updated restart test to use the new table comparison facilities and check the new basic solver (part 1, there is some problem with renaming)
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@ -214,8 +214,8 @@ class Test():
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data = [[] for i in xrange(dataLength)]
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maxError = [0.0 for i in xrange(dataLength)]
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maxNorm = [0.0 for i in xrange(dataLength)]
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column = [[length],[length]]
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column = [[1 for i in xrange(dataLength)] for j in xrange(2)]
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for i in xrange(dataLength):
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if headings0[i]['shape'] != headings1[i]['shape']:
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raise Exception('shape mismatch when comparing ', headings0[i]['label'], ' with ', headings1[i]['label'])
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@ -225,7 +225,6 @@ class Test():
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else:
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raise Exception('trying to compare ', len(headings0), ' with ', len(headings1), ' data sets')
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table0 = damask.ASCIItable(open(file0))
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table0.head_read()
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table1 = damask.ASCIItable(open(file1))
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@ -236,7 +235,6 @@ class Test():
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False:'%s' }[length[i]>1]%headings0[i]['label']
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key1 = {True :'1_%s',
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False:'%s' }[length[i]>1]%headings1[i]['label']
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if key0 not in table0.labels:
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raise Exception('column %s not found in 1. table...\n'%key0)
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elif key1 not in table1.labels:
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@ -246,6 +244,7 @@ class Test():
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column[1][i] = table1.labels.index(key1) # remember columns of requested data in second column
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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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line0 +=1
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for i in xrange(dataLength):
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@ -253,6 +252,7 @@ class Test():
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column[0][i]+length[i]]),'d')
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maxNorm[i] = max(maxNorm[i],numpy.linalg.norm(numpy.reshape(myData,shape[i])))
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data[i]=numpy.append(data[i],myData)
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for i in xrange(dataLength):
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data[i] = numpy.reshape(data[i],[line0,length[i]])
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if maxNorm[i] == 0.0:
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@ -264,12 +264,11 @@ class Test():
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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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column[1][i]+length[i]]),'d')
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maxError[i] = max(maxError[i],numpy.linalg.norm(numpy.reshape(myData-data[i][line1,:],shape[i])))
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line1 +=1
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line1 +=1
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if (line0 != line1): raise Exception('found ', line0, ' lines in 1. table and ', line1, ' in 2. table')
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print ' ********'
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for i in xrange(dataLength):
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maxError[i] = maxError[i]/maxNorm[i]
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