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https://github.com/vale981/master-thesis
synced 2025-03-05 10:01:43 -05:00
figsaver is now plot_utils
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b1b4005d70
commit
56facd27cd
2 changed files with 38 additions and 40 deletions
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@ -270,15 +270,15 @@ fs.tex_value(
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cycles
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ts = np.linspace(0,50,1000)
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fig, ax = fs.plot_complex(ts, model.bcf(0)(ts))
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fs.plot_complex(ts, model.thermal_correlations(0)(ts))
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fig, ax = pu.plot_complex(ts, model.bcf(0)(ts))
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pu.plot_complex(ts, model.thermal_correlations(0)(ts))
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ts = np.linspace(0,10,1000)
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proc = model.thermal_process(1)
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import hops
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z=hops.core.utility.uni_to_gauss(np.random.rand(proc.get_num_y() * 2))
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proc.new_process(z)
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fs.plot_complex(ts, proc(ts) * model.bcf_scales[1])
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pu.plot_complex(ts, proc(ts) * model.bcf_scales[1])
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G_h = model.thermal_spectral_density(1)
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G_c = model.spectral_density(0)
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@ -291,17 +291,17 @@ plt.plot(vs, G_h(vs))
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aux.integrate(model, 10000)
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#_, ax = fs.plot_energy_overview(model, markersize=1, ensemble_args=dict(gc_sleep=0.05))
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#_, ax = pu.plot_energy_overview(model, markersize=1, ensemble_args=dict(gc_sleep=0.05))
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fig, ax = plt.subplots()
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with aux.get_data(model) as data:
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fs.plot_with_σ(model.t, model.total_power(data), ax=ax)
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fs.plot_with_σ(model.t, model.total_energy_from_power(data), ax=ax)
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pu.plot_with_σ(model.t, model.total_power(data), ax=ax)
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pu.plot_with_σ(model.t, model.total_energy_from_power(data), ax=ax)
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#ax.legend()
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with aux.get_data(model) as data:
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fs.plot_with_σ(
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pu.plot_with_σ(
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model.t,
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EnsembleValue(
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(data.rho_t_accum.mean[:, 0, 0], data.rho_t_accum.ensemble_std[:, 0, 0])
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@ -317,16 +317,16 @@ plt.plot(model.t, ut.smoothen(model.t, ρ_ee, frac=.06, it=0))
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with aux.get_data(model) as data:
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_, ax = plt.subplots()
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fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
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fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
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fs.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax)
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fs.plot_with_σ(model.t, model.total_energy_from_power(data), ax=ax)
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pu.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
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pu.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
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pu.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax)
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pu.plot_with_σ(model.t, model.total_energy_from_power(data), ax=ax)
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fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
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#fs.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax)
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pu.plot_with_σ(model.t, model.total_energy(data), ax=ax)
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#pu.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax)
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#fs.plot_with_σ(model.t, model.system_energy(data), ax=ax)
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#fs.plot_with_σ(model.t, model.system_energy(data) + model.bath_energy(data).sum_baths(), ax=ax)
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#pu.plot_with_σ(model.t, model.system_energy(data), ax=ax)
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#pu.plot_with_σ(model.t, model.system_energy(data) + model.bath_energy(data).sum_baths(), ax=ax)
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#ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1))
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#ax.plot(model.t, np.gradient(model.total_energy(data).value))
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@ -343,7 +343,6 @@ with aux.get_data(model) as data:
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tot_power = (model.total_power(data).sum_baths()) #model.total_energy(data).value
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fig, ax = plt.subplots()
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fig.set_size_inches(fs.get_figsize("poster", 0.49))
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for t in ts_begin:
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plt.axvline(t, linestyle="dashed", color="orangered", linewidth=1)
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@ -351,7 +350,7 @@ for t in ts_begin:
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for t in ts_end:
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plt.axvline(t, linestyle="dotted", color="lightblue", linewidth=1)
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fs.plot_with_σ(
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pu.plot_with_σ(
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model.t[ind_begin[0]:], tot_power.slice(ind_begin[0], None, 1), ax=ax, linewidth=0.5
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)
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ax.plot(
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@ -396,15 +395,15 @@ 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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_, ax = fs.plot_energy_overview(model, markersize=1)
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_, ax = pu.plot_energy_overview(model, markersize=1)
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model, params = anti_zeno_engine(ω_0=6)
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_, ax = fs.plot_energy_overview(model, markersize=1)
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_, ax = pu.plot_energy_overview(model, markersize=1)
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model, params = anti_zeno_engine(ω_0=6, ε=1e-1)
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_, ax = fs.plot_energy_overview(model, markersize=1)
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_, ax = pu.plot_energy_overview(model, markersize=1)
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model, params = anti_zeno_engine(ε=1e-1, ε_couple=1/4)
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@ -372,8 +372,8 @@ Let's test the assumptions of the paper.
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#+begin_src jupyter-python
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ts = np.linspace(0,50,1000)
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fig, ax = fs.plot_complex(ts, model.bcf(0)(ts))
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fs.plot_complex(ts, model.thermal_correlations(0)(ts))
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fig, ax = pu.plot_complex(ts, model.bcf(0)(ts))
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pu.plot_complex(ts, model.thermal_correlations(0)(ts))
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#+end_src
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#+RESULTS:
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@ -389,7 +389,7 @@ Let's test the assumptions of the paper.
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import hops
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z=hops.core.utility.uni_to_gauss(np.random.rand(proc.get_num_y() * 2))
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proc.new_process(z)
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fs.plot_complex(ts, proc(ts) * model.bcf_scales[1])
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pu.plot_complex(ts, proc(ts) * model.bcf_scales[1])
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#+end_src
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#+RESULTS:
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@ -512,12 +512,12 @@ Let's test the assumptions of the paper.
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#+Begin_src jupyter-python
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#_, ax = fs.plot_energy_overview(model, markersize=1, ensemble_args=dict(gc_sleep=0.05))
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#_, ax = pu.plot_energy_overview(model, markersize=1, ensemble_args=dict(gc_sleep=0.05))
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fig, ax = plt.subplots()
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with aux.get_data(model) as data:
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fs.plot_with_σ(model.t, model.total_power(data), ax=ax)
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fs.plot_with_σ(model.t, model.total_energy_from_power(data), ax=ax)
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pu.plot_with_σ(model.t, model.total_power(data), ax=ax)
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pu.plot_with_σ(model.t, model.total_energy_from_power(data), ax=ax)
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#ax.legend()
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#+end_src
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@ -537,7 +537,7 @@ Let's test the assumptions of the paper.
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#+begin_src jupyter-python
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with aux.get_data(model) as data:
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fs.plot_with_σ(
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pu.plot_with_σ(
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model.t,
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EnsembleValue(
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(data.rho_t_accum.mean[:, 0, 0], data.rho_t_accum.ensemble_std[:, 0, 0])
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@ -573,16 +573,16 @@ We need the time points where we sample the total energy.
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#+begin_src jupyter-python
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with aux.get_data(model) as data:
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_, ax = plt.subplots()
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fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
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fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
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fs.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax)
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fs.plot_with_σ(model.t, model.total_energy_from_power(data), ax=ax)
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pu.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
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pu.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
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pu.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax)
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pu.plot_with_σ(model.t, model.total_energy_from_power(data), ax=ax)
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fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
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#fs.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax)
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pu.plot_with_σ(model.t, model.total_energy(data), ax=ax)
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#pu.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax)
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#fs.plot_with_σ(model.t, model.system_energy(data), ax=ax)
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#fs.plot_with_σ(model.t, model.system_energy(data) + model.bath_energy(data).sum_baths(), ax=ax)
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#pu.plot_with_σ(model.t, model.system_energy(data), ax=ax)
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#pu.plot_with_σ(model.t, model.system_energy(data) + model.bath_energy(data).sum_baths(), ax=ax)
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#ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1))
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#ax.plot(model.t, np.gradient(model.total_energy(data).value))
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#+end_src
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@ -606,7 +606,6 @@ We need the time points where we sample the total energy.
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#+begin_src jupyter-python
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fig, ax = plt.subplots()
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fig.set_size_inches(fs.get_figsize("poster", 0.49))
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for t in ts_begin:
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plt.axvline(t, linestyle="dashed", color="orangered", linewidth=1)
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@ -614,7 +613,7 @@ We need the time points where we sample the total energy.
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for t in ts_end:
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plt.axvline(t, linestyle="dotted", color="lightblue", linewidth=1)
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fs.plot_with_σ(
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pu.plot_with_σ(
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model.t[ind_begin[0]:], tot_power.slice(ind_begin[0], None, 1), ax=ax, linewidth=0.5
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)
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ax.plot(
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@ -690,7 +689,7 @@ We need the time points where we sample the total energy.
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#+end_src
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#+begin_src jupyter-python
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_, ax = fs.plot_energy_overview(model, markersize=1)
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_, ax = pu.plot_energy_overview(model, markersize=1)
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#+end_src
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#+RESULTS:
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@ -705,7 +704,7 @@ model, params = anti_zeno_engine(ω_0=6)
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#+end_src
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#+begin_src jupyter-python
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_, ax = fs.plot_energy_overview(model, markersize=1)
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_, ax = pu.plot_energy_overview(model, markersize=1)
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#+end_src
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#+RESULTS:
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@ -720,7 +719,7 @@ _, ax = fs.plot_energy_overview(model, markersize=1)
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#+begin_src jupyter-python
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_, ax = fs.plot_energy_overview(model, markersize=1)
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_, ax = pu.plot_energy_overview(model, markersize=1)
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#+end_src
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#+RESULTS:
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