scientific-viz

Claude Code skill for publication-quality scientific visualizations — charts (matplotlib/seaborn), neural-network architecture diagrams (matplotlib patches or Mermaid), block-scheme flowcharts, training-pipeline diagrams, and benchmark comparison figures. Tuned for the CVGL / NADEZHDA project but reusable for any deep-learning paper.

Behaviour spec — see SKILL.md. This file is the human-facing entry point.

When to use

• Need a figure for a paper / report / slide deck and want it consistent with project style.
• Want a Pareto plot (params vs R@1), grouped bar, radar, heatmap, or training curve.
• Need a Teacher–Student / multi-modal fusion architecture diagram.
• Need a Mermaid flowchart embeddable in Obsidian for an LUPI / KD pipeline.
• Want a quick comparison figure across CVGL benchmarks (University-1652, GeoText-1652, GTA-UAV, VisLoc).

Invocation

/scientific-viz <type> "<description>"

Where <type> ∈ {chart, architecture, flowchart, comparison, pipeline}.

Examples

/scientific-viz chart        "R@1 comparison across methods on University-1652"
/scientific-viz architecture "Teacher-Student LUPI pipeline"
/scientific-viz flowchart    "Training progressive staging 3 phases"
/scientific-viz comparison   "Backbone candidates: params vs R@1"
/scientific-viz pipeline     "LUPI distillation flow with 7 losses"

Five visualisation types

Type	Output	Use for
chart	Python script (.py) + .png (300 DPI) + .pdf (vector)	metric comparisons, ablations, distributions, training curves
architecture	matplotlib patches Python (complex) or Mermaid (Obsidian-embeddable)	Teacher/Student, backbone stages, fusion modules, head designs
flowchart	Mermaid (graph TD / graph LR) + optional Python	training/inference pipeline, experimental workflow, data processing
comparison	Python .py + .png/.pdf	Pareto front, grouped bar, radar/spider, heatmap
pipeline	Mermaid + matplotlib	LUPI distillation flow, augmentation chain, edge deployment

Publication-quality defaults (charts)

Every generated chart / comparison / pipeline Python script starts with this rcParams block:

import matplotlib
matplotlib.rcParams.update({
    'font.family': 'serif',
    'font.size': 11,
    'axes.labelsize': 12,
    'axes.titlesize': 13,
    'legend.fontsize': 10,
    'xtick.labelsize': 10,
    'ytick.labelsize': 10,
    'figure.dpi': 300,
    'savefig.dpi': 300,
    'savefig.bbox': 'tight',
    'axes.grid': True,
    'grid.alpha': 0.3,
})

Hard requirements baked in:

• ≥ 300 DPI, vector PDF + raster PNG saved side-by-side.
• Times New Roman / DejaVu Serif, ≥ 10 pt.
• Colorblind-safe palette (Okabe-Ito or tab10).
• English axis labels for publications, optional Russian for internal reports.
• Light-gray grid (alpha=0.3), tight layout, no clipped labels.

NADEZHDA project palette (consistent across figures)

Component	Colour	Hex
Teacher	Deep blue	#1f77b4
Student	Orange	#ff7f0e
Satellite modality	Green	#2ca02c
Drone modality	Red	#d62728
Street-view	Purple	#9467bd
Depth	Brown	#8c564b
Text	Pink	#e377c2
Loss / gradient	Gray	#7f7f7f
Edge / Jetson	Teal	#17becf

Mermaid conventions (architecture / flowchart / pipeline)

• graph TD — vertical flow (Teacher → Student stack).
• graph LR — horizontal pipeline (input → backbone → neck → heads).
• Thick arrows = main data flow · dashed arrows = gradient / loss signals.
• Loss labels on arrows: L_task, L_LUPI, L_feat, L_RKD, L_seg, L_CVD_MI, L_CVD_Recon.
• style / classDef to mark Teacher (blue), Student (orange), shared (green).

Ready-to-use templates: templates/architecture_nadezhda.md.

Standard tensor shapes (for architecture annotations)

Input:      [B, 3, 256, 256]
Stage 1:    [B,  32,  96,  96]
Stage 2:    [B,  64,  48,  48]
Stage 3:    [B, 128,  24,  24]
Stage 4:    [B, 256,  12,  12]
Descriptor: [B, 512]   L2-normalized

Installation

Drop this repo into your vault's Claude Code skills directory:

git clone https://git.lissad.keenetic.name/Pikaliov/scientific-viz.git \
    .claude/skills/scientific-viz

Or as a submodule:

git submodule add https://git.lissad.keenetic.name/Pikaliov/scientific-viz.git \
    .claude/skills/scientific-viz

Claude Code picks up the skill on the next session. Verify with /help — /scientific-viz should appear in the user-invocable list.

Runtime dependencies (charts & comparison types):

pip install matplotlib seaborn numpy

Mermaid output requires no Python — the diagram block embeds directly into Obsidian, GitHub-flavoured Markdown, or Quarto.

File layout

scientific-viz/
├── README.md                                — this file
├── SKILL.md                                 — behaviour spec (5 types, generation pipeline)
├── reference/
│   └── chart_patterns.md                    — canonical Pareto/grouped-bar/radar/heatmap snippets
└── templates/
    └── architecture_nadezhda.md             — Mermaid templates for Teacher-Student / LUPI / fusion

Hard constraints

• ❌ Никаких внешних API (OpenRouter, Gemini и т. п.) — только локальные библиотеки.
• ❌ Никакой растровой генерации архитектур через PIL.ImageDraw — только matplotlib patches или Mermaid.
• ✅ Всегда сохранять и .py скрипт, и результат (.png/.pdf) — рядом, рядом с заметкой / в attachments/figures/.
• ✅ Каждое значение в графике должно иметь источник (комментарий в коде или caption под фигурой).
• ✅ Для Obsidian — Mermaid-блок встраивается прямо в .md, без ссылок на внешние файлы.
• ✅ Для публикаций — английский язык подписей; для внутренних отчётов — RU допустим.

Worked example

/scientific-viz comparison "Pareto front: backbone params vs R@1 on University-1652, mark NADEZHDA target at 8.5M"

Expected output:

• pareto_params_r1.py — full matplotlib script with palette, grid, edge-budget shaded region, NADEZHDA marker as star.
• pareto_params_r1.png (300 DPI) + pareto_params_r1.pdf (vector) saved next to the script.
• Inline annotation list (Sample4Geo, VimGeo, QDFL, MobileGeo, (MGS)², NADEZHDA target).
• Source comment at the top of the script linking to 1_lit_research/СИНТЕЗ_всех_статей_для_LUPI_CVGL.md rows.

See reference/chart_patterns.md for the canonical pattern this expands from.

Allowed tools

Read, Write, Edit, Bash, Glob, Grep — the skill writes Python scripts and Mermaid blocks directly into the vault, then optionally executes the script via Bash to produce the rendered .png/.pdf.

Related skills

• /analyze-paper — surfaces the numbers this skill plots.
• /synthesize-review — produces the cross-paper tables that feed comparison figures.
• /generate-hypothesis — the hypotheses whose evidence is plotted.