Figure correctness checklist

This skill makes one plot trustworthy and readable. It deliberately has no house aesthetic — frame, font family, and sizes are all parameters of apply_figure_style(), which must run before the first plotting call. Multi-panel assembly is figure-composer's job; deciding what each figure in a paper should argue is paper-narrative's.

Loading the Python helpers only defines them; it does not select a matplotlib backend or apply a style. Call apply_figure_style(...) explicitly for plotting. The optional figure_style_self_check() applies the defaults and checks font wiring when called; it is not a load-time check.

Two tiers of rule live below. Hard rules — everything under Tell the truth, Never do, and Prove the render, plus any rule stating a perceptual or factual invariant (semantic-zero centring, colour-vision safety, leader-line anchoring) — apply to every plot with no override. Everything else is a default: deviate when you have a deliberate reason, not by accident.

Tell the truth about the data

• Excluded means excluded. A row the source data marks excluded either disappears from the plot or appears as a clearly distinct open/hatched marker named in the key — and it never contaminates a summary statistic drawn next to included rows.
• Peers must be comparable. Arms measured under different N, budget, initialisation, or protocol don't sit side by side as if equivalent. Facet them apart or mark the label, and state the difference once in the caption.
• The figure can't contradict itself. Before saving, trace every categorical label, threshold, and title back to the rule that defines it and check each plotted row satisfies it. A row that contradicts its label means the figure is wrong.
• A sentence-title is a claim — test it. Check the claim against every category on the axis. One counterexample means qualifying ("on 3 of 4 pairs") or demoting the title to a description.
• State n and what's held fixed. Any summary mark comes with n and the unit of replication; any small-multiple that fixes a variable names the fixed value — in-panel, or in the caption when the label budget is tight.
• Context structure comes from references. A tree, ordering, or topology drawn as background (scale bar, category strip) uses an established reference. Infer it from the plotted data only when the structure is itself the finding.
• One claim, one number. Each quantitative claim (accuracy, runtime, count) has a single canonical value reused identically in every panel, caption, and the abstract — with a definition of what it measures.

Say less, and say it in the right place

The panel shows the pattern; the caption carries the context. Design for a general scientific reader, not for yourself.

• Floor. Every visually distinct mark must be identifiable from the figure alone. Deleting a label may only ever leave the reader asking "why is that there?" — never "what is that?". Comparators are named for what they are ("no joint training", "prior method"), not a role word ("baseline"). Gloss any term a general scientist can't parse.
• Ceiling. Per panel: title, axis labels, ticks, series identity (labelled once per row of small multiples), and at most 2–3 narrative annotations. More than ~6 strings beyond axes/ticks means over budget. Identity labels are floor, not budget.
• Caption material: n=, held-fixed values, abbreviation expansions, exclusion rationale, non-comparability footnotes, methods caveats.
• Titles state takeaways. "Robust to gene dropout" works; "Fewer genes" doesn't — read it aloud and if a listener would ask "fewer genes what?", rewrite. A row of small multiples varying one thing gets one row header, not per-panel titles.
• Numbers on marks: headline only. Print the one value a reader would quote; the axis serves the rest.
• Tie-break: delete and re-read. If the message survives without the label, the label stays gone.

Axes and scales

• Limits clear the data by at least a marker radius on every side — ax.margins(0.04) — and no mark or text touches a spine.
• Data using under 40% of an axis calls for a break or a data-floor start with an explicit non-zero tick. Nothing may be drawn inside a break gap: the gap has no coordinates.
• Log ticks read as 10²/1k/10k/100k, never raw exponents. Filled bars on a log value axis are banned outright — bar length would encode the ratio to an arbitrary floor. Points with a median tick replace them.
• In a row/column of small multiples, tick labels appear once (leftmost or bottommost); interior panels keep tick marks only. Panels sharing y and differing only in x abut (wspace≤0.06) under one row header.
• A panel's data envelope fills ≥75% of its rectangle; dead bands mean reshaping the grid, not padding the panel.
• When better-is-up/down isn't obvious from the axis label, put an upright "higher = better" cue in the margin — once per row, never per panel, never caption-only, and never rotated with rotated text (goodness_arrow).
• The full-width figure must fit the venue's double-column width at 300 dpi, and adding a schematic or label never squeezes the data panels narrower.

Colour

• A colour is a binding. Once an entity gets a colour, every mark for that entity — line, fill, marker, text, heatmap row — reuses it exactly. Colour is the cross-reference; nobody should read a legend twice.
• Few hues, one dominant. Use the minimum hue count. A focal series is saturated and heavy; comparators desaturate and thin (focal_palette). The focal hue may not collide with any categorical palette in the same figure, and the focal series must stay identifiable even at zero width or full overlap — outline, marker, or tinted band.
• Nested categories: outer level chooses the hue family, inner level samples within it.
• Continuous data: perceptually uniform sequential map; single-hue ramp for rank/size; diverging map for signed values, centred on the semantic zero (0, 1.0, median) — never the data midpoint.
• Colour-vision safety. No red/green binary. Every binary pair survives a deuteranopia simulation. One alarm hue is reserved for error/anomaly/perturbation and never doubles as a series colour.
• Two palettes ⇒ two legends, each adjacent to the first panel using its palette.

