diff --git a/python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py b/python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
index 15a24a5..6b2d38a 100644
--- a/python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
+++ b/python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
@@ -232,6 +232,7 @@ with aux.get_data(model) as data:
 
 plt.plot(model.t, mean_norm / mean_norm.max())
 plt.yscale("log")
+mean_norm.max()
 
 model, _ = anti_zeno_engine(Δ=5, γ=0.1 / 2, ω_c=1, ω_0=2, ε=1e-1, ε_couple=1 / 3, n=6, cycle_scale=1, switch_cycles=3)
 
diff --git a/python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org b/python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org
index 25af8a1..2837c5a 100644
--- a/python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org
+++ b/python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org
@@ -294,8 +294,8 @@ Let's test the assumptions of the paper.
 
 #+RESULTS:
 :RESULTS:
-: <matplotlib.legend.Legend at 0x7f132fe19460>
-[[file:./.ob-jupyter/ba6c791f1832931e5812afc9a68b21595f3bbba8.svg]]
+: <matplotlib.legend.Legend at 0x7f4260457790>
+[[file:./.ob-jupyter/6b5cd3972ff3a7b14357d87084276dcb5da496d3.svg]]
 :END:
 
 - **too fast decoupling kills it**
@@ -377,10 +377,15 @@ We need the time points where we sample the total energy.
 
   plt.plot(model.t, mean_norm / mean_norm.max())
   plt.yscale("log")
+  mean_norm.max()
 #+end_src
 
 #+RESULTS:
-[[file:./.ob-jupyter/64ac8170bee42810cf16ee89546c935e035b762c.svg]]
+:RESULTS:
+[[file:./.ob-jupyter/bdb31ef8b89503ed0bbada8f235f6c547e142c3b.svg]]
+: 8.916284416712514e-05
+[[file:./.ob-jupyter/22379dd36f375ead2a12699f7dab26107e94bb2f.svg]]
+:END:
 
 * Too long modulation?, more likely to small spectral sep or to strong coupling
 - definitely not too long mod