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continued work on test class and adopted some tests

This commit is contained in:
Martin Diehl 2015-12-04 22:40:39 +00:00
parent 7eb7a4e8cd
commit c3dc05c53a
1 changed files with 16 additions and 13 deletions
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29
lib/damask/test/test.py
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@ -418,19 +418,23 @@ class Test():
compare tables with np.allclose
threshold can be used to ignore small values (a negative number disables this feature)
'''
#http://stackoverflow.com/questions/8904694/how-to-normalize-a-2-dimensional-numpy-array-in-python-less-verbose
import numpy as np
from collections import Iterable
if not (isinstance(files, Iterable) and not isinstance(files, str)): # check whether list of files is requested
files = [str(files)]
if isinstance(columns, (str,int,float)): # single item --> one copy per file
columns = [str(columns)]*len(files)
tables = [damask.ASCIItable(name = filename,readonly = True) for filename in files]
for table in tables:
table.head_read()
columns += [None]*(len(files)-len(columns)) # extend to same length as files
columns += [columns[0]]*(len(files)-len(columns)) # extend to same length as files
columns = columns[:len(files)] # truncate to same length as files
for i,column in enumerate(columns):
if column is None: columns[i] = tables[i].labels # if no column is given, read all
logging.info('comparing ASCIItables')
for i in xrange(len(columns)):
columns[i] = columns[0] if not columns[i] else \
@ -439,31 +443,30 @@ class Test():
)
logging.info(files[i]+':'+','.join(columns[i]))
if len(files) < 2: return True # single table is always close to itself...
tables = [damask.ASCIItable(name = filename,readonly = True) for filename in files]
if len(files) < 2: return True # single table is always close to itself...
maximum = np.zeros(len(columns[0]),dtype='f')
data = []
for table,labels in zip(tables,columns):
table.head_read()
table.data_readArray(labels)
data.append(table.data)
maximum += np.abs(table.data).max(axis=0)
table.close()
maximum /= len(tables)
maximum = np.where(maximum >0.0, maximum, 1)
for i in xrange(len(data)):
data[i] /= maximum
mask = np.zeros_like(table.data,dtype='bool')
for table in data:
mask |= np.where(np.abs(table)<threshold) # mask out (all) tiny values
allclose = True # start optimistic
for table in data:
mask |= np.where(np.abs(table)<threshold,True,False) # mask out (all) tiny values
allclose = True # start optimistic
for i in xrange(1,len(data)):
allclose &= np.allclose(np.where(mask,0.0,data[i-1]),
np.where(mask,0.0,data[i ]),rtol,atol) # accumulate "pessimism"
np.where(mask,0.0,data[i ]),rtol,atol) # accumulate "pessimism"
return allclose