Until now, we just had to specify a few parameters and everything fell into place for us. However, zasim offers a whole toolkit for assembling StepFunc objects from simpler parts that all influence the behavior of the result. In this chapter, the StepFunc object, the StepFuncVisitor objects and the Simulator interface will be explored. The next chapter will be about replacing those StepFuncVisitors with our own creations and seeing what happens.
The job of the StepFunc object is to make sure the individual StepFuncVisitor objects you decide to throw into the blender all play nice together or, if they won’t, report incompatibilities.
It has pre-set slots for a few different types of StepFuncVisitor objects, each contributing a different kind of functionality, but with a somewhat fixed interface. Those slots are:
A CellLoop instance (as the loop keyword argument) decides, what cells have to be considered for the step and in what order. Here are some examples of CellLoop classes that are already there:
- OneDimCellLoop and TwoDimCellLoop loop over all cells in the configuration.
- OneDimNondeterministicCellLoop and the two-dimensional version have a parameter that defines with what probability each of the cells will be considered.
Other possibilities include a loop that only considers cells for an update if there were changes in their neighbourhood in the last step.
A StateAccessor instance (as the accessor keyword argument) has all knowledge about how the state is kept and offers access to the data as well as meta-data. Beyond that, it creates code, that writes the result of the computation into the state data storage. These are two of the StateAccessor classes that can already be found in zasim:
- SimpleStateAccessor keeps the configuration in a numpy array with one or two dimensios.
- BetaAsynchronousAccessor works almost the same as the SimpleStateAccessor, but takes care of correctly handling the internal and external state of each cell, as described in the beta_async module.
Possible future ideas are storing the data in a sparse way and not limiting the size of the configuration.
A Neighbourhood instance (as the neighbourhood keyword argument), whose sole responsibility is to make the data from all neighbours available to the computational part of the step func later on. In its most basic form, the SimpleNeighbourhood, it stores a list of names for the neighbourhood cells and their positional offsets and just reads them at the beginning of the loop.
A BorderHandler instance (as the border keyword argument), that does things like copying the borders from the sides and at the same time ensuring the size of the configuration storage is big enough to keep those extra cells. It could also do things like set the border cells to changing values or anything you could think of.
They also offer functions is_position_valid and correct_position for other components to figure out if positions are valid and if they are invalid, what to do with them. The SparseCellLoop uses these functions to correctly mark neighbourhood cells as active if there was a change.
A ExtraStats instance (in the visitors list, as well), which gathers some additional statistics about the step function execution. The following two ExtraStats classes are already available:
Finally, a Computation instance (in the visitors list), that does the actual computation. Examples include:
- ElementaryCellularAutomatonBase, a class, that implements elementary cellular automatons for any neighbourhood and number of dimensions
- CountBasedComputationBase, a base class, that offers a local variable nonzerocount inside the loop, that holds the number of cells from the neighbourhood, that are not zero.
- Based on the CountBasedComputationBase, the LifeCellularAutomatonBase, that implements Conways Game of Life.
The other possibilities are almost limitless. CAs like the SandPile CA, the cellular automaton originally envisioned by Von Neumann, Langton’s ant or any other would have the bulk of their implementation in one of those. Some may also need special instances of CellLoop or StateAccessor to work.
The StepFunc works by calling each of the following methods on all `StepFuncVisitor`s:
- bind with itself as the code argument. This binds the StepFuncVisitor to the StepFunc. The StepFuncVisitor should not allow another StepFunc to bind it to itself after this.
- visit, to let it generate any code for the step function body
- set_target with the target instance as the target argument. This makes the target accessible to the StepFuncVisitor, so that any new attributes  can be set.
- init_once, which allows for actions, that depend on the target, but are only needed to be run once, not whenever the configuration has changed.
- new_config, in which the StepFuncVisitor can perform any tasks necessary to bring a changed configuration into a sane state. Any BorderHandler, for instance, would work their magic here.
|||For an example of this, see zasim.cagen.nondeterministic.NondeterministicCellLoopMixin.set_target, which populates the randseed attribute, that was previously added to the targets attribute list in the visit method.|
There are other actions, that happen, which don’t fit this pattern:
- At the very beginning, the StepFunc will tell the StateAccessor, how big the configuration array is, by calling its set_size method.
- After new_config has been called on all visitors, the StepFunc will call multiplicate_config, which takes care of populating a kind of history of configs. Usually, a StateAccessor would keep at least a current config and a next config internally. This is the code, that makes sure, that every external change to the configuration will be reflected in both of these.
- After setting the size on the accessor, it will extract the possible_values property of the target instance and set self.possible_values to it.
- After calling bind on all visitors, the StepFunc will run a compatibility check of all StepFuncVisitors, to make sure simple errors like using a loop for one dimension with a configuration, that’s two-dimensional, will get noticed straight away.
And StepFunc has another neat feature. Each visitor is able to contribute a little part to a common name for the StepFunc. Such a name is generated when calling str on the StepFunc and will call build_name on all StepFuncVisitor objects that are part of the StepFunc. A name could be, for instance:
2d with VonNeumannNeighbourhood (copy borders) calculating rule 0x915b8b0a (histogram) 1d with ElementaryFlatNeighbourhood calculating rule 0xa5 2d with MooreNeighbourhood calculating game of life (activity)
In the previous section, the target instance has been mentioned, but there was not yet any explanation for what it is or does. The target is, however, very simple. All it has to do is basically keep the configuration and a bunch of additional attributes together in one namespace. The only class currently useful as a target is the Target, which takes a config - or a size, which will generate a random config - and a base as arguments and offers the attributes cconf and possible_values.
