Scientific Visualization

Build figures that preserve scientific meaning before optimizing appearance. Separate universal principles from dated publisher rules, preserve raw data and transformations, use color redundantly, and inspect delivered files rather than trusting plotting defaults.

Non-negotiable guardrails

• Never alter, hide, invent, or selectively enhance data to improve a figure.
• Preserve raw tables/images, exclusions, missing-value codes, analysis code, normalization, binning, image adjustments, and random seeds.
• Do not infer journal requirements. Identify the exact journal, article type, figure type, and submission phase; verify its live official guidance.
• Do not claim that a palette, DPI value, format, or automated report makes a figure accessible or journal-compliant.
• Do not silently connect missing observations, suppress inconvenient points, upsample images as if detail increased, or tune axes/dual axes to exaggerate a conclusion.
• Keep interactive and static outputs as distinct deliverables. Interactive hover is not a substitute for labels, alt text, keyboard access, an accessible data table, or a static fallback.

Read references/publication_guidelines.md for deceptive-encoding and integrity checks. Read references/journal_requirements.md only after the target and phase are known.

Workflow

1. Define the evidence and destination

Record:

• audience and medium: manuscript, web, slide, poster, supplement;
• exact publisher/journal, article type, submission phase, and intended final width;
• variable semantics, units, sample/replicate structure, missing/censored values;
• estimator and uncertainty definition;
• transformations: filtering, aggregation, normalization, smoothing, bins, image processing;
• source-data paths/identifiers and output provenance.

If requirements are not known, create a provisional general figure and label all publisher choices as pending verification.

2. Choose an honest encoding

Prefer position on a common scale. Before coding, check:

• Bars/areas: normally include zero because length/area is measured from a baseline.
• Points/lines: nonzero limits can be valid; show context and disclose breaks.
• Uncertainty: name SD, SE, CI, percentile, posterior, or another interval; state n and the unit of replication.
• Raw observations: show them when feasible; do not let jitter obscure categories/values.
• Missing data: distinguish missing, zero, censored, and excluded; use gaps or explicit model/interpolation styling.
• Area/volume: scale area/volume, not radius/diameter; avoid decorative 3D.
• Log axes: label the base/transform and declare how zero/negative values are handled.
• Binning/smoothing: record edges, bandwidth/window, method, and sensitivity.
• Normalization: state formula/reference and keep limits consistent across compared panels.
• Dual axes: prefer aligned panels; if unavoidable, justify units and do not engineer apparent correlation.
• Images: preserve originals, disclose whole-image adjustments, show scale bars, and avoid clipped/erased background.

3. Design accessibility in, not after

• Use color plus marker, line style, hatching, direct label, or panel separation.
• Choose qualitative, sequential, diverging, or cyclic color according to data semantics.
• Audit foreground/background contrast at the rendered size.
• Make missing and out-of-range values explicit.
• Provide alt text, a longer description for complex figures, and underlying data for web delivery.
• Treat WCAG 2.2 as web guidance: 4.5:1 normal text, 3:1 large text, and 3:1 for graphical objects required for understanding; color cannot be the only cue. Applicability and exceptions matter.

See references/color_palettes.md. A grayscale screen is useful but is not a complete color-vision or accessibility test.

4. Implement with scoped styles

Run shell examples from this skill directory. For your own figure script, add scripts/ and assets/ to the import path (see the bootstrap in references/matplotlib_examples.md) and create the output directory. Use Matplotlib's object-oriented API and temporary style contexts:

import matplotlib.pyplot as plt

from style_presets import style_context

with style_context("default", palette_name="okabe_ito_on_white"):
    fig, ax = plt.subplots(
        figsize=(89 / 25.4, 60 / 25.4),
        layout="constrained",
    )
    ax.plot(x, y, marker="o", label="Observed")
    ax.set(xlabel="Time (hours)", ylabel="Response (unit)")
    ax.legend()

layout="constrained" supports colorbars, nested GridSpec, subfigures, and subplot_mosaic. Do not call tight_layout() afterward; it disables constrained layout.

For exact physical dimensions, do not use bbox_inches="tight" unless the changed page size is intentional.

