From 69a042b47b0bc3266c4bff943382b65510867eab Mon Sep 17 00:00:00 2001 From: Valentin Boettcher Date: Tue, 12 Jul 2022 17:15:30 +0200 Subject: [PATCH] anti zeno without cooldown 1000 --- .../10_antizeno_engine/10_first_anti_zeno.py | 2 +- .../10_antizeno_engine/anti_zeno_engine.org | 25 ++++++++++--------- 2 files changed, 14 insertions(+), 13 deletions(-) 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 d59aecd..44e63f9 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 @@ -260,7 +260,7 @@ with aux.get_data(model) as data: #ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1)) #ax.plot(model.t, np.gradient(model.total_energy(data).value)) -ts_begin = τ_init + ((τ_c + τ_off) * np.arange(0, n) - Δ_switch) +ts_begin = τ_init + ((τ_c + τ_off) * np.arange(0, n-1) - Δ_switch) ts_end = τ_c + ts_begin + 2 * Δ_switch ind_begin = np.searchsorted(model.t, ts_begin) ind_end = np.searchsorted(model.t, ts_end) 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 4884ba9..6e14354 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 @@ -248,8 +248,8 @@ Let's test the assumptions of the paper. #+RESULTS: :RESULTS: -| | -[[file:./.ob-jupyter/9cf2dc28bc9a8a8123042f79d9702fa25b04f99a.svg]] +| | +[[file:./.ob-jupyter/724673c144d604c0d468da57794f75631fa59464.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/381fba40c5134ab6ddf8744ef0c980fcf689c875.svg]] +[[file:./.ob-jupyter/db4cca12d9a50dc32fd834c080a4b36a1802d4a7.svg]] - **too fast decoupling kills it** - no anti-zeno effects without detuning? @@ -508,13 +508,7 @@ We need the time points where we sample the total energy. #+end_src #+RESULTS: -:RESULTS: -: Loading: 98% 49/50 [00:07<00:00, 6.37it/s] -: [INFO root 537693] Writing cache to: results/3b50a2bb478fafb22b3d2f6fd3f25200c1483b9fc544a4a96e70dc7984ce195f_b7cd558634733fdb6d35da1623675474d43c06c4b9a8b27b62b299138d6a643c_op_exp_task_100_None_1ff6d34b7de893995dfd0ae8efe2ed770a6773ef52b6e717b4b4ee9b9e5d285d.npy -: Loading: 98% 98/100 [00:20<00:00, 4.67it/s] -: [INFO root 537693] Writing cache to: results/interaction_b7cd558634733fdb6d35da1623675474d43c06c4b9a8b27b62b299138d6a643c_interaction_energy_task_100_None_a1ebae168ba6027d405a66e94ca963c84f8aedb16621da5954b5631bcd1636c4.npy -[[file:./.ob-jupyter/600931f9143f8f955aa7938fb5c31af0f1d81b83.svg]] -:END: +[[file:./.ob-jupyter/f8f7bc3f1b8fa44fe49bcce62f47ce418c3aca97.svg]] @@ -526,7 +520,7 @@ We need the time points where we sample the total energy. One cycle power. #+begin_src jupyter-python - ts_begin = τ_init + ((τ_c + τ_off) * np.arange(0, n) - Δ_switch) + ts_begin = τ_init + ((τ_c + τ_off) * np.arange(0, n-1) - Δ_switch) ts_end = τ_c + ts_begin + 2 * Δ_switch ind_begin = np.searchsorted(model.t, ts_begin) ind_end = np.searchsorted(model.t, ts_end) @@ -552,9 +546,16 @@ One cycle power. #+RESULTS: :RESULTS: +: -0.0440125572928138 +[[file:./.ob-jupyter/5dc7ccf0f740aa282c134ea13c897f79ccfb5de1.svg]] +:END: + + + + + : -0.10190789225469887 [[file:./.ob-jupyter/a731a44cf0b555b2b4bda0d0d99ea0e1ad57f2c1.svg]] -:END: