petaurus 0.3.0

Custom Altair/Vega-Lite themes, perceptually uniform palettes, and chart utilities for publication-ready scientific figures.

petaurus

An Altair configuration wrapper with perceptually uniform palettes and chart utilities for publication-ready figures.

This is a personal project under active development, so there may be breaking changes between minor versions.

Installation

# uv
uv pip install git+https://github.com/dkkung/altair_custom_themes.git

# pip
pip install git+https://github.com/dkkung/altair_custom_themes.git

Requires Python 3.11+. Dependencies: altair, numpy, polars, scipy.

---

Quick start

import altair as alt
import polars as pl
import petaurus as theme  # or: import petaurus

theme.options(chartWidth=300, chartHeight=200)

chart = (
    alt.Chart(df)
    .mark_point()
    .encode(
        x=alt.X("x:Q"),
        y=alt.Y("y:Q"),
        color=alt.Color("y:Q", scale=alt.Scale(range=theme.palette("blues"))),
    )
)

theme.save(chart, "plots/myplot")
# writes: plots/myplot_light.png, plots/myplot_light.svg
#         plots/myplot_dark.png,  plots/myplot_dark.svg
#         plots/myplot_vegalite.json

---

petaurus.options()

Call before building any Altair charts to configure global theme defaults.

theme.options()  # apply defaults

theme.options(   # custom configuration
    chartWidth=400,
    chartHeight=250,
    fontSize=8,
    grid=True,
    palette="blues",
)

Parameter	Default	Description
angledX	False	Angle x-axis labels 45°
axisOffset	tickSize	Distance between axis line and data area
axisWidth	0.25	Stroke width of axes, ticks, and rules
bandPadding	0.1	Inner and outer padding for ordinal bands
chartFill	"white"	Background fill of the entire chart
chartHeight	100	Default chart height in pixels
chartWidth	100	Default chart width in pixels
closed	auto	Draw a border around the plot area. Auto-enabled when viewFill is set
darkmode	False	Invert text and axis colors for dark backgrounds
dashedLine	False	Render line marks dashed
dashedRule	True	Render rule marks dashed
dashedWidth	[2, 2]	Dash/gap pattern [dash, gap] in pixels
font	"HelveticaNeue"	Font family for all labels and titles
fontSize	7	Font size in points
fontWeight	400	Font weight: 300 = light, 400 = normal, 700 = bold
grid	False	Show axis grid lines
gridColor	"darkGray"	Grid line color
legend	True	Show legends
legendOffset	tickSize	Distance between legend and chart edge
legendStroke	False	Draw a border around the legend box
markFill	"black"	Default fill color for marks
markFillOpacity	1.0	Default mark fill opacity
markSize	min(W, H) × 0.1	Mark size; for points, this is area in sq px
markStroke	"black"	Default stroke color for marks
markStrokeOpacity	1	Default mark stroke opacity
palette	None	Default color scheme applied to category, diverging, heatmap, and ramp scales. Accepts a key from colors or a raw list
strokeCap	"round"	Stroke end cap: "butt", "round", or "square"
ticks	True	Show axis ticks
tickSize	5	Tick length in pixels
transparentBackground	False	Transparent chart background (overrides chartFill)
verticalY	False	Rotate y-axis labels 90°
viewFill	None	Fill color of the plot area only. Setting this auto-enables closed
xTicks	True	Show ticks on the x-axis
yTicks	True	Show ticks on the y-axis

---

Palettes

All custom palettes are built in Oklab (Ottosson, A perceptual color space for image processing, 2020) for perceptual uniformity. They are stored in petaurus.colors, a plain dict[str, list[str]] mapping palette names to 12-stop hex lists (13 stops for diverging palettes).

Accessing palettes

from petaurus.palettes import colors

blues = colors["blues"]   # list of 12 hex strings, light → dark

petaurus.palette()

Samples a slice or subset from any named palette.

theme.palette("blues")                     # all 12 stops
theme.palette("blues", n=5)                # 5 evenly-spaced stops
theme.palette("blues", start=3)            # stops 3–11
theme.palette("blues", end=6, step=2)      # indices 0, 2, 4, 6
theme.palette("blues", n=4, reverse=True)  # reversed

Parameter	Default	Description
name	required	Key in colors
n	None	Return n evenly-spaced stops (overrides step)
start	0	Index of the first stop to include
end	last	Index of the last stop to include (inclusive)
step	1	Step between indices (used when n is not set)
reverse	False	Reverse the returned list

Theme defaults

When no explicit scale= is set on a color encoding, Vega-Lite falls back to the theme's range defaults:

Range type	Default palette	Used for
category	blues (even indices: 0, 2, 4, 6, 8, 10)	Nominal/unordered groups
ordinal	blues	Ordered discrete values
ramp	blues	Sequential continuous (legend ramps)
heatmap	blues	Rect/heatmap marks
diverging	redsblues	Diverging scales

Setting theme.options(palette="mypalette") overrides all five types simultaneously.

