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A Better Default Colormap for Matplotlib | SciPy 2015 | Nathaniel Smith and Stéfan van der Walt

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  • Recommended by 16 people: 15 on Hacker News and 1 on Lobsters.
  • A classic. People kept recommending it from 2016 to 2026.
  • Presented by Nathaniel Smith and St fan van der Walt

Intermediate. Assumes working knowledge.

177K views11 years ago16 recommendations

Graphics and audioProgramming languagesData visualizationPython

Nathaniel Smith and St fan van der Walt explain the design of Viridis, a perceptually uniform colormap created to replace Jet in Matplotlib.

Complete SciPy 2015 Talk & Tutorial Playlist here: ow.ly/PHjEN

Why it's here

16 people recommend this

What people say. Highly recommended as a brilliant explanation of perceptual color spaces and how to design accessible colormaps for data visualization.

vitaliqueon Hacker News • Recommends

On “New Map Scale Is More Readable by People Who Are Color Blind”

I am not colorblind, but work with data visualization in Matlab a lot. This hilarious SciPy2015 talk1 convinced me to switch to the Parula color scheme. There's no way I'm going back to Jet. 1 link to this video
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Mizzaon Hacker News • Recommends

On “Unveiling the first-ever image of a black hole [video]”

Grrr, why aren't scientists all using perceptually uniforms color schemes! This would still look awesome in `plasma`. Is there any where I can get the data and do it myself? One of my favorite talks ever is on this subject: link to this video
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kbdon Hacker News • Recommends

On “Turbo, an Improved Rainbow Colormap for Visualization”

If you have any interest in this topic, I’d strongly recommend watching this presentation at SciPy 2015 where Viridis and Inferno were first presented, when the developers of Matplotlib were coming up with a new default colormap to replace Jet: link to this video I'd love to see Turbo analyzed in the same fashion.
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spectramaxon Hacker News • Recommends

On “Turbo, an Improved Rainbow Colormap for Visualization”

…of Jet while also addressing some of its shortcomings, such as false detail, banding and color blindness ambiguity. What are the desirable properties of Jet? Why not use Viridis or Magma or any of the recent linear colormaps from matplotlib 2.0? These shortcomings were the exact reason why Viridis was created as exemplified in this brilliant talk: link to this video I don't understand the point of Turbo. > Turbo mimics the lightness profile of Jet, going from low to high back down to low, without banding. As such, its lightness slope is generally double that of Viridis, allowing subtle changes to be more easily seen. This is a valuable feature, since it greatly enhances detail when color can be used to…
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neilpanchalon Hacker News • Recommends

On “Designing Accessible Color Systems”

…ting proper colors but if the end user's calibration is not good, all bets are off. Colors are a problem for interface design (and accessibility), it becomes a real challenge when dealing with quantitative data and build perceptually uniform colormaps with accessibility considerations, here is an amazing presentation of Matplotlib's new colormaps: link to this video…
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azepoion Hacker News • Recommends

On “How to Avoid Equidistant HSV Colors (2011)”

I'm impressed by the other mentioned picker, for HCL this time link This tool is also useful link I have to mention this fantastic presentation on colormaps link to this video (A Better Default Colormap for Matplotlib, SciPy 2015, Nathaniel Smith and Stéfan van der Walt). It is about perceptually uniform colormaps. link
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asthasron Lobsters • Recommends

A Better Default Colormap for Matplotlib
5View on Lobsters

elsherbinion Hacker News • Recommends

On “HCL: a color model that actually matches our perceptions (2011)”

This talk from the matplotlib people on making a better color map goes into some details about physical perception and what it means for picking colors: link to this video
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vanderZwanon Hacker News • Recommends

On “Sorting Algorithms Revisualized”

There is the cube-helix approach, but I suspect you're thinking about something else. The linked talk about the creation of the viridis colour map0 actually spends a good ten minutes on explaining colour theory, and makes it look very simple in the process. About ten minutes in it shows the "perceptual colour space" of a typical non-colourblind person. A few minutes later it shows the color spaces for people with various types of colourblindness. Perhaps one could project one perceptual space into the other in a way that more-or-less preserves the original information, but essentially with more banding? 0 link to this video
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hprotagoniston Hacker News • Recommends

On “Color: From Hex codes to Eyeballs”

Color is way more fascinating than I had previously thought. A few years ago, i watched this pydata talk on designing colormaps for matplotlib that was pretty engrossing: link to this video
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cosmic_quantaon Hacker News • Recommends

On “Ask HN: What are some of the best technical talks you've heard?”

A Better Default Colormap for Matplotlib ( link to this video ) This talk is made better by the fact that it's rather brief (~20min).
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malikeron Hacker News • Recommends

On “Which color scale to use when visualizing data”

There’s a great talk 1 on how SciPy chose their new default colormap viridis to replace the rainbow colored former default “jet”. Turns out that there have been good studies on how the wrong colormap can lead to bad decisions in medicine, and designing a replacement took a lot of careful work. 1 link to this video
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CorrectHorseBaton Hacker News • Recommends

On “Rainbow Color Map Still Considered Harmful (2007) [pdf]”

…ee how that is a good tradeoff. As a comparison, what would you rather have: a non-distorted plot or a plot that is on average about three times as big, but it's heavily distorted. At some places it's smaller than the original and on other places it's 5 times bigger. Would you really choose the bigger figure? That is what jet is. Look at this plot link to this video (watch the whole video if you have the time), ideally you want the derivatives (the 2 plots on the left top) to be constant so not to distort your data. Jet is all over the place while viridis is basically constant (not shown in the slides, but the derivative stays all the way around 120).…
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quietbritishjimon Hacker News • Recommends

On “Why Japan Has Blue Traffic Lights Instead of Green”

Depending on what exactly your figures were/are, you might benefit from the viridis colour map, available in matplotlib and R (and probably in many other places). It's colourful, so those without colour blindness get a richer display than if you used greyscale, and the colours chosen aren't too affected by most common forms of colour blindness. But for those that really can't see the colours, even if you literally just convert to monochrome, it still works. It also looks nice :-) Here's a talk by the creators: link to this video Here's a little article about it (with some R specifics): link
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qwertoxon Hacker News • Recommends

On “Issues with color spaces and perceptual brightness”

If this submission has put you into a mindset of wanting to know more about colormaps, here's a good video about how Matplotlib ended up having `vividris` as its default colormap: A Better Default Colormap for Matplotlib | SciPy 2015 | Nathaniel Smith and Stéfan van der Walt link to this video
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carpdiemon Hacker News • Recommends

On “Show HN: Ember – Redshift safe color palettes”

…I'm still very much learning, but if you like this stuff and want to learn more yourself there's a couple sources I can recommend: - Interaction of Color, by Josef Albers. Amazing book, but you really want to get a physical copy. - I really like this talk from SciPy 2015 to get a sense of how people work with and talk about color maps in practice: link to this video - This is a really, really thorough (and beautifully illustrated) article on what color spaces are: link - And last, I wrote a very technical blog post a few years back, if you want to see some math on a bunch of this: link…
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