refactor core python package, implement base CLI

monobiome/__init__.py

@@ -0,0 +1,3 @@
+from importlib.metadata import version
+
+__version__ = version("monobiome")

monobiome/__main__.py

@@ -1,5 +1,6 @@
 from monobiome.cli import create_parser, configure_logging
 
+
 def main() -> None:
     parser = create_parser()
     args = parser.parse_args()
@@ -16,4 +17,3 @@ def main() -> None:
 
 if __name__ == "__main__":
     main()
-

monobiome/cli/__init__.py

@@ -1,7 +1,7 @@
-import argparse
 import logging
+import argparse
 
-from monobiome.cli import generate, scheme
+from monobiome.cli import scheme, palette
 
 logger: logging.Logger = logging.getLogger(__name__)
 
@@ -26,7 +26,7 @@ def create_parser() -> argparse.ArgumentParser:
 
     subparsers = parser.add_subparsers(help="subcommand help")
 
-    generate.register_parser(subparsers)
+    palette.register_parser(subparsers)
     scheme.register_parser(subparsers)
 
     return parser

monobiome/cli/generate.py

@@ -1,31 +0,0 @@
-import argparse
-
-def generate_scheme(args: argparse.Namespace) -> None:
-    run_from_json(args.parameters_json, args.parameters_file)
-
-
-def register_parser(subparsers: _SubparserType) -> None:
-    parser = subparsers.add_parser(
-        "generate",
-        help="generate theme variants"
-    )
-
-    parser.add_argument(
-        "-m",
-        "--contrast-method",
-        type=str,
-        help="Raw JSON string with train parameters",
-    )
-    parser.add_argument(
-        "-c",
-        "--contrast-level",
-        type=str,
-        help="Raw JSON string with train parameters",
-    )
-    parser.add_argument(
-        "-b",
-        "-base-lightness",
-        type=str,
-        help="Minimum lightness level",
-    )
-    parser.set_defaults(func=generate_scheme)

monobiome/cli/palette.py

@@ -0,0 +1,51 @@
+import argparse
+from pathlib import Path
+
+from monobiome.util import _SubparserType
+from monobiome.palette import generate_palette
+
+
+def register_parser(subparsers: _SubparserType) -> None:
+    parser = subparsers.add_parser(
+        "palette",
+        help="generate primary palette"
+    )
+
+    parser.add_argument(
+        "-n",
+        "--notation",
+        type=str,
+        default="hex",
+        choices=["hex", "oklch"],
+        help="Color notation to export (either hex or oklch)",
+    )
+    parser.add_argument(
+        "-f",
+        "--format",
+        type=str,
+        default="toml",
+        choices=["json", "toml"],
+        help="Format of palette file (either JSON or TOML)",
+    )
+    parser.add_argument(
+        "-o",
+        "--output",
+        type=str,
+        help="Output file to write palette content",
+    )
+
+    parser.set_defaults(func=handle_palette)
+
+
+def handle_palette(args: argparse.Namespace) -> None:
+    notation = args.notation
+    file_format = args.format
+    output = args.output
+
+    palette_text = generate_palette(notation, file_format)
+
+    if output is None:
+        print(palette_text)
+    else:
+        with Path(output).open("w") as f:
+            f.write(palette_text)

monobiome/cli/scheme.py

@@ -0,0 +1,154 @@
+import argparse
+from pathlib import Path
+
+from monobiome.util import _SubparserType
+from monobiome.scheme import generate_scheme
+from monobiome.constants import monotone_h_map
+
+
+def register_parser(subparsers: _SubparserType) -> None:
+    parser = subparsers.add_parser(
+        "scheme",
+        help="create scheme variants"
+    )
+
+    parser.add_argument(
+        "mode",
+        type=str,
+        choices=["dark", "light"],
+        help="Scheme mode (light or dark)"
+    )
+    parser.add_argument(
+        "biome",
+        type=str,
+        choices=list(monotone_h_map.keys()),
+        help="Biome setting for scheme."
+    )
+    parser.add_argument(
+        "-m",
+        "--metric",
+        type=str,
+        default="oklch",
+        choices=["wcag", "oklch", "lightness"],
+        help="Metric to use for measuring swatch distances."
+    )
+
+    # e.g., wcag=4.5; oklch=0.40; lightness=40
+    parser.add_argument(
+        "-d",
+        "--distance",
+        type=float,
+        default=0.40,
+        help="Distance threshold for specified metric",
+    )
+    parser.add_argument(
+        "-o",
+        "--output",
+        type=str,
+        help="Output file to write scheme content",
+    )
+
+    # these params remain rooted in lightness; no need to accommodate metric
+    # given these are monotone adjustments. You *could* consider rooting these
+    # in metric units, but along monotones, distance=lightness and WCAG isn't a
+    # particularly good measure of perceptual distinction, so we'd prefer the
+    # former.
+    parser.add_argument(
+        "--l-base",
+        type=int,
+        default=20,
+        help="Minimum lightness level (default: 20)",
+    )
+    parser.add_argument(
+        "--l-step",
+        type=int,
+        default=5,
+        help="Lightness step size (default: 5)",
+    )
+
+    # gaps
+    parser.add_argument(
+        "--fg-gap",
+        type=int,
+        default=50,
+        help="Foreground lightness gap (default: 50)",
+    )
+    parser.add_argument(
+        "--grey-gap",
+        type=int,
+        default=30,
+        help="Grey lightness gap (default: 30)",
+    )
+    parser.add_argument(
+        "--term-fg-gap",
+        type=int,
+        default=60,
+        help="Terminal foreground lightness gap (default: 60)",
+    )
+
+    parser.set_defaults(func=handle_scheme)
+
+
+def handle_scheme(args: argparse.Namespace) -> None:
+    output = args.output
+
+    mode = args.mode
+    biome =  args.biome
+    metric = args.metric
+    distance = args.distance
+    l_base = args.l_base
+    l_step = args.l_step
+    fg_gap = args.fg_gap
+    grey_gap = args.grey_gap
+    term_fg_gap = args.term_fg_gap
+
+    full_color_map = {
+        "red": "red",
+        "orange": "orange",
+        "yellow": "yellow",
+        "green": "green",
+        "cyan": "cyan",
+        "blue": "blue",
+        "violet": "violet",
+        "magenta": "orange",
+    }
+    term_color_map = {
+        "red": "red",
+        "yellow": "yellow",
+        "green": "green",
+        "cyan": "blue",
+        "blue": "blue",
+        "magenta": "orange",
+    }
+    vim_color_map = {
+        "red": "red",
+        "orange": "orange",
+        "yellow": "yellow",
+        "green": "green",
+        "cyan": "green",
+        "blue": "blue",
+        "violet": "blue",
+        "magenta": "red",
+    }
+    # vim_color_map = full_color_map
+
+    scheme_text = generate_scheme(
+        mode,
+        biome,
+        metric,
+        distance,
+        l_base,
+        l_step,
+        fg_gap,
+        grey_gap,
+        term_fg_gap,
+        full_color_map,
+        term_color_map,
+        vim_color_map,
+    )
+
+    if output is None:
+        print(scheme_text)
+    else:
+        with Path(output).open("w") as f:
+            f.write(scheme_text)

monobiome/constants.py

@@ -1,157 +1,121 @@
+import tomllib
+from importlib.resources import files
+
 import numpy as np
 
