582 lines
23 KiB
Python
582 lines
23 KiB
Python
import os
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import sys
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import shutil
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import logging
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import logging.config
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from collections.abc import Iterable
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from optparse import OptionParser
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from pathlib import Path
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import numpy as np
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import damask
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class Test:
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"""
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General class for testing.
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Is sub-classed by the individual tests.
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"""
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variants = []
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def __init__(self, **kwargs):
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"""New test."""
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defaults = {'description': '',
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'keep': False,
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'accept': False,
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'updateRequest': False,
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'show': False,
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'select': None,
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}
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for arg in defaults.keys():
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setattr(self,arg,kwargs.get(arg) if kwargs.get(arg) else defaults[arg])
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fh = logging.FileHandler('test.log') # create file handler which logs even debug messages
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fh.setLevel(logging.DEBUG)
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fh.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s: \n%(message)s'))
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ch = logging.StreamHandler(stream=sys.stdout) # create console handler with a higher log level
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ch.setLevel(logging.INFO)
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ch.setFormatter(logging.Formatter('%(message)s'))
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logger = logging.getLogger()
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logger.addHandler(fh)
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logger.addHandler(ch)
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logger.setLevel(0)
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logging.info('\n'.join(['+'*40,
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'-'*40,
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'| '+self.description,
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'-'*40,
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]))
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self.dirBase = os.path.dirname(os.path.realpath(sys.modules[self.__class__.__module__].__file__))
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self.parser = OptionParser(option_class=damask.extendableOption,
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description = f'{self.description} (Test class version: {damask.version})',
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usage = './test.py [options]')
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self.parser.add_option("-k", "--keep",
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action = "store_true",
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dest = "keep",
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help = "keep current results, just run postprocessing")
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self.parser.add_option("--ok", "--accept",
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action = "store_true",
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dest = "accept",
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help = "calculate results but always consider test as successful")
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self.parser.add_option("-l", "--list",
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action = "store_true",
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dest = "show",
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help = "show all test variants without actual calculation")
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self.parser.add_option("-s", "--select",
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dest = "select",
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action = 'extend', metavar = '<string LIST>',
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help = "run test(s) of given name only")
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self.parser.set_defaults(keep = self.keep,
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accept = self.accept,
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update = self.updateRequest,
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show = self.show,
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select = self.select,
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)
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def variantName(self,variant):
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"""Generate name of (numerical) variant."""
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return str(variant)
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def execute(self):
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"""Run all variants and report first failure."""
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if not self.options.keep:
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if not self.feasible(): return -1
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self.clean()
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self.prepareAll()
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for variant,object in enumerate(self.variants):
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name = self.variantName(variant)
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if self.options.show:
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logging.critical(f'{variant+1}: {name}')
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elif self.options.select is not None \
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and not (name in self.options.select or str(variant+1) in self.options.select):
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pass
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else:
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try:
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if not self.options.keep:
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self.prepare(variant)
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self.run(variant)
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self.postprocess(variant)
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if self.options.update:
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if self.update(variant) != 0: logging.critical(f'update for "{name}" failed.')
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elif not (self.options.accept or self.compare(variant)): # no update, do comparison
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return variant+1 # return culprit
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except Exception as e:
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logging.critical(f'exception during variant execution: "{e}"')
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return variant+1 # return culprit
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return 0
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def feasible(self):
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"""Check whether test is possible or not (e.g. no license available)."""
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return True
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def clean(self):
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"""Delete directory tree containing current results."""
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try:
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shutil.rmtree(self.dirCurrent())
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except FileNotFoundError:
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logging.warning(f'removal of directory "{self.dirCurrent()}" not possible...')
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try:
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os.mkdir(self.dirCurrent())
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return True
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except FileExistsError:
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logging.critical(f'creation of directory "{self.dirCurrent()}" failed.')
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return False
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def prepareAll(self):
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"""Do all necessary preparations for the whole test."""
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return True
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def prepare(self,variant):
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"""Do all necessary preparations for the run of each test variant."""
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return True
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def run(self,variant):
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"""Execute the requested test variant."""
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return True
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def postprocess(self,variant):
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"""Perform post-processing of generated results for this test variant."""
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return True
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def compare(self,variant):
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"""Compare reference to current results."""
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return True
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def update(self,variant):
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"""Update reference with current results."""
