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   # 4 questions -> a ready command\npython3 scripts/gen_figure.py --quick -d data.csv # v3.1 one-command figure: auto-pick + render\npython3 scripts/gen_figure.py -t line -d series.json -o fig.png --direct-label  # v3.4 label series at line ends (default under glm-brand/nature-clean)\npython3 scripts/gen_figure.py --demo --cjk        # pick a type, renders sample data\npython3 scripts/gen_figure.py --explain bar       # one type's usage & limits\n# 0️⃣ Call from Python: see references/python-api.md (subprocess recommended;\n#    import-embedding needs matplotlib.use(\"Agg\") on your side)\n\n# Not sure which chart fits? Let the analyzer recommend one\npython3 scripts/gen_figure.py --suggest -d data.json\n\n# Bar chart with default glm palette (muted, colorblind-safe)\npython3 scripts/gen_figure.py -t bar -d data.json -o figure.png \\\n  --title \"Figure 2 / Subtitle\" --ylabel \"Accuracy (%)\"\n\n# v2.7: raw per-subject survival data -> automatic multi-variable Cox -> HR forest (with PH test)\npython3 scripts/gen_figure.py -t forest --data patient.csv --stats cox \\\n  --cox-time time --cox-event event --cox-cols \"age,sex,treat\"\n\n# Forest plot for meta-analysis (PDF output)\npython3 scripts/gen_figure.py -t forest -d forest.json -o forest.pdf --theme okabe-ito\n\n# Kaplan-Meier survival curve with log-rank test\npython3 scripts/gen_figure.py -t km -d survival.json -o km.png --theme okabe-ito\n\n# ROC curve with AUC\npython3 scripts/gen_figure.py -t roc -d roc.json -o roc.png --theme okabe-ito\n\n# v2.8: 4-set ellipse venn (data format: templates/venn.json)\npython3 scripts/gen_figure.py -t venn -d venn.json -o venn4.png\n\n# PRISMA 2020 systematic-review flow diagram (arithmetic auto-validated)\npython3 scripts/gen_figure.py -t prisma -d prisma.json -o flow.pdf --theme"}]}}