Thanks Amit -- I think you just saved future me a lot of frustration :)
On Mon, Feb 3, 2014 at 7:27 PM, Amit Murthy amit.mur...@gmail.com wrote:
Would like to mention that the non-reducer version of @parallel is
asynchronous. Before you can use Ans1 and Ans2, you should wait for
completion.
I don't know matlab but if variable explorer is a view into
all variables then yes in julia you can use whos function
and for plotting
Winston, pyplot ... google for more
Thanks Kevin,
Follow the link you've posted found an explanation why
@printf() constant format string is not allowed
https://github.com/JuliaLang/julia/issues/4248
and the useful trick:
julia const fmt_ = %4d%4d%4d\n
%4d%4d%4d\n
julia @eval f(x,y,z) = @printf($fmt_,x,y,z)
f (generic function
I this case I think you need to look into how whos() is implemented
in base
As always when choosing a library to use; check the
number of stars, forks, last update, number of issues open to
close and good documentation; so try them all, it will
help you make the right choice
It already works. But as this is on julia-users I just wanted to mention
that this is only a prototype and getting all the dependencies working is
non-trivial (at least on windows and OSX). The code lives here:
https://github.com/tknopp/Julietta.jl
Am Dienstag, 4. Februar 2014 13:13:22 UTC+1
will make time to install and test it.
Is there an easier method to obtain the sorting index given a column of
data? In Matlab you can add a second output and it'll give you an index
which you can apply to other related arrays.
using Datetime
using DataFrame
D = # load your data, DataFrame
time = # Parse time from your loaded data
I think you want sortperm.
— John
On Feb 4, 2014, at 6:24 AM, RecentConvert giz...@gmail.com wrote:
Is there an easier method to obtain the sorting index given a column of data?
In Matlab you can add a second output and it'll give you an index which you
can apply to other related arrays.
Hello,
On Saturday, February 1, 2014 8:42:07 PM UTC+1, Eric Davies wrote:
Please forgive me if I'm giving too much obvious info; I'm not aware of
your background and I want to answer this as completely as possible.
Thanks, it is fine, if only for future reference for others who happen to
That's what I was looking for.
using Datetime
using DataFrame
D = # load your data, DataFrame
time = # Parse time from your loaded data
I = sortperm(time) # Sorting index
time = time[I] # Sort time by time
Thanks.
There is also PyPlot https://github.com/stevengj/PyPlot.jl. If you're
already accustomed to Python's
Matplotlibhttp://matplotlib.org/gallery.htmllibrary then this one might be
for you. There is some translation from
Python examples of course but I've done some work creating Julia
ccall automatically calls convert(Ptr{Uint8}, fname), which fails if the
string is a non-terminal SubString not because the conversion is
impossible, but because it would need to return a pointer to a copy but not
the copy itself, there's no guarantee that copy wouldn't be
garbage-collected
I was astonished to see the following functions in the Julia Standard
Library,
functions that accept (or return) degrees instead of radians:
sindasind secdasecd
cosdacosd cscdacscd
tandatand cotdacotd
I didn't find these function names in any other
These functions are included in Matlab as convenience functions. They
do get used frequently by folks working with real-world measurements.
It is true that one can always do rad = (x/180)*pi, but this
introduces the possibility of one ULP error in the calling arg. Not
big, admittedly, but try
Seems like a reasonable suggestion to me, and it's certainly good to bring
it up for discussion.
It doesn't have to be immediate, but if there is further consensus, it
would be good to file an issue, or this is likely to get lost.
Cheers,
Kevin
On Tue, Feb 4, 2014 at 9:09 AM, Johan
introduces the possibility of one ULP error in the calling arg. Not
big, admittedly, but try doing sind(180) vs. sin(pi) and see what the
results are.
Slightly off topic, but I just wanted to note that are also sinpi and
cospifunctions (although no other related functions) which give more
You are right. I have done real-world projects with Matlab, and I am giving
lectures on Matlab, but I have never seen or used these functions. I guess
removing them now they are here will not be reasonable.
I fear, some of my students will be glad to hear about these functions.
On Tuesday,
As someone who doesn't have to work with the functions very often or deal
with degrees/radians conversions, I actually have found it convenient to
have the sind functions. It saves me time from having to remember what the
conversion is or make my code uglier littered with degrees2radians()
Solved.
I compiled the Julia source code - 0.3.0 pre-release version.
Then everything began to work.
On Friday, January 31, 2014 4:58:45 PM UTC-5, Sung Soo Kim wrote:
Hi,
In addition to using Gadfly error, there is another using RDatasets
error.
julia using RDatasets
ERROR: data not
Looking forward to this. In the meantime, as our lab has an older, fixed
version of ipython, a colleague found a workaround: select the parts of the
notebook that are to be printed and then print the selection. Works with
chrome, which is what we have installed in our labs. --J
On Sunday,
Yes, that was the problem.
I spend a few hours to figure out how to make my system to perceive
gfortran, then compiled the source codes 0.3.0 pre-release.
Then all work fine now.
I think it would be a good idea to provide pre-release version as a binary.
A simple overnight automatic build
Great, I'm glad it worked out for you.
On Feb 3, 2014 2:46 AM, Alex alexc...@googlemail.com wrote:
Hi,
Updating the command line tools worked. Now I have
git version 1.8.3.4 (Apple Git-47)
and there are no errors anymore.
Thanks!
- Alex.
Thanks for the hint. Getting rid of 'mx' and 'my' definitely helps.
I couldn't figure out how to implement the parallel version of tuple
adding. This is what I've got. It crashes my Julia Studio console when I
try to run it. What am I missing?
add_two_tuple(x,y) = (x[1]+y[1], x[2]+y[2],
Yihui Xie, the author of the knitr package for R, was kind enough to write
another R package https://github.com/yihui/runr that allows an author to
specify
engine='julia'
as a knitr chunk option.
knitr allows for literate programming by processing a file in LaTeX or
Markdown with embedded
I'm playing around with calling some objective-c code in the Mac system
frameworks, because there are a lot of useful goodies in there. I'm
generally having more success than I deserve. Thanks to some code that I
saw in Tk.jl, I am able to send messages to objects, using various
frameworks,
huh.
maybe @everywhere in front of the function definition? I'm not sure
On Tue, Feb 4, 2014 at 10:53 AM, Alex C alex@gmail.com wrote:
Thanks for the hint. Getting rid of 'mx' and 'my' definitely helps.
I couldn't figure out how to implement the parallel version of tuple
adding. This
IJulia?
On Tuesday, February 4, 2014 3:02:08 PM UTC-5, Douglas Bates wrote:
It occurs to me that I may be reinventing the wheel here. Are there
already implementations of some sort of Julia server in the sense of a
process that can be running in the background listening on a socket and,
Maybe you should take a look, its written entirely in Julia :-) You will
have to follow the message spec, but that is well
documentedhttp://ipython.org/ipython-doc/dev/development/messaging.html.
On Tuesday, February 4, 2014 3:41:14 PM UTC-5, Douglas Bates wrote:
On Tuesday, February 4,
I have also kind of reinvented the wheel for my Gtk based terminal but this
was on purpose to get something quickly working and later replace it with
the proper solution.
But its still not entirely clear what the best solution is. The REPL.jl
package also seems to have some kind of client
Sorry for the confusion. I was trying to get a simple example to work so I
wouldn't get distracted by details. The @everywhere did the trick.
This is the fastest parallel version of the code that I was able to get
working. However, I easily run into memory limitations (8GB RAM) as I
increase
1. You should change:
C = complex(zeros(Float64, Ly, Lx)
to:
C = zeros(Complex{Float64}, Ly, Lx)
[the way you are doing it there creates a float version, then a complex
version, then trashes it]
2. The algorithm after the above change allocates 3 * limit * (limit/2) *
samples * 16 bytes in
Woah, also:
A = B = zeros(Float64,Ly,Lx);
is almost surely not what you intended.
julia A = B = [1 2]
1x2 Array{Int64,2}:
1 2
julia A[1] = 10
10
julia B
1x2 Array{Int64,2}:
10 2
On Tue, Feb 4, 2014 at 2:15 PM, David Salamon d...@lithp.org wrote:
1. You should change:
C =
On Tuesday, February 4, 2014 3:21:12 PM UTC-6, Tobias Knopp wrote:
I have also kind of reinvented the wheel for my Gtk based terminal but
this was on purpose to get something quickly working and later replace it
with the proper solution.
But its still not entirely clear what the best
Yes, assuming we can get the builds working smoothly, it would be really great
to offer stable and unstable binaries right on the main downloads page.
— John
On Feb 4, 2014, at 11:52 AM, Eric Davies iam...@gmail.com wrote:
On Tuesday, 4 February 2014 12:35:19 UTC-6, Sung Soo Kim wrote:
I
julia immutable Foo{T,N}
x::Vector{Int}
y::Array{T,N}
z::Vector{ASCIIString}
function Foo(x::Vector{Int}, y::Array{T,N}, z::Vector{ASCIIString})
new(x,y,z)
end
end
julia foo = Foo([1], [2], [bar])
ERROR: no method Foo{T,N}(Array{Int64,1},
I’m sure you are. :)
— John
On Feb 4, 2014, at 6:37 PM, Elliot Saba staticfl...@gmail.com wrote:
We're working on it, I promise. :)
On Feb 4, 2014 6:01 PM, John Myles White johnmyleswh...@gmail.com wrote:
Yes, assuming we can get the builds working smoothly, it would be really
great to
Thanks John, I finally figured it out after hacking around for a couple
hours. It's not intuitive, that's for sure!
On Tuesday, February 4, 2014 10:57:52 PM UTC-5, John Myles White wrote:
Yup, this is one of the quirkier things about parametric types. You need
to echo the inner constructor
btw, why don't constraints get handled by an outer constructor in the first
place?
It makes sense to have inner constructors for unitialized
On Tuesday, February 4, 2014 11:23:51 PM UTC-5, milktrader wrote:
Thanks John, I finally figured it out after hacking around for a couple
hours. It's
... happy fingers problem ...
it makes sense to have inner constructors for uninitialized *fields*
On Wednesday, February 5, 2014 12:11:10 AM UTC-5, milktrader wrote:
btw, why don't constraints get handled by an outer constructor in the
first place?
It makes sense to have inner
Can anyone explain what's going on here? :)
julia a = [1,2,3]
3-element Array{Int64,1}:
1
2
3
julia b = [4,5,6]
3-element Array{Int64,1}:
4
5
6
julia a / b
3x3 Array{Float64,2}:
0.0519481 0.0649351 0.0779221
0.103896 0.129870.155844
0.155844 0.194805 0.233766
I get the
Hello Fil,
Generally, division is like multiplying by the inverse of the divisor. For
vectors, the inverse is actually a pseudoinverse or generalized
inversehttp://en.wikipedia.org/wiki/Generalized_inverse.
In this case,
a / b = a * pinv(b)
where
julia pinv(b)
1x3 Array{Float64,2}:
julia foo = Foo([1], [2], [bar])
ERROR: no method Foo{T,N}(Array{Int64,1}, Array{Int64,1},
Array{ASCIIString,1})
Without the inner constructor, an object is created no problem.
julia foo = Foo{Int64, 1}([1],[2], [bar])
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