DAMASK_EICMD/lib/damask/test/test.py

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# -*- coding: UTF-8 no BOM -*-
import os,sys,shutil
import logging,logging.config
import damask
import numpy as np
from collections import Iterable
from optparse import OptionParser
class Test():
"""
General class for testing.
Is sub-classed by the individual tests.
"""
variants = []
def __init__(self, **kwargs):
defaults = {'description': '',
'keep': False,
'accept': False,
'updateRequest': False,
'show': False,
'select': None,
}
for arg in defaults.keys():
setattr(self,arg,kwargs.get(arg) if kwargs.get(arg) else defaults[arg])
fh = logging.FileHandler('test.log') # create file handler which logs even debug messages
fh.setLevel(logging.DEBUG)
fh.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s: \n%(message)s'))
ch = logging.StreamHandler(stream=sys.stdout) # create console handler with a higher log level
ch.setLevel(logging.INFO)
ch.setFormatter(logging.Formatter('%(message)s'))
logger = logging.getLogger()
logger.addHandler(fh)
logger.addHandler(ch)
logger.setLevel(0)
logging.info('\n'.join(['+'*40,
'-'*40,
'| '+self.description,
'-'*40,
]))
self.dirBase = os.path.dirname(os.path.realpath(sys.modules[self.__class__.__module__].__file__))
self.parser = OptionParser(description = '{} (Test class version: {})'.format(self.description,damask.version),
usage = './test.py [options]')
self.parser.add_option("-k", "--keep",
action = "store_true",
dest = "keep",
help = "keep current results, just run postprocessing")
self.parser.add_option("--ok", "--accept",
action = "store_true",
dest = "accept",
help = "calculate results but always consider test as successful")
self.parser.add_option("-l", "--list",
action = "store_true",
dest = "show",
help = "show all test variants without actual calculation")
self.parser.add_option("-s", "--select",
dest = "select",
help = "run test of given name only")
self.parser.set_defaults(keep = self.keep,
accept = self.accept,
update = self.updateRequest,
show = self.show,
select = self.select,
)
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def execute(self):
"""Run all variants and report first failure."""
if not self.options.keep:
if not self.feasible(): return -1
self.clean()
self.prepareAll()
for variant,name in enumerate(self.variants):
if self.options.show:
logging.critical('{}: {}'.format(variant+1,name))
elif self.options.select is not None \
and not (name == self.options.select or str(variant+1) == self.options.select):
pass
else:
try:
if not self.options.keep:
self.prepare(variant)
self.run(variant)
self.postprocess(variant)
self.compare(variant)
if self.options.update:
if self.update(variant) != 0: logging.critical('update for "{}" failed.'.format(name))
elif not (self.options.accept or self.compare(variant)): # no update, do comparison
return variant+1 # return culprit
except Exception as e:
logging.critical('exception during variant execution: "{}"'.format(e.message))
return variant+1 # return culprit
return 0
def feasible(self):
"""Check whether test is possible or not (e.g. no license available)."""
return True
def clean(self):
"""Delete directory tree containing current results."""
try:
shutil.rmtree(self.dirCurrent())
except:
logging.warning('removal of directory "{}" not possible...'.format(self.dirCurrent()))
try:
os.mkdir(self.dirCurrent())
return True
except:
logging.critical('creation of directory "{}" failed.'.format(self.dirCurrent()))
return False
def prepareAll(self):
"""Do all necessary preparations for the whole test"""
return True
def prepare(self,variant):
"""Do all necessary preparations for the run of each test variant"""
return True
def run(self,variant):
"""Execute the requested test variant."""
return True
def postprocess(self,variant):
"""Perform post-processing of generated results for this test variant."""
return True
def compare(self,variant):
"""Compare reference to current results."""
return True
def update(self,variant):
"""Update reference with current results."""
logging.critical('update not supported.')
return 1
def dirReference(self):
"""Directory containing reference results of the test."""
return os.path.normpath(os.path.join(self.dirBase,'reference/'))
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def dirCurrent(self):
"""Directory containing current results of the test."""
