Project: s41467-024-50605-5 | Function: encode (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Core cation encoding algorithm that transforms perovskite oxide compositions into high-dimensional numerical feature vectors by computing physicochemical properties including valence states, ionic radii, electronegativity, and ionization energies for transfer learning-based OER catalyst prediction

**KEY TERMS:** {
  "V_A": "average A-site cation valence weighted by stoichiometry",
  "V_B": "average B-site cation valence weighted by stoichiometry",
  "R_A": "Shannon radius of A-site cations with XII coordination",
  "R_B": "Shannon radius of B-site cations with VI coordination",
  "X_A": "A-site cation electronegativity from custom database",
  "X_B": "B-site cation electronegativity from custom database",
  "AIE": "A-site cation ionization energy from NIST database",
  "BIE": "B-site cation ionization energy from NIST database"
}

**CONTEXT CODE:**     R_B = {}
    for k,v in oxi_Bs.items():
        try:
            R_B[k] = np.sum([p.Species(k,oxidation_state=abs(k_)).get_shannon_radius(cn="VI",spin="Low Spin",radius_type="ionic")*v_ for k_,v_ in v.items()])
        except:
            print("%s radius error in R_B"%k)
            R_B[k] = np.sum([p.Species(k,oxidation_state=abs(k_)).get_shannon_radius(cn="VI",spin="",radius_type="ionic")*v_ for k_,v_ in v.items()])
    R_B = np.sum([B_ion[1][B_ion[0].index(k)]*v for k,v in R_B.items()])

    # Average cation electronegativity 
    X_A = {}
    for k,v in oxi_As.items():
        X_A[k] = np.sum([A_site_cation_electronegativity[k][k_]*v_ for k_,v_ in v.items()])
    X_A = np.sum([A_ion[1][A_ion[0].index(k)]*v for k,v in X_A.items()])

    X_B = {}
    for k,v in oxi_Bs.items():
        X_B[k] = np.sum([B_site_cation_electronegativity[k][k_]*v_ for k_,v_ in v.items()])
    X_B = np.sum([B_ion[1][B_ion[0].index(k)]*v for k,v in X_B.items()])

    # ionization energies
    # List of ionization energies. First value is the first ionization energy, second is the second ionization energy, etc. Note that this is zero-based indexing! So Element.ionization_energies[0] refer to the 1st ionization energy. Values are from the NIST Atomic Spectra Database. Missing values are None.
    # For a M cation with N+ oxidation state, its ionization energy denotes the Nth ionization energy of M atom in this work.
    AIE = {}
    for k,v in oxi_As.items():
        AIE[k] = np.sum([p.Species(k,oxidation_state=abs(k_)).ionization_energies[int(abs(k_))-1]*v_ for k_,v_ in v.items()])
    AIE = np.sum([A_ion[1][A_ion[0].index(k)]*v for k,v in AIE.items()])

    BIE = {}
    for k,v in oxi_Bs.items():
        BIE[k] = np.sum([p.Species(k,oxidation_state=abs(k_)).ionization_energies[int(abs(k_))-1]*v_ for k_,v_ in v.items()])
    BIE = np.sum([B_ion[1][B_ion[0].index(k)]*v for k,v in BIE.items()])

    # weighted_RO normalized by 3
    R_O = 1.4*guess_O_no/3
    # t(tolerance factor)
    t = (R_A + R_O)/(np.sqrt(2) * (R_B + R_O))
    t_ = (R_A + 1.4)/(np.sqrt(2) * (R_B + 1.4))
    # μ(Octahedral_factor)
    u = R_B / R_O
    u_ = R_B / 1.4

    # entropy
    E_A = np.sum([-i*np.log(i) for i in A_ion[1]])
    E_B = np.sum([-i*np.log(i) for i in B_ion[1]])

    E = np.sum([-(i/(2.0+guess_O_no))*np.log(i/(2.0+guess_O_no)) for i in A_ion[1]]) + np.sum([-(i/(2.0+guess_O_no))*np.log(i/(2.0+guess_O_no)) for i in B_ion[1]]) + (-guess_O_no/(2.0+guess_O_no)*np.log(guess_O_no/(2.0+guess_O_no)))

    return weight, round(V_A,2), round(V_B,2), round(V_A+V_B,2), R_A, R_B, R_A+R_B, R_O, X_A, X_B, X_A+X_B, AIE, BIE, t, t_, u, u_, E_A, E_B, E, {**oxi_As,**oxi_Bs}

def encode(file_name, predict=False, file_path_head="./", vsum_index=7, oxi_state_index=-3, guess_O_range=np.arange(2.0,3.1,0.1),guessor=oxi_state_guessor()):
    # MASKED: Core cation encoding algorithm that transforms perovskite oxide compositions into high-dimensional numerical feature vectors by computing physicochemical properties including valence states, ionic radii, electronegativity, and ionization energies for transfer learning-based OER catalyst prediction
    raise NotImplementedError("Function has been masked for testing purposes")


Complete the function implementation replacing "raise NotImplementedError".