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DAMASK_EICMD
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verbose option for compareTables, new option to filter (now before and after normalizing)

This commit is contained in:
Martin Diehl 2015-12-11 17:23:36 +00:00
parent 6de6b8b87f
commit 1f08d0fbf6
1 changed files with 13 additions and 5 deletions
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16
lib/damask/test/test.py
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@ -411,7 +411,8 @@ class Test():
columns = [None], # list of list of column labels (per file) columns = [None], # list of list of column labels (per file)
rtol = 1e-5, rtol = 1e-5,
atol = 1e-8, atol = 1e-8,
threshold = -1.0, preFilter = -1.0,
postFilter = -1.0,
debug = False): debug = False):
''' '''
@ -449,22 +450,29 @@ class Test():
data = [] data = []
for table,labels in zip(tables,columns): for table,labels in zip(tables,columns):
table.data_readArray(labels) table.data_readArray(labels)
data.append(table.data) data.append(np.where(np.abs(table.data)<preFilter,np.zeros_like(table.data),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) maximum = np.where(maximum >0.0, maximum, 1) # do not devide by zero for empty columns
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: for table in data:
mask |= np.where(np.abs(table)<threshold,True,False) # mask out (all) tiny values mask |= np.where(np.abs(table)<postFilter,True,False) # mask out (all) tiny values
allclose = True # start optimistic allclose = True # start optimistic
for i in xrange(1,len(data)): for i in xrange(1,len(data)):
if debug:
t0 = np.where(mask,0.0,data[i-1])
t1 = np.where(mask,0.0,data[i ])
j = np.argmin(np.abs(t1)*rtol+atol-np.abs(t0-t1))
print np.amin(np.abs(t1)*rtol+atol-np.abs(t0-t1))
logging.info('%f %f'%((t0*maximum).flatten()[j],(t1*maximum).flatten()[j]))
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"