plotting

As a follow-up to #11540, we would like to add metadata to as many examples as possible. This will not only make the examples more usable as they are right now, but it will also open up new possibilities to search for and crosslink examples.

For the purpose of this issue, 'examples' are all .py files in the subfolders of the examples folder in this repository.

Prerequisites

Adding

Bug summary

The ax.invertxaxis() and ax.invert_yaxis() function both produce the same output, a scatterplot with a flipped X axis.

Code for reproduction

from mpl_toolkits import mplot3d
import matplotlib.pyplot as plt

ax = plt.axes(projection='3d')
plt.title("Invert Z")
ax.scatter3D(1,1,1)
# ax.invert_xaxis()
ax.invert_yaxis()
# ax.invert_zaxis()

Actual o

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/

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

Annotations don't look good in a Heatmap with a large amount of data and those that are rendered in the high values range are barely visible with the default color map. Heatmaps generated with the Bokeh backend don't have annotations by default.

See this example taken from the reference gallery:
![image](https://user-images.githubusercontent.com/35924738/140487561-3cdb38ea-8694-4328-ab9f-bfdda

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

It would be nice to support adding arrow annotations to plots, similar to pyplot.arrow (from #245).

From here: http://makie.juliaplots.org/stable/plotting_functions/heatmap.html#MakieCore.heatmap there is really no info, e.g. how to choose colormap, how to set color range.

The doctest of find_closest_cell has random data:

        Find nearest cell to a point on a sphere, centered on the
        origin.

        >>> import pyvista
        >>> mesh = pyvista.Sphere()
        >>> index = mesh.find_closest_cell([0, 0, 0.5])
        >>> index
        30

        Find the nearest cells to several random points that
        are centered on the origin.

As noted in this Stack Overflow question it would be good to be clear what’s going on in this function, including the equation.