orientate on the paper

python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py

@@ -32,7 +32,8 @@ def anti_zeno_engine(
     detune=0,
     δ=[2] * 2,
     T_h=5,
-    therm_initial_state=False
+    therm_initial_state=False,
+    s=1
 ):
     # τ_bath = 1 / ω_c
     τ_mod = 2 * np.pi / Δ
@@ -97,15 +98,15 @@ def anti_zeno_engine(
         therm_methods=["tanhsinh", "fft"],
     )
 
-    return model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s)
+    return model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_mod)
 
-model, Δ, params = anti_zeno_engine(ε=1/10, ε_couple=1/2, n=10, detune=4, ω_0 = 10, δ=[2]*2, γ=.05/2, therm_initial_state=True)
+model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s) = anti_zeno_engine(Δ = 15, ε=1/6, ω_c=1, ε_couple=.7, n=5, detune=-1, ω_0 = 20, δ=[2]*2, γ=.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
 
+cycles
 
-
+aux.integrate(model, 1000)
-aux.integrate(model, 5000)
 
 _, ax = fs.plot_energy_overview(model, markersize=1)
 # with aux.get_data(model) as data:

python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org

@@ -48,7 +48,8 @@ Init ray and silence stocproc.
       detune=0,
       δ=[2] * 2,
       T_h=5,
-      therm_initial_state=False
+      therm_initial_state=False,
+      s=1
   ):
       # τ_bath = 1 / ω_c
       τ_mod = 2 * np.pi / Δ
@@ -113,13 +114,13 @@ Init ray and silence stocproc.
           therm_methods=["tanhsinh", "fft"],
       )
 
-      return model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s)
+      return model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_mod)
 #+end_src
 
 
 * Model Definition
 #+begin_src jupyter-python
-  model, Δ, params = anti_zeno_engine(ε=1/10, ε_couple=1/2, n=10, detune=4, ω_0 = 10, δ=[2]*2, γ=.05/2, therm_initial_state=True)
+  model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s) = anti_zeno_engine(Δ = 15, ε=1/6, ω_c=1, ε_couple=.7, n=5, detune=-1, ω_0 = 20, δ=[2]*2, γ=.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
@@ -129,10 +130,13 @@ Init ray and silence stocproc.
 
 Let's test the assumptions of the paper.
 
-#+begin_src jupyter-python :results none
+#+begin_src jupyter-python
-
+cycles
 #+end_src
 
+#+RESULTS:
+: 13
+
 ** BCFs and Modulations
 #+begin_src jupyter-python :tangle nil
   def thermal_bcf(t):
@@ -145,28 +149,31 @@ Let's test the assumptions of the paper.
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7f5d260e80a0> |
+| <matplotlib.lines.Line2D | at | 0x7f5cd835aa30> |
-[[file:./.ob-jupyter/726ed9600beddd1075ec34af565217476ba75ded.svg]]
+[[file:./.ob-jupyter/58c1849bdc141b930cd11e4a0772548a750999af.svg]]
 :END:
 
 #+begin_src jupyter-python :tangle nil
-  ωs = np.linspace(0, 20, 1000)
+  ωs = np.linspace(0, 40, 10000)
-  plt.plot(ωs, model.spectral_density(0)(ωs))
+  mx = model.spectral_density(0)(ωs).max()
-  plt.plot(ωs, model.spectral_density(1)(ωs))
+  plt.plot(ωs, model.spectral_density(0)(ωs)/mx)
+  plt.plot(ωs, model.spectral_density(1)(ωs)/mx)
   plt.axvline(model.H.operator_norm(0) * 2 + Δ)
   plt.axvline(model.H.operator_norm(0) * 2 - Δ)
+  plt.plot(ωs, np.sinc((ωs - ω_0 + Δ) * τ_s * cycles))
+  plt.plot(ωs, np.sinc((ωs - ω_0 - Δ) * τ_s * cycles))
 #+end_src
 
 #+RESULTS:
 :RESULTS:
-: <matplotlib.lines.Line2D at 0x7f5d260d9940>
+| <matplotlib.lines.Line2D | at | 0x7f5cd83dcca0> |
-[[file:./.ob-jupyter/fd95c61584398b96afe53c29cb8817e37a58f8bf.svg]]
+[[file:./.ob-jupyter/113b340dc8e93c0b6d1687dc2a92b9f3e746509b.svg]]
 :END:
 
 
 ** TODO Integration
 #+begin_src jupyter-python
- aux.integrate(model, 5000)
+ aux.integrate(model, 1000)
 #+end_src
 
 
@@ -184,7 +191,7 @@ Let's test the assumptions of the paper.
 #+begin_example
   ---------------------------------------------------------------------------
   RuntimeError                              Traceback (most recent call last)
-  Input In [69], in <cell line: 1>()
+  Input In [176], in <cell line: 1>()
   ----> 1 _, ax = fs.plot_energy_overview(model, markersize=1)
         2 # with aux.get_data(model) as data:
         3 #     fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
@@ -207,9 +214,9 @@ Let's test the assumptions of the paper.
       145 else:
   --> 146     raise RuntimeError(f"No data found for model with hash '{hexhash}'.")
 
-  RuntimeError: No data found for model with hash 'bd9e2510c88d49b8cb8938ff8510c4dd61a92ce8746d623e126886d9972ba9a0'.
+  RuntimeError: No data found for model with hash 'da33647fd18c4384c29267fdef75d3a92854ae89e407f1c0e6d97499bf5462f9'.
 #+end_example
-[[file:./.ob-jupyter/5977a58c06c307c5917aa9e110ab85b47f060ba4.svg]]
+[[file:./.ob-jupyter/107b34b88483dd236391c2dc9151730a6d4fd8d5.svg]]
 :END:
 
 - **too fast decoupling kills it**