We are happy about feedback in any form, but you can make our lives a little easier by opening issues over at GitHub - julia-vscode/julia-vscode: Julia extension for Visual Studio Code, ideally one issue for each problem you encounter If that is too cumbersome, keep responding here, more important than our workflow is that we hear the feedback!
If I use Julia REPL (started by F1, then “Julia: Start REPL” or by Ctrl-Enter to execute selected lines of code), my PyPlot plotting will take long time and will not generate any window with plot. However, if I go with the standard terminal and start Julia manually there, the PyPlot plotting works fine. This is a big problem for me as my script does a lot of plots.
This sounds to me like this? Does it also now show anything when you call gcf()? Or is this another, distinct problem?
The whole environment support right now is half baked, and we won’t fix this before we ship v0.11.0… I know pretty much what needs to be done to get this all into shape (some of it is really tricky), and that will be the main thing for v0.12.0 for me. But right now, we mainly want to ship
Thanks! Calling “gcf()” did show the plot in a new plane.
I almost thought “gcf()” didn’t work when I followed it by a semicolon “;” in REPL habitually. This “;” suppressed the plot output. When I removed “;” it worked.
Sorry for so many questions - I’m new to VS plot planes.
(1) How do I interact with a PyPlot plot in a plot plane? E.g. pan, zoom, change to full screen, save as figure, …
(2) If I cannot do that for a plot plane, am I able to still use the F5 (and thus the REPL) but not the plot plane (which is triggered by gcf())?
Thanks! I’m newbie regarding this. Can you explain or point me to any document that explains how to “pop the VS Code display from the julia base display stack”?
On Windows, it still seems to be pasting the code into the REPL.
julia> module Aretto
using Distributed
import Distributed.launch, Distributed.manage, Distributed.kill, Distributed.init_worker, Distributed.connect
export SlurmManager
export launch, manage
worker_arg = `--worker`
function __init__()
global worker_arg
worker_arg = `--worker=$(Distributed.cluster_cookie())`
end
struct ArettoManager <: ClusterManager
np::Integer
end
function launch(manager::ArettoManager, params::Dict, launched::Array, c::Condition)
## get the keyword arguments passed to addprocs()
exehome = params[:dir]
exename = params[:exename]
exeflags = params[:exeflags]
julia> module Aretto using Distributed
import Distributed.launch, Distributed.manage, Distributed.kill, Distributed.init_worker, Distributed.connect export SlurmManager
export launch, manage
worker_arg = `--worker`
function __init__()
global worker_arg worker_arg = `--worker=$(Distributed.cluster_cookie())`
end
struct ArettoManager <: ClusterManager
np::Integer
end
function launch(manager::ArettoManager, params::Dict, launched::Array, c::Condition)
## get the keyword arguments passed to addprocs()
exehome = params[:dir]
exename = params[:exename]
exeflags = params[:exeflags]
## get the default arguments passed to addprocs()
stdkeys = keys(Distributed.default_addprocs_params())
p = filter((x,y) -> !(x
in stdkeys), params) #get the keys passed in params that are not in stdkeys
srunargs = []
return nothing
end
function manage()
error("not implemented")
end
end
WARNING: replacing module Aretto.
Main.Aretto
If that is too cumbersome, keep responding here, more important than our workflow is that we hear the feedback!