forked from IPKM/nmreval
51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
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"""
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============
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Log-Gaussian
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============
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Example for Log-Gaussian distributions
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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from nmreval.distributions import LogGaussian
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x = np.logspace(-5, 5, num=101)
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lg = LogGaussian
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sigma_lg = [1, 3, 5]
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fig, axes = plt.subplots(2, 3, constrained_layout=True)
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lines = []
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for s in sigma_lg:
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axes[0, 0].plot(np.log10(x), lg.correlation(x, 1, s))
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axes[1, 0].plot(np.log10(x), np.log10(lg.specdens(x, 1, s)))
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axes[0, 1].plot(np.log10(x), np.log10(lg.susceptibility(x, 1, s).real))
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axes[1, 1].plot(np.log10(x), np.log10(lg.susceptibility(x, 1, s).imag))
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l, = axes[0, 2].plot(np.log10(x), lg.distribution(x, 1, s),
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label=rf'$\sigma={s}$')
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lines.append(l)
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fig_titles = ('Correlation function', 'Susceptibility (real)', 'Distribution',
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'Spectral density', 'Susceptibility (imag)')
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fig_xlabel = (r'$\log(t/\tau_\mathrm{LG})$', r'$\log(\omega\tau_\mathrm{LG})$',
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r'$\log(\tau/\tau_\mathrm{LG})$', r'$\log(\omega\tau_\mathrm{LG})$',
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r'$\log(\omega\tau_\mathrm{LG})$')
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fig_ylabel = (r'$C(t)$', r"$\log(\chi'(\omega))$", r'$G(\ln\tau)$',
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r'$\log(J(\omega))$', r"$\log(\chi''(\omega))$")
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for title, xlabel, ylabel, ax in zip(fig_titles, fig_xlabel, fig_ylabel, axes.ravel()):
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ax.set_title(title)
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ax.set_xlabel(xlabel)
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ax.set_ylabel(ylabel)
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labels = [l.get_label() for l in lines]
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leg = fig.legend(lines, labels, loc='center left', bbox_to_anchor=(1.05, 0.50),
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bbox_transform=axes[1, 1].transAxes)
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fig.delaxes(axes[1, 2])
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plt.show()
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