetic being performed before the function was even called: macro round_down(expr)quote with_rounding(BigFloat, RoundDown) do $expr end endend (Not sure what happened to the indentation when copying from GitHub, sorry.) This works great when everything is BigFloats. But now I want to add the possibility that intervals store either Float64s or BigFloats, so I wanted the macro to also check the type of its argument, which is where my original code came from. One solution would be to add an explicit parameter T to the @round_down macro, e.g. the following which avoids eval at the expense of a repetitive code smell: macro new_round_down(expr, T) if T == :Float64 quote with_rounding(Float64, RoundDown) do $expr end end elseif T == :BigFloat quote with_rounding(BigFloat, RoundDown) do $expr end end end end But this again makes the code more messy, since the Interval type must now be parametrised (which may be a good idea anyway!)
Re: [julia-users] How to define rounding macros depending on type
Ah, I see. I don't have any good suggestions I'm afraid (other than change both rounding modes), but you can simplify your macro slightly: macro new_round_down(expr, T) quote with_rounding($T, RoundDown) do $expr end end end As someone who cares about rounding modes, you might also be interested in this discussion on the julia-dev list: https://groups.google.com/d/topic/julia-dev/yuq-2phXon0/discussion Simon On Friday, 10 October 2014 14:03:02 UTC+1, David P. Sanders wrote: El viernes, 10 de octubre de 2014 05:15:48 UTC-5, Simon Byrne escribió: On Friday, 10 October 2014 04:12:37 UTC+1, David P. Sanders wrote: I believe (but please correct me if I'm wrong) that I do need a macro, since I use it with whole expressions to ensure the correct rounding, for example (simplifying) something like @round_down( min(a*b, c*d) ) You can use with_rounding for this: with_rounding(() - min(a*b,c*d), T, RoundDown) or equivalently, with do syntax: with_rounding(T, RoundDown) do min(a*b,c*d) end Thanks. I should have been more clear. I was previously using with_rounding, but in order to avoid this being littered through the code, I wanted to hide it somewhere, and this somewhere thus has to be a macro, in order to avoid the arithmetic being performed before the function was even called: macro round_down(expr)quote with_rounding(BigFloat, RoundDown) do $expr end endend (Not sure what happened to the indentation when copying from GitHub, sorry.) This works great when everything is BigFloats. But now I want to add the possibility that intervals store either Float64s or BigFloats, so I wanted the macro to also check the type of its argument, which is where my original code came from. One solution would be to add an explicit parameter T to the @round_down macro, e.g. the following which avoids eval at the expense of a repetitive code smell: macro new_round_down(expr, T) if T == :Float64 quote with_rounding(Float64, RoundDown) do $expr end end elseif T == :BigFloat quote with_rounding(BigFloat, RoundDown) do $expr end end end end But this again makes the code more messy, since the Interval type must now be parametrised (which may be a good idea anyway!)
Re: [julia-users] How to define rounding macros depending on type
El viernes, 10 de octubre de 2014 08:16:26 UTC-5, Simon Byrne escribió: Ah, I see. I don't have any good suggestions I'm afraid (other than change both rounding modes), Actually my current branch does change both rounding modes ;) I guess the correct way to do this would be to nest `with_rounding(Float64, RoundDown)` inside `with_rounding(BigFloat, RoundDown)`? [By the way, does the `try` block inside `with_rounding` not lead to a performance penalty compared to just using `set_rounding`? But for some reason `with_rounding` avoids the problem of returning the rounding mode that was set instead of value I'm trying to calculate.) but you can simplify your macro slightly: macro new_round_down(expr, T) quote with_rounding($T, RoundDown) do $expr end end end Oh yes, that was exactly what I was looking for but for some reason I hadn't hit on that version. Many thanks! As someone who cares about rounding modes, you might also be interested in this discussion on the julia-dev list: https://groups.google.com/d/topic/julia-dev/yuq-2phXon0/discussion I had seen it but will take another look, thanks. David. Simon On Friday, 10 October 2014 14:03:02 UTC+1, David P. Sanders wrote: El viernes, 10 de octubre de 2014 05:15:48 UTC-5, Simon Byrne escribió: On Friday, 10 October 2014 04:12:37 UTC+1, David P. Sanders wrote: I believe (but please correct me if I'm wrong) that I do need a macro, since I use it with whole expressions to ensure the correct rounding, for example (simplifying) something like @round_down( min(a*b, c*d) ) You can use with_rounding for this: with_rounding(() - min(a*b,c*d), T, RoundDown) or equivalently, with do syntax: with_rounding(T, RoundDown) do min(a*b,c*d) end Thanks. I should have been more clear. I was previously using with_rounding, but in order to avoid this being littered through the code, I wanted to hide it somewhere, and this somewhere thus has to be a macro, in order to avoid the arithmetic being performed before the function was even called: macro round_down(expr)quote with_rounding(BigFloat, RoundDown) do $expr end endend (Not sure what happened to the indentation when copying from GitHub, sorry.) This works great when everything is BigFloats. But now I want to add the possibility that intervals store either Float64s or BigFloats, so I wanted the macro to also check the type of its argument, which is where my original code came from. One solution would be to add an explicit parameter T to the @round_down macro, e.g. the following which avoids eval at the expense of a repetitive code smell: macro new_round_down(expr, T) if T == :Float64 quote with_rounding(Float64, RoundDown) do $expr end end elseif T == :BigFloat quote with_rounding(BigFloat, RoundDown) do $expr end end end end But this again makes the code more messy, since the Interval type must now be parametrised (which may be a good idea anyway!)
