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try longtime
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2 changed files with 9 additions and 3 deletions
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@ -232,6 +232,7 @@ with aux.get_data(model) as data:
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plt.plot(model.t, mean_norm / mean_norm.max())
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plt.plot(model.t, mean_norm / mean_norm.max())
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plt.yscale("log")
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plt.yscale("log")
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mean_norm.max()
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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)
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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)
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@ -294,8 +294,8 @@ Let's test the assumptions of the paper.
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#+RESULTS:
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#+RESULTS:
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:RESULTS:
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:RESULTS:
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: <matplotlib.legend.Legend at 0x7f132fe19460>
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: <matplotlib.legend.Legend at 0x7f4260457790>
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[[file:./.ob-jupyter/ba6c791f1832931e5812afc9a68b21595f3bbba8.svg]]
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[[file:./.ob-jupyter/6b5cd3972ff3a7b14357d87084276dcb5da496d3.svg]]
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:END:
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:END:
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- **too fast decoupling kills it**
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- **too fast decoupling kills it**
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@ -377,10 +377,15 @@ We need the time points where we sample the total energy.
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plt.plot(model.t, mean_norm / mean_norm.max())
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plt.plot(model.t, mean_norm / mean_norm.max())
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plt.yscale("log")
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plt.yscale("log")
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mean_norm.max()
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#+end_src
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#+end_src
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#+RESULTS:
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#+RESULTS:
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[[file:./.ob-jupyter/64ac8170bee42810cf16ee89546c935e035b762c.svg]]
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:RESULTS:
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[[file:./.ob-jupyter/bdb31ef8b89503ed0bbada8f235f6c547e142c3b.svg]]
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: 8.916284416712514e-05
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[[file:./.ob-jupyter/22379dd36f375ead2a12699f7dab26107e94bb2f.svg]]
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:END:
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* Too long modulation?, more likely to small spectral sep or to strong coupling
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* Too long modulation?, more likely to small spectral sep or to strong coupling
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- definitely not too long mod
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- definitely not too long mod
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