-# SET LIGHTNESS CONTROL POINTS
+from monobiome.curve import (
-# L_points = [10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 98]
+    l_maxC_h,
-L_points = list(range(10, 98+1))
+    bezier_y_at_x,
+)
 
-# FIXED MONOBIOME PARAMETERS
+parameters_file = files("monobiome.data") / "parameters.toml"
-L_resolution = 5  # step size along lightness dim
+parameters = tomllib.load(parameters_file.open("rb"))
-L_space = np.arange(0, 100+L_resolution, L_resolution)
 
-monotone_C_map = {
+L_min: int = parameters.get("L_min", 10)
-    "alpine": 0,
+L_max: int = parameters.get("L_max", 98)
-    "badlands": 0.011,
+L_step: int = parameters.get("L_step", 5)
-    "chaparral": 0.011,
-    "savanna": 0.011,
-    "grassland": 0.011,
-    "tundra": 0.011,
-}
 
-h_weights = {
+L_points: list[int] = list(range(L_min, L_max+1))
-    "red": 3.0,
+L_space = np.arange(0, 100 + L_step, L_step)
-    "orange": 3.8,  # 3.6
-    "yellow": 3.8,  # 4.0
-    "green": 3.8,
-    "blue": 3.4,  # 3.8
-}
-h_L_offsets = {
-    "red": 0,  # -1,
-    "orange": -5.5,  # -3,
-    "yellow": -13.5,  # -8
-    "green": -11,  # -8
-    "blue": 10,  # 14
-}
-h_C_offsets = {
-    "red": 0,  # 0
-    "orange": -0.01,  # -0.02
-    "yellow": -0.052,  # -0.08
-    "green": -0.088,  # -0.105
-    "blue": 0.0,  # 0.01
-}
 
-monotone_h_map = {
+monotone_C_map = parameters.get("monotone_C_map", {})
-    "alpine": 0,
+h_weights = parameters.get("h_weights", {})
-    "badlands": 29,
+h_L_offsets = parameters.get("h_L_offsets", {})
-    "chaparral": 62.5,
+h_C_offsets = parameters.get("h_C_offsets", {})
-    "savanna": 104,
+monotone_h_map = parameters.get("monotone_h_map", {})
-    "grassland": 148,
+accent_h_map = parameters.get("accent_h_map", {})
-    "tundra": 262,
-}
-accent_h_map = {
-    "red": 29,
-    "orange": 62.5,
-    "yellow": 104,
-    "green": 148,
-    "blue": 262,
-}
 h_map = {**monotone_h_map, **accent_h_map}
 
+"""
+Compute chroma maxima at provided lightness levels across hues.
 
-v111_L_space = list(range(15, 95+1, 5))
+A map with max chroma values for each hue across lightness space
-v111_hC_points = {
+
-    "red": [
+{
-        0.058,
+   "red": [ Cmax@L=10, Cmax@L=11, Cmax@L=12, ... ],
-        0.074,
+   "orange": [ Cmax@L=10, Cmax@L=11, Cmax@L=12, ... ],
-        0.092,
+   ...
-        0.11,
-        0.128,
-        0.147,
-        0.167,
-        0.183,
-        0.193,
-        0.193,
-        0.182,
-        0.164,
-        0.14,
-        0.112,
-        0.081,
-        0.052,
-        0.024,
-    ],
-    "orange": [
-        0.030,
-        0.038,
-        0.046,
-        0.058,
-        0.07,
-        0.084,
-        0.1,
-        0.114,
-        0.125,
-        0.134,
-        0.138,
-        0.136,
-        0.128,
-        0.112,
-        0.092,
-        0.064,
-        0.032,
-    ],
-    "yellow": [
-        0.02,
-        0.024,
-        0.03,
-        0.036,
-        0.044,
-        0.05,
-        0.06,
-        0.068,
-        0.076,
-        0.082,
-        0.088,
-        0.088,
-        0.086,
-        0.082,
-        0.072,
-        0.058,
-        0.04,
-    ],
-    "green": [
-        0.0401,
-        0.048,
-        0.056,
-        0.064,
-        0.072,
-        0.08,
-        0.09,
-        0.098,
-        0.104,
-        0.108,
-        0.11,
-        0.108,
-        0.102,
-        0.094,
-        0.084,
-        0.072,
-        0.05,
-    ],
-    "blue": [
-        0.06,
-        0.072,
-        0.084,
-        0.096,
-        0.106,
-        0.116,
-        0.124,
-        0.13,
-        0.132,
-        0.128,
-        0.122,
-        0.11,
-        0.096,
-        0.08,
-        0.064,
-        0.044,
-        0.023,
-    ],
 }
+"""
+Lspace_Cmax_Hmap = {
+    h_str: [l_maxC_h(_L, _h) for _L in L_space]
+    for h_str, _h in h_map.items()
+}
+
+
+"""
+Set QBR curves, *unbounded* chroma curves for all hues
+
+1. Raw bezier chroma values for each hue across the lightness space
+
+   Lpoints_Cqbr_Hmap = {
+      "red": [ Bezier@L=10, Bezier@L=11, Bezier@L=12, ... ],
+      ...
+   }
+
+2. Three bezier control points for each hue's chroma curve
+
+   QBR_ctrl_Hmap = {
+      "red": np.array([
+          [ x1, y1 ],
+          [ x2, y2 ],
+          [ x3, y3 ]
+       ]),
+      ...
+   }
+"""
+Lpoints_Cqbr_Hmap = {}
+QBR_ctrl_Hmap = {}
+
+for h_str, _h in monotone_h_map.items():
+    Lpoints_Cqbr_Hmap[h_str] = np.array(
+        [monotone_C_map[h_str]]*len(L_points)
+    )
+    
+for h_str, _h in accent_h_map.items():
+    Lspace_Cmax = Lspace_Cmax_Hmap[h_str]
+    
+    # get L value of max chroma; will be a bezier control
+    L_Cmax_idx = np.argmax(Lspace_Cmax)
+    L_Cmax = L_space[L_Cmax_idx]
+
+    # offset control point by any preset x-shift
+    L_Cmax += h_L_offsets[h_str]
+
+    # and get max C at the L offset
+    Cmax = l_maxC_h(L_Cmax, _h)
+
+    # set 3 control points; shift by any global linear offest
+    C_offset = h_C_offsets.get(h_str, 0)
+    
+    p_0 = np.array([0, 0])
+    p_Cmax = np.array([L_Cmax, Cmax + C_offset])
+    p_100 = np.array([100, 0])
+    
+    B_L_points = bezier_y_at_x(
+        p_0, p_Cmax, p_100,
+        h_weights.get(h_str, 1),
+        L_points
+    )
+    Lpoints_Cqbr_Hmap[h_str] = B_L_points
+    QBR_ctrl_Hmap[h_str] = np.vstack([p_0, p_Cmax, p_100])
+
+
+"""
+Bezier chroma values, but bounded to attainable gamut colors (bezier fit
+can produce invalid chroma values)
+
+h_L_points_Cstar = {
+   "red": [ bounded-bezier@L=10, bounded-bezier@L=11, ... ],
+   ...
+}
+"""
+Lpoints_Cstar_Hmap = {}
+
+for h_str, L_points_C in Lpoints_Cqbr_Hmap.items():
+    _h = h_map[h_str]
+
+    Lpoints_Cstar_Hmap[h_str] = [
+        max(0, min(_C, l_maxC_h(_L, _h)))
+        for _L, _C in zip(L_points, L_points_C, strict=True)
+    ]

