223031012
/
DAMASK_EICMD
mirror of https://github.com/achalhp/DAMASK_EICMD.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

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
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

27
lib/damask/test/test.py
View File

@ -418,19 +418,23 @@ class Test():
compare tables with np.allclose compare tables with np.allclose
threshold can be used to ignore small values (a negative number disables this feature) 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 import numpy as np
from collections import Iterable from collections import Iterable
if not (isinstance(files, Iterable) and not isinstance(files, str)): # check whether list of files is requested if not (isinstance(files, Iterable) and not isinstance(files, str)): # check whether list of files is requested
files = [str(files)] files = [str(files)]
if isinstance(columns, (str,int,float)): # single item --> one copy per file tables = [damask.ASCIItable(name = filename,readonly = True) for filename in files]
columns = [str(columns)]*len(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 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') logging.info('comparing ASCIItables')
for i in xrange(len(columns)): for i in xrange(len(columns)):
columns[i] = columns[0] if not columns[i] else \ columns[i] = columns[0] if not columns[i] else \
@ -439,31 +443,30 @@ class Test():
) )
logging.info(files[i]+':'+','.join(columns[i])) logging.info(files[i]+':'+','.join(columns[i]))
if len(files) < 2: return True # single table is always close to itself... if len(files) < 2: return True # single table is always close to itself...
tables = [damask.ASCIItable(name = filename,readonly = True) for filename in files]
maximum = np.zeros(len(columns[0]),dtype='f') maximum = np.zeros(len(columns[0]),dtype='f')
data = [] data = []
for table,labels in zip(tables,columns): for table,labels in zip(tables,columns):
table.head_read()
table.data_readArray(labels) table.data_readArray(labels)
data.append(table.data) data.append(table.data)
maximum += np.abs(table.data).max(axis=0) maximum += np.abs(table.data).max(axis=0)
table.close() table.close()
maximum /= len(tables) maximum /= len(tables)
maximum = np.where(maximum >0.0, maximum, 1)
for i in xrange(len(data)): for i in xrange(len(data)):
data[i] /= maximum data[i] /= maximum
mask = np.zeros_like(table.data,dtype='bool') 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)): for i in xrange(1,len(data)):
allclose &= np.allclose(np.where(mask,0.0,data[i-1]), 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 return allclose