Maybe PlotlyJS.jl can provide the mapping functionality you need? It allows you
to create chloropleth maps as well as scatter plots (line, bubble, or dots) on
top of maps.
Thanks for letting us know about the broken link.
I'll be travelling today, but will get this fixed up tomorrow and the fix will
be included the next time we publish the site.
er-clever?
>
> Best,
> --Tim
>
> On Friday, March 11, 2016 08:18:14 AM Spencer Lyon wrote:
> > New package announcement. I've just registered PlotlyJS.jl with
> metadata.
> > The package leverages the plotly.js javascript library to provide a
>
The Federal Reserve bank of New York has finished moving their fairly large
DSGE model from Matlab to Julia. This model is used inside the Fed for
forecasting and policy analysis.
As part of the move to Julia, the code base has been open sourced.
A blog post announcing the release is
here:
Ahh nice,
You found the draft of our next post ;)
Good point about matlab.jl. we did make heavy use of that when transitioning to
julia.
Another one would be to call local t, data just before the try block,
though I like Matt’s better.
On Thursday, September 3, 2015 at 5:21:31 PM UTC-4, Spencer Russell wrote:
Ah yes. That works. Good idea, thanks for the tip.
>
> -s
>
>
> On Thu, Sep 3, 2015, at 05:12 PM, Mike Innes
It's not quite ready for public consumption, which is why it isn't released
in metadata yet, but you can look at
https://github.com/spencerlyon2/CompEcon.jl. It works quite well, but the
API isn't totally fleshed out yet.
This has support for an arbitrary number of dimensions.
If you do want
Yep, constructing a 1d interpoland is about the same as constructing a 6d
one!
On Monday, August 31, 2015 at 8:26:34 AM UTC-4, Nils Gudat wrote:
>
> Sounds interesting, I'd definitely give it a go! I'd be doing 3 to 6
> dimensional interpolation, but I'm sure a 3D example would scale up
Reach out of you need help!
And, just a minor clarification, the interpolation bits are in the CompEcon
(not QuantEcon) repo. Eventually they will probably make there way under
the QuantEcon umbrella, but they aren't quite polished enough. Sorry for
the unfortunate similarity between the
It would be awesome to get this under AD. I'm not sure I follow with how to
get DF(y) at each output point. Can you provide a quick example (made up data
is totally fine)? Thanks
// Spencer
On August 25, 2015 at 4:15:28 PM EDT, Christoph Ortner
christophortn...@gmail.com wrote:In this case
Thanks Cristoph. You are totally right that my problem is a different class
of problem than what the packages within JuliaDiff are designed to solve.
That being said, I had a hunch that members of the JuliaDiff community
might have a bit more expertise than my for my particular problem!
About
Hey Mauro,
That’s right. The ODE solver does give me the first derivative. The problem is
that I need the first two derivatives!
So what I’ve done to test all my numerical tools for accuracy is to compare the
approximated first derivative with the actual one given my by the ODE solver.
That’s
Probably the wrong place to post this, but I couldn't find a julia-diff
list :)
I'm having stability issues computing the derivative of functions I only
know on (non-uniform) grids. For example, I have a grid of x (I can't
choose this) and the associated values of y = f(x). I've tried a few
The problem here is that the third argument is actually not the variance of
epsilon, but rather the standard deviation (see docs
http://quantecon.github.io/QuantEcon.jl/api/QuantEcon/#type__arma.1):
julia ρ = 0.8;
julia σ_ϵ = 0.03;
julia theoretical_variance = σ_ϵ^2/(1 - ρ^2) # change from
Great point, that is definitely a typo in the docs. I'll fix that now.
Thanks!
On Monday, August 17, 2015 at 8:38:43 AM UTC-4, Nils Gudat wrote:
Ah, I should have checked the docs more closely. I was confused because I
went by the fields listed in the type defintion of ARMA (e.g. here in the
Great point. Definitely looks like a docstring error here.
I was speaking somewhat off topic. What I had in mind was the proposed
solution in the link to the other github issue
(https://github.com/QuantEcon/QuantEcon.jl/issues/64#issuecomment-130149602).
Here for some reason I could call
Hmm. Too bad it didn't fix all the problems!
I would guess you are hitting some strange bug in the automatic
pre-compilation mechanism recently merged into Julia master.
Disclaimer: this is all unfounded speculation, but here are my thoughts on
what might be happening. It seems that until
We saw problems trying to load on very recent master the other day.
I was able to fix it using the steps outlined here:
https://github.com/QuantEcon/QuantEcon.jl/issues/64#issuecomment-130149602.
Can you give that a try and report back?
