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.LinearSample(pyomo_object, *args, **kwargs)[source]

Bases: watertap.tools.parameter_sweep.FixedSample

class watertap.tools.parameter_sweep.NormalSample(pyomo_object, *args, **kwargs)[source]

Bases: watertap.tools.parameter_sweep.RandomSample

class watertap.tools.parameter_sweep.RandomSample(pyomo_object, *args, **kwargs)[source]

Bases: watertap.tools.parameter_sweep._Sample

class watertap.tools.parameter_sweep.SamplingType(value)[source]

Bases: enum.Enum

An enumeration.

class watertap.tools.parameter_sweep.UniformSample(pyomo_object, *args, **kwargs)[source]

Bases: watertap.tools.parameter_sweep.RandomSample

watertap.tools.parameter_sweep.parameter_sweep(model, sweep_params, outputs, results_file=None, optimize_function=<function _default_optimize>, optimize_kwargs=None, reinitialize_function=None, reinitialize_kwargs=None, 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 samples num_samples will be take between the lower_limit and upper_limit.

  • outputs – A 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.

  • results_file (optional) – The path and file name where the results are to be saved; subdirectories will be created as needed.

  • optimize_function (optional) – A user-defined function to perform the optimization of flowsheet model and loads the results back into model. The first argument of this function is model. 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 be model, 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 to optimize_function fails for any reason. After reinitialize_function, the parameter sweep tool will immediately call optimize_function again.

  • reinitialize_kwargs (optional) – Dictionary or kwargs to pass into every call to reinitialize_function. The first arg will always be model, e.g., reinitialize_function(model, **reinitialize_kwargs). The default uses no kwargs.

  • 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 the outputs argument.

Return type

save_data

Module contents