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try faster modulation

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This commit is contained in:
Valentin Boettcher 2022-07-11 16:08:41 +02:00
parent 01350fff3d
commit 109903d184
2 changed files with 18 additions and 17 deletions
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4
python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
View file

@ -178,7 +178,7 @@ def anti_zeno_engine(
Δ,
(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=5,
Δ=10,
ε=1,
ω_c=1,
ε_couple=0.2,
@ -273,7 +273,7 @@ for t in ts_end:
plt.axvline(t, color="blue")
with aux.get_data(model) as data:
tot_e = (model.system_energy(data) + model.bath_energy(data).sum_baths()).value #model.total_energy(data).value
tot_e = (model.total_energy_from_power(data)).value #model.total_energy(data).value
diff = (tot_e - tot_e[-1])
plt.plot(model.t, diff / diff[0])
plt.plot(model.t, ut.smoothen(model.t, diff / diff[0] , frac=.9))

31
python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org
View file

@ -198,7 +198,7 @@ Init ray and silence stocproc.
Δ,
(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=5,
Δ=10,
ε=1,
ω_c=1,
ε_couple=0.2,
@ -229,7 +229,7 @@ Let's test the assumptions of the paper.
#+end_src
#+RESULTS:
: 19
: 39
** BCFs and Modulations
#+begin_src jupyter-python :tangle nil
@ -245,8 +245,8 @@ Let's test the assumptions of the paper.
#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7fc87a521700> |
[[file:./.ob-jupyter/5ea0a35cd05cf2cf4c0126978e35a10d11416e28.svg]]
| <matplotlib.lines.Line2D | at | 0x7fc84443f850> |
[[file:./.ob-jupyter/99eef71824c4bba4e10908bdebb9f52cb2ec3cc9.svg]]
:END:
#+begin_src jupyter-python :tangle nil
@ -259,13 +259,13 @@ Let's test the assumptions of the paper.
plt.plot(ωs, np.sinc((ωs - ω_0 - Δ) * τ_s * cycles))
plt.plot(ωs, np.sinc((ωs - ω_0 - Δ) * τ_s * cycles * 2), color="orange", linewidth=.5)
plt.plot(ωs, np.sinc((ωs - ω_0 - Δ) * τ_s * cycles * 10), color="yellow", linewidth=.4)
plt.xlim(20, 30)
#plt.xlim(20, 30)
#+end_src
#+RESULTS:
:RESULTS:
| 20.0 | 30.0 |
[[file:./.ob-jupyter/3a1786b8d1a6a7908c454f193523beda1ce82a2e.svg]]
| <matplotlib.lines.Line2D | at | 0x7fc880d29d30> |
[[file:./.ob-jupyter/aade01d9e60146b2c865140bab73ec1a58a82b0a.svg]]
:END:
#+begin_src jupyter-python
@ -347,8 +347,9 @@ Let's test the assumptions of the paper.
#+RESULTS:
:RESULTS:
: <matplotlib.legend.Legend at 0x7fc87c4a29a0>
[[file:./.ob-jupyter/937986fe8e46f733a643430ae62eb572ff64fca6.svg]]
: [WARNING matplotlib.legend 35102] No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
: <matplotlib.legend.Legend at 0x7fc879935fa0>
[[file:./.ob-jupyter/ddf025e019b5263ef3c09c16c7c88caa71609683.svg]]
:END:
- **too fast decoupling kills it**
@ -425,7 +426,7 @@ We need the time points where we sample the total energy.
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/7e5d23cb9e46290af7792e33bd244b6f476d990e.svg]]
[[file:./.ob-jupyter/7da4d68c0d2592ebdf69ace2067f107f23b6593f.svg]]
@ -451,7 +452,7 @@ One cycle power.
plt.axvline(t, color="blue")
with aux.get_data(model) as data:
tot_e = (model.system_energy(data) + model.bath_energy(data).sum_baths()).value #model.total_energy(data).value
tot_e = (model.total_energy_from_power(data)).value #model.total_energy(data).value
diff = (tot_e - tot_e[-1])
plt.plot(model.t, diff / diff[0])
plt.plot(model.t, ut.smoothen(model.t, diff / diff[0] , frac=.9))
@ -463,8 +464,8 @@ One cycle power.
#+RESULTS:
:RESULTS:
: -0.03996888620839219
[[file:./.ob-jupyter/c1504df12b138f8ca873b863d4bf605aa127341b.svg]]
: -0.03467681556531242
[[file:./.ob-jupyter/5bdb4e604aa258376721b1b1f98f6e1c7023e2e3.svg]]
:END:
@ -507,8 +508,8 @@ It is anti zeno, tested it with turning coupling on and off but no cooldown
#+RESULTS:
:RESULTS:
: 5.741482766795489e-05
[[file:./.ob-jupyter/97d96e8931b5b208c905061d9e9ee7a15b401324.svg]]
: 0.0005854498090217866
[[file:./.ob-jupyter/fad8f6abf218b6736cacbb9c1fe37ca825cf26d8.svg]]
:END: