Figure Style

Publication-Grade Figure Rules

*A checklist for correct, legible, internally-consistent scientific figures. This

skill does not impose a visual house style — frame, font, and palette are

parameters. Load it and call apply_figure_style() before any plot.*

§0 Scope

§1–§3, §8, and §9 are correctness — they apply to every plot, in every

context, and have no aesthetic content. §4–§7 are guidance — defaults that

produce a clean result but that a deliberate alternative can override

(individual rules inside §4–§7 that state a factual/perceptual invariant — e.g.

§4.4 semantic-zero centring, §4.5 CVD, §6.9 leader anchoring — still bind). On

its own, this skill is the inner tier (make one plot good); figure-composer

and paper-narrative supply multi-panel and whole-paper context.

§1 Data fidelity & self-consistency

1.1 Excluded rows. A row marked excluded or flagged in the source data is

either omitted entirely or drawn with a visually distinct open/hatched marker

and named in the key. It never enters a summary statistic plotted alongside

the included rows.

1.2 Comparable conditions only. Arms measured under non-comparable

conditions (different N, epoch budget, initialisation, protocol) are not plotted

as visual peers. Separate them with a facet break or a marker on the label, and

state the difference once in the caption.

1.3 Self-consistency. Every key, threshold, and title inside the figure must

be satisfied by every plotted row. Before saving, walk each categorical outcome

label back to the rule that defines it; if a row's value contradicts its label

or the title, the figure is wrong, not the data.

1.4 Claim-titles must be true. A sentence-title (§5.1) is tested against

every category on the axis before rendering. If any contradicts it, qualify the

title ("on 3 of 4 pairs") or downgrade it to a description.

1.5 State n and what was held fixed. Every panel that draws a summary mark

states n and the unit of replication, and every small-multiple that holds a

variable fixed states the fixed value — in the panel or, when §2 budget is

tight, in the caption.

1.6 Reference structure is reference. A tree, ordering, or topology drawn as

*context* (a scale bar, a category strip) uses an established reference, not

one inferred from the plotted data. Infer the structure only when the structure

*is* the result.

1.7 One number per claim. A quantitative claim (runtime, accuracy, count)

has exactly one canonical value across every panel, caption, and the abstract.

Define what it measures and use that value everywhere.

§2 Label economy — floor and ceiling

The figure shows the pattern; the caption carries the context. Design for a

general scientific reader, not the author.

2.1 Floor (non-removable). Every distinct mark, series, glyph, or comparator

must be identifiable from the figure alone. The caption explains *why it

matters*, not *what it is*. A label is non-removable if deleting it leaves a

reader asking "what is that?"; it is removable only if the question becomes "why

is that there?". Comparator labels name the thing ("prior method", "no joint

training"), never a bare role word ("baseline", "previous"). Any term a general

scientist can't parse gets a one-word gloss.

2.2 Ceiling. Per panel: title + axis labels + tick labels + series identity

(labelled once per row of small multiples) + at most 2–3 result annotations.

Count the strings; >6 beyond axes/ticks means you're over. The ceiling counts

*narrative* annotations (callouts, value labels, brackets) — identity labels are

floor, not budget.

2.3 Move to the caption: n=, what's-held-fixed, abbreviation expansions,

non-comparable footnotes, exclusion rationale, methodological caveats.

2.4 Titles are takeaways. A reader seeing only the title knows what the

panel shows. "Robust to gene dropout" passes; "Fewer genes" fails. Test: read it

aloud cold — if the listener asks "fewer genes *what*?", rewrite. For a row of

small multiples that vary one thing, drop per-panel titles for one row-header.

2.5 Value-on-mark only for the headline number — the one a reader would

quote. Everything else is read off the axis.

2.6 When in doubt, delete the label and re-read. If the message survives, it

stays deleted.

§3 Axes, scales, small multiples

3.1 Axis padding. Axis limits clear the data by ≥ one marker radius on every

side; markers and text never touch a spine. ax.margins(0.04) after plotting,

or extend the limit past any annotation.

3.2 Axis breaks over wasted range. When data occupy <40 % of an axis, break

the axis or start it at the data floor with a clear non-zero tick. Never draw a

reference line, threshold, or annotation inside a broken-axis gap — the gap has

no coordinate.

3.3 Log axes get human-readable ticks — 10², 10³, or 1k / 10k / 100k,

not raw exponents. Never draw filled bars on a log-scaled value axis (bar

length encodes ratio to an arbitrary floor); use points + median tick instead.

3.4 Shared axes across small multiples. A row or column of small multiples

shows tick labels once (leftmost / bottommost panel); interior panels keep ticks

but drop labels. When the panels share a y-axis and differ only by x-variable,

render them as abutting subplots (wspace≤0.06) with one row-header title.

3.5 Fill the box. A panel's data envelope occupies ≥75 % of its allotted

rectangle. If a panel's natural aspect leaves dead bands, reshape the grid

(rowspan, stacked complementary panels) — don't pad the panel.