return os.path.normpath(os.path.join(self.dirBase,'current/'))
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def dirProof(self):
"""Directory containing human readable proof of correctness for the test."""
return os.path.normpath(os.path.join(self.dirBase,'proof/'))
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def fileInRoot(self,dir,file):
"""Path to a file in the root directory of DAMASK."""
return os.path.join(damask.Environment().rootDir(),dir,file)
def fileInReference(self,file):
"""Path to a file in the refrence directory for the test."""
return os.path.join(self.dirReference(),file)
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def fileInCurrent(self,file):
"""Path to a file in the current results directory for the test."""
return os.path.join(self.dirCurrent(),file)
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def fileInProof(self,file):
"""Path to a file in the proof directory for the test."""
return os.path.join(self.dirProof(),file)
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def copy(self, mapA, mapB,
A = [], B = []):
"""
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Copy list of files from (mapped) source to target.
mapA/B is one of self.fileInX.
"""
if not B or len(B) == 0: B = A
for source,target in zip(list(map(mapA,A)),list(map(mapB,B))):
try:
shutil.copy2(source,target)
except:
logging.critical('error copying {} to {}'.format(source,target))
def copy_Reference2Current(self,sourcefiles=[],targetfiles=[]):
if len(targetfiles) == 0: targetfiles = sourcefiles
for i,file in enumerate(sourcefiles):
try:
shutil.copy2(self.fileInReference(file),self.fileInCurrent(targetfiles[i]))
except:
logging.critical('Reference2Current: Unable to copy file "{}"'.format(file))
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def copy_Base2Current(self,sourceDir,sourcefiles=[],targetfiles=[]):
source=os.path.normpath(os.path.join(self.dirBase,'../../..',sourceDir))
if len(targetfiles) == 0: targetfiles = sourcefiles
for i,file in enumerate(sourcefiles):
try:
shutil.copy2(os.path.join(source,file),self.fileInCurrent(targetfiles[i]))
except:
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logging.error(os.path.join(source,file))
logging.critical('Base2Current: Unable to copy file "{}"'.format(file))
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def copy_Current2Reference(self,sourcefiles=[],targetfiles=[]):
if len(targetfiles) == 0: targetfiles = sourcefiles
for i,file in enumerate(sourcefiles):
try:
shutil.copy2(self.fileInCurrent(file),self.fileInReference(targetfiles[i]))
except:
logging.critical('Current2Reference: Unable to copy file "{}"'.format(file))
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def copy_Proof2Current(self,sourcefiles=[],targetfiles=[]):
if len(targetfiles) == 0: targetfiles = sourcefiles
for i,file in enumerate(sourcefiles):
try:
shutil.copy2(self.fileInProof(file),self.fileInCurrent(targetfiles[i]))
except:
logging.critical('Proof2Current: Unable to copy file "{}"'.format(file))
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def copy_Current2Current(self,sourcefiles=[],targetfiles=[]):
for i,file in enumerate(sourcefiles):
try:
shutil.copy2(self.fileInReference(file),self.fileInCurrent(targetfiles[i]))
except:
logging.critical('Current2Current: Unable to copy file "{}"'.format(file))
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def execute_inCurrentDir(self,cmd,streamIn=None):
logging.info(cmd)
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out,error = damask.util.execute(cmd,streamIn,self.dirCurrent())
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logging.info(error)
logging.debug(out)
return out,error
def compare_Array(self,File1,File2):
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import numpy as np
logging.info('\n '.join(['comparing',File1,File2]))
table1 = damask.ASCIItable(name=File1,readonly=True)
table1.head_read()
len1=len(table1.info)+2
table2 = damask.ASCIItable(name=File2,readonly=True)
table2.head_read()
len2=len(table2.info)+2
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refArray = np.nan_to_num(np.genfromtxt(File1,missing_values='n/a',skip_header = len1,autostrip=True))
curArray = np.nan_to_num(np.genfromtxt(File2,missing_values='n/a',skip_header = len2,autostrip=True))
if len(curArray) == len(refArray):
refArrayNonZero = refArray[refArray.nonzero()]
curArray = curArray[refArray.nonzero()]
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max_err=np.max(abs(refArrayNonZero[curArray.nonzero()]/curArray[curArray.nonzero()]-1.))
