def test_no_warning_on_same_version(self): with tempfile.TemporaryFile() as tmp: write_experiment(self.experiment, tmp) with warnings.catch_warnings(record=True) as record: warnings.simplefilter('ignore', DeprecationWarning) read_experiment(tmp) self.assertFalse(record)
def test_warning_on_newer_version(self): with tempfile.TemporaryFile() as tmp: write_experiment(self.experiment, tmp) with zipfile.ZipFile(tmp, 'r', allowZip64=True) as file: data = json.loads( file.read(EXPERIMENT_DATA_FILE).decode("utf-8")) data[extrap.__title__] = '1' + data[extrap.__title__] with zipfile.ZipFile(tmp, 'w', compression=zipfile.ZIP_DEFLATED, compresslevel=1, allowZip64=True) as file: file.writestr(EXPERIMENT_DATA_FILE, json.dumps(data)) self.assertWarnsRegex(UserWarning, 'newer version', read_experiment, tmp)
def test_no_warning_on_developer_version(self): with tempfile.TemporaryFile() as tmp: write_experiment(self.experiment, tmp) with zipfile.ZipFile(tmp, 'r', allowZip64=True) as file: data = json.loads( file.read(EXPERIMENT_DATA_FILE).decode("utf-8")) data[extrap.__title__] = data[extrap.__title__] + '-alpha1' with zipfile.ZipFile(tmp, 'w', compression=zipfile.ZIP_DEFLATED, compresslevel=1, allowZip64=True) as file: file.writestr(EXPERIMENT_DATA_FILE, json.dumps(data)) with warnings.catch_warnings(record=True) as record: warnings.simplefilter('ignore', DeprecationWarning) read_experiment(tmp) self.assertFalse(record)
def _save(file_name): with ProgressWindow(self, "Saving Experiment") as pw: write_experiment(self.getExperiment(), file_name, pw) self._set_opened_file_name(file_name)
def setUpClass(cls) -> None: cls.experiment = read_text_file("data/text/two_parameter_3.txt") ModelGenerator(cls.experiment).model_all() with tempfile.TemporaryFile() as tmp: write_experiment(cls.experiment, tmp) cls.reconstructed = read_experiment(tmp)
def main(args=None, prog=None): # argparse modelers_list = list(set(k.lower() for k in chain(single_parameter.all_modelers.keys(), multi_parameter.all_modelers.keys()))) parser = argparse.ArgumentParser(prog=prog, description=extrap.__description__, add_help=False) positional_arguments = parser.add_argument_group("Positional arguments") basic_arguments = parser.add_argument_group("Optional arguments") basic_arguments.add_argument('-h', '--help', action='help', default=argparse.SUPPRESS, help='Show this help message and exit') basic_arguments.add_argument("--version", action="version", version=extrap.__title__ + " " + extrap.__version__, help="Show program's version number and exit") basic_arguments.add_argument("--log", action="store", dest="log_level", type=str.lower, default='warning', choices=['debug', 'info', 'warning', 'error', 'critical'], help="Set program's log level (default: warning)") input_options = parser.add_argument_group("Input options") group = input_options.add_mutually_exclusive_group(required=True) group.add_argument("--cube", action="store_true", default=False, dest="cube", help="Load data from CUBE files") group.add_argument("--text", action="store_true", default=False, dest="text", help="Load data from text files") group.add_argument("--talpas", action="store_true", default=False, dest="talpas", help="Load data from Talpas data format") group.add_argument("--json", action="store_true", default=False, dest="json", help="Load data from JSON or JSON Lines file") group.add_argument("--extra-p-3", action="store_true", default=False, dest="extrap3", help="Load data from Extra-P 3 experiment") input_options.add_argument("--scaling", action="store", dest="scaling_type", default="weak", type=str.lower, choices=["weak", "strong"], help="Set weak or strong scaling when loading data from CUBE files (default: weak)") modeling_options = parser.add_argument_group("Modeling options") modeling_options.add_argument("--median", action="store_true", dest="median", help="Use median values for computation instead of mean values") modeling_options.add_argument("--modeler", action="store", dest="modeler", default='default', type=str.lower, choices=modelers_list, help="Selects the modeler for generating the performance models") modeling_options.add_argument("--options", dest="modeler_options", default={}, nargs='+', metavar="KEY=VALUE", action=ModelerOptionsAction, help="Options for the selected modeler") modeling_options.add_argument("--help-modeler", choices=modelers_list, type=str.lower, help="Show help for modeler options and exit", action=ModelerHelpAction) output_options = parser.add_argument_group("Output