facelessuser

# pragma: init
"""Custom sRGB color class."""
from coloraide import Color as Base
from coloraide.spaces.srgb.css import sRGB
from coloraide.css import serialize
from coloraide import algebra as alg
import re

RE_CUSTOM = re.compile(r'(?i)\b([a-z][a-z_]{2,})\b')

facelessuser

DPS CONTRAST (DELTA PHI STAR) LICENSE

DPS Contrast, also known as Delta Phi Star, is a simple formula for predicting human visual perception of contrast between text and background.

• WHAT WHERE HOW

  • Delta Phi Star predicts the contrast of text against the background for a given pair of colors.
  • Delta Phi Star emerged from the SAPC/APCA project developing new standards for better beat ability on the web.
  • Delta Phi Star takes in two $L^*$ (Lstar) values, and returns an $L^c$ (lightness contrast) value.

• UNIFORMITY:

• DPS Contrast is quasi-uniform for human perception of text against a background on a self-illuminated display

facelessuser

/// tab | Tab 1

//// tab | Tab A content ////

//// tab | Tab B content //// ///

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()])

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

# 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

# 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

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 __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

"""

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),