max_loc=np.argmax(abs(refArrayNonZero[curArray.nonzero()]/curArray[curArray.nonzero()]-1.))
refArrayNonZero = refArrayNonZero[curArray.nonzero()]
curArray = curArray[curArray.nonzero()]
print(' ********\n * maximum relative error {} between {} and {}\n ********'.format(max_err,
refArrayNonZero[max_loc],
curArray[max_loc]))
return max_err
else:
raise Exception('mismatch in array size to compare')
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def compare_ArrayRefCur(self,ref,cur=''):
if cur =='': cur = ref
refName = self.fileInReference(ref)
curName = self.fileInCurrent(cur)
return self.compare_Array(refName,curName)
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def compare_ArrayCurCur(self,cur0,cur1):
cur0Name = self.fileInCurrent(cur0)
cur1Name = self.fileInCurrent(cur1)
return self.compare_Array(cur0Name,cur1Name)
def compare_Table(self,headings0,file0,headings1,file1,normHeadings='',normType=None,
absoluteTolerance=False,perLine=False,skipLines=[]):
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import numpy as np
logging.info('\n '.join(['comparing ASCII Tables',file0,file1]))
if normHeadings == '': normHeadings = headings0
# check if comparison is possible and determine lenght of columns
if len(headings0) == len(headings1) == len(normHeadings):
dataLength = len(headings0)
length = [1 for i in range(dataLength)]
shape = [[] for i in range(dataLength)]
data = [[] for i in range(dataLength)]
maxError = [0.0 for i in range(dataLength)]
absTol = [absoluteTolerance for i in range(dataLength)]
column = [[1 for i in range(dataLength)] for j in range(2)]
norm = [[] for i in range(dataLength)]
normLength = [1 for i in range(dataLength)]
normShape = [[] for i in range(dataLength)]
normColumn = [1 for i in range(dataLength)]
for i in range(dataLength):
if headings0[i]['shape'] != headings1[i]['shape']:
raise Exception('shape mismatch between {} and {} '.format(headings0[i]['label'],headings1[i]['label']))
shape[i] = headings0[i]['shape']
for j in range(np.shape(shape[i])[0]):
length[i] *= shape[i][j]
normShape[i] = normHeadings[i]['shape']
for j in range(np.shape(normShape[i])[0]):
normLength[i] *= normShape[i][j]
else:
raise Exception('trying to compare {} with {} normed by {} data sets'.format(len(headings0),
len(headings1),
len(normHeadings)))
table0 = damask.ASCIItable(name=file0,readonly=True)
table0.head_read()
table1 = damask.ASCIItable(name=file1,readonly=True)
table1.head_read()
for i in range(dataLength):
key0 = ('1_' if length[i]>1 else '') + headings0[i]['label']
key1 = ('1_' if length[i]>1 else '') + headings1[i]['label']
normKey = ('1_' if normLength[i]>1 else '') + normHeadings[i]['label']
if key0 not in table0.labels(raw = True):
raise Exception('column {} not found in 1. table...\n'.format(key0))
elif key1 not in table1.labels(raw = True):
raise Exception('column {} not found in 2. table...\n'.format(key1))
elif normKey not in table0.labels(raw = True):
raise Exception('column {} not found in 1. table...\n'.format(normKey))
else:
column[0][i] = table0.label_index(key0)
column[1][i] = table1.label_index(key1)
normColumn[i] = table0.label_index(normKey)
line0 = 0
while table0.data_read(): # read next data line of ASCII table
if line0 not in skipLines:
for i in range(dataLength):
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myData = np.array(map(float,table0.data[column[0][i]:\
column[0][i]+length[i]]),'d')
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normData = np.array(map(float,table0.data[normColumn[i]:\
normColumn[i]+normLength[i]]),'d')
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data[i] = np.append(data[i],np.reshape(myData,shape[i]))
if normType == 'pInf':
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norm[i] = np.append(norm[i],np.max(np.abs(normData)))
else:
