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DAMASK_EICMD
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[skip ci] added "list(map..." required since Python3

This commit is contained in:
Philip Eisenlohr 2020-04-22 21:36:05 -04:00
parent 898cf42aca
commit f0e2437d75
1 changed files with 7 additions and 7 deletions
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14
processing/post/permuteData.py
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@ -60,7 +60,7 @@ for name in filenames:
table.head_read() table.head_read()
# ------------------------------------------ process labels --------------------------------------- # ------------------------------------------ process labels ---------------------------------------
errors = [] errors = []
remarks = [] remarks = []
@ -80,7 +80,7 @@ for name in filenames:
damask.util.croak(errors) damask.util.croak(errors)
table.close(dismiss = True) table.close(dismiss = True)
continue continue
# ------------------------------------------ assemble header --------------------------------------- # ------------------------------------------ assemble header ---------------------------------------
randomSeed = int(os.urandom(4).hex(), 16) if options.randomSeed is None else options.randomSeed # random seed per file randomSeed = int(os.urandom(4).hex(), 16) if options.randomSeed is None else options.randomSeed # random seed per file
@ -97,17 +97,17 @@ for name in filenames:
for col,dim in zip(columns,dims): for col,dim in zip(columns,dims):
if options.unique: if options.unique:
s = set(map(tuple,table.data[:,col:col+dim])) # generate set of (unique) values s = set(map(tuple,table.data[:,col:col+dim])) # generate set of (unique) values
uniques = np.array(map(np.array,s)) # translate set to np.array uniques = np.array(list(map(np.array,s))) # translate set to np.array
shuffler = dict(zip(s,np.random.permutation(len(s)))) # random permutation shuffler = dict(zip(s,np.random.permutation(len(s)))) # random permutation
table.data[:,col:col+dim] = uniques[np.array(map(lambda x: shuffler[tuple(x)], table.data[:,col:col+dim] = uniques[np.array(list(map(lambda x: shuffler[tuple(x)],
table.data[:,col:col+dim]))] # fill table with mapped uniques table.data[:,col:col+dim])))] # fill table with mapped uniques
else: else:
np.random.shuffle(table.data[:,col:col+dim]) # independently shuffle every row np.random.shuffle(table.data[:,col:col+dim]) # independently shuffle every row
# ------------------------------------------ output result ----------------------------------------- # ------------------------------------------ output result -----------------------------------------
table.data_writeArray() table.data_writeArray()
# ------------------------------------------ output finalization ----------------------------------- # ------------------------------------------ output finalization -----------------------------------
table.close() # close ASCII tables table.close() # close ASCII tables