HydroCNHS.calibration package
Submodules
HydroCNHS.calibration.convertor module
- class HydroCNHS.calibration.convertor.Convertor
Bases:
object- gen_cali_inputs(wd, df_list, par_bound_df_list, fixed_par_list=None)
Generate inputs dictionary required for calibration.
Parameters
- wdstr
Working directory.
- df_listlist
A list of parameter dataframes. Dataframe index is parameter names.
- par_bound_df_listlist
A list of parameter bound dataframes.
- fixed_par_listlist, optional
A list contains a list of tuples of fixed parameter location like [([“CN2”], [“outlet1”, “outlet2”]), ([“b”], [“outlet1”])], by default None. The number of tuples in the list is corresponding to the number of dataframes.
Returns
- dict
Input for calibration.
- gen_formatter(df_list, fixed_par_list=None)
Generate formatter for given list of dataframe objects. This is already included in gen_cali_inputs().
Parameters
- df_listlist
A list of parameter dataframes. Dataframe index is parameter names.
- fixed_par_listlist, optional
A list contains a list of tuples of fixed parameter location. For example, we want to fix par1 of outlet1 & 2 and all par2 in dataframe 1 and not to fix df2. We can input the following list, [[([“par1”], [“outlet1”, “outlet2”]), ([“par2”], [“:”])], []]. fixed_par_list is by default None. The number of lists in the list is corresponding to the number of dataframes.
Returns
- dict
formatter.
- static to_1D_array(df_list, formatter)
Convert a list of dataframes to a 1D array.
Parameters
- df_listlist
A list of parameter dataframes. Dataframe index is parameter names.
- formatterdict
Formatter, generated by gen_formatter or gen_cali_inputs. It is stored in attributes of the Convertor object.
Returns
- array
1D array.
HydroCNHS.calibration.ga_deap module
- class HydroCNHS.calibration.ga_deap.GA_DEAP(evaluation_func, rn_gen=None)
Bases:
object- auto_save()
- find_best_and_record(pop)
- gen_rn_gens(seed, size)
- load(GA_auto_save_file, max_gen=None)
Load save pickle file (i.e., continue previous run).
Parameters
- GA_auto_save_filestr
Filename.
- max_genint, optional
This allow user to increase max_gen for continuing calibration for a longer searching, by default None.
- run(guess_pop=None)
Run calibration.
Parameters
- guess_pop2darray, optional
Assigned initial guesses, by default None. guess_pop has the size = [number of guesses, number of parameters]
- run_individual(individual='best', name='best')
Run the evaluation for a given individual.
Warning! run_individual() does not generantee the same rn_gen will be assign to the evaluation, but the same one will be used for run_individual()
Parameters
- individual1darray, optional
Individual or solution, by default “best”.
- namestr, optional
This will be sent to the evaluation function through info = (cali_wd, name, name, formatter, rn_gen), by default “best”.
- set(inputs, config, formatter=None, name='Calibration')
Setup the GA calibration.
Parameters
- inputsdict
Calibration input dictionary generated by Convertor. Or, get the template by calling get_inputs_template().
- configdict
Calibration configuration dictionary. Get the template by calling get_config_template().
- formatterdict, optional
Formatter generated by Convertor, by default None.
- namestr, optional
Name of the calibration, by default “Calibration”.
- HydroCNHS.calibration.ga_deap.cxUniform(ind1, ind2, indpb, rn_gen_gen)
- HydroCNHS.calibration.ga_deap.descale(individual, bound_scale, lower_bound)
individual is 1d ndarray.
- HydroCNHS.calibration.ga_deap.gen_init_pop(creator, size, method='LHC', guess_pop=None, rn_gen_gen=None)
- HydroCNHS.calibration.ga_deap.logger = <Logger HydroCNHS.GA (INFO)>
- inputs = {“par_name”: [“a”,”b”,”c”],
“par_bound”: [[0,1],[0,1],[0,1]], “wd”: r””}
- config = {“min_or_max”: “min”,
“pop_size”: 100, “num_ellite”: 1, “prob_cross”: 0.5, “prob_mut”: 0.1, “stochastic”: False, “max_gen”: 100, “sampling_method”: “LHC”, “drop_record”: False, “paral_cores”: -1, “paral_verbose”: 1, “auto_save”: True, “print_level”: 10, “plot”: True }
- HydroCNHS.calibration.ga_deap.mut_middle(individual, p1, p2, prob_mut, rn_gen_gen)
- HydroCNHS.calibration.ga_deap.mut_uniform(individual, prob_mut, rn_gen_gen)
- HydroCNHS.calibration.ga_deap.sample_by_LHC(size, rn_gen_gen)
- HydroCNHS.calibration.ga_deap.sample_by_MC(size, rn_gen_gen)
- HydroCNHS.calibration.ga_deap.scale(individual, bound_scale, lower_bound)
individual is 1d ndarray.
- HydroCNHS.calibration.ga_deap.selRoulette(individuals, k, rn_gen_gen, fit_attr='fitness')
Module contents
- HydroCNHS.calibration.get_config_template()
- HydroCNHS.calibration.get_inputs_template()
- HydroCNHS.calibration.helper()