import numpy
import pandas as pd

s = input("Input Excel data file (*.xlsx) = ")
xlsx_fl = pd.ExcelFile(s)
data_xlsx = pd.read_excel(xlsx_fl,  header = None)
data = data_xlsx.values

s_out = input("Output text data file (*.txt) = ")
fout = open(s_out, "w")
fout.write("Input data file = " + s + "\n")

33

data = data.T
n_vars = 0
var_names = []
X = []
for v in data:
    if v[1] == 1:
        var_names.append(v[0])
        X.append(list(v[2:]))
        n_vars += 1        

n_data = len(X[0])
print('n_data =', n_data)
print('n_vars =', n_vars)
print('var_names = :', var_names)
fout.write('\nn_data ={}\n'.format(n_data))
fout.write('n_vars = {}\n'.format(n_vars))
fout.write('var_names: {}\n'.format(var_names))

X = numpy.array(X)
sx2 = numpy.cov(X.sum(axis = 0)) 
S = numpy.cov(X)

n_data = 50
n_vars = 8
var_names = : ['Item_1', 'Item_2', 'Item_3', 'Item_4', 'Item_5', 'Item_7', 'Item_8', 'Item_10']

gc_alpha = (1.0 - (numpy.diagonal(S).sum() / sx2)) * n_vars / (n_vars - 1)
print('\nα = {0:.3f}'.format(gc_alpha))
fout.write('\nα = {0:.3f}\n'.format(gc_alpha))

α = 0.940

11

print('\nVar_names...')
for i, vnm in enumerate(var_names):
    print(f'{vnm} ==> X[{i+1}]')
fout.write('\n\nVar_names...\n')
for i, vnm in enumerate(var_names):
    fout.write(f'{vnm} ==> X[{i+1}]\n')
    
psi2 = numpy.zeros(n_vars)
A = numpy.zeros(n_vars)
while True:
    A0 = A.copy()
    C = S.copy()
    for i in range(n_vars):
        C[i][i] -= psi2[i]
    w, vctr = numpy.linalg.eigh(C)
    A = vctr[:, -1] * (w[-1] ** 0.5)
    for i in range(n_vars):
        psi2[i] = S[i][i] - (A[i] ** 2)
    diff = ((A0 - A) ** 2).sum()
    if diff < 1.0e-7:
        break

print()
fout.write('\n')
for i in range(n_vars):
    print('Lambda[{0}]**2 / Var(X[{0}]) = {1:.3f}'.format(
            i+1, A[i]**2 / S[i][i]))
    fout.write('Lambda[{0}]**2 / Var(X[{0}]) = {1:.3f}\n'.format(
            i+1, A[i]**2 / S[i][i]))
print()
fout.write('\n')
for i in range(n_vars):
    print('{0}: Lambda[{1}] = {2:.3f}'.format(
            var_names[i], i+1, A[i]))
    fout.write('{0}: Lambda[{1}] = {2:.3f}\n'.format(
            var_names[i], i+1, A[i]))

Var_names...
Item_1 ==> X[1]
Item_2 ==> X[2]
Item_3 ==> X[3]
Item_4 ==> X[4]
Item_5 ==> X[5]
Item_7 ==> X[6]
Item_8 ==> X[7]
Item_10 ==> X[8]

Lambda[1]**2 / Var(X[1]) = 0.618
Lambda[2]**2 / Var(X[2]) = 0.683
Lambda[3]**2 / Var(X[3]) = 0.546
Lambda[4]**2 / Var(X[4]) = 0.709
Lambda[5]**2 / Var(X[5]) = 0.659
Lambda[6]**2 / Var(X[6]) = 0.653
Lambda[7]**2 / Var(X[7]) = 0.749
Lambda[8]**2 / Var(X[8]) = 0.715

Item_1: Lambda[1] = 12.968
Item_2: Lambda[2] = 12.648
Item_3: Lambda[3] = 12.685
Item_4: Lambda[4] = 16.145
Item_5: Lambda[5] = 13.350
Item_7: Lambda[6] = 12.306
Item_8: Lambda[7] = 15.736
Item_10: Lambda[8] = 15.817

sum_num = 0.0
sum_den = 0.0
for i in range(n_vars):
    for j in range(n_vars):
        if i == j:
            sum_den += S[i][i] ** 2
        else:
            sum_den += S[i][j] ** 2
            sum_num += (S[i][j] - A[i] * A[j]) ** 2
GFI = 1.0 - sum_num / sum_den
print('\nGFI = {0:.3f}'.format(GFI))
fout.write('\nGFI = {0:.3f}\n'.format(GFI))

GFI = 0.999

13

omega = (A.sum() ** 2) / sx2
print('\nρE= {0:.3f}'.format(omega))
fout.write('\nρE= {0:.3f}\n'.format(omega))

fout.close()
print()
print(s_out + " was saved.\n")

ρE= 0.942

temp69.txt was saved.