Imported from shasyanrz/g2-sclc-physicell-dt (
figures/AGENTS.md). Install upstream withnpx skills add shasyanrz/g2-sclc-physicell-dt --skill figures. Copyright stays with the author.
AGENTS.md — figures/
What This Directory Is
Generated output figures from scripts/visualize_scenarios.py and scripts/visualize_substrate_spatial.py. All files are PNG images produced after simulation runs complete. They are not committed as source — regenerate them by running the visualization scripts.
Current Figures (generated 2026-07-01)
| File | Script | Content |
|---|---|---|
fig1_population_dynamics.png |
visualize_scenarios.py |
Line plot: tumor_total, CD8_total, TAM_total vs time for all 9 scenarios (S0-S8) |
fig2_endpoint_comparison.png |
visualize_scenarios.py |
Bar chart: tumor count at t=360 and total killing events |
fig3_substrate_dynamics.png |
visualize_scenarios.py |
Time series: mean oxygen, glucose, lactate, serplulimab for each scenario |
fig4_S1_spatial_snapshots.png |
visualize_scenarios.py |
Placeholder (SVG disabled in fast runs; shows "SVG saves disabled" message) |
fig5_subtype_comparison.png |
visualize_scenarios.py |
SCLC-A vs SCLC-N tumor fraction comparison by scenario |
fig6_resistance_mechanisms.png |
visualize_scenarios.py |
2×2 panel for S1/S6/S7/S8: tumor_total, mean_effective_effector, treg_total, mean_IL2 |
fig6_S2_substrate_colormap.png |
visualize_substrate_spatial.py |
Serplulimab proxy spatial colormap at t=0 (S2 gradient scenario) |
fig7_S2_substrate_contour.png |
visualize_substrate_spatial.py |
Contour plot of serplulimab gradient |
fig8_S2_substrate_surface.png |
visualize_substrate_spatial.py |
3D surface plot of serplulimab gradient |
fig9_S1vsS2_substrate_compare.png |
visualize_substrate_spatial.py |
Side-by-side comparison of S1 (uniform) vs S2 (gradient) |
fig10_S0_oxygen_colormap.png |
visualize_substrate_spatial.py |
Oxygen concentration heatmap showing hypoxic tumor core (S0) |
Regeneration
# After running simulations
python3 scripts/visualize_scenarios.py
# Spatial substrate figures (requires snapshot XMLs — SVG saves must be enabled)
python3 scripts/visualize_substrate_spatial.pyInterpreting Results From 6h Fast Runs
These figures reflect 6h (360 min) fast-test runs:
killing_events = 0for all scenarios — expected because tumor (9392 cells, 200 µm radius) is too large for CD8 cells seeded outside to infiltrate in 6h.- S6 ICD effect (3795 apoptoses at t=0) IS visible in fig2 tumor endpoint comparison.
- S7 Treg IL-2 depletion IS visible in fig6 (mean_IL2 drops from 0.1 → 8e-6 ng/mL).
- S8 EZH2 effect requires full 7-day runs to see in killing_events.
Disk and Storage Note
Each figure is ~300–800 KB. Regenerating does not require re-running simulations (reads from output_*/g2_summary.csv). Delete figures freely — they can always be regenerated.