Color normalization

import matplotlib as mpl

norm = mpl.colors.TwoSlopeNorm(vmin=-2, vcenter=0, vmax=5)
cmap = mpl.colormaps["RdBu_r"].with_extremes(bad="#777777")
image = ax.imshow(values, norm=norm, cmap=cmap, interpolation="nearest")
fig.colorbar(image, ax=ax, label="Change (unit)")

Use LogNorm, CenteredNorm, SymLogNorm, BoundaryNorm, or TwoSlopeNorm only when its mapping matches the scientific meaning.

Seaborn

Seaborn 0.13.2 uses the current errorbar API:

sns.lineplot(
    data=frame,
    x="time",
    y="response",
    hue="treatment",
    style="treatment",
    markers=True,
    errorbar=("ci", 95),
    n_boot=5000,
    seed=20260723,
    ax=ax,
)

For repeated measurements, preserve the subject/sample identifier. To show individual trajectories, use Seaborn's units with estimator=None; this draws one line per sampling unit instead of an aggregate mean. For an aggregate uncertainty band, compute intervals using the actual independent sampling unit (for example, a subject-level bootstrap) and plot those intervals explicitly. A row-wise CI and a fixed random seed do not account for within-subject dependence. See the relational tutorial.

Seaborn drops missing rows before drawing lines, so it can bridge a missing observation. For visible gaps, use explicit NaNs/masks with Matplotlib or draw contiguous observed segments separately.

Axes-level functions fit custom Matplotlib layouts; figure-level functions create their own figures/facets. Do not customize Seaborn's internal artist lists as if they were stable API.

Plotly

• Use write_html() for interaction and write_image()/plotly.io.write_images() for static output.
• Kaleido 1.4.0 requires Chrome/Chromium; it no longer bundles Chrome. Plotly 7 requires MathJax 3/4, not 2.
• Current static formats: PNG, JPEG, WebP, SVG, PDF. EPS is Kaleido v0-only.
• Plotly 7 removes engine=, Orca, Kaleido <1, and plotly.io.kaleido.scope; use plotly.io.defaults.
• width, height, and scale control pixels; scale=3 is not inherently “300 DPI.”
• WebGL traces embed raster content in PDF/SVG. Plotly Express may select WebGL above 1,000 rows; use render_mode="svg" when vector markers are required.
• Fully offline exports need local external assets when a figure references MathJax/topojson/tiles.

5. Export explicitly and record provenance

from figure_export import export_figure

report = export_figure(
    fig,
    "outputs/figure1",
    formats=["pdf", "png"],
    dpi=600,
    bbox_inches=None,  # preserve figure page dimensions
    provenance={
        "raw_data": "data/source.csv",
        "transformations": ["predeclared QC filter", "group mean"],
        "uncertainty": "95% bootstrap CI; seed 20260723",
        "missing_data": "retained as gaps",
    },
    write_manifest=True,
)

The exporter refuses implicit overwrite and publishes each file atomically (a multi-format batch is not a transaction). It preserves vector DPI for embedded rasters and uses TIFF LZW. Matplotlib normally writes RGBA TIFF even on white; request tiff_rgb=True for an RGB TIFF without alpha. This verifies opacity before removing the alpha channel. It does not validate scientific content or publisher acceptance.

For editable fonts:

• PDF/PS Type 42 embeds TrueType fonts.
• svg.fonttype="none" keeps text editable/searchable but does not embed fonts; appearance depends on installed fonts.
• svg.fonttype="path" preserves glyph appearance as paths but loses editable/searchable text.

Use an opaque explicit background unless transparency is required; blending against another background changes apparent contrast.

6. Inspect, compare, and review

1. Inspect file metadata.
2. Audit palette contrast/grayscale separation.
3. Compare against a dated publisher snapshot.
4. View at final size in the manuscript/web context.
5. Manually review fonts, embedded rasters, clipping, legends, scale bars, image integrity, caption, alt text, and source data.
6. Re-check the live target-journal page immediately before upload.

Pinned snapshot

The examples and native smoke tests use these direct package pins, reviewed 2026-10-01:

uv run --isolated --no-project --python 3.13 \
  --with "matplotlib==3.11.2" \
  --with "seaborn==0.13.2" \
  --with "plotly==7.1.0" \
  --with "kaleido==1.4.0" \
  --with "pillow==12.3.0" \
  --with "pypdf==6.19.0" \
  python your_figure.py

This is a dated direct-dependency snapshot, not a transitive lock. Create a lock in the consuming figure project for exact replay; the repository lock does not cover these isolated scientific dependencies.

Bundled CLIs

Bundled CLIs make no network calls and apply input byte/pixel limits; these are not general parser-memory or CPU limits. They reject final-path symlinks and refuse overwrite unless --force is explicit. Matplotlib file timestamps and backend versions can vary, so identical inputs do not guarantee byte-identical output.

Inspect raster/vector metadata

uv run --isolated --no-project --python 3.13 \
  --with "pillow==12.3.0" \
  python scripts/image_metadata.py figure.tiff \
  --format tiff --mode RGB --min-dpi 300 --target-width-mm 85 \
  --alpha-policy forbid

Supports raster images (Pillow), SVG, PDF (pypdf), and EPS/PS. Reports dimensions, DPI/effective DPI, mode, alpha, ICC presence, compression, and first-page PDF fonts. PDF dimensions include UserUnit and rotation, with CropBox separate from MediaBox. It does not fully decode every raster frame, inspect all PDF pages/fonts, or measure embedded-raster DPI.

Audit palette contrast and grayscale

uv run --isolated --no-project --python 3.13 \
  python scripts/palette_audit.py \
  --palette okabe_ito_on_white \
  --background FFFFFF \
  --role graphical

Reports exact WCAG sRGB contrast plus pairwise CIE L* grayscale screening. The grayscale threshold is a heuristic, not a standard.

Plan/screen publisher export

uv run --isolated --no-project --python 3.13 \
  python scripts/export_plan.py \
  --publisher nature \
  --figure-type combination \
  --width single \
  --phase final

Add --input figure.pdf to screen machine-readable properties. Profiles record source dates individually. Most were checked 2026-10-01; Science retains its 2026-07-23 snapshot because live access failed, and ACS is explicitly legacy guidance. Phase mismatches and advisory rules require review; a zero failure count is not acceptance.

Preview styles

uv run --isolated --no-project --python 3.13 \
  --with "matplotlib==3.11.2" \
  python scripts/style_preview.py \
  --output outputs/style-preview \
  --style default \
  --palette okabe_ito_on_white \
  --formats png,svg

Inspect/write styles and smoke-test export

uv run --isolated --no-project --python 3.13 \
  python scripts/style_presets.py --list
uv run --isolated --no-project --python 3.13 \
  python scripts/style_presets.py --show nature
uv run --isolated --no-project --python 3.13 \
  --with "matplotlib==3.11.2" \
  python scripts/figure_export.py --demo outputs/export-smoke --manifest

Assets

• assets/publication.mplstyle: general print starting point.
• assets/nature.mplstyle: dated flagship Nature visual starting point, not a compliance preset.
• assets/presentation.mplstyle: larger projected-display style.
• assets/color_palettes.py: importable Okabe-Ito and Paul Tol values with metadata.
• assets/publisher_profiles.json: dated, machine-readable planning snapshots.

Matplotlib style files use double-quoted hex strings (for example, "#0072B2"); quotes preserve # instead of starting a comment.

References

• references/publication_guidelines.md: integrity, deceptive encodings, accessibility, static/interactive output.
• references/color_palettes.md: palette semantics, exact values, WCAG contrast, grayscale caveats, color management.
• references/journal_requirements.md: phase-specific official publisher snapshots.
• references/matplotlib_examples.md: current, runnable Matplotlib/Seaborn/Plotly patterns.
• references/sources.md: official URLs, dates, versions, and research basis.

Final review checklist

• [ ] Raw data/images and transformation code are preserved.
• [ ] Missing values, exclusions, bins, normalization, and uncertainty are explicit.
• [ ] Baselines, scales, limits, and area/volume encodings are honest.
• [ ] Color is redundant and rendered contrast was reviewed.
• [ ] Figure has an accessible description/data alternative where applicable.
• [ ] Physical dimensions, DPI, format, fonts, transparency, and file size were inspected after export.
• [ ] Publisher rules were verified for the exact journal and phase.
• [ ] No automated report is presented as a scientific, accessibility, or compliance certification.