Development of ruleset

src/cell_plane.py

@@ -8,29 +8,29 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 
-class Bit:
+class Cell:
     """
-    Represents a "bit" in a bitplane.
+    Represents a "cell" in a CellPlane.
 
     Note we keep track of the index for vectorization purposes. By maintaining each index
     and batch updating via the given index, we can much more efficiently update the entire
-    bitplane.
+    cell plane.
     """
     def __init__(self, value, *index):
         self.value = value
         self.index = index
 
 
-class BitPlane:
+class CellPlane:
     """
-    A BitPlane represents a layer of the grids that can be placed on top of one another in a 2D CAM.
+    A CellPlane represents a layer of the grids that can be placed on top of one another in a 2D CAM.
 
-    The use of multiple bit plane allow for more intricate states of life and death, though there
-    exists only a single master bit plane that controls the others. That is, the master bit plane has
-    a CAM ruleset applied to it, and the other bit planes merely copy the master, though this can
+    The use of multiple cell plane allow for more intricate states of life and death, though there
+    exists only a single master cell plane that controls the others. That is, the master cell plane has
+    a CAM ruleset applied to it, and the other cell planes merely copy the master, though this can
     be delayed and have different color mappings.
 
-    For example, by setting a delay of two ticks on the second bit plane of a 2-level CAM configuration,
+    For example, by setting a delay of two ticks on the second cell plane of a 2-level CAM configuration,
     one can allow for ECHOing, providing a more intuitive sense of "velocity" based on the master.
 
     That is not to say one could not have multiple CAM's operating simultaneously though. We can consider
@@ -44,18 +44,18 @@ class BitPlane:
         """
         The following joins indices in N-dimensions together.
 
-        This information is stored in a bit (with initial value False) in order for batch processing
-        to be performed when actually updating values and computing whether a bit is on or off. For
-        example, if exploring a 4D array, we want to be able to know which bits we need to check the
-        status of, but this is relative to the current bit, whose position we do not know unless that
-        information is stored with the current bit.
+        This information is stored in a cell (with initial value False) in order for batch processing
+        to be performed when actually updating values and computing whether a cell is on or off. For
+        example, if exploring a 4D array, we want to be able to know which cells we need to check the
+        status of, but this is relative to the current cell, whose position we do not know unless that
+        information is stored with the current cell.
         """
-        return Bit(False, *indices)
+        return Cell(False, *indices)
 
 
     def __init__(self, dimen):
         """
 
         """
-        self.grid = BitPlane._populate(*np.indices(dimen))
+        self.grid = CellPlane._populate(*np.indices(dimen))
 

src/neighborhood.py

@@ -1,6 +1,8 @@
 """
 
 
+@author: jrpotter
+@date: May 31st, 2015
 """
 import itertools
 
@@ -70,25 +72,25 @@ class Neighborhood:
         self.offsets = {}
 
 
-    def neighbors(self, bit, grid, wrap_around=True):
+    def neighbors(self, cell, grid, wrap_around=True):
         """
-        Returns all bits in the given neighborhood.
+        Returns all cells in the given neighborhood.
 
-        The returned cells are grouped with the value the cell is checked to be (a 2-tuple (Bit, value) pair).
+        The returned cells are grouped with the value the cell is checked to be (a 2-tuple (Cell, value) pair).
         These are sorted based on the NeighborhoodKey comparison class defined above.
         """
-        bits = []
+        cells = []
         for k in sorted(self.offsets.keys()):
-            position = [sum(x) for x in zip(bit.index, k)]
+            position = [sum(x) for x in zip(cell.index, k)]
             for i in range(len(position)):
                 if wrap_around:
                     position[i] = position[i] % grid.shape[i]
                 elif i < 0 or i >= grid.shape[i]:
                     break
             else:
-                bits.append(grid[tuple(position)])
+                cells.append((grid[tuple(position)], self.offsets[k]))
 
-        return bits
+        return cells
 
 
     def extend(self, offsets, strict=False):

src/ruleset.py

@@ -0,0 +1,70 @@
+"""
+
+
+
+@author: jrpotter
+@date: May 31st, 2015
+"""
+
+class Ruleset:
+    """
+    The following determines the next state of a given cell in a CAM.
+
+    Given a neighborhood and a tolerance level, the ruleset determines whether a given cell should be on or off after a tick.
+    For example, if the tolerance level is set to 100% (i.e. neighborhoods must exactly match desired neighborhood to be on),
+    then the ruleset iterates through all neighbors and verifies a match.
+
+    For the sake of clarity, we consider a neighborhood to actually contain the "rules" for matching, and a ruleset to be the
+    application of the rules as defined in the neighborhood. We state this since the actual expected values are declared in
+    a neighborhood instance's offsets member.
+    """
+
+    def __init__(self, grid, wrap_around=True):
+        """
+
+        """
+        self.grid = grid
+        self.wrap_around = wrap_around
+
+
+    def matches(self, cell, neighborhood):
+        """
+        Determines that neighborhood matches expectation exactly.
+
+        Note this is just like the tolerate method with a tolerance of 1, but
+        recoding allows for short circuiting.
+        """
+        residents = neighborhood.neighbors(cell, self.grid, self.wrap_around)
+        for resident in residents:
+            if resident[0].value != resident[1]:
+                return False
+
+        return True
+
+
+    def tolerate(self, cell, neighborhood, tolerance):
+        """
+        Determines that neighborhood matches expectation within tolerance.
+
+        We see that the percentage of actual matches are greater than or equal to the given tolerance level. If so, we
+        consider this cell to be alive. Note tolerance must be a value 0 <= t <= 1.
+        """
+        matches = 0
+        residents = neighborhood.neighbors(cell, self.grid, self.wrap_around)
+        for resident in residents:
+            if resident[0].value == resident[1]:
+                matches += 1
+
+        return (matches / len(residents)) >= tolerance
+
+
+    def satisfies(self, cell, neighborhood, valid_func):
+        """
+        Allows custom function to relay next state of given cell.
+
+        The passed function is supplied the list of 2-tuple elements, of which the first is a Cell and the second is
+        the expected state as declared in the Neighborhood, as well as the grid and cell in question.
+        """
+        residents = neighborhood.neighbors(cell, self.grid, self.wrap_around)
+
+        return valid_func(cell, self.grid, residents)