now: no cooldown

python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py

@@ -179,7 +179,7 @@ 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,
+    ε=1,
     ω_c=2,
     ε_couple=0.5,
     n=6,
@@ -283,26 +283,6 @@ powers = (tot_e[ind_end] - tot_e[ind_begin]) / (model.t[ind_end[0]] - model.t[in
 
 (powers[2:]).mean()
 
-with aux.get_data(model) as data:
-  _, ax = plt.subplots()
-  #fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
-  fs.plot_diff_vs_sigma(model.t, model.total_energy(data, every=1000)[:-1], model.total_energy(data, every=1000)[-1], ax=ax)
-  ax.legend()
-
-mean_norm = np.zeros_like(model.t)
-with aux.get_data(model) as data:
-    for i in range(min(data.samples, 2)):
-        aux_state = data.aux_states[i, :]
-        # plt.plot(model.t, np.linalg.norm(aux_state, axis=1))
-
-        mean_norm += np.linalg.norm(aux_state, axis=1)
-
-    mean_norm /= data.samples
-
-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)
 
 _, ax = fs.plot_energy_overview(model, markersize=1)

python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org

@@ -202,7 +202,7 @@ 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,
+      ε=1,
       ω_c=2,
       ε_couple=0.5,
       n=6,
@@ -248,8 +248,8 @@ Let's test the assumptions of the paper.
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7f519129d190> |
+| <matplotlib.lines.Line2D | at | 0x7f518ead6f70> |
-[[file:./.ob-jupyter/bd8dd45c519728f3c2034c29c3683783266b287f.svg]]
+[[file:./.ob-jupyter/9cf2dc28bc9a8a8123042f79d9702fa25b04f99a.svg]]
 :END:
 
 #+begin_src jupyter-python :tangle nil
@@ -443,7 +443,7 @@ Let's test the assumptions of the paper.
 #+end_src
 
 #+RESULTS:
-[[file:./.ob-jupyter/4f9c64f7f8eb5b4c06d7f920b6c43c17260d0122.svg]]
+[[file:./.ob-jupyter/381fba40c5134ab6ddf8744ef0c980fcf689c875.svg]]
 
 - **too fast decoupling kills it**
 - no anti-zeno effects without detuning?
@@ -552,70 +552,12 @@ One cycle power.
 
 #+RESULTS:
 :RESULTS:
-: -0.05407415408283555
+: -0.10190789225469887
-[[file:./.ob-jupyter/85cfe9187eafaf87bd20fea4b9a164f3fe6c2e44.svg]]
+[[file:./.ob-jupyter/a731a44cf0b555b2b4bda0d0d99ea0e1ad57f2c1.svg]]
-
-
-
-
-[[file:./.ob-jupyter/401b41048b1b2afe539bfddb6780a8a2e5ee4480.svg]]
-
-
-
-: -0.10798709005058302
-[[file:./.ob-jupyter/833b64a5b95211ec1753bc3c4f064c5b53836f48.svg]]
-
-
-
-
-: -0.03467681556531242
-[[file:./.ob-jupyter/5bdb4e604aa258376721b1b1f98f6e1c7023e2e3.svg]]
-
-
-
-: -0.03951090584819158
-[[file:./.ob-jupyter/673437250abea43fbff9003efe88dafac1cd98a4.svg]]
-
-
-
-
-: -0.00032248844096049805
-[[file:./.ob-jupyter/9ac6d0cae41eda15950b000878165c8e21547539.svg]]
-
-Slightly better than constant coupling. But due to on-off or anti-zeno?
-It is anti zeno, tested it with turning coupling on and off but no cooldown
-
-#+begin_src jupyter-python
-  with aux.get_data(model) as data:
-    _, ax = plt.subplots()
-    #fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
-    fs.plot_diff_vs_sigma(model.t, model.total_energy(data, every=1000)[:-1], model.total_energy(data, every=1000)[-1], ax=ax)
-    ax.legend()
-#+end_src
-
-#+begin_src jupyter-python
-  mean_norm = np.zeros_like(model.t)
-  with aux.get_data(model) as data:
-      for i in range(min(data.samples, 2)):
-          aux_state = data.aux_states[i, :]
-          # plt.plot(model.t, np.linalg.norm(aux_state, axis=1))
-
-          mean_norm += np.linalg.norm(aux_state, axis=1)
-
-      mean_norm /= data.samples
-
-  plt.plot(model.t, mean_norm / mean_norm.max())
-  plt.yscale("log")
-  mean_norm.max()
-#+end_src
-
-#+RESULTS:
-:RESULTS:
-: 0.0005854498090217866
-[[file:./.ob-jupyter/fad8f6abf218b6736cacbb9c1fe37ca825cf26d8.svg]]
 :END:
 
 
+
 * Too long modulation?, more likely to small spectral sep or to strong coupling
 - definitely not too long mod
 #+begin_src jupyter-python :results none