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Event loops for tests, wx, and twisted. No docs, and only minimal testing so far, but much hacking was done to get these to work more-or-less correctly. wx needs some mocking, and it turns out that the Time service really needs to make next_event_time() dirty when new events are added, even if self._tick doesn't change. That'll have to be fixed later, though (as with the wx mocks issue).
===================================================================
Event-Driven Programming The Easy Way, with ``peak.events.trellis``
===================================================================
Whether it's an application server or a desktop application, any sufficiently
complex system is event-driven -- and that usually means callbacks.
Unfortunately, explicit callback management is to event-driven programming what
explicit memory management is to most other kinds of programming: a tedious
hassle and a significant source of unnecessary bugs.
For example, even in a single-threaded program, callbacks can create race
conditions, if the callbacks are fired in an unexpected order. If a piece
of code can cause callbacks to be fired "in the middle of something", both that
code *and* the callbacks can get confused.
Of course, that's why most GUI libraries and other large event-driven systems
usually have some way for you to temporarily block callbacks from happening.
This lets you fix or workaround your callback order dependency bugs... at the
cost of adding even *more* tedious callback management. And it still doesn't
fix the problem of forgetting to cancel callbacks... or register needed ones
in the first place!
The Trellis solves all of these problems by introducing *automatic* callback
management, in much the same way that Python does automatic memory management.
Instead of worrying about subscribing or "listening" to events and managing
the order of callbacks, you just write rules to compute values. The Trellis
"sees" what values your rules access, and thus knows what rules may need to be
rerun when something changes -- not unlike the operation of a spreadsheet.
But even more important, it also ensures that callbacks *can't* happen while
code is "in the middle of something". Any action a rule takes that would cause
a new event to fire is *automatically* deferred until all of the applicable
rules have had a chance to respond to the event(s) in progress. And, if you
try to access the value of a rule that hasn't been updated yet, it's
automatically updated on-the-fly so that it reflects the current event in
progress.
No stale data. No race conditions. No callback management. That's what the
Trellis gives you.
Here's a super-trivial example::
>>> from peak.events import trellis
>>> class TempConverter(trellis.Component):
... trellis.values(
... F = 32,
... C = 0,
... )
... trellis.rules(
... F = lambda self: self.C * 1.8 + 32,
... C = lambda self: (self.F - 32)/1.8,
... )
... @trellis.action
... def show_values(self):
... print "Celsius......", self.C
... print "Fahrenheit...", self.F
>>> tc = TempConverter(C=100)
Celsius...... 100
Fahrenheit... 212.0
>>> tc.F = 32
Celsius...... 0.0
Fahrenheit... 32
>>> tc.C = -40
Celsius...... -40
Fahrenheit... -40.0
As you can see, each attribute is updated if the other one changes, and the
``show_values`` action is invoked any time the dependent values change... but
not if they don't::
>>> tc.C = -40
Since the value didn't change, none of the rules based on it were recalculated.
Now, imagine all this, but scaled up to include rules that can depend on things
like how long it's been since something happened... whether a mouse button was
clicked... whether a socket is readable... or whether a Twisted "deferred"
object has fired. With automatic dependency tracking that spans function
calls, so you don't even need to *know* what values your rule depends on, let
alone having to explicitly code any dependencies in!
Imagine painless MVC, where you simply write rules like the above to update
GUI widgets with application values... and vice versa.
And then, you'll have the tiny beginning of a mere glimpse... of what the
Trellis can do for you.
Other Python libraries exist which attempt to do similar things, of course;
PyCells and Cellulose are two. However, only the Trellis supports fully
circular rules (like the temperature conversion example above), and intra-pulse
write conflict detection. The Trellis also uses less memory for each cell
(rule/value object), and offers many other features that either PyCells or
Cellulose lack.
The Trellis also boasts an extensive `Tutorial and Reference Manual`_, and
can be `downloaded from the Python Package Index`_ or installed using
`Easy Install`_.
Questions, discussion, and bug reports for the Trellis should be directed to
the `PEAK mailing list`_.
.. _downloaded from the Python Package Index: http://pypi.python.org/pypi/Trellis#toc
.. _Easy Install: http://peak.telecommunity.com/DevCenter/EasyInstall
.. _PEAK mailing list: http://www.eby-sarna.com/mailman/listinfo/PEAK/
.. _Tutorial and Reference Manual: http://peak.telecommunity.com/DevCenter/Trellis#toc
.. _toc:
.. contents:: **Table of Contents**
------------------------------
Developer's Guide and Tutorial
------------------------------
Creating Components, Cells, and Rules
=====================================
A ``trellis.Component`` is an object that can have its attributes automatically
maintained by rules, the way a spreadsheet is maintained by its formulas.
These managed attributes are called "cell attributes", because the attribute
values are stored in ``trellis.Cell`` objects. The ``Cell`` objects can
contain preset values, values computed using rules, or even both at the same
time. (Like in the temperature converter example above.)
To define a simple cell attribute, you can use the ``trellis.rules()`` and
``trellis.values()`` functions inside the class body to define multiple rules
and values. Or, you can use the ``@trellis.rule`` decorator to turn an
individual function into a rule, or define a single value attribute by calling
``trellis.value``. Last, but not least, you can use ``@trellis.action`` to
define a rule that does something other than just computing a value. Here's an
example that uses all of these approaches, simply for the sake of
illustration::
>>> class Rectangle(trellis.Component):
... trellis.values(
... top = 0,
... width = 20,
... )
... left = trellis.value(0)
... height = trellis.value(30)
...
... trellis.rules(
... bottom = lambda self: self.top + self.height,
... )
...
... @trellis.rule
... def right(self):
... return self.left + self.width
...
... @trellis.action
... def show(self):
... print self
...
... def __repr__(self):
... return "Rectangle"+repr(
... ((self.left,self.top), (self.width,self.height),
... (self.right,self.bottom))
... )
>>> r = Rectangle(width=40, height=10)
Rectangle((0, 0), (40, 10), (40, 10))
>>> r.width = 17
Rectangle((0, 0), (17, 10), (17, 10))
>>> r.left = 25
Rectangle((25, 0), (17, 10), (42, 10))
By the way, any attributes for which you define an action or a rule (but *not*
a value) will be read-only::
>>> r.bottom = 99
Traceback (most recent call last):
...
AttributeError: can't set attribute
However, if you define both a rule *and* a value for the attribute, as we did
in the ``TemperatureConverter`` example, then you'll be able to both read and
write the attribute's value.
Note, by the way, that you aren't required to make everything in your program a
``trellis.Component`` in order to use the Trellis. The ``Component`` class
does only four things, and you are free to accomplish these things some other
way if you need or want to:
1. It sets ``self.__cells__ = trellis.Cells(self)``. This creates a special
dictionary that will hold all the ``Cell`` objects used to implement cell
attributes.
2. It takes any keyword arguments it receives, and uses them to initialize any
named attributes. (Note that you don't necessarily have to do this, but it
often comes in handy.)
3. It creates a cell for each of the object's non-optional cell attributes,
in order to initialize their rules and set up their dependencies. We'll
cover this in more detail in the next section, `Automatic Activation and
Dependencies`_.
4. It wraps the entire object creation process in a ``@modifier``, so that all
of the above operations occur in a single logical transaction. We'll cover
this more in a later section on `Managing State Changes`_.
In addition to doing these things another way, you can also use ``Cell``
objects directly, without any ``Component`` classes. This is discussed more
in the section below on `Working With Cell Objects`_.
Automatic Activation and Dependencies
-------------------------------------
You'll notice that each time we change an attribute value, our Rectangle
instance above prints itself -- including when the instance is first created.
That's because of two important Trellis principles:
1. When a ``Component`` instance is created, all its "non-optional" cell
attributes are calculated at the end of ``Component.__init__()``. That is,
if they have a rule, it gets invoked, and the result is used to determine
the cell's initial value.
2. While a cell's rule is running, *any* trellis Cell that is looked at becomes
a dependency of that rule. If the looked-at cell changes later, it triggers
recalculation of the rule that looked. In Trellis terms, we say that the
first cell has become an "observer" of the second cell.
The first of these principles explains why the rectangle printed itself
immediately: the ``show`` rule was calculated. We can see this if we look at
the rectangle's ``show`` attribute::
>>> print r.show
None
(The ``show`` rule didn't return a specific value, so the resulting attribute
value is ``None``. Also notice that *rules are not methods* -- they are more
like properties.)
The second principle explains why the rectangle re-prints itself any time one
of the attributes changes value: all six attributes are referenced by the
``__repr__`` method, which is called when the ``show`` rule prints the
rectangle. Since the cells that store those attributes are being looked at
during the execution of another cell's rule, they become dependencies, and the
``show`` rule is thus recalculated whenever the observed cells change.
Each time a rule runs, its dependencies are automatically re-calculated --
which means that if you have more complex rules, they can actually depend on
different cells every time they're calculated. That way, the rule is only
recalculated when it's absolutely necessary.
By the way, an observed cell has to actually *change* its value (as determined
by the ``!=`` operator), in order to trigger recalculation. Merely setting a
cell doesn't cause its observers to recalculate::
>>> r.width = 17 # doesn't trigger ``show``
But changing it to a non-equal value *does*::
>>> r.width = 18
Rectangle((25, 0), (18, 10), (43, 10))
Note that if a cell rule ever has *no* dependencies -- that is, does not look
at any other cell attributes -- then it will not be recalculated. This means
you can use trellis rules to create attributes that are automatically
initialized, but then keep the same value thereafter::
>>> class Demo(trellis.Component):
... aDict = trellis.rule(lambda self: {})
>>> d = Demo()
>>> d.aDict
{}
>>> d.aDict[1] = 2
>>> d.aDict
{1: 2}
A rule like this will return the same object every time, because it doesn't
use any other cells to compute its value. So it runs once, and never again.
If we also defined a ``trellis.value`` for ``aDict``, then the attribute
would also be writable, and we could put a different value there. But since
we didn't, it becomes read-only::
>>> d.aDict = {}
Traceback (most recent call last):
...
