Chris, thank you for the information!!

21 сент. 2016 г. 23:07 пользователь "Chris Rackauckas" <rackd...@gmail.com>
написал:

> 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.
>>
>

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