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norm[i] = np.append(norm[i],np.linalg.norm(np.reshape(normData,normShape[i]),normType))
line0 += 1
for i in range(dataLength):
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if not perLine: norm[i] = [np.max(norm[i]) for j in range(line0-len(skipLines))]
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data[i] = np.reshape(data[i],[line0-len(skipLines),length[i]])
if any(norm[i]) == 0.0 or absTol[i]:
norm[i] = [1.0 for j in range(line0-len(skipLines))]
absTol[i] = True
if perLine:
logging.warning('At least one norm of {} in 1. table is 0.0, using absolute tolerance'.format(headings0[i]['label']))
else:
logging.warning('Maximum norm of {} in 1. table is 0.0, using absolute tolerance'.format(headings0[i]['label']))
line1 = 0
while table1.data_read(): # read next data line of ASCII table
if line1 not in skipLines:
for i in range(dataLength):
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myData = np.array(map(float,table1.data[column[1][i]:\
column[1][i]+length[i]]),'d')
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maxError[i] = max(maxError[i],np.linalg.norm(np.reshape(myData-data[i][line1-len(skipLines),:],shape[i]))/
norm[i][line1-len(skipLines)])
line1 +=1
if (line0 != line1): raise Exception('found {} lines in 1. table but {} in 2. table'.format(line0,line1))
logging.info(' ********')
for i in range(dataLength):
if absTol[i]:
logging.info(' * maximum absolute error {} between {} and {}'.format(maxError[i],
headings0[i]['label'],
headings1[i]['label']))
else:
logging.info(' * maximum relative error {} between {} and {}'.format(maxError[i],
headings0[i]['label'],
headings1[i]['label']))
logging.info(' ********')
return maxError
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def compare_TablesStatistically(self,
files = [None,None], # list of file names
columns = [None], # list of list of column labels (per file)
meanTol = 1.0e-4,
stdTol = 1.0e-6,
preFilter = 1.0e-9):
"""
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Calculate statistics of tables
threshold can be used to ignore small values (a negative number disables this feature)
"""
if not (isinstance(files, Iterable) and not isinstance(files, str)): # check whether list of files is requested
files = [str(files)]
tables = [damask.ASCIItable(name = filename,readonly = True) for filename in files]
for table in tables:
table.head_read()
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(raw = True) # if no column is given, read all
logging.info('comparing ASCIItables statistically')
for i in range(len(columns)):
columns[i] = columns[0] if not columns[i] else \
([columns[i]] if not (isinstance(columns[i], Iterable) and not isinstance(columns[i], str)) else \
columns[i]
)
logging.info(files[i]+':'+','.join(columns[i]))
if len(files) < 2: return True # single table is always close to itself...
data = []
for table,labels in zip(tables,columns):
table.data_readArray(labels)
data.append(table.data)
table.close()
for i in range(1,len(data)):
delta = data[i]-data[i-1]
normBy = (np.abs(data[i]) + np.abs(data[i-1]))*0.5
normedDelta = np.where(normBy>preFilter,delta/normBy,0.0)
mean = np.amax(np.abs(np.mean(normedDelta,0)))
std = np.amax(np.std(normedDelta,0))
logging.info('mean: {:f}'.format(mean))
logging.info('std: {:f}'.format(std))
return (mean<meanTol) & (std < stdTol)
def compare_Tables(self,
files = [None,None], # list of file names
columns = [None], # list of list of column labels (per file)
rtol = 1e-5,
atol = 1e-8,
debug = False):
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"""Compare multiple tables with np.allclose"""
if not (isinstance(files, Iterable) and not isinstance(files, str)): # check whether list of files is requested
files = [str(files)]
if len(files) < 2: return True # single table is always close to itself...
