Neighborhood logic established. Optimization needed

README.md

@@ -7,11 +7,41 @@ though mentioned as reasonably priced, a CAM Forth machine is out of my price ra
 
 The following uses numpy/matplotlib underneath, and will ideally incorporate the following:
 
-* Arbitrary description of neighborhoods
+* N-Dimensional Cellular Automata
-* Arbitrary leveling of bit planes
+* Arbitrary count of bit planes and description of neighborhoods
-* Arbitrary description of rulesets
+* Timing specifications and control for granular viewing
-* 2D and 3D cellular automata
+* ECHOing and TRACing in library for 2D CAMs
-* Timing specifications for granular viewing
+
-* Echoing and Tracing (for 2D)
+Documentation will be made available at fuzzykayak.com/... but a quickstart will be provided below.
+There are also a variety of examples given to demonstrate different means of building CAMS.
+
+Quickstart
+----------
+
+To begin construction of a CAM, we need two objects: a CAM and a Ruleset.
+
+A CAM can be broken down into a list of cell planes, each of which contain the same number of states.
+Of these planes, the first is considered the master, and all others are mirrors of the master at an
+earlier stage in time (this allows for methods such as ECHOing).
+
+A ruleset can further be broken down into a list of configurations, of which one must pass
+for the state of a cell to change. During application of a ruleset, each cell is described by
+a neighborhood, which packages all other cells considered in the given plane.
+
+The following will construct Conway's Game of Life, as shown in the provided GIF:
+
+```
+import cam
+import ruleset as rs
+
+# View the different formats the CAMParser can parse. Manual construction for
+# more complicated rulesets are also a possibility
+c = cam.CAM(1, 100, 2)
+p = u.CAMParser('B3/S23', c)
+
+# 400 represents the time, in milliseconds, before the next tick occurs
+c.randomize()
+c.start_plot(400, p.ruleset)
+```
 
 ![alt tag](https://raw.githubusercontent.com/jrpotter/fifth/master/rsrc/demo.gif)

src/cam.py

@@ -1,11 +1,10 @@
 """
 Top level module representing a Cellular Automata Machine.
 
-The CAM consists of any number of cell planes that allow for increasingly complex cellular automata.
+The CAM consists of a number of cell planes that allow for increasingly complex cellular automata.
 This is the top-level module that should be used by anyone wanting to work with fifth, and provides
-all methods needed (i.e. supported) to interact/configure the cellular automata as desired.
+all methods needed (i.e. supported) to interact/configure with the cellular automata directly.
 
-@author: jrpotter
 @date: June 01, 2015
 """
 import time
@@ -22,9 +21,9 @@ class CAM:
     directly, but instead mirror the master plane, and reflect these changes after a given number of
     "ticks."
 
-    A tick represents an interval of time after which all states should be updated, and, therefore, all
+    A tick represents an interval of time after which all states of a given set of cell planes should be
-    cell planes should be updated. Certain planes may or may not change every tick, but instead on every
+    updated. should be updated, Certain planes may or may not change every tick, but instead on every
-    nth tick, allowing for more sophisticated views such as ECHOing and TRACE-ing.
+    nth tick, allowing for more sophisticated views such as ECHOing and TRACing.
     """
 
     def __init__(self, cps=1, states=100, dimen=2):
@@ -49,7 +48,7 @@ class CAM:
         The tick function should be called whenever we want to change the current status of the grid.
         Every time the tick is called, the ruleset is applied to each cell and the next set of states
         is placed into the master grid. Depending on the timing specifications set by the user, this
-        may also change secondary cell planes (the master is always updated on each tick).
+        may also change secondary cell planes (the master, by default, is always updated on each tick).
         """
         self.total += 1
         for i, j in self.ticks:

src/neighborhood.py

@@ -8,7 +8,7 @@ class Neighborhood:
     """
 
     """
-    def __init__(self, index, offsets):
+    def __init__(self, f_index, b_offset, states, indices):
         """
 
         """

src/ruleset.py

@@ -4,11 +4,9 @@ The following determines the next state of a given cell in a CAM.
 The ruleset takes in a collection of rules specifying neighborhoods, as well as the configurations of
 said neighborhood that yield an "on" or "off" state on the cell a ruleset is being applied to.
 
-@author: jrpotter
 @date: May 31st, 2015
 """
 import enum
-import itertools as it
 
 import numpy as np
 
@@ -27,19 +25,20 @@ class Ruleset:
 
     must match exactly for the center cell to be a 1, then each cell is checked for this configuration, and its
     state is updated afterward (note the above is merely for clarity; a configuration is not defined as such). Note
-    configurations are checked until a match occurs, in a FIFO manner.
+    configurations are checked until a match occurs, in order of the configurations list.
     """
 
     class Method(enum.Enum):
         """
         Specifies how a ruleset should be applied to a given cell.
 
