Functional Game of Life

src/cam.py

@@ -2,7 +2,11 @@
 
 
 """
-from cell_plane import CellPlane
+import copy
+import numpy as np
+import ruleset as rs
+import cell_plane as cp
+import neighborhood as nh
 
 
 class CAM:
@@ -18,9 +22,10 @@ class CAM:
         """
 
         """
+        cps = max(cps, 1)
         self._dimen = dimen
-        self._planes = [CellPlane(dimen) for i in cps]
+        self._planes = [cp.CellPlane(dimen) for i in range(cps)]
-        self._master = self._planes[0].grid if cps > 0 else None
+        self.master = self._planes[0].grid
 
 
     def tick(self, ruleset, neighborhood, *args):
@@ -31,5 +36,28 @@ class CAM:
         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).
         """
-        self._master = ruleset.update(self._master, self.master, neighborhood, *args)
+        self.master[:] = rs.Ruleset.update(self.master, ruleset, neighborhood, *args)
+
+
+    def randomize(self):
+        """
+        Set the master grid to a random configuration.
+        """
+        @np.vectorize
+        def v_random(cell):
+            cell.value = np.random.random_integers(0, 1)
+            return cell
+
+        v_random(self.master)
+
+
+    def console_display(self):
+        """
+        A convenience method used to display the grid onto the console.
+
+        This should not be called frequently; I haven't benchmarked it but I do not anticipate this
+        running very well for larger grids.
+        """
+        vfunc = np.vectorize(lambda x: int(x.value))
+        print(vfunc(self.master))
 

src/life.py

@@ -0,0 +1,30 @@
+import cam
+import ruleset as rs
+import neighborhood as nh
+
+def game_of_life(cell, neighbors):
+    """
+    Rules of the Game of Life.
+
+    Note we ignore the second component of the neighbors tuples since
+    life depends on all neighbors
+    """
+    total = sum(map(lambda x: int(x[0].value), neighbors))
+    if cell.value:
+        if total < 2 or total > 3:
+            return False
+        else:
+            return True
+    elif total == 3:
+        return True
+    else:
+        return False
+
+if __name__ == '__main__':
+    c = cam.CAM(1, (10, 10))
+    c.randomize()
+    c.console_display()
+    r = rs.Ruleset(rs.Rule.SATISFY)
+    n = nh.Neighborhood.moore(c.master, True)
+    c.tick(r, n, game_of_life)
+    c.console_display()

src/neighborhood.py

@@ -61,7 +61,7 @@ class Neighborhood:
             return 0
 
 
-    def __init__(self):
+    def __init__(self, grid, wrap_around=True):
         """
         Sets up an empty neighborhood.
 
@@ -69,10 +69,12 @@ class Neighborhood:
         Note the offsets have a tuple as a key representing the position being offsetted by, and as a value,
         the current state the given cell at the offset is checked to be.
         """
+        self.grid = grid
         self.offsets = {}
+        self.wrap_around = wrap_around
 
 
-    def neighbors(self, cell, grid, wrap_around=True):
+    def neighbors(self, cell):
         """
         Returns all cells in the given neighborhood.
 
@@ -83,12 +85,12 @@ class Neighborhood:
         for k in sorted(self.offsets.keys()):
             position = [sum(x) for x in zip(cell.index, k)]
             for i in range(len(position)):
-                if wrap_around:
+                if self.wrap_around:
-                    position[i] = position[i] % grid.shape[i]
+                    position[i] = position[i] % self.grid.shape[i]
-                elif i < 0 or i >= grid.shape[i]:
+                elif i < 0 or i >= self.grid.shape[i]:
                     break
             else:
-                cells.append((grid[tuple(position)], self.offsets[k]))
+                cells.append((self.grid[tuple(position)], self.offsets[k]))
 
         return cells
 
@@ -111,7 +113,7 @@ class Neighborhood:
 
 
     @classmethod
-    def moore(cls, dimen, value=True):
+    def moore(cls, grid, wrap_around=True, value=True):
         """
         Returns a neighborhood corresponding to the Moore neighborhood.
 