On Friday, August 14, 2015 at 3:34:51 PM UTC-4, Nils
I'll keep an eye on it and potentially pitch in!
On Wednesday, July 29, 2015 at 12:47:07 PM UTC-4, Tom Breloff wrote:
If anyone wants to follow, comment, or contribute:
https://github.com/tbreloff/Unums.jl
On Wed, Jul 29, 2015 at 12:01 PM, Job van der Zwan j.l.van...@gmail.com
javascript:
It's currently going through a major overhaul to get ready for public
consumption, but CompEcon.jl https://github.com/spencerlyon2/CompEcon.jl
provides
a Julia implementation of the popular (amongst economists) CompEcon matlab
toolbox.
It does irregular interpolation for an arbitrary number
I would love to read a summary. I don't think I have time to read the whole
book AND contribute, but I might be able to find time to read a summary and
contribute.
// Spencer
BTW, Tom, I was already working on a summary of the book (on an IJulia
notebook). I'm on mobile right now so
John,
Do you have create permissions within the JuliaLang github org?
If not, who should we contact to create the repo/set up permissions?
I guess I could just request that my repo be transferred to the organization,
but I'd want whoever gets that message to have a heads up.
I've been wondering the same. I don't think it would be too difficult to hook
Lint.jl up
Excellent. Thank you.
I've been traveling all day, but will push the code within the next 24 hours.
I have some experimental ideas implemented right now so my initial push will
probably be accompanied by some pull requests to get comments from other users.
. The important thing is that the package is named language-julia, not the
repo.On Thursday, June 11, 2015 at 8:05:20 PM UTC-7, Spencer Lyon wrote:Good to
see something for atom-language-julia. That is what the package was named
originally, but it wasn't maintained so it was forked and renamed
Good to see something for atom-language-julia. That is what the package was
named originally, but it wasn't maintained so it was forked and renamed
language-julia.
I don't think there will really be any reusable components. I just dont know
another editor/tool that uses textmate style grammar
Right now the atom package for adding syntax highlighting for Julia is
https://github.com/tpoisot/language-julia
Based on issues like this one
(https://github.com/tpoisot/language-julia/issues/8) it seems like the
grammar.cson is incorrect.
I did a fresh restart of the grammar based on the
and the constructors chapters to get a good
understanding).
andrew
On Saturday, 19 April 2014 21:50:05 UTC-3, Spencer Lyon wrote:
Say I have a type that defines a model. Something like this:
abstract Model
type Results
# Details not shown here
end
type IFP{T : FloatingPoint} : Model
On Thursday, May 7, 2015 at 4:17:20 PM UTC-4, Spencer Lyon wrote:
Uninitialized fields need to be declared last because the way you
construct an incompletely initialized type is to call `new` with less than
the total number of fields. Then, elsewhere (preferably via a function call
later
Consider the following expression:
julia macroexpand(:(@nexprs 3 j-(out[r, ix] *= z[r, i_{3-j+1}]) ))
quote
out[r,ix] *= z[r,i_3]
out[r,ix] *= z[r,i_2]
out[r,ix] *= z[r,i_1]
end
What I would really like to be able to do is have a macro generate the
expression
julia ... some magic
, 2015 09:03:08 AM Spencer Lyon wrote:
Consider the following expression:
julia macroexpand(:(@nexprs 3 j-(out[r, ix] *= z[r, i_{3-j+1}]) ))
quote
out[r,ix] *= z[r,i_3]
out[r,ix] *= z[r,i_2]
out[r,ix] *= z[r,i_1]
end
What I would really like to be able to do
fast Julia on OS X again!
On Thursday, April 30, 2015 at 12:10:37 PM UTC-4, Spencer Lyon wrote:
Thanks for the tip.
I rebuilt my docker image to have a 1-day old master and am getting the
same results (see updated gist). So, unfortunately the puzzle isn't
resolved yet...
On Wednesday
Thanks for the tip.
I rebuilt my docker image to have a 1-day old master and am getting the
same results (see updated gist). So, unfortunately the puzzle isn't
resolved yet...
On Wednesday, April 29, 2015 at 4:50:03 PM UTC-4, Spencer Lyon wrote:
I ran into strange performance issues
I ran into strange performance issues in an algorithm I have been working
on.
