try without init

python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py

@@ -165,7 +165,7 @@ model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ
     ε=.7,
     ω_c=0.1,
     ε_couple=0.8,
-    n=4,
+    n=8,
     detune=-0.08,
     ω_0=2,
     T_c=.8,
@@ -175,7 +175,7 @@ model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ
     switch_cycles=1,
     therm_initial_state=False,
     s=[1]*2,
-    ε_init=.3
+    #ε_init=.3
 )
 model.k_max = 4
 # model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-4, n=1, detune=.5, δ=[.1,.1])

python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org

@@ -185,7 +185,7 @@ Init ray and silence stocproc.
       ε=.7,
       ω_c=0.1,
       ε_couple=0.8,
-      n=4,
+      n=8,
       detune=-0.08,
       ω_0=2,
       T_c=.8,
@@ -195,7 +195,7 @@ Init ray and silence stocproc.
       switch_cycles=1,
       therm_initial_state=False,
       s=[1]*2,
-      ε_init=.3
+      #ε_init=.3
   )
   model.k_max = 4
   # model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-4, n=1, detune=.5, δ=[.1,.1])
@@ -226,8 +226,8 @@ Let's test the assumptions of the paper.
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7feeacbd7550> |
-[[file:./.ob-jupyter/b31fd85d99945713755c9f15799276f11ded9c72.svg]]
+| <matplotlib.lines.Line2D | at | 0x7feeaba77700> |
+[[file:./.ob-jupyter/454724eb455c76efa6cc6b66440e9c606981c971.svg]]
 :END:
 
 #+begin_src jupyter-python :tangle nil
@@ -415,6 +415,12 @@ Let's test the assumptions of the paper.
   ax.legend()
 #+end_src
 
+#+RESULTS:
+:RESULTS:
+: <matplotlib.legend.Legend at 0x7feeae3e8730>
+[[file:./.ob-jupyter/7ab9215394ea95c4840ebe39eea48b1ae06bcf26.svg]]
+:END:
+
 - **too fast decoupling kills it**
 - no anti-zeno effects without detuning?
 - **spectral separation seems to be important** or maybe it's just the coupling strenght
@@ -423,6 +429,7 @@ Let's test the assumptions of the paper.
 - making an initial coupling period messes up the accuracy
 - too weak coupling -> time scale too long
 - at temperatures around 5 x sys scale -> 1000 samples not engough
+- init breaks it?
 
 #+begin_src jupyter-python
   with aux.get_data(model) as data:
@@ -449,7 +456,7 @@ Let's test the assumptions of the paper.
 :   pts[1:N+1, 1] = dep1slice
 : /nix/store/5s7kjvxclc9c53l6jzzp3c6fhbc52myg-python3-3.9.13-env/lib/python3.9/site-packages/matplotlib/axes/_axes.py:5224: ComplexWarning: Casting complex values to real discards the imaginary part
 :   pts[N+2:, 1] = dep2slice[::-1]
-[[file:./.ob-jupyter/89b0a921a192ca2c9302f224ee44de11d3ef769f.svg]]
+[[file:./.ob-jupyter/8ebd7afec954939e9224b91b21abcf6de8b9c2f1.svg]]
 :END:
 
 - no steady state ... but we have to average...
@@ -462,8 +469,8 @@ Let's test the assumptions of the paper.
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7fdba036e490> |
-[[file:./.ob-jupyter/a6a5cf21051c53115e4b272f924f107fa8309303.svg]]
+| <matplotlib.lines.Line2D | at | 0x7feeabad3340> |
+[[file:./.ob-jupyter/ed1068cfde19ceb3b502aef89f715f63b411350b.svg]]
 :END:
 
 ** TODO Power and Efficiency
@@ -488,7 +495,7 @@ We need the time points where we sample the total energy.
 #+end_src
 
 #+RESULTS:
-[[file:./.ob-jupyter/bd6ac52527ec691daf8ab0be3e900e82a2c2ae62.svg]]
+[[file:./.ob-jupyter/1b8ad650d9876909c1915d0cf9b93ff62df16fb4.svg]]