AttributeError: Constants can't be changed
...even though we can override the initial value when the component is created,
or any time before it is first read::
>>> d = Demo(aDict={3:4})
>>> d.aDict
{3: 4}
However, since this rule is not an "optional" rule, the ``Component.__init__``
method will read it, meaning that the only chance we get to override it is
via the keyword arguments. In the next section, we'll look at how to create
"optional" rules: ones that don't get calculated the moment a component is
created.
"Optional" Rules and Subclassing
--------------------------------
The ``show`` rule we've been playing with on our ``Rectangle`` class is kind of
handy for debugging, but it's kind of annoying when you don't need it. Let's
turn it into an "optional" action, so that it won't run unless we ask it to::
>>> class QuietRectangle(Rectangle):
... @trellis.optional
... @trellis.action
... def show(self):
... print self
By subclassing ``Rectangle``, we inherit all of its cell attribute definitions.
We call our new optional rule ``show`` so that its definition overrides the
noisy version of the rule. And, because it's marked optional, it isn't
automatically activated when the instance is created. So we don't get any
announcements when we create an instance or change its values::
>>> q = QuietRectangle(width=18, left=25)
>>> q.width = 17
Unless, of course, we activate the ``show`` rule ourselves::
>>> q.show
Rectangle((25, 0), (17, 30), (42, 30))
And from now on, it'll be just as chatty as the previous rectangle object::
>>> q.left = 0
Rectangle((0, 0), (17, 30), (17, 30))
While any other ``QuietRectangle`` objects we create will of course remain
silent, since we haven't activated *their* ``show`` cells::
>>> q2 = QuietRectangle()
>>> q2.top = 99
Notice, by the way, that rules are more like properties than methods, which
means you can't use ``super()`` to call the inherited version of a rule.
(Later, we'll look at other ways to access rule definitions.)
Model-View-Controller and the "Observer" Pattern
------------------------------------------------
As you can imagine, the ability to create rules like this can come in handy
for debugging. Heck, there's no reason you have to print the values, either.
If you're making a GUI application, you can define rules that update displayed
fields to match application object values.
For that matter, you don't even need to define the rule in the same class!
For example::
>>> class Viewer(trellis.Component):
... trellis.values(model = None)
...
... @trellis.action
... def view_it(self):
... if self.model is not None:
... print self.model
>>> view = Viewer(model=q2)
Rectangle((0, 99), (20, 30), (20, 129))
Now, any time we change q2, it will be printed by our ``q2_view`` rule, even
though we haven't activated q2's ``show`` rule::
>>> q2.left = 66
Rectangle((66, 99), (20, 30), (86, 129))
This means that we can automatically update a GUI (or whatever else might need
updating), without adding any code to the thing we want to "observe". Just
use cell attributes, and *everything* can use the "observer pattern" or be a
"Model-View-Controller" architecture. Just define rules that can read from the
"model", and they'll automatically be invoked when there are any changes to
"view".
Notice, by the way, that our ``Viewer`` object can be repointed to any object
we want. For example::
>>> q3 = QuietRectangle()
>>> view.model = q3
Rectangle((0, 0), (20, 30), (20, 30))
>>> q2.width = 59 # it's not watching us any more, so no output
>>> view.model = q2 # watching q2 again
Rectangle((66, 99), (59, 30), (125, 129))
>>> q3.top = 77 # but we're not watching q3 any more
See how each time we change the ``model`` attribute, the ``view_it`` rule is
recalculated? The rule references ``self.model``, which is a value cell
attribute. So if you change ``view.model``, this triggers a recalculation,
too.
Remember: once a rule observes another cell, it will be recalculated whenever
the observed value changes. Each time ``view_it`` is recalculated, it renews
its dependency on ``self.model``, but *also* acquires new dependencies on
whatever the ``repr()`` of ``self.model`` looks at. Meanwhile, any
dependencies on the attributes of the *previous* ``self.model`` are dropped,
so changing them doesn't cause the rule to be recalculated any more. This
means we can even do things like set ``model`` to a non-component object, like
this::
>>> view.model = {}
{}
But since dictionaries don't use any cells, changing the dictionary won't do
anything:
>>> view.model[1] = 2
To be able to observe mutable data structures, you need to use data types like
``trellis.Dict`` and ``trellis.List`` instead of the built-in Python types.
We'll cover how that works in the section below on `Mutable Data Structures`_.
By the way, the links from a cell to its observers are defined using weak
references. This means that views (and cells or components in general) can
be garbage collected even if they have dependencies. For more information
about how Trellis objects are garbage collected, see the later section on
`Garbage Collection`_.
Accessing a Rule's Previous Value
---------------------------------
Sometimes it's useful to create a rule whose value is based in part on its
previous value. For example, a rule that produces an average over time, or
that ignores "noise" in an input value, by only returning a new value when the
input changes more than a certain threshhold since the last value. It's fairly
easy to do this, using rules that refer to their previous value::
>>> class NoiseFilter(trellis.Component):
... trellis.values(
... value = 0,
... threshhold = 5,
... filtered = 0
... )
... @trellis.rule
... def filtered(self):
... if abs(self.value - self.filtered) > self.threshhold:
... return self.value
... return self.filtered
>>> nf = NoiseFilter()
>>> nf.filtered
0
>>> nf.value = 1
>>> nf.filtered
0
>>> nf.value = 6
>>> nf.filtered
6
>>> nf.value = 2
>>> nf.filtered
6
>>> nf.value = 10
>>> nf.filtered
6
>>> nf.threshhold = 3 # changing the threshhold re-runs the filter...
>>> nf.filtered
10
>>> nf.value = -3
>>> nf.filtered
-3
As you can see, referring to the value of a cell from inside the rule that
computes the value of that cell, will return the *previous* value of the cell.
Notice, by the way, that this technique can be extended to keep track of an
arbitrary number of variables, if you create a rule that returns a tuple.
We'll use this technique more later on.
Beyond The Spreadsheet: "Receiver" Cells
----------------------------------------
So far, all the stuff we've been doing isn't really any different than what you
can do with a spreadsheet, except maybe in degree. Spreadsheets usually don't
allow the sort of circular calculations we've been doing, but that's not really
too big of a leap.
But practical programs often need to do more than just reflect the values of
things. They need to *do* things, too.
While rule and value cells reflect the current "state" of things, discrete and
receiver cells are designed to handle things that are "happening". They also
let us handle the "Controller" part of "Model-View-Controller".
For example, suppose we want to have a controller that lets you change the
size of a rectangle. We can use "receiver" attributes to do this, which are
sort of like an "event", "message", or "command" in a GUI or other event-driven
system::
>>> class ChangeableRectangle(QuietRectangle):
... trellis.receivers(
... wider = 0,
... narrower = 0,
... taller = 0,
... shorter = 0
... )
... trellis.rules(
... width = lambda self: self.width + self.wider - self.narrower,
... height = lambda self: self.height + self.taller - self.shorter,
... )
>>> c = ChangeableRectangle()
>>> view.model = c
Rectangle((0, 0), (20, 30), (20, 30))
A ``receiver`` attribute (created with ``trellis.receiver()`` or
``trellis.receivers()``) works by "receiving" an input value, and then
automatically resetting itself to its default value after its dependencies are
updated. For example::
>>> c.wider
0
>>> c.wider = 1
Rectangle((0, 0), (21, 30), (21, 30))
>>> c.wider
0
>>> c.wider = 1
Rectangle((0, 0), (22, 30), (22, 30))
Notice that setting ``c.wider = 1`` updated the rectangle as expected, but as
soon as all updates were finished, the attribute reset to its default value of
zero. In this way, every time you put a value into a receiver, it gets
processed and discarded. And each time you set it to a non-default value,
it's treated as a *change*. Which means that any rule that depends on the
receiver will be recalculated. If we'd used a normal ``trellis.value`` here,
then set ``c.wider = 1`` twice in a row, nothing would happen the second time!
Now, we *could* write methods for changing value cells that would do this sort
of resetting for us, but why? We'd need to have both the attribute *and* the
method, and we'd need to remember to never set the attribute directly. It's
much easier to just use a receiver as an "event sink" -- that is, to receive,
consume, and dispose of any messages or commands you want to send to an object.
But why do we need such a thing at all? Why not just write code that directly
manipulates the model's width and height? Well, sometimes you *can*, but it
limits your ability to create generic views and controllers, makes it
impossible to "subscribe" to an event from multiple places, and increases the
likelihood that your program will have bugs -- especially order-dependency
bugs.
If you use rules to *compute* values instead of writing code to *manipulate*
values, then all the code that affects a value is in *exactly one place*. This
makes it very easy to verify whether that code is correct, because the way
the value is arrived at doesn't depend on what order a bunch of manipulation
methods are being called in, and whether those methods are correctly updating
everything they should.
Thus, as long as a cell's rule doesn't modify *anything* except local
variables, there is no way for it to become "corrupt" or "out of sync" with the
rest of the program. This is a form of something called "referential
transparency", which roughly means "order independent". We'll cover this topic
in more detail in the later section on `Managing State Changes`_. But in the
meantime, let's look at how using receivers instead of methods also helps us
implement generic controllers.
Creating Generic Controllers by Sharing Cells
---------------------------------------------
Let's create a couple of generic "Spinner" controllers, that take a pair of
"increase" and "decrease" receivers, and hook them up to our changeable
rectangle::
>>> class Spinner(trellis.Component):
... """Increase or decrease a value"""
... increase = trellis.receiver(0)
... decrease = trellis.receiver(0)
... by = trellis.value(1)
...
... def up(self):
... self.increase = self.by
...
... def down(self):
... self.decrease = self.by
>>> cells = trellis.Cells(c)
>>> width = Spinner(increase=cells['wider'], decrease=cells['narrower'])
>>> height = Spinner(increase=cells['taller'], decrease=cells['shorter'])
The ``trellis.Cells()`` API returns a dictionary containing all active cells
for the object. (We'll cover more about this in the section below on `Working
With Cell Objects_`.) You can then access them directly, assigning them to
other components' attributes.
