watertap.tools package
Submodules
watertap.tools.parameter_sweep module
- class watertap.tools.parameter_sweep.FixedSample(pyomo_object, *args, **kwargs)[source]
Bases:
watertap.tools.parameter_sweep._Sample
- class watertap.tools.parameter_sweep.LatinHypercubeSample(pyomo_object, *args, **kwargs)[source]
Bases:
watertap.tools.parameter_sweep._Sample
- class watertap.tools.parameter_sweep.RandomSample(pyomo_object, *args, **kwargs)[source]
Bases:
watertap.tools.parameter_sweep._Sample
- watertap.tools.parameter_sweep.parameter_sweep(model, sweep_params, outputs=None, csv_results_file=None, h5_results_file=None, optimize_function=<function _default_optimize>, optimize_kwargs=None, reinitialize_function=None, reinitialize_kwargs=None, reinitialize_before_sweep=False, mpi_comm=None, debugging_data_dir=None, interpolate_nan_outputs=False, num_samples=None, seed=None)[source]
This function offers a general way to perform repeated optimizations of a model for the purposes of exploring a parameter space while monitoring multiple outputs. If provided, writes single CSV file to
results_file
with all inputs and resulting outputs.- Parameters
model – A Pyomo ConcreteModel containing a watertap flowsheet, for best results it should be initialized before being passed to this function.
sweep_params – A dictionary containing the values to vary with the format
sweep_params['Short/Pretty-print Name'] = (model.fs.variable_or_param[index], lower_limit, upper_limit, num_samples)
. A uniform number of samplesnum_samples
will be take between thelower_limit
andupper_limit
.outputs – An optional dictionary containing “short names” as keys and and Pyomo objects on
model
whose values to report as values. E.g.,outputs['Short/Pretty-print Name'] = model.fs.variable_or_expression_to_report
. If not provided, i.e., outputs = None, the default behavior is to save all model variables, parameters, and expressions which provides very thorough results at the cost of large file sizes.csv_results_file (optional) – The path and file name where the results are to be saved; subdirectories will be created as needed.
h5_results_file (optional) – The file name without the extension where the results are to be saved; The path is identified from the arguments of csv_results_file. This filename is used when creating the H5 file and the companion text file which contains the variable names contained within the H5 file.
optimize_function (optional) – A user-defined function to perform the optimization of flowsheet
model
and loads the results back intomodel
. The first argument of this function ismodel
. The default uses the default IDAES solver, raising an exception if the termination condition is not optimal.optimize_kwargs (optional) – Dictionary of kwargs to pass into every call to
optimize_function
. The first arg will always bemodel
, e.g.,optimize_function(model, **optimize_kwargs)
. The default uses no kwargs.reinitialize_function (optional) – A user-defined function to perform the re-initialize the flowsheet
model
if the first call tooptimize_function
fails for any reason. Afterreinitialize_function
, the parameter sweep tool will immediately calloptimize_function
again.reinitialize_kwargs (optional) – Dictionary or kwargs to pass into every call to
reinitialize_function
. The first arg will always bemodel
, e.g.,reinitialize_function(model, **reinitialize_kwargs)
. The default uses no kwargs.reinitialize_before_sweep (optional) – Boolean option to reinitialize the flow sheet model before every parameter sweep realization. The default is False. Note the parameter sweep model will try to reinitialize the solve regardless of the option if the run fails.
mpi_comm (optional) – User-provided MPI communicator for parallel parameter sweeps. If None COMM_WORLD will be used. The default is sufficient for most users.
debugging_data_dir (optional) – Save results on a per-process basis for parallel debugging purposes. If None no debugging data will be saved.
interpolate_nan_outputs (optional) – When the parameter sweep has finished, interior values of np.nan will be replaced with a value obtained via a linear interpolation of their surrounding valid neighbors. If true, a second output file with the extension “_clean” will be saved alongside the raw (un-interpolated) values.
num_samples (optional) – If the user is using sampling techniques rather than a linear grid of values, they need to set the number of samples
seed (optional) – If the user is using a random sampling technique, this sets the seed
- Returns
- A list were the first N columns are the values of the parameters passed
by
sweep_params
and the remaining columns are the values of the simulation identified by theoutputs
argument.
- Return type
save_data