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logging.critical('update not supported.')
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return 1
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def dirReference(self):
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"""Directory containing reference results of the test."""
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return os.path.normpath(os.path.join(self.dirBase,'reference/'))
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def dirCurrent(self):
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"""Directory containing current results of the test."""
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return os.path.normpath(os.path.join(self.dirBase,'current/'))
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def dirProof(self):
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"""Directory containing human readable proof of correctness for the test."""
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return os.path.normpath(os.path.join(self.dirBase,'proof/'))
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def fileInRoot(self,dir,file):
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"""Path to a file in the root directory of DAMASK."""
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return str(Path(os.environ['DAMASK_ROOT'])/dir/file)
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def fileInReference(self,file):
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"""Path to a file in the refrence directory for the test."""
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return os.path.join(self.dirReference(),file)
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def fileInCurrent(self,file):
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"""Path to a file in the current results directory for the test."""
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return os.path.join(self.dirCurrent(),file)
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def fileInProof(self,file):
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"""Path to a file in the proof directory for the test."""
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return os.path.join(self.dirProof(),file)
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def copy(self, mapA, mapB,
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A = [], B = []):
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"""
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Copy list of files from (mapped) source to target.
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mapA/B is one of self.fileInX.
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"""
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if not B or len(B) == 0: B = A
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for source,target in zip(list(map(mapA,A)),list(map(mapB,B))):
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try:
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shutil.copy2(source,target)
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except FileNotFoundError:
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logging.critical(f'error copying {source} to {target}')
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raise FileNotFoundError
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def copy_Reference2Current(self,sourcefiles=[],targetfiles=[]):
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if len(targetfiles) == 0: targetfiles = sourcefiles
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for i,f in enumerate(sourcefiles):
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try:
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shutil.copy2(self.fileInReference(f),self.fileInCurrent(targetfiles[i]))
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except FileNotFoundError:
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logging.critical(f'Reference2Current: Unable to copy file "{f}"')
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raise FileNotFoundError
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def copy_Base2Current(self,sourceDir,sourcefiles=[],targetfiles=[]):
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source = os.path.normpath(os.path.join(self.dirBase,'../../..',sourceDir))
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if len(targetfiles) == 0: targetfiles = sourcefiles
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for i,f in enumerate(sourcefiles):
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try:
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shutil.copy2(os.path.join(source,f),self.fileInCurrent(targetfiles[i]))
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except FileNotFoundError:
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logging.error(os.path.join(source,f))
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logging.critical(f'Base2Current: Unable to copy file "{f}"')
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raise FileNotFoundError
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def copy_Current2Reference(self,sourcefiles=[],targetfiles=[]):
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if len(targetfiles) == 0: targetfiles = sourcefiles
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for i,f in enumerate(sourcefiles):
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try:
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shutil.copy2(self.fileInCurrent(f),self.fileInReference(targetfiles[i]))
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except FileNotFoundError:
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logging.critical(f'Current2Reference: Unable to copy file "{f}"')
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raise FileNotFoundError
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def copy_Proof2Current(self,sourcefiles=[],targetfiles=[]):
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if len(targetfiles) == 0: targetfiles = sourcefiles
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for i,f in enumerate(sourcefiles):
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try:
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shutil.copy2(self.fileInProof(f),self.fileInCurrent(targetfiles[i]))
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except FileNotFoundError:
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logging.critical(f'Proof2Current: Unable to copy file "{f}"')
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raise FileNotFoundError
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def copy_Current2Current(self,sourcefiles=[],targetfiles=[]):
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for i,f in enumerate(sourcefiles):
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try:
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shutil.copy2(self.fileInReference(f),self.fileInCurrent(targetfiles[i]))
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except FileNotFoundError:
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logging.critical(f'Current2Current: Unable to copy file "{f}"')
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raise FileNotFoundError
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def execute_inCurrentDir(self,cmd,streamIn=None,env=None):
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logging.info(cmd)
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out,error = damask.util.execute(cmd,streamIn,self.dirCurrent())
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logging.info(error)
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logging.debug(out)
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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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absoluteTolerance=False,perLine=False,skipLines=[]):
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import numpy as np
