skills/scientific-visualization/SKILL.md
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.
Read references/publication_guidelines.md for deceptive-encoding and integrity checks. Read references/journal_requirements.md only after the target and phase are known.
Record:
If requirements are not known, create a provisional general figure and label all publisher choices as pending verification.
Prefer position on a common scale. Before coding, check:
n and the unit of replication.See references/color_palettes.md. A grayscale screen is useful but is not a complete color-vision or accessibility test.
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.
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 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,
)
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.
write_html() for interaction and write_image()/plotly.io.write_images() for static output.engine= or use Orca/plotly.io.kaleido.scope.width, height, and scale control pixels; scale=3 is not inherently “300 DPI.”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, writes atomically, keeps vector DPI for embedded rasters, uses TIFF LZW, and can use PDF/PS Type 42 fonts. It does not validate scientific content or publisher acceptance.
For editable 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.
The examples and smoke tests use direct package pins current on 2026-07-23:
uv run --isolated --no-project --python 3.13 \
--with "matplotlib==3.11.1" \
--with "seaborn==0.13.2" \
--with "plotly==6.9.0" \
--with "kaleido==1.3.0" \
--with "pillow==12.3.0" \
--with "pypdf==6.14.2" \
python your_figure.py
This is a dated direct-dependency snapshot, not a transitive lock. Use the project's uv lock for exact replay; this skill intentionally ships no dependency lock.
All helpers are deterministic, network-free, bounded, reject symlink inputs/destinations where relevant, and refuse overwrite unless --force is explicit.
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, page size, and conservative first-page PDF font resources. It does not inspect every embedded raster in a vector container.
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.
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 are official-source snapshots accessed 2026-07-23, not automatic compliance rules.
uv run --isolated --no-project --python 3.13 \
--with "matplotlib==3.11.1" \
python scripts/style_preview.py \
--output outputs/style-preview \
--style default \
--palette okabe_ito_on_white \
--formats png,svg
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==3.11.1" \
python scripts/figure_export.py --demo outputs/export-smoke --manifest
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 omit # in hex colors because # begins comments in .mplstyle parsing.
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.