-        * A match declares that a given configuration must match exactly for the cell to be considered on.
+        * A match declares that a given configuration must match exactly for a configuration to pass
-        * A tolerance specifies that a configuration must match within a given percentage to be considered on.
+        * A tolerance specifies that a configuration must match within a given percentage to pass
         * A specification allows the user to define a custom function which must return a boolean, declaring
-          whether a cell should be on or off. This function is given the current cell's state, as well as
+          whether a configuration passes. This function is given a neighborhood with all necessary information.
-          the state of the cell's neighbors.
+        * Always passing allows the first configuration to always yield a success. It is redundant to add
-
+          any additional configurations in this case (in fact it is inefficient since neighborhoods are computer
+          in advance).
         """
         MATCH       = 0
         TOLERATE    = 1
@@ -48,66 +47,24 @@ class Ruleset:
 
     def __init__(self, method):
         """
-        @grid: Every ruleset is bound to a grid, which a ruleset is applied to.
+        A ruleset does not begin with any configurations; only a means of verifying them.
-        @method: One of the values defined in the RulesetMethod enumeration. View class for description.
+
+        @method: One of the values defined in the Ruleset.Method enumeration. View class for description.
         """
         self.method = method
         self.configurations = []
 
-    def addConfiguration(self, grid, next_state, offsets):
-        """
-        Creates a configuration and saves said configuration.
-        """
-        config = Configuration(grid, next_state, offsets)
-        self.configurations.append(config)
-
     def applyTo(self, plane, *args):
         """
         Depending on the set method, applies ruleset to each cell in the plane.
 
-        Note we first compute all neighborhoods in a batch manner and then test that a configuration
-        passes on the supplied neighborhood.
-
         @args: If our method is TOLERATE, we pass in a value in set [0, 1]. This specifies the threshold between a
                passing (i.e. percentage of matches in a configuration is > arg) and failing. If our method is SATISFY,
                arg should be a function returning a BOOL, which takes in a current cell's value, and the
                value of its neighbors.
         """
-        master = plane.grid.flat
-
-        for config in self.configurations:
-
-            # Construct neighborhoods
-            #
-            # After profiling with a previous version, I found that going through each index and totaling the number
-            # of active states was taking much longer than I liked. Instead, we compute as many neighborhoods as possible
-            # simultaneously, avoiding explicit summation via the "sum" function, at least for each state separately.
-            #
-            # Because the states are now represented as numbers, we instead convert each number to their binary representation
-            # and add the binary representations together. We do this in chunks of 9, depending on the number of offsets, so
-            # no overflowing of a single column can occur. We can then find the total of the ith neighborhood by checking the
-            # sum of the ith index of the summation of every 9 chunks of numbers (this is done a row at a time).
-
-            # TODO: Config offsets should be flat index, bit offset
-
-
-
-            neighborhoods = []
-
-            values = []
-            for f_index, offset in config.offsets:
-                val = plane.f_bits([f_index])
-                values.append(int(val[offset+1:] + val[:offset]))
-
-            # Chunk into groups of 9 and sum all values
-            chunks = [values[i:i+9] for i in range(0, len(values), 9)]
-            summands = map(sum, chunks)
-
-            # Construct neighborhoods for each value in list
-
-
-
 
+        # Determine which function should be used to test success
         if self.method == Ruleset.Method.MATCH:
             vfunc = self._matches
         elif self.method == Ruleset.Method.TOLERATE:
@@ -117,13 +74,57 @@ class Ruleset:
         elif self.method == Ruleset.Method.ALWAYS_PASS:
             vfunc = lambda *args: True
 
-            # Apply the function if possible
+        # Find the set of neighborhoods for each given configuration
-            passed, state = config.passes(f_index, grid, vfunc, *args)
+        neighborhoods = [self._construct_neighborhoods(plane, config) for c in self.configurations]
+        for f_idx, value in enumerate(self.plane.flat):
+            for b_offset in len(self.plane.shape[-1]):
+                for c_idx, config in enumerate(self.configurations):
+                    n_idx = f_idx * self.plane.shape[-1] + b_offset
+                    passed, state = config.passes(neighborhoods[c_idx][n_idx], vfunc, *args)
                     if passed:
-                return state
+                        plane[f_idx][b_offset] = state
+                        break
 
-        # If no configuration passes, we leave the state unchanged
+    def _construct_neighborhoods(self, plane, config):
-        return grid.flat[f_index]
+        """
+        Construct neighborhoods
+
+        After profiling with a previous version, I found that going through each index and totaling the number
+        of active states was taking much longer than I liked. Instead, we compute as many neighborhoods as possible
+        simultaneously, avoiding explicit summation via the "sum" function, at least for each state separately.
+
+        Because the states are now represented as numbers, we instead convert each number to their binary representation
+        and add the binary representations together. We do this in chunks of 9, depending on the number of offsets, so
+        no overflowing of a single column can occur. We can then find the total of the ith neighborhood by checking the
+        sum of the ith index of the summation of every 9 chunks of numbers (this is done a row at a time).
+
+        TODO: Config offsets should be flat offset, bit offset
+        """
+        neighborhoods = []
+
+        for f_idx, row in enumerate(plane.grid.flat):
+
+            # Construct the current neighborhoods of each bit beforehand
+            row_neighborhoods = [Neighborhood(f_idx, i) for i in range(plane.shape[-1])]
+
+            # Note: config's offsets contain the index of the number in the plane's flat iterator
+            # and the offset of the bit referring to the actual state in the given neighborhood
+            offset_totals = []
+            for f_offset, b_offset in config.offsets:
+                row_offset = plane.f_bits(f_idx + f_offset)
+                offset_totals.append(int(row_offset[b_offset+1:] + row_offset[:b_offset]))
+
+            # Chunk into groups of 9 and sum all values
+            # These summations represent the total number of states in a given neighborhood
+            chunks = map(sum, [offset_totals[i:i+9] for i in range(0, len(offset_totals), 9)])
+            for chunk in chunks:
+                for i in range(len(row_neighborhoods)):
+                    row_neighborhoods[i].total += int(chunk[i])
+
+            # Lastly, keep neighborhoods 1D, to easily relate to the flat plane grid
+            neighborhoods += row_neighborhoods
+
+        return neighborhoods
 
     def _matches(self, f_index, f_grid, indices, states):
         """