v2.2.0 — Plot it. Audit it. Prove it.

v2.2.0 — Plot it. Audit it. Prove it.

This release turns the project from a collection of AR6-oriented plotting
helpers into an installable, auditable scientific-figure toolkit.

Highlights

• Fidelity audit as a product surface. AuditCheck, AuditReport,
  and audit_figure_report() provide structured pass/fail/skip results
  with stable check codes, actual values, and expected values.
• New ar6plot CLI. Audit a Matplotlib figure factory from the
  terminal, emit human-readable or JSON reports, and use exit codes in CI.
• One-line installation. The distribution is ar6-sciplot; the
  stable Python import remains ipcc_sciplot.
• A stronger visual story. The README now shows the progression from
  Matplotlib defaults to appearance-only “IPCC-ish” styling, explicit
  adapted profiles, and a fidelity-aware AR6 contract.
• Reference-first evidence. Four figures remain reproducible from
  pinned official AR6 WGI source repositories with recorded provenance.
• Release engineering. Versions now derive from Git tags, package
  artifacts are built and checked in CI, and original project code is
  MIT-licensed with explicit third-party/IPCC licensing boundaries.

Install

python -m pip install ar6-sciplot

Audit a figure

ar6plot audit examples/audit_demo.py --strict-dimensions

Machine-readable output:

ar6plot audit examples/audit_demo.py \
  --strict-dimensions \
  --format json \
  --output audit.json

Exit codes are 0 for pass, 1 for fidelity failure, and 2 when
an audit cannot run.

Fidelity boundary

Machine checks do not replace reference-specific scientific review.
Projection choices, panel geometry, scientific methods, and exact
reproduction still require evidence and visual comparison. Strict
typography also requires Arial to be legally installed locally.

This is an independent project. It is not an official IPCC product and
does not imply IPCC endorsement.