@@ -122,19 +124,19 @@ class Neighborhood:
         Note the center cell is excluded, so the total number of offsets are 3^N - 1.
         """
         offsets = {}
-        variants = ([-1, 0, 1],) * dimen
+        variants = ([-1, 0, 1],) * len(grid.shape)
         for current in itertools.product(*variants):
             if any(current):
                 offsets[current] = value
 
-        m_neighborhood = cls()
+        m_neighborhood = cls(grid, wrap_around)
         m_neighborhood.extend(offsets)
 
         return m_neighborhood
 
 
     @classmethod
-    def neumann(cls, dimen, value=True):
+    def neumann(cls, grid, wrap_around=True, value=True):
         """
         Returns a neighborhood corresponding to the Von Neumann neighborhood.
 
@@ -145,14 +147,15 @@ class Neighborhood:
         Note the center cell is excluded, so the total number of offsets are 2N.
         """
         offsets = {}
-        variant = [0] * dimen
+        variant = [0] * len(grid.shape)
         for i in range(len(variant)):
             for j in [-1, 1]:
                 variant[i] = j
                 offsets[tuple(variant)] = value
             variant[i] = 0
 
-        n_neighborhood = cls()
+        n_neighborhood = cls(grid, wrap_around)
         n_neighborhood.extend(offsets)
 
         return n_neighborhood
+

src/ruleset.py

@@ -5,9 +5,12 @@
 @author: jrpotter
 @date: May 31st, 2015
 """
-from enum import Enum
+import copy
+import enum
+import numpy as np
 
-class Rule(Enum):
+
+class Rule(enum.Enum):
     MATCH    = 0
     TOLERATE = 1
     SATISFY  = 2
@@ -26,22 +29,41 @@ class Ruleset:
     a neighborhood instance's offsets member.
     """
 
-    def __init__(self, method, wrap_around=True):
+    @staticmethod
+    @np.vectorize
+    def update(cell, rules, neighborhood, *args):
+        """
+        Allow for batch processing of rules.
+
+        We choose our processing function based on the specified rule and update every cell in the grid simultaneously
+        via a vectorization.
+        """
+        tmp = copy.deepcopy(cell)
+        if rules.method == Rule.MATCH:
+            tmp.value = rules.matches(cell, neighborhood, *args)
+        elif rules.method == Rule.TOLERATE:
+            tmp.value = rules.tolerate(cell, neighborhood, *args)
+        elif rules.method == Rule.SATISFY:
+            tmp.value = rules.satisfies(cell, neighborhood, *args)
+
+        return tmp
+
+
+    def __init__(self, method):
         """
 
         """
         self.method = method
-        self.wrap_around = wrap_around
 
 
-    def _matches(self, cell, grid, neighborhood):
+    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, grid, self.wrap_around)
+        residents = neighborhood.neighbors(cell)
         for resident in residents:
             if resident[0].value != resident[1]:
                 return False
@@ -49,7 +71,7 @@ class Ruleset:
         return True
 
 
-    def _tolerate(self, cell, grid, neighborhood, tolerance):
+    def tolerate(self, cell, neighborhood, tolerance):
         """
         Determines that neighborhood matches expectation within tolerance.
 
@@ -57,7 +79,7 @@ class Ruleset:
         consider this cell to be alive. Note tolerance must be a value 0 <= t <= 1.
         """
         matches = 0
-        residents = neighborhood.neighbors(cell, grid, self.wrap_around)
+        residents = neighborhood.neighbors(cell)
         for resident in residents:
             if resident[0].value == resident[1]:
                 matches += 1
@@ -65,32 +87,15 @@ class Ruleset:
         return (matches / len(residents)) >= tolerance
 
 
-    def _satisfies(self, cell, grid, neighborhood, valid_func):
+    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, grid, self.wrap_around)
+        residents = neighborhood.neighbors(cell)
 
-        return valid_func(cell, grid, residents)
+        return valid_func(cell, residents)
 
 
-    @np.vectorize
-    def update(self, cell, *args):
-        """
-        Allow for batch processing of rules.
-
-        We choose our processing function based on the specified rule and update every cell in the grid simultaneously
-        via a vectorization.
-        """
-        if self.method == Rule.MATCH:
-            cell.value = self._matches(cell, *args)
-        elif self.method == Rule.TOLERATE:
-            cell.value = self._tolerate(cell, *args)
-        elif self.method == Rule.SATISFY:
-            cell.value = self._satisfy(cell, *args)
-
-        return cell
-