wip: doc for color scales

[mattijn]

fix: #2861
preview: https://mattijn.github.io/altair-docs/user_guide/color_usage.html

[mattijn]

I'm struggling a bit with this one. @joelostblom, can you have a look if this is going in the right direction? Its maybe a bit too lengthy. I also doubt on the order of the sections. Currently the page starts with some color-theory including examples of good vs bad practices to continue with the predefined color schemes in Altair. I can imagine that one does not want to scroll through the theory each time to reach the available color schemes in Altair..

[joelostblom]

Sorry for the delay in reviewing this @mattijn I had a go now but something is up with my environment, so I will try again tomorrow or during next week.

[joelostblom]

This section is great! I would love to have more explanatory sections for good choices to make effective visualizations somehow linked to the Altair docs. I don't know exactly how this section will fit with the current color sections, but I think that is one of the things we will discuss on our call, so I just made some comments for now without knowing exactly where this will go in the end. Thanks for working on this!

[joelostblom]

Suggested change

	Effective visualization of scientific data requires careful attention
	to color selection. Choosing the right colors can be a complex task,
	involving considerations such as color perception, accessibility,
	and aesthetics. Fortunately, there are a number of resources available
	to help simplify this process, including pre-designed color schemes
	and tools for exploring and modifying color scales. In this guide,
	we'll explore some of the basics of color selection for scientific
	data visualization, including different types of color scales and
	examples of their use.
	Effective data visualization requires careful attention
	to color selection. Choosing the right colors can be a complex task,
	involving considerations such as color perception, accessibility,
	and aesthetics. Fortunately, there are a number of resources available
	to help simplify this process, including pre-designed color schemes
	and tools for exploring and modifying color scales. In this guide,
	we'll explore some of the basics of color selection for
	data visualization, including different types of color scales and
	examples of their use.

[joelostblom]

Suggested change

	Color selection is an essential aspect of creating clear and informative
	visualizations of scientific data. To make the most effective choices,
	it's important to understand the basics of color perception and theory.
	Color selection is an essential aspect of creating clear and informative
	data visualizations. To make the most effective color choices,
	it's important to understand the basics of color perception and theory.
	In this section we start by explaining colors as perceived with standard color vision
	and proceed to discuss how common color vision deficiencies alter color perception.

[joelostblom]

In all the plots in this section, could we remove the "normal vision" label and only include it in the section that talks about different types of color vision? I think the blue and orange rectangles could look better if they were made a bit smaller as well.

In the later plots, we might also want to consider calling it something like "Standard/reference/perfect color vision" instead of "normal". And likewise change "color blindness" to "color vision deficiencies" which is more descriptive of the partial loss in color vision that most people with this condition have (rather than a complete color blindness which is quite rare)

[joelostblom]

Instead of starting with talking about how to define a path between two colors, what do you think about having a more data-centric explanation that explains the different scenarios for how color could be used with data? These would be varying the hue/color to represent categorical values and varying mainly luminance to represent change in a quantitative value (with two sub categories: sequential and diverging colormaps, both which should be perceptually uniform). Personally, I'm a fan of how the seaborn docs introduce this topic as it becomes really tangible how the concepts of color applies to data right across the bath. I think talking about categorical and numerical data also maps nicely to that these concepts are already used in channel encodings and maybe our categories would be nominal data on the one hand, and then ordinal and quantitative together (as discrete and continuous sequential (or diverging) color scales). What do you think?

The section you have is really interesting and I love that the color space plot is made directly in Altair, so I want to keep it as part of the "Sequential" subsection where we talk more about how these color palettes are defined.

[joelostblom]

I start my class on this with these three slides:

[joelostblom]

I also really like these from the mpl docs on what perceptually uniform looks like in terms of the lightness increase:

[joelostblom]

Suggested change

	Now, lets say we want to define a gradient between ``blue`` and ``orange``.
	That means we have to go gradually from ``blue`` to ``orange``.
	The interpolation between colors can follow several methods.
	Now, lets say we want to define a gradient between ``blue`` and ``orange``.
	That means we have to gradually transition from ``blue`` to ``orange``,
	which is also called an "interpolation" between the two colors.

Then after this I would have the paragraph that starts with "So what is the correct interpolation...", then show the color space plot, then show the ppaths and the corresponding sequential color schemes created by each path.

[joelostblom]

Suggested change

	:hide-code:
	:remove-code:

[joelostblom]

Suggested change

	:hide-code:
	:remove-code:

[joelostblom]

Suggested change

	:hide-code:
	:remove-code:

[joelostblom]

This might need some more explanation on how to use these custom palettes. Maybe the section could start with an intro of how to make your own palette (like what is in the color customization section right now) and then show some from this other source. Also, I am not familiar with Paul Tol, but maybe we can think about the presentation of his palettes so that it doesn't seem like the properties listed above are not true for the built-in altair color schemes, because in many cases they are.

[joelostblom]

Suggested change

	color=alt.expr("luminance(scale('color', datum.sum_yield)) > 0.5 ? 'black' : 'white'")
	)
	bars + text
	The lighter the bar, the higher the luminance. If the bar is light
	we like a text overlay that is black. The darker the bar, the lower
	the luminance. If the bar is dark, we like a text overlay that is white.
	In the expression above we have written this as a predicate. The text
	appear ``black`` if the luminance is above ``0.5`` and ``white`` when
	the luminance is below ``0.5``. The luminance is computed using the
	color=alt.expr("luminance(scale('color', datum.sum_yield)) > 0.408 ? 'black' : 'white'")
	)
	bars + text
	The lighter the bar, the higher the luminance. If the bar is light
	we like a text overlay that is black. The darker the bar, the lower
	the luminance. If the bar is dark, we like a text overlay that is white.
	In the expression above we have written this as a predicate. The text
	appear ``black`` if the luminance is above ``0.408`` and ``white`` when
	the luminance is below ``0.408``. The luminance is computed using the

This section is great! I love that expression can be used for this! The reason for the change I suggest here is to stay consistent with what is used in seaborn, which is also what I chose when adding this to the text of pandas heatmaps. We could add a note about this? (I was trying to look up the reason for picking this exact number in the first place, but based on my comment here I don't think I ever knew =) )