def test_plot_box(self): """测试可视化代码""" # plot box,使用seaborn库 keys = ["Bias", "NSE", "FLV", "FHV"] inds_test = subset_of_dict(self.inds, keys) plot_diff_boxes(inds_test, title_str="Metrics of streamflow prediction") plt.savefig(os.path.join(self.dir_out, 'boxes.png'), dpi=500, bbox_inches="tight") plt.show()
def test_ensemble_results_plot_box(self): preds = [] obss = [] # cases_exps = ["basic_exp11", "basic_exp17"] cases_exps = [ "basic_exp12", "basic_exp13", "basic_exp14", "basic_exp15", "basic_exp16", "basic_exp18" ] for case_exp in cases_exps: config_data_i = load_dataconfig_case_exp(case_exp) pred_i, obs_i = load_result(config_data_i.data_path['Temp'], self.test_epoch) pred_i = pred_i.reshape(pred_i.shape[0], pred_i.shape[1]) obs_i = obs_i.reshape(obs_i.shape[0], obs_i.shape[1]) print(obs_i) preds.append(pred_i) obss.append(obs_i) preds_np = np.array(preds) obss_np = np.array(obss) pred_mean = np.mean(preds_np, axis=0) obs_mean = np.mean(obss_np, axis=0) inds = statError(obs_mean, pred_mean) keys = ["Bias", "RMSE", "NSE"] inds_test = subset_of_dict(inds, keys) box_fig = plot_diff_boxes(inds_test)
def plot_we_need(data_model_test, obs, pred, show_me_num=5, point_file=None, **kwargs): pred = pred.reshape(pred.shape[0], pred.shape[1]) obs = obs.reshape(pred.shape[0], pred.shape[1]) inds = statError(obs, pred) # plot box keys = ["Bias", "RMSE", "NSE"] inds_test = subset_of_dict(inds, keys) box_fig = plot_diff_boxes(inds_test) box_fig.savefig( os.path.join(data_model_test.data_source.data_config.data_path["Out"], "box_fig.png")) # plot ts t_s_dict = data_model_test.t_s_dict sites = np.array(t_s_dict["sites_id"]) t_range = np.array(t_s_dict["t_final_range"]) ts_fig = plot_ts_obs_pred(obs, pred, sites, t_range, show_me_num) ts_fig.savefig( os.path.join(data_model_test.data_source.data_config.data_path["Out"], "ts_fig.png")) # plot nse ecdf sites_df_nse = pd.DataFrame({"sites": sites, keys[2]: inds_test[keys[2]]}) plot_ecdf(sites_df_nse, keys[2]) # plot map if point_file is None: gauge_dict = data_model_test.data_source.gage_dict plot_map(gauge_dict, sites_df_nse, **kwargs) else: plot_ind_map(point_file, sites_df_nse, percentile=25)
def test_inv_plot(self): data_model = GagesModel.load_datamodel( self.config_data_2.data_path["Temp"], "2", data_source_file_name='test_data_source.txt', stat_file_name='test_Statistics.json', flow_file_name='test_flow.npy', forcing_file_name='test_forcing.npy', attr_file_name='test_attr.npy', f_dict_file_name='test_dictFactorize.json', var_dict_file_name='test_dictAttribute.json', t_s_dict_file_name='test_dictTimeSpace.json') flow_pred_file = os.path.join( data_model.data_source.data_config.data_path['Temp'], 'epoch' + str(self.test_epoch) + 'flow_pred.npy') flow_obs_file = os.path.join( data_model.data_source.data_config.data_path['Temp'], 'epoch' + str(self.test_epoch) + 'flow_obs.npy') pred = unserialize_numpy(flow_pred_file) obs = unserialize_numpy(flow_obs_file) pred = pred.reshape(pred.shape[0], pred.shape[1]) obs = obs.reshape(obs.shape[0], obs.shape[1]) inds = statError(obs, pred) inds['STAID'] = data_model.t_s_dict["sites_id"] inds_df = pd.DataFrame(inds) inds_df.to_csv( os.path.join(self.config_data_2.data_path["Out"], 'data_df.csv')) # plot box,使用seaborn库 keys = ["Bias", "RMSE", "NSE"] inds_test = subset_of_dict(inds, keys) box_fig = plot_diff_boxes(inds_test) box_fig.savefig( os.path.join(self.config_data_2.data_path["Out"], "box_fig.png")) # plot