I would also be for coming up with our own colormap. That said, I think simply modifying algae won't be enough, since it is too perceptually nonlinear.
On Wed, Jan 6, 2016 at 10:32 AM, John ZuHone email@example.com wrote:
I would go for modifying algae.
On Jan 6, 2016, at 11:30 AM, Matthew Turk firstname.lastname@example.org wrote:
For a long time we've used "algae," which was designed by Britton about eight years ago, as the default colormap. This has been really nice for "branding" yt -- if you see an algae plot, it's probably (not definitely) made with yt. But it's also not accessible from a colorblindness perspective. Stefan van der Walt has been giving some really great talks lately about building a better colormap for matplotlib (e.g., https://www.youtube.com/watch?v=xAoljeRJ3lU ) which culminated in viridis, which is shipping in recent versions of matplotlib and will become the default.
In support of this, he built a tool called viscm which can generate reduced versions of colormaps to show what they would be like with varying degrees of insensitivity to color. I've generated outputs from viscm of three of the custom colormaps we ship with yt:
Algae: https://images.hub.yt/u/fido/m/d275d5e1-png/ Cubehelix: https://images.hub.yt/u/fido/m/8e698928-png/ (I believe this is now also shipped with MPL) Kamae: https://images.hub.yt/u/fido/m/e0e40efa-png/
I love algae, but it's not the best from an accessibility perspective.
I'd like to propose that we use a new default colormap. If we do this, I see two options:
- Retain a "branding" by developing a new one either by using the
techniques used by matplotlib (or one of the maps they opted not to use) or by modifying algae to be more accessible; looking at the response functions, I suspect it would be reasonably possible to modify it. (Modifying algae is my preference.)
- Use viridis (which we may then have to ship if we have older
versions of matplotlib to support)
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