monobiome/curve.py

@@ -0,0 +1,77 @@
+from functools import cache
+
+import numpy as np
+from coloraide import Color
+
+
+def quad_bezier_rational(
+    P0: float,
+    P1: float,
+    P2: float,
+    w: float,
+    t: np.array,
+) -> np.array:
+    """
+    Compute the point values of a quadratic rational Bezier curve.
+
+    Uses `P0`, `P1`, and `P2` as the three control points of the curve. `w`
+    controls the weight toward the middle control point ("sharpness" of the
+    curve"), and `t` is the number of sample points used along the curve.
+    """
+
+    t = np.asarray(t)[:, None]
+    num = (1-t)**2*P0 + 2*w*(1-t)*t*P1 + t**2*P2
+    den = (1-t)**2 + 2*w*(1-t)*t + t**2
+    
+    return num / den
+    
+def bezier_y_at_x(
+    P0: float,
+    P1: float,
+    P2: float,
+    w: float,
+    x: float,
+    n: int = 400,
+) -> np.array:
+    """
+    For the provided QBR parameters, provide the curve value at the given
+    input.
+    """
+
+    t = np.linspace(0, 1, n)
+    B = quad_bezier_rational(P0, P1, P2, w, t)
+    x_vals, y_vals = B[:, 0], B[:, 1]
+    
+    return np.interp(x, x_vals, y_vals)
+
+@cache
+def l_maxC_h(
+    _l: float,
+    _h: float,
+    space: str = 'srgb',
+    eps: float = 1e-6,
+    tol: float = 1e-9
+) -> float:
+    """
+    Binary search for max attainable OKLCH chroma at fixed lightness and hue.
+
+    Parameters:
+        _l: lightness
+        _h: hue
+
+    Returns:
+        Max in-gamut chroma at provided lightness and hue
+    """
+
+    def chroma_in_gamut(_c: float) -> bool:
+        color = Color('oklch', [_l/100, _c, _h])
+        return color.convert(space).in_gamut(tolerance=tol)
+
+    lo, hi = 0.0, 0.1
+    while chroma_in_gamut(hi):
+        hi *= 2
+    while hi - lo > eps:
+        m = (lo + hi) / 2
+        lo, hi = (m, hi) if chroma_in_gamut(m) else (lo, m)
+
+    return lo

