Reproducibility bundle and run instructions

A single self-contained Python bundle (numpy, scipy, pandas, matplotlib) reproduces every pillar of the paper: axioms.py recovers the No-Go and U3 reduction, validate_all.py returns 5/5 PASS on Pillars I and IV, and verify_rotcore.py runs the Pillar II RCCI audit, alongside the marginal-substrate result c² = B/ρ.

A single self-contained Python bundle (numpy, scipy, pandas, matplotlib) reproduces every pillar of the paper: axioms.py recovers the No-Go and U3 reduction, validate_all.py returns 5/5 PASS on Pillars I and IV, and verify_rotcore.py runs the Pillar II RCCI audit, alongside the marginal-substrate result c² = B/ρ. Each script is listed with its one-line purpose and expected output.

The scripts below form a single self-contained bundle. With a standard scientific Python stack (numpy, scipy, pandas, matplotlib):

python axioms.py                  # Axiomatic core: No-Go + U3 reduction
python validate_all.py            # Pillars I & IV: 5/5 PASS
python verify_rotcore.py <data>   # Pillar II: RCCI audit (<=3e-12)
python length_selection.py        # Pillar III: one-half law
python universality.py            # Reach U1-U3 (Burgers, d-capacity, Turing)
python extensibility.py           # Cross-scale E1-E3 (DNA, droplet, 2D/3D)
python rigid_shell.py             # G-S quantification E4 (82-core, n-fold)
python corotation.py              # Mechanism M (co-rotation, merger, Ekman)
python lattice_inflow.py          # G-Q test: 81+1 nozzle (2D/3D)
python event_flux.py              # Pillar IV: events carry flux (Burgers)
python multid_flux.py             # Pillar IV: 2D NS flux on events (N=192)
python ns3d.py                    # Pillar IV: 3D NS solver (validated)
python event_rg.py                # Event-RG fixed point = Pi_L plateau
python marginal_fluidity.py       # Substrate: G_rel -> 0, B finite, c^2 = B/rho
python unjam_inflow.py            # Step 0: rotation unjams; forced-radius attractor
python transition_dp.py           # Transition test stage 1: G-SOC rules -> DP class
python transition_dp_refine.py    # stage 2: lambda_c refine (curvature criterion)
python transition_dp_final.py     # stage 3: spreading, correlation, consolidated table

[1] 2D Euler energy+enstrophy conserved (nu=0):    PASS  (dE=2.9e-08, dZ=5.4e-06)
[2] Energy budget dE/dt = -eps_nu (NS identity):   PASS  (max rel=4.4e-06)
[3] Enstrophy monotone => max_eps proportional nu:  PASS
[4] Metriplectic budget closes (eps_tot=I):        PASS
[5] Onsager saturation eps_bind->I as nu->0:       PASS