ts show_me_num = 5 t_s_dict = data_model.t_s_dict sites = np.array(t_s_dict["sites_id"]) t_range = np.array(t_s_dict["t_final_range"]) time_seq_length = self.config_data_1.model_dict['model']['seqLength'] time_start = np.datetime64(t_range[0]) + np.timedelta64( time_seq_length - 1, 'D') t_range[0] = np.datetime_as_string(time_start, unit='D') ts_fig = plot_ts_obs_pred(obs, pred, sites, t_range, show_me_num) ts_fig.savefig( os.path.join(self.config_data_2.data_path["Out"], "ts_fig.png")) # plot nse ecdf sites_df_nse = pd.DataFrame({ "sites": sites, keys[2]: inds_test[keys[2]] }) plot_ecdf(sites_df_nse, keys[2]) # plot map gauge_dict = data_model.data_source.gage_dict plot_map(gauge_dict, sites_df_nse, id_col="STAID", lon_col="LNG_GAGE", lat_col="LAT_GAGE")
def test_forecast_test(self): sim_df = GagesModel.load_datamodel( self.sim_config_data.data_path["Temp"], "1", data_source_file_name='test_data_source.txt', stat_file_name='test_Statistics.json', flow_file_name='test_flow.npy', forcing_file_name='test_forcing.npy', attr_file_name='test_attr.npy', f_dict_file_name='test_dictFactorize.json', var_dict_file_name='test_dictAttribute.json', t_s_dict_file_name='test_dictTimeSpace.json') df = GagesModel.load_datamodel( self.config_data.data_path["Temp"], "2", data_source_file_name='test_data_source.txt', stat_file_name='test_Statistics.json', flow_file_name='test_flow.npy', forcing_file_name='test_forcing.npy', attr_file_name='test_attr.npy', f_dict_file_name='test_dictFactorize.json', var_dict_file_name='test_dictAttribute.json', t_s_dict_file_name='test_dictTimeSpace.json') data_input = GagesForecastDataModel(sim_df, df) pred, obs = test_lstm_forecast(data_input) pred = pred.reshape(pred.shape[0], pred.shape[1]) obs = obs.reshape(obs.shape[0], obs.shape[1]) inds = statError(obs, pred) show_me_num = 5 t_s_dict = data_input.model_data.t_s_dict sites = np.array(t_s_dict["sites_id"]) t_range = np.array(t_s_dict["t_final_range"]) time_seq_length = data_input.model_data.data_source.data_config.model_dict[ 'model']['seqLength'] time_start = np.datetime64(t_range[0]) + np.timedelta64( time_seq_length, 'D') t_range[0] = np.datetime_as_string(time_start, unit='D') ts_fig = plot_ts_obs_pred(obs, pred, sites, t_range, show_me_num) ts_fig.savefig( os.path.join(self.config_data.data_path["Out"], "ts_fig.png")) # # plot box,使用seaborn库 keys = ["Bias", "RMSE", "NSE"] inds_test = subset_of_dict(inds, keys) box_fig = plot_diff_boxes(inds_test) box_fig.savefig( os.path.join(self.config_data.data_path["Out"], "box_fig.png")) # plot map sites_df = pd.DataFrame({"sites": sites, keys[2]: inds_test[keys[2]]}) plot_ind_map(df.data_source.all_configs['gage_point_file'], sites_df)
def test_explore_test(self): models_num = 0 dirs = os.listdir(self.config_data.data_path["Temp"]) for dir_temp in dirs: if os.path.isdir( os.path.join(self.config_data.data_path["Temp"], dir_temp)): models_num += 1 for count in range(models_num): print("\n", "testing model", str(count + 1), ":\n") data_model = GagesModel.load_datamodel( self.config_data.data_path["Temp"], str(count), data_source_file_name='test_data_source.txt', stat_file_name='test_Statistics.json', flow_file_name='test_flow.npy', forcing_file_name='test_forcing.npy', attr_file_name='test_attr.npy', f_dict_file_name='test_dictFactorize.json', var_dict_file_name='test_dictAttribute.json', t_s_dict_file_name='test_dictTimeSpace.json') pred, obs = master_test(data_model) pred = pred.reshape(pred.shape[0], pred.shape[1]) obs = obs.reshape(obs.shape[0], obs.shape[1]) inds = statError(obs, pred) show_me_num = 5 t_s_dict = data_model.t_s_dict sites = np.array(t_s_dict["sites_id"]) t_range = np.array(t_s_dict["t_final_range"]) ts_fig = plot_ts_obs_pred(obs, pred, sites, t_range, show_me_num) ts_fig.savefig( os.path.join( data_model.data_source.data_config.data_path["Out"], "ts_fig.png")) # # plot box,使用seaborn库 keys = ["Bias", "RMSE", "NSE"] inds_test = subset_of_dict(inds, keys) box_fig = plot_diff_boxes(inds_test) box_fig.savefig( os.path.join( data_model.data_source.data_config.data_path["Out"], "box_fig.png")) # plot map sites_df = pd.DataFrame({ "sites": sites, keys[2]: inds_test[keys[2]] }) plot_ind_map(data_model.data_source.all_configs['gage_point_file'], sites_df)
def test_plot_pretrained_model_test(self): data_model_test = GagesModel.load_datamodel( self.majordam_config_data.data_path["Temp"], data_source_file_name='test_data_source.txt', stat_file_name='test_Statistics.json', flow_file_name='test_flow.npy', forcing_file_name='test_forcing.npy', attr_file_name='test_attr.npy', f_dict_file_name='test_dictFactorize.json', var_dict_file_name='test_dictAttribute.json', t_s_dict_file_name='test_dictTimeSpace.json') pretrained_model_name = "nodam" + self.nomajordam_subdir.split( "/")[1] + "_pretrained_model" save_dir = os.path.join( data_model_test.data_source.data_config.data_path['Out'], pretrained_model_name) pred, obs = load_result(save_dir, self.test_epoch) pred = pred.reshape(pred.shape[0], pred.shape[1]) obs = obs.reshape(pred.shape[0], pred.shape[1]) inds = statError(obs, pred) # plot box,使用seaborn库 keys = ["Bias", "RMSE", "NSE"] inds_test = subset_of_dict(inds, keys) box_fig = plot_diff_boxes(inds_test) box_fig.savefig(os.path.join(save_dir, "box_fig.png")) # plot ts show_me_num = 5 t_s_dict = data_model_test.t_s_dict sites = np.array(t_s_dict["sites_id"]) t_range = np.array(t_s_dict["t_final_range"]) ts_fig = plot_ts_obs_pred(obs, pred, sites, t_range, show_me_num) ts_fig.savefig(os.path.join(save_dir, "ts_fig.png")) # plot nse ecdf sites_df_nse = pd.DataFrame({ "sites": sites, keys[2]: inds_test[keys[2]] }) plot_ecdf(sites_df_nse, keys[2]) # plot map gauge_dict = data_model_test.data_source.gage_dict plot_map(gauge_dict, sites_df_nse, id_col="STAID", lon_col="LNG_GAGE", lat_col="LAT_GAGE")
def plot_region_seperately(gages_data_model, epoch, id_regions_idx, preds, obss, inds_dfs): df_id_region = np.array(gages_data_model.t_s_dict["sites_id"]) regions_name = gages_data_model.data_source.all_configs.get("regions") for i in range(len(id_regions_idx)): # plot box keys = ["Bias", "RMSE", "NSE"] inds_test = subset_of_dict(inds_dfs[i], keys) box_fig = plot_diff_boxes(inds_test) box_fig.savefig( os.path.join( gages_data_model.data_source.data_config.data_path["Out"], regions_name[i] + "epoch" + str(epoch) + "box_fig.png")) # plot ts sites = np.array(df_id_region[id_regions_idx[i]]) t_range = np.array(gages_data_model.t_s_dict["t_final_range"]) show_me_num = 5 ts_fig = plot_ts_obs_pred(obss[i], preds[i], sites, t_range, show_me_num) ts_fig.savefig( os.path.join( gages_data_model.data_source.data_config.data_path["Out"], regions_name[i] + "epoch" + str(epoch) + "ts_fig.png")) # plot nse ecdf sites_df_nse = pd.DataFrame({ "sites": sites, keys[2]: inds_test[keys[2]] }) plot_ecdf( sites_df_nse, keys[2], os.path.join( gages_data_model.data_source.data_config.data_path["Out"], regions_name[i] + "epoch" + str(epoch) + "ecdf_fig.png")) # plot map gauge_dict = gages_data_model.data_source.gage_dict save_map_file = os.path.join( gages_data_model.data_source.data_config.data_path["Out"], regions_name[i] + "epoch" + str(epoch) + "map_fig.png") plot_map(gauge_dict, sites_df_nse, save_file=save_map_file, id_col="STAID", lon_col="LNG_GAGE", lat_col="LAT_GAGE")
