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refactor all output interface; add evaluate script for sceua calibrat…
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| Original file line number | Diff line number | Diff line change |
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| """ | ||
| Author: Wenyu Ouyang | ||
| Date: 2022-10-25 21:16:22 | ||
| LastEditTime: 2024-03-26 21:52:05 | ||
| LastEditors: Wenyu Ouyang | ||
| Description: Plots for calibration and testing results | ||
| FilePath: \hydro-model-xaj\hydromodel\trainers\evaluate.py | ||
| Copyright (c) 2021-2022 Wenyu Ouyang. All rights reserved. | ||
| """ | ||
|
|
||
| import pathlib | ||
| import pandas as pd | ||
| import os | ||
| import numpy as np | ||
| import xarray as xr | ||
| import spotpy | ||
|
|
||
| from hydroutils import hydro_file | ||
| from hydrodata.utils.utils import streamflow_unit_conv | ||
|
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||
| from hydromodel.datasets import FLOW_NAME, remove_unit_from_name | ||
| from hydromodel.datasets.data_preprocess import get_basin_area | ||
| from hydromodel.models.model_config import MODEL_PARAM_DICT | ||
| from hydromodel.models.xaj import xaj | ||
|
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| def read_save_sceua_calibrated_params(basin_id, save_dir, sceua_calibrated_file_name): | ||
| """ | ||
| read the parameters' file generated by spotpy SCE-UA when finishing calibration | ||
| We also save the parameters of the best model run to a file | ||
| Parameters | ||
| ---------- | ||
| basin_id | ||
| id of a basin | ||
| save_dir | ||
| the directory where we save params | ||
| sceua_calibrated_file_name | ||
| the parameters' file generated by spotpy SCE-UA when finishing calibration | ||
| Returns | ||
| ------- | ||
| """ | ||
| results = spotpy.analyser.load_csv_results(sceua_calibrated_file_name) | ||
| bestindex, bestobjf = spotpy.analyser.get_minlikeindex( | ||
| results | ||
| ) # 结果数组中具有最小目标函数的位置的索引 | ||
| best_model_run = results[bestindex] | ||
| fields = [word for word in best_model_run.dtype.names if word.startswith("par")] | ||
| best_calibrate_params = pd.DataFrame(list(best_model_run[fields])) | ||
| save_file = os.path.join(save_dir, basin_id + "_calibrate_params.txt") | ||
| best_calibrate_params.to_csv(save_file, sep=",", index=False, header=True) | ||
| return np.array(best_calibrate_params).reshape(1, -1) # 返回一列最佳的结果 | ||
|
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|
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| def read_all_basin_params(basins, save_dir): | ||
| params_list = [] | ||
| for basin_id in basins: | ||
| db_name = os.path.join(save_dir, basin_id) | ||
| # 读取每个流域的参数 | ||
| basin_params = read_save_sceua_calibrated_params(basin_id, save_dir, db_name) | ||
| # 确保basin_params是一维的 | ||
| basin_params = basin_params.flatten() | ||
| params_list.append(basin_params) | ||
| return np.vstack(params_list) | ||
|
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|
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| def convert_streamflow_units(test_data, qsim, data_type, data_dir): | ||
| times = test_data["time"].data | ||
| basins = test_data["basin"].data | ||
| flow_name = remove_unit_from_name(FLOW_NAME) | ||
| flow_dataarray = xr.DataArray( | ||
| qsim.squeeze(-1), coords=[("time", times), ("basin", basins)], name=flow_name | ||
| ) | ||
| flow_dataarray.attrs["units"] = test_data[flow_name].attrs["units"] | ||
| ds = xr.Dataset() | ||
| ds[flow_name] = flow_dataarray | ||
| target_unit = "m^3/s" | ||
| basin_area = get_basin_area(data_type, data_dir, basins) | ||
| ds_simflow = streamflow_unit_conv( | ||