Assigning a ``Cell`` *object* to a cell *attribute* allows two components to
**share** the same cell. In this case, that means setting the ``.increase``
and ``.decrease`` attributes of our ``Spinner`` objects will set the
corresponding attributes on the rectangle object, too::
>>> width.up()
Rectangle((0, 0), (23, 30), (23, 30))
>>> width.down()
Rectangle((0, 0), (22, 30), (22, 30))
>>> height.by = 5
>>> height.down()
Rectangle((0, 0), (22, 25), (22, 25))
>>> height.up()
Rectangle((0, 0), (22, 30), (22, 30))
Could you do the same thing with methods? Maybe. But can methods be linked
the *other* way?::
>>> width2 = Spinner()
>>> height2 = Spinner()
>>> controlled_rectangle = ChangeableRectangle(
... wider = trellis.Cells(width2)['increase'],
... narrower = trellis.Cells(width2)['decrease'],
... taller = trellis.Cells(height2)['increase'],
... shorter = trellis.Cells(height2)['decrease'],
... )
>>> view.model = controlled_rectangle
Rectangle((0, 0), (20, 30), (20, 30))
>>> height2.by = 10
>>> height2.up()
Rectangle((0, 0), (20, 40), (20, 40))
A shared cell is a shared cell: it doesn't matter which "direction" you share
it in! It's a simple way to create an automatic link between two parts
of your program, usually between a view or controller and a model. For
example, if you create a text editing widget for a GUI application, you can
define a value cell for the text in its class::
>>> class TextEditor(trellis.Component):
... text = trellis.value('')
...
... @trellis.action
... def display(self):
... print "updating GUI to show", repr(self.text)
>>> te = TextEditor()
updating GUI to show ''
>>> te.text = 'blah'
updating GUI to show 'blah'
And then you'd write some additional code to automatically set ``self.text``
when there's accepted input from the GUI. An instance of this editor can then
either maintain its own ``text`` cell, or be given a cell from an object whose
attributes are being edited.
This allows you to independently test your models, views, and controllers, then
simply link them together at runtime in any way that's useful.
"Discrete" Rules
----------------
Receiver attributes are designed to "accept" what might be called events,
messages, or commands. But what if you want to generate or transform such
events instead?
Let's look at an example. Suppose you'd like to trigger an action whenever a
new high temperature is seen::
>>> class HighDetector(trellis.Component):
... value = trellis.value(0)
... max_and_new = trellis.value((None, False))
...
... @trellis.rule
... def max_and_new(self):
... last_max, was_new = self.max_and_new
... if last_max is None:
... return self.value, False # first seen isn't a new high
... elif self.value > last_max:
... return self.value, True
... return last_max, False
...
... trellis.rules(
... new_high = lambda self: self.max_and_new[1]
... )
...
... @trellis.action
... def monitor(self):
... if self.new_high:
... print "New high"
The ``max_and_new`` rule returns two values: the current maximum, and a flag
indicating whether a new high was reached. It refers to itself in order to
see its own *previous* value, so it can tell whether a new high has been
reached. We set a default value of ``(None, False)`` so that the first time
it's run, it will initialize itself correctly. We then split out the "new
high" flag from the tuple, using another rule.
The reason we do the calculation this way, is that it makes our rule
"re-entrant". Because we're not modifying anything but local variables,
it's impossible for an error in this rule to leave any corrupt data behind.
We'll talk more about how (and why) to do things this way in the section below
on `Managing State Changes`_.
In the meantime, let's take our ``HighDetector`` for a test drive::
>>> hd = HighDetector()
>>> hd.value = 7
New high
>>> hd.value = 9
Oops! We set a new high value, but the ``monitor`` rule didn't detect a new
high, because ``new_high`` was *already True* from the previous high.
Normal rules return what might be called "continuous" or "steady state" values.
That is, their value remains the same until something causes them to be
recalculated. In this case, the second recalculation of ``new_high`` returns
``True``, just like the first one... meaning that there's no change, and no
observer recalculation.
But "discrete" rules are different. Just like receivers, their value is
automatically reset to a default value as soon as all their observers have
"seen" the original value. Let's try a discrete version of the same thing::
>>> class HighDetector2(HighDetector):
... new_high = trellis.value(False) # <- the default value
... new_high = trellis.discrete(lambda self: self.max_and_new[1])
>>> hd = HighDetector2()
>>> hd.value = 7
New high
>>> hd.value = 9
New high
>>> hd.value = 3
>>> hd.value = 16
New high
As you can see, each new high is detected correctly now, because the value
of ``new_high`` resets to ``False`` after it's calculated as (or set to) any
other value::
>>> hd.new_high
False
>>> hd.new_high = True
New high
>>> hd.new_high
False
Wiring Up Multiple Components
-----------------------------
Over the course of this tutorial, we've created a whole bunch of different
objects, like the temperature converter, high detector, changeable rectangle,
and a simple viewer. Let's link them up together to make a rectangle that
gets wider and taller whenever the Celsius temperature reaches a new high::
>>> tc = TempConverter()
Celsius...... 0
Fahrenheit... 32
>>> hd = HighDetector2(value = trellis.Cells(tc)['C'])
>>> cr = ChangeableRectangle(
... wider = trellis.Cells(hd)['new_high'],
... taller = trellis.Cells(hd)['new_high'],
... )
>>> viewer = Viewer(model = cr)
Rectangle((0, 0), (20, 30), (20, 30))
>>> tc.F = -40
Celsius...... -40.0
Fahrenheit... -40
>>> tc.F = 50
Celsius...... 10.0
Fahrenheit... 50
New high
Rectangle((0, 0), (21, 31), (21, 31))
Crazy, huh? None of these components were designed with any of the others in
mind, but because they all "speak Trellis", you can link them up like building
blocks to do new and imaginative things.
By the way, although in this demonstration we saw the three outputs in one
particular order, in general the Trellis does not guarantee what order rules
will be recalculated in, so it's unwise to assume that your program will
always produce results in a certain order, unless you've taken steps to ensure
that it will.
That's why managing the order of Trellis output (and dealing with state changes
in general) is the subject of our next major section.
Managing State Changes
======================
Time is the enemy of event-driven programs. They say that time is "nature's
way of keeping everything from happening at once", but in event-driven programs
we usually *want* certain things to happen "at once"!
For example, suppose we want to change a rectangle's top and left
co-ordinates::
>>> r.top = 66
Rectangle((25, 66), (18, 10), (43, 76))
>>> r.left = 53
Rectangle((53, 66), (18, 10), (71, 76))
Oops! If we were updating a GUI like this, we would see the rectangle move
first down and then sideways, instead of just going to where it belongs in one
movement.
Therefore, in most practical event-driven systems, certain kinds of changes
are automatically deferred, usually by adding them to some kind of event queue
so that they can happen later, after all the desired changes have happened.
That way, they don't take effect until the current event is completely
finished.
The Trellis actually does the same thing, but its internal "event queue" is
automatically flushed whenever you set a value from outside a rule. If you
want to set multiple values, you need to use a ``@modifier`` function or
method like this one, which we could've made a Rectangle method, but didn't::
>>> @trellis.modifier
... def set_position(rectangle, left, top):
... rectangle.left = left
... rectangle.top = top
>>> set_position(r, 55, 22)
Rectangle((55, 22), (18, 10), (73, 32))
Changes made by a ``modifier`` function do not take effect until the current
recalculation sweep is completed, which will be no sooner than the *outermost*
active ``modifier`` function returns. (In other words, if one ``modifier``
calls another ``modifier``, the inner modifier's changes don't take effect
until the same time as the outer modifier's changes do.)
Now, pay close attention to what this delayed update process means. When
we say "changes don't take effect", we *really* mean, "changes don't take
effect"::
>>> @trellis.modifier
... def set_position(rectangle, left, top):
... rectangle.left = left
... rectangle.top = top
... print rectangle
>>> set_position(r, 22, 55)
Rectangle((55, 22), (18, 10), (73, 32))
Rectangle((22, 55), (18, 10), (40, 65))
Notice that although the ``set_position`` had just set new values for ``.left``
and ``.top``, it printed the *old* values for those attributes! In other
words, it's not just the notification of observers that's delayed, the actual
*changes* are delayed, too.
Why? Because the whole point of a ``modifier`` is that it makes all its
changes *at the same time*. If the changes actually took effect one by one
as you made them, then they wouldn't be happening "at the same time".
In other words, there would be an order dependency -- the very thing we want
to **get rid of**.
The Evil of Order Dependency
----------------------------
The reason that time is the enemy of event driven programs is because time
implies order, and order implies order dependency -- a major source of bugs
in event-driven and GUI programs.
Writing a polished GUI program that has no visual glitches or behavioral quirks
is difficult *precisely* because such things are the result of changes in the
order that events occur in.
Worse still, the most seemingly-minor change to a previously working version of
such a program can introduce a whole slew of new bugs, making it hard to
predict how long it will take to implement new features. And as a program
gets more complex, even fixing bugs can introduce new bugs!
Indeed, Adobe Systems Inc. estimates that nearly *half* of all their reported
desktop application bugs (across all their applications!) are caused by such
event-management problems.
So a major goal of the Trellis' is to not only **wipe out** these kinds of
bugs, but to prevent most of them from happening in the first place.
And all you have to do to get the benefits, is to divide your code three ways:
* Input code, that sets trellis cells or calls modifier methods (but does not
run inside trellis rules)
* Processing rules that compute values, but do not make changes to any other
cells, attributes, or other data structures (apart from local variables)
* Action rules that send data on to other systems (like the screen, a socket,
a database, etc.). This code may appear in ``@trellis.action`` rules, or it
can be "application" code that reads results from a finished trellis
calculation.
The first and third kinds of code are inherently order-dependent, since
information comes in (and must go out) in a meaningful order. However, by
putting related outputs in the same action rule (or non-rule code), you can
ensure that the required order is enforced by a single piece of code. This
approach is highly bug-resistant.
Second, you can reduce the order dependency of input code by making it do as
little as possible, simply dumping data into input cells, where they can be
handled by processing rules. And, since input controllers can be very generic
and highly-reusable, there's a natural limit to how much input code you will
need.
By using these approaches, you can maximize the portion of your application
that appears in side effect-free processing rules, which the Trellis makes 100%
immune to order dependencies. Anything that happens in Trellis rules, happens
*instantaneously*. There is no "order", and thus no order dependency.
In truth, of course, rules do execute in *some* order. However, as long as the
rules don't do anything but compute their own values, then it cannot matter
what order they do it in. (The trellis guarantees this by automatically
recalculating rules when they are read, if they aren't already up-to-date.)
The Side-Effect Rules
---------------------
To sum up the recommended approach to handling side-effects in Trellis-based
programs, here are a few brief guidelines that will keep your code easy to
write, understand, and debug.
Rule 1 - If Order Matters, Use Only One Action
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
If you care what order two "outside world" side-effects happen in, code them
both in the same action rule.
For example, in the ``TempConverter`` demo, we had a rule that printed the
Celsius and Fahrenheit temperatures. If we'd put those two ``print``
statements in separate actions, we'd have had no control over the output order;
either Celsius or Fahrenheit might have come first on any given change to the
temperatures. So, if you care about the relative order of certain output or
actions, you must put them all in one rule. If that makes the rule too big or
complex, you can always refactor to extract new rules to calculate the
intermediate values. Just don't put any of the *actions* (i.e. side-effects or
outputs) in the other rules, only the *calculations*. Then have an action rule
that *only* does the output or actions.
Rule 2 - Return Values, Don't Set Them
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Rules should always *compute* a value, rather than changing other values. If
you need to compute more than one thing at once, just make a rule that returns
a tuple or some other data structure, then make other rules that pull the
values out. E.g.::
>>> class Example(trellis.Component):
... trellis.rules(
... _foobar = lambda self: (1, 2),
... foo = lambda self: self._foobar[0],
... bar = lambda self: self._foobar[1]
... )
In other words, there's no need to write an ``UpdateFooBar`` method that
computes and sets ``foo`` and ``bar``, the way you would in a callback-based
system. Remember: rules are not callbacks! So always *return* values instead
of *assigning* values.
If you need to keep track of some value between invocations of the same rule,
make that value part of the rule's return value, then refer back to that value
each time. See the sections above on `Accessing a Rule's Previous Value`_ and
`"Discrete" Rules`_ for examples of rules that re-use their previous value,
and/or use a tuple to keep track of state.
Rule 3 - If You MUST Set, Do It From One Place or With One Value
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
If you set a value from more than one place, you are introducing an order
dependency. In fact, if you set a value more than once in an action or
modifier, the Trellis will stop you. After all, all changes in an action or
modifier happen "at the same time". And what would it mean to set a value to
22 and 33 "at the same time"? A conflict error, that's what it would mean::
>>> @trellis.modifier
... def set_twice():
... set_position(r, 22, 55)
... set_position(r, 33, 66)
>>> set_twice()
Traceback (most recent call last):
...
InputConflict: (22, 33)
This rule is for your protection, because it makes it impossible for you to
accidentally set the same thing in two different places in response to an
event, and then miss the bug or be unable to reproduce it because the second
change masks the first!
Instead, what happens is that assigning two different values to the same cell
in response to the same event always produces an error message, making it
easier to find the problem. Of course, if you arrange your input code so that
only one piece of input code is setting trellis values for a given event, and
you don't change values from inside of computations (rule 2 above), then you'll
never have this problem.
Of course, if all of your code is setting a cell to the *same* value, you won't
get a conflict error either. This is mostly useful for e.g. receiver cells
that represent a command the program should do. If you have GUI input code
that triggers a command by setting some receiver to ``True`` whenever that
command is selected from a menu, invoked by a keyboard shorcut, or accessed
with a toolbar button click, then it doesn't matter which event happens or
even if all three could somehow happen at the same time, because the end result
is exactly the same: the receiver processes the ``True`` message once and then
discards it.
Rule 4 - Change Takes Time
~~~~~~~~~~~~~~~~~~~~~~~~~~
Be aware that if you ever change a cell or other Trellis data structure from
inside an ``@action`` rule, this will trigger a recalculation of the trellis,
once all current action rules are completed. This effectively causes a loop,
which *may not terminate* if your action rule is triggered again. So beware of
making such changes; there is nearly always a better way to get the result
you're looking for -- i.e., one that doesn't involve action rules.
Mutable Data Structures
-----------------------
So far, all of our Trellis examples have worked with atomic cell values, like
integers, strings, and so forth. We've avoided working with lists, sets,
dictionaries, and similar structures, because the standard Python
implementations of these types can't be "observed" by rules, which means that
they won't be automatically updated.
But this doesn't mean you can't use sets, lists, and dictionaries. You just
need to use Trellis-ized ones. Of course, all the warnings above about
changing values still apply; just because you're modifying something other
than attributes, doesn't mean you're not still modifying things!
The Trellis package provides three mutable types for you to use in your
components: ``Set``, ``List``, and ``Dict``. You can also subclass them or
create your own mutable types, as we'll discuss in a later section.
trellis.Dict
~~~~~~~~~~~~
The ``trellis.Dict`` type looks pretty much like any dictionary, but it can
be observed by rules. Any change to the dictionary's contents will result
in its observers being recalculated. For example, if we use our ``view``
object (defined way back in the section on `Model-View-Controller and the
"Observer" Pattern`_), we can print it whenever it changes, no matter how it
changes::
>>> d = trellis.Dict(a=1)
>>> view.model = d
{'a': 1}
>>> del d['a']
{}
>>> d['a'] = 2
{'a': 2}
Unlike normal values, however, even changing a dictionary entry to the same
value will trigger a recalculation::
>>> d['a'] = 2
{'a': 2}
This is because the ``Dict`` type doesn't try to compare the values you put
into it. If you need to prevent such recalculations from happening, you can
always check the dictionary contents first, or create a subclass and override
``__setitem__`` (but be sure to read the section on `Creating Your Own Data
Structures`_ for some important information first).
In addition to these basic features, the ``Dict`` type provides three receiver
attributes (``added``, ``changed``, and ``deleted``) that reflect changes
currently in progress. Ordinarily, they are empty dictionaries, but while a
change is taking place they temporarily become non-empty. For example::
>>> view.model = None
>>> class Dumper(trellis.Component):
... @trellis.action
... def dump(self):
... for name in 'added', 'changed', 'deleted':
... if getattr(d, name):
... print name, '=', getattr(d, name)
>>> dumper=Dumper()
>>> del d['a']
deleted = {'a': 2}
>>> d[3] = 4
added = {3: 4}
>>> d[3] = 5
changed = {3: 5}
>>> @trellis.modifier
... def two_at_once():
... del d[3]
... d[4] = 5
>>> two_at_once()
added = {4: 5}
deleted = {3: 5}
These dictionaries immediately reset to empty as soon as a change has been
fully processed, so you'll never see anything in them if you look from non-rule
code::
>>> d.added
{}
Also note that you cannot use the ``.pop()``, ``.popitem()``, or
``.setdefault()`` methods of ``Dict`` objects::
>>> d.setdefault(1, 2)
Traceback (most recent call last):
...
InputConflict: Can't read and write in the same operation
Remember: the trellis wants all changes to be deferred until the next
recalculation. That means you can't see the effect of a change in the same
moment during which you *make* the change, so operations like ``pop()`` are
disallowed, because they would have to return the same value no matter how
many times you called it during the same recalculation! (Otherwise, the
change hasn't really been deferred.)
This limitation also applies to the ``pop()`` method of ``List`` and ``Set``
objects, as we'll see in the next two sections.
trellis.Set
~~~~~~~~~~~
Trellis ``Set`` objects offer nearly all the comforts of the Python standard
library's ``sets.Set`` objects (minus ``.pop()``, and support for sets of
mutable sets), but with observability::
>>> s = trellis.Set("abc")
>>> view.model = s
Set(['a', 'c', 'b'])
>>> s.add('d')
Set(['a', 'c', 'b', 'd'])
>>> s.remove('c')
Set(['a', 'b', 'd'])
>>> s -= trellis.Set(['a', 'b'])
Set(['d'])
Similar to the ``Dict`` type, the ``Set`` type offers receiver set attributes,
``added`` and ``removed``, that reflect changes-in-progress to the set::
>>> view.model = None
>>> class Dumper(trellis.Component):
... @trellis.action
... def dump(self):
... for name in 'added', 'removed':
... if getattr(s, name):
... print name, '=', list(getattr(s, name))
>>> dumper=Dumper()
>>> s.add('a')
added = ['a']
>>> s.remove('d')
removed = ['d']
Note, however, that you cannot use the ``.pop()`` method of ``Set`` objects::
>>> s.pop()
Traceback (most recent call last):
...
InputConflict: Can't read and write in the same operation
Remember: as with ``trellis.Dict``, operations like ``pop()`` are disallowed
here because they would require reading the effect of a change, before the
logical future moment in which the change actually takes effect.
trellis.List
~~~~~~~~~~~~
A ``trellis.List`` looks and works pretty much the same as a normal Python
list, except that it can be observed by rules::
>>> myList = trellis.List([1,2,3])
>>> myList
[1, 2, 3]
>>> myList.reverse() # no output while not being observed
>>> view.model = myList
[3, 2, 1]
>>> myList.reverse() # but now we're being watched
[1, 2, 3]
>>> myList.insert(0, 4)
[4, 1, 2, 3]
>>> myList.sort()
[1, 2, 3, 4]
``trellis.List`` objects also have a receiver attribute called ``changed``.
It's normally false, but is temporarily ``True`` during the recalculation
triggered by a change to the list. But as with all receiver attributes, you'll
never see a value in it from non-rule code::
>>> myList.changed
False
Only in rule code will you ever see it true, a moment before it becomes false::
>>> view.model = None # quiet, please
>>> class Watcher(trellis.Component):
... @trellis.action
... def dump(self):
... print myList.changed
>>> watcher=Watcher()
False
>>> del myList[0]
True
False
>>> myList
[2, 3, 4]
Note, however, that you cannot use the ``.pop()`` method of ``List`` objects::
>>> myList.pop()
Traceback (most recent call last):
...
InputConflict: Can't read and write in the same operation
Remember: as with ``trellis.Dict`` and ``trellis.Set``, operations like
``pop()`` are disallowed here because they would require reading the effect of
a change, before the logical future moment in which the change actually takes
effect.