Re: [julia-users] How to define rounding macros depending on type
On 10 October 2014 14:21, David P. Sanders dpsand...@gmail.com wrote: Actually my current branch does change both rounding modes ;) I guess the correct way to do this would be to nest `with_rounding(Float64, RoundDown)` inside `with_rounding(BigFloat, RoundDown)`? That should work. [By the way, does the `try` block inside `with_rounding` not lead to a performance penalty compared to just using `set_rounding`? But for some reason `with_rounding` avoids the problem of returning the rounding mode that was set instead of value I'm trying to calculate.) Possibly (I don't know enough about the internals), but the reason for it is so that if the function throws an exception, the rounding mode still gets reset.
[julia-users] Re: Structure and Interpretation of Classical Mechanics
Any updates on this? Amuthan On Tuesday, January 14, 2014 3:31:16 PM UTC-8, Brian Cohen wrote: Most code examples for Julia are aimed at users of existing statistical and numerical software without demonstrating how functional programming can be substantially more useful for their field. In many ways, Julia is a Lisp without S-Expressions, so I didn't think it would be unwise to port code examples from Structure and Interpretation of Classical Mechanics http://en.wikipedia.org/wiki/Structure_and_Interpretation_of_Classical_Mechanics from Scheme to Julia. This book shows how functional programming can be directly applied to the formalism of Lagrangian and Hamiltonian Mechanics, but Scheme SCMUtils http://groups.csail.mit.edu/mac/users/gjs/6946/refman.txt may be too obscure and syntactically different from what people are used to for most people in the physical sciences. Book review: http://www.ids.ias.edu/~piet/publ/other/sicm.html I was impatient, and decided to just start porting code, so the first example was easy enough: (define ((L-free-particle mass) local) (let ((v (velocity local))) (* 1/2 mass (dot-product v v would become something like lFreeParticle(mass) = tuple - begin v = velocity(tuple); mass * dot(v,v) / 2 end. So far so good. But it's not long that I encounter usage of SCMUTILS (define q (up (literal-function ’x) (literal-function ’y) (literal-function ’z))) So I turn to the Appendix which talks about Symbolic values in Scheme, and confirming it in the SCMUTILS documentation, it appears that symbols have the same type as real numbers, which seems very different than symbolic expressions as described in the Julia documentation. Here, up constructs a tuple, and literal-function is a constructor for symbolic manipulation. At this point, I'm not sure how to proceed, but am still looking into the matter. I see that the only packages that mention symbolic manipulation are a SymPy interface and a Calculus package.
Re: [julia-users] Re: Function roots() in package Polynomial
Related topic: I'd like to propose that roots and polyval be part of base. I can promise firsthand that they are among the first things a 12 year old user of Julia would just want to be there. On Tuesday, June 17, 2014 11:30:04 AM UTC-4, Stefan Karpinski wrote: On Tue, Jun 17, 2014 at 10:53 AM, Iain Dunning iaind...@gmail.com javascript: wrote: I see both Polynomial and Polynomials in METADATA - is Polynomials a replacement for Polynomial? Yes, Polynomials is the newer version with good indexing order – i.e. p[0] is the constant term. We should probably get this in better order. It may make sense to break the connection with the old repo and put it under some organization so that more people can work on it. What org would be most appropriate?
Re: [julia-users] Re: Structure and Interpretation of Classical Mechanics
I'm not quite certain about Scheme syntax. So L-free-particle is a function accepting one argument mass that returns another function accepting an argument local? And you are renaming local to tuple? (I would have gone with loc.) What do you mean by constructor for symbolic manipulation? Julia does support symbolic manipulation; this is e.g. used in macros, and there is a class Expr that holds unevaluated expressions. However, there are no built-in functions in Julia e.g. for differentiation or simplification of such expressions as you would find in a computer algebra system. Do SCMUTILS provide these? Let me take a wild guess: The function q assembles a tuple consisting of three functions. These functions are known by their names, but their definitions may not be available yet. This would read the following way in Julia: ``` q = quote (x,y,z) end ``` or in a more concise notation ``` q = :(x,y,z) ``` Later, in an environment where x, y, and z are defined, you can eval(q), as in eval(q)[1](value), which would be equivalent to x(value). -erik On Fri, Oct 10, 2014 at 9:32 AM, Amuthan A. Ramabathiran apar...