monobiome/curves.py

@@ -1,178 +0,0 @@
-import json
-from pathlib import Path
-from functools import cache
-
-import numpy as np
-
-from pprint import pprint
-from coloraide import Color
-
-from monobiome.constants import (
-    L_points,
-    L_resolution,
-    L_space,
-
-    monotone_C_map,
-    h_weights,
-    h_L_offsets,
-    h_C_offsets,
-
-    monotone_h_map,
-    accent_h_map,
-    h_map,
-)
-
-
-@cache
-def L_maxC_h(L, h, space='srgb', eps=1e-6, tol=1e-9):
-    """
-    Binary search for max attainable OKLCH chroma at fixed lightness and hue.
-
-    Parameters:
-        L: lightness percentage
-    """
-
-    def C_in_gamut(C):
-        return Color('oklch', [L/100, C, h]).convert(space).in_gamut(tolerance=tol)
-
-    lo, hi = 0.0, 0.1
-    while C_in_gamut(hi):
-        hi *= 2
-    while hi - lo > eps:
-        m = (lo + hi) / 2
-        lo, hi = (m, hi) if C_in_gamut(m) else (lo, m)
-
-    Cmax = lo
-    # c_oklch = Color('oklch', [L, Cmax, h])
-    # c_srgb  = c_oklch.convert('srgb')
-    
-    return Cmax
-
-def quad_bezier_rational(P0, P1, P2, w, t):
-    """
-    Compute the point values of a quadratic rational Bezier curve.
-
-    Uses `P0`, `P1`, and `P2` as the three control points of the curve. `w`
-    controls the weight toward the middle control point ("sharpness" of the
-    curve"), and `t` is the number of sample points used along the curve.
-    """
-
-    t = np.asarray(t)[:, None]
-    num = (1-t)**2*P0 + 2*w*(1-t)*t*P1 + t**2*P2
-    den = (1-t)**2 + 2*w*(1-t)*t + t**2
-    
-    return num / den
-    
-def bezier_y_at_x(P0, P1, P2, w, x_query, n=400):
-    """
-    For the provided QBR parameters, provide the curve value at the given
-    input.
-    """
-
-    t = np.linspace(0, 1, n)
-    B = quad_bezier_rational(P0, P1, P2, w, t)
-    x_vals, y_vals = B[:, 0], B[:, 1]
-    
-    return np.interp(x_query, x_vals, y_vals)
-
-def Lspace_Cmax_Hmap(h_map: dict[str, float], L_space):
-    """
-    Compute chroma maxima at provided lightness levels across hues.
-
-    Parameters:
-        h_map: map from hue names to hue values
-        L_space: array-like set of lightness values
-
-    Returns:
-        A map with max chroma values for each hue across lightness space
-
-        {
-           "red": [ Cmax@L=10, Cmax@L=11, Cmax@L=12, ... ],
-           "orange": [ Cmax@L=10, Cmax@L=11, Cmax@L=12, ... ],
-           ...
-        }
-    """
-    # compute C max values over each point in L space
-
-    h_Lspace_Cmax = {
-        h_str: [max_C_Lh(_L, _h) for _L in L_space]
-        for h_str, _h in h_map.items()
-    }
-
-    return h_Lspace_Cmax
-
-def ():
-    """
-    
-
-    raw bezier chroma values for each hue across the lightness space
-    h_L_points_C = {
-       "red": [ Bezier@L=10, Bezier@L=11, Bezier@L=12, ... ],
-       ...
-    }
-    
-    three bezier control points for each hue's chroma curve
-    h_ctrl_L_C = {
-       "red": np.array([
-           [ x1, y1 ],
-           [ x2, y2 ],
-           [ x3, y3 ]
-        ]),
-       ...
-    }
-    """
-
-    # compute *unbounded* chroma curves for all hues
-    h_L_points_C = {}
-    h_ctrl_L_C = {}
-
-    for h_str, _h in monotone_h_map.items():
-        h_L_points_C[h_str] = np.array([monotone_C_map[h_str]]*len(L_points))
-        
-    for h_str, _h in accent_h_map.items():
-        Lspace_Cmax = h_Lspace_Cmax[h_str]
-        
-        # get L value of max chroma; will be a bezier control
-        L_Cmax_idx = np.argmax(Lspace_Cmax)
-        L_Cmax = L_space[L_Cmax_idx]
-
-        # offset control point by any preset x-shift
-        L_Cmax += h_L_offsets[h_str]
-
-        # and get max C at the L offset
-        Cmax = max_C_Lh(L_Cmax, _h)
-
-        # set 3 control points; shift by any global linear offest
-        C_offset = h_C_offsets.get(h_str, 0)
-        
-        p_0 = np.array([0, 0])
-        p_Cmax = np.array([L_Cmax, Cmax + C_offset])
-        p_100 = np.array([100, 0])
-        
-        B_L_points = bezier_y_at_x(p_0, p_Cmax, p_100, h_weights.get(h_str, 1), L_points)
-        h_L_points_C[h_str] = B_L_points
-        h_ctrl_L_C[h_str] = np.vstack([p_0, p_Cmax, p_100])
-
-
-def ():
-    """
-    bezier chroma values, but bounded to attainable gamut colors (bezier fit can produce invalid chroma values)
-    h_L_points_Cstar = {
-       "red": [ bounded-bezier@L=10, bounded-bezier@L=11, ... ],
-       ...
-    }
-    """
-
-    # compute full set of final chroma curves; limits every point to in-gamut max
-    h_LC_color_map = {}
-    h_L_points_Cstar = {}
-
-    for h_str, L_points_C in h_L_points_C.items():
-        _h = h_map[h_str]
-
-        h_L_points_Cstar[h_str] = [
-            max(0, min(_C, max_C_Lh(_L, _h)))
-            for _L, _C in zip(L_points, L_points_C)
-        ]
-
-# if __name__ == "__main__":

monobiome/palette.py

@@ -0,0 +1,54 @@
+import json
+from functools import cache
+from importlib.metadata import version
+
+from coloraide import Color
+
+from monobiome.constants import (
+    h_map,
+    L_points,
+    Lpoints_Cstar_Hmap,
+)
+
+
+@cache
+def compute_hlc_map(notation: str) -> dict[str, dict[int, str]]:
+    hlc_map = {}
+
+    for h_str, Lpoints_Cstar in Lpoints_Cstar_Hmap.items():
+        _h = h_map[h_str]
+        hlc_map[h_str] = {}
+        
+        for _l, _c in zip(L_points, Lpoints_Cstar, strict=True):
+            oklch = Color('oklch', [_l/100, _c, _h])
+
+            if notation == "hex":
+                srgb = oklch.convert('srgb')
+                c_str = srgb.to_string(hex=True)
+            elif notation == "oklch":
+                ol, oc, oh = oklch.convert('oklch').coords()
+                c_str = f"oklch({ol*100:.1f}% {oc:.4f} {oh:.1f})"
+            
+            hlc_map[h_str][_l] = c_str
+
+    return hlc_map
+
+def generate_palette(
+    notation: str,
+    file_format: str,
+) -> str:
+    mb_version = version("monobiome")
+    hlc_map = compute_hlc_map(notation)
+            
+    if file_format == "json":
+        hlc_map["version"] = mb_version
+        return json.dumps(hlc_map, indent=4)
+    else:
+        toml_lines = [f"version = {mb_version}", ""]
+        for _h, _lc_map in hlc_map.items():
+            toml_lines.append(f"[{_h}]")
+            for _l, _c in _lc_map.items():
+                toml_lines.append(f'l{_l} = "{_c}"')
+            toml_lines.append("")
+
+        return "\n".join(toml_lines)

monobiome/plotting.py

@@ -1,131 +1,176 @@
+import numpy as np
 import matplotlib.pyplot as plt
-from scipy.interpolate import interp1d, CubicSpline, BSpline
 
 from monobiome.constants import (
-    L_points,
-    L_space,
-    h_weights,
-    monotone_h_map,
-    accent_h_map,
     h_map,
-)
+    L_space,
-from monobiome.curves import (
+    L_points,
-    h_L_points_Cstar,
+    accent_h_map,
-    h_Lspace_Cmax,
+    monotone_h_map,
+    Lspace_Cmax_Hmap,
+    Lpoints_Cstar_Hmap,
 )
 
-ax_h_map = {}
+
-fig, axes = plt.subplots(
+def plot_hue_chroma_bounds() -> None:
+    name_h_map = {}
+    ax_h_map = {}
+    fig, axes = plt.subplots(
         len(monotone_h_map),
         1,
         sharex=True,
         sharey=True,
-    figsize=(4, 8)
+        figsize=(4, 10)
-)
+    )
 
-for i, h_str in enumerate(h_L_points_Cstar):
+    for i, h_str in enumerate(Lpoints_Cstar_Hmap):
         _h = h_map[h_str]
-    L_points_Cstar = h_L_points_Cstar[h_str]
+
-    L_space_Cmax = h_Lspace_Cmax[h_str]
+        l_space_Cmax = Lspace_Cmax_Hmap[h_str]
+        l_points_Cstar = Lpoints_Cstar_Hmap[h_str]
         
         if _h not in ax_h_map:
             ax_h_map[_h] = axes[i]
         ax = ax_h_map[_h]
         