``trellis.List`` objects also have some inherent inefficiencies due to the wide
variety of operations supported by Python lists. While ``trellis.Set``
and ``trellis.Dict`` objects update themselves in place by applying change
logs, ``trellis.List`` has to use a copy-on-write strategy to manage updates,
because there isn't any simple way to reduce operations like ``sort()``,
``reverse()``, ``remove()``, etc. to a meaningful change log. (That's why
it only provides a simple ``changed`` flag.)
So if you need to use large lists in an application, you may be better off
creating a custom data structure of your own design. That way, if you only
need a subset of the list interface, you can implement a changelog-based
structure. In the next section, we'll see how to create a simple
``SortedList`` type that tracks inserted and removed items, maintaining them
in a sorted order and issuing change events.
Creating Your Own Data Structures
---------------------------------
If you want to create your own data structures along the lines of ``Dict``,
``List``, and ``Set``, you have a few options. First, you can just build
components that use those existing data types, and use ``@modifier`` methods
to perform operations on them. (If you just directly perform operations, then
observers of your data structure may be recalculated in the middle of the
changes.)
Depending on the nature of the data structure you need, however, this may not
be sufficient. For example, when you perform multiple operations on a
``trellis.Dict``, the later operations need to know about changes made by the
earlier ones. If you add some items and then delete one, for example, the dict
needs to know whether the item you're deleting is one of the ones that you
added.
But, if you use normal read operations on the dictionary (like ``.has_key()``),
these will only reflect the "before" state -- what the dictionary had in it
during the current recalculation, before any new changes were made.
So, the Trellis-supplied data types use a couple of special tools to allow them
to "see the future" (and change it).
Let's suppose that we're creating a simple "queue" type, that keeps track of
items added to it. Its output is a list of the most-recently added items,
and the list becomes empty in the next recalculation if nobody adds anything to
it::
>>> class Queue(trellis.Component):
... items = trellis.todo(lambda self: [])
... to_add = items.future
...
... @trellis.modifier
... def add(self, item):
... self.to_add.append(item)
...
... def __repr__(self):
... return str(self.items)
>>> q = Queue()
>>> view.model = q
[]
>>> q.add(1)
[1]
[]
>>> @trellis.modifier
... def add_many(*args):
... for arg in args: q.add(arg)
>>> add_many(1,2,3)
[1, 2, 3]
[]
Let's break down the pieces here. First, we create a "todo" cell. A todo
cell is discrete (like a ``receiver`` cell or ``@discrete`` rule), which means
it resets to its default value after any changes. (By the way, you can define
todo cells with either a direct call as shown here, a ``@trellis.todo``
decorator on a function, or using ``trellis.todos(attr=func, ...)`` in your
class body.)
The default value of a ``@todo`` cell is determined by calling the function it
wraps when the cell is created. This value is then saved as the default value
for the life of the cell.
The second thing that we do in this class is create a "future" view. Todo
cell properties have a ``.future`` attribute that returns a new property. This
property accesses the "future" version of the todo cell's value.
Next, we define a modifier method, ``add()``. This method accesses the
``to_add`` attribute, and gets the *future* value of the ``items`` attribute.
This future value is initially created by calling the "todo" cell's function.
In this case, the todo function returns an empty list, so that's what ``add()``
sees, and adds a value to it. As a side effect of accessing this future value,
the Trellis schedules a recalculation to occur after the current recalculation
is finished.
(Note, by the way, that you cannot access future values except from inside
a ``@modifier`` function, and these in turn can only be called from ``@action``
or non-Trellis code.)
In our second example above, we create another ``@modifier`` that adds more
than one item to the ``to_add`` attribute. This works because only a single
"future value" is created during a given recalculation sweep, and ``@modifier``
methods guarantee that no new sweeps can occur while they are running. Thus,
the changes made in the modifier don't take effect until it returns.
Finally, after each change, the queue resets itself to empty, because the
default value of the ``items`` cell is the empty list created when the cell
was initialized.
Of course, since "todo" attributes are discrete (i.e., transient), what we've
seen so far isn't enough to create a data structure that actually *keeps* any
data around. To do that, we need to combine "todo" attributes with a rule to
update an existing data structure::
>>> class Queue2(Queue):
... added = trellis.todo(lambda self: [])
... to_add = added.future
...
... @trellis.rule
... def items(self):
... items = self.items
... if items is None:
... items = []
... if self.added:
... return items + self.added
... return items
>>> q = Queue2()
>>> view.model = q
[]
>>> q.add(1)
[1]
>>> add_many(2, 3, 4)
[1, 2, 3, 4]
This version is very similar to the first version, but it separates ``added``
from ``items``, and the ``items`` rule is set up to compute a new value that
includes the added items.
Notice, by the way, that the ``items`` rule returns a new list every time there
is a change. If it didn't, the updates wouldn't be tracked::
>>> class Queue3(Queue2):
... @trellis.rule
... def items(self):
... items = self.items
... if items is None:
... items = []
... if self.added:
... items.extend(self.added)
... return items
>>> q = Queue3()
>>> view.model = q
[]
>>> q.add(1)
>>> add_many(2, 3, 4)
Why are no updates displayed here? Because ``items`` is being modified
in-place, and when the trellis compares the "before" and "after" versions of
its value, it concludes they are the *same*. This didn't happen when we
returned a new list, because the old list still had its old contents, and the
new list was different.
If you are modifying a return value in place like this, you should use the
the ``trellis.dirty()`` API to flag that your return value has changed, even
though it's the same object::
>>> class Queue4(Queue2):
... @trellis.rule
... def items(self):
... items = self.items
... if items is None:
... items = []
... if self.added:
... items.extend(self.added)
... trellis.dirty()
... return items
>>> q = Queue2()
>>> view.model = q
[]
>>> q.add(1)
[1]
>>> add_many(2, 3, 4)
[1, 2, 3, 4]
Please note, however, that using this API is as "dirty" as its name
implies. More precisely, the dirtiness is that we're modifying a value inside
a rule -- the worst sort of no-no. You must take extra care to ensure that
all your dependencies have already been calculated before you perform the
modification, otherwise an unexpected error could leave your data in a
corrupted state. In this example, the modification is the last thing that
happens, and ``self.added`` has already been read, so it should be pretty safe.
On the whole, though, it's best to stick with immutable values as much as
possible, and avoid mutating data in place if you can.
Other Things You Can Do With A Trellis
======================================
XXX This section isn't written yet and should include examples
* MVC/Live UI Updates
* Testable UI Models
* Live Object Validation
* Persistence/ORM
* Async I/O
* Process Monitoring
* Live Business Statistics
---------------------------------
Advanced Features and API Details
---------------------------------
Working With Cell Objects
=========================
Throughout the main tutorial, we worked only with component attributes. But
it's also possible to work directly with ``Cell`` objects. For example, here's
a temperature converter implemented directly with cells::
>>> F = trellis.Cell(lambda: C.value * 1.8 + 32, 32)
>>> C = trellis.Cell(lambda: (F.value - 32)/1.8, 0)
>>> F.value
32
>>> C.value
0
>>> F.value = 212
>>> C.value
100.0
The ``trellis.Cell()`` constructor takes three arguments: a zero-argument
callable (or ``None``), an optional value, and an optional "discrete" flag.
In our example above, we created a pair of cells with both rules and values,
that are not discrete.
Notice, by the way, that when you are directly creating cells, you must use
zero-argument callables. That is, Cell objects don't pass in a "self" argument
to their rules. (The reason rules in a component use a "self" is that those
rules are turned into methods before the cell is created. The Cell doesn't
pass in a "self", but it's already bound to the method, so it shows up anyway.)
The ``value`` attribute of a ``Cell`` can be read or set, to get or change the
value of the cell, and it works just like getting or setting a component cell
attribute (except that setting a cell's value to another cell doesn't cause the
cell to be replaced!). In addition to the ``.value`` attribute, there are also
``get_value()`` and ``set_value()`` methods::
>>> C.set_value(-40)
>>> F.get_value()
-40.0
These can be useful if you need to register callbacks with other systems. For
example, you could use a cell's ``set_value()`` method as a callback for a
Twisted "deferred" object, so that the cell would receive the deferred's value
when it became available.
Here's our earlier "noise filter" example, reconstituted as a set of cells::
>>> value = trellis.Cell(value=0)
>>> threshhold = trellis.Cell(value=5)
>>> def filtered():
... if abs(value.value - filtered.value) > threshhold.value:
... return value.value
... return filtered.value
>>> filtered = trellis.Cell(filtered, 0)
>>> filtered.value
0
>>> value.value = 1
>>> filtered.value
0
>>> value.value = 6
>>> filtered.value
6
Read-Only and Constant Cells
----------------------------
As you can see, you can provide either a value only, or a rule and a value when
you create a cell. However, if you provide just a rule and no value, you end
up with a read-only cell whose value can't be set::
>>> roc = trellis.Cell(lambda: 123)
>>> roc.value = 456
Traceback (most recent call last):
...
AttributeError: can't set attribute
In fact, it's not even a ``Cell`` instance, but of a different type
altogether::
>>> roc
ReadOnlyCell(<function <lambda> at ...>, None [out-of-date])
What the above means is that you have a read-only cell whose current value is
``None``, but is out-of-date. This means that if you actually try to *read*
the value of this cell, it may or may not match what the ``repr()`` showed.
(This is because simply looking at the cell shouldn't cause the cell to
recalculate; that would be very painful when debugging).
If we actually read the value of this cell, the rule will be run::
>>> roc.value
123
But since the rule doesn't depend on any other cells, the cell changes type
again, to a ``Constant``::
>>> roc
Constant(123)
Since the rule didn't depend on any other cells, there is never any way that
it could be meaningfully recalculated. Thus, it becomes constant, and cannot
be observed by any other rules. If we create another rule that reads this
cell, it will not end up depending on it::
>>> cell2 = trellis.Cell(lambda: roc.value)
>>> cell2.value
123
>>> cell2
Constant(123)
Thus, constant values propagate automatically through the cell network,
eliminating dependencies on things that can't possibly change. Of course, if a
read-only cell depends on a cell that *can* change, it remains a read-only
cell, and will be recalculated whenever its dependencies change::
>>> c1 = trellis.Cell(value=0)
>>> c2 = trellis.Cell(lambda: c1.value * 2)
>>> c2.value
0
>>> c1.value = 27
>>> c2
ReadOnlyCell(<function <lambda>...>, 54)
Note that you can take advantage of constant propagation by explicitly setting
a component attribute to a ``trellis.Constant`` at creation time. For example,
if for some reason you wanted a temperature converter that could only be used
once::
>>> tc = TempConverter(C=trellis.Constant(100))
Celsius...... 100
Fahrenheit... 212.0
>>> tc.C = -40
Traceback (most recent call last):
...
