untested and unused code
This commit is contained in:
Martin Diehl
parent
a587e70704
commit
5ea2fa97a0
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@ -97,7 +97,7 @@ processing:
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script:
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script:
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- cd $DAMASKROOT/python
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- cd $DAMASKROOT/python
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- pytest --basetemp=${TESTROOT}/python -v --cov --cov-report=term
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- pytest --basetemp=${TESTROOT}/python -v --cov --cov-report=term
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- coverage report --fail-under=85
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- coverage report --fail-under=90
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except:
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except:
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- master
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- master
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- release
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- release
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@ -85,7 +85,6 @@ class Test:
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def execute(self):
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def execute(self):
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"""Run all variants and report first failure."""
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"""Run all variants and report first failure."""
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if not self.options.keep:
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if not self.options.keep:
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if not self.feasible(): return -1
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self.clean()
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self.clean()
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self.prepareAll()
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self.prepareAll()
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@ -114,10 +113,6 @@ class Test:
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return variant+1 # return culprit
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return variant+1 # return culprit
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return 0
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return 0
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def feasible(self):
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"""Check whether test is possible or not (e.g. no license available)."""
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return True
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def clean(self):
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def clean(self):
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"""Delete directory tree containing current results."""
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"""Delete directory tree containing current results."""
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try:
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try:
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@ -172,11 +167,6 @@ class Test:
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return os.path.normpath(os.path.join(self.dirBase,'current/'))
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return os.path.normpath(os.path.join(self.dirBase,'current/'))
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def dirProof(self):
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"""Directory containing human readable proof of correctness for the test."""
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return os.path.normpath(os.path.join(self.dirBase,'proof/'))
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def fileInRoot(self,dir,file):
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def fileInRoot(self,dir,file):
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"""Path to a file in the root directory of DAMASK."""
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"""Path to a file in the root directory of DAMASK."""
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return str(Path(os.environ['DAMASK_ROOT'])/dir/file)
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return str(Path(os.environ['DAMASK_ROOT'])/dir/file)
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@ -192,11 +182,6 @@ class Test:
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return os.path.join(self.dirCurrent(),file)
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return os.path.join(self.dirCurrent(),file)
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def fileInProof(self,file):
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"""Path to a file in the proof directory for the test."""
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return os.path.join(self.dirProof(),file)
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def copy(self, mapA, mapB,
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def copy(self, mapA, mapB,
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A = [], B = []):
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A = [], B = []):
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"""
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"""
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@ -249,17 +234,6 @@ class Test:
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raise FileNotFoundError
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raise FileNotFoundError
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def copy_Proof2Current(self,sourcefiles=[],targetfiles=[]):
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if len(targetfiles) == 0: targetfiles = sourcefiles
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for i,f in enumerate(sourcefiles):
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try:
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shutil.copy2(self.fileInProof(f),self.fileInCurrent(targetfiles[i]))
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except FileNotFoundError:
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logging.critical(f'Proof2Current: Unable to copy file "{f}"')
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raise FileNotFoundError
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def copy_Current2Current(self,sourcefiles=[],targetfiles=[]):
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def copy_Current2Current(self,sourcefiles=[],targetfiles=[]):
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for i,f in enumerate(sourcefiles):
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for i,f in enumerate(sourcefiles):
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@ -281,158 +255,6 @@ class Test:
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return out,error
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return out,error
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def compare_Table(self,headings0,file0,
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headings1,file1,
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normHeadings='',normType=None,
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absoluteTolerance=False,perLine=False,skipLines=[]):
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import numpy as np
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logging.info('\n '.join(['comparing ASCII Tables',file0,file1]))
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if normHeadings == '': normHeadings = headings0
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# check if comparison is possible and determine length of columns
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if len(headings0) == len(headings1) == len(normHeadings):
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dataLength = len(headings0)
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length = [1 for i in range(dataLength)]
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shape = [[] for i in range(dataLength)]
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data = [[] for i in range(dataLength)]
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maxError = [0.0 for i in range(dataLength)]
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absTol = [absoluteTolerance for i in range(dataLength)]
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column = [[1 for i in range(dataLength)] for j in range(2)]
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norm = [[] for i in range(dataLength)]
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normLength = [1 for i in range(dataLength)]
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normShape = [[] for i in range(dataLength)]
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normColumn = [1 for i in range(dataLength)]
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for i in range(dataLength):
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if headings0[i]['shape'] != headings1[i]['shape']:
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raise Exception(f"shape mismatch between {headings0[i]['label']} and {headings1[i]['label']}")
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shape[i] = headings0[i]['shape']
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for j in range(np.shape(shape[i])[0]):
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length[i] *= shape[i][j]
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normShape[i] = normHeadings[i]['shape']
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for j in range(np.shape(normShape[i])[0]):
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normLength[i] *= normShape[i][j]
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else:
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raise Exception(f'trying to compare {len(headings0)} with {len(headings1)} normed by {len(normHeadings)} data sets')
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table0 = damask.ASCIItable(name=file0,readonly=True)
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table0.head_read()
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table1 = damask.ASCIItable(name=file1,readonly=True)
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table1.head_read()
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for i in range(dataLength):
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key0 = ('1_' if length[i]>1 else '') + headings0[i]['label']
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key1 = ('1_' if length[i]>1 else '') + headings1[i]['label']
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normKey = ('1_' if normLength[i]>1 else '') + normHeadings[i]['label']
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if key0 not in table0.labels(raw = True):
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raise Exception(f'column "{key0}" not found in first table...')