def test_plot_pretrained_model_test(self): data_model_test = GagesModel.load_datamodel( self.config_data.data_path["Temp"], data_source_file_name='test_data_source.txt', stat_file_name='test_Statistics.json', flow_file_name='test_flow.npy', forcing_file_name='test_forcing.npy', attr_file_name='test_attr.npy', f_dict_file_name='test_dictFactorize.json', var_dict_file_name='test_dictAttribute.json', t_s_dict_file_name='test_dictTimeSpace.json') save_dir = data_model_test.data_source.data_config.data_path['Temp'] pred, obs = load_result(save_dir, self.config_data.config_file.TEST_EPOCH) pred = pred.reshape(pred.shape[0], pred.shape[1]) obs = obs.reshape(pred.shape[0], pred.shape[1]) inds = statError(obs, pred) keys = ["Bias", "RMSE", "NSE"] t_s_dict = data_model_test.t_s_dict sites = np.array(t_s_dict["sites_id"]) t_range = np.array(t_s_dict["t_final_range"]) # plot nse ecdf sites_df_nse = pd.DataFrame({"sites": sites, keys[2]: inds[keys[2]]}) plot_ecdf(sites_df_nse, keys[2]) # plot map gauge_dict = data_model_test.data_source.gage_dict plot_map(gauge_dict, sites_df_nse, id_col="STAID", lon_col="LNG_GAGE", lat_col="LAT_GAGE") # plot box,使用seaborn库 inds_test = subset_of_dict(inds, keys) box_fig = plot_diff_boxes(inds_test) # plot ts show_me_num = 5 ts_fig = plot_ts_obs_pred(obs, pred, sites, t_range, show_me_num) plt.show()
def plot_gages_attrs_boxes(sites1, sites2, attr_lst, attrs1, attrs2, diff_str, row_and_col=None): """plot boxplots of GAGES model results""" if type(sites1) == list or type(sites2) == list: sites1 = np.array(sites1) sites2 = np.array(sites2) sites1diff = np.tile(0, sites1.size).reshape(sites1.size, 1) site2diff = np.tile(1, sites2.size).reshape(sites2.size, 1) attrs1_new = np.concatenate((attrs1, sites1diff), axis=1) attrs2_new = np.concatenate((attrs2, site2diff), axis=1) diff_str_lst = [diff_str] df1 = pd.DataFrame(attrs1_new, columns=attr_lst + diff_str_lst) df2 = pd.DataFrame(attrs2_new, columns=attr_lst + diff_str_lst) result = pd.concat([df1, df2]) box_fig = plot_diff_boxes(result, row_and_col=row_and_col, y_col=np.arange(len(attr_lst)).tolist(), x_col=len(attr_lst)) return box_fig
def test_purposes_seperate(self): quick_data_dir = os.path.join(self.config_data.data_path["DB"], "quickdata") data_dir = os.path.join(quick_data_dir, "allnonref-dam_95-05_nan-0.1_00-1.0") data_model_test = GagesModel.load_datamodel( data_dir, data_source_file_name='test_data_source.txt', stat_file_name='test_Statistics.json', flow_file_name='test_flow.npy', forcing_file_name='test_forcing.npy', attr_file_name='test_attr.npy', f_dict_file_name='test_dictFactorize.json', var_dict_file_name='test_dictAttribute.json', t_s_dict_file_name='test_dictTimeSpace.json') data_model = GagesModel.update_data_model(self.config_data, data_model_test) nid_dir = os.path.join( "/".join(self.config_data.data_path["DB"].split("/")[:-1]), "nid", "quickdata") gage_main_dam_purpose = unserialize_json( os.path.join(nid_dir, "dam_main_purpose_dict.json")) gage_main_dam_purpose_lst = list(gage_main_dam_purpose.values()) gage_main_dam_purpose_unique = np.unique(gage_main_dam_purpose_lst) purpose_regions = {} for i in range(gage_main_dam_purpose_unique.size): sites_id = [] for key, value in gage_main_dam_purpose.items(): if value == gage_main_dam_purpose_unique[i]: sites_id.append(key) assert (all(x < y for x, y in zip(sites_id, sites_id[1:]))) purpose_regions[gage_main_dam_purpose_unique[i]] = sites_id id_regions_idx = [] id_regions_sites_ids = [] df_id_region = np.array(data_model.t_s_dict["sites_id"]) for key, value in purpose_regions.items(): gages_id = value c, ind1, ind2 = np.intersect1d(df_id_region, gages_id, return_indices=True) assert (all(x < y for x, y in zip(ind1, ind1[1:]))) assert (all(x < y for x, y in zip(c, c[1:]))) id_regions_idx.append(ind1) id_regions_sites_ids.append(c) pred_all, obs_all = load_result(self.config_data.data_path["Temp"], self.test_epoch) pred_all = pred_all.reshape(pred_all.shape[0], pred_all.shape[1]) obs_all = obs_all.reshape(obs_all.shape[0], obs_all.shape[1]) for i in range(9, len(gage_main_dam_purpose_unique)): pred = pred_all[id_regions_idx[i], :] obs = obs_all[id_regions_idx[i], :] inds = statError(obs, pred) inds['STAID'] = id_regions_sites_ids[i] inds_df = pd.DataFrame(inds) inds_df.to_csv( os.path.join( self.config_data.data_path["Out"], gage_main_dam_purpose_unique[i] + "epoch" + str(self.test_epoch) + 'data_df.csv')) # plot box,使用seaborn库 keys = ["Bias", "RMSE", "NSE"] inds_test = subset_of_dict(inds, keys) box_fig = plot_diff_boxes(inds_test) box_fig.savefig( os.path.join( self.config_data.data_path["Out"], gage_main_dam_purpose_unique[i] + "epoch" + str(self.test_epoch) + "box_fig.png")) # plot ts sites = np.array(df_id_region[id_regions_idx[i]]) t_range = np.array(data_model.t_s_dict["t_final_range"]) show_me_num = 1 ts_fig = plot_ts_obs_pred(obs, pred, sites, t_range, show_me_num) ts_fig.savefig( os.path.join( self.config_data.data_path["Out"], gage_main_dam_purpose_unique[i] + "epoch" + str(self.test_epoch) + "ts_fig.png")) # plot nse ecdf sites_df_nse = pd.DataFrame({ "sites": sites, keys[2]: inds_test[keys[2]] }) plot_ecdf( sites_df_nse, keys[2], os.path.join( self.config_data.data_path["Out"], gage_main_dam_purpose_unique[i] + "epoch" + str(self.test_epoch) + "ecdf_fig.png")) # plot map gauge_dict = data_model.data_source.gage_dict save_map_file = os.path.join( self.config_data.data_path["Out"], gage_main_dam_purpose_unique[i] + "epoch" + str(self.test_epoch) + "map_fig.png") plot_map(gauge_dict, sites_df_nse, save_file=save_map_file, id_col="STAID", lon_col="LNG_GAGE", lat_col="LAT_GAGE")
inds_df[show_ind_NSE] <= nse_range[1])].index.tolist() plot_gages_map(data_model, inds_df, show_ind_NSE, idx_lstl_nse, cbar_font_size=14) plt.savefig(os.path.join(config_data.data_path["Out"], 'map_NSE.png'), dpi=FIGURE_DPI, bbox_inches="tight") # plt.figure() # plot box,使用seaborn库 keys = ["Bias", "NSE", "FHV", "FLV"] inds_test = subset_of_dict(inds_df, keys) plot_diff_boxes(inds_test) plt.savefig(os.path.join(config_data.data_path["Out"], 'boxes.png'), dpi=FIGURE_DPI, bbox_inches="tight") # plt.figure() ############################ plot map box ########################### # plot NSE nse_range = [0, 1] # nse_range = [-10000, 1] # nse_range = [-10000, 0] idx_lstl_nse = inds_df[(inds_df[show_ind_NSE] >= nse_range[0]) & ( inds_df[show_ind_NSE] <= nse_range[1])].index.tolist() plot_gages_map_and_box(data_model, inds_df, show_ind_NSE,