df = DataFrame(A=rand(10), B=rand(10))
df[:C] = df[:A] .+ df[:B]
does the trick in DataFrames v0.5.4.
On Thursday, May 15, 2014 7:59:54 PM UTC-7, Jason Solack wrote:
So i feel like this a simple question, but i can't find reference to it.
Lets say i have a DataFrame with columns A and B
But do you agree that the usage of x::T as a formal parameter is quite
different when T is a type parameter compared to when it is a plain type?
I'm not 100% sure I grok what you're getting at, but *if *what you're
asking is whether I see a difference between foo(x::Real) and
Hello all,
I wanted to create an array of an immutable type and initialize an empty
copy in each (with the default constructor).
I am wondering which is the best way to do it, so far:
immutable ChannVals
taus::Vector{Float64}
alphas::Vector{Float64}
ChannVals() = new( Float64[], Float64[] )
* correction, 'allVals' is 'arr' in the last line of code.
--
Carlos
On Fri, May 16, 2014 at 10:27 AM, Carlos Becker carlosbec...@gmail.comwrote:
Hello all,
I wanted to create an array of an immutable type and initialize an empty
copy in each (with
Here is an example of the difference Toivo refers to (I think):
julia foo{T:Real}(a::Array{T},b::T) = T
foo (generic function with 1 method)
julia bar(a::Array{Real},b::Real) = Real
This is just an anouncement that I have been working on a package for
performing non-equidistant fast Fourier transforms (NFFT). The package
should now be accessible using Pkg.add(NFFT). Some basic usage examples
can be found here: https://github.com/tknopp/NFFT.jl
The NFFT (also known as
Try
arr = [ChannVals() for i = 1:10]
On Friday, May 16, 2014 01:27:18 AM Carlos Becker wrote:
Hello all,
I wanted to create an array of an immutable type and initialize an empty
copy in each (with the default constructor).
I am wondering which is the best way to do it, so far:
Correct me if I am wrong, but IMHO the comparison of the two functions in
Mauro's last post is not entirely fair for :: because the parametrized version
imposed an additional constraint which has nothing to do with ::
Essentially in the two cases the following types are required:
foo: T :
How close did you get to c speed?
I might try something like (note the code code is written for clarity, not
for compactness or completeness):
function encode_name(name::String)
if name == setosa
return [1 0 2];
elseif name == versicolor
return [2 1 0];
else
return [0 2 1];
end
end
output = map(encode_name,
Well, this is actually an interesting story. In my first test the code was
about 100 times slower for which reason I did not investigate Julia further
and concentrated on a Numpy like multidimensional array package called
numcpp: https://github.com/tknopp/numcpp. And with C++11 I got actually
Thank you so much!
I am very busy with other things at the moment, but will dive into the
code, attempt to update as necessary in a few weeks and share the results,
if any.
On Monday, 12 May 2014 22:52:17 UTC+2, Ariel Keselman wrote:
Just looked for the code in some old backup drive and
Hi all,
I have been using julia and ijulia for a while and everything worked fine.
over time I get more and more issues, trying to upgrade/reinstall etc and
now I can't get it to work at all anymore. As I intend to reinstall osx
anyway, I was wondering if you good people have any good setup
Hello, new user and subscriber here. Is there a way to decompose a
UnionType back into its constituent type(s)?
I tried names():
dt = Union(Int,Float64)
names(dt)
1-element Array{Symbol,1}:
:types
convert() seems to hit dead-ends.
Help appreciated.
Tony
On Friday, May 16, 2014 2:09:03 AM UTC-5, Tony Fong wrote:
Hello, new user and subscriber here. Is there a way to decompose a
UnionType back into its constituent type(s)?