Available palettes

See the palette gallery for a visual overview of all palettes, or open docs/index.html locally.

Sequential — Single-hue (12 stops, light → dark): blues, greens, purples, lavenders, violets, greys, reds, rose, oranges, browns, yellows, cyans, magentas, neongreens

Sequential — Single-hue 2 (12 stops, deeper saturation built with Oklab arc-length resampling): blues2, greens2, purples2, lavenders2, violets2, greys2, reds2, roses2, oranges2, browns2, yellows2, cyans2, magentas2, neongreens2

Sequential — Multi-hue (12 stops, two or more hues blended in Oklab): yellowgreen, ember, dusk, shoal, moss, GnBu, YlGnBu, candy, lagoon, bluestlagoon, bluerlagoon, bluelagoon

Diverging (13 stops, exact-white pivot at stop 6): RdBu, RdYlBu, PuGn, MgGn, PkTe, GdBu, BrTe, BrGn

Diverging — Sequential pairs (13 stops, one sequential hue per arm): greensblues, redsblues, redsgreens, redscyans, redslavenders, redsviolets, redsneongreens, rosesblues, rosescyans, rosesgreens, rosesneongreens, orangesblues, orangescyans, orangespurples, orangeslavenders, orangesviolets, orangesneongreens, yellowsblues, yellowspurples, yellowslavenders, brownsblues, brownsgreens, brownscyans, brownsneongreens, magentasneongreens, magentasgreens, magentasblues, magentascyans, violetsoranges, violetsyellows, purplesgreens, purplesblues, purplesneongreens, lavendersgreens, lavendersblues, lavendersneongreens, cyanspurples, cyanslavenders, cyansviolets, greysblues, greysreds, greysgreens, greyscyans, greysyellows, greysoranges, greysmagentas, greysviolets, greysneongreens, greyspurples, greyslavender, greysrose

Discrete: nucleotides (5 colors: A, T, G, C, U), proteins (8 biochemical groups: hydrophobic, aromatic, positive, negative, polar, proline, glycine, cysteine)

Matplotlib ported (prefixed with mpl_): mpl_viridis, mpl_plasma, mpl_inferno, mpl_magma, mpl_cividis, mpl_turbo, mpl_Blues, mpl_Greens, mpl_Greys, mpl_Oranges, mpl_Purples, mpl_Reds, mpl_YlGnBu, mpl_YlOrBr, mpl_YlOrRd, and more.

cmocean ported (prefixed with cmocean_): cmocean_algae, cmocean_amp, cmocean_balance, cmocean_curl, cmocean_deep, cmocean_delta, cmocean_dense, cmocean_diff, cmocean_gray, cmocean_haline, cmocean_ice, cmocean_matter, cmocean_oxy, cmocean_phase, cmocean_rain, cmocean_solar, cmocean_speed, cmocean_tarn, cmocean_tempo, cmocean_thermal, cmocean_topo, cmocean_turbid

---

Saving charts

theme.save(chart, "plots/myplot")
# writes: plots/myplot_light.png, plots/myplot_light.svg
#         plots/myplot_dark.png,  plots/myplot_dark.svg
#         plots/myplot_vegalite.json

Produces light and dark PNG and SVG files from a single call. SVG output is post-processed to flatten Vega's redundant <g> wrappers, making it easier to navigate in Illustrator. A Vega-Lite JSON spec is also saved by default for full reproducibility.

theme.save(chart, "myplot", ppi=1200)               # default PPI; reduce for faster exports
theme.save(chart, "myplot", save_vega_spec=False)    # skip the JSON spec
theme.save(chart, "myplot", description="Figure 1")  # embed a description in the SVG

---

Custom marks

petaurus.mark_violin()

Violin plot with an embedded boxplot.

theme.options(chartWidth=300)
palette = theme.palette("lavenders", n=len(CATEGORIES))

chart = theme.mark_violin(df, "group", "value", CATEGORIES, palette=palette)
theme.save(chart, "violin")

Parameter	Default	Description
df	required	Polars DataFrame
x_col	required	Grouping column name
y_col	required	Value column name
categories	required	Ordered list of group labels
palette	None	Single color or list of colors for violin fills
boxplot_size	markSize × 0.8	Boxplot box width in pixels
boxplot_color	"black"	Boxplot fill color
fillOpacity	theme default	Violin fill opacity
stroke	None	Violin outline color (None = no outline)
strokeWidth	theme default	Violin outline width
legend	False	Show a color legend
angledX	theme default	Angle x-axis labels
steps	200	KDE grid resolution per group

petaurus.mark_strip()

