TDA Figure Specification

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Scaffolds matplotlib figure code following established TDA visualization conventions. Uses predefined style constants for consistent publication-ready figures.

Sby Skills Guide Bot
Data & AIIntermediate
007/27/2026
Claude Code
#tda#matplotlib#figure-generation#publication#data-visualization

Recommended for

Our review

Generates publication-ready TDA figures using predefined style conventions from the trajectory_tda codebase.

Strengths

  • Enforces codebase style conventions
  • Produces publication-ready figures
  • Uses standardized size and color constants

Limitations

  • Requires familiarity with trajectory_tda codebase
  • Only works for specified figure types
  • Working figures must not be saved to production folders
When to use it

Use this skill when you need to create TDA figures consistent with the project's publication style.

When not to use it

Do not use for quick exploratory visualizations or figures that do not follow project conventions.

Security analysis

Safe
Quality score92/100

The skill provides declarative instructions for generating figure code; it does not declare any executable tools, so there is no risk of destructive actions or data exfiltration.

No concerns found

Examples

Persistence diagram
/tda-figure-spec persistence-diagram P01-B
Null histogram
/tda-figure-spec null-histogram P01-A
Barcode
/tda-figure-spec barcode P02-C

/tda-figure-spec — Generate Publication-Ready TDA Figures

Scaffold matplotlib figure code following the established trajectory_tda/viz/ conventions. Uses PUBLICATION_RC, DPI, FIGSIZE_*, STATE_COLORS, and _save_figure from the codebase — never ad-hoc sizes or colours.

Usage

/tda-figure-spec [figure-type] [paper-number]

Example: /tda-figure-spec persistence-diagram P01-B Example: /tda-figure-spec null-histogram P01-A Example: /tda-figure-spec (interactive)


Style constants (always import from trajectory_tda/viz/constants.py)

from trajectory_tda.viz.constants import (
    DPI, FIGSIZE_FULL, FIGSIZE_WIDE, FIGSIZE_HALF, FIGSIZE_SQUARE,
    PUBLICATION_RC, STATE_COLORS, STATES, STATE_LABELS, REGIME_LABELS,
)
import matplotlib.pyplot as plt
plt.rcParams.update(PUBLICATION_RC)

Figure size guide

| Use case | Constant | Size | |---|---|---| | Single full-width plot | FIGSIZE_FULL | 190mm × 120mm | | Wide two-panel | FIGSIZE_WIDE | 190mm × 104mm | | Half-width single panel | FIGSIZE_HALF | 90mm × 120mm | | Square heatmap | FIGSIZE_SQUARE | 90mm × 90mm |

Save function (always use this pattern)

def _save_figure(fig, output_dir: Path, name: str) -> None:
    output_dir.mkdir(parents=True, exist_ok=True)
    fig.savefig(output_dir / f"{name}.pdf", format="pdf")
    fig.savefig(output_dir / f"{name}.png", format="png", dpi=DPI)

Colour rules

  • State space: always STATE_COLORS dict — never tab10 or default cycle
  • Observations: #2d6a2e (green); Null: steelblue; Dim 0: green; Dim 1: #1a237e
  • Regimes: plt.cm.tab10 with REGIME_LABELS order
  • Sequential/diverging: viridis, plasma, RdBu_rnever jet

Annotation standards

  • p-values: write p=0.003, not p<0.01
  • Axes: remove top and right spines; keep bottom and left
  • Legends: frameon=False
  • Subplot labels: ax.text(0.02, 0.97, "(a)", transform=ax.transAxes, ...)

Figure templates available

| Template | When to use | |---|---| | plot_persistence_diagram(diagram, dim, ax) | Scatter birth-death pairs | | plot_barcode(diagram, dim, ax) | Feature lifetimes sorted by persistence | | plot_null_histogram(observed, null_dist, p_value, dim, ax) | Permutation test result | | plot_landscape_comparison(obs, null, grid, ax) | L² landscape distance (mandatory) |


Output paths

| Context | Path | Naming | |---|---|---| | Production paper figure | papers/PXX/figures/ | fig{N}_{desc}.{pdf\|png} | | Supplement | papers/PXX/figures/ | figS{N}_{desc}.{pdf\|png} | | Working / exploratory | figures/{domain}/ | YYYYMMDD_{desc}.png |

Working figures must not be saved to papers/PXX/figures/ — that is for production only.

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