I have a test case as well as some timing and profiler results at this
gist: https://gist.github.com/spencerlyon2/d21d6368a2ccbf6f1e7b
I summarize the issues here. Consider the following code (note I am
defining
I have a function that will compute the derivative matrix operator that
transforms coefficients of a Chebyshev interpolant on interval [a, b] of
degree n (so n+1 total Chebyshev basis functions T_0, T_1, … T_n) into the
coefficients of a degree n-1 chebyshev interpolant on [a, b] that is the
Oh I forgot to say how to use it. If c are your coefficients for a degree n
interpolant on [a, b] then the coefficients of the derivative of that
interpolant are der_matrix(n, a, b) * c
On Saturday, April 4, 2015 at 11:39:49 PM UTC-4, Spencer Lyon wrote:
I have a function that will compute
Seems like you have a good answer already, but for what it’s worth I have
the following code in a package I’m developing:
# Some helpful typealiases to keep things tidy
typealias SymExpr Union(Symbol, Expr)
typealias VecSymExpr Union(Vector{Symbol}, Vector{Expr})
# Take a name and type and
22, 2015 12:32:34 AM Viral Shah wrote:
Best to file an issue.
-viral
On Sunday, February 22, 2015 at 12:52:17 PM UTC+5:30, Spencer Lyon
wrote:
Bump on this thread. I need this functionality again...
On Friday, December 6, 2013 at 11:04:44 AM UTC-5, Spencer Lyon wrote
Bump on this thread. I need this functionality again...
On Friday, December 6, 2013 at 11:04:44 AM UTC-5, Spencer Lyon wrote:
I am working with tensors with more than 2 dimensions. I would like to
find a Julia equivalent to the numpy function tensordot. The docstring
for the function
I live in NYC and will be back in a few weeks. Happy to meet up. Also
interested in a meetup.
On Tuesday, January 6, 2015 2:38:02 PM UTC-7, Stefan Karpinski wrote:
I'm actually planning on jump-starting this and having the first meetup in
late January (I'll be giving the first talk). I've
I am trying to construct a type with this definition:
# are two ranges disjoint?
isdisjoint(r1::UnitRange, r2::UnitRange) = isempty(intersect(r1, r2))
# are a collection of ranges all disjoint?
function all_disjoint(rs::UnitRange...)
n = length(rs)
for i=1:n, j=i+1:n
if
would like
to make this general enough to support time-invaraint matrices F, V, G, W,
but if one or more matrices happen to be constant, I don’t want the user to
have to worry about specifying for which time periods the matrices apply.
On Thursday, November 13, 2014 10:24:44 AM UTC-5, Spencer Lyon
UTC-5, Spencer Lyon wrote:
I am trying to construct a type with this definition:
# are two ranges disjoint?
isdisjoint(r1::UnitRange, r2::UnitRange) = isempty(intersect(r1, r2))
# are a collection of ranges all disjoint?
function all_disjoint(rs::UnitRange...)
n = length(rs)
for i=1:n
What are the differences/advantages of following:
```
# 1
abstract MyType
#2
type MyType end
```
Is there any reason to prefer one over the other?
I am doing some numerical work and then plotting some results.
I really don’t need to load the plotting packages on all processes that are
used to do the computation. Is there a way to execute import or using on
one process only?
I have tried include(joinpath(Pkg.dir(PackageName),
typo in my hacky function. Should have written
include(joinpath(Pkg.dir(PackageName),
src, PackageName.jl))
On Wednesday, October 22, 2014 11:33:11 AM UTC-4, Spencer Lyon wrote:
I am doing some numerical work and then plotting some results.
I really don’t need to load the plotting packages
Thanks, I hadn't seen that issue yet.
I'm curious why loading color would cause this. I didn't losd it directly, but
I precompile gadfly which causes color to be precompiled also. Could that have
the same effect as loading color?
, I'd like to write my code in a way that chooses the
better option in each iteration, but at the moment I'm stumped as for how
to figure out which situation suits which routine.
Thanks!
On Saturday, September 20, 2014 1:06:41 AM UTC+1, Spencer Lyon wrote:
Hi David,
Thanks for the questions
That’s great!
One more tip. If you know that the integration bounds are going to be
constant over certain iterations (which is likely given that you are doing
value function iteration), it helps performance significantly if you
precompute the nodes and weights before the iterations begin,
Oh ok. Yeas, the helper function is just that: a helper to reduce
repetitive code. It should have no impact on performance.
On Wednesday, October 8, 2014 11:27:55 AM UTC-4, Nils Gudat wrote:
I do have changing bounds unfortunately, so I'm stuck with computing the
nodes and weights each
Right now we don’t have any documentation specific to QuantEcon.jl. We are
waiting for this issue https://github.com/JuliaLang/julia/issues/8514 to
be closed in the main Julia repo so that we can have a more stable backend
for generating documentation.
