cassowary 0.3.0+4
Cassowary in Dart #
This is an implementation of the Cassowary constraint solving algorithm in Dart. The initial implementation was based on the Kiwi toolkit written in C++. Implements a subset of the functionality described in the Cassowary paper and makes specific affordances for the needs of Flutter
The Solver #
A solver is the object that accepts constraints and updates member variables in an attempt to satisfy the same.
Parameters #
In order to create constraints, the user needs to take specific parameter objects vended by elements in the view hierarchy and create expression from these. Constraints can then be obtained from these expressions. If the solver needs to update these parameters to satisfy constraints, it will call callbacks on these parameters.
Constructing Constraints #
A constraint is a linear equation of the form ax + by + cz + ... + k == 0
. Constraints need not be equality relationships. Less/greaterthanorequalto (<=
or >=
) relationships can also be specified. In addition, each constraint is specified at a given priority to help resolve constraint ambiguities. A system can also be overconstrained.
The constraint as a whole is represented by an instance of the Constraint
object. This in turn references an instance of an Expression
(ax + by + cz + ... k
), a realtionship and the finally the priority.
Each expression in turn is made up of a list of Term
s and a constant. The term ax
has the coefficient a
and Variable
x
. The Param
that is vended to the user is nothing but a wrapper for this variable and deals with detecting changes to it and updating the underlying view in the hierarchy.
Once the user obtains specific parameter objects, it is straightforward to create constraints. The following example sets up constraints that specify that the width of the element must be at least 100 units. It is assumed that the left
and right
Param
objects have been obtained from the view in question.
var widthAtLeast100 = right  left >= cm(100.0);
Lets go over this one step at a time: The expression right  left
creates an instance of an Expression
object. The expression consists of two terms. The right
and left
params wrap variables. The coefficients are 1.0 and 1.0 respectively and the constant 100.0. Constants need to be decorated with CM
to aid with the operator overloading mechanism in Dart.
All variables are unrestricted. So there is nothing preventing the solver from making the left and right edges negative. We can specify our preference against this by specifying another constraint like so:
var edgesPositive = (left >= cm(0.0));
When we construct these constraints for the solver, they are created at the default Priority.required
. This means that the solver will resist adding constraints where there are ambiguities between two required constraints. To specify a weaker priority, you can use the priority
setter or use the 
symbol with the priority while constructing the constraint. Like so:
var edgesPositive = (left >= cm(0.0))..priority = Priority.weak;
Once the set of constraints are constructed, they are added to the solver and the results of the solution flushed out.
solver
..addConstraints([widthAtLeast100, edgesPositive])
..flushUpdates();
Edit Constraints #
When updates need to be applied to parameters that are a part of the solver, edit variables may be used. To illustrate this, we try to express the following case in terms of constraints and their update: On mouse down, we want to update the midpoint of our view and have the left
and right
parameters automatically updated (subject to the constraints already setup).
We create a parameter that we will use to represent the mouse coordinate.
var mid = new Variable(coordinate);
Then, we add a constraint that expresses the midpoint in terms of the parameters we already have.
solver.addConstraint((left + right).equals(Term(mid, 1.0) * cm(2.0)));
Then, we specify that we intend to edit the midpoint. As we get updates, we tell the solver to satisfy all other constraints (admittedly our example is trivial).
solver.addEditVariable(mid, Priority.strong);
and finally
solver.flushUpdates();
Changelog #
0.3.0+4 #
 Updating dependencies.
0.3.0+3 #
 Adding linter rules.
0.3.0+2 #
 Adding example code and correcting sample code in the README.md.
0.3.0+1 #
 Fix package homepage and description in pubspec.yaml.
0.3.0 #
 Republishing as a standalone package
 Upgraded implementation to Dart 2
0.1.7 #
 Minor API updates to address code review concerns
0.1.6 #
 Add bulk remove options
0.1.5 #
 Bulk update options on solver
0.1.4 #
==
operator override on expression returns a constraint
0.1.3 #
 Parameters contexts are non final
0.1.2 #
 Parameters have contexts associated with them
0.1.1 #
 When updating parameters, the set of updates is returned
0.1.0 #
 Initial version
// Copyright 2018 The Chromium Authors. All rights reserved.
// Use of this source code is governed by a BSDstyle license that can be
// found in the LICENSE file.
import 'package:cassowary/cassowary.dart';
void main() {
final solver = Solver();
final left = Param(10);
final right = Param(20);
final widthAtLeast100 = right  left >= cm(100);
final edgesPositive = (left >= cm(0))..priority = Priority.weak;
solver
..addConstraints([widthAtLeast100, edgesPositive])
..flushUpdates();
print('left: ${left.value}, right: ${right.value}');
final mid = Variable(15);
// It appears that == isn't defined
solver
..addConstraint((left + right).equals(Term(mid, 1) * cm(2)))
..addEditVariable(mid, Priority.strong)
..flushUpdates();
print('left: ${left.value}, mid: ${mid.value}, right: ${right.value}');
}
Use this package as a library
1. Depend on it
Add this to your package's pubspec.yaml file:
dependencies:
cassowary: ^0.3.0+4
2. Install it
You can install packages from the command line:
with pub:
$ pub get
with Flutter:
$ flutter pub get
Alternatively, your editor might support pub get
or flutter pub get
.
Check the docs for your editor to learn more.
3. Import it
Now in your Dart code, you can use:
import 'package:cassowary/cassowary.dart';
Popularity:
Describes how popular the package is relative to other packages.
[more]

25

Health:
Code health derived from static analysis.
[more]

100

Maintenance:
Reflects how tidy and uptodate the package is.
[more]

100

Overall:
Weighted score of the above.
[more]

62

We analyzed this package on Jan 24, 2020, and provided a score, details, and suggestions below. Analysis was completed with status completed using:
 Dart: 2.7.0
 pana: 0.13.4