facelessuser

##  MIT License
##
##  Copyright (c) 2023 Ronald van Wijnen
##
##  Permission is hereby granted, free of charge, to any person obtaining a
##  copy of this software and associated documentation files (the "Software"),
##  to deal in the Software without restriction, including without limitation
##  the rights to use, copy, modify, merge, publish, distribute, sublicense,
##  and/or sell copies of the Software, and to permit persons to whom the
##  Software is furnished to do so, subject to the following conditions:

facelessuser

##  MIT License
##
##  Copyright (c) 2023 Ronald van Wijnen
##
##  Permission is hereby granted, free of charge, to any person obtaining a
##  copy of this software and associated documentation files (the "Software"),
##  to deal in the Software without restriction, including without limitation
##  the rights to use, copy, modify, merge, publish, distribute, sublicense,
##  and/or sell copies of the Software, and to permit persons to whom the
##  Software is furnished to do so, subject to the following conditions:

facelessuser

Interpolation with Kubelka-Munk Theory

Approach

This is an experiment showcasing interpolation via Kubelka-Munk theory. Approach generally employs the method as outlined in Spectral.js. Most of foundational topics are covered in Mixbox's paper, though Spectral.js uses an approach less focused on specific paints and instead opts to generalize and simplify the approach by simply generating reflectance curves directly from the spectral data. Results don't necessarily mimic specific paints, but give a pigment like feel.

facelessuser

"""

Fancy lists in the style of Pandoc.

---
# A Python implementation of John Gruber's Markdown.

# Started by Manfred Stienstra (http://www.dwerg.net/).
# Maintained for a few years by Yuri Takhteyev (http://www.freewisdom.org).
# Currently maintained by Waylan Limberg (https://github.com/waylan),

facelessuser

# pragma: init
from __future__ import annotations
from coloraide.spaces.okhsl import Okhsl, okhsl_to_oklab, oklab_to_okhsl
from coloraide.spaces.okhsv import Okhsv, okhsv_to_oklab, oklab_to_okhsv

P3L_TO_LMS = [
    [0.4813798527499543, 0.4621183710113182, 0.05650177623872754],
    [0.2288319418112447, 0.6532168193835677, 0.11795123880518772],
    [0.08394575232299314, 0.22416527097756647, 0.6918889766994405]
]

facelessuser

from coloraide.gamut import Fit
from coloraide.spaces import RGBish
from coloraide import algebra as alg

class OkLChScale(Fit):
    """
    Gamut mapping by scaling.
    Expected gamut mapping spaces are RGB type spaces.
    For best results, linear light RGB spaces are preferred.
    """

facelessuser

# pragma: init
from coloraide.gamut import Fit
from coloraide.spaces import RGBish

class OkLChScale(Fit):
    """
    Gamut mapping by scaling.

    Expected gamut mapping spaces are RGB type spaces.
    For best results, linear light RGB spaces are preferred.

facelessuser

# pragma: init
from coloraide.gamut import Fit
from coloraide.spaces import RGBish
from coloraide import algebra as alg

class OkLChScale(Fit):
    """
    Gamut mapping by scaling.

    Expected gamut mapping spaces are RGB type spaces.

facelessuser

Exploring Tonal Palettes

HCT

HCT is a color model developed by [Google][material-hct]. It aims to solve a problem related to generating color palettes with good contrast. While HCT may seem like a revolutionary color model, the idea behind it is quite simple, take the perceptually uniform color model CAM16 and combine it with the CIE Lab's lightness.

Upside of HCT

facelessuser

from coloraide import Color as Base

class Color(Base):
    FIT = 'oklch-chroma'
    POWERLESS = True
    CARRYFORWARD = True

print('==== Case 1 ====')
color = Color.interpolate(['oklch(100% 50% 60deg)', 'oklch(50% 50% 0deg)'], space='oklch', out_space='oklch')(0.5)
Row([color.to_string(percent=True), color.convert('srgb').to_string()])