Also... the really important benefit that I see in Plots.jl (if people start using it, of course) is that the authors of each backend package don't need to focus all their energy on supporting 101 ways to create the same plot. They can focus on adding the core features to their backend instead of wasting time on API code/design. This way, when there's some cool new graphics backend, it's straightforward to get a working version and there's very low barrier to entry to get new users to try it out. To use a new graphics library the user has to both install and learn it. If they no longer need to learn it, then adoption will be simple.
On Monday, September 14, 2015 at 10:04:08 AM UTC-4, Tom Breloff wrote: > > Hi Daniel... ambitious but I think it will be incredibly useful (even if > I'm the only one that ever uses it). Did you see my last post about > PyPlot? I was working on PyPlot support yesterday, and you can do a ton of > stuff already. See the examples: > https://github.com/tbreloff/Plots.jl/blob/master/docs/pyplot_examples.md > > Matplotlib's subplot command is very clunky and I haven't figured out the > right way to implement yet, but it already has support for: > > - Many lines, styles, symbols > - Bars, histograms, heatmaps > - 2-Axis support (which was always so annoying to do... I can't tell > you how many times I looked at this example > <http://matplotlib.org/examples/api/two_scales.html> throughout my > career...) > > > In reference to: > >> The priority for me is to be able to fiddle with the details of the >> plot: change the font, define a new colour, remove the tick marks, have two >> y-axes, change the aspect ratio, insert formulas in LaTeX, etc. > > > You can already pass in arbitrary colors, and change the sizes, etc. If > there's functionality you can't accomplish through Plots, you can always > adjust it directly: > > using Plots > pyplot!() > > # use Plots to create a base figure > plt = scatter(randn(1000),randn(1000), background_color = :red) > > # say you want the background color outside the canvas to be a different > color than inside (can't do this directly right now) > fig = plt.o.o # fig is the PyCall wrapper around the matplotlib.Figure > instance > fig[:set_facecolor]("blue") > > # refresh > display(plt) > > which displays a scatter plot with a blue exterior background and red > interior background. > > Long story short, you get all the benefits of Plots, and you can still > access all the functionality of matplotlib. Please try it out and open > issues if you find yourself wanting features or if you find bugs. And once > you're comfortable, try out some other backends! You might find Gadfly > plots look great when you have trouble with matplotlib, or vice versa. > Also I recommend trying the UnicodePlots backend... it's not as > feature-full as others, but it has minimal dependencies and is pretty cool! > >
