2015-05-31 22:13:14 +00:00
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"""
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2015-06-02 20:35:16 +00:00
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The following determines the next state of a given cell in a CAM.
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2015-05-31 22:13:14 +00:00
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2015-06-02 20:35:16 +00:00
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The ruleset takes in a collection of rules specifying neighborhoods, as well as the configurations of
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said neighborhood that yield an "on" or "off" state on the cell a ruleset is being applied to.
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2015-05-31 22:13:14 +00:00
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@date: May 31st, 2015
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"""
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2015-06-01 00:58:09 +00:00
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import enum
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2015-06-02 20:35:16 +00:00
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import numpy as np
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2015-05-31 22:27:47 +00:00
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2015-05-31 22:13:14 +00:00
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class Ruleset:
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"""
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2015-06-02 20:35:16 +00:00
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The primary class of this module, which saves configurations of cells that yield the next state.
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The ruleset will take in configurations defined by the user that specify how a cell's state should change,
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depending on the given neighborhood and current state. For example, if I have a configuration that states
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2015-05-31 22:13:14 +00:00
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2015-06-02 20:35:16 +00:00
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[[0, 0, 0]
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,[1, 0, 1]
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,[1, 1, 1]
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]
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2015-05-31 22:13:14 +00:00
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2015-06-02 20:35:16 +00:00
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must match exactly for the center cell to be a 1, then each cell is checked for this configuration, and its
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state is updated afterward (note the above is merely for clarity; a configuration is not defined as such). Note
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2015-06-06 03:19:57 +00:00
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configurations are checked until a match occurs, in order of the configurations list.
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2015-05-31 22:13:14 +00:00
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"""
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2015-06-02 20:35:16 +00:00
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class Method(enum.Enum):
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2015-05-31 22:13:14 +00:00
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"""
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2015-06-02 20:35:16 +00:00
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Specifies how a ruleset should be applied to a given cell.
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2015-06-06 03:19:57 +00:00
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* A match declares that a given configuration must match exactly for a configuration to pass
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* A tolerance specifies that a configuration must match within a given percentage to pass
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2015-06-02 20:35:16 +00:00
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* A specification allows the user to define a custom function which must return a boolean, declaring
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2015-06-06 03:19:57 +00:00
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whether a configuration passes. This function is given a neighborhood with all necessary information.
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* Always passing allows the first configuration to always yield a success. It is redundant to add
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any additional configurations in this case (in fact it is inefficient since neighborhoods are computer
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in advance).
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2015-06-02 20:35:16 +00:00
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"""
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2015-06-04 18:16:25 +00:00
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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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2015-06-02 20:35:16 +00:00
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def __init__(self, method):
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"""
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2015-06-06 03:19:57 +00:00
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A ruleset does not begin with any configurations; only a means of verifying them.
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@method: One of the values defined in the Ruleset.Method enumeration. View class for description.
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2015-05-31 22:13:14 +00:00
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"""
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2015-05-31 22:27:47 +00:00
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self.method = method
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2015-06-02 20:35:16 +00:00
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self.configurations = []
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2015-05-31 22:13:14 +00:00
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2015-06-06 13:26:08 +00:00
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def apply_to(self, plane, *args):
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2015-05-31 22:13:14 +00:00
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"""
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2015-06-06 00:45:57 +00:00
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Depending on the set method, applies ruleset to each cell in the plane.
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2015-05-31 22:13:14 +00:00
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2015-06-06 00:45:57 +00:00
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@args: If our method is TOLERATE, we pass in a value in set [0, 1]. This specifies the threshold between a
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passing (i.e. percentage of matches in a configuration is > arg) and failing. If our method is SATISFY,
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arg should be a function returning a BOOL, which takes in a current cell's value, and the
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value of its neighbors.