3.6 Direction of goodness. When higher- or lower-is-better is not obvious

from the axis label, place a small upright cue ("higher = better") in the

margin — once per row of panels, never per panel, and never only in the caption.

A directional glyph embedded in rotated text rotates with it; set the cue

upright.

3.7 Physical width. A single-row figure at 300 dpi fits the venue's

double-column width. Adding a schematic or labels does not push data panels narrower than

they were before.

§4 Colour

4.1 Threading. Once a colour is bound to an entity (a method, a feature, a

condition), reuse that exact colour for every mark representing that entity

across the figure — line, fill, marker, text, heatmap row. Colour *is* the

cross-reference; a reader should never have to consult a legend twice.

4.2 Limit hues. Use as few distinct hues as the data require. When the

figure compares a focal series against others, make the focal series visually

dominant (saturated, heavier weight) and render comparators with lower visual

weight (desaturated, lighter, or thinner). The focal hue must not coincide with

any hue in a categorical palette used in the same figure. The focal series must

remain identifiable even when its mark is zero-width or coincident with others —

via outline, marker, or a light tinted band.

4.3 Hierarchical categories. When categories nest (groups within groups),

the outer level picks the hue family and the inner level samples within it.

4.4 Continuous and diverging. Use a perceptually uniform sequential map for

generic continuous values; a single-hue ramp for ordinal rank or size; a

diverging map for signed quantities — always centred at the semantically

meaningful zero (0, 1.0, or median), never the data midpoint.

4.5 CVD safety. Never rely on a red/green contrast for a binary or opposing

distinction. Any binary pair should remain distinguishable in deuteranopia

simulation. Reserve one alarm hue for error/anomaly/perturbation marks and do

not reuse it as a data-series colour.

4.6 Two palettes, two legends. When a figure uses two categorical colour

systems, each legend sits adjacent to the first panel where its palette applies.

§5 Typography

5.1 Sentence titles. A panel title states the comparison in plain language,

regular weight, left-aligned. Metric names go on the axis, not in the title.

5.2 Role-mapped size ladder. A figure uses at most three font sizes,

mapped to *role* not space: titles/axis-labels/series-identity at the base size;

legend/annotation text one step down; tick labels one step further. Panel

letters are the only exception (bold, larger). If a label doesn't fit at its

role's size, fix the layout or shorten the text — don't reach for an

intermediate size. apply_figure_style(sizes=(8,7,6)) sets the ladder.

5.3 Nomenclature. Species, gene, and variable names that scientific

convention italicises are italicised. Abbreviated codes inherit the rule; expand

once on first appearance.

5.4 Magnitude suffixes. Large counts use k / M / B (4.2B, 120 kb),

not comma-grouped full numerals.

5.5 Numeric annotations. On-mark numbers use at most 2 significant figures —

unless 2-sf rounding would make two distinct rows print the same value, in which

case show the digit that separates them. Text on a filled mark reaches ≥4.5:1

contrast; if it doesn't, place the text outside the mark.

5.6 No internal codes. Axis labels use plain-language names; codebase

abbreviations appear only in parentheses after the readable name or in the

caption. Comparator series are labelled with what they *are*, not a role word.

5.7 Panel letters. Bold, top-left, outside the axes box. Case follows the

target venue's convention; panel_letter(ax, 'a', case=...) handles either.

§6 Chart-family guidance (by data shape)

6.1 Categorical × numeric. Show the distribution, not just the summary.

Chart choice follows n: jittered strip with a median tick for small n; box or

violin for large n; bar + overlaid raw points or bar + interval when the mean is

the message. The errorbar='ci95' interval is the t-distribution 95% CI of the

mean (half-width t_{0.975,n−1}·s/√n), so it is valid at small n. Error bars

and raw-point overlays are alternatives — showing both is usually redundant. A

category absent from a group is marked (n.d., —, or a hatched ghost) at its

slot; an empty slot reads as zero. A zero-valued bar

gets a visible stub or dot at the baseline.

6.2 Single-observation categories. A filled dot with a thin neutral stem to

the semantic zero (lollipop). Value labels sit beside the dot.

6.3 Continuous series. Mean-per-x as a line with markers; individual runs as

thin translucent lines or points behind it. Label each series with direct text

at the right end of its line in preference to a legend box. Summary glyphs

(per-bin mean/median) use a shape that cannot be mistaken for a raw observation,

identical across series, drawn below the raw points in z-order.

6.4 Distributions on shared support. When two distributions overlap heavily,

stack them as small panels with a shared x-axis or use a ridgeline. Overlay only

when the separation is visually clear.

6.5 Matrices. When a heatmap is small enough to read (< ~200 cells), print

the value in every cell. State the threshold once in the colourbar label.

