Esempio n. 1
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    def test_trans_all_forcing_file_to_camels(self):
        data_source_dump = os.path.join(self.config_data.data_path["Temp"],
                                        'data_source.txt')
        source_data = unserialize_pickle(data_source_dump)
        output_dir = os.path.join(self.config_data.data_path["DB"],
                                  "basin_mean_forcing", "daymet")
        if not os.path.isdir(output_dir):
            os.mkdir(output_dir)
        region_names = [
            region_temp.split("_")[-1]
            for region_temp in source_data.all_configs['regions']
        ]
        # forcing data file generated is named as "allref", so rename the "all"
        region_names = ["allref" if r == "all" else r for r in region_names]
        year_start = int(source_data.t_range[0].split("-")[0])
        year_end = int(source_data.t_range[1].split("-")[0])
        years = np.arange(year_start, year_end)
        assert (all(x < y for x, y in zip(source_data.gage_dict['STAID'],
                                          source_data.gage_dict['STAID'][1:])))

        config_dir = definitions.CONFIG_DIR
        for i in range(len(region_names)):
            config_file_i = os.path.join(
                config_dir, "transdata/config_exp" + str(i + 1) + ".ini")
            subdir_i = "transdata/exp" + str(i + 1)
            config_data_i = GagesConfig.set_subdir(config_file_i, subdir_i)
            source_data_i = GagesSource(
                config_data_i,
                config_data_i.model_dict["data"]["tRangeTrain"],
                screen_basin_area_huc4=False)
            for year in years:
                trans_daymet_to_camels(source_data.all_configs["forcing_dir"],
                                       output_dir, source_data_i.gage_dict,
                                       region_names[i], year)
Esempio n. 2
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 def test_trans_all_forcing_file_to_camels(self):
     """the function need to be run region by region"""
     data_source_dump = os.path.join(self.config_data.data_path["Temp"], 'data_source.txt')
     source_data = unserialize_pickle(data_source_dump)
     output_dir = os.path.join(self.config_data.data_path["DB"], "basin_mean_forcing", "daymet")
     if not os.path.isdir(output_dir):
         os.mkdir(output_dir)
     region_names = [region_temp.split("_")[-1] for region_temp in source_data.all_configs['regions']]
     # forcing data file generated is named as "allref", so rename the "all"
     region_names = ["allref" if r == "all" else r for r in region_names]
     year_start = int(source_data.t_range[0].split("-")[0])
     year_end = int(source_data.t_range[1].split("-")[0])
     years = np.arange(year_start, year_end)
     assert (all(x < y for x, y in zip(source_data.gage_dict['STAID'], source_data.gage_dict['STAID'][1:])))
     for year in years:
         trans_daymet_to_camels(source_data.all_configs["forcing_dir"], output_dir, source_data.gage_dict,
                                region_names[0], year)