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supy.run_supy¶
-
supy.
run_supy
(df_forcing: pandas.core.frame.DataFrame, df_state_init: pandas.core.frame.DataFrame, save_state=False, n_yr=10, logging_level=20, check_input=False, serial_mode=False) → Tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame][source]¶ Perform supy simulation.
Parameters: - df_forcing (pandas.DataFrame) – forcing data for all grids in
df_state_init
. - df_state_init (pandas.DataFrame) – initial model states; or a collection of model states with multiple timestamps, whose last temporal record will be used as the initial model states.
- save_state (bool, optional) – flag for saving model states at each time step, which can be useful in diagnosing model runtime performance or performing a restart run. (the default is False, which instructs supy not to save runtime model states).
- n_yr (int, optional) – chunk size (
n_yr
years) to split simulation periods so memory usage can be reduced. (the default is 10, which implies 10-year forcing chunks used in simulations). - logging_level (logging level) – one of these values [50 (CRITICAL), 40 (ERROR), 30 (WARNING), 20 (INFO), 10 (DEBUG)]. A lower value informs SuPy for more verbose logging info.
- check_input (bool, optional) –
- flag for checking validity of input:
df_forcing
anddf_state_init
. - If set to
True
, any detected invalid input will stop SuPy simulation; aFalse
flag will bypass such validation and may incur kernel error if any invalid input. Note: such checking procedure may take some time if the input is large. (the default isFalse
, which bypasses the validation). - serial_mode : bool, optional
- If set to
True
, SuPy simulation will be conducted in serial mode; aFalse
flag will try parallel simulation if possible (Windows not supported, i.e., always serial). (the default isFalse
).
- flag for checking validity of input:
Returns: df_output, df_state_final –
- df_output: output results
- df_state_final: final model states
Return type: Tuple[pandas.DataFrame, pandas.DataFrame]
Examples
>>> df_output, df_state_final = supy.run_supy(df_forcing, df_state_init)
- df_forcing (pandas.DataFrame) – forcing data for all grids in