I am setting up some Jupyter notebooks to go over in my classes, and give students files to start working with.
My constraints are:
- I will do a precompilation step offline, and tell users they will do the same (and to not get irritated in the middle).
- Relatively simple plotting requirements, but don’t want bleeding edge.
- Focus on Jupyter in new notebooks, but need to at least work in Juno
- I will tell people to use this as the “quick and dirty” solution, and that they can use
Plots.jlor something fancier for real work.
- My main criteria is: start up a notebook, type in the text, and how long does it take?
Main Question: What is the best quick-and-dirty library for Jupyter tutorials? As a more general point for Julia in the short-term, there is no reason we need to use the same plots library for “real work” and for tutorials. Pick something stable and fast for all of the tutorials, and tell people other libraries are useful for production work (i.e. see where
Plots.jl and others go).
As an example, after the precompilation for
PlotlyJS, I booted up a new notebook on JuliaBox and used
import PlotlyJS x = linspace(0, 10, 200) y = sin.(x) # specify which module scatter belongs to since both have scatter PlotlyJS.plot(PlotlyJS.scatter(x=x, y=y, marker_color="blue", line_width=2))
and it took 18-20 seconds before the plot showed. With pyplot (after precompilation) a new notebook took about 16 seconds before this plot showed.
using PyPlot x = linspace(0, 10, 200) y = sin.(x) plot(x, y, "b-", linewidth=2)