Our review
Generates publication-ready TDA figures by scaffolding matplotlib code based on project-specific style constants and templates.
Strengths
- Enforces consistent publication style through shared constants.
- Provides ready-to-use templates for persistence diagrams, barcodes, null histograms, and landscape comparisons.
- Automatically saves figures as PDF and PNG at appropriate DPI.
- Explicit color and annotation rules avoid ad-hoc choices.
Limitations
- Requires the project's trajectory_tda/viz/constants.py module.
- Limited to the listed figure types.
- No support for interactive plotting or non-matplotlib backends.
Use when creating figures for a TDA paper or supplement in this codebase.
Do not use for exploratory working plots outside the project or with other plotting libraries.
Security analysis
SafeThe skill provides formatting and style guidelines for generating matplotlib figures using an internal codebase. It contains no destructive, exfiltrating, or obfuscated instructions. It does not instruct the AI to perform any risky system operations.
No concerns found
Examples
/tda-figure-spec persistence-diagram P01-B/tda-figure-spec null-histogram P01-A/tda-figure-spec/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_COLORSdict — nevertab10or default cycle - Observations:
#2d6a2e(green); Null:steelblue; Dim 0: green; Dim 1:#1a237e - Regimes:
plt.cm.tab10withREGIME_LABELSorder - Sequential/diverging:
viridis,plasma,RdBu_r— neverjet
Annotation standards
- p-values: write
p=0.003, notp<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.
Prompt Engineering
Data & AI
Prompt engineering best practices and templates to maximize AI outputs.
Data Visualization
Data & AI
Generates data visualizations and charts tailored to your data.
RAG Architecture Setup
Data & AI
Setup guide for RAG (Retrieval-Augmented Generation) architectures.