import numpy as np
from scipy.optimize import curve_fit
Cp2fit = lambda T, P0, P1, P2, P3, P4, P5: P0*T**0 + P1*T**1 + P2*T**(-2) + P3*T**2 + P4*T**(-.5) + P5*T**(-3)
alpha2fit = lambda T, Q0, Q1, Q2, Q3: Q0*T**0 + Q1*T**1 + Q2*T**(-1) + Q3*T**(-2)
Ksinv2fit = lambda T, R0, R1, R2, R3: R0*T**0 + R1*T**1 + R2*T**2 + R3*T**3
Ksp2fit = lambda T, S0, S1: S0 + S1*(T-298.15)*np.log(T/298.15)
[docs]def fit_FS(tprops: dict, T_from: float, T_to: float) -> dict:
"""
Procedure for the fitting of FS compound database parametes to thermodynamic properties.
:param tprops: Dictionary with the thermodynamic properties.
:type tprops: dict
:param T_from: Initial temperature.
:type T_from: float
:param T_to: Final temperature.
:type T_to: float
:return: Dictionary with the optimal parameters.
:rtype: dict
"""
from_ix = np.where(np.round(tprops['T'],2) == np.round(T_from,2))[0][0]
to_ix = np.where(np.round(tprops['T'],2) == np.round(T_to,2))[0][0]
fs_params_Cp, c = curve_fit(Cp2fit, tprops['T'][from_ix:to_ix+1], tprops['Cp'][from_ix:to_ix+1])
fs_params_alpha, c = curve_fit(alpha2fit, tprops['T'][from_ix:to_ix+1], tprops['a'][from_ix:to_ix+1])
fs_params_Ksinv, c = curve_fit(Ksinv2fit, tprops['T'][from_ix:to_ix+1], [1/ksi for ksi in tprops['Ks'][from_ix:to_ix+1]])
fs_params_Ksp, c = curve_fit(Ksp2fit, tprops['T'][from_ix:to_ix+1], tprops['Ksp'][from_ix:to_ix+1])
return {'Cp': fs_params_Cp, 'a':fs_params_alpha, '1/Ks': fs_params_Ksinv, 'Ksp':fs_params_Ksp}