I have a weird bug in the promotion system where promote_type(a,b) is not
equal to promote_type(b,a). It looks like the function call on line 101 of
promotion.jl for one ordering is being called even though a≠b. a and b
are different template variable choices of the same type. @code_typed
H, somehow changing it so that one templated parameter was
consistently Domain{T:Number} instead of a mix of Domain{T:Number} and
Interval{T:Number} fixed the bug.
Is it true that I should only be using concrete types as template
parameters? So Interval{Float64} instead
To expand a little:
julia 0.3: doesn't (and won't) support irregular (Vector{Int}) indexes in
SubArrays
julia 0.4: does support Vector{Int} indexes in SubArrays
BLAS/LAPACK: does not support, and presumably never will support, subarrays
with irregular indexing.
So as Andreas said, you can do
It's not a 4-by-2 array, it's a 4-vector of 2-vectors. If you want to create a
4-by-2 array, use
chunky = [a*b for a in arr, b in (, d)]
--Tim
On Saturday, March 28, 2015 05:36:50 PM Anthony Voutas wrote:
None of the above are working for me.
s = string
arr = [s,s,s,s]
transform = (x -
Makes sense. Was just thinking naively of the start and next functions
using tuples, as they would know the proper dimensions etc but I bet this
is what Cartesian index does and now I should look at it :-)
On Saturday, March 28, 2015, Tim Holy tim.h...@gmail.com wrote:
Right now
Le dimanche 29 mars 2015 à 09:33 -0400, Stefan Karpinski a écrit :
Why is it odd?
I understand that this behavior can be confusing the first time you
experience it. Since multiple dispatch is one of Julia strong points, I
also expected at first that all methods with the same name would be
merged
Thank you for the info.
On Saturday, March 28, 2015 at 7:56:54 PM UTC-5, Yee Sian Ng wrote:
If you use tab-completion in an interactive session,
julia spones(
spones{T}(S::SparseMatrixCSC{T,Ti:Integer}) at sparse/sparsematrix.jl:401
you'll see that the spones() function in Base only
Thank you Big Stone, I removed 0.3.7, Ipython, .julia/0.3 and the .ipython
folder in my home folder and the installed from fresh and it works:)
Why is it odd?
On Mar 28, 2015, at 10:16 PM, kevin.dale.sm...@gmail.com wrote:
On Saturday, March 28, 2015 at 4:19:44 PM UTC-5, Mauro wrote:
Now, generic functions carry
around with them the module in which they were first defined. To extend
such a function with another method in
Thanks for the info. I suspected it might have something to do with JIT
compilation.
On Saturday, March 28, 2015 at 2:16:54 PM UTC+1, Isaiah wrote:
This delay is due to parsing and JIT'ing a bunch of code in both Gadfly
and dependencies.
There is a work-in-progress caching process you
Thanks Tim!
I wouldn't say that generic functions remember in which module they were first
defined. Rather, a `using` declaration is kind of a weak import that says to
only look for a name in the given module if it is not found in the current
module. So when defining a method in the current module, no
Btw there's still some deprecations. Not sure how to get rid of them,
especially while keeping compatibility with Julia 0.3.
Le dimanche 29 mars 2015 à 09:46 -0700, Philip Tellis a écrit :
On Sunday, March 29, 2015 at 6:47:32 AM UTC-4, Milan Bouchet-Valat
wrote:
Le samedi 28 mars 2015 à 21:35 -0700, Philip Tellis a écrit :
I've written the following code:
So I'm building a program that does the following:
* Have data stored either in a file (CSV or gzipped CSV) or a Julia
structure (Array{T,2}, or other structures that support getindex(A,i,j))
* Need to do a POST request over HTTPS with Content-Type: text/csv, and
ideally always as
0.3 does not have the ARM related patches in Julia. I doubt it will work on
0.3 even if you get it compiled.
Even on 0.4, we have some ways to pass all tests before trying to use
packages. Do you see success for `make testall`?
-viral
On Sunday, March 29, 2015 at 7:11:37 PM UTC+2, Kevin Owens
All our beautifull continuous integration systems, Travis, AppVeyor ... ,
do only check that the clean install procedure works.
You pretty much nailed it on the head as to why I thought it was odd. I
still have some work to do to get my head around what the best way is to
work around my current issue. It still seems like most of the DataFrame
methods should be defined in AbstractDataFrame to make it easier to
I think it's purposely slow, just to annoy Julia developers into
implementing a system that saves compiles from previous sessions
On Sunday, March 29, 2015 at 12:00:28 AM UTC+11, Steven Sagaert wrote:
Hi,
I use Gadfly to create simple barplots save them as SVG. Since this is
for usage
I did a clean re-install of Julia 0.3.7 + friends packages + manually
edited the kernel.json file that was looking odd to my eyes.
