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, 'standalone examples' are all .py files in these folders in this repository:

• examples/plotting/file
• `examples/mode

It was decided in the dev call today that we want to deprecate arrow() because of its awkward dependence on the axis scales. ...

As an alternative, we want to introduce a similar replacement vector(x, y, dx, dy, ...) where the parameters are still in data space, but the arrow shape is not tied to the data. This should be implemented based on FancyArrowPatch which is also the basis for `an

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

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

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

Details

This shouldn't happen

using Plots
plot(rand(4,4), layout = 2, series_annotations = [1 2; 3 4])
Error showing value of type Plots.Plot{Plots.GRBackend}:
ERROR: type Array has no field baseshape
Stacktrace:
  [1] getproperty(x::Vector{Plots.SeriesAnnotations}, f::Symbol)
    @ Base ./Base.jl:42
  [2] serie

@piever @kimlaberinto

Compare https://github.com/JuliaPlots/Makie.jl/blob/master/src/stats/boxplot.jl#L31-L64 with http://makie.juliaplots.org/stable/plotting_functions/boxplot.html#Makie.boxplot
This should be a good beginner issue :)

Discussed in pyvista/pyvista#2225

What can be better?

metpy.calc.thermo.lcl has conflicting documentation. The header for the function is accurate - if given a multidimensional array, the function will calculate the LCL pointwise for each index of the arrays. This means it works for 2D horizontal arrays, 3D [time, experiment, station] arrays, or any other n-dimensional array.

The footer of the docstring says