AttributeError: Constants can't be changed
(This would probably be more useful with something like the ``NoiseFilter``
example, in that you could set its ``threshhold`` to a ``Constant()``,
eliminating the need for the ``filtered`` rule to check for changes to the
``threshhold`` in order to know if it should be recalculated.)
Working With A Component's Cells
--------------------------------
As we saw in the main tutorial, the ``trellis.Cells()`` API returns a
dictionary of active cells for a component::
>>> trellis.Cells(view)
{'model': Cell(None, [1, 2, 3, 4] [out-of-date]),
'view_it': ActionCell(<bound method Viewer.view_it of
<Viewer object at 0x...>>, None [out-of-date])}
In the case of a ``Component``, this data is also stored in the component's
``__cells__`` attribute::
>>> trellis.Cells(view) is view.__cells__
True
This makes it possible for you to set up direct links between components using
shared cells. It also lets you access cell objects directly, in order to e.g.
register their ``set_value()`` methods as callbacks for other systems.
Discrete and Action Cells
-------------------------
To make a cell "discrete" (i.e. a receiver or discrete rule), you set its
third constructor argument (i.e., ``discrete``) to true::
>>> aReceiver = trellis.Cell(value=0, discrete=True)
>>> aReceiver.value
0
>>> v = Viewer(model = aReceiver)
0
>>> aReceiver.value = 42
42
0
As you can see, the value a discrete cell is created with, is the default value
it resets to between received (or calculated) values. If you want to make
a discrete rule, just include a rule in addition to the default value and the
discrete flag.
"Action" cells are implemented with the ``trellis.ActionCell`` class::
>>> trellis.Cells(view)['view_it']
ActionCell(<bound method Viewer.view_it of
<Viewer object at 0x...>>, None [out-of-date])
The ActionCell constructor takes only one parameter: a zero-argument callable,
such as a bound method or a function with no parameters. You can't set a value
for an ``ActionCell`` (because it's not writable), nor can you make it discrete
(since that would imply a readable value, and action cells exist only for their
side-effects).
Cell Attribute Metadata
-----------------------
The various decorators and APIs that set up cell attributes in components all
work by registering metadata for the enclosing class. This metadata can be
accessed using various ``peak.util.roles.Registry`` objects. (See the
documentation of the ``ObjectRoles`` package at the Python Package Index for
more info on registries.)
In the current version of the Trellis library, these registries should mostly
be considered implementation details; they are not officially documented and
may change in a future release. However, if you need to be able to access a
superclass' definition of a rule, you can do so using the ``CellRules``
registry::
>>> trellis.CellRules(NoiseFilter)
{'filtered': <function filtered at 0x...>}
As you can see, calling ``trellis.CellRules(sometype)`` will return you a
dictionary of rules for that type. You can then pull out the definition you
need and call it. This particular registry should be a relatively stable API
across releases.
Co-operative Multitasking
=========================
The Trellis allows for a limited form of co-operative multitasking, using
generator functions. By declaring a generator function as a ``@task`` method,
you can get it to run across multiple trellis recalculations, retaining its
state along the way. For example::
>>> class TaskExample(trellis.Component):
... trellis.receivers(
... start = False,
... stop = False
... )
... @trellis.task
... def demo(self):
... print "waiting to start"
... while not self.start:
... yield trellis.Pause
... print "starting"
... while not self.stop:
... print "waiting to stop"
... yield trellis.Pause
... print "stopped"
>>> t = TaskExample()
waiting to start
>>> t.start = True
starting
waiting to stop
waiting to stop
>>> t.stop = True
stopped
A ``@trellis.task`` is like a ``@trellis.action``, in that it is allowed to
modify other cells, and its output cannot be observed by normal rules. The
function you decorate it with, however, must be a generator. The generator
can yield ``trellis.Pause`` in order to suspend itself until a cell it depends
on has changed.
In the above example, the task initially depends on the value of the ``start``
cell, so it is not resumed until ``start`` is set to ``True``. Then it prints
"starting", and waits for ``self.stop`` to become true. However, at this point
it now depends on both ``start`` *and* ``stop``, and since ``start`` is a
"receiver" cell, it resets to ``False``, causing the task to resume. (Which is
why "waiting to stop" gets printed twice at that point.)
We then set ``stop`` to true, which causes the loop to exit. The task is now
finished, and any further changes will not re-invoke it. In fact, if we
examine the cell, we'll see that it has become a ``CompletedTask`` cell::
>>> trellis.Cells(t)['demo']
CompletedTask(None)
even though it's initially a ``TaskCell``::
>>> trellis.Cells(TaskExample())['demo']
waiting to start
TaskCell(<function step...>, None)
Invoking Subtasks
-----------------
Tasks can invoke or "call" other generators by yielding them. For example, we
can rewrite our example like this, for more modularity::
>>> class TaskExample(trellis.Component):
... trellis.receivers(
... start = False,
... stop = False
... )
...
... def wait_for_start(self):
... print "waiting to start"
... while not self.start:
... yield trellis.Pause
...
... def wait_for_stop(self):
... while not self.stop:
... print "waiting to stop"
... yield trellis.Pause
...
... @trellis.task
... def demo(self):
... yield self.wait_for_start()
... print "starting"
... yield self.wait_for_stop()
... print "stopped"
>>> t = TaskExample()
waiting to start
>>> t.start = True
starting
waiting to stop
waiting to stop
>>> t.stop = True
stopped
Yielding a generator from a ``@task`` causes that generator to temporarily
replace the main generator, until the child generator returns, yields a
non-``Pause`` value, or raises an exception. At that point, control returns to
the "parent" generator. Subtasks may be nested to any depth.
Receiving Values and Propagating Exceptions
-------------------------------------------
If you are targeting Python 2.5 or higher, you don't need to do anything
special to receive values yielded by subtasks, or to ensure that subtask
exceptions are propagated. You can receive values using expressions like::
result = yield someGenerator(someArgs)
However, in earlier versions of Python, this syntax doesn't exist, so you must
use the ``trellis.resume()`` function instead, e.g.::
yield someGenerator(someArgs); result = trellis.resume()
If you are writing code intended to run on Python 2.3 or 2.4 (as well as 2.5),
you should call ``trellis.resume()`` immediately after a subtask invocation
(preferably on the same line, as shown), *even if you don't need to get the
result*. E.g.::
yield someGenerator(someArgs); trellis.resume()
The reason you should do this is that Python versions before 2.5 do not allow
you to pass exceptions into a generator, so the Trellis can't cause the
``yield`` statement to propagate an error from ``someGenerator()``. If the
subtask raised an exception, it will silently vanish unless the ``resume()``
function is called.
The reason to put it on the same line as the yield is so that you can see the
subtask call in the error's traceback, instead of just a line saying
``trellis.resume()``! (Note, by the way, that it's perfectly valid to use
``trellis.resume()`` in code that will also run under Python 2.5; it's just
redundant unless the code will also be used with older Python versions.)
The ability to receive values from a subtask lets you create utility functions
that wait for events to occur in some non-Trellis system. For example, you
could create a function like this, to let you wait for a Twisted "deferred" to
fire::
def wait_for(deferred):
result = trellis.Cell(None, trellis.Pause)
deferred.addBoth(result.set_value)
while result.value is trellis.Pause:
yield trellis.Pause
if isinstance(result.value, failure.Failure):
try:
result.value.raiseException()
finally:
del result # get rid of the traceback reference cycle
yield trellis.Return(result.value)
You would then use it like this (Python 2.5)::
result = wait_for(someTwistedFuncReturningADeferred(...))
Or like this (compatible with earlier Python versions)::
wait_for(someTwistedFuncReturningADeferred(...)); result = trellis.resume()
``wait_for()`` creates a cell and adds its ``set_value()`` method as a callback
to the deferred, to receive either a value or an error. It then waits until
the callback occurs, by yielding ``Pause`` objects. If the result is a Twisted
``Failure``, it raises the exception represented by the failure. Otherwise,
it wraps the result in a ``trellis.Return()`` and yields it to its calling
task, where it will be received as the result of the ``yield`` expression
(in Python 2.5) or of the ``trellis.resume()`` call (versions <2.5).
Time, Tasks, and Changes
------------------------
Note, by the way, that when we say the generator above will "wait" until the
callback occurs, we actually mean no such thing! What *really* happens is that
this generator yields ``Pause``, recalculation finishes normally, and control
is returned to whatever non-Trellis code caused a recalculation to occur in
the first place. Then, later, when the deferred fires and a callback occurs to
set the ``result`` cell's value, this *triggers a recalculation sweep*, in
which the generator is resumed in order to carry out the rest of its task!
When it yields the result or raises an exception, this is propagated back to
whatever generator "called" this one, which may then go on to do other things
with the value or exception before it pauses or returns. The recalculation
sweep once again finishes normally, and control is returned back to the code
that caused the deferred to fire.
Thus, "time" in the Trellis (and especially for tasks) moves forward only when
something *changes*. It's the setting of cell values that triggers
recalculation sweeps, and tasks only resume during sweeps where one of their
dependencies have changed.
A task is considered to depend on any cells whose value it has read since the
last time it (or a subtask) yielded a ``Pause``. Each time a task pauses, its
old dependencies are thrown out, and a new set are accumulated.
A task must also ``Pause`` in order to see the effects of any changes it makes
to cells. For example::
>>> c = trellis.Cell(value=27)
>>> c.value
27
>>> def demo_task():
... c.value = 19
... print c.value
... yield trellis.Pause
... print c.value
>>> trellis.TaskCell(demo_task).value
27
19
As you can see, changing the value of a cell inside a task is like changing it
inside a ``@modifier`` or ``@action`` -- the change doesn't take effect until
a new recalculation occurs, and the *current* recalculation can't finish until
the task yields a ``Pause`` or returns (i.e., exits entirely).
