ok lets try anti zeno

python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py

@@ -183,10 +183,10 @@ def anti_zeno_engine(
     (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ_switch, τ_init),
 ) = anti_zeno_engine(
     Δ=11,
-    ε=1,#.1,
+    ε=.01,#.1,
     ω_c=2,
     ε_couple=0.8,
-    n=10,
+    n=5,
     detune=-1.6,
     ω_0=20,
     T_c=8,

python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org

@@ -202,10 +202,10 @@ Init ray and silence stocproc.
       (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ_switch, τ_init),
   ) = anti_zeno_engine(
       Δ=11,
-      ε=1,#.1,
+      ε=.01,#.1,
       ω_c=2,
       ε_couple=0.8,
-      n=10,
+      n=5,
       detune=-1.6,
       ω_0=20,
       T_c=8,
@@ -285,8 +285,8 @@ Let's test the assumptions of the paper.
 
 #+RESULTS:
 :RESULTS:
-: <matplotlib.legend.Legend at 0x7fb969110df0>
-[[file:./.ob-jupyter/ba478ef1f6169d3ce3dfc837b3704ee5f56784dc.svg]]
+: <matplotlib.legend.Legend at 0x7fb968d6ce20>
+[[file:./.ob-jupyter/d22a71e35fdcf05377bc97f8aa21fb5852ea41bf.svg]]
 :END:
 
 #+begin_src jupyter-python :tangle nil
@@ -329,8 +329,8 @@ Let's test the assumptions of the paper.
 
 #+RESULTS:
 :RESULTS:
-[[file:./.ob-jupyter/19795d1c9861cd98fce1989b910994894ef0326b.svg]]
-[[file:./.ob-jupyter/23d9b268b1bd736dceee49186bd61bd36a8d9d31.svg]]
+[[file:./.ob-jupyter/940407a7f40b4ac8018fcfe86a8ea1de0d617263.svg]]
+[[file:./.ob-jupyter/6ee005961252c5deaf785398f657e9ca56732eeb.svg]]
 :END:
 
 #+begin_src jupyter-python
@@ -737,11 +737,11 @@ We need the time points where we sample the total energy.
   )
 #+end_src
 
-#+RESULTS:
-:RESULTS:
-: \(P=-0.066\pm 0.006\)
-[[file:./.ob-jupyter/1d490921fae7062908ff727cf6f73831470e2921.svg]]
-:END:
+
+: \(P=-0.047\pm 0.027\)
+[[file:./.ob-jupyter/4a53f8963bf2bcd9ac5940030087ac34305d2372.svg]]
+
+