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2015-05-31 22:13:14 +00:00
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"""
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2015-06-06 13:26:08 +00:00
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next_grid = []
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2015-06-06 00:45:57 +00:00
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2015-06-06 03:19:57 +00:00
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# Determine which function should be used to test success
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if self.method == Ruleset.Method.MATCH:
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vfunc = self._matches
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elif self.method == Ruleset.Method.TOLERATE:
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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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2015-06-06 13:26:08 +00:00
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# We apply our method a row at a time, to take advantage of being able to sum the totals
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# of a neighborhood in a batch manner. We try to apply a configuration to every bit of a
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# row, mark those that fail, and try the next configuration on the failed bits until
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# either all bits pass or configurations are exhausted
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for flat_index, value in enumerate(plane.grid.flat):
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next_row = bitarray(self.N)
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to_update = range(0, self.N)
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for config in self.configurations:
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next_update = []
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# After profiling with a previous version, I found that going through each index and totaling the number
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# of active states was taking much longer than I liked. Instead, we compute as many neighborhoods as possible
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# simultaneously, avoiding explicit summation via the "sum" function, at least for each state separately.
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#
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# Because the states are now represented as numbers, we instead convert each number to their binary representation
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# and add the binary representations together. We do this in chunks of 9, depending on the number of offsets, so
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# no overflowing of a single column can occur. We can then find the total of the ith neighborhood by checking the
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# sum of the ith index of the summation of every 9 chunks of numbers (this is done a row at a time).
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neighboring = []
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for flat_offset, bit_offset in config.offsets:
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neighbor = str(plane.grid.flat[flat_index + flat_offset])
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neighboring.append(int(neighbor[bit_offset+1:] + neighbor[:bit_offset]))
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# Chunk into groups of 9 and sum all values
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# These summations represent the total number of active states in a given neighborhood
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totals = [0] * self.N
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chunks = map(sum, [offset_totals[i:i+9] for i in range(0, len(neighboring), 9)])
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for chunk in chunks:
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totals = list(map(sum, zip(totals, chunk)))
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# Apply change to all successful configurations
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for bit_index in to_update:
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neighborhood = Neighborhood(flat_index, bit_index, totals[bit_index])
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success, state = config.passes(neighborhood, vfunc, *args)
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if success:
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next_row[bit_index] = state
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else:
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next_update.append(bit_index)
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# Apply next configuration to given indices
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to_update = next_update
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# We must update all states after each next state is computed
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next_grid.append(next_row)
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# Can now apply the updates simultaneously
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for i in range(plane.grid.size):
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plane.grid.flat[i] = next_grid[i]
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2015-05-31 22:13:14 +00:00
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2015-06-02 20:35:16 +00:00
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def _matches(self, f_index, f_grid, indices, states):
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2015-05-31 22:13:14 +00:00
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"""
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2015-06-02 20:35:16 +00:00
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Determines that neighborhood matches expectation exactly.
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2015-05-31 22:13:14 +00:00
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2015-06-02 20:35:16 +00:00
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Note this functions like the tolerate method with a tolerance of 1.
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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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2015-05-31 22:13:14 +00:00
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"""
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2015-06-02 20:35:16 +00:00
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Determines that neighborhood matches expectation within tolerance.
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2015-06-01 12:30:00 +00:00
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2015-06-02 20:35:16 +00:00
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We see that the percentage of actual matches are greater than or equal to the given tolerance level. If so, we
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consider this cell to be alive. Note tolerance must be a value 0 <= t <= 1.
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"""
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2015-06-05 12:57:14 +00:00
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non_matches = np.count_nonzero(f_grid[indices] ^ states)
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2015-06-02 20:35:16 +00:00
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return (non_matches / len(f_grid)) >= tolerance
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2015-05-31 22:27:47 +00:00
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2015-06-02 20:35:16 +00:00
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def _satisfies(self, f_index, f_grid, indices, states, valid_func):
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2015-06-01 12:30:00 +00:00
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"""
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2015-06-02 20:35:16 +00:00
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Allows custom function to relay next state of given cell.
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2015-06-01 12:30:00 +00:00
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2015-06-02 20:35:16 +00:00
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The passed function is supplied the list of 2-tuple elements, of which the first is a Cell and the second is
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the expected state as declared in the Neighborhood, as well as the grid and cell in question.
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2015-06-01 12:30:00 +00:00
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"""
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2015-06-02 20:35:16 +00:00
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return valid_func(f_index, f_grid, indices, states)
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2015-06-01 12:30:00 +00:00
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