AFL.automation.mixing.MassBalanceBase#

Functions

lsq_linear(A, b[, bounds, method, tol, ...])

Solve a linear least-squares problem with bounds on the variables.

Classes

Any(*args, **kwargs)

Special type indicating an unconstrained type.

BalanceDiagnosis(success, details, ...)

Full diagnosis of a single balance result.

Bounds([lb, ub, keep_feasible])

Bounds constraint on the variables.

FailureCode(value)

Machine-readable codes describing why a mass balance failed.

FailureDetail(code, description, ...)

One specific failure mode detected during diagnosis.

MassBalanceBase()

PipetteAction(source, dest, volume[, ...])

Solution(name[, total_mass, total_volume, ...])

class AFL.automation.mixing.MassBalanceBase.MassBalanceBase[source]#
__init__()[source]#
property components: Set[str]#
property stock_components: Set[str]#
property target_components: Set[str]#
mass_fraction_matrix() → ndarray[source]#
make_target_names(n_letters: int = 2, components=None, name_map: Dict | None = None)[source]#
balance_report()[source]#

Returns a json serializable structure that has all of the balanced targets that can be reconstituted by the user back into solution objects.

failure_summary() → str[source]#

Return a human-readable summary of all failed balance entries.

Returns an empty string if all balances succeeded or no balances have been run yet.

balance(tol=0.05, return_report=False, progress_callback: Callable[[...], Any] | None = None)[source]#