sync version with pypi

.gitignore

@@ -17,7 +17,6 @@ docs/_build/
 # local
 /Makefile
 
-data/
 archive/
 
 notebooks/color_spaces_manyview.ipynb

README.md

@@ -32,11 +32,11 @@ smoothly as a function of lightness within sRGB gamut bounds.
 
 | Chroma curves                                            | Color trajectories                       |
 |----------------------------------------------------------|------------------------------------------|
-| ![Chroma curves](images/release/1.5.1/chroma-curves.png) | ![Trajectories](images/trajectories.gif) |
+| ![Chroma curves](images/release/1.5.4/chroma-curves.png) | ![Trajectories](images/trajectories.gif) |
 
 | Palette                                      |
 |----------------------------------------------|
-| ![Palette](images/release/1.5.1/palette.png) |
+| ![Palette](images/release/1.5.4/palette.png) |
 
 Chroma curves are designed specifically to establish a distinct role for each
 accent and are non-intersecting over the lightness domain (hence the distinct
@@ -143,6 +143,13 @@ using the `monobiome` CLI:
   (`grassland`). Every part of this process can be customized: the scheme
   parameters, the scheme definitions/file, the app template.
 
+The separation of duties here facilitates robustness: the palette colors can be
+tweaked (across versions, say) without changing how those colors get used in
+app themes. Scheme files don't hardcode color values, and app templates don't
+refer to palette variables at all. Scheme files bridge palette colors with
+*intended uses*, and templates need only align those uses under an app's
+expected config syntax.
+
 Running these commands in sequence from the repo root should work
 out-of-the-box after having installed the CLI tool.
 

colors/hex-palette.toml

@@ -1,4 +1,4 @@
-version = "1.5.1"
+version = "1.5.4"
 
 [alpine]
 l10 = "#030303"

colors/oklch-palette.toml

@@ -1,4 +1,4 @@
-version = "1.5.1"
+version = "1.5.4"
 
 [alpine]
 l10 = "oklch(10.0% 0.0000 0.0)"

monobiome/data/parameters.toml

@@ -0,0 +1,65 @@
+L_min = 10
+L_max = 98
+L_step = 5
+
+[monotone_C_map]
+alpine = 0
+badlands = 0.011
+chaparral = 0.011
+savanna = 0.011
+grassland = 0.011
+reef = 0.011
+tundra = 0.011
+heathland = 0.011
+moorland = 0.011
+
+[h_weights]
+red = 3.0
+orange = 3.8
+yellow = 3.8
+green = 3.8
+cyan = 3.8
+blue = 3.6
+violet = 3.0
+magenta = 3.6
+
+[h_L_offsets]
+red = 0
+orange = -5.5
+yellow = -13.5
+green = -12.5
+cyan = -10.5
+blue = 9
+violet = 7
+magenta = 2
+
+[h_C_offsets] 
+red = 0
+orange = -0.015
+yellow = -0.052
+green = -0.08
+cyan = -0.009
+blue = -0.01
+violet = -0.047
+magenta = -0.1
+
+[monotone_h_map]
+alpine = 0
+badlands = 29
+chaparral = 62.5
+savanna = 104
+grassland = 148
+reef = 205
+tundra = 262
+heathland = 306
+moorland = 350
+
+[accent_h_map]
+red = 29
+orange = 62.5
+yellow = 104
+green = 148
+cyan = 205
+blue = 262
+violet = 306
+magenta = 350

monobiome/palette.py

@@ -5,6 +5,10 @@ from importlib.metadata import version
 
 from coloraide import Color
 
+from monobiome.util import (
+    hex_from_rgb8,
+    srgb8_from_color,
+)
 from monobiome.constants import (
     h_map,
     L_points,
@@ -24,8 +28,8 @@ def compute_hlc_map(notation: str) -> dict[str, Any]:
             oklch = Color('oklch', [_l/100, _c, _h])
 
             if notation == "hex":
-                srgb = oklch.convert('srgb')
+                rgb8 = srgb8_from_color(oklch)
-                c_str = srgb.to_string(hex=True)
+                c_str = hex_from_rgb8(rgb8)
             elif notation == "oklch":
                 ol, oc, oh = oklch.convert('oklch').coords()
                 c_str = f"oklch({ol*100:.1f}% {oc:.4f} {oh:.1f})"

monobiome/plotting.py

@@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
 from coloraide import Color
 from matplotlib.collections import LineCollection
 
+from monobiome.util import srgb8_from_color
 from monobiome.palette import compute_hlc_map
 from monobiome.constants import (
     h_map,
@@ -72,13 +73,12 @@ def plot_hue_chroma_bounds() -> tuple[plt.Figure, plt.Axes]:
 
     return fig, axes
 
-
 def plot_hue_chroma_star() -> tuple[plt.Figure, plt.Axes]:
     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"])
+    # uncomment to preview just the 5 core term colors
+    # colors = set(["red", "orange", "yellow", "green", "blue"])
 
     for h_str, _ in max_Cstar_Horder:
         Lpoints_Cstar = Lpoints_Cstar_Hmap[h_str]
@@ -88,34 +88,22 @@ def plot_hue_chroma_star() -> tuple[plt.Figure, plt.Axes]:
 
         _h = h_map[h_str]
         h_colors = [
-            Color('oklch', [_l/100, _c, _h]).convert("srgb")
+            Color(
+                'oklch', [_l/100, _c, _h]
+            ).convert("srgb").fit(method="oklch-chroma")
             for _l, _c in zip(L_points, Lpoints_Cstar, strict=True)
         ]
         
