def test_save_results(self): # test for 1d # test for 2d # test for 3d # test for very large nr_experiments = 10000 experiments = np.recarray((nr_experiments,), dtype=[('x', float), ('y', float)]) outcome_a = np.random.rand(nr_experiments,1) results = (experiments, {'a': outcome_a}) fn = u'../data/test.tar.gz' save_results(results, fn) os.remove(fn) # ema_logging.info('1d saved successfully') nr_experiments = 10000 nr_timesteps = 100 experiments = np.recarray((nr_experiments,), dtype=[('x', float), ('y', float)]) outcome_a = np.zeros((nr_experiments,nr_timesteps)) results = (experiments, {'a': outcome_a}) save_results(results, fn) os.remove(fn) # ema_logging.info('2d saved successfully') nr_experiments = 10000 nr_timesteps = 100 nr_replications = 10 experiments = np.recarray((nr_experiments,), dtype=[('x', float), ('y', float)]) outcome_a = np.zeros((nr_experiments,nr_timesteps,nr_replications)) results = (experiments, {'a': outcome_a}) save_results(results, fn) os.remove(fn) # ema_logging.info('3d saved successfully') nr_experiments = 500000 nr_timesteps = 100 experiments = np.recarray((nr_experiments,), dtype=[('x', float), ('y', float)]) outcome_a = np.zeros((nr_experiments,nr_timesteps)) results = (experiments, {'a': outcome_a}) save_results(results, fn) os.remove(fn)
def test_save_results(self): # test for 1d # test for 2d # test for 3d # test for very large nr_experiments = 10000 experiments = np.recarray((nr_experiments, ), dtype=[('x', float), ('y', float)]) outcome_a = np.random.rand(nr_experiments, 1) results = (experiments, {'a': outcome_a}) fn = u'../data/test.tar.gz' save_results(results, fn) os.remove(fn) # ema_logging.info('1d saved successfully') nr_experiments = 10000 nr_timesteps = 100 experiments = np.recarray((nr_experiments, ), dtype=[('x', float), ('y', float)]) outcome_a = np.zeros((nr_experiments, nr_timesteps)) results = (experiments, {'a': outcome_a}) save_results(results, fn) os.remove(fn) # ema_logging.info('2d saved successfully') nr_experiments = 10000 nr_timesteps = 100 nr_replications = 10 experiments = np.recarray((nr_experiments, ), dtype=[('x', float), ('y', float)]) outcome_a = np.zeros((nr_experiments, nr_timesteps, nr_replications)) results = (experiments, {'a': outcome_a}) save_results(results, fn) os.remove(fn) # ema_logging.info('3d saved successfully') nr_experiments = 500000 nr_timesteps = 100 experiments = np.recarray((nr_experiments, ), dtype=[('x', float), ('y', float)]) outcome_a = np.zeros((nr_experiments, nr_timesteps)) results = (experiments, {'a': outcome_a}) save_results(results, fn) os.remove(fn)
def test_load_results(self): # test for 1d # test for 2d # test for 3d nr_experiments = 10000 experiments = np.recarray((nr_experiments,), dtype=[('x', float), ('y', float)]) outcome_a = np.zeros((nr_experiments,1)) results = (experiments, {'a': outcome_a}) save_results(results, u'../data/test.tar.gz') experiments, outcomes = load_results(u'../data/test.tar.gz') logical = np.allclose(outcomes['a'],outcome_a) os.remove('../data/test.tar.gz') # if logical: # ema_logging.info('1d loaded successfully') nr_experiments = 1000 nr_timesteps = 100 nr_replications = 10 experiments = np.recarray((nr_experiments,), dtype=[('x', float), ('y', float)]) outcome_a = np.zeros((nr_experiments,nr_timesteps,nr_replications)) results = (experiments, {'a': outcome_a}) save_results(results, u'../data/test.tar.gz') experiments, outcomes = load_results(u'../data/test.tar.gz') logical = np.allclose(outcomes['a'],outcome_a) os.remove('../data/test.tar.gz')
def test_load_results(self): # test for 1d # test for 2d # test for 3d nr_experiments = 10000 experiments = np.recarray((nr_experiments, ), dtype=[('x', float), ('y', float)]) outcome_a = np.zeros((nr_experiments, 1)) results = (experiments, {'a': outcome_a}) save_results(results, u'../data/test.tar.gz') experiments, outcomes = load_results(u'../data/test.tar.gz') logical = np.allclose(outcomes['a'], outcome_a) os.remove('../data/test.tar.gz') # if logical: # ema_logging.info('1d loaded successfully') nr_experiments = 1000 nr_timesteps = 100 nr_replications = 10 experiments = np.recarray((nr_experiments, ), dtype=[('x', float), ('y', float)]) outcome_a = np.zeros((nr_experiments, nr_timesteps, nr_replications)) results = (experiments, {'a': outcome_a}) save_results(results, u'../data/test.tar.gz') experiments, outcomes = load_results(u'../data/test.tar.gz') logical = np.allclose(outcomes['a'], outcome_a) os.remove('../data/test.tar.gz')
ParameterUncertainty((0.5,2), "susceptible to immune population delay time region 2"), ParameterUncertainty((0.01, 5), "root contact rate region 1"), ParameterUncertainty((0.01, 5), "root contact ratio region 2"), ParameterUncertainty((0, 0.15), "infection ratio region 1"), ParameterUncertainty((0, 0.15), "infection rate region 2"), ParameterUncertainty((10, 100), "normal contact rate region 1"), ParameterUncertainty((10, 200), "normal contact rate region 2")] if __name__ == "__main__": ema_logging.log_to_stderr(ema_logging.INFO) model = FluModel(r'./models/flu', "fluCase") ensemble = ModelEnsemble() ensemble.model_structure = model ensemble.parallel = True #turn on parallel processing nr_experiments = 1000 results = ensemble.perform_experiments(nr_experiments) fh = r'./data/{} flu cases no policy.tar.gz'.format(nr_experiments) save_results(results, fh)