n_cells = 200 n_features = 5 dataM = DataManager(path="data/", filter_items=["pm25", "temperature", "wind"]) station82_pm25 = dataM.get_pm25("84") station82_t = dataM.get_temperature("82") station82_w = dataM.get_wind("82") n_size = 100 n_input_steps = 24 * 7 * semana n_output_steps = 24 * 3 data_pm25 = np.copy(station82_pm25.CONCENTRATION.values) data_t = np.copy(station82_t.Value.values) data_w = np.copy(station82_w.Value.values) ensembles_pm25 = dataM.generate_ensambles(data_pm25, 2.4, n_size) ensembles_t = dataM.generate_ensambles(data_t, 0.5, n_size) ensembles_w = dataM.generate_ensambles(data_w, 0.05, n_size) file_name = "m_Weeks_" + str(semana) + "_hidden_" + str( n_LSTM_hidden_layers) + "_cells_" + str( n_cells) + "_" + mls_label[param] + ".h5" titulo = mls_label[param] + str(semana) + "_hidden_" + str( n_LSTM_hidden_layers) + "_cells_" + str(n_cells) mls = CnnMeteo(n_input_steps, n_features, n_output_steps, drop=True, n_LSTM_hidden_layers=n_LSTM_hidden_layers, n_cells=n_cells) mls[param].load(file_name)
for physical_device in physical_devices: tf.config.experimental.set_memory_growth(physical_device, True) warnings.filterwarnings('ignore') os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ['CUDA_VISIBLE_DEVICES'] = '-1' dataM = DataManager(path="data/", filter_items=["pm25", "temperature", "wind"]) station82_pm25 = dataM.get_pm25("82") station82_t = dataM.get_temperature("271") station82_w = dataM.get_wind("271") weeks = 2 n_input_steps = 24 * 7 * weeks data = station82_pm25.CONCENTRATION.values[:n_input_steps] n_size = data.size ensembles = dataM.generate_ensambles(data, 2.4, 100) cov1 = dataM.calculate_cov(ensembles) cov2 = dataM.calculate_cov2(ensembles) ##Make a 3D plot fig = plt.figure() ax = fig.gca(projection='3d') x = np.linspace(-10, 10, n_size) y = np.linspace(-10, 10, n_size) X, Y = np.meshgrid(x, y) ax.plot_surface(X, Y, cov2, cmap='viridis', linewidth=0) ax.set_xlabel('X axis') ax.set_ylabel('Y axis') ax.set_zlabel('Z axis') plt.show()