@gmail.com wrote: Any updates on this? Amuthan On Tuesday, January 14, 2014 3:31:16 PM UTC-8, Brian Cohen wrote: Most code examples for Julia are aimed at users of existing statistical and numerical software without demonstrating how functional programming can be substantially more useful for their field. In many ways, Julia is a Lisp without S-Expressions, so I didn't think it would be unwise to port code examples from Structure and Interpretation of Classical Mechanics from Scheme to Julia. This book shows how functional programming can be directly applied to the formalism of Lagrangian and Hamiltonian Mechanics, but Scheme SCMUtils may be too obscure and syntactically different from what people are used to for most people in the physical sciences. Book review: http://www.ids.ias.edu/~piet/publ/other/sicm.html I was impatient, and decided to just start porting code, so the first example was easy enough: (define ((L-free-particle mass) local) (let ((v (velocity local))) (* 1/2 mass (dot-product v v would become something like lFreeParticle(mass) = tuple - begin v = velocity(tuple); mass * dot(v,v) / 2 end. So far so good. But it's not long that I encounter usage of SCMUTILS (define q (up (literal-function 'x) (literal-function 'y) (literal-function 'z))) So I turn to the Appendix which talks about Symbolic values in Scheme, and confirming it in the SCMUTILS documentation, it appears that symbols have the same type as real numbers, which seems very different than symbolic expressions as described in the Julia documentation. Here, up constructs a tuple, and literal-function is a constructor for symbolic manipulation. At this point, I'm not sure how to proceed, but am still looking into the matter. I see that the only packages that mention symbolic manipulation are a SymPy interface and a Calculus package. -- Erik Schnetter schnet...@cct.lsu.edu http://www.perimeterinstitute.ca/personal/eschnetter/
Re: [julia-users] Re: ANN: GeometricalPredicates.jl
just opened the PR for METADATA.jl
Re: [julia-users] Re: Function roots() in package Polynomial
Polynomials is a good candidate for “default packages” however that ends up being implemented. Installed by default for the vast majority of users who aren’t trying to do something with a “minimal Julia,” but not strictly necessary for the rest of the language to function. From: Alan Edelman Sent: Friday, October 10, 2014 8:42 AM To: julia-users@googlegroups.com Subject: Re: [julia-users] Re: Function roots() in package Polynomial Related topic: I'd like to propose that roots and polyval be part of base. I can promise firsthand that they are among the first things a 12 year old user of Julia would just want to be there. On Tuesday, June 17, 2014 11:30:04 AM UTC-4, Stefan Karpinski wrote: On Tue, Jun 17, 2014 at 10:53 AM, Iain Dunning iaind...@gmail.com wrote: I see both Polynomial and Polynomials in METADATA - is Polynomials a replacement for Polynomial? Yes, Polynomials is the newer version with good indexing order – i.e. p[0] is the constant term. We should probably get this in better order. It may make sense to break the connection with the old repo and put it under some organization so that more people can work on it. What org would be most appropriate?
[julia-users] Multi-OS (Linux + Mac) testing in Travis
Heads up for package developers - looks like Travis got some additional
capacity and is accepting new repositories for multi-OS support. See
http://docs.travis-ci.com/user/multi-os/ - you need to send an email to
supp...@travis-ci.com asking them to enable multi-OS support for your
package, with a link to the repository. Then change your .travis.yml as
follows:
Add
```
os:
- linux
- osx
```
Replace
```
before_install:
- sudo add-apt-repository ppa:staticfloat/julia-deps -y
- sudo add-apt-repository ppa:staticfloat/${JULIAVERSION} -y
- sudo apt-get update -qq -y
- sudo apt-get install libpcre3-dev julia -y
```
with
```
before_install:
- if [ `uname` = Linux ]; then
sudo add-apt-repository ppa:staticfloat/julia-deps -y;
sudo add-apt-repository ppa:staticfloat/${JULIAVERSION} -y;
sudo apt-get update -qq -y;
sudo apt-get install libpcre3-dev julia -y;
elif [ `uname` = Darwin ]; then
if [ $JULIAVERSION = julianightlies ]; then
wget -O julia.dmg http://status.julialang.org/download/osx10.7+;;
else
wget -O julia.dmg http://status.julialang.org/stable/osx10.7+;;
fi;
hdiutil mount julia.dmg;
cp -Ra /Volumes/Julia/*.app/Contents/Resources/julia ~;
export PATH=$PATH:$(echo ~)/julia/bin;
fi
```
Elliot got this working a while back when Travis originally announced the
feature, I just tweaked it a little so it works whether or not you've
enabled multi-OS support. To force a build on a Mac worker before Travis
responds to your email, you can temporarily change `language: cpp` to
`language: objective-c`. I think you'll need to change it back to get
builds on both Linux and Mac once support is enabled.
I'll probably make a PR to base to turn this on in the default .travis.yml
from Pkg.generate, since it doesn't hurt anything.
-Tony
[julia-users] Re: JUNO: Couldn't Connect to Julia
Hi Thomas,
I've decided to wait until 0.4 has settled down a bit before supporting it
in Juno, so it's best to grab the latest release of Julia (v0.3). Building
from git should work fine but you could also try using the Ubuntu packages
https://launchpad.net/~staticfloat/+archive/ubuntu/juliareleases if
that's not working for you.
On Thursday, 9 October 2014 03:15:13 UTC+1, Thomas Moore wrote:
I've been trying to get Juno to work (a Julia plugin for Light Table), but
after following the installation instructions on their website
http://junolab.org/docs/installing.html I end up getting the following
error, both on startup and when attempting to execute Julia commands:
Couldn't connect to Julia
WARNING: deprecated syntax (eltype(xs)=Int)[] at
/home/thomas/.julia/v0.4/Lazy/src/collections.jl:23.
Use Dict{eltype(xs),Int}() instead.
WARNING: deprecated syntax (String=Vector{Function})[] at
/home/thomas/.julia/v0.4/Jewel/src/lazymod.jl:8.
Use Dict{String,Vector{Function}}() instead.
ERROR: InexactError()
in char at char.jl:1
in include at ./boot.jl:245
in include_from_node1 at ./loading.jl:128
in include at ./boot.jl:245
in include_from_node1 at ./loading.jl:128
in reload_path at ./loading.jl:152
in _require at ./loading.jl:67
in require at ./loading.jl:54
in require_3B_3948 at /home/thomas/julia/usr/bin/../lib/julia/sys.so
in require at /home/thomas/.julia/v0.4/Jewel/src/lazymod.jl:2
in include at ./boot.jl:245
in include_from_node1 at ./loading.jl:128
in include at ./boot.jl:245
in include_from_node1 at ./loading.jl:128
in include at ./boot.jl:245
in include_from_node1 at ./loading.jl:128
in reload_path at ./loading.jl:152
in _require at ./loading.jl:67
in require at ./loading.jl:52
in require_3B_3948 at /home/thomas/julia/usr/bin/../lib/julia/sys.so
in include at ./boot.jl:245
in include_from_node1 at loading.jl:128
in process_options at ./client.jl:293
in _start at ./client.jl:362
in _start_3B_3774 at /home/thomas/julia/usr/bin/../lib/julia/sys.so
while loading /home/thomas/.julia/v0.4/JuliaParser/src/lexer.jl, in
expression starting on line 12
while loading /home/thomas/.julia/v0.4/JuliaParser/src/JuliaParser.jl, in
expression starting on line 5
while loading /home/thomas/.julia/v0.4/Jewel/src/parse/scope.jl, in
expression starting on line 4
while loading /home/thomas/.julia/v0.4/Jewel/src/parse/parse.jl, in
expression starting on line 1
while loading /home/thomas/.julia/v0.4/Jewel/src/Jewel.jl, in expression
starting on line 7
while loading /home/thomas/.config/LightTable/plugins/Julia/jl/init.jl, in
expression starting on line 27
I'm on Ubuntu 14.04 and have tried re-installing Julia (now on Version
0.4.0-dev+1021 (2014-10-08 21:56 UTC) though I was on v0.3 before (it seems
github automatically gave me the 0.4 version :) )) and also LightTable, but
I haven't had any luck.
Any advice?
Re: [julia-users] Behaviour of expanduser is inconsistent between platforms.
Hmmm... looking at how bash handles this it doesn't seem too difficult, I might give it a go over the weekend. I have no idea how to handle it on Windows though. On Thursday, 9 October 2014 18:06:07 UTC+1, Stefan Karpinski wrote: This is definitely a bug. The implementation on both platforms seems to be a hack – a more correct implementation would be a great contribution. It should also be possible to map arbitrary user names to user IDs and user IDs to user metadata. On Thu, Oct 9, 2014 at 1:00 PM, Sean Marshallsay srm@gmail.com javascript: wrote: I noticed the other day that calling expanduser on an empty string throws a BoundsError when using a UNIX platform. Whether this is a bug or a feature will probably depend entirely on who you ask. More importantly though, in my eyes, doing the same thing on Windows returns an empty string instead. This inconsistency definitely seems like a bug to me. In my eyes the UNIX implementation should be changed to check for a empty string and return it but I was wondering what other people thought of this?
Re: [julia-users] Behaviour of expanduser is inconsistent between platforms.
How does bash handle it? On Fri, Oct 10, 2014 at 3:22 PM, Sean Marshallsay srm.1...@gmail.com wrote: Hmmm... looking at how bash handles this it doesn't seem too difficult, I might give it a go over the weekend. I have no idea how to handle it on Windows though. On Thursday, 9 October 2014 18:06:07 UTC+1, Stefan Karpinski wrote: This is definitely a bug. The implementation on both platforms seems to be a hack – a more correct implementation would be a great contribution. It should also be possible to map arbitrary user names to user IDs and user IDs to user metadata. On Thu, Oct 9, 2014 at 1:00 PM, Sean Marshallsay srm@gmail.com wrote: I noticed the other day that calling expanduser on an empty string throws a BoundsError when using a UNIX platform. Whether this is a bug or a feature will probably depend entirely on who you ask. More importantly though, in my eyes, doing the same thing on Windows returns an empty string instead. This inconsistency definitely seems like a bug to me. In my eyes the UNIX implementation should be changed to check for a empty string and return it but I was wondering what other people thought of this?
[julia-users] more implementations of convert()
Hi, I seem to need convert() a lot, and especially for arrays this is somewhat of a nuisance. E.g., convert(Float32, rand(2,3)) does not work out of the box. Luckily, for the numeric types there are the functions float64(), float32() etc that can operate on arrays. This is all good, but for parameterized functions/types that need to convert to type T : Real it seems a bit of a hassle to find the accompanying convenience function for type T. So I wonder, would it be possible to include in Base a function convert(T::Type) = the convenience function for T so that you could say t = Float32 convert(t)(randn(2,3)) My current solution for such conversion is map(x-convert(t,x), randn(2,3)) but that doesn't seem great. An alternative would be to include in Base a number of declarations like convert(Float32, x) = float32(x) What is your view on this? ---david
Re: [julia-users] PPA for Ubuntu 14.04 (Trusty Tahr)
