Model Evaluation

Getter Functions


qsdsan.utils.AttrGetter(obj, attr, hook=<function AttrGetter.<lambda>>, hook_param=())


qsdsan.utils.FuncGetter(func, params=())

Setter Functions


qsdsan.utils.AttrFuncSetter(obj, attrs, funcs)


qsdsan.utils.AttrSetter(obj, attrs)


qsdsan.utils.DictAttrSetter(obj, dict_attr, keys)


qsdsan.utils.MethodSetter(obj, method, key=None, **kwargs)

Sample Manipulation


qsdsan.utils.copy_samples(original, new, exclude=(), only_same_baseline=False)

Copy samples of the shared parameters in the original model to the new model. Parameters in exclude will be excluded (i.e., will not be copied).

  • original (obj) – Original model where the samples will be copied from.

  • new (obj) – New model whose samples of the shared parameters will be copied from the original model.

  • exclude (tuple(obj)) – Parameters that will be excluded from copying.

  • only_same_baseline (bool) – If True, will only copy parameters with the same name, units, and baseline values.


Create two models with shared and different parameters

>>> from qsdsan.utils import create_example_model, copy_samples
>>> original = create_example_model()
>>> # You might get some replacing warnings as we are making two exact systems,
>>> # it's OK
>>> new = create_example_model()
>>> # Sort the parameter orders
>>> for model in (original, new):
...    model.parameters = sorted(model.parameters, key=lambda p:

Let’s make the 1st/2nd parameters of the original model the same as the 0th/1st parameters of the new model.

>>> original.parameters = original.parameters[:3]
>>> original.parameters 
(<Parameter: [HXutility-H1] Heat exchanger temperature (K)>,
 <Parameter: [Mix tank-M1] Mix tank mixer power usage (kW/m3)>,
 <Parameter: [Mix tank-M1] Mix tank retention time (hr)>)
>>> new.parameters = new.parameters[1:4]
>>> new.parameters 
(<Parameter: [Mix tank-M1] Mix tank mixer power usage (kW/m3)>,
 <Parameter: [Mix tank-M1] Mix tank retention time (hr)>,
 <Parameter: [Pump-P1] Pump design head (kPa)>)

Before copying, samples of the shared parameter are not the same

>>> original_samples = original.sample(N=100, rule='L')
>>> original.load_samples(original_samples)
>>> new_samples = new.sample(N=100, rule='L')
>>> new.load_samples(new_samples)
>>> (original.table.values[:, 1:3]==new.table.values[:, 0:2]).all()

Let’s copy the samples, but exclude the “Mix tank retention time”

>>> copy_samples(original, new, exclude=(original.parameters[2],))
>>> # After copying, all of the samples of the "Mix tank mixer power usage"
>>> # parameter are the same
>>> (original.table.values[:, 1]==new.table.values[:, 0]).all()
>>> # But the samples are not the same for the excluded parameter
>>> # "Mix tank retention time" parameter
>>> (original.table.values[:, 2]==new.table.values[:, 1]).all()

When only_same_baseline is True, only values for samples with the same name and unit will not be copied if their baseline values are different.

>>> # Let's change the baseline values for the "Mix tank retention time" parameter
>>> # to be different for the two models
>>> new.parameters[1].baseline = original.parameters[2].baseline + 10
>>> copy_samples(original, new, exclude=(), only_same_baseline=True)
>>> # Samples are not copied even if they have the same names due to the unequal baselines
>>> (original.table.values[:, 2]==new.table.values[:, 1]).any()