| ds, basin_area, target_unit=target_unit, inverse=True | ||
| ) | ||
| ds_obsflow = streamflow_unit_conv( | ||
| test_data[[flow_name]], basin_area, target_unit=target_unit, inverse=True | ||
| ) | ||
| return ds_simflow, ds_obsflow | ||
|
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||
|
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| def summarize_parameters(result_dir, model_name, basin_ids): | ||
| """ | ||
| output parameters of all basins to one file | ||
| Parameters | ||
| ---------- | ||
| result_dir | ||
| the directory where we save results | ||
| model_name | ||
| the name of the model | ||
| Returns | ||
| ------- | ||
| """ | ||
| params = [] | ||
| for basin_id in basin_ids: | ||
| columns = MODEL_PARAM_DICT[model_name]["param_name"] | ||
| params_txt = pd.read_csv( | ||
| os.path.join(result_dir, basin_id + "_calibrate_params.txt") | ||
| ) | ||
| params_df = pd.DataFrame(params_txt.values.T, columns=columns) | ||
| params.append(params_df) | ||
| params_dfs = pd.concat(params, axis=0) | ||
| params_dfs.index = basin_ids | ||
| print(params_dfs) | ||
| params_dfs_ = params_dfs.transpose() | ||
| params_csv_file = os.path.join(result_dir, "basins_params.csv") | ||
| params_dfs_.to_csv(params_csv_file, sep=",", index=True, header=True) | ||
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| def renormalize_params(result_dir, model_name, basin_ids): | ||
| renormalization_params = [] | ||
| for basin_id in basin_ids: | ||
| params = np.loadtxt( | ||
| os.path.join(result_dir, basin_id + "_calibrate_params.txt") | ||
| )[1:].reshape(1, -1) | ||
| param_ranges = MODEL_PARAM_DICT[model_name]["param_range"] | ||
| xaj_params = [ | ||
| (value[1] - value[0]) * params[:, i] + value[0] | ||
| for i, (key, value) in enumerate(param_ranges.items()) | ||
| ] | ||
| xaj_params_ = np.array([x for j in xaj_params for x in j]) | ||
| params_df = pd.DataFrame(xaj_params_.T) | ||
| renormalization_params.append(params_df) | ||
| renormalization_params_dfs = pd.concat(renormalization_params, axis=1) | ||
| renormalization_params_dfs.index = MODEL_PARAM_DICT[model_name]["param_name"] | ||
| renormalization_params_dfs.columns = basin_ids | ||
| print(renormalization_params_dfs) | ||
| params_csv_file = os.path.join(result_dir, "basins_renormalization_params.csv") | ||
| renormalization_params_dfs.to_csv(params_csv_file, sep=",", index=True, header=True) | ||
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| def summarize_metrics(result_dir, model_info: dict): | ||
| """ | ||
| output all results' metrics of all basins to one file | ||
| Parameters | ||
| ---------- | ||
| result_dir | ||
| the directory where we save results | ||
| Returns | ||
| ------- | ||
| """ | ||
| path = pathlib.Path(result_dir) | ||
| all_basins_files = [file for file in path.iterdir() if file.is_dir()] | ||
| train_metrics = {} | ||
| test_metrics = {} | ||
| count = 0 | ||
| basin_ids = [] | ||
| for basin_dir in all_basins_files: | ||
| basin_id = basin_dir.stem | ||
| basin_ids.append(basin_id) | ||
| train_metric_file = os.path.join(basin_dir, "train_metrics.json") | ||
| test_metric_file = os.path.join(basin_dir, "test_metrics.json") | ||
| train_metric = hydro_file.unserialize_json(train_metric_file) | ||
| test_metric = hydro_file.unserialize_json(test_metric_file) | ||
|
|
||
| for key, value in train_metric.items(): | ||
| if count == 0: | ||
| train_metrics[key] = value | ||
| else: | ||
| train_metrics[key] = train_metrics[key] + value | ||
| for key, value in test_metric.items(): | ||
| if count == 0: | ||
| test_metrics[key] = value | ||
| else: | ||
| test_metrics[key] = test_metrics[key] + value | ||
