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supy.run_supy

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6.1.3.1.3. supy.run_supy#

supy.run_supy(df_forcing: DataFrame, df_state_init: DataFrame, save_state=False, chunk_day=3660, logging_level=20, check_input=False, serial_mode=False) → Tuple[DataFrame, DataFrame][source]#

Perform supy simulation.

6.1.3.1.3. Parameters#

df_forcingpandas.DataFrame

forcing data for all grids in df_state_init.

df_state_initpandas.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_statebool, 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).

chunk_dayint, optional

chunk size (chunk_day days) to split simulation periods so memory usage can be reduced. (the default is 3660, 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_inputbool, optional

flag for checking validity of input: df_forcing and df_state_init. If set to True, any detected invalid input will stop SuPy simulation; a False 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 is False, which bypasses the validation).

serial_modebool, optional

If set to True, SuPy simulation will be conducted in serial mode; a False flag will try parallel simulation if possible (Windows not supported, i.e., always serial). (the default is False).

6.1.3.1.3. Returns#

df_output, df_state_finalTuple[pandas.DataFrame, pandas.DataFrame]

6.1.3.1.3. Examples#

>>> df_output, df_state_final = supy.run_supy(df_forcing, df_state_init)
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