Writing to CSV files and plotting are getting slower in every run

General Usage
17

Another possibility for your consideration :slight_smile:

I am using PyPlot backend, but I do not believe as intensely as you are,
and this worked out OK to list the Options about managing a Python installation

pkg> add PyPlot

==THEN I chose==>

 pkg> add Conda ## whereupon Julia configured PyCall to use a Julia-specific Python 
and so Julia Pkg Handled everything beautifully

Here is why I selected “pkg> Add Conda” :
Another alternative is to configure PyCall to **use a Julia-specific Python **
**distribution via the Conda.jl package (which installs a private Anaconda **
Python distribution), which has the advantage that packages can be installed
and kept up-to-date via Julia. As explained in the PyCall documentation,
set ENV[“PYTHON”]=“” , ## ## Run ONCE THEN Comment out – ENV[“PYTHON”]=“”
run Pkg.build(“PyCall”)

Below are just some notes I scratched down , HTH

(v1.4-EXP3) pkg> add PyCall

  • PyCall v1.92.2
    build PyCall ## again
    julia> using PyCall
    [ Info: Precompiling PyCall

(v1.4-EXP3) pkg> build PyCall
Building Conda ─→ ~/.julia/packages/Conda/tJJuN/deps/build.log
Building PyCall → ~/.julia/packages/PyCall/tqyST/deps/build.log

.
and re-launch Julia. Then,
To install the matplotlib module, you can use
pyimport_conda("matplotlib", PKG) ,
where PKG is the Anaconda package that contains the module matplotlib,
.
julia> pyimport_conda("matplotlib", "PKG")
[ Info: Installing matplotlib via the Conda PKG package…