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logging.info('\n '.join(['comparing ASCII Tables',file0,file1]))
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if normHeadings == '': normHeadings = headings0
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# check if comparison is possible and determine length of columns
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if len(headings0) == len(headings1) == len(normHeadings):
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dataLength = len(headings0)
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length = [1 for i in range(dataLength)]
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shape = [[] for i in range(dataLength)]
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data = [[] for i in range(dataLength)]
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maxError = [0.0 for i in range(dataLength)]
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absTol = [absoluteTolerance for i in range(dataLength)]
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column = [[1 for i in range(dataLength)] for j in range(2)]
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norm = [[] for i in range(dataLength)]
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normLength = [1 for i in range(dataLength)]
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normShape = [[] for i in range(dataLength)]
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normColumn = [1 for i in range(dataLength)]
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for i in range(dataLength):
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if headings0[i]['shape'] != headings1[i]['shape']:
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raise Exception(f"shape mismatch between {headings0[i]['label']} and {headings1[i]['label']}")
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shape[i] = headings0[i]['shape']
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for j in range(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 range(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(f'trying to compare {len(headings0)} with {len(headings1)} normed by {len(normHeadings)} data sets')
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table0 = damask.ASCIItable(name=file0,readonly=True)
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table0.head_read()
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table1 = damask.ASCIItable(name=file1,readonly=True)
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table1.head_read()
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for i in range(dataLength):
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key0 = ('1_' if length[i]>1 else '') + headings0[i]['label']
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key1 = ('1_' if length[i]>1 else '') + headings1[i]['label']
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normKey = ('1_' if normLength[i]>1 else '') + normHeadings[i]['label']
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if key0 not in table0.labels(raw = True):
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raise Exception(f'column "{key0}" not found in first table...')
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elif key1 not in table1.labels(raw = True):
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raise Exception(f'column "{key1}" not found in second table...')
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elif normKey not in table0.labels(raw = True):
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raise Exception(f'column "{normKey}" not found in first table...')
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else:
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column[0][i] = table0.label_index(key0)
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column[1][i] = table1.label_index(key1)
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normColumn[i] = table0.label_index(normKey)
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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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if line0 not in skipLines:
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for i in range(dataLength):
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myData = np.array(list(map(float,table0.data[column[0][i]:\
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column[0][i]+length[i]])),'d')
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normData = np.array(list(map(float,table0.data[normColumn[i]:\
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normColumn[i]+normLength[i]])),'d')
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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] = np.append(norm[i],np.max(np.abs(normData)))
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else:
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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 range(dataLength):
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if not perLine: norm[i] = [np.max(norm[i]) for j in range(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 range(line0-len(skipLines))]
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absTol[i] = True
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logging.warning(f'''{"At least one" if perLine else "Maximum"} norm of
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"{headings0[i]['label']}" in first table is 0.0, using absolute tolerance''')
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line1 = 0
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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 range(dataLength):
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myData = np.array(list(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],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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if (line0 != line1): raise Exception(f'found {line0} lines in first table but {line1} in second table')
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logging.info(' ********')
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for i in range(dataLength):
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logging.info(f''' * maximum {'absolute' if absTol[i] else 'relative'} error {maxError[i]}
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between {headings0[i]['label']} and {headings1[i]['label']}''')
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logging.info(' ********')
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return maxError
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def compare_TablesStatistically(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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meanTol = 1.0e-4,
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stdTol = 1.0e-6,
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preFilter = 1.0e-9):
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"""
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Calculate statistics of tables.
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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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tables = [damask.Table.load(filename) for filename in files]
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for table in tables:
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table._label_discrete()
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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].data.columns) # if no column is given, read all
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logging.info('comparing ASCIItables statistically')
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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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if len(files) < 2: return True # single table is always close to itself...