It is 0.3.1~trusty2. The crash message in the terminal only says core generated (in Spanish). I suppose it means that there is a dump file somewhere with the details, but I cannot find it. If somebody knows where should I look for it, I would be happy to post those details. Thanks Helios On Thu, Oct 9, 2014 at 8:47 AM, Elliot Saba staticfl...@gmail.com wrote: Hello Helios, can you tell me what version of the package you have installed? You can get this via dpkg -l julia. I'm looking for a version number like 0.3.1~trusty1 or similar. Also, can you copy the message given at the crash here? Thanks, -E On Wed, Oct 8, 2014 at 5:56 PM, K Leo cnbiz...@gmail.com wrote: On Xubuntu 14.04 64, it seems to work fine: _ _ _ _(_)_ | A fresh approach to technical computing (_) | (_) (_)| Documentation: http://docs.julialang.org _ _ _| |_ __ _ | Type help() for help. | | | | | | |/ _` | | | | |_| | | | (_| | | Version 0.3.1 (2014-09-21 21:30 UTC) _/ |\__'_|_|_|\__'_| | Official http://julialang.org release |__/ | x86_64-linux-gnu On 2014年10月09日 08:50, Helios De Rosario wrote: Hi again, I saw that the 0.3.1 version of Julia was already available to install from the juliareleases repository, and tried to install it. But when I start Julia now it crashes. It generates a core file and suggests sending a report (I accepted). Somebody else has experienced this? I am using Lubuntu 14.04 in an i386 machine. Julia 0.2.1 from the main Ubuntu repository worked normally. How could I report more details to help debug this problem? Thanks for your support Helios De Rosario
Re: [julia-users] PPA for Ubuntu 14.04 (Trusty Tahr)
What kind of computer do you have? Is it a 32-bit machine or 64-bit? Can you try running julia inside of gdb and giving any information on what gdb says such as a backtrace showing where it crashed? To run julia inside of gdb, just run gdb julia, then when gdb is loaded press r to run julia, and when it crashes, enter bt to get a backtrace. On Fri, Oct 10, 2014 at 12:58 PM, Helios De Rosario helios.derosa...@gmail.com wrote: It is 0.3.1~trusty2. The crash message in the terminal only says core generated (in Spanish). I suppose it means that there is a dump file somewhere with the details, but I cannot find it. If somebody knows where should I look for it, I would be happy to post those details. Thanks Helios On Thu, Oct 9, 2014 at 8:47 AM, Elliot Saba staticfl...@gmail.com wrote: Hello Helios, can you tell me what version of the package you have installed? You can get this via dpkg -l julia. I'm looking for a version number like 0.3.1~trusty1 or similar. Also, can you copy the message given at the crash here? Thanks, -E On Wed, Oct 8, 2014 at 5:56 PM, K Leo cnbiz...@gmail.com wrote: On Xubuntu 14.04 64, it seems to work fine: _ _ _ _(_)_ | A fresh approach to technical computing (_) | (_) (_)| Documentation: http://docs.julialang.org _ _ _| |_ __ _ | Type help() for help. | | | | | | |/ _` | | | | |_| | | | (_| | | Version 0.3.1 (2014-09-21 21:30 UTC) _/ |\__'_|_|_|\__'_| | Official http://julialang.org release |__/ | x86_64-linux-gnu On 2014年10月09日 08:50, Helios De Rosario wrote: Hi again, I saw that the 0.3.1 version of Julia was already available to install from the juliareleases repository, and tried to install it. But when I start Julia now it crashes. It generates a core file and suggests sending a report (I accepted). Somebody else has experienced this? I am using Lubuntu 14.04 in an i386 machine. Julia 0.2.1 from the main Ubuntu repository worked normally. How could I report more details to help debug this problem? Thanks for your support Helios De Rosario
Re: [julia-users] more implementations of convert()
I think the problem is in deciding when do I use the equivalent of map? and when do I convert as a whole object? For example, if I say convert(Image, A) I'm not asking to convert each _element_ of A to an Image, I'm asking to convert _A as a whole_ to an Image. But now let's say I had an array-of- arrays; then I might wish this would convert each element of the outer array to its own Image. Of course, this would go horribly awry if I had decided to represent a single image as an array of 3-vectors (for r,g,b)---I'd get something completely unexpected out of that. So basically, once you can have arrays-of-arrays-of-arrays-of..., it gets to be a little tricky to read the mind of whoever called that function. That said, there is a place for convenience functions that make certain assumptions, especially in limited domains. Images, in fact, does that. But the base convert function, being so fundamental to so many things, is purposefully a bit finicky. --Tim On Friday, October 10, 2014 12:54:57 PM David van Leeuwen wrote: Hi, I seem to need convert() a lot, and especially for arrays this is somewhat of a nuisance. E.g., convert(Float32, rand(2,3)) does not work out of the box. Luckily, for the numeric types there are the functions float64(), float32() etc that can operate on arrays. This is all good, but for parameterized functions/types that need to convert to type T : Real it seems a bit of a hassle to find the accompanying convenience function for type T. So I wonder, would it be possible to include in Base a function convert(T::Type) = the convenience function for T so that you could say t = Float32 convert(t)(randn(2,3)) My current solution for such conversion is map(x-convert(t,x), randn(2,3)) but that doesn't seem great. An alternative would be to include in Base a number of declarations like convert(Float32, x) = float32(x) What is your view on this? ---david
[julia-users] QZ Decomposition Reordering
Can the schurfact(A,B) function reorder its output such that generalized eigenvalues appear in descending (or ascending) magnitude order? I'm thinking on something like MATLAB's ordqz or R's ordqz (in QZ package). I'm really new to Julia (installed it just a few minutes ago, actually), but as far as I can tell there is no select option for this function. best, Ricardo
[julia-users] Advise on Julia composite type to replace MATLAB struct