| count = count + 1 | ||
| metric_dfs_train = pd.DataFrame(train_metrics, index=basin_ids).transpose() | ||
| metric_dfs_test = pd.DataFrame(test_metrics, index=basin_ids).transpose() | ||
| metric_file_train = os.path.join(result_dir, "basins_metrics_train.csv") | ||
| metric_file_test = os.path.join(result_dir, "basins_metrics_test.csv") | ||
| metric_dfs_train.to_csv(metric_file_train, sep=",", index=True, header=True) | ||
| metric_dfs_test.to_csv(metric_file_test, sep=",", index=True, header=True) | ||
|
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| def save_evaluate_results(result_dir, model_name, qsim, qobs, obs_ds): | ||
| ds = xr.Dataset() | ||
|
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||
| # 添加 qsim 和 qobs | ||
| ds["qsim"] = qsim["flow"] | ||
| ds["qobs"] = qobs["flow"] | ||
|
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| # 添加 prcp 和 pet | ||
| ds["prcp"] = obs_ds["prcp"] | ||
| ds["pet"] = obs_ds["pet"] | ||
|
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||
| # 保存为 .nc 文件 | ||
| file_path = os.path.join(result_dir, f"{model_name}_evaluation_results.nc") | ||
| ds.to_netcdf(file_path) | ||
|
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| print(f"Results saved to: {file_path}") | ||
|
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| def read_and_save_et_ouputs(result_dir, fold: int): | ||
| prameter_file = os.path.join(result_dir, "basins_params.csv") | ||
| param_values = pd.read_csv(prameter_file, index_col=0) | ||
| basins_id = param_values.columns.values | ||
| args_file = os.path.join(result_dir, "args.json") | ||
| args = hydro_file.unserialize_json(args_file) | ||
| warmup_length = args["warmup_length"] | ||
| model_func_param = args["model"] | ||
| exp_dir = pathlib.Path(result_dir).parent | ||
| data_info_train = hydro_file.unserialize_json( | ||
| exp_dir.joinpath(f"data_info_fold{fold}_train.json") | ||
| ) | ||
| data_info_test = hydro_file.unserialize_json( | ||
| exp_dir.joinpath(f"data_info_fold{fold}_test.json") | ||
| ) | ||
| train_period = data_info_train["time"] | ||
| test_period = data_info_test["time"] | ||
| # TODO: basins_lump_p_pe_q_fold NAME need to be unified | ||
| train_np_file = os.path.join(exp_dir, f"data_info_fold{fold}_train.npy") | ||
| test_np_file = os.path.join(exp_dir, f"data_info_fold{fold}_test.npy") | ||
| # train_np_file = os.path.join(exp_dir, f"basins_lump_p_pe_q_fold{fold}_train.npy") | ||
| # test_np_file = os.path.join(exp_dir, f"basins_lump_p_pe_q_fold{fold}_test.npy") | ||
| train_data = np.load(train_np_file) | ||
| test_data = np.load(test_np_file) | ||
| es_test = [] | ||
| es_train = [] | ||
| for i in range(len(basins_id)): | ||
| _, e_train = xaj( | ||
| train_data[:, :, 0:2], | ||
| param_values[basins_id[i]].values.reshape(1, -1), | ||
| warmup_length=warmup_length, | ||
| **model_func_param, | ||
| ) | ||
| _, e_test = xaj( | ||
| test_data[:, :, 0:2], | ||
| param_values[basins_id[i]].values.reshape(1, -1), | ||
| warmup_length=warmup_length, | ||
| **model_func_param, | ||
| ) | ||
| es_train.append(e_train.flatten()) | ||
| es_test.append(e_test.flatten()) | ||
| df_e_train = pd.DataFrame( | ||
| np.array(es_train).T, columns=basins_id, index=train_period[warmup_length:] | ||
| ) | ||
| df_e_test = pd.DataFrame( | ||
| np.array(es_test).T, columns=basins_id, index=test_period[warmup_length:] | ||
| ) | ||
| etsim_train_save_path = os.path.join(result_dir, "basin_etsim_train.csv") | ||
| etsim_test_save_path = os.path.join(result_dir, "basin_etsim_test.csv") | ||
| df_e_train.to_csv(etsim_train_save_path) | ||
| df_e_test.to_csv(etsim_test_save_path) |
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