-        # # ax.fill_between(
-        # ax.scatter(
-        #     L_points,
-        #     Lpoints_Cstar,
-        #     alpha=0.7,
-        #     c=h_colors,
-        #     #alpha=0.2,
-        #     #color='grey',
-        #     label=h_str
-        # )
-        # x, y = L_points, Lpoints_Cstar_Hmap[h_str]
-        # n = int(0.45*len(x))
-        # ax.text(x[n], y[n]-0.01, h_str, rotation=10, va='center', ha='left')
-        
         x = np.asarray(L_points)
         y = np.asarray(Lpoints_Cstar)
         pts = np.column_stack([x, y]).reshape(-1, 1, 2)
         segs = np.concatenate([pts[:-1], pts[1:]], axis=1)
         rgb = np.asarray(h_colors)
         seg_colors = (rgb[:-1] + rgb[1:]) / 2
-
         lc = LineCollection(segs, colors=seg_colors, linewidth=3,
                             capstyle="round", joinstyle="round",
                             label=h_str)
+
         ax.add_collection(lc)
         ax.autoscale_view()
         
@@ -126,12 +114,11 @@ def plot_hue_chroma_star() -> tuple[plt.Figure, plt.Axes]:
 
     return fig, ax
 
-
 def palette_image(
     palette: dict[str, dict[int, str]],
     cell_size: int = 40,
     keys: list[str] | None = None
-) -> tuple[np.ndarray, list[str], list[list[int]], int, int]:
+) -> tuple[np.ndarray, list[str], list[list[int]]]:
     names = list(palette.keys()) if keys is None else keys
         
     row_count = len(names)
@@ -140,7 +127,7 @@ def palette_image(
 
     h = row_count * cell_size
     w = max_cols * cell_size
-    img = np.ones((h, w, 3), float)
+    img = np.ones((h, w, 3), int)
 
     lightness_keys_per_row = []
 
@@ -149,14 +136,12 @@ def palette_image(
         lkeys = sorted(shades.keys())
         lightness_keys_per_row.append(lkeys)
         for c, k in enumerate(lkeys):
-            col = Color(shades[k]).convert("srgb").fit(method="clip")
+            rgb = srgb8_from_color(shades[k])
-            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
+    return img, names, lightness_keys_per_row
-
 
 def show_palette(
     palette: dict[str, dict[int, str]],
@@ -165,9 +150,7 @@ def show_palette(
     show_labels: bool = True,
     dpi: int = 100,
 ) -> tuple[plt.Figure, plt.Axes]:
-    img, names, keys, cell_size, max_cols = palette_image(
+    img, names, _ = palette_image(palette, cell_size, keys=keys)
-        palette, cell_size, keys=keys
-    )
 
     fig_w = img.shape[1] / 100
     fig_h = img.shape[0] / 100
@@ -175,7 +158,7 @@ def show_palette(
     if show_labels:
         fig, ax = plt.subplots(figsize=(fig_w, fig_h), dpi=dpi)
 
-        ax.imshow(img, interpolation="none", origin="upper")
+        ax.imshow(img, interpolation="nearest", origin="upper")
         ax.set_xticks([])
 
         ytick_pos = [(i + 0.5) * cell_size for i in range(len(names))]

monobiome/util.py

@@ -2,6 +2,7 @@ import math
 from types import GenericAlias
 from argparse import ArgumentParser, _SubParsersAction
 
+import numpy as np
 from coloraide import Color
 
 _SubParsersAction.__class_getitem__ = classmethod(GenericAlias)
@@ -33,3 +34,13 @@ def oklch_distance(xc: Color, yc: Color) -> float:
     dz = l1 - l2
     
     return (dx**2 + dy**2 + dz**2)**0.5
+
+def srgb8_from_color(c: str | Color) -> np.ndarray:
+    c = Color(c).convert("srgb").fit(method="oklch-chroma")
+    rgb = np.array([c["r"], c["g"], c["b"]], dtype=float)
+    rgb8 = np.clip(np.round(rgb * 255), 0, 255).astype(np.uint8)
+
+    return rgb8
+
+def hex_from_rgb8(rgb8: np.ndarray) -> str:
+    return f"#{int(rgb8[0]):02x}{int(rgb8[1]):02x}{int(rgb8[2]):02x}"

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "monobiome"
-version = "1.5.1"
+version = "1.5.4"
 description = "Monobiome color palette"
 requires-python = ">=3.12"
 authors = [

scripts/plots.py

@@ -17,8 +17,8 @@ fig.savefig(Path(figure_dir, "chroma-curves.png"))
 # "oklch" causes some slight hex drift when later using an eyedropper
 hlc_map = compute_hlc_map("hex") # ("oklch")
 
-fig, ax = plotting.show_palette(hlc_map)
+fig, ax = plotting.show_palette(hlc_map, cell_size=25)
-fig.savefig(Path(figure_dir, "palette.png"))
+fig.savefig(Path(figure_dir, "palette.png"), pad_inches=0)
 
 fig, ax = plotting.show_palette(hlc_map, show_labels=False)
 fig.savefig(Path(figure_dir, "palette-bare.png"), pad_inches=0)

scripts/prepare.sh

@@ -11,3 +11,6 @@ uv run monobiome palette -n oklch -f toml -o colors/oklch-palette.toml
 
 # generate provided app config
 "$script_dir/generate.sh"
+
+# generate release plots
+uv run "$script_dir/plots.py"

uv.lock

@@ -882,7 +882,7 @@ wheels = [
 
 [[package]]
 name = "monobiome"
-version = "1.5.1"
+version = "1.5.4"
 source = { editable = "." }
 dependencies = [
     { name = "coloraide" },