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elif key1 not in table1.labels(raw = True):
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raise Exception(f'column "{key1}" not found in second table...')
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elif normKey not in table0.labels(raw = True):
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raise Exception(f'column "{normKey}" not found in first table...')
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else:
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column[0][i] = table0.label_index(key0)
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column[1][i] = table1.label_index(key1)
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normColumn[i] = table0.label_index(normKey)
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line0 = 0
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while table0.data_read(): # read next data line of ASCII table
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if line0 not in skipLines:
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for i in range(dataLength):
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myData = np.array(list(map(float,table0.data[column[0][i]:\
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column[0][i]+length[i]])),'d')
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normData = np.array(list(map(float,table0.data[normColumn[i]:\
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normColumn[i]+normLength[i]])),'d')
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data[i] = np.append(data[i],np.reshape(myData,shape[i]))
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if normType == 'pInf':
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norm[i] = np.append(norm[i],np.max(np.abs(normData)))
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else:
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norm[i] = np.append(norm[i],np.linalg.norm(np.reshape(normData,normShape[i]),normType))
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line0 += 1
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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]])
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if any(norm[i]) == 0.0 or absTol[i]:
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norm[i] = [1.0 for j in range(line0-len(skipLines))]
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absTol[i] = True
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logging.warning(f'''{"At least one" if perLine else "Maximum"} norm of
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"{headings0[i]['label']}" in first table is 0.0, using absolute tolerance''')
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line1 = 0
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while table1.data_read(): # read next data line of ASCII table
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if line1 not in skipLines:
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for i in range(dataLength):
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myData = np.array(list(map(float,table1.data[column[1][i]:\
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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]))/
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norm[i][line1-len(skipLines)])
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line1 +=1
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if (line0 != line1): raise Exception(f'found {line0} lines in first table but {line1} in second table')
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logging.info(' ********')
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for i in range(dataLength):
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logging.info(f''' * maximum {'absolute' if absTol[i] else 'relative'} error {maxError[i]}
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between {headings0[i]['label']} and {headings1[i]['label']}''')
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logging.info(' ********')
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return maxError
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def compare_TablesStatistically(self,
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files = [None,None], # list of file names
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columns = [None], # list of list of column labels (per file)
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meanTol = 1.0e-4,
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stdTol = 1.0e-6,
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preFilter = 1.0e-9):
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"""
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Calculate statistics of tables.
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threshold can be used to ignore small values (a negative number disables this feature)
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"""
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if not (isinstance(files, Iterable) and not isinstance(files, str)): # check whether list of files is requested
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files = [str(files)]
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tables = [damask.Table.load(filename) for filename in files]
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for table in tables:
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table._label_discrete()
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columns += [columns[0]]*(len(files)-len(columns)) # extend to same length as files
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columns = columns[:len(files)] # truncate to same length as files
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for i,column in enumerate(columns):
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if column is None: columns[i] = list(tables[i].data.columns) # if no column is given, read all
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logging.info('comparing ASCIItables statistically')
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for i in range(len(columns)):
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columns[i] = columns[0] if not columns[i] else \
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([columns[i]] if not (isinstance(columns[i], Iterable) and not isinstance(columns[i], str)) else \
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columns[i]
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)
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logging.info(files[i]+':'+','.join(columns[i]))
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if len(files) < 2: return True # single table is always close to itself...
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data = []
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for table,labels in zip(tables,columns):
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table._label_uniform()
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data.append(np.hstack(list(table.get(label) for label in labels)))
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for i in range(1,len(data)):
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delta = data[i]-data[i-1]
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normBy = (np.abs(data[i]) + np.abs(data[i-1]))*0.5
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normedDelta = np.where(normBy>preFilter,delta/normBy,0.0)
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mean = np.amax(np.abs(np.mean(normedDelta,0)))
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std = np.amax(np.std(normedDelta,0))
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logging.info(f'mean: {mean:f}')
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logging.info(f'std: {std:f}')
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return (mean < meanTol) & (std < stdTol)
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def report_Success(self,culprit):
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def report_Success(self,culprit):
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ret = culprit
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ret = culprit
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