Dear Julia users, For a computationally challenging problem I'm trying to port an existing design-optimization from MATLAB to Julia. Currently, the designs are organized in a MATLAB struct and I am looking for some advice on how to efficiently store and distribute the data for a parallel evaluation in Julia. The idea of the MATLAB algorithm is to make some random changes in each design, re-calculate their effiency (using everything in the MATLAB struct) within a parallel loop, and sorting the designs based on their updated efficiency. Below is how the data is organized in Matlab. I figured a composite type ( http://julia.readthedocs.org/en/latest/manual/types/ ) would be the relevant Julia structure, but I am not sure how to create a vector of composite types and whether to use type or immutable. Hence, a specific suggestion about how to do this in Julia would be much appreciated! Thank you and best regards, Marcel % setup structure to save designs (only once) for d = 1:100 design(d).efficiency = 99; design(d).ind_eff= zeros(8,1); design(d).design = zeros(14,12,8); design(d).X = zeros(196,22,8); design(d).dX= zeros(144,22,8); for subNr=1:8 for drawNr = 1:1 design(d).draws(subNr,drawNr).V = zeros(144,1); design(d).draws(subNr,drawNr).expV= zeros(144,1); design(d).draws(subNr,drawNr).sumExpV = zeros(144,1); design(d).draws(subNr,drawNr).P = zeros(144,1); design(d).draws(subNr,drawNr).PdX = zeros(144, 22); design(d).draws(subNr,drawNr).sumPdX = zeros(144, 22); design(d).draws(subNr,drawNr).ZZ = zeros(22,22); end end end % update efficiency parfor d = 1:100 updateEff( design(d) ); end % sort designs based on updated efficiencies [~,v]=sort( [design.efficiency] ); design=design(v);
Re: [julia-users] ANN: GeometricalPredicates.jl
This is pretty cool. Writing a robust set of geometric predicates requires quite an attention to detail. Some questions: Restricting to the float range 1.0=x2.0 essentially makes the input a fixed point representation, with fixed point scaling factor eps(1.0) = 2.220446049250313e-16. How does this compare to telling the user to rescale their variables onto an integer lattice? Using an integer lattice arguably exposes the exact nature of the rescaling a little more clearly though it might just be a pain to work with. I expect that rescaling a set of points fails to preserve geometric predicates between them. If so, a comparison to CGAL performance may not be entirely fair if they keep their vertices in the original position. Having said that, I think it's a totally practical tradeoff - one that I'd certainly be willing to make for a bit of extra speed. ~Chris On Fri, Oct 10, 2014 at 5:34 AM, Ariel Keselman skar...@gmail.com wrote: Fast and robust 2D 3D geometrical predicates. For documentation see here: https://github.com/skariel/GeometricalPredicates.jl This is used in a Delaunay/Voronoi implementation which I'll also package which is faster than CGAL. In addition, it could be used as the basis for a fast and robust geometry library, much like CGAL. read about how here: https://www.cgal.org/philosophy.html
[julia-users] About usage in Emacs (not display prompt of Julia)
Hello I like Julia very much. I switched from Scheme. By the way, please tell me usage in Emacs(on WIndows7). I'm in trouble that prompt is not displayed in the Shell. I'm guessing Julia display prompt without STDIO. Yours sincerely example of running in shell Copyright (c) 2009 Microsoft Corporation. All rights reserved. C:\Program Files\Julia\Julia-0.3.1\binjulia julia 1 1 println(Hello world) Hello world sin(1) 0.8414709848078965 exit() C:\Program Files\Julia\Julia-0.3.1\bin
Re: [julia-users] iterating through permutations in parallel
thank you, this is very helpful. On Wednesday, October 8, 2014 9:18:14 AM UTC-4, David Gonzales wrote: here is more source code sample for parallel permutation processing. this code goes over all permutation of `keys` and counts the number of cycles into `resall`: https://gist.github.com/dvdgonzales17/2ebb7fd07af9c93994fc#file-parallel-permutation-loop On Wednesday, October 8, 2014 3:20:12 AM UTC+3, Jason Solack wrote: thank you for your input! I will try your suggestions! On Tuesday, October 7, 2014 3:39:46 PM UTC-4, Jiahao Chen wrote: This is not a good strategy since your code generates all the permutations explicitly in memory, and there are an exponentially large number of them. Instead you could loop though k=1:factorial(n) and generate the kth permutation programmatically using nthperm(the_keys, k). If your computation performs a reduction you can use the @parallel (+) construct (or something similar), otherwise you can distribute the work manually across the p processors and write a loop like for p in 1:nprocs() #You'll have to do the rounding more carefully to avoid missing permutations on the edges @spawnat procs()[p] for k=iround((p-1)*n/nprocs())+1:iround(p*n/nprocs()) do_stuff() end end Thanks, Jiahao Chen Staff Research Scientist MIT Computer Science and Artificial Intelligence Laboratory On Mon, Oct 6, 2014 at 8:57 PM, Jason Solack jays...@gmail.com wrote: Hello everyone, I'm trying to iterate through a collection of permutations in parallel and i'm having trouble iterating through the collection. In the code below i'm using next(p) in the place i'd like to grab the next permutation. This is also the first bit of processing i've done in parallel in Julia so if have any pointers on how i could do this more easily i'd appreciate any advice. np = nprocs() output = Dict() p = permutations(the_keys) on_perm = 1 @sync begin for on_proc=1:np if p != myid() || np == 1 the_perm = next(p) @async begin while true output[on_perm] = remotecall_fetch(on_proc, do_calcs, the_perm) on_perm += 1 if on_perm length(p) break end end end end end end return output Thank you for your help. Jason
[julia-users] Re: About usage in Emacs (not display prompt)
