dev #284
@ -156,3 +156,20 @@ double energyDistCorrelation(double x, void *user_data) {
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return normalDist(x, e_m, e_b) * exp(-t * r);
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}
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// Generalised Gamma Function
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double genGammaAlphaDist(double x, void *user_data) {
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double *c = (double *)user_data;
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double omega = c[0];
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double tau0 = c[1];
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double alpha = c[2];
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double beta = c[3];
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double b_to_a = beta / alpha;
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double tau_to_tau0 = tau / tau0;
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double norm = exp(-lgamma(b_to_a) + b_to_a * log(b_to_a)) * alpha;
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return norm * exp(-b_to_a * pow(tau_to_tau0, alpha)) * pow(tau_to_tau0, beta);
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}
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@ -36,7 +36,10 @@ class AbstractGG(Distribution, ABC):
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taus, ln_tau = AbstractGG._prepare_integration(tau0)
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g_tau = cls.distribution(taus, tau0, *args)
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ret_val = np.array([simpson(g_tau / (1 - 1j*w_i*taus), ln_tau) for w_i in w]).squeeze()
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ret_val = np.zeros_like(omega, dtype=np.complex128)
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ret_val.real = np.array([simpson(g_tau * taus / (1 + (w_i*taus)**2), ln_tau) for w_i in w]).squeeze()
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ret_val.imag = np.array([simpson(g_tau * w_i * taus / (1 + (w_i*taus)**2), ln_tau) for w_i in w]).squeeze()
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return ret_val
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@ -50,7 +53,7 @@ class AbstractGG(Distribution, ABC):
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ret_val = np.zeros((w.size, _t.size))
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for i, tau_i in enumerate(_t):
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taus, ln_tau = AbstractGG._prepare_integration(tau_i)
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taus, ln_tau = AbstractGG._prepare_integration(tau_i, limits=limits, num_steps=num_steps)
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g_tau = cls.distribution(taus, tau_i, *args)
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ret_val[:, i] = np.array([simpson(g_tau * taus / (1 + (w_i*taus)**2), ln_tau) for w_i in w]).squeeze()
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