Implement logarithmic law?

[gmorello]

Below I report the corresponding functions with the usual approach, however a change is necessary to account for the fact that log is not defined in 0. My proposal is:
np.log(mu24) -> np.max([ np.log(mu24), min_log ])
np.log(1.0 - rsq) -> np.max([ np.log(1.0 - rsq), 2*min_log ])
Where min_log could depend on precision.

integral definitions for classic_log method

def integral_r_classic_log(limb_darkening_coefficients, r):
a1, a2 = limb_darkening_coefficients
rsq = r * r
mu44 = 1.0 - rsq
mu24 = np.sqrt(mu44)
return 0.5 * (a1 - 1.0) * mu44 - (a1/3.0 + a2/9.0) * mu44 * mu24 + (a2/3.0) * mu44 * mu24 * np.log(mu24)

def num_classic_log(r, limb_darkening_coefficients, rprs, z):
a1, a2 = limb_darkening_coefficients
rsq = r * r
#mu = np.sqrt(1.0 - rsq)
return (1.0 - a1 * (1.0 - (1.0 - rsq)**0.5 ) - 0.5 * a2 * ((1.0 - rsq)**0.5) * np.log(1.0 - rsq) ) * r * np.arccos(np.minimum((-rprs ** 2 + z * z + rsq) / (2.0 * z * r), 1.0))

def integral_r_f_classic_log(limb_darkening_coefficients, rprs, z, r1, r2, precision=3):
x1 = (r2 - r1) / 2.0
x2 = (r2 + r1) / 2.0
#return x1 * np.sum(gausstab[precision][0][:, None] * num_classic_log(x1[None, :] * gausstab[precision][1][:, None] + x2[None, :], limb_darkening_coefficients, rprs, z), 0)
return gauss_numerical_integration(num_classic_log, r1, r2, precision, limb_darkening_coefficients, rprs, z)