6.6 Embeddings. Dimensionality-reduction scatters (UMAP, t-SNE, PCA) drop

ticks and tick labels; a small corner arrow pair names the axes. Clusters are

labelled by thin leader lines to text in surrounding whitespace.

6.7 Paired prediction vs. observation. Stack the two as adjacent tracks with

identical x and colour; let the alignment carry the comparison. Target regions

are translucent spans registered in the legend.

6.8 Insets. Connect a detail inset to its source region visibly — a bounding

box with connector lines, or a translucent wedge.

6.9 Label the extremes. On a scatter of named observations, direct-label at

least the maximum, minimum, and any flagged point with a thin leader line. After

rendering, verify every leader endpoint terminates within one marker radius of

the row it names.

§7 Layout & narrative

7.1 Show what is measured before the result. A reader should grasp what's

being compared before seeing the comparison — via a plain-language title, a

labelled schematic, or panel ordering. Any schematic uses the same words and

glyphs as the data panels' labels.

7.2 One figure, one message. A multi-panel figure has a single sentence it

is trying to make true. Every panel either states it, supports it, or bounds it;

panels that do none of these belong in supplement.

7.3 Legends live in whitespace. Frameless, placed inside the figure's

natural whitespace, or replaced by direct labelling. Legend entries are

swatch-first, left-aligned, and resolve every visually distinct glyph on the

panel.

7.4 Row-band headers for nested faceting. When small multiples are grouped,

each group gets one spanning header, not repeated per-panel titles.

7.5 The figure arc. For a paper: Figure 1 renders the paper's one-sentence

pitch as data — scope, not architecture. Subsequent figures cover mechanism,

evidence, robustness, application. A panel is judged against the paper's pitch,

not just its own figure's claim; content moves between figures if that's where

the story needs it. (paper-narrative runs this review.)

7.6 Don't re-decorate a passing panel. Between revision rounds, a panel that

already passes is not made more visually complex to fix nothing. Adding marks or

labels to a clean panel is a regression.

§8 Anti-patterns

These are correctness failures, not style preferences:

• Red and green as opposing categories.
• Filled bars on a log-scaled value axis.
• Colourbar ticks that are evenly spaced but miss the semantic centre.
• A diverging colormap whose centre is the data midpoint, not the semantic zero.
• An axis title that restates the tick labels.
• Explaining the direction of goodness only in the caption.
• A "reference" line drawn at a value that is itself one of the plotted points.
• An excluded row that enters a plotted summary statistic.
• A leader line whose nearest mark is not the row it labels.

§9 Render-then-verify

After fig.savefig(...), before presenting the file:

9.1 Geometric (bbox) check.

r = fig.canvas.get_renderer()
texts = [(t, t.get_window_extent(r)) for t in fig.findobj(mpl.text.Text)
         if t.get_text().strip() and t.get_visible()]
spines = [(s, s.get_window_extent(r)) for ax in fig.axes
          for s in ax.spines.values() if s.get_visible()]
ticklabels = {ax: set(ax.get_xticklabels(which='both') + ax.get_yticklabels(which='both'))
              for ax in fig.axes}
overlaps  = [(a, b) for i, (a, ba) in enumerate(texts) for b, bb in texts[i+1:] if ba.overlaps(bb)]
overlaps += [(t, s) for t, bt in texts for s, bs in spines
             if bt.overlaps(bs) and t not in ticklabels[s.axes]]
# assert: overlaps == [] and every text box lies within fig.bbox

Overlap is defined between *visible* boxes, and a tick label sitting on its own

spine is not a finding. Fix (move, shorten, stagger) and re-save until clean.

9.2 Perceptual check. The bbox check is geometric, not perceptual — it will

not catch a low-contrast label, a leader that crosses three others, or a series

colour mistakable for another. Save every panel crop as a concrete file with

Pillow, then inspect each crop with Wisp's view_image tool:

from PIL import Image

fig.savefig("figure.png")
for letter, box in panel_crops(fig).items():
    Image.open("figure.png").crop(box).save(f"figure-{letter}.png")

Leave Python after creating the files and call view_image once per crop.

For each crop: Is every glyph and mark legible against its background? Does the

smallest plotted element have a stroke or stub? Do any leaders cross? Could any

series colour be mistaken for another? Does the legend sit beside what it keys?

A perceptual defect that passes §9.1 is still a defect.

9.3 Reliable R export. For ggplot objects, prefer an explicit

ggsave(filename, plot = p, dpi = 300, bg = "white", ...) call over relying on

the current graphics device. For base graphics or packages that draw only to an

active device, open png(..., bg = "white", res = 300), draw explicitly, and

always call dev.off(). After either path, assert that the file exists and is

non-empty, then inspect the saved image. A successful R call with a missing,

zero-byte, transparent-on-transparent, or visually blank file is a failed

render.

*When in doubt: fewer hues, more direct labels, raw data over summary stats, and

state what is being measured before showing the result.*