mirror of
https://github.com/vale981/master-thesis
synced 2025-03-05 10:01:43 -05:00
try very long coupling
This commit is contained in:
parent
ed6e5beb45
commit
d2b1ccae61
2 changed files with 24 additions and 59 deletions
|
|
@ -82,7 +82,7 @@ def anti_zeno_engine(
|
|||
|
||||
return model, (τ_mod, τ_c, τ_bath, cycles, model.ω_s)
|
||||
|
||||
model, params = anti_zeno_engine(ε=1e-1, ε_couple=1/4)
|
||||
model, params = anti_zeno_engine(ε=1e-1, ε_couple=1e-3)
|
||||
params
|
||||
|
||||
|
||||
|
|
@ -109,7 +109,7 @@ with aux.get_data(model) as data:
|
|||
plt.plot(model.t, ρ_ee)
|
||||
|
||||
from statsmodels.nonparametric.smoothers_lowess import lowess
|
||||
filtered = lowess(ρ_ee, model.t, is_sorted=True, return_sorted=False, frac=0.05/5, it=0)
|
||||
filtered = lowess(ρ_ee, model.t, is_sorted=True, return_sorted=False, frac=0.05, it=0)
|
||||
plt.plot(model.t, filtered)
|
||||
|
||||
with aux.get_data(model) as data:
|
||||
|
|
@ -151,3 +151,5 @@ _, ax = fs.plot_energy_overview(model, markersize=1)
|
|||
model, params = anti_zeno_engine(ω_0=6, ε=1e-1)
|
||||
|
||||
_, ax = fs.plot_energy_overview(model, markersize=1)
|
||||
|
||||
model, params = anti_zeno_engine(ε=1e-1, ε_couple=1/4)
|
||||
|
|
|
|||
|
|
@ -103,12 +103,12 @@ Init ray and silence stocproc.
|
|||
|
||||
* Model Definition
|
||||
#+begin_src jupyter-python
|
||||
model, params = anti_zeno_engine(ε=1e-1, ε_couple=1/4)
|
||||
model, params = anti_zeno_engine(ε=1e-1, ε_couple=1e-3)
|
||||
params
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
| 1.2566370614359172 | 10.053096491487338 | 10.466663926059324 | 8 | (0 10) |
|
||||
| 1.2566370614359172 | 104.30087609918112 | 104.30851486716159 | 83 | (0 10) |
|
||||
|
||||
Let's test the assumptions of the paper.
|
||||
|
||||
|
|
@ -128,8 +128,8 @@ Let's test the assumptions of the paper.
|
|||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
| <matplotlib.lines.Line2D | at | 0x7f960ac80b50> |
|
||||
[[file:./.ob-jupyter/be0ec9303d06001ae4918a09abc91df4dee35a7a.svg]]
|
||||
| <matplotlib.lines.Line2D | at | 0x7f9609c57be0> |
|
||||
[[file:./.ob-jupyter/1e2486aa48b134e6e04e86c4e8a61e2765670df7.svg]]
|
||||
:END:
|
||||
|
||||
#+begin_src jupyter-python :tangle nil
|
||||
|
|
@ -140,8 +140,8 @@ Let's test the assumptions of the paper.
|
|||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
| <matplotlib.lines.Line2D | at | 0x7f960bd896a0> |
|
||||
[[file:./.ob-jupyter/a1a3bf487639295688b6e279e56b9037bc41f08b.svg]]
|
||||
| <matplotlib.lines.Line2D | at | 0x7f960a86b460> |
|
||||
[[file:./.ob-jupyter/ef06a83eb4d653f3f747482f90c756a426f41326.svg]]
|
||||
:END:
|
||||
|
||||
|
||||
|
|
@ -168,8 +168,8 @@ Let's test the assumptions of the paper.
|
|||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
: <matplotlib.legend.Legend at 0x7f960aca70d0>
|
||||
[[file:./.ob-jupyter/a8946060252eb98d118b65dfa24a9ecda1d12bf2.svg]]
|
||||
: <matplotlib.legend.Legend at 0x7f960a48a7f0>
|
||||
[[file:./.ob-jupyter/0ca3fd474f532dcad50671f0cc152d660b37d1bf.svg]]
|
||||
:END:
|
||||
|
||||
- **too fast decoupling kills it**
|
||||
|
|
@ -190,7 +190,7 @@ Let's test the assumptions of the paper.
|
|||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
[[file:./.ob-jupyter/294d83bf1986bdafb1661ff20830beb942df9723.svg]]
|
||||
[[file:./.ob-jupyter/1fa5fc5caa749e69ae16f07ffbd67b6420f54156.svg]]
|
||||
|
||||
|
||||
- no steady state ... but we have to average...
|
||||
|
|
@ -199,14 +199,14 @@ Let's test the assumptions of the paper.