I tried names():
dt = Union(Int,Float64)
names(dt)
1-element Array{Symbol,1}:
:types
convert() seems to hit
the `names` function is indeed the right way to go about it:
julia un = Union(Int, Float64)
Union(Int64,Float64)
julia names(un)
1-element Array{Symbol,1}:
:types
julia un.types
(Int64,Float64)
julia un.types[1]
Int64
julia un.types[2]
Float64
For datatypes:
julia type A
a::Int
I am not sure if this appeals to you, but I'm happy to share my
configuration. I just use the REPL and a decent editor (vim), which I'm
happy with.
I've been using this setup for the past couple months (mid-March). I've
only had occasionally issues, but I follow HEAD so that is expected. I am
Hi,
I'm struggling to understand the scheduling subsection (in the parallel
computation section) of the docs; specifically how the pmap function as
shown there works. I've copy-pasted the code I used below my questions.
Question 1) Is the purpose of the @sync block just to wait for all the
I follow an identical process to Cameron, with the same results.
- Adrian.
On Fri, May 16, 2014 at 3:37 PM, Cameron McBride
cameron.mcbr...@gmail.comwrote:
I am not sure if this appeals to you, but I'm happy to share my
configuration. I just use the REPL and a decent editor (vim), which I'm
+1 for Cameron. I use the same workflow.
Hey guys,
I'm confused by this dictionary behaviour.
I create a dictionary like this:
for line=readlines(open(string(directo, 4_column_file.tab)))[2:end]
tmp = split(line, '\t')
ste_data[(tmp[1], tmp[2], tmp[3])] = tmp[4]
end
Then I run
v = collect(keys(ste_data))
to get an array
We really need to make that work the same way – can you open an issue?
On Fri, May 16, 2014 at 10:42 AM, Andrew B. Martin
andrew.brown.mar...@gmail.com wrote:
Found the problem.
The indices are tuples of substrings and I'm querying using strings.
Should this be mentioned in the documentation?
Currently it reads that split returns an array of strings, but I'm finding
that it returns an array of SubStrings.
Since they are immutable, fill! did exactly what you wanted
On Friday, May 16, 2014, Tim Holy tim.h...@gmail.com wrote:
Try
arr = [ChannVals() for i = 1:10]
On Friday, May 16, 2014 01:27:18 AM Carlos Becker wrote:
Hello all,
I wanted to create an array of an immutable type and
Well, SubStrings are Strings. The issue here is that we're hashing tuples
based on their type, which is problematic. We very much need to (re)think
how collection hashing works. The basic question is whether containers
should hash differently based on their type, their eltype, or just their
Issue filed:
https://github.com/JuliaLang/julia/issues/6870
I'm new to filing issues on github, so please feel free to clarify if I'm
not expressing the problem well.
No, that's perfect. Thanks!
On Fri, May 16, 2014 at 11:14 AM, Andrew B. Martin
andrew.brown.mar...@gmail.com wrote:
Issue filed:
https://github.com/JuliaLang/julia/issues/6870
I'm new to filing issues on github, so please feel free to clarify if I'm
not expressing the problem well.
Hello all
I'm trying to index a dataframe in the folowing manner:
df = DataFrame(A = round(rand(1000) * 10), B = round(rand(1000) * 10))
df[:C] = 0
df[(df[:A] .== 1 df[:B] .== 1),: C] = 1
I get an error ERROR: no method (Int64, DataArray{Float64, 1})
if i index like this, it works but i
This is a result of the weird precedence of . Try this:
(df[:A] .== 1) (df[:B] .== 1)
— John
On May 16, 2014, at 8:25 AM, Jason Solack jaysol...@gmail.com wrote:
Hello all
I'm trying to index a dataframe in the folowing manner:
df = DataFrame(A = round(rand(1000) * 10), B =
perfect, thank you!
On Friday, May 16, 2014 11:28:46 AM UTC-4, John Myles White wrote:
This is a result of the weird precedence of . Try this:
(df[:A] .== 1) (df[:B] .== 1)
— John
On May 16, 2014, at 8:25 AM, Jason Solack jays...@gmail.com javascript:
wrote:
Hello all
I'm
Yes, that is exactly the kind of example that I'm referring to. (As is the
example that started this thread as well.)
