def main():
    # Now let's make use of logging
    logger = logging.getLogger()

    # Create folders for data and plots
    folder = os.path.join(os.getcwd(), 'experiments', 'ca_patterns_pypet')
    if not os.path.isdir(folder):
        os.makedirs(folder)
    filename = os.path.join(folder, 'all_patterns.hdf5')

    # Create an environment
    env = Environment(trajectory='cellular_automata',
                      multiproc=True,
                      ncores=4,
                      wrap_mode='QUEUE',
                      filename=filename,
                      overwrite_file=True)

    # extract the trajectory
    traj = env.traj

    traj.par.ncells = Parameter('ncells', 400, 'Number of cells')
    traj.par.steps = Parameter('steps', 250, 'Number of timesteps')
    traj.par.rule_number = Parameter('rule_number', 30, 'The ca rule')
    traj.par.initial_name = Parameter('initial_name', 'random', 'The type of initial state')
    traj.par.seed = Parameter('seed', 100042, 'RNG Seed')

    # Explore
    exp_dict = {'rule_number' : [10, 30, 90, 110, 184],
                'initial_name' : ['single', 'random'],}
    # # You can uncomment the ``exp_dict`` below to see that changing the
    # # exploration scheme is now really easy:
    # exp_dict = {'rule_number' : [10, 30, 90, 110, 184],
    #             'ncells' : [100, 200, 300],
    #             'seed': [333444555, 123456]}
    exp_dict = cartesian_product(exp_dict)
    traj.f_explore(exp_dict)

    # Run the simulation
    logger.info('Starting Simulation')
    env.run(wrap_automaton)

    # Load all data
    traj.f_load(load_data=2)

    logger.info('Printing data')
    for idx, run_name in enumerate(traj.f_iter_runs()):
        # Plot all patterns
        filename = os.path.join(folder, make_filename(traj))
        plot_pattern(traj.crun.pattern, traj.rule_number, filename)
        progressbar(idx, len(traj), logger=logger)

    # Finally disable logging and close all log-files
    env.disable_logging()
Ensures that you can filter by objects that have an 'evaluate' attr
dataset/ETHPy150Open SmokinCaterpillar/pypet/examples/example_17_wrapping_an_existing_project/pypetwrap.py/main--crossover with--dataset/ETHPy150Open AppScale/appscale/AppServer/lib/django-1.4/tests/regressiontests/model_regress/tests.py/EvaluateMethodTest.test_model_with_evaluate_method