Type

• Panel titles are plain-language sentences, regular weight, left-aligned; metric names live on the axis.
• Three sizes, mapped to roles: base for titles/axis labels/series identity, one step down for legends/annotations, one more for ticks (apply_figure_style(sizes=(8,7,6))). Panel letters alone break the rule (bold, larger). A label that doesn't fit gets a layout fix or a shorter string, never a fourth size.
• Species, genes, and variables that convention italicises are italicised; abbreviations inherit the style and expand once on first use.
• Large numbers wear magnitude suffixes — 4.2B, 120 kb — not comma grouping.
• On-mark values: ≤2 significant figures, unless rounding would collapse two distinct rows, in which case show the separating digit. Text on a fill needs 4.5:1 contrast or it moves outside the mark.
• No codebase identifiers as labels: readable name first, code in parentheses or the caption.
• Panel letters: bold, top-left, outside the axes box; case per venue (panel_letter(ax, 'a', case=...)).

Match the chart to the data

• Category × number: show the distribution. Small n → jittered strip with median tick (strip_with_median); large n → box/violin; mean-as- message → bar with raw points or interval (bar_with_points), not both. errorbar='ci95' is the t-interval, valid at small n. A missing category is marked n.d./—/hatched ghost — an empty slot reads as zero — and a true zero gets a visible stub.
• One observation per category: lollipop (dot plus thin stem to the semantic zero), value beside the dot.
• Series over a continuum: mean line with markers, raw runs as thin translucent traces behind it, series named by text at the line's right end (end_of_line_labels) rather than a legend box. Per-bin summary glyphs are unmistakable-for-raw, identical across series, and drawn under the raw points.
• Overlapping distributions: stacked panels with shared x, or a ridgeline; overlay only when separation is obvious.
• Matrices: under ~200 cells, print every value; state the threshold in the colourbar label.
• Embedding scatters (UMAP/t-SNE/PCA): no ticks or tick labels, a corner arrow pair for axes, clusters labelled by thin leaders into whitespace.
• Prediction vs. observation: adjacent tracks, identical x and colours, alignment carries the comparison; target regions as translucent spans in the legend.
• Insets connect visibly to their source region: box plus connectors, or a wedge.
• Named-point scatters direct-label at least max, min, and every flagged point via thin leaders — and after rendering, confirm each leader ends within a marker radius of its row.

Composition

• Show what is being measured before the result — plain title, labelled schematic, or panel order — and any schematic reuses the exact words and glyphs of the data panels.
• A multi-panel figure exists to make one sentence true. Panels that neither state, support, nor bound that sentence move to the supplement.
• Legends are frameless, sit in natural whitespace or become direct labels, read swatch-first left-aligned, and resolve every distinct glyph.
• Grouped small multiples take one spanning header per group, not repeated titles.
• Across a paper, Figure 1 renders the pitch as data (scope, not architecture); later figures carry mechanism, evidence, robustness, application. Panels are judged against the paper's pitch and move between figures when the story requires (paper-narrative runs that review).
• Between revision rounds, a passing panel is left alone — decorating a clean panel is a regression.

Never do

Each of these is a correctness failure:

• red vs. green as an opposing pair;
• filled bars on a log value axis;
• a diverging map centred on the data midpoint, or a colourbar whose ticks skip the semantic centre;
• an axis title that repeats the tick labels;
• direction-of-goodness explained only in the caption;
• a "reference" line at a value that is one of the plotted points;
• an excluded row inside a plotted summary;
• a leader line whose nearest mark is not its target.

Prove the render

Run both checks after fig.savefig(...) and before presenting the file.

1. Collision scan. Assert no visible text box overlaps another or a spine (a tick label touching its own spine doesn't count), and every text box sits inside fig.bbox:

rend = fig.canvas.get_renderer()
labels = [(t, t.get_window_extent(rend)) for t in fig.findobj(mpl.text.Text)
          if t.get_text().strip() and t.get_visible()]
frames = [(s, s.get_window_extent(rend)) for ax in fig.axes
          for s in ax.spines.values() if s.get_visible()]
own_ticks = {ax: set(ax.get_xticklabels(which='both') + ax.get_yticklabels(which='both'))
             for ax in fig.axes}
hits  = [(a, b) for i, (a, ba) in enumerate(labels)
         for b, bb in labels[i+1:] if ba.overlaps(bb)]
hits += [(t, s) for t, bt in labels for s, bs in frames
         if bt.overlaps(bs) and t not in own_ticks[s.axes]]
assert not hits

Move, shorten, or stagger until the scan is clean, re-saving each time.

2. Visual pass. Geometry can't see a low-contrast label, crossing leaders, or two confusable series colours. Crop each panel to its own file and inspect every 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, then view_image each crop asking: every glyph legible against its background? smallest element still has a stroke or stub? leaders uncrossed? any two series colours confusable? legend beside what it keys? A visual defect that passed the collision scan is still a defect.

3. R output. Prefer explicit ggsave(filename, plot = p, dpi = 300, bg = "white", ...) over the active device; for base graphics open png(..., bg = "white", res = 300), draw, and always dev.off(). Then assert the file exists and is non-empty and inspect it — a "successful" R call with a missing, zero-byte, or blank file is a failed render.

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Defaults when unsure: fewer hues, direct labels over legends, raw data over summaries, and name the measurement before showing its result.