try very long coupling

python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py

@@ -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)

python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org

@@ -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> |
+| <matplotlib.lines.Line2D | at | 0x7f9609c57be0> |
-[[file:./.ob-jupyter/be0ec9303d06001ae4918a09abc91df4dee35a7a.svg]]
+[[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> |
+| <matplotlib.lines.Line2D | at | 0x7f960a86b460> |
-[[file:./.ob-jupyter/a1a3bf487639295688b6e279e56b9037bc41f08b.svg]]
+[[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>
+: <matplotlib.legend.Legend at 0x7f960a48a7f0>
-[[file:./.ob-jupyter/a8946060252eb98d118b65dfa24a9ecda1d12bf2.svg]]
+[[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> |
+| <matplotlib.lines.Line2D | at | 0x7f9609df5070> |
-[[file:./.ob-jupyter/e06edb6435c22251a533a36ac6109265c7efeb19.svg]]
+[[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:
+[[file:./.ob-jupyter/d2a4d830b7965689e1085ea5b80633907f7f75f5.svg]]
-: 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
-  ---------------------------------------------------------------------------
-  TypeError                                 Traceback (most recent call last)
-  Input In [62], in <cell line: 1>()
-        2 _, ax = plt.subplots()
-        3 #fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
-  ----> 4 fs.plot_diff_vs_sigma(model.t, model.total_energy(data, every=50)[:-1], model.total_energy(data, every=50)[-1], ax=ax)
-        5 ax.legend()
-
-  File ~/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/10_antizeno_engine/figsaver.py:142, in wrap_plot.<locals>.wrapped(ax, setup_function, *args, **kwargs)
-      139 if not ax:
-      140     fig, ax = setup_function()
-  --> 142 ret_val = f(*args, ax=ax, **kwargs)
-      143 return (fig, ax, ret_val) if ret_val else (fig, ax)
-
-  File ~/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/10_antizeno_engine/figsaver.py:326, in plot_diff_vs_sigma(x, y, reference, ax, label, transform, ecolor, ealpha, ylabel, bath)
-      323 current = y[i]
-      325 not_last = current.N < y[-1].N
-  --> 326 consistency = current.consistency(reference)
-      327 diff = abs(current - reference)
-      329 ax.plot(
-      330     x,
-      331     diff.value,
-     (...)
-      335     color=None if not_last else "red",
-      336 )
-
-  File ~/src/hopsflow/hopsflow/util.py:129, in EnsembleValue.consistency(self, other)
-      126 def consistency(self, other: EnsembleValue) -> float:
-      127     diff = abs(self[-1] - other[-1])
-  --> 129     return (diff.value < diff.σ).sum() / len(diff.for_bath(0).value) * 100
-
-  TypeError: object of type 'numpy.float64' has no len()
-#+end_example
-[[file:./.ob-jupyter/6c90699e7bcb14a58349734ba00cb508989be023.svg]]
-:END:
 
 #+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