Additional attributes will then be added by the StepFunc on an as-needed basis. These include nconf, the “next configuration” set by the SimpleStateAccessor, randseed, the random seed to be used in the next step of the step function, set by NondeterministicCellLoopMixin, activity, or histogram, set by the stats classes or anything else.
In order for displays and controls to work, there is a unified interface for all kinds of simulators, wether they are based on a StepFunc class and a Target, or any other class you can come up with. This interface is defined and documented in zasim.simulator. There is a special class for a Simulator built from a StepFunc and a Target, which is the CagenSimulator and a class for a StepFunc and Target based simulator, that also offers a rule number, like the elementary cellular automaton would, called ElementaryCagenSimulator.
In fact, the simulators from zasim.cagen.simulators are all derived from either the ElementaryCagenSimulator or the CagenSimulator.
The CagenSimulator and the ElementaryCagenSimulator are both constructed from a StepFunc and a Target.
The Simulator grants access to the extra attributes of the target via the t property. It is a TargetProxy object, that will allow access to the extra attrs and nothing else.
The Simulator interface offers a couple of signals, most notably updated and changed, which you can connect any python function or Qt slot to. updated will be emitted, when the simulator has made a step and changed will be emitted when the configuration has changed due to some other event, such as the user drawing on the image. Connecting functions to those signals works like this:
>>> from zasim.cagen.simulators import ElementaryCagenSimulator >>> sim = ElementaryCagenSimulator(size=(10,), rule=110) >>> def fizzbuzz(): >>> if sim.step_number % 3 == 0: >>> if sim.step_number % 5 == 0: >>> print "fizzbuzz" >>> else: >>> print "fizz" >>> elif sim.step_number % 5 == 0: >>> print "buzz" >>> sim.updated.connect(fizzbuzz) >>> sim.step() >>> sim.step() >>> sim.step() fizz >>> sim.step() >>> sim.step() buzz >>> # and disconnect the function again >>> sim.updated.disconnect(fizzbuzz)
Before doing too much, the StepFunc constructor will check compatibility between the StepFuncVisitors. The way this works is, that each StepFuncVisitor has three properties, that have to be set after bind has been set. Those are:
The only features, that are not provided by any StepFuncVisitors, but by the StepFunc itself, are one_dimension and two_dimensions.
The StepFunc goes through all StepFuncVisitors and adds up the provides_features into one big set, then goes through all the requires_features and checks if any are missing and finally goes through the incompatible_features to make sure, that none of them are present.
If neither the missing nor the incompatible list have any entries, normal construction of the StepFunc will continue. Otherwise, a CompatibilityException will be raised.
The best way to figure out, what’s going on is to just plug a couple different StepFuncVisitors together and see what comes out. The interesting parts are the properties pure_py_code_text for the generated python code and code_text for the generated C++ code:
>>> from zasim.cagen import * >>> # create a random configuration, base 2, 15 cells wide >>> t = Target(size=(15,), base=2) >>> a = SimpleStateAccessor() >>> # Create a border of constant zeros around the configuration >>> b = BorderSizeEnsurer() >>> # Calculate the normal elementary cellular automaton number 99 >>> c = ElementaryCellularAutomatonBase(rule=99) >>> # loop over a one-dimensional space >>> l = OneDimCellLoop() >>> # Take the first neighbour from the right and left >>> n = ElementaryFlatNeighbourhood() >>> # finally, compose the parts into a whole >>> sf = StepFunc(loop=l, accessor=a, neighbourhood=n, ... visitors=[b, c], target=t) >>> sf.gen_code() >>> print sf.pure_py_code_text def step_pure_py(self): # from hook init result = None for pos in self.loop.get_iter(): # from hook pre_compute l = self.acc.read_from(offset_pos(pos, (-1,))) m = self.acc.read_from(offset_pos(pos, (0,))) r = self.acc.read_from(offset_pos(pos, (1,))) # from hook compute result = self.target.rule[l, m, r] # from hook post_compute self.acc.write_to(pos, result) # from hook loop_end # from hook after_step # from hook finalize self.acc.swap_configs()
As you can see, the generated python code is divided into multiple sections. This is due to the way the visitors work. Their visit methods are called in order, so if they just appended their code, it would come out interleaved, so instead, there are the sections init, pre_compute, compute, post_compute, after_step and finalize. Each StepFuncVisitor will call add_py_code with a section name and a string containing the python code to add and the StepFunc will correct the indentation of the code and add it to the given category.
The C++ code, that gets generated works the same way, although the sections are not the same.
Using a wrong combination of StepFuncVisitors will result in such an exception:
>>> from zasim.cagen import * >>> # this time, the configuration is two-dimensional >>> t = Target(size=(15,15), base=2) >>> a = SimpleStateAccessor() >>> # we carelessly forgot to use the correct loop for the two-dimensional >>> # config >>> l = OneDimCellLoop() >>> n = ElementaryFlatNeighbourhood() >>> sf = StepFunc(loop=l, accessor=a, neighbourhood=n, target=t) Traceback (most recent call last): ... File "zasim/cagen/stepfunc.py", line 114, in __init__ raise CompatibilityException(conflicts, missing) CompatibilityException: <Compatibility Exception: feature conflicts: missing features: (<zasim.cagen.loops.OneDimCellLoop object at 0x31eca90>, ['one_dimension']) >
This exception shows, that the OneDimCellLoop misses the feature one_dimension.