Jittered or beeswarm points with a median tick and optional mean ± error bars.

chart = theme.mark_strip(df, "group", "value", CATEGORIES)
chart = theme.mark_strip(df, "group", "value", CATEGORIES, scatter="beeswarm")

Parameter	Default	Description
scatter	"jitter"	"jitter" (fast, random Gaussian) or "beeswarm" (collision-avoidance)
palette	None	List of colors for points
point_size	theme markSize	Point size in sq px
jitter_scale	4.0	Jitter standard deviation in pixels
errorbars	True	Show mean ± error bars
errorbar_extent	"sem"	"sem" or "sd"

---

Statistical annotations

Add a p-value bracket between two groups using petaurus.pvalue_layer(). Combine with any chart using +.

ann = theme.pvalue_layer(
    df, "group", "value", "Control", "Drug A",
    test="mannwhitneyu",
    categories=["Control", "Drug A", "Drug B"],
    chartWidth=300,
    y=210,
)
chart + ann

From a pre-computed p-value:

ann = theme.pvalue_layer(
    group1="Control", group2="Drug A",
    pvalue=0.023, y=210,
    categories=CATEGORIES,
    chartWidth=300,
)

Parameter	Default	Description
df	None	Polars DataFrame (required unless pvalue and y are both provided)
x_col, y_col	None	Column names for groups and values
group1, group2	required	Group labels to compare
test	"mannwhitneyu"	Statistical test: "mannwhitneyu", "ttest_ind", "ttest_rel", "wilcoxon", "tukey_hsd"
pvalue	None	Pre-computed p-value (skips the test)
correction	None	"bonferroni" or None
n_comparisons	1	Number of comparisons for Bonferroni correction
y	auto	Y position of the bracket in data units
y_pad	5	Padding above the group max when y is auto-placed
style	"line"	"line" (bar only) or "bracket" (bar + end ticks)
categories	inferred	Ordered list of all x-axis categories
chartWidth	theme default	Chart width used to compute text x position
reverse	False	Flip the annotation below the bar
decimals	3	Decimal places in the p-value label

---

Data transforms

Jitter

Adds random Gaussian x-offsets to each row, useful for strip plots.

df = theme.add_jitter_offsets(df, scale=5)

alt.Chart(df).mark_circle().encode(
    x=alt.X("group:N"),
    y=alt.Y("value:Q"),
    xOffset=alt.XOffset("jitter_x:Q"),
)

Parameter	Default	Description
scale	5.0	Standard deviation of jitter in pixels
out_col	"jitter_x"	Output column name
seed	20220701	Random seed

Beeswarm

Computes collision-avoiding x-offsets per group. Better than jitter for small n, but slower.

df = theme.add_beeswarm_offsets(
    df,
    y_col="value",
    group_by=["group"],
    height_px=200,
    markSize=10,
)

Parameter	Default	Description
y_col	required	Value column
group_by	required	Column(s) defining each beeswarm group
height_px	theme chartHeight	Chart height in pixels
markSize	10	Point size (area in sq px)
out_col	"beeswarm_x"	Output column name

---

Development

Building palettes

scripts/build/build_palettes.py documents the Oklab recipes for all custom palette families and prints updated hex literals to stdout. Use this to calibrate or extend palettes.

# uv
uv run python scripts/build/build_palettes.py

# pip
python scripts/build/build_palettes.py

The four recipes are:

1. Sequential single-hue — fix hue; sweep L from light to dark with C = frac × Cmax(L, hue); arc-length resample to 12 stops.
2. Sequential multi-hue — interpolate (L, hue) between keyframes; same chroma and arc-length logic.
3. Diverging — two arms meeting at an exact-white pivot; 13 stops so the white center lands exactly on the V-corner.
4. Chroma-scaling — preserve L, scale (a, b) by a constant to derive lighter variants.

Palette hex values live in petaurus/palettes.py as plain lists — no color math runs at import time.

Building the gallery

# uv
uv run python scripts/build/build_gallery.py

# pip
python scripts/build/build_gallery.py

Writes docs/index.html. Open in a browser to browse all palettes across 11 chart types.

Exporting swatches for Adobe Illustrator

# uv
uv run python scripts/build/build_swatches_for_illustrator.py

# pip
python scripts/build/build_swatches_for_illustrator.py

Generates scripts/import_palettes_to_illustrator.jsx. To import into Illustrator:

1. Open or create a document in Adobe Illustrator.
2. Go to File > Scripts > Other Script...
3. Select scripts/import_palettes_to_illustrator.jsx.
4. All palettes are added as named swatch groups in the Swatches panel.

Re-run this script after adding or modifying palettes in petaurus/palettes.py.