-    # plot Cmax and Cstar
+        if _h not in name_h_map:
-    ax.plot(L_space, L_space_Cmax, c="b", alpha=0.2)
+            name_h_map[_h] = []
-    ax.plot(L_points, L_points_Cstar, alpha=0.7)
+        name_h_map[_h].append(h_str)
-    
-    ax.title.set_text(f"Hue [${_h}$]")
-    
-axes[-1].set_xlabel("Lightness (%)")
-axes[-1].set_xticks([L_points[0], L_points[-1]])
-
-fig.tight_layout()
-fig.subplots_adjust(top=0.9)
-
-plt.suptitle("$C^*$ curves for hue groups")
-plt.show()
-
-
-
-ax_h_map = {}
-fig, axes = plt.subplots(
-    len(monotone_h_map),
-    1,
-    sharex=True,
-    sharey=True,
-    figsize=(5, 10)
-)
-
-for i, h_str in enumerate(h_L_points_Cstar):
-    _h = h_map[h_str]
-    L_points_Cstar = h_L_points_Cstar[h_str]
-    L_space_Cmax = h_Lspace_Cmax[h_str]
-    
-    if _h not in ax_h_map:
-        ax_h_map[_h] = axes[i]
-    ax = ax_h_map[_h]
 
         # plot Cmax and Cstar
-    ax.plot(L_space, L_space_Cmax, c="b", alpha=0.2, label='Cmax')
+        ax.plot(L_space, l_space_Cmax, c="g", alpha=0.3, label="Cmax")
-    ax.plot(L_points, L_points_Cstar, alpha=0.7, label='C*')
         
-    if h_str in v111_hC_points:
+        cstar_label = f"{'accent' if h_str in accent_h_map else 'monotone'} C*"
-        ax.scatter(v111_L_space, v111_hC_points[h_str], s=4, label='Cv111')
+        ax.plot(L_points, l_points_Cstar, alpha=0.7, label=cstar_label)
         
-    if h_str in h_ctrl_L_C:
+        ax.title.set_text(f"Hue [${_h}$] - {'|'.join(name_h_map[_h])}")
-        cpts = h_ctrl_L_C[h_str]
-        cpt_x, cpt_y = cpts[:, 0], cpts[:, 1]
-        h_w = h_weights.get(h_str, 1)
         
-        P0, P1, P2 = cpts[0], cpts[1], cpts[2]
+    axes[-1].set_xlabel("Lightness (%)")
-        d0 = 2 * h_w * (P1 - P0)
+    axes[-1].set_xticks([L_points[0], L_points[-1]])
-        d2 = 2 * h_w * (P2 - P1)
 
-        handle_scale = 0.25
+    fig.tight_layout()
-        H0 = P0 + handle_scale * d0
+    fig.subplots_adjust(top=0.9)
-        H2 = P2 - handle_scale * d2
 
-        # ax.plot([P0[0], H0[0]], [P0[1], H0[1]], color='tab:blue', lw=1)
+    handles, labels = axes[-1].get_legend_handles_labels()
-        # ax.plot([P2[0], H2[0]], [P2[1], H2[1]], color='tab:orange', lw=1)
+    unique = dict(zip(labels, handles))
+    fig.legend(unique.values(), unique.keys(), loc='lower center', bbox_to_anchor=(0.5, -0.06), ncol=3)
 
-        ax.plot(cpt_x, cpt_y, '--', color='gray', lw=1, label='Bezier polygon')
+    plt.suptitle("$C^*$ curves for hue groups")
-        ax.scatter(cpt_x, cpt_y, color='red', zorder=5, s=2, label='Control points')
+    plt.show()
-        
-    ax.title.set_text(f"Hue [${_h}$]")
-    
-axes[-1].set_ylabel("Chroma (C)")
-axes[-1].set_xlabel("Lightness (%)")
-axes[-1].set_xticks([L_points[0], 50, 65, L_points[-1]])
-
-fig.tight_layout()
-fig.subplots_adjust(top=0.9)
-
-handles, labels = axes[-1].get_legend_handles_labels()
-unique = dict(zip(labels, handles))
-fig.legend(unique.values(), unique.keys(), loc='lower center', bbox_to_anchor=(0.5, -0.06), ncol=3)
-
-plt.suptitle("$C^*$ curves for hue groups + v111 5% lightness")
-plt.show()
 
 
+def plot_hue_chroma_star() -> None:
+    fig, ax = plt.subplots(1, 1, figsize=(8, 6))
 
+    # uncomment to preview 5 core term colors
+    colors = accent_h_map.keys()
+    #colors = set(["red", "orange", "yellow", "green", "blue"])
 
-from numpy import arctan2, degrees
+    for h_str in Lpoints_Cstar_Hmap:
-
+        if h_str not in accent_h_map or h_str not in colors:
-fig, ax = plt.subplots(1, 1, figsize=(8, 6))
-
-for h_str in h_L_points_Cstar:
-    if h_str not in accent_h_map:
             continue
-    ax.fill_between(L_points, h_L_points_Cstar[h_str], alpha=0.2, color='grey', label=h_str)
+        ax.fill_between(
+            L_points,
+            Lpoints_Cstar_Hmap[h_str],
+            alpha=0.2,
+            color='grey',
+            label=h_str
+        )
 
-    x, y = L_points, h_L_points_Cstar[h_str]
+        x, y = L_points, Lpoints_Cstar_Hmap[h_str]
-    n = int(0.5*len(x))
+        n = int(0.45*len(x))
         ax.text(x[n], y[n]-0.01, h_str, rotation=10, va='center', ha='left')
         
