Apr-30-2019, 12:40 PM
I am creating a matrix array by reading two different lists with for loop. But the second for loop stops after the first iteration.
The print output is only with one parameter *1 instead of the all other that are in the lists in the list f1.
The print output is only with one parameter *1 instead of the all other that are in the lists in the list f1.
import numpy as np
f1 = [['N1M2', 'N1M3', 'N1M4', 'N1M5'], ['M1N2', 'N2M3', 'N2M4', 'N2M5'], ['N1M3', 'N1M4', 'N1M5', 'N2M3', 'N2M4', 'N2M5'], ['M1N3', 'M2N3', 'N3M4', 'N3M5'], ['N1M2', 'N1M4', 'N1M5', 'M2N3', 'N3M4', 'N3M5'], ['M1N2', 'M1N3', 'N2M4', 'N2M5', 'N3M4', 'N3M5'], ['N1M4', 'N1M5', 'N2M4', 'N2M5', 'N3M4', 'N3M5'], ['M1N4', 'M2N4', 'M3N4', 'N4M5'], ['N1M2', 'N1M3', 'N1M5', 'M2N4', 'M3N4', 'N4M5'], ['M1N2', 'M1N4', 'N2M3', 'N2M5', 'M3N4', 'N4M5'], ['N1M3', 'N1M5', 'N2M3', 'N2M5', 'M3N4', 'N4M5'], ['M1N3', 'M1N4', 'M2N3', 'M2N4', 'N3M5', 'N4M5'], ['N1M2', 'N1M5', 'M2N3', 'M2N4', 'N3M5', 'N4M5'], ['M1N2', 'M1N2', 'M1N3', 'N2M5', 'N3M5', 'N4M5'], ['N1M5', 'N2M5', 'N3M5', 'N4M5'], ['M1N5', 'M2N5', 'M3N5', 'M4N5'], ['N1M2', 'N1M3', 'N1M4', 'M2N5', 'M3N5', 'M4N5'], ['M1N2', 'M1N5', 'N2M3', 'N2M4', 'M3N5', 'M4N5'], ['N1M3', 'N1M4', 'N2M3', 'N2M4', 'M3N5', 'M4N5'], ['M1N3', 'M1N5', 'M2N3', 'M2N5', 'N3M4', 'M4N5'], ['N1M2', 'N1M4', 'M2N3', 'M2N5', 'N3M4', 'M4N5'], ['M1N2', 'M1N3', 'M1N5', 'N2M4', 'N3M4', 'M4N5'], ['N1M4', 'N2M4', 'N3M4', 'M4N5'], ['M1N4', 'M1N5', 'M2N4', 'M2N5', 'M3N4', 'M3N5'], ['N1M2', 'N1M3', 'M2N4', 'M2N5', 'M3N4', 'M3N5'], ['M1N2', 'M1N4', 'M1N5', 'N2M3', 'M3N4', 'M3N5'], ['N1M3', 'N2M3', 'M3N4', 'M3N5'], ['M1N3', 'M1N4', 'M1N5', 'M2N3', 'M2N4', 'M2N5'], ['N1M2', 'M2N3', 'M2N4', 'M2N5'], ['M1N2', 'M1N3', 'M1N4', 'M1N5']]
f2 = ['M1N2','M1N3','M1N4','M1N5','M2N3','M2N4','M2N5','M3N4','M3N5','M4N5','N1M2','N1M3','N1M4','N1M5','N2M3','N2M4','N2M5','N3M4','N3M5','N4M5']
def get_matrix(f1, f2):
new = []
for j in f1:
for x in j:
for i in f2:
if i == x:
new.append('%s*1' % i)
#new.append('0')
else:
new.append('%s*0' % i)
#new.append('1')
break
new1 = np.array(new)
shape = ( 30, 20 )
new1.reshape( shape )
return new1.reshape( shape )
print (get_matrix(f1, f2))Output:[['M1N2*0' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*1' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*1' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*1' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*1' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*1' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*1' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*1' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*0' 'M1N4*1' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*1' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*1' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*1' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*1' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*1' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*1' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*0' 'N1M5*1' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*0' 'M1N4*0' 'M1N5*1' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*1' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*1' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*1' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*1' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*1' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*1' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*1' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*0' 'M1N4*1' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*1' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*1' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*1' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*1' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*0' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*1' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']
['M1N2*1' 'M1N3*0' 'M1N4*0' 'M1N5*0' 'M2N3*0' 'M2N4*0' 'M2N5*0' 'M3N4*0'
'M3N5*0' 'M4N5*0' 'N1M2*0' 'N1M3*0' 'N1M4*0' 'N1M5*0' 'N2M3*0' 'N2M4*0'
'N2M5*0' 'N3M4*0' 'N3M5*0' 'N4M5*0']]
