untested and unused code

This commit is contained in:
Martin Diehl 2021-03-28 12:46:26 +02:00
parent a587e70704
commit 5ea2fa97a0
2 changed files with 1 additions and 179 deletions

View File

@ -97,7 +97,7 @@ processing:
script: script:
- cd $DAMASKROOT/python - cd $DAMASKROOT/python
- pytest --basetemp=${TESTROOT}/python -v --cov --cov-report=term - pytest --basetemp=${TESTROOT}/python -v --cov --cov-report=term
- coverage report --fail-under=85 - coverage report --fail-under=90
except: except:
- master - master
- release - release

View File

@ -85,7 +85,6 @@ class Test:
def execute(self): def execute(self):
"""Run all variants and report first failure.""" """Run all variants and report first failure."""
if not self.options.keep: if not self.options.keep:
if not self.feasible(): return -1
self.clean() self.clean()
self.prepareAll() self.prepareAll()
@ -114,10 +113,6 @@ class Test:
return variant+1 # return culprit return variant+1 # return culprit
return 0 return 0
def feasible(self):
"""Check whether test is possible or not (e.g. no license available)."""
return True
def clean(self): def clean(self):
"""Delete directory tree containing current results.""" """Delete directory tree containing current results."""
try: try:
@ -172,11 +167,6 @@ class Test:
return os.path.normpath(os.path.join(self.dirBase,'current/')) return os.path.normpath(os.path.join(self.dirBase,'current/'))
def dirProof(self):
"""Directory containing human readable proof of correctness for the test."""
return os.path.normpath(os.path.join(self.dirBase,'proof/'))
def fileInRoot(self,dir,file): def fileInRoot(self,dir,file):
"""Path to a file in the root directory of DAMASK.""" """Path to a file in the root directory of DAMASK."""
return str(Path(os.environ['DAMASK_ROOT'])/dir/file) return str(Path(os.environ['DAMASK_ROOT'])/dir/file)
@ -192,11 +182,6 @@ class Test:
return os.path.join(self.dirCurrent(),file) return os.path.join(self.dirCurrent(),file)
def fileInProof(self,file):
"""Path to a file in the proof directory for the test."""
return os.path.join(self.dirProof(),file)
def copy(self, mapA, mapB, def copy(self, mapA, mapB,
A = [], B = []): A = [], B = []):
""" """
@ -249,17 +234,6 @@ class Test:
raise FileNotFoundError raise FileNotFoundError
def copy_Proof2Current(self,sourcefiles=[],targetfiles=[]):
if len(targetfiles) == 0: targetfiles = sourcefiles
for i,f in enumerate(sourcefiles):
try:
shutil.copy2(self.fileInProof(f),self.fileInCurrent(targetfiles[i]))
except FileNotFoundError:
logging.critical(f'Proof2Current: Unable to copy file "{f}"')
raise FileNotFoundError
def copy_Current2Current(self,sourcefiles=[],targetfiles=[]): def copy_Current2Current(self,sourcefiles=[],targetfiles=[]):
for i,f in enumerate(sourcefiles): for i,f in enumerate(sourcefiles):
@ -281,158 +255,6 @@ class Test:
return out,error return out,error
def compare_Table(self,headings0,file0,
headings1,file1,
normHeadings='',normType=None,
absoluteTolerance=False,perLine=False,skipLines=[]):
import numpy as np
logging.info('\n '.join(['comparing ASCII Tables',file0,file1]))
if normHeadings == '': normHeadings = headings0
# check if comparison is possible and determine length 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(f"shape mismatch between {headings0[i]['label']} and {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(f'trying to compare {len(headings0)} with {len(headings1)} normed by {len(normHeadings)} data sets')
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(f'column "{key0}" not found in first table...')
elif key1 not in table1.labels(raw = True):
raise Exception(f'column "{key1}" not found in second table...')
elif normKey not in table0.labels(raw = True):
raise Exception(f'column "{normKey}" not found in first table...')
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):
myData = np.array(list(map(float,table0.data[column[0][i]:\
column[0][i]+length[i]])),'d')
normData = np.array(list(map(float,table0.data[normColumn[i]:\
normColumn[i]+normLength[i]])),'d')
data[i] = np.append(data[i],np.reshape(myData,shape[i]))
if normType == 'pInf':
norm[i] = np.append(norm[i],np.max(np.abs(normData)))
else:
norm[i] = np.append(norm[i],np.linalg.norm(np.reshape(normData,normShape[i]),normType))
line0 += 1
for i in range(dataLength):
if not perLine: norm[i] = [np.max(norm[i]) for j in range(line0-len(skipLines))]
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
logging.warning(f'''{"At least one" if perLine else "Maximum"} norm of
"{headings0[i]['label']}" in first table is 0.0, using absolute tolerance''')
line1 = 0
while table1.data_read(): # read next data line of ASCII table
if line1 not in skipLines:
for i in range(dataLength):
myData = np.array(list(map(float,table1.data[column[1][i]:\
column[1][i]+length[i]])),'d')
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(f'found {line0} lines in first table but {line1} in second table')
logging.info(' ********')
for i in range(dataLength):
logging.info(f''' * maximum {'absolute' if absTol[i] else 'relative'} error {maxError[i]}
between {headings0[i]['label']} and {headings1[i]['label']}''')
logging.info(' ********')
return maxError
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):
"""
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.Table.load(filename) for filename in files]
for table in tables:
table._label_discrete()
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] = list(tables[i].data.columns) # 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._label_uniform()
data.append(np.hstack(list(table.get(label) for label in labels)))
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(f'mean: {mean:f}')
logging.info(f'std: {std:f}')
return (mean < meanTol) & (std < stdTol)
def report_Success(self,culprit): def report_Success(self,culprit):
ret = culprit ret = culprit