-ax.set_xlabel("Lightness (%)")
+    ax.set_xlabel("Lightness (%)")
-ax.set_xticks([L_points[0], 45, 50, 55, 60, 65, 70, L_points[-1]])
+    ax.set_xticks([L_points[0], 45, 50, 55, 60, 65, 70, L_points[-1]])
-plt.suptitle("$C^*$ curves (v1.3.0)")
+    plt.suptitle("$C^*$ curves (v1.4.0)")
-fig.show()
+    fig.show()
+
+
+def palette_image(palette, cell_size=40, keys=None):
+    if keys is None:
+        names = list(palette.keys())
+    else:
+        names = keys
+        
+    row_count = len(names)
+    col_counts = [len(palette[n]) for n in names]
+    max_cols = max(col_counts)
+
+    h = row_count * cell_size
+    w = max_cols * cell_size
+    img = np.ones((h, w, 3), float)
+
+    lightness_keys_per_row = []
+
+    for r, name in enumerate(names):
+        shades = palette[name]
+        keys = sorted(shades.keys())
+        lightness_keys_per_row.append(keys)
+        for c, k in enumerate(keys):
+            col = Color(shades[k]).convert("srgb").fit(method="clip")
+            rgb = [col["r"], col["g"], col["b"]]
+            r0, r1 = r * cell_size, (r + 1) * cell_size
+            c0, c1 = c * cell_size, (c + 1) * cell_size
+            img[r0:r1, c0:c1, :] = rgb
+
+    return img, names, lightness_keys_per_row, cell_size, max_cols
+
+
+def show_palette(palette, cell_size=40, keys=None):
+    img, names, keys, cell_size, max_cols = palette_image(palette, cell_size, keys=keys)
+
+    fig_w = img.shape[1] / 100
+    fig_h = img.shape[0] / 100
+    fig, ax = plt.subplots(figsize=(fig_w, fig_h))
+
+    ax.imshow(img, interpolation="none", origin="upper")
+    ax.set_xticks([])
+
+    ytick_pos = [(i + 0.5) * cell_size for i in range(len(names))]
+    ax.set_yticks(ytick_pos)
+    ax.set_yticklabels(names)
+
+    ax.set_ylim(img.shape[0], 0)   # ensures rows render correctly without half-cells
+
+    plt.show()
+
+
+if __name__ == "__main__":
+    from monobiome.constants import OKLCH_hL_dict
+
+    keys = [
+        "alpine",
+        "badlands",
+        "chaparral",
+        "savanna",
+        "grassland",
+        "reef",
+        "tundra",
+        "heathland",
+        "moorland",
+        "orange",
+        "yellow",
+        "green",
+        "cyan",
+        "blue",
+        "violet",
+        "magenta",
+        "red",
+    ]
+    term_keys = [
+        "alpine",
+        "badlands",
+        "chaparral",
+        "savanna",
+        "grassland",
+        "tundra",
+        "red",
+        "orange",
+        "yellow",
+        "green",
+        "blue",
+    ]
+
+    show_palette(OKLCH_hL_dict, cell_size=25, keys=keys)
+    # show_palette(OKLCH_hL_dict, cell_size=1, keys=term_keys)