tables = [damask.ASCIItable(name = filename,readonly = True) for filename in files]
for table in tables:
table.head_read()
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(raw = False) # if no column is given, use all
logging.info('comparing ASCIItables')
for i in range(len(columns)):
columns[i] = columns[0] if not columns[i] else \
([columns[i]] if not (isinstance(columns[i], Iterable) and not isinstance(columns[i], str)) else \
columns[i]
)
logging.info(files[i]+': '+','.join(columns[i]))
dimensions = tables[0].label_dimension(columns[0]) # width of each requested column
maximum = np.zeros_like(columns[0],dtype=float) # one magnitude per column entry
data = [] # list of feature table extracted from each file (ASCII table)
for i,(table,labels) in enumerate(zip(tables,columns)):
if np.any(dimensions != table.label_dimension(labels)): # check data object consistency
logging.critical('Table {} differs in data layout.'.format(files[i]))
return False
table.data_readArray(labels) # read data, ...
data.append(table.data) # ... store, ...
table.close() # ... close
for j,label in enumerate(labels): # iterate over object labels
maximum[j] = np.maximum(
maximum[j],
np.amax(np.linalg.norm(table.data[:,table.label_indexrange(label)],
axis=1))
) # find maximum Euclidean norm across rows
maximum = np.where(maximum > 0.0, maximum, 1.0) # avoid div by zero for zero columns
maximum = np.repeat(maximum,dimensions) # spread maximum over columns of each object
for i in range(len(data)):
data[i] /= maximum # normalize each table
logging.info('shape of data {}: {}'.format(i,data[i].shape))
if debug:
violators = np.absolute(data[0]-data[1]) > atol + rtol*np.absolute(data[1])
logging.info('shape of violators: {}'.format(violators.shape))
for j,culprits in enumerate(violators):
goodguys = np.logical_not(culprits)
if culprits.any():
logging.info('{} has {}'.format(j,np.sum(culprits)))
logging.info('deviation: {}'.format(np.absolute(data[0][j]-data[1][j])[culprits]))
logging.info('data : {}'.format(np.absolute(data[1][j])[culprits]))
logging.info('deviation: {}'.format(np.absolute(data[0][j]-data[1][j])[goodguys]))
logging.info('data : {}'.format(np.absolute(data[1][j])[goodguys]))
# for ok,valA,valB in zip(allclose,data[0],data[1]):
# logging.debug('{}:\n{}\n{}'.format(ok,valA,valB))
allclose = True # start optimistic
for i in range(1,len(data)):
allclose &= np.allclose(data[i-1],data[i],rtol,atol) # accumulate "pessimism"
return allclose
def compare_TableRefCur(self,headingsRef,ref,headingsCur='',cur='',normHeadings='',normType=None,\
absoluteTolerance=False,perLine=False,skipLines=[]):
if cur == '': cur = ref
if headingsCur == '': headingsCur = headingsRef
refName = self.fileInReference(ref)
curName = self.fileInCurrent(cur)
return self.compare_Table(headingsRef,refName,headingsCur,curName,normHeadings,normType,
absoluteTolerance,perLine,skipLines)
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def compare_TableCurCur(self,headingsCur0,Cur0,Cur1,headingsCur1='',normHeadings='',normType=None,\
absoluteTolerance=False,perLine=False,skipLines=[]):
if headingsCur1 == '': headingsCur1 = headingsCur0
cur0Name = self.fileInCurrent(Cur0)
cur1Name = self.fileInCurrent(Cur1)
return self.compare_Table(headingsCur0,cur0Name,headingsCur1,cur1Name,normHeadings,normType,
absoluteTolerance,perLine,skipLines)
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def report_Success(self,culprit):
ret = culprit
if culprit == 0:
msg = 'The test passed.' if (self.options.select is not None or len(self.variants) == 1) \
else 'All {} tests passed.'.format(len(self.variants))
elif culprit == -1:
msg = 'Warning: Could not start test...'
ret = 0
else:
msg = ' * Test "{}" failed.'.format(self.variants[culprit-1])
logging.critical('\n'.join(['*'*40,msg,'*'*40]) + '\n')
return ret