|
|||
plt.plot(model.t, ρ_ee)
|
||||
|
||||
from statsmodels.nonparametric.smoothers_lowess import lowess
|
||||
filtered = lowess(ρ_ee, model.t, is_sorted=True, return_sorted=False, frac=0.05/5, it=0)
|
||||
filtered = lowess(ρ_ee, model.t, is_sorted=True, return_sorted=False, frac=0.05, it=0)
|
||||
plt.plot(model.t, filtered)
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
| <matplotlib.lines.Line2D | at | 0x7f960a4e3ac0> |
|
||||
[[file:./.ob-jupyter/e06edb6435c22251a533a36ac6109265c7efeb19.svg]]
|
||||
| <matplotlib.lines.Line2D | at | 0x7f9609df5070> |
|
||||
[[file:./.ob-jupyter/4b8e6f94660d0abbeb3ff93d88e088a8249afb69.svg]]
|
||||
:END:
|
||||
|
||||
** TODO Power and Efficiency
|
||||
|
|
@ -227,7 +227,7 @@ We need the time points where we sample the total energy.
|
|||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
[[file:./.ob-jupyter/9f6d051adfd9c9f45c3a20a6c03097ee06fa9906.svg]]
|
||||
[[file:./.ob-jupyter/66981899ba65d026196d29f54e45b3663b179d43.svg]]
|
||||
|
||||
#+begin_src jupyter-python
|
||||
with aux.get_data(model) as data:
|
||||
|
|
@ -238,50 +238,7 @@ We need the time points where we sample the total energy.
|
|||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
: Loading: 100% 17/17 [00:00<00:00, 171.09it/s]
|
||||
: [INFO root 863133] Writing cache to: results/ba7df99368964b9933f29b81136f37a9d573fe6ed91440ed48906c0dbdc087dc_4679484025cd9cc8d82e069b9d17885c456e6960c3260612d36c5b4c14ba0a48_op_exp_task_100_50_340c6375eb3a3e5b329053f541b602d84b3bf13b6c8911851fb6befcb823823e.npy
|
||||
: Loading: 97% 33/34 [00:00<00:00, 82.97it/s]
|
||||
: [INFO root 863133] Writing cache to: results/flow_4679484025cd9cc8d82e069b9d17885c456e6960c3260612d36c5b4c14ba0a48_flow_worker_100_50_9e0987996ea000d9bc276dffc5621585bc35dc00c1ecd305b40fa693d9a2a051.npy
|
||||
: Loading: 97% 33/34 [00:00<00:00, 95.97it/s]
|
||||
: [INFO root 863133] Writing cache to: results/interaction_4679484025cd9cc8d82e069b9d17885c456e6960c3260612d36c5b4c14ba0a48_interaction_energy_task_100_50_013ca26cb226de02fec37a78c300c480879ef32f60def77beef092023ba1f46a.npy
|
||||
# [goto error]
|
||||
#+begin_example
|
||||
[0;31m---------------------------------------------------------------------------[0m
|
||||
[0;31mTypeError[0m Traceback (most recent call last)
|
||||
Input [0;32mIn [62][0m, in [0;36m<cell line: 1>[0;34m()[0m
|
||||
[1;32m 2[0m _, ax [38;5;241m=[39m plt[38;5;241m.[39msubplots()
|
||||
[1;32m 3[0m [38;5;66;03m#fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)[39;00m
|
||||
[0;32m----> 4[0m [43mfs[49m[38;5;241;43m.[39;49m[43mplot_diff_vs_sigma[49m[43m([49m[43mmodel[49m[38;5;241;43m.[39;49m[43mt[49m[43m,[49m[43m [49m[43mmodel[49m[38;5;241;43m.[39;49m[43mtotal_energy[49m[43m([49m[43mdata[49m[43m,[49m[43m [49m[43mevery[49m[38;5;241;43m=[39;49m[38;5;241;43m50[39;49m[43m)[49m[43m[[49m[43m:[49m[38;5;241;43m-[39;49m[38;5;241;43m1[39;49m[43m][49m[43m,[49m[43m [49m[43mmodel[49m[38;5;241;43m.[39;49m[43mtotal_energy[49m[43m([49m[43mdata[49m[43m,[49m[43m [49m[43mevery[49m[38;5;241;43m=[39;49m[38;5;241;43m50[39;49m[43m)[49m[43m[[49m[38;5;241;43m-[39;49m[38;5;241;43m1[39;49m[43m][49m[43m,[49m[43m [49m[43max[49m[38;5;241;43m=[39;49m[43max[49m[43m)[49m
|
||||
[1;32m 5[0m ax[38;5;241m.[39mlegend()
|
||||
|
||||
File [0;32m~/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/10_antizeno_engine/figsaver.py:142[0m, in [0;36mwrap_plot.<locals>.wrapped[0;34m(ax, setup_function, *args, **kwargs)[0m
|
