Created parser for generic life automata
parent
ead7725eb1
commit
bde75c012e
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@ -10,26 +10,11 @@ if __name__ == '__main__':
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sys.path.append(os.path.abspath('src'))
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import cam
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import util as u
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import ruleset as rs
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def high_life(f_index, f_grid, indices, states, *args):
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total = sum(f_grid[indices])
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if not f_grid[f_index]:
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if total == 3 or total == 6 or total == 8:
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return rs.Configuration.OFF
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else:
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if total == 2 or total == 3:
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return rs.Configuration.ON
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return rs.Configuration.OFF
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c = cam.CAM(1, 100, 2)
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p = u.CAMParser('B368/S23', c)
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c.randomize()
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r = rs.Ruleset(rs.Ruleset.Method.SATISFY)
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offsets = rs.Configuration.moore(c.master)
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r.addConfiguration(c.master, high_life, offsets)
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c.start_plot(100, r, lambda *args: True)
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c.start_plot(100, p.ruleset)
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@ -1,5 +1,5 @@
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"""
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B3/S34: Game of Life
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B3/S23: Game of Life
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@author: jrpotter
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@date: June 01, 2015
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@ -10,33 +10,11 @@ if __name__ == '__main__':
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sys.path.append(os.path.abspath('src'))
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import cam
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import util as u
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import ruleset as rs
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def game_of_life(f_index, f_grid, indices, states, *args):
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"""
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Rules of the Game of Life.
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Note we ignore the second component of the neighbors tuples since
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life depends on all neighbors
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"""
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total = sum(f_grid[indices])
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if f_grid[f_index]:
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if total < 2 or total > 3:
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return rs.Configuration.OFF
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else:
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return rs.Configuration.ON
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elif total == 3:
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return rs.Configuration.ON
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else:
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return rs.Configuration.OFF
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c = cam.CAM(1, 100, 2)
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p = u.CAMParser('B3/S23', c)
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c.randomize()
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r = rs.Ruleset(rs.Ruleset.Method.SATISFY)
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offsets = rs.Configuration.moore(c.master)
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r.addConfiguration(c.master, game_of_life, offsets)
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c.start_plot(100, r, lambda *args: True)
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c.start_plot(100, p.ruleset)
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@ -10,22 +10,11 @@ if __name__ == '__main__':
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sys.path.append(os.path.abspath('src'))
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import cam
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import util as u
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import ruleset as rs
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def lwd(f_index, f_grid, indices, states, *args):
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total = sum(f_grid[indices])
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if not f_grid[f_index] and total == 3:
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return rs.Configuration.ON
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else:
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return f_grid[f_index]
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c = cam.CAM(1, 100, 2)
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p = u.CAMParser('B3/S012345678', c)
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c.randomize()
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r = rs.Ruleset(rs.Ruleset.Method.SATISFY)
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offsets = rs.Configuration.moore(c.master)
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r.addConfiguration(c.master, lwd, offsets)
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c.start_plot(100, r, lambda *args: True)
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c.start_plot(100, p.ruleset)
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@ -10,26 +10,11 @@ if __name__ == '__main__':
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sys.path.append(os.path.abspath('src'))
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import cam
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import util as u
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import ruleset as rs
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def morley(f_index, f_grid, indices, states, *args):
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total = sum(f_grid[indices])
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if not f_grid[f_index]:
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if total == 3 or total == 6 or total == 8:
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return rs.Configuration.ON
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else:
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if total == 2 or total == 4 or total == 5:
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return rs.Configuration.ON
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return rs.Configuration.OFF
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c = cam.CAM(1, 100, 2)
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p = u.CAMParser('B368/S245', c)
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c.randomize()
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r = rs.Ruleset(rs.Ruleset.Method.SATISFY)
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offsets = rs.Configuration.moore(c.master)
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r.addConfiguration(c.master, morley, offsets)
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c.start_plot(100, r, lambda *args: True)
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c.start_plot(100, p.ruleset)
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@ -10,26 +10,11 @@ if __name__ == '__main__':
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sys.path.append(os.path.abspath('src'))
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import cam
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import util as u
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import ruleset as rs
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def replicator(f_index, f_grid, indices, states, *args):
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total = sum(f_grid[indices])
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if not f_grid[f_index]:
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if total % 2 == 1:
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return rs.Configuration.ON
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else:
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if total % 2 == 1:
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return rs.Configuration.ON
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return rs.Configuration.OFF
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c = cam.CAM(1, 100, 2)
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p = u.CAMParser('B1357/S1357', c)
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c.randomize()
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r = rs.Ruleset(rs.Ruleset.Method.SATISFY)
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offsets = rs.Configuration.moore(c.master)
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r.addConfiguration(c.master, replicator, offsets)
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c.start_plot(100, r, lambda *args: True)
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c.start_plot(100, p.ruleset)
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@ -10,22 +10,11 @@ if __name__ == '__main__':
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sys.path.append(os.path.abspath('src'))
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import cam
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import cam_util as u
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import ruleset as rs
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def seeds(f_index, f_grid, indices, states, *args):
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total = sum(f_grid[indices])
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if not f_grid[f_index] and total == 2:
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return rs.Configuration.ON
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else:
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return rs.Configuration.OFF
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c = cam.CAM(1, 100, 2)
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p = u.CAMParser('B2/S', c)
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c.randomize()
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r = rs.Ruleset(rs.Ruleset.Method.SATISFY)
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offsets = rs.Configuration.moore(c.master)
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r.addConfiguration(c.master, seeds, offsets)
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c.start_plot(100, r, lambda *args: True)
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c.start_plot(100, p.ruleset)
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@ -0,0 +1,25 @@
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"""
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@author: jrpotter
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@date: June 4th, 2015
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"""
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class InvalidFormat(Exception):
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"""
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Called when parsing an invalid format.
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For example, in MCell and RLE, numbers should be in ascending order.
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"""
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def __init__(self, value):
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"""
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"""
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self.value = value
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def __str__(self):
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"""
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"""
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return repr(self.value)
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@ -12,22 +12,7 @@ import itertools as it
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import numpy as np
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def flatten(coordinates, grid):
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"""
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Given the coordinates of a matrix, returns the index of the flat matrix.
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This is merely a convenience function to convert between N-dimensional space to 1D.
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"""
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index = 0
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gridprod = 1
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for i in reversed(range(len(coordinates))):
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index += coordinates[i] * gridprod
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gridprod *= grid.shape[i]
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return index
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import util
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class Configuration:
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@ -39,20 +24,11 @@ class Configuration:
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the next state of a cell depending on a configuration.
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"""
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# Possible states a cell can take
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#
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# If a configuration passes, the cell's state will be on or off if ON or OFF was passed respectively.
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# If IGNORE, then the state remains the same, but no further configurations will be checked by the
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# ruleset.
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ON = 1
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OFF = 0
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def __init__(self, grid, next_state, offsets={}):
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"""
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@next_state: Represents the next state of a cell given a configuration passes.
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This should be an [ON|OFF|Function that returns ON or Off]
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This should be an [0|1|Function that returns 0 or 1]
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@offsets: A dictionary of offsets containing N-tuple keys and [-1, 0, 1] values.
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Note N must be the same dimension as the grid's dimensions, as it specifies
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@ -68,12 +44,11 @@ class Configuration:
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f_offsets = []
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for k, v in offsets.items():
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states.append(v)
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f_offsets.append(flatten(k, grid))
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f_offsets.append(util.flatten(k, grid))
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self.states = np.array(states)
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self.offsets = np.array(f_offsets)
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def passes(self, f_index, grid, vfunc, *args):
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"""
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Checks if a given configuration passes, and if so, returns the next state.
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else:
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return (success, self.next_state)
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@classmethod
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def moore(cls, grid, value=ON):
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def moore(cls, grid, value=1):
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"""
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Returns a neighborhood corresponding to the Moore neighborhood.
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return offsets
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@classmethod
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def neumann(cls, grid, value=ON):
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def neumann(cls, grid, value=1):
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"""
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Returns a neighborhood corresponding to the Von Neumann neighborhood.
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@ -139,7 +112,6 @@ class Configuration:
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return offsets
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class Ruleset:
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"""
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The primary class of this module, which saves configurations of cells that yield the next state.
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@ -171,7 +143,7 @@ class Ruleset:
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MATCH = 0
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TOLERATE = 1
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SATISFY = 2
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ALWAYS_PASS = 3
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def __init__(self, method):
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"""
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@ -181,7 +153,6 @@ class Ruleset:
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self.method = method
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self.configurations = []
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def addConfiguration(self, grid, next_state, offsets):
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"""
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Creates a configuration and saves said configuration.
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config = Configuration(grid, next_state, offsets)
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self.configurations.append(config)
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def applyTo(self, f_index, grid, *args):
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"""
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Depending on a given method, applies ruleset to a cell.
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vfunc = self._tolerates
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elif self.method == Ruleset.Method.SATISFY:
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vfunc = self._satisfies
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elif self.method == Ruleset.Method.ALWAYS_PASS:
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vfunc = lambda *args: True
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# Apply the function if possible
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if vfunc is not None:
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return grid.flat[f_index]
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def _matches(self, f_index, f_grid, indices, states):
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"""
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Determines that neighborhood matches expectation exactly.
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@ -233,7 +204,6 @@ class Ruleset:
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"""
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return not np.count_nonzero(f_grid[indices] ^ states)
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def _tolerates(self, f_index, f_grid, indices, states, tolerance):
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"""
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Determines that neighborhood matches expectation within tolerance.
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non_matches = np.count_nonzero(f_grid[inices] ^ states)
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return (non_matches / len(f_grid)) >= tolerance
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def _satisfies(self, f_index, f_grid, indices, states, valid_func):
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"""
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Allows custom function to relay next state of given cell.
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@ -254,4 +223,3 @@ class Ruleset:
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"""
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return valid_func(f_index, f_grid, indices, states)
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"""
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A collection of utilities that can ease construction of CAMs.
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@author: jrpotter
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@date: June 4th, 2015
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"""
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import re
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import ruleset as rs
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import exceptions as ce
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def flatten(coordinates, grid):
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"""
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Given the coordinates of a matrix, returns the index of the flat matrix.
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This is merely a convenience function to convert between N-dimensional space to 1D.
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"""
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index = 0
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gridprod = 1
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for i in reversed(range(len(coordinates))):
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index += coordinates[i] * gridprod
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gridprod *= grid.shape[i]
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return index
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class CAMParser:
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"""
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The following builds rulesets based on the passed string.
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Following notation is supported:
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* MCell Notation (x/y)
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* RLE Format (By/Sx)
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For reference: http://en.wikipedia.org/wiki/Life-like_cellular_automaton
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"""
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RLE_FORMAT = r'B\d*/S\d*$'
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MCELL_FORMAT = r'\d*/\d*$'
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def __init__(self, notation, cam):
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"""
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Parses the passed notation and saves values into members.
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@sfunc: Represents the function that returns the next given state.
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@ruleset: A created ruleset that matches always
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@offsets: Represents the Moore neighborhood corresponding to the given CAM
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"""
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self.sfunc = None
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self.offsets = rs.Configuration.moore(cam.master)
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self.ruleset = rs.Ruleset(rs.Ruleset.Method.ALWAYS_PASS)
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if re.match(CAMParser.MCELL_FORMAT, notation):
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x, y = notation.split('/')
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if all(map(self._numasc, [x, y])):
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self.sfunc = self._mcell(x, y)
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else:
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raise ce.InvalidFormat("Non-ascending values in MCELL format")
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elif re.match(CAMParser.RLE_FORMAT, notation):
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B, S = map(lambda x: x[1:], notation.split('/'))
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if all(map(self._numasc, [B, S])):
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self.sfunc = self._mcell(S, B)
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else:
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raise ce.InvalidFormat("Non-ascending values in RLE format")
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else:
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raise ce.InvalidFormat("No supported format passed to parser.")
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# Add configuration to given CAM
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self.ruleset.addConfiguration(cam.master, self.sfunc, self.offsets)
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def _numasc(self, value):
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"""
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Check the given value is a string of ascending numbers.
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"""
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if all(map(str.isnumeric, value)):
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return ''.join(sorted(value)) == value
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else:
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return False
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def _mcell(self, x, y):
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"""
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MCell Notation
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A rule is written as a string x/y where each of x and y is a sequence of distinct digits from 0 to 8, in
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numerical order. The presence of a digit d in the x string means that a live cell with d live neighbors
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survives into the next generation of the pattern, and the presence of d in the y string means that a dead
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cell with d live neighbors becomes alive in the next generation. For instance, in this notation,
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Conway's Game of Life is denoted 23/3
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"""
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x, y = list(map(int, x)), list(map(int, y))
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def next_state(f_index, f_grid, indices, states, *args):
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total = sum(f_grid[indices])
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if f_grid[f_index]:
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return int(total in x)
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else:
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return int(total in y)
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return next_state
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