In this example, the task is resumed immediately after the pause because the
task depended on ``c.value`` (by printing it), and its value *changed* in the
subsequent sweep (because the task set it). So the task was resumed
immediately, as part of the second recalculation sweep (which happened only
because there was a change in the first sweep).
But what if a task doesn't have any dependencies? If it doesn't depend on
anything, how does it get resumed after a pause? Let's see what happens::
>>> def demo_task():
... print 1
... yield trellis.Pause
... print 2
>>> trellis.TaskCell(demo_task).value
1
2
As you can see, a task with no dependencies, (i.e., one that hasn't looked at
any cells since its last ``Pause``), is automatically resumed. The Trellis
effectively pretends that the task both set and depended on an imaginary cell,
forcing another recalculation sweep (if one wasn't already in the works due
to other changes or the need to reset some discrete cells). This prevents
tasks from accidently suspending themselves indefinitely.
Notice, by the way, that this makes Trellis-style multitasking rather unique
in the world of Python event-driven systems and co-operative multitasking
tools. Most such systems require something like an "event loop", "reactor",
"trampoline", or similar code that runs in some kind of loop to manage tasks
like these. But the Trellis doesn't need a loop of its own: it can use
whatever loop(s) already exist in a program, and simply respond to changes as
they occur.
In fact, you can have one set of Trellis components in one thread responding to
changes triggered by callbacks from Twisted's reactor, and another set of
components in a different thread, being triggered by callbacks from a GUI
event loop. Heck, you can have them both happening in the *same* thread! The
Trellis really doesn't care. (However, you can't share any trellis components
across threads, or use them to communicate between threads. In the future,
the ``TrellisIO`` package will probably include mechanisms for safely
communicating between cells in different threads.)
Managing Activities in "Clock Time"
===================================
(NEW in 0.6a1)
Real-life applications often need to work with intervals of physical or "real"
time, not just logical "Trellis time". In addition, they often need to manage
sequential or simultaneous activities. For example, a desktop application may
have background tasks that perform synchronization, download mail, etc. A
server application may have logical tasks handling requests, and so on. These
activities may need to start or stop at various times, manage timeouts, display
or log progress, etc.
So, the ``peak.events.activity`` module includes support for time tracking as
well as controlling activities and monitoring their progress.
Timers and the Time Service
---------------------------
The Trellis measures time using "timers". A timer represents a moment in time,
but you can't tell directly *what* moment it represents. All you can do is
measure the interval between two timers, or tell whether the moment defined by
a timer has been reached.
The "zero" timer is ``activity.EPOCH``, representing an arbitrary starting
point in relative time::
>>> from peak.events.activity import EPOCH
>>> t = EPOCH
>>> t
<...activity._Timer object at ...>
Static Time Calculations
~~~~~~~~~~~~~~~~~~~~~~~~
As you can see, timer objects aren't very informative by themselves. However,
you can use subscripting to create new timers relative to an existing timer,
and subtract timers from each other to produce an interval in seconds, e.g.::
>>> t10 = t[10]
>>> t10 - t
10
>>> t10[-10] - t
0
>>> t10[3] - t
13
Timers compare equal to one another, if and only if they represent the same
moment::
>>> t==t
True
>>> t!=t
False
>>> t10[-10] == t
True
>>> t10[-10] != t
False
And the other comparison operators work on timers according to their relative
positions in time, e.g.:
>>> t[-1] < t <= t[1]
True
>>> t[-1] > t[-2]
True
>>> t[-2] > t[-1]
False
>>> t[-1] >= t[-1]
True
>>> t<=t
True
>>> t<=t[1]
True
>>> t<=t[-1]
False
Dynamic Time Calculations
~~~~~~~~~~~~~~~~~~~~~~~~~
Of course, if arithmetic were all you could do with timers, they wouldn't be
very useful. Their real value comes when you perform dynamic time calculations,
to answer questions like "How long has it been since X happened?", or "Has
Y seconds elapsed since X happened?" And of course, we want any rules that
ask these questions to be recalculated if the answers change!
This is where the ``activity.Time`` service comes into play. The ``Time``
class is a ``context.Service`` (see the Contextual docs for more details) that
tracks the current time, and takes care of letting the Trellis know when a rule
needs to be recalculated because of a change in the current time.
By default, the ``Time`` service uses ``time.time()`` to track the current
time, whenever a trellis value is changed. But to get consistent timings
while testing, we'll turn this automatic updating off::
>>> from peak.events.activity import Time
>>> Time.auto_update = False
With auto-update off, the time will only advance if we explicitly call
``Time.tick()`` or ``Time.advance()``. ``tick()`` updates the current time
to match ``time.time()``, while ``Time.advance()`` moves the time ahead by a
specified amount (so you can run tests in "simulated time" with perfect
repeatability).
So now let's do some dynamic time calculations. In most programs, what you
need to know in a rule is whether a certain amount of time has elapsed
since something has happened, or whether a certain future time has arrived.
To do that, you can simply create a timer for the desired moment, and check its
boolean (truth) value::
>>> twenty = Time[20] # go off 20 secs. from now
>>> bool(twenty) # but we haven't gone off yet
False
>>> Time.advance(5)
>>> bool(twenty) # not time yet...
False
>>> Time.advance(15) # bingo!
>>> bool(twenty)
True
>>> Time.advance(7)
>>> bool(twenty) # remains true even after the exact moment has passed
True
And of course, you can use this boolean test in a rule, to trigger some action::
>>> class AlarmClock(trellis.Component):
... trellis.values(timeout = None)
... def alert(self):
... if self.timeout:
... print "timed out!"
... alert = trellis.rule(alert)
>>> clock = AlarmClock(timeout=Time[20])
>>> Time.advance(15)
>>> Time.advance(15)
timed out!
Notice, by the way, that the ``Time`` service can be subscripted with a value
in seconds, to get a timer representing that many seconds from the current
time. (However, ``Time`` is not really a timer object, so don't try to use it
as one!)
Elapsed Time Tracking
~~~~~~~~~~~~~~~~~~~~~
This alarm implementation works by getting a future timer (``timeout``), and
then "goes off" when that future moment is reached. However, we can also
create an "elapsed" timer, and trigger when a certain amount of time has
passed::
>>> class Elapsed(trellis.Component):
... trellis.values(duration = 20)
... trellis.rules(has_run_for = lambda self: Time[0])
...
... def alarm(self):
... if self.has_run_for[self.duration]:
... print "timed out!"
... alarm = trellis.rule(alarm)
>>> t = Elapsed() # Capture a start time
>>> Time.advance(15) # duration is 20, so no alarm yet
>>> t.duration = 10 # duration changed, and already reached
timed out!
>>> t.duration = 15 # duration changed, but still reached
timed out!
>>> t.duration = 20 # not reached yet...
>>> Time.advance(5)
timed out!
As you can see, the ``has_run_for`` attribute is a timer that records the
moment when the ``Elapsed`` instance is created. The ``alarm`` rule is then
recalculated whenever the ``duration`` changes -- or elapses.
Of course, in complex programs, one usually needs to be able to measure the
amount of time that some condition has been true (or false). For example, how
long a process has been idle (or busy)::
>>> from peak.events.activity import NOT_YET
>>> class IdleTimer(trellis.Component):
... trellis.values(
... idle_for = NOT_YET,
... idle_timeout = 20,
... busy = False,
... )
... trellis.rules(
... idle_for = lambda self:
... self.idle_for.begins_with(not self.busy)
... )
... def alarm(self):
... if self.idle_for[self.idle_timeout]:
... print "timed out!"
... alarm = trellis.rule(alarm)
The way this code works, is that initially the ``idle_for`` timer is equal to
the special ``NOT_YET`` value, representing a moment that will never be
reached.
The ``begins_for()`` method of timer objects takes a boolean value. If the
value is false, ``NOT_YET`` is returned. If the value is true, the lesser of
the existing timer value or ``Time[0]`` (the present moment) is returned.
Thus, a statement like::
a_timer = a_timer.begins_with(condition)
ensures that ``a_timer`` equals the most recent moment at which ``condition``
was observed to become true. (Or ``NOT_YET``, in the case where ``condition``
is false.)
So, the ``IdleTimer.alarm`` rule effectively checks whether ``busy`` has been
false for more than ``idle_timeout`` seconds. If ``busy`` is currently true,
then ``self.idle_for`` will be ``NOT_YET``, and subscripting ``NOT_YET``
always returns ``NOT_YET``. Since ``NOT_YET`` is a moment that can never be
reached, the boolean value of the expression is always false while ``busy``
is true.
Let's look at the ``IdleTimer`` in action::
>>> it = IdleTimer()
>>> it.busy = True
>>> Time.advance(30) # busy for 30 seconds
>>> it.busy = False
>>> Time.advance(10) # idle for 10 seconds, no timeout yet
>>> Time.advance(10) # ...20 seconds!
timed out!
>>> Time.advance(15) # idle 35 seconds, no new timeout
>>> it.busy = True # busy again
>>> Time.advance(5) # for 5 seconds...
>>> it.busy = False
>>> Time.advance(30) # idle 30 seconds, timeout!
timed out!
>>> it.idle_timeout = 15 # already at 30, fires again
timed out!
Automatically Advancing the Time
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In our examples, we've been manually updating the time. But if ``auto_update``
is true, then the time automatically advances whenever a trellis value is
changed::
>>> Time.auto_update = True
>>> c = trellis.Cell()
>>> c.value = 42
>>> now = Time[0]
>>> from time import sleep
>>> sleep(0.1)
>>> now == Time[0] # time hasn't actually moved forward yet...
True
>>> c.value = 24
>>> now == Time[0] # but now it has, since a recalculation occurred
False
This ensures that any rules that use a current time value, or that are waiting
for a timeout, will see the correct time.
Note, however, that if your application doesn't change any trellis values for a
long time, then any pending timeouts may not fire for an excessive period of
time. You can, however, force an update to occur by using the ``Time.tick()``
method::
>>> now = Time[0]
>>> sleep(0.1)
>>> now == Time[0] # time hasn't actually moved forward yet...
