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try longtime coupling

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Valentin Boettcher 2022-06-15 13:26:49 +02:00
parent 2ed5ac8481
commit 3ed5efef27
2 changed files with 16 additions and 16 deletions
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4
python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
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@ -99,7 +99,7 @@ def anti_zeno_engine(
return model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s)
model, Δ, params = anti_zeno_engine(ε=1/10, ε_couple=1/5, n=3, detune=4, ω_0 = 10, δ=[2]*2, γ=.05/2, therm_initial_state=True)
model, Δ, params = anti_zeno_engine(ε=1/10, ε_couple=1/100, n=1, detune=4, ω_0 = 10, δ=[2]*2, γ=.05/2, therm_initial_state=True)
#model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-4, n=1, detune=.5, δ=[.1,.1])
params
@ -135,7 +135,7 @@ with aux.get_data(model) as data:
# 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.system_energy(data), ax=ax)
#fs.plot_with_σ(model.t, model.system_energy(data) + model.bath_energy(data).sum_baths() , ax=ax)

28
python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org
View file

@ -119,13 +119,13 @@ Init ray and silence stocproc.
* Model Definition
#+begin_src jupyter-python
model, Δ, params = anti_zeno_engine(ε=1/10, ε_couple=1/5, n=3, detune=4, ω_0 = 10, δ=[2]*2, γ=.05/2, therm_initial_state=True)
model, Δ, params = anti_zeno_engine(ε=1/10, ε_couple=1/100, n=1, detune=4, ω_0 = 10, δ=[2]*2, γ=.05/2, therm_initial_state=True)
#model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-4, n=1, detune=.5, δ=[.1,.1])
params
#+end_src
#+RESULTS:
| 1.2566370614359172 | 23.876104167282428 | 24.302841029661206 | 19 | (0 10) |
| 1.2566370614359172 | 69.11503837897544 | 69.53900991144106 | 55 | (0 10) |
Let's test the assumptions of the paper.
@ -145,8 +145,8 @@ Let's test the assumptions of the paper.
#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f5d29585190> |
[[file:./.ob-jupyter/f2a3a305b1c8e2555319cfe942929d6b6fb71814.svg]]
| <matplotlib.lines.Line2D | at | 0x7f5d26109a00> |
[[file:./.ob-jupyter/7d3f9d656abe461b9d80e8a469377ec02d56b350.svg]]
:END:
#+begin_src jupyter-python :tangle nil
@ -159,8 +159,8 @@ Let's test the assumptions of the paper.
#+RESULTS:
:RESULTS:
: <matplotlib.lines.Line2D at 0x7f5d3943ef10>
[[file:./.ob-jupyter/f10d4889fa3ba5e14cf18b06786c8c3abbb543ef.svg]]
: <matplotlib.lines.Line2D at 0x7f5d270bbeb0>
[[file:./.ob-jupyter/d62f60794f36943c81759c729e59039f3cdbf796.svg]]
:END:
@ -180,8 +180,8 @@ Let's test the assumptions of the paper.
#+RESULTS:
:RESULTS:
: <matplotlib.legend.Legend at 0x7f5d2992bdc0>
[[file:./.ob-jupyter/0040db619c407e843d5f1711167511524917f68c.svg]]
: <matplotlib.legend.Legend at 0x7f5d299afe20>
[[file:./.ob-jupyter/b31f863ff350e2002444fc6e5f5c398035071564.svg]]
:END:
- **too fast decoupling kills it**
@ -203,7 +203,7 @@ Let's test the assumptions of the paper.
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/d25a3e1939e0694038483d889536b52c437cfb69.svg]]
[[file:./.ob-jupyter/36f1d98c27e9a08a89189a71ce9aabd4ca74ab99.svg]]
- no steady state ... but we have to average...
@ -218,8 +218,8 @@ Let's test the assumptions of the paper.
#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f5d26d1a0d0> |
[[file:./.ob-jupyter/aaa084f342224bfa6006d9151ade77649362eb61.svg]]
| <matplotlib.lines.Line2D | at | 0x7f5d264c5790> |
[[file:./.ob-jupyter/894624558751612376703eec2db3e20b0511fad3.svg]]
:END:
** TODO Power and Efficiency
@ -233,7 +233,7 @@ We need the time points where we sample the total energy.
# 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.system_energy(data), ax=ax)
#fs.plot_with_σ(model.t, model.system_energy(data) + model.bath_energy(data).sum_baths() , ax=ax)
@ -241,7 +241,7 @@ We need the time points where we sample the total energy.
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/ccb90328ceddb2217a5835d5afa984188ccfccbb.svg]]
[[file:./.ob-jupyter/28e4057ba538aa0c153b63e83d600698e1015e47.svg]]
#+begin_src jupyter-python
with aux.get_data(model) as data:
@ -270,7 +270,7 @@ We need the time points where we sample the total energy.
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/914086091aa9249ecbf1e295919a1f66b1720b55.svg]]
[[file:./.ob-jupyter/19cba09b910c6f21e12b91ab1795922a83f7698a.svg]]
* Too long modulation?, more likely to small spectral sep or to strong coupling
- definitely not too long mod