||||
[1;32m 139[0m [38;5;28;01mif[39;00m [38;5;129;01mnot[39;00m ax:
|
||||
[1;32m 140[0m fig, ax [38;5;241m=[39m setup_function()
|
||||
[0;32m--> 142[0m ret_val [38;5;241m=[39m [43mf[49m[43m([49m[38;5;241;43m*[39;49m[43margs[49m[43m,[49m[43m [49m[43max[49m[38;5;241;43m=[39;49m[43max[49m[43m,[49m[43m [49m[38;5;241;43m*[39;49m[38;5;241;43m*[39;49m[43mkwargs[49m[43m)[49m
|
||||
[1;32m 143[0m [38;5;28;01mreturn[39;00m (fig, ax, ret_val) [38;5;28;01mif[39;00m ret_val [38;5;28;01melse[39;00m (fig, ax)
|
||||
|
||||
File [0;32m~/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/10_antizeno_engine/figsaver.py:326[0m, in [0;36mplot_diff_vs_sigma[0;34m(x, y, reference, ax, label, transform, ecolor, ealpha, ylabel, bath)[0m
|
||||
[1;32m 323[0m current [38;5;241m=[39m y[i]
|
||||
[1;32m 325[0m not_last [38;5;241m=[39m current[38;5;241m.[39mN [38;5;241m<[39m y[[38;5;241m-[39m[38;5;241m1[39m][38;5;241m.[39mN
|
||||
[0;32m--> 326[0m consistency [38;5;241m=[39m [43mcurrent[49m[38;5;241;43m.[39;49m[43mconsistency[49m[43m([49m[43mreference[49m[43m)[49m
|
||||
[1;32m 327[0m diff [38;5;241m=[39m [38;5;28mabs[39m(current [38;5;241m-[39m reference)
|
||||
[1;32m 329[0m ax[38;5;241m.[39mplot(
|
||||
[1;32m 330[0m x,
|
||||
[1;32m 331[0m diff[38;5;241m.[39mvalue,
|
||||
[0;32m (...)[0m
|
||||
[1;32m 335[0m color[38;5;241m=[39m[38;5;28;01mNone[39;00m [38;5;28;01mif[39;00m not_last [38;5;28;01melse[39;00m [38;5;124m"[39m[38;5;124mred[39m[38;5;124m"[39m,
|
||||
[1;32m 336[0m )
|
||||
|
||||
File [0;32m~/src/hopsflow/hopsflow/util.py:129[0m, in [0;36mEnsembleValue.consistency[0;34m(self, other)[0m
|
||||
[1;32m 126[0m [38;5;28;01mdef[39;00m [38;5;21mconsistency[39m([38;5;28mself[39m, other: EnsembleValue) [38;5;241m-[39m[38;5;241m>[39m [38;5;28mfloat[39m:
|
||||
[1;32m 127[0m diff [38;5;241m=[39m [38;5;28mabs[39m([38;5;28mself[39m[[38;5;241m-[39m[38;5;241m1[39m] [38;5;241m-[39m other[[38;5;241m-[39m[38;5;241m1[39m])
|
||||
[0;32m--> 129[0m [38;5;28;01mreturn[39;00m (diff[38;5;241m.[39mvalue [38;5;241m<[39m diff[38;5;241m.[39mσ)[38;5;241m.[39msum() [38;5;241m/[39m [38;5;28;43mlen[39;49m[43m([49m[43mdiff[49m[38;5;241;43m.[39;49m[43mfor_bath[49m[43m([49m[38;5;241;43m0[39;49m[43m)[49m[38;5;241;43m.[39;49m[43mvalue[49m[43m)[49m [38;5;241m*[39m [38;5;241m100[39m
|
||||
|
||||
[0;31mTypeError[0m: object of type 'numpy.float64' has no len()
|
||||
#+end_example
|
||||
[[file:./.ob-jupyter/6c90699e7bcb14a58349734ba00cb508989be023.svg]]
|
||||
:END:
|
||||
[[file:./.ob-jupyter/d2a4d830b7965689e1085ea5b80633907f7f75f5.svg]]
|
||||
|
||||
#+begin_src jupyter-python
|
||||
mean_norm = np.zeros_like(model.t)
|
||||
|
|
@ -302,6 +259,7 @@ We need the time points where we sample the total energy.
|
|||
[[file:./.ob-jupyter/26492ec360e7a3661c31c6377825d8e3061fc33f.svg]]
|
||||
|
||||
* Too long modulation?, more likely to small spectral sep or to strong coupling
|
||||
- definitely not too long mod
|
||||
#+begin_src jupyter-python :results none
|
||||
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)
|
||||
#+end_src
|
||||
|
|
@ -342,3 +300,8 @@ _, ax = fs.plot_energy_overview(model, markersize=1)
|
|||
|
||||
#+RESULTS:
|
||||
[[file:./.ob-jupyter/8d54f5d626c1ae1332fce377387c60385dfca9ba.svg]]
|
||||
|
||||
* Longer modulation -> more harvest
|
||||
#+begin_src jupyter-python :results none
|
||||
model, params = anti_zeno_engine(ε=1e-1, ε_couple=1/4)
|
||||
#+end_src
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue