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DAMASK_EICMD
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migrating to new table class

This commit is contained in:
Martin Diehl 2020-03-18 23:43:56 +01:00
parent cbe5944390
commit f7a6ac4a0e
1 changed files with 22 additions and 23 deletions
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33
python/damask/test.py
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@ -1,11 +1,16 @@
import os,sys,shutil
import logging,logging.config
import damask
import numpy as np
import os
import sys
import shutil
import logging
import logging.config
from collections.abc import Iterable
from optparse import OptionParser
class Test():
import numpy as np
import damask
class Test:
"""
General class for testing.
@ -494,15 +499,13 @@ class Test():
if len(files) < 2: return True # single table is always close to itself...
tables = [damask.ASCIItable(name = filename,readonly = True) for filename in files]
for table in tables:
table.head_read()
tables = [damask.Table.from_ASCII(filename) for filename in files]
columns += [columns[0]]*(len(files)-len(columns)) # extend to same length as files
columns = columns[:len(files)] # truncate to same length as files
for i,column in enumerate(columns):
if column is None: columns[i] = tables[i].labels(raw = False) # if no column is given, use all
if column is None: columns[i] = list(tables[i].shapes.keys()) # if no column is given, use all
logging.info('comparing ASCIItables')
for i in range(len(columns)):
@ -512,22 +515,20 @@ class Test():
)
logging.info(files[i]+': '+','.join(columns[i]))
dimensions = tables[0].label_dimension(columns[0]) # width of each requested column
dimensions = [np.prod(tables[0].shapes[c]) for c in columns[0]] # width of each requested column
maximum = np.zeros_like(columns[0],dtype=float) # one magnitude per column entry
data = [] # list of feature table extracted from each file (ASCII table)
for i,(table,labels) in enumerate(zip(tables,columns)):
if np.any(dimensions != table.label_dimension(labels)): # check data object consistency
if np.any(dimensions != [np.prod(table.shapes[c]) for c in labels]): # check data object consistency
logging.critical('Table {} differs in data layout.'.format(files[i]))
return False
table.data_readArray(labels) # read data, ...
data.append(table.data) # ... store, ...
table.close() # ... close
data.append(np.hstack(list(table.get(label) for label in labels))) # store
for j,label in enumerate(labels): # iterate over object labels
maximum[j] = np.maximum(
maximum[j],
np.amax(np.linalg.norm(table.data[:,table.label_indexrange(label)],
np.amax(np.linalg.norm(table.get(label),
axis=1))
) # find maximum Euclidean norm across rows
@ -549,8 +550,6 @@ class Test():
logging.info('data : {}'.format(np.absolute(data[1][j])[culprits]))
logging.info('deviation: {}'.format(np.absolute(data[0][j]-data[1][j])[goodguys]))
logging.info('data : {}'.format(np.absolute(data[1][j])[goodguys]))
# for ok,valA,valB in zip(allclose,data[0],data[1]):
# logging.debug('{}:\n{}\n{}'.format(ok,valA,valB))
allclose = True # start optimistic
for i in range(1,len(data)):