def test_read_config_illegal_value_high(self): """Tests reading config json file with illegal high value""" test_dict = {"polling_interval_in_minutes": 180, "api_key": "Your API key goes here", "days_checked": 20 } with open("config_test.json", "w") as test_file: json.dump(test_dict, test_file) with self.assertRaises(ValueError): main.read_config("config_test.json")
def test_process_event_pivotal_tracker(mocker): mock_client = mocker.patch.object(main.boto3, 'client').return_value mock_client.get_parameter.return_value = { 'Parameter': { 'Value': 'some secret' } } # called in create_story mock_post = mocker.patch.object(requests, 'post') # called in _get_current_top_of_backlog mock_get = mocker.patch.object(requests, 'get') mock_get.return_value.json.return_value = [{'id': 1}] # called in _move_to_top_of_backlog mock_put = mocker.patch.object(requests, 'put') config = main.read_config('config.json.sample') with open('test_event_one.json', 'r') as f: mock_event = json.load(f) rv = main.process_event(config, mock_event) mock_post.assert_called() mock_get.assert_called() mock_put.assert_called() assert len(rv) > 0
def test_check_location_exists_false(self): """Tests that check_location is able to verify that provided location is supported by OpenWeatherMap API """ point = Observer("qwert123", -2, 11) api_key = read_config("config.json")["api_key"] self.assertFalse(point.check_location_exists(api_key))
def test_read_config(self): """Tests reading config json file""" config = main.read_config("config_template.json") self.assertEqual(config["api_key"], "Your API key goes here") self.assertEqual(config["polling_interval_in_minutes"], 180) self.assertEqual(config["locations"][0]["name"], "Vantaa") self.assertEqual(config["days_checked"], 5)
def test_read_config(self): """ Tests the read_config function :return: Assertion results """ config = read_config() self.assertNotEqual(None, config) self.assertEqual('tcp://*:2500', config[Config.PUB_ADDR]) self.assertEqual('tcp://172.31.32.23:2360', config[Config.SUB_ADDR]) self.assertEqual('BidChanged', config[Config.TOPIC]) self.assertEqual('https://auctionapp.firebaseio.com', config[Config.FIREBASE_URL])
def test_process_event_targetprocess(mocker): mock_client = mocker.patch.object(main.boto3, 'client').return_value mock_client.get_parameter.return_value = { 'Parameter': { 'Value': 'some secret' } } # called in create_story mock_post = mocker.patch.object(requests, 'post') config = main.read_config('config.json.sample') with open('test_event_two.json', 'r') as f: mock_event = json.load(f) rv = main.process_event(config, mock_event) mock_post.assert_called() assert len(rv) > 0
def test_read_config(): """Test read_config().""" config = read_config(os.path.join("test", "test_config.ini")) assert config["file_names"]["dataset_name"] == "hello_world" assert config["file_locations"]["image_directory"] == "foo" assert config["file_locations"]["faa_aircraft_db"] == "bar"
def main(checkpoint, config_path, output_dir): config = read_config(config_path) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print('Initializing parameters') template_file_path = config['template_fname'] template_mesh = Mesh(filename=template_file_path) print('Generating transforms') M, A, D, U = mesh_operations.generate_transform_matrices( template_mesh, config['downsampling_factors']) D_t = [scipy_to_torch_sparse(d).to(device) for d in D] U_t = [scipy_to_torch_sparse(u).to(device) for u in U] A_t = [scipy_to_torch_sparse(a).to(device) for a in A] num_nodes = [len(M[i].v) for i in range(len(M))] print('Preparing dataset') data_dir = config['data_dir'] normalize_transform = Normalize() dataset = ComaDataset(data_dir, dtype='test', split='sliced', split_term='sliced', pre_transform=normalize_transform) loader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1) print('Loading model') model = Coma(dataset, config, D_t, U_t, A_t, num_nodes) checkpoint = torch.load(checkpoint) state_dict = checkpoint['state_dict'] model.load_state_dict(state_dict) model.eval() model.to(device) print('Generating latent') data = next(iter(loader)) with torch.no_grad(): data = data.to(device) x = data.x.reshape(data.num_graphs, -1, model.filters[0]) z = model.encoder(x) print('View meshes') meshviewer = MeshViewers(shape=(1, 1)) for feature_index in range(z.size(1)): j = torch.range(-4, 4, step=0.1, device=device) new_z = z.expand(j.size(0), z.size(1)).clone() new_z[:, feature_index] *= 1 + 0.3 * j with torch.no_grad(): out = model.decoder(new_z) out = out.detach().cpu() * dataset.std + dataset.mean for i in trange(out.shape[0]): mesh = Mesh(v=out[i], f=template_mesh.f) meshviewer[0][0].set_dynamic_meshes([mesh]) f = os.path.join(output_dir, 'z{}'.format(feature_index), '{:04d}.png'.format(i)) os.makedirs(os.path.dirname(f), exist_ok=True) meshviewer[0][0].save_snapshot(f, blocking=True)
def test_get_forecast(self): """Tests that get_forcast is able to get reply from API""" point = Observer("Talin", -5, 12) api_key = read_config("config.json")["api_key"] self.assertEqual(point.get_forecast(3, api_key), 200)