import pandas as pd
import numpy as np
from matrix_vec import *
from bags_vec import *



def read_data(xlsx_file): 
    data = pd.read_csv(xlsx_file)
    mol_mass = np.zeros(len(data))
    num_mols = len(data)
    for i, refcode in enumerate(data['KOJIN ID']):
        print(refcode)
       #filename = '../sep531xyz/'+str(refcode)+'.xyz'
       #molecule = atom_type_list(filename)
       #mass = molecule[0] * 12.011 + molecule[1] * 1.00797 + molecule[2]* 14.0067 + molecule[3] * 15.9994
       #atoms = molecule.sum()
       #mol_mass[i] = mass

read_data('../list/H531_b3lyp.csv')

max_atoms = 50
##Determine target property




#def generate_bags(data, type):
#    X_summedBoH = []
#    for i, refcode in enumerate(data['KOJIN ID']):
#        filename = '../sep531xyz/'+str(refcode)+'.xyz'
#        summed_BoH_feature_names, summedBoH = type(filename)
# 
#        X_summedBoH += [summedBoH]
#    return X_summedBoH
#
#
#def generate_eigen(data, type):
#    X_Vmat_eigs = np.zeros((num_mols, max_atoms))
#    
#    for i, refcode in enumerate(data['KOJIN ID']):
#        filename = '../sep531xyz/'+str(refcode)+'.xyz'
#        this_atom_type_vec = atom_type_list(filename)
#        this_volu_Cmat_eigs, this_volu_Cmat_as_vec = houlombmat3_and_eigenvalues_as_vec(filename, max_atoms )
#    
#    
#        X_Vmat_eigs[i,:] = this_volu_Cmat_eigs
#    return X_Vmat_eigs
#
##X_HBmat_eigs = np.concatenate((X_Hmat_eigs, X_Bmat_eigs),axis=1)
#
## Estimated data
#data, mol_mass, num_mols = read_data('../list/H531_b3lyp.csv')
#target_cep= np.array(data['Nzpe energy'].values)
#X_summedBoH = generate_bags(data,summed_bag_of_heat3)
#X_PlusCeP = np.insert(X_summedBoH, 0, values=target_cep, axis=1)
#
## benchmark data
#data2, mol_mass2, num_mols2 = read_data('../list/H531_b3lyp.csv')
#enthalpy = data2['DFT enthalpy'].values
#y = enthalpy
#
#
#x_train = X_PlusCeP
#y_train = y
#
##print(X_PlusCeP[0])
