make table compare normalize data by type (scaler, vector, tensor)
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d9077805e4
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@ -5,6 +5,7 @@ import os,sys,shutil
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import logging,logging.config
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import damask
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import numpy as np
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import itertools
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from collections import Iterable
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from optparse import OptionParser
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@ -467,17 +468,15 @@ class Test():
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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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preFilter = -1.0,
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postFilter = -1.0,
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debug = False):
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"""
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compare tables with np.allclose
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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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if len(files) < 2: return True # single table is always close to itself...
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tables = [damask.ASCIItable(name = filename,readonly = True) for filename in files]
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for table in tables:
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table.head_read()
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@ -486,7 +485,7 @@ class Test():
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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] = tables[i].labels(raw = True) # if no column is given, read all
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if column is None: columns[i] = tables[i].labels(raw = True) # if no column is given, use all
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logging.info('comparing ASCIItables')
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for i in xrange(len(columns)):
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@ -496,37 +495,37 @@ class Test():
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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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maximum = np.zeros(len(columns[0]),dtype='f')
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data = []
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for table,labels in zip(tables,columns):
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# peek into the ASCII table to figure out real table size
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# the cryptic table header does not share the same size as real
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# table
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table.data_readArray(columns[0])
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maximum = np.zeros(table.data.shape[1], dtype='f')
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data = [] # list of feature table extracted from each file (ASCII table)
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for table, labels in zip(tables, columns):
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table.data_readArray(labels)
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data.append(np.where(np.abs(table.data)<preFilter,np.zeros_like(table.data),table.data))
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maximum += np.abs(table.data).max(axis=0)
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for label in labels:
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idx = table.label_indexrange(label)
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maximum[idx] = np.maximum(maximum[idx],
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np.amax(np.linalg.norm(table.data[:,idx],axis=1)))
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data.append(table.data)
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table.close()
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maximum /= len(tables)
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maximum = np.where(maximum >0.0, maximum, 1) # avoid div by zero for empty columns
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maximum = np.where(maximum > 0.0, maximum, 1) # avoid div by zero for empty columns
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# normalize each table
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for i in xrange(len(data)):
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data[i] /= maximum
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mask = np.zeros_like(table.data,dtype='bool')
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for table in data:
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mask |= np.where(np.abs(table)<postFilter,True,False) # mask out (all) tiny values
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if debug:
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logging.debug(str(maximum))
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allclose = np.absolute(data[0]-data[1]) <= (atol + rtol*np.absolute(data[1]))
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for ok,valA,valB in zip(allclose,data[0],data[1]):
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logging.debug('{}:\n {}\n{}\n'.format(ok,valA,valB))
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allclose = True # start optimistic
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for i in xrange(1,len(data)):
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if debug:
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t0 = np.where(mask,0.0,data[i-1])
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t1 = np.where(mask,0.0,data[i ])
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j = np.argmin(np.abs(t1)*rtol+atol-np.abs(t0-t1))
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logging.info('{:f}'.format(np.amax(np.abs(t0-t1)/(np.abs(t1)*rtol+atol))))
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logging.info('{:f} {:f}'.format((t0*maximum).flatten()[j],(t1*maximum).flatten()[j]))
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allclose &= np.allclose(np.where(mask,0.0,data[i-1]),
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np.where(mask,0.0,data[i ]),rtol,atol) # accumulate "pessimism"
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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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@ -554,14 +553,16 @@ class Test():
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def report_Success(self,culprit):
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ret = culprit
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if culprit == 0:
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logging.critical(('The test' if len(self.variants) == 1 else 'All {} tests'.format(len(self.variants))) + ' passed')
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logging.critical('\n!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n')
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return 0
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if culprit == -1:
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logging.warning('Warning: Could not start test')
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return 0
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msg = 'The test passed' if len(self.variants) == 1 \
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else 'All {} tests passed.'.format(len(self.variants))
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elif culprit == -1:
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msg = 'Warning: Could not start test...'
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ret = 0
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else:
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logging.critical(' ********\n * Test {} failed...\n ********'.format(culprit))
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logging.critical('\n!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n')
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return culprit
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msg = ' * Test "{}" failed.'.format(self.variants[culprit-1])
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logging.critical('\n'.join(['*'*40,msg,'*'*40]) + '\n')
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return ret
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