I would like to know if I can speedup this code using numpy...
The code is actually running but I know it's possible to do better with np.where, which I've tried but without success :)
For each syn position I want to compare the string on first positions ('000','001'...) with the variable syndrome (casted to string) and get the int on the second position when match
Like if I have a syndrome '100' I will get the 4 so I know I've to flip the 4th bit in a 8 bit codeword
def recover_data(noisy_data):
syn=[['000','none'],['001',6],['010',5],['011',3],['100',4],['101',0],['110',1],['111',2]]
for ix in range(noisy_data.shape[0]):
unflip=0 #index that will be flipped
for jx in range(len(syn)):
if(syn[jx][0] == ''.join(syndrome.astype('str'))):
unflip = syn[jx][1]
if(str(unflip)!='none'):
noisy_data[ix,unflip]=1-noisy_data[ix,unflip]
looks like a dictionary would help
syn=dict([['000','none'],['001',6],['010',5],['011',3],['100',4],['101',0],['110',1],['111',2]])
syn
{'000': 'none',
'001': 6,
'010': 5,
'011': 3,
'100': 4,
'101': 0,
'110': 1,
'111': 2}
syn.get('011') # .get(key) will return None if the key isn't in the dict
3