paper-plot

# Paper Plot Use this skill when the job is to turn measured data into a paper-quality figure quickly and consistently. This companion skill is adapted from `Trae1ounG/paper-plot-skills/tree/main/plot-from-data`. ## Interaction discipline - Follow the shared interaction contract injected by the system prompt. - If chart semantics, units, grouping, or the intended comparison are ambiguous, ask the user a focused follow-up question instead of guessing. - When the first durable render is ready, send a concise progress update that says which style was chosen, what data source was used, and where the output was written. ## Use when - the user provides measured values, arrays, tables, or CSV-like data and wants a publication-quality figure - the chart can be expressed as a bar, line, scatter, or radar plot using one of the bundled styles - `write`, `analysis-campaign`, or `experiment` needs a first-pass paper-facing figure from structured results ## Do not use when - the job is only final visual QA or last-mile refinement of an already rendered figure; use `figure-polish` - the figure is a disposable debug plot with no durable value - the figure requires a custom multi-panel composition that clearly does not fit any bundled template All bundled templates emit a `dpi=300` PNG first. If a paper-facing final export needs vector output or further visual refinement, hand the result to `figure-polish` after the first-pass render. ## Available Styles | Style | Type | Script | Best for | |-------|------|--------|----------| | `bar_paired_delta` | Bar | `scripts/bar_memevolve.py` | Baseline vs. method paired comparison with explicit gain arrows | | `bar_grouped_hatch` | Bar | `scripts/bar_spice.py` | Multi-method comparison or ablation with highlighted primary method | | `line_confidence_band` | Line | `scripts/line_selfdistill.py` | Training or scaling curves with uncertainty bands | | `line_training_curve` | Line | `scripts/line_aime.py` | Ordered curves with reference lines or breakpoint markers | | `line_loss_with_inset` | Line | `scripts/line_loss_inset.py` | Curves that need a local zoomed inset | | `scatter_tsne_cluster` | Scatter | `scripts/scatter_tsne.py` | Clustered embedding plots with annotations | | `scatter_broken_axis` | Scatter | `scripts/scatter_break.py` | Scatter plots with broken-axis layout for outliers or large gaps | | `radar_dual_series` | Radar | `scripts/radar_dora.py` | Two-method multi-dimension comparison | ## Workflow ``` 1. Confirm the chart question, units, grouping, and preferred output location. 2. Choose the closest bundled style; if two or more styles fit, ask the user or state the rationale. 3. Read `references/<style_name>.md` for the exact layout, color, and rcParams expectations. 4. Copy `scripts/<script>.py` into a quest-local figure workspace such as `paper/figures/scripts/<figure_id>.py`. 5. Replace only the clearly marked data and label section in the copied script; keep the bundled template immutable. 6. Run the copied script and inspect the rendered output. 7. If the figure is durable or paper-facing, hand the result to `figure-polish` before treating it as final. ``` ## Data Substitution Tips Each template script keeps the editable data block near the top, usually as `np.array(...)` declarations or a small dictionary. - Keep array rank and basic types stable unless you intentionally refactor the plotting logic. - If the number of categories changes, update width calculations, color lists, tick labels, and legend labels together. - Replace labels and legends directly in the copied script instead of post-editing the exported figure. - Keep the source data path and generated script path next to the figure output so the figure remains reproducible. ## Detailed Style Parameters Read the corresponding file in `references/` for exact `rcParams`, colors, font sizes, spine settings, and tick directions before generating: - Bar: `references/bar_paired_delta.md`, `references/bar_grouped_hatch.md` - Line: `references/line_confidence_band.md`, `references/line_training_curve.md`, `references/line_loss_with_inset.md` - Scatter: `references/scatter_tsne_cluster.md`, `references/scatter_broken_axis.md` - Radar: `references/radar_dual_series.md` ## Relationship to other skills - Use `paper-plot` for first-pass figure generation from structured data, especially for standard bar, line, scatter, and radar figure families. - Use `figure-polish` for final render-inspect-revise work on durable milestone or paper-facing figures. - In `write`, prefer this skill before inventing a new plotting stack for standard bar, line, scatter, or radar figures.