There is not currently a solution to this problem. There is an issue tracking it: https://github.com/JuliaLang/julia/issues/5271 On Friday, October 10, 2014 8:52:00 PM UTC-5, kenichi sasagawa wrote: Hello I like Julia very much. I switched from Scheme. By the way, please tell me usage in Emacs(on WIndows7). I'm in trouble that prompt is not displayed in the Shell. I'm guessing Julia display prompt without STDIO. Yours sincerely example of running in shell Copyright (c) 2009 Microsoft Corporation. All rights reserved. C:\Program Files\Julia\Julia-0.3.1\binjulia julia 1 1 println(Hello world) Hello world sin(1) 0.8414709848078965 exit() C:\Program Files\Julia\Julia-0.3.1\bin
[julia-users] This week in Julia
This is an experiment. I think it'd be really amazing to have weekly updates about what's going on in Julia master, particularly during this crazy 0.4-dev period. So I figured I'd give it a shot. Take a look: http://thisweekinjulia.github.io/julia/2014/10/10/October-10.html I first tried a post like this two weeks ago over on reddit and it was pretty well received. But I think GitHub pages will make creating these posts much simpler. No, this doesn't replace NEWS.md (that's where I glean a lot of this information from!), and I *really* don't expect folks who are implementing the features and changes to be updating this blog. But I think it'd be great if other folks would help me keep it up-to-date. Pull requests and collaborators are very welcome! https://github.com/thisweekinjulia/thisweekinjulia.github.io
Re: [julia-users] This week in Julia
This is great. Honestly, I can't believe that some of these things were only 2 weeks ago. It feels so much longer. ;) -E On Fri, Oct 10, 2014 at 7:30 PM, Matt Bauman mbau...@gmail.com wrote: This is an experiment. I think it'd be really amazing to have weekly updates about what's going on in Julia master, particularly during this crazy 0.4-dev period. So I figured I'd give it a shot. Take a look: http://thisweekinjulia.github.io/julia/2014/10/10/October-10.html I first tried a post like this two weeks ago over on reddit and it was pretty well received. But I think GitHub pages will make creating these posts much simpler. No, this doesn't replace NEWS.md (that's where I glean a lot of this information from!), and I *really* don't expect folks who are implementing the features and changes to be updating this blog. But I think it'd be great if other folks would help me keep it up-to-date. Pull requests and collaborators are very welcome! https://github.com/thisweekinjulia/thisweekinjulia.github.io
[julia-users] Code slower if not enclosed in a function?
This code runs in 0.5 sec in v0.3.2, but takes 0.64 s in v0.4: tic() N = 256 n = 80 x = rand(N,N) + 1im*randn(N,N) f = zeros(Complex128, N, N, n) for t = 1:n f[:,:,t] = fft(x + float(t)) end toc() If I enclose it in a function, as in the following, and then run it by calling the function, it runs in 0.3 s in both v0.3.2 and v0.4. function bench() tic() N = 256 n = 80 x = rand(N,N) + 1im*randn(N,N) f = zeros(Complex128, N, N, n) for t = 1:n f[:,:,t] = fft(x + float(t)) end toc() end bench() What is going on?
[julia-users] Re: Advise on Julia composite type to replace MATLAB struct
A composite type is the right data structure, given that you have large arrays you need to store in your type. -viral On Saturday, October 11, 2014 4:43:50 AM UTC+5:30, mfjo...@hotmail.com wrote: Dear Julia users, For a computationally challenging problem I'm trying to port an existing design-optimization from MATLAB to Julia. Currently, the designs are organized in a MATLAB struct and I am looking for some advice on how to efficiently store and distribute the data for a parallel evaluation in Julia. The idea of the MATLAB algorithm is to make some random changes in each design, re-calculate their effiency (using everything in the MATLAB struct) within a parallel loop, and sorting the designs based on their updated efficiency. Below is how the data is organized in Matlab. I figured a composite type ( http://julia.readthedocs.org/en/latest/manual/types/ ) would be the relevant Julia structure, but I am not sure how to create a vector of composite types and whether to use type or immutable. Hence, a specific suggestion about how to do this in Julia would be much appreciated! Thank you and best regards, Marcel % setup structure to save designs (only once) for d = 1:100 design(d).efficiency = 99; design(d).ind_eff= zeros(8,1); design(d).design = zeros(14,12,8); design(d).X = zeros(196,22,8); design(d).dX= zeros(144,22,8); for subNr=1:8 for drawNr = 1:1 design(d).draws(subNr,drawNr).V = zeros(144,1); design(d).draws(subNr,drawNr).expV= zeros(144,1); design(d).draws(subNr,drawNr).sumExpV = zeros(144,1); design(d).draws(subNr,drawNr).P = zeros(144,1); design(d).draws(subNr,drawNr).PdX = zeros(144, 22); design(d).draws(subNr,drawNr).sumPdX = zeros(144, 22); design(d).draws(subNr,drawNr).ZZ = zeros(22,22); end end end % update efficiency parfor d = 1:100 updateEff( design(d) ); end % sort designs based on updated efficiencies [~,v]=sort( [design.efficiency] ); design=design(v);
[julia-users] Re: Code slower if not enclosed in a function?
http://julia.readthedocs.org/en/release-0.3/manual/performance-tips/#avoid-global-variables On Friday, October 10, 2014 10:56:36 PM UTC-5, David Smith wrote: This code runs in 0.5 sec in v0.3.2, but takes 0.64 s in v0.4: tic() N = 256 n = 80 x = rand(N,N) + 1im*randn(N,N) f = zeros(Complex128, N, N, n) for t = 1:n f[:,:,t] = fft(x + float(t)) end toc() If I enclose it in a function, as in the following, and then run it by calling the function, it runs in 0.3 s in both v0.3.2 and v0.4. function bench() tic() N = 256 n = 80 x = rand(N,N) + 1im*randn(N,N) f = zeros(Complex128, N, N, n) for t = 1:n f[:,:,t] = fft(x + float(t)) end toc() end bench() What is going on?
