Cassowary supports linear equations and non-strict inequalities. Additionally, a strength may be associated with each constraint in the system, constrolling its importance to the overall solution to the system.
Variables are the values which the solver is trying to resolve. These
correspond to the
Variable type in the implementation. The variables can be
used to create the expressions which form constraints of the system. These must
be added to an instance of the solver.
import cassowary let simplex: Solver = Solver() let x_l: Variable = Variable("x_l") let x_r: Variable = Variable("x_r") let x_m: Variable = Variable("x_m") simplex.add(constraint: 2.0 * x_m == x_l + x_r) simplex.add(constraint: x_l + 10.0 <= x_r) simplex.add(constraint: x_l >= -10.0) simplex.add(constraint: x_r <= 100.0)
This creates a system with three variables (xl, xr,
xm) representings points on a line segment. xm is
constrained to the midpoint between xl and xr,
xl is constrained to be at least 10 to the left of xr, and
all variables must lie in the range [-10, 100]. All constraints must be
satisfied and are considered as
required by the cassowary algorithm.
NOTE The same constraint in the same form cannot be added to the solver multiply. Redundant constraints, as per cassowary, are supported. That is, the following set of constraints can be added to the solver:
x == 10 x + y == 30 y == 20
Cassowary supports constraints which are not required but are handled as
best-effort. Such a constraint is modelled as having a strength other than
required. The constraints are considered in order of the value of their
strengths. Three standard strengths are defined by default:
We can add a constraint to our previous example to place xm at 50 by
adding a new
simplex.add(constraint: (x_m == 50.0) | Strength.weak)
The system described thus far has been static. In order to find solutions for
particular value of xm, Cassowary provides the concept of edit
variables which allows you to suggest values for the variable before evaluating
the system. These variables can have any strength other than
Continuing our example, we could make xm editable and suggest a value
60 for it.
simplex.add(variable: x_m, .strong) simplex.suggest(value: 60.0, for: x_m)
This implementation solves the system each time a constraint is added or removed, or when a new value is suggested for an edit variable. However, the variable values are not updated automatically and you must request the solver to update the values.
simplex.suggest(value: 90, for: x_m) simplex.update()