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data = []
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for table,labels in zip(tables,columns):
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table._label_uniform()
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data.append(np.hstack(list(table.get(label) for label in labels)))
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for i in range(1,len(data)):
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delta = data[i]-data[i-1]
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normBy = (np.abs(data[i]) + np.abs(data[i-1]))*0.5
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normedDelta = np.where(normBy>preFilter,delta/normBy,0.0)
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mean = np.amax(np.abs(np.mean(normedDelta,0)))
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std = np.amax(np.std(normedDelta,0))
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logging.info(f'mean: {mean:f}')
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logging.info(f'std: {std:f}')
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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]
|
|
|
|
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] = list(tables[i].shapes.keys()) # if no column is given, use all
|
|
|
|
logging.info('comparing ASCIItables')
|
|
for i in range(len(columns)):
|
|
columns[i] = columns[0] if not columns[i] else \
|
|
([columns[i]] if not (isinstance(columns[i], Iterable) and not isinstance(columns[i], str)) else \
|
|
columns[i]
|
|
)
|
|
logging.info(files[i]+': '+','.join(columns[i]))
|
|
|
|
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 != [np.prod(table.shapes[c]) for c in labels]): # check data object consistency
|
|
logging.critical(f'Table {files[i]} differs in data layout.')
|
|
return False
|
|
data.append(np.hstack(list(table.get(label) for label in labels)).astype(np.float)) # store
|
|
|
|
for j,label in enumerate(labels): # iterate over object labels
|
|
maximum[j] = np.maximum(
|
|
maximum[j],
|
|
np.amax(np.linalg.norm(table.get(label),
|
|
axis=1))
|
|
) # find maximum Euclidean norm across rows
|
|
|
|
maximum = np.where(maximum > 0.0, maximum, 1.0) # avoid div by zero for zero columns
|
|
maximum = np.repeat(maximum,dimensions) # spread maximum over columns of each object
|
|
|
|
for i in range(len(data)):
|
|
data[i] /= maximum # normalize each table
|
|
logging.info(f'shape of data {i}: {data[i].shape}')
|
|
|
|
if debug:
|
|
violators = np.absolute(data[0]-data[1]) > atol + rtol*np.absolute(data[1])
|
|
logging.info(f'shape of violators: {violators.shape}')
|
|
for j,culprits in enumerate(violators):
|
|
goodguys = np.logical_not(culprits)
|
|
if culprits.any():
|
|
logging.info(f'{j} has {np.sum(culprits)}')
|
|
logging.info(f'deviation: {np.absolute(data[0][j]-data[1][j])[culprits]}')
|
|
logging.info(f'data : {np.absolute(data[1][j])[culprits]}')
|
|
logging.info(f'deviation: {np.absolute(data[0][j]-data[1][j])[goodguys]}')
|
|
logging.info(f'data : {np.absolute(data[1][j])[goodguys]}')
|
|
|
|
allclose = True # start optimistic
|
|
for i in range(1,len(data)):
|
|
allclose &= np.allclose(data[i-1],data[i],rtol,atol) # accumulate "pessimism"
|
|
|
|
return allclose
|
|
|
|
|
|
def compare_TableRefCur(self,headingsRef,ref,headingsCur='',cur='',
|
|
normHeadings='',normType=None,
|
|
absoluteTolerance=False,perLine=False,skipLines=[]):
|
|
|
|
return self.compare_Table(headingsRef,
|
|
self.fileInReference(ref),
|
|
headingsRef if headingsCur == '' else headingsCur,
|
|
self.fileInCurrent(ref if cur == '' else cur),
|
|
normHeadings,normType,
|
|
absoluteTolerance,perLine,skipLines)
|
|
|
|
|
|
def compare_TableCurCur(self,headingsCur0,Cur0,Cur1,
|
|
headingsCur1='',
|
|
normHeadings='',normType=None,
|
|
absoluteTolerance=False,perLine=False,skipLines=[]):
|
|
|
|
return self.compare_Table(headingsCur0,
|
|
self.fileInCurrent(Cur0),
|
|
headingsCur0 if headingsCur1 == '' else headingsCur1,
|
|
self.fileInCurrent(Cur1),
|
|
normHeadings,normType,absoluteTolerance,perLine,skipLines)
|
|
|
|
|
|
def report_Success(self,culprit):
|
|
|
|
ret = culprit
|
|
|
|
if culprit == 0:
|
|
count = len(self.variants) if self.options.select is None else len(self.options.select)
|
|
msg = ('Test passed.' if count == 1 else f'All {count} tests passed.') + '\a\a\a'
|
|
elif culprit == -1:
|
|
msg = 'Warning: could not start test...'
|
|
ret = 0
|
|
else:
|
|
msg = f'Test "{self.variantName(culprit-1)}" failed.'
|
|
|
|
logging.critical('\n'.join(['*'*40,msg,'*'*40]) + '\n')
|
|
return ret
|