In all other cases, x::T is covariant in the sense that it allows typeof(x)
: T. But in an argument list when T is a type parameter to the same
method, x::T is instead invariant
That form of iteration is common from Ruby, which is where I'm guessing
this interest is coming from? Ruby has the block/proc/lambda distinction
that makes the for loop and .each forms behaviorally similar, whereas in
Julia, there's just the one kind of anonymous function. As a result, if
this
I think it is great to have most topics advanced, it is a fun and
educational way to engage with contributors and see all the power of the
language in action.
However if one of the objectives of the conference is also to broaden a bit
the audience -- e.g., spark the interest of a few more
in matlab :
x=[1,4]
B=repmat(A,x)
/(B==)
how to replicate A in Julia
Paul
Julia has repmat. You can look up its methods like this:
julia methods(repmat)
# 3 methods for generic function repmat:
repmat(a::AbstractArray{T,1},m::Int64) at abstractarray.jl:964
repmat(a::Union(AbstractArray{T,2},AbstractArray{T,1}),m::Int64) at
abstractarray.jl:950
B=repmat(A,x...)
On Friday, May 16, 2014 8:18:14 PM UTC+2, paul analyst wrote:
in matlab :
x=[1,4]
B=repmat(A,x)
/(B==)
how to replicate A in Julia
Paul
Are you going to film/livestream it? Are you going to have lightning talks?
If the answer to both questions is yes, don't forget to film/stream the
latter! Apparently the GopherCon last month had some amazing lightning
talks but nobody remembered to film them.
On Tuesday, 13 May 2014 05:43:34
On Fri, 2014-05-16 at 17:18, Toivo wrote:
Yes, that is exactly the kind of example that I'm referring to. (As is the
example that started this thread as well.)
Haha, well sometimes I just take a bit longer to figure things
out... ;-)
I suspect that the pragmatic rule that Julia follows is
We're going to film but not live stream. We'll film the lightning talks too
– good to have a reminder.
On Fri, May 16, 2014 at 3:50 PM, Job van der Zwan
j.l.vanderz...@gmail.comwrote:
Are you going to film/livestream it? Are you going to have lightning
talks? If the answer to both questions
Hi,
I am starting to use Julia, and I would like to learn and contribute a bit.
As I have some experience in numerics I am thinking of contributing to the
ODE package.
I've read the ideas for the API, and I believe that we can still improve
it. Usually, for this kind of solver, we could
Hi Sorami,
Yes, JuliaText is meant to be the repository of Julia NLP packages and I
agree with you about Julia's potential in the NLP domain. There hasn't been
a lot of action there since I think there aren't many people using Julia
for NLP yet (although I hope that changes). Any contributions
@Jameson They are immutable, but they contain references to mutable arrays,
and all the immutable types will reference the same arrays. That way you
would not just need a copy but a deepcopy. That will probably be too much
overhead for fill!(), and will be problematic if someone decided to
Comprehensions and for loops do not perform nested looping in the same
order:
julia [begin println((i,j)); (i,j) end for i = 1:3, j = 1:4]
(1,1)
(2,1)
(3,1)
(1,2)
(2,2)
(3,2)
(1,3)
(2,3)
(3,3)
(1,4)
(2,4)
(3,4)
3x4 Array{(Int64,Int64),2}:
(1,1) (1,2) (1,3) (1,4)
(2,1) (2,2) (2,3) (2,4)
It's less about the array ordering and more about the fact that mathematically
the row index comes before the column index and doing it the other way would be
very confusing. It's a shame these don't match, but there's not much to do
about it.
On May 16, 2014, at 4:57 PM,
I am new to GitHub. Is there some kind of forum, or do you mean to create a
new file with my thoughts on it ?
On Friday, May 16, 2014 11:26:21 PM UTC+2, Alex wrote:
so if you have a github account it might be good to post your thoughts
there as well.
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