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try stronger coupling

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Valentin Boettcher 2022-06-17 16:14:04 +02:00
parent 620357bc8d
commit 976ff6dbc4
2 changed files with 23 additions and 23 deletions
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8
python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
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@ -110,7 +110,7 @@ model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s) = anti_zeno_e
detune=-0.1,
ω_0=2,
T_h=6,
δ=[0.1] * 2,
δ=[1] * 2,
γ=0.5/10,
switch_cycles=4,
therm_initial_state=False,
@ -167,14 +167,14 @@ plt.plot(model.t, ut.smoothen(model.t, ρ_ee, frac=.06, it=0))
with aux.get_data(model) as data:
_, ax = plt.subplots()
#fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
#fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
#fs.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax)
#fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
#fs.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax)
#fs.plot_with_σ(model.t, model.system_energy(data), ax=ax)
#ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1))
ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1))
#ax.set_xlim(10,40)
with aux.get_data(model) as data:

38
python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org
View file

@ -133,7 +133,7 @@ Init ray and silence stocproc.
detune=-0.1,
ω_0=2,
T_h=6,
δ=[0.1] * 2,
δ=[1] * 2,
γ=0.5/10,
switch_cycles=4,
therm_initial_state=False,
@ -167,8 +167,8 @@ Let's test the assumptions of the paper.
#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f13362ab700> |
[[file:./.ob-jupyter/ebf1ccf4953fdb0eea4e52a7358cb566b6f6591a.svg]]
| <matplotlib.lines.Line2D | at | 0x7f133599c520> |
[[file:./.ob-jupyter/3be2cf929cf6586c3c81582ffb7981d59c77af40.svg]]
:END:
#+begin_src jupyter-python :tangle nil
@ -187,8 +187,8 @@ Let's test the assumptions of the paper.
#+RESULTS:
:RESULTS:
: 0.03678793854981345
| <matplotlib.lines.Line2D | at | 0x7f133622cca0> |
[[file:./.ob-jupyter/9d17015b386c7827affcb914735cb8f1b08167bd.svg]]
| <matplotlib.lines.Line2D | at | 0x7f133571f3d0> |
[[file:./.ob-jupyter/67f1354bb1ef5997cbab5399b0606b75243f249a.svg]]
:END:
#+begin_src jupyter-python
@ -200,8 +200,8 @@ Let's test the assumptions of the paper.
#+RESULTS:
:RESULTS:
| <Figure | size | 432x288 | with | 1 | Axes> | <AxesSubplot:> |
[[file:./.ob-jupyter/2aa53e083163511a9e669c2c2c9d15d0776fce5e.svg]]
[[file:./.ob-jupyter/ad580ed8db1bdd84c95f146cbc1050911be4d598.svg]]
[[file:./.ob-jupyter/55e68865c5aeb072d1e8b8a5ff968d9091555d60.svg]]
[[file:./.ob-jupyter/783a5539352463838f2a8999a0737122240781e6.svg]]
:END:
#+begin_src jupyter-python
@ -215,7 +215,7 @@ Let's test the assumptions of the paper.
#+RESULTS:
:RESULTS:
| <Figure | size | 432x288 | with | 1 | Axes> | <AxesSubplot:> |
[[file:./.ob-jupyter/07a4e821f4be1e01b72a39dea2bc9e02573ca8b0.svg]]
[[file:./.ob-jupyter/b8d43755ff7b21e2b252456dfcbdef071732d4bf.svg]]
:END:
@ -234,8 +234,8 @@ Let's test the assumptions of the paper.
#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f1348633190> |
[[file:./.ob-jupyter/63a4a5df348b95cbd45a2b23faa7d2ec6ec5484b.svg]]
| <matplotlib.lines.Line2D | at | 0x7f133616ea30> |
[[file:./.ob-jupyter/b7f29e0ce64ae030cdaae0342020edd5c640a559.svg]]
:END:
@ -256,8 +256,8 @@ Let's test the assumptions of the paper.
#+RESULTS:
:RESULTS:
: <matplotlib.legend.Legend at 0x7f134820a490>
[[file:./.ob-jupyter/7507e88998a03dcf3476c4c51c78c5601eff4be3.svg]]
: <matplotlib.legend.Legend at 0x7f13357e3430>
[[file:./.ob-jupyter/9da382c6216c78288adbd322652b4f969cca0bac.svg]]
:END:
- **too fast decoupling kills it**
@ -290,7 +290,7 @@ Let's test the assumptions of the paper.
: pts[1:N+1, 1] = dep1slice
: /nix/store/pwhaggpgvxhy410r6vlx448v9lp8n08s-python3-3.9.12-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/9047b9ad6cc065564020f725a9b8f90e9086515b.svg]]
[[file:./.ob-jupyter/7cea930d8a6359a571a49609c4100a880e9ee5e0.svg]]
:END:
@ -304,8 +304,8 @@ Let's test the assumptions of the paper.
#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f144d0a90a0> |
[[file:./.ob-jupyter/7435a7152b387b1e4e27c02c9c97e065f0c303bb.svg]]
| <matplotlib.lines.Line2D | at | 0x7f13351e9dc0> |
[[file:./.ob-jupyter/bb31c5cce39d7b273e1734a9994beca2baa5d6e3.svg]]
:END:
** TODO Power and Efficiency
@ -317,19 +317,19 @@ We need the time points where we sample the total energy.
with aux.get_data(model) as data:
_, ax = plt.subplots()
#fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
#fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
#fs.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax)
#fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
#fs.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax)
#fs.plot_with_σ(model.t, model.system_energy(data), ax=ax)
#ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1))
ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1))
#ax.set_xlim(10,40)
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/5a11a6d238cdffc2dba72c3b55b910fbda560c94.svg]]
[[file:./.ob-jupyter/11d924a25bd5729cf677db8f54c7db6412412883.svg]]
#+begin_src jupyter-python
with aux.get_data(model) as data: