create Name <-> tags csv
so one day it would be possible to only get nature related names, or only technical names and so on

I would be useful to implement support for various photoreceptor models so that it is possible to generate custom cone fundamentals down the road. I have started with Caroll et al. (2000), Stockman and Sharpe (2000), Lamb (1995) photoreceptor models in that notebook: https://colab.research.google.com/drive/1snhtUdUxUrTnw_B0kagvfz015Co9p-xv

We will obviously need support for various pre-receptor

Two screenshots, could not get image saving to work. One is MLV App 1.3 (should be same as 1.4), other is 1.5 - the new one is a bit less sharp, sharpening for both was set at 60 (same MASXML)

![screenshot from 2019-02-03 23-25-28](https://user-images.git

I could not find any documentation of the range of the channels in JCh variant the CICAM02. Or is there no such thing as a range for this values?

Additionally, is there a way to calculate ranges that produce valid SRGB?

pillar::pillar(567.89)
#> <dbl>
#>  568.

Created on 2018-08-05 by the reprex package (v0.2.0).

@hadley: Would it work to use a different symbol instead of the decimal point here, to indicate that the true value is lower?

The default image extension mode used in scipy.ndimage.convolve is 'reflect', which reflects the image about the edge of the pixels. This introduces artefacts in the edge of the resulting RGB layers when demosaicing:

# Constant ones as image
orig = np.ones((2,2)) 
R, G, B 

@ekmett mentioned on reddit that he'd like some color distance computation added to Color:

One thing I'd really like to have would be DIN99o color distance computations

It seems like a pretty good idea, I wanna record it before I forget about it. There are sample

Right now the K-means clustering method is used to find K distinct colors from the album artwork. This method has its flaws, more specifically:

• Need to know K (how many distinct colors the image has)
• Always learns convex clusters
• Sensitive to outliers

Therefore, the accuracy of the system could potentially be significantly increased by changing the clustering method. Potential clusteri

Observed behaviour

In the "Help" > "Minimum ratio" > " Level AA" and "Text size understanding" sections, it is implied that 14pt is equivalent to 14px and that 18pt is equivalent to 18px.

Expected behaviour

I believe this is incorrect if you follow the W3C's own "[Contrast (Minimum) Understanding SC 1.4.3](https://www.w3.org/