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19 lines
1.1 KiB
Text
19 lines
1.1 KiB
Text
def thermal_bcf(t):
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return model.bcf(1)(t) + 2 * (model.thermal_correlations(1)(t).real)
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plt.plot(model.t, np.abs(thermal_bcf(model.t))/np.abs(thermal_bcf(0)), alpha=.5)
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plt.plot(model.t, abs(model.bcf(0)(model.t)) / abs(model.bcf(0)(model.t)).max())
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plt.plot(model.t, model.L[0].operator_norm(model.t))
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plt.plot(model.t, model.H.operator_norm(model.t) - ω_0/2)
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plt.plot(model.t, np.exp(- model.t * np.min(np.array(model.bcf_coefficients()[1][0]).real)))
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#plt.xlim(0,10)
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ωs = np.linspace(0.01, 4*ω_0, 10000)
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mx = (model.spectral_density(1)(ωs) + model.thermal_spectral_density(1)(ωs)).max()
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plt.plot(ωs, model.spectral_density(0)(ωs)/mx + model.thermal_spectral_density(0)(ωs)/mx)
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plt.plot(ωs, model.spectral_density(1)(ωs)/mx + model.thermal_spectral_density(1)(ωs)/mx)
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plt.axvline(model.H.operator_norm(0) * 2 + Δ)
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plt.axvline(model.H.operator_norm(0) * 2 - Δ)
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plt.plot(ωs, np.sinc((ωs - ω_0 - Δ) * τ_s * cycles))
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plt.plot(ωs, np.sinc((ωs - ω_0 - Δ) * τ_s * cycles * 2), color="orange", linewidth=.5)
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plt.plot(ωs, np.sinc((ωs - ω_0 - Δ) * τ_s * cycles * 10), color="yellow", linewidth=.4)
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#plt.xlim(2, 4)
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