(4 times '\', instead of 2 '\' , in .ipython\kernels\julia\kernel.json path)
On Sunday, March 29, 2015 at 9:36:48 AM UTC+2, Daniel Høegh wrote:
I installed
This is great news, and apologies are not warranted: it's a wonderful gift
you've given the community!
Best,
--Tim
On Sunday, March 29, 2015 12:37:49 AM Toivo Henningsson wrote:
I just released Debug https://github.com/toivoh/Debug.jl v0.1.2 with
Julia 0.4 support. I think this has been some
I installed 0.3.7 removed 0.3.6 and did a Pkg.build(IJulia) in 0.3.7
I just released Debug https://github.com/toivoh/Debug.jl v0.1.2 with
Julia 0.4 support. I think this has been some time overdue now, sorry for
the long wait!
/ Toivo
For the record, this works as desired in 0.4. I'm not sure how it got fixed,
but if you file an issue on GitHub perhaps the fixer might be able to backport
it?
Best,
--Tim
On Saturday, March 28, 2015 09:35:15 PM Philip Tellis wrote:
I've written the following code:
import Base.convert
No, it gets an error because there is a build option with gcc and g++ of
-m32 (32 being the word length). Now that I see this, I remember this
being an issue when I installed 0.4* about a month ago. (Sorry if this
would be more appropriate in the julia-dev list.)
Here's the output from
This does bring up a question about writing methods though. What happens
if two completely unrelated packages define a function 'foobar' (that isn't
part of Base or any other Julia standard package) and someone tries to use
both packages? It seems like this couldn't work. You would only get
This does bring up a question about writing methods though. What happens
if two completely unrelated packages define a function 'foobar' (that isn't
part of Base or any other Julia standard package) and someone tries to use
both packages? It seems like this couldn't work. You would only
I think that you could use sub(A,:,2:2:4) for BLAS, but not sub(A,:,[2,4])
because the indexing has to be with ranges for BLAS to be able to extract
the right elements of the matrix.
2015-03-29 17:34 GMT-04:00 Dominique Orban dominique.or...@gmail.com:
Sorry if this is another [:] kind of
C[:,idx] creates a copy, so gemm updates a copy of part of C and then
discarts it after the computation. sub(C,:,idx) creates a view instead of a
copy.
2015-03-29 18:10 GMT-04:00 Dominique Orban dominique.or...@gmail.com:
Sorry there was a typo in my example. It should have been
Sorry if this is another [:] kind of question, but I can't seem to find the
right syntax to call BLAS.gemm! on part of an array. The piece of code
julia n = 10; m = 5; idx = [2, 4];
julia C = rand(n, m); A = rand(n, m); B = rand(m, m);
julia BLAS.gemm!('N', 'N', 1.0, A[:,idx], B[:,idx], 1.0,
Sorry there was a typo in my example. It should have been
BLAS.gemm!('N', 'N', 1.0, A[:,idx], B[idx,idx], 1.0, C[:,idx]);
for the dimensions to work out.
The gemm! command works fine, is happy with the dimensions, and produces
the correct update. As far as I can tell,
C1 = C[:,idx];
Unfortunately, my idx will be computed on the fly and there's zero chance
that it would be a range. Is there a plan to support more general indexing
in subarrays and/or ArrayView?
On Sunday, March 29, 2015 at 6:13:16 PM UTC-4, Andreas Noack wrote:
C[:,idx] creates a copy, so gemm updates a
Shhh, no one is supposed to know that.
On Sunday, March 29, 2015 at 4:47:46 AM UTC-7, Sheehan Olver wrote:
I think it's purposely slow, just to annoy Julia developers into
implementing a system that saves compiles from previous sessions
On Sunday, March 29, 2015 at 12:00:28 AM UTC+11,
It is supported in SubArrays and you can also multiply with these in place
with A_mul_B!, but this will be calling a generic multiplication method
implemented in Julia instead of BLAS. So far it doesn't support the α and β
arguments of C:=αAB+βC, but I'm working on that.
2015-03-29 18:23
How can we do the following *Matlab* code in *Julia*?
A=rand(4); A(:,A(1,:)0.7)
I've tried this in *Julia*:
A=rand(4,4); A[:,A[1,:].0.7]
And how would we combine multiple *boolean comparisons* in the indexing such as
*A(:,A(1,:)0.7 A(1,:)0.9)*?
OK Here’s the final version
arglength(f)=length(Base.uncompressed_ast(f.code.def).args[1])
function Fun(f::Function)
if (isgeneric(f)applicable(f,0)) || (!isgeneric(f)arglength(f)==1)
# check for tuple
try
f(0)
catch ex
if isa(ex,BoundsError)
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