monobiome/scheme.py

@@ -0,0 +1,238 @@
+from functools import cache
+from collections.abc import Callable
+
+from coloraide import Color
+
+from monobiome.util import oklch_distance
+from monobiome.palette import compute_hlc_map
+from monobiome.constants import (
+    accent_h_map,
+    monotone_h_map,
+)
+
+
+@cache
+def compute_dma_map(
+    dT: float,
+    metric: Callable | None = None
+) -> dict[str, dict]:
+    """
+    For threshold `dT`, compute the nearest accent shades that exceed that
+    threshold for every monotone shade.
+
+    Returns: map of minimum constraint satisfying accent colors for monotone
+        spectra
+
+        {
+            "alpine": {
+                "oklch( ... )": {
+                    "red": *nearest oklch >= dT from M base*,
+                    ...
+                },
+                ...
+            },
+            ...
+        }
+    """
+
+    if metric is None:
+        metric = oklch_distance
+    
+    oklch_hlc_map = compute_hlc_map("oklch")
+    oklch_color_map = {
+        c_name: [Color(c_str) for c_str in c_str_dict.values()]
+        for c_name, c_str_dict in oklch_hlc_map.items()
+    }
+    
+    dT_mL_acol_map = {}
+    for m_name in monotone_h_map:
+        mL_acol_map = {}
+        m_colors = oklch_color_map[m_name]
+        
+        for m_color in m_colors:
+            acol_min_map = {}
+            
+            for a_name in accent_h_map:
+                a_colors = oklch_color_map[a_name]
+                oklch_dists = filter(
+                    lambda d: (d[1] - dT) >= 0,
+                    [
+                        (ac, metric(m_color, ac))
+                        for ac in a_colors
+                    ]
+                )
+                oklch_dists = list(oklch_dists)
+                if oklch_dists:
+                    min_a_color = min(oklch_dists, key=lambda t: t[1])[0]
+                    acol_min_map[a_name] = min_a_color
+    
+            # make sure the current monotone level has *all* accents; o/w
+            # ignore
+            if len(acol_min_map) < len(accent_h_map):
+                continue
+
+            mL = m_color.coords()[0]
+            mL_acol_map[int(mL*100)] = acol_min_map
+        dT_mL_acol_map[m_name] = mL_acol_map
+
+    return dT_mL_acol_map
+
+def generate_scheme_groups(
+    mode: str,
+    biome: str,
+    metric: str,
+    distance: float,
+    l_base: int,
+    l_step: int,
+    fg_gap: int,
+    grey_gap: int,
+    term_fg_gap: int,
+    accent_color_map: dict[str, str],
+) -> tuple[dict[str, str], ...]:
+    """
+    Parameters:
+        mode: one of ["dark", "light"]
+        biome: biome setting
+        metric: one of ["wcag", "oklch", "lightness"]
+    """
+
+    metric_map = {
+        "wcag": lambda mc,ac: ac.contrast(mc, method='wcag21'),
+        "oklch": lambda mc,ac: mc.distance(ac, space="oklch"),
+        "lightness": lambda mc,ac: abs(mc.coords()[0]-ac.coords()[0])*100,
+    }
+    
+    metric_func = metric_map[metric]
+    dT_mL_acol_map = compute_dma_map(distance, metric=metric_func)
+    Lma_map = {
+        m_name: mL_acol_dict[l_base]
+        for m_name, mL_acol_dict in dT_mL_acol_map.items()
+        if l_base in mL_acol_dict
+    }
+    
+    # the `mL_acol_dict` only includes lightnesses where all accent colors were
+    # within threshold. Coverage here will be partial if, at the `mL`, there is
+    # some monotone base that doesn't have all accents within threshold. This
+    # can happen at the edge, e.g., alpine@L15 has all accents w/in the
+    # distance, but the red accent was too far under tundra@L15, so there's no
+    # entry. This particular case is fairly rare; it's more likely that *all*
+    # monotones are undefined. Either way, both such cases lead to partial
+    # scheme coverage.
+    if len(Lma_map) < len(monotone_h_map):
+        print(f"Warning: partial scheme coverage for {l_base=}@{distance=}")
+    if biome not in Lma_map:
+        print(f"Biome {biome} unable to meet {metric} constraints")
+    accent_colors = Lma_map.get(biome, {})
+    
+    meta_pairs = [
+        ("mode", mode),
+        ("biome", biome),
+        ("metric", metric),
+        ("distance", distance),
+        ("l_base", l_base),
+        ("l_step", l_step),
+    ]
+
+    # note how selection_bg steps up by `l_step`, selection_fg steps down by
+    # `l_step` (from their respective bases)
+    term_pairs = [
+        ("background", f"f{{{{{biome}.l{l_base}}}}}"),
+        ("selection_bg", f"f{{{{{biome}.l{l_base+l_step}}}}}"),
+        ("selection_fg", f"f{{{{{biome}.l{l_base+term_fg_gap-l_step}}}}}"),
+        ("foreground", f"f{{{{{biome}.l{l_base+term_fg_gap}}}}}"),
+        ("cursor", f"f{{{{{biome}.l{l_base+term_fg_gap-l_step}}}}}"),
+        ("cursor_text", f"f{{{{{biome}.l{l_base+l_step}}}}}"),
+    ]
+
+    monotone_pairs = []
+    monotone_pairs += [
+        (f"bg{i}", f"f{{{{{biome}.l{l_base+i*l_step}}}}}")
+        for i in range(4)
+    ]
+    monotone_pairs += [
+        (f"fg{3-i}", f"f{{{{{biome}.l{fg_gap+l_base+i*l_step}}}}}")
+        for i in range(4)
+    ]
+ 
+    accent_pairs = [
+        ("black", f"f{{{{{biome}.l{l_base}}}}}"),
+        ("grey", f"f{{{{{biome}.l{l_base+grey_gap}}}}}"),
+        ("white", f"f{{{{{biome}.l{l_base+term_fg_gap-l_step}}}}}"),
+    ]
+    for color_name, mb_accent in accent_color_map.items():
+        aL = int(100*accent_colors[mb_accent].coords()[0])
+        accent_pairs.append(
+            (
+                color_name,
+                f"f{{{{{mb_accent}.l{aL}}}}}"
+            )
+        )
+    
+    return meta_pairs, term_pairs, monotone_pairs, accent_pairs
+
+def generate_scheme(
+    mode: str,
+    biome: str,
+    metric: str,
+    distance: float,
+    l_base: int,
+    l_step: int,
+    fg_gap: int,
+    grey_gap: int,
+    term_fg_gap: int,
+    full_color_map: dict[str, str],
+    term_color_map: dict[str, str],
+    vim_color_map: dict[str, str],
+) -> str:
+    meta, _, mt, ac = generate_scheme_groups(
+        mode, biome, metric, distance,
+        l_base, l_step,
+        fg_gap, grey_gap, term_fg_gap,
+        full_color_map
+    )
+
+    _, term, _, term_norm_ac = generate_scheme_groups(
+        mode, biome, metric, distance,
+        l_base + l_step, l_step,
+        fg_gap, grey_gap, term_fg_gap,
+        term_color_map
+    )
+    _, _, _, term_bright_ac = generate_scheme_groups(
+        mode, biome, metric, distance,
+        l_base + l_step + 10, l_step,
+        fg_gap, grey_gap, term_fg_gap,
+        term_color_map
+    )
+
+    _, _, vim_mt, vim_ac = generate_scheme_groups(
+        mode, biome, metric, distance,
+        l_base + l_step, l_step,
+        fg_gap, grey_gap, term_fg_gap,
+        vim_color_map
+    )
+
+    def pair_strings(pair_list: list[tuple[str, str]]) -> list[str]:
+        return [
+            f"{lhs:<12} = \"{rhs}\""
+            for lhs, rhs in pair_list
+        ]
+
+    scheme_pairs = []
+    scheme_pairs += pair_strings(meta)
+    scheme_pairs += pair_strings(mt)
+    scheme_pairs += pair_strings(ac)
+
+    scheme_pairs += ["", "[term]"]
+    scheme_pairs += pair_strings(term)
+
+    scheme_pairs += ["", "[term.normal]"]
+    scheme_pairs += pair_strings(term_norm_ac)
+
+    scheme_pairs += ["", "[term.bright]"]
+    scheme_pairs += pair_strings(term_bright_ac)
+
+    scheme_pairs += ["", "[vim]"]
+    scheme_pairs += pair_strings(vim_mt)
+    scheme_pairs += pair_strings(vim_ac)
+
+    return "\n".join(scheme_pairs)

