standard name for numpy is np
This commit is contained in:
Martin Diehl
parent
cd9a802a2b
commit
a29240242b
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@ -246,7 +246,7 @@ class Test():
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def compare_Array(self,File1,File2):
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import numpy
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import numpy as np
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logging.info('comparing\n '+File1+'\n '+File2)
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table1 = damask.ASCIItable(File1,readonly=True)
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table1.head_read()
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@ -255,14 +255,14 @@ class Test():
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table2.head_read()
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len2=len(table2.info)+2
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refArray = numpy.nan_to_num(numpy.genfromtxt(File1,missing_values='n/a',skip_header = len1,autostrip=True))
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curArray = numpy.nan_to_num(numpy.genfromtxt(File2,missing_values='n/a',skip_header = len2,autostrip=True))
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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=numpy.max(abs(refArrayNonZero[curArray.nonzero()]/curArray[curArray.nonzero()]-1.))
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max_loc=numpy.argmax(abs(refArrayNonZero[curArray.nonzero()]/curArray[curArray.nonzero()]-1.))
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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(' ********\n * maximum relative error %e for %e and %e\n ********'
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@ -289,7 +289,7 @@ class Test():
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def compare_Table(self,headings0,file0,headings1,file1,normHeadings='',normType=None,\
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absoluteTolerance=False,perLine=False,skipLines=[]):
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import numpy
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import numpy as np
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logging.info('comparing ASCII Tables\n %s \n %s'%(file0,file1))
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if normHeadings == '': normHeadings = headings0
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@ -311,17 +311,17 @@ class Test():
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if headings0[i]['shape'] != headings1[i]['shape']:
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raise Exception('shape mismatch when comparing %s with %s '%(headings0[i]['label'],headings1[i]['label']))
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shape[i] = headings0[i]['shape']
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for j in xrange(numpy.shape(shape[i])[0]):
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for j in xrange(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 xrange(numpy.shape(normShape[i])[0]):
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for j in xrange(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('trying to compare %i with %i normed by %i data sets'%(len(headings0),len(headings1),len(normHeadings)))
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table0 = damask.ASCIItable(file0)
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table0 = damask.ASCIItable(file0,readonly=True)
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table0.head_read()
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table1 = damask.ASCIItable(file1)
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table1 = damask.ASCIItable(file1,readonly=True)
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table1.head_read()
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for i in xrange(dataLength):
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@ -346,20 +346,20 @@ class Test():
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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 xrange(dataLength):
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myData = numpy.array(map(float,table0.data[column[0][i]:\
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myData = np.array(map(float,table0.data[column[0][i]:\
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column[0][i]+length[i]]),'d')
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normData = numpy.array(map(float,table0.data[normColumn[i]:\
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normData = np.array(map(float,table0.data[normColumn[i]:\
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normColumn[i]+normLength[i]]),'d')
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data[i] = numpy.append(data[i],numpy.reshape(myData,shape[i]))
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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] = numpy.append(norm[i],numpy.max(numpy.abs(normData)))
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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] = numpy.append(norm[i],numpy.linalg.norm(numpy.reshape(normData,normShape[i]),normType))
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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 xrange(dataLength):
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if not perLine: norm[i] = [numpy.max(norm[i]) for j in xrange(line0-len(skipLines))]
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data[i] = numpy.reshape(data[i],[line0-len(skipLines),length[i]])
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if not perLine: norm[i] = [np.max(norm[i]) for j in xrange(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 xrange(line0-len(skipLines))]
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absTol[i] = True
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@ -372,9 +372,9 @@ class Test():
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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 xrange(dataLength):
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myData = numpy.array(map(float,table1.data[column[1][i]:\
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myData = np.array(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],numpy.linalg.norm(numpy.reshape(myData-data[i][line1-len(skipLines),:],shape[i]))/
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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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