options") output_options.add_argument("--out", action="store", metavar="OUTPUT_PATH", dest="out", help="Specify the output path for Extra-P results") output_options.add_argument("--print", action="store", dest="print_type", default="all", choices=["all", "callpaths", "metrics", "parameters", "functions"], help="Set which information should be displayed after modeling " "(default: all)") output_options.add_argument("--save-experiment", action="store", metavar="EXPERIMENT_PATH", dest="save_experiment", help="Saves the experiment including all models as Extra-P experiment " "(if no extension is specified, '.extra-p' is appended)") positional_arguments.add_argument("path", metavar="FILEPATH", type=str, action="store", help="Specify a file path for Extra-P to work with") arguments = parser.parse_args(args) # set log level loglevel = logging.getLevelName(arguments.log_level.upper()) # set output print type printtype = arguments.print_type.upper() # set log format location etc. if loglevel == logging.DEBUG: # import warnings # warnings.simplefilter('always', DeprecationWarning) # check if log file exists and create it if necessary # if not os.path.exists("../temp/extrap.log"): # log_file = open("../temp/extrap.log","w") # log_file.close() # logging.basicConfig(format="%(levelname)s - %(asctime)s - %(filename)s:%(lineno)s - %(funcName)10s(): # %(message)s", level=loglevel, datefmt="%m/%d/%Y %I:%M:%S %p", filename="../temp/extrap.log", filemode="w") logging.basicConfig( format="%(levelname)s - %(asctime)s - %(filename)s:%(lineno)s - %(funcName)10s(): %(message)s", level=loglevel, datefmt="%m/%d/%Y %I:%M:%S %p") else: logging.basicConfig( format="%(levelname)s: %(message)s", level=loglevel) # check scaling type scaling_type = arguments.scaling_type # set use mean or median for computation use_median = arguments.median # save modeler output to file? print_path = None if arguments.out is not None: print_output = True print_path = arguments.out else: print_output = False if arguments.path is not None: with ProgressBar(desc='Loading file') as pbar: if arguments.cube: # load data from cube files if os.path.isdir(arguments.path): experiment = read_cube_file(arguments.path, scaling_type) else: logging.error("The given path is not valid. It must point to a directory.") sys.exit(1) elif os.path.isfile(arguments.path): if arguments.text: # load data from text files experiment = read_text_file(arguments.path, pbar) elif arguments.talpas: # load data from talpas format experiment = read_talpas_file(arguments.path, pbar) elif arguments.json: # load data from json file experiment = read_json_file(arguments.path, pbar) elif arguments.extrap3: # load data from Extra-P 3 file experiment = read_extrap3_experiment(arguments.path, pbar) else: logging.error("The file format specifier is missing.") sys.exit(1) else: logging.error("The given file path is not valid.") sys.exit(1) experiment.debug() # initialize model generator model_generator = ModelGenerator( experiment, modeler=arguments.modeler, use_median=use_median) # apply modeler options modeler = model_generator.modeler if isinstance(modeler, MultiParameterModeler) and arguments.modeler_options: # set single-parameter modeler of multi-parameter modeler single_modeler = arguments.modeler_options[SINGLE_PARAMETER_MODELER_KEY] if single_modeler is not None: modeler.single_parameter_modeler = single_parameter.all_modelers[single_modeler]() # apply options of single-parameter modeler if modeler.single_parameter_modeler is not None: for name, value in arguments.modeler_options[SINGLE_PARAMETER_OPTIONS_KEY].items(): if value is not None: setattr(modeler.single_parameter_modeler, name, value) for name, value in arguments.modeler_options.items(): if value is not None: setattr(modeler, name, value) with ProgressBar(desc='Generating models') as pbar: # create models from data model_generator.model_all(pbar) if arguments.save_experiment: try: with ProgressBar(desc='Saving experiment') as pbar: if not os.path.splitext(arguments.save_experiment)[1]: arguments.save_experiment += '.extra-p' experiment_io.write_experiment(experiment, arguments.save_experiment, pbar) except RecoverableError as err: logging.error('Saving experiment: ' + str(err)) sys.exit(1) # format modeler output into text text = format_output(experiment, printtype) # print formatted output to command line print(text) # save formatted output to text file if print_output: save_output(text, print_path) else: logging.error("No file path given to load files.") sys.exit(1)