All the functionality from QuantEcon.py
considerably.
the documentation is sparse and it's not quite ready to be advertised, but
the tests are quite complete, so check it out at
https://github.com/floswald/ApproXD.jl
On Saturday, 20 September 2014 02:42:01 UTC+1, Tim Holy wrote:
On Friday, September 19, 2014 05:06:41 PM Spencer
:
arbitrary precision linear algebra
i think most linear algebra operations are defined in julia and thus will
work for bignums. sticking a call to big() somewhere in the code is usually
enough to promote everything
On Fri, Sep 19, 2014 at 8:06 PM, Spencer Lyon spence...@gmail.com
javascript
hilsen
Andreas Noack
2014-09-19 21:09 GMT-04:00 Spencer Lyon spence...@gmail.com javascript:
:
Hey Jameson,
Thanks for the tip, but I don’t think we have “most linear algebra
operations” defined on BigFloat types. See the example below (on Julia
master, one day old):
julia P1
3x3 Array
New package QuantEcon.jl https://github.com/QuantEcon/QuantEcon.jl.
This package collects code for quantitative economic modeling. It is
currently comprised of two main parts:
1.
A toolbox of routines useful when doing economics
2.
Implementations of types and solution
I often solve fixed point problems where I apply a contraction mapping to
an array and`` iterate until convergence. There is nothing in the algorithm
that requires the new value at one array index to depend on neighboring
indices — only on values obtained at the same index on the previous
10:57:59 AM UTC-4, Spencer Lyon wrote:
For displaying a string representation of custom types I have always
defined show(io::IO, x::MyType)
What is the difference between this and defining writemime(io::IO,
mime::MIMEtext/plain, x::MyType) ?
Which is preferred and why?
I am working on a library that defines various types as well as a few
“helper” functions to plot those types with PyPlot.
If I do [import|using] PyPlot at the top level of any file in my package,
PyPlot is loaded when I do [using|import] MyPackage. This makes the startup
time for my package
, Spencer Lyon wrote:
I am working on a library that defines various types as well as a few
“helper” functions to plot those types with PyPlot.
If I do [import|using] PyPlot at the top level of any file in my
package, PyPlot is loaded when I do [using|import] MyPackage. This makes
the startup
is not
possible.
On Fri, Aug 22, 2014 at 11:56 AM, Spencer Lyon spence...@gmail.com
javascript: wrote:
I am working on a library that defines various types as well as a few
“helper” functions to plot those types with PyPlot.
If I do [import|using] PyPlot at the top level of any file in my
.
On Fri, Aug 22, 2014 at 12:16 PM, Peter Simon psimo...@gmail.com
javascript: wrote:
From https://groups.google.com/d/topic/julia-users/AWCerAdDLQo/discussion
:
eval(Expr(:using,:PyPlot))
can be used inside a conditional.
--Peter
On Friday, August 22, 2014 8:56:53 AM UTC-7, Spencer
issue, as much as it is intrinsic to the eval and compilation steps.
On Fri, Aug 22, 2014 at 12:49 PM, Spencer Lyon spence...@gmail.com
javascript: wrote:
I’m actually still having issues with both of these options — I’ll try to
enumerate what I think the problem is here.
- When I do
Consider the following code:
function periodogram(x::Array)
n = length(x)
I_w = abs(fft(x)).^2 ./ n
w = 2pi * [0:n-1] ./ n # Fourier frequencies
# int rounds to nearest integer. We want to round up or take 1/2 + 1 to
# make sure we get the whole interval from [0, pi]
That's what I thought. I'm happy to do that
To do this I just need to do
`Pkg.tag(PDMats)`
Then push that commit to my fork of METADATA and submit a PR, right?
On Tuesday, August 19, 2014 10:13:06 AM UTC-7, Spencer Lyon wrote:
Is there a way to specify that a package depends on the current
Done: https://github.com/JuliaLang/METADATA.jl/pull/1273
Thanks guys
On Tuesday, August 19, 2014 10:58:46 AM UTC-7, Leah Hanson wrote:
That should work. :)
-- Leah
On Tue, Aug 19, 2014 at 12:28 PM, Spencer Lyon spence...@gmail.com
javascript: wrote:
That's what I thought. I'm happy
Suppose I am defining a function that operates on any two real numbers.
Which of the following ways of specifying types is preferred (in terms of
idomatic Julia and performance considerations) and why?
```julia
function foo1(x::Real, y::Real) = ...
function foo2{ T: Real}(x::T, y::T) = ...
will make library code less
generic for reuse by others (don't overtype is a phrase you might find
when searching the forum or the github project pages).