[julia-users] Re: This week in Julia
Really nice to have. Perhaps publish as a blog and post on juliabloggers.com? -viral On Saturday, October 11, 2014 8:00:11 AM UTC+5:30, Matt Bauman wrote: This is an experiment. I think it'd be really amazing to have weekly updates about what's going on in Julia master, particularly during this crazy 0.4-dev period. So I figured I'd give it a shot. Take a look: http://thisweekinjulia.github.io/julia/2014/10/10/October-10.html I first tried a post like this two weeks ago over on reddit and it was pretty well received. But I think GitHub pages will make creating these posts much simpler. No, this doesn't replace NEWS.md (that's where I glean a lot of this information from!), and I *really* don't expect folks who are implementing the features and changes to be updating this blog. But I think it'd be great if other folks would help me keep it up-to-date. Pull requests and collaborators are very welcome! https://github.com/thisweekinjulia/thisweekinjulia.github.io
[julia-users] Re: This week in Julia
Is there an RSS feed already? -viral On Saturday, October 11, 2014 9:34:47 AM UTC+5:30, Viral Shah wrote: Really nice to have. Perhaps publish as a blog and post on juliabloggers.com? -viral On Saturday, October 11, 2014 8:00:11 AM UTC+5:30, Matt Bauman wrote: This is an experiment. I think it'd be really amazing to have weekly updates about what's going on in Julia master, particularly during this crazy 0.4-dev period. So I figured I'd give it a shot. Take a look: http://thisweekinjulia.github.io/julia/2014/10/10/October-10.html I first tried a post like this two weeks ago over on reddit and it was pretty well received. But I think GitHub pages will make creating these posts much simpler. No, this doesn't replace NEWS.md (that's where I glean a lot of this information from!), and I *really* don't expect folks who are implementing the features and changes to be updating this blog. But I think it'd be great if other folks would help me keep it up-to-date. Pull requests and collaborators are very welcome! https://github.com/thisweekinjulia/thisweekinjulia.github.io
[julia-users] Re: Multi-OS (Linux + Mac) testing in Travis
Now, if only they had Windows!
-viral
On Friday, October 10, 2014 11:19:26 PM UTC+5:30, Tony Kelman wrote:
Heads up for package developers - looks like Travis got some additional
capacity and is accepting new repositories for multi-OS support. See
http://docs.travis-ci.com/user/multi-os/ - you need to send an email to
supp...@travis-ci.com asking them to enable multi-OS support for your
package, with a link to the repository. Then change your .travis.yml as
follows:
Add
```
os:
- linux
- osx
```
Replace
```
before_install:
- sudo add-apt-repository ppa:staticfloat/julia-deps -y
- sudo add-apt-repository ppa:staticfloat/${JULIAVERSION} -y
- sudo apt-get update -qq -y
- sudo apt-get install libpcre3-dev julia -y
```
with
```
before_install:
- if [ `uname` = Linux ]; then
sudo add-apt-repository ppa:staticfloat/julia-deps -y;
sudo add-apt-repository ppa:staticfloat/${JULIAVERSION} -y;
sudo apt-get update -qq -y;
sudo apt-get install libpcre3-dev julia -y;
elif [ `uname` = Darwin ]; then
if [ $JULIAVERSION = julianightlies ]; then
wget -O julia.dmg http://status.julialang.org/download/osx10.7+
;
else
wget -O julia.dmg http://status.julialang.org/stable/osx10.7+;;
fi;
hdiutil mount julia.dmg;
cp -Ra /Volumes/Julia/*.app/Contents/Resources/julia ~;
export PATH=$PATH:$(echo ~)/julia/bin;
fi
```
Elliot got this working a while back when Travis originally announced the
feature, I just tweaked it a little so it works whether or not you've
enabled multi-OS support. To force a build on a Mac worker before Travis
responds to your email, you can temporarily change `language: cpp` to
`language: objective-c`. I think you'll need to change it back to get
builds on both Linux and Mac once support is enabled.
I'll probably make a PR to base to turn this on in the default .travis.yml
from Pkg.generate, since it doesn't hurt anything.
-Tony
[julia-users] Re: About usage in Emacs (not display prompt)
Thank you On Saturday, October 11, 2014 11:27:29 AM UTC+9, Patrick O'Leary wrote: There is not currently a solution to this problem. There is an issue tracking it: https://github.com/JuliaLang/julia/issues/5271 On Friday, October 10, 2014 8:52:00 PM UTC-5, kenichi sasagawa wrote: Hello I like Julia very much. I switched from Scheme. By the way, please tell me usage in Emacs(on WIndows7). I'm in trouble that prompt is not displayed in the Shell. I'm guessing Julia display prompt without STDIO. Yours sincerely example of running in shell Copyright (c) 2009 Microsoft Corporation. All rights reserved. C:\Program Files\Julia\Julia-0.3.1\binjulia julia 1 1 println(Hello world) Hello world sin(1) 0.8414709848078965 exit() C:\Program Files\Julia\Julia-0.3.1\bin
[julia-users] Re: Multi-OS (Linux + Mac) testing in Travis
Wow, this is exactly what I need. As I just got Travis functional last night for the first time @ 3 (you should see the crazy binding.gyp file), I feel the universe is reaching out to me. Thanks, Tony, thanks, universe.
Re: [julia-users] Re: Structure and Interpretation of Classical Mechanics
PS I didn't know about JuliaDiff, but it looks like an interesting effort to do in Julia some of the same things that ScmUtils does in Scheme. See http://www.juliadiff.org/ haven't used any of the packages, but one may be able to port some of ScmUtils using this.
