make() Rules

Some rules, like x = trellis.rule(lambda self: {}), are meant only to initialize an attribute value, which will then remain constant -- or at least will not be recalculated. In the new API, the equivalent construct would be x = trellis.make(dict).

The argument to make() can currently be a function or a type. If it's a function (or any other object with a __get__ method), its __get__ method will be called to bind it to the object instance before calling it. Thus, you can use something like x = trellis.make(lambda self: self.foo), and have it work correctly as well.

Individual make rules can also be made writable, by passing in a writable=True keyword argument. This allows you to translate code like this:

trellis.values(x = None)
trellis.rules(x = lambda self: {})

into:

x = trellis.make(dict, writable=True)

which is more compact and clearer about the code's intent.

When writable, the created cell will be a Value instead of a Constant. Either way, the cell will be "optional" (i.e., created only when needed), unless you pass in optional=False to the constructor, and the supplied rule will only be invoked once, before the cell is created.

Note that in general, make rules should not read any other trellis values, since they will never be recalculated, and anything they do read will be treated as dependencies of any rule that caused the compute rule to be invoked. (A future version of the code may prevent make rules from accessing trellis values, by raising an error after the fact.)

Finally, note that there is also a make.attrs() form available:

trellis.make.attrs(
    x = dict,
    y = list,
    ...
)

And that make() can also be used as a decorator, either as @make or using a 2.3-compatible make() form. (And both decorator forms allow keyword arguments to be specified.)

compute Rules

Rules that simply compute a value, with no side-effects or dependency on their previous value, can be specified using @compute or compute.attrs().

Compute rules are both optional and "lazy". If a compute rule has no listeners, in other words, it is not recalculated until the next time it is read. Thus, it cannot enforce side effects or rely on its previous value.

You can make a compute rule discrete, by passing in resetting_to=val, where val is the value you would like the rule to automatically reset itself to between recalculations.

However, if a compute rule is not discrete, it has the option of becoming Constant if at runtime it stops depending on any other trellis values.

You can specify multiple compute rules using compute.attrs() in place of the old rules() function.

maintain Rules

The maintain constructor should be used for rules that must be recalculated every time their dependencies change, even if there are no rules depending on their return value. Unlike compute rules, maintain rules always have a writable value, and can read their previous value as well as modify other trellis values and objects.

A maintain rule can have an initial value (using the initially= or make= keywords), or can be discrete (using the resetting_to= keyword). It can also be optional (by specifying optional=True).

The make keyword argument allows you to specify an initialization function, similar to the make() constructor, to initialize the value of the rule. This can be helpful for translating code like this:

@trellis.rule
def data(self):
    data = self.data
    if data is None:
        data = {}
    ...

into this:

@trellis.maintain(make=dict)
def data(self):
    data = self.data
    ...

In other words, the make keyword lets you initialize the value the attribute will have, before the first invocation of the rule. (The initially keyword does the same thing, but specifies a constant value instead of a callable that creates the value.)