I never use Jupyter notebooks so I cannot really help you there. linearize_symbolic is not exported, so you’d have to call it using ModelingToolkit.linearize_symbolic
When you activate a temp environment in the REPL that is not carried over to the notebook.
IJulia notebooks support REPL mode though, so you can do ] activate --temp in a notebook cell.
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You can call it programatically through Pkg, though it will have a little different syntax compared to the pkg mode.
From the docstring we see that Pkg.activate(temp=true) is one way to activate a temp environment.
help?> Pkg.activate
Pkg.activate([s::String]; shared::Bool=false, io::IO=stderr)
Pkg.activate(; temp::Bool=false, shared::Bool=false, io::IO=stderr)
Activate the environment at s. The active environment is the environment that is modified
by executing package commands. The logic for what path is activated is as follows:
• If shared is true, the first existing environment named s from the depots in the
depot stack will be activated. If no such environment exists, create and
activate that environment in the first depot.
• If temp is true this will create and activate a temporary environment which will
be deleted when the julia process is exited.
• If s is an existing path, then activate the environment at that path.
• If s is a package in the current project and s is tracking a path, then activate
the environment at the tracked path.
• Otherwise, s is interpreted as a non-existing path, which is then activated.
If no argument is given to activate, then activate the home project. The home project is
specified by either the --project command line option to the julia executable, or the
JULIA_PROJECT environment variable.
Examples
≡≡≡≡≡≡≡≡≡≡
Pkg.activate()
Pkg.activate("local/path")
Pkg.activate("MyDependency")
Pkg.activate(; temp=true)
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That’s what I did.
Thanks!
How can I then add the master version of ModelingToolkit?
Pkg.add("ModelingToolkit#master")
does not work.
Command line version is:
pkg> add ModelingToolkit#master
As I said, you can just use the REPL mode in Jupyter by starting your cell with ].
If you want to use the functional API check the docs:
help?> Pkg.add
(...)
Pkg.add(name="Example", version="0.3") # Specify version; latest release in the 0.3 series
Pkg.add(name="Example", version="0.3.1") # Specify version; exact release
Pkg.add(url="https://github.com/JuliaLang/Example.jl", rev="master") # From url to remote gitrepo
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OK – some bugs in linearize_partial_model, in addition to relying on an ODESystem model which may have equations and variables auto-removed.
I fixed the code by (i) making it based purely on the equation vector (for which I have full control), and (ii) fixing the bug. The new function is named linearize_symbolic_dae01 (indicating that it only works for DAEs of index 0 or 1) + a helper function to pick out the variables and parameters from equation (get_symbols).
I can make the code available on request, but won’t take up more space here now.