not needed anymore
This commit is contained in:
Martin Diehl
parent
f69d1029e6
commit
051e3ed0ae
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@ -283,40 +283,6 @@ class Test:
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return out,error
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def compare_Array(self,File1,File2):
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import numpy as np
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logging.info('\n '.join(['comparing',File1,File2]))
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table = damask.Table.load(File1)
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len1 = len(table.comments)+2
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table = damask.Table.load(File2)
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len2 = len(table.comments)+2
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refArray = np.nan_to_num(np.genfromtxt(File1,missing_values='n/a',skip_header = len1,autostrip=True))
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curArray = np.nan_to_num(np.genfromtxt(File2,missing_values='n/a',skip_header = len2,autostrip=True))
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if len(curArray) == len(refArray):
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refArrayNonZero = refArray[refArray.nonzero()]
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curArray = curArray[refArray.nonzero()]
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max_err = np. max(abs(refArrayNonZero[curArray.nonzero()]/curArray[curArray.nonzero()]-1.))
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max_loc = np.argmax(abs(refArrayNonZero[curArray.nonzero()]/curArray[curArray.nonzero()]-1.))
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refArrayNonZero = refArrayNonZero[curArray.nonzero()]
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curArray = curArray[curArray.nonzero()]
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print(f' ********\n * maximum relative error {max_err} between {refArrayNonZero[max_loc]} and {curArray[max_loc]}\n ********')
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return max_err
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else:
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raise Exception(f'mismatch in array sizes ({len(refArray)} and {len(curArray)}) to compare')
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def compare_ArrayRefCur(self,ref,cur=''):
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if cur == '': cur = ref
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refName = self.fileInReference(ref)
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curName = self.fileInCurrent(cur)
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return self.compare_Array(refName,curName)
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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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@ -469,101 +435,6 @@ class Test:
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return (mean < meanTol) & (std < stdTol)
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def compare_Tables(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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rtol = 1e-5,
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atol = 1e-8,
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debug = False):
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"""Compare multiple tables with np.allclose."""
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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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if len(files) < 2: return True # single table is always close to itself...
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tables = [damask.Table.load(filename) for filename in files]
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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].shapes.keys()) # if no column is given, use all
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logging.info('comparing ASCIItables')
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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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dimensions = [np.prod(tables[0].shapes[c]) for c in columns[0]] # width of each requested column
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maximum = np.zeros_like(columns[0],dtype=float) # one magnitude per column entry
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data = [] # list of feature table extracted from each file (ASCII table)
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for i,(table,labels) in enumerate(zip(tables,columns)):
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if np.any(dimensions != [np.prod(table.shapes[c]) for c in labels]): # check data object consistency
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logging.critical(f'Table {files[i]} differs in data layout.')
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return False
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data.append(np.hstack(list(table.get(label) for label in labels)).astype(np.float)) # store
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for j,label in enumerate(labels): # iterate over object labels
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maximum[j] = np.maximum(
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maximum[j],
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np.amax(np.linalg.norm(table.get(label),
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axis=1))
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) # find maximum Euclidean norm across rows
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maximum = np.where(maximum > 0.0, maximum, 1.0) # avoid div by zero for zero columns
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maximum = np.repeat(maximum,dimensions) # spread maximum over columns of each object
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for i in range(len(data)):
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data[i] /= maximum # normalize each table
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logging.info(f'shape of data {i}: {data[i].shape}')
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if debug:
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violators = np.absolute(data[0]-data[1]) > atol + rtol*np.absolute(data[1])
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logging.info(f'shape of violators: {violators.shape}')
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for j,culprits in enumerate(violators):
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goodguys = np.logical_not(culprits)
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if culprits.any():
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logging.info(f'{j} has {np.sum(culprits)}')
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logging.info(f'deviation: {np.absolute(data[0][j]-data[1][j])[culprits]}')
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logging.info(f'data : {np.absolute(data[1][j])[culprits]}')
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logging.info(f'deviation: {np.absolute(data[0][j]-data[1][j])[goodguys]}')
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logging.info(f'data : {np.absolute(data[1][j])[goodguys]}')
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allclose = True # start optimistic
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for i in range(1,len(data)):
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allclose &= np.allclose(data[i-1],data[i],rtol,atol) # accumulate "pessimism"
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return allclose
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def compare_TableRefCur(self,headingsRef,ref,headingsCur='',cur='',
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normHeadings='',normType=None,
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absoluteTolerance=False,perLine=False,skipLines=[]):
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return self.compare_Table(headingsRef,
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self.fileInReference(ref),
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headingsRef if headingsCur == '' else headingsCur,
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self.fileInCurrent(ref if cur == '' else cur),
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normHeadings,normType,
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absoluteTolerance,perLine,skipLines)
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def compare_TableCurCur(self,headingsCur0,Cur0,Cur1,
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headingsCur1='',
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normHeadings='',normType=None,
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absoluteTolerance=False,perLine=False,skipLines=[]):
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return self.compare_Table(headingsCur0,
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self.fileInCurrent(Cur0),
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headingsCur0 if headingsCur1 == '' else headingsCur1,
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self.fileInCurrent(Cur1),
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normHeadings,normType,absoluteTolerance,perLine,skipLines)
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def report_Success(self,culprit):
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ret = culprit
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