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A good understanding of SUEWS is a prerequisite to the proper use of SuPy.
supy.save_supyΒΆ
- supy.save_supy(df_output: pandas.core.frame.DataFrame, df_state_final: pandas.core.frame.DataFrame, freq_s: int = 3600, site: str = '', path_dir_save: str = PosixPath('.'), path_runcontrol: Optional[str] = None, save_tstep=False, logging_level=50, output_level=1, debug=False) → list[source]ΒΆ
Save SuPy run results to files
- Parameters
df_output (pandas.DataFrame) β DataFrame of output
df_state_final (pandas.DataFrame) β DataFrame of final model states
freq_s (int, optional) β Output frequency in seconds (the default is 3600, which indicates hourly output)
site (str, optional) β Site identifier (the default is ββ, which indicates site identifier will be left empty)
path_dir_save (str, optional) β Path to directory to saving the files (the default is Path(β.β), which indicates the current working directory)
path_runcontrol (str, optional) β Path to SUEWS RunControl.nml, which, if set, will be preferably used to derive
freq_s
,site
andpath_dir_save
. (the default is None, which is unset)save_tstep (bool, optional) β whether to save results in temporal resolution as in simulation (which may result very large files and slow progress), by default False.
logging_level (logging level) β one of these values [50 (CRITICAL), 40 (ERROR), 30 (WARNING), 20 (INFO), 10 (DEBUG)]. A lower value informs SuPy for more verbose logging info.
output_level (integer, optional) β option to determine selection of output variables, by default 1. Notes: 0 for all but snow-related; 1 for all; 2 for a minimal set without land cover specific information.
debug (bool, optional) β whether to enable debug mode (e.g., writing out in serial mode, and other debug uses), by default False.
- Returns
a list of paths of saved files
- Return type
Examples
save results of a supy run to the current working directory with default settings
>>> list_path_save = supy.save_supy(df_output, df_state_final)
save results according to settings in RunControl.nml
>>> list_path_save = supy.save_supy(df_output, df_state_final, path_runcontrol='path/to/RunControl.nml')
save results of a supy run at resampling frequency of 1800 s (i.e., half-hourly results) under the site code
Test
to a customised location βpath/to/some/dirβ
>>> list_path_save = supy.save_supy(df_output, df_state_final, freq_s=1800, site='Test', path_dir_save='path/to/some/dir')