I think a few clarifications are necessary here:
1. The linked-cell code can mimic the naive O(N^2) algorithm if you make
the following changes to md_main(...) (In main.jl)
n_cell = [ 1 for dim = 1:DIM ]
l_cell = [ 2r_cutoff for dim = 1:DIM ]
With this modification the code
Just a reminder: the call for proposals for OSCON closes Feb 2. You've got
one more weekend to get your Julia talk proposals in. It's great
visibility for the language as well as for presenters.
http://www.oscon.com/open-source-2015/public/cfp/360
The problem:
I have 2 types. They have the same internal structure, and both can be
thought of as subtypes of an abstract root.
abstract Root
type Foo : Root
...
end
type Bar : Root
...
end
Because they have the same internal structure, I could equally define a
parametric type
This is a long pending issue - how do we make it easy for packages to link
to Julia's libraries, especially openblas. One quick thing we could do is
have something like julia-config that can provide the right include paths
and ldflags and such.
-viral
On Friday, January 30, 2015 at 4:30:24 AM
Very nice! Thanks for doing this. It is great to see that DASSL is largely
competitive. I am also happy that ode23s isn't completely useless :-) In ODE.jl
we have still the problem that we don't use in-place rhs functions or
Jacobians. I guess this becomes problematic for larger systems
Very nice! Thanks for doing this. It is great to see that DASSL is
largely competitive. I am also happy that ode23s isn't completely
useless :-)
Yep, DASSL seems good and ode23s is fine too.
In ODE.jl we have still the problem that we don't use in-place rhs
functions or Jacobians. I guess
Woops, example 2 should be:
type Root{T}
...
end
On Fri, Jan 30, 2015 at 4:53 PM, James Crist crist...@umn.edu wrote:
The problem:
I have 2 types. They have the same internal structure, and both can be
thought of as subtypes of an abstract root.
abstract Root
type Foo : Root
In sundials, is there anything equivalent to Matlab's odeprint, which
tells the progress?
Apologies for the mistaken post. This was a note to myself that Gmail
suggested a recipient for and I hit send by accident.
Jeremy gave a talk today on associative arrays and said this during the QA:
Python's mathematics is fundamentally broken; there are too many ways to
represent 2D arrays due to decisions made 15 years ago... For Python
programmers we recommend them to use Julia. We find that Julia gives Python
You can use a macro. I've written short-circuiting 'any' and 'all' macros
that are available in this gist:
https://gist.github.com/grayclhn/5e70f5f61d91606ddd93
I'm sure they can be substantially improved; the usage would be
if @all [f(x) for x in 1:10]
##
It looks like there is at least one Julia talk
http://www.oreilly.com/conferences/sample_proposals.html#tech :)
On Saturday, January 31, 2015 at 6:32:53 AM UTC+8, Phil Tomson wrote:
Just a reminder: the call for proposals for OSCON closes Feb 2. You've got
one more weekend to get your Julia
I have a newbie-type performance question.
In some of my code there is a structure that looks like this:
type FourierPoly
periods :: Vector{Int}
radiuses :: Vector{Float64}
phase_offsets :: Vector{Float64}
end
and the following two functions that operate on it:
- function
I've got lots of multi-field types in a package I'm developing
https://github.com/imanuelcostigan/FinancialMarkets.jl and would like to
understand how I might best show() them optimally. The default method
applied to some types prints what is effectively unreadable garbage to the
REPL. Has
Hi,
I am generating two functions and the outcome of the first function will be
use as input values of the second function.
Like Matlab's case of [x,y,z] =test_function(input), I used Julia's tuple
function to generate [x,y,z] and it worked well.
function test_function(input)
Hi all,
those of you who solve initial value problems (IVP) of ordinary and
algebraic differential equations (ODE/DAE) might be interested in:
https://github.com/mauro3/IVPTestSuite.jl It provides several test cases
for ODE and DAE solvers based on previous, well-known test sets. It can
easily
Do Pkg.update() and it will start working.
--Tim
On Friday, January 30, 2015 03:20:27 AM paul analyst wrote:
But why not work this : dset[:,1] ?
julia dset[:,1]
ERROR: `size` has no method matching size(::JldDataset, ::Int64)
julia dset[1:k,1]
30070x1 Array{Float64,2}:
How about `all(f, values)`?
On 30 January 2015 at 06:51, Wai Yip Tung w...@tungwaiyip.info wrote:
I want to apply function f() over a range of value. f() returns true for
success and false for failure. Since f() is expensive, I want short circuit
computation, i.e. it stops after the first
Hi Guillaume: I've added the serial version of the linked-cell MD code
here: https://gist.github.com/Amuthan
The code is pretty basic and needless to say, a lot more functionality
needs to be added (though I should admit that I'm more interested in
extending this code to a parallel version than
any() is short-circuiting if the length of array is more than 16. That
seems like dubious semantics to me.
Ivar
fredag 30. januar 2015 09.39.28 UTC+1 skrev Mike Innes følgende:
Ok, I now see that `all` isn't short-circuiting, which is kind of
surprising/annoying. Anyone know why that is?
Ok, I now see that `all` isn't short-circuiting, which is kind of
surprising/annoying. Anyone know why that is?
You can easily define your own short-circuiting `all` to do this:
all′(f, xs) =
isempty(xs) ? true :
f(xs[1]) all′(f, xs[2:end])
The other thing you could try is to check out
I think that LAPACK error codes above 0 are about ill-conditioning
and/or non-convergence. Perhaps you could share the matrix (in a binary
format, since copy-pasting decimal floats can change the values and
thus the conditioning).
Best,
Tamas
On Fri, Jan 30 2015, Steven Sagaert
That's already way better than the simple and naive version ! On my
computer, this gives me something 4x slower than serial LAMMPS code.
I'll try to integrate this into the package. It will need changes in the
data structures, but this is worth it !
I'll have to think a bit about how I can
when doing an SVD of a large matrix I get
ERROR: LAPACKException(1)
in gesdd! at linalg/lapack.jl:1046
in svdfact! at linalg/factorization.jl:660
in svdfact at linalg/factorization.jl:664
It's definitely something related to the data because it works on different
matrices the code has been
Is it possible to file jld only read range ? Without reading the entire file
into memory ?
julia using HDF5,JLD
julia load(C1o2.jld,C1,(1,1))
julia load(C1o2.jld,C1)[1:1]
Above methods ar reading all file .
Paul
But why not work this : dset[:,1] ?
julia dset[:,1]
ERROR: `size` has no method matching size(::JldDataset, ::Int64)
julia dset[1:k,1]
30070x1 Array{Float64,2}:
I have , it work!
dset2 = jldopen(C1o2.jld)[C1]
dset2[1:3,1]
Paul
W dniu piątek, 30 stycznia 2015 12:06:29 UTC+1 użytkownik paul analyst
napisał:
Is it possible to file jld only read range ? Without reading the entire file
into memory ?
julia using HDF5,JLD
julia
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