Cheers,
Tobi
Am Dienstag, 19. August 2014 21:54:37 UTC+2 schrieb Spencer Lyon:
I just read Tobais’ comment more closely:
Specifying
So I read through issue 1974
https://github.com/JuliaLang/julia/issues/1974. It seems like what I am
trying to do is not possible before that gets implemented. Is that correct?
On Thursday, July 10, 2014 1:15:14 AM UTC-4, Spencer Lyon wrote:
I have a very simple composite type:
type
:40 AM Spencer Lyon wrote:
So I read through issue 1974
https://github.com/JuliaLang/julia/issues/1974. It seems like what I
am
trying to do is not possible before that gets implemented. Is that
correct?
On Thursday, July 10, 2014 1:15:14 AM UTC-4, Spencer Lyon wrote:
I have
I have a very simple composite type:
type DiscreteRv{T : Real}
q::Vector{T}
Q::Vector{T}
end
DiscreteRv{T : Real}(x::Vector{T}) = DiscreteRv(x, cumsum(x))
Here q represents a probability vector for a discrete random variable. Q is
the associated cdf.
I want to make sure that the
outputs from the types of the inputs. If you have to
look at the actual values of the inputs, it's not type-stable.
--Tim
On Monday, June 30, 2014 09:17:45 AM Spencer Lyon wrote:
Does this function suffer from type instability?
function bellman_operator{T : FloatingPoint}(g
of the inputs. If you have
to
look at the actual values of the inputs, it's not type-stable.
--Tim
On Monday, June 30, 2014 09:17:45 AM Spencer Lyon wrote:
Does this function suffer from type instability?
function bellman_operator{T : FloatingPoint}(g::GrowthModel,
w::Vector
I need to find/write some code in julia that can compute the quadrature
nodes and weights for various distributions. Some example distributions are
Beta, Gamma, Normal, LogNormal, Uniform...
I imagine that this functionality already exists somewhere and I don't want
to duplicate code, but
I am having issues defining constructors for parametric types. I’lll borrow
an example from the docs to illustrate my issue:
Say I define this type:
type Point1{T}
x::T
y::T
end
I can then create an object of this type in many ways
julia Point1(1.0, 2.0)
Point1{Float64}(1.0,2.0)
julia
-read that section every now and again to
remind myself. But I don't think I can say it any better myself, at least
not yet.
On Monday, April 21, 2014 3:47:40 PM UTC-4, Spencer Lyon wrote:
I am having issues defining constructors for parametric types. I’lll
borrow an example from the docs
Say I have a type that defines a model. Something like this:
abstract Model
type Results
# Details not shown here
end
type IFP{T : FloatingPoint} : Model
# Model parameters
rho::T
beta::T
r::T
R::T
gamma::T
# Grid parameters for a
n_a::Integer
a_max::T
I'd love to beef up this wrapper type and add it to grid, but unfortunately
I wont' be able to get to it for a while -- probably late June.
On Tuesday, April 15, 2014 9:06:57 AM UTC-4, Tim Holy wrote:
On Tuesday, April 15, 2014 05:35:27 AM Spencer Lyon wrote:
It seems to me that this would
Hey Tim,
Looks like that worked well:
X = [3:15];
V = sin(X);
Xq = [4:.25:12];
ig = InterpGrid(V, BCnil, InterpLinear);
y=[ig[x - 2.0] for x in Xq];
plot(X,V,o,Xq,y, orange, Xq, sin(Xq), red);
legend([Data, Interp, Actual])
Also,
The getindex method for the InterpGrid object can accept more than one
point. There is no need to evaluate the points using a comprehension:
julia y2 = ig[Xq - 2.0];
julia y=[ig[x - 2.0] for x in Xq];
julia maximum(y - y2)
0.0
julia minimum(y - y2)
0.0
Also Tim,
I am not sure I understand what you mean by
You could also scale the values if they aren’t usually spaced by 1.
This example now fails again:
X = [3:0.5:15];
V = sin(X);
Xq = [4:.25:12];
ig = InterpGrid(V, BCnil, InterpLinear);
y = ig[Xq - 2.0]
plot(X,V,o,Xq,y, orange, Xq,
Thanks for the response. I think I have settled on how to get this to work.
This is what I have right now
immutable ShiftedInterpGrid{T,N,BC,IT}
IG::InterpGrid{T,N,BC,IT}
m::Number
b::Number
end
function ShiftedInterpGrid{T,BC,IT}(V::Vector{T}, X::Vector{T}, ::Type{BC},
I need to do some basic interpolation and think Grid.jl should provide all
the functionality I need, but I can’t quite get it to work for me.
I am essentially looking to do something like what is described for this
form of the interp1 matlab function (copied and pasted excerpt from
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