Usually I'm plotting the run from really long differential equations solution. The one I am mentioning is from a really long stochastic differential equation solution (publication coming soon). 19 lines with likely millions of dots, thrown together into one figure or spanning multiple. I can't really explain "faster" other than, when I ran the plot command afterwards (on smaller test cases) PyPlot would take forever but GR would get the plot done much quicker, so for the longer run I went with GR and it worked. I am not much of a plot guy so my method is, use Plots.jl, switch backends to find something that works, and if I can't find an easy solution like this, go ask Tom :). What I am saying is, if you do some experiments, GR will plot faster than something like Gadfly, PyPlot, (Plotly gave issues, this was in June so it may no longer be present) etc., so my hint is to give the GR backend a try if you're ever in a similar case.
On Wednesday, September 21, 2016 at 11:54:11 AM UTC-7, Andreas Lobinger wrote: > > Hello colleague, > > On Wednesday, September 21, 2016 at 8:34:21 PM UTC+2, Chris Rackauckas > wrote: >> >> I've managed to plot quite a few large datasets. GR through Plots.jl >> works very well for this. I tend to still prefer the defaults of PyPlot, >> but GR is just so much faster that I switch the backend whenever the amount >> of data gets unruly (larger than like 5-10GB, and it's worked to save a >> raster image from data larger than 40-50 GB). Plots + GR is a good combo >> > > Could you explain this in more length, especially the 'faster'? It sounds > like your plotting a few hundred million items/lines. >