monobiome/schemes.py

@@ -1,140 +0,0 @@
-def compute_dma_map(dT, metric=None):
-    """
-    For threshold `dT`, compute the nearest accent shades
-    that exceed that threshold for every monotone shade.
-
-    Returns:
-
-    Map like
-    { "alpine": {
-        "oklch( ... )": {
-            "red": *nearest oklch >= dT from M base*
-    """
-
-    if metric is None:
-        metric = lambda mc,ac: mc.distance(ac, space="oklch")
-    
-    oklch_color_map = {
-        c_name: [Color(c_str) for c_str in c_str_dict.values()]
-        for c_name, c_str_dict in oklch_hL_dict.items()
-    }
-    
-    dT_mL_acol_map = {}
-    for m_name in monotone_h_map:
-        mL_acol_map = {}
-        m_colors = oklch_color_map[m_name]
-        
-        for m_color in m_colors:
-            acol_min_map = {}
-            
-            for a_name in accent_h_map:
-                a_colors = oklch_color_map[a_name]
-                oklch_dists = filter(
-                    lambda d: (d[1] - dT) > 0,
-                    [
-                        (ac, metric(m_color, ac))
-                        for ac in a_colors
-                    ]
-                )
-                oklch_dists = list(oklch_dists)
-                if oklch_dists:
-                    min_a_color = min(oklch_dists, key=lambda t: t[1])[0]
-                    acol_min_map[a_name] = min_a_color
-    
-            # make sure the current monotone level has *all* accents; o/w ignore
-            if len(acol_min_map) < len(accent_h_map):
-                continue
-
-            mL = m_color.coords()[0]
-            mL_acol_map[int(mL*100)] = acol_min_map
-        dT_mL_acol_map[m_name] = mL_acol_map
-    return dT_mL_acol_map
-
-
-
-    
-
-
-mode = "dark"  # ["dark", "light"]
-biome = "alpine"  # [ ... ]
-metric = "wcag"  # ["wcag", "oklch"]
-metric_map = {
-    "wcag": lambda mc,ac: ac.contrast(mc, method='wcag21'),
-    "oklch": lambda mc,ac: mc.distance(ac, space="oklch"),
-}
-metric_func = metric_map[metric]
-
-term_color_map = {
-    "red": "red",
-    "organge": "orange",
-    "yellow": "yellow",
-    "green": "green",
-    "cyan": "green",
-    "blue": "blue",
-    "violet": "blue",
-    "magenta": "red",
-}
-
-L = 20
-d = 4.5
-I = 5
-fg_gap = 50
-grey_gap = 30
-
-dT_mL_acol_map = compute_dma_map(d, metric=metric_func)
-Lma_map = {
-    m_name: mL_acol_dict[L]
-    for m_name, mL_acol_dict in dT_mL_acol_map.items()
-    if L in mL_acol_dict
-}
-
-# the `mL_acol_dict` only includes lightnesses where all accent
-# colors were within threshold. Coverage here will be partial if,
-# at the `mL`, there is some monotone base that doesn't have all
-# accents within threshold. This can happen at the edge, e.g., alpine@L15
-# has all accents w/in the distance, but the red accent was too far under
-# tundra@L15, so there's no entry. This particular case is fairly rare; it's
-# more likely that *all* monotones are undefined. Either way, both such
-# cases lead to partial scheme coverage.
-if len(Lma_map) < len(monotone_h_map):
-    print(f"Warning: partial scheme coverage for {mL=}@{dT=}")
-if biome not in Lma_map:
-    print(f"Biome {biome} unable to meet {metric} constraints")
-accent_colors = Lma_map.get(biome, {})
-
-scheme_pairs = []
-for i in range(4):
-    scheme_pairs.append(
-        (
-            f"bg{i}",
-            f"f{{{{{biome}.l{L+i*I}}}}}"
-        )
-    )
-for i in range(4):
-    scheme_pairs.append(
-        (
-            f"fg{3-i}",
-            f"f{{{{{biome}.l{fg_gap+L+i*I}}}}}"
-        )
-    )
-for term_color, mb_accent in term_color_map.items():
-    aL = int(100*accent_colors[mb_accent].coords()[0])
-    scheme_pairs.append(
-        (
-            f"{term_color}",
-            f"f{{{{{mb_accent}.l{aL}}}}}"
-        )
-    )
-
-term_fg_gap = 60
-scheme_pairs.extend([
-    ("background", f"f{{{{{biome}.l{L}}}}}"),
-    ("selection_bg", f"f{{{{{biome}.l{L+I}}}}}"),
-    ("selection_fg", f"f{{{{{biome}.l{L+term_fg_gap}}}}}"),
-    ("foreground", f"f{{{{{biome}.l{L+term_fg_gap+I}}}}}"),
-])
-
-scheme_toml = [
-    f"{lhs:<12} = {rhs:<16}"
-    for lhs, rhs in scheme_pairs
-] 

monobiome/util.py

@@ -0,0 +1,10 @@
+from types import GenericAlias
+from argparse import ArgumentParser, _SubParsersAction
+
+from coloraide import Color
+
+_SubParsersAction.__class_getitem__ = classmethod(GenericAlias)
+_SubparserType = _SubParsersAction[ArgumentParser]
+
+def oklch_distance(mc: Color, ac: Color) -> float:
+    return mc.distance(ac, space="oklch")

monobiome/writer.py

@@ -1,39 +0,0 @@
-
-# put together objects for output formats
-toml_lines = []
-oklch_hL_dict = {}
-
-for h_str, L_points_Cstar in h_L_points_Cstar.items():
-    _h = h_map[h_str]
-    toml_lines.append(f"[{h_str}]")
-    oklch_hL_dict[h_str] = {}
-    
-    for _L, _C in zip(L_points, L_points_Cstar):
-        oklch = Color('oklch', [_L/100, _C, _h])
-        srgb = oklch.convert('srgb')
-        
-        hex_str = srgb.to_string(hex=True)
-        
-        l, c, h = oklch.convert('oklch').coords()
-        # oklch_str = oklch.to_string(percent=False)
-        oklch_str = f"oklch({l*100:.1f}% {c:.4f} {h:.1f})"
-        
-        toml_lines.append(f'l{_L} = "{hex_str}"')
-        oklch_hL_dict[h_str][_L] = oklch_str
-        
-    toml_lines.append("")
-
-
-
-# write files -- QBR = "quadratic bezier rational"
-PALETTE_DIR = "palettes"
-
-toml_content = '\n'.join(toml_lines)
-with Path(PALETTE_DIR, 'monobiome-vQBRsn-130.toml').open('w') as f:
-    f.write(toml_content)
-print("[TOML] written")
-    
-with Path(PALETTE_DIR, 'monobiome-vQBRsn-130-oklch.json').open('w') as f:
-    json.dump(oklch_hL_dict, f)
-print("[JSON] written")
-

pyproject.toml

@@ -1,7 +1,11 @@
+[build-system]
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"
+
 [project]
 name = "monobiome"
-version = "1.2.0"
+version = "1.4.0"
-description = "Add your description here"
+description = "Monobiome color palette"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
@@ -21,3 +25,39 @@ dependencies = [
 dev = [
     "ipykernel>=7.0.1",
 ]
+
+[project.scripts]
+monobiome = "monobiome.__main__:main"
+
+[project.urls]
+Homepage = "https://doc.olog.io/monobiome"
+Documentation = "https://doc.olog.io/monobiome"
+Repository = "https://git.olog.io/olog/monobiome"
+Issues = "https://git.olog.io/olog/monobiome/issues"
+
+[tool.setuptools.packages.find]
+include = ["monobiome*"]
+
+[tool.setuptools.package-data]
+"monobiome" = ["data/*.toml"]
+
+[tool.ruff]
+line-length = 79
+
+[tool.ruff.lint]
+select = ["ANN", "E", "F", "UP", "B", "SIM", "I", "C4", "PERF"]
+
+[tool.ruff.lint.isort]
+length-sort = true
+order-by-type = false
+force-sort-within-sections = false
+
+[tool.ruff.lint.per-file-ignores]
+"tests/**" = ["S101"]
+"**/__init__.py" = ["F401"]
+
+[tool.ruff.format]
+quote-style = "double"
+indent-style = "space"
+docstring-code-format = true
+

uv.lock

@@ -622,8 +622,8 @@ wheels = [
 
 [[package]]
 name = "monobiome"
-version = "1.2.0"
+version = "1.4.0"
-source = { virtual = "." }
+source = { editable = "." }
 dependencies = [
     { name = "coloraide" },
     { name = "imageio", extra = ["ffmpeg"] },