True
>>> Time.tick()
>>> now == Time[0] # but now it has!
False
So, an application's main loop can call ``Time.tick()`` repeatedly in order to
ensure that any pending timeouts are being fired.
You can reduce the number of ``tick()`` calls significantly, however, if you
make use of the ``next_event_time()`` method. If there are no scheduled events
pending, it returns ``None``::
>>> print Time.next_event_time()
None
But if anything is waiting, like say, our ``IdleTimeout`` object from the
previous section, it returns the relative or absolute time of the next time
``tick()`` will need to be called::
>>> it = IdleTimer(idle_timeout=30)
>>> Time.next_event_time(relative=True)
30.0
>>> when = EPOCH[Time.next_event_time(relative=False)]
>>> when - Time[0]
30.0
>>> Time.advance(30)
timed out!
(We can't show the absolute time in this example, because it would change every
time this document was run. But we can offset it from ``EPOCH``, and then
subtract it from the current time, to prove that it's equal to an absolute time
30 seconds after the current time.)
Armed with this method, you can now write code for your application's event
loop that calls ``tick()`` at the appropriate interval. You will simply need
to define a Trellis rule somewhere that monitors the ``next_event_time()`` and
schedules a call to ``Time.tick()`` if the next event time is not None. You
can use whatever scheduling mechanism your application already includes, such
as a ``wx.Timer`` or Twisted's ``reactor.callLater``, etc.
When the scheduled call to ``tick()`` occurs, your monitoring rule will be
run again (because ``next_event_time()`` depends on the current time), thus
repeating the cycle as often as necessary.
Note, however, that your rule may be run again *before* the scheduled
``tick()`` occurs, and so may end up scheduling extra calls to ``tick()``.
This should be harmless, however, but if you want to avoid the repeats you can
always write your rule so that it updates the existing scheduled call time, if
one is pending. (E.g. by updating the ``wx.Timer`` or changing the Twisted
"appointment".)
Event Loops
-----------
>>> def hello(*args, **kw):
... print "called with", args, kw
>>> from peak.events.activity import EventLoop
>>> Time.auto_update = False # test mode
>>> EventLoop.call(hello, 1, a='b')
>>> EventLoop.call(hello, 2)
>>> EventLoop.call(hello, this=3)
>>> EventLoop.call(EventLoop.stop)
>>> EventLoop.run()
called with (1,) {'a': 'b'}
called with (2,) {}
called with () {'this': 3}
>>> EventLoop.stop()
Traceback (most recent call last):
...
AssertionError: EventLoop isn't running
>>> EventLoop.call(EventLoop.run)
>>> EventLoop.call(hello, 4)
>>> EventLoop.call(EventLoop.stop)
>>> EventLoop.run()
Traceback (most recent call last):
...
AssertionError: EventLoop is already running
>>> it = IdleTimer(idle_timeout=5)
>>> EventLoop.run()
called with (4,) {}
timed out!
Garbage Collection
==================
Cells keep strong references to all of the cells whose values they accessed
during rule calculation, and weak references to all of the cells that accessed
them. This ensures that as long as an observer exists, its most-recently
observed subject(s) will also continue to exist.
Cells whose rules are effectively methods (i.e., cells that represent component
attributes) also keep a strong reference to the object that owns them, by
way of the method's ``im_self`` attribute. This means that as long as some
attribute of a component is being observed, the component will continue to
exist.
In addition, a component's ``__cells__`` dictionary keeps a reference to all
its cells, creating a reference cycle between the cells and the component.
Thus, Component instances can only be reclaimed by Python's cycle collector,
and are not destroyed as soon as they go out of scope. You should therefore
avoid giving Component objects a ``__del__`` method, and should explicitly
dispose of any resources that you want to reclaim early.
You should NOT, however, attempt to break the cycle between a component and its
cells. If the cells have any observers, this will just cause the rules to
break upon recalculation, or else recreate some of the cells, depending on how
you tried to break the cycle. It's better to simply let Python detect the
cycle and get rid of it itself.
However, if you absolutely MUST mess with this, the best thing to do is delete
the component's ``__cells__`` attribute with ``del ob.__cells__``, as this will
ensure that any dangling observers will at least get attribute errors when
recalculation occurs. Thus, if the component is really still in use, at least
you'll get an error message, instead of weird results. But it still won't be a
fun problem to debug, so it's highly recommended that you leave the garbage
collection to Python. Python always knows more about what's happening in your
program than you do!
----------
Appendices
----------
The "Trellis" Name
==================
The "Trellis" name comes from Dr. David Gelernter's 1991 book, "Mirror Worlds",
where he describes a parallel programming architecture he called "The Trellis".
In the excerpted passages below, he describes the portions of his architecture
that are roughly the same as in this Python implementation:
"Consider an upward-stretching network of infomachines tethered together,
rung-upon-rung (billowing slightly in the breeze?) No two rungs need have
exactly the same number of machines.... There might be ten rungs in all or
hundreds or thousands, and the average rung might have anywhere from a
handful to hundreds of members. This architecture spans a huge range of
shapes and sizes....
So, these things are "tethered together" -- meaning? Those lines are
*lines of communication*. Each member of the Trellis is tethered to some
lower-down machines and to some higher-ups.... A machine deals *only* with
the machines to which it is tethered. So far as it's concerned, the rest
don't exist. It deals with inferiors in a certain way and superiors in a
certain other way, and that's it....
Information rushes upward through the network, and the machines on each
rung respond to it on their own terms.... Each machine focuses on one
piece of the problem -- on answering a single question about the thing out
there...that is being monitored. Each machine's entire and continuous
effort is thrown into answering its one question. You can query a machine
at any time -- what's the current best answer to your particular question?
-- and it will produce an up-to-the-second response....
So data flows upward through the ensemble; there's also a reverse, downward
flow of what you might call "anti-data" -- *inquiries* about what's going
on. A high ranking element might attempt to generate a new value, only to
discover it's missing some key datum from an inferior. It sends a query
downward.... The inferior tries to come up with some new data.... If a
bottom-level machine is missing data,.... It can ask the outside world
directly for information....
The fact that data flows up and anti-data flows downwards means that, in a
certain sense, a Trellis can run either forwards or backwards, or both at
the same time....
A Trellis, it turns out, is a lot like a crystal.... When you turn it on,
it vibrates at a certain frequency.
Meaning? In concept, each Trellis element is an infomachine. All these
infomachines run separately and simultaneously.
In practice, we do things somewhat differently....
We run the Trellis in a series of sweeps. During the first sweep, each
machine gets a chance to [produce one output value]. During the second,
each [produces a second value], and so on. No machine [produces] a
second [value] until every [machine] has [produced] a *first* [value]."
While Dr. Gelernter's Trellis was designed to be run by an arbitary number of
parallel processors, our Trellis is scaled down to run in a single Python
thread. But on the plus side, our Trellis automatically connects its "tethers"
as it goes, so we don't have to explicitly plot out an entire network of
dependencies, either!
The Implementation
==================
Ken Tilton's "Cells" library for Common Lisp inspired the implementation of
the Trellis. While Tilton had never heard of Gelernter's Trellis, he did
come to see the value of having synchronous updates, like the "sweeps" of
Gelernter's design, and combined them with automatic dependency detection to
create his "Cells" library.
I heard about this library only because Google sponsored a "Summer of Code"
project to port Cells to Python - a project that produced the PyCells
implementation. My implementation, however, is not a port but a re-visioning
based on native Python idioms and extended to handle mutually recursive rules,
and various other features that do not precisely map onto the features of
Cells, PyCells, or other Python frameworks inspired by Cells (such as
"Cellulose").
While the first very rough drafts of this package were done in 2006 on my own
time, virtually all of the work since has been generously funded by OSAF, the
Open Source Applications Foundation.
Roadmap
=======
Open Issues
* Debugging code that does modifications can be difficult because it can be
hard to know which cells are which. There should be a way to give cells
an identifier, so you know what you're looking at.
* Coroutine/task rules and discrete rules are somewhat unintuitive as to
their results. It's not easy to tell when you should ``poll()`` or
``repeat()``, especially since things will sometimes *seem* to work without
them. In particular, we probably need a way to return *multiple* values
from a rule via an output queue. That way, a discrete rule or task's
recalculation can be separated from mere outputting of queued values.
* Errors in rules can currently clog up the processing of rules that observe
them. Ideally, errors should cause a rollback of the entire recalculation,
or at least the parts that were affected by an error, so that the next
recalculation will begin from the pre-error state.
* Currently, there's no protection against accessing Cells from other
threads, nor support for having different logical tasks in the same thread
with their own contexts, services, etc. This should be fixed by using
the "Contextual" library to manage thread-local (and task-local) state for
the Trellis, and by switching to the appropriate ``context.State`` whenever
non-rule/non-modifier code tries to read or write a cell. If combined with
a lockable cell controller, and the rollback capability mentioned above,
this would actually allow the Trellis to become an STM system -- a Software
Transactional Memory.
* There should probably be a way to tell if a Cell ``.has_listeners()`` or
``.has_dependencies()``. This will likely become important for TrellisIO,
if not TrellisDB.
* There should probably be an easier way to reference cells directly, instead
of using Cells(ob)['name'] -- perhaps a ``.link`` property, similar to the
``.future`` of "todo" cells, would make this easier.
* Currently, you can set the value of a new cell more than once, to different
values, as long as it hasn't been read yet. This provides some additional
flexibility to constructors, but isn't really documented or fully
specified yet.
* The ``poll()`` and ``repeat()`` functions, as well as the
``.ensure_recalculation()`` method of cells, are undocumented in this
release.
TrellisDB
* A system for processing relational-like records and "active queries" mapped
from zero or more backend storage mechanism.
TrellisUI
* Framework for mapping application components to UI views.
* Widget specification, styling, and layout system that's backend-agnostic,
ala Adobe's "Eve2" layout constraint system. Should be equally capable of
spitting out text-mode drawings of a UI, as it is of managing complex wx
"GridBagSizer" layouts.
TrellisIO
* Time service & timestamp rules
* IO events
* Cross-thread bridge cells
* signal() events
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