def test_configuration_file_correct_types(correct_arguments, correct_types,
tmpdir):
"""Checks that the configuration file was saved to the directory"""
parsed_arguments = arguments.parse(correct_arguments)
directory_prefix = str(tmpdir) + "/"
configuration.save(directory_prefix + constants.CONFIGURATION,
vars(parsed_arguments))
assert len(tmpdir.listdir()) == 1
tada_configuration_dict = configuration.read(directory_prefix +
constants.CONFIGURATION)
assert configuration.get_types(tada_configuration_dict) == correct_types
analyzed_module = importlib.import_module(
configuration.get_module(tada_configuration_dict))
# reflectively access the chosen function
analyzed_function = getattr(
analyzed_module, configuration.get_function(tada_configuration_dict))
# read the chosen_size
chosen_size = read.read_experiment_size()
# configure perf
runner = pyperf.Runner()
# give a by-configuration name to the experiment
current_experiment_name = configuration.get_experiment_name(
tada_configuration_dict, chosen_size)
# set the name of the experiment for perf
runner.metadata[constants.DESCRIPTION_METANAME] = current_experiment_name
# read the chosen types
func_type = configuration.get_types(tada_configuration_dict)
# initialize path for schema
path = None
gen_func = None
# using hypothesis to generate experiment data
if func_type[0] == "hypothesis":
# read path from arguments
path = configuration.get_schema_path(tada_configuration_dict)
if func_type[0] == "custom":
data_directory = configuration.get_data_directory(
tada_configuration_dict)
if data_directory != "":
package.add_data_sys_path(
configuration.get_data_directory(tada_configuration_dict))
data_module = importlib.import_module(
configuration.get_data_module(tada_configuration_dict))
if __name__ == "__main__":
# read the configuration file to access the configuration dictionary
tada_configuration_dict = configuration.read(constants.CONFIGURATION)
# add the specified directory to the system path
package.add_sys_path(configuration.get_directory(tada_configuration_dict))
# reflectively import the chosen module
analyzed_module = importlib.import_module(
configuration.get_module(tada_configuration_dict))
# reflectively access the chosen function
analyzed_function = getattr(
analyzed_module, configuration.get_function(tada_configuration_dict))
# read the chosen_size
chosen_size = read.read_experiment_size()
# configure perf
runner = perf.Runner()
# give a by-configuration name to the experiment
current_experiment_name = configuration.get_experiment_name(
tada_configuration_dict, chosen_size)
# set the name of the experiment for perf
runner.metadata[constants.DESCRIPTION_METANAME] = current_experiment_name
# run the benchmark using the bench_func from perf
current_benchmark = runner.bench_func(
current_experiment_name,
run.run_benchmark,
analyzed_function,
*generate.generate_data(
configuration.get_types(tada_configuration_dict), chosen_size),
)
# save the perf results from running the benchmark
save.save_benchmark_results(current_benchmark, current_experiment_name)