plotting

Currently the polling_interval is only used as an initial configuration and can not be dynamically changed.

The following is a discourse link where I have asked the question whether this is a supported feature.
https://discourse.bokeh.org/t/dynamically-changing-bokeh-ajaxdatasource-polling-interval/7737

Problem

3d axes don't support the data kwarg:

gcf().add_subplot(projection="3d").scatter("a", "b", "c", data={"a": [0], "b": [1], "c": [2]})

results in

ValueError: could not convert string to float: 'a'

Proposed solution

I think it's "mostly" a matter of adding a bunch of @_preprocess_data decorators to 3D plotting methods similarly to what's done for 2D plots

it's becoming more time-consuming and error-prone to manually re-test all the demos following internal refactorings and API adjustments.

now that the API is fleshed out a bit, it's possible to test a large amount of code (non-granularly) without having to simulate all interactions via Puppeteer or similar.

a lot of code can already be regression-tested by simply running all the demos and val

https://readthedocs.org/projects/chartify/builds/

What's wrong?

It is currently a pain to use an OxyPlot.WinfowsForms.PlotView with a transparent background: it throws if you set its BackColor to transparent. To avoid this, it is necessary to set the ControlStyles.SupportsTransparentBackColor style to true on the PlotView; however, Control.SetStyle is protected, so consumers must resort to reflection or extending PlotView to do so. This could be

I think it could be useful, when one wants to plot only e.g. class 1, to have an option to produce consistent plots for both plot_cumulative_gain and plot_roc

At the moment, instead, only plot_roc supports such option.

Thanks a lot

Michael Bostock has made some major improvements to the overlap avoidance algorithms in d3-sankey over the last week. Since the code in holoviews is a fairly straightforward Python port of that code it should be reasonably straightforward to update our code to match. Here's a link to the diff for all the updates.

I don't think this is a duplicate ticket, even though it's quite an obvious feature after that cat demo at SciPy2018.

The code that selects and renders the different geos is here in the THREE.js code
https://github.com/maartenbreddels/ipyvolume/blob/master/js/src/scatter.js#L57

I don't know quite where/how to inject the mesh definition compatible with the data file of this form, but i

What is the feature ?

I'm trying out this library for the first time and there's some places where it would be delightful to have shorthand functions:

1. Shorthand functions for Pos: Pos::top_right, Pos::bottom_right, Pos::bottom_center etc..
2. Short way to set colors for components. Currently, lots of boilerplate is required for e.g. white-on-black plotos
   • a cool start would b

Details

(This is related to JuliaPlots/Plots.jl#1706)

For example scatter(skipmissing([1.0, 2.0, missing, 1.0])) fails with Cannot convert Base.SkipMissing{Vector{Union{Missing, Float64}}} to series data for plotting.

Generally skipmissing is not required but it seems odd to see this fail.

Also I stumbled on this while trying to make a histogram, e.g.

The following code gives a segfault with Makie 0.15 and Julia 1.6.1 (also reproduced with 1.7-beta2):

using Makie

xs = range(150, 160, length=100)
ys = range(7, 9, length=100)
points = [(x, y, 0.0) for x in xs for y in ys]
x, y, z = map(i -> getindex.(points, i), 1:3)

flags = (153 .< x .< 153.5) .& (7 .< y .< 7.3)
surface(y[flags], x[flags], z[flags]);

Starting Julia

See pyvista/pyvista#1583

Would be nice to have:

p = pv.Plotter(notebook=True)
p.add_mesh(pv.Cube())
p.enable_stereo_render()
p.show()

We don't currently demonstrate how to:

• Use bounds and colors
• Pass in a custom norm and colormap