bids / colormap Goto Github PK

SSIA. It strikes me as implausible that MATLAB owns the exclusive use of an array of numbers, and even more implausible that if an perceptually very similar colormap to parula were to live in this repository (so as to be made from non-identical RGB values) whether that would be covered by any claimed ownership over the 'original' parula.

What do the authors believe on this topic?
Is the parula.py file here a recreation of the parula colormap from MATLAB by independent means? (I can't check I don't have a MATLAB license)

I need to visualize data that fits the circular color space, much like the wind directions in this talk. (My use case is the complex argument, i.e., the angle, in a complex-valued function.) Right now, I'm using the hsv color map aka the Comic Sans of circular color map.

Now that matplotlib will have four awesome colormaps for linear space, is there a recommendation you can give for circular color maps as well?

Is there any interest in adding somewhere in MPL a tool to equalize poor colormaps?
I've been working on a prototype replicating Peter Kovesi's Matlab function:
https://github.com/mycarta/rainbowbot/blob/master/equalize_colormap_test.ipynb

My idea is actually a full automatic 'rainbowbot':
https://github.com/mycarta/rainbowbot
https://github.com/mycarta/rainbowbot/blob/master/color_reduction_and_perceptual_test.ipynb

Is there any particular reason this repo hasn't been put into PyPI? Would love to be able to import without copy/pasting the colormaps.py file into my repos. Happy to help build a setup.py if there is interest in posting it on PyPI.

Just tried cloning this repo and it took much longer then expected owing to a large pack size... i.e. 17Mb 99% stored in .git/objects/pack/pack-6b24ce3d7a0ffb8b614f9cdcc5e68dfd610187a9.pack, this seems crazy for just a few short text files and a handful of commits...

On a fresh install of WinPython-64bit-3.4.3.4 and viscm:

In the editor the gamut slice is rendered with blue and red in the correct place, but green and pink inverted (i.e. x & y interchanged). The spline and colourmap therefore do not correspond to the slice. The following is a replication of the example at the bottom of http://bids.github.io/colormap/.

Is there any pip installable source for these colormaps? Including colormaps.py from here, or extracting them from matplotlib seem suboptimal. It doesn't seem to be specified anywhere, but I gather that colormaps.py arrays are in sRGB1 (non-linear), is this correct?

Also, are the equations (code) used for creating the maps available anywhere?

Very nice but under presented.

I would update README to include:

• Link to demo http://bids.github.io/colormap/
• Link to presentation recording https://youtu.be/XjHzLUnHeM0
• Simple install/usage for iPython
• Link to https://github.com/matplotlib/cmocean

Apologies if this is the wrong place to ask, but since this seems to be the origin of viridis, it seemed a good place.

If I already have viridis-encoded data, what's an easy way to convert a given color to the scalar value that produced it? in other words, if viridis(x) gives (r,g,b), how would you write an inverse_viridis(r, g, b) that gives x (assuming the range of x is known to be [0,1]).

A brute-force method would be to find the closest rgb value in the _viridis_data list to your input rgb, and then do a linear interpolation between the two adjacent values in the data list to get the exact value of x. However, this seems quite slow, and given the structure of viridis it looks like there could be assumptions to make for a simpler algorithm that doesn't involve a brute force search?

Given that this is the new default for matplotlib, it's quite possible a lot of viridis-encoded data will end up out there in the